From a8aa6904034da74b91f8046e2440ebb4359d63e2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dav=C3=ADd=20Brakenhoff?= Date: Sun, 8 Oct 2023 17:13:38 +0300 Subject: [PATCH 1/4] black formatting + isort - remove all ttim star imports - move some data files --- .git-blame-ignore-revs | 1 + .../html/_sources/models/modelindex.rst.txt | 2 +- .../html/_sources/models/modelindex.txt | 2 +- docs/conf.py | 118 +- docs/models/modelindex.rst | 2 +- examples/linesinkex.py | 98 +- examples/test1.py | 17 +- examples/test2.py | 31 +- examples/test3.py | 12 +- examples/test4.py | 58 +- examples/test5.py | 49 +- notebooks/aem_ttim_sol.ipynb | 96 +- notebooks/circareasink_example.ipynb | 70 +- notebooks/compare_wells_linesink.ipynb | 32 +- notebooks/{ => data}/pumptest_neuman.txt | 0 notebooks/drawdown.txt | 2 - notebooks/hobs.txt | 1 - notebooks/line-sink-ditch.ipynb | 16 +- notebooks/line_sink_well_sol.ipynb | 86 +- notebooks/meandering_river.ipynb | 24 +- notebooks/pathline_trace.ipynb | 256 ++-- notebooks/pumpingtest.ipynb | 173 ++- notebooks/pumpingtest_hypothetical.ipynb | 93 +- notebooks/theis_storage.ipynb | 40 +- notebooks/time.txt | 0 notebooks/ttim_exercise1_sol.ipynb | 144 +- notebooks/ttim_figures.ipynb | 69 +- notebooks/ttim_neuman_comparison.ipynb | 62 +- notebooks/ttim_pumptest_neuman.ipynb | 104 +- notebooks/ttim_slugtest.ipynb | 102 +- notebooks/well_example.ipynb | 202 +-- notebooks/well_near_river_or_wall.ipynb | 74 +- notebooks/well_near_wall.ipynb | 26 +- notebooks/wells_in_different_systems.ipynb | 58 +- pumpingtest_benchmarks/0_synthetic_data.ipynb | 214 +-- pumpingtest_benchmarks/10_moench_test.ipynb | 216 +-- .../11_slug_test_pratt_county.ipynb | 110 +- .../12_falling-head_slug_test.ipynb | 160 +- .../13_multiwell_slug_test-.ipynb | 158 +- .../14_dawsonville_slug_test.ipynb | 128 +- .../1_test_of_oude_korendijk.ipynb | 228 +-- pumpingtest_benchmarks/2_test_of_dalem.ipynb | 486 +++--- .../3_test_of_vennebulten.ipynb | 204 +-- .../4_test_of_gridley.ipynb | 138 +- pumpingtest_benchmarks/5_test_of_sioux.ipynb | 127 +- .../6_test_of_schroth.ipynb | 298 ++-- .../7_test_of_neveda_double-porosity.ipynb | 136 +- .../8_test_of_hardinxveld_recovery.ipynb | 183 ++- .../9_test_of_texas_hill.ipynb | 203 +-- .../confined1_oude_korendijk.ipynb | 305 ++-- setup.py | 18 +- tests/test_import.py | 3 +- tests/test_notebooks.py | 48 +- tests/test_theis.py | 50 +- ttim/__init__.py | 31 +- ttim/aquifer.py | 255 ++-- ttim/aquifer_parameters.py | 149 +- ttim/aquifernew.py | 207 +-- ttim/besselnumba.py | 1158 +++++++------- ttim/besselnumba_total.py | 1337 +++++++++-------- ttim/circareasink.py | 153 +- ttim/circinhom.py | 538 ++++--- ttim/element.py | 259 ++-- ttim/equation.py | 489 +++--- ttim/fit.py | 264 ++-- ttim/invlapnumba.py | 185 +-- ttim/kuhlman_invlap.py | 222 +-- ttim/linedoublet.py | 368 +++-- ttim/linesink.py | 972 ++++++++---- ttim/model.py | 609 +++++--- ttim/src/test_bessel.py | 544 ++++--- ttim/trace.py | 216 +-- ttim/util.py | 76 +- ttim/version.py | 4 +- ttim/well.py | 347 +++-- ttimman/ttimman.tex | 12 +- 76 files changed, 7894 insertions(+), 6034 deletions(-) create mode 100644 .git-blame-ignore-revs rename notebooks/{ => data}/pumptest_neuman.txt (100%) delete mode 100644 notebooks/drawdown.txt delete mode 100644 notebooks/hobs.txt delete mode 100644 notebooks/time.txt mode change 100644 => 100755 ttim/fit.py diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 0000000..b4ebe8b --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1 @@ +# Migrate code style to black diff --git a/docs/builddocs/html/_sources/models/modelindex.rst.txt b/docs/builddocs/html/_sources/models/modelindex.rst.txt index 5558265..40b97a8 100644 --- a/docs/builddocs/html/_sources/models/modelindex.rst.txt +++ b/docs/builddocs/html/_sources/models/modelindex.rst.txt @@ -8,7 +8,7 @@ The top of the system can be either an aquifer or a leaky layer. The head is com .. code-block:: python - ml = ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], \ + ml = ttim.ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], \ c=[2000, 5000], Saq=[0.1, 1e-4, 2e-4], \ Sll=[1e-4, 4e-4], phreatictop=True, \ tmin=0.01, tmax=10)) diff --git a/docs/builddocs/html/_sources/models/modelindex.txt b/docs/builddocs/html/_sources/models/modelindex.txt index 5558265..40b97a8 100644 --- a/docs/builddocs/html/_sources/models/modelindex.txt +++ b/docs/builddocs/html/_sources/models/modelindex.txt @@ -8,7 +8,7 @@ The top of the system can be either an aquifer or a leaky layer. The head is com .. code-block:: python - ml = ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], \ + ml = ttim.ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], \ c=[2000, 5000], Saq=[0.1, 1e-4, 2e-4], \ Sll=[1e-4, 4e-4], phreatictop=True, \ tmin=0.01, tmax=10)) diff --git a/docs/conf.py b/docs/conf.py index 253216a..55b6d3e 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,48 +23,48 @@ # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. -sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath(".")) # -- General configuration ------------------------------------------------ extensions = [ - 'alabaster', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.napoleon', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', - 'sphinx.ext.mathjax', - 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode' + "alabaster", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.napoleon", + "sphinx.ext.doctest", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.coverage", + "sphinx.ext.mathjax", + "sphinx.ext.ifconfig", + "sphinx.ext.viewcode", ] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] -source_suffix = '.rst' +templates_path = ["_templates"] +source_suffix = ".rst" # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'ttim' -copyright = u'2017, Mark Bakker' -author = u'Mark Bakker' -rst_epilog = '.. |project| replace:: %s' % project +project = "ttim" +copyright = "2017, Mark Bakker" +author = "Mark Bakker" +rst_epilog = ".. |project| replace:: %s" % project # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. -version = '0.4' +version = "0.4" # The full version, including alpha/beta/rc tags. -release = '0.4.a1' +release = "0.4.a1" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -81,7 +81,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ['_build'] +exclude_patterns = ["_build"] # The reST default role (used for this markup: `text`) to use for all # documents. @@ -99,7 +99,7 @@ # show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] @@ -112,30 +112,30 @@ # -- Options for HTML output ---------------------------------------------- -html_theme = 'alabaster' +html_theme = "alabaster" html_theme_path = [alabaster.get_path()] -html_static_path = ['_static'] +html_static_path = ["_static"] html_theme_options = { - 'logo': False, - 'travis_button': False, - 'logo_name': False, - 'github_user': 'mbakker7', - 'github_repo': 'ttim', - 'github_banner': False, - 'github_button': True, - 'github_type': 'watch', - 'github_count': True, - 'description': "TTim is a transient multi-layer analytic element model", - 'codecov_button': False, + "logo": False, + "travis_button": False, + "logo_name": False, + "github_user": "mbakker7", + "github_repo": "ttim", + "github_banner": False, + "github_button": True, + "github_type": "watch", + "github_count": True, + "description": "TTim is a transient multi-layer analytic element model", + "codecov_button": False, } html_sidebars = { - '**': [ - 'about.html', - 'navigation.html', - 'relations.html', - 'searchbox.html' + "**": [ + "about.html", + "navigation.html", + "relations.html", + "searchbox.html", ] } @@ -173,20 +173,17 @@ html_show_copyright = True # Output file base name for HTML help builder. -htmlhelp_basename = 'ttimdoc' +htmlhelp_basename = "ttimdoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # 'papersize': 'letterpaper', - # The font size ('10pt', '11pt' or '12pt'). # 'pointsize': '10pt', - # Additional stuff for the LaTeX preamble. # 'preamble': '', - # Latex figure (float) alignment # 'figure_align': 'htbp', } @@ -195,13 +192,18 @@ # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'ttimman.tex', u'TTim Documentation', - u'Mark Bakker', 'manual'), + ( + master_doc, + "ttimman.tex", + "TTim Documentation", + "Mark Bakker", + "manual", + ), ] # The name of an image file (relative to this directory) to place at the top of # the title page. -#latex_logo = 'logo.png' +# latex_logo = 'logo.png' # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. @@ -224,10 +226,7 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'ttim', u'TTim Documentation', - [author], 1) -] +man_pages = [(master_doc, "ttim", "TTim Documentation", [author], 1)] # If true, show URL addresses after external links. # man_show_urls = False @@ -239,9 +238,15 @@ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'TTim', u'TTim Documentation', - author, 'TTim', 'TTim is a transient multi-layer analytic element model', - 'Miscellaneous'), + ( + master_doc, + "TTim", + "TTim Documentation", + author, + "TTim", + "TTim is a transient multi-layer analytic element model", + "Miscellaneous", + ), ] # Documents to append as an appendix to all manuals. @@ -258,8 +263,7 @@ # Example configuration for intersphinx: refer to the Python standard library. -#intersphinx_mapping = {'https://docs.python.org/3': None, +# intersphinx_mapping = {'https://docs.python.org/3': None, # 'http://pandas.pydata.org/pandas-docs/stable/': None, # 'https://docs.scipy.org/doc/scipy/reference/': None, # 'https://docs.scipy.org/doc/numpy/': None} - diff --git a/docs/models/modelindex.rst b/docs/models/modelindex.rst index 5558265..40b97a8 100644 --- a/docs/models/modelindex.rst +++ b/docs/models/modelindex.rst @@ -8,7 +8,7 @@ The top of the system can be either an aquifer or a leaky layer. The head is com .. code-block:: python - ml = ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], \ + ml = ttim.ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], \ c=[2000, 5000], Saq=[0.1, 1e-4, 2e-4], \ Sll=[1e-4, 4e-4], phreatictop=True, \ tmin=0.01, tmax=10)) diff --git a/examples/linesinkex.py b/examples/linesinkex.py index cc80d71..80d4f3f 100644 --- a/examples/linesinkex.py +++ b/examples/linesinkex.py @@ -1,47 +1,92 @@ -import numpy as np import matplotlib.pyplot as plt -from ttim import * +import numpy as np + +import ttim xls = 100 * np.cos(np.linspace(np.pi, 0, 7)) yls = 50 * np.ones(len(xls)) -ml1 = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[100, 200], - Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True, - tmin=0.1, tmax=10, M=20, f2py=False) -ls1 = HeadLineSinkString(ml1, list(zip(xls, yls)), tsandh=[(0, 2)], layers=0, label='river') +ml1 = ttim.ModelMaq( + kaq=[1, 20, 2], + z=[25, 20, 18, 10, 8, 0], + c=[100, 200], + Saq=[0.1, 1e-4, 1e-4], + Sll=[0, 0], + phreatictop=True, + tmin=0.1, + tmax=10, + M=20, + f2py=False, +) +ls1 = ttim.HeadLineSinkString( + ml1, list(zip(xls, yls)), tsandh=[(0, 2)], layers=0, label="river" +) ml1.solve() -ml2 = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[100, 200], - Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True, - tmin=0.1, tmax=10, M=20, f2py=True) -ls2 = HeadLineSinkString(ml2, list(zip(xls, yls)), tsandh=[(0, 2)], layers=0, label='river') +ml2 = ttim.ModelMaq( + kaq=[1, 20, 2], + z=[25, 20, 18, 10, 8, 0], + c=[100, 200], + Saq=[0.1, 1e-4, 1e-4], + Sll=[0, 0], + phreatictop=True, + tmin=0.1, + tmax=10, + M=20, + f2py=True, +) +ls2 = ttim.HeadLineSinkString( + ml2, list(zip(xls, yls)), tsandh=[(0, 2)], layers=0, label="river" +) ml2.solve() -print('ml1:', ml1.disvec(40, 20, 10)) -print('ml2:', ml2.disvec(40, 20, 10)) +print("ml1:", ml1.disvec(40, 20, 10)) +print("ml2:", ml2.disvec(40, 20, 10)) -ml3 = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[100, 200], - Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True, - tmin=0.1, tmax=10, M=20, f2py=False) -ls3 = HeadLineSinkHo(ml3, -100, 0, 100, 0, tsandh=[(0, 2)], layers=0, order=5, label='river') +ml3 = ttim.ModelMaq( + kaq=[1, 20, 2], + z=[25, 20, 18, 10, 8, 0], + c=[100, 200], + Saq=[0.1, 1e-4, 1e-4], + Sll=[0, 0], + phreatictop=True, + tmin=0.1, + tmax=10, + M=20, + f2py=False, +) +ls3 = ttim.HeadLineSinkHo( + ml3, -100, 0, 100, 0, tsandh=[(0, 2)], layers=0, order=5, label="river" +) ml3.solve() -ml4 = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[100, 200], - Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True, - tmin=0.1, tmax=10, M=20, f2py=True) -ls4 = HeadLineSinkHo(ml4, -100, 0, 100, 0, tsandh=[(0, 2)], layers=0, order=5, label='river') +ml4 = ttim.ModelMaq( + kaq=[1, 20, 2], + z=[25, 20, 18, 10, 8, 0], + c=[100, 200], + Saq=[0.1, 1e-4, 1e-4], + Sll=[0, 0], + phreatictop=True, + tmin=0.1, + tmax=10, + M=20, + f2py=True, +) +ls4 = ttim.HeadLineSinkHo( + ml4, -100, 0, 100, 0, tsandh=[(0, 2)], layers=0, order=5, label="river" +) ml4.solve() -print('ml3:', ml3.disvec(40, 20, 10)) -print('ml4:', ml4.disvec(40, 20, 10)) +print("ml3:", ml3.disvec(40, 20, 10)) +print("ml4:", ml4.disvec(40, 20, 10)) -#x = np.linspace(-200, 200, 101) -#h1 = ml1.headalongline(x, 50, t=100) +# x = np.linspace(-200, 200, 101) +# h1 = ml1.headalongline(x, 50, t=100) -# ml2 = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[100, 200], +# ml2 = ttim.ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[100, 200], # Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True, # tmin=0.1, tmax=1000, M=20, f2py=False) -# ls2 = HeadLineSinkHo(ml2, x1=-100, y1=50, x2=100, y2=50, tsandh=[(0.0,2.0)],\ +# ls2 = ttim.HeadLineSinkHo(ml2, x1=-100, y1=50, x2=100, y2=50, tsandh=[(0.0,2.0)],\ # order=5, layers=0) # ml2.solve() @@ -61,4 +106,3 @@ # # plt.plot(x, h2[i, 0]) # # plt.grid() # # plt.show() - diff --git a/examples/test1.py b/examples/test1.py index 1fdb256..e6b01c8 100644 --- a/examples/test1.py +++ b/examples/test1.py @@ -1,5 +1,14 @@ -from ttim import * -ml = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=1000, M=20) -w1 = DischargeWell(ml, xw = 0, yw = 0, rw = 1e-5, tsandQ = [(0, 1)], layers = 0) -ml.solve() +import ttim +ml = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=10, + tmax=1000, + M=20, +) +w1 = ttim.DischargeWell(ml, xw=0, yw=0, rw=1e-5, tsandQ=[(0, 1)], layers=0) +ml.solve() diff --git a/examples/test2.py b/examples/test2.py index a929daa..bfe537f 100644 --- a/examples/test2.py +++ b/examples/test2.py @@ -1,14 +1,33 @@ -from ttim import * import numpy as np -ml = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=1000, M=20) -w1 = HeadWell(ml, xw=0, yw=0, rw=0.3, tsandh=[(0, 1)], layers=0) +import ttim + +ml = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=10, + tmax=1000, + M=20, +) +w1 = ttim.HeadWell(ml, xw=0, yw=0, rw=0.3, tsandh=[(0, 1)], layers=0) ml.solve() -ml2 = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=1000, M=20) -w2 = DischargeWell(ml2, xw=0, yw=0, rw=0.3, tsandQ=[(0, 0), (100, 2.15)], layers=0) +ml2 = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=10, + tmax=1000, + M=20, +) +w2 = ttim.DischargeWell(ml2, xw=0, yw=0, rw=0.3, tsandQ=[(0, 0), (100, 2.15)], layers=0) ml2.solve() x = np.linspace(-10, 10, 101) h1 = ml.headalongline(x, np.zeros_like(x), 110, [0, 1]) -h2 = ml2.headalongline(x, np.zeros_like(x), 110, [0, 1]) \ No newline at end of file +h2 = ml2.headalongline(x, np.zeros_like(x), 110, [0, 1]) diff --git a/examples/test3.py b/examples/test3.py index 4af72a3..14b1d26 100644 --- a/examples/test3.py +++ b/examples/test3.py @@ -1,25 +1,31 @@ import numpy as np from scipy.integrate import quad + def func1(tau, p0, p1, f): rv = np.exp(-tau) * np.cos(-p1 / p0 * tau) * f(tau / p0) return rv + def func2(tau, p0, p1, f): rv = np.exp(-tau) * np.sin(-p1 / p0 * tau) * f(tau / p0) return rv + def func(t): return np.exp(-t) + p0 = 0.2 p1 = 0.4 + def quadfunc(): f1 = quad(func1, 0, np.inf, args=(p0, p1, func))[0] f2 = quad(func2, 0, np.inf, args=(p0, p1, func))[0] return (f1 + 1j * f2) / p0 + f = quadfunc() @@ -27,10 +33,12 @@ def gausslag1(tau, p0, p1, f): rv = np.cos(-p1 / p0 * tau) * f(tau / p0) return rv + def gausslag2(tau, p0, p1, f): rv = np.sin(-p1 / p0 * tau) * f(tau / p0) return rv + def lag(func): x, w = np.polynomial.laguerre.laggauss(50) g1 = 0.0 @@ -39,6 +47,6 @@ def lag(func): g1 += w[i] * gausslag1(x[i], p0, p1, func) g2 += w[i] * gausslag2(x[i], p0, p1, func) return (g1 + 1j * g2) / p0 - -g = lag(func) + +g = lag(func) diff --git a/examples/test4.py b/examples/test4.py index 54c671b..d62e754 100644 --- a/examples/test4.py +++ b/examples/test4.py @@ -1,44 +1,78 @@ import numpy as np from scipy.integrate import quad from scipy.interpolate import interp1d -from ttim import * -ml = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=1e-4, tmax=1e5, M=20) -w1 = HeadWell(ml, xw=0, yw=0, rw=0.3, tsandh=[(0, 1)], layers=0) +import ttim + +ml = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=1e-4, + tmax=1e5, + M=20, +) +w1 = ttim.HeadWell(ml, xw=0, yw=0, rw=0.3, tsandh=[(0, 1)], layers=0) ml.solve() + def func1(tau, p0, p1, f): rv = np.exp(-tau) * np.cos(-p1 / p0 * tau) * f(tau / p0) return rv + def func2(tau, p0, p1, f): rv = np.exp(-tau) * np.sin(-p1 / p0 * tau) * f(tau / p0) return rv + t = np.linspace(100, 1e5, 1000) Q = w1.strength(t) -func = interp1d(t - 100, Q[0], 'cubic') +func = interp1d(t - 100, Q[0], "cubic") + def funcnew(t): - print('time:', t) + print("time:", t) if t > 5000: - print('t too large:', t) + print("t too large:", t) return func(t) + def quadfunc(p0, p1, func): f1 = quad(func1, 0, np.inf, args=(p0, p1, func))[0] f2 = quad(func2, 0, np.inf, args=(p0, p1, func))[0] return (f1 + 1j * f2) / p0 -ml2 = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=100, M=20) + +ml2 = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=10, + tmax=100, + M=20, +) ml2.initialize() -fp = np.zeros(41, 'D') +fp = np.zeros(41, "D") p = ml2.p for i in range(41): p0 = p[i].real p1 = p[i].imag fp[i] = quadfunc(p0, p1, funcnew) - -ml2 = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=100, M=20) -w2 = WellTest(ml2, xw=0, yw=0, rw=0.3, tsandQ=[(0, 1)], layers=0, fp=fp) -ml2.solve() \ No newline at end of file + +ml2 = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=10, + tmax=100, + M=20, +) +w2 = ttim.WellTest(ml2, xw=0, yw=0, rw=0.3, tsandQ=[(0, 1)], layers=0, fp=fp) +ml2.solve() diff --git a/examples/test5.py b/examples/test5.py index 75f9a7e..ca099cd 100644 --- a/examples/test5.py +++ b/examples/test5.py @@ -1,20 +1,33 @@ import numpy as np from scipy.integrate import quad from scipy.interpolate import interp1d -from ttim import * -ml = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=1e-3, tmax=1e3, M=20) -w1 = HeadWell(ml, xw=0, yw=0, rw=0.3, tsandh=[(0, 1)], layers=0) +import ttim + +ml = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=1e-3, + tmax=1e3, + M=20, +) +w1 = ttim.HeadWell(ml, xw=0, yw=0, rw=0.3, tsandh=[(0, 1)], layers=0) ml.solve() + def gausslag1(tau, p0, p1, f): rv = np.cos(-p1 / p0 * tau) * f(tau / p0) return rv + def gausslag2(tau, p0, p1, f): rv = np.sin(-p1 / p0 * tau) * f(tau / p0) return rv + def glag(p0, p1, func): x, w = np.polynomial.laguerre.laggauss(50) g1 = 0.0 @@ -23,22 +36,34 @@ def glag(p0, p1, func): g1 += w[i] * gausslag1(x[i], p0, p1, func) g2 += w[i] * gausslag2(x[i], p0, p1, func) return (g1 + 1j * g2) / p0 - -#t = np.linspace(100, 1e5, 1000) -#Q = w1.strength(t) -#func = interp1d(t - 100, Q[0], 'cubic') + + +# t = np.linspace(100, 1e5, 1000) +# Q = w1.strength(t) +# func = interp1d(t - 100, Q[0], 'cubic') + def func(t): return w1.strength(t + 100)[0, 0] -ml2 = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=0.1, tmax=100, M=20) + +ml2 = ttim.ModelMaq( + kaq=[1, 5], + z=[3, 2, 1, 0], + c=[10], + Saq=[0.3, 0.01], + Sll=[0.001], + tmin=0.1, + tmax=100, + M=20, +) ml2.initialize() -fp = np.zeros(len(ml2.p), 'D') +fp = np.zeros(len(ml2.p), "D") p = ml2.p for i in range(len(p)): p0 = p[i].real p1 = p[i].imag fp[i] = glag(p0, p1, func) -#ml2 = ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=100, M=20) -w2 = WellTest(ml2, xw=0, yw=0, rw=0.3, tsandQ=[(0, 1)], layers=0, fp=fp) -ml2.solve() \ No newline at end of file +# ml2 = ttim.ModelMaq(kaq=[1, 5], z=[3, 2, 1, 0], c=[10], Saq=[0.3, 0.01], Sll=[0.001], tmin=10, tmax=100, M=20) +w2 = ttim.WellTest(ml2, xw=0, yw=0, rw=0.3, tsandQ=[(0, 1)], layers=0, fp=fp) +ml2.solve() diff --git a/notebooks/aem_ttim_sol.ipynb b/notebooks/aem_ttim_sol.ipynb index de9d6b5..42bef77 100644 --- a/notebooks/aem_ttim_sol.ipynb +++ b/notebooks/aem_ttim_sol.ipynb @@ -9,7 +9,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -31,9 +31,15 @@ "source": [ "def generate_data():\n", " # 2 layer model with some random error\n", - " ml = ModelMaq(kaq=[10, 20], z=[0, -20, -22, -42], c=[1000], \n", - " Saq=[0.0002, 0.0001], tmin=0.001, tmax=100)\n", - " w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", + " ml = ttim.ModelMaq(\n", + " kaq=[10, 20],\n", + " z=[0, -20, -22, -42],\n", + " c=[1000],\n", + " Saq=[0.0002, 0.0001],\n", + " tmin=0.001,\n", + " tmax=100,\n", + " )\n", + " w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", " ml.solve()\n", " t = np.logspace(-2, 1, 100)\n", " h = ml.head(10, 0, t)\n", @@ -43,12 +49,12 @@ " alpha = 0.8\n", " for i in range(1, len(n)):\n", " n[i] = 0.8 * n[i - 1] + r[i]\n", - " ho = h[0] + n\n", - " plt.plot(t, ho, '.')\n", + " ho = h[0] + n\n", + " plt.plot(t, ho, \".\")\n", " data = np.zeros((len(ho), 2))\n", " data[:, 0] = t\n", " data[:, 1] = ho\n", - " #np.savetxt('pumpingtestdata.txt', data, fmt='%2.3f', header='time (d), head (m)')\n", + " # np.savetxt('pumpingtestdata.txt', data, fmt='%2.3f', header='time (d), head (m)')\n", " return data" ] }, @@ -67,7 +73,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -92,8 +98,8 @@ "def func(p, to=to, ho=ho, returnmodel=False):\n", " k = p[0]\n", " S = p[1]\n", - " ml = ModelMaq(kaq=k, z=[0, -20], Saq=S, tmin=0.001, tmax=100)\n", - " w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", + " ml = ttim.ModelMaq(kaq=k, z=[0, -20], Saq=S, tmin=0.001, tmax=100)\n", + " w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", " ml.solve(silent=True)\n", " if returnmodel:\n", " return ml\n", @@ -121,9 +127,10 @@ ], "source": [ "from scipy.optimize import fmin\n", + "\n", "lsopt = fmin(func, [10, 1e-4])\n", - "print('optimal parameters:', lsopt)\n", - "print('rmse:', np.sqrt(func(lsopt) / len(ho)))" + "print(\"optimal parameters:\", lsopt)\n", + "print(\"rmse:\", np.sqrt(func(lsopt) / len(ho)))" ] }, { @@ -133,7 +140,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -145,12 +152,12 @@ "source": [ "ml = func(lsopt, returnmodel=True)\n", "plt.figure()\n", - "plt.plot(data[:, 0], data[:, 1], '.', label='observed')\n", + "plt.plot(data[:, 0], data[:, 1], \".\", label=\"observed\")\n", "hm = ml.head(10, 0, to)\n", - "plt.plot(to, hm[0], 'r', label='modeled')\n", + "plt.plot(to, hm[0], \"r\", label=\"modeled\")\n", "plt.legend()\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\");" ] }, { @@ -169,12 +176,12 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=10, pmin=0.1, pmax=1000)\n", - "cal.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "cal.series(name='obs1', x=10, y=0, layer=0, t=to, h=ho)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10, pmin=0.1, pmax=1000)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "cal.series(name=\"obs1\", x=10, y=0, layer=0, t=to, h=ho)\n", "cal.fit(report=False)\n", - "print('rmse:', cal.rmse())" + "print(\"rmse:\", cal.rmse())" ] }, { @@ -273,8 +280,9 @@ " k = p[0]\n", " S = p[1]\n", " c = p[2]\n", - " ml = ModelMaq(kaq=k, z=[2, 0, -20], Saq=S, c=c, topboundary='semi', \n", - " tmin=0.001, tmax=100)\n", + " ml = ttim.ModelMaq(\n", + " kaq=k, z=[2, 0, -20], Saq=S, c=c, topboundary=\"semi\", tmin=0.001, tmax=100\n", + " )\n", " w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", " ml.solve(silent=True)\n", " if returnmodel:\n", @@ -303,8 +311,8 @@ ], "source": [ "lsopt2 = fmin(func2, [10, 1e-4, 1000])\n", - "print('optimal parameters:', lsopt2)\n", - "print('rmse:', np.sqrt(func2(lsopt2) / len(ho)))" + "print(\"optimal parameters:\", lsopt2)\n", + "print(\"rmse:\", np.sqrt(func2(lsopt2) / len(ho)))" ] }, { @@ -314,7 +322,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -326,12 +334,12 @@ "source": [ "ml = func2(lsopt2, returnmodel=True)\n", "plt.figure()\n", - "plt.plot(data[:, 0], data[:, 1], '.', label='observed')\n", + "plt.plot(data[:, 0], data[:, 1], \".\", label=\"observed\")\n", "hm = ml.head(10, 0, to)\n", - "plt.plot(to, hm[0], 'r', label='modeled')\n", + "plt.plot(to, hm[0], \"r\", label=\"modeled\")\n", "plt.legend()\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\");" ] }, { @@ -340,8 +348,10 @@ "metadata": {}, "outputs": [], "source": [ - "ml = ModelMaq(kaq=10, z=[2, 0, -20], Saq=1e-4, c=1000, topboundary='semi', tmin=0.001, tmax=100)\n", - "w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", + "ml = ttim.ModelMaq(\n", + " kaq=10, z=[2, 0, -20], Saq=1e-4, c=1000, topboundary=\"semi\", tmin=0.001, tmax=100\n", + ")\n", + "w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", "ml.solve(silent=True)" ] }, @@ -441,11 +451,11 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=10)\n", - "cal.set_parameter(name='Saq0', initial=1e-4)\n", - "cal.set_parameter(name='c0', initial=1000)\n", - "cal.series(name='obs1', x=10, y=0, layer=0, t=to, h=ho)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "cal.set_parameter(name=\"c0\", initial=1000)\n", + "cal.series(name=\"obs1\", x=10, y=0, layer=0, t=to, h=ho)\n", "cal.fit(report=False)\n", "cal.parameters" ] @@ -477,7 +487,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -488,12 +498,12 @@ ], "source": [ "plt.figure()\n", - "plt.plot(data[:, 0], data[:, 1], '.', label='observed')\n", + "plt.plot(data[:, 0], data[:, 1], \".\", label=\"observed\")\n", "hm = ml.head(10, 0, to)\n", - "plt.plot(to, hm[0], 'r', label='modeled')\n", + "plt.plot(to, hm[0], \"r\", label=\"modeled\")\n", "plt.legend()\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\");" ] } ], diff --git a/notebooks/circareasink_example.ipynb b/notebooks/circareasink_example.ipynb index 39630de..da516d8 100644 --- a/notebooks/circareasink_example.ipynb +++ b/notebooks/circareasink_example.ipynb @@ -9,7 +9,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -35,7 +35,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -49,8 +49,8 @@ "source": [ "N = 0.001\n", "R = 100\n", - "ml = ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=1e-3, tmax=1e4)\n", - "ca = CircAreaSink(ml, 0, 0, 100, tsandN=[(0, 0.001)])\n", + "ml = ttim.ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=1e-3, tmax=1e4)\n", + "ca = ttim.CircAreaSink(ml, 0, 0, 100, tsandN=[(0, 0.001)])\n", "ml.solve()\n", "ml.xsection(-200, 200, 0, 0, t=[0.1, 1, 10], figsize=(12, 4), sstart=-200)" ] @@ -62,7 +62,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -79,13 +79,13 @@ "for t in [0.1, 1, 10]:\n", " for i in range(len(x)):\n", " qx[i], qy = ml.disvec(x[i], 1e-6, t)\n", - " plt.plot(x, qx, label='time is ' + str(t))\n", - "qxb = N * np.pi * R ** 2 / (2 * np.pi * R)\n", - "plt.axhline(qxb, color='r', ls='--')\n", - "plt.axhline(-qxb, color='r', ls='--')\n", - "plt.xlabel('x (m)')\n", - "plt.ylabel('Qx (m^2/d)')\n", - "plt.legend(loc='best');" + " plt.plot(x, qx, label=\"time is \" + str(t))\n", + "qxb = N * np.pi * R**2 / (2 * np.pi * R)\n", + "plt.axhline(qxb, color=\"r\", ls=\"--\")\n", + "plt.axhline(-qxb, color=\"r\", ls=\"--\")\n", + "plt.xlabel(\"x (m)\")\n", + "plt.ylabel(\"Qx (m^2/d)\")\n", + "plt.legend(loc=\"best\");" ] }, { @@ -115,9 +115,9 @@ "source": [ "N = 0.001\n", "R = 100\n", - "Q = N * np.pi * R ** 2\n", - "ml = ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=1e-3, tmax=1e4, M=10)\n", - "ca = CircAreaSink(ml, -200, 0, 100, tsandN=[(0, 0.001)])\n", + "Q = N * np.pi * R**2\n", + "ml = ttim.ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=1e-3, tmax=1e4, M=10)\n", + "ca = ttim.CircAreaSink(ml, -200, 0, 100, tsandN=[(0, 0.001)])\n", "w = Well(ml, 200, 0, rw=0.1, tsandQ=[(0, Q)])\n", "ml.solve()" ] @@ -129,7 +129,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -166,9 +166,9 @@ "t = np.logspace(-3, 4, 100)\n", "h = ml.head(-200, 0, t)\n", "plt.semilogx(t, h[0])\n", - "plt.xlabel('time')\n", - "plt.ylabel('head')\n", - "plt.title('head at center of area-sink');" + "plt.xlabel(\"time\")\n", + "plt.ylabel(\"head\")\n", + "plt.title(\"head at center of area-sink\");" ] }, { @@ -186,7 +186,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -200,10 +200,10 @@ "source": [ "N = 0.001\n", "R = 100\n", - "Q = N * np.pi * R ** 2\n", - "ml = ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=10, tmax=100, M=10)\n", - "ca = CircAreaSink(ml, -200, 0, 100, tsandN=[(0, 0.001)])\n", - "w = Well(ml, 200, 0, rw=0.1, tsandQ=[(0, Q)])\n", + "Q = N * np.pi * R**2\n", + "ml = ttim.ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=10, tmax=100, M=10)\n", + "ca = ttim.CircAreaSink(ml, -200, 0, 100, tsandN=[(0, 0.001)])\n", + "w = ttim.Well(ml, 200, 0, rw=0.1, tsandQ=[(0, Q)])\n", "ml.solve()\n", "ml.contour([-300, 300, -200, 200], ngr=40, t=20)" ] @@ -233,10 +233,18 @@ "source": [ "N = 0.001\n", "R = 100\n", - "Q = N * np.pi * R ** 2\n", - "ml = ModelMaq(kaq=[5, 20], z=[20, 12, 10, 0], c=[1000], Saq=[2e-4, 1e-4], tmin=1e-3, tmax=1e4, M=10)\n", - "ca = CircAreaSink(ml, 0, 0, 100, tsandN=[(0, 0.001)])\n", - "w = Well(ml, 0, 0, rw=0.1, tsandQ=[(0, Q)], layers=1)\n", + "Q = N * np.pi * R**2\n", + "ml = ttim.ModelMaq(\n", + " kaq=[5, 20],\n", + " z=[20, 12, 10, 0],\n", + " c=[1000],\n", + " Saq=[2e-4, 1e-4],\n", + " tmin=1e-3,\n", + " tmax=1e4,\n", + " M=10,\n", + ")\n", + "ca = ttim.CircAreaSink(ml, 0, 0, 100, tsandN=[(0, 0.001)])\n", + "w = ttim.Well(ml, 0, 0, rw=0.1, tsandQ=[(0, Q)], layers=1)\n", "ml.solve()" ] }, @@ -247,7 +255,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -269,7 +277,7 @@ "outputs": [ { "data": { - "image/png": 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55ORvNzqK8BNNUQgygI6HdScDh1pgXCHQWvPuga+5qPeFRkdpNUIjUjnFnsyHyx80OorwE01RCNYA3ZVSnZVSNmAa8EULjCsEqze9g097GT3sBqOjtCoXjLiNT4o2UeUsMzqK8AONLgRaaw9wA7AA2A7M1VpvVUrNVErNBFBKJSilMoBbgXuVUhlKqbCjjdvYTKL9+O+ml7kkcSzKbDE6SqvSuesk+puC+GzZ/UZHEX6gSb49Wuv5wPwj+r182Otsqpt96jSuEHWxeecX7HWXMuuEh4yO0ipdPfB6bt/wJGe7q7Ba7UbHEa2YnFks/NbsNU9wefwx2AIjjI7SKg0YeCmpBPDF8geMjiJaOSkEwi/t2LuAba5Czh73iNFRWi+lmDnwal7b/yUej8voNKIVk0Ig/NLslY8wPWYEAUExRkdp1YYMvopELMz/8WGjo4hWTAqB8Du793/Pemc+5574qNFRWj+luKbv5by67zO8Xo/RaUQrJYVA+J2XfnyAS6MGt/kb0zeVEcOuJ1Ir2SoQRyWFQPiVjTvmscmZywUT5LaMdaVMJm4edB3P7/0Yp6vc6DiiFZJCIPyG1pqnV/2b65MmyNZAPQ0ZfBW9TUG8v/gOo6OIVkgKgfAb36+ZRZnXwenjZN9AvSnFzWPu57/ZyyguzTA6jWhlpBAIv+D2OHlm6xvc2utSzLZAo+P4pc7dTmJSQAKzF91sdBTRykghEH7h0yX/JB4zY0bdZnQUvzbzxKf4onQH6VnrjY4iWhEpBKLVKyo5yH8Ozuf2EXeiTPIv2xgxCQOZHtGPxxffbHQU0YrIt0q0es9+M5OT7cn06nue0VHahOlTXmK/q5Dv1zxvdBTRSkghEK3ahm0fsbziANefNNvoKG2GLTCSe/texaNbZlNZVWJ0HNEKSCEQrZbb4+TB1Y9wR+pphES073sRN7WRI25ikDmMV76ZaXQU0QpIIRCt1vuLbyNam5g8Vs6IbXJKccfEF5lXtJk9B5YYnUYYTAqBaJUOZK7mtUM/cO/x/0aZzUbHaZNiEgdxQ9wY/vXDrXJ10nZOCoFodbxeD/d8ez0zY4aT0nWS0XHatHMnv0CID/67UG712Z5JIRCtzluLbsTm83DBSS//9cCiUZTFygMTXuTdnJ/Ymfat0XGEQaQQiFZl9/4feDNrGQ+Oew6TJcDoOO1CQsdR3NphAncvuQO3u8roOMIAUghEq+F0VXD3D7dyU8LxJKUeb3ScduX0CU/RASvPf3W50VGEAaQQiFbjiS8upKOycfakWUZHaXeU2cz/nfwG8ws3s3S9nLPR3kghEK3CNyue4KfSvfzf6R+gzBaj47RLUXH9eHzADfxr4yyy87YZHUe0ICkEwnAHMlfzyI63eHL4PYRGdjY6Trs2ZNhMLgnvxx3zp+P2OI2OI1qIFAJhqMqqEm5ddA3Xx42mT/8LjI4jgMtPf5tQn+aZ/11idBTRQqQQCMP4fF7u+fQM+phDOW+KHCraWpgsNv592nssKdrGvKX3Gx1HtAApBMIwL351BQVVRfzz7M/k7OFWJjymJ88f+yjP7v2Y9ds/MjqOaGZSCIQh5v/0GF/lreOZU97GFhRldBxRiy49TuXhHhdz28oHyMzZZHQc0YykEIgWt3brhzy28x2eG3U/0fEDjI4j/sSxY+7kyughXPf1pZSUZhodRzQTKQSiRe1M+5bbVj/IY72voGefc4yOI+rgotPeZKw9kevnnSH3L2ijpBCIFpOevYHrfriFuzudyqhRtxodR9SVUtxyzhekmuzc9vEpclhpG9QkhUApNUUptVMptUcpdWct7yul1Kya9zcppYYc9t5+pdRmpdTPSqm1TZFHtD45BTu55uvLuDpmBJPHP2p0HFFPymLl/vPmY/Y4uefjU/F6PUZHEk2o0YVAKWUGXgROAvoAFyil+hwx2ElA95rH1cBLR7w/Tms9SGs9rLF5ROuTU7CTGV+cx9lhPTn/1NeMjiMayBIQypNnf0mRo4B7PjpFikEb0hTn8o8A9mit9wEopeYAU4HDz1GfCryttdbASqVUhFIqUWud1QTzF61Ydv4OZvzvfM4O78UVZ84BpYyO1Ghen2Z3bhmbMkrILHKQW+Ykr8yJ2+v7dZiQAAuxoQHEhQXQJSaYAckRJIbbUX6+/PbQeJ4/52tu/Pgk7v7oFB4+90ssZqvRsUQjNUUhSALSD+vOAEbWYZgkIAvQwEKllAZe0VrXesUrpdTVVG9N0KmT3L/WHxzK3cJVX13E2eG9ueLMD/y6CGSXVPH1liwWbs1hU0Yx8WF2BiSH0ykqiD4dwogNsRFgrTkXQkOZ00NuaRV5ZU7mrs3g3s+2AIpRXaI4uX8i43rGEWjzz3Mn7KHxPH/uN9z00RTunnsyD5/zJVarXDLcnzVFIajt263rMcwYrfUhpVQcsEgptUNrvfQPA1cXiNkAw4YNO3L6opXZfWAJ1353A5dFDuLiqW/7ZRHw+TQLt+XwxvI0duaUMb53HDOO7czwzlGEB9ZvLVhrTWaxg6W78vlg9UH+8fEmJvaJ56rju9A7MayZlqD52EPimHXuN9zx8Snc+OF4nj77S4ICI4yOJRqoKQpBBtDxsO5k4FBdh9Fa//Kcq5SaR3VT0x8KgfAf63d8wi0r7uPvSZM4ZdLTRsepN69P88n6DF5espfQAAvXjO3KhN7x2CwN36WmlCI5MogLR3biwpGdKKxw8eGadKa/sZo+HcK48cRuDE3xrxPr7CFxPHPBdzww9xSunDuBF8/8jMiwZKNjiQZQ1c32jZiAUhZgFzAeyATWABdqrbceNswpwA3AyVQ3G83SWo9QSgUDJq11Wc3rRcADWutv/myew4YN02vXygFGrdGi1c/x0NZX+XePSzhmzD+MjlNvmzKKuWfeFgKtZm6e2J3RXaKbtV2/yu1l3oZMZi3ezZhuMdx1Ui+iQ/yrmUV73Dz38VQWOw7x4pTX6ZQ41OhI4iiUUutqOyin0UcNaa09VP/ILwC2A3O11luVUjOVUjNrBpsP7AP2AK8C19X0jweWK6U2AquBr/6qCIjWSWvNS/Ov5vEts3l52F1+VwQcLi/3fb6FGW+t5bJjUvnwmlEc0zWm2Xfu2q1mLhjRiUW3jiU80MrkZ5fy0dp0GruC1pKUxcrN53/FJbHDuOSb6aza8r7RkUQ9NXqLwAiyRdC6OJxl3DfvbDIrsnl2yuvEJg03OlK97M+vYOa76+iVEMr9p/clIshmWJZth0q5+cMNDEiO4KEz+mG3+tcO5dUrnuaO7W9wfZepnHfCw0bHEUdoti0C0b4dyFrLxR+MxeQs5/XzFvldEVi0LYezX/qJi0al8Mz5gwwtAgB9OoTx2fVjcHl8nPWfnzhYUGlonvoaMfpW3jn+Kd7f+zn3zj0Fh7PM6EiiDqQQiAZbuPpZLvnmMs6PHsS/L16KPTTe6Ej18tZP+/nX51t4dfowLhmV0mqO8Q+yWXhu2iDOHZbMWS/9xJZM/7q+T6duk3n/nK/xOAq56IOx7M9cY3Qk8RekEIh6czjLeOiTs3h6y2u8NPh2zjvtDb+7z/ArS/by+vI05l4zmiGdIo2O8wdKKS4f05mHzujLZf9dzYaDRUZHqpeg8I78++JlTIsezKULL+fz5Q/71X6P9kYKgaiXrXsXcN4Hx1FensXc0+fRd9BlRkeqF601sxbv5sM16Xx4zSg6RgUZHelPTemXyOPnDODKt9ayOq3Q6Dj1oswWzjvtdV4ddg9v7vyA2+ZMoLhMLibQGkkhEHXiclfxn6+u5Lqlt3Fdh/E8esmPhMV0NzpWvb2+PI3/bTzEnGtGkRgeaHScOjmxVzzPTRvMzHfX+V0zEUDP/hcw5/zFJGgTZ388ie/Xv2J0JHEEKQTiL23c/SXnvTea7bk/8+GJr3DSpKfA5H//Ogu2ZvPqsn28ecUI4kLtRsepl2O7x/DQGf246u21ZJdUGR2n3gJC4vn7hYt4tNcVPLnheW6fM5H8kgNGxxI1/O/bLFpMSUUuD396Njcv+wczk8Yz69KVJKSMMTpWg2zOKOGuTzfz6qXDSIrwjy2BI53cP5FLRqcw4601VDj988qfw0ffwifnLaKDtnD2p6fy8dL75CqmrYAUAvEHPp+XT5f9H1PnjkdXFjDv1A+ZMvFJv9sh/Ivskiquenstj5zZnwHJEUbHaZRrx3alX4dw/jZnAz6ff+58tYcmcusFX/PK0H/wxe7PuOjdUWzaPd/oWO2anFAmfmfV1jk8tfZJbF4Xdw+9jT4DpxsdqVG8Ps1Fr63kmK4x3DTe//Zp1Mbt9TFt9kqm9E3gquO7GB2nUbTHxZff3s4zhxYzIrgjN574FEmxfY2O1WbJCWXiT+06uIxr3zue+1c9yBUdxvHOpWv9vggAvLJ0Lz4N14/rZnSUJmM1m3j2/EG8vGSvX+48Ppyy2Dhtyiz+d+ZXdFQBnP/l+Tz1+YWUlGcbHa1dkULQzu3J+InbPpjA1d/O5JjgTnx+/hKmTHwCZTH2DNumsDG9mNeXpfHM+YMwm1rHyWJNpWNUEP86rQ9/m7OBSpf/t7EHR6Zy/XmfM2/i65SXZnLKRxN44csrKKnINTpauyBNQ+3U9v3f88bKf7O6MpPpEf2YduITBEW0nRv+VDg9nDJrGXdM7sUpAxKNjtNsbvnwZ+xWM/8+q7/RUZpUxt6FzP7xQb73FnFuzDAuGvsg0WEd/3pE8aeO1jQkhaAd0VqzatuH/HfDC+xxFXFp5ADOGfsIwVGdjY7W5O79bDNVbh9PnjvQ6CjNqqzKzUnPLePBM/oxrmec0XGaXPrur3lz1WN87c7n5LAeTD/2fjrGDzA6lt+SQtCOOVzlfLXyCd7b9z98XheXJRzLKWMfwBbS9n44AFbuK+DmOT+z4Jbj630nMX+0fHc+//hkE9/cfByh9ra5vPkZq3lv+f18UnmAgQGxXDR4JiN7n99qrg/lL6QQtEN7M1fz8eqn+Kp4GwOwcVH38xg18mZUG76/rMPlZcpzS7n3lD5M7ONfF8FrjH98vAmLWfHwmW2riehIjuJ0vlr+AO/lrMBntnJO8omcPurvhIe0n8+6MaQQtBNljkIWrX2ez9O+5qCnjDODUzlr6I0kd5tidLQW8fBX28gudfL8BYONjtKiShxuJj+zlGfOH8TortFGx2l22uNi/bqX+GjHHJb6yjg2MImpfS9mVJ8LMPvp+S4tQQpBG+byOPlp8zvM3/kRyx2ZjCCQ01Imcfyo27AG+dd9cBtj/cEirn57HQtuPs7vbvfYFBZty+Ghr7Yx/6bjCA5oPz+Gxdkbmb/qab7IX0+eSXFyVD9OGngVvVNOkKajI0ghaGNcHicrt77Lop3z+L7iAF19ipNihzJ5+E1EJravtWGAvDInZ7z4I/86rQ+T+yYYHccwf/94IxVOLy9cOLj9/Qj6fOzd/glfbXmTr8v3YzJbmRw9iAkDLqN3p7Ht7+9RCykEbUBJRR7LN73JDwcW8aPjEN19JsZH92fSwCtJSD0B2uk/usvj4+LXVjGqSxS3TuppdBxDVbm9nD97JZP6xLepk+jqS7udbNv8Lgt2fsS3lel4zRZODO/J2J5nMbTHWVjbwHkyDSGFwA/5tI/t+7/npx0fszxvAzu95QwngONjhzBu4Axikke22x//w/3zsy0cKnbw6qXDMLWxE8caIrukiqkvLufRswYwrlfbPDKsPrTHxe7tH/Pdjo9YVrqHNJNmlD2eY5KO5Zi+F9EhuofREVuMFAI/oLVmf+5GVm+fy6qs1aypyiHK5+OYwA4ck3w8wwdejj082eiYrcrbK/bz5k/7+ez6MYS10UMnG2LdgUKufnsd7145kt6JYUbHaT20Jj9jFT9ueYefctez0ltKmMnGiNBURnQ6geG9zycqpO02LUohaIXcHhe70pexYd/XrM/dwPqqPGw+L8OtEYyMHcyInmeSkDrOL6/93xL++2Mary1L4/2rRpISHWx0nFbnq01Z3PfFVv572XD6J4cbHadV8jnL2bn9Y1anLWRN8U7WU0WssjE4pBNDO4xiUI+pJEf3bjP7F6QQGExrTUbBdrbs+Zot2WvYXJrGDl8FSV7NIFsMQ+IGMbTryXTofCKYZc32r7z0w14+WH2Q968aSXJk677dpJEWbs3m7nmbeeWSYQxNaX33Zm5tvJVF7Nr1Oev3L2Zd8U42+ipwm0wMsEbRL7In/ZKPpW/XKUQG+2eTmxSCFuTyOEnLWsOu9GVsz93IjrID7PCWE+T10s8UTN/QFPolDKd/j9MIie0t7fz14PH6eGLhThZty+H9K0eREO5fdxozwvc7c7l97kYeP2cA43vLiVf14nWTfWAZG/d9w9a8TWytzGKbyUOostDLFkXviK70jB9Gj5QT6BDdA5Nq3VvvUgiaQZXbwYGcn0k7tIq0vC3sKd3PPmch6bhI9vjobgmlZ2gnescNolfKiUQnDQc52aXBskuquOmDDQRYTTxz/iBi2uG5Ag217kAhN76/gVMHduCOyT2xmlv3D1Zr5qsqJX3/92xPX86Ogq3sqsxml66i3GymqymQroFxdI3oRpf4QaQmjaZDZDfMJrPRsQEpBA1W5XaQmb+NjJyfSS/YzoGSNA5W5nDAU0YeXpK9mlRTIF2D4uka0Z2u8YPpnDqOgPCOsqbfhBZty+GuTzdz2TEpXHdCNzk6qAEKK1zc/tFGiipdPHPeIFJjZL9Kk/F5KcneyN705ezJ28TekjTSnIWkaSfFZhPJykaKNYJOIR3oFNGVjjF96Zg4lPjw1BYtElIIaqG1prSqmOyCHeQUbCOraC9ZpRkcqszhkKuYTJ+DUnx08PpIUnaSbeGkhCSREtmDlPiBJCWNwhIqm9rNaXdOGQ99tZ2DhZU8elZ/RnZp+5dPaE4+n+aNH9N44fs9nDesIzec2E2OtmpOPh+VhXs4mLGCA/lbOVC8l4OVOWS4S0nHQ5HZRDwWksxBdLBHkxicQGJYColRPYiP6UV8ZDcCrU23D0wKAbBl1xe8vf4Fct1l5Hkd5OLFqn3E+yBe2Ui0htEhMJaEkESSIrvTIbYfsfH9MQfHNsNSiD+zL6+c15en8fWWbK47oSuXjk7FZpHmjKaSW1rFkwt38t2OPGaO7cK5wzq2iyu1tio+H87iA2TnbiIzfzuZxXvJrsgmq6qALE8FObjJMZuwo4jDSpwliFhbOP+c+gEBAQ07JPhohaBdNVhHBERyfGRv4sI6EhvRhbjoHgRHdgOr7HBsDVweH8v35PH2igNsySxh2vBOLLrl+HZ53aDmFhdm5/FzBrIls4RXl+3j+e/2MHVQBy4Y0YleCaFt5nDJVs1kIiCqMylRnUlhaq2DaEcJRfk7yCvaTW5xGnllGdgsTd+k1yRbBEqpKcBzgBl4TWv96BHvq5r3TwYqgcu01uvrMm5tWsvOYtF4RRUuVqUVsnBrNot35NI1Nphpwztx+qAO2K2tYwdbe5BTWsW7Kw8wb0MmFpNicr8EJvSOZ0ByOAEW+RzaimZrGlJKmYFdwEQgA1gDXKC13nbYMCcDN1JdCEYCz2mtR9Zl3NpIIfBPJQ43u3LK2JFdxvasUtbuL+RQcRWDO0UwoXc8k/smyOGgBtNas/VQKV9vyWLprnz25pXTLymcIZ0i6ZUQSo/4ULrGBUtx8FPN2TQ0Atijtd5XM6M5wFTg8B/zqcDburrqrFRKRSilEoHUOowrWhGvT+Py+HB5fTg9XhwuLw63lwqnl9IqN2VVHkoqXeSXu8gvd5JX5iSz2EFGkQOP10f3+FB6xofSMyGUacM70icxDIscythqKKXolxROv6Rw7phcfSvM9QeL2ZhezKLtObzw/R4OFlQSFWwjKTKQDhGBxITYiAkJIDrYRliglTC7lVC7hSCbGbvVTJDNjM1iqn6YTdLs1Ao1RSFIAtIP686geq3/r4ZJquO4TWbZ+19R/sKzaBRaqd+eD3vt+11/E75f+5mq3zf90v+Xh8KrzGil8JrM1cObzHiVGa+pehiPyVz9Xs2zx2TBU/PsNZnxmC14TBbc5uqHx2LFbbLWdFtxW6y4zFY8Zmuth6Q29IultUYDWoNG1zxX9/fp6h99n9Z4fRqPV+Px+dCAzVz9hQ6wmgi0mQmyWggKMBNqtxJmtxAWaCUmJICeCaGM6RZDcmQgyZFBRAZZ5UfAz4TarYztEcvYHr8dMOHx+sgpc5JRWMmhEgcF5dWF/0BBBaUOD2VON6UODw539YpCpcvz68qD26sxmxSWmofJpDCbFGalUEqhFJgUKKpfKxr+/w3V/8tHUtqH1ePG5nVh8XqweVxYvR4sPg8Wjxur143V58Hi9WDxebH4PJi9Nc8+H2afB7P2YvZ5a7q9mLUXk8+HWfswaR8mX/WzWftQNa9NWmPSPtRhzwqNyeerfta6pp/v19cAJu2redaA5oRFXxAYHNjgv0ltmqIQ1PYpHfnXP9owdRm3egJKXQ1cDdCpU6f65PuV1+1me6dQUGaUMoEyoWpeVz+bUabq1yaTCaUsmEwmTKbq/uaa/iaTGYvJhNlkwmI2YVImLEphMZswK7CYFFYFFqWxorFqH1blxeKr+dA9HvBUod1u8LjRLjc4XeByoWseuFxopxNczurnqirweMBuR9ntEGBHBQZCYCAqMAgVHAT2IAgORgUHo4JDICQEFRKCCglFhYWhQsMgLAxTeBjKYv3l71rzZav+8plU9adiVgqT+u2L+ssX12xS8mPezlnMJpIiAkmKqP+PkdYaj6965cLt9eHzgbdmZeOXlRGf/v1KyS98Dge6pARdVoYuLYGyMnR5Gbq8HCrK0RUV6IoKqKhAOyrB4UBXVkBVFdrhqH52VoHLBbYAVEAABASgAmzV3yebDay26m6rDWWzgt0GVivKYgGrFZ/Jgs9kw6MULmXGgwm3NuHW4Pnl4fNVrzz5fPi0D49P4/N58fl8eH2+mtfV3T6fF61/efaia7q1rnmu6Ybf+p/g9TXdh1mjKQpBBtDxsO5k4FAdh7HVYVwAtNazgdlQvY+gIUFHnXMSfcYei9vlwuNy43F68LjdeN0e3E4XXnd1t8flqnmufng9Na/dLnxud804blweNw6PG6/bhc/rxuvx4PO68Hnc+HxufF4P2udG+1xo7QbtATygLCiTFZPJhsliw2wJwGwPwRIagMVmxxoQiNVuJyAomIDgIOzBwQSGBmMPCSY4yE5goJWgADM2FMrpRDsceCsq8P3yKK/AV16GNzMfX1k53rJSfCWleEpL8ZaU4C0txWS3Y46MxBwZiSUqCnNUFOboaCyxMVhiYrDExmKJj8cSF4cpQNrtRdNQSmE1K6xmCLCY8JWV4cnJwZ2biycvD29+Pp68fDyFhXgLCvAUFeGteQCYw8Mxh4djCg/DHBqGOSwMU2goppBgzMkJmIJDMAUH//pQgXZ8VitOr4+KKjeVDieVFU6qystxlFfirKjAWVGJy1GJq8qB21mF11WFx+3E63Lic1RUf6e9LrTPDWhQFsCKMllrvsdWTObq12ZL9WuTpfq12WLFHGDDbLFgs9qqu61WrDYrZqsNs9WCxWbFYrNhsVqrX1urH2abBWuADYvVgsVmw2yzYLFaCGjirQFomkKwBuiulOoMZALTgAuPGOYL4IaafQAjgRKtdZZSKq8O4zYZe3AA9mBjDkXUPo3H48Pt9FBV7qCqzIGjvBJHefU/YlWFA2dFJc7KSpyVDtyOSsqLKijKysftqsLjdOBxO/C5q/B6q9C+KtBOUBbMlkDM1kAstmBsgcEEBIVgDwklKLYzId0jCImOIDwumsiEGMJiojBbLPjKy/EWFVV/4YqK8BQU4C0owHXgIJVr1+HJy8OTk4MnLw9TcDCWxESsvzySk7EmJ2Hr2BFbx46YguUMVfFHWmu8hYW4DhzEnZmBOyMDV2YmnkNZuLOycGdno0ymX1c4LLGx1SshcXHY+/SuXjmJjMISVb3Coux2nJWVlOQVUpyVT0leAeWFxVQUl1BZWkJVVgauynLcVRV4XJV4PQ58HgdgAhWAyRyAyWLHbAnEYrNjsQVitQdiDQgkIDCMoPAEbEGBBAQFYQ8OIiA4iMCQIOwhQdiDAwkMDcIWFIA1wIzZ0rb2dTS6EGitPUqpG4AFVB8C+obWeqtSambN+y8D86k+YmgP1YePXv5n4zY2U2ukTAqrzYzVZiYoNAASIxo1PZ9P46x0U15URnlBCWWFxZQXldZ8KUqpKi0l70AWGdt34XKU43FV4POUo30OlMmCxRaC1R6GPSScoPBIQqKiiYjvSFT/YcSmdCAyIRazxYr2+fAWFeE+lIU7OwvPoUO4MjKpXL0aV/pB3OkZmMJCCUhJxdalC7YunQno2o2AHt2xxMW1qS+LqJ32eHAdPIhz126c+/bi2peGa98+XAcPgtmMrVMnbB2TsSYlE9ivP9bJk7EmJGBJTMQcEgKAs7KCgowcctKzKDyUS+m+XMrX7qCytAhnRQnuqlK8nnLQJpQ5CLM1BGtAMDZ7KAEhYdiDQ4lOSiQoPIzgiHBCoyIIiQ4jNDqC4PAgrAFm+V/8E+3qzOL2TmuN0+GhJKeIwkO5FOcUUJqXT1lBARXFhTjKinFVFuNxlaB9FZgswdgCIwgKjyE0Jo7IxETiOiXRoWcqkYlxmExmtM+HJycH1/79OPftw7UvDefevTh370Z7PNi7dyegd2/svXtj79uHgK5dq9tbhV/yVVRQtX07Vdu2UbVtO1U7duDavx9LXBwB3bsT0KULti5dCOjSGVtKCuaICADcTie5BzLI2n2Q/PRMirOzKS/MxVFWiLuqGO3zYLKEYg0IxxYUQVB4FMERUYRGRxEeF0tEQgxRHWIIjwnDYpNDVxtKLjEh6kxrTWVpFbn7s8k7kElBZhYlubmUF+TiKMvDXVWI9jmw2iMJCo8nPD6J2JSOJPfqSsc+3QgIqr42iqegAOfOnTU/HNup2roVT24u9j59sA8cQNCQIQQOGYIlUq6T3xpprXEfrG4qdPy8AcfGTbjS0wno3h17n97Ye/fB3rsXAd26YQoKQmtNWX4hBzbv4tDuNAoy0ynNy6KqLBevuwKTJQxbUAxBYTGERscRmZhAdMcOxHXqQHTHGALkEhfNTgqBaDLapynOLSVjRxrZew9QkJFBad4hKkuy8boKMFuDCY5KIjophQ49utF1SF9iOiWhlMJbUoJj8xYcG3/GsX4Djp9/xpKYQPDIUQSPHkXQiBGYw+TWikZxZWRQsWIFlStWUrFmNcpkJmjoUAKHDCFw4ADsPXuibDY8bhcZ2/aStnEbOfvSKMo6gKM0G63BFhhLcEQCEfFJxKR0JLF7Z5J6dCQoTA46MJoUAtEiHBVODm5OI33bLnL3p1GcfZCqskzAQ3BEMjEp3Ugd0I+eowcREhmB9nio2r6DylUrqfhpBY6ffyagVy9Cxo4l5IQTCOjRXdp2m5F2uahcu5byJUso/2EJ3vJygkePri7KI0diTUoCIHd/OrtW/UzGtu0UZu6jqiIHkzmc4MhkojqkktCtC6kDepLQNRGLXBqk1ZJCIAzj8/rI3HWIveu2kLlzB0WH9uKsyMQaEEZ0cg869e9Hv3GjiIyPw+d0Url6NeU/LKH8hx/AZCJs8iRCJ0/G3q+fFIUm4HM6qfjxR8oWLKDshyXYUlOqC+/Ysdj79AEgfdsuti9fS+b2rZTk7kVrE0ERnYjt1I2OfXvTbXg/IhPC5fPwM1IIRKtSVelk54qt7F27gZy0HVQWp2G1hxKX2peex4yk3/EjsATYcG7fTumChZQtWID2+Qg//XTCp56OrWPHv56J+JX2+XCsX0/J559TunAR9h49CJ08mdCJE7DGx1OaV8CGhcvZt2EtxYd2AVbC4rrToWcfeo4eQmr/zpjkUiB+TwqBaNWqKp1sX7aJXavWkLNvM+6qHMLjutFt+GiGn3oiQRFhVG3ZWv1DNn8+Ad26EXnhBYSOH4+yyk7Go/EUFVHy6acUzfkQkz2A8KlTCTv1VKwJCRzancbaL78lfet6qspzCYroQnLvwfQ5biSdB3WRu8C1QVIIhF/JTsvh5wXLSft5FZXFewiJSqHnqOMYceYkAgNslC1eTNH7H+Dav5+IaecTeeGFcvTRYap27qLwjTco+/57Qk88kcgLpmEfMIDc/Rms/PRrDm5eidtZQWSHAfQYfQyDJo4kOLzp7oQlWicpBMJvFeeWsOZ/P7Bn9TIcJWlEJvVh6MlT6D9uNK59+yh4803KFn1L+OmnE33F5VgTE42ObJjKdevInz2bqm3biLr4EiLOOxevLYAfP57Pjh9/oKosl8gOA+l3wjgGTRyOVW5T2a5IIRBtQnZaDis++ZoDG5eilIfuI8dz/IVTCXC5KHz7LUo+/oTwM6YSfc01WKKijI7bYhxbt5L3zLO49u8n+qqrCD9jKpn70ln+wadk7V6NPTSF3seNZ+QZ4wgKbfpr1Qj/IIVAtClej5cNC1az/uuvKC/YQYeeozjx8guICrKR//IrlH75JZHTLyV6xgxMAW33VpfuQ4fIeeIJHGvXEX3tTCLOPpsdqzfx49y5lOUfJL7baMacN5XU/p2NjipaASkEos06uO0gS97+kLz9q4hO6c9J115JhEWT+9jjVO3cScK99xBy/PFGx2xS2uWi4M23KHz9dSIvvpjoGVewdeVGln3wDs7KMroOn8yJl51JSIRcEFD8RgqBaPMKMgtY8Mq7ZO9eRmL34Uy57gqsu7aT/dDD2Pv0IeG+f7WJHcqOzZs5dOddWJOTSLjnHg5kF7P4v2/gKC2gz/FnMG76adjsNqNjilZICoFoN7L3ZbPg5TcpSF9H7+NOY8L0syl84UVK588n8eGHCTl2jNERG0R7PBS8+iqF77xL/D134xs2gv89/R/y07fTfcRpTLjybAJD5DIO4uikEIh2Z9eq7Syc/RI+bzkTZlxHisXNobvuJmzyJOJuv92vzj9w5+SQecutqAAbCQ89xJKvlrLl+3nEpAzj9FuvISIu3OiIwg9IIRDtktfr47s3/8fmbz8gue8oTrvqQvL+eS/a5Sbpmaf9oqnIsXEjGTf9jcgLLsA3+RQ+fexxXFUuJl55A72P7Wd0POFHjlYI5Jxx0aaZzSYmzpjKRY88S0F6Bq/94y58192CvU8f9p8/DeeePUZH/FMlX3xB+sxrSbjvPrZHdODdu28lKrk3M1+ZJUVANBnZIhDthtfr45v/zGXnT58w8qzL6Wt1kfv4E3R86T8EDhxodLw/KHj9DYo++ICEWc/xxQdfkJu2jRNn3MyAcUOMjib8lGwRiHbPbDZxyo3TmDTzHlbPe5fFOzKIf+hB0q+9jsr1G4yO9zv5s1+leO5cIme9wDvPvUxxTi7Tn3xOioBoFlIIRLvTb+wgLnz4aQ7t3MpHny0g5sEHybjhBirXrTM6GgD5L71Eybx5WO5/hHceeZCQ6BSuev4xIhNa//4M4Z+kEIh2Kb5zAlc+/zReD3z0yZfEPPwQGTfcSNXOnYbmKnz7HUr+9yWmex/g0xeeoOuIk7n44VuxBvjPEU7C/0ghEO1WYEgglz/9IMoUyNwPPyPy9jvIuO56PIWFhuQpX/4j+a/ORt9+N5+9+CS9xpzJaTddKDd/Ec1OCoFo12x2G5c/dT9mWxgfL/yeoJNOJuPGm9AuV4vmcO5L49Df/4665U7+99os+pxwLiddd16LZhDtlxQC0e5ZA6xc/uR9KJOdRbllmCLCyXrggRabv7ekhIxrryXwmmv5at4cehxzFpOvPrvF5i+EFAIhAIvNwkUP/5Pi7HQ2dR2EY/0GShcsbJF55zzybwJGjeTLn1YRnTyAk6+XLQHRsqQQCFEjNCqUc+6+j11rF5N9+jRyHnoIb3Fxs86zfOlSKtau5QeXFa2tTPu/m2WfgGhxUgiEOExSr46ceNltrFryBa6xE8l59LFmm5e3vIKs++9n38Qzyc/Yw0UP/QurTY4OEi1PCoEQRxg0aTgd+49nWX4+FWvWUL5sWbPMJ+/pp/AMHcXWjUuYdM2thMdFNMt8hPgrUgiEqMXUW6/A6ahg34QzybrvPnwOR5NOv3L9Bkq/Xcxyh5vE7mPoc+yAJp2+EPUhhUCIWtjsNiZeeSNbNy3B3bMfxR993KTTz3t+FukTzqCiOJcz7ri6SactRH1JIRDiKPocN5CE7sewXFvIf+ONJju3wLFxI8XpWWzcsZKxl1xHoNxMXhisUYVAKRWllFqklNpd81zrxVCUUlOUUjuVUnuUUnce1v9+pVSmUurnmsfJjckjRFM7845rqCjLIzO1L8Wff94k08x/+RU29RxEVNIABk8e2STTFKIxGrtFcCewWGvdHVhc0/07Sikz8CJwEtAHuEAp1eewQZ7RWg+qecxvZB4hmlRgaCC9jj2N7WZNwauvoT2eRk2vaudO8rfvIq9gD5Ount5EKYVonMYWgqnAWzWv3wLOqGWYEcAerfU+rbULmFMznhB+4cTpp+N0FJEVm0LpNwsaNa2CV2azvd8IIpMG0KF7chMlFKJxGlsI4rXWWQA1z3G1DJMEpB/WnVHT7xc3KKU2KaXeOFrTEoBS6mql1Fql1Nq8vLxGxhai7gKC7HQdPpltQYEUvPIK2udr0HRc+/dTsGot2QW7OXH6xU2cUoiG+8tCoJT6Vim1pZZHXdfqaztN8pfbor0EdAUGAVnAU0ebiNZ6ttZ6mNZ6WGxsbB1nLUTTmDDjHCrKs8m2hlDZwLvjFX04lx2DjiEsticpA7o0cUIhGs7yVwNorScc7T2lVI5SKlFrnaWUSgRyaxksA+h4WHcycKhm2jmHTetV4Mu6BheiJQWFBZMycDxbd22h6+LvCB4xol7ja60pWPwDGdEhnHrz/c0TUogGamzT0BfAL3u8pgO1HVaxBuiulOqslLIB02rGo6Z4/OJMYEsj8wjRbCbOmEaZI4uDS1ZS33t9O3fvZmtIPEERKfQY2eevRxCiBTW2EDwKTFRK7QYm1nSjlOqglJoPoLX2ADcAC4DtwFyt9daa8R9XSm1WSm0CxgG3NDKPEM0mLDaMyA792BsSjXPXrnqNW754MTnBZvqOPeoGthCG+cumoT+jtS4AxtfS/xBw8mHd84E/HBqqtb6kMfMXoqX1GHUMG3LTKfv2W+w9e9Z5vOzFS3BaXAw9+dhmTCdEw8iZxULUw5Apx+J0F5L13dI6j+POymKX00JIVDeCwoKbMZ0QDSOFQIh6CAwNJDS6O7vdNtyZmXUap2zxd+RGhNB5cP12MAvRUqQQCFFPnYeMIjcimLLF39Vp+PxvF1PhzmXYqeOaOZkQDSOFQIh6GnbKWCpdueR/+9eFwFtSws6sMuwhSUQlRrdAOiHqTwqBEPUUmRCFPSSZnXkVeIqK/nTY8iVLyI6LpWPfYS2UToj6k0IgRAN06j+c7OgoHBs3/ulwZavXUubOY8gUaRYSrZcUAiEaYMhJYynz5OHYl/anw+0+kI/ZGkpy704tlEyI+pNCIEQDJPXohNkSxu6t+/90uANVLuJT+rZMKCEaSAqBEA0UHBJLbt7R9xH4HA4qlIfEXnU/8UwII0ghEKKBwuISKak6+k3tXQfTcZvcJHZLbblQQjSAFAIhGii2S2cq8eA7yr2MHfv24dXlJPXu3MLJhKgfKQRCNFB89864TG7c6em1vp+9bQ/KZCc4XC4rIVo3KQRCNFByz854dTlVaftrfT9r/yFstogWzSREQ0ghEKKBwmLCUMpGztbdtb5fWFRKUKicTSxaPykEQjSC1RZOVlrtF58rdVYRHt+hhRMJUX9SCIRohMDgKPILi//Q31dZiUN5iO0q9yYWrZ8UAiEaITw+kdKqqj/0dx08iNvkIrF7asuHEqKepBAI0QixXbrgwIPP6fxdf8feNLy6guReqcYEE6IepBAI0QgJ3VNxmVy4Dx78Xf9D2/agTIEEhgYZlEyIupNCIEQjJPeq/RDS7ANZcuio8BtSCIRohJDIUJSyk3XEIaSFxaUEy6Gjwk9IIRCikaoPIT30u36lzioiEpMMSiRE/UghEKKRgkKjKSws+bXbW15OlclDbLeuBqYSou6kEAjRSBHxHShx/nYVUteBA7iUUw4dFX5DCoEQjRTTtQsO5cHnqC4Gjn378OlKOvRMMTiZEHUjhUCIRkrsmoLb5Ma5Zy8+p5PMzbsxmYIIDLYbHU2IOrEYHUAIf5fUu/oQ0rSLLsaE5kBcN2zJ8UbHEqLOZItAiEYKDg9GmYOI+OQLem3aCKdPJSRejhgS/kO2CIRoAgFBMXzx5JPYgkIoK8gkddAJRkcSos6kEAjRBCbMmMnBbXt+7R560rEGphGifhpVCJRSUcCHQCqwHzhPa11Uy3BvAKcCuVrrfvUdX4jWrufovvQc3dfoGEI0SGP3EdwJLNZadwcW13TX5k1gSiPGF0II0UwaWwimAm/VvH4LOKO2gbTWS4HCho4vhBCi+TS2EMRrrbMAap7jmmt8pdTVSqm1Sqm1eXl5DQ4shBDi9/5yH4FS6lsgoZa37mn6OEentZ4NzAYYNmyYbsl5CyFEW/aXhUBrPeFo7ymlcpRSiVrrLKVUIpBbz/k3dnwhhBCN1NimoS+A6TWvpwOft/D4QgghGqmxheBRYKJSajcwsaYbpVQHpdT8XwZSSn0ArAB6KqUylFIz/mx8IYQQLadR5xForQuA8bX0PwScfFj3BfUZXwghRMtRWvvfflelVB5wwOgcDRAD5BsdogW1t+UFWeb2wl+XOUVrHXtkT78sBP5KKbVWaz3M6Bwtpb0tL8gytxdtbZnl6qNCCNHOSSEQQoh2TgpBy5ptdIAW1t6WF2SZ24s2tcyyj0AIIdo52SIQQoh2TgqBEEK0c1IImpFS6nallFZKxRzW7y6l1B6l1E6l1OTD+g9VSm2ueW+WUkoZk7phlFJPKKV2KKU2KaXmKaUiDnuvTS7zkZRSU2qWcY9Sqk3cW0Mp1VEp9b1SartSaqtS6m81/aOUUouUUrtrniMPG6fWz9vfKKXMSqkNSqkva7rb7jJrreXRDA+gI7CA6hPfYmr69QE2AgFAZ2AvYK55bzUwGlDA18BJRi9DPZd3EmCpef0Y8FhbX+Yjlt9cs2xdAFvNMvcxOlcTLFciMKTmdSiwq+YzfRy4s6b/nXX5vP3tAdwKvA98WdPdZpdZtgiazzPA34HD98ZPBeZorZ1a6zRgDzCi5sqrYVrrFbr6P+tt/OwmPVrrhVprT03nSiC55nWbXeYjjAD2aK33aa1dwByql92vaa2ztNbra16XAduBJI5+U6laP+8WDd0ElFLJwCnAa4f1brPLLIWgGSilTgcytdYbj3grCUg/rDujpl9Szesj+/urK6hew4f2s8xHW842QymVCgwGVnH0m0q1lb/Ds1SvyPkO69dml7lRF51rz/7ihj13U91U8ofRaumn/6R/q/Jny6y1/rxmmHsAD/DeL6PVMrzfLHM9tLXl+R2lVAjwCXCz1rr0T3bn+P3fQSl1KpCrtV6nlDqhLqPU0s+vllkKQQPpo9ywRynVn+p2wo01X5ZkYL1SagTVawodDxs8GThU0z+5lv6tytGW+RdKqenAqcD4muYe8PNlroejLaffU0pZqS4C72mtP63pfbSbSrWFv8MY4HSl1MmAHQhTSr1LW15mo3dStPUHsJ/fdhb35fc7lfbx247TNcAofttxerLR2eu5nFOAbUDsEf3b7DIfsZyWmmXrzG87i/sanasJlktRvf/m2SP6P8Hvd5w+/leftz8+gBP4bWdxm11m2SJoQVrrrUqpuVT/YHqA67XW3pq3rwXeBAKp/lH8utaJtF4vUP1FWFSzJbRSaz2zjS/zr7TWHqXUDVQfKWYG3tBabzU4VlMYA1wCbFZK/VzT726qbyI1t+YmUweBc+Ev/8f9XZtdZrnEhBBCtHNy1JAQQrRzUgiEEKKdk0IghBDtnBQCIYRo56QQCCFEOyeFQAgh2jkpBEII0c79P0Q0yh59m2V9AAAAAElFTkSuQmCC\n", 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", 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" ] diff --git a/notebooks/compare_wells_linesink.ipynb b/notebooks/compare_wells_linesink.ipynb index bd09bcf..8d0143b 100644 --- a/notebooks/compare_wells_linesink.ipynb +++ b/notebooks/compare_wells_linesink.ipynb @@ -16,7 +16,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -34,8 +34,8 @@ } ], "source": [ - "ml = ModelMaq(tmin=0.01, tmax=10)\n", - "ls1 = LineSink(ml, -1, 0, 1, 0, tsandQ=[(0, 10)])\n", + "ml = ttim.ModelMaq(tmin=0.01, tmax=10)\n", + "ls1 = ttim.LineSink(ml, -1, 0, 1, 0, tsandQ=[(0, 10)])\n", "ml.solve()" ] }, @@ -54,13 +54,13 @@ } ], "source": [ - "ml2 = ModelMaq(tmin=0.01, tmax=10)\n", + "ml2 = ttim.ModelMaq(tmin=0.01, tmax=10)\n", "x = np.arange(-0.9, 1, 0.2)\n", "nwells = len(x)\n", "Qtot = 10\n", "for i in range(nwells):\n", - " Well(ml2, x[i], 0, tsandQ=[(0, Qtot / nwells)])\n", - "ml2.solve() " + " ttim.Well(ml2, x[i], 0, tsandQ=[(0, Qtot / nwells)])\n", + "ml2.solve()" ] }, { @@ -73,7 +73,7 @@ "h1a = ml.head(0.5, 1, t)\n", "h1b = ml.head(0.5, 10, t)\n", "h2a = ml2.head(0.5, 1, t)\n", - "h2b = ml2.head(0.5, 10, t) " + "h2b = ml2.head(0.5, 10, t)" ] }, { @@ -83,7 +83,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -95,12 +95,12 @@ } ], "source": [ - "plt.semilogx(t, h1a[0], 'b', label='line-sink')\n", - "plt.semilogx(t, h2a[0], 'r--', label='wells')\n", - "plt.semilogx(t, h1b[0], 'b')\n", - "plt.semilogx(t, h2b[0], 'r--')\n", - "plt.xlabel('time')\n", - "plt.ylabel('head')\n", + "plt.semilogx(t, h1a[0], \"b\", label=\"line-sink\")\n", + "plt.semilogx(t, h2a[0], \"r--\", label=\"wells\")\n", + "plt.semilogx(t, h1b[0], \"b\")\n", + "plt.semilogx(t, h2b[0], \"r--\")\n", + "plt.xlabel(\"time\")\n", + "plt.ylabel(\"head\")\n", "plt.legend();" ] }, @@ -120,7 +120,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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" ] diff --git a/notebooks/pumptest_neuman.txt b/notebooks/data/pumptest_neuman.txt similarity index 100% rename from notebooks/pumptest_neuman.txt rename to notebooks/data/pumptest_neuman.txt diff --git a/notebooks/drawdown.txt b/notebooks/drawdown.txt deleted file mode 100644 index b72205d..0000000 --- a/notebooks/drawdown.txt +++ /dev/null @@ -1,2 +0,0 @@ -0.25 -0.08 diff --git a/notebooks/hobs.txt b/notebooks/hobs.txt deleted file mode 100644 index c6281aa..0000000 --- a/notebooks/hobs.txt +++ /dev/null @@ -1 +0,0 @@ --0.0400,-0.0800,-0.1300,-0.1800,-0.2300,-0.2800,-0.3300,-0.3600,-0.3900,-0.4200,-0.4500,-0.5000,-0.5400,-0.5700,-0.5800,-0.6000,-0.6400,-0.6800,-0.7420,-0.7530,-0.7790,-0.7930,-0.8190,-0.8550,-0.8730,-0.9150,-0.9350,-0.9660,-0.9900,-1.0070,-1.0500,-1.0530,-1.0720,-1.0880 diff --git a/notebooks/line-sink-ditch.ipynb b/notebooks/line-sink-ditch.ipynb index fd5115d..4c5217d 100644 --- a/notebooks/line-sink-ditch.ipynb +++ b/notebooks/line-sink-ditch.ipynb @@ -17,7 +17,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -35,10 +35,10 @@ } ], "source": [ - "ml = ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", + "ml = ttim.ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", "x = np.linspace(-100, 100, 21)\n", "y = np.zeros(len(x))\n", - "lsd = LineSinkDitchString(ml, xy=list(zip(x, y)), tsandQ=[(0, 100)])\n", + "lsd = ttim.LineSinkDitchString(ml, xy=list(zip(x, y)), tsandQ=[(0, 100)])\n", "ml.solve()" ] }, @@ -57,7 +57,7 @@ ], "source": [ "t = 2\n", - "print(f'Discharge at time t={t}:, {lsd.discharge(t)}')" + "print(f\"Discharge at time t={t}:, {lsd.discharge(t)}\")" ] }, { @@ -94,7 +94,7 @@ ], "source": [ "for i, Q in enumerate(lsd.discharge_list(t=t)):\n", - " print(f'Discharge of segment {i}: {Q}')" + " print(f\"Discharge of segment {i}: {Q}\")" ] }, { @@ -112,10 +112,10 @@ } ], "source": [ - "ml = ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", + "ml = ttim.ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", "x = np.linspace(-100, 100, 21)\n", "y = np.zeros(len(x))\n", - "lsd = LineSinkDitchString(ml, xy=list(zip(x, y)), tsandQ=[(0, 100)], Astorage=100)\n", + "lsd = ttim.LineSinkDitchString(ml, xy=list(zip(x, y)), tsandQ=[(0, 100)], Astorage=100)\n", "ml.solve()" ] }, @@ -134,7 +134,7 @@ ], "source": [ "t = 2\n", - "print(f'Discharge at time t={t}:, {lsd.discharge(t)}')" + "print(f\"Discharge at time t={t}:, {lsd.discharge(t)}\")" ] }, { diff --git a/notebooks/line_sink_well_sol.ipynb b/notebooks/line_sink_well_sol.ipynb index 958d630..0d7f8a1 100755 --- a/notebooks/line_sink_well_sol.ipynb +++ b/notebooks/line_sink_well_sol.ipynb @@ -15,7 +15,7 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -52,7 +52,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -62,15 +62,24 @@ } ], "source": [ - "ml = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[1000, 2000],\n", - " Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True,\n", - " tmin=0.1, tmax=1000)\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, 1000)], layers=1, label='well 1')\n", + "ml = ttim.ModelMaq(\n", + " kaq=[1, 20, 2],\n", + " z=[25, 20, 18, 10, 8, 0],\n", + " c=[1000, 2000],\n", + " Saq=[0.1, 1e-4, 1e-4],\n", + " Sll=[0, 0],\n", + " phreatictop=True,\n", + " tmin=0.1,\n", + " tmax=1000,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, 1000)], layers=1, label=\"well 1\")\n", "yls = [-500, -300, -200, -100, -50, 0, 50, 100, 200, 300, 500]\n", "xls = 50 * np.ones(len(yls))\n", - "ls1 = HeadLineSinkString(ml, list(zip(xls, yls)), tsandh='fixed', layers=0, label='river')\n", + "ls1 = ttim.HeadLineSinkString(\n", + " ml, list(zip(xls, yls)), tsandh=\"fixed\", layers=0, label=\"river\"\n", + ")\n", "ml.solve()\n", - "ml.xsection(x1=-200, x2=200, npoints=100, t=100, layers=[0, 1, 2], sstart=-200) " + "ml.xsection(x1=-200, x2=200, npoints=100, t=100, layers=[0, 1, 2], sstart=-200)" ] }, { @@ -88,7 +97,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -101,8 +110,8 @@ "t = np.logspace(-1, 3, 100)\n", "Q = ls1.discharge(t)\n", "plt.semilogx(t, Q[0])\n", - "plt.ylabel('Q [m$^3$/d]')\n", - "plt.xlabel('time [days]');" + "plt.ylabel(\"Q [m$^3$/d]\")\n", + "plt.xlabel(\"time [days]\");" ] }, { @@ -135,7 +144,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -145,8 +154,17 @@ } ], "source": [ - "ml.contour(win=[-200, 200, -200, 200], ngr=[20, 20], t=100, layers=0,\n", - " levels=20, color='b', labels='True', decimals=2, figsize=(8, 8))" + "ml.contour(\n", + " win=[-200, 200, -200, 200],\n", + " ngr=[20, 20],\n", + " t=100,\n", + " layers=0,\n", + " levels=20,\n", + " color=\"b\",\n", + " labels=\"True\",\n", + " decimals=2,\n", + " figsize=(8, 8),\n", + ")" ] }, { @@ -172,7 +190,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -182,23 +200,41 @@ } ], "source": [ - "ml = ModelMaq(kaq=[1, 20, 2], z=[25, 20, 18, 10, 8, 0], c=[1000, 2000],\n", - " Saq=[0.1, 1e-4, 1e-4], Sll=[0, 0], phreatictop=True,\n", - " tmin=0.1, tmax=2000)\n", - "tsandQ=[(0, 1000), (100, 0), (365, 1000), (465, 0), \n", - " (730, 1000), (830, 0), (1095, 1000), (1195, 0),\n", - " (1460, 1000), (1560, 0)]\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, tsandQ=tsandQ, layers=1, label='well 1')\n", + "ml = ttim.ModelMaq(\n", + " kaq=[1, 20, 2],\n", + " z=[25, 20, 18, 10, 8, 0],\n", + " c=[1000, 2000],\n", + " Saq=[0.1, 1e-4, 1e-4],\n", + " Sll=[0, 0],\n", + " phreatictop=True,\n", + " tmin=0.1,\n", + " tmax=2000,\n", + ")\n", + "tsandQ = [\n", + " (0, 1000),\n", + " (100, 0),\n", + " (365, 1000),\n", + " (465, 0),\n", + " (730, 1000),\n", + " (830, 0),\n", + " (1095, 1000),\n", + " (1195, 0),\n", + " (1460, 1000),\n", + " (1560, 0),\n", + "]\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=tsandQ, layers=1, label=\"well 1\")\n", "yls = [-500, -300, -200, -100, -50, 0, 50, 100, 200, 300, 500]\n", "xls = 50 * np.ones(len(yls))\n", - "ls1 = HeadLineSinkString(ml, list(zip(xls, yls)), tsandh='fixed', layers=0, label='river')\n", + "ls1 = ttim.HeadLineSinkString(\n", + " ml, list(zip(xls, yls)), tsandh=\"fixed\", layers=0, label=\"river\"\n", + ")\n", "ml.solve()\n", "\n", "t = np.linspace(0.1, 2000, 200)\n", "Q = ls1.discharge(t)\n", "plt.plot(t, Q[0])\n", - "plt.ylabel('Q [m$^3$/d]')\n", - "plt.xlabel('time [days]');" + "plt.ylabel(\"Q [m$^3$/d]\")\n", + "plt.xlabel(\"time [days]\");" ] } ], diff --git a/notebooks/meandering_river.ipynb b/notebooks/meandering_river.ipynb index c4dbcf7..3269882 100644 --- a/notebooks/meandering_river.ipynb +++ b/notebooks/meandering_river.ipynb @@ -8,7 +8,7 @@ "source": [ "%matplotlib inline\n", "import numpy as np\n", - "from ttim import *" + "import ttim" ] }, { @@ -17,11 +17,17 @@ "metadata": {}, "outputs": [], "source": [ - "ml = Model3D(kaq=[2, 1, 5, 10, 4], z=[10, 8, 6, 4, 2, 0], \\\n", - " Saq=[.1, .0001, .0002, .0002, .0001], \\\n", - " phreatictop=True, kzoverkh=0.1, topboundary='conf', \\\n", - " tmin=1, tmax=10)\n", - "w = Well(ml, -25, 0, rw=.3, tsandQ=[(0, 100)], layers=[2, 3])\n", + "ml = ttim.Model3D(\n", + " kaq=[2, 1, 5, 10, 4],\n", + " z=[10, 8, 6, 4, 2, 0],\n", + " Saq=[0.1, 0.0001, 0.0002, 0.0002, 0.0001],\n", + " phreatictop=True,\n", + " kzoverkh=0.1,\n", + " topboundary=\"conf\",\n", + " tmin=1,\n", + " tmax=10,\n", + ")\n", + "w = ttim.Well(ml, -25, 0, rw=0.3, tsandQ=[(0, 100)], layers=[2, 3])\n", "\n", "dxdy = 30 * np.pi / 100 * np.cos(np.pi)\n", "x1 = np.arange(-150, -100, 10)\n", @@ -32,7 +38,7 @@ "xls = np.hstack((x1, x2))\n", "xy = np.array([xls, yls]).T\n", "\n", - "ls1 = HeadLineSinkString(ml, xy=xy, tsandh='fixed', layers=[0, 1])" + "ls1 = ttim.HeadLineSinkString(ml, xy=xy, tsandh=\"fixed\", layers=[0, 1])" ] }, { @@ -60,7 +66,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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sSBTZBQq+fbGNxjOwx3V2ZMXRKNafimHGgOqt2BQKBVu2bOHAgQMcP36c8PBwAPT09Gjfvj3vvPMOXbt2pUuXLtjbV81EqFQqycjIKFcQp0+fZvXq1bz++uu8//77DBs2jAkTJtCrVy+NJ0Dp6ek9stWHlicgj6Aq/PDDD/j6+jJo0CC2bNmCkZGRxseoa/hfTeKN9ef4emgrxnV2qrFx/IITeP+vi7g3MGfNxA7YaGgzm8x8OcHx2QTHZhEcn83luGySKtj1y9pEHzuzEqVga6qPjakBze1NGeTREFOD6s1zhBAs2BfB4oPXGNK2IT+/3E7jJbqnbjjH8WvpnP6090PtAaFUKtmwYQNfffUVUVFRWFtb06VLF7p27UrXrl3x9vbW6P+7EIKzZ8+yZs0aNm7cSFZWFo0bN2b8+PGMHz8eV1dXjY2lpepUN4/gqVAEAL/99htvv/02PXr0YOfOnZiZ1ayppDYRQjBw0TGKFSoCPuyObg3MmqFkJfD6unN4NrFk9cSO1XrwypVqdlyM53BEKsFxWcRm3Nrdq2k9EzwaWeDhYEnL+mYUyFWk5BaTmltMSm4RKbnFpOWVHrlyChUqTPRlvODZiDHPOFZ7u81fD1/jh73hTH3WhWn9W1Z+wQNwLDKNsatO89sYLwY8QH6HWq1m8+bNfPnll4SHh+Pp6clXX33FwIEDH1l5gqKiInbs2MGaNWsICAhArVbTrVs3JkyYwIgRI57o71hdo7qKoNplqDV5VFaGurr88ccfQiaTiY4dO4r09PQaHas2CbiaJBx9/cTWoNgaG+Pk9TTR/LPdYvCioyKnUP7Q/RQrVGLDqRjRZe4B4ejrJzrP2S/eXB8kfj10TRyPTBXZD9i3Wq0W52MyxP82XxTNP9stHH39xAu/HBNbgmJFoVz5UDKq1Wox/e9g4ejrJzafvflQfdwLpUot2n+9T7z1R1CVZdm2bZto3bq1AESrVq3E33//XeslruPi4sTcuXNF8+bNBSCMjY3F+++/L3JycmpVrqcFqlmGutYf/rcfNa0IhBBi+/btQl9fX7Ru3VokJCTU+Hi1wdAlx0S37w8KhVJVI/1HJOWIVjP3it7zD4v0vOKH7ifgapLo+l2JAnh+yTFxMDRZow+0rHy5WHU0SvT68ZBw9PUTHl/6i/kB4SIz/8FllitVYsyKU8L1013i4s1MjckohBAzt18WzT/bLfKKFPdso1arhZ+fn/Dy8hKAaN68ufjzzz+FUvlwyq2mUKvV4sSJE2LixIlCkiTh4OAgdu7cWdtiPfFoFcFDsH//fmFiYiJcXFzEjRs3HsmYj4pLsZnC0ddPrDl+o0b6zy9WiD7zDwuvrwJEQlbBQ/VxMz1fTF5zRjj6+om+Cw6LQ2GaVQD/Ra1Wi5PX08SUtWeFo6+fcP9ij1h5NOqBx8zKl4uO3+4Tzy08IooVmlOyJ66lCUdfP7HncuJd59RqtQgICBCdOnUSgHB2dhZr1qwRCsW9lUZd4dSpU6JNmzYCECNGjHhiJ151gVpXBEBj4BAQClwF3i993xrYB0SW/rSqrK9HpQiEEOLEiRPC0tJSODg4iLCwsEc2bk0zbcsl0fLzPQ9sUqkqH226KJym+4mjEakPfG2RQimWHIwULT7fLdy+2COWH7ku5DW0arkXYYk5YsLvp4Wjr5+YvOaMyHjAFc2+UrPbz/sjNCaTXKkSbWbtFR9vvnjH+6dOnRLdu3cXgHBwcBDLli0TcnnN/F1rCrlcLubMmSMMDAyEhYWFWL58uVCpHu3f/GmgLiiCBoBX6WszIAJwB34Appe+Px34vrK+HqUiEEKIixcvCjs7O1GvXj1x4cKFRzp2TZBbpBAtP98jfLdeqpH+/S4lCEdfPzE/IPyBr72RmieeW3hEOPr6ibf+CHro1YQmzF1qtVqsOholXD/dJXy+PyCiUvMe6Pp3/jwvXD/dJWLS8qstSxnv/nleeH0VIJQqtVAqleLrr78WOjo6on79+mLRokWisLBQY2PVBhEREaJnz54CEN27dxehoaG1LdITRXUVgcajhiRJ2gEsKT2eFUIkSpLUADgshGhxv2trMmroXkRERNCnTx9ycnLYvXs3Xbp0eaTja5IdF+N5/6+LbHmzMx2cNJutWiBX0nv+EayM9fn3XZ8Hiqm/GJvF5DVnUQvBjyPa0tut6qUuVGrBpbgsjoSncqQ0oshEX5eGlkY0tDQs/Vn62qLkdQMLwypFSl24mcnktUHoSLBmYkdaN6paglRKThHdfjhE/9b1+XmUZ5U/y/3491IC7268wG/DmzJ/xrscOHCA0aNHs3TpUszNqxf1VFcQQrB69Wo+/vhj8vPz+fzzz/H19UVfX7+2RXvsqVPho5IkOQGBQGvgphDC8rZzmUKI++7RVxuKAODmzZv06dOH+Ph4duzYQZ8+fR65DJrg9XVBXIrL4uT03hqPdy/by2Drm53xfgAlczAsmbc3XMDWTJ+1EzvStJ5ppdek5BZxuPTBfywyjexCBZIE7Rpb8oyzDUUKFfFZhSSUHpkFdxaJszHRZ5KPM692dqy0xPX11DxeXXWG7EIFy19tTxcX2yp9rrL7sf3trrRrXP19BXKLFLSc9APZexaiLi5g8eLFTJo06Yksr5CcnMwHH3zAX3/9hbu7OytWrHisJ2B1gToTPgqYAueAYaW/Z/3nfOY9rnsdCAKCmjRpouEFU9VJSkoSHh4eQl9fX/zzzz+1JsfDUlCsFM0+2y1m7bii8b6TsgtFs892iw/+ejDz2ZHwFOEyY5cYvOioSMkpum9blUot/jwdI1745Zhw9PUTjr5+osM3+8THmy+KnRfj72vLLyhWimspuSIwIkVsPB0jXl1V4gNo//U+8deZGKFU3d8pnJhVKPouOCxafL5bXInPqtJnyy1SiPZf7xOjl5+sUvv7oVAoxKeffiqQJGFk5yiCg4Or3efjwK5du0STJk2EJEnirbfe0oaaVgNq20dQIgN6gD/w0W3vhQMNxC0/Qnhl/TxqH8F/ycjIEM8884yQyWRi/fr1tSrLg3IoLFk4+vqJI+EpGu975vbLoumMXeJmetVt4lfis4T7F3vEcwuPVJpnEJWaJ0b8dkI4+vqJAT8FikX7I8TV+OxqRRJduJkphv16XDj6+omBPweK01H3zxtJySkSz3y7X/h8f6DK4aVLD18Tjr5+VVYeFXHz5k3RtWtXAYjeQ0eJxh9tFUHRT26Oy3/Jzc0VH3zwgdDR0RGtWrUS169fr22RHkuqqwiqnYIolaxdVwGhQogFt53aCYwvfT0e2FHdsWoaKysr9u3bR48ePRg3bhy//vprbYtUZY5FpqGvq0NHZ836Bq6l5PHH6ZuM7ti4yhu/x2cVMnH1WcyN9FgzseM9zTNqtWBFYBT9fwokLCmHeS95sOs9H97t3Qz3hubVMou0a2zJ1jc7s2i0Jxn5ckYuO8nbG86TkFVYYft6Zgb8NtaL5Oxi3t14AZW6cpPpqI5NMNGXsfLojYeScefOnbRr145Lly6xYcMGtm9aj7GxMdsvJDxUf48jpqamLFy4kH379pGQkEDHjh05fPhwbYv19FEdLVKiiPABBBAMXCw9BgI2wAFKwkcPANaV9VXbK4IyCgsLxZAhQwQg5s6dW9viVImBPweKUcuqb6b4L29vOCdazdwrUnPvb9opQ6VSixG/nRCtZ+4VYYn3XuoXKZTinT/PC0dfP/Ha2rMiOfveUTFKlVoERqSID/+6IDy/ChA9fzwkJq4+I2btuCJWH4sSB0OTxfWU3HvG9hcUK8XCfeGixee7RbvZ/iIw4t6rpj9PxzxQHsaXO68Ilxm7HihJraioSLz//vsCEJ6eniIi4lYo6jt/nhdtZ/uLIkXdShR7FERGRgo3Nzehq6srfvvtt9oW57GCaq4Iql19VAhxDLjX1K13dfuvDQwNDfn7778ZP348M2bMIDs7mzlz5tRZx12RQkVYUi5v9miq0X5vphew+3IiU7o1xbaKxeQ2nI7hTHQG817yoEX9imvNZBcqeHP9OU5GpTN9QEve6N60wnsbkpDDPxfi2HExgZTcYswMdenjZk+hXEVMRgGnotIpkKvK2+tIJdtQtne04uN+LcpXMEb6Mj7o05yh7Rrx5vpzjP/9DLOHtmZcJ8e7xhzVoTF+wQnMDwhnYJsG1DO7/+d+0bMRq49HExCSzEjvxpXen+vXr/Pyyy9z7tw53n33XebNm4eBwa0xRrR34N9LCQRcTWZI28ezlPfD4urqysmTJ3nllVd46623uHz5Mj/99NNTUzG4Nnliy1BXFz09PdavX4+5uTnfffcdOTk5LF68+JEV9HoQriZko1IL2jpUP3rldlYei0KmIzGxa9XKAcdnFfLdnjC6NbPlpfYV10xPyS3i1VVnuJ6ax08vt+MFz0Z3nM8pUrDpTCx/n48jLCkXPVnJDmLDPBvRs6XdHRU6hRCk5cmJSc8nJr2AmPR8otMLCLiazN4rSUx91pWpPV3KS1U725rwz9tdeG/jBb7YfgUdCcY8c6cykCSJ2c+3ZuDPR/lmV0il4aFtGlngYGXEruDEShXBjh07GDduHDKZjG3btvHiiy/e1cbH1ZZGlkZsDop96hQBgIWFBTt37mT69On8+OOPhIWFsXnzZmxsbGpbtCeb6iwnNH3UFdPQ7ajVavHJJ58IQIwbN65OpvavO3FDOPr6PXSSVkUUFCuF+xd7xEebLlbeuJQZ24JFs89239OprFCqxIilJ0TLz/fclZmsUqnFb4evidaz9pYXilt34sYd0UKFcqU4GpEqIpJy7ptYFp9ZIKZuOCccff3ES78dF4lZd5qdihUqMXH1GeE83U8cj6w4Q3q+f5hw9PUTl+MqdwR/43dVuH66SxQU39ucs2TJEiFJkujQoYOIjo6+b3/zA8KF03S/u+R+2lizZo3Q19cXLi4u4urVq7UtTp2G2nYWP+lIksT333/PN998w/r16xkxYkT5Tk91heup+Zjoy6hvrrkt7PaHJpMvVzG8faPKGwOpucVsPRfHcC+HezqVFx2I5MyNDL59sTU+zW7F6xfIlbz953m+2xPGM87W/PuOD/9M7cq4zk5YmegTm1HAd3vC6PLdQcauOk3fhYG0muXPkMXHmLb1EmuO3+B0VHr5pvMNLY345RUvfh7VjqsJOQxcdJTAiFs7Xenr6rBotCdN65nyzsYLFTqQX+veFDNDXX45dK3Sz97ZxQaFSnChgv2H1Wo106dP55133mHw4MEcPnwYR8e7TVK380K7hghRkmT2NDN+/HgOHz5MXl4enTp1Yvfu3bUt0hOLVhFUAUmS+Oyzz1i0aBHbt29nyJAh5Ofn17ZY5VxPzcPFzlSjPowdF+Opb25IJ+eqLcnXnohGoVIzpVvFZqTj19JYfOgaI9o7MMzrltkoPa+YEUtPsvdqEp8NdGPFq960cSjJ8I1MzuW1tUF0n3eI5YHX6eBkxYpXvZk/oi3jOjlibqTL/tAUvvw3hJeXn6Lt7AC6fneQGduCySlSMLRdI3a+44OtqT7jV5+546FuaqDLsnHtkSvVvLXhPAqV+g55zQ31mNjFiT1XkohIzr3vZ/d2skZHglM3Mu54Xy6XM378eL7//nveeOMNtm3bhrFx5ZFXTeuZ4uFgwfaL8ZW2fdLp3LkzZ8+exdXVlcGDBzNv3ryyIBUtGkSrCB6Ad999l9WrV3PgwAH69etHVlZWbYsEQGxGAU2qGNpZFYoUKo5GpjGgTf0qZSgrVWq2nIulVwu7CjOHC+RKpm0NxtnWhNlDW5W/XyhXMXltENdS8lg13psppU7jYqWKhfsiGLjoKGejM3j7WVeO+fZixgA3whJz0NGBVzs78cfkZzj3eR9Of9qb1RM7MK1/C9o1tmRLUByDFx3jSnw2rnam7Hjbh8EeDZnnH87O22bZLvVMmTusDZdis9gcFHuX3BO7OqMnk9h6Lu6+n9/cUI+W9c3vWBHk5OQwaNAg/vjjD7755ht+++03dHWr7pIb4tGQqwk5xGUWVPmaJ5XGjRtz9OhRXnrpJaZNm8b48eMpKrp7tzotD4/WWfyATJgwATMzM0aPHk3Pnj3x9/fHzs6u1uQRQpCUU0SfB6jfUxnnYzIpVtXK51gAACAASURBVKrp1qxq5Rb2Xk0iOaeYb15oUuH5BQERxGcVsvmNzhjrl/zLqdSC9/+6wKW4LJaObU+vliXyn43OYPrfwVxPzWdou4Z8MdidzHw5P+wNY+elBG4P77c1NaC9oyVeTaxo72jFpK7OGOrJOBeTyTt/nmfYryf4Yog7Y59pwvwRbUnMKsR3azAt7M3KI5oGezRg7Yloft4fyTBPB4z0bzmjrUz06dasHruCE5kx4P4b2LdpZEFASBJCCBITExk4cCBXr15l9erVTJgwoUr38XZ6udnx7e5QDoWl1OhWo48LJiYmbNq0idatWzNr1iwiIyPZuXMn9erVq23Rngi0K4KHYPjw4ezcuZPw8HC6d+9OXNz9Z4w1SU6RkiKFmvoWmvMPHL+ehq6ORMcqmoVWH4/G0caY3i3vVoghCTn8fvwGozs2uSPZbZ5/OAEhycwa7M5zreojhOD7vWGMWHqSIoWa1RM78PULrfnGL4S+CwMJCElmUldnFr7cjjUTO/DNC63p3syW8KRc5u4J46WlJ2nzpT8v/HKcCzcz+fcdH7q42vDF9itM2xqMnkzi1zFemBrq8sb6IIoUJWGnkiQxfUBLUnKLWXMi+i75B7VpQHxWIRdj77/6a+1gQWaBgkOnztO5c2euX7+On5/fQykBgKa2JjjaGHMwLOWhrn8SkSSJmTNnsnXrVi5evEi3bt24efNmbYv1RKBVBA9J//79CQgIIDExER8fH65dq9ypWBNk5ssBsDLWXAXH4LhsWjYwq9IexAlZhZyLyWRUhyYVmpGWB17HWF+X6bft9XsjLZ+VR6MY6e3AhNLQ1JVHb/Db4eu87N2YgA+7U9/ckOcXH+Pf4ESmPuvCV0Nbsz80mQ83XWTC6rOsPn4DKxN9vhramuO+PVk+rj2TfJwRQvDNrlBm/XuVX1/x4p2ermw5F8eywCjszA1ZMLIt0ekFbDh96wHi7WRNp6bWbDkXe5f9uWepcjvzH/v/f3FvYEZR7BWG9u9NcXExR44c4bnnnqv0/t0LSZLwcbUlKDqzSlnOTxPDhw8v/+517dqVsLCw2hbpsUerCKqBj48Phw4dIi8vj27dunHlypVHLkNesRIAM0PNWfnCknJpYV+10sf7Q5MB6NfqbtNUSk4Ruy4nMsLbAQvjW0lB8/zD0NfV4ZPnSpTDnsuJzNkTysA29Zk7rA3+V5N48dfjFMhVfPtCa4KiM/l4yyVUArq62tDP3R4rY33Wn4rh1d/P0HvBETaeuUkDc0MWvtyOTwe2ZFdwImNXnWZCF0cGezTgh71hHItMw8fVFh9XWxYdiCSrQF4u02CPhkSl5hOWdKdj2NpEn8bWRgTHZd/3Plw9sY/kTV9gYmHDyZMn8fLyqtL9ux9eTazILVYSmXJ/Z/XTSLdu3Thy5AhyuRwfHx9qo2rxk4RWEVQTLy8vAgMD0dHRoUePHpw5c+aRjl+mCKoye68KmflyUnOLaXmPrOD/sj80hab1THCpwEm88UwsSrVgQhen8veuJmSz+3ISU7o1pZ6ZAddS8vhg00XaNbZkwch2rDgaxUebL9HWwZLBHg2Yvu0y11LzeMbZmtiMAs7cyCAgJJmgmEwaWBgy0tuBF9o1Ijq9gC//DaHX/CPsvZLE/BFtuZqQw4hlp/h8sBvN7Mx4d+N5cgqVfDbIjdwiBb8dvl4uV//W9dGRYM+VpLs+h0cjS4Lj720aWrRoERPGjMa0UTNenLUKZ+eqJeBVRnvHkqrtZ6PvDkvVAu3atePYsWOYmprSs2dPDh06VNsiPbZoFYEGcHd359ixY1haWtK7d+9HWjSrLOxRT1czf8r40pj6qhSYE0JwKTaLZ+7hSzgYnoJXEyscbUzK39t9ObE0W9kJgIX7I5DpSCwb157MAjkL90fQx82ebs1s+f14NH3d7LEy1uP0jQzszQxQqASGejq0a2yJoa6MzUFx/HMhnvaOViwb58VnA924HJ/NhtMxrHi1PTHp+SwMiGT+yLZkFij4+3wcbg3M6etuz7YL8eVmF1tTA1rUNyc47u4HftN6JsRnFt5lolGr1XzyySe8//77DB06lOc++YUUueZMdI42xtQzM+BCjFYR3ItmzZpx/PhxHB0d6d+/P9u3b69tkR5LtIpAQzg7O3P06FGaNGnCgAED2LVr1yMZt8ykrakMgpTckrA8O/PKawslZheRXajAveHdZqScIgWX47Lo6nKnkjgQmoK3oxWWxvqEJuawKziRiV2dsDMzZJ5/OGo1eDhY8GNABB4OFhyJSCEhqwgdCTJKTTk6kkRIQg7hybnYmRnQzM4Uv+AE3lh/Hr/gBCZ2deZibBYrjt7gtW7ObAqKJTWvmHaNLdlwOgYhBEPaNiQ1t5jTN9LLZWtZ34zwpLvNMHbmhqhFSc5DGUqlkkmTJvHjjz8ydepUtm7dStP6VtzM0Fy4pyRJtGtsWamj+mmnUaNGBAYG4unpyfDhw1m9enVti/TYoVUEGqRhw4YcOXKEVq1a8cILL7Bp06baFumBSc0tedjZVVJsDSh/aLpVYEY6F52JWkCn2xRBSk4RYUm59HYrccAuD4zCzECXKd2aciU+m23n4xnStgEL90fQ1NaE4LhsDPVkFCpU6OrooFIJdCQw1JMhL10JyXQkQhJzKFKo6etuT26RkuWBUUz2ceZoZBpFCjXN7U35/J8rjO7YmOup+VyMzaJ3S3uM9GT432YKam5vRmJ2EblFd+54Zl96L5JzSu5NcXExI0eOZO3atcyePZslS5Ygk8loYm1Mam4xBXJlle93ZbRrbElUWn551rSWirG2tmb//v307t2bSZMmMX/+/NoW6bGixhWBJEn9JUkKlyTpmiRJ02t6vNrG1taWgwcP0qVLF0aPHs3KlStrdDyDUpNQsVJdScuqUaQo6acs3v9+lCmNikJXy2bGze1vKYmotJJsbPcGFgghOHE9jV5udlga67PjYjx6Mgl9XR30dHSIzSyggYUhOUVK9GQSSKAG1OJWpJSjjTFJ2UU0tDSks4sN+0KSmezjjKONMUciUhnm2Yit5+J4tbMT8VmF5RVUw5JyMdKX0by+GddTb2WI25iUmHX++9A1KfW/FMiV5OfnM2TIEP755x9+/vlnZs6cWZ5f0NDSCICkbM0lOzWzK/G93EirO5nsdRVTU1P+/fdfRowYwccff8ynn36qzUKuIjWqCCRJkgG/AAMAd2C0JEnuNTlmXcDc3Jw9e/bQv39/pkyZwoIFCyq/6CEpe2DfXo65OpT7HGSVG5vKHpgWRneXCU7ILkRfpoP1bWGt8Zkl/odGVkbEZxWSnFNMe0crhBDsvpyEj6stB0JTsCyNMErMLkKiJPlMfpuiK/tq30wvQADpeQpOXU+nibUxX+y4Qv/W9YlIzqO+pSEFchUZpYrjSnw2+ro6RJc+VBtbGd2RuVuWTFb4n3tZpmzTMzLo27cvBw4cYPXq1bz33nt3tCtTiJpUBM62Jf6VaK0iqBIGBgZs3LiR119/nblz5/Lmm2+iUmnmu/EkU9Mrgo7ANSFElBBCDvwFDK3hMesExsbGbN++nREjRvC///2PL7/8skZmJyYGJQ+vvGLNmA4UqhIZZVUoLVFmQqkoYik1t5h6ZgZ35BYk5ZQ8IBtYGHI1IQcADwdLrqfmE59ViIeDJSm5xaTkFpfLIeCObOKyh7UyIYysk5spjg9FoVIh05G4mVGAvbkhW4Ni8Wpiyd4rSXg4WHAwLAW3BuYExWTiYGVEbOnDv765YblMcO/Vla5MB1V+Ju+OfYGgoCC2bNlSYaJYA4uSFUGiBhVBmdNek76HJx2ZTMbSpUuZMWMGy5cvZ/To0XWuUGRdo6ZLTDQCbi/iEgc8U8Nj1hn09fXZuHEjZmZmzJ49m6ysLBYsWKDRPQ1sSs0d6XnySlpWDePbZsWVmYfK6vwr1eKuFYRMku6r+NSlT3dDPZ3yaBx93cqVzyf9WvDt79uJ++szVEoFkkyX7h8sIk6vpJBdIysjwpNycbQxITUmAxsTfdLy5Bjqld5zUeJsBihQqO5QYrlFFedkREVHk7TBF92iLPz8/OjXr1+FslmWrow0ac831JNhZqhbvqrRUjUkSWLOnDnY2Njw8ccfk5WVxT///IOJiUnlFz+F1LQiqOibfcfTQZKk14HXAZo0qbhWzeOMTCZjxYoVmJub89NPP5GZmcnKlSs1tuuSuaEu+ro65fb66lL2YMwrVpYrmXtRNjsvkKuwMLpTuRno6VD035l16epAqRbolioRhVJgYlDyWlUFN4daCHKjg1EpFSDUCJWStIgLyNo0RqkWqNUCA10ZxUpV6U81Bro65MtV2JiUOJ6NSje3yciT35GRnVkalWRpdOu98PBw3hw5CFVBNqs3/XNPJQC3FEhOkWYdu9Ym+lpF8JD873//w9ramk2bNj1Q0b+njZo2DcUBt2/b5ADcUWRdCLFcCOEthPB+UgtI6ejosGDBAr7++mvWrVvH8OHDKSyseBP1B0WSJOzNDTRmjjAvndVmFVT+MCt/8FUwAzbR1yWvSHlH7H254ihWlpu00vOLMS3tJ6uw8ofdN7tCkTVqhSTTBUkHSaZLpoUrZXOOyJQ8rE30uBKfg4WRLjfS8jE30iU5pwgzQ12yChSYGZZ8xjJTUhkpucXoy3TKP1dZPRu5XE790XPp7uNzX9l0ZToY6umQX6y5qCEo8cFoWrk8TUycOJE9e/bcsSWoljupaUVwFmgmSZKzJEn6wChgZw2PWSeRJInPP/+cX375BT8/PwYMGEB29v3LFlQVJxsTotM140xsVBr5EpdZuaJyKk0Uu56ad9c5FztT5Co1MbfJVeb4vJaaR7vGlujLdDh+LQ07M0PaO1pxODwVJxtjbEz1KXMt6EjwX3eFQSM37Ed9i2W3sdiP+hYjB3eEEFga6SFREnJ5M6MADwdLErOL8HCwJCNfjlsDMwoVKto2tiA5p4iQxBy6uN4Kbw1NzKF5fVN0dCSOHz/Os88+i6GhIa99vw6Thi40tKy8sJ+ksYyOW8h0JG29oWpSV/cbryvUqCIQQiiBdwB/IBTYLIS4WpNj1nWmTp3Khg0bOH78OD179iQlpfrVJZvamnAjNV8jzujG1iWKILYKdfBdS0Mbbw/BLMOtfkmS2e0JWmWhpNdS8jDW18XbyYrAiDQAXu7QmKjUfDo1tSE9T46Jvi52ZgaoRcmOYrL//KcaNnKjeb9xGDRyo0V9U2xMDVALwacD3dh2Pp6h7RrifyUJzyaWRCTnYm1yy9zT0dmaQ6VVPXuXlr8WQnA1IQf3BuYEBATQr18/7O3tOXbsGLkG9WhibVxuzrofaiHKfRCaQlerCLTUMDWeRyCE2C2EaC6EcBFCfFvT4z0OjB49mp07dxIWFoaPjw8xMTHV6s/VzpTcYqVGzENmhnrYmhoQmXz3LP+/2JgaUM/MgEsVZL42szdFX1eHU1G3MnftzAywNdUvf6+vuz3hybmcuJbGoDYNsDTWIyQxB32ZhKmhLim5xZga6FKkUKNX6mDXl+lgZqCLADILFNiY6HM1IZesAgWNrIyYvu0yFkZ6pOfJScguondLO/ZcSWKYVyO2nY/HpZ4JdmaGrD0Zg5ONMc3tS5TZtZQ8MvLlyK+dYsiQITRr1ozAwECaNGlCaGIuzewqr72kVotSx7lmv1baUHgtNY02s7iWGDBgAPv27SM1NZWuXbsSEhLy0H21blSytWNFdXIehrYOFlyMrVp9m26uthyNTL1rxmqoJ6NXCzt2XU4qPydJEoM9GrI/NIXsQgWjOzahsbURX/57FX1dHT4d6EZwXDbjuziRVaCggYUhRnoydKSSCAMJkJUmlwHIVWr0dXUw1i9xAhcp1Ex7rjluDcw5di2Nj/o0Z9mRKNwbmONSz5SwpFze79Ocfy7EE5qYw4d9m5ebDHZfTiL34m4WfzYVb29vDh8+jL29PUnZRdzMKMDbyarSe5FdqEClFnesPjRBXrGySgl+WrQ8LFpFUIt07dqVI0eOoFQq6d69+0NXLnVrYI6ujsSlSkolV5V2jUti+6sSBtmjRT0yCxRcqkAJDWnbkLS84jtWBS96NkKuVOMXnIChnozPB7kTkZzHmuPRvOTlQAcnK7aci+P74W3ILVJioKfDsy3sKFaqMTfSQ08mYaCrQ2OrEhNWWl4xz7aoxzdDWzG0bUM2BcVx7Foanw5049/gBHRlEgtHtmXJwWu0aWRBPzd75geE09bBgiEeDYGS4nE/f/8VGf6/MnDgQAICArC0tATgTHTJPgT3Kqx3O+mlkT02pppVBAVyFaYGssobatHykGgVQS3j4eHB8ePHMTc3p1evXhw4cOCB+zDUk9GqoTlB0fffPKWqdCjdSezk9bRK2/ZoXg99XR22nb97l7ZeLe2wMta7o9yzh4MFrRuZ8/P+SLILFfRzt6ePmx1z94QSEJLMty+2QaUWTPs7mJHeDhTKlRwMS6F9Eyt0dSCnUElaac7EYI8G9HGz53RUBp/vuMrPByOxNzdk1mB31p6IJiajgPkj2vLZ9isk5RTx+SA3Vp+IJjG7iI+fa4GOjoRCoWDAsNHc2P8HPYeOvivWfF9IMlbGerg1qNw0FHdbopomySyQV5i9rUWLptAqgjqAi4sLx44dw9nZmYEDB7Jt27YH7qOrqy0XbmaV709QHbwdrbAw0iPganKlbS2N9RnatiF/n4u/awVhpC/jnV7NOHYtjcCIVKA00efFNqTlFTN3dyiSJPHzKE/aNrbkvY0XSMouYt+HPejc1Ibfj0fjaGPC+C6OhCXlkFlqLmpZ34y4rEL8ghM5fSODbs1smT+iLate9aaLiw0/+IejVKtZP6kjywKjOBeTyc+j2uFgbcyiA5H0dbenW7N65ObmMnDgIAJ2bMax70T2bFl/R6x5XrGSfSFJDPJoUCVHcURyiWP89vpK1SWvWElukZIGpdFcWrTUBFpFUEcoq1zavn17RowYwapVqx7oeh9XW5Rqwanr6ZU3rgRdmQ693ew4EJZSXnvofozv4kShQsWWoNi7zo3t1AQHKyPm7A4t78vDwZIp3Zvy19lYAq4mYWKgy5oJHXGxM2Xy2rP8fT6OxaM9+XFEWyJT8th0NpZRHZsw0rsxduaGGOrJePtZF+a82IbJPs5kFyqYueMKk9YG8dP+SLwcLVkzoQML9kVwNjqDhS+347lW9fn8n8sIBLOGuJOYmEiPHj04eOggNgPeZ9G8bzDQu9P8svdKEkUKNS+0a1Sl+xaelIedmQFWGvQRlNUtaqDBPam1aPkvWkVQh7C2tmbfvn307duX1157ja+//rrKIaHtnawwNdAlIOTuHbYehsEeDcguVOB/tfL+WjeyoL2jFetPxZSXjijDQLfEDxCWlMuP/uHl73/YpzmtG5nz9p/n2RWciIWxHn+93olnW9gxzz+cHvMOk5FfzL/v+NCrpR2/H7/BtvPx5Bcr0dWRWB54g0//ucw8/3BiMwt50asRv43x4vwXfZnYxZmxq85wMTaLBSPb0b91faZuOM+h8FQ+HehGXvJNOnfuTFh4BA1HzqLX8y8zuE2DO+RWqwXLA6/jamdavlNYZZyLycDDwaJKbatKWdVRB6vKNwrSouVh0SqCOoaJiQk7d+5k7NixzJw5k1deeYWCgspj+g10ZfR1t8f/anKVZvGV0aO5HQ5WRqw7WbXQ1gldnIhJL+Bg2N15Ef1b12dspyYsC4ziRKnfwVBPxobXOtHWwZJ3N55nc1AsFkZ6rHjVm21Tu+De0Jw5u8N4aelJ2jtas+e9bozq2BhLIz1UQjChqxMrXvXm3Od9WPmqNx2crAmKyeT1dUG8ti4IO3ND/n3Xh36t7HltbRD7QpKZ/XwrXNTxdO3alfyCQlwmzqNxmy78MsbrjuJ4ULJlZURyHu/1blalZKTYjAKi0wvwcbWt0v2qKrfMTXdvBapFi6bQKoI6iL6+PuvWrWPu3Lls2rSJ7t27Exd3tzP2vwxqUzKLPxZZuZO3MmQ6EmM7OXLmRgZXEyqPRurfuj4NLAxZHhhV4Srms4HuONkYM21rcHldJAsjPdZN7khXV1umbQ3mp/0RqNQCryZWrJ/8DFve7Exze1O+9gth3O9nMNKX8b9+LXizhwt6Mom1J6Lp+eNhnv3xMO//dZE/TsWgEoKP+jZn+9tdUKkFo5af4vi1NOa95IF58gX69OmDlbUNnu8sRmHlxPJXvan3n014FCo1P+2PwNXOlEH/WSnci2PXSu65TzPNKoKwpFwaWRqVl8XQoqUm0CqCOookSUyfPp0dO3YQHh5Ohw4dOH369H2v6dbcFmsTfTadvdtW/zCM7tAEc0Nd5t1m0rkXejId3u7pypnoDLadj7/rvJG+jIUvtyM9T84rK06Vb/torK/LyvHeDPNsxE/7Ixn481F+P3aDzHw5HZys+XNKJ/56vRMejSxYefQGo1ec4o3151h6JIqMfDmDPBowd1gbdr3nw5XZz/HP1K5M9nHmR/9wBi8+RlxmIb+NbU/yqR0MHz6cNh5tcXv9J64XmfLTy57lORi3s+5kDJEpeUx7rkWVynED7LgYj5ONMS71NDtzv3AzkzYVyKhFi0YRQtSZo3379kLL3Vy+fFk4OzsLAwMDsX79+vu2nbM7RDSdsUskZRdqZOxlR64JR18/cSwytdK2KpVaDPv1uGg721+k5hZV2Ob4tVTR4vPd4rmFR0RGXnH5+2q1Wuy4GC+GLD4qHH39RLNPd4upG86JI+EpQqVSCyGESM0tEmdupIug6AxRKFfecW1CVoE4GJoslhyMFJ3m7BeOvn5i+t+XRHpuoZg2bZoAxMDBQ8TA+fuEy4xdYldwQoXyZRfKRdvZ/mLsylNVvkc30/OFo6+fWLQ/osrXVIXk7ELh6OsnVgRe12i/Wp48gCBRjWevNl3xMaB169acOXOGl156iXHjxnHlyhW+/fZbZLK7k4xe6diEZUei2HD6Jh/1bV7tsV/t7MTaEzF8uyuUf9/1ue8MWUdH4rthbRi46CjTtgazfFz7u8Iuu7jYsvLVDkxae5bhv53gh5c88HayRpIknm/bkOfbNiQkIYfNQbFsvxjPruBE6pkZ0KuFHT1b2tHF1Ybk7CL2XkkiJDGHqwnZhCSUhJaW0aaRBUte8aSZlS5TJo9n8+bNvPDKBDI8xpCYrmDp2Pb0cbev8DMsCIggq0CBb/+WVb5HZdFSL3pVLbqoqpRt3qNdEWipaSRRhwqZeHt7i6CgoNoWo84il8t57733WLZsGYMHD2bDhg2Ym5vf1W7KuiBOR6VzfHovjdiWd15K4L2NF/hxRFteau9Qafv1p2L4YvsVRno78P1wjwqdraei0vnf5kskZBfyaidHPunf8q6dzoqVKvaFJLP3ShJHIlLLN44pQ1+mQ4v6ZrRqaI5bg5KjZQMzzA31OH/+PC+//DJRUVE8M/Id4pv0pWk9U74b1oZnmlacJewXnMA7f15gQhcnvny+VZXuTU6RAp/vDvJMUxtWvOpdpWuqyo/+4fx25DrBs/qV75usRUtFSJJ0Tgjx8P+A1VlOaPrQmoaqxi+//CJkMplo1aqVuH79brPBpdhM4ejrJ345FKmR8VQqtXh+8VHRac7+O0wy92O+f5hw9PUTP+wNvWebvCKFmLXjinCa7ic6z9kvDoYl37OtXKkSJ66lia1BsWJrUKwITcwWcqXqrnZqtVosWrRI6OvrC8t69sJx/DzR/LPdYtH+CFGkuLfsEUk5wu2LPeLFX46JYsXd/d6LxQcihKOvn7gcl1Xla6rKyKUnxJDFRzXer5YnD6ppGqqWs1iSpHmSJIVJkhQsSdI/kiRZ3nZuhiRJ1yRJCpck6bnqjKPlTqZOnYq/vz8JCQl07NiRw4cP33Hew8GSHs3rsSIwiqyC6u9spaMjMWOgG4nZRSw6EFmlaz7s25zRHRvzy6HrfO0XgrKCkFYTA12+fL4VW9/sjJG+jImrz9L9h0N84xfC3itJpOXd2nVNT6ZDZxcbhrd3YHh7B1rWN7+rymdGRgaDn3+B9957D5OmXpiOWkCvZ3uw78MevNu7GQa6FdfruZaSx4TVZzHWl/HrmPbo61bta5GWV8yKozfo1dKuQqdzdShSqLgYm0UHJ2uN9qtFS0VUyzQkSVI/4KAQQilJ0vcAQghfSZLcgY2UbF7fENgPNBdCqO7Xn9Y09GBcu3aN559/nsjISBYvXswbb7xRboYJSchh8OKjjO3kyFdDW2tkvGlbL7HlXBwbXnuGLi6Vh0mq1IKv/UJYcyKabs1sWTLaCwvjik1VxUoVOy4msDUojotxWchLt7l0tjWhvaMV3o5WeDtZ41LPhAK5ihtp+XccQWdOc3rVTBS5GVg9O4Fnh0/g3d7NeLZ5vfvmAZy/mcnkNWeR6UismdjxgR7oH/x1gV2XE9n9XjeaabCsBMChsBQmrjnL2kkd6dH8ydy5T4vmqK5pSGM+AkmSXgReEkKMkSRpBoAQYm7pOX/gSyHEyfv1oVUED052djavvPIKu3fvZujQoSxZsgQHhxI7/qwdV1h/Kga/d7vh3vBuX8KDUiBXMnjRMfLlSva+373KpRT+OnOTL3ZcwcHKmPkj2+LV5P6ZusVKFVficwiKziAoJpOg6IxyZ7CxvowC+a35hBBqCN5JbMBqLOrV5+2vljCsf49KxwA4EJrM23+ex97ckHWTOuJoU/WNzY9GpjJu1Rne6+XKR/1aVPm6qvLF9iv8fT6O81/0xVBPW3lUy/2pMz4C4F9gbOnrJWWvS39fRYmS0PoIagClUinmzZsnjIyMhJmZmViyZIlQqVQiK18uPL8KECN+OyHUarVGxroclyVcP90lhv96XOQXK6p83Zkb6eVhnW+uDxIRSTlVvlatVotrKbli05mbYtaOK2LJwUixKzhBHA2+Jvr07ScAMWLECJGVVTU7vVypEj/skW75GgAAEtpJREFUDRVO0/3E4EVH7xnqei9yixSiy9wDoue8Q1X2mTwISpVaPPPtfjFl7VmN963lyYRq+giq8oDfD1yp4Bh6W5vPgH+4tcL4pQJFMPwe/b8OBAFBTZo0qfEb9iRz/fp10bdvXwGIzp07iytXroh1J6OFo6+fCIxI0dg4fpcShPN0PzF25an7OmD/S06hXCwICBetZu4VTtP9xAd/XRDRaXkPJcPBgwdF/fr1hYGBgVi6dGmVFJ1arRYHw5LF86W5CtO2XHogZVbGjG3Bwnm6nwiKzngY0SvlaESqcPT1E/9eiq+R/rU8eVRXEVTqFRNC9BFCtK7g2FG6JBkPDAbGlAoEEAc0vq0bByDhHv0vF0J4CyG869XT2kKrQ9OmTfH392fdunVERETg6elJqN8K6hvrsHBfBLf+PNVjkEcDvh/uwdHINN7980K5Pb8yzAz1+LBvcwKn9eT1bk3ZcyWR3vOPMGPbZaLT7t73uCJUKhWzZs2id+/eWFpacubMmTt8IxVRKFfx97k4Bvx8lImrz5KcU8zi0Z58/5LHA+/8FRiRyp+nbzKlW9MqF6N7ULZdiMPMUJc+bhXnOmjRommq6yzuDywAegghUm97vxXwJ7ecxQeAZkLrLH5kpKWl8dFHH7F+/XoaOLqg7jqFPz6bcM9Eqodh7YloZu28SuemNiwd2/6ejuB7kZJTxJJD19h45iYKlaBH83r0drOjg5P1/9u787Cqqr2B498fYwIqwhHBERDNcChnBLXuW+ZYZJm3HDNT6+qtbk+vr/el4TY/1Xu7aail5ZjavWVezaEytRScUQEFuTKYAioCCiqTcNb7x9n4kAEi0znA+jzPfs4+65x9zvq53fzOXnvttejc2u03vXfMZjMbNmzgtddeIy4ujqlTpxIeHo6bW/lDOiilOHLmEl8fTmVzzDmuFhbTxcuNWfd25uG721a5Z1BZKZnXeHRRJJ5uzmz+8+A6abu/nFdE0Hs7GNu7He892qvWP19rnKx6sVhEEgFnoHQQ/P1KqWeN18KAp4Fi4EWl1LZbfZ5OBLVv+/btzJo1i5SUFNoOHE30llWYPGuvS+K3R1L5n/UxdPBwYdnU/viaqn7BtdSF3ALWHTzDpuh0ki9azgwc7YXOrd24y7s511MO8+PqBZyKP063bt14++23eeyxxyguMXOtsIRrRcXkFRVb1guLOXr2MuujUknOvIaLkz2jevowrm97Bvh6/G6U0arKulrIo4v3cqWgmG+fC65WnFWxcFciH/6QwPcvDqGbd80v8GtNg830GqoNOhHUjby8PKbMfpn1Kz+jZStPln66kHHjxlVpeOWqOJCcxawvoygxKz4cdzcjenhX63NKzIqUzKvEnbtCXHoOO37azr5/LeZa6kkc3L1pGTKBjv2HocSOa0UllTZJDfDz4PG+7RnV06fGd+VeKyxm8hcHOJGey7qZQVXqkVQdRcVmhnywk65tmrN6+sA6+Q6tcappItD3rTcBLi4ufL1sIfd79+fAqvcYP348w4cPZ/bs2YwYMQJHx5oNQzHQ35Pv5gzmuTVRPPtlFA/d3ZY3Hu6Ox23O1GVvJwR4NSct/ghb3n2FiIgIOnbsyDvhi7nnv0I5lZlP0sWrONrb4eLkgKuTPS7OlkdXZwdcne1xcXKgfatmtTaRS+bVQp5ecYgT6bksnNC7zpIAwD8PneFCbiEfjLu7zr5D08qjzwiakLj0XMYs+AXf879wYttqMjIyaNOmDZMmTWLatGl071618XUqcr3EzOKfk/hk5yla3OHIm6E9GN2rauP5A+zbt49XX32VHTt24OPjQ1hYGM888wzOzs633rgOnMnKY8qyA5zPLWDhhD7cX4cXb/OKirn3w5/x83Tln7OCau1sTWsaanpGoOcjaEIC27ZgxtAAkr3/wIY9MWzcuJHg4GDmz59Pjx496N+/P4sWLSI7O7tan+9ob8fz93fhuz8Ppq17M2avPcKs1YdJu5xf6XZRUVGMHj2a4OBgYmJi+Oijj0hKSmL27NlWSwLRZy/z6OK9XM6/zppnguo0CQAsjzzNxSuFzB1xp04CWr3TZwRNTF5RMcM/3o29CNteGEozJ3suXrzI2rVrWb58OdHR0Tg5OREaGsq0adMYNmwYDg6334JYXGJmyZ5k5v90ChGYfV8AM4b64yCKmJgYIiMjbyypqam0atWKuXPnMmfOnAp7AtWHErNixd7TvP/9SbyaO7NiWn8CvGp3+Iibnc3O48F/7GZwF1Otj2CqNQ36YrF22/YlZfHk0v1MC/Hl9Yd+2xx07NgxVqxYwZo1a8jMzMTHx4fJkyczbNgwWrdujclkwtPTkzvuuKNK3xX/63n+e9F6IiIiISOBgrSTFOZb5mBu3749ISEhDBkyhEmTJtGypXXH3Y8/l8u89TFEp+Zwfzcv/u/xu6s8jEZ1KaV4esUhDqRks/2le2nn3qxOv09rnHQi0Krl9Y3HWbnvV9bNCGJQ59+Pz19UVMSWLVtYvnw5W7dupaTkt7eAuLm5YTKZbiSG0nWTyYS7uzvx8fFERkYSGxuL2WzGzs4Ot7adUV5d8fDvxVNjh/OXscG0bGb9uXgzrxayaFcSq/adpmUzR15/uDsP9fKplyaa0rkeXh0TyPTBfnX+fVrjpBOBVi15RcWMXhBBflEJ214YUukv34yMDOLj48nMzCQzM5OsrKwb62WXrKwscnMts2q5ubkRFBRESEgIISEhDBw4kObNm3Po9CU+/SWJnSczaO7swORBnZg+2A9Pt/q/FpCTd50le5JYHnmaguslPN63A/NGdqvzs4BSaZfzGfnxbvxbu/HNs4N+N5ubplWVTgRatR1Py2Hsokjuu9OLJZP71sov4KKiIrKzszGZTJVeWziRnsOiXUlsPX4OZwc7JgzoxMyh/ni3rFqTU01cKyxmeWQKS3Ynk1tQzJhePvxlWNdan3i+MkopJn5+gOizl9n6wpDbGvlU026mE4FWI19EpPDW5jjefqQHk4I61fv3J2ZcZfHPSfz7WBr2Igz092BYYBuC/D3p4uVWK8lJKUVy5jX2JmYSkZjJ3qQsrhQUc383L156sCvd29b/tYnS6TzfGduDiQPr/99da1x0ItBqRCnFlGUHOXz6ElueH4x/Pf4qLutsdh5fHTrDt0fSOJdTAICnqxMD/DwI8ve8kRiqOkRERm4BkUmZRJzKYm9S5o3PbOfejMEBJsb371Bng8bdysnzuYSGRzLQ35MVT/Wv9rAXmlZKJwKtxi7kFjD849108nDhm+eCfzcFZH1SSnE2O5/9KVnsT85if1IW6cYf8VYujvTt5IGrsz0lZoVSYFbKWCzbmpUlqZzKuAqAu4sjIZ1NBAd4MjjAREcPF6v2088rKubh8Ehy8q+z7YUhmKxwbURrfPQQE1qNtWlxB++O7cmf1hzh/W0neWVMoNXqIiJ09HSho6cL4/t1QClF6qV8S1JIzubY2UsUmxX2IoiAnYhlsRPsjOdt3Zsxrm97QgJMBPq0sJlf3GazYu43MSRdvMqX0wfqJKDZDJ0INABG9fRh6qBOfB6RQlfv5ozv1+HWG9UDEaGDhwsdPFx43EbqVF3vf3+SzTHnmDeyGyEBt57zWdPqi+6vpt3w6phAhnQxEbYhloMp1RtmQivfqn2n+Wx3MpODOjFrqL+1q6Npv1EriUBEXhYRJSIm47mIyAIRSRSRGBHpUxvfo9UtB3s7wp/sQ4dWLsxYdZjjaTnWrlKj8NXBM7y+6QQP3OXF6w8F6rGENJtT40QgIh2AYcCZMsUjgS7GMhNYXNPv0epHSxdHVj49ADdnByZ9cYC49FxrV6lBW7XvNPO+jWVol9aET+ijbxrTbFJt/K/8BzAXKNv9KBRYZcyrvB9wF5Gqj0esWVUHDxfWzQiimaM9Ez/fz8nzOhlUx5LdSby28QTDAtuwZErfOpnaUtNqQ40SgYg8DKQppaJveqkdcLbM81SjTGsgOnpakoGzgz1//Gw/Ub/qawZVZTYr3tkSx7tbTzK6lw+LJvbB2UEnAc123TIRiMhPInK8nCUUCANeK2+zcsrKvWFBRGaKyGEROXzx4sXbq71Wp3xNrnz97CA8XJ2YsPQA3x8/b+0q2bz8ohL+tOYIS/ek8FSwLwue6G3V+zI0rSqqfUOZiPQEdgB5RlF7IB0YALwB/KyUWme8NwG4Tyl1rrLP1DeU2aasq4VMX3mY6NTLhI26i+mD/fQFz3Jk5BYwc3UU0amXeWW0Hk1Uqz9Wm6FMKRWrlPJSSvkqpXyxNP/0UUqdBzYBU4zeQ0FAzq2SgGa7PN2cWTcjiOGB3ry9JZ45a4+SW3Dd2tWyKbtOZjBi/h4Szl9h8cS+OgloDUpd3VC2FRgFJGI5Y5hWR9+j1ZNmTvYsmtiHpXuS+eCHBI6n57BwQh96tLPuZDLWVlhcwgffJ/BFRArdvJsTPqF3nc9opmm1TY81pN22w6ezmbP2KNnXipg3shtPBfvazDAO9enUhSu89K9oYtNymDKoE/876i7dM0izCj15vVbv+vl6sOX5wYQEePLm5jj+uGQfSRevWrta9abgegnhO08xekEEqZfy+GxyX94M7aGTgNZg6TMCrdqUUqw/ksab352goNjMs/d2ZuZQf9ycG+cQVmazYlN0Oh/+kEDa5XxG9fTmzdAeevA4zer0MNSa1WVcKeCN7+LYEnMOT1cnZt3rz8SBnXBtJAlBKcWO+Azm7zhFbFoOPdq1IGxUYLlzPWuaNehEoNmMo2cu8fcf/0NEYibuLo5MC/bjqWBfWrpYf4L66jCbFT/GneeTnYmcSM+lo4cLLz7QhUfuadckr4lotksnAs3mHD1ziYW7EvkpPgO3MhPUN5QmlOISM9uOnyd8ZyIJF67gZ3Jlzh8CCL2nrR4rSLNJemIazeb07tiKz6f2J/5cLgt3JfLpL0mkXsrnkyd7W7tqVfJ1VCp//TaWAC835j9xD2N6tcVenwFojZhNnRGIyBUgwdr1qEMmINPalahDOr6GqzHHBo0/vjuVUtW+gcXWzggSanJ6Y+tE5LCOr+FqzPE15tigacRXk+11g6emaVoTpxOBpmlaE2driWCJtStQx3R8DVtjjq8xxwY6vkrZ1MViTdM0rf7Z2hmBpmmaVs+slghE5HEROSEiZhHpV6bcV0TyReSYsXxa5rW+IhIrIokiskBsdHaUimIzXvurUf8EERlepnyEUZYoIvPqv9bVIyJ/E5G0MvtrVJnXyo21oWmo+6YyInLaOJaOlfY4EREPEdkuIqeMx1bWrmdVicgyEckQkeNlysqNx5gnZYGxP2NEpI/1al41FcRXe8eeUsoqC3AXcCfwM9CvTLkvcLyCbQ4Cg7BMhbkNGGmt+lcztkAgGnAG/IAkwN5YkgB/wMl4T6C146hirH8DXi6nvNxYrV3fasTXYPfNLeI6DZhuKvsAmGeszwPet3Y9byOeoUCfsn87KooHy1wp24y/I0HAAWvXv5rx1dqxZ7UzAqVUvFKqyjePiYgP0EIptU9Zol0FPFJnFayBSmILBb5SShUqpVKwTNwzwFgSlVLJSqki4CvjvQ1ZRbE2NI1x31QkFFhprK/ERo+v8iildgPZNxVXFE8osEpZ7Afcjb8vNquC+Cpy28eerV4j8BORoyLyi4gMMcraYZkOs1SqUdaQtAPOlnleGkNF5Q3FHOMUe1mZ5oSGHlOpxhLHzRTwo4hEichMo6yNMqaUNR69rFa72lFRPI1pn9bKsVendxaLyE+AdzkvhSmlNlaw2Tmgo1IqS0T6Av8Wke5YTuNuZrUuT9WMraIYykvINtOdq7JYgcXAW1jq+xbwd+BpbGx/1UBjieNmIUqpdBHxAraLyElrV6geNZZ9WmvHXp0mAqXUA9XYphAoNNajRCQJ6Iolq7Uv89b2QHpt1LM6qhMblhg6lHleNoaKyq2uqrGKyFJgs/G0slgbksYSx28opdKNxwwR2YCl6eCCiPgopc4ZTSUZVq1kzVUUT6PYp0qpC6XrNT32bK5pSERai4i9se4PdAGSjVO7KyISZPQWmgJU9MvbVm0CnhARZxHxwxLbQeAQ0EVE/ETECXjCeK/Nu6ltdSxQ2quholgbmga7byoiIq4i0rx0HXgQy37bBEw13jaVhnd83ayieDYBU4zeQ0FATmkTUkNSq8eeFa+Cj8WSuQqBC8APRvljwAksV72PAA+V2aafEWwSEI5xQ5ytLRXFZrwWZtQ/gTK9nrD0ZPiP8VqYtWO4jVhXA7FAjPEf0OdWsTa0paHum0ri8TeOr2jjWAszyj2BHcAp49HD2nW9jZjWYWlWvm4ce9MrigdL08lCY3/GUqZnn60uFcRXa8eevrNY0zStibO5piFN0zStfulEoGma1sTpRKBpmtbE6USgaZrWxOlEoGma1sTpRKBpmtbE6USgaZrWxOlEoGma1sT9P7eKRitrvwgZAAAAAElFTkSuQmCC\n", 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", "text/plain": [ "
" ] diff --git a/notebooks/pathline_trace.ipynb b/notebooks/pathline_trace.ipynb index bdbdd05..560382a 100644 --- a/notebooks/pathline_trace.ipynb +++ b/notebooks/pathline_trace.ipynb @@ -54,15 +54,15 @@ "outputs": [], "source": [ "# parameters\n", - "Q = 100 # discharge of well, m^3/d\n", - "k = 10 # hydraulic conductivity, m/d\n", - "H = 10 # thickness of aquifer, m\n", - "Ss = 1e-4 # specific storage, m^(-1)\n", - "npor = 0.3 # porosity, -\n", - "xw = 0 # x-location of well\n", - "yw = 0 # y-location of well\n", - "rw = 0.3 # radius of well, m\n", - "tmin = 0.001 # first time of simulation after change in bc, d" + "Q = 100 # discharge of well, m^3/d\n", + "k = 10 # hydraulic conductivity, m/d\n", + "H = 10 # thickness of aquifer, m\n", + "Ss = 1e-4 # specific storage, m^(-1)\n", + "npor = 0.3 # porosity, -\n", + "xw = 0 # x-location of well\n", + "yw = 0 # y-location of well\n", + "rw = 0.3 # radius of well, m\n", + "tmin = 0.001 # first time of simulation after change in bc, d" ] }, { @@ -80,8 +80,7 @@ } ], "source": [ - "ml = tt.ModelMaq(kaq=[k], z=[H, 0], Saq=[Ss], poraq=npor,\n", - " tmin=tmin, tmax=1000, M=10)\n", + "ml = tt.ModelMaq(kaq=[k], z=[H, 0], Saq=[Ss], poraq=npor, tmin=tmin, tmax=1000, M=10)\n", "w = tt.Well(ml, xw=0, yw=0, tsandQ=[(0, Q)], rw=rw)\n", "ml.solve()" ] @@ -108,8 +107,16 @@ } ], "source": [ - "trace = tt.timtrace(ml, xstart=10, ystart=10, zstart=0.5 * H, tstartend=[0, 10], \n", - " tstartoffset=tmin, tstep=1, hstepmax=2)" + "trace = tt.timtrace(\n", + " ml,\n", + " xstart=10,\n", + " ystart=10,\n", + " zstart=0.5 * H,\n", + " tstartend=[0, 10],\n", + " tstartoffset=tmin,\n", + " tstep=1,\n", + " hstepmax=2,\n", + ")" ] }, { @@ -147,10 +154,10 @@ } ], "source": [ - "print('xyzt array of pathline:')\n", - "print(trace['xyzt'])\n", - "print('trace message:', trace['message'])\n", - "print('trace status:', trace['status'])" + "print(\"xyzt array of pathline:\")\n", + "print(trace[\"xyzt\"])\n", + "print(\"trace message:\", trace[\"message\"])\n", + "print(\"trace status:\", trace[\"status\"])" ] }, { @@ -174,8 +181,16 @@ } ], "source": [ - "trace = tt.timtrace(ml, xstart=10, ystart=10, zstart=0.5 * H, tstartend=[0, 20], \n", - " tstartoffset=tmin, tstep=1, hstepmax=2)" + "trace = tt.timtrace(\n", + " ml,\n", + " xstart=10,\n", + " ystart=10,\n", + " zstart=0.5 * H,\n", + " tstartend=[0, 20],\n", + " tstartoffset=tmin,\n", + " tstep=1,\n", + " hstepmax=2,\n", + ")" ] }, { @@ -194,8 +209,8 @@ } ], "source": [ - "print('last two entries in xyzt:')\n", - "print(trace['xyzt'][-2:])" + "print(\"last two entries in xyzt:\")\n", + "print(trace[\"xyzt\"][-2:])" ] }, { @@ -219,8 +234,16 @@ } ], "source": [ - "trace = tt.timtrace(ml, xstart=10, ystart=10, zstart=0.5 * H, tstartend=[0, 1], \n", - " tstartoffset=tmin, tstep=1, hstepmax=0.1)" + "trace = tt.timtrace(\n", + " ml,\n", + " xstart=10,\n", + " ystart=10,\n", + " zstart=0.5 * H,\n", + " tstartend=[0, 1],\n", + " tstartoffset=tmin,\n", + " tstep=1,\n", + " hstepmax=0.1,\n", + ")" ] }, { @@ -244,12 +267,12 @@ } ], "source": [ - "xyzt = trace['xyzt']\n", - "print('xyzt:')\n", - "print(trace['xyzt'])\n", + "xyzt = trace[\"xyzt\"]\n", + "print(\"xyzt:\")\n", + "print(trace[\"xyzt\"])\n", "x0, y0, z0, t0 = xyzt[1]\n", "x1, y1, z1, t1 = xyzt[2]\n", - "print(f'length of first step: {np.sqrt((x1 - x0) ** 2 + (y1 - y0) ** 2):.2f}')" + "print(f\"length of first step: {np.sqrt((x1 - x0) ** 2 + (y1 - y0) ** 2):.2f}\")" ] }, { @@ -282,8 +305,7 @@ } ], "source": [ - "ml = tt.ModelMaq(kaq=[k], z=[H, 0], Saq=[Ss], poraq=npor, \n", - " tmin=tmin, tmax=1000, M=10)\n", + "ml = tt.ModelMaq(kaq=[k], z=[H, 0], Saq=[Ss], poraq=npor, tmin=tmin, tmax=1000, M=10)\n", "w = tt.Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (10, -Q)], rw=rw)\n", "ml.solve()" ] @@ -310,8 +332,16 @@ } ], "source": [ - "trace = tt.timtrace(ml, xstart=10, ystart=10, zstart=0.5 * H, tstartend=[0, 10, 20], \n", - " tstartoffset=tmin, tstep=1, hstepmax=2)" + "trace = tt.timtrace(\n", + " ml,\n", + " xstart=10,\n", + " ystart=10,\n", + " zstart=0.5 * H,\n", + " tstartend=[0, 10, 20],\n", + " tstartoffset=tmin,\n", + " tstep=1,\n", + " hstepmax=2,\n", + ")" ] }, { @@ -359,9 +389,9 @@ } ], "source": [ - "xyzt = trace['xyzt']\n", - "print('xyzt:')\n", - "print(trace['xyzt'])" + "xyzt = trace[\"xyzt\"]\n", + "print(\"xyzt:\")\n", + "print(trace[\"xyzt\"])" ] }, { @@ -379,17 +409,17 @@ "outputs": [], "source": [ "# parameters\n", - "k = 20 # hydraulic conductivity aquifer, m/d\n", - "H = 10 # thickness of aquifers, m\n", - "Hstar = 2 # thickness of leaky layer, m\n", - "c = 100 # resistance of leaky layer, d\n", - "Ss = 1e-4 # specific storage of both aquifers, m^(-1)\n", - "npor = 0.3 # porosity of both aquifers, -\n", - "Q = 1000 # discharge of well in aquifer 1, m^3/d\n", - "xw = 0 # x-location of well\n", - "yw = 0 # y-location of well\n", - "rw = 0.3 # radius of well, m\n", - "tmin = 0.001 # first time of simulation after change in bc, d" + "k = 20 # hydraulic conductivity aquifer, m/d\n", + "H = 10 # thickness of aquifers, m\n", + "Hstar = 2 # thickness of leaky layer, m\n", + "c = 100 # resistance of leaky layer, d\n", + "Ss = 1e-4 # specific storage of both aquifers, m^(-1)\n", + "npor = 0.3 # porosity of both aquifers, -\n", + "Q = 1000 # discharge of well in aquifer 1, m^3/d\n", + "xw = 0 # x-location of well\n", + "yw = 0 # y-location of well\n", + "rw = 0.3 # radius of well, m\n", + "tmin = 0.001 # first time of simulation after change in bc, d" ] }, { @@ -407,8 +437,17 @@ } ], "source": [ - "ml = tt.ModelMaq(kaq=[k], z=[H + Hstar, H, 0], c=[c], Saq=Ss, poraq=npor,\n", - " topboundary='semi', tmin=tmin, tmax=1000, M=10)\n", + "ml = tt.ModelMaq(\n", + " kaq=[k],\n", + " z=[H + Hstar, H, 0],\n", + " c=[c],\n", + " Saq=Ss,\n", + " poraq=npor,\n", + " topboundary=\"semi\",\n", + " tmin=tmin,\n", + " tmax=1000,\n", + " M=10,\n", + ")\n", "w = tt.Well(ml, xw=0, yw=0, tsandQ=[(0, -Q)], layers=0, rw=0.3)\n", "ml.solve()" ] @@ -442,15 +481,24 @@ "plt.subplot(111, aspect=10)\n", "zstart = np.arange(1.0, 10, 1)\n", "for zs in zstart:\n", - " trace = tt.timtrace(ml, xstart=rw, ystart=0, zstart=zs, \n", - " tstartend=[0, 1000], tstartoffset=tmin, tstep=100, \n", - " nstepmax=100, hstepmax=2, silent=True)\n", - " xyzt = trace['xyzt']\n", + " trace = tt.timtrace(\n", + " ml,\n", + " xstart=rw,\n", + " ystart=0,\n", + " zstart=zs,\n", + " tstartend=[0, 1000],\n", + " tstartoffset=tmin,\n", + " tstep=100,\n", + " nstepmax=100,\n", + " hstepmax=2,\n", + " silent=True,\n", + " )\n", + " xyzt = trace[\"xyzt\"]\n", " plt.plot(xyzt[:, 0], xyzt[:, 2])\n", "for y in [0, H, H + Hstar]:\n", - " plt.axhline(y, color='k')\n", - "plt.xlabel('$x$ (m)')\n", - "plt.ylabel('$z$ (m)');" + " plt.axhline(y, color=\"k\")\n", + "plt.xlabel(\"$x$ (m)\")\n", + "plt.ylabel(\"$z$ (m)\");" ] }, { @@ -474,17 +522,17 @@ "outputs": [], "source": [ "# parameters\n", - "k0 = 20 # hydraulic conductivity aquifer 0, m/d\n", - "k1 = 40 # hydraulic conductivity aquifer 1, m/d\n", - "H = 10 # thickness of both aquifers, m\n", - "Hstar = 2 # thickness of leaky layer, m\n", - "c = 100 # resistance of leaky layer, d\n", - "Ss = 1e-4 # specific storage of both aquifers, m^(-1)\n", - "npor = 0.3 # porosity of both aquifers, -\n", - "Q = 100 # discharge of well in aquifer 1, m^3/d\n", - "xw = 0 # x-location of well\n", - "yw = 0 # y-location of well\n", - "rw = 0.3 # radius of well" + "k0 = 20 # hydraulic conductivity aquifer 0, m/d\n", + "k1 = 40 # hydraulic conductivity aquifer 1, m/d\n", + "H = 10 # thickness of both aquifers, m\n", + "Hstar = 2 # thickness of leaky layer, m\n", + "c = 100 # resistance of leaky layer, d\n", + "Ss = 1e-4 # specific storage of both aquifers, m^(-1)\n", + "npor = 0.3 # porosity of both aquifers, -\n", + "Q = 100 # discharge of well in aquifer 1, m^3/d\n", + "xw = 0 # x-location of well\n", + "yw = 0 # y-location of well\n", + "rw = 0.3 # radius of well" ] }, { @@ -502,8 +550,17 @@ } ], "source": [ - "ml = tt.ModelMaq(kaq=[k0, k1], z=[H + Hstar + H, H + Hstar, H, 0], c=[c], Saq=[Ss, Ss], \n", - " poraq=npor, porll=npor, tmin=0.01, tmax=10000, M=10)\n", + "ml = tt.ModelMaq(\n", + " kaq=[k0, k1],\n", + " z=[H + Hstar + H, H + Hstar, H, 0],\n", + " c=[c],\n", + " Saq=[Ss, Ss],\n", + " poraq=npor,\n", + " porll=npor,\n", + " tmin=0.01,\n", + " tmax=10000,\n", + " M=10,\n", + ")\n", "w = tt.Well(ml, xw=0, yw=0, tsandQ=[(0, Q)], layers=1, rw=0.3)\n", "ml.solve()" ] @@ -537,15 +594,24 @@ "zstart = np.hstack((np.arange(1, 10, 2), np.arange(13, 22, 2)))\n", "plt.subplot(111, aspect=5)\n", "for zs in zstart:\n", - " trace = tt.timtrace(ml, xstart=200, ystart=0, zstart=zs, \n", - " tstartend=[0, 10000], tstartoffset=0.01, tstep=100, \n", - " nstepmax=100, hstepmax=10, silent=True)\n", - " xyzt = trace['xyzt']\n", + " trace = tt.timtrace(\n", + " ml,\n", + " xstart=200,\n", + " ystart=0,\n", + " zstart=zs,\n", + " tstartend=[0, 10000],\n", + " tstartoffset=0.01,\n", + " tstep=100,\n", + " nstepmax=100,\n", + " hstepmax=10,\n", + " silent=True,\n", + " )\n", + " xyzt = trace[\"xyzt\"]\n", " plt.plot(xyzt[:, 0], xyzt[:, 2])\n", "for y in [0, H, H + Hstar, H + Hstar + H]:\n", - " plt.axhline(y, color='k')\n", - "plt.xlabel('$x$ (m)')\n", - "plt.ylabel('$z$ (m) - VE:5');" + " plt.axhline(y, color=\"k\")\n", + "plt.xlabel(\"$x$ (m)\")\n", + "plt.ylabel(\"$z$ (m) - VE:5\");" ] }, { @@ -568,12 +634,12 @@ "metadata": {}, "outputs": [], "source": [ - "k = 10 # hydraulic conductivity of aquifer, m/d\n", - "H = 5 # thickness of each layer, m\n", - "Q = 100 # injection rate of well, m^3/d\n", - "Ss = 1e-4 # specific storage of both aquifers, m^(-1)\n", - "npor = 0.3 # porosity of both aquifers, -\n", - "tmin = 0.01 # minimum time, d" + "k = 10 # hydraulic conductivity of aquifer, m/d\n", + "H = 5 # thickness of each layer, m\n", + "Q = 100 # injection rate of well, m^3/d\n", + "Ss = 1e-4 # specific storage of both aquifers, m^(-1)\n", + "npor = 0.3 # porosity of both aquifers, -\n", + "tmin = 0.01 # minimum time, d" ] }, { @@ -591,8 +657,9 @@ } ], "source": [ - "ml = tt.Model3D(kaq=k, z=np.arange(4, -1, -1) * H, Saq=Ss, poraq=npor,\n", - " tmin=tmin, tmax=1000)\n", + "ml = tt.Model3D(\n", + " kaq=k, z=np.arange(4, -1, -1) * H, Saq=Ss, poraq=npor, tmin=tmin, tmax=1000\n", + ")\n", "w = tt.Well(ml, xw=0, yw=0, tsandQ=[(0, -Q)], layers=1, rw=0.1)\n", "ml.solve()" ] @@ -625,16 +692,25 @@ "source": [ "zstart = np.linspace(10.01, 14.99, 11)\n", "for zs in zstart:\n", - " trace = tt.timtrace(ml, xstart=1, ystart=0, zstart=zs, \n", - " tstartend=[0, 100], tstartoffset=tmin, tstep=5, \n", - " nstepmax=200, hstepmax=2, silent=True)\n", - " xyzt = trace['xyzt']\n", + " trace = tt.timtrace(\n", + " ml,\n", + " xstart=1,\n", + " ystart=0,\n", + " zstart=zs,\n", + " tstartend=[0, 100],\n", + " tstartoffset=tmin,\n", + " tstep=5,\n", + " nstepmax=200,\n", + " hstepmax=2,\n", + " silent=True,\n", + " )\n", + " xyzt = trace[\"xyzt\"]\n", " plt.plot(xyzt[:, 0], xyzt[:, 2])\n", "for z in np.arange(4, -1, -1) * H:\n", - " plt.axhline(z, color='k')\n", - "plt.axis('scaled')\n", - "plt.xlabel('$x$ (m)')\n", - "plt.ylabel('$z$ (m)');" + " plt.axhline(z, color=\"k\")\n", + "plt.axis(\"scaled\")\n", + "plt.xlabel(\"$x$ (m)\")\n", + "plt.ylabel(\"$z$ (m)\");" ] } ], diff --git a/notebooks/pumpingtest.ipynb b/notebooks/pumpingtest.ipynb index 3300e9d..4eb376a 100644 --- a/notebooks/pumpingtest.ipynb +++ b/notebooks/pumpingtest.ipynb @@ -15,8 +15,9 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "\n", "%matplotlib inline\n", - "from ttim import *" + "import ttim" ] }, { @@ -32,14 +33,14 @@ "metadata": {}, "outputs": [], "source": [ - "drawdown = np.loadtxt('data/oudekorendijk_h30.dat')\n", - "to1 = drawdown[:,0] / 60 / 24\n", - "ho1 = -drawdown[:,1]\n", + "drawdown = np.loadtxt(\"data/oudekorendijk_h30.dat\")\n", + "to1 = drawdown[:, 0] / 60 / 24\n", + "ho1 = -drawdown[:, 1]\n", "ro1 = 30\n", "\n", - "drawdown = np.loadtxt('data/oudekorendijk_h90.dat')\n", - "to2 = drawdown[:,0] / 60 / 24\n", - "ho2 = -drawdown[:,1]\n", + "drawdown = np.loadtxt(\"data/oudekorendijk_h90.dat\")\n", + "to2 = drawdown[:, 0] / 60 / 24\n", + "ho2 = -drawdown[:, 1]\n", "ro2 = 90" ] }, @@ -81,8 +82,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=0)\n", + "ml = ttim.ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=0)\n", "ml.solve()" ] }, @@ -198,16 +199,16 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=10)\n", - "cal.set_parameter(name='Saq0', initial=1e-4)\n", - "cal.series(name='obs1', x=ro1, y=0, layer=0, t=to1, h=ho1)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "cal.series(name=\"obs1\", x=ro1, y=0, layer=0, t=to1, h=ho1)\n", "cal.fit(report=True)\n", "display(cal.parameters)\n", - "print('rmse:', cal.rmse())\n", - "print('mse:', cal.rmse() ** 2 * len(ho1))\n", - "h1a = ml.head(ro1, 0, to1, 0) # simulated head\n", - "h2a = ml.head(ro2, 0, to2, 0) # simulated head" + "print(\"rmse:\", cal.rmse())\n", + "print(\"mse:\", cal.rmse() ** 2 * len(ho1))\n", + "h1a = ml.head(ro1, 0, to1, 0) # simulated head\n", + "h2a = ml.head(ro2, 0, to2, 0) # simulated head" ] }, { @@ -294,14 +295,14 @@ ], "source": [ "# second observation well\n", - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=50)\n", - "cal.set_parameter(name='Saq0', initial=1.5e-5)\n", - "cal.series(name='obs1', x=ro2, y=0, layer=0, t=to2, h=ho2)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=50)\n", + "cal.set_parameter(name=\"Saq0\", initial=1.5e-5)\n", + "cal.series(name=\"obs1\", x=ro2, y=0, layer=0, t=to2, h=ho2)\n", "cal.fit(report=False)\n", "display(cal.parameters)\n", - "h1b = ml.head(ro1, 0, to1, 0) # simulated head\n", - "h2b = ml.head(ro2, 0, to2, 0) # simulated head" + "h1b = ml.head(ro1, 0, to1, 0) # simulated head\n", + "h2b = ml.head(ro2, 0, to2, 0) # simulated head" ] }, { @@ -311,7 +312,7 @@ "outputs": [ { "data": { - "image/png": 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LBtRhYg8fsmdz5IOl+6n/1SambD7BjXtRlg7TvsTVACpHcHAAHSMbbaxYjdL5eKNJ+Qwps6pevTp9+vTBz8+PWrVqERAQgI+PDwCVK1dmzpw5mEwmrl69ysCBA7l16xbt2rXDZDLRqFEjxo8fD8DEiRMJDQ3FZDLh6enJ1KlTk3zOzp07s2DBArp27fr4ZyNGjCAqKgqTyYSXlxcjRowAYODAgdy+fRuTycTYsWPx80t8tm7ixImUKFGCs2fPYjKZCAgIeOb3RvznWQYKbrlc6OFXip/7+RH2cQu+6VKNykVz80tIBJ2nhlD7yw188tsBtp24QvSjmEx8FVamaDVoMxb+dxS6zIZCnhA8Hr6vDj+1hj3zICqJoxLj52xHZ8ju9t9RrTI5YnUkZz8ts3K2srVjp2vWrKkTOwMxLbTWbD0eybTAEwQdu0IuFyd6+JWkX/0yFH3u6aUMkQni6vuyuxn1grIMaRaHDx+mcuXKlg7DbiT2fiulwrTWNS0UkkVkRN5+VrfuR7HxyCXW7L/A5n8ucT8qBreczvhXKUwrr6LULedGNkc7m5e6eQ72LoA9cyHyOGTPDzV6g19/yFPsyWvj12SHBxkDbP3IGHg3HQ4N/meZ12DjJGebV1pztt3XZCdGKUV9jwLU9yjAgX9vMD3wJD9tDWfW1nBe8C5O/4ZlqVhEdqlnqvg1gIU9n95QI5tshBAZKLdrNl7wLs4L3sW5+zCazUcvs+bABVbuOcf8nWd4Lns2+tR159UGZcjjms3S4ZpHnmLQ4B2o/7aRb3dMg63fwbZJYOoKdd+EQrEDjoR12wnP35acLeyQzGSn0pmrd5kZfIqFu85wL+oRTSsVYkDDsviVyZ/monqbPjJQa8jsTQayySbTyKyIeclMtsEaZrKTcj/qEUHHrjAj6CQ7Tl0ll4sTbzQpT++6pcnhbIfzVNfCIeQH+PtniL4HHi2h3lAoXffJ3B9/UA2SszOJ5GzzSmvOtrO1r/QrmT8Ho9pXYduwprzTogJ7zlyn2/TtvPjDNtbsP8+jVG6WiTt66tt1R+k5Y3uGHQZvNXZMg4Uvw8VDKV+bXqltBSyEEM/INZsj+XM6s/fsdRRw92E0X/15hIZjN/FT8CnuR9nZBvl87kbt9tsHofFH8G8ozG4DM5rB4d8hJraOvaSfUSJS0k9ytrBbMshOo3w5nRnSzIOtHzTl0w5eXL3zkIFz/6bZt5uZuyMixYSb2NFTNkXHwIlNMKUuLO4Dl45k/HMk3GQTtxQpO9mFEJkgLm9rjF61L/mVwqNQbv7vj0M0+WYz83acJsqeNkkC5HSDxh/AWweg7bdw9yos7AXTGsDBFf8NtkFytrBbdrjWlTGyOzvycu3SvORXij8PXGBa4AmGLz/A+L/+oU9dd3rVLk3eHM5PPS6jz6i0OnUGQbXuEDIZdkw1kq1XJ2j0ARSskDHPkVhzBFmKFEJkkoR5u1ONEtQonY9tx6/wzbqjfLR8P1O3nGBoMw86+BTH0Z76LDjnAN8AqN4HDiyFwLGwuLdxOknjD6Hy85Kzhd2SmuwMorVm+8mrTAs8weajl8nh7Eh331K82qAMxfM+eSKJTddkx3cnEkK+hx3Tjdq9ql2MwbZbuYx9nqBvn9zJXuMVeK6kbLBJB6nvMy+pyTZYc012nKTyttaazUcv8826oxw8d5NyBXPyTouKtPYqYp9NzWIewcHlsHkMRB6DYj7QdASUa/pfzXbCnC2nj6Sb5GzzkppsC1FKUaecG7P7+rFmaANaVSnCzyHhNBy7ibcW7ObQuZuPr83IMyqtWk43aD4K3toHdQbDoZUwyReWD4SrpzLueZ44V9sRds+T81kFAO7u7ly5ciVd1wwfPpySJUuSK1euzApPZCFJ5W2lFE0qFeKPN+sztVd1HJTijXl/0/b7YNYfuphsRzyb5OAIVTvDoO3wwg/GZMuvHWF2Ozi93bgmsXO1pXREYHs5WwbZmaBy0TyM6+bNlveb0KeuO+sOXaTNxCBe+Wkn245fsb+km7MA+H9qDLZrD4SDy2BSTVj9Hty+/Oz3j1uKbDocfHpBTLRssBHP7Pnnn2fnTvmjL1JHKUUrr6L8+VZDJnTz5u7DaAJ+DuXFH7YRfMwO876jk9Ge/c1QaPONMav9U0tY0BOy5/svZ7caY/RCkIkR8YysMWfLIDsTFc+bnRHtPAkZ1oz3Wlbk0LmbvDRjB89PCmbVvvP2l3RzFYKWn8OQPeDzMuyaCRO9IfAbiLr3bPeO28lercfTG2xElhEeHk6lSpUICAjAy8uLnj17sn79eurVq4eHh8fjBHr16lU6dOiAyWSidu3aj1vlRkZG4u/vj4+PDwMGDHji39ivv/6Kn58f3t7eDBgwgEePkt+kXLt2bYoWLZp5L1bYJEcHRQef4qx/pxFjOlbl0s379Jq5g+7Tt7Mr/KqlwzM/Jxfwe83I+01HwMktMLmW0eTG52W4Fyknj2RhkrOTJzXZZnQ/6hHLd//L9MCTnLpyBz/3/PxfhypUKpLH0qFZxuV/YP1IOLraqKFuPsrYJPms52xL04N0i19vNvr3g0+UOWUEz2J5GPl8lSR/Hx4eTvny5dm9ezdVqlTB19eXatWqMXPmTFauXMmsWbNYsWIFb775JgUKFGDkyJFs3LiRd955hz179jBkyBAKFCjAJ598wqpVq2jXrh2XL1/m8uXLvP/++yxbtoxs2bIxaNAgateuzSuvvIK7uzuhoaEUKFAg0Zhy5crF7du3M/R9iCM12QZrztvP6kH0IxbsPMOkTce5fOsBjSoU5H/+FTCVyGvp0Czj9mXY8hWEzQInV2Ovzp75EBMlmyDTQXL206wpZ8vpImbkms2RHn6l6FqzJItCz/DVn0doOzGYvnXdGdrcg9zP2EUsy22oLFgBesw3ZjbWDoelrxonkrT84tmSbMLOY/HJANzqlSlThqpVqwJQpUoVmjVrhlKKqlWrEh4eDkBwcDBLly4FoGnTpkRGRnLjxg0CAwNZtmwZAG3btiVfPuPfwYYNGwgLC8PX1xeAe/fuUahQITO/MmGPXJwc6V3Xna41S/LL9nCmbD5B+0lb8fcszDv+FexvkiVXQWj7DdR63ZhkCZsFOQtBmQbgN0DychYkOTtpMsi2AEcHRQ+/UrSqUoSxa48yc+spVu49x/C2lWlfrViaO0jCf01uHkbH4OzkwNyA2lljoA1QthEM2AJ75sHGT2FmC2NGu/loyFsy455HOkWmSXKzF5nJxcXl8dcODg6Pv3dwcCA6Ohog0VKruH83if370VrTu3dvvvzyy8wIWYgUZXd2pH/DcrxUqzQ/BZ/ix8CTtJ4QRJXieXineQWaVi5s6RDNq0B56D4XTgXB2g+N4/9uX4LWY6Gw53/XycRIqknOtj5Sk21B+XI682XHqiwfVI/CeVwZumAPL/24g2MXb6X5Xlm+yY2DI1R/Gd78Gxq+D0dWGSeRbBkLUfcz5jmk65jNaNiwIXPnzgVg8+bNFChQgDx58jzx8zVr1nDtmtFRtVmzZixZsoRLly4BRn1gRESEZYIXdi2XixNDmnnw/Us+ODjAgX9v0m9OKJ+vOpTqzsE2pUwD6L8F2o6Diwdgan1Y8wHcu/7fxIhsiszy7DVnyyDbCniXzMuKN+rxaQcvDp67QevvgvhyzWHuPIhO9T3imiU4KrJ2kxuXXMaO88G7oII/bPocJvsZg+5n3T+QWNcxkSWNGjWK0NBQTCYTw4YNY86cOQCMHDmSwMBAqlevzrp16yhVqhQAnp6efPbZZ/j7+2MymWjRogXnz59P9jnef/99SpQowd27dylRogSjRo3K7Jcl7MjBczefSGk/Bp3ihcnB7D59zXJBWYqDI/i+akyy1OgDO6fD9zVg20SIfiATIzbAXnO2bHy0MpG3HzBmzREWh52l6HOujGjnSWuvIqkqIclyNdmpcXKzMatx+Qh4+BtLifnLpP9+8ZceQZYhE5DGBuYlGx8NWT1vp0dciV9UdAxOjoo3mngwd0cEF28+oLtvSd5vVYn8OZ/uGmwXzu+F1e/Dme0YjeyVcUqJlPg9RXK2eaU1Z8sg20qFRVzl4xUHOXz+Jg08CjC6fRXKFrSeA9bN6lEU7JgGm780zsCu/w7UGwrZXNN/T6nPTpQkbPOSQbbBVvJ2WiWcGLn9IJqJG47xU/Apcrk68X7LSnTzLWlfbdrjaA37FsHaYXD3KlRqCzVfhfN7ZGIkHsnZ5iUdH21EjdL5+X1wPUY978me09dpOSGQr9ce4d7D5M+JtEmO2aDuYKOEpGJr2PwFTKkDx9en/55Sny2EsLCEXSRzuTjxUZvKrB7agIqFc/PR8v10/GEre89ct3CkFqAUVOsGQ/dB7Tfg6Bqjc+SGT2H281KfLbIEGWRbMSdHB/rUK8OGdxvxvKkYkzedoPm4Law9eMH+GtkA5CkGXWbDy8sBBb92giWvwq2Lab+X1GcLIaxUhcK5WdC/Nt919+bcjft0+GErw5fv5+b9KEuHZn4uuaHVF+AbEPsDDY/uw6HfLBqWEKkhg+wsoFBuV8Z182Zh/9rkcnFiwC9h9Ju9i4jIO+m6X1jENSZvOk5YRBbdYFOuKQwKgcYfwuGVMNkXQmdBTEzq7xG/FXvCUpEzOyHoW5kpEUJYjFKKF7yLs/F/jehbtwzzd56mxbgtrDt4wdKhWUbVLuDoilGjjbE5Mni8UU4ohJWSmuwsJupRDHO2hTP+r3+IitEMbFSOgY3L4ZrNMVWPz9LnaSfmyjH4422j3KNkbXj+OyhUKf33s/NabanvMy+pyTbYet7OCHvPXOeDpfs4cuEWbasWZVT7KhTM7ZLyA21J3Mb1gpVh7zw4/DsU9jLyvo6xy43skrPNS2qybVw2RwcCGpRl47uNaVWlCN9tOEaL8VvYcDh1JRNZ/jzthAp4QO/focMUuHLUOGN105fGsU/pIbXaQggrExZxjeDjVxjdvgrv+lfgr0MXaT5uC4tCz9hX6WBJP2jwP6jUBrr9Ct3nGZsiZzSHn1rBhs/kPG1hVSwyyFZK5VdK/aWUOhb7+ampVKWUt1IqRCl1UCm1TynVzRKxWqvCeVyZ2MOHeQG1cHFy5NU5oQTMCeXc9XvJPs5mztOOTynwfgne2AVVOsCWMTCtIZxNx8yY1GpbpfDwcLy8vCwdxlMaN25MYjOwPXv2pGLFinh5edGvXz+iomRJW6RP3Orjt+uO0nvWTuqUK8DqoQ2oUDgX7y/Zx8szd3I68q6lw7SMSm3hjR1QwteYGCHGmGCRyRGLk5xtsNRM9jBgg9baA9gQ+31Cd4FXtNZVgFbABKVUXjPGmCXULV+A1UMa8GHrSmw9fgX/8YHM23E6ydmNGqXzMTegNu/4V8z6pSIJ5SoInWbAS4vhwS2jPfva4fAwDX+AkqvVFjYlrt1vZujZsydHjhxh//793Lt3jxkzZmTac5mLTI5YRmKrj+UL5WJh/zp82sGLPWeu4z9hCz8GniT6URr2pdgK1zzQ8nNwjCudiYGIEGOGW9iUrJizLTXIfgGYE/v1HKBDwgu01v9orY/Ffn0OuAQUNFuEWYizkwMDGpVj7VsNqVr8OT5avp9eM3dw5mrig8uEx0bZnAr+MGi70TksZBJMqQun0jCzEbckmXCALRsiE5fB78u4cePw8vLCy8uLCRMmPP55dHQ0vXv3xmQy0blzZ+7eNf77HjZsGJ6enphMJt59910ALl++TKdOnfD19cXX15etW7cCRtex/v374+/vzyuvvEKtWrU4ePDg4+do3LgxYWFh3Llzh379+uHr64uPjw+//WacZHDv3j26d++OyWSiW7du3LuX+MpRmzZtUEqhlMLPz4+zZ89myHtjYTI5YgFJrT46OCherl2av95pSP3yBfh89WE6TtnGoXM3LRyxBZT0gz5/QJOPwLsnnNwEk2vJCSRJkZz9lEzL2Vprs38A1xN8fy2F6/2Aw4BDEr/vD4QCoaVKldL27NGjGP1LSLj2HLFGVx6xRs/Zdko/ehRj6bAs52Sg1hOqaT0yj9Yrh2h973r67nN6h9afFtZ6VD7j8+kdGRunlTh06FDaHpDB70toaKj28vLSt2/f1rdu3dKenp7677//1qdOnfLMQnUAACAASURBVNKADg4O1lpr3bdvX/3111/ryMhIXaFCBR0TY/w3fu3aNa211j169NBBQUFaa60jIiJ0pUqVtNZajxw5UlevXl3fvXtXa631uHHj9CeffKK11vrcuXPaw8NDa631hx9+qH/55ZfH9/Tw8NC3b9/W3377re7bt6/WWuu9e/dqR0dHvWvXriRfz8OHD7WPj48ODAxM9PeJvd9AqLZAXk7pAzgKFI39uihwNBWP2Qt4pHRdjRo1knwPhdah4Vf1pI3HdGj41UR/HxMTo3/f+6+u8ek6Xe7DVfrrP4/o+1HRZo7Sipzbq/WU+kbeX9BL61uXLB1RppGcbd05O9NmspVS65VSBxL5eCGN9ykK/AL01VonuhamtZ6uta6pta5ZsKB9T3Y7OCh61S7NuncaUaN0Pj757SA9ftxO+JX0HfeX5ZVpAAO3QZ3B8PfPMLk2/LMu7feRDZGJy+D3JTg4mBdffJGcOXOSK1cuOnbsSFCQcc+SJUtSr149AHr16kVwcDB58uTB1dWVgIAAli1bRo4cOQBYv349gwcPxtvbm/bt23Pz5k1u3boFQPv27cmePTsAXbt2ZfHixQAsWrSILl26ALBu3TrGjBmDt7c3jRs35v79+5w+fZrAwEB69eoFgMlkwmQyJft6Bg0aRMOGDWnQwCZq+wtrrc8DxH4ulNzFSik/wBk4YYbYbFpKq49KKdqZivHX241o712MSZuO88KkrRz494aZI7USRU3w2kao2Q+OrIJJNeDgCktHZR0kZyf7ejI6ZztlyF0SobVuntTvlFIXlVJFtdbnYwfRl5K4Lg+wCvhYa709k0K1ScXzZufnfn4sDj3Lp6sO0eq7QN5rWYk+dd3tr0Wvcw6jZq/Ki/DbYJjXxVhSbPkFZE/lSnbchsi4o/1kQ6Qhg98XncxJCUqpp753cnJi586dbNiwgQULFjBp0iQ2btxITEwMISEhjxNzfDlz5nz8dfHixXFzc2Pfvn0sXLiQadOmPY5j6dKlVKxYMcU4kjJ69GguX778+J5ZgVJqPVAkkV8NT+N94iZHeic1OaKU6o+xCkmpUqXSGKlITL6czozr6k3bqkUZtmw/HSZvZXDT8rzRpDzZHO3sMLFzu2HPfKM9+/2bsLg3HO4Ebb6BHPktHZ3lSM5OUmbkbEv9q1sJ9I79ujfwVOGUUsoZWA78rLVebMbYbIZSiq6+Jfnr7UbULVeAT/84RNdpIZy4fDvV98jyjWviK1ETBmwx6q33LoAf0jCrLRsiE5fB70vDhg1ZsWIFd+/e5c6dOyxfvvzxjMLp06cJCQkBYP78+dSvX5/bt29z48YN2rRpw4QJE9izZw8A/v7+TJo06fF9436emO7duzN27Fhu3LhB1apVAWjZsiXff//94z8gu3fvfhzf3LlzAThw4AD79u1L9J4zZsxg7dq1zJ8/HweHrDO40Vo311p7JfLxG3AxdvBMRkyOyApk5mlWuTB/vd2QdqaiTFh/jA6Tt3Lkgp3VasfN2BIDKCjbGA6tNGq1j6y2bGyWJDk70XtmVs62VPYfA7RQSh0DWsR+j1KqplIqbktnV6Ah0EcptSf2w9sy4WZtRZ5zZWbvmozrWo3jl27T5rsgpgee4FFM8uerxj86queM7bYx0HZygWafwGsbwDWvMau98k1jpiMlSW2ItHcZ+L5Ur16dPn364OfnR61atQgICMDHxweAypUrM2fOHEwmE1evXmXgwIHcunWLdu3aYTKZaNSoEePHjwdg4sSJhIaGYjKZ8PT0ZOrUqUk+Z+fOnVmwYAFdu3Z9/LMRI0YQFRWFyWTCy8uLESNGADBw4EBu376NyWRi7Nix+Pkl/ppff/11Ll68SJ06dfD29ub//u//nvm9sQIyOZKF5M3hzITuPkztVYMLN+7z/PfBTN503H5OIIl/HKuTCzQZDv03Qa5CsKAHLH8d7l23dJSWITn7KZmVs6Xjo525dPM+w1cc4K9DF/Fzz883XapRyi1HotdO3nScb9cdJUaDo4J3/CvyRpPyZo44E0U/gM1fwtbvIE9xeGEylG1k6agsSrqHmVdW6violHIDFgGlgNNAF631VaVUTeB1rXWAUqoXMAs4GO+hfbTWSU9LIXk7I4VFXGP7yUhql3V7XMMdefsBn/x2kFX7z1OtZF6+7WKifKHcFo7UDOI6RMbvAhn9EAK/Nk7XyFUY2n8PHklWt1o9ydnmJR0fRbIK5XFl+ss1+LZLNQ6fv0mr75I+V9smG9fE5+QCzUdBv7XGjMfP7WH1e/DQTjeJCpEMrXWk1rqZ1toj9vPV2J+Haq0DYr/+VWudTWvtHe8j2QG2yDhJrT665XJhcs/qfN/Dh4jIO7SZGJyq1cwsL7EZWydno1QiYL1xxvbcTqlfzRQijWSQbYeUUnSqUYI/326IT6m8fLR8P31n7+LizftPXGfTjWviK+kHrwdDrYGwc7rRmv30jme7p5ypLYQws8Qa18T3fLVirHu7IY0qFOSL1UfoOi2EiEg7nVQoXh36b4F6b8HuX41+Cic3WzoqYWNkkG3HiufNzi/9ajHqeU+2n4zEf3wgv+8998Q1Nt+4Jo5zDmg9Bnr/ATHR8FNL+GukUVKSVmd2wpz2sPFz47MMtIUQZpCa1cdCuY3VzPHdqvHPxVu0/i6I+TuT7hJs07K5QovRxmqmkwv8/AKsfj9tXYKFSIYMsu2cg4OiT70yrB7SgDIFcvLm/N0Mnvc31+48tHRolhF3rnb1l2HrBPixKVw8mPLj4pMztYUQFpDa1UelFC/6lGDtWw3xLpmXD5ftJ2BOKJdvpWNSwRaU9IN246GEH+ycBtMbwb9/WzoqYQNkkC0AKFswF0ter8N7LSuy9uAF/CcEsulIoid02T6X3MZmmB4L4PZFmN7Y2BwZ8yh1j4+/q13O1BZCmFFaVh+L5c3Or6/W4pN2ngQdv0LLCYGsPXjBDFFamTM7YW5X+DcMHJzh3jWY2QI2fwWPoi0dncjCZJAtHnNydOCNJuVZ8UY98udwpu/sXXy4bB+3H9hpkqnYGgZthwot4a9PYHY7uBae8uOSOodU6rSFEFbGwUHRr34ZVr1Zn6LPuTLglzDeX7KXW/ejLB2a+cRffdSPoEYfo3nZ5i+M0sFIaVoq0kcG2eIpVYo9x8o36/F6o3Is2HWGNt8FpXhGtk01rYkvZwHo+gt0mAoXD8CU+rBnntFFLDkJd7VLnbZFuLu7c+XKlTRfc/fuXdq2bUulSpWoUqUKw4YNy8wwhbA4j8K5WT6oHm80KceSsLO0/i6InaeuWjos80i4+ujhD51mQOdZEHnc2AwfNjvlvC+ema3lbBlki0S5ODkyrHUlFg2oQ4zWdJm6jXF//UNUIo0MbLJpTXxKgXcPGLgVippgxUCjRe/dNPwBkjrtLOfdd9/lyJEj7N69m61bt7JmzRpLhyREpnJ2cuC9lkbed1CKbtNDGLPmCA+iU1kql1Ultfr4XAljVrtgJfh9KCx4CW5ftmioImnWmLNlkC2S5euen9VDG9DBpzgTNxyjy9QQwq88eeRTSsdG2Yy8paD379B8tNGWd0pdOLExdY+VOu1UCQ8Pp1KlSgQEBODl5UXPnj1Zv3499erVw8PDg507jRWAq1ev0qFDB0wmE7Vr137cKjcyMhJ/f398fHwYMGDAEycm/Prrr/j5+eHt7c2AAQN49CjpgUOOHDlo0qQJAM7OzlSvXp2zZ89m4isXwrySW32sGZv3u/uWZOqWE7w4eRvHL92yQJRmlNTq47bv4eIh44jX4xtgSh34Z61lY7UikrOT52TpAIT1y+OajXFdvWlaqRDDlx+gzcQgPmnnSTffkiilHh8bFRUdY5tNa+JzcIT6b0HZxrDsNfjlRag9CJqNNI6DSkrcTEnC7mPWbM0wuLA/Y+9ZpKpxVGIyjh8/zuLFi5k+fTq+vr7MmzeP4OBgVq5cyRdffMGKFSsYOXIkPj4+rFixgo0bN/LKK6+wZ88eRo8eTf369fnkk09YtWoV06dPB4wuXQsXLmTr1q1ky5aNQYMGMXfuXF555ZUUQ75+/Tq///47Q4cOzZC3QAhLi1t9fBgdg7OTQ6InkeRyceLLjiaaVirMB0v30XZiMB+3rUyv2qVRSlkocjOKv/oYA+QqCP03w7L+MK8r+AZAi0+N41+theRswLpytgyyRaq1MxWjRul8vLt4L8OW7WfDkUuM6Vj18bFRCVv52rRi3kYjg/UjYfsPcHILdPoRCldJ+jEl/Z4eXCfW9tfOlSlThqpVqwJQpUoVmjVrhlKKqlWrEh4eDkBwcDBLly4FoGnTpkRGRnLjxg0CAwNZtmwZAG3btiVfPuO/xQ0bNhAWFoavry8A9+7do1ChQinGEh0dTY8ePRgyZAhly5bN6JcqhEUktvqYVN5u4VmYaiUb8N7ifYz47SCbjl7mq04mCuZ2MXPUZha3+vjo4X+rj4U94bUNsPFTY4b7VJBRu13UZOloLUpydtJkkC3SpOhzRgObn7aeYuyfR2k5IYivu5hoUrGQfQyu43POAW2+NjbJrBhkHPXXfJSxrOiQikqsuOXIuCQevxbQGqQwe5FZXFz+++Pt4ODw+HsHBweio42TbhJrnBE3u5bYLJvWmt69e/Pll1+mKZb+/fvj4eHBW2+9labHCWHN0rr6WCi3K7P7+jJnWzhfrDlCqwmBfN3FmOW2WUmtPjq5gP9nUK4ZLH8dZjQzVjJrD0pd3s9MkrOtLmdLTbZIMwcHRUCDsvw2uB5uOZ3pO2sXI387wP0oG98ckxSPFjAoBMq3gLUfwS8d4Oa5lB8nmyHTrWHDhsydOxeAzZs3U6BAAfLkyfPEz9esWcO1a0a9abNmzViyZAmXLhlnv1+9epWIiIhkn+Pjjz/mxo0bTJgwIRNfiRDml9qmNfEpZTQu++PN+hTM7UK/2aGMWGHjeT9hnXZ85ZoYed/DH9YNh19fhJvnzR9jFmGvOVsG2SLdKhfNw2+D6/Fq/TLMCYmg3ffBHDx3w9JhWUbOAtB9Ljz/HZzdBT/UgYMrkn+MbIZMt1GjRhEaGorJZGLYsGHMmTMHgJEjRxIYGEj16tVZt24dpUqVAsDT05PPPvsMf39/TCYTLVq04Pz5pP8gnj17ls8//5xDhw5RvXp1vL29mTFjhllemxDmkJamNfFVKJyb3wbXI6B+GX7ZHsHz3wdz+PzNTIrSCsXvd5AjP3T71cj7Z3YamyIP/2HpCK2SveZsldgUflZWs2ZNHRoaaukw7E7Qscv8b9Fert+N4v1WFelXrwwODnawOSYxkSeMTZH/hoF3L2j9FbjkSvxaK6vJPnz4MJUrV7Z0GHYjsfdbKRWmta5poZAsQvJ21hQ/73/QuhJ967rbdt5PrsTvyjFY+iqc32sc+9fyC3DOmekhSc42r7TmbJnJFhmigUdB/nyrIQ0rFOSzVYfpM3sXl27df+Iam21Yk5BbOei3Fhq+B3vmwrQGcDYs8WuTW44UQggrFj/vf/rHoUTzvk1JrsSvgAe8uh7qDYWwOTCtIZzbbblYhVWQQbbIMPlzOvPjKzX4rIMXO09F0npCEBuPXATsoGFNQo7ZoOnH0GcVPIqCn/wh8BuIseH6RSGE3Uku79uclEr8nJyhxf/BK7/Bw7swozkET4CYp5u4Cfsgg2yRoZRS9Kpdmj/erE+hPK70mx3KJ78dIOjYZftoWJOQez14PRgqtzeOfZrTHm5Y/oB8IYRIjdSsQMbP+3GbIketPGh7myKT6gyZUNlGRofgiq2NY15/fRFuXTBvrMIqyCBbZIryhXKz4o26vFq/DD+HRLA07CxOjgpHhe03rEkoe17o/BN0mGIsH06pB4dWpu0e8TfbZDJb26dhreR9FtYurSuQRt6vR5+67szeFk6HyVttr1Nkakv8cuSHrr8YmyJP7zA6BB/NnDbfkkvMIz3vswyyRaZxcXJkRDtP5vTz415UDFpDo4qF+PXVWvZ3prZS4P0SvB4E+cvAopfh96Hw8E7Kj43bbLPxc+NzJg60XV1diYyMlKSdybTWREZG4uqaTJdQISwssaY1KXHN5sio9lX4qU9NLt16QLvvg5m347R95hSljE2QAwIhTzGY3x1WvwdRGVe3LjnbPNKbs6UZjch0jSoU5M+3GvD+kn1sPGKcefl155y45frvAPuwiGv20THSrRz0WwebPoet30HENug0M/mOYYlttsmkjZIlSpTg7NmzXL58OVPuL/7j6upKiRIlLB2GEElKa9Oa+JpWKsyfQxvwv8V7+Wj5fgL/ucyYTlXJm8M5EyO2UgUrQMAGWD/K6BAcvtVY3SxU6ZlvLTnbfNKTs+UIP2E2Wmt+Dong89WHeS57NsZ39aa+R4HHS5IPo2NwdnJIdXOELO/kZlg2AO5dNTbL1HrdmPlIKLljo6zsCEDx7OQIP2FNnnUCJCZG82PQSb5ee5RCuV2Y0N0HvzL5MyFSK5NUbj72l9Ep8uEdaPWlMdOdWN4XWUZyOVsG2cLsDp+/yZvzd3Pi8m36NyxLTmcnJqz/hxgNjgre8a/IG03KWzpM87gTCb+9Af+sAY+W0OEHo7FNQoklbGtvyy7SRQbZwhbtPXOdIQt2c+bqXYY082Bwk/I4OdpoxWpKufnWRVg+AE5uAs8XjLrt7HYwsWSj5JxsYVUqF83D74Pr0923FNO2nGTl3nP2uykypxv0mA+tvzZmtqfUMz4nlNhmG2nLLoSwkLT2PahWMi+rhjSgg3dxJqw/xks/7uDc9XuZHKWFpJSbcxeGXsug+Wg4sgqmNjA2RwqbI4NsYRHZnR35smNVpvaqzuVbD3BQipZeReynVCQ+paBWf3htA7jmgZ87GLV7j6KSf5y0ZRdCWEB6+x7kcnFiXDdvxnerxsFzN2j9XRB/HrDBo+2Sy81xJ0X9Gwr13zL26Dg4wqzWEPi19FKwMbLxUVhUK6+imErk5a0Fe1i9/wKuTo5ULJKbXC5O9rMZMk6RqtB/M/z5IQSPh1OBxqbI/GUSvz7uzFapyRZCmFFip46kJUe/6FMCn5L5GLJgN6//GsbLtUszvG1lXLM5ZmLUZpRUbk6qjGRAEPzxNmz8DE5ugY7TjdNIRJZnkZlspVR+pdRfSqljsZ+T/NeplMqjlPpXKTXJnDEK8ymWNzvzXqvFW809WLHnX9pNDGJR6Bn76hAZxzkntJ8IXWbDlePGMuL+JUlfL23ZhRBmFnfqyLOU+LkXyMmS1+vyWoMy/LI9wvbO1E5LiZ9rHug0w+il8O/fmXqmtjAvS5WLDAM2aK09gA2x3yflU2CLWaISFuPk6MBbzSuwoH8dHkTHMGzpPh5E2WGHyDhVXoSBwVDYE5a+CisGwYPblo5KCCGoUTofcwNq845/xWcq8XN2cmB4W09m9fXl8q0HPP/9VhbusuEztZMrI4nrpTAgEJ4raZypvWYYRD+wXLzimVlqkP0CMCf26zlAh8QuUkrVAAoD68wUl7AwvzL5WTO0Ab7u+YlLs06Oyr42Q8bJWwr6rIaG78GeeTCtIZzfm/b7mLFbpBDCPtQonY83mpTPkFK+JhULsWZoA6qXzssHS/fz5vzd3Lyfwp6UrCg1bdkLlIeA9VBrIOyYAjOaQ+QJ88cqMoSlBtmFtdbnAWI/F0p4gVLKAfgWeC+lmyml+iulQpVSoXIge9aXN4czC/rX5rUGZXF0UGR3duJ+lJ1uBnF0gqYfQ+/fIequkXC3T4XUzvSYsVuksG1S5idSktYTR+IrlMeVn/vV4r2WFVlz4AJtJwax58z1TIjSwlJT4ufkAq3HQPf5cOOMMcGyd6H5YhQZJtMG2Uqp9UqpA4l8vJDKWwwCVmutz6R0odZ6uta6pta6ZsGCBZ8tcGEVlFIMb1uZVUPqUyCXC71m7mDsn0eIehRj6dAso0wDeH0rlGsKf34A83sYZ2ynRI75ExlHyvxEktJ74kh8jg6KN5qUZ9GA2sTEQOcp25i25QQxMTZaPpKSSm2MvF/EBMv7G01spGwwS8m0QbbWurnW2iuRj9+Ai0qpogCxny8lcos6wGClVDjwDfCKUmpMZsUrrFOlInlYObge3WqW5IfNJ+g6LYQ1+8+ne7YkS8vpBj0WQKuv4MQGmFofwoOTf4wc8ycyjpT5iSQlduJIetUonZ/VQxrQwrMwX645Qt/Zu7hy245qk+OX+D1X3FjJbDQM9i6A6Y3g/D5LRyhSySIdH5VSXwORWusxSqlhQH6t9fvJXN8HqKm1HpzSvaVzmO36fe85Pli6j7sPH6EAl2x21II9ofN7YXFfuHYKGr4Pjd43zlpNTEqt16U1u1Wx1o6PSqnrWuu88b6/prXOl+AaB2Aj8DLQjGTytlKqP9AfoFSpUjUiIiIyLXaR+eJmsqOiY8jmlDG5WWvN3B2n+b8/DvFc9mx8182buuUT6YhrS5LrFnkqCJa9Bncjwf8z8OsvLdmtgDV2fBwDtFBKHQNaxH6PUqqmUmqGhWISVu75asXoWasUABp4EBVD0DE7rcEvWs3YhW7qBlvGwJzn4ca/iV+bXA2g1GyLeKTMT6RXRp04Ep9Sil61S/PbG/XI4+pEz5k7+HbdUaJtuWwwuRK/uLLBsk1gzfuwoCfcvWq5WEWKLDLI1lpHaq2baa09Yj9fjf15qNY6IJHrZ6dmFlvYvlZeRXFxUiiMgfayv//l2EUbOls1LVxywYtT4cVpcG6PUT5y9M+03UNqtkU8UuYnnkVSJ448y4ZIgMpF8/D7m/XpVL0E3288btst2VMq8cvpBi8thJZfwLF1sS3Zt1smVpEii5SLZCYpF7F9cZ0gXZwcmLL5BHceRvN/7b3oUrMEyl6Xzq4chyV94MJ+qD0Imo8ydqinJLGlSZDyEQuy4nIRKfMTaRZXRvIwOgbnDCgjWb77LB8vP0A2Jwe+7lyNFp6FMzBaK5HaEr5//4YlfeH6GWjyEdR/BxwsVaBgv5LL2TLIFlnapZv3eXvRHrYej6RSkdwMb1uZBh52uvQc/QD++gR2TDXKSTrPArdyKT8ufkKHpOsBhVlY8SDbDVgElAJOA1201leVUjWB1xOuQsogWwBM3nScb9cdJUaDo4J3/CvyRpPyz3TPU1fuMHje3xw8d5O+9dwZ1roSLk420pI9NeLn7IKV4I+34MBSo4yk43TI9dSpyCITWWNNthAZolAeV4Y2q4CTg+LIhVu8MnMnC3elWA5qm5xcoPVX0H0eXIswzlbdtzjlx8Wv2ZbyEZEEKfMT6ZERLdgTKlMgJ8sG1aVPXXdmbQ2n85QQIiLvZEC0WUDCfTSXj0CnmfD8RDgdAlPqwYlNlo5SxJJBtsjydoVfJSZ2RUYDHy7bx8zgU7bbmjclldrCwK1QpCosC4AVb8DDVP4BkiP/hBAZKDM2RAK4ODkyqn0Vpr1cg4jIO7SdGMzKvecy5N5WLbGJkLO74O4VY6CdIz/88iJs+BQeRVs6WrvnZOkAhHhWcTMlUdExODkqTCXy8ukfh9h2/ArfdKlGvpzOlg7R/J4rAb3/ME4eCfwGzu40ykeKeCX/uLi2v0nVA8pxf0KINKpROl+mHbXaskoRqhTLw5D5uxkyfzchJ67wSbsqZHe20fKRuImQuJK+7G5Plvi9tBD2L4KgbyBiqzHL/VxxS0dtt1KsyVZKuQLtgAZAMeAecABYpbU+mOkRppHU9tmnuM2Qtcu6Ub1UXmZvC+fL1Udwy+XMd9198CuT39IhWs7JLcbZqvdvQKsvoUbf9J2tmtz5rSLDPGtNdlbL2SB52x7Fz9kZMQCPehTDuL/+YcrmE1QonIvJL1XHo3DuDIjUCsWf7AgPMkpH9CNjBbLpcKP8b98i+ONtI1d3mAIVW1k6apuV7ppspdQoYCvGsUw7gGkYG1+igTFKqb+UUqaMDVeItIt/dJRSir71yrBsUF1cnBzoPj2E7zcc45G9tuYt2wheD4bSdY2ku6SvMeBOK6nXtnqSs0VWkBEt2BPK5ujAB60qMaefH5G3H9J+0lYWh9ro/pz4+2iSKvEzdYX+W4xZ7PndYO1wiH5o2bjtUErlIru01qOS+N04pVQhjJ3mQlgdr+LP8ceQBgxfvp9v//qHkJORTOjmTaE8rpYOzfxyFYKeS2Hbd0at3rnd0PknKF4j9fdIuEwp9drWSHK2sHqJtWDPqHKSRhUKsnpoA95asIf3luwj5EQkn3bwIqeLjVbHJlfiV6A8vLoe/hoBIZMgYht0mQX53C0Wrr2RI/yEzdNaszjsLCN/O0gOZ0fGdfOmUQU7PeYP4PQOWPoq3LpgnKdd543Ul49ITXams9Yj/DKT5G37khkt2BN6FKOZtPE43234B/cCOZnUozqexfJk6HNkCXE5WwNbvzN+1n4iVOlg0bBsyTOfkx17DupwoDTG7LcCtNba6pYdJVmLpBy/dIs35u7m6MVbDGxcjndaVCCbo50esHP3Kqx8E478AR4tjZq9nOk8WksG3hkqIwbZWSlng+Rte5TRNdlJCTkRydAFu7l+L4pP2nnSs1Yp+2lalnAfTcdpxkD73zDwDQD/zyGbHa7sZrDkcnZq10/mAu8B+4GYjApMCHO6cS+a1lWLUMotB1M2n2DnqatM7OFD8bzZLR2a+eXID91+hZ3TYd3HRkv2zjONuu20kM2Q1kpytrBqmXniSHx1yrmxemgD/rdoLx+vOEDIyUi+7FiVPK7ZMv25LS7hPprI49D3T9gw2igfObMDusxJXdMykS6pnca7rLVeqbU+pbWOiPvI1MiEyEBxy5MTNxwj6Nhl3m5egaMXbtHmuyDWHbxg6fAsQymoNQBe/cuYzZjdFgK/hphHqb9HUpshz+yEoG+Nz8ISJGcLEatALhdm9fHlg1aV+PPABdpNDGbf2euWDivzJbYp0skZWn4OPRbCjbNG07L9Sywdqc1K7SB7pFJqhlKqh1KqY9xHpkYmRAZKuNHG8c0I7AAAIABJREFUyVHxx5v1KZU/B/1/CWP07wd5EJ2GwaUtKeYNAwKhSkfY+Bn82hFuXUzdYxNL4gk7kslA2xIkZwsRj4ODYmDjcizsX5uoRzF0mrKNWVttvGlZ3KbIpsOfXmWs2Mo4daqwl7FHZ+Wb8PCu5WK1UaktF+kLVAKy8d/SowaWZUZQQmS0+A1r4lr7uhfIyZKBdRiz5giztoYTGn6N73v44F4gp6XDNT+X3NBphnHc3+r3jfKRjtOhXJPkH5fYzvagb5+e3ZYSEnOTnC1EImq652f1kAa8u3gvo38/xPaTkYztVI3nctho+UhJv8Tzb9xemuaj4Ng6CB4HZ0Ohy2woWNHMQdqu1G583K+1rmqGeJ6ZbKARSUluo826gxd4b8k+HsVoxnSqSjtTMQtFaQUuHYbFfeDyUWj4LjQaBo5pOP5K6rSfSQZtfMwyORskb4vEZebmSK01M4NPMWbNEQrncWXSSz74lMr8GnGrkFiOfnALlvWHqLvQ9lvwfsnSUWYZ6W5GE892pZRnBsYkhNnFb1iTkH+VIqwaUp8KhXMxeN5uPlq+n/tRdlo+UqgyvLYJfHoaNdpznocb/6b+8YktUUqNtrlJzhZZWmY0rIlPKUVAg7Isfr0OAF2mhvBj4EnbLh+Jk9heGpfcUP1lKOABKwbC8oHw8I6lI83yUjvIrg/sUUodVUrtU0rtV0rty8zAhDC3EvlysHBAHV5vVI55O07TYfJWjl+6bemwLMM5B7wwGV6cDuf3GuUj/6xN/ePjdySTGm1LkJwtsrTEGtZkBp9S+Vg9pAHNKhfi89WHCZgTyrU7Nt4ZMeFemuxuRm7eOhEuHQWfl2HvfJjeBC4esnS0WVpqB9mtAA/AH3geaBf7WQibEBZxjcmbjrPv7A2Gta7E7L6+XLr1gPaTglkadtbS4VlOtW7Gpsg8/9/efcdXVWX9H/+shJDQa0AEQgd1LAkEQQgqKjyKOqijVAvOKBZAHHV+w4yjzxQd22MBxFHEOiN2OtgHFFQ6Kl0pgggqRSnSAtm/P06uXuK9IeWeW3K/79crL3JPTu7ZnLks9+yz9lqNYXwfr9xfaVvzqh17LChmS0IL7KNJNX7aR+OXWlXTePzyDvz1whOY/cU2zh81m0Ubdvh2vZgr+rRx3/afY3RBPtRtAVdOhn3fw5PdYfHzkAwr/D4oNifbzKo754pdyivJOdGk3D4prcBjyYOHCqgc1H3sm537uemlJcxfv4NLOzTh771/RdXKFbQ179Hk74e3b4cF46BxrteSvU6zkv2ucrRLpTw52YkYs0FxW0KLVsOaYEs37WTI+MV8/cM+/vA/7RjcrSUpKRW8eU24GL37W5hwLax/H07qAxc8DOnVYz3auFPmjo9m9h7wCTAZWOSc+7HweEugO9AHeNI5FzdFFhWspbTGzFzDg2+vpsBBqsEtPdsxpHtrwGvNO/K9Lxj93y9oWb8aYwa257hjkrA1b8DyiTDlJq/Gdu8xcHwJF0eL6wqpjpFHKOckO+FiNihuS3zZtT+fP72+lOlLt3Bmu0we6pNN3WqVYz0sfxWNw4HXWV28P2fdA3Vbes1rjjkx1qONK+Vqq25mvYCBQFegDnAIWA1MB55yzsVVJw8FaymtwEp2oLxfYCU72EdrtjH85U/YtS+fv/36V/Tt2DR5WvMWtWM9vHY1bF4Cp14HPf8BldLL9l5a5f6F8lYXSbSYDYrbEn+cc/xn7gb+MW0ldatVZlT/HE5tUTfWw4qOUHH58EF47XdeCsl590GHQd5ii5SvuohzboZzbqBzrrlzrpZzrp5zrotz7u54DNYipdWhWR1euKYzt/RsF3KCDdCldX1m3NSNjs3rMmLCUoa/9Al7DhyKwWjjQN0W8Nu3oPONMP8JeKoHbF9btvdSvnbEKWZLsgjspYl05RHwqo9ccVpzJtzYhYy0FPo/OZcxM9dQUJAEucmh4nLzPK95TfOuMO1mr4HN/l2xHmncK+nGR5EKrbjyfoFAvnHHXp7/7anc1rMt0z7bzIWj57Ds650xGG0cqJQO594D/V6E7zfAE2fAsjL0OQnVMTJAZf9EJAy/S/wFnNi4FlOH5dHrpEY88NZqrnpmPtv2HPDlWnEjXFyungkDX4ez74Tlk2DsGV71KQlLk2yRYhQN5Eu++oGhZ7XhpcGnse/gYS7510f8++Mvk6O2aijH9YLrZ0OD47wUkmm/h/x9Jf/9cG1/VfZPRIoRrRJ/ADUy0hjVL5t/XnwS89bvoNfI2Xy81r/rxVxx7dhTUrxJd87l3kr2uB7ehvhk/W/gUWiSLVKM4EB+IL+A1xd75fxObVGX6Tfl0aVVPe6YvJwh4xeza39+jEcbI7Wz4Oo3oOtwWPg0jDsHtn1R8t8PrqkdoDQSESlGNEv8gZc+MqBTFpNu7Er19EoMHDeX0e99weGKmj4SKi7DzwsgS/4DB/ZAo5Nh+q3eIovSR36h2Em2mdUt7itagxSJlc4t61GpsHyTA15btOmnx5L1qqfz9FUdGXHecby1/FsuGDWHzzb9EMPRxlBqGvT4Owx4FXZt9tJHPnul7O9XXBqJhKWYLcmiJHtp/HDCsTWZMiyPC085lgff+Zyrnp7P1t0VPH0kWPACSEE+tD0XzvkrrJgCT5wOmz+J9QjjytFWshcBCwv/3Ap8DnxR+P2isl60MOC/Y2ZfFP4Z8l+HmWWZ2dtmttLMVphZ87JeU6QsOjSrw2W5TQnsoT58+MjHkikpxvVntOKV6zpz6HABv/nXRzw9Z33ypo+07eltjml0sldfdfJQOLi39O9T3OPKYMrbLsqXmC0Sj4rbS+On6umVeKRvNvdechILvtxBr1EVPH0kWNEFkBanQ97v4eoZ3uT7qR4w/0mljxQqdpLtnGvhnGsJvAVc6Jyr75yrh9c9rAy7nH4yAnjPOdcGeK/wdSjPAw84544HTgW+K8c1RcrkkvZNSE878rFk0V3tHZrVZfpN3TijbSZ/n7aC6/69iJ17kzR9pFZjuGqa96hxyX/gybPgu1Wlf59wjysDlLf9Cz7GbJGE4WfVkQAzo9+pWUwa0pUaGV76yKiKnD4SEG4BJKszXDjKSx+ccRu8ehXsT9LCAEGOWicbfqoB2KHIsYXlaJiwGjjTObfFzBoBs5xz7YqccwIw1jmXV5r3Vr1V8UNw5zEgZIdI8GqrPjVnPfe+sYqGNTMYPSCH9lnRXWWJK2vegwmDIX8v9Po/yBkYufee/aA3wXaHvVWVs273JuUJrrx1soPeI2Ix22+K2xIp4Tr4+unHA4e4feJSJn2ymbzW9Xm4bzaZNcrYOyBRBRY9Dh3wNke6Am/CfdmzcGxOrEfnq3LVyS60zcz+YmbNzayZmd0OlOfZSEPn3BaAwj8bhDinLfCDmU0wsyVm9oCZpYZ6MzMbbGYLzWzh1q1byzEskdCCH0sWt6vdzLimW0teu6ELAH0e/5hxs9clb/pI67O99JHGHWDyjTDxBjj4Y2TeW3nbxYl0zBZJCOE2q/upWnolHu6bzX2/+Tl95KO123y/blwJ5GpT4KWKtL8KDufDUz1h3tikTR8p6SS7P5AJTAQm4U2K+xf3C2b2rpktC/HVu4TXrAR0A24DOgItgUGhTnTOjXXO5TrncjMzM0v49iJlU5Jd7dlNazPjpm6cdVwD7pq+kmufX8gPew/GYLRxoGYjuHIynPFH+PRFGNsdvl1R/vctLm9budqljtkiFUFxm9X9ZGb07ZjF5KFdqZlRicvHzWPku0mQPhJQdNEje4C3wNKyO7zxB3jlyqRMHylRukjEL1qydJHOwL3OuTMLX18BdHbODSnuvfXYUaIhOH2kuEeRzjme/ehL/jljJZnV0xk9IIcOzZK4yMO6WfD6tXBgN/S6H3KuiHxr3gRv1R6JdBE/FFYneRloDnwJ9HHO/WL2YmZZwDigKd48p5dz7svi3ltxWyLp9olLGT9vIw5INbilZzuGdG8dtev/eOAQf5m0jIlLvqZr63o80jcnOdJHvppf2B2y288xt6AAPn4U3v0r1G7qpY8czv/leQms3OkiZpZZmK4xw8z+G/gqx5imAFcVfn8VMDnEOQuAOmYWWJo+C4jA8pdI+ZV0V7uZcXXXFrx+QxdSU40+T8zl8ffXJkdr3lBanumtbjQ9FaYMg4nXebVWI0k1tv2I2aAN65IgSrJZ3U/V0ivxUJ9TuP83J7Now/ecN3I2H61JgvSRUJvVU1Kg601eL4XDh7zmNc/0gvfuSooN6yVNF3kBWAW0AP6Gt4qxoBzXvRfoYWZfAD0KX2NmuWY2DsA5dxgvVeQ9M1sKGPBkOa4pEjMnN6nNtGHd6HlCQ+59YxW/fW4BO35M0vSRGg3hionQ/XZY+iqMPRO+WRa591euNkQ+ZgP0Bp4r/P454KKiJxRuWK/knHsHwDm3xzlXhhqOImVXtIY2EJUW7MHMjD4dmzJ5SB61qlRi4FPzeOTdz5MnfaSorE5ed+A6zb362hR4myQr+CJIqaqLmNlnzrmTC4+975w7w/cRlpIeO0o8CJdO4pzj33M3cNe0ldStVpnRA3Lo2DyJ00fWz4bXr4H9P8C590KHQZFJHwn12LK443EkktVFIhmzzewH51ztoNffO+fqFDnnIuAa4CDeBP9dYEThoknR9xsMDAbIysrqsGHDhrIOTaRYY2au4cG3V1PgYpc+csekZUxY8jVdWtXjkX7ZNKiREbXrx5WNc+HZ86HgEGDQ+1GvRXsCi0R1kUDB3y1mdr6Z5QBNIjI6kQomUEIq1KqJmXHlac2ZcGMX0tNS6Dd2LmNmrkne9JEW3bz0kazTYNrN3oT7wO7yv2+ox5bJVVe7TDFbG9alIop2C/aiqqVX4sE+p3D/pSezeOP39Bo5JznSR0LJ6uyljnS4GqrVh2m//7n6SAXcsF6phOfdZWa1gFuB0UBN4Pe+jUokgYUq8Vc0d/vExrWYNiyPP01YygNvrWbe+h083OcU6lVPgs0xRVXPhMsnwJyHYObdsHmJtzmm0cmRvU6oXO04Xc2OgDLFbOfcOeF+ZmbfmlmjoA3roXKtNwFLnHPrCn9nEtAZeKoMfweRiAikj5Rks7pfzIw+uU05pUltbnxhEQOfmsfws9sw7Kw2pKZEePN3vGt6qvd19p0w8Xqv+sjKKfDVAi+VJAE3rIdTopVs59w059xO59wy51x351wH59wUvwcnkohKumpSIyON0f1zuOuiE5m7bju9Rs1m3rokLWWckgKn3+Z1iszfC+POgQVPRba2arhc7Qq4euJTzNaGdUlYsWrBXlS7Y2owZWgeF2U35pF3v+CKp+bx3e79MR1TzFStC/1fgh7/gA1z4PD+CrdhvaQ52W2Bf+E1kTnRzE4Gfu2cu8vvAZaWcrIlHpS0xF/A8s07GTp+CRu2/8gtPdpy45mtSUm21Y2AH7d5XSLXvge/uthr1ZtRMzLvXTQnOw7L/UUoJzviMdvM6gGvAFnARuAy59wOM8sFrnfOXVN4Xg/gQbzN6ouAwc65Ynf5Km5LsnHO8erCTdwxeRk1q6Qxsl82XVrVj/WwYmfhM17qCA5S0mDQdG+zZAIoLmaXdJL9PvAH4AnnXE7hsWXOuRMjOtIIULCWRLXnwCH+PGEpUz7dTLc29XmoTxK25g0oKIAPH/byp2tnQZ/noNEpkb9OHLZmj9AkO2FiNihuS/Ja9c0uhrywmPXbfmT42W0Zelbr5EsfCfjiXXhzBGz/Ak7oDb8eDVtXJ/Rm9ZJufKzqnCv6LPVQ+YYlIsGqp1diZL9s7rnkJOavT9LWvAEpKd5kd9B0r8zTuHNg/pORb81bccv9KWaLlFI0a2kHHHdMTaYMzaN3dmMefvdzrnx6Hlt3H4ja9eNKm3NgyHzo8XdYOQ3GdIJnL0jozeolnWRvM7NWeN27MLNLgS2+jUokSRQN6mZG/1OzmDSkKzXSk7A1b1HNTitszXsmzLgNXr0qsq15i2vNDomcr62YLVIKxVWF8lugec19vzmJhV9+rwWWrsO9CiQHf4TDB7wnjQlaU7ukk+whwBPAcWb2NXAzcINvoxJJAsUF9eMb1WTKsDx+fcqxPPzu58m9OaZaPej/MpzzN29144nTvQokkRKq3B8kesk/xWyRUghVFSqazIy+HbOYPLQrNTK0wEJWJ7j0GbDCaaoZHNs+tmMqg5JWF1lXWNopEzjOOZfnnPvS15GJVHBHC+rV0yvxcN9s7vvNSSzakOS1VVNSIO/mn1vzPtXz59qqfglV8i9BVrYVs0VKJ9a1tAOOO6YmU4f+vMBy1dPzkzt95Oo3oPU5gPM2Rm7+JGHiMJR842M68BugOUG1tZ1zf/dtZGWkDTSSKAIr2fmHCkirlMIL13QOW4kksDlm3bYfGXZWG4afnYS1VQP27oBJN8Dnb8LxF8KvH4UqtY/+e6VVtPLIufd6m3J8rkQSoY2PCROzQXFb4kNpq0L5yTnHKwu/4s7Jy6lZJY1R/XI4rVVsJv5xYeNcePVq+HGr97rgMFRKj/uKUCWdZL8J7MQrx/RTe1zn3IORGmSkKFhLIilNUA9uzXtay3qM7JdNg5pJ2prXOfj4UXj3r1CzMVz2DDTuEPnrBJf8+3L2kZVIOlwJtZpGfNd7hCbZCROzQXFbJJyVW7wFli+3/8jvz2nLkO7JXN51OzzdE7avKTyQAmf/Ja4rQpV0kh23pZ+KUrCWisw5x6uLNnHn5GVUT6/EI31zyGuTxLVVv1oAr10Nu7+BnndBp+u83D1frhW0sp2SChgUHIr4qnaEJtkJE7NBcVukOEXLuz7cN5v6ydgdGLwV7WfP92IvBr0fg5wBMR1SJEr4fWRmJ0VwTCJSBoHWvFOG5lG7amWueHoeD729Onk3xzTtCNd94OXsvflHePly2PeDT9cKqkSSc7kX5OO3O5litogPYlHmL7i867z1O+g1cnbUN2bGjazOXp52h0FQtR5MGw4Lxh25PyeOcraLXck2s6V4JaAqAW2AdcABvE5ezjl3cjQGWRpaEZFksffgIe6YtJzXF2+iU4u6jOqfQ8NkTh+Z+xi8cyfUPBYufRaa+JA+EhCuU2QgvaRKPdi3vUypJOVZyU7EmA2K25IYAvtoDh4qoPJR9tH4ZcXmXQwd76WPqDvwdpg4GNa8C7+6BC4cCVtX/XIvTRljcUmVOV3EzJoV98bOuQ3lHFvEKVhLsnl14VfcMXkZ1SpX4pF+2XRrkxnrIcXOpoXe5pjdW7yGBp1v8Dd9JFSL9kMHgAKv9FRq6TfmlHOSnXAxGxS3JTGMmbmGB99eTYGDVINberZjSPfWUR/HngOH+NOEpUwtTB95pG829ZI1faSgAD58BP57F9Rp5j3VXPCU95SRFK8ylXMx26xebLqIc25DcV8RH6mIlNpluU2ZOjSPutUqc+XT83nw7dUcOlwQ62HFRpNcuP4DaNMT3vpTYfqIT491i9bXDpT8o/Deu4Kop5IoZov4J17K/FVPr8Softn88+LC9JFRs5mXrOkjKSnQ7RYYNA3y98GiQG3twAS7IKZpfSXNyRaRONamYQ0mD+3Kpe2bMPq/axg4bh7f7krS5jVV6kC/F+B/7oHP34LHT/dWuP0WaNEeCKuWUtFatYsktQ7N6vDCNZ25pWe7mKSKBDMzBnTKYuKNXahauRL9n5zLmJlrKEjW/TnNunjdgZufDgX50PB46Hm39zTRUn+OxVHO1y5RdZFEoseOUtEdrezfa4s2ccekZVStnMrDfbM5vW0yp48sglcHwe7NXsfI04b4lz4CMc/JTlSK2yJlt3t/Pn+asJRpn23hjLaZPNTnlOROH5nzEMy8G+q0gNP/4MX/wGJHqL005RSJ6iIiEgeKa8UecGmHJkwZ2pV61Stz1TPz+b+3kjl9pENh+sj/wNu3w0sDvGY2fgmkkOQOCt2qXUQkwmpkpDG6fw53XXQiH6/bzvmj5rDgSx/jXDxLSYHTb4OrpsHBH2HazV4VkiYdQ3fx9Xs4vl9BRCKmuFbswaWl2jSsweQheVzWoQmPzlzDAKWPeLvMv3gHnjjdq68tIlJBmBmXd27GhBu6kJ6WQr+xc/nXrLXJmz7SvKuXPtKsC0wdDhMGQ+NcbwU7VPrIwmd9SSNRuohIAgnXir240lITFm/i9olKHwG89JHXBsGuzXDOX+G0of6mj5SB0kVEpDx2789nxOtLmb50C93bZfJgn2zqVqsc62HFRkEBzHkQZv4T6rbyVrl3fX1k+oiPFaG0ki2SQMJtvCluhfuS9k2YOqwr9aunK32kSQe4bja0PRfe/gu82N/f9BERkSirkZHGowNy+EfvX/Hhmu2cP2o2C5M6feQPcOUUOLDLW9WuWv/I9BEfK0Jpki2SYDo0q8OQ7q2P2PR4tNJSrRvUYNKQrvTNbeqljzw5j292Jmv6SG3o+x849z6viYHSR0SkgjEzrjitORNu7EJaagp9x87l8feTOH2kRTcvfSSrM0y9CSZe93P6iI8VoZQuIlJBHK3qSMDEJV76SEaalz5yRjKnj3y9yGtes+vruEkfUbqISMVS0tjsl1378xnx+mfMWPoNZx3XgAcvO4U6SZs+chg++D+YdQ/UbwPdboNdm3yrCKVJtkgFFyrAr/luD0PHL2bVN7sZ0r0Vvz+nLZVSk/TB1r4fYMpQWDkV2p4HFz0GVevGbDiaZItUHPHQih3AOcfzH2/grukryKyezqMD29M+K3Z1vmNu3fvw+jVwYDf0egByLi/zAotyskWSVLiSf60bVGfijV3p17EpY2auVfpIn3/DefcHpY9Ep1GBiFRsJa0I5Tcz46ouzXn9hi6kphp9Hv+YJz9YR0VbaC2xlmd46SNNO3qLLJNu8Er+RVhMJtlmVtfM3jGzLwr/DPl/p8zsfjNbbmYrzWyUWZyVARCJc+EC/KIN3/P0h+u5LLcpj/TNZtnmnfQaNZtZq7+L8YhjxAw6XQe/e9vLy3vmPPhwlLczXUSkjMLtlylJzwM/nNykNtOGdePs4xtw94yVXPv8In7YezAq1447NRrCFZPgjBHw6UswtnvEN8LHaiV7BPCec64N8F7h6yOYWRegK3AycCLQETgjmoMUSXShAnzR4N60blWmDsujQY10Bj2zgPvfXJW81Ucat4frPoB258E7d8BLqj4iImVXlopQfqtVJY3HL+/AnRecwPuff8f5o+awZGN0JvlxJyUVuv8JrpjobY6sEtkUmlhNsnsDzxV+/xxwUYhzHJABVAbSgTTg26iMTqSCCBXgQwX3VpnVmTSkK/1Pbcpjs9bS/8m5bNm5L9bDj42f0kcegDXvwePdYOO8WI9KRBJUWSpC+c3M+G1eC169vgsAfZ74mKfmrE/e9JFW3eH8ByO+8T1Wk+yGzrktAIV/Nih6gnPuY2AmsKXw6y3n3MqojlKkAiga4MMF94y0VO655GRG9stm+eZdnD9qTpKnjwz20kdSUuHZXkofEZGICbfCHW3ZTWsz46ZunNmuAf+YtoLr/r2InXvzYzKWisi36iJm9i5wTIgf3Q4855yrHXTu9865Iz5hZtYaGAn0LTz0DvBH59wHIa41GBgMkJWV1WHDhg2R+UuIVFBHKym1dusehrzgVR+54cxW3Noj2auPDIOVU7wmNhf9y9fqI6ouIiLRLvvnnOOpOeu5941VHFMrgzED2nNK09pH/0WJvxJ+ZrYaONM5t8XMGgGznHPtipzzByDDOfePwtd3Avudc/cX994K1iLlEwjuOVm1mfrpFl6cv5GOzeswqn8OjWpVifXwYsM5WDAO3vozVGsAlz1T6lqqJaVJtkhyi2XZv8Ubv2fY+CV8t3s/f+51PIO6NEc1J4oXjyX8pgBXFX5/FTA5xDkbgTPMrJKZpeFtelS6iIiPgjdF/vbZBVzaoQkj+2WzYvMueo2czcxkTh859VovfSS1UmH1kZFKHxGRiIvlpsj2WXWYflMeZ7TN5G9TV3DDfxazc5/SR8oqVpPse4EeZvYF0KPwNWaWa2bjCs95DVgLLAU+BT51zk2NxWBFkkWo4N47uzFThuXRsGYGVz+zgPuSufrIsTle9ZHjzod37oQX+yVN9RGVXhWJjnBVoaJVU7t21co8eWUut/c6nndXfsuFo+ewdNNO369bEanjo4j8JLCSnX+ogLQijyn35x/mb1NX8OL8jeQ2q8PoAUof8dJHMuHSZyCrU0TeOl7TRczsfmCHc+5eMxsB1HHO/bHIOV2AB4DTCw/NAf7knJtV3HsrboscKTgnG4hZ+siiDd8zbPxitu05yF8uOJ4rOjdT+kgR8ZguIiJxqLgd7171kZMY2S+blVuUPuKlj7wDqWnJkj6i0qsiURJcFSqW6SMdmtVh+k3dyGtTnzsnL2fo+CXs2q/0kZLSJFtEjhCqpmuwoukj976xivykTR/J9tJHjr8gGdJHIlp61cwGm9lCM1u4detWH4ctkthiXVO7TrXKjLsylxHnHceby7/hwtFzWPa10kdKQpNsESm1H/bmc+6Jx9DzhIY8/v5a+o+dy+YfkrR5TUYtuOw56PV/sG4mPJ6XsM1rzOxdM1sW4qt3CX+/NXA80ARoDJxlZqeHOtc5N9Y5l+ucy83MzIzcX0KkgomHmtopKcb1Z7Ti5cGdOZBfwCWPfcS/525I3uY1JaRJtoiUSiBve9R7X/DBF1u5+Zw2rNyyi/NHzWbmqmRPH3n75/SROY94udsJxDl3jnPuxBBfk4FvC0uuUvhnqP+xLwbmOuf2OOf2AG8AnaP3NxCpmIp7whjNTZG5zesyY3g3TmtVjzsmLWPYi0vYrfSRsDTJFpFSKZofmJaawtRheRxTqwpXP5vs6SNB1Ud2rI14i94YU+lVkTgTXHZ14Li5UZlo161WmWcGdeT/nduON5Z9w68f/ZAVm3f5ft1EpEm2iJRKqPzAlpnVueOC4zmlSS2lj2TUgj7Pw/kPxXpavxwQAAATGElEQVQkkabSqyJxJlabIlNSjBvPbM2L13Zm78FDXPTYh4yft1HpI0WohJ+IlFrRlr/BHcpSU4zUFKNKWioP9cmm+3G/2B8nxYjXEn5+UtwWKZviyq5Gy7Y9B/j9y58w+4tt9M4+lrsvPonq6ZWiOoZYKi5mJ89dEJGI6dCszhGBPHg1xQocV57WnI/WbufqZxdw/RmtuLVnW9JS9eBMRCSSApsigxc9oq1+9XSeu/pUxsxcw8Pvfs7STTsZM7A9xzeqGfWxxBv9V09Eyq1oCkmvkxox8cYuDOiUpfQREREfHa3sajSkpBjDzm7DC9d0ZveBQ1w05kNemq/0EU2yRaTcQpWYykhL5Z8X/9y8Jqmrj4iIxFC0KpCc1qoeM27qRm7zOoyYsJRbXvmUHw8c8vWa8UzpIiISEUVTSAJ6ZzfmpMa1GDJ+idJHRESiLHjPTDTasmfWSOf533bi0f+u4ZH3PuezTT/w2MAOtDumhm/XjFf6r5yI+K5lZnWlj4iIxEAsKpCkphjDz2nDC7/rxM59h+g9Zg6vLvzK9+vGG02yRSQqlD4iIhJ9sWzL3qV1fWYMzyOnaR3+8Npn3PrKp+w9mDzpI5pki0hU9c5uzNRhedSqksbVzy7g5pc/Sd7mNSIiPgvXlj1aedoNamTwn2s6cdPZbZiwZBO9H/2QNd/t9vWa8UI52SISdd/vzWfLTi9dZNKSr1m5ZRfPDOrIsbWrxHhkIiIVT9E9M9HO005NMW7p0ZaOzetw80ufcOHoD7n74hO5pH0T364ZD7SSLSJRN3fddvIPe6WdDFi3dQ+9lD4iIhIVseoU2a1NJjOGd+OkJrW45ZVP+eNrn7E//3BUrh0LmmSLSNQF5wimp6XwUJ9sGtWqwtXPLuDeN1aRf7ggao8yRUSSTSzztBvWzGD8NZ0Y0r0VLy/8iovGfMjarXuidv1oUlt1EYmJoq3Z9+cf5u/TVjB+3kbaHVODL7ftIf+wi8qjzHiituoiEg1FY3AszFr9Hb9/+RMOHCrgnktOond245iMozzUVl1E4k7RHMFA9ZFOLepy26uf/pROEniUmSyTbBGRaAjX2yCazmzXgBnDuzFs/BKGv/QJc9ft4H8vPIGMtNSYjitSlC4iInGld3ZjHuqTjQUOGORqgi0iUiE1qlWFFwd35vozWvHi/I1c/NhHrN/2Y6yHFRGaZItI3LnwlGN54dpOnNKkFocL4IG3Vqt5jYhIDPm5TyYtNYUR5x3H04Ny2bJzHxeOnsO0zzZH/DrRpkm2iMSlLq3qM3lonprXiIjEWKDk34Nvr2bguLm+bUg/67iGTL+pG20bVmfo+CXcMWlZQlcf0SRbROJaoHlNw5oZR1Qfgeg1UxARSWbRLPnXuHYVXr7uNAaf3pJ/z93ApY9/xIbtiZk+okm2iMS9lpnVmTSkK/1PzeLx99fSf+xc3l7+TVRWVkREkl20S/6lpabw517HM+7KXL7asY8LRs3hjaVbfL2mHzTJFpGEkJGWyj2XnPRT+sjwlz7hQH70mymIiCSbcK3Zg/nxZPGcExoy/aY8Wjaozg0vLOavU5Zz4FDipI+ohJ+IJJTe2Y05sXEtfvvsAjZs34sBlVItqs0URESSTXEl//xs096kTlVeve407ntzFU/NWc/ijd8zZkB7mtatGpH391NMVrLN7DIzW25mBWYWtumCmZ1rZqvNbI2ZjYjmGEUkfrXKrM5bN59OjxMa4oAW9atzbO2Mn36uXG0RkejxO2e7cqUU7rjgBJ64ogPrt/1Ir1GzeXPZNxG9hh9ilS6yDLgE+CDcCWaWCowBzgNOAPqb2QnRGZ6IxLuMtFSevDKXkf2y+WrHXnqNnM3M1d9FbRe8iIh4opWz/T+/OoYZN3WjRf1qXP+fRfx96goOHirw5VqREJN0EefcSgAzK+60U4E1zrl1hee+BPQGVvg+QBFJGIH0kSEvLObqZxbQqUVdDuQX4FC3SBGRaAjkbEejTXvTulV59frTuGfGKp7+cD2LNn7Po/1z4jJ9JJ43PjYGvgp6vanwmIjIEVoFVR+Zt34HmBfciq6oKI1ERMQfHZrVYUj31lFZ1EivlMpff/0rHhvYnnXf7eH8UbN5Z8W3vl+3tHybZJvZu2a2LMRX75K+RYhjLsy1BpvZQjNbuHXr1rIPWkQSVnD1kfRKKaSnpXJrj3Y/BXylkYiIxFakFzp6ndSIaTflkVWvKtc+v5B/zlj5Ux+FeOBbuohz7pxyvsUmoGnQ6yZAyB6bzrmxwFiA3NzckBNxEUkOwekjd89YyY69B7m1R9uQG3OURiIiEh1+VSBpVq8ar13fhbunr2TsB+tY+OUOHh3QnmNrV4nAqMsnntNFFgBtzKyFmVUG+gFTYjwmEUkAwekj/5q1ln5j59KmQfWoNlMQEZGf+VmBJCMtlX9cdCKj++fw+bd76DVqNjNXfRex9y+rWJXwu9jMNgGnAdPN7K3C48ea2QwA59whYCjwFrASeMU5tzwW4xWRxFO0ec0fX/+MW3u0K7aZgoiI+CMaFUguPOVYpg7L49haVbj62QXc+8aqmKaPmHMVK7siNzfXLVy4MNbDEJE4snbrHoa8sJhV3+zm+jNacWvPtqSlxueDPDNb5JwL2z+gIlLcFkkOizZ8H7YCSXE/K639+Yf5+7QVjJ+3kY7N6zC6f3uOqZVx9F8sg+Jidnz+V0ZEJIJ+Th9pyuPvr6X/2Lls2bkv1sNKKGoiJiLlFa4CSaQ3pmekpfLPi70nmSs276LXqNm8/3n0C2Noki0iScFLHzmZR/pms2LLrp+a10iJqYmYiPjCr3zt3tmNmTIsjwY10rnq6fk88NYqDkUxfUSTbBFJKhflNGbqsDwa1szg6mcWcN+bRwbd4kpMJXOdbefcSufc6qOc9lMTMefcQSDQRExEJKxw+dqRiLmBJ5n9OjZlzMy1DBw3j2937Y/U0IsVk46PIiKxFAi6f5u6gn/NWsvCL3cwqn8Om3/YH7bElF/lpyqYUE3EOoU60cwGA4MBsrKy/B+ZiMStUB0jIxlzM9JSufc3J9OpZV3+PGEZvUbO5pF+2XRrkxnhv8mRtJItIkkpuPrIis27OH/UHF6ctzHsI0s/y0/Fi2g2EXPOjXXO5TrncjMz/f0PnYjEv6L52n7E3ItzmjB1WFfqVa/MlU/P56G3V3O4wL8CIFrJFpGkFty85rXFm0hNgRT3yxJTgceZ+YcKfvE4M1I74mMtmk3ERESKEy7mllfrBjWYPCSPOycvY9R/17Dgy+8Z2T+bBjUiX31EJfxERPBKPv1t6gpenL+RxrWr8L8XnkDPXx1zxDlFJ9R+pJDEewk/M5sF3Oac+0WgNbNKwOfA2cDXeE3FBhytx4HitoiE4vcixqsLv+KOycuonp7GqP7ZdGlVv9TvoRJ+IiJHEZw+8sPeg4yYsJRZRaqPRONxZrxSEzERibZwJf8i5bLcpkwZmkedqmkcOBT5qiNKFxERCRKcPjLomQXceGYrbunRlkohmteEepxZkdJHgjnnJgITQxzfDPQKej0DmBHFoYmIlFnbhjV4Y3i3kDG+vDTJFhEpIrj6yGOz1rLgyx0hO4YV3REPqAKJiEiC8WOCDUoXEREJKZA+8kjfbJYXdgwrmj4CRz7OTKb0ERGReBDP/Qs0yRYRKUageU2DGukMemYB978ZvmNYuIYKIiISeUdrxx7rCbjSRUREjiKQPvLXKct5bNZaFn75PaP65xw1fUSpIiIi/gn19DCeGohpJVtEpAQCHcMe6ZvNss07S5Q+IiIi/inu6WE8pO9pki0iUgoX5TRmytA8Mqt76SMPvBU+fURERPwTeHp4S892v1ipLm4CHq00EqWLiIiUUusGgeojyxkzcy0L1odOHxEREX91aFYn5JPDcOl70Uwj0Uq2iEgZVKnspY883PeUYtNHREQkNkKl70UzjUSTbBGRcrg4p4nSR0REEkSoNBK/0keULiIiUk5KHxERSQzRbCKmlWwRkQgITh/ZsONH9ucfjvWQREQkhGg1EdNKtohIBF2c04TzTmxERlpqrIciIiJHEUgfyT9UEPEmYppki4hEmCbYIiKJwc8mYppki4iIiEjSClcGsLyUky0iIiIiEmGaZIuIiIiIRJgm2SIiIiIiEaZJtoiIiIhIhGmSLSIiIiISYZpki4iIiIhEmDnnYj2GiDKzrcCGoEO1gJ0hTg11vOix4NfB39cHtpV7sCUbU3nPL+6cktyDUMeKe+3HvSntfSnJ75T2voQ6frR7pc9MyV7Hw2fGj/sS6nhJXtd2zmUeZSwVSpG47UfMBn3O4vnfX0l/pzSfjXDH9ZmpGDG7JL8TiX9LoY4V/cyEj9nOuQr9BYwt6fGix4JfF/l+YTTHWp7zizunJPfgaPclGvemtPelJL9T2vtSkvugz0zifmb8uC+lvQ9lGXdF/PIjZhe+1ucs9H2K+X0p773RZyb5YnZJficS/5ZK+5kp+pUM6SJTS3G86LGpxfzMD6W9RknOL+6cktyDUMeO9jrSyvL+R/ud0t6XUMePdq/0mSn560iLh/sS6nis70siUMwu/c/i7XPmR8wu7hx9Zkr3s4oQs0vyO5H4txTqWIk/MxUuXSQazGyhcy431uOIR7o3oem+hKd7I9Ggz1loui/h6d6EpvtScsmwku2HsbEeQBzTvQlN9yU83RuJBn3OQtN9CU/3JjTdlxLSSraIiIiISIRpJVtEREREJMI0yRYRERERiTBNskVEREREIkyTbB+YWTUzW2RmF8R6LPHEzI43s8fN7DUzuyHW44kXZnaRmT1pZpPNrGesxxNPzKylmT1lZq/FeixScSlmh6aYHZpidniK2UfSJDuImT1tZt+Z2bIix881s9VmtsbMRpTgrf4IvOLPKGMjEvfGObfSOXc90AeoEOV/InRfJjnnrgUGAX19HG5URejerHPO/c7fkUqiUswOTzE7NMXs8BSzI0/VRYKY2enAHuB559yJhcdSgc+BHsAmYAHQH0gF7inyFr8FTsZrOZoBbHPOTYvO6P0ViXvjnPvOzH4NjAAedc6Nj9b4/RKp+1L4ew8CLzjnFkdp+L6K8L15zTl3abTGLolBMTs8xezQFLPDU8yOvEqxHkA8cc59YGbNixw+FVjjnFsHYGYvAb2dc/cAv3i0aGbdgWrACcA+M5vhnCvwdeBREIl7U/g+U4ApZjYdSPiAHaHPjAH3Am9UlGANkfvMiISjmB2eYnZoitnhKWZHnibZR9cY+Cro9SagU7iTnXO3A5jZILxVkYQP1sUo1b0xszOBS4B0YIavI4utUt0XYBhwDlDLzFo75x73c3AxVtrPTD3gbiDHzP5UGNhFiqOYHZ5idmiK2eEpZpeDJtlHZyGOHTXHxjn3bOSHEndKdW+cc7OAWX4NJo6U9r6MAkb5N5y4Utp7sx243r/hSAWkmB2eYnZoitnhKWaXgzY+Ht0moGnQ6ybA5hiNJd7o3oSm+xKe7o34TZ+x8HRvQtN9CU/3phw0yT66BUAbM2thZpWBfsCUGI8pXujehKb7Ep7ujfhNn7HwdG9C030JT/emHDTJDmJmLwIfA+3MbJOZ/c45dwgYCrwFrARecc4tj+U4Y0H3JjTdl/B0b8Rv+oyFp3sTmu5LeLo3kacSfiIiIiIiEaaVbBERERGRCNMkW0REREQkwjTJFhERERGJME2yRUREREQiTJNsEREREZEI0yRbRERERCTCNMkWEREREYkwTbKlwjOz2mZ2Y9DrY83sNZ+udZGZ3RnmZ3sK/8w0szf9uL6ISKJTzJaKQpNsSQa1gZ8CtnNus3PuUp+u9f+Ax4o7wTm3FdhiZl19GoOISCJTzJYKQZNsSQb3Aq3M7BMze8DMmpvZMgAzG2Rmk8xsqpmtN7OhZnaLmS0xs7lmVrfwvFZm9qaZLTKz2WZ2XNGLmFlb4IBzblvh6xZm9rGZLTCzfxQ5fRIw0N+/tohIQlLMlgpBk2xJBiOAtc65bOfcH0L8/ERgAHAqcDew1zmXA3wMXFl4zlhgmHOuA3AboVc+ugKLg16PBP7lnOsIfFPk3IVAtzL+fUREKjLFbKkQKsV6ACJxYKZzbjew28x2AlMLjy8FTjaz6kAX4FUzC/xOeoj3aQRsDXrdFfhN4ff/Bu4L+tl3wLGRGb6ISFJRzJaEoEm2CBwI+r4g6HUB3r+RFOAH51z2Ud5nH1CryDEX5tyMwvNFRKR0FLMlIShdRJLBbqBGWX/ZObcLWG9mlwGY55QQp64EWge9/hDoV/h90Vy+tsCyso5JRKQCU8yWCkGTbKnwnHPbgQ/NbJmZPVDGtxkI/M7MPgWWA71DnPMBkGM/P58cDgwxswX8crWkOzC9jGMREamwFLOlojDnwj0ZEZHSMrORwFTn3LtHOe8DoLdz7vvojExERIpSzBY/aSVbJLL+CVQt7gQzywQeUrAWEYk5xWzxjVayRUREREQiTCvZIiIiIiIRpkm2iIiIiEiEaZItIiIiIhJhmmSLiIiIiESYJtkiIiIiIhH2/wGTPJIps5tVJwAAAABJRU5ErkJggg==\n", + "image/png": 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", "text/plain": [ "
" ] @@ -325,22 +326,22 @@ "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(121)\n", - "plt.semilogx(to1, ho1, 'C0.', label='observed 1')\n", - "plt.semilogx(to1, h1a[0], 'C0', label='model 1')\n", - "plt.semilogx(to2, ho2, 'C1.', label='observed 2')\n", - "plt.semilogx(to2, h2a[0], 'C1', label='model 2')\n", - "plt.title('calibrated well 1')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to1, ho1, \"C0.\", label=\"observed 1\")\n", + "plt.semilogx(to1, h1a[0], \"C0\", label=\"model 1\")\n", + "plt.semilogx(to2, ho2, \"C1.\", label=\"observed 2\")\n", + "plt.semilogx(to2, h2a[0], \"C1\", label=\"model 2\")\n", + "plt.title(\"calibrated well 1\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.subplot(122)\n", - "plt.semilogx(to1, ho1, 'C0.', label='observed 1')\n", - "plt.semilogx(to1, h1b[0], 'C0', label='model 1')\n", - "plt.semilogx(to2, ho2, 'C1.', label='observed 2')\n", - "plt.semilogx(to2, h2b[0], 'C1', label='model 2')\n", - "plt.title('calibrated well 2')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to1, ho1, \"C0.\", label=\"observed 1\")\n", + "plt.semilogx(to1, h1b[0], \"C0\", label=\"model 1\")\n", + "plt.semilogx(to2, ho2, \"C1.\", label=\"observed 2\")\n", + "plt.semilogx(to2, h2b[0], \"C1\", label=\"model 2\")\n", + "plt.title(\"calibrated well 2\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend();" ] }, @@ -366,8 +367,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.1, rc=0.2, tsandQ=[(0, 788)], layers=0)\n", + "ml = ttim.ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.1, rc=0.2, tsandQ=[(0, 788)], layers=0)\n", "ml.solve()" ] }, @@ -466,15 +467,17 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=10)\n", - "cal.set_parameter(name='Saq0', initial=1e-5)\n", - "cal.set_parameter_by_reference(name='rc', parameter=w.rc[:], initial=0.2, pmin=0.01, pmax=1)\n", - "cal.series(name='obs1', x=ro1, y=0, layer=0, t=to1, h=ho1)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-5)\n", + "cal.set_parameter_by_reference(\n", + " name=\"rc\", parameter=w.rc[:], initial=0.2, pmin=0.01, pmax=1\n", + ")\n", + "cal.series(name=\"obs1\", x=ro1, y=0, layer=0, t=to1, h=ho1)\n", "cal.fit(report=False)\n", "display(cal.parameters)\n", - "h1a = ml.head(ro1, 0, to1, 0) # simulated head\n", - "h2a = ml.head(ro2, 0, to2, 0) # simulated head" + "h1a = ml.head(ro1, 0, to1, 0) # simulated head\n", + "h2a = ml.head(ro2, 0, to2, 0) # simulated head" ] }, { @@ -572,15 +575,17 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=10)\n", - "cal.set_parameter(name='Saq0', initial=1e-5)\n", - "cal.set_parameter_by_reference(name='rc', parameter=w.rc[:], initial=0.2, pmin=0.01, pmax=1)\n", - "cal.series(name='obs2', x=ro2, y=0, layer=0, t=to2, h=ho2)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-5)\n", + "cal.set_parameter_by_reference(\n", + " name=\"rc\", parameter=w.rc[:], initial=0.2, pmin=0.01, pmax=1\n", + ")\n", + "cal.series(name=\"obs2\", x=ro2, y=0, layer=0, t=to2, h=ho2)\n", "cal.fit(report=False)\n", "display(cal.parameters)\n", - "h1b = ml.head(ro1, 0, to1, 0) # simulated head\n", - "h2b = ml.head(ro2, 0, to2, 0) # simulated head" + "h1b = ml.head(ro1, 0, to1, 0) # simulated head\n", + "h2b = ml.head(ro2, 0, to2, 0) # simulated head" ] }, { @@ -590,7 +595,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -604,22 +609,22 @@ "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(121)\n", - "plt.semilogx(to1, ho1, 'C0.', label='observed 1')\n", - "plt.semilogx(to1, h1a[0], 'C0', label='model 1')\n", - "plt.semilogx(to2, ho2, 'C1.', label='observed 2')\n", - "plt.semilogx(to2, h2a[0], 'C1', label='model 2')\n", - "plt.title('calibrated well 1')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to1, ho1, \"C0.\", label=\"observed 1\")\n", + "plt.semilogx(to1, h1a[0], \"C0\", label=\"model 1\")\n", + "plt.semilogx(to2, ho2, \"C1.\", label=\"observed 2\")\n", + "plt.semilogx(to2, h2a[0], \"C1\", label=\"model 2\")\n", + "plt.title(\"calibrated well 1\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend()\n", "plt.subplot(122)\n", - "plt.semilogx(to1, ho1, 'C0.', label='observed 1')\n", - "plt.semilogx(to1, h1b[0], 'C0', label='model 1')\n", - "plt.semilogx(to2, ho2, 'C1.', label='observed 2')\n", - "plt.semilogx(to2, h2b[0], 'C1', label='model 2')\n", - "plt.title('calibrated well 2')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to1, ho1, \"C0.\", label=\"observed 1\")\n", + "plt.semilogx(to1, h1b[0], \"C0\", label=\"model 1\")\n", + "plt.semilogx(to2, ho2, \"C1.\", label=\"observed 2\")\n", + "plt.semilogx(to2, h2b[0], \"C1\", label=\"model 2\")\n", + "plt.title(\"calibrated well 2\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend();" ] }, @@ -725,12 +730,14 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=10)\n", - "cal.set_parameter(name='Saq0', initial=1e-5)\n", - "cal.set_parameter_by_reference(name='rc', parameter=w.rc[:], initial=0.2, pmin=0.01, pmax=1)\n", - "cal.series(name='obs1', x=ro1, y=0, layer=0, t=to1, h=ho1)\n", - "cal.series(name='obs2', x=ro2, y=0, layer=0, t=to2, h=ho2)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=10)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-5)\n", + "cal.set_parameter_by_reference(\n", + " name=\"rc\", parameter=w.rc[:], initial=0.2, pmin=0.01, pmax=1\n", + ")\n", + "cal.series(name=\"obs1\", x=ro1, y=0, layer=0, t=to1, h=ho1)\n", + "cal.series(name=\"obs2\", x=ro2, y=0, layer=0, t=to2, h=ho2)\n", "cal.fit(report=False)\n", "display(cal.parameters)" ] @@ -742,7 +749,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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b1uqp4pWg7WgIGqBtQ5KJPXyEMwvmc3bJUszlyxS/owv+w0fgW6d2ms+bvXc27+98Hzt2PMSDvoF9qVC0gha78zFNFP/RRJFHE0WKjIFDm6yEcfhnKFEV2j0NN94DHhkv5hYE8WfOcObj+UQvWoT9wgWK3taRMo8+im+dOilenziiiLPH4SFWd+B4e7yOLvKx/JYoqlevTlhYGKVLl870NVrMzk9ErL0XQ76F+5ZBkdIQ+ih80Ax2LYKEeFdH6DY8S5Wi7KgnqbVhPaUffpiL23/ir17BHH7ySa4cPHjd9Yl9pB5p/Ai9avUi3h6vxW6lUqGJIi8QsXZ/D90I9yy2GhKuGgkfNodfFlsb/BQAHiVLUubRR6i1YT3+I4Zz4bstHOrRkyNjxhAbEXHNtUFlgwhpGELPmj1TLHYr5WwRERHUrVuXkJAQGjRowH333cf69etp3bo1gYGB7NixgzNnzhAcHEyjRo1o1aoVe/bsASAqKorOnTvTuHFjhg8ffk3fqE8++YQWLVoQFBTE8OHDSUhw7nuCTj3lRcbAvq9g03g4sRf8A+HWsVanWz1k6Rrx0dFEzZ5N9KJPMXFxlOzXj9IPj8SrbNlrrtMNefnfNdMtX4+F43ud+wLlG8IdE9K8JCIiglq1arFr1y7q169P8+bNufHGG5kzZw6hoaHMmzePKlWqULp0aV5++WU2btzIqFGj2L17N4899hilS5fmpZdeYs2aNXTv3p1Tp05x6tQpxowZw/Lly/Hy8mLkyJG0atWKgQMHOm3qSXs95UUi1rkYte+AP1ZbCWPZENgy0UoYN/SCJIea5MThK3mFp58f5Z5+Gv/Bgzk9fQbRixcTs2oVpQYOxD9kCB7Frf5bGTkfQylnCAgIoGHDhgDUr1+fjh07IiI0bNiQiIgIIiMjWbZsGQAdOnQgKiqKmJgYtmzZwvLlywHo1q0bfn7W/8sbNmwgPDyc5s2bA3Dp0iXKJvtFKLs0UeRlNpvVBqRuD2u39+YJsGQwlK0P7Z+Fut0J//us0w9fyYs8y5Sh/IsvUGrQQE69+x5RM2dydvFi/EeMwO++e7F5p72aTEcc+Uw6v/nnJJ8kHZJtNtvVxzabjfj4eDw9r39bTmxdk1ILG2MMgwYNYvz48TkUsdYo8gebDRr0gZE/Qp/ZkHAFFg+AGW05vmM5sfEJ15yzUZB5V61KpcmTqL5sKb7163Pyrbc41LUb5776KtWzAhJXSL2/832GrhuqG/RUjmrbti2LFi0CYPPmzZQuXZrixYtf8/mvv/6a6OhoADp27MjSpUs5efIkAGfOnCEyMtKpMWmiyE9sHtDoThj5E/SeAVf+pduvo1jl/SK32PY67fCV/KBQ/fpUnTuHKrNnYytShCOjniLy3vu4tPf6eWttAaJy0yuvvEJYWBiNGjVi7NixzJ8/H4CXX36ZLVu20KRJE9atW0fVqlUBqFevHq+//jqdO3emUaNGdOrUiWPHjjk1Ji1m52cJ8bDnc65sGI/P+cNEV70dv95vg191V0fmVkxCAjErVnDynakkREVRIjiYMqOevFrwTr7noletXvSs2VOnoPKY/LaPIjt0H4X6j4cnNB6Az+Ph0PEl/I5thQ9bWhv4Yi+6Ojq3IR4elOzXj5prv8E/ZAjn1qzhUJc7OD1jJvYrV67uuegb2BeAZfuX6RSUKlA0URQEXr5Wz6hHwqBud/juLfigOfy6wlpqqwDwKFqUsqNHU2PNlxS++SZOvfMOh7p159zaddxY5kYqFK2gG/NUgaSJoiApUQn6zYEHvrZO2FsyGOb3gBM5e4xiXuNdtSpVPviAqvPmYitUiCOPP87fgwbT/HxZ3ZinCiRNFAVRtZth+HfQbQqc+B9MbwNfPQ0Xz7g6MrdS5KabCFixnPIvv8SV/fvxDnmOub+34fHaIdoPShUomigKKpsHNB8Cj+6EZkPg59nwflMi137ARxv3ER4Z7eoI3YJ4euJ3zz3U/OZrSva/E8/la2k96jOq/xCR4nJaPeNC5UeaKAq6wqWg2yQYvpV/SwRS7cfnabu5P+NnL9JkkYRHyZJUePllqi9dgneVKhwb+yx/DxzElQMHrl6j+y1UfuWSRCEipUTkWxH50/H3dVuFRSRIRH4UkV9FZI+I3OWKWAuM8g1YUPtDHo17lDJyli9sL+C19hm4fM7VkbmVQvXrU+2zTyn/6qtc3r+fQ8G9OTl5MvaLF3W/hcqSiIgIGjRokOHrp0yZQr169WjUqBEdO3Z0+ua6lLhqRDEW2GCMCQQ2OB4ndxEYaIypD3QBpopIyVyMscBpVbM039pa0zl2Ep+azjQ8+gV82AJ+W6Wro5IQmw2/u/pT8+uvKNGjB1GzZnOoew9a/IkWu1WOa9y4MWFhYezZs4d+/foxZsyYHH9NVyWKXsB8x8fzgeDkFxhj9htj/nR8fBQ4CZTJtQgLoKbV/FgU0ophnRtzw5AZSMgGKFwavhgIn94F0Tn/m0te4lmqFBXHv0m1TxZiK1IYr+cmM29THZ6qPFCL3flETtScpkyZQoMGDWjQoAFTp04FID4+nkGDBtGoUSP69evHxYvWPqexY8deHT2MHj0agPbt21O4cGEAWrVqxeHDh50WW6qMMbn+Bzib7HF0Ote3AH4HbOndu2nTpkY5UXycMd+/b8zr5a0/2941Jj7W1VG5HXtsrDk9e7b5Paix+T2osTk9d56xx8Vd/fquE7vMrD2zzK4Tu1wYZcH222+/Zer6XSd2mWYLm5lGHzcyzRY2c8q/XVhYmGnQoIE5f/68+ffff029evXMzp07DWC2bdtmjDHmgQceMBMnTjRRUVGmdu3axm63G2OMiY6Ovu5+Dz/8sBk3blym40jpZwGEmVTeV3NsRCEi60Xkfyn86ZXJ+1QAFgIPGGPsqVwzTETCRCTs1KlTzghfJfLwhJsfgYd3QEA7+PZFmHkr/POzqyNzK+Llhf+QIdRc8yVFWrbk5FtvEdH/Li7971ctcudROVFz2rZtG71796ZIkSIULVqUPn36sHXrVqpUqULr1q0BGDBgANu2baN48eL4+voSEhLC8uXLr44iEn3yySeEhYXx9NNPZzuu9ORYojDG3GaMaZDCn1XACUcCSEwEJ1O6h4gUB9YALxhjtqfxWjONMc2MMc3KlNHZqRxRsgrc8xnc9Ym132JOJ1jzFFyOcXVkbsWrYkUqT/uISlOnEnfqJBH9+3P6rUnIpSta5M5jmpVr5vSak0ml1pe8fbiI4OnpyY4dO+jbty8rV66kS5cuV7++fv163njjDUJDQ69pW55TXFWjCAUGOT4eBKxKfoGIeAMrgAXGmCW5GJtKjQjc0AMe2QEtR0DYXKsVyP+Wa7E7CRGheJfbqblmDSX730mlNeFMnhVP04NokTsPSXquurNqTm3btmXlypVcvHiRCxcusGLFCm655Rb+/vtvfvzxRwA+++wz2rRpw/nz54mJiaFr165MnTqV3butkeiuXbsYPnw4oaGhTj+gKDUu6R4rIv7AF0BV4G/gTmPMGRFpBowwxoSIyABgHpC0v8RgY0ya43btHpuLju7iwrJHKBL1P85W7UTJfu9D8QqujsrtXNy5k7+eG4Mt4ggJnVpT97WJePoVvMOjXM1dusdOmTKFuXPnAhASEkJwcDBdu3albdu2/PDDDwQGBrJw4UJiYmLo1asXly9fxhjD6NGjGTRoELfddht79+6lQgXr/7WqVasSGhqaqRgy2z1W24yrLAuPjGbg7O+5z6xhlMcSPH188ezyJjS+3xp9qKtMbCynZ8zk9IwZeBQvTvkXX6BYly4pnlimcoa7JAp3oG3GVa7ZfiiKS/HCzPjudIubwIlCgRD6KCwMhugIV4fnVsTbmzKPPkLAsmV4VazIkSdHcfjRR4k7mWJ5Tim3oolCZVmrGv54e9rwEDjiUYnjvZdCt8lwOAw+ugm2Twd7gqvDdCu+dWpT/fPPKPv0aC5s3cah7j04u2x5qkVOpdyBJgqVZYkb9EZ1rsOikFY0re4PzUNg5HarQ+03z8C8O+DUPleH6lbE0xP/IUMIWLkCn9qBHHv+ef4ZEkLs4SPXXKcNBpW70BqFyhnGwJ7F8M1YiL0A7Z6B1o+Dh5erI3Mrxm4n+vPPOTVpMgYo++ST+N13L7+c3sPQdUOJTYjF28Nbd3o7gdYo/qM1CuUeRODGu62NenXugI3jYFZ7OPaLqyNzK2KzUeree6mxOpTCTZpw4o03iBxwP7/uXKcNBpXb0EShclbRstB/AfRfCOdPwsz2sPF1iI91dWRuxatSJarMmkmF8eO5cvAgjcd8Qp/t4GVsuvdCuZwmCpU76vWEh3+CRv1hy0QdXaRARCjZO5iaX66meLtb6b8plulL/Jgd+BJBZYO0ZqGuUb16dU6fPp3tazLCM9t3UCqjCvkR3mQ8x+Nb0PnQm3jN6gBtn4ZbntLaRRKeZcpQ+f33OPfNNxx/9TXsIc+z94Fwhpf+kssmTmsWKtfpiELlmvDIaO6bvZ1Hd5ajzfnxRAX0gM3jrdHF8b2uDs/tFO/ShRpfrqZohw54zlzMC/MuUvFUgtYs8rCIiAjq1q1LSEgIDRo04L777mP9+vW0bt2awMBAduzYwZkzZwgODqZRo0a0atWKPXv2ABAVFUXnzp1p3Lgxw4cPv2ZJ9SeffEKLFi0ICgpi+PDhJCQ4d1m6jihUrtl+KIrYeDt2A6fjC/N55Rd4uPmdsPoJq3bRbgy0eVJHF0l4+vtT+d2p7Pl8GmXffo+35iWwvJ0HzTo1cXVoedrxN9/kyu9/OPWePjfUpfxzz6V73YEDB1iyZAkzZ86kefPmfPrpp2zbto3Q0FDefPNNqlSpQuPGjVm5ciUbN25k4MCB7N69m1dffZU2bdrw0ksvsWbNGmbOnAlYK5gWL17M999/j5eXFyNHjmTRokUMHDjQad+bJgqVaxI36MXF2/HytNGqhj9U6wZVb4KvnoZNb8AfX0LwdChXz9XhupVGdz/E7sb1iHlzMndt/JNCUW9zZcJ4fAICXB2ayqSAgAAaNmwIQP369enYsSMiQsOGDYmIiCAyMpJly5YB0KFDB6KiooiJiWHLli0sX74cgG7duuHn6Be2YcMGwsPDad68OQCXLl1yerNATRQq1yRu0Nt+KIpWNfxpWs3RGK9wKeg3B+r1gi+fhBlt4dax0PoJ6zwMBUBQnXaYj9ty7ss1HH/9df4K7k3Zp0bhN2AAYtNZ5MzIyG/+OSVpW3CbzXb1sc1mIz4+Hk/P6/+bT+wJllJvMGMMgwYNYvz48TkUsdYoVC5rWs2Ph9vX+i9JJJW4MuqG7ta+izmd4NT+3A/SjYkIJXp0p0ZoKEVateLEm+P57d5+LFw/SVdD5RNt27Zl0aJFAGzevJnSpUtTvHjxaz7/9ddfEx0dDUDHjh1ZunQpJx19w86cOUNkpHOPLdZEodxLkdJw58fQb57VWHDGLfDjR2BP8XDDAsurXFkqT59G3NjhXP79dxo8OYdFEwaz+8QuV4emsumVV14hLCyMRo0aMXbsWObPnw/Ayy+/zJYtW2jSpAnr1q2jatWqANSrV4/XX3+dzp0706hRIzp16sSxY8ecGpO28FDu698TsPox2P8NVGsDwR+CX3VXR+VWZu+dzaeb32P4mngaRRjONA6gxdSP8SqXOwfa5CXawuM/2sJD5R/FysE9n0OvD63NedNaQ/jHeppeEs3KNeNfPx8m3OPF/C7e+P12hEM9e3Luq69cHZrKRzRRKLcUHhnNh5sOEP73WWg8AEb+ABUbw+rH4dP+cM65Q+u8KvG4zoebPMrdz82n5sqVeFevxpFRT3Fk1CjiHfPYSmWHLilRbidxY15svB1vT5vVwrxaVRgYCj/Pgm9fhmk3Qbcp0KCPq8N1uaCyQdfs0q6+aBFRs2dz6oMPufhzGBXeeJ2ibdu6MEKV1+mIQrmdpBvz4uLtbD8UZX3BZoOWw2HEVihVA5Y+AMtC4JL+1pyUeHpSesQIAr5YjEfJkvwzbDjHXnoZ+4ULrg7N5fJbTTYrsvIz0ESh3E7Sk/OubsxLqnQgPLgO2j8Pv66Aj26GgxtdE6wb861Xj+pLl1BqyIOcXbKEQ8G9uRgeXmCbC/r6+iMg/vkAACAASURBVBIVFVWgk4UxhqioKHx9fTP1PF31pNxSeGT09RvzUnJkJ6wYDqf3Q/Oh0OlV8C6Se4HmERfDwjj67HPEHj7MV608+awN2Hx8ClRzwbi4OA4fPszly5ddHYpL+fr6UrlyZby8rm2Vk9aqJ00UKs+5LonEXYINr8H2j8C/FvSeCZWbujpMt2O/cIHvxj5I+W/3EFkGPurlRc/bHyOkYYirQ1NuQJfHqnwjsdA9ed0+7pu9nfDIaPAqBF3GW8XuuMvWju7NEyAhztXhuhVbkSL4vfgsk+72pfhFeGNuHC03HMU4Oo0W1CkplT5NFCpPSbXQDVCjHTz0PTTsZ7Uvn3s7nD7gumDdUFDZIB4dOY/97z2E3NICz+mfETloELt3r2PouqG8v/N9hq4bqslCXUMThcpT0i10FyoJfWZaLUCiDsL0NrBjlm7SSyKobBCDWz9G/ekfU/GtCVz5Yx+eg0dz065L2I2ed6GupzUKledkuNB97hisGmmtiKp1m7XDu1j53As0j4g7coT9Tz2Kbffv/FzbxrzuhXgneHaBKXIrixazVcFlDPw8G9a9AF6Foce7VpdadQ1jt/O/D97ENvNzpHgxqk54SzfpFTBuV8wWkVIi8q2I/On4O9VfC0WkuIgcEZEPcjNGlfdcbfsRmWQDngi0GArDt0LJqvDF/bByJFw+57pA3ZDYbDR87AVqLluGr38Z/hk2nOOvvYb90iVXh6bcgKtqFGOBDcaYQGCD43FqxgHf5UpUKs9KcTVUUmVqw5Bv4ZbR8MtnML01RP7ommDdmG+dOtYmvcGDif70M/7q05dL//vV1WEpF3NVougFzHd8PB8ITukiEWkKlAPW5VJcKo9KczVUIk9v6PgiPPA1IPBxV1j/KsTH5nq87szm40O5sc9Qdd5c7BcvEnH33ZyePh2TkKBLaAsoVzUFLGeMOQZgjDkmItc1zxcRGzAZuB/omMvxqTwmxfO4U1O1lbWM9uuxsG2KVezuM8sadairitx0EzVCV3H81dc4NfVdTqz/mufaHeZI8Xi8PbwL1K7ugi7HRhQisl5E/pfCn14ZvMVI4CtjzD8ZeK1hIhImImGnTp3KXuAqT0o8j3tU5zqObrNprIYC8ClmHYTUfyGcjbTO6f55ji6jTcajRAkqTZlMxYkTsR+K5PVZF2m7J564hFhdQluAuGTVk4jsA251jCYqAJuNMXWSXbMIuAWwA0UBb+AjY0xa9Qxd9aSuk+5y2nPHYOVDcGgT1O4CPT+AomVyP1A3t3vveg499Tg3/G3n57oeNJo4jaDAW1wdlnISt1v1BIQCgxwfDwJWJb/AGHOfMaaqMaY6MBpYkF6SUCq5dIvcAMUrwIDl0OUtOLjJOuti3ze5H6ybC2p4GzUWLCRiQFuaHYAiQ17g/PffuzoslQtclSgmAJ1E5E+gk+MxItJMRGa7KCaVD2WoyA3WWRetRsCwzVC0HHx2F3w5CmIv5ma4bi+ofBPueGEGNb74AluxYvwzJIQT48djv3Llmuu06J2/6IY7la8ljigSi9wZql/EX4GN4+CH98E/EPrOso5hVdewX7rEyYmTiP70U3xq16bixIn41qnN7pO7GbpuKLEJsVr0zkPccepJqVyR6SI3gKcPdH7d6kYbewFm3wZbJ4M9IecDzkNshQpR/qUXqTJjOvFnzhBx552cmT+fsGM/E5sQix279o3KJ3REoVRaLkXDl09aJ+lVaw29p1s7vNU14qOiOPbCi5zftAl7s4Y80fogp4ok4GXzYlbnWQCEnQijWblmOrpwU9rrSakUZLi5oDHwy+fw1WgQD+g+xWplrq5hjOHs4i84MWECdm9P/hjWgRo97wHQqag8QKeelEomQ6uhEolA0D0wYhuUqQPLhsDyYXA5JvcCzgNEBL+77yJg+XIKVa3ODZNCKfveUnZG/KBTUXlcuolCRHxFpJ+IvCsiS0RkgYiMEZH6uRGgUjkhw6uhkioVYLX/uPU52LsUprXRflEp8KkRQPVPF+E/bBgxy5bT8tkl1D3ugYd44GXzolm5FH9pVW4szUQhIq8A3wM3AT8BM4AvgHhggqPza6OcDlIpZ0v3AKTUeHjCrc/Ag2utJbUfd4WNr+uxq8mItzdlRz1J1fkf420XXl5whQmHmjGr44wUp510Oa17S7NGISLdjDFr0vh6WaCqMcZtxpJao1AZleEaRWqu/AtfPwO7F0Glpla/KP+azg80j0s4d47jr7zKua++olCzplR66y28KlW6+nVdTuseslyjSCtJOL5+0p2ShFKZ0bSaHw+3r5W1JAGOflEfwZ0fQ9QBmH4L7PpE+0Ul41G8OBUnT7KOXf39Dw4F9ybmy//eWsJOhGkNw81lqJjt2DG9QkR2isgeEdkrIntyOjil8oT6veGhH6BSE1j1MCwZDBfPuDoqtyIilOjVi4CVK/CpWZOjo0dzZMwYEv79l2blmuHt4a01DDeWoeWxjiZ+TwN7sZr0AWCMicy50LJGp56Uy9gTrN3cG8dZbUB6T4cAPU40ORMfz+npMzj90Ud4VahAxYlvs7+yTfdZuFi291GIyDZjTBunR5YDNFEolzu6C5aFQNRBaP0YtH/BOjRJXePizl0cHTOGuKNHKT1iBKVHPoR4XntEzu6TuzWB5BJnJIqOwD1Yx5Ze7f5ljFnurCCdRROFcrYsFb1jL8A3z8LO+VDhRug7B0oH5mygeVDC+fOcGDeOmFWhFAoKouLEt/GuUgXQInduc8aGuweAIKAL0MPxp7tzwlPKfWVqY15S3kWg53tw1yI4+491MFL4x1roTsajaFEqvvUWFSdP4srBg/wV3JuYVaswxqRa5NaltLkvo0eh3miMaZijkSjlhlLamJepVVI3dLeWzq4YDqsfhz+/hZ7vQ+FSORd0HlSiWzcKBwVxZMwzHH1mLOe3bKXZyN54e3gTZ4+7WuTWUYZrZHREsV1E6uVoJEq5oSxvzEuqeAW4f6XVkXb/WpjWGg595/xg8zivSpWotmA+ZR5/jHPffEPRoS8xu/xTPNL4kasJQZfSukZGE0UbYLeI7NPlsaogyVKb8pTYbHDzozB0A/gUhQW94NuXID7WuQHnceLhQemHHqL6p4vAwwPvx8bRa9NFbizVAECX0rpIRovZ1VL6vC6PVSoLYi/C2ucgfB5UCHIUumu5Oiq3k3D+Aidef52YlSspdOONVJw0Ee8qVa5bCaUro5wjy6ueRKSoMeZ8OjdP95rcpIlC5Rm/fwmhj1gn6nWZAE0GWp1q1TXOffUVx15+Bex2yr/0IsV79kQcPyetWThPdlY9rRKRySLSVkSKJLlhDREZIiJrsVZCKVVghUdG8+GmAxlfEZXohu7Wju7KzWD1Y/DFQN3RnYLiXbtSY+UKfOrW5egzYzk6+mkS/v0X0PYfuSW9Xk8dsfZODAd+FZEYEYkCPgHKA4OMMUtzPkyl3FOWl88mKl4R7l8FnV6DfV9bhe6/tuRMsHlY8kL3X72Cubhzp9YscomecKdUNny46QCT1+3DbsBDYFTnOjzcPov1hqQ7uts8Ae2fBw8v5wacD1zavZsjo5+2dnQ/9BBH+t5EWNSuVGsUWsPIGD3hTqkc4pTls4kqNobhW6DJ/bDtHZjTyUoa6hqFgoIIWLmCEj26c/rDDyn51GQG+t2RapIYum4o7+98n6HrhuomvSzSRKFUNjht+Wwi7yLWhrz+C+DMX47W5Yt0R3cyV3d0T5zIlT//5K/gYGLWXH8qgtYwnEMThVLZlNq5FlkucgPU65WkdflIWPoAXDrrpIjzjxI9ulutywMDOfrUaI4+M5aE8xeufj2tGoa2Asm49JbHptlnwBjjdks0tEah3EFikTs23o63py3row17Anw/FTa9CcUqQJ+ZUO1m5wecx5n4eE5Pm87padPwqlyZSpMmUqiRdUpzSjUKXVZ7vezUKMKBMMffp4D9wJ+Oj8OdGaRS+UnSHlGxcXamrt+ftZGFzQNueQoeXAc2T/i4G2x8AxLinR90HiaenpR59BGqLVwA8fFE3Hsfp6fPwCQkEFQ2iJCGIdckAp2Sypz0lscGGGNqAGuBHsaY0sYYf6zOsW7XYlwpd5FY5LZhnfT1/YHTWVs+m6hyUxixFRrdDVvehnl3QHSEEyPOHwo3bUrAyhUU79yZU1On8vfgB4g7duy663RZbeZktIVHuDGmabLPhaU2TMnA/UoBi4HqQATQ3xhz3f9BIlIVmA1UAQzQ1RgTkda9depJuYvwyGimrt/P9wdOO2f5bKK9S+HLJ62Pu02BRndmP9h8xhhDzKpVnHhtHHh5UeHVVyne5fZrrsnMstmCsMTWGQcXrQW2Ym20M8AAoK0x5vY0n5j6/d4GzhhjJojIWMDPGPNMCtdtBt4wxnwrIkUBuzHmYlr31kSh3ElirSIu3o5XdmoVyUVHwvJh8M92aHQXdJ0EvsWzf998JjYykiOjn+by3r2U6NeX8s89h61w4Uzdo6DUM5yxj+IeoAywAlgJlHV8Lqt6AfMdH88HgpNf4Ghr7mmM+RbAGHM+vSShlLtJbflstlZEAfhVg8Fr4NZnYe8SmN4G/vnZiZHnD97VqlH900X4DxtGzLLl/NWnL5d+/TVT99B6hot2ZovIWWNMySSPo40xfsmuCQZCgFggAFgPjDXGJKR1bx1RKHfntBVRif7eDsuGwrkj0P5ZaDPKKoKra1zY/hNHn3mG+DNnKPvEE5R6YDBiS/935cQRReIBSjqiSP0GZURkooh8JSIbE/+k85z1IvK/FP70ymDcnsAtwGigOVADGJzKaw0TkTARCTt16lQGb6+Ua6R0al62VG1lFbrrB8PG12F+D4g57Jxg85EirVoSsHIFxW5tx8mJE/knJIS4kyfTfV5Q2SBmdZ51zQFKiQrKXoyM1ijWYRWfRwMjgEHAqZTqChl6UZF9wK3GmGMiUgHYbIypk+yaVsAEY8ytjsf3A62MMQ+ndW8dUSh3l2N1C2Pgl8/hq9HWUtqe71kb99Q1jDGc/WIJJ8aPx1aoEBXeeINiHdpn+j75rXbhjBqFvzFmDhBnjPnOGPMg0CobMYViJRscf69K4ZqfAT8RKeN43AH4LRuvqZRbcHrbj0QiEHSP1S+qVIDVtjz0MYi9kP5zCxARwe+u/gQsW4pnuXIcHjmS4+Nex375cqbuU5BqFxlNFHGOv4+JSDcRaQxUzsbrTgA6icifQCfHY0SkmYjMBnDUIkYDG0RkLyDArGy8plJuI7W2H07hX9PaoNfmSdi5AGa0g2O/OP918jifmjWp/sViSg0aRPSiRUTc2Z/L+/dn+PkFaS9GRqeeumMtj60CvA8UB141xoTmbHiZp1NPSiVxaDOsGAEXo+C2V6DlQ9b53eoa57du5ejYZ7H/+y9lnxmD3733Xj1FLy1p7a/Ia3svsr2PIi/RRKHyi/DIaLYfiqJVDf/sjTwuREHoo7BvDdTsCMHToFg55wWaT8SfPs3R557jwpatFG3fngpvvoGnX9Z+7nmxfuGMVU+1RWSDiPzP8biRiLzgzCCVUv/J9sl5SRXxh7sXWbu4I7+HaTfD/nXOCzaf8CxdmirTp1PuuWe5sG0bf/XsxYUffsjSvfJb/SKjY9BZwLM4ahXGmD3A3TkVlFIFndOX0IpA8yEw7DsoWg4+vRO+fgbiMlfAze/EZqPUwIFUX/IFtuLF+XtICCcnTcLExmbqPvmtfpHRRFHYGLMj2ee0faVSOcSpJ+clVbYuDN0ILUfAT9NhVgc4+Ydz7p2P+NatS8DSJZS8qz9Rs+cQcc+9xEZEZPj5qe29yKv7LjJazP4aeARYYoxpIiL9gCHGmDtyOsDM0hqFyi+cVqNIzf61sHIkxJ6H29+EZg9aIw91jX/Xr+fY8y9gj4uj/AsvUKJ3cIYK3cm5e93CGfsoHgZmAHVF5AjwBPCQk+JTSqUgR5fQAtS+3TpFr9rNsGYULB4AF93uLDKXK3bbbQSsWkmhBg049txzHH3qKRLOncv0ffJy3SJDicIYc8gYcxtWY8C6xpg26bX7VkrlAcXKwX3LoPMb1ghj2s3w1xZXR+V2vMqXp+q8uZR58knOrV3HX8G9ubhzZ6bukZfrFhmdevIB+mKdH+GZ+HljzGs5FlkW6dSTKmicNkV17BdYOgSiDlib9do/Bx5ezgs0n7j0yy8cGf00cUeOUHrkSEqPGI54eqb/RNLfW+HKvRfOOI/iGyAG6/jTq91bjTGTnRWks2iiUAWJ0zvRxl6Ab8ZaO7orNYW+s6FUDecFnE8knD/P8dde41zoago1bUqlt9/Cq1KlbN0zpRoGkGuJI61EkbE0CJWNMV2cGJNSyglSWkabrUThXQR6vm9tzFv9GEy/BbpNhht1NXxSHkWLUunttynapg3HX32NQ8G9qTDuNYp3yfrbZPIaxuqDqwk9GOoWxe+MFrN/EJGGORqJUirTcmwZbf1gGPE9lG8EK4Zb511cznwBN78r0bMnAStX4F0jgCNPPMnR55/HfjFr56slr2EYjNsUv9OcenI04zNYI49A4BBwBatBnzHGNMqNIDNDp55UQZOjy2jtCbB1MmyeACUqQ985UKW5c18jHzBxcZz68EOiZszEu1o1Kk6eRKH69TN9n6Q1CuC6A5Mg56aislyjEJFqad3YGBOZzdicThOFUjng759gWYieopeOCz/t4OiYMdYpek8+SanBgzJ0il5qUkocOTUVleV9FMaYyLT+OC1CpZR7q9oy2Sl6PSHmiKujcjtFWrb47xS9t9/mn6HDiM/GqZtBZYMIaRhCUNmgdPdh5OSub+03rJTKmEIlramnXh/B0V3WnovfV7s6Krfj6edHpffeo/yrr3IxPJxDvYI5/9132b5vavswdp/czbgfxzFk7RDe3/k+Q9cNdXqy0DbjSqnMizoISx+EY7uh6QNWCxDvwq6Oyu1cOXCAI0+N5sq+ffgNvJ+yTz2Fzccny/dLvs8icUntlYQrGKz3cg/x4JHGjxDSMCRT93ZGCw+llPqPf00Y8i3c/BiEz4OZt8Lxva6Oyu341KpF9S8W4zfwfqIXLCTirru5cvBglu+XdCoK/ltSm5gkBMmRXd+aKJRSWePpDZ3Hwf0r4PJZqxPt9mmQz2Ypssvm40P5556jyozpxJ88yV99+xG9+AucMZuTdDrK2+ZNv9r9cmS/hU49KVVAOXVZ7YXTVifaP9dCYGerjlG0jHMCzUfiT53i6DNjufDDDxTr1IkK417Do2TJbN3TWW0/9ChUpdQ1nN76A6yRxI6ZsO5Fq/AdPA1qdXROwPmIsds58/F8Tr7zDp7+/lR8+y2KtGjh6rC0RqGUulZ6J+iFR0bz4aYDmTuCVQRaDrcORirkB5/0gXUvQHzmTofL78Rmw//BB6j+2WfYfHz4e9BgTr77LiYuztWhpUoThVIFUFqtP7J9Xnf5BjB0k3UQ0g/vw5xOcPqAk7+DvK9Qg/oELF9Gid69iZo2ncgB9xN7+LCrw0qRJgqlCqCm1fxYFNKKUZ3rXDft5JTzur0LQ/d34K5FcDYSZrSFXYu00J2MrUgRKr75BpWmTObKwYP8FdybmC/XuDqs62iiUKqASu0EPac2Gryhu9VcsFITWDUSlg2BS2ezGXn+U7xrVwJWrsQnMJCjo0dzdOyzJJy/4OqwrtJitlLqOk5vNGhPgG3vwKY3oXgl65yLqi2zf998xsTHc/qjaZyePh2vKpWpNGkShRrmTuNuXfWklHKabCWRf362RhUxh+HWsXDLU9pcMAUXf/6ZI2OeIf7UKco+8TilHnwwW80FM0JXPSmlnCLbhe4qza3mgg36wKY3YH4PK2moaxRu3pwaK1dQrEMHTk6azD8hIcSdPOmyeFySKESklIh8KyJ/Ov5O8dcSEXlbRH4Vkd9F5D0RkdyOVSn1n9QK3ZlaTutbAvrMguDp1jnd01rDb6E5HHne41GiBJXenUr5ca9xcddu/uoVzL+bNrkkFleNKMYCG4wxgcAGx+NriMjNQGugEdAAaA60y80glVLXSqnQnaVRhggE3QPDt1hncn9xP6x+HGKzdjpcfiUi+N15JwHLluJZvjyHHxrJ8XGvY79yJVfjcFWi6AXMd3w8HwhO4RoD+ALegA/gBZzIleiUUilKaVlttpbT+teEB9dC6ycgfD7MbKfNBVPgU6MG1Rd/TqlBA4letIiIO/tz5UDu7U1xVaIoZ4w5BuD4u2zyC4wxPwKbgGOOP2uNMb+ndDMRGSYiYSISdiobh4QopdKXfFlttpfTenpDp1dh4ErrXO5ZHWD7dN1zkYzN25tyzz5LlZkziD992mou+PnnTmkumJ4cW/UkIuuB8il86XlgvjGmZJJro40x19QpRKQW8C5wl+NT3wLPGGO2pPW6uupJqdzntOW0F07Dqkdg/9faXDAN8adOcXTss1z4/nuKdbqNCuPGZbu5oNstjxWRfcCtxphjIlIB2GyMqZPsmqcBX2PMOMfjl4DLxpi307q3Jgql3Eumk4gxsGOW1SeqUEnoPR1qdsj5QPOYa5oLlipFxbffpkjLrDcXdMflsaHAIMfHg4BVKVzzN9BORDxFxAurkJ3i1JNSyj1ludDdcth/zQUX9tbmgim4prmgry9/Dx7MyalTMXa701/LVYliAtBJRP4EOjkeIyLNRGS245qlwEFgL/AL8IsxRg/oVSoPyVah+2pzwSH/NReMyvrpcPlV0uaCcf8cthKtk+nObKVUjkkcUcTF2/HKzrkXv38JoY9Yo4quEyHo3hx5Q8zrTHw84umZpee6XY0iJ2miUMq9OK3QHXMElg+DyG3QoK/Vnda3hPMCLeA0USil8gd7AmybApvGQ4lK0HcOVHH96XD5gTsWs5VS6jrptgKxeUDbp+HBb6zHc7vAdxOtBKJyTNYms5RSyskydY53lRYwYht8OQo2vQ6HNkOfmdYoQzmdjiiUUm4h0yukfEtY51oET4Oju2DazfC7LozMCZoolFJuIUutQESsFVAjtkKpAFg8AFY/oc0FnUyL2Uopt5HSCqkMr5qKj7Wmob5/F8rUtQrd5RvkUuR5n656UkrlSZmqWyQ6uBFWjLDO5u48DloM0z0XGaCrnpRSeVKWdnbX7AAP/QA1boWvx8Bnd1vNBlWWaaJQSrmtLLcwL1Ia7l0MXd6yRhjTWsNB15wOlx/o1JNSyq1le2f38b2w9EE4/Se0fgzav2CdgaGuoTUKpVTBFnsR1j4L4R9DxcZWodu/pqujcitao1BKFWzehaHHu9B/AZz5C2a0hd2f6Sl6GaSJQimV76TaCqReL3joe6hwI6wcAcuHWsevqjRpCw+lVL6S7pLaEpVh0GrYOhk2T4B/djiaCzZ3XdBuTkcUSql8JUNLam0e0G4MPPC1Nf0093bYMkmbC6ZCE4VSKl/J1JLaqi2t9h/1esHGcbCgF5w7mnvB5hG66kkple9kZEntNddULQm7PrE26Hn6QK8PoW63XI7atXR5rFJKJZFqHeP0n9aei+N7oHkIdH4dvAq5OtxcoctjlVIqiVTrGKUDIWQ93PQI/DwbZnWAE7+5Nlg3oIlCKVXgpFnH8PSB29+A+5bBhVMwqz3smFWg91zo1JNSqkDKUGuQ8ydh5UNwYD3U6Qa9PoDCpXI30FyS1tST7qNQShVITav5pd87qmhZwtvM5JL5gJv/fB/btJutI1cD2uZOkG5Cp56UUioV4ZHR3DdnBwN/a0bfuNe4bCsE83vChtcgIc7V4eUaTRRKKZWKpEXvPfHVmN9wATQeYO3qntvF6htVAGiiUEqpVCQvejcLrGzVKfrNs5bSTr8F9ixxdZg5TovZSimVhlSL3tGRVlPBf36CG++BrhPBp5jrAs0mt9tHISJ3isivImIXkRQDc1zXRUT2icgBERmbmzEqpRRYRe+H29e6vvDtV43wDov4udpQzJ7FVuvyIztdE2QOc9XU0/+APsCW1C4QEQ/gQ+AOoB5wj4jUy53wlFIqbeGR0dw3N4y79rdnQPyLxF65DHM6wffvgt3u6vCcyiWJwhjzuzFmXzqXtQAOGGMOGWNigc+BXjkfnVJKpS9poXt7fB0W3LgI6nSFb1+CT/rAv8ddHaLTuHMxuxLwT5LHhx2fu46IDBORMBEJO3XqVK4Ep5Qq2JIXuhvXCbBO0OvxLvbIH7n0Xkv+3LbM1WE6RY5tuBOR9UD5FL70vDFmVUZukcLnUqy8G2NmAjPBKmZnOEillMqiptX8WBTS6rpCd3jpXrwYG88keY966x/kxJEtlOvzFnj5ujjirMuxRGGMuS2btzgMVEnyuDKgjeKVUm4jpd3d2w9F8Ud8BXqbV3nW8zMG//4xzA6HfnOgTB3XBJpN7jz19DMQKCIBIuIN3A2EujgmpZRKU+KUVLx4M0Ee4M/b5sK/R2FGOwj/OE82F3RJrycR6Q28D5QB1ojIbmPM7SJSEZhtjOlqjIkXkUeAtYAHMNcY86sr4lVKqYxKPiUVWM0PbmwNK4bD6sfh4Ebo8S4USqfPlBvRDXdKKZUb7Hb44T3ryNWi5aHvbKh2k6ujusrtNtwppVSBY7NBmydgyDrw8IKPu8Km8ZAQ7+rI0qWJQimlclOlpjBiKzS6C76bwNF3O7D3172ujipNmiiUUiq3+RQjvMl4RtsfoVjMfqp90ZmDmz+57rLwyGg+3HSA8MhoFwT5Hz24SCmlXGD7oSiWx93MT9Tkfa8PCNr8MMRshzveAu8iVouQ2duJjbfj7WljUUir9A9ayiE6olBKKRdIXEZ7lHIMMK9yrNFI2PWJtYz22C/XtAiJi7ez/VCUy2LVEYVSSrlA8mW0Far1gKAu1jLa2bfRs+kzvO9Zj7h4g5enjVY1/K8+N0PnfTuRLo9VSil3ciEKQh+BfV8RU+lWllV9nhvrBv7XIiSHpqR0eaxSSuUVRfzh7k+h6yRKHP+RB/fcR9O47ClZgQAABo9JREFU/865cMWUlCYKpZRyNyLQYigM2wSF/a225Wufh/jY67rWtqrhn+Oro3TqSSml3FncJStJhM2BCjdC37mEX/C/WqMAnDIVpVNPSimVV3kVgu5T4K5FcPZvmNGWpme+4uFba9K0ml+uTEVpolBKqbzghu4w4nuo1ARWjYRlQ+ByTIpTUc6mU09KKZWX2BNg2xSrT1SJStZUlL1WtpfL6tSTUkrlFzYPaPs0PPiN9Xju7TSNnM3D7QJybE+FJgqllMqLqrSAEdugfm/Y+Dos6AXncuYQUE0USimVV/mWsM61CJ4GUQcg/kqOvIy28FBKqbxMBILuhfp9wMs3R15CRxRKKZUf5FCSAE0USiml0qGJQimlVJo0USillEqTJgqllFJp0kShlFIqTZoolFJKpUkThVJKqTTlu6aA/2/vXkOkqsM4jn9/GSVZKJUvsgJNswtiSlSQFAkVvYiM7iWEJIGVEkQXI+hNlIYUKFFiFFF0wXxhbppBkBhisHYjRQpTosVCjTIlEcKnFzPWMDvznz1nz5nL+vvAsvv/n+ecefbhvzycs8P8Je0Hfq6bHgscbBDeaL5+rtX4bOBArmRba5Z3Ueel4oZTs0ZzteMya9Ysn6LOaRXntZY9zmstX1zRa21cRIxv+EoRMeK/gFVDna+fG8J4W7vzLuq8VNxwataqbmXWLG/diqjZcOvmtea1liWuzLVW/3WiPHrqyzBfP9dqXKa8rzXU81Jxw6lZo7lur1sRNUsd91rLfsxrLd/xwtfaiHv01G6StkWTz3C3xlyzfFy37FyzYpwodxRlWtXpBHqQa5aP65ada1YA31GYmVmS7yjMzCzJjcLMzJLcKMzMLMmNomSSxkj6StLNnc6lF0i6RNJKSWskPdTpfHqFpFslvS7pI0k3djqfXiDpAklvSFrT6Vy6nRtFE5LelLRP0va6+Zsk/SBpl6TFQ7jUU8DqcrLsLkXULCJ2RsQC4C7ghHhbY0F1WxsRDwLzgLtLTLcrFFSz3RExv9xMRwa/66kJSdcCh4G3I2JadW4U8CNwAzAA9AP3AqOAJXWXeACYTuUjBEYDByLi4/Zk3xlF1Cwi9km6BVgMvBIR77Ur/04pqm7V814C3o2Ir9uUfkcUXLM1EXFHu3LvRSd3OoFuFRGbJU2sm74S2BURuwEkfQDMiYglwKBHS5JmA2OAS4EjkjZExLFSE++gImpWvc46YJ2k9cCIbxQFrTUBS4FPRnqTgOLWmg2NG0U25wK/1IwHgKuaBUfEMwCS5lG5oxixTSIhU80kXQfcBpwKbCg1s+6WqW7AIuB6YKykKRGxsszkulTWtXYW8DwwU9LT1YZiDbhRZKMGcy2f3UXEW8Wn0jMy1SwiNgGbykqmh2St2wpgRXnp9ISsNfsdWFBeOiOH/5mdzQBwfs34PGBvh3LpFa5ZPq5bdq5ZSdwosukHLpQ0SdIpwD3Aug7n1O1cs3xct+xcs5K4UTQh6X1gK3CRpAFJ8yPiH2Ah8CmwE1gdETs6mWc3cc3ycd2yc83ay2+PNTOzJN9RmJlZkhuFmZkluVGYmVmSG4WZmSW5UZiZWZIbhZmZJblRmJlZkhuFWYKkcZIerhlPKGujm+rmQ882OXa4+n28pI1lvL5ZM24UZmnjgP8aRUTsLXHvgieBV1MBEbEf+FXSrJJyMBvEjcIsbSkwWdK3kpZJmnh8VzVJ8yStldQnaY+khZIek/SNpC8lnVmNmyxpY3VL3C8kXVz/IpKmAkcj4kB1PEnSVkn9kp6rC18LzC331zb7nxuFWdpi4KeImBERTzQ4Pg24j8qmOc8Df0fETCqfQ3R/NWYVsCgiLgcep/FdwyygdsOh5cBrEXEF8Ftd7Dbgmpy/j1lm3o/CbHg+j4hDwCFJB4G+6vz3wHRJpwNXAx9WNqEDKpsy1TsH2F8zngXcXv35HeDFmmP7gAnFpG/WmhuF2fAcrfn5WM34GJW/r5OAPyNiRovrHAHG1s01+8TO0dV4s7bwoyeztEPAGXlPjoi/gD2S7oTK3taSLmsQuhOYUjPeQmU/BRj8/4ipwPa8OZll5UZhllDdLnOLpO2SluW8zFxgvqTvgB3AnAYxm6ns3Xz8+dSjwCOS+hl8pzEbWJ8zF7PMvB+FWZeQtBzoi4jPWsRtBuZExB/tycxOdL6jMOseLwCnpQIkjQdedpOwdvIdhZmZJfmOwszMktwozMwsyY3CzMyS3CjMzCzJjcLMzJL+BWFj0WJ6PRCHAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -755,14 +762,14 @@ ], "source": [ "h1 = ml.head(ro1, 0, to1, 0)\n", - "plt.semilogx(to1, ho1, '.', label='obs1')\n", - "plt.semilogx(to1, h1[0], label='model')\n", + "plt.semilogx(to1, ho1, \".\", label=\"obs1\")\n", + "plt.semilogx(to1, h1[0], label=\"model\")\n", "h2 = ml.head(ro2, 0, to2, 0)\n", - "plt.semilogx(to2, ho2, '.', label='obs2')\n", - "plt.semilogx(to2, h2[0], label='model')\n", - "plt.title('two wells simulaneously')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + "plt.semilogx(to2, ho2, \".\", label=\"obs2\")\n", + "plt.semilogx(to2, h2[0], label=\"model\")\n", + "plt.title(\"two wells simulaneously\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend();" ] } diff --git a/notebooks/pumpingtest_hypothetical.ipynb b/notebooks/pumpingtest_hypothetical.ipynb index 4e63cf5..0892433 100644 --- a/notebooks/pumpingtest_hypothetical.ipynb +++ b/notebooks/pumpingtest_hypothetical.ipynb @@ -16,7 +16,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -32,8 +32,8 @@ "metadata": {}, "outputs": [], "source": [ - "drawdown = np.loadtxt('data/oudekorendijk_h30.dat')\n", - "tobs = drawdown[:,0] / 60 / 24\n", + "drawdown = np.loadtxt(\"data/oudekorendijk_h30.dat\")\n", + "tobs = drawdown[:, 0] / 60 / 24\n", "robs = 30\n", "Q = 788" ] @@ -60,8 +60,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=0)\n", + "ml = ttim.ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=0)\n", "ml.solve()\n", "np.random.seed(2)\n", "hobs = ml.head(robs, 0, tobs)[0] + 0.05 * np.random.randn(len(tobs))" @@ -103,10 +103,10 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=100)\n", - "cal.set_parameter(name='Saq0', initial=1e-3)\n", - "cal.series(name='obs1', x=robs, y=0, layer=0, t=tobs, h=hobs)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=100)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "cal.series(name=\"obs1\", x=robs, y=0, layer=0, t=tobs, h=hobs)\n", "cal.fit()" ] }, @@ -203,7 +203,7 @@ } ], "source": [ - "print('rmse:', cal.rmse())" + "print(\"rmse:\", cal.rmse())" ] }, { @@ -223,7 +223,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -236,8 +236,8 @@ ], "source": [ "hm = ml.head(robs, 0, tobs, 0)\n", - "plt.semilogx(tobs, hobs, '.k')\n", - "plt.semilogx(tobs, hm[0], 'r')" + "plt.semilogx(tobs, hobs, \".k\")\n", + "plt.semilogx(tobs, hm[0], \"r\")" ] }, { @@ -256,7 +256,7 @@ } ], "source": [ - "print('correlation matrix')\n", + "print(\"correlation matrix\")\n", "print(cal.fitresult.covar)" ] }, @@ -293,10 +293,10 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=100)\n", - "cal.set_parameter(name='Saq0', initial=1e-3)\n", - "cal.series(name='obs1', x=robs, y=0, layer=0, t=tobs, h=hobs)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=100)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "cal.series(name=\"obs1\", x=robs, y=0, layer=0, t=tobs, h=hobs)\n", "cal.fit_least_squares(report=True)" ] }, @@ -323,8 +323,15 @@ } ], "source": [ - "ml = ModelMaq(kaq=[10., 10.], z=(-10, -16, -18, -25), c=[10.], Saq=[0.1, 1e-4], tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=1)\n", + "ml = ttim.ModelMaq(\n", + " kaq=[10.0, 10.0],\n", + " z=(-10, -16, -18, -25),\n", + " c=[10.0],\n", + " Saq=[0.1, 1e-4],\n", + " tmin=1e-5,\n", + " tmax=1,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)], layers=1)\n", "ml.solve()\n", "hobs0 = ml.head(robs, 0, tobs, layers=[0])[0]\n", "hobs1 = ml.head(robs, 0, tobs, layers=[1])[0]" @@ -516,13 +523,15 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0_1', initial=20., pmin=0., pmax=30.) # layers 0 and 1 have the same k-value\n", - "cal.set_parameter(name='Saq0', initial=1e-3, pmin=1e-5, pmax=0.2)\n", - "cal.set_parameter(name='Saq1', initial=1e-3, pmin=1e-5, pmax=0.2)\n", - "cal.set_parameter(name='c1', initial=1., pmin=0.1, pmax=200.)\n", - "cal.series(name='obs0', x=robs, y=0, layer=0, t=tobs, h=hobs0)\n", - "cal.series(name='obs1', x=robs, y=0, layer=1, t=tobs, h=hobs1)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(\n", + " name=\"kaq0_1\", initial=20.0, pmin=0.0, pmax=30.0\n", + ") # layers 0 and 1 have the same k-value\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-3, pmin=1e-5, pmax=0.2)\n", + "cal.set_parameter(name=\"Saq1\", initial=1e-3, pmin=1e-5, pmax=0.2)\n", + "cal.set_parameter(name=\"c1\", initial=1.0, pmin=0.1, pmax=200.0)\n", + "cal.series(name=\"obs0\", x=robs, y=0, layer=0, t=tobs, h=hobs0)\n", + "cal.series(name=\"obs1\", x=robs, y=0, layer=1, t=tobs, h=hobs1)\n", "cal.fit(report=False)\n", "display(cal.parameters)" ] @@ -544,7 +553,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -556,12 +565,12 @@ } ], "source": [ - "plt.semilogx(tobs, hobs0, '.C0', label=\"obs layer 0\")\n", - "plt.semilogx(tobs, hobs1, '.C1', label=\"obs layer 1\")\n", + "plt.semilogx(tobs, hobs0, \".C0\", label=\"obs layer 0\")\n", + "plt.semilogx(tobs, hobs1, \".C1\", label=\"obs layer 1\")\n", "\n", "hm = ml.head(robs, 0, tobs)\n", - "plt.semilogx(tobs, hm[0], 'C0', label=\"modelled head layer 0\")\n", - "plt.semilogx(tobs, hm[1], 'C1', label=\"modelled head layer 1\")\n", + "plt.semilogx(tobs, hm[0], \"C0\", label=\"modelled head layer 0\")\n", + "plt.semilogx(tobs, hm[1], \"C1\", label=\"modelled head layer 1\")\n", "\n", "plt.legend(loc=\"best\")" ] @@ -597,8 +606,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.3, res=0.02, tsandQ=[(0, 788), (0.6, 0)], layers=0)\n", + "ml = ttim.ModelMaq(kaq=60, z=(-18, -25), Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.3, res=0.02, tsandQ=[(0, 788), (0.6, 0)], layers=0)\n", "ml.solve()\n", "np.random.seed(2)\n", "hobs2 = w.headinside(tobs2)[0] + 0.05 * np.random.randn(len(tobs2))" @@ -636,11 +645,11 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0', initial=100)\n", - "cal.set_parameter(name='Saq0', initial=1e-3)\n", - "cal.set_parameter_by_reference(name='res', parameter=w.res[:], initial=0.05)\n", - "cal.seriesinwell(name='obs1', element=w, t=tobs2, h=hobs2)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0\", initial=100)\n", + "cal.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "cal.set_parameter_by_reference(name=\"res\", parameter=w.res[:], initial=0.05)\n", + "cal.seriesinwell(name=\"obs1\", element=w, t=tobs2, h=hobs2)\n", "cal.fit()" ] }, @@ -661,7 +670,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -674,8 +683,8 @@ ], "source": [ "hm = w.headinside(tobs2)\n", - "plt.semilogx(tobs2, hobs2, '.k')\n", - "plt.semilogx(tobs2, hm[0], 'r')" + "plt.semilogx(tobs2, hobs2, \".k\")\n", + "plt.semilogx(tobs2, hm[0], \"r\")" ] }, { diff --git a/notebooks/theis_storage.ipynb b/notebooks/theis_storage.ipynb index 28b83ea..f0939c3 100644 --- a/notebooks/theis_storage.ipynb +++ b/notebooks/theis_storage.ipynb @@ -41,12 +41,13 @@ "outputs": [], "source": [ "def headtheis(r, t, T=T, S=S, Q=Q):\n", - " u = r ** 2 * S / (4 * T * t)\n", + " u = r**2 * S / (4 * T * t)\n", " h = -Q / (4 * np.pi * T) * exp1(u)\n", " return h\n", "\n", + "\n", "def voltheis(r, t, T, S, Q):\n", - " u = r ** 2 * S / (4 * T * t)\n", + " u = r**2 * S / (4 * T * t)\n", " h = -Q / (4 * np.pi * T) * exp1(u)\n", " vol = h * 2 * np.pi * r\n", " return vol * S" @@ -70,7 +71,7 @@ ], "source": [ "# demonstrate headtheis works correctly\n", - "quad(voltheis, a=1e-3, b=10000, args=(10, T, S, Q)) # gives 1000 m^3" + "quad(voltheis, a=1e-3, b=10000, args=(10, T, S, Q)) # gives 1000 m^3" ] }, { @@ -80,7 +81,7 @@ "outputs": [], "source": [ "def volume(r, t, T=T, S=S, Q=Q):\n", - " return quad(voltheis, a=1e-3, b=r, args=(10, T, S, Q))[0] + npor * np.pi * (r ** 2)" + " return quad(voltheis, a=1e-3, b=r, args=(10, T, S, Q))[0] + npor * np.pi * (r**2)" ] }, { @@ -91,8 +92,8 @@ "source": [ "def vxytheis(t, xy):\n", " x, y = xy\n", - " r = np.sqrt(x ** 2 + y ** 2)\n", - " u = S * r ** 2 / (4 * T * t)\n", + " r = np.sqrt(x**2 + y**2)\n", + " u = S * r**2 / (4 * T * t)\n", " Qr = -Q / (2 * np.pi) / r * np.exp(-u)\n", " vr = Qr / (H * npor)\n", " vx = vr * x / r\n", @@ -108,8 +109,8 @@ "source": [ "def vxytheisnew(t, xy):\n", " x, y = xy\n", - " r = np.sqrt(x ** 2 + y ** 2)\n", - " u = S * r ** 2 / (4 * T * t)\n", + " r = np.sqrt(x**2 + y**2)\n", + " u = S * r**2 / (4 * T * t)\n", " Qr = -Q / (2 * np.pi) / r * np.exp(-u)\n", " vr = Qr / (H * (npor + S * headtheis(x, t)))\n", " vx = vr * x / r\n", @@ -135,9 +136,9 @@ ], "source": [ "# trace pathline for 10 days\n", - "path0 = solve_ivp(vxytheis, (1e-5, 10), y0=[1e-5, 0], method='DOP853')\n", + "path0 = solve_ivp(vxytheis, (1e-5, 10), y0=[1e-5, 0], method=\"DOP853\")\n", "R0 = path0.y[0, -1]\n", - "R0, np.pi * R0 ** 2 * npor * H" + "R0, np.pi * R0**2 * npor * H" ] }, { @@ -157,9 +158,9 @@ } ], "source": [ - "path0 = solve_ivp(vxytheisnew, (1e-5, 10), y0=[1e-5, 0], method='DOP853')\n", + "path0 = solve_ivp(vxytheisnew, (1e-5, 10), y0=[1e-5, 0], method=\"DOP853\")\n", "R1 = path0.y[0, -1]\n", - "R1, np.pi * R1 ** 2 * npor * H" + "R1, np.pi * R1**2 * npor * H" ] }, { @@ -217,7 +218,7 @@ ], "source": [ "R = path0.y[0, -1]\n", - "print('R, volume', R, np.pi * R ** 2 * npor * H)" + "print(\"R, volume\", R, np.pi * R**2 * npor * H)" ] }, { @@ -238,7 +239,8 @@ ], "source": [ "from scipy.integrate import quad\n", - "quad(headtheis, 1e-5, R, args=(100, T, S, Q))[0] + np.pi * R ** 2 * npor * H" + "\n", + "quad(headtheis, 1e-5, R, args=(100, T, S, Q))[0] + np.pi * R**2 * npor * H" ] }, { @@ -249,11 +251,13 @@ "source": [ "t0 = 0\n", "t1 = 10\n", + "\n", + "\n", "def vxytheis2(t, xy):\n", " x, y = xy\n", - " r = np.sqrt(x ** 2 + y ** 2)\n", - " u1 = S * r ** 2 / (4 * T * (t - t0))\n", - " u2 = S * r ** 2 / (4 * T * (t - t1))\n", + " r = np.sqrt(x**2 + y**2)\n", + " u1 = S * r**2 / (4 * T * (t - t0))\n", + " u2 = S * r**2 / (4 * T * (t - t1))\n", " Qr = -Q / (2 * np.pi) / r * np.exp(-u1)\n", " if t >= t1:\n", " Qr += 2 * Q / (2 * np.pi) / r * np.exp(-u2)\n", @@ -280,7 +284,7 @@ } ], "source": [ - "path1 = solve_ivp(vxytheis2, (10 + 1e-6, 20), y0=[R, 0], method='DOP853')\n", + "path1 = solve_ivp(vxytheis2, (10 + 1e-6, 20), y0=[R, 0], method=\"DOP853\")\n", "path1.y[0, -1]" ] }, diff --git a/notebooks/time.txt b/notebooks/time.txt deleted file mode 100644 index e69de29..0000000 diff --git a/notebooks/ttim_exercise1_sol.ipynb b/notebooks/ttim_exercise1_sol.ipynb index 36bf647..6fda529 100755 --- a/notebooks/ttim_exercise1_sol.ipynb +++ b/notebooks/ttim_exercise1_sol.ipynb @@ -16,7 +16,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -59,7 +59,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -69,35 +69,35 @@ } ], "source": [ - "ml = ModelMaq(kaq=[1, 20, 2],\n", - " z=[25, 20, 18, 10, 8, 0],\n", - " c=[1000, 2000],\n", - " Saq=[1e-4, 1e-4, 1e-4],\n", - " Sll=[0, 0],\n", - " phreatictop=False,\n", - " tmin=0.1,\n", - " tmax=1000)\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, \n", - " tsandQ=[(0,1000)], \n", - " layers=1)\n", + "ml = ttim.ModelMaq(\n", + " kaq=[1, 20, 2],\n", + " z=[25, 20, 18, 10, 8, 0],\n", + " c=[1000, 2000],\n", + " Saq=[1e-4, 1e-4, 1e-4],\n", + " Sll=[0, 0],\n", + " phreatictop=False,\n", + " tmin=0.1,\n", + " tmax=1000,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, 1000)], layers=1)\n", "ml.solve()\n", "\n", "t = np.logspace(-1, 3, 100)\n", "h = ml.head(50, 0, t)\n", "plt.figure(figsize=(12, 6))\n", "plt.subplot(121)\n", - "plt.plot(t, h[0], label='layer 0')\n", - "plt.plot(t, h[1], label='layer 1')\n", - "plt.plot(t, h[2], label='layer 2')\n", - "plt.legend(loc='best')\n", - "plt.ylabel('head [m]')\n", - "plt.xlabel('time [days]')\n", + "plt.plot(t, h[0], label=\"layer 0\")\n", + "plt.plot(t, h[1], label=\"layer 1\")\n", + "plt.plot(t, h[2], label=\"layer 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.xlabel(\"time [days]\")\n", "plt.subplot(122)\n", - "plt.semilogx(t, h[0], label='layer 0')\n", - "plt.semilogx(t, h[1], label='layer 1')\n", - "plt.semilogx(t, h[2], label='layer 2')\n", - "plt.legend(loc='best')\n", - "plt.xlabel('time [days]');" + "plt.semilogx(t, h[0], label=\"layer 0\")\n", + "plt.semilogx(t, h[1], label=\"layer 1\")\n", + "plt.semilogx(t, h[2], label=\"layer 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xlabel(\"time [days]\");" ] }, { @@ -115,7 +115,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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" ] @@ -135,12 +135,12 @@ } ], "source": [ - "ml.xsection(x1=-1000, x2=1000, y1=0, y2=0, npoints=100, t=10, layers=[0, 1, 2]) \n", - "plt.xlabel('distance (m)')\n", - "plt.ylabel('head (m)')\n", - "ml.xsection(x1=-1000, x2=1000, y1=0, y2=0, npoints=100, t=1000, layers=[0, 1, 2]) \n", - "plt.xlabel('distance (m)')\n", - "plt.ylabel('head (m)');" + "ml.xsection(x1=-1000, x2=1000, y1=0, y2=0, npoints=100, t=10, layers=[0, 1, 2])\n", + "plt.xlabel(\"distance (m)\")\n", + "plt.ylabel(\"head (m)\")\n", + "ml.xsection(x1=-1000, x2=1000, y1=0, y2=0, npoints=100, t=1000, layers=[0, 1, 2])\n", + "plt.xlabel(\"distance (m)\")\n", + "plt.ylabel(\"head (m)\");" ] }, { @@ -166,7 +166,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -176,24 +176,26 @@ } ], "source": [ - "ml = ModelMaq(kaq=[1, 20, 2],\n", - " z=[25, 20, 18, 10, 8, 0],\n", - " c=[1000, 2000],\n", - " Saq=[1e-4, 1e-4, 1e-4],\n", - " Sll=[0, 0],\n", - " phreatictop=False,\n", - " tmin=0.1,\n", - " tmax=1000)\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0,1000), (100,0)], layers=1)\n", + "ml = ttim.ModelMaq(\n", + " kaq=[1, 20, 2],\n", + " z=[25, 20, 18, 10, 8, 0],\n", + " c=[1000, 2000],\n", + " Saq=[1e-4, 1e-4, 1e-4],\n", + " Sll=[0, 0],\n", + " phreatictop=False,\n", + " tmin=0.1,\n", + " tmax=1000,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, 1000), (100, 0)], layers=1)\n", "ml.solve()\n", "t = np.logspace(-1, 3, 100)\n", "h = ml.head(50, 0, t)\n", - "plt.semilogx(t, h[0], label='layer 0')\n", - "plt.semilogx(t, h[1], label='layer 1')\n", - "plt.semilogx(t, h[2], label ='layer 2')\n", - "plt.legend(loc='best')\n", - "plt.ylabel('head [m]')\n", - "plt.xlabel('time [days]');" + "plt.semilogx(t, h[0], label=\"layer 0\")\n", + "plt.semilogx(t, h[1], label=\"layer 1\")\n", + "plt.semilogx(t, h[2], label=\"layer 2\")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.xlabel(\"time [days]\");" ] }, { @@ -219,7 +221,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -230,14 +232,14 @@ ], "source": [ "h = w.headinside(t)\n", - "plt.semilogx(t, h[0], label='res=0') # head from previous solution\n", + "plt.semilogx(t, h[0], label=\"res=0\") # head from previous solution\n", "w.res = 0.1\n", "ml.solve()\n", "h = w.headinside(t)\n", - "plt.semilogx(t, h[0], label='res=0.1')\n", - "plt.legend(loc='best')\n", - "plt.ylabel('head [m]')\n", - "plt.xlabel('time [days]');" + "plt.semilogx(t, h[0], label=\"res=0.1\")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.xlabel(\"time [days]\");" ] }, { @@ -266,7 +268,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -277,27 +279,29 @@ ], "source": [ "tmin = 1.0 / 24 / 60 # 1 minute\n", - "ml = ModelMaq(kaq=[1, 20, 2],\n", - " z=[25, 20, 18, 10, 8, 0],\n", - " c=[1000, 2000],\n", - " Saq=[1e-4, 1e-4, 1e-4],\n", - " Sll=[0, 0],\n", - " phreatictop=False,\n", - " tmin=1e-4,\n", - " tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0,1000)], layers=1)\n", + "ml = ttim.ModelMaq(\n", + " kaq=[1, 20, 2],\n", + " z=[25, 20, 18, 10, 8, 0],\n", + " c=[1000, 2000],\n", + " Saq=[1e-4, 1e-4, 1e-4],\n", + " Sll=[0, 0],\n", + " phreatictop=False,\n", + " tmin=1e-4,\n", + " tmax=1,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, 1000)], layers=1)\n", "ml.solve()\n", "t = np.logspace(np.log10(tmin), 0, 100)\n", "h = w.headinside(t)\n", - "plt.semilogx(t, h[0], label='no wellbore storage') # head from previous solution\n", + "plt.semilogx(t, h[0], label=\"no wellbore storage\") # head from previous solution\n", "w.rc = 0.2\n", "ml.solve()\n", "h = w.headinside(t)\n", - "plt.semilogx(t, h[0], label='wellbore storage')\n", - "plt.legend(loc='best')\n", - "plt.ylabel('head [m]')\n", - "plt.xlabel('time [days]');\n", - "plt.xticks([tmin, 10 * tmin, 60 * tmin, 1], ['1 min','10 min','1 hr','1 day']);" + "plt.semilogx(t, h[0], label=\"wellbore storage\")\n", + "plt.legend(loc=\"best\")\n", + "plt.ylabel(\"head [m]\")\n", + "plt.xlabel(\"time [days]\")\n", + "plt.xticks([tmin, 10 * tmin, 60 * tmin, 1], [\"1 min\", \"10 min\", \"1 hr\", \"1 day\"]);" ] } ], diff --git a/notebooks/ttim_figures.ipynb b/notebooks/ttim_figures.ipynb index b7615ac..af167a6 100644 --- a/notebooks/ttim_figures.ipynb +++ b/notebooks/ttim_figures.ipynb @@ -7,6 +7,7 @@ "outputs": [], "source": [ "from pylab import *\n", + "\n", "%matplotlib inline" ] }, @@ -31,28 +32,28 @@ "source": [ "# ModelMaq figure\n", "figure()\n", - "axes(frameon = 0)\n", - "grey = [.9, .9, .9]\n", - "plot([-1, 1], [0, 0], 'k', lw=2)\n", + "axes(frameon=0)\n", + "grey = [0.9, 0.9, 0.9]\n", + "plot([-1, 1], [0, 0], \"k\", lw=2)\n", "axhspan(-5, -10, color=grey)\n", "axhspan(-20, -25, color=grey)\n", - "plot([-1, 1], [-35, -35], 'k', lw=2)\n", - "text(-0.5, -2.5, '$k$ = 10 m/d', ha='center', va='center')\n", - "text(-0.5, -15, '$k$ = 30 m/d', ha='center', va='center')\n", - "text(-0.5, -30, '$k$ = 20 m/d', ha='center', va='center')\n", - "text(-0.5, -7.5, '$c$ = 2000 d', ha='center', va='center')\n", - "text(-0.5, -22.5, '$c$ = 5000 d', ha='center', va='center')\n", - "text(0.5, -2.5, '$S$ = 0.1', ha='center', va='center')\n", - "text(0.5, -15, '$S_s$ = 0.0001', ha='center', va='center')\n", - "text(0.5, -30, '$S_s$ = 0.0002', ha='center', va='center')\n", - "text(0.5, -7.5, '$S_s$ = 0.0001', ha='center', va='center')\n", - "text(0.5, -22.5, '$S_s$ = 0.0004', ha='center', va='center')\n", + "plot([-1, 1], [-35, -35], \"k\", lw=2)\n", + "text(-0.5, -2.5, \"$k$ = 10 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -15, \"$k$ = 30 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -30, \"$k$ = 20 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -7.5, \"$c$ = 2000 d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -22.5, \"$c$ = 5000 d\", ha=\"center\", va=\"center\")\n", + "text(0.5, -2.5, \"$S$ = 0.1\", ha=\"center\", va=\"center\")\n", + "text(0.5, -15, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", + "text(0.5, -30, \"$S_s$ = 0.0002\", ha=\"center\", va=\"center\")\n", + "text(0.5, -7.5, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", + "text(0.5, -22.5, \"$S_s$ = 0.0004\", ha=\"center\", va=\"center\")\n", "xlim(-1, 1)\n", "yticks([0, -5, -10, -20, -25, -35])\n", - "ylabel('elevation (m)')\n", + "ylabel(\"elevation (m)\")\n", "xticks([])\n", - "savefig('../docs/models/modelmaq.png', bbox_inches='tight')\n", - "#ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], c=[2000, 5000])" + "savefig(\"../docs/models/modelmaq.png\", bbox_inches=\"tight\")\n", + "# ModelMaq(kaq=[10, 30, 20], z=[0, -5, -10, -20, -25, -35], c=[2000, 5000])" ] }, { @@ -76,30 +77,30 @@ "source": [ "# ModelMaq figure\n", "figure()\n", - "axes(frameon = 0)\n", - "grey = [.9, .9, .9]\n", - "plot([-1, 1], [0, 0], 'k', lw=2)\n", + "axes(frameon=0)\n", + "grey = [0.9, 0.9, 0.9]\n", + "plot([-1, 1], [0, 0], \"k\", lw=2)\n", "axhspan(-5, -5, color=grey)\n", "axhspan(-10, -10, color=grey)\n", "axhspan(-20, -20, color=grey)\n", "axhspan(-25, -25, color=grey)\n", - "plot([-1, 1], [-35, -35], 'k', lw=2)\n", - "text(-0.5, -2.5, '$k$ = 10 m/d', ha='center', va='center')\n", - "text(-0.5, -7.5, '$k$ = 0.025 m/d', ha='center', va='center')\n", - "text(-0.5, -15, '$k$ = 30 m/d', ha='center', va='center')\n", - "text(-0.5, -22.5, '$k$ = 0.01 m/d', ha='center', va='center')\n", - "text(-0.5, -30, '$k$ = 20 m/d', ha='center', va='center')\n", - "text(0.5, -2.5, '$S_s$ = 0.0001', ha='center', va='center')\n", - "text(0.5, -7.5, '$S_s$ = 0.0001', ha='center', va='center')\n", - "text(0.5, -15, '$S_s$ = 0.0001', ha='center', va='center')\n", - "text(0.5, -22.5, '$S_s$ = 0.0001', ha='center', va='center')\n", - "text(0.5, -30, '$S_s$ = 0.0001', ha='center', va='center')\n", + "plot([-1, 1], [-35, -35], \"k\", lw=2)\n", + "text(-0.5, -2.5, \"$k$ = 10 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -7.5, \"$k$ = 0.025 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -15, \"$k$ = 30 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -22.5, \"$k$ = 0.01 m/d\", ha=\"center\", va=\"center\")\n", + "text(-0.5, -30, \"$k$ = 20 m/d\", ha=\"center\", va=\"center\")\n", + "text(0.5, -2.5, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", + "text(0.5, -7.5, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", + "text(0.5, -15, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", + "text(0.5, -22.5, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", + "text(0.5, -30, \"$S_s$ = 0.0001\", ha=\"center\", va=\"center\")\n", "xlim(-1, 1)\n", "yticks([0, -5, -10, -20, -25, -35])\n", - "ylabel('elevation (m)')\n", + "ylabel(\"elevation (m)\")\n", "xticks([])\n", - "savefig('../docs/models/model3d.png', bbox_inches='tight')\n", - "#Model3D(kaq=[10, 0.0025, 30, 0.001, 20], z=[0, -5, -10, -20, -25, -35], kzoverkh=0.1)" + "savefig(\"../docs/models/model3d.png\", bbox_inches=\"tight\")\n", + "# Model3D(kaq=[10, 0.0025, 30, 0.001, 20], z=[0, -5, -10, -20, -25, -35], kzoverkh=0.1)" ] }, { diff --git a/notebooks/ttim_neuman_comparison.ipynb b/notebooks/ttim_neuman_comparison.ipynb index 6b7a9be..494c23d 100644 --- a/notebooks/ttim_neuman_comparison.ipynb +++ b/notebooks/ttim_neuman_comparison.ipynb @@ -32,7 +32,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", - "from ttim import *" + "import ttim" ] }, { @@ -53,7 +53,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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PaN68OU888QQzZszgnnvuYcqUKXh6ekrHtvPVV1+xe/duoqKimDRpEm3btpXWOTo6cvjwYfLz8ykuLkYQBDIzM9m+fTtZWVn89NNPfP7554Ct+rW0tBRBEC47KabiDb+iLQDNmzeXwrZOTk6SaKwPIqG+hU7t17awsJCs7H/yKgsKCsAtFECaMfpf+0+cOJGAgAB8fX1p3bo1M3/NRavVsnZFjrRdTV/7elk5WoWnTVxuizjl5+eTm5tLVlaWNALs7mU28Vzxt2HHSNughx4rbakD8+bNkx5CAgICaNOmDRnv+UkV0/Pnz+fVV1+9JpNlQSdTK/Tu3ZsePXrUtRm3DL169cLLy6uuzbipmThxIl988YXk+XBzc2P06NG8+OKLJCYm8tVXX0njlQYOHIirqyvffPMN/fr1AyAhIYFOnTpJFafOzs5ERUURGBhIdHQ0M2fOlMKHzZo1o1WrVigUCiZMmMCwYcMQBKHK6r7Ro0cTHR3Ne++9x+OPP07Xrl2JjY0lJiaGnTt3smjRokqerh07dmC1WlEqlSgUCsrLy1m3bh1gEwnz588HbO0n7KOQ/svDdscdd0h5VIMGDeKOO+4AbF63io1pG/L0geqkKk/bbS99bhNqF7FYzJhMtrB05tppl/W0AXQp/wONRkOfPn1o164dWq0Wf39/23VfMQmVSlVJsGkvtg6pDwK6KmrSy2bHKlqlHoRmsxn/i+/v2rUL+Ee0ubq6otFoEASBRx65nylTpkgedH9/f5Rf2Dzdhk+/BWrmey8LOplaYXaF2XYyNc8777xT9UYy1018fDxLlizBycmJX375BYvFQp8+fVi8eDFDhw4FKt8QBUHAx8eHpKQkBgwYgLu7O/n5+dxxxx0IgkBCQgJlZWWsXLmSNm3a0KZNG06cOEGvXr3o1q0bgiBgMpnQaDRMnTr1qqv7goODmTZtGu+//z6TJk3i448/ZvDgwfTv3x+TySQlp1+4cIFTp06xdu1afv/9d2n/s2fPXjE5u2vXrgiCgFqtZv78+ej1ejw8PNDpdGg0mnobBq1vnrasrCwuXLggheesVqvUC08T2BKgUj5jRbRaLXfffTcAc+fOxcXFBUdHR6b+ZCscWf/0lJo2/4apUWFWhafNuLQXpWW20GZmZiZFRUVYrVZMJhO9VhmxWq2Ul5cTEBBAUFAQH3W0FZs6PfctarWa4OBgNBoNDg4Okkd+7eXOJ9jWKWrwAaZ+/muTkZGRqQO++uor/vrrL0aOHEmLFi3+czv7GKG2bdvSsWNHAGJiYjCZTJJnNC4ujnvuuQcfHx/AJn6WL1/OkSNHANuT/bFjx6TXAN27d8fFxYWAgABSUlJYt26dJBDt3e4rsnbtWg4fPswrr7xy2RYPgiDwwAMPcODAAX766Sfpffv4qGnTbJ6dkydPcvjwYXJzcysNgb9cuBTg3nvv5f777+ehhx6SzmOvUGwInp2a4moGwWdkZFBSWlJpfVlpGTnrX6tUvWu/9pfr0dazZ0969eoFwG23taBZM1ubmICAAEnsK7b/E4KtLepl6PQy5BfkY68NPxV/ipIS29/DZLJX89qufWlpKY8//jitW7emTZs2hIeHE7T9KQA0t99ebdXt1YUs6GRkZG5JjEYjcXFxtGrVipKSElq1akVGRgaiKLJgwQKGDh3KqlWrLrvvypUr8ff3p1+/fpIX6n//+5+0vm/fvpw6dYqsrCxJ0Gk0GiwWCydOnOCxxx7jww8/ZNSoUUyfPp0ePXqQkpLCli1bGDduHM899xzz58+v1LuxdevWlJWVodPppPcsFgtOTk5MnDjxEhuPHj3KqVOneP/990lMTCQ1NfWSsUiX6yXm4OBAq1atCA0Nxd3dnVdeeQWwVTjau/DbQ7Iyl6eoqIjy8nLy8vJsBSxettDzrl27/lMkZ1y4gE6no23btvj5+dGoUSMmT57MS98no9VqWb3inxFj9fX615in7SoqRwFy83KxmCsXG+wMe4VFixZJXlCDwYDFYmH7CFvldfcVKQwcOBCVSsXUqVNxcXGRwqTGT7kkJM0e237U4vUfI2RwQafWVbWdLOhkZGRuWgwGA6mpqezYsYMlS5ZgMpnIzc0lPDychx56iAULFjB9+nSMRiOZmZl8/PHH9O7du8qqSU9PTywWy3+OiLJareTk5FRqOTJmzBiSk5P55ZdfiIyMtLV/6NGDiIgINm/ezOLFi3nxxRfp1asXzz//PFarlenTp0tJ0j4+Prz00kvs37+f9957D71ej6enJ2fOnOH06dPExsYyd+5cKafPYDBc0vojPDz8ksrE+fPno9PpcHFxwcfHB4VCgUajuaGmrbVFXYROrVYrJ06cIC8vj2XLlnHo0CEA0tPTKS4uRhRFSTTYqehl69Spk3RtFyxYILV0ifjkOZRK5SXXXqezedpqK9ewIXjZjCYjZWVl2OXjb7//htVqvWzl6MOvPgzYZuq2adMGgFdffZV25xagUCjIfXdjpWrteolva8rMx+RJETIyMrcWFouF1157jVdffZW3336bBQsWYDKZUCgUtGnThjFjxjBy5Eh+/fVX8vLySE5OxsfHB0EQpCa99kkMoaGhlz3H/ffbkp63bNnCpEmTpBuw1WqlsLCQfv368dxzz5Gbm8vOnTtxcHDg66+/5o8//uDee++ld+/e7N37T7f91q1b8+abb/LHH39gsVjQ6/VMmTKFZ555BkCaNXr77bdz4sQJHn/8cUksVuxnpVAopB5sQ4cO5Z577kGpVBIcHIyPjw9OTk71Nq+tPpCXl0dBQQEPLdkjtYywh9dFUUTl3xyAnm/svjiHNBKaXxxU3xycAMWvMVKj5ldffZXQ0FDePSLi5OTEsiV5V2wEXR+oy3w2+3qT2YTFbCEvL49zSeewWCz0j/3HI1ZeXk55eTm/jHJEq9Uy/GebzTqdjoULF+Lo6CiJt7z3bPs4ODhI4tl2ro8BcKzvYu4akP9ly8jINCiMRiNpaWnMnTuXSZMm0atXL0aMGMG8eTYvSH5+Pu+88w4hISGYzWZMJhPz589n5MiRlTrWOzk58dRTT/H111+zYcMGmjZtyujRowGk/cLDwxk5ciQzZ86sZEOLFi2IjIzkl19+YcqUKURHR7Nr1y4WLVpEQkICR48e5csvv2Tfvn0EBATg4OBAbm4uOp2OmTNnEhYWxvLly6UbjNlsxtnZmf379zNnzhwyMzMpLCzk999/p6SkpJJoU6vVqFQqVCoVCxYsoFGjRnTq1AmwhXUbWnVzbXrZ7MUGGRkZFBQWMG7cMrZv3y4J6/z8fEpKSiSP2r+x3zAFQYGrq4sUTtdqtej1tgKT9SsnXyKalyfZPmN9F3M1jVW0Vgo5X7hwgeLiYppeHPm7d5/tOplMJqlq1x5aNhhsf6Px48fTsmVLHnjgATy/H4VGo+Hs0h9q8VNcH2P8bL89y2vwHLKgk5GRqfcUFBRw8uRJABYuXMjff//N2bNnGTVqFOnp6VJSM9jaB9xzzz3s2bNH8la5u7vj7e2N1WolLS2NwMDASjeWyMhIYmNjpePs37+fhQsXkpiYSFJS0iX2qNVqRo8eTUxMDJ9++im//PILeXl5pKWl8cILL6DX65k0aRKrVq1ix44d5ObmMnDgQAYOHEhYWBhqtRoPDw/++OMPRFHkxx9/ZOvWrZhMJs6cOSPd0Co2/fXz8yM4OJixY8dK1bGRkZEolcpKeXUy/1CxSMFkMpGZmcmFjAuY3RuDypMfDZFYOkdI2zhd/B8uHW/VsWNHclyciQgPZ+7DPfHw8JAeENRq9SX5iXXNOs3ci68216kddrKzs5mw3YNTp05J76Wl2R5cfhyqwGKx0H1FYaV9vLy8eP/994mIiGD3s7b3AgMD0ev1ODk5gUZbmx+h3iMLOhkZmXrP3r176dOnj7SsUqkYNmwY4eHhAGzYsIGYmBhpnaenJwUFBXTr1o1du3bxwgsvMHnyZKkFwYIFC3j22WdtN+mLTXqbNWsmHf+OO+5g/PjxeHt7s3r1apYuXXqJ12XKlCmMHj2anj17kpGRgYODA08++STR0dGUlpbSqVMnWrRoIZ3XbDazfv16PvzwQxISEoDK4VI7TZs2ZeHChYDt5mXvNXdJyEjmEgoLC5k1axYJCQkYDAb+9rtPWldxpJLDxSibxWKVeocBNG7cGLVajYuLC8uW5En7CoKAq6urJBDtPfVuWaoIneYu6kx2djYGg4GSkhLMZjNWq5Xxaivd/7SFshUKBf7+/gQEBBASYpsIcurUykpe9Hr5nR9TPwTy5ZAFnYyMTL2ne/fupKamkpOTQ5s2bYiIiOCLL77AbDbTsWNH9u/fT79+/QgMDCQxMZE9e/YgiiIzZszA39+f33//nTlz5uDp6UmTJk3o2LEjCoUCURQ5ffo08fHx/PDDD0ycOBGVSoXVamXevHkUFhbSoUOHy1YUCoKAn58fJ06cwGAwsHfvXtatW0d4ePglYdKK2POrwFYZ2759e5o2bUpERITUr64h5rnVZujUYDDYRldduEB+fj6BgbbE98LCwkp981SqX1EqlYwePVqaUvPAAw/w/NenAfjg+RH4+vrW6wKQ+tIrz47BYKAgK5OcnJxLvuOdL/aLjouLAy4dide8eXNSUzcB/0oPuCgQG0VG1qDl1cOYLWMAWH5/9QdP7ccGSHFPofGUxizJWcIZ4xkEreB0hV0BWdDJyMg0ALRaLQEBAZIn5cyZM5w8eZJmzZoRGRnJ3r17SUpKon379vj5+eHi4kJsbCweHh40b96c++67j5deeumS4z7++OOcO3eOI0eOsHHjRqKjo3FxccFkMpGdnc2jjz7Kyy+/XOnGNHfuXMxmMxs2bKCkpASTyUR6ejqiKFYK4w4aNEhKzA4NDZUaBAcGBkqfo771saqPHD16lHGrj1FYVEhZaRlWq1WaaesQ3Bq03pi7Poe3tzeBjo4IgiB5eTQaDesuDoqveJ01mhQAqQ1LvcbuDatuz9BVFCgUFReRk5NDXl4eBoPh4rU30WNlaaXveqdOnejcuTN+bAXg9F0TadeuHa1bt6503etVq5V66GlLSkpCFEUyMzMv21KoKmRBJyMj02Dw8PCgWbNmnDx5UmrJYQ+jjR8/nhEjRgDw448/Ehsbi0ajoUmTJnz00UdSD7fi4mKKiopQKpV4eXnh5OTE4cOHGTx4MCdOnCA9PR2tVsugQYOYM2cO7u7ubN26lY8++gir1cqePbYbXkFBQaVxS/ZxVr169WLw4MG0bNlSahFScci8zJUpLS2loKCAxMREvv32W3bs2EF686FYrRasVpuIUCqVlbxqrVq1Qq/XS57Nih7O+iAi6puX7XKYzLa8Tbv/+NjxY1I1qcVikQScWq3G19eXxo0bSz0KGzdujJ+fH27f2BplDxs2DJ1OV689n/WF9PR0jh07ZmsS/vVhwJaKYb/eH2/9mDXWNSQYEoqvdByQBZ2MjEwDwsHBgQEDBpCens6ePXto1aoVAwYM4NChQ2RnZ0v9pIYMGcKIESNYt24doaGhZGZmEh4ejiiKFBUVUVJSQnBwMOfOneOBBx5AFEWcnZ3p1q0b33zzDenp6fz888907tyZwsJCysvLJRsUCgVBQUHMnWtLOm/ZsiVNmzbF19e32j9vfn4+3bt3Z+DAgTz11FNSVWVDpGKBgiiK5Ofnk5efR9bFnv0hYxYBtrYzZqk5rDtC5EP0UZ2gXbt2APTv3x93d3ecnJzq3QivhoLJZGJxTnfOnj3LkSNHOHfuHDk5OVIrEIDeq9MBeOqpp2jWrBkRERG0bNkSX19f0v5LqKlsvfLUTlVGB6uHG/Cy1VTotGLY1I4oivyZ+ScAd71/F3l5eVLPPLu3WfAR6NSpE/369cPT05M///zzmkc4yoJORkamQWH3xtjHZtkbrp45c0baRqPRMHToUKxWK4MHD2bYsGGcP3+e/v37ExUVRdOmTUlNTWXu3Lnk5+ezceNGMjMzKS8vr+R1u/fee+ncuTMhISH07NkTsFXMOjs714rnp6SkhMOHDxMfH48gCJe0T6kJakIkLVy4kKNnnaXebkajQfK2OQTbBJ3BYMTVzRU3Vzfc3d2lXEOtVsuyJ19p2KHpmgqbVjz2v0n+t1IAACAASURBVLkYOjUsvZf09HQKCwspK/snZN1OFHl5he3v0a5dO2bMmEH79u1p+ccrCAqBsmW28KlCoWjY176WMRgMXLhwgczMTKxWq9RoWt/M5qFPS0sDbHOWBYWAr48vCoUClUrF5298Ll1re+/Ja0EWdDIyMg0Kb2/vSrlP/fv3B5DamoAtN23FihUIgoBSqWTs2LFs2rSJ5cuXU1paSkZGBmazWQrbKpVK7r33XkaMGEH79u3R6/U0atQItVpdp2Ejf39/kpOTCQ0NlSpj6ysmk5Hi4mK2bt3KZ599xh9//CENlK/o4WzWrBnNQkJ49dVXCQgIYMq2dJRKFV+8/mKdX++qqM+eQLPFLE0JOXPmDO0v1hvs27cfUbQ9pCiVSlxdXQkOCsbdw52sBeulZtNSmDruYlVpbf4d6mE+W1X88ccf5OXlsX37djZt2iQ9EFosFkwmE926dcPBwQFvb29mzJjBzOMzEQSB5bNsHsGaqN6VBZ2MjEyDolevXrRo0UIKVSgUCh588EG6d+8uee0++eQTNm3aRF6erfVExcpHR0dHIiIimDVrFq1atcLX1xeVSmXra1UPiImJYcmSJWRkZGCxWFizZg1du3blxx9/pKioCGdn5zq1b9D7OzGZTKSeT6Ws1NYnr6ysDIVvUwBGrTgEytsRurTF39ERJ2cn/Hz90Gq1rPpfe7RabaUxVupfbG1j6l17inrM2bNnKSkpYcKECRiNRk6fPk1BQYHUt1CtVvPbeJvn0/jYKpo2bYq7uztKpRJHR0fpOI6XPXrDobYqTo1Go6346UI6+U62yt5hO4fZVirBcbgj3SK6SZ62Zb2X4eTkVMmzqUu09Yqsye+5LOhkZGQaFAqFopKH7vz588TFxbFjxw5iY2MBWxNT+1xNO1FRUbRu3ZrIyEhGjBiBn58fGo2mXiTNV2TFihUkJCRgtdp6pAUGBtK2bVvi4uI4cOAAPXr0qPIYNRE2LS8vZ/Xq1Zw+I1JUWITJZJTCpqIoUvE25e3tjdZBi7+fP0qlApVKjSAI9UY010tP21X0dpMmWqSlYTAYmBlsmyXc/Tdb1Wnnzp0JDQ0lLCyMVgG/A6C6555LB8zLXBU5OTmYzWZycnIoKirCZDLh0MT2TVer1Ti7OKNSqggNDZWEmiAIdfbQJQs6GRmZBkF5eTl79uzh3XffJTExkaIiW0sFWxK9WRJvAQEBdOzYkUcffZQuXbogCALBwcHSyKz6giiKjB07lvXr1yMIAnfffTfff/8969evx2g0otFoCAgIQKvVMmDAAAoKCmpFEImiSFlZGZ9//jlvv/02hYWFFBQUYDabMRgMlbbt2bMnrq6uvPHGG0zZdgG1Ws0Xr79483rbajIXrgL5BfkYDUbOJZ2jw8U+u/bebnacnJzw8PDA09OTEyeWExwcjFar/Sdkbbe1gje0xmmAoVOA4uJi9u3bx9q1azl48CBFRUWkXRTNoijSpEkTQkJCGD16NN/qvrXldT62rN4J5frz6yYjIyNzkeLiYlJTU5k8eTI5OTlkZGSQmZlZacRXSEgIkZGRxMTEoFaradWqFWBLoq/YvLe2SU1NJTAw8D/X2/vnbd26lRUrVjBt2jRKSkpYuXIlAwYM4Pvvv79kn27dutGtW7dqt7WkpISUlBR27drF6tWrSUpKwnjXM1itVr5fPUXaztvbGw8PD6ZPn05QUBCtW7e2eSgqeCK0O23h7ZtWzNUAaWlpFBYWsmfPHr78soS4uDgsFouUCwfw62g9Li4uqMZ9RVRUlPS+Xq+XxJtHrVvecLhc1WlpaSknik4A0DK6pdRjDy/QDtTi4uBCVGgUizoukiqq7Q+DP2/5GaBS2kBNs/z+5Xye/vmpqraTBZ2MjEydYrFYMBgMLFmyhL/++otff/2VsrIyqQu9RqNBp9MRGBjIrFmzCAwMJDw8HG9v73rlcdu8eTNr165l3bp1fPPNN/Tt2/eSbVatWsXw4cP56KOPcHR0pFevXkRHRyOKIhMmTKBdu3Z89tlnjB07ttrtKy4uprS0lIkTJyKKIgdd7qK0tERqESIE9EEZrEDt3wKAe6K/QafT4eDggFKpRKFQ8F0JcBLWdasfEqLehU6rCJtmL+hIZmYmZWVlGAwGaSRWBLBzZymurq7ceeedvPDCC9x2220ANPpuJGqVGu66q3Y+Qx142WoqF85kMmGxWMjKyiIvL0/q72YymaSqU71eT2hoKK6urlIOXMVRcA2J+vNrKCMjc0shiiK//vorf//9N9999x0HDhygtLRUSux2c3Pj9ttvp3v37gwYMABHR0cCAwNRqVS1+nR8NRw7doynn36anJwcFAqF1A+vIvHx8Rw9ehSwefG8vb3Zt28fhYWFuLm5ERwcjCiKJCcnS/tURy7coUOHyM/PZ+7cuaSnp0vtEFwHtamUYxgQEICfny9HM2xhVU9PT3mSRTVQXFKMPVCekJCAyWS6ZKqIm5sbY8cO46WXXsLFxcWWg2j3Mqvq13e9oXDw4EE2btzI6V9Pk5iYKLVtAWjbti2im0hYWBiLhy3Gw8MDnU5XxxbfOLKgk5GRqRXs/d5WrVpFYmKilGhsx9PTk969e/PCCy/g6elJREREpYq8+kpcXBx33nknxcXFeHh4sHTpUjp3vlSArVmzhtjYWPR6PTExMWi1WgoLC8nLy8PNzQ2FQsHw4cNZvnw5r7/++nXZYrFYOHv2LFlZWaSlpeHp2Z+8vDypt56Pj49UUDKhs55u3boREhICgIuLC1C7M1lr8zyVqKFcuOz+K0hLS2PVqlX89ttvHD16lJKSEn4ebhNn/WOtdOvWjYEDBxIWFkbbtm1RKBS4uLiwrFotubm4XNgU4GDGQQAe/eZR0tLSyMrKkgpH7I17z+49S0REBG+//TZRUVEEBATQqFEjxv00Dqi98W81UYn7b2RBJyMjU+3YhdrUqVM5duwYJ0+epKSkhOJi2/QaZ2dnnJyc+PDDD2nZsiV+fn64u7s3yPyrp556CkdHR44cOYKPj89/jviaNWsWs2bN4uuvv2bIkCH06NFDGlE2efJkwFYhV7GxcVU88vFvWK0i586do7ikmJJi22xZh+DW4BKEvv90Wl0MG7m5uaLRaCWP23P1LVzZACkpKWHVqlXs3buXQ4cOER8fL3mBwPY9nzp1Ks30P+Do6Mj5mM2ScK5xGmiBQlVYLBby8/PJysqCi47w/fv3A7YK+EaNGuHq5opapcbNzY39mfvx8vKqQ4trD1nQycjI3DA///wz27dvJy0tjZ9++klqKGsPK7Vp04a+ffvSoUMH2rVrh7+//00Tzjtz5gyjR48mICCAHTt2cOjQIaZNmyatP3jwIAsWLKBnz56MHz+eQYMG4ebmxrZt2+jXrx8LFixg7NixeHh4XNX1WLt2Ldu3bycuLo7UyCFYLP8IwEaNGuHi4kL6xclZnTp1qvbPe0tQRS7cmdeakZmZidFoJFIUiQRG3Qaqdmr2RMzE09OTvn374ufnZ/ubLv/Ltn9tibk6oiZy4U6fPk2r461YvHgxBoOBrKws6Xcl8rVIAgMDGdF0BD179sTHxweNRlNt525oyIJORkbmujl79iyrV69m1apVpKWlYTabpUpUe9uNWbNm0aZNG4KDg3F0dGyQXrirwWQysXfvXlauXMmLL77Ili1b+Pbbb/nrr7/IjhpOSrIb4y9u26NHD7755hs8PT3Jzc3lk08+YciQIfz888/S9IqK5ObmUlBQwPvvv8/XX39NdnY25eXlmP/4A6VSSdu2bRkwYABDh96Dt7c341YfQ6FQ1Go4s94VKFQTFqutLU5Odg7+F99LT0+v9HdSq9UEBQbh4upC6zFPo1Qq603PvYZIdnY2RUVFzJs3j0OHDpGSkkJGRgZgq7h2dHRk8uTJbPfcjl6v5+HuD/+nZ7y6qY3Q6fUiCzoZGZkqsY+z+f3333nrrbc4duwYVqtV+pEFUKlUDB8+nH79+tGlSxe0Wi0eHvWjGrImsVqtKJVKtFot7dq1Y86cOWzbto1Ro0ZJzYFve7gpHh4emEwm1Go1Q4cOJTExEQcHB9544w1eeeUVpk6dilqtJjY2FqPRiNlsJvGsLdcw4MUe0vgstVpNeHg4DzzwAMOHDycwMBBPT89KNtW3Zsk1Rg3kwhmNRt7N7MrJkyfZu3cv6enplJSUYLFY+GWUI4KgYMBGBYsWxXDHHXcQEBCAi4tLzbXKuYlCp1XlwnV+rzN5eXlS9S+NQNFbgbPgzJthb9KpUyfCw8Ola31wi22/2hJzdcHatWtp+sWXhGk0TavaVhZ0MjIyl+XYsWP88MMPrFy5koyMDKm5rCiKhIeH07t3b4KDg3nsscdQq9W4uLjg6Oh4U4RRr4Vhw4axfv163nzzTbp06YK3tzfff/89BQUFlJaW8sYbb7DmbCLx8fE0mvoAubm5dO3alUOHDnH8+HEWL17M7w4dEEWR9PR0Jmw6S/m6d7FaRVsuXCM3Ip78ABdnF7y8vFCrVSgUSlKAefvLWHebZ5U2ylyZmJgYMjMz2bBhA0lJSZVmz4JNMEydOpW2zlvQ6/XkLfvh1hHNNUBOTg7FxcVkZGRgtVpRNbZJkYyMDFxcXXB1ccXd3R1HR0c0Gg2CIDD2/upv5dMQ2L9/P7S946q2lQWdjIwMAMnJyRw5coTXXnuN06dPS32yADp37oy3tzeTJk2idevWlRpt3uqEh4dz7tw5fvjhB+677z68vW2t/Tds2EB0dDTHjx8naNQCfHx9WPn116hUKnx8fJg7dy5Hjx5l9OjRHBRbY7GYpVFaDg4O6Bx12NPrWzRvUUefroFTRS7cvqcaYTQaibpYiHJvJ3C9z52Q4BDyB60CwMvLCwcHB1urnOW2cVrcxGKuJvLgtmzZgsFgYP/U/Zw9exaj0SgV/9x7772Ue5YTGBjIG+PfwN/fv1bSMupz6LQi1/KALP8iy8jcohw+fJi//vqLJUuWEB8fj8FgkAbee3p6Mnz4cIYPH87tt9+OTqe75T0S/9XOY8KECXz11Vc8+OCDaDQaysvLmTx5MoMHD6Z///5YrVbGfHEYQVDQs2dnli1bxqJFi8jOziY3Nxez2UyrVq3Q6XS888470vXWaDS13kKkts9VWxQVF2G1Wjl9+jSlpaXcHWS7SZaXl6PX6wlrHIZGa2tgrVQqERDwCAurPgNuorDp1fDXX39RXl7Oiy++yN9//y1Vt4OtQGr+/PkEBQURFBSEo6MjY7favG9h1XnNb0HqtaATBOF+YDGgBP5PFMW3/rV+NDAfOH/xrQ9EUfy/WjVSRqaBYbVamTNnDitWrKCkpITs7OxKrTJefvll2rdvzz333IObm1u9a+Jb3xAEgUmTJvHQQw9hNpvp0qULnTt3RqVSSV5MQVCQnJzMjBmb+fTTTytd8/vvv59Fixah1+vx8/OTr3c1Yh31HdHR0axYsQKLxUJaWraUC6fX69kdPplHHnkEp9BQ1Gr1Lf/QcrVUlQs3fP9wRFHE0NOAV08vvPDC2dmZRo0asbTHUkJCQlCr1bdUekbSiJEAhHyxssbOUW8FnSAISuBDoBeQChwQBOFbURRP/GvTdaIoPlfrBsrINBAsFgvffvstr7zyCiUlJeTk5EieOC8vL/r27cvcuXPx8fHBzc3tpq1CrUmGDBlCeXk5I5YfRBAEXvslh5wN35NfkE92djZ4NQGc+L9kJzT3v0KUlyce7h64ubmhUil5fVc+655sXtcfo26o5sKG/fv3M3fuXI4dO0ZaWpr0XQfo27cvb7/9Nk33voRKqaL92Neq5Zy3Knl5eSQlJUnpGdoIW7FCeXk5Op2OsLAwvL29JfEmCAJNmjSptvM3lLBpbVFvBR3QATgtimIigCAIa4EHgX8LOhkZmQpYLBZ+++03Jk+ejNFo5PTp05WG2rdv356YmBj8/Pzw9fWt00H2NxMpKSnk5eVfHO9krNQfzi6RO3TogIODFkGoWU/QTRc2rSIX7tjkEAoKCrBarVgsViY1EtH00uDg4IHp8W9o0aIFCoXin2rIA7IX9HqIi4tj0qRJHDx4UGpRVHGEWdSbUUSER/D5tM//yTuUuWrsXjyAzikpfB4UjN5sofTAAXSCoso+OPVZ0AUAKRWWU4GOl9luiCAIXYF4YKIoiimX2QZBEJ4AngCkpGUZmZuFsrIyJk+ezPnz59m5c6c02B5sSfsLFy6kadOm+Pn54erqWoeW3jyUlpZSXFzM5MmT2bJlC7m5udK4oaCgID6MiSEkJISwsDCeWGt7Dr3phFYdUFxSzLlz5ygrK6P9xQEA2dnZUuuY8LBw26B1pQKFoICOl7ttXCcNKBeuOoobjEYjs2fPZv369WRlZVFQUCAJOFdXV5YvX06vXr1wdHREoVAw4fcJgG1ChsyNcT3R6Pos6C73ccR/LX8HrBFF0SAIwlPACqDH5Q4miuInwCcAkZGRYkWPhYxMQ8VsNpOcnMyHH37IypUrMZlMGAy24ep33HEHzZs3Jzo6Gn9/f1QqFUqlso4trnuqo9CgrKyMGTNm8OOPP5KcnExpaam0rk+fPnzwwQcEBASgVCrlauDqYMxmTp06xblz5/j000/5/vv9mM1maUbq9ITbmDJliq0C288Plex1rpKq8uAGrR1EXl4e54XzWIZYcL/4n16vJ/JQJCNHjqRXr17odLpbKheuJrHn1x08eJAVS5fyf7/+SsuWLdl8e1vKziYWV7F7vRZ0qUBQheVAIK3iBqIo5lRY/BR4uxbskpGpU4qLizl79izPPfccf/75J0ajEZPJhJubGyEhIcTExNChQwfpqVnm2rALvn+z/2wuAGHjYigvL8dq9YbbR+HRQU0LX1+Cg4NYMaoter2+QdzgUlJSUKlU+Pn51bUpl8VkMpGSksLIkSM5d+4cmZmZ0uD1yMhIBg4cSJTPLlycXdj92Y/yd/0GsVqtlfINjxw5AtiKftzd3fHx8cHd3R21Ws3nMz6v1nM3pFy4mihuMBgMJCcnM2XKFHbv3l3J238t1GdBdwBoIghCY2xVrMOAxypuIAiCnyiK6RcXBwB/166JMjI1j9FoJDc3lxdeeIHt27djMBgoKSlBEAQ8PDxo1aoVH374Ic2bN8fR0bGuzb2pKCkpJjs7h8zMTPAMB2yhVr1eT1hYmK1fnM5Byomr7nFPNRGiNRgMrFmzhvHjxyMIAt9//z29e/e+8QNXU3HDV199xcKFCzlx4oRtvJnZjF6vZ9y4cXTr1g1PT0+6d+9uE3D2c8pi7pqxi6i0tDQGDBjAqVOnKCsrI/jlYJRKJTObziQqKorIyEh5jNk1UDEPriKlBw5UWm+2mLFYrByNi6OsvAyrxcrGlGQ8PDxYvnw527Zt48svv7ymc9dbQSeKolkQhOeArdjalnwmiuJxQRCigYOiKH4LvCAIwgDADOQCo+vMYBmZaqSkpIQpU6awceNGysrKyMvLA2xPy82bN+eJJ55g6NCh+Pr61rGlNx/vDwrn9ttvB+DChQtSe5Hg0Qtxd/dgyXNd6dSpU4P1CG3cuJF33nmH//3vf2RlZREdHY0gCPTq1atmT1xFYcPvT7gD4G008nZzcLjdgUaNbIU7yv9tqd6HlQaUC1fdiKLI5s2bmTZtGpmZmdL4vqZNm9K/f3+S2iWh0+kY3Xd03Rp6kxJ39CglxcUYTSYpHzEoKAgHrZbUvb/j7++PIAikp6dXcaRLqbeCDkAUxR+AH/713swKr6cCU2vbLhmZmsI+Dmf69Ols3ry5UkPOZs2a8e677xIYGEhISIiceFzNFBQUsHnzZmJjY0lLs2V3eHp64u3tTVRUFIWtWqPRaGjfvn2DFXNg68x/6tQp2rVrxxdffIFer6+TVjVFxUVkZWURdvFS2sN9KpUKT09PAgMD0Wg0qFVqhFvY81ydkxvOnz/Phx9+yJo1a0hJSZHCes2bN2flypWEhITw8sGXG0TKQH3lcqHY48ePkzBsGACPxds8oVqtloCAAN566y1adeuGTqe74d/0ei3oZGRuBaxWK19//TXvv/8+R44cobCwEKvVipOTE2vWrMHPz4/mzZvL1dk1QElJCSkpKUydOpUdO3ZQWFgIgE6n48knn2Tu3LlSqwt7bl1Db8WgVquZMmUKDg4OfPnllzz00EPcfffdNX7eoofW8vrrr/P3339z4MABW38+YNdYZ4KCgsjoM5OIiAgiIyPlh5VroKrihkdiHyEpKYnCwkKMRiOiKKJ4VEG30G484foEHTp0oHHjxtJ+NyLmGlIuXE1SUlLCSy+9RGJiIkePHiU7O5tl/gEoFQrGjBlD165due+++3B2dq7WQjVZ0MnI1BGvv/46q1evJj8/n6ysLABatGjBc889x+OPP05wcLCcE3cFqqpWraq4ocVzn5CXl2cLe+g64tivE63CwnB0dOTbF3ug0+mqzdb60K7krbfeYu3atcTFxREUFMTJkyf59ttvufvuu+nTpw/jx49n8ODB1XrO7777jt27d7N27VrS09Ol2cDu7u7ExMRw2223cVfCPARBIHTo0Go9961Meno6XPR8/vXXX1itVhQKBT4+Pvj5+aHX61EqlQy9X77m1cUnn3xCQkICW7duJSEhgfLycgDGjh3LE088gfuid1GpVAz/8MMas0EWdDIytUROTg4//fQT8+fP5+zZs1JenIeHB08//TRTp07F399fbi1SQ5SWlpCaeh7UngDk5ubi4OCAm7sboSEhqNUaKZRanWKurikuLmbGjBksXryY8PBw3nrrLR588EFEUaSsrIznn3+exo0b/1PteoPFDWfOnGHChAns2bOHoiLbDFUXFxceeughXnrpJSIjI9Hr9f+0czn91pUPeCVu4Vw4O3avWGxsLCtWrGDfvn1kZ2cT+mooALfH387s2bOJiIhAq9XK4dSLVFWterXFDfHx8WTn5KA1GmkFtAI29elDdHQ0zs7OBAYGolQqSdJortvWkC9WkvjlF6eq2k4WdDIyNUhRURFvv/02GzduJDExEYPBgJeXFy1btsTb25vly5ej1WrlaQ01wLonO7N371727dtHTEwMqampmM1mfB6dRyPPRmx7fTCNGzdu8CHUf5OZmUlMTAzvvfceVqsVb29vxo0bR0JCAqGhoZX64tn756lUqqu/DpcpbsjLz8O94DgAqXPbMNlT5LVHdQQEtKJRo0ZotVoUQiG0b3/jH1AGgGPHjrFv3z42btzIr7/+SllZmbRu5MiRGDoYUKvVfDHvizq08uZl9+7diCAVTTVv3hwnvR4HnY5Rq66tOrW6kAWdjEwNER8fzxtvvMGmTZsoLCxEFEUaNWrExx9/TMeOHVEqlbi4uNS1mTctGzZs4KWXXqKkpEQKrd59992YGjfG19eXJk2a3JTeit27d7N582Zp3FtKSgpLly7lySefvKTJ8Y16IouKi8jJySEtLY0u/rb3RFGUQntOTk4olUqEy/aJl7leTCYTo0aNIjk5WcqNA3jttddwdnZm5MiRTDk05bqOLefB2fi3527Hjh1s27aN2/aZMRlNjExOAqBbt2488MADDH7qKfR6fZ0WTMmCTkamGvnrr79477332LZtG9nZ2VgsFhwdHWnbti3r168nICAAzQ243mX+m5SUFPbv38/vv//OsmXLKC4ulp6ep02bxjPPPIOvry+P/d8fwLUnf9eHPLj/Yv78+SxdupThw4cD8Pfff5OYmEhgYCBvv/027777LpMmTWLlyhtrhnr69GlOej3NN998w7p16ygtLcVqtRIWFsYvIx3x8fWh3eRvGkxz5driaipVqypu6LK4C/n5+YiiaCtu6C3irnInyCmI+e3m06FDB7mYpJr5+eefOX36NG+99RYpKSlYrVY+DwpGp9MxY8YMRowYQURERL2pepcFnYzMDXLq1CkSEhIYPnw4xcXFUiuArl27snjxYiIjI9FqtXJuXA1gMpl48skn2bJlCxcuXKjU6X78+PHMnj0bV1fXm764ZM2aNSQmJvL2228zcuRI1Go15eXlqNVqJk6cCMDs2bN5+umn6dz52oRpeno6+/fvZ+rUqZw+fVoqbOjSpQtPP/003bt3x9vbG/WXAwHQyk1oq4Xy8nJOxZ8CH9vyhQsXpHVubm6Eh4dL02B69uxZR1befBw/fpw5c+bw3XffXZwIY3so7Ny5M+PHj6fL1q2oVWoemTPnmo5bnZMl/gtZ0MnIXAcmk4lVq1ZJrUbsIq5v377MmjWLxo0b4+XlVcdW1n+uZq7qv6tVDQYD2dnZpBpt4cLyosZw59N4qVS4urri5eWFp6cnBUplvR1rVd1kZ2ej1WoxmUxs3ryZsrIyNm3aRPPmzdHr9TzyyCNER0ezbdu2qxJ0VtHK73v2MH36dPbv3y/NB46MjGTIkCG8/PLLuLu7V4/xcmEDYPPeHTp0iJUrV/LNN99w/vx5TCYTjac0Rq/X8+m9n9KuXTuUSqU8uaGa2blzJ2+++SYHDhygsLBQ+j0PDg7mqaeeYty4cdLvedKOX+rS1CsiCzoZmWvkl19+IS4ujrlz55KXlyf94+/Tpw/Lli3D09NTHsheQ5SXl3Py5ElKSkpQ+TeX3ndwcCAiIgJnZ2dUKlW9CYHUFn379sXf35/FixeTnp7O9hEOuKbHALY8qtDQUIKDg6X2OMAVJzcoAOX+AUSHGREbK1EqnfHy8iI0NBCl8iDq6hJzMhIHDhxg9OjRpKSkUFJSgtVqtY3z83AkLCyMrl273nQFPDfK1cxVrapa9c8+fclNSeHxoiIec3IGJ2dcnJ3R6XQ027AeDw+PBhPKlu86MjJXQVFRET/88AOvvPIKKSkpiKKISqWSms9qtVp0Op2cN1QDxAwMY+vWrcybN4/ExEQprBo4cgHe3t788O4YPDw8bumb3ccffwzYKu6WLl2Ks7NIQUEhf/zxBx06dECj0aDRaKTw0b8xoTl0iAAAIABJREFUmUwknk0kJydHKm4wGk00Dm2Mt483arUapUJOGahOcnNz+fHHH4mNjWXbtm2Ul5cjiiJhYWFMnz6d+++/H09PT8b/PB649obWcnHDf1NSWiqV6Rw7bqvOdnJyoklEBI56R1QqNQIQEhpaVyZeF7Kgk5G5Ar/99hvPPPMMp0+fprS0FEEQaNq0KQMHDmTatGkN5smtIfLTTz8xZ84cDhw4IDXp1Ov1LFy4kKioKN4/rpSapV4L9bm44UZ5+OGHeeONN1CrffHy8qRD9+7cddddJCUlkZCQwIIFC6RtS4euZ968eaxdu5bU1FTKy8txcXEhfmoTHPWO3DH5Bzm0V82UlZWxd+9eNmzYwPLly6Xvtbu7O9OmTePZZ5+t3KNP5oaxe+8+++wzli5dSlJSkjS5wdnZifLp07jtttvo0KFDg38gl781MjIVEEWR+Ph4Zs+eTXx8PH/++Sdg69O1du1aevToIefG1SDHjh3j6aefJiEhQRoarlKpiIqK4oMPPqBNmzZSq5cP/778JIhbmZYtW/LII4+Ql2fLnxsypCvr1q0DYNGiRdx55508+eSTnDhxgri4OAoLC1Gr1dx1110sXLiQZs2aoVv7kO1gspi7hKqqVS9XqSqKIn9m2n5Hot6IsqVouIL/RH+aNWuGk5MTOp2OaX2m1ZzhtyjR0dGkpqayfft2EhMTAdvvybRp0+hyKh61RkPok09e0zFro7jhepEFnYxMBVavXs3cuXNJSkrCZDIB0L17dwYMGMCAAQNuqgkC9Y2UlBTee+899u/fL117e/PliIiIm7IJcE0QEhIC6bYw6htvvMHLL7+MRqMhLS2N7t27Ex8fj9FoxGQyERQUxOrVqwkMDCQoKOjGKrHl4oZLMBqNnDlzBjxsyxaLBQcHB9QaNU0imtR537KbkaysLC5cuPD/7J13eBR1/sdfsyW7yaY3ElJISAgJvQkKKIoCigceKCDSFAQsICiKivSTUwRUkCKIFOnnIcIpyPmjKQLK0XsNJAES0tsm2TLz+2OSJSGB9Mq8noeHzcx8Z747mcy851NJS0tjwYIFZGdnYzQa8fDwoGfPngwYMIBHHnmE9HHjq3uqFY4i6BQeeBITE+nbty8XLlywWYX0ej2dOnVi8eLFNGnSpJpnWPMpb1/ViDFLSUlOQZJC8ez/Ma6urjRo0AAnJ0d69uxUOZOuo3z00UeIK/aj1WiJyclh8ODBREdHk5qaiiiK9OrVi169etGhQwciIiIUkVyBrHx6JZmZmRiNRsaMGcPWrVvJyckh+INgdDodPwz4gWbNmiku1UogNTWVFStW2JLVJElCr9fToEEDPv/8c7p27Yper7dtn16Nc60slKtK4YFEkiSSkpIYPnw4O3futJVlGDJkCL1796ZHjx5KfFwlkpmZSWJiIiC79ZKTklGpVDRp0gQ3NzelZl8exfVVLSJT1QEJIfEEADdmtWJ+awlaCzRs2AQfHx/stFbgR2gxspIm/WCybNkyVqxYwZkzZ2wFlw0GAz/99BMrMleg0+lo1apVqff7ICQ3lLev6vY2bUlMSsJdFPnC0QmfkFDq16+PvYMDwWvXPDBWUEXQKTxQpKWlMXbsWHbs2EFKSopc5yk4mEWLFtGiRQvq169f6wNjayKbRj9CcnIys2fPZu3atdy6dQtRFKk38BPCwsJYPOlvREREKEKujFisFlJSUrh8+TI5OSa6NJAfYP7+/rbm4Bq1cruvaDZv3syiRYs4efIkSUlJSJJEly5daN++PaNHjyYoKAi1Ws26X9ZV91TrFOnp6Vy8dIm8wkXxCQkE+Pvj4+uLg4M95Gs296CIOVAEncIDQmRkJAsXLmTJkiVkZWXh7u7Oww8/zMyZM+nUqZPidqpEDh48yMSJEzly5IitgXiPHj3o1KkTJzw6oVZraNasWan3W5ezVUvK2Q5z6Nu3L5GRkVgsFkRRpHfv3oSH38DT05OQEb9U9xTrHJmZmfz888+MGjWK2NhYWymY4cOH89lnn+Hq6qq8mFQwDdZ8R0xMDK+++iq//fYbJpMJq9XKthYtCQsLo8+3y5W+2CiCTuEBIDExkXHjxrF3716ysrIwGAwsW7aMDh06UK9ePUXMVSLbt29n7NixxMTE2OrHTZgwgXfffRd7e3tGbTxbzTOsnYiiyKFDhxg3bhyXLl1CFEUEQWDAgAHMmTMHz/+OLHvduBqY3HDt2jXmzZsHwKefforBYCjX/sqSrQp3+qpOODkB3VAdDWiAVqvFy8uLnKAc3j387gPhIq1qTCYTr7zyik3MAYwbN46QqGj0en2ZxFxNzlYtK4qgU6izxMfHM3HiRNatW4fZbMbNzY0xY8YwZ86cAsGxChXL2bNn+eqrr9iyZQvx8fGIoki3bt1o2rQpkydPxsPDo7qnWCsxmUz861//YuXKlezbtw9JkhBFkbZt27JmzRoCAgIwGAxyyEAdKgIcGRlJu3btcHd3x2g0curUKfbu3Vtlx8/KyiIzM5Po6GiwFV024eLqQkR4BHZ2dkqYRiUxatQotm7dSlpaGtnZ2Xh5eTFs2DCGDBlC8+bNiRo6rLqnWKNQBJ1CnWPVqlV8+OGHJCYmYjab6dWrF0uWLMHT0xOdTlfd06vVFJetGjT8S3JycpCkBtB1PBGenjRq1Ag7Ozu+UFykZSLHlMP8zz5j/fr1nDghJzuEhITw0ksv8fbbb+Ps7FynXXyXL1+mdevWfPvtt/z4448sWbIEs9lcqZb18b7jWbp0KT/88AOJiYlYLBYAWn/amrCwMJZ9uAxHR8cHKj6rqvjggw/YvXs3586dIyMjg1atWjFlyhT69ev3wHeEKQ5F0CnUCXJycti3bx8jRowgJiYGlUpFu3btWLp0aZkyyx5Uiis/cjcWi4XTp0+DSyAg91rV2+sJDQnFYDAoltCSUkS2qoSEcP0PdED7awdp3woMnbxo0qQJ9vb2qIQj8OPgGukirUi6detGt27dyMrKwsXFhezsbJ566in27dtXocexWq3MmjWLhQsXkpSUZOvRHBYWRv/+/XnrrbeYeGQiQKldfIobVqa4bNW1wQ2JsFqJAAz+ATRr2hS9vR7hz7+oN2ZMFc60dqIIOoU6wYIFC/j++++5ceMGAC+//DK9e/emefPm1TyzukV+oXft2jWWLl3KtuVz8ej3DwB8z21i48aNBAUFKa6o/BRXfuQukpKTMGYa8c+3zMnJiaZNm6LT6fLl8NV9rl+/jtVqJTg4mBdeeAFvb2/Gjh1bocfYsWMHp0+f5vPPPyctLQ1JkgB45pln+Prrr3F2dsbFxaVCj1kXKa78yN1YRZH427fJk8dWqxUHB3ucnZwJDg7GTqd7gK708qMIOoVaS0ZGBlu3buWtt94iKUl2+XXv3p01a9bg7e1dzbOrm8TFxTFhwgR27txJamoqZrOZoKAg6oeE4OXlxeY175XK/adkqubyys+kpKSwfft2JkyYQFxcHJIkcWCUG6GhobT4/BecnZ0fqIK0w4cP55dffiE+Pp6ePXuyceNGrl27RnR0tK2TSHlZv349o0aNwmg0IkkSKpWKTz/9lFGjRqHX67Gzs1PcqhVIntDbs2cPCxYsYOfOnWRlZbEqIBAPDw86/mcbwcHBD9R1XpEoZ02h1mE0Gpk4caKtubUoirz33nu89NJLNG3aVImxqGDS09PZvn07q1evZvfu3eTk5ODq6kpgYCAffvghQ4cOZfAKOfuvLsdyVRb79+/nvffeIyoqips3bwLw6quv8uKLL9Lh+lxUggrc3Uu301ruhl2xYgUrV67EycmJuXPn8vrrr3Pz5k0WLVrEr7/+yg8//FCi/RSVrWo2mzmRJMci7ju/D++x3giCgH+APw0CG3BOfY63D72tuEkrmIyMDN577z02bNhgs4K2b9+eefPm4f3VQuzs7Ahq1KhU+6yLmarlQRF0CrWGzMxM/vjjD4YNG0ZsbCwajYb27duzbNkyxbVaCfz222+sXLmSzZs3k54uN8rx9PTkl19+oUOHDkpf23JgMpn47bffmDBhAqdOnbJZh1555RVmzZqFr6+vvOHKz6t3olWAJElMnz6dJUuWACAIAtOnT+f27dvY29vj6Ch3EwkKCrJtk0dx5UfyEEWRpKQkIiMjycrKwqGxAwB+fn4EBgYiCAJqtVqxxlUw2dnZzJkzh8WLF5ORkUFGRgYNGzZk0aJFeHh40L17d1QqFdeXLqvuqdZotsw7Sj3XwMbFbacIOoVagSRJvPzyyxw6dMjWb3X27Nn07duXgICAap5d3eO3335jyJAhJCQkYDQaAfD19eW7775TCjGXk5SUFCZPnsy2bdu4efMmkiTh7+/Ps88+y4wZMx64cIHbt2+zYMECUlJSbMt27NjB66+/Xu59r3x6JQkJCUyYMIGj/3fUVgi47WdtqVevHqtfXI2np2e5j6NQNP3792f//v0kJycD0KRJE9auXUt4eLgioCsBRdAp1Hg2btzI7NmzOX78OCBnvX333Xf4+PhU88xqL0WVHzEajZyKywZg375T0Ol1XNVqGnl5ERoaikol8M0VFU89pYi50iKKImnpacx85x2+/vprW8eMwMBAVqxYwaOPPoqdnV01z7JqWb9+PQcOHOD1118nJSWF77//nh49enDjxg3atm3LSy+9xPvvv0/Lli1LvW+j0UhqaiozZsxg9erVZGfL1/XAgQP55JNPmH5uOkCpxZzihi2eW7du0adNG2JiYoiPj8fb25vZs2fTq1cvwsLClLCMSkQRdAo1lv379zNlyhR+++03RFGkefPmTJ48mb59+ypBs8VQmvIjmZmZXLx0kfS0NHQBsuvazc2NsLAwtFqtcgMuDXeVH5GQiI6OJlCMwhXonXSc5waoCA9vi6OjIwYHA0R9Dus+r/Vxb6Vl8+bNbN++nV27dgFgb2+Pk5MT4eHhLF68mJEjR+Ll5cX8+fNLvE+LxcKsWbOYN28eRqMRq9XKE088wRdffEH9+vXx8vKSNzxXGd+oblCSTNW7y48kJiWRnJRE/eRkXIBxRiNqg4EWDz+Ck7MzqlOn4dRpYorZr0JhSmPFVJ6KCjWS4cOHs27dOkwmE87OzixZsoS+ffsqdc0qiE2jH+G9997j4MGDHDx4EFEUcXJyot6rC2jYMJjNb44qVcmRByJbtRSlR27F3iIyMhJRFLFYLAQGybfapk2b4u7uXva2XHWE6Ohotm3bhsVi4fz58wD89NNPRERE0LBhQ4YOHcrixYtZunQp8+bNK/YFbsGCBaxZs4YLFy7Y4j27d+/OZ599RosWLZTyOZVEVFQUcXFxZGVlIUoS9R3k2MSOHTui0WhQKee93Jw7dw6z2VSibRVBp1BjyMnJYdeuXQwZMoSkpCQ0Gg3BwcH8+uuvhISEVPf06gQxMTGsXLmSefPmkZqaCkB4eDjbtm0jKCjIlq2qPABLT1RUFHvVA5g0aZItNg7kh9uejg5yz89Xtpdup3XUaufv709UVBRms5nOnTsTHR3NsmXLMJvNLF++HEEQCAsL4/Dhw+zevZvu3bsX2selS5e4desW165dY9XMVQBERESwZMkSHnnkEVlQKDFaFc7Ro0cZfuokV69etYnnzp0789Zbb6H7cSsqlYqgUlrhFKvdvbFYLEgl3FYRdAo1AkmS+OWXX5g6daqtptzMmTN59NFHCQ4OrubZ1X4kSWLNmjWsWbOGw4cPk5qaikqlonXr1ixevJiQkBDl4VcO4uLiGDp0KGfOnCEhIQGADh068Le//Y3nn38eu0PvVvMMaxaCINgyeTt27MimTZsQRZGdO3cW2EaSJIxGY6HyIyaTidNnTiPWF9E21BL8QTD+/v54e3uz0riSVbtX2bZV4t4qBkmS+OKLL1i9ejVnzpzBarWi1WqZPHkyAwcOJCAggLht/6nuadZqtsw7avusv9mYcb3mAZBwzYidRu9Y3HhF0ClUO5Ik0bNnT3bu3IkkSfj4+PDXX38p2asVQGpqKh9//DErV64kKSkJSZIQBIHly5fTr18/nJycFGtcGUlLS2Pv3r288cYb3Lp1C1EUAXj//fcZN24cPj4+d87toWqcaA1HM0hDREQE56afIyYmBi8vL0aPHs2WLVvQaDT06NGDrfu2AnK856VLl2x1zAz1DQB06tRJiautJKKiohgwYAAXLlwgJSUFFxcX/vnPf/L444/TunVrJeO9hOSJtT4T2hS7rdlsJiMjo9THUP4CFKqNtLQ0jh07xsCBA7l16xY6nY6uXbuydetW5SZRTpKSkrhw4Tw+47vaMvzeeecd/v73v9OyZctS96JUuENCQgI//PAD48ePJycnB1EU6dKlC3PnzqVZs2ZKnGcp0Wq1ODk54ejoaOsOMWvWLEaPHs2QIUNISUmh3ZV2TJ48mYyMDCwWC/369WPevHl8dPIj1Gp1qa1witWueF566SUOHTpETEwMZrMZPz8/1q1bR6dOnZT7RyXh8VAGw4cPt3VDyQvb2PjJQUyW7GIVniLoFKqF33//ne7du2MymRBFkV69erFhwwZ0Op3ypl0K8pcfyc7OJjY2Vq7T590IlU84nv3/gZeXN0FBQcRotSw8C5w9AzwgiQwVREZGBtmJCSQmJtLS35+cnBwEQaBbt258++23+Pn5KZbOctCwYUP+yvgLd3d3zpw5Y4unXbx4Mf/+978xmUzUr1+fwMBANm/eTEhIiFwM+EzdTC4ZOHAgffr0oX///pV2jLszVQFMZjOW3PJQPYxGegCGkFDCwsIwGBzQrN9A8voNOCsxbxVGQkICI0eO5Pr165w4cQJRFGnWrBlJSUm2zjElRXlyKlQ5JpOJSZMm2SxHDRo0YPny5RgMhmqeWc2iNKVH0tLSiImJISkpCavVSp6NKCysMa6uropIzk9x2apFlB6JuXCBcH0Cnjr45UU1Wq0LgYGBODtn4vzrKIS8FuJ1NImhstHpdMyYMYPHH38ctVrN3Llz+frrr0lNTcVkMtmKWoeHhxd0ZddRcnJybElLZaUk5UfyExcXR9zt24Tm/uzl6YnBYMDH1xednR3U8XNeHezYsYNFixbxf//3f1gsFkRRZNSoUXz44YesXLmSmTNnlmp/yl1eoco4fvw4CxcuZNOmTWRkZNC5c2emTZtGly5dFBdrGcjOziYsZjtr1qwhJiYGq9UKwPPPP4/o/wharV2prXCK1e4OmcZMTp8+jclkxmq1EJ5beiQiIgIvLy+5x6pChSAIAiNHjmT+/Pk8++yztvihgIAAvvrqK5555pkHrvDy448/zv79+wkNDa2UIuoN1nyHKIqMHTuW77//nsTERERRZFuLloSGhtJ900blRbASOHfuHCNGjODUqVMYjUZEUeSRRx7h1Vdf5bnnnsPd3R1BEMpk4FB+WwpVwtatW+nbty+iKGIwGFi4cCGjRo1ShFwpsVgsJCYmsnDhQr744gsyMzMBCAsLY+7cubRs2ZLAwMAiO0EoFE9sbCyb0rqzcOFCrl27hsViwdnZmenTP+ZR552oVCrqlbb0CCiWu3tgtVq5fv06sbGxBD0XhNlsxtXVlZEjRzJ9+nS8vLweyHvE//73P5ycnOjWrRuJiYlcvnyZevXqVej++/XrR0JCAhkZGTRo0IBff/2VgIAA0seNByiTmFPKjxTNyZMnOXrsKKIo0u/9N7BarTRs2JCRI0cyaNAg/Pz8KqTKQI0WdIIgPA3MB9TAckmSPr1rvQ74DmgLJAIDJEm6VtXzVLg3WVlZ/Pe//+WFF15ApVIxfvx43n///QeuX2V5SUpKYtOmTSxdupTz58+Tk5NDWFgYX3zxBf7+/jRv3rzOu6Eqk8jISLp06UJcXBwmkwmtVstjjz3Gpk2b0Ov1coP4lf9X3dOs0eSVFrlXwkH+0iPZ2dlcvnwZo9GIqoEKdZCaBhMb4OnpSXBwMGY7Mx+d/Mi2fV1OYsjOzmbnzp1MnDiRxMREBg4ciNVqJTw83OZ2/f3333nhhRfKdZyYmBi+/fZb1q9fz8WLF7Gzs8PPz49Dhw7RuHFjm4BLL/c3qtsUl62at14URTIyMjh37hzZOdk08pVb2H0ycj0BAQHY6ewQEPjfv+L5H/GF9mmxWEo9txor6ARBUAOLgG5ADHBYEIRtkiSdzbfZCCBZkqRQQRBeBGYDA6p+tgpFce7cOf7+979z5coV7OzsOHbsGI0bN67uadUaJEni3LlzhIYO4cqVK7blERERLFu2jI4dOyq148pBSkoKAwcO5NixY3IiCdCiRQv69u3L22+/rWTyVTBms5nIa5FkZGSQnnZHNhi4U3rkQWT+/Pl88MEHNGzYkB07dtC4cWOeeeYZvLy8CAwMBMDJyanM+1+6dCkzZ860Bdh7eHjwxx9/0Lx5cxwcHJTWfhWMMcvImTNnyM7Kzi1lJMm1VHPk9aGhofcdn8e4ceP4+OOPbX2fS0KNFXRAe+CyJElXAQRB2Ag8B+QXdM8B03M//xtYKAiCIOXl+t4HURRtLWeKIyEhgYkTJ9KsWTMWLFiglCUoAaIoMnHiRC5fvowkSaxYsaLEF7ICpKenc/XqVRITE4mLjARAr9czZswYXnnlFRo1aqSIuXKQlZ3FkCFD2L17NyaT3FYnJCSETZs2Ub9+fUXMVTC3b9/m9le3Of7HcUwmExaLhbCwMF555RXOep4tU+kRqH2Wu/T0dHr37k1Ojvx0X7NmDS+++CKPPfaYrY+tRqOha9euaDQaNBoN14cMJZvSuzNNJhOJiYlMnz6duLg4PD09cXNzY8mSJTz00EMPpCu7MomJiSEzM5N5P7zFyZMnsVqtqNVqFi5cyIsvPsGeb68CJatDB2AwGGxel8dfCSbuw6gLxY2pyYLOD4jO93MM0OFe20iSZBEEIRXwABLu3pkgCKOAUQDe3t5kZ2fz8MMPl2pCBoPBVjxU4d5cu3aNl156iYMHD9KyZUtmzpxJ7969q3ta1UZx2ap56yVJ4vr166RnpJOakorWrwl2/m6Ejl5IcFAwjo6ORKnVzPg9FX7/3333+UBQymxVURK5cuUKjbRx2AMTPCy8+5IWvd6JiIgInBydEA5OuP8+FUpMdnY2o0aN4vDhw0RGRpKTk4PBYCA8PJw1a9bQvHlz1Gp1oS4QdQ2TyWT77i+88AJ79+5lwIABREdHEx4ezvjx45kzZ06BMaUxGuQvP2IVRVKSk4mJiaGRyYQLMEenJ6xbdzw9PVGpVAirVnNz1WpAiXmrCFJTU+nQoQNXr17FbDYDMHXqVF5//XUcHBxwdnZGkiQsVgs3b9ykX79PAPj666+LjVMsgW2qADVZ0BUVEHT3tyvJNvJCSVoGLAMICwuTMjMzSU5OLv2klDil+zJ+/HhWrFhBeno6c+fOZcyYMeh0uuqeVo3n6tWr3Lp1E4tFzlTNb31r2aKlct2VAwmJq1evcvPmLaxWC41ys1WDgoLw9/dHrVbfKTuiUG6sViv9+vXjl19+sbmL/P39Wbduna3H6oN0Pb/11lts3LiRSZMmIYoiGzdupG/fvmg0Gnr16sW8efPo2bMnTzzxRLmOc/78eW7Hx98xOjg4ALIrW7HmVyzR0dH06/cJx44d4+rVq0iShK+vL4MHD+b999/Hw8OjwPYmk4nYW7GoVCpbaMF3391fTO/atatU7lao2YIuBsjf+8kfuLvKXt42MYIgaAAXIKmkB3iQbiqVTWpqKi1btuT69esAfPzxx0yYMKGaZ1WzWblyJR999BFGo5HU1FQ0Gg3Tpk2jX79+hISEMOjbw0DZrHAPtOUul6Mt/0Hfvn1JTEy0lcFo3749/2ki4unpSdDwHdU8w5pJcckN9yI+Pp4rV67g/LwzRqMRgGHDhtG/f3+6dev2QLr4MjIyWLVqFTk5OXz11VeEhYURFRVFWloaHh4ezJkzB3t7e2bNmkXnzp1LfY42b97Mz1oNmzZtwmg04uDgQLt27Vi3bh2WDychUHorXGVb7Q4ePCj//QUFVes1UZpWXCAL5gsXLnDkSByZmUb+ve3fAHTv3p2VK1dSr1492QJahK7Q6XT4+/vLxxtfMm/V+PHjcXR0tP0tlYSaLOgOA40EQQgGbgAvAi/dtc02YBhwEHgB2F2S+DmFiuf7778nOjoaZ2dnunfvztixY6t7SjWWuLg4du7cyXvvvUdiYiIglwiYM2cOw4cPx9HRUXmjLgcWi4Uvv/yS7777jqioKCRJQqVSMWTIEN588008Tk4pWw05xQ0LFMxWlSQJs9ksZ6x6GNEEa6j3Vj0EQaBRWCMsHhb+JfyL73d9D9S+mLfyIkkSFosFjUbD7du3MZlMqNVqunTpgoeHB6GhobRq1coW/F5SgWM2m/nll194/fXXyczMxGg00qJFC2bNmkXjxo3x8/MjqpK/W1k4duwYw4cPZ/z48QwcOLBGi/w8wQfyPeXEiROYTDk08GwCzjBl0Dd4eXnj6enJnxtigVjb9iUVifejLAanGivocmPixgA7kcuWrJAk6YwgCDOB/0mStA34FlgjCMJlZMvci9U34weTPXv2MG3aNPbv34+TkxOnTp0iICBAsX4Wwfbt2xk5ciTx8fG2WIuPPvqIrl278tBDD5Urk+1B5+DBg2zevJkDBw5w5swZ0tLSAOjZsycjR46kS5cuuLm5yRufVrL6yovVaiUyMpLY2FhEUUSSJAwecrZq8+bNcXV1VV5KkLNTb9++TfKYsZw4cZznz55l37597Nixg/bt26PVanniiSeYOnVqsfFSZrOZSZMmsX//fk6fPm2zOj/11FPMmzePxo0b1/jwli5dutCnTx/69etX4+93oiRy6uQpsrKyMJlyCv1+Wrcuv2iraGqsoAOQJGk7sP2uZVPzfc4G+lX1vBRk9u3bR48ePTCbzYSEhLB7925bmr2CTFJSEhcvXSQhPoF/r52IKIr07duX3r1707t37zsi40GluMSCAF81AAAgAElEQVSG+yBJEsdPHKeHtzdpaWm2zMHg4GB69uzJggUL8PDwUIRFBXH27FkWLVrEwV0HuXbtmu18R0REsHjxYlZlrUIQhAfOClcc7u7upKvVuLnKf+smk4l//vOfvPbaa9SrV49HHnkEtVpNwuuvk6Iu+EjONGYinJELO2wIbUQzSaIZ4N4whMaNG6O1s0MQBIJatKjqr1UmRFHk+vXrtMid7+7duwkLC6vmWRVk3759jBgxguvXr9tqwbVs2ZLly5fj7+/PwXVy5FdFWOEqmhot6BRqLt9++y2vvfYaAJ07d2b37t012nxeEZQ0W9VqtZKTk0NMTAxxcbHY+TdD7etJ07e+IaRhCBo7O7Znw/Z/3Smbo8S83Yd82aoms4kLFy5gNBrpUM9Mazf4V08Ldnb2hDRshru7O2qNGpWQBj+9rLhJy4kkScycOZMtW7Zw5syZAg+46dOn06NHD7RaLRqNhtW/rC7TMR4UAdggKAjXs2dISUnBZDIRERGBr68vSUlJtnZPedyOv01k5DWys7N4yF5ObnBxcaFRo1C0Wju0Wm2tSePJysri559/5rvvvqNz585kZWVx/vx5du7cSa9evbhwodhqHJXO2bNnmTJlCtu3b8dkMiGKIkFBQSxbtow2bdrg6uqar17f3aH8JaMqBKAi6BRKzcWLF5k4cSIWi4UuXbowa9asOi/mSo7EpcuXSE9LJzs7G1G8Y6ZvFBqKRqOcp7KSnJxMzI0YkpOTczP55NtXw4YNMTgYcHVzRa1S3KlQ9sSG/Ny6dYuPP/6YtWvXkpmZidVqRavV8swzz/D555/j7+9f4118NQm1SkWLFi2Ii4vD3t6e48ePk5qaiiRJDBkyhOBVq0hKSmLr1q18+OGHxMfHI0kSG0JDCQwM5OH16/Hy8kIQhFoT0rJ//36WLVvGjh07SE9PZ9GiRbRp0wZHR0e8vb1LXZbjbkqb2HA3ZrOZr776im+++YZLly6RnZ0NwODBg3njjTdo165drXq2KYJOoVSMGDGCDRs2kJWVxbhx45g3b55SaRy5qGT06gmcPn2a9HS5Cn6bNm348ccfmfBzNIIgKFa4MrB9+3Y+WnCTyMhI0tPTEUWRgIAAmjZtStu2mRgMBgKVbNUyUVT9N6vVyrGEYwB0/qozkrOE/wR/XF1dbVmJgiAQEhJS1dOtEzz77LNMnTqVDz/8kJ07d2IymRAEgfnz5+Pr60tiYiJWq5V27drx3HPP8dlnn5E8ZiwqQajQXq6Vxblz5zh//jy+vr40adKE5ORkMjIyWL16Nc2aNcPPzw+j0ciWLVs4d+5clcwpf3JDHgkJCZiSZflz5aaFrkEv82RDFRHhETg7O6PT6Yg9ANpHao+YgxIIOkEQ+pZhvzskSSpdARWFGs/o0aNZsWIFIBdFHD58+AMt5hITE9m0aROTJk3CaDRiNpvx9/dn+vTp9OnTh8DAQLnGmRBTpv3XKgFYjli4/Ny8eZO//vqL9evX89NPP9nqMDk6OvK3v/2NhQsX4uPjI78131U4uMQoblgbFouF5ORkEhISSE1NxWQy4dBYdvGFhITg5uaGvb19rbEIVTZ5RXzLWtrjnXfeYezYsSQnJzN27Fh2795NZmYmWVlZNGzYkKlTp9KxY0eaNWtmKzqbWsZzX9VFg48cOcKjjz5KVlYWKpUKOzs7bt++Ta9evQpsp9VqWbZsGYmJiaxbt65K5pZpzCQhPoHY2FuYzRa5HmX9VgAEBgbi6+uLnc6ubNnvNYiSWOj+Xcp9SkAj4Grpp6NQUxk8eDDr1q0jNDSUKVOmMHTo0OIH1UFEUWTkyJGcPn2aY8eOkZOTg52dHa6urnz66acMHTq02OrfCncQRZGU1BQ+HD2aP//8k7Nnz9oygA0GA2PGjGH06NGEhIRgb29fzbOteirCdZqfrKwskpOTSU1NJWZeDPv377e5mZydnVm1ahU/aH9ApVKxumfZYuIeZPJ3bciP8bBcU/LHZs3JzslBEkV6ShJ/d3HFo2FDgoKCaLhuXa27d5w8eZLOnTvz119/sXjxYqZOncqbb77J4cOHee+995gwYQJLly4t8EKg1+vZsaOgVb28rtOiuHr1Km+88QZnz57lxo0biKKISqWiXr16rFixgtRjDtjZ2dFnQtcKO2Z1U9Krx0eSpNsl2VAQhPTit1KoLVy6dIkZM2bYxNyJEydwyK1AXhspLrHhXlgsFs6ePUtSUiI31y4H5DpBs2bNYsyYMUrtuOLIZ02TkIiNjSUlJYUI+0TcgYE5JxnYChw7u+Hn54dOp8PVxRWV6io0a1Z9864D/Prrr+zYsYPvv/+elJQUW7kLQRDo0KEDEydOJDw8nIYNG6LT6dj2y7ZqnnHdwGQyyf2Yk5JonRuHZcwy4mDvgKeXF/5+fmjyJTfUNjEH8Le//Y309PQCXZecnJzo2rUr+/btw8XFhbFjx9K8efNKnIVc6y8uLo5Jkyaxfft2rFarbU5qtZr+/fszduxYmjVrhsFgQK1Ws+VMYVdsbackV9BqoDTu07VAWtmmo1DTmDJlCtu2yTf4d955p1aLuZKSJ/pAtiClpqaSmJRIur0PWj8f6g38BJVKhZ+fH0ddGzBywxnb9rXKTVoUFeQ6LQoJiYyMDKKiokhNTcVisRAReEcE+/r6EhgYiF1uKYba7v6obkwmE7t27WLEiBEkJSVhNBptQeje3t4MHjyYt956y+bCLu8LSW3KVi2v6/ReNFjzHefPn+fs2bNMnjyZa9eukZWVxaqAQOzt7bGbPZtW7drh6emJq6trhR67OjCZTIDcpiotLa1AkoydnR2Ojo7Ex8dX2vGzs7O5cSOGzp3fJDU1levXrxdol9W6dWtGjRpFnz598PDwqBDRXBPLleRR7LeTJKlUnZMlSXq97NNRqEkMGTKETZs2AbBq1SqGDRtWzTOqGiRJIivLSGJiEteuXbP1RtQHugN3eiMqFrniyUue2bbtCBkZGWRmZgLg4OCAr68vf77uKbcrencbjo6O1TzbyqOiXad379disSBJEpcuXcJsNmP2kd3WIw+OxG6IHfVV9W0xcRqNBpVKxbxn5lXoXB5U0tPljPZBgwZx6NAhMjMzEUURd3d3Bg8ezLRp08h+byJqtYqgF+tW7fvLly8zadIkVq1aRe/evYnep2Kz9X88/2477OzsaNKkCZs2baJr1/K5NfNcsmazmZiYGDIzM8nISCfQIwJ7PHnYuz9qXw2+T/gSEBhgy3ZXq9X0ea3mCrCKplRyVRCEjkBLwAKckCTpr0qZlUK1creb9UGImZMkiRMnTjBt2jR+++03UlJSANkt9eSTTzJmzBjW3pSr39d6K1wlIooiP/30E/Pnz+fUqVMYjUabiFOpVEydOpXBgwcTEBCAXq+/YxGsw2KuMrBarVgsFi5dulSg80geBh+5a8PDDz+MRqN5oJOXKhoJCZPJxNtvv82BAwe4cOEC2dnZCIKAl5cXX375JW3atKF58+a28369HOe/qpMbSoOjoyMzZsxg8eLFXLp0iSb6UDLSM2zrBUEoc525vA4kb7/9Ni6JLcjOzs69zvNKndyJy+vYsSNqjeaBt+qXSNAJgqAHtgA98i2WBEG4ALwmSdJvlTE5haonMjKSli1bkpWVRVBQECdPnqyRwehljYW7m+joaJYuXcqCBQtsb9YAjRs3ZvDgwYwbNw5HR0cEQWB9PldsaagWAVhZrtMiMktj42Lxyb4MwO8jnHGWJKYEAAHg5eWFm1sjvLy84OWfK7am0wOUrfr7779z9epVVq5cycmTJ22tzaxWKyAX+m3evDl9+/bl2WefZdSuUQ9E14bKcp3mT25ITknGmGnkelQUrXJddhsbNaK9BJ20GjyaNsPJyQkfX19UKhVBw4dX6FxqOm5ubrRt25ZDhw7Rue9LnDx5gv/+14ROp+PIkSN8//33Jd7Xtm3bmDlzJjdv3iQpKQmLxWK7xsPDw+nQoQNTp07F3t4eLy8v/jP/JFCz3aBVSUktdNOB1sALwB7AHmgLvAn8VxCEQZIkba6UGSpUKdOmTbPFIEyfPr1GirmKIDY2lgMHDjBv3jxOnTplqx1Xr149lixZQoMGDWjUqFGN7zdY1YiSiMVs4eKli7Z4rNTUVHz85fV5y7y8vPD29rb19FSr1FCLCnQWRWW5TfPvOw9RFBFFkeOJxwH46/pfmM1mrB2tuD7iiity/JVGo2FGkxk0bdoUf39/DAaDLQZRoWxkZGQQnxBPUmISZrOZjMxMLBaL3CUjV9CpVGrcXF3xDwjAYDCgUgmo60gIRlkyTjt37kxkZCTu7h7ExsYxcuRIBEHAZDLh4uJSaN95WEUrFrOFxCj5mXP9UDSd/QZBffk+olZrbEWIHx0WiL+/P25ubsr1fQ9KKugGAOMkSfoh9+dk5P4X/xEEYQSwRhCEw0AO0EaSJKXSZy2kLpUmGXAPa9qfkUkAtHpnn7ygQS88IwbQLigIJycn9Ho9ffrUEJdqJSYolIQ8cfbJJ59w4MABjh07Rnp6uk385ufkOwG4u7ujf+1rwsLCcHd3r+rplpjKFGblITMzk5s3b5KYmGizTBjCZddpVlYWarWakNAQ9Do9zs7OgFzTa+DTAyt0HlV9XirLylYcEnDt2jXmzJmD2Wzm6NGjREVFkZKSUsiFLQgCb7/9Ni0vXMTJyYkB362u8R0EKqMUyL0YMmQIx48fJysri9atWxPafSIATz75JI0bNwbyYpOziImJIS1d7r1stVgQRdFWE86UYyIgIAAHBwdbEkOeeGtRwf1q66JVr6SCrj7wZ1ErJEn6VhCE1sjZsGHAl4Ai6GoRkZGRzJo1i3Xr1tVoN2tZMBozuXHjJrdv38ZqtaILkEtguLq6YjAYaNAgEK3WrppnWQ3c5Tq1Wq3EJ8TbXKd/jHS19e3sKEl09AS6yQ+2b61D6Nu3Lw8//DAAnp6eqNf0RkDAL3eZwr25ceMGa9as4ddff+Xs2bMkJiba4oXyXP5PP/00Xl5eZLfLRqvV8t3HstipybFw1SXM7kfenMwWCxkZ6ZhyTERei6RFbtD84ce60D53206Ap78/2uBggoOCcZr/JYIg4OnpCcjnPm9/NV3MVTWtWrXihx9+4NdlF1Gr1Ax9801OnjzJ4cOH6d69O4mJibYeqXnXOMDzzz+Pu7s7Bu/WODjY89qit1CpVIoFroyUVNAlAF7AtXusX40s+L4APi//tBSqisjISJo2bUpWVhbBwcGcOXOmSsVcRcXC5bFr1y62bt3Kno0bSUtLs1k6VCoVPj4+NH1nJQ4ODvzrtZ5Vd9OoZktbHikpKVy5coWEhARc//oTURSxWq02MSFJEj5B8i1Br9ej1Wpxc3OTBZtKjVqjRhAEugwv6oFd9TfgmmRpE0WRPXv28NXtr7BarVy5cqXAenWQLCBCJsktsyRJgvZg396eUH0oERERzH1oLj4+PrYq+4Ig2L5jVQq5mijMSsKpU6e4fVsul7pgwQJOnTrFx4IKq9WCJN2xOKtVKtDL57Ndu3bY6XSo1bK7NH9QvY+PT4XN7Wir8QA0KMPYqrS0lZQt847KJZ3SUgFITkomOTkZX2f5+n77718iiRIg0bfVeFxcXBBUKhqFNqLLK0FyTC3Yypzkfcea/MJSGyipoNsNDAUO32N9AmCRJGlChcxKocqYPHmyLWZu5syZtdYy99NPP7F9+3b+/e9/k5mZidFotK3r1KkTQ4YMISwsDO+DUyAHBGF/qY8xNfG93E+lH1sdZGRmMHf6dFuBzZs3b3Ls2DG5rl5iUpGNsQ+95oGfnx8eE7bi6OiIXq9HbzAUK35fEeIAKK20esXXu0zjagpnz55l6dKlGI1Gtm7dittrbkiSZLNu5mFAdp3efc5DQkJwcnLCycmJoKCgQuf5xa9yaxw+Xbp5lXVcbeTa9et8Pm4ce/bsITY2FpDjOs1mM4Pyne/HH3+cPn364O3tjebHrQCErF9XqvJDB/TyCS2LMKsrTJ06Faf4ZpjNJpKT5WoAomjFKoogRwIg5VrhXFxc8fLywsvLE0FQodVq8Pf3r66p13lKKug+A/4nCMJxSZK+LWJ9eyC64qalUBXs37+fjRs3EhQUxIcffsjgwYOre0qFKCoWTpIk3rn5DgCBL/fHbDbnPigDUT31Dk5AIx8fpnRyolu3bgW6OJwpW6IqAE19XYrfqAjKLHbuM+7kyZOcOXOGrlHzcotr3rCtM5strOrgAxoYdm3OnUEGoLP80dHRi8+TnkStVjN79mwEQcDd3Z3XNnSSjxkUVMrZVj1VIXY2b95sK57a59ujWCwW6g+vT0JCgs1Fmt+FFP+PO0VUO3ToQMOGDQHod+gker2e1it/tbnwoGZ2ByiraKnIcfv37yc6OhqLxcL777/PPAeDbV1+wax+5AOo35E2Wz6hDYBefiH1DgwEBNxcXWn8/b9wcnLK7assC+YNu+TfWaM6kshQ0aSmprBhwwb279/Pli1bADmOMyMjo9DLCsDAgXIcZ1vfNtjZ6Ri98C2AAudcofIp0d1EkqRTgiC8CqwQBKE/sAg4itxBoguym3VVZU1SoWKxWCwcOnSIJ554gsDAQM6ePVujLXPp6emkpaURHR1tq0PUQyW7okyWZPR6PTq9DoPBQGBAIHZ2WgRBxfPPF3bjHrowBICmZZjHt3t7AzCiVKW2ofOekfKHl+8sy8jI4MiRI/ccc/jwYTqffBWARrMaAZCcnGxr25TnSt4zrOjOHZ0PyMZyV/d/4OzsjK+PXFJBo9EgqAS5DMsr60s015JQ5nEbZQFZFktSjNeo0g+yjZPYt09OjImNjWXy5Mm29drBWnJycoCCFrUP/ORAb/0jn+GPbGXQ6/XodDrc3d3x8vLi4+Yf4+TkhKura4HuC9++LLeLe6aUbryyfsek+uPLNK6yyM7O5s8/74Rh//DDD2zfvh2A99rkXquurra+smazuYBQNgUE2j6rVCpccpNC8iqedejQHp2dzub5z+86dXNzq/DvU9u4ePEit27dsv28ZcsW7G81LrCNKfech/rIyQfHj99CSwj9270LgoDBwYGgoGAMBgN/f6d1Affo3a7TqnpRqUlu6JpAic+6JElrBEGIBOYDP1Kwut92YGbFT0+hojGZTHTu3Jljx47h5+dXIWKuPHFw3Q/IdYTIHZuTk8ONGzcYP348165dIzIy0iZi8vDx8YFu00GAc4tG4+TkVC2WjoyMDFvMTh5ms5lRo0YVEALDgl5CggKdEKxWq+3hVZQoawdcQa7k/02nm/nWqAv8/8RqI05OTjarT1hYGB999BEsPwtAqy9vUFeQXcWJ+ZbI57hLly5Fuo/Te97JxjWbzbbzPVF6F7irVMgLdz5qvbVIUQX3FxAQIOf1c6dYryAItn95hIaGluWr1UiSkpJsBbbzmDp1KlFRUYW2vXDhAjO6TAMosuOHKIos8fSy/dwm9x+AJvf0zXd2Aec7VnBBAK1Gi0qtZlOzpqxZswaQrT56vR6ADSPll5Lwb0pe66z2IZGdnc3Vq1eLXPvnhjtC7ebNW6Slp4EE3oYgAN7pMx8pN072DoG4e/hxI/GybYl8Tu9cy61atQaw1eFUqQTb+gehBWRtpFRPQUmS9gNtBUFoBrQC7JA7Rtzb1KBQo1i7di2HDx9Go9GwadOmarXM2eLcJHj22Wc5dOgQFovFVjQV5Ju3n58fX375JU888QQALi4urB4he/7L+/adP9auKCIjI/nnP/+JJEl01TwGgIeHByCLz7wuCPdjaNBLACXaFvLdOHMNFBEREQW2yOuRCJD0xSb0en2h3+PFXEFXZQj3DmbOycmxFQctjOy2HDRoUIGlMTExnD59usCyrKysAn0avx68BIDffiu6rnlwp+ASTJwCMVS+vr44G5wZ1HwQ9evXtzUVd3R0ZO1fsqBY/1xhy2Z1k52dXcCiRe7D+8aNG0ycOLHY8VevXuXixYsA/PPJjwGoX7++zVKZn1X5rGU2dHo0uedxkbtHkccI1+m4lK8ciEqlwsvLi7xXorzr3NnZKVcwy+31VILAgBqSoKEPDy/xtmaz2Vb+RBTl6z/vfrN9+3abKzM/u3btKlQy5eUu07BaLLw0uegQ9XG9im7h5p3rpRbv+tsTBDmL9FZyJJuPfcHs2bMB6N+/PxqNpkYmYSiUjJJ2ipiHbJX7Q5IkUZKk08DpYoYp1DDS0tL46KOP0Gg0tG/fnrZt21bIfkuSLJA/Fs5isZCZmcnFixf5SJJN9UddH0Pb41G0yFWrnZyc0Gg1uLq4sm7EQ/j5+RVo/Fwajhw5YrPO5PHHH3+QnJzMhAn3z+PJzs62WeG69pMFXVJSUpHb3sv9eUW833oBOzstE8/KTuCWLVsyaNAgPD09uTRrDwAu4wqeV7VabSubUB3OJFEUOXToUIE3finXWvbHH38U2n7KlCkFYvzyM6H924DsgsuP1Wq1PdiCP5CFmQsFYxiFXItZ3vqiiPw0svDCwQAC9X+vT+fOnen7XF9bo3QvLy+bOM5/nqsLB618zRw4cKBIK2Qe77//foEm6BNayef1scce4+bNm0WOWVbvjvtXkiQkg/ySoFHJVpil3vWKHBeu03G+CKFXFHq9Djs7HQKQoNXSftNG2zqVSoWbmxv/ff8XAFqs+hGQX1hqQ5/kixcvFmo8f+m/Jtvn23G3bQlJ/m5hAPzjFVmYWiwW6lkfKrTPF9sXvic39JZLLd1LuAHM/0/h+1je9vP/M4HQ0FDq1ZN/ny+//DKPPSbfy8ZoelG/fn2geuM5FfFYMZT0N+gAbAB0giD8jCzudkqSlHX/YQo1hbS0NJo2bUpsbCz79++nffv2FfYHfL+4NJPJRGJiIklJSURGRpKVZUSSuGNN0Mgizc7OjkaNGqHRqHF0dLLFZ4iiiEajIS4ujnPnzrF8+XLi4+MZEig3uX700Uc5duxYoePuGHDngVDAOiTJNznzN91xBJaWyEss7+v+wgxa+ag5Hlvwbdjb25srcuId4eHhcvp+PhedgICExPrJ906ZuNutWxqKco99/fXXHDhwoMjt887r448/XuT61KflMgWpqakFln8gyVagIT8NKTyoA+gD9WRHZRdel6s1fMf7Fnm8sqBWq2nUSI47nL56ulxqpUsX23n/7xRZQKxfX9jSdq/CyTKyoCrqnBZP4bFms5lWrVohSRJfe3kVOUro8D4Al14sunhwnrgaedfyPDfmVLMFvLyLHFvAolcKzufk8HJ0FDqdjqVLl9rOq2WXiFqtousfBV9A7idEMzMzEXPXl+U6z/sOpf2dFDfuwNoY2+cLFy6Qmus1CKknW2zf6TNf3sdd383PI7SAG9NG7ptXVtb9PQL3Q6fTF1rmYHDA28ubPe/sKbA8LCyMA+vkl6hZGzLQ6XQ1MgFHoWIpaVLE68DrgiC0B54DPgbWCYKwC1nc/UeSpPj77UOh+lixYgVTp07lxo0bDBs2jI4dO1Za5pEoipjNZiZOnMjmzZtJT08nIyPDdgPV6XS4uroye/ZsGjVqxLmlpwDoykms548hSRI7duyw1UX78e9mrv6ZOxZ40wAY4IooC49/hByFkMLzaO5FIXFV2bzi480JofBD8oPcU90xNRlSkwut1wfqyV7yeOFxyCLp8SLWFUdxY/UDihZY1lyBFfVI0Q86vcs9hFklMm1d0b9HTcd7rbcCZ3LFzhQk5H6Feeg7fgjAns6PFrnfe1mgiht3P+41dqGb3FGjkUZb5DEr8xF8rNW4Ipc3cpWtSZfusd7PI5RxuaLl2OY7MY151f7nvVl0PNu9xE7euM/H/LuEMy/52LIe857CLJe73Zh53Ei8XKy1rCgmTJhAy5YtbT/37HmnTubu5ZcRVCpenT/invMpCiHXhK3RaLBarYXCHu7l8ZCQiI6KZujQL0t0nOeff56nn5azmkRJvv/d7aoXBAGNRlOk5TV/v9aK5n5enaLCCSr7mGWlpM9r4X5vT8UcIBRZ3D0HdECuUfcjsEGSpBodiR0WFibdvHmzULB9XeTYsWO0bdsWSZIYNmwYK1eurHAx9+2wpQD8nL6TPXv2kJaWZhNwarWa5s2bM2jQIJ5++mk6dOiAg4ODLR5q2aBFALy65vUi9/3NkCVFLs9xkB86OuPFItdb7PwRcmIKLzfI4zSZRY+7H8WNlXRlO6ak80djLuwWy7GXA+x1Wfd+sNyL4sZatPXLdEyLtj56U2FLSqZDEAAG4zV5P9k5NssLAM6BkFZYJIoeckySKvF8kcfT63X3vF7vPqbVYsWUP/7ormNqNRo0Wk2hcXeTbVcPvSmu2OOVhuLGZul8MBtMhZars+VsTqv+Tlypi6sr9rlJASm3snH11ZORkZl7P5PuOa6kFDdWleOAqCtsaaqscdUxV0mCf/1vTiFLdnHC7F4UN27EiBEsX7680PLGjRvzTOPXAPjq53dLdcyxz8695ziDwUBKSkqRAmvhO//h8uVLJf6OdycGFUV4eDjffPMNHTt2LLRu/PjxrF69ukDsdEXg4OBAamrqPd33jo6OBWJyK4q0tDQMhjtldi5evMijj5b+JRAgPj4eSZJYt24dgwYNOiJJUrv7bV/mF0BJki4D84B5giB4Ab2A3rmr55Z1vwoVy+bNm5EkCWdnZ2bOnFkuMVdUTTiz2cSzuc/tQw4d0D3Tnjznkb29PeHh4ajVatxOXOHPk/uZ/3zBOJDsXGFWlHATALOdP6qcmNybRuE/TKt0Z9uixuch/1EL5FVQUqtKX5G8uLEWwEFTOMkk7zZV1DqATAQMWkOh5Xnvj0WtK47ixqaW8ZipCHJ5iLvIS/fIW6dRa2xxdQCZgoDBsfA+886NYxHrQG7ObRcWVuS6zKuya9SxmewGs1jMCMY7N+icFND53inNoNfrsbOzKzTubkw3jTjWL+ymLG7c/SjJMb3qF3a7xjdF3JYAACAASURBVOWOq1/fz7Ysf2skQZWDVmuHq6sGZxdnW+2BosbdzdNvRBS5/D/j5aK7vT5+7n5fqdrHlWfsL4vPAfD0G+3vuU23qLBC8YdRe+Xz/utbv5bqeMWN8/Mr+vfk7e2NnU5uTejrW7qwhPuNM9ynWLinpycODg4l/o5TpkwhOvr+ZWhHjx5N+D0SSlxcXPDx8SkggiqC+31HkM9LZQi6u4+pVqtJSUmx1bMsC3cnytyLUgk6QRC+B36VJGlZ7s+NgRbAXkmSVgArSjlPhUpk7969zJo1i6ZNm7JgwQICA4vITstHacqPZGVlcf78edLS0nhWIweIaLVagoMbotfriY+Px2wxc/r0KURR4u+CU6F9FIglu8cfntZ8gxFrXiu0PM8q+MLCgVgsFhwcHEqUsZs3bsTq0cVuW1Fj88YNKeO4gWWZa27ds4GrXq0V44aUclz+sS9MLNsxq2oc3Cmv8cLErjV6HCDXC6Ho8iM1ahygz82MLe3YvBjd+41r0qQJTZo0KbBsywk5A/Spp0oXxF/Wcb///rst6/Sz7wtb/+97zDKO0+l06HS6Es/1qaeeKtX+72bGjBnMmDGjXPsoC5cuXaqS44SEhJCUlFQoMa8kBAYGFluJIT+ltdA9BswBEATBA7l/qwDkCILQNTf7VaGaEUWR48eP8+STTxIQEMDBgwdLdMO7uybc3Wy0+wenTp0iPT0ds9kCSDh7OnMsRa4/tV4zE+s1qy3+DUBQCTg4OrAveCJDhw7FwcHBVsNIpVKVS2CB/HanoFAV5GWcKijUZZSM04rHYDBUuAWyKEor6JyAvCqGzwPXkGugzgD+yR2Xq0I1MmLECFsG38KFC3FyKmwdKw1Xr16lc+fOzHxqKoV+xSl34tnOGT8Fcl2deRY3CciAd98tm2B7oLlPbTeFsqMIs5pFaWq7KZQcRZg9eJRW0EUh5xRGI9dW/06SJIsgCKuAwgWoFKqcrKws9u7di8lkokmTJnTv3r1c+5s/fz7bt2/n1q1btni1eyLIcWq5iq5cx1VQUKhdKMJMQaF6Ka2gWwEsEgRhO/AEkBfcpEGuVadQzfTu3ZuoqCiaNGnC0aNHy5RCLUkSMTEx9OzZs0C1/tfWFsxEHTZsGDNnzuTXKTuAsrtN6zyKpa1SUCxtlYMizCqe8ljLFEubQkkpaaeIQcAmSZI+yw1e7wG8K0lSXnO59sD1ypmiQkmwWCzs3r2bXbt20adPH/7xj3+UuR6Ok5MT2dnZhWoDhYaG8vbbb/Pcc8/h5eWFnZ1dRUy9aimPuFKE2T0pq7hSRNn9Kau4UkRZ5aCIK4WaTEktdN8BvwK3JUn6DPjsrvX1gI2FRilUGT169GD//v24u7uzYsWK+yYLFFV+RJIkeuS6VB17TyYvhcLgaMDJ0YnQ0FDUahVvjC5cR6haqEXiqjrEzoMgsKpa7JRHJNUmgVVW0VLV46rrmAoKNZUSFRYWBEEEfCRJKnsPohpEXSssPGrUKL755hsADh48yMMPP3zf7fMyS23kXgN5NeH0ecV6iyglUpRbtTpKgSgoKCgoKNRlDAYDRqORVatW8fLLL1deYWGFmoHFYuH3339HpVLRsGFD2rQp5ZunlL/8ay6V1BasKBQhp6CgoKCgUH5KI+gmCIJwADgiSVLpKhUqVBrLli3j/PnzdOzYkblz55Yoru2VlSNJSUnhoYce4upVOQxSq9Wy5MWvQFBEloKCgoKCQm2jNILuReBdAEEQEoGjwJHc/49KkhRZ8dNTuB8XLlxgzJgxNGvWjL1796LVaoH7uzGjoqJo2bIlWVlZ5OTk4OHhwcqVKwkICODIF3+WbSK1KJ5NQUFBQUGhLlIaQfcQkA20Bdrk/usLvA+oBEFIkSTJveKnqFAUEyZMYOnSpRgMBnbs2GETc/fjm2++Yfz48bZWIn379mX16tW2LhJHKJugG1GGtk0KCgoKCgoKFUdJBZ0EIElSGrAn9x8AgiAYkMVd6wqfnUKRSJLEzp07yczM5Mknn7xnY+c8zGYzhw4dYtq0aRiNRlQqFc2aNePzzz8vU/9EBQUFBQUFhcpFFMVSbV9SQXfPKHlJkjKB33P/KVQBa9eu5cyZM/j4+PD/7d13fFRV/v/x1ycVSDNAICBIk15EEFARRRcVsYCsCPpFKYICFlbxh4gsCKuAZV3dlRULVVBhV0GaKKygIiqCAtKLSFlqQFpCQmbm8/tjhmyGTCCBJHeS+Twfj3kkc+6Ze98z4RE+Offec2bPnp3rwvYA27Zto3nz5qSlpeFyuahatSozZsygWbNmFzxPnTHGGGMKV7169Vi9enWe+4flsd+twLELSnQBRKSsiCwSka2+r4m59HOLyGrfY05R5XPS1q1b6dWrFw0aNGDixInnHGHbunUrTZo04fjx44SFhfHaa6+xceNGrrnmGivmjDHGmCB2rsGaQPI0Qqeqi3w7Hw68pKoZ+Y+WL0OA/6jqWBEZ4nv+TIB+p1S1aSFnCRoej4err76aiIgI5syZQ61atXLvrFC/fn3cbjePP/443bt3p2XLlkUX1hhjjDFFJr/z0D0P7BSRb1V1W/YNInKrqn5eQLk6Am19308BlhK4oAspu3bt4ujRo1SoUIHq1avnnCDYJ710bQDevv9Nb8Mx4ZdxP/PLuJ+z+gS6A9amKzHGGGOKp7yecs1uHLBFRE6IyHciMl5EngVmFGCuiqq6D8D3tUIu/UqJyEoR+V5EOp1rhyLysK/vymPHiuzscYH56KOPaNiwIR6Ph+XLlxMenstUIX6zBEuRThJsjDHGGGdcyEoRtYA44Aqgqe9xPZCv0TkRWQwkB9j0XD52c5mq7hWRmsCXIvKLqm4P1FFV3wHeAe/SX6mpqfmJ66gtW7bQv39/0tLS+Oc//0mNGjUA/xG15cuXM3/+fEaPHs17D4z3ThA81UbcjDHGmFBwIQWd+k63bgM+vtADq2q73LaJyAERqaSq+0SkEhBwDVlV3ev7+quILMU7dUrAgq64UlVuvPFGTpw4QevWrenfv3+OPj/99BNt2rTB4/HQsWPHc9yTbIwxxpiS6EJOuTYVkTIFnsTfHKCH7/sewKdndxCRRBGJ9n1fHmgNbCjkXEVu1KhR7N27l4cffpglS5bk2L506VJatGhBdHQ0nTp14pNPPnEgpTHGGGOcdCEjdJ8BKiK/AmuBNb6vawtw+a+xwEwReQjYBXQBEJGrgH6q2geoD7wtIh68helYVS1RBd2BAwd49dVXiY2N5dlnn82xGkRqaiqfffYZHo+HDh06MGbMGMLCLqRGN8YYY0xxlt+CbiPQCe+1b018jw7AYKAMUCCLeqrqYeAPAdpXAn183y8HGhfE8YJRRkYGK1asIDU1lZEjR1K1alW/7fv37+f111/n5ZdfpmPHjsycOdOKOWOMMSZE5augU9WGvm+3km1lCPHOfneOSdFMfv3lL39hzJgx1KhRg2HDhvltS0lJoU6dOpw4cYJOnTrx8ccfWzFnjDHGhLALOeWaRUQiVTVTVRXvTRKmAKxZs4YOpz+lw6AGlLn/fb/Zovfv359VzNWtW9eKOWOMMcZcXEEHpIrIFaq6sUDSGAA+++wzkvY/CkDPxv87q6yqvPfee5w4cYKOHTsyatSogMWcTRBsjDHGhJY8FXQi8vdcNoUDz4nIEQBVfaKggoWqDRs2MGLECP7Z9Q1AsiYQPnr0KHPmzGHEiBGUK1eOKVOmkJCQ4GxYY4wxxgSFvI7QPYb3btajZ7ULUAdI5aw1Ckz+paam0r59e1wuV44VHvr27cu8efNISkpiy5YtxMfHO5TSGGOMMcEmrwXdMLx3lz6pqkvPNIpIJtCzpE0X4gSXy8Xy5cvZvXs3jRo18tv25ptv8u9//xuAd99914o5Y4wxxvjJ09X0qjoauB94T0RGi0iBTE9i/mfevHn06tULgAcffDCrfdmyZfzpT3+ievXqvPfee3Tv3t2piMYYY4wJUnm+KUJVvxeR5njXQ/1eRP6v8GKFjvWjrwPg0tQ0Zt6fSFhYWcqkf8KP6l0oI3L+Y3z9VH3KlClD5P5JrB89Keu1DYcucySzMcYYY4JLfuehOwZ0FZG+wDIubOkwk833mx/wfqO+SxB9186ll6kNwLr/9vNrz65hjhZjjDHGhKILmrZEVd8Vka+Aa4A9BRspxEg4brcLBQQhPMz/bLY7QJsxxhhjTHbnLehEpCWwSlXd2dtVdQuwJUD/5njXdc0ssJQlWKV7K3P77bdz3XXXMW3aNKpVq8aQIUOovdcFwG1j76Jy5coOpzTGGGNMMMvLKdPvgLL52OcSoOp5exkAVqxYQUREBC+88AJVq1Zl3759/OMf//BtFZKTkx3NZ4wxxpjgl5dTrgKMEZG0PO4z6iLyhAy3283q1asZOXIk99xzDzfccAMej4fbbruNtLQ0QECwZb2MuQAffPABI0aMACA8PJxZs2ZRv359h1MZY0zhyUtB9zVQKx/7/A44dWFxQsdXX33FwIEDEREGDx5MWloas2bNYu3atbz44ouw2emExhRPn3zyCQ8++CBlypQhISGBEydO0KhRIyZPnswDDzzgdDxjjCkU5x3+UdW2qnpjPh/7iiJ8cfbss8+ybt06OnfuTIsWLejevTsPPvggDRs2ZOjQoU7HMyaoqSqjRo3K0T5//nxeeOEFWrVqRUpKCrt37+bQoUO0bduWV155xYGkxhhTNOx8ngOOHj3K2rVrARg8eDApKSksWrQIj8fDn//8Z4fTGRP8fvvtN9566y1WrVrl166qqCrVqlUjKsp79UdkZCQtWrRAAkz9Y4wxJUW+pi0RkYm5bFIgHdgGzFDVvRcbrCSbPXs2GRkZ1KhRgyZNmtCmTRvcbjd33XUX99xzj7eTLcZhTK5iY2OJiIjgqquuIjk5mYYNG2YtjwfQpk0bv/5nRug2b95M3bp1izquMcYUuvzOQ5cEtAE8wDpfWyO8N06sAjoDo0SkjaquLrCUJcj27dt56KGHiI+P5/bbb6d3796sXLmSESNG8NRTT2XdBPHQ5D4OJzUm+CxatIjevXuTkpKC2+2dSWn//v3s37+fRx99lPvuuw+AXbt2Zb1GVXG73Xg8HhulM8aUWPk95fot8BlQRVWvV9XrgSrAAuALoBowH/hrgaYsITZt2sRTTz2Fx+Nh1qxZ3H333cyYMYNatWrx/PPPEx8f73REY4LW1q1b6dChA0899RS//fYbDRt610rp3Lkz8+bNY+LEidStW5eWLVuyYMEC1Lf6yowZM7jzzjudjG6MMYUuvyN0A4GbVDVrChNVTRORF4H/qOrLIvISsLggQ5YU/fv3Z+nSpdSrV489e/YwaNAgEhISWL9+vdPRjAl6o0ePpnfv3vzpT39CRFi8eDFhYWEkJCRkjWzXrl2bYcOGcdlllzFkyBBq1qzJ3Llzs4q7rl27MmjQILp37+7kWzHGmAKX3xG6WKBSgPZk3zaA41zgkmIl3YkTJwBo2LAh48ePJyUlhQYNGhAdHe1wMmOC38KFC4mOjs46bVquXDkSExNzzNWYlJRE8+bNefPNN/l//+//8cUXX2Rt27ZtG8uWLSvS3MYYUxTyW9DNAiaISBcRqS4i1USkCzAB+MTXpyUBlgQLdWvXrmXVqlXEx8dz9913s3z5cmJjY5k9e7bT0YwJenv37iUtLY1HHnnkvH1LlSrF8uXLeeONN+jSpQs9e/bk+PHj7Nixg3vvvZc6deoUQWJjjCla+R1J6we8BkzL9loXMBF42vd8I9C3QNKVABN6vA3AC1+PJT4+nk6dOjFo0CCSk5N58sknKV++vMMJjQl+69evp0KFCnm+QzUqKoo+ffrQp8//bi6Ki4tjwoQJhRXRGGMcla+CznftXD8RGYR39QgBtqlqarY+dnfr2dQ7b9add95JpUqVOHjwIGvWrKFx48ZOJzMm6B04cIAlS5YQERFBRIRdzWGMMYFc0MTCqpqqqmtVdU32Ys6cW1RUFG+//TYNGjSgQYMGTscxJmjt3LmTL7/8EoDMzEyOHz/ucCJjjAlu+S7oRKSiiIwSkX+LyL9EZKSIVCyMcCWHcumllzJv3jyOHj3K9OnTCQ+3iYONyc2PP/5Iu3btWL3aO+B/5i5VgI8//pirrrqKYcOGORXPGGMK3XPPPZev/vkq6ESkNd7VIO4HTuFdHeL/gK0ick2+jhwC9uzZ411DA+/kp+Hh4XTr1o0mTZo4G8yYICciqCrz58/PWs7rjLS0NPbt28fRo0cdTGiMMYUrISEhX/3ze0HKq8CHQD9V9QCISBgwHu9kwtfmc38lWu/evema3BkAt9tN69atmThxos1Wb0w+VKlShTvuuCNr2h9jjDE55begawr0PFPMAaiqR0ReA34u0GTFzJm7WbPrmtyZ9DLeKRLee+AtEOGDflNz9HtoyvmnYjAmVIkISUlJXH/99U5HMcaYoJXfgu4YUAPYfFZ7DcDOfxhjCkWLFi1o0aKF0zGMMSZo5beg+wjvxMKDgeV4rxC7DhiL91RsyMo+yuZyuWjVqhU//fSTd2QOuP/tHpQuXdqpeMYUS263O0eby+Xyu6bOGGNM/gu6wXjnnpvoe60Ap4G3gCEFG634mj59Oj/99NP/GkSsmDMmj/bv38+aNWsAePjhh3Nsf/fdd9m3b19RxzLGmKCWr7tcVfW0qg4EEvFeT9cUKKuqT6rq6cIIWNykp6fTr18/AFuj1ZgLcPr0adLS0gACTu/zwAMPUK5cOTweT45txhgTqs47Qicic/LQBwBVvasAMhVre/bsIT09HYDExES8g5jGmLyqUqUKzZs3z/Vu8Pbt25OcnEzVqlWLOJkxxgSvvJxyPVzoKUqIlJQUPvzwf5cSvvbaa6QttBnujcmPsLAw7rnnHu68805iY2NzbK9RowY1atRwIJkxxgSv8xZ0qtqrKIKUBDNmzGDUqFEAtGnThvvuu48JC3NOZ2KMObfIyEgiIyOdjmGMMcXGBa3lanJyu90MHDgQl8tFTEwMM2fOdDqSMcXW77//zo4dOwJue+edd6hbty6bN589e5IxxoSuoCzoRKSLiKwXEY+IXHWOfu1FZLOIbBMRR++yPXDgQNZF2tWqVaNiRe/ytg9NecQmDjYmn5YuXcqdd95Jampqjm3Tp09n27ZtLFu2zIFkxhgTnPI7bUlRWQd0BnI9Xyki4cA44GZgD/CjiMxR1Q15OUBaWhpJSUl5CpOenk6rVq3417/+5bvRIafhw4ejqoSHh/OXv/zFlvcyjtq9e3fWwvbF0YoVK1i/fj2zZ88mPj7eb9uhQ4fweDzMmjWLChUqOJTQBINLLrmENm3aOB3DmKAQlAWdqm4EzlcUtQS2qeqvvr4fAR2BPBV0qkpKSgpNmjTh1KlTbN26lSZNmuR6zC+//JJdu3YFLOgOHz7MpEmTAKhXrx6dO3fOSwRjCs3nn39O3759nY5x0bp3757rtvnz5zN//vwiTGOCTePGjVm7dq3TMYw5pxMnTnDNNdfke0L0QGcoziUoC7o8uhTYne35HqBVbp1F5GHgYYAKFSoQExPDgQMHiImJwePxcOrUKWJiYnI9WFpaGmXKlAm47ZdffsHj8dCwYUPeeuutC3kvxhSonj170q1bN6djXLC5c+dy//33s3fvXuLi4vy2zZgxg+HDh9O5c2fGjBnjUEJzLmXLliUzM5MtW7ZQqVKlQjtOoHkKjQk2pUuXJjw8nP/+97/07t2bP/zhDwwdOpT9+/czYcKEXAeSfv75Z5599tk8H8exgk5EFgPJATY9p6qf5mUXAdpyLX9V9R3gHYA6depoampqVgEXFhZ2zmIOyLWYA/jb3/4GwPXXX8/VV199vtzGFLqIiIiAU34UF1FRUUDg93FmG1Cs32NJlX3C56ioKPsZmZAXERHBl19+idvtJiYmhujoaBYtWoTb7c71Mi7w1ib5Os7FBr1QqtruInexB8g+s2gVYO9F7jNfVJX//ve/zJs3j9jYWP7+978TEVGcBz2NKT5sPdfgNH78eEqVKoWq8sgjj/D000/Trt3F/ro3pngrV66c3/OyZcue9zX5vRY/KO9yzaMfgdoiUkNEooBuwHlXtShIGRkZfP/993g8HgYPHmzFnDEmpH399dd8/PHHvPvuu8TGxvLFF19kXV9sjClcQVnQicjdIrIHuAaYLyKf+9ori8gCAFV1AY8BnwMbgZmqur4oc/7973+nT58+xMTE5Os8tzHGlEQTJ05k2bJldO7cGRGha9euzJs3j8OHbcEhYwpbUBZ0qjpLVauoarSqVlTVW33te1W1Q7Z+C1S1jqrWUtUXizLjyZMnGTZsGMeOHaN79+42OmdMAXK5XFSqVIno6Ogc26pUqUKtWrW4/PLLHUhmzuWf//wnv//+e9YqHy+++CJ79+7N0+klY8zFsSrkAnk8HjIzMwF48MEHHU5jTMly7bXX0qpVqxx3uAJcd911vP3221SpUsWBZDmlp6dTqlSpXJ+HkrNvHgsPDz/vDWfGmIIRlCN0xcHo0aOJjIzk1ltv5dprr3U6jjElStWqVfnkk08CXhQcGRlJ/fr1AxZ7RenIkSOMHj2auLg4Nm/eTGZmJlOmTCEuLs7mxzPGFDkbobsAGRkZzJo1i9q1a/Pqq686HceYEinYV1u55JJLaNiwIS6XC4Cbb76ZZcuWMWDAADp06HCeVxtjTMGyEboL8N1337F9+3ZGjx5NvXr1nI5jTImyceNGevToQVRUFIcOHcqx/d///jf16tVjyBBHl28mLCws61qxkSNH8vXXX3P55Zfz17/+NeiLUWNMyWMF3QWYMmUKpUuXpkOHDnYzhDEFrEKFCtSvXz/rGtWzpaWlceLECU6ePFnEyXL38ccfo6ocOnSII0eOOB3HGBOCrKDLowk93mZCj7dJTU1lwYIF1KtXL+uvc2NMwSlXrhy1a9fOdXswjn6dPn2ayy+/nIyMDMaNG+d0HGNMCLKCLp8+/vhjDh48yNChQ52OYowJEsOHD+ebb76hZ8+eWVN3bN26le+++47vvvuO7t278+uvvzod0xhTgtn5wnx69dVXKVu2LB07dnQ6ijEmCNSrV4/hw4cD0LlzZ8aNG8crr7zCvn37mDJlStYSZTfeeCM1a9Z0MqoxpgSzEbr8UOWXX36hTZs2+V401xhTMm3evJnt27cTHh5O27ZtadSoEQcOHODdd99l165dALz00kv07t3b4aTGmJLMRugugC3zZYwB74oW1atXp3r16oD3ztf+/ftz5MgR/vWvfzF+/HiSk5N54okngvLaP2NMyWEFXT5Vr16dli1bOh3DGBME7rjjDtq3b09UVBQHDx4kJiaGAQMGkJ6eTtWqVUlJSeGKK64gMTGRqVOn0qVLF6cjG2NKKCvozjKhx9sB29NLe++6G9ZmCBN7vhOwz0NTHim0XMaEmiZNmgRcDaJ58+a0bduWm266yYFU/sLCwoiKiuKll17iz3/+M7feeitz586lVKlSfPLJJ+zYsQOAlStXsm/fPofTGmNKMivo8svOmhhT6Dp37kznzp0DnqZs0KAB06ZNC6pTmFu3biUzM5N58+axYcMGGjRoQJs2bWjTpg3gXe/5zM0RxhhTGKygO0ugUbYZM2bA/C0gYqNwxhSB8xVrwVTMud1uli5dmvU8t8nGgymzMabksVs1z0NVeeutt5yOYYwJUps2bWL79u1ZzydMmOBgGmNMqLKC7jy2bdvGV199hZ1rNabo7Ny5k88//zzgtuHDh3PppZfyySefFHGqwN5//30AoqOjqVu3LtOmTXM4kTEmFFlBdw5nph4oXbq01XPGFJF169YxePBghg4dyunTp3NsX7x4MXv37uXLL790IF1OkyZNom/fvkRFRdGzZ09KlSrF7NmznY5ljAkxVtCdg8fjIT09nYoVKzodxZiQIiKsWbOGY8eO5dgWbDcXlC9fnuTkZFSVsLAwxo4dS7NmzZyO5bhg+zkZU9JZQXcOy5cv54UXXuDWW291OooxIcftdgds79OnD5UqVSriNLlbt26ddxTfp0uXLlx22WUOJgoOhw8fplq1ak7HMKbYW7VqVZ76WUGXi1OnTtGzZ09EhFGjRjkdx5iQc8MNN1CuXLkc7VFRUUF1x6iI8OCDDzodI+jY8ojGFIzp06fnqZ9NW5KLmTNn8vvvvxMdHU1CQoJNV2JMEYuOji42RYGdXjTGFJa8/gFrBV0uDh06RFxcHF26dCE6OtrpOMaYIPbNN984HSFouN1uXC6X/d40pogVjz9/i5iq8vLLL1OxYkXatm3rdBxjTAAej8fpCFm2bdvmdISgcPz4cd544w2eeuopp6MYE3KsoAtgy5YtHDp0iBtvvJEHHnjA6TjGhJSoqCiio6NzLdhiYmKoWrVqUBUNDz30kNMRgsLGjRtZuHAhR44ccTqKMSHHTrmeJSUlhenTpyMijBw50uk4xoScOnXqMHHiRFwuV8DtZ9Z5DSbBNFoYDL799lvuueeerOcTJkwgISHBwUTGlHxW0J3lm2++YezYsVSvXj2opkYwJpSEh4cTHh7udIx8ycjIYMWKFU7HCAq7d+9m9+7dWc8TEhJsSTRjCpmdcj3LSy+9RGZmJtddd53TUYwxxUhmZibr1q1zOkZQKF26NIsXL2bWrFl06NCBDz74gIMHDzody5gSzQq6bA4ePMiaNWsIDw/nscceczqOMSErMzOTsWPHBtz21Vdf0b9/f3bu3FnEqc6vuEyzUtg6duzIH/7wBzp16sSDDz5Ieno6kydPdjqWMSWa/fbJJjU1lfT0dC6//HJatmzpdBxjQtL69evp0aMHw4YN49ChQzm2z5kzhw8++IBXXnnFgXTOW7NmDQkJCQwYMCCrbcyYMTz99NOkpqY6mOx/she2Z77Pfk2dMabg2TV0PqrKCy+8QLly5ejWrZvTcYwJWaqKx+PJgG9KqAAAHq1JREFUdemvpk2bEhsbW8SpgseWLVs4fvw4p0+fBmDu3LkMHTqUihUrMmLECIfTeX344Yds2LCBunXrMmfOHGJjY0P6Z2ZMUbCCzsftdvP++++TmJhIqVKlnI5jjDF5MmrUKCpWrMiTTz4ZNEWTqrJ27VrWr19PZmYmQ4YMISkpyelYxpRoVtD5HD9+HLfbTevWrXnmmWecjmOMCWGtW7dm7dq1jBw5Mtf59sLDw2nbti0rV65k27Zt1KxZ0/E1blu1asX8+fPJyMjgz3/+M6dOnaJZs2b07dvX8WzGlHRW0Pm88cYblCpVismTJ9svHmOCwI033ki5cuWcjpEvBTXVyn/+8x8+/vhjBgwYwODBg4mNjSU5OZlNmzZl9Xn33Xez1pDdt28fNWrUCIrfXZGRkURGRvK3v/3N6SjGhBS7KQJIT0/nzTffpE6dOsTHxzsdxxiDtzAobneNrl+/nqioqIt+xMfH06tXL06cOIHb7ebYsWNs3ryZRx55JOtYqkrfvn2Jj49n4MCBLFiwwMF3boxxmo3Q4Z0E88iRI/Tr18/pKMaEvDM3RRQn5cuX54svviiw/R07doypU6cyd+5cAIYMGcJNN93EpZdeyvr16wF4+OGHefvtt4mLi+Nvf/sb48eP5/bbb6dnz57s27ePZcuW8fLLLzs2BVP58uVZuXIl1atXd+T4xhR3ERER+Rp1t4IO7whdeHi4XTtnTBCIj4+ncuXKxWo90FKlSnHTTTcV2P6mTJnCL7/8Anjv6m3fvj3XXXcdIpJV0J25C7hz58689tprLFq0iN9//53ly5ezY8cO3G63o6dgnT6+McVd69at87VkXsgXdDt27ODJJ58kKiqKmJgYp+MYE/KqVavGyJEjc5225Oabb2bHjh20b9++iJOdW0EuVXb33Xdz3XXXER8fT8WKFf0Ko/j4eL9T0ddeey39+vVj/PjxeDweVJW2bduyYMECIiMjCyxTflkxZ8zFiYyMtBG6/Pj0009ZsmQJAwYMKHZrRxpTUp3rr9Lk5GSGDx9ehGmKXmJiIomJiQG33XrrrXz++edUrFgR8BZOr7/+OgMGDMi6BjguLo6oqKgiy2uMcV5QFnQi0gV4HqgPtFTVlbn0+w04AbgBl6pelZ/juFwuBg8ejMfjoXLlyhcX2hhTJL7//ntmzZrFlVdeGbKTgLdr187veXR0NI0bNwa81yCeufvVGBM6gvUWsnVAZ+DrPPS9UVWb5reYAzh9+jRut5uoqCj69OmT75DGmMJx4sQJjh8/HnDbpk2bmDp1KsuWLSviVMXD/fffb3+gGhOCgnKETlU3QuFegxEWFsZf//pXPB4PdevWtVnMjQkSO3fu5I033mDatGls3bo1x+nX8PDwYjedSVH6y1/+4nQEY4wDivtvRQW+EJFVIvLwuTqKyMMislJEVh47dgwRYfv27dSoUcMmwDQmiJw4cYK9e/dy6NChrPVKjTHGnJtjBZ2ILBaRdQEeHfOxm9aq2gy4DXhURK7PraOqvqOqV6nqVQkJCRw/fpypU6dStWrVrIuLjTHGGGOKI8dOuapqu/P3Ou8+9vq+HhSRWUBL8nbd3ZnX07x5cxo0aHCxUYwxxhhjHFNsT7mKSIyIxJ35HrgF780U53X6SAbgnYX5pZdesutxjDHGGFOsBWUlIyJ3i8ge4Bpgvoh87muvLCJnFiysCCwTkTXACmC+qi7Mz3Fq1qzp6MSbxhhjjDEFIVjvcp0FzArQvhfo4Pv+V+CKCz1GWFgYTZs2veCMxpjCUaZMGcqVK0dcXFzAP7guueQSkpKSKF++vAPpjDEmOEkoTkBZvXw1TUk/zMmTJ52OYowJ4PDhw2RmZpKcnBxwu8fjQURseakgVrZsWX7++WeqVavmdBRjiq2yZcsSFhbG4cOHV51vvt2gHKEzxoS2smXLnnO7XfdqjDH+7LeiMSbonG/07cCBA+zatasIExljTHALyRE6j5Tijc6vMqHH2wG3PzTlkSJOZIw5Y8eOHbz88svMnDmT7du3c8kll/htnzRpEkOGDOG+++7j9ddfdyilyavMzExcLhelS5d2OooxJZqN0Bljgkpqaiq///47R44cITMzM8f2iIgIIiIi+OCDDxxIZ87lu+++47bbbuNf//oXAJMnT6ZJkyZ8+OGHDiczpuQLyRG6ME1n4CdP200RxhRj57vOzhS92NhYYmNjWbx4MZGRkWzdupXffvstYGFujClYNkJnjCmW2rW76MVmTAFr3Lgx7du3Z86cORw9epQPP/wQVaVbt25ORzOmxLOCzhgTVC699FKuuuqcd+ebINazZ0+qVq3K6dOn8Xg8dOrUiYSEBKdjGVPiWUFnjAkqiYmJ1KhR47z9QmUOurVr1xIdHU14eDhhYWFce+21nD592ulYuQoPD+eyyy4D4PHHH+fpp592OJExoSEkr6EzpqTLzMwkLS3N6RgXLDU1FYDjx48TFRXlty0tLQ2Px0NGRgbHjh1zIl6RWrRokV8B98MPP7B///6gHvU6c83cY489RqVKlQr15xQXF2fzEhqDrRThdBRjCsXkyZPp1auX0zGMKXTbt2+nZs2aTscwplDkZ6UI+7PGmBLo7rvvpkePHvTs2ZOkpCSqVq1Kjx49sh6NGjUiNjaWRx55hLS0NNLS0vj1118ZOHAgMTEx/Prrr1ntR48epW7dupQpU4YtW7Zw/PhxevbsSc+ePalduzatWrXy23fPnj1JTEwkOTnZr71Hjx4kJiZSvXp1evTowXvvvUdaWhqTJk3ihhtuAOCBBx7I6tu7d29Kly5NgwYNuOWWW0hISGDKlCm89957VKpUiRYtWmRlTEtLY9CgQQDUqVPH75gPPPAAYWFhXHHFFX7tO3bs4JprriE2Ntbv/aalpdGpUycee+wxv7a0tDQmT54MwMyZM7PaVq9eTZkyZdi4caNf365duwLw3HPPkZaWRr9+/ejRowc1a9akUaNGfPjhh1l9t2/fTu/evWnRogU7d+7Mar/99tsD/nzvuOMOqlWrRps2bTh8+HBW/5UrVxITE8Pjjz/ul2XNmjVERUWxatUqvvrqK5555hm6d++OiNC1a1dWrlyZ1XfSpEnUq1ePP/7xj377+O2334iIiMjRPnz4cBo3bpzjs7rkkkvYvHmzX9s333xDu3btGDVqlF/7999/j4hwww03+LWvXLmS8PBw9u3b59derVo1WrRowYIFC/J0et6YkKCqIfeoXbu2xsTEqDElmcvl0h9++EETEhI0IyMjq33v3r0aFhamu3btUo/Hk9X+/vvva2Jior755pt++5k+fbpWqVJFX3/9db/2zZs3a3h4uJ44ccKvfcmSJZqUlKQvvviiX/vOnTu1fPny6nK5/NqnTJmi9evX14ULF/q1T5s2TWvVqqUul0sXLVqk9evXV1XVW265RWvXrp1jP126dNF//OMfOdo//fRTLVeunB4/ftyvfdWqVVqrVi2dMGGCX/vatWu1WrVqmpqa6tf+yy+/ZB0ju2effVbHjBnj17Zjxw4tXbq0Nm7c2O8z9ng8Ghsbq3v27PFr79+/v0ZEROjOnTv99tO0aVOtXLmyJiUlKaCAfvbZZ9q5c+eAGbt06aKvv/66389ky5Yt2q1bNx03bpxf3ylTpuhdd93ll0NVtVevXpqcnJzj59qnTx99/vnndd++fVltr7zyitasWVOXL1/u17dv376akJCg+/fv92tv2bKl1qtXT91ut1/7bbfdps8884zu2bMnq2316tV655136qBBg/z6zp07V6tUqZLj52xMSZSYmKjlypVTYKWep7ZxvLhy4mEFnQkFixcv1ubNm+ujjz7q1/7BBx/oXXfd5dd2+vRprVWrlt5+++052uvVq6c//vijX/sPP/ygt912W44iQVW1fv36OnToUM3MzMxqmzt3rjZp0kSHDBni1zc9PV2TkpL0nnvu8WvPzMzUSpUq6fr161VV9YsvvtB69erp7t27NSkpSYcNG+bXf/r06ZqUlJSjUFi2bJm2b99ex48f79eekZGhV111lX766ad6+vTprPYvv/xSb7jhhhxFnqq3SClbtqzfMZYsWaI1a9bU9PR0v77333+/jhs3zq+QTk9P19deey1HwZyRkaFNmjTRl19+2a9927ZtmpycrCdPntSZM2dqo0aNtF+/frpgwQKtW7euTp061a//yZMntVmzZn7HVFVt166d1qpVyy+jy+XS1q1b64cffujX94cfftDk5OQcGTdt2qSVK1fWtLS0rLa0tDRNTEzUAQMG+PU9dOiQxsXF6U8//ZTj/cTExOhbb73l175u3TqNjIzU33//3a/9sssu07CwME1JSfFr/+Mf/6ivvfaaGhMK8lPQ2SlXY0qoo0ePcvz4cZ588km/9nnz5tGmTRu/tu3bt3Py5Elat27t175r1y5SUlJo1qyZX/vatWvZvn07V155ZY7jnjm9GBHxv3uuDh8+zN69e+nSpYtfX4/HQ0RERI5pSjweD6pK1apV/dpjY2MZOXIkAwcO9GtPSUnhpptuynFx/Ny5c1m6dCk9evTI8X7XrVtHw4YNiYyM9NvPwYMHadq0aY73NXPmTG6++Wa/Y1xyySV06dKF6Ohov76HDx+madOmfjd0rFu3jiVLlhATE+PXd8OGDSQmJub4DNLS0qhVqxYxMTGoKh6PB7fbTdmyZbn33ntp3LixX/+FCxeSmZmZ4yaSQ4cO0aBBA7+MBw8eZMWKFTlOV6oqjRs3zvH+T506Rd26df2W73K73cTHx9O8eXO/vh999BG33HILV1xxBWerW7cu1atX92ubPn06DRs2zHGTR0pKCvXr1ycxMdGv/T//+U+OfxfGmBC9KaJOnTq6d+9euynClGjp6emkpaXlWFHB4/EgIjmm/XC73YSFhfm1u91uTp48meM/2+PHj5Oenk6FChVyHPfw4cOUK1fOr+3MX5CBjuvxeALepZi9fdGiRTzxxBNs3Lgx4HvN+gv1rP2cOHGCU6dOkZSU5Hdcl8vFqVOniIuLy9N+wPtZiEie7qgM9J5y+wzO/A4ONA3Lmf4zZ85k5MiRtG7dmnfeeSfgMXPLntvP+9ixYzl+rrllcblcpKenExsbGzBfXnLk1t/j8ZCWlpZj38eOHSMsLCzHz+jYsWPExsYSHh6eY//GlDR2U4QxhlKlSgVcHuvsou2M8PDwHO3h4eEBp8eIj48PWMwBOYo5IKsQCnTc3Aqk/ExFkVuhFRcXR4UKFXIcNyIiIkehcK79AFnzwAGsXr2a22+/ndmzZ+c5e26fQaBiK/u2vMote26fe6Cfa25ZIiIichRcueU712eY288/0L4TEhIC/owSEhKsmDMmACvojDEmn95//33WrVtHSkqK01GMMQawgs4YYy5IWFiYTWhrjAka9tvIGFMspaSksG/fvgLb31133UVkZGTWY9KkSefs/9tvv9G3b18iIyOz5ngrCAcPHqRt27aMHz++QPZnjAkNVtAZY4qNhQsX0qJFC8qUKUOFChWoV69ege37iiuuwOVy4XK5eOqpp7jxxhuZMmUK3bp183ssWbIk6zVNmzZl2rRpJCcnc/311zN27NiLzpGQkMDhw4d54okniIqKok2bNvz888923Zgx5pxsLVdjTLGwadMmbrvtNr+2kydPUqpUKQBuueUWLr/88jzt6/vvv+enn34CoHbt2vzyyy/UqlUL8C4o/9JLLwGwbNky1q1bx/r167NeO2TIEAAuu+wy+vfvT9euXWndujVVq1Zl6tSpDBkyhPfee48NGzYA3mliLr/88qzic9GiRSQlJflNDTJ16lS/u+6zT1OybNkyli1bBsDVV1+dx0/LGBNqrKAzxhQLsbGxtGvXDpfLxdatW9m8eTMi4lfk7dixI0/7qlixYtbrqlSpktV+99138/DDD2c9b9GiRY5RtzPzomW/m/PMXHYulwuA/fv3Z2Vp2LChX7YzRWf2rGfPCxgVFcW6devYsGED4eHhNGvWjE2bNuXpvRljQpMVdMaYYuGyyy5jxowZnD59mtdff53hw4cTGRnJjBkzLmq/Z88Jl31uzvvvv59169b59R83bhzgnbD3hx9+oGfPnixdupSoqCjatm0LeEfxPB7PBWcaPXp01qhgmTJlGDRoEC+88MIF788YU/LZNXSmUKWnpzN9+nRatGjhdJSQctVVV9GlSxd27drldJQC4/F4iIqKIjY2lmHDhnH69GmOHj1KVFTURT2yrxRxdkH3zDPP0KtXL79HgwYNUFXS0tJ45513iIiIoFu3bowePZpXXnkF8M7bdqF5UlNT+cc//kHp0qXZs2cPx44dQ0QuqkAsicaMGUOlSpXyPCprLt7+/fv56KOPnI5hcmEjdKbQud1uNm/e7HSMkOJ2u3G5XJTklWAiIiL8lhe7WP/3f/9H165d/ZbOuvfee7n33ntz9L366qsZOXIkP//8My+//DKdOnWiT58+BZIjMTExa367/EwsHIrOnOI2RWP79u0MHz6cbt26OR3FBGAjdKZInL1GqDH5ERsbW+j/eUdGRlK6dOk83U16Zkmq66+/nnnz5nHHHXfQtGlT3n333QLJcvaKDQkJCSQmJpboAt0Yc3FshM4UCfuPqGiVtM+7VatW9OrVy+kYuXK5XGzfvp0jR44Uyv7btWvHd999ZxMZG2NyZQWdKRJHjx51OkJIGTBgAJ999pnTMQpMWFgYQ4cOdTrGeRXWKdLw8HBGjBhhp2CNMbmyP/dMkbBr6IpWdHS00xFMAbNizpjQk5+boaygM8YYY4wJQvmZTNwKOmOMMcaYIJSf66GtoDNFwu12Ox3BGGOMKbGsoDNF4uyljYwxxhhTcKygM0Xi22+/dTqCMcYYU2JZQWeKhN2hZ4wxxhQeK+iMMcYYY4q5oJxYWEReAe4ETgPbgV6qmmNmWhFpD7wBhAPvqerYvB4jIyODxx9/vIASm9y43W62bt3KqVOn7PMuQps2beLXX39lxIgRxMXFOR2nxEtNTSUjI4PZs2eze/dup+OEhFWrVpGamsrzzz9PfHy803FCwr59+zh06NBF/y6PiYmhV69e1K1bt4CSlVz5ObslwbhEkIjcAnypqi4ReQlAVZ85q084sAW4GdgD/Ajcp6obzrf/OnXq6NatWws+uDHGGGPOK5QuwwkPD7/gZftcLheJiYkcPnx4lapeda6+QTlCp6pfZHv6PXBPgG4tgW2q+iuAiHwEdATOW9DFx8fbNBrGGGNMCdOjR48StzJRQkICixcvPm+/oCzoztIbmBGg/VIg+7mNPUCr3HYiIg8DD/ueZoSHh68rsIQmL8oDKU6HCDH2mRc9+8yLnn3mRc8+86J33vPTjhV0IrIYSA6w6TlV/dTX5znABUwPtIsAbbmeP1bVd4B3fPtdeb6hS1Ow7DMvevaZFz37zIuefeZFzz7zoiciK8/Xx7GCTlXbnWu7iPQA7gD+oIEv9NsDVM32vAqwt+ASGmOMMcYUD0E5bYnv7tVngLtUNS2Xbj8CtUWkhohEAd2AOUWV0RhjjDEmWARlQQe8CcQBi0RktYiMBxCRyiKyAEBVXcBjwOfARmCmqq7P4/7fKYTM5tzsMy969pkXPfvMi5595kXPPvOid97PPCinLTHGGGOMMXkXrCN0xhhjjDEmj6ygM8YYY4wp5kKyoBORLiKyXkQ8ImK3XhciEWkvIptFZJuIDHE6TygQkYkiclBEbK7FIiAiVUVkiYhs9P1eGeh0ppJOREqJyAoRWeP7zEc6nSlUiEi4iPwsIvOczhIKROQ3EfnFdz/BOacuCcmCDlgHdAa+djpISeZbnm0ccBvQALhPRBo4myokTAbaOx0ihLiAQapaH7gaeNT+nRe6DOAmVb0CaAq0F5GrHc4UKgbivRHRFJ0bVbXp+eb+C8mCTlU3qmrJWhskOGUtz6aqp4Ezy7OZQqSqXwNHnM4RKlR1n6r+5Pv+BN7/7C51NlXJpl4nfU8jfQ+7w6+QiUgV4HbgPaezmJxCsqAzRSbQ8mz2H50psUSkOnAl8IOzSUo+36m/1cBBYJGq2mde+F4HBgMep4OEEAW+EJFVviVMc1Uc1nK9IHlZWswUunwtz2ZMcSYiscDHwJ9U9bjTeUo6VXUDTUXkEmCWiDRSVbtutJCIyB3AQVVdJSJtnc4TQlqr6l4RqYB3bt5NvrMwOZTYgu58S4uZImHLs5mQICKReIu56ar6idN5QomqHhWRpXivG7WCrvC0Bu4SkQ5AKSBeRKapaneHc5VoqrrX9/WgiMzCeylTwILOTrmawmTLs5kST0QEmABsVNXXnM4TCkQkyTcyh4iUBtoBm5xNVbKp6rOqWkVVq+P9Xf6lFXOFS0RiRCTuzPfALZzjj5aQLOhE5G4R2QNcA8wXkc+dzlQSXeTybOYCiciHwHdAXRHZIyIPOZ2phGsNPADc5JtaYLVvFMMUnkrAEhFZi/cPx0WqatNomJKmIrBMRNYAK4D5qrowt8629JcxxhhjTDEXkiN0xhhjjDEliRV0xhhjjDHFnBV0xhhjjDHFnBV0xhhjjDHFnBV0xhhjjDHFnBV0xhhjjDHFnBV0xhhjjDHFnBV0xhhTTIhIVRFZKiIbRGSNiHR2OpMxJjjYxMLGGFNMiEgloKKqrvYt1r0KqKuqaQ5HM8Y4zEbojDHFgohMFpECXd7JN9qlvsfVBbnvbMdIFJEDIlLrYvelqvtUdbXv+4PA70D5bMf6t4g8ddbxJ2d7j/dcbAZjTHCygs4YE+om4V0bdJWI3JKt+Mnt8QCAiLwqIjnWVRSR8SLyt2xNQ4EFqrr9rH6Xisg4EdkqIukiclBEvhSRVnkJLSJXAZHA7mzNI4FhIpKQrW2g7/0ZY0owK+iMMaEuTVX3q2om8A3e4ufMYzfw17PaZvhe1wLvgtlZRESAO4FPfc/LAH2ACWf1qwb8DFwK9ATqAXcDK4HMQCFFJCLb9+WAqcBDmu26GVX9BfgV6J6t7Ziq7s/rh2GMKZ6soDPGFDsiEi0ir/tOZaaLyPcict1ZfWJEZKqInPT1e1ZE5onI5Nz2q6qnfMXdfuAUUAX49kybr11F5DRwPfBn36jdet8uWgClgGW+5x0AD/DtWYd6AnADf1TVb1X1N9/Xwar6k4hU8e23m2/ULh148Mx7B2YBY1R1eYC3MQe4L48fpTGmhLCCzhhTHL0MdAV6A1cCvwALfTcNnPFX4Aa8I183AVcAbfJxjGaA4L3xIDs3cI3v+1Z4R+3OFJOdgPmq6vI9bwOs0px3nyUCUUD1XI7d1Pf1GeBVoCHwqW8EcDLwpaq+n8trVwAtRaR0ru/MGFPiWEFnjClWRCQG6A88o6rzVXUj0A84ADzq6xOLt9h7RlUXqep64CG8o2V51Rw4rKq7sjeqqgdvEXcC+NE3cve7b3NHYHa27tWAfQH2/XfgOLBVRH7yXY/XNNv2K4B0oIuqLlDV7ap6GGiNt5DtJCKrfY/GZ+17L95r6yrn470aY4q5iPN3McaYoFILb8GSdRpTVd0i8h3Q4Kw+K7L1SRWRdfk4TjPgp1y2XQmsyT7yJiKXAzWBz7P1K4230PTjm3bkcuBa4GbgHuBJEemtqlPwjtAtUNVtZ71uGef/Q/xUtmMbY0KEjdAZY4ob8X0NNImm5qFPXjUj5+nWM5rivakhu07Af1Q1NVtbCt7TqzmoqltVv1HV4UBj/G9muAL46gJzl/V9PXSBrzfGFENW0BljipttwGn+d90aIhKO97q2Ddn6ZAIts/UpAzTKywF8p2xrk/sI3RXA2rPazj7dCt6irwHnJ3hvpjjkO6Vc6xzHPp9GwF5VzTEyaIwpueyUqzGmWPGdOn0LGCsiKcAO4EmgIvBPX5+TIjIReMnXZx8wDO8fsXkZtbvS1ze3oioCqCcilYE0vKd3r8Z76jS7z30ZyvmugUNEpgEbgf/4clUHBgMJwFigie+1q/OQM5A2QI758YwxJZuN0BljiqNngJl4JwVejbcIaq+q2W9AeBrvvHJzgCV4R9RW4r3Z4HyaAcfwngYN5DmgG7AHGIN37rkfzx4V880Lt8LX94xVeKczmQtsBt4D/gs0VdW1eEf/tqrqyTzk9CMipfDe1ftufl9rjCnebC1XY0xI8M3fthN4RVX/6mtbCqxT1ccuct+f4p2v7uUA29oDbwANVNV9McfJQ45HgY6qekuAbYr3rtl/F2YGY4wzbITOGFMiiciVInK/iFwuIlcCU4A4/rfSwxkP+yYfbnERh/sW+DDQBlVdCIzDO0lxYcsEHs/e4FuKLN+jfcaY4sVG6IwxJZKviHsXqAu48J6afVpVV2Xrcyn/m95jt6pmFHnQQiYiFYB439N9Z92Fa4wpIaygM8YYY4wp5uyUqzHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMWcFnTHGGGNMMff/AYSY4/hBBZd2AAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -66,8 +66,8 @@ ], "source": [ "plt.figure(figsize=(10, 6))\n", - "img = mpimg.imread('figs/neuman.png')\n", - "plt.imshow(np.flipud(img), origin='lower', extent=(-1, 5, -2, 1))\n", + "img = mpimg.imread(\"figs/neuman.png\")\n", + "plt.imshow(np.flipud(img), origin=\"lower\", extent=(-1, 5, -2, 1))\n", "\n", "kaq = np.ones(11)\n", "z = np.hstack((10.01, np.arange(10, -1, -1)))\n", @@ -75,24 +75,25 @@ "T = 10\n", "\n", "for Ss in [1e-3, 1e-4, 1e-5, 1e-6, 1e-7]:\n", - "#for Ss in [1e-3]:\n", + " # for Ss in [1e-3]:\n", " S[1:] = Ss\n", - " Stot = 10 * Ss\n", + " Stot = 10 * Ss\n", " sig = Stot / S[0]\n", - " print('sigma equals:',sig)\n", + " print(\"sigma equals:\", sig)\n", " ts = np.logspace(-1, 5, 40)\n", - " t = ts * Stot * 10 ** 2 / T\n", + " t = ts * Stot * 10**2 / T\n", " #\n", - " ml = Model3D(kaq, z, S, kzoverkh=1, phreatictop=True,\n", - " tmin=t[0], tmax=t[-1])\n", - " w = DischargeWell(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 1)], layers=np.arange(1, 11))\n", + " ml = ttim.Model3D(kaq, z, S, kzoverkh=1, phreatictop=True, tmin=t[0], tmax=t[-1])\n", + " w = ttim.DischargeWell(\n", + " ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 1)], layers=np.arange(1, 11)\n", + " )\n", " ml.solve(silent=True)\n", " h = ml.head(10, 0, t)\n", - " d = -h * 4 * np.pi * T / 10 \n", - " plt.plot(np.log10(ts), np.log10(d[-1]), '+', markersize=15, mew=1.5)\n", - " \n", - "plt.xlabel('$\\log[Tt/(Sr^2)]$', fontsize=14)\n", - "plt.ylabel('$\\log[4\\pi Ts/Q]$', fontsize=14);" + " d = -h * 4 * np.pi * T / 10\n", + " plt.plot(np.log10(ts), np.log10(d[-1]), \"+\", markersize=15, mew=1.5)\n", + "\n", + "plt.xlabel(\"$\\log[Tt/(Sr^2)]$\", fontsize=14)\n", + "plt.ylabel(\"$\\log[4\\pi Ts/Q]$\", fontsize=14);" ] }, { @@ -121,7 +122,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -134,8 +135,8 @@ ], "source": [ "plt.figure(figsize=(10, 6))\n", - "img = mpimg.imread('figs/neuman.png')\n", - "plt.imshow(np.flipud(img), origin='lower', extent=(-1, 5, -2, 1))\n", + "img = mpimg.imread(\"figs/neuman.png\")\n", + "plt.imshow(np.flipud(img), origin=\"lower\", extent=(-1, 5, -2, 1))\n", "\n", "kaq = np.ones(11)\n", "z = np.hstack((10.01, np.arange(10, -1, -1)))\n", @@ -143,24 +144,23 @@ "T = 10\n", "\n", "for Ss in [1e-3, 1e-4, 1e-5, 1e-6, 1e-7]:\n", - "#for Ss in [1e-3]:\n", + " # for Ss in [1e-3]:\n", " S[1:] = Ss\n", - " Stot = 10 * Ss\n", + " Stot = 10 * Ss\n", " sig = Stot / S[0]\n", - " print('sigma equals:',sig)\n", + " print(\"sigma equals:\", sig)\n", " ts = np.logspace(-1, 5, 40)\n", - " t = ts * Stot * 10 ** 2 / T\n", + " t = ts * Stot * 10**2 / T\n", " #\n", - " ml = Model3D(kaq, z, S, kzoverkh=1, phreatictop=True,\n", - " tmin=t[0], tmax=t[-1])\n", - " w = Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 10)], layers=np.arange(1, 11))\n", + " ml = ttim.Model3D(kaq, z, S, kzoverkh=1, phreatictop=True, tmin=t[0], tmax=t[-1])\n", + " w = ttim.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 10)], layers=np.arange(1, 11))\n", " ml.solve(silent=True)\n", " h = ml.head(10, 0, t)\n", - " d = -h * 4 * np.pi * T / 10 \n", - " plt.plot(np.log10(ts), np.log10(d[-1]), '+', markersize=15, mew=1.5)\n", - " \n", - "plt.xlabel('$\\log[Tt/(Sr^2)]$', fontsize=14)\n", - "plt.ylabel('$\\log[4\\pi Ts/Q]$', fontsize=14);" + " d = -h * 4 * np.pi * T / 10\n", + " plt.plot(np.log10(ts), np.log10(d[-1]), \"+\", markersize=15, mew=1.5)\n", + "\n", + "plt.xlabel(\"$\\log[Tt/(Sr^2)]$\", fontsize=14)\n", + "plt.ylabel(\"$\\log[4\\pi Ts/Q]$\", fontsize=14);" ] } ], diff --git a/notebooks/ttim_pumptest_neuman.ipynb b/notebooks/ttim_pumptest_neuman.ipynb index 047e3a2..e9345ad 100755 --- a/notebooks/ttim_pumptest_neuman.ipynb +++ b/notebooks/ttim_pumptest_neuman.ipynb @@ -26,7 +26,7 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from scipy.optimize import fmin\n", - "from ttim import *" + "import ttim" ] }, { @@ -45,7 +45,7 @@ "xw, yw = 0, 0 # location well\n", "xp, yp = 63 * 0.3048, 0 # Location piezometer [meter]\n", "Qw = 1170 * 5.45 # discharge well in [m3/d]\n", - "z_obswell = -19.7 * 0.3048 # elevation of observation well" + "z_obswell = -19.7 * 0.3048 # elevation of observation well" ] }, { @@ -68,11 +68,11 @@ ], "source": [ "# loading data\n", - "data = np.loadtxt('pumptest_neuman.txt') # time and drawdown\n", - "time, dd = data[:,0], data[:,1] \n", - "td = time / 60 / 24 # t in [days]\n", - "ho = -dd * 0.3048 # observed head [meter]\n", - "print('minimum and maximum time:', td.min(), td.max())" + "data = np.loadtxt(\"./data/pumptest_neuman.txt\") # time and drawdown\n", + "time, dd = data[:, 0], data[:, 1]\n", + "td = time / 60 / 24 # t in [days]\n", + "ho = -dd * 0.3048 # observed head [meter]\n", + "print(\"minimum and maximum time:\", td.min(), td.max())" ] }, { @@ -112,10 +112,10 @@ "source": [ "Saq = 1e-4 * np.ones(nlay)\n", "Saq[0] = 0.2\n", - "ml = Model3D(kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.2,\n", - " phreatictop=True, tmin=1e-4, tmax=10) \n", - "w = Well(ml, xw=xw, yw=yw, rw=0.3, tsandQ=[(0, Qw)], \n", - " layers=range(nlay))\n", + "ml = ttim.Model3D(\n", + " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.2, phreatictop=True, tmin=1e-4, tmax=10\n", + ")\n", + "w = ttim.Well(ml, xw=xw, yw=yw, rw=0.3, tsandQ=[(0, Qw)], layers=range(nlay))\n", "ml.solve()" ] }, @@ -153,12 +153,14 @@ } ], "source": [ - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0_11', initial=100, pmin=10, pmax=400)\n", - "cal.set_parameter(name='Saq0', initial=0.1, pmin=0.01, pmax=1)\n", - "cal.set_parameter(name='Saq1_11', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "cal.set_parameter_by_reference(name='kzoverkh', parameter=ml.aq.kzoverkh[:], initial=0.2, pmin=0.01, pmax=1)\n", - "cal.series(name='obs1', x=xp, y=yp, layer=layer_obswell, t=td, h=ho)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0_11\", initial=100, pmin=10, pmax=400)\n", + "cal.set_parameter(name=\"Saq0\", initial=0.1, pmin=0.01, pmax=1)\n", + "cal.set_parameter(name=\"Saq1_11\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "cal.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml.aq.kzoverkh[:], initial=0.2, pmin=0.01, pmax=1\n", + ")\n", + "cal.series(name=\"obs1\", x=xp, y=yp, layer=layer_obswell, t=td, h=ho)\n", "cal.fit()" ] }, @@ -169,7 +171,7 @@ "outputs": [], "source": [ "cal.parameters\n", - "k, Sy, Ss, kzoverkh = cal.parameters['optimal'].values" + "k, Sy, Ss, kzoverkh = cal.parameters[\"optimal\"].values" ] }, { @@ -179,7 +181,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -192,21 +194,21 @@ ], "source": [ "hm1 = ml.head(xp, yp, td, layers=layer_obswell)\n", - "plt.figure(figsize=(14,6))\n", + "plt.figure(figsize=(14, 6))\n", "plt.subplot(121)\n", - "plt.plot(time, ho, 'ko', label='Observed')\n", - "plt.plot(time, hm1[0], 'b', label='TTim')\n", - "plt.xlabel('time [min]')\n", - "plt.ylabel('Drawdouwn (m)')\n", - "plt.legend(loc='best');\n", + "plt.plot(time, ho, \"ko\", label=\"Observed\")\n", + "plt.plot(time, hm1[0], \"b\", label=\"TTim\")\n", + "plt.xlabel(\"time [min]\")\n", + "plt.ylabel(\"Drawdouwn (m)\")\n", + "plt.legend(loc=\"best\")\n", "plt.subplot(122)\n", - "plt.loglog(time, -ho, 'ko', label='Observed')\n", - "plt.loglog(time, -hm1[0], 'b', label='TTim')\n", + "plt.loglog(time, -ho, \"ko\", label=\"Observed\")\n", + "plt.loglog(time, -hm1[0], \"b\", label=\"TTim\")\n", "plt.ylim(10, 0.01)\n", - "plt.xlabel('time [min]')\n", - "plt.ylabel('Drawdouwn (m)')\n", - "plt.legend(loc='best')\n", - "plt.suptitle('TTim Aquifer Test Analysis in Unconfined Aquifer');" + "plt.xlabel(\"time [min]\")\n", + "plt.ylabel(\"Drawdouwn (m)\")\n", + "plt.legend(loc=\"best\")\n", + "plt.suptitle(\"TTim Aquifer Test Analysis in Unconfined Aquifer\");" ] }, { @@ -285,11 +287,13 @@ } ], "source": [ - "r = pd.DataFrame(columns=['$T$ [ft$^2$/day]', '$S_y$', '$S$','$k_h/k_r$'],\n", - " index=['Lohman (1972)', 'AQTESOLV', 'TTim'])\n", - "r.loc['Lohman (1972)'] = [22000, 0.2, 0, 0.3]\n", - "r.loc['AQTESOLV'] = [22980, 0.15, 0.008166, 0.25]\n", - "r.loc['TTim'] = [k * H / 0.0929, Sy, Ss * H, kzoverkh]\n", + "r = pd.DataFrame(\n", + " columns=[\"$T$ [ft$^2$/day]\", \"$S_y$\", \"$S$\", \"$k_h/k_r$\"],\n", + " index=[\"Lohman (1972)\", \"AQTESOLV\", \"TTim\"],\n", + ")\n", + "r.loc[\"Lohman (1972)\"] = [22000, 0.2, 0, 0.3]\n", + "r.loc[\"AQTESOLV\"] = [22980, 0.15, 0.008166, 0.25]\n", + "r.loc[\"TTim\"] = [k * H / 0.0929, Sy, Ss * H, kzoverkh]\n", "r" ] }, @@ -338,18 +342,20 @@ } ], "source": [ - "ml = Model3D(kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.2,\n", - " phreatictop=True, tmin=1e-4, tmax=10) \n", - "Qp = Qw / nlay #deviding Qw over the layers equal\n", - "w = DischargeWell(ml, xw=xw, yw=yw, rw=0.3, tsandQ=[(0, Qp)], \n", - " layers=range(nlay))\n", + "ml = ttim.Model3D(\n", + " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.2, phreatictop=True, tmin=1e-4, tmax=10\n", + ")\n", + "Qp = Qw / nlay # deviding Qw over the layers equal\n", + "w = ttim.DischargeWell(ml, xw=xw, yw=yw, rw=0.3, tsandQ=[(0, Qp)], layers=range(nlay))\n", "ml.solve()\n", - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0_11', initial=100, pmin=10, pmax=400)\n", - "cal.set_parameter(name='Saq0', initial=0.1, pmin=0.01, pmax=1)\n", - "cal.set_parameter(name='Saq1_11', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "cal.set_parameter_by_reference(name='kzoverkh', parameter=ml.aq.kzoverkh[:], initial=0.2, pmin=0.01, pmax=1)\n", - "cal.series(name='obs1', x=xp, y=yp, layer=layer_obswell, t=td, h=ho)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0_11\", initial=100, pmin=10, pmax=400)\n", + "cal.set_parameter(name=\"Saq0\", initial=0.1, pmin=0.01, pmax=1)\n", + "cal.set_parameter(name=\"Saq1_11\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "cal.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml.aq.kzoverkh[:], initial=0.2, pmin=0.01, pmax=1\n", + ")\n", + "cal.series(name=\"obs1\", x=xp, y=yp, layer=layer_obswell, t=td, h=ho)\n", "cal.fit()" ] }, @@ -433,8 +439,8 @@ ], "source": [ "cal.parameters\n", - "k, Sy, Ss, kzoverkh = cal.parameters['optimal'].values\n", - "r.loc['TTim uniform discharge well'] = [k * H / 0.0929, Sy, Ss * H, kzoverkh]\n", + "k, Sy, Ss, kzoverkh = cal.parameters[\"optimal\"].values\n", + "r.loc[\"TTim uniform discharge well\"] = [k * H / 0.0929, Sy, Ss * H, kzoverkh]\n", "r" ] } diff --git a/notebooks/ttim_slugtest.ipynb b/notebooks/ttim_slugtest.ipynb index 72d121a..1b50635 100755 --- a/notebooks/ttim_slugtest.ipynb +++ b/notebooks/ttim_slugtest.ipynb @@ -22,7 +22,7 @@ "import matplotlib.pyplot as plt\n", "from scipy.optimize import fmin\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -32,15 +32,15 @@ "outputs": [], "source": [ "# problem definitions\n", - "rw = 0.125 # well radius\n", - "rc = 0.064 # well casing radius\n", - "L = 1.52 # screen length\n", + "rw = 0.125 # well radius\n", + "rc = 0.064 # well casing radius\n", + "L = 1.52 # screen length\n", "zbot = -47.87 # aquifer thickness\n", "welltop = -16.77 # top of screen\n", - "delh = 0.671 # slug displacement in the well\n", + "delh = 0.671 # slug displacement in the well\n", "#\n", "wellbot = welltop - L # bottom of screen\n", - "Q = np.pi * rc**2 * delh # volume of slug" + "Q = np.pi * rc**2 * delh # volume of slug" ] }, { @@ -58,10 +58,10 @@ ], "source": [ "# loading data\n", - "data = np.loadtxt('data/slugtest.txt') # time and drawdouwn\n", - "time, dd = data[:,0], data[:,1]\n", - "td = time/60/60/24 #time in days\n", - "print('minimum and maximum time:', td.min(), td.max())" + "data = np.loadtxt(\"data/slugtest.txt\") # time and drawdouwn\n", + "time, dd = data[:, 0], data[:, 1]\n", + "td = time / 60 / 60 / 24 # time in days\n", + "print(\"minimum and maximum time:\", td.min(), td.max())" ] }, { @@ -135,22 +135,29 @@ } ], "source": [ - "ml = Model3D(kaq=100, z=[0, -0.5, welltop, wellbot, zbot],\n", - " Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01) \n", - "w = Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0.0, -Q)],\n", - " layers=2, rc=rc, wbstype='slug')\n", + "ml = ttim.Model3D(\n", + " kaq=100,\n", + " z=[0, -0.5, welltop, wellbot, zbot],\n", + " Saq=1e-4,\n", + " kzoverkh=1,\n", + " tmin=1e-6,\n", + " tmax=0.01,\n", + ")\n", + "w = ttim.Well(\n", + " ml, xw=0, yw=0, rw=rw, tsandQ=[(0.0, -Q)], layers=2, rc=rc, wbstype=\"slug\"\n", + ")\n", "ml.solve()\n", - "print('k:', ml.aq.kaq)\n", - "print('T: ', ml.aq.T)\n", - "print('c: ', ml.aq.c)\n", - "cal = Calibrate(ml)\n", - "cal.set_parameter(name='kaq0_3', initial=10)\n", - "cal.set_parameter(name='Saq0_3', initial=1e-3)\n", - "cal.series(name='obs1', x=0, y=0, layer=2, t=td, h=dd)\n", + "print(\"k:\", ml.aq.kaq)\n", + "print(\"T: \", ml.aq.T)\n", + "print(\"c: \", ml.aq.c)\n", + "cal = ttim.Calibrate(ml)\n", + "cal.set_parameter(name=\"kaq0_3\", initial=10)\n", + "cal.set_parameter(name=\"Saq0_3\", initial=1e-3)\n", + "cal.series(name=\"obs1\", x=0, y=0, layer=2, t=td, h=dd)\n", "cal.fit()\n", - "print('k:', ml.aq.kaq)\n", - "print('T: ', ml.aq.T)\n", - "print('c: ', ml.aq.c)" + "print(\"k:\", ml.aq.kaq)\n", + "print(\"T: \", ml.aq.T)\n", + "print(\"c: \", ml.aq.c)" ] }, { @@ -160,7 +167,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -174,13 +181,13 @@ "source": [ "hm = ml.head(0, 0, td, layers=2)\n", "plt.figure(figsize=(12, 6))\n", - "plt.semilogx(time, dd / delh, 'ko', label='Observed')\n", - "plt.semilogx(time, hm[0] / delh, 'b', label='TTim')\n", + "plt.semilogx(time, dd / delh, \"ko\", label=\"Observed\")\n", + "plt.semilogx(time, hm[0] / delh, \"b\", label=\"TTim\")\n", "plt.ylim([0, 1])\n", - "plt.xlabel('time [s]')\n", - "plt.ylabel('h / delh')\n", - "plt.legend(loc='best')\n", - "plt.title('TTim Slug Test Analysis');" + "plt.xlabel(\"time [s]\")\n", + "plt.ylabel(\"h / delh\")\n", + "plt.legend(loc=\"best\")\n", + "plt.title(\"TTim Slug Test Analysis\");" ] }, { @@ -240,10 +247,9 @@ } ], "source": [ - "r = pd.DataFrame(columns=['Kr [m/day]','Ss [1/m]'],\n", - " index=['TTim', 'AQTESOLV'])\n", - "r.loc['TTim'] = cal.parameters['optimal'].values\n", - "r.loc['AQTESOLV'] = [4.034, 0.000384]\n", + "r = pd.DataFrame(columns=[\"Kr [m/day]\", \"Ss [1/m]\"], index=[\"TTim\", \"AQTESOLV\"])\n", + "r.loc[\"TTim\"] = cal.parameters[\"optimal\"].values\n", + "r.loc[\"AQTESOLV\"] = [4.034, 0.000384]\n", "r" ] }, @@ -261,14 +267,22 @@ "outputs": [], "source": [ "def sse(p, returnheads=False):\n", - " ml = Model3D(kaq=p[0], z=[0, -0.5, welltop, wellbot, zbot],\n", - " Saq=p[1], kzoverkh=1, tmin=1e-6, tmax=0.01) \n", - " w = Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0.0, -Q)],\n", - " layers=2, rc=rc, wbstype='slug')\n", - " ml.solve(silent = '.')\n", + " ml = ttim.Model3D(\n", + " kaq=p[0],\n", + " z=[0, -0.5, welltop, wellbot, zbot],\n", + " Saq=p[1],\n", + " kzoverkh=1,\n", + " tmin=1e-6,\n", + " tmax=0.01,\n", + " )\n", + " w = ttim.Well(\n", + " ml, xw=0, yw=0, rw=rw, tsandQ=[(0.0, -Q)], layers=2, rc=rc, wbstype=\"slug\"\n", + " )\n", + " ml.solve(silent=\".\")\n", " hm = ml.head(0, 0, td, 2)\n", - " if returnheads: return hm\n", - " se = np.sum((hm[0] - dd)**2)\n", + " if returnheads:\n", + " return hm\n", + " se = np.sum((hm[0] - dd) ** 2)\n", " return se" ] }, @@ -292,8 +306,8 @@ ], "source": [ "popt = fmin(sse, [3, 1e-4])\n", - "print('optimal parameters:', popt)\n", - "print('sse:', sse(popt))" + "print(\"optimal parameters:\", popt)\n", + "print(\"sse:\", sse(popt))" ] } ], diff --git a/notebooks/well_example.ipynb b/notebooks/well_example.ipynb index 74f0a72..09930fc 100644 --- a/notebooks/well_example.ipynb +++ b/notebooks/well_example.ipynb @@ -9,7 +9,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -26,13 +26,16 @@ "outputs": [], "source": [ "from scipy.special import exp1\n", + "\n", + "\n", "def theis(r, t, T, S, Q):\n", - " u = r ** 2 * S / (4 * T * t)\n", + " u = r**2 * S / (4 * T * t)\n", " h = -Q / (4 * np.pi * T) * exp1(u)\n", " return h\n", "\n", + "\n", "def theisQr(r, t, T, S, Q):\n", - " u = r ** 2 * S / (4 * T * t)\n", + " u = r**2 * S / (4 * T * t)\n", " return -Q / (2 * np.pi) * np.exp(-u) / r" ] }, @@ -74,8 +77,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=1e-5, tmax=1)\n", - "w = Well(ml, tsandQ=[(0, Q)], rw=1e-5)\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, tsandQ=[(0, Q)], rw=1e-5)\n", "ml.solve()\n", "h = ml.head(r, 0, t)\n", "Qx, Qy = ml.disvec(r, 0, t)" @@ -88,7 +91,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -102,17 +105,17 @@ "source": [ "plt.figure(figsize=(12, 4))\n", "plt.subplot(121)\n", - "plt.semilogx(t, htheis, 'b', label='theis')\n", - "plt.semilogx(t, h[0], 'r--', label='ttim')\n", - "plt.xlabel('time (day)')\n", - "plt.ylabel('head (m)')\n", - "plt.legend();\n", + "plt.semilogx(t, htheis, \"b\", label=\"theis\")\n", + "plt.semilogx(t, h[0], \"r--\", label=\"ttim\")\n", + "plt.xlabel(\"time (day)\")\n", + "plt.ylabel(\"head (m)\")\n", + "plt.legend()\n", "plt.subplot(122)\n", - "plt.semilogx(t, Qrtheis, 'b', label='theis')\n", - "plt.semilogx(t, Qx[0], 'r--', label='ttim')\n", - "plt.xlabel('time (day)')\n", - "plt.ylabel('head (m)')\n", - "plt.legend(loc='best');" + "plt.semilogx(t, Qrtheis, \"b\", label=\"theis\")\n", + "plt.semilogx(t, Qx[0], \"r--\", label=\"ttim\")\n", + "plt.xlabel(\"time (day)\")\n", + "plt.ylabel(\"head (m)\")\n", + "plt.legend(loc=\"best\");" ] }, { @@ -122,8 +125,8 @@ "outputs": [], "source": [ "def test(M=10):\n", - " ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=1e-5, tmax=1, M=M)\n", - " w = Well(ml, tsandQ=[(0, Q)], rw=1e-5)\n", + " ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=1e-5, tmax=1, M=M)\n", + " w = ttim.Well(ml, tsandQ=[(0, Q)], rw=1e-5)\n", " ml.solve(silent=True)\n", " h = ml.head(r, 0, t)\n", " return htheis - h[0]" @@ -136,7 +139,7 @@ "outputs": [ { "data": { - "image/png": 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Y98L0/pEeA+CzEXFcetw2is9heGpbA9sEfQeVc22amVnS7lvwmIiYFhEHRURPRExueD7azo6lwHVp/TrgTU32OR24MyL6I2IDcCewJN1cc1pE3BsRAVzf8P5W5S4Fro/McmBGKmckx6hQjS9kdu3LzErWLoH9S4HHfWFErAVIyxc02ecQ4ImG52vStkPS+tDt7cptV9ZwjwHw/tQUeU2r5k8ASRdKWiFpxbp161rtll9dp5LyTPRmVoF234KjGuYm6S5JDzd5LM1bRJNt0Wb7WJbV7hhXAkcAx5FNavy3rQ4aEVdFxKKIWDRnzpwOIeZR0z4wz0RvZhVoNwrxEElXtHoxIj7QruCIOLXVa5J+KWluRKxNzXVPNdltDfC6hufzgHvS9nlDtj+Z1luVuwY4tMl7hn2MiPhlw3lcDdza6jzHXCVTSeVoQty2CabMKj4eM7MG7b4FtwL3t3mMxjJgcMTfeWTXmg11B3CapJmpme404I7UNLhZ0uI0MvDchve3KncZcG4ajbgY2JjKGfYxUmIc9DvAw6P7KIajgibEvFNJuQ/MzErWrgb2dERc1+b10fgk8DVJFwCPA28BkLQI+MOIeFdE9Ev6OHBfes9lEdGf1t8DXAtMBm5Pj5blArcBZwIrgS3AOwBGeIxPSTqOrEnxZ8AfjPbDyK2SG1q6CdHM6qldAst5L/nhi4ingVOabF8BvKvh+TXANS32O2YY5QbZfc2axTLcY/x+s3L2S8NpQvQgDjMrWct2qIhYXGYglkMZta5GeaaS2rkDBrbCJN9KxczKVUJHio2ZMvq9GvX2ZXda3rWr9T6eRsrMKuIE1lVKroH1TsyW7QZybEu3UnETopmVLFcCk/Trkt6R1udIWlBsWNZU2U2IvX3Zsl0/mGeiN7OKdExgki4BLgI+kjZNBL5aZFDWSlUJrE0/mO8FZmYVyVMD+x3gjcBzABHxJOAOjyqUXgNLTYhta2DuAzOzauRJYM+nYegBIGlqsSFZa1UlsDY1MDchmllF8iSwr0n6AtkM7u8muzPz1cWGZU1V1geWpwnRw+jNrFwd78gcEX8j6TeBTcBLgY9FxJ2FR2ZNVDUKcaD1PtvTKEQ3IZpZyTomsDTi8LuDSUvSZEnzI+JnRQdnQ9SxBrZ9M/ROggmTyonJzCzJ04T4daDxStadaZuVroYJzNNImVlF8iSwCRGx+xssrfcVF5K1VPpUUqmC3uk6MA/gMLMK5Elg6yS9cfBJuiHl+uJCstZqWgNz/5eZVaBjHxjwh8A/SfoHsm/QJ8juj2Vlq+VMHJvdhGhmlcgzCvFRYLGkAwFFxObiw7Lm6ngh8yaYeng58ZiZNcgzCnES8GZgPjBBqRYQEZcVGpntq+T8lX8Qh68BM7Py5WlCvBnYCNwPbC82HGuritupQOeZONwHZmYVyJPA5kXEksIjsRzKbkLsMApx166sD8yjEM2sAnl+0v+HpF8tPBLrrG4XMj//LBAexGFmlchTA/t14HxJq8maEAVERBxbaGTWREUJrNUNLT2Rr5lVKE8CO6PwKCyfut1OxbdSMbMKdWxCjIjHgEOB16f1LXneZ0WoWROib2ZpZhXyHZm7SelTSXW4H9juJkQPozez8vmOzF2lZk2I29KtVFwDM7MK+I7M3aTsGpiU1cLcB2ZmNeQ7MneVsqfiIOsH69iE6BqYmZXPd2TuJmXXwCBrRmzZhLgJ1At9rpSbWfnaJjBJvcAdEXEq4KRVuRrWwCYdVE1iNbNxr20TYkTsBLZI8jCzOqhbDczTSJlZhfJcyLwNeEjSnaSRiAAR8YHCorIWqkpgba4D8whEM6tIngT2zfSwqlVSA+trP5WUa2BmVpE8gziukzQZOCwiflpCTNZSSmA9eX53jJHevvbXgR00t7xYzMwa5JmJ47eBHwHfSs+Pk7Ss6MCsidiVLWeVeAfkdk2I2ze7CdHMKpPnOrBLgVcDzwBExI+ABQXGZK1s3ZAtX3R8ecfsaZfA3IRoZtXJ0xY1EBEbtXf/SxQUj7VzxMlw0kXw2veVd8xWTYgRHsRhZpXKk8AelvR2oFfSQuADwH8UG5Y11TsRTv7z8o/5/HP7bh/Ylg3u8DRSZlaRPE2IfwS8nOxmlv8MbAT+uMigrEZaXci8ex5E18DMrBota2CSvhIRvw+8OyI+Cny0vLCsNlpdyLz7XmC+xt3MqtGuBvZKSS8G3ilppqRZjY/RHDSVcaekR9JyZov9zkv7PCLpvIbtr5T0kKSVkq5Q6qBrVa4yV6T9H5R0Qo5jfELSE5KeHRLTJEk3prK+L2n+aD6L2mtZA0u3UnENzMwq0i6BfZ5s6PxRwP1DHitGedyLgbsjYiFwd3q+l5QkLwFeQzYK8pKGRHclcCGwMD2WdCj3jIZ9L0zv73SMW9K2oS4ANkTES4DPApeP4Py7R6th9IM1MPeBmVlF2iWwWyLiZcA1EXF4RCxoeIz2QqSlwHVp/TrgTU32OR24MyL6I2ID2WTCSyTNBaZFxL3pPmXXN7y/VblLgesjs5zs1jBzWx0DICKWR8TaDrHfBJwyWAPcL7VqQhzsA/MoRDOrSLsEdlNaHlnAcV84mBzS8gVN9jkEeKLh+Zq07ZC0PnR7u3LbldVsezu73xMRA2SDWmY321HShZJWSFqxbt26DsXWVKuppHwvMDOrWLth9D2SLgGOlPShoS9GxGfaFSzpLuBXmryUdzBIs1pNtNledVn7boy4CrgKYNGiRd157VyrPrDdgzicwMysGu0S2NlkTXATgGF3dKR7iDUl6ZeS5kbE2tSU91ST3dYAr2t4Pg+4J22fN2T7k2m9VblrgEObvKfVMdoZLGuNpAnAdKC/w3u6V6cmxD73gZlZNVo2IUbETyPicuCdEfGXQx+jPO4yYHDE33nAzU32uQM4LY2AnAmcRnZzzbXAZkmLU9/TuQ3vb1XuMuDcNBpxMbAxldP0GMOI/Szg31Jf3P6p1VRS2zfBxKnQW+LEwmZmDdpdB3ZORHwVOFrSy4a+3qkJsYNPAl+TdAHwOPCWdMxFwB9GxLsiol/Sx4H70nsui4jBms57gGuBycDt6dGyXOA24ExgJbAFeEc6h5bHkPQp4O3AFElrgC9GxKXAl4CvSFpJVvM6exSfQ/319sGugWzqqMaxKts2uvnQzCrV7ufz1LQ8cKwPGhFPA6c02b4CeFfD82uAa1rsd8wwyg2g6QSCbY7xYeDDTbZvY09i3P/1TsyWO3fAhL492z2Rr5lVrGUCi4gvpOVomwutm/WmpLXz+SEJbLOvATOzSrVrQryi3Rsj4gNjH47VTmMCa+SZ6M2sYu2uAxucdeMA4ATgkfQ4DthZfGhWC41NiI3chGhmFWvXhHgdgKTzgZMjYkd6/nngX0uJzqq3O4G5BmZm9ZLndiovYu/rwA5M22w8GGxCHDobh2tgZlaxPBfxfBL4oaRvp+cnAZcWFpHVS7MmxB3bYMcWmDKqmxKYmY1KxwQWEV+WdDvZjO0AF0fEL4oNy2qj2SCOrRuy5eSmd8ExMytFrmkUUsJqNluG7e+cwMyspvL0gdl41pN+4zQ2ITqBmVkNOIFZe01rYGlGr8nuAzOz6rS7kLntt1PDvIS2P9udwFwDM7N6adcHdj977pl1GLAhrc8gmyh3QeHRWfWajUJ0AjOzGmh3O5UFEXE42e1FfjsiDo6I2cAbgP9bVoBWsWZNiFv6s+19U5u/x8ysBHn6wF4VEbcNPomI28muBbPxoNUoxMkz9769iplZyfIMo18v6S+Ar5I1KZ4DPF1oVFYfvS1GIbr50MwqlqcG9jZgDvAvwDeAF6RtNh40m0rKCczMaiDPTBz9wAdLiMXqqFUT4owXVxOPmVnSMYFJmkN2Z+KXk91aBYCIeH2BcVldtBqFOPe4auIxM0vyNCH+E/DfZMPm/xL4GXBfgTFZnbQcxDGjmnjMzJI8CWx2RHwJ2BER/x4R7wQWFxyX1UXPkPuBeSZ6M6uJPKMQB9uO1kr6LeBJYF5xIVmtDG1C9EXMZlYTeRLYX0maDvwp8PfANOBPCo3K6kPKamFOYGZWM3lGId6aVjcCJxcbjtVSb9+eJsTdE/k6gZlZtTr2gUk6UtLdkh5Oz49NFzbbeNHbrAbmPjAzq1aeQRxXAx8h9YVFxIPA2UUGZTWzVw3MTYhmVg95EtiUiPjPIdsGigjGaqppDcwJzMyqlSeBrZd0BNk8iEg6C1hbaFRWL70T90wl5Znozawm8oxCfB9wFXCUpJ8Dq8km9LXxYmgTomeiN7MayDMKcRVwqqSpQE9EbC4+LKuV3r69mxDdfGhmNZBnLsRJwJuB+cAEpV/eEXFZoZFZffRO3LcGZmZWsTxNiDeTXQN2P7C92HCslnqGJDDPRG9mNZAngc2LiCWFR2L11dsHO9PAU89Eb2Y1kWcU4n9I+tXCI7H62qcJ0TPRm1n1WtbAJD1ENnR+AvAOSavImhAFREQcW06IVrnePti5wTPRm1mttGtCfENpUVi9DV7I7IuYzaxGWiawiHiszECsxgavA3MCM7MaydMHZuPdYB/Y7pno3YRoZtVzArPOeifCrgHXwMysVipJYJJmSbpT0iNp2fQbUdJ5aZ9HJJ3XsP2Vkh6StFLSFUpXV7cqV5kr0v4PSjohxzE+IekJSc8Oiel8Sesk/Sg93jXWn0/tuAnRzGqoqhrYxcDdEbEQuDs934ukWcAlwGuAVwOXNCS6K4ELgYXpMXidWqtyz2jY98L0/k7HuCVta+bGiDguPb44/NPvMk5gZlZDVSWwpcB1af064E1N9jkduDMi+iNiA3AnsETSXGBaRNwbEQFc3/D+VuUuBa6PzHJgRiqn6TEAImJ5RHjWfdgzCtEz0ZtZjVSVwF44mBzS8gVN9jkEeKLh+Zq07ZC0PnR7u3LbldVseydvTk2RN0k6tNVOki6UtELSinXr1uUotqYaa2Ceid7MaqKwBCbpLkkPN3kszVtEk23RZntZZd0CzE8Xct/FnhrfvgVFXBURiyJi0Zw5czoUW2M9g4M4+t18aGa1kWcuxBGJiFNbvSbpl5LmRsTa1JT3VJPd1gCva3g+D7gnbZ83ZPuTab1VuWuAQ5u8p9Ux2p3X0w1PrwYub7f/fqF3YrZ89ikPoTez2qiqCXEZMDji7zyyGe+HugM4TdLMNLDiNOCO1DS4WdLiNPrw3Ib3typ3GXBuGo24GNiYyml6jHaBp8Q46I3Af+U+627V25ctn/2la2BmVhtVJbBPAr8p6RHgN9NzJC2S9EWAiOgHPg7clx6XpW0A7wG+CKwEHgVub1cucBuwKu1/NfDeTseQ9ClJa4ApktZIujSV9QFJP5b0APAB4Pwx/FzqaXcCe8oJzMxqQ9lAPivaokWLYsWKFVWHMTL3fQm++aFs/bXvh9M/UW08ZjZuSLo/IhY1e80zcVhng31g4Jnozaw2nMCss8EmRHATopnVhhOYddZYA3MCM7OacAKzzvaqgbkJ0czqwQnMOnMTopnVkBOYdeYmRDOrIScw66zHCczM6scJzDobbEL0TPRmViNOYNbZYBOiZ6I3sxpxArPOBmtgbj40sxpxArPOdicwD6E3s/pwArPOetNdd1wDM7MacQKzztyEaGY15ARmne1OYDOqjcPMrIETmHXWOArRzKwmnMCsswNmwMkfhWN+t+pIzMx2m1B1ANYFJDjpw1VHYWa2F9fAzMysKzmBmZlZV3ICMzOzruQEZmZmXckJzMzMupITmJmZdSUnMDMz60pOYGZm1pUUEVXHMC5IWgc8NsK3HwysH8NwuoHPeXzwOY8PoznnF0fEnGYvOIF1AUkrImJR1XGUyec8Pvicx4eiztlNiGZm1pWcwMzMrCs5gXWHq6oOoAI+5/HB5zw+FHLO7gMzM7Ou5BqYmZl1JScwMzPrSk5gNSJpiaSfSlop6eImr0+SdGN6/fuS5pcf5djKcc4fkvQTSQ9KulvSi6uIcyx1OueG/c6SFJK6fsh1nnOW9Nb0t/6xpH8uO8axluPf9mGSvi3ph+nf95lVxDlWJF0j6SlJD7d4XZKuSJ/Hg5JOGPVBI8KPGjyAXuBR4HCgD3gAOHrIPu8FPp/WzwZurDruEs75ZGBKWn/PeDjntN9BwEFlVAkAAATUSURBVHeA5cCiquMu4e+8EPghMDM9f0HVcZdwzlcB70nrRwM/qzruUZ7zbwAnAA+3eP1M4HZAwGLg+6M9pmtg9fFqYGVErIqI54EbgKVD9lkKXJfWbwJOkaQSYxxrHc85Ir4dEVvS0+XAvJJjHGt5/s4AHwc+BWwrM7iC5DnndwOfi4gNABHxVMkxjrU85xzAtLQ+HXiyxPjGXER8B+hvs8tS4PrILAdmSJo7mmM6gdXHIcATDc/XpG1N94mIAWAjMLuU6IqR55wbXUD2C66bdTxnSccDh0bErWUGVqA8f+cjgSMlfU/ScklLSouuGHnO+VLgHElrgNuAPyontMoM9/97RxNGFY6NpWY1qaHXOOTZp5vkPh9J5wCLgJMKjah4bc9ZUg/wWeD8sgIqQZ6/8wSyZsTXkdWyvyvpmIh4puDYipLnnN8GXBsRfyvptcBX0jnvKj68Soz595drYPWxBji04fk89m1S2L2PpAlkzQ7tqux1l+eckXQq8FHgjRGxvaTYitLpnA8CjgHukfQzsr6CZV0+kCPvv+2bI2JHRKwGfkqW0LpVnnO+APgaQETcCxxANunt/irX//fhcAKrj/uAhZIWSOojG6SxbMg+y4Dz0vpZwL9F6h3tUh3POTWnfYEseXV7vwh0OOeI2BgRB0fE/IiYT9bv98aIWFFNuGMiz7/tb5AN2EHSwWRNiqtKjXJs5Tnnx4FTACS9jCyBrSs1ynItA85NoxEXAxsjYu1oCnQTYk1ExICk9wN3kI1guiYifizpMmBFRCwDvkTWzLCSrOZ1dnURj17Oc/40cCDw9TRe5fGIeGNlQY9SznPer+Q85zuA0yT9BNgJ/FlEPF1d1KOT85z/FLha0p+QNaWd380/SCX9b7Im4INTv94lwESAiPg8WT/fmcBKYAvwjlEfs4s/LzMzG8fchGhmZl3JCczMzLqSE5iZmXUlJzAzM+tKTmBmZtaVnMDMupCkGZLe2/D8RZJuKuhYb5L0sRavPZuWcyR9q4jjm7XiBGbWnWaQ3Z0AgIh4MiLOKuhYHwb+sd0OEbEOWCvpxIJiMNuHE5hZd/okcISkH0n6tKT5g/dhknS+pG9IukXSaknvT/dV+2GaKHdW2u8ISd+SdL+k70o6auhBJB0JbI+I9en5Akn3SrpP0seH7P4N4PeKPW2zPZzAzLrTxcCjEXFcRPxZk9ePAd5OdluPTwBbIuJ44F7g3LTPVcAfRcQrgf9J81rWicAPGp7/HXBlRLwK+MWQfVcA/2OE52M2bJ5Kymz/9O2I2AxslrQRuCVtfwg4VtKBwK+xZ4ougElNypnL3vPznQi8Oa1/Bbi84bWngBeNTfhmnTmBme2fGmft39XwfBfZ//se4JmIOK5DOVvJ7nrQqNX8cwek/c1K4SZEs+60mezWKyMSEZuA1ZLeApBmCH9Fk13/C3hJw/PvsWcS6aH9XUcCD480JrPhcgIz60JppvbvSXpY0qdHWMzvARdIegD4Mfve8h7gO8Dx2tPO+EHgfZLuY9+a2cnAN0cYi9mweTZ6M2tL0t8Bt0TEXR32+w6wNCI2lBOZjXeugZlZJ/8LmNJuB0lzgM84eVmZXAMzM7Ou5BqYmZl1JScwMzPrSk5gZmbWlZzAzMysKzmBmZlZV/r/MxZkhX9PpwgAAAAASUVORK5CYII=\n", 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", 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" ] @@ -149,9 +152,9 @@ ], "source": [ "enumba = test(M=10)\n", - "plt.plot(t, enumba, 'C1')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head difference Thies - Ttim');" + "plt.plot(t, enumba, \"C1\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head difference Thies - Ttim\");" ] }, { @@ -161,7 +164,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -174,8 +177,8 @@ ], "source": [ "plt.plot(t, Qrtheis - Qx[0])\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('Qx difference Thies - Ttim');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"Qx difference Thies - Ttim\");" ] }, { @@ -185,7 +188,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -198,22 +201,25 @@ ], "source": [ "def compare(M=10):\n", - " ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=1e-5, tmax=1, M=M)\n", - " w = Well(ml, tsandQ=[(0, Q)], rw=1e-5)\n", + " ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=1e-5, tmax=1, M=M)\n", + " w = ttim.Well(ml, tsandQ=[(0, Q)], rw=1e-5)\n", " ml.solve(silent=True)\n", " h = ml.head(r, 0, t)\n", - " rmse = np.sqrt(np.mean((h[0] - htheis)**2))\n", + " rmse = np.sqrt(np.mean((h[0] - htheis) ** 2))\n", " return rmse\n", "\n", + "\n", "Mlist = np.arange(1, 21)\n", "rmse = np.zeros(len(Mlist))\n", "for i, M in enumerate(Mlist):\n", " rmse[i] = compare(M)\n", "plt.semilogy(Mlist, rmse)\n", - "plt.xlabel('Number of terms M')\n", + "plt.xlabel(\"Number of terms M\")\n", "plt.xticks(np.arange(1, 21))\n", - "plt.ylabel('relative error')\n", - "plt.title('comparison between TTim solution and Theis \\n solution using numba and M terms')\n", + "plt.ylabel(\"relative error\")\n", + "plt.title(\n", + " \"comparison between TTim solution and Theis \\n solution using numba and M terms\"\n", + ")\n", "plt.grid()" ] }, @@ -237,7 +243,9 @@ "def volume(r, t=1):\n", " return -2 * np.pi * r * ml.head(r, 0, t) * ml.aq.Scoefaq[0]\n", "\n", + "\n", "from scipy.integrate import quad\n", + "\n", "quad(volume, 1e-5, np.inf)" ] }, @@ -248,9 +256,11 @@ "outputs": [], "source": [ "from scipy.special import exp1\n", + "\n", + "\n", "def theis2(r, t, T, S, Q, tend):\n", - " u1 = r ** 2 * S / (4 * T * t)\n", - " u2 = r ** 2 * S / (4 * T * (t[t > tend] - tend))\n", + " u1 = r**2 * S / (4 * T * t)\n", + " u2 = r**2 * S / (4 * T * (t[t > tend] - tend))\n", " h = -Q / (4 * np.pi * T) * exp1(u1)\n", " h[t > tend] -= -Q / (4 * np.pi * T) * exp1(u2)\n", " return h" @@ -271,8 +281,8 @@ } ], "source": [ - "ml2 = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=1e-5, tmax=10)\n", - "w2 = Well(ml2, tsandQ=[(0, Q), (1, 0)])\n", + "ml2 = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=1e-5, tmax=10)\n", + "w2 = ttim.Well(ml2, tsandQ=[(0, Q), (1, 0)])\n", "ml2.solve()" ] }, @@ -294,7 +304,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -306,9 +316,9 @@ } ], "source": [ - "plt.plot(t2, htheis2, 'b', label='theis')\n", - "plt.plot(t2, h2[0], 'r--', label='ttim')\n", - "plt.legend(loc='best');" + "plt.plot(t2, htheis2, \"b\", label=\"theis\")\n", + "plt.plot(t2, h2[0], \"r--\", label=\"ttim\")\n", + "plt.legend(loc=\"best\");" ] }, { @@ -339,15 +349,19 @@ "outputs": [], "source": [ "from scipy.integrate import quad\n", + "\n", + "\n", "def integrand_hantush(y, r, lab):\n", - " return np.exp(-y - r ** 2 / (4 * lab ** 2 * y)) / y\n", + " return np.exp(-y - r**2 / (4 * lab**2 * y)) / y\n", + "\n", "\n", "def hantush(r, t, T, S, c, Q, tstart=0):\n", " lab = np.sqrt(T * c)\n", - " u = r ** 2 * S / (4 * T * (t - tstart))\n", + " u = r**2 * S / (4 * T * (t - tstart))\n", " F = quad(integrand_hantush, u, np.inf, args=(r, lab))[0]\n", " return -Q / (4 * np.pi * T) * F\n", "\n", + "\n", "hantushvec = np.vectorize(hantush)" ] }, @@ -366,8 +380,10 @@ } ], "source": [ - "ml = ModelMaq(kaq=25, z=[21, 20, 0], c=[1000], Saq=S/20, topboundary='semi', tmin=1e-5, tmax=1)\n", - "w = Well(ml, tsandQ=[(0, Q)])\n", + "ml = ttim.ModelMaq(\n", + " kaq=25, z=[21, 20, 0], c=[1000], Saq=S / 20, topboundary=\"semi\", tmin=1e-5, tmax=1\n", + ")\n", + "w = ttim.Well(ml, tsandQ=[(0, Q)])\n", "ml.solve()" ] }, @@ -378,7 +394,7 @@ "outputs": [ { "data": { - "image/png": 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kX+8CTLt0BWNMOPAt8Jm19is3tyc+rvPbdYhrPZz6O75k2U0P6N07Ij7E3cIfArQwxiQBLdJvY4yJNsb8b3rFDkBjoKsxJjH9EuXmdsWHxU5/irhWQ2iwbRJLqz6ok7NEfISx1jfnRImOjrYJCfrwDX8Wd9urNJ4zkGGtfqLfjFhcOq9bxOuMMSuttdEZLdN/QfGa2NnP8uHDq+n/YyyPPgo+um8hEjRU+OJVj3xQgwED4I/Rs5lXu59m2RRxkApfvMoYeOUVeKbOfJqvHs6CW59R6Ys4RIUvXmcMNF3yKgsiexG7YhhxsYOcjiQSlFT4kiOMy9Bo1bv8XLk7TX5+mbiWrzkdSSToqPAlx7hCXdRf+yGLynZm8+wtvPO2Du2I5CSdBik5KiQ8hLobx/FmJxffPGnInyuZBx+NcDqWSFDQHr7kuNCIUCZ+4eK+mB00fuxmFj063ulIIkFBhS+OyJULRn9TlKMFy1B3VFeWPvOt05FEAp4KXxxzTeFruGHDdDbkvYWaQ+9h5WuznY4kEtBU+OKovNfm5fpfZrI14iaqPNuWxA+XOR1JJGCp8MVxBcsXovCK2czJdzdt+lVm5UqnE4kEJhW++IRi1UpQ69fPMYULcddtySTN+t3pSCIBR4UvPuP662HuXHjvZHfy3tGIHQv/cDqSSEBR4YtPqVQJqo3vT4Q9hW3WjL2rdjsdSSRgqPDF51S+O5LdY36kUMp+jtW/jUNJB52OJBIQVPjik25+sA6b3/iO609vZuMt93HsmNOJRPyfCl98Vs3/NCHx+W949Ngw2rSB5GSnE4n4NxW++LRbX76DfuOqMX++ZUTDL/Sh6CJuUOGLz7vvPpjyeDx9V3ZiabUepKWkOR1JxC+p8MUv3P12I+KavkzDLeP4+Zb/6FOzRLJBhS9+I2bOQBbUfIKYxHdY0Hyw03FE/I4KX/yGcRkaLX+D+IpdaDT/JcYP3Oh0JBG/osIXv+IKdXHruo95ruFC7n/lJiZOdDqRiP9Q4YvfCY0IZdCcBjRpAl/eP50Vg2Y4HUnEL6jwxS9FRMDUb9J4+ZpXqfZSe34ZGe90JBGfp8IXv5W/oIvrVnzH3rAylO3Vmk1TfnE6kohPU+GLXytapRjhC+ZwIiQfBTvexraftjgdScRnqfDF75WqV4ZTU2cTYlP4pv0E9uxxOpGIb1LhS0Co2LoK26cl8vzp52nZEv76y+lEIr5HhS8Bo+Y/SjN1moENG1hZ+R5OHjjpdCQRn6LCl4DSvDmMenwDTQ98ya9V/8XZk2edjiTiM1T4EnDqD29HfOcPuGX/TJZX66bJ1kTSqfAlIDWe8DBxzQbT4I/x/FznKU22JoIKXwJYzOznWFCjD6xexbD/nnY6jojjQp0OIOItxmVolPAW3e8/w9gXIyhc0tKjp3E6lohjtIcvAc0V6mL0ZxG0b36YSg81YenT3zgdScQxKnwJeGFhMHZCGEXynqHmsE6sfnO+05FEHKHCl6CQp3geSid+z/ZcN1DxqTZsmLDK6UgiOc6twjfGFDbGzDHGJKV/LXSZdfMbY3YZY0a4s02R7CpUsTB5f/6RoyGFKHp/K7bOSXI6kkiOcncPvz8wz1p7AzAv/XZmBgML3NyeiFtK3lKaM9NnsyukDN0fTGP3bqcTieQcdwu/DTAu/fo4oG1GKxljagMlgNlubk/EbRVuv5GUxStYfuRGWt5m+WuXpmCQ4OBu4Zew1u4BSP9a/NIVjDEu4A2gn5vbEvGY6FsM334LD294nO1VW2reHQkKVyx8Y8xcY8y6DC5trnIbjwEzrbU7rmJbDxljEowxCfv377/KpxfJnubNoVafhkQeXcS6mztq3h0JeFc88cpa2zyzZcaYfcaYktbaPcaYksCfGaxWD2hkjHkMyAuEG2OOW2v/drzfWjsaGA0QHR2tc+HF6+q/1YGFew/S+IvHiK/ekwabPsW4dHKWBCZ3D+l8B3RJv94FmHbpCtbae621Zay15YC+wGcZlb2IUxpPepS42EE03DKOuHoDnI4j4jXuFv4QoIUxJglokX4bY0y0MeZjd8OJ5JSYeS8wN6ovw5Y3Ztgwp9OIeIex1jePnERHR9uEhASnY0gQSUuDzp1h8mT4YvhOOj5V2ulIIllmjFlprY3OaJnOtBVJ53LBZ5/BwKjvadu3Isue+87pSCIepcIXuUB4ODw9I4bNeWpQ/dWOrBnxs9ORRDxGhS9yiXzX5aPEihnsCS9Lud7/4Lcv1zgdScQjVPgiGShapRjhP83ipCsvBTu1YmvCAacjibhNhS+SidINynLi29m8l6sfzTsWYe9epxOJuEeFL3IZle6qSuuf/sOevYZeTdZzZPsRpyOJZJsKX+QKbr0Vpo4/zsiNTfijehuSDyc7HUkkW1T4IlehxT/zsrnX20QdWUBilU6kJKc4HUkky1T4IlepwYhOLLj7XW7dO5XFNR7BpvnmSYsimVHhi2RBzJTexDV6nsabxvD1HWOcjiOSJSp8kSyKiXuJz2LHcO+s+xk+3Ok0IldPhS+SRcZluHduN9p2yMVr/Q4y73FNwSD+QYUvkg0hIefm3fm09PPEvNtO8+6IX1Dhi2RTrlzQdMVQNuapTY1XO5D4zgKnI4lclgpfxA15r81LyZUz2BVegfJP3MXGSaudjiSSKRW+iJuK3FiU3PGzORZSkGMP9GLTb3q7pvgmFb6IB5S8pTRnZszlwfxfc1tLw65dTicS+TsVvoiHVGh5A+Nml+TIwRSmRb3IoaSDTkcSuYgKX8SDateGOW+to9uB19ld806O7z3udCSR81T4Ih4W3SOKNQMmc9OJBH67+Z+cPnra6UgigApfxCvqvtqGpT0/ofahuayqeq8mWxOfoMIX8ZKGox9gQdu3iNz1Iy932oDVm3fEYSp8ES+K+fYJPnj8NwZPjaRfP1T64qhQpwOIBLq+b5ViWwokvzGCuLXHaDJrgNORJEhpD1/Ey4yBd9+xdCq3lCazn2Vhpw+cjiRBSoUvkgNcIYY6v37KshL/oOEXvVjUa6LTkSQIqfBFckhY7jCqr5/MmoIx1B35AMufn+50JAkyKnyRHHRN4Wuo9Ot3bMxTmy9e+4P5851OJMFEhS+Sw/Jdl4+Sm+OZfVMf7roLlsefcTqSBAkVvogDilwbxpw5cHv+eK6Nqcymr9c6HUmCgApfxCElS8Ibk0oRRgoFO7Rg65wkpyNJgFPhizjo+sblOTltDi6bSujtzdm1ZLvTkSSAqfBFHFaxdRUOTJhNvtQjnIlpzr51+52OJAFKhS/iA27qVJPto2YSlxZDq3sKclBT6YsXqPBFfETkw/UpM+sjNmwOo3OzfRzZcdTpSBJgVPgiPqRZM/h6cgpvrGnG1mp3cuLPE05HkgCiwhfxMXe2CeXYky9S7ehifqvShuTDyU5HkgChwhfxQfXe/BdLen5KrUPz+OXG9pw5rpOzxH0qfBEf1XD0AyzsPIo6f87gx1rPkqIPzRI3uVX4xpjCxpg5xpik9K+FMlmvjDFmtjFmgzFmvTGmnDvbFQkWjSc8zKx7P6N70jN06QKpqU4nEn/m7h5+f2CetfYGYF767Yx8Bgyz1lYB6gB/urldkaDRcvz99B1SjK8mnmF8w1GkpaQ5HUn8lLuF3wYYl359HND20hWMMVWBUGvtHABr7XFr7Uk3tysSVJ55Bibe/Q1dlj5KfOSj2DR9VqJknbuFX8Jauwcg/WvxDNapDBw2xnxjjFltjBlmjAlxc7siQefuLzsSV28AjTeOZmFUb5W+ZNkVP9PWGDMXuDaDRc9lYRuNgJrAdmAy0BUYk8G2HgIeAihTpsxVPr1IcDAuQ0z8K8TVOUPsyjdYUDuUxivfwriM09HET1xxD99a29xaWy2DyzRgnzGmJED614yOze8EVltrf7fWpgBTgVqZbGu0tTbaWhtdrFix7I9KJEAZlyFm+TAWRD1OVOKnvPrwNqx29OUquXtI5zugS/r1LsC0DNZZARQyxvyvwZsC693crkjQMi5D45Vv8cb9axj4cTkGDEClL1fF3cIfArQwxiQBLdJvY4yJNsZ8DGCtTQX6AvOMMWsBA3zk5nZFgppxGV4aV45HHgH7+ussqD9Ax/Tliq54DP9yrLUHgWYZ3J8A9Ljg9hygujvbEpGLGQPvj7DEL/iDxks/JK4BxCx6Vcf0JVM601bEj7lCDA1/GcnCKg8Tu3QICxo8qz19yZQKX8TPuUJdF5V+XP0BOqYvGVLhiwSA/5V+XLVeTFpWgb599UKu/J0KXyRAuEJdxPwygojeD/Hmm/DqAxt1eEcuosIXCSDGwDvvwCtdk3hyfC1+jnxMc+/IeSp8kQBjDAwYU4nldfvQeP0oFlXtQeoZTbMpKnyRgGRchpjFrxEX8yKNkj5laeUHSEnWhPrBToUvEqCMyxAbN4i4lq/RYNtEPq73MadPO51KnKTCFwlwsT/2Z9rDM+mV2JO77oKTmpw8aKnwRYJAm1G389GYEDbO2UF8ufs4sv2I05HEASp8kSDRrRuM75dIk/2T2X1TE/b/qg+eCzYqfJEg0uj1f7DmpWmUPbWR4zUbsWvJdqcjSQ5S4YsEmegX7mDz+7MpfHYfplEDNv+42elIkkNU+CJBqPpjDdk7eQG/hVSlRaeiLF3qdCLJCSp8kSB1Y4calFk/i5AiBbmjySmWDP3Z6UjiZSp8kSBWsSIsWgRvFhjELc80Ib7HWKcjiRep8EWCXIkScPeqgawp3ISGYx4krunLmnQtQKnwRYR81+UjctsM4is8QOz8F4mv0pOzJ886HUs8TIUvIgCE5w2nQdJY4hoOpMqmqXRrtZsjOj8roKjwReQ84zLE/jyYWW+u54slZWlQ37I9QSdoBQoVvoj8zb1PFmfWLPjX768TUbc668YsczqSeIAKX0Qy1LQp3D+lDadceajUI4ZFj453OpK4SYUvIpmqcGcV8q5bxoaC9Wgw6n7ibu2vD1PxYyp8EbmsIjcWpdqu2Sys+ggNlw3niWZrOXzY6VSSHSp8EbmisNxhNP71A74emMiHy6KIjob1Cw84HUuySIUvIlet4+BqxMVBvYPfc31MeRb3+cLpSJIFKnwRyZL69WH4/Nr8ka8G9d/rxILIXpw+qs9O9AcqfBHJshJRJamydz5x0X2JWTeSLSUbsD3ud6djyRWo8EUkW8JyhxG7YhjLBkzlulNbGHr7fCZNcjqVXI4KX0TcUvfVNhxdtpHVNbvRuTO82mohx3YfczqWZECFLyJuK3NLCRYsNLzy9BEem3UXh8pGsWaE5tf3NSp8EfGI0FB49vUCbH//ewyWyN4xxEX3JflwstPRJJ0KX0Q8qvpjDSm84xfiqz5M7Mo32FmiFkvmnnA6lqDCFxEvyHttXhr/+gEJ//2RmRF3U79FHnr1gqMHNce+k1T4IuI10c+1pNuuwTz+OCwfmcDREpVY3OcLfaKWQ1T4IuJVefPC22/DJ+NCOBZelPrvdSKxSFOSvl3ndLSgo8IXkRwR+UBNKh9ezsLOoyh75BfKt4tiTo2n2LvX6WTBQ4UvIjkmJDyExhMeht82EV+jFwvWFaFSJXjpxTSO7jzqdLyAp8IXkRxX+IYixCa+Q9ffnuX22yHx5WmklilHXItXVPxe5FbhG2MKG2PmGGOS0r8WymS9ocaYX40xG4wx7xpjjDvbFZHAUKkSfPUV/PeLSmwq3pDYuQPPFX+j59m/bp/T8QKOu3v4/YF51tobgHnpty9ijKkPNACqA9WAW4AYN7crIgHk5o6R1N37Hes/S+C3a2NoHP8K+6s3pWcPy5o1TqcLHO4WfhtgXPr1cUDbDNaxQAQQDuQCwgD96haRv6l6f21u3f0t22dtZNY/3mf8BMOtUadYWrAl8T3GcuJPncDlDncLv4S1dg9A+tfil65grV0CzAf2pF9mWWs3uLldEQlg5W6rzJPTYtm1C0b130axU9tpOOZBKFGcReXvJeHlmZw9ccbpmH7nioVvjJlrjFmXwaXN1WzAGFMJqAKUBkoBTY0xjTNZ9yFjTIIxJmH//v1ZGYeIBKDChaHLazdR4dR61ry3kJU33UfVbT8Q/eKdNLv2Vzp3hmkf7ObwH385HdUvGGuzf8abMeY3INZaux1nEvwAAAZQSURBVMcYUxKIs9beeMk6/YAIa+3g9NsvAMnW2qGXe+7o6GibkJCQ7WwiEphOHz1N4ps/MXp7K6Z/b3hlf0+68Qm/5a7Fn5HNyHNHDJXurUuhioWdjuoIY8xKa210hsvcLPxhwEFr7RBjTH+gsLX26UvW6Qj0BFoBBvgReNtaO/1yz63CF5ErSU2FdWMT+Gv8DAqtmkfVo0sII4UkKtG6chLR0dDBTua6G/NRsllVSta5npDwEKdje5U3C78I8CVQBtgO/Mtae8gYEw08Yq3tYYwJAUYCjTn3Au6P1tr/XOm5VfgiklUn/jxB0sQVbF51lM+P3EViIizeXopS7AbgNOHsDK/AkrL38HOTFyldGhrumESeEnnJXbYY+coXJc91BchfpiDhecIcHk32eK3wvUmFLyKecGTbYbZOX8uR5RtJ+20zETuSWMEtDE4ZwMH9qZwmF6GkXvSY9/g3T0e8R6Hcp1l+pDKnQ3KT4srF2ZBcpIbkYvZ1XZlbphsF7GGe+qUL1uXCukIAg3W5iC97H6tL/4OCyXu5b/VTcMGpR9YYfq7QlfUlm1HsxFbarXnhb5nnd/6Y5weHZ2u8lyv80Gw9o4iInyhQtiA1/t0IaHT+vluB3kDyKRd7V27m6Jb9nNi6n9O7D5J66AgF81fj34Xh7JFUtv7UhJDTJ3GlnCbk7GlCUk+Tkmo4ehRST56lwNHtuGwqLpuKsRawHDnzJyv3wXVnkim9a9kFac7tYO87cTuLfofKZ47zxL74v2Ve+4t3dsS1hy8iEkAut4evuXRERIKECl9EJEio8EVEgoQKX0QkSKjwRUSChApfRCRIqPBFRIKECl9EJEj47IlXxpj9wDagAHDkgkWXu33h9aLAAQ9EuXR72V03s2UZ3a8xX92YPTXezDJlZz1PjTmzZcEyZl/+ub7ccl8Yc1lrbbEMl1hrffoCjL7a25dcT/DG9rO7bmbLMrpfY766MXtqvFkZ85XW89SYM1sWLGP25Z9rfxrzpRd/OKRz6TTKl7t92SmXPbT97K6b2bKM7teYfXfMV1rPU2O+0r+HJ/jymH355/pyy31tzBfx2UM67jLGJNhM5pMIVME25mAbL2jMwcJbY/aHPfzsGu10AAcE25iDbbygMQcLr4w5YPfwRUTkYoG8hy8iIhdQ4YuIBAkVvohIkAi6wjfGxBpjfjbGjDLGxDqdJ6cYY/IYY1YaY1o7nSUnGGOqpH+PpxhjHnU6T04wxrQ1xnxkjJlmjLnN6Tw5wRhTwRgzxhgzxeks3pT+/3dc+vf33uw+j18VvjHmE2PMn8aYdZfc38oY85sxZrMxpv8VnsYCx4EIYKe3snqKh8YM8AzwpXdSepYnxmyt3WCtfQToAPj8W/o8NOap1tqeQFegoxfjeoSHxvy7tba7d5N6RxbH3w6Ykv79vSvbG/XG2VzeugCNgVrAugvuCwG2ABWAcGANUBWIBL6/5FIccKU/rgQwwekx5dCYmwP3cK4IWjs9ppwYc/pj7gIWA52dHlNOjTn9cW8AtZweUw6PeYrT4/Hy+AcAUenrTMzuNkPxI9bahcaYcpfcXQfYbK39HcAY8wXQxlr7GnC5wxd/Abm8kdOTPDFmY0wTIA/nfnBOGWNmWmvTvBrcDZ76PltrvwO+M8bMACZ6L7H7PPR9NsAQ4Adr7SrvJnafh/8/+52sjJ9zRyNKA4m4cWTGrwo/E6WAHRfc3gnUzWxlY0w7oCVQEBjh3Whek6UxW2ufAzDGdAUO+HLZX0ZWv8+xnPszOBcw06vJvCdLYwZ6c+6vuQLGmErW2lHeDOclWf0+FwFeAWoaYwak/2LwZ5mN/11ghDHmTtyYgiEQCt9kcF+mZ5NZa78BvvFenByRpTGfX8HasZ6PkmOy+n2OA+K8FSaHZHXM73KuGPxZVsd8EHjEe3FyXIbjt9aeAB5098n96kXbTOwErr/gdmlgt0NZcorGrDEHqmAc84W8Ov5AKPwVwA3GmPLGmHDOvTj5ncOZvE1j1pgDVTCO+ULeHb/Tr1Rn8VXtScAe4CznfhN2T7//DmAT517dfs7pnBqzxqwxa8y+OH5NniYiEiQC4ZCOiIhcBRW+iEiQUOGLiAQJFb6ISJBQ4YuIBAkVvohIkFDhi4gECRW+iEiQUOGLiASJ/wMBIhct6gK7pQAAAABJRU5ErkJggg==\n", + "image/png": 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", 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" ] @@ -392,9 +408,9 @@ "source": [ "hhantush = hantushvec(30, t, T, S, c, Q)\n", "h = ml.head(r, 0, t)\n", - "plt.semilogx(t, hhantush, 'b', label='hantush')\n", - "plt.semilogx(t, h[0], 'r--', label='ttim')\n", - "plt.legend(loc='best');" + "plt.semilogx(t, hhantush, \"b\", label=\"hantush\")\n", + "plt.semilogx(t, h[0], \"r--\", label=\"ttim\")\n", + "plt.legend(loc=\"best\");" ] }, { @@ -421,7 +437,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -439,20 +455,22 @@ "rw = 0.3\n", "Q = 788\n", "\n", - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=1e-5, tmax=1)\n", - "w = Well(ml, rw=rw, tsandQ=[(0, Q)])\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, rw=rw, tsandQ=[(0, Q)])\n", "ml.solve()\n", "hnostorage = ml.head(rw, 0, t)\n", "\n", - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=1e-5, tmax=1)\n", - "w = Well(ml, rw=rw, tsandQ=[(0, Q)], rc=rw)\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, rw=rw, tsandQ=[(0, Q)], rc=rw)\n", "ml.solve()\n", "hstorage = ml.head(rw, 0, t)\n", "\n", - "plt.semilogx(t, hnostorage[0], label='no storage')\n", - "plt.semilogx(t, hstorage[0], label='with storage')\n", - "plt.legend(loc='best')\n", - "plt.xticks([1/(24*60*60), 1/(24 * 60), 1/24, 1], ['1 sec', '1 min', '1 hr', '1 d']);" + "plt.semilogx(t, hnostorage[0], label=\"no storage\")\n", + "plt.semilogx(t, hstorage[0], label=\"with storage\")\n", + "plt.legend(loc=\"best\")\n", + "plt.xticks(\n", + " [1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24, 1], [\"1 sec\", \"1 min\", \"1 hr\", \"1 d\"]\n", + ");" ] }, { @@ -477,7 +495,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -496,14 +514,16 @@ "rw = 0.2\n", "rc = 0.2\n", "delh = 1\n", - "ml = ModelMaq(kaq=k, z=[H, 0], Saq=S, tmin=1e-7, tmax=1)\n", - "Qslug = np.pi * rc ** 2 * delh\n", - "w = Well(ml, tsandQ=[(0, -Qslug)], rw=rw, rc=rc, wbstype='slug')\n", + "ml = ttim.ModelMaq(kaq=k, z=[H, 0], Saq=S, tmin=1e-7, tmax=1)\n", + "Qslug = np.pi * rc**2 * delh\n", + "w = ttim.Well(ml, tsandQ=[(0, -Qslug)], rw=rw, rc=rc, wbstype=\"slug\")\n", "ml.solve()\n", "h = w.headinside(t)\n", "plt.semilogx(t, h[0])\n", - "plt.xticks([1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24], \n", - " ['0.1 sec', '1 sec', '1 min', '1 hr']);" + "plt.xticks(\n", + " [1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24],\n", + " [\"0.1 sec\", \"1 sec\", \"1 min\", \"1 hr\"],\n", + ");" ] }, { @@ -529,7 +549,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -548,17 +568,20 @@ "rw = 0.2\n", "rc = 0.2\n", "delh = 1\n", - "ml = Model3D(kaq=k, z=np.linspace(H, 0, 6), Saq=Ss, tmin=1e-7, tmax=1)\n", + "ml = ttim.Model3D(kaq=k, z=np.linspace(H, 0, 6), Saq=Ss, tmin=1e-7, tmax=1)\n", "Qslug = np.pi * rc**2 * delh\n", - "w = Well(ml, tsandQ=[(0, -Qslug)], rw=rw, rc=rc, layers=[0, 1], wbstype='slug')\n", + "w = ttim.Well(ml, tsandQ=[(0, -Qslug)], rw=rw, rc=rc, layers=[0, 1], wbstype=\"slug\")\n", "ml.solve()\n", "hw = w.headinside(t)\n", - "plt.semilogx(t, hw[0], label='inside well')\n", + "plt.semilogx(t, hw[0], label=\"inside well\")\n", "h = ml.head(0.2 + 1e-8, 0, t)\n", "for i in range(2, 5):\n", - " plt.semilogx(t, h[i], label='layer' + str(i))\n", + " plt.semilogx(t, h[i], label=\"layer\" + str(i))\n", "plt.legend()\n", - "plt.xticks([1/(24*60*60)/10, 1/(24*60*60), 1/(24 * 60), 1/24], ['0.1 sec', '1 sec', '1 min', '1 hr']);" + "plt.xticks(\n", + " [1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24],\n", + " [\"0.1 sec\", \"1 sec\", \"1 min\", \"1 hr\"],\n", + ");" ] }, { @@ -583,7 +606,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -602,17 +625,22 @@ "rw = 0.2\n", "rc = 0.2\n", "delh = 1\n", - "ml = Model3D(kaq=k, z=np.linspace(H, 0, 21), Saq=S, tmin=1e-7, tmax=1)\n", + "ml = ttim.Model3D(kaq=k, z=np.linspace(H, 0, 21), Saq=S, tmin=1e-7, tmax=1)\n", "Qslug = np.pi * rc**2 * delh\n", - "w = Well(ml, tsandQ=[(0, -Qslug)], rw=rw, rc=rc, layers=np.arange(8), wbstype='slug')\n", + "w = ttim.Well(\n", + " ml, tsandQ=[(0, -Qslug)], rw=rw, rc=rc, layers=np.arange(8), wbstype=\"slug\"\n", + ")\n", "ml.solve()\n", "hw = w.headinside(t)\n", - "plt.semilogx(t, hw[0], label='inside well')\n", + "plt.semilogx(t, hw[0], label=\"inside well\")\n", "h = ml.head(0.2 + 1e-8, 0, t)\n", "for i in range(8, 20):\n", - " plt.semilogx(t, h[i], label='layer' + str(i))\n", + " plt.semilogx(t, h[i], label=\"layer\" + str(i))\n", "plt.legend()\n", - "plt.xticks([1/(24*60*60)/10, 1/(24*60*60), 1/(24 * 60), 1/24], ['0.1 sec', '1 sec', '1 min', '1 hr']);" + "plt.xticks(\n", + " [1 / (24 * 60 * 60) / 10, 1 / (24 * 60 * 60), 1 / (24 * 60), 1 / 24],\n", + " [\"0.1 sec\", \"1 sec\", \"1 min\", \"1 hr\"],\n", + ");" ] }, { @@ -639,7 +667,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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uLmbKlCmsW7eO66+//kq+o0u6bFhWSq0FvKp56skrvYjWOhHoqpTyAZYrpb7VWqdXc63aW69TiEZGa03CmSL2JGSz51QOexOzickoJMzLke7+LtzQ2Yu5w9sT0MpOJt+J5mPoC/B2X2OjkpH/ufz5QogGwd7e/vz9Pn36sHjxYtq1a0dUVBQffvghW7duZf78+cDle5b9/PxITEw8/3hSUhI+Pj4XvbaNjQ1jxoxhxYoV9ROWtdZDLvacUir93NJDSilvIOMybaUopQ4DUcC31Txfu+t1CtGAlZRXcDApl92nstl9Kps9CdlYWiiuaeNKd39XbuzuQycfZ9ncQzRvnu0h4k7Y9aGxYYlne3NXJETjcpke4PowYMAA5s2bx7x58+jevTvr16/H1tYWZ2dn4PI9y2PGjOGNN95g0qRJbN++HWdn578MwTjXe+3t7U15eTmrVq06P8yjpmo6DON7YCrwUtVxxZ9PUEr5AVla67NKKVegH/BqDa8rRKOTW1TGrlNn2Bmfza74MxxOySPYw56INq6M6ubDM2M64eNsI73GQvzZwCfgwDew5km4bam5qxFCmCgqKorExEQGDBiApaUl/v7+tG9/5b/4jhgxglWrVhESEoKdnR2LFy8+/1x4eDj79u2jsLCQMWPGUFJSQkVFBddddx333ntvrdSvarJVslLKDfgaCAASgAla6zNKqQjgXq31dKXU9RhbqGqMLVTfqOpBvqSIiAi9a9euq65NCHNLyy1me1wWO+PPsDMum+Scs3TzdyaiTSsiAl3pHuCKg6xO0WQppXZrrSPMXUd9qtP37d/fMMLylG8htOYfqwrRlB09epQOHTqYu4wGq7o/n0u9Z9foJ7XWOgsYXM3ju4DpVfd/AbrW5DpCNAaJZ4rYFpvF9rgz7Ig7Q35xGT0DW9ErqBUTrvGno48TVrIDnhBXp9cMYyjGz09A8ECwlNVehBD1Q7q1hLhKSdlFbIs9w9aYLLbFZlFSXkHvIDd6B7fi7qhgQj0dsLCQIRVC1IoW1jDs/+DLibDjPegzy9wVCSGaCQnLQlyhjPxitsZksTUmi99jsigsKScy2I0+bd2YObAtbT3sZbyxEHUpbBi0HQwbXoKut4C9u7krEkI0AxKWhbiIgpJydsRlsTk6iy0nT5Oae5bewW70bevGnf2CCGvtIOFYiPqkFNzworGU3LrnYfRr5q5IiAZLay0/o6pxNXP1JCwLUaWiUnMwOZdNJzLZFH2awym5dPVzoX+oOy/f3JXOPk60kDHHQpiXRztj/PK2tyFiGnjLlBgh/szGxoasrCzc3NwkMF9Aa01WVhY2NjYmvU7CsmjW0vOK+e1EJr+dyGTLydN4OrYkKtSDWYPa0iuoFXbW8l9EiAbn2sfgwNew6h8w7Sejx1kIcZ6fnx9JSUlkZmaau5QGx8bGBj8/P5NeI0lANCtlFZXsis/mtxOZbDieQWpuMf1D3bk21IOnR3bEy9m03zaFEGZg6wJDnoXv7zNCc7eJ5q5IiAbFysqKoKAgc5fRZEhYFk3e6YIS1h/LYP3xDDZHn6aNmz0D23nwwrjOdPNzkaEVQpjRyYx8QjwdTX9h+BTYvRh+eRraDQcbp9ovTgghkLAsmiCtNcfT81l7JJ1fj2VwMqOAfm3dua6DJ8+O6YSno/QeiytUUWbcrO3MXUmT9NuJTKZ+uIP374hgSMfWpr3YwgJGvALvDYbfXoZhL9RNkUKIZk/CsmgSyisq2RF/hl+OpPPLkXQAhnRozcPXt6NXUCusW0jvsahGZSXkp0LOKcg+dcExwbhfkG583N9ntrkrbZL6tnUjrLUDz/5wmH4h7thaW5rWgO810ON22P4OdL8NPGXHMiFE7ZOwLBqts6UVbIzO5OfDaaw/loGfqx3Xd2zN+1MjaNfaUWYAC0NxHmTHQXa8EYSz46tCcTzkJBrjX13agGsb4xgQaYyBdWkDzn6yU1wdsrK04F9jOzNp0Tbe2nCSh4e2M72Rwc/C0R9g5cPwt5Uy2U8IUeskLItGpaCknF+PpvPToTQ2R5+mi58zwzp58cjQdvi42Jq7PGEO53qHs+PgTNz/gvGZqmN5CbgGGmHYNchYeixsmPGYs78MsTCzyGA3xnX35d3fYhnfw48gd3vTGrB3g8HPwI8PymQ/IUSdkLAsGrz84jJ+PZrByoOpbIvJIiLQleGdvXlhXBda2VubuzxRHyrKITcBzsQaIfjCY84psHE2gnCrIOMYdkNVQA4Eew/pbWzgHh/RnrVH0pm34hCfTOtl+qdCPabC3k9hzZPGL0K2LnVTqBCiWZKwLBqkotJy1h7N4Mf9KWyNyaJnUCtGdvHmPxO64WwrH4s3SecCcVYsnIkxgnBWjHE/NxkcW0Or4KpQHAxt+lZ9HQjWJvZGigbF09GGvw8N458/HGHVwTRGdvU2rQELCxg5HxYNgvUvGBP/hBCilkhYFg1GaXklm6IzWbEvhfXHM+ge4Mqort68cnM3nO0kIDcJWhtDJrJOVt1i/nc/JxEcWoNbMLRqawTh4IHGfdc20KKluasXdej2yDZ8uzuJf/5wmAFh7jjamPh/3qc79JwOO96DbpPBt0fdFCqEaHYkLAuz0lqz+1Q2y/Yms/pQGsHu9ozt7sszozvi5iDhqNEqKTAC8OloyIquOlaFY2t7cAsBt7bGMSDSOLoGgZUs69dctbC04P/GdeHGt7Ywf80Jnh3TyfRGBj8NR783xi9PXweW8iNOCFFz8k4izCIhq4ile5JYtjcZK0vF+B5+rJjdD/9WMtmq0TjXS5x53AjDp09U3aLhbPb/wrB7qDGG2D3E+NrG2dyViwaqm78Lt0e24ZOt8dzUw48ufib+W7FxhhtehG+nwc73IfLeOqlTCNG8SFgW9aawpJyVB1P5dncSMRkFjO7mwxu3dqeLr7Ms89aQVVYYq0pkHofMY0YgzjwGp0+Cla0Rht3DjFu7G8At1FhlwkLWthame2RYO1YfSuPJ5QdZNqsflhYmvjd0Gg97P4d1z0PHMeDkUzeFCiGaDQnLok5prdmTkMNXOxP46VAavYLcmNYviOvae8pGIQ1NRbkxqS7z6P+CccYx4zEHT2PJNfcwaNMPrrnTCMl2rcxdtWhinGysmDeqI/d/uZePf49nWv8g0xpQypjs91YkrH4UJn5WN4UKIZoNCcuiTmQXlvLd3mSW7EigolJzS09/1j58rWw13RBUVkJOPGQchYwjRiDOOGqsOuHoBR4dwLM9hA6FvvcbAVlWmxD1aFRXb77bk8R/1hxnWGcvfE1dQ71VEFz7GPz6Tzi2EtqPrJtChRDNgoRlUWuMXuRsPtuWwNqj6Qxu78nzN3amV1ArGWZhLgUZkH7YCMXpR4xj5nGjR9izA3i0h5Ah0Pc+cG8nG3SIBkEpxb/Gdmbogo3MW36I96dGmP4e0vd+OPgtrHwEAqPAxqluihVCNHkSlkWNFZWWs3xvCp9sjae4rIIpvdswb1RHXGXDkPpTXmKE4PRDRjg+d6wog9adwLMj+F0DPe4weo1lkp1o4Pxb2fHw0DCeX3n06tZetrSCMa/D+0OM8csj/l03hQohmjwJy+KqJWQV8cnWeJbuSSIisBVPjuxAv7buWJg6IUeYpjAL0g5A2kEjFKcdNMYVuwZC687QuiNEzgavzuDoLbvXiUbrb30DWb4vmWe+P0y/EDdc7Ez8BdwvAnrNgB2LoMsE8O9ZN4UKIZo0CcvCJFprtsWe4cMtceyKP8MtEf58f19/WfKtLmgNOQmQut8Ix6lVAbm0ALy6GME4MAoiZxrjjGWNYnEZSql4IB+oAMq11hFKqVbAV0AgEA/corXOVsa4h4XACKAI+JvWek991tvC0oKXb+rKmDe28MLKo7wyoZvpjQx+Go79CN/fB/dslM1thBAmk7AsrkhZRSUrD6Ty/uZYikormNYviIWTwrGzln9CtaKy0ugdTt1nhONzNytb8OoK3l2h+21GSHYNlN5iURODtNanL/h6LvCr1volpdTcqq8fA4YDoVW33sDbVcd61cnHmRkDgnl7Qwxjw33pH+puWgMtHWHUAvjiFtj0Kgx6vG4KFUI0WTVKOhfrkbjIuU7AUWCZ1vq+mlxX1J/CknKW7Ezkg02xBLjZ8dCQMAa185ShFjWhtRGMU/Ya4TilKiDbuIBPN/AOh74PGAHZwdPc1YqmbywwsOr+x8AGjLA8FvhEa62BbUopF6WUt9Y6tb4LnDM4lJ8OpfH4sgP8/OAA039JDxsGXW6BTfONtZdbX8XugEKIZqum3YIX65GoznPAbzW8nqgn2YWlLN4Sx2fbE4gMbsXbt11DN38Xc5fVOOWnQfLuqtseIyRb24NPd+PW/0Hw7g72buauVDR9GlijlNLAu1rrRUDrcwFYa52qlDr3G5ovkHjBa5OqHqv3sGxjZclL47swcdE25q85wdOjOpreyA0vQcyvsOI+mL4WLCxrv1AhRJNU07B8sR6JP1BKXQO0Bn4CImp4TVGHMvKLeW9jLF/vSmJ4Zy+WzuxLkLussXvFSgqM3uKkXZC8ywjHZWfB9xrw7QG97zUCsmNrc1cqmqd+WuuUqkD8i1Lq2CXOre7jI/2Xk5SaAcwACAgIqJ0qq9E72I3bIgP4cEscI7p4cU0bEzfEsXeD4f+GpXfB1jeh3wN1U6gQosmpaVi+WI/EeUopC2A+cDswuIbXE3UkPa+Yd36L4bs9yYzr7svqOVH4mLoRQHOjNWSdhKSdkLjDCMhnYoyPeH0joOONcP1zMsZYNBha65SqY4ZSahnQC0g/N7xCKeUNZFSdngT4X/ByPyClmjYXAYsAIiIi/hKma9Pc4R1YfyyTf3x7gFUPRGFjZWLvcOeb4NB3sP4FaDfc2IVSCCEu47JhWSm1FvCq5qknr/Aas4BVWuvEyy0qX189FOJ/TheU8PaGGL7dncRNPfz45aEBeDrJqgrVKi2ClD2QsK0qHO80hlP49wK/nv+bgCez7UUDpJSyByy01vlV94cC/wK+B6YCL1UdV1S95HvgPqXUEoyJfbnmGK98IYeWLXjppi7c/sEOFqw9wePDO5jWgFIw6lV4szesmA13rpbhGEKIy7psWNZaD7nYc0qpi/VIXKgPEKWUmgU4ANZKqQKt9dxqrlVvPRTNXe7ZMt79LYYvdiRwY7ivhOTqFGQYwThhGyRuM7aE9uwIAZEQfiuMXghOJm6UIIT5tAaWVXVatAC+0Fr/pJTaCXytlLoLSAAmVJ2/CmPZuJMYS8fdWf8l/1VUqAeTevrz3sZYbujkRfcAV9MacPSC4S/Dsntg+7vQZ1bdFCqEaDKUMdH5Kl+s1CtA1gUT/FpprR+9xPl/AyKuZDWMiIgIvWvXrquuTVSvuKyCj36PZ9HGWIZ08GTOkDB8ZbhF1ZrGp+DU78YtYSsUZoJ/byMc+0caY46t5M9KXBml1G6tdbOao1Ff79t5xWUMW7ARO2tLVl7NcAyt4ctJEPsb3LsZ3EPqplAhRKNxqffsmo5ZfolqeiSUUhHAvVrr6TVsX9SSikrNsr3JzF9znG5+Lnx9Tx9CPB3MXZb5nFu+LX4znNpiHCvLoU1fCOhrTMTz7AgWFuauVNSjkooS0gvTSStMI60ojfTCdMI9w+npJTu/NSRONla8fFNX7vhwB6/+coInRlzNcIzX4K1IWD4Tpv0kwzGEEBdVo7Cstc6imkl7WutdwF+Cstb6I+CjmlxTmO73mNM8/+NRbKwseOPW7qbPIm8qsuMhbiPEbTLCMUBgfwjsBwMeBbe2MhGvCavUlWSdzSK1MJXUwlTSCtP+cswvzcfTzpPWdq3xsvfCy94LKwsrc5cuqjEgzINbewfw3qZYhnVqbfr7mpM3jHgFvrsbtr4B/ebUTaFCiEZPtl9rwhKyivi/VUc5lJLL48M7MKKLF5ebZNmkFGQY4Th2A8T9BmXFEDQAgqJg4FxoFSzhuAkprSglrTCNlMIUUgtSzx/PheP0wnQcrB3wsvfCx94HL3svvO296eHZ43wwdrd1x0LJpwmNxRMjOrDxRCaPfHOAlQ/0N32zki4T4MgKWPc8hA4FTxN7qIUQzYKE5SbobGkFb284yafbTjE9KpjXJoWbPqavMSotMtpnf8MAACAASURBVMYbx643AnJuIrTpD8HXQp/Z4NFewnEjVlxeTGphKikFKSQXJJNSkGLcCo1jTkkOnnae+Dj44G3vbQTh1j3O3/ey98KmhUxibUocWrbglZu7Mfm9bby8+hj/HNvZtAbOD8foDcvuNTYrsZRPEoQQfyRhuYlZcziNf/14hHB/F1bNicLbuQlPSNMaMo7AybVw8ldjhzyvLtD2OuMHoE93sJR/4o1FWWUZaQVpJBUkkVyQ/IdbSkEKeSV5Rq+wgw++Dr5423szwH8APvY++Dj44GHrgaWMO212+rR1Y1q/ID7cEseQjq2JCvUwrQEHD+P94uvbYeMrMOiJuilUCNFoSZJoIlJyzvLM94eJySzg5Zu60i/E3dwl1Y3iXIhZDyd/MQJyi5YQMgR63wOBUWDjZO4KxUVorckuySYxP5Gk/CTjVhWMk/KTOH32NB62Hvg6+uLrYNyifKPwdfDFx8EHTztPGSIhqvXoDe347UQG//jmAD8/OABnOxN7hzuOga6TYON/IHQY+F1TN4UKIRolCcuNXEWl5pOt8bz+azRT+wbyxq3dadmiCfWuaQ2nT8CJn+DEGmMr6YBIY3xh/78bk/JEg1FeWU5qYer5QJyYn3j+lpSfRAuLFvg7+uPn6Ievgy/dPLoxImgEfo5+MplOXDUbK0sWTAxn3Fu/88z3h3htUnfTGxnxb2Pi77IZcM8msLar/UKFEI2ShOVG7GRGAY8tPYCFgm9n9qWtRxNZCq6izPihdeIn41ZRDmHDoO/9xgQ9+SFmVmUVZSQVGEE4IS+BhHzjlpiXSFphGm62bvg7+p8PxZ3dO5+/72QtPf+ibnT1c+H+60J4bW00gzu0ZnQ3H9MasHGGG9+CT8bAL0/DyPl1U6gQotGRsNwIVVRq3t8Uyzu/xfDQ9WHc1rsNFhaNfOJacZ4xtOLYKmMMsltbaDccJn4OrTvJxLx6Vl5ZTkpBCqfyTpGQn0B8bjwJ+QmcyjtFRlEGre1a08apDf6O/gQ4BdDftz9+jn74OfhhbWlt7vJFM3XfoBA2HM/kyWUHuaaNKz6mbrgUfC1EzoZtbxrDMcKG1k2hQohGRcJyI3Mqq5BHvtmPhVJ8f19//Fs14l7WwtNwbCUc+xFObYWA3tB+JAx9XraRrgdaa7KKs4jLjeNU3inic+ONY148KQUpuNu6E+AUQBunNgQ6BRLlF0WAYwC+Dr5YyYoBogFqYWnBaxPDGfH6Jh7+ej+fT+9tekfC4HnGajorZsHMrcYEQCFEsyZhuZHQWvPNriRe+ukYswa2ZVq/oMbZm5yfDsd+MNY2TdlnrFzRdSLc9IFMzqsjZRVlJOQnEJcbd/4WnxdPXG4clhaWBDoFGjfnQMI9wwlwCiDAMUCWWRONUqC7Pc+M7shjSw/y/uZYZgwwcV6DlQ3c9B4sGgTf3w+Tv5RPtoRo5iQsNwK5Z8t4YtlBYjIKWDIjkrDWjuYuyTQFmXB0BRxeDqkHjI82e80wVrGwasJL29WzwrJCYnNiic2NJSY3hricOOLy4kgtSMXbwZsg5yCCnIPo6dWTCe0mEOgUiKuNq7nLFqLW3RLhz7pjGbzy83H6tnWns6+zaQ207gRDnoWfH4ddH0LPu+qiTCFEIyFhuYHbm5DN/V/uZXB7T+bP7td4NhcpzoWjP8KhbyFpN4QOgd73VgVk6bGsifzSfGJyYoxbbsz5+3mleQQ6BRLkHESwczBjQsYQ7BxMgGOADJsQzYpSipfGd+WGhRt5YMlefrz/Knb3632vMY/i5yehTT/wbF83xQohGjwJyw2U1prFW+J5c/1JXhzfhaGdvMxd0uWVlxo/XA58ZayFHBgF3W83JunJChYmKyorIjY3lujsaE7mnDx/yy/NJ9g5mLYubWnr0pZI70iCnIPwdfCVdYiFqOJqb82CW8KZ8sF2nvvxCC+O72paAxYWcOM78HZfWHoXTP9VftEXopmSsNwAFZSU8+i3+0k4U8SyWf0IcGvAQVNrSNkD+76Ew9+BezvoeouxI5ZdK3NX1yiUV5aTkJ/AiewTRGdHnw/HmUWZBDoHEuoSSluXtkzpMIW2Lm3xtveWUCzEFegb4s49A9ryzm8xDAj1YHgXEycOO7aGG9+GLybA2mdh+Et1UqcQomGTsNzAxJ8uZManu+ju78q39/ZtuMMu8tPhwBLY+zlUlEL4rXD3enBtY+7KGrTcklyOnznOiewTHM82jnG5cbjbuhPqEkqoaygjg0cS6hpKgGMALSzkv6gQNfH368P4PeY0jy09QBc/Z/xcTex8CBtqDMnY/ja0HWSs+S6EaFbkJ3ED8tuJTB7+eh9zhoRxW+8AVEObgV1RbqyBvOdjOLUFOoyG0QuNHfUaWq1mprUmqSCJ42eOc+zMMeOYfYz80nzCXMMIcw2jq0dXbg67mVCXUOysGvCnB0I0YtYtLPjv5O6MfH0zc5bs46sZkbSwNPGTmSH/hPgtsOxemLkFnEzc8EQI0ahJWG4gPtkaz3/XneStKdfQK6iBDV/ITYY9nxg3Jx+4ZiqMfw9aNpEdA2uoorKC+Lx4jmQd4eiZoxzNOsrxM8exs7Kjfav2tGvVjrEhY3nU9VF8HWVcsRD1rY2bPS+M68ycJft4bW00jwxrZ1oDVjYwYTG8ey0svRumfg8WDfRTPyFErZOwbGYVlZrnVx5h44lMlt7bt+GMT66shNj1sPMDSPgdukyA2741llRqxioqK4jLjeNw1mGOZB3hcNZhTmSfwMPWgw5uHejQqgN3d72b9q3a08qmgf3SI0QzNjbcl83Rp3lzw0kig93oH+puWgPuoTDyP7B8Jmx8BQbOrZtChRANjoRlMyouq+D+L/dSWFLOd7P64WzbAJb3Ks6DfV/AzveghS30mg7jFzXLXmStNYn5iRw6fYhDWYc4fPowx84cw8POg46tOtLJvRND2gyhfav2OFo3srWvhWiG/jm2E3sTc3jwq32smtMfT0cTV7foNtnY3e+3l43l5IKi6qROIUTDImHZTHKLypj+yU68nW1589YeWLcw80fzWTGwYxHsX2Lsqjf2TfDv3azGImcXZ3Pw9EEOZB44H5BtW9jS2a0zndw7MTN8Jh3dOuJkLTsNCtEY2Vm34M1bezD2zc08uGQfn97VG0tTdkJVCka+Csl7jOXk7t0MDp51V7AQokGQsGwG6XnF3PHBDvqGuPH0yI7m27Zaa0jcDr//FxK2Qo+pMPN3cPY1Tz31qKyyjBPZJziQeeD87UzxGTq5d6Kre1cmtpvIc+7P4WHnYe5ShRC1qJ2XI/8a05lHlx7gjXUnmTMk1LQGWjrAhI/g/cHw3d1w23cyflmIJk7Ccj1LzjnLlPe2MSHCn1kD25pnxYvKSjixGjYvgMLT0Ge2MdTC2r7+a6knuSW57M/cz76MfezL3Mfh04fxcfChq0dXenr1ZFrnaQQ7B2MpP/SEaPImRPixLTaLhb+eoGegK31DTBy/7NUZhv8bfngANs2Hax+tm0KFEA2ChOV6lJBVxK3vb+POfkHc1T+o/guoKIOD3xoh2coW+j9kLP/WxAKi1pqUwhT2pO9hT8Ye9mXsI7Uwlc7unQn3CGda52l0ce+Cc0tnc5cqhDADpRTP3diZA8m5PLBkLysfiKK1k4njl3vcAfGbYcOL4N8LggfWRalCiAZAwnI9OZVVyORF25g5KITbI+t5447yUtj/BWx6FVwCYPjLxht7ExmPrLUmLi+OXWm72J2+m93puymvLKdH6x5c0/oaJoRNIMw1TDb4EEKcZ9+yBW9P6cGYN7Zw/5d7+WJ6b9PWX1YKRi2A1P2wdDrcs1HWXxaiiZL0UA+Sc85y63vbmX1dCFN612NQriiDfZ/Dxv+AexiMexfa9Km/69cRrTVxuXHsTNvJzvSd7EzbiY2lDRFeEfTy6sWs8FkEODbATV2EEA1KaGtHXhzfhQe/2scra47z+PAOpjXQ0gEmfgqLBsG302DqD2DZAFY1EkLUqhqFZaVUK+ArIBCIB27RWmdXc14FcLDqywSt9ZiaXLcxSc8r5tb3tjGtf1D9BeXKCjjwFWx4CVoFw80fGh8TNmJJ+UlsT93O9rTt7EzbiZWFFT29ehLlG8VD1zyEr0PTn5QohKh9N3b3ZUf8Gd79LZYeAa4M6+RlWgMe7YydTL+bDmufhWEv1EmdQgjzqWnP8lzgV631S0qpuVVfP1bNeWe11uE1vFajk1NUym3vb+eWCP/6GaOsNRxbCeueA1tXGPcOtOlb99etA7kluWxL3ca21G1sTdlKcXkxvbx7Eekdyf3d78fPwU96joUQteKZ0R05nJzLI1/vJ+x+R4LcTZzs3HWCsbLQ1jfALwI6jaubQoUQZlHTsDwWGFh1/2NgA9WH5WanuKyC6R/vYmA7D2YPCqn7CybuhDVPQUk+XP8chF7fqMYkl1WWcSDzAL+n/M7vyb8TlxdHd8/u9PHuw+T2kwl1CZVwLISoEy1bWPLWbdcw6vVN3PvpbpbN7oudtYk/Hof9H6Tug+WzwbOj0eMshGgSahqWW2utUwG01qlKqYutzm6jlNoFlAMvaa2X1/C6DVpFpWbOkr34uNiaPgbOVNmnYO0zkLAdrnvS2GGqkaxukV6YzubkzWxO3sz21O34OfrR16cvf4/4O908umFtaW3uEoUQzYSviy0LJ3Vn6uIdzF16kIWTwk37Bb2FNUz4GBZdC0umwN3rwEY2MBKiKbhsWFZKrQWqG8T1pAnXCdBapyilgoF1SqmDWuuYaq41A5gBEBAQYELzDctzPx4hv7ic1+/sXncbjpQWGkvA7Xwfes+EsW+BtV3dXKuWVFRWcPD0QTYmbWRj0kZSC1Pp69OXgf4DeTLySdxtTVzrVIhmRinlB0wCogAf4CxwCFgJrNZaV17m9ZbALiBZaz1KKfURcC2QW3XK37TW+5SREhcCI4Ciqsf31MG31KAMCPPgkaHteOXn43T1c2Z6VLBpDTj7ws2L4ZOxsHwm3PIpWJh5d1YhRI1dNixrrYdc7DmlVLpSyruqV9kbyLhIGylVx1il1AagO/CXsKy1XgQsAoiIiNBX9B00MJ9vP8Wm6EyWze5HyxZ10MOrNRz9Hn56AgIije1Wnf1q/zq1pKisiN9TfmdD4gY2JW+ilU0rrvW7lid6P0FXj66ynJsQV0gptRjwBX4EXsZ4v7UBwoAbgCeVUnO11hsv0cwc4ChwYZfnP7TW3/7pvOFAaNWtN/B21bHJmzWwLQeScnhx9TE6+jjRt62Jv8QHRcH1/4I1T8LmV2HAI3VTqBCi3tQ0qXwPTAVeqjqu+PMJSilXoEhrXaKUcgf6Af+u4XUbpO2xWSz45QTf3NsXJ5s6WD7oTCysfATyUmD8uxDYv/avUQuyzmbxW9JvrEtYx670XXRx78JA/4HMDJ8pq1YIcfXma60PVfP4IeA7pZQ1cNGP5Kp6pUcCLwB/v8y1xgKfaK01sE0p5XKuY+Qqa280lFL8Z0I3bnxzC/d/sZfv7++Pr4utaY30mQ0pe2Hd8+DdzZhDIoRotGr6+dBLwPVKqWjg+qqvUUpFKKXerzqnA7BLKbUfWI8xZvlIDa/b4CSeKeK+L/eyYGK46TOpL6eizNhQ5L3BEHwt3LupwQXltMI0Pj/6OXf+dCejl41mS/IWRgSNYM3Na3hv6HtM6TBFgrIQNXCRoHzh86Va65OXOOU14FHgz0M1XlBKHVBKLVBKtax6zBdIvOCcpKrHmgVHGysW3RFBSXkl93y6i+KyCtMaUArG/Bdad4ald0HWXz5IFUI0IjXqWdZaZwGDq3l8FzC96v7vQJeaXKehKymvYNbne7hnQDBRoR6123jKPlhxHzh4woz14BpYu+3XQHphOmtOrWFN/Bpic2MZ6D+QqZ2m0senDy0tW16+ASHEFVNKHQQuOjxNa931Eq8dBWRorXcrpQZe8NTjQBpgjTEE7jHgX0B1ky2qvXZTmWvyZ209HHhtYjh3f7qLuUsPsGCiiRP+rO1g0mewaCAsuRWmr4WWjnVWrxCi7siA0Vrw4qpj+LjY1O5ayuWlsPEV2L0Yhj4PXSc2iKXgzhSfYU38GlbHreZkzkkG+Q9iRtcZRHpHYiU7VwlRl0ZVHWdXHT+tOk7BmIR3Kf2AMUqpERjjnJ2UUp9prW+rer6kakz0uQG2SYD/Ba/3A1Kqa7gpzDW5mCEdW/P3IWHM/+UEnXycuXuAiRP+XANhwkfw6Xj47h6Y+JlM+BOiEZKwXEOrDqby67F0frw/qvbWAU4/At/NMCbu3bsZHE3cUaqWFZUV8WvCr6yKW8X+jP309+vPnZ3vpK9PX1neTYh6orU+BaCU6qe17nfBU3OVUlsweoQv9trHMXqRqepZfkRrfdsFE7QVcCPG+Gcw5qPcp5RagjGxL7c5jFeuzn3XhXAkNY8XVx8lzMuRa8NM/PQweKCxBvNPj8GGF40lPoUQjYqE5RpIyCri6eWHWHxnT5xta6FXtbISdrxr9CgP+Sd0v81svcmVupLtqdv5IeYHNiRuINwznFHBo5h/7XzsrBr2EnVCNHH2Sqn+WuvNAEqpvsDVTpT4XCnlgTHsYh9wb9XjqzCWjTuJ0Wt9Z81KbrzOTfiLO13IfV/sYfnsfrT1cDCtkd73QNpB2PhvaN1RdvgTopFRxmTnhiciIkLv2rXL3GVcVEWlZvKibVzfsbXpH81Vp/A0LLsHzubA+EXg1rbmbV6FxPxElp9czvcx3+PS0oUxbccwPGi4rIEshImUUru11hF10O41wIeAM8Y44lxgWkNYB7mhv2/XRFJ2EWPf2IKTrRXLZ/XD2c7EDpLyEvholBGap/0EPuF1U6gQ4qpc6j1bBk9dpcVb4gCYVhvjlOO3wLsDwKuL8SZaz0G5pKKElbErmf7zdKasnEJRWRH/ve6/fDP6G27veLsEZSEaAKVUH6WU0lrv1lp3A7oC4Vrr8IYQlJs6P1c73rn9GpKyi7jvyz2UV1xy/5e/atESJn0O9u7GhL/8tLopVAhR62QYxlU4mZHPWxtiWD6rH5Y12aFPa9jyGmx9C258G0Ivuv9LnTiVd4pvjn/DD7E/EOYaxs3tbuY6/+tkHLIQDdNU4E2l1AngJ+AnrbUkrnrUM7AVL9zYhUeXHuBfPx7hX2M7m9aAgydM/hI+GGoE5r+tBCsT13AWQtQ7CcsmKq+o5O9f7+fhoWEEuNVg7G5JAayYBblJMGODsU1qPSivLGdj0kaWHFvC8ezjjA0Zy6fDPyXAqeks+SREU6S1vhdAKdUeY4e9j5RSzhjr1/8EbNFam7ggsDDVLT39ic7I571NcYR4OnBHn0DTGvDqAuPfg69uM7bEvulDWSFDiAZOwrKJPt56CoeWLbi1Vw3C5ZlY+PJW8LsG/rYKrGxqr8CLyC3JZVn0Mr489iXutu5Maj+JNwLfkF5kIRoZrfUx4BiwQCllCwwCJgCvArU+Rlr81dzhHYg7Xcg/fzhCGzd701fI6DAKhjwLa58B9zAY9ERdlCmEqCUSlk2QllvMG+ui+XZm36tfJu7U7/D1VLj2Ueg5vc5Xu0jMS+STI5+wMm4lA/wGMH/gfDq7m/jRoRCiQVBKWQBorSurtrfuAGzTWq8yb2XNi6WFYuGk7tz09u/c9/kels7qS1hrEzcc6TcHsqLht5fBLQS63lI3xQohakw++zHB8yuPMKV3G9OXDTpn/1fw1e0w7m3odXedBuWDmQd5aP1DTFk1BQdrB5aPXc5LUS9JUBaikVJK3QikAslKqbHAJuA/wAGl1GizFtcM2bdswYd/64mttSV3Lt5JZn6JaQ0oBSMXQGAUrJhtdKQIIRokCctXaFN0JvsSc5g9KMT0F2sNm16Fdc/D336EkLqZyKe1ZmvKVu76+S4e/u1hIrwi+Ommn5jTYw6edp51ck0hRL15BugG9MXYve8OrfV1GLvzPWPOwporHxdbPpjakzOFpUz/ZBdnS00cMt7CGm75BFwCjAl/WTF1U6gQokYkLF+BsopKnllxmGdHd8LW2tK0F1dWws9PwsFv4K414Nmh1uvTWrMxaSO3rbqNF3e8yJi2Y1g5fiVTOkyRDUSEaEK01mla6zggQWt9vOqxU8h7udl08XNm4aRwDiTl8OBXe6moNHHvArtWMOUbUBbw+c1QdKZuChVCXDV5g70CX+1MxMfFliEdW5v2wopyY7Zz8m64cxU4eddqXVprNiVtYvLKyby25zVu73Q7y8YsY2zIWKwsamFHQSFEg3JuzDIw7YLHLAGZqWtGQzt58fTIjvx8OJ0XVh41vYFWwTDpS8hNhi8nQdnZ2i9SCHHVZILfZRSWlPP6r9F8MLWnaS+sKIPv7obiXLh9GVjXbg/vzrSdvL7ndfJK85gVPovr21yPhZLffYRowmZghOJirfWOCx73B14yT0ninGn9g0jMLuLDLXH4udqavmFVQG8Y/y58c6exm+vNH8mSckI0EBKWL+ODzXFEBrvRxc/5yl9UUQbfTjN6ByZ9WatLwx0/c5zX9rxGXG4cs8NnMyJoBJYWJg4NEUI0OlrrnRd5PB6Ir9diRLWeGtmRlJyzPLfyCD4uNtzQ2cRPEzuNM3qX1zwJa56CG/6vbgoVQphEfm29hKyCEhZvieORoe2u/EUV5UZQrig1tjatpaCcUZTBU5uf4p5f7qG/b39+uPEHRrcdLUFZiGZGKTVKKbVXKXVGKZWnlMpXSuWZuy7xvyXlwv1deGDJPnbGX8X44z6zodc9sO1NY3dXIYTZSVi+hDfWn2RsuO+V79RXWQk/PAClBcYM5xYta1xDcXkx7+x/h/Hfj8fN1o0fx/3IlA5TsLKUMclCNFOvYWx97aa1dtJaO2qtncxdlDDYWFnywdSe+LnYMv3jXZzMyDetAaXghhehwxj4+XE4tLRuChVCXDEJyxdxuqCE7/YkM2tg2yt7gdbGR2dZJ2HiZzUOylpr1ies58YVN3Ii+wRLRi7hoWsewsH6Ktd4FkI0FYnAIa21icsuiPrSyt6aj6f1wsrSgqkf7iQ9r9i0BiwsjS2xA/rCsnshbmPdFCqEuCISli/i49/jGdnVG0+nKxxGsWk+xG2CW78Ga/saXTspP4nZv85mwZ4FzOszj1cHvoqfo1+N2hRCNBmPAquUUo8rpf5+7mbuosQf+bey46M7e5JTVMrUD3eQe7bMtAasbGDyF8ZKGUumQNrBuilUCHFZEparkV9cxmfbTnHPgOAre8GhpbD7I7jtW7B1uerrlleW89Ghj5i8cjI9Wvdg6eil9PXpe9XtCSGapBeAIsAGcLzgJhqYzr7OLLojgpjMAu7+eBfFZSZuWmLrCrcthZaO8NlNcCaubgoVQlySrIZRjS93JNA/1IM2blfQQ5ywHVY9CnesAEevq77m8TPHeXrL07i0dOGLEV/g7+R/1W0JIZq0VlrroeYuQlyZfiHuLJgYzv1f7uWBL/fy1pQetLA0oZ/K2Q9u+w4W3wCfjYdpa8DBo+4KFkL8hfQs/0lJeQUfbI7j3muvoFc5+xR8fTvc+DZ4db6q65VVlvHO/neY8csMJrefzLvXvytBWQhxKWuVUhKWG5FRXX14dnQn1hxJ54llBzF5uLlne2OIX16qEZiLZfETIeqThOU/Wb43mXZeTnTyucy6ymXFRlDuNwfCru7nVnxuPLevup19mfv4atRXjAsdh1LqqtoSQjQbs4GflFJnZem4xmNq30DmDA7l611JvLj6mOmB2b8XTPwUMo7Al5Nllz8h6pGE5T/5fHsCd/YLvPyJq/9hTLyInGXyNbTWLD2xlDtW38GNITfy9uC38bK/+iEcQojmo2qpOAutta0sHde4PDgklKl92rBoYyxv/xZjegOh18O4d+HUFmOnvwoTJw0KIa5KjcYsK6VaAV8BgRg7SN2itc6u5rwA4H2MbVk1MKJq16kG5VByLlkFpQwIvcx4sD2fQsI2uHudsSamCQpKC3h267PE58az+IbFtHW5wqXphBDNmlIq8FLvm8r4WMpXa51Uf1UJUyileGZ0J3LOlvHvn47jaGPF7ZFtTGuky81QnAMrH4YVs+HGd2RbbCHqWE3/h80FftVahwK/Vn1dnU+AV7TWHYBeQEYNr1snvtiRwKSe/lhaXCIAZxyFtc/ALZ8aM5RNcPzMcSavnIyTtROfj/xcgrIQwhSvKKWWKqXuUEp1Ukp5KqUClFLXKaWeA7YAHcxdpLg0CwvFfyZ0Y3B7T+atOMTyvcmmN9JzOlz3NBz4ClY9YqzzL4SoMzVdDWMsMLDq/sfABuCxC09QSnUEWmitfwHQWhfU8Jp1orCknB/3p7DmoWsvflJ5KXx3Nwx51phwYYKVsSt5ecfL/KPnPxjddnSNahVCND9a6wlV76dTgGmAN8YSckeBVcALWmsTd78Q5mBlacGbU3pw5+KdPPzNfuysLRnaycSheFEPQ0kebFkINk7GzyUhRJ2oaVhurbVOBdBapyqlPKs5JwzIUUp9BwQBa4G5WmsTF5ysWz/sT6F3sBtezpfYhGTDi+DsD91vv+J2KyorWLh3IWvi1/De0Pdo16pdLVQrhGiOtNZHgCfNXYeoORsrS96bGsGU97dz3xd7eX9qBAPCTFgSTv0/e/cdHkW5PXD8e9ILJLTQEpqACAJSAgQQkCpWiogVFSk2bHj1Wu5Pr/fqtXexINgRVERBBBUBUaRI6E2lCwQBgVDTOb8/ZtCIKbskm0025/M88+zUd87sxvUwe+Z9BXo9DBmHYf5zzmBYXe/2XcDGlGOFlmGIyDcisiaPqZ+H5wgBugD/ANoBpwHX5XOukSKSLCLJe/fu9bD54jHxx1+5sn3d/HfYthBWTICLXvS4Tvlo1lFunXMra39fy8QLJlqibIwx5g8VwkN4Z2g7TouLZuR7ySzevM+7BkTg/Geg5eUw5xFYOMY3gRpTzhWaLKtqL1Vtnsc0FdgtRaRkNwAAIABJREFUIrUA3Ne8apF3AMtVdbOqZgOfAW3yOddYVU1U1cS4uJLrdH1dyiH2Hs7I/1/1WWnw2U1w4fMedwa/59gervvyOqpHVee13q9ROaJyMUZsjDEmEFSKCuP94R2IrxTJ9W8vYfmvf3tGvmBBQdBvDDTrB1/dD0vG+yZQY8qxoj7gNw241p2/Fpiaxz5LgMoiciLL7AGsK+J5i9X0VSlc3Co+/wf7vnsaareCM873qL1NqZsYMmMIfer14aGODxEaFFqM0RpjjAkk1SqEM2F4ElUrhHPNmz+yZudB7xoIDoGB46DxufDFaFj+vm8CNaacKmqy/DjQW0Q2AL3dZUQkUUTGAbi1yf8AZovIakCAN4p43mKjqsxYvYvzW+TzcMXeX2DpW3DuYx61t+b3NQz7ahijWo9iRMsRNsiIMaZYieNqEXnQXa4rIu39HZcpmpqxEUwcmURMRChXj1/MuhQvx5kJCYPB78Jp3WHqKFj5oW8CNaYcKlKyrKr7VLWnqjZ2X/e765NVdXiu/WapaktVbaGq16lqZlEDLy4//XaYrBylRXweI/apOv9K73oPxNQqtK1lu5dxy+xbeKjjQ9bjhTHGV14BOgJXuMuHAStWDQDxlSKZOCKJyNBgrh6/mJ9/O+xdA6ERcPkHUP9s+OxGWPOJbwI1ppwp9z2Zz3TvKud5B3jVR5B+0OnTshCLdi3izm/v5LEuj9G9bncfRGqMMQB0UNVbgHQAdyCoMP+GZIpL3apRfDAiidBg4co3FvHLbi8T5rAouPJDqNsRPhkBaz/1TaDGlCPlPlmeseY3zmuRx13jrDSY/TBc8IxTD1aApbuXcs+8e3im2zN0qt3JR5EaYwwAWSISjDMaKu7zIMc9OVBEgkVkuYhMd5cbiMhiEdkgIh+KSJi7Ptxd3uhur++bSzF5aVAtmg9GJBEc5CTMG7xOmKOdhDmhHUweBms/802gxpQT5TpZ/mX3YY5lZNO6TqW/b1z8OsS3gToFlwKu3rua0d+O5omuT5BYM9FHkRpjzB9eBD4FqovIo8B84H8eHns7ziAmJzwBPOeOwnoAGOauHwYcUNVGwHPufqYENYyrwAcjkgDhijcWe58wh1eEqye7CfP1ljAbUwTlOlmesXoXfZvX+nsJRtoBWPCiM5xoATYc2MCtc27lP53+Q8faHX0YqTHGOFR1AnAP8BiwC+ivqh8XdpyIJAAXAOPcZcHpnWiyu8s7QH93vp+7jLu9p9jTyiWuUfUKTBrZARG44lRKMv5ImBOdhHnNFN8EakyAK9fJ8szVv+XdC8YPL0CT8yEu/0FE9hzbwy2zb+HudnfTrU4BQ2QbY0wxEpEqOH3aTwQ+wOnv3pP+KZ/HSbJPlGxUBVLd/u/B6RM/3p2PB7YDuNsPuvubEtaoekUmjUwiSIQrxi7ip9+87CUjvCJc/YnzK+knw2H15MKPMcb8RblNlrfvP8a+oxm0qXvSYCGHdsHSt+Gc+/I99ljWMUbNHsXgJoO54LQLfBuoMcb81TJgL/ALsMGd3yIiy0SkbV4HiMiFwB5VXZp7dR67qgfbTm7bbyOvlhcN4yowaWQSIcFOwrw2xct+mMMrwlWToW4STBlh3coZ46Vymywv3LSPjg2rEXTyQCQ/PA+troLY+DyPyzmewz/m/YNmVZsxrPmwPPcxxhgf+hI4X1WrqWpV4DzgI+BmnG7l8tIZuFhEtgKTcMovngcqiciJJ5gTgBR3fgdQB8DdHgvsz6thf428Wt6cFleBD0d2JDI0mCvfWMyqHaneNRBeAa76GOp1hk9vgGXv+SZQYwJQuU2WF2z6nU4NT/pV8dh+WDkJOt6S73Evr3iZzJxMHkh6wAYcMcb4Q6KqfnViQVW/Brqq6iIgPK8DVPU+VU1Q1frA5cAcVb0KmAsMcnfLPQpr7tFZB7n753ln2ZSc+tWi+fCGjsREhnDVG4tZus3LobHDop2EuWEPmDYKlozzTaDGBJhymSyrKgs27ft7spz8plOrHFM7z+Pm/DqH6Zun82S3J20Ia2OMv+wXkX+KSD13ugc44HYn51EXcrn8ExgtIhtxapLHu+vHA1Xd9aOBe4sreFM0dapE8eHIjlSrGM6Q8YtZuGmfdw2ERjoDl5x+HnxxFyx42TeBGhNAymWyvGnvUUKDg6hbJerPldkZ8ONY6DQqz2O2HdrGwwsf5pluz1AlokoJRWqMMX9zJU7JxGfuVMddFwwMLuxgVf1WVS905zerantVbaSql6pqhrs+3V1u5G7f7LOrMV6rXSmSD0cmkVA5kuve+pG5P+/xroHQCGdo7Gb94OsHYN6Tzoi1xpg8lctkeaFbgvGXMopVH0GN5lDjzL/tn5mTyehvR3PzWTfTMq5lCUZqjDF/cu8e/1NVb1XV1u50q6ruVdVMVd3o7xhNyageE8GkkR1pXKMCI99NZubqXd41EBIGl7wJZ10Jcx+FWQ9awmxMPsplsvzDxn10apSrBOP4cVjwEnS+Lc/9X17xMnUr1mVwk0Jv2hhjjM+oag6QZ48XpvypEh3GByOSaJlQiVs+WMbHydu9ayA4BPqNgXbDnbEFpt8Jx3N8E6wxZVjB4zgHoOPHlUVb9vHvi3PdQd48F4LDoMHf+0teunsp0zdNZ/LFk+2BPmNMabBcRKYBHwNHT6xUVRtxohyKiQjlvWHtueG9pdw9eRWH07O5/uwGnjcQFATnP+10Lzf/Ocg4DANeg2B7LseYE8pdsrxu1yGqRIdRMzbiz5UrJkDba+GkZPho1lEemP8AD3Z80OqUjTGlRRVgH073bycoYMlyORUVFsK4axO5beJy/jN9HQfTsrijV2PPb/CIQK9/Q0QsfPNvJ2G+9G0Iiyr4OGPKiXKXLC/ctI/ODav9uSItFTbMcv5lfZJnk5+lfc32nFPnnJIL0BhjCqCqQ/0dgyl9wkOCGXNlG+6bspoXZm8g9VgmD1105t/HEijI2Xc6CfP00fD+QLhiEkRW8l3QxpQR5S5ZXrDpdwYn1vlzxdop0LA7RP31zvHqvauZu30un/X/rIQjNMaY/IlIBDAMOBP44ycyVb3eb0GZUiEkOIgnB7WkUlQob3y/hdS0LJ4adBZhIV48npR4PURUgikj4e0LnKGyK9b0XdDGlAHl6gE/VWX59lTa1s81xPWKic6IfbnkHM/h0cWPckfbO4gJiynhKI0xpkDvATWBc4F5ON3IHfZrRKbUEBHuP78p9/RtwtQVKQx/N5mjGdneNdJ8IFz5IezfDG+eC/s2+SZYY8qIcpUs7zqYTmhwENUrujdjft8AqdugYc+/7Ddl4xTCg8O56LSL/BClMcYUqJGq/h9wVFXfAS4AWvg5JlOKiAg3n9OIJy5pwfwNe7ly3GL2H830rpFGPeHa6ZB+yEmYU1b4JlhjyoBylSyvSzlEs1q57hSv+ABaDna6z3Glpqfy8vKXub/D/db7hTGmNMpyX1NFpDkQC9T3XzimtLqsXV1eH5LIT7sOMejVBWzff8y7BhLawrCvISTSKcnYNNc3gRpTypWvZHnXIZqeSJaP58DKSU6H7Lm8tuo1+tTrQ5MqTfwQoTHGFGqsiFQG/g+YBqwDnvRvSKa06t2sBhOGd2Df0UwGvrqAtSkHvWugWmMnYa5UDyZc6gzgZUw5U76S5ZRDNKvtJss7lzlP/dZo9sf2347+xuebPueGs27wU4TGGFMwVR2nqgdUdZ6qnqaq1VX1NX/HZUqvxPpVmHxjR0KDhMteX8T8Db9710BMLbh+JtRNgikjYP7zNtqfKVfKV7K8K1cZxoav4PQ+f9n+xqo3uKTxJVSLrJbH0cYY438iEi4iV4rI/SLy4InJ33GZ0q1xjYpMubkzCZUjue6tH/lk6Q7vGoiIdXrGOHMgfPMQzLjbRvsz5Ua5SZYPpWex93AGDapFOyt++QpO7/vH9p1HdvLVtq8Y2ty6MDXGlGpTgX5ANs4IficmYwpUMzaCj27sSIfTqnDXxyt5afYG1Js7xCHhcMl46DgKlrwBHw6BTC/roI0pg8pNP8s/7TpMk5oVCQ4SOJQCB7dDQvs/to9dNZbBpw+mckTlAloxxhi/S1DVvoXvZszfxUSE8tZ17bn3k1U8M+sXdhxI45EBzQkN9vDeWVAQnPsoVKoLM/8J71zoDF5SobpvAzfGj4p0Z1lEqojILBHZ4L7+LdMUke4isiLXlC4i/Yty3lOxLuXgn/XKG76Ghj3+6AVj++HtzPl1DteeeW1Jh2WMMd5aICLWVZw5ZWEhQTwz+Cxu69GID5O3M/StJRxKzyr8wNw63ACXvQ+718G4nrD3Z98Ea0wpUNQyjHuB2araGJjtLv+Fqs5V1Vaq2groARwDvi7ieb32l3rlX76Gxuf+sW3ST5MY0GgAseGxJR2WMcZ4RERWi8gq4GxgmYj8LCKrcq03xmMiwug+TXhqUEsWbd53al3LNb0Qhn4BWekwvjds+c43wRrjZ0VNlvsB77jz7wCF3TEeBMxU1RIvclq3y+0JIysdtn4PjXoBkJadxtRNUxncZHBJh2SMMd64ELgIOA9oBPRxl0+sN8ZrlybW4Z3r27PrYDoDXvmB5b8e8K6B+LYw/BuoWAveGwDL3vNNoMb4UVGT5RqqugvAfS2saOlyYGIRz+m1rJzjbNxzhDNqVoRt8yHuDIiuCsCMzTNoHdeahIoJJR2WMcZ4TFW3qeo2oBawP9fyfpzhr405JZ0bVePTmzsRGRbM5WMXMX1VincNVK7n9MVcvwtMGwWzHoTjx30TrDF+UGiyLCLfiMiaPKZ+3pxIRGrhDMn6VQH7jBSRZBFJ3rt3rzfNF2jT3iPUrhRJVFiIU4JxulOCoapM/Gkil59xebGdyxhjfOxV4Eiu5aPuOmNOWaPqFfns5s40j49l1AfLedHbnjIiYuGqjyHxevjhBfhoCGQcKfw4Y8qAQpNlVe2lqs3zmKYCu90k+EQyvKeApgYDn6pqvk8RqOpYVU1U1cS4uDhvryVffxnmetNsaNwbgBV7V5CWnUbH2h2L7VzGGONjormyGFU9Tjnq2cj4TtUK4UwY3oEBreN5dtYv3D5pBelZXvSlHBwKFzwL5z4GP8+At/rCQS/7czamFCpqGcY04EQXEtfi9P+ZnyvwQwkGOMly01oxcGw/HP4NajQH+OOucpCUm+6mjTFl32YRuU1EQt3pdmCzv4MygSEiNJhnB5/F3ec2YdrKFC4bu4g9h9I9b0AEOt4MV34E+7fC2O6wfYnP4jWmJBQ1S3wc6C0iG4De7jIikigi407sJCL1gTrAvCKe75Rs3XeUhnHRkLIMarWCoGBS01OZv2M+/Rp5VU1ijDH+diPQCdgJ7AA6ACP9GpEJKCLCLd0b8drVbdmw+zAXvTyfVTtSvWukcW/nwb+wKHj7Aljhl3tlxhSLIiXLqrpPVXuqamP3db+7PllVh+fab6uqxrs/F5a4lNR04itFwY6lkNAWgDnb55BUO4mYsBh/hGSMMadEVfeo6uWqWl1Va6jqlapaUAmcMaekb/OafHJTJ0KCgrj0tYVMXbHTuwaqnwEj5kKd9vDZjfD1v2yIbFMmlYv6g5SDadSqFAE7lzrd3ABfb/uaPvX6+DkyY4zxjog8KSIxbgnGbBH5XUSu9ndcJjA1rRXDtFGdOatOJW6ftILHZq4n57gXD/5FVYEhn0L7kbDgJZhwKaR52T2dMX4W8Mnyscxs0jJzqBoVCjuTIT6RgxkHWblnJV0Tuvo7PGOM8VYfVT2E07/yDuB04G7/hmQCWdUK4bw/rANXJ9Xl9Xmbuf7tJRw85sWIf8GhcP5TcNELzsAlY7vDnvW+C9iYYhbwyXJKajq1K0UiB3+F4DCIjWfu9rl0qNWBqNAof4dnjDHeCnVfzwcmnih/M8aXwkKCeKR/C/43oAULNv1OvzHz+fm3w9410vY6uG46ZB6Fcb1g3TSfxGpMcSsHyXIatWIjYEfyHyUYs7bNone93n6OzBhjTsnnIvITkAjMFpE4wIvuCow5dVd2qMukkUkczcxhwCs/MGP1Lu8aqJsEI7+FuCZOX8zfPGx1zKbUC/hkedfBNGpXioSdyyC+LYcyD7F091K6JXTzd2jGGOM1Vb0X6Agkuv3WHwWsWx9TYtrWq8L0W8+mSc2K3DxhGY/NXE92jhfP78fGw9CZ0OYamP+sU8d8zH4gMaVXwCfLO90yDHYmQ0Ii87bPo13NdlQIq+Dv0IwxxmMi0sN9HQh0B/q5831xupIzpsTUiIlg0sikP+qYr3nzR/YdyfC8gZBwuPgluPB5t465G+xa6buAjSmCgE+WU1LTiK8YDL+thlqtrBcMY0xZdeLnsIvymC70V1Cm/AoPCeaR/i14alBLkrcd4MKX5rPsVy97ukgcCtd/6ZRijO8Dyyf4JlhjiiDgk+VdB9NozK9QqR7ZYVEs+W0JXeK7+DssY4zxiqo+5L4OzWO6vrDjRSRCRH4UkZUislZEHnbXvy0iW0RkhTu1cteLiLwoIhtFZJWItPHtFZqy6tLEOky5qRMhwcJlry/k3YVbyTUie+ESEuGG75z+mKfeDNNugywrwzelR4i/A/C1lNR0Eo6tg4S2/LT/J2pF16JSRCV/h2WMMV4RkdEFbVfVZwtpIgPooapHRCQUmC8iM91td6vq5JP2Pw9o7E4dgFfdV2P+pnl8LNNHdeHOj1bw4NS1JG89wGMDWxAd7mGaEV0NhnwGcx+F75+BlOUw+F2o0sC3gRvjgYC+s6yqpKSmUeXgWqjdhqW7l9K2Rlt/h2WMMaeiojslAjcB8e50I9CssIPVccRdDHWngm7/9QPedY9bBFQSkVpFiN8EuNioUMZdk8g/+pzO9FUpXPzyfH7Z7UX3ckHB0PNBuGISpG6D17vBT1/4LmBjPBTQyfL+o5lEhgUTkroVqjVm2e5ltKluvyQaY8oeVX1YVR8GqgFtVPUuVb0LaAskeNKGiASLyApgDzBLVRe7mx51Sy2eE5Fwd108sD3X4TvcdcbkKyhIGNWjMe8P68DBtCz6vfwDnyzd4V0jTc5zyjKqNIBJV8JXD0COF4OgGFPMAjpZTklNp3ZsJBzYxvHYOizbs4w2NSxZNsaUaXWBzFzLmUB9Tw5U1RxVbYWTXLcXkebAfcAZQDugCvBPd3fJq4mTV4jISBFJFpHkvXv3enwRJrB1alSNGbd1oWVCLHd9vJJ7Jq8kLdOL/pQr14dhX0O7EbDwZXjrPEj91WfxGlOQwE6WD6ZRNzYIju5hi2YSHRpNzeia/g7LGGOK4j3gRxH5t4g8BCwG3vGmAVVNBb4F+qrqLrfUIgN4C2jv7rYDqJPrsAQgJY+2xqpqoqomxsXFeX81JmBVj4lgwvAO3NqjER8v3UG/MfPZ4E1ZRkg4XPA0DHoL9vwEr3WBn2cWfpwxxSywk+XUNJpGHoSYeJb+vtLqlY0xZZ6qPgoMBQ4AqcBQVX2ssONEJE5EKrnzkUAv4KcTdcgiIkB/YI17yDTgGrdXjCTgoKp6OVybKe9CgoO4q08T3hnann1HMrno5fl8tGS7d71lNB8IN8yDyvVg4uXw5X2Q7UWfzsYUUcAny41Cf4fK9Z0SDKtXNsYEAFVdpqovuNNyDw+rBcwVkVXAEpya5enABBFZDazGqYd+xN1/BrAZ2Ai8AdxcrBdhypWup8cx8/YutKlbmXs+WcUdH67gcLoXdchVG8KwWdDhRlj0itMn875NvgvYmFwCO1k+mE6C7IXK9ZyH+6xe2RhTTqnqKlVtraotVbW5qv7HXd9DVVu4664+0WOGW5pxi6o2dLcn+/cKTFlXPSaC94Z1YHTv0/l8ZQoXvjSfldtTPW8gJBzOewIu/wAObIXXu8LKD30WrzEnBHaynJpGjexdpFSoRkZOBvVj6vs7JGOMMabcCg4SbuvZmA9v6Eh2jnLJqwt4fd4mjh/3oizjjAvgxvlQsyV8OhKmjIT0Q74L2pR7AZ8sx2aksDQ4hzbV2+CU5BljjDHGn9rVr8KM27rQu1kNHpv5E0PeXMzuQ16M2lepDlz7OZxzH6z+GF7vAjvsxw/jGwGbLGflHGf/0UwiDv/Kisz9VoJhjDHGlCKxUaG8clUbnrikBcu2pdL3+e/4au1vnjcQHALn3AvXzYDjx5065nlPwXEvuqgzxgMBmyz/djCduOgwJHUbmzL207hyY3+HZIwxxphcRITL2tVl+m1nE185khveW8q9n6ziaEa2543U6wg3fg9n9oe5j8DbF8CBbb4L2pQ7AZss7zqYzumxOYCw7ehOq1c2xhhjSqmGcRWYclNnbuzWkA+Tt3PBi9+z/NcDnjcQWQkuGQ8DXofda+HVzrByEnjTRZ0x+QjYZDklNY1mUfs5UrkuRzKPUD2qur9DMsYYY0w+wkKCuPe8M5g4IonM7OMMem0hz836hayc4541IAJnXe4+/NcCPr0BPr4Wju33beAm4AVssrzvaCYNgvbya2x16sbUJUgC9lKNMcaYgJF0WlVm3tGVfmfV5oXZGxj06gI27jnieQOV68F106HnQ/DTDHilI2yY5buATcAL2AwyPSuHuOzf2BYVQ72Yev4OxxhjjDEeio0M5dnLWjHmyjZs23+MC178nrd+2OJ5F3NBwdBlNIyYA1FVYMIg+PwOyPAi6TbGVaRkWUSqiMgsEdngvlbOZ78nRWStiKwXkRelBPpwS8vMoVpWCltDQ61e2RhjjCmDLmhZi6/v6EqnhlV5+PN1XD1+MTsOHPO8gVotYcRc6HQbLH0bXusM2xb4LF4TmIp6Z/leYLaqNgZmu8t/ISKdgM5AS6A50A7oVsTzFiotK4fKmSlsI8vuLBtjjDFlVPWYCN68rh2PDWzByu2p9H3+eyb9+Cvq6cN7oRHQ578wdKaz/Nb58OX9kJXmu6BNQClqstwPeMedfwfon8c+CkQAYUA4EArsLuJ5C5WWlUNMegrbMg9asmyMMcaUYSLCFe3r8uUdXWkeH8O9U1Yz9O0l7DroRcJbryPc+AO0GwaLxsBrZ8P2Jb4L2gSMoibLNVR1F4D7+rcuJ1R1ITAX2OVOX6nq+iKet1CZGRlEpv/GtrTdliwbY4wxAaBOlSg+GJ7EwxefyeLN++nz7Hd8tGS753eZwyvABc/ANVMhOwPe7ANf/8vuMpsCFZosi8g3IrImj6mfJycQkUZAUyABiAd6iEjXfPYdKSLJIpK8d+9eb67jb8LTdrMnohqCUCm8UpHaMsYYY0zpEBQkXNupPl/e0YWmtWO455NVXPvWEnamepHwnnYO3LQA2lwLC15y7jL/ushXIZsyrtBkWVV7qWrzPKapwG4RqQXgvu7Jo4kBwCJVPaKqR4CZQFI+5xqrqomqmhgXF3fqVwXEpO1gY8Ua1I+pTwk8T2iMMcaYElSvajSTRjh3mZO37qfPs/N4f9E2z3vMiIiBi56HIZ9Bdia82Rdm/tN6zDB/U9QyjGnAte78tcDUPPb5FegmIiEiEorzcJ/PyzBiM1LYat3GGWOMMQHrxF3mr+7oSuu6lfnXZ2u44o1FbPn9qOeNNOwONy+E9iNg8WvwakfYNNd3QZsyp6jJ8uNAbxHZAPR2lxGRRBEZ5+4zGdgErAZWAitV9fMinrdQ4VmH2BEqliwbY4wxAa5OlSjeG9aeJy5pwbpdh+j7/He8Nm8T2Z6O/hdeAc5/yukxIzgM3usPn91so/8ZoIjJsqruU9WeqtrYfd3vrk9W1eHufI6q3qCqTVW1maqOLo7ACxOSc4wdZFAv1pJlY4wxJtCJCJe1q8s3o7vR7fQ4Hp/5E/3G/MCanQc9b6ReJ6fHjLNHw8pJMKYDrJkCnj5AaAJSwI7gF5KTxk5No15FS5aNMcaY8qJGTASvD2nLq1e1Yc/hDC5+eT6PfrGOY5nZnjUQGgG9HoKRcyGmFkweChMvh4M7fBu4KbUCOFk+xs6cw1aGYYwxxpQzIsJ5LWrxzehuXNauDm98v4Xez37H3J/z6ocgH7XOguFzoM8jsOU75y7zolfheI7vAjelUsAmy2lylJjgCKJCo/wdijHGGGP8IDYylMcGtuSjGzoSERrE0LeWMOqDZew5lO5ZA8Eh0OlW5wHAuh3hy3vhjR6QssK3gZtSJSCTZVUlNfQo9SKK1v2cMcYYY8q+9g2qMOP2LtzZ63S+Xrebns/M472FW8nxtJu5yvXhqo9h0FtwKAXe6O50M5d+yJdhm1IiIJPlrBzlSGg69aJq+DsUY4wxxpQC4SHB3N6rMV/d0ZWWdWL5v6lrGfjKD6ze4eEDgCLQfCCMWgKJ18Pi12FMe1j7qT0AGOACMllOy8ohOziLmPBYf4dijDHGmFKkQbVo3h/WgRcub8XO1HT6jZnPQ1PXcDAty7MGIis5Q2YPnw3RcfDxdfD+JbBvk0/jNv4TkMlyelYOx4OyiQ6v6O9QjDHGGFPKiAj9WsUz+65uDEmqx3uLttHzmXlMWbYD9fQucUJbGDEX+j4B23+EV5JgzqOQecy3wZsSF5DJclpmDtlBOUSHxfg7FGOMMcaUUrGRoTzcrznTRp1NfOVIRn+0ksteX8T6XR7WIgeHQNKNcGsyNL0YvnsSXukAP82w0owAEpjJclYOmXKcKCvDMMYYY0whmsfH8ulNnXh8YAs27DnMhS/N59/T1npemlGxJgwaD9dOh9AomHQFfDDYSjMCRMAmyxlBx4mKqOTvUIwxxhhTBgQFCZe3r8ucu87hivZ1eGfhVno+8y0fLdnOcU97zWjQBW6cD+f+D7YtdEozZv8HMo74NHbjWwGZLKdnZJMuSnREFX+HYowxxpgypHJ0GI/0b8Hno86mXtVo7vlkFQNe+YHlvx7wrIHgUOh4i1OaceZA+P4ZeLkdrPrYSjPKqIBMljPSj3EsKIjocKtZNsYYY4z3msfHMvnGjjx32VnsOpjOgFcWMPqjFez2dECTijVh4Otw/ddQoToLTXy0AAAdPklEQVRMGQ5v9oWU5b4N3BS7gEyWM9OPcCQo2EbvM8YYY8wpExEGtE5gzj/O4aZzGjJ95S66P/0tY+ZuJD3Lw2Gv63aAEXPgohdh/yYY2x2mjoLDu30bvCk2AZksZ6cd4WhQENEh0f4OxRhjSgURiRCRH0VkpYisFZGH3fUNRGSxiGwQkQ9FJMxdH+4ub3S31/dn/Mb4U4XwEP7Z9wxmje7K2Y2q8dRXP9Pr2XnMWL3Ls67mgoKh7bVw61LoNApWToKX2sD3z0KWh3eqjd8EZLKck3GYYyJ2Z9kYY/6UAfRQ1bOAVkBfEUkCngCeU9XGwAFgmLv/MOCAqjYCnnP3M6Zcq1c1mrHXJPLB8A5UCA/h5gnLuOz1RazakepZAxGx0OcRuHkRNOgKsx+GMe1gzRSrZy7FAjRZPkpaEESH2p1lY4wBUMeJR/JD3UmBHsBkd/07QH93vp+7jLu9p4hICYVrTKnWqVE1vritC48NbMHm349w8cs/MPrDFaSkpnnWQLVGcMVEuGYqhMfA5KHw5rmwI9m3gZtTEpDJclbaEbKB8OBwf4dijDGlhogEi8gKYA8wC9gEpKpqtrvLDiDenY8HtgO42w8CVUs2YmNKr+Ag4Yr2dZl7op55tVPP/NRXP3E43cP+mU87B274Di5+CfZvgXE94eOhcGCrDyM33grIZDkjcz+RCHYTxBhj/qSqOaraCkgA2gNN89rNfc3rC/RvvxOLyEgRSRaR5L179xZfsMaUERUjQvln3zOYc1c3+javyZi5mzjnqW95b+FWsnKOF95AUDC0uQZuWwZd74GfZzpdzX31AKR52F2d8amATJYzsw4SocH+DsMYY0olVU0FvgWSgEoiEuJuSgBS3PkdQB0Ad3sssD+PtsaqaqKqJsbFxfk6dGNKrYTKUbxweWum3tKZRtUr8H9T13Luc9/x5ZrfPHsIMLwi9HjASZpbDIaFY+CFVrDgJXsI0M8CNFk+ROQf3/3GGGNEJE5EKrnzkUAvYD0wFxjk7nYtMNWdn+Yu426fox79H9+Y8u2sOpWYNDKJcdckEhQk3Pj+Uga9tpDkrX/7t2beYmpD/zHOSIAJifD1v+DlRKcHjeMe3Kk2xS4wk+WcI0RKqL/DMMaY0qQWMFdEVgFLgFmqOh34JzBaRDbi1CSPd/cfD1R1148G7vVDzMaUSSJCr2Y1+PL2Ljw+sAU7Dhxj0GsLGf7OEn7+7bBnjdRsDld/AkM+g6gq8OkN8HpX2DDLes4oYQF5+zUr5wiRTlehxhhjAFVdBbTOY/1mnPrlk9enA5eWQGjGBKyQ4CAub1+Xfq3iefOHLbz27Sb6vvAdA1sncGfvxiRU9qCL24bdoUE3WDsF5vwXJgyCep2h17+hzt/+0zU+EJB3lrOOHyPSesIwxhhjTCkQGRbMLd0b8d093Rl+dgM+X5VCj6fn8e9pa/n9SEbhDQQFQYtBcMsSOP9p+H0DjO8NE6+A3et8fwHlXIAmy2lEBUf4OwxjjDHGmD9Ujg7jgQua8e0/zmFA63jeXbiVrk/O5Zmvf+ZgmgfdzYWEQfsRcNty6PEv2DofXu0EU0bC/s0+j7+8KlKyLCJVRGSWO0zqLBGpnM9+T4jIGne6rCjn9EQO6USH2Oh9xhhjjCl9aleK5IlBLZk1uhvdm1TnpTkb6frkXF75diPHMrMLbyC8AnS9G25fCZ1vg3XTnO7mPr8DDu70/QWUM0W9s3wvMNsdJnU2eTwAIiIXAG1whlftANwtIjFFPG+Bssi0oa6NMcYYU6o1jKvAmKvaMP3Ws2lTtxJPfvkzXZ+cy/j5W0jPyim8gagq0Ps/cPsKaDsUlr8PL7aGmffC4d2+v4ByoqjJcu7hUHMPk5pbM2Ceqmar6lFgJdC3iOctUJZkUiGsoi9PYYwxxhhTLJrHx/LW0PZ8clMnmtSsyH+nr6PbU3N5b+FWMrI9SJor1oQLnoZbl0LLwfDjWHjhLPj6/+Do7z6PP9AVNVmuoaq7ANzX6nnssxI4T0SiRKQa0B23o3tfySaLCuGWLBtjjDGm7GhbrzIThicxcUQS9apE839T19L9qW+ZsHgbmdke9LFcuR70exlGLYGmFzkDmjzfEr55GI552M+z+ZtCk2UR+SZXvXHuqZ8nJ1DVr4EZwAJgIrAQyLMgp7iGTc2SbGIiYk/5eGOMMcYYf+nYsCof3pDE+8M6UCM2ggc+XUP3p7/lg8W/epY0V20Il7wBtyyGJn1h/nPwfAuY/R9Lmk9BocmyqvZS1eZ5TFOB3SJSC8B93ZNPG4+qaitV7Q0IsCGf/Yo8bOrx40pWUA4xUXk+a2iMMcYYU+qJCGc3rsaUmzrxzvXtiasYzv2frqb709/y/qJtnpVnxDWBQW/CzQuhcR/4/lknaf7mYTi6z/cXESCKWoaRezjU3MOk/kFEgkWkqjvfEmgJfF3E8+YrI/s4mUFKtN1ZNsYYY0wZJyJ0Oz2OT2/uxNtD21E9Jpx/fbaGc576lncXbvXsQcDqTeHSt+CmBU7SfOJO86wH4cip/5JfXhQ1WX4c6C0iG4De7jIikigi49x9QoHvRWQdMBa4WlU96Bfl1KRl5ZARpERHVPHVKYwxxhhjSpSIcE6T6ky5qRPvXt+e2pUieXDqWro+OZdx328mLdODpLlGMydpvnkRNDnPrWluAV/eD4d2+f4iyqgiDXetqvuAnnmsTwaGu/PpOD1ilIi0rBwyRImOqlpSpzTGGGOMKREiQtfT4+jSuBoLN+/jxdkbeOSL9bz67SaGdWnAkKR6VIwILbiR6mfAoPFwzn3w/TOw+DVY8ga0HgKdb3ceFDR/CLgR/NLSMzgWJERHWM2yMcYYYwKTiNCpYTUmjezI5Bs70iIhlie//JnOj8/hma9/Zv/RzMIbqdYIBrzqdDnX6kpY9i681AY+vQn2/uL7iygjAi5Zzkw7zNGgICJtUBJjjDHGlAOJ9avw9tD2fD7qbDo1rMZLczbS+fE5PPz5WlJS0wpvoEoDuOgFZ0TAdiNg7acwpj18OARSlvv+Akq5AEyWj3BUhOjQaH+HYowxxhhTYlokxPLakLbMurMr57WoybsLt9Htqbnc/fFKNu45UngDsfFw3uNw5xro+g/YPA/GngPv9nPmVX1+DaVRwCXLaUcPki0QERzh71CMMcYYY0pc4xoVeXZwK+bdfQ5Xtq/LtJUp9H5uHje8l8zyXw8U3kB0NejxLydp7vUw7F4H714Mb/SAtZ/BcQ8eJgwgAZcsHz32O5EqiIi/QzHGGGOM8ZuEylE83K85P9zbg1HdG7Fo834GvLKAy15fyNyf9qCF3SmOiIGz74A7VsOFz0F6Knx8LbycCMlvQpYHJR4BIPCS5fT9RKglysYYY4wxANUqhHNXnyYsuLcH/7qgKdv3H2Po20s49/nv+Ch5e+EDnIRGQOL1MCoZLn0bImJh+p1Ot3Pzngr4UQEDLlk+lp5KhAbcZRljjDHGFEl0eAjDu5zGvHu68+zgswgS4Z7Jq+jyxFxe+XYjB49lFdxAUDCcOQBGzIVrp0OtVjD3EXi2GXzxD9i/uWQupIQVqZ/l0igt8yARBPs7DGOMMcaYUik0OIiBbRIY0Dqe7zf8ztjvNvPklz/z8pyNDE6sw/WdG1C3agG9iolAgy7OtHsdLBwDS9+GJeOg6YXQcRTU6eDsFwACLlnOyDpMROBdljHGGGNMsToxwEnX0+NYl3KIcfM3M2HxNt5duJU+zWoyvEsD2tarXPBzYDWaQf8x0PP/4MexsGQ8rP8c4ttCx1ugaT8ILtt5WcDVK6RnHSZCwvwdhjHGGGNMmdGsdgzPDm7F9/f04MZuDVm4eR+DXltI/zE/MHXFTjKzjxfcQMWa0PNBGL0Ozn8a0g7A5OvhhbPghxec5TIq4JLljOwjhFuybIwxxhjjtZqxEdzT9wwW3teD//ZvzuH0bG6ftIIuT85hzNyNhY8MGBYN7UfAqKVw+URnwJNZD7p1zXfB7xtK5kKKUdm+L56HjONpRIaE+zsMY4wxxpgyKyoshCFJ9biqfV3m/bKXN3/YwlNf/cyLszfQv1U813aqT7PaMfk3EBQEZ5zvTL+thkWvOsNpLxkHjXpBhxuhYU9nv1Iu4JLlrJw0IoIj/R2GMcYYY0yZFxQkdD+jOt3PqM6G3Yd5a8FWpizbwYfJ22nfoArXdapPn2Y1CAkuIOmt2QL6v+IMcLL0LSdhnjAIqjSE9iOh1ZVOn86lVOlP572USQaRIQU8wWmMMcYYY7zWuEZF/jegBYvu68n955/BzgNp3DxhGV2enMuYuRvZdySj4AYqxEG3e+CONTBwHERVgS//Cc82dbqe2/tzyVyIlwLuznKGZhAXUsHfYRhjjDHGBKRKUWGM7NqQYWefxuz1u3l34Tae+upnXvhmAxe0rMWQjvVoXadS/r1ohIRBy0udaedS+HEcLHsHlrwBDbpCuxHQ5PxS04tG6YiiGGWSRVSYJcvGGGOMMb4UHCT0ObMmfc6sycY9h3lv4TY+WbaTT5fvpHl8DNck1eeis2oTGVbA+BfxbWFAW+jzXydhTn4LPhoCMfHQdii0uQYq1ii5i8pDAJZhZBEdXtHfYRhjjDHGlBuNqlfk4X7NWXR/T/7b70wys49zzyer6PC/b/jP5+vYuOdIwQ1EV4Mud8FtK+CyCVDtdGd0wOeawcfXwZbvQbVEruVkgXdnWXKoEF7J32EYY4wxxpQ7FcJDGNKxPlcn1WPxlv1MWPwr7y3ayps/bKHjaVW5KqkufZrVJCwkn/u1wSHOKIBNL4R9m5xBTlZMgLWfOgl04vVw1uUQWbnEringkuUMyaFilCXLxhhjjDH+IiIknVaVpNOqsvdwMz5K3s7EH39l1AfLqVYhjEFt63Bl+7oFD6tdtSH0/Z8zOuDaT53E+ct74Zt/w5kDIXEoJLTz+bDaAVeGkSHHiYmq6u8wjDGm1BCROiIyV0TWi8haEbndXf9vEdkpIivc6fxcx9wnIhtF5GcROdd/0Rtjyrq4iuHc0r0R8+7uzltD29G6bmXGfreJrk/NZcj4xcxYvavgEQJDI53u5UbMhhu+d+bXT4PxveHVTrD4dZ+OEBhwd5bTgiA22pJlY4zJJRu4S1WXiUhFYKmIzHK3PaeqT+feWUSaAZcDZwK1gW9E5HRVzSnRqI0xASU4SOjepDrdm1Tnt4PpfJS8nUk//srNE5ZRrUIYl7RN4LLEOpwWV0BHDbVawoXPQe//wJpPYOnbMPMeZ5TAZv2h7XVQN6lY7zYHXLKcLlC5Ypy/wzDGmFJDVXcBu9z5wyKyHogv4JB+wCRVzQC2iMhGoD2w0OfBGmPKhZqxEdzWszG3dG/Edxv2MnHxr4z7fguvz9tMhwZVuLx9Hc5rXouI0Hx60giv6CTGba+DXSth6Tuw6iNY/zn842dnezEJqGQ5KzvHubNsybIxxuRJROoDrYHFQGdglIhcAyTj3H0+gJNIL8p12A4KTq6NMeaU5L7bvOdQOpOX7eDDJdu588OVPDh1Lf1a1eayxLo0j4/Jv9/mWmfBhc863c/tWlWsiTIEWM3ykaMHyRQhMsy6jjPGmJOJSAXgE+AOVT0EvAo0BFrh3Hl+5sSueRyeZ59NIjJSRJJFJHnv3r0+iNoYU15Uj4ng5nMaMfeuc/hgRAd6nlGdj5N3cNHL8zn/xfm89cMWDhzNzL+BsGio17HY4ypSsiwil7oPixwXkcQC9uvrPiSyUUTuLco5C5J6eA+Rqvn/y8MYY8opEQnFSZQnqOoUAFXdrao5qnoceAOn1AKcO8l1ch2eAKTk1a6qjlXVRFVNjIuzX/WMMUUXFCR0aliN5y9vzY8P9OK//ZsTGiw8/Pk62v/vG256fylzftpNdk4BDwUWo6KWYawBBgKv57eDiAQDY4DeOF/AS0RkmqquK+K5/+bgkf1EHrdE2RhjchPnDsJ4YL2qPptrfS23nhlgAM53OsA04AMReRbnAb/GwI8lGLIxxgAQGxnKkKR6DEmqx/pdh/g4eQefrdjJzDW/EVcxnIGt4xnUNoHGNXxXVVCkZFlV1wOF3cltD2xU1c3uvpNwHh4p9mT50NHfifDP4C7GGFOadQaGAKtFZIW77n7gChFphVNisRW4AUBV14rIRzjf09nALdYThjHG35rWiuHBi5px73lnMOenPXyybAfj52/h9e820zIhlkvaJHDxWbWpHB1WrOctiQf84oHtuZZ3AB18caLDx/YTcTygyrCNMabIVHU+edchzyjgmEeBR30WlDHGnKKwkCD6Nq9J3+Y1+f1IBlNXpPDJ0h08NG0tj8/8iSX/6kWF8OJLcQttSUS+AWrmsekBVZ3qwTm8elAEGAlQt25dD5r+q7NbX0ydGk28Ps4YY4wxxpQ91SqEM+zsBgw7uwHrdx1i5fbUYk2UwYNkWVV7FfEcXj0oAowFSExM9LqgIrZCFVqeXvxPQRpjjDHGmNKtaa0YmtaKKfZ2S6JmYQnQWEQaiEgYzqhQ00rgvMYYY4wxxhRJUbuOGyAiO4COwBci8pW7vraIzABQ1WxgFPAVsB74SFXXFi1sY4wxxhhjfK+ovWF8Cnyax/oU4PxcyzMo4EESY4wxxhhjSiPrOsIYY4wxxph8WLJsjDHGGGNMPixZNsYYY4wxJh+WLBtjjDHGGJMPS5aNMcYYY4zJhyXLxhhjjDHG5MOSZWOMMcYYY/Ihql6PKl0iRGQvsO0UDq0G/F7M4ZQWgXxtENjXF8jXBoF9fad6bfVUNa64gynNTvrejgUOFjCfe92pvMe5j/dmn8LWeTN/qn8bhcV+KnHnF6O/4y4szpOXy3rcuectbu+2+zvu/L+zVTWgJiDZ3zHYtdn1ladrC/TrC+Rr8/H7Nrag+ZPWef0e5z7em30KW+fN/Kn+bRQW+6nE7eH7XOJxFxZnoMWd1zVY3GUj7oImK8MwxhjjC58XMp97XVHb92afwtZ5O38qCjv+VOI+eTmv99kfcee1PpDjzj1vcXu33d9x56vUlmGcKhFJVtVEf8fhC4F8bRDY1xfI1waBfX2BfG2lRVl9jy3ukmVxlyyL+0+BeGd5rL8D8KFAvjYI7OsL5GuDwL6+QL620qKsvscWd8myuEuWxe0KuDvLxhhjjDHGFJdAvLNsjDHGGGNMsQiYZFlE+orIzyKyUUTu9Xc8RSUidURkroisF5G1InK7u76KiMwSkQ3ua2V/x3qqRCRYRJaLyHR3uYGILHav7UMRCfN3jKdKRCqJyGQR+cn9DDsGymcnIne6f5NrRGSiiESU5c9ORN4UkT0isibXujw/K3G86H7PrBKRNv6L3BhjTEkIiGRZRIKBMcB5QDPgChFp5t+oiiwbuEtVmwJJwC3uNd0LzFbVxsBsd7msuh1Yn2v5CeA599oOAMP8ElXxeAH4UlXPAM7Cuc4y/9mJSDxwG5Coqs2BYOByyvZn9zbQ96R1+X1W5wGN3Wkk8GoJxWiMMcZPAiJZBtoDG1V1s6pmApOAfn6OqUhUdZeqLnPnD+MkW/E41/WOu9s7QH//RFg0IpIAXACMc5cF6AFMdncpy9cWA3QFxgOoaqaqphIgnx0QAkSKSAgQBeyiDH92qvodsP+k1fl9Vv2Ad9WxCKgkIrVKJtLyR0Saishr7q80N/k7Hk+JSH8ReUNEpopIH3/H4w0ROU1ExovI5ML39h8RiRaRd9z3+Sp/x+ONsvIen6ys/l0Xx/dIoCTL8cD2XMs73HUBQUTqA62BxUANVd0FTkINVPdfZEXyPHAPcNxdrgqkqmq2u1yWP8PTgL3AW26ZyTgRiSYAPjtV3Qk8DfyKkyQfBJYSOJ/dCfl9VgH9XVOc8ipvcdd7XDKnqutV9UZgMFAiXVgVU9yfqeoI4DrgMh+G+xfFFPtmVfXLL0Nexj8QmOy+zxeXeLAn8SZ2f77HJ/Mybr/8XefFy7iL/D0SKMmy5LEuILr5EJEKwCfAHap6yN/xFAcRuRDYo6pLc6/OY9ey+hmGAG2AV1W1NXCUMlhykRe3drcf0ACoDUTjlCacrKx+doUJpL9TX3ubk8pb8iuZE5EWIjL9pKm6e8zFwHyccpgyE7frX+5xJeVtii92f3gbD+MHEvjzH645JRhjft7G89hLk7fxPu6S/rvOy9t4EXdRv0dCihJpKbIDqJNrOQFI8VMsxUZEQnES5QmqOsVdvVtEaqnqLvfn3z3+i/CUdQYuFpHzgQggBudOcyURCXHvUJblz3AHsENVF7vLk3GS5UD47HoBW1R1L4CITAE6ETif3Qn5fVYB+V3jC6r6nfurWG5/lMwBiMgkoJ+qPgZcmE8704BpIvIF8IHvIv7jfEWOW0QEeByYeaKcriQU13vuL97Ej/PfYgKwglJw48/L2NeVbHT58yZuEVmPH/6u8+Lt+13U7xG//4EVkyVAY3GeyA/DeeBomp9jKhL3y3Y8sF5Vn821aRpwrTt/LTC1pGMrKlW9T1UTVLU+zmc1R1WvAuYCg9zdyuS1Aajqb8B2EWniruqJ8+VY5j87nPKLJBGJcv9GT1xbQHx2ueT3WU0DrhFHEnDwRLmG8YhXZSwico44vY+8DszwdXAF8Lb85lacf1gOEpEbfRmYB7x9z6uKyGtAaxG5z9fBeSC/+KcAl4jIq/hwmOMiyjP2Uvgenyy/97w0/V3nJb/3u8jfIwFxZ1lVs0VkFPAVztP5b6rqWj+HVVSdgSHAahFZ4a67H+dfdR+JyDCcxOVSP8XnC/8EJonII8By3AfkyqhbgQnuP942A0Nx/nFapj87VV0szkMpy3B6bFmOM1rSF5TRz05EJgLnANVEZAfwEPn/dzYDOB/YCBzD+VyN57wqY1HVb4FvfRWMF7yN+0XgRd+F4xVvY98HlKZEKM/4VfUopf+/v/xiL23v8cnyi7s0/V3nJb+4v6WI3yMBkSwDqOoM/HvnoVip6nzy/uDBuZsXEHL/Ebs/nbT3ZzzFRVVXkPeDBGX+s1PVh3ASytzK7Genqlfks+lvn5U6Q57e4tuIAlpZLWMpq3FD2Y4dynb8ZTV2i/skgVKGYYwxpvQrqyVzZTVuKNuxQ9mOv6zGbnGfxJJlY4wxxc4tb1kINBGRHSIyzH0A9ETJ3Hrgo9JWMldW44ayHTuU7fjLauwWt4fnc35VNMYYY4wxxpzM7iwbY4wxxhiTD0uWjTHGGGOMyYcly8YYY4wxxuTDkmVjjDHGGGPyYcmyMcYYY4wx+bBk2RhjjDHGmHxYsmyMMcaYYicilUTk5lzLtUVkso/O1V9EHsxn2xH3NU5EvvTF+U1gs2TZGGOMMb5QCfgjWVbVFFUd5KNz3QO8UtAOqroX2CUinX0UgwlQliwbY4wxxhceBxqKyAoReUpE6ovIGgARuU5EPhORz0Vki4iMEpHRIrJcRBaJSBV3v4Yi8qWILBWR70XkjJNPIiKnAxmq+ru73EBEForIEhH570m7fwZc5dvLNoHGkmVjjDHG+MK9wCZVbaWqd+exvTlwJdAeeBQ4pqqtcYYxvsbdZyxwq6q2Bf5B3nePOwPLci2/ALyqqu2A307aNxnocorXY8qpEH8HYIwxxphyaa6qHgYOi8hB4HN3/WqgpYhUADoBH4vIiWPC82inFrA313Jn4BJ3/j3giVzb9gC1iyd8U15YsmyMMcYYf8jINX881/JxnPwkCEhV1VaFtJMGxJ60TvPZN8Ld3xiPWRmGMcYYY3zhMFDxVA9W1UPAFhG5FEAcZ+Wx63qgUa7lH4DL3fmT65NPB9acakymfLJk2RhjjDHFTlX3AT+IyBoReeoUm7kKGCYiK4G1QL889vkOaC1/1mrcDtwiIkv4+x3n7sAXpxiLKadENb9fKowxxhhjSj8ReQH4XFW/KWS/74B+qvr/7dihDYBQEETBPYunZSSt0sKvYEkwJJAZfeLky17vfMYfWJYBgK87kmx3BzOzJzmFMk9ZlgEAoLAsAwBAIZYBAKAQywAAUIhlAAAoxDIAABRiGQAAigVsG1kirPorLAAAAABJRU5ErkJggg==", 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" ] @@ -651,23 +679,23 @@ } ], "source": [ - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=1e-5, tmin=1e-3, tmax=1000)\n", - "w = HeadWell(ml, tsandh=[(0, -1)], rw=0.2)\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=1e-5, tmin=1e-3, tmax=1000)\n", + "w = ttim.HeadWell(ml, tsandh=[(0, -1)], rw=0.2)\n", "ml.solve()\n", - "plt.figure(figsize=(12,5))\n", - "plt.subplot(1,2,1)\n", + "plt.figure(figsize=(12, 5))\n", + "plt.subplot(1, 2, 1)\n", "ml.xsection(0.2, 100, 0, 0, 100, t=[0.1, 1, 10], sstart=0.2, newfig=False)\n", "t = np.logspace(-3, 3, 100)\n", "dis = w.discharge(t)\n", - "plt.subplot(1,2,2)\n", - "plt.semilogx(t, dis[0], label='rw=0.2')\n", - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=1e-5, tmin=1e-3, tmax=1000)\n", - "w = HeadWell(ml, tsandh=[(0, -1)], rw=0.3)\n", + "plt.subplot(1, 2, 2)\n", + "plt.semilogx(t, dis[0], label=\"rw=0.2\")\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=1e-5, tmin=1e-3, tmax=1000)\n", + "w = ttim.HeadWell(ml, tsandh=[(0, -1)], rw=0.3)\n", "ml.solve()\n", "dis = w.discharge(t)\n", - "plt.semilogx(t, dis[0], label='rw=0.3')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('discharge (m3/d)')\n", + "plt.semilogx(t, dis[0], label=\"rw=0.3\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"discharge (m3/d)\")\n", "plt.legend();" ] }, diff --git a/notebooks/well_near_river_or_wall.ipynb b/notebooks/well_near_river_or_wall.ipynb index 82997db..da66783 100644 --- a/notebooks/well_near_river_or_wall.ipynb +++ b/notebooks/well_near_river_or_wall.ipynb @@ -16,7 +16,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -41,7 +41,7 @@ }, { "data": { - "image/png": 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\n", 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lBPe7KPQK8FYzS5hZA8GB7KJdT9V20hHTaYibBggUkboQVVBcD1xgZquAC8J5zKzLzG4I29wCvAQ8DTwJPOnud0ZRbKFUMs7r50zTmU8iUhcq1vVUirtvAc4rsrwbuCqczgB/VeXSynbaghn86MG1DI5kaGrQSLIicujSldn7qWt+G8OZLCte2xF1KSIiFaWg2E+nzg8uvPuDup9E5BCnoNhP7a2NHNnZwkMvbYm6FBGRilJQHIDzXzeLh17awvb+4ahLERGpGAXFAbh48WzSWefeZyIfWUREpGIUFAfgxDnTOGJGirue2hB1KSIiFaOgOABmxsUnHs6DL25m2251P4nIoUlBcYAuGe1+2hh1KSIiFaGgOEAnHD6V+e3N/FLdTyJyiFJQHKCg+2k2v39pC1vV/SQihyAFxSS4ePFsMlnnnpXqfhKRQ4+CYhIcP3sqCztauP2J16IuRURk0ikoJoGZcXnXXB5evZWV6zX2k4gcWhQUk+SKN82nJRnnht+tiboUEZFJpaCYJNNSDfzZafO488n1rN8+EHU5IiKTRkExiT7ylgU48MMHtVchIocOBcUkmtvWzMUnzuZnf3iVnYMjUZcjIjIpFBST7OqzF9E3lOZnj7wSdSkiIpNCQTHJXj9nGmcc2c73freGXdqrEJFDgIKiAj5/4XFs7hvi2795MepSREQOmIKiAk46Yjrv65rLD/57DS9u6ou6HBGRA6KgqJDPX3gcqWScv79zJe4edTkiIvtNQVEhHa2NfPqCY/jdqs3cs1J3wBORg5eCooI+ePp8jp01hS//8hl2DOjAtogcnBQUFZSIx7j+PSfSs3OQz9/ypLqgROSgpKCosJPntfGFC4/jnpU9/Pihl6MuR0RknykoquCqsxZy3nEz+cpdz7LiNY0uKyIHFwVFFZgZX7/8DXS0JrnmpsfZ3DcUdUkiImVTUFRJW0uS71xxCpt2DXLlD/+gq7ZF5KChoKiik+e18d2/OJXnNuziL3/czeBIJuqSREQmpKCosnOOncnXL38DD6/eyid+9keG09moSxIRKUlBEYHLTp7Dl951PMue6eGjNz5K31A66pJERMaloIjIlWcu5KvvXczvX9rCB5Y+TO8uHeAWkdqkoIjQ+7qOYOkHT2XVpl28999+z3Mbd0ZdkojIXhQUETvvdbP46V+ezu6hDJd950F+3v1q1CWJiOxBQVEDTpnXxt2ffAsnHTGdz93yFJ/9+ZM6biEiNUNBUSNmTmnipqtO5xPnHsV/Pr6Od/zvB7j/+U1RlyUiEk1QmNnlZrbSzLJm1lWi3YVm9ryZvWhm11azxijEY8Zn3n4st3zszaSSca784aN86uYndKBbRCIV1R7FCuBPgQfGa2BmceA7wDuB44EPmNnx1SkvWqfOn8Fdf/MW/ubco7jzyfW87Wu/5V9/s4qBYV2gJyLVF0lQuPuz7v78BM3eCLzo7qvdfRj4D+DSyldXGxoTcT799mO591Nn85ajO/j6vS9w7jfu56ZHXmYorcAQkeqp5WMUc4D8U4DWhcv2YmZXm1m3mXX39vZWpbhqWdTZyv/9YBc3X306s6Y2cd2tK3jrV+/nhw+uoX9YB7xFpPIqFhRmdp+ZrSjyU+5egRVZVvTOP+6+1N273L2rs7Nz/4uuYW9a1M6t15zBTz76Rua1N/P3dz7D6f+4nK/c9Qyvbu2PujwROYQlKvXC7n7+Ab7EOuCIvPm5wPoDfM2Dmplx1tGdnHV0J91rt/LD36/lBw+u5Yb/XsPZR3dyeddcLjh+Fo2JeNSlisghpGJBMQkeBY42s4XAa8D7gT+PtqTa0bVgBl0LZrBhxwA/feQVbnlsHR//6R+Zlmrg4sWzuWTxbN60sJ14rNiOmYhI+SyK+zib2buBbwOdwHbgCXd/h5kdDtzg7heF7S4C/hmIAz9w969M9NpdXV3e3d1dueJrVCbrPPjiZm55bB3LnulhYCRDR2sj7zhhFucfP4s3L2qnqUF7GiJSnJk95u5FL1eIJCgqqV6DIt/AcIbfPLeJu55ez/3P99I/nKE5GefMozo4+5hOzj66g/ntLVGXKSI1pFRQ1HLXk+ynVDLOxYtnc/Hi2QyOZHho9Rbue6aH+5/vZdkzPQDMbUtx+qJ2Tl/UzpsWzmBuWwozdVOJyN4UFIe4poY45xw7k3OOnYm7s3ZLPw+80MtDL21h+bM93PLYOgA6pzRy6rw2Tpk/ncVzp3PinGm0NOrjISIKirpiZizsaGFhRwtLzlhANuus2tTHo2u38vjL23jslW38euVGAGIGR3a2csLhUznh8Gkcf/hUjj1sCh2tjRH/K0Sk2nSMQvawuW+Ip9ft4Ml123l63Q5Wrt/Jxp2Do+s7WpMce9gUjp45haNmtnLUzFYWdbbQ2dqoriuRg5iOUUjZOlobOee4mZxz3MzRZVv6hnhmw06e37iL5zfu4oWeXfy/7lfpzxt7akpjgoWdLSxob2FBezPz2luY397MEW3NzJzSSEyn6YoctBQUMqH21sbRC/1y3J0NOwZZtamPNb19rN68mzWbd/PHV7fxy6fWk83bUU0mYsydnmJOW4o501McnvuZ1sRh4U9zUh9FkVqlv07ZL2Y2+oX/1mP2HDZlOJ1l3bZ+Xtnaz6vbBnh1az+vbRtg3fYBnn22h819w3u93tSmBIdNa2LW1CY6pzQyc0oTM6c00hn+dLQGj1ObEuriEqkyBYVMumQixqLOVhZ1thZdPziSoWfnIK9tH2DjjkE27hxk445BenYO0rNziJc29dHbN8RIZu/jZ8l4jBktSWa0JGlvTY5Oz2hO0taSpK05SVtzA9Obk7S1NDA9lSSV1IWGIgdCQSFV19QQZ357S8mL/tyd7f0j9PYN0btriM2jj8Ns3T3Elr5hNu8e5uUt/WzdPVzy1rHJRIzpqQamFfxMTTUwtSnB1FQDU5oSTGkqeGwMppsaYtqLkbqmoJCaZGbBHkJLkmNmTZmw/VA6w47+Ebb2D7N19zA7+kfYPjDCtv5hdgyMBPP9I+wYGGHjzkGe79nFzoERdg2lmejEv3jMaG1M0NqYoKUxHj4G883JBK2NcZobE7Qk4zQngza5x1RDguZknOZknFQyTqohWKfwkYOJgkIOCY2JODOnxpk5tWmfnpfNOruG0uwaHGHXYJpdg2n6hoLpnYNpdg+l6RsM1vcNZYL58Kdn5yC7hzL0DaXpH04X7SorJdUwFh6jjw1xGhtipBriNDXEaWqIhY9xmhIxGvOXJ4LpxkSMxoYYjYki0+FzGhMxEjFTOMl+UVBIXYvFbLQr6kANp7PsHkrTP5JhYDhN31CG/uE0A8MZ+ocz4WOwfnA4w8BIsHxwJMvgSLBucCTLrsE0vbuGGEpnGQjbDY5kGEpnD6g+M2hMxEjGYyTDIEmOzheZzs2HyxriMRoSRmM8Nx08JuMWzIfL8ucTcSMZj5GIx2jIXx6z0fUN4bpELHhUmNUeBYXIJAm+XJO0Vej13Z2hdJahkSyD6bHwyM0PjWQZSgfBM5wJ5oczQQgNp7NB23R2dHo4HawfTgevM5IJlvX3p4P14fxIJstIxvPaH1hgTSQes9EgicdsNERyoZKIGYnRx6Btbn0iZsRjBevCtrnXjYdtx+b3fF48fG5uecyC+ZgF7zPaPm7ELWwTPi8eC5bF8143VrAsHr5mvOA5sRij7WotLBUUIgcJMxvthprGge8B7S93J5110mF4DGUypDMeBkoQKiOjIeOks2PL89uls2Ptgtcbax+0czLZLCPhunTGx6b3eHQyWWcwnSETzqezwbpM3vqgDiebDevPZslkfY9rfmqFGWF4jIVMzNgraGJ562Ix44TDp/HtD5w86fUoKERkn5hZ2I1EeOpxdKE1GXLBkR0NwCBAMtkgmLK5wAkfx9ZlR9dlsk7G91yfyXvd3HzuPUaf55DJZslk2atd7jWDthRZNjaddci4M29GqiLbSEEhInUtFjOSGmKmpFjUBYiISG1TUIiISEkKChERKUlBISIiJSkoRESkJAWFiIiUpKAQEZGSFBQiIlKS+URjLB9kzKwXeHk/ntoBbJ7kciZDrdYFtVub6to3tVoX1G5th2Jd8929s9iKQy4o9peZdbt7V9R1FKrVuqB2a1Nd+6ZW64Lara3e6lLXk4iIlKSgEBGRkhQUY5ZGXcA4arUuqN3aVNe+qdW6oHZrq6u6dIxCRERK0h6FiIiUpKAQEZGS6i4ozOxCM3vezF40s2uLrG80s5vD9Y+Y2YIq1HSEmf3WzJ41s5Vm9skibd5mZjvM7Inw5+8qXVf4vmvN7OnwPbuLrDcz+1a4vZ4ys1OqVNexedviCTPbaWZ/W9CmKtvMzH5gZpvMbEXeshlmtszMVoWPRW+lbWZLwjarzGxJFer6mpk9F/6ubjWz6eM8t+TvvUK1fcnMXsv7fV00znNL/g1XoK6b82paa2ZPjPPcim2z8b4jqvY5c/e6+QHiwEvAIiAJPAkcX9DmGuDfwun3AzdXoa7ZwCnh9BTghSJ1vQ34ZQTbbC3QUWL9RcCvAANOBx6J6Pe6keCCoapvM+Bs4BRgRd6yrwLXhtPXAv9U5HkzgNXhY1s43Vbhut4OJMLpfypWVzm/9wrV9iXgs2X8rkv+DU92XQXrvwH8XbW32XjfEdX6nNXbHsUbgRfdfbW7DwP/AVxa0OZS4MZw+hbgPDOr6H0S3X2Duz8eTu8CngXmVPI9J9GlwI898DAw3cxmV7mG84CX3H1/rsg/YO7+ALC1YHH+5+hG4LIiT30HsMzdt7r7NmAZcGEl63L3e909Hc4+DMydrPfbF+Nss3KU8zdckbrC74H3AT+brPcrV4nviKp8zuotKOYAr+bNr2PvL+TRNuEf1A6gvSrVAWFX18nAI0VWv9nMnjSzX5nZCVUqyYF7zewxM7u6yPpytmmlvZ/x/3ij2GYAs9x9AwR/5MDMIm2i3nYfIdgbLGai33ulfDzsFvvBON0oUW6zs4Aed181zvqqbLOC74iqfM7qLSiK7RkUnh9cTpuKMLNW4D+Bv3X3nQWrHyfoWnkD8G3gtmrUBJzp7qcA7wT+2szOLlgf2fYCMLMk8CfAz4usjmqblSvKz9p1QBq4aZwmE/3eK+G7wJHAScAGgm6eQlF+3j5A6b2Jim+zCb4jxn1akWX7tM3qLSjWAUfkzc8F1o/XxswSwDT2bxd5n5hZA8EH4CZ3/0Xhenff6e594fTdQIOZdVS6LndfHz5uAm4l2PXPV842raR3Ao+7e0/hiqi2Wagn1wUXPm4q0iaSbRcezLwEuMLDTuxCZfzeJ52797h7xt2zwPfGec+otlkC+FPg5vHaVHqbjfMdUZXPWb0FxaPA0Wa2MPyf6PuBOwra3AHkzgp4L/Cb8f6YJkvY9/l94Fl3/+Y4bQ7LHSsxszcS/O62VLiuFjObkpsmOBC6oqDZHcCHLHA6sCO3K1wl4/4vL4ptlif/c7QEuL1Im3uAt5tZW9jN8vZwWcWY2YXAF4A/cff+cdqU83uvRG35x7bePc57lvM3XAnnA8+5+7piKyu9zUp8R1Tnc1aJI/S1/ENwls4LBGdOXBcu+weCPxyAJoJujBeBPwCLqlDTWwh2BZ8Cngh/LgI+BnwsbPNxYCXBWR4PA2dUoa5F4fs9Gb53bnvl12XAd8Lt+TTQVcXfZTPBF/+0vGVV32YEQbUBGCH439tHCY5rLQdWhY8zwrZdwA15z/1I+Fl7EfhwFep6kaC/Ovc5y53hdzhwd6nfexVq+0n4GXqK4AtwdmFt4fxef8OVrCtc/qPc5yqvbdW2WYnviKp8zjSEh4iIlFRvXU8iIrKPFBQiIlKSgkJEREpSUIiISEkKChERKUlBIVKCmU03s2vy5g83s1sq9F6X2Tgj3JpZX/jYaWa/rsT7i4xHQSFS2nSCEYWB4Opbd39vhd7r88D/KdXA3XuBDWZ2ZoVqENmLgkKktOuBI8N7DHzNzBbk7lVgZlea2W1mdqeZrTGzj5vZp83sj2b2sJnNCNsdaWa/DgeL+52ZHVf4JmZ2DDDk7pvD+YVm9pCZPWpmXy5ofhtwRWX/2SJjFBQipV1LMIT5Se7+uSLrXw/8OcG4Pl8B+t39ZOAh4ENhm79pJ50AAAE7SURBVKXAJ9z9VOCzFN9rOJNgEMOcfwG+6+6nEdxrI183wUimIlWRiLoAkYPcbz24P8AuM9sB3BkufxpYHI72eQbw87zbmjQWeZ3ZQG/e/JnAe8LpnxDcZChnE8HwESJVoaAQOTBDedPZvPkswd9XDNju7idN8DoDBCMV5xtvfJ2msL1IVajrSaS0XQS3ntwvHtwzYI2ZXQ6j9xh/Q5GmzwJH5c0/SDAyKux9POIYqjCaq0iOgkKkBHffAjxoZivM7Gv7+TJXAB81s9zIosVu3fkAcHLebXc/SXDzm0fZe0/jHOCu/axFZJ9p9FiRGmFm/wLc6e73TdDuAeBSD+5/LFJx2qMQqR3/SHCPjXGZWSfwTYWEVJP2KEREpCTtUYiISEkKChERKUlBISIiJSkoRESkJAWFiIiU9P8BsKLAGgDUmDoAAAAASUVORK5CYII=", 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" ] @@ -53,15 +53,17 @@ } ], "source": [ - "ml1 = ModelMaq(kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100)\n", - "w1 = Well(ml1, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", - "w2 = Well(ml1, 100, 0, rw=0.3, tsandQ=[(0, -1000)])\n", + "ml1 = ttim.ModelMaq(\n", + " kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100\n", + ")\n", + "w1 = ttim.Well(ml1, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", + "w2 = ttim.Well(ml1, 100, 0, rw=0.3, tsandQ=[(0, -1000)])\n", "ml1.solve()\n", "t = np.linspace(0.1, 20, 100)\n", "h1 = ml1.head(20, 0, t)\n", - "plt.plot(t, h1[0], label='river modeled with image well')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)');" + "plt.plot(t, h1[0], label=\"river modeled with image well\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\");" ] }, { @@ -81,7 +83,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -93,19 +95,21 @@ } ], "source": [ - "plt.plot(t, h1[0], label='river modeled with image well')\n", + "plt.plot(t, h1[0], label=\"river modeled with image well\")\n", "for ystart in [-50, -100]:\n", - " ml2 = ModelMaq(kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100)\n", - " w = Well(ml2, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", + " ml2 = ttim.ModelMaq(\n", + " kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100\n", + " )\n", + " w = ttim.Well(ml2, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", " yls = np.arange(ystart, -ystart + 1, 20)\n", " xls = 50 * np.ones(len(yls))\n", - " lss = HeadLineSinkString(ml2, xy=list(zip(xls, yls)), tsandh='fixed')\n", + " lss = ttim.HeadLineSinkString(ml2, xy=list(zip(xls, yls)), tsandh=\"fixed\")\n", " ml2.solve()\n", " h2 = ml2.head(20, 0, t)\n", - " plt.plot(t, h2[0], '--', label=f'line-sink string from {ystart} to {-ystart}')\n", - "plt.title('head at (x,y)=(20,0)')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + " plt.plot(t, h2[0], \"--\", label=f\"line-sink string from {ystart} to {-ystart}\")\n", + "plt.title(\"head at (x,y)=(20,0)\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend();" ] }, @@ -131,7 +135,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -143,15 +147,17 @@ } ], "source": [ - "ml1 = ModelMaq(kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100)\n", - "w1 = Well(ml1, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", - "w2 = Well(ml1, 100, 0, rw=0.3, tsandQ=[(0, 1000)])\n", + "ml1 = ttim.ModelMaq(\n", + " kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100\n", + ")\n", + "w1 = ttim.Well(ml1, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", + "w2 = ttim.Well(ml1, 100, 0, rw=0.3, tsandQ=[(0, 1000)])\n", "ml1.solve()\n", "t = np.linspace(0.1, 20, 100)\n", "h1 = ml1.head(20, 0, t)\n", - "plt.plot(t, h1[0], label='impermeable wall modeled with image well')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)');" + "plt.plot(t, h1[0], label=\"impermeable wall modeled with image well\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\");" ] }, { @@ -173,7 +179,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -185,19 +191,21 @@ } ], "source": [ - "plt.plot(t, h1[0], label='river modeled with image well')\n", + "plt.plot(t, h1[0], label=\"river modeled with image well\")\n", "for ystart in [-100, -200, -400]:\n", - " ml2 = ModelMaq(kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100)\n", - " w = Well(ml2, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", + " ml2 = ttim.ModelMaq(\n", + " kaq=10, z=[20, 0], Saq=[0.1], phreatictop=True, tmin=0.001, tmax=100\n", + " )\n", + " w = ttim.Well(ml2, 0, 0, rw=0.3, tsandQ=[(0, 1000)])\n", " yls = np.arange(ystart, -ystart + 1, 20)\n", " xls = 50 * np.ones(len(yls))\n", - " lss = LeakyLineDoubletString(ml2, xy=list(zip(xls, yls)), res='imp')\n", + " lss = ttim.LeakyLineDoubletString(ml2, xy=list(zip(xls, yls)), res=\"imp\")\n", " ml2.solve()\n", " h2 = ml2.head(20, 0, t)\n", - " plt.plot(t, h2[0], '--', label=f'line-doublet string from {ystart} to {-ystart}')\n", - "plt.title('head at (x,y)=(20,0)')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('head (m)')\n", + " plt.plot(t, h2[0], \"--\", label=f\"line-doublet string from {ystart} to {-ystart}\")\n", + "plt.title(\"head at (x,y)=(20,0)\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"head (m)\")\n", "plt.legend();" ] } diff --git a/notebooks/well_near_wall.ipynb b/notebooks/well_near_wall.ipynb index ccfcdf3..0cbaf4a 100644 --- a/notebooks/well_near_wall.ipynb +++ b/notebooks/well_near_wall.ipynb @@ -9,7 +9,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -27,8 +27,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", - "w = Well(ml, 0, 20, rw=0.3, tsandQ=[(0, 100)], layers=0)\n", + "ml = ttim.ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", + "w = ttim.Well(ml, 0, 20, rw=0.3, tsandQ=[(0, 100)], layers=0)\n", "ml.solve()" ] }, @@ -59,7 +59,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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/4sPfcimoaeHzO6ZYXJrS6vTct/EYdS0avlhjvkaNKIp8nlTAv39MQy4VeHLpWG6aGmzT90kURdq1ehRSicW1d29nFf9aPo41s8N49ddM3t6TTUpxPetuTMDZjPdbEAT+vXwcjW0dPLctnWg/F2ZaSDK4akIAXx8r4uUdmSyP87dKruOWaSG8vSebL5IKePTSnmSyBo5Lxvny9y0pbDtTxlg/Z5uu3Rumhrnz2q9ZJOfXWlX3v3b9QZtdU7tWz7MrzJvRUKlUfPvttzg7O1NVVcXUqVNZtmwZq1atYsWKFTzwwAPo9Xq++OILkpKS2LFjB1lZWSQlJSGKIsuWLSMxMZGgoCAyMjL48MMPeeutt7qco7q6GrVajUxm+NwEBARQXFzc7VoEQUAmlZBXZb3EyLAM/o1tHWRWNHLJ+MERu+oJh3NqaNHouGgQmlJbjhej04tcO8m6rL+hrYP1e3OYF+nJ9AH0Qv67+yxH8mp59dpYxgeYVy7q0Ol5ZPMpvjlezKxwD168Ktbi/kB1UzvfnShhX1Ylze06Wjs6fzQ62jrO/b/YyRCaG+HJvCgvZkd4WkSHDXSz55Vr4pgS6sbj36Zw9dsH+eD2Sfir7fp9rVQi8OJVsWSUNfLglyfY+sAsi3jggiDw14ujWPHWb3x8MN+qGQ5PJyXzorz49ngxf1kcaVNZEzcHBfFBruzNqBgURltPiAkwNP5PFNYNm6avRDB8tnV6PVJJ3++vKIo89thjJCYmIpFIKC4upry8nJCQENzd3Tl+/Djl5eXEx8fj7u7Ojh072LFjB/HxhtnXpqYmsrKyCAoKIjg4mKlTp/Z4jgvRW8NfJhEotIG+1LAM/qeK6hFFw+j7UGFXegUqucTman+iKPLV0SISgtRWl0fe25dLfWsHaweg25OUW8MbO7NYEe/PFfHmDa+1agwDZLvSDYHiT/NGm52Vd+j07E6vYHNyEbvSK9DqRcK9HPFwVOLppMROLkUllyKRiJTUtlHe2I6jUoaHo4JdGRV8c7wYqURgQrAr86O8mB/lRbiXo1kMmGsnBeGvtufuT5NZvu4A79860azyoZ1Cyn9vSGDZf/fz8KYTbLh9skW7kIQgV+ZGerI+MZubpwXjaEX2f2WCP7+klnMgu5o5EdZPc56PeZGevLQjk8rG9iEZdLJXyIjwduJEYZ1V63x55zQbXRE0tHaQV91MW4ceB2Xfwf+zzz6jsrKS5ORk5HI5ISEhpqnj1atX89FHH1FWVsbKlSsBw3f+0Ucf5c477+yyTl5eHg4OPU/0e3h4UFdXh1arRSaTUVRUhJ9fz71HqUSguLbVarrnsKz5Hy8wMB3ihqjeL4oiuzMqmD7Kw+Ysn5NF9WRVNHHVBOuy/ppmDe/vy+GScT4WN3nrWzp48AuDQNvTy8eZ/Zqb3z/M7owKnrliPPcvCDcrEKaWNPD0D6lMfWYnaz5J5lhBHStnhrL9wdn88vAcNq6ZygMLwrl+chAvXxOLr7MdHk5K1i6MYFmsHy0aHbvXzuXru6dx15wwGtu0PLc1nUWvJjLz+d3847sUs5g9M8M9+Pqe6SikEq5Zf5DtZ8rM+r0jfZx4cmk0+7KqeH8AYmgPXRRBXUsHG37Ls/i152NelBcudnK+OVZk1To9YW6kYWgyMbPS5mv3hrhAtWH+w8qGuK2gkhtCnzkaP/X19Xh5eSGXy9m9ezf5+efUk6+44gq2bdvGkSNHWLx4MQCLFy/mgw8+oKnJUJcvLi6moqKiz3MIgsC8efPYvNngf7VhwwYuv/zyHo+VSQS0epHS+tb+f9E+MCwz/xOF9YR5OuBiPzT8/vzqFvKrW1g9M9Tma285XoxSJmFJrHVzAx8dyKWlQzegrfq/f0qlorGdr++eblY22tahY+WGI5wuqmfdDQn9ituJosi2lDLe3ZfDsYI6FFIJC8d6c9WEAGaFeyARBDYnF7Fu91lyqprILG/ilqnBzAz3YMEYb2I7lUJbNFqO5tdwpqSBmeEeTAh24y+Loyitb2V3eiW7MyrYdLSQTUcLefzSMdw0NbjPnUCEtxNb7p3B6g1HuPvTZN6/dRLzovqfFr9+ciB7Myt4cXsGF431tkh/KTZQzYIoL97dl8OqmaEDTiaUMilLY33ZnFxEi0ZrUxOjaD9nPJ2U7M2s5MpBkjC5EPGBajYmFZBbPTCdH1tDLpUgEQTatf0TA2688UaWLl3KxIkTiYuL43zDKYVCwbx581Cr1Uilhr/1okWLSEtLY9o0w07F0dGRTz/91PR8b3j++ee57rrreOKJJ4iPj2fVqp6db43ZfmFN64Cn+sEGwV8QhEDgY8AH0APviKL4uiAITwF3AMb04jFRFH82Z8200gYmBA9dyedwroGGNs3GMwWiKPJLajmzwj3Najz2hg6dno1HCpkf6WXxQNPpono2HytizawwU5DtC6Io8tg3p0nOr+WtG/sP/C0aLU9sSeGbY8WEejjw9yVjuTLBv5vbUGFtC3MiPHnj+nie2HIav846/PjzdjEFNS1IBIFQz67B1tfFjhumBHHDlCBK61t55OvT/P27M+xILe+3B+HppGTjmqlc9fZB7t94nG/umd7veygIAv9aPo75L+3l6R/O8OHtlomtrZoZys73Kvj5dCkrEgYeXC8Z58unhwrYl1XF4mjb9b8EQWBKqBtH8moQRXFIzFaMzeX00sZhEfwFQUApk/QZ/I2Zu4eHBwcP9txs1uv1HDp0iK+++qrL4w888AAPPPBAt+NTUlJ6PV9YWBhJSUn9XrusM/iXNViX+dui7KMF1oqiOAaDicu9giCM7XzuVVEU4zp/zAr89a0dFNe1EuU7dBLCh3NrcHdQMMrTtraEaaWNFNe1snCsddpEO86UU9nYzo1Tgyx6nSiK/OvHVNzsFdw7f7RZr3l7bzbfHC/m4YUR/Qb+zPJGlr65n2+PF7Nmdhh3zQljSqibKfCf38RauyjSlGU6q+QU1BhKN4Jw7riMskbaOnR9Nmh9XezYcPsk/rV8HEfzaln8WiLfn+xbM95eIePdWyeilEtZ/fFRs/TQvZxUPHhROLszKtmVbpkRyrRR7oR6OPD5Yeton5ND3XCxk7M9xbySlSWYFOJGaX0bxXXWBRBzMdrLEYkAGWW2MyOxFgqZBI0VstypqamMHj2aBQsWEB5uOe16oDCWX0stnGnpto61FyKKYqkoisc6/92IwchlwDKHGWUG/vEY36GhoQEcyathcqibzTOgX1LLEQSsdr369FA+/mo75kRYdhP56XQpSXk1rF0UadbOY/uZMl7cnsHSWD/u6+dmsTm5iMv/e4D61g6eXBLNkbwatqWU8dFveazbfRYweMheiBaNlhaNDnu5lF3p5TRrdAiCQLtWxzuJOfxlcf9SFYIgcPPUYH5+YBahHg7cv/E492083ieP3F9tx/qbJ1Ba18Y9nx0zaw7glmkhjPJ04OkfUi3S7hcEgesnB3I0v9b0eR4I5FIJF43x5te0cjRmlCcswaROifQjeTU2Xbc3qORSQj0cSLfi/bA1lDIpGq2IfoADb2PHjiUnJ4eXX37ZxlfWNySCgIud3OKBxm7r2Oh6ABAEIQSDtv/hzof+JAjCKUEQPhAEocc6jiAIawRBOCoIwtHKykrSOzODKJ+hyfxL61sprGk1fRlsiZ3p5cQHqq1iVORUNnEwp5obpgRZ1NnXaPU8+3M6Y3ydzaKY5lY189CXJ4jxd+HFq2J6vRGKosi/f0zlz1+dJDbQhZ/vn4VSLiE2QM2Ht0/m9hkhbDleTGNbR48NYrlUwt5MQ/1+w2/5XLf+oGkOYm6kJ5E+TuzPqjIFpb4mUUM9HNh81zTWLoxg6+lSLn5tX5/BdkKwK89dOZ6DOdW8sC293/dEIZPw5NJo8qpb+OhAXr/Hn4+rJgSikEn49JDF1qpdcMk4HxratDZ3n4r0ccJJKRsUGYneEOXrbHHwH8xJZIVMgohIh41vrIMJ4/vh46z6/TN/IwRBcAS+Bh4URbEBeBsYBcQBpUCPt0dRFN8RRXGiKIoTPT09ya5owlEpw2eIpAJOdbobxQfZllnU0NbB6WLrTRe2dm75VyRYtpn64WQJxXWt/PXiyH5vGqIo8tT3Z5AIAutvnthnk/Ltvdm8tz+XW6YF8+mqKXg4KimoaSE+SI1Gqyfaz4VAN3sOnK3q8sXt0OkRRZHfsqvR60UmhLixYeVklsT48uH+XD47VMAnB/O5fN0B1idmU1jTQlppQ7+7MZlUwn0Lwvn2nhnoRZHr3jnYpyPWioQAbpwSxPv7czlpBvVwdoQnM0d78MGBXIuybzcHBRdH+/DjqRKzp417wsxwDxRSCfvPVg14jZ4glQjEBLoMintYbxjt6UhhbYvZLloqlYrq6upBuwEYZVY0Vvx9hhKiKFJdXY1KpcLLWUllo3VWojahEAiCIMcQ+D8TRfEbAFEUy897/l3gR3PWyq1uIcTDfkiaUABnShqQCBDlY9syU3J+LaJoqNtagx2p5cQGuODr0v+gkhGiKPLuvhwivZ2YawZH/Ne0CvZmVvLEZWP6bJ5+dbSQF7ZlcHmcH08tjQYM9cdWjY66lg7T9jk+UM2ponoWR/uYvrjGaeAdKWW4OSqY3jlPUdbQjlwqEBek5q65o5gS6oaDUsbJwjoe+OI4jkoZfmo7LhrjzYIxXr1OSI8PcGHTndO48b3D3PT+Yb69Z0avLJ1HLonil9Ry/vbNab7/04x+J5VXzQzl9o+O8PPpUpbHm38TXhrrx/cnSzhwtspEr7QUKrmUhGA1v2XbNvgDjPV1ZsPBfLQ6/ZD4Y4d5OiCKkFfdbNb3LSAggKKiIiorB4eSqtXrKa9vR1Mlt2oieyihUqkICAjA3aGOvGrr5D9swfYRMPjzpomi+Mp5j/uKomiUD7wC6L3NfR7yqpqJMXP61BZILalnlKejSenPVjiSW4NMIli1oyirb+NkYR1/WWzZUFdiVhXpZY28dHVsvzfRtg4dT/94hghvR26dHtLrcbvTK/jbN6dNU77nl3SmjXJnd3oFDa0dBiGvIFfe2JVlOvemo4XUNGuYE+FJaUMbhTUtJpZPeUMby2L9uunXxwaq2fHQHADu/ewY63afxbXTH6BDq+NAdjVxgeourKIQDwc+Wz2FK946wMqPjvDN3dNx7cFDwFkl5+nLo7nr02N8sD+XO+f0PY07J8KTME8H3t+fy+VxfmYnJrMjPHBSyfjxVOmAgz/A9FEevPprJnUtmm4sKmsw1s8ZjVZPTlXzkHg0G1k+uZXmBX+5XE5oqO3p10ZotHqW/X0r988P56Ehmna2FTwclVQ1WqeVZIvb/QzgZmC+IAgnOn8uBV4QBOF0p7H7POCh/hYSRSiqbbGJr625OFPSQPQgaJwczasl2t/FKn72L2mGzdMiC41a3k3MwdtZaZY66fq9ORTWtPLUsuheM+BTRXXc89kxxvg68fZNE85tl7V6EjMr2ZNRgUwqmJQbR3s50tahMw30fJFUQHZFEy9uzyDE3YF1NyQQ6GZPfUsHTe3aHsXldOcNA1U2tvPAReGmSde7Pj3G7R8eYf7Le7n1gyRSS84xSEI8HHjnlokU17Zy5yfJaHvZ0i+O9uGiMd68+msmhTV9D41JJAIrZ4RyutgyqWWlTMriaB+2p5RZZRg+bZQ7oojN6/5jfQ034PPfv8GE8XudMwiCdQOBQibBy0lJyRAxnmwJd0clrR26TkeygcEWbJ/9oigKoijGnE/rFEXxZlEUx3c+vuy8XUCv6NDp0YsQNAj2iT2hsa2D0vo2Im1c8tHpRU4V15FgZR9hf1YlAa52FlkEltS1sv9sFTdNCe5XOrqxrYP39uVwcbRPr1pB7VodD286iau9nA9vm9xlSGxzchGfHc4n0tuJeZFebE4uYv3ebB7edILrJwdhTJBfvTaOF6+OZf1NCbRrddS3dgCGhrhcKukxC5RKBNo6dLyTmI1KIWVJjOFG9sH+XOwVUmaGe1DboiEuUN2tVDUpxI3nrhxPUl4NH/UyaSsIAk9fHo1eb/BZ6A9XJgTgoJDy7XHLJm4vGedDY7uWY/kDb6zGBqhRyCQcK7BOHuFChHk6IJMIZFrpQ2AuHJQyPJ2U5FtZrrAlfLGi7Y4AACAASURBVJxVlDXYziB+qODeuaOtbhp49j+s5B2MjTE/M4S4bAGjMp6tdxq5VU20degZawVdVa8XOZpXazEFdVtng3iJGVn/xqQCGtu13Duvd1rn+r05nK1o4j9XjO/GWrp+ciDrb57IbTNCWTDGmzWzwyhvaGdJjB9XxPsjCAKiKBLkZo9Gq0cuk+LvasfXx4r4IqmAbSllLI727taQNvYJdqdXsC+ryjTVnFfVzOHcauZEenHf/HAmBLny9p5sE0PsfFwR78/8KC9e+SWz18zOT23H5XF+bE4u6pf7b6eQMjfKi19Sy7vsSvrD5FA3pBLBKj17hUxClI8TZ0ps25yVSyUEuNqR38/Ox5bwU9tZzVKxJTwclVYF0N8Lzp2Ch8ZEaiAYZsG/k8Y0ADvBgSC3MwOxdfBPKTYEI3OVM3tCZkUj1c0ai9U7fz5dyhhf535/J41Wzwf785gW5t7rdWZXNvHfXWdZEuPboyyCMbgbb9qzwj35x9Kx3DAlyMQYEgQBQRBMu5AbpwQzY7QHiVmV3DE7jCviAwzaSmkVnK1opLZFgyAIVDa2s+loIXMiPInrnExOK20gv7qFlOJ6Xv0lE39XOwLd7Ljz4+RugVEQBP65LBq9KPL0D6m9vg+rZoXS1qHn86T+B7IWR/tQ1aQxaU+ZAyeVnHH+LlY7WUX7OZNS3GBz5kuwuwN5Q1iG8VerhmywzBy4OyqobraONfN7wKh22/D/T/A3BJGhonkaP/TBFhiamIOU4nqUMgmjrRhj/+2sUXLCfJXRsvo2jubXcsm4/qUAfjhZQllDG2vmhPX4vFHmQSWX8I+lY3s8BgxB1hxd/6Z2LV8nF/HJwTzOVjSh18OzP6cx/+U9RDyxlds3HOGiVxKZ9fxuXvs1k//8lEqYpyNXnac9k1PVzORQN+6eO4qNa6ZS39rBzdOCcVLJuO3DI91q94Fu9ty/IJxtZ8p6ndKN8nFm5mgPPj6Y1y+Vc16kJ3KpYLZInBHTwtw5WVRnVX12rJ+Lafrdlghxtye/umXInL18XeworWsbsvP1B2PmP1wE58yFy/9/mb8eJ6VsyGhX+dUt+DirbK7kmVbWQJSPk1X0uaP5NQS62ZmlRW/EngyDcuDFZgT/z5MKCPdy7JUKuiejksO5NfztkjF4OVl3M86pbGL5ugOs/eokL+3IZMvxYrIrm7BTSJkU4sbKGaE8cdkYXrs2jlnhHrz2axY7UstZNNa7C7sl0tsJe4UM787kQCYRCFDb8/GqyWi0ev72zalu5149M4xRng68sC2j14Bz+4wQyhva2duPyqWTSs7UMHf2ZFhGPZwS5kaHTrRK0thYQky3sQNXkLsDTe1aalsGHkQsga+LitYOHQ2tA78R2hJuDgq0epHGtuFxPebCKHr5/03w1+lF3BxtR2XrDxWNbYNSYsqvbiHEylJSWmkj0b6WlY2OF9ShtpcT3k+DuLqpnWMFtVwW49trP+HzpAI8HJVcPbGrMJlOL/Yoo9Ci0fLlkYJuAXZXejmXrztAdVM7H9w2kfR/Xcyppxbzy8Nz+Gz1VF69No5HLo5iSYwf/q52PLUsmk9WTcZeIeW2D4/w1dFzVncR3k5klTey40wZezIqSCttJCbQhdFeTtw7bxQHzlZ3K8koZBJumxFKelkjZ3phtcwK98RBITXdPPvCxGA3zlY20dRufrCI7gzcmVZIGxjLeAU2rs97Oxv6OBWNQ1OHd+/8fg+XUoupfNI2NDc/W8Gxk0XYbKYVZU8YVsFfqxdtymPuDxUN7XjZ2MyiQ6enpK6VYCsYS60aHXnVzURaKHFxorCOuEB1vw3iPRmViCK9upaV1bexK72CqycGdCvpfH+ymFkv7OZsxTkPUVEUWbvpJI9+c5rUUkOA1etF3tyZxaoNRwlys+f7P83E21nFrvQK1u/N5vFvT3PLB0nMf2kPUf/YxtRnd3L1/x1k7ot7yKls5sc/zSQ20IW/bD7F2k0nadFo8VOruHZSIJ8nFfDjqVJevCoGLycVoihy45Rg1PZyk67Q+VgW44dCKmFzcs9MHYVMwvTRHuzNrOy3HBET4IIoYtFkrKeTEhc7OZkVA/dddbWX46CQ2jz4G3d1FQ1DE4zdHAzft9ph4udri8bp7wHjXFKrFaXEYTXWptOLuA6Rhj9AeWMbk0JtKx1dXNuKXjTUmweKrIpGRNEyfSOj9WV/SpxgcC3zdlb2Ot/w1dFCdHqR6y7QBNJo9bz6SxaBrvaEnbez+exwAVtTynjs0iii/VxoateydtMJtp8pZ3mcH49fNpYntpxm+5lzdXe1vZxAV3uifJ1YONabADd7fJ1VfHwonye/P8PPp0t5bkUM354o5s1dWZwqqmPdjQksivZh0QXyxoIg4KCUsXJGKK/8kklqSUMXf1oXezkLo7357kQxj106pkcK7JwIT35JLSe7srlPaq2xOX6qqI6pYeb1YwRBINLbyarMXxAEAt3s+51JsBTG5KfCSqkAc+HWmdzVNA+PYOukMoTAP1rZRyGTIJcK/39l/q5DlPm3aw2SBNbWsy+E0VvTmuCfWW7IECMsCP4GJgjE9TNboNOLJGZWMi/Sq9cdwuZjRUwf5U6we9fS1Q8nSyioaeEviyNNE74VjW38+6dUZoV7sHpmGKIocvuHSfyaVsETl43h38vHcVvn//9lcSQ/3T+TU08t4sQ/FvHtPdNZuyiSuEA11U3tfHu8mGlhbjy7YjypJQ1c9uY+LhrjxScrp1DbomH5ugN9unjdOj0EJ6WM9/bndHvuqgkB1LZ09OpeNTfS0PvYl9V3Pd/DUYm/2o7TxZYNRoV7O1rNpw90s7eJd+v58Oos+1irE2MuXB0MyV3NMCn7GNVu/2hlHzBIlbdYUH68EMMq89eLolW+p5bAeKdX23inUdVk+FBbU04q7WR0WNLsLagxMJfC+uk1lNS10tiuNdEnL0RlYzv51S3cPDW423N7MyvxdFKaAiXAV0eLaOvQ89SyaCQSgV3p5RzJq+Xfy8dxw+Qg7v38GGmlDay/eQJKmZR9WVVkljWSUd5IVkWTiWEjCODtpOKn06VcMs6HLffO4JYPkljzcTLf3zeDLffOYMHLe/nv7ixeuCq2x2t3sZMzL8qLfVlV3UxKpoW5I5UInCyq46IeJqYDXO1xc1CYbrx9IdTDgSILg7C/qx0NbVqrXLk8nZQW0UzNgZ1cilwqDFnwc1Iavm9N7QPPWG0JY/nEmgns3wv9mdH0h2EX/G2tsdMbmjqDv61vNrWd21lrdjAVje242MktYiEV17UhCP3PSBhr9b2VNk4VGRgpFxqei6LI4dxqppw3dKbXi2xMKmBamDujPB0RRZHXf80i0M2OaycF8sauLFM56KdTpWw5YTBd8XFWEenjxIzRHoR5OKDVi+RXN3M4twZvZyXbzpRRXNfK81eO546Pk7nzk2S+WDOV6ycH8cmhfP40L7xHSQgwqHB+f7KEMyUNXbyOVXIpoz0de236giGo51b1H/yXxvpazFYx7jCrGjUEuQ/sM6e2k1PX0mFT9y1BEHBUymi2IoO0BLaoVdsSxu9Ye8cfQ9nzfChkEqsUSYdV2UcUDZnIUMDI1rB18K9r0SAI5xpJA0Flo+WN6NK6VryclP1y7rMrDcGtNyu9U0X1SAS69QPyq1sob2hnynl17sSsSopqW7lhisFhbE9GJSeL6rl37mh+TS3ntV+zuDLBn7yqZracKOGhiyI48Y+FfLJqMrMjPMkqb+SfP6TyxJYUPuzUyz9VXE+wmz1Z5U38ZfMpHloYzvGCOh7/NoW75oQhkwi8vjPLdA15Vc28tD3DNHU7N9ITQYCdad2ZO2P9nPvUsTEE//4Hnq6dFMQds3uej+gNxunoyqaBs2pc7Q20REuYRubAQSkzJUODDVvUqm0JZWf/p80KR6/fCwqpxCqTn2EV/IEhy/yNZR9HlY0z/5YOXOzkFhmvXIiKxjaLDWBK6lvNksU4W9GEm4OiR7VLgNPF9Yz2cuw2a5GUazBXmXqeRPUXSYW4OyhM/rJv7MoiwNWO+CA1D286SXyQmkBXOz5PKuTuuaOQywQufm0fC19N5F8/ppJf3cI1EwN48/o4Lh3vS25VM6M8HKhobMdeIUWnF3nt1yyumRjA5uQifk2r4OapwXx7vMgUpNPLGvnv7rMkdtbqPRyVxAWqexzqivZzpqyhjeqmnuvNoR4OlDe0D0oW7OnY2Vi1glVj5HbX2ZiT76iU2fyG0hfs5FJah0nw/6Nn/tZ4RVgd/AVBCBQEYbcgCGmCIJwRBOGBzsfdBEH4RRCErM7/mkWrUQyBrjhgsuVTymx7s2nWaHGwQskTDLsSSw3f61s7zCo1VTVpTENSPaGkrpUgt+59g4KaFgSha7nodHE9syM8Ucgk6PSGIabL4/z4Lbua1g4db1wXz+ZjxcwK92BSsCsvbMsg3NuR51aMZ/8j89j157ncPXc0b+3J4efTpUT7OXO2spmVM0KpadFweZwfUomAgGHAa/uZMq6fEoReNPglwLlG7fkZfWyAmuzK7hl8gKuhVNSbkJdxt1VjhsevpTCySqwJssbPVYuNA6dSLrWqdmwpFLKhPV9fMJqh/1EMXc6HVCKg1Q18MnkwDdz/BuwURTEc2Nn5//3CmozZEhiNR2x9Pr1eRCa1bk2tzvI1OrSiWTfOdq0Olbz349o6dNj3sPtq0ehwUMi61JprWzS4de4g6ls7EEVD5p1b1YyTSoZCJlBU28qcCE9+TinDWSXjw9smcd3kIAJc7cksb2Th397l2Hcf8HAsbLxjKtF+zmxNKWVyiBu70iuYFOzKkfxaRns7UljTYmqCl3cGcJVcip1c2mXwzNlOTlO7tpsAm/H3busly1Mas8A+ApNeLw5ImkApk+DrojKdYyAwflYtEZYza12BAfvYDuh8ksG1Z7QEkiEyjRoMCAJY8y5aXfPolGou7fx3oyAIRgP3y4G5nYdtAPYAj/S33hDFfow3eqmN//havWj1DaVDrzdLL6fLa3R65P1IOIMhsCn7OO616+J7DP6tHdouj7d16GjR6EzB3zi04+agYFd6BWEeDpwsNAxCxQaoWZ+Yw+wIT5PkxcHsam565mNyP3kUQa/lz4e+JKdqAwlBY/jkUD7TR3lwOLeG2eGe7MqoZHa4J8V1rcilEtT2ckrrz2ncqO3lXUohzudl2S7n9V7ObfF7zpyNN8++6qg9+RKbAy9nFQcfXTCg1xohG6zgLxFsvmZfkAjCkN5s+oLxz/lH0/YBw/tozU3UpgXvCwzcvY0a/qIolgqC0KOVkSAIa4A1AAqf0UNm32j8sEtsXGXS6UWrbyhanWj6opsLjU6P3IzdQrtWj7qPZnRvFNDm9q47AmOwNVJljZLIansFOZXNTApx5VhBHXKpgFRiUOk0ulltPV3KA1+cQChLQ6LXotPpaGvX8P6mH/Gd7YiPs4rU0nqkEsGkOdOh09OhEylraDNosNefq5272Mm7aNOcG9zp6DH499bcM94U23t4PrO8kf/bk01WRRPhXo6EezsRH6QmJsA6wx5LYEwqtHrbligE4fcI/kN2uj5hzPyHy/VYAgHrrnswDdzNwvkG7mDdNuZ/GaKIeW/eADOFC5c3BiDjh88YPHR6vWlX0NjWgUQQTAyj1s6M+6vkIjQ6PU6hMUhkciRSKUikqILG09JZkkkraUAqCKb5BeP4fXuHDq1e7FKjFUXDeS9EbzGyv7fgwqfTyxp46MsTLIr24W+XRBET4EJOZRNPfX+Gf3x3hsY/4IDQ741hkvibIP4PRp5BM3AHyo0+voIg+AL9q2ZBr7Z7toZS3v8WfyCwk0tNQW6gcFDKaLaQB+1sJzdrUMfdUTkgEa8AVzu2pZQadjYSAX+1HR6OCo4X1HLz1GBiAtTIpQKHc2u4ZlIg6/dms3HNVL47UcI3x4qZPsqNv29J4WheDUtifJkf5cV3J1wpu/pfaApTmDB1BmPiJnI0r5bKxnYEwaD9nl3ZTLSfE2dKGogLVCNiYCzd2EkvbW7XklXRyOLoc4NbmeVNqOQS/F27sp+MfYLeGt7VnbsX9wuYUGmlDYR5OprUUmeMPuex8MqODJ7fls6/l4+3+D21FMbPla0ZcRqt3ipqsqVo1+r7dZkbKnR0ZgiWllmHA3Si5RWC82ELtk+PBu7A98Ctnf++FfjOnPWsoS5ZAhO/18YUL3ul1Go2hpNKZrHWiNpObpY4laejckCj/CHu9nToRJMrliAITAx242inp62dQkpcoJpD2dXcPj0EmUTCDydLePyyMRzMqWZ+lDf3LwgnMbOShzed5OkfUnFQynjk1mXc/cBaqh1C+O5ECcV1rUgk4KdW4eGkRKPVc9PUEHKrmrlteojJqcwYiE8V1aMXIT74HJksvayBCG+nbr2Xsk4Hqd4G4YwUUA/HrjRbNwcljW0dbD9TZioJGWvEjipZj1vvVo3O5pRR4+fKXm7bMtO6GxN45opxNl2zL3To+u47DSXkEgm//W0+N0/rPtE+3KHVWddftMWnyGjgfloQhBOdjz0GPAdsEgRhFVAAXG3OYrbOxHuDqfln4+EOe4XMKtMOMOjGW6oyqLaXd1Ha7A2eTkqqmjSmDP58iKJokqPW6vRd/AiM9M/86haTbtHEEFe2nSmjoqENL2cV08Lc+e/us6gUUlYk+PPV0SIeeCScn8M9eOWXTLY/OJv7548mOb+WX1LL2ZFabtLGjw1wYX6UF1tTyvBwVLAkxo83d53l2RXj2XGmDE8nJZeO92X5ugMkBKnxdTFk9ccLDTefuPMmkjPKGpnfg/NYaX0bCqnEJC52IaqbNajkki69DVEUmRPhiVQQ2JhUwKYjhfi4qAjtnEdIK21g5czQbmv9dLqUsxVN/O2SKERRZF9WFVkVTYR62DMnwmtAX1rjVKytM39LZERsAY3WvP7UUEAiEYbMNtbW0FnJLBxMA/dqURQXiKIY3vnfGnPWGyr+r0o2OJoe9gopbR16q8pXTiqZxfZsanuFWfx0L2clOr3YY/YvCAKv/JJJSnG9KfCX1rfS1K4lsLOEcr5f7qQQw8DX9lTDQNX00R7oRfgiqYA7Zoeh0el55OvTBt0fQWDWC7t55ZdMKpvauWFKELvXzmH7g7P586IIRAy9ALW9jKWxfry9J5tFY72obmpnd0YlN0wO4kheDamlDSblUlEU2ZVmYBYZh9YKa1qoatL0aApfWNOCj4uqV8ZOWX0bHo7KLqQDQRBo1+qYGe7Bk8vGcuPUICJ9nGhq1zLay5HXr4tnXmT3G01iZiWhHoab5CeH8vkquYgThXW8sfMsD286MaA+gXEq1kE5NIOQgwFRNPRrhkvZ548MA7Nw4O/jsNL2kQjCkE0a2sIJpycYSwZVTZoBG8X4q+34JbUcvV40m1oY4m5PdbOG+pYO0+/WE4yOUCeL6vBxOSeNbDxXfJCa9/blcMfsMD49VIBeL9LYbniPwjwc+P5kCatnGaQNYgJcmBjsyuu/ZnJ5nB+TQ9xYNNab57amE+njzNOXj+Mf36VQ39rBK9fE8vWxIt5JzEHbWSexV0iJ8nFirJ8z100K4t55CjYmFfDmrrPMjfSkqV3HSzsyWRrrx5IYX65Zf5BwL0eum2yo929LKeNofi3/Wn6uZPHV0UIkAiy+wM1Mrxc5mFPdq3MZQEpJfbebhlanZ3NyEUvG++HlpGJ+lIrjBbUIgkCoh0MXNlGX84miqbew9XQZTy2LNvkzrProCKklDV2kMsxBZefksy0kUPR6EUE458M8VCy7tg49Or2Ik4VDjLbG+RTJofrdbY32Dp1V5bNhdfuVSoQh09UeLF1xo/9weS9TpOYgyM0ejVZPuQWN2XBvw+Tt2cq+ZYPH+bsglwocu0Ad0vj5v2qCwcDlrT3ZjPJ04LrJgdwzdzTTwtzRiSKniupN0sSCIPDk0miqmzW8uTMLiUTg1WvjiPB24k+fH2NamDvrbkjgdFE9L27P4Mml0Zx5ejE/3jeTF66K4ZqJgcikEr47XsJj357mzk+SOXC2ilunBZNa0sDR/FqeuWI8z68Yz/1fnECrF3nnlok4KmW0dej4z89pRHo7cX2n74Aoimw5UcKM0R7dShlnShqoadYwK8KDnlDf0kFOZTPxF0hip5U28vnhAlzs5VQ1tfPc1nTeSczh5R0Z/OO7lB5dzQBWzQzlpR0Z/HiqhGg/Z9LLGkw9hZL6tm7NaHNQ3tCGt7PKJsFKIhFM6wxl8DOSEiydYLcVjEFfEATTzx8VaxdFsiLBf8CvH2aZP0NGm7NTSFHKJL1+eQcKY7Zf1tBGz8LD/cNoKJ9f3WKqbfeHcC9DVplZ3sSEYLdej1PJpUT7uXAs/8Lgb/gS7M+qQi6TsHpKEBHeTiaNH3dHBXsyKymqaeHr5CIevXQMYDA3uWZCIB8eyOO6yUGM8nTkvVsncvl/D7B6wxG23DuDDSsns+bjo0x/bhezwj3wcVbh62JQ9pwT6YmPsxJRhPyaFpJya9jwWx4h7g58dPtkxvg6cf8XJ0gva+CDWyeZ7Azf359LUW0rn62eYipRnSiso6Cmhfvmj+72exu1f2aF95z5n+xUM429QM00v6bZ9Pc4mF1NRlkD625MwF4h4/Vfs3h5R2aXnYcR8UGuvHhVLB8fzKOkro39Z6vIrWomp7KZKaFuJqkJS2AL57n61g6+SCrgaH4tj14ShUou5VRRPaM8HRjl6TjgITZzYSxnOtlYU8tcCILAwexqThXVoZBJ8HZWEehqT5C7fa+7uOGKKycE9H9QHxhWwX8oM38wTKNW21jHxdtGmT9AQXWL2W5R/mo77ORSMsxwi5oQ7Monh/Jp69B1kY2ub+1g09FCVs0MIz7oHHtmV3o5L+/I5MqEAAQEvj5WzP0Lwk03hj8vjuTn06U8+vVpPlo5iQBXe9bfPIHr3z3Eqg1HWXdDAl/fM50fTpaQmFVFZnklFY3t3bjeCplBpXBZrB/PrBiPTi/y+JYUfjhZwl8WRzKvs4mbU9nEut1nWTTWuwvtcnNyEQqppJvTF8Du9Aqi/Zy7MXmMOF5QhyBATGBX3+QAV3vkUglnK5rwdlYxM9zTNNTlpJL1Sa8d4+vMQwsjyKtqobyhjQ6d4XcLcR+Yv3N5Y1u3m5Ol+M9PqbjaK5gV7sEHB3LRiwZdpMrGdm6fEcKqmaGDmg0b36/fK/i/uTOLkvo25FKBqqZ2Khvb0Wj1RPk488BF4X/Y5u9AMKyCv0wiGRRRrd7g5aS0Kkj3BHcHBfYKqVnSwL0hwNUeB4WUMyX1QGC/x4NhGz8+wIUjef331edGevL+/lx2plVwWcw520cXOzlavUhudTMzwz34+XQp28+UUd/awe0zQrlqQgDj/F249p2D/PunNJ5dYeC2ezop+dfycTy86QTXv3OI92+bxMQQN169No6/fHWKi19P5LkV41m7KJK1iyIBA92vorGdsvpWSuvbKKtvo6SujZgAF5bG+PL18WJe2JZOdbOGlTNCuWfuKMDQgL75/STs5FL+vmSs6dqT82v4PKmAGyYHdcvgjhfUmjLd3rA1pZTYAHWXcoQoisQFqlke788DXxynRaNDo9VTWNPClFA3jhXUmiin5+NQTjVhHg54OavwcjL8FNW24KSSDzi71Gj1FNe2siSmf5vOvpBf3cLKmaFE+Tgz58XdPHJxFM9cMZ52rY6b30ti2ih3ov1c+l9ogKhqMs5S2NY72xxodXq+PlbExjVTu+2oNyYV8NCXJ/jyzmlDfl2/F4ZX8O+8Gw8V/F3tSC+1zlrvQkgkAuFejmSZ4QjVG6Sdgfx4YZ1Fr5sd7sFLOzKpbmrHvZcMF2D6KA98XVRsTi7sEvwBHrwogs8PF/DlkQIcFDKWx/szLcydkM5yy8RgV9bMCmN9Yg7zo7xY2OmKtTzeHweljPs2HuOqt39jw8rJLInxY4yvMw9+cYK7Pj1GbKCaK+L88HBS4uFo+Bnt6URCkKsp2zxVVMeV/3eQE4V1JASp+ej2ySZTlvzqZlZtOGooXayZaqKctmp0/PmrU/i52JnKUefjzV1nUdvLubEHdzIwMJjSyxr557LoLo8bm6HzIr2YFOJGSnE9yfm1nC6q59e0Cu6bH06Ed3dfhGd+TuPJpdF4Oav4LbuKb48VI2LY2dw1e1SvRjR9Ib+6Ga1eNJX3BgpRNPQ38qubqW/tMPlEK2VS2nV6k/T0YMHoFWy0jxxKNLRpkUklPco3Lxrrzfv7c4f8mszFYDTlh1fwlwjUNPfMQR8M+Kvt2JlWYfM3drSXU79esP0hPsiVdxNzupVm+sLMcE9e2pHJgexqlsX69XqcVCKwIsGft/dkmzj6RkwIdiU2wIXS+rYefYglEoGHF0WQmFXFI1+fIjZwlsmlauFYbz5bPZXVG46w4q3f+PD2ScQEqPn67um89msmW1PKeOqH1G5ryqUC7g5KXB0UpJc14O6g5OWrY7ki3h+JRECvF/nscD7P/JyOTCLw3q0Tu7h0vbA9ndyqZj5fPaWbOU9KcT270itYuzCiV+OeLcdLkEqEHrNq4+fCUSljapi7WWU4UTxnhvPGziyWxPgx3t+FHallvPZrJk8vH2exiVB/Dmzm4pFLInluazoudgrWzA5jy/FiYgPVBm0kUezyWRgMVDYYHOcunKIeCjirZNw3fzRv7MrCu7Pv5OOsQi/C/rOVVpfUBhODUYobXsFfKkEvQnVzu82N1XuCv9qOdq2eqiaNxeYpfSHc25GvjxX1S7vsC3GBarR6kTMl9X02cM/HeH8XXOzk7Mus7DP4A6xICGDd7my+PV7MnXNGdXlOJpV0C/zGiVaJREApk/L6dXEseWMff918ivdvnWS6WU8IdmXz3dO59YMkrnvnEC9cFcNl433568VRrF0USW2LhqqmdqoaNVQ3G2quVU2djzW1MzsijHvnjTaV0AbuRAAAIABJREFUX86U1POfn9L4Lbua2RGePH/l+C5b9v1ZVXx4II9bpgUzfXR3Js8bO7NwUsm4dUZIr++FKIpcMs6nz92S8Ti9aCAmaHR6tDqxm+lNQXULNc0a0kobaNUYNI5u6txxxAaqWfxq4oDc47I6g3+Y58D6BUZMCHbj9eviaWrXEuxuzycH83ljZxaeTipevibOqrXNQUVjO+4Oyi4DhEMFmVTCxeN8UMmlHMyuZld6Bc3tWtwdlFw8zofl8QNnzgw2tp8pY9ood5xVcrQ6PUl5NSBCmKfjgCnlwyr4yyUCGgzDNkMR/INMrJpmmwZ/41b6TEl9jwHJHEzolCv47Wy12cFfKhGYE+HJL2nltGt1fRrVjPJ0ZHKIGx8eyOPW6SHddhfrdp8lyseJBWO8u8wblNa3klfVQkKwmseXjOXJ787w4JcneOWaWJM+yihPR765ezqrNhzlT58f57OwAv6xdCxjfJ1N5R66l8q7ICm3hrf2nGVPRiVOShnPrhjPdZMCu2RAP58u5cEvTzDK04G/9VDP33K8mB2p5axdGNEntfDRS8eYJY0rCALGgcrTRfWsT8zh3VsmdjlGKhWYFe7But3ZlDW0dmnuHs2rwcNpYBlvakkDwe72VimIGj8T5zc1V88KM81tDAVK69vw/h1KPkYoZVIWR/uY3Of+CGjr0PH+/lzmR3mh1ekNQ4NHi1DIDD3S564cmK7U8Ar+MgkaoLi2tZuB+GDAWD/NqmhiYoh5AdYcGJkyxwpqBxz8PRyVxAS4kJhVyX0Lws1+3dUTA/j+ZAk7zpSztJ/s/+FFEVz3ziE++i2Puy7I/meHe5q46BKJQFJuDV8kFbD/bBXzo7z4PKmAuEA1f7skiue2ptOh1fPG9fGmyU0vZxXf3jOdz5MKeOWXTC57Yx/L4/0J93LCxU6O2t7Q/DT92MtxVMjYm1nJW3vOciSvFjcHBX9eFMHN00K6NEpFUeS9fbk8szWN+EA17906qVtQzCpv5NFvTjM5xI2753b93XpCb9vq+pYOBImh4SqK57x4J4a49fiZ8Vfb8dyVMYCBttzcfm6CPK20YUA1e1EUSS6oZeYAP0tGvLM3h41JBYR4OOCkkhEToCY5vxa1nZxXrh38rB8MU9ZRvtb1LQaKXenl7DhTjqeTEqVMQpSPM7+klqMTRV66OtbgiTEMBd6Mu2K5VEJZfRubjhax9YFZAJytaOQvm08NaN1hFfwVUgnNQGFty5Ccz0iPNA4t2QoudnKDmUlRvVXrzBztwTuJOTS2dZg9ETljlGHAadPRwn6D/9Qwd+ZGevLW7rNcPymoS4lqfIChpl7Z2M7ft6SwPbWMMT7O3L8gnJumBpNV3shtHx7h5/tnoZBKePrHVK58+zeeuWK86bUyqYRbpoWwLNaP137N4osjBbR1FPd6PYJgqJf7uah4culYrpsU1E3HRqcX+ecPZ/j4YD6XjvfhlWviuu1aWjRa7vnsGA5KKW/eEG8qMbRqdBbp4hw4W8XGpAJOFNbhp7bDy0mJq72CuEA1i6K9e/ybnF8ec1LJTcfo9CI3TwsZkJxIUW0rlY3tJASb5YTaK+yVMnxcVFw90TDIl1XeRFppg6Gf8cxO/rV8nKmBPxjQ6UWKaltZGD145+gLqSUN/HCyhCsS/HFUyvnxVAmHc2uQSyVMf3YnD14UwTWTzGPXDSXkUsn/Y++8w6Mq0z58n5n0MimkV0gnISRACL1IUaT3piKCYO+6tl3XthbsLlhQUVHpICAKghCK9HQCpPdCem+TmTnfH5NkAwZImcZ+e18X14QwOeeQmXne9zzl9yPAyZr9F4rwd7Ym1F1GU+t0b32zsscLlkEFf6lEQGZmRH5l482frAEkEgF/59515lyPgR42nM4s79UxRvs78NnRDM5kVnT5QymRCCyM8OSjP1LJq2jotGjbkb/dEcTUT0/w5fEM/jblr6mTxPwqApyt+PfSO5ErVKz+IZr+rjKGeNsxOdiZE+mlrBjdDxcbM17Zc5FZ6/5k2Yi+PH37f1ItthYmvDozhH/OCKapRUVVo5zqxhaqGlqobmyhuvWxqlGOj4MVM8LcOtV+yato4NW9FzmcXMLqsT68MCXoL0NJoijy95+TSC+t48eVw9rnLt7/PYVLRTW8NjPkpr+TNl7cdYGPFoUzxNuOK9VNZJXVk5Bfxe74AurlCu4e5v2X80skf3VXEkV1A0NF/X9sL7tD2zT2YK+e3w2LosjK0f0IcZPxzZ9ZLBvhzVOTAzidUc7zdwYR4ibTusb+lZom5EoV3p14ROuCu4Z542lvQcqVWib2d2aItx1Pb40nsp89iyO9NK7CqimcZWasHNOPnTH5xOdXUVLbzDPbExgX4EhMdiXjAx3Z3oPjakrPfwMwHSgRRXFA6/deBVYBbW0vL4mi+NvNjuVpb0FOuW52/gABztYcTdF8x0+Ypy274wvJr2zo0TQnqPP+FiZSjiQXd2tHtiDCg08Op/LdqeyreuE7I9hNxvSBrmw4mcW8IR74Ol7dTbL5XC6Lh3phLJVgLJXgaWdBWnEtQ7zteHCcb7sJxtRQV0b5OfDe78l8fzqbnbH53DuiL/eN6tteSBUEAXMTKeYm5l2eXG5WKDl4sZht0Xn8mV6GVBB4fVYIy0b0/ctzVSqR9w+msCuugCcn+V81ACaVCKRcqeWHMzksGur5l//ntcgVKqzNjPBpbXF1sTHDxcaMEb59eHCcL6PfPcK8wR5/Kfi2/T+v/btCqWL5t+fY88iobr/PzmZVYGkiJdC55+mStnMO62ePzMyYnbH5FFU3kVlWh5+TVZc7ynpDTuvsi1cXF19NY2dpwp0DXFGJIr9dKEIURVKKa5k7WD0p25l9qaEwtK89LjIz9iUW4WVvQXOLiuyyeu4Z4c0Adxse6cExNbXz/w5YC2y85vsfiaL4fncO5Oto9RfdGW0S5mHDjph8CqoaexykO2OkrzrwnEwvY9FQrx4dw9RIyuRgZ35rFQa7UQG3I2625swd7MEPZ3K4f0y/mwbav08L5kRaGU9vjWfHQyOvuo28PdiFH8/m4ONoSXpJHRX1cia1LkQuNmZXCfHZmBvz5uxQFg/1Yl1UOuuOpvPNn1ksifRi1dibX0dHLhfVsPV8HrvjC6hqaMHd1pwnJvqzMMKz0ynMphYlz2xP4NfEIpZEevL4hKvrJGP8HZArVVQ3tvDRoVT+MT2YjNI6BnvZdRr4RESmhrryt52JLB3mRd8+lpgbS6lubCGvogGZmXGngf9KdROltc1YmRkhFQScZKaYGUsxkkrY++joLv//269DFDmaXMIoPweNdMgIgkCwm4wmhSu/JhYR5CLTmaxBW8eSfyezEbpAFEVMjCTMGeSBs6yMfYlFNMqV7YuRIev8NMqVCALt9as2ddSuxoTO0EjwF0XxeKt/b6/xd7Jib0IhDXKFTrxRwz3VedS43CqNBv8AZyucrE05kdbz4A8wZ5A7e+ILOZpS2q0OhScm+rMnvoBPD6fx9tyBN3yui40Zb80J5ZFNsaw9ks5TkwPa/23hUE8ySutYF5VBbkU9D47zxcHKlJqmFt7cdwljqQRTIyl/n9a/PQUywN2Gz+8eQnpJLZ8fzeT709n8cCabqaGuuNuaY2lqhJWpUeujFMvWry1NjDifXcG26DwS86tbpRqcWTTUk1G+DtfVnSmpbWL1xhgS8qt44c4gHhjrgyAIyDs4RlmbGdPQrOCtOaF8ejiN+V+cwtLEiH2PdR6QTY2kPHKbH3sTCtkZk0+DXImLjRk25sbkVjTwYCdF5MOXizl0qZj4vCpMjaU4WJpgYWpEkIs1swe590g3P6W4lsLqJh7vRtG/Kwz2ssPXwYrcCt3dZacW1yIzM+q1PlFPaQvuKpXISF8HBnupU5c9bZXUFRfyq/klsZDM0npuD3FGZmbEmcwK6psVLBrq2eNmFW1H10cFQVgGRAPPiKL4ly19RwN3Ly+v9iGWjJL69sKhNglytcbUSEJ8XtVNC6TdQRAERvs7cDSltFvSzNcy2s8BBysTdscVdCv4e9pbcNcwb344k8Pqsb7tgmjXY9pAV/647M7aKLWcckdtnxen9m9fjA8kXeHnuHwsTIwwMZIwO9yd9ccz2Xg6m+WjrjY18XOy5oOFYTw5yZ/1xzPZl1hITZPipmbhQS7WvDI9mDmD3Nt1+q9H8pUaVn4XTUW9nM/vGtIut3A2s5y0kjpmhrshMzMm0MUaqUStHWRrYYxCKeJobcqlohqCXWV/2VW3pQFnhrlxe7AzzS0qPjmchpWpEeuWDu70WtZFpbNqjE97t09WWT0XCqo5ePEKNU0tPDkxoNtGLFHJ6qzpbZ2Y0/QGURSxsTAm1MKGmqYWzI2lWu90SSupw9/ZWu877LbBQTNjaXtqsKapRW9Kozdj07lc+liasGJUX/59JB17SxNmhbtRWNXI2qh0nm2VTOku2gz+nwNvoPbDfgP4AFhx7ZNEUVwPrAeIiIgQ224J00pqdRL8jaUSQt1ttJJqGuPvwK7YApIKq3vcumoklTAjzI2fzuR2u2D48G2+bD2fx3u/J/PZXUNu+vzXZoVwLquCx7fEsePBkVd53ZobS/nnniQySuuZEebK7rhczI2lRPS1R2ZuzLPbE/4S/NvwtLfgjdkDeGP2AERRpFmhor5ZQX2zkrpmBfVyhfqxWYGXvQWh7jY3DRDK1qnfd/cnY2VmxPYHR7RP/V4srObxLXG8PTf0qg+0s8yUBV+eplGuYP8TY9gZW8DvF690+tp0PL+pkQQzYymVDXIGtr4nO5tCtzE3vuqOo5+DJf0cLJkZ5saUj48zY6DbVZPJXeFIcjEhbrLr+g73FEEQ2jcl287nUdukuOqOT9OIokhGSR2T+uun06eNttet42asrlnBPd+o6zGGSFF1I4uHehLmacu7v6fwxAjv9knzkxnlPdZD01rwF0WxuO1rQRC+AvZ15ef69rHEzFjCxcIa5na+wdI4kf3s+bKbLZVdYXyAE0YSgd8udB5gusrSSC++PZnND6dzeGJS12//nazNeHi8Lx8cSiUqueSmu0eZmTFrlw7i7q/PsuSrM2xZPbx92C6zrJ68ykY+XBSGk7UZs8LdGfjqQQ5dKiYmp5IRPn26dIcjCAJmxlLMjKX06WHq93JRDS/uukB8XhWj/Rx4b8HA9nqCSiWyIyafFaP6MSHImZqmFq5UNyEAdw/3JrGgmtVjfLC1MGFlJ/aLN8LWwrg9+HcmP7J8VD82nc2hskFOiJsMW3MTaprUnUwqUey2NENhVSPnsyt5WkNBue1uprqxBUH4j6a+Loa8SmqbKa+X019PPf5ttL1uHVOCVqZGBhv44T/5foBlw73bmxBALZHd0wFVrQV/QRBcRVEsav3rHCCpSxckldDfVcaFgt71yHeHtpbKc1kVTNTgzsTO0oRRfg7sSyzk+SmBPb7d9Xe2ZmKQE9+fzmb1WJ9upQ4eGOfLnoRC/r47iUNPj71pHWWQlx3frYjk3g3nWPrVWTavGo6jtSle9hYUVf9HAbVRrmT6QFeu1DRxsbCaf0wPvirwp1ypJcDZSmO3+KIoklFax7bofL75Mwtbc2M+XhTOrHC3q84hkQj4OVm1B7aV353Hzdac8jo5Xn0sWLtk0F/qAZ2xLTqPK9VNPDzetz0l9PTkgBtuDsYFOGJlKmVHjPqOwtrMmL59LMgub2DFqH7d7qj57YL646OJdGRsbiWfHk4jr6IB7z6WOFqZ4iQzJaKvPcN97HtVOOwKSa2f5+7e+WiShLwqfjyTg7mJFFsLE+wsjOnrYMlAd5ubSnvok79PC26X9eio4d8oV/f497Hq2dS4plo9NwPjAQdBEPKBfwLjBUEIR532yQYe6OrxQt1t2BmT36tceXdQd3xI+DO9TKPBH2D6QFee25FIfF7VVXn07vLgeF8WfHGa7TF5nbY5Xg8TIwlvzw1lwRen+ehQKi9Pu3HrJ6jbyjYsH8p9357nrq/PsHnVcPpYmTIxyInXf1HrwR9LLeWOEGfuGe7NPdeoZeaWN3DHx8fxdbRk/hBP5g5271HaokGu4HRGOVEpJRxNKW2f/1gY4cGLd/a/bj3A3FjKF8cyKKpu5PZgF1aN9aGqQc7zOxP543IJk4Odbxj4C6saef2XSwz0sLnKGKYrd4VDvO3b5Tg+O5pOdWMLny4O79Ei+NuFIvq7ym5ar+kKT2yJ499LBuMiM+NKTRO5FQ3E51bx+dF0GuX9OpWm1iRJBTUIgtrjQB+cTC9j07lcQtxkmEglyJUqMkrr2JtQiLe9BS9O7a/x1Jqm6Jj+7hgTBQG+vjeix226mur2WdLJt7/p6fEGuNuw8XQOmWX1vVYx7ApmxlKG9rXnz7QyjR/79hAXXv45ib0Jhb0K/hHedgz2smX98UyWRHp1qzg3tK89SyK9+ObPLKYPdCPM8+YpqOE+ffhmeQQrvjvPXV+fZeOKSB6f6M+ZzHIOXSpuH35qo+OchIO1CWvmDWR7TB7vHkjmvd+TGRfgyKT+zjjLzFp7/aXtfrTqr40wM5KQWVbP8dRSjqWWcjarArlChYWJlJG+Djw03pfxgU437ZqZO9iD7PIG1kVl8Owd6mKYrYUJXvYWNx3kUalEnt+ZiFIl8s7cgd0O2qIoolSJGEklFFU1td/9KJSqbrVqFlQ1EptbxXN39KyY15HCqkZkZsaEt77uLjZmhHvaMjPMjSvVTSz56ozWg/+Fgmp8HCw7bY/VBTE5lThbm/Hw+L+6vL2zP5kvj2Xyyoybb4z0QcfPVsfNsJmxlJqmFv0Gf03T9iaNy63USfAH9S37m79eJre8oUd669fDxtyYCUFO7I4r4PkpQT1+oQRB4NEJfqz4LpofTuewopv56hemBHEspYQHf4xhz6OjuiScN9LXga+XDWXVxmhmrj3JW3MHMCHImbEdTNDb3pgdg6SFiRELh3qycKgnWWX17IjJY1dsAfXNCs5ld62w7utoybLh3owPdGJoP7supSXaVDelEoGnJwegUom89etlfB0tqaxv4URaWftAz/V4/2AKJ9LK+NecAT16HwiCgFGr+puthTEjfNWFue5KlO+IzgfotXkLqAvWIW4yntmWwF3DvXCzMUcigYZmJYkF1VrX8BdFkfi8Ksb6906bqDe425oTl1vJ0ZQSQtxssDSVIleoMDOW0tSi1IvEdFcRBKHTBoOS2iYe+jGWnQ+N7Nlxu6JmqCsiIiLE6OhoVCqRwW8e4vZgZ9bM76kTbvfIq2hgzJooXpoaxOqxNxcC6w4n08u46+uzfLAgrFe+m6IosmzDOeLzqjj67Phu5ykvFlYz//PTBLpYs2X18C4vREkF1Ty1NZ60kjpmhbvxyvRg7C1NurUrVqlECqoaqGxooUGupLFFSaNc/aehRUmjXKHupZeZMSbAscs98ZX1cpoVqqt6tTvulPYmFHKxsJrqhhamD3Rj9A0C0J74Ap7YEs+SSC/emjOg27v+c1kVDPSwaf+9dseLoSMKpYoxa6Lwc7Lih5XDuv3zndEgV7D5XB6xOZWIiHjaWaASRQqqGpkd7t6p9aWmyC6rZ/z7R3lrTihLh/V85qU3NCuU/JJQRGxuJZatd5yOMjPic6toVih55DY/vaWkusP1alWCIMSIohjRyY9cF4MM/gD3fx9NRmkdUc+O19n5p//7BCZSCbse1mzlXxRFJn54DGsz4153FaQV1zLlkxMsHurJv+Z0X8r1QNIVHvwxhtnhbny0qOu56GaFks+iMvjsaDrWZsb8c0YwM8PcNFbQ7Qkv7EykpFateHh7sDMrRvdrL2h3VGjsSsolMb+KBV+cJszDlh/vH3bDmkBnFFQ1MuH9oyyJ9OLVaxzBusvBi1dY/UMMX9w9RCPpmLbFUBRFCqubaJQrWH88k2BXGfeO7Kv113B7dB7P7Ujk0FNj8e+FREVvEUWRCwXVXCqsoUGu5HRmObPD3f/iZmdoxOdVsT06DzNjKbatirj9HKwY4C7D1kJ9x9KT4G94+qWtRPazI6usnpJazXrs3ogpIS7E5lZp3NdXEATuGe5NQl4Vifnds2a8Fn9na+4Z7s3mc7lcKqzp9s9PGeDCs7cHsDu+kM+OZnT550yNpDw1OYB9j43B096CJ7bEs+K78+TpcEK0I4cvF5NeUseG5UP5eFE4ZzIr+PBgKoVV6qKwsVRCeV0zlfXymwb+khr1hLCDlSmf3z2424Ef4N39yQCsGtv7tsmfzubiLDNlUn/NDHa1BXdBEHC3NcfPyZq6ZgUedhbt/f7aJDq7ElsL45vqKWkbQRAY6GHL4kgvVozuR1WDnEAX9TUZ0ia4I8dSS9nwZxZ9rEzpY2WCSoSLhTWs+T2ZN3+9TGltz21vDTj4q3OlZzJvbkiuKaYMUO8A9iUW3eSZ3WfeEA8sTaSsP57Z62M9NSkAWwsTXtiVSIvyr36kN+OR2/yYGebGe7+nsPF0drd+NtDFml0PjeQf04M5k1nBxA+O8ea+S1Q19GzQpKdYmRphZWZEg1yBj6MVn909mIoGOZ8eTmt/zi8JheyOv76ENKh37IvWn6GmqYWvlkX0qOXvSHIxexMKeWCsT48kHDqSWlzL8bRSFg/10orbVVugtzE3IaB1F67tjrozWeVEeNvppHPvZoiiiKL1MyMzM+64c9bnZV2XM5nluNma8/TkAB4e78cTk/x5Z95A9j46GpmZMRtO9tx32GCDf5sl4fHU3nnhdgc/J6v2NlNNIzMzZtnIvvx6oYi0XvoH2FgY8+bsASTmV/NZVNd3720IgsD7C8KYHOzMK3su8l0330BSicDK0f048uw4ZoW7seFkFmPWRPHPPUlEJZfQINe+NK6vkxWedhbE5lTR1KJEZmbMe/PDyK1o4K3fLgPqoav7rjN1DJBRWseCz09RVtfMxhWRBLt1P+dbVtfM33YkEuRizSMT/tpJ0l3WHknH3FjK8pF9e32szmgLwM/dEYinfe8Wqq6QU15PTnkDY/wdb/5kHaAuyKvD3nNTAg260AvqQnVmaR2nM8opq2umUa6kqkGOXKGiWaHEroc2sWCg3T6gDjCj/Rw4kVaqFef66zFvsDuv/nKJy0U1Gi8ArRrjw8ZT2Xx6JJ1/LxnUq2NNDXVlVrgbnx5J47Ygx25PEJsYSVi3dDCPbY7l1V8uUdOk4LEJft36PbvamPPegjDuH+PDJ4dT2Radz/enczCRSojsZ8+4AEfGBTri76S5Ya82HKxM8XOyYtO5HCQS6O8iw87ShNdmhrC9C4t3UkE19244hyDAltXDCXHr/vCRKIq8sPMCNU0Kfrx/WK8HpTJK6/glsZAHxvreVNOoJ3x9IhPvPpZMDnbuka9ATzjR2j59o0K7rknIq8LCREqQi+EXeOe0+gr/kliIzMxYXai2NiU6p4IWpcj4wJ6nBg02+AOMDXDg1wtFpBTX6uyFmhnuzpu/XmZ3XIHGg7+9pQn3juzL58cyeHyCX6+LX6/PHMC5rAqe3BrPr4+N6bZomImRhLVLB/P8jkQ+PJRKVlk978wL7XYQC3Sx5rO7htDUoiQ6u5JjqSUcSy3lX79d5l+/XcbVxoxxAY4M87HH2tQYU2MJJlIJJkZqRVD1owSpBEpq5cTnVpKQX83qsT6dvgZtm4F7R/bF3ETKtvN5hHva4utkxc6Y/JsuNNHZFdz33XmsTY348f5h+PQwF731fB5/XC7m79P6a+T9uS4qHTMjKfeP6V4bb1eoa1bwwcFU5gx216pb17WcSCvF3db8KkkCffOvXy/TolLxs4YbO7SBpakRSyK9iM+r5FJhDfVyJVEpJcwZ5M7U0N4Vqg08+KtvFY+mlOos+NtbmjAuwJHd8QU8d0egxvOu94/x4ftT2XxwMJUv7rm52NqNsLEw5oMFYSz9+iwv777ABwvCur3DNpZK+GBhGL5OVrz3ewppJbV8tDC8RwuTmbGU0f4OjPZ34OVp6uGitoGtXy8UseV83o2vRSLQ0pqTdrQ2ZfpA106Df8ehKUdr03ZPga3n87A2M+btuZ13QSlVIhv+zOK9gyl42Jrzw/3DepyjT7lSy+v7LjHKrw8rbpBa6irpJbXsiS9kxai+aoN7DbMvoZDGFiXzbjLnoEnkChWn0suZNtDVYHLqCqWKpMJqFkYYnl3j9ZBKhKsmx+d/fopAF/XnszdZEYMO/q425gxwl3Hw4pW/GIxrk4VDPTn8QwmHk0u6JaPcFewtTXhwnFps7XRGefsQUE8Z6efAk5P8+fiPNAKdrXmgB78nQRB45DY/ApyteX5nItP+/SfPTwnivpF9e1Wkc7M1Z3GkF4sjvVAoVWSW1dPUomzNV6raH5sV6u+pRBEnazOCXK1xkZnd8E1tJJVwIb+ad/cn88HCMELcbK6rKgpqeeVntycQk1PJ5GBn3pkb2mM9l8p6Oat/iMbS1IgPF4b3upApiiKv/XIJCxOpVt7noijy/ekcglyse2UF2V3OZpVT26zQuGRKb7hcVEuDXNlrP2Rd03FyXGZujG2rAU9vFlWDDv6gbr98/2AqxTVNOtPemBjkhKuNGT+eydF48Ad1O+CW83m89stFfn18TLenP6/liYn+pJXU8c6BZHwdrdqdtrrL5GBnwj3H8uKuRN7Yd4nDl4t5f0FYp85Z3cVIKmnvLtEU5iYSPlwYTrCb7Kq+/o6oVCLfncpmze/JmEglfLQojNnh7j3+0DQrlDzwYwxF1U1sXjVcI+/JQ5eKOZFWxj9nBGtFYOxcVgWXi2p4Z26oTnfgv1+8grmxlDEGlO8/l63uHhza99YK/uopevUi8MztARqp2Rhst08bbUMuBy9e0dk5jaQSFg/14kRaGdmtvqOaxMxYyotTg0i+UsvWm6RCuoIgCLw/P4xQdxue2BJH8pXu9/+34WhtylfLInhnbijxeVXc8fFxdscVGGQftJ+TdXuHTmfJE76NAAAgAElEQVSBP6e8nsVfneH1fZcY6evAoafHMWeQR48DoCiKvLQriXNZFbw3f+BV2kY9palFyZu/XsbfyYq7rxHI0xTfncrG1sKYWeHuWjl+Z6hUIocuFTMuwFEn/sBdJTq7Ag+7rntIGxJ74gsY8uYf7b4RvUUjwV8QhA2CIJQIgpDU4Xv2giAcEgQhrfWxR58UPydrfBwtOaDD4A+wONITqUTgxzM5Wjn+tFBXIvva8/7BFI30yJubSFl/TwSWpkas/C6aourGHh9LEAQWR3qx/4kxBDhb8+TWeFZtjCFOh97KvUGuULH+eAZTPj7B5cIa3ps/kG/ujej1Lv2Tw2nsjM3nyUn+GgukXx3PJLeigX/OCNGKk1ZhVSO/X7zCoqGe3W4I6A0J+VUU1zRze4jhpHxEUeR8diWRPbQ91DeXi2qoa1bgoqEMiKbebd8BU6753gvAYVEU/YHDrX/vEdNDXTmdUU6Jhidvb4SzzIypoa5sOZ9HdUOLxo8vCAL/nBlMTWMLr++7pJFjutiY8c29Q6lpbGHx+jPt0649xbuPJdseGMHzU4I4m1nOnM9OMfezk+xLLGwflDEkGuVKdscVMOXj47z1WzIjfPvw+1NjWRDh2audkiiKfHQolY//SGPeYA+e0JCfbnpJLf8+ks7UUBettUJ+82dW+4S5LtmbUIiJVGJQ+f60kjrK6pqJ7HdrBv/E/Gr6u1hrrAlFI0cRRfE4cO0o7izg+9avvwdm9/T4swe5oxJhT3xhTw/RIx4e70tds6LbU7BdJcTNhofG+7IrtoAjycU3/4EuEOphw8aVkVTUyTWyAEglAg+N9+X0SxN5dUYw5fVyHt0Ux9g1UXx5LEMrC2N3EEWRmJxKXtx1gch//cGTW+NBgG/vG8qG5UN7Xa8QRZH3D6bwyeE0FgzxYM387ss8d4ZSJfLs9kQsTaW8NnNAr4/XGVUNcjafy2VmmBsedppTqr0ZCqWKXxIKmdjfCRtzw/HF/dMAZw66ilIlklRQ3SU59q6izZy/c5uTV+tjp9MIgiCsFgQhWhCE6NLSzqd5fRytCPO0ZVfcjUf1NU1/VxkTgpzYcDJLa1Orj07wI8DZipd2JVHTpJlAOsjLjh/uH0Zlg3oBKOjlAgBqOYXlo/px5JnxrL9nCF59LHh7fzIj3jnMK3uSyCyt08CVd50r1U18djSdiR8eY97np9gdV8DkEGc2rRrGH0+N47ZeDL+0IYoi7+xPZl1UBksivXh33sBeF+fb+OpEJvF5Vbw2a0CPbfhuxsbTOTTIlTwwTvs2jR05mVFOWZ2c2YN0V2PoCifTy+jnYKnThVBTZJbWUS9X9soO9lr0XvAVRXG9KIoRoihGODpefwR87iB3LhfVcLmo58XMnvDIbb5UNrSw+VzvC7OdYWok5b35YZTUNvGmhtI/oPZE+HGlegFY+MVpUq70TlKiDalE4PYQF7asHsG+x0Zz5wBXtpzLY8IHx5j26Qn+sTuJXbH5ZJfVa7RIrFSJZJXVszuugHs3nGPkO4dZcyAFB0tT1swbyPm/T+LDheGM9HXQiIaMSiXy+r5LfHk8k3uGe/Ov2QM0pk2TVlzLh4dSmRLiwgwtKUo2ypV8dyqbiUFOOp9k3R1XgMzMiPGBhiHpAGqV1zOZ5Yzy611rtb6Iz1MLQoZ7as4GU5utnsVtPr6CILgCJb052IwwN9789RI7YvL5x3TdOe4M8VZ7nH5+NIPFQz214kQU5mnLg+N8+exoBiN9HTS2YwrztGXzquHc9915Zq87yTvzQjXa8THA3YYPFobx/J2BbD2Xx5mscnbF5vNDa5Hc3tKEQZ62DPKyZbCXHSHuNliaSJFKhOumTkRRpKi6iZTiWlKv1Kofi2tJK66jWaGuM7jZmPHIbX7MG+xBXy1Mjja1KHl2ewL7EotYMaof/5jeX2Mtkg1yBY9sisXK1Ig3ZnffM6CrfHcqm4p6OQ+N1918DEB1Ywv7k4qYO9hD677A3SE6u5J6uZLRfoazIHWH+LwqrE2N8HHQnDKqNoP/XuBe4J3Wxz29OZi9pQmTg535Oa6Av00J1Okb6/kpQcz57BRfncjkyUkBWjnHU5MDiM5W5677u8raJ/h6ywB3G/Y9NppHforliS3xxOVW8dLU/j2SLb4eTtZmPDbRn8fwR6kSSS2uJS63itjcSuJyKzmcfPW6Lwjq1kwTqQRjqYCxVKL+u5GEstpmajtYLTrLTAl0kbFsRB8CnK0JcpER4ibTmkJkZmkdj26K41JRDS/eGcTqsT4aNaF/adcF0krq2LgiUmvpnqoGuTolFuREhI47W36OzaepRcXSSP2YtlyPI8nFmEglt2S+H9SL1yANK6Nq08D9HWCbIAgrgVxgQW/Ps2ioF79duMIfl0p0asAwyMuOqaEurD+eydJhXl2yQOwuxlIJa5cOYtq//2y3WpR1wTC8KzjLzNi8ejhv/5bMhpNZXCio5rO7BmtlaE4qEejvKqO/q6zdtamqQU58XhWpxbU0t6hoUaqQK0ValKr2P20Tv7YWxgS6yAh0tibA2apdclcX/ByXz8s/J2FiJOGbeyM03qny49lcdscX8szkAK2qXH5+NIO6ZgXPTem9/293EEWRn87mEuZhwwB3zaUnNMHh5BKG+dhjpScP4d5Q3dBCakmtRiw9O6JNA3eAiZo4fhuj/RxwszFjy/lcnbvvPHdHEAcvFvPJH2k9ctDqCk4yM9YtHcySr87w7LYEvrxniMZ2ncZSCa/MCGaQl61awuHTP1m7dBDDfbSfA7W1MGF8oFOvFAi1SYNcwT/3XGR7TD6Rfe35ZEm4xoeA4vOqeP2Xi9wW6Mgjt/Ve+vl6FFY18u2pbOYO8tB5rj86p5K0kjrenaedz0dPySqrJ7O0nntH9NX3pfSI2NxKRBGGaHgqWe8F3+4glQgsiPDkz/Qycso1P3l7I/o5WHLXMC+2nM/r1QTtzYjsZ8+LdwZx8FIxH/2RdvMf6CYzwtzY/cgoZOZGLP3qDGsOJNOsUGr8PLcKp9LLmPLxCXbE5vPYBD82rRqm8cBfUNXIAz9E42RtxkeLeq8FdCPePZAMIjw1WTOzCN1h4+kcrE2NmBHmpvNz34g/LqnbqCcEGebm42acz67ASCIQrsE2T7jFgj/A0mFeSAWB709pZ/L2Rjw5KQAbc2P+/nOSVq3vVo7ux4IhHnx6OI1t0ZrvMgpwtmbvo6NZMMSTz45mMGvtSWJydOeYZghUN7bw/I5Eln59FokAm1cN55nbNa/iWlkvZ9k3Z2mQK/lmeYRW01gn08vYE1/Ig+N8dN7OWFjVyG8Xilgc6dnuo2wo/HqhiBA3GZ72t16LJ8DpzHIGetho/Pd6ywV/Z5kZ0we6si06j1oN9cV3FTtLE164M4jonEp2aMHtqw1BEHhrbihj/B14cdeF9p2LJrEyNeLd+QP5elkEFfVy5n1+mvu/jya1ly5jho4oiuy/UMTkD4+xIzafB8f5cuDJsVpJfzXKlaz8/jx5FY18tSxCq2mYZoWSf+xOwruPBQ9rMa10PTaezkEURe7VkgNZTymoaiQ+r6rX2vf6oq5ZQWJ+da/Vfzvjlgv+ACtG96OuWcH2aO0F4Osxf7AHQ/va8fb+y1TWa8+31lgq4Yu7hzDATcYjm2I5m1mulfNMCnbm6HPjee6OQM5mlnPHx8d5ZlsC+ZX6MWbXFiqVyIGkK8xY+ycP/RSLg5Upex4ZxQt3BmlFeEyhVPHY5jji8qr4eHG41msr649lkllWz+uzBuhcSK1RrmTzuVzuCHExuAGq/RfUftzTbtHgfz67AqVKZISP5ruUbsngP9DDliHednx7KkvnGjMSicAbswdQ06Ro94rVFpamRnx7XyQedubc/300sVoSVrMwMeKR2/w4/rfbuH90P35JLGTC+8d4ZU9Sr+Uh9I1coWJHTD53fnKCB3+MobZJwZp5A9nz6CitdaQolCqe2Z7AH5eLeXVGiNZ3nVll9ayNSmdaqCvjAnTfx74rLp/qxpYb+iXri18vFBHsKtPKPIguOJNZjrFU0IiC7LXcksEfYPVYH/IqGvklUbd6PwBBLjJWj/Vhe0w+Ucm9ml27KfaWJvywchj2Vibc/fVZTmdo5w4A1Gmtl6cFE/XseOYOdmfT2VzGvRfFi7sukFt+a90JVDe28PnRDMasOcKz2xMQBPhoURiHnx7HwqGeWlHQBHX65eGfYtkTX8hzdwRqPQ2iVIk8sy0eUyN1N5euUShVfHEsgzAPG4PTyM8uqycut8rgCtDd4WR6GYM87bSiyHrLBv/J/Z0JdLZm7ZF0lFosvl6PJyf5E+BsxfM7EzUiyXwj3GzN2f7ACDzszHlkUyz1zdrRGWrD3dacd+YN5Ohz41k01JOdMfmMfS+K2etO8uWxDINdCERRPWD2+i+XGPn2Yd49kIy/kzUbV0Sy/4kxzBnkofGCbkca5Aru/z6ag5eKeXVGsFZbOttYfzyT2NwqXp81QGdmRx3Zm1BIXkUjj9zmZzBWjW38HFeAIPzHBP1Wo7yumYuFNVobTDOssnw3kEgEHp3gx2Ob49ifVMT0gbpd3U2NpHy4MJzZ607y6t6LfLx4kFbP5yQzY8vqEWSV1WlFYqIzPOwseHN2KI/e5s/O2HwOJF3h7f3JvL0/mRA3GVNDXZkywAXfHhqga4K6ZgUn08s4mlLKsZQSCqubMJIIzAhz4/4x/Qhx082wUU1TCyu+PU9sbiXvzR/IAh14xCZfqeGjVo2gWeG6392qVCLrotIJcrFmkgFJN4N6I/BzXAGjfB1wsdH9oqgJTmaUI4pozQntlg3+AFNDXfnoj1TWHkln6gBXrfZPd8YAdxseuc2PTw6ncUeIC3dqObdrb2mCvaXutchdWrV0HrnNj7yKBg4kXWF/UhHv/Z7Ce7+nEOhszR0DXIjsa0+AixWOVqZa2wWqd/d1HEst4WhKKeezK2hRiliaSBnl58CjE/yZ2N9Jp7vgktomVnx3npQrtaxdOlgnnSXNCiVPb01AZm7Ev+ZoTyPoRhy4eIWM0no+XTJI55+9mxGTU0luRQNPTtL9vIOm+DOtFJmZkUaVPDtySwd/qUTgiYn+PLElnl8SC3VqU9fGoxP8iEop4W87Ewlxs8Grj/66HZQqsV1yuFGu5ExmOe525hr1zvW0t2DVWB9WjfWhqLqR35Ou8FvSFdYeSaMt+2ZnYYy/s3W7REOAszUBztbYddF3VBRFyurk5FY0kF/ZQG55A3mVDeRWNJBZWk9JbTMAgc7WrBjVj3GBjkR422tUr6irJBVUs3pjNJUNLaxfFqERKemu8K9fL3OpqIavl0Voxff3ZiiUKj44mIKPo6VBdtJsOZ+HpYlUKx7cukAURY6lljLKz0FjMuLXcksHf4AZA9348lgm7/2ewpQBLjpXEjSWSli3dDDTPj3BQz/FsPOhkTpvtWsL+m1vkm3RecTmVFJW10xaSR33DPfm/jGa13R3tTFn+ah+LB/Vj8p6OZeKakhtVeFMLa5jd1zBVSJtdhbGmBpJMWoVc5NKBIwk//naWCpQ3dhCXkUjjS1XTx07WZviZW/BaD8HhvnYMzbAUe8+rPsvFPH0tgTsLIzZ8dAInaWY9iYUsvF0DqvG9GNSsH7SLTtj88koreeLuwdrLTj1lOrGFvYlFjJ3sIfOUqSa5mJhDcU1zVqdSr41fzMdkEgEXrgziGUbzvHTmVxWjNZ9u5mnvQUfLQpn5ffRvLr3Iu/MG6izc6vFtHIwN5ayIMKTA0lXOJZayqwwN24PcSGjtI6vT2Rq/TrsLE0Y5efAKL//5Cfb5JnbFoSc8gZalCoUShGFSkShUtGiFFGqxPbve9lbMsbfEU87c7z6WOBlb4GHnYVBmYArVSKfHk7jk8NpDPay5ct7IrSm0Hkt6SW1vLAzkQhvO/42JUgn57yWphYlHx1KI9zT1iB31rvjCgxSWbQ7tHURalMPS+vBXxCEbKAWUAIKURQjNH2OsQGOjPZz4N9H0pgf4aExNczuMLG/Mw+PV2vyD/G200nBD9TTwOMDnHhyaxynM8upamhhwRCP9iBcVNXEpcIamlqUOg+ggiDgZmuOm625wYq6dZfCqkae2hrP2awK5g324K25A3R2t9kgV/DwT7GYG0tZu3Sw1tpVb8Z3p7K5UtPEx4vDDa7DRxRFNp/LJdTd8JRFu8Ph5BLCPGy0uqnQ1bvnNlEUw7UR+Nt44c4gKhtaWHckXVunuClPTw5ghE8fXt6dpLWBrGsRRRGvPhbsengUPg6WjPZzYEhfOyxNjZArVHx2NJ3FkV6YGUu1qkf0345Kpb7Duv2j41woqOb9BWG8v2CgzgK/UiXy1NZ40krq+GTxIL11sJTWNrMuKp3xgY46UYTtLrG5VSRfqWVxpG42X9qgtLaZhPwqJgRpN6V3y/b5X8sAdxsWRniw4WSWzv1k2zCSSlh312BcZGas3hitE4kEQRBoaZ1ylitFGluU7X4DHx5KxcfRsr37RCIR2mciCqoaqW7Ur/n6rUJOeT13fX2Wl39OYqCHDQeeGMv8IR463fW+9dtlfr9YzCvTg/VqSLLmQDJNLUr+Pk33A2VdYePpbKxNjZith+YPTXHoUjGiCHcMuPWDvwgcFAQhRhCE1df+Y1cM3LvKc3cEYWYk5Q0NeuF2F3tLEzYsj6BZoWLFd+d1EmCNpRJEUaS2qaXd6vCr45nE5laybERfbMzVabC2wnBTi5JntsUz//NTRKVod0L5VkapEvn6RCZ3fHycpIJq3p4byk/3D9N5R9fG09l882cWy0f21auEQmxuJdtj8lkxuh9+Tvqb7bgeJbVN/HahiPkRt26hF9QttH37WBCowS69ztBF8B8liuJg4E7gEUEQxnb8x64auHcFR2tTnpjkT1RKKUeSNa+E2VX8nKz58u4hZJXV8+APMcgV2tcfEgSB1WN9OHy5mPu/j27XlenY5tnWlbHmQApDvO14anIAHx9K5W0taxTdiqSX1DL/i1O8+etlRvo6cPDpsSyJ9NJ5jvvw5WJe3XuRSf2ddepdfS1Klcg/91zEWWbKYxMMs3d+y7k8WpQi9wz31vel9JjqxhZOpZdxxwAXrb/XtL48iqJY2PpYIgjCz0AkcFxb51s2oi+bzuXy2i+XGOnroLcukZF+DqyZP5Cntibw1LZ4Pl08SOstca425vz6+BgKqxqxNDVq3/F35FxWBQcvXWHN/IGM9HVgfKAje+N1r49kqBTXNPHFsQx+OpOLpamUjxeFMyvcTS+FzZicCh7bHEeImw2fLgnXa0vlpnO5XCio5tMlgwzSClGuUPHT2RzGBjjio8eJ895y+HIxCpWoky4qre78BUGwFATBuu1r4HYgSZvnNDGS8OasAeSUN/DJYc07YXWHOYM8eHlqf35NLOK5HQk6K7i62ZpjY26MKIrtbZ6iqD53f1dr7hnuzWt7L3E2sxwLEyMWt7bEnUwv41R6mU6u0dAorGrklT1JjFkTxcbTOcwKd+PQ0+OYPchdL4E/Ia+K5RvO4ywz45vlEXo1SLlS3cS7+5MZ5deHGTq2T+0qe+ILKK5pZsWovvq+lF7xS0Ih7rbmhGtpqrcj2n5HOQM/t354jIBNoige0PI5GennwIIhHqw/nsn0ga46G77pjFVjfWhqUfLBoVRMpBLemhOqk1F4URSpbVbQKFcPSylVIkZSAWszYx4Y54u1mTFHU0sZ1tqxUVkvp6yuma9PZDHE245XZ4Zo/RoNgbyKBj4/lsH26DxEERZEePDQOD+9TmpfLKxm2YZz2Foas2nVsPYCvr54ZU8SCpWKt+aEGlxrJ6g7sdYfzyTIxVovktaaorJezom0MlaO6aeTGKHV4C+KYiYQps1zXI+Xp/UnKqWEF3Ze4OeHR2pVzfFmPDbRn2aFirVR6ZgYSXhtZojWP0SCICAzM+axier87PsHUwl0sWLOIA9ArQ3TsRZhZ2nCrHB3ZoW7s3pjNOui0nWiSqkvcsrr+Swqg52x+UgEgUVDPXlwnK/ezUhSi2u555tzWJpI2XT/cL1PMR9IKuLgpWJeuDMI7z6GqYkflVJCWkkdHy8yvLmD7rA/6QoKlchMHUlQG17yTkPYWpjw6swQtp7Po7ZJ0WVdGW3xzO0ByJUq1h/PRCoReGV6sE7fqGP8HVhzIJmssgbsLYzJKqvHszXQiaKIKNK+27gz1IW8ilvbxKUzFEoVpzLK2RWbzy+JRRhJBO4e7s0D43z0HmRBHfiXfnUWI4nAplXD9e45W93Qwit7LhLsKuN+PUzOd5UvjmXgbmvONANNSXWVvQkF+DhaEuyqPbvPjvzXBn9QW7dNC3U1iN2AIAi8eGcQLUoV357MplGu5F9zQnVWxBvl58BPq4az5kAydU0Khvv0YWSrL6ggCAgC1DcrOJtVzu9JxYR7aT/nqAtUKpHY3Er2JhTy24UiyurkWJsasXxkXx4Y64OTHjTwOyMmp4IV30VjaiRh06pheneeEkWRl3ZfoKJezoblQ/V653wjTmWUcT67kldnBOtt4lkT5Fc2cCazgqcnB+gsXv1XB39DCPodEQT1jt/SxIi1UenUNSv4YGGYzqZErUyNeH3WAERRRBAESmqaOJtZTl5lIweSrtAgV+AsMyOir51WhOB0hSiKXCqqYW9CIfsSiiioasTUSMKk/s7MCHNjfKCjQWkFRSWX8NBPMbjamLNxRaTed/wAu+ML1I0KdwQarEyCKIp8/EcazjLT9qaFW5WfYwsA3RrP/FcHf0NEEASevSMQazMj3t6fTFldM1/eHYGNhe70iERRnfP/x54kdT53ShD3jPDGz8kKd1vzDs8T/7KARqWUEJtTyXCfPgz20o69XHdpE5CLz6siLreSqJRS0kvqkEoExvg78MztAdwe4mKQLYq7YvN5bkci/V2t+e6+SBz0IM98LXkVDbyy+yIR3nY8OM5X35dzXU5nlHMuq4LXZoYY1GLeXURRZEdsPsN97HW68Bvep0EHqFQiEolaFkFft4oPjPPFxcaM57YnMu+LU3y7fKjOXniJRMBMImXd0sG8vu8S57MrmdjfuT3wt/1+OrtzisutYl1UOv8+ko6xVGCghy3D+tkzzKcP/V2ttWrk0kaDXMGF/Gri8qqIz60iLq+S4hq1xr+JkYRBnrYsnz2AqaGu2Ou51nM9RFHdofL2/mRG+vbhy3uGYK0HQcJrUXsCJyACHy3S72zBjRBFkY/+SMVZZsqiobeujg9AdE4lOeUNOh+eE9r6vw2BiIgIMTo6WqvnqKiXs+V8Lg+P/08ny6XCGiQSqG1SMLSvbp2yzmSWs3pjNBKJwKeLBzFWD61qW87lsvF0Dh8sDKN/F4pNtU0tROdUcjazgrNZ5STmV7drBpkbS/G0N8fTzgJPe7Ukc9ujq60ZJq3a/VJB6LSdTaUSqWiQU1zTRElNM8U1TRTXNFNc20RJTRP5lY2kldS1n8+7jwWDPG0J97RlkJcd/V1lejF16Q4NcgUv7LzA3oRCpoW68uEi3aX+bsb7v6ewNiqdDxaEMW+Ih74v57pEpZRw37fneW1mCPeO7Kvvy+kVz25P4LcLRZx/eVKPZSkEQYjprnDm/7vgD/DoplhC3Gy4b1Rf3vrtMs0tKioa5DTIFQS7ynhZx6JV2WX1PPhjDKnFtTx7RyAPjfPVeb2isKoRN9uedbzUNyuIy60io7SOvAq141ZuRQN5FQ3Uy5XX/TlBACOJgERQm7pIJQINciWKTobh+lia4CQzw0VmSoibDYO9bQnzsNWLi1VvaHutU4prefZ29WttKBaIhy8Xs/L7aBZGeLBmvl46tLuEUiUy7dMTNLYoOfTUOINf7G9EdWMLw976gzmD3Hl7bs99QHoS/P9fpX3a0hlvzh7Ak1vjicmpJNDFikVDXXGzMcfO0oQxa45w1zBvnXZb9HWwZNfDI/nbjkTWHEjhQn417y0I02mOui3wd5bnvxmWpkaM9nf4i9qkKIpUNrS0LwTFNU2t5i0qlCpQqlQoVCJKUUTZavBibiLFRWaGs8wUJ5kZzjIzHK1Mb+kPeBuHLxfz5NZ4pBKB7++L1Mtd3vXILW/gqa3xhLjJeH3WAH1fzg3ZFZtP8pVa1i4ddMu/L/bEtxnP6F6P6P9V8G/bYdmYGxPqboOfkxWj/Bzai2xF1Y0M9rLDSab73aSFiRH/XjKIgR42vLM/mfSSOr68Z4jOdUo0ecchCEKr6bwJ4Z7/Ha2jPUGlEvn4cBqfHk4jxE3GF3cPMYiOnjaaWpQ89FMMAJ/fNcSgi6dNLUo+PJRKmIeNQXoHdwdRFNl0Vm08E+qh+46qW3vZ7CHHUktJyK9mhE+f9sB/LLWU+749j5utud50VNTKnL78sHIYZXXNzFx7kt1xBRhSau5/dI/imibu/uYsnx5OY/4QD3Y+NNKgAr8oiry46wIXC2v4aFG4XmUtusKGk1kUVTfxwp39Da6Vu7vE5enXeOb/1c6/jWaFiuYWJU4yMxrlSj7+I5W0kjpWj/Vh7mD9F7lG+Tmw7/ExPL45jie3xrMvsYi35gwwmIGk/9E1/rhUzHM7EmhqUfHuvFAWRngaXMD67GgGP8cV8MzkACb2148ZfFcpqWli3ZF0JvV3ZoSv4bmIdZcfT+dgZWrELD0Zz/y/3PnfEeKCiZGEp7fFM/XTE1Q2yHl6cgDTB6o1NQxhp+1ua862B0bw8tT+nEgrZdKHx9gRk28Q1/Y/bkxFvZxntiVw/8ZoXG3M+eWx0SwaqnsvgJtxIKmI935PYVa4G49OMHwdp/cPpiBXqnh5Wn99X0qvKa9rZl9iEXMHu+tt/kQXBu5TgE8AKfC1KIrvaPucXeGrZRGU18upapD/RfXTUD6kUonAqrE+TOzvxPM7E3l2ewK/Jhby1txQg9Ci+R9XI4oiu+MLeGPfZWoaW3h4vC9PTPI3mDbOjiQVVPPU1jhtp1EAAB4ySURBVAQGedny7ryBBvOevx5JBdVsj8ln1Rgf+ulZ+kITbI3OQ65U6dV4RqutnoIgSIFUYDKQD5wHloii2KnPoq5aPbtCT7petIlKJfL96WzWHEjBSCLwtymBLB3mbbBDOP/fSC+p5dW9l/gzvYxBXra8PTeUIBfdCHR1l4KqRuZ9dgqpRGD3I6NwtDbsdllRFFn45WkyS+uJem48MgMYhusNCqWKce8dxcvegs2rh2vkmIbY6hkJpLdKOyMIwhZgFqA/k91rOJWuFoZ6YpI/oihSL1dS26T23a1tUmBuLKW0rpkQN5led3ASicB9o/oxIciJl36+wD/2XGRHTD5vzg7VS6fA/1BT29TCp4fT+PZkNhYmUt6YFcJdw7wNpnf/Wsrrmrnnm7PUyxVse2CEwQd+gO0x+ZzPruSduaG3fOAHOHipmIKqRr3acoL2g787kNfh7/nAsI5PaDV1Xw3g5aV7caYQdxssWnNucXlVrD+WiZFUoG8fSy4UVBPhbceGk1lMH+jGG7P13//s3ceSH1cOY29CIW/su8zMdX8yO9ydJyf5G6ze+n8jKpXIz3EFvHNArc+0KMKT5+4INOihs9qmFpZ/e56CykZ+vH9Yl6a59U1FvZy3f7tMhLcdCyNubRmHNr4+kYl3HwsmB+u3wK7t4N/Z9ueqPJMoiuuB9aBO+2j5ev6Cjblxew96uIct6aV1rB7jw8KhnhTXNLHhzyy8+1gyPtBwBnIEQWBWuDvjA51YF5XO96ey+SWhkIVDPXlsgt//6gFaRBRFjqaW8vEfaSTkVRHuacvXyyIIM/A5hqYWJas3xnC5qIb1y4boXMakp7z922VqmxT8S0cOeNomJqeS2NwqXpsZoveUrbaDfz7Qcbn2AAzSLVyhVGEklfDqjBBe33cRcxMpP5zJwcnalO9XRHZqhq5vbMyNeWlqf1aO7sfaI+lsOZ/Ljph87h7mzYPjffRu//ffhEol8sflYtZGpZOYX427rTlr5g9k/mAPgw9KLUoVj2+O43RmOR8vCmdCkGG3dLZxNrOc7TH5PDjOl0AXa31fjkb45s9MbMyNWRCh/5ZybQf/84C/IAj9gAJgMbBUy+fsEW1mFaP9HTAzlvLyzxdYM38gUwb8Z4qwbYEwNJxlZrwxewCrx/rwyeE0vjuVxU9nc1gS6cVD431x/t98QI9pUarYG1/IF8cySCupw8vegnfnhTJnkMctIS3QolTx5NZ4Dl4q5tUZwczWoV58b2iUK3l+ZyIeduY8PtHw21C7QmZpHfuTrvDQOF+9DZJ2RNsevgpBEB4Ffkfd6rlBFMWL2jxnbyira2b5t+fw7mOJqZGEkX5qrZpGuRKVKCKVCNQ1y1GqRIPM7XraW/D+gjAevc2PdVHp/HAmh03ncpk32IOlkV7/Kwx3g0a5kq3nc/nqRBYFVY0EuVjzyeJwpoW6GuQGoDPkChVPbIljf9IVXpoaxPJRhmvFeC1rfk8mu7yBTauGGUSg1ARfHMvARCphhYFYYv6/VPW8Eacyyhjp60CLUsWxlFI+O5rO2ABHzmVVMDnYmT3xhRRUNfLb42MMvlMit7yBz46m83NcAc0KFSFuMhZHejEr3O2/omtCG6QV17IjNp/t0flU1MsZ2teOh8f7MT7Q0aBaf29Gs0LJIz/F8sflEv4xPZiVBhJwusK5rAoWrT/NPcO9DV5krqsUVDUybk0Udw/35tWZIRo/viG2et5yjPRV7/aNpRKSCqupamjhyUkBtChVHL5cTEW9nDH+DliaGt7gzrV49bHgnXkDefHO/uxJKGDzuTz+sTuJt369zLSBriyJ9GSwl90tFdS0QXldM78kFLIztoALBdUYSQRuC3Ji9VifW6Yw2pGmFiUP/BDDsdRS3pg9QK+DRN2lQa7guR0JeNiZ8/yUIH1fjsb46ngmAKvGGo496v+Cfye0DXg9OSmAk+ll/HA6m6yyBo6mlPDS1P5MGeCi70vsFjYWxiwb0Zd7hnuTmF/NlvO57I0vZEdMPj6OliyM8OTOAS7/r1pFm1qUHLpUzO64Ao6llqJQiYS4yXhlejAzw90Mwk6xJ1Q1yFm1MZronErenRfKoqG3lrft278lk1PewOZVw3tsbGJolNQ0sflcLnMGuV9lk6pv/jt+uxqm4054Zpgbb/56mbEBjvz+1Fi92T5qAkEQCPO0JczTlpenBfNbYhHbovN4Z38y7+xPxsfBknGBjtwW6ERkP3uDlvbtCcU1TZzKKONEahkHLxVT16zARWbGyjH9mDPI3WAncrtKQVUj9244R255A/9eMqhdq+pW4fDlYn44k8PK0f3+K4Tb2vjsaAYKlahzm8ab8b/gfwM+PJjCtuh8hvn0wcPOHGOpxOBkH3qKlakRC4d6snCoJznl9UQllxCVUspPZ3P59mQ25sZSRvr2YXygI+MDnQxKhrirVDe0cDqznNMZZZzMKCe9pA4AWwtjpoa6MHuQO8P69dF7v7UmuFxUw/Jvz9EgV/L9ishbLniW1jbztx2JBLlY87cpgfq+HI1RVN3IprO5LBjiYXBy2f8L/jdgZrg7d4/wxsnajAa5Arj6riAquYSRfn0MUrirO3j3sWT5qH4sH9WPRrmSM5nlRKWUEJVSwuHkEuAifftYEOJuQ4ibjBA3G4JdZQZV8FaqRHIrGkgtriUut4pTGWUkFVSjEtW+wpH97FkY4cFIXweCXWUG35vfHU6ll/HADzFYmhqx/cERt9wdjCiKPLcjgbpmBZtXD7/lP08dWXskHRHRIFVT/xf8b4Cfk9pFS6US/9JullVWz8rvzzPIy47P7xr8X6O1b24i5bYgJ24LckIURTLL6jmaUsrZzHIS8qr4NbGo/blO1qaEuMkY0LooePexxFlmhp2FsdbujlQqkYKqRlKLa0kpriWtuI6UK7VklNbRrFABal/gQV62PDbBn1F+DoR72t4SPfk9YUdMPi/tuoB3Hwu+XxHZYx9mfbLhZDZHU0p5fVYIAc7/HcNcAHkVDWyLzmPRUE887P6vvfMOj6rM/vjnJCG9V0MCkgRC7xiaYENBQBEUGyoqCGLBtq7ysGtZdV3QXSw/V1BhRVFZRFSaFBcVEBBDgARIIKGlEBJCKpMymcz7+2NuMMuCISHJ3Jncz/PMc9vMne/ce+fc9573vOfoq9UPRqjnJbEmOZc/fLkXfy833r+nP/3aB9lbUrNTUl7N/twSDpwoZf+JUvafKCEj/wx1a663cRXC/TwJ9/cgwu+3erwhPu64ubrg6gIuYivY7iJydt7VBSrMVgrLzRSZzBRp08LyatvUZKbgTNVZIw/QNsCTThF+xEf4Eh/hd3beWWLDL4TZYuXVNQf4ZPtxhsSF8P6k/gR4O1747q7jRdyxYDvXdAnng3v7O4VLtZanl+1hTXIuPz17DZcFNG/jsDGhnobxv0RSc0uZ/ukucksq+Mu4HtyV4FjRFU1BZXUNB0+WkVNcQX5pJXllVeSVVpJfapvmlVZSWmlp8H79PNwI8nEnyMedYO82BPm4E+LjTmxYraH3bZXjFfJLK3nksyQSjxcxbXgsfxzZ2WEGntWl0GRmzDtbcHMVVj8+TJcpVBrLwZNljHp7M9OGxzLrxuYvPmPE+duBrpH+rHxsKI9/sZtZK1JIySnhpZu6O62b4Xx4tnE9G0V0ISrMNRSWm6mpUdQoRY1VodRv81Yr1CiFh5sLIT7uBHq7t6pjeLHsOl7IjCVJlFVaePeuvtzU27EiemqpsSqeWLqb0yYzK2YMcSrDD/DG+oP4ergx46o4e0u5IIbxbwICvd35+IEE3txwkPd/PExabinvTepnZNesg5e7K1HuxvFoLEopPtl+nFfXHKBtoBefTElwuI7dury7KZ0t6QW8Nr4HPaKcK+3IruOFfJ+ax7MjOxPo7W5vORdEV02rM1UWqiw19pbRKFxdhOdGdeG9u/uRdrKMMe9sZVNanr1lGTgBJeXVPLxkFy+u3M+wTmGsfPRKhzb86/ad5K3v05nQN4q7ncxNqpTildWphPt58MDQDvaW87voyvgfLTAx++t9Dl2kfEyvSFY9fiXhfh48+HEis1akYKpquL/bwABsrcjR72xhU1o+fxrTlY/uG+CQHbu1pJ0s5elle+jdLpC/TujpVB28AKuSc9mTVcwfRnbWfdBBsxl/EXlJRHJEZI/2Gl3fZ8L9PFi+K5uFW482l6wWIS7Ml28fG8r0q2JZ+msmN769hcRjhfaWZeBAVFlqeGN9Grcv2IGLCyx/eAhTh8U69PiEQpMt9YSvhxsf3Nvf6UaQV1bXMOe7NLq39ee2fvbP118fzd3yn6eU6qO91tb35gh/T0Z1v4y/rk3lp0Onmlla8+Lh5sqsG7vy72mDsWoFqOesS3NYt5ZBy5GSXcLN7/7Mez8cZnzfKNbMHKb7SmH1YbZYeeSzXeSVVrHg3v5OWWNi4VZb+u/ZY7o6xE1aV24fgL/f3pvOl/nz6GdJpOaW2lvOJZMQE8y6J4czsX873v/xMGPe2UpSZpG9ZRnoEFOVhVdWH2Dce1spKjez6P4BvDmxt8OHsyqleH5FMjuOFDLn1p70dcLxMLklFbz3QwbXd4s4mxlY7zS38X9MRJJFZJGInPeMi8g0EUkUkcRTp07h4+HGovsH4OvhxgP/+pXckopmltj8+Hq4Mee2XvzrgSsor7Jw6/vb+MuqA2dTRhgYbErL44Z5m1m49Sh3JbRn49NXOUy5xfqYt/EQK5JyeGpEPOP76t8d0hheW5NKjVXxwthu9pZy0VzSIC8R+R44X37j2cAOoABbwfZXgEil1IO/t7+6g7xSc0u5ff522gZ68eWMwQ7f+qmlrLKauesO8umO40QHefGnMd0Y2T3C6Tq+DC6O/LJKXl51gDXJuXQK9+X1CT0Z4IA1BC7E0p2ZPL8ihdsHRDPn1l5OeZ1vyyjg7o9+4ckRnXhyRLxdNOh2hK+IdABWK6V+tyzPuSN8f84oYPKinQyMDeZf9yc41aCfX46c5s/f7uNQ3hmGdgzhhbHdnaZItUH9WGqsfLEzk7nrD1JlsTLz2o5MGx7nVNf4jwfzmbI4kaEdQ1k4eYBDp0O/EGaLldHvbKHKUsPGp66yWyd2Y4x/c0b7RNZZHA/sa+g+hnYMZe5tvfg54zTPfLmXGqvjhoCey8DYENbOHMZfxnVnX04pN769mVkrkskvrbS3NINmRCnF2pRcbpi3mT9/u5+eUQGse2IYj13byakMf1JmEY98lkTnCD/+OamfUxp+gEU/HyUj/wwv3dTd4aKXmjMQda6I9MHm9jkGTG/MTib0iya/rIq/fZeGv6cbr97Sw2keHd1cXbhvcAdu6tWWt/+TzpIdx/l2zwkeGhbLtOGxTlPJyMDGzxkFzFmXRnJ2CfERvnx43wBGdA13muu5lgMnSrl/0U7C/Dz4+IEr8HXS6/j4aRNvfX+IG7pFcF1Xx+ufcZjEbn/7Lo35Px3m4avieG5UZ6f7wwAcKzAxd30aa1NOEubnwdPXxzOxf7RDJu0y+I2U7BLmrEtja0YBUYFePHV9POP7RjlFEZlzOXzqDLfP3467mwvLpg92yCJAF4NSivsW7WR3ZjEbnx5u91QuTp3Y7blRnSmrrGb+T4fxdndl5nX6KonWFHQI9eGfk/qz63gRf12byqwVKSzQbnjj+0U5VZGL1sCRU2f4+4ZDrEnJJdjHnT+P7cakge0dzj1wsWQVlnPPR78AsGTqQKc1/ADf7MlhS3oBL9/c3e6Gv7E4TMsfbIU8nl2ezFdJ2Tw3qgszrtZvxrxLRSnFhgN5/N+mDFJySmy1Zq+M4c6Edvg5SeSTs7I7s4hFPx9jbUouHm4uTB0Wy0PDYpz6vOWWVHDnBzsoMplZOm0w3do6bu6h+jh9porr523m8hBvlj88RBdPcE7d8gdwcRHm3taL6horc9alUWO18pjOiiI3FSLCyO6XcUO3CLakF/DPHzN4bW0q72xKZ9LAy3lwaAenqR7mDFhqrKzfn8fCrUdIyizGz8ONB4d2YPpVcYT66qfcZXOQU1zBXZrhXzwlwakNP8CLK/dTVlnN6xN66sLwNxaHMv5gy575j9t74+oivLnhEBar4onrOjllHwDYbgLD48MYHh9GcnYxCzYf4YPNh1m09Si39G3LtOGxdAw3QkTtRWllNf/emcXH246RU1xB+2BvXrypGxMHtHPajs66ZBWWc9eHOyitqObTqQPp4+BpKOpj3b5cVifn8sz18Q6dWRUc0PiDLUrmzYm2G8Bb36djqVE8c0O8094AaukVHch7d/fj+GkTC7ceZVliFssSs0mICWZi/2hG94w0IoRaiKMFJhZvO8aXiVmYzDUkxATzwk3dGNE1wqFbgw3hWIGJuz/cgclcw+cPDXK6vPznUmQy86dv9tG9rT8PO4HL2aF8/uditSpmf5PCFzuzmD48ludv7OL0N4C6FJrMfLEzk+W7sjlaYMLH3ZXRPSO5rX80CTHBrepYtASFJjNrkk/w9e4ckjKLcXMRburdlilXxji94TuXw6fOcPeHOzBbrHw2dZDTu3oAnli6mzXJuax6/Eq6Rurr9zq9z/9cXFyE127piZuLCws2H6GsysIr43q0mpZXsI87j17TkUeujiPxeBFfJmaxOjmXL3dl0z7Ym1v7RTOhX5RTR100N+VmCxsP5PHNblt0h8WqiI/w5blRXZjQL8ops1PWx96sYh74+FdcBL6YNsjh3R8Xw6q9J/h2zwmeGhGvO8PfWBy65V+LUoq5620lFMf2iuTvt/dutWGRpioL6/ad5KukbLYfOY1S0P/yIK7tEs61XcLpcpmf8URQD2aLlZ8PF/Dt7hw2HMij3FxDZIAnN/dpyy19olr1MdySforpn+4i2MedT6cMJCbUx96Smp3ckgpGzttMbJgvyx8erMtxN7rN7XOxNNb417Lgp8O8/l0aQ+JCmH9vf6dJBtdYcoor+Dopm3X7T7Ivx5Yeu22AJ9doN4IhcaF4ubfOm2RdlFIcLTCx+dAptqQXsP3IacrNNfh7ujGmVyTj+kSR0CHYIXK0Nycr957gmWV7iAvz5ZMHE1pFtJnVqrhn4S/sySpm7cxhdNDpza7VG3+AFUnZ/HF5Mh3Dffn4gQQuC3D+C/RiyCut5MeD+WxKy2dLegHl5ho83FwYHBfCtV3CGRgTQqdw31Zj4EoqqtmWUcDm9AI2HzpFTrEtdXiHEG+GdQrjqvgwhsWHttonyHNZvO0YL63azxWXB/Ph5AEEeLWOhtWHm4/w2tpU5tzakzuu0G+9YcP4a2xJP8WMJUn4erjx0eQBra4zrj6qLDXsPFrIprR8fkjL59jpcgD8PNzo0z6Qvu2D6Ns+kH7tghy6XmwtNVbF4VNnSM4uISW7mD3a1Kpsv3lwXIgtnLZTGO1DjP6RulhqrLy6JpWPtx3j+m4RvHtXX6cdoXwuKdkl3Pr+Nq7pEsb8e/rr2tVnGP86pOaWMnVxIoUmM/Pu6M2oHpH1f6iVcqzAxK7jRSRlFrE7s5i0k6XUJlCNC/Ohb/sgekcHEBvmS4dQHyL9PXX7hFBjtblwUnKKSc4uYV9OCftySqmotpXP9HF3pXtUAINiQxjWKZQ+7QKdNuPkpVJSUc1jnyexJb2AqVfGMGt011YTTFFWWc3Yd7ditlhZO3MYQT7u9pb0u7S48ReRicBLQFcgQSmVWGfbLGAKUAPMVEqtr29/TWn8AU6VVTH900SSMot5dmRnHrk6Ttd3b71gqrKwN7uY3ZnF7M4sIimzmEKT+ex2DzcXYkJ9iAn1oYM2jQ31IcLfkyAfd3zcXZvtOCulKDhjJquonKzCcrKLKsgqLNeWKzhRXIFFu3N5tnGhR9sAekYH0Cs6gJ5RgcSG+uj2xqUnjhWYeHDxr2QVlvPqLT107fJoapRSPLF0D2tSclk6bRBXOEBxHXuEeu4DJgALzhHSDbgT6A60Bb4XkXilVItWLw/z8+Dzhwbx/FfJvLH+IBn5Z3h9Qs9W89jaWHw83BgSF3q2FqlSipOllRwtMNlep2zTg3llbDyQd9bY1uLu6kKQTxuCvN0J8nYn2MedQO82BHi1wc1FEBFcRHARW7iuCGeXzRYrpZUWyiqrtak2X1FNWaWFkopqqizW//q+UF93ooO86d0ukLG9IokJ9aFXdCBxYT66jMzQO9sOFzBjSRIuAkumDGRgbIi9JbUoS3/NYuXeE/zhhniHMPyN5ZKMv1IqFThfK28csFQpVQUcFZEMIAHYfinf1xg827gy744+dAz35R8bDzGxfzRDOjpGgWW9ICJEBngRGeD1P8WpLTVWsosqOFpg4tSZKopMZorKq7Wp7XUwr4wik5mSimqsSlFfTR4PNxf8PNvg7+mGn6cb/l5tiAzwxM+jDf5ebrQN9KJdkDftgr2JDvIyRjU3IUop5m08RLifBwsnX9Hq+kCUUmw8kMewTqE8cnVHe8tpVprrXxOFrYZvLdnauv9BRKYB07TFKhFpcMWvhjD0b02ym1Bs9Yn1jqGzaXEEnU2m8fJnmmIvF0T3x3LJVMABdGp0bugH6jX+v1ekXSn17YU+dp51523vKaU+AD7QviuxoX4re2DobFoMnU2HI2gEQ2dTIyIN7iyt1/grpUY0Qks20K7OcjRwohH7MTAwMDBoBpqrN2wlcKeIeIhIDNAJ2NlM32VgYGBg0EAuyfiLyHgRyQYGA2tEZD2AUmo/sAw4AKwDHr3ISJ8PLkVPC2LobFoMnU2HI2gEQ2dT02CduhrkZWBgYGDQMhhB0AYGBgatEMP4GxgYGLRCdGH8ReQVEUkWkT0iskFE2mrrRUTeEZEMbXs/O+t8Q0TSNC1fi0hgnW2zNJ0HRWSknXVOFJH9ImIVkQHnbNOTzlGajgwRed6eWuoiIotEJL/umBMRCRaRjSKSrk2D7KlR09RORH4QkVTtfD+hR60i4ikiO0Vkr6bzZW19jIj8oun8t4jYPYGOiLiKyG4RWa1jjcdEJEWzl4nauoafc6WU3V+Af535mcB8bX408B22cQODgF/srPMGwE2bnwPM0ea7AXsBDyAGOAy42lFnV2yDPn4EBtRZrxudgKv2/bGAu6armz3Pbx1tw4F+wL466+YCz2vzz9eeezvrjAT6afN+wCHtHOtKq/b/9dXm2wC/aP/nZcCd2vr5wAwdHNOngc+B1dqyHjUeA0LPWdfgc66Llr9SqrTOog+/DQgbB3yibOwAAkXEbuk5lVIblFIWbXEHtvELUCedhVLqKFCbzsIuKKVSlVIHz7NJTzoTgAyl1BGllBlYqumzO0qpzUDhOavHAYu1+cXALS0q6jwopXKVUknafBmQim0kva60av/fM9piG+2lgGuB5dp6u+sUkWhgDPCRtizoTOPv0OBzrgvjDyAir4lIFjAJeEFbHQVk1XnbBdNE2IEHsT2VgL511kVPOvWk5WKIUErlgs3oAuF21vNfiEgHoC+2VrXutGrulD1APrAR21NfcZ3GlB7O/1vAH4HazIEh6E8j2G6cG0Rkl5YeBxpxzlssI1Z9aSKUUrOB2Voq6MeAF2lAmoim4mLSWYjIbMACfFb7sfO83+46z/ex86yzV6yvnrQ4NCLiC3wFPKmUKtVj2nJlG+fTR+sn+xqba/J/3tayqn5DRMYC+UqpXSJyde3q87xVD9foUKXUCREJBzaKSFpjdtJixl9dfJqIz4E12Ix/i6eJqE+niEwGxgLXKc3Bhg51XgA9pd3Qk5aLIU9EIpVSuZrrMd/eggBEpA02w/+ZUmqFtlqXWgGUUsUi8iM2n3+giLhpLWt7n/+hwM0iMhrwBPyxPQnoSSMASqkT2jRfRL7G5kJt8DnXhdtHRDrVWbwZqL2TrQTu06J+BgEltY829kBERgHPATcrpcrrbHKUdBZ60vkr0EmLpnDHVv9hpZ20XAwrgcna/GTgQk9XLYbmk14IpCql/lFnk660ikhYbWSciHgBI7D1T/wA3Ka9za46lVKzlFLRSqkO2K7FTUqpSehII4CI+IiIX+08tiCUfTTmnNu751prPH+l/YBkYBUQpX6LEngPm38whTqRK3bSmYHNT71He82vs222pvMgcKOddY7H1rKuAvKA9TrVORpbhMphbO4qu1+Lmq4vgFygWjuOU7D5f/8DpGvTYB3ovBKbGyK5zjU5Wm9agV7Abk3nPuAFbX0stsZHBvAl4GHvY6rpuprfon10pVHTs1d77a/93zTmnBvpHQwMDAxaIbpw+xgYGBgYtCyG8TcwMDBohRjG38DAwKAVYhh/AwMDg1aIYfwNDAwMWiGG8TcwMDBohRjG38DAwKAV8v/KOCY6xfnpvQAAAABJRU5ErkJggg==", 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" ] @@ -111,9 +111,11 @@ } ], "source": [ - "ml2 = ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", - "w2 = Well(ml2, 0, 20, rw=0.3, tsandQ=[(0, 100)])\n", - "wall = LeakyLineDoubletString(ml2, xy=[(-20, 0), (20, 0), (40, 20)], res='imp', order=5, layers=0)\n", + "ml2 = ttim.ModelMaq(kaq=10, z=[10, 0], Saq=1e-4, tmin=0.01, tmax=10)\n", + "w2 = ttim.Well(ml2, 0, 20, rw=0.3, tsandQ=[(0, 100)])\n", + "wall = ttim.LeakyLineDoubletString(\n", + " ml2, xy=[(-20, 0), (20, 0), (40, 20)], res=\"imp\", order=5, layers=0\n", + ")\n", "ml2.solve()" ] }, @@ -143,7 +145,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -155,8 +157,8 @@ } ], "source": [ - "plt.plot(t, h[0], label='no wall')\n", - "plt.plot(t, h2[0], label='wall')\n", + "plt.plot(t, h[0], label=\"no wall\")\n", + "plt.plot(t, h2[0], label=\"wall\")\n", "plt.legend()" ] }, @@ -167,7 +169,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] diff --git a/notebooks/wells_in_different_systems.ipynb b/notebooks/wells_in_different_systems.ipynb index 7f19d9e..66c0643 100644 --- a/notebooks/wells_in_different_systems.ipynb +++ b/notebooks/wells_in_different_systems.ipynb @@ -7,7 +7,7 @@ "outputs": [], "source": [ "%matplotlib inline\n", - "from ttim import *\n", + "import ttim\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] @@ -47,8 +47,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=0.01, tmax=100)\n", - "w = DischargeWell(ml, tsandQ=[(0, Q)], rw=1e-5)\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=0.01, tmax=100)\n", + "w = ttim.DischargeWell(ml, tsandQ=[(0, Q)], rw=1e-5)\n", "ml.solve()\n", "h1 = ml.head(r, 0, t)" ] @@ -82,8 +82,10 @@ "clist = [1e2, 1e3, 1e4]\n", "hhantush = np.zeros((len(clist), len(t)))\n", "for i, c in enumerate(clist):\n", - " ml = ModelMaq(kaq=25, z=[21, 20, 0], c=c, Saq=S/20, topboundary='semi', tmin=0.01, tmax=100)\n", - " w = DischargeWell(ml, tsandQ=[(0, Q)], rw=1e-5)\n", + " ml = ttim.ModelMaq(\n", + " kaq=25, z=[21, 20, 0], c=c, Saq=S / 20, topboundary=\"semi\", tmin=0.01, tmax=100\n", + " )\n", + " w = ttim.DischargeWell(ml, tsandQ=[(0, Q)], rw=1e-5)\n", " ml.solve()\n", " hhantush[i] = ml.head(r, 0, t)[0]" ] @@ -95,7 +97,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -107,11 +109,11 @@ } ], "source": [ - "plt.semilogx(t, h1[0], label='Theis')\n", + "plt.semilogx(t, h1[0], label=\"Theis\")\n", "for i in range(3):\n", - " plt.semilogx(t, hhantush[i], label='c=' + str(int(clist[i])))\n", - "plt.legend(loc='best')\n", - "plt.title('head at r=20');" + " plt.semilogx(t, hhantush[i], label=\"c=\" + str(int(clist[i])))\n", + "plt.legend(loc=\"best\")\n", + "plt.title(\"head at r=20\");" ] }, { @@ -143,8 +145,16 @@ "clist = [1e2, 1e3, 1e4]\n", "htwolayer = np.zeros((len(clist), len(t)))\n", "for i, c in enumerate(clist):\n", - " ml = ModelMaq(kaq=[25, 25], z=[20, 0, -5, -25], c=c, Saq=S/20, topboundary='conf', tmin=0.01, tmax=100)\n", - " w = DischargeWell(ml, tsandQ=[(0, Q)], rw=1e-5)\n", + " ml = ttim.ModelMaq(\n", + " kaq=[25, 25],\n", + " z=[20, 0, -5, -25],\n", + " c=c,\n", + " Saq=S / 20,\n", + " topboundary=\"conf\",\n", + " tmin=0.01,\n", + " tmax=100,\n", + " )\n", + " w = ttim.DischargeWell(ml, tsandQ=[(0, Q)], rw=1e-5)\n", " ml.solve()\n", " htwolayer[i] = ml.head(r, 0, t)[0]" ] @@ -156,7 +166,7 @@ "outputs": [ { "data": { - "image/png": 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6/lFi/WJ5rO9jDO482PqE/zLeXWHwTG2rLNJKPS+3e9i/EGwdICS+9g7B08JlqQqFFTBq1Chuu+02nnzySXx8fMjPz7/pGb4QgsTERBYtWsT06dOZP38+t96qucBOmTKF+fPnM2TIEBYtWkRSUpJFtKRN1uHfDBdLKvnmva/oteQLOlUUUBo/mp6vP8eK4i18sv8TzpefZ0CnATza51EGdK6zxNU6MRoge5t5wdcqyD+u7e/cq7bRm3L3UlgAa6nDnz9/Pu+88w62trb07duXefPm1XvOZT/b0tJSfHx8mDt3LuPGjePEiRNMnz6d/Px8+vbty4IFC3B0dKSyspL77ruPvXv30qFDB7755hu6devW6NhvtA6/UYIvhJgGvIJmUD7IbHpS13HjgX8DtsAcKWWD7t8s0R55Z8YZNr/+Hol71yDt7HCdMYugGfewOGspcw7O4VLFJQZ3HsyjfR+lb8e+LRpbk3Aps7bVQ84OkCZw979ite8IsG+mpnMKxRVYi+C3Zlpa8KMBE/AJ8HRdgi+EsAWOAmOAHGAncJeUMr2+8S3VD99gNPHNki3wn3/S/2w6pZ0DCZ/9Ks5x/fjuyHfMTZtLfmU+cV3ieLj3w/Tr1K/FY2wSyvIg82ftC+B4MlSXgr2Llu+PnKC5fLn5WTpKRRtFCX7jaVHBv+ICKVxb8IcAr0gpx5l//xuAlPKt+sa1tAHK+eJKFry3gL7LvqBLeT4VQxPo9cZL6H09+f7o93ye9jn5lfkM7jKYWbGzWleq52oMVXByozn1sxqKcwABgQNr8/7K4EXRhCjBbzzWKPh3AOPNZuYIIe4DBksp/3SNsWYCMwGCg4P7nzp1qtHxNZYt6blsfuNfjNm7GhtbG1z/OIPQR2ZSZWPkuyPf8UXaF+RV5tG/U39m9Z5l3Q93G4KUWk30kZXaF8BZs2mzdwh0N4t/16HK4EXRKJTgN54mF3whxDqgri5ef5dSLjUfk8K1BX8aMO4qwR8kpXysvjdj6Rn+lVQbTCxYshXx338Tl3uQcr8udHvtZbwTR1JpqGRx5mI+P/g5FyouEOsXy8xeMxkROKJ1C/9lagxeVsOJFDBWgaMnRIypNXhxth7fTkXrQAl+47HGGX6rTenURW5hBV/86xsGLf+CoNKLVA8eTvTsl3AICqLKWMXSY0v5PO1zcktzifSO5MFeDzKm65jmN2FpKS4bux9ZrX0JlF/SDF66Dq2d/XcItXSUilaAEvzGY42Cb4f20HYUkIv20PZuKeWh+sa1RsG/jO5gLpv/8R8m7l2JPRL33/+e4D89jI2zM3qTnhUnVjD34FxOFp8k2D2YB2IeYErYFBxsHSwdetNhMkLOLjhqzvtfPKzt94sy5/0nQkB/tdpXUSdK8BtPi7ZHFkLcJoTIAYYAK4QQa8z7/YUQKwGklAbgT8Aa4DDwXUPE3tpJ7BXA01/MZtsrH7PZvxcVcz/jwKhxFKxajZ2wY2r4VJbcuoT3Et7DzcGNV7e+yvjF4/k87XNKqkssHX7TYGMLwYNh9Cvw6Db4814Y9xa4+sHm92HuGK3N89JH4fBy7e5AoWgDNGV75Lfeeovw8HAiIyNZs2ZNs8atFl41AScvlTHnw8UMWTGPbsVnMfYZQMTsl3EMDwe05dvbzm7j87TP2XZ2G272bkzrPo27o++ms2sbNTmpKNBW+x5ZBZlroaoIbB2h20ht9t99vOYFrGi3tOYZ/vr16ykvL+eTTz5h+fLlNfvvvPNObr/9dqZPn86sWbPo3bs3Dz/8MB999BEHDhzg448/5ptvvuHHH3/k22+/JT09nbvuuosdO3Zw5swZRo8ezdGjR7G1bdhd8Y3O8JFSWu3Wv39/2VowmUxy1b5s+fx9L8rtMX1lWnRPmfXaG9JQUvKL49IupcmnU56WsfNjZZ/5feTzG5+XGXkZFoq6hTBUS3k8RcpVz0n5z15SvuyhbZ+MlFL3tpRn9ktpMlk6SkULk56ebukQpJRSzp8/X/bq1UvGxsbKe++9t8Hn6XQ6OWnSpJrfTSaT9PHxkXq9Xkop5ZYtW+TYsWOllFKOHTtWbtmyRUoppV6vlz4+PtJkMsk333xTvvnmmzVjXHlcQ6jrMwR2yWtoarvupdOUCCEY3zuQ+M9e4uNlE7Gd+zFjvl7AoZ+WE/js03jfNhVhY0NPn568M/Idnih9ggXpC1icuZhlx5cxuPNgftfzdwwPGG5dHTqbAlt7bWbfbSSMexMuHK7N+6e8BSlvgkegZu0YOUHr+WPnaOmoFS3IP3b8g4z8jCYdM6pDFM8Our7RiKXbI+fm5hIXF1czxpXnNAdK8JsYV0c7npoWR+aInvz3k5+IXzUf+7//nbMLFtLttZdx7hUDQIBbAM8OepZZvWfxQ+YPfH34ax5d/yghHiHcFXUXt4bfiqu9q4XfTTMgBHTqoW3xT0HpBa3L59HVsO9/sHMOOLhB+Cit6idiLLj6WDpqRRvF0u2Rr3dOc6AEv5mI6OTO/714F8smx/PJB18ybc9Ssu68E9eptxHwzFPYmVujejp68kDMA9zb417WnlzL14e/5q0db/HB3g+YGj6Vu6LuItgj2MLvphlx66h5+fa7D/QVkLXhCoOXpSBsIGhwbdWPb4SlI1Y0A/XNxJsLaeH2yJf3X+bKc5qFa+V6rGFrTTn861FUUS3f+Ha7nD3pIXkgqoc82Le/vDR/vjSZc31Xs+/CPvlM6jOyz/w+ste8XvLhtQ/L1OxUaTAaWjhyC2IySZm7R8rkN6T877DavP+/+0q5+nkpszZJaaj781O0Dqwhh5+WliYjIiLkpUuXpJRS5uXlNfjcq3P4Ukp5xx13yIULF0oppXzooYfkhx9+KKWU8j//+Y986KGHpJRSLly4UE6bNq3m+rGxsbKyslKeOHFChoaGSoOh4X/nN5rDt7ioX29rK4J/mbTcQjnz9e/kV/FTZHpklEwfN1GWbt16zeMvlF2QH+79UCZ8myBj5sXIcYvGybkH58q8iob/T9lmKDgt5fZPpfxyqpSv+mji/3ZXKRfPkPLgYikriiwdoeIGsQbBl1LKefPmyZ49e8rY2Fh5//33N+ic4cOHS19fX+nk5CQDAgLk6tWrpZRSHj9+XA4cOFCGhYXJO+64Q1ZWVkoppayoqJB33HGHDAsLkwMHDpTHjx+vGWv27NmyW7dusnv37nLlypU3FPuNCr4qy2xhTCbJol3Z/Dzne+7Z9QOdy/NxHj0G/+eexSEwoM5z9EY967PX823Gt+w6vwt7G3tGdx3Nnd3vpH+n/m2jfcONUFViXu27Ssv/V+SDjT2EDDf3+B8PXm04DdZGaM1lmdaCRVbaNhdtUfAvU1BWzbvLD2L6ZgG/zUzGXoDfjD/iO2MGNs7O1zzveOFxvj/6PcuOL6OkuoQQjxB+E/EbpoRPoYNTy1umWRyTEbJ3wJEVWtVPXqa2v1OMud5/Avj31SwhFVaFEvzGowS/lbH3dAHvfLWB+ORvSMjdBx07EfDcX3GfMOG6M/cKQwVrTq5h8dHF7Lu4DzsbOxKDErkt/DaG+g9tO717bpRLx7Qun0dXw+mtmsGLW6daY/duI8H+2l+oipZDCX7jUYLfCjGaJF9vP8VPX63k97sX063oDI79B+D/wvM4NeAP4ljBMX449gPLjy+noKqAji4duTXsVqaETSHEM6T534C1Up5vNnhZpa36rS4BO2cIS6w1eHHvZOko2y1K8BuPEvxWzMWSKt5efojypT/ywOFVuOkr8J42Db/H/1xTxnk99EY9KTkp/Jj5I5vPbMYkTfT2682UsCmMCxmHp6NnC7wLK8VQrRm8XG7zXHRa2x8wwLzgayJ07KEMXloQJfiNRwl+G2D7iTze/G47cRuWMCVrMzauLnR67E943303wr5hpiMXyy+y/MRylh5byvGi49jb2JMQlMDkbpOJD4jHvj2bl0gJ5w+ZV/uugtzd2n6vYE34u4+HrsPArg11NrVClOA3HiX4bQS90cQXm7NYtHgjv9+3hL7nj2AfGkrnvz2H24gRDR5HSsnh/MP8dPwnVmatJL8yHw8HD8aGjGVS6CT6derX9lo53CjFZyFzjSb+J1LAUAmOHtpq38iJmsGLSzt8IN7MKMFvPC3aHlnRfNjb2jBzRBjzX/stm2a8wMtxfyA3r5TsmQ9x+qGHqDqR1aBxhBD08OnBs4OeZd20dXw06iOGBwxnxYkVPLDmAcYuGsu7O9/lUN6hOpd5tws8ukD/38Pd38Jfs2D6Qug5FU5uhh9mwDvhMG8ybP0Q8o5bOlqFFdAS7ZFXr15NZGQk4eHhvP32200St5rhtxI2HL3Iaz/up/eONdx3bD2OBj0d7r0b30cewdbzxnPz5fpyUrJTWJW1ik1nNmEwGQhyD2J8yHjGhYyju3f39lfffzUmE5zZU+vteyFd2+8bWZv3DxyoDF5uktY8w2/u9sgA3bt3Z+3atQQGBjJw4EAWLlxIjx49fhGHao/chqnUG+T7647KgU99K/894ffyUFS0zBgcJ/P/979rtmloCIWVhXLx0cVyxpoZMnZ+rIyZFyMn/zBZvr/nfZmRlyFNqnWxRn6WlFv/K+W8W6R8tYO22vcfoVL++LCUh5ZKWVlS7xCKWqxlpa01tke+8lwp5a+Ou0yLtkc2G5S/AkSjGZPXZXEYBHyJZoRuAj6VUv67Mddtrzja2fLYqAim9g3g1Z9CWbJ1CE8cXYHp1dco+N9COv3tOVyHDr3hcT0dPbk94nZuj7idvIo81p9ez88nf2bOwTl8euBTunp0ZUzXMYzuOpoeHXq035m/dwjEzdK2yiI4tk6b+Wcsh31fg60DhI6oXfDlWffKacWvOffmm1Qdbtr2yI7RUXR+/vnrHmPN7ZEvH395//bt2xv4zq9NY7tlpgG3A59c5xgD8JSUco8Qwh3YLYRYK6VMb+S12y1BHVyYc/9A1g4M5pWlYXQ9vJPHM1dR9Yc/4paYSMe/PoNj6M0Zifs4+3Bn5J3cGXkneRV5JGcns/bkWr5I+4I5B+fg7+rPqK6jGNN1DL39erffB75OnhDzG20z6uH0Nk38j6yEFU9pW+dYc6uHCdCltyr5tEKstT2yyWS65liNoVGCL6U8XF8gUsqzwFnzzyVCiMNAAKAEv5GM6dGJ4eG+fKgL5AFdNLdlbeauresovWUK3nffhd8jj2Dr5XXT4/s4+zCt+zSmdZ9GYWUhumwd606v45uMb/gq/St8nHxICk5iVPAoBnUe1H5LPW3tITRe28a9AZeOmvP+qyH1H5D6NngEQPdx2hdASDzYO1k6aquivpl4cyGl9bZHbpa2ydfK9dzIBqQAAxpwXAhwGvBoyLgqh99wjl0okfd8tk32fnyhnHvbDHkouoc8MmiwzJv/pTRVVzfptUqqSuTKEyvlX3R/kQMXDJQx82Jk3Ndx8pnUZ+TqrNWytLq0Sa/Xqim9KOWeBVJ+c4+Us7toef/ZXaRceLe2v/SipSO0GNaQw7fW9sh6vV6GhobKEydOyKqqKhkbGyvT0tJ+FUOTt0cG1qGlbq7ebr3imHoFH3ADdgO313PcTGAXsCs4OLjBH75Ce2i0bF+uHPTGWpnw8Kdy3aRpMj0ySh4bP0EWr09uloevFfoKmXI6Rb60+SU54psRMmZejOz7ZV85a+0s+W3Gt/JC2YUmv2arpbpCyqM/S/nTk1K+G2Xu8e8p5ZwxUm58T8oLGe3K29caBF9K622PvGLFChkRESG7desmZ8+eXWccFmmPLIRIAZ6WdTy0Nb9uDywH1kgp32vouKos8+YorTLwr7VH+WJzFiPzj/DY0VU4nc3BJS6OTs/+tUH9eW4Go8nIvov7SD6dTPLpZHJKcwCI9Y0lMTiRhMAEwrzC2u9D3yuREs7uN7d4XqX9DOAdWpv3Dx4Ctm3XlK41l2VaCxZZaXs9wRfaX/d8IF9K+cSNjKsEv3FknCvmxSVp7DlxiYeK9jN57wpESTGeU6fi98Tj2HdqvsZhUkqOFR5Dl61Dd1pHWl4aAEHuQSQGJZIQlEDfjn2xs2m7gnZDFOXU9vnJSgVjNTh5QcQYTfzDR2sPitsQSvAbT4sKvhDiNuADwA8oBPZJKccJIfyBOVLKiUKI4UaurbIAACAASURBVMBG4CBaWSbA81LKlfWNrwS/8UgpWbwnl7dWHqa6sIjXSnYStXUVws4Onwd+T4c//BFbt+Y3Sz9fdp7UnFR02Tq2n92O3qTHw8GDEYEjSAxKZFjAsLZp2n4zVJVqBi9HV2tbeR7Y2GkGL90naIu+vEMsHWWjUYLfeFQvHUWdFJXreefnDL7efpooWcIr51Px2JqCra8vfo89htdvbkfYtcxsu0xfxpYzW9Cd1rEhdwNFVUXY29gzqPMgEoISSAhKoLNr5xaJxeoxGSFnp7nkcxVcOqLt79ij1tjdv1+rNHhRgt94lOArrsuBnEJeXJLG/pwi7nAq5MFDPyEP7schPIyOTz2FW0JCi+bYDSYD+y7sq5n9nyo+BUB0h+ga8Y/uEK3y/pfJO25O/ayCU1tAGsG1Y23JZ7cEcHCxdJQN4vDhw0RFRan/tjeJlJKMjAwl+IrrYzRJFu44zTtrjlBWqecFrwsMW/8thtOncBk0iI7PPINzrxiLxJZVlIUuW0dKdgr7L+7HJE10dOlIQqAm/oO6DMLR1tEisVkdFQWQuU6r+T+2DqqKwc5JE/3ICVqbZ3frvVPKysrC3d0dHx8fJfo3iJSSvLw8SkpKCL1qkaUSfEWd5JVW8faqDL7fnUOQuz1vORyn448LMObn4zFxIn5PPoHDFcu7W5r8ynw25mwkJTuFzWc2U2GowNnOmaH+Q0kISmBE4Ij26eNbF4ZqOL2ldrVvodngxb9fbdVPp55WtdpXr9eTk5NDZWWlpUNplTg5OREYGIj9VR4ZSvAV12XXyXxeWJJGxrkSxoa48UzBTozf/Q9pMOA9fTq+D89qkONWc1JlrGLnuZ3oTutIyUnhQvkFBILefr1JCEogMSiRUM9QNVMEreTzwmHzat+VtQYvnsHaA9/u47XVvsrgpU2iBF9RLwajiflbT/HPtUepNpp4oo8XU/aupOTHH7BxdqbDH/+Az/33Y+Nq+UoaKTVTl5TsFFKyUzicfxiAYPdgRgaNJDEoUZV8XknJ+VqDl+M6MFSAg3utwUvEGGXw0oZQgq9oMOeLK3ljxWGW7T9DVx8XXu/rSrelX1Gydh22vr74PvIw3nfcgXCwntnhubJzpGankpKT8ouSz/jAeBICExgWMAx3B3dLh2kd6CvgRCocWQFH10DpeRC2EBxX2+XTN9zSUSoagRJ8xQ2z+dglXlyaxomLZYzv2ZnnQ/TITz6kfNcu7IOC8Pvzn/GYNBFhZeWAl0s+U7JT2JizkYKqAuyEHQM6DyAhKIGRgSMJdA+0dJjWgckEZ/fWlnye1xbH4RNRW/IZNEgZvLQylOArbopqg4nPNp7gg+RMBII/J4UzXeZQ8O9/UXXkCI5RUfg98ThuI0daZe7caDJy4NKBmtTPiaITAIR7hdes9o3xjWm/LZ6vpvB0rfif3AQmPTh30Eo+u4/XUkCO6k7J2lGCr2gU2fnlvLY8nbXp5wnv6Mbrt/SgR8Z2Ln7wAfrTp3Hu14+OTz6By8CBlg71upwqPkVKdgqpOansOb8HozTi4+TDyKCRJAQmEOcfh7Ods6XDtA4qi7VSz6OrIfNnrQTU1kF72Hu55NPLchVcimujBF/RJCRnnOflZYfIzq/g1j7+PD82HIefV3Dpo/9iuHAB12HD8HvicZx79bJ0qPVSVFXEptxNpGSnsCl3E6X6UhxtHYnrEleT+vFz8bN0mNaB0QDZ27UmbxkrId9s5N65l5b26T4euvRplat92yJK8BVNRqXeyEe6Y3ycegJHOxv+MrY79/TpRMm335L36acYCwtxGzUKvz8/hlNkpKXDbRB6o57dF3bXpH5ySzWLuRifmJrVvsrU/QouHtXE/8gq7YtAmsC9S+1q39ARYK/ulCyFEnxFk5N1qYyXlqaxMfMSPbp48PrUGPr42JP/5Zfkf/4FptJS3CeMx+/RR3EMbz1VH1JKMgsztaqf7BQOXjqIRNLFtUuN+A/sNLD9untdTVkeHFtrXu27HqpLwd4FwpK01E/EOHBTd0otiRJ8RbMgpWRV2jle+ymdc8WV/HZAEM9OiMLTUEHeF19Q8OVXmCoq8Jg0Cd9HHsGx28357FqSSxWX2JCzAd1pHdvObqPSWImrvSvD/IeREJRAfEA8Xk43byPZpjBUwcmN5ge/q6E4BxAQOKC26scvyqpW+7ZFlOArmpXSKgPvr8/k801ZuDnZ8ddxUUwfGISpqJD8zz8nf8HXyKoqPCZPwnfWw61S+AEqDBXsOLsDXbaODTkbuFhxERthQ9+OfWuqfrp6dLV0mNaBlHDuYG2rh7P7tP3eIbV5/65DNT9gRZOiBF/RIhw5V8KLS9PYkZVPnyAvZk+NISbAE0NeHnmff07B/xZqwj9pEr4Pz8KxWzdLh3zTmKSJ9Lx0dNk6UrNTOVKgtS0O8QipEf/efr2xVTXsGsVnart8nkgFYxU4ekLEaO0LIHw0OKs7paZACb6ixZBS8uPeXN5ceZj8smrui+vKX8ZG4uls/0vhr6zEY8J4fGbNwql7d0uH3WjOlJ6pEf+d53diMBnwcvQiPiCehKAEZfByJdVlWouHIyu11b7llzSDl+AhtY3eOrTOu0BroDkdr6YBrwDRwKBredqaj7VFMyfPlVJObsj4SvBbL0UVev659ihfbj1JB1dH/j4piql9AhBCYMjPJ3/efAoWLMBUXo77mNH4PDQL55ielg67SSitLmXzmc2kZqcqg5f6MBm15m5HVmp5/4taXyT8orVGb5ETIaC/Wu17AzSn4Eej2RZ+wnVMzM3H/gUYAHgowW8/pOUW8cKSNPZlFzI4tAOvT42heydttaaxsJD8rxaQ/9VXmIqLcY2Px/ehmbgMqPP/1VaJwWRg74W9Nb1+Lhu8RHWIYmSg1ugt2idarfa9TH6WlvrJWFFr8OLiq+X8IydAWCI4qDul62FRE3Pz64FoRuZvAH9Rgt++MJkk3+7K5h+rMyitNPDH4aH8eVQEro5aN0tjaSkF/1tI/rx5GPPzce7XD5+ZM6y2ZUNjyCrKIjVbc/fad3GfZvDi3FFb7RuUwKDOg3Cyc7J0mNZBRYFW6nlkpWb0UlUEto7QbWTtal8Pf0tHaXVYg+AvAt4C3M3HKcFvh+SXVfOPVRl8uyubLp5OvDS5B+NjOteIuqmigsLFP5D3+VwMZ87iGBGBz4N/xGPiRIR926vmKKgsYEPOBlJzUtmcu5lyQznOds7EdYkjMSiR+MB4fJ19LR2mdWDUw+mt2krfIyuhULtTokuf2rx/516q5JNGCr4QYh1QV8Lx71LKpeZjUriG4AshJgMTpZSPCCESqEfwhRAzgZkAwcHB/U+dOnXd+BStj92n8nlhySEOny1mRHc/Xp3Sk1Df2tt0qddTtGIF+XPnUpV5DDv/Lvjcfz+ev7kDW7e2eTtfbaxmx7kdNat9z5efRyCI9YvV8v6BCYR5hbW5O56bQkq4mFGb98/ZCUjwCDTn/SeYDV7apxWmRWf4Qoi3gPsAA+AEeAA/SCnvrW9cNcNvuxiMJr7ador3fj5KlcHErIQwHkkIw8m+9uGcNJko3bCBvDlzqNi1Gxt3d7yn/xbve+/FvlMnC0bfvEgpycjPICUnhdTsVA7lHQIg0C2w5qFvv079sLdpe3c9N0XpBa3a5+hqOJ4M+nJwcNO6e3afABFjwdXH0lG2GBZP6VxxXAIqpaO4ggvFlbyx8jBL950hqIMzr07pSVLUr8W8Yv9+8j7/gpK1a8HGBs9JE+lw//049ehhgahblvNl50nN0Vo9bD+7nWpTNe727gwPGE5CUALDA4fj4eBh6TCtA30FZG3Q6v2ProaSsyBsIGhw7Wpf3whLR9msNGeVzm3AB4AfUAjsk1KOE0L4A3OklBOvOj4BJfiKOthy/BIvLT3EsQuljOnRiZdv6UGgt8uvjqvOzib/q68oWrQYU3k5LgMH4v27+3BPSkLYtv3SvXJ9OVvPbiUlO4UNORvIr8zHTtjRv1P/mjbPQR6qbTFgNnjZZ17wtVJb+QvQIcws/hMgKA5s25YVplp4pWgVVBtMzN2UxfvrM5FIHkuKYEZ8Nxzsfl2yaCwupvD7RRR8/TX6M2ewDwjA++678frN7dh6tY8Vm0aTkYOXDpKak4rutI7jRVrb4jDPsJrUTy/fXmq172UKs2tX+2Zt0AxenLzMXT4nQNgocGr9d0pK8BWtitzCCl776RBrDp2nm58rr98aw7DwuqtVpMFASXIyBV9+RfmuXQgnJzxvuQXve+7GKSqqhSO3LNkl2TUln7vP78YojXRw6sCIwBEkBCUwpMsQXOx/fdfULqkq0fL9R1Zp+f+KfLCxh5Dh5qqf8eAVbOkobwol+IpWiS7jAi8vO8Tp/HJu6e3PC5Oi6eRx7Rr1yowMCr7+mqKfliMrK3Hu1w/vu6bjPm4cNlZkut4SFFcXszl3M7rTOjblbqJEX4KDjQODuwyumf13dOlo6TCtA5MRsneYq35WQt4xbX+nmNrUT5e+rcbgRQm+otVSqTfy35Tj/Df1OA62Njw5pjv3D+mKne21//iMhYUULllCwcKF6E+dxtbbG8/bb8P7zjtx6Nr+ulnqTXr2nN9DSnYKumxdjcFLT5+ejAzSVvtGekeqks/LXDpWa/Byeqtm8OLWudbgpdtIqzZ4UYKvaPWcvFTGy8sOkXr0IlGd3Zk9NYYBIR2ue440mSjbspXCb7+lJDkZjEZchsThPW0abqNHt7tZP2gln8cLj5OSo9X7H7h4AImks2vnmlYPAzsPxMG2/X02dVKeD5lr4cgKOJYM1SVg56y1eLhs8OJuXSXCSvAVbQIpJWsOaYYrZ4oqmdY/kOcmROHjVv8CG/35CxQuXkTRosXoz5zRZv1TpuD5m9vbRLfOm+VSxSU25mwkJTuFrWe3UmGowMXOhWEBtQYv3k7elg7TOjBUw6lN5h7/q6AoW9sfcIXBS8doi6/2VYKvaFOUVxt4f/0x5mw8gaujHX8dH8n0gcHY2tT/h1Yz6//+e23Wr9fjFBuL1+234TFhAraeni3wDqyTSkNlzWrf1OxULlRcwEbY0MevT02vn26erdfDoEmREs4fMuf9V8GZPdp+r+ArDF6GgV3L3ykpwVe0STLPa4Yr207k0zvQk9lTe9ErsOGCbSgooHjZMgoXLaYqMxPh4IDbqCS8pk7FddgwhF3bqs++EaSUpOel16R+MvIzAOjq0ZWEQO2hb5+OfbCzab+f0S8oPquVfB5dDSdSwFAJjh6asUvkBO1fl+unIJsKJfiKNouUkqX7zjB7xWHyyqq4d3BXnh4biadLw9sOSCmpTE+n6MclFP/0E8aiImx9ffGcNBGPKVNw6tGj3T/QPFt6tma1745zO9Cb9Hg6etYavPgPw83BzdJhWgfV5ZroHzV7+5ZdAGGrWTpe7vLpE9Zsl1eCr2jzFFfqee9nzXDF28WB5ydGc3u/gBsWalldTemGDRQtXUZJSgro9TiEheF5y2Q8Jk/GITCwWeJvTZTpy9hyZkvNat/CqkLsbOwY2GlgTcmnv5tqWwxoq31zd9eK/wWtLxK+kbUGL4EDm9TgRQm+ot2QllvEi0vT2Hu6kEEhmuFKZGf3mxrLWFhI8ZqfKfppGRW7dgPg3Ls3HpMm4j5+PPYdVR270WRk/8X9NSWfJ4tPAtDduzsJQQkkBiXSw6eHMni5TMFJbaHXkZVwchOYDODio836u4+HsCRwbNydkhJ8RbvCZJJ8vzubt1dlUFxp4A/DQnh8dHfcHG8+36zPzaVo5UqKV6ykKiMDhMBl0CA8JkzAfdxY7LxVJQvAyaKTpOakknw6ucbgxdfZt6bkc3CXwcrg5TIVhXB8vfbQN/NnqCwCWwcIHanN/vv9/qb6/CjBV7RLCsqq+cfqDL7ZmU0nD0denNyDSb26NDofX3XsGMUrV1G8ciXVJ0+CrS2ugwfjPn4c7mPGKPE3U1hZyMZcreRz85nNlOnLcLJ1Is4/joTABEYGjVQGL5cx6uH0tlp7R2mExw/cVImnEnxFu2bP6QJeXJLGoTPFDA/35bVbe9LNr/EPGKWUVGVkULxqNcVrVqM/dRpsbXEZNBCPceNwHz0aO18laAB6o56d53fWlHyeKTsDQKxvbE3JZ4RXRLt/OA5oJZ/l+Tfdw18JvqLdYzRJvtp6kv8zG67MHNGNRxPDcXZomodlNeK/Zg0lq9doM38hcO7fD4+xY3EfPRp7f/UgE7TP6mjBUXTZOlKzU0nLSwMgwC2AhKAERgaOZECnAdjbKoOXm0EJvkJh5kJJJW+tzODHvbkEejvzyi09Gd2jaZfGSympOppJyc8/U7J2LVVHjwLgFBOD++jRuI8dg2M3tYDpMhfLL5Kak0pqdipbz26lyliFm71brcFLwHA8HdvvgrgbRQm+QnEVW4/n8dLSNDIvlDI6uiMv39KToA7N0zq4KiuLknXrKFm3jsr9BwBwCA3VxH/0KJx69UK0kk6MzU2FoYJtZ7bV2DvmVeZhK2zp16lfzYPfYI/W2ba4pVCCr1DUgd5o4vNNWfx7fSZGk+SxpHBmjOiGo13zGYboz52jZP16Stevp2zHTjAYsPPzwy0pCffRo3AZPLhdNnWrC5M0kXYpTTN2z0khsyATgG6e3Wrq/WN9Y5XBy1U0p8XhNOAVIBoYdC1PWyGEFzAHiAEk8Acp5db6xleCr2gJzhRWMHtFOisPnqObryuv3RrD8Ijmf9hqLCqiNDWVkvXJlG7ciCwvx8bVFdf4eNxHJeE2YkS77u1zNTklOTXiv/vcbgzSgLejN/GB8SQGJTLUf6gyeKF5BT8aMAGfcB0TcyHEfGCjlHKOEMIBcJFSFtY3vhJ8RUuSevQiLy9N42ReOZNiu/DipB509myZmnFTVRVlW7dSuj6ZEp0O46VLYGeHy4ABuCcl4ZaUhENgQIvE0hooqS5hU+4mUrJT2Ji7kZLqEuxt7BncZTCJQYmMCBxBZ9fOlg7TIjR7SkcIkcI1BF8I4QHsB7rJG7yYEnxFS1OpN/LphhN8qDuGnY3QDFeGhmB/HcOVpkaaTFQeOEDJ+mRKdMlUH9O8ah0jI3FLSsQ9KQmnnj1V3t+M3qRn7/m9NY3esku0tsXRHaJrUj/RHaLbTcmnpQW/D/ApkA70BnYDj0spy64x1kxgJkBwcHD/U6dONTo+heJGOZ1Xzis/HSI54wJRnd15fWoMA+sxXGkuqk+doiRZR2lyMuW7d4PJpOX9ExNxS0rENS4OGye1ehW0CqkTRSe0ev+cVPZd2IdE0smlU03J56Aug3C0rd9DobXSKMEXQqwD6ro3+ruUcqn5mBSuLfgDgG3AMCnldiHEv4FiKeWL9QWuZvgKSyKlZG36eV79KZ3cwgp+0y+Qv02MwrcBhivNhaGggLINGyhJ1lG2cSOm8nKEszOuQ4finpSI28iRarHXFeRV5LExdyOp2alsPrOZCkMFznbODPUfSkJQAiMCR9DByTJf5M2FpWf4nYFtUsoQ8+/xwHNSykn1jasEX2ENlFcb+CBZM1xxtrflmXGR3D24a4MMV5oTU3U15dt3UKpLpiRZh+HcOW2xV+/eWtVPUiIOYWHtJpVRH1XGKnac3UFqTiq6bB0Xyi8gEPT2613T6C3UM7TVf14WFXzz6xuBB6WUR4QQrwCuUspn6htXCb7Cmjh2oZSXlqax5XgevQI8mT01ht5BXpYOCzAv9jp8mBKdjtL1yVSmpwNgHxyMe2ICbolJuPTvh7BXq1dB+7wO5x/Wqn6yUzicfxiAYPfgGmP3vh37tkqDl+as0rkN+ADwAwqBfVLKcUIIf2COlHKi+bg+aGWZDsAJ4AEpZUF94yvBV1gbUkqW7dcMVy6VVnHXoGD+Oi4SLxfrqp3XnztHaUoKJcnJlG/bjqyuxsbDA7f4eNySEnGLj8fWw8PSYVoN58rOsSFnA8nZyew4qxm8eDh4MDxgOIlBiQwLGIa7w8212W5p1MIrhaKJKa7U88+1R5m/5SReLg48NyGKO/oFYmPhNE9dmMrKKN2yhdJkHaWpqRjz87WSz4EDcE9MxC0xEYegIEuHaTWU68vZemYrydnJbMzZSEFVAXY2dvTv1J/EoERGBo4k0N16jXCU4CsUzUT6mWJeWHKQPacLGdDVm9enxhDdxXpnztJopGL/AS3vr9PVlnxGROCWmIh7UiJOsbGq5NOM0WTkwKUDNY3eThSdACDcK5zEoEQSghKI8Y2xKoMXJfgKRTNiMkkW7c7hrVWHKa40cP+QEJ4cE4G7k/Xny6tPndLy/roUynftAqMRW19f3EaOwD0pCdchQ7BxUatXL3Oq+FRNyeee83swSiM+Tj5ai+fABOL843C2c7ZojErwFYoWoKCsmv+35gjf7DyNn5sjL0zuwS2xjTdcaSmMRUWUbthIqU5H6YYNmEpLEY6OuA4ZotX8JyYoW8crKKoqqin53JS7iVJ9KY62jsR1iav5AvBz8WvxuJTgKxQtyL7sQl5YcpC03GKGhfvw2q0xhDWB4UpLIqurKd+1ixJdCqXJyehzcwGtxfPl1b6OkZGt5susudEb9ew6v4vUnFRSslPILdU+rxifmJrVvt29u7fI56UEX6FoYYwmydfbT/HOmiNU6o3MiO/GY0kRTWa40pJIKanKzKTULP4V+/cDYOffBffEJNwSE3EdNBChunwC2ueVWZhJarYm/gcvHUQi6eLapUb8B3Ya2GwGL0rwFQoLcbGkirdWHeaHPbkEeDnzypSejGliw5WWxnDxotblU5dC2ebNyMpKrcvn8OG4JyXiOmKE8vW9gksVl9iQswFdto5tZ7ZRaazE1d6VYf7DSAhKID4gHi+nplvPoQRfobAwO7LyeXFJGkfOlzAqqiOvTGk+w5WWxFRZSdm2bVrJp06H4eJFsLHBuV9fbfaflIhjaKilw7QaKgwV7Di7A122jg05G7hYcREbYUPfjn1JCNRm/yGeIY26hhJ8hcIK0BtNzNt8kn+tO4rBJHk0MZyHRjav4UpLIk0mKg+layWf65OpOnIEAIeQkJpWD859+iDsWt/q1ebAJE2k56Wjy9ahy9bVGLyEeISQEJTAn/v9GXubG0/7KMFXKKyIs0UVzF5+mBUHzxLq68qrU3oyonvLV3M0N/rcXEpSUihN1lG2Ywfo9dh6euKWMBK3xCRchw/H1s3V0mFaDbmluTV5/4KqAr6/5fubGkcJvkJhhWw4epGXlx0i61IZk3p14YXJ0XTxtGwNd3NhLC2lbNNmSpLXU5a6AWNREcLeHpdBg7Sqn8RE7P39LR2m1WA0GW/aulEJvkJhpVQZjHyaeoL/6I5hayN4YnQEDwwLbVHDlZZGGgxU7N2r9fjX6ag+eRIAx6gorcVzYhJOPXuo1b43iRJ8hcLKyc4v55Vlh1ifcYHundx4/dYYBnfzsXRYLULViayaVg8Ve/ZqBi8dO+KWkKAMXm4CJfgKRSthbfp5Xll2iNzCCm7vG8DfJkbj59523ZmupsbgZX0yZZs2KYOXm0AJvkLRiqioNvIfXSafbjiBk9lw5R4rMFxpaUzV1ZTv2Elp8npKdCkYzp7VDF5iY3FLSsItMQHHiAi12vcqlOArFK2QYxdKeXlZGpuPaYYrr0+NoY+VGK60NFJKqjIyKElOplSXQmVaGgD2gYE1XT5dBgxQBi8owVcoWi1SSpYfOMvry9O5WFrF9IGa4Yq3a/tuY6A/f0Fr8qbTUbZ1q2bw4u5uNnhJwi1+OLaenpYO0yI0p+PVNOAVIBoYdB2LwyeBBwEJHERzvKqsb3wl+AqFRkmlnn+ty2TelpN4ONnx3IQopvUPskrDlZbGVF5O2ZYtWpvnlFSMeXlga4vLgAG4JSbgnpSEQ3CwpcNsMZpT8KMBE/AJ1zYxDwA2AT2klBVCiO+AlVLKefWNrwRfofglh88W8+KSNHadKqBfsBezp/aih7/1Gq60NNJkomL/fq3Rm05HVaa2etUhPKym0Ztz71iEbdtY3VwXFjUxNwv+NqA3UAwsAd6XUv5c37hK8BWKX2MySRbvyeGtVRkUlldz/9AQ/jKme6swXGlpqrOzKdXpKNHpKN+5CwwGbDt0wG3kSM3bd+hQbFzb1mpfiwq++fXHgTeACuBnKeU91xlrJjATIDg4uP+pU6caHZ9C0RYpLK/mnTVH+N+O0/i6OfLCpGim9PZXVSvXwFhcTOnGjVqjtw0bMJWUIBwccBkSZ579J2DfqXV3MoVGCr4QYh3QuY6X/i6lXGo+JoVrz/C9gcXAb4FC4HtgkZRyQX2Bqxm+QlE/+7MLeXFpGgdyihjSzYfXp/YkvKO7pcOyaqReT/nuPdqCr2Qd+uxsAJx69qw1eImKapVfnpZO6UwDxksp/2j+/XdAnJTykfrGVYKvUDQMo0mycMdp/t/qDCr0Rh6M78ZjSeG4OKjOlPUhpaT6+HGt1cNlgxcpsevSBffEBNwSk3AZPAibVmLwYmnBHwx8DgxES+nMA3ZJKT+ob1wl+ArFjXGptIq3VmaweE8OAV7OvDi5B+N6dmqVM1VLYcjLozQllRJdMmWbtyArKrBxccF1+HDN23fkCOw6dLB0mNekOat0bgM+APzQ0jX7pJTjhBD+wBwp5UTzca+ipXQMwF7gQSllVX3jK8FXKG6OnSc1w5WMcyUkRvrx6pQYgn1av+FKS2OqqqJ827aaRm+GCxe01b59+2qtHpKScAgNtaovVLXwSqFoh+iNJuZvOck/1x5Fb5I8khDGrJFhONm33ZLE5kRKqRm8JCdTkqKjKv0wAA5du2oz/6REXPr1s7jBixJ8haIdc66oktkr0ll+4CxdfVx4dUpPEiI7WjqsVo/+7FltsZcuhfJt25B6PTaenriNGKF5+8bHY+vm1uJxKcFXKBRsyrzES8vSOHGxjAkxnXlxcg/8vdqm4UpLYywto2zTJq3dQ2oqxsJC/T/dmgAAD4NJREFUsLfHdeBAbfafmIhDYECLxKIEX6FQAJrhypyNWXyQnImNEDw+KoI/DG/bhistjTQaqdi3T1vwlayj+sQJABwjI2vcvZxiYprN4EUJvkKh+AXZ+eW8+lM66w6fJ6KjG69PjSGunRiutDRVWVk1rR7Kd+8GkwlbP1/cE7SST9chcdg4N92dlhJ8hUJRJ+vSz/PKT4fIKajgtr4B/G1iFB3dlbtUc2EsLKR040ZKkpMp27ARU1kZwskJ1yFDtNl/QgJ2fo0ztFeCr1AorklFtZGPUo7xSeoJHO1seGpsd+6N64qdSvM0K7K6mrKdO7VWDzod+jNnAHCKjcU9KRGfBx+8qYofJfgKhaJeTlws5eVlh9iYeYkeXTyYfVsM/YK9LR1Wu0BKSdXRozV5f1NJCWGrVt7UWErwFQpFg5BSsvLgOV5bfojzxVVMHxjEs+Oj2r3hSktjqqy8aeP26wm+umdTKBQ1CCGYFNvl/7d379FRlecex79PEiKIAUEEhHCTe7hYJCByCwKWBDFUsCKt1mIVsaKIOR6gaHu0p/VUBQRBEW+oRaw3ELkHkHApUCKiBCKXokAQBZWbCkjwPX8kXSf1TC6TPZOZyfw+a2Ut9rvf2e/Ds4aHvd6Z7IeVGX24rWcz3ng/j6smrea1f+znhx/C9+awsilvsS/1ukG5qohEtAvOi+OBQUksuqcnreomMP7tbQyd+XdyDh4PdWjigQq+iBSrTf0a/O2Obkz6+WUc+Po70qev478WbOfE6bOhDk3KQQVfREpkZgztnMjKjD7c1K0JL234lL6PZzH/g4OE82eA8v+p4ItImdSsVoWHB7dnwV09aVirGvf+bSvDn93I7i9Ohjo0KSMVfBHxS4fEmsy7szt/vq4DuYdOkjZ1LY8syeXbM/mhDk1KoYIvIn6LiTF+cUVjVmWkMOTyhjyTtZerJ2exNOeQtnnCmAq+iJTbRRecx6PXX8abo66kRrUqjPrrFkbM3sy+r74NdWjig6eCb2aPmdnHZvaRmc0zswuLmZdqZjvNbI+ZjfeypoiEn+SmtVl4d09+PyiJ7E+PcvWUNUzJ3MXps+dCHZoU4fUOPxNo75zrCOwCJvx4gpnFAjOANCAJGG5mSR7XFZEwExcbw609m7EyI4UB7eozdeVuBjyxhvd2Hg51aFLIU8F3zi13zv3rk5qNQKKPaV2BPc65vc6574HXgMFe1hWR8FWvRlWeHN6JObddQWyMMeLFzYx65X0OHjsV6tCiXiD38G8FlvgYbwgcKHKcVzjmk5mNNLNsM8s+cuRIAMMTkYrUo0Udlo7pzf0DWrN612H6T8riqdV7+D7/h1CHFrVKLfhmtsLMcnz8DC4yZyKQD8zxdQkfY8V+jO+cm+WcS3bOJV/s8bnQIhJa8XEx3HVVCzLHptCzZR0eXbqTtKlr+PueL0MdWlQq9WHLzrn+JZ03s1uAQUA/5/v7WHlAoyLHicBn/gQpIpGtUe3zefZXyazMLWi48ovnNpF+WQMeuKYtdWuo4UpF8fotnVRgHJDunPuumGmbgZZm1szM4oEbgQVe1hWRyNSvbT0yx6ZwT98WLM35nH6Tsnhh3Sfkn9M2T0Xwuoc/HUgAMs1sq5nNBDCzBma2GKDwQ93RwDIgF3jdObfd47oiEqGqVonlvp+2ZtnY3nRqUouHF+7g2unreX/f0VCHVumpAYqIhIxzjqU5n/Pwwh0cOn6aYcmNGJfWhtpquFJuaoAiImHJzEjrcAkr7kvhjpRLeWtLHn0nrebVTWq4Egwq+CISctXPi2NCWlsWj+lF63oJ/G7eNoY8rYYrgaaCLyJho1W9BF4b2Y0pwy4j7+gp0qev4w/v5HD8lBquBIIKvoiEFTPjuk6JrMxI4eZuTXhl4z76Tcri7S15ehKnRyr4IhKWalarwkOD27NgdEHDlfte/5BhszaySw1Xyk0FX0TCWvuGBQ1XHhnSgV1fnGTg1LX8ebEarpSHCr6IhL2YGGN418asyujD0MsTmbVmL/0nZ7F4mxqu+EMFX0QiRu3q8fzl+o68deeVXHh+PL+ds4VbXtzMJ1+q4UpZqOCLSMTp3KQ2747uwR+uTWLLvqMMmLKGyct3quFKKVTwRSQixcXGMKJHM1ZlpJDWoT7TVu3h6ilZrPr4i1CHFrZU8EUkotWtUZWpN3bi1duv4Ly4WG6dnc3tL2eTd7S45zlGLxV8EakUujevw+J7ejEutQ3rdn9J/8lZzHhPDVeKUsEXkUojPi6GO/s0Z0VGCimtLuaxZTtJnbqG9Wq4Aqjgi0gl1PDCajxzczIvjuhC/jnHL5/bxN1zP+CLE6dDHVpIqeCLSKV1Veu6LB/bmzH9WrJse0HDleejuOGKCr6IVGpVq8Qy9upWLL+3N52b1OKPC3cw6Ml1ZH/6dahDq3BeWxw+ZmYfm9lHZjbPzC70MaeRmb1nZrlmtt3MxnhZU0SkPJrWqc7sEV2YedPlnDh1lutnbuD+Nz7kq2/OhDq0CuP1Dj8TaO+c6wjsAib4mJMPZDjn2gLdgLvMLMnjuiIifjMzUttfwoqMFEalNGfeBwfpOymLOZv2cS4KGq54KvjOueWFPWsBNgKJPuYccs5tKfzzSQr62jb0sq6IiBfnx8cxPq0NS8b0ou0lCUycl8OQp9azLa9yN1wJ5B7+rcCSkiaYWVOgE7ApgOuKiJRLy3oJzL29G1Nv/AkHj50mfcY6Hpyfw/HvKmfDlVKbmJvZCqC+j1MTnXPvFM6ZCCQDQ1wxFzSzC4As4E/OubdLWG8kMBKgcePGnfft21eWv4eIiCcnTp9l8vJdvLzhU2qdH8/vBrZlyOUNMbNQh+aXkpqYl1rwy3DxW4BRQD/nnM/fZTazKsBCYJlzbnJZr52cnOyys7M9xSci4o/tnx3ngfk5fLD/GF2b1uaPP2tP6/oJoQ6rzEoq+F6/pZMKjAPSSyj2BjwP5PpT7EVEQqFdg5q8Nao7fxnagd2HTzJw2lr+tGgH31SChite9/CnAwlAppltNbOZAGbWwMwWF87pAdwM9C2cs9XMBnpcV0QkaGJijGFdChqu3JCcyLNrP6H/pCwWfRTZDVc8b+kEk7Z0RCQcbNl/lAfn57D9sxP0almHh9LbcenFF4Q6LJ+CtqUjIhINLm9ciwWje/JQeju27j9G6hNreXzZTk59H1kNV1TwRUTKIDbGuKV7U1b+RwrXdLyE6e8VNFxZsSNyGq6o4IuI+KFuQlWmDPsJc2/vRrUqsdz2cja3vbSZA1+Hf8MVFXwRkXK4svlFLLqnF+PT2rB+z1f0n5zF9FW7OZMfvts8KvgiIuUUHxfDqJTmrMxIoW+bujy+fBdpT6xl3e7wbLiigi8i4lGDC6vx9E2dmT2iC+ec46bnN3HXq1v4/Hh4NVxRwRcRCZA+reuy7N7ejO3fiswdX9Bv0mqeW7uXs2HScEUFX0QkgKpWiWVM/5Zkju1N12a1+e9FuQyato7NYdBwRQVfRCQImlxUnRd+3YVnbu7MN2fy+fnMDWS8/iFfhrDhigq+iEiQmBkD2tUn877e3NmnOQs+PEjfx1fzysbQNFxRwRcRCbLz4+MYl9qGJWN6075hTR6cn8N1T63no7xjFRqHCr6ISAVpUfcC5tx2BdOGd+LQ8dMMnrGeifO2VVjDFRV8EZEKZGakX9aAVRkpjOjejLn/2M9Vk1bzRvYBfgjyNo8KvohICCRUrcLvr01i4d29aFanOve/+RE3PLOB3EMngramCr6ISAglNajBG3dcyaNDO/LPI98w6Ml1PPzuDk6fDfwjGuICfkUREfFLTIxxQ5dGXJ1Uj0eX7WTL/qNUiQ38/bingm9mjwHXAt8D/wRGOOd8fuxsZrFANnDQOTfIy7oiIpVRrerxPDKkA2fyzxEbE/jm6V7/C8kE2jvnOgK7gAklzB0D5HpcT0Sk0jsvLjYo1/VU8J1zy51z/+rsuxFI9DXPzBKBa4DnvKwnIiLlF8hNoluBJcWcewL4TyA8niAkIhKFSt3DN7MVQH0fpyY6594pnDMRyAfm+Hj9IOCwc+59M+tThvVGAiMBGjduXNp0EREpo1ILvnOuf0nnzewWYBDQzznn67cGegDpZjYQqArUMLO/OuduKma9WcAsgOTk5Ip/2ISISCXlaUvHzFKBcUC6c85nQ0fn3ATnXKJzrilwI7CquGIvIiLB43UPfzqQAGSa2VYzmwlgZg3MbLHn6EREJGA8fQ/fOdeimPHPgIE+xlcDq72sKSIi5WO+t93Dg5kdAfYVHtYEjhc5XdpxHSBYnYR/vFYgX1PSvOLO+RovbUz58m9M+fJ/rOix8lVx+WrinLvY5xnnXET8ALP8PM6uqFgC+ZqS5hV3ztd4aWPKl/IVzHz5yJ/yFQb5iqSHp73r53EwlWetsr6mpHnFnfM1XtqY8uXfmPLl/1hF5Uz5KqOw3tLxwsyynXPJoY4jUihf/lG+/KN8+SdY+YqkO3x/zQp1ABFG+fKP8uUf5cs/QclXpb3DFxGRf1eZ7/BFRKQIFXwRkSihgi8iEiWisuCb2c/M7Fkze8fMfhrqeMKdmV1qZs+b2ZuhjiVcmVl1M3up8H31y1DHE+70nvJPoGpWxBV8M3vBzA6bWc6PxlPNbKeZ7TGz8SVdwzk33zl3O/BrYFgQww25AOVrr3PuN8GNNPz4mbshwJuF76v0Cg82DPiTr2h9TxXlZ74CUrMiruADs4HUogOF/XJnAGlAEjDczJLMrIOZLfzRT90iL32g8HWV2WwCl69oM5sy5o6Cbm8HCqedq8AYw8lsyp4vKV++PNUsTw9PCwXn3Boza/qj4a7AHufcXgAzew0Y7Jx7hIJn9f8bMzPgf4AlzrktwY04tAKRr2jlT+6APAqK/lYi80bKMz/ztaNiows//uTLzHIJQM2qLG/Mhvzf3RUU/ONrWML8u4H+wPVmNiqYgYUpv/JlZhcVPvq6k5mV1Kg+GhSXu7eBoWb2NBX7GIZw5zNfek8Vq7j3V0BqVsTd4RfDfIwV+xtlzrlpwLTghRP2/M3XV0A0/sfoi8/cOee+BUZUdDARoLh86T3lW3H5CkjNqix3+HlAoyLHicBnIYolEihf5afc+Uf58k9Q81VZCv5moKWZNTOzeApaKS4IcUzhTPkqP+XOP8qXf4Kar4gr+GY2F9gAtDazPDP7jXMuHxgNLANygdedc9tDGWe4UL7KT7nzj/Lln1DkSw9PExGJEhF3hy8iIuWjgi8iEiVU8EVEooQKvohIlFDBFxGJEir4IiJRQgVfRCRKqOCLiEQJFXwRkSjxv+p2qyfEmYkaAAAAAElFTkSuQmCC\n", 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", 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" ] @@ -168,11 +178,11 @@ } ], "source": [ - "plt.semilogx(t, h1[0], label='Theis')\n", + "plt.semilogx(t, h1[0], label=\"Theis\")\n", "for i in range(3):\n", - " plt.semilogx(t, htwolayer[i], label='c=' + str(int(clist[i])))\n", - "plt.legend(loc='best')\n", - "plt.title('head at r=20');" + " plt.semilogx(t, htwolayer[i], label=\"c=\" + str(int(clist[i])))\n", + "plt.legend(loc=\"best\")\n", + "plt.title(\"head at r=20\");" ] }, { @@ -198,8 +208,8 @@ } ], "source": [ - "ml = ModelMaq(kaq=25, z=[20, 0], Saq=S/20, tmin=0.01, tmax=100, tstart=10)\n", - "w = DischargeWell(ml, tsandQ=[(10, Q)], rw=1e-5)\n", + "ml = ttim.ModelMaq(kaq=25, z=[20, 0], Saq=S / 20, tmin=0.01, tmax=100, tstart=10)\n", + "w = ttim.DischargeWell(ml, tsandQ=[(10, Q)], rw=1e-5)\n", "ml.solve()\n", "ht10 = ml.head(r, 0, t)" ] @@ -211,7 +221,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -223,10 +233,10 @@ } ], "source": [ - "plt.semilogx(t, ht10[0], label='time shifted in model')\n", - "plt.semilogx(t + 10, h1[0], 'r--', label='time shifted by hand')\n", - "plt.legend(loc='best')\n", - "plt.title('note that heads are not computed at the same times');" + "plt.semilogx(t, ht10[0], label=\"time shifted in model\")\n", + "plt.semilogx(t + 10, h1[0], \"r--\", label=\"time shifted by hand\")\n", + "plt.legend(loc=\"best\")\n", + "plt.title(\"note that heads are not computed at the same times\");" ] } ], diff --git a/pumpingtest_benchmarks/0_synthetic_data.ipynb b/pumpingtest_benchmarks/0_synthetic_data.ipynb index 19cdccb..fa1ecfe 100755 --- a/pumpingtest_benchmarks/0_synthetic_data.ipynb +++ b/pumpingtest_benchmarks/0_synthetic_data.ipynb @@ -17,7 +17,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *" + "import ttim" ] }, { @@ -33,12 +33,12 @@ "metadata": {}, "outputs": [], "source": [ - "H = 7 #aquifer thickness\n", - "k = 70 #hydraulic conductivity\n", - "S = 1e-4 #specific storage\n", - "Q = 788 #constant discharge\n", - "d1 = 30 #observation well 1\n", - "d2 = 90 #observation well 2 (positions same as for Oude Korendijk)" + "H = 7 # aquifer thickness\n", + "k = 70 # hydraulic conductivity\n", + "S = 1e-4 # specific storage\n", + "Q = 788 # constant discharge\n", + "d1 = 30 # observation well 1\n", + "d2 = 90 # observation well 2 (positions same as for Oude Korendijk)" ] }, { @@ -54,8 +54,8 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/piezometer_h30.txt', skiprows = 1)\n", - "t = data1[:, 0] / 60 / 24 # convert min to days" + "data1 = np.loadtxt(\"data/piezometer_h30.txt\", skiprows=1)\n", + "t = data1[:, 0] / 60 / 24 # convert min to days" ] }, { @@ -71,9 +71,9 @@ "metadata": {}, "outputs": [], "source": [ - "ml = ModelMaq(kaq=70, z =[-18, -25], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)])\n", - "ml.solve(silent='True')\n", + "ml = ttim.ModelMaq(kaq=70, z=[-18, -25], Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.1, tsandQ=[(0, 788)])\n", + "ml.solve(silent=\"True\")\n", "h1 = ml.head(d1, 0, t)\n", "h2 = ml.head(d2, 0, t)" ] @@ -91,9 +91,9 @@ "metadata": {}, "outputs": [], "source": [ - "np.savetxt('data/syn_30_0.0.txt', h1[0])\n", - "np.savetxt('data/syn_90_0.0.txt', h2[0])\n", - "#print(h2[0])" + "np.savetxt(\"data/syn_30_0.0.txt\", h1[0])\n", + "np.savetxt(\"data/syn_90_0.0.txt\", h2[0])\n", + "# print(h2[0])" ] }, { @@ -105,8 +105,8 @@ "np.random.seed(5)\n", "he12 = h1[0] - np.random.randn(len(t)) * 0.02\n", "he22 = h2[0] - np.random.randn(len(t)) * 0.02\n", - "np.savetxt('data/syn_p30_0.02.txt', he12)\n", - "np.savetxt('data/syn_p90_0.02.txt', he22)" + "np.savetxt(\"data/syn_p30_0.02.txt\", he12)\n", + "np.savetxt(\"data/syn_p90_0.02.txt\", he22)" ] }, { @@ -118,8 +118,8 @@ "np.random.seed(4)\n", "he15 = h1[0] - np.random.randn(len(t)) * 0.05\n", "he25 = h2[0] - np.random.randn(len(t)) * 0.05\n", - "np.savetxt('data/syn_p30_0.05.txt', he15)\n", - "np.savetxt('data/syn_p90_0.05.txt', he25)" + "np.savetxt(\"data/syn_p30_0.05.txt\", he15)\n", + "np.savetxt(\"data/syn_p90_0.05.txt\", he25)" ] }, { @@ -129,7 +129,7 @@ "outputs": [ { "data": { - "image/png": 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qvcQkppdR0MrPvUF96nzxBfW+nYHZ15fk4cPZP2QotoQEo6MJUSnIFb0QWsO2ebDyFUf/fbM+0O4NqFLjnNMMW/LXhei8PNK/m8Whzz5D5+TgP2Qw/kOHYvLwMDqaEOWaXNELURSloGkPeHoj3PI0bJkNnzeHDZPAnn/mtMKW/BXOpdzcqN6vLw2WL8OnUycOf/kVcXffQ+avq42OJkSFJYVeiNM8fKHjWHj8Dwi6EZY/BxNvh33rAWOX/HU1loAAgj94n7rTp6M8PUh68kn2P/EktqQko6MJUeFI070QhdEadi6Cn16GjGS4sRe0f4OYIxbDlvx1VTo3l6PfzODQ+PGQn0+Nxx+j+qBBmGQ5XSHOkFH3QlypnCxY+yH8+YVjzfy2o+GmIbKcrgFyDx4k9d33yFyxAku9utR65VW8W99mdCwhygXpoxeiEMUaQe/uDe3GwJN/QUgErBgFX7e55Oh8cfli02KJ3BZJbFpsoccttWoRMu4T6kRGopSJ/UOGkDRsOLkpKWWcVIiKRa7ohUu6ohH0WsPfP8CPL0LGAcfOeHe9Cu4+F7y2NO9fnnP2ty/G/vV2m42jU6ZyeMIEUIqaTz1J9f79URZLGaYWovyQK3ohznNFI+iVguvvhSejoMUQx2I741vC7hVnTjn9AeKjlbvpHRkl8+2L6YL97S+xf73JaqXG44/R4IcfyAtvRNqHH7Gz631k79pVRomFqDik0AuXVKIR9B6+0OUDGLTScTU/qyfMHQCZqTIF7wpFBEZgNVsxKzMWk4WIwEIvTC6w03qIgXf8zUdd3cg4kEhc9x4c+uwz7DZbKScWouKQpnvhspzSxJ5ngz8+hTXvg8WThPDRdFpTl9w8jUUW1bksl9rfvjCR2yL5fNPn2LHjm23i7U0NCfh9J+5XNyRo7Fg8mzYt5dRClA8y6l6I0nZ4DywdDol/kFmrJYvrvMj1jZtJkS9lp/v2c+25WEwWJnWYRMO/M0h57XXy0tKoPnAANf/3v2KvrHclHzaEKA+k0AtRFux22PwNrHwN8rIdU/FueVqm4pWywopzflYWaR98yLE5c7DWq0fQ2Lfwiii6O+ByBwQKUZ7IYDwhyoLJBOED4OkNcHV7+Pl1mNwe0mSAWGkKCwhjcJPB5xRls7c3QW+Moe7UKej8fBL79OXg/72F/cSJi77O5Q4IFKKikEIvhLP51IKe30L3KXAs0THvfs2HkJ9ndDKXU+WWW2iweBF+ffuS/t13xN13Pyf+/LPQc690QKAQ5Z003QtRmrIOOdbM37nIsX7+/V9CrcZGp3JJJzdtImX0y9gSEqjWozsBL7yA2efcNRCkj15UVNJHL4TRdiyCZc9C9nFo8zy0HglmWdylrNmzszk8fjxHJk/BrWZNao15HZ+2bY2OJUSJSR+9EEZr9AA8tQFuuB9+e5uT41sze+lyWVCnjJk8PAh49llC58zG7OtL0hNPkvzCC+Sly99BVF5S6IUoK1X8oftk9t75NSeOHKBrdB/WTH6RmPhDRidzOZ5NmlB//jxqPPUUGct/JO6ee8lY8ZPRsYQoFVLohShjP+VH0NH2PivtNzHCNIfgBQ845uGLMqWsVmr+72nqz5uLJTCQ5GeeIWnYcPKOyGqGonKRQi9EGWvZwJ+TblUZnjeMEfZh1LAlwYTbIOorx1x8irmznnAKj+uuI/T7OdQcOZKs334j7t77yPzlF6NjCeE0hgzGU0pVB+YAoUAC8JDWOv28c+oA3wC1ADswUWv9aXFeXwbjifLunOV3q+fAkmGw5ycIbc22m96hx+ykInfWkx3ySkfOnj0kv/giOTt3UfXBBwl8eTRmb+9zzpGR+aI8Knej7pVS7wNHtdbvKqVGAX5a6xfPOycICNJab1JK+QAxwANa652Xen0p9KLC0Ro2z4AVL2HL17yW3YvZ+XdgVoqRHa7lqbYNz5x6RVvsimLTNhuHvvySIxMnYalVi6B33qHKzS0AWT1PlF/lcdT9/cD0gq+nAw+cf4LWOkVrvang60xgFxBcZgmFKEtKQfN+8MSf5NRswruWSUy2fEgtt4wLdtaTHfJKl7JaCXjmGUK/mwkWN/YNGEDqu+9hz8mR1fNEhWRUoQ/UWqeAo6ADAUWdrJQKBZoB60s9mRBG8quHz9Af2d/iNW5328FvVV4m/NRf55xSoi12RbF5hoXRYOFCqj3ck6PTphHfrRs3HfeX1fNEhVNqTfdKqZ9x9K+f72Vguta62lnnpmutC217VEp5A78DY7XWC4p4v6HAUIC6deuGJyYmliS+EMZL2wULhsDBbY6r/Y7vgLujv1j66MtW1tp1pIweTV56OnkDurK+XTARtVtIs70oN8pjH/1u4A6tdUpBX/xvWutrCznPAvwA/KS1/ri4ry999KLSyLPB6rGOPe/9QqHrRKjTwuhULin/2DEOvvkmGct/xLNZM2p/8D7WkBCjYwkBlM8++iVA/4Kv+wOLzz9BKaWAycCuyynyQlQqblZo/wYMWAb2fJjSEX4dC/m5RidzOeZq1Qj++GNqf/ABOXv2EH//AxxbtIjKuIy4qFyMKvTvAu2VUnuA9gWPUUrVVkotLzjnVqAvcKdSKrbg1sWYuEIYLPRWeGIdNO0Ja96HyR3g8F6jU7mkqvfeQ4PFi3C//jpSRr1E8siR5B8/fs45sWmxRG6LJDYt1qCUQvxHNrURoqLZsQh+eAbycqDz+9Csj2PUvihTOj+fI5MiOfTFF7jVqEHt996jys0tZAqeMER5bLoXQlypRg/AE39CcDgseRrmDoBTsoJeWVNmMzUef4zQWbMwubuzb8AA0j78kJik9TIFT5QrUuiFqIh8a0O/xdBuDPz9A3x1GyT8YXQql+TZpDH1Fy6gWo8eHImcTMsxi6mX7iZT8ES5IU33QlR0yTEwfzCkJ0DrZ+H2F2Wve4Nk/vILKS+/Qt6pk8T1bU1I30GEBTYzOpZwAdJ0L0RlFhwOj62FG3vBmg9gamc4Gm90Kpfkc9dd1F+yGO+bWnBV5C/4vxEpe90Lw0mhF6IycPeGB8ZD96lw6B+Y0Bq2zDE6lUuyBARQZ+LXBL40ihNr1xJ//wOciIoyOpZwYVLohahMGnd1TMOr1RgWDoX5QyD7+KW/TziVMpmo3r8/oXNmY6pShX0DHyXto4/RubL+gSh7UuiFqGyq1XUssNP2Fdg+37HX/f4Nsse9ATxuuIH68+dRrXs3jkyaRELvPtj27TM6lnAxUuiFqIxMZrj9eXh0BQB6SifWTX6eT1buondklBT7MmTy8iLo//6P4HHjsCUkEP9gV44vWWJ0LOFCpNALUZnVaQGPr+Ofmh0YbprLTMtb+Ocdkq1tDeDbqSMNFi3E/brrOPDCixx4cRT2EyeMjiVcgBR6ISo7j6pk3f0VL9ifpLGKZ5n1JTq6bTY6lUuy1K5NvenTqPHUUxxfupT4bt3J3rnT6FiikpNCL4QLCK/nR89BL7CwxSys/vVo+Mtg+HGUYxldUaaUmxs1//c0dadNxX7qFAk9H+bojG9lcxxRamTBHCFcTV4OrHod1n8FtZpCj2ngf5XRqVxSXno6KS+NJuu33/C+806Cxr6Fm5+f0bFEBSQL5ggh/uPmDp3fhYdnwfH98HUbmXNvEDc/P0K++pLA0S+RtXYt8Q925eTGjUbHEpWMFHohXNV1XeDxPxxX9QuHwsInICcLQKbilbKzt7FVSlG9Xz9CZ81CuVtJ7D+AQ+PHE5uySba6FU4hTfdCuLr8PMfSub+/B/5XsfPWT+m6MBNbnh2rm4mZg1sSXu/izckxielExR2hZQP/Is8TDkVtY5ufdYKDb75BxpKl7Kpr4rP7zJyo5i5b3YpLkqZ7IcTFmd2g7UvQfynYTnDt0gfoaf8Ru9bk5tmLnIoXk5hO78goPlq5W+bnF1N0avRFt7E1e1ch+P33+efpTtRPsfPe5Fxu2JMjW92KEpFCL4RwqN8aHl9HVvBtvGGZzteWcfi7naRlA/+LfktU3BFseXbsmkt+KBAOEYERWM3WIrexrfdQf14f5MUxb8WoObncvHCPLJ8rrpib0QGEEOVIlRpUHbSQ/cs/pF30O9xRZQzu5gZAeKGnt2zgj9XNRG6eHYubqcgPBcIhLCCMSR0mEZ0aTURgRKFN8mEBYbzZazKbbvuL4Dk7cPtuCYm79hP88UdYgoIMSC0qMumjF0IULika5g6EzAPQ7g245SlQ6oLTpI++9B1ftoyDr76GslgIevcdfNq2NTqSKGeK6qOXQi+EuLhT6bD4afj7B7imMzzwJXhVNzqVS7IlJJD0zAhy/v6b6gMHEjByBMpiMTqWKCdkMJ4Q4sp4+kHPb6HTe7D3Z8c+9/vWG53KJVlDQwmdM5tqjzzM0alTSezTl9zkZKNjiQpACr0QomhKQcvHYdBKxwj9qZ1h3Tiw241O5nJM7u4Evf46wZ98TM7evcR17UbmL78YHUuUc1LohRDFE9wcHlsD198DP78O3z0EJ2SUvRF8O3em/sIFWIODSXrqaVLfeQdtsxkdS5RTUuiFEMXnURV6TIe7P4L432HCbbAvyuhULslaty71Zs/Cr08fjk7/hoTefbAlJRkdS5RDUuiFEJdHKbhpMAz+2bFu/tQusO6TC5ryZRnd0meyWqn1yssEf/YptoQE4h/sSsbKlUbHEuWMFHohxBWJsdVl4g3TOFqvE/w8BmY9DCePOo7JinllyrdDB0dTfmgoycOGc3Ds29KUL86QQi+EuGynC/m7vybT6t8+7Gv5JsStdozK379RVswzgDUkhNCZ3+LXry/pM2aQ0KcvtiQZlS+KWeiVUialVDOl1N1KqTuVUoGlHUwIUX6dW8g1S93vhkd/ApMZpnbivpMLsbopzApZMa8MKauVWqNHE/zpp9ji4ojv1o3MX1cbHUsYrMhCr5S6Sik1EdgLvAs8AjwJrFJKRSmlBiqlpFVACBdzeunbcwr56VH513Sizsa3iKo/hZfaBl1y9zvhfL4dO1B/wXzHqPwnnyT1gw9krXwXVuTKeEqpWcBXwFp93olKqQCgF5CutZ5eqikvk6yMJ0Tpu+jSt1pD1Few6lXwre0YpR/c3LigLsyek0Pae++R/t0sPJs1I/iTj7HUqmV0LFEKZAlcIUTZS4qG7/vDiTTo9A5EDCp0rXxR+s6sle/uTu3338e79W1GRxJOVuIlcJVSZqXUfUqpYUqpkadvzo0phKhUQiLg8bVQ/3ZY9izMHww5WUancklV776b0HnzcKtZk/1Dh3Los8/Q+flGxxJlpLj960uBAYA/4HPWTQghLs6rOvT6Hu58FXYsgEltIW2X0alcknuD+oR+P4eqXR/k8JdfsW/wYPKOyGwIV1Cspnul1FatddMyyOMU0nQvRDkUvwbmDQJbFtzzCdz4sNGJXE5sWizRqdG0iM7EOm465qpVCR73CV7NZQxFReeM3et+VEp1cGImIYSrqd/G0ZRfuzksfAyWDIPcbKNTuYzYtFiGrBzC55s+Z5DlO3K+egPl6UFi334cmTKVyjheSzgUt9BHAQuVUqeUUhlKqUylVEZpBhNCVEI+taDfYrhtJGyaDpPbwdE4o1O5hOjUaGz5NuzYybXnstHnEPXnzcPnrrtIe/99kocNIz9D/luvjIpb6D8CbgG8tNa+WmsfrbVvKeYSQlRWZjdo97qj7/7Yfvj6dti5xOhUlUJsWiyR2yKJTYu94FhEYARWsxWzMmMxWYgIjMDs40Pwp+MIfGkUmat/I757D7J3yRiKyqa4ffQ/AZ211hViA2rpoxeigji2D+YOgOQYaPkUtH8DzBajU1VIp5vmbfk2rGYrkzpMIiwg7IJzolOjiQiMuODYyU2bSR4xgvz0dGq99ipVu3VDyXTICqOoPnq3Yr5GCvCbUupHIOf0k1rrj52QTwjhqqrVhYErYOUrEDUekjZCj2lQNdjoZBXO+U3z0anRFxTzsICwC547zat5M+ovXMCB554n5ZVXORkdQ63XX8Pk6VkW8UUpKm7TfTzwC2BFptcJIZzJzQpd3ofuUyFtJ3zdBv6V9dkvV2FN85fLrXp16kyaSI2nn+b44sUk9HyYnPj4UkgrypKsjCeEKD8O/QPf94VDu6Hty9D6WdbZa/MAACAASURBVDDJdhrFVVTT/OXKWvcHB557Dp2bS9DYt/Dt1MlJKUVpuOIlcAs2tPlca72tkGNVgJ5AjtZ6prPCOoMUeiEqsJws+OEZ2DYXru4AD37tWHhHlLnclBSSR4zkVGwsfn37Evj8cyir1ehYohAlmUf/JfCqUmqXUmquUupLpdQUpdRa4E8czffznJxXCOHK3L2h6yS4+yNHE/7Xt0PyJqNTuSRLUBD1vplO9f79SJ8xg8S+/cg9cMDoWOIyFXfUvTcQAQQBp4BdWuvdpZztiskVvRAVT6G74SXFwNz+kJUKnd6FiEdlYxyDZPy0kpTRo8l3U+x8qgMNO/UocfeAcB7ZvU4IUa7FJKbTOzIKW54dq5vp3D3sTx6FBUNg78/QtKdj+VxrFWMDu6gtMT9ycMRzhKTZWdzaQpc3pxEWJMvnlgfOWAJXCCFKTVTcEWx5duwacvPsRMWdtdmKV3XoNdcxOG/r9xDZDg7vMS6sC9toTeaVfmbWNFE8uDaXk8+8TF56utGxxCUYUuiVUtWVUquUUnsK7v2KONeslNqslPqhLDMKIcpOywb+WN1MmBVY3Ey0bOB/7gkmE9z+AvSZD5kHYWJb2LHImLAuLCIwAjzc+foeC1O6uFNtZxLx3bpxautWo6OJIhjSdK+Ueh84qrV+Vyk1CvDTWr94kXNH4hgf4Ku1vqc4ry9N90JUPIX20RfmeBJ83x+So2U1PQOcPYXv2lQ3kocPJ/fQIWqNfolqDz8sq+kZpMR99Eqpa4DngXqctZqe1vrOKwy0G7hDa52ilAoCftNaX1vIeSHAdGAsMFIKvRACgDybYzW9DV9DnZsdq+n51jY6lUvKP3aM5Bde4MSatfjedy9BY8Zg8vIyOpbLcUYf/VxgE/AKjoJ/+nalArXWKQAF9wEXOW8c8AJQIdbYF0KUkdOr6XWbDAe3w4TWEPe70alckrlaNepMmECNYf8jY+kPF6ymV9RGO6JsFHet+zyt9VeX88JKqZ+BWoUcermY338PkKa1jlFK3VGM84cCQwHq1q17GUmFEBVWk+5QqwnM6QszHoC7XoNbn5EpeGVMmUzUfPJJPG+8kQPPPkdC9x4EvfM2cWEBl9xoR5S+4l7RL1VKPamUCioYSFddKVXkUlVa63Za68aF3BYDqQVN9hTcpxXyErcC9ymlEoDZwJ1KqW+LeL+JWusIrXVEzZo1i/ljCSEqvJrXwpBf4Ib74ecxMLs3ZB83OpVL8r71VuovmI/1qqtIHjacwx98SL4t55yNdkTZK26h74+jqf5PIKbgVpK/2JKC1zz92ovPP0Fr/ZLWOkRrHQo8DPyqte5TgvcUQlRW7j6OTXE6vgN7foKJdzia9EWZs9SuTb1vZ+DXqxfBS2N4dVY+/idMV7zRjii5YhV6rXX9Qm4NSvC+7wLtlVJ7gPYFj1FK1VZKLS/B6wohXJVScMuT0P8HsJ10zLffMsfoVC7JZLVS67VXqf3B+1x7yMKn092IDBgpzfYGKe6o+7XAGmAt8IfWOrO0g5WEjLoXwsVlpsK8RyFxHdw0GDq+DW7ul/y2Yk/xE8WW/c8/JA8bjm3/fgKefZbqAwfIFLxS4IxR9/2B3UA34E+lVLRS6hNnBRRCCKfyCYR+i6HV/2BjJEzt4ph/X4TTy/B+tHI3vSOjiEmUFd+cweOaawidNxefO9uS9v77JD8zgvysE0bHcinFbbqPA1YBv+C4svcCri/FXEIIUTJmN+jwFjz0jWN/+6/bQNxvFz29yGV4RYmYvb0J/uwzAp5/jsxVq0h46CFy/v3X6Fguo1iFXin1L7AICAQmA4211p1KM5gQQjjFDffD0NVQpSbMeBDWfAj2C5fmuOQyvKJElFL4DxpE3alTyT9+nPgeD5Hx449Gx3IJxe2jHw7cBtQB/gZ+B9ZorcvlRzLpoxfCdRS7Xz0nC5YOg+3z4dou8MBX4Fntyl5LlEhuairJz4zg1ObNVO/fj4DnnkNZZBnjknDaNrUF+9IPBJ4DQrTWZudEdC4p9EK4hiK3ty2M1rD+a1j5MlStAz1nOBbcEWVO22ykfvAh6TNm4BkeTvAnH2MJuNgiqeJSSjwYTyn1kVJqPbAeCANeA652XkQhhLh8l92vrhS0fBwGLIO8bMcUvNhZZRNWnENZrdR6eTS1P/yQ7J07ie/ajZMbNxodq1Iq7qj7KOA+rXUjrfUgrfX0ggF6QghhmCvuV6/bEh5bAyE3waLH4YcRkJdTumFFoareczehc2Zj9vYmccBAjkyZihG7qlZmxW66V0rdB7QpePi71nppqaUqIWm6F8J1lKhfPT8Pfn0T/vgUajd3jNCvVqd0gooi5WdlkfLSaDJXrcKnY0eCxo7F7F3F6FgVhjO2qX0HaAHMLHjqESBaa/2S01I6kRR6IcRl2bUUFj7h2Ne++2S46op24BYlpLXm6JQppH30MdZ69Qj5/DPcGzY0OlaF4IwFc+4G2mutp2itpwCdCp4TQoiK7/p7Yehv4FMLZnS96BQ8UbrOmYKXkUH8Qz1lCp4TFLfQA5w9D6Wqs4MIIYShajSEwT9D427w6//BnN5w6pjRqVxSlZtbUH/BfDyuuYbkESNJffc9dG6u0bEqrOIW+neAzUqpaUqp6Th2r3u79GIJIYQBrFWgWyR0eg/2rIRJbSF1h9GpXJIlMJB630zHr3dvjk6bxr6Bj5J36JDRsSqkyxmMFwTcBChgvdb6YGkGKwnpoxdClNi+KPi+P+RkwL2fQdMeRidyWceXLCHltdcx+/gQ/Ok4vJo3NzpSuXPFffRKqeanb0AQkATsB2oXPCeEEJXT6Sl4tZvBgsGw/AXIsxmdymXEpsUSuS2S2LRYqt53H6FzZqM8PUns15+jM75Fa33OOeLiiryiV0qtLvjSA4gAtuC4om+K46r+tlJPeAXkil4I4TT5ufDzGPjrC6hzM/SYDr5BRqeq1GLTYhmycgi2fBtWs5VJHSYRFhBGfkYGB14cRdbq1eS3u5XHmseSZc495xxXdcVX9FrrtlrrtkAi0FxrHaG1DgeaAXudH1UIIcoZswU6joXuU+HgdscueAl/GJ2qUotOjcaWb8OOnVx7LtGpjgs3s68vIeO/oOYzwzH98gevTT1JwNH8c84RFyruYLzrtNbbTj/QWm/HsRSuEEK4hsZdYciv4FEVpt8Lf413rJ1fiJjEdMav3it72l+hiMAIrGYrZmXGYrIQEfjfhaoymajx+OPkfjCK6lmad6flc9Nedc45p0nTvkNxF8yZBZwAvgU00Afw1lo/Urrxrow03QshSk12Bix+0rHITqMH4b4vwN37zOHL3mhHFCo2LZbo1GgiAiMu2iQfu3UVuaPG4h2Xiv8Tj1Pz6adRZvOZ7y+s+b+ycsaCOQOBHcBw4BlgZ8FzQgjhUmJS8xlf83WSwl+EnYsh8i44vOfM8cveaEcUKiwgjMFNBhdZnMOatid84U9U7daVI19NYP/Qx8hLd7SiXKz53xUVt9C3Ar7WWj9YcPtEa51dmsGEEKK8OX21/tGqf2i3oRn/dJgBJw7BxLaOK3xKsNGOuCImd3eC3nqLWm++wckNG0jo1p1TO3YU2fzvaorbdP8N0BI4AqwtuK3TWpfLDihpuhdClIbxq/fy0crd2DWYFYzscC1PNXOH7/vBgU1w2who+woxSZlXvtGOuGKntm4lafgz5B85Qq3XXyehdYNLNv9XFiXe1OasF6oNdAeeA2prrd2cE9G5pNALIUrD6Sv63Dw7lrP73/Ny4McXIGYa1L8duk+BKjWMjuuS8o4eJfnZZzn5VxTVHnqIwFdexmS1Gh2r1Dlj97o+QGugCXAYWAes1Vr/5cygziKFXghRWorcFnfTDFj2LFSp6djyNiTcmJAuTuflcejTzzgyaRIeTZsS8uk4LEGVe+0DZxT6w8C/wARgtdY6wakJnUwKvRDCMAc2w5x+kHUQunwA4QMuOKXIDwvCaTJWrSJl1Esod3eCP/6IKi1bGh2p1JR41L3WugbwKI4V8sYqpTYopWY4MaMQQlQOtZvBY79D6G2wdDgsfhpy/xu7fGZA38rd9I6Mkrn2pci3fXtC536P2c+PfY8O4sjkyVxOd3VlUaxCr5TyBeoC9YBQHNvUymbNQghRGK/q0HsetH4WNs+AqZ3g2D5Apt+VNfcGDQidMwef9u1J++BDkp8ZQX7WCaNjlaniTq9bB9wLbAV6aq2v1Vr3L71YQghRwZnMcNdr8PB3cORf+Pp2+He1TL8zgNm7CsHjPiHg+efJXLWKhJ49yYmLMzpWmbmsUfcVhfTRCyHKlcN7YU4fOLwb7nyVmDoDiIo/Kn30BjgRtZ7kkSPROTkEvfM2vh06GB3JKZwxGK8m8ALQCEc/PQBa6zudFdKZpNALIcqdnCxY8j/YsQCuuwce+Ao8fI1O5ZJyU1JIGv4M2Vu34j9kMDWHD0e5lcvZ4sXmjCVwZwJ/A/WBN4AEYKNT0gkhhCtw93bMr+/4Nuz+ESbdCYd2G53KJVmCgqj37Qyq9ezJkUmR7BsyhLyjR42OVWqKW+j9tdaTgVyt9e9a60dxrJQnhBCiuJSCW56C/ksg+5ij2O9cXOipsgNe6TJZrQS9MYagsWM5FbOJ+G7dObVt26W/sQIqbqHPLbhPUUrdrZRqBoSUUiYhhKjcQm+Dob9DwPWO5XNXvgr5eWcOyxS8slOtW1fqffcdSikSe/Umfe5coyM5XXEL/VtKqarAsziWv40ERpRaKiGEqOyqBsOAZRAxCP78DL59EE4cBmQKXlnzbNyI0Pnz8LrpJg6++hopr76G3WYzOpbTXLLQK6XMwNVa6+Na6+1a67Za63Ct9ZIyyCeEEJWXmzvc8zHc/yXsW++YgpccI1PwDODm50edSRPxf+wxjs2dS2LvPuSmpBgdyykuWei11vnAfWWQRQghXFOz3jBoJSgTTOlE+OElzBzckpEdrv1v4xxR6pTZzIHed7Dr+fs49e9e4rt150RUlNGxSqy40+vG4lgNbw5wZkkhrfWm0ot25WR6nRCiQjp5FOY9CnGroXk/6PwBWDwu/X3CKWLTYhmycgi2fBt1j7nx7nI/TPtTCHj2Wao/OhCllNERL6qo6XXFnTjYquD+zbOe00C5nEcvhBAVkld16DMfVo+FtR/Bwe3QcwZUlbHPZSE6NRpbvg07dvb75bPh/7rSfuY/pH3wAae2bSPorbcwe1cxOuZlK+6mNm0LuUmRF0IIZzu9dG7PmXB4j6PfPn6N0alcQkRgBFazFbMyYzFZaBbaqmDp3OfIXLmShId7khMXb3TMy1Zk071SamRR36y1/tjpiZxAmu6FEJXC4T0wuzcc2QPt3oBW/3PMxRelJjYtlujUaCICIwgLCDvz/ImoKJJHjETbbNR+71182rUzMOWFrngJXKXU6wVfXgvcBJweaX8vsEZrPdiZQZ1FCr0QotLIyYTFTzkW1rnhAbh/vGOVPVHmcg8ccCydu20beX0fYP3d9Ymo3eKcDwRGccZa9yuBblrrzILHPsBcrXUnpyZ1Ein0QohKRWvHXPufx0CNaxzN+jUaGp3KJdlzctj58gjMP6xmS33F1w968ckDkYYXe2esdV8XOHv1ABuOfemFEEKUNqXg1uHQdyFkpcGktvD3MqNTuSSTuztR/ZszsbOZG/Zpxkw+wa6o8v23KG6hnwFsUEqNKWjOXw9ML71YQgghLtDgDnjsd6jeAGb3gl/fAnu+0alcTkRgBOvCPXmjrwWzXXHjK3M4tmiR0bEuqtj70SulmgOtCx6u0VpvLrVUJSRN90KISi03G5Y/C5u/havugm6R4FWdmMR0ouKOyD73ZeDMoD3r1fi9PZWT69fj16sXgaNeRFmtZZ6nxH30FY0UeiFEpac1xEyD5c+Db212tvmSrgszseXZsbqZZEW9MqTz8kj7+BOOTpmCZ7NmBI8bhyUwoEwzOKOPXgghRHmiFEQMhEdXQH4u1/zQlS7232UjHAMoNzcCX3ie4E8+Jnv3buK7d+NkTIzRsc6QQi+EEBVZSAQ89junAsL42PIVb1qm4eVml41wDODbuTOhs2dh9qpCYv8BHJ3xLeWh1dyQQq+Uqq6UWqWU2lNwX2j7klKqmlJqnlLqb6XULqXULWWdVQghyj3vAHyGLCe10WD6mVfyV9A4wqvnGJ3KJXlccw2h8+bi3aYNqWPHcuDFF7GfOmVoJqOu6EcBv2itrwZ+KXhcmE+BFVrr64AbgV1llE8IISoWsxuBPT6CbpPxTt8JX7eBfRV/57WKyOzjQ8gXn1Nj2P/IWPoDCY/0wrZ/v2F5jCr09/Pf9LzpwAPnn6CU8gXaAJMBtNY2rfWxMksohBAVUZPuMPhnsHjBtLthwyTHwD1RppTJRM0nn6TO1xPIPXCA+O49yFq71pAsRhX6QK11CkDBfWHDExsAh4CpSqnNSqlIpVTF2zZICCHKWmAjGPqbY+rd8udg0ROQa2zzsavybtOG+vPmYqlVi/1DH+PwhK+JPbiJyG2RxKbFlkmGUptep5T6GahVyKGXgela62pnnZuutT6nn14pFQFEAbdqrdcrpT4FMrTWr17k/YYCQwHq1q0bnpiY6KSfRAghKii7Hda8D7+9A7WaOra89Qs1OpVLsp88Scprr5Pxww9EX2tm/N0m8r3cmdRhklOWzzVkep3Wup3WunEht8VAqlIqqCBcEJBWyEskAUla6/UFj+cBzYt4v4la6witdUTNmjWd/eMIIUTFYzLBHaPgkTmQnggT74C9vxidymXEpsWeuXI3eXlR+4P3iRvQlmb/5NNpYz659lyiU0t/zRejmu6XAP0Lvu4PLD7/BK31QWC/UuragqfuAnaWTTwhhKhEru0EQ1eDTxB82w3WfiT99qUsNi2WISuH8PmmzxmycgixabEopQh+dChvDvRkaSs3LCYLEYGFXoQ7lVGF/l2gvVJqD9C+4DFKqdpKqeVnnfc/YKZSaisQBrxd5kmFEKIy8L/KMUivcVf45U2Y0weyM4xOVWlFp0Zjy7dhx37OlXtYQBijB0zhiYj/Oa3Z/lLcSv0dCqG1PoLjCv385w8AXc56HAuU/scdIYRwBdYq0G0yBIfDylch8i7Hlrc1rzE6WaUTERiB1Wwl1557wZV7WEBYmW5rK2vdCyGEK4pfA3MHQl4OPPgVXH+v0YkqnTMb3wRGlHphl01thBBCXLi73fEkmNMXDmyC1s9C25fBZDY6prgCRRV6Q5ruhRBClK2YxHR6R0adt7tdCAz8EX583jFA78BmR9O+V3Wj4wonkk1thBDCBUTFHcGWZ79wdzuLB9z3Odz7KSSsg4m3Q8oWY8MKp5JCL4QQLqBlA3+sbibMCixupgt3twsf4Li6z8+DyR1gyxxDcgrnkz56IYRwERf00RcmK80xSC9xHdz8OHR4C8yWsg0qLpv00QshhCC8nt/FC/xp3gHQbxGseh2ixkPKVugxDXwCyySjcD5puhdCCHEuswU6ve0YmHdgs6Pffv8Go1OJKySFXgghROFOb3nr5g5Tu8DGybJ0bgUkhV4IIcTF1Wrs2PK2wR2wbCQsfhpys43NJC6LFHohhBBF8/SDXt/D7S9C7LcwpSMc22d0KlFMUuiFEEJcmskEbUfDw7PgaJxjy9u434xOJYpBCr0QQojiu64LDFkNVWrCjAfhj0+l376ck0IvhBCi2GIS0xm/DTZ3nO/YCGfVazBvIORkGR1NXITMoxdCCFEsZ6+X/7mbiZmDxhEeHA4/j4G0v+HhmY5970W5Ilf0QgghiuWC9fLjj8Ktw6HPAshKhYltYfcKo2OK80ihF0IIUSwXXS//qrbw2O9QPRRm9YTV74DdbmhW8R9Z614IIUSxFblefu4p+GEkbPkOru4IXSeCZzVjgrqYota6l0IvhBDCebSGjZGwYhRUqws9Z0LgDUanqvRkUxsgNzeXpKQksrNlRSdRvnh4eBASEoLFIjuEiUpAKWgxBGo1ge/7kT/pTn6++jVqtHzk0hvqiFLhMoU+KSkJHx8fQkNDUUoZHUcIALTWHDlyhKSkJOrXr290HCGcp25Ltty9lLzZfem46yUm7/gDBo4jvH5No5O5HJcZjJednY2/v78UeVGuKKXw9/eXliZRKa07aKaX7WWm57VnkOkHAhc/AicOGx3L5bhMoQekyItySf5disqqZQN/lJuVN/MHMsr+BLUztzqWzk3eZHQ0l+JShb48SkhIoHHjxk59zdjYWJYvX17osQ0bNhAWFkZYWBg33ngjCxcuPHMsJiaGJk2a0LBhQ4YNG0ZZDdScMGEC33zzDQDTpk3jwIEDZ46FhoZy+PCVXQG0atWqxNm01gwbNoyGDRvStGlTNm0q/D+o+Ph4br75Zq6++mp69uyJzWYDYObMmTRt2pSmTZvSqlUrtmzZUuJMQlQU4fX8mDm4JSM7XEuPQS9iGrTScWBKJ9j8rbHhXInWutLdwsPD9fl27tx5wXPlQXx8vG7UqJFTX3Pq1Kn6qaeeKvTYiRMndG5urtZa6wMHDuiaNWueeXzTTTfpP//8U9vtdt2pUye9fPlyp+Yqjttvv11v3LjxzON69erpQ4cOlXmO05YtW6Y7deqk7Xa7/uuvv3SLFi0KPa9Hjx561qxZWmutH3vsMf3ll19qrbX+448/9NGjR7XWWi9fvvyi319e/30K4XRZh7Wedq/Wr/tqvXSE1rk5RieqFIBofZGaKFf0Zejjjz+mcePGNG7cmHHjxp15Pi8vj/79+9O0aVO6d+/OyZMnARg1ahQ33HADTZs25bnnnrvg9TZs2ECrVq1o1qwZrVq1Yvfu3dhsNl577TXmzJlDWFgYc+bMOed7vLy8cHNzjMHMzs4+02yckpJCRkYGt9xyC0op+vXrx6JFiy54zzFjxtC/f386dOhAaGgoCxYs4IUXXqBJkyZ06tSJ3Nzcc85PS0sjPDwcgC1btqCUYt8+x/aWV111FSdPnmTMmDF8+OGHzJs3j+joaHr37k1YWBinTp0C4PPPP6d58+Y0adKEv//++4JMO3bsoEWLFoSFhdG0aVP27NkDgLe3NwB2u50nn3ySRo0acc8999ClSxfmzZt3qT8XAIsXL6Zfv34opWjZsiXHjh0jJSXlnHO01vz66690794dgP79+5/53bVq1Qo/P8dI45YtW5KUlFSs9xWi0qri71hJ79bhED0Zpt0NGSmX/j5xxaTQFyEmMZ3xq/cSk5he8teKiWHq1KmsX7+eqKgoJk2axObNmwHYvXs3Q4cOZevWrfj6+vLll19y9OhRFi5cyI4dO9i6dSuvvPLKBa953XXXsWbNGjZv3sybb77J6NGjsVqtvPnmm/Ts2ZPY2Fh69ux5wfetX7+eRo0a0aRJEyZMmICbmxvJycmEhIScOSckJITk5ORCf5Z///2XZcuWsXjxYvr06UPbtm3Ztm0bnp6eLFu27JxzAwICyM7OJiMjg7Vr1xIREcHatWtJTEwkICAALy+vM+d2796diIgIZs6cSWxsLJ6engDUqFGDTZs28cQTT/Dhhx9ekGfChAkMHz6c2NhYoqOjz/k5ABYsWEBCQgLbtm0jMjKSv/7668yxESNGnOnKOPv27rvvApCcnEydOnWK/L0cOXKEatWqnfkAdbHf3eTJk+ncuXOhv1MhXIrZDdq/CT2mQeoOmHg7JP51yW8TV8ZlptddrrM3b7C6mZg5uGWJ5oCuW7eOBx98kCpVqgDQtWtX1q5dy3333UedOnW49dZbAejTpw+fffYZzzzzDB4eHgwePJi7776be+6554LXPH78OP3792fPnj0opS64mr6Ym2++mR07drBr1y769+9P586dC+2Pv9ggsc6dO2OxWGjSpAn5+fl06tQJgCZNmpCQkHDB+a1ateKPP/5gzZo1jB49mhUrVqC1pnXr1sXK27VrVwDCw8NZsGDBBcdvueUWxo4dS1JSEl27duXqq68+5/i6devo0aMHJpOJWrVq0bZt2zPHPvnkkyLfuzi/l+Kcs3r1aiZPnsy6deuKfD8hXEqjB6HmdTC7N0y/Bzq+45iDr1TRK/CJyyJX9BdxweYNcUdK9HqFFYPTzi8KSinc3NzYsGED3bp1Y9GiRWeK6dleffVV2rZty/bt21m6dOllT9G6/vrrqVKlCtu3byckJOScZuWkpCRq165d6Pe5u7sDYDKZsFgsZ/KbTCby8vIuOL9169ZnruLvv/9+tmzZwrp162jTpk2xcp5+P7PZXOjr9+rViyVLluDp6UnHjh359ddfzzle1O/+Ulf0ISEh7N+//8z5hf1eatSowbFjx85kO/+crVu3MnjwYBYvXoy/v3+xfmYhXEbA9TDkV2jYDn58HhY9waZ/U+gdGcVHK3fTOzLKKa2qrkwK/UVcdPOGK9SmTRsWLVrEyZMnOXHiBAsXLjxzRbtv374zzcmzZs3itttuIysri+PHj9OlSxfGjRtHbGzsBa95/PhxgoODAcdo9dN8fHzIzMwsNEd8fPyZgpSYmMju3bsJDQ0lKCgIHx8foqKi0FrzzTffcP/995foZz77Z//222+5+uqrMZlMVK9eneXLl59pxThbUdkvJi4ujgYNGjBs2P+3d+9xUdX548dfH0RTsxA0V4tW42KSOAwKYqAG67JuxmKpfbUspEQTaynaMLS01bbEH361tTTT3EDT1tYL6C8zu3kPFWpEvEWat2zVBjGJCpXP9w9w4jJy0YHBmffz8eABc87nnPOe+TC8+Zxz5vNOIDo6mtzc3Err+/bty8qVKyktLeXUqVNs3LjRsm727NmYTKZqX8nJyQBER0ezePFitNZkZWXh5uZGp06dKu1fKUVERITlun96errltTt27BhDhgxhyZIldO3atV7PSwin0aotjHgPwifB7n9z++rB3HLxlM0GWs5OEv0VVPxYyLWetgfo2bMnsbGx9O7dm5CQEOLiu3AaUgAAGoVJREFU4ggMDATKRtbp6ekYDAYKCgqIj4/n/PnzREVFYTAYuOeee6yeYp4wYQITJ04kLCyMS5cuWZZHRESwb98+qzfjbd26lYCAAIxGIw888ADz5s2jffv2ALz55pvExcXh4+ODt7e3za4nd+nSBcAygu/bty9t27a13KRWUWxsLOPGjat0M15tli9fjr+/P0ajkQMHDhATE1Np/dChQ/H09MTf358nnniCkJAQ3Nzc6rTvQYMG4eXlhY+PD2PGjGHevHmV1l3+KOCMGTOYNWsWPj4+mM1mRo8eDcC0adMwm82MHz8eo9FIUJDVqaiFEC4uEP48PLwc95KTrG3xAv1d9thkoOXsnKaozf79+/Hz87NTRMLeioqKaNOmDWazmd69e7Nt2zY6duxo77As5PdTiArMh/h5yUPcUJjPyaAJeN6XXDaHvrgiKWojnF5UVBSFhYWUlJQwefLkJpXkhRBVtPOmVfxnsOYpPLNToHg/DJ4LN9xk78iuS5LohVOoeF1eCHEduKENDHsHbu0Jn7wEZw6Wlbxt72PvyK47co1eCCFE06QUhCXAo6uh6DQsjICDH9o7quuOJHohhBBNm1c4PLEJPLzgvRHw+atQWmrvqK4bkuiFEEI0fW1/D4+vB+NI2DQD3huB6esjNpu91JFJohdCCHF9aN6q7Ka8QTPR33yK+9I/sfbjT2RSnVpIom8khYWFlT6DfeTIEZYtW2Z5nJ2dTUJCgs2Pm5GRwb59+6yumz9/Pj169MBoNNK3b99K7dLT0/H19cXX15f09HSbxyWEEFdFKeg9hlUBb9GKX1nVfAqRpdtlUp0aSKJvJLUl+qCgIObMmWPz49aU6B9++GH27NmDyWRiwoQJPPvsswAUFBQwdepUduzYwc6dO5k6dSpnz8p/y0KIpqNL4ACGlb7Kft2Z15vPYaj5LbhUfYpsIYm+0SQnJ3Po0CGMRiNJSUkkJyezZcsWjEYjs2fPZuPGjZbCNfUtBQuwcOFCgoODCQgIYOjQoRQXF7N9+3bWrFlDUlISRqORQ4cOVdrm5ptvtvz8008/Weas/+ijj4iMjMTDwwN3d3ciIyNZv359tWOGh4eTmJhI//798fPzY9euXZaiMtaq7QkhhK306uzO7LhB7LxnMae7PULHvAXw7hD4SUb2VUmibyQpKSl4e3tjMplITU0lJSWFfv36YTKZSExMrNa+PqVgoazC265du9i9ezd+fn4sWrSI0NBQoqOjSU1NxWQy4e3tXW27uXPn4u3tzYQJEyxnFOpSmvWyFi1asHnzZsaNG8fgwYOZO3cueXl5pKWlYTbLG04I0XB6dXYnfoAfHUbMLbt2fyyrrOTtya/sHVqT4pwT5nyYDP/dY9t9duwB96bYbHf1LQWbl5fHiy++SGFhIUVFRQwcOLBOx3nyySd58sknWbZsGf/4xz9IT0+vV8na6OhoS1zdu3e3FHzx8vLi+PHjUq1NCNE4Ah+BDnfB8kdh0UD4y2vkuN8rpW6REX2TVd9SsLGxsbzxxhvs2bOHl156qd4la0eMGEFGRgZQt9Ks1uK8/HNNcQohRIO5rWfZ5+1/HwIZ8RxYNJY5G/Y6/V35zjmit+HIu66qll+9mnKsNTl//jydOnXiwoULLF261FK+tqbj5Ofn4+vrC8AHH3xg+XngwIFMmjTJcgPehg0bmD59us1iFUKIBnNje3hkNV+98wwjTyyha/Mj/PXC02QdNjvtqF5G9I2kXbt2hIWF4e/vT1JSEgaDAVdXVwICAqyWoK2vl19+mZCQECIjI+nWrZtl+YgRI0hNTSUwMLDazXhvvPEG3bt3x2g0MmvWLMvH6Dw8PJg8eTLBwcEEBwczZcoUPDw8rjlGIYRoFM1cKY18mcTSBLqro6xp8SIDbjxi76jsRsrUCtEEyO+nELaXc/QsX+dm8cDXz9Pyp5Pw5+kQHOeQJW9rKlNrlxG9UspDKfWxUiq//LvV8ylKqUSl1F6lVJ5S6j2lVMvGjlUIIcT1qVdndx76y720HL8JvCNg3XOQ+SRcqN89TNc7e526TwY+1Vr7Ap+WP65EKXUbkAAEaa39gWbAiEaNUgghxPWvlTs8tBz6TwDTUvjXQCg8Xvt2DsJeiX4wcHle1XTg/iu0cwVaKaVcgdbAyUaITQghhKNxcYE/vAAjloH5UNnn7Q9vsndUjcJeif53WuvvAcq/d6jaQGv9HTATOAZ8D5zTWm9o1CiFEEI4lm73wdjPoXV7WHI/bH8dHPBetYoaLNErpT4pv7Ze9WtwHbd3p2zkfwdwK3CjUuqRGtqPVUplK6Wyz5w5Y5snIYQQwvG094Uxn5Yl/Q0vwsrRUPKTvaNqMA2W6LXWf9Ra+1v5ygROKaU6AZR/P21lF38EvtVan9FaXwBWAaE1HG+B1jpIax10yy23NMRTEkII4ShuuAn+ZwkMeAnyVsHbkVBw2N5RNQh7nbpfA4wq/3kUkGmlzTGgj1KqtSqbFm4AsL+R4ms0R44cwd/f36b7NJlMrFu3zuq6kpISHnvsMXr06EFAQAAbN260rMvJyaFHjx74+PiQkJBgdSrchjB//nwWL14MQFpaGidP/nYrRpcuXfjhhx+uar+hoVf8v7DOtNYkJCTg4+ODwWDgyy+/tNru22+/JSQkBF9fX4YPH05JSQkAS5cuxWAwYDAYCA0NZffu3dcckxDCRpSCfs/CIyvhx+9gQTjkf2zvqGzOXok+BYhUSuUDkeWPUUrdqpRaB6C13gGsAL4E9pTHusA+4V5fakr0CxcuBGDPnj18/PHH/O1vf6O0tBSA+Ph4FixYQH5+Pvn5+VYr1jWEcePGERMTA1RP9Ndi+/bt17yPDz/80PJ6LFiwgPj4eKvtnn/+eRITE8nPz8fd3Z1FixYBcMcdd7Bp0yZyc3OZPHkyY8eOveaYhBA25jOgbOpct9/D0gdh0/+D8r+LjsAuiV5rbdZaD9Ba+5Z/LyhfflJrPahCu5e01t3KT/k/qrX+1R7x2sqsWbPw9/fH39+f1157zbL84sWLjBo1CoPBwLBhwyguLgbKStveddddGAwGnnvuuWr727lzJ6GhoQQGBhIaGsrBgwcpKSlhypQpLF++HKPRyPLlyytts2/fPgYMGABAhw4daNu2LdnZ2Xz//ff8+OOP3H333SiliImJscx9X1F9S+iePn2aXr16AbB7926UUhw7dgwAb29viouL+fvf/87MmTNZsWIF2dnZjBw5EqPRyM8//wzA66+/Ts+ePenRowcHDhyoFtPevXvp3bs3RqMRg8FAfn4+AG3atAGgtLSU8ePH0717d6Kiohg0aBArVqyoQ49BZmYmMTExKKXo06cPhYWFfP/995XaaK357LPPGDZsGACjRo2yvHahoaG4u5dNE9GnTx9OnDhRp+MKIRqZexcYvQF6PAifvwLLH4Ffztk7KpuQKXBrYDpt4u09b2M6bbrmfeXk5PDOO++wY8cOsrKyWLhwIV99VVZK8eDBg4wdO5bc3Fxuvvlm5s2bR0FBAatXr2bv3r3k5uZare/erVs3Nm/ezFdffcW0adOYNGkSLVq0YNq0aQwfPhyTycTw4cMrbRMQEEBmZiYXL17k22+/JScnh+PHj/Pdd9/h6elpaVdTadr6lNDt0KEDv/zyCz/++CNbtmwhKCiILVu2cPToUTp06EDr1q0tbYcNG0ZQUBBLly7FZDLRqlUrANq3b8+XX35JfHw8M2fOrBbP/PnzefrppzGZTGRnZ1d6HgCrVq3iyJEj7Nmzh7fffpsvvvjCsi4xMRGj0VjtKyWlrB5CXUr2ms1m2rZti6ura42v3aJFi7j33nutvqZCiCagRWsYsgD+nAJfr4eFf4AzB+0d1TVzzqI2dWA6bWLMhjGUXCqhRbMWLPzTQowdjFe9v61bt/LAAw9w4403AmX147ds2UJ0dDS33347YWFhADzyyCPMmTOHZ555hpYtWxIXF8d9991HVFRUtX2eO3eOUaNGkZ+fj1Kq2mjamscff5z9+/cTFBRE586dCQ0NxdXVtV6laetbQjc0NJRt27axefNmJk2axPr169Fa069fv1rjhbLXCqBXr16sWrWq2vq7776bV155hRMnTjBkyBBLcZ7Ltm7dyoMPPoiLiwsdO3YkIiLCsq62OgN1eV3q0ubzzz9n0aJFbN26tcbjCSHsTCnoE19Wevw/sWXJ/v434a5oe0d21WREfwXZp7IpuVRCKaVcKL1A9qns2jeqQU03tlVNCkopXF1d2blzJ0OHDiUjI8OSTCuaPHkyERER5OXlsXbt2jqVpnV1dWX27NmYTCYyMzMpLCzE19cXT0/PSqeV61qati4ldPv162cZxQ8ePJjdu3ezdetW+vfvX2u8FY/XrFkzq/t/+OGHWbNmDa1atWLgwIF89tlnldbX9NrXNqKvS8ne9u3bU1hYaImtapvc3Fzi4uLIzMykXbt2dXrOQgj7yDl6lrmff0OO6g5jN8Etd8L7j8InU6H0UvV210H5W0n0VxD0uyBaNGtBM9WM5i7NCfqd1VoBdda/f38yMjIoLi7mp59+YvXq1ZYR7bFjxyynk9977z369u1LUVER586dY9CgQbz22muYTNUvH5w7d85SjjYtLc2yvKbStJePD/Dxxx/j6urKXXfdRadOnbjpppvIyspCa83ixYsZPLhOUx7U6bm/++67+Pr64uLigoeHB+vWrbOcxajoasr3Hj58GC8vLxISEoiOjiY3N7fS+r59+7Jy5UpKS0s5depUpU8aXP6np+pXcnLZrMzR0dEsXrwYrTVZWVm4ubnRqVOnSvtXShEREWG57p+enm557Y4dO8aQIUNYsmQJXbt2rdfzEkI0rpyjZxn5dhb/u+FgWQ37wtbw2IfQMwa2ziq7Ua+4oHq7Jp7sJdFfgbGDkYV/WshTgU9d82l7gJ49exIbG0vv3r0JCQkhLi6OwMBAAPz8/EhPT8dgMFBQUEB8fDznz58nKioKg8HAPffcY/UU84QJE5g4cSJhYWFcuvTbf5oRERHs27fP6s14p0+fpmfPnvj5+TFjxgyWLFliWffmm28SFxeHj48P3t7eNrue3KVLFwDLCL5v3760bdvWcpNaRbGxsYwbN67SzXi1Wb58Of7+/hiNRg4cOGC5g/+yoUOH4unpib+/P0888QQhISG4ubnVad+DBg3Cy8sLHx8fxowZw7x58yqtu/wJgRkzZjBr1ix8fHwwm82MHj0agGnTpmE2mxk/fjxGo5GgoGv7h1EI0XCyDpspuVhKqYYLF0vJOmwG1xsg+nX4yz/hyBZYEE5+7hfV2zVhUqZWOIWioiLatGmD2Wymd+/ebNu2jY4dO9o7LAv5/RTC/i6P1C9cLKW5qwtL4/rQq3OFAcnxXfB+DKXFBUy4EMfqC6HW29lBTWVq5WY84RSioqIoLCykpKSEyZMnN6kkL4RoGnp1dmdpXB+yDpvp49WuevK+PRie2ITL+6OYeewNHvUyc/GP0+ye5GsjiV44hYrX5YUQ4kp6dXavOXG36QCj1sCGFwnYMR82Hodb3ilb3kTJNXohhBCiPpo1h3tnwAML4LtseOseOJFj76iuSBK9EEIIcTUChpfNptfMFd75M3y52N4RWSWJXgghhLhanQLKPm/fOQzW/BXWPgMXm9Zs7ZLohRBCiGvR2qOsAl7YM5DzDqTdBz/apjiXLUiibySFhYWVPoN95MgRli1bZnmcnZ1NQkKCzY+bkZHBvn37rK47evQoAwYMwGAwEB4eXmlmvPT0dHx9ffH19SU9Pd3mcQkhhENxaQaRU+HBdDi1D966h4M7PmoSs+dJom8ktSX6oKAg5syZY/Pj1pTon3vuOWJiYsjNzWXKlClMnDgRgIKCAqZOncqOHTvYuXMnU6dO5ezZpj3zkxBCNAnd74cxn/JLsxvxWvcQZz6Zw8i3v7BrspdE30iSk5M5dOgQRqORpKQkkpOT2bJlC0ajkdmzZ7Nx40ZL4Zr6loKFsjrzwcHBBAQEMHToUIqLi9m+fTtr1qwhKSkJo9HIoUOHKm1TsWRtREQEmZmZAHz00UdERkbi4eGBu7s7kZGRVmvTh4eHk5iYSP/+/fHz82PXrl2WojLWqu0JIYRT6ODHkh7vsLnUwN+bpzOduezKt14NtDFIom8kKSkpeHt7YzKZSE1NJSUlhX79+mEymUhMTKzWvj6lYKGswtuuXbvYvXs3fn5+LFq0iNDQUKKjo0lNTcVkMuHt7V1pm4CAAFauXAnA6tWrOX/+PGazuU6lWS9r0aIFmzdvZty4cQwePJi5c+eSl5dHWloaZnPTnhZSCCEaSs+uXXiKJF67OJQHmm0lZt9YOHvULrE45YQ5/331VX7df8Cm+7zBrxsdJ02y2f7qWwo2Ly+PF198kcLCQoqKihg4cGCtx5g5cyZPPfUUaWlp9O/fn9tuu63eJWujo6MtcXXv3t1S8MXLy4vjx49LtTYhhFPq1dmdd+NCyTp8J980H4TPlmdhQTgM+xc5rsYrz77XAJwy0V8P6lsKNjY2loyMDAICAkhLS6vTTHC33nqrpb57UVERK1euxM3NDU9Pz0rbnzhxgvDw8FrjvPxzTXEKIYSz+G2WPR/wC4R/j0S/O4TPLo3gzQv30cK1WaPMk++Uid6WI++6qlp+9WrKsdbk/PnzdOrUiQsXLrB06VJL+dqajvPDDz/g4eGBi4sL06dP5/HHHwdg4MCBTJo0yXID3oYNG5g+fbrNYhVCCKfTzhviPuHQ26NIOrMM7XKRty7eT9Zhc4MnerlG30jatWtHWFgY/v7+JCUlYTAYcHV1JSAgwGoJ2vp6+eWXCQkJITIykm7dulmWjxgxgtTUVAIDA6vdjLdx40buvPNOunbtyqlTp3jhhRcA8PDwYPLkyQQHBxMcHMyUKVPw8PC45hiFEMKp3dCGc/ctZFrpY/yn9A80d3Whj1fDX96UMrVCNAHy+ymE88g5etbm1+ilTK0QQgjRRNRaIc/G5NS9EEII4cAk0QshhBAOzKkSvSPejyCuf/J7KYRoSE6T6Fu2bInZbJY/qqJJ0VpjNptp2bKlvUMRQjgop7kZz9PTkxMnTnDmzBl7hyJEJS1btsTT09PeYQghHJTTJPrmzZtzxx132DsMIYQQolE5zal7IYQQwhlJohdCCCEcmCR6IYQQwoE55BS4SqkzQNXCv27AuRo2q2n9ldbVdXl74Icajt2QanveDb2vum7TWP1jbZkj9I8j9I215fbsG5D+qW2ZvHeurZ0t3zu+Wms3q3vSWjvFF7DgatdfaV1dlwPZTfV5N/S+6rpNY/XPFZZd9/3jCH1jbbk9+0b6p07L5L3TBPqmtn0506n7tdew/krr6rvcHmwZy9Xsq67bNFb/NKW+AdvF4wh9U5djNTbpn7ofp7FJ39RxXw556r6pUUpl6ytUFRL2J/3TdEnfNG3SP9cHZxrR29MCewcgaiT903RJ3zRt0j/XARnRCyGEEA5MRvRCCCGEA5NEL4QQQjgwSfRCCCGEA5NE3wQopW5USuUopaLsHYuoTCnlp5Sar5RaoZSKt3c84jdKqfuVUguVUplKqT/ZOx5RmVLKSym1SCm1wt6xODtJ9NdAKfUvpdRppVReleV/VkodVEp9o5RKrsOungfeb5gonZct+kdrvV9rPQ74H0A+RmQjNuqbDK31GCAWGN6A4TodG/XPYa316IaNVNSF3HV/DZRS/YEiYLHW2r98WTPgayASOAHsAh4CmgHTq+ziccBA2TSSLYEftNb/v3Gid3y26B+t9WmlVDSQDLyhtV7WWPE7Mlv1Tfl2/wss1Vp/2UjhOzwb988KrfWwxopdVOc09egbgtZ6s1KqS5XFvYFvtNaHAZRS/wYGa62nA9VOzSulIoAbgbuAn5VS67TWpQ0auJOwRf+U72cNsEYp9QEgid4GbPTeUUAK8KEkeduy1XtHNA2S6G3vNuB4hccngJArNdZavwCglIqlbEQvSb5h1at/lFLhwBDgBmBdg0Ym6tU3wF+BPwJuSikfrfX8hgxO1Pu90w54BQhUSk0s/4dA2IEkettTVpbVen1Ea51m+1CEFfXqH631RmBjQwUjKqlv38wB5jRcOKKK+vaPGRjXcOGIupKb8WzvBHB7hceewEk7xSKqk/5puqRvmjbpn+uUJHrb2wX4KqXuUEq1AEYAa+wck/iN9E/TJX3TtEn/XKck0V8DpdR7wBfAnUqpE0qp0Vrri8BTwEfAfuB9rfVee8bprKR/mi7pm6ZN+sexyMfrhBBCCAcmI3ohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRyYJHohnJhSqq1SanyFx7c2VP3w8vrxU66wrqj8+y1KqfUNcXwhnJUkeiGcW1vAkui11icbsKToBGBeTQ201meA75VSYQ0UgxBORxK9EM4tBfBWSpmUUqlKqS5KqTwoq6iolMpQSq1VSn2rlHpKKfWsUuorpVSWUsqjvJ23Umq9UipHKbVFKdWt6kGUUl2BX7XWP5Q/vkMp9YVSapdS6uUqzTOAkQ37tIVwHpLohXBuycAhrbVRa51kZb0/8DBltchfAYq11oGUTY8aU95mAfBXrXUv4Dmsj9rDgIo14/8JvKm1Dgb+W6VtNtDvKp+PEKIKKVMrhKjJ51rr88B5pdQ5YG358j2AQSnVBggF/qOUpYrpDVb20wk4U+FxGDC0/OclwIwK604Dt9omfCGEJHohRE1+rfBzaYXHpZT9/XABCrXWxlr28zPgVmXZlQpttCxvL4SwATl1L4RzOw/cdLUba61/BL5VSj0IoMoEWGm6H/Cp8HgbZWVOofr1+K5A3tXGJISoTBK9EE5Ma20Gtiml8pRSqVe5m5HAaKXUbmAvMNhKm81AoPrt/P7TwJNKqV1UH+lHAB9cZSxCiCqkTK0QolEopf4JrNVaf1JLu83AYK312caJTAjHJiN6IURjeRVoXVMDpdQtwCxJ8kLYjozohRBCCAcmI3ohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRzY/wFC4L76RtWovwAAAABJRU5ErkJggg==\n", 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qvcQkppdR0MrPvUF96nzxBfW+nYHZ15fk4cPZP2QotoQEo6MJUSnIFb0QWsO2ebDyFUf/fbM+0O4NqFLjnNMMW/LXhei8PNK/m8Whzz5D5+TgP2Qw/kOHYvLwMDqaEOWaXNELURSloGkPeHoj3PI0bJkNnzeHDZPAnn/mtMKW/BXOpdzcqN6vLw2WL8OnUycOf/kVcXffQ+avq42OJkSFJYVeiNM8fKHjWHj8Dwi6EZY/BxNvh33rAWOX/HU1loAAgj94n7rTp6M8PUh68kn2P/EktqQko6MJUeFI070QhdEadi6Cn16GjGS4sRe0f4OYIxbDlvx1VTo3l6PfzODQ+PGQn0+Nxx+j+qBBmGQ5XSHOkFH3QlypnCxY+yH8+YVjzfy2o+GmIbKcrgFyDx4k9d33yFyxAku9utR65VW8W99mdCwhygXpoxeiEMUaQe/uDe3GwJN/QUgErBgFX7e55Oh8cfli02KJ3BZJbFpsoccttWoRMu4T6kRGopSJ/UOGkDRsOLkpKWWcVIiKRa7ohUu6ohH0WsPfP8CPL0LGAcfOeHe9Cu4+F7y2NO9fnnP2ty/G/vV2m42jU6ZyeMIEUIqaTz1J9f79URZLGaYWovyQK3ohznNFI+iVguvvhSejoMUQx2I741vC7hVnTjn9AeKjlbvpHRkl8+2L6YL97S+xf73JaqXG44/R4IcfyAtvRNqHH7Gz631k79pVRomFqDik0AuXVKIR9B6+0OUDGLTScTU/qyfMHQCZqTIF7wpFBEZgNVsxKzMWk4WIwEIvTC6w03qIgXf8zUdd3cg4kEhc9x4c+uwz7DZbKScWouKQpnvhspzSxJ5ngz8+hTXvg8WThPDRdFpTl9w8jUUW1bksl9rfvjCR2yL5fNPn2LHjm23i7U0NCfh9J+5XNyRo7Fg8mzYt5dRClA8y6l6I0nZ4DywdDol/kFmrJYvrvMj1jZtJkS9lp/v2c+25WEwWJnWYRMO/M0h57XXy0tKoPnAANf/3v2KvrHclHzaEKA+k0AtRFux22PwNrHwN8rIdU/FueVqm4pWywopzflYWaR98yLE5c7DWq0fQ2Lfwiii6O+ByBwQKUZ7IYDwhyoLJBOED4OkNcHV7+Pl1mNwe0mSAWGkKCwhjcJPB5xRls7c3QW+Moe7UKej8fBL79OXg/72F/cSJi77O5Q4IFKKikEIvhLP51IKe30L3KXAs0THvfs2HkJ9ndDKXU+WWW2iweBF+ffuS/t13xN13Pyf+/LPQc690QKAQ5Z003QtRmrIOOdbM37nIsX7+/V9CrcZGp3JJJzdtImX0y9gSEqjWozsBL7yA2efcNRCkj15UVNJHL4TRdiyCZc9C9nFo8zy0HglmWdylrNmzszk8fjxHJk/BrWZNao15HZ+2bY2OJUSJSR+9EEZr9AA8tQFuuB9+e5uT41sze+lyWVCnjJk8PAh49llC58zG7OtL0hNPkvzCC+Sly99BVF5S6IUoK1X8oftk9t75NSeOHKBrdB/WTH6RmPhDRidzOZ5NmlB//jxqPPUUGct/JO6ee8lY8ZPRsYQoFVLohShjP+VH0NH2PivtNzHCNIfgBQ845uGLMqWsVmr+72nqz5uLJTCQ5GeeIWnYcPKOyGqGonKRQi9EGWvZwJ+TblUZnjeMEfZh1LAlwYTbIOorx1x8irmznnAKj+uuI/T7OdQcOZKs334j7t77yPzlF6NjCeE0hgzGU0pVB+YAoUAC8JDWOv28c+oA3wC1ADswUWv9aXFeXwbjifLunOV3q+fAkmGw5ycIbc22m96hx+ykInfWkx3ySkfOnj0kv/giOTt3UfXBBwl8eTRmb+9zzpGR+aI8Knej7pVS7wNHtdbvKqVGAX5a6xfPOycICNJab1JK+QAxwANa652Xen0p9KLC0Ro2z4AVL2HL17yW3YvZ+XdgVoqRHa7lqbYNz5x6RVvsimLTNhuHvvySIxMnYalVi6B33qHKzS0AWT1PlF/lcdT9/cD0gq+nAw+cf4LWOkVrvang60xgFxBcZgmFKEtKQfN+8MSf5NRswruWSUy2fEgtt4wLdtaTHfJKl7JaCXjmGUK/mwkWN/YNGEDqu+9hz8mR1fNEhWRUoQ/UWqeAo6ADAUWdrJQKBZoB60s9mRBG8quHz9Af2d/iNW5328FvVV4m/NRf55xSoi12RbF5hoXRYOFCqj3ck6PTphHfrRs3HfeX1fNEhVNqTfdKqZ9x9K+f72Vguta62lnnpmutC217VEp5A78DY7XWC4p4v6HAUIC6deuGJyYmliS+EMZL2wULhsDBbY6r/Y7vgLujv1j66MtW1tp1pIweTV56OnkDurK+XTARtVtIs70oN8pjH/1u4A6tdUpBX/xvWutrCznPAvwA/KS1/ri4ry999KLSyLPB6rGOPe/9QqHrRKjTwuhULin/2DEOvvkmGct/xLNZM2p/8D7WkBCjYwkBlM8++iVA/4Kv+wOLzz9BKaWAycCuyynyQlQqblZo/wYMWAb2fJjSEX4dC/m5RidzOeZq1Qj++GNqf/ABOXv2EH//AxxbtIjKuIy4qFyMKvTvAu2VUnuA9gWPUUrVVkotLzjnVqAvcKdSKrbg1sWYuEIYLPRWeGIdNO0Ja96HyR3g8F6jU7mkqvfeQ4PFi3C//jpSRr1E8siR5B8/fs45sWmxRG6LJDYt1qCUQvxHNrURoqLZsQh+eAbycqDz+9Csj2PUvihTOj+fI5MiOfTFF7jVqEHt996jys0tZAqeMER5bLoXQlypRg/AE39CcDgseRrmDoBTsoJeWVNmMzUef4zQWbMwubuzb8AA0j78kJik9TIFT5QrUuiFqIh8a0O/xdBuDPz9A3x1GyT8YXQql+TZpDH1Fy6gWo8eHImcTMsxi6mX7iZT8ES5IU33QlR0yTEwfzCkJ0DrZ+H2F2Wve4Nk/vILKS+/Qt6pk8T1bU1I30GEBTYzOpZwAdJ0L0RlFhwOj62FG3vBmg9gamc4Gm90Kpfkc9dd1F+yGO+bWnBV5C/4vxEpe90Lw0mhF6IycPeGB8ZD96lw6B+Y0Bq2zDE6lUuyBARQZ+LXBL40ihNr1xJ//wOciIoyOpZwYVLohahMGnd1TMOr1RgWDoX5QyD7+KW/TziVMpmo3r8/oXNmY6pShX0DHyXto4/RubL+gSh7UuiFqGyq1XUssNP2Fdg+37HX/f4Nsse9ATxuuIH68+dRrXs3jkyaRELvPtj27TM6lnAxUuiFqIxMZrj9eXh0BQB6SifWTX6eT1buondklBT7MmTy8iLo//6P4HHjsCUkEP9gV44vWWJ0LOFCpNALUZnVaQGPr+Ofmh0YbprLTMtb+Ocdkq1tDeDbqSMNFi3E/brrOPDCixx4cRT2EyeMjiVcgBR6ISo7j6pk3f0VL9ifpLGKZ5n1JTq6bTY6lUuy1K5NvenTqPHUUxxfupT4bt3J3rnT6FiikpNCL4QLCK/nR89BL7CwxSys/vVo+Mtg+HGUYxldUaaUmxs1//c0dadNxX7qFAk9H+bojG9lcxxRamTBHCFcTV4OrHod1n8FtZpCj2ngf5XRqVxSXno6KS+NJuu33/C+806Cxr6Fm5+f0bFEBSQL5ggh/uPmDp3fhYdnwfH98HUbmXNvEDc/P0K++pLA0S+RtXYt8Q925eTGjUbHEpWMFHohXNV1XeDxPxxX9QuHwsInICcLQKbilbKzt7FVSlG9Xz9CZ81CuVtJ7D+AQ+PHE5uySba6FU4hTfdCuLr8PMfSub+/B/5XsfPWT+m6MBNbnh2rm4mZg1sSXu/izckxielExR2hZQP/Is8TDkVtY5ufdYKDb75BxpKl7Kpr4rP7zJyo5i5b3YpLkqZ7IcTFmd2g7UvQfynYTnDt0gfoaf8Ru9bk5tmLnIoXk5hO78goPlq5W+bnF1N0avRFt7E1e1ch+P33+efpTtRPsfPe5Fxu2JMjW92KEpFCL4RwqN8aHl9HVvBtvGGZzteWcfi7naRlA/+LfktU3BFseXbsmkt+KBAOEYERWM3WIrexrfdQf14f5MUxb8WoObncvHCPLJ8rrpib0QGEEOVIlRpUHbSQ/cs/pF30O9xRZQzu5gZAeKGnt2zgj9XNRG6eHYubqcgPBcIhLCCMSR0mEZ0aTURgRKFN8mEBYbzZazKbbvuL4Dk7cPtuCYm79hP88UdYgoIMSC0qMumjF0IULika5g6EzAPQ7g245SlQ6oLTpI++9B1ftoyDr76GslgIevcdfNq2NTqSKGeK6qOXQi+EuLhT6bD4afj7B7imMzzwJXhVNzqVS7IlJJD0zAhy/v6b6gMHEjByBMpiMTqWKCdkMJ4Q4sp4+kHPb6HTe7D3Z8c+9/vWG53KJVlDQwmdM5tqjzzM0alTSezTl9zkZKNjiQpACr0QomhKQcvHYdBKxwj9qZ1h3Tiw241O5nJM7u4Evf46wZ98TM7evcR17UbmL78YHUuUc1LohRDFE9wcHlsD198DP78O3z0EJ2SUvRF8O3em/sIFWIODSXrqaVLfeQdtsxkdS5RTUuiFEMXnURV6TIe7P4L432HCbbAvyuhULslaty71Zs/Cr08fjk7/hoTefbAlJRkdS5RDUuiFEJdHKbhpMAz+2bFu/tQusO6TC5ryZRnd0meyWqn1yssEf/YptoQE4h/sSsbKlUbHEuWMFHohxBWJsdVl4g3TOFqvE/w8BmY9DCePOo7JinllyrdDB0dTfmgoycOGc3Ds29KUL86QQi+EuGynC/m7vybT6t8+7Gv5JsStdozK379RVswzgDUkhNCZ3+LXry/pM2aQ0KcvtiQZlS+KWeiVUialVDOl1N1KqTuVUoGlHUwIUX6dW8g1S93vhkd/ApMZpnbivpMLsbopzApZMa8MKauVWqNHE/zpp9ji4ojv1o3MX1cbHUsYrMhCr5S6Sik1EdgLvAs8AjwJrFJKRSmlBiqlpFVACBdzeunbcwr56VH513Sizsa3iKo/hZfaBl1y9zvhfL4dO1B/wXzHqPwnnyT1gw9krXwXVuTKeEqpWcBXwFp93olKqQCgF5CutZ5eqikvk6yMJ0Tpu+jSt1pD1Few6lXwre0YpR/c3LigLsyek0Pae++R/t0sPJs1I/iTj7HUqmV0LFEKZAlcIUTZS4qG7/vDiTTo9A5EDCp0rXxR+s6sle/uTu3338e79W1GRxJOVuIlcJVSZqXUfUqpYUqpkadvzo0phKhUQiLg8bVQ/3ZY9izMHww5WUancklV776b0HnzcKtZk/1Dh3Los8/Q+flGxxJlpLj960uBAYA/4HPWTQghLs6rOvT6Hu58FXYsgEltIW2X0alcknuD+oR+P4eqXR/k8JdfsW/wYPKOyGwIV1Cspnul1FatddMyyOMU0nQvRDkUvwbmDQJbFtzzCdz4sNGJXE5sWizRqdG0iM7EOm465qpVCR73CV7NZQxFReeM3et+VEp1cGImIYSrqd/G0ZRfuzksfAyWDIPcbKNTuYzYtFiGrBzC55s+Z5DlO3K+egPl6UFi334cmTKVyjheSzgUt9BHAQuVUqeUUhlKqUylVEZpBhNCVEI+taDfYrhtJGyaDpPbwdE4o1O5hOjUaGz5NuzYybXnstHnEPXnzcPnrrtIe/99kocNIz9D/luvjIpb6D8CbgG8tNa+WmsfrbVvKeYSQlRWZjdo97qj7/7Yfvj6dti5xOhUlUJsWiyR2yKJTYu94FhEYARWsxWzMmMxWYgIjMDs40Pwp+MIfGkUmat/I757D7J3yRiKyqa4ffQ/AZ211hViA2rpoxeigji2D+YOgOQYaPkUtH8DzBajU1VIp5vmbfk2rGYrkzpMIiwg7IJzolOjiQiMuODYyU2bSR4xgvz0dGq99ipVu3VDyXTICqOoPnq3Yr5GCvCbUupHIOf0k1rrj52QTwjhqqrVhYErYOUrEDUekjZCj2lQNdjoZBXO+U3z0anRFxTzsICwC547zat5M+ovXMCB554n5ZVXORkdQ63XX8Pk6VkW8UUpKm7TfTzwC2BFptcJIZzJzQpd3ofuUyFtJ3zdBv6V9dkvV2FN85fLrXp16kyaSI2nn+b44sUk9HyYnPj4UkgrypKsjCeEKD8O/QPf94VDu6Hty9D6WdbZa/MAACAASURBVDDJdhrFVVTT/OXKWvcHB557Dp2bS9DYt/Dt1MlJKUVpuOIlcAs2tPlca72tkGNVgJ5AjtZ6prPCOoMUeiEqsJws+OEZ2DYXru4AD37tWHhHlLnclBSSR4zkVGwsfn37Evj8cyir1ehYohAlmUf/JfCqUmqXUmquUupLpdQUpdRa4E8czffznJxXCOHK3L2h6yS4+yNHE/7Xt0PyJqNTuSRLUBD1vplO9f79SJ8xg8S+/cg9cMDoWOIyFXfUvTcQAQQBp4BdWuvdpZztiskVvRAVT6G74SXFwNz+kJUKnd6FiEdlYxyDZPy0kpTRo8l3U+x8qgMNO/UocfeAcB7ZvU4IUa7FJKbTOzIKW54dq5vp3D3sTx6FBUNg78/QtKdj+VxrFWMDu6gtMT9ycMRzhKTZWdzaQpc3pxEWJMvnlgfOWAJXCCFKTVTcEWx5duwacvPsRMWdtdmKV3XoNdcxOG/r9xDZDg7vMS6sC9toTeaVfmbWNFE8uDaXk8+8TF56utGxxCUYUuiVUtWVUquUUnsK7v2KONeslNqslPqhLDMKIcpOywb+WN1MmBVY3Ey0bOB/7gkmE9z+AvSZD5kHYWJb2LHImLAuLCIwAjzc+foeC1O6uFNtZxLx3bpxautWo6OJIhjSdK+Ueh84qrV+Vyk1CvDTWr94kXNH4hgf4Ku1vqc4ry9N90JUPIX20RfmeBJ83x+So2U1PQOcPYXv2lQ3kocPJ/fQIWqNfolqDz8sq+kZpMR99Eqpa4DngXqctZqe1vrOKwy0G7hDa52ilAoCftNaX1vIeSHAdGAsMFIKvRACgDybYzW9DV9DnZsdq+n51jY6lUvKP3aM5Bde4MSatfjedy9BY8Zg8vIyOpbLcUYf/VxgE/AKjoJ/+nalArXWKQAF9wEXOW8c8AJQIdbYF0KUkdOr6XWbDAe3w4TWEPe70alckrlaNepMmECNYf8jY+kPF6ymV9RGO6JsFHet+zyt9VeX88JKqZ+BWoUcermY338PkKa1jlFK3VGM84cCQwHq1q17GUmFEBVWk+5QqwnM6QszHoC7XoNbn5EpeGVMmUzUfPJJPG+8kQPPPkdC9x4EvfM2cWEBl9xoR5S+4l7RL1VKPamUCioYSFddKVXkUlVa63Za68aF3BYDqQVN9hTcpxXyErcC9ymlEoDZwJ1KqW+LeL+JWusIrXVEzZo1i/ljCSEqvJrXwpBf4Ib74ecxMLs3ZB83OpVL8r71VuovmI/1qqtIHjacwx98SL4t55yNdkTZK26h74+jqf5PIKbgVpK/2JKC1zz92ovPP0Fr/ZLWOkRrHQo8DPyqte5TgvcUQlRW7j6OTXE6vgN7foKJdzia9EWZs9SuTb1vZ+DXqxfBS2N4dVY+/idMV7zRjii5YhV6rXX9Qm4NSvC+7wLtlVJ7gPYFj1FK1VZKLS/B6wohXJVScMuT0P8HsJ10zLffMsfoVC7JZLVS67VXqf3B+1x7yMKn092IDBgpzfYGKe6o+7XAGmAt8IfWOrO0g5WEjLoXwsVlpsK8RyFxHdw0GDq+DW7ul/y2Yk/xE8WW/c8/JA8bjm3/fgKefZbqAwfIFLxS4IxR9/2B3UA34E+lVLRS6hNnBRRCCKfyCYR+i6HV/2BjJEzt4ph/X4TTy/B+tHI3vSOjiEmUFd+cweOaawidNxefO9uS9v77JD8zgvysE0bHcinFbbqPA1YBv+C4svcCri/FXEIIUTJmN+jwFjz0jWN/+6/bQNxvFz29yGV4RYmYvb0J/uwzAp5/jsxVq0h46CFy/v3X6Fguo1iFXin1L7AICAQmA4211p1KM5gQQjjFDffD0NVQpSbMeBDWfAj2C5fmuOQyvKJElFL4DxpE3alTyT9+nPgeD5Hx449Gx3IJxe2jHw7cBtQB/gZ+B9ZorcvlRzLpoxfCdRS7Xz0nC5YOg+3z4dou8MBX4Fntyl5LlEhuairJz4zg1ObNVO/fj4DnnkNZZBnjknDaNrUF+9IPBJ4DQrTWZudEdC4p9EK4hiK3ty2M1rD+a1j5MlStAz1nOBbcEWVO22ykfvAh6TNm4BkeTvAnH2MJuNgiqeJSSjwYTyn1kVJqPbAeCANeA652XkQhhLh8l92vrhS0fBwGLIO8bMcUvNhZZRNWnENZrdR6eTS1P/yQ7J07ie/ajZMbNxodq1Iq7qj7KOA+rXUjrfUgrfX0ggF6QghhmCvuV6/bEh5bAyE3waLH4YcRkJdTumFFoareczehc2Zj9vYmccBAjkyZihG7qlZmxW66V0rdB7QpePi71nppqaUqIWm6F8J1lKhfPT8Pfn0T/vgUajd3jNCvVqd0gooi5WdlkfLSaDJXrcKnY0eCxo7F7F3F6FgVhjO2qX0HaAHMLHjqESBaa/2S01I6kRR6IcRl2bUUFj7h2Ne++2S46op24BYlpLXm6JQppH30MdZ69Qj5/DPcGzY0OlaF4IwFc+4G2mutp2itpwCdCp4TQoiK7/p7Yehv4FMLZnS96BQ8UbrOmYKXkUH8Qz1lCp4TFLfQA5w9D6Wqs4MIIYShajSEwT9D427w6//BnN5w6pjRqVxSlZtbUH/BfDyuuYbkESNJffc9dG6u0bEqrOIW+neAzUqpaUqp6Th2r3u79GIJIYQBrFWgWyR0eg/2rIRJbSF1h9GpXJIlMJB630zHr3dvjk6bxr6Bj5J36JDRsSqkyxmMFwTcBChgvdb6YGkGKwnpoxdClNi+KPi+P+RkwL2fQdMeRidyWceXLCHltdcx+/gQ/Ok4vJo3NzpSuXPFffRKqeanb0AQkATsB2oXPCeEEJXT6Sl4tZvBgsGw/AXIsxmdymXEpsUSuS2S2LRYqt53H6FzZqM8PUns15+jM75Fa33OOeLiiryiV0qtLvjSA4gAtuC4om+K46r+tlJPeAXkil4I4TT5ufDzGPjrC6hzM/SYDr5BRqeq1GLTYhmycgi2fBtWs5VJHSYRFhBGfkYGB14cRdbq1eS3u5XHmseSZc495xxXdcVX9FrrtlrrtkAi0FxrHaG1DgeaAXudH1UIIcoZswU6joXuU+HgdscueAl/GJ2qUotOjcaWb8OOnVx7LtGpjgs3s68vIeO/oOYzwzH98gevTT1JwNH8c84RFyruYLzrtNbbTj/QWm/HsRSuEEK4hsZdYciv4FEVpt8Lf413rJ1fiJjEdMav3it72l+hiMAIrGYrZmXGYrIQEfjfhaoymajx+OPkfjCK6lmad6flc9Nedc45p0nTvkNxF8yZBZwAvgU00Afw1lo/Urrxrow03QshSk12Bix+0rHITqMH4b4vwN37zOHL3mhHFCo2LZbo1GgiAiMu2iQfu3UVuaPG4h2Xiv8Tj1Pz6adRZvOZ7y+s+b+ycsaCOQOBHcBw4BlgZ8FzQgjhUmJS8xlf83WSwl+EnYsh8i44vOfM8cveaEcUKiwgjMFNBhdZnMOatid84U9U7daVI19NYP/Qx8hLd7SiXKz53xUVt9C3Ar7WWj9YcPtEa51dmsGEEKK8OX21/tGqf2i3oRn/dJgBJw7BxLaOK3xKsNGOuCImd3eC3nqLWm++wckNG0jo1p1TO3YU2fzvaorbdP8N0BI4AqwtuK3TWpfLDihpuhdClIbxq/fy0crd2DWYFYzscC1PNXOH7/vBgU1w2who+woxSZlXvtGOuGKntm4lafgz5B85Qq3XXyehdYNLNv9XFiXe1OasF6oNdAeeA2prrd2cE9G5pNALIUrD6Sv63Dw7lrP73/Ny4McXIGYa1L8duk+BKjWMjuuS8o4eJfnZZzn5VxTVHnqIwFdexmS1Gh2r1Dlj97o+QGugCXAYWAes1Vr/5cygziKFXghRWorcFnfTDFj2LFSp6djyNiTcmJAuTuflcejTzzgyaRIeTZsS8uk4LEGVe+0DZxT6w8C/wARgtdY6wakJnUwKvRDCMAc2w5x+kHUQunwA4QMuOKXIDwvCaTJWrSJl1Esod3eCP/6IKi1bGh2p1JR41L3WugbwKI4V8sYqpTYopWY4MaMQQlQOtZvBY79D6G2wdDgsfhpy/xu7fGZA38rd9I6Mkrn2pci3fXtC536P2c+PfY8O4sjkyVxOd3VlUaxCr5TyBeoC9YBQHNvUymbNQghRGK/q0HsetH4WNs+AqZ3g2D5Apt+VNfcGDQidMwef9u1J++BDkp8ZQX7WCaNjlaniTq9bB9wLbAV6aq2v1Vr3L71YQghRwZnMcNdr8PB3cORf+Pp2+He1TL8zgNm7CsHjPiHg+efJXLWKhJ49yYmLMzpWmbmsUfcVhfTRCyHKlcN7YU4fOLwb7nyVmDoDiIo/Kn30BjgRtZ7kkSPROTkEvfM2vh06GB3JKZwxGK8m8ALQCEc/PQBa6zudFdKZpNALIcqdnCxY8j/YsQCuuwce+Ao8fI1O5ZJyU1JIGv4M2Vu34j9kMDWHD0e5lcvZ4sXmjCVwZwJ/A/WBN4AEYKNT0gkhhCtw93bMr+/4Nuz+ESbdCYd2G53KJVmCgqj37Qyq9ezJkUmR7BsyhLyjR42OVWqKW+j9tdaTgVyt9e9a60dxrJQnhBCiuJSCW56C/ksg+5ij2O9cXOipsgNe6TJZrQS9MYagsWM5FbOJ+G7dObVt26W/sQIqbqHPLbhPUUrdrZRqBoSUUiYhhKjcQm+Dob9DwPWO5XNXvgr5eWcOyxS8slOtW1fqffcdSikSe/Umfe5coyM5XXEL/VtKqarAsziWv40ERpRaKiGEqOyqBsOAZRAxCP78DL59EE4cBmQKXlnzbNyI0Pnz8LrpJg6++hopr76G3WYzOpbTXLLQK6XMwNVa6+Na6+1a67Za63Ct9ZIyyCeEEJWXmzvc8zHc/yXsW++YgpccI1PwDODm50edSRPxf+wxjs2dS2LvPuSmpBgdyykuWei11vnAfWWQRQghXFOz3jBoJSgTTOlE+OElzBzckpEdrv1v4xxR6pTZzIHed7Dr+fs49e9e4rt150RUlNGxSqy40+vG4lgNbw5wZkkhrfWm0ot25WR6nRCiQjp5FOY9CnGroXk/6PwBWDwu/X3CKWLTYhmycgi2fBt1j7nx7nI/TPtTCHj2Wao/OhCllNERL6qo6XXFnTjYquD+zbOe00C5nEcvhBAVkld16DMfVo+FtR/Bwe3QcwZUlbHPZSE6NRpbvg07dvb75bPh/7rSfuY/pH3wAae2bSPorbcwe1cxOuZlK+6mNm0LuUmRF0IIZzu9dG7PmXB4j6PfPn6N0alcQkRgBFazFbMyYzFZaBbaqmDp3OfIXLmShId7khMXb3TMy1Zk071SamRR36y1/tjpiZxAmu6FEJXC4T0wuzcc2QPt3oBW/3PMxRelJjYtlujUaCICIwgLCDvz/ImoKJJHjETbbNR+71182rUzMOWFrngJXKXU6wVfXgvcBJweaX8vsEZrPdiZQZ1FCr0QotLIyYTFTzkW1rnhAbh/vGOVPVHmcg8ccCydu20beX0fYP3d9Ymo3eKcDwRGccZa9yuBblrrzILHPsBcrXUnpyZ1Ein0QohKRWvHXPufx0CNaxzN+jUaGp3KJdlzctj58gjMP6xmS33F1w968ckDkYYXe2esdV8XOHv1ABuOfemFEEKUNqXg1uHQdyFkpcGktvD3MqNTuSSTuztR/ZszsbOZG/Zpxkw+wa6o8v23KG6hnwFsUEqNKWjOXw9ML71YQgghLtDgDnjsd6jeAGb3gl/fAnu+0alcTkRgBOvCPXmjrwWzXXHjK3M4tmiR0bEuqtj70SulmgOtCx6u0VpvLrVUJSRN90KISi03G5Y/C5u/havugm6R4FWdmMR0ouKOyD73ZeDMoD3r1fi9PZWT69fj16sXgaNeRFmtZZ6nxH30FY0UeiFEpac1xEyD5c+Db212tvmSrgszseXZsbqZZEW9MqTz8kj7+BOOTpmCZ7NmBI8bhyUwoEwzOKOPXgghRHmiFEQMhEdXQH4u1/zQlS7232UjHAMoNzcCX3ie4E8+Jnv3buK7d+NkTIzRsc6QQi+EEBVZSAQ89junAsL42PIVb1qm4eVml41wDODbuTOhs2dh9qpCYv8BHJ3xLeWh1dyQQq+Uqq6UWqWU2lNwX2j7klKqmlJqnlLqb6XULqXULWWdVQghyj3vAHyGLCe10WD6mVfyV9A4wqvnGJ3KJXlccw2h8+bi3aYNqWPHcuDFF7GfOmVoJqOu6EcBv2itrwZ+KXhcmE+BFVrr64AbgV1llE8IISoWsxuBPT6CbpPxTt8JX7eBfRV/57WKyOzjQ8gXn1Nj2P/IWPoDCY/0wrZ/v2F5jCr09/Pf9LzpwAPnn6CU8gXaAJMBtNY2rfWxMksohBAVUZPuMPhnsHjBtLthwyTHwD1RppTJRM0nn6TO1xPIPXCA+O49yFq71pAsRhX6QK11CkDBfWHDExsAh4CpSqnNSqlIpVTF2zZICCHKWmAjGPqbY+rd8udg0ROQa2zzsavybtOG+vPmYqlVi/1DH+PwhK+JPbiJyG2RxKbFlkmGUptep5T6GahVyKGXgela62pnnZuutT6nn14pFQFEAbdqrdcrpT4FMrTWr17k/YYCQwHq1q0bnpiY6KSfRAghKii7Hda8D7+9A7WaOra89Qs1OpVLsp88Scprr5Pxww9EX2tm/N0m8r3cmdRhklOWzzVkep3Wup3WunEht8VAqlIqqCBcEJBWyEskAUla6/UFj+cBzYt4v4la6witdUTNmjWd/eMIIUTFYzLBHaPgkTmQnggT74C9vxidymXEpsWeuXI3eXlR+4P3iRvQlmb/5NNpYz659lyiU0t/zRejmu6XAP0Lvu4PLD7/BK31QWC/UuragqfuAnaWTTwhhKhEru0EQ1eDTxB82w3WfiT99qUsNi2WISuH8PmmzxmycgixabEopQh+dChvDvRkaSs3LCYLEYGFXoQ7lVGF/l2gvVJqD9C+4DFKqdpKqeVnnfc/YKZSaisQBrxd5kmFEKIy8L/KMUivcVf45U2Y0weyM4xOVWlFp0Zjy7dhx37OlXtYQBijB0zhiYj/Oa3Z/lLcSv0dCqG1PoLjCv385w8AXc56HAuU/scdIYRwBdYq0G0yBIfDylch8i7Hlrc1rzE6WaUTERiB1Wwl1557wZV7WEBYmW5rK2vdCyGEK4pfA3MHQl4OPPgVXH+v0YkqnTMb3wRGlHphl01thBBCXLi73fEkmNMXDmyC1s9C25fBZDY6prgCRRV6Q5ruhRBClK2YxHR6R0adt7tdCAz8EX583jFA78BmR9O+V3Wj4wonkk1thBDCBUTFHcGWZ79wdzuLB9z3Odz7KSSsg4m3Q8oWY8MKp5JCL4QQLqBlA3+sbibMCixupgt3twsf4Li6z8+DyR1gyxxDcgrnkz56IYRwERf00RcmK80xSC9xHdz8OHR4C8yWsg0qLpv00QshhCC8nt/FC/xp3gHQbxGseh2ixkPKVugxDXwCyySjcD5puhdCCHEuswU6ve0YmHdgs6Pffv8Go1OJKySFXgghROFOb3nr5g5Tu8DGybJ0bgUkhV4IIcTF1Wrs2PK2wR2wbCQsfhpys43NJC6LFHohhBBF8/SDXt/D7S9C7LcwpSMc22d0KlFMUuiFEEJcmskEbUfDw7PgaJxjy9u434xOJYpBCr0QQojiu64LDFkNVWrCjAfhj0+l376ck0IvhBCi2GIS0xm/DTZ3nO/YCGfVazBvIORkGR1NXITMoxdCCFEsZ6+X/7mbiZmDxhEeHA4/j4G0v+HhmY5970W5Ilf0QgghiuWC9fLjj8Ktw6HPAshKhYltYfcKo2OK80ihF0IIUSwXXS//qrbw2O9QPRRm9YTV74DdbmhW8R9Z614IIUSxFblefu4p+GEkbPkOru4IXSeCZzVjgrqYota6l0IvhBDCebSGjZGwYhRUqws9Z0LgDUanqvRkUxsgNzeXpKQksrNlRSdRvnh4eBASEoLFIjuEiUpAKWgxBGo1ge/7kT/pTn6++jVqtHzk0hvqiFLhMoU+KSkJHx8fQkNDUUoZHUcIALTWHDlyhKSkJOrXr290HCGcp25Ltty9lLzZfem46yUm7/gDBo4jvH5No5O5HJcZjJednY2/v78UeVGuKKXw9/eXliZRKa07aKaX7WWm57VnkOkHAhc/AicOGx3L5bhMoQekyItySf5disqqZQN/lJuVN/MHMsr+BLUztzqWzk3eZHQ0l+JShb48SkhIoHHjxk59zdjYWJYvX17osQ0bNhAWFkZYWBg33ngjCxcuPHMsJiaGJk2a0LBhQ4YNG0ZZDdScMGEC33zzDQDTpk3jwIEDZ46FhoZy+PCVXQG0atWqxNm01gwbNoyGDRvStGlTNm0q/D+o+Ph4br75Zq6++mp69uyJzWYDYObMmTRt2pSmTZvSqlUrtmzZUuJMQlQU4fX8mDm4JSM7XEuPQS9iGrTScWBKJ9j8rbHhXInWutLdwsPD9fl27tx5wXPlQXx8vG7UqJFTX3Pq1Kn6qaeeKvTYiRMndG5urtZa6wMHDuiaNWueeXzTTTfpP//8U9vtdt2pUye9fPlyp+Yqjttvv11v3LjxzON69erpQ4cOlXmO05YtW6Y7deqk7Xa7/uuvv3SLFi0KPa9Hjx561qxZWmutH3vsMf3ll19qrbX+448/9NGjR7XWWi9fvvyi319e/30K4XRZh7Wedq/Wr/tqvXSE1rk5RieqFIBofZGaKFf0Zejjjz+mcePGNG7cmHHjxp15Pi8vj/79+9O0aVO6d+/OyZMnARg1ahQ33HADTZs25bnnnrvg9TZs2ECrVq1o1qwZrVq1Yvfu3dhsNl577TXmzJlDWFgYc+bMOed7vLy8cHNzjMHMzs4+02yckpJCRkYGt9xyC0op+vXrx6JFiy54zzFjxtC/f386dOhAaGgoCxYs4IUXXqBJkyZ06tSJ3Nzcc85PS0sjPDwcgC1btqCUYt8+x/aWV111FSdPnmTMmDF8+OGHzJs3j+joaHr37k1YWBinTp0C4PPPP6d58+Y0adKEv//++4JMO3bsoEWLFoSFhdG0aVP27NkDgLe3NwB2u50nn3ySRo0acc8999ClSxfmzZt3qT8XAIsXL6Zfv34opWjZsiXHjh0jJSXlnHO01vz66690794dgP79+5/53bVq1Qo/P8dI45YtW5KUlFSs9xWi0qri71hJ79bhED0Zpt0NGSmX/j5xxaTQFyEmMZ3xq/cSk5he8teKiWHq1KmsX7+eqKgoJk2axObNmwHYvXs3Q4cOZevWrfj6+vLll19y9OhRFi5cyI4dO9i6dSuvvPLKBa953XXXsWbNGjZv3sybb77J6NGjsVqtvPnmm/Ts2ZPY2Fh69ux5wfetX7+eRo0a0aRJEyZMmICbmxvJycmEhIScOSckJITk5ORCf5Z///2XZcuWsXjxYvr06UPbtm3Ztm0bnp6eLFu27JxzAwICyM7OJiMjg7Vr1xIREcHatWtJTEwkICAALy+vM+d2796diIgIZs6cSWxsLJ6engDUqFGDTZs28cQTT/Dhhx9ekGfChAkMHz6c2NhYoqOjz/k5ABYsWEBCQgLbtm0jMjKSv/7668yxESNGnOnKOPv27rvvApCcnEydOnWK/L0cOXKEatWqnfkAdbHf3eTJk+ncuXOhv1MhXIrZDdq/CT2mQeoOmHg7JP51yW8TV8ZlptddrrM3b7C6mZg5uGWJ5oCuW7eOBx98kCpVqgDQtWtX1q5dy3333UedOnW49dZbAejTpw+fffYZzzzzDB4eHgwePJi7776be+6554LXPH78OP3792fPnj0opS64mr6Ym2++mR07drBr1y769+9P586dC+2Pv9ggsc6dO2OxWGjSpAn5+fl06tQJgCZNmpCQkHDB+a1ateKPP/5gzZo1jB49mhUrVqC1pnXr1sXK27VrVwDCw8NZsGDBBcdvueUWxo4dS1JSEl27duXqq68+5/i6devo0aMHJpOJWrVq0bZt2zPHPvnkkyLfuzi/l+Kcs3r1aiZPnsy6deuKfD8hXEqjB6HmdTC7N0y/Bzq+45iDr1TRK/CJyyJX9BdxweYNcUdK9HqFFYPTzi8KSinc3NzYsGED3bp1Y9GiRWeK6dleffVV2rZty/bt21m6dOllT9G6/vrrqVKlCtu3byckJOScZuWkpCRq165d6Pe5u7sDYDKZsFgsZ/KbTCby8vIuOL9169ZnruLvv/9+tmzZwrp162jTpk2xcp5+P7PZXOjr9+rViyVLluDp6UnHjh359ddfzzle1O/+Ulf0ISEh7N+//8z5hf1eatSowbFjx85kO/+crVu3MnjwYBYvXoy/v3+xfmYhXEbA9TDkV2jYDn58HhY9waZ/U+gdGcVHK3fTOzLKKa2qrkwK/UVcdPOGK9SmTRsWLVrEyZMnOXHiBAsXLjxzRbtv374zzcmzZs3itttuIysri+PHj9OlSxfGjRtHbGzsBa95/PhxgoODAcdo9dN8fHzIzMwsNEd8fPyZgpSYmMju3bsJDQ0lKCgIHx8foqKi0FrzzTffcP/995foZz77Z//222+5+uqrMZlMVK9eneXLl59pxThbUdkvJi4ujgYNGjBs2P+3d+9xUdX548dfH0RTsxA0V4tW42KSOAwKYqAG67JuxmKpfbUspEQTaynaMLS01bbEH361tTTT3EDT1tYL6C8zu3kPFWpEvEWat2zVBjGJCpXP9w9w4jJy0YHBmffz8eABc87nnPOe+TC8+Zxz5vNOIDo6mtzc3Err+/bty8qVKyktLeXUqVNs3LjRsm727NmYTKZqX8nJyQBER0ezePFitNZkZWXh5uZGp06dKu1fKUVERITlun96errltTt27BhDhgxhyZIldO3atV7PSwin0aotjHgPwifB7n9z++rB3HLxlM0GWs5OEv0VVPxYyLWetgfo2bMnsbGx9O7dm5CQEOLiu3AaUgAAGoVJREFU4ggMDATKRtbp6ekYDAYKCgqIj4/n/PnzREVFYTAYuOeee6yeYp4wYQITJ04kLCyMS5cuWZZHRESwb98+qzfjbd26lYCAAIxGIw888ADz5s2jffv2ALz55pvExcXh4+ODt7e3za4nd+nSBcAygu/bty9t27a13KRWUWxsLOPGjat0M15tli9fjr+/P0ajkQMHDhATE1Np/dChQ/H09MTf358nnniCkJAQ3Nzc6rTvQYMG4eXlhY+PD2PGjGHevHmV1l3+KOCMGTOYNWsWPj4+mM1mRo8eDcC0adMwm82MHz8eo9FIUJDVqaiFEC4uEP48PLwc95KTrG3xAv1d9thkoOXsnKaozf79+/Hz87NTRMLeioqKaNOmDWazmd69e7Nt2zY6duxo77As5PdTiArMh/h5yUPcUJjPyaAJeN6XXDaHvrgiKWojnF5UVBSFhYWUlJQwefLkJpXkhRBVtPOmVfxnsOYpPLNToHg/DJ4LN9xk78iuS5LohVOoeF1eCHEduKENDHsHbu0Jn7wEZw6Wlbxt72PvyK47co1eCCFE06QUhCXAo6uh6DQsjICDH9o7quuOJHohhBBNm1c4PLEJPLzgvRHw+atQWmrvqK4bkuiFEEI0fW1/D4+vB+NI2DQD3huB6esjNpu91JFJohdCCHF9aN6q7Ka8QTPR33yK+9I/sfbjT2RSnVpIom8khYWFlT6DfeTIEZYtW2Z5nJ2dTUJCgs2Pm5GRwb59+6yumz9/Pj169MBoNNK3b99K7dLT0/H19cXX15f09HSbxyWEEFdFKeg9hlUBb9GKX1nVfAqRpdtlUp0aSKJvJLUl+qCgIObMmWPz49aU6B9++GH27NmDyWRiwoQJPPvsswAUFBQwdepUduzYwc6dO5k6dSpnz8p/y0KIpqNL4ACGlb7Kft2Z15vPYaj5LbhUfYpsIYm+0SQnJ3Po0CGMRiNJSUkkJyezZcsWjEYjs2fPZuPGjZbCNfUtBQuwcOFCgoODCQgIYOjQoRQXF7N9+3bWrFlDUlISRqORQ4cOVdrm5ptvtvz8008/Weas/+ijj4iMjMTDwwN3d3ciIyNZv359tWOGh4eTmJhI//798fPzY9euXZaiMtaq7QkhhK306uzO7LhB7LxnMae7PULHvAXw7hD4SUb2VUmibyQpKSl4e3tjMplITU0lJSWFfv36YTKZSExMrNa+PqVgoazC265du9i9ezd+fn4sWrSI0NBQoqOjSU1NxWQy4e3tXW27uXPn4u3tzYQJEyxnFOpSmvWyFi1asHnzZsaNG8fgwYOZO3cueXl5pKWlYTbLG04I0XB6dXYnfoAfHUbMLbt2fyyrrOTtya/sHVqT4pwT5nyYDP/dY9t9duwB96bYbHf1LQWbl5fHiy++SGFhIUVFRQwcOLBOx3nyySd58sknWbZsGf/4xz9IT0+vV8na6OhoS1zdu3e3FHzx8vLi+PHjUq1NCNE4Ah+BDnfB8kdh0UD4y2vkuN8rpW6REX2TVd9SsLGxsbzxxhvs2bOHl156qd4la0eMGEFGRgZQt9Ks1uK8/HNNcQohRIO5rWfZ5+1/HwIZ8RxYNJY5G/Y6/V35zjmit+HIu66qll+9mnKsNTl//jydOnXiwoULLF261FK+tqbj5Ofn4+vrC8AHH3xg+XngwIFMmjTJcgPehg0bmD59us1iFUKIBnNje3hkNV+98wwjTyyha/Mj/PXC02QdNjvtqF5G9I2kXbt2hIWF4e/vT1JSEgaDAVdXVwICAqyWoK2vl19+mZCQECIjI+nWrZtl+YgRI0hNTSUwMLDazXhvvPEG3bt3x2g0MmvWLMvH6Dw8PJg8eTLBwcEEBwczZcoUPDw8rjlGIYRoFM1cKY18mcTSBLqro6xp8SIDbjxi76jsRsrUCtEEyO+nELaXc/QsX+dm8cDXz9Pyp5Pw5+kQHOeQJW9rKlNrlxG9UspDKfWxUiq//LvV8ylKqUSl1F6lVJ5S6j2lVMvGjlUIIcT1qVdndx76y720HL8JvCNg3XOQ+SRcqN89TNc7e526TwY+1Vr7Ap+WP65EKXUbkAAEaa39gWbAiEaNUgghxPWvlTs8tBz6TwDTUvjXQCg8Xvt2DsJeiX4wcHle1XTg/iu0cwVaKaVcgdbAyUaITQghhKNxcYE/vAAjloH5UNnn7Q9vsndUjcJeif53WuvvAcq/d6jaQGv9HTATOAZ8D5zTWm9o1CiFEEI4lm73wdjPoXV7WHI/bH8dHPBetYoaLNErpT4pv7Ze9WtwHbd3p2zkfwdwK3CjUuqRGtqPVUplK6Wyz5w5Y5snIYQQwvG094Uxn5Yl/Q0vwsrRUPKTvaNqMA2W6LXWf9Ra+1v5ygROKaU6AZR/P21lF38EvtVan9FaXwBWAaE1HG+B1jpIax10yy23NMRTEkII4ShuuAn+ZwkMeAnyVsHbkVBw2N5RNQh7nbpfA4wq/3kUkGmlzTGgj1KqtSqbFm4AsL+R4ms0R44cwd/f36b7NJlMrFu3zuq6kpISHnvsMXr06EFAQAAbN260rMvJyaFHjx74+PiQkJBgdSrchjB//nwWL14MQFpaGidP/nYrRpcuXfjhhx+uar+hoVf8v7DOtNYkJCTg4+ODwWDgyy+/tNru22+/JSQkBF9fX4YPH05JSQkAS5cuxWAwYDAYCA0NZffu3dcckxDCRpSCfs/CIyvhx+9gQTjkf2zvqGzOXok+BYhUSuUDkeWPUUrdqpRaB6C13gGsAL4E9pTHusA+4V5fakr0CxcuBGDPnj18/PHH/O1vf6O0tBSA+Ph4FixYQH5+Pvn5+VYr1jWEcePGERMTA1RP9Ndi+/bt17yPDz/80PJ6LFiwgPj4eKvtnn/+eRITE8nPz8fd3Z1FixYBcMcdd7Bp0yZyc3OZPHkyY8eOveaYhBA25jOgbOpct9/D0gdh0/+D8r+LjsAuiV5rbdZaD9Ba+5Z/LyhfflJrPahCu5e01t3KT/k/qrX+1R7x2sqsWbPw9/fH39+f1157zbL84sWLjBo1CoPBwLBhwyguLgbKStveddddGAwGnnvuuWr727lzJ6GhoQQGBhIaGsrBgwcpKSlhypQpLF++HKPRyPLlyytts2/fPgYMGABAhw4daNu2LdnZ2Xz//ff8+OOP3H333SiliImJscx9X1F9S+iePn2aXr16AbB7926UUhw7dgwAb29viouL+fvf/87MmTNZsWIF2dnZjBw5EqPRyM8//wzA66+/Ts+ePenRowcHDhyoFtPevXvp3bs3RqMRg8FAfn4+AG3atAGgtLSU8ePH0717d6Kiohg0aBArVqyoQ49BZmYmMTExKKXo06cPhYWFfP/995XaaK357LPPGDZsGACjRo2yvHahoaG4u5dNE9GnTx9OnDhRp+MKIRqZexcYvQF6PAifvwLLH4Ffztk7KpuQKXBrYDpt4u09b2M6bbrmfeXk5PDOO++wY8cOsrKyWLhwIV99VVZK8eDBg4wdO5bc3Fxuvvlm5s2bR0FBAatXr2bv3r3k5uZare/erVs3Nm/ezFdffcW0adOYNGkSLVq0YNq0aQwfPhyTycTw4cMrbRMQEEBmZiYXL17k22+/JScnh+PHj/Pdd9/h6elpaVdTadr6lNDt0KEDv/zyCz/++CNbtmwhKCiILVu2cPToUTp06EDr1q0tbYcNG0ZQUBBLly7FZDLRqlUrANq3b8+XX35JfHw8M2fOrBbP/PnzefrppzGZTGRnZ1d6HgCrVq3iyJEj7Nmzh7fffpsvvvjCsi4xMRGj0VjtKyWlrB5CXUr2ms1m2rZti6ura42v3aJFi7j33nutvqZCiCagRWsYsgD+nAJfr4eFf4AzB+0d1TVzzqI2dWA6bWLMhjGUXCqhRbMWLPzTQowdjFe9v61bt/LAAw9w4403AmX147ds2UJ0dDS33347YWFhADzyyCPMmTOHZ555hpYtWxIXF8d9991HVFRUtX2eO3eOUaNGkZ+fj1Kq2mjamscff5z9+/cTFBRE586dCQ0NxdXVtV6laetbQjc0NJRt27axefNmJk2axPr169Fa069fv1rjhbLXCqBXr16sWrWq2vq7776bV155hRMnTjBkyBBLcZ7Ltm7dyoMPPoiLiwsdO3YkIiLCsq62OgN1eV3q0ubzzz9n0aJFbN26tcbjCSHsTCnoE19Wevw/sWXJ/v434a5oe0d21WREfwXZp7IpuVRCKaVcKL1A9qns2jeqQU03tlVNCkopXF1d2blzJ0OHDiUjI8OSTCuaPHkyERER5OXlsXbt2jqVpnV1dWX27NmYTCYyMzMpLCzE19cXT0/PSqeV61qati4ldPv162cZxQ8ePJjdu3ezdetW+vfvX2u8FY/XrFkzq/t/+OGHWbNmDa1atWLgwIF89tlnldbX9NrXNqKvS8ne9u3bU1hYaImtapvc3Fzi4uLIzMykXbt2dXrOQgj7yDl6lrmff0OO6g5jN8Etd8L7j8InU6H0UvV210H5W0n0VxD0uyBaNGtBM9WM5i7NCfqd1VoBdda/f38yMjIoLi7mp59+YvXq1ZYR7bFjxyynk9977z369u1LUVER586dY9CgQbz22muYTNUvH5w7d85SjjYtLc2yvKbStJePD/Dxxx/j6urKXXfdRadOnbjpppvIyspCa83ixYsZPLhOUx7U6bm/++67+Pr64uLigoeHB+vWrbOcxajoasr3Hj58GC8vLxISEoiOjiY3N7fS+r59+7Jy5UpKS0s5depUpU8aXP6np+pXcnLZrMzR0dEsXrwYrTVZWVm4ubnRqVOnSvtXShEREWG57p+enm557Y4dO8aQIUNYsmQJXbt2rdfzEkI0rpyjZxn5dhb/u+FgWQ37wtbw2IfQMwa2ziq7Ua+4oHq7Jp7sJdFfgbGDkYV/WshTgU9d82l7gJ49exIbG0vv3r0JCQkhLi6OwMBAAPz8/EhPT8dgMFBQUEB8fDznz58nKioKg8HAPffcY/UU84QJE5g4cSJhYWFcuvTbf5oRERHs27fP6s14p0+fpmfPnvj5+TFjxgyWLFliWffmm28SFxeHj48P3t7eNrue3KVLFwDLCL5v3760bdvWcpNaRbGxsYwbN67SzXi1Wb58Of7+/hiNRg4cOGC5g/+yoUOH4unpib+/P0888QQhISG4ubnVad+DBg3Cy8sLHx8fxowZw7x58yqtu/wJgRkzZjBr1ix8fHwwm82MHj0agGnTpmE2mxk/fjxGo5GgoGv7h1EI0XCyDpspuVhKqYYLF0vJOmwG1xsg+nX4yz/hyBZYEE5+7hfV2zVhUqZWOIWioiLatGmD2Wymd+/ebNu2jY4dO9o7LAv5/RTC/i6P1C9cLKW5qwtL4/rQq3OFAcnxXfB+DKXFBUy4EMfqC6HW29lBTWVq5WY84RSioqIoLCykpKSEyZMnN6kkL4RoGnp1dmdpXB+yDpvp49WuevK+PRie2ITL+6OYeewNHvUyc/GP0+ye5GsjiV44hYrX5YUQ4kp6dXavOXG36QCj1sCGFwnYMR82Hodb3ilb3kTJNXohhBCiPpo1h3tnwAML4LtseOseOJFj76iuSBK9EEIIcTUChpfNptfMFd75M3y52N4RWSWJXgghhLhanQLKPm/fOQzW/BXWPgMXm9Zs7ZLohRBCiGvR2qOsAl7YM5DzDqTdBz/apjiXLUiibySFhYWVPoN95MgRli1bZnmcnZ1NQkKCzY+bkZHBvn37rK47evQoAwYMwGAwEB4eXmlmvPT0dHx9ffH19SU9Pd3mcQkhhENxaQaRU+HBdDi1D966h4M7PmoSs+dJom8ktSX6oKAg5syZY/Pj1pTon3vuOWJiYsjNzWXKlClMnDgRgIKCAqZOncqOHTvYuXMnU6dO5ezZpj3zkxBCNAnd74cxn/JLsxvxWvcQZz6Zw8i3v7BrspdE30iSk5M5dOgQRqORpKQkkpOT2bJlC0ajkdmzZ7Nx40ZL4Zr6loKFsjrzwcHBBAQEMHToUIqLi9m+fTtr1qwhKSkJo9HIoUOHKm1TsWRtREQEmZmZAHz00UdERkbi4eGBu7s7kZGRVmvTh4eHk5iYSP/+/fHz82PXrl2WojLWqu0JIYRT6ODHkh7vsLnUwN+bpzOduezKt14NtDFIom8kKSkpeHt7YzKZSE1NJSUlhX79+mEymUhMTKzWvj6lYKGswtuuXbvYvXs3fn5+LFq0iNDQUKKjo0lNTcVkMuHt7V1pm4CAAFauXAnA6tWrOX/+PGazuU6lWS9r0aIFmzdvZty4cQwePJi5c+eSl5dHWloaZnPTnhZSCCEaSs+uXXiKJF67OJQHmm0lZt9YOHvULrE45YQ5/331VX7df8Cm+7zBrxsdJ02y2f7qWwo2Ly+PF198kcLCQoqKihg4cGCtx5g5cyZPPfUUaWlp9O/fn9tuu63eJWujo6MtcXXv3t1S8MXLy4vjx49LtTYhhFPq1dmdd+NCyTp8J980H4TPlmdhQTgM+xc5rsYrz77XAJwy0V8P6lsKNjY2loyMDAICAkhLS6vTTHC33nqrpb57UVERK1euxM3NDU9Pz0rbnzhxgvDw8FrjvPxzTXEKIYSz+G2WPR/wC4R/j0S/O4TPLo3gzQv30cK1WaPMk++Uid6WI++6qlp+9WrKsdbk/PnzdOrUiQsXLrB06VJL+dqajvPDDz/g4eGBi4sL06dP5/HHHwdg4MCBTJo0yXID3oYNG5g+fbrNYhVCCKfTzhviPuHQ26NIOrMM7XKRty7eT9Zhc4MnerlG30jatWtHWFgY/v7+JCUlYTAYcHV1JSAgwGoJ2vp6+eWXCQkJITIykm7dulmWjxgxgtTUVAIDA6vdjLdx40buvPNOunbtyqlTp3jhhRcA8PDwYPLkyQQHBxMcHMyUKVPw8PC45hiFEMKp3dCGc/ctZFrpY/yn9A80d3Whj1fDX96UMrVCNAHy+ymE88g5etbm1+ilTK0QQgjRRNRaIc/G5NS9EEII4cAk0QshhBAOzKkSvSPejyCuf/J7KYRoSE6T6Fu2bInZbJY/qqJJ0VpjNptp2bKlvUMRQjgop7kZz9PTkxMnTnDmzBl7hyJEJS1btsTT09PeYQghHJTTJPrmzZtzxx132DsMIYQQolE5zal7IYQQwhlJohdCCCEcmCR6IYQQwoE55BS4SqkzQNXCv27AuRo2q2n9ldbVdXl74Icajt2QanveDb2vum7TWP1jbZkj9I8j9I215fbsG5D+qW2ZvHeurZ0t3zu+Wms3q3vSWjvFF7DgatdfaV1dlwPZTfV5N/S+6rpNY/XPFZZd9/3jCH1jbbk9+0b6p07L5L3TBPqmtn0506n7tdew/krr6rvcHmwZy9Xsq67bNFb/NKW+AdvF4wh9U5djNTbpn7ofp7FJ39RxXw556r6pUUpl6ytUFRL2J/3TdEnfNG3SP9cHZxrR29MCewcgaiT903RJ3zRt0j/XARnRCyGEEA5MRvRCCCGEA5NEL4QQQjgwSfRCCCGEA5NE3wQopW5USuUopaLsHYuoTCnlp5Sar5RaoZSKt3c84jdKqfuVUguVUplKqT/ZOx5RmVLKSym1SCm1wt6xODtJ9NdAKfUvpdRppVReleV/VkodVEp9o5RKrsOungfeb5gonZct+kdrvV9rPQ74H0A+RmQjNuqbDK31GCAWGN6A4TodG/XPYa316IaNVNSF3HV/DZRS/YEiYLHW2r98WTPgayASOAHsAh4CmgHTq+ziccBA2TSSLYEftNb/v3Gid3y26B+t9WmlVDSQDLyhtV7WWPE7Mlv1Tfl2/wss1Vp/2UjhOzwb988KrfWwxopdVOc09egbgtZ6s1KqS5XFvYFvtNaHAZRS/wYGa62nA9VOzSulIoAbgbuAn5VS67TWpQ0auJOwRf+U72cNsEYp9QEgid4GbPTeUUAK8KEkeduy1XtHNA2S6G3vNuB4hccngJArNdZavwCglIqlbEQvSb5h1at/lFLhwBDgBmBdg0Ym6tU3wF+BPwJuSikfrfX8hgxO1Pu90w54BQhUSk0s/4dA2IEkettTVpbVen1Ea51m+1CEFfXqH631RmBjQwUjKqlv38wB5jRcOKKK+vaPGRjXcOGIupKb8WzvBHB7hceewEk7xSKqk/5puqRvmjbpn+uUJHrb2wX4KqXuUEq1AEYAa+wck/iN9E/TJX3TtEn/XKck0V8DpdR7wBfAnUqpE0qp0Vrri8BTwEfAfuB9rfVee8bprKR/mi7pm6ZN+sexyMfrhBBCCAcmI3ohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRyYJHohnJhSqq1SanyFx7c2VP3w8vrxU66wrqj8+y1KqfUNcXwhnJUkeiGcW1vAkui11icbsKToBGBeTQ201meA75VSYQ0UgxBORxK9EM4tBfBWSpmUUqlKqS5KqTwoq6iolMpQSq1VSn2rlHpKKfWsUuorpVSWUsqjvJ23Umq9UipHKbVFKdWt6kGUUl2BX7XWP5Q/vkMp9YVSapdS6uUqzTOAkQ37tIVwHpLohXBuycAhrbVRa51kZb0/8DBltchfAYq11oGUTY8aU95mAfBXrXUv4Dmsj9rDgIo14/8JvKm1Dgb+W6VtNtDvKp+PEKIKKVMrhKjJ51rr88B5pdQ5YG358j2AQSnVBggF/qOUpYrpDVb20wk4U+FxGDC0/OclwIwK604Dt9omfCGEJHohRE1+rfBzaYXHpZT9/XABCrXWxlr28zPgVmXZlQpttCxvL4SwATl1L4RzOw/cdLUba61/BL5VSj0IoMoEWGm6H/Cp8HgbZWVOofr1+K5A3tXGJISoTBK9EE5Ma20Gtiml8pRSqVe5m5HAaKXUbmAvMNhKm81AoPrt/P7TwJNKqV1UH+lHAB9cZSxCiCqkTK0QolEopf4JrNVaf1JLu83AYK312caJTAjHJiN6IURjeRVoXVMDpdQtwCxJ8kLYjozohRBCCAcmI3ohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRyYJHohhBDCgUmiF0IIIRzY/wFC4L76RtWovwAAAABJRU5ErkJggg==", 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" ] @@ -142,13 +142,13 @@ ], "source": [ "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, he12, '.', label='obs at 30 m with sig=0.02')\n", - "plt.semilogx(t, h1[0], label='ttim at 30 m')\n", - "plt.semilogx(t, he22, '.', label='obs at 90 m with sig=0.02')\n", - "plt.semilogx(t, h2[0], label='ttim at 90 m')\n", + "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with sig=0.02\")\n", + "plt.semilogx(t, h1[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with sig=0.02\")\n", + "plt.semilogx(t, h2[0], label=\"ttim at 90 m\")\n", "plt.legend()\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\");" ] }, { @@ -158,7 +158,7 @@ "outputs": [ { "data": { - "image/png": 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2bnucaUtKIjU+HtP1eEzx10m9Ho/p+nVS469juh5P6vVrJJ8+za3r183Hxcebv2jcQdnbZ0j89pU9MXpWxqF6NRx8fbGvVg2Dg4MN3hFhFT+OgnP7rHtOL3/oPPnex+VQbkvB7t+/nzFjxnDlyhXi4+Pp2LFjjq7z8ssv8/LLL/P111/zwQcfEBoamquStd26dbPEVb9+fUvBlxo1anDq1ClJ9AXElon+T6C2Uup+4DTQD3jqjmPWAsOUUt9i7ta/qrUutG77O0eVN6vlBU7u5s1aUpNz9oUg6QYkXb/tcdq+xKtw7TQc3Qw755nPae8M3g3/TfxVGoFHDXPXdwmm7OxQdnaQVqbS2rTWmG4kkHrlCimxsWnbeVJiY0k+b35+6/Bhbmzdiikh4bbAFPZVquDg64tD9ermn/f7UuaBBzB6ekpvgMi33JaCHThwIKtXr6Zhw4YsXLgw1yvB9evXj6FDhwLmFvztr4+JiaFdu3b3jLPMbf+f3i1OYR02S/Ra6xSl1DDgJ8zT677UWh9QSg1J2z8X2IB5at0/mKfXPVuYMVq7IlWW7Oyt8+XBlApx/8CZ3f9uEV9Cyk3z/jKu/yb/qo3NP8tXl7EEuaCUws7FGTsXZxx8qmZ7bOq1ayRFnyTpxAnzFh1N0okTXF271twzkMbOwwPHunVxrFc37Wc97KtVY8/FvUScjyCwcmCW0wajYqOy3S+syIot75y6s/xqXsqxZuf69et4e3uTnJzM0qVLLeVrs7vOkSNHqF27NgDr16+3PO7YsSOjR4+2DMDbtGkTkyZNslqsIv9sOo9ea70BczK//Xdzb3usgZcLO67bNanuXjxWezPYQaU65q1hP/PvUlPgwl8Zk/+OuZCaZN7v5J6x1V+lEbhWleRvBXaurjj5++Hk75fh91prUi9dIun4cRL/OkzioYMkHjpE3MJQSE42H1PWiRMVbnHZGz6v7sDQAbMIqN3Gcg5ZQ6Dkq1ChAq1atcLPz4/OnTvz4YcfYjQaadiwIQMHDqRRo0b5Ov+ECRNo3rw51atXx9/f35Lc+/Xrx+DBg5k1axbLly/PMBjv008/5ZdffsHe3h53d3fLNDoPDw/Gjh1L06ZNARg3bpxlYJ4oGqRMbWmTkgSxB+HMrn+T//mDoNMGsjlXSkv6jf9N/uUq2zbmAhIZfblge2tyQSclceuff0g8dIhdW5dzff8efM9pHNL+s5SpXQunJk0oG9iUNS5/My16ISZM2Ck7hjUaxiD/QTaNv6SRMrWiKJMytSJ7RgeoEmDe0iXfhPMH/k38p3fBP7+ATht4Vq5K5pa/c/EeNFPU1ulXDg441quHY716eLSpyVubBkNSEg+cs2NUmW44HzrNtXU/cOXbZTQFZrsp9tRQRNUxEhjUwGZxCyGKPkn0JUC+W6b2TuATaN7S3Yo3jzS+vdv/8Pp/95ev9m/S92kG1VoUq5H+Wa3Tb61En9/75wGeAYR0CLGcwz/tHDolhcTDh7kZEUHqtl8I2rmH/+xORK0ZQkzrVri0C8Kl3cMYZeSyEOI2kuiLuQJrmZZxgeoPmbd0iVfh7J6Myf/gGvM+l8pQrwf49TIn/iI+wr+g1um31v3zAM+ATK9TRiNO9evjVL8+HsHBmG7dImHnTuJ/+43rm3/j+s+/gFI4BQTg8kgQ5YKCcKhZU0b1C1HKSaIv5sKPxZFiPI7R9RipCTUIP1a74LqgHd3g/rbmLV3CJTi2BQ6shMiFsPMLcPWB+j3Ar7e5xV8EE02T6u68/4QLm479QYcaLa32nmW1Bn9BDZQzlCmDS5s2uLRpQ+WxY7l16BDXN/9G/ObNXJg2nQvTpmNfrRrlgoJweeQRyjZpjDLm7H95GdUvRMkhib6Yq1jhLI7V5oNKAW2kYgV/oFbhBVDWw9yK9+sFidfg8I/mpL/jC9j+Kbj7mhN+/V5QuX6GpG/LZBIVG8XUvW+QlJrE3r3LqeNlnZHr6WvwJ5uSC3UNfqWU5R5/pWEvk3zuHPFbtnB982Yuf/01l0JDMbi6Uq79fyjfsydOTZrctaUvo/qFKFkk0Rdz19VhDIZUNBqDSuW6Ogy0uefrCoSjKzTsa95uXoZDP8D+FbBtJoRNg4p10r4U9CbKdMOmyaSgWt533l+3VYK09/LCvV8/3Pv1w3TjBvG//078r5u59uNGrq5YiX21arj16E757t2xr5pxTYDC7JWwBul9ECJ7RftGqrinwMqBlEmr4uZg51B0qrg5uUPjZ2DAavjvYXhsmnnq3pbJ8GkgEWsHkZR6y2ZlZguy+l2AZwCD/AcVmaRjcHbGtUMHqnw0mQfCtuI9eRL23t5cnDWbfx79D9EDn+XqmjWW1fyKU2XA9N6H2btmM3jTYKJio2wdUoE6ceIEfn5+9z4wF6KiotiwYUOW+5KSknj22Wfx9/enYcOGGVbAi4yMxN/fn1q1ajF8+PAsl8ItCHPnzmXRokUALFy4kDNn/l0V3dfXl4sXL+bpvC1btsx3bFprhg8fTq1atWjQoAG7du3K8rjjx4/TvHlzateuTd++fUlKMq9tsmXLFtzc3AgICCAgIIDx48fnOyaQFn2xV1RakNlyqQRNB5m3a2fh4GoCD3yLg+kmyUphj4nAZMzryOdiEF9+ZhsUi/etABicnSnfowfle/QgKeY0V9eu4eqq1Zx5axSG98dTrlMnHujZg5D284iIjSzy701x630oiqKiooiIiKBLly6Z9oWEhACwb98+YmNj6dy5M3/++ScGg4GhQ4cyb948WrRoQZcuXdi4cSOdO3cu8HiHDBliebxw4UL8/PyoUqVKvs/7xx9/5PscP/74I0eOHOHIkSPs2LGDoUOHsmPHjkzHvfXWW7z++uv069ePIUOGsGDBAsuSwm3atLEUL7IWadGXAEWtBZktV29oMZSA5/9HyMPTGeYeQMjlWwSs/S/MaQo75sGtey/1mT7bYNqmwzw9P5zI6NzXvy5W71sBcPCpSqWXXqLmpp+ovmQx5Tp34vrGjUQ/MwCX/m/Rcwf4O/jaOsxsFafeh9yaPn06fn5++Pn5MXPmTMvvU1JSCA4OpkGDBvTp04eEtJ6YUaNGUa9ePRo0aMCbb76Z6Xw7d+6kZcuWNGrUiJYtW3L48GGSkpIYN24cy5YtIyAggGXLlmV4zcGDB3n00UcB8PT0pHz58kRERHD27FmuXbvGQw89hFKKAQMGsHr16kzXfO+99wgODqZDhw74+vqycuVKRo4cib+/P506dSI5bTXIdLGxsTRp0gSAPXv2oJTi5MmTANSsWZOEhATee+89pk6dyvLly4mIiODpp58mICCAmzfNy33Pnj2bxo0b4+/vz19//ZUppgMHDtCsWTMCAgJo0KABR44cAcDFxQUAk8nESy+9RP369enatStdunRh+fLlOfgvBmvWrGHAgAEopWjRogVXrlzh7NmM5Vm01mzevNlSITA4ODjL986aJNELmwmo0ZFB3ZcQMGwf9JoPjuXhxxEwra65Yljc0bu+Nqt58CJvlFKUDQykysSJ1N4WRpWPJmNfuTKxU6dxpF0QZ8eOI/Hw33d9fVRsFPP3zbdJt3l6z8ywRsNsPmjQmu9DZGQkX331FTt27CA8PJyQkBB2794NwOHDh3nhhRfYu3cvrq6ufPbZZ1y6dIlVq1Zx4MAB9u7dy5gxYzKd88EHH2Tr1q3s3r2b8ePHM3r0aBwcHBg/fjx9+/YlKiqKvn37ZnhNw4YNWbNmDSkpKRw/fpzIyEhOnTrF6dOn8fHxsRyXXWnao0ePsn79etasWUP//v0JCgpi3759ODk5sX79+gzHenp6kpiYyLVr1wgLCyMwMJCwsDCio6Px9PSkbNmylmP79OlDYGAgS5cuJSoqCicnJwAqVqzIrl27GDp0KFOnTs0Uz9y5c3n11VctPRm3/x0AK1eu5MSJE+zbt4/58+ezfft2y77XX3/d0q1++zZ5srkeQk5K9sbFxVG+fHmMaTNg7jxm+/btNGzYkM6dO3PgwIEs39Pckq57YXtGB2jwhHmLiTCP2P9zvnld/todoPmLUPORDCP2C2oefGlnKFsWt+7dcevencTDh7m8ZAlX167lyvffU7Z5czye6Y9LUJC5SiBFY4R+VmsOFDZrvw/btm2jZ8+eODs7A+b68WFhYXTr1o377ruPVq1aAdC/f39mzZrFa6+9hqOjI4MGDeKxxx6ja9eumc559epVgoODOXLkCEqpTK3prDz33HMcOnSIwMBAqlevTsuWLTEajbkqTZvbErotW7bk999/Z+vWrYwePZqNGzeitaZNm5wNMu7VqxcATZo0YeXKlZn2P/TQQ0ycOJGYmBh69eplKc6Tbtu2bTzxxBMYDAa8vLwICgqy7JsxY0a2187J+5LdMY0bNyY6OhoXFxc2bNhAjx49LD0O+SEtelG0+ARC7xB4fT88/JZ5UZ4lvWBOM9gZYunWT68s+EaHOjZfvrakcqxTB+8JE6i15Tcq/fcNkk6eJGbYKxzt0JG4BV+SevVqlvfISyNrvw/ZDWy7M3EopTAajezcuZPevXuzevVqSzK93dixYwkKCmL//v2sW7eOxMTEe8ZhNBqZMWMGUVFRrFmzhitXrlC7dm18fHyIiYmxHBcTE3PX++S5LaHbpk0bSyu+e/fu7Nmzh23bttG2bdtMx2Z3PTs7uyzP/9RTT7F27VqcnJzo2LEjmzdvzrA/u/f+Xi16Hx8fTp06ZTk+q/elYsWKXLlyxRLb7ce4urpabiF06dKF5OTkPA8uvJ0kelE0lfOCoLfNCb/nPHBwhg1vwvR6sPFtuHSMJtXdeTmoliT5AmZ0d6fi4MHU+nkTVWd9gn2VKsROmcKRh9vR4ut9VL1mLJH3yHPD2mMF2rZty+rVq0lISODGjRusWrXK0qI9efKkpTv5m2++oXXr1sTHx3P16lW6dOnCzJkziYrKfPvg6tWrlnK0CxcutPw+u9K06dcH+PnnnzEajdSrVw9vb2/KlStHeHg4WmsWLVpE9+7d8/U33/63L1myhNq1a2MwGPDw8GDDhg2WXozb5aV877Fjx6hRowbDhw+nW7du7N27N8P+1q1bs2LFCkwmE+fPn88w0yD9S8+d26hRowDo1q0bixYtQmtNeHg4bm5ueHt7Zzi/UoqgoCDLff/Q0FDLe3fu3DnLF42dO3diMpmoYIUlrSXRi6LNWMY8L3/wb/D8L+au/J3zYFZj+LovHN0MJbACY0HI7z1kZTTi2qED1Rcv4v7Vq3Dt0gW7H35j6uc3mfU/X+bXHGvzLnRbsfZYgcaNGzNw4ECaNWtG8+bNGTRokKU0bd26dQkNDaVBgwZcunSJoUOHcv36dbp27UqDBg14+OGHs+xiHjlyJG+//TatWrUiNTXV8vugoCAOHjyY5WC82NhYGjduTN26dfnoo49YvHixZd/nn3/OoEGDqFWrFjVr1rTaiHtfX18ASwu+devWlC9fHnf3zF/oBw4cyJAhQzIMxruXZcuW4efnR0BAAH/99RcDBgzIsL937974+Pjg5+fHiy++SPPmzXFzc8vRubt06UKNGjWoVasWgwcP5rPPPsuwL30q4EcffcT06dOpVasWcXFxPP/88wAsX74cPz8/GjZsyPDhw/n222+tsoS1lKkVxc+1sxDxpXlLuAhVA82t/5qPFsnldouCgrqXnnw+lkuLQrnyzbeYEhJwCQqi4pAXcWrY0ApR246UqS3d4uPjcXFxIS4ujmbNmvH777/j5eVl67AsclumVlr0wmYioy8z57d/cj81ztUbHnkHXj8AXWfC9XOwpDcs6CAt/LsoqHvp9pU9qTxiBLU2/0rFV4Zxc9cuTvTtR/TAZ7mxfXuhLaIihDV17dqVgIAA2rRpw9ixY4tUks8LGXUvbMIqVffsHSHwWQh4CnYvhrDpsLgnVHsIgkZnLL5TyhX0Gvx25ctT6eWXqTBwIJeXfUfcV19y8tnncGrUiEqvvopzi+ZWvZ4QBen2+/IlgbTohU1YdR68sYx51b3hu6HLVLh8AkIfh68egxPbrBZzcVZY880Nzs5UeO5Zav3yC17vjiP5zBlODhxI9LPPcnPPngK5phAie5LohU2kz4O3U1hvHryxDDQbDMOjoNNHEA8C4ekAACAASURBVHcEFj5mTvrR2+/9+hKuMFcCNJQpg/uTT1Lzp414jnqLW4f/5kTffpwa+hKJWaxWJoQoODIYT9hMftaqz5HkmxDxFWybATdioUY7aDcaquW9G7nAYy6hTDducGnxYuIWfInp+nVcu3Sm4rBXKFPjfluHliUZjCeKstwOxpNEL/KlWJQITUqAiAXmcrkJF82j84NGmxfnyQWrjCso5VKvXiXuq6+4tGgxOjERtx49qPTyS5lK5dqaJHpRlMmoe1Foik2JUIey0PIVeG0v/Od9OBsF8x+FpU/Auf05Po2sr59/dm5ueL72GrV+3oTHM89w7Ycf+KdTZ86Nn0BybKytwysyrly5kmEO9okTJ/j6668tzyMiIhg+fLjVr7t69WoOHjyY5b7o6GgeffRRGjRoQLt27TKsjBcaGkrt2rWpXbs2oaGhVo9L5I8kepFnxW75UwdnaP0avLoXHn0XTu2EL9rA2lfg+vl7vrxAxhWUUsYKFaj89ihqbvqJ8r16cfm77zjaoSOx06aRGh9v6/Bs7l6JPjAwkFmzZln9utkl+jfffJMBAwawd+9exo0bx9tvvw3ApUuXeP/999mxYwc7d+7k/fff5/Ll3FeTFAVHEr3Is2JbIrSMC7R5wzxKv/lQiPoGZjeGrVPM9/XvQtbXtz57Ly+833+PmhvW49qxA3HzF3C0Yycuf/cd+rbV20qbUaNGcfToUQICAhgxYgSjRo0iLCyMgIAAZsyYwZYtWyyFa3JbChbMdeabNm1Kw4YN6d27NwkJCfzxxx+sXbuWESNGEBAQwNGjGatH3l6yNigoiDVr1gDw008/0b59ezw8PHB3d6d9+/Zs3Lgx0zXbtWvH66+/Ttu2balbty5//vmnpahMVtX2hBVprUvc1qRJEy0Kx+7zu3XI3hC9+/xuW4eSdxf/0fqbp7R+11XrafW03rNM69RUW0dVKiXs26+PP/20PljnQX20W3cdv327TeI4ePCgTa6b7vjx47p+/fqW57/99pt+7LHHsnz+7rvv6latWumkpCQdFRWlnZyc9IYNG7TWWvfo0UOvWrUq0/kvXrxoefzOO+/oWbNmaa21Dg4O1t9//32WMT355JN65syZWmutV6xYoQF98eJFPWXKFD1hwgTLcePHj9dTpkzJ9PqHH35Yjxw5Umut9cyZM7W3t7c+c+aMTkxM1FWrVs0Qk8heVp9PIELfJSfKgjkiX4pCidB8q1AT+i01z7n/aTSsHGwukdvxQ6jWwtbRlSpOfvWpvngx13/aROyUKZwc+Cwujz5K5RFv4pC2BnphO/fhh9w6ZN0pgWXqPojX6NFWO19uS8Hu37+fMWPGcOXKFeLj4+nYseM9rzF16lSGDRvGwoULadu2LVWrVs11ydpu3bpZ4qpfv76l4EuNGjU4deqUVQq4iMyk616IdL6tYfAW6DHXvJ7+lx3huwFw6bitIyuW8lpERymFa6eO1Niwnkr/fYOE7ds5+ng3zk/+iNRr1woo2uItt6VgBw4cyKeffsq+fft49913c1SytkqVKqxcuZLdu3czceJEANzc3HJUmjWrONMfZxensA5p0QtxO4MBAp6Eet3gj0/h95lw+Edo/iK0eROcyts6wiIju6mV1iiiYyhThoqDB1O+Rw8uzJrFpdBQrq5ZQ6Xhr1D+iSdQxsL558uaLe+curP8al7KsWbn+vXreHt7k5yczNKlSy3la7O7zsWLF/Hw8MBgMDBp0iSee+45ADp27Mjo0aMtA/A2bdrEpEmTrBaryD9p0QuRFQdnaPcWvLIL/P/PnPRnNYKdIZCaeXBTaXOvqZXWnJFhrFQJ7wkTuH/lCsrUrs2598dzvGdP4rf9nt8/o8iqUKECrVq1ws/PjxEjRtCgQQOMRiMNGzbMsgRtbk2YMIHmzZvTvn17HnzwQcvv+/Xrx5QpU2jUqFGmwXhbtmyhTp06PPDAA5w/f5533nkHAA8PD8aOHUvTpk1p2rQp48aNw8PDI98xCuuRBXOEyImze+Cnd+BEGFSsA10+Nq+0V0CK+gp88/fNZ/au2ZgwYafsGNZoGIP8B1n2p38RSC+iY6319bXWxP/6K+c/nkLyyZO4PPwwnm+NpEyNGvk+9+1kwRxRlOV2wRzpuhciJ7wbQvA6OLzBPGBvUXeo3xM6TAQ3667qVhxW4LtXNbz0IjrWXjVRKUW5//wH57Ztubx4CRc//5xj3brj/tSTVHrlFezKlbPKdYQoSSTRC5FTSsGDj5mX0P1jFoRNg783wcMjocVLYHSwymWyWoGvqCX6nCTy3M7IyM1yygYHByo8/xxuPbpzYdZsLi9ZyrUff8Tr7bcp17nzXUd9C1EayT16IXLL3tGc3F/eATUehl/ehbmt4NgWq5y+uKzAZ81qeHldTtlYoQLe77+H77Jl2HtW5vQb/+XUoMEkRUfnOyYhSgpJ9ELklbsvPPkNPPUdpCaZu/O/HwhXT+frtKVxBb78Dt5z8vfD97tlVB4zhptRURx7vBsXP/8cU1JSnmMqieOXRPGXl8+lJHoh8uuBjvDSDgh6xzwV79Om5kp5+Rid36S6Oy8H1SoVSR6ss5yysrPDo//T1NiwAZdHH+HCJ7M43r0HN8J35Ppcjo6OxMXFSbIXRYrWmri4OBwdHXP1Ohl1L4Q1XT4BG982D9rzrAddZ0K15raOqliwdsnj+LAwc1W8U6dw694dz5EjMOZw5bXk5GRiYmJytJCMEIXJ0dERHx8f7O3tM/xe6tELUdgO/wgbRsDVU9BkIPznPXAqHa3zosSUmMjFuXOJW/AlhrJl8fzvG5Tv0wdlkM5MUbJIPXohCludzvBSODw0DHYtNnfn71sOJfCLdVFmcHTE87XXqLF6FY4PPMC5ce8S/XR/Eg//bevQhCg0kuiFKChlXKDjRHhhC5SvBiueh8U94dIxW0dW6pSpWZNqi0LxnjSJpBMnON6rF+enTMGUkGDr0IQocJLohSho3g3g+Z+hy1SIiYDPHoKtUyAl7yPCRe4ppSjfswc1NqynfK+eXFrwJUe7duX65t9sHZoQBUoSvRCFwWAHzQbDsD/No/Q3fwDzHjYnflGojO7ueE+YQPWlS7BzdibmpZc4NWwYyWfP2jo0IQqEDMYTwspytE794Y2w/g24dgaaD4FHxpi7+kWh0snJXAoN5cKnc8BgIPnZXuxoU4nAKs2stmyvEIVBRt0LUUhytU594jX4dTz8GQJu1eDxGVDrP4UbsAAgKeY0f48dgd323RzzUix43JFxz3wpyV4UG0Vu1L1SykMp9bNS6kjaz0z/Eiql7lNK/aaUOqSUOqCUetUWsQqRG1mtU5+VyOjLzNkeS6TfO/DcT+ZldZf0hpUvwo2sXyMKjoNPVcJfD2JGDyMe1zTvL7hJ7Cef5HllvajYKObvm5/jpXyFKEi2ukc/CvhVa10b+DXt+Z1SgP9qresCLYCXlVL1CjFGIXItJ+vUp7f6p206zNPzw4nUdeDFMGg7EvYvhznNZCqeDQR6NWW3nyMjXnAgvL6R+1aEc7xnLxJ2787VefK6br8QBcVWib47EJr2OBTocecBWuuzWutdaY+vA4cA69YDFcLKcrJOfZatfntHeOQdeHEruFc3T8X7pp/5Hr4oFOkV+Qa2eoWmcxZz37wvMCUkEP3U05z78MMcT8XL77r96aRXQFiLrcrUVtZanwVzQldKeWZ3sFLKF2gE5H7RaiEKWZPq7tmuUZ/e6k9OMWVu9Veub56Kt2Mu/DoB5rQwz8Vv1N9cJlcUqAyldT2hxrp1XJg+jcuLFhP/62a8J4zHuWXLbM+Rvm5/sik5z+v2p/cKJKUm4WDnQEiHEBkvIPKswAbjKaV+Abyy2PUOEKq1Ln/bsZe11ln+y6iUcgH+B0zUWq/M5novAC8AVKtWrUm0lKkURViORubHHYW1wyF6G9R8BB6fBeXvK9xABQAJf/7J2TFjSYqOJrVLO/78v/o0qtH6rsk3v+v2z983n9m7ZmPChJ2yY1ijYQzyH5TfP0OUYEVu1L1S6jDQLq017w1s0VrXyeI4e+AH4Cet9fScnl9G3YsSw2SCiAXw87vmFn378dDkWZC12gudKTGRAx+Nw/DtOi67wFePOfLKS18VSEs7vUWf3isgLXpxL0Vu1D2wFghOexwMrLnzAKWUAhYAh3KT5IUoUQwG80I7L20Hn0Dz3PtF3eDScVtHVuoYHB3Z0esBxgQbSSgDb36byJV3PyD12jWrXyt9vMCwRsMkyYt8s1WLvgLwHVANOAk8obW+pJSqAszXWndRSrUGwoB9gCntpaO11hvudX5p0YsSSWvYFQo/jQGdaq6I13SwtO4LUXpLWycl8cQ2zePhKdhX8sR7wnhc2ra1dXiiFCtyXfcFTRK9KNGuxsC6V+GfX8C3DXSfYx6pLwrF7fff65y348zbb5P0z1Hcevei8qhR2JUrZ+sQRSkkiV6IkkZr2LUIfnoH0OaR+Y2DZWS+DZhu3eLip3OIW7AAo6cn3hMm4NKmta3DEqVMUbxHL4TID6WgSTC89AdUbWxu4S/tI/PubcBQpgye/30D32XfYnBx5tTgwZwZM4bU69dtHZoQgCR6IYq38tXgmTXmErjRf8BnLWDPt7Kqng04+ftz/4oVVBg8mKsrV3Hs8W7E//67rcMSQhK9EMVe+sj8IdugUl1Y9SJ8+zTEx9o6slLH0rr/9hsMZcty6vlBnBs/HtONG7YOTZRikuiFKCkq1IRnN0CHD8wD9eY0hwOrbB1VqeTUoAH3r1yBx8CBXP7mW4717EVCZKStwxKllCR6IYqhyOjLzPntHyKjL2fcYbCDlq/AkDBw94XvB8L3z0LCpbu/RhQIg6MjlUe9RfVFoWAyEd3/Gc5/PAXTrVu2Dk2UMjLqXohiJsc171NT4PeZsGUyyWXcGB4/kJ9SGmf/mjzGc8/lfEs5040bnJ8yhSvfLsOhVk2qTP4IJ7/6tg5LlCAy6l6IEiSnNe+xM0LbN+GFLVyxq8DndlP52DiXMinxd39NLmUquSu9BVkyODvj/d573BcSgunadU707cuFT+egk5NtHZooBSTRC1HM5KTmfQZefpzqtY45pl70MGxjg8Mo/lP2iFViyfGXDgGAS5vW1Fi3ltRHWnDx0085+ERPbh2T5YxFwZJEL0Qxk5Oa93dqXKMyLZ6fzurGX1LBrRx1fnzSvNhOcmK+Ysn1lw7BvlvHebb5Hmb0MJIQfZSjPXtwaclStMl07xcLkQdyj16I0ibpBvw8Dv6cD571oOcX4N0gz6eTe/S5c3sJ2orxBiaGVcE96gTOLVviPelD7CtXtnWIohiSe/RCiH85OMNj0+DpFZBwCUIegbBpYErN0+maVHfn5aBakuRzKLByIA52DtgpO+LdHCj7yYd4vfcuCbt3c+zxblz9Yb2tQxQljLTohSjNEi7BD6/DwdVwX3PoORc8atg6qhLv9sI46SVok06c4Mxbo7i5Zw+uXTrjNW4cduXL2zhSUVxIURshxN1pDfu+h/VvgikFOn0oBXJsRKekEDd/Phc+nYPRwwPvDz/EpXUrW4cligHpuhdC3J1S0OD/zAVyfALNBXK+6SdL6NqAMhqpOGSIuUCOazlODRrEuYkfYkrM36BJUbpJohdCmLn5wDOrodNkOLYFPnsI/tpg66hKJaf69bl/+XLcn3mGy4sXc7xPHxIPHbJ1WKKYylGiV0oZlFKNlFKPKaUeUUrJsFAhSiKDAVoMhRe2gKs3fPukuYWfJEVZCpvB0RGvd0ZzX0gIqVevcvz/+hK34EuZhidyLdtEr5SqqZSaB/wDTAaeBF4CflZKhSulnlVKSa+AECWNZ10Y9Cu0ehUiQ2FuG4iRoiy24NKmNTXWrqVcu4eJnTKFk88+R/LZs7YOSxQj2Q7GU0p9A3wOhOk7DlRKeQJPAZe11qEFGmUuyWA8IazoeBisGgLXz8LDb0Gb/5qX1xWFSmvN1ZWrOD9xIhiNeL07DrfHHrN1WKKIkFH3QohcybQIzs0rsOFN8+h8n6bQa55Mw7ORpJMnOTPyLW5GReH6+ON4jR2DnaurrcMSNpbvRK+UsgMeA3wBy1d5rfV0K8VoVZLohci7bKvj7VsOP7wBOtU8aK9Rf5mGZwM6JYWLX3zBxc8+x1jZk6offUTZpk1tHZawIWtMr1sHDAQqAOVu24QQJUy2hWr8+8DQ36FKI1g7DL57xrzojihUymik0ssv4/v1UpS9PdEDgomdNh2dlGTr0EQRlNMbbT5a67wvhi2EKDbSC9Ukp5iyLlRT/j4YsBa2z4ZfJ0BMS/OKejXa2SLcUs2pYUNqrFzJ+ckfERcSQvzv26g6ZQplata0dWiiCMlp1/1HwK9a600FH1L+Sde9EPmT40I1Z/fAikFw8W9o+Qo8MhaMZQovUGFx/ddfOTtmLKaEBDzfGon7k0+i5LZKqWGNe/Q9gSWYu/qTAQVorXWRHAEiiV6Igpf+ZeChamVpfGgqRCwAL3/ovQAq1bF1eKVSyoULnBn9DjfCwnAJCsJ74gcYPTxsHZYoBNZI9MeAHsC+O6fZFUWS6IUoWFkO2EsMhzUvQ1ICdJwIgc/JQD0b0CYTl5csJXbqVAyurlSZNAmXNq1tHZYoYNYYjHcE2F8ckrwQouBlOWCvTmcYuh2qt4T1b8A3T0L8hRyfMzL6MnN++4fI6MsFGHnJpwwGPAY8g+/332N0L8+pwYM5P2kSplu3bB2asJGcJvqzwBal1NtKqTfSt4IMTAhRdKUP2LNTZBywV64yPL3cPPXu6K8wtxX88+s9z5feQzBt02Genh8uyd4KHOs8gO/33+Pevz+XQhdx4v/6cuvIEVuHJWwgp4n+OPAr4IBMrxOi1GtS3Z2lg1rwRoc6GefZw7/r5Q/+DZzcYUkv+OkdSLl7izLbKX0izwyOjniNeYf7vphLysWLHO/zBJeWLEU6Z0sXWRlPCFFwkm/CpjHw5/y0gXpfQqUHMh2W3qJPn9KX6cuDyJGo2CgizkcQWDmQAM+ADPtSLl7kzDvvcON/W3Fp1w7vDyfKQL0SJM+D8dIK2szWWu/LYp8z0Be4pbVeaq1grUESvRC2l2GK3s3t5oF6yTeh82RoHJxpoF6Op/SJLEXFRjF402CSUpNwsHMgpENIpmSvteby4iXETpmCXfnyVPloMs4tW9ooYmFN+RmM9xkwVil1SCn1vVLqM6XUl0qpMOAPzN33y60crxCimMt0z93pIRj6B1Rrbi57m8WKek2qu/NyUC1J8nkUcT6CpNQkTJhINiUTcT5zY0cplTZQ7zsMrq6cfO55zk+ZIivqlXDZJnqtdZTW+v+ApsAcIAxYCwzSWjfUWn+itZahnEKIDLK85+7qDf1XQfsJcHgjfN7KXBlPWEVg5UAc7BywU3bYG+wJrJxl4w4Axwcf5P7l31O+b18uLfiSE08+RdKJE4UXrChUco9eCGF197znfma3eUW9uKPQ+nUIGg129rYLuITI7h793VzbtImzY8ehk5PxGjMGt549ZEW9YkjK1AohCt0977nfioeNo2D3YqjaBHrPl9K3BexuXwSSz53jzIiRJPz5J65duuD13rtS+raYkUQvhCi6DqyGdcPBlApdpkLDfrKiXgG412A9nZpKXMh8LsyejX3lylSZOpWyjRvZMGKRG9ZYGU8IIQpG/R7mgXreDWH1EFg5GBKv2TqqEudeg/WUnR0Vh7yI79IlYDAQ/cwzXJgzB52aaqOIhbXkKNErpR5QSoUopTYppTanbwUdnBCilHDzgeB1EDQG9q+EL9pAjPTKWVNOB+s5BQRw/6qVuHbuzMXZnxIdHEzymTOFHK2wppwWtdkDzAUiAcvXO611ZMGFlnfSdS9EMXYy3DxQ7/pZCHoHWr1mXm1P5FtuB+tdXbOGc++PB6MR7/Hjce3UsRCiFHlhjep1kVrrJlaPrIBIoheimLt5xTzf/uBquP9h6DUPynnZOqpSKenkSU6/OYLEvXsp/0QfKr/9NoayZW0dlriDNe7Rr1NKvaSU8lZKeaRvVoxRCCH+5VQenlgIj8+CUzvh85bw90+2jqpUcqhWDd+lS6gweDBXlq/geO8+JB48aOuwRC7ktEV/PItfa611kZwLIy16IUqQC4dh+fNwfh80Hwrt3wdjGVtHVSrdCA/nzMi3SL18Gc83/4v7gAEy576IyHeLXmt9fxZbkUzyQogSplIdGPQLNB8COz6HkEfhwt+2jqpUcm7RgvvXrMa5TRvOT5rMqSFDSLl06d4vFDaV01H3YUqpiUqpTkqpfJenTev6/1kpdSTt510Xt1ZK2SmldiulfsjvdYUQxZS9I3T+CJ5cBtfPwLyHYdciKIHrgBR1Rnd3fOZ8SuUxY0jYHs7x7j24sX27rcMS2cjpPfpg4DDQG/hDKRWhlJqRj+uOAn7VWtfGXOd+VDbHvgocyse1hBAlRZ1OMOR38GkKa1+B7weaB+6JQhMVG8WC/Qs42aE+vt8tw1CuHCefe57Y6TPQycm2Dk9kIadd98eAnzEn5a1AWaBuPq7bHQhNexwK9MjqIKWUD/AYMD8f1xJClCSu3vDManj0XTi0Dua2gZM7bB1VqZC+ut7sXbMZvGkwf3kkmovj9OlN3Lx5RPd/hqSYGFuHKe6Q0677o8BqoDKwAPDTWnfKx3Ura63PAqT99LzLcTOBkYApH9cSQpQ0BgO0eQOe32ReLverzvC/KeZldEWByWp1PUPZsnhPmEDVGdO5dewYx3v05NqGDbYOVdwmp133s4CTwJPAcCBYKVUzuxcopX5RSu3PYuuekwsqpboCsTldlEcp9ULaLYWICxcu5OQlQojizicQhoRB/Z7w2wewqDtcO2vrqEqs7FbXc+3cmftXraJMzZqcfuO/nHnnHUwJCTaMVqTLVVEbpZQL8CzwJuCjtbbL00WVOgy001qfVUp5A1u01nXuOGYS8AyQAjgCrsBKrXX/e51fptcJUcpoDVFLYcMIsHeCHp/DA7KKW0G41+p6OjmZC5/OIW7ePBx8fak6fRqOdfNzp1fkhDVWxpsGtAZcgHDM9+nD0u7d5yWgKUCc1nqyUmoU4KG1HpnN8e2AN7XWXXNyfkn0QpRSFw7D8ufg/H54aJj5Pr7RwdZRlUo3wsM5M2IkqVeu4DliBO7P9Jc59wXIGivjhQPdtNb1tdbPa61D85rk00wG2iuljgDt056jlKqilJKbO0KIvKlUBwb9Ck0Hw/ZPYUF7iDtq66hKJecWLbh/7RqcW7Xi/IcfEvPSy6RcvmzrsEqlHHfdK6W6AW3Tnv5Pa72uwKLKJ2nRCyE49AOseRlMKdB1BjT4P1tHVCpprbm8eDHnp0zF6OFB1alTKNu0qa3DKnHy3aJPu1/+KnAwbRue9jshhCia6naFIdvAy99c437VULgVb+uoSh2lFB4DBuD7zTcoxzJEBw/kwqdS574w5fQe/V4gQGttSntuB+zWWjco4PjyRFr0QgiL1BTY+jH872OoUBP6fAXeRfKfrhIvNf4G58a/z7W16yjbtClVpnyMvZdUJbQGa9yjByh/22O3/IUkhBCFxM4IQaMheB0k3YD5j8KOL2T5XBuwc3Gm6scf4z15EjcPHOB4j55c/+03W4dV4uU00U8CdiulFiqlQoFI4MOCC0sIIazs/jbm5XNrPgI/joRvn4IEKchiC+V79OD+5csxensTM/Qlzn34IaakJFuHVWLlZjCeN9AUUMAOrfW5ggwsP6TrXghxV1rDjrnw8zhwrgS9QsC3la2jKpVMt24RO2Uql5csoUy9ulSdNo0y999v67CKpTx33SulGqdvgDcQA5wCqqT9TgghiheloMVQeP5nMDpCaFfYMlmWz7UBQ5kyeI15B585n5Jy+gzHe/fh6po1tg6rxMm2Ra+USr954ggEAnswt+gbYG7Vty7wCPNAWvRCiBy5dR3Wvwl7v4XqraHXPHCrauuoSqXks2c5PWIENyMiceveHa9xYzE4O9s6rGIjzy16rXWQ1joIiAYaa60DtdZNgEbAP9YPVQghClGZctDrC+j5BZzZDXNbweEf833ayOjLzPntHyKjZYGYnLL39qb6woVUfOklrq5dy/FevUk8eNDWYZUIOR2M96DWel/6E631fiDzIsdCCFEcNexnLo7jdh980w82vg0pt/J0qsjoyzw9P5xpmw7z9PxwSfa5oIxGKg1/hWoLF2K6eZMTfftxafESclOTRWSW00R/SCk1XynVTin1sFIqBDhUkIEJIUShqlATBv0CzYdA+Gd5Xj43/FgcSSkmTBqSU0yEH4srgGBLNufmzbh/zWqcW7bk/MSJxAx7hdQrV3J9nqjYKObvm09UbFQBRFl85DTRPwscwLw63muYV8d7tqCCEkIImzCWgc4fQb9v4MpJ+KIt7P0+V6doUaMCDkYDdgrsjQZa1KhQQMGWbEZ3d3zmfo7nqLeI37qVYz17kRCZo6rlgDnJD940mNm7ZjN40+BSnexzmuhbAl9orXumbTO01okFGZgQQtjMg11uWz53EKx+2bzYTg40qe7O0kEteKNDHZYOakGT6u4FHGzJFBUbxYL9CzjVJQDfr79G2dsTPSCYi3Pn5mj53IjzESSlJmHCRLIpmYjzpXeAtjGHxw0E5iql4oCwtG2b1lpuPgkhSiY3Hwj+Af43GbZOhZid5uVzvfzu+dIm1d0lwedDems8KTUJBzsHQjqE4L9yBefGvcuFmZ9wY8cOqnz0Efaennc9R2DlQBzsHEg2JWNvsCewcpYD0kuFHLXotdYDtNYPAL0xz6WfA1woyMCEECInCnSEu50RHhkDA9ZA4lUIeYTon2YzZ/MRGWRXgLJqjdu5uFBl2lS8P5jAzd1RHO/Zi/iwbXc9R4BnACEdQhjWaBghHUII8Cy948dzWtSmP9AG8AcuAtuAMK319oINL29kHr0QpUP6CPekFBMO3jfuYwAAGwVJREFURkPBdpXHX+DqN8/hdnorP6Y2YxwvMnfQo9JyLwDpLfr01vidifrWP/9w+vU3uHXkCDHdm1Lp1eEEVCm9LXawTlGbmZin04UAw7XWHxfVJC+EKD2sPcI9294Bl0osqTmNSSlP8h9DJCsNozgetSVf1xNZu1drvEytWlz7bAybGxnxWfMnp54JJmrPzzaKtujLadd9ReA5zCvkTVRK7VRKLS7QyIQQ4h6sOcI9J/PfW9SsRKjqTr/kcSigd9Qg+P0TMJny8VeIrAR4BjDIf9Bdu9wjruxlXicDM3oYqHLRhN1zI7i2aVMhR1k85CjRK6VcgWpAdcAXc5la+WQLIWzKmiPcc9I7kH69R9p35eLTv6Ae7GIujvP1/8GNi/n5U0QupQ+221nPnrGDymK4ryqnh7/KufHjMd3K22JHJVVO79HvxXxffhuwVWsdU9CB5YfcoxdC5FZ6iz45xYR9Tu/3aw0RC2DjaHByh97zzeVwRaGIio0i4nwEgZUDaVi+HrHTZ3Bp4ULKPPggVadPp0yN0lMJL7t79DkuU1ucSKIXQuRFZPRlwo/F0aJGhdz1DpzbB98/C3H/wMMj4eG3wGBXcIGKu7q+ZQtnR72NKSkJr3FjKd+jh61DKhT5TvRKqUr/396dh0dV3X8cf3+TEDDIvoigYlCsIipKitSFRQU3bGTVggoICC60FUXxp1bRWrdqRWtBFuOGouwIqAjiRoslKCoW3CAIIiRiZBNIQs7vjzvIloRJMjN3ls/reeZJZubOvZ/xPPHLPffcc4DbgJPxrtMD4Jw7L1QhQ0mFXkQibtc2mDscPn3ZWwmv+zio2djvVAmpcMMG1t86nF+ysxNmJbxQjLqfCKwE0oGRQA6wJCTpRETiQdXDoevovSvhjT4bvtLgMD9UadSIY57L2rsSXo+e7PzyS79j+SbYQl/POTcBKHTOveecuxZoG8ZcIiKx6bQrYfB7ULMJvNwT5t0FRQV+p0o4v66El5VF8bZt5PTsRf6kSQm5El6whb4w8PMHM7vUzE4HjgpTJhGR2Fa/ubcS3m8Hwr+fgqyLID/H71QJqXrbM0mfMZ20Nm3YcO9Ivr95GLu3bq30fmNpZbxgr9F3wZvf/mjgKaAmMNI5Nyu88SpG1+hFJGr8bybMHOr9nvkUtMj0N0+CcsXFbJowgbwnRlHlyCNp8o/HOeyUUyq0r5Lm4vd7it1KXaM3s2SguXNus3NuuXOuo3OudbQWeRGRqNIiE4a8D/WPh9eugdnDoFCLf0aaJSVRf9Agmr74Iq54Nzm9+7Ap67kKdeXH2sp4hyz0zrndwO8jkEVEJD7VORb6vwlnDYXsCfwyugMT58wP+cI4YV3gJ06knXE6zaZN4/D27ch9+GHWXX8DRfnl+++1Z7KeZEuOiZXxgu26fwBvNrxXgV8XZXbOfRy+aBWnrnsRiVZffziN+m//kVQKGOkGcMWA20KyME5EF/iJA8458l+aSO4jj5Bcrx5N/v4oaRnBF+x9J+vxu9seQnN73Vl499DfBzwWePw9NPFERBLHvMJTuaTgQZa7dB5J+hdpc4dCwfZDf/AQQr3AT7wzM+pefRVNJ72CVU1lzTV9+XH0aNzu3UF9/lBz8UeTYBe16VjCIyonyxERiWZtm9UjP6U+VxfeydPF3Tlx42wY2wE2flHp/YZqgZ9QivbR6YedfDLpU6dS8+KLyRv1JN8NHEhRXp7fsUKqzK57MxtW1oedc4+HPFEIqOteRKLZflPt7v4Mpg2CnZvhooegdT8wq/x+o6DbPhpHp5fGOcfmqVPZ8NcHSKpenSaPPkL1s87yO1bQKtN1XyPwyACuB5oEHkOAFqEMKSKSKFo3rcONHY/3inGz9jDkQzjmdzD7zzDlWti5pfL7jQKxNDrdzKjdowfpk18juU5tvhswkNwnnsAVFfkdrdLKLPTOuZHOuZFAfeAM59wtzrlbgNZowhwRkdA4vCFcNQ3Ov8e77/6Zdt40ujEu1kanA1Rt3pz0yZOp1b0bm8Y8w5q+/SjcsMHvWJUS7Kj7lcBpzrldgedVgU+dcyeGOV+FqOteRGLWd4thygDYthE63w9nDqlwV340iLbR6Yeyb970j9ay4Z57sdRUjnzoQWp06OB3vFKFYvW6O4FewHTAAV2BV51zD4YyaKio0ItITPvlJ35+ZRC1187n52M6UfvKsZBW1+9Uca+kMQUnba/F98NuYdeKFdTt35+GN/8ZS031O+pBKn17nXPuAaA/kA/8DPSP1iIvIhLrluYZbXMGcH/R1aSteYddT58Da//rd6y4V9KYgqrp6Rw76RXq9O7NT1lZ5Fx1NQXr1vkdtVyCvY8e59zHzrlRgUfsXzwSEYlS3j3xjglFF9Or8F527gaevQg+fAKKi/2OF7dKG1OQVLUqjf5yN01GjaJg9WpWd+3GlrdiZwnioAu9iIhExr73xK9Mbs6q7m/ASV1g/j3e0rfbf/Q7Ylxq1bAV4zqP46bTbyrxVsCaF3Ymffo0UtPT+f5Pf2LDffdRvGuXT2mDF9Q1+lija/QiEusOuifeOch+Ft68w7te3308HHuO3zETkisoIPcfT/BTVhZVTzqJJo8/RtX09KA/H44BipUejBdrVOhFJG5t+Bwm94OfVkGHO+DcWyAp2e9UCWnrwoX8MOIOXGEhjUaOpNZlXQ75mXBNIhSKue5FRCQaNDoFrnsXWvaAhQ/Ai5fD1o1+p0pINTp2JH3mDKqedBLrhw9n/V13UbxjR5mf8WMSIRV6EZFYU7UGdBsLmU/D2iUw5mz49h2/UyWkKo0a0fT556g3eDCbp04jp1cvdn3zTanb+zGJkLruRURiWe4KmNwf8lZ63fgd7oDkFL9TJaRtHy5i/e23U7x9O43uvpta3bpiJUx2pGv0IaBCLyIJpeAXeOM2+ORFb8787hOgVhO/UyWkwtxc1g+/jV8++oiav7+MRn+5h+TDq4f9uFF3jd7M6prZ22b2deBniSswmFltM5tiZivNbIWZ/S7SWUVEol5qGmT+E7qN8wbrjTkHvnrL71QJqUrDhhzz7ATqD72JLbPnkNOjBztXrvQ1k1/X6EcAC5xzzYEFgeclGQW8GZhT/zRgRYTyiYjEnlN7wXXvQc0m8HIvmHcX7C70O1XCseRkGtx4I8dkZVG8fTs5va4gf9Ik/OpB96vQZwLPB35/Hrj8wA3MrCbQDpgA4JwrcM79HLGEIiKxqP7xMHA+/HYg/Pspb0a9/DV+p0pI1c9sQ/qM6aS1acOGe0fy/bBh7N66NeI5/Cr0RzjnfgAI/GxYwjbNgDwgy8w+MbPxZhb+Cx0iIrGuSjW49DHo+Rz8+BU8cy6smO13qoSUUq8eR499hgbDhrF13tus7tadHZ8vj2iGsBV6M5tvZstLeGQGuYsU4AxgtHPudGA7pXfxY2bXmVm2mWXn5eWF4BuIiMS4k7vC4PehbjN4tQ+8cTsURf+UrfHGkpKof90gmr74Aq6oiJzevdk8c2bEjh+2Qu+cu8A517KEx0xgo5kdCRD4mVvCLtYB65xzHwWeT8Er/KUdb6xzLsM5l9GgQYNQfx0RkdhUNx2ufQva3gAfjYEJnWDTt36nSkhpZ5xB+rSp1OjYkdTjjo/Ycf3qup8F9A383hc46J82zrkNwFoz+03gpfOB/0UmnohIHEmpChc9CFe+DPk58Ex7WD7N71QJKaVOHY56chSHtTw5Ysf0q9A/BHQys6+BToHnmFljM5u7z3ZDgYlm9hnQCvhbxJOKiMSLEy+FIR9CwxNhSn+YfTMUlj1lq8Q+TZgjIpJodhfCgvvg30/CES29QXv1m/udSioh6ibMERERHyVXgc73Q+/XYMt6ryv/s9f8TiVhokIvIpKoTrjQ68o/8lSYNghm3uRNpytxRYVeRCSR1WoCfWfDubfCJy/BuPMg198pWyW0VOhFRBJdcgqcfzdcPQ2258HYDl7Rj8MxXIlIhV5ERDzHnQfXL4KjMmDmjTB9COza5ncqqSQVehER2atGI7hmpreu/WevwriOsCGyU7ZKaKnQi4jI/pKSocMI6DsLdm6G8edDdpa68stpWe4yxn8+nmW5y3zNkeLr0UVEJHqlt4Mhi2D6dTD7z5DzAXR5AqrV9DtZ1FuWu4xB8wZRsLuA1ORUxnUeR6uGrXzJojN6EREp3eENoM9UOO9u+GI6jG0PP3zqd6qol70xm4LdBRRTTGFxIdkb/ZvETYVeRETKlpQE7W6FfnOgcCeMvwD+O05d+WXIOCKD1ORUki2ZKklVyDiixEnrIkJT4IqISPC2b4IZ18PXb0GLTLjsSTistt+potKy3GVkb8wm44iMsHfblzUFrgq9iIiUT3Ex/OefsGAk1GwCPbOgSWu/UyU0zXUvIiKhk5QEZ/8R+r8BrhgmXAiLR4e0K3/pmnyeXvgNS9fkh2yfiUqj7kVEJGhL1+SzeNUm2jarR+umbWDw+97kOm+OgNUfQOY/Ia1upY/RZ/xiCoqKSU1JYuLAtrRuWidE3yDx6IxeRESCsqcAPzbvS/qMX+ydbafVhStfhosegq/nwTPtYO2SSh1n8apNFBQVU+ygsKiYxas2hegbJCYVehERCUqpBdgM2l4PA94CS4Ksi2DRk961/Apo26weqSlJJBtUSUmibbN6IfwWiUdd9yIiEpQ9BbiwqLjkAtyktdeVP2sovH035HwIXceUuyu/ddM6TBzYdp9LBOq2rwyNuhcRkaDtf42+lALsHCwZD2/9H1RvAD2ehWPaRjZogtGoexERCYnWTetwY8fjyz7LNoM2g2DA25CcClmXwAePV7grXypHhV5ERMKjcSuvK79FpnfP/cs9YfuPfqdKOCr0IiISPtVqel33Xf7h3X435hzIWeR3qoSiQi8iIuFlBhnXwqAFkFodnu8C7z0Kxbv9TpYQVOhFRCQyGp0C170LLXvAwr/CS91gW27YDqfZ9Ty6vU5ERCKnag3oNhbSz4W5w72u/G7joFn7kB5Gs+vtpTN6EZEEETVnuGZwxjUwaCFUqwUvZMLCB0Pala/Z9fbSGb2ISAKIyjPcI1p4XflzboX3HoI1i6D7eKjRqNK7PuTkPglEZ/QiIgkgas9wU6tD19GQ+S/4fqnXlf/tO5Xe7Z7Z9YZ1/k10/KPGRyr0IiIJIOrnjz+9j9eVn1YfXuwGC+6H3UWV2mVQk/skAE2BKyKSIIKavtZvBb/AG8Phk5fgmLOgxwSo2djvVFGvrClwVehFRCT6fPoqzL4ZqlSDrmOh+QV+J4pqmuteRERiy2lXeAP1Dm8EE7vD2/fA7kK/U8UkFXoREYlODU7wZtNr3Q8WPQHPXQqb1/mdKuao0IuISPSqchhcNgq6T4CNX3ij8r980+9UMUWFXkREot8pPbyV8GodBa9cAfPuUld+kFToRUQkNtQ7DgbMh98OhH8/BVkXw8/f+Z0q6qnQi4hI7KhSDS59DHo+B3lfwphzYeUcv1NFNRV6ERGJPSd3hcHvQZ2mMKk3vHkHFBX4nSoqqdCLiEhsqtsMBrwNbQbD4n/BsxdCfo7fqQ4p0osLqdCLiEjsSqkKlzwCvV6ETd/CmHaw4nW/U5Vqz+JCj837kj7jF0ek2KvQi4hI2ETs7LXF72HI+96AvVevgrm3QdGu8B6zAvxYXEjL1IqISFhEfGncOsfCtW/B/Hu8rvy1H0HPLK+LP0r4sXyuzuhFRCQsfFkaNyUVLnoQrnwZ8lfDM+3hixlBfzzcPRB+LJ+rM3oREQkLP85ef3XipTDkQ5jcHyb3hZyB0PkB7/a8UkSqB6J10zoRXT3QlzN6M6trZm+b2deBnyV+YzO72cy+MLPlZvaKmZXeQiIiElX8OHvdT+1joP8b8LubYMl4mNDJG7BXCl96ICLAr677EcAC51xzYEHg+X7MrAnwRyDDOdcSSAaujGhKERGplNZN63Bjx+MjX+T3SEmFCx+AP0yCzWu9rvzlU0vcdE8PRLIR+R6IMPKr0GcCzwd+fx64vJTtUoDDzCwFSAPWRyCbiIjEm99cDIM/gIYnwZRr4fU/Q+GO/TbxvQciTPy6Rn+Ec+4HAOfcD2bW8MANnHPfm9nfge+AHcA859y8COcUEZF4Ufto6D8X3rkfFo2CdUu8qXTrN/91k0hfP4+EsJ3Rm9n8wLX1Ax+ZQX6+Dt6ZfzrQGKhuZleVsf11ZpZtZtl5eXmh+RIiIhJfkqtAp/ug92TYst7ryv/sNb9ThVXYCr1z7gLnXMsSHjOBjWZ2JEDgZ24Ju7gAWO2cy3POFQLTgLPKON5Y51yGcy6jQYMG4fhKIiISL07o7I3Kb3QKTBsEs4Ye1JUfL/y6Rj8L6Bv4vS8ws4RtvgPamlmamRlwPrAiQvlERCTe1WoC/ebAOcPg4xdg3HmQ95XfqULOr0L/ENDJzL4GOgWeY2aNzWwugHPuI2AK8DHweSDrWH/iiohIXEpOgQvugT5TYdtGGNsePp3kd6qQMuec3xlCLiMjw2VnZ/sdQ0REYsmW9TB1IKxZBK2ugksehdQ0v1MFxcyWOucySnpPU+CKiIgA1GwM18yCdsNh2UQY1xFyV/qdqtJU6EVERPZIToHz7oKrp8H2H2FsB/hkot+pKkWFXkRE5EDHnQfXL4KjMmDmDTB9COza5neqClGhFxGRmBT2te5rNIJrZkL7Ed4AvXEdYeP/wnOsMFKhFxGRmLNnpbnH5n1Jn/GLw1fsk5Kh4x1ewd/xs1fsP34BYmgguwq9iIjEnIivNNesvTfBztFnepPrTLsOdm0N7zFDRIVeRERiji8rzdU4Aq6eDh3vhOVTvIF6G5aH/7iVpPvoRUQkJi1dk8/iVZto26xe5BeiWf2Bd8/9jny4+GFo3Q/MIpthH2XdR69CLyIiUhHb8rx58lcthJbdocsTUK2mL1E0YY6IiEioHd4ArpoG590NX0z3ps/94VO/Ux1EhV5ERKSikpKg3a3Qd7a3+t34TrBkfFSNylehFxERqaxjz/ZG5aefC3NugSn9YecWv1MBKvQiIiLlVuJkPdXrQ+/JcMG98L9Z7Hz6HF6d9Xr47vEPkgq9iIhIOZQ5WU9SEpxzMysvfpX8LVu5fGk/5k64l6U5P/mWV4VeRESkHIKZrGfB9nS6FPyND4tP4e6k56g1eyDs3OxDWhV6ERGRcglmsp62zeqxPaU2g4tu4ZHiPhy36V0Ycy58/3HE8+o+ehERkXIKZrKe/bZJ+hom94dtG6Hz/XDmkJBOsKMJc0RERPz2y08w4wb46g3v3vt2t4Zs12UV+pSQHUVERERKl1YX/vCKd599i8sjdlgVehERkUgxgzaDInpIDcYTERGJYyr0IiIicUyFXkREJI6p0IuIiMQxFXoREZE4pkIvIiISx1ToRURE4pgKvYiISBxToRcREYljKvQiIiJxLC4XtTGzPGDNAS/XAspaDLis90t7L9jX6wM/lnHscDrU9w73voL9TKTap6TX4qF94qFtSnrdz7YBtc+hXtPfTuW2C+XfTnPnXK0S9+ScS4gHMLai75f2XrCvA9nR+r3Dva9gPxOp9inltZhvn3hom5Je97Nt1D5Bvaa/nShom0PtK5G67l+vxPulvVfe1/0QyiwV2Vewn4lU+0RT20Do8sRD2wRzrEhT+wR/nEhT2wS5r7jsuo82ZpbtSlknWPyn9oleapvopvaJDYl0Ru+nsX4HkDKpfaKX2ia6qX1igM7oRURE4pjO6EVEROKYCr2IiEgcU6EXERGJYyr0UcDMqpvZUjPr4ncW2Z+ZnWRmY8xsipld73ce2cvMLjezcWY208w6+51H9mdmzcxsgplN8TtLolOhrwQze9bMcs1s+QGvX2RmX5rZN2Y2Iohd3Q68Fp6UiSsU7eOcW+GcGwL0AnQbUYiEqG1mOOcGAf2AK8IYN+GEqH1WOecGhDepBEOj7ivBzNoB24AXnHMtA68lA18BnYB1wBLgD0Ay8OABu7gWOBVvGslqwI/OudmRSR//QtE+zrlcM/s9MAL4p3Pu5Ujlj2ehapvA5x4DJjrnPo5Q/LgX4vaZ4pzrEanscrAUvwPEMufc+2Z27AEvtwG+cc6tAjCzSUCmc+5B4KCueTPrCFQHWgA7zGyuc644rMETRCjaJ7CfWcAsM5sDqNCHQIj+dgx4CHhDRT60QvW3I9FBhT70mgBr93m+DjiztI2dc3cCmFk/vDN6FfnwKlf7mFkHoBtQFZgb1mRSrrYBhgIXALXM7Hjn3JhwhpNy/+3UAx4ATjezOwL/IBAfqNCHnpXw2iGvjzjnngt9FClBudrHOfcu8G64wsh+yts2TwJPhi+OHKC87bMJGBK+OBIsDcYLvXXA0fs8PwpY71MWOZjaJ3qpbaKb2idGqdCH3hKguZmlm1kqcCUwy+dMspfaJ3qpbaKb2idGqdBXgpm9AvwH+I2ZrTOzAc65IuAm4C1gBfCac+4LP3MmKrVP9FLbRDe1T3zR7XUiIiJxTGf0IiIicUyFXkREJI6p0IuIiMQxFXoREZE4pkIvIiISx1ToRURE4pgKvUgCM7PaZnbDPs8bh2v98MD68X8p5b1tgZ8NzOzNcBxfJFGp0IskttrAr4XeObc+jEuK3gb8q6wNnHN5wA9mdnaYMogkHBV6kcT2EHCcmS0zs0fN7FgzWw7eiopmNsPMXjez1WZ2k5kNM7NPzGyxmdUNbHecmb1pZkvN7AMzO/HAg5jZCcAu59yPgefpZvYfM1tiZvcfsPkMoE94v7ZI4lChF0lsI4BvnXOtnHPDS3i/JdAbby3yB4BfnHOn402Pek1gm7HAUOdca+BWSj5rPxvYd834UcBo59xvgQ0HbJsNnFvB7yMiB9AytSJSloXOua3AVjPbDLweeP1z4FQzOxw4C5hs9usqplVL2M+RQN4+z88Gugd+fxF4eJ/3coHGoYkvIir0IlKWXfv8XrzP82K8/38kAT8751odYj87gFoHvFbaQhvVAtuLSAio614ksW0FalT0w865LcBqM+sJYJ7TSth0BXD8Ps8X4S1zCgdfjz8BWF7RTCKyPxV6kQTmnNsELDKz5Wb2aAV30wcYYGafAl8AmSVs8z5wuu3t3/8TcKOZLeHgM/2OwJwKZhGRA2iZWhGJCDMbBbzunJt/iO3eBzKdc/mRSSYS33RGLyKR8jcgrawNzKwB8LiKvEjo6IxeREQkjumMXkREJI6p0IuIiMQxFXoREZE4pkIvIiISx1ToRURE4pgKvYiISBz7fxZjRDvEGsWzAAAAAElFTkSuQmCC\n", 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", 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" ] @@ -171,13 +171,13 @@ ], "source": [ "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, he15, '.', label='obs at 30 m with sig=0.05')\n", - "plt.semilogx(t, h1[0], label='ttim at 30 m')\n", - "plt.semilogx(t, he25, '.', label='obs at 90 m with sig=0.05')\n", - "plt.semilogx(t, h2[0], label='ttim at 90 m')\n", + "plt.semilogx(t, he15, \".\", label=\"obs at 30 m with sig=0.05\")\n", + "plt.semilogx(t, h1[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t, he25, \".\", label=\"obs at 90 m with sig=0.05\")\n", + "plt.semilogx(t, h2[0], label=\"ttim at 90 m\")\n", "plt.legend()\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\");" ] }, { @@ -293,10 +293,10 @@ } ], "source": [ - "ca23 = Calibrate(ml)\n", - "ca23.set_parameter(name='kaq0', initial=10)\n", - "ca23.set_parameter(name='Saq0', initial=1e-3)\n", - "ca23.series(name='obs1', x=d1, y=0, t=t, h=he12, layer=0)\n", + "ca23 = ttim.Calibrate(ml)\n", + "ca23.set_parameter(name=\"kaq0\", initial=10)\n", + "ca23.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "ca23.series(name=\"obs1\", x=d1, y=0, t=t, h=he12, layer=0)\n", "ca23.fit(report=True)\n", "display(ca23.parameters)" ] @@ -315,7 +315,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -327,17 +327,17 @@ } ], "source": [ - "print('rmse:', ca23.rmse())\n", + "print(\"rmse:\", ca23.rmse())\n", "h123 = ml.head(d1, 0, t)\n", - "h223 = ml.head(d2, 0 ,t)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t, he12, '.', label='obs at 30 m with sig=0.02')\n", - "plt.semilogx(t, h123[0], label='ttim at 30 m')\n", - "plt.semilogx(t, he22, '.', label='obs at 90 m with sig=0.02')\n", - "plt.semilogx(t, h223[0], label='ttim at 90 m')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)')\n", - "plt.title('ttim analysis with synthetic data, sig=0.02 errors at 30 m.')\n", + "h223 = ml.head(d2, 0, t)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he12, \".\", label=\"obs at 30 m with sig=0.02\")\n", + "plt.semilogx(t, h123[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t, he22, \".\", label=\"obs at 90 m with sig=0.02\")\n", + "plt.semilogx(t, h223[0], label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"ttim analysis with synthetic data, sig=0.02 errors at 30 m.\")\n", "plt.legend();" ] }, @@ -438,10 +438,10 @@ } ], "source": [ - "ca29 = Calibrate(ml)\n", - "ca29.set_parameter(name='kaq0', initial=10)\n", - "ca29.set_parameter(name='Saq0', initial=1e-3)\n", - "ca29.series(name='obs2', x=d2, y=0, t=t, h=he22, layer=0)\n", + "ca29 = ttim.Calibrate(ml)\n", + "ca29.set_parameter(name=\"kaq0\", initial=10)\n", + "ca29.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "ca29.series(name=\"obs2\", x=d2, y=0, t=t, h=he22, layer=0)\n", "ca29.fit(report=True)\n", "display(ca29.parameters)" ] @@ -460,7 +460,7 @@ }, { "data": { - "image/png": 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Pz6/YskkpmTRpEi4uLnh4eHD06FGj8S5duoSvry+urq4MHTqU5ORkANauXYuHhwceHh74+fnx119/FVsmUIo+X8JuhrH8+HLCboaZWxRFGcBU9UHVK0VZIS9F7+bmRkhICGFhYezatYsxY8aQmpoKwLhx41i6dCnnzp3j3Llz7Nq1q1TkHTt2LCNGjAByKvri8NtvvxU7jR9//DGjPJYuXcq4ceOMxvu///s/XnvtNc6dO0etWrVYsWIFAM2aNWP//v2Eh4czbdo0Ro8eXWyZQCn6PAm7Gcao3aP47OhnjNo9Sr2UKzmmqg+qXlVMQiNj+XzveUIjY02S3vz583Fzc8PNzY2FCxdmnE9NTSUwMBAPDw+GDBlCUlISAJMnT6ZNmzZ4eHjw5ptv5kjvyJEj+Pn50a5dO/z8/Dh79izJycm8++67bNiwAS8vLzZs2JDlmipVqmBpqU3Oun//fkb/8PXr14mPj6dz584IIRgxYgRbt27Nkef06dMJDAykZ8+eODs7s3nzZt566y3c3d3p3bs3KSkpWeLfvHkTb29vAP766y+EEFy+fBmAFi1akJSUxPTp05k3bx4bN24kJCSEYcOG4eXlxb179wD47LPPaN++Pe7u7pw5cyaHTCdPnqRjx454eXnh4eHBuXPnAKhWrRoAer2e8ePH07ZtWwICAujbty8bN27M73EBsG3bNkaMGIEQgk6dOnHnzh2uX7+eJY6Ukj179jBkyBAAAgMDM8rOz8+PWrVqAdCpUyeioqIKlG9+KEWfByE3QkhOS0aPnhR9CiE31Pr5lRlT1QdVryoeoZGxDFsezMe7zzJseXCxlX1oaChff/01f/zxB8HBwSxbtoxjx44BcPbsWUaPHk14eDjVq1dn8eLFxMTEsGXLFk6ePEl4eDhTp07NkWarVq04cOAAx44dY8aMGUyZMgVra2tmzJjB0KFDCQsLY+jQoTmu++OPP2jbti3u7u4sWbIES0tLrl69ipOTU0YcJycnrl69avReLly4wI4dO9i2bRvDhw/H39+f48ePY2dnx44dO7LErVu3Lvfv3yc+Pp6DBw/i4+PDwYMHiYyMpG7dulSpUiUj7pAhQ/Dx8WHt2rWEhYVhZ2cHQO3atTl69Cjjxo1j3rx5OeRZsmQJr7zyCmFhYYSEhGS5D4DNmzcTERHB8ePHWb58Ob///ntG2GuvvZbRlZF5mzNnDgBXr16lcePGeZZLdHQ0NWvWzPiAyq3sVqxYQZ8+fYyWaWFR8+jzwKeeD9YW1qToU7DSWeFTz8fcIinMiKnqg6pXFY/gi9Ekp+rRS0hJ1RN8MRrvprWKnN6hQ4cYNGgQVatWBWDw4MEcPHiQ/v3707hxY7p06QLA8OHD+fTTT3n11VextbUlKCiIfv36ERAQkCPNuLg4AgMDOXfuHEKIHNZ0bvj6+nLy5ElOnz5NYGAgffr0Mdofn9to8D59+mBlZYW7uztpaWn07t0bAHd3dyIiInLE9/Pz4/Dhwxw4cIApU6awa9cupJR069atQPIOHjwYAG9vbzZv3pwjvHPnzsyaNYuoqCgGDx6Mq6trlvBDhw7x1FNPodPpqF+/Pv7+/hlhCxYsyDPvgpRLQeLs3buXFStWcOjQoTzzKyhK0eeBV10vlvVcRsiNEHzq+eBV18vcIinMiKnqg6pXFY9OzR2xttSRkqrHylJHp+aOxUovr4Ft2ZWCEAJLS0uOHDnCr7/+yrfffsuiRYvYs2dPlnjTpk3D39+fLVu2EBERwSOPPFIomVq3bk3VqlU5ceIETk5OWZqVo6KiaNiwodHrbGxsANDpdFhZWWXIr9PpMvr7M9OtW7cMK37AgAHMnTsXIYTRj5e88rOwsDCa/nPPPYevry87duygV69eLF++nEcffTQjPK+yf+2119i7d2+O88888wyTJ0/GycmJK1euZJw3Vi61a9fmzp07pKamYmlpmSNOeHg4QUFB/Pjjjzg6Fq8epaOa7vPBq64XQe5B6mWsAExXH1S9qlh4N63F2qBOvN6zJWuDOhXLmgd4+OGH2bp1K0lJSdy9e5ctW7ZkWLSXL1/OaE5ev349Xbt2JTExkbi4OPr27cvChQsJC8s57iMuLo5GjRoB2iC2dOzt7UlISDAqx6VLlzKUZWRkJGfPnsXZ2ZkGDRpgb29PcHAwUkpWr17NgAEDinXPme/9m2++wdXVFZ1Oh4ODAzt37sxoxchMXrLnxsWLF2nevDmTJk2if//+hIeHZwnv2rUrmzZtQq/Xc+PGDfbt25cRtmDBAsLCwnJskydPBqB///6sXr0aKSXBwcHUqFGDBg0aZElfCIG/v39Gv/+qVasyyu7y5csMHjyYNWvW8NBDDxXqvvJCKXqFQqEwAd5NazHB36XYSh6gffv2jBw5ko4dO+Lr60tQUBDt2rUDNMt61apVeHh4EBMTw7hx40hISCAgIAAPDw+6d+9utIn5rbfe4u2336ZLly6kpaVlnPf39+fUqVNGB+MdOnQIT09PvLy8GDRoEIsXL6Z27doAfPHFFwQFBeHi4kKLFi1M1p/s7OwMaAofNMVbs2bNjEFqmRk5ciRjx47NMhgvPzZs2ICbmxteXl6cOXMmYwR/Ok8++SROTk64ubkxZswYfH19qVGjRoHS7tu3L82bN8fFxYVRo0axePHiLGHpMwTmzp3L/PnzcXFxITo6mpdeegmAGTNmEB0dzfjx4/Hy8sLHxzTdeqK05j6WJj4+PjIkRA1wUigUReP06dO0bt3a3GIozERiYiLVqlUjOjqajh07cvjwYerXr29usTIwVj+FEKFSSqNfBqqPXqFQKBSKTAQEBHDnzh2Sk5OZNm1amVLyRUEpekWxCI2MJfhiNJ2aO5qkyVKhUCjMTeZ++YqAUvSKIpM+dzg5VY+1pc4kg5AUCoVCYVrUYDxFkTE2d7g0UcvIKhQKRf4oi15RZEw9d7gwpC8jm5yWjLWFNct6LlNT1RQKhcIIZrXohRC9hRBnhRDnhRCTjYQLIcSnhvBwIUR7c8ipMI6p5w4XBrWMrEKhUBQMsyl6IYQF8DnQB2gDPCuEaJMtWh/A1bCNBr4oVSEV+WLKucOFIX0ZWQthoZaRVVQ47ty5k2UOdkREBOvWrcs4DgkJYdKkSSbPd+vWrZw6dcpo2JIlS3B3d8fLy4uuXbtmibdq1SpcXV1xdXVl1apVJpdLUTzMNo9eCNEZmC6l7GU4fhtASjk7U5wvgX1SyvWG47PAI1LK60aSzMCU8+gvjOjDvdi72NjYYGdrAzor0FmChZFfYWGSPMs9mZfozLKf+bT496ROBzqBELp/93U60FnkPJ9p/+a921y/d4MGtRrT0KEpwtoGYW2NsLFBWFuhs7HR9q2sETbWCGtr7VxGHGss7O0RlqoHS5EVc8+jj4iIICAggBMnTgDaKPB58+bxww8/lGi+I0eOJCAgIMOzWmbi4+OpXr06ANu3b2fx4sXs2rWLmJgYfHx8CAkJQQiBt7c3oaGhRhe4UZiG8jSPvhFwJdNxFOBbgDiNgByKXggxGs3qp0mTJiYRMDQyliqRkdjeS0EnJMn5XiFA6DSFr9P9u5+unDL2Db+5xTXuG6JckOXDUWYJML6v1yORoJfavtRr+2lpWlp6veF8tv20NKz0ehrr9ZD2BzeLIbPO3h6LGjWwqFlT+61RA4uaNdCl79eoiUXNTL+1amFRs2auTjwUiuIyefJkLly4gJeXFz169ODgwYOcPn0aLy8vAgMDadeuXYbinz59OpcuXeL69ev8/fffzJ8/n+DgYH788UcaNWrE999/j5WVVZb0ly1bxtKlS0lOTsbFxYU1a9YQFhbG9u3b2b9/PzNnzmTTpk20aNEi45p0JQ9w9+7djPr/008/0aNHDxwcHADo0aMHu3bt4tlnn82S5yOPPEK7du0IDQ3l1q1brF69mtmzZ3P8+HGGDh3KzJkzS6o4Kz3mVPTG3pLZmxcKEkc7KeVSYCloFn3xRNMIvhjNx34foZdgLdJ4y78RQR0c4UEC3I+HB/GG/bhM+/FG9uP/3Zf6/DO2tgfb6mBjDzbVjexXz3m+hhM4NM9qQVcSZGoqMjkZmZyM/kEyMvmBdvzggdFz+uRk5INk5IP7pMXFkxYXZ9jukBYXR0pUlHYcH699XBhB2Nlh1aCBtjVsiFVD7deyQQOsGjbCql5dRLaXq6Kc8uNk+Oe4adOs7w595uQaPGfOHE6cOJGxZn12iz77PO8LFy6wd+9eTp06RefOndm0aRMffvghgwYNYseOHQwcODBL/MGDBzNq1CgApk6dyooVK3j55Zfp379/rhY9wOeff878+fNJTk7OcJpTENes6VhbW3PgwAE++eQTBgwYQGhoKA4ODrRo0YLXXnvNZE5cFFkxp6KPAhpnOnYCrhUhTomReVS5ztKKdi2bQ3Gao6SElCQjHwkJmT4GErJ+GDyIh6QYiI3893xqLms62zmAUwdo3AGcOkKj9tqHQAVHWFpqze9VqmDKzhOp16NPTNSU/p040u5oHwJp0bdJuf4PKdeukXL9OvfPnCEtOtvUQgsLrJs0wcalBdYtWmDTwkXbb9YMna2tCaVUKArvCvbEiRNMnTqVO3fukJiYSK9evQqUz4QJE5gwYQLr1q1j5syZrFq1qlAua/v3758hV9u2bTMcvjRv3pwrV64oRV9CmFPR/wm4CiGaAVeBZ4DnssXZDkwUQnyL1qwfl1//vClJH1VuspXfhADrqtpGg3yj50paSs6PhOgLEHUErvwJ534y5KeDum0Myr+jpvwdW1RKq78oCJ0Oi+rVsaheHRo3zjOu/v59Uq5fJ/X6dVKuXSM5KorkCxd5cOECCXv2QroTEZ0Oq8ZO2LRwwbZ1a+w83LF1d8fS0OyZTtjNsDzd2OYXrjAheVjeZYXCuoIdOXIkW7duxdPTk5UrVxZ6JbhnnnmGcePGAZoFn/n6qKioXF3gZpYzfT8vORWmwWyKXkqZKoSYCPwEWABfSSlPCiHGGsKXADuBvsB5IAl4obTl9G5aq+yt9mZhBVUctC0d567gHajt34uFqFCI+lNT/ic2QejXWlgWq78DNPKuFFZ/SaOztcWmWTNsmjXLESaTk0mOjOTBhQs8OH+BBxfO8+DcORL37cvoGrBq3Bg7d3dsPdyJamzHhMsfkahLMbpGgFpDoOKT3f1qUdyx5kVCQgINGjQgJSWFtWvXZrivzSufc+fO4erqCsCOHTsy9nv16sWUKVOIjY0FYPfu3cyePdtoGgrzYNbhxlLKnWjKPPO5JZn2JTChtOUq99jVAtfHtQ00ZXL7LFw5oqz+TJTWOv3C2hobV1dsDC/GdNIS73L/1EnuHz/OvfDjJIUdI37nTqyBLwVcqg8nndM4z0Y8Bj+ErkoVwPgaAkrRVywcHR3p0qULbm5u9OnThw8++ABLS0s8PT0ZOXJkhsvaovL+++/j6+tL06ZNcXd3z1DuzzzzDKNGjeLTTz9l48aNWQbjLVq0iF9++QUrKytq1aqVMY3OwcGBadOm0aFDBwDefffdjIF5irKBclNbWbl3B66GaEo/6ojWAvAgTguzq6UpfqeOmuVfAa3+srpOf+qtW5w6tJ2dP3xCy8hUXK5JLPWAlRVVPD2p0qkT11o6MubaPO6LVKx0VsqiLwHMPb1OociL8jS9TmEiimSZ2tUEl8e1Df61+qP+NFj+f8K53VpYhtXvY1D+HcHRpVxb/cbW6TeVoi9O/7llnTp4DHoJfRdvQm6E0MzejWZXUkn6I5i7vwdz+/PPsZaSr2xtiHF3onrPXrS1ydldoFAoFOkoRV/OMZllqtNB3dba1n6Edi671X9iC4Su1MIyW/1N/aBJJ219gHJCSa3Tb6r+c6+6Xv9e5wzVunUFIC0ujqQ//+Tub79h9eseUmct5u+5S6nq64t9z57YP/YolrVrm+ReFApFxUAp+nJO8MVoUi0vYVn9ImlJzQm+6Gq6JmijVv/fhn7+bFa/fUNwHwIeQ6G+m2nyL0G8m9biv09VY/fF3+jZ3M9kZVbS/ecWNWpg//jj2D/+OPWmTuX+iRMk7N5N/O6f+ee99/hn+nSqeHtj37MH9j16YNWgaLM71Kh+haLioBR9Oae243VsmywHkWzM4gQAACAASURBVArSktqO7oBLyWSm00HdVtqW2eq/8CuEfwfBi+G3T7Vmfo+nwf0pbSGfXDCnMgm7Gca88NdJTksmPHwjLeubpp87fQ3+FH1Kia/BL3Q67Dw8sPPwoM4bb/Dg779J2P0zCT//zI0PZnPjg9nYurtj36MH1Xv2wNrZuUDpqlH9CkXFQin6ck6COItOl4ZEohNpJIizQLfSE8CuJrg9qW13o+HkZgj/H/wyHX75rzbtz+NpaN1fi2vA3MqkpCxvr7peLOu5rNQ/YIQQ2LZsiW3LltR5eSIPLl0i4ZdfSNj9M7fmz+fW/PnYuLpSPSCAGoMGYlW3bq5plbdR/ar1QaHIG6Xoyzk+9XywKSULMl+qOkLHUdoWcxGOb4TwDbD9ZdjxJjzUS2vad+1hdmVSkpZ3lv51M2HTrBk2o0ZRe9QoUq5dI+GXX4n/6SduLVjArU8/pVr37tQcMoRqD3fL4dSnNFsliou5PxgVivKAWf3RK4pPugU5sd3EsvWSc2gO3d+CiSEwag/4vACRv8GGYTDvIXzOHcZaZ1EsN7OhkbF8vvc8oZGxhb62zJZbCWDVsCEOI57Hee03tNj1I44vvsi98HCixo/nvP+j3FywkOTLlzPil6eyMfbBWJGJiIjAzc20Y2DCwsLYuXOn0bDk5GReeOEF3N3d8fT0zLICXmhoKO7u7ri4uDBp0iSjS+GWBEuWLGH16tUArFy5kmvX/l0V3dnZmdu3bxcpXT8/v2LLJqVk0qRJuLi44OHhwdGjR43Gu3TpEr6+vri6ujJ06FCSkzWXaWvXrsXDwwMPDw/8/Pz466+/ii1ThmAVbfP29paKMkhqipR//yzlxiApZ9aXxz6oLZd94SaP/fKOlPfuFCqpkIgY2XLqTtls8g+y5dSdMiQipoSErpjok5Nl/C+/yMtjxspTrdvIUy1byYjAkfLO9z/ItPv3zS1egTl245j0WeMjPVd5Sp81PvLYjWMmSffUqVMmScfUXLp0SbZt29akaX799ddywoQJRsMWLVokR44cKaWU8saNG7J9+/YyLS1NSillhw4d5G+//Sb1er3s3bu33Llzp0nlKgjdu3eXf/75Z8Zx06ZN5a1bt0pdjnR27Nghe/fuLfV6vfz9999lx44djcZ76qmn5Pr166WUUo4ZM0YuXrxYSinl4cOHZUyM9i7buXNnrtcbq59AiMxFJyqLXlF6WFhqq/U9uQzePIdX30UE2TTG6+BnML8t7HobYiMKlJSxefCKgiOsrLB/7DEaL/kCl717qPPqK6RERXHtzTc593B3/pn1AQ8uXipQWmE3w1h+fDlhN8NKWOqclKXWB1OXw/z583Fzc8PNzY2FCxdmnE9NTSUwMBAPDw+GDBlCUlISoLm2bdOmDR4eHrz55ps50jty5Ah+fn60a9cOPz8/zp49S3JyMu+++y4bNmzAy8uLDRs2ZLnm1KlTPPbYYwDUrVuXmjVrEhISwvXr14mPj6dz584IIRgxYgRbt27Nkef06dMJDAykZ8+eODs7s3nzZt566y3c3d3p3bs3KSkpWeLfvHkTb29vAP766y+EEFw2tDa1aNGCpKQkpk+fzrx589i4cSMhISEMGzYMLy8v7t3TnH199tlntG/fHnd3d86cOZNDppMnT9KxY0e8vLzw8PDg3LlzAFSrVg0AvV7P+PHjadu2LQEBAfTt25eNGzcW4InBtm3bGDFiBEIIOnXqxJ07d7h+Pat7Fikle/bsyfAQGBgYmFF2fn5+1DI4TuvUqRNRUVEFyjc/lKJXmAebauA5FF7YCaP3Q6u+cGQpfNoONgyHy8FZ/dZnI30evIXApPPgKyNW9epRe+xYWuz+iSZff0W1Ll248+23XOzblytjxnL3999zbZZN7yP/7OhnjNo9ymzKPsg9yOxK3pTlEBoaytdff80ff/xBcHAwy5Yt49ixYwCcPXuW0aNHEx4eTvXq1Vm8eDExMTFs2bKFkydPEh4eztSpU3Ok2apVKw4cOMCxY8eYMWMGU6ZMwdramhkzZjB06FDCwsIYOnRolms8PT3Ztm0bqampXLp0idDQUK5cucLVq1dxcvp3Rk1ermkvXLjAjh072LZtG8OHD8ff35/jx49jZ2fHjh07ssStW7cu9+/fJz4+noMHD+Lj48PBgweJjIykbt26VDEsAw0wZMgQfHx8WLt2LWFhYdjZ2QFQu3Ztjh49yrhx45g3b14OeZYsWcIrr7xCWFgYISEhWe4DYPPmzURERHD8+HGWL1/O77//nhH22muv4eXllWObM0dzfFQQl73R0dHUrFkTS8PYmNzKbsWKFfTp08domRYWNRhPYX4aesHgpfD4dDiyDEK+gtPfQ8P20HkCtBmgOfLJhMk9CyoQOh1VO3emaufOpEZHE7v+W2LXrePyCy9i07IlDoGBVA/oh87aOuMacw+qLCuYuhwOHTrEoEGDqFq1KqD5jz948CD9+/encePGdOnSBYDhw4fz6aef8uqrr2Jra0tQUBD9+vUjICAgR5pxcXEEBgZy7tw5hBA5rGljvPjii5w+fRofHx+aNm2Kn58flpaWhXJNW1gXun5+fhw+fJgDBw4wZcoUdu3ahZSSbt0KNpto8ODBAHh7e7N58+Yc4Z07d2bWrFlERUUxePDgDOc86Rw6dIinnnoKnU5H/fr18ff3zwhbsGBBnnkXpFwKEmfv3r2sWLGCQ4cO5ZlfQVEWvaLsUL0hPP4evH4K+n2sueHd9BJ84gmHP9Hm7GfCu2ktJvi7KCVfAlg6OlJn4gRc9u6hwayZoE/j+pQpnH/0MW4tXkxqTAzw7wj94gyqrAiYuhxya0GBnEpBCIGlpSVHjhzhySefZOvWrRnKNDPTpk3D39+fEydO8P3333P//v185bC0tGTBggWEhYWxbds27ty5g6urK05OTlmalaOiomjYsKHRNArrQrdbt24ZVvyAAQP466+/OHToEA8//HC+8mbOz8LCwmj6zz33HNu3b8fOzo5evXqxZ8+eLOF5lX1+Fr2TkxNXrlzJiG+sXGrXrs2dO3cyZMseJzw8nKCgILZt24ajo2laKpWiV5Q9rKtChyBtxP6zGzRvej+/C/PbwM7/QPQFc0tYLilKH7LOxoaaTz5Js+3babxiObatW3P708847/8o16e9S+vEGmWmj9ycmHqswMMPP8zWrVtJSkri7t27bNmyJcOivXz5ckZz8vr16+natSuJiYnExcXRt29fFi5cSFhYzmccFxeX4Y525cqVGefzck2bnj/Azz//jKWlJW3atKFBgwbY29sTHByMlJLVq1czYMCAYt1z5nv/5ptvcHV1RafT4eDgwM6dOzNaMTJTFPe9Fy9epHnz5kyaNIn+/fsTHh6eJbxr165s2rQJvV7PjRs3ssw0SP/oyb5NnjwZgP79+7N69WqklAQHB1OjRg0aZFudUgiBv79/Rr//qlWrMsru8uXLDB48mDVr1vDQQw8V6r7yQil6RdlFp4OWvSHwexhzUGvCD/kaPvOG9c9BxOE8+/EV/1LcPmQhBNW6dKHJsqU0/+F7agwYQNz27Vzs14/aM79mmK5zpVXy6ZhyrED79u0ZOXIkHTt2xNfXl6CgoAzXtK1bt2bVqlV4eHgQExPDuHHjSEhIICAgAA8PD7p37260ifmtt97i7bffpkuXLqSlpWWc9/f359SpU0YH4928eZP27dvTunVr5s6dy5o1azLCvvjiC4KCgnBxcaFFixYm6092NqzgmG7Bd+3alZo1a2YMUsvMyJEjGTt2bJbBePmxYcMG3Nzc8PLy4syZM4wYMSJL+JNPPomTkxNubm6MGTMGX19fatSoUaC0+/btS/PmzXFxcWHUqFEsXrw4S1j6VMC5c+cyf/58XFxciI6O5qWXXgJgxowZREdHM378eLy8vPDxMU0LmXJTqzAbRfK6l/AP/Lkc/lwB92KggRc8Mhke6l2uvemVNMuPL+ezo5+hR4+FsGBiu4kEuQcVK83U6Ghi1qwhdu069AkJVOvendrjx2Hn6Wkiqc2HclNbuUlMTKRatWpER0fTsWNHDh8+TP369c0tVgbKTa2iXFBkr3v29eHRqdD1dW3Vvd8+hfXPQCNv8H8HWjyqFL4RSmK1O0tHR+q++iqOL71E7Nq1xHy9koihz1C1Sxdqjx9HFcM0KYWivBEQEMCdO3dITk5m2rRpZUrJFwVl0SvMwud7z/Px7rPoJVgIeL1nSyb4F8EZT1oq/LUe9s+FuCvQxE/7EHDO2Z9X2SnpNeH1d+8S++23RH/1NWnR0VTp2JHaEydQtWNHk+dV0iiLXlGWKaxFr/roFWbBZPPgLSyh/fPwcij0naetsb+yL6weCFHqYy8zJT3fXFe1Ko4vvYTLLz9T7+3JJF+6xOURgVwOGsW9kydLJE+FQpE/yqJXmI0i9dHnR8o9rf/+0AJIuq313ftPgQam6TcuEZkrKPr794ldu47opUtJi4vDvndv6kyahE3zZuYWLV+URa8oyxTWoleKXlEsyqyL0AeJcORLbf79/ThtxP4jU6BuqyInWeRxBZWctIQEYr7+muiVq5APHlBj0EDqTJiAVbZpR2UJpegVZRnVdK8oNcrC8qe5YlMNur0Br4RD9/+D83tgcSfYNKrI8/DV+vpFw8LenjqTJuHy825qDXuO+G3budCrNzfmzCU1tvCeBxUKReFQil5RZMqFi1C7mlrT/avh0OUVbWndRR1g20S4czn/6zOh1tcvHpaOjtSfMoUWu36kekAAMatXc+HxHtxa9DlpiXfNLV6Z4s6dO1nmYEdERLBu3bqM45CQECZNmmTyfLdu3cqpU6eMhkVGRvLYY4/h4eHBI488kmVlvFWrVuHq6oqrqyurVq0yuVyKYpKbW7vyvCk3taVDSbkILVHi/5Fy5/9JOaO2lP91lHLXFCmTYgt8eUhEjFy055xyi2sC7p8/L69MfFmeatlKnu3UWd7++usy4yLX3G5qs7uj3bt3r+zXr1+J5xsYGCi/++47o2FDhgyRK1eulFJK+euvv8rhw4dLKaWMjo6WzZo1k9HR0TImJkY2a9Ysw9WqomQorJtasyvlktiUoi89jt04JpeFLysfSj4zd6Kk3DpByvdqSDm3mZRHlkmZmmJuqSolSeHhMvKFF+Splq3kOf9HZdzOnVKv15tVJnMr+qFDh0pbW1vp6ekp33zzTenr6yurV68uPT095fz587Mo/vfee0+OGDFC9ujRQzZt2lRu2rRJ/uc//5Fubm6yV69eMjk5OUf6S5culT4+PtLDw0MOHjxY3r17Vx4+fFjWqlVLOjs7S09PT3n+/Pks17Rp00ZeuXJFSimlXq+X9vb2Ukop161bJ0ePHp0Rb/To0XLdunU58uzevbt89dVXZbdu3WSrVq3kkSNH5KBBg6SLi4t85513TFZ2lYHCKnq1YI6iWHjV9Spbg/AKSo1GMGARdBwNP02BHW/AkeXQaya4PG5u6SoVdu7uNPnqK+7+/js35n7I1ddex27tWuq9/TZ2bduaWzz++eADHpzO6de8ONi0bkX9KVNyDZ8zZw4nTpzIWLN+3759zJs3jx9++CHjODMXLlxg7969nDp1is6dO7Np0yY+/PBDBg0axI4dOxg4cGCW+IMHD2bUqFEATJ06lRUrVvDyyy/Tv39/AgICMnylZ8bT05NNmzbxyiuvsGXLFhISEoiOji6Qa9Z0rK2tOXDgAJ988gkDBgwgNDQUBwcHWrRowWuvvWYyJy6KrKg+ekXlpoGHtpb+0LWQeh++eRLWPgW3/ja3ZOWewjrRqdq5M802baT+f/9L8sVLRAx5imtTp5J6+3YJS1r+Kawr2BMnTtCtWzfc3d1Zu3YtJwuwzsG8efPYv38/7dq1Y//+/TRq1KjQLmv79++fIVfbtm1p0KABNjY2NG/ePIvXN4VpURa9QiEEtA4A1x7wx5dw4CNthH6HIG0d/SoO5pawTJLX1Mr0GRnJaclYW1gX2KObsLCg1tCnqd63D7cXf0HMmjUk/LiL2uPGUmvECHTW1iV1O7mSl+VdViisK9iRI0eydetWPD09WblyZY4WAmM0bNgww797YmIimzZtokaNGjg5OWW5PioqikceeSRfOdP385JTYRqURa9QpGNpA10mwaRj4D0S/lwGn7aD4C8gLcXc0pUp8ptaWdwZGRb29tT7v7do/v12qnTowM15H3Mx4AkSfv01T3/hFYXs7leL4o41LxISEmjQoAEpKSmsXbu2QPncvn0bvV4PwOzZs3nxxRcB6NWrF7t37yY2NpbY2Fh2795Nr169TCarovgoRa9QZKdqbQiYD2MPQ8N2sGsyLO4MZ3eVmlvc0MhYPt97ntDIsjnPPD9Fnu5Ex0JYFMuJjk2zZjRe8gWNly9HWFsRNWEil198kftnK3bXiqOjI126dMHNzY3//Oc/eHh4YGlpiaenp1EXtIXl/fffx9fXlx49etCq1b+LSD3zzDN89NFHtGvXjgsXsq43sW/fPlq2bMlDDz3EjRs3eOeddwBwcHBg2rRpdOjQgQ4dOvDuu+/i4KBawcoSamU8hSIvpIRzu+GndyD6HDT3h14fQL02JZZleViBL92iT/eGZ6xp3tSrJsqUFGK/3cCtRYvQJyRQc+jT1Jk0CUsjfsqLi1oZT1GWUW5qFQpTIgQ81Etzf/vnCtg3G5Z01Ubr+78NtjVMnqWxFfjKmqL3quvFsp7L8lTkhZ2Rkd+HgbCywuH54VQP6MftRZ8T++23xO/YSZ2JE6j17LMIK6ti3ZNCUVFRTfcKRUGwsIJOY//tv/9jibbCXvj/TN6cX15W4DOlN7zCLKdsWasW9adNpfnWLdi5uXHjg9lcHDCQu8F/FFsOhaIiohS9QlEYqjho/fej9kD1RrB5FKwMgJunTZaFd9NarA3qxOs9W5bJZvuSoCiD92xcXWm8YjlOixcjU1K4PHIk1/5vMqkxMSaRqSJ2ayrKP0Wpl0rRKxRFoVF7CPoVAhbCzZNac/5P78AD04yM9m5aiwn+LpVCyUPRB+8JIbB/1J/m32/HcewY4nbu5EKfvsR+9x3SMEK8KNja2hIdHa2UvaJMIaUkOjoaW1vbQl2nBuMpFMXlbjT8Oh2Orgb7BtBrFrQdrPXvKwqMKQbvPTh/nn+m/5ekkBDs2renwX+nY+PqWuh0UlJSiIqK4v79+0WSQ6EoKWxtbXFycsIq25gU5Y9eoSgNrvwJO9+A639Bs+7Qbz7UdjG3VJUOKSVxm7dw86OPSEtMxPGFF6g9fhw6Oztzi6ZQlBjKH71CURo07gCj9kLfeXAtDL7oDPvmQuoDc0tWqRBCUPPJwTT/cSc1nniC6GXLuBjwBIn795tbNIXCLChFr1CYEp0FdBwFE/+E1k/Avg/giy4QccjcklU6LGvVouHsD2iyehXC1pYrY8YS9cqrpNy4aW7RFIpSRSl6haIksK8HQ76CYZsgLRlW9oOtEyDJNCPCFQWnaseONN+ymTqvvkLivn1c7NuXmDXfINPSzC2aQlEqKEWvUJQkro/D+GDo+hqEfwuLfCBsfaktpavQENbW1B47lubfb8fOy4sbs2YR8fRQ7p3I32ubQlHeUYpeoTAxOdapt64Cj0+HMQfAoQVsHQur+0P0hbySUZQA1k2a0Hj5MhrN/5iUmzeIePpp/pn1AWmJiUDhXesqFOUBNepeoTAh+a5Tr9fD0ZXw83RIe6C5we08UVt5T1GqpMXHc2vhQmLXf4tlnTrcm/gcLz1YRrI+pVCudRWKsoAada9QlBLG1qnPgk4HPi/y18CfuVDTD36ZDsv8tVH6ilLFonp16r/7Ls4bvsXC0RGrdxfy6rdJOMSlFcm1bmZUy4CiLGEWRS+EcBBC/CyEOGf4zbH8lxCisRBirxDitBDipBDiFXPIqlAUhoKsUx8aGcvQdRfpcTWIl9NeJyXuH1j2KOyeBslJZpC6cmPn4UGz7/5H6sTnaXNZMn9ZGn1CJT512hcpvcKs269QlAbmsugnA79KKV2BXw3H2UkF3pBStgY6AROEECXnG1ShMAEFWac+s9W/M9WHle2/g3bD4LdP4Qs/uKjme5c2wtIS94lTEGs/5V4bZ0b8lEzN1z/mwcVLhU6rKOv2G0O1CihMhbkU/QBglWF/FTAwewQp5XUp5VHDfgJwGmhUahIqFEUkv3Xqs1v97R9yhv6fQeD3WoTV/WHbRLgXW3pCKwDw8uhB52930mD2bB5cuMClgQO5/eVSZEpKgdMo6rr9mVGtAgpTYpbBeEKIO1LKmpmOY6WUuXrvEEI4AwcANyllfC5xRgOjAZo0aeIdGRlpUpkVClMSGhlL8MVoOjV3zPpBkJwE++fAb4ugah0IWACt+ppP0EpM6q1b/DNzFgk//YRNm9Y0nDkT2zZtCrQmf3HX7V9+fDmfHf0MPXoshAUT200kyD2ouLekqMCYZa17IcQvQH0jQe8Aqwqq6IUQ1YD9wCwp5eaC5K1G3SvKPdeOaVb9jRPg/hT0ngtVy6Zf+opO/O7d/PP++6TFxJL6bABjnH7hrijZkfnpFn2KPgUrnZWaAaDIlzLn1EYIcRZ4REp5XQjRANgnpWxpJJ4V8APwk5RyfkHTV4peUSFITYZDC+DAR2BXU1tDv22OXi5FKZAWF8eNuR8St3kzVx3gi34WXGhsWaKWtim8+SkqD2VR0X8EREsp5wghJgMOUsq3ssURaP33MVLKVwuTvlL0igrFjZOwdTxcD4PW/aHfx1CtrrmlqpT8tWM1cTPm4Bgn+cnXikdnLsercUdzi6VQlMl59HOAHkKIc0APwzFCiIZCiJ2GOF2A54FHhRBhhk11VioqH/XaQtCv8Nh78PdP8HlH+GuDWkbXDHj2G0H1DSu40dOTPn+kYD/qPZKOHjW3WApFnqiV8RSK8sSts1rffdQRaNkXAhZqDnQUpc7d4GCuT3mHlOvXcRgxgjqvvqJ83ivMRlm06BUKRVGo0xJe3AU9Z8GFPbDYF45vVNa9GajaqRPNtm+n1rPPELNqFZcGDlLWvaJMohS9QlHe0FmA30QYewgcXWDTS/C/EXD3trklq3RYVKtK/XffpcnKr5GpqUQOG86N2XPQ37tnbtEUigyUolcoyiu1XeHFnzTPeH/vgs994dQ2c0tVKanaqRPNt2/LsO4vDhxIUmioucVSKACl6BWK8o3OQvN1P+YA1HDSLPuNL0FSjLklq3ToqqZb9yshNY3I4c9zY85c9Pfvm1s0RSVHKXqFohySw+d93dYQ9Av4T9Ws+s994czOvK9RlAhVO/nSfPs2aj4zlJiVK7k0+EnuhYebWyxFJUaNulcoyhn5+rz/5zhsGQc3joPns9B7DqE3Zd7XFFMeo8v5Kkg8fJjr70wl9eZNHEePos748Qhra3OLpaiAqFH3CkUFIl+f9/XdYdQeePgtCP8fLO7MtZDv876miKR/dHy8+yzDlger1oJsVOvShebfb6fGwIFEL/mSS089zf0zZ8wtlqKSoRS9QlHOKIjPeyyt4dF3YNSvYFuDJ46/zByr5dQQSblfUwTy/ehQYGFvT8MPZuH0xWJSY6K59NTTHP/oXZaHLVVe6RSlgmq6VyjKIYVqLk99APtmIw9/QoJ1Pa4/uoCWvn1MJsew5cGkpOqxMnGXQEUkNTaWM1PfwOLX3znfQLB0oB3vP7tCrWWvKDZlbq37kkYpeoXCCFf+hC2jIeYSdJ4Aj04DK9tiJ6v66AvH8uPL+XPtQl7alYZ1Klwd4U/fNxchdKqBVVF0VB+9QqGAxh20RXY6vAS/L4Kl3eFa8ZuOvZvWYoK/i1LyBcSnng9H3ez4v1HWnGlqQfOv9nIlKIiU69fNLZqigqIseoWiMnL+F23N/Lu3oPtkbS6+haW5pao0ZLigretN0/3nuDFnLsLCgvrTplL9iSfQnHcqFAVHNd0rFIqc3IuFHW/CiY3QyAcGfQm1XcwtVaUk+fJlrk1+m3tHj2Lfsyf1p7+HpYODucVSlCNU071CociJXS0YsgKGfAXR52FJVziyDPR6c0tW6bBu0oSma1ZT9803SNy7l4v9B5CwZ6+5xVJUEJSiVygqO25PwvhgcO4KO9+EbwZD3FVzS1XpEBYWOAYF4bxxI5a1axM1fjzXpk5Ff/euuUVTlHOUolcoFFC9AQz7DgIWwJU/YHFnzf2totSxbfkQzf63AcdRo4jbtJmLAweRdOyYucVSlGMKpOiFEDohRDshRD8hxKNCiHolLZhCoShlhACfF7WR+XVaau5vN76o9eUrShVhbU3dN16n6TdrQK8ncthwbn7yCTIlxdyiKcoheSp6IUQLIcRS4DwwB3gWGA/8LIQIFkK8IIRQrQIKRUXCsQW88CM8anCQ80UXuLjP3FJVSqp4e9Ns21ZqDBhA9BdLiHj2OR5cvGRusRTljPyU9EzgG6CFlLKXlHK4lHKIlNID6A/UAJ4vaSEVCkXpEhqVwOdpgzjdbzNYVYHVA2DXFEhRLldLG4tq1Wg4+wMaffIJKVFRXBo8mJh166iIM6YUJYOaXqdQKLKQ3TveupGetD8zH/5cBnVaw+Cl0MDD3GJWSlJu3uT6O1O5e/AgVbt1o8GsmVjVrWtusRRlgGJPrxNCWAgh+gshJgkhXk/fTCumQqEoC2R3VPP75SToNw+GbYJ7MbDsUTi0EPRp5ha10mFVty6Nl35JvWlTSTpyhEv9BxD/88/mFktRxilo//r3wEjAEbDPtCkUigpGrt7xXB+Hcb9Dy97wy3uw6gmIjTSvsJUQIQQOw4bRbMtmrBo14urLk7g25R3SEhPNLZqijFKgpnshRLihX75coJruFYrikaejGinhr/Ww8y1tpH7feeDxtLavKFVkSgq3Fi8m+sulWDVoQMMP51LF29vcYinMgClWxvtRCNHThDIpFIoyTJ6OaoQAr+c43n8n12yaaR7x1DQ8syCsrKj7LX7jzAAAIABJREFUyis0/eYb0OmIfH4EN+cvQCYnm1s0RRmioIo+GNgihLgnhIgXQiQIIeJLUjCFQlF2CY2M5akNV3n41n9YoB+KPLVdm4Z36YC5RauUVGnfjmZbtlBj8CCily5V0/AUWSioov8Y6AxUkVJWl1LaSymrl6BcCoWiDJM+YC9V6liUMoDv2n0NVnawqj/sngapDwqdZmhkLJ/vPU9opGoZKAoW1arScOZMGn32acY0vNhvv1XT8BQFVvTngBNS1RiFQkHOAXstPLvBmAPgPRJ++xSWPwa3zhY4vfQpfR/vPsuw5cFK2ReD6j160Gz7dqq0b88/0/9L1PgJpMbEmFsshRkpqKK/DuwTQrytptcpFArvprVYG9SJ13u2ZG1QJ60v37oqPLEQnlkP8dfgy4c1b3gFsA+yT+kLvhhdCndRcbGqV5fGy5dR7+3J3D18mIv9B5B4QHWrVFYKqugvAb8C1qjpdQqFgjwG7LXqq03Dc+6mecNbNxQSb+WZVq5T+hSFIuxmGMuPLyfsZhhCp8MhMBDn777DslYtrowewz/vz0R/X61uWNlQK+MpFIoSITQihruHvqDrpYXobGvAwC/AtUfu8fOa0qfIl7CbYYzaPYrktGSsLaxZ1nMZXnW9ANA/eMCt+fOJWbUaa5cWNJo3D9tWrcwsscKUFHl6nRBiqRDCPZewqkKIF4UQw0whpEKhqDiERsYybMUfjDzpxYDkWSRZO8LaIdrc+1zWy89zSp8iX0JuhJCclowePSn6FEJu/Gvs6GxsqPf22zRevhx9XDwRTz1N9MqVSL3ejBIrSov8mu4XA9OEEKeFEN8JIRYLIb4SQhwEfkNrvldOqxUKRRYy97mfSm3EarevoNN4OPIlLPOHGyfNLWKFw6eeD9YW1lgIC6x0Vvx/e/ceJ+Xc/3H89dlTaTufT3RQwu0Q7U1KKpRDqCR3QpFKEfeNuIsbPzcKHW5uh1JbFDkkdCCJ5C6HsLGEhNrSSaWTdNrDfH9/zCyV3W12d2aunZn38/GYx+7MXHNdn3E91qfre32/n09anT9f3FU8sy1NZs8itV07Nj/0MGsHXk/ulqJvq0j0C7YyXkUgDagH7AWWO+eCn1IbYRq6F/FW/iz6nFwfyUkJf0zY++FdmDkY9u2ETvfB6YNUUS+EMjdnkrEpg7Q6ab8P2xfEOceOl19m00MPk1ChAvUefIBKHTtGMFIJtaKG7nWPXkTCotB77rt/gVlD4Pu3oNm50PUpqFTHu0DjyKH/ENi/ciXrbxvK/u++o1rv3tS+43YSypf3OkwpASV6ESlbnIOMSfD2Xf5leV2f8jfLkbApbLKeLzubLWPGsm3KFMo1b0b90aMp36KF1+FKMYWi1r2ISOiYwV/7+4vsVK4PL/4N3rwNsvd4HVnMKmyyXkJKCnWGD+PI9HRyd+xgdc/L2TZ1qibqxRAlehHxTq0W0H8BnDEEPkuHCR3g52VeRxWTDjdZr+KZbWk6axapbdqwacRITdSLIcFOxjsGuB1oBCTlv+6cOzt8oZWchu5FotDKhfD6INi7DTr9WxP1wiCYyXrOOXa89JJ/ol5qKvVGPEilDh0iG6gUW6nv0ZvZl8B4YCmQl/+6c25pqIIMJSV6kSi1eyvMHgIr5kKzTtDtKahY2+uo4tL+H39k/dDbNVEvSoTiHn2uc26cc+5T59zS/EcIYxQRgdQa0OsF6DIGVi+GcW38S/Ik4so1a0bj6S9TvW9ftr/wAqt79mTfiu+9DktKINhEP8fMbjCzemZWPf8R1shEJD7lT9Qb+D6k1oZpPWDe8BK1vpXS+X2i3sSJ5G7fweqePdk29Tm1vo0ywQ7dZxXwsnPONQ19SKWnoXuRGJGzD965x19Rr86JcNkk/wQ+ibjcbdvYeOdd/Pb++6Se1Y76I0aQVLOm12FJQKmH7p1zTQp4lMkkLyIxJLk8XPgI9J4OuzbA0+0hY3JQrW8ltJKqV6fhuKeoc8/d7PnkU1Z17cZviz/wOiwJQlCJ3swWm9mDZna+mZW6PW1g6P8dM/sh8LPQLhZmlmhmX5jZG6U9rohEqWPOg8EfQaMz4I1b4OWrYM82r6OKO2bGT+cez+cjepFTuQJrBwxg00MP48vO9jo0KUKw9+j7AiuAHsBHZpZhZv8pxXGHAQucc83x97kfVsS2fweWl+JYIhILKtWFK1+Fzg/C92/DuLaQtcjrqOJKfnW9h395kQGXbyOve2e2Pfssq3v1Yv+qgu7wSlkQ7ND9KuAd/El5EVABOK4Ux+0KTAn8PgXoVtBGZtYQ6AKkl+JYIhIrEhKgzRAYsMBfOnfKJfDufZCX43VkceHA6np7EnNZ0vtEGj71JLkbNpLVowc7ZszQRL0yKNih+5XATKAOMAk4wTlXmsLUdZxzGwECPwtbKPsocAdw2FqMZjYwMNKQsUXVnERiW72T4fr/wal94IOxMKkzbFvldVQxr6DqepXOPpsms2ZyxMkns/Ffd7P+llvJ27nT61DlAMHOuv87cCZwJPAd8D9gkXNuZRGfeReoW8BbdwFTnHNVD9h2u3PuoPv0ZnYRcKFz7gYz6wAMdc5ddPivpFn3InHl21kw+ybw+eCisXDS5V5HFNMKq67nfD62TprElsf+S1LtWjQYNYoKrVp5GGl8CVn3ukBf+muBoUBD51xiCQNaAXRwzm00s3rA+865FodsMxK4GsgFygOVgdecc1cdbv9K9CJxZsdaeG0g/PQRnNQLuoyGcqWeNywlsHfZMtbfNpScdeuoOXgwNQcPwpKSDv9BKZVSL68zszFm9gnwCdASuAdoXoqYZuOf4Efg56xDN3DODXfONXTONQZ6Ae8Fk+RFJA5VPRL6zoEOw2HZdBjfDtareKcXjjjxRJq89hpVLr6YX558kjV9+pKzfr3XYcW1YGfdLwEucc79xTl3nXNuSmCCXkk9BHQysx+AToHnmFl9M5tbiv2KSLxKTIIOw+Cauf7JeZM6w4eP+Yf0JaISK6ZS/+GHqD9qFPtXrGBVt+78Om+e12HFraCH7s3sEuCswNP/OefmhC2qUtLQvUic27sdZt8My2dD047Qfbx/eZ5EXPbatawfOpR9X35F1Z6XUWf4cBIqVPA6rJgTiqH7kfjXs38beNwceE1EpOw5ohpcPhUufgx+WuJfc//9fK+jikspRx5J4+efp8bAgeyY8SpZl/Vk33ffeR1WXAl26L4L0Mk5N9k5Nxk4P/CaiEjZZAatrvEvw6tUF17oqeY4HrHkZGrfegtHPTMZ365drO55uZrjRFCwiR6g6gG/Vwl1ICIiYVGrBfRfAKcPgiVPQfo5sEXtVr2Q2ro1TWbPIrVtWzaNGMG6wTeQu02ljMMt2EQ/EvjCzJ41synAUmBE+MISEQmh5PJwwcNwxUuwcz1MaA+fT1VzHA8kVavmb45z113s/vBDsrp2Y/fHH3sdVkwrzmS8esBfAQM+cc79HM7ASkOT8USkUL9uhNcH+uvk/6U7XPQoHFH18J+TkNv33Xesv/U2srOyqDFgALVuGoIlJ3sdVlQq8WQ8Mzs1/wHUA9YBa4H6gddERKJL5Xpw9Uw45174drZ/zf1Pn3gdVVwqf+yxNJnxClUvu4ytEyaw+qqryF671uuwYk6RV/RmtjDwa3kgDfgS/xX9Sfiv6s8Me4QloCt6EQnKugyY0Q92rvOvwW93GySUqODnQZau2c6SVVtp3bQGrRoV2oVbDvDrvLfZeM89kJdH3fvuo8pFmu9dHCW+onfOdXTOdQTWAKc659Kcc62AU4AfQx+qiEgENUyDQYv9Q/gLH/R3w9tZuipuS9ds58r0JYyZv4Ir05ewdM32EAUb2yqffx5NX3+Nci1asGHoUDYMG45v926vw4oJwU7GO9Y5tyz/iXPua/ylcEVEolv5KtAjHbqNgw1fwPi28N2bJd7dklVbyc714XOQk+tjyaqtIQw2tiU3aECjqVOoecMN7Jw9m6xLe7D3m29KvL/MzZmkL0snc3NmCKOMPsEm+uVmlm5mHcysvZlNBJaHMzARkYgxg5a94fpFUOVIeKk3zL0dcvYVe1etm9YgJSmBRIPkpARaN60RhoBjlyUlUevmmzjq2Wfw7dvH6l5XsG3KlGKvuc/cnMmA+QN4/PPHGTB/QFwn+2AT/bXAN/ir4/0Df3W8a8MVlIiIJ2o2g/7vwhlD4NMJMPFs2LKiWLto1aga0/q35tbOLZjWv7Xu0ZfQD41T+HBkD/JOP5lNIx9i7aBBxVpzn7Epg+y8bHz4yPHlkLEpfudtBduP/mxgiXNuT/hDKj1NxhORUvt+PswcDNm7/WvwT+3jv/KXsMu/Gs/OyyYlIZlJuy8n5alpJFSpTINHHiH1jDOC3keOL4fkhGQmdp5Iy9qxe8e51LXugWuATDP72MweMbOLzUz/TBURzy1ds50nF/4Y+klvx3SGwR/CkafBnJthxrWwd0f4jie/O+hq3OXyadvqNJ7+MomVKvNTv+vYPPY/uJycIvfRsnZLJnaeyJBThsR8kj+cpGA2cs71AX8bWeAy4EmgfrCfFxEJh/wZ7tm5PlKSEkI/VF6prn/N/YePwnsPsH/NZ4zaOZBPc5uF53gCQFqdNFISU36/Gk+rk0b52v4195tGjmTrhAns+eQTdgzvR0biT6TVSSswkbes3TKuE3y+YLvXXWVmTwMzgHOBJ4B24QxMRORwQj3DvcCr9YQEaHcr9Hub/bk+nk/4PwYlzCQvN1cz6sOksKvxhAoVqHf//TQYO4Y9P35PXt+/k/H8o3E/2e5wgr0ifxRYCYwHFjrnVoctIhGRIOXPcM/J9ZV6hvthRweO/Csre8xjw3PXc0fydM5035Jad3IIvoUUpKir8coXXsjSlOVUHjGJv8/K473Ve/i8xce6ei9EUFf0zrmaQD/8FfIeNLNPzey5sEYmInIYoZzhHszowCnNG1G33wu8d8zdtE76kZPnXAg/vFOaryAldOJJ5zCybwVmtkmkw5c+Tr9rBvtWFG+FRLwIdui+MnAU0AhojL9NrS98YYmIBKdVo2rc2LFZqe+VB7v+vVXj6pzdeygJgxb57+FPuwzevgtys0t1fCmelrVbMv6CdGrd8g9yx95J8t5sf5/7adPU5/4QwS6v+wr4IPBY5JxbF+7ASkPL60SkJIpdoz5nH8z/F3w2Eeq1hMsmQ42jwx+o/Enu1q1sGD6c3YsWU/Hcc6j/wAMkVo2froRFLa8Luk1tNFGiF5GIWv4GzLoRfLnQZSyc/DevI4pLzudj25SpbB47lqSaNWkw6hEqpBWY+2JOqdfRm1ktMxtlZnPN7L38R2jDFBGJUsdd5F9zX/ckf6/71wfB/t+8jiruWEICNa69hsYvvIAlJ7OmT1+2PPkkLi/P69A8FWzBnGnAd0AT4D5gNfBZmGISEYk+VRpC3znQfhh89TI8fRZs0JIvLxxx4gk0ee01Knfpwi+PP8FP11xLzs8/ex2WZ4JN9DWcc5OAHOfc/5xz/YDWYYxLRCT6JCZBx+H+hJ+zFyZ1giXjIAZvkZZ1iRVTqf/Iw9QbOZK933xDVrfu7Fq4MGT7j6bOeMEm+vxagxvNrIuZnQI0DFNMIiLRrfGZ/qH8o8+BecPgxStgt4rrRJqZUbV7N5rMmEFSvXqsG3wDP48YgS+7dCskoq0zXrCJ/gEzqwLcBgwF0oFbwhaViEi0q1AdrngRzn8YVi7w97nPWux1VHGpXNMmNH7pRapdfTXbpz7H6l692J+VVeL9RVtnvMMmejNLBJo753Y65752znV0zrVyzs2OQHwiItHLDFoP8re+TUmFKRfDwhEszdoSlsY4arhTuIRy5ah71500fOpJctdvIKvHZeyYObNE+8qvxZ9oib/X4i/Lgl1Hv9A51zEC8YSElteJSJmz/zeYezt8+QKfuWP5R/aNbE2qFbLGOGFv8BNDcn7+mQ1Db2dPRgZVul5CnbvvIbFiarH2kbk5k4xNGYU21Im0ULSp/cjMnjCzdmZ2av4jhDGKiMS2chWh+zjeOfZ+jmM1b6YMo73v05A1xgl1g59Ylly3LkdNeZaaQ4awc84brO7Rg73ffFOsfbSs3ZL+J/YvE0n+cIJN9G2AvwD/BsYEHqPDFZSISKyqfsbV9PCNZJ2rxdPJY+m5+b/+CnulFGwJ30grq7PTLTGRWkNu5Khnn8G3bx+re13BtqlTY7J8rirjiYhE2NI12/n0x41cunUidb6dDHVOhJ7PQM3mpd5vsUr4hln+7PTsvGxSElMOajlbluRu387GO+/it4ULqdihA/VGjiCpmvf//YqjxCVwzezWonbsnBtbytjCQoleRKLGinkwczDk7ocuo+HkK/yT+GJA+rJ0Hv/8cXz4SLREhpwyhP4n9vc6rAI559j+3PNsHjWKxGrVqD96FKmnneZ1WEErzT36SoFHGjAYaBB4DAKOD2WQIiJxqcX5/jX39U/xJ/zXr4f9u7yOKiSiaXa6mVG9z9XsH3cfuxJzWHPNNWx5/Alcbq7XoZVasLPu5wM9nHO7As8rAa84584Pc3wloit6EYk6vjxYPAbeHwnVGvs74dU/xeuoSq2szU4vSv6tBtu7n/7vONp9lUuFtDTqjx5Fct26XodXpFDMuj8KOLCUUDb+vvQiIhIKCYnQ/g645k3I3Y8vvRMfPHcfS1dv8zqyUomm2en5hXD2pjieuiiR74dcwN5vvyWrazd2vRe68rmRFmyifw741Mz+z8zuBT4BpoQvLBGRONWoDZld3uC9vJM5c+VYfn2mB5krVnodVVw49FZDo8v70OTVGSQ1qM+6G0JTPtcLQc+6D6ybbxd4usg590XYoiolDd2LSDR7cuGPjJn/HVcnzOfOpGnklKtGxd7P+mvoS1gVdKvBl53N5tGj2T71OcodfxwNxoyhXJMmHkd6sBLPuo9WSvQiEs3yq9zl5Po4KWkNL1abSPldq+GsO/zD+wmJXocYl3a99x4bh9+JLyeHevfeQ5WuXb0O6XdK9CIiUeagNfF1k2HuUPjyRWjUFi6dCFUaeB1iXDq4fG5X6t5zNwmp3pfPVaIXEYkFmS/Cm7dBUgp0GwctLvA6orjk8vL45alx/DJuHClHHUWD/4yl/HHHBfXZcBURCsWsexER8VrLK+D6RVClIbzYC+YN9xfakYiyxERq3TTEXz53zx5WX/43tj0/LajyuV60uFWiFxGJJjWbQf8FcPogWPIUTOoEWzUr3wupp51Gk1kzSW3Thk0PPMC6ITeRt2NHkZ/xooiQhu5FRKLVd3Nh1g2QlwMX/QdOutzriOKSc47tU6eyafQYkmrWpMHoUVRo1arQ7XWPPgSU6EUkbuxcB68OgJ8+gpZXwoWjIKV4k8MkNPYu+5r1t91Gzrp11LppCDUGDsQSI7NCQvfoRURiVZWG0HcOtP8nZL4AT7eHn5d5HVVcOuLEE2jy2qtUvuACtjz2X37qdx05mzZ7HZY3id7MqpvZO2b2Q+Bngf0Azayqmc0ws+/MbLmZnRHpWEVEyrzEJOh4J/Sd7W+IM/Ec+HQixOCIbVmXWLEi9UePot6DD7L3q6/I6t6d3xYt8jQmr67ohwELnHPNgQWB5wV5DJjnnDsWOBlYHqH4RESiT5Oz/J3wmpzlX3f/8lWwd7vXUcUdM6Nqj0tpMuMVkmrWZO3A69n0yCicR+VzvUr0XfmjVv4UoNuhG5hZZeAsYBKAcy7bOVf0dEYRkXiXWhN6T4fOD8L3b8P4dvDTJ15HFZfKHX00jae/TNUrerFt8mRWX3U12WvXRjwOrxJ9HefcRoDAz9oFbNMU2AI8Y2ZfmFm6mRU6w8TMBppZhpllbNmyJTxRi4hEg4QEaDMErnvbXy73mQv8LXB9Pq8jizsJ5ctT7957afDYY2RnZZHV/VJ+feutyMYQrh2b2btm9nUBj2CLAycBpwLjnHOnALspfIgf59wE51yacy6tVq1aIfgGIiJRrkErf4Gd47vCgn/D891h1yavo4pLlc/rTJPXX6fc0Uez/pZb2frMsxE7dlK4duycO7ew98xsk5nVc85tNLN6QEHTEtcB65xz+WNOMygi0YuISAHKV4HLJkPTDvDWP2F8W+g+HpoV+r9oCZOUhg1o9Pxz/DJuPJU7d4rYcb0aup8N9A383heYdegGzrmfgbVm1iLw0jnAt5EJT0QkhphBq74wcCFUqAnP94B37vUX2pGIsuRkat18E8kNIteUyKtE/xDQycx+ADoFnmNm9c1s7gHb3QRMM7OvgJbAiIhHKiISK2ofBwPeg1P7woeP+u/db1/jdVQSZqqMJyISj75+Feb8w3+1f8nj/vv4ErVUGU9ERA52Qg//RL3qR8P0PvDGrZCzz+uoJAyU6EVE4lX1JtDvbThjCGRMgvRzYMv3XkclIaZELyISz5JS4LwHofcrsGsjTGgPX0xT+dwYokQvIiJwTGcY9IF/7f2sG+D16/118yXqKdGLiIhf5frQZxZ0uBOWveLvhLfxS6+jklJSohcRkT8kJEKHf/pb3+bshfRzYcl4DeWXQObmTNKXpZO5OdPTOMJWGU9ERKJY4zP9Q/mzboB5/4SsRdD1CahQ3evIokLm5kwGzB9Adl42KYkpTOw8kZa1W3oSi67oRUSkYKk14IqX4LyR8MN8fye8NR97HVVUyNiUQXZeNj585PhyyNjkXW0XJXoRESmcGZxxA1w3HxKT4dkusGgU+PK8jqxMS6uTRkpiComWSHJCMml1CqxlExGqjCciIsHZ9yu8cQt8PQOatIdLJ0Clul5HVWZlbs4kY1MGaXXSwj5sX1RlPCV6EREJnnPwxXMw9w4oV1Gd8MoIlcAVEZHQMINT+8DA9yG1Vtg64S1ds50nF/7I0jXbQ7rfeKRZ9yIiErSla7azZNVWWjetQ6sB78G8Yf5OeGs+hB6ToFqjkBzjyvQlZOf6SElKYFr/1rRqVC0E0ccnXdGLiEhQ8hPwmPkruDJ9CUs37IOLH4PLnoEtK+DpdvDt7FIfZ8mqrWTn+vA5yMn1sWTV1hBEH7+U6EVEJCiFJuATLj2gE97V8OZtpeqE17ppDVKSEkg0SE5KoHXTGiH6BvFJQ/ciIhKU/ASck+v7cwLO74S34D74+An46RPo+QzUbF7s47RqVI1p/VsHbhHU0LB9KWnWvYiIBO2Pe/RFJODv34bXB0HufugyGlr2jmyQcUjL60REJLJ+3QCvDoA1H8BJvfwJv1wlr6OKWVpeJyIikVW5PvSdDR2Gw7LpgU54X3kdVVxSohcRkfBISIQOwwKd8PZA+jnwyQR1woswJXoREQmv/E54TTvAW7fDy1fBXhXCiRQlehERCb/UmnDFy9D5Qf9kvfHt/DPzw0jV9fyU6EVEJDISEqDNELjubbAEeOYCWDwWfL6QH+pPxX3iONkr0YuIxIkyc4XboBUMWgzHX+Jfd//8pfDb5pAeQtX1/qBELyISB8rcFW75Kv7SuRc9Cj99DOPawsqFIdu9quv9QYleRCQOlMkrXDNIuxYGvAdHVIPnusOCf0Nebql3nV9d79bOLeK+KY5K4IqIxIEiy9d6rc5fYOBCeOufsHgMrP4QeqRD1SNLtdtWjarFdYLPp8p4IiJxIqjytV5bNgPm/MO/Br/bU3BsF68jigoqgSsiItFj60qY0Q82ZsJp10Pn+yGpnNdRlWkqgSsiItGjxtFw3XxofQN8+jSkn+tP/lIiSvQiIlL2JJWD80dCrxdh51p4+iz4arrXUUUlJXoRESm7jr3QXz637onw2gCYeSNk7/Y6qqiiRC8iImVblYbQ9w0463bInAYTOsKmb7yOKmoo0YuISNmXmARn/wv6zPQ3xJl4NmRMVie8ICjRi4hI9GjaAQZ/CI3awBu3wCvXwL6dHgdVtinRi4hIdKlYG658Fc65F5bP8XfCW7/U66jKLCV6ERGJPgkJ0O5WuPYtcD6Y1Bk+ejwsnfBCLdLNhVQCV0REotdRp8P1i2D2TTD/X5C1CLqNh9QyVOL3APnNhbJzfaQkJUSkDr+u6EVEJGwicvVaoTr87Xm4YBSseh/Gt4XVH4TveKXgRXMhJXoREQmLiLbGNYPTB0L/dyG5Aky5GN5/GHx54TtmCXjRPleJXkREwsKT1rj1Tobr/wcn9oT3R8DUrvDrxqA/Hu4RCC/a5+oevYiIhIVnrXHLVYLuT0OT9jB3qH8ov/sEaH5ukR+L1P3zSLfP1RW9iIiEhRdXr78zg1OuhIHvQ8W6MK0HzL8b8nIK/YgnIxAR4EmiN7PqZvaOmf0Q+Fng2TezW8zsGzP72sxeNLPykY5VRERKrlWjatzYsVlkk/yBarWAAQsgrR989F+YfD5sX1Pgpl7cP48Er67ohwELnHPNgQWB5wcxswbAzUCac+4EIBHoFdEoRUQk+iUfARf9B3o+C7987y+w8+2sP23m6QhEGHmV6LsCUwK/TwG6FbJdEnCEmSUBFYANEYhNRERi0V+6w6DFULMZTO8Db9wKOfsO2sTzEYgw8CrR13HObQQI/Kx96AbOufXAaOAnYCOw0zk3v7AdmtlAM8sws4wtW7aEKWwREYlq1RrDtfOgzU2QMQnSz4Et33sdVViFLdGb2buBe+uHProG+flq+K/8mwD1gVQzu6qw7Z1zE5xzac65tFq1aoXmS4iISOxJSoHOD0DvV2DXRpjQATJf8DqqsAlbonfOneucO6GAxyxgk5nVAwj83FzALs4FspxzW5xzOcBrQJtwxSsiInHmmM4w6AOofwrMHAyvXQ/7f/M6qpDzauh+NtA38Htf4M+zIvxD9q3NrIKZGXAOsDxC8YmISDyoXB/6zob2w2DZdJjQHjZ+5XVUIeVVon8I6GRmPwCdAs8xs/pmNhfAOfcJMAP4HFgWiHWCN+GKiEjMSkiEjsOhz2zI3g3p58KnE8E5ryMLCXMx8kUOlJaW5jIyMrwOQ0REos3uX+CLXkR9AAAH6ElEQVT1QfDjO3DcxXDJE3BEVa+jOiwzW+qcSyvoPVXGExERyZdaE3pPh073w4q3/Gvu137mdVSlokQvIiJyoIQEaHsz9HsbDHjmfPjgUfD5vI6sRJToRUQkKoW9133DNLh+MRzbBd69F17oCb9FX50WJXoREYk6Eet1f0RV6DkFuoyFrMUw/kzIWhSeY4WJEr2IiESdiHaaM4O/XudvjlOuEky5BBaOgLzc8B0zhJToRUQk6njSaa7uif62tydfAf97GKZeAjvXh/+4paTldSIiEpWWrtnOklVbad20RuSb0Hz5kr8pTlI56D4ejjkvssc/RFHL65ToRURESuKXH+CVa2HTMjhjCJxzr7+Ovge0jl5ERCTUajaH/u/CXwfAx0/A5PNgW5bXUf2JEr2IiEhJJZeHLqPh8udg20p4+iz4+lWvozqIEr2IiEhpHX+Jf819rRYwox/M+Tvk7PU6KkCJXkREpNgKLNZTrRFc+xa0/QcsfZa9T7bnhTfmh2+Nf5CU6EVERIqhyGI9icnQ6T5+6DyFPds30v2zK3l10kiWrt7mWbxK9CIiIsUQTLGe+ftPoEv2SL7wNWNEwtNUmjsY9u/yIFolehERkWIJplhP66Y12JFUg765d/KYryfNt8z3T9TbkBnxeLWOXkREpJiCKdZz0DbuW3j1OtizFTo/AKcN9JfWDREVzBEREfHa7q0wczD88DacfTecNTRkuy4q0SeF7CgiIiJSuNQa0Ptl+Cwdju8WscMq0YuIiESKGZw2IKKH1GQ8ERGRGKZELyIiEsOU6EVERGKYEr2IiEgMU6IXERGJYUr0IiIiMUyJXkREJIYp0YuIiMQwJXoREZEYpkQvIiISw2KyqY2ZbQHWHPJyFWBnER8r6v3C3gv29ZrAL0UcO5wO973Dva9gPxOp81PQa7FwfmLh3BT0upfnBnR+Dvea/nZKt10o/3aaO+eqFLgn51xcPIAJJX2/sPeCfR3IKKvfO9z7CvYzkTo/hbwW9ecnFs5NQa97eW50foJ6TX87ZeDcHG5f8TR0P6cU7xf2XnFf90IoYynJvoL9TKTOT1k6NxC6eGLh3ARzrEjT+Qn+OJGmcxPkvmJy6L6sMbMMV0ifYPGezk/ZpXNTtun8RId4uqL30gSvA5Ai6fyUXTo3ZZvOTxTQFb2IiEgM0xW9iIhIDFOiFxERiWFK9CIiIjFMiV5ERCSGKdGXAWaWamZLzewir2ORg5nZcWY23sxmmNlgr+ORP5hZNzObaGazzKyz1/HIwcysqZlNMrMZXscS75ToS8HMJpvZZjP7+pDXzzezFWb2o5kNC2JX/wSmhyfK+BWK8+OcW+6cGwRcDmi9cIiE6NzMdM4NAK4B/hbGcONOiM7PKufcdeGNVIKh5XWlYGZnAb8BU51zJwReSwS+BzoB64DPgCuARGDkIbvoB5yEv150eeAX59wbkYk+9oXi/DjnNpvZJcAw4Ann3AuRij+WhercBD43BpjmnPs8QuHHvBCfnxnOucsiFbv8WZLXAUQz59wiM2t8yMunAT8651YBmNlLQFfn3EjgT0PzZtYRSAWOB/aa2VznnC+sgceJUJyfwH5mA7PN7E1AiT4EQvS3Y8BDwFtK8qEVqr8dKRuU6EOvAbD2gOfrgNML29g5dxeAmV2D/4peST68inV+zKwDcClQDpgb1sikWOcGuAk4F6hiZs2cc+PDGZwU+2+nBvAgcIqZDQ/8g0A8oEQfelbAa4e9P+Kcezb0oUgBinV+nHPvA++HKxg5SHHPzX+B/4YvHDlEcc/PVmBQ+MKRYGkyXuitA4484HlDYINHscif6fyUXTo3ZZvOT5RSog+9z4DmZtbEzFKAXsBsj2OSP+j8lF06N2Wbzk+UUqIvBTN7EfgYaGFm68zsOudcLjAEeBtYDkx3zn3jZZzxSuen7NK5Kdt0fmKLlteJiIjEMF3Ri4iIxDAlehERkRimRC8iIhLDlOhFRERimBK9iIhIDFOiFxERiWFK9CJxzMyqmtkNBzyvH67+4YH+8fcU8t5vgZ+1zGxeOI4vEq+U6EXiW1Xg90TvnNsQxpaidwBPFbWBc24LsNHM2oYpBpG4o0QvEt8eAo42s0wzG2Vmjc3sa/B3VDSzmWY2x8yyzGyImd1qZl+Y2RIzqx7Y7mgzm2dmS81ssZkde+hBzOwYYL9z7pfA8yZm9rGZfWZm9x+y+UzgyvB+bZH4oUQvEt+GASudcy2dc7cX8P4JQG/8vcgfBPY4507BXx61T2CbCcBNzrlWwFAKvmpvCxzYM/4xYJxz7q/Az4dsmwG0K+H3EZFDqE2tiBRloXNuF7DLzHYCcwKvLwNOMrOKQBvgFbPfu5iWK2A/9YAtBzxvC/QI/P4c8PAB720G6ocmfBFRoheRouw/4HffAc99+P//kQDscM61PMx+9gJVDnmtsEYb5QPbi0gIaOheJL7tAiqV9MPOuV+BLDPrCWB+Jxew6XKg2QHPP8Tf5hT+fD/+GODrksYkIgdToheJY865rcCHZva1mY0q4W6uBK4zsy+Bb4CuBWyzCDjF/hjf/ztwo5l9xp+v9DsCb5YwFhE5hNrUikhEmNljwBzn3LuH2W4R0NU5tz0ykYnENl3Ri0ikjAAqFLWBmdUCxirJi4SOruhFRERimK7oRUREYpgSvYiISAxTohcREYlhSvQiIiIxTIleREQkhv0/DfV3V9d1raIAAAAASUVORK5CYII=\n", + "image/png": 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", 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" ] @@ -472,19 +472,19 @@ } ], "source": [ - "print('rmse:', ca29.rmse())\n", + "print(\"rmse:\", ca29.rmse())\n", "h129 = ml.head(d1, 0, t)\n", "h229 = ml.head(d2, 0, t)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t, he15, '.', label='obs at 30 m with sig=0.02')\n", - "plt.semilogx(t, h129[0], label='ttim at 30 m')\n", - "plt.semilogx(t, he25, '.', label='obs at 90 m with sig=0.02')\n", - "plt.semilogx(t, h229[0], label='ttim at 90 m')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he15, \".\", label=\"obs at 30 m with sig=0.02\")\n", + "plt.semilogx(t, h129[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t, he25, \".\", label=\"obs at 90 m with sig=0.02\")\n", + "plt.semilogx(t, h229[0], label=\"ttim at 90 m\")\n", "plt.legend()\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)');\n", - "plt.title('ttim analysis with synthetic data, sig=0.02 errors at 90 m.')\n", - "plt.legend(loc = 'best');" + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"ttim analysis with synthetic data, sig=0.02 errors at 90 m.\")\n", + "plt.legend(loc=\"best\");" ] }, { @@ -598,11 +598,11 @@ } ], "source": [ - "ca0 = Calibrate(ml)\n", - "ca0.set_parameter(name='kaq0', initial=10)\n", - "ca0.set_parameter(name='Saq0', initial=1e-3)\n", - "ca0.series(name='obs1', x=d1, y=0, t=t, h=h1[0], layer=0)\n", - "ca0.series(name='obs2', x=d2, y=0, t=t, h=h2[0], layer=0)\n", + "ca0 = ttim.Calibrate(ml)\n", + "ca0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca0.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "ca0.series(name=\"obs1\", x=d1, y=0, t=t, h=h1[0], layer=0)\n", + "ca0.series(name=\"obs2\", x=d2, y=0, t=t, h=h2[0], layer=0)\n", "ca0.fit(report=True)\n", "display(ca0.parameters)" ] @@ -623,7 +623,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -635,17 +635,17 @@ } ], "source": [ - "print('rmse:', ca0.rmse())\n", + "print(\"rmse:\", ca0.rmse())\n", "h1n = ml.head(d1, 0, t)\n", "h2n = ml.head(d2, 0, t)\n", - "plt.figure(figsize = (7, 4))\n", - "plt.semilogx(t, h1[0], 'b.', label='obs at 30 m no errors')\n", - "plt.semilogx(t, h1n[0], color = 'r', label = 'ttim at 30 m')\n", - "plt.semilogx(t, h2[0], 'g.', label='obs at 90 m no errors')\n", - "plt.semilogx(t, h2n[0], color='orange', label = 'ttim at 90 m')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)')\n", - "plt.title('ttim analysis with synthetic data without errors.')\n", + "plt.figure(figsize=(7, 4))\n", + "plt.semilogx(t, h1[0], \"b.\", label=\"obs at 30 m no errors\")\n", + "plt.semilogx(t, h1n[0], color=\"r\", label=\"ttim at 30 m\")\n", + "plt.semilogx(t, h2[0], \"g.\", label=\"obs at 90 m no errors\")\n", + "plt.semilogx(t, h2n[0], color=\"orange\", label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"ttim analysis with synthetic data without errors.\")\n", "plt.legend();" ] }, @@ -753,11 +753,11 @@ } ], "source": [ - "ca2 = Calibrate(ml)\n", - "ca2.set_parameter(name='kaq0', initial=10)\n", - "ca2.set_parameter(name='Saq0', initial=1e-3)\n", - "ca2.series(name='obs1', x=d1, y=0, t=t, h=he12, layer=0)\n", - "ca2.series(name='obs2', x=d2, y=0, t=t, h=he22, layer=0)\n", + "ca2 = ttim.Calibrate(ml)\n", + "ca2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "ca2.series(name=\"obs1\", x=d1, y=0, t=t, h=he12, layer=0)\n", + "ca2.series(name=\"obs2\", x=d2, y=0, t=t, h=he22, layer=0)\n", "ca2.fit()\n", "display(ca2.parameters)" ] @@ -776,7 +776,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -788,17 +788,17 @@ } ], "source": [ - "print('rmse:', ca2.rmse())\n", + "print(\"rmse:\", ca2.rmse())\n", "h12 = ml.head(d1, 0, t)\n", "h22 = ml.head(d2, 0, t)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t, he12, 'b.', label='obs at 30 m, sig=0.02')\n", - "plt.semilogx(t, h12[0], color = 'r', label = 'ttim at 30 m')\n", - "plt.semilogx(t, he22, 'g.', label='obs at 90 m, sig=0.02')\n", - "plt.semilogx(t, h22[0], color='orange', label = 'ttim at 90 m')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)')\n", - "plt.title('ttim analysis with synthetic data and errors with sig=0.02')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, he12, \"b.\", label=\"obs at 30 m, sig=0.02\")\n", + "plt.semilogx(t, h12[0], color=\"r\", label=\"ttim at 30 m\")\n", + "plt.semilogx(t, he22, \"g.\", label=\"obs at 90 m, sig=0.02\")\n", + "plt.semilogx(t, h22[0], color=\"orange\", label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"ttim analysis with synthetic data and errors with sig=0.02\")\n", "plt.legend();" ] }, @@ -906,11 +906,11 @@ } ], "source": [ - "ca5 = Calibrate(ml)\n", - "ca5.set_parameter(name='kaq0', initial=10)\n", - "ca5.set_parameter(name='Saq0', initial=1e-3)\n", - "ca5.series(name='obs1', x=d1, y=0, t=t, h=he15, layer=0)\n", - "ca5.series(name='obs2', x=d2, y=0, t=t, h=he25, layer=0)\n", + "ca5 = ttim.Calibrate(ml)\n", + "ca5.set_parameter(name=\"kaq0\", initial=10)\n", + "ca5.set_parameter(name=\"Saq0\", initial=1e-3)\n", + "ca5.series(name=\"obs1\", x=d1, y=0, t=t, h=he15, layer=0)\n", + "ca5.series(name=\"obs2\", x=d2, y=0, t=t, h=he25, layer=0)\n", "ca5.fit()\n", "display(ca5.parameters)" ] @@ -929,7 +929,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -941,17 +941,17 @@ } ], "source": [ - "print('rmse:', ca5.rmse())\n", + "print(\"rmse:\", ca5.rmse())\n", "h15 = ml.head(d1, 0, t)\n", "h25 = ml.head(d2, 0, t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, he15, 'b.', label='obs at 30 m')\n", - "plt.semilogx(t, h15[0], color = 'r', label = 'ttim at 30 m')\n", - "plt.semilogx(t, he25, 'g.', label='obs at 90 m')\n", - "plt.semilogx(t, h25[0], color='orange', label = 'ttim at 90 m')\n", - "plt.xlabel('time (d)')\n", - "plt.ylabel('drawdown (m)')\n", - "plt.title('ttim analysis with synthetic data and errors with sig=0.05')\n", + "plt.semilogx(t, he15, \"b.\", label=\"obs at 30 m\")\n", + "plt.semilogx(t, h15[0], color=\"r\", label=\"ttim at 30 m\")\n", + "plt.semilogx(t, he25, \"g.\", label=\"obs at 90 m\")\n", + "plt.semilogx(t, h25[0], color=\"orange\", label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time (d)\")\n", + "plt.ylabel(\"drawdown (m)\")\n", + "plt.title(\"ttim analysis with synthetic data and errors with sig=0.05\")\n", "plt.legend();" ] } diff --git a/pumpingtest_benchmarks/10_moench_test.ipynb b/pumpingtest_benchmarks/10_moench_test.ipynb index 6a268cf..4da1c85 100755 --- a/pumpingtest_benchmarks/10_moench_test.ipynb +++ b/pumpingtest_benchmarks/10_moench_test.ipynb @@ -17,8 +17,8 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -34,10 +34,10 @@ "metadata": {}, "outputs": [], "source": [ - "b = 10 #aquifer thickness in m\n", - "Q = 172.8 #constant discharge rate in m^3/d\n", - "rw = 0.1 #well radius in m\n", - "rc = 0.1 #casing radius in m" + "b = 10 # aquifer thickness in m\n", + "Q = 172.8 # constant discharge rate in m^3/d\n", + "rw = 0.1 # well radius in m\n", + "rc = 0.1 # casing radius in m" ] }, { @@ -53,22 +53,22 @@ "metadata": {}, "outputs": [], "source": [ - "r1 = 3.16 \n", + "r1 = 3.16\n", "r2 = 31.6\n", - "data0 = np.loadtxt('data/moench_pumped.txt', skiprows=1)\n", - "t0 = data0[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "data0 = np.loadtxt(\"data/moench_pumped.txt\", skiprows=1)\n", + "t0 = data0[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h0 = -data0[:, 1]\n", - "data1 = np.loadtxt('data/moench_ps1.txt', skiprows=1)\n", - "t1 = data1[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "data1 = np.loadtxt(\"data/moench_ps1.txt\", skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h1 = -data1[:, 1]\n", - "data2 = np.loadtxt('data/moench_pd1.txt', skiprows=1)\n", - "t2 = data2[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "data2 = np.loadtxt(\"data/moench_pd1.txt\", skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h2 = -data2[:, 1]\n", - "data3 = np.loadtxt('data/moench_ps2.txt', skiprows=1)\n", - "t3 = data3[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "data3 = np.loadtxt(\"data/moench_ps2.txt\", skiprows=1)\n", + "t3 = data3[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h3 = -data3[:, 1]\n", - "data4 = np.loadtxt('data/moench_pd2.txt', skiprows=1)\n", - "t4 = data4[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "data4 = np.loadtxt(\"data/moench_pd2.txt\", skiprows=1)\n", + "t4 = data4[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h4 = -data4[:, 1]" ] }, @@ -85,11 +85,11 @@ "metadata": {}, "outputs": [], "source": [ - "#Set kaq, Saq, Sy and kzoverkh as given in Moench (1997)\n", - "kaq = 1e-4 * 60 * 60 * 24 #convert from m/s to m/d\n", + "# Set kaq, Saq, Sy and kzoverkh as given in Moench (1997)\n", + "kaq = 1e-4 * 60 * 60 * 24 # convert from m/s to m/d\n", "Sy = 0.2\n", "Saq = 2e-5\n", - "zh = 0.5 #kzoverkh" + "zh = 0.5 # kzoverkh" ] }, { @@ -107,9 +107,15 @@ } ], "source": [ - "ml1 = Model3D(kaq=kaq, z=[0, -0.1, -2.1, -5.1, -10.1], Saq=[Sy, Saq, Saq, Saq], \\\n", - " kzoverkh=zh, tmin=1e-5, tmax=3)\n", - "w1 = Well(ml1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml1 = ttim.Model3D(\n", + " kaq=kaq,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[Sy, Saq, Saq, Saq],\n", + " kzoverkh=zh,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", "ml1.solve()" ] }, @@ -120,7 +126,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -138,16 +144,16 @@ "hm4 = ml1.head(r2, 0, t4, layers=3)[0]\n", "hm0 = ml1.head(0, 0, t0, layers=3)[0]\n", "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t0, -h0, '.', label='pumped well')\n", - "plt.loglog(t0, -hm0, label='ttim pumped well')\n", - "plt.loglog(t1, -h1, '.', label='PS1')\n", - "plt.loglog(t1, -hm1, label='ttim PS1')\n", - "plt.loglog(t2, -h2, '.', label='PD1')\n", - "plt.loglog(t2, -hm2, label='ttim PD1')\n", - "plt.loglog(t3, -h3, '.', label='PS2')\n", - "plt.loglog(t3, -hm3, label='ttim PS2')\n", - "plt.loglog(t4, -h4, '.', label='PD2')\n", - "plt.loglog(t4, -hm4, label='ttim PD2')\n", + "plt.loglog(t0, -h0, \".\", label=\"pumped well\")\n", + "plt.loglog(t0, -hm0, label=\"ttim pumped well\")\n", + "plt.loglog(t1, -h1, \".\", label=\"PS1\")\n", + "plt.loglog(t1, -hm1, label=\"ttim PS1\")\n", + "plt.loglog(t2, -h2, \".\", label=\"PD1\")\n", + "plt.loglog(t2, -hm2, label=\"ttim PD1\")\n", + "plt.loglog(t3, -h3, \".\", label=\"PS2\")\n", + "plt.loglog(t3, -hm3, label=\"ttim PS2\")\n", + "plt.loglog(t4, -h4, \".\", label=\"PD2\")\n", + "plt.loglog(t4, -hm4, label=\"ttim PD2\")\n", "plt.legend();" ] }, @@ -185,11 +191,11 @@ "for i in range(len(h0)):\n", " r = (h0[i] - hm0[i]) ** 2\n", " res0 = res0 + r\n", - " \n", + "\n", "n = len(h1) + len(h2) + len(h3) + len(h4) + len(h0)\n", "residuals = res1 + res2 + res3 + res4 + res0\n", - "rmse = np.sqrt(residuals/n)\n", - "print('RMSE:', rmse)" + "rmse = np.sqrt(residuals / n)\n", + "print(\"RMSE:\", rmse)" ] }, { @@ -214,9 +220,15 @@ } ], "source": [ - "ml2 = Model3D(kaq=1, z=[0, -0.1, -2.1, -5.1, -10.1], Saq=[0.1, 1e-4, 1e-4, 1e-4], \\\n", - " kzoverkh=1, tmin=1e-5, tmax=3)\n", - "w2 = Well(ml2, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml2 = ttim.Model3D(\n", + " kaq=1,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", + " kzoverkh=1,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w2 = ttim.Well(ml2, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", "ml2.solve()" ] }, @@ -254,17 +266,18 @@ } ], "source": [ - "ca2 = Calibrate(ml2)\n", - "ca2.set_parameter(name='kaq0_3', initial=1)\n", - "ca2.set_parameter(name='Saq0', initial=0.2)\n", - "ca2.set_parameter(name='Saq1_3', initial=1e-4, pmin=0)\n", - "ca2.set_parameter_by_reference(name='kzoverkh', parameter=ml2.aq.kzoverkh, \\\n", - " initial=0.1, pmin=0)\n", - "ca2.series(name='pumped', x=0, y=0, t=t0, h=h0, layer=3)\n", - "ca2.series(name='PS1', x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca2.series(name='PD1', x=r1, y=0, t=t2, h=h2, layer=3)\n", - "ca2.series(name='PS2', x=r2, y=0, t=t3, h=h3, layer=1)\n", - "ca2.series(name='PD2', x=r2, y=0, t=t4, h=h4, layer=3)\n", + "ca2 = ttim.Calibrate(ml2)\n", + "ca2.set_parameter(name=\"kaq0_3\", initial=1)\n", + "ca2.set_parameter(name=\"Saq0\", initial=0.2)\n", + "ca2.set_parameter(name=\"Saq1_3\", initial=1e-4, pmin=0)\n", + "ca2.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml2.aq.kzoverkh, initial=0.1, pmin=0\n", + ")\n", + "ca2.series(name=\"pumped\", x=0, y=0, t=t0, h=h0, layer=3)\n", + "ca2.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca2.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", + "ca2.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", + "ca2.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", "ca2.fit()" ] }, @@ -375,7 +388,7 @@ ], "source": [ "display(ca2.parameters)\n", - "print('RMSE:', ca2.rmse())" + "print(\"RMSE:\", ca2.rmse())" ] }, { @@ -385,7 +398,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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m0uoNUps6f94SG4t97FhissZiH5tFzNgx5jRrLPZRo8yXF4lBp6FhC5UnSzjqKOagMYbtTS2UNbVyoLUtvE9CwEdK4ynSGutIb6on0+9lQnpquCWemZlJfHx8FL+LziSwe0kbBjUVB6kIhffRPZ0vn+cUFJE9bTppY7MH9JmbEENZx74DzqCFb4/6Ete5ZuOvOozv8GH8h09PO71/wGrFPmpU12E+dixWtzt635S4KE2BIGVNrZR5Wtje1Mr2phb2t7SFX2TjDvjCrfC0pgZytJ9JGWmMCbXER48eHbWe6hLYEeb3eqku3xEO8E6Xz0P3vrPk8rkQ/aqnl+S1YRA4cQJ/VRW+qsP4qg+fDvWqKoL19Z32tyYnY88aS8zYLOxjxxDTIcxt6enyiNwg0RwIstPTynaPGeDbGlvY19IWvj8eG/CR2lhHmqee9KYGLrXBlIw0MkOX0keOHIn9jCsxfdFLXQK7jzXVnqRy+1YzwMtK8Z5x+TynYDqZk6Zgi4mJck2FGNoicUk+2NRktsarDuOvNqe+w1X4D1fjP3q00xgCyuEwQ3zMWOrTnBxKN8iefQUFM69GDbLOTsNRS9CgvGOINzSzp6WN9ofQnAEfqU31pDfVk9HcyESHlfxQSzw5+RQVld/CMHxYLDERe7OeBHY/OvPy+ZHduzCCAZzueIqWXUvhldcSm3D+e+hCiIFH+/34jx7Fd7ga/+GqcKg3HNyLr6oKpz+0n8tBXF4BzoJ8XPkFuArysY0aJbfMBgFv0GB3s5ftTebl9NKGJva0+Aj9aIkJ+Enz1DOO/UxL+IhJaiepNDA+dyU5Od/odfkS2FHk87ZS9vZHlL39OrWHy7A5HOR/ainF19xAQnpGtKsnhIiAx8se55HND5NxymDCMcUN/nxyjwZp21WO9pt/6q1pabjy83EV5OPML8CVNxVrUlKUay56wmcY7Gn2hu+Hb6lrZHdrG34s3KZ/zzWWt/qlhS0vc+1jp476KHkVgoFP40qZwaicA2x74xVKX1/L5PmLmHndjaRl5US7mkKIXigeUYzd5qAmzU9dhp3bl36fcRmFaJ8P7569tJZtx7u9jNayMjzr1oUfUbNnZ4Vb4M78fJyTJ2NxnnvwDtH/YiwW8uNjyY+P5VZSAfAbmi3Ht2JrzueS9OX98qY1aWH3sa5e2nJpcSyb166h7B+v42/zkjt9JjOv/zxjJk09/wGFEANST++fB5ua8O7cSWtZWTjEA8eOmRttNpwTJnS6lB6Tmyv3w4cRuSQeRed6aUtrUyOlr69ly2sv4W1qZPSEycz67OfJLZopPU+FGEb8x2vw7iijdXsZ3rLttG4vw/B4APMZcmde3ulL6QX52EaOlPvhQ5QEdpSd76Ut/jYvO955k5KX/07jiRpSx2Qx87obmTR/EVYZgk6IYUcbBr6KynB4t5aV0Vbe4X54ehquvNP3w/eP0pS07I7Km/VEZElgDxLBQIC9G95n04vPcbKqgvjUdGZc81nyL19KjHP4DjcnhDBf6dq2Zw+t20/fD/cdPBjefjhNsSvXxqdu/DZ5V3wBS1xcFGsrLpYE9iCjteZQaQkfr3mO6vIdON3xFF55LUXL5JEwIcRpwaYmnn/pp+x49+9MqjKYclgTEwDsdmILC4mbP4+4efNwTp0q98EHCQnsQezo3nI2rXmOAyUbscWEHgm7Vh4JE0KYOr7hLdaw8X8Z32LUruN41q+nbVc5AJbEROLmzCFu3jzi5s8jZsyYKNdadEcCewiora7i4xefp/yDd9BaMyn0SFi6PBImxLDXXQ/1QG0tzRs20rx+Pc3r14d7o9uzs4ibNw/3/PnEzp6NdQANfjHcSWAPIY0nT7DllRfY/laHR8Kuu5HMSVOl16gQoltaa3wHD9L8oRnezZs2oVtawGrFlZ9P3Pz5xM2fhys/X0YviyIJ7CGo1dNE6esvs/XVl2htamTUhEnMuv4mxk+XR8KEEOenfT5at23Ds349zR+ux7tjBxgGlrg4YufMIW7eXOLmzSMmJ0caA/1IAnsIO/1I2As0njje4ZGwhVhtcpYshOiZYEMDzRs/ovnDD2levx5/dTUA9tGjw53XYufMwZYsoxD2JQnsYcAIBtmz4X02rXmWk1UVuFPTKL7ms+RffqU8EiaEuGC+qqpweDdv/AijqQmUwjl1aqjz2nxcRYVsr9/V6xHSxGkS2MOI1pqK0s1sevFZqnftwBnnpnDZZ5hx9fU43e5oV08IMQjpQADvjh14PvyQ5vUbaN22DQIBtNPB9swApTmwY6KDH3/xtxLavSSBPUwd3bub95/6K9W7SrA7XBR/5gZmXHM9jlh5oYIQ4uIFPR5aNm1i04uPoTeVknnKXN86KpnMpdfhXrKY2OnTUTEx0a3oICSBPUy1v8fc7z1OoG0jwbZ9OOPcFH/mcxQtu5YYV2y0qyiEGMTanwFPOuWj+ADcVj8Z69ZytM+HJS6OuMsuw71oEe6FC7ClpUW7uoOCBPYwdeZIYVPm2ak78jYHt3yMMz6BmZ/5HEVXXotdhvMTQlykM58BN1paaN64Ec876/C8+y6Bmhrz3nd+Pu5FC3EvXoxzyhTped4NCexhqruRwj7Zv4f1zzxJRelmYhOTmHX95yn49FXYYxzRrrIQYgjRWtNWXk7TOjO8vdvLQGtsGRnh8I6bM0fee96BBPYwdq6Rwo7s3sX6Z56gasc24pJTmHX9TRRcfiU2ue8khOgDgdpaPO+9j2fdOpo//BDD40HZ7cTOnm1eOl+8iJixY6NdzaiSwBbndHhXGev/9gTV5Ttwp6Yx54abyVvyaXmOWwjRZ7TPR8uWLXjWvYtn3Tp8FRUAxIwfj3vxItyLFhFbVDTs3romgS3OS2vN4Z3b+fDpv3B0bznxaenM+dwXmbrochmTWwjR53wVFXjefRfPu+/S/HEJ+P1Y4uNxLzA7rsUtXDgsXtrSZ4GtlEoBngZygArgZq11XRf7BYGy0GKV1vq6nhxfArv/aa2p3LaFD595gmP795I4YiRzb7yFyZctxiLD8wkh+kHQ00zz+g/N1vd77xE8eRKUwjVtGu7Fi3EvXoRj4sQh2XGtLwP7p8AprfVqpdQqIFlr/e9d7OfRWl/wWzsksKNHa82hrSV8+Le/UHPoAMmjRjP3xluYOH8hFosEtxCif2jDwLtzZ/jSuXfnTgBsI0fSNmsqe6bEM/5Tn6Uwa3aUaxoZfRnYe4DFWutPlFKjgHVa64ld7CeBPUhprdlfspENf3uCE1UVpGSOZd5NX2LC7PkyyIgQot/5a2pofu89ql9/Ef/Gj3H6wWcD68wiMq+8DveiRdhHjYp2NS9aXwZ2vdY6qcNyndb6rJsMSqkAUAoEgNVa6xd6cvxIBvbmyjo2HqxlTm4qM7KH/n2QSNOGwd6P1rPh2Sepra4ibWw28266lUtmzR2Sl6WEEAPb42WP83+bHmZSVZAZBxRLqtw4axoAcEyahHvxIuIXL8ZZUDCoGhe9Cmyl1FvAyC423Qv8sYeBPVprfVQplQu8DVyutT7QTXl3AncCZGVlzaisrDxn/Xpic2Udv3j8t1QGUjhuG8UTt8+R0L5IhhFkz4YP2PDMk9R9coSMnPHMu/lL5E6fJcEthOg37W9Z8xt+7BY7j336USZ7EvC88w5N69bRumUrGAbWlJTQI2OLiZs/D+sAH1Mh6pfEz/jMH4CXtdbPnu/4kWph/+atMj73/jXUazc3+e/jjqXT+Zcll/T6uMOZEQyy+8N32fDsU9Qf/4QRuZcy/+ZbySmcIcEthOgXZ75lraNgfT2e9z/As24dnvffx2hsBLuduJnFoY5ri4nJyopSzbvXl4H9AFDbodNZitb6+2fskwy0aK3blFJpwAbgeq31rvMdP1KB3d7CftxyPx8zBeeK55kxLqPXxxUQDATY9f7bbHzuaRpPHGfUhEnMv+k2svKnSXALIQYEHQjQunWr+ca1de/iO2Be4I3JzQ33Oo+dPh01AB5h7cvATgX+BmQBVcBNWutTSqli4Ota69uVUvOA3wAGYAF+rrX+bU+OH+l72PXrf8/le34ExV+Fa34GEigREwz42bnuH2x8/mmaak+QOWkq82++lbFTC6JdNSGE6MRXVRXudd788cfmM98JCbgXLDADfMFlWJOSzn+gPiAvTunozR/Chz+HK/8H5v5zZI8tCPj9lL39Oh/9/W80151iRO5k4tPzmDhnBhPnTBlUnT+EEENfp2e+332XYG0tWCy4phcR337pfPz4frtiKIHdkWHAM1+B8pfhlr/CxGWRPb4AwO9r48OnX2DLK2vQRiMAjlg3Y6fmM3ZqPmMm55GelSMBLoQYMLRh4C0rC186bysvB8A+Zkz4vnfsrJlY+nC8BQnsM/la4PdXwcl98LXXYWR+5MsQbH6tgo0vHMAINqKD1aSMqqe1oYLGE8cBcLrjyZw0lbFT8hkzJY/07Bx5KYsQYsDwHzt2+tL5hg3otjZUbCzu+fNwL16Ce9HCiI/zLYHdlcZP4PHLzfk73ob4rp5cE73R3fCejSdqOLyrjOryHRzeVUbD8WMAOOLizACfnMfYqQWk54yTABdCDAhGayvNH31k9jpf9y6BY+bfrYMrFpP51bvO6qV+sSSwu/PJdvjdMkifACtegZjYvitrmDrX8J7tGk+eoLp8B9WhEK/75CgAMa5YxkyeypjJeYydkk/GuPHyPnMhRNRprdm+4UXW/Ok/2TzO4Gimk8eWPhaR0D5XYEe/D3s0jSqAz/8WnroF/n4n3PQnkHuqETUyN7HboG6XkJbOlAVLmLJgCQCeU7UcLt9B9c4yDpfv4OCWjwGIcbnInDiFMVPMe+Ajci+RkcSEEP1OKcXH8Sd4bq75+JPV8FNyvCRirezuyF+7iVfBlT+B1/8D3v4vuOK+aNdo2HOnpDJ5/iImz18EQHN9nXkJfdcOqst38P6TfwDA7nAyeuLk0D3wfEaOv0TG8BZC9IviEcXEWGPCb1orHtFloziihvcl8XZaw8srYfPv4fpfQtFtfV+muGgtDfXh+9/Vu3Zw8rD5+lqbw8HoCZMZOzmPMVPzUZYRHDvoOefleCGEuFjnetPaxZJ72D0R9MMTN0HFB/CVFyDnsv4pV/RaS2MDR8p3cri8jOqdZZyoqghtsaEsLlA2EtPjcbqd2OwxWO12bDExWO0x2ELzNnsM1hhz2Vwfgy3GHt7HGhODPbzP6WOceTyrzYZSqkf37oUQ4kwS2D3VWg+/XQrNNXD7PyB1fP+VLSKmtamRd598hz3rt6ANLxAgeaQDd4qdoM9HwO8j4PcT8PkI+n2hqZ+A35z2ltVmJxi0AhaUUjjdMVjtViwWc1kpC8rSPg2ts1g6rD9juX2+fX34OGd+ztzW1hyg1RMgLsmFO8mFxWbFYrVhsVpDXzYsVgsWqw1rx3Vn7Ge12lBWa2if0HaLtcv92udPVrdwvMLDmEkZjL40RV5PK8QFkk5nPeVKgi89bT7u9cRNcPtbEJsS7VqJC+SKT6Dw04up3JEYfqTsym8U9ailqw2DYCAQDu+Azwx0c9lHwOc3p6HQN08A/OHgD/h9HNlzkqP7ToI2QGmSRsaTMjoWtEYbBoZhoLVGaw2dlg3Q2lxuX2cYaG1gBIOh5fbp6W3h/QwDvy+Ip67VLBuNI9YCmJ83vwIYwWCf/wzA7JhjczixOxzm1YgYRxfz7V8xHZZD86Fle2idzdE+7+iwPQZbTEz48T+5siGGMmlhd6VqI/zxMzB2Ntz2PNj67q02ou9E6493d8+f94fNr1Xw0ZqDaA3KArOvy2XGspxO+7QHfDAYwAgEMYwgRiBwVqgHA4HwCYy5zji9LRhAB4MEg6HPGkEqttVwaNtx88SDAKMvdZM+1om/rc08mWlrI+D3mcttbQR8bfh97fM+/L62i77CYbXbsVrt+H0WUA6UxcXoS0eSNCIFp9uNKz7BnLoTcMbH43TH43KbU1sfvrVKiAslLewLlTXH7Hz2/B1mZ7TrH5GBQgahnjxS1lflXr+yKConC5kTkrHaLOGThcwJZ4/7rpRChS5hE8GsSs9u4OiB0ycqC2658BMVwwgS9PnDQd5p2h76ZwZ9aN3RfSc5duAkWreBbqXu6O7V9AQAACAASURBVBHqPjmIt6mRYCDQbZk2hyMc4K74eJxx8WeFujM+ITzvio/HEec+65FCad2LviYt7HN5+yfw3k/NR70uWxm9eghxAaIZHNEuu6srG1prAm1ttHqa8Ia+Wps6zjfi9Xho9ZhTb1Mj3mYPrU2NaMPotrwYV2w4wC1WFyeq/KDisNiSmHdjETkFuSSkZWB3OvvxX0EMdtLp7GJpDc99DXY8Bzf/GaZcF726CCHOK5InDFprfK2teD2N4YAPh/4ZyyerT+KprUcbTUDnPgKuhEQSM0aQkD6CxIwRJKZnkJg+goSMESSkZcgledGJXBK/WErB9b+C+ip4/k5IHAOZ06NdKyFENyJ5G0QphSM2FkdsLIkZ5x5roL11HwgEsSgvC24egdXeTGPNcRpOHKfxRA01h/azf9MGjGDny/NxySkkhEI8HOzpI0jIyCAhLV1eBiTCpIXdE54aeOxyCPrMgUISM6NdIyHEANOT1r02DDz1p2ioMUO8oeYYjSdqaDxx3Fx38kTny/BK4U5JNcM8PYOEjJHmNBTu8alpWKxWuX8+hMgl8Ug4vst8RjslB/7pNXC4o10jIcQQYwSDeE7V0tAe4Cfag91sqXtqa0O98E3KYiE2MRWvxwmWJGwxI1h822VMnJuH3SH3zgcjCexI2fcWPHkTXHolfPEJkKEfhRD9KBjw01Rb26llfrD0ICerjmAEa0G3AaCUheTRmWTk5JIxbjwZ2blkjMvFFZ8Q5e9AnI8EdiRtegxe+S7MvdscNEQIIaKo8/1zD8VXufG1HuNE5UFqDh2kqfZEeN/41HQyxuWSkZNLek4uI3LGE5+WLm+kG0Ck01kkzboDTu6DDY+Yry4t/mq0aySEGMbO99x/S2MDJyoOUVN5kJpDB6ipOMjBzR+HL60749xkjMslPTvUGs/JJWX0GBl7fgCSFvbFCAbgqS/Cgbfhtmdh/KeiXSMhhOgxf5uXE5UV4VZ4TcUBTlZVEvD7ALDZY0jLzglfSs/IGU9aVrbcF+8Hckm8L3gb4XfLoKEabn8T0idGu0ZCCHHRjGCQU0cOU1N5KNwSr6k4QFtzM9DFffEc89J6wwktPdQjSAK7r9Qfhsc+BXaX+bhXXFq0aySEEBGjtabp5AmOVxwIt8RPVBzqdF9cWeJR1gxsjrF8avnlTJ5fIJfTe0ECuy9Vb4Y/XA2jCmH5i2BzRLtGQgjRp9rvi295vYTK7bsxAp+gjQYA7E4XmRMnM2ZyHpmTpzJy/ARsdnn5S09Jp7O+NGYG3PBreGYFvPivcMNvZKAQIcSQFpuQSHZBIQ73OGqqzPe3K1ooWmrDU3uII+U7+OCvfwLMkdRGXTqRMZPzGDMpj1ETJhLjdEX5OxicJLAjYeoNULsf3v4xpF4Ci74f7RoJIUSfO1cP9damRo7s3kV1+Q6qy3fy0d//xkbjryiLhRG5l5gBPnkqmROn4nTLi6h6Qi6JR4rW8MI3YNtT8PnfQd6N0a6REEIMGL7WFo7u3U11+U6qy3dwbP8ec9hTpUgfm03m5LxwiMclnT0s7HAh97D7S6AN/vRZOLIZVrwMY2dFu0ZCCDEgBXw+ju3fa7bAd+/k6J5y/G1eAJJHZTJm8tRQgOeRkJ4R5dr2Hwns/tRcC49fDm1NZs/x5Oxo10gIIQa8YCBATcWBcAv8yO6d4UfK4lPTwwGeOXkqKaPHoJQakoOeSGD3txN74bdXQPxo+Nrr4Bwav0hCCNFftGFw8nAl1bt3miG+q4yWhnrAHGM8LWsCxyviUZbR2Bwj+Ox3pg+J0JbAjoaD6+AvN8K4RWy+7DdsrGhgTm4qM7KH770ZIYS4WFpr6o8dDbfAD27ZhtdTa25ULjJyplB45Xyy84tISEuPbmV7oc8CWyl1E3AfMBmYpbXuMl2VUsuAXwBW4HGt9eqeHH9QBzbA5j/CS9/kCWMpP/CvIMZm4Ynb50hoCyFELx072MDfH3wXf1sVOlCFzX4Ub5PZAk8ePYbs/EKyC4oYOyUfR2xslGvbc335HPYO4HPAb85RuBX4JfBpoBr4WCn1otZ6Vy/LHvhmLGfr1o+5tfrP7LGM4onAlWw8WCuBLYQQvTQyN5EbvrsofA97xLgEag9XUrF9K5Vlpex4501KX38Zi9XKqEsnkp1fRHZBISPHTxi0b2LrVWBrrcuB8w3NNgvYr7U+GNr3r8D1wNAPbMC4/D7e+cNO7rU9yYfGDObkzot2lYQQYkgYmZvY6b51WlYOaVk5FF97AwG/n6N7yqks20rl9lLWP/sk6595AkdsHGOn5psBPq2IpBGjBs3wov3x4pRM4HCH5Wpgdj+UOyDMGJfG9i/8EsuzS3h23GskZy+PdpWEEGLIs9ntZOUVkJVXwIJbltPa1EjVju2hAN/K/o83ApCQPoLsgkKy84vIyp+Gyx0f5Zp377yBrZR6CxjZxaZ7tdZrelBGV6cu3d44V0rdCdwJkJWV1YPDD3wFU6bAon8j+Z2fQMUHkHNZtKskhBDDiis+gYlzL2Pi3MvCHdgqt5dSWbaVPevfp+wfr4NSjBh3CdkFheQUFDFqwuQB9R70iPQSV0qtA77bVaczpdRc4D6t9ZWh5XsAtNb/c77jDvpOZx35W+GRmeBMgrveBcvgvIcihBBDjREMcuzA3nCAH927G20Y2BwOxk7OI7ugiOz8QlLHZvf55fNoD/7xMXCpUmoccAT4IvClfih3YLG74NP/Bc/+E2z9M8xYEe0aCSGEACxWK6MnTGb0hMnM/fwttLW0cHhXGZWhDmyH/vQ4AHHJKWTnTSN72nSy8wtpOmXp1xe39PaxrhuA/wXSgXqgVGt9pVJqNObjW1eH9rsa+DnmY12/01r/pCfHH1ItbDDfN/77q+HkXvjXzeBKinaNhBBCnEfjyRoqy0qp3F5KVVkprU2NAFisadhcc4iJncT1K4siEtry4pSB5JNt8JtFMPdf4MoenbcIIYQYILRhUFNxkPXPvkPFtq1YHYXYnJcw+7pcZizL6fXxzxXYll4fXVyYUdNg+pfho1/DyX3Rro0QQogL0D486NzP30xsys3YnJdgtVrInND379eQwI6GT/0AbC54/d5o10QIIcRFaB8LfPZ1uRG7HH4+EtjR4M6ARd+Hfa/DvreiXRshhBAXYWRuIjOW5fTboCMS2NEy++uQkguv3wNBf7RrI4QQYoCTwI4WWwxceb/ZY/zj30a7NkIIIQY4CexomrAMcpfAuvuhuTbatRFCCDGASWBHk1Kw7H+gzWOGthBCCNENCexoy5gMM78GJb+D4zujXRshhBADlAT2QLD4HnAmwmurzLehCSGEEGeQwB4IYlNg8X/Aofdg99po10YIIcQAJIE9UBR/FdInwxv3QqAt2rURQggxwEhgDxRWGyy7H+oqYOOvol0bIYQQA4wE9kAy/lMw8Wp470FoOh7t2gghhBhAJLAHmqU/Ni+J/+O/ol0TIYQQA4gE9kCTOh7mfB1Kn4AjW6JdGyGEEAOEBPZAtPB7EJcGr90jj3kJIYQAJLAHJmeiOQTn4Y2w47lo10YIIcQAYIt2BS6U3++nuroar9cb7ar0LddMuPoFaDFg105QA+/cyul0MmbMGOx2e7SrIoQQQ96gC+zq6mri4+PJyclBKRXt6vSttiyo3QfxKRA/Ktq16URrTW1tLdXV1YwbNy7a1RFCiCFv4DXbzsPr9ZKamjr0wxrA4QZnEjTVQMAX7dp0opQiNTV16F/pEEKIAWLQBTYwPMK6XcJoQEPj0WjX5CzD6ucghBBRNigDe1ixOcA9Arx15jCc3cjJyeHkyZP9WDEhhBD9SQJ7MHBngMUOjdXymJcQQgxTEtgXqKKigkmTJrF8+XIKCgr4/Oc/T0tLS6cWbklJCYsXLwbgvvvuY/ny5SxdupScnByef/55vv/975Ofn8+yZcvw+/2A2UL+93//d2bNmsWsWbPYv38/ACdOnODGm25m5jVfZuanb+TDt18FoLa2lqVLl1JUVMRdd92FliAXQoghbVgE9ubKOn75zn42V9ZF5Hh79uzhzjvvZPv27SQkJPCrX517sI4DBw6wdu1a1qxZw2233caSJUsoKyvD5XKxdu3p4TQTEhLYtGkTd999N9/+9rcB+Na3vsXKlSv5uGQLz/3+EW7/+t1gBPnRj37EZZddxtatW7nuuuuoqqqKyPcmhBBiYBp0j3VdqM2Vddz6+EZ8AYMYm4Unbp/DjOzkXh1z7NixzJ8/H4DbbruNhx9++Jz7X3XVVdjtdvLz8wkGgyxbtgyA/Px8Kioqwvvdcsst4enKlSsBeOutt9i1a5e5gzZo9Hho+mQ/7733Hs8//zwA11xzDcnJvfuehBBCDGxDPrA3HqzFFzAwNPgDBhsP1vY6sM/sHa2UwmazYRgGwFmPOjkcDgAsFgt2uz38eYvFQiAQ6PK47fOGYbBhwwZcLpe5oa4SWutAa+mlLYQQw8iQvyQ+JzeVGJsFqwK7zcKc3NReH7OqqooNGzYA8NRTT3HZZZeRk5PD5s2bAXjuuYt7nejTTz8dns6dOxeApUuX8sgjj4T3KT1YA0qxcHYhTzzxBACvvvoqdXWRudwvhBBiYBrygT0jO5knbp/Dd5ZOjMjlcIDJkyfzxz/+kYKCAk6dOsU3vvENfvjDH/Ktb32LBQsWYLVaL+q4bW1tzJ49m1/84hc89NBDADz88MOUlJRQUFDAlClT+PVjvwX3CH74zRW8t+5tpk+fzhtvvEFWVlavvy8hhBADlxrIvYuLi4t1SUlJp3Xl5eVMnjw5SjUye4lfe+217NixI6LHzcnJoaSkhLS0tPPvbBhwotx8v3j6JIjipfFo/zyEEGIoUUpt1loXd7WtVy1spdRNSqmdSilDKdVlAaH9KpRSZUqpUqVUSXf7iR6yWCAhEwJeaJGXpQghxHDQ205nO4DPAb/pwb5LtNaDPl1ycnIi3roGOvUW7xFnIsS4ofETcCWDZcj3HxRCiGGtVy1srXW51npPpCojLoBSkJgJOghNx6JdGyGEEH2svzqdaeANpdRmpdSd/VTm0GePhdhUaD4Bfhk1SwghhrLzXkdVSr0FjOxi071a6zU9LGe+1vqoUioDeFMptVtr/V435d0J3AlIz+eeiB8FrfXme8ZTxke1A5oQQoi+c97A1lpf0dtCtNZHQ9MapdTfgVlAl4GttX4UeBTMXuK9LXvIs9ohfiQ0HoG2RvPethBCiCGnzy+JK6XilFLx7fPAUszOaoNSfX19p3eHV1RU8OSTT4aXS0pK+OY3v9m/lYpLA6sDGo6ANvqkiPvuu48HH3ywT44thBDi/Hr7WNcNSqlqYC6wVin1emj9aKXUK6HdRgAfKKW2AZuAtVrr13pTbjSdL7CLi4vP+27xiFMWswNasA2aB31HfCGEEF3obS/xv2utx2itHVrrEVrrK0Prj2qtrw7NH9RaTwt9TdVa/yQSFY+WVatWceDAAQoLC/ne977HqlWreP/99yksLOShhx5i3bp1XHvttUDPh9bsaPHixXz7299m3rx55OXlsWnTpvCxOrZw8/LyqKioCA/3efvd/0be5V/g1q/8E2+9/hrz58/n0ksv7fT5L3/5y3zqU5/i0ksv5bHHHgsf64EHHmDmzJkUFBTwwx/+MLz+Jz/5CRMnTuSKK65gzx55GEAIIaJJHt69QKtXr2bHjh2UlpYCsG7dOh588EFefvnl8HJHBw4c4J133mHXrl3MnTuX5557jp/+9KfccMMNrF27ls9+9rNnldHc3Mz69et57733+OpXv3re577379/PM888w6O/fJiZM4t58s+/44MPPuDFF1/k/vvv54UXXgBg+/btbNy4kebmZoqKirjmmmvYsWMH+/btY9OmTWitue6663jvvfeIi4vjr3/9K1u3biUQCDB9+nRmzJgRgX9BIYQQF2NwB/arq+BYWWSPOTIfrlodscP1dGjNjtqH2Vy4cCGNjY3U19efs4xx48aRn58PwNQpU7h8zjSUv/WsMq6//npcLhcul4slS5awadMmPvjgA9544w2KiooA8Hg87Nu3j6amJm644QZiY2MBuO6663rzzyCEEKKXBndgDwI9HVqzo/MN3wmdh/BsLwPA4ojF4XBC4xEsytbt8J3ty1pr7rnnHu66665O237+85/L8J1CCDGADO7AjmBLuKfi4+NpamrqdjkSnn76aZYsWcIHH3xAYmIiiYmJ5OTkhC+7b9myhUOHDnX9YWUxX1Xq80Bb51HD1qxZwz333ENzczPr1q1j9erVuFwufvCDH3Drrbfidrs5cuQIdrudhQsXsmLFClatWkUgEOCll146K9SFEEL0n8Ed2FGQmprK/PnzycvL46qrruL+++/HZrMxbdo0VqxYEb603BvJycnMmzePxsZGfve73wFw44038qc//YnCwkJmzpzJhAkTuj+AIx5sTmg6jKE1NY1efAGDWbNmcc0111BVVcUPfvADRo8ezejRoykvLw+Pv+12u/nLX/7C9OnT+cIXvkBhYSHZ2dksWLCg19+XEEKIiyfDaw4wixcv5sEHH6S4uNvBz3qmrQlq93NcJ1Ojk/i/n61mTEYK/7Hq+5GpaMhQ/3kIIUR/6rPhNcUA5ojHa4snjXpsBNGALxCMdq2EEEJcJLkkPsCc+VhYbxjxo1Gn9jJSneJf/u0exqXFRezYQggh+pe0sIewWFcsgdg0kpWH8UmKOIecnwkhxGAlgT3ExSSOAosNV8snMID7KwghhDg3CeyhzmKFhNHgb4HWumjXRgghxEWSwB4OXClgj4XGo2BIxzMhhBiMJLAvgtVqpbCwkLy8PG666SZaWloAc7CMqVOnUlBQQGFhIR999BEAjzzyCJdccglKKU6ejMJoWkpBQiYYfvAc7//yhRBC9Jr0QroILpcrPPjHrbfeyq9//Wvmzp3Lyy+/zJYtW3A4HJw8eRKfzwfA/Pnzufbaa1m8eHH0Ku1wgzMZPDUQmwo2x/k/I4QQYsCQwO6lBQsWsH37dnJyckhLSwu/1zstLS28TyTefhYRCaPB22BeGk8ZF+3aCCGEuADD4pJ4aU0pj5c9TmlNaUSPGwgEePXVV8nPz2fp0qUcPnyYCRMm8M///M+8++67ES0rImwxEJ8B3nrzTWhCCCEGjSEf2KU1pdzxxh3875b/5Y437ohIaLe2tlJYWEhxcTFZWVl87Wtfw+12s3nzZh599FHS09P5whe+wB/+8IfefwORFpcBFjs0HJHHvIQQYhAZ8pfES46X4Av6MDDwG35KjpdQmFHYq2N2vIfdkdVqZfHixSxevJj8/Hz++Mc/smLFil6VFXEWKyRmQl0FtNRCXNp5PyKEECL6hnwLu3hEMTHWGKzKit1ip3hELwfV6MaePXvYt29feLm0tJTs7Ow+KavXnEkQEwdNn4DR9ZjcQgghBpYhH9iFGYU8tvQx7i66m8eWPtbr1nV3PB4Py5cvZ8qUKRQUFLBr1y7uu+8+AB5++GHGjBlDdXU1BQUF3H777X1Shx5TChLGmGHdJI95CSHEYCDDaw5n9ZXQUgfpk8DuvKhDyM9DCCEiR4bXFF2LH222thuPRLsmQgghzkMCeziz2iF+JLQ1grcx2rURQghxDhLYw11cOlhjzFa2NqJdGyGEEN2QwB7ulMXsgBbwQnMU3nMuhBCiRySwBTgTICYemo5BUB7zEkKIgUgCW5gdzxIzQQfNZ7OFEEIMOBLYF6i+vp5f/epX4eWKigqefPLJ8HJJSQnf/OY3L/r4K1asYNy4cRQWFjJ9+nQ2bNgAwMaNG5k9ezaFhYVMnjw5/Iz37t27mTt3Lg6HgwcffPCiy8Xugtg0aDkJ/taLP44QQog+IYF9gc4X2MXFxTz88MO9KuOBBx6gtLSU1atXc9dddwGwfPlyHn30UUpLS9mxYwc333wzACkpKTz88MN897vf7VWZAMSPAmWFhmp5z7gQQgwwQ/5d4pG2atUqDhw4QGFhIZ/+9Kd5//33KS8vp7CwkOXLl1NUVMSDDz7Iyy+/zH333cehQ4f45JNP2Lt3Lz/72c/YuHEjr776KpmZmbz00kvY7fZuy1q4cCH79+8HoKamhlGjRgHmO8unTJkCQEZGBhkZGaxdu7b335zVZoZ2Y7U5DKcrqffHFEIIERHSwr5Aq1evZvz48ZSWlvLAAw+wevVqFixYQGlpKStXrjxr/wMHDrB27VrWrFnDbbfdxpIlSygrK8Plcp03ZF966SXy8/MBWLlyJRMnTuSGG27gN7/5DV6vt0++P+LSwOaUx7yEEGKA6VULWyn1APAZwAccAP5Ja13fxX7LgF8AVuBxrfXq3pTb7tj999NWvjsShwpzTJ7EyP/4j4gd76qrrsJut5Ofn08wGGTZsmUA5OfnU1FR0eVnvve97/HjH/+Y9PR0fvvb3wLwn//5n9x666288cYbPPnkkzz11FOsW7cuYvUMUwoSMuHUAfCcgPgRkS9DCCHEBettC/tNIE9rXQDsBe45cwellBX4JXAVMAW4RSk1pZflDhoOhwMAi8WC3W5HKRVeDgS6foSq/R72m2++SV5eXnj9+PHj+cY3vsE//vEPtm3bRm1tbd9U2pkAjgTwHIOgv2/KEEIIcUF61cLWWr/RYXEj8PkudpsF7NdaHwRQSv0VuB7Y1ZuygYi2hHsqPj6epqambpf7ytq1a7n66qtRSrFv3z6sVitJSX14jzkxE2p2Q+NRSB6gw4QKIcQwEslOZ18Fnu5ifSZwuMNyNTA7guX2q9TUVObPn09eXh5XXXUV999/PzabjWnTprFixQqKior6pNw///nPrFy5ktjYWGw2G0888QRWq5Vjx45RXFxMY2MjFouFn//85+zatYuEhITeFWhzmq8tba4x72vHxEXmGxFCCHFRzju8plLqLWBkF5vu1VqvCe1zL1AMfE6fcUCl1E3AlVrr20PLXwZmaa3/tZvy7gTuBMjKyppRWVnZabsM59iPjADUlIPVAWmXmve3zyA/DyGEiJxzDa953ha21vqK8xx8OXAtcPmZYR1SDYztsDwGOHqO8h4FHgVzPOzz1U/0IUvoMa+Gw9BaB7Ep0a6REEIMW73qdBbq/f3vwHVa65ZudvsYuFQpNU4pFQN8EXixN+WKfhSbCjaXeS/bCEa7NkIIMWz1tpf4I0A88KZSqlQp9WsApdRopdQrAFrrAHA38DpQDvxNa72zl+WK/qIUJI4Bww+emmjXRgghhq3e9hK/pJv1R4GrOyy/ArzSm7JEFDnc4EwyAzs2FWwx0a6REEIMO/KmM9EzCaMBbV4aF0II0e8ksEXP2BzgHgHeOmjzRLs2Qggx7EhgXwSr1UphYSF5eXncdNNNtLS0dFo/depUpk2bxs9+9jMMw3wfd21tLUuWLMHtdnP33XdHs/oXz50BFrs5OIiM5iWEEP1KAvsiuFyu8DCXMTEx/PrXv+60fufOnbz55pu88sor/OhHPwLA6XTy3//9370bszraLFbz0ri/FVpORbs2QggxrEhg99KCBQvCQ2B2lJGRwaOPPsojjzyC1pq4uDguu+wynE5nFGoZQa5ksMdBkzzmJYQQ/WlYBPaxgw1sfq2CYwcbInrcQCDAq6++Gh4C80y5ubkYhkFNzRB6HEop8z3jRsAcHEQIIUS/iOS7xAekYwcbWPPQVoIBA6vNwvUrixiZm9irY7a2tlJYWAiYLeyvfe1r3e57vle/DkoxceBKMYffDA6Lcz4hhIi6IR/YR/bWEQwYaA3BoMGRvXW9Duz2e9Xnc/DgQaxWKxkZGb0qb0BKGA3eevBG9qqFEEKIrg355lHmhGSsNgvKAlarhcwJyf1S7okTJ/j617/O3XffHR4De0ix2s3HvPwtcOCdaNdGCCGGvCHfwh6Zm8j1K4s4sreOzAnJvW5dn0v7pXK/34/NZuPLX/4y3/nOd8Lbc3JyaGxsxOfz8cILL/DGG28wZcqUPqtPn4vLMAcIee0e+PoHYB3yv05CCBE1w+Iv7MjcxIgGtcfT9YtDgsFz95quqKiIWB0GBIsFXElwohw2/x5m3RHtGgkhxJA15C+Jiz5mj4WcBfDOT+TZbCGE6EMS2KL3lq02O5+tWx3tmgghxJAlgS16b2QezFgBHz8ONbujXRshhBiSJLBFZCy51xyG8/V75D3jQgjRBySwRWTEpcGiVXDgbdj7erRrI4QQQ44EtoicWXdA2gSzlR3wRbs2QggxpEhgX6D6+np+9atfhZcrKip48sknw8slJSV885vfvOjjr1ixgnHjxlFYWMj06dPZsGFDp/XTpk1jwoQJfOUrX+HIkSPhz917772MHTsWt9t90WX3mtUOV/4PnDoIH5kjmG2urOOX7+xnc2Vd9OolhBBDgAT2BTpfYBcXF/Pwww/3qowHHniA0tJSVq9ezV133dVp/bZt29izZw9FRUUsWbIEn89syX7mM59h06ZNvSo3Ii69Ai5dCu89wLbde7n18Y38/2/s4dbHN0poCyFEL0hgX6BVq1Zx4MABCgsL+d73vseqVat4//33KSws5KGHHmLdunVce+21ANx3330sX76cpUuXkpOTw/PPP8/3v/998vPzWbZsGX6//5xlLVy4sMuhO5VSrFy5kpEjR/Lqq68CMGfOHEaNGhX5b/hiXHk/+FuwrfsJvoCBocEfMNh4sDbaNRNCiEFLAvsCrV69mvHjx1NaWsoDDzzA6tWrWbBgAaWlpaxcufKs/Q8cOMDatWtZs2YNt912G0uWLKGsrAyXy8XatWvPWdZLL73U7dCdAP+vvXuPi6rcFz/+eeaCiJgi4hUlNDWCgRHIa7q10i3ayex0OeUp+KVZu4tlvx9lr9ptSytf2bHfb9dul1v30dxap7JOobHVTmhWUuINCS+hQlKURGAqiAzz/P5YIzdBBWYYZvi+X6/1WrOeWZfvemZe8521nrXWEx8fz4ED7fA2Ypx+sQAAG9VJREFUqp5DYMR9XPXTR8RZCjArsFpMjBoU6u3IhBDCZ/n0o0kzVi7jeMERt66zV8QgJqbMcdv6kpKSsFqt2Gw2qqurmTJlCgA2m63JR5WmpqayaNEiwsLCWLFiRZPrbtddd/7ucVT2O7zV9UPeGvY6owb3JCGibTpeEUIIfyRH2B7WqVMnAEwmE1artabnLpPJhMPhaHSZc23YmzdvJiYmpsl17969m6ioKPcH7Q6du8O1f6Trz9/w4Om/kNC/i7cjEkIIn+bTR9juPBK+VF27duXkyZNNTrcFrTWvvvoqRUVFNUfs7VJ8MpTmw5f/F4oPwG1vQbAf9g0uhBBtQI6wmyk0NJSxY8cSExNDamoqsbGxWCwW4uLieOWVVzy67dTU1Jrbunbs2EFGRgYBAQEAPP7444SHh1NeXk54eDgLFizwaCyXxGSCSc/Cv66AH/fAsgnwwy5vRyWEED5Jted20MTERJ2VlVWvbP/+/e33NHAHdMmfR9FeeGcmnC6GG1+F2Ns8H5wQQvgYpdROrXViY+/JEbZoG33jYM4W6J8IH9wLG5+C6sbb8IUQQpxPErZoO116wt3/DSPmwPbXYM0t0oe2EEJcIknYom2ZrTB1Cdz4GhR8CX+bCD/nejsqIYRo9yRhC++IvwtSNkDVGVh+PeR+7O2IhBCiXZOELbxnwAijXbtXFLx7F3z2PDid3o5KCCHapVYlbKXUEqXUAaVUtlLqQ6VU9ybmy1dK7VNK7VFKZTU2j+igLutrHGnb/x0+fwn+ayac+c3bUQkhRLvT2iPszUCM1joWOAQ8eYF5J2qt7U1dru5LzGYzdrudmJgYbr31VsrLywF4/vnniY6OJjY2Frvdztdffw3AzJkzGTZsGDExMdxzzz0X7fSjw7EGwvTXIOklOLTROEVectjbUQkhRLvSqoSttd6ktT53b04mEN76kNq/zp07s2fPHnJycggICOCNN95g+/btrF+/nl27dpGdnc2nn37KgAEDACNhHzhwgH379lFRUcHy5cu9vAftkFIw8j7jKvLTxbBsInz3qbejEkKIdsOdbdj3AOlNvKeBTUqpnUqptn+eqAeNGzeOvLw8ioqK6NmzZ82zw3v27Em/fv0AmDp1KkoplFKMGDGCwsJCb4bcvkWON9q1uw80bvv64hVoxw/3EUKItnLRhK2U+lQpldPIML3OPE8BDmBNE6sZq7WOB5KAB5VS4y+wvTlKqSylVFZxcXEzd6dtORwO0tPTsdlsTJ48mWPHjjF06FAeeOABtm7det78VVVVrF69un0//7s9CImAWRshegZ8ugDWzYKz5d6OSgghvOqiCVtrfb3WOqaR4SMApVQycAMwUzfxnFOt9Y+u8XHgQ2DEBba3TGudqLVODAsLa8k+Napw/ja3rauiogK73U5iYiIDBw5k1qxZBAcHs3PnTpYtW0ZYWBi33347K1eurLfcAw88wPjx4xk3bpzbYvFbAV3glr/DdX+CnA/g75Oh7HtvRyWEEF7Tqt66lFJTgCeA32mtGz0EUkp1AUxa65Ou15OB51qz3ZYIX+y+JHmuDbshs9nMhAkTmDBhAjabjVWrVpGSkgLAs88+S3FxMW+++abb4vB7SsG4x6B3DKybbXQecusqiJQ/PEKIjqe1bdivAV2Bza5btt4AUEr1U0p94pqnN/CFUmov8A2wQWv9z1Zut905ePAg3333Xc30nj17iIiIAGD58uVs3LiRt99+G5NJbn1vtqGT4d7PICgU3poOXy+Tdm0hRIfTqiNsrfUVTZT/CEx1vT4CxLVmO77g1KlTPPzww5SVlWGxWLjiiitYtmwZAPfffz8RERGMHj0agJtvvplnnnnGm+H6np5XwOxP4YP7ID0VftoL05aCpZO3IxNCiDbRqoTdUZ06deq8soSEBL766qtG53c4pFcqtwjsBv+2Fra8AJ8vgeKDcNtq4+ErQgjh5+T8rPAtJhNc+7TRlv1zrtGuXSgPzxNC+D9J2MI3Rd8Eszcbp8T/Mwl2/8PbEQkhhEdJwha+q3e08ZCVgaPhowfhk8ehWh77KoTwT5KwhW8L6gH//gGMehC+eRNWz4DTJQDsLCjlLxl57Cwo9XKQQgjRenLRmfB9ZgtMeQH62CDtEVg2gdzf/ZWZH57krMNJgMXEmtmjSIgI8XakQgjRYnKELfyH/Q64Jx2cDoasv5lJzi9xaqhyOMk8UuLt6IQQolUkYTdTWVkZr7/+es10fn4+a9eurZnOyspi7ty5LV5/SkoKkZGR2O124uPj2b59OwCZmZmMHDkSu91OVFQUCxYsAGDNmjXExsYSGxvLmDFj2Lt3b4u37Rf6J8CcLVT2tPGq9VW2dprHuwHPcmf+07Dh/8DWJbBzFRz8J/ywC04UguOst6MWQoiLklPizXQuYT/wwANAbcK+8847AUhMTCQxsXVdfi9ZsoRbbrmFTZs2cd9995GdnU1ycjLvvvsucXFxVFdXc/DgQQAiIyPZunUrISEhpKenM2fOnJp+uDusrr0JnpPOD+kvUXksmyGm37js9BHYtx3OlDW+TOcQCO4Nwb1c497QJez8sqAeYDK37f4IIQSSsJtt/vz5HD58GLvdzqRJk9i2bRv79+/HbreTnJzM8OHDefnll1m/fj0LFizg6NGjFBUVcejQIZYuXUpmZibp6en079+ftLQ0rFZrk9saP348eXl5ABw/fpy+fY0HhJjNZq666ioAxowZUzP/qFGjpOvOcywB9P+Xp88vd1TCqePGcPo4nPrZNf1z7evCHca4qpHH4yuzK5GH1SbxmoTuGnfpxZ7SAL4qrGTkoDASLu/h+f2tY2dBKZlHShg1KFTa7YXwI5Kwm2nx4sXk5OTUdP6xZcuWmgR9brquw4cPk5GRQW5uLqNHj2bdunW89NJLzJgxgw0bNnDTTTc1ua20tDRsNhsA8+bNY9iwYUyYMIEpU6aQnJxMYGBgvflXrFhBUlKSG/fWD1k6QfcBxnAxlacaJPQ6if10sTE+vt8YO+s/zc7uGvgcNAplMoMyGQlfmYyjdKUaTNd93+Sabt4yv1U6Kf/hJFc5FaVbLJReHkZI1yAwWY2L80xWMFuNsclc+7rhe2YLmOqWWS7pvX0/lbOr8BRxg/pij+wLlkBjX4QQrebTCTs9PZ2ffvrJrevs06ePW5NeUlISVqsVm81GdXV1TV/YNpuN/Pz8RpdJTU1l0aJFhIWFsWLFCgCeeeYZZs6cyaZNm1i7di1vv/12vT8HGRkZrFixgi+++MJtsXd4nYKNIXTwhedzOo1T7a5kvumbfezI2U9nXYlFORk9KISrB3YD7aw/OKtdr6vrTOsG03Xe17qJZVzrq67i9KnTBOtyzKoaq65GF/8KJxU4q6Da4RpXGX8wnA7jta52W5XZXAN1W2UsncHaGaxBrnHD1w3LGo4blFkCz3/P0sn4MyOEH/PphO0LOnUyOqcwmUxYrVaU60fFZDI1+Yzxc23YDQ0ePJg//OEP3HvvvYSFhVFSUkJoaCjZ2dnMnj2b9PR0QkNDPbczonEmk9G2HdQDekURao5j9beZVFU7sVpMjL1uFLTRqekfC0qZuTyTKoex7TW3j6LHxbbtdLoSeJ1kXpPUG0n01VXGtNNR771NOYVszD6GhWqCOMukoZcxZmCQ0bRQVQFVZ+q8Loezp+H0L/XLqiqgurIFe67AGkSVOZBTKhhrcCjBIb2gs+tz6RziGvcwen2red1DOpARPsOnE7Y3Tv927dqVkydPNjntKRs2bGDq1Kkopfjuu+8wm810796d77//nptvvpnVq1czdOhQj8chLi4hIoQ1s0d5pR25Rds2mcAUAAS0atuhwaVsyKn9s3DDhBb+UXFWuxJ4nSTuaDBd77Ux/rmklIx9+XTVJ+lRfoq46gKCHPug/Fdj+aZYuzSS1JsahxjjwG7nHdHLtQPC03w6YXtDaGgoY8eOJSYmhqSkJF544QUsFgtxcXGkpKQwfPhwj2x39erVzJs3j6CgICwWC2vWrMFsNvPcc89RUlJSc9W6xWIhK0s6w/C2hIgQr/1oe2vbbvujYjLXNkc0w/sZefxH1UGcGswKHrMN48GJrh6AqyqMxF3xayPj0vrTZceMcUUZ0ES/68pcL5mXEczR/CqqnSG8n9Gb4GkTGDYsBi7rJ3cVCLdRWjfxhWwHEhMTdcPks3//fqKiorwUkWhIPg/RXuxs2BzQ2qfbOavhzIkLJPra8S/FRThOlRBGGWZV5zfVZIVu4RASAd0j6owjjddBodL2LupRSu3UWjd6b7AcYQsh/ILbmyJM5tprEy6iwPVnAcdZBlp+5bWkUIYGlEBZAZQWGOMDG6D8l/oLWrs0kszrjDt1bd0+CL8iCVsI4TfaS3PA0KZiqDwFZd/XJvLS/NrX+dvg7Kn683fuYSTukMsbJPPLodsAsBjXHUj7eccgCVsIIdzgkv4sdAqG3lcZQ0NaG6fZy/Jrj8rPjYuyYf9644r8Ggou68fJzv05VhRIhbMPf/1sEA/P/FfirpQLUP2RJGwhhGgPlIIuocbQP+H8953VcLLovGR+Mv8AI1QeN1k+N+Z7ZzF07Qt9YqFvHPR1jbsNkPZyHycJWwghfIHJbFzA1i0cGFtTXORqP+/kOEWs5XteGO1kwJk8KNoLeZuNh+oABHavTd594oxx6GC5it2HSMIWQggfVr/9/HoG1D0tX1UBP+dC0R74KdtI4l8vq304jTXI6Ee+T2xtMg+LqmkbF+2LJOwWMJvN2Gw2HA4HUVFRrFq1iqCgoJryqqoqLBYLycnJPProo5hMJjZv3sz8+fM5e/YsAQEBLFmyhGuvvdbbuyKE8ANNtp9bO0N4gjGcU10FxQddCdyVxPe+Azv+ZrxvskKvKFcCtxvJvE8MBHRpm50RTZKE3QKdO3eu6fxj5syZvPHGGzz22GP1yo8fP86dd97JiRMnePbZZ+nZsydpaWn069ePnJwcfv/73/PDDz94czeEEB2R2Wok4D4xYDe6BcbphNKjRvIu2msk84PpsPsfroUU9BziOp3uOhrvEwtBPeQK9TYkCbuVxo0bR3Z29nnlvXr1YtmyZVx99dUsWLCg3hPQoqOjOXPmDJWVlTXPGhdCCK8xmYz27NDBEHOzUaY1/PZjbQIvyoaC7bDvvZrFKoP7U3KyHxXVkSz9LIr/nXIH8YP7emkn/F+HSNgnTuyitPRrQkJG0q1bvNvW63A4SE9Pr+mBq6FBgwbhdDo5fvw4vXv3rilft24dw4cPl2QthGi/lIJu/Y3hyqm15adL4Ke9UJTN93u/5Irf9jHZsgMAxz9ehIFXQ8RYiBgDA0bIqXQ38vuEfeLELnbtvgun8ywmUwDxw1e3OmlXVFRgt9sB4wh71qxZTc7b8NGv3377LU888QSbNm1qVQxCCOEVXUJh8LUw+Fp+G5DMzOWZBFX+xkjLIf4Ue4I+pVmw7WX43Gn0ld5vuJG8I66BgSONjlNEi/h9wi4t/Rqn8yzgxOmsorT061Yn7Lpt1Rdy5MgRzGYzvXr1AqCwsJAZM2bw1ltvMXjwRfpYFkKIdq7+FeqT6XOuDfvMb3DsGyj4Egq+gu2vw5f/D5QJesfA5dcYSXzgGOMPgLgkfp+wQ0JGYjIF4HRWYTJZCQkZ2SbbLS4u5v777+ehhx5CKUVZWRnTpk3jxRdfZOzYsRdfgRBC+IBGr1APvAyGXG8MAGfL4YcsI3nnfwFZf4fM1433wqKM5H35WONUetc+bbsDPsTvE3a3bvHED1/tkTbshs6dKj93W9ddd93FY489BsBrr71GXl4eCxcuZOHChQBs2rSp5uhbCCH8VkAQRI43BgBHJfy4u/YIPPu/IGuF8V6PQa42cFc7eEiE9+JuZ1rdvaZSaiEwHXACx4EUrfWPjcyXDDztmlyktV51sXVL95rtn3weQohWq3YYV6KfS+AFX8GZMuO9bgNcbeCudvDQwX79iFVPd6+5RGv9R9eG5gLPAPc3CKAH8CcgEaNH+J1KqY+11qVu2L4QQghfZrZA/3hjGPOwcV/48VxX8v4SDn9mHIUDBPd2Je+xfGuNYUtpKKMGh3WIe8BbnbC11r/VmeyCkZAb+j2wWWv9K4BSajMwBXi7tdsXQgjhZ0ym2oe7jJxj3BNekmck7/wvjfG3HxIN9NPBZG6JoeCa6USMuAG6D/R29B7jljZspdTzwN3ACWBiI7P0B47VmS50lQkhhBAXplxPWus5BBJSQGveSv+c7C8/YZQpl7GmHPp+9SR89ST0GOy67WwiXD7OuADOT1xSwlZKfQo0duneU1rrj7TWTwFPKaWeBB7COP1dbxWNLNto47lSag4wB2DgQP/9pySEEKKFlCI6OpYXtpfzoWM8Voti3S09ia7YaZw+37PGeDa6MkP41UbyHnwt9Is3Tr/7qFZfdFZvZUpFABu01jENyu8AJmit73NNvwls0Vpf8JS4XHTW/snnIYTwliafY+44C4XfGMn7cIZxRToaOnWDyHFGAh800bgivZ1dwObRi86UUkO01t+5Jm8EDjQy20bgBaXUuRqdDDzZ2m0LIYTouJrspcwSYDyc5fJr4LpnoPxXOLrVSN6HM+DAemO+7gONI+9BE41bzoJ6tO0ONJPJDetYrJTKUUplYyTiRwCUUolKqeUArovNFgI7XMNz5y5A8zVlZWW8/vrrNdP5+fmsXbu2ZjorK4u5c+e2eP0pKSlERkZit9uJj49n+/bt9crj4uIYOnQod999d01vX+Xl5UybNo0rr7yS6Oho5s+f3+LtCyGE3wnqAdEz4MY/w6PZ8PAumPoy9LZBzgfwXjIsGQx/uxb+Z6FxYZvjrLejPp/Wut0OCQkJuqHc3NzzytrS0aNHdXR0dM10RkaGnjZtmtvWn5ycrN977z2ttdYbN27UNpvtvHKn06mXLl2qhwwZoisrK/Xp06f1Z599prXWurKyUl9zzTX6k08+cVtMF+Ltz0MIIVrFUaV1QabWn72g9fJJWi8I0fpPl2m9qK/Wa27TevtftT5+QGuns03CAbJ0EznRd1vfvWT+/PkcPnwYu93OpEmT2LZtG/v378dut5OcnMzw4cN5+eWXWb9+PQsWLODo0aMUFRVx6NAhli5dSmZmJunp6fTv35+0tDSsVmuT2xo/fjx5eXnnlSulmDdvHh9++CHp6elMnz6diRONi/MDAgKIj4+nsLDQY3UghBB+w2wxOiUZOBImPglnTsDRbXAkw2gDP/RPY77L+hunzgdPhEEToEvPNg9VEnYzLV68mJycnJrOP7Zs2VKToM9N13X48GEyMjLIzc1l9OjRrFu3jpdeeokZM2awYcMGbrrppia3lZaWhs1ma/L9+Ph4Dhw4wPTp02vKysrKSEtL45FHHmnFXgohRAcV2A2ibjAGgNKC2uR9YD3s+YdR3ifWSN62W6FP07/T7uTTCfuP3xWSc6rCreuMCe7MwiHhbltfUlISVqsVm81GdXV1Td/ZNpuN/Pz8RpdJTU1l0aJFhIWFsWLFiibXrRtc4e9wOLjjjjuYO3cugwYNcts+CCFEhxUSYdz7nZACzmoo2uO6+nyL0QtZ9whJ2P6iU6dOAJhMJqxWK8p1C4HJZMLhcDS6zJIlS7jlllsuuu7du3dz3XXX1UzPmTOHIUOG8Oijj7ohciGEEPWYzNA/wRjGp7I7r5Ad+b+SUFDaJo9G9emE7c4j4UvVtWtXTp482eR0W9Ba8+qrr1JUVFRzxP70009z4sQJli9f3qaxCCFER7SzoJSZq/Zx1uEkYOsPrJk9yuNJ2x23dXUooaGhjB07lpiYGFJTU4mNjcVisRAXF8crr7zi0W2npqbW3Na1Y8cOMjIyCAgIoLCwkOeff57c3Fzi4+Ox2+2SuIUQwoMyj5Rw1uHEqaHK4STzSInHt+nWJ525mzzprP2Tz0MI0RHtLChl5vJMqhxOrBaT246wPd29phBCCNGhJESEsGb2qMYfjeohkrCFEEKIFmjy0ageIm3YQgghhA/wyYTdntvdOxL5HIQQou34XMIODAykpKREkoWXaa0pKSkhMDDQ26EIIUSH4HNt2OHh4RQWFlJcXOztUDq8wMBAwsPb/l54IYToiHwuYVutViIjI70dhhBCCNGmfO6UuBBCCNERScIWQgghfIAkbCGEEMIHtOtHkyqlioECoBtwopFZGitvWNYT+MUjATauqVg9tY5Lmfdi8zSnfhsrb2w+X6v35i7f2npv7nvyXb/0+VtS7/Ib07p53fkbc6llvlbvl7p8hNY6rNF3tNbtfgCWXWp5wzIgqz3E6ql1XMq8F5unOfXbRB039jn4VL03d/nW1ntz35PvumfrXX5jPFfnra3fC5T5VL2743PzlVPiac0ob2retuKO7TdnHZcy78XmaU79Nlbu7TqH1sfQ3OVbW+/NfU++65c+f0vqXX5jWjevO39j2mOdQ9v/xpynXZ8SdwelVJZuoucT4TlS721P6tw7pN69oyPWu68cYbfGMm8H0EFJvbc9qXPvkHr3jg5X735/hC2EEEL4g45whC2EEEL4PEnYQgghhA+QhC2EEEL4gA6fsJVSXZRSO5VSN3g7lo5AKRWllHpDKfW+UuoP3o6no1BK3aSU+ptS6iOl1GRvx9NRKKUGKaVWKKXe93Ys/sz1O77K9R2f6e14PMVnE7ZS6u9KqeNKqZwG5VOUUgeVUnlKqfmXsKongHc9E6V/cUeda633a63vB24DOtQtGS3lpnr/b631vUAKcLsHw/Ubbqr3I1rrWZ6N1D81s/5vBt53fcdvbPNg24jPJmxgJTClboFSygz8BUgCrgLuUEpdpZSyKaXWNxh6KaWuB3KBn9s6eB+1klbWuWuZG4EvgP9p2/B91krcUO8uT7uWExe3EvfVu2i+lVxi/QPhwDHXbNVtGGOb8rn+sM/RWn+ulLq8QfEIIE9rfQRAKfUOMF1r/SJw3ilvpdREoAvGB1+hlPpEa+30aOA+zB117lrPx8DHSqkNwFrPRewf3PRdV8BiIF1rvcuzEfsHd33fRcs0p/6BQoykvQffPhC9IJ9N2E3oT+2/LDA+xJFNzay1fgpAKZUC/CLJukWaVedKqQkYp686AZ94NDL/1qx6Bx4Grge6KaWu0Fq/4cng/Fhzv++hwPPAcKXUk67ELlquqfr/M/CaUmoa7eMxph7hbwlbNVJ20SfDaK1Xuj+UDqNZda613gJs8VQwHUhz6/3PGD9qonWaW+8lwP2eC6fDabT+tdangf/V1sG0NX87dVAIDKgzHQ786KVYOgqpc++QevcOqXfv6tD1728JewcwRCkVqZQKAP4N+NjLMfk7qXPvkHr3Dql37+rQ9e+zCVsp9TawHRimlCpUSs3SWjuAh4CNwH7gXa31t96M059InXuH1Lt3SL17l9T/+aTzDyGEEMIH+OwRthBCCNGRSMIWQgghfIAkbCGEEMIHSMIWQgghfIAkbCGEEMIHSMIWQgghfIAkbCGEEMIHSMIWQgghfIAkbCGEEMIH/H/M0TQe3C9qBQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -403,16 +416,16 @@ "hm3_2 = ml2.head(r2, 0, t3, layers=1)[0]\n", "hm4_2 = ml2.head(r2, 0, t4, layers=3)[0]\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t0, h0, '.', label='pumped')\n", - "plt.semilogx(t0, hm0_2, label='ttim pumped')\n", - "plt.semilogx(t1, h1, '.', label='PS1')\n", - "plt.semilogx(t1, hm1_2, label='ttim PS1')\n", - "plt.semilogx(t2, h2, '.', label='PD1')\n", - "plt.semilogx(t2, hm2_2, label='ttim PD1')\n", - "plt.semilogx(t3, h3, ',', label='PS2')\n", - "plt.semilogx(t3, hm3_2, label='ttim PS2')\n", - "plt.semilogx(t4, h4, '.', label='PD2')\n", - "plt.semilogx(t4, hm4_2, label='ttim PD2')\n", + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0_2, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1_2, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2_2, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3_2, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4_2, label=\"ttim PD2\")\n", "plt.legend();" ] }, @@ -438,9 +451,15 @@ } ], "source": [ - "ml3 = Model3D(kaq=1, z=[0, -0.1, -2.1, -5.1, -10.1], Saq=[0.1, 1e-4, 1e-4, 1e-4], \\\n", - " kzoverkh=1, tmin=1e-5, tmax=3)\n", - "w3 = Well(ml3, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", + "ml3 = ttim.Model3D(\n", + " kaq=1,\n", + " z=[0, -0.1, -2.1, -5.1, -10.1],\n", + " Saq=[0.1, 1e-4, 1e-4, 1e-4],\n", + " kzoverkh=1,\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w3 = ttim.Well(ml3, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, Q)], layers=3)\n", "ml3.solve()" ] }, @@ -480,18 +499,19 @@ } ], "source": [ - "ca3 = Calibrate(ml3)\n", - "ca3.set_parameter(name='kaq0', initial=1, pmin=0)\n", - "ca3.set_parameter(name='kaq1_3', initial=1)\n", - "ca3.set_parameter(name='Saq0', initial=0.2, pmin=0)\n", - "ca3.set_parameter(name='Saq1_3', initial=1e-4, pmin=0)\n", - "ca3.set_parameter_by_reference(name='kzoverkh', parameter=ml3.aq.kzoverkh, \\\n", - " initial=0.1, pmin=0)\n", - "ca3.series(name='pumped', x=0, y=0, t=t0, h=h0, layer=3)\n", - "ca3.series(name='PS1', x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca3.series(name='PD1', x=r1, y=0, t=t2, h=h2, layer=3)\n", - "ca3.series(name='PS2', x=r2, y=0, t=t3, h=h3, layer=1)\n", - "ca3.series(name='PD2', x=r2, y=0, t=t4, h=h4, layer=3)\n", + "ca3 = ttim.Calibrate(ml3)\n", + "ca3.set_parameter(name=\"kaq0\", initial=1, pmin=0)\n", + "ca3.set_parameter(name=\"kaq1_3\", initial=1)\n", + "ca3.set_parameter(name=\"Saq0\", initial=0.2, pmin=0)\n", + "ca3.set_parameter(name=\"Saq1_3\", initial=1e-4, pmin=0)\n", + "ca3.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml3.aq.kzoverkh, initial=0.1, pmin=0\n", + ")\n", + "ca3.series(name=\"pumped\", x=0, y=0, t=t0, h=h0, layer=3)\n", + "ca3.series(name=\"PS1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca3.series(name=\"PD1\", x=r1, y=0, t=t2, h=h2, layer=3)\n", + "ca3.series(name=\"PS2\", x=r2, y=0, t=t3, h=h3, layer=1)\n", + "ca3.series(name=\"PD2\", x=r2, y=0, t=t4, h=h4, layer=3)\n", "ca3.fit()" ] }, @@ -614,7 +634,7 @@ ], "source": [ "display(ca3.parameters)\n", - "print('RMSE:', ca3.rmse())" + "print(\"RMSE:\", ca3.rmse())" ] }, { @@ -624,7 +644,7 @@ "outputs": [ { "data": { - "image/png": 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zScibgTt7Kq6pU3FNzcY91Rp3jLGzLtG7xsad1Na9Q3PsJRxjOnua2ylvaWNPcxuNIaujK0NrJvlaSWmoJb3FCvEZToPpU7I6z8KzsrJwj5JLNhLYQ6Cx+gwVe8qo2LOLir1l+Fut5vPM6TM6r39PmTMPp0uaz4UYKToQ6AzxZ3Y+xstlT5HWZJLZqFiupzOl2UmwqgqzpaXb94zERCvAs7Nx9Qz07GyMmBibfpEYClprTvgCkQBvZ09zG3ua2qgNWU3rSmtSfa2kNtUzqbmBjNZG5npd5GdN7gzxjIwMW+5Ul8AeYqYZ5uyRw1Ts2cXxPbs4/d67mOEwTo+HnHmFnde/06ZOk+ZzIUZIX/cOaK0xGxsJnDxJsOokwaoqgierCFRVWdMnT6L9/m7bckyahCt7Sq9n566sLKsLYTGmaK057Q92Bnh5czu7m1o5G3V9PNnXSlpTPenNDWS0NVMQ52FWVIinpaV16+VtOO5Sl8AeZoH2Nk7sL++8/l1/qgqA+JTUzvDOW7iEmARphhNiOF1sk3z43LmoAI8K86oqgqdPd7+pzjBwTs60wjw7uzPQK+J87IqrYdHMVfJI2xhSHRXie5rb2d3YwqmoEE/0tTGpuZ5JLY1M9rVQFB/L7MkZpKc3U9/wNbQOYhjuIetZTwJ7hDXVVFvXvsvLqCwvw9fSjNPlpmD1VZTceCvJmZPtLlEIMUA6FCJ09iyBPs7OQ9XV3Tp5P5uimLR4OVklq/AWFuCdX4AjPs7GXyAuVF0wxN7mdnY3W9fDdze2UBnoCvE4fzs5oUrmx+yiWG0njxPMyP8CeXn/Z9D7lsC2kWmGOfDWbna/+hJnj7yDNjWzV1zGsptvIyNvxF/jJIQYYmYgwBMbfsz6TY8yrdpk5mlYWBuH91yztYJSuPPziSksxFtYaIX4vHkYXq+9hYsL0hQKUx7VlL6rvpaKoMHd/JJrjI1yhj0eArvzkbKQiVJt5Bac4Mj21wm0t5O3cAlLb7qNnIIFcq1biDGst+vnhY4cfHv30r53L769+2jfW0645pz1BYcDz6xZxCwoxFtQiHdBId5Zs6xX6oox41TdTuobtjM1balcwx4Pgd1bpy0Fqyaxe/0L7HzxOdoaG5g8czbLbrqNmUuXW/1UCyHGnPNdP9daE6quxldeboV4+V58e/cSbmwErFfleubOJaawAG/hAryFBXhmzBh0d7FibJHAtlF/nbYEA372v/Ea2//6FxrPniFlylSW3ngr81atkcfDhJgAtNYEq6qsM/FIgPv27cNsbQWs3uG88+d3C3F3bq4c2I9jEtg2O1+nLWY4zKF3NrH92T9TffwI8SmpLLnhFoquXItnFPfII4QYeto0CRw/HjkT34evvBzfgQOdj54ZCQl4Cwo6m9NjFhSyz1nNjupSeenKOCCBPUZoranYs4vtzz1F5d49eGLjWHTtDSxeeyNxySl2lyeEsIkOhfAfOdK9Of3Qoc6XvTTFwqFsxYF8F3d87EGKSq6X+2LGKAnsMejM4UNse+4p3tu2BYfTSeHqq+WRMCFEJzMQwH/wIK+9/BCV2zYwr9JkcoO1zJmVRdzKFcStXEncihU4U1PtLVYMmAT2GFZ36iQ7/vpn9r/5OmbYlEfChBDdRN+hPqXRwfe8d5JafoLWrVsxm5oA8MyfR/zKlcStXElMcTGGx2Nz1aIvEtjjQEtdLaUvPMueV18k0N5ObtFilt18uzwSJoTo9Q51HQ7j27uX1s2bad20mbayMgiFUB4PsSUl1tn3pSvxzJ4tN7GNIhLY44ivtaX7I2EzZrHs5tuZsfQSDEMe/xBC9M5sbaV1+3ZaN22mdfNmAkeOAOBISyNuxYrOAHdlZtpc6cQmgT0OhQIB9r3xGjv++hcazp4mJSubpTf9nTwSJoQYkOCZM7Ru3mKdgW/ZQri2FgD3zBlWeK9cSdzSpRhx0q3qSJLAHsdMM8x772xm2zNPySNhQoiLok0T/6FDtG7aZDWfl5Zaj5G5XMQuXEjcpVaAewsLpSOXYSaBPQForakoL2P7s3/qfCRs4TXXs+S6m+SRMCHEBTH9ftpLS2ndvJmWzZvx7z8AWO8Rj1u+vLP53J2Tc1FvSBN9k8CeYM4cPsT25/7MoW2bOx8JW3bL7SROSre7NCHEGBSqraV1y1ar+XzzZkJnzgCgp2SwYXIdu/Lg4AwPP73xEQntQZLAnqDqTp3krSf+wJHSN1FKsfDq61h2y+3Ep8gzmUKIi6O1JnDsGK2bNnPgpd8TU36UmACEDGguzGXuB+8ifs1q3Dk5dpc6JklgT1Ad/ZgH/Q2E/e8QDuzD4XCy8NobWHbzbcQmvr+bVCGEGKiy6jI+8+I6plcGKDkCa0+lY1SeAqyb1xJWryZ+9WpiFi1COZ02Vzs2SGBPUD3fFFa0OoHmmrc48NZGnG43i6+7kZIbbyUmPsHuUoUQY1TPa9iB48dpeeMNmjdupG37DgiFMJKSiF+1ivjVq4lfdRmOJDlZ6IsE9gTV15vCak+eYMufnuDglrdwx8RSfMMtFN9wM55YeXxDCDF0ws3NtG7aTMvGjbS88Qbh+npwOIhdssQK7zWrcU+fLp0/RZHAnsD6e1NYTeVxNv/xcQ5v34I3Lp6SG29l8XU34vbG2FStEGK80uEw7Xv20LLxDVo2bsR/8CAArmnTiF99BQlr1hBbXIxyu22u1F4S2KJfZ48eZvOfHufozu3EJCax7Ka/Y+G1N+ByS3/DQojhETx1qqvpfMtWdCCAERdH3GWXWWffl6/CmZZmd5kjTgJbDMipQwfY9MfHqSwvIy4llUtuuZ0FV66VntOEEMPKbGujdetWWjZspGXjRkI1NaAUMUVFxK9ZQ/ya1Vaf5xOg6XzYAlsplQo8CeQBx4EPa63re1kvDJRHJiu11jcNZPsS2Pao2r+XTX98jKoDe0lIS2f5rXdQsPoqHHKXpxBimGmt8e3fb1333vgGvnIrOpxZWVbT+erVxF5yCYbXa3Olw2M4A/sHQJ3W+ntKqfuAFK31l3tZr0VrHX+h25fAto/Wmsry3Wz646Ocfu8gSRmZrLjto8y7bDWGdE0ohBghwepqWt98k+aNG2ndvAXd1oaKiSFuxQrqi/Mpm+mgaN7qcdNhy3AG9kFgtdb6tFIqC9iotZ7Ty3oS2GOU1ppju3aw6Y+PUX3sCClZ2ay4/aPMXbFKXsknhBhRpt9P27bttGzcSN1rL6POnAPg2GSDnLUfYuYH78RbMH9MN50PZ2A3aK2To6brtdbv67haKRUCyoAQ8D2t9TMD2f5QBnZpRT1bj9ayPD+N4lzpW/tCaa05vH0Lm//4OOdOVJA2dRorP3wXs5aukOAWQoy4R/Y8zNPrf8LiwyYlhzWzT2qUBmdGhnXT2urVxK1YjhEztp56GVRgK6VeBSb3suh+4LcDDOwpWutTSql84HXgSq31kT72dy9wL8C0adOKKyoq+q1vIEor6vnJI49QEUrjjDOLx9ctl9C+SNo0ObjlLTY/9XvqT1WRnpfPpR/+GPlLlo7po1ohxNhSVl3GPevvIWgGcRkuHl72I/L21dGyYQOtb7+N2daG8nqJW77cunFt9WpcmRl2l31etjeJ9/jOb4C/aa2fOt/2h+oM+6FXy7n1rRto1HHcHnyAddcU89k1Mwe93YnMDIc58PZGtvz59zSePUPWzDms/PBd5BYtluAWQoyIvt4UZgYCtG3fbt11vmEDwZMnAfAWFEQ6bFkzapvOhzOwfwjURt10lqq1/lKPdVKANq21Xyk1CdgC3Ky13n++7Q9VYJdW1POfj/yaXxnfppS5uO9+huLpo/9IaywIh0Lse+M1tv7lDzSfqyF77nwuvePj5MxfYHdpQgiB1hr/e+91PjLWXlYGWnc1na9ZTdyKFaPmrvPhDOw04I/ANKASuF1rXaeUKgE+o7Vep5RaCTwEmIAB/Fhr/cuBbH+or2HXb/4NVx18AJZ8Am78LxiFR1djVSgYZO/r69n69JO01tcxrXAhl97xMabMnmd3aUII0SlUV0fLG2+O2qZz6Tgl2mvfhLd+BNd8G1Z+fmi3LQgG/Ox55UW2PfsUbY0NZM0qIjGjkDkrlzKzeOaobIISQkxMZiDQedf5+5rOIx22eOePbNO5BHY004Sn7ob9z8Edj8G8Dw7t9gUAQZ+PN3//FLvX/xVttgIQm5RG3sKF5MxfQE5BEUkZmTZXKYQQlm5N5xs20L57ty1N5xLYPQXa4Dc3QM278MkXYcr4eOB+tCl96ThbnzmCGa5Dh0+QnF5PS+0R2pubAEhMz4yEt/VJnCT3FQghRodQba3VdL5xY/em8xUriF9jPTbmyhj6f7MksHvTfBYeuRLMENzzOiROGZ79TGC9vd4zMy+B2qpKTuwv58S+ck7sL8fX0gxAUuZkcuYXdQZ4Quokm3+BEEJENZ1v2GA1nZ86Zc2fM52DNy0g/8Y7h6ynNQnsvpzZC7+6FlLzrTNtzwV3xibOo7/Xe4L1XPe5ExWc2LeHyn3lVB0ox99qNaEnT84ip6DI+sxfQHxK6kiXL4QQ3Wit8R96j/eef4JjL/yJvy41KCvw8vA1Dw9JaEtg9+fQevj9HTD7OrjjUTCkn2w7mWaYmorjVO0vp3LfHk4e2Ie/zQrwlClTyZlf2BngccnS+Y0Qwh6PlD/CT3f+FBMTh3LwucWfY92CdYPebn+BLa9fmn0NrP0evPglePUb1t3jwjaG4SBz+gwyp8+g+IZbrAA/fozKfXuo2l/Ou5veYM+rLwGQmp3TGd45BQuITXz/GbwQQgyHkswS3A53Z09rJZm9ZuyQkjPsDs//C2x/GG78CRR/YmT2KS6YGQ5TfexIZ4BXvbufoK8dgLSp08gpKGJaQRHZ8wpoOke/zfFCCDEYffW0NhjSJD4Q4ZDVNH50I3zsz5C/emT2KwYlHApx9ujhyE1sezh5cD8hvx8AwzEJjASU4SRnbjrxqfE43S4cLjdOlxun243T5cLh7hi3Pg63C6fL07Wuu2t9h8uF0+3B4XT2+2zm+a7dCyFEbySwB8rXCL+8FppOwbpXIX32yO1bDIlwKMiZI4d555k3qSjfgzZ9QAhPrMLp0oSCQUIBP6FAAAb5d78zxDuCPzJtmgb1p/xoDJRSTM5PxhvvRimFUgbKsOZ3DpVCGQ6UoaLWUaAMjM51rWmllDUvMr9jnjIUhuEApWip99Nc6yc5M57kjHgMh6PzoxwOHJ1DJ4ZhYDidGIYDw+nAMHpZJ+r7hsOJ4TAiQ0dXHRFyoCLE4Mg17IHyJsFHn7Qe93ridlj3OsSl2V2VuAAOp4vsOfNYefsUaqrmdHukLDpAtNaY4TDhYIBQIEAoGCAUsMI8HAxGpq354UAgEvSBHusHuq0bjsyvO92E1iG0NtFoGmtCBHwutGlaH63RWoM20abGNE1rXa0harlpmlHT1ro6al1tWvPtZjisoMcwCAUADJRykpAWjzcuBqfHY7VSuN043R5cneNunG5vt2Ud4y6PJ9LK0ccytwfD8f4bROWAQYxncobdmxPbrY5VspfA3z8LTs/I1yAGza5/vHt7/nw492+FtxXkpS8dZ/vfjqK1iVImS66dRuEVWeiwSTgcwgyH0eEw4fcNQ53TphnGDFnDrmVm93XCYcxwCDNsos0w4VCIM0frOX24HnQYVJjULA8JqU5CAT/BQORAJxDobOEIBQKE/P6LPugwHI5uYQ5OWurDgBvliGH6wqmkZqXijU/AExdPTHwC3vj4btNOj0e6yxWjijSJX4y9f4anPgVFd8CHHpIXhYgLMlEOFga7b6ulI9RrmAf9vt5DPjIe7JxnDWsq66k92YA2/YAPpztIONCOGQ71uX+H04k3PqEzxL3xXcFuTVvLYuLi8cR3TXtiY61WhR6/X87uxWBJYF+sN34AG74Da/4NrvhX++oQ4gLYGRx27/t9PetNTyTk99Pe0oyvpRlfSwu+1siwpRlfa2TY0oy/tYX2jvktLZ1PH/RKKbyxcVaIxyVgOLzUVAZBxWE4k1jxoUVMK8wjKSMTT2zcyP0hiDFPAvtiaQ1Pfxr2PAm3/RoKb7WvFiHEeQ3lAUM4FMTf2hoJ+65gt0I/arq1hdqqc7TUNaLNZpXIZAkAACAASURBVCDcbTueuDgSJ2WQmJ5JYno6SemZkWnr441PkGZ50UluOrtYSsFNP4WGSnj6M5CUAzlL7a5KCNGHyflJQ3Zm73C6iE1KJjYp+bzrdpzdh0JhDMPHqg9n4XK30VRzlqZz1TTVVNNw5hSVe3e/78zd5Y0hcVI6SRmZJEzKICm9K8wTJ2UQm5QsgS4AOcMemNZaeOQDEGiFda9BSq7dFQkhRpmBnN1rrfG1NNNUY4V407lqGmvO0lRT0xnuHX3pd3C6PSROSu8W4okZ1ll6UnoGcckpnD3eLNfPxwlpEh8KNYfgkasgKRs+9TJ4E+2uSAgxDvnbWmmqqaaxI9SjztKbaqo7X0/bwXA40cSjjGQcrkwuueUSZi8rIDkzC2UYNv0KcbEksIfK0Y3w2N9ZvaDd+SQ45IqCEGJkBXztNJ+r6TwzP/TOIU4dqoy8d74WsP5Nd3m8pOdOJ2N6Pum5+WTk5TMpJzfyCJwYrSSwh1Lpb+Cv/wxL74Eb/t3uaoQQE1z03fGGYXL5HZMIh6qpPnaUmoqj1FQcI9BuXTdXhkFadg7peflk5E63hnn5xCRIi+FoITedDaXiu+Hce7DlZzBpFlzyabsrEkJMYJPzk7j5C4vffw17jTXQpklj9VmqK452hviJfXs48NaGzm0kpKWTnjedjLx8MnLzyZieT2J6ptzsNsrIGfbFMMPw5Mfg0EtW0/jsa+yuSAghLkhbUyPVx49Sc/yoNaw4Rt3Jqs6e59wxsWTk5UeCfAYZefmkTc3B4XTZXPn4Jk3iw8HfAr++DuqOWjehTS60uyIhhBiUoN/HuRMV1Bw/RvXxo1RHmtS73oDnJG1qjnUmnpdPel4+WqdRcyIgd6gPEQns4dJ0Ch7+ACgH3PM6JGTaXZEQQgwp0wzTcOZ0t7Px6uNHaWts6FxHGUk43FMpuWElBZcvIylzsjSnXyQJ7OF0qsw6086YB3c/D64YuysSQohh19pQz6antnDg7T2YodOYoZOgfQDEp6SSPa+QqfMKmTqvgLTsHHnEbIDkprPhNGUR3PqwdU376c9YXZjKX0whxDgXl5xC0Qcu5dju2Mgd6oo1d2Xga6mg6sA+qvaXc3DzmwB4ExKZOnd+JMALSc+d3uvrUUX/5Ax7qGz6CbzyNVj1L3Dl1+yuRgghRkRfPbxprWmsPkvVgb1UHdjLyQP7aDh7GgB3TAxT5sxn6twCps4rJHPGLJwuuZkNpEl8ZGgNf/0n2Pk7uOV/YNFH7a5ICCFGlea6c5w8sM86Az+wl9qqSgCcLjdZs+ZEmtELmDJrLi6v1+Zq7SGBPVLCQXjsVqjYAn//LORdandFQggxarU1NXLy4H5OHthL1YF9VB87itYmhsNBZv7Mzib0KXPm4Y2Lt7vcESGBPZLa6+GRq6HtnPWikLQZdlckhBBjgr+tjVOHDkSa0fdx5vAhzHAIlCI9dzpT51lN6FPnFhCblGzr+9eHiwT2SKs9Yr0oJDYV/uEVayiEEOKCBAN+zrx3sLMJ/dShdwkFrGfCE9On0NY8CeXIxuXN40P/ctm4CG0JbDtUbIbf3gTTlrPz8l+ypaKZ5flpFOem2F2ZEEKMSeFQkLNHj1B1YC/73txO3cnDoK0Aj0vJZu6ly8grWkz2vAJcbo/N1V6cYQtspdTtwAPAPGCZ1rrXdFVKrQX+C3AAj2itvzeQ7Y/pwAYo+z088xn+ZK7hy8F1uJ0OHl+3XEJbCCEG6czRRp75j1JCgbPoUCUpk+s4V3GQcCiEw+Uie24BeUWLyS1aTPq0vDHzHPhwPoe9F7gVeKifnTuAnwNXA1XAdqXUc1rr/YPc9+i36E527NzO7ZW/5JCRxa9CH2Tr0VoJbCGEGKTJ+Unc8sXiyDXs65mcn0TQ56Pq3X1U7NlJxZ4y3nz81/D4r4lNSiZ3wSJyixaTu2AR8alpdpd/UQYV2FrrA8D5uqBbBhzWWh+NrPsH4GZg/Ac2oD5wP+t/Xc6/Op/kTXMpy/NX2l2SEEKMC5Pzk7pdt3Z5vUxfVMz0RcUAtNTVUlFeRsWeXVSUl3Hg7Y0ApE2dRt7CxeQWLWHqvAJcnrHxCNlI9HSWDZyImq4CLhmB/Y4KxXlp7P7Iz+Cp1Tw55XmScz9ld0lCCDEhxKemUXDFlRRccSXaNKmpPE7Fnl0c37OLsvUvUPr8szicTrLnzie3aAm5CxaRkZc/apvPzxvYSqlXgcm9LLpfa/3sAPbR2+l3nxfOlVL3AvcCTJs2bQCbH/0WzpsDa76E+9UH4MjrMOMDdpckhBATijKMzreMLb3p7wgG/Jw8sM86A9+9k7ee+A1vATEJiUxbsKjz+ndC2iS7S+80JHeJK6U2Av/S201nSqkVwANa62sj018B0Fp/93zbHfM3nUUL+eHny8Dphc9sAod04y6EEKNFa0N9Z3hXlJfR2lAPQGp2Tmd4T51fiNs7vC94GvbHus4T2E7gEHAlcBLYDnxUa73vfNsdV4ENcOBv8ORdcN0P4ZJ77a5GCCFEL7TWnDtR0RneVfv3EgoGMBxOsufMs25eK1qMqSdx+nDjkHbcMpyPdX0I+CmQDjQAZVrra5VSU7Ae37o+st71wI+xHuv6ldb6OwPZ/rgLbK3hdzfD6d3wT7ukQxUhhBgDQoEAJw/u77z+XXP8qLVAeXHFXo4nroibv7B4SEJbOk4ZTc7ug/+9DJaug+t/aHc1QgghLlBbYwMbH3uFQ1t3YLjn4fRM45Kb8ilemzfobfcX2KPzVrjxLLMASj4F238J1QfsrkYIIcQFik1KZsl11xCTfB1OzzQcDoPs2cPfv4acYduhtRZ+uhimLIGPPw39P8cuhBBiFBqOl4/IGfZoE5cGq78KRzfAwRftrkYIIcRFmJyfRPHavBF76YgEtl2W/gNMmgPr77ce+RJCCCH6IYFtF4cL1j4IdUfhnf+1uxohhBCjnAS2nWZeBbPXwhs/hJZqu6sRQggxiklg2+2a70DIB6990+5KhBBCjGIS2HabNBMu+TTsegxOldldjRBCiFFKAns0uOJLEJsGL91n9YYmhBBC9CCBPRp4k+DKr0HlFtj3F7urEUIIMQpJYI8Wiz8OkxfA+q9DoM3uaoQQQowyEtijheGAtd+HpirY/FO7qxFCCDHKSGCPJnmXwvxb4O3/hMYqu6sRQggxikhgjzZXfxPQ8OoDdlcihBBiFJHAHm1ScmHl56H8T1C51e5qhBBCjBIS2KPRZV+AhCnw4pfBNO2uRgghxCgggT0auePgqgfgdBns/r3d1QghhBgFJLBHqwW3w9Sl1rVsX5Pd1QghhLCZBPZoZRjWY16t1fDWj+yuRgghhM0ksEezqcWw8E7Y+t/WaziFEEJMWBLYo92V3wDDBeu/ZnclQgghbCSBPdolZsGqL8K7f4OjG+2uRgghhE0ksMeCFZ+D5Gnw0lcgHLK7GiGEEDaQwB4LXF645jtQvR9Kf213NUIIIWwggT1WzLsR8lbBhgehrc7uaoQQQowwCeyxQilY+13wNcAb37e7GiGEECNMAnssmbwAlnwCtj0M1e/aXY0QQogRJIE91nzg38AdDy9/BbS2uxohhBAjRAJ7rImbBKvvgyOvw6GX7a5GCCHECJHAHouW3QNps+Dlr0IoYHc1QgghRsCgAlspdbtSap9SylRKlfSz3nGlVLlSqkwptWMw+xSAw2XdgFZ3BLY9ZHc1QgghRsBgz7D3ArcCbw5g3TVa60Va6z6DXVyAWVfDzKvhjR9AS43d1QghhBhmgwpsrfUBrfXBoSpGXKBrH4RgG7z+LbsrEUIIMcxG6hq2BtYrpUqVUveO0D7Hv/TZsOzTsPN3cHq33dUIIYQYRucNbKXUq0qpvb18br6A/VyqtV4CXAd8Vil1eT/7u1cptUMptaOmRpp6z+uKL0FsqtXPuDzmJYQQ49Z5A1trfZXWurCXz7MD3YnW+lRkWA08DSzrZ91faK1LtNYl6enpA93FxBWTbD2bXbEJ9j9jdzVCCCGGybA3iSul4pRSCR3jwDVYN6uJobLkE5BZCOu/DsF2u6sRQggxDAb7WNeHlFJVwArgeaXUy5H5U5RSL0RWywTeVkrtBrYBz2utXxrMfkUPhsN6zKuxEjb/zO5qhBBCDAOlR/F1z5KSEr1jhzy2PWBPfhwOvwqfL4XEKXZXI4QQ4gIppUr7evxZejobT675FphhePUBuysRQggxxCSwx5OUPFj5OdjzJJzYZnc1QgghhpAE9nhz2RchfjK8+GUwTburEUIIMUQksMcbTzxc9QCc2mmdaQshhBgXJLDHo6I7ILsYXn2AXYer+PmGw5RW1NtdlRBCiEGQwB6PDAPWfh9azvDO7+7nR+sPctcjWyW0hRBiDJPAHq9ylnIw43o+qZ4nm7MEQyZbj9baXZUQQoiLJIE9jvlXf50wBvc7n8DlNFien2Z3SUIIIS6SBPY4VjR/HvVLPsdax3aevcGkODfF7pKEEEJcJAnscS77+n+FpGnM2fWg1amKEEKIMUkCe7xzxcA134Sze2Hnb+2uRgghxEWSwJ4I5t8CuZfC69+G9ga7qxFCCHERJLAnAqWst3m11cEbP7C7GiGEEBdBAnuiyFoIS/4etj0ENYfsrkYIIcQFksCeSD7wNXDFwvr77a5ECCHEBZLAnkji0+GKL8F76+G9V+yuRgghxAWQwJ5oln0aUmfAS1+BcNDuaoQQQgyQBPZE43TDtQ9C7Xuw7WG7qxFCCDFAEtgT0exrYcYHYOP3oPWc3dUIIYQYAAnsiUgpuPa7EGiBDd+xuxohhBADIIE9UWXMhaXroPQ3cGav3dUIIYQ4DwnsiWz1feBNgpfuA63trkYIIUQ/JLAnsthUWHM/HH8L3v2b3dUIIYTohwT2RFf8SUifBy/fD0Gf3dUIIYTogwT2ROdwWv2MN1TA1v+2uxohhBB9kMAWMGMNzLkB3vx3aD5jdzVCCCF6IYEtLNd8C8IBeO2bdlcihBCiFxLYwpI2A1b8I5Q9DidL7a5GCCFEDxLYosuqf4G4DHhRHvMSQojRRgJbdPEmwpVfh6ptUP6U3dUIIYSIIoEtult0F2QthFe/AYFWu6sRQggRMajAVkr9UCn1rlJqj1LqaaVUch/rrVVKHVRKHVZK3TeYfYphZhiw9nvQdBI2/Zfd1QghhIgY7Bn2K0Ch1roIOAR8pecKSikH8HPgOmA+cKdSav4g9yuGU+5KKLjVCuyGE3ZXI4QQgkEGttZ6vdY6FJncCkztZbVlwGGt9VGtdQD4A3DzYPYrRsDVkce7Xvm6vXUIIYQAhvYa9qeAF3uZnw1En6ZVReaJ0Sw5By79Z9j3F6jYbHc1Qggx4Z03sJVSryql9vbyuTlqnfuBEPB4b5voZV6fzwwppe5VSu1QSu2oqakZyG8Qw+XSf4aEKdbbvEzT7mqEEGJCc55vBa31Vf0tV0p9AvggcKXWvT68WwXkRE1PBU71s79fAL8AKCkpkYeB7eSOs5rG/7LO6lBlycftrkgIISaswd4lvhb4MnCT1rqtj9W2A7OUUtOVUm7gI8Bzg9mvGEELboOpy+C1/we+JrurEUKICWuw17B/BiQAryilypRS/wuglJqilHoBIHJT2ueAl4EDwB+11vsGuV8xUpSC674HrTXw1r/bXY0QQkxY520S74/WemYf808B10dNvwC8MJh9CRtlF8PCj8KW/4Yln7D6HRdCCDGipKczMTBXfQOcHlj/NbsrEUKICUkCWwxMwmRY9UU4+Dwc2WB3NUIIMeFIYIuBW/5ZSM6Fl74C4dD51xdCCDFkJLDFwLm8cM23oeYAlP7a7mqEEGJCkcAWF2bejZC3CjZ8B9rq7K5GCCEmDAlscWGUst7m5WuEN75vdzVCCDFhSGCLCze5EIrvhm0PQ/W7dlcjhBATggS2uDhr7gd3PLz8Fei1R1ohhBBDSQJbXJy4SbD6PjjyOhx62e5qhBBi3JPAFhdv2T2QNgte/iqEAnZXI4QQ45oEtrh4Dhes/S7UHYFtD9ldjRBCjGsS2GJwZl0NM6+GN34ALfL+ciGEGC4S2GLwrn0Qgm3w+rfsrkQIIcYtCWwxeOmzYdm9sPN3cHqP3dUIIcS4JIEthsYVX4LYVHjpPnnMSwghhoEEthgaMSnWs9kVm2D/s3ZXI4QQ444Ethg6xXdDZqH1zuxgu93VCCHEuCKBLYaO4bAe82qshC0/A6C0op6fbzhMaUW9zcUJIcTY5rS7ADHOTL/ceqPXW//Bnkkf5K7fHycQMnE7DR5ft5zi3BS7KxRCiDFJzrDF0Lv6W2CGcL/xLQIhE1NDMGSy9Wit3ZUJIcSYJYEthl7qdFjxOeaefZ6lziM4FLicBsvz0+yuTAghxixpEhfDY9UXoexxfhnzZ34772GWz0iX5nAhhBgEOcMWw8OTAFc9QHxNGZ+t/yHFU7x2VySEEGOanGGL4bPwTmg8CRu+DbXvwR2PQ1K23VUJIcSYJGfYYvgoBVf8K3zkCTj3HvxiNVS+Y3dVQggxJklgi+E39wZY9yq44+C3H4Sdj9pdkRBCjDkS2GJkZMyDe16H3JXw3OfghS9BOGh3VUIIMWZIYIuRE5sKd/0Zln8Wtj0Ej34I2ursrkoIIcYECWwxshxOWPsg3PI/cGKbdV377D67qxJCiFFPAlvYY9FH4ZMvQMgPj1wN+5+zuyIhhBjVJLCFfaaWwL0brevbf/w4bPgumKbdVQkhxKg0qMBWSv1QKfWuUmqPUupppVRyH+sdV0qVK6XKlFI7BrNPMc4kZsHdz8PCj8Ib37OC299sd1VCCDHqDPYM+xWgUGtdBBwCvtLPumu01ou01iWD3KcYb1xeuOW/4drvwsEX4JfXQN0xu6sSQohRZVCBrbVer7UORSa3AlMHX5KYkJSCFf8IH/sLNJ2Ch9fA0Y12VyWEEKPGUF7D/hTwYh/LNLBeKVWqlLp3CPcpxpsZa+DeDRA/GR69Fbb+D2htd1VCCGG78wa2UupVpdTeXj43R61zPxACHu9jM5dqrZcA1wGfVUpd3s/+7lVK7VBK7aipqbnAnyPGhdR8WPcKzF4LL90Hz37OuptcCCEmMKUHefailPoE8BngSq112wDWfwBo0Vr/+/nWLSkp0Tt2yD1qE5ZpWjeivfF9mLoU7ngMEibbXZUQQgwbpVRpX/d6DfYu8bXAl4Gb+gprpVScUiqhYxy4Btg7mP2KCcIwYM1X4fbfWp2r/GI1VJXaXZUQQthisNewfwYkAK9EHtn6XwCl1BSl1AuRdTKBt5VSu4FtwPNa65cGuV8xkRTcAv/wCjhc8OvrYPcf7K5ICCFG3KDeh621ntnH/FPA9ZHxo8DCwexHCCYXwj0b4U+fgKc/DWfK4ar/Z3V1KoQQE4D0dCbGjrg0+PjTsOxe2PIzeOJ2aK+3uyohhBgREthibHG44Pofwo0/gWNvwcMfgOp37a5KCCGGnQS2GJuKPwF3/w38LfDIVXCwry4AhBBifJDAFmPXtOVWJytpM+D3d8KbP5ROVoQQ45YEthjbkqbCp16CBbfB69+GP90NgVYASivq+fmGw5RWyHVuIcTYJ7fYirHPFQO3PgyTF8Ar34DaI5Rf/j/c9YcqAiETt9Pg8XXLKc5NsbtSIYS4aHKGLcYHpeDSf4a7noKGSmY+80EWhfdhagiGTLYerbW7QiGEGBQ5wxbjy6yr4J7XUY/ezqOBBynVczhHMkvPzYNNuRCfCfEZ1stF4jMhJsXqUU0IIUY5CWwx/kyaiff/bKT6ma8y7VQ5i/RJvIfKYF8vvecarkiAZ0TCPLMr1BMmRwV8ptX0LoQQNpHAFuOTN4mMj/y8+zx/M7RUQ8tZaD7TNd7xaToJJ3dCaw3WG2F78CRBQmaPs/RImCdEhX1MKqUnGtl6tJbl+Wkjfu28tKLetn0LIYaPBLaYODwJ1idtRv/rhUPQVgstPUK9uSPcq+HULmsYaHnf17Vykm0mcq32Ym40aEuNJ9bjAmVEPo6ucaNjXPUxv8en23xH5Htd86tbguzdV43HVOzc4GDKolyyUhOs5YYLDKfV+Yzh7GfcZa3vcEV9x9ljPDLtcHXbdmlVC1uPN8jBghDDQAJbiJ4cTuuMOSHz/Ov6W7pCPBLspfve5dixI8Tgx9AmDkccM5JiQYdBm9bHjBrXpvUqUe2Pmt8x1D3WDUet32OeNkkMBPmQCuFwhHFi4toTBsxh/yPrUAws0gr/RjdBbxwuT6x1KcHlBVcsOL2R6RhwxkSNR83vXBb9nVhrOvo7Hd9TasR+nxB2ksAWYjA88dYn6qxdTa7na49sJRgycTkNHr9xOYzQ2ea+inruit73uuUU5ySBGYp8gtYBQDgYGQ9ZLQody8KR5Z3LoqbDwajthLpPh4NsOXyWLe+dwUmIGIIsT4tlQYYHgm0Q8lnDoA98DRBst8ajl+mLPLDoCHdnDD7loVHH4k2cRFJqBsSkWjcWdnxie0x7k6wWAiHGAAlsIYZYcW4Kj69bbst15D73bbgB97Du2z21nl8cjjpYuPYCDlS0tg4AQu2RMI98Qh3B3t7PMiv0z9U38M7BKuJpIaWpitlNx/EGG8HXSK/3JACgrNDuK9D7Cvxegl7uHRDDTelR3JVjSUmJ3rFjh91lCCEGyM7Q+vmGw/xo/UFMDQ4FX7xmDp9dM9NqIfA1Wm92a6+Htrqu8fZ6aO8x3bG836AnEvRWgDeqeN46EeKUmcJpI5OPXrOKWbPnQ3IOuONG7M9AjH1KqVKtdUlvy+QMWwgxZIpzU2w7u1yen4bbaXSe4S/PT7MWGA7rzDg29cI22DPo+wl739nTFKpzXO2ow6OC8Oqv4NXIdmInQfI0SMm1hsnTIDkynpQD7tgh/XMQ45cEthBiXBjySxEXEPRVkXsHQqEQWc5mHr4pg7neBmiogIZKqK+A03vg3echHOj+5biMqCCPCvSUXKuvfHn+X0RIk7gQQgyBAV0OME3raYKGykiYRwK983PCusEvWnzm+8O8Y5g0FVxeuX4+jvTXJC6BLYQQo4UZtjr16RbiUcHeWGXdlR8lEJPBvrYkjphZHFR53HbDDcxZuBK8ifb8BjEoEthCCDEemGFoPt3VzN5QyYF399Jw6jAzVRXpqqlr3ZTpkFUEkyOfrCKru10xqslNZ0IIMR4YDqsZPGkq5K4EoG16PZ98ZCvBoMkUZyO/utbLLPOIdc389G7Y/2zX9+MyukK8Y5gyXV6AM0ZIYAshxBjW82a7WT2vYfsa4Uy5FeBn9ljDIxusHvIA3AnWu+Sziqzh5CJInwvO4X1uX1w4aRIXQoiJJuiDmgPdQ/zsXqsTGgCH2wrtrCKYvNAaZhZavfqJYSVN4kIIIbq4vDBlsfXpYIah9kgkwHdbZ+UHX4Rdj0VWUFYXvJOLos7IF1Ja65Q71EeIBLYQQgjr+nj6bOuz4DZrntbQdKrrLPzMHqjaAfv+0vm1qTqFOeZ0Xtswj9ibbmfe4lXWC3TEkJMmcSGEEBemrQ7OlPP22xuoeW87C9Vh8o0z1jJ3POQsg9xLrU/2EnB67K13DJEmcSGEEEMnNhXyryDGUcRX3uu6Q/23V4bJby2D45vg9W9Z6zq9MHVpJMBXWuPSHetFkTNsIYQQF63PXtZaa6FyM1RshopN1jVxbYLhss66O87Ap10CngT7fsAoIx2nCCGEsJevESrfgYq3rRA/tcvqtU0ZkLWwK8BzV1ivMJ2gJLCFEEKMLv4WqNpunX1XbLZuZgv7AQWZBV1N6LmXQny63dWOmGG9hq2U+hZwM2AC1cDdWutTvaz3CeDfIpPf1lr/drD7FkIIMUZ54mHGGusD1rPhJ0sjAb4Jdj0K2x6ylk2a3XUGnncpJE6xr24bDfoMWymVqLVuioz/EzBfa/2ZHuukAjuAEqw3wpcCxVrr+v62LWfYQggxQYUC1vPgHU3olVvBH+krPSUPci/jePxC3gzMoaCgaNw8Az6sZ9gdYR0RhxXIPV0LvKK1rosU9AqwFvj9YPcvhBBiHHK6IWep9bnsC1bHLmfKO29iCx34G3n+x8gDKt7JpGbuVaQvug6mXw7eJLurHxZD8liXUuo7wN8DjcCaXlbJBk5ETVdF5gkhhBDnZzhgyiLrs+Ifeej1Q/z11de5RO1nlVHO5YefhoOPg3LA1BKY8QHrM2XJuOnIZUBN4kqpV4He3st2v9b62aj1vgJ4tdbf6PH9fwU8WutvR6a/BrRprX/Uy77uBe4FmDZtWnFFRcUF/BwhhBATQWlFPXc9spVgyMTlNHjik0tYYhyGI69bn1O7AA2eJJi+qivAU6fbXXq/RuwucaVULvC81rqwx/w7gdVa609Hph8CNmqt+20Sl2vYQggh+tLnM+Bg9cZ27A0rvA+/Dk1V1vyU6V3hPX3VqGs+H9bAVkrN0lq/Fxn/PHCF1vq2HuukYt1otiQyayfWTWd1/W1bAlsIIcSgaQ21UWffx96CYOuobD4f7sD+MzAH67GuCuAzWuuTSqmSyPi6yHqfAr4a+dp3tNa/Pt+2JbCFEEIMuVDAega8t+bz/Mu7Ajwlb8RLk45ThBBCiL601cHRjZEA39DVfJ6aD/lrRrT5XAJbCCGEGIh+m8+XRjWfLx6W5nMJbCGEEOJihAJQtS2q+byMbs3nS++B/CuGbHfyek0hhBDiYjjdkHeZ9bny6z2az1+HOe/riXv4ShmxPQkhhBBjXWwqFN4KhbdSeryOd47W/P/t3U9oHGUcxvHnSWM89FBK1IuVaFDEggep2BwVilSUKh78Qy+VVqigZ5V6FT0XhVKpxENVSg4ateJBLCJYsRUPahFKIBgEtaF4EKHG/DxkwZLsNju7M/PuO/P9QA55553ZH8+8zC8zkFntXrxcy6tRadgAABR0fvGy9p/4RldWVjXxxYJOHpqpvGmPVXp0AAAa6OzCsq6srGo1pH9WVnV2Ybnym1mrRgAAA/pJREFUz6RhAwBQ0Mz0pCbGx7TF0nXjY5qZnqz8M3kkDgBAQbumtuvkoZner0atAA0bAIAB7JraXuv3cPNIHACADNCwAQDIAA0bAIAM0LABAMgADRsAgAzQsAEAyAANGwCADNCwAQDIwEh/H7btPyQtStom6c8uU7qNrx+7QdKlSgrsrletVR2jn7mbzSmSb7fxbvNyy73o/sPmXnQba73/+YPkzjVmuLllXmP6Hcst9373n4qIG7tuiYiR/5F0vN/x9WOSzo1CrVUdo5+5m80pkm+PjLudh6xyL7r/sLkX3cZarzZ3rjHVZT5svtcYyyr3Ms5bLo/EPyow3mtuXcr4/CLH6GfuZnOK5NttPHXm0vA1FN1/2NyLbmOt9z9/kNy5xgw3t8xrzChmLtV/jdlgpB+Jl8H2uYi4N3UdbUPu9SPzNMg9jTbmnssd9jCOpy6gpci9fmSeBrmn0brcG3+HDQBAE7ThDhsAgOzRsAEAyAANGwCADLS+Ydveavu87UdS19IGtu+yfcz2nO3nUtfTFrYfs/2W7Q9tP5i6nrawPW37hO251LU0Wec6/k5nje9PXU9Vsm3Ytt+2/bvtH9aN77X9s+2Ltl/q41AvSjpVTZXNUkbmEXEhIg5LekJSq/4lY1Al5f5BRDwr6YCkJysstzFKyn0hIg5WW2kzFcz/cUlznTW+r/Zia5Jtw5Y0K2nv1QO2t0h6U9JDknZKetr2Ttt32/543c9NtvdI+knSb3UXn6lZDZl5Z599kr6S9Hm95WdrViXk3vFKZz9sblbl5Y7iZtVn/pJ2SPqlM+3fGmus1XjqAgYVEV/avnXd8H2SLkbEgiTZfl/SoxHxmqQNj7xtPyBpq9ZO/N+2T0fEaqWFZ6yMzDvHmZc0b/sTSe9WV3EzlLTWLel1SZ9GxHfVVtwMZa13DKZI/pKWtNa0v1feN6LXlG3D7uFm/f9XlrR2Enf3mhwRRyTJ9gFJl2jWAymUue37tfb46npJpyutrNkK5S7pBUl7JG2zfXtEHKuyuAYrut4nJb0q6R7bL3caOwbXK/+jkt6w/bBG4zWmlWhaw3aXsU3fDBMRs+WX0hqFMo+IM5LOVFVMixTN/ajWLmoYTtHclyUdrq6c1umaf0T8JemZuoupW9MeHSxJuuWq33dI+jVRLW1B5mmQexrknlar829aw/5W0h22b7M9IekpSfOJa2o6Mk+D3NMg97RanX+2Ddv2e5K+lnSn7SXbByNiRdLzkj6TdEHSqYj4MWWdTULmaZB7GuSeFvlvxJd/AACQgWzvsAEAaBMaNgAAGaBhAwCQARo2AAAZoGEDAJABGjYAABmgYQMAkAEaNgAAGaBhAwCQgf8AiI0ublSj1NMAAAAASUVORK5CYII=\n", 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", "text/plain": [ "
" ] @@ -642,16 +662,16 @@ "hm3_3 = ml3.head(r2, 0, t3, layers=1)[0]\n", "hm4_3 = ml3.head(r2, 0, t4, layers=3)[0]\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t0, h0, '.', label='pumped')\n", - "plt.semilogx(t0, hm0_3, label='ttim pumped')\n", - "plt.semilogx(t1, h1, '.', label='PS1')\n", - "plt.semilogx(t1, hm1_3, label='ttim PS1')\n", - "plt.semilogx(t2, h2, '.', label='PD1')\n", - "plt.semilogx(t2, hm2_3, label='ttim PD1')\n", - "plt.semilogx(t3, h3, ',', label='PS2')\n", - "plt.semilogx(t3, hm3_3, label='ttim PS2')\n", - "plt.semilogx(t4, h4, '.', label='PD2')\n", - "plt.semilogx(t4, hm4_3, label='ttim PD2');" + "plt.semilogx(t0, h0, \".\", label=\"pumped\")\n", + "plt.semilogx(t0, hm0_3, label=\"ttim pumped\")\n", + "plt.semilogx(t1, h1, \".\", label=\"PS1\")\n", + "plt.semilogx(t1, hm1_3, label=\"ttim PS1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"PD1\")\n", + "plt.semilogx(t2, hm2_3, label=\"ttim PD1\")\n", + "plt.semilogx(t3, h3, \",\", label=\"PS2\")\n", + "plt.semilogx(t3, hm3_3, label=\"ttim PS2\")\n", + "plt.semilogx(t4, h4, \".\", label=\"PD2\")\n", + "plt.semilogx(t4, hm4_3, label=\"ttim PD2\");" ] }, { @@ -679,7 +699,7 @@ } ], "source": [ - "ca3.parameters['optimal'].values" + "ca3.parameters[\"optimal\"].values" ] }, { @@ -770,12 +790,14 @@ } ], "source": [ - "ta = pd.DataFrame(columns=['Moench', 'TTim', 'TTim-stratified'],\\\n", - " index=['k0[m/d]', 'k[m/d]', 'Sy[-]', 'Ss[1/m]', 'kz/kh'])\n", - "ta.loc[:, 'TTim-stratified'] = ca3.parameters['optimal'].values\n", - "ta.loc[1:, 'TTim'] = ca2.parameters['optimal'].values\n", - "ta.loc[1:, 'Moench'] = [8.640, 0.2, 2e-5, 0.5]\n", - "ta.loc['RMSE'] = [0.061318, ca2.rmse(), ca3.rmse()]\n", + "ta = pd.DataFrame(\n", + " columns=[\"Moench\", \"TTim\", \"TTim-stratified\"],\n", + " index=[\"k0[m/d]\", \"k[m/d]\", \"Sy[-]\", \"Ss[1/m]\", \"kz/kh\"],\n", + ")\n", + "ta.loc[:, \"TTim-stratified\"] = ca3.parameters[\"optimal\"].values\n", + "ta.loc[1:, \"TTim\"] = ca2.parameters[\"optimal\"].values\n", + "ta.loc[1:, \"Moench\"] = [8.640, 0.2, 2e-5, 0.5]\n", + "ta.loc[\"RMSE\"] = [0.061318, ca2.rmse(), ca3.rmse()]\n", "ta" ] }, diff --git a/pumpingtest_benchmarks/11_slug_test_pratt_county.ipynb b/pumpingtest_benchmarks/11_slug_test_pratt_county.ipynb index aaad43d..af512ec 100755 --- a/pumpingtest_benchmarks/11_slug_test_pratt_county.ipynb +++ b/pumpingtest_benchmarks/11_slug_test_pratt_county.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -34,13 +34,13 @@ "metadata": {}, "outputs": [], "source": [ - "rw = 0.125 # well radius\n", - "rc = 0.064 # well casing radius\n", - "L = 1.52 # screen length\n", - "b = -47.87 # aquifer thickness\n", - "zt = -16.77 # depth to top of screen\n", - "H0 = 0.671 # initial displacement in the well\n", - "zb = zt - L # bottom of screen" + "rw = 0.125 # well radius\n", + "rc = 0.064 # well casing radius\n", + "L = 1.52 # screen length\n", + "b = -47.87 # aquifer thickness\n", + "zt = -16.77 # depth to top of screen\n", + "H0 = 0.671 # initial displacement in the well\n", + "zb = zt - L # bottom of screen" ] }, { @@ -64,8 +64,8 @@ } ], "source": [ - "Q = np.pi * rc ** 2 * H0\n", - "print('slug:', round(Q, 5), 'm^3')" + "Q = np.pi * rc**2 * H0\n", + "print(\"slug:\", round(Q, 5), \"m^3\")" ] }, { @@ -81,9 +81,9 @@ "metadata": {}, "outputs": [], "source": [ - "data = np.loadtxt('data/slug.txt', skiprows = 1)\n", - "t = data[:, 0] / 60 / 60 / 24 #convert time to days\n", - "h = data[:, 1] " + "data = np.loadtxt(\"data/slug.txt\", skiprows=1)\n", + "t = data[:, 0] / 60 / 60 / 24 # convert time to days\n", + "h = data[:, 1]" ] }, { @@ -108,8 +108,8 @@ } ], "source": [ - "ml = Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", - "w = Well(ml, xw=0, yw=0, rw=rw,rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype='slug')\n", + "ml = ttim.Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, kzoverkh=1, tmin=1e-6, tmax=0.01)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\")\n", "ml.solve()" ] }, @@ -142,10 +142,10 @@ } ], "source": [ - "ca = Calibrate(ml)\n", - "ca.set_parameter(name='kaq0_2', initial=10)\n", - "ca.set_parameter(name='Saq0_2', initial=1e-4)\n", - "ca.series(name='obs', x=0, y=0, layer=1, t=t, h=h)\n", + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq0_2\", initial=10)\n", + "ca.set_parameter(name=\"Saq0_2\", initial=1e-4)\n", + "ca.series(name=\"obs\", x=0, y=0, layer=1, t=t, h=h)\n", "ca.fit(report=True)" ] }, @@ -234,7 +234,7 @@ ], "source": [ "display(ca.parameters)\n", - "print('RMSE:', ca.rmse())" + "print(\"RMSE:\", ca.rmse())" ] }, { @@ -244,7 +244,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -257,12 +257,12 @@ ], "source": [ "hm = ml.head(0, 0, t, layers=1)\n", - "plt.figure(figsize = (10, 5))\n", - "plt.semilogx(t, h/H0, '.', label='obs')\n", - "plt.semilogx(t, hm[-1]/H0, 'r', label='ttim')\n", + "plt.figure(figsize=(10, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm[-1] / H0, \"r\", label=\"ttim\")\n", "plt.ylim([0, 1])\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -282,12 +282,12 @@ "n1 = 18\n", "n2 = 3\n", "n3 = 29\n", - "nlay = n1 + n2 + n3 #number of layers\n", + "nlay = n1 + n2 + n3 # number of layers\n", "zlay1 = np.linspace(0, zt, n1 + 1)\n", "zlay2 = np.linspace(zt, zb, n2 + 1)\n", "zlay3 = np.linspace(zb, b, n3 + 1)\n", "layers = np.append(zlay1[:-1], zlay2[:-1])\n", - "layers = np.append(layers, zlay3) #elevation of each layer\n", + "layers = np.append(layers, zlay3) # elevation of each layer\n", "Saq = 1e-4 * np.ones(nlay)\n", "Saq[0] = 0.1" ] @@ -307,10 +307,19 @@ } ], "source": [ - "M_nlay = Model3D(kaq=10, z=layers, Saq=Saq, kzoverkh=1, phreatictop=True,\\\n", - " tmin=1e-6, tmax=0.01)\n", - "W_nlay = Well(M_nlay, xw=0, yw=0, rw=rw, tsandQ=[(0, -Q)], layers=[18,19,20], \\\n", - " rc=rc, wbstype='slug')\n", + "M_nlay = ttim.Model3D(\n", + " kaq=10, z=layers, Saq=Saq, kzoverkh=1, phreatictop=True, tmin=1e-6, tmax=0.01\n", + ")\n", + "W_nlay = ttim.Well(\n", + " M_nlay,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " tsandQ=[(0, -Q)],\n", + " layers=[18, 19, 20],\n", + " rc=rc,\n", + " wbstype=\"slug\",\n", + ")\n", "M_nlay.solve()" ] }, @@ -343,10 +352,10 @@ } ], "source": [ - "cM = Calibrate(M_nlay)\n", - "cM.set_parameter(name='kaq0_49', initial=10)\n", - "cM.set_parameter(name='Saq0_49', initial=1e-4, pmin=0)\n", - "cM.series(name='obs', x=0, y=0, layer=[18,19,20], t=t, h=h)\n", + "cM = ttim.Calibrate(M_nlay)\n", + "cM.set_parameter(name=\"kaq0_49\", initial=10)\n", + "cM.set_parameter(name=\"Saq0_49\", initial=1e-4, pmin=0)\n", + "cM.series(name=\"obs\", x=0, y=0, layer=[18, 19, 20], t=t, h=h)\n", "cM.fit()" ] }, @@ -433,7 +442,7 @@ ], "source": [ "display(cM.parameters)\n", - "print('RMSE:', cM.rmse())" + "print(\"RMSE:\", cM.rmse())" ] }, { @@ -443,7 +452,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -455,13 +464,13 @@ } ], "source": [ - "hM = M_nlay.head(0, 0, t, layers = 20)\n", - "plt.figure(figsize = (10, 5))\n", - "plt.semilogx(t, h / H0, '.', label = 'obs')\n", - "plt.semilogx(t, hM[0] / H0, label = 'ttim')\n", + "hM = M_nlay.head(0, 0, t, layers=20)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hM[0] / H0, label=\"ttim\")\n", "plt.ylim([0, 1])\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -539,12 +548,13 @@ } ], "source": [ - "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]'], \\\n", - " index=['AQTESOLV', 'ttim-three', 'ttim-multi'])\n", - "ta.loc['ttim-three'] = ca.parameters['optimal'].values\n", - "ta.loc['ttim-multi'] = cM.parameters['optimal'].values\n", - "ta.loc['AQTESOLV'] = [4.034, 3.834E-04]\n", - "ta['RMSE'] = [0.002976, ca.rmse(), cM.rmse()]\n", + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\"], index=[\"AQTESOLV\", \"ttim-three\", \"ttim-multi\"]\n", + ")\n", + "ta.loc[\"ttim-three\"] = ca.parameters[\"optimal\"].values\n", + "ta.loc[\"ttim-multi\"] = cM.parameters[\"optimal\"].values\n", + "ta.loc[\"AQTESOLV\"] = [4.034, 3.834e-04]\n", + "ta[\"RMSE\"] = [0.002976, ca.rmse(), cM.rmse()]\n", "ta" ] }, diff --git a/pumpingtest_benchmarks/12_falling-head_slug_test.ipynb b/pumpingtest_benchmarks/12_falling-head_slug_test.ipynb index 0fc46aa..c9852c4 100755 --- a/pumpingtest_benchmarks/12_falling-head_slug_test.ipynb +++ b/pumpingtest_benchmarks/12_falling-head_slug_test.ipynb @@ -15,10 +15,10 @@ "outputs": [], "source": [ "%matplotlib inline\n", - "from ttim import *\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -34,13 +34,13 @@ "metadata": {}, "outputs": [], "source": [ - "rw = 0.127 # well radius\n", - "rc = 0.0508 # well casing radius\n", - "L = 4.20624 # screen length\n", - "b = -9.9274 # aquifer thickness\n", - "zt = -0.1433 # depth to top of the screen\n", - "H0 = 0.4511 # initial displacement in the well\n", - "zb = zt - L # bottom of the screen" + "rw = 0.127 # well radius\n", + "rc = 0.0508 # well casing radius\n", + "L = 4.20624 # screen length\n", + "b = -9.9274 # aquifer thickness\n", + "zt = -0.1433 # depth to top of the screen\n", + "H0 = 0.4511 # initial displacement in the well\n", + "zb = zt - L # bottom of the screen" ] }, { @@ -64,8 +64,8 @@ } ], "source": [ - "Q = np.pi * rc ** 2 * H0\n", - "print('Slug:', round(Q, 5), 'm^3')" + "Q = np.pi * rc**2 * H0\n", + "print(\"Slug:\", round(Q, 5), \"m^3\")" ] }, { @@ -81,9 +81,9 @@ "metadata": {}, "outputs": [], "source": [ - "data = np.loadtxt('data/falling_head.txt', skiprows = 2)\n", - "t = data[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", - "h = (10 - data[:, 1]) * 0.3048 #convert drawdown from ft to meters" + "data = np.loadtxt(\"data/falling_head.txt\", skiprows=2)\n", + "t = data[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", + "h = (10 - data[:, 1]) * 0.3048 # convert drawdown from ft to meters" ] }, { @@ -108,8 +108,10 @@ } ], "source": [ - "ml_0 = Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", - "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype='slug')\n", + "ml_0 = ttim.Model3D(kaq=10, z=[0, zt, zb, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w_0 = ttim.Well(\n", + " ml_0, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=1, wbstype=\"slug\"\n", + ")\n", "ml_0.solve()" ] }, @@ -142,10 +144,10 @@ } ], "source": [ - "ca_0 = Calibrate(ml_0)\n", - "ca_0.set_parameter(name='kaq0_2', initial=10)\n", - "ca_0.set_parameter(name='Saq0_2', initial=1e-4)\n", - "ca_0.series(name='obs', x=0, y=0, t=t, h=h, layer=1)\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0_2\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0_2\", initial=1e-4)\n", + "ca_0.series(name=\"obs\", x=0, y=0, t=t, h=h, layer=1)\n", "ca_0.fit(report=True)" ] }, @@ -232,7 +234,7 @@ ], "source": [ "display(ca_0.parameters)\n", - "print('RMSE:', ca_0.rmse())" + "print(\"RMSE:\", ca_0.rmse())" ] }, { @@ -250,7 +252,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -263,11 +265,11 @@ ], "source": [ "hm_0 = ml_0.head(0, 0, t, layers=1)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t, h/H0, '.', label='obs')\n", - "plt.semilogx(t, hm_0[0]/H0, label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_0[0] / H0, label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -284,14 +286,14 @@ "metadata": {}, "outputs": [], "source": [ - "#Determine elevation of each layer. \n", - "#Thickness of each layer is set to be 0.5 m.\n", + "# Determine elevation of each layer.\n", + "# Thickness of each layer is set to be 0.5 m.\n", "z0 = np.arange(zt, zb, -0.5)\n", "z1 = np.arange(zb, b, -0.5)\n", "zlay = np.append(z0, z1)\n", "zlay = np.append(zlay, b)\n", "zlay = np.insert(zlay, 0, 0)\n", - "nlay = len(zlay) - 1 #number of layers\n", + "nlay = len(zlay) - 1 # number of layers\n", "Saq_1 = 1e-4 * np.ones(nlay)\n", "Saq_1[0] = 0.1" ] @@ -311,10 +313,19 @@ } ], "source": [ - "ml_1 = Model3D(kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, \\\n", - " tmin=1e-5, tmax=0.01, phreatictop=True)\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, -Q)], layers=[1,2,3,4,5,6,7,8], rc=rc, \\\n", - " wbstype='slug')\n", + "ml_1 = ttim.Model3D(\n", + " kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", + ")\n", + "w_1 = ttim.Well(\n", + " ml_1,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " tsandQ=[(0, -Q)],\n", + " layers=[1, 2, 3, 4, 5, 6, 7, 8],\n", + " rc=rc,\n", + " wbstype=\"slug\",\n", + ")\n", "ml_1.solve()" ] }, @@ -347,11 +358,11 @@ } ], "source": [ - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0_21', initial=10, pmin=0)\n", - "ca_1.set_parameter(name='Saq0_21', initial=1e-4, pmin=0)\n", - "ca_1.series(name='obs', x=0, y=0, layer=[1,2,3,4,5,6,7,8], t=t, h=h)\n", - "ca_1.fit(report = True)" + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0_21\", initial=10, pmin=0)\n", + "ca_1.set_parameter(name=\"Saq0_21\", initial=1e-4, pmin=0)\n", + "ca_1.series(name=\"obs\", x=0, y=0, layer=[1, 2, 3, 4, 5, 6, 7, 8], t=t, h=h)\n", + "ca_1.fit(report=True)" ] }, { @@ -437,7 +448,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -455,7 +466,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -468,11 +479,11 @@ ], "source": [ "hm_1 = ml_1.head(0, 0, t, layers=8)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t, h/H0, '.', label='obs')\n", - "plt.semilogx(t, hm_1[0]/H0, label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_1[0] / H0, label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -498,10 +509,20 @@ } ], "source": [ - "ml_2 = Model3D(kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, \\\n", - " tmin=1e-5, tmax=0.01, phreatictop=True)\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, tsandQ=[(0, -Q)], layers=[1,2,3,4,5,6,7,8], \\\n", - " rc=rc, res=0.1, wbstype='slug')\n", + "ml_2 = ttim.Model3D(\n", + " kaq=10, z=zlay, Saq=Saq_1, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", + ")\n", + "w_2 = ttim.Well(\n", + " ml_2,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " tsandQ=[(0, -Q)],\n", + " layers=[1, 2, 3, 4, 5, 6, 7, 8],\n", + " rc=rc,\n", + " res=0.1,\n", + " wbstype=\"slug\",\n", + ")\n", "ml_2.solve()" ] }, @@ -539,12 +560,12 @@ } ], "source": [ - "ca_2 = Calibrate(ml_2)\n", - "ca_2.set_parameter(name='kaq0_21', initial=10, pmin=0)\n", - "ca_2.set_parameter(name='Saq0_21', initial=1e-4, pmin=0)\n", - "ca_2.set_parameter_by_reference(name='res', parameter=w_2.res, initial=0, pmin=0)\n", - "ca_2.series(name='obs', x=0, y=0, layer=[1,2,3,4,5,6,7,8], t=t, h=h)\n", - "ca_2.fit(report = True)" + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0_21\", initial=10, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq0_21\", initial=1e-4, pmin=0)\n", + "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0, pmin=0)\n", + "ca_2.series(name=\"obs\", x=0, y=0, layer=[1, 2, 3, 4, 5, 6, 7, 8], t=t, h=h)\n", + "ca_2.fit(report=True)" ] }, { @@ -642,7 +663,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('RMSE:', ca_2.rmse())" + "print(\"RMSE:\", ca_2.rmse())" ] }, { @@ -652,7 +673,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -665,11 +686,11 @@ ], "source": [ "hm_2 = ml_2.head(0, 0, t, layers=8)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t, h/H0, '.', label='obs')\n", - "plt.semilogx(t, hm_2[0]/H0, label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t, h / H0, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_2[0] / H0, label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -754,12 +775,13 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]'], \\\n", - " index=['AQTESOLV', 'ttim-single', 'ttim-multi'])\n", - "t.loc['AQTESOLV'] = [2.616, 7.894E-5]\n", - "t.loc['ttim-single'] = ca_0.parameters['optimal'].values\n", - "t.loc['ttim-multi'] = ca_1.parameters['optimal'].values\n", - "t['RMSE'] = [0.001197, round(ca_0.rmse(), 6), round(ca_1.rmse(), 6)]\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\"], index=[\"AQTESOLV\", \"ttim-single\", \"ttim-multi\"]\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [2.616, 7.894e-5]\n", + "t.loc[\"ttim-single\"] = ca_0.parameters[\"optimal\"].values\n", + "t.loc[\"ttim-multi\"] = ca_1.parameters[\"optimal\"].values\n", + "t[\"RMSE\"] = [0.001197, round(ca_0.rmse(), 6), round(ca_1.rmse(), 6)]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/13_multiwell_slug_test-.ipynb b/pumpingtest_benchmarks/13_multiwell_slug_test-.ipynb index 102ccd2..52b1f4b 100755 --- a/pumpingtest_benchmarks/13_multiwell_slug_test-.ipynb +++ b/pumpingtest_benchmarks/13_multiwell_slug_test-.ipynb @@ -15,10 +15,10 @@ "outputs": [], "source": [ "%matplotlib inline\n", - "from ttim import *\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -34,13 +34,13 @@ "metadata": {}, "outputs": [], "source": [ - "H0 = 2.798 #initial displacement in m\n", - "b = -6.1 #aquifer thickness\n", - "rw1 = 0.102 #well radius of Ln-2 Well\n", - "rw2 = 0.071 #well radius of observation Ln-3 Well\n", - "rc1 = 0.051 #casing radius of Ln-2 Well\n", - "rc2 = 0.025 #casing radius of Ln-3 Well\n", - "r = 6.45 #distance from observation well to test well" + "H0 = 2.798 # initial displacement in m\n", + "b = -6.1 # aquifer thickness\n", + "rw1 = 0.102 # well radius of Ln-2 Well\n", + "rw2 = 0.071 # well radius of observation Ln-3 Well\n", + "rc1 = 0.051 # casing radius of Ln-2 Well\n", + "rc2 = 0.025 # casing radius of Ln-3 Well\n", + "r = 6.45 # distance from observation well to test well" ] }, { @@ -64,8 +64,8 @@ } ], "source": [ - "Q = np.pi * rc1 ** 2 * H0\n", - "print('Slug:', round(Q, 5), 'm^3')" + "Q = np.pi * rc1**2 * H0\n", + "print(\"Slug:\", round(Q, 5), \"m^3\")" ] }, { @@ -81,10 +81,10 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/ln-2.txt')\n", - "t1 = data1[:, 0] / 60 / 60 / 24 #convert time from seconds to days\n", + "data1 = np.loadtxt(\"data/ln-2.txt\")\n", + "t1 = data1[:, 0] / 60 / 60 / 24 # convert time from seconds to days\n", "h1 = data1[:, 1]\n", - "data2 = np.loadtxt('data/ln-3.txt')\n", + "data2 = np.loadtxt(\"data/ln-3.txt\")\n", "t2 = data2[:, 0] / 60 / 60 / 24\n", "h2 = data2[:, 1]" ] @@ -111,9 +111,10 @@ } ], "source": [ - "ml_0 = ModelMaq(kaq=10, z=[0, b], Saq=1e-4, \\\n", - " tmin=1e-5, tmax=0.01)\n", - "w_0 = Well(ml_0, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype='slug')\n", + "ml_0 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w_0 = ttim.Well(\n", + " ml_0, xw=0, yw=0, rw=rw1, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\"\n", + ")\n", "ml_0.solve()" ] }, @@ -153,12 +154,12 @@ } ], "source": [ - "#unknown parameters: kaq, Saq\n", - "ca_0 = Calibrate(ml_0)\n", - "ca_0.set_parameter(name='kaq0', initial=10)\n", - "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_0.series(name='Ln-2', x=0, y=0, layer=0, t=t1, h=h1)\n", - "ca_0.series(name='Ln-3', x=r, y=0, layer=0, t=t2, h=h2)\n", + "# unknown parameters: kaq, Saq\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"Ln-2\", x=0, y=0, layer=0, t=t1, h=h1)\n", + "ca_0.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", "ca_0.fit(report=True)" ] }, @@ -245,7 +246,7 @@ ], "source": [ "display(ca_0.parameters)\n", - "print('RMSE:', ca_0.rmse())" + "print(\"RMSE:\", ca_0.rmse())" ] }, { @@ -255,7 +256,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ "
" ] @@ -270,12 +271,12 @@ "hm1_0 = ml_0.head(0, 0, t1, layers=0)\n", "hm2_0 = ml_0.head(r, 0, t2, layers=0)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1/H0, '.', label='obs ln-2')\n", - "plt.semilogx(t1, hm1_0[0]/H0, label='ttim ln-2')\n", - "plt.semilogx(t2, h2/H0, '.', label='obs ln-3')\n", - "plt.semilogx(t2, hm2_0[0]/H0, label='ttim ln-3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1_0[0] / H0, label=\"ttim ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2_0[0] / H0, label=\"ttim ln-3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -301,9 +302,10 @@ } ], "source": [ - "ml_1 = ModelMaq(kaq=10, z=[0, b], Saq=1e-4, \\\n", - " tmin=1e-5, tmax=0.01)\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw1, res=0, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype='slug')\n", + "ml_1 = ttim.ModelMaq(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-5, tmax=0.01)\n", + "w_1 = ttim.Well(\n", + " ml_1, xw=0, yw=0, rw=rw1, res=0, rc=rc1, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\"\n", + ")\n", "ml_1.solve()" ] }, @@ -338,13 +340,13 @@ } ], "source": [ - "#unknown parameters: kaq, Saq, res\n", - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0', initial=10)\n", - "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0)\n", - "ca_1.series(name='Ln-2', x=0, y=0, layer=0, t=t1, h=h1)\n", - "ca_1.series(name='Ln-3', x=r, y=0, layer=0, t=t2, h=h2)\n", + "# unknown parameters: kaq, Saq, res\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res, initial=0)\n", + "ca_1.series(name=\"Ln-2\", x=0, y=0, layer=0, t=t1, h=h1)\n", + "ca_1.series(name=\"Ln-3\", x=r, y=0, layer=0, t=t2, h=h2)\n", "ca_1.fit(report=True)" ] }, @@ -443,7 +445,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -455,7 +457,7 @@ "outputs": [ { "data": { - "image/png": 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rZS9SyizbtYyJKyaybNeyYjnfmDFjiI6OJjo6mpdeein/ebfbzZAhQ2jTpg2DBg3i6NGjQN7ytK1ataJNmzY8+OCD5z330KFDGTlyJJ07dyYiIoLPPvvsrPtVr149//GRI0fy58QvKWrJS7l0+nX7II73fonLJ/ZisP2aW53fUn3prfyUEsOCnH6Mc0YzKbGTWvYiPrRs1zLunHUn2bnZ+Dv9efPqN4mtE1vk86WkpPDOO++waNEirLV07NiR7t27ExQUxLp163jrrbfo0qULw4YNY/z48QwbNozPP/+ctWvXYoxh//79hb5HRkYG8+fPZ+3atfTr149Bgwaddb9XX32VMWPGkJ2dzQ8//FDkz1QUaslLhRAXFsTLib2wVzzGpluTWNBkJJFsY5L/0/zXPM725C9ISdurlr2IjyRnJpOdm40HDzmeHJIzL2768vnz5zNgwACqVKlC1apVGThwIPPmzQMgNDSULl26AHDrrbcyf/58qlevTmBgIImJiUydOpXKlSsX+h7XXnstDoeDVq1akZmZec797rvvPjZt2sRzzz3HU089dVGf6/dSyEuFERcWxH09mtEuMoyAyx7gSs/LPJ4zlIZmD/1W/AnXO1ey5NtPGDxxoYJepITF143H3+mP0zjxc/gRX7dIU7XnO9+6LGd2mRtjcLlcLF68mOuuu45p06blryd/PqeWly34fo888gixsbHExv62F+Kmm25i2rRpF/oRioVCXiqkuLAg3knsRp0r/0TGkIXMjnyEWhzkXf/neMs8xcZffvJ1iSIVSmydWN68+k1GtBtx0V31AN26dWPatGkcPXqUI0eO8Pnnn3PppZcCsHXrVhYuXAjA5MmT6dq1K4cPH+bAgQP06dOHl156iWXLijYu4Omnn2bZsmX5x2/YsCH/ta+++orIyMiL+ly/l67JS4V12oh85530WRvF9XYWf3J+Tpelg8F9I1z+T6jZ2MeVilQMsXViLzrcT2nfvj1Dhw6lQ4cOACQmJtKuXTvS0tKIiorivffe4+677yYyMpJ77rmHAwcO0L9/f44fP461lrFjxxZLHa+88grfffcdfn5+BAUF8d577xXLeS+UlpoVOSllyz6SUrPo0tBF7NZ3Iek1sBY63g2XPgCVNDBP5EJpqdmi0VKzIl5y2oj85k/AJYkw+xlYMA6Wvg/dHoIOd4Ir4LznEREpLXRNXuRcajSCa8fDH+dDo3iY9Qi8Eg/LPwWPx9fViYgUyqshb4zpZYxZZ4zZaIwZdZbXGxtjZhtjfjbGLDfG9PFmPSJFUi8abp0Ct02DwBowNRHe7AGbf/R1ZSIi5+W1kDfGOIFXgd5AK+BmY0yrM3b7J/CJtbYdcBMw3lv1iFy0pj3grh9hwAQ4mgXv/QEmXQ+Zv85FrVn0RKQ08eY1+Q7ARmttKoAx5iOgP1Bwdn4LnJrzrwaww4v1iFw8hwPa3git+sPiCTBvNLzeBWIHszzyPgZPTiPb7cHf5dD8+CLic97srm8IbCuwnX7yuYKeAG41xqQDM4A/ebEekeLjFwhdRsLIZZBwLyz/mKjPujPCfkSAPU6O20NSapavqxSRCs6bIX+2WfjPvF/vZuBda20joA/wgTHmNzUZY+4yxiQbY5J3797thVJFiqhyLej5NIxYwsHwXoxwTWNWwN/o4VpBQkSwr6sTqbD279/P+PG/XgFOS0vjww8/zN9OTk5m5MiRRT7/0KFDz7kozbkMHz6ctm3b5i+Mc/jw4SK//4XyZsinA6EFthvx2+744cAnANbahUAgEHLmiay1E6y18dba+Nq1a3upXJGLEBRO8O3vs7bPp1SpUoWJzv8jLvlhOLLH15WJVEiFhXx8fDwvv/xyidY0duxYfvnlF5YvX07jxo155ZVXvP6e3gz5JUCkMaaJMcafvIF108/YZytwBYAxJoq8kFdTXcqslh2uptYDi6H732DV5/DKJfDLR3mT6ohIiRk1ahSbNm0iNjaWhx56iFGjRjFv3jxiY2MZO3Ysc+bMoW/fvgA88cQTDBkyhKuvvprw8HCmTp3Kww8/TExMDL169SInJ+e87xUeHs7jjz9O+/btiYmJYe3atWfd79Sys9Zajh07ViLLznpt4J211m2MGQHMBJzA29baVcaYfwPJ1trpwF+BN40xfyGvK3+oLWtT8ImcyRUAPf4BrQfA9JHw+d2w/GPoOxaCwn1dnUiJ2/nMM5xYc/bgK6qAqJbU+8c/zvn6s88+y8qVK/PnkJ8zZw6jR4/myy+/zN8uaNOmTcyePZvVq1fTqVMnpkyZwvPPP8+AAQP46quvuPbaa89bT0hICEuXLmX8+PGMHj2aiRMnnnW/O+64gxkzZtCqVStefPHF3/GJi8ar98lba2dYa5tba5taa58++dxjJwMea+1qa20Xa21ba22stXaWN+sRKVF1omDYTOgzGrYthvGdYMErkOv2dWUicobevXvj5+dHTEwMubm5+avQxcTEkJaWVujxAwcOBCAuLu68+7/zzjvs2LGDqKgoPv744+Io/bw0ra2INzkceVPhtugNXz2YN2veik+h3zio38bX1YmUiPO1uEuLU8vGOhwO/Pz88rvSHQ4HbnfhX8xPHe90OvP379mzJ5mZmcTHx5/Wsnc6ndx444288MIL3HHHHcX9UU6jkBcpCTUawc2TYfU0mPEwTLgMOo+A7qNIyThBUmoWCRHBuq9epJhUq1aNQ4cOnXO7JMycOTP/sbWWTZs20axZM6y1fPHFF7Rs2dLrNSjkRUqKMXnX6SMug1mPwk//4fjyaYzbfys/ultrAh2RYhQcHEyXLl2Ijo6md+/ePPPMM7hcLtq2bcvQoUNp165didZjrWXIkCEcPHgQay1t27bltdde8/r7aqlZEV/Z/CP7P7mXmse2Mcl9Bc/k3sq9V7fhvh7NfF2ZyEXTUrNFU9xLzWoVOhFfadKN1EHf8panL4Nd3zPd/xF61Njp66pEpBxRyIv4UPum9Ykd/gr/azOe0MpuWn01ABa+qqVsRaRYKORFfCwuLIj+AwfjPyIJml0FM/8Bk66DQ5m+Lk3kopS1y8G+5o3/Xgp5kdKiSjDcNAmuGQNbFsJrnWH9zMKPEymFAgMDycrKUtBfIGstWVlZBAYGFut5NbpepDQxBi4ZDmFdYEoifHgDdLgLrvo3+FXydXUiF6xRo0akp6ejRcUuXGBgII0aNSrWcyrkRUqjOi3hzu/hu39B0quQNh+ue4uU4/V1T72UCX5+fjRp0sTXZVR4CnmR0soVAL2egaaXw7R78EzozoycW3gn5yr8XU7dUy8ihdI1eZHSLvJKuGcBW2tcwqOOd3nNNZYA92GSUrN8XZmIlHIKeZGyoGptsvr9l//z3MYVjqV87v8Yl9Xa6+uqRKSUU8iLlBFx4bW4eviTfBH7OqGVsmn95bWwapqvyxKRUkwhL1KGxIUFMWDAjfjdOw/qtoJPh+TNg6/la0XkLBTyImVR9QYw9CuIHw4LXob/DoAje3xdlYiUMgp5kbLKFQB9x0D/8bB1EbzRHbankLJlH6/O3kjKln2+rlBEfEy30ImUde0GQ93W8PFteN7qxefuoXyYc5mWrhURteRFyoUGsXD3XNJrtOMpxwSecr4J7mzdZidSwSnkRcqLyrXY3e9D3vD05xbXbN71f44uDZ2+rkpEfEghL1KOxDUJIX74f/i25ZN0cG4gduYgyNrk67JExEcU8iLlTFxYEFfdNBLHkOlwbB9MvCJv7nsRqXAU8iLlVVgnSPweqtSG96+Fnyf5uiIRKWEKeZHyrFYTGP4thHeB/90L3z1BSlqWbrETqSAU8iLlXaWaMPgziBsK88ey552beHXWcgZPTFLQi5RzCnmRisDpB31fYn7EX7iKJUz2e5Ia7r26xU6knFPIi1QUxlCp+5+5z/NXIs12PvV/gu4hh3xdlYh4kUJepAKJCwsiMXEEM9q/QYPAHKK/uR52/OzrskTESxTyIhVMXFgQg/oPwHXnt+CqBO/2hU0/+LosEfEChbxIRRUSCcNnQc0wmHQDrPjM1xWJSDFTyItUZNXrwx0zILQjTBkOC1/VKnYi5YhWoROp6CrVhFunwNQ7YeY/+NmzkDE5N+LncmkVO5EyTi15EQG/QLj+XVY0GESi4wuedU7A487RLXYiZZxa8iKSx+Ek++oXeOVtywjXFKo6sqkb9r6vqxKRi6CWvIjkiwuvRafhL/JTxP30diTRfuGfIOeYr8sSkSJSyIvIaeLCguhy+7+g70uwYRZMuh5OaNIckbJIIS8iZxd/BwycAFsWwAcDWLY+TaPuRcoYhbyInFubG+CG9/Ds+IWASf14d9ZiLWwjUoYo5EXk/KL+wJetxxBOBh/5/Zsgd5ZG3YuUEQp5ESlUw/i+JHr+Tl2zj8n+T3Jp3RxflyQiF0AhLyKFigsL4oHEO5jZbjyh/odp890tcGC7r8sSkUIo5EXkgsSFBXHdtdfhvH0aHNkD714DB9J9XZaInIdCXkR+n9BL4LbP4WgWvHsNy1et1Kh7kVLKqyFvjOlljFlnjNlojBl1jn1uMMasNsasMsZ86M16RKSYNIqH26fhPpJF0CcDmDxrvkbdi5RCXgt5Y4wTeBXoDbQCbjbGtDpjn0jg70AXa21r4H5v1SMixaxhHFOjx1Odw3zk/yS13ZkadS9SynizJd8B2GitTbXWZgMfAf3P2OdO4FVr7T4Aa+0uL9YjIsWsadtLucPzKNU4yiT/pzXqXqSU8WbINwS2FdhOP/lcQc2B5saYn4wxScaYXmc7kTHmLmNMsjEmeffu3V4qV0R+r7iwIB5JvJlZ7V+jof9R2nx/Kxza6euyROQkb4a8Octz9oxtFxAJXAbcDEw0xtT8zUHWTrDWxltr42vXrl3shYpI0cWFBXF9/2tx3jYVDmbA+/3zRt+LiM95M+TTgdAC242AHWfZ53/W2hxr7WZgHXmhLyJlTeOOMPgT2LcF3u/PsnWpGnUv4mPeDPklQKQxpokxxh+4CZh+xj7TgB4AxpgQ8rrvU71Yk4h4U3hXuHkynj3r8ftwIBNmLdWoexEf8lrIW2vdwAhgJrAG+MRau8oY829jTL+Tu80Esowxq4HZwEPWWg3PFSnLmvZgRtQLRLKVd/yew899VKPuRXzE5c2TW2tnADPOeO6xAo8t8MDJPyJSTtS/pD8P/LKFlxwvMcH/RQIaT/F1SSIVkma8E5FiFxcWxB2JI5kd9QSdzCraL34AcnV7nUhJ82pLXkQqrriwIAj7MyyuDDMehGn3kNL+WZLS9pMQEZz3uoh4lUJeRLyrw52QfRi+e4KNv+zlxZxh+LucTEpMUNCLeJm660XE+7r+hZTQO7jR8T0POyeT4/ZoMJ5ICVBLXkRKxhWPMent7fzR9SWHHNVJiOjs64pEyj215EWkRMSF16LlsNdZX7snDzk+JC7rC1+XJFLuqSUvIiUmLjwY7v4vTL4JvvgzVAoipXJXklKzNBhPxAsU8iJSslz+cOMH8H5/PJ8N4+XsvzHPHYW/y6HBeCLFTN31IlLy/KvALZ+wLyCUVxyjiWKzBuOJeIFCXkR8o3IttvedxEGq8J7/c0S4dpEQEezrqkTKFYW8iPhMm1ZR7Bv4EVX8DF/UHEtcsGbFEylOCnkR8anotpdQacgUAo/tgkmD+HnjNi1RK1JMFPIi4nuhl8AN72F3ruToBzfz8qxVWqJWpBgo5EWkdGjekx+a/5MuZgXPuV7H7XZrIJ7IRdItdCJSatTsfAdj16zlL86P2W2CaR8xztcliZRp5w15Y4wLGA4MABoAFtgB/A94y1qrUTIiUmziwoJg2LOsnOnmzowpsLMbhN3l67JEyqzCWvIfAPuBJ4D0k881AoYA/wVu9FplIlIhxYXXgjvfhI+PwdcPs+lENb7JvUQz4okUQWEh395a2+KM59KBJGPMei/VJCIVncMJ173F4Td70/D7PzE75xHGOVtqRjyR36mwgXf7jDHXG2Py9zPGOIwxNwIa9ioi3uNfmY+bjWanrcWbfqNpmLtdA/FEfqfCQv4mYBCQaYxZb4zZAOwEBp58TUTEa2JbNuNO+3c8GN7xe46uDXxdkUjZYqy1F7ajMcEn99/j3ZLOLz4+3nSrk9cAACAASURBVCYnJ/uyBBEpQSlb9rF52RwGLr8bR/02MOQL8Kvk67JESowxJsVaG1+UYwsbXT/wLM/lP7bWTi3Km4qIXKi4sCDiwgZAcxd8fBv7PhjC5PAn6di0jq7PixSisIF3fzjj8RcFti2gkBeRkhH1B7Z1eJTQxf8mMNXB4NlDNBBPpBDnDXlr7R2nHhtjfi64LSJS0qZX6k9l92KGub5ma04dklIjFfIi5/F7Zry7sIv3IiJekhARzG0/3E6j3N086nqfzX7dgGa+Lkuk1NK0tiJSZsSFBfFBYmeSNzSm85p7aDb3T6yuVJfZB+trshyRszjv6HpjzBf82oLvBvxY8HVrbT/vlXZ2Gl0vIgAc2smJ1y/nwOGjDMj+N1mu2rpGL+WS10bXA6MLPH6xKG8gIuIV1eoxNWoMfZcMZaLfC9yY8zhJqVkKeZECCgv5wcDXwHfW2kMlUI+IyAVrHtOR+xfdzxuO5xjnP46q4Z/5uiSRUqWwGe/eBtoCM4wx3xtj/maMaVsCdYmIFCouLIh7E+9mfvNRdDfLCF38JK/O3kjKFs26LQKF30KXBCQBT5yc8e5q4K/GmBjgZ+Aba+0n3i9TROTs4sKCIOxvZH66i7qrJrJnhWXwD310fV6EwlvyABhjAoCeQBNgE3nryddG966ISCkxJfguZuXG8U/nB3TxpGgxGxEuMOTJC/X+gBs4DBwC5lhrn/FWYSIiv0fHpnX4G39ijQ3jZdc4etTc5euSRHzughaoMcastNZGl0A9hdItdCJyLilb9rFizRpuWT4UjJNJMW/TJqqluu2lTLuYW+gutCW/4OR1eBGRUisuLIihvTqz8Yq3cB/Oov2Cexk+ca4G4kmFdd6QN8asMMYsB7oCS40x64wxyws8LyJS6sw+WJ/73fcRYzbzNONJ2rTb1yWJ+ERh98n3LZEqRESKUUJEMOMcHXgu92b+7vqQHQc+AJ70dVkiJa6wW+i2lFQhIiLFJS4siEmJCSRtasaejFwa/PIy356oTa1Ot+n6vFQoF3pNXkSkTIkLC+K+yyPZ2ulJFtlWdFvzL0ZPfE/X56VCUciLSLm2cMsh/ph9P9ttCK84XmTV6hW+LkmkxCjkRaRcS4gI5pirOne6H8JFLtevexCOH/R1WSIlQiEvIuXaqevzA6+6jMxeEwg8sJG0CTeRsnmPr0sT8TqvhrwxptfJ2+42GmNGnWe/QcYYa4wp0s3+IiLnExcWxH09mnGoQReeyL2D8L0/seKdEbo+L+We10LeGOMEXgV6A62Am40xrc6yXzVgJLDIW7WIiAAkpWbxQc7lvO3uxVDH1xyc/6avSxLxKm+25DsAG621qdbabOAj8ua/P9OTwPPAcS/WIiJCQkQw/i4Hz+YOZq6NpfvG55g2dbJa9FJueTPkGwLbCmynn3wunzGmHRBqrf3yfCcyxtxljEk2xiTv3q2Zq0SkaE5dn//z1a3YddV4Nnnqcdkvf+WRiVMV9FIueTPkzVmey18NxxjjAMYCfy3sRNbaCdbaeGttfO3atYuxRBGpaE5dn9+VE8Dw7AfJxcF48wI/r9vs69JEip03Qz4dCC2w3QjYUWC7GhANzDHGpAEJwHQNvhORkpAQEcwuVz3uyfkLjcxu+q4bxWvfr1GLXsoVb4b8EiDSGNPEGOMP3ARMP/WitfaAtTbEWhturQ0HkoB+1lqtIysiXneq6777Vf1Jafsv6mUtovqcRxg8caGCXsqNwhaoKTJrrdsYMwKYCTiBt621q4wx/waSrbXTz38GERHvigsLIi4siFdn92KZexH3uKazyd2QpNTmmuNeygWvhTyAtXYGMOOM5x47x76XebMWEZFzSYgI5tYfbiIiN4NHnB+Q6t8DaObrskQumma8E5EKLy4siP8mdiat+1hOBLcicu5IyFzt67JELppCXkSEvKC/+8oYKg/5FPyrwOQb4bBu2ZWyTSEvIlJQjYZw84dweBd8PBhyNE+XlF0KeRGRMzWMgwGvw7ZF8MVIsLbwY0RKIa8OvBMRKbNaD4A9G2H2UxASSUpYIkmpWSREBGvkvZQZCnkRkXPp9iDsWQ8/PMX7uYf5wt0Bf5eDSYkJCnopE9RdLyJyLsZAv3FkVG/Ds47xtCaVHLeHpNQsX1cmckEU8iIi5+MXyK4+b7GX6kz0H00j1z4SIoJ9XZXIBVHIi4gUom3L5uwfMIkgVw6f1xxHyoZtmvpWygSFvIjIBWgdm8CWy1+lxsF1RMz9M7dNXKCgl1JPIS8icoFmZcfwb/ftXOlcyl/tB7o2L6WeQl5E5AIlRATzsaM377l7Mtz1NY02fqjWvJRqCnkRkQt0anna9e3/wWxPO65JH8trE99Q0EuppZAXEfkd4sKCaBBUlZE5I1hvQxnreInPZsxU0EuppJAXEfmdEiKCyXFVITH7QY4QyIidj/Dnid8o6KXUUciLiPxOp7rtm0a24M6cBwniEOPN8yRvSPd1aSKnUciLiBRBXFgQ91/ZnA3OpvzZPYJos5k+6x9l/A/r1KKXUkMhLyJSRKda9LFX3sLSVn8jdNdsAn94nMETkxT0Uioo5EVELkJcWBD39WjGojrX8467J8NcX3OT52te+m69gl58TiEvIlIMEiKCecEMYVZuHI+63icwdaZa9OJzCnkRkWIQFxbEB4md+aTxY6y0TXjZNY6W7g1q0YtPKeRFRIpJXFgQ91zdlnvtw+y2NXjT/wW2blylFr34jEJeRKQYxYUF8XJiL8Y1eBYXHt7xe45q7v2a5158QiEvIlLM4sKCuKn3FdzjeZj6Zi9v+b9A59BAX5clFZBCXkTEC+LCgngo8XZ+iH6WGMdm2iXdD7k5vi5LKhiFvIiIl8SFBXHN9cMxfcfCxm/hiz+Dtb4uSyoQl68LEBEp9+KGwqFMmPMMVKsHVzzm64qkglDIi4iUhO4Pw6EdMO9FqFoPOt7l64qkAlDIi4iUBGOgz4tweDd8/TBUrQOtr/V1VVLO6Zq8iEhJcbpg0FsQ2gGm3glp831dkZRzCnkRkZLkVwlu/giCmsDkW1j180Jenb1Rk+WIVyjkRURKWuVacOsUsp2BhEy7mcmz5mtWPPEKhbyIiC/UDGVK1H+oxAne9/s/arj3alY8KXYKeRERH2neJoG7PX+jrtnH+/7P0qWh09clSTmjkBcR8ZG4sCAeTBzCd21fItK5k9i5w+HEIV+XJeWIQl5ExIfiwoLoP/AWHDe8BzuWwYc3QfZRX5cl5YRCXkSkNGjZBwZOgC0/wSe3gzvb1xVJOaCQFxEpLWIGwR/+kzfP/ZThkOv2dUVSxinkRURKk7gh0PMZWDMdpv8JPB5fVyRlmKa1FREpbTrdBycOw5xn2HXCxad1RpLQNIS4sCBfVyZljFryIiKlUfeH2Rl9F3XWvo/fD48zeOJCTZYjv5ta8iIipZExTKl1F9VyN3OX6yscbkvSpki15uV38WpL3hjTyxizzhiz0Rgz6iyvP2CMWW2MWW6M+d4YE+bNekREypKEpiE8wzDec/ck0TWDQXvGg7W+LkvKEK+FvDHGCbwK9AZaATcbY1qdsdvPQLy1tg3wGfC8t+oRESlr4sKCmJTYicOXP01mqzuou/pt+GaUgl4umDe76zsAG621qQDGmI+A/sDqUztYa2cX2D8JuNWL9YiIlDlxYUF5XfR2LMyqAgtfAU8u9Hkhb416kfPwZsg3BLYV2E4HOp5n/+HA116sR0Sk7DIGrn4q7+eCcWA90Gc0ODR+Ws7NmyF/tq+YZ+1jMsbcCsQD3c/x+l3AXQCNGzcurvpERMoWY+CqJ8E44aeX2H3oGJ/U/QsJTWtrQJ6clTe/AqYDoQW2GwE7ztzJGHMl8AjQz1p74mwnstZOsNbGW2vja9eu7ZViRUTKBGPgyifIaHMftdd9SO3ZD3H7xJ90e52clTdb8kuASGNME2A7cBNwS8EdjDHtgDeAXtbaXV6sRUSk/DCGqTXvwO3O4M+uqdTIPcKSjU3Umpff8FpL3lrrBkYAM4E1wCfW2lXGmH8bY/qd3O0FoCrwqTFmmTFmurfqEREpTxKahvCauYF/u2+np3MJt2x8UMvUym8YW8ZuxYiPj7fJycm+LkNExOdStuwjKTWLPvZHmsx7EOq3gcGfQZUQX5cmxcgYk2KtjS/KsZrxTkSkjMq/vY5m0LABfDoE3u4Ft0+DGo18XZ6UArr3QkSkPGjRC26dCoczyZ5wFZO++laD8UQhLyJSboR3YXXPyRw8fITei4cyeuJ7CvoKTiEvIlKOzD5Qj+uzH+eArcK7jqfYkzTZ1yWJDynkRUTKkYSIYDJcDbg+51+sIIKea/4OP47WfPcVlAbeiYiUI3mL2iSQlJqFI2w6/Pwo/PAk7N0MfceCy9/XJUoJUsiLiJQzv466B5pMgFoRMPdZDu7cxCcRz9CuhSbOqSjUXS8iUp4ZAz3+zuZLxxCYsZge82/hnxOnaEBeBaGQFxGpAGY4unNbzj+oYY7wieOfZC6e4uuSpAQo5EVEKoCEiGB+cbbm2uynSaM+fVb9FX54Km9teim3dE1eRKQCKDggL6fxVbDiKfjxBdixjGUdRvPTdjcJEcG6Vl/OKORFRCqI0wbkRbwCDdvh+XoUQRuuZkbOnxnnjGBSYoKCvhxRd72ISEVkDFySyOdt3iCAbKb6PcZNnq9J2rTH15VJMVLIi4hUYOHtLmeA5zkWeKJ5wu89Bqf9HY7u9XVZUkwU8iIiFVhcWBCvJF7N6h4T2dbhn9RMnwOvd2Xdopm8OnujbrUr4xTyIiIVXFxYEPddHklon4cg8VuOWxfNZtyI+f5fDJ04T0FfhmngnYiUast2LSM5M5n4uvHE1ok97zZw2mtSBA3a8X6b96n146Pc65rO5Z6f+WX5c8SFXePryqQIFPIi4lXW7Sb30CE8Bw+yJi2Z9duX0SIgjDD/uniOHCE9cyPb96XR0K82dVxB7N6/nd0HM6gdUAtyPaSkLwSPh6XGwbHgKNZkrQE8LHE6OFo7hl/2ruCEw8P3fg7cTjjutGwJcLKnWU+OuNw0qd8KW6USq4+nERUWD9WqkHx4NfENOgDoC8JZxDUPY/CP9/JNTgeecU3khqW3s8PzJ/5X9UY6NKun0fdliLFlbGWi+Ph4m5yc7OsyRCosay2eI0dw79qFe9cuNm1cwva0lTRyVyf4uAv3vr3kZu3l2J6d5B44iOtY9gWd12PABvhzzGST4wCPwxAYUJmDuUewBsAQ6ArkuPsYxoLDQiUTgDvnBK5c8HeDKxdcngt4L+BIJThcyXCwEhyq4uBgFcPeyh4O1vDjtu5/pnmLTrjq1sVZsybGmPwegxr+NTiQfSD/Z3n9UpCyZR9JqVl0aeAgdNETBKf+j5WecB63d/GPxFsU9CXIGJNirY0v0rEKeREpyGZnk7NzJzk7dpCzI+Pkzx3sS1vHiYwdBO4/ijl24jfHZTvBLziYSiF1OVrVxcKjqzkQ6OF4ZRf9Ym8m1ZPJtJ3fcSTAkh3gYFDbW/EE+jN+3dsc8/NgnC461u9I0o4kPHhwGifXRV7H9E3TyfHk4Ofw4+FLHub5Jc+fddtpnHn157oJzDH4ZXvwz/ZQOdtQ6YSl0glLleOGqsctlY9bqh0zVD9mqXYUqh+11DgC1Y/+dqCS8ffHE1KTdc497Kpu2V0DdtV0sKumZXdNJwdquOjX/Fr6Ne1XLsMe4NXZG1n53Qf8y/UuwRxgZaMb8Vz2CAvSszWBTgm4mJBXd71IBeQ5epTsbdvI3rqVnK1byd66jeytW8jZspXsnRkYz+lf/m1wTVIDD7K7Jhxo7OTK9oNpHBHLzCNLeHvH5+yt6iE7wMmI9kNJjElk4oqJjFu6Fg/gNIa67UKIr9uLlbPm5wd0dLurAcjdNglz8rkrG1/J0syl+fv8oekf+EPTP5zWjR4ZFHnObSC/tf38kuc5UCD8c21uoY9xuwk56uL5qL/RJLsG7l2Z5OzMZN26Bditu4naZum6Ghz21FSwHnKcOeyqOZnVtT7hUKuO7K9bGWfjUHbXCaBynfocyDlY5lv7CRHBjHMksCA7mof9PuGW7R+T+d9ZrHTfzjhHRyYldlLQl1JqyYuUU9Za3Lt2k705lROpqWSnbiY7dROHN6zD7D79PmhnzZr4hTXmSO1qfHF8MRk1POyv6ccDfZ+lTesevL3ufcYtHZffwh7RbgSJMYks27WMO2fdmR/Kb179Zv5guHM9f+Z178IG1hXVuQbkXcjjM9/31OfJzs3G5OYSfMhQZ7+HevsMdfd7qLcP6u2F+vss/u5fjzscCOkhhu11nHTrdhvN4noQEBmJs2bNIn8uXznVfZ8QEczmZXNonfIYUY6tzPPEkN7hUW7u29PXJZZb6q4XqcBOhfmJDRs4sX49GSsWcXT9OqpmHMAcPZa/n6NKFdyh9Vjgt4X0IMueEBd39X6M6LZX4qxeHeBkC/zCwxx+G9KnFFdYlxZnuya/du9apm2cRq7NxWCwnlyCDnpokGVpmAWNsiyNdlsa74YqBa5wuOrUISAykoDISDLrB7Iq6DBRcT2JbVik3+MlLmXLPoZMnM+NdiYjnVOp7jiOiR/Gsmb38tMOj7rwi5lCXqSCWLZ5AeuSvyVqfxXqZBzjxPr1nNiwgdwDB/L32V/VsC0Edoa4uKzrrTRteyn+ERG46tThrZVvnTXE889fhDCv6AqG//NLnic7NxsPnrzQx+LAgb/DjzfbPUezff55f2frN3BiwwaObdyAyc4BwO0AR9Nwgtq0JzCqFYGtoghs0YLlRzaUyv/uvw7MM8RufA2b/DYHPYGMz+3HR6Y3byd2U9AXE4W8SDljrcWdmcnxNWs4sXYtx1ev4eCqXzA7dv26T+VKVG7egoDmzfNahc2b86k7ibEb3ypSiBfcpzSGSlnwe0fgT1w2gc9+eIXGmblE7DJ0O9KIkG0Hyd2XN/mMNYaMWpBa17CloYvr+/+dmM79cFSuXNIfrVCTv5xJ/UVPc5nzF3baIJJCh5PRZJBuuSsGCnmRMmxZxlJWLfuWmL1VqbvjKCfWrOH4mrX5v+gB/MIak9EgkO/8NpFWx7K9roubuo8gsc2dp59LIV6mnO3vq23ttnlf8FavYcGcD8j8OYmInZbgQycPcjgIaNqUwOho9oTXZHWdbFpe0pPYRpf49LOkbNnH4IlJxOau4iHXx8Q51pNm6zLBDuC6O/5KXJM6Pq2vLFPIi5QR1u3mRGoqx1et5viqVWQtW0zO2vUEnBysZf1cVIpsTkCrKAJbRhEY1ZKAFi1xVq1yQQEOCvGy5nx/XwX/zkOOOBlT717qbzvKsVUrOfzLMsz+gwDkOMHZvCnB7ROo1CaGtIZ+LPHbTnz9S0r038CpLvwd+46SmfI//uL8lNaOLRwMqM++9vcxw3UFHZrVV8v+d1LIi5QSBX9ht60VfVqgH1+1iuNr1mCPHwfAVK7M/sZB/FQ1g9R6sK2ek/5X3Mfwdndf0PkV4BXDuf7OJy5/k8lzxtEkI5fmGYZuB+tTM20v9uhRAI4EQGoDJ1GX9iM84SoqtW2DKzi4RGo+1arPcedypesXnq71DSEHlrPL1mSS7UnoVX8i011JA/QukEJexMdsbi7Lk2fwzpRHabw9h6aZELnHD3M8b0i1qVyZwKgoAlu3olLr1gRGR+MfHs4vWSsuqHUucqazdvUHxzD5mxdY9P1/abrDQ+QOCNtD/rwHtl5t9kTUIqh9B5p27k1gqygcgYFeqa/gLXdJm/aQ9N0U7nR+STfnCo7aAD7zdGMyvXgq8ToFfSEU8iIlyHo8ZG/ZwvGVqzi+ciXHVq7Ma6GfbEEd94O0uobqbdoR3/2G/EA3TudZz6fWuRTVueYdOC38Lx1H890uNv30DSlzPiZiu5vaB0+ewOUisEULjkTWZ1NDF+Edr6RNfG+Mo3gXKP21Ze+hpWMrdzhm0M/xE/4ml/Sal3Ci3TBmudtpkN45KORFisHZfmFaa8lJT/81zFfmdbt7Dh8GwAQG5rXQo6PZ1bgaj+55l61Bblwuf7XKxWfO9m+54BwIwUccjAzsTdf9ddmdsoATK1ZS6eQSA7ZKJarEtOVg0zpsqA8RHa+mbfTlGGMuqqZTLfugyv78+8tVVHfv4ya/udxdeS5Vjmewx1bnf7YbdbslssUZqq78AhTyIkVw5oxod85MpNq+bFrscnJvYE9qpO7m2KpVeE7eg278/AiIiqJSdGsCW0cTGB1NQNMIjMt11nMq4KU0OdfAzYkrJvJKysvUy/LQPMNwbXZr6m45iGfD5vzFfmytmlRrE0tgTDSVovP+7V/M9f2CXfmLNu1i6fefMMgxlyscS/EzuSz3RPAVXWhxxR1keGpU+MBXyIucobDpU7GWUZ8l0mh7Ns0yDR0OhlB5UwY18nrc8TgdVG7RksDoaAKjW1MpOpqAZs0w/v4+/FQiF+eCuvevfpPkzGReX/wyoZkeIncarjnRnJC0A5it2zEnI8PWCWZvWBA12rSjSYcrCGzVClft2r+7poJd+cHmIP0c8+nvmE+MI41ca1hso/iWjkRfcVuFDXyFvFR4v2mVn5xn3N/pz5tXvgEZu5nw6ShCM7JpmmlovstFwOG8QXG5Bg41qsnyWofZWA+2NPTjHze/TmxoB19+JJESc7b1AwoG/6nV/hxHTxC528Uwv26kLfqe8B1u6v86nQM2uCb7GgdRPbotYXGXERjVEr/Q0EKv8Z/ZlZ/j9tDU7KCvYwG9HYuIdGzHYw3LbFNm2zhcUdfQtfOlYEx+j0B5Dn6FvFQo5/qFlJ2bTSX8uLnSpWxc9C1hmR4iTo5yP7WmudsB6bUNtnkEP1ZJZ2NdDzvq+TP+monAuRcoEaloCv5/lpyZfNp0yAWXBK6a7eAvNQYQtTuApDkfErozl0Z7LM5TLf5KgVRu0ZLDjYNJqwOhbbsS3bFP/noJZzpb4Eea7VztWMTljqXEOlIByLC1mGdjmZsbzRKiuSq+FQPbNyqXYa+Ql3LpfF2L2bnZBGX78Z/GD5D+83y2LJ1LWKaH0D3gd3IV0BMu2FbXQZNLLscTGc5TWZNIDcnF+OcNigOFusiFOFfL/swu/lNfBALchsZ7DI0zc2my20GXow1g0xaqHP/1nCeCq+LXNIKQVu3JrOvP6uqHaNnuSmIjOufvc2bgn8jxUJt99HAu4zLHL3RxrKC6OYbHGlbbMJKJIiK+JxsCoqlUsy77jpaP9e4V8lLqFTar1/nCvLL14/XmjxKe5WDx/E/ZtTKZxrssIYd+PceBKoa0OrCtnoueV92NbRbOEv/txDfooAVWRIpBYUsCF/wiYDB4rOf0lv/2hQQd8hC2yxC2Bxrt8hCaBY33OnBk/7o+b06tahxpEES1Zi1o0OoS/Js0wb9JOCuyA5nySwafpaSTm+vB6TA4jYeo3A10dqyis2MV7R0bCDR5C/5s8tTnZxvJcppRp2VXQiJiyTpuy2ToK+SlVCsY2P5O/9+sbFawq/2Nlo/TZK+LhQs+ZfuKRYTu9lB/L792/bmcbKtl2Vob0uu5GNj7r7RO6MNKT7oCXMTHzlyR72wt/zO/ACTU7cDm1UnUz8oldI+hUZalfpalQZY9bXle6+fiWJ3quOs1Yn/NJtRr2QxP/YZ8s9fBB5uzOYGDAOMmmo20N+uJc6ynvWMDwSavNXDCulhnG7OWcMKjO7PNvwmekCh25QQQVNm/VLf6FfLidRfSCj7n9JtnrFF+f7Ph3FCpK9mb01iyeBo7Vi+hQZY9PcyNIbMmpIcYttdx0ufKe2gRf2XeLHH7VinQRUq5c7X8L/QLgMMago46qJuVS6N9Durt9VBnby719kOD/Q5c2bn572UNHK5Wmew69ViRHcTOwJpkVg5id6WaOCp7aFQlixb+22hjUmnl2EJNcyT/2O02mA2eRmyyDUijIY1btCW4cWsycqsTVCWgVIS/Ql686nwt8XPu0+klWhytQc62rWxZvYgFS6ZSJyuXBnst1Y/+epx1Ocmo4WF7sGFHbSd9Lr+b5nFX4B8ezvKDaxXmIuXQhXwBKBj4hryJeCx5eWUs1P7/9u49Rq6yjOP49zlnZmd2O8teym5ht11aSmlLSwWL4AUaNaCo2KoRixJFUBGxGGPkYkzUaJSq8Q8JGAXFW7xEa8RWICReADEkUrFIgYptBSmFLu12273O7sw8/jGz63S63Vtn9nL6+ySTOZd33vOe7tN5zvuemXl74zR35pjbkaGp05nXCc2H4bSeFMmDXQS5I3Nbd7yK/ckGOqrr6E0mCKqdVE0/jdWHaak+QFvNK6SS/Vjhhyl7PMHzfgoveDMv0kRT21KWL1tBOjWf3+xy0kGKFa31U3IRoCQvx2WsXnppT3zDuRu45owrGdy7N/948UW2bruf5/+1lebOHKd0Qm3fkXX43HoON88htfhMTl1+HlWnLyKxaBHx+fN5omO7krmIACMP+YeFzDuYG/x/oi9J/AABAYEFeDZDfZfTdBjmHnZOPgwndxltvSdzSm8Cbz9AXX/P8MhhsZ5ESF8ypL8GPJljsDpDdXKA3pocbeEAi4MBwkSOdFWcffEG2oNG9tHIsiVLqG1awCMvB3TH5rJ6zTvKlviV5OUo4/2Q2Ui99FWpMxnct4/MvnYyr7Tz313/4I+Pb6L+UJamw7CoLzU8xeUQj4W0p3K018MrjSFrLljPwhWvJb6gjaoF8wlqaip9yiISMaW/f7Fl1xbu2XkPWc8OJ/5MLpMf3i8k+KHe/0gMIx7EybmTyw5S1+M0dENDt1PfA3XdUN/j1PVAXY9T3wsn9UKqf8TqABiMQTrpVMezWCLHSzUhvdVw47mf4ecfXF+WRD9jk7yZXQp8GwiB77v7xpL9CeAnwGrgALDe3Z8brc4TLclP5hPhRyXui7/H2dWLyXR0kD14kGxHR2G5k23PPszu5/5BXY/T2AWnMgRwmwAACPBJREFU9P7/O+XFvDpJX0M1ydYFNC5cSry1lXhrS/65pYVYUxNPHHhSPXIRqajSxD/U6z80cGi49z+QHRge5nd8+BlGHgEYS5BxTuqH2p6hpO/U9uVHLFN9+eU5/fmLgTn9Ti6AG69O8u5Tv8JXLr3suM95RiZ5MwuBZ4FLgD3AY8D73f3pojLXA6vc/TozuwJ4t7uvH63ecib5Sn+l6njrH/5KSibNSZkqbrvg6yxLLCDX3U2uu5tsVxe5rm5yPYXl7h5yXV3s3rudl/ftprYvf/+7rs+Gp5os5dUJ9lcNcDAFnbUBr15xMacuXEG8uZnYvHnEmpuJNc8jTM053n8OEZGKKx7uH0r8Ozp2HHMEoPRC4FjPYIQWkPXsiMcdSqVD8/i4w+WnX8sX19xw3Od0PEk+NnaRSTsf2OnuuwHM7JfAOuDpojLrgC8VljcBt5uZ+RTcQ9jWvo1P3vtRqroH2GRxNl74Nc6qX4pns3g2C7lc/nnE9RzkirZnsoX1wvZMlv92PsfPtv8YG8zwQjYkXLiWebFGPN1PLp3G+9P4QJpcfxpP5x+54ed+PD1A0NPJnf39VA1CwCCwgf8c64SCgCCVIkylaKiOsd+NfQ3GrvkBF628jNb5ywgbGggbGgkbG4g1NhI2NBAkk2xr38azhYuRs9UDF5FZ7Jzmc0bsVL1z8TtHHQEY7/PGv21kIHf0aGescPGQJX8REA/irFt2UQXPdHwqmeRbgReK1vcAFxyrjLtnzOwQMBfYX8F2Afk/8Gv+2c/H788Cg3D7p9lVxvrjwDXDa1lgEwficYKqKiyRwJIJgqoElkxiiSqCRJKwvn542RIJ0rku/vDSg/TGcqSrQy5/9VUsbDmLYE6KsDZFUFs7nNitpuaIqSAHC1ezrx9H4j7WfwoRkagofZ+b7HvekoYlbNm1BcdZ3ricHR07cJy1i9cCDO9bu3jtjHhfrWSSH2ny4dIe+njKYGbXAtcCtLW1HX/LgPPmncfm0xLc9bYBLAy56uyrOa3xdAgCLAzzz7HYkethmF8Ow/yEC6OsP31wBzf95Wb6ggy5qjh3XPo9zmlZPaE2tgCZoiH/VRMIGCVuEZHyG+u9daa971bynvzrgC+5+1sL658DcPdbi8o8UCjzqJnFgJeBptGG60+ke/IiIiIz9Z78Y8ASM1sEvAhcAXygpMxm4CrgUeC9wJ+m4n78kEr3dtWbFhGR6VSxJF+4x74BeID8V+judvenzOzLwFZ33wz8APipme0EOshfCIiIiEgZVLInj7vfB9xXsu0LRcv9wOWVbIOIiMiJKpjuBoiIiEhlKMmLiIhElJK8iIhIRCnJi4iIRJSSvIiISEQpyYuIiESUkryIiEhEVXQ++Uows1eA56e7HUAdcGgWHmuydU30deMtP55yY5UZbf/JTMGER2Wm2CpPecXW0RRb5Sk/1bF1mrs3jaNdR3N3PSbxAO6cjceabF0Tfd14y4+n3FhlRttP/tcVpz1epuvvPZXHUmzN/IdiqzzlZ1Nsabh+8rbM0mNNtq6Jvm685cdTbqwyU/m3mAqKrfKUV2wdTbFVnvKzJrZm3XC9yESY2Vaf5OxNIqNRbEmllDO21JOXqLtzuhsgkaXYkkopW2ypJy8iIhJR6smLiIhElJK8iIhIRCnJi4iIRJSSvJywzGyOmf3dzC6b7rZIdJjZcjP7rpltMrNPTHd7JFrM7F1mdpeZ/c7M3jJWeSV5mXXM7G4zazez7SXbLzWzf5nZTjO7ZRxV3Qz8qjKtlNmoHLHl7s+4+3XA+wB9xU6GlSm+7nH3jwEfBtaPeUx9ul5mGzNbA3QDP3H3lYVtIfAscAmwB3gMeD8QAreWVHENsIr8T0cmgf3u/vupab3MZOWILXdvN7O1wC3A7e7+86lqv8xs5Yqvwuu+BfzM3R8f7Zixsp6ByBRw94fNbGHJ5vOBne6+G8DMfgmsc/dbgaOG483sTcAc4Cygz8zuc/dcRRsuM145YqtQz2Zgs5ndCyjJC1C29y4DNgL3j5XgQUleoqMVeKFofQ9wwbEKu/vnAczsw+R78krwciwTii0zeyPwHiAB3FfRlkkUTCi+gBuAi4E6MzvD3b87WuVK8hIVNsK2Me9FufuPyt8UiZgJxZa7Pwg8WKnGSORMNL5uA24bb+X64J1ExR5gQdH6fGDvNLVFokWxJZVU0fhSkpeoeAxYYmaLzKwKuALYPM1tkmhQbEklVTS+lORl1jGzXwCPAkvNbI+ZfcTdM8AG4AHgGeBX7v7UdLZTZh/FllTSdMSXvkInIiISUerJi4iIRJSSvIiISEQpyYuIiESUkryIiEhEKcmLiIhElJK8iIhIRCnJi4iIRJSSvEjEmVm9mV1fWG4xs01lrPvTZvahEbYvHJoz28zONrMfleuYIjJ+SvIi0VcPXA/g7nvd/b3lqNTMYsA1jDGVqrs/Ccw3s7ZyHFdExk+z0IlE30ZgsZltA/4NLHf3lYVpdt8FhMBK4FtAFfBBIA283d07zGwxcAfQBPQCH3P3HcCbgccLP8uJma0G7i6UeaSkDVvI/yb3Nyp5oiJyJPXkRaLvFmCXu58D3FiybyXwAeB84KtAr7ufS/73tYeG4e8EbnD31cBnge8Utr8B+HtRXT8EPuXurxuhDVuBi8pwLiIyAerJi5zY/uzuXUCXmR0i3+MGeBJYZWYp4PXAr82Gp71OFJ5PJT+hBmZWB9S7+0OFfT8F3lZ0nHagpWJnISIjUpIXObGli5ZzRes58u8PAdBZGAUo1QckC8sGjDbbVbJQXkSmkIbrRaKvC6idzAvd/TDwHzO7HMDyXlXY/QxwRqFcJ3DIzC4s7LuypKozge2TaYOITJ6SvEjEufsB4K+Fr7R9cxJVXAl8xMyeAJ4C1hW23w+sKSp3NXCHmT3K0b32NwH3TuLYInIcNJ+8iEyamf0WuMnd/z1KmQTwEHDh0CfxRWRqKMmLyKSZ2VJgnrs/PEqZJUCruz84ZQ0TEUBJXkREJLJ0T15ERCSilORFREQiSkleREQkopTkRUREIkpJXkREJKL+B3aPZck8UfYrAAAAAElFTkSuQmCC\n", + "image/png": 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", "text/plain": [ "
" ] @@ -470,12 +472,12 @@ "hm1_1 = ml_1.head(0, 0, t1, layers=0)\n", "hm2_1 = ml_1.head(r, 0, t2, layers=0)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1/H0, '.', label='obs ln-2')\n", - "plt.semilogx(t1, hm1_1[0]/H0, label='ttim ln-2')\n", - "plt.semilogx(t2, h2/H0, '.', label='obs ln-3')\n", - "plt.semilogx(t2, hm2_1[0]/H0, label='ttim ln-3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1_1[0] / H0, label=\"ttim ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2_1[0] / H0, label=\"ttim ln-3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -499,13 +501,13 @@ "metadata": {}, "outputs": [], "source": [ - "#Determine elevations of each layer.\n", - "#Thickness of each layer is set to be 0.5 m.\n", + "# Determine elevations of each layer.\n", + "# Thickness of each layer is set to be 0.5 m.\n", "z = np.arange(0, b, -0.5)\n", "zlay = np.append(z, b)\n", "nlay = len(zlay) - 1\n", "Saq_2 = 1e-4 * np.ones(nlay)\n", - "n = np.arange(0, 13,1)" + "n = np.arange(0, 13, 1)" ] }, { @@ -523,10 +525,12 @@ } ], "source": [ - "ml_2 = Model3D(kaq=10, z=zlay, Saq=Saq_2, kzoverkh=1, tmin=1e-5, tmax=0.01, \\\n", - " phreatictop=True)\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=rw1, tsandQ=[(0, -Q)], layers=n, rc=rc1, \\\n", - " wbstype='slug')\n", + "ml_2 = ttim.Model3D(\n", + " kaq=10, z=zlay, Saq=Saq_2, kzoverkh=1, tmin=1e-5, tmax=0.01, phreatictop=True\n", + ")\n", + "w_2 = ttim.Well(\n", + " ml_2, xw=0, yw=0, rw=rw1, tsandQ=[(0, -Q)], layers=n, rc=rc1, wbstype=\"slug\"\n", + ")\n", "ml_2.solve()" ] }, @@ -566,11 +570,11 @@ } ], "source": [ - "ca_2 = Calibrate(ml_2)\n", - "ca_2.set_parameter(name='kaq0_12', initial=10)\n", - "ca_2.set_parameter(name='Saq0_12', initial=1e-4, pmin=0)\n", - "ca_2.series(name='Ln-2', x=0, y=0, layer=n, t=t1, h=h1)\n", - "ca_2.series(name='Ln-3', x=r, y=0, layer=n, t=t2, h=h2)\n", + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0_12\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0_12\", initial=1e-4, pmin=0)\n", + "ca_2.series(name=\"Ln-2\", x=0, y=0, layer=n, t=t1, h=h1)\n", + "ca_2.series(name=\"Ln-3\", x=r, y=0, layer=n, t=t2, h=h2)\n", "ca_2.fit(report=True)" ] }, @@ -657,7 +661,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('RMSE:', ca_2.rmse())" + "print(\"RMSE:\", ca_2.rmse())" ] }, { @@ -667,7 +671,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -682,12 +686,12 @@ "hm1_2 = ml_2.head(0, 0, t1, layers=n)\n", "hm2_2 = ml_2.head(r, 0, t2, layers=n)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1/H0, '.', label='obs ln-2')\n", - "plt.semilogx(t1, hm1_2[0]/H0, label='ttim ln-2')\n", - "plt.semilogx(t2, h2/H0, '.', label='obs ln-3')\n", - "plt.semilogx(t2, hm2_2[0]/H0, label='ttim ln-3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('h/H0')\n", + "plt.semilogx(t1, h1 / H0, \".\", label=\"obs ln-2\")\n", + "plt.semilogx(t1, hm1_2[0] / H0, label=\"ttim ln-2\")\n", + "plt.semilogx(t2, h2 / H0, \".\", label=\"obs ln-3\")\n", + "plt.semilogx(t2, hm2_2[0] / H0, label=\"ttim ln-3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"h/H0\")\n", "plt.legend();" ] }, @@ -772,13 +776,15 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]'], \\\n", - " index=['MLU', 'AQTESOLV', 'ttim-single', 'ttim-multi'])\n", - "t.loc['AQTESOLV'] = [1.166, 9.368E-06]\n", - "t.loc['MLU'] = [1.311, 8.197E-06]\n", - "t.loc['ttim-single'] = ca_0.parameters['optimal'].values\n", - "t.loc['ttim-multi'] = ca_2.parameters['optimal'].values\n", - "t['RMSE'] = [0.010373, 0.009151, ca_0.rmse(), ca_1.rmse()]\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", + " index=[\"MLU\", \"AQTESOLV\", \"ttim-single\", \"ttim-multi\"],\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [1.166, 9.368e-06]\n", + "t.loc[\"MLU\"] = [1.311, 8.197e-06]\n", + "t.loc[\"ttim-single\"] = ca_0.parameters[\"optimal\"].values\n", + "t.loc[\"ttim-multi\"] = ca_2.parameters[\"optimal\"].values\n", + "t[\"RMSE\"] = [0.010373, 0.009151, ca_0.rmse(), ca_1.rmse()]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/14_dawsonville_slug_test.ipynb b/pumpingtest_benchmarks/14_dawsonville_slug_test.ipynb index af6d86c..de3ab70 100755 --- a/pumpingtest_benchmarks/14_dawsonville_slug_test.ipynb +++ b/pumpingtest_benchmarks/14_dawsonville_slug_test.ipynb @@ -15,10 +15,10 @@ "outputs": [], "source": [ "%matplotlib inline\n", - "from ttim import *\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -34,12 +34,12 @@ "metadata": {}, "outputs": [], "source": [ - "b = 98 #aquifer thickness\n", + "b = 98 # aquifer thickness\n", "zt = -24\n", "zb = zt - b\n", - "rw = 0.076 #well radius of Ln-2 Well\n", - "rc = 0.076 #casing radius of Ln-2 Well\n", - "Q = 0.01016 #slug volume in m^3" + "rw = 0.076 # well radius of Ln-2 Well\n", + "rc = 0.076 # casing radius of Ln-2 Well\n", + "Q = 0.01016 # slug volume in m^3" ] }, { @@ -55,7 +55,7 @@ "metadata": {}, "outputs": [], "source": [ - "data = np.loadtxt('data/dawsonville_slug.txt')\n", + "data = np.loadtxt(\"data/dawsonville_slug.txt\")\n", "t = data[:, 0]\n", "h = data[:, 1]" ] @@ -82,8 +82,10 @@ } ], "source": [ - "ml = ModelMaq(kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary='conf')\n", - "w = Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype='slug')\n", + "ml = ttim.ModelMaq(\n", + " kaq=10, z=[zt, zb], Saq=1e-4, tmin=1e-6, tmax=1e-3, topboundary=\"conf\"\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=0, wbstype=\"slug\")\n", "ml.solve()" ] }, @@ -116,11 +118,11 @@ } ], "source": [ - "#unknown parameters: kay, Saq\n", - "ca = Calibrate(ml)\n", - "ca.set_parameter(name='kaq0', initial=10, pmin=0)\n", - "ca.set_parameter(name='Saq0', initial=1e-4)\n", - "ca.series(name='obs', x=0, y=0, layer=0, t=t, h=h)\n", + "# unknown parameters: kay, Saq\n", + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq0\", initial=10, pmin=0)\n", + "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca.series(name=\"obs\", x=0, y=0, layer=0, t=t, h=h)\n", "ca.fit(report=True)" ] }, @@ -207,7 +209,7 @@ ], "source": [ "display(ca.parameters)\n", - "print('rmse:', ca.rmse())" + "print(\"rmse:\", ca.rmse())" ] }, { @@ -217,7 +219,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -231,10 +233,10 @@ "source": [ "hm = ml.head(0, 0, t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h, '.', label='obs')\n", - "plt.semilogx(t, hm[0], label='ttim')\n", - "plt.xlabel('times(d)')\n", - "plt.ylabel('displacement(m)')\n", + "plt.semilogx(t, h, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm[0], label=\"ttim\")\n", + "plt.xlabel(\"times(d)\")\n", + "plt.ylabel(\"displacement(m)\")\n", "plt.legend();" ] }, @@ -251,8 +253,8 @@ "metadata": {}, "outputs": [], "source": [ - "nlay = 49 #number of layers\n", - "zlayers = np.linspace(zt, zb, nlay + 1) #elevation of each layer\n", + "nlay = 49 # number of layers\n", + "zlayers = np.linspace(zt, zb, nlay + 1) # elevation of each layer\n", "Saq = 1e-4 * np.ones(nlay)" ] }, @@ -271,9 +273,10 @@ } ], "source": [ - "ml_1 = Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=True)\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=range(nlay), \\\n", - " wbstype='slug')\n", + "ml_1 = ttim.Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=True)\n", + "w_1 = ttim.Well(\n", + " ml_1, xw=0, yw=0, rw=rw, rc=rc, tsandQ=[(0, -Q)], layers=range(nlay), wbstype=\"slug\"\n", + ")\n", "ml_1.solve()" ] }, @@ -306,10 +309,10 @@ } ], "source": [ - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0_48', initial=10, pmin=0)\n", - "ca_1.set_parameter(name='Saq0_48', initial=1e-4)\n", - "ca_1.series(name='obs', x=0, y=0, layer=range(nlay), t=t, h=h)\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0_48\", initial=10, pmin=0)\n", + "ca_1.set_parameter(name=\"Saq0_48\", initial=1e-4)\n", + "ca_1.series(name=\"obs\", x=0, y=0, layer=range(nlay), t=t, h=h)\n", "ca_1.fit(report=True)" ] }, @@ -396,7 +399,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -413,7 +416,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -427,10 +430,10 @@ "source": [ "hm_1 = ml_1.head(0, 0, t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h, '.', label='obs')\n", - "plt.semilogx(t, hm_1[0], label='ttim')\n", - "plt.xlabel('times(d)')\n", - "plt.ylabel('displacement(m)')\n", + "plt.semilogx(t, h, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_1[0], label=\"ttim\")\n", + "plt.xlabel(\"times(d)\")\n", + "plt.ylabel(\"displacement(m)\")\n", "plt.legend();" ] }, @@ -456,9 +459,18 @@ } ], "source": [ - "ml_2 = Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=True)\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, rc=rc, res=0.1, tsandQ=[(0, -Q)], \\\n", - " layers=range(nlay), wbstype='slug')\n", + "ml_2 = ttim.Model3D(kaq=10, z=zlayers, Saq=Saq, tmin=1e-6, tmax=1e-3, phreatictop=True)\n", + "w_2 = ttim.Well(\n", + " ml_2,\n", + " xw=0,\n", + " yw=0,\n", + " rw=rw,\n", + " rc=rc,\n", + " res=0.1,\n", + " tsandQ=[(0, -Q)],\n", + " layers=range(nlay),\n", + " wbstype=\"slug\",\n", + ")\n", "ml_2.solve()" ] }, @@ -494,11 +506,11 @@ } ], "source": [ - "ca_2 = Calibrate(ml_2)\n", - "ca_2.set_parameter(name='kaq0_48', initial=10, pmin=0)\n", - "ca_2.set_parameter(name='Saq0_48', initial=1e-4)\n", - "ca_2.set_parameter_by_reference(name='res', parameter=w_2.res, initial=0)\n", - "ca_2.series(name='obs', x=0, y=0, layer=range(nlay), t=t, h=h)\n", + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0_48\", initial=10, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq0_48\", initial=1e-4)\n", + "ca_2.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0)\n", + "ca_2.series(name=\"obs\", x=0, y=0, layer=range(nlay), t=t, h=h)\n", "ca_2.fit(report=True)" ] }, @@ -597,7 +609,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('RMSE:', ca_2.rmse())" + "print(\"RMSE:\", ca_2.rmse())" ] }, { @@ -607,7 +619,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -621,10 +633,10 @@ "source": [ "hm_2 = ml_2.head(0, 0, t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t, h, '.', label='obs')\n", - "plt.semilogx(t, hm_2[0], label='ttim')\n", - "plt.xlabel('times(d)')\n", - "plt.ylabel('displacement(m)')\n", + "plt.semilogx(t, h, \".\", label=\"obs\")\n", + "plt.semilogx(t, hm_2[0], label=\"ttim\")\n", + "plt.xlabel(\"times(d)\")\n", + "plt.ylabel(\"displacement(m)\")\n", "plt.legend();" ] }, @@ -716,14 +728,16 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]'], \\\n", - " index = ['MLU', 'ttim', 'ttim-multilayer', 'ttim-res'])\n", - "tr = np.delete(ca_2.parameters['optimal'].values, 2)\n", - "t.loc['MLU'] = [0.4133, 1.9388E-05]\n", - "t.loc['ttim'] = ca.parameters['optimal'].values\n", - "t.loc['ttim-multilayer'] = ca_1.parameters['optimal'].values\n", - "t.loc['ttim-res'] = tr\n", - "t['RMSE'] = [0.004264, ca.rmse(), ca_1.rmse(), ca_2.rmse()]\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\"],\n", + " index=[\"MLU\", \"ttim\", \"ttim-multilayer\", \"ttim-res\"],\n", + ")\n", + "tr = np.delete(ca_2.parameters[\"optimal\"].values, 2)\n", + "t.loc[\"MLU\"] = [0.4133, 1.9388e-05]\n", + "t.loc[\"ttim\"] = ca.parameters[\"optimal\"].values\n", + "t.loc[\"ttim-multilayer\"] = ca_1.parameters[\"optimal\"].values\n", + "t.loc[\"ttim-res\"] = tr\n", + "t[\"RMSE\"] = [0.004264, ca.rmse(), ca_1.rmse(), ca_2.rmse()]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/1_test_of_oude_korendijk.ipynb b/pumpingtest_benchmarks/1_test_of_oude_korendijk.ipynb index 77bdd40..0a016f7 100755 --- a/pumpingtest_benchmarks/1_test_of_oude_korendijk.ipynb +++ b/pumpingtest_benchmarks/1_test_of_oude_korendijk.ipynb @@ -17,7 +17,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *\n", + "import ttim\n", "import pandas as pd" ] }, @@ -34,10 +34,10 @@ "metadata": {}, "outputs": [], "source": [ - "H = 7 #aquifer thickness\n", - "zt = -18 #top boundary of aquifer\n", - "zb = zt - H #bottom boundary of aquifer\n", - "Q = 788 #constant discharge" + "H = 7 # aquifer thickness\n", + "zt = -18 # top boundary of aquifer\n", + "zb = zt - H # bottom boundary of aquifer\n", + "Q = 788 # constant discharge" ] }, { @@ -53,10 +53,10 @@ "metadata": {}, "outputs": [], "source": [ - "#unkonwn parameters: kaq, Saq\n", - "ml = ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", - "ml.solve(silent='True')" + "# unkonwn parameters: kaq, Saq\n", + "ml = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml.solve(silent=\"True\")" ] }, { @@ -72,14 +72,14 @@ "metadata": {}, "outputs": [], "source": [ - "#time and drawdown of piezometer 30m away from pumping well\n", - "data1 = np.loadtxt('data/piezometer_h30.txt', skiprows = 1)\n", - "t1 = data1[:, 0] / 60 / 24 #convert min to days\n", + "# time and drawdown of piezometer 30m away from pumping well\n", + "data1 = np.loadtxt(\"data/piezometer_h30.txt\", skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 24 # convert min to days\n", "h1 = data1[:, 1]\n", "r1 = 30\n", - "#time and drawdown of piezometer 90m away from pumping well\n", - "data2 = np.loadtxt('data/piezometer_h90.txt', skiprows = 1)\n", - "t2 = data2[:, 0] / 60 / 24 #convert min to days\n", + "# time and drawdown of piezometer 90m away from pumping well\n", + "data2 = np.loadtxt(\"data/piezometer_h90.txt\", skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 24 # convert min to days\n", "h2 = data2[:, 1]\n", "r2 = 90" ] @@ -188,10 +188,10 @@ } ], "source": [ - "ca1 = Calibrate(ml)\n", - "ca1.set_parameter(name='kaq0', initial=10)\n", - "ca1.set_parameter(name='Saq0', initial=1e-4)\n", - "ca1.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca1 = ttim.Calibrate(ml)\n", + "ca1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", "ca1.fit(report=True)\n", "display(ca1.parameters)" ] @@ -210,7 +210,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -222,14 +222,14 @@ } ], "source": [ - "print('rmse:', ca1.rmse())\n", + "print(\"rmse:\", ca1.rmse())\n", "hm1 = ml.head(r1, 0, t1)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm1[0], label='ttim at 30 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm1[0], label=\"ttim at 30 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -337,10 +337,10 @@ } ], "source": [ - "ca2 = Calibrate(ml)\n", - "ca2.set_parameter(name='kaq0', initial=10)\n", - "ca2.set_parameter(name='Saq0', initial=1e-4)\n", - "ca2.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca2 = ttim.Calibrate(ml)\n", + "ca2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca2.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca2.fit(report=True)\n", "display(ca2.parameters)" ] @@ -359,7 +359,7 @@ }, { "data": { - "image/png": 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BggW4O88++yz9+2deB67wqUZfCLJqGYhHa4GISFHWqm7luP3/1rJlSwYNGkTbtm0BGDx4MEcffTSrVq3iyCOPZMKECVx66aU0bNiQYcOGsXHjRvr378+2bdtw9z1q4eluvPFGBg4cyIMPPsjxxx+/+/muXbsyYsQIWrRowb/+9S/OOuvv1dd//fVXevXqRalSpahZsybPPffc7tcef/xxBg0axNatWznhhBM44YQT4vLe88OKwhy/eGrdurUnJyeHHYaISML5+uuvOfLII8MOo8SL9ncwsxR3bx2tvJruc7Jja9gRiIiI5Jma7rOzcwc81AQObgyNT4Yj+sL+4S6xqyl5IiKSG0r02dmxFVoNhCWT4c1/wFvXQd1OQdI/sh/sf1ChhhPvOakiIpL41HSfnfIVodutcGUKDP0QOl8Hm34JEv4Dh8P4k2DhU8FzhSCraXoiIiJZUY0+FmZwSJPg1vVm+PVr+GpyUNOfdj1MuwHqdPi7pl+xeoGEEe85qSIikviU6HPLDA4+Krh1vQl+/ebvpP/2jfD2P6FOeziqf5D0D6gZt0vHsoCP+vBFRCQjNd3n10FHQJfhcPkCuHxhkPy3/QHvDIeHjoJxPWH+aNi4JudzxSC7BXy0CY+IJLINGzYwevTo3Y9XrVrFCy+8sPtxcnIyV111VdyvO3nyZL766quor61evZpu3brRrFkzunTpssfKeBMmTKBhw4Y0bNiQCRMmxD2uWCnRx1O1RnDcjXDZR3BFMhx/C2zfAu/+Cx5qHCT9T8bB1oJJwOrDF5FEllOib926NY8++mjcr5tdor/++uu54IIL+OKLL7j11lv517/+BcD69eu5/fbb+fjjj1m4cCG33347qanhVL6U6AvKgQ3h2Btg2Adw5afBoL5tf8Bb18IDjeClgfDtu7Azfmsaa1ldEUlkw4cPZ8WKFbRo0YIbbriB4cOHM2/ePFq0aMFDDz3EnDlzdm9cc9tttzFw4EB69uxJvXr1eO2117jxxhtp2rQpvXv3jrp5zVNPPUWbNm1o3rw5p512Glu2bOGjjz5i6tSp3HDDDbRo0YIVK1bscUzGLWu7du3KlClTAHj33Xfp0aMHVapUoXLlyvTo0SPq3vT16tXjpptuokOHDrRu3ZpPP/2UXr16cdhhh/HEE0/E5femPvrCUPWwYMT+MdfCz4tg0Yvw5ctB3/7+h8DR50HLC6By3XxdJl6b8IiI5Ojt4fDLl/E95yFN4YQRWb48YsQIFi9evHvN+jlz5vDAAw/w5ptv7n6c0YoVK3jvvff46quv6NChA6+++ir33Xcfp5xyCm+99RYnn3zyHuVPPfVULrnkEgBuueUWxo0bx5VXXkm/fv3o06cPp59++l4xNW/enFdffZWrr76a119/nU2bNrFu3Tp+/PFHateuvbtcdlvW1q5dm/nz5/OPf/yDQYMG8eGHH7Jt2zYaN27M0KFDc/695UCJvjCZQY2jg1vPu2DZdPj0WZg3Mrg16AatBsHhvaF0mTxdIp7rSouIFGcnnHACZcqUoWnTpuzcuZPevXsD0LRpU1atWrVX+cWLF3PLLbewYcMGNm/eTK9evXK8xgMPPMAVV1zBM888w7HHHkvNmjVzvWVtv379dse1efNmKlSoQIUKFShfvjwbNmygUqVKuXjXe1OiD0tSWTiyT3Db8AN89hx8+hxMOi+utXwRkQKRTc27qMi4FWyZMmV2J9qstoIdNGgQkydPpnnz5jzzzDN7tRBEU6NGDV577TUANm/ezKuvvsoBBxxArVq19jh+zZo1WW6BW9Bb1qqPviioVDsYrX/Nl3D2i1C9eVDDf6Q5PH8afP1GsBxvHKWsTmXUe8s1Ml9Eio3MW8dmt5VsXmzatInq1auzY8cOJk6cGNN1fv/9d3bt2gXAPffcw0UXXQRAr169mD59OqmpqaSmpjJ9+vSYWggKghJ9UVI6CY44EQa8FCT9426EtUuCWv5DTWD2XZC6Ot+X0TQ8ESmOqlatSqdOnWjSpAk33HADzZo1IykpiebNm0fdgja37rzzTtq1a0ePHj044ogjdj9/9tlnc//993P00UfvNRhvzpw5NGrUiMMPP5y1a9dy8803A1ClShX+/e9/06ZNG9q0acOtt95KlSpV8h1jXmib2qJuZ1rQl58yHpbNCJ5r0A3aXgoNukOp3H9XG/XeckZOX8ouh9IG1/ZsxOVdG8Q5cBFJNNqmtmjI7Ta16qMv6tJr+UecCBu+D/rxP30WXjgDqhwG7S6FFudCuQoxn1JL6YqIlByq0RdHadvh66mw4HH4MRnKVggG77W9JJjKFwMtlSsiuaUafdGgGn1JkFQWmp4e3NYkw8dPwidj4eMn4PBe0G4o1O8STOfLQizT8PRlQEQyc/csp4lJwctL5VyJvrir1Tq49bwTkp8Obs+dHCw80fEqaHxKnubkpw/Y2562i7JJpZg4uL2SvUgJV758edatW0fVqlWV7EPg7qxbt47y5cvn6rhQEr2ZVQEmAfWAVcCZ7r7X0G8zewdoD3zg7n0KM8Zip8IhwRS9ztfBFy/BR/+F1y6BmbdD+2HBnPzyFWM+XbR185XoRUq2WrVqsWbNGn777bewQymxypcvT61atXJ1TFg1+uHALHcfYWbDI4//GaXc/cC+wKWFGVyxllQOWp4PLQbA8hnw4aMw/WZ4/z5oPSho1q9YI8fTaMCeiGRWpkwZDj300LDDkFwKZTCemS0Furj7z2ZWHZjj7o2yKNsFuD7WGn2JGIyXWz+mBDX8r6aAlYamZ0DHK+Hgo7I9LFofvfrtRUSKnuwG44WV6De4e6UMj1PdPWrWiCXRm9kQYAhAnTp1Wq1enf9FZRLS+u+CkfqfPQc7tkDDnkFTf532MR2ufnsRkaIpu0RfYCvjmdlMM1sc5dY/3tdy9zHu3trdW1erVi3ep08cVQ6FE++DfyyBrrcENf2ne8H4E2H5LMjhS5/2uxcRKX4KrI/e3btn9ZqZrTWz6hma7n8tqDgkin2rwHE3QIfLgsV3PnwUnj812FWv83XQ6KSoK+6p315EpPgJa637qcDAyP2BwJSQ4ijZyu4XjMi/ehH0fRS2bgjW1X+8A3w+KVh+N4P0/e6v7dlIzfYiIsVEWH30VYGXgDrA98AZ7r7ezFoDQ919cKTcPOAIYH9gHXCxu7+b3bk1GC8fdqbBV5Nh3oPw6xKoVBc6XwvNzw0W6RERkSKpyA3GK0hK9HGwaxcsexfm3h/04x9QG475R7DMblK5nI8XEZFCFcpgPCnGSpWCRifA4Flw3qtQoTq8dS080gI+HgM7toUdoYiIxEiJXrJmFmyFe/F0OH8yVK4Lb98A/20ZLLWbtj3sCEVEJAdK9JIzMzisK1z4NlwwBSrWhDf/AY+1hkUv7DVoT0REig4leomdWbAr3sXT4dyXYZ9KMHkYjG4PX74S9O1nI2V1KqPeW07K6r22NRARkQKiRC+5ZwaH94Qh78NZz0OpJHj1YnjiGPj6zagL76Svqjdy+lIGjF2gZC8iUkiU6CXvzODIvjDsQzhtHOz8CyYNgKe6wrIZeyR8raonIhIOJXrJv1KloenpcNnH0H80bFkHE08Pltf9bi7w96p6pQ2tqiciUog0j17iL217sHHO3Adg009w6LHQ9RZS/HDtfCciUgC0YI6EY8c2SBkP80bCn79Bgx5w/C1Qo0XYkYmIJBQtmCPhKFM+spb+59D9NvgxGcYcBy9fCOtWhB2diEiJoEQvBa/sfsESuld/DsfeAN++A6PaBnPxN/2iaXciIgVITfdS+DatDdbRTxnPLkviqR29GL2jL38l7a9d8URE8kBN91K0VDgYTnoArviE5VWP49JSU3iv7DWc69NYuPyXsKMTEUkoSvQSnir12XTSk5y68x6+8brcmvQsFy46G5ZMjrrojoiI5J4SvYSqVd3K3Dz4HD7rMoFl3cdTvvw+8PJAGNcTvl8QdngiIsWe+uilaNmZBp+/ALPvhs2/wBF9oPvtcGCDsCMTESmy1EcvxUfpJGh5AVz1KXS9GVbOgdHt4K3rYfNvYUcnIlLsKNFL0VR2PzjuRrjqM2g5EJKfhkePDkbrb98SdnQiIsWGEr0UbfsfBH0ehMsWBEvpzr4L/tsKPnuelO9+1/x7EZEcKNFL8VDtcDjnBRg0DSpWhymXs98zXfl4xiva9lZEJBtK9FK81OsEg2fx7pH3sA/beLbsPYzmXr5ZrAGYIiLRKNFL8WPGge3Poc+ukYxIO4c2pb7h3OSzYdqNsGV92NGJiBQpSvRSLLWqW5lnBnemQrfrWXnOPKzl+fDJU8GAvQWPw84dYYcoIlIkaB69JI61S+Ddm4IpeVUbQs+74PBeYBZ2ZCIiBUrz6KVkOLgxnD8ZzpkEOLx4Fjx3Cqz9KuzIRERCE0qiN7MqZjbDzJZFfu61XZmZtTCz+Wa2xMy+MLOzwohVihkzaNQbhs2H3iPgp8/giU7wxjVacEdESqSwavTDgVnu3hCYFXmc2RbgAndvDPQGHjazSoUYoxRnSWWh/bBgwZ02l8Cnz8J/W8JHj6n/XkRKlLASfX9gQuT+BODkzAXc/Vt3Xxa5/xPwK1Ct0CKUxLBvFTjxPrhsPtRuC9Nvhsc7wYrZYUcmIlIowkr0B7v7zwCRnwdlV9jM2gJlgRVZvD7EzJLNLPm339Q8K1FUa0TKMU/xVuOH+OuvrUHf/f8GQOqqsCMTESlQBTbq3sxmAodEeelmYIK7V8pQNtXd9+qnj7xWHZgDDHT3HPct1ah7iSZldSoDxi5ge9ou9k9K4+02X1Lzy1Hgu6DT1dDpGii7b9hhiojkSSij7t29u7s3iXKbAqyNJPD0RP5rFoFXBN4CboklyYtkZcHKdWxP28Uuhz/Tkphc4Wy44hNodCK8fy+MagtLJkOCTTcVEQmr6X4qMDByfyAwJXMBMysLvA486+4vF2JskoDa169K2aRSlDYok1SK9vWrwgG14IzxMOgtKFcRXh4Iz/aDX78OO1wRkbgJZcEcM6sKvATUAb4HznD39WbWGhjq7oPN7DxgPLAkw6GD3H1RdudW071kJWV1KgtWrqN9/aq0qpupp2hnGqSMD3bH+2sTtB0CXYbDPproISJFX3ZN91oZTySjP9fB7Dsh5RnYtyp0/z9ocR6U0tpSIlJ0aWU8kVjtVxX6PgxD5kDVw2DqlTC2G6zRl0cRKZ6U6EWiqdECLnoXThkDf/wUJPvJl8HmqONGRUSKLCV6kayYQfOz4MrkYAreFy/Bf1tpdT0RKVaU6EVyUq4C9LgDLlsAtdtpdT0RKVaU6EVidWADGPBysDvezu0ZVtdbHXZkIiJZUqIXyY303fEuWwDH/zuo1Y9qC+/9B7ZvCTs6EZG9KNGL5EWZ8nDs9XBFMhzRJ7K6Xjv4+k2triciRYoSvUh+HFATTh8Hg6ZBuf1h0gB44UxYvzLsyEREACV6kfio1wkunQu9/gOr58Oo9kFz/o6tYUcmIiWcEr1IHKSsTmXU3NWk1Dg32CznyL5/N+cvfSfs8ESkBFOiF8mn9C1wR05fyoCxC0hJLR805w98A5LKw4tnwQtnQ+qqsEMVkRJIiV4knzJugbsjbRcLVq4LXjj0WBj6QTAH/7u5Qe3+/fsg7a9wAxaREkWJXiSfom6Bmy6pbLCq3hWfwOG94b27YXQHLbYjIoVGu9eJxEG2W+BmtHwWTLs+GJXf+NRg8F7F6oUXqIgkJG1TK1KU7NgGHz4C80ZC6bJw/M3Q5hIonRR2ZCJSTGmbWpGipEx56PJPuGw+1G4L7wyHp7rAD5+EHZmIJCAlepGwVD0MznsVznwW/lwH47rD1Ktgy/qwIxORBKJEL1JIUlanMuq95aSsTv37STM4qj9csRA6XAGfPQ+PtQ5+7toVXrAikjCU6EUKwV5z7TMmewi2wu11d7C6XtUGMOVyeOZEWLsknIBFJGEo0YsUgizn2md2SBO48B3oPwp+WwpPHgszboXtfxZuwCKSMJToRQpBtnPtMytVCo4+D65MgeZnByP0R7WHb98tvIBFJGFoep1IIYl5rn1mqz+CN66B35fCkf3ghHuhYo2CC1REih3Noxcp7tK2w0ePwtz7oVQSHH8LtB0CpVX1EKYAACAASURBVEqHHZmIFAGaRy9S3CWVhWOvj8y9bxeZe98Vfvw07MhEpIiLOdGbWWUza2xm9c0sX18QzKyKmc0ws2WRn3u1Y5pZXTNLMbNFZrbEzIbm55oiCaFK/WDu/enjYdMvMLYbTLsRtv0RdmQiUkRlm7DN7AAzu8nMvgQWAE8CLwGrzexlM+uax+sOB2a5e0NgVuRxZj8DHd29BdAOGG5m6pgUMYMmpwYb5bS+GBaOgVFtYclkSLCuOBHJv5xq5q8APwCd3b2Rux/j7q3dvTYwAuhvZhfn4br9gQmR+xOAkzMXcPft7p6+n2e5GGIVKVnKHwAnPQCDZ8F+B8LLA+GFM7XvvYjsIZTBeGa2wd0rZXic6u7Rmu9rA28BDYAb3H1UFucbAgwBqFOnTqvVq1cXTOAihShXo/R3psHCJ2H23eC74LgboeOVULpM4QQrIqGKy6h7M2sG1AN2b7Hl7q9lU34mcEiUl24GJsSS6DO8XgOYDPR197XZxalR95II0lfS2562i7JJpZg4uH1sU/I2roG3/wnfvAnVjoS+D0Od9gUfsIiEKrtEH9O+mGb2NNAMWAKkL8DtQJaJ3t27Z3O+tWZW3d1/NrPqwK/ZXd/dfzKzJUBngu4EkYQWbSW9mBL9AbXg7InwzTSYdgM83QtaXgDdb4d9qxR84CJS5MS6AXZ7dz8qjtedCgwk6OcfCEzJXMDMagHr3H1rZFR+J+DBOMYgUmSlr6S3I21XzivpRXPEiXDosfD+CJg/Okj8ve6GZmcFg/lEpMSIqenezMYBI939q7hc1Kwqwej9OsD3wBnuvt7MWgND3X2wmfUARhK0HBjwmLuPyencarqXRJHnlfQy++XLYGW9H5OhXmfo8xAc2DB+gYpI6PLdR29mxwJvAL8AfxEkXnf3ZvEMNB6U6EWi2LULUsbDzNshbSt0vh6OuQaSyoUdmYjEQb776IGngfOBL/m7j15EiotSpaDNxXBEn2BVvTn/gcWvQN9HoG7HsKMTkQIU69z07919qrt/5+6r028FGpmIxF+Fg+GM8TDgFdixDcafAFOvhK2pYUcmIgUk1kT/jZm9YGbnmNmp6bcCjUxECk7DHnD5gmCu/WcT4bE28OUrWllPJAHFmuj3Ieib7wn0jdz6FFRQIlIIyu4HPe+CIXPggNrw6sXw/Gmw/ruwIxORONI2tSICu3bCJ2Nh1h3B/S7/hA5XaGU9kWIiz9vUmtktZpblKhtmdryZqWYvUtyVKg3tLoXLF0KDbjDzNhjTBdboS7NIcZfTqPsvgTfMbBvwKfAbUB5oCLQAZgL/KdAIRaTwHFAzWFnv6zeC7W/Hdoe2l0C3W6FchbCjE5E8iHUefUOClemqA1uBr4G57r61YMPLPTXdi8TJtj9g9p2w8CmoWANOfCBYcU9Eipy4bGpTXCjRi8TZD5/AG1fBr1/BUf3hhPugQrT9qkQkLHnuo89wgsPNbIyZTTez2em3+IYpIgUtZXUqo95bTsrqXMybr90GLp0Lx/8blr4Dj7WF5PHBansiUuTF2nT/OfAEkALsTH/e3VMKLrS8UY1eJLo8b32b0boV8MbVsGoe1OkQrKxXrVHBBCwiMct3jR5Ic/fH3X2hu6ek3+IYo4gUsGhb3+Za1cNg4BvQfxT8+jU8cQzMGQFpf8U/YBGJi1gT/RtmdpmZVTezKum3Ao1MROIqfevb0kbetr5NZwZHnwdXJMOR/WDOPfBEZ1g9P74Bi0hcxNp0H22pLHf3+vEPKX/UdC+StbhtfZvRshnw5rWw8XtodSF0vw32qRSfc4tITDTqXkTyJOYvBn9tDmr2C0bDfgfBifcFtX2zwgtWpASLx6j7eWZ2t5n1NjOtmiFSAqQP3hs5fSkDxi7IfqR+uf2h191wyWzY/yB46QL437mw8cfCC1hEooq1j34gsBQ4DfjIzJLN7KGCC0tEwpanwXs1joZL3oMed8KK92BUO/h4TLB+voiEIqZE7+4rgRnALGAusC9wZAHGJSIhy/PgvdJJ0OmqYBvc2m3g7Rvg6V6wdknBBiwiUcU6GG8F8DvwAjAPWOTuRXK1DPXRi8RPvgfvucOXL8M7w2HbRjjmH9D5eihTPv7BipRg+R6MZ2ZXA8cAtYFvgPcJ1rpfEc9A40GJXqQI+nMdTL8ZPn8RqjaEfo9C3Y5hRyWSMPI9GM/dH3H3M4DuBKvj3QZ8G7cIRSSx7VcVTnkCznsNdv4F40+AN64JavkiUqBiHXU/0sw+Bj4m2J72VoKtakVEYtegG1y2ADpcAZ9OCAbrff1m2FGJJLRYR90vAPq5e2N3v9jdJ0QG6ImI5E7Z/YKpeINnwb4HwqQBMOl82PRL2JGJJKRYm+5fBtqZ2QORW98CjktEEl3NljDkPej2f/Dtu8GueCkTggF8IhI3sTbd3wNcDXwVuV0VeU5EJO9Kl4HO18Jl86F6s2Df+2f6wO/Lw45MJGHE2nR/EtDD3Z9296eB3pHn8iSyKc4MM1sW+ZnlvB0zq2hmP5rZY3m9nogUcem74vX7L/zyJTzeEeaNhJ07wo5MpNiLNdEDZNyl4oB8Xnc4MMvdGxIswjM8m7J3EkznE5FEZgYtL4ArFkKj3jDrDhjTBX7Ujtgi+RFror8H+MzMnjGzCQRT7P6Tj+v2ByZE7k8ATo5WyMxaAQcD0/NxLREJWcrqVEa9tzz79fLTVTgEznwWzpoIW9bB2O7w7s2w/c+CD1QkAcW8e52ZVQfaAAZ87O55HiJrZhvcvVKGx6nuXjlTmVLAbOB8oBvQ2t2vyOJ8Q4AhAHXq1Gm1evXqvIYmInGWvjnO9rRdlE0qxcTB7WNfZW/bRph5GyQ/DZXqQJ+HoEH3Ao1XpDjK84I5ZtYy/QZUB9YAPwA1Is9ld+xMM1sc5dY/xrgvA6a5+w85FXT3Me7e2t1bV6tWLcbTi0hhyNPmOOnKHxAk9wvfhtLl4PnT4LVLg5X2RCQmSTm8PjLyszzQGvicoEbfjGDxnGOyOtDds/zabWZrzay6u/8caSn4NUqxDkBnM7sM2B8oa2ab3T27/nwRKWLSN8fZkbYrd5vjZFS3Iwz9IBig98GDsHwmnHAvNDlNe96L5CDWte7/B9zt7l9GHjcBrnf3QXm6qNn9wDp3H2Fmw4Eq7n5jNuUHkU3TfUZa616k6Mn35jgZrV0CU68MBuk17AUnjYRKteMTqEgxle+17oEj0pM8gLsvJlgKN69GAD3MbBnQI/IYM2ttZmPzcV4RKYJa1a3M5V0b5D/JAxzcGC6eAb3ugVXzYHT7yJ73RXJDTZHQxVqjfxH4E3gecOA8YH93P6dgw8s91ehFSpDU1fDmNbBiNtRuF8zDr9Yo7KhECl08avQXAksIVse7hmB1vAvjE56ISB5VrhvsiHfyE/D7t/DEMTDnXkjbHnZkIkVGrDX644EF7r6l4EPKH9XoRUqozb/BO/+Exa/CQUcFtftaUSs4IgknHjX6QcAiM5tvZveZWd/slq0VESkI2S68s381OP1pOGcSbN0QLLTzzr+00I6UeDlNrwPA3S8AMLMawOnAKKBGrMeLiORXzAvvNOodTMebeRssGA3fvAl9H4HDji/0mEWKglh3rzvPzJ4EXgG6A48BnQsyMBGRjHK18E75itDnwchCO2XhuVPg9WGwZX3hBSxSRMRaI38YWAE8Abzn7qsKLCIRkSjytPBO3Y4w9EN4/1748BFYPgNOvB+OOlkL7UiJkZu17hsDxxKshtcQWOru5xdgbHmiwXgiiStfC+/8/AVMvQJ+/hwanQQnPQAVaxRMoCKFLN+D8cysIlAHqAvUI9imVqtTiEihymnhnWwH61VvBoNnQ487YMUsGNUOksdroR1JeLFOr/sC+CBym+vuawo6sLxSjV6kZMrVLnnrVsAbVwcr69U9Bvo9ClUPK9yAReIo3zV6d2/m7pe5+wtFOcmLSMmVq8F6VQ+DgW8Eo/F/+RIe7wgfPAQ70wovYJFCEmvTfTUzu9/MppnZ7PRbQQcnIhKr9MF6pY3YBuuZQatBcPnHwR73M2+DsccHffkiCSTWBXMmAt8AhwK3A6uATwooJhGRXGtVtzITB7fn2p6Nsm+2z6xidTjreThjAvzxE4zpArPugB3bCjRekcISax99iru3MrMv3L1Z5Ln33f24Ao8wl9RHLyJ5tmU9TL8FFk2Eqg2DZXTrdgg7KpEcxWMJ3B2Rnz+b2UlmdjRQKy7RiYgUFftWgZNHBxvl7PwLxveGt66HvzaFHZlInsWa6O8yswOA64DrgbHAPwosKhGRMDXoBsPmQ7th8MlYGNUels0IOyqRPMkx0ZtZaaChu29098Xu3tXdW7n71EKIT0SkwEWdf19ufzhhBFw8HcruBxNPh9eGwJ/ZjOYXKYJyTPTuvhPoVwixiIgUuvT59yOnL2XA2AV7L7ZTuy0MnQfHRbbAHdU2+BnjqqIiYYu16f4jM3vMzDqbWcv0W4FGJiJSCGKaf59UDrreBJfOhUp14JWL4MVzglH6IkVcrJvadIz8vD3y0wAHtO+jiBRrudos5+DGMHgmLHgcZt8VLKPb805oOVCb5EiRle30OjO7Nv0uQWLP+El2d3+wAGPLE02vE5HcytNmOetXwtSrgmV063UOVtnTMroSkuym1+VUo68Q+dkIaANMIUj2fYG5cYtQRCRErepWzv1ueFXqB8vofvpsMPf+8U5w/M3BSP3SsTaWihS8WBfMmQ6c5u6bIo8rAC+7e+8Cji/XVKMXkUL3x0/w1nWwdBrUaAn9Hwua+UUKSTwWzKkDbM/weDvBdrUiIlKxBpz9Apw+HjZ8D08eC+/9B9L+CjsykZgH4z0HLDSz1wn66k8BJhRYVCIixY0ZNDkV6neBd/4F798LX02Bfo9B7TZhRyclWKzb1N4NXAikAhuAC939noIMTESkqIq6wE66favAqU/CgFfgr80wrkeQ+Lf/WfiBihBjH33cL2pWBZhE0Py/CjjT3ff6F2NmO4EvIw+/d/ccF+5RH72IFKT0BXa2p+2ibFKp7HfK2/YHzLo9WEa3Ul3o92hQ4xeJs3j00cfbcGCWuzcEZkUeR7PV3VtEblqdT0RCF9MCO+nKV4STRsKgaVAqCZ7tD1OugK0bCi9gKfHCSvT9+buPfwJwckhxiIjkSvoCO6WNnBfYSVevEwz7EDpdA4teCBba+eatgg9WhPCa7je4e6UMj1Pdfa+2LzNLAxYBacAId5+cxfmGAEMA6tSp02r16tUFE7iICLEvsBO13E+fwZQrYe2X0PgUOOE+2P+gQopcElV2TfcFlujNbCZwSJSXbgYmxJjoa7j7T2ZWH5gNdHP3FdldV330IlIUZNuXv3MHfPgwvH9fsDNe73uh2ZlaRlfyLJQ+enfv7u5NotymAGvNrHokuOrAr1mc46fIz5XAHODogopXRCSesu3LL10Gjr0Bhn4AVRvC60PghTNh45rwApaEFVYf/VRgYOT+QIKldfdgZpXNrFzk/oFAJ+CrQotQRCQfYurLr9YILnoHeo+AVR/AqPbwyTjYtavwA5aEFVYffVXgJYIV974HznD39WbWGhjq7oPNrCPwJLCL4AvJw+4+Lqdzq+leRIqKXG2Wk7oK3rgaVs6BuscEU/G0SY7EKJQ++rAo0YtIseUOnz0P794MO/+CrjdD+8u0SY7kqCjOoxcRkczMoOX5cPnHcFg3mPFvGNcd1i4JOzIpxpToRURCFHU53YrV4eyJkU1yftAmOZIvag8SEQlJtlPw0jfJOfQ4eDd9k5ypwRa4taK20IpEpRq9iEhIYlpOd7+qcOoYOPcl+OuPYJOcd2+G7VsKP2AplpToRURCkqvldA/vBZctgFaDYP5j8HgH+G5uocUqxZdG3YuIhChXU/DSrfoApl4J61cGib/HHVD+gAKNU4o2Ta8TEUk027fAnP/A/FGw/yHQ9+Gg1i8lkqbXiYgkmrL7Qs+74OKZsE+lYAndVy+BP7PZNldKJCV6EZFiaPe0vJ31Ycj7cNxwWPIajGoLi18LFt8RQdPrRESKnajT8rr+C47qB1Muh1cuhMWvwokPBHPypURTjV5EpJjJclrewY2Dpvwed8LymTCqHXz6nGr3JZwSvYhIMZPttLzSSdDpKhj2UZD4p14Bz50CqavDC1hCpVH3IiLFUEzT8nbtgpSnYcb/BbX67v8HbS6BUqrjJRpNrxMRKck2/ABvXhM059fpAP3+Cwc2DDsqiSNNrxMRKckq1YYBr8DJT8CvX8PjneCDh2BnWtiRSSFQohcRSXApq1MZNWcFKZV7w+ULg4V1Zt4GY7vBL1+GHZ4UMCV6EZEElj4Vb+T0pQwYu4CU9WXhrOfgjAnwx48wpgvMvltb4CYwJXoRkQSW5VS8xicHtfsmp8Pc+4I979dofFMiUqIXEUlg2U7F27cKnPoknPsy/LVJW+AmKI26FxFJcDFNxdv2B8z8P0h+GiofCv0fg3rHFG6gkmeaXiciIrH5bl6wBW7qd9D6Iuh+O5SvGHZUkgNNrxMRkdgc2jlYVa/DFZDyDIzuAMtmhB2V5IMSvYiI7KnsvtDrbrh4BpTbHyaeDq9dClvWhx2Z5IESvYiIRFerNVw6F469ERa/EmyBu2QykGGb3NWpIQcpOQllm1ozqwJMAuoBq4Az3X2vT4uZ1QHGArUBB05091WFFqiISEmXVA6Ov/nvLXBfHkjqwt5cvfJkfkqr+Pc2uVkN8pPQhVWjHw7McveGwKzI42ieBe539yOBtsCvhRSfiIhkdEhTGDwbut9Ghe9n82ap6zjZ5rIjbeffc/OlSAor0fcHJkTuTwBOzlzAzI4Cktx9BoC7b3Z3Te4UEQlL6SQ45h98c8rbrKAWD5Z9gvFl76PzQdvCjkyyEVaiP9jdfwaI/DwoSpnDgQ1m9pqZfWZm95tZ6WgnM7MhZpZsZsm//fZbAYYtIiJNmrWGC99mboMb6FRmGc2m9IZPxgXb4kqRU2Dz6M1sJnBIlJduBia4e6UMZVPdfY8OHjM7HRgHHA18T9CnP83dx2V3Xc2jFxEpRKmrYOpV8N37UPcY6PcoVD0s7KhKnFDm0bt7d3dvEuU2BVhrZtUjwVUnet/7GuAzd1/p7mnAZKBlQcUrIiJ5ULkeXDAl2OP+ly+DLXDnj4JdO8OOTCLCarqfCgyM3B8ITIlS5hOgsplVizw+HviqEGITEZHcMIOWF8DlC6B+F3j3Jni6F/z6ze4imo4XnlCWwDWzqsBLQB2CZvkz3H29mbUGhrr74Ei5HsBIwIAUYIi7b8/u3Gq6FxEJkTssfhWm3QDbN8NxN5JSeyADnk5he9ouTccrINk13Ycyj97d1wHdojyfDAzO8HgG0KwQQxMRkfwwg6anw6HHwds3wuy7qLP/yzTYOZDFXm/3VrlK9IVHK+OJiEj87V8NzhgPZ02k0s71TC5zCzcmTWK/pLQ9t8qVAqdELyIiBefIPpS56hM2NDyVy5Km8HGVO2hVannYUZUoSvQiIlKw9qnMgeeNg/NeZR/+gnE94J1/wfY/w46sRFCiFxGRwtGgO1w2H9pcDAtGw+Md4bu5YUeV8JToRUSk8JSrACeNhEFvgZWCCX3hjWtg2x97FNN0vPgJZdS9iIiUcPWOgaEfwpz/BAvsLJsOfR+Bhj1IWZ3KgLELNB0vTlSjFxGRcJTdF3reBRfPhHIVYeLp8NqlfLZ0JdvTdrHL2T0dT/JOiV5ERMJVqxVc+j4ceyMsfoWBn51Jn6RPKG1QJqmUpuPlkxK9iIiEL6kcHH8zDJlDmUo1ebT0Q7xbcxyTBhymZvt8UqIXEZGi45CmMHg2dPs/GqR+QPMpPeHzScHSupInSvQiIlK0lE6CztfC0A+gakN4fQi8cBZs/DHsyIolJXoRESmaqh0OF70DvUfAqnkwuj0kj9+jdq9peDnT9DoRESm6SpWG9sPg8N4w9Up48xpY8hr0fZSUTZU0DS8GqtGLiEjRV+VQGPgG9HkYfvwMHu/Ilrn/JS0tTdPwcqBELyIixYMZtL4QLl8A9Y6h84qRvFT2DhqW+lHT8LKhRC8iIsXLAbXg3JfglCdpVm4tb5e7mZltP6VVrQphR1YkKdGLiEjxYwbNzybpyk9IOqI3tVLug7Hd4JfFYUdW5CjRi4hI8VXhYDjrOThjAvzxI4w5Dt77D6RtDzuyIkOJXkREir/GJ8PlC6HJafD+vUHC/zEl7KiKBCV6ERFJDPtWgVPHBP33WzfA2O4w/d+wY2uWh5SEefiaRy8iIonl8F7ByPzp/4aPHoVv3oL+j0HdjnsUKynb4apGLyIiiaf8AdDvUbhgCuzaAeNPgGk3wF+bdxdZsHJdidgOV4leREQSV/0uMGw+tBsKC5+C0R1gxWwA2tevStmkUlluh5sozfrmCbYjUOvWrT05OTnsMEREpKj5fgFMuRzWLYejz4Oed5Pyq7Ng5Tra16+6R7N9cWvWN7MUd28d7TXV6EVEpGSo0z7YEa/TNbDoBRjdnlZb53N51wZ7JfFEatYPJdGbWRUzm2FmyyI/9/qaZGZdzWxRhts2Mzs5jHhFRCRBlNkHetwOg2fCPpXhf+fAKxfDn3sm8pya9YuTUJruzew+YL27jzCz4UBld/9nNuWrAMuBWu6+Jbtzq+leRERikrYd5o2EeQ9A+Upw4v3Q+JRg1T2C5vtozfrpcnq9MGXXdB9Wol8KdHH3n82sOjDH3RtlU34IcJy7D8jp3Er0IiKSK78sDvruf14ER/SBk0ZChUOyPaSo9eEXxT76g939Z4DIz4NyKH828GJWL5rZEDNLNrPk3377LY5hiohIwjukCQyeBd1vh2UzYFQ7WPQiZFMRLk59+AWW6M1sppktjnLrn8vzVAeaAu9mVcbdx7h7a3dvXa1atfyGLiIiJU3pJDjmGhj2IVRrBJOHwsTTYeOaqMWLUx9+kW+6N7OrgcbuPiSWc6vpXkRE8mXXzmDO/azbwUpDzzug5SAotWfduLj00YfVdD8VGBi5PxCYkk3Zc8im2V5ERCSuSpWG9kPhsvlQ82h48x/wbD9Y/90exVrVrRx1al5OCnshnrAS/Qigh5ktA3pEHmNmrc1sbHohM6sH1AbeDyFGEREpySrXgwumQt9H4OfP4fGOsODxoMYfo8xJPX0Q38jpSxkwdkGhJPtQNrVx93VAtyjPJwODMzxeBdQsvMhEREQyMINWg6BBD3jzGnhnOCx5Hfo9BtUOz/bQaCPzow3iK+hmf62MJyIikpMDagbb354yBn7/Fp44BuY9CDvTsjwkWlIPYxCftqkVERGJhRk0PwsO6wrTrg8G6301BfqPCqboZZKe1Hek7dqd1FvVrby7Zl9Yg/i0qY2IiEheLJkcJPytqdD5euh8HSSV3aNIYY3ML3Ir4xUkJXoRESk0W9YH/fZfTIKDjgpq9zVbFnoYRXF6nYiISPG3bxU4dQycMwm2boCx3WDGrbBja9iR7aZELyIikl+NesPlC+Do8+HDR4LBet8vCDsqQIleREQkPsofAP0ehfMnw87t8HRvePuf8NfmUMNSohcREYmnw7rCsPnQdgh8/ESw0M7KOaGFo0QvIiISb+X2hxPvgwvfgdJl4Nn+MPUq2Lax0ENRohcRESkodTvA0A+g09Xw2XMwqj18m+VmrAVCiV5ERKQgldkHetwBg2cG/fgvnAlfvFRol9fKeCIiIoWhZiu49H1YOAaO6FNol1WiFxERKSxJ5aDjlYV6STXdi4iIJDAlehERkQSmRC8iIpLAlOhFREQSmBK9iIhIAlOiFxERSWBK9CIiIglMiV5ERCSBKdGLiIgkMCV6ERGRBGbuHnYMcWVmvwGrMzx1AJDVvoDZvXYg8HscQysM2b2fonidvJ4nt8fFWj6ncvl5vbh9nkrKZym3x8brs5RTmUT6LEHJ+TyF+X9TJXevFvVVd0/oGzAmj68lhx17PN9rUbxOXs+T2+NiLZ9Tufy8Xtw+TyXls5TbY+P1WcqpTCJ9luL5dy6s6yTa/00loen+jTy+VhwV1vuJ13Xyep7cHhdr+ZzK5ff14qSkfJZye2y8Pks5lUmkzxKUnM9Tkfy/KeGa7uPFzJLdvXXYcUhi0OdJ4kWfJcmtklCjz6sxYQcgCUWfJ4kXfZYkV1SjFxERSWCq0YuIiCQwJXoREZEEpkQvIiKSwJToRUREEpgSfR6Y2ZFm9oSZvWJmw8KOR4ovMzvZzJ4ysylm1jPseKR4M7P6ZjbOzF4JOxYpOkpcojezp83sVzNbnOn53ma21MyWm9nw7M7h7l+7+1DgTEDzWUuoOH2WJrv7JcAg4Kz/b+9+Xq2owziOvz8p2qJIiIjUwjCT4PZDhKAyqahFEdnCoJKilCAko0WF/QFhFG4ko1pUFP2ggsAwaZVK4cKSwsTIskUXF4KhGYoQPi3OmKfbvVe8nnN/nPN+be7Md75n5hl4mOd859yZbxfD1STXoXzaX1Wruhupppq+e7wuyVLgL+Ddqhpo2qYBPwN3AYPATuAhYBqwbsguVlbVwST3AWuBV6vqg/GKX5NHp3Kp+dx64P2q2jVO4WuS6XA+fVpVy8crdk1u0yc6gPFWVduTzBvSfCPwS1XtB0jyEbCsqtYB946wn03ApiSbAQt9H+pELiUJ8BKwxSLf3zp1bZKG6rtb9yOYA/zetj7YtA0ryW1JNiR5A/ii28FpSjmrXALWAHcCy5M82c3ANCWd7bXp4iSvA4uSvNDt4DQ19N2IfgQZpm3E3zSqaiuwtVvBaEo721zaAGzoXjia4s42nw4BfmHUfziibxkELm9bnwscmKBYNLWZS+ok80nnzELfshNYkOTKJDOAB4FNExyTpiZzSZ1kPumc9V2hT/IhsANYmGQwyaqq+ht4CvgS2At8XFV7JjJOTX7mkjrJfFK39N3jdZIk9ZO+G9FLktRPLPSSJPUwC70kST3MQi9JUg+z0EuS5k66JQAAAf9JREFU1MMs9JIk9TALvSSSzEqyulme3cn5zJM8k+TRYdrnnZqSNcm1Sd7p1DElnWahlwQwC1gNUFUHOjXFaZLpwErOMMNjVe0G5ia5ohPHlXSak9pIgtZUufOTfA/sA66pqoEkjwH305r/fABYD8wAHgFOAPdU1R9J5gMbgUuAY8ATVfUTcAewq3nDG0kWA281fb4eEsPntF7x+nI3T1TqN47oJQGsBX6tqhuA54ZsGwAepjU3+ovAsapaROt1raduyb8JrKmqxcCzwGtN+y3Ad237eht4uqpuGiaGb4FbO3Aukto4opd0Jl9V1VHgaJIjtEbeALuB65JcANwMfJL8O6vqzObvZbTe0U6Si4BZVbWt2fYecHfbcQ4Cs7t2FlKfstBLOpMTbcsn29ZP0rqGnAccbu4GDHUcOL9ZDqPMpd70O35uoUoaylv3kgCOAheO5YNV9SfwW5IHANJyfbN5L3BV0+8wcCTJkmbbiiG7uhr4cSwxSBqZhV4SVXUI+KZ53O2VMexiBbAqyQ/AHmBZ074FWNrW73FgY5Id/H/0fjuweQzHljQKp6mV1FVJPgOer6p9o/SZCWwDlpz6D31JnWGhl9RVSRYCl1bV9lH6LADmVNXWcQtM6hMWekmSepi/0UuS1MMs9JIk9TALvSRJPcxCL0lSD7PQS5LUw/4B0eof6SzCEbkAAAAASUVORK5CYII=\n", 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BggW4O88++yz9+2deB67wqUZfCLJqGYhHa4GISFHWqm7luP3/1rJlSwYNGkTbtm0BGDx4MEcffTSrVq3iyCOPZMKECVx66aU0bNiQYcOGsXHjRvr378+2bdtw9z1q4eluvPFGBg4cyIMPPsjxxx+/+/muXbsyYsQIWrRowb/+9S/OOuvv1dd//fVXevXqRalSpahZsybPPffc7tcef/xxBg0axNatWznhhBM44YQT4vLe88OKwhy/eGrdurUnJyeHHYaISML5+uuvOfLII8MOo8SL9ncwsxR3bx2tvJruc7Jja9gRiIiI5Jma7rOzcwc81AQObgyNT4Yj+sL+4S6xqyl5IiKSG0r02dmxFVoNhCWT4c1/wFvXQd1OQdI/sh/sf1ChhhPvOakiIpL41HSfnfIVodutcGUKDP0QOl8Hm34JEv4Dh8P4k2DhU8FzhSCraXoiIiJZUY0+FmZwSJPg1vVm+PVr+GpyUNOfdj1MuwHqdPi7pl+xeoGEEe85qSIikviU6HPLDA4+Krh1vQl+/ebvpP/2jfD2P6FOeziqf5D0D6gZt0vHsoCP+vBFRCQjNd3n10FHQJfhcPkCuHxhkPy3/QHvDIeHjoJxPWH+aNi4JudzxSC7BXy0CY+IJLINGzYwevTo3Y9XrVrFCy+8sPtxcnIyV111VdyvO3nyZL766quor61evZpu3brRrFkzunTpssfKeBMmTKBhw4Y0bNiQCRMmxD2uWCnRx1O1RnDcjXDZR3BFMhx/C2zfAu/+Cx5qHCT9T8bB1oJJwOrDF5FEllOib926NY8++mjcr5tdor/++uu54IIL+OKLL7j11lv517/+BcD69eu5/fbb+fjjj1m4cCG33347qanhVL6U6AvKgQ3h2Btg2Adw5afBoL5tf8Bb18IDjeClgfDtu7Azfmsaa1ldEUlkw4cPZ8WKFbRo0YIbbriB4cOHM2/ePFq0aMFDDz3EnDlzdm9cc9tttzFw4EB69uxJvXr1eO2117jxxhtp2rQpvXv3jrp5zVNPPUWbNm1o3rw5p512Glu2bOGjjz5i6tSp3HDDDbRo0YIVK1bscUzGLWu7du3KlClTAHj33Xfp0aMHVapUoXLlyvTo0SPq3vT16tXjpptuokOHDrRu3ZpPP/2UXr16cdhhh/HEE0/E5femPvrCUPWwYMT+MdfCz4tg0Yvw5ctB3/7+h8DR50HLC6By3XxdJl6b8IiI5Ojt4fDLl/E95yFN4YQRWb48YsQIFi9evHvN+jlz5vDAAw/w5ptv7n6c0YoVK3jvvff46quv6NChA6+++ir33Xcfp5xyCm+99RYnn3zyHuVPPfVULrnkEgBuueUWxo0bx5VXXkm/fv3o06cPp59++l4xNW/enFdffZWrr76a119/nU2bNrFu3Tp+/PFHateuvbtcdlvW1q5dm/nz5/OPf/yDQYMG8eGHH7Jt2zYaN27M0KFDc/695UCJvjCZQY2jg1vPu2DZdPj0WZg3Mrg16AatBsHhvaF0mTxdIp7rSouIFGcnnHACZcqUoWnTpuzcuZPevXsD0LRpU1atWrVX+cWLF3PLLbewYcMGNm/eTK9evXK8xgMPPMAVV1zBM888w7HHHkvNmjVzvWVtv379dse1efNmKlSoQIUKFShfvjwbNmygUqVKuXjXe1OiD0tSWTiyT3Db8AN89hx8+hxMOi+utXwRkQKRTc27qMi4FWyZMmV2J9qstoIdNGgQkydPpnnz5jzzzDN7tRBEU6NGDV577TUANm/ezKuvvsoBBxxArVq19jh+zZo1WW6BW9Bb1qqPviioVDsYrX/Nl3D2i1C9eVDDf6Q5PH8afP1GsBxvHKWsTmXUe8s1Ml9Eio3MW8dmt5VsXmzatInq1auzY8cOJk6cGNN1fv/9d3bt2gXAPffcw0UXXQRAr169mD59OqmpqaSmpjJ9+vSYWggKghJ9UVI6CY44EQa8FCT9426EtUuCWv5DTWD2XZC6Ot+X0TQ8ESmOqlatSqdOnWjSpAk33HADzZo1IykpiebNm0fdgja37rzzTtq1a0ePHj044ogjdj9/9tlnc//993P00UfvNRhvzpw5NGrUiMMPP5y1a9dy8803A1ClShX+/e9/06ZNG9q0acOtt95KlSpV8h1jXmib2qJuZ1rQl58yHpbNCJ5r0A3aXgoNukOp3H9XG/XeckZOX8ouh9IG1/ZsxOVdG8Q5cBFJNNqmtmjI7Ta16qMv6tJr+UecCBu+D/rxP30WXjgDqhwG7S6FFudCuQoxn1JL6YqIlByq0RdHadvh66mw4HH4MRnKVggG77W9JJjKFwMtlSsiuaUafdGgGn1JkFQWmp4e3NYkw8dPwidj4eMn4PBe0G4o1O8STOfLQizT8PRlQEQyc/csp4lJwctL5VyJvrir1Tq49bwTkp8Obs+dHCw80fEqaHxKnubkpw/Y2562i7JJpZg4uL2SvUgJV758edatW0fVqlWV7EPg7qxbt47y5cvn6rhQEr2ZVQEmAfWAVcCZ7r7X0G8zewdoD3zg7n0KM8Zip8IhwRS9ztfBFy/BR/+F1y6BmbdD+2HBnPzyFWM+XbR185XoRUq2WrVqsWbNGn777bewQymxypcvT61atXJ1TFg1+uHALHcfYWbDI4//GaXc/cC+wKWFGVyxllQOWp4PLQbA8hnw4aMw/WZ4/z5oPSho1q9YI8fTaMCeiGRWpkwZDj300LDDkFwKZTCemS0Furj7z2ZWHZjj7o2yKNsFuD7WGn2JGIyXWz+mBDX8r6aAlYamZ0DHK+Hgo7I9LFofvfrtRUSKnuwG44WV6De4e6UMj1PdPWrWiCXRm9kQYAhAnTp1Wq1enf9FZRLS+u+CkfqfPQc7tkDDnkFTf532MR2ufnsRkaIpu0RfYCvjmdlMM1sc5dY/3tdy9zHu3trdW1erVi3ep08cVQ6FE++DfyyBrrcENf2ne8H4E2H5LMjhS5/2uxcRKX4KrI/e3btn9ZqZrTWz6hma7n8tqDgkin2rwHE3QIfLgsV3PnwUnj812FWv83XQ6KSoK+6p315EpPgJa637qcDAyP2BwJSQ4ijZyu4XjMi/ehH0fRS2bgjW1X+8A3w+KVh+N4P0/e6v7dlIzfYiIsVEWH30VYGXgDrA98AZ7r7ezFoDQ919cKTcPOAIYH9gHXCxu7+b3bk1GC8fdqbBV5Nh3oPw6xKoVBc6XwvNzw0W6RERkSKpyA3GK0hK9HGwaxcsexfm3h/04x9QG475R7DMblK5nI8XEZFCFcpgPCnGSpWCRifA4Flw3qtQoTq8dS080gI+HgM7toUdoYiIxEiJXrJmFmyFe/F0OH8yVK4Lb98A/20ZLLWbtj3sCEVEJAdK9JIzMzisK1z4NlwwBSrWhDf/AY+1hkUv7DVoT0REig4leomdWbAr3sXT4dyXYZ9KMHkYjG4PX74S9O1nI2V1KqPeW07K6r22NRARkQKiRC+5ZwaH94Qh78NZz0OpJHj1YnjiGPj6zagL76Svqjdy+lIGjF2gZC8iUkiU6CXvzODIvjDsQzhtHOz8CyYNgKe6wrIZeyR8raonIhIOJXrJv1KloenpcNnH0H80bFkHE08Pltf9bi7w96p6pQ2tqiciUog0j17iL217sHHO3Adg009w6LHQ9RZS/HDtfCciUgC0YI6EY8c2SBkP80bCn79Bgx5w/C1Qo0XYkYmIJBQtmCPhKFM+spb+59D9NvgxGcYcBy9fCOtWhB2diEiJoEQvBa/sfsESuld/DsfeAN++A6PaBnPxN/2iaXciIgVITfdS+DatDdbRTxnPLkviqR29GL2jL38l7a9d8URE8kBN91K0VDgYTnoArviE5VWP49JSU3iv7DWc69NYuPyXsKMTEUkoSvQSnir12XTSk5y68x6+8brcmvQsFy46G5ZMjrrojoiI5J4SvYSqVd3K3Dz4HD7rMoFl3cdTvvw+8PJAGNcTvl8QdngiIsWe+uilaNmZBp+/ALPvhs2/wBF9oPvtcGCDsCMTESmy1EcvxUfpJGh5AVz1KXS9GVbOgdHt4K3rYfNvYUcnIlLsKNFL0VR2PzjuRrjqM2g5EJKfhkePDkbrb98SdnQiIsWGEr0UbfsfBH0ehMsWBEvpzr4L/tsKPnuelO9+1/x7EZEcKNFL8VDtcDjnBRg0DSpWhymXs98zXfl4xiva9lZEJBtK9FK81OsEg2fx7pH3sA/beLbsPYzmXr5ZrAGYIiLRKNFL8WPGge3Poc+ukYxIO4c2pb7h3OSzYdqNsGV92NGJiBQpSvRSLLWqW5lnBnemQrfrWXnOPKzl+fDJU8GAvQWPw84dYYcoIlIkaB69JI61S+Ddm4IpeVUbQs+74PBeYBZ2ZCIiBUrz6KVkOLgxnD8ZzpkEOLx4Fjx3Cqz9KuzIRERCE0qiN7MqZjbDzJZFfu61XZmZtTCz+Wa2xMy+MLOzwohVihkzaNQbhs2H3iPgp8/giU7wxjVacEdESqSwavTDgVnu3hCYFXmc2RbgAndvDPQGHjazSoUYoxRnSWWh/bBgwZ02l8Cnz8J/W8JHj6n/XkRKlLASfX9gQuT+BODkzAXc/Vt3Xxa5/xPwK1Ct0CKUxLBvFTjxPrhsPtRuC9Nvhsc7wYrZYUcmIlIowkr0B7v7zwCRnwdlV9jM2gJlgRVZvD7EzJLNLPm339Q8K1FUa0TKMU/xVuOH+OuvrUHf/f8GQOqqsCMTESlQBTbq3sxmAodEeelmYIK7V8pQNtXd9+qnj7xWHZgDDHT3HPct1ah7iSZldSoDxi5ge9ou9k9K4+02X1Lzy1Hgu6DT1dDpGii7b9hhiojkSSij7t29u7s3iXKbAqyNJPD0RP5rFoFXBN4CboklyYtkZcHKdWxP28Uuhz/Tkphc4Wy44hNodCK8fy+MagtLJkOCTTcVEQmr6X4qMDByfyAwJXMBMysLvA486+4vF2JskoDa169K2aRSlDYok1SK9vWrwgG14IzxMOgtKFcRXh4Iz/aDX78OO1wRkbgJZcEcM6sKvATUAb4HznD39WbWGhjq7oPN7DxgPLAkw6GD3H1RdudW071kJWV1KgtWrqN9/aq0qpupp2hnGqSMD3bH+2sTtB0CXYbDPproISJFX3ZN91oZTySjP9fB7Dsh5RnYtyp0/z9ocR6U0tpSIlJ0aWU8kVjtVxX6PgxD5kDVw2DqlTC2G6zRl0cRKZ6U6EWiqdECLnoXThkDf/wUJPvJl8HmqONGRUSKLCV6kayYQfOz4MrkYAreFy/Bf1tpdT0RKVaU6EVyUq4C9LgDLlsAtdtpdT0RKVaU6EVidWADGPBysDvezu0ZVtdbHXZkIiJZUqIXyY303fEuWwDH/zuo1Y9qC+/9B7ZvCTs6EZG9KNGL5EWZ8nDs9XBFMhzRJ7K6Xjv4+k2triciRYoSvUh+HFATTh8Hg6ZBuf1h0gB44UxYvzLsyEREACV6kfio1wkunQu9/gOr58Oo9kFz/o6tYUcmIiWcEr1IHKSsTmXU3NWk1Dg32CznyL5/N+cvfSfs8ESkBFOiF8mn9C1wR05fyoCxC0hJLR805w98A5LKw4tnwQtnQ+qqsEMVkRJIiV4knzJugbsjbRcLVq4LXjj0WBj6QTAH/7u5Qe3+/fsg7a9wAxaREkWJXiSfom6Bmy6pbLCq3hWfwOG94b27YXQHLbYjIoVGu9eJxEG2W+BmtHwWTLs+GJXf+NRg8F7F6oUXqIgkJG1TK1KU7NgGHz4C80ZC6bJw/M3Q5hIonRR2ZCJSTGmbWpGipEx56PJPuGw+1G4L7wyHp7rAD5+EHZmIJCAlepGwVD0MznsVznwW/lwH47rD1Ktgy/qwIxORBKJEL1JIUlanMuq95aSsTv37STM4qj9csRA6XAGfPQ+PtQ5+7toVXrAikjCU6EUKwV5z7TMmewi2wu11d7C6XtUGMOVyeOZEWLsknIBFJGEo0YsUgizn2md2SBO48B3oPwp+WwpPHgszboXtfxZuwCKSMJToRQpBtnPtMytVCo4+D65MgeZnByP0R7WHb98tvIBFJGFoep1IIYl5rn1mqz+CN66B35fCkf3ghHuhYo2CC1REih3Noxcp7tK2w0ePwtz7oVQSHH8LtB0CpVX1EKYAACAASURBVEqHHZmIFAGaRy9S3CWVhWOvj8y9bxeZe98Vfvw07MhEpIiLOdGbWWUza2xm9c0sX18QzKyKmc0ws2WRn3u1Y5pZXTNLMbNFZrbEzIbm55oiCaFK/WDu/enjYdMvMLYbTLsRtv0RdmQiUkRlm7DN7AAzu8nMvgQWAE8CLwGrzexlM+uax+sOB2a5e0NgVuRxZj8DHd29BdAOGG5m6pgUMYMmpwYb5bS+GBaOgVFtYclkSLCuOBHJv5xq5q8APwCd3b2Rux/j7q3dvTYwAuhvZhfn4br9gQmR+xOAkzMXcPft7p6+n2e5GGIVKVnKHwAnPQCDZ8F+B8LLA+GFM7XvvYjsIZTBeGa2wd0rZXic6u7Rmu9rA28BDYAb3H1UFucbAgwBqFOnTqvVq1cXTOAihShXo/R3psHCJ2H23eC74LgboeOVULpM4QQrIqGKy6h7M2sG1AN2b7Hl7q9lU34mcEiUl24GJsSS6DO8XgOYDPR197XZxalR95II0lfS2562i7JJpZg4uH1sU/I2roG3/wnfvAnVjoS+D0Od9gUfsIiEKrtEH9O+mGb2NNAMWAKkL8DtQJaJ3t27Z3O+tWZW3d1/NrPqwK/ZXd/dfzKzJUBngu4EkYQWbSW9mBL9AbXg7InwzTSYdgM83QtaXgDdb4d9qxR84CJS5MS6AXZ7dz8qjtedCgwk6OcfCEzJXMDMagHr3H1rZFR+J+DBOMYgUmSlr6S3I21XzivpRXPEiXDosfD+CJg/Okj8ve6GZmcFg/lEpMSIqenezMYBI939q7hc1Kwqwej9OsD3wBnuvt7MWgND3X2wmfUARhK0HBjwmLuPyencarqXRJHnlfQy++XLYGW9H5OhXmfo8xAc2DB+gYpI6PLdR29mxwJvAL8AfxEkXnf3ZvEMNB6U6EWi2LULUsbDzNshbSt0vh6OuQaSyoUdmYjEQb776IGngfOBL/m7j15EiotSpaDNxXBEn2BVvTn/gcWvQN9HoG7HsKMTkQIU69z07919qrt/5+6r028FGpmIxF+Fg+GM8TDgFdixDcafAFOvhK2pYUcmIgUk1kT/jZm9YGbnmNmp6bcCjUxECk7DHnD5gmCu/WcT4bE28OUrWllPJAHFmuj3Ieib7wn0jdz6FFRQIlIIyu4HPe+CIXPggNrw6sXw/Gmw/ruwIxORONI2tSICu3bCJ2Nh1h3B/S7/hA5XaGU9kWIiz9vUmtktZpblKhtmdryZqWYvUtyVKg3tLoXLF0KDbjDzNhjTBdboS7NIcZfTqPsvgTfMbBvwKfAbUB5oCLQAZgL/KdAIRaTwHFAzWFnv6zeC7W/Hdoe2l0C3W6FchbCjE5E8iHUefUOClemqA1uBr4G57r61YMPLPTXdi8TJtj9g9p2w8CmoWANOfCBYcU9Eipy4bGpTXCjRi8TZD5/AG1fBr1/BUf3hhPugQrT9qkQkLHnuo89wgsPNbIyZTTez2em3+IYpIgUtZXUqo95bTsrqXMybr90GLp0Lx/8blr4Dj7WF5PHBansiUuTF2nT/OfAEkALsTH/e3VMKLrS8UY1eJLo8b32b0boV8MbVsGoe1OkQrKxXrVHBBCwiMct3jR5Ic/fH3X2hu6ek3+IYo4gUsGhb3+Za1cNg4BvQfxT8+jU8cQzMGQFpf8U/YBGJi1gT/RtmdpmZVTezKum3Ao1MROIqfevb0kbetr5NZwZHnwdXJMOR/WDOPfBEZ1g9P74Bi0hcxNp0H22pLHf3+vEPKX/UdC+StbhtfZvRshnw5rWw8XtodSF0vw32qRSfc4tITDTqXkTyJOYvBn9tDmr2C0bDfgfBifcFtX2zwgtWpASLx6j7eWZ2t5n1NjOtmiFSAqQP3hs5fSkDxi7IfqR+uf2h191wyWzY/yB46QL437mw8cfCC1hEooq1j34gsBQ4DfjIzJLN7KGCC0tEwpanwXs1joZL3oMed8KK92BUO/h4TLB+voiEIqZE7+4rgRnALGAusC9wZAHGJSIhy/PgvdJJ0OmqYBvc2m3g7Rvg6V6wdknBBiwiUcU6GG8F8DvwAjAPWOTuRXK1DPXRi8RPvgfvucOXL8M7w2HbRjjmH9D5eihTPv7BipRg+R6MZ2ZXA8cAtYFvgPcJ1rpfEc9A40GJXqQI+nMdTL8ZPn8RqjaEfo9C3Y5hRyWSMPI9GM/dH3H3M4DuBKvj3QZ8G7cIRSSx7VcVTnkCznsNdv4F40+AN64JavkiUqBiHXU/0sw+Bj4m2J72VoKtakVEYtegG1y2ADpcAZ9OCAbrff1m2FGJJLRYR90vAPq5e2N3v9jdJ0QG6ImI5E7Z/YKpeINnwb4HwqQBMOl82PRL2JGJJKRYm+5fBtqZ2QORW98CjktEEl3NljDkPej2f/Dtu8GueCkTggF8IhI3sTbd3wNcDXwVuV0VeU5EJO9Kl4HO18Jl86F6s2Df+2f6wO/Lw45MJGHE2nR/EtDD3Z9296eB3pHn8iSyKc4MM1sW+ZnlvB0zq2hmP5rZY3m9nogUcem74vX7L/zyJTzeEeaNhJ07wo5MpNiLNdEDZNyl4oB8Xnc4MMvdGxIswjM8m7J3EkznE5FEZgYtL4ArFkKj3jDrDhjTBX7Ujtgi+RFror8H+MzMnjGzCQRT7P6Tj+v2ByZE7k8ATo5WyMxaAQcD0/NxLREJWcrqVEa9tzz79fLTVTgEznwWzpoIW9bB2O7w7s2w/c+CD1QkAcW8e52ZVQfaAAZ87O55HiJrZhvcvVKGx6nuXjlTmVLAbOB8oBvQ2t2vyOJ8Q4AhAHXq1Gm1evXqvIYmInGWvjnO9rRdlE0qxcTB7WNfZW/bRph5GyQ/DZXqQJ+HoEH3Ao1XpDjK84I5ZtYy/QZUB9YAPwA1Is9ld+xMM1sc5dY/xrgvA6a5+w85FXT3Me7e2t1bV6tWLcbTi0hhyNPmOOnKHxAk9wvfhtLl4PnT4LVLg5X2RCQmSTm8PjLyszzQGvicoEbfjGDxnGOyOtDds/zabWZrzay6u/8caSn4NUqxDkBnM7sM2B8oa2ab3T27/nwRKWLSN8fZkbYrd5vjZFS3Iwz9IBig98GDsHwmnHAvNDlNe96L5CDWte7/B9zt7l9GHjcBrnf3QXm6qNn9wDp3H2Fmw4Eq7n5jNuUHkU3TfUZa616k6Mn35jgZrV0CU68MBuk17AUnjYRKteMTqEgxle+17oEj0pM8gLsvJlgKN69GAD3MbBnQI/IYM2ttZmPzcV4RKYJa1a3M5V0b5D/JAxzcGC6eAb3ugVXzYHT7yJ73RXJDTZHQxVqjfxH4E3gecOA8YH93P6dgw8s91ehFSpDU1fDmNbBiNtRuF8zDr9Yo7KhECl08avQXAksIVse7hmB1vAvjE56ISB5VrhvsiHfyE/D7t/DEMTDnXkjbHnZkIkVGrDX644EF7r6l4EPKH9XoRUqozb/BO/+Exa/CQUcFtftaUSs4IgknHjX6QcAiM5tvZveZWd/slq0VESkI2S68s381OP1pOGcSbN0QLLTzzr+00I6UeDlNrwPA3S8AMLMawOnAKKBGrMeLiORXzAvvNOodTMebeRssGA3fvAl9H4HDji/0mEWKglh3rzvPzJ4EXgG6A48BnQsyMBGRjHK18E75itDnwchCO2XhuVPg9WGwZX3hBSxSRMRaI38YWAE8Abzn7qsKLCIRkSjytPBO3Y4w9EN4/1748BFYPgNOvB+OOlkL7UiJkZu17hsDxxKshtcQWOru5xdgbHmiwXgiiStfC+/8/AVMvQJ+/hwanQQnPQAVaxRMoCKFLN+D8cysIlAHqAvUI9imVqtTiEihymnhnWwH61VvBoNnQ487YMUsGNUOksdroR1JeLFOr/sC+CBym+vuawo6sLxSjV6kZMrVLnnrVsAbVwcr69U9Bvo9ClUPK9yAReIo3zV6d2/m7pe5+wtFOcmLSMmVq8F6VQ+DgW8Eo/F/+RIe7wgfPAQ70wovYJFCEmvTfTUzu9/MppnZ7PRbQQcnIhKr9MF6pY3YBuuZQatBcPnHwR73M2+DsccHffkiCSTWBXMmAt8AhwK3A6uATwooJhGRXGtVtzITB7fn2p6Nsm+2z6xidTjreThjAvzxE4zpArPugB3bCjRekcISax99iru3MrMv3L1Z5Ln33f24Ao8wl9RHLyJ5tmU9TL8FFk2Eqg2DZXTrdgg7KpEcxWMJ3B2Rnz+b2UlmdjRQKy7RiYgUFftWgZNHBxvl7PwLxveGt66HvzaFHZlInsWa6O8yswOA64DrgbHAPwosKhGRMDXoBsPmQ7th8MlYGNUels0IOyqRPMkx0ZtZaaChu29098Xu3tXdW7n71EKIT0SkwEWdf19ufzhhBFw8HcruBxNPh9eGwJ/ZjOYXKYJyTPTuvhPoVwixiIgUuvT59yOnL2XA2AV7L7ZTuy0MnQfHRbbAHdU2+BnjqqIiYYu16f4jM3vMzDqbWcv0W4FGJiJSCGKaf59UDrreBJfOhUp14JWL4MVzglH6IkVcrJvadIz8vD3y0wAHtO+jiBRrudos5+DGMHgmLHgcZt8VLKPb805oOVCb5EiRle30OjO7Nv0uQWLP+El2d3+wAGPLE02vE5HcytNmOetXwtSrgmV063UOVtnTMroSkuym1+VUo68Q+dkIaANMIUj2fYG5cYtQRCRErepWzv1ueFXqB8vofvpsMPf+8U5w/M3BSP3SsTaWihS8WBfMmQ6c5u6bIo8rAC+7e+8Cji/XVKMXkUL3x0/w1nWwdBrUaAn9Hwua+UUKSTwWzKkDbM/weDvBdrUiIlKxBpz9Apw+HjZ8D08eC+/9B9L+CjsykZgH4z0HLDSz1wn66k8BJhRYVCIixY0ZNDkV6neBd/4F798LX02Bfo9B7TZhRyclWKzb1N4NXAikAhuAC939noIMTESkqIq6wE66favAqU/CgFfgr80wrkeQ+Lf/WfiBihBjH33cL2pWBZhE0Py/CjjT3ff6F2NmO4EvIw+/d/ccF+5RH72IFKT0BXa2p+2ibFKp7HfK2/YHzLo9WEa3Ul3o92hQ4xeJs3j00cfbcGCWuzcEZkUeR7PV3VtEblqdT0RCF9MCO+nKV4STRsKgaVAqCZ7tD1OugK0bCi9gKfHCSvT9+buPfwJwckhxiIjkSvoCO6WNnBfYSVevEwz7EDpdA4teCBba+eatgg9WhPCa7je4e6UMj1Pdfa+2LzNLAxYBacAId5+cxfmGAEMA6tSp02r16tUFE7iICLEvsBO13E+fwZQrYe2X0PgUOOE+2P+gQopcElV2TfcFlujNbCZwSJSXbgYmxJjoa7j7T2ZWH5gNdHP3FdldV330IlIUZNuXv3MHfPgwvH9fsDNe73uh2ZlaRlfyLJQ+enfv7u5NotymAGvNrHokuOrAr1mc46fIz5XAHODogopXRCSesu3LL10Gjr0Bhn4AVRvC60PghTNh45rwApaEFVYf/VRgYOT+QIKldfdgZpXNrFzk/oFAJ+CrQotQRCQfYurLr9YILnoHeo+AVR/AqPbwyTjYtavwA5aEFVYffVXgJYIV974HznD39WbWGhjq7oPNrCPwJLCL4AvJw+4+Lqdzq+leRIqKXG2Wk7oK3rgaVs6BuscEU/G0SY7EKJQ++rAo0YtIseUOnz0P794MO/+CrjdD+8u0SY7kqCjOoxcRkczMoOX5cPnHcFg3mPFvGNcd1i4JOzIpxpToRURCFHU53YrV4eyJkU1yftAmOZIvag8SEQlJtlPw0jfJOfQ4eDd9k5ypwRa4taK20IpEpRq9iEhIYlpOd7+qcOoYOPcl+OuPYJOcd2+G7VsKP2AplpToRURCkqvldA/vBZctgFaDYP5j8HgH+G5uocUqxZdG3YuIhChXU/DSrfoApl4J61cGib/HHVD+gAKNU4o2Ta8TEUk027fAnP/A/FGw/yHQ9+Gg1i8lkqbXiYgkmrL7Qs+74OKZsE+lYAndVy+BP7PZNldKJCV6EZFiaPe0vJ31Ycj7cNxwWPIajGoLi18LFt8RQdPrRESKnajT8rr+C47qB1Muh1cuhMWvwokPBHPypURTjV5EpJjJclrewY2Dpvwed8LymTCqHXz6nGr3JZwSvYhIMZPttLzSSdDpKhj2UZD4p14Bz50CqavDC1hCpVH3IiLFUEzT8nbtgpSnYcb/BbX67v8HbS6BUqrjJRpNrxMRKck2/ABvXhM059fpAP3+Cwc2DDsqiSNNrxMRKckq1YYBr8DJT8CvX8PjneCDh2BnWtiRSSFQohcRSXApq1MZNWcFKZV7w+ULg4V1Zt4GY7vBL1+GHZ4UMCV6EZEElj4Vb+T0pQwYu4CU9WXhrOfgjAnwx48wpgvMvltb4CYwJXoRkQSW5VS8xicHtfsmp8Pc+4I979dofFMiUqIXEUlg2U7F27cKnPoknPsy/LVJW+AmKI26FxFJcDFNxdv2B8z8P0h+GiofCv0fg3rHFG6gkmeaXiciIrH5bl6wBW7qd9D6Iuh+O5SvGHZUkgNNrxMRkdgc2jlYVa/DFZDyDIzuAMtmhB2V5IMSvYiI7KnsvtDrbrh4BpTbHyaeDq9dClvWhx2Z5IESvYiIRFerNVw6F469ERa/EmyBu2QykGGb3NWpIQcpOQllm1ozqwJMAuoBq4Az3X2vT4uZ1QHGArUBB05091WFFqiISEmXVA6Ov/nvLXBfHkjqwt5cvfJkfkqr+Pc2uVkN8pPQhVWjHw7McveGwKzI42ieBe539yOBtsCvhRSfiIhkdEhTGDwbut9Ghe9n82ap6zjZ5rIjbeffc/OlSAor0fcHJkTuTwBOzlzAzI4Cktx9BoC7b3Z3Te4UEQlL6SQ45h98c8rbrKAWD5Z9gvFl76PzQdvCjkyyEVaiP9jdfwaI/DwoSpnDgQ1m9pqZfWZm95tZ6WgnM7MhZpZsZsm//fZbAYYtIiJNmrWGC99mboMb6FRmGc2m9IZPxgXb4kqRU2Dz6M1sJnBIlJduBia4e6UMZVPdfY8OHjM7HRgHHA18T9CnP83dx2V3Xc2jFxEpRKmrYOpV8N37UPcY6PcoVD0s7KhKnFDm0bt7d3dvEuU2BVhrZtUjwVUnet/7GuAzd1/p7mnAZKBlQcUrIiJ5ULkeXDAl2OP+ly+DLXDnj4JdO8OOTCLCarqfCgyM3B8ITIlS5hOgsplVizw+HviqEGITEZHcMIOWF8DlC6B+F3j3Jni6F/z6ze4imo4XnlCWwDWzqsBLQB2CZvkz3H29mbUGhrr74Ei5HsBIwIAUYIi7b8/u3Gq6FxEJkTssfhWm3QDbN8NxN5JSeyADnk5he9ouTccrINk13Ycyj97d1wHdojyfDAzO8HgG0KwQQxMRkfwwg6anw6HHwds3wuy7qLP/yzTYOZDFXm/3VrlK9IVHK+OJiEj87V8NzhgPZ02k0s71TC5zCzcmTWK/pLQ9t8qVAqdELyIiBefIPpS56hM2NDyVy5Km8HGVO2hVannYUZUoSvQiIlKw9qnMgeeNg/NeZR/+gnE94J1/wfY/w46sRFCiFxGRwtGgO1w2H9pcDAtGw+Md4bu5YUeV8JToRUSk8JSrACeNhEFvgZWCCX3hjWtg2x97FNN0vPgJZdS9iIiUcPWOgaEfwpz/BAvsLJsOfR+Bhj1IWZ3KgLELNB0vTlSjFxGRcJTdF3reBRfPhHIVYeLp8NqlfLZ0JdvTdrHL2T0dT/JOiV5ERMJVqxVc+j4ceyMsfoWBn51Jn6RPKG1QJqmUpuPlkxK9iIiEL6kcHH8zDJlDmUo1ebT0Q7xbcxyTBhymZvt8UqIXEZGi45CmMHg2dPs/GqR+QPMpPeHzScHSupInSvQiIlK0lE6CztfC0A+gakN4fQi8cBZs/DHsyIolJXoRESmaqh0OF70DvUfAqnkwuj0kj9+jdq9peDnT9DoRESm6SpWG9sPg8N4w9Up48xpY8hr0fZSUTZU0DS8GqtGLiEjRV+VQGPgG9HkYfvwMHu/Ilrn/JS0tTdPwcqBELyIixYMZtL4QLl8A9Y6h84qRvFT2DhqW+lHT8LKhRC8iIsXLAbXg3JfglCdpVm4tb5e7mZltP6VVrQphR1YkKdGLiEjxYwbNzybpyk9IOqI3tVLug7Hd4JfFYUdW5CjRi4hI8VXhYDjrOThjAvzxI4w5Dt77D6RtDzuyIkOJXkREir/GJ8PlC6HJafD+vUHC/zEl7KiKBCV6ERFJDPtWgVPHBP33WzfA2O4w/d+wY2uWh5SEefiaRy8iIonl8F7ByPzp/4aPHoVv3oL+j0HdjnsUKynb4apGLyIiiaf8AdDvUbhgCuzaAeNPgGk3wF+bdxdZsHJdidgOV4leREQSV/0uMGw+tBsKC5+C0R1gxWwA2tevStmkUlluh5sozfrmCbYjUOvWrT05OTnsMEREpKj5fgFMuRzWLYejz4Oed5Pyq7Ng5Tra16+6R7N9cWvWN7MUd28d7TXV6EVEpGSo0z7YEa/TNbDoBRjdnlZb53N51wZ7JfFEatYPJdGbWRUzm2FmyyI/9/qaZGZdzWxRhts2Mzs5jHhFRCRBlNkHetwOg2fCPpXhf+fAKxfDn3sm8pya9YuTUJruzew+YL27jzCz4UBld/9nNuWrAMuBWu6+Jbtzq+leRERikrYd5o2EeQ9A+Upw4v3Q+JRg1T2C5vtozfrpcnq9MGXXdB9Wol8KdHH3n82sOjDH3RtlU34IcJy7D8jp3Er0IiKSK78sDvruf14ER/SBk0ZChUOyPaSo9eEXxT76g939Z4DIz4NyKH828GJWL5rZEDNLNrPk3377LY5hiohIwjukCQyeBd1vh2UzYFQ7WPQiZFMRLk59+AWW6M1sppktjnLrn8vzVAeaAu9mVcbdx7h7a3dvXa1atfyGLiIiJU3pJDjmGhj2IVRrBJOHwsTTYeOaqMWLUx9+kW+6N7OrgcbuPiSWc6vpXkRE8mXXzmDO/azbwUpDzzug5SAotWfduLj00YfVdD8VGBi5PxCYkk3Zc8im2V5ERCSuSpWG9kPhsvlQ82h48x/wbD9Y/90exVrVrRx1al5OCnshnrAS/Qigh5ktA3pEHmNmrc1sbHohM6sH1AbeDyFGEREpySrXgwumQt9H4OfP4fGOsODxoMYfo8xJPX0Q38jpSxkwdkGhJPtQNrVx93VAtyjPJwODMzxeBdQsvMhEREQyMINWg6BBD3jzGnhnOCx5Hfo9BtUOz/bQaCPzow3iK+hmf62MJyIikpMDagbb354yBn7/Fp44BuY9CDvTsjwkWlIPYxCftqkVERGJhRk0PwsO6wrTrg8G6301BfqPCqboZZKe1Hek7dqd1FvVrby7Zl9Yg/i0qY2IiEheLJkcJPytqdD5euh8HSSV3aNIYY3ML3Ir4xUkJXoRESk0W9YH/fZfTIKDjgpq9zVbFnoYRXF6nYiISPG3bxU4dQycMwm2boCx3WDGrbBja9iR7aZELyIikl+NesPlC+Do8+HDR4LBet8vCDsqQIleREQkPsofAP0ehfMnw87t8HRvePuf8NfmUMNSohcREYmnw7rCsPnQdgh8/ESw0M7KOaGFo0QvIiISb+X2hxPvgwvfgdJl4Nn+MPUq2Lax0ENRohcRESkodTvA0A+g09Xw2XMwqj18m+VmrAVCiV5ERKQgldkHetwBg2cG/fgvnAlfvFRol9fKeCIiIoWhZiu49H1YOAaO6FNol1WiFxERKSxJ5aDjlYV6STXdi4iIJDAlehERkQSmRC8iIpLAlOhFREQSmBK9iIhIAlOiFxERSWBK9CIiIglMiV5ERCSBKdGLiIgkMCV6ERGRBGbuHnYMcWVmvwGrMzx1AJDVvoDZvXYg8HscQysM2b2fonidvJ4nt8fFWj6ncvl5vbh9nkrKZym3x8brs5RTmUT6LEHJ+TyF+X9TJXevFvVVd0/oGzAmj68lhx17PN9rUbxOXs+T2+NiLZ9Tufy8Xtw+TyXls5TbY+P1WcqpTCJ9luL5dy6s6yTa/00loen+jTy+VhwV1vuJ13Xyep7cHhdr+ZzK5ff14qSkfJZye2y8Pks5lUmkzxKUnM9Tkfy/KeGa7uPFzJLdvXXYcUhi0OdJ4kWfJcmtklCjz6sxYQcgCUWfJ4kXfZYkV1SjFxERSWCq0YuIiCQwJXoREZEEpkQvIiKSwJToRUREEpgSfR6Y2ZFm9oSZvWJmw8KOR4ovMzvZzJ4ysylm1jPseKR4M7P6ZjbOzF4JOxYpOkpcojezp83sVzNbnOn53ma21MyWm9nw7M7h7l+7+1DgTEDzWUuoOH2WJrv7JcAg4Kz/b+9+Xq2owziOvz8p2qJIiIjUwjCT4PZDhKAyqahFEdnCoJKilCAko0WF/QFhFG4ko1pUFP2ggsAwaZVK4cKSwsTIskUXF4KhGYoQPi3OmKfbvVe8nnN/nPN+be7Md75n5hl4mOd859yZbxfD1STXoXzaX1Wruhupppq+e7wuyVLgL+Ddqhpo2qYBPwN3AYPATuAhYBqwbsguVlbVwST3AWuBV6vqg/GKX5NHp3Kp+dx64P2q2jVO4WuS6XA+fVpVy8crdk1u0yc6gPFWVduTzBvSfCPwS1XtB0jyEbCsqtYB946wn03ApiSbAQt9H+pELiUJ8BKwxSLf3zp1bZKG6rtb9yOYA/zetj7YtA0ryW1JNiR5A/ii28FpSjmrXALWAHcCy5M82c3ANCWd7bXp4iSvA4uSvNDt4DQ19N2IfgQZpm3E3zSqaiuwtVvBaEo721zaAGzoXjia4s42nw4BfmHUfziibxkELm9bnwscmKBYNLWZS+ok80nnzELfshNYkOTKJDOAB4FNExyTpiZzSZ1kPumc9V2hT/IhsANYmGQwyaqq+ht4CvgS2At8XFV7JjJOTX7mkjrJfFK39N3jdZIk9ZO+G9FLktRPLPSSJPUwC70kST3MQi9JUg+z0EuS5k66JQAAAf9JREFU1MMs9JIk9TALvSSSzEqyulme3cn5zJM8k+TRYdrnnZqSNcm1Sd7p1DElnWahlwQwC1gNUFUHOjXFaZLpwErOMMNjVe0G5ia5ohPHlXSak9pIgtZUufOTfA/sA66pqoEkjwH305r/fABYD8wAHgFOAPdU1R9J5gMbgUuAY8ATVfUTcAewq3nDG0kWA281fb4eEsPntF7x+nI3T1TqN47oJQGsBX6tqhuA54ZsGwAepjU3+ovAsapaROt1raduyb8JrKmqxcCzwGtN+y3Ad237eht4uqpuGiaGb4FbO3Aukto4opd0Jl9V1VHgaJIjtEbeALuB65JcANwMfJL8O6vqzObvZbTe0U6Si4BZVbWt2fYecHfbcQ4Cs7t2FlKfstBLOpMTbcsn29ZP0rqGnAccbu4GDHUcOL9ZDqPMpd70O35uoUoaylv3kgCOAheO5YNV9SfwW5IHANJyfbN5L3BV0+8wcCTJkmbbiiG7uhr4cSwxSBqZhV4SVXUI+KZ53O2VMexiBbAqyQ/AHmBZ074FWNrW73FgY5Id/H/0fjuweQzHljQKp6mV1FVJPgOer6p9o/SZCWwDlpz6D31JnWGhl9RVSRYCl1bV9lH6LADmVNXWcQtM6hMWekmSepi/0UuS1MMs9JIk9TALvSRJPcxCL0lSD7PQS5LUw/4B0eof6SzCEbkAAAAASUVORK5CYII=", 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" ] @@ -371,14 +371,14 @@ } ], "source": [ - "print('rmse:', ca2.rmse())\n", + "print(\"rmse:\", ca2.rmse())\n", "hm2 = ml.head(r2, 0, t2)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t2, h2, '.', label='obs at 90 m')\n", - "plt.semilogx(t2, hm2[0], label='ttim at 90 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t2, hm2[0], label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -486,11 +486,11 @@ } ], "source": [ - "ca = Calibrate(ml)\n", - "ca.set_parameter(name='kaq0', initial=10)\n", - "ca.set_parameter(name='Saq0', initial=1e-4)\n", - "ca.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq0\", initial=10)\n", + "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca.fit(report=True)\n", "display(ca.parameters)" ] @@ -509,7 +509,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -521,17 +521,17 @@ } ], "source": [ - "print('rmse:', ca.rmse())\n", + "print(\"rmse:\", ca.rmse())\n", "hs1 = ml.head(r1, 0, t1)\n", - "hs2 = ml.head(r2, 0 ,t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30m')\n", - "plt.semilogx(t1, hs1[0], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 90m')\n", - "plt.semilogx(t2, hs2[0], label = 'ttim at 90m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "hs2 = ml.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", + "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", + "plt.semilogx(t2, hs2[0], label=\"ttim at 90m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -555,10 +555,10 @@ "metadata": {}, "outputs": [], "source": [ - "#unknown parameters: kaq, Saq and rc\n", - "ml1 = ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w1 = Well(ml1, xw=0, yw=0, rw=0.2, rc=0.2, tsandQ=[(0, Q)], layers=0)\n", - "ml1.solve(silent='True')" + "# unknown parameters: kaq, Saq and rc\n", + "ml1 = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.2, rc=0.2, tsandQ=[(0, Q)], layers=0)\n", + "ml1.solve(silent=\"True\")" ] }, { @@ -680,11 +680,11 @@ } ], "source": [ - "ca3 = Calibrate(ml1)\n", - "ca3.set_parameter(name='kaq0', initial=10)\n", - "ca3.set_parameter(name='Saq0', initial=1e-4)\n", - "ca3.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca3.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca3 = ttim.Calibrate(ml1)\n", + "ca3.set_parameter(name=\"kaq0\", initial=10)\n", + "ca3.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca3.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", "ca3.fit(report=True)\n", "display(ca3.parameters)" ] @@ -703,7 +703,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -715,14 +715,14 @@ } ], "source": [ - "print('rmse:', ca3.rmse())\n", + "print(\"rmse:\", ca3.rmse())\n", "hm3 = ml1.head(r1, 0, t1)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm3[0], label='ttim at 30 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm3[0], label=\"ttim at 30 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -845,11 +845,11 @@ } ], "source": [ - "ca4 = Calibrate(ml1)\n", - "ca4.set_parameter(name='kaq0', initial=10)\n", - "ca4.set_parameter(name='Saq0', initial=1e-4)\n", - "ca4.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca4.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca4 = ttim.Calibrate(ml1)\n", + "ca4.set_parameter(name=\"kaq0\", initial=10)\n", + "ca4.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca4.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca4.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca4.fit(report=True)\n", "display(ca4.parameters)" ] @@ -868,7 +868,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -880,14 +880,14 @@ } ], "source": [ - "print('rmse:', ca4.rmse())\n", + "print(\"rmse:\", ca4.rmse())\n", "hm4 = ml1.head(r2, 0, t2)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t2, h2, '.', label='obs at 90 m')\n", - "plt.semilogx(t2, hm4[0], label='ttim at 90 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t2, hm4[0], label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -1010,12 +1010,12 @@ } ], "source": [ - "ca0 = Calibrate(ml1)\n", - "ca0.set_parameter(name='kaq0', initial=10)\n", - "ca0.set_parameter(name='Saq0', initial=1e-4)\n", - "ca0.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca0.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca0.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca0 = ttim.Calibrate(ml1)\n", + "ca0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca0.set_parameter_by_reference(name=\"rc\", parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca0.fit(report=True)\n", "display(ca0.parameters)" ] @@ -1034,7 +1034,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1046,17 +1046,17 @@ } ], "source": [ - "print('rmse:', ca0.rmse())\n", + "print(\"rmse:\", ca0.rmse())\n", "hs1 = ml1.head(r1, 0, t1)\n", - "hs2 = ml1.head(r2, 0 ,t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30m')\n", - "plt.semilogx(t1, hs1[0], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 90m')\n", - "plt.semilogx(t2, hs2[0], label = 'ttim at 90m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "hs2 = ml1.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", + "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", + "plt.semilogx(t2, hs2[0], label=\"ttim at 90m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -1141,14 +1141,17 @@ } ], "source": [ - "t0 = pd.DataFrame(columns=['obs 30 m', 'obs 90 m', 'obs simultaneously'], index=['without rc', 'with rc'])\n", - "t0.loc['without rc', 'obs 30 m'] = ca1.rmse()\n", - "t0.loc['without rc', 'obs 90 m'] = ca2.rmse()\n", - "t0.loc['without rc', 'obs simultaneously'] = ca.rmse()\n", - "t0.loc['with rc', 'obs 30 m'] = ca3.rmse()\n", - "t0.loc['with rc', 'obs 90 m'] = ca4.rmse()\n", - "t0.loc['with rc', 'obs simultaneously'] = ca0.rmse()\n", - "print('RMSE of two conceptual models:')\n", + "t0 = pd.DataFrame(\n", + " columns=[\"obs 30 m\", \"obs 90 m\", \"obs simultaneously\"],\n", + " index=[\"without rc\", \"with rc\"],\n", + ")\n", + "t0.loc[\"without rc\", \"obs 30 m\"] = ca1.rmse()\n", + "t0.loc[\"without rc\", \"obs 90 m\"] = ca2.rmse()\n", + "t0.loc[\"without rc\", \"obs simultaneously\"] = ca.rmse()\n", + "t0.loc[\"with rc\", \"obs 30 m\"] = ca3.rmse()\n", + "t0.loc[\"with rc\", \"obs 90 m\"] = ca4.rmse()\n", + "t0.loc[\"with rc\", \"obs simultaneously\"] = ca0.rmse()\n", + "print(\"RMSE of two conceptual models:\")\n", "t0" ] }, @@ -1242,12 +1245,13 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'RMSE'], \\\n", - " index=['K&dR', 'ttim', 'AQTESOLV', 'MLU'])\n", - "t.loc['ttim'] = np.append(ca.parameters['optimal'].values, ca.rmse())\n", - "t.loc['AQTESOLV'] = [66.086, 2.541e-05, 0.05006]\n", - "t.loc['MLU'] = [66.850, 2.400e-05, 0.05083]\n", - "t.loc['K&dR'] = [55.71429, 1.7E-4, '-']\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE\"], index=[\"K&dR\", \"ttim\", \"AQTESOLV\", \"MLU\"]\n", + ")\n", + "t.loc[\"ttim\"] = np.append(ca.parameters[\"optimal\"].values, ca.rmse())\n", + "t.loc[\"AQTESOLV\"] = [66.086, 2.541e-05, 0.05006]\n", + "t.loc[\"MLU\"] = [66.850, 2.400e-05, 0.05083]\n", + "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, \"-\"]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/2_test_of_dalem.ipynb b/pumpingtest_benchmarks/2_test_of_dalem.ipynb index cd04ffd..2bea0e1 100755 --- a/pumpingtest_benchmarks/2_test_of_dalem.ipynb +++ b/pumpingtest_benchmarks/2_test_of_dalem.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -34,11 +34,11 @@ "metadata": {}, "outputs": [], "source": [ - "H = 37 #aquifer thickness [m]\n", - "zt = - 8 #top boundary of aquifer\n", + "H = 37 # aquifer thickness [m]\n", + "zt = -8 # top boundary of aquifer\n", "zb = zt - H\n", - "Q = 761 #constant pumping rate [m^3/d]\n", - "t = 0.34 #time start pumping [d]" + "Q = 761 # constant pumping rate [m^3/d]\n", + "t = 0.34 # time start pumping [d]" ] }, { @@ -54,11 +54,12 @@ "metadata": {}, "outputs": [], "source": [ - "#unkonwn parameters: kaq, Saq, c\n", - "ml = ModelMaq(kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary='semi', \\\n", - " tmin=0.001, tmax=0.5)\n", - "w = Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", - "ml.solve(silent = 'True')" + "# unkonwn parameters: kaq, Saq, c\n", + "ml = ttim.ModelMaq(\n", + " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.001, tmax=0.5\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "ml.solve(silent=\"True\")" ] }, { @@ -74,23 +75,23 @@ "metadata": {}, "outputs": [], "source": [ - "#data of observation well 30 m away from pumping well\n", - "data1 = np.loadtxt('data/dalem_p30.txt', skiprows = 1)\n", + "# data of observation well 30 m away from pumping well\n", + "data1 = np.loadtxt(\"data/dalem_p30.txt\", skiprows=1)\n", "t1 = data1[:, 0]\n", "h1 = data1[:, 1]\n", "r1 = 30\n", - "#data of observation well 60 m away from pumping well\n", - "data2 = np.loadtxt('data/dalem_p60.txt', skiprows = 1)\n", + "# data of observation well 60 m away from pumping well\n", + "data2 = np.loadtxt(\"data/dalem_p60.txt\", skiprows=1)\n", "t2 = data2[:, 0]\n", "h2 = data2[:, 1]\n", "r2 = 60\n", - "#data of observation well 90 m away from pumping well\n", - "data3 = np.loadtxt('data/dalem_p90.txt', skiprows = 1)\n", + "# data of observation well 90 m away from pumping well\n", + "data3 = np.loadtxt(\"data/dalem_p90.txt\", skiprows=1)\n", "t3 = data3[:, 0]\n", "h3 = data3[:, 1]\n", "r3 = 90\n", - "#data of observation well 120 m away from pumping well\n", - "data4 = np.loadtxt('data/dalem_p120.txt', skiprows = 1)\n", + "# data of observation well 120 m away from pumping well\n", + "data4 = np.loadtxt(\"data/dalem_p120.txt\", skiprows=1)\n", "t4 = data4[:, 0]\n", "h4 = data4[:, 1]\n", "r4 = 120" @@ -215,13 +216,13 @@ } ], "source": [ - "ca1 = Calibrate(ml)\n", - "ca1.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", - "ca1.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca1.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", - "ca1.series(name='obs2', x=r2, y=0, layer=0, t=t2, h=h2)\n", - "ca1.series(name='obs3', x=r3, y=0, layer=0, t=t3, h=h3)\n", - "ca1.series(name='obs4', x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca1 = ttim.Calibrate(ml)\n", + "ca1.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca1.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca1.series(name=\"obs2\", x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca1.series(name=\"obs3\", x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca1.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", "ca1.fit()\n", "display(ca1.parameters)" ] @@ -240,7 +241,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -252,23 +253,23 @@ } ], "source": [ - "print('rmse:', ca1.rmse())\n", + "print(\"rmse:\", ca1.rmse())\n", "plt.figure(figsize=(8, 5))\n", "ha1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, ha1[0], label='ttim at 30 m')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, ha1[0], label=\"ttim at 30 m\")\n", "ha2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, ha2[0], label='ttim at 60 m')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, ha2[0], label=\"ttim at 60 m\")\n", "ha3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, ha3[0], label='ttim at 90 m')\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, ha3[0], label=\"ttim at 90 m\")\n", "ha4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, ha4[0], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim fit exceppt for data of obs1')\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, ha4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit exceppt for data of obs1\")\n", "plt.legend();" ] }, @@ -384,13 +385,13 @@ } ], "source": [ - "ca2 = Calibrate(ml)\n", - "ca2.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", - "ca2.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca2.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", - "ca2.series(name='obs1', x=r1, y=0, layer=0, t=t1, h=h1)\n", - "ca2.series(name='obs3', x=r3, y=0, layer=0, t=t3, h=h3)\n", - "ca2.series(name='obs4', x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca2 = ttim.Calibrate(ml)\n", + "ca2.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca2.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca2.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca2.series(name=\"obs3\", x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca2.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", "ca2.fit()\n", "display(ca2.parameters)" ] @@ -409,7 +410,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -421,23 +422,23 @@ } ], "source": [ - "print('rmse:', ca2.rmse())\n", + "print(\"rmse:\", ca2.rmse())\n", "plt.figure(figsize=(8, 5))\n", "hb1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hb1[0], label='ttim at 30 m')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hb1[0], label=\"ttim at 30 m\")\n", "hb2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hb2[0], label='ttim at 60 m')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hb2[0], label=\"ttim at 60 m\")\n", "hb3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hb3[0], label='ttim at 90 m')\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hb3[0], label=\"ttim at 90 m\")\n", "hb4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hb4[0], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim fit exceppt for data of obs2')\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hb4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit exceppt for data of obs2\")\n", "plt.legend();" ] }, @@ -553,13 +554,13 @@ } ], "source": [ - "ca3 = Calibrate(ml)\n", - "ca3.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", - "ca3.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca3.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", - "ca3.series(name='obs1', x=r1, y=0, layer=0, t=t1, h=h1)\n", - "ca3.series(name='obs3', x=r2, y=0, layer=0, t=t2, h=h2)\n", - "ca3.series(name='obs4', x=r4, y=0, layer=0, t=t4, h=h4)\n", + "ca3 = ttim.Calibrate(ml)\n", + "ca3.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca3.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca3.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca3.series(name=\"obs3\", x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca3.series(name=\"obs4\", x=r4, y=0, layer=0, t=t4, h=h4)\n", "ca3.fit()\n", "display(ca3.parameters)" ] @@ -578,7 +579,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -590,23 +591,23 @@ } ], "source": [ - "print('rmse:', ca3.rmse())\n", + "print(\"rmse:\", ca3.rmse())\n", "plt.figure(figsize=(8, 5))\n", "hc1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hc1[0], label='ttim at 30 m')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hc1[0], label=\"ttim at 30 m\")\n", "hc2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hc2[0], label='ttim at 60 m')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hc2[0], label=\"ttim at 60 m\")\n", "hc3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hc3[0], label='ttim at 90 m')\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hc3[0], label=\"ttim at 90 m\")\n", "hc4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hc4[0], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim fit exceppt for data of obs3')\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hc4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit exceppt for data of obs3\")\n", "plt.legend();" ] }, @@ -722,13 +723,13 @@ } ], "source": [ - "ca4 = Calibrate(ml)\n", - "ca4.set_parameter(name='kaq0', initial=10, pmin=1, pmax=100)\n", - "ca4.set_parameter(name='Saq0', initial=1e-4, pmin=1e-5, pmax=1e-3)\n", - "ca4.set_parameter(name='c0', initial=1000, pmin=100, pmax=1e6)\n", - "ca4.series(name='obs1', x=r1, y=0, layer=0, t=t1, h=h1)\n", - "ca4.series(name='obs3', x=r2, y=0, layer=0, t=t2, h=h2)\n", - "ca4.series(name='obs4', x=r3, y=0, layer=0, t=t3, h=h3)\n", + "ca4 = ttim.Calibrate(ml)\n", + "ca4.set_parameter(name=\"kaq0\", initial=10, pmin=1, pmax=100)\n", + "ca4.set_parameter(name=\"Saq0\", initial=1e-4, pmin=1e-5, pmax=1e-3)\n", + "ca4.set_parameter(name=\"c0\", initial=1000, pmin=100, pmax=1e6)\n", + "ca4.series(name=\"obs1\", x=r1, y=0, layer=0, t=t1, h=h1)\n", + "ca4.series(name=\"obs3\", x=r2, y=0, layer=0, t=t2, h=h2)\n", + "ca4.series(name=\"obs4\", x=r3, y=0, layer=0, t=t3, h=h3)\n", "ca4.fit()\n", "display(ca4.parameters)" ] @@ -747,7 +748,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -759,23 +760,23 @@ } ], "source": [ - "print('rmse:', ca4.rmse())\n", + "print(\"rmse:\", ca4.rmse())\n", "plt.figure(figsize=(8, 5))\n", "hd1 = ml.head(r1, 0, t1)\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hd1[0], label='ttim at 30 m')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hd1[0], label=\"ttim at 30 m\")\n", "hd2 = ml.head(r2, 0, t2)\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hd2[0], label='ttim at 60 m')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hd2[0], label=\"ttim at 60 m\")\n", "hd3 = ml.head(r3, 0, t3)\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hd3[0], label='ttim at 90 m')\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hd3[0], label=\"ttim at 90 m\")\n", "hd4 = ml.head(r4, 0, t4)\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hd4[0], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim fit exceppt for data of obs4')\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hd4[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim fit exceppt for data of obs4\")\n", "plt.legend();" ] }, @@ -865,15 +866,21 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]'], \\\n", - " index=['Data at 30 m removed', 'Data at 60 m removed', \\\n", - " 'Data at 90 m removed', 'Data at 120 m removed'])\n", - "t.loc['Data at 30 m removed'] = ca1.parameters['optimal'].values\n", - "t.loc['Data at 60 m removed'] = ca2.parameters['optimal'].values\n", - "t.loc['Data at 90 m removed'] = ca1.parameters['optimal'].values\n", - "t.loc['Data at 120 m removed'] = ca4.parameters['optimal'].values\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\"],\n", + " index=[\n", + " \"Data at 30 m removed\",\n", + " \"Data at 60 m removed\",\n", + " \"Data at 90 m removed\",\n", + " \"Data at 120 m removed\",\n", + " ],\n", + ")\n", + "t.loc[\"Data at 30 m removed\"] = ca1.parameters[\"optimal\"].values\n", + "t.loc[\"Data at 60 m removed\"] = ca2.parameters[\"optimal\"].values\n", + "t.loc[\"Data at 90 m removed\"] = ca1.parameters[\"optimal\"].values\n", + "t.loc[\"Data at 120 m removed\"] = ca4.parameters[\"optimal\"].values\n", "rmse = [ca1.rmse(), ca2.rmse(), ca3.rmse(), ca4.rmse()]\n", - "t['RMSE'] = rmse\n", + "t[\"RMSE\"] = rmse\n", "t" ] }, @@ -897,11 +904,12 @@ "metadata": {}, "outputs": [], "source": [ - "#unkonwn parameters: kaq, Saq, c\n", - "m_1 = ModelMaq(kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary='semi', \\\n", - " tmin=0.001, tmax=0.5)\n", - "w_1 = Well(m_1, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", - "m_1.solve(silent = 'True')" + "# unkonwn parameters: kaq, Saq, c\n", + "m_1 = ttim.ModelMaq(\n", + " kaq=10, z=[0, zt, zb], c=500, Saq=0.001, topboundary=\"semi\", tmin=0.001, tmax=0.5\n", + ")\n", + "w_1 = ttim.Well(m_1, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "m_1.solve(silent=\"True\")" ] }, { @@ -1016,14 +1024,14 @@ } ], "source": [ - "c0 = Calibrate(m_1)\n", - "c0.set_parameter(name='kaq0', initial=10)\n", - "c0.set_parameter(name='Saq0', initial=1e-4)\n", - "c0.set_parameter(name='c0', initial=500, pmin=0)\n", - "c0.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=0)\n", - "c0.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=0)\n", - "c0.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=0)\n", - "c0.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=0)\n", + "c0 = ttim.Calibrate(m_1)\n", + "c0.set_parameter(name=\"kaq0\", initial=10)\n", + "c0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "c0.set_parameter(name=\"c0\", initial=500, pmin=0)\n", + "c0.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", + "c0.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", + "c0.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0)\n", + "c0.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0)\n", "c0.fit(report=True)\n", "display(c0.parameters)" ] @@ -1042,7 +1050,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1058,19 +1066,19 @@ "hm_12 = m_1.head(r2, 0, t2)\n", "hm_13 = m_1.head(r3, 0, t3)\n", "hm_14 = m_1.head(r4, 0, t4)\n", - "print('rmse:', c0.rmse())\n", + "print(\"rmse:\", c0.rmse())\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm_11[0], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hm_12[0], label='ttim at 60 m')\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hm_13[0], label='ttim at 90 m')\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hm_14[0], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('model with leakage only')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_11[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_12[0], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_13[0], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_14[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with leakage only\")\n", "plt.legend();" ] }, @@ -1087,11 +1095,19 @@ "metadata": {}, "outputs": [], "source": [ - "#unkonwn parameters: kaq, Saq, c, Sll\n", - "m_2 = ModelMaq(kaq=10, z=[0, zt, zb], c=500, Saq=0.001, Sll=0.001, \\\n", - " topboundary='semi', tmin=0.001, tmax=0.5)\n", - "w_2 = Well(m_2, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", - "m_2.solve(silent = 'True')" + "# unkonwn parameters: kaq, Saq, c, Sll\n", + "m_2 = ttim.ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " c=500,\n", + " Saq=0.001,\n", + " Sll=0.001,\n", + " topboundary=\"semi\",\n", + " tmin=0.001,\n", + " tmax=0.5,\n", + ")\n", + "w_2 = ttim.Well(m_2, xw=0, yw=0, tsandQ=[(0, Q), (0.34, 0)])\n", + "m_2.solve(silent=\"True\")" ] }, { @@ -1222,15 +1238,15 @@ } ], "source": [ - "c1 = Calibrate(m_2)\n", - "c1.set_parameter(name='kaq0', initial=10)\n", - "c1.set_parameter(name='Saq0', initial=1e-4)\n", - "c1.set_parameter(name='c0', initial=500, pmin=0)\n", - "c1.set_parameter_by_reference(name='Sll', parameter=m_2.aq.Sll[:], initial=1e-5)\n", - "c1.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=0)\n", - "c1.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=0)\n", - "c1.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=0)\n", - "c1.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=0)\n", + "c1 = ttim.Calibrate(m_2)\n", + "c1.set_parameter(name=\"kaq0\", initial=10)\n", + "c1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "c1.set_parameter(name=\"c0\", initial=500, pmin=0)\n", + "c1.set_parameter_by_reference(name=\"Sll\", parameter=m_2.aq.Sll[:], initial=1e-5)\n", + "c1.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=0)\n", + "c1.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=0)\n", + "c1.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=0)\n", + "c1.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=0)\n", "c1.fit(report=True)\n", "display(c1.parameters)" ] @@ -1249,7 +1265,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -1265,19 +1281,19 @@ "hm_22 = m_2.head(r2, 0, t2)\n", "hm_23 = m_2.head(r3, 0, t3)\n", "hm_24 = m_2.head(r4, 0, t4)\n", - "print('rmse:', c1.rmse())\n", + "print(\"rmse:\", c1.rmse())\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm_21[0], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hm_22[0], label='ttim at 60 m')\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hm_23[0], label='ttim at 90 m')\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hm_24[0], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('model with both leakage and storage')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_21[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_22[0], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_23[0], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_24[0], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with both leakage and storage\")\n", "plt.legend();" ] }, @@ -1303,11 +1319,19 @@ } ], "source": [ - "#unkonwn parameters: kaq1, Saq1, c, Sll\n", - "m_3 = ModelMaq(kaq=[0.01, 10], z=[0, -0.001, -8.001, -45.001], c = 500, \\\n", - " Saq = [0, 0.001], Sll = 1e-4, topboundary = 'conf', tmin=0.001, tmax=0.5)\n", - "w_3 = Well(m_3, xw = 0, yw = 0, tsandQ = [(0, 761), (0.34, 0)], layers = 1)\n", - "m_3.solve(silent = 'True')" + "# unkonwn parameters: kaq1, Saq1, c, Sll\n", + "m_3 = ttim.ModelMaq(\n", + " kaq=[0.01, 10],\n", + " z=[0, -0.001, -8.001, -45.001],\n", + " c=500,\n", + " Saq=[0, 0.001],\n", + " Sll=1e-4,\n", + " topboundary=\"conf\",\n", + " tmin=0.001,\n", + " tmax=0.5,\n", + ")\n", + "w_3 = Well(m_3, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", + "m_3.solve(silent=\"True\")" ] }, { @@ -1437,15 +1461,15 @@ } ], "source": [ - "c2 = Calibrate(m_3)\n", - "c2.set_parameter(name='kaq1', initial=10)\n", - "c2.set_parameter(name='Saq1', initial=1e-4)\n", - "c2.set_parameter(name='c1', initial=500, pmin=0)\n", - "c2.set_parameter_by_reference(name='Sll', parameter=m_3.aq.Sll[:], initial=1e-5, pmin=0)\n", - "c2.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=1)\n", - "c2.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=1)\n", - "c2.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=1)\n", - "c2.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=1)\n", + "c2 = ttim.Calibrate(m_3)\n", + "c2.set_parameter(name=\"kaq1\", initial=10)\n", + "c2.set_parameter(name=\"Saq1\", initial=1e-4)\n", + "c2.set_parameter(name=\"c1\", initial=500, pmin=0)\n", + "c2.set_parameter_by_reference(name=\"Sll\", parameter=m_3.aq.Sll[:], initial=1e-5, pmin=0)\n", + "c2.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=1)\n", + "c2.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=1)\n", + "c2.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=1)\n", + "c2.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=1)\n", "c2.fit(report=True)\n", "display(c2.parameters)" ] @@ -1464,7 +1488,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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whzdtiAhtbW3ekHDz0dHLli3TugSlVMBo8aEfGgwmzu1y0XLuNDXlB6g5fIDe9jZsYWEsWbmGvJLN5BRtIn6unmB4O4aPjj537hx2u917dHReXh7Lli3TugSl1IRoMPBDg0FgiQitdTXUlL9DTfkBOi+2gDEsyltGXslmcku2MCd1YbCHOa0MDg5it9u9dQl9fX3YbDYyMzO93Re1LkEpdTs0GPihwWDyiAgdLU3UHLJCwuWGOgDmZ2aTV7yZvJLNzFuyVNfQb4Pb7aa5udm75NDe3g7grUsoKChg4cKF+jNVSvkVUsHAGJMMvABkAg3Ab4lI5wjXPQb8mefmX4rIj4wxCcCbPpelA/8uIn9gjPkE8DdAi+drz4rIc7cajwaDqdN1+ZK13FB+gAvVZ0GEuWmLyC3ZQl7xZhbm5GFstmAPc1oZ7pdQVVVFU1MToHUJSqlbC7Vg8A2gQ0SeMcZ8EZgrIl+46ZpkoAIoAgSoBApvDhDGmErgKRF5wxMMikTkc7czHg0GwXHtaie1hw9SU/4OTadP4Ha5iE+eR27xZvJKtpC+fCU2fUO7Jd9OjEPzbO+qS9B+CUqpkYRaMKgCtorIRWNMGrBfRApuuuYjnms+47n9z57r/tPnmjzgVSBDRESDwfQ10NuL/Ug5NeUHaDh+BOegg+iERHIKS8gr2cLS1esIj4wM9jBDjr9OjMP9EobPcRiuS9B+CUopCL1gcFVE5vjc7hSRuTdd8zQQLSJ/6bn9ZaBfRL7pc81XgEQRedpz+xPAXwNXgGqsmYSmUcbwBPAEQEZGRuH58+cD+B2qiRgaGKDh+BFqyt+hrrKcwf4+IqJjyF5fRG7JZrLXFxEZExvsYYaE7tea6N7dYM2pGUjckUnivUvedd1wv4ThFs0dHR2AVZcwXLyo/RKUml2mPBgYY/YCI5Wefwn40RiCwf8Fom4KBn0i8rc+15wBPiYilZ7b84BeEXEYY57Eql2471Zj1RmD0OVyDtF46gS15QeorThIX9dVwiIiWLp6HXklW4hPWU57i3NWnNMwkrGe3eBrtH4Jeo6DUrNLqM0YTHgpwRizFvgvEckf5TXCsOoYbvluocFgenC7XVyoOktN+QFqDx+g+8plwGALTyc8Oo/3fu5hstdnBXuYU2600x7Havgch6qqKurq6nC5XN66hOF+CVFRUZMwcqVUMIVaMPgboN2n+DBZRP74pmuSsQoON3juOoJVfNjh+fozgENE/tznMWkictHz+cPAF0Rk063Go8Fg+hERXv/pWxzf8xquwVrE7Zkazysgr2QLuSWbmbtwUZBHOf3cfI5Df38/YWFhN9QlJCbefvhQSoWeUAsG84CfARlAI/CoiHQYY4qAJ0Xkcc91nwL+1POwr4vID32eww68R0TO+dz318CDgBPoAH7P9+uj0WAwPXlPdnS5MdJJXtE1LtUeodVeC8D8jExrG+TGLaRor4Tb5nK5bqhL6Oy0NgQtWrTIuxVywYIF+nNVapoKqWAQajQYTF+X7F20VHfeUGPQdbnVuw2ypeoMiDBnYRp5JVvIK9mivRLGQUS8/RLOnTtHS4vVKmTOnDne4sWMjAytS1BqGtFg4Md0DgbHLh+jorWCotQi1i1YF+zhhJxrVzupqzhETfk7NJ46fkOvhPyNW1i8THsljEdPT4+3qZLdbsflchETE+OtS8jJydG6BKVCnAYDP6ZrMDh2+Ri/u/t3GXQNEhkWyfd3fF/DgR8Dvb3Yjx6m5tA73l4JMQmJ5BRtIm/jZjJWrSM8IiLYw5x2HA6Hty6hpqbmhrqE4dmEhISEYA9TKXUTDQZ+TNdg8NzJ5/jOke/gxk2YCeNz6z/H46sfD/awpoWhgQHqj1dSc+gd7EcOM9jfR2RMDNkbSsgr2UzmukIio2OCPcxpZ7guYXgr5HBdwuLFi73Fi1qXoFRo0GDgx3QNBsMzBkPuISJsETpjME7OoSGaTh2npvwdag8fpL+nm/CISJau3WAdGV24kej4+BHrGdToRITLly97lxyG6xLmzp3rDQlal6BU8Ggw8GO6BgPQGoNAc7tctFSd8R4Z3dvehi0sjNTsFXRcWogJyyY8MoGHnlqv4eA2dXd3e/sl3FyXMHyOg9YlKDV1NBj4MZ2DgZo8IkJrXQ015e9w8rXX6e++AoAtfDFZGzZy78feS9KC1CCPcnoarksYPsdB+yUoNfU0GPihwUDdysW6q/zym3sY6q/BPVSL23kZgAVZOd5tkPPS331Ggbo17ZegVHBoMPBDg4EaC98ag+i4fmo9yw0Xqs8CkLwonbyNVkhYkJWjb2Qet9Oy2V+/hOGQoHUJSgWGBgM/NBioiejtaKfW0yuh6fQJxO0mIWW+ZyZhM4sKlmOzzc43svEc8uRr+ByHc+fOeesShs9xGK5LiI6OnsTvQKmZS4OBHxoMVKD093RjP3KYmnKrV4JraIjYpDnkFm0it2QzGavWEBY+e3oljPVY6LEYPsdhuC6hr68Pm812Q11CUpIWhCo1VhoM/NBgoCbDYH8f9ceOUFNu9UoYGugnKjaO7A3F5JVsIXPtBiJm+G+7E50xGI3b7b6hLqGjw3OIVlqad8khNTVVl3OU8kODgR8aDNRkcw4O0jjcK6HiEAM93YRHRpG5dgN5G7eQvaGY6Lj4YA9zUkz0WOhbERHa2tq8IaG5uRmApKQkb0hYunSp1iUodRMNBn5oMLh92j9h/NwuF81nT1khofwAvZ0d2MLCyVi1hrySLeQUbSRuztxgD3Pa6u3tvaEuwel0EhUV5T3HQesSlLJoMPBjugYDEaHjh/9G/D13E5WTM2Wvq2c0BI643VysrfaGhKutF8EYFhes8BYvJs5fEOxhTluDg4PY7XbOnTt3Q11CZmYmy5YtIz8/nzlz5gR7mEoFhQYDP6ZrMHDU1mJ//4MgQlReHgkP7CRx5wNEZWdN6uvqGQ2TQ0Roa2zwdl1sa2wAIDU7l7ySLeQWb9ZeCRPgdrtpbm72nuPQ3t4OwMKFC73Fi2lpaVqXoGYNDQZ+TNdgADDUepme3bvpLttFf+URKyQUFJD4wE4SSkuJygp8SNAzGqZG58UWjux6jbrKg/RcaQCsXgm5JZvJK9lCanauvolNwHBdQlVVFU1NTYgICQkJ5OfnU1BQQFZWFhF62qaawTQY+DGdg4GvodZWesp2071rF/1HjgAQtWwZiTt3krizlMjMzIC9ltYYTL5L9i5e+vZRXE43xlxjxR0DXLYfo+nMSatXwrz55JZsIq9kC4uXrZi1vRLG4+aCyGvXrlFTU0NVVRV1dXUMDg4SERFBTk4OBQUF5OfnExcXF+xhKxVQGgz8mCnBwNfQpUv0lJXRvauM/qNHAYhasZzEnQ9YISEjI8gjVLdSuauBQy/ZEQFjg40PZlO4M5P+nm7qKsupPXzA2yshJiGRnKJN5G3cTMaqdYTrb7qjutUWSqfTSUNDg3c2obu7G4AlS5Z4lxxSUlJ0tkZNexoM/JiJwcDX0MWLdJeV0fPKLvqPHwcgesUKT03CTiKX6Lp1KPLOGLjchIXZRjzRcXCgn4ZjldSUH8B+5DCD/X1ExsSQtb6YvJLNZK0rJDImNkjfQWi6naZLIsKlS5e8IeHixYsAJCcne0PCkiVLdCukmpY0GPgx04OBr6GWFrrLrJqEgeMnAIhetYrEnaUk7NxJZHp6kEd4a7NpGcP3fIZbHfPsHBqiyadXQn93F2ERESxdvY68ki1kF5YQm6idASfSdKmrq8t7dHR9fb23RfNwXUJOTo5uhVTThgYDP2ZTMPA12NziWW7YxcDJkwBEr17trUmIWLw4yCN8N90qOTZut4sL585Sc/gANeXv0NN2BWOzkb58FXklm8kt3kzCvJRgDzNoAtF0aaSjo31bNOtWSBXqNBj4MVuDga/B5mZ6du2ie1cZA6dOARC9dg2JpZ6QsGhRkEdo0a2St09EuFxfR025FRI6WpoAWJib790Gmbwo9ELgdOLbotl3K2RqauoNWyFtNluQR6rUdRoM/NBgcKPBpia6d+2i55VdDJw5A0DM2rVWTUJpKRFpaUEbm26VnLj2liZqyw9QU36AVnsNAPPSM8jbaIWEBZnZWlg3QcNbIaurq2lsbNStkCokaTDwQ4PB6AYbG+neVUb3rldwnDkLQMz69VZNQmkpEQsXTvmYZlONwWTrbrtM7eGD1JYfoPnsaUTcJM5PJa9kE7klW1iUv0y3QU5QX1+fdytkbW2tboVUIUODgR8aDMZmsKHBExJ24Th3DoCYDRuuh4TU1CCPUE1EX3cXdRWHqD18gPMnjuJyOr1HRueVbGbJLDsyejLoVkgVSjQY+KHB4PY56uutwsVXduGoqgIgprCQxNLhkKD9/aczR18f9ccqqCk/QP2Rwww5BrxHRueWbCZrbeGMPzJ6sulWSBVsIRcMjDHJwAtAJtAA/JaIdI5w3S5gE/CWiLzP5/4s4HkgGTgCfExEBo0xUcCPgUKgHfhtEWnwNxYNBhPjsNfTU7bLCgnV1WCMZyZhJwk7dmhImOacg4OcP3mMmvJ3qKsst46Mjogkc90Gcos3k1O4kej4mXlk9FQabStkXl4eBQUFeiqkCrhQDAbfADpE5BljzBeBuSLyhRGu2wbEAp+5KRj8DHhRRJ43xnwPOC4i/2SM+d/AGhF50hjzYeBhEfltf2PRYBA4DrvdKlzcVXY9JBRuIHHnAyTs2E7EAg0J05l1ZPRpaj3bIHs72rGFhZG+YrVnh8Mm4ucme6+/nT4M6rrRtkIuXbrUW5eQnJx86ydSyo9QDAZVwFYRuWiMSQP2i0jBKNduBZ4eDgbGWoC7AiwUEacxZjPwFyJSaowp83x+wBgTDlwC5oufb1SDweRw1NVdDwk1NWAMsYWF1u6GHTsInz8/2ENUEyBuN5fsNd4dDp0XW8AY0vIKyCvZwpyFq3j1xxdwOd2EhY/cuVHd2vCpkMMh4cqVKwCkpKR4Q0J6erouOajbForB4KqIzPG53Skic0e5dis3BoMU4KCI5HpuLwFeEZFVxphTwE4RafZ8rQ7YKCJtNz3nE8ATABkZGYXnz58P+PeornPU1tK9q4yesl04amqtkFBUZIWE7dtDNiToDoixERE6WpqoOWQdGX25oQ4AE5aCLSKX8Kg8Nj1cQtEDk3sk+GzQ0dFBdXU11dXVNDQ04Ha7iYmJIS8vj/z8fF1yUGMWlGBgjNkLjLSf7UvAjyYQDOYDB24KBi+LyGpjzGmg9KZgUCIi7aONU2cMptZwSOh+5RUG6+qskFBcTMLOUmsmISU0OvJpl8Xx67rcypFdr3Fs92u4h1oAiJ87n4ItVq+ERQXLdRtkAAwMDHiXHGpqanTJQd2WUJwx0KUEhaOmxrsFcrCuDmw2YouLrS2Q27cHNSRol8WJu2Tvov54I67BOi7XH6Px5DFcTicxiUnkFG4kr2QzGavWEh4ZGeyhTntjWXJYsmSJdl9UXqEYDP4GaPcpPkwWkT8e5dqt+AQDz33/BfzCp/jwhIh81xjzWWC1T/HhIyLyW/7GosEg+EQER00NPcMhwW63QkJJyfWQMG/elI5JuywGnqOvj4bj1mmQ9UcPM9jfT0R0DFnrCskt2Uz2+iKiYrXZTyDokoO6lVAMBvOAnwEZQCPwqIh0GGOKgCdF5HHPdW8Cy4B4rO2HnxaRMmNMNte3Kx4FPioiDmNMNPATYD3QAXxYROz+xqLBILSICI7qGu8WyMH6ep+QsJOEHdsJn6LpUa0xmDzOoSGaTp+gtvwAtRUH6eu6ii0snIzVa8kt2kRu8Sbi5oy4uqhuky45qJGEXDAIJRoMQpcVEqq9ZzcMNjRYIWFjCYmlUxsS1ORxu11crKmm9vABassPcLX1IhjDorxl5JZsJrd4E3MXhsZBXlMtECdB+ho+8Gl4NkGXHGYvDQZ+aDCYHrwh4ZVX6NlVpiFhhhIR2prOe0LCQe8Oh5QlSz0hYfYc9OQ4303bcycRpxsTbiPl8dUBCQe+hpccqqqqOH/+vC45zCIaDPzQYDD9jBoShpcbtt8/5TUJanJ0XW6lruIgNYcP0HL2jOegpwXWckPJZhYXrMA2Q/fwd7/WRPfuBhDAQOKOTBLvXTJpr6dLDtJZPysAACAASURBVLOLBgM/NBhMb6MuN2hImHH6uruoqzxE7eGD1kFPQ0PEJCSSXVhi7XBYvY6IyKhgDzNgpmLGYDS65DDzaTDwQ4PBzHFDSNhVdlPhYnB2N6jJMTjQT8Ox4R0OFTj6rhERFU3mug3kFW8ma0Mx0XHT/wyHQNcYjJe/JYe8vDxyc3OJiYkJ2vjU7dNg4IcGg5lJQ8Ls4XIO0XT6pFWXUHGIa50d2MLCWLJyDbnFm8kt2kh8sv5dB4rvkkNtbS19fX0YY8jIyCA/P5/8/Hw9Pnoa0GDghwaDmW94C2T3rlduDAnFxSQ+sDOkQ4Jumbw94nZzsdazw+HwATovXgAgLa+A3OLNzFm4ip7OaD3YKUDcbjctLS3eJYfW1lYA5s6d6y1gzMzMJDw8PMgjVTfTYOCHBoPZZdQ+CSHScdGXtmWeGO8ZDuVWSGi11wJgwuYRHpXLfZ94LyvvWofRdfKAuXr1KjU1NVRXV1NfX4/T6SQiIoKcnBzy8/PJy8sjISEh2MNUaDDwS4PB7HVDSNhVdr3jYoiEBG3LHFhv//wIlb95DddgLW5nMyDEz00mp2gTuUUbWbJqDWHhEcEe5owxODhIQ0ODdzahu7sbgEWLFnlDQlpamhYwBokGAz80GCjwbct8U0goKrIOeArCKZDaljmwLtm7eOnbR3G53NiMgzX3umhrPEHDsSMMOQaIjIm12jMXbyJL2zMHlIjQ2trqDQnNzc0AxMfHe5ccsrOziYqaObtKQp0GAz80GKib3XB2Q1nZ9VMgi4pIKC0lYcd2IhYsmJKxaI1BYF2yd9FS3XlDjYFzcJDGU8epPXyAuspyb3vmJStXk1u0iZyijSTMC43lpZni2rVr1NbWUl1dTW1tLQ6Hg7CwMDIzM71BQXsmTC4NBn5oMFC3MnwKZM/uMhw1tWAMMYUbSNxRSkLpDiJSU4M9RBUgw+2Z6yoOUnv4IJ0XrWOjU7PzyC22lhzmLVmqFfcB5HK5aGxspLq6mpqaGtra2gCrZ8LwLoclS5YQNkMbWQWLBgM/NBio2+Goq/NugXTU1AAQs2EDiaU7SNixg4i0tCCPUAVSe0sTtYcPUnf4IBdrqwCYk5pGTtFGcos3sahgOTbb7HvDmsz+Cu3t7d4CxuGTIaOjo70FjLm5ucTF6TLPRGkw8EODgRovh91OT1kZ3bvKcFRZbxoxa9eSsHMniaU7iFg0Ow/+mal6OzuoqzhEXcVBGk8dx+V0ejsv5hZvZunqtUREzfxzBaayI6PD4aCurs47m3Dt2jUA0tPTvQWMCxcu1BmccdBg4IcGAxUIjvp6esp2011WhuPsWQCi16whsbSUhNJSItMXB3mEKpAG+/uoP3aE2sPXOy+GR0aRuXY9OUWbyN5QTGzizOyTMNVnOAxzu91cvHjRGxIuXLB6VCQkJHjrErKysrSAcYw0GPihwUAF2uD583SX7aZn1y4GzpwBIHrVKmsLZGkpkUsm/x9RNXVcziGaz5ymtsLqvNjb3oYxNhYvW0Fu8SZyijYxJ3VhsIcZMME8w8FXT0+Pt4Cxrq6OwcFBwsLCWLp0qXc2YV6INi4LBRoM/NBgoCbTYFOTd7lh4NQpAKJXrLCWG3aWEpmREeQRqkASES7X11HrKV5sa2wAICUjk9yijdax0Vk5037qO1TOcBjmdDppbGz01ia0t7cDMG/ePO9sQkZGhnZg9KHBwA8NBmqqDDa3WCGhrIyBEycAiFq+nMTSUiskZGYGd4Aq4K62XrJ2OFQc9B4bHT8vxQoJRZtJX7GKMH2zCrjhQ59qampoaGjA5XIRGRlJdna299CnpKSZudQzVhoM/NBgoIJhqKWF7t176Nm1i/7jxwGIKijwLDfsJCo7K8gjVIHW191F/dEKag8foOH4UZyDDqJi48haX0Ru8SYy1xYSFRsb7GHOOA6Hg4aGBu9swnAHxtTUVO/pkOnp6bNuO6QGAz80GKhgG7p4kZ7du+neVUb/0aMAROXlWR0Xd+4kKicnyCNUgTbkGOD8Saupkr2ynP6ebsLCw1myaq23qVL8XG3wE2giwpUrV6ipqaGmpobGxsYbtkMOzybEx0//I7tvRYOBHxoMVCgZam317m7oP3IERIjMzfE0UyolKj9v2q9Pqxu53S4uVJ2ltuIQdYcPcrX1IgBpuQWefgmbSV6crn/vk2BgYAC73e4NCr29vYB1nsPwbMKiRYtm5HkOEw4Gxpi5wCKgH2gQEXdghxg8GgxUqBpqvUzPnj30lJXRV1FhhYSlS622zKU7iF6xIiTfLLSN8/iJCO1N56mtOETt4YO02q0mWgnzUsnftJncIk9TpVk27T0V3G43ly5dora2lpqaGpqbmxERYmNjyc3NJS8vj5ycHGJnyHLPuIKBMSYJ+CzwESASuAJEA6nAQeC7IvLapIx4CmkwUNOBs62Nnr376NldxrVD5eByEbF4MQmlpSSW7iB6zZqQCAl6VHTgXLJ38ctvvsFQfy1uZx3iasbtchIdn0D2+iJyijaSuXYDkTEz440q1PT19XmbK9XW1tLf348xhvT0dO9swnRurjTeYLAH+DHwaxG5etPXCoGPASdF5AcBHu+U0mCgphtnZye9r75Kd1kZ1w4chKEhwhcuJGHHdhJLS4lZvx4TpKlPPSo6cCp3NXDoJTsiYGxQ+MAi5i5op67iEPYjhxno7bHqElauIadwI9mFJSSmTO0JoLOF2+2mpaXFu+Rw8aK13DN8OmReXh7Z2dlER0+fzpdaY+CHBgM1nbm6u+l97TW6y3Zz7a23kMFBwufPJ2H7/STsKCW2qBAzhdvh9KjowPE9JjoszMZDT633ngjpdrm4UO2pS6g4yNVL1hvVgswccoo2klO0kQWZ2dP2t9mRhFLvhOHmSjU1NdTV1eFwOLDZbGRkZHiDwvz580P65x+IGoM1QCbg/RdGRF4M1ACDSYOBmilcvdfofX0/PWW76X3jDWRggLDkZBK2bSOhtJS4jSWYiIhJH4fWGATOSMdE30xE6LjQbJ3jUFnOheqzIEL8vBRyCjeSW1hC+so1hE/B3/1kCZVuiyNxuVw0NTV5ZxMuX74MQFJSkjckZGVlERkZGeSR3mhCwcAY86/AGuA0MFx0KCLyqYCOMkg0GKiZyN3XR++bb9FTVkbv/v24+/qwJSVZIWHHduK2bMEWYv9QqcDo67qK/WgFdRUHaThxFKfDQUR0DFlrN5BTtJGs9UXEJITGm+pYBet8hvHo6uryhgS73c7Q0JC3VfPwdsiUlJSgzyZMNBicEZEVARxMMvAC1gxEA/BbItI5wnW7gE3AWyLyPp/7fwoUAUNAOfAZERkyxmwFXgLqPZe+KCJfu9V4NBiomc7tcHDt7bfpKSuj59XXcPf0YIuPJ/6+e0ksLSXujjuwTaO1UTV2Q4MOmk6dsGYTjpRzrbMDY7POccgptJYc5i4M/VNAQ3nGwB+n08n58+e9yw5tbW0AzJkzx7vTIVizCRMNBj8A/lZEzgRoMN8AOkTkGWPMF4G5IvKFEa7bBsRivfH7BoP3AK94bv4H8IaI/JMnGDzte+1YaDBQs4l7cJC+Awfo3r2b3r37cHV1YWJjSdh6Dwk7Som/+y5sM2Q7lrqRuN202mupqzxEXcUhrnjOcUhevMSqSyjcSFpePjZbaG6FHG+NQSjVJly9epWamhpqa2vfNZswHBSmajZhosHgbuDXwCXAARispYQ14xxMFbBVRC4aY9KA/SJSMMq1W/HzZm+MeQpIEZEvaTBQ6vbI0BDXysvp2b2Hnj17cHV0YKKjib/rLhJKS4nfeg9hs6AD3GzVdfkSdZXl1FUcovnsKdwuF7FJc8jeUExO4UaWrl5HxDSfSQrlmQbfg59qa2u5cuUKcL02ITc3d1KPkZ5oMKgF/hA4yfUaA0Tk/DgHc1VE5vjc7hSRuaNcu5VR3uyNMRHAIeD3ReRNz7W/AJqBC57HnR7leZ8AngDIyMgoPH9+XN+KUjOGuFz0VVTSs3s3Pbt347xyBRMRQdydd5JQuoOEe+8lbJYfOjOTDVzrpeFYJXWV5dQfrcDRd43wiEgyVq8lp2gj2RtKpmWL5ulUm3D16lXvkoPvbMLwTocNGzYEdDvkRIPBqyJy322+4F5gpAPIvwT8KEDB4PvANRH5A8/tRMAtIr2e5Ya/F5G8W41VZwyUupG43fQfO2adBLl7D86LFyEigrhNm0gs3UH8tm2Ezx3xf9mQpTslxs7ldNJ89pRnyaGc7iutACzMzbd2ORRtZN6SpUEvnhuLYM8YjHcZY3g2YTgotLe384UvfCGgswcTDQbfBeZgLSc4hu8f73bFQCwlGGP+HFgPPDJae2ZjTANQJCJt/sajwUCp0YkIAydP0l1WRs/uPQw1NUFYGLElxSSWlpJw//2Ep6QEe5h+aTfG8RMR2prOe7ZCHuJSbTUASQtSyS4sIWfDRtJXrCQsPHS3QgarxiCQoeTatWvExcUFdHyjBYOxdj2JwQoEO3zuE2C8fQx+BTwGPOP586XbebAx5nGgFNjmGwqMMQuBVhERY0wJYAPaxzlGpRRgjCFmzRpi1qxhwdNP4zh7lu7du+nZVcalv/gql776NWILC63zG3ZsJyI1NdhDfpeK1goGXYO4cTPkHqKitUKDwRgZY5ifkcn8jEw2PfLb9HZ2YD9i1SWc2LuLo6/8msiYWDLXbiCnsITMdYXEJobWklPU0sSg1BU47F2I0w0C4nRb4WSc4wh0KPAnKJ0PjTHzgJ8BGUAj8KiIdBhjioAnReRxz3VvAsuAeKw3+E+LSJkxxgmcB3o8T/miiHzNGPM54PcAJ9ZhT38oIu/cajw6Y6DU7RMRHDU1VuFiWRmOGuuwn5h166zzG3ZsJ2Lx4iCP0qLdGAPvkr2LxtOXsJkWOlpOYT9ymGtXOzHGRlr+MnIKS8gpLCF58ZJpseQwGQI1YzBZMx7jPSvhz7AOSuoY5ev3AbEi8j8BG2kQaDBQauIcdjs9u3fTvXs3jjNnAYheuZKEHTtI2L6dqOysoI5PawwCx9uq2ekmLNxq1ZyamWBthTxyGHtlOZcb6gBISl1o7XKYBksOk2Gib+qTWSMx3mDwEPDHwABwhOunK+YB64C9wF+JyJWAjDJINBgoFViDjY1WSNizh4HjJwCIzM0hcTgkLFs2a3+LnAluPtxp44PZFO7MvOGanvY2a8mhspzGU8dxDQ2F/JJDKJrMXRUTLT7MA+4A0rCm6M9iNRXqD8jogkyDgVKTZ+jSJXr27KVnzx76KirA7SZiyRIStm8nYfv9xKxdG7STINX4+DvcaSRDAwOcP3Uce+UhXXK4TSE3YzBbBDQYOHrh6L/DyochIfSKsJQKJmdHBz379tGzZ8/146IXLCDh/vtJ2LFjyk+CVOM3lsOdRiJuN631ddRVluuSwxiFVI2Bz4Pzgad59+mKt9XbIFQFNBicexme/4g1v5Z1N6z6ECx/P8TMufVjlZpFXD099O7fT8/uPfS++aZ1EuScOcRvu4/EHTuI3bxZD3maBYaXHOxHDtN48jjOoUFdcpgiEw0Gx4HvAZWAa/h+EakM5CCDJeBLCVeq4OTP4dTPocMOYZGQtwNWfRDyd0Kk9qFXype7r4/et96yQsL+/bh7e7HFxRG/dSsJO3YQf9eden7DLKBLDlNrosGgUkQKJ2VkIWDSagxE4MIROPkLOPUL6L0EkfGw7L2w+lHI3gphOl2mlC/34CB9Bw9ahzztexVXZycmKoq4u+4kcccO4rduJSwxNHrdq8njd8lhfTHZG4pJX7Ga8Aj9N3S8JhoM/gK4DPySGzsfjriNcbqZkuJDtwvOvw0n/wvOvAQDXRA7D1Z8AFZ/CJZsAi3AUuoG4nTSV3nEOr9hzx6cly97WzMnbL+fhG3bCJ83L9jDVFPAWnI4jP1IuXfJISIqmqVr1pG9oYSs9UXT8iyHYJpoMKgf4W4RkexADC7YpnxXgtMBtfuskFD1Cjj7ITEdVn/QqklYuBp0qkypG4jbzcCJE3Tv2XO9NbPNZnVd9OxwiEhLC/Yw1RQYcgzQdPqkJygcpqfd2jGfmp1L9oZisjeUkJqVo7tdbkF3JfgR1O2Kjl6oetmqSajbB24npBRYswirPgjzcoIzLqVCmIjgqKryHBe9G0dNLQDRa9aQsP1+EnfsIHLp0iCPUgXaSDshRIS2xgZvSLhQcw5EvMdHZ28oZunqdUTGaI3KzSY6Y/Am8AbwJvC2iPTc4iHTSsj0MbjWDmdfskLC+bet+xZtsELCykcgUX8bUmokDns9PXv30rN7NwOnTgEQlZ9vzSTs2EFUft60LVjTjo2WkbotjrRNsq+7i4bjR7BXltNw/AiOvmvYwsJZsnK1FRTWFzNnof5bChMPBtnAncBdwCasOoM3ReSpQA80GEImGPjqaoZTL1o7Gy4eBwxk3mmFhOUPQqyupSk1kqELF+jZu5fu3bvprzwCIkQszSBx+3YStm8nevXqaTPFrKdCXjeWbos3czmdXKg+651N6GhpAiB5UTrZhSVkry9iUcEKwmZp74wJLyV4jke+Bysc3As0isjOgI4ySAIZDCrPd3LQ3s6m7HkULg3QefVtNdYswsn/go46sEVA7v3WUkPBAxAVH5jXUWqGcba10bPX01Dp0CFwOglPTSVh230k3H8/scXFmBCuan/u5HN858h3cOMmzITxufWf4/HVjwd7WEFxu90WR3L10kXsR62Q0HzmJC6nk6jYODLXbiB7Q/Gs65kw0RmDOqAN+A+s5YRjvscdT3eBCgaV5zv5necOMuh0Exlu46ePbwpcOABr++PFY54eCS9CzwWIiLV6I6z+kBUWwqMC93pKzSCuri6rodLevfS++RYyMIAtKYmErfcQf//9xN9xR8j1StBTIW803m6LIxns7+P8yWPYj1RQf9TqmYAxpOUVeLdDzl+aNW2XoMZiosHg97GWEpYA54DXsc5KqAv0QIMhUMHgH1+r5W93V+EWCDPwhzsK+Oy9uQEY4Qjcbmg8YC01nP5v6O+A6CRrmWH1hyDzLrCFTc5rKzXNufv7ufb229YZDvv34+7qwkRHE3fHHVZ75nu3EjYnNLqVao3B5BvumTC85NBqt44Qj5+bTNb6IrKGCxijY4I80sAKyK4EY0w88Ems9sjpIjIj3nkCPWMw5HQTMRkzBqNxDYF9vzWTcO5/YLAX4lOt8xpWfRDSi3X7o1KjkKEh+ioqrCWHfftwXroEYWHEFhdbIeH+bUQsXBjsYaopdO1qJ/XHKqk/cpiGE0cZ7O8jLDycxctXkb2+mKz1RSQvWhzsYU7YRGcM/hZrxiAeOIhnh4KI2AM90GAI+RqD23nuoX6oLrNmEqp3g8sBczKsgLD6UUhdGdAxKTWTiAgDp05ZMwl79zJot/6Ji161ygoJ2+8nKke3EM9kNy9XuJxOLlSdwX60gvqjFbQ3NwIwZ2GaNyRM1w6MEw0Gj2ItHbROxuCCLSR3JdxkXPULA11w7jfWTIJ9P4gL5i/3NFL6ICTPiP5USk0ah91uzSTs3cvAiRMARGZleUNC9KpV02aHg7q1sWyJ7Lp8yRsSmk6d8HZgzFi9juz1RWStLyJhXsoNzxmouohAC8SuhAeBuz03XxeRXwdwfEE1HYLBhOsXeq/Amf+2zmxoPGDdt7jQ6rS48mHtkaDULQxdumQdGb13L33lh8HlmlY7HNSt3e6WyCHHAE1nTnoLGLuvXAZg/tIsstYXkZS6nHde7MXtwm/vhWCZ6IzBXwMlwE89d30EqBCRPwnoKINkOgSDgNYvXG2C0y9aMwmXTqA9EpS6Pa6rV+nZv5/efftG3uFw553YYmZWodpsMJEtkSJCR0sT9iOHqT9aQUvVGdwuF5hobOFLCYvKovj997D5A6vHNa7JmHWYaDA4Aawb3qJojAkDjorImoCNMIimQzCASapfuFJtzSKc+jm013p6JGyzZhK0R4JStzTqDoc77yBhW2jtcFC3Fqg3YUffNY7teZuDL+7D6agH6QNjWJiTR9a6IrLXF5GanXvLpaixdnwcj0AEg63DpykaY5KB/RoMZhARq8PiKU+PhO4WCI+xwoH2SFBqTLw7HPbstXY4tLbqDodZ7pK9i+aqdmLiu+m+fJb6oxVcrK0GEWISk8hau4Gs9UUsXbuBmPiEdz1+PB0fx2qiweAjwDPAa4DBqjX4ExF5PiCjCzINBjdxu6HpoLXUcOa/oa8dopJg+futwsXMuyFsdrYQVWqsRt3hsHq1NyToDofZqa+7i/MnjlJ/tIL640cY6OnGGBtp+cvIWldI1voiFmRmY4wJSMfH0QSqJXIxVjA4JCKXAjKyEKDBwA/XENhft2YSzv4PDPZAbAqs/IC1s2HJJtCqbKVuyWG3e0PCwMmTgO5wUOB2u7hUW0P9sQrqj1Z6myvFzU32hoTohGyuNDpCo8bAGLPB35OKyJEAjC3oNBiM0dAA1O6xahKqdoGzHxIXX2+ktGi9NlJSagxG3+GwjYT7t+kOh1ns2tVO63TIoxWc954OGcaiguW89/N/TPzcwBWHjzcYvOb5NBooAjzH/LEGa9bgzoCNMIhGCgZDQ0M0NzczMDAQpFGFOHFbzZSG+qzAgFiFi5ExEBEHYZP3j1p0dDTp6elE6D+cagYY3uHQs3cv1956W3c4KC+3y8WFmnPUH63gQtVZHv3K17EFsNX9RGsMnge+LiInPbdXAU+LyCcCNsIgGikY1NfXk5CQwLx582b0IRoB4XLCwFXov2otNQCER0PMXOsjgEWLIkJ7ezs9PT1kZWUF7HmVCgXu/n5633qL3r373n2Gw7ZtxN+7lfC5U9BmfQz0DIfpb7RgMNYKsmXDoQBARE4ZY2b0fwkDAwNkZmZqKBiLsHCIS7E+XENWSOjrhJ6L1kdErBUQoudAeOSEXsoYw7x587hy5UqABq9U6LDFxJC4fTuJ27ffuMPh1Vfp3bcPbDZiNqwn4b5tJGy7j8ilS4MyzuFTHwddg0SGRc76Ux9nmrFWupw1xjxnjNlqjLnHGPN94Ox4X9QYk2yM2WOMqfH8OWIENsbsMsZcNcb8z033/5sxpt4Yc8zzsc5zvzHG/IMxptYYc+JWNRJjGOdEHj47hUVA3HyYnw8LVkLiIkCs7Y+XT0NbNVy7YgWIcdK/FzUbmIgI4jZvZuFXvkzua6+S+fOfk/LkZ3D39HL5G9+grnQnde97H5e/9W36T5xA3O4pG1tFawWDrkHcuBlyD1HRqjVaM8lYZww+Cfwe8Pue228A/zSB1/0isE9EnjHGfNFz+wsjXPc3QCzwmRG+9n9F5Oc33fcAkOf52OgZ48YJjFN5XHM4ueZwEhcVTlzUGP+zCY+0TnmMT7XqEAauQn8ndDVbH1EJnpmEJLDp9kelRmOMIWbVSmJWrWT+5z/PYHMzva++Ss/efbT/4Ae0/8u/ED5/PvH33UfCtvuI3bQJW+TEZuf8KUotIjIskiH3EBG2CIpS3zUbraaxsc4YbAH+WUQe9nx8W0QmUpX3EPAjz+c/Aj4w0kUisg/ouc3n/bFYDgJzPNssZ4yGhgZWrVoV0Oc8duwYL7/88ohfKy8vZ83atRRuWM9dm4p47ifPc83hBGDXrl0UFBSQm5vLM8884/9FIqIhYSHMX2Z9xKeC0wFXG+HSKWi3Q18HuF0B/d6Umoki09NJ/vjHWfrjH5H/9lss+n/PELN+PV2//jVNT3yGmk2baf79P6DrV7/C1dUV8Ndft2Ad39/xfT63/nO6jDADjfXXtE8A3zPGtANvej7eEpHOcb5uqohcBBCRi8aYBeN4jq8bY74C7AO+KCIOYDHQ5HNNs+e+izc/2BjzBPAEQEZGxjhefuY4duwYFRUVvOc973nX11atWsWu196mvc/J5dZLPFp6F48+8gGiw2P47Gc/y549e0hPT6e4uJgHH3yQFStW+H8xYyAixvpISLN2NfR3WoWLji6rtVdUEsTMgehE67ZSalRhc+aQ9NBDJD30EG6Hg76DB+nZ9yo9r71KT1mZ1XmxqIiEbfcRf982ItMXB+R11y1Yp4FghhrTv7oi8nERyQc+iPVm+4+A3+ovY8xeY8ypET4emviw+RNgGVbDpWSuL0OMtPg84rYLEfkXESkSkaL58+cHYEjWWQb/+FotlefHm5du9K1vfYtVq1axatUq/u7v/s57v9Pp5LHHHmPNmjV86EMfoq+vD4AvfvGLrFixgjVr1vD000+/6/nKy8vZsmUL69evZ8uWLVRVVTE4OMhXvvIVXnjhBdatW8cLL7xww2NiY2NJiovGGMOgw4ExhriocMrLy8nNzSU7O5vIyEg+/OEP89JLL73rNbdu3cpTTz3F3XffzfLlyzl8+DCPPPIIeXl5/NmXvwyRcZCUDqkrYV4uxCSDoxs6662ZhM7zMNBttWxWSvlli4oi/p57SPvaV8l7/XUyX3ieeZ/+NM62Nlr/6q+pu/9+7B94mCv/8B36T59mrA3u1OwyphkDY8xHgbuA1UAb8CzWrMGoROR+P8/XaoxJ88wWpAGXxz5ka5bB86nDGPNDYPhdsBlY4nNpOnDhdp57vIZPPxx0uomc6OmHQGVlJT/84Q85dOgQIsLGjRu55557mDt3LlVVVfzgBz/gjjvu4FOf+hTf/e53+dSnPsUvf/lLzp07hzGGq1evvus5ly1bxhtvvEF4eDh79+7lT//0T/nFL37B1772NSoqKnj22WdHHMupY5V84pOforHxPM/967+RFBdNS0sLS5Zc/1Gnp6dz6NChER8fGRnJG2+8wd///d/z0EMPUVlZSXJyMjk5OTz11FPMmzfPmkmISrA+khaDo9eaSRjogv4OqwYheo41kxCpBzspdSvGZiNm7Vpi1q5lwR8+xWBDgzWT8OqrtH3ve7R997uEp6WRcO+9xG+7j7jiYswk1iWo6WOs87R/B6wDvg98d4fEQwAAIABJREFUXkS+ISIHJvC6vwIe83z+GPDuXzX9GK4bMFZ5+geAUz7P+3HP7oRNQJdPiJhUB+3tDDrduAWGnG4O2tsn9HxvvfUWDz/8MHFxccTHx/PII4/w5ptWFluyZAl33HEHAB/96Ed56623SExMJDo6mscff5wXX3yR2NjYdz1nV1cXjz76KKtWreKpp57i9OnTYxrLxo0bOXvmNBWHD/Ptb36DgYGBEX/TGG23wIMPPgjA6tWrWblyJWlpaURFRZGdnU1TU9O7H2Bs1jLC3KWQugrmZllhoK/DOgGy9bQVGprKdSZBqTGKzMxk3qc/ReZP/528t94k7etfJ3rFCq6++CJNn36c6jvupOWPnqb75Zdx9fYGe7gqiMY0YyAiKcaYlViHJ33dGJMHVInIx8b5us8APzPGfBpoBB4FMMYUAU+KyOOe229iLRnEG2OagU+LSBnwU2PMfKylg2PAk57nfRl4D1AL9GHtppgSm7LnERluY8jpJiLcxqbseRN6vlt0pHzX7fBwa3p/3759PP/88zz77LO8+uqrN1z35S9/mXvvvZdf/vKXNDQ0sHXr1tsa0/Lly4mLi+PUqVOkp6ff8Kbe3NzMokWLRnxcVJTV4Mhms3k/H77tdDr9v6jNZs0SxMyxChMHuqzdDY4L8INHISnj+rkNaWu1JbNSYxCenMycDz7CnA8+Yh0bfeAAPfv20fvafrp/8xuIiCCupIT4bfeRcN99eiLkLDPWpYREIANYCmQCScC4N82KSDuwbYT7K4DHfW7fNcrj7xvlfgE+O95xTUTh0rn89PFNHLS3syl73oSWEQDuvvtuPvGJT/DF/7+9Ow+rskwfOP594KCIoKJmmpgbqMjuhuSKhrjgmqU1FWS0OJWjpY411vjLZrJ01BqzxjJFx8rKxN3UBLd0DJSKcMEFTbONAEVUBJ7fH+/xiGyyHDig9+e63kvO+77nOc/hKNw+y31Pm4bWmtWrV7N8+XIATp8+zd69ewkKCuLjjz+mZ8+eZGZmkpWVxeDBg+nevTvu7u6F2szIyKB5c2Ph0dKlSy3nXVxcuHCh6M0fJ0+epEWLFphMJk6dOsWRI0do1aoVDRo0IDk5mZMnT9K8eXM++eQTPvroowq955uyswenhsZR/xKMeA9++AL2LYSv34aGbcBrlBEk3HmTRZBCCMBIquTSzwgAdG4ulxISuPCVkVDpl1dn8surM3H08jKChP73Urudh81yiUi2xapR2l0Ju/MdC7TWZyqvSzVX55auFQ4IrunUqRMRERF069YNgMjISAICAkhJScHT05OoqCieeuopPDw8GD9+PBkZGQwfPtwyzD9v3rxCbU6dOpXw8HDmzp1Lv37XY6vg4GBmzZqFv78/L774ImPGjLFc2717N7NmzcLBwQE7OzsWLlxI48aNAViwYAGhoaHk5uYybtw4vLy8rPLei1Ioj4KyA/8HjSPrDzi0zijutHsu7JpjbIn0vs8IFBoXDpKEEIUpe3ucOnfGqXNnmkyZTPaJE5Yg4fe3/83vb/8bBzc3yw4Hp86dUKaqyUEi2RarTqnLLt/KiqqVcOjQITw9PW3UI5HfxSs5nPz9IlprlFK0blyX0yeSi/58Mn+FpDWQ+AWc3gtoaOoL3qOMIMHVNilkhajpcn77jQsxMWR+tZ2Le/eis7Oxr1+fun164xIcTN1evbB3rryFwR98/wH/PvBv8sjDXtnzbMCzRPpE3vyJolgVqpVgns+fCnhhVFoEih/SF8KaLl7JQWtt7DvV2pJgqUjOTaDbE8aRcRaSoo0gYdsM42jexTySMMKcrlkIURqmO+7A9YEHcH3gAfIuXiRzzx4yv/qKzB07Ob92nbEuoWtXnIODcQ4Otlq+hGsk22LVKW11xS3ASoxtgU9j7CT4TWtdVBrjGkdGDKq3Mo0YFCctBX5YbQQJP38HKLg7yBhJ6DgCnK2Ty0KI241lXcL27WTGxJJ94gQAtdu3xzm4Ly79+uHo7Y2yq3iysqpYY3A7rWOoaNnleK11Z6XUd1prX/O5HVrrPpXQ1yongUH1V3CNQYU+n9+PGYsWE1fBb4eN9QqtextTDZ5DjcWNQohyuXLyJJkxsWTGxJAVHw95edjf0RiXvn1xDu5H3aDu2NWpY+tuFul2W8dQ0bLL10rhnVNKDcFIGuRmrc4JcTNlKt50M43doc9U4/gl6XqQsG4CbHge2vYzgoQOg40CT0KIUqvdujW1W7em0bjHyElL4+KuXVyIieH8xk2kf/Y5ytGRukFBOPcLxqVvX0xWyjxrDUVVjbyVA4PilPYn7WtKqfrAC8C/gXrApErrlRBV5c6OxhH8Nzj3rTlIWA3JT4N9bXC/F7xGQvuBRlZGIUSpmVxdqT9sGPWHDUNnZ3Pxm2+M0YTt28mMieFnwNHXF5d+xrqE2u3a2bSsemWsY6iJUxM3nUpQStljZDssvP/tFiFTCTVPpX4+WsOZOCNI+CEaLvwEJkfwCDFGEtqFGjUehBDlorXmytFkMmO2c2F7DJe/+w4Ah7vuwrlfP5yD+9osRbM1f5FX96mJ4qYSbroaRGudCwyrlF6JYqWnp7Nw4ULL45SUlBsSCMXFxTFhwgSrv250dDRJSUlFXnvvvffw8fHB39+fnj173nDf66+/jru7O+3bt+fLL7+0er+qlFLQoisMfB0m/QCPbYZO4UYK5s8fg9nu8FkEJK2Fq5ds3VshahylFI7t29H46adp/elK3HfuoOnMV6ndvj3pn39upGgOuoczEycZpaOLqP1SWfyb+BPpE2mVX+BFTU3UBKVdfPgPjGyHK4GL185rrQ9UXteqTnUcMUhJSSEsLIzERKMMRGxsLHPmzGH9+vWV+roRERGEhYUxevToQtfOnz9PvXr1AFi7di0LFy5k8+bNJCUl8eCDD7J//35++ukn7r33Xo4ePYq9vX2l9dMmn09erpEbIfELI1dC1u9GDYf2g4zphrb9wcHx5u0IIYplpGjeZ4wmxMaS+9vvRunogADLaELt1q1t3c1SuTZicG1qoqaMGJQ2MIgxf3ntZoWRgfiWyGNQHQODa2WM27dvT0hICLt27eLQoUO0bt2a8PBwAgICLIHCjBkzOHnyJOfOnePo0aPMnTuXffv2sWnTJpo3b866detwcHC4of3333+fRYsWkZ2djbu7O8uXLychIYGwsDDq169P/fr1WbVqFW3bti2yfx9//DHLli1j06ZNvP766wC8+OKLAISGhjJjxgyCgoJueI6zszPPPPMM27Ztw9XVlX/+859MnTqV06dPM3/+fEuxpdKo7M8n/lRayemtc3Pg1G5jC2TSWqMCZO160H6wsQWyTTCYpFKdEBWh8/K4nJho2Qp55cgRwCgI5dy3L87BwTh1CkAV+PlWnVTnNQbl2pWglHre/OV6jKAg/6qQ2ydl4qZp8PP31m2zqQ8MmlXs5VmzZpGYmEhCQgJQeMQgNjb2hvuPHz9OTEwMSUlJBAUFsWrVKt58801GjhzJhg0bGDFixA33jxo1iieeeAKA6dOns3jxYp577jmGDRtW7IgBwDvvvMPcuXPJzs62FGk6e/Ys3bt3t9zj5ubG2bNnCz334sWL9O3blzfeeIORI0cyffp0tm7dSlJSEuHh4WUKDCpTqUpo25ugTV/jGDwHTu401iQcWgfffWLsZugw1BhJaNMH7KvvDy4hqitlZ0cdX1/q+PrSZOJErp49y4WYWDJjY0lbsYI/li7FzsUF5149cQ4Opm7PnphcrZOW3lr8m/hbZa1CVQYXN9uVcG0ZdnugK0Z5ZAUMBXZWYr9EGQ0aNAgHBwd8fHzIzc1l4MCBgFHqOCUlpdD9iYmJTJ8+nfT0dDIzMwkNDS3V6zzzzDM888wzfPTRR7z22mtERUWVugRzrVq1buhX7dq1LX0uqo+2UlQJ7RJrYNg7gHt/4xgyD07EGiMJh9ZCwn+hjquRH8FrFLTqZQQVQogyc2jenIYP/4mGD//JyL749ddkxsYa2Rc3bgI7O+oEBOAS3Bfnvn2p1batTXc5WIMtFjCW+BNKa/1/YMl82ElrfcH8eAbwWaX2rDop4X/21UX+0sYODg6WfwzFlTaOiIggOjoaPz8/li5dWmgE4mbGjh3L+PHjAUpdgrlgv/L3+abll6tQhUpom2pBuwHGkTMfjn11PePigWXg1Bg6DjNGElr2MCpGCiHKzK5uXeqFhFAvJMQy5ZAZG8uF2Fh+nfMvfp3zLxxatDCmHPr2sdkuh4qyRW6F0v7X5W4gO9/jbIzyy6KSFCyFXFJp5PK4cOECzZo14+rVq6xYscJSjrmk10lOTsbDwwOADRs2WL4eNmwYDz30EM8//zw//fQTycnJlqqQNZHVSmibahtJkjoMNnYvHNtmBAjffgJxH0LdJtBxuLEmoUV3sELKWCFuR/mnHO6YMIGrP/9MZuwOMmNiSP/0U9KWL8eubl3q9uhhBAp9emNqVIaA34ZsUSOitIHBcmC/Umo1xtqCkUBUpfVK0KhRI3r06IG3tzeDBg3in//8JyaTCT8/PyIiIggICKhQ+zNnziQwMJCWLVvi4+NjCQbGjh3LE088wdtvv83nn39+w+LDBQsWsG3bNhwcHHB1dSUqyvgr4OXlxQMPPEDHjh0xmUy88847lbojoSpYs4Q2AA51jOkEz6GQnQXJXxojCQf/C9+8Dy7NjJoNXiPBrasECUJUgEPTpriOHYPr2DHGLod9+4zESrGxXNiyBZSijq8vzsF9q0VipZL4N/Hn/QHvV+kag1KXXVZKdQJ6mR/u1FofrLReVbHquCtBlOyW+XyuZMLRzUaQkLwVcq9APTdjJMFrJLh1MfIqCCEqTGvNlUOHjPLRsTu4/L2xqNzUrBnOffvgEhyMU2AgduZpzltdhbYr3uokMKh5bsnP5/J5OLLJ2N1wfDvkZucLEkYYJaNlJEEIq8n57Tcyd+zgQmwsF7/ei87KQtWpQ93u3S1rExzuvNPW3aw0EhiUQAKDmueW/3wuZ5iDhGg4/pU5SGh+fSRBggQhrCrvyhWy9n9DZkwMmTt2cNW85bq2pyfOfXrj3KcPdXx9UTV8mjQ/CQxKIIFBzXNbfT6XM+CIebqhYJDQcYSsSRDCyrTWZB87RuaOHWTG7iDr4EHIzcXe1RXn3r1w7tOHuj17Ym/OBFtTSWBQAgkMap7b9vO5FiQkRRu7HHKzweWu69MNbt0kSBDCynIzMsjcvZvMHTu4uHOXUbvB3h6nTp1w7tvHyJnQpk21XcBYHAkMSiCBQc1zK38+N03HfM3l8+aFi9eChCsSJAhRyXRuLpe+/c4YTdixgyuHDwPg4OaGcx8jSHDq1rVGLGCUwKAEEhjUPLfq51OqdMxFKTZIGGZMN7QIlCBBiEpw9dw5MnfsNEYT9u5FX75sLGAMCjJGE/pU3wWM5aqVIKqfglUXrSEhIYGffvqJwYMHF3n9u+++46mnnuL8+fPY2dnxzTff4OjoSHx8PBEREVy6dInBgwfz1ltv1bihtOqmzOmYr3GsB74PGMfl83D0S2O6IW4J/O89I0+CpznjogQJQliNQ7Nm13MmXL5M1v79RnKl2FgyzfVkant64tzbvIDRr/ovYJTAQJCQkEBcXFyRgUFOTg4PP/wwy5cvx8/Pj9TUVEulxvHjx7No0SK6d+/O4MGD2bx5M4MGDarq7t9SKpSO+RrHeuB7v3HkDxLil8L+/+QLEkZIxkUhrMjO0dEIAHr3Rr88/YYFjKkffEDqf/6Dff361O3Z00jTXA2LPoFMJQDWm0qwdgWsuXPn8uGHHwIQGRnJxIkTSUlJYeDAgQQGBnLw4EHatWvHsmXLcHJyYtq0aaxduxaTycSAAQOYM2fODe3t37+fiRMncunSJerUqcOSJUto3bo17u7uXLp0iebNm/Piiy8yZswYy3M2btzIRx99xH//+98b2jp37hzBwcEcNs+vffzxx8TGxvKf//znhvsiIiKoU6cOhw8f5tSpUyxZsoSoqCj27t1LYGAgS5cuLdf35ladSoAyrDEoqysXjCAhfzIl56bXazdIkCBEpck9f56LX39tjCbs2kVuaur1DIx9+1C3d28cPT1RVfhvsLipBLTWVX4ADYGtQLL5T9di7tsMpAPrC5zfBSSYj5+AaPP5vkBGvmuvlKY/nTt31gUlJSUVOleSg78c1F2Wd9G+S311l+Vd9MFfDpbp+QXFxcVpb29vnZmZqS9cuKA7duyoDxw4oE+ePKkBvXv3bq211o899piePXu2Tk1N1e3atdN5eXlaa63T0tIKtZmRkaGvXr2qtdZ669atetSoUVprrZcsWaKfeeaZIvsxb948/fDDD+sBAwbogIAA/cYbb2ittf7mm290//79Lfft3LlTDxkypNDzw8PD9ZgxY3ReXp6Ojo7WLi4u+rvvvtO5ubm6U6dO+uDB8n2fyvr5iAIun9f6u8+0/vghrWc20frv9bSe3U7rDZO1Prlb69wcW/dQiFtWXm6uzvruO/3rvxfoE/c/oJM6eOqk9h30kZ499dmXXtIZm7/UOefPV3o/gDhdxO9EW00lTAO+0lrPUkpNMz/+axH3zQacgKfyn9RaX0vNjFJqFUY56Gt2aa3DrN/lklm7Atbu3bsZOXIkdevWBWDUqFHs2rWLYcOG0aJFC3r06AHAww8/zNtvv83EiRNxdHQkMjKSIUOGEBZW+FuQkZFBeHg4ycnJKKW4evXqTfuRk5PD7t27+eabb3BycqJ///507tyZekXs3y1ufcHQoUNRSuHj48Odd96Jj48PYNRYSElJwd+/8nN/iwJqu4DPaOO4NpKQFG1UgNy/CJzvvD7dcHeQVIEUwoqUnR11fHyo4+PDHc8+Q05qKpm7dhlZGLdsJWPVF2Ay4dS5s3ltQu8qLSFtq3HD4VwvwhQFjCjqJq31V0CxJQWVUi5APyDa2h0sq2sVsOyVvVUqYOkSpngK/uVQSmEymdi/fz/33Xcf0dHRDBw4sNDzXn75ZYKDg0lMTGTdunVcvnz5pv1wc3OjT58+NG7cGCcnJwYPHsyBAwdwc3PjzJkzlvuKK7UMN5aErp1vC091K7d827oWJIz5L0w5DqM/hBbd4OByWDoE5nrChhcgZTfk5dq6t0LcckyNGtFgxAjc5s2j3d6vafnf5TR67DFy09L4dfZsToQN5fi9IVz95Zcq6Y+tAoM7tdbnAMx/NilnOyMxRh7O5zsXpJT6Vim1SSnlVdwTlVJPKqXilFJxv/32Wzlf/rprFbCeDXiW9we8X+E1Br179yY6OpqsrCwuXrzI6tWr6dXLGCg5ffo0e/fuBYy5/Z49e5KZmUlGRgaDBw9m/vz5JCQkFGozIyPDUl45/9x+SaWWQ0ND+e6778jKyiInJ4cdO3bQsWNHmjVrhouLC/v27UNrzbJlyxg+fHiF3rOoBmo7g/d9BYKEQDi4wggS/tXBCBJO7pIgQYhKoEwmnLp0ockLz9Nm7RrcY7bTdMYMnLp0xtSkvL8qy6bSphKUUtuApkVc+psVX+ZB4IN8jw8ALbXWmUqpwRgjCR5FPVFrvQhYBMbiQ2t0xr+Jv9VKYnbq1ImIiAi6desGGIsPAwICSElJwdPTk6ioKJ566ik8PDwYP348GRkZDB8+nMuXL6O1Zt68eYXanDp1KuHh4cydO5d+/fpZzgcHBzNr1iz8/f0LLT50dXXl+eefp2vXriilGDx4MEOGDAHg3XfftWxXHDRokOxIuNVcCxK87zOqQCZvMaYbDq6Abz6Auk2MMtJeI6HlPTLdIEQlyL8dsqrYZFeCUuoI0FdrfU4p1QyI1Vq3L+bevsDkgusGlFKNgKNAc611kWPiSqkUoIvW+veS+iMJjmoe+XxsKPvi9TUJR7dAzqV8QcIIaNlDggQhaoDqluBoLRAOzDL/uabk24t0P8ZuBUtQoJRqCvyitdZKqW4YUyWpVuivEOKaWnXBe5RxZF80RhJ+iIaEjyBuMdS9wwgSOg6Hlj3BXtKlCFGT2Opf7CzgU6XU48BpjF/yKKW6AE9rrSPNj3cBHQBnpdQZ4HGt9ZfmNsaa28lvNDBeKZUDXALGalsMiQhxu6hV15hK8Bp5Y5Dw7ScQ9yHUaQgdhhhBQus+YKpl6x4LIW7CJoGB1joV6F/E+TggMt/jXgXvyXetbxHnFgALrNNLIUSZmIOEeOe+xDU8w721Emn721dGoHBwOdSuD+0HGUFC237g4GjrHgshiiBjfEIIq8lfBGqeqQErImfTefg7cDwGDq2Fw+vhu0+gljO0CzVyJXiEGEGFEKJakMBACGE1xRaBaj/QOHLmQ8pOSDIHCYmrwFQHPO41qkB6DDBqPQghbEYCAyGE1dy0CJSpFrjfaxxD5sLpr40g4dBaOLQO7GtB2/5G/Yb2g6BO9SswI8StTiqmVFPp6eksXLjQ8jglJYWPPvrI8jguLo4JEyZY/XWjo6NJSkoq9vqnn35Kx44d8fLy4qGHHrKcj4qKwsPDAw8PD6Kioop9vri1dW7pyorI7jw/oD0rIruXXATK3gSte8OQOfD8YRj3JXSNhJ+/h+jxMNsdlo+C+Ci4WOKOYyGEFUl1RapnHoOUlBTCwsJITEwEIDY2ljlz5rB+/fpKfd2IiAjCwsIYPXp0oWvJyck88MADbN++HVdXV3799VeaNGnCH3/8QZcuXYiLi0MpRefOnYmPj8e1EsuJ2vrzEZVIazh7wMiTcGgtpKWAsoNWPY01CZ5DwaWo3GlCiLIoLo+BjBhUU9OmTeP48eP4+/szZcoUpk2bxq5du/D392fevHnExsZaCiXNmDGD8PBwBgwYQKtWrfjiiy+YOnUqPj4+DBw4sMhiSe+//z5du3bFz8+P++67j6ysLL7++mvWrl3LlClT8Pf35/jx44We88wzz1h+4Tcxp+f88ssvCQkJoWHDhri6uhISEsLmzZsLvWarVq146aWXCAoKokuXLhw4cIDQ0FDatm3Le++9Z+1voaiplAK3zjBgJkxIgKd2Qs/n4fw52DjZSMv84SDY9y5knLl5e0KIMpE1BqXw8z//yZVDh63aZm3PDjR96aVir8+aNYvExERLzYOCIwaxsbE33H/8+HFiYmJISkoiKCiIVatW8eabbzJy5Eg2bNjAiBE31qkaNWoUTzzxBADTp09n8eLFPPfccwwbNqzYEYOjR48C0KNHD3Jzc5kxYwYDBw7k7NmztGjRwnKfm5sbZ8+eLfJ9tWjRgr179zJp0iQiIiLYs2cPly9fxsvLi6effvom3zVx21EKmvkZR7/p8NthSFpjrEvYPM04mncx1iR4DoOGrW3dYyFqPAkMbhGDBg3CwcEBHx8fcnNzLdUVfXx8SElJKXR/YmIi06dPJz09nczMTEJDQ2/6Gjk5OSQnJxMbG8uZM2fo1asXiYmJRVaCLK486LBhwyz9yszMxMXFBRcXFxwdHUlPT6dBgwZleNfitqIUNPE0jr7T4PdjcGiNEShsfcU4mvoaeRI6DofGRZZJEULchAQGpVDS/+yri/yljR0cHCy/mIsrbRwREUF0dDR+fn4sXbq00AhEUdzc3OjevTsODg60bt2a9u3bk5ycjJub2w3PP3PmDH379r1pP6UEs6iQxu7Q6wXjSEu5vrth+0zjaNLRCBA8hxnBRBXVsheippM1BtVUwVLIJZVGLo8LFy7QrFkzrl69yooVK0r1OiNGjCAmJgaA33//naNHj9KmTRtCQ0PZsmULaWlppKWlsWXLllKNQAhhNa6toMcEiNwGk36AgW+AYwOInQXvBsGCLrBtBpyNNxY3CiGKJYFBNdWoUSN69OiBt7c3U6ZMwdfXF5PJhJ+fX5Ellctq5syZBAYGEhISQocOHSznx44dy+zZswkICCi0+DA0NJRGjRrRsWNHgoODmT17No0aNaJhw4a8/PLLdO3ala5du/LKK6/QsGHDCvdRiHKp7wbdn4Zxm+CFIzDkX8a5PW/D+/1gnjdsnAond0GujFIJUZBsV6R6blcUJZPPR5RZ1h9wdLORSOn4dsi5DE6NoP1gYwtkm75gqn2zVoS4ZVS3sstCCFG1nBqC/0PGcSUTjm0zgoRrRZ5quUC7AUaQ4B4CtZ1t3WMhbEICAyHELS3+VBr7TqTSvU2j65kYazuD1wjjyLkCJ3eaizxtMOo32Nc2KkB6DjVSMzvJ1Ji4fUhgIIS4ZeWv9ljLZFd0mmZTbaPCo0cIhM2H03vh0HpjNOHoJlD20KqHsbuhwxCod5dt3owQVUQCAyHELavYao/FsbM3Ui+36gkDX4efDhoBwqF1RtbFjZONhEqeQ42jUduqezNCVBEJDIQQt6ybVnssiVLQvJNx3Pt3+O3I9SqQ2/5uHE28zEFCGNzpLbkSxC1BAgMhxC3rWrXHQmsMyuOO9nDHFOg9BdJOGesRDq2DHW/AjllGLgXPocaUQ/MuYCe7wUXNJH9za5iUlBS8vb2t2mZCQgIbN24s8lp2djaPPfYYPj4++Pn53ZDhMD4+Hh8fH9zd3ZkwYUKRqZGFsLXOLV15Jti9YkFBQa4tIejPRq6EyUdh6FvQyB32vQeLQ2CuJ6x/Ho7HQG7hImZCVGcSGIgSA4P3338fgO+//56tW7fywgsvkJeXB8D48eNZtGgRycnJJCcnF1lRUYhbnnMT6BwBD6+Cqcdh1AfQoht8+zEsHwGz3WH108YIw9VLtu6tEDclgYEV/Xwig/jNKfx8IsMq7c2dOxdvb2+8vb2ZP3++5XxOTg7h4eH4+voyevRosrKyAKNUc8eOHfH19WXy5MmF2tu/fz/33HMPAQEB3HPPPRw5coTs7GxeeeUVVq5cib+/PytXrrzhOUlJSfTv3x8wyiw3aNCAuLg4zp07x/nz5wkKCkIpxaOPPkp0dHSh14yIiGD8+PEEBwfTpk0bduzYwbhx4/D09CQiIsIq3ychqg3H+uB7P4xYWOuBAAAd30lEQVRZDlOOw5gVxnbHIxvhk4fgzTaw8hH47jO4bJ2fE0JYm6wxsJKfT2SwZt5BcnPysDfZMXxSAE3b1C93e/Hx8SxZsoT//e9/aK0JDAykT58+uLq6cuTIERYvXkyPHj0YN24cCxcuZNy4caxevZrDhw+jlCI9Pb1Qmx06dGDnzp2YTCa2bdvGSy+9xKpVq3j11VeJi4tjwYIFhZ7j5+fHmjVrGDt2LD/++CPx8fH8+OOP2NnZ4ebmZrmvpFLLaWlpbN++nbVr1zJ06FD27NnDBx98QNeuXUlISMDf37/c3ychqq1aTsaiRM8wYzohZZexJuHwBmMRo52DkW3RMwzaDwHnO2zdYyEAGTGwmrNH08jNyUNryM3N4+zRtAq1t3v3bkaOHEndunVxdnZm1KhR7Nq1C4AWLVrQo0cPAB5++GF2795NvXr1cHR0JDIyki+++AInJ6dCbWZkZHD//ffj7e3NpEmT+OGHH27aj3HjxuHm5kaXLl2YOHEi99xzDyaTqUyllocOHYpSCh8fH+688058fHyws7PDy8uryJLQQtxy7B2MhElh8+D5wzBuCwQ+Bb8fhXV/gX+1gyWDYd+7kP6jrXsrbnMSGFhJ83au2JvsUHZgb29H83YVW+hU0kK+gr+AlVKYTCb279/PfffdR3R0NAMHDiz0vJdffpng4GASExNZt24dly9fvmk/TCYT8+bNIyEhgTVr1pCeno6Hhwdubm6cOXPGct+ZM2e4666iE79IqWUh8rGzg7sDIfQf8Jdv4endxk6HS2mweRrM94b/9IGdc4wtkkJUMQkMrKRpm/oMnxRA4LA2FZ5GAOjduzfR0dFkZWVx8eJFVq9eTa9evQA4ffo0e/fuBeDjjz+mZ8+eZGZmkpGRweDBg5k/fz4JCQmF2szIyKB58+YALF261HK+pFLL114fYOvWrZhMJjp27EizZs1wcXFh3759aK1ZtmwZw4cPr9B7FuK2oxQ09YHgl+DPe+G5A3Dv/4GdCbbPhHe6wYKusO3/jJLR5oW/QlQmCQysqGmb+nQe2KrCQQFAp06diIiIoFu3bgQGBhIZGUlAQAAAnp6eREVF4evryx9//MH48eO5cOECYWFh+Pr60qdPnyJLM0+dOpUXX3yRHj16kJubazkfHBxMUlJSkYsPf/31Vzp16oSnpydvvPEGy5cvt1x79913iYyMxN3dnbZt2zJo0KAKv28hbmuN2kLPifDEVzApCQbPAec7Yc9b5pLRXsY2yGNfQU62rXsrblE2K7uslGoIrARaASnAA1rrtAL3+APvAvWAXOAfWuuV5mutgU+AhsAB4BGtdbZSqjawDOgMpAJjtNYpJfVFyi7XPPL5iNtK1h9w9Es4vN4oGX01C2rXA48B0GGwUQ3SsZ6teylqmOLKLttyxGAa8JXW2gP4yvy4oCzgUa21FzAQmK+UamC+9gYwz/z8NOBx8/nHgTSttTswz3yfEELUXE4Nwf9BGLsCpp6ABz+BjsPgRAx8Ps7YBvnf+yDuQ7jws617K2o4W44YHAH6aq3PKaWaAbFa6/Y3ec63wGjgGPAb0FRrnaOUCgJmaK1DlVJfmr/eq5QyAT8Dd+gS3qiMGNQ88vkIAeTlwo/7jZGEw+shLcU437yLUQmyQxjc0c6mXRTVV3EjBrbMY3Cn1vocgDk4aFLSzUqpbkAt4DjQCEjXWl9b0n4GaG7+ujnwo7ndHKVUhvn+3wu09yTwJMDdd99tlTckhBDWFn8qrfhaD3b20DLIOAa8Br8eMvIkHNkAX/2fcTTyMAcJQ6SGgyiVSg0MlFLbgKZFXPpbGdtpBiwHwrXWearoDfPXRgRKunb9hNaLgEVgjBiUpT9CCFEV4k+l8acP9pGdk0ctkx0rIrsXX/NBKbizo3H0mQIZZ+DIJmMkYe8C2DPfWMjYfpAxktC6N5hqF92WuK1VamCgtb63uGtKqV+UUs3yTSX8Wsx99YANwHSt9T7z6d+BBkopk3nUwA34yXztDNACOGOeSqgP/GGddySEEFVn34lUsnPyyNNwNSePfSdSS18Mqr4bdHvCOC6lQ/JWI0j4/nOIXwq1nMH9XiNI8AiBOg1u2qS4PdhyKmEtEA7MMv+5puANSqlawGpgmdb6s2vntdZaKRWDsd7gkwLPv9buXvP17SWtLxBCiOqqe5tG1DLZcTUnDweTHd3bNCpfQ3UaGDUcfO+Hq5eN9MyH18PhjZAUbeRNaNXLmG5oPxjqN795m+KWZcvJpllAiFIqGQgxP0Yp1UUp9YH5ngeA3kCEUirBfFxLrP9X4Hml1DGMNQSLzecXA43M55+n6N0O1V56ejoLFy60PE5JSeGjjz6yPI6Li2PChAlWf93o6GiSkpKKvHbq1Cn69++Pr68vffv2vSHzYVRUFB4eHnh4eBAVFWX1fglxO+rc0pUVkd15fkD7kqcRysLB0RghGPoWvHAEHt8KQc9Axo+wcTLM6wiL+sLO2caaBfl/1W3HZrsSqpPquCshJSWFsLAwEhMTAYiNjWXOnDmsX7++Ul83IiKCsLAwRo8eXeja/fffT1hYGOHh4Wzfvp0lS5awfPly/vjjD7p06UJcXBxKKTp37kx8fDyurlb4IVYMW38+QtySfjtqHknYAGfNPxMbtjFGETqEGeWk7ext20dhNdUxj4EowbRp0zh+/Dj+/v5MmTKFadOmsWvXLvz9/Zk3bx6xsbGEhYUBMGPGDMLDwxkwYACtWrXiiy++YOrUqfj4+DBw4ECuXr1aqP3333+frl274ufnx3333UdWVhZff/01a9euZcqUKfj7+3P8+PEbnpO/BHNwcDBr1hizN19++SUhISE0bNgQV1dXQkJC2Lx5c6HXbNWqFS+99BJBQUF06dKFAwcOEBoaStu2bXnvvfes/S0UQpTVHe2g1/NG5sXnDxtFnxq2gf/9B5YMhDntYM2zcGQzXL1k696KSiJll0shZukifj11wqptNmnZhuCIJ4u9PmvWLBITEy01DwqOGMTGxt5w//Hjx4mJiSEpKYmgoCBWrVrFm2++yciRI9mwYQMjRoy44f5Ro0bxxBNPADB9+nQWL17Mc889x7Bhw4odMfDz82PVqlX85S9/YfXq1Vy4cIHU1FTOnj1LixYtLPeVVIK5RYsW7N27l0mTJhEREcGePXu4fPkyXl5ePP300zf/xgkhqka9ZtBlnHFcPg/HtprXJKyBg8vBwQnc+xujCR6hULec6x9EtSOBwS1i0KBBODg44OPjQ25urqW6oo+PT5GljRMTE5k+fTrp6elkZmYSGhp609eYM2cOzz77LEuXLqV37940b968zCWYhw0bZulXZmYmLi4uuLi44OjoSHp6Og0ayMpoIaodx3rgfZ9x5GSbFy9ugCMb4dA6UHbQoruxFbL9YGjsbuseiwqQwKAUSvqffXWRv7Sxg4OD5RdzcaWNIyIiiI6Oxs/Pj6VLlxYagSjKXXfdxRdffAFAZmYmq1aton79+ri5ud3w/DNnztC3b9+b9lNKMAtRA5lqGSMF7v1hyL/gXIKRL+HIRtj6snE08jDnSxgCbl1lXUINI2sMqqmCpZBLKo1cHhcuXKBZs2ZcvXqVFStWlOp1fv/9d/LMZV9ff/11xo0bB0BoaChbtmwhLS2NtLQ0tmzZUqoRCCFEDacU3BVglI1+ejdM/B4GzTZyKOx7Fz4MhTkeEP1nY2ThSqateyxKQQKDaqpRo0b06NEDb29vpkyZgq+vLyaTCT8/vyJLKpfVzJkzCQwMJCQkhA4dOljOjx07ltmzZxMQEFBo8WFsbCzt27enXbt2/PLLL/ztb0YCy4YNG/Lyyy/TtWtXunbtyiuvvELDhg0r3EchRA3T4G4IfBIejYapx2H0Emjbz9jpsPJho9jTivuNYk/nz9m6t6IYsl2R6rldUZRMPh8hbKvEGg4F5V6F03vNKZo3QPop4/xdAdB+iDHtcKeXMQIhqkx1LKIkhBCiBipTDQcAewejNkPr3hD6T/jtsLEm4fBGiHnNOOrfbV68OAha9jDWMgibkMBACCFEmVSohoNS0MTTOHq9ABd+gaObjdGEA1Gw/z9Quz543GvscHC/V+o4VDEJDIQQQpSJ1Wo4ALjcCZ3DjSM7C07EGmWjj2yGxFVGHYeW9xhBQvtB4NrKWm9DFEMCAyGEEGVyrYZDqdcYlFYtJ+gw2DjycuFsvDHlcGQTbJ5mHE06QruBRpDQvLNshawEEhgIIYQos84tXa0XEBTFzt6ozdCiG9w7A1KPG1MOhzfCnrdg91xwagztQo2jbT+o7VJ5/bmNSGAghBCi+mvU1qgCGfQMXEqDY1+Zdzmsh4QVYF8LWvWEdoOg/UBj66QoF8ljUMOkpKTg7e1t1TYTEhLYuHFjkddSU1MJDg7G2dmZZ5991nI+KyuLIUOG0KFDB7y8vJg27Xp16ytXrjBmzBjc3d0JDAwsMiWzEEKUWx1X8BkNoxfDlOMQvh66PQlpp2DTFJjvAwvvga9ehR/3G9MSotQkMBAlBgaOjo7MnDmTOXPmFLo2efJkDh8+zMGDB9mzZw+bNm0CYPHixbi6unLs2DEmTZrEX//610rtvxDiNmbvAK17Qeg/YMIBeDYeBrxmBA+758PiEKMqZPSfIWmtZF8sBQkMrOjKqfOcj/mRK6fOW6W9uXPn4u3tjbe3N/Pnz7ecz8nJITw8HF9fX0aPHk1WVhZglGru2LEjvr6+TJ48uVB7+/fv55577iEgIIB77rmHI0eOkJ2dzSuvvMLKlSvx9/dn5cqVNzynbt269OzZE0dHxxvOOzk5ERwcDECtWrXo1KkTZ86cAWDNmjWEh4cDMHr0aL766qtChZZiY2Pp06cPDzzwAO3atWPatGmsWLGCbt264ePjUyjrohBClEpjd7jnOXhsA0w5BqM+gDZ9jSmHTx+BN1vD8lGw/31IP23r3lZLssbASq6cOs/vH3yPzslDmexoHOlD7Zb1yt1efHw8S5Ys4X//+x9aawIDA+nTpw+urq4cOXKExYsX06NHD8aNG8fChQsZN24cq1ev5vDhwyilSE9PL9Rmhw4d2LlzJyaTiW3btvHSSy+xatUqXn31VeLi4liwYEG5+pqens66dev4y1/+AnBDGWaTyUT9+vVJTU2lcePGNzzv22+/5dChQzRs2JA2bdoQGRnJ/v37eeutt/j3v/99QzAkhBBlyrYI4NQQfO83jtyrcHrf9ZwJGycbRxMvY01Cu2u7HOT/y/IdsJIrJzLQOXmgQefkceVERoXa2717NyNHjqRu3bo4OzszatQodu3aBUCLFi3o0aMHAA8//DC7d++mXr16ODo6EhkZyRdffIGTk1OhNjMyMrj//vvx9vZm0qRJ/PDDDxXqIxijFw8++CATJkygTZs2AKUuw9y1a1eaNWtG7dq1adu2LQMGDACKLxUthLh9Xcu2+K8tR/jTB/uIP5VWtgYKTTnEFZhyuBf+1Q6inzFPOVivaF1NI4GBldRuUx9lsgMFymRH7Tb1K9ReSTUsCv6SVUphMpnYv38/9913H9HR0QwcOLDQ815++WWCg4NJTExk3bp1XL58uUJ9BHjyySfx8PBg4sSJlnNubm78+OOPgBE4ZGRkFFlUqWDZ5fwlmaUEsxAiv6KyLVZIY4/CUw6textVID99BN5oDcuGG1Ui/zhhnTdRQ0hgYCW1W9ajcaQP9Qa0qvA0AkDv3r2Jjo4mKyuLixcvsnr1anr16gXA6dOn2bt3LwAff/wxPXv2JDMzk4yMDAYPHsz8+fNJSEgo1GZGRgbNmzcHYOnSpZbz5S3pPH36dDIyMgoN+Q8bNoyoqCgAPv/8c/r161fkiIEQQpTWtWyL9oqKZ1ss6NqUw+gPjaqQERug+9NGBcjN0+DtAFjQFb78G5zcZUxL3MJkjYEV1W5Zr8IBwTWdOnUiIiKCbt26ARAZGUlAQAApKSl4enoSFRXFU089hYeHB+PHjycjI4Phw4dz+fJltNZFlmaeOnUq4eHhzJ07l379+lnOBwcHM2vWLPz9/XnxxRcZM2bMDc9r1aoV58+fJzs7m+joaLZs2UK9evX4xz/+QYcOHejUqRMAzz77LJGRkTz++OM88sgjuLu707BhQz755BOrfE+EELevSsu2WED8mUz2nWxK9/Yv0HnAa8ZowdEtkPwl7F8EexcYtRzc+xkZGN1DoK4Vg5RqQMouI2WXayL5fIQQ1nbTqpFXLhi1HI5uNoKFi78CysjO6DHACBRqUPloKbsshBBClOCmVSNru4DnUOPIy4NzCXD0SyNQ2D7TOOq5QTtzkNC6NzjUsd0bKicJDIQQQgjKWDXSzg6adzKO4Bfhws+QvMUIFL5dCXEfgqkOtOljjCZ4DIAGLaruzVSABAZCCCEEFVzH4NIUOj1qHDlXIGX39dGEo5uNe5p4gUeIUfTJrRvYV89fwdWzV0IIIYQNWKVqpKk2uPc3jkFvwO9HjSAheYuxeHHPfHBsYFz3GFDtFjDaJDBQSjUEVgKtgBTgAa11WoF7/IF3gXpALvAPrfVK87UVQBfgKrAfeEprfVUp1RdYA5w0N/OF1vrVyn4/QgghRFHiT6ez74Q93ds8QuceE+ByBhyPMYKE5C2QuApQ4NYFPEKNEYVmfjZdwGirEYNpwFda61lKqWnmxwUr7WQBj2qtk5VSdwHxSqkvtdbpwArgYfN9HwGRGEEEwC6tdVjlvwUhhBCieMXucvAaYRx5eXDuICRvNUYUYl4zDuem16cc2vQ1Fj1WIVslOBoORJm/jgJGFLxBa31Ua51s/von4FfgDvPjjdoMY8TArUp6XYXS09NZuHCh5XFKSgofffSR5XFcXBwTJkyw+utGR0eTlJRU5LWdO3fSqVMnTCYTn3/+ueV8QkICQUFBeHl54evre0MhppMnTxIYGIiHhwdjxowhOzvb6n0WQojq6KbZGu3sjPoMfafBkzEwORmGL4S7u0PSGlj5sJGBMWoY7H0HLlunQN/N2CowuFNrfQ7A/GeTkm5WSnUDagHHC5x3AB4BNuc7HaSU+lYptUkp5VVCm08qpeKUUnG//fZbed9HpblZYNClSxfefvttq79uSYHB3XffzdKlS3nooYduOO/k5MSyZcv44Ycf2Lx5MxMnTrQUcfrrX//KpEmTSE5OxtXVlcWLF1u9z0IIUR2VOVujcxMI+BM8EAVTT0D4eiMDY+YvsPXvVdNpMHLyV8YBbAMSiziGA+kF7k0roZ1mwBGgexHX3gfm53tcD3A2fz0YSC5NXzt37qwLSkpKKnSuKo0ZM0Y7OjpqPz8/PXnyZB0YGKjr1aun/fz89Ny5c3VMTIweMmSI1lrrv//97/rRRx/VISEhumXLlnrVqlV6ypQp2tvbW4eGhurs7OxC7S9atEh36dJF+/r66lGjRumLFy/qPXv2aFdXV92qVSvt5+enjx07VmTfwsPD9WeffVZs3319ffXRo0d1Xl6ebtSokb569arWWuuvv/5aDxgwoND95em/rT8fIYQojbiUP/SC7ck6LuWPijV04RfrdCgfIE4X8Tux0tYYaK3vLe6aUuoXpVQzrfU5pVQzjGmCou6rB2wApmut9xW49neMqYWn8r3m+Xxfb1RKLVRKNdZa/16R97Jp0yZ+/vnnijRRSNOmTRk0aFCx12fNmkViYqKl5kFsbCxz5sxh/fr1lsf5HT9+nJiYGJKSkggKCmLVqlW8+eabjBw5kg0bNjBixI2zNaNGjeKJJ54AjJoHixcv5rnnnmPYsGGEhYUxevTocr2v/fv3k52dTdu2bUlNTaVBgwaYTMZfMzc3N86ePVvk88rafyGEqAmssssBjNGEKmKrqYS1QLj563CMnQQ3UErVAlYDy7TWnxW4FgmEAg9qrfPynW+qzNV6zNMPdkAFS3DVDIMGDcLBwQEfHx9yc3Mt1RWLK2GcmJhIr1698PHxYcWKFVYpwXzu3DkeeeQRlixZgp2dXanLL5en/0IIcbuIP5XGOzHHyl5qupxstSthFvCpUupx4DRwP4BSqgvwtNY6EngA6A00UkpFmJ8XobVOAN4DTgF7zb9orm1LHA2MV0rlAJeAsbqo305lVNL/7KuL/CWLHRwcLL+AiythHBERQXR0NH5+fixdurTQCERZnT9/niFDhvDaa6/RvXt3ABo3bkx6ejo5OTmYTCbOnDnDXXfdZZX+CyHE7eCm9RsqgU0CA611KtC/iPNxGFsP0Vr/F/hvMc8vst9a6wXAAuv11HYKlkIub2nk4ly4cIFmzZpx9epVVqxYYSnHXJ7Xyc7OZuTIkTz66KPcf//9lvNKKYKDg/n8888ZO3YsUVFRDB8+3GrvQQghbnU3rd9QCWw1lSBuolGjRvTo0QNvb2+mTJmCr68vJpMJPz+/Iksql9XMmTMJDAwkJCSEDh06WM6PHTuW2bNnExAQwPHjN2wC4ZtvvsHNzY3PPvuMp556Ci8vY9PHp59+ys6dO1m6dCn+/v74+/tb1ka88cYbzJ07F3d3d1JTU3n88ccr3HchhLhdlHlngxVI2WWk7HJNJJ+PEOJ2EX8qrXz1G25Cyi4LIYQQNZDVdjaUkkwlCCGEEMJCAoMSyDRL9SSfixBCVB4JDIrh6OhIamqq/BKqZrTWpKam4ujoaOuuCCHELUnWGBTDzc2NM2fOUB3rKNzuHB0dcXO75epmCSFEtSCBQTEcHBxo3bq1rbshhBBCVCmZShBCCCGEhQQGQgghhLCQwEAIIYQQFpL5EFBK/YZRlOlWVR/IsHUnrKy6vidb9auyX9fa7VujvYq2Ud7nNwYqVMpdlFp1/XdeUdXlfbXUWt9R8KQEBrcBpdQirfWTtu6HNVXX92SrflX261q7fWu0V9E2yvt8pVRcUWlkhfVV13/nFVXd35dMJdwe1tm6A5Wgur4nW/Wrsl/X2u1bo72KtlFd/w6J627Vz6havy8ZMRBCiDKQEQNxq5MRAyGEKJtFtu6AEJVJRgyEEEIIYSEjBkIIIYSwkMBACCGEEBYSGAghhBDCQgIDIYQQQlhIYCCEEFailGqjlFqslPrc1n0RorwkMBBCCEAp9aFS6lelVGKB8wOVUkeUUseUUtNKakNrfUJr/Xjl9lSIymWydQeEEKKaWAosAJZdO6GUsgfeAUKAM8A3Sqm1gD3weoHnj9Na/1o1XRWi8khgIIQQgNZ6p1KqVYHT3YBjWusTAEqpT4DhWuvXgbCq7aEQVUOmEoQQonjNgR/zPT5jPlckpVQjpdR7QIBS6sXK7pwQlUFGDIQQoniqiHPFpovVWqcCT1ded4SofDJiIIQQxTsDtMj32A34yUZ9EaJKSGAghBDF+wbwUEq1VkrVAsYCa23cJyEqlQQGQggBKKU+BvYC7ZVSZ5RSj2utc4BngS+BQ8CnWusfbNlPISqbVFcUQgghhIWMGAghhBDCQgIDIYQQQlhIYCCEEEIICwkMhBBCCGEhgYEQQgghLCQwEEIIIYSFBAZCiDJTSjVQSv3Z/PVdSqnPrdj2RKXUo0Wcb3WtJLJSykcptdRarymEuE4CAyFEeTQA/gygtf5Jaz3aGo0qpUzAOOCjku7TWn8PuCml7rbG6wohrpMiSkKI8pgFtFVKJQDJgKfW2lspFQGMAOwBb+BfQC3gEeAKMFhr/YdSqi3wDnAHkAU8obU+DPQDDpgzDqKU6gx8aL5nd4E+rMNIUfxmZb5RIW43MmIghCiPacBxrbU/MKXANW/gIaAb8A8gS2sdgJFu+NoUwSLgOa11Z2AysNB8vgcQn6+tJcAErXVQEX2IA3pZ4b0IIfKREQMhhLXFaK0vABeUUhkY/7MH+B7wVUo5A/cAnyllqWpc2/xnM4yaBCil6gMNtNY7zNeWA4Pyvc6vwF2V9i6EuE1JYCCEsLYr+b7Oy/c4D+Nnjh2Qbh5tKOgS4Gj+WgElFXNxNN8vhLAimUoQQpTHBcClPE/UWp8HTiql7gdQBj/z5UOAu/m+dCBDKdXTfO1PBZpqBySWpw9CiOJJYCCEKDOtdSqwx7x9cHY5mvgT8LhS6lvgB2C4+fwmoHe++x4D3lFK7aXw6EAwsKEcry2EKIGUXRZCVCtKqdXAVK11cgn31AZ2AD2v7WAQQliHBAZCiGpFKdUeuFNrvbOEezyA5lrr2CrrmBC3CQkMhBBCCGEhawyEEEIIYSGBgRBCCCEsJDAQQgghhIUEBkIIIYSwkMBACCGEEBb/DxWkC5mZ/nnIAAAAAElFTkSuQmCC", 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" ] @@ -1480,19 +1504,19 @@ "hm_32 = m_3.head(r2, 0, t2)\n", "hm_33 = m_3.head(r3, 0, t3)\n", "hm_34 = m_3.head(r4, 0, t4)\n", - "print('rmse:', c2.rmse())\n", + "print(\"rmse:\", c2.rmse())\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm_31[-1], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hm_32[-1], label='ttim at 60 m')\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hm_33[-1], label='ttim at 90 m')\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hm_34[-1], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('model with storage only')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_31[-1], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_32[-1], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_33[-1], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_34[-1], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with storage only\")\n", "plt.legend();" ] }, @@ -1518,11 +1542,19 @@ } ], "source": [ - "#unkonwn parameters: kaq1, Saq1, c, Sll\n", - "m_4 = ModelMaq(kaq=[0.01, 10], z=[0, -0.001, -8.001, -45.001], c = 500, \\\n", - " Saq = [0, 0.001], Sll = 0.1, topboundary = 'conf', tmin=0.001, tmax=0.5)\n", - "w_4 = Well(m_4, xw = 0, yw = 0, tsandQ = [(0, 761), (0.34, 0)], layers = 1)\n", - "m_4.solve(silent = 'True')" + "# unkonwn parameters: kaq1, Saq1, c, Sll\n", + "m_4 = ttim.ModelMaq(\n", + " kaq=[0.01, 10],\n", + " z=[0, -0.001, -8.001, -45.001],\n", + " c=500,\n", + " Saq=[0, 0.001],\n", + " Sll=0.1,\n", + " topboundary=\"conf\",\n", + " tmin=0.001,\n", + " tmax=0.5,\n", + ")\n", + "w_4 = ttim.Well(m_4, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", + "m_4.solve(silent=\"True\")" ] }, { @@ -1637,14 +1669,14 @@ } ], "source": [ - "c3 = Calibrate(m_4)\n", - "c3.set_parameter(name='kaq1', initial=10)\n", - "c3.set_parameter(name='Saq1', initial=1e-4)\n", - "c3.set_parameter(name='c1', initial=500, pmin=0)\n", - "c3.series(name='obs1', x=30, y=0, t=t1, h=h1, layer=1)\n", - "c3.series(name='obs2', x=60, y=0, t=t2, h=h2, layer=1)\n", - "c3.series(name='obs3', x=90, y=0, t=t3, h=h3, layer=1)\n", - "c3.series(name='obs4', x=120, y=0, t=t4, h=h4, layer=1)\n", + "c3 = ttim.Calibrate(m_4)\n", + "c3.set_parameter(name=\"kaq1\", initial=10)\n", + "c3.set_parameter(name=\"Saq1\", initial=1e-4)\n", + "c3.set_parameter(name=\"c1\", initial=500, pmin=0)\n", + "c3.series(name=\"obs1\", x=30, y=0, t=t1, h=h1, layer=1)\n", + "c3.series(name=\"obs2\", x=60, y=0, t=t2, h=h2, layer=1)\n", + "c3.series(name=\"obs3\", x=90, y=0, t=t3, h=h3, layer=1)\n", + "c3.series(name=\"obs4\", x=120, y=0, t=t4, h=h4, layer=1)\n", "c3.fit(report=True)\n", "display(c3.parameters)" ] @@ -1663,7 +1695,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1679,19 +1711,19 @@ "hm_r2 = m_4.head(r2, 0, t2)\n", "hm_r3 = m_4.head(r3, 0, t3)\n", "hm_r4 = m_4.head(r4, 0, t4)\n", - "print('rmse:', c3.rmse())\n", + "print(\"rmse:\", c3.rmse())\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm_r1[-1], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 60 m')\n", - "plt.semilogx(t2, hm_r2[-1], label='ttim at 60 m')\n", - "plt.semilogx(t3, h3, '.', label='obs at 90 m')\n", - "plt.semilogx(t3, hm_r3[-1], label='ttim at 90 m')\n", - "plt.semilogx(t4, h4, '.', label='obs at 120 m')\n", - "plt.semilogx(t4, hm_r4[-1], label='ttim at 120 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('model with storage only')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm_r1[-1], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 60 m\")\n", + "plt.semilogx(t2, hm_r2[-1], label=\"ttim at 60 m\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t3, hm_r3[-1], label=\"ttim at 90 m\")\n", + "plt.semilogx(t4, h4, \".\", label=\"obs at 120 m\")\n", + "plt.semilogx(t4, hm_r4[-1], label=\"ttim at 120 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"model with storage only\")\n", "plt.legend();" ] }, @@ -1808,14 +1840,16 @@ } ], "source": [ - "t1 = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]', 'Sll [1/m]'], \\\n", - " index=['Hantush', 'ttim', 'MLU', 'AQTESOLV'])\n", - "t1.loc['Hantush'] = [45.332, 4.762E-5, 331.141, '-']\n", - "t1.loc['ttim'] = np.append(c3.parameters['optimal'].values, '-')\n", - "t1.loc['MLU'] = [45.186, 3.941e-05, 769.200, 3.611e-04]\n", - "t1.loc['AQTESOLV'] = [49.286, 4.559e-05, 745.156, '-']\n", + "t1 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"Sll [1/m]\"],\n", + " index=[\"Hantush\", \"ttim\", \"MLU\", \"AQTESOLV\"],\n", + ")\n", + "t1.loc[\"Hantush\"] = [45.332, 4.762e-5, 331.141, \"-\"]\n", + "t1.loc[\"ttim\"] = np.append(c3.parameters[\"optimal\"].values, \"-\")\n", + "t1.loc[\"MLU\"] = [45.186, 3.941e-05, 769.200, 3.611e-04]\n", + "t1.loc[\"AQTESOLV\"] = [49.286, 4.559e-05, 745.156, \"-\"]\n", "rmse = [0.005917, c3.rmse(), 0.005941, 0.007245]\n", - "t1['RMSE'] = rmse\n", + "t1[\"RMSE\"] = rmse\n", "t1" ] }, @@ -1901,12 +1935,14 @@ } ], "source": [ - "t2 = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]', 'Sll [1/m]'],\\\n", - " index=['ttim', 'MLU', 'AQTESOLV'])\n", - "t2.loc['MLU'] = [45.335, 4.668e-05, 331.400, 1.284e-05]\n", - "t2.loc['AQTESOLV'] = [45.159, 4.100e-05, 367.577, 2.868e-05]\n", - "t2.loc['ttim'] = c2.parameters['optimal'].values\n", - "t2['RMSE'] = [c2.rmse(), 0.004941, 0.005861]\n", + "t2 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"Sll [1/m]\"],\n", + " index=[\"ttim\", \"MLU\", \"AQTESOLV\"],\n", + ")\n", + "t2.loc[\"MLU\"] = [45.335, 4.668e-05, 331.400, 1.284e-05]\n", + "t2.loc[\"AQTESOLV\"] = [45.159, 4.100e-05, 367.577, 2.868e-05]\n", + "t2.loc[\"ttim\"] = c2.parameters[\"optimal\"].values\n", + "t2[\"RMSE\"] = [c2.rmse(), 0.004941, 0.005861]\n", "t2" ] } diff --git a/pumpingtest_benchmarks/3_test_of_vennebulten.ipynb b/pumpingtest_benchmarks/3_test_of_vennebulten.ipynb index 7edc108..b3a0f5e 100755 --- a/pumpingtest_benchmarks/3_test_of_vennebulten.ipynb +++ b/pumpingtest_benchmarks/3_test_of_vennebulten.ipynb @@ -17,8 +17,8 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -34,9 +34,9 @@ "metadata": {}, "outputs": [], "source": [ - "b = -21 #aquifer thickness in m\n", - "r = 90 #distance from observation wells to pumping well in m\n", - "Q = 873 #constant discharge in m^3/d" + "b = -21 # aquifer thickness in m\n", + "r = 90 # distance from observation wells to pumping well in m\n", + "Q = 873 # constant discharge in m^3/d" ] }, { @@ -52,12 +52,12 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/venne_shallow.txt', skiprows=1)\n", - "ts = data1[:, 0] / 60 / 24 #convert min to days\n", + "data1 = np.loadtxt(\"data/venne_shallow.txt\", skiprows=1)\n", + "ts = data1[:, 0] / 60 / 24 # convert min to days\n", "hs = data1[:, 1]\n", "\n", - "data2 = np.loadtxt('data/venne_deep.txt', skiprows=1)\n", - "td = data2[:, 0] / 60 / 24 #convert min to days\n", + "data2 = np.loadtxt(\"data/venne_deep.txt\", skiprows=1)\n", + "td = data2[:, 0] / 60 / 24 # convert min to days\n", "hd = data2[:, 1]" ] }, @@ -83,8 +83,8 @@ } ], "source": [ - "ml_1 = Model3D(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-4, tmax=1.1)\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)])\n", + "ml_1 = ttim.Model3D(kaq=10, z=[0, b], Saq=1e-4, tmin=1e-4, tmax=1.1)\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)])\n", "ml_1.solve()" ] }, @@ -124,12 +124,12 @@ } ], "source": [ - "#calibrate with data of shallow piezometer\n", - "#unknown parameters: kaq, Saq\n", - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0', initial=10)\n", - "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_1.series(name='obs', x=r, y=0, t=ts, h=hs, layer=0)\n", + "# calibrate with data of shallow piezometer\n", + "# unknown parameters: kaq, Saq\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.series(name=\"obs\", x=r, y=0, t=ts, h=hs, layer=0)\n", "ca_1.fit()" ] }, @@ -212,7 +212,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -222,7 +222,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -237,13 +237,13 @@ "hs_1 = ml_1.head(r, 0, ts)\n", "hd_1 = ml_1.head(r, 0, td)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(ts, hs, '.', label='shallow obs')\n", - "plt.semilogx(ts, hs_1[0], label='shallow ttim')\n", - "plt.semilogx(td, hd, '.', label='deep obs')\n", - "plt.semilogx(td, hd_1[0], label='deep ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis of shallow piezometer')\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(ts, hs_1[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(td, hd_1[0], label=\"deep ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis of shallow piezometer\")\n", "plt.legend();" ] }, @@ -276,12 +276,12 @@ } ], "source": [ - "#Calibrate with deep piezometer\n", - "#unknown parameters: kay, Saq\n", - "ca_2 = Calibrate(ml_1)\n", - "ca_2.set_parameter(name='kaq0', initial=10)\n", - "ca_2.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_2.series(name='obs', x=r, y=0, t=td, h=hd, layer=0)\n", + "# Calibrate with deep piezometer\n", + "# unknown parameters: kay, Saq\n", + "ca_2 = ttim.Calibrate(ml_1)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_2.series(name=\"obs\", x=r, y=0, t=td, h=hd, layer=0)\n", "ca_2.fit()" ] }, @@ -368,7 +368,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('RMSE:', ca_2.rmse())" + "print(\"RMSE:\", ca_2.rmse())" ] }, { @@ -378,7 +378,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -393,13 +393,13 @@ "hd_2 = ml_1.head(r, 0, td)\n", "hs_2 = ml_1.head(r, 0, ts)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(td, hd, '.', label='deep obs')\n", - "plt.semilogx(td, hd_2[0], label='deep ttim')\n", - "plt.semilogx(ts, hs, '.', label='shallow obs')\n", - "plt.semilogx(ts, hs_2[0], label='shallow ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis of deep piezometer')\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(td, hd_2[0], label=\"deep ttim\")\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(ts, hs_2[0], label=\"shallow ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis of deep piezometer\")\n", "plt.legend();" ] }, @@ -416,8 +416,8 @@ "metadata": {}, "outputs": [], "source": [ - "nlay = 21 #number of layers\n", - "zlayers = np.linspace(0, b, nlay + 1) #elevation of each layer\n", + "nlay = 21 # number of layers\n", + "zlayers = np.linspace(0, b, nlay + 1) # elevation of each layer\n", "Saq = 1e-4 * np.ones(nlay)\n", "Saq[0] = 0.1" ] @@ -437,9 +437,10 @@ } ], "source": [ - "ml_2 = Model3D(kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, \\\n", - " tmin=1e-4, tmax=1.1)\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", + "ml_2 = ttim.Model3D(\n", + " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, tmin=1e-4, tmax=1.1\n", + ")\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", "ml_2.solve()" ] }, @@ -493,14 +494,15 @@ } ], "source": [ - "ca_3 = Calibrate(ml_2)\n", - "ca_3.set_parameter(name='kaq0_20', initial=10)\n", - "ca_3.set_parameter(name='Saq0', initial=0.2)\n", - "ca_3.set_parameter(name='Saq1_20', initial=1e-4)\n", - "ca_3.set_parameter_by_reference(name='kzoverkh', parameter=ml_2.aq.kzoverkh[:], \\\n", - " initial=0.1, pmin=0.01)\n", - "ca_3.series(name='obs1', x=r, y=0, layer=1,t=ts, h=hs)\n", - "ca_3.series(name='obs2', x=r, y=0, layer=15, t=td, h=hd)\n", + "ca_3 = ttim.Calibrate(ml_2)\n", + "ca_3.set_parameter(name=\"kaq0_20\", initial=10)\n", + "ca_3.set_parameter(name=\"Saq0\", initial=0.2)\n", + "ca_3.set_parameter(name=\"Saq1_20\", initial=1e-4)\n", + "ca_3.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml_2.aq.kzoverkh[:], initial=0.1, pmin=0.01\n", + ")\n", + "ca_3.series(name=\"obs1\", x=r, y=0, layer=1, t=ts, h=hs)\n", + "ca_3.series(name=\"obs2\", x=r, y=0, layer=15, t=td, h=hd)\n", "ca_3.fit(report=True)" ] }, @@ -611,7 +613,7 @@ ], "source": [ "display(ca_3.parameters)\n", - "print('RMSE:', ca_3.rmse())" + "print(\"RMSE:\", ca_3.rmse())" ] }, { @@ -623,7 +625,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -637,13 +639,13 @@ "source": [ "hs_3 = ml_2.head(x=r, y=0, t=ts, layers=1)\n", "hd_3 = ml_2.head(x=r, y=0, t=td, layers=15)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(ts, hs, '.', label='shallow obs')\n", - "plt.semilogx(td, hd, '.', label='deep obs')\n", - "plt.semilogx(ts, hs_3[0], label='shallow ttim')\n", - "plt.semilogx(td, hd_3[0], label='deep ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(ts, hs_3[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd_3[0], label=\"deep ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", "plt.legend();" ] }, @@ -670,9 +672,10 @@ } ], "source": [ - "ml_3 = Model3D(kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, \\\n", - " tmin=1e-4, tmax=1.1)\n", - "w_3 = Well(ml_3, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", + "ml_3 = ttim.Model3D(\n", + " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.1, phreatictop=True, tmin=1e-4, tmax=1.1\n", + ")\n", + "w_3 = ttim.Well(ml_3, xw=0, yw=0, rw=0.1, tsandQ=[(0, Q)], layers=range(nlay))\n", "ml_3.solve()" ] }, @@ -717,15 +720,16 @@ } ], "source": [ - "ca_4 = Calibrate(ml_3)\n", - "ca_4.set_parameter(name='kaq0_20', initial=50)\n", - "ca_4.set_parameter(name='Saq0', initial=0.1)\n", - "ca_4.set_parameter(name='Saq1_7', initial=1e-4, pmin=0)\n", - "ca_4.set_parameter(name='Saq7_20', initial=1e-4, pmin=0)\n", - "ca_4.set_parameter_by_reference(name='kzoverkh', parameter=ml_3.aq.kzoverkh[:], \\\n", - " initial=0.1, pmin=0)\n", - "ca_4.series(name='obs1', x=r, y=0, layer=1,t=ts, h=hs)\n", - "ca_4.series(name='obs2', x=r, y=0, layer=15, t=td, h=hd)\n", + "ca_4 = ttim.Calibrate(ml_3)\n", + "ca_4.set_parameter(name=\"kaq0_20\", initial=50)\n", + "ca_4.set_parameter(name=\"Saq0\", initial=0.1)\n", + "ca_4.set_parameter(name=\"Saq1_7\", initial=1e-4, pmin=0)\n", + "ca_4.set_parameter(name=\"Saq7_20\", initial=1e-4, pmin=0)\n", + "ca_4.set_parameter_by_reference(\n", + " name=\"kzoverkh\", parameter=ml_3.aq.kzoverkh[:], initial=0.1, pmin=0\n", + ")\n", + "ca_4.series(name=\"obs1\", x=r, y=0, layer=1, t=ts, h=hs)\n", + "ca_4.series(name=\"obs2\", x=r, y=0, layer=15, t=td, h=hd)\n", "ca_4.fit(report=True)" ] }, @@ -848,7 +852,7 @@ ], "source": [ "display(ca_4.parameters)\n", - "print('RMSE:', ca_4.rmse())" + "print(\"RMSE:\", ca_4.rmse())" ] }, { @@ -858,7 +862,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -872,13 +876,13 @@ "source": [ "hs_4 = ml_3.head(x=r, y=0, t=ts, layers=1)\n", "hd_4 = ml_3.head(x=r, y=0, t=td, layers=15)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(ts, hs, '.', label='shallow obs')\n", - "plt.semilogx(td, hd, '.', label='deep obs')\n", - "plt.semilogx(ts, hs_4[0], label='shallow ttim')\n", - "plt.semilogx(td, hd_4[0], label='deep ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(ts, hs, \".\", label=\"shallow obs\")\n", + "plt.semilogx(td, hd, \".\", label=\"deep obs\")\n", + "plt.semilogx(ts, hs_4[0], label=\"shallow ttim\")\n", + "plt.semilogx(td, hd_4[0], label=\"deep ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", "plt.legend();" ] }, @@ -980,13 +984,15 @@ } ], "source": [ - "t1 = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'Sy [-]', 'kz/kh'], \\\n", - " index=['K&dR', 'AQTESOLV', 'MLU', 'ttim'])\n", - "t1.loc['K&dR'] = [73, 2.476e-05, 0.005, 0.000548]\n", - "t1.loc['AQTESOLV'] = [63.805, 2.663e-05, 0.011, 0.000690]\n", - "t1.loc['MLU'] = [74.657, 2.767e-05, 0.005, 0.000737]\n", - "t1.iloc[3, 0:2] = ca_2.parameters['optimal'].values \n", - "t1['RMSE'] = ['-', 0.003041, 0.003216, ca_2.rmse()]\n", + "t1 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"Sy [-]\", \"kz/kh\"],\n", + " index=[\"K&dR\", \"AQTESOLV\", \"MLU\", \"ttim\"],\n", + ")\n", + "t1.loc[\"K&dR\"] = [73, 2.476e-05, 0.005, 0.000548]\n", + "t1.loc[\"AQTESOLV\"] = [63.805, 2.663e-05, 0.011, 0.000690]\n", + "t1.loc[\"MLU\"] = [74.657, 2.767e-05, 0.005, 0.000737]\n", + "t1.iloc[3, 0:2] = ca_2.parameters[\"optimal\"].values\n", + "t1[\"RMSE\"] = [\"-\", 0.003041, 0.003216, ca_2.rmse()]\n", "t1" ] }, @@ -1072,13 +1078,15 @@ } ], "source": [ - "t2 = pd.DataFrame(columns=['k [m/d]', 'Sy [-]', 'Ss [1/m]','kzoverkh'], \\\n", - " index=['MLU', 'ttim-multilayer', 'ttim-stratified Ss'])\n", - "t2.loc['MLU'] = [62.657, 0.0012, 2.790e-05, 0.002595]\n", - "t2.loc['ttim-multilayer'] = ca_3.parameters['optimal'].values\n", - "t2.iloc[2, 0:2] = ca_4.parameters['optimal'].values[0:2]\n", - "t2.iloc[2, 2:4] = ca_4.parameters['optimal'].values[3:5]\n", - "t2['RMSE'] = [0.013540, ca_3.rmse(), ca_4.rmse()]\n", + "t2 = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Sy [-]\", \"Ss [1/m]\", \"kzoverkh\"],\n", + " index=[\"MLU\", \"ttim-multilayer\", \"ttim-stratified Ss\"],\n", + ")\n", + "t2.loc[\"MLU\"] = [62.657, 0.0012, 2.790e-05, 0.002595]\n", + "t2.loc[\"ttim-multilayer\"] = ca_3.parameters[\"optimal\"].values\n", + "t2.iloc[2, 0:2] = ca_4.parameters[\"optimal\"].values[0:2]\n", + "t2.iloc[2, 2:4] = ca_4.parameters[\"optimal\"].values[3:5]\n", + "t2[\"RMSE\"] = [0.013540, ca_3.rmse(), ca_4.rmse()]\n", "t2" ] }, diff --git a/pumpingtest_benchmarks/4_test_of_gridley.ipynb b/pumpingtest_benchmarks/4_test_of_gridley.ipynb index ad3567e..84d19a2 100755 --- a/pumpingtest_benchmarks/4_test_of_gridley.ipynb +++ b/pumpingtest_benchmarks/4_test_of_gridley.ipynb @@ -34,10 +34,10 @@ "metadata": {}, "outputs": [], "source": [ - "b = -5.4846 #aquifer thickness in m\n", - "Q = 1199.218 #constant discharge in m^3/d\n", - "r = 251.1552 #distance between observation well to test well in m\n", - "rw = 0.1524 #screen radius of test well in m" + "b = -5.4846 # aquifer thickness in m\n", + "Q = 1199.218 # constant discharge in m^3/d\n", + "r = 251.1552 # distance between observation well to test well in m\n", + "rw = 0.1524 # screen radius of test well in m" ] }, { @@ -53,10 +53,10 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/gridley_well_1.txt')\n", + "data1 = np.loadtxt(\"data/gridley_well_1.txt\")\n", "t1 = data1[:, 0]\n", "h1 = data1[:, 1]\n", - "data2 = np.loadtxt('data/gridley_well_3.txt')\n", + "data2 = np.loadtxt(\"data/gridley_well_3.txt\")\n", "t2 = data2[:, 0]\n", "h2 = data2[:, 1]" ] @@ -83,7 +83,7 @@ } ], "source": [ - "ml = ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "ml = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", "w = Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml.solve()" ] @@ -124,11 +124,11 @@ } ], "source": [ - "#unknown parameters: kaq, Saq\n", - "ca_0 = Calibrate(ml)\n", - "ca_0.set_parameter(name='kaq0', initial=10)\n", - "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_0.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0)\n", + "# unknown parameters: kaq, Saq\n", + "ca_0 = ttim.Calibrate(ml)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", "ca_0.fit(report=True)" ] }, @@ -215,7 +215,7 @@ ], "source": [ "display(ca_0.parameters)\n", - "print('rmse:', ca_0.rmse())" + "print(\"rmse:\", ca_0.rmse())" ] }, { @@ -238,11 +238,11 @@ ], "source": [ "hm_0 = ml.head(r, 0, t1)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, hm_0[0], label = 'ttim')\n", - "plt.semilogx(t1, h1, '.', label = 'obs1')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, hm_0[0], label=\"ttim\")\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", "plt.legend();" ] }, @@ -275,11 +275,11 @@ } ], "source": [ - "#unknown parameters: kaq, Saq\n", - "ca_1 = Calibrate(ml)\n", - "ca_1.set_parameter(name='kaq0', initial=10)\n", - "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_1.series(name='obs2', x=0, y=0, t=t2, h=h2, layer=0)\n", + "# unknown parameters: kaq, Saq\n", + "ca_1 = ttim.Calibrate(ml)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.series(name=\"obs2\", x=0, y=0, t=t2, h=h2, layer=0)\n", "ca_1.fit(report=True)" ] }, @@ -366,7 +366,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('rmse:', ca_1.rmse())" + "print(\"rmse:\", ca_1.rmse())" ] }, { @@ -389,11 +389,11 @@ ], "source": [ "hm_1 = ml.head(0, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t2, hm_1[0], label = 'ttim')\n", - "plt.semilogx(t2, h2, '.', label = 'obs2')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t2, hm_1[0], label=\"ttim\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", "plt.legend();" ] }, @@ -419,7 +419,9 @@ } ], "source": [ - "ml_1 = ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "ml_1 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", + ")\n", "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml_1.solve()" ] @@ -453,11 +455,11 @@ } ], "source": [ - "ca_2 = Calibrate(ml_1)\n", - "ca_2.set_parameter(name='kaq0', initial=10)\n", - "ca_2.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca_2.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0)\n", - "ca_2.series(name='obs2', x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_2 = ttim.Calibrate(ml_1)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_2.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", + "ca_2.series(name=\"obs2\", x=0, y=0, t=t2, h=h2, layer=0)\n", "ca_2.fit(report=True)" ] }, @@ -544,7 +546,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('rmse:', ca_2.rmse())" + "print(\"rmse:\", ca_2.rmse())" ] }, { @@ -568,13 +570,13 @@ "source": [ "hm1_2 = ml.head(r, 0, t1)\n", "hm2_2 = ml.head(0, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, hm1_2[0], label = 'ttim1')\n", - "plt.semilogx(t1, h1, '.', label = 'obs1')\n", - "plt.semilogx(t2, hm2_2[0], label = 'ttim3')\n", - "plt.semilogx(t2, h2, '.', label = 'obs3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, hm1_2[0], label=\"ttim1\")\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, hm2_2[0], label=\"ttim3\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", "plt.legend();" ] }, @@ -600,7 +602,9 @@ } ], "source": [ - "ml_2 = ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary='conf')\n", + "ml_2 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", + ")\n", "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n", "ml_2.solve()" ] @@ -643,12 +647,12 @@ } ], "source": [ - "ca_3 = Calibrate(ml_2)\n", - "ca_3.set_parameter(name = 'kaq0', initial = 10)\n", - "ca_3.set_parameter(name = 'Saq0', initial = 1e-4, pmin=0)\n", - "ca_3.set_parameter_by_reference(name='res', parameter=w_2.res, initial =0.2)\n", - "ca_3.series(name='obs1', x=r, y=0, t=t1, h=h1, layer=0)\n", - "ca_3.series(name='obs3', x=0, y=0, t=t2, h=h2, layer=0)\n", + "ca_3 = ttim.Calibrate(ml_2)\n", + "ca_3.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_3.set_parameter_by_reference(name=\"res\", parameter=w_2.res, initial=0.2)\n", + "ca_3.series(name=\"obs1\", x=r, y=0, t=t1, h=h1, layer=0)\n", + "ca_3.series(name=\"obs3\", x=0, y=0, t=t2, h=h2, layer=0)\n", "ca_3.fit(report=True)" ] }, @@ -747,7 +751,7 @@ ], "source": [ "display(ca_3.parameters)\n", - "print('rmse:', ca_3.rmse())" + "print(\"rmse:\", ca_3.rmse())" ] }, { @@ -771,13 +775,13 @@ "source": [ "hw1 = ml_2.head(r, 0, t1)\n", "hw2 = ml_2.head(0, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, hw1[0], label = 'ttim1')\n", - "plt.semilogx(t1, h1, '.', label = 'obs1')\n", - "plt.semilogx(t2, hw2[0], label = 'ttim3')\n", - "plt.semilogx(t2, h2, '.', label = 'obs3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, hw1[0], label=\"ttim1\")\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, hw2[0], label=\"ttim3\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", "plt.legend();" ] }, @@ -879,14 +883,16 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'res'], \\\n", - " index=['MLU', 'AQTESOLV', 'ttim', 'ttim-res&rc'])\n", - "t.loc['MLU'] = [38.094, 1.193E-06, '-']\n", - "t.loc['AQTESOLV'] = [37.803, 1.356E-06, '-']\n", - "t.loc['ttim'] = np.append(ca_2.parameters['optimal'].values, '-')\n", - "t.loc['ttim-res&rc'] = ca_3.parameters['optimal'].values \n", - "t['rc'] = ['-', '-', '-', 0.2]\n", - "t['RMSE'] = [0.259, 0.270, 0.272, 0.192]\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\"],\n", + " index=[\"MLU\", \"AQTESOLV\", \"ttim\", \"ttim-res&rc\"],\n", + ")\n", + "t.loc[\"MLU\"] = [38.094, 1.193e-06, \"-\"]\n", + "t.loc[\"AQTESOLV\"] = [37.803, 1.356e-06, \"-\"]\n", + "t.loc[\"ttim\"] = np.append(ca_2.parameters[\"optimal\"].values, \"-\")\n", + "t.loc[\"ttim-res&rc\"] = ca_3.parameters[\"optimal\"].values\n", + "t[\"rc\"] = [\"-\", \"-\", \"-\", 0.2]\n", + "t[\"RMSE\"] = [0.259, 0.270, 0.272, 0.192]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/5_test_of_sioux.ipynb b/pumpingtest_benchmarks/5_test_of_sioux.ipynb index 1c907a7..e000ee1 100755 --- a/pumpingtest_benchmarks/5_test_of_sioux.ipynb +++ b/pumpingtest_benchmarks/5_test_of_sioux.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -34,9 +34,9 @@ "metadata": {}, "outputs": [], "source": [ - "Q = 6605.754 #constant discharge in m^3/d\n", - "b = -15.24 #aquifer thickness in m\n", - "rw = 0.1524 #well radius in m" + "Q = 6605.754 # constant discharge in m^3/d\n", + "b = -15.24 # aquifer thickness in m\n", + "rw = 0.1524 # well radius in m" ] }, { @@ -52,20 +52,20 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/sioux100.txt')\n", + "data1 = np.loadtxt(\"data/sioux100.txt\")\n", "t1 = data1[:, 0]\n", "h1 = data1[:, 1]\n", - "r1 = 30.48 #distance between obs1 to pumping well\n", + "r1 = 30.48 # distance between obs1 to pumping well\n", "\n", - "data2 = np.loadtxt('data/sioux200.txt')\n", + "data2 = np.loadtxt(\"data/sioux200.txt\")\n", "t2 = data2[:, 0]\n", "h2 = data2[:, 1]\n", - "r2 = 60.96 #distance between obs2 to pumping well\n", + "r2 = 60.96 # distance between obs2 to pumping well\n", "\n", - "data3 = np.loadtxt('data/sioux400.txt')\n", + "data3 = np.loadtxt(\"data/sioux400.txt\")\n", "t3 = data3[:, 0]\n", "h3 = data3[:, 1]\n", - "r3 = 121.92 #distance between obs3 to pumping well" + "r3 = 121.92 # distance between obs3 to pumping well" ] }, { @@ -90,8 +90,10 @@ } ], "source": [ - "ml_0 = ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary='conf')\n", - "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers = 0)\n", + "ml_0 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\"\n", + ")\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml_0.solve()" ] }, @@ -131,13 +133,13 @@ } ], "source": [ - "#unknown parameters: k, Saq\n", - "ca_0 = Calibrate(ml_0)\n", - "ca_0.set_parameter(name='kaq0', initial=10)\n", - "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_0.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_0.series(name='obs3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "# unknown parameters: k, Saq\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", "ca_0.fit(report=True)" ] }, @@ -224,7 +226,7 @@ ], "source": [ "display(ca_0.parameters)\n", - "print('RMSE:', ca_0.rmse())" + "print(\"RMSE:\", ca_0.rmse())" ] }, { @@ -242,7 +244,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -257,17 +259,17 @@ "hm1_0 = ml_0.head(r1, 0, t1)\n", "hm2_0 = ml_0.head(r2, 0, t2)\n", "hm3_0 = ml_0.head(r3, 0, t3)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t1, hm1_0[0], label='ttim1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t2, hm2_0[0], label='ttim2')\n", - "plt.semilogx(t3, h3, '.', label='obs3')\n", - "plt.semilogx(t3, hm3_0[0], label='ttim3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t1, hm1_0[0], label=\"ttim1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t2, hm2_0[0], label=\"ttim2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", + "plt.semilogx(t3, hm3_0[0], label=\"ttim3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/siouxsfit1.eps')\n", + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/siouxsfit1.eps\")\n", "plt.show();" ] }, @@ -293,8 +295,10 @@ } ], "source": [ - "ml_1 = ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary='conf')\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=0, tsandQ=[(0, Q)], layers=0)\n", + "ml_1 = ttim.ModelMaq(\n", + " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=10, topboundary=\"conf\"\n", + ")\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=0, tsandQ=[(0, Q)], layers=0)\n", "ml_1.solve()" ] }, @@ -344,15 +348,15 @@ } ], "source": [ - "#unknown parameters: k, Saq, res, rc\n", - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0', initial=10)\n", - "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", - "#ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0, pmin = 0)\n", - "ca_1.set_parameter_by_reference(name='rc', parameter=w_1.rc, initial=0)\n", - "ca_1.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_1.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_1.series(name='obs3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "# unknown parameters: k, Saq, res, rc\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "# ca_1.set_parameter_by_reference(name='res', parameter=w_1.res, initial=0, pmin = 0)\n", + "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc, initial=0)\n", + "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name=\"obs3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", "ca_1.fit(report=True)" ] }, @@ -451,7 +455,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -471,7 +475,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -486,17 +490,17 @@ "hm1_1 = ml_1.head(r1, 0, t1)\n", "hm2_1 = ml_1.head(r2, 0, t2)\n", "hm3_1 = ml_1.head(r3, 0, t3)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t1, hm1_1[0], label='ttim1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t2, hm2_1[0], label='ttim2')\n", - "plt.semilogx(t3, h3, '.', label='obs3')\n", - "plt.semilogx(t3, hm3_1[0], label='ttim3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t1, hm1_1[0], label=\"ttim1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t2, hm2_1[0], label=\"ttim2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"obs3\")\n", + "plt.semilogx(t3, hm3_1[0], label=\"ttim3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/siouxfit2.eps')\n", + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/siouxfit2.eps\")\n", "plt.show();" ] }, @@ -586,13 +590,14 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'rc'], \\\n", - " index=['AQTESOLV', 'MLU', 'ttim', 'ttim-rc'])\n", - "t.loc['AQTESOLV'] = [282.659, 4.211E-03, '-']\n", - "t.loc['ttim'] = np.append(ca_0.parameters['optimal'].values, '-')\n", - "t.loc['ttim-rc'] = ca_1.parameters['optimal'].values\n", - "t.loc['MLU'] = [282.684, 4.209e-03, '-']\n", - "t['RMSE'] = [0.003925, 0.003897, ca_0.rmse(), ca_1.rmse()]\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"rc\"], index=[\"AQTESOLV\", \"MLU\", \"ttim\", \"ttim-rc\"]\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [282.659, 4.211e-03, \"-\"]\n", + "t.loc[\"ttim\"] = np.append(ca_0.parameters[\"optimal\"].values, \"-\")\n", + "t.loc[\"ttim-rc\"] = ca_1.parameters[\"optimal\"].values\n", + "t.loc[\"MLU\"] = [282.684, 4.209e-03, \"-\"]\n", + "t[\"RMSE\"] = [0.003925, 0.003897, ca_0.rmse(), ca_1.rmse()]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/6_test_of_schroth.ipynb b/pumpingtest_benchmarks/6_test_of_schroth.ipynb index dc5cf70..b9df833 100755 --- a/pumpingtest_benchmarks/6_test_of_schroth.ipynb +++ b/pumpingtest_benchmarks/6_test_of_schroth.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -41,12 +41,12 @@ "metadata": {}, "outputs": [], "source": [ - "Q = 82.08 #constant discharge in m^3/d\n", - "zt0 = -46 #top boundary of upper aquifer in m\n", - "zb0 = -49 #bottom boundary of upper aquifer in m\n", - "zt1 = -52 #top boundary of lower aquifer in m\n", - "zb1 = -55 #bottom boundary of lower aquifer in m\n", - "rw = 0.05 #well radius in m" + "Q = 82.08 # constant discharge in m^3/d\n", + "zt0 = -46 # top boundary of upper aquifer in m\n", + "zb0 = -49 # bottom boundary of upper aquifer in m\n", + "zt1 = -52 # top boundary of lower aquifer in m\n", + "zb1 = -55 # bottom boundary of lower aquifer in m\n", + "rw = 0.05 # well radius in m" ] }, { @@ -62,14 +62,14 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/schroth_obs1.txt', skiprows = 1)\n", + "data1 = np.loadtxt(\"data/schroth_obs1.txt\", skiprows=1)\n", "t1 = data1[:, 0]\n", "h1 = data1[:, 1]\n", - "r1 = 0 \n", - "data2 = np.loadtxt('data/schroth_obs2.txt', skiprows = 1)\n", + "r1 = 0\n", + "data2 = np.loadtxt(\"data/schroth_obs2.txt\", skiprows=1)\n", "t2 = data2[:, 0]\n", "h2 = data2[:, 1]\n", - "r2 = 46 #distance between observation well2 and pumping well" + "r2 = 46 # distance between observation well2 and pumping well" ] }, { @@ -94,8 +94,8 @@ } ], "source": [ - "ml_0 = ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", - "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, tsandQ = [(0, Q), (1e+08, 0)])\n", + "ml_0 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)])\n", "ml_0.solve()" ] }, @@ -128,11 +128,11 @@ } ], "source": [ - "ca_0 = Calibrate(ml_0)\n", - "ca_0.set_parameter(name='kaq0', initial=10)\n", - "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_0.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca_0.fit(report=True)" ] }, @@ -215,7 +215,7 @@ ], "source": [ "display(ca_0.parameters)\n", - "print('RMSE:', ca_0.rmse())" + "print(\"RMSE:\", ca_0.rmse())" ] }, { @@ -233,7 +233,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -247,15 +247,15 @@ "source": [ "hm1_0 = ml_0.head(r1, 0, t1)\n", "hm2_0 = ml_0.head(r2, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t1, hm1_0[-1], label='ttim1')\n", - "plt.semilogx(t2, hm2_0[-1], label='ttim2')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t1, hm1_0[-1], label=\"ttim1\")\n", + "plt.semilogx(t2, hm2_0[-1], label=\"ttim2\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_one1.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_one1.eps\");" ] }, { @@ -282,8 +282,8 @@ } ], "source": [ - "ml_1 = ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=5, tsandQ = [(0, Q), (1e+08, 0)])\n", + "ml_1 = ttim.ModelMaq(z=[zt1, zb1], kaq=10, Saq=1e-4, tmin=1e-4, tmax=1)\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, rc=0, res=5, tsandQ=[(0, Q), (1e08, 0)])\n", "ml_1.solve()" ] }, @@ -321,13 +321,13 @@ } ], "source": [ - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0', initial=10)\n", - "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_1.set_parameter_by_reference(name='rc', parameter=w_1.rc[:], initial=0.2)\n", - "ca_1.set_parameter_by_reference(name='res', parameter=w_1.res[:], initial=3)\n", - "ca_1.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_1.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter_by_reference(name=\"rc\", parameter=w_1.rc[:], initial=0.2)\n", + "ca_1.set_parameter_by_reference(name=\"res\", parameter=w_1.res[:], initial=3)\n", + "ca_1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca_1.fit(report=True)" ] }, @@ -438,7 +438,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -456,7 +456,7 @@ }, { "data": { - "image/png": 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\n", 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OJiIiARR0BYC19mtrbW7+w2SgsZN5Ql1yWgbZuV5yM1twaP2ttIrrz2vLXuPyGZerb4CISAgLugLgOOOAL4rZZoGvjTGpxphrKjBTSOnZrBaRbhcRBjyuWO7r/hDPDXiOHVk7uHj6xby1/C3yvHlOxxQRET9zZBigMWYWUL+ITQ9Yaz/L3+cBoBtwgS0ipDGmobV2qzGmLr7bBjdZa+cXsd81wDUACQkJXdPT0/34TkJDUXMIZBzK4NHkR5m9cTZd6nbhsT6P0aRqE4eTiohIaVS6eQCMMWOB64BB1tqsEuz/EHDQWvv0yfYL93kASstay7S0aTyx8AnybB53db+LC1teqJUGRUQqiUo1D4AxZhhwD3BecR/+xpg4Y0zVoz8DQ4HlFZcyPBhjOK/5eUweNZnT6pzGIz88wg2zb+C3rN+cjiYiIuUUdAUA8CJQFZiZP8Tv3+Br8jfGzMjfpx6wwBizFFgEfG6t/dKZuKGvflx9XhvyGvf1uI+U7SlcOPVCftrxk9OxRESkHILyFkCg6BZA+aXtS+OWObew5eAWHj7zYUY2H+l0JBERKUalugUgwW3P3nj6V32E5tU6cP+C+3lh8Qt4rdfpWCIiUkpupwNI5XF02eHsXC+R7jEM7NOA15a9Rvr+dB7r/RjR7minI4qISAmpBUBK7OikQV4LObku2nnGc1vX2/h6w9dc/dXV7Dq0y+mIIiJSQioApMSOmTTI7aJX89qM6zCOCf0nsGbPGi7/XLMHiohUFuoEKKVS1KRBAL9k/MJNs28iKzeLp896mj6N+jiYUkREQJ0AxY+6Jsbz5wEtjvnwB2hfqz0fnPsBTao24c+z/8wHKz9wKKGIiJSECgDxm/px9Xl72Nv0a9yPJxY9wd8X/p1cb+6pDxQRkQqnAkD8KtYTy7/6/4ux7cby31X/5aY5N3Ew+6DTsURE5DgqAMTvIlwR3Nn9Th7s9SA/bP2BK7+4kq0HtzodS0REClEBIAEzptUYXhn8Cjsyd3Dp55eydOdSpyOJiEg+FQASUL0a9uK9c94j1h3LuC/H8eV6LdkgIhIMVABIwDWr0YwPzv2ADrU7cNf8u3h16auE0/BTEZFgpAJAKkR8dDyvD32dEc1G8OKSF3lgwQPk5OU4HUtEJGxpLQCpMJERkfy9z99JqpbEi0te5FDuIf551j9xu/SfoYhIRVMLgFQoYwzXnn4t93S/h1kbZ/Hgdw9qNUEREQfoTy9xRNu4c+lefRPT0j4g1hPLA2c8gDHG6VgiImFDBYBUuN+XFe5IdL3+fLj6Q2I9sdzW5TYVASIiFUQFgFS435cVNhzZcTbdm0Tz1vK3iHPHce3p1zodT0QkLKgAkAp3dFnhnFwvHncEd3S9lymbI3lxyYvEemK5st2VTkcUEQl5KgCkwnVNjOf98T2PWVa4c8LDHMo9xFM/PkWsO5Y/tPqD0zFFREKaCgBxRNfE+GOWFHa73Pyj7z/Iys3i4R8eJsYdwznNznEwoYhIaNMwQAkanggPE/pPoEu9Lty/4H7mbpzrdCQRkZClAkCCSow7hhcHvkjbmm2545s7+GHrD05HEhEJSSoAJOhUiazCv4f8m6TqSdwy9xYW/7bY6UgiIiFHBYAEpepR1XltyGvUja3LDbNuYEXGCqcjiYiEFBUAErRqx9Tm9SGvUzWyKtfOvJZ1e9c5HUlEJGSoAJCg1qBKAyYOnYjb5eZPX/+JTfs3OR1JRCQkBF0BYIx5yBizxRizJP+ryLFgxphhxpjVxpi1xph7KzqnVJyEagm8NuQ1sr3ZjP96PNsztzsdSUSk0gu6AiDfBGttp/yvGcdvNMZEAC8Bw4F2wKXGmHYVHVIqTsv4lrw65FX2Z+/nT1//iV2HdjkdSUSkUgvWAuBUegBrrbVp1tpsYBIwyuFMEmDta7XnpUEvsT1zO9fOvJZ9R/aRmr6Hl+auJTV9j9PxREQqlWAtAG40xiwzxrxpjIkvYnsjoPDN4M35z0mI61KvC88NfI71+9bzf5//icvfmMczX6/m8onJKgJERErBkQLAGDPLGLO8iK9RwCtAc6ATsA14pqhTFPGcLea1rjHGpBhjUnbu3Om39yDOObPhmTx91tOsP7AaV/3/4CWHnFwvyWkZTkcTEak0HCkArLWDrbUdivj6zFq7w1qbZ631Aq/ja+4/3magSaHHjYGtxbzWa9babtbabnXq1PH/mxFHDEwYyDVtH8AVu56YRpPwuH2rDIqISMkE3S0AY0yDQg9HA8uL2O1HoKUxpqkxJhK4BJhaEfkkeNzY4yIua/Fn3FV/Yczg1ccsLiQiIicXjKsBPmWM6YSvSX8DcC2AMaYhMNFae461NtcYcyPwFRABvGmt/cWpwOKc+3pfQyYbmbLuLfonnc6gxEFORxIRqRSMtUXeOg9J3bp1sykpKU7HED87kneEq764irR9abx3znu0jG/pdCQRkaBgjEm11nYralvQ3QIQKa2oiCj+NeBfxHpiuXnOzew7ss/pSCIiQU8FgISEenH1mNB/AjuydnDnN3eS6811OpKISFBTASAho1PdTvy1519J3pbMhNQJTscREQlqwdgJUKTMRrcczardq3hnxTu0rtma85qf53QkEZGgpBYACTl3dr+THvV78PD3D7N8V1GjSEVERAWAhByPy8PTZz1N7Zja3DLnFnZmaQZIEZHjqQCQkBQfHc/zA5/nQM4Bbpt3G9l52U5HEhEJKioAJGS1rtmaR3s/ytKdS3l84eOE05wXIiKnok6AEtLOTjqb1btX8/rPr9OmZhsubXOp05FERIKCWgAk5N3Y+Ub6N+7PPxb9gx+3/+h0HBGRoKACQEKey7h4ou8TJFRL4I55d7Dl4BanI4mIOE4FgISFKpFVeH7A8+R6c7llzi1k5WQ5HUlExFEqACRsJFVP4qmznuLXvb/y4PcPqlOgiIQ1FQASVvo06sMtXW7hqw1f8cbyN5yOIyLiGBUAEnb+2P6PDG86nOd/ep75m+c7HUdExBEqACTsGGN4+MyHaVOzDffMv4e0fWnHbE9N38NLc9eSmr7HoYQiIoGnAkDCUow7hucGPEdkRCS3zLmF/dn7Ad+H/+UTk3nm69VcPjFZRYCIhCwVABK2GlRpwLP9n2Xzgc3cM/8e8rx5JKdlkJ3rxWshJ9dLclqG0zFFRAJCBYCEta71unLfGfexYMsCXlj8Aj2b1SLS7SLCgMftomezWk5HFBEJCE0FLGHvotYXsWr3Kt5Y/gat+7Xm/fE9SU7LoGezWnRNjHc6nohIQKgFQAS4r8d9dKnbhQe/e5DYKtv584AW+vAXkZCmAkAE8ER4eKb/M9SIrsEtc28h45Du/YtIaFMBIJKvdkxtnhvwHLsP7+bu+XeT6811OpKISMCoABAppF2tdvy1519ZtH0RLy15yek4IiIBowJA5DijWoziwlYXMvHniczdONfpOCIiAaECQKQI9/a4l3a12vHAggfYtH+T03FERPxOBYBIEaIioni2/7MYY7j9m9s5nHvY6UgiIn5V4gLAGNPNGHObMeafxphHjDEXGWNq+juQMeZDY8yS/K8Nxpglxey3wRjzc/5+Kf7OIdKoSiOe6PsEq3av4u8L/+50HBERvzplAWCMucoY8xNwHxADrAZ+A/oAM40xbxtjEvwVyFp7sbW2k7W2E/Ap8L+T7D4gf99u/np9kcL6Ne7Htaddy+S1k/nfryf7T1FEpHIpyUyAcUBva+2hojYaYzoBLYGN/gxmjDHARcBAf55XpLSuP/16lu1cxuPJj9O2Zlva1mrrdCQRkXI7ZQuAtfal4j7887cvsdbO9m8sAPoCO6y1vxb30sDXxphUY8w1AXh9EQAiXBE82e9J4qPjuW3ebew7ss/pSCIi5VaaPgBNjTHPGmP+Z4yZevSrLC9qjJlljFlexNeoQrtdCvz3JKfpba3tAgwH/myM6VfMa11jjEkxxqTs3LmzLHFFqBldk2f6P8OOrB38ZcFf8Fqv05FERMrFWGtLtqMxS4E3gJ+Bgn/9rLXf+D2UMW5gC9DVWru5BPs/BBy01j59sv26detmU1LUX1DK7oOVH/DEoie4pcstjO843uk4IiInZYxJLa6fXGlWAzxsrX3eT5lOZTCwqrgPf2NMHOCy1h7I/3ko8EgFZZMwdmmbS1mycwkvLH6BDrU70LNBT6cjiYiUSWnmAXjOGPM3Y0wvY0yXo18BynUJxzX/G2MaGmNm5D+sByzIb5VYBHxurf0yQFlEChhjeKjXQzSt1pR75t/DjswdTkcSESmT0twCeAK4EljH77cArLW20vTS1y0A8Ze0fWlcOv1SWsa35K2z38IT4XE6kojICU52C6A0LQCjgWbW2rOstQPyvyrNh7+IPzWr3oyHez/M0p1LeTb1WafjiIiUWmkKgKVAjUAFEalshiUN44q2V/Deyvf4coPvDlRq+h5emruW1PQ9DqcTETm50nQCrAesMsb8CBw5+qS19jy/pxKpJG7vdjvLdy3nb9/9jZysetz13+1k53qJdLt4f3xPuibGOx1RRKRIpSkA/hawFCKVlMfl4emznuai6Rfxz8X3k533J7w2ipxcL8lpGSoARCRolWQtAAO+8f5FfRXeRyQc1Yurx1P9nmJv7lZiGk4mwlg8bhc9m9VyOpqISLFK0gdgrjHmpuMX/DHGRBpjBhpj3gbGBiaeSOVwRoMzuLnLTbiqLmFQz9Vq/heRoFeSAmAYkAf81xiz1RizwhizHvgV33S9E6y1/wlgRpFKYVyHcQxJHMLCfe+QGfGz03FERE6qxPMAABhjPEBt4JC1dm/AUgWI5gGQQDuUe4ixX4xl44GNvDf8PVrEt3A6koiEsXLNA2CMqXn0C6iKbwSAq9BzIpIvxh3D8wOfJ9Ydy41zbmT34d1ORxIRKVJJbgGkAin533cCa/A1/+/Mf05ECqkfV5/nBz7PrkO7uG3ubeTk5TgdSUTkBKcsAKy1Ta21zYCvgJHW2trW2lrACOB/gQ4oUhl1qN2Bx3o/xk+//cQjyY9QmlttIiIVoTQzAXa31h5djAdr7RfAWf6PJBIahjUdxnWnX8eUtVN4Z8U7TscRETlGaSYC2mWM+QvwHmCBK4CMgKQSCRHXn3496/au45mUZ0iqlsRZTVQzi0hwKE0LwKVAHWAyMAWom/+ciBTDZVw83udx2tZqy93z7+bXPb86HUlEBChFAWCt3W2tvcVa2zn/6xZrrbo4i5xCjDuG5wc8T5wnjpvm3KSRASISFEpcABhj6hhj/mmMmWGMmXP0K5DhREJFvbh6x4wMyM7LdjqSiIS50twCeB9YBTQFHgY2AD8GIJNISCo8MuDR5Ec1MkBEHFWaAqCWtfYNICd/IaBxQM8A5RIJScOaDuP6069nytopvP3L207HEZEwVppRAEdnM9lmjDkX2Ao09n8kkdB23enXsW7vOp5NfZam1ZtqZICIOKI0LQCPGWOqA3cAdwITgdsCkkokhLmMi8f6PFYwMmDNnjVORxKRMFSaUQDTrbX7rLXLrbUDrLVdrbVTAxlOJFQVHhlw85ybyTikKTVEpGKVZhRAK2PMbGPM8vzHp+VPDCQiZVAvrh4vDHzBNzJgnkYGiEjFKs0tgNeB+8jvC2CtXQZcEohQIuGife32PNbnMRb/tphHfjhxzYDU9D28NHctqel7HEooIqGqNJ0AY621i4wxhZ/L9XMekbAzLGkYaXvTeGXpKzSv0Zw/dvgj4Pvwv3xiMtm5XiLdLt4f35OuifEOpxWRUFGaFoBdxpjm+NYBwBhzIbAtIKlEwsx1p1/H2UlnMyF1AvM2zQMgOS2D7FwvXgs5uV6S09RPQET8pzQFwJ+BV4E2xpgtwK3AdQFJJRJmXMbFo70fpV2tdtwz/x7W7FlDz2a1iHS7iDDgcbvo2ayW0zFFJISYks5GZoyJAi4EkoCawH7AWmsfCVg6P+vWrZtNSUlxOoZIsXZk7uCyzy/D7XLzwbkfsOE3F8lpGfRsVkvN/yJSasaYVGttt6K2laYF4DNgJL5OgFuBg0Bm+eOJyFFH1wzYfXg3t827jY6N4/jzgBb68BcRvytNAdDYWnuJtfYpa+0zR7/K+sLGmDHGmF+MMV5jTLfjtt1njOH2f08AACAASURBVFlrjFltjDm7mOObGmMWGmN+NcZ8aIyJLGsWkWDSvnZ7Hu3zKIt/W8zDPzysNQNEJCBKUwB8b4zp6MfXXg5cAMwv/KQxph2+4YXtgWHAy8aYiCKO/wcwwVrbEtgDXO3HbCKOGpY0jBtOv4Gp66by1i9vOR1HRELQKYcBGmN+xtfz3w380RiTBhwBDL4+AKeV5YWttSvzz3/8plHAJGvtEWC9MWYt0AP4oVAmAwwELst/6m3gIeCVsmQRCUbXnX4dafvS+Ffqv6gRVYMLWl7gdCQRCSElmQdgRMBTHKsRkFzo8eb85wqrBey11uaeZB+RSs0Yw6O9H+VA9gH+9v3fyMnL4eI2FzsdS0RCxCkLAGttellPboyZBdQvYtMD1trPijusqBhl2OdohmuAawASEhKKeUmR4BTtjua5gc9xx7w7eGzhY2R7s7my3ZVOxxKREFCamQBLzVo7uAyHbQaaFHrcGN+og8J2ATWMMe78VoCi9jma4TXgNfANAyxDHhFHRUVEMaH/BO6efzdP/fgU2XnZXN1RXV5EpHxK0wmwokwFLjHGRBljmgItgUWFd7C+btFz8c1LADAW3zBFkZDkifDw1FlPMTxpOP/66V/8e+m/nY4kIpWcYwWAMWa0MWYz0Av43BjzFYC19hfgI2AF8CXwZ2ttXv4xM4wxDfNPcQ9we34nwVrAGxX9HkQqksfl4Ym+T3Be8/N4aclLPP/T8xoiKCJlFtBbACdjrZ0MTC5m2+PA40U8f06hn9PwjQ4QCRsRrgge7f0oHpeH139+nRxvDrd3vb2o0TQiIiflWAEgImXjMi4e7PUgHpeH//zyH7Lzsrm3x70qAkSkVFQAiFRCLuPi/jPuJzIikndWvEO2N5u/9vwrLhOM3XpEJBipABCppIwx3NntTiIjIpn480Ry8nJ4+MyHiXAVNXGmiMixVACIVGLGGG7ufDORrkheXvoyOd4cHu/zOG6XfrVF5OT0r4RIJWeM4fpO1+OJ8PDcT8+R483hH/3+gcflcTqaiAQxFQAiIWJ8x/FEuiL5Z8o/yZmXwzNnPUNkhBbJFJGiqceQSAj5v/b/x/1n3M+8TfO4Ze4tHM497HQkEQlSKgBEQsylbS7lb73+xndbvmPUJ39k3q/rnY4kIkFIBYBICGoaNYic7Rez5dBKbvzmSj5ctsDpSCISZFQAiISg5LQMjuztRFb69Vgbwd8X38T7K9/X1MEiUkAFgEgI6tmsFpFuF+ZIY/I238zptc7gyUVPcuc3d3Iw+6DT8UQkCGgUgEgI6poYz/vje5KclkHPZrXoknAB//nlPzz303Os3rOaZ/s/S6v4Vk7HFBEHmXBqEuzWrZtNSUlxOoaIY1K2p3DX/Ls4mH2Qv/T8C6NajHI6kogEkDEm1VrbrahtugUgEka61e/GxyM/5rQ6p/GX7/7CQ98/pKGCImFKBYBImKkdU5tXh7zKnzr+iU9//ZQrv7iSTfs3OR1LRCqYCgCRMOR2ubm5y828NOglth7cykXTL2J2+mynY4lIBVIBIBLG+jXux0cjPyKpWhK3zruVBxY8wG9ZvzkdS0QqgAoAkTDXqEoj3h7+Nld3uJov1n/BiMkjeHXpq+obIBLiVACICJERkdza9VY+O/8z+jTqw4tLXmTklJF8sf4LTR4kEqJUAIhIgSZVm/Bs/2d58+w3qRFVg7vn382VX1zJzzt/djqaiPiZCgAROUH3+t2ZdO4kHjnzETYf2MxlMy7j/m/vZ0fmDqejiYifqAAQkSJFuCIY3XI0n1/wOVd3uJovN3zJyCkjeWXpKxzKPeR0PBEpJxUAIgJAavoeXpq7ltT0Pcc8H+eJ49autzL1/Kn0adSHl5e8zMjJI/k87XP1DxCpxDQVsIiQmr6Hyycmk53rJdLt4v3xPemaGF/kvinbU3jqx6dYuXslp9U5jXu638NpdU6r4MQiUhKaClhETio5LYPsXC9eCzm5XpLTMordt1v9bkwaMYlHez/K1oNbuXzG5dz77b1sz9xegYlFpLxUAIhIwfLBEQY8bhc9m9U66f4u4+L8FuczffR0/tTxT8zcMJORk0fy8pKXycrJqqDUIlIeugUgIoDvNsDR5YOLa/4vzpaDW5iQOoGvNnxFjagaXNDyAi5ufTENqzQMUFoRKYmT3QJwpAAwxowBHgLaAj2stSn5zw8BngQigWzgLmvtnCKOfwj4E7Az/6n7rbUzTvW6KgBEAmvJb0t4+5e3mbPJ92vbv3F/Lmt7GT3q98AY43A6kfBzsgLAXdFh8i0HLgBePe75XcBIa+1WY0wH4CugUTHnmGCtfTqAGUWklDrV7USnup3YdnAbH635iE/WfMKcTXNoXr05l7a5lJHNRxLriXU6pojgUB8Aa+1Ka+3qIp5fbK3dmv/wFyDaGBNVselEpLwaVGnALV1uYdaYWTza+1EiIyJ5bOFjDPp4EP9Y9A/S96c7HVEk7AVzJ8A/AIuttUeK2X6jMWaZMeZNY0zpbliKSIWIioji/Bbn8+GID3l3+Lv0a9yPSasnMWLyCK6bdR3zN8/Ha71OxxQJSwHrA2CMmQXUL2LTA9baz/L3mQfcebQPQKFj2wNTgaHW2nVFnLsevtsFFngUaGCtHVdMjmuAawASEhK6pqfrLw8RJ+3M2sknv37Cx6s/ZuehnTSp2oRLWl/C+S3Pp1pkNafjiYSUoOsEWPDiRRQAxpjGwBzgj9ba70pwjiRgurW2w6n2VSdAkeCRk5fDrI2z+O+q/7L4t8XEuGMY0WwEl7a5lJbxLZ2OJxISgrETYJGMMTWAz4H7Tvbhb4xpYK3dlv9wNL5OhSJSiXgiPAxvOpzhTYezImMFk1ZNYuq6qXy85mO61+/OpW0u5azGZxEZEel0VJGQ5NQwwNHAC0AdYC+wxFp7tjHmL8B9wK+Fdh9qrf3NGDMR+Le1NsUY8y7QCd8tgA3AtYUKgmKpBUAkuO09vJdPf/2UD1d/yLbMbVSLrMawpGGMbD6S0+ucrqGEIqUUtLcAKpoKAJHKIc+bx/dbv2da2jTmbpzL4bzDNK7SmBHNRzCy2UgSqiU4HVGkUlABkE8FgEhglWc2weJk5mQyK30W09KmsWjbIiyW0+qcxshmIxmWNIwa0TX88joioUgFQD4VACKBU5oVBctqe+Z2ZqyfwbR101i7dy1ul5u+jfoysvlI9RcQKUKl6QQoIpVXUSsK+rsAqB9Xn3EdxvHH9n9k9Z7VTFs3jRnrZzB301yqRlZlaOJQRjYfSee6nXGZYJ7mRMR5KgBExC+OriiYk+st0YqC5WGMoU3NNrSp2Ybbu97Owm0LmZbmKwY+/fVTGlVpxLnNzmVks5EkVU8KWA6Ryky3AETEbwLRB6A0snKymL1xNtPWTWPh9oV4rZeOtTsyotkIhjUdRs3omhWeScRJ6gOQr6gCICcnh82bN3P48GGHUjkjOjqaxo0b4/F4nI4iEhC/Zf3GF+u/YNq6aazesxq3cXNmozMZ3nQ4A5oMIM4T53REkYBTAZCvqAJg/fr1VK1alVq1aoXNGGNrLRkZGRw4cICmTZs6HUck4NbsWcP0ddOZsX4GO7J2EBURRb/G/RjedDh9G/Ul2h3tdESRgFABkK+oAmDlypW0adMmbD78j7LWsmrVKtq2bet0FJEK47Velu5cyhfrv+CrDV+x+/BuYt2xDEwYyPCmw+nVoBeeCLWKSejQKIBTCLcPfwjP9yziMi461+1M57qdubv73aTsSOHL9V8yM30m09OmUy2yGoMTBzMsaRjd63fH7dI/kRK6NE4mCG3YsIEOHU65tlGB+fPn06VLF9xuN5988kkAk4mEDrfLTc8GPXnozIeYd9E8Xhr0Ev0a9+PL9V9yzcxrGPTxIB5PfpyfdvykJYslJKm8DQEJCQn85z//4emnn3Y6ikil5Inw0K9xP/o17sfh3MMs2LKAL9Z/weS1k5m0ehL1YusxNGkoZyedzWm1T1MLmoQEFQBB4Nlnn+XNN98EYPz48Zx//vnk5uYyduxYFi9eTKtWrXjnnXeIjY3l3nvvZerUqbjdboYOHcrTTz9NUlISAC6XGnREyivaHc3gxMEMThxMZk4m8zbN48v1XzJp1STeXfEu9ePqMzRxKEOThqoYkEpNBUAZ+HOsc2pqKm+99RYLFy7EWssZZ5zBWWedxerVq3njjTfo3bs348aN4+WXX2bcuHFMnjyZVatWYYxh7969fnpHIlKUOE8c5zY7l3Obncv+7P3M2zSPrzd8zQerPuCdFe/QIK4BQxKHqBiQSkkFQCn5e77zBQsWMHr0aOLifGOSL7jgAr799luaNGlC7969Abjiiit4/vnnufXWW4mOjmb8+PGce+65jBgxwi/vSUROrVpkNc5rfh7nNT/vpMXA2Uln07F2RxUDEvRUAJSSv+c7L24Y5vH/eBhjcLvdLFq0iNmzZzNp0iRefPFF5syZU+bXFpGyKUkxcPQ2gYoBCVa6aVxKR+c7jzD4Zb7zfv36MWXKFLKyssjMzGTy5Mn07duXjRs38sMPPwDw3//+lz59+nDw4EH27dvHOeecw7/+9S+WLFnij7ckEnRS0/fw0ty1pKbvcTrKKR0tBl4c9CLfXPwNj/d5nJbxLXl/1ftcPuNyhn06jKd/fJplO5cVW/CLOEETAa1cWerJcPw933lRnQDPOecc+vXrx/fff0/Lli1599132bdvH6NGjeLw4cNYa7nzzjsZO3YsP/74I6NHj2bPnj1ER0dTv359fvnll1O+blneu0igVcSywhVhf/Z+5m6cy9fpX/P91u/J9ebSMK5hQZ8BtQxIRdBMgPn8VQCEinB+7xK8Xpq7lme+Xo3XQoSB24e25s8DWjgdq1xOVgwMSRpCx9odtXyxBIRmAhSRSqMilxWuKNUiqzGqxShGtRjFviP7mLdpHl9t+Ir3V73P2yvepl5sPYYkDmFw4mA61elEhCvC6cgSBtQCEMZ/BYfze5fg5vSywhVlf/Z+vtn0DTPTZ/Ldlu/I9mZTO6Y2gxIGMTRxKF3qddF0xFIuagEQkUqla2J8SH/wH1Utshojm49kZPORZOZkMn/zfGamz+SztZ/x4eoPqRldk4EJAxmSOITu9bvjcWmhIvEfFQAiIkEgzhPH8KbDGd50OFk5WXy39TtmbpjJjLQZfLLmE6pHVWdAkwEMSRyiVQvFL1QAiEhYKM9thYq+JRHrifV1EEwcwuHcw3y/9Xtmps9kVvospqydQlVPVc5qchaDEwfTu2Fvot3RAc8koUcFgIiEvPIMLXR6WGK0O5qBCQMZmDCQ7LxskrclMzN9JnM3zWV62nRi3DH0adSHIYlD6Ne4H3GeuArLJpWbCoAgtGHDBkaMGMHy5ctLtP+zzz7LxIkTcbvd1KlThzfffJPExMQApxSpPMozg6e/Z/8sj8iIyIJVC3O8OaRsT2FW+ixmb5zNzPSZRLoi6dWwF4MTBzOgyQCqR1V3JKdUDioAQkDnzp1JSUkhNjaWV155hbvvvpsPP/zQ6VgiQaM8QwuDdViix+WhV8Ne9GrYi/vPuJ+lO5cyM30mszfO5pvN3xBhIuhevztDEocwMGEgtWNqOx1ZgoyGAQbBULiiZgIcNmwYZ5xxRomWAy5s8eLF3HjjjXz33XenfN1geO8iFaUy9QEoD2stKzJWMGvjLGalz2LD/g0YDJ3rdvYtc5wwmAZVGjgdUypI0M0EaIwZAzwEtAV6WGtT8p9PAlYCq/N3TbbWXlfE8TWBD4EkYANwkbX2lJOG+60A2LQINnwLSX2hSY/SHXuc1NRUrrrqKpKTkwuWA37vvffo0qULCxYsKFgOuF27dowbN45evXodsxxwjRo1jjnfjTfeSP369fnLX/5yytdWASAS2qy1rN27llnps5i1cRZr9qwBoH2t9gxOHMyQxCEkVtPtwlB2sgLAqbknlwMXAPOL2LbOWtsp/+uED/989wKzrbUtgdn5jyvGpkXw9nkw53Hf902LynW6wssBV6lSpdjlgBcsWEC1atUKlgP+3//+R2xs7DHneu+990hJSeGuu+4qVyYRCQ3GGFrGt+T6Ttfz6XmfMn30dG7tcisu4+K5n55jxOQRXDD1Al5e8jJr9qzRYkVhxpECwFq70lq7+tR7FmsU8Hb+z28D55c/VQlt+BbyssHm+b5v+LZcpyvLcsB/+MMfmDJlCsOGDSvYPmvWLB5//HGmTp1KVFRUuTKJSOCVZ8XDsh6bWC2RqztezQfnfsDMC2dyT/d7qBZZjX8v/Td/mPoHRk4ZyYTUCSzftfyYf5v8uTpjZVrpMdQFYyfApsaYxcB+4C/W2qI+YetZa7cBWGu3GWPqVli6pL4QEen78I+I9D0uh379+nHVVVdx7733Yq1l8uTJvPvuu9xyyy388MMP9OrV65jlgLOysjjnnHPo2bMnLVr4FkhZvHgx1157LV9++SV161bc/xUiUjbBMCyxflx9rmh3BVe0u4Jdh3YxZ+McZm+czTu/vMOby9+kQVwDBiUMoknUGTzySSbZuZR7GKTTQyrlWAErAIwxs4D6RWx6wFr7WTGHbQMSrLUZxpiuwBRjTHtr7f5y5LgGuAYgISGhrKf5XZMeMHaq3/oAdOnShauuuooePXznGT9+PPHx8bRt25a3336ba6+9lpYtW3L99defsBzwhAkTALjrrrs4ePAgY8aMAXzvc+rUqeV7nyISMME2LLF2TG0uan0RF7W+qGCxolkbZ/HR6o/I9r5HRFIVPAfak3egA9+ta1rm1wumIZUSwALAWju4DMccAY7k/5xqjFkHtAJSjtt1hzGmQf5f/w2A305yzteA18DXCbC0mYrUpEe5P/gLu/3227n99tuPeW7FihUn7BcbG8uiRSf2OZg1a5bfsohI4AXzsMTqUdULVi7MzMnk7SVf8NLCyXiqL8bEL+T9bR+yY8FABiUM4syGZ5ZqFsJgHVIZroLqFoAxpg6w21qbZ4xpBrQE0orYdSowFngy/3txLQoiIkGna2I874/vWaahheU5trTiPHHc0P1Czqg7iAVrtxJXI431h5KZt2keU9dNLZiFcFDCIPo17kfVyKpBk11OzalhgKOBF4A6wF5gibX2bGPMH4BHgFwgD/ibtXZa/jETgX9ba1OMMbWAj4AEYCMwxlq7+1SvG6zzADglnN+7iJRdjjeH1B2pzEqfxZyNc9h5aCdul5ueDXoyOGEw/Zv0p1aM/roPBkE3D4BTVAAcK5zfu4j4h9d6WbZzGbM3zmZW+iw2H9yMy7joUrcLgxIGMShhkCYecpAKgHwqAI4Vzu9dRPzPWsuaPWsKZiFcu3ct8PvEQ4MSBtG0elOHU4aXkxUAQdUHQEREKi9jDK1rtqZ1zdb8udOf2bBvA7M3zmb2xtk899NzPPfTczSv3pyBCQMZnDiYtjXbnjDnSVmUdarmQEzxXJmmnFYLQBj/FRzO711EKtb2zO0FxUDqjlS81kvDuIYMTPCNKOhctzMRrohSn7escwsEYk6CYJjf4XjBOBWw5Nu7dy8vv/wy4FsG+IMPPijYlpKSws0331yq87344ou0aNECYwy7du3ya1YRkbKqH1efy9tezptnv8m8i+bxyJmP0DK+JR+u/pA/fvVHBn48kIe+f4hvN39Ldl52ic9b1NwCgTwuUOcMRJ5T0S0Ahx0tAG644YaCAuCyyy4DoFu3bnTrVmThVqzevXszYsQI+vfvH4C0IiLlFx8dz+iWoxndcjSZOZl8u+Vb5qTP4csNX/Lpr58S54mjX+N+DEoYRN9GfYn1xBZ7rrLOLRCIOQmCeX6HougWgMPN4JdccgmfffYZrVu3xuPxsGbNGpo2bcrYsWPp3LkzTz/9NNOnT+ehhx5i/fr1bNu2jTVr1vDss8+SnJzMF198QaNGjZg2bRoej6fgvElJSaSkpFC7dvFrgDv93kVECsvOyyZ5WzKzN85m7sa57Dmyh0hXJGc2PJOBCQMZ0GQANaJrnHCc+gAUT50Ag9iTTz7J8uXLWbJkCfPmzSv4wAeYN2/eMfuuW7eOuXPnsmLFCnr16sWnn37KU089xejRo/n88885//yKWxNJRMTfIiMi6de4H/0a9+OvPf/K4t8WM2fjHGZtnMW8zfOIMBF0rdeVQQmDGJgwkPpxvtnmuybGl+kDs6zHBeqcgchzMioACvnHon+wavcqv56zTc023NPjHr+ca/jw4Xg8Hjp27EheXl7BaoAdO3Zkw4YNfnkNEZFg4Ha56V6/O93rd+fu7nezYvcKZqf7OhE+segJnlj0BB1rd/SNKEgYTFL1JKcjVzoqACqRo8v8ulwuPB5PwfAZl8tFbm6uk9FERALGGEP7Wu1pX6s9N3e5mbR9ab7VC9NPHF44KHEQ7Wq288vwwlCnAqAQf/2lXhpVq1blwIEDJ/wsIiJFa1a9Gc06NmN8x/EFwwvnbJzDm8vf5PWfXy9YynhgwkC61O1SpuGF4UAFgMNq1apF79696dChA0OGDMHtdnP66adz1VVX0blz51Kf7/nnn+epp55i+/btnHbaaZxzzjlMnDgxAMlFRJx3dHjh5W0vZ8/hPczbNI85G+fw0eqPeG/le8RHxdO/SX8GJQyiZ8OeREVEOR05aGgUQBj3hA/n9y4ioS0rJ4sFWxYwe+Ns5m+ez8Gcg8S6Y+nbuG/B8MIqkVWcjhlwGgUgIiJhJdYTy9CkoQxNGkpOXg4Lty8sGF741Yav8Lg8nNHgDAYlDKJ/k/7Ujil+yHSoUgtAGP8VHM7vXUTCU543j2W7ljE7fTazNs5iy8EtGAyd63YumJa4cdXGTsf0G60GmE8FwLHC+b2LiBxdvfDoGgVr9qwBoHV864JOhK3iW1XqEQW6BSAiInKcwqsX3tDpBjYd2OQbXrhxNq8sfYWXl75Mk6pNGJQwiEEJgzitzmm4TOgsoaMCQEREBGhStQlj249lbPux7Dq0i7mb5jJ742zeW/ke//nlP9SOqc2AJgMYlDCIHvV74InwnPqkQUwFgIiIyHFqx9RmTKsxjGk1hgPZB/h287fM3jib6WnT+XjNx1T1VC0YUdCnUZ+TLlgUrEKnLaOS8vdywJdffjmtW7emQ4cOjBs3jpycHL/mFREJN1Ujq3JOs3N4pv8zfHvJt7w48EUGJw7m+63fc8c3d9B3Ul9umn0Tk3+dzJ7De5yOW2LqBOhwR7gNGzYwYsQIli9ffsJiQGUxY8YMhg8fDsBll11Gv379uP7664vc1+n3LiJSmeV6c1n82+KCmQi3ZW7DZVy/L1jUZCANqjRwNKM6AQaxe++9l3Xr1tGpU6eC5YA7depU5uWAzznnnIJz9+jRg82bNzv47kREQlfhBYvu6X5PwYJFczbO4clFT/LkoidpV6tdQSfCZtWbBdWIAt0CcNiTTz5J8+bNWbJkCf/85z/p27cvS5Ys4bbbbjth33Xr1vH555/z2WefccUVVzBgwAB+/vlnYmJi+Pzzz4/ZNycnh3fffbdgxUAREQmcowsW3dzlZqacP4Vp50/jtq634Xa5eWHxC5z/2fmcN+U8nk19lqU7l+K1XqcjqwWgsO1//ztHVvp3OeCotm2of//9fjlXaZYDvuGGG+jXrx99+/b1y2uLiEjJJVVPYlz1cYzrMI7fsn5j7kbfiIJ3f3mXt5a/Rd2YugxIGMDAhIF0r98dj6viRxSoAKhESroc8MMPP8zOnTt59dVXHckpIiK/qxtbl4vbXMzFbS5m35F9zN88nzkb5zB13VQ+XP0hVSOrclbjsxiUMIgzG55ZYSMKVAAU4q+/1EvD38sBT5w4ka+++orZs2fjcukOj4hIMKkeVZ2RzUcysvlIDuce5vut3zN742y+2fwN09Omk1QtiWmjp1VIFhUADvP3csDXXXcdiYmJ9OrVC4ALLriABx980N+xRUSknKLd0QxMGMjAhIHkenP5acdP7D2yt8JeX8MAw3goXDi/dxGRcHCyYYBqIxYREQlDjhQAxpgxxphfjDFeY0y3Qs9fboxZUujLa4zpVMTxDxljthTa75zj9xEREZHiOdUHYDlwAXBMN3Vr7fvA+wDGmI7AZ9baJcWcY4K19umAphQREQlRjhQA1tqVwKlmRLoU+G8F5Qmq2ZkqQjj1/RARkRMFcx+Aizl5AXCjMWaZMeZNY0x8WV8kOjqajIyMsPpAtNaSkZFBdHS001FERMQhAWsBMMbMAuoXsekBa+1npzj2DCDLWru8mF1eAR4FbP73Z4BxxZzrGuAagISEhBO2N27cmM2bN7Nz586TRQo50dHRNG7c2OkYIiLikIAVANbaweU4/BJO8te/tXbH0Z+NMa8DxS6fZ619DXgNfMMAj9/u8Xho2rRpOaKKiIhUPkF3C8AY4wLGAJNOsk/h9RVH4+tUKCIiIiXk1DDA0caYzUAv4HNjzFeFNvcDNltr0447ZmKhIYNPGWN+NsYsAwYAJy6dJyIiIsUK+5kARUREQtXJZgIMqwLAGLMTSC9mc3VgXwmfrw3s8mO08iouu1PnLM2xJdn3VPucbHtprisE17UN9etakv38dW2D6bpCcF3b0h6n39niBdN1PXpsDWttnSK3Wmv15SuCXivp80CK03lLkt2pc5bm2JLse6p9Tra9NNc12K5tqF/Xiry2wXRdg+3alvY4/c5WjutakmODrhOgg4pbf7Fi1mUsn0BkLM85S3NsSfY91T4n267r6r9z+vu6lmQ/XdvAn7O0x+l3tnjBdF1PeWxY3QLwF2NMii3mnopUbrq2oUnXNXTp2padWgDK5jWnA0jA6NqGJl3X0KVrW0ZqARAREQlDagEQEREJQyoAREREwpAKABERkTCkAiAAjDFxxphUY8wIp7OIfxhj2hpj/m2M+cQYc73TecR/jDHnG2NeN8Z8ZowZ6nQe8Q9jTDNjzBvGmE+czhKsVAAUYox50xjzmzFm+XHPDzPGXl4ewgAABGNJREFUrDbGrDXG3FuCU90DfBSYlFJa/riu1tqV1trrgIsADTkKEn66tlOstX8CrgIuDmBcKSE/Xdc0a+3VgU1auWkUQCHGmH7AQeAda22H/OcigDXAEGAz8CNwKRABPHHcKcYBp+GbmjIa2GWtLXapYqkY/riu1trfjDHnAfcCL1prP6io/FI8f13b/OOeAd631v5UQfGlGH6+rp9Yay+sqOyVidvpAMHEWjvfGJN03NM9gLU2f3VCY8wkYJS19gnghCZ+Y8wAIA5oBxwyxsyw1noDGlxOyh/XNf88U4GpxpjPARUAQcBPv7MGeBL4Qh/+wcFfv7NycioATq0RsKnQ483AGcXtbK194P/bu38XOeowjuPvj0jOQsmBiBijKEkMwvkjpFKjqNhoowiCGBQlCBKMWJigf0BAFJtARCw0YCFoISgqVl5ESeEPlBgiBLEJKYRI4kFCQPJY7JxZzrs9dm82pzvvV3OzM9+beYaH3X32uzvzACR5mt4MgG/+/01D5TXJvcCjwBTw2Vgj00oNlVtgF/AAsDbJxqp6a5zBaWTDPmevBPYCW5K80hQK6mMBsLwssm7Z702q6kD7oahFQ+W1qmaB2XEFo1YNm9t9wL7xhaOWDJvXk8Bz4wvn/88fAS7vOHBd3+P1wIlVikXtMa+Ty9xOJvPaMguA5X0LbEpyY5I1wOPAx6sck1bOvE4uczuZzGvLLAD6JHkfOARsTnI8yY6q+gt4HvgCOAp8UFVHVjNODce8Ti5zO5nM68XhZYCSJHWQMwCSJHWQBYAkSR1kASBJUgdZAEiS1EEWAJIkdZAFgCRJHWQBIGlJSaaT7GyW17XZWz3Ji0meWmT9DfNtYJPckuRAW8eUdIEFgKRBpoGdAFV1oq22qkkupdc+e2BXxao6DKxPcn0bx5V0gc2AJA3yKrAhyY/AMeDmqpppul0+Qq8X+wzwBrAGeBI4BzxUVX8k2QDsB64CzgDPVtUvwP3AD83d3UiyFXinGfP1ghg+oXfb19fGeaJS1zgDIGmQl4Ffq+p2YPeCbTPAE/T6tO8FzlTVFnq3cJ2f2n8b2FVVW4GXgDeb9XcB3/ft613ghaq6Y5EYvgPubuFcJPVxBkDSqL6sqjlgLslpep/UAQ4Dtya5HLgT+DD5p5PrVPP3Gnr3cyfJWmC6qg42294DHuw7zu/AurGdhdRRFgCSRnWub/l83+Pz9F5bLgFONbMHC50FLmuWw4C+7s24sysLVdJCfgUgaZA54IpR/rGq/gR+S/IYQHpuazYfBTY2404Bp5Nsa7ZtX7Crm4CfR4lB0tIsACQtqapOAt80l+W9PsIutgM7kvwEHAEebtZ/DtzTN+4ZYH+SQ/z70/59wKcjHFvSALYDlrQqknwE7KmqYwPGTAEHgW3zVwxIaocFgKRVkWQzcHVVfTVgzCbg2qqavWiBSR1hASBJUgf5GwBJkjrIAkCSpA6yAJAkqYMsACRJ6iALAEmSOsgCQJKkDvobH38xhrFRnZ8AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -470,15 +470,15 @@ "source": [ "hm1_1 = ml_1.head(r1, 0, t1)\n", "hm2_1 = ml_1.head(r2, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t1, hm1_1[-1], label='ttim1')\n", - "plt.semilogx(t2, hm2_1[-1], label='ttim2')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t1, hm1_1[-1], label=\"ttim1\")\n", + "plt.semilogx(t2, hm2_1[-1], label=\"ttim2\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_one2.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_one2.eps\");" ] }, { @@ -503,9 +503,17 @@ } ], "source": [ - "ml_2 = ModelMaq(kaq=[17.28, 2], z=[zt0, zb0, zt1, zb1], c=200, Saq=[1.2e-4, 1e-5],\\\n", - " Sll=3e-5, topboundary='conf', tmin=1e-4, tmax=0.5)\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, tsandQ = [(0, Q), (1e+08, 0)], layers=1)\n", + "ml_2 = ttim.ModelMaq(\n", + " kaq=[17.28, 2],\n", + " z=[zt0, zb0, zt1, zb1],\n", + " c=200,\n", + " Saq=[1.2e-4, 1e-5],\n", + " Sll=3e-5,\n", + " topboundary=\"conf\",\n", + " tmin=1e-4,\n", + " tmax=0.5,\n", + ")\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, tsandQ=[(0, Q), (1e08, 0)], layers=1)\n", "ml_2.solve()" ] }, @@ -552,16 +560,17 @@ } ], "source": [ - "ca_2 = Calibrate(ml_2)\n", - "ca_2.set_parameter(name= 'kaq0', initial=20, pmin=0)\n", - "ca_2.set_parameter(name='kaq1', initial=1, pmin=0)\n", - "ca_2.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca_2.set_parameter(name='Saq1', initial=1e-5, pmin=0)\n", - "ca_2.set_parameter_by_reference(name='Sll', parameter=ml_2.aq.Sll[:],\\\n", - " initial=1e-4, pmin=0)\n", - "ca_2.set_parameter(name='c1', initial=100, pmin=0)\n", - "ca_2.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca_2.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=20, pmin=0)\n", + "ca_2.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_2.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", + "ca_2.set_parameter_by_reference(\n", + " name=\"Sll\", parameter=ml_2.aq.Sll[:], initial=1e-4, pmin=0\n", + ")\n", + "ca_2.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca_2.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_2.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", "ca_2.fit(report=True)" ] }, @@ -696,7 +705,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('RMSE:',ca_2.rmse())" + "print(\"RMSE:\", ca_2.rmse())" ] }, { @@ -714,7 +723,7 @@ }, { "data": { - "image/png": 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\n", 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zNWb2tNd5pHwlxifyfLfn6du0Ly+vfZnfLfmd7ruLiJRQ0BV2M+sC9AJaOueaAX/1OJJ4IDIiklHtRjGq3SiW7FjCgIUD2Ht0r9exRESCXtAVdmAY8JRz7jiAc263x3nEQ32b9mVil4lsObCFvgv6siljk9eRRESCWjAW9iZAJzP7r5ktNbNLvA4k3upcvzOzeswiMzeT2969jeXfL/c6kohI0PKksJvZIjNbXcirFxAFJADtgfuBf5iZFbKNwWaWamape/bsKedPIOWtWWIzXv35q5xV+SyG/XsYczbO8TqSiEhQCrrR3czsPfIuxS/Jf78ZaO+cK7J6a3S3iuNg5kF+v+T3fPr9p9zR4g7ubn03hXzvExEJe6E0uttc4EoAM2sCxAB6akoAqBpTlUndJnFD4xuYvmo6D3z8AMdzjnsdS0QkaER5HaAQM4GZZrYayAT6u2C7rCCeio6IZkyHMdSvWp8JX0zgh8M/MKHLBBLiEryOJiLiuaA7Y3fOZTrn+jnnmjvn2jjnPvQ6kwQfM2Ngi4GMvWIsq/eupt87/dj24zavY4mIeC7oCruIL3o07MEL3V/gYOZB+r3Tjy9++MLrSCIinlJhl5DXqk4rXvn5K9SIrcGg9wfx7jfvFrpc2rYMJi3eRNq2jHJOKCJSflTYJSzUr1af2T+fTYtaLRj50Uimr5z+k7Hd07Zl0HfGcp55fz19ZyxXcReRsKXCLmGjemx1pl89nV8k/4Jnv3yWMZ+MISs3C4DlW9LJzM4l10FWdi7Lt6R7nFZEJDBK/FS8maUAnYBzgaPAamCRc25fgLKJ+CwmMoYnL3uS+lXrM+WrKew8vJNxncfRPjmRmKgIsrJziY6KoH1yotdRRUQC4owd1JjZ7cA9wDdAGrAbiCOv69eO5BX4Pzrnvg1o0mKogxopzLxN83jk00doULUBk7tN5vv0eJZvSad9ciJtG6hpnIiEtqI6qCnJGXtloKNz7mgRG24FNAY8K+wihel1fi/OqXwO9y65lz4L+vBc1+cY3qW517FERALqjPfYnXOTiirq+fNXOOc+8G8sEf9od047Zl8zm7ioOH7z3m/44Fv9UxWR8Fbih+fMrJGZjTOzf5nZ/IJXIMOJ+ENyjWRm/3w2TRKa8Lslv+OtzW95HUlEJGB86VJ2LvAC8BaQG5g4IoFRK74W06+ezj0f3sPoZaM5nnOcG5vc6HUsERG/86WwH3POPRuwJCIBVim6Es91fY7fLfkdj376KMdzjtO3aV+vY4mI+JUv7dgnmtkYM+tgZm0KXgFLJhIAcVFxTOgyga5JXXnqs6eYuXqm15FERPzKlzP2FsCt5A2pWnAp3uW/FwkZMZExjL1iLKOXjWZ82niOZR9j2MXDNK67iIQFXwp7byDZOZcZqDAi5SU6IponL3uS2MhYnv/qeY7lHOO+NvepuItIyPOlsH8F1CCvgxqRkBcZEcmjP3uU2MhYZq2exfHs4zzQ7gEiTD0ti0jo8qWwnwV8bWafA8cLJjrnfun3VCLlJMIiGH3paGIjY3lp7UsczznOH9v/kciISK+jiYiUii+FfUzAUoh4yMwYkTKCuKg4pq2cxvGc4/yp45+IivDlz0NEJDic8X8uMzOXZ+mZlvFvNJHyY2bc3fpuYiNj+b8v/4/jOcf5S6e/EB0Z7XU0ERGflOSUZLGZvQnMO3mgFzOLAS4D+gOLgb8FJKFIORrccjBxkXGMTR1LZk4mz3R+htjIWK9jiYiUWEmeEuoB5ACvmdlOM1trZt8AG4FfA+Odc38LYEaRcnVbs9t46NKHWLpjKXd/cDdHs4scKkFEJOic8YzdOXcMmAxMNrNooBZw1Dm3P9DhRLxy84U3ExsVy5hPxjBs0TAmdZ1E5ejKXscSETmjM56xm1nNghdQlbwn4iNOmiYSlq47/zqe6vQUK3avYPD7g/kx80evI4mInFFJLsWnAan5P/cAG8i7DL8nf5pI2Lqm0TU8c8UzrN23lsHvD+ZQ5iGvI4mIFKsk47E3cs4lAwuBa51ztZxziUBP4F/+DmRmrcxsuZmtMLNUM2vn732I+KJrg66M7zye9fvWM/yD4brnLiJBzZcuti5xzr1T8MY59y5whf8j8TTwqHOuFfBw/nsRT3Wu35knOz3Jl7u/5N7F95KZo56VRSQ4+VLY95rZQ2bW0MwamNloID0AmRxQLf/36sDOAOxDxCdp2zLYvPV8fnPBSD7Z+Qkjlo4gKzfL61giIqfxpWutX5PX+9yc/Pcf5U/zt3uBhWb2V/K+ePwsAPsQKbG0bRn0nbGczOxcYqISub3Hb3ll00QeWvYQf77sz+p+VkSCSokLu3NuH/Bbf+zUzBYBZxcyazTQFbjPOfemmf0KeAHoVsg2BgODAZKSkvwRS6RQy7ekk5mdS66DrOxcqmZ25rdtYOIXE4mPimdMhzEaFU5EgkaJC7uZ1QZGAs2AuILpzjmfx2N3zp1WqE/az0v87wvEP4EZRWxjGjANICUlRd3ZSsC0T04kJiqCrOxcoqMiaJ+cSNsGgziSdYTpq6YTHxXPyEtGqriLSFDw5VL8K8Dr5D0NP5S8rmT3BCDTTvIeylsCXEle0zoRz7RtkMArg9qzfEt6flFPAODu1ndzJPsIs9fNpnJ0Ze5qfZfHSUVEfCvsic65F8zst/kDwiw1syIHhimDO4CJZhYFHCP/cruIl9o2SDhR0AuYGSMvGcmRrCNMXTmVStGVGNB8gEcJRUTy+FLYCx4B/t7MfkHemXU9fwdyzi0D2vp7uyKBEGERjOkwhqPZRxmfNp5KUZW45cJbvI4lIhWYL4X9cTOrDvwe+D/ymqTdF5BUIiEkMiKSP3f6M8eyj/HEf58gPiqeXuf38jqWiFRQvjwV/3b+rweALoGJIxKaoiOi+Wvnv3LXB3fx8CcPExcVR/eG3b2OJSIVUIk7qDGzJmb2gZmtzn/f0sweClw0kdASGxnLxC4Tubj2xYz6aBQf7fjI60giUgH50vPcdOBB8u+1O+dWArqZKHKSStGVmNR1Eo0TGnPf4vv47/f/9TqSiFQwvhT2Ss65z06Zlu3PMCLhoGpMVaZeNZWkaknc/eHdrNi9wutIIlKB+NpX/Hnk9eWOmd0IfB+QVCIhLiEugWlXTaN2fG3uXHQn69LXeR1JRCoIXwr7cGAqcKGZfUden+5DA5JKJAzUrlSb6VdPp3JMZYb8ewhb9m/xOpKIVAC+FPbvgFnAE8DfgX+T1/uciBTh3CrnMuPqGURYBIPeH8T2H7d7HUlEwpwvhX0ecC15D8/tBA4BhwMRSiScNKjWgOlXTyczN5NB7w9i1+FdXkcSkTBmzpVs/BQzW+2cax7gPKWSkpLiUlNTvY4hUqw16WsYtHAQteJrMavHLGrF1/I6koiEMDNLc86lnDrdlzP2T8yshR8ziVQozRKbManrJHYd3sWQfw/hwPEDXkcSkTB0xsJuZqvMbCVwGfCFma03s5UnTReREmpzVhsmXjmRbw58w7BFwziUecjrSCISZs54Kd7MGhQ33zm3za+JSkGX4iXULP52MfctuY+2Z7Xl+W7PExMZ43UkEQkxpb4U75zbVtwrMHFFwluXpC78qeOf+GzXZ/xh2R/IdbleRxKRMOHL6G4i4kfXnncte4/uZVzaOGrF1+KBSx7AzLyOJSIhToVdxEO3N7ud3Ud2M3vdbGrH12Zgi4FeRxKREKfCLuIhM+P+S+4n/Wg6E76YQK34WhrLXUTKRIVdxCNp2zJYviWd9smJPH7Z4+w7vo8xn4yhZlxNOtXr5HU8EQlRvrRjFxE/SduWQd8Zy3nm/fX0nbGcVTsOM6HzBJokNOH3S3/Pqj2rvI4oIiFKhV3EA8u3pJOZnUuug6zsXJZvSadKTBUmd5tMzbiaDP9gOFsPbPU6poiEIBV2EQ+0T04kJiqCSIPoqAjaJycCUCu+FlOvmoqZMXTRUPYc2eNxUhEJNSXuKz6YqYMaCUUn32Nv2yDhJ/PW7F3Dbxb+hqSqSczqMYuqMVU9SikiwcoffcWLiB+1bZDA8C7nn1bUAZrVasb4zuPZvH8z9y2+j8ycTA8Sikgo8qSwm9lNZrbGzHLNLOWUeQ+a2ab8Pum7e5FPJBh0rNuRxzo+xn93/Ve904lIiXnV3G01cD0w9eSJZnYRcAvQDDgXWGRmTZxzOeUfUcR71553LelH03km7Rn1TiciJeJJYXfOrQMK+w+qF/B359xx4Bsz2wS0Az4t34QiwaN/s/7sPrqbl9e+rN7pROSMgq2DmrrA8pPe78ifJlJhmRkjUkaw9+he9U4nImcUsMJuZouAswuZNdo5N6+o1QqZVuhj+2Y2GBgMkJSUVKqMIqEiwiJ4ouMTZBzLUO90IlKsgD0855zr5pxrXsirqKIOeWfo9U96Xw/YWcT2pznnUpxzKbVr1/ZndJGgFB0ZzYQu6p1ORIoXbM3d5gO3mFmsmTUCGgOfeZxJJGhUjq7M5G6TSYxLZPgHw/n2x2+9jiQiQcar5m69zWwH0AFYYGYLAZxza4B/AGuB94DheiJe5KdqxddiylVTABi6aCjpR9M9TiQiwUQ9z4mEqJV7VjJw4UDOq3EeM7vPpFJ0Ja8jiUg5Us9zImGmZe2WjL1iLOv2reP3S39PVm6W15FEJAiosIuEsM71O/NQ+4dY9t0y/vTpnwiHK3AiUjbB1o5dRHx0U5Ob+OHwD0xdOZWzK5/Nna3u9DqSiHhIhV0kDAxvNZwfjvzA8189T51KdbixyY1eRxIRj6iwi4QBM+PhDg+z5+geHl/+OLXja3NF/Su8jiUiHtA9dpEwER0RzbgrxnFBzQu4/6P71YGNSAWlwi4SRipFV2JS10nqwEakAlNhFwkz6sBGpGJTYRcJQw2qNeC5rs+x58gehn8wnCNZR7yOJCLlRIVdJASlbctg0uJNpG3LKHIZdWAjUjGpsIuEmLRtGfSdsZxn3l9P3xnLiy3unet35o/t/8iy75Yx5j9jyHW55ZhURLygwi4SYpZvSSczO5dcB1nZuSzfUvw99Bub3Mhdre7irS1vMS51XDmlFBGvqB27SIhpn5xITFQEWdm5REdF0D458YzrDG45mPRj6by49kUS4xP5TfPflENSEfGCCrtIiGnbIIFXBrVn+ZZ02icn0rZBwhnXMTNGtRtFxrEMxqWNo2ZcTXqd36sc0opIeVNhFwlBbRsklKignyzCInjisifYf3w/Yz4ZQ43YGuqdTiQM6R67SAUSExnDhC4TuLDmhYxYOoIvd3/pdSQR8TMVdpEKpnJ0ZSZ3m8zZlc9m+AfD2Zix0etIIuJHKuwiFVDNuJpMuWoK8ZHxDP33UHYe2ul1JBHxExV2kQqqbpW6PH/V8xzNOcqQfw8h41jR7eFFJHSosItUYE0SmvDclc/x/eHvuXPRnep6ViQMqLCLVHBtzmrD2Mvzup69b8l9ZOWo61mRUKbCLiJ0SerCmA5j+GTnJ4z+z2h1PSsSwtSOXUQA6N24N+nH0pn4xUQS4xIZeclIzMzrWCLiI0/O2M3sJjNbY2a5ZpZy0vSrzCzNzFbl/7zSi3wiFdXA5gPp17Qfs9fN5oXVL3gdR0RKwasz9tXA9cDUU6bvBa51zu00s+bAQqBueYcTqajMjPsvuZ+M4xlM/GIiNeNqcn3j672OJSI+8KSwO+fWAadd5nPOndwN1hogzsxinXPHyzGeSFhJ25bhU7/yERbBn372J/Yf28+jnz5K9djqdE3qWg5JRcQfgvnhuRuAL1XURUrPl7HbTxYdGc24zuNoltiMkUtHkrorNcBJRcRfAnbGbmaLgLMLmTXaOTfvDOs2A/4CXF3MMoOBwQBJSUmnzc/KymLHjh0cO3bMl9ghLy4ujnr16hEdHe11FAkChY3dXtLBYypFV2JS10n0f68/93x4D7N6zOKCmhcEOLGIlFXACrtzrltp1jOzesAc4Dbn3OZitj8NmAaQkpLiTp2/Y8cOqlatSsOGDSvMk73OOdLT09mxYweNGjXyOo4EgdKM3X6yhLgEpnabyq3v3srQRUOZ1X0WDas3DExYEfGLoLoUb2Y1gAXAg865/5RlW8eOHSMxMbHCFHXIe2YhMTGxwl2lkKIVjN3+u6sv4JVB7brA8IgAABY9SURBVH0e6hXgnCrnMPWqqeS6XAYsHMDWA1v9H1RE/Mar5m69zWwH0AFYYGYL82fdBZwP/NHMVuS/6pRhP35IG1oq4meW4rVtkMDwLueXqqgXOK/Gecy4egY5LocBCwfwzYFv/JhQRPzJk8LunJvjnKvnnIt1zp3lnOueP/1x51xl51yrk167vcgYKFu3bqV58+YlXv6jjz6iTZs2REVF8cYbbwQwmUjxGic05oWrXyDH5TBw4UAVd5EgFVSX4uV0SUlJ/O1vf6NPnz5eRxHh/ITzmdl95okz9y0HtngdSUROocIeYOPGjaN58+Y0b96cCRMmAJCdnU3//v1p2bIlN954I0eO5I2oNWrUKC666CJatmzJiBEjAGjYsCEtW7YkIkKHSoLDeTXOY2b3mTjnGPDeALbsV3EXCSaqFidJ25bBpMWbStzW94zbS0tj1qxZ/Pe//2X58uVMnz6djIwM1q9fz+DBg1m5ciXVqlVj8uTJ7Nu3jzlz5rBmzRpWrlzJQw895JcMIoFQUNwBBiwcwOb9RTZgEZFypsKer7QdeRRn2bJl9O7dm8qVK1OlShWuv/56Pv74Y+rXr0/Hjh0B6NevH8uWLaNatWrExcUxaNAg/vWvf1GpUqUy718kkJJrJDOzx0zMTMVdJIiosOcrrCOPsnLutOb1wOlPrpsZUVFRfPbZZ9xwww3MnTuXHj16lHn/IoGWXD2ZF7q/QIRFMGDhADZlbPI6kkiFp8Ker6Ajj0ijVB15FObyyy9n7ty5HDlyhMOHDzNnzhw6derEt99+y6effgrAa6+9xmWXXcahQ4c4cOAAP//5z5kwYQIrVqwo8/5FykNy9WRmdp9JpEUy8P2BKu4iHlNhz+ePjjxO1aZNG26//XbatWvHpZdeyqBBg0hISKBp06a8+OKLtGzZkn379jFs2DAOHjxIz549admyJVdccQXjx48H4PPPP6devXr885//ZMiQITRr1qzMuUT8rVH1RrzQ/YUTxX1jxkavI4lUWFbU5eJQkpKS4lJTfzpIxbp162jatKlHibxVkT+7eGvrga0MXDiQbJfNjKtn0DihsdeRRMKWmaU551JOna4zdhHxm4bVGzKzx0yiLIqBCweyIWOD15FEKhwVdhE5I1+agjao1oCZPWYSHRnNoIWDWL9vfTkkFJECKuwiUqzSNAVtUK0Bs7rPyivu76u4i5QnFXYRKVZpm4ImVUtiVvdZxEbGqriLlCMVdhEpVlmaghYU97ioOAa+P5Cv930dwKQiAirsInIGZW0KWr9afWZ2n0l8VDyD3h/EuvR1AUoqIqDCXu58HbZ13LhxJwaG6dq1K9u2bQtgOpHClXVM9/pV84p7pahKDHp/ECv3rPRzQhEpoMIe5Fq3bk1qaiorV67kxhtvZOTIkV5HEimVguJeNaYqt793O29ueNPrSCJhSYU9wMo6bGuXLl1ODAjTvn17duzY4c0HEfGDelXr8fdf/J1Lzr6ERz59hEc+eYTMnEyvY4mEFRX2k23/DD5+Ju+nH/h72NYXXniBa665xi/ZRLxSI64Gk7tOZlCLQby58U1uf+92dh3e5XUskbChwl5g+2fw4i/hwyfyfvqhuPtz2NbZs2eTmprK/fffX+ZcIl6LjIjkt21+y/jO49m8fzM3v30zn+/63OtYImFBhb3A1o8hJxNcTt7PrR+XeZP+GrZ10aJFPPHEE8yfP5/Y2Ngy5xIJFt0adOO1X7xGtZhq3PH+Hby05qUi/25EpGRU2As07ASRMWCReT8bdirzJv0xbOuXX37JkCFDmD9/PnXq1ClzJpFgk1wjmdd+8RpX1LuCsaljeeDjBziSdcTrWCIhK8rrAEGjfjvoPz/vTL1hp7z3ZXTysK3AacO2DhkyhMaNGzNs2DAOHDhAr169OHbsGM65E8O23n///Rw6dIibbroJgKSkJObPn1/mbCLBpEpMFcZ3Gc/M1TN59otn2bR/ExM7T6R+tfpeRxMJORq2NQxV5M8uoe8/3/2HkR+NxOF4qtNTXF7vcq8jiQSloBq21cxuMrM1ZpZrZqeFMrMkMztkZiO8yCci3ulYtyOv93ydulXqctcHdzHlqynkulyvY4mEDK/usa8Grgc+KmL+eODd8osjIsGkXtV6vHTNS/wi+RdMWjGJ3374Ww5mHvQ6lkhI8KSwO+fWOecKHerJzK4DtgBryjeViJSHko7tHh8Vz58v+zOj2o1i2XfL+PWCX7MpY1M5pRQJXUH1VLyZVQYeAB71OouI+J+vY7ubGX2b9mVG9xkcyjxEn3f6sHDrwnJKKxKaAlbYzWyRma0u5NWrmNUeBcY75w6VYPuDzSzVzFL37Nnjv+AiEjClHdu97Vlt+ce1/6BJQhNGLB3BuNRxZOdmBzitSGgKWHM351y3Uqx2KXCjmT0N1AByzeyYc+65QrY/DZgGeU/FlymsiJSLgrHds7JzfR7bvU6lOszqPou/fP4XZq2Zxdr0tTx9xdPUjKsZwMQioSeoLsU75zo55xo65xoCE4A/F1bUQ8X+/fuZPHkykDdc66uvvnpiXmpqKvfcc49P23vuuec4//zzMTP27t3r16wi5aGsY7tHR0bzUPuHeOxnj/Hl7i+55e1bWLNXj+OInMyr5m69zWwH0AFYYGZhedOsuMKekpLCs88+69P2OnbsyKJFi2jQoIFfc4qUp7KO7Q7Qu3FvXvr5SwDc9u5tzNk4x1/xREKeJz3POefmAMX+JTrnHimfNIEzatQoNm/eTKtWrYiOjmbDhg20atWK/v3707p1a/7617/y9ttv88gjj/DNN9/w/fffs2HDBsaNG8fy5ct59913qVu3Lm+99RbR0dG0bt3a648kEjSaJTbj7z3/zsiPRvLwJw+zcNtC7mtzHxfUvMDraCKeCqpL8eHmqaee4rzzzmPFihWMHTuWTp06sWLFCu67777Tlt28eTMLFixg3rx59OvXjy5durBq1Sri4+NZsGCBB+lFgl/NuJpM6TaFESkjWLVnFTe9dRMPLXtIw8BKhVYh+or/y2d/4et9X/t1mxfWvJAH2j3gt+1dc801REdH06JFC3Jyck6M7taiRQu2bt3qt/2IhJuoiCiaV/klN9S5mF32Du9+8ybvbX2PWy+6lQHNB1A1pmqR66Zty2D5lnTaJyeW6daASDCpEIU9FBQMxxoREUF0dPSJoV0jIiLIzlazHpGiFLSNz8zOJSaqNRP79WbJ7peYsWoGb2x4g6EXD+VXTX5FdGR0MetFlOphPpFgVCEKuz/PrH1RtWpVDh48eNrvIuI/p7aN37Qzmie7PMmtF93KuLRxPPXZU8xeO5vftv0t3Rt0P/GlubA29SrsEg50jz2AEhMT6dixI82bN2f27NlERUVx8cUXnxiS1VfPPvss9erVY8eOHbRs2ZJBgwb5ObFI6CloGx9p/KRt/EWJFzH9quk83+154qPjuX/p/fR9py9pP6QVu55IqNOwrWGoIn92qZjOdK88JzeH+Zvn89yK59h9ZDed63fmvjb3kXEgQffYJWQVNWxrhbgULyLhrW2DhGILc2REJL0b96ZHox68su4VZqyaQe/5vbm+8fUMbz+cWvEq6hI+VNhFpMKIj4pnUItBXN/4eqatnMbrX7/O/E3zufScS+mS1IXO9TpTu1Jtr2OKlIkKu4hUODXjajKq3Sj6XNiH175+jcXbF/Pxpx/zGI/RslZLuiR1oUv9LiRXTz7xsJ1IqNA99jBUkT+7SGk459i4fyOLv13M4u2LWZOe1/98UtUkutTvQpekLrSq3YrIiMgzbqssbeO9aFfvr32qT4Dyp3vsIiJFMDOaJDShSUIThlw8hB8O/8CS7UtYvH0xr3z9Ci+ufZEasTW4vN7lXFn/Sjqc24FK0ZVO205Z2sZ70a7eX/tUnwDBRYVdROQUZ1U+i5svvJmbL7yZQ5mH+M/O/7B4e97Z/PzN84mNjKX9Oe3pUr8LV9S/glrxtYCytY33ol29v/apPgGCiwp7AO3fv59XX32VO++8k61bt/LJJ5/Qp08fIG/Y1pdeesmnEd769u1Lamoq0dHRtGvXjqlTpxIdHX3mFUWk1KrEVKF7w+50b9idrNwsvvjhi7wi/+1ilu5Yin1qtKzdki71u5B0VttSjzdflrHqS8tf+/QiuxRN99gDaOvWrfTs2ZPVq1ezZMmSE6O5ldY777zDNddcA0CfPn24/PLLGTZs2GnLBcNnFwl3zjk2ZGzgw+0fsvjbxazbtw6As+PrUyuyBS3OSqZ9UmPqVqlL3Sp1qRJT5Yzb1D128UVR99hV2APolltuYd68eVxwwQUnhm1t1KhRqYdtPdn48ePZu3cvTzzxxGn7DYbPLlLR7Dq868SZ/Io9KziaffQn86vHVj9R5Ate51Y5l3pV6nFulXOJi4rzKLmEKj0854GnnnqK1atXs2LFitPO2JcsWfKTZTdv3szixYtZu3YtHTp04M033+Tpp5+md+/eLFiwgOuuu+7EsllZWbz88stMnDixPD+OiBTj7Mpn8+sLf82vL/w1zjn2H9/Pd4e++9/r4Hd8d/g7NmZsZOn2pWTmZv5k/cS4ROpWrUvN2JrERMYQExlDbGQs0RHRxEbGEhMZQ3Rk/u8RMSeWiYnIXy7yf8udPP+0bUREqwlfmKsQhX3Xn//M8XX+HbY1tumFnP2HP/hte74M23rnnXdy+eWX06lTJ7/tX0T8x8xIiEsgIS6B5rWanzY/1+WSfjSd7w59x45DO9h5aOeJ4r/ryC4yczL/98rN5HjOcbJyssh2/hnp8SdfDCJjiLTTm/EZduKznPy+qGmnzvOXwvbhpbLkGddlHMnVk/2YpnAVorCHgpIO2/roo4+yZ88epk6d6klOESm7CIugdqXa1K5Um1Z1WpV4vZzcHDJzCy/6x3OO/2R6Zk7+vNzC5xW8juccx5F3S7bg1uyJ96dMP3na/34UMs9P9h3OJP3wcRIrx5JQybcHhTOOZJV63SLzHMlk36Hj1Kzi2zYzjmSx79BxNnx/jOTqfolSrApR2P15Zu0Lfw/bOmPGDBYuXMgHH3xARIQG5hOpaCIjIomPiCc+Kt7rKAEXbH0ClHabJ69376ptvDLonIA/XKjqEED+HrZ16NCh/PDDD3To0IFWrVrx2GOP+TmxiEhwKKxtfHms6+9tBiLLmVSIM3Yvvfrqq0XO69y5MwCPPPLIT6YfOnToxO8nzzv5kryISDgrS9v4QLSrL+02vWjjr+ZuYagif3YRCR/B1u9+abcZqDb+au4mIiIhpW2DhFIXwrKs6+9tBiJLcTy5x25mN5nZGjPLNbOUU+a1NLNP8+evMjP12iAiIlJCXp2xrwauB37SZsvMooDZwK3Oua/MLBHIKu1OnHMVriOGcLi1IiIipefJGbtzbp1zbn0hs64GVjrnvspfLt05l1OafcTFxZGenl6hCp1zjvT0dOLidJFDRKSiCrZ77E0AZ2YLgdrA351zT5dmQ/Xq1WPHjh3s2bPHrwGDXVxcHPXq1fM6hoiIeCRghd3MFgFnFzJrtHNuXjF5LgMuAY4AH+Q/9fdBIdsfDAwGSEpKOm1D0dHRNGrUqJTpRUREQlPACrtzrlspVtsBLHXO7QUws3eANsBphd05Nw2YBnnN3coQVUREJGwEW89zC4GWZlYp/0G6K4C1HmcSEREJGV41d+ttZjuADsCC/HvqOOcygHHA58AK4Avn3AIvMoqIiISisOh5zsz2ANuKmF0dOODD9FrAXj9FK6uiMnq1TV/WLemyZ1quuPm+HNtgOq4QXMfW1/VKsryOa3BsM5j+ZkP5/2Lw/7H1x3Ft4Jyrfdpc51xYv4BpPk5P9TrzmTJ6tU1f1i3psmdarrj5vhzbYDquwXZsfV2vJMvruAbHNoPpbzaU/y8OxLEN5HENtnvsgfCWj9ODSSAylmWbvqxb0mXPtFxx83Vs/bNNX9cryfI6rsGxzWD6mw3l4wr+zxmw4xoWl+L9ycxSXSGd6kto03ENTzqu4UvHtvQqwhm7r6Z5HUACQsc1POm4hi8d21LSGbuIiEgY0Rm7iIhIGFFhFxERCSMq7CIiImFEhd0HZlbZzNLMrKfXWcR/zKypmU0xszfMbJjXecQ/zOw6M5tuZvPM7Gqv84j/mFmymb1gZm94nSUYVYjCbmYzzWy3ma0+ZXoPM1tvZpvMbFQJNvUA8I/ApJTS8Mexdc6tc84NBX4FqHlNEPDTcZ3rnLsDuB24OYBxxQd+OrZbnHMDA5s0dFWIp+LN7HLgEPCSc655/rRIYANwFXmjyn0O/BqIBJ48ZRMDgJbkdXEYB+x1zr1dPumlOP44ts653Wb2S2AU8Jxz7tXyyi+F89dxzV/vGeAV59wX5RRfiuHnY/uGc+7G8soeKgI2bGswcc59ZGYNT5ncDtjknNsCYGZ/B3o5554ETrvUbmZdgMrARcBRM3vHOZcb0OByRv44tvnbmQ/MN7MFgAq7x/z0N2vAU8C7KurBw19/s1K0ClHYi1AX2H7S+x3ApUUt7JwbDWBmt5N3xq6iHrx8OrZm1hm4HogF3gloMikLn44rcDfQDahuZuc756YEMpyUia9/s4nAE0BrM3sw/wuA5KvIhd0KmXbG+xLOub/5P4r4mU/H1jm3BFgSqDDiN74e12eBZwMXR/zI12ObDgwNXJzQViEenivCDqD+Se/rATs9yiL+pWMbnnRcw5eOrR9V5ML+OdDYzBqZWQxwCzDf40ziHzq24UnHNXzp2PpRhSjsZvYa8ClwgZntMLOBzrls4C5gIbAO+Idzbo2XOcV3OrbhScc1fOnYBl6FaO4mIiJSUVSIM3YREZGKQoVdREQkjKiwi4iIhBEVdhERkTCiwi4iIhJGVNhFRETCiAq7SAVkZjXM7M7838/157jWZnavmd1WyPSGBUN1mlkLM/ubv/YpIv+jwi5SMdUA7gRwzu3019CXZhZF3jDHxY6Q55xbBdQzsyR/7FdE/qciDwIjUpE9BZxnZiuAjUBT51zz/NELryNvHOzmwDNADHArcBz4uXNun5mdB0wCagNHgDucc18DVwJf5Pckhpm1BWbmL7PslAxvkdd16NOB/KAiFY3O2EUqplHAZudcK+D+U+Y1B/qQN0b2E8AR51xr8roBLbjEPg242znXFhgBTM6f3hFIO2lbs4B7nHMdCsmQCnTyw2cRkZPojF1ETrXYOXcQOGhmB8g7swZYBbQ0syrAz4B/mp0YbTM2/+c55PX1jZlVB2o455bmz3sZuOak/ewGzg3YpxCpoFTYReRUx0/6Pfek97nk/Z8RAezPP9s/1VEgLv93o5gxtfOXO1q2qCJyKl2KF6mYDgJVS7Oic+5H4BszuwnA8lycP3sdcH7+cvuBA2Z2Wf68vqdsqgmwujQZRKRoKuwiFZBzLh34T37zs7Gl2ERfYKCZfQWsAXrlT38XuPyk5X4DTDKzTzn97LwLsKAU+xaRYmjYVhHxKzObA4x0zm0sZplYYClwWcET9CLiHyrsIuJXZnYBcJZz7qNilmkM1HXOLSm3YCIVhAq7iIhIGNE9dhERkTCiwi4iIhJGVNhFRETCiAq7iIhIGFFhFxERCSMq7CIiImHk/wGONGw0eOHrJwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -728,15 +737,15 @@ "source": [ "hm1_2 = ml_2.head(r1, 0, t1)\n", "hm2_2 = ml_2.head(r2, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t1, hm1_2[-1], label='ttim1')\n", - "plt.semilogx(t2, hm2_2[-1], label='ttim2')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t1, hm1_2[-1], label=\"ttim1\")\n", + "plt.semilogx(t2, hm2_2[-1], label=\"ttim2\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three1.eps');" + "# plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three1.eps\");" ] }, { @@ -761,10 +770,19 @@ } ], "source": [ - "ml_3 = ModelMaq(kaq=[19, 2], z=[zt0, zb0, zt1, zb1], c=200, Saq=[4e-4, 1e-5],\\\n", - " Sll=1e-4, topboundary='conf', tmin=1e-4, tmax=0.5)\n", - "w_3 = Well(ml_3, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ = [(0, Q), (1e+08, 0)], \\\n", - " layers=1)\n", + "ml_3 = ttim.ModelMaq(\n", + " kaq=[19, 2],\n", + " z=[zt0, zb0, zt1, zb1],\n", + " c=200,\n", + " Saq=[4e-4, 1e-5],\n", + " Sll=1e-4,\n", + " topboundary=\"conf\",\n", + " tmin=1e-4,\n", + " tmax=0.5,\n", + ")\n", + "w_3 = ttim.Well(\n", + " ml_3, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n", + ")\n", "ml_3.solve()" ] }, @@ -828,18 +846,19 @@ } ], "source": [ - "ca_3 = Calibrate(ml_3)\n", - "ca_3.set_parameter(name= 'kaq0', initial=20, pmin=0)\n", - "ca_3.set_parameter(name='kaq1', initial=1, pmin=0)\n", - "ca_3.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca_3.set_parameter(name='Saq1', initial=1e-5, pmin=0)\n", - "ca_3.set_parameter_by_reference(name='Sll', parameter=ml_3.aq.Sll[:],\\\n", - " initial=1e-4, pmin=0)\n", - "ca_3.set_parameter(name='c1', initial=100, pmin=0)\n", - "ca_3.set_parameter_by_reference(name='res', parameter=w_3.res[:], initial=0, pmin=0)\n", - "ca_3.set_parameter_by_reference(name='rc', parameter=w_3.rc[:], initial=0.2, pmin=0)\n", - "ca_3.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca_3.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_3 = ttim.Calibrate(ml_3)\n", + "ca_3.set_parameter(name=\"kaq0\", initial=20, pmin=0)\n", + "ca_3.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca_3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca_3.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", + "ca_3.set_parameter_by_reference(\n", + " name=\"Sll\", parameter=ml_3.aq.Sll[:], initial=1e-4, pmin=0\n", + ")\n", + "ca_3.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca_3.set_parameter_by_reference(name=\"res\", parameter=w_3.res[:], initial=0, pmin=0)\n", + "ca_3.set_parameter_by_reference(name=\"rc\", parameter=w_3.rc[:], initial=0.2, pmin=0)\n", + "ca_3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_3.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", "ca_3.fit(report=True)" ] }, @@ -1000,7 +1019,7 @@ ], "source": [ "display(ca_3.parameters)\n", - "print('RMSE:', ca_3.rmse())" + "print(\"RMSE:\", ca_3.rmse())" ] }, { @@ -1018,7 +1037,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1032,15 +1051,15 @@ "source": [ "hm1_3 = ml_3.head(r1, 0, t1)\n", "hm2_3 = ml_3.head(r2, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t1, hm1_3[-1], label='ttim1')\n", - "plt.semilogx(t2, hm2_3[-1], label='ttim2')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t1, hm1_3[-1], label=\"ttim1\")\n", + "plt.semilogx(t2, hm2_3[-1], label=\"ttim2\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three2.eps');" + "# plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three2.eps\");" ] }, { @@ -1065,10 +1084,19 @@ } ], "source": [ - "ml_4 = ModelMaq(kaq=[17.28, 2], z=[zt0, zb0, zt1, zb1], c=200, Saq=[1.2e-4, 1e-5],\\\n", - " Sll=3e-5, topboundary='conf', tmin=1e-4, tmax=0.5)\n", - "w_4 = Well(ml_4, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ = [(0, Q), (1e+08, 0)], \\\n", - " layers=1)\n", + "ml_4 = ttim.ModelMaq(\n", + " kaq=[17.28, 2],\n", + " z=[zt0, zb0, zt1, zb1],\n", + " c=200,\n", + " Saq=[1.2e-4, 1e-5],\n", + " Sll=3e-5,\n", + " topboundary=\"conf\",\n", + " tmin=1e-4,\n", + " tmax=0.5,\n", + ")\n", + "w_4 = ttim.Well(\n", + " ml_4, xw=0, yw=0, rw=rw, rc=None, res=0, tsandQ=[(0, Q), (1e08, 0)], layers=1\n", + ")\n", "ml_4.solve()" ] }, @@ -1114,13 +1142,13 @@ } ], "source": [ - "ca_4 = Calibrate(ml_4)\n", - "ca_4.set_parameter(name='kaq1', initial=1, pmin=0)\n", - "ca_4.set_parameter(name='Saq1', initial=1e-5, pmin=0)\n", - "ca_4.set_parameter(name='c1', initial=100, pmin=0)\n", - "ca_4.set_parameter_by_reference(name='rc', parameter=w_4.rc[:], initial=0.2, pmin=0)\n", - "ca_4.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=1)\n", - "ca_4.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=1)\n", + "ca_4 = ttim.Calibrate(ml_4)\n", + "ca_4.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca_4.set_parameter(name=\"Saq1\", initial=1e-5, pmin=0)\n", + "ca_4.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca_4.set_parameter_by_reference(name=\"rc\", parameter=w_4.rc[:], initial=0.2, pmin=0)\n", + "ca_4.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=1)\n", + "ca_4.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=1)\n", "ca_4.fit(report=True)" ] }, @@ -1231,7 +1259,7 @@ ], "source": [ "display(ca_4.parameters)\n", - "print('RMSE:', ca_4.rmse())" + "print(\"RMSE:\", ca_4.rmse())" ] }, { @@ -1249,7 +1277,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1263,15 +1291,15 @@ "source": [ "hm1_4 = ml_4.head(r1, 0, t1)\n", "hm2_4 = ml_4.head(r2, 0, t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs1')\n", - "plt.semilogx(t2, h2, '.', label='obs2')\n", - "plt.semilogx(t1, hm1_4[-1], label='ttim1')\n", - "plt.semilogx(t2, hm2_4[-1], label='ttim2')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs2\")\n", + "plt.semilogx(t1, hm1_4[-1], label=\"ttim1\")\n", + "plt.semilogx(t2, hm2_4[-1], label=\"ttim2\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three3.eps');" + "# plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/schroth_three3.eps\");" ] }, { @@ -1412,21 +1440,31 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k0[m/d]','k1[m/d]','Ss0[1/m]','Ss1[1/m]','Sll[1/m]','c[d]',\\\n", - " 'res', 'rc'], \\\n", - " index=['MLU', 'MLU-fixed k1','ttim','ttim-rc','ttim-fixed upper'])\n", - "t.loc['ttim-rc'] = ca_3.parameters['optimal'].values\n", - "t.iloc[2,0:6] = ca_2.parameters['optimal'].values\n", - "t.iloc[4,5] = ca_4.parameters['optimal'].values[2]\n", - "t.iloc[4,7] = ca_4.parameters['optimal'].values[3]\n", - "t.iloc[4,0] = 17.28\n", - "t.iloc[4,1] = ca_4.parameters['optimal'].values[0]\n", - "t.iloc[4,2] = 1.2e-4\n", - "t.iloc[4,3] = ca_4.parameters['optimal'].values[1]\n", - "t.iloc[4,4] = 3e-5\n", + "t = pd.DataFrame(\n", + " columns=[\n", + " \"k0[m/d]\",\n", + " \"k1[m/d]\",\n", + " \"Ss0[1/m]\",\n", + " \"Ss1[1/m]\",\n", + " \"Sll[1/m]\",\n", + " \"c[d]\",\n", + " \"res\",\n", + " \"rc\",\n", + " ],\n", + " index=[\"MLU\", \"MLU-fixed k1\", \"ttim\", \"ttim-rc\", \"ttim-fixed upper\"],\n", + ")\n", + "t.loc[\"ttim-rc\"] = ca_3.parameters[\"optimal\"].values\n", + "t.iloc[2, 0:6] = ca_2.parameters[\"optimal\"].values\n", + "t.iloc[4, 5] = ca_4.parameters[\"optimal\"].values[2]\n", + "t.iloc[4, 7] = ca_4.parameters[\"optimal\"].values[3]\n", + "t.iloc[4, 0] = 17.28\n", + "t.iloc[4, 1] = ca_4.parameters[\"optimal\"].values[0]\n", + "t.iloc[4, 2] = 1.2e-4\n", + "t.iloc[4, 3] = ca_4.parameters[\"optimal\"].values[1]\n", + "t.iloc[4, 4] = 3e-5\n", "t.iloc[0, 0:6] = [17.424, 6.027e-05, 1.747, 6.473e-06, 3.997e-05, 216]\n", "t.iloc[1, 0:6] = [2.020e-04, 9.110e-04, 3.456, 6.214e-05, 7.286e-05, 453.5]\n", - "t['RMSE'] = [0.023452, 0.162596, ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", + "t[\"RMSE\"] = [0.023452, 0.162596, ca_2.rmse(), ca_3.rmse(), ca_4.rmse()]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb b/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb index 6a7a797..bc56217 100755 --- a/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb +++ b/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb @@ -15,10 +15,10 @@ "outputs": [], "source": [ "%matplotlib inline\n", - "from ttim import *\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -34,8 +34,8 @@ "metadata": {}, "outputs": [], "source": [ - "H = 400 #aquifer thickness [m]\n", - "Q = 3093.12 #constant pumping rate [m^3/d]" + "H = 400 # aquifer thickness [m]\n", + "Q = 3093.12 # constant pumping rate [m^3/d]" ] }, { @@ -51,16 +51,16 @@ "metadata": {}, "outputs": [], "source": [ - "#Pumped well UE-25b#1\n", - "data1 = np.loadtxt('data/double-porosity-pumpingwell.txt', skiprows = 1)\n", + "# Pumped well UE-25b#1\n", + "data1 = np.loadtxt(\"data/double-porosity-pumpingwell.txt\", skiprows=1)\n", "t1 = data1[:, 0]\n", "h1 = data1[:, 1]\n", "\n", - "#Observation well UE-25a#1\n", - "data2 = np.loadtxt('data/double-porosity-110m.txt', skiprows = 1)\n", + "# Observation well UE-25a#1\n", + "data2 = np.loadtxt(\"data/double-porosity-110m.txt\", skiprows=1)\n", "t2 = data2[:, 0]\n", "h2 = data2[:, 1]\n", - "r = 110 #distance from obs to pumped well" + "r = 110 # distance from obs to pumped well" ] }, { @@ -76,8 +76,8 @@ "metadata": {}, "outputs": [], "source": [ - "km = 0.1 / H #hydraulic conductivity of matrix calculated by K&dR\n", - "Sm = 3.85e-4 #specific storage of matrix calculated by" + "km = 0.1 / H # hydraulic conductivity of matrix calculated by K&dR\n", + "Sm = 3.85e-4 # specific storage of matrix calculated by" ] }, { @@ -95,9 +95,17 @@ } ], "source": [ - "ml = ModelMaq(kaq=[km, 1], z=[0, -400, -401, -801], c=5, Saq=[Sm, 1e-3],\\\n", - " Sll=0, topboundary='conf', tmin=1e-5, tmax=3)\n", - "w = Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", + "ml = ttim.ModelMaq(\n", + " kaq=[km, 1],\n", + " z=[0, -400, -401, -801],\n", + " c=5,\n", + " Saq=[Sm, 1e-3],\n", + " Sll=0,\n", + " topboundary=\"conf\",\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", "ml.solve()" ] }, @@ -242,16 +250,16 @@ } ], "source": [ - "ca = Calibrate(ml)\n", - "ca.set_parameter(name='kaq1', initial=10)\n", - "ca.set_parameter(name='Saq1', initial=1e-4, pmin=0)\n", - "ca.set_parameter(name='c1', initial=10)\n", - "ca.set_parameter_by_reference(name='rc', parameter=w.rc, initial=0)\n", - "ca.series(name='UE-25b#1', x=0, y=0, t=t1, h=h1, layer=1)\n", - "ca.series(name='UE-25a#1', x=110, y=0, t=t2, h=h2, layer=1)\n", + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq1\", initial=10)\n", + "ca.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n", + "ca.set_parameter(name=\"c1\", initial=10)\n", + "ca.set_parameter_by_reference(name=\"rc\", parameter=w.rc, initial=0)\n", + "ca.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n", + "ca.series(name=\"UE-25a#1\", x=110, y=0, t=t2, h=h2, layer=1)\n", "ca.fit(report=True)\n", "display(ca.parameters)\n", - "print('RMSE:', ca.rmse())" + "print(\"RMSE:\", ca.rmse())" ] }, { @@ -271,7 +279,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -286,14 +294,14 @@ "hm1 = ml.head(0, 0, t1)\n", "hm2 = ml.head(110, 0, t2)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs UE-25b#1')\n", - "plt.semilogx(t1, hm1[-1], label='TTim UE-25b#1')\n", - "plt.semilogx(t2, h2, '.', label='obs UE-25a#1')\n", - "plt.semilogx(t2, hm2[-1], label='TTim UE-25a#1')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n", + "plt.semilogx(t1, hm1[-1], label=\"TTim UE-25b#1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n", + "plt.semilogx(t2, hm2[-1], label=\"TTim UE-25a#1\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/neveda_1.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/neveda_1.eps\");" ] }, { @@ -318,8 +326,16 @@ } ], "source": [ - "ml1 = ModelMaq(kaq=[1, 1], z=[0, -400, -401, -801], c=5, Saq=[1e-3, 1e-3],\\\n", - " Sll=0, topboundary='conf', tmin=1e-5, tmax=3)\n", + "ml1 = ttim.ModelMaq(\n", + " kaq=[1, 1],\n", + " z=[0, -400, -401, -801],\n", + " c=5,\n", + " Saq=[1e-3, 1e-3],\n", + " Sll=0,\n", + " topboundary=\"conf\",\n", + " tmin=1e-5,\n", + " tmax=3,\n", + ")\n", "w1 = Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", "ml1.solve()" ] @@ -489,18 +505,18 @@ } ], "source": [ - "ca1 = Calibrate(ml1)\n", - "ca1.set_parameter(name='kaq0', initial=1, pmin=0)\n", - "ca1.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca1.set_parameter(name='kaq1', initial=1, pmin=0)\n", - "ca1.set_parameter(name='Saq1', initial=1e-4, pmin=0)\n", - "ca1.set_parameter(name='c1', initial=100, pmin=0)\n", - "ca1.set_parameter_by_reference(name='rc', parameter=w1.rc, initial=0, pmin=0)\n", - "ca1.series(name='UE-25b#1', x=0, y=0, t=t1, h=h1, layer=1)\n", - "ca1.series(name='UE-25a#1', x=110, y=0, t=t2, h=h2, layer=1)\n", + "ca1 = ttim.Calibrate(ml1)\n", + "ca1.set_parameter(name=\"kaq0\", initial=1, pmin=0)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca1.set_parameter(name=\"kaq1\", initial=1, pmin=0)\n", + "ca1.set_parameter(name=\"Saq1\", initial=1e-4, pmin=0)\n", + "ca1.set_parameter(name=\"c1\", initial=100, pmin=0)\n", + "ca1.set_parameter_by_reference(name=\"rc\", parameter=w1.rc, initial=0, pmin=0)\n", + "ca1.series(name=\"UE-25b#1\", x=0, y=0, t=t1, h=h1, layer=1)\n", + "ca1.series(name=\"UE-25a#1\", x=110, y=0, t=t2, h=h2, layer=1)\n", "ca1.fit(report=True)\n", "display(ca1.parameters)\n", - "print('RMSE:', ca1.rmse())" + "print(\"RMSE:\", ca1.rmse())" ] }, { @@ -518,7 +534,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -533,14 +549,14 @@ "ht1 = ml1.head(0, 0, t1)\n", "ht2 = ml1.head(110, 0, t2)\n", "plt.figure(figsize=(8, 5))\n", - "plt.semilogx(t1, h1, '.', label = 'obs UE-25b#1')\n", - "plt.semilogx(t1, ht1[-1], label = 'TTim UE-25b#1')\n", - "plt.semilogx(t2, h2, '.', label = 'obs UE-25a#1')\n", - "plt.semilogx(t2, ht2[-1], label = 'TTim UE-25a#1')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs UE-25b#1\")\n", + "plt.semilogx(t1, ht1[-1], label=\"TTim UE-25b#1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs UE-25a#1\")\n", + "plt.semilogx(t2, ht2[-1], label=\"TTim UE-25a#1\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/neveda_2.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/neveda_2.eps\");" ] }, { @@ -674,16 +690,18 @@ } ], "source": [ - "t = pd.DataFrame(columns=['km [m/d]', 'Sm [1/m]', 'kf [m/d]', 'Sf [1/m]', 'c', 'rc'], \\\n", - " index=['K&dR', 'Moench', 'AQTESOLV', 'MLU', 'TTim1', 'TTim2'])\n", - "t.loc['TTim2'] = ca1.parameters['optimal'].values\n", - "t.loc['K&dR'] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, '-', '-']\n", - "t.loc['Moench'] = [0.1728, 3.000e-4, 0.864, 1.500e-6, '-', '-']\n", - "t.loc['AQTESOLV'] = [0.149, 5.512e-4, 0.937, 5.533e-6, '-', 0.11]\n", - "t.loc['MLU'] = [0.00025, 3.850e-04, 0.874, 8.053e-6, 12.380, 0.1]\n", + "t = pd.DataFrame(\n", + " columns=[\"km [m/d]\", \"Sm [1/m]\", \"kf [m/d]\", \"Sf [1/m]\", \"c\", \"rc\"],\n", + " index=[\"K&dR\", \"Moench\", \"AQTESOLV\", \"MLU\", \"TTim1\", \"TTim2\"],\n", + ")\n", + "t.loc[\"TTim2\"] = ca1.parameters[\"optimal\"].values\n", + "t.loc[\"K&dR\"] = [0.8325, 3.750e-4, 0.8325, 4.000e-6, \"-\", \"-\"]\n", + "t.loc[\"Moench\"] = [0.1728, 3.000e-4, 0.864, 1.500e-6, \"-\", \"-\"]\n", + "t.loc[\"AQTESOLV\"] = [0.149, 5.512e-4, 0.937, 5.533e-6, \"-\", 0.11]\n", + "t.loc[\"MLU\"] = [0.00025, 3.850e-04, 0.874, 8.053e-6, 12.380, 0.1]\n", "t.iloc[4, 0:2] = [km, Sm]\n", - "t.iloc[4, 2:6] = ca.parameters['optimal'].values\n", - "t['RMSE'] = ['-', '-', 0.031736, 0.434638, ca.rmse(), ca1.rmse()]\n", + "t.iloc[4, 2:6] = ca.parameters[\"optimal\"].values\n", + "t[\"RMSE\"] = [\"-\", \"-\", 0.031736, 0.434638, ca.rmse(), ca1.rmse()]\n", "t" ] }, diff --git a/pumpingtest_benchmarks/8_test_of_hardinxveld_recovery.ipynb b/pumpingtest_benchmarks/8_test_of_hardinxveld_recovery.ipynb index 98036e9..fd102d2 100755 --- a/pumpingtest_benchmarks/8_test_of_hardinxveld_recovery.ipynb +++ b/pumpingtest_benchmarks/8_test_of_hardinxveld_recovery.ipynb @@ -17,7 +17,7 @@ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *\n", + "import ttim\n", "import pandas as pd" ] }, @@ -34,12 +34,12 @@ "metadata": {}, "outputs": [], "source": [ - "H = 27 #aquifer thickness [m]\n", - "zt = -10 #upper boundary of aquifer\n", + "H = 27 # aquifer thickness [m]\n", + "zt = -10 # upper boundary of aquifer\n", "zb = zt - H\n", - "rw = 0.155 #well screen radius [m]\n", - "Q = 1848 #constant discharge rate [m^3/d]\n", - "t0 = 0.013889 #time stop pumping [d]" + "rw = 0.155 # well screen radius [m]\n", + "Q = 1848 # constant discharge rate [m^3/d]\n", + "t0 = 0.013889 # time stop pumping [d]" ] }, { @@ -55,7 +55,7 @@ "metadata": {}, "outputs": [], "source": [ - "data = np.loadtxt('data/recovery.txt', skiprows=1)\n", + "data = np.loadtxt(\"data/recovery.txt\", skiprows=1)\n", "t = data[:, 0]\n", "h = data[:, 1]" ] @@ -82,9 +82,16 @@ } ], "source": [ - "ml1 = ModelMaq(kaq=[50, 40], z=[0, zt, zb, -68, -88], c=[1000, 1000], Saq=[1e-4, 5e-5],\\\n", - " topboundary='semi', tmin=1e-4, tmax=0.04)\n", - "w1 = Well(ml1, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml1 = ttim.ModelMaq(\n", + " kaq=[50, 40],\n", + " z=[0, zt, zb, -68, -88],\n", + " c=[1000, 1000],\n", + " Saq=[1e-4, 5e-5],\n", + " topboundary=\"semi\",\n", + " tmin=1e-4,\n", + " tmax=0.04,\n", + ")\n", + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", "ml1.solve()" ] }, @@ -120,11 +127,11 @@ } ], "source": [ - "ca1 = Calibrate(ml1)\n", - "ca1.set_parameter(name='kaq0', initial=50, pmin=0)\n", - "ca1.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca1.set_parameter_by_reference(name='res', parameter=w1.res[:], initial=1, pmin=0)\n", - "ca1.seriesinwell(name='obs', element=w1, t=t, h=h)\n", + "ca1 = ttim.Calibrate(ml1)\n", + "ca1.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca1.set_parameter_by_reference(name=\"res\", parameter=w1.res[:], initial=1, pmin=0)\n", + "ca1.seriesinwell(name=\"obs\", element=w1, t=t, h=h)\n", "ca1.fit()" ] }, @@ -223,7 +230,7 @@ ], "source": [ "display(ca1.parameters)\n", - "print('RMSE:', ca1.rmse())" + "print(\"RMSE:\", ca1.rmse())" ] }, { @@ -241,7 +248,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -255,12 +262,12 @@ "source": [ "hm1 = w1.headinside(t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, '.', label='obs')\n", - "plt.loglog(t, -hm1[0], label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.loglog(t, -h, \".\", label=\"obs\")\n", + "plt.loglog(t, -hm1[0], label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double.eps\");" ] }, { @@ -285,9 +292,18 @@ } ], "source": [ - "ml2 = ModelMaq(kaq=[50, 40], z=[0, zt, zb, -68, -88], c=[1000, 1000], Saq=[1e-4, 5e-5],\\\n", - " topboundary='semi', tmin=1e-4, tmax=0.04)\n", - "w2 = Well(ml2, xw=0, yw=0, rw=rw, rc=0.155, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml2 = ttim.ModelMaq(\n", + " kaq=[50, 40],\n", + " z=[0, zt, zb, -68, -88],\n", + " c=[1000, 1000],\n", + " Saq=[1e-4, 5e-5],\n", + " topboundary=\"semi\",\n", + " tmin=1e-4,\n", + " tmax=0.04,\n", + ")\n", + "w2 = ttim.Well(\n", + " ml2, xw=0, yw=0, rw=rw, rc=0.155, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0\n", + ")\n", "ml2.solve()" ] }, @@ -325,12 +341,12 @@ } ], "source": [ - "ca2 = Calibrate(ml2)\n", - "ca2.set_parameter(name='kaq0', initial=50, pmin=0)\n", - "ca2.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca2.set_parameter_by_reference(name='rc', parameter=w2.rc[:], initial=0.1, pmin=0)\n", - "ca2.set_parameter_by_reference(name='res', parameter=w2.res[:], initial=1, pmin=0)\n", - "ca2.seriesinwell(name='obs', element=w2, t=t, h=h)\n", + "ca2 = ttim.Calibrate(ml2)\n", + "ca2.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca2.set_parameter_by_reference(name=\"rc\", parameter=w2.rc[:], initial=0.1, pmin=0)\n", + "ca2.set_parameter_by_reference(name=\"res\", parameter=w2.res[:], initial=1, pmin=0)\n", + "ca2.seriesinwell(name=\"obs\", element=w2, t=t, h=h)\n", "ca2.fit()" ] }, @@ -441,7 +457,7 @@ ], "source": [ "display(ca2.parameters)\n", - "print('RMSE:', ca2.rmse())" + "print(\"RMSE:\", ca2.rmse())" ] }, { @@ -459,7 +475,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -473,12 +489,12 @@ "source": [ "hm2 = w2.headinside(t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, '.', label='obs')\n", - "plt.loglog(t, -hm2[0], label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.loglog(t, -h, \".\", label=\"obs\")\n", + "plt.loglog(t, -hm2[0], label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double rc.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double rc.eps\");" ] }, { @@ -510,9 +526,10 @@ } ], "source": [ - "ml0 = ModelMaq(kaq=50, z=[0, zt, zb], c=1000, Saq=1e-4, topboundary='semi', \\\n", - " tmin=1e-4, tmax=0.04)\n", - "w0 = Well(ml0, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml0 = ttim.ModelMaq(\n", + " kaq=50, z=[0, zt, zb], c=1000, Saq=1e-4, topboundary=\"semi\", tmin=1e-4, tmax=0.04\n", + ")\n", + "w0 = ttim.Well(ml0, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", "ml0.solve()" ] }, @@ -548,11 +565,11 @@ } ], "source": [ - "ca0 = Calibrate(ml0)\n", - "ca0.set_parameter(name='kaq0', initial=50, pmin=0)\n", - "ca0.set_parameter(name='Saq0', initial=1e-4, pmin=0)\n", - "ca0.set_parameter_by_reference(name='res', parameter=w0.res[:], initial=1)\n", - "ca0.seriesinwell(name='obs', element=w0, t=t, h=h)\n", + "ca0 = ttim.Calibrate(ml0)\n", + "ca0.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca0.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca0.set_parameter_by_reference(name=\"res\", parameter=w0.res[:], initial=1)\n", + "ca0.seriesinwell(name=\"obs\", element=w0, t=t, h=h)\n", "ca0.fit()" ] }, @@ -651,7 +668,7 @@ ], "source": [ "display(ca0.parameters)\n", - "print('RMSE:', ca0.rmse())" + "print(\"RMSE:\", ca0.rmse())" ] }, { @@ -669,7 +686,7 @@ }, { "data": { - "image/png": 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\n", 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DnwR+AzwG/CzG+GgS9UlS2egeo3cyXlolMkYfYzx1M9t/CfxykMuRpPJl0KfeUOq632YhhONDCFe2tLQkXYokDQ+Vozs/G/SplaqgjzEuiDGeNWbMmKRLkaThwcvrUi9VQS9J6qfuy+ts0aeVQS9J5awiB5mcXfcpZtBLUrlzqdpUM+glqdzlRtqiTzGDXpLKnUGfaqkKei+vk6QBqBxt132KpSrovbxOkgbAFn2qpSroJUkDkBtpiz7FDHpJKne50S5Tm2IGvSSVO1v0qWbQS1K58zr6VDPoJanc5UZCm133aWXQS1K5q7RFn2apCnqvo5ekAciNgvx66OhIuhKVQKqC3uvoJWkAupaqzduqT6NUBb0kaQBcqjbVDHpJKnddQe/d8VLJoJekctfVde+EvFQy6CWp3FWO7vzsJXapZNBLUrmzRZ9qBr0klbvuMXqDPo0Mekkqd90teifjpVGqgt4b5kjSAHh5XaqlKui9YY4kDYCX16VaqoJekjQATsZLNYNeksqdLfpUM+glqdxVZKGi0qBPKYNeklRYk96u+zQy6CVJnd33tuhTyaCXJHUGvZfXpZJBL0kqtOjtuk+jbF93DCHUAocBuwPrgEeA38UYXylRbZKkwZIbadd9Sm21RR9COCOE8CDwBWAk8ASwHDgUuDOE8MMQwh6lLbNvvDOeJA1QpWP0adWXFv1o4JAY4yb7dEII+wN7Ac8Ws7CBiDEuABbU1taemXQtkjSs5EbB2qakq1AJbDXoY4yXbeX5h4pXjiQpEV5el1r9GaPfEzgXmNzzuBjje4tfliRpUBn0qdXnoAduAa4GFgAdpSlHkpSI3GhoXZN0FSqB/gT9+hjjJSWrRJKUHFv0qdWfoP+fEMJXgd8CG7o2xhgfLHpVkqTBlRsF7Rugox0yFUlXoyLqT9DvC3wIOJLXuu5j4WtJ0nBW2WMFuxHbJ1uLiqo/Qf8+YEqMsbVUxUiSEtJzTXqDPlX6cwvcvwE7lqoQSVKCXJM+tfrTot8VeDyE8AAbj9F7eZ0kDXc9W/RKlf4E/VdLVoUkKVm50Z2fXcEudbYa9CGEEDv9fmv7FLc0SdKg6W7RG/Rp05cx+oUhhHN7L1wTQqgMIRwZQvghcHppyusfF7WRpAHqHqO36z5t+hL0s4B24IYQwgshhH+EEJ4BFgOnAhfHGK8rYY19FmNcEGM8a8yYMUmXIknDS/fldd4dL236sqjNeuBy4PIQQg7YGVgXY1xZ6uIkSYPEyXip1Zcx+p16bdoAZLq2xxhfKUVhkqRB5OV1qdWXWfcNdN4BLwB7AM2FxzvSuQb9niWrTpI0OAot+j899hxVuzRTM2lswgWpWLY6Rh9j3DPGOAX4DXB8jHHnGGM1cBzwi1IXKEkqvYYXOm+P8pcnG5l9VT0Ny5oTrkjF0p87470lxvjLri9ijL8C3lH8kiRJg61+6Uo2xBxVbKAt30H9kqakS1KR9CfoXw4h/HsIYXIIYVII4UuA/xMkKQXqplTzKqPYLTSTy2aom1KddEkqkv4E/anAOOBm4BZgl8I2SdIwVzNpLJlpR3HMiL8zf06NY/Qp0udb4BZm13+6hLVIkhJU/ZZ/gad/QU37w8BRSZejIulz0IcQxgGfA94MVHVtjzG6Hr0kpcHUI6FyO/jHrTDNoE+L/nTdzwcep/Nyuv8AlgIPlKAmSVISclWw9yx4/HZozyddjYqkP0FfHWO8GmiLMf4+xjgHqCtRXZKkJOxzAqxtgmV/SroSFUl/gr6t8PnFEMJ7QggHABNKUJMkKSnT3tl5l7x/3Jp0JSqS/gT9f4YQxgD/BpwPXAV8piRVSZKSUTkK9joaHlsAHe1JV6Mi6M+s+9sLD1uAI0pTjiQpcfucAP+4BZ6th8mHJF2NtlGfW/QhhL1DCHeFEB4pfD0zhPDvpStNkpSIvY6GbJXd9ynRn677HwBfoDBWH2P8O3BKKYoaqBDC8SGEK1taWpIuRZKGrxHbdY7VP3YbdHQkXY22UX+CflSM8S+9tg2p6y9ijAtijGeNGTMm6VIkaXjb50RY9SI09v61r+Gmv/e6n0rnkrWEEE4GXixJVZKkZO39bqio5J/1P+WyhU+5mt0w1ufJeMA5wJXAm0IIzwPPALNLUpUkKVlVO7By97fT8egtXNR6JLlslvlz67wH/jDUnxb988C1wDeAG4E7gdNLUZQkKXmLRh3GG8IrzORpl64dxvoT9LcCx9M5Ge8FYDWwphRFSZKSt3PNiayLlXwwe7dL1w5j/em6nxBjnFWySiRJQ8r+e09m+fRTOOmJ69n7Xy9kP7vth6X+tOj/HELYt2SVSJKGnF2O/gwVsZ39nr8x6VI0QFsN+hDCwyGEvwOHAg+GEJ4IIfy9x3ZJUlrtNAWmHw+LroYNq5OuRgPQl67740pehSRp6Dr4U503z/nrT6Du40lXo37aatDHGJcNRiGSpCFq4ltgYh3UXwZvmQsV/ZnepaT1Z4xeklSuDj4XVj7Lkj/e4A10hhn/LJMkbd0bj2H9DnuyeuHFfKd1LJXZCm+gM0zYopckbV2mgvpdT2FmeJpaHvcGOsOIQS9J6pMd6j7MK3F7zsre4Q10hhGDXpLUJwdO3Z31B3yUd1Y8yM0njrLbfpgw6CVJfbb7rH+DUdVMf/hCiDHpctQHBr0kqe+qdoB3zIOlf4TFdyZdjfrAoJck9U/NGZ13zPvdV6GjPelqtBUGvSSpf7KVcNRXYfk/4G83JF2NtsKglyT13z4nwPhauPsb0Lo26Wq0BQa9JKn/QoB3fQ1WvQD3fy/parQFBr0kaWAmHwJvPBb+eDGseTnparQZBr0kaeDeeQG0rYW7vpZ0JdqMVAV9COH4EMKVLS0tSZciSeVh3Buh7mx48Ifw7P1JV6NNSFXQxxgXxBjPGjNmTNKlSFLZ+OvUs1k1YlfW/eJcaG9Luhz1kqqglyQNroZlzZz6w4f5t9WzGbnyCRp/9e2kS1IvBr0kacDqlzTRmu/gt+213Nlew64PXgzNy5IuSz0Y9JKkAaubUk1lNkNFgG/wETKZCvjlZ70P/hCSTboASdLwVTNpLPPn1lG/pIm6KdVUPN8Cv/0SPHZb5011lDiDXpK0TWomjX1tydoJH2ftouvh5s/wZMUM9n/j1GSLk133kqTiaWhcxSkrPky2dSUvzv8EDUtfSbqksmfQS5KKpn5JE4/kJ/Ld/Mkck6nn5ftd9CZpBr0kqWi6Judd1XEcD8W9OOrp/4JXX0y6rLJm0EuSiqZrct6nj96H7Pu/T7ajlZafnc1ldy+mYVlz0uWVJYNeklRUNZPGcs4R05gxs4Znaz7HmMaFNN51BbOvqjfsE2DQS5JKZsGI4/hzx5v5cvbHTGx/jvolTUmXVHYMeklSydRNHce8eA7ryXFJ7lLetsfopEsqOwa9JKlkaiaN5eK5x/Lnfb/O9LCMA5+4OOmSyo43zJEklVTNpLEwaQ6MfhLu/x5MORzedGzSZZUNW/SSpMHxrv+A3WaSv/lsrvv1n5yYN0gMeknS4MiO4JGDv8uG9et4858/w+lX3WvYDwKDXpI0aH7fNIYv5s/kLZknOC9e7yz8QWDQS5IGTd2Uan6TOZQftR/N3OwvmZW5P+mSUs+glyQNmq475605/GusHncAU//0OXh5cdJlpZpBL0kaVDWTxnL2UdPZ7rT5kB0BPz0NNqxOuqzUMuglSckYMx5OvgZefhJuPQdiTLqiVDLoJUnJmXI4jTWfh3/cwvMLvpF0Nalk0EuSEtOwrJl33j+T29oP5g0N3+ape29KuqTUMeglSYmpX9JEaz7yubYz+UecxB4LPwUrnky6rFQx6CVJiambUk1lNkNbGMG58XxCrgpuPBXWeSOdYvFe95KkxHRdble/pIm6KdUseWkXpv36g6z50Wx2mHsrVOSSLnHYs0UvSUpUzaSxnHPENABOuL2Dea0fZYcX/8SKn33KmfhFYNBLkoaEzvH6Dn7e/g6uyB/PuCeuh/uvSLqsYc+glyQNCV3j9RUB/iecSvMeRxN/80Vuv+laF7/ZBo7RS5KGhN7j9Uvb9qfyx+/hiIc/z4f/tpYvzv1g59r26hdb9JKkIaNrvL5m0lj+/Nw65rSeTzPbc0Xmv3j00b8nXd6wZNBLkoakuinVtGSrmdP2eSrJ869PnAdrX0m6rGHHoJckDUldXfknvOtIXjr2GqpWN8KNH4S29UmXNqw4Ri9JGrJqJo0tjMtPg9FXwE1z4OaPwcnXQsa2al8M+Z9SCGFKCOHqEII3QJakcjbj/fCur8M/boE7v5x0NcNGSYM+hHBNCGF5COGRXttnhRCeCCE8FUKYt6VzxBiXxBg/Wso6JUnDxMHnwkFnwX2X8uyvLuKyhU956d1WlLrr/jrgUuBHXRtCCBXAZcC7gEbggRDCbUAF8M1ex8+JMS4vcY2SpOEiBJj1LZpfWsqE+q/xWL6Z/5c5mPlz67z0bjNKGvQxxj+EECb32nwQ8FSMcQlACOFG4IQY4zeB40pZjyQpBTIV/GyPr1CzdCkXZS9jbtto6pfsZdBvRhJj9OOB53p83VjYtkkhhOoQwhXAASGEL2xhv7NCCItCCItWrFhRvGolSUNO7V4TODt+nqfjeL6Xu4gjt3826ZKGrCSCPmxi22ZXLYgxNsUYPx5jnFpo9W9uvytjjLUxxtpx48YVpVBJ0tBUM2ksV8w9ivqDryS7w65Mv2sOLH886bKGpCSCvhGY2OPrCcALCdQhSRrGaiaN5SOz3saIj9wGFZXw4/fBSlv2vSUR9A8Ae4UQ9gwhVAKnALclUIckKQ122hNO+wW0rekM+zUvJ13RkFLqy+tuAO4D3hhCaAwhfDTGmAc+CfwGeAz4WYzx0VLWIUlKud1mwAd/Bi3Pw0/eD+tfTbqiISPEuNnh8WGrtrY2Llq0KOkyJEmD7cnfwo2nwsS3wuyboHJU0hUNihBCQ4yxdlPPDfk74/VHCOH4EMKVLS0tSZciSUrC3kfD+74Py/4MPz0N8huSrihxqQr6GOOCGONZY8aMSboUSVJCGnY4irv3/jI8fVfnvfHb25IuKVGpCnpJUnlrWNbM7Kvqmfvwm/jPjjPg8dvhlrOhoz3p0hJj0EuSUqN+SROt+Q46IlzbdjT37flJePjncPt5kMI5aX3hMrWSpNSom1JNZTZDW76DXDZD5eHnw8SR8IcLITcaZn2z8375ZcSglySlRs2kscyfW0f9kibqplR33v9+jy9B6xqov5yGF9bDUV+hZvJOSZc6aAx6SVKq1Ewau/ECNyHQ8KbPsvjPT3LKc9dy6TWvwke/UzaL4KRqjN7L6yRJm1L/zCt8qe0Mfpo/nE9m/pe2hf+ddEmDJlVB7+V1kqRNqZtSTS6b5d/b53JLx2HULb0c/vQ/SZc1KOy6lySlXs+x+4mTr4OGz8GdX+G5ljZuG3nia+P5KWTQS5LKwkZj93t8n+ZVa5n4l6+zvO05ZmdmMX9uXSrD3qCXJJWfihw37vFVpi1Zzn/krqO9LUP9kr1SGfSpGqOXJKmvDpq2G/+H87ir/QD+M3cNx7X+OumSSsKglySVpZpJY7lu7mE8efjlrJxwJJPu+xIsujbpsorOrntJUtnqHrfP3wg//VDnrXKBhnEnbnzTnWEsVUEfQjgeOH7atGlJlyJJGk6yI+ADP+5c2vb287il41Hmtx1BZTbTPUmvYVnzsAz/VHXdex29JGnAsiPgAz9h6U6H8PXMD/hA5i7a8h3UL2nqXhXvO799gtlX1dOwrDnpavssVUEvSdI2yY7glfdczT3xAL6Zu5oP5X5H3ZTqjVbF6wr/4cKglySphwOnvoEdTr+RZ3Y6jAsy11Dzz5u6V8WrCJDLZqibUp10mX2WqjF6SZKK4cApu8En/hd+fgb88nxqZrUzf+4HhuUYvUEvSdKmZEfAv/wQbvoI/Prz1Ly7nZojzkm6qn6z616SpM3JVsK/XAfT3wu/+eLrFsJpWNbMZQufGtKT82zRS5K0JRU5OPka+MWZcOdXoL0V3v7Z7pn4rfmOjS7DG2oMekmStqYiByddBZkc3P2f0J6nPp78upn4QzHoU9V1H0I4PoRwZUtLS9KlSJLSpiIL77sC9p8Nv/8WJ628lvhdlMIAAAb/SURBVMpsGPIz8VPVoo8xLgAW1NbWnpl0LZKkFMpUwHsvhUyWNzx4Gffs18b/7nQWdVN3HpKteUhZ0EuSVHKZDBz3XaioZLcHruSct2Zgj28BDMnb5Br0kiT1VyYDx14IFZVQfxnkN9Cw75eZffVfhtzkPINekqSBCAHe/Y3O6+3vvYhRjSvI50+hI2aG1OS8VE3GkyRpUIUAR30FDv8i0/95O/+Tu5wRIT+kJufZopckaVuEAId/HrKVvOd3F/Cm8VW0HHsFBw6B1jwY9JIkFcehn4FsFVN/PQ/uOxcm/ghyVUlXZde9JElFU3c2HHcxLP4N3PABaF2T+G1ybdFLklRMtXMgWwW3nsOqq0/krBfOpjk/IrGZ+Klq0XtnPEnSkLD/B+H9VzNqeQNXhW+wXVzdPRN/sKUq6GOMC2KMZ40ZMybpUiRJ5W7GSTxzxPd4c1jK9ZX/l3HZ1YnMxLfrXpKkEpn29g+wODuCN/3uLBbucCEjd3rHoNeQqha9JElDzV4Hn0j2Q//LyDUvwLXHQkvjoL6+QS9JUqnteRh8+BZYswKuOQZeeWbQXtqglyRpMEw8CE6/DVpXwZ//36C9rGP0kiQNlt0P4JFjb+aPy6s4aFnzoFxqZ9BLkjRIGpY1M/tnL3WucHfP0kG5rt6ue0mSBkn9kiZa8x10RAbtunqDXpKkQVI3pZrKbIaKwKCtcGfXvSRJg6Rm0ljmz62jfkkTdVOqHaOXJCltaiaNHdT73dt1L0lSiqUq6F3URpKkjaUq6F3URpKkjaUq6CVJ0sYMekmSUsyglyQpxQx6SZJSzKCXJCnFQowx6RqKLoSwAljWa/MYYFuuuxvo8f09rj/77wy83O+Kyse2/psPlqTqLOXrFvPcvnfLj+/d/r/mpBjjuE0eEWMsiw/gyiSO7+9x/dkfWJT0z3Uof2zrv3na6yzl6xbz3L53y+/D925xX7Ocuu4XJHR8f4/b1jr1muHys0yqzlK+bjHP7Xu3/AyXn2USdfb7NVPZdV8uQgiLYoy1SdchqX9872owlVOLPo2uTLoASQPie1eDxha9JEkpZotekqQUM+glSUoxg16SpBQz6FMqhDA9hHBFCOGmEMLZSdcjqW9CCCeGEH4QQrg1hHB00vVo+DPoh6AQwjUhhOUhhEd6bZ8VQngihPBUCGHels4RY3wsxvhx4F8BL+ORBkGR3ru3xBjPBM4APlDCclUmnHU/BIUQ3g6sBn4UY5xR2FYBPAm8C2gEHgBOBSqAb/Y6xZwY4/IQwnuBecClMcbrB6t+qVwV671bOO47wPwY44ODVL5SyqAfokIIk4Hbe/yyeBtwQYzx3YWvvwAQY+z9i2JT57ojxvie0lUrqcu2vndDCAH4FnBnjPF3g1Gz0i2bdAHqs/HAcz2+bgTeurmdQwiHAycBI4BflrQySVvSr/cucC7wTmBMCGFajPGKUhan9DPoh4+wiW2b7Y6JMd4D3FOqYiT1WX/fu5cAl5SuHJUbJ+MNH43AxB5fTwBeSKgWSX3ne1eJMuiHjweAvUIIe4YQKoFTgNsSrknS1vneVaIM+iEohHADcB/wxhBCYwjhozHGPPBJ4DfAY8DPYoyPJlmnpI353tVQ5Kx7SZJSzBa9JEkpZtBLkpRiBr0kSSlm0EuSlGIGvSRJKWbQS5KUYga9JEIIO4YQPlF4vHsI4aYinvu8EMKHN7F9ctdyriGEfUMI1xXrNSW9xqCXBLAj8AmAGOMLMcaTi3HSEEIWmANscZnkGOPDwIQQwh7FeF1Jr3FRG0nQuSzq1BDCQ8BiYHqMcUYI4QzgRDrXTp8BfAeoBD4EbACOjTG+EkKYClwGjAPWAmfGGB8HjgQeLNwdjhBCDXBNYZ97e9WwgM7bw/53Kb9RqdzYopcEMA94Osa4P/DZXs/NAD4IHAR8A1gbYzyAzlu9dnXJXwmcG2OsAc4HLi9sPwRo6HGua4FPxRjftokaFgGHFeF7kdSDLXpJW7MwxrgKWBVCaKGz5Q3wMDAzhLAdcDDw8xC6V2QdUfj8Bjrv704IYQywY4zx94Xnfgwc0+N1lgO7l+y7kMqUQS9pazb0eNzR4+sOOn+HZICVhd6A3tYBVYXHgS2sw17Yb922lSqpN7vuJQGsArYfyIExxleBZ0II/wIQOu1XePoxYFphv5VASwjh0MJzs3udam/gkYHUIGnzDHpJxBibgD8VLne7cACnmA18NITwN+BR4ITC9l8Bb++x30eAy0II9/H61vsRwB0DeG1JW+AytZJKKoRwM/C5GOPiLewzAvg9cGjXDH1JxWHQSyqpEMIbgV1jjH/Ywj57AeNjjPcMWmFSmTDoJUlKMcfoJUlKMYNekqQUM+glSUoxg16SpBQz6CVJSjGDXpKkFPv/aNx0z+/3mqEAAAAASUVORK5CYII=", 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" ] @@ -683,12 +700,12 @@ "source": [ "hm = w0.headinside(t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, '.', label='obs')\n", - "plt.loglog(t, -hm[0], label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.loglog(t, -h, \".\", label=\"obs\")\n", + "plt.loglog(t, -hm[0], label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-single.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-single.eps\");" ] }, { @@ -699,15 +716,6 @@ "fitm is a version with changed objective function of the Calibrate function. The original objective function is 'h_observed - h_predicted', while for log drawdown curve fitting solution, the objective function has been changed to 'log10(-h_observed) - log10(-h_predicted)'." ] }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from fitm import Calibrate" - ] - }, { "cell_type": "code", "execution_count": 17, @@ -723,9 +731,16 @@ } ], "source": [ - "ml3 = ModelMaq(kaq=[50, 40], z=[0, zt, zb, -68, -88], c=[1000, 1000], Saq=[1e-4, 5e-5],\\\n", - " topboundary='semi', tmin=1e-4, tmax=0.04)\n", - "w3 = Well(ml3, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", + "ml3 = ttim.ModelMaq(\n", + " kaq=[50, 40],\n", + " z=[0, zt, zb, -68, -88],\n", + " c=[1000, 1000],\n", + " Saq=[1e-4, 5e-5],\n", + " topboundary=\"semi\",\n", + " tmin=1e-4,\n", + " tmax=0.04,\n", + ")\n", + "w3 = ttim.Well(ml3, xw=0, yw=0, rw=rw, res=1, tsandQ=[(0, Q), (t0, 0)], layers=0)\n", "ml3.solve()" ] }, @@ -761,11 +776,11 @@ } ], "source": [ - "ca3 = Calibrate(ml3)\n", - "ca3.set_parameter(name='kaq0', initial=50, pmin=0)\n", - "ca3.set_parameter(name='Saq0', initial=1e-4,pmin=0)\n", - "ca3.set_parameter_by_reference(name='res', parameter=w3.res[:], initial=1,pmin=0)\n", - "ca3.seriesinwell(name='obs', element=w3,t=t, h=h)\n", + "ca3 = ttim.Calibrate(ml3)\n", + "ca3.set_parameter(name=\"kaq0\", initial=50, pmin=0)\n", + "ca3.set_parameter(name=\"Saq0\", initial=1e-4, pmin=0)\n", + "ca3.set_parameter_by_reference(name=\"res\", parameter=w3.res[:], initial=1, pmin=0)\n", + "ca3.seriesinwell(name=\"obs\", element=w3, t=t, h=h)\n", "ca3.fit(report=True)" ] }, @@ -864,7 +879,7 @@ ], "source": [ "display(ca3.parameters)\n", - "print('RMSE:', ca3.rmse())" + "print(\"RMSE:\", ca3.rmse())" ] }, { @@ -882,7 +897,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -896,12 +911,12 @@ "source": [ "hm3 = w3.headinside(t)\n", "plt.figure(figsize=(8, 5))\n", - "plt.loglog(t, -h, '.', label='obs')\n", - "plt.loglog(t, -hm3[0], label='ttim')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.loglog(t, -h, \".\", label=\"obs\")\n", + "plt.loglog(t, -hm3[0], label=\"ttim\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend()\n", - "plt.savefig('C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double log.eps');" + "plt.savefig(\"C:/Users/DELL/Python Notebook/MT BE/Fig/recovery-double log.eps\");" ] }, { @@ -989,12 +1004,14 @@ } ], "source": [ - "ta = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'res'], \\\n", - " index=['MLU-log', 'TTim-single layer', 'TTim-two layers'])\n", - "ta.loc['TTim-single layer'] = ca0.parameters['optimal'].values\n", - "ta.loc['TTim-two layers'] = ca1.parameters['optimal'].values\n", - "ta.loc['MLU-log'] = [51.530, 8.16E-04, 0.022]\n", - "ta['RMSE [m]'] = [0.00756, ca0.rmse(), ca1.rmse()]\n", + "ta = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"res\"],\n", + " index=[\"MLU-log\", \"TTim-single layer\", \"TTim-two layers\"],\n", + ")\n", + "ta.loc[\"TTim-single layer\"] = ca0.parameters[\"optimal\"].values\n", + "ta.loc[\"TTim-two layers\"] = ca1.parameters[\"optimal\"].values\n", + "ta.loc[\"MLU-log\"] = [51.530, 8.16e-04, 0.022]\n", + "ta[\"RMSE [m]\"] = [0.00756, ca0.rmse(), ca1.rmse()]\n", "ta" ] }, diff --git a/pumpingtest_benchmarks/9_test_of_texas_hill.ipynb b/pumpingtest_benchmarks/9_test_of_texas_hill.ipynb index a17a9f2..12ed555 100755 --- a/pumpingtest_benchmarks/9_test_of_texas_hill.ipynb +++ b/pumpingtest_benchmarks/9_test_of_texas_hill.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -34,12 +34,12 @@ "metadata": {}, "outputs": [], "source": [ - "Q = 24464.06 #constant discharge in m^3/d\n", - "b1 = 6.096 #overlying aquitard thickness in m\n", - "b2 = 15.24 #aquifer thickness in m\n", - "zt = -b1 #top boundary of aquifer\n", - "zb = -b1 - b2 #bottom boundary of aquifer\n", - "rw = 0.1524 #well radius in m" + "Q = 24464.06 # constant discharge in m^3/d\n", + "b1 = 6.096 # overlying aquitard thickness in m\n", + "b2 = 15.24 # aquifer thickness in m\n", + "zt = -b1 # top boundary of aquifer\n", + "zb = -b1 - b2 # bottom boundary of aquifer\n", + "rw = 0.1524 # well radius in m" ] }, { @@ -55,20 +55,20 @@ "metadata": {}, "outputs": [], "source": [ - "data1 = np.loadtxt('data/texas40.txt')\n", + "data1 = np.loadtxt(\"data/texas40.txt\")\n", "t1 = data1[:, 0]\n", "h1 = data1[:, 1]\n", - "r1 = 12.191 #distance between obs1 to pumping well in m\n", + "r1 = 12.191 # distance between obs1 to pumping well in m\n", "\n", - "data2 = np.loadtxt('data/texas80.txt')\n", + "data2 = np.loadtxt(\"data/texas80.txt\")\n", "t2 = data2[:, 0]\n", "h2 = data2[:, 1]\n", - "r2 = 24.383 #distance between obs2 to pumping well in m\n", + "r2 = 24.383 # distance between obs2 to pumping well in m\n", "\n", - "data3 = np.loadtxt('data/texas160.txt')\n", + "data3 = np.loadtxt(\"data/texas160.txt\")\n", "t3 = data3[:, 0]\n", "h3 = data3[:, 1]\n", - "r3 = 48.766 #distance between obs3 to pumping well in m" + "r3 = 48.766 # distance between obs3 to pumping well in m" ] }, { @@ -93,9 +93,17 @@ } ], "source": [ - "ml_0 = ModelMaq(kaq=10, z=[0, zt, zb], Saq=0.001, Sll=0, c=10, tmin=0.001, \\\n", - " tmax=1, topboundary='semi')\n", - "w_0 = Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_0 = ttim.ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Saq=0.001,\n", + " Sll=0,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w_0 = ttim.Well(ml_0, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml_0.solve()" ] }, @@ -138,16 +146,17 @@ } ], "source": [ - "#unknown parameters: kaq, Saq, c, Sll\n", - "ca_0 = Calibrate(ml_0)\n", - "ca_0.set_parameter(name='kaq0', initial=10)\n", - "ca_0.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_0.set_parameter_by_reference(name='Sll0', parameter=ml_0.aq.Sll, \\\n", - " initial=1e-4, pmin=0)\n", - "ca_0.set_parameter(name='c0', initial=100)\n", - "ca_0.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_0.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_0.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "# unknown parameters: kaq, Saq, c, Sll\n", + "ca_0 = ttim.Calibrate(ml_0)\n", + "ca_0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_0.set_parameter_by_reference(\n", + " name=\"Sll0\", parameter=ml_0.aq.Sll, initial=1e-4, pmin=0\n", + ")\n", + "ca_0.set_parameter(name=\"c0\", initial=100)\n", + "ca_0.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_0.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_0.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", "ca_0.fit(report=True)" ] }, @@ -258,7 +267,7 @@ ], "source": [ "display(ca_0.parameters)\n", - "print('RMSE:', ca_0.rmse())" + "print(\"RMSE:\", ca_0.rmse())" ] }, { @@ -268,7 +277,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -283,15 +292,15 @@ "hm1_0 = ml_0.head(r1, 0, t1)\n", "hm2_0 = ml_0.head(r2, 0, t2)\n", "hm3_0 = ml_0.head(r3, 0, t3)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label = 'OW1')\n", - "plt.semilogx(t1, hm1_0[0], label = 'ttim OW1')\n", - "plt.semilogx(t2, h2, '.', label = 'OW2')\n", - "plt.semilogx(t2, hm2_0[0], label = 'ttim OW2')\n", - "plt.semilogx(t3, h3, '.', label = 'OW3')\n", - "plt.semilogx(t3, hm3_0[0], label = 'ttim OW3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_0[0], label=\"ttim OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_0[0], label=\"ttim OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_0[0], label=\"ttim OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend();" ] }, @@ -317,9 +326,17 @@ } ], "source": [ - "ml_1 = ModelMaq(kaq=10, z=[0, zt, zb], Saq=0.001, Sll=0, c=10, tmin=0.001, \\\n", - " tmax=1, topboundary='semi')\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "ml_1 = ttim.ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Saq=0.001,\n", + " Sll=0,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml_1.solve()" ] }, @@ -355,14 +372,14 @@ } ], "source": [ - "#unknown parameters: kaq, Saq, c\n", - "ca_1 = Calibrate(ml_1)\n", - "ca_1.set_parameter(name='kaq0', initial=10)\n", - "ca_1.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_1.set_parameter(name='c0', initial=100)\n", - "ca_1.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_1.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_1.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "# unknown parameters: kaq, Saq, c\n", + "ca_1 = ttim.Calibrate(ml_1)\n", + "ca_1.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_1.set_parameter(name=\"c0\", initial=100)\n", + "ca_1.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_1.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_1.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", "ca_1.fit(report=True)" ] }, @@ -461,7 +478,7 @@ ], "source": [ "display(ca_1.parameters)\n", - "print('RMSE:', ca_1.rmse())" + "print(\"RMSE:\", ca_1.rmse())" ] }, { @@ -471,7 +488,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -486,15 +503,15 @@ "hm1_1 = ml_1.head(r1, 0, t1)\n", "hm2_1 = ml_1.head(r2, 0, t2)\n", "hm3_1 = ml_1.head(r3, 0, t3)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label = 'OW1')\n", - "plt.semilogx(t1, hm1_1[0], label = 'ttim OW1')\n", - "plt.semilogx(t2, h2, '.', label = 'OW2')\n", - "plt.semilogx(t2, hm2_1[0], label = 'ttim OW2')\n", - "plt.semilogx(t3, h3, '.', label = 'OW3')\n", - "plt.semilogx(t3, hm3_1[0], label = 'ttim OW3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_1[0], label=\"ttim OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_1[0], label=\"ttim OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_1[0], label=\"ttim OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend();" ] }, @@ -527,9 +544,17 @@ } ], "source": [ - "ml_2 = ModelMaq(kaq=10, z=[0, zt, zb], Sll=0, Saq=0.001, c=10, tmin=0.001, \\\n", - " tmax=1, topboundary='semi')\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, res=0, rc=None, tsandQ=[(0, Q)], layers=0)\n", + "ml_2 = ttim.ModelMaq(\n", + " kaq=10,\n", + " z=[0, zt, zb],\n", + " Sll=0,\n", + " Saq=0.001,\n", + " c=10,\n", + " tmin=0.001,\n", + " tmax=1,\n", + " topboundary=\"semi\",\n", + ")\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, res=0, rc=None, tsandQ=[(0, Q)], layers=0)\n", "ml_2.solve()" ] }, @@ -583,15 +608,15 @@ } ], "source": [ - "#unknown parameters: kaq, Saq, c, rc\n", - "ca_2 = Calibrate(ml_2)\n", - "ca_2.set_parameter(name='kaq0', initial=10)\n", - "ca_2.set_parameter(name='Saq0', initial=1e-4)\n", - "ca_2.set_parameter(name='c0', initial=10)\n", - "ca_2.set_parameter_by_reference(name='rc', parameter=w_2.rc, initial=0)\n", - "ca_2.series(name='OW1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca_2.series(name='OW2', x=r2, y=0, t=t2, h=h2, layer=0)\n", - "ca_2.series(name='OW3', x=r3, y=0, t=t3, h=h3, layer=0)\n", + "# unknown parameters: kaq, Saq, c, rc\n", + "ca_2 = ttim.Calibrate(ml_2)\n", + "ca_2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca_2.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca_2.set_parameter(name=\"c0\", initial=10)\n", + "ca_2.set_parameter_by_reference(name=\"rc\", parameter=w_2.rc, initial=0)\n", + "ca_2.series(name=\"OW1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca_2.series(name=\"OW2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca_2.series(name=\"OW3\", x=r3, y=0, t=t3, h=h3, layer=0)\n", "ca_2.fit(report=True)" ] }, @@ -702,7 +727,7 @@ ], "source": [ "display(ca_2.parameters)\n", - "print('RMSE:', ca_2.rmse())" + "print(\"RMSE:\", ca_2.rmse())" ] }, { @@ -712,7 +737,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -727,15 +752,15 @@ "hm1_2 = ml_2.head(r1, 0, t1)\n", "hm2_2 = ml_2.head(r2, 0, t2)\n", "hm3_2 = ml_2.head(r3, 0, t3)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='OW1')\n", - "plt.semilogx(t1, hm1_2[0], label='ttim OW1')\n", - "plt.semilogx(t2, h2, '.', label='OW2')\n", - "plt.semilogx(t2, hm2_2[0], label='ttim OW2')\n", - "plt.semilogx(t3, h3, '.', label='OW3')\n", - "plt.semilogx(t3, hm3_2[0], label='ttim OW3')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('head(m)')\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"OW1\")\n", + "plt.semilogx(t1, hm1_2[0], label=\"ttim OW1\")\n", + "plt.semilogx(t2, h2, \".\", label=\"OW2\")\n", + "plt.semilogx(t2, hm2_2[0], label=\"ttim OW2\")\n", + "plt.semilogx(t3, h3, \".\", label=\"OW3\")\n", + "plt.semilogx(t3, hm3_2[0], label=\"ttim OW3\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"head(m)\")\n", "plt.legend();" ] }, @@ -821,12 +846,14 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'c [d]', 'rc'], \\\n", - " index=['AQTESOLV', 'ttim', 'ttim-rc'])\n", - "t.loc['AQTESOLV'] = [224.726, 2.125e-4, 43.964, '-']\n", - "t.loc['ttim'] = np.append(ca_1.parameters['optimal'].values, '-')\n", - "t.loc['ttim-rc'] = ca_2.parameters['optimal'].values\n", - "t['RMSE'] = [0.059627, ca_1.rmse(), ca_2.rmse()]\n", + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"c [d]\", \"rc\"],\n", + " index=[\"AQTESOLV\", \"ttim\", \"ttim-rc\"],\n", + ")\n", + "t.loc[\"AQTESOLV\"] = [224.726, 2.125e-4, 43.964, \"-\"]\n", + "t.loc[\"ttim\"] = np.append(ca_1.parameters[\"optimal\"].values, \"-\")\n", + "t.loc[\"ttim-rc\"] = ca_2.parameters[\"optimal\"].values\n", + "t[\"RMSE\"] = [0.059627, ca_1.rmse(), ca_2.rmse()]\n", "t" ] }, diff --git a/pumpingtests_new/confined1_oude_korendijk.ipynb b/pumpingtests_new/confined1_oude_korendijk.ipynb index 588e237..c73d69e 100644 --- a/pumpingtests_new/confined1_oude_korendijk.ipynb +++ b/pumpingtests_new/confined1_oude_korendijk.ipynb @@ -41,8 +41,8 @@ "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from ttim import *\n", - "import pandas as pd" + "import pandas as pd\n", + "import ttim" ] }, { @@ -58,10 +58,10 @@ "metadata": {}, "outputs": [], "source": [ - "H = 7 #aquifer thickness in meters\n", - "zt = -18 #top boundary of aquifer (m)\n", - "zb = zt - H #bottom boundary of aquifer (m)\n", - "Q = 788 #constant discharge m3/d" + "H = 7 # aquifer thickness in meters\n", + "zt = -18 # top boundary of aquifer (m)\n", + "zb = zt - H # bottom boundary of aquifer (m)\n", + "Q = 788 # constant discharge m3/d" ] }, { @@ -99,9 +99,9 @@ "metadata": {}, "outputs": [], "source": [ - "#unkonwn parameters: kaq, Saq\n", - "ml = ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", - "w = Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", + "# unkonwn parameters: kaq, Saq\n", + "ml = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=0.2, tsandQ=[(0, Q)], layers=0)\n", "\n", "# Here we are setting everything in meters for length and days for time" ] @@ -119,7 +119,7 @@ "metadata": {}, "outputs": [], "source": [ - "ml.solve(silent='True')" + "ml.solve(silent=\"True\")" ] }, { @@ -146,14 +146,14 @@ "metadata": {}, "outputs": [], "source": [ - "#time and drawdown of piezometer 30m away from pumping well\n", - "data1 = np.loadtxt('data/piezometer_h30.txt', skiprows = 1)\n", - "t1 = data1[:, 0] / 60 / 24 #convert min to days\n", + "# time and drawdown of piezometer 30m away from pumping well\n", + "data1 = np.loadtxt(\"data/piezometer_h30.txt\", skiprows=1)\n", + "t1 = data1[:, 0] / 60 / 24 # convert min to days\n", "h1 = data1[:, 1]\n", "r1 = 30\n", - "#time and drawdown of piezometer 90m away from pumping well\n", - "data2 = np.loadtxt('data/piezometer_h90.txt', skiprows = 1)\n", - "t2 = data2[:, 0] / 60 / 24 #convert min to days\n", + "# time and drawdown of piezometer 90m away from pumping well\n", + "data2 = np.loadtxt(\"data/piezometer_h90.txt\", skiprows=1)\n", + "t2 = data2[:, 0] / 60 / 24 # convert min to days\n", "h2 = data2[:, 1]\n", "r2 = 90" ] @@ -207,10 +207,10 @@ "metadata": {}, "outputs": [], "source": [ - "ca1 = Calibrate(ml) # Calibrate object\n", - "ca1.set_parameter(name = 'kaq0', initial=10) # Setting parameters\n", - "ca1.set_parameter(name = 'Saq0', initial=1e-4)\n", - "ca1.series(name = 'obs1', x=r1, y=0, t=t1, h=h1, layer=0) # Adding observations\n" + "ca1 = ttim.Calibrate(ml) # Calibrate object\n", + "ca1.set_parameter(name=\"kaq0\", initial=10) # Setting parameters\n", + "ca1.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca1.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0) # Adding observations" ] }, { @@ -249,7 +249,9 @@ } ], "source": [ - "ca1.fit(report = True) # Fitting the model. We can hide the message below setting report = False" + "ca1.fit(\n", + " report=True\n", + ") # Fitting the model. We can hide the message below setting report = False" ] }, { @@ -361,7 +363,7 @@ } ], "source": [ - "print('rmse:', ca1.rmse())" + "print(\"rmse:\", ca1.rmse())" ] }, { @@ -398,8 +400,8 @@ } ], "source": [ - "hm1 = ml.head(x = r1, y = 0, t = t1) #Using the head method to calculate model resuts\n", - "hm1.shape #Demonstration of the output shape" + "hm1 = ml.head(x=r1, y=0, t=t1) # Using the head method to calculate model resuts\n", + "hm1.shape # Demonstration of the output shape" ] }, { @@ -416,7 +418,7 @@ "outputs": [ { "data": { - "image/png": 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WLVqE9JirV69m3rx5BW5btWoVrVu3pnXr1pxxxhnMnj370La0tDRatmxJs2bNuPnmm/G7vrlqd4qIiEiFUlSS1qJFC1JTU1m9ejXz58/n+uuvJzs7G4DRo0czadIkNmzYwIYNG5g/f34kwz6MkjQREREpUFp6JuOXbCQtPTMkx3vsscdo0aIFLVq04Iknnjj0fnZ2NkOGDKFVq1ZcfPHF7Nu3D4CxY8dy2mmn0apVK/7whz8cdrxVq1bRoUMH2rRpQ4cOHVi/fj0HDhzg3nvv5fXXX6d169a8/vrrv9unevXqxMV5d3vt37//0H1iP/zwA3v27KF9+/aYGddccw1z5sw57Jz33XcfQ4YMoUePHjRt2pQ333yTu+66i5YtW9KrVy+ysrJC8rWCClq7U0RERMomLT2TwVNSOJCdQ5W4GGYMT6ZtkzqlP15aGs8//zwrV67EOUe7du3o3LkzderUYf369UydOpWOHTsybNgwnn32WYYNG8bs2bP5+uuvMTN27dp12DFPOeUUli1bRlxcHIsWLeJPf/oTs2bN4oEHHiA1NZVnnnmmwFhWrlzJsGHDSE9P56WXXiIuLo5t27bRqFGjQ20aNWrEtm3bCtx/06ZNLFmyhK+++or27dsza9Ys/vGPfzBw4EDeffddBgwYUOqvU17qSRMREZHDpGzO4EB2DjkOsrJzSNmcUabjrVixgoEDB1KjRg2OPPJIBg0axPLlywE4/vjj6dixIwBXXXUVK1asoFatWlSrVo3hw4fz5ptvUr169cOOuXv3bi655BJatGjBbbfdxtq1a4OKpV27dqxdu5ZPP/2UcePGsX///gLvPytsNmbv3r2Jj4+nZcuWHDx4kF69egHQsmVLtmzZElQMwVCSJiIiIodJTkygSlwMsQbxcTEkJyaU6XhF3YSfPxkyM+Li4li1ahUXXXQRc+bMOZQI5fWXv/yFrl27smbNGt5+++0SV1M49dRTqVGjBmvWrKFRo0Zs3br10LatW7dy3HHHFbhf1apVAYiJiSE+Pv5Q/DExMYfubwsFJWmlEOoxehERkWjTtkkdZgxP5vYeJ5d5qBPg3HPPZc6cOezbt49ffvmF2bNn06lTJwC+/fZbPvnkEwBeffVVzjnnHH7++Wd2795Nnz59eOKJJ1i9evVhx9y9ezcNGzYE4IUXXjj0fs2aNdm7d2+BcXzzzTeHEqn09HTWr19P06ZNadCgATVr1iQlJQXnHC+++CL9++cvKR5ZStJKyBuj/4RHF65n8JQUJWoiIlJhtW1ShzFdm5U5QQM488wzGTp0KGeffTbt2rVj+PDhtGnTBvB6tKZPn06rVq3YuXMno0ePZu/evfTt25dWrVrRuXNnHn/88cOOedddd3H33XfTsWNHDh48eOj9rl278tVXXxU4cWDFihWcccYZtG7dmoEDB/Lss89Sr149ACZMmMDw4cNp1qwZJ554Ir179y7z5y4L83sNkHBISkpyqanhWVJt/JKNNP7gRva7eKbl9KFv9+6M6dosLOcSEREJlXXr1nHqqaf6HUalV9D3wczSnHNJ+duqJ62Ekk+oS6bV5oLYlbxXZSxXbbgVNi6GCpjsioiIiH+UpJVQ26Z1Of26ibzS8T22nXkntff8B14eBBM6wOcvQ/ZvfocoIiIiFYDWSSuFtk3q0LZJW6AtZN8JX86ET8bDW2Ng8QNw9ghIug6q1/U7VBERESmn1JNWVnFVoc1gGP0RXD0bjmkBHzwIj50G794BGZv8jlBERETKIfWkhYoZnHie9/jxK69n7bMX4dOpcHIf6HAjNG7vtRMREREphnrSwuGY02DAeLh1DXS6A779GJ7vDZPPgzWz4GDoFroTERGRiklJWjjVPAbO/wvc9hVc8Cjs3w0zh8FTbbyetv17/I5QREQkInbt2sWzzz576PWWLVt45ZVXDr1OTU3l5ptvDvl558yZw1dffVXgtokTJ9KyZUtat27NOeec87t206dPp3nz5jRv3pzp06eHPK5gKEmLhCrV4azhcGMqXP4K1G4EC/4Ej58OC/4Mu77zO0IREZGwKi5JS0pK4qmnngr5eYtK0q688kq+/PJLVq9ezV133cXtt98OwM6dO7n//vtZuXIlq1at4v777yczM/KL1ytJi6SYGDjlAhj2Hoz4AJp1g5QJ8OQZMPM6+P5zvyMUEREJi7Fjx7Jp0yZat27NnXfeydixY1m+fDmtW7fm8ccfZ+nSpfTt2xeA++67jyFDhtCjRw+aNm3Km2++yV133UXLli3p1asXWVlZhx1/8uTJnHXWWZxxxhlcdNFF7Nu3j48//pi5c+dy55130rp1azZt+v1kvlq1ah16/ssvvxyqwblgwQK6d+9O3bp1qVOnDt27d2f+/PmHnbNLly7cdtttnHvuuZx66ql8+umnDBo0iObNm3PPPfeU+WumiQN+adgWLnkedn0LK5+DtOmwZiY0OQfaj4GTenlJXZilpWeSsjmD5MSEkJT9EBGRcuC9sfDfL0N7zGNbQu+HC9388MMPs2bNmkM1OJcuXcojjzzCO++8c+h1Xps2bWLJkiV89dVXtG/fnlmzZvGPf/yDgQMH8u677zJgwIDftR80aBAjRowA4J577mHq1KncdNNN9OvXj759+3LxxRcXGNf48eN57LHHOHDgAB988AEA27Zt4/jjjz/UplGjRmzbtq3A/atUqcKyZct48skn6d+/P2lpadStW5cTTzyR2267jYSE0hemV0+a345qDD0fgtvXQo8HIXMLvHYFjD/Lmxl6YF/YTu3VIU1RHVIREYk6vXv3Jj4+npYtW3Lw4EF69eoFQMuWLdmyZcth7desWUOnTp1o2bIlM2bMYO3atUGdZ8yYMWzatIm///3vPPjggwAUVDLTClmdoV+/fofiOv3002nQoAFVq1YlMTGR774r2+1M6kmLFtVqQ4eboN0o+Oot+OQZePd2b821s4Z7C+QeeXRIT5myOYMD2TnkOMjKziFlc4Z600REKoMieryiRdWqVQGIiYkhPj7+UJIUExNDdvbhqyQMHTqUOXPmcMYZZ/DCCy8c1jNXnMsvv5zRo0cDXs9Z3v23bt1Kly5dio0z93lRcZaEetKiTWw8tLwYRiyBofOgcTIs+yc83gLeuhF+WheyUyUnJlAlLoZYg/i4GJITS98lKyIiUpSaNWuyd+/eQl+X1d69e2nQoAFZWVnMmDEjqPNs2LDh0PN3332X5s2bA9CzZ08WLlxIZmYmmZmZLFy4kJ49e4Ys1mCpJy1amUHTjt5jx0ZIGQ+rX4XPX/ImHLS/ERK7lGlx3LZN6jBjeLLuSRMRkbBLSEigY8eOtGjRgt69e/O3v/2NuLg4zjjjDIYOHUqbNm3KdPy//vWvtGvXjiZNmtCyZctDidnll1/OiBEjeOqpp5g5cyYnnnjioX2eeeYZFi1aRHx8PHXq1Dm01EbdunX5y1/+wllnnQXAvffeS926kS/1aAWNu5Z3SUlJLjU11e8wQu+XDEidBqsmwS8/wTEtvUkGLS6CuCp+RyciIlFs3bp1nHrqqX6HUekV9H0wszTnXFL+thruLE9qJEDnO+HWL6HfM5CTDXNGwRMtYfmjsG+n3xGKiIhIiChJK4/iq8GZV8MNn8DgWXD0qbD4AW9x3Hl3ws7NfkcoIiIiZaR70sozM2jezXv8d41Xair1eVg1GU7tC+1vgsbt/I5SRESihHOu0KUkJPxKeouZetIqimNbwMAJ3lDoObfBN8thWg+Y0g3WzlZRdxGRSq5atWpkZGSUOFGQ0HDOkZGRQbVq1YLeRxMHKqoDv8DqV7zetcxvvEVzk2+ANldB1Zp+RyciIhGWlZXF1q1b2b9/v9+hVFrVqlWjUaNGxMfH/+79wiYOKEmr6HIOwvp58PEz8F0KVK0NSUPh7OuhdkO/oxMREan0NLuzsoqJhVMvhOsWwPDFcGJX+PhpeLIVzBoBP3zhd4QiIiJSACVplUmjJLh0Otz8OZw90uthe+5cmH4hbPvM7+hEREQkDyVplVGdptBrHNy2Fro/4JWamnwezL0Jftnhd3QiIiKCkrTK7YijoOMtcFOaV7lg9Svw1JmQMlGzQUVERHymJE2gWm3o+RCM/hgangnz/wgTz4FvlvkaVlp6JuOXbCQtPdPXOERERPygJE3+p/7JcPVsuGwGZP3i3av2xhDY9V3EQ0lLz2TwlBQeXbiewVNSlKiJiEiloyRNfs/Mq1YwZhV0/TP8ZwE8cxZ8+A/I+jViYaRszuBAdg45DrKyc0jZnBGxc4uIiEQDJWlSsPgjoPNdcOMqOKknLHkIxp8N696BCKytl5yYQJW4GGIN4uNiSE5MCPs5RUREookWs5XgbP4Q3vsjbF8HJ54HvR72hkfDKC09k5TNGSQnJtC2SZ2wnktERMQvqjggZXcwG1Kner1qB36BdqOg8x+hWi2/IxMRESm3VHFAyi42DtpdDzd9Bq2v9OqCPt0WPp8BOTl+RyciIlKhKEmTkqtRD/o9DSM+gDpN4K0bYGp32Jbmd2QiIiIVhpI0Kb2GZ8KwhTBgIuz6FiafD2/dCD9v9zsyERGRck9JmpRNTAy0vsKrWtDhRvjiVW8INGUCHMzyOzoREZFyS0mahEa1WtDjQRj9iVfIff5YmNgJNi/1OzIREZFySUmahFb9k+CqWXD5q5D9K7zYH16/2hsOFRERkaApSZPQM4NT+sANK+G8e2DD+17VgqUPR7RqgYiISHmmJE3CJ74anHsn3JQKJ/eBpePgmbPhq7kRqVogIiJSnilJk/Cr3QgueR6GvANVa8IbV8NLA+Cnr/2OTEREJGopSZPIOaETXL8Mev8Tvv8cJnaE+X+C/bv9jkxERCTqKEmTyIqNg3Yj4abPoc1VkPJsoGrBy2GtWpCWnsn4JRtJS88M2zlERERCSUma+KNGAlz4JIxcCnUT4a0xMLUbbA191YK09EwGT0nh0YXrGTwlRYmaiIiUC0rSxF/HtYZhC2DgJNi9DaacB3PGwM8/hewUKZszOJCdQ46DrOwcUjZnhOzYIiIi4aIkTfxnBmdc5s0C7XAz/Pt1bwj0k/EhqVqQnJhAlbgYYg3i42JITkwIQdAiIiLhZa4CLoWQlJTkUlNT/Q5DSmvHBq9iwcZFUO9kb1i0SfsyHTItPZOUzRkkJybQtkmdEAUqIiJSdmaW5pxLyv++Lz1pZlbXzN43sw2Bfw/7q2lmx5vZEjNbZ2ZrzewWP2IVH9RrDoNnwhWveYvfPt8L3r0D9u8p9SHbNqnDmK7NlKCJiEi54ddw51hgsXOuObA48Dq/bOAO59ypQDIwxsxOi2CM4iczOLk33PAJJN8An06FZ5Nh/Xy/IxMREYkIv5K0/sD0wPPpwID8DZxzPzjnPgs83wusAxpGKkCJElWPhF7jYPgiqFYbXr0MZg6Dn7f7HZmIiEhY+ZWkHeOc+wG8ZAw4uqjGZtYUaAOsLKLNSDNLNbPU7dv1B7zCaZQEIz+Ern+GdW/D+LNg9SsqLyUiIhVW2JI0M1tkZmsKePQv4XGOBGYBtzrnCr0pyTk3yTmX5JxLql+/flnDl2gUVwU63wWjVngTCuaMhpcHQeYWvyMTEREJubhwHdg5162wbWb2o5k1cM79YGYNgAIXxTKzeLwEbYZz7s0whSrlTf2T4dr3IHUqLLoPnm0P590D7UZBTKzf0YmIiISEX8Odc4EhgedDgLfyNzAzA6YC65xzj0UwNikPYmLg7BEwZiU07QQL/gRTusF/1/gdmYiISEj4laQ9DHQ3sw1A98BrzOw4M5sXaNMRuBo4z8xWBx59/AlXolbtRnDl63DRVNj1LUzqDB88CFn7/Y5MRESkTLSYrVQc+3Z6PWpfvAr1ToILnyrzIrgiIiLhFlWL2YqERfW6MHAiXDXL60kLwSK4IiIiflGSJhVPs25aBFdERMo9JWlSMRW2CO4vO0Jy+LT0TMYv2UhaemZIjiciIpJf2JbgEIkKuYvgfvQEfPgP2Pwh9H0MTivRcn2/k5aeyeApKRzIzqFKXAwzhierJqiIiIScetKk4stdBPf6Zd5s0DeugX8NhV8ySnW4lM0ZHMjOIcdBVnYOKZtLdxwREZGiKEmTyuOY07zhz/PugXXvwPiz4avDlugrVnJiAlXiYog1iI+LITkxIQzBiohIZaclOKRy+nGtV1bqhy/g9EHQ5xGoEXyylZaeScrmDJITEzTUKSIiZVLYEhxK0qTyOpgFK56AD//uTS4o471qIiIipaF10kTyi42HznfCyKVQu6F3r9rMYaW+V01ERCSUlKSJHNsChi+GrvfAV3Ph2XbevyIiIj5SkiYCv+9Vq9kA3rhavWoiIuIrJWkieR3bAkZ8AF3//L9etXVv+x2ViIhUQkrSRPKLjffWVcvtVXv9Kq9Xbd9OvyMTEZFKREmaSGHy96qNP1u9aiIiEjFK0kSKUmCv2nXqVRMRkbBTkiYSjNxetS5/gq/mwPh2XtUCERGRMFGSJhKs2Hjo8sdAr9ox8PpgePN6+HWX35GJiEgFpCRNpKSObQkjlkDnsfDlv2BCB9i0pNSHS0vPZPySjaSlZ4YwSBERKe+UpImURmw8dL0bhr8PVWrASwNg3p1wYF+JDpOWnsngKSk8unA9g6ekKFETEZFDlKSJlEXDtnD9Mki+AVZNgonnwHefBr17yuYMDmTnkOMgKzuHlM1aPFdERDxK0kTKKv4I6DUOhrwNBw/AtB6w+K+QfaDYXZMTE6gSF0OsQXxcDMmJCREIWEREygNzzvkdQ8glJSW51NRUv8OQymj/HlhwN3z+snfv2sDn4JjTi9wlLT2TlM0ZJCcm0LZJnQgFKiIi0cLM0pxzSYe9ryRNJAzWvwdzb4L9u73FcDvcBDGxfkclIiJRqLAkTcOdIuFwcm+4IQVO6gmL/g+e7wM7N/sdlYiIlCNK0kTCpUY9uPQlGDgJfloHE86BT6dCBey9FhGR0FOSJhJOZnDGZXDDJ3D82fDu7TDjYtjzvd+RiYhIlFOSJhIJtRvC1bOhzyOQ/jE82x6+nOl3VCIiEsWUpIlEihmcPQJGrYB6J8Gs6+BfQ1WsXURECqQkTSTSEk6EYfPh/P/zirQ/mwz/WeB3VCIiEmWUpIn4ISYWOt0OI5dAjfrwyqXw9q1w4Be/IxMRkSihJE3ET8e2hBEfQMdbIO0Fr6zUVq3xJyIiStJE/BdXFbo/AEPfgYNZMLUHLBnnPRcRkUpLSZpItGh6Doz+CFpdCh8+7CVrOzb6HZWIiPhESZpINKlWGwZOhEumQ+Y33vDnp1OKXQA3LT2T8Us2kpaeGaFARUQk3OL8DkBECnD6ADi+Hbw1Bt69A9bPh/7joeYxhzVNS89k8JQUDmTnUCUuhhnDk1WoXUSkAlBPmki0qtUArprlLYC7Zbm3VMe6tw9rlrI5gwPZOeQ4yMrOIWVzhg/BiohIqClJE4lmuQvgXr8cjmoMr18Fc8bA/j2HmiQnJlAlLoZYg/i4GJITE3wMWEREQsVcBSz2nJSU5FJTtYyBVDAHs+DDv8PyR6F2I69we5P2gDfkmbI5g+TEBA11ioiUM2aW5pxLOux9JWki5cy3K2H2SNj1LXS8FbrcDXFV/I5KRERKqbAkTcOdIuVN43Ze/c82V8GKx2DK+fDT135HJSIiIaYkTaQ8qloT+j0Nl78Ce76HSZ0hZSLk5PgdmYiIhIiSNJHy7JQL4IZPILELzP8jvDwI9vzgd1QiIhICStJEyrsjj4YrXoO+j8O3KTChPXw11++oRESkjJSkiVQEZpA0DEYthzpN4Y2rvYVwf9tbYHNVKBARiX6qOCBSkdRrDte9D0sf9iYVbPkIBk2G48861EQVCkREygf1pIlUNLHxcP5fYOi7kHMQpvWEJePgYDagCgUiIuWFkjSRiqpJBxi9AlpeAh8+7CVrGZtUoUBEpJzQYrYilcGaWfDObV5vWu+/k1b3AlK+2akKBSIiUaCwxWx1T5pIZdDiIji+HcweBXNvpO2pC2jb90mooQRNRCRaabhTpLKo3QiumQvd/wrr58OEDrDpA7+jEhGRQihJE6lMYmKg480w4gOoVhteGggL/gzZv/kdmYiI5KMkTaQyatAKRi6Fs0bAJ8/A5PPgp3V+RyUiInkoSROprKpUhwsegSvfgJ9/hEldYOUkyDOZSIveioj4RxMHRCq7k3rC6I+9CgXv3Qkb34f+40nLiNeityIiPvKlJ83M6prZ+2a2IfBvob/5zSzWzD43s3ciGaNIpXLk0V6PWp9H4Jtl8Gx7fvh0tha9FRHxkV/DnWOBxc655sDiwOvC3ALoZhmRcDODs0fAyA+hZgP6rrmNv8a/QA37TYveioj4wK8krT8wPfB8OjCgoEZm1gi4AJgSmbBEhKNPgRGLocNNDI5ZyPI6D/DmwJoa6hQRiTC/krRjnHM/AAT+PbqQdk8AdwE5xR3QzEaaWaqZpW7fvj1kgYpUSnFVoceDcM1b1I3dz2nvDICPn4acYn8URUQkRMKWpJnZIjNbU8Cjf5D79wV+cs6lBdPeOTfJOZfknEuqX79+mWIXkYDELt6kgpN6wsJ74OWBsOcHv6MSEakUwja70znXrbBtZvajmTVwzv1gZg2Anwpo1hHoZ2Z9gGpALTN72Tl3VZhCFpGCVK8Ll70Mn02H+Xd7lQr6PQ2n9vU7MhGRCs2v4c65wJDA8yHAW/kbOOfuds41cs41BS4HPlCCJuITM2g7FK5fBkcdD68PhrdvgQO/+B2ZiEiF5VeS9jDQ3cw2AN0DrzGz48xsnk8xiUhx6jWH6xZBx1sgbTo81xm+Xw1o4VsRkVAzl2d18YoiKSnJpaam+h2GSMW2+UOYfT38soOtZ/6B7itb8ls2WvhWRKSEzCzNOZeU/32VhRKR0knsfGhSQaPUcUy1h6jvdmrhWxGREFGSJiKlF5hUsKXj32ltG5lfdSy94tK08K2ISAgoSRORsjGjafdRbL5oHlk1GzE+9lHa/vs+TSoQESkjJWkiEhItWiVx9K3Lfz+p4Icv/A5LRKTcUpImIqETVwW6PwDXzIEDP8Pk81WpQESklJSkiUjoHVapYBDs/a/fUYmIlCtK0kQkPHIrFfR9Ar5NgWfbw9daBlFEJFhK0kQkfMwg6VqvUkHthvDaFfDO7ZD1q9+RiYhEPSVpIhJ+9U+C4Yv57+kjIHUqv47vBP9d43dUIiJRTUmaiERE2rZ9dPn3+VxzYCw/Z/5EzuSusPI5qIBVT0REQkFJmohERMrmDA5k57AspxUXHHiYb2ufDe/dBa9cCj9v9zs8EZGooyRNRCIiOTGBKnExxBrsiatDRr+XoPc/vRqgEzrAxkV+hygiElVUYF1EIiYtPZOUzRkkJyb8rwD7j2th5nWwfR20vxHOvxfiqvobqIhIBBVWYF1Jmoj4L+tXWPgX+HQyHNsSLprmTTYQEakECkvSNNwpIv6LPwIueAQufxV2b4NJnb3SUhXwP5EiIsFSkiYi0eOUPl6lgkZnwds3wxvXwL6dfkclIuILJWkiEl1qNYCr53g1QNfPg4nnwJYVfkclIhJxStJEJPrExEDHW+C6971JBC/0hcV/hYNZfkcmIhIxStJEJHo1PBOuXw6tB8PyR2BaL9j5jd9RiYhEhJI0EYluVY+EAePh4mmwYwNM7ARfvO53VCIiYRdX1EYzuz2IY/zinHsuRPGIiBSsxUXehIJZI2D2SNi0GPo8AtVq+R2ZiEhYFNeTdidwJFCziMcd4QxQROSQoxrD0Hehy93w5b/guU6wNc3vqEREwqLInjTgJefcA0U1MLMaIYxHRKRosXHQZSyc0BneHAHTekDXP0PHW70JByIiFUSRv9Gcc3cVd4Bg2oiIhFyT9jBqOZzSFxbfDy/1hz3f+x2ViEjIFNeTBoCZHQVcAzTNu49z7uawRCUiUojD6n9e8gJ8/hK890eY0BH6j/cWxRURKeeCStKAeUAK8CWQE75wREQKl5aeyeApKRzIzqFKXAwzhid7idqZ10Dj9jBzGLx2BZw1HHo86JWbEhEpp4JN0qo554KZ6SkiEjYpmzM4kJ1DjoOs7BxSNmd4SRpAveYwfBEsfgA+eQbSP4aLpsIxp/kbtIhIKQV7l+1LZjbCzBqYWd3cR1gjExHJJzkxgSpxMcQaxMfFkJyY8PsGcVWh50MweBb8sh0md4VVk1WoXUTKJXNB/PIyszHAQ8AuIHcH55xLDF9opZeUlORSU1P9DkNEwuCwe9IK8/NPMOcG2Pg+nNwH+j0DNRIKby8i4hMzS3POJR32fpBJ2iagnXNuRziCCzUlaSICQE4OrJwIi/4PqifAwOcgsbPfUYmI/E5hSVqww51rgX2hDUlEJMxiYqD9DTB8MVQ5El7sD4vuV6F2ESkXgp04cBBYbWZLgN9y39QSHCJSLjRoBdd/CPPHworH4JtlcNEUqHuC35GJiBQq2CRtTuAhIlI+VakB/Z6GE8+Dubd4hdr7PgatLvU7MhGRAgWVpDnnpoc7EBGRiDh9IDRs6xVqf3MEbPoA+vwTqtb0OzIRkd8p8p40M5tU3AGCaSMiElVyC7V3Hgv/fh2eOxe2feZ3VCIiv1NcT9oAM9tfxHYDuoYwHhGRyIiNg653e7M9Z42Aqd3h/Huh/U0q1C4iUaG4JO3OII6xPBSBiIiES5FrqzXpAKNXwNyb4f17YdMSGDgRah7rT7AiIgFBrZNW3midNBHJVWi9z/ycg8+mw3tjoUp1GDARTuoR+YBFpNIp6zppIiLlUkH1PgtkBm2Hekt11GwAr1ziJWzZvxXcXkQkzJSkiUiFVmy9z/zqn+wtfttuFKycAJPPh+3/iUywIiJ5aLhTRCq8oOt95rd+PswZDdn7offfoc3VXo+biEgIlbV250l4kwiakGeygXPuvFAGGSpK0kQkZPb8ALNHelUKTh8IfZ+AI47yOyoRqUAKS9KCrTjwL2AiMBmvRJSISOVQqwFcPQc+ehI+eBC2pnklpRq38zsyEanggr0nLds5N8E5t8o5l5b7CGtkIiLRIiYWOt0OwxZ4w53P94YP/wk5+j+riIRPsEna22Z2g5k1MLO6uY+wRiYiEm2OPwtGLfeGPZc8CNP7we5tfkclIhVUsPekfVPA2845lxj6kMpO96SJSFg5B1+8Cu/+AeKqQP/xcMoFfkclIuVUmdZJc86dUMAjKhM0EZGySEvPZPySjaSlZxbeyAxaXwnXL/PqgL52Jbx7B2T9GrlARaTCC2rigJktB5bhlYD6yDm3N6xRiYj4IOjqBLnqNYPr3ofFD8Anz0D6x3DxNDj61MgFLSIVVrD3pA0B1gMXAR+bWaqZPR6+sEREIi/o6gR5xVWFng/B4Fnwy3aY1AVSp3lDoiIiZRDscOdm4H1gMV6PWnVA/1UUkQolf3WCOtWrFD/0mat5Nxj1ETRuD+/cBm9cDft2hj9oEamwgp04sAnYAbyCN+S52jmXE+bYSk0TB0SktHKrE9SpXoUH3lkb/NBnrpwcb+hz8f1w5LFw0WRo0iH8gYtIuVXWAutPAd8CVwA3A0PM7MQQxiciEhXaNqnDmK7NyNx3oORDnwAxMdDxZrhuIcTGwwsXwJJxcDA7vIGLSIUT7HDnk865S4BuQBpwH6CKwyJSYZW4MHt+Ddt6a6q1vBQ+fBimXwi7vgtPsCJSIQU73PkocA5wJJBCYKZn4F61qKPhThEJhVIXZs/vi9e8JTpiYqHf03Ba/9AFKSLlXlkLrF8CLHPO/RiiYOoCrwNNgS3Apc65w+7MNbOjgClAC8ABw5xznxR3fCVpIhJ1MjbBrOvg+8+h7bXQ829QpbrfUYlIFCjrYrb/AtqZ2SOBx4VljGcssNg51xxvxujYQto9Ccx3zp0CnAGsK+N5RURCJqiFb3MlnAjDFkKHmyHteZjcFX5cG/4gRaTcCrYnbRxwNjAj8NYVQKpz7u5SndRsPdDFOfeDmTUAljrnTs7XphbwBZDoggkyD/WkiUi4lXjh27w2LobZo+C3Pd4aa0nXeVUMRKRSKuvszguA7s65ac65aUCvwHuldYxz7geAwL9HF9AmEdgOPG9mn5vZFDOrUdgBzWxkYJHd1O3bt5chNBGR4pVq4dtczc6H0R9D03O8e9Vev0prqonIYYJN0gCOyvO8dnGNzWyRma0p4BHsHbNxwJnABOdcG+AXCh8WxTk3yTmX5JxLql+/fpCnEBEpnTLP/jyyPlz5L+jxEPxnAUw8B7Z8FJ5gRaRcCqp2JzAO+NzMlgAGnAsUOdTpnOtW2DYz+9HMGuQZ7vypgGZbga3OuZWB1zMpIkkTEYmktk3qMGN4ctlmf8bEQIcboWlHmDkMpveFc++Ec++C2GB/PYtIRRXsxIFXgWTgzcCjvXPutTKcdy5ePVAC/75VwDn/C3xnZrn3qp0PfFWGc4qIhFTuwrdlWp4D4Lg2cP0yaHU5fPh3L1nTmmoilV6REwfM7MyidnbOfVaqk5olAG8AjfEqGVzinNtpZscBU5xzfQLtWuMtwVEF2AxcW9BSHflp4oCIlFv/fsOr/ak11UQqjVKtkxYY3gSoBiThzbY0oBWw0jl3ThhiLTMlaSJSru3cDDOvg+8/05pqIpVAqWZ3Oue6Oue6AunAmYEb89sCbYCN4QlVRKSSq5sIwxZAx1sCa6qdBz/qbg+RyibY2Z2nOOe+zH3hnFsDtA5LRCIiAnFVoPsDcNWbsC/DW/z20ylQsmUjRaQcCzZJWxdYp6yLmXU2s8lo9X8RkfDTmmoilVawSdq1wFrgFuBWvFmW14YpJhERyUtrqolUSsEmaR2A55xzAwOPx51z+8MZmIiI5JG7ptrw9yGuqrdMx5JxcDDb78hEJEyCTdKGAqvN7BMz+4eZXWhmZVwYSESk8ipRcfa8frem2sNaU02kAgtqSWvn3DUAgXXMLgbGA8cFu7+IiPxPmYqzA1StCQMnwIldvTXVJp4D/Z+BUy8MX9AiEnFB9aSZ2VVm9hxeaaZuwDNAp3AGJiJSUZWpOHterS6FUcuh7gnehIJ3boesX0MbrIj4JtiesCeATcBEYIlzbku4AhIRqehyi7NnZeeUrjh7XnUTYdhC+OCv8PFT8O0ncPE0OPrU0AUsIr4osuLA7xqanY5XWP0coDmw3jl3dRhjKzVVHBCRaJeWnlm24uwF2bgIZo+C3/ZCr3FetQKz0BxbRMKmsIoDQfWkmVktvDqbTYCmQG0gJ5QBiohUJm2b1AldcparWTcY9RHMGeXdq7ZpCfR7Co7QPC+R8ijY2Z0rgAuBfwOXOedOds4NCV9YIiJSKjWPgcGzvGoF6+fBxE7wbYrfUYlIKQSVpDnnWjnnbnDOveKc2xruoEREpAxiYry6n9cthJhYeL43fPgPyDnod2QiUgLBzu6sb2b/NLN5ZvZB7iPcwYmISBk0bAvXL4cWF8GSh+DF/rDne7+jEpEgBTvcOQP4GjgBuB/YAnwapphERCRUqtWCQZNhwATY9hlM6ABfz/M7KhEJQrBJWoJzbiqQ5Zz70Dk3DEgOY1wiIhIqZtD6Sq9SQe3j4bUrYN6dkKXqfiLRLNgkLSvw7w9mdoGZtQEahSkmEREJh3rNYPgiSL4BVk2CKd1g+3/8jkpEChFskvagmdUG7gD+AEwBbgtbVCIiEpQS1wCNq+qtoXblG7D3e5jUGT57EYJcM1NEIqfYddLMLBZo7px7B9gNdA17VCIiUqwy1QA9qae3ptrskTD3Jm9NtQufgGq1wxqziASv2J4059xBoF8EYhERkRIocw3QWg3g6jlw/r3w1VteofbvNCdMJFoEO9z5sZk9Y2adzOzM3EdYIxMRkSLl1gCNNUpfAzQmFjrdAcPmgwOm9YTlj0GOisqI+C2o2p1mtqSAt51z7rzQh1R2qt0pIpVFSGuA/roL3rkV1s6GEzrDoElQ89hQhCkiRSisdmfQBdbLEyVpIiKl5Bx8/hLMuwuqVIcBE+GkHn5HJVKhlarAupndXtR259xjZQ1MRESiiBmceQ0c3w5mDoNXLoHkMdDt/7yZoSISMcXdk1Yz8EgCRgMNA49RwGnhDU1ERHxT/2QYvhjOHgkp42Fqd9ix0e+oRCqVIpM059z9zrn7gXrAmc65O5xzdwBt0WK2IiIVW3w16PNPuPwV2PUtPHcurH7V76hEKo1gZ3c2Bg7keX0AaBryaEREJPqccoG3ptpxrWHOKHhzJPy21++oRCq8YhezDXgJWGVms/EmaQ8EpoctKhERiS61G8KQt2HZI/Dhw/DdKrh4GjTUakwi4RJUT5pz7iHgWiAT2AVc65wbF8a4REQk2sTEQpc/wtB34WAWTO0BHz+tNdVEwiTYnjScc58Bn4UxFhERKQ+adIBRy71yUgvvgc1LvaU6jqzvd2QiFUqw96SJiIj8T/W6cNnLcMGj8M1ymNABNn3gd1QiFYqSNBERKR0zOGs4jFziJW0vDYL3/88bChWRMlOSJiIiZXPM6TBiCbQdAh89AdN6QeYWv6MSKfeUpImISNlVqQ4XPgmXvAA7NsDETvDlTL+jEinXlKSJiEjonD4QRq+Ao0+FWdfBW2PgwC9+RyVSLilJExGR0DqqMQydB53+AJ/PgOc6ww//9jsqkXJHSZqIiIRebByc/xe45i2vOsGU82Hlc+Cc35GJlBtK0kREpEBp6ZmMX7KRtPTM0h8ksTOM/hhOPA/euwteuxL27QxdkCIVmJI0ERE5TFp6JoOnpPDowvUMnpJStkStRgJc8Rr0ehg2LoIJHb211USkSErSRETkMCmbMziQnUOOg6zsHFI2Z5TtgGaQPBqGL/Jmgk6/ED54CA5mhyZgkQpISZqIiBwmOTGBKnExxBrEx8WQnJhQZPugh0YbnAEjP4TWV8Kyf8ALF8Cu70IYuUjFYa4C3sSZlJTkUlNT/Q5DRKRcS0vPJGVzBsmJCbRtUqfIdoOnpHAgO4cqcTHMGJ5cZPtD/v0GvHM7xMRAv2fgtH4hjF6k/DCzNOdcUv731ZMmIiIFatukDmO6Nis24Sr10GirS2HUMqibCG9c7SVsWb+GIHKRikFJmoiIlElJh0Z/p24iDFsIHW6C1Kkw+Tz4aV34ghUpRzTcKSIiZRbs0GiRNiyCOaPgt5+h1zhoO9SbcCBSwRU23KkkTUREosfeH2H2SNi8FE7r79UDPaKUSZ9IOaF70kREJPrVPAaumg3d7oOv3/UKtX+70u+oRHyhJE1ERKJLTAyccxsMWwAWA8/3hmX/hJyDfkcmElFK0kREJDo1SoJRy71hzw8ehBf7w54f/I5KJGKUpImISMQFvfhttdpw8TRvHbVtaTChA6yfH5kgRXymJE1ERCKqxHVBzeDMq2HkUqh1HLx6Gbw3FrJ/i0i8In5RkiYiIhFV6sVv658MwxfD2SNh5QSY0g12bAxvsCI+UpImIiIRVabFb+OrQZ9/wuWvwO7v4LlzYfWr4QtWxEdaJ01ERCIumMVvi22zexu8OQLSP4JWl8EFj0LVmmGOXCT0omoxWzOrC7wONAW2AJc65w67KcHMbgOGAw74ErjWObe/uOMrSRMRKd+CLtqec9BbnuPDv8NRTbxJBg3PjHzAImUQbYvZjgUWO+eaA4sDr3/HzBoCNwNJzrkWQCxweUSjFBERXwR931pMLHQZC0PegYMHYGoP+PhpyMmJbMAiYeBXktYfmB54Ph0YUEi7OOAIM4sDqgPfhz80ERHxW4nvW2vaEUatgOY9YOE98Mol8PP2yAQrEiZ+DXfucs4dled1pnPusH5sM7sFeAj4FVjonBtcxDFHAiMBGjdu3DY9PT3kcYuISOSUqmi7c/DpFFjwZzjiKBj4HJzYNaxxipRVxO9JM7NFwLEFbPozML24JM3M6gCzgMuAXcC/gJnOuZeLO7fuSRMRqeT++yXMHAY7NsA5t0LXP0NsvN9RiRSosCQtLlwndM51KyKYH82sgXPuBzNrAPxUQLNuwDfOue2Bfd4EOgDFJmkiIlLJHdvSW/x2/lhY8ThsWQEXTYE6Tf2OTCRoft2TNhcYEng+BHirgDbfAslmVt3MDDgfWBeh+EREpLyrUgP6Pe3N+Ny+HiZ2gjVv+h2VSND8StIeBrqb2Qage+A1Znacmc0DcM6tBGYCn+EtvxEDTPInXBERiXaF1gNtcZFXqL3eSTDzWph7ExzY50+QIiWgxWxFRKTcC2pdtYNZsOQhWPGEl7Bd8jwcc7ov8YrkFW3rpImIiIRMUOuqxcZDt/vg6jdh/y6Y1BVWTfZmhIpEISVpIiJS7pVoXbUTz4NRH8EJnWDeH+D1q2DfzsgFKxIkDXeKiEiFUOJ11XJyIGU8LLofjjwGLpoMTTqEP1CRfKKqdme4KUkTEZGgbfvMW1NtVzp0Hgvn/sErNyUSIbonTUREpCANz4Trl0GLi2Hp32B6P9i9ze+oRJSkiYiIUK0WDJoEAybA95/DxI7w9Ty/o5JKTkmaiIgIgBm0vtLrVat9PLx2Bcy7C7L2+x2ZVFJK0kRERPKq1wyGL4J2o2HVczClm1cDVCTClKSJiIjkF1cVej8MV7wOe7bBc+fC5y9rTTWJKCVpIiIiAYeVljq5F4z+CBq2hbfGwKzhsH+Pv0FKpRHndwAiIiLRoNDSUrWOg2veghWPwZJxsC0VLpoGjdr6HbJUcOpJExERoZjSUjGxcO6dcO08yDkI03rAR096C+KKhImSNBEREYIsLdU4GUYth5P7wPv3woyL4eefIh+sVAqqOCAiIhIQdGkp5yDteZh/N1StBQMnQrPzIxeoVCgqCyUiIhJqP34FM6+F7V9Dx1vgvL9AbLzfUUk5o7JQIiIiIfC7GaDHnAYjlkDbod49atN6ws5v/A5RKgglaSIiIkHKnQH66ML1DJ6S4iVqVarDhU/CJS/Ajo3emmpfzvQ7VKkAlKSJiIgEqcgZoKcP9CYV1D8FZl3nrat24Bf/gpVyT0maiIhIkIqdAVqnibdMR6c74PMZMKkL/PdLX2KV8k8TB0REREog6Bmgm5fCm9fDr5nQ40E4e4RXxF0kH83uFBERibRfdsCc0bBhIZx8AfR/BqrX9TsqiTKa3SkiIhJpNep5Rdp7/s1L1CaeA1s+8jsqKSeUpImIiIRTTAy0HwPD34e4qjC9Lyx92CsvJVIEJWkiIiKRcFwbuH4ZtLwUlo6D6RfC7m1+RyVRTEmaiIhIpFStCYOegwET4fvVMLEjfP2u31FJlFKSJiIiEmmtr/B61WofD69dCfPuhKz9fkclUUZJmoiIiB/qNYPhiyD5Blg1CaZ0g+3/8TsqiSJK0kRERPwSVxV6jYMr34C938OkzvDZS1ABl8eSklOSJiIi4reTevLvC+extcZpMPdGr6zU/t1+RyU+U5ImIiLis7T0TC595Ru6/Hgrj+dchls7ByZ2gq1pfocmPlKSJiIi4rPcwu3ZLoZnsvrz5hmTweXAtB7w0ZOQk+N3iOIDJWkiIiI+y1+4vWmb82DUcji5D7x/L8y4GH7+ye8wJcJUu1NERCQKFFi43TlIex7m3w1Va8HAidDsfH8DlZBTgXUREZHy6sevYOa1sP1r6HgLnPcXiI33OyoJERVYFxERKa+OOQ1GLIG213r3qE3rCTu/8TsqCTMlaSIiIuVBlepw4RNwyXTYsRGeOxfWzAK8odLxSzaSlp7pb4wSUnF+ByAiIiIlcPoAr1j7rOEwcxg7vljA8K97szu7ClXiYpgxPPl/97RJuaaeNBERkfKmThO4dh50uoOEDW/wL/sTJ/EtWdk5pGzO8Ds6CRElaSIiIuVRbDycfy8bes6glv3KW1X+wpD490k+oa7fkUmIKEkTEREpx05qfwE/XLmIHxLO5t6Y52mbchPs2+l3WBICStJERETKuTNObkbTG9+Bnn+D/yyAiedA+sd+hyVlpCRNRESkIoiJgfZjYPj7EFcVXrgAlj4MOQf9jkxKSUmaiIhIRXJcG7h+GbS8FJaOg+n9YPc2v6OSUlCSJiIiUtFUrQmDnoMBE+H7z2FiR/h6nt9RSQkpSRMREamoWl/h9arVPh5euwLm3QVZ+wtsqgVxo48WsxUREanI6jWD4Ytg0X2Q8qw3oeCS56Fe80NN0tIzGTwlhQPZOVoQN4qoJ01ERKSii6sKvcbBlW/A3u+9klKfvwzOAZCyOYMD2TnkOLQgbhRRkiYiIlJZnNQTRn0EDdvCW2O80lL795CcmECVuBhiDeLjYkhOTPA7UgHMBbLoiiQpKcmlpqb6HYaIiEh0yjkIKx6DJePgqOPh4mmkZSeSsjmD5MQEDXVGmJmlOeeS8r+vnjQREZHKJiYWzr0Trn3PS9im9qDt1hcZ0zlRCVoUUZImIiJSWTVuB6OWw8l94P17YcbF8PNPfkclAUrSREREKrMj6sClL0LfxyH9I5jQETZ94HdUgpI0ERERMYOkYTBiCVSvCy8N9HrWDmYV2FxrqkWG1kkTERERzzGneYnagrvhoydhywq4aCrUPeFQE62pFjm+9KSZ2SVmttbMcszssNkMedr1MrP1ZrbRzMZGMkYREZFKqUp1uPBJuOQF2LHRW1NtzaxDm7WmWuT4Ndy5BhgELCusgZnFAuOB3sBpwBVmdlpkwhMREankTh/oTSqofwrMHAZv3QgHftGaahHky3Cnc24dgJkV1exsYKNzbnOg7WtAf+CrsAcoIiIiUKcJXDsPlo6D5Y/Bdytpe/HzzBierDXVIiCaJw40BL7L83pr4L0CmdlIM0s1s9Tt27eHPTgREZFKITYezr8XrpkD+3fD5PNo++NMxnQ5sdgETRMMyiZsPWlmtgg4toBNf3bOvRXMIQp4r9DyCM65ScAk8CoOBBWkiIiIBCexi1dSas5omPcH2LwU+j3tzQYtgCYYlF3YetKcc92ccy0KeASToIHXc3Z8nteNgO9DH6mIiIgE5cj6XpH2Hg/BfxbAxHMg/eMCm2qCQdlF83Dnp0BzMzvBzKoAlwNzfY5JRESkcouJgQ43wvD3Ia4qvHABLH3YKy+VhyYYlJ1fS3AMNLOtQHvgXTNbEHj/ODObB+CcywZuBBYA64A3nHNr/YhXRERE8jmuDVy/DFpe4k0smN4Pdm87tLltkzrMGJ7M7T1O1lBnKZlzFe/2raSkJJeamup3GCIiIpXD6lfh3Tsgrgr0fxZO6eN3ROWKmaU55w5bNzaahztFRESkPGh9hderVvt4eO0KmHcXZO33O6pyT0maiIiIlF29ZjB8ESTfAKuegyndYMcGv6Mq15SkiYiISGjEVYVe4+CK12HPNq+k1OcvQwW8tSoSlKSJiIhIaJ3cC0Z/BA3bwltj4M0RsH+P31GVO0rSREREJPRqHQfXvAVd7/EKtD93LmxL8zuqckVJmoiIiIRHTCx0vhOufQ9ysmFqD/joKcjJ8TuyckFJmoiIiIRX42QYtRxO7g3v/wVmXAw//+R3VFFPSZqIiIiE3xF14NKX4ILHYMsKmNARNn3gd1RRTUmaiIiIRIYZnHUdjFziJW0vDYJF98HBrAKbp6VnMn7JRtLSMyMbZ5SI8zsAERERqWSOOR1GLoX5Y2HF417P2kVToE7TQ03S0jMZPCWFA9k5VImLCbq0VFp6JimbM0hOTCj3pajUkyYiIiKRV6U69HsKLn4etq+HiZ1gzZuHNqdszuBAdg45DrKyc0jZnFHsIXMTu0cXrmfwlJRy3wOnJE1ERET802KQN6mg3kkw81qYezMc2EdyYgJV4mKINYiPiyE5MaHYQ5UmsYtmGu4UERERf9VpCsPmw5KHYMUT8N1K2l48jRnDk0s0dJmb2GVl5wSd2EUzcxWwVENSUpJLTU31OwwREREpqU0fwOxRsH839HwIkq7zJhwEqTzek2Zmac65pMPeV5ImIiIiUeXn7TBnFGxcBKf0hX5PQ/W6fkcVNoUlabonTURERKLLkfXhyn9BjwfhPwu8SQXpn4T8NNG+xIeSNBEREYk+MTHQ4Sa4bgHExsELfeDDf0LOwZAcvjzMBFWSJiIiItGrYVu4fjm0uAiWPAgv9oc935f5sOVhJqiSNBEREYlu1WrBoMnQ/1nYluaVlFo/v0yHLM0SH5GmiQMiIiJSfuzY4K2n9t8vod1o6H4/xFUt1aGiZSaoZneKiIhIxZC1Hxb9H6ycCMe28qoW1GsW0lNEMoHT7E4RERGpGOKrQe+/w+Wvwu7v4LlzYfWrITt8tEwqUJImIiIi5dMpfWDUR3BcG29dtTdHwm97y3zYaJlUoCRNREREyq/aDWHIXOjyJ/jyX96aats+K9Mho2VSge5JExERkYoh/WOYNRx+/gm63QfJN3jrrZVCNNyTpiRNREREKo59O2HuTfD1O9CsOwyY4FUwiGKaOCAiIiIVX/W6cNnL0OcR+GYZTOwIm5f6HVWpKEkTERGRisUMzh4BIxZDtdrw4gBY/AAczPI7shJRkiYiIiIV07EtYeRSaHMVLH8Unu8Dmel+RxU0JWkiIiJScVWpAf2fgYumwvavvdmfa+f4HVVQlKSJiIhIxdfyYrh+mVeZ4F9D4O1bIetXv6MqkpI0ERERqRzqngDDFkDHWyDteZjUFX5a53dUhVKSJiIiIpVHbDx0fwCuehP27YBJXSB1GkThkmRK0kRERKTyaXY+jP4YmnSAd27zhkB/9adGZ2GUpImIiEjldOTRMHiW17P29bvepIJvV/od1SFK0kRERKTyionx7lEbthBiYuH53rDsEcg56HdkStJEREREaNTWm/15+gD44K/w0gDY84OvISlJExEREQGvOsFFU6HfM7A1FSae49UC9Umcb2cWERERiTZmcObVcHw72LTYqwXqEyVpIiIiIvnVP8l7+EjDnSIiIiJRSEmaiIiISBRSkiYiIiIShZSkiYiIiEQhJWkiIiIiUUhJmoiIiEgUUpImIiIiEoWUpImIiIhEISVpIiIiIlFISZqIiIhIFFKSJiIiIhKFlKSJiIiIRCElaSIiIiJRSEmaiIiISBRSkiYiIiIShcw553cMIWdm24H0AjbVBnYHeZji2pZle0Hb6gE7gowt0krydYv0sUuzf7D7BNOupN/n4rbpOojM/tHwu0DXQGiPrd8FkVORroNIXQNFba8NHOWcq3/YFudcpXkAk0LVtizbC9oGpPr99QnF1y3Sxy7N/sHuE0y7kn6fi9um6yAy+0fD7wJdA/5eAyXZR78LKu51EKlroJjvdaH7VbbhzrdD2LYs20sSRzQIZ7xlPXZp9g92n2Dalfb7XN6uAahY10E0/C7QNRDaY+t3QeRUpOsgUtdAUdsL3a9CDneWR2aW6pxL8jsO8ZeuA9E1IKDrQDyVrSctmk3yOwCJCroORNeAgK4DQT1pIiIiIlFJPWkiIiIiUUhJmoiIiEgUUpImIiIiEoWUpImIiIhEISVp5YSZ1TCzNDPr63cs4g8zO9XMJprZTDMb7Xc8EnlmNsDMJpvZW2bWw+94xB9mlmhmU81spt+xSHgpSQszM5tmZj+Z2Zp87/cys/VmttHMxgZxqD8Cb4QnSgm3UFwHzrl1zrlRwKWA1k8qZ0J0Dcxxzo0AhgKXhTFcCZMQXQebnXPXhTdSiQZagiPMzOxc4GfgRedci8B7scB/gO7AVuBT4AogFhiX7xDDgFZ4ddyqATucc+9EJnoJlVBcB865n8ysHzAWeMY590qk4peyC9U1ENjvUWCGc+6zCIUvIRLi62Cmc+7iSMUukRfndwAVnXNumZk1zff22cBG59xmADN7DejvnBsHHDacaWZdgRrAacCvZjbPOZcT3sgllEJxHQSOMxeYa2bvAkrSypEQ/S4w4GHgPSVo5VOofhdI5aAkzR8Nge/yvN4KtCussXPuzwBmNhSvJ00JWsVQouvAzLoAg4CqwLxwBiYRU6JrALgJ6AbUNrNmzrmJ4QxOIqakvwsSgIeANmZ2dyCZkwpISZo/rID3ih13ds69EPpQxEclug6cc0uBpeEKRnxR0mvgKeCp8IUjPinpdZABjApfOBItNHHAH1uB4/O8bgR871Ms4h9dB6JrQEDXgRRCSZo/PgWam9kJZlYFuByY63NMEnm6DkTXgICuAymEkrQwM7NXgU+Ak81sq5ld55zLBm4EFgDrgDecc2v9jFPCS9eB6BoQ0HUgJaMlOERERESikHrSRERERKKQkjQRERGRKKQkTURERCQKKUkTERERiUJK0kRERESikJI0ERERkSikJE1EKiwzO8rMbsjz+jgzmxmG89xnZtvM7IFCtm8xs3pmdoSZrTazA2ZWL9RxiEjFoiRNRCqyo4BDSZpz7nvn3MVhOtfjzrl7i2rgnPvVOdcalfwRkSCowLqIVGQPAyea2WrgfWA88I5zroWZDQUGALFAC+BRoApwNfAb0Mc5t9PMTgzsVx/YB4xwzn1d1EnNLAF4NbDPKgouoC0iUiT1pIlIRTYW2OSca+2cu7OA7S2AK4GzgYeAfc65Nnhle64JtJkE3OScawv8AXg2iPP+H7AicKy5QOOyfQwRqYzUkyYildkS59xeYK+Z7QbeDrz/JdDKzI4EOgD/MjvUGVY1iOOeCwwCcM69a2aZoQ1bRCoDJWkiUpn9lud5Tp7XOXi/H2OAXYH7yEpKhZFFpEw03CkiFdleoGZpd3bO7QG+MbNLAMxzRhC7LgMGB/bpDdQpbQwiUnkpSRORCss5lwF8ZGZrzOyfpTzMYOA6M/sCWAv0D2Kf+4FzzewzoAfwbSnPLSKVmDmnHnkRkbIws/uAn51zjwTZfguQ5JzbEc64RKR8U0+aiEjZ/QyMLGwx21y5i9kC8Xj3vYmIFEo9aSIiIiJRSD1pIiIiIlFISZqIiIhIFFKSJiIiIhKFlKSJiIiIRCElaSIiIiJR6P8BzDHMgg9YkJ0AAAAASUVORK5CYII=\n", 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", 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" ] @@ -428,13 +430,13 @@ } ], "source": [ - "#matplotlib plot for calibration, \n", + "# matplotlib plot for calibration,\n", "plt.figure(figsize=(10, 7))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m') #Plotting the observed drawdown\n", - "plt.semilogx(t1, hm1[0], label='ttim at 30 m') #Simulated drawdown\n", - "plt.xlabel('time [d]')\n", - "plt.ylabel('drawdown [m]')\n", - "plt.title('ttim analysis for Oude Korendijk - Piezometer 30 m')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\") # Plotting the observed drawdown\n", + "plt.semilogx(t1, hm1[0], label=\"ttim at 30 m\") # Simulated drawdown\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 30 m\")\n", "plt.legend();" ] }, @@ -551,10 +553,10 @@ } ], "source": [ - "ca2 = Calibrate(ml)\n", - "ca2.set_parameter(name='kaq0', initial=10)\n", - "ca2.set_parameter(name='Saq0', initial=1e-4)\n", - "ca2.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca2 = ttim.Calibrate(ml)\n", + "ca2.set_parameter(name=\"kaq0\", initial=10)\n", + "ca2.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca2.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca2.fit(report=True)\n", "ca2.parameters" ] @@ -573,7 +575,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -585,14 +587,14 @@ } ], "source": [ - "print('rmse:', ca2.rmse())\n", + "print(\"rmse:\", ca2.rmse())\n", "hm2 = ml.head(r2, 0, t2)\n", "plt.figure(figsize=(10, 7))\n", - "plt.semilogx(t2, h2, '.', label='obs at 90 m')\n", - "plt.semilogx(t2, hm2[0], label='ttim at 90 m')\n", - "plt.xlabel('time [d]')\n", - "plt.ylabel('drawdown [m]')\n", - "plt.title('ttim analysis for Oude Korendijk - Piezometer 90 m')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t2, hm2[0], label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 90 m\")\n", "plt.legend();" ] }, @@ -710,11 +712,11 @@ } ], "source": [ - "ca = Calibrate(ml)\n", - "ca.set_parameter(name='kaq0', initial=10)\n", - "ca.set_parameter(name='Saq0', initial=1e-4)\n", - "ca.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0) # Adding well 1\n", - "ca.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0) # Adding well 2\n", + "ca = ttim.Calibrate(ml)\n", + "ca.set_parameter(name=\"kaq0\", initial=10)\n", + "ca.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "ca.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0) # Adding well 1\n", + "ca.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0) # Adding well 2\n", "ca.fit(report=True)\n", "ca.parameters" ] @@ -733,7 +735,7 @@ }, { "data": { - "image/png": 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\n", 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r2DycwHYypehW0Eqlmoh8Bpw2xoxydyzKtUTkONDbGLPB3bEopeLn9tGaVMZmF3d3J4O1QlVpJ9ZAKYWxSjuUUumUFt2rVBORcVjFju8aY464Ox7lOnZ9/wGsPtTHk1pfKeU+WnSvlFJKZWJ6Ra+UUkplYpmyjr5QoUKmbNmy7g5DKaWUcokdO3ZcMMbEO8FQpkz0ZcuWJTAw0N1hKKWUUi4hIgmOHqhF90oppVQmpoleKaWUysQ00SullFKZWKaso1dKKeV44eHhnDx5kps3kxylWjlJ9uzZKVmyJN7e3sl+jlsTvYi0xRon2hOYHT3jVIzlYi9vjzWFYV9jzB8uD1QppRQnT54kT548lC1bljsnOFSuYIzh4sWLnDx5knLlyiX7eW4ruhdrfvNpWJM53A08Ys+WFFM7rMlRKgJPYk17qJRSyg1u3rxJwYIFNcm7iYhQsGDBFJeouLOO/l7goDHmsD1j1rdYs1DF1AX4wli2APlFpJirA1VKKWXRJO9eqXn/3ZnoS2BNKRntpP1YStcBQESeFJFAEQk8fz6hWROVUkqprMWdiT6+05LYA+8nZx3rQWNmGmPqGGPqFC4c7+BASimlMqGjR49SvXp1h25z586drFq1Kt5l27ZtIyAggICAAGrWrMnixYtvL9uxYwf+/v5UqFCBZ599lvQwn4w7E/1JoFSM+yWB06lYR6k02XluJ7N3zWbnuZ3uDkUplU4kluirV69OYGAgO3fu5Mcff2TQoEFEREQAMGTIEGbOnMmBAwc4cOAAP/74oyvDjpc7E/12oKKIlBMRH+BhYFmsdZYBj4ulPhBijDnj0ihDTkJUlEt3qVxn57mdDFw9kCl/TGHg6oHJTvaxTw70ZEGp+O04Fsy0dQfZcSzYIdubNGkS1atXp3r16kyePPn24xEREfTp04caNWrQs2dPrl+/DsCIESO4++67qVGjBi+99FKc7W3bto2GDRtSq1YtGjZsyP79+wkLC2PMmDHMnz+fgIAA5s+ff8dzcubMiZeX1Wnt5s2bt+vNz5w5w5UrV2jQoAEiwuOPP86SJUsA6Nu3L0OGDKFZs2b4+fnx66+/0r9/f6pWrUrfvn0d8t4kxG3d64wxESLyNPATVve6z4wxe0RksL18BrAKq2vdQazudf1cHCR83h7Cb0DltlC5Pfg1Be8cLg1DOU9gUCBhkWFEEUV4VDiBQYEEFAlI9DnRJwdhkWH4ePowvO5wJm6fePv+rNazktyGUlnBjmPB9Jq9hbCIKHy8PJg3oD61yxRI/fZ27ODzzz9n69atGGOoV68e999/PwUKFGD//v18+umnNGrUiP79+zN9+nT69+/P4sWL+eeffxARLl++HGebVapUYcOGDXh5ebF27VpeeeUVFi5cyBtvvEFgYCBTp06NN5atW7fSv39/jh07xpdffomXlxenTp2iZMmSt9cpWbIkp06dun0/ODiYX375hWXLltGpUyc2bdrE7NmzqVu3Ljt37iQgICDV701i3DoynjFmlTGmkjGmvDHmLfuxGXaSx25tP9Re7m+Mce1MNSYKmo+Gso1hzxL45mGY6Aff9oI/v4Kr2ugvo6tTtA4+nj54iifeHt7UKVonyefEPjlYe3xtnJMFpRRsOXyRsIgoogyER0Sx5fDFNG1v48aNdOvWjVy5cpE7d266d+/Ob7/9BkCpUqVo1KgRAL1792bjxo3kzZuX7NmzM2DAABYtWkTOnDnjbDMkJIQHHniA6tWr88ILL7Bnz55kxVKvXj327NnD9u3bGT9+PDdv3oy3Pj5mK/lOnTohIvj7+1O0aFH8/f3x8PCgWrVqHD16NBXvSPLoyHiJ8fCEGg9Yt4gwOLYR/lkF+3+Af1YAAqXqQZX21tV+oYrujlilUECRAGa1nkVgUCB1itZJ1pV49MlBeFQ43h7etCzdkj+C/rh9PzknC0plBfX9CuLj5UF4RBTeXh7U9yuYpu0l1rAtdrczEcHLy4tt27bx888/8+233zJ16lR++eWXO9YbPXo0zZo1Y/HixRw9epSmTZumKKaqVauSK1cudu/eTcmSJTl58uTtZSdPnqR48eK372fLlg0ADw+P2/9H34+u43cGTfRJ2HEsmC2HL1LfryC1yzeH8s2h/btw9m874a+ENWOsW8GKULkdVOkAJetaJwoq3QsoEpCiovb4Tg4qFqiYopMFpbKC2mUKMG9A/f9+Q9NQbA/QpEkT+vbty4gRIzDGsHjxYr788ksAjh8/zubNm2nQoAHffPMNjRs35urVq1y/fp327dtTv359KlSoEGebISEhlChh9dqeM2fO7cfz5MlDaGhovHEcOXKEUqVK4eXlxbFjx9i/fz9ly5alUKFC5MmThy1btlCvXj2++OILnnnmmTS9ZkfQRJ+IBOuXRKBYTevWdARcPgH//mgl/S0fw+8fQc5CUKmtlfjLNwOfXO5+OcqBYp8cJOdkYee5nXoyoLKc2mUKpDnBR7vnnnvo27cv9957LwADBgygVq1aHD16lKpVqzJ37lwGDRpExYoVGTJkCCEhIXTp0uV2sfoHH3wQZ5vDhw+nT58+TJo0iebNm99+vFmzZkyYMIGAgABGjhzJQw89dHvZxo0bmTBhAt7e3nh4eDB9+nQKFSoEwMcff0zfvn25ceMG7dq1o127dg557Wkh6aGPn6PVqVPHBAamvZ502rqDvL96P1EGPAX+17oyQ5vFPSO8w80QOLjWutr/dzXcCgGv7FYjvsrtreSfp2iaY1OOE3X9OuGnTpGtovOqXmI34NMGeyoj2rdvH1WrVnV3GFlefMdBRHYYY+KtN9Qr+kSkqn4pez6o3sO6RYbDsd9h/yqrbv/fHwGBknWsK/3KHaBwZauEQLlN6M8/c3rYcLJVqUK+Th3J27493sUcO9JyYq379UpfKeVMekWfhDvq6NNS/GQMBO2xrvT3r4TTf1qP+/pZV/qV21sN+zz13MvVIi5e5MqqHwhZsZybf/0NIuSsU4e8nTqSt00bPPPlS/M+oq/ooxvsRV/R65W+ykj0ij59SOkVvSZ6d7ly2k76q+DIBogMgxy+UKmNXa/fArLldneUWU7YsWOErFjBleUrCDt6FPH2Jtf9TcjXsRO5mzXFI0ZL2ZSK78p99q7ZTPljClFE4SmePF3raQb4D3DMi1HKwTTRpw9adJ9R5C0OdZ+wbrdC4eDPdr3+j/DXN+DpA+Xut7ruVWoHeXXSPlfwKVOGwkOHUuipp7i5Zy9Xli/nyqpVXF37Mx65c5OnVSvydepIznr1EM+U9aqIr8Fe7K562jVPKeVoekWf3kRGwIktdn/9lRB81Hq8+D1W8X6V9lDkbq3XdyETGcn1rVsJWbGS0NWribp6Fa/ChcnboQP5e/YgWzxddlJC6+hVRqFX9OmDFt2TwRN9TMbA+X/+a8x3yn5N+cvY9frtoExD8PR2b5xZSNTNm1xd/yshK5Zz9dcNEB5Ojlq1yP/gg+Rt2waPHI4bHllPAFR6o4k+fUhponfrELgqCSJQpCrc9yIM/Ble3A+dPoTCVSDwM/iiM7xbnotf9GHFgjnsOHrJ3RFneh7Zs5O3bRtKTZ1KxV/XU2T4cCKDgzkzciQHmzYj6N13CTt5KukNJSG1k+0olZldvnyZ6dOn375/9OhRvv7669v3AwMDefbZZx2+3yVLlrB37954l82YMQN/f38CAgJo3LjxHevNnTuXihUrUrFiRebOnevwuJJLr+gzqrBrcOgXLuxYgseBn/CVUP4xpfFp+iJ+TXpr630XMsZwfft2gud9TejatRAVRe5mzfDt3Yuc9ixWKaWN9FR65O4r+qNHj9KxY0d2794NwPr163nvvfdYsWKFU/fbt29fOnbsSM+ePeMsu3LlCnnz5gVg2bJlTJ8+nR9//JFLly5Rp04dAgMDERFq167Njh07KFAg7YMH6RV9VuGTC6p2Yn6JkdQPm8b/wgbjaSLx+/U5mFoHdsyBiFvujjJLEBFy3XsvJT+cTIW1ayg46Elu7NzJ8f5PcLhjJy59/TWRV6+laJuJTbajU+KqrGrEiBEcOnSIgIAAhg0bxogRI/jtt98ICAjggw8+YP369XTs2BGAsWPH0qdPH1q3bk3ZsmVZtGgRw4cPx9/fn7Zt2xIeHh5n+7NmzaJu3brUrFmTHj16cP36dX7//XeWLVvGsGHDCAgI4NChQ3c8JzrJA1y7du32if1PP/1Eq1at8PX1pUCBArRq1SreuembNm3KCy+8QJMmTahatSrbt2+ne/fuVKxYkVGjRjnkfdPLvgyuvl9Bpnj5sDSiCT+YJqxofoXy/3wCy5+D9ROgwVCo3U+76rmId7FiFHn+eQoNGcKVH34g+Kt5BL0xjvOTPiBft2749noUn7Jlk9xOQpPtaL97lW78MALO7nLsNu/yh3YTElw8YcIEdu/ezc6dO4G4V/Tr16+/Y/1Dhw6xbt069u7dS4MGDVi4cCETJ06kW7durFy5kq5du96xfvfu3Rk4cCAAo0aN4tNPP+WZZ56hc+fOCV7RA0ybNo1JkyYRFhZ2e9KcU6dOUapUqdvrxJ6yNiYfHx82bNjAhx9+SJcuXdixYwe+vr6UL1+eF154gYIF0zYZkF7RZ3DRk0b8r3VlvhrQkPJNHoGB6+CxJVCwAqweBZOrw7rxcF3r8F3FI1s28nftSrkF31N2/rfkbtaM4G+/5VC79px4+mmu//FnktsIKBLAAP8BdyTy+EbYU0rFr127dnh7e+Pv709kZCRt27YFwN/fP95pYXfv3s19992Hv78/8+bNS/aUtUOHDuXQoUO88847vPnmm0D8M+0lVI3XuXPn23FVq1aNYsWKkS1bNvz8/Dhx4kSyYkiMXtFnAnEmjRCxJtIp3wxObIeNk+DXCfD7FKjTDxo8rf3yXShHzZqUqFmTosOHcenrr7n89TccW/szOWrVwrd/P/I0b57sPvna716lG4lceacXMaeF9fb2vp1oE5oWtm/fvixZsoSaNWsyZ86cOCUESXn44YcZMmQIYF3Bx3z+yZMnE5wC19nT1+oVfWZXqi488g0M2WxNn7vlY/iwhlW0f+mwu6PLUrwKF6bIc89RYd0vFH31VSLOnePUM89yuH0HLi9YgAkLS3Ib0UX6T9d6+o5hdLXOXmUFsaeOTWwq2dQIDQ2lWLFihIeHM2/evGTt58CBA7f/X7lyJRXtybHatGnD6tWrCQ4OJjg4mNWrV9OmTRuHxZoSmuiziqJ3Q49Z8MwOqNUbdn4DU2rDgifg7G53R5eleOTMie9jvSn/04+U+GASHrlycWbUaA62bsOlL74k6saNRJ8fs0hfu+GprKRgwYI0atSI6tWrM2zYMGrUqIGXlxc1a9aMdwralBo3bhz16tWjVatWVKlS5fbjDz/8MO+++y61atWK0xhv6tSpVKtWjYCAACZNmnS7G52vry+jR4+mbt261K1blzFjxuDr65vmGFNDu9dlVaFnYfM0qz9+2FVr+tzG/4PS9dwdWZZjjOHaxo1cmPEJN3bswLNgQXz79KHAo4/gmTvxRpTaDU+5kru71ymLdq9TyZPnLmg9Dl7YDc1ehRPb4LPW8HkHOLjWGpVPuYSIkPu++yg77yvKfPUl2atW5fykSRxs3oLzH31E5OXLCT43sW54SikFekWvooVdgx1zrQZ7oaehWE3rCr9qJ/BI2eQtKu1u7NrNxZmfELpmLR65c+P7+GP49ukT75S5OlSuchW9ok8fdKx7NNGnScQt+Hs+bPzAaqxXsCI0fh78HwQvnzir7zgWzJbDF6nvV/DOlv/KIW7u/5cL06cT+tNPdsJ/HN++ffCMMUiHUq6iiT590KJ7lTZe2eCex+HpQOj5OXhnh6VD4aNasPUTCLt+e9Udx4LpNXsL76/eT6/ZW9hxLNiNgWdO2StXouSHkym3dAm5GjTgwvTpHGzRkvNTpxGZitbG2kJfqaxHE72Kn4cnVO8Og36DXgsgf2n4YThM9ocN78GNy2w5fJGwiCiiDIRHRLHl8EV3R51pZa9cmZJTPqLcksXkql+PC1OncqhlKy7MmkXU9etJbwCdKEeprEoTvUqcCFRsBf1/gH4/QPFa8Ms4mOxP90uzKeZ1BU8Bby8P6vulbZhGlbTsVapQcsoUyi5cQPaAmpx/f5LVLe/Lr5Lsh6+j6imVNWmiV8lXpiH0XgCDNkCFFhTbNYPffJ5jsd9S5veuqHX0LpSjWjVKf/IJZb7+mmx+fgS99RaH2ncgZMVKTFRUvM/RFvoqszp69CjVq1d36DZ37tzJqlWr4l0WFhZGv3798Pf3p2bNmneMgLdjxw78/f2pUKECzz77bLxD4bqaJnqVcsVqwgNz4JkdeNR4kBqnF1BzYVOrSD8secXIyjFy3lOL0nPnUGrWLDxy5+b0Sy9xpGdPrm7aFGfd+EbVU0rFL7FEP2vWLAB27drFmjVrePHFF4myT7CHDBnCzJkzOXDgAAcOHIh3xjpX00SvUq9geegyFZ7aAuWaWEX6U2rDn19BVKS7o8syrH74jSm3aCHF351IVMgVTjwxgGP9+nFj952TcsSeKEcb5ylnc/RnbNKkSVSvXp3q1aszefLk249HRETQp08fatSoQc+ePblut10ZMWIEd999NzVq1OCll16Ks71t27bRsGFDatWqRcOGDdm/fz9hYWGMGTOG+fPnExAQwPz58+94zt69e2nRogUARYoUIX/+/AQGBnLmzBmuXLlCgwYNEBEef/xxlixZAljj6A8ZMoRmzZrh5+fHr7/+Sv/+/alatSp9+/Z1yHuTIGNMprvVrl3bKDc4stGYmc2MeS2vMdMaGPPvGmOiotwdVZYTeeuWuTh3rtlfr77ZW7mKOfnCC+bW0aNx1vsz6E9T58s6psacGqbOl3XMn0F/uj5YlaHs3bs3Res7+jMWGBhoqlevbq5evWpCQ0PN3Xffbf744w9z5MgRA5iNGzcaY4zp16+feffdd83FixdNpUqVTJT9OxQcHBxnmyEhISY8PNwYY8yaNWtM9+7djTHGfP7552bo0KHxxvHJJ5+Ynj17mvDwcHP48GGTL18+s2DBArN9+3bTokWL2+tt2LDBdOjQwRhjTJ8+fcxDDz1koqKizJIlS0yePHnM33//bSIjI80999xj/vwz+e9NfMcBCDQJ5ES9oleOU7YRDPjZ6pYXfg3m9YAvusCZv9wdWZbi4eOD7+OPU37tGgoOGUzouvUc6tCRs2+MI+LChdvraeM85WyO/oxt3LiRbt26kStXLnLnzk337t357bffAChVqhSNGjUCoHfv3mzcuJG8efOSPXt2BgwYwKJFi8iZM2ecbYaEhPDAAw9QvXp1XnjhhWRNTdu/f39KlixJnTp1eP7552nYsCFeXl5JTk3bqVMnRAR/f3+KFi2Kv78/Hh4eVKtWLd5pcx1FE71yLBGrW97Q7dB2Apz9Gz65HxYNgsvH3R1dluKZOzdFnnuO8j/9SP6ePQieP5+Drdtwfto0oq5f18Z5yukc/RmLL5FGiz3Xu4jg5eXFtm3b6NGjB0uWLLk9H31Mo0ePplmzZuzevZvly5dz8+bNJOPw8vLigw8+YOfOnSxdupTLly9TsWJFSpYsycmTJ2+vd/LkSYoXL377vrOno02IJnrlHF4+UH8IPLsTGj0HexbDlDqwejTcuOzu6LIU7yJFKDZ2LH4rlpO7cWMuTJnKobbtKLvxCLNafKKN85TTOLoBaJMmTViyZAnXr1/n2rVrLF68mPvuuw+A48ePs3nzZgC++eYbGjduzNWrVwkJCaF9+/ZMnjyZnTt3xtlmSEgIJUqUAGDOnDm3H09satro/QOsWbMGLy8v7r77booVK0aePHnYsmULxhi++OILunTpkqbX7Aia6JVz5cgPrV63pset3t0aS/+jANg83RpuV7lMtnLlKPnRh5T5eh5exe7izCuvkPepN3nkmn+SP8DaaE+lVuwGoGlxzz330LdvX+69917q1avHgAEDqFWrFgBVq1Zl7ty51KhRg0uXLjFkyBBCQ0Pp2LEjNWrU4P777493Ktvhw4czcuRIGjVqRGTkf42ImzVrxt69e+NtjHfu3DnuueceqlatyjvvvMOXX355e9nHH3/MgAEDqFChAuXLl6ddu3Zpft1ppWPdK9c68xesGQOH13MrdynWl3qKQvUepnZZ98zTnFUZY7iyahXn359E+OnT5GnVkiLDh+NTqlScdaNH1AuLDMPH00ev/rMwHes+fdCx7lX6VqwmPL6UA63ncjRUaLNvJFGft2PvHxvdHVmWIiLk69ABv1UrKfz8c1zd9DuH23fg3PvvE3n16h3raqM9pTI2TfTKLVbfqk6HsLcZGf4EfpyiyrJOsOIFuKbj5buSR/bsFBo8mPI//EDe9u25OGs2h9q24/KCBRi7GFMb7SmVsbkl0YuIr4isEZED9t84Y6eKSCkRWSci+0Rkj4g8545YlXPU9yuIl5cX30W1oF3UB5yv+jjsmAtTasHWmRDpvBaoKi7vokUo/s4Eyn7/HT6lSnFm1GiOPPAA1wMDdUQ9pTI4t9TRi8hE4JIxZoKIjAAKGGNejrVOMaCYMeYPEckD7AC6GmP2JrV9raPPGOLMZX9uH/zwMhz5FYrcbXXP87vf3WFmOcYYrqxcxbn33iPi7FnytG1LkZdewqdkCXeHptxM6+jTh4xSR98FmGv/PxfoGnsFY8wZY8wf9v+hwD5Af2kykdplCjC0WYX/JsMpUhUeXwoPfglhV+GLzjD/MQg+5t5AsxgRIV/HDpT/YRWFnn6aq+vXc7h9e85/9BFRN27E+xxtla9U+uWuRF/UGHMGrIQOFElsZREpC9QCtiayzpMiEigigefPn3dkrMqVRODuzjB0GzR7FQ6sgWn3wrq3dcIcF/PIkYPCTw+l/A+ryNOqFRemf8yhDh24snr1HQOX6Dz3SqVvTkv0IrJWRHbHc0vR6AEikhtYCDxvjLmS0HrGmJnGmDrGmDqFCxdOa/jK3bxzwP3D4ZlAqNIBfn0Hpta1Bt7JhF1C0zPvYsUo8f57lP5iLp6583Dq2ec48cQT3Dp0CNBW+cp1Ll++zPTp02/fP3r0KF9//fXt+4GBgTz77LMO3++SJUvYuzf+WuNjx47RokULatSoQdOmTe8YGW/u3LlUrFiRihUrMnfu3Hif7wpOS/TGmJbGmOrx3JYCQXYdfHRd/Ln4tiEi3lhJfp4xZpGzYlXpWL6S0PMz6LsKchSA7/vCnI5wdre7I8tyct17L+UWLaToqFHc2L2Hw126EvTOROrkvltb5SuXSCrR16lTh48++sjh+00s0b/00ks8/vjj/P3334wZM4aRI0cCcOnSJV5//XW2bt3Ktm3beP311wkODnZ4bMnhrqL7ZUAf+/8+wNLYK4g1cPGnwD5jzCQXxqbSo7KNYNCv0GESnNsDn9wHK1+E65fcHVmWIl5e+PbuRfkffyB/t65cmjOHnI+/zKfmcZ4OGKqt8pVTjRgxgkOHDhEQEMCwYcMYMWIEv/32GwEBAXzwwQesX7+ejh07AjB27Fj69OlD69atKVu2LIsWLWL48OH4+/vTtm1bwsPD42x/1qxZ1K1bl5o1a9KjRw+uX7/O77//zrJlyxg2bBgBAQEcskuyosWcsrZZs2YsXWqls59++olWrVrh6+tLgQIFaNWq1e256cuWLcsrr7xCgwYNqFOnDn/88Qdt2rShfPnyzJgxw+Hvm5fDt5g8E4DvROQJ4DjwAICIFAdmG2PaA42Ax4BdIrLTft4rxphVbohXpQcenlD3CajWDdaPh+2zYfdCaPk61HoMPHRYCFfx8vWl2Lhx5H/wQc6Oe5PIt6bTsnZt7hpzfxItblRmcfbtt7m17x+HbjNb1Src9corCS6fMGECu3fvvj1m/fr163nvvfdYsWLF7fsxHTp0iHXr1rF3714aNGjAwoULmThxIt26dWPlypV07dr1jvW7d+/OwIEDARg1ahSffvopzzzzDJ07d6Zjx4707NkzTkw1a9Zk4cKFPPfccyxevJjQ0FAuXrzIqVOnKBVjpMmSJUty6tSp2/dLlSrF5s2beeGFF+jbty+bNm3i5s2bVKtWjcGDB6fkbUuSW34ZjTEXjTEtjDEV7b+X7MdP20keY8xGY4wYY2oYYwLsmyZ5BTl9of277Om8itM+ZWH5s/BZGzi7y92RZTk5/P0p++03FHtzHGGHDnGke3eCJr5LlD3hh1Lu1K5dO7y9vfH39ycyMvL27HX+/v7xTgu7e/du7rvvPvz9/Zk3b16ypqx97733+PXXX6lVqxa//vorJUqUSNaUtZ07d74dS7169ciTJw+FCxcme/bsXL58OXUvOAHuuqJXKk12HAum16LLhEW8xIPeGxl34Tu8P2kC9QZD05GQPa+7Q8wyxMOD/D17krtFC85PmsSlzz7jyqpVFB05kjytW8WZPlRlDoldeacXMaeF9fb2vv1ZTGha2L59+7JkyRJq1qzJnDlz4pQQxKd48eIsWmQ1Ibt69SoLFy4kX758lCxZ8o7nnzx5kqZNm8Ybm7OnrNWyTpUhbTl8kbCIKKKM8H34fcy953uo3Re2fGx1x9u9SFvnu5hXgQIUGzeOMt98jWf+/Jx67jlODBpE2PHj7g5NZRKxp45NbCrZ1AgNDaVYsWKEh4czb968ZO3nwoULREVFATB+/Hj69+8PQJs2bVi9ejXBwcEEBwezevVq2rRp47BYU0ITvcqQ6vsVxMfLA08Bby8PalX2g44fwICfIXcRWNAPvuwGFw66O9QsJ2etWpRb8D1FR47gRuAODnfqzPnp04kKC3N3aCqDK1iwII0aNaJ69eoMGzaMGjVq4OXlRc2aNeOdgjalxo0bR7169WjVqhVVqlS5/fjDDz/Mu+++S61ateI0xlu/fj2VK1emUqVKBAUF8eqrrwLg6+vL6NGjqVu3LnXr1mXMmDH4+rpnlk6dplZlWHGG0I0WFQmBn8HPb0DETWj0PNz3P6tvvnKp8KAggiZMIPSHH/EpU4aiY0aTu1GjOOvtPLeTwKBA6hSto6320zEdAjd9SOkQuJroVeYVGgRrRsPf86FAWWj/HlRs5e6osqSrGzdxdtwbhB87Tt727Sjy8gi8i1rN83W++4xDE336kFHGulfK+fIUhe4zoc9y8PSBeT1hfm8IOZn0c5VD5W7cCL9lyyj0zNOErv2Zw+3bc+mLLzARETqynlJOpoleZX7lmsDgTdDiNTiwFqbeC5s+hMi4A2Yo5/HIlo3CQ4fit3wZOWrVIujt8Rx54EHqXsinI+tlIJmxFDgjSc37r0X3KmsJPgY/joT9K6FwVeg0GUrXd3dUWY4xhtCfVhM0fjwR584R2bE5gd0qE1DhPi22T8eOHDlCnjx5KFiwoHabdANjDBcvXiQ0NJRy5crdsUzr6JWKbf8PsGoYhJyA2v2g5VjIkd/dUWU5kVevcWHqVC59+SWeefNS5OXh5OvSRZNIOhUeHs7Jkye5efOmu0PJsrJnz07JkiXx9va+43FN9ErF59ZVayjdLdMhZyFoNwGqdbemylUudXP/fs6+NpYbO3eSs3597nptDNnsKxZtka9U0jTRKxXLHV3zfI7DsmfhzE6o2NpqnV+gjLtDzHJMVBSXv/uOc+9Pwty6RcHBgzjVuS4D1z+lLfKVSoK2ulcqhh3Hguk1ewvvr95Pr9lb2BFWGgb+Am0nwLHfYXp92PQRRDp2GEqVOPHwoMDDD+O3cgV5WrbgwkdTiOzzPH5HbmqLfKXSQBO9ynL+Gz4XwiOi2HL4ojUzXv0hMHQr+DW1+t/Pagqndrg73CzHu0gRSkyaRKmZn5Aj0pOx8yIYvCqK/Le8tEW+UqmgiV5lObGHz63vV/C/hflKwsNfw0NfwbULMKsFrBoOtxw3nrZKntxNmlDlh5+IeKQjzXYZpn/miV/gae3epVQKaR29ypISHD43pptX4JdxsG0W5CkG7d+Fqh1dG6gC4Oa+fZwZPYabu3eT6/4mFBszBu8SJdwdllLphjbGUyotTgbC8ucgaDdU6QjtJkI+TTKuZiIjCf7qK859+BEYQ+HnnsW3d2/ES2fbVkoTvVJpFRkOm6fB+gng4QUtX4M6T4CH1n65WvipU5x54w2u/bqB7NWqceWFXmzPd1G736ksTRO9Uo4SfBRWvACHfoFS9aDTR1CkSpJPU45ljCH0xx85Oe51ooJDWHmvB8ua5mBah9ma7FWWpN3rlHKUAmWh9yLo9glc+BdmNLau8iNuuTuyLEVEyNuuHVvf7836Gh503hrFmzOvcWDtIneHplS6o4leqZQSgZoPw9OBUK2bNbrejPvg+FZ3R5bl1CrfmLmdcjLuUW8Eofob33N61CgiQ0LcHZpS6YYW3SuVVgfWWMX5ISeh7gBoMQay53V3VFnG7SFy89eg+PwNXPx8Dp6+Bbhr1Gjytmnt7vCUcgmto1fKQRLslnfrKvzyJmydYXXF6zgJKrdzX6BZ2I09ezgzajS39u0jT6uWFB01Gu+iRdwdllJOpYleKQeIHjo3LCIKHy8P5g2oH7cP/slAa9z8c3usCXLaTYTchd0TcBZmwsO5OGcOF6ZOQ3x8KDLsJfL37IloLwmVSWljPKUcIN6hc2MrWQeeXA/NRsE/K2BaXfjrW8iEJ9TpmXh7U2jgQPyWLiF71aqcHfMax/v2I+zoUXeHppTLaaJXKpkSHTo3Ji8fuH8YDN4IhSrB4kHwVQ+4fNy1ASt8ypal9JzPueuN17m5bx+Hu3TlwqxZmPBwd4emlMto0b1SKZCsoXNjioqC7bPh59etq/qWr1kN9jw8nR+sukN40DmC3hxH6Jq1ZLu7KsXGjWN/4XCd615lClpHr5S7XT5utcw/uBZK3gudp+hAO25y5afVnH1zHBGXLrHyXk/mNwLJnk3nulcZmtbRK+Vu+UtDrwXQbSZcPACf3Ae/TrSG1lUulbdNa8qvWMG5+++m4+ZwJn4aTvmjt3Sue5VpaaJXysF2HAtm2rqD7DgWfOcCEaj5EAzdbk2Os+4tmNkUTv/pljizMs98+cg/9lXG98qOh4HXvgqn/ry/iLx61d2hKeVwWnSvlAMlqwtetH9WWcX5185Dw2eg6QjwzuHagLO4ned28sexzdRdfhCv73/A6667KPb6WHI3aXJ7udbhq4wgsaJ7nd9RKQeKrwtegom+Snso0xBWj4JNk63ueJ2nWI8plwgoEmAl8Lpwo/tjnB41ihNPDiJfl86cG9CRgVv/R1hkGD6ePlqHrzIsLbpXyoGS3QUvWo780GUqPL7Uqq//vB2sfAluhbokXvWfHAEBlFu0iEJPDSFk5Sqk9/PU2nOTKKIIjwrXOnyVYWnRvVIOluIueNHCrsHP46xhdPOVhE6ToUJLp8WpEnbzn3849PL/8Nh/hK2VPfiqXXbe6/6pXtGrdEu71ymVkZzYBkufhgv7IaA3tHkTcqTghEE5hImIYPfUt/H49Dske3ZKjBpN3s6dERF3h6ZUHNq9TqmMpNS9MGgD3Pci/PUNTKsH/6x0d1RZjnh54f/8GCosXU7OCpU4/fIITgweTPjZs+4OTakU0USvlIsl2P0uJu/s1nS3A3+BXIXh20dhQX+4dsF1gSoAsvmVo8xXX1L0lVe4vm07hzt2Ivj778mMpaEqc3JLohcRXxFZIyIH7L8JlkuKiKeI/CkiK1wZo1LOEN397v3V++k1e0viyR6geAAMXAfNXoW9y2DavbBrgU6S42Li6Ynv44/ht2wp2atV4+zoMRzv35+wkyfdHZpSSXLXFf0I4GdjTEXgZ/t+Qp4D9rkkKqWcLFkz4MXm5QP3D7eK8/OXgYVPwLe9IFSLkF3Np1QpSn/+GXeNHcvNv3dxuHMXLn01DxMV5e7QlEqQuxJ9F2Cu/f9coGt8K4lISaADMNs1YSnlXCnufhdT0bvhiTXQahwc+tm6ut/5tV7du5h4eFDg4YfwW76MnLVrE/Tmmxx77HFuHTkCWIPszN41m53ndro3UKVsbml1LyKXjTH5Y9wPNsbEKb4XkQXAeCAP8JIxpmMi23wSeBKgdOnStY8dO+bwuJVyhFR3v4vpwkFYOhRObIGKraHjZMhXwqFxqqQZYwhZspSg8eMxt24R3r8nTxRYwi0TroPsKJdyS6t7EVkrIrvjuXVJ5vM7AueMMTuSs74xZqYxpo4xpk7hwoXTFLtSzlS7TAGGNquQ+iQPUKgC9FsFbSfAkd9gen344wu9uncxESF/t674rVhOrsaN8fp4HqM/v07x85E6yI5KN5yW6I0xLY0x1eO5LQWCRKQYgP33XDybaAR0FpGjwLdAcxH5ylnxKpXheHhC/SHw1O9wVw1Y9gx81R0un3B3ZFmOd5EilJw6hfDXnqVIiOGdzyPp/ruhTsFa7g5NKbfV0S8D+tj/9wGWxl7BGDPSGFPSGFMWeBj4xRjT23UhKuU+yeqCF83XD/osh/bvwfGt1tX99k9BG4i5lIhQ45EhZPv2E0LurcQD68PI9/Tb3Ny/392hqSzOXYl+AtBKRA4Arez7iEhxEVnlppiUShdS3AUPwMMD7h0IT22GErVh5f/gi84QfNTp8ao7BVRqwn2fL6XEhx8SHhTEkZ4PcH7aNEx4uLtDU1mUWxK9MeaiMaaFMaai/feS/fhpY0z7eNZfn1hDPKUyk1R1wYtWoIw1QU7HyXB6J0xvCFtn6tW9G+Rt0xq/FcvJ27o1F6ZM5cgDD3JzX9yewtpKXzlbshO9iBQQkWoi4iciOqKeUk6Spi54ACJQpx8M3QJlGsAPw2BuR7h4yDkBqwR5FShAifffo+TUKURcuMCRBx7k/EcfYcLCACvJD1w9kCl/TGHg6oGa7JVTJJqwRSSfiLwiIruALcAnwHfAMRH5XkSauSJIpbKS2mUKMG9Aff7XujLzBtRPfev8fCWh1wLoMh3O7oaPG8HmaRAV6diAVZLytGxJ+RXLydehPRemf8yRng9wY/ceAoMCCYsM06lwlVMl2o9eRNYAXwDLjTGXYy2rDTwG7DLGfOrMIFNKZ69TKpYrZ2DF8/Dvj1CqPnSZZnXRUy4Xum4dZ18bS8TFi0Q80pEnS67lhkcE3h7ed/S733luJ4FBgdQpWkf74qsk6TS1SmUiqR5wxxj4ez78MBwibkHz0Vb3PA9P5wWr4hUZEkLQhHcIWbyYqLIl2PXk/VRp3OmOJD9w9UDCIsN04B2VLA4ZMEdEaohIZxHpHn1zXIhKqeRIVYv8aCJQ82EYug3KN4fVr8JnbeHCAecFrOLlmS8fxce/TamZn+BzM5Kao76l+Bdribp1C0CL9JVDJSvRi8hnwGdAD6CTfdNW8Eq5WJpa5EfLcxc8/DV0nwUX/oUZjWHTR1p37wa5mzTBb/ky8vfozsXZn3KkW3du/PUXdYrWwcfTB0/xxNvDmzpF471QUypZklV0LyJ7jTF3uyAeh9Cie5VZRV/Rh0dE4e3lkbbGegChQbDiBdi/EkrWtRruFa7kuIBVsl39bSNnxowhIigI3359OfPw/QRe/lvr6FWypLmOXkQ+Bd43xux1dHDOoIleZWYOmRQnJmNg90JY9RKEXYdmr0DDZ7Tu3g0iQ0M5N/FdLn//PT5+fhR/+y1yBAQk+hxttKfAMYm+CbAcOAvcAgQwxpgajgzUUTTRK5UKV89ZV/f/rLBG1+v6MRSu7O6osqSrGzdxZvTo21f3hZ99Fo9s2eKsp432VDRHNMb7DKsrXVv+q5/v5JjwlFLpQu4i8NBX0ONTuHQEZtwHGydr3b0b5G7cyK6778GlTz+z6u537oyznjbaU8mR3ER/3BizzBhzxBhzLPrm1MiUUq4nAv49YehWqNQa1r4Gn7aG8zoxi6t55s5NsXFvUGr2bKJu3ODoo70Ievfd2y3zAW20p5IluUX304H8WMX3tz9lxphFTossDbToXikHuF13PwzCrmndvRtFXr3KuXcmxlt3r3X0ChxTR/95PA8bY0z/tAbnDJrolXKgO+ru60DX6Vp37ybJrbtXWY+OjKeUShu9uk83Eru6j49e8WcNqW6MJyKjRMQ3keXNRUQHzlEqndtxLJhp6w6mbCS9mGLW3VdspXX3bpScuvtoOjuegqQb4+0ClovIzyLyrogMF5ExIvKlPaNdJ2Cr88NUSqVWmobNje2OlvmHtGW+G8XbMv+vv+5YR1vlK0gi0RtjlhpjGgGDgT2AJ3AF+Aq41xjzgjHmvPPDVEqllkOGzY3p9tX9tv+u7j9rA+f/dUzAKtluX93PmkXU9escfeRRzr3//u2r+6Ra5e88t5PZu2brlX4mp3X0SmVyDh82NyZjYNcC+GGYNapei9FQ/ymtu3eDyNBQgt55h5AFC/GpUJ7i48eTw98/wTp6HWwnc3FEq/tKwEtAWcAr+nFjTHMHxehQmuiVupPDh82NLeaY+aXqWWPm63z3bnH1t984M3oMEefPU/CJJyj09FA8fHzirDd712ym/DGFKKLwFE+ervU0A/wHuCFi5QiOSPR/ATOAHcDtyjhjzA5HBelImuiVcgNj4O/v7Pnub+p8924UGRpK0IQJhCxcRLaKFSj29nhy+Fe/Y53oK/rwqHC8Pbz1ij6Dc0Si32GMqe3wyJxEE71SbhR6FpY/D//+AKXqW/3uC5Z3d1RZ0tVff7Wu7i9epOCTAyk8ZAgS4+peu95lHo5I9GOBc8Bi7hwZ75KDYnQoTfRKuZkx8Ne38OPLEBEGLcfCvU+CR3JH3VaOEnnlCkHjJxCyeDHZKlWi2Pi3yVGtWqLP0ROAjMcRif5IPA8bY4xfWoNzBk30SqUTV07D8ufgwGoo0wi6TAPfcu6OKksKXb+es6PHEBEcTKEnn6TQ4EF3XN1H00Z6GVOaZ68zxpSL55Yuk7xSKm3SPLhOTHmLw6PfWQn+7C74uCFsmwVRUWnftkqRPE2b4rdiOfk6tOfC9OkcefAhbv7zT5z1tO995pOsRC8iv4nIWyLSVkTyODsopZR7OHRwnWgiUKs3PLUZSjeAVS/BF50hWCfAdDXPfPko/s47lJw2lYgLFzjS8wHOT5uGCQ+/vY7OiJf5JLfCrA+wH+gB/C4igSLygfPCUkq5g8MH14kpX0novRA6fQSnd1pX94GfWfX5yqXytGiB3/Jl5G3ThgtTpnL0oYe5+a814FFAkQBmtZ7F07We1mL7TCK5RfeHgTXAz8AGICdQ1YlxKaXcoL5fQXy8PPAU8PbyoL5fQcfuQARq94GnfocSta2+9192hcvHHbsflSSvAgUo8f57lPjoQ8LPnuVIj55cmPEJJiKCgCIBDPAfkGSS15H1MobkNsY7BFwAvgZ+A3YaY9JtJZs2xlMq9Zw+uE40Y6wr+tWjQTygzZtwTx/rZEC5VMSlS5wdN47QH34ke/XqFJ8wnmwVEh/wSBvtpS9pbowHfAQcBx4BngX6iIh2jFUqE6pdpgBDm1VwbpIHK6HXfcK6ui8eYLXO/6oHhJxy7n5VHF6+vpT84ANKfDCJ8JMnOdKtOxdnz8ZEJjxZkTbayziSW3T/oTHmAaAl1uh4YwGdwUIplXYFysLjy6Ddu3B8M0xvAH/O07p7N8jbrh1+K5aTu2lTzr33Psce7cWtw/H1rtZGexlJcovu3wcaA7mBLVj19L/ZdffpjhbdK+VcTivev3gIlg61En6lttBxMuQt5rjtq2QxxnBl5SrOjhuHuXmTwi88j+9jjyGedw5nnNTAOjrwjus4YsCcB4ANxpggRwfnDJrolXKe6C54YRFR+Dh6Njyw+thvnQE/vw5e2aH9u+D/gNbdu0H4uXOcfW0sV9etI0ft2hR/+y18ypRJ1nO1Dt+1HDFgzvdAPRF5z751cmiESqkMI2YXvLDwKCav/dcx/e2jeXhAg6dg8CYoVAkWDYT5veHqOcftQyWLd5EilJw+jeLvTODWv/9yuEtXLn35FSYZAx5pHX76kdwBc8YDzwF77duz9mNKqSwmugueBxAFbDp4wXGD68RUqAL0/xFajYMDa2BaPdi90LH7UEkSEfJ16YLfiuXkvLcuQW+9xfG+/Qg7eTLR52kdfvqR3KL7v4GA6C51IuIJ/GmMqeHk+FJFi+6Vcq4dx4KZvPZfNh28QJQBT4H/ta7M0GZOmoP+3D+wZAic/gPu7god3odchZyzL5UgYwwhixYRNH4CJiqKosOHkf+hh5AEqlW0jt51HNG9DiB/jP/zpSkipVSGVrtMAZ5vWcm5g+vEVKQKPLEGWoyBf1ZaV/d7lzlvfypeIkL+Hj3wW7aUnAEBnB37OieeeILw06fjXT+5A+/ERwfjcZzkXtE/AkwA1gECNAFGGmO+TdVORXyB+UBZ4CjwoDEmTrmfiOQHZgPVAQP0N8ZsTmr7ekWvlGu4bHCdmIL2wOLBcPZvqN7TaqyX09c1+1a3GWO4PP87zk2cCCIUHTmCfD16JHh1nxLakC/lHNEY7xugPrDIvjVIbZK3jQB+NsZUxBpWd0QC630I/GiMqQLUBPalYZ9KKQdLbHAdh86CF1PRajDwF2j6CuxdYl3d/7PKsftQSRIRCjz8EOWWLSN79eqcGTWaE4MGER6U9s5Z2pDPsRJN9CJyT/QNKAacBE4Axe3HUqsLMNf+fy7QNZ5958UqOfgUwBgTZoy5nIZ9KqVcxCmz4MXk6Q1NX4aB6yB3Efj2Eesq/8Zlx+5HJcmnZAlKf/4ZRUeN4vr2QA537MTlJUtITmlxQrQhn2MlWnQvIuvsf7MDdYC/sIruawBbjTGNU7VTkcvGmPwx7gcbYwrEWicAmInVyr8m1oh8zxljriWwzSeBJwFKly5d+9gxnQJTKXeZtu4g76/e75qGehFhsGEi/DYJcheFzlOgYkvn7EslKuzYMU6/8io3duwgd/PmFHt9LF6FC6dqW9qQL2VSXXRvjGlmjGkGHAPuMcbUMcbUBmoBB5PY6VoR2R3PrUsy4/YC7gE+NsbUAq6RcBE/xpiZdnx1Cqfyg6WUcgynz4IXk5cPNB8FA9ZA9rwwrwcsewZuXnHePlW8fMqUocwXcyky4mWubdrE4Y6dCFmxMlVX90k15NPGesmX3MZ4O40xAUk9luydiuwHmhpjzohIMWC9MaZyrHXuArYYY8ra9+8DRhhjOiS1fW2Mp5T7uaWhXvhNWP82/D4F8paALlPBr6lr9q3ucOvwEc6MHMmNv/4iT+vW3PXaGLwKOuaETxvrxeWI7nX7RGS2iDQVkftFZBZpaxi3DOhj/98HWBp7BWPMWeCEiESfALTAKsZXSmUALpsFLybv7NDqDej/E3j6wBddYOWLcOuq62JQAGTzK0eZr+dR+MX/cXXdOg536syVn1Y7ZNvaWC9lkpvo+wF7sEbHex4r4fZLw34nAK1E5ADQyr6PiBQXkZjNZ58B5kUP2AO8nYZ9KqWyilL3wuCNUP8p2P4pzGgERze5O6osRzw9KTRwIGUXLsD7rrs49dxznHrxJSKC09Y4UxvrpUxyi+6bYxWjX3d+SGmnRfdKqduOboKlT0HwMag/BJqPBp+c7o4qyzHh4VyYNYsL0z/Gs0B+ir3+BnmaN0v19rSx3p0cMXvdF1j96C8Cv9m3jfENcpMeaKJXSt3h1lVY+xpsnw0FK0DXj62rfuVyN/ft4/SIkdzav598XbtS9JWReObN65R9ZaWTgTQn+hgbKg70BF4CihtjvBwTomNpolcq43FJ473D62Hp03DlFDR8xhp0xzu7c/alEmTCwjg/fToXZ83Gq1Ahir35JrnvS1Vv7QRltQZ7aW6MJyK9ReQTYAHQEpgK3Oe4EJVSWZnTB9iJ5tcUhvwOtXrDpg9h5v1w6g/n7EslSHx8KPL885T99hs8cufmxMCBnBk9hsir8Q6TkiraYO8/yW2MNxmrMdws4FljzMTkjDmvlFLJEXOO+/CIKLYcvui8nWXPaw2q02sB3AyB2S3hlzetgXeUS+Xw96fcooX4PtGfywsWcKRzZ65t2eKQbWuDvf8kd6z7QkB/rBHy3hKRbSLypVMjU0plGS4dYCdaxVbw1Gao8SBseBdmNYMzfzt/v+oOHtmyUXTYMMrMm4d4e3O8bz/OvjGOqOtpa/sdUCSAWa1n8XStpzN9sX1SktsYLy/QCLgfq8i+EFYr/D6JPtFNtI5eqYzHLQPsRPtnFSx/Dm5cgvtfhsYvWOPpK5eKunGDc5M+IPjLL/EuVYri498mZx3nXIlntoZ6jmh1/zew0b5tMMacdGyIjqWJXimVYtcvwaqXYPdCKBYA3WZAkarujipLurZtG2deeZXwU6fw7dOHws8/h0d2xzWazIwN9RwxTW0NY8xTxpiv03uSV0qpVMnpCz0/gwfmQsgJ+KQJbJwMUZHujizLyXXvvfgtXUL+hx7k0pw5HOnWnRt//eWw7We1hnrJbXVfWETeFZFVIvJL9M3ZwSmllMtV6wpPbYWKra2+95+1hQuJzuGlnMAjVy6KjR1LqU9nE3XzJkcfeZRz708iKiztjSazWkO95BbdrwbmY/WfH4w1Pv15Y8zLzg0vdbToXimVZsbArgVWcX7ELWj5Gtw7CDyS21lJOUpkaChBEyYQsnAR2SpWpNiE8eSoVi1N29Q6+vg3UFtE/jbG1LAf+9UYc7+DY3UITfRKZR1Ob8R35YzVUO/AT1CmMXSdBgXKOn4/Kkmh69dzdvQYIoKDKTR4MIUGPYl4O7/RZEY4KXDE7HXh9t8zItJBRGoBJR0SnVJKpZJLBtrJWwwenQ9dpsHZv2F6Q2uinFTMsa7SJk/TpvgtX0bedu24MHUqRx56iJv7/3XqPqMb7k35YwoDVw9k57mdTt2fMyQ30b8pIvmAF7GK72cDLzgtKqWUSgaXDbQjYo2mN+R3KFUXVv4PvuwGIdo22dU88+enxLsTKTHlIyKCznG0Z08ufDITExHhlP3F13Bv57mdzN41O8Mk/SQTvYh4AhWNMSHGmN3GmGbGmNrGmGUuiE8ppRLk8oF28peCx5ZAh/fhxDaY3gD+nKdX926Qt1Ur/JYvI3fz5pz/4AOO9urFrcOHHb6f2A338vnky3BX+Mmto19njEn9fIIupnX0SmUdbhto59IRWDoUjm2CSm2h04eQ5y7X7V8BYIzhyqpVBL0xjqibNyn8/PP4Pv4Y4unpsH3ErKMPDApkyh9TiCIKT/Hk6VpPM8B/gMP2lVqOaIz3FpAPq+X97VkHjDHpcjYITfRKKZeIioKtM+Dn18Eru3WlX72HVdSvXCri/HnOjHmNq+vWkaN2bYqPfxuf0qUdvp/oOvvwqHC8PbzvGGzHnY32HJHo19n/Rq8sgDHGNHdMiI6liV4p5VIXDsCSIXByO1TtDB0/gFyF3B1VlmOMIWTJUoLefhsTEUGRl16kwCOPIA7uEhlfQnf3aHuJJfpE55MXkf/Z/67ASvIxT1O1UkoppQAKVYT+P8HvH8G6t2Ha71ayv7uzuyPLUkSE/N26kqtBfc6MGk3QuDcJXbOW4m+9iXeJEg7bT0CRgDhJPL5Ge+mlK15Spzl57FttYAhQDCgODALudm5oSimVgXh4WpPhPPkr5CsB3z0GCwdYY+grl/K+6y5KzZrJXa+/zs2//+Zw5y5cXrCA5JRgp1Zio+3FbqXv6lb7KRkZr4cxJtS+nwf43hjT1snxpYoW3Sul3CoyHH6bBBsmQs5C0PkjqNTG3VFlSWEnT3HmlVe4vm0bue5vQrE3xuFdtIhT9pWcIv3hdYczcftEhxfxO2LAnNJAzAGGw4CyaYxLKaUyJ09vaPoyDPzFmizn6wdhyVC4GeLuyLIcn5IlKD3nc4q++irXt27jcOfOhCxf7pSr+4AiAQzwH3BH4o5dpL/2+FqXT6iT3ET/JbBNRMaKyGvAVmCu88JSSqlMoFhNeHI9NP4f/PW1NareIZ0PzNXEwwPfx3rjt2Qx2cqV4/Sw4Zx69lkiLjppgKUYYhfptyzd0uUT6iSr6B5ARO4B7rPvbjDG/Om0qNJIi+6VUunOyUBYPBguHoA6/aHVOMiW291RZTkmMpJLc+ZwfvKHeOTOzV2vvUbets6tVoldpO+Mbnhp7l6X0WiiV0qlS+E34Jc3YfM0KFAGukyHso3cHVWWdOvgQU6PGMnN3bvJ26EDRUe9ilcBFw645GCOqKNXSqlMbcexYKatO+iciXGieeeANm9Bv1XW/Tkd4MeR1gmAcqlsFSpQ9puvKfzcs1z56ScOd+5M6Lp1ST8xA9IreqVUlhc9C15YRBQ+Xh7MG1Df+cPp3roKa1+D7bOhYAXoOsOaMEe53M19+zg9YiS39u8nX7duFH1lJJ558rg7rBTRK3qllEpEcmfBc+hVf7bc1pC5jy2BiFvwWWtYO9b6X7lU9qpVKff9dxQcPIiQZcs43KkzVzducndYDqOJXimV5SVnFrzoq/73V++n1+wtjiviL9/Mmv42oBds/ABmNoXTOx2zbZVs4uNDkeefp+w3X+ORMycnBgzgzNixRF27lvST0zlN9EqpLK92mQLMG1Cf/7WunGCxfXKv+lMle17oMhUe/c4aSW92C1g/wRp4R7lUjho1KLdoIb79+nF5/ncc7tKVa9u2uTusNNFEr5RSWMl+aLMKCdbNJ+eqP80qtYGnNkO17rB+vJXwg/Y6fj8qUR7Zs1P05eGU+epL8PDg+ON9OPv220TdyJiNJrUxnlJKJdOOY8FsOXyR+n4Fnd9Yb99yWP483LoCzV6Bhs9a4+krl4q6fp1z771P8Ndf41O2LMUnjCdHQIC7w4pD+9ErpVRGdO0CrHgB9i2DknWtlvmFKrg7qizp2ubNnH71VSLOBlHwif4UeuYZPHx83B3WbdrqXimlXMhhrfNzFYIHv4Aen1pz3s9oBFs+hqgoxwSqki1Xgwb4LVtG/h7duThrNkd79ODGnj3uDitZ9IpeKaUcyGl98q+cgeXPwYGfoExj6DoNCpRN+3ZVil3dsIEzo0YTcekShQYNotDgQYi3t1tj0it6pZRyEae1zs9bDB6dD12mwdm/rQlyAj+DTHixlt7lbtIEv+XLyNu+HRemTePIQw9xc/+/7g4rQZrolVLKgZJqnZ+mYn0RqNXb6ndfqq5Vf/9Vdwg56aDoVXJ55stHiYkTKTHlIyLOBnG0Z08uzJyFiYhwd2hxuKXoXkR8gflYc9ofBR40xsT51IvIC8AAwAC7gH7GmJtJbV+L7pVS7pRQ63yHFusbA4Gfwuox4OEFbcdDwKPWyYByqYhLlzg79nVCV68me80aFB8/gWx+5VwaQ3osuh8B/GyMqQj8bN+/g4iUAJ4F6hhjqgOewMMujVIppVIhoT75Di3WF4G6A2DIRihaDZY+Bd88AqFBaYxepZSXry8lPpxM8ffeI+zoMY5068aluXMx6aTRpLsSfRdgrv3/XKBrAut5ATlExAvICZx2fmhKKeUcThl0x9cP+q6ENm/D4XUwvR7sXpj27aoUERHydeyA37Jl5Kpfn6DxEzj+eB/CTpxwd2huK7q/bIzJH+N+sDEmTvmViDwHvAXcAFYbY3olss0ngScBSpcuXfvYsWMOj1sppdLKqYPunP8XlgyBU4Fwd1foMAlyOWEEP5UoYwwhixYTNH48JiqKosOHkf+hhxAnVqu4ZcAcEVkL3BXPoleBuUklehEpACwEHgIuA98DC4wxXyW1b62jV0plWZER8PuHsG485MgPHSdD1Y7ujipLCj99mjOjRnHt983katiQYm+9iXexYk7Zl1vq6I0xLY0x1eO5LQWCRKSYHVwx4Fw8m2gJHDHGnDfGhAOLgIbOilcppdKLNLXM9/SC+16EJ9dDnrtgfi9YNAhuXHZ0mCoJ3sWLU+rTT7nrtTFc37mTw506c3nRYlxdku6uOvplQB/7/z7A0njWOQ7UF5GcYpV3tAD2uSg+pZRyC4dNh3tXdRjwC9z/Muz6HqY3gINrHRusSpKIUOCRR/BbsphsVSpz5pVXOPnUUCLOn3dZDO5K9BOAViJyAGhl30dEiovIKgBjzFZgAfAHVtc6D2Cme8JVSinXcGjLfC8fa0KcAWutqXC/6mGNrncr1HEBq2TxKV2aMl98QZERL3Nt0yZClsZ3fescOgSuUkqlI9FX9OERUXg7cgjd8Juw7i34fQrkLwVdpkO5+9K+XZViYUeP4l2yJOLl5bBt6ux1SimVgTi1Zf7xrbBkMFw6DPUGQ4vXwCenY/ehXE4TvVJKqf+EXYO1r8O2T8C3PHT9GErXc3dUKg3S48h4Siml3MUnF7SfCI8vg8hw+LwtrBljFe+rTEcTvVJKZVV+98OQTVDrMdj0IcxsCqf/dHdUysE00SulVCaRqv732fNC54+g1wK4eRlmtbAG24kMd1qcyrUc1+RPKaWU26R5ZryKreCpzfDDCPh1AuxfBd1mWBPmqAxNr+iVUioTcEj/+xwFoPsn8NA8CD1jFeX/NskaVldlWJrolVIqE3DozHhVO8JTW6BSW/j5dfisDVw44LhglUtp9zqllMokkup/n+L++cZYU96ufBEiblp97usNBg+9RkxvtB+9UkplcWmqww89aw2d+++PUKYxdJ0GBco6NV6VMtqPXimlsrg01eHnuQse+Ra6TIOzf8P0hhD4mXXFr9I9TfRKKZUFpLkOXwRq9YYhv0OpurDiBfiqO4ScdE7AymG06F4ppbIIh42hbwwEfgqrR4OHN7SbADUfsU4GlFtoHb1SSinHu3QYlgyF479D5fbQcTLkKeruqLIkraNXSinleL5+0HcFtH4LDv4M0+vD7kXujkrFooleKaVU6nl4QsOnYfBG8C0HC/rB933hWioG7FFOoYleKaVU2hWuBP1XQ/PRsG+FdXX/zyp3R6XQRK+UUspRPL2gyUvw5DrIXRS+fQQWD4Ybl90dWZamiV4ppVSyJWuGvLv8YeAv0GQ4/P0dfNzQqsNXbqGJXimlVLJEj673/ur99Jq9JfFk7+UDzV+FAWvAJ7fV537583Ar1GXxKosmeqWUUsmSqtH1StSGQRug4TOwYw583AiObnR6rOo/muiVUkolS6pH1/PODq3fhH4/gHjAnA7w40gIv+HcgBWgA+YopZRKgZSOrhdn/bBrsOY12D4LClaArjOsIXVVmujIeEoppVwu0RnzDq+HpU/DlVPQ6DloOhK8srk13oxMR8ZTSinlconW6fs1tSbICegFGz+AmU3hzF/uCjVT00SvlFLKKZKs08+eF7pMhUe/g+uXYFZzWP8ORIa7J+BMSovulVJKOU2y6/SvX4IfhsOu76FYAHSbAUWquizOjE7r6JVSSmUMe5dac93fCoXmo6DB09Z4+ipRWkevlFIqY7i7Czy1FSq2hjVj4PN2cPGQu6PK0DTRK6WUSld2XPRiWpGxHGkyGc7/Yw2ys/UTiIpyd2gZkiZ6pZRS6cbtYXbX/Eu7dXfxd+efoGxjq/7+i84QfMzdIWY4muiVUkqlG7G75P0W5A29vofOU+D0n9YEOTvmQiZsX+YsmuiVUkqlG/F2yROBex63+t0XrwXLn4V5D8CVM+4ON0PQVvdKKaXSlUS75EVFwfbZVkM9Lx9o/x74P2CdDGRh2r1OKaVU5nLxECwZAie2QpWO0HEy5C7s7qjcRrvXKaWUylR2XPVlerkpnKwzEg6shun1YO8yd4eVLrkl0YvIAyKyR0SiRCTeMxB7vbYisl9EDorICFfGqJRSKn2Kbpn/3pqDtNxakz2dVkC+UvDdY7BwgDXKnrrNXVf0u4HuwIaEVhART2Aa0A64G3hERO52TXhKKaXSq9gt89cHF4QBa6HpK7BnMUxvAP+udneY6YZbEr0xZp8xZn8Sq90LHDTGHDbGhAHfAl2cH51SSqn0LN6W+Z7e0PRlGPgL5PSFrx9g74zH+fPAcXeH63bpuY6+BHAixv2T9mPxEpEnRSRQRALPnz/v9OCUUkq5R+0yBZg3oD7/a135zjnuAYrV5I+2i5kZ1YXKZ5ZR5Ktm/Lt5hfuCTQecluhFZK2I7I7nltyr8vj6SiTYRcAYM9MYU8cYU6dw4azb8lIppbKC2mUKMLRZhXhnxNt87CoTwh+iZ9hYbhlvKv3UC1YNg7BrbojU/byctWFjTMs0buIkUCrG/ZLA6TRuUymlVCYXXbT/d0RFupsJ/HT3rxTdNhMOroWuH0Pp+u4O0aWclugdYDtQUUTKAaeAh4FH3RuSUkqp9C66aD960J2iZbrCke6w9Cn4rC00fBqajQLv7O4O1SXc1b2um4icBBoAK0XkJ/vx4iKyCsAYEwE8DfwE7AO+M8bscUe8SimlMpY4Rfvl7rOG0K3dF36fAp80gVN/uDVGV9GR8ZRSSmUtB9fC0mfgahDc9yI0GWYNp5uB6ch4SimlVLQKLeGpzVDjQdgwEWY3h7O73R2V02iiV0oplfXkyA/dZsDDX0PoWZjZFH57HyIj3B2Zw2miV0oplXVV6QBPbbX+/vwGfNYazv/r7qgcShO9UkqprC1XQXhwLofv/4ibQQeJmtEYNk+zpsTNBDTRK6WUyvJ2HAum/S9FuP/a2/waUQ1+egXmdoRLR+Jdd9q6g+w4FuyGSFNOE71SSqksL3qinCBTgAFhL/Jz5dfg7C74uBFs/xTsHmrRM+e9v3o/vWZvyRDJXhO9UkqpLO/OiXI8yd+wn9XvvlRdWPk/+Ko7hJyMM3PelsMX3R16kjTRK6WUyvLinSgnfyl4bAl0eB+Ob4HpDWgfuQ4fL7lz5rx0TgfMUUoppZJy6TAsGQrHf+dyqZYsLjmMGlUqxzupDlhF/NFD8Ca0jiMlNmBOeh7rXimllEoffP2g7wrY8jH5f36Dfhf+gBLvA93jrBpdjx8WEYWPl0fcqXRdTIvulVJKqeTw8LQmxBn8GxQoCwv6wff94PqlO1ZLb/X4muiVUkqplChcGZ5YA81Hw77lMK0e/LPq9uI7G/a5vx5f6+iVUkqp1Dq7CxYPhqDdENAL2o6H7PkSrKN3Vt19YnX0muiVUkqptIgIsybH+W0S5LkLOk+BCi3irObMunudvU4ppZRyFi8faD4KBqwBn9xWn/sVL8Ctq3es5q66e030SimllCOUqA2DNkDDZyDwc/i4IRzddHuxu+ruteheKaWUcrRjm2HJEAg+CvWfghajwTuH1tE7iiZ6pZRSbhd2Dda8BttnQcGK0G0GlIw3F6eZ1tErpZRSruaTCzq8Zw2jG34DPm1lzXkfcculYWiiV0oppZypfDN46neo+Sj89j4sHODS3esQuEoppZSzZc8HXadB1U6Qq5BLd62JXimllHKVym1dvkstuldKKaUyMU30SimlVCamiV4ppZTKxDTRK6WUUpmYJnqllFIqE9NEr5RSSmVimuiVUkqpTEwTvVJKKZWJaaJXSimlMjFN9EoppVQmlimnqRWR88CxWA/nA0ISeVpiyxNaltzHCwEXEtm3MyX1up25reSun5z1HHV84nssMxyf1GzHUcdHj43jt5Pejk18j2eGY5OabaXX37X8xpjC8W7JGJMlbsDM1C5PaFlyHwcC0+vrdua2krt+ctZz1PFJ4LEMf3xSsx1HHR89Npn/2MT3eGY4NqnZVkb5XYt5y0pF98vTsDyhZSl93B0cGUtKt5Xc9ZOznqOOT3o6NuC4eFKzHUcdHz02jt9Oejs2ydmXK+nvWgr2lSmL7tMbEQk0xtRxdxwqfnp80i89NumXHpuMIytd0bvTTHcHoBKlxyf90mOTfumxySD0il4ppZTKxPSKXimllMrENNErpZRSmZgmeqWUUioT00SvlFJKZWKa6NMBEcklIjtEpKO7Y1H/EZGqIjJDRBaIyBB3x6PuJCJdRWSWiCwVkdbujkf9R0T8RORTEVng7liUJvo0EZHPROSciOyO9XhbEdkvIgdFZEQyNvUy8J1zosyaHHFsjDH7jDGDgQcB7S/sQA46PkuMMQOBvsBDTgw3S3HQsTlsjHnCuZGq5NLudWkgIk2Aq8AXxpjq9mOewL9AK+AksB14BPAExsfaRH+gBtaY0dmBC8aYFa6JPnNzxLExxpwTkc7ACGCqMeZrV8Wf2Tnq+NjPex+YZ4z5w0XhZ2oOPjYLjDE9XRW7ip+XuwPIyIwxG0SkbKyH7wUOGmMOA4jIt0AXY8x4IE7RvIg0A3IBdwM3RGSVMSbKuZFnfo44NvZ2lgHLRGQloIneQRz03RFgAvCDJnnHcdR3R6UfmugdrwRwIsb9k0C9hFY2xrwKICJ9sa7oNck7T4qOjYg0BboD2YBVzgxMASk8PsAzQEsgn4hUMMbMcGZwWVxKvzsFgbeAWiIy0j4hUG6iid7xJJ7HkqwfMcbMcXwoKpYUHRtjzHpgvbOCUXGk9Ph8BHzkvHBUDCk9NheBwc4LR6WENsZzvJNAqRj3SwKn3RSLupMem/RNj0/6pccmA9NE73jbgYoiUk5EfICHgWVujklZ9Nikb3p80i89NhmYJvo0EJFvgM1AZRE5KSJPGGMigKeBn4B9wHfGmD3ujDMr0mOTvunxSb/02GQ+2r1OKaWUysT0il4ppZTKxDTRK6WUUpmYJnqllFIqE9NEr5RSSmVimuiVUkqpTEwTvVJKKZWJaaJXSiEi+UXkKfv/4o6cR1xEnheRx+N5vGz0VKgi4i8icxy1T6XUfzTRK6UA8gNPARhjTjtqalER8cKajjnRmf+MMbuAkiJS2hH7VUr9Rye1UUqBNd1reRHZCRwAqhpjqtuzKnbFmne8OvA+4AM8BtwC2htjLolIeWAaUBi4Dgw0xvwDNAf+sEdWQ0RqA5/Z62yMFcNyrKFVJzrvZSqV9egVvVIKYARwyBgTAAyLtaw68CjWnORvAdeNMbWwhkmNLpKfCTxjjKkNvARMtx9vBOyIsa3PgWeNMQ3iiSEQuC/tL0UpFZNe0SulkrLOGBMKhIpICNaVN8AuoIaI5AYaAt+L3J7NNJv9txjW2OiISD4gvzHmV3vZl0C7GPs5BxR32qtQKovSRK+USsqtGP9HxbgfhfUb4gFctksDYrsBZLf/FxKZw9xe70aaIlVKxaFF90opgFAgT2qeaIy5AhwRkQcAxFLTXrwPqGCvdxkIEZHG9rJesTZVCdidmhiUUgnTRK+UwhhzEdhkd3d7NxWb6AU8ISJ/AXuALvbjPwBNYqzXD5gmIpuJe/XeDFiZin0rpRKh09QqpZxKRBYDw40xBxJZJxvwK9A4uoW+UsoxNNErpZxKRCoDRY0xGxJZpyJQwhiz3mWBKZVFaKJXSimlMjGto1dKKaUyMU30SimlVCamiV4ppZTKxDTRK6WUUpmYJnqllFIqE/s/UPvSHKI/QJoAAAAASUVORK5CYII=", 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" ] @@ -745,17 +747,17 @@ } ], "source": [ - "print('rmse:', ca.rmse())\n", + "print(\"rmse:\", ca.rmse())\n", "hs1 = ml.head(r1, 0, t1)\n", - "hs2 = ml.head(r2, 0 ,t2)\n", - "plt.figure(figsize = (8, 5))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30m')\n", - "plt.semilogx(t1, hs1[0], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 90m')\n", - "plt.semilogx(t2, hs2[0], label = 'ttim at 90m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk - Piezometers at 30 and 90 m')\n", + "hs2 = ml.head(r2, 0, t2)\n", + "plt.figure(figsize=(8, 5))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", + "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", + "plt.semilogx(t2, hs2[0], label=\"ttim at 90m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk - Piezometers at 30 and 90 m\")\n", "plt.legend();" ] }, @@ -781,8 +783,8 @@ "metadata": {}, "outputs": [], "source": [ - "#unknown parameters: kaq, Saq and rc\n", - "ml1 = ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)" + "# unknown parameters: kaq, Saq and rc\n", + "ml1 = ttim.ModelMaq(kaq=60, z=[zt, zb], Saq=1e-4, tmin=1e-5, tmax=1)" ] }, { @@ -805,8 +807,8 @@ "metadata": {}, "outputs": [], "source": [ - "w1 = Well(ml1, xw=0, yw=0, rw=0.2, rc=0.3, tsandQ=[(0, Q)], layers=0)\n", - "ml1.solve(silent='True')" + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.2, rc=0.3, tsandQ=[(0, Q)], layers=0)\n", + "ml1.solve(silent=\"True\")" ] }, { @@ -927,11 +929,11 @@ } ], "source": [ - "ca3 = Calibrate(ml1)\n", - "ca3.set_parameter(name='kaq0', initial=10)\n", - "ca3.set_parameter(name='Saq0', initial=1e-4)\n", - "#ca3.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca3.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca3 = ttim.Calibrate(ml1)\n", + "ca3.set_parameter(name=\"kaq0\", initial=10)\n", + "ca3.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "# ca3.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca3.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", "ca3.fit(report=True)\n", "ca3.parameters" ] @@ -950,7 +952,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -962,14 +964,14 @@ } ], "source": [ - "print('rmse:', ca3.rmse())\n", + "print(\"rmse:\", ca3.rmse())\n", "hm3 = ml1.head(r1, 0, t1)\n", "plt.figure(figsize=(10, 7))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30 m')\n", - "plt.semilogx(t1, hm3[0], label='ttim at 30 m')\n", - "plt.xlabel('time(d)')\n", - "plt.ylabel('drawdown(m)')\n", - "plt.title('ttim analysis for Oude Korendijk - Piezometer 30 m and Wellbore Storage')\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30 m\")\n", + "plt.semilogx(t1, hm3[0], label=\"ttim at 30 m\")\n", + "plt.xlabel(\"time(d)\")\n", + "plt.ylabel(\"drawdown(m)\")\n", + "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 30 m and Wellbore Storage\")\n", "plt.legend();" ] }, @@ -1080,11 +1082,11 @@ } ], "source": [ - "ca4 = Calibrate(ml1)\n", - "ca4.set_parameter(name='kaq0', initial=10)\n", - "ca4.set_parameter(name='Saq0', initial=1e-4)\n", - "#ca4.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca4.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca4 = ttim.Calibrate(ml1)\n", + "ca4.set_parameter(name=\"kaq0\", initial=10)\n", + "ca4.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "# ca4.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca4.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca4.fit(report=True)\n", "ca4.parameters" ] @@ -1103,7 +1105,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -1115,14 +1117,14 @@ } ], "source": [ - "print('rmse:', ca4.rmse())\n", + "print(\"rmse:\", ca4.rmse())\n", "hm4 = ml1.head(r2, 0, t2)\n", "plt.figure(figsize=(10, 7))\n", - "plt.semilogx(t2, h2, '.', label='obs at 90 m')\n", - "plt.semilogx(t2, hm4[0], label='ttim at 90 m')\n", - "plt.xlabel('time [d]')\n", - "plt.ylabel('drawdown [m]')\n", - "plt.title('ttim analysis for Oude Korendijk - Piezometer 90 m and Wellbore Storage')\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90 m\")\n", + "plt.semilogx(t2, hm4[0], label=\"ttim at 90 m\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"ttim analysis for Oude Korendijk - Piezometer 90 m and Wellbore Storage\")\n", "plt.legend();" ] }, @@ -1233,12 +1235,12 @@ } ], "source": [ - "ca0 = Calibrate(ml1)\n", - "ca0.set_parameter(name='kaq0', initial=10)\n", - "ca0.set_parameter(name='Saq0', initial=1e-4)\n", - "#ca0.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", - "ca0.series(name='obs1', x=r1, y=0, t=t1, h=h1, layer=0)\n", - "ca0.series(name='obs2', x=r2, y=0, t=t2, h=h2, layer=0)\n", + "ca0 = ttim.Calibrate(ml1)\n", + "ca0.set_parameter(name=\"kaq0\", initial=10)\n", + "ca0.set_parameter(name=\"Saq0\", initial=1e-4)\n", + "# ca0.set_parameter_by_reference(name='rc', parameter=w1.rc[0:], initial=0.2, pmin=0.01)\n", + "ca0.series(name=\"obs1\", x=r1, y=0, t=t1, h=h1, layer=0)\n", + "ca0.series(name=\"obs2\", x=r2, y=0, t=t2, h=h2, layer=0)\n", "ca0.fit(report=True)\n", "ca0.parameters" ] @@ -1257,7 +1259,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -1269,17 +1271,17 @@ } ], "source": [ - "print('rmse:', ca0.rmse())\n", + "print(\"rmse:\", ca0.rmse())\n", "hs1 = ml1.head(r1, 0, t1)\n", - "hs2 = ml1.head(r2, 0 ,t2)\n", - "plt.figure(figsize = (10, 7))\n", - "plt.semilogx(t1, h1, '.', label='obs at 30m')\n", - "plt.semilogx(t1, hs1[0], label='ttim at 30 m')\n", - "plt.semilogx(t2, h2, '.', label='obs at 90m')\n", - "plt.semilogx(t2, hs2[0], label = 'ttim at 90m')\n", - "plt.xlabel('time [d]')\n", - "plt.ylabel('drawdown [m]')\n", - "plt.title('ttim analysis for Oude Korendijk')\n", + "hs2 = ml1.head(r2, 0, t2)\n", + "plt.figure(figsize=(10, 7))\n", + "plt.semilogx(t1, h1, \".\", label=\"obs at 30m\")\n", + "plt.semilogx(t1, hs1[0], label=\"ttim at 30 m\")\n", + "plt.semilogx(t2, h2, \".\", label=\"obs at 90m\")\n", + "plt.semilogx(t2, hs2[0], label=\"ttim at 90m\")\n", + "plt.xlabel(\"time [d]\")\n", + "plt.ylabel(\"drawdown [m]\")\n", + "plt.title(\"ttim analysis for Oude Korendijk\")\n", "plt.legend();" ] }, @@ -1345,15 +1347,18 @@ } ], "source": [ - "t0 = pd.DataFrame(columns=['obs 30 m', 'obs 90 m', 'obs simultaneously'], index=['without rc', 'with rc'])\n", - "t0.loc['without rc', 'obs 30 m'] = ca1.rmse()\n", - "t0.loc['without rc', 'obs 90 m'] = ca2.rmse()\n", - "t0.loc['without rc', 'obs simultaneously'] = ca.rmse()\n", - "t0.loc['with rc', 'obs 30 m'] = ca3.rmse()\n", - "t0.loc['with rc', 'obs 90 m'] = ca4.rmse()\n", - "t0.loc['with rc', 'obs simultaneously'] = ca0.rmse()\n", + "t0 = pd.DataFrame(\n", + " columns=[\"obs 30 m\", \"obs 90 m\", \"obs simultaneously\"],\n", + " index=[\"without rc\", \"with rc\"],\n", + ")\n", + "t0.loc[\"without rc\", \"obs 30 m\"] = ca1.rmse()\n", + "t0.loc[\"without rc\", \"obs 90 m\"] = ca2.rmse()\n", + "t0.loc[\"without rc\", \"obs simultaneously\"] = ca.rmse()\n", + "t0.loc[\"with rc\", \"obs 30 m\"] = ca3.rmse()\n", + "t0.loc[\"with rc\", \"obs 90 m\"] = ca4.rmse()\n", + "t0.loc[\"with rc\", \"obs simultaneously\"] = ca0.rmse()\n", "\n", - "t0.style.set_caption('RMSE of two conceptual models')\n" + "t0.style.set_caption(\"RMSE of two conceptual models\")" ] }, { @@ -1396,7 +1401,7 @@ }, { "data": { - "image/png": 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\n", 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PH96rmrc09tRJkiRWtdTWS7eufY29em0mTZrE3LlzWbFiBcOGDWPKlCnMmTOH2bNnbxDqOnPggQcyduxYhgwZQnNzMwsXLuy03fHHHw/AI488wmte8xoOOOAAALbddluGDq12X1a1X50kSapJU2MDK3sR7JqGNvRq+Y2NjYwbN46rrrqKqVOn8sY3vpHbbruNxx57jL322qvH5w8bNmzd44aGhi6Pmdtmm22A4sSPze2SI/VmT50kSeLQ8WMYUmMGGhJw2F5jer2O6dOnc9FFFzF9+nSmTZvGZZddRnNz8wbha8SIEZt8/Nv48eN55plnuOeeewBYsWJF5U+eMNRJkiROmbYrw2rsfRs2tIFTpu3W63VMmzaNxYsXM2XKFHbccUeampo6HXodNWoUBx10EPvss8+6EyV6a6uttmLGjBmcddZZTJw4kSOOOGKzuvtDPUQ9rjPT1yZPnpxz5swZ6DIkVcjxX7sbgBmnTxngSqT+sWDBgm6HOTOTs6+9j5vnL+n2siZNjUM4YsKOXHrCfoNueLMznW3XiJibmZP7uxZ76iRJEhHBxcc1c8SEHRne2LDBUOyQgOGNDRwxYUcuPm7DIVMNPE+UkCRJADQ2DOHSE/bj/kXPc8Xtj3Prw0tZtWYtTUMbOGyvMZw6bTcm7jJyoMtUFwx1kiRpnYigeZeRfPnE/Qe6FPWSw6+SJEkVYE+dJElaLxOengt3XQqP/hxaVkFjE+zxVph6Nuy8P3g83WbJUCdJkgprW+D6M+CRG2HNKsjyLNiWlbDgx0XI2/NIOOYyaGgc2Fq1AYdfJUlS0UN3/RnwyA3Q8tL6QLdufmsx/eEbinZ1uCTakUceyfLly1m+fDlf+cpX1k2fNWsW73jHO/pkHbNmzeKuu+7qk2Vtbgx1kiSpGHJ95MaiV647a1YW7Z6+t89LuPHGGxk5cuQGoa4v1RrqtsS7TxjqJEkS3PXFYsi1FmtWwd1f6tXiP/OZz3DppZcCcM4553DooYcCMHPmTE466SQAxo0bx7PPPst5553HY489RnNz87o7Srz44osce+yxjB8/nhNPPJG2myfMnDmT/fbbj3333ZePfOQjrF69+hXLApgzZw6HHHIICxcu5LLLLuOSSy6hubmZ2bNnv6LGCy64gNNOO42/+Iu/4IMf/CBLlizhmGOOYeLEiUycOHGz7+Ez1EmSJHj0ZxsOuXYlW+G3P+3V4qdPn74uRM2ZM4cXX3yRlpYW7rjjjg1uFXbhhRey++67M2/ePD772c8CcN999/H5z3+e+fPn8/jjj3PnnXeyatUqTj75ZGbMmMEDDzzAmjVr+OpXv9plDePGjeOMM87gnHPOYd68eZ3eomzu3Ln86Ec/4tvf/jZnn302Bx98MPfffz/33nsve++9d69ec3+ra6iLiI9GxIMR8VBEfKyctkNE3BwRj5bft69nDZIkqQYtvbwv6poehmk7mDRpEnPnzmXFihUMGzaMKVOmMGfOHGbPnt1puOrowAMPZOzYsQwZMoTm5mYWLlzII488wq677sob3vAGAD70oQ9x++239+51dPCud72L4cOHA3Drrbfy13/91wA0NDSw3XbbbdKy661uoS4i9gFOBQ4EJgLviIg9gPOAmZm5BzCz/FmSJA2kxqbetR86vHeLb2xk3LhxXHXVVUydOpVp06Zx22238dhjj3V7T9o2w4YNW/e4oaGBNWvW0N3964cOHUpra9HzuGpV7YF1m222qbnt5qaePXV7Ab/MzJcycw3wC+AY4GjgmrLNNcC761iDJEmqxR5vhagxFsQQeMPber2K6dOnc9FFFzF9+nSmTZvGZZddRnPzhveRHTFiBCtWrOhxeePHj2fhwoX87ne/A+Ab3/gGBx98MFAMtc6dOxeA73//+71eNsBhhx22bjh37dq1vPDCCzU9b6DUM9Q9CEyPiFERsTVwJLALsGNmLgYov4+pYw2SJKkWU8+CoTX21g1tgiln9noV06ZNY/HixUyZMoUdd9yRpqamTodeR40axUEHHcQ+++yz7kSJzjQ1NXHVVVfxvve9j3333ZchQ4ZwxhlnAHD++efz0Y9+lGnTptHQ0LDuOe985zu5/vrrOz1RoqMvfOEL3Hbbbey7775MmjSJhx56qNevuT9Fd12Xm7zwiL8C/gZ4EZgPrAQ+nJkj27X5Y2ZucFxdRJwGnAbw2te+dtKTTz5ZtzolDT7Hf+1uAGacPmWAK5H6x4IFC7of5syE759SXIeuu+Plhg6H8UfBe6/0zhJ0vl0jYm5mTu7vWup6okRm/ndm7p+Z04E/AI8CSyLiNQDl96VdPPfyzJycmZNHjx5dzzIlSVJEcaeI8UdB49YbDsXGkGL6+KOKdga6zU5dbxMWEWMyc2lEvBZ4DzAF2BX4EHBh+f1H9axBkiTVqKGx6IF7+t71935ds7LonXvD22DqmbDzpIGuUl2o971fvx8Ro4AW4G8y848RcSFwXTk0+3vgfXWuQZIkAZm5wUkJG4iAsZPguGu6b6duz74dCHUNdZm5wdGPmfkccFg91ytJkl6pqamJ5557jlGjRvUc7NSjzOS5556jqamXl4Kpo3r31EmSpM3A2LFjWbRoEcuWLRvoUiqjqamJsWPHDnQZ6xjqJEkaBBobG9l1110HugzVkfd+lSRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFeDFhyUNGpnJvKeWc8Xsx7ln4R9oTdjrX37KoePHcOr03Zg4djtvnyRpi2WokzQotKxt5dzr5nHL/KWsXrOW1vI+3Ctb1nLTg4u59eGlHD5hDBcf10xjg4MYkrY8/uaSVHmZybnXzePm+UtY2bI+0LVpzSLc3Tx/CedeN4/M7HxBkrQZM9RJqrx5Ty3nlvlLWdXS2m27VS2t3DJ/Kfcver6fKpOkvmOok1R5V85+gtVr1tbUdvWatVw5+/E6VyRJfc9QJ6nybn146QZDrl1pTZi5YGl9C5KkOjDUSaq8VS219dKta19jr54kbU4MdZIqr6mxoXfth/auvSRtDgx1kirv0PFjGFLj5eeGBBy215j6FiRJdWCok1R5p0zblWE19r4NG9rAKdN2q3NFktT3DHWSKq95l5EcPmEMTY3d/8prahzC4RPGMHHsdv1UmST1HUOdpMqLCC4+rpkjJuzI8MaGDYZihwQMb2zgiAk7cvFxzd4qTNIWyduESRoUGhuGcOkJ+3H/oue54vbHuenBxbRmEeYO22sMp07bjYm7jBzoMiVpoxnqJA0aEUHzLiP58on7c/zX7gZgxulTBrgqSeobDr9KkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqoCh9Vx4RJwDnAIk8ADwYeA84FRgWdnsE5l5Yz3rkKSOZpw+ZaBLkKQ+VbdQFxE7A2cDEzJzZURcB5xQzr4kMy+q17olSZIGm3oPvw4FhkfEUGBr4Jk6r0+SJGlQqluoy8yngYuA3wOLgecz8+fl7DMj4jcR8fWI2L6z50fEaRExJyLmLFu2rLMmkiRJKtUt1JVh7WhgV2AnYJuIOAn4KrA70EwR9j7X2fMz8/LMnJyZk0ePHl2vMiVJkiqhnsOvhwNPZOayzGwBfgBMzcwlmbk2M1uBK4AD61iDJEnSoFDPUPd74E0RsXVEBHAYsCAiXtOuzTHAg3WsQZIkaVCo29mvmfmriPgecC+wBrgPuBy4MiKaKS5zshA4vV41SJIkDRZ1vU5dZp4PnN9h8gfquU5JkqTByDtKAMd/7W6O/9rdA12GJEnSRjPUSZIkVYChTpIkqQIMdZIkSRVgqJMkSaoAQ50kSVIFGOokSZIqwFAnSZJUAYY6SZKkCjDUSZIkVYChTpIkqQIMdZIkSRVgqJMkSaoAQ50kSVIFGOokSZIqwFAnSZJUAYY6SZKkCjDUSZIkVYChTpIkqQIMdZIkSRVgqJMkSaoAQ50kSVIFGOokSZIqwFAnSZJUAYY6SZKkCjDUSZIkVYChTpIkqQIMdZIkSRVgqJMkSaoAQ50kSVIFGOokSZIqwFAnSZJUAYY6SZKkCjDUSZIkVYChTpIkqQIMdZIkSRUwdKALGCiZybynlnPF7Me5Z+EfaE3Y619+yqHjx3Dq9N2YOHY7ImKgy5QkSarJoAx1LWtbOfe6edwyfymr16ylNYvpK1vWctODi7n14aUcPmEMFx/XTGODnZmSJGnzN+gSS2Zy7nXzuHn+Ela2rA90bVqzCHc3z1/CudfNIzM7X5AkSdJmZNCFunlPLeeW+UtZ1dLabbtVLa3cMn8p9y96vp8qkyRJ2niDLtRdOfsJVq9ZW1Pb1WvWcuXsx+tckSRJ0qYbdKHu1oeXbjDk2pXWhJkLlta3IEmSpD4w6ELdqpbaeunWta+xV0+SJGkgDbpQ19TY0Lv2Q3vXXpIkaSAMulB36PgxDKnx8nNDAg7ba0x9C5IkSeoDgy7UnTJtV4bV2Ps2bGgDp0zbrc4VSZIkbbpBF+qadxnJ4RPG0NTY/UtvahzC4RPGMHHsdv1UmSRJ0sYbdKEuIrj4uGaOmLAjwxsbNhiKHRIwvLGBIybsyMXHNXurMEmStEUYlLcJa2wYwqUn7Mf9i57nitsf56YHF9OaRZg7bK8xnDptNybuMnKgy5QkSarZoAx1UPTYNe8yki+fuD/Hf+1uAGacPmWAq5IkSdo4g274VZIkqYrqGuoi4pyIeCgiHoyI70REU0TsEBE3R8Sj5fft61mDJEnSYFC3UBcROwNnA5Mzcx+gATgBOA+YmZl7ADPLnyVJkrQJ6j38OhQYHhFDga2BZ4CjgWvK+dcA765zDZIkSZVXt1CXmU8DFwG/BxYDz2fmz4EdM3Nx2WYx4C0bJEmSNlE9h1+3p+iV2xXYCdgmIk7qxfNPi4g5ETFn2bJl9SpTkiSpEuo5/Ho48ERmLsvMFuAHwFRgSUS8BqD8vrSzJ2fm5Zk5OTMnjx49uo5lSpIkbfnqGep+D7wpIraO4rYMhwELgB8DHyrbfAj4UR1rkCRJGhTqdvHhzPxVRHwPuBdYA9wHXA68CrguIv6KIvi9r141SJIkDRZ1vaNEZp4PnN9h8mqKXjtJkiT1Ee8oIUmSVAGGOkmSpAow1EmSJFWAoU6SJKkCDHWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAGGOkmSpAow1EmSJFWAoU6SJKkCDHWSJEkVYKiTJEmqgKHdzYyIHWpYRmtmLu+bciRJkrQxug11wDPlV3TTpgF4bZ9VJEmSpF7rKdQtyMz9umsQEff1YT2SJEnaCD0dUzelhmXU0kaSJEl11FNP3dYRsXVXMzPzD5m5qo9rkiRJUi/1FOrmAklxTN1rgT+Wj0cCvwd2rWdxkiRJqk23w6+ZuWtm7gb8DHhnZr46M0cB7wB+0B8FSpIkqWe1XqfugMy8se2HzLwJOLg+JUmSJKm3ehp+bfNsRPwz8E2K4diTgOfqVpUkSZJ6pdaeuvcDo4Hry6/R5TRJkiRtBnq6o8Q/Aj/NzPuAj/ZPSZIkSeqtnoZfnwA+GhETgfuBm4CfZ+Yf615ZP5pxupfakyRJW7ZuQ11mXgtcCxAR+wFvA34QEQ3ALRS9eL+ue5WSJEnqVq0nSlAOwd4H/GdEbAscAZwCGOokSZIGWE2hruyZOwoY1/45mXlafcqSJElSb9TaU/cTYBXwANBaTsu6VCRJkqReqzXUjc3MN9a1EkmSJG20Wq9Td1NE/EVdK5EkSdJGq7Wn7pfA9RExBGgBAsjM3LZulUmSJKlmtYa6zwFTgAcy02PpJEmSNjO1Dr8+CjxooJMkSdo81dpTtxiYFRE3AavbJmbmxXWpSpIkSb1Sa6h7ovzaqvySJEnSZqSmUJeZ/1rvQiRJkrTxuj2mLiIu6GkBtbSRJElSffXUU3dKRLzQzfwATgAu6LOKJEmS1Gs9hborgBE1tJEkSdIA6jbUeSydJEnSlqHW69RJkiRpM2aokyRJqoCezn4d2828d/Z9OZIkSdoYPfXUzYyIcR0nRsRHgM/XoyBJkiT1Xk+h7hzg5ojYo21CRPxjOf3gehYmSZKk2vV09uuNEbEauCki3g2cAhwATM/MP/ZDfZIkSapBjydKZOZM4GRgFrAbcJiBTpIkafPSbU9dRKwAkuLOEcOAw4ClERFAZua29S9RkiRJPelp+LWnu0lIkiRpM+B16iRJkiqgp3u/brSI2BOY0W7SbsAngZHAqcCycvonMvPGetUhSZI0GNQt1GXmI0AzQEQ0AE8D1wMfBi7JzIvqtW5JkqTBpr+GXw8DHsvMJ/tpfZIkSYNKf4W6E4DvtPv5zIj4TUR8PSK27+wJEXFaRMyJiDnLli3rrIkkSZJKdQ91EbEV8C7gu+WkrwK7UwzNLgY+19nzMvPyzJycmZNHjx5d7zIlSZK2aP3RU/d24N7MXAKQmUsyc21mtgJXAAf2Qw2SJEmV1h+h7v20G3qNiNe0m3cM8GA/1CBJklRpdTv7FSAitgaOAE5vN/kzEdFMcaeKhR3mSVL/uOqo4vuHbxjYOiSpj9Q11GXmS8CoDtM+UM91SpIkDUbeUUKSJKkCDHWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAGGOkmSpAow1EmSJFWAoU6SJKkCDHWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAGGOkmSpAow1EmSJFWAoU6SJKkCDHWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAGGOkmSpAow1EmSJFWAoU6SJKkCDHWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAFDB7oASeo3mfD0XLjrUvj9XZCt8Ok/gz3eClPPhp33h4iBrlKSNoqhTtLgsLYFrj8DHrkR1qwqAh1Ay0pY8GN49Oew55FwzGXQ0DiwtUrSRnD4VVL1ZZaB7gZoeWl9oFs3v7WY/vANRbvMgalTkjaBoU5S9T09t+iha1nZfbs1K4t2T9/bP3VJUh8y1Emqvru+WAy51mLNKrj7S/WtR5LqwFAnqfoe/dmGQ65dyVb47U/rW48k1YGhTlL1tdTYS9dmTQ/DtJK0GTLUAVx1VPElqZoam3rXfujw+tQhSXVkqJNUfXu8FaLGX3cxBN7wtvrWI0l1YKiTVH1Tz4KhNfbWDW2CKWfWtx5JqgNDnaTq23lScWHhnoZVhw4v2u28f//UJUl9yFAnqfoiijtFjD8KGrfecCg2hhTTxx9VtPNWYZK2QN4mTNLg0NAI772yuLDwXZcWtwbL1iLMveFtMPXMokdPkrZQhjpJg0cEjJ0Ex12z/oz3D98wsDVJUh9x+FWSJKkCDHWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAGGOkmSpAqoW6iLiD0jYl67rxci4mMRsUNE3BwRj5bft69XDZIkSYNF3UJdZj6Smc2Z2QxMAl4CrgfOA2Zm5h7AzPJnSZIkbYL+Gn49DHgsM58EjgauKadfA7y7n2qQJEmqrP4KdScA3ykf75iZiwHK72P6qQZJkqTKqnuoi4itgHcB3+3l806LiDkRMWfZsmX1KU6SJKki+qOn7u3AvZm5pPx5SUS8BqD8vrSzJ2Xm5Zk5OTMnjx49uh/KlCRJ2nL1R6h7P+uHXgF+DHyofPwh4Ef9UIMkSVKl1TXURcTWwBHAD9pNvhA4IiIeLeddWM8aJEmSBoOh9Vx4Zr4EjOow7TmKs2ElSZLUR7yjhCRJUgUY6iRJ0qBw/Nfu5viv3T3QZdRNXYdfJUmSNheffO7j5aM7BrSOerGnTpIkqQIMdZIkSRVgqJMkSaqAwXtMXSY8PRfuuhR+fxdkK3z6z2CPt8LUs2Hn/SFioKuUJEmqyeAMdWtb4Poz4JEbYc2qItABtKyEBT+GR38Oex4Jx1wGDY0DW6skSVINBt/wa2YZ6G6AlpfWB7p181uL6Q/fULTLHJg6JUmSemHwhbqn5xY9dC0ru2+3ZmXR7ul7+6cuSZKkTTD4Qt1dXyyGXGuxZhXc/aX61iNJktQHBl+oe/RnGw65diVb4bc/rW89kiRJfWDwhbqWGnvp2qzpYZhWkiRpMzD4Ql1jU+/aDx1enzokSZL60OC7pMkeby0uW1LLEGwMgTe8rf41Sep/H75hoCuQpD41+Hrqpp4FQ2vsrRvaBFPOrG89kiRJfWDwhbqdJxUXFu5pWHXo8KLdzvv3T12SJEmbYPCFuojiThHjj4LGrYsh1lfMH1JMH39U0c5bhUmSpC3A4DumDopbf733yuLCwndduv4Yu8ati2Popp5Z9OhJkiRtIQZnqIOiB27sJDjuGrjqqGKaB05LkqQt1OAbfpUkSaogQ50kSVIFGOokSZIqwFAnSZJUAYY6SZKkCjDUSZIkVYChTpIkqQIMdZIkSRVgqJMkSaoAQ50kSVIFGOokSZIqwFAnSZJUAYY6SZKkCjDUSZIkVYChTpIkqQIMdZIkSRUwdKALkCRJqpdsbeW3987iT7MuYa/VjzCMl1l5/mjmj5jCiEPPZY/m6cSQavRxGeokSVIltby8mvu/eAITXriTYbxMQyQAw3mZ5hW3s/pHv2TubQcx8axradxq2ABXu+mqEU0lSZLaydZW7v/iCez9wh1sHavXBbo2DZFsHavZ+4U7uP+LJ5CtrQNUad8x1EmSpMr57b2zmPDCnQyPl7ttNzxeZsILd/LovNv7qbL6MdRJkqTKeXHW5xlG94GuzTBeZsWtn69vQf3AUCdJkipnwoq7Nxhy7UpDJHutuKvOFdWfoU6SJFVOrb10bZp62X5zZKiTJEmVs5qtetV+VS/bb44MdZIkqXLmj5jC2oya2q7NYMGIqXWuqP4MdZIkqXJedcjHau6tW81WjDj0Y/UtqB8Y6gA+fEPxJUmSKuEN+x/C/G0PYmV2H+xW5lbM3/Yg9mie3k+V1Y+hTpIkVU4MGcLEs67loW3fzEs5bIOh2LUZvJTDeGjbNzPxrGsrcauwLf8VSJIkdaJxq2FMOuf7LDr6OuaNmM5LuRWtZZibN+IQFh39XSb/7fWVuEUYeO9XSZJUYTFkCG/Y/xDY/xAe+o83A7D3J+5g0sCWVRf21EmSJFWAoU6SJKkC6hrqImJkRHwvIh6OiAURMSUiLoiIpyNiXvl1ZD1rkCRJGgzqfUzdF4CfZuaxEbEVsDXwVuCSzLyozuuWJEkaNOoW6iJiW2A6cDJAZr4MvBxR29WdJUmSVLt6Dr/uBiwDroqI+yLiyojYppx3ZkT8JiK+HhHb17EGSZKkQaGeoW4osD/w1czcD/gTcB7wVWB3oBlYDHyusydHxGkRMSci5ixbtqyOZUqSJG356hnqFgGLMvNX5c/fA/bPzCWZuTYzW4ErgAM7e3JmXp6ZkzNz8ujRo+tYpiRJ0pavbqEuM/8PeCoi9iwnHQbMj4jXtGt2DPBgvWqQJEkaLOp99utZwLfKM18fBz4MXBoRzUACC4HT61yDJElS5dU11GXmPGByh8kfqOc6JUmSBiPvKCFJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqoChA12AJElSf/jUqM8CMGOA66gXQ50kSRoUZpw+ZaBLqCuHXyVJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgUY6iRJkirAUCdJklQBhjpJkqQKMNRJkiRVgKFOkiSpAgx1kiRJFWCokyRJqgBDnSRJUgVEZg50DT2KiGXAk3VezauBZ+u8DkmbF/d7afDpj/3+dZk5us7r2MAWEer6Q0TMyczJA12HpP7jfi8NPlXe7x1+lSRJqgBDnSRJUgUY6ta7fKALkNTv3O+lwaey+73H1EmSJFWAPXWSJEkVUIlQFxHjIuLBjXzu0RHxm4iYFxFzIuLN7ea9LSIeiYjfRcR5fVexpE21ifv99hFxfbnv/zoi9mk3z/1e6qVN2R+7WN5OEfG9PlrWIRHxv+0eT+2L5dZD+1o3RiVC3SaaCUzMzGbgI8CVABHRAHwZeDswAXh/REwYqCIl9alPAPMy843AB4EvgPu9tLnIzGcy89g6LPoQYLMNdZtqiwt1EXFuRDxYfn2s3ayhEXFN+Z/39yJi67L9hRExv5x+UcflZeaLuf7Awm2AtscHAr/LzMcz82XgWuDoTuq5OiK+GhG3RcTjEXFwRHw9IhZExNV9+NKlQauv93uKwDYTIDMfBsZFxI6430s96uv9sdx/5pVf90XEiPY9fxFxckT8MCJ+EhFPRMSZZQ33RcQvI2KHst2siJhcPn51RCzssJ5xwBnAOeW6pkXEOyPiV+Wybil/DxARF5T79KxyHz+73XJOKnv450XE18p/BomI90fEA+V2+a927V9s9/jYtt8REfG+su39EXF7h1qHRMSjETG63c+/i4hXd/febFGhLiImAR8G/hx4E3BqROxXzt4TuLz8z/sF4P+Vb/QxwN7l9H/vYrnHRMTDwA0UvXUAOwNPtWu2qJzWme2BQ4FzgJ8AlwB7A/tGRPNGvFRJpTrt9/cD7ymXfyDwOmAs7vdSt+q0P/4d8DfliNk0YGUnbfYB/pLiH69PAy9l5n7A3RS97T3KzIXAZcAlmdmcmbOBO4A3lcu6Fvj7dk8ZD7y1XOf5EdEYEXsBxwMHlfWuBU6MiJ2A/6L4ndAMHBAR7+6hpE8Cb83MicC7OtTaCnwTOLGcdDhwf2Z2eyeMLSrUAW8Grs/MP2Xmi8APKD4AAE9l5p3l42+WbV8AVgFXRsR7gJc6W2hmXp+Z44F3A/9WTo7OmnZR10/K3r4HgCWZ+UD5hjwEjOvF65O0oXrs9xcC20fEPOAs4D5gDe73Uk/qsT/eCVxc9oaNzMw1nbS5LTNXZOYy4HmKf6Sg2P/GbcLrGQv8LCIeAD5O8Y9Zmxsyc3UZpJYCOwKHAZOAe8rfH4cBuwEHALMyc1lZ/7eA6T2s+07g6og4FWjoZP7XWR9YPwJc1dOL2dJCXWe/cNt0/MWb5YY9EPg+RWD7aXcLz8zbgd3L7s1FwC7tZo8FnuniqavL763tHrf9PLS7dUrqUZ/v95n5QmZ+uPxP+4PAaOAJ3O+lntRjf7wQOAUYDvwyIsZ3suyO+1j7/a9tf1vD+lzT1E2d7X0R+FJm7guc3uF57de5tlxPANeUPX3NmblnZl5A7dtl3fIz8wzgnyl+58yLiFGveFLmU8CSiDiUomf0pp5ezJYW6m4H3h0RW0fENhRdurPLea+NiCnl4/cDd0TEq4DtMvNG4GMUXaKvEBGvj4goH+8PbAU8B9wD7BERu0bEVsAJwI/r9sokdaUe+/3Icr+G4o/J7Zn5Au73Uk/qsT/uXvZ0/xcwh2LYc2MspOhFA+jqJIsVwIh2P28HPF0+/lAN65gJHBsRYwAiYoeIeB3wK+Dg8li+BorX/4vyOUsiYq+IGEKxvSifu3tm/iozPwk8yyv/oWxzJUWv53WZuban4raoUJeZ9wJXA7+m2IBXZuZ95ewFwIci4jfADsBXKd64/y2n/YLi2JeO3gs8WHajfhk4PgtrgDOBn5XLvi4zH6rXa5PUuTrt93sBD5XH0r4d+Gi5Lvd7qRt12h8/1nbCAMXxdD32SHXhIuCvI+IuoKsTCn4CHNN2ogRwAfDdiJhNEay6lZnzKXrXfl6+ppuB12TmYuAfgdsojtm9NzN/VD7tPOB/gVuBxe0W99m2EysowvL9nazyx8CrqGHoFbyjhCRJ0mapPJv3ksyc1mNjPO5DkiRpsxPFxc//mvVnwPb8HHvqJEmStnxb1DF1kiRJ6pyhTpIkqQIMdZIkSRVgqJO0SSLizyLi2oh4LIr7O94YEW/o4Tkvlt93iojvlY9PjogvbWItH4vyfpPlzzdGxMhNWWa5nAsi4unyMgiPRsQPImJCDc87ubx9UJ+JiOaIOLIvlympGgx1kjZaeeHu6yluj7N7Zk4APkFxO50eZeYzmdnVRUI7XV95Ac+ufAxYF+oy88jMXF7r8nvQdr/IPYAZwK1tN9vuxslAn4Y6iou3GuokbcBQJ2lTvAVoyczL2iZk5rzMnB0Rr4qImRFxb3mBzaM7PjkixpUX3myzS0T8NCIeiYjz27VZEBFfAe4t23w1IuZExEMR8a9lu7MpAtRtEXFbOW1heds/IuLc8gKnD0bExzos+4pyWT+PiOE9vejMnAH8nOIG40TEJyPinnLZl5fh81hgMvCtsodveGft2movezl/ExHXltO2iYivl+3vi4ijy7tcfAo4vlzm8b14ryRVnKFO0qbYB5jbxbxVwDGZuT9F+PtcW4jpxoEU12RqBt5XXngTYE/gfzJzv8x8EvinzJwMvJHi1jxvzMxLKe7T+pbMfEv7hUbEJODDFPdPfBNwakTsV87eA/hyZu4NLKe4y0wt7mX97Yy+lJkHZOY+FPevfEdmfo/ilkcnlj18KztrVz7/PGC/zHwjcEY57Z+AWzPzAIrt91mgEfgkMKNc5owaa5U0CBjqJNVLAP9R3krnFmBneh6WvTkznysD0A+AN5fTn8zMX7Zrd1xE3AvcB+wN9HR825uB6zPzT5n5Yrnstiu0P5GZ88rHc4FxPb6yQvuA+paI+FVEPAAcWtbUma7a/YaiR+8kipuSA/wFcF55C8NZFDcCf22NtUkahLyjhKRN8RBd3zj7RGA0MCkzWyJiIUUw6U7Hq6G3/fyntgkRsSvwd8ABmfnHiLi6huV210O4ut3jtRQ9aLXYD5gTEU3AV4DJmflURFzQWT09tDsKmA68C/iXiNi7rPm9mflIh+X8eY31SRpk7KmTtCluBYZFxKltEyLigIg4GNgOWFoGurcAr6theUdExA7lcW3vBu7spM22FCHv+YjYEXh7u3krKG4g3tHtwLsjYuuI2AY4BphdQz2dioj3UvSkfYf1wezZiHgVrwy57evptF154scumXkb8PfASIobeP8MOKvdcXf7dbJMSVrHUCdpo2Vxn8FjKMLYYxHxEHABxbFt3wImR8Qcil67h2tY5B3AN4B5wPczc04n67yfYtj1IeDrvDL4XQ7c1HaiRLvn3AtcDfwa+BVwZWbeV/MLLZzTdkkT4CTg0MxcVp5dewXwAPBD4J52z7kauKwcQl3dRbsG4JvlkOx9FGfZLgf+jeIYut+UJ5P8W9n+NmCCJ0pI6sh7v0qSJFWAPXWSJEkVYKiTJEmqAEOdJElSBRjqJEmSKsBQJ0mSVAGGOkmSpAow1EmSJFWAoU6SJKkC/j/WUUpWN9CvEAAAAABJRU5ErkJggg==", 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" ] @@ -1409,27 +1414,62 @@ ], "source": [ "# Preparing the DataFrame:\n", - "t1 = pd.DataFrame(columns=['kaq - opt', 'kaq - min', 'kaq - max', 'W. Storage', 'Calib. Dataset']) \n", - "w_storage = ['without rc','without rc','without rc','with rc','with rc','with rc',]\n", - "obs_dataset = ['obs 30 m','obs 90 m','obs simultaneously','obs 30 m','obs 90 m','obs simultaneously']\n", + "t1 = pd.DataFrame(\n", + " columns=[\"kaq - opt\", \"kaq - min\", \"kaq - max\", \"W. Storage\", \"Calib. Dataset\"]\n", + ")\n", + "w_storage = [\n", + " \"without rc\",\n", + " \"without rc\",\n", + " \"without rc\",\n", + " \"with rc\",\n", + " \"with rc\",\n", + " \"with rc\",\n", + "]\n", + "obs_dataset = [\n", + " \"obs 30 m\",\n", + " \"obs 90 m\",\n", + " \"obs simultaneously\",\n", + " \"obs 30 m\",\n", + " \"obs 90 m\",\n", + " \"obs simultaneously\",\n", + "]\n", "\n", "# Looping through all calibration objects and fetching the desired values\n", - "for calib,w_sto,obs_dts in zip([ca1,ca2,ca,ca3,ca4,ca0],w_storage,obs_dataset):\n", - " p = calib.parameters #Accessing the parameters Dataframe inside the Calibrate object\n", - " tab = pd.DataFrame([[p.loc['kaq0','optimal'], 2*p.loc['kaq0', 'std'], 2*p.loc['kaq0', 'std'],w_sto,obs_dts]],\n", - " columns=['kaq - opt', 'kaq - min', 'kaq - max', 'W. Storage', 'Calib. Dataset'])\n", + "for calib, w_sto, obs_dts in zip([ca1, ca2, ca, ca3, ca4, ca0], w_storage, obs_dataset):\n", + " p = (\n", + " calib.parameters\n", + " ) # Accessing the parameters Dataframe inside the Calibrate object\n", + " tab = pd.DataFrame(\n", + " [\n", + " [\n", + " p.loc[\"kaq0\", \"optimal\"],\n", + " 2 * p.loc[\"kaq0\", \"std\"],\n", + " 2 * p.loc[\"kaq0\", \"std\"],\n", + " w_sto,\n", + " obs_dts,\n", + " ]\n", + " ],\n", + " columns=[\"kaq - opt\", \"kaq - min\", \"kaq - max\", \"W. Storage\", \"Calib. Dataset\"],\n", + " )\n", " t1 = t1.append(tab)\n", "\n", "# Plotting\n", - "groups = t1.groupby('W. Storage')\n", - "plt.figure(figsize = (10,7))\n", + "groups = t1.groupby(\"W. Storage\")\n", + "plt.figure(figsize=(10, 7))\n", "for name, group in groups:\n", - " plt.errorbar(x = group['Calib. Dataset'], y = group['kaq - opt'], yerr = [group['kaq - min'], group['kaq - max']],\n", - " marker='o', linestyle='', markersize=12, label=name)\n", + " plt.errorbar(\n", + " x=group[\"Calib. Dataset\"],\n", + " y=group[\"kaq - opt\"],\n", + " yerr=[group[\"kaq - min\"], group[\"kaq - max\"]],\n", + " marker=\"o\",\n", + " linestyle=\"\",\n", + " markersize=12,\n", + " label=name,\n", + " )\n", "plt.legend()\n", "plt.suptitle(\"Error bar plot of calibrated hydraulic conductivities\")\n", - "plt.ylabel('K [m/d]')\n", - "plt.xlabel('Calibration Dataset')" + "plt.ylabel(\"K [m/d]\")\n", + "plt.xlabel(\"Calibration Dataset\")" ] }, { @@ -1506,13 +1546,14 @@ } ], "source": [ - "t = pd.DataFrame(columns=['k [m/d]', 'Ss [1/m]', 'RMSE'], \\\n", - " index=['K&dR', 'TTim', 'AQTESOLV', 'MLU'])\n", - "t.loc['TTim'] = np.append(ca.parameters['optimal'].values, ca.rmse())\n", - "t.loc['AQTESOLV'] = [66.086, 2.541e-05, 0.05006]\n", - "t.loc['MLU'] = [66.850, 2.400e-05, 0.05083]\n", - "t.loc['K&dR'] = [55.71429, 1.7E-4, '-']\n", - "t.style.set_caption('Comparison of Model Results with different Softwares')" + "t = pd.DataFrame(\n", + " columns=[\"k [m/d]\", \"Ss [1/m]\", \"RMSE\"], index=[\"K&dR\", \"TTim\", \"AQTESOLV\", \"MLU\"]\n", + ")\n", + "t.loc[\"TTim\"] = np.append(ca.parameters[\"optimal\"].values, ca.rmse())\n", + "t.loc[\"AQTESOLV\"] = [66.086, 2.541e-05, 0.05006]\n", + "t.loc[\"MLU\"] = [66.850, 2.400e-05, 0.05083]\n", + "t.loc[\"K&dR\"] = [55.71429, 1.7e-4, \"-\"]\n", + "t.style.set_caption(\"Comparison of Model Results with different Softwares\")" ] }, { diff --git a/setup.py b/setup.py index 7efe4a8..6032295 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ l_d = "" this_directory = path.abspath(path.dirname(__file__)) -with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f: +with open(path.join(this_directory, "README.md"), encoding="utf-8") as f: l_d = f.read() @@ -17,14 +17,20 @@ version=version["__version__"], description="Transient multi-layer AEM Model", long_description=l_d, - long_description_content_type='text/markdown', + long_description_content_type="text/markdown", author="Mark Bakker", author_email="markbak@gmail.com", url="https://github.com/mbakker7/ttim", license="MIT", packages=["ttim"], - python_requires='>=3.7', - install_requires=["numpy>=1.17", "scipy>=1.5", "numba>=0.5", - "matplotlib>=3.1", "lmfit>=1.0", "pandas>=1.1"], - classifiers=['Topic :: Scientific/Engineering :: Hydrology'], + python_requires=">=3.7", + install_requires=[ + "numpy>=1.17", + "scipy>=1.5", + "numba>=0.5", + "matplotlib>=3.1", + "lmfit>=1.0", + "pandas>=1.1", + ], + classifiers=["Topic :: Scientific/Engineering :: Hydrology"], ) diff --git a/tests/test_import.py b/tests/test_import.py index 35f9692..3650e22 100644 --- a/tests/test_import.py +++ b/tests/test_import.py @@ -1,5 +1,6 @@ def test_import(): import ttim -if __name__ == '__main__': + +if __name__ == "__main__": test_import() diff --git a/tests/test_notebooks.py b/tests/test_notebooks.py index a786887..854163c 100644 --- a/tests/test_notebooks.py +++ b/tests/test_notebooks.py @@ -5,46 +5,58 @@ import pytest -nbdir = os.path.join('notebooks') +nbdir = os.path.join("notebooks") testdir = tempfile.mkdtemp() + def get_notebooks(): - return [f for f in os.listdir(nbdir) if f.endswith('.ipynb')] + return [f for f in os.listdir(nbdir) if f.endswith(".ipynb")] + def get_jupyter_kernel(): try: - jklcmd = ('jupyter', 'kernelspec', 'list') - b = subprocess.Popen(jklcmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT).communicate()[0] + jklcmd = ("jupyter", "kernelspec", "list") + b = subprocess.Popen( + jklcmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT + ).communicate()[0] if isinstance(b, bytes): - b = b.decode('utf-8') + b = b.decode("utf-8") print(b) for line in b.splitlines(): - if 'python' in line: + if "python" in line: kernel = line.split()[0] except: kernel = None - - return kernel + + return kernel + @pytest.mark.notebooks @pytest.mark.parametrize("fn", get_notebooks()) def test_notebook(fn): - kernel = get_jupyter_kernel() - print('available jupyter kernel {}'.format(kernel)) + print("available jupyter kernel {}".format(kernel)) pth = os.path.join(nbdir, fn) - - cmd = 'jupyter ' + 'nbconvert ' + \ - '--ExecutePreprocessor.timeout=600 ' + '--to ' + 'notebook ' + \ - '--execute ' + '{} '.format(pth) + \ - '--output-dir ' + '{} '.format(testdir) + \ - '--output ' + '{}'.format(fn) + + cmd = ( + "jupyter " + + "nbconvert " + + "--ExecutePreprocessor.timeout=600 " + + "--to " + + "notebook " + + "--execute " + + "{} ".format(pth) + + "--output-dir " + + "{} ".format(testdir) + + "--output " + + "{}".format(fn) + ) ival = os.system(cmd) - assert ival == 0, 'could not run {}'.format(fn) + assert ival == 0, "could not run {}".format(fn) -if __name__ == '__main__': +if __name__ == "__main__": test_notebook() shutil.rmtree(testdir) diff --git a/tests/test_theis.py b/tests/test_theis.py index bf656b1..962ee45 100644 --- a/tests/test_theis.py +++ b/tests/test_theis.py @@ -1,17 +1,20 @@ import numpy as np from scipy.special import exp1 -from ttim import * + +import ttim + def theis(r, t, T, S, Q): - u = r ** 2 * S / (4 * T * t) + u = r**2 * S / (4 * T * t) h = -Q / (4 * np.pi * T) * exp1(u) return h + def theisQr(r, t, T, S, Q): - u = r ** 2 * S / (4 * T * t) + u = r**2 * S / (4 * T * t) Qr = -Q / (2 * np.pi) * np.exp(-u) / r return Qr - + T = 500 S = 1e-3 @@ -21,8 +24,8 @@ def theisQr(r, t, T, S, Q): h1 = theis(r, t, T, S, Q) -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) ml.solve() h2 = ml.head(r, 0, t)[0] @@ -34,8 +37,8 @@ def theisQr(r, t, T, S, Q): h1 = theis(r, t, T, S, Q) h1[t > 5] -= theis(r, t[t > 5] - 5, T, S, Q) -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) ml.solve() h2 = ml.head(r, 0, t)[0] @@ -46,8 +49,8 @@ def theisQr(r, t, T, S, Q): h1 = theis(r, t, T, S, Q) tmin = 0.1 -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) ml.solve() h2 = ml.head(r, 0, t)[0] @@ -64,8 +67,8 @@ def theisQr(r, t, T, S, Q): h1[t > 5] -= theis(r, t[t > 5] - 5, T, S, Q) tmin = 0.1 -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) ml.solve() h2 = ml.head(r, 0, t)[0] @@ -79,13 +82,12 @@ def theisQr(r, t, T, S, Q): Qr1 = theisQr(r, t, T, S, Q) -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) ml.solve() Qr2 = ml.disvec(r, 0, t)[0][0] -assert np.allclose(Qr1, Qr2, atol=1e-4), \ -"Qr1 and Qr2 not all close Theis well 1" +assert np.allclose(Qr1, Qr2, atol=1e-4), "Qr1 and Qr2 not all close Theis well 1" # turn Theis off t = np.logspace(-1, 1, 10) @@ -93,21 +95,20 @@ def theisQr(r, t, T, S, Q): Qr1 = theisQr(r, t, T, S, Q) Qr1[t > 5] -= theisQr(r, t[t > 5] - 5, T, S, Q) -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=1e-4, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) ml.solve() Qr2 = ml.disvec(r, 0, t)[0][0] -assert np.allclose(Qr1, Qr2, atol=1e-4), \ -"Qr1 and Qr2 not all close Theis well 2" +assert np.allclose(Qr1, Qr2, atol=1e-4), "Qr1 and Qr2 not all close Theis well 2" # test nan values for Theis well 1 t = np.array([0.08, 0.09, 0.1, 1, 5, 9]) Qr1 = theisQr(r, t, T, S, Q) tmin = 0.1 -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q)]) ml.solve() Qr2 = ml.disvec(r, 0, t)[0][0] @@ -124,8 +125,8 @@ def theisQr(r, t, T, S, Q): Qr1[t > 5] -= theisQr(r, t[t > 5] - 5, T, S, Q) tmin = 0.1 -ml = ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) -w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) +ml = ttim.ModelMaq(kaq=50, z=[10, 0], Saq=S / 10, tmin=tmin, tmax=10) +w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, Q), (5, 0)]) ml.solve() Qr2 = ml.disvec(r, 0, t)[0][0] @@ -134,4 +135,3 @@ def theisQr(r, t, T, S, Q): assert np.all(a == b), "nans not in the right spot for tmin" assert np.allclose(Qr1[~b], Qr2[~b], atol=1e-4) - diff --git a/ttim/__init__.py b/ttim/__init__.py index 7a1e4d9..cf66a8b 100644 --- a/ttim/__init__.py +++ b/ttim/__init__.py @@ -1,4 +1,4 @@ -''' +""" Copyright (C), 2017, Mark Bakker. Mark Bakker, Delft University of Technology mark dot bakker at tudelft dot nl @@ -6,19 +6,24 @@ TTim is a computer program for the simulation of transient multi-layer flow with analytic elements and consists of a library of Python scripts and FORTRAN extensions. -''' +""" -#--version number -__name__='ttim' -__author__='Mark Bakker' -from .version import __version__ +# --version number +__name__ = "ttim" +__author__ = "Mark Bakker" +from .circareasink import CircAreaSink +from .fit import Calibrate +from .linedoublet import LeakyLineDoublet, LeakyLineDoubletString +from .linesink import ( + HeadLineSink, + HeadLineSinkHo, + HeadLineSinkString, + LineSink, + LineSinkDitchString, +) # Import all classes and functions -from .model import ModelMaq, Model3D +from .model import Model3D, ModelMaq +from .trace import timtrace, timtraceline +from .version import __version__ from .well import DischargeWell, HeadWell, Well, WellTest -from .linesink import LineSink, HeadLineSink, HeadLineSinkString, \ - LineSinkDitchString, HeadLineSinkHo -from .linedoublet import LeakyLineDoublet, LeakyLineDoubletString -from .circareasink import CircAreaSink -from .fit import Calibrate -from .trace import timtraceline, timtrace \ No newline at end of file diff --git a/ttim/aquifer.py b/ttim/aquifer.py index 876ecd8..99dc0b6 100644 --- a/ttim/aquifer.py +++ b/ttim/aquifer.py @@ -1,55 +1,74 @@ -import numpy as np +import inspect # Used for storing the input + import matplotlib.pyplot as plt -import inspect # Used for storing the input +import numpy as np + class AquiferData: - def __init__(self, model, kaq, z, Haq, Hll, c, Saq, Sll, poraq, porll, - ltype, topboundary, phreatictop, kzoverkh=None, model3d=False): - '''kzoverkh and model3d only need to be specified when model - is model3d''' + def __init__( + self, + model, + kaq, + z, + Haq, + Hll, + c, + Saq, + Sll, + poraq, + porll, + ltype, + topboundary, + phreatictop, + kzoverkh=None, + model3d=False, + ): + """kzoverkh and model3d only need to be specified when model + is model3d""" self.model = model - self.kaq = np.atleast_1d(kaq).astype('d') - self.z = np.atleast_1d(z).astype('d') + self.kaq = np.atleast_1d(kaq).astype("d") + self.z = np.atleast_1d(z).astype("d") self.naq = len(self.kaq) self.nlayers = len(self.z) - 1 - self.Haq = np.atleast_1d(Haq).astype('d') - self.Hll = np.atleast_1d(Hll).astype('d') + self.Haq = np.atleast_1d(Haq).astype("d") + self.Hll = np.atleast_1d(Hll).astype("d") self.T = self.kaq * self.Haq self.Tcol = self.T.reshape(self.naq, 1) - self.c = np.atleast_1d(c).astype('d') - self.c[self.c > 1e100] = 1e100 - self.Saq = np.atleast_1d(Saq).astype('d') - self.Sll = np.atleast_1d(Sll).astype('d') - self.Sll[self.Sll < 1e-20] = 1e-20 # Cannot be zero - self.poraq = np.atleast_1d(poraq).astype('d') - self.porll = np.atleast_1d(porll).astype('d') + self.c = np.atleast_1d(c).astype("d") + self.c[self.c > 1e100] = 1e100 + self.Saq = np.atleast_1d(Saq).astype("d") + self.Sll = np.atleast_1d(Sll).astype("d") + self.Sll[self.Sll < 1e-20] = 1e-20 # Cannot be zero + self.poraq = np.atleast_1d(poraq).astype("d") + self.porll = np.atleast_1d(porll).astype("d") self.ltype = np.atleast_1d(ltype) - self.zaqtop = self.z[:-1][self.ltype == 'a'] - self.zaqbot = self.z[1:][self.ltype == 'a'] - self.layernumber = np.zeros(self.nlayers, dtype='int') - self.layernumber[self.ltype == 'a'] = np.arange(self.naq) - self.layernumber[self.ltype == 'l'] = np.arange(self.nlayers - self.naq) - if self.ltype[0] == 'a': - self.layernumber[self.ltype == 'l'] += 1 # first leaky layer below first aquifer layer + self.zaqtop = self.z[:-1][self.ltype == "a"] + self.zaqbot = self.z[1:][self.ltype == "a"] + self.layernumber = np.zeros(self.nlayers, dtype="int") + self.layernumber[self.ltype == "a"] = np.arange(self.naq) + self.layernumber[self.ltype == "l"] = np.arange(self.nlayers - self.naq) + if self.ltype[0] == "a": + self.layernumber[ + self.ltype == "l" + ] += 1 # first leaky layer below first aquifer layer self.topboundary = topboundary[:3] self.phreatictop = phreatictop self.kzoverkh = kzoverkh if self.kzoverkh is not None: - self.kzoverkh = np.atleast_1d(self.kzoverkh).astype('d') - if len(self.kzoverkh) == 1: + self.kzoverkh = np.atleast_1d(self.kzoverkh).astype("d") + if len(self.kzoverkh) == 1: self.kzoverkh = self.kzoverkh * np.ones(self.naq) self.model3d = model3d if self.model3d: - assert self.kzoverkh is not None, \ - "model3d specified without kzoverkh" - #self.D = self.T / self.Saq - self.area = 1e200 # Smaller than default of ml.aq so that inhom is found - + assert self.kzoverkh is not None, "model3d specified without kzoverkh" + # self.D = self.T / self.Saq + self.area = 1e200 # Smaller than default of ml.aq so that inhom is found + def __repr__(self): - return 'Inhom T: ' + str(self.T) - + return "Inhom T: " + str(self.T) + def initialize(self): - ''' + """ eigval[naq, npval]: Array with eigenvalues lab[naq, npval]: Array with lambda values lab2[naq, nint, npint]: Array with lambda values reorganized per @@ -58,141 +77,179 @@ def initialize(self): coef[naq ,naq, npval]: Array with coefficients; coef[ilayers, :, np] are the coefficients if the element is in ilayers belonging to Laplace parameter number np - ''' + """ # Recompute T for when kaq is changed self.T = self.kaq * self.Haq self.Tcol = self.T.reshape(self.naq, 1) # Compute Saq and Sll self.Scoefaq = self.Saq * self.Haq self.Scoefll = self.Sll * self.Hll - if (self.topboundary == 'con') and self.phreatictop: + if (self.topboundary == "con") and self.phreatictop: self.Scoefaq[0] = self.Scoefaq[0] / self.Haq[0] - elif (self.topboundary == 'lea') and self.phreatictop: + elif (self.topboundary == "lea") and self.phreatictop: self.Scoefll[0] = self.Scoefll[0] / self.Hll[0] self.D = self.T / self.Scoefaq # Compute c if model3d for when kaq is changed if self.model3d: - self.c[1:] = \ - 0.5 * self.Haq[:-1] / (self.kzoverkh[:-1] * self.kaq[:-1]) + \ - 0.5 * self.Haq[1:] / (self.kzoverkh[1:] * self.kaq[1:]) + self.c[1:] = 0.5 * self.Haq[:-1] / ( + self.kzoverkh[:-1] * self.kaq[:-1] + ) + 0.5 * self.Haq[1:] / (self.kzoverkh[1:] * self.kaq[1:]) # - self.eigval = np.zeros((self.naq, self.model.npval), 'D') - self.lab = np.zeros((self.naq, self.model.npval), 'D') - self.eigvec = np.zeros((self.naq, self.naq, self.model.npval), 'D') - self.coef = np.zeros((self.naq, self.naq, self.model.npval), 'D') + self.eigval = np.zeros((self.naq, self.model.npval), "D") + self.lab = np.zeros((self.naq, self.model.npval), "D") + self.eigvec = np.zeros((self.naq, self.naq, self.model.npval), "D") + self.coef = np.zeros((self.naq, self.naq, self.model.npval), "D") b = np.diag(np.ones(self.naq)) for i in range(self.model.npval): - w, v = self.compute_lab_eigvec(self.model.p[i]) + w, v = self.compute_lab_eigvec(self.model.p[i]) # Eigenvectors are columns of v - self.eigval[:, i] = w; self.eigvec[:, :, i] = v + self.eigval[:, i] = w + self.eigvec[:, :, i] = v self.coef[:, :, i] = np.linalg.solve(v, b).T self.lab = 1.0 / np.sqrt(self.eigval) - self.lab2 = self.lab.copy() + self.lab2 = self.lab.copy() self.lab2.shape = (self.naq, self.model.nint, self.model.npint) - self.lababs = np.abs(self.lab2[:, :, 0]) # used to check distances + self.lababs = np.abs(self.lab2[:, :, 0]) # used to check distances self.eigvec2 = self.eigvec.copy() - self.eigvec2.shape = (self.naq, self.naq, - self.model.nint, self.model.npint) - - def compute_lab_eigvec(self, p, returnA = False, B = None): - sqrtpSc = np.sqrt( p * self.Scoefll * self.c ) + self.eigvec2.shape = (self.naq, self.naq, self.model.nint, self.model.npint) + + def compute_lab_eigvec(self, p, returnA=False, B=None): + sqrtpSc = np.sqrt(p * self.Scoefll * self.c) a, b = np.zeros_like(sqrtpSc), np.zeros_like(sqrtpSc) small = np.abs(sqrtpSc) < 200 a[small] = sqrtpSc[small] / np.tanh(sqrtpSc[small]) b[small] = sqrtpSc[small] / np.sinh(sqrtpSc[small]) - a[~small] = sqrtpSc[~small] / ((1.0 - np.exp(-2.0*sqrtpSc[~small])) / - (1.0 + np.exp(-2.0*sqrtpSc[~small]))) - b[~small] = sqrtpSc[~small] * 2.0 * np.exp(-sqrtpSc[~small]) / \ - (1.0 - np.exp(-2.0*sqrtpSc[~small])) - if (self.topboundary[:3] == 'sem') or (self.topboundary[:3] == 'lea'): + a[~small] = sqrtpSc[~small] / ( + (1.0 - np.exp(-2.0 * sqrtpSc[~small])) + / (1.0 + np.exp(-2.0 * sqrtpSc[~small])) + ) + b[~small] = ( + sqrtpSc[~small] + * 2.0 + * np.exp(-sqrtpSc[~small]) + / (1.0 - np.exp(-2.0 * sqrtpSc[~small])) + ) + if (self.topboundary[:3] == "sem") or (self.topboundary[:3] == "lea"): dzero = sqrtpSc[0] * np.tanh(sqrtpSc[0]) d0 = p / self.D if B is not None: d0 = d0 * B # B is vector of load efficiency paramters d0[:-1] += a[1:] / (self.c[1:] * self.T[:-1]) - d0[1:] += a[1:] / (self.c[1:] * self.T[1:]) - if self.topboundary[:3] == 'lea': - d0[0] += dzero / ( self.c[0] * self.T[0] ) - elif self.topboundary[:3] == 'sem': - d0[0] += a[0] / ( self.c[0] * self.T[0] ) - + d0[1:] += a[1:] / (self.c[1:] * self.T[1:]) + if self.topboundary[:3] == "lea": + d0[0] += dzero / (self.c[0] * self.T[0]) + elif self.topboundary[:3] == "sem": + d0[0] += a[0] / (self.c[0] * self.T[0]) + dm1 = -b[1:] / (self.c[1:] * self.T[:-1]) dp1 = -b[1:] / (self.c[1:] * self.T[1:]) - A = np.diag(dm1,-1) + np.diag(d0,0) + np.diag(dp1,1) - if returnA: return A + A = np.diag(dm1, -1) + np.diag(d0, 0) + np.diag(dp1, 1) + if returnA: + return A w, v = np.linalg.eig(A) # sorting moved here index = np.argsort(abs(w))[::-1] w = w[index] v = v[:, index] return w, v - + def head_to_potential(self, h, layers): return h * self.Tcol[layers] - + def potential_to_head(self, pot, layers): return pot / self.Tcol[layers] - - def isInside(self,x,y): - print('Must overload AquiferData.isInside method') + + def isInside(self, x, y): + print("Must overload AquiferData.isInside method") return True - + def inWhichLayer(self, z): - '''Returns -9999 if above top of system, - +9999 if below bottom of system, + """Returns -9999 if above top of system, + +9999 if below bottom of system, negative for in leaky layer. - leaky layer -n is on top of aquifer n''' + leaky layer -n is on top of aquifer n""" if z > self.zt[0]: return -9999 - for i in range(self.naq-1): + for i in range(self.naq - 1): if z >= self.zb[i]: return i - if z > self.zt[i+1]: - return -i-1 - if z >= self.zb[self.naq-1]: + if z > self.zt[i + 1]: + return -i - 1 + if z >= self.zb[self.naq - 1]: return self.naq - 1 return +9999 - + def findlayer(self, z): - ''' - Returns layer-number, layer-type and model-layer-number''' + """ + Returns layer-number, layer-type and model-layer-number""" if z > self.z[0]: modellayer = -1 - ltype = 'above' + ltype = "above" layernumber = None elif z < self.z[-1]: modellayer = len(self.layernumber) - ltype = 'below' + ltype = "below" layernumber = None else: - modellayer = np.argwhere((z <= self.z[:-1]) & - (z >= self.z[1:]))[0, 0] + modellayer = np.argwhere((z <= self.z[:-1]) & (z >= self.z[1:]))[0, 0] layernumber = self.layernumber[modellayer] - ltype = self.ltype[modellayer] + ltype = self.ltype[modellayer] return layernumber, ltype, modellayer - + + class Aquifer(AquiferData): - def __init__(self, model, kaq, z, Haq, Hll, c, Saq, Sll, poraq, porll, - ltype, topboundary, phreatictop, kzoverkh=None, model3d=False): - AquiferData.__init__(self, model, kaq, z, Haq, Hll, c, Saq, Sll, - poraq, porll, ltype, topboundary, phreatictop, kzoverkh, model3d) + def __init__( + self, + model, + kaq, + z, + Haq, + Hll, + c, + Saq, + Sll, + poraq, + porll, + ltype, + topboundary, + phreatictop, + kzoverkh=None, + model3d=False, + ): + AquiferData.__init__( + self, + model, + kaq, + z, + Haq, + Hll, + c, + Saq, + Sll, + poraq, + porll, + ltype, + topboundary, + phreatictop, + kzoverkh, + model3d, + ) self.inhomlist = [] - self.area = 1e300 # Needed to find smallest inhomogeneity - + self.area = 1e300 # Needed to find smallest inhomogeneity + def __repr__(self): - return 'Background Aquifer T: ' + str(self.T) - - + return "Background Aquifer T: " + str(self.T) + def initialize(self): AquiferData.initialize(self) for inhom in self.inhomlist: inhom.initialize() - + def find_aquifer_data(self, x, y): rv = self for aq in self.inhomlist: if aq.isInside(x, y): if aq.area < rv.area: rv = aq - return rv \ No newline at end of file + return rv diff --git a/ttim/aquifer_parameters.py b/ttim/aquifer_parameters.py index 165e0ff..fcbf257 100644 --- a/ttim/aquifer_parameters.py +++ b/ttim/aquifer_parameters.py @@ -1,19 +1,30 @@ import numpy as np -def param_maq(kaq=[1], z=[1, 0], c=[], Saq=[0.001], Sll=[0], - poraq=[0.3], porll=[0.3], topboundary='conf', phreatictop=False): + +def param_maq( + kaq=[1], + z=[1, 0], + c=[], + Saq=[0.001], + Sll=[0], + poraq=[0.3], + porll=[0.3], + topboundary="conf", + phreatictop=False, +): # Computes the parameters for a TimModel from input for a maq model - z = np.atleast_1d(z).astype('d') - kaq = np.atleast_1d(kaq).astype('d') - Saq = np.atleast_1d(Saq).astype('d') - poraq = np.atleast_1d(poraq).astype('d') - c = np.atleast_1d(c).astype('d') - Sll = np.atleast_1d(Sll).astype('d') - porll = np.atleast_1d(porll).astype('d') + z = np.atleast_1d(z).astype("d") + kaq = np.atleast_1d(kaq).astype("d") + Saq = np.atleast_1d(Saq).astype("d") + poraq = np.atleast_1d(poraq).astype("d") + c = np.atleast_1d(c).astype("d") + Sll = np.atleast_1d(Sll).astype("d") + porll = np.atleast_1d(porll).astype("d") H = z[:-1] - z[1:] - assert np.all(H >= 0), 'Error: Not all layer thicknesses are' + \ - ' non-negative' + str(H) - if topboundary[:3] == 'con': + assert np.all(H >= 0), ( + "Error: Not all layer thicknesses are" + " non-negative" + str(H) + ) + if topboundary[:3] == "con": naq = int(len(z) / 2) if len(kaq) == 1: kaq = kaq * np.ones(naq) @@ -27,34 +38,32 @@ def param_maq(kaq=[1], z=[1, 0], c=[], Saq=[0.001], Sll=[0], Sll = Sll * np.ones(naq - 1) if len(porll) == 1: porll = porll * np.ones(naq - 1) - assert len(kaq) == naq, 'Error: Length of kaq needs to be ' + \ - str(naq) - assert len(Saq) == naq, 'Error: Length of Saq needs to be ' + \ - str(naq) - assert len(poraq) == naq, 'Error: Length of poraq needs to be ' + \ - str(naq) - assert len(c) == naq - 1, 'Error: Length of c needs to be ' + \ - str(naq - 1) - assert len(Sll) == naq - 1, 'Error: Length of Sll needs to be ' + \ - str(naq - 1) - assert len(porll) == naq - 1, 'Error: Length of porll needs to be ' + \ - str(naq - 1) + assert len(kaq) == naq, "Error: Length of kaq needs to be " + str(naq) + assert len(Saq) == naq, "Error: Length of Saq needs to be " + str(naq) + assert len(poraq) == naq, "Error: Length of poraq needs to be " + str(naq) + assert len(c) == naq - 1, "Error: Length of c needs to be " + str(naq - 1) + assert len(Sll) == naq - 1, "Error: Length of Sll needs to be " + str(naq - 1) + assert len(porll) == naq - 1, "Error: Length of porll needs to be " + str( + naq - 1 + ) Haq = H[::2] - assert np.all(Haq > 0), 'Error: Some thicknesses of aquifer layers ' + \ - 'are negative' + assert np.all(Haq > 0), ( + "Error: Some thicknesses of aquifer layers " + "are negative" + ) Hll = H[1::2] - Hll = np.maximum(Hll, 1e-20) # make sure none are negative - assert np.all(Hll > 0), 'Error: Some thicknesses of leaky layers ' + \ - 'are negative' - c = np.hstack((1e100, c)) - Sll = np.hstack((1e-20, Sll)) + Hll = np.maximum(Hll, 1e-20) # make sure none are negative + assert np.all(Hll > 0), ( + "Error: Some thicknesses of leaky layers " + "are negative" + ) + c = np.hstack((1e100, c)) + Sll = np.hstack((1e-20, Sll)) Hll = np.hstack((1e-20, Hll)) porll = np.hstack((1e-20, porll)) # layertype nlayers = len(z) - 1 - ltype = np.array(nlayers * ['a']) - ltype[1::2] = 'l' - else: # leaky layers on top + ltype = np.array(nlayers * ["a"]) + ltype[1::2] = "l" + else: # leaky layers on top naq = int(len(z - 1) / 2) if len(kaq) == 1: kaq = kaq * np.ones(naq) @@ -68,61 +77,67 @@ def param_maq(kaq=[1], z=[1, 0], c=[], Saq=[0.001], Sll=[0], Sll = Sll * np.ones(naq) if len(porll) == 1: porll = porll * np.ones(naq) - assert len(kaq) == naq, 'Error: Length of kaq needs to be ' + \ - str(naq) - assert len(Saq) == naq, 'Error: Length of Saq needs to be ' + \ - str(naq) - assert len(poraq) == naq, 'Error: Length of poraq needs to be ' + \ - str(naq) - assert len(c) == naq, 'Error: Length of c needs to be ' + \ - str(naq) - assert len(Sll) == naq, 'Error: Length of Sll needs to be ' + \ - str(naq) - assert len(porll) == naq, 'Error: Length of porll needs to be ' + \ - str(naq) + assert len(kaq) == naq, "Error: Length of kaq needs to be " + str(naq) + assert len(Saq) == naq, "Error: Length of Saq needs to be " + str(naq) + assert len(poraq) == naq, "Error: Length of poraq needs to be " + str(naq) + assert len(c) == naq, "Error: Length of c needs to be " + str(naq) + assert len(Sll) == naq, "Error: Length of Sll needs to be " + str(naq) + assert len(porll) == naq, "Error: Length of porll needs to be " + str(naq) Haq = H[1::2] Hll = H[::2] # layertype nlayers = len(z) - 1 - ltype = np.array(nlayers * ['a']) - ltype[0::2] = 'l' + ltype = np.array(nlayers * ["a"]) + ltype[0::2] = "l" return kaq, Haq, Hll, c, Saq, Sll, poraq, porll, ltype - -def param_3d(kaq=[1], z=[1, 0], Saq=[0.001], kzoverkh=1, poraq=0.3, - phreatictop=False, topboundary='conf', topres=0, topthick=0, - topSll=0, toppor=0.3): + + +def param_3d( + kaq=[1], + z=[1, 0], + Saq=[0.001], + kzoverkh=1, + poraq=0.3, + phreatictop=False, + topboundary="conf", + topres=0, + topthick=0, + topSll=0, + toppor=0.3, +): # Computes the parameters for a TimModel from input for a 3D model - kaq = np.atleast_1d(kaq).astype('d') - z = np.atleast_1d(z).astype('d') + kaq = np.atleast_1d(kaq).astype("d") + z = np.atleast_1d(z).astype("d") naq = len(z) - 1 - if len(kaq) == 1: + if len(kaq) == 1: kaq = kaq * np.ones(naq) - Saq = np.atleast_1d(Saq).astype('d') - if len(Saq) == 1: + Saq = np.atleast_1d(Saq).astype("d") + if len(Saq) == 1: Saq = Saq * np.ones(naq) - kzoverkh = np.atleast_1d(kzoverkh).astype('d') - if len(kzoverkh) == 1: + kzoverkh = np.atleast_1d(kzoverkh).astype("d") + if len(kzoverkh) == 1: kzoverkh = kzoverkh * np.ones(naq) - poraq = np.atleast_1d(poraq).astype('d') + poraq = np.atleast_1d(poraq).astype("d") if len(poraq) == 1: poraq = poraq * np.ones(naq) Haq = z[:-1] - z[1:] - c = 0.5 * Haq[:-1] / (kzoverkh[:-1] * kaq[:-1]) + \ - 0.5 * Haq[1:] / (kzoverkh[1:] * kaq[1:]) + c = 0.5 * Haq[:-1] / (kzoverkh[:-1] * kaq[:-1]) + 0.5 * Haq[1:] / ( + kzoverkh[1:] * kaq[1:] + ) # Saq = Saq * H - #if phreatictop: + # if phreatictop: # Saq[0] = Saq[0] / H[0] c = np.hstack((1e100, c)) Hll = 1e-20 * np.ones(len(c)) Sll = 1e-20 * np.ones(len(c)) porll = np.zeros(len(c)) nlayers = len(z) - 1 - ltype = np.array(nlayers * ['a']) - if (topboundary[:3] == 'sem') or (topboundary[:3] == 'lea'): + ltype = np.array(nlayers * ["a"]) + if (topboundary[:3] == "sem") or (topboundary[:3] == "lea"): c[0] = np.max([1e-20, topres]) Hll[0] = np.max([1e-20, topthick]) Sll[0] = np.max([1e-20, topSll]) porll[0] = toppor - ltype = np.hstack(('l', ltype)) + ltype = np.hstack(("l", ltype)) z = np.hstack((z[0] + topthick, z)) - return kaq, Haq, Hll, c, Saq, Sll, poraq, porll, ltype, z \ No newline at end of file + return kaq, Haq, Hll, c, Saq, Sll, poraq, porll, ltype, z diff --git a/ttim/aquifernew.py b/ttim/aquifernew.py index b71566b..fd11b06 100644 --- a/ttim/aquifernew.py +++ b/ttim/aquifernew.py @@ -1,188 +1,207 @@ # flake8: noqa -import numpy as np +import inspect # Used for storing the input + import matplotlib.pyplot as plt -import inspect # Used for storing the input +import numpy as np + class AquiferData: def __init__(self, model, kaq, c, z, npor, ltype, Saq, Sll, phreatictop): self.model = model - self.kaq = np.atleast_1d(kaq).astype('d') + self.kaq = np.atleast_1d(kaq).astype("d") self.Naq = len(kaq) - self.c = np.atleast_1d(c).astype('d') + self.c = np.atleast_1d(c).astype("d") self.c[self.c > 1e100] = 1e100 # Needed for tracing - self.z = np.atleast_1d(z).astype('d') + self.z = np.atleast_1d(z).astype("d") self.Hlayer = self.z[:-1] - self.z[1:] # thickness of all layers self.nlayers = len(self.z) - 1 self.npor = np.atleast_1d(npor) self.ltype = np.atleast_1d(ltype) # - self.layernumber = np.zeros(self.nlayers, dtype='int') - self.layernumber[self.ltype == 'a'] = np.arange(self.Naq) - self.layernumber[self.ltype == 'l'] = np.arange(self.nlayers - self.Naq) - if self.ltype[0] == 'a': - self.layernumber[self.ltype == 'l'] += 1 # first leaky layer below first aquifer layer - self.zaqtop = self.z[:-1][self.ltype == 'a'] - self.zaqbot = self.z[1:][self.ltype == 'a'] + self.layernumber = np.zeros(self.nlayers, dtype="int") + self.layernumber[self.ltype == "a"] = np.arange(self.Naq) + self.layernumber[self.ltype == "l"] = np.arange(self.nlayers - self.Naq) + if self.ltype[0] == "a": + self.layernumber[ + self.ltype == "l" + ] += 1 # first leaky layer below first aquifer layer + self.zaqtop = self.z[:-1][self.ltype == "a"] + self.zaqbot = self.z[1:][self.ltype == "a"] self.Haq = self.zaqtop - self.zaqbot self.T = self.kaq * self.Haq self.Tcol = self.T[:, np.newaxis] - self.zlltop = self.z[:-1][self.ltype == 'l'] - self.zllbot = self.z[1:][self.ltype == 'l'] - if self.ltype[0] == 'a': + self.zlltop = self.z[:-1][self.ltype == "l"] + self.zllbot = self.z[1:][self.ltype == "l"] + if self.ltype[0] == "a": self.zlltop = np.hstack((self.z[0], self.zlltop)) self.zllbot = np.hstack((self.z[0], self.zllbot)) self.Hll = self.zlltop - self.zllbot - self.nporaq = self.npor[self.ltype == 'a'] - if self.ltype[0] == 'a': - self.nporll = np.ones(len(self.npor[self.ltype == 'l']) + 1) - self.nporll[1:] = self.npor[self.ltype == 'l'] + self.nporaq = self.npor[self.ltype == "a"] + if self.ltype[0] == "a": + self.nporll = np.ones(len(self.npor[self.ltype == "l"]) + 1) + self.nporll[1:] = self.npor[self.ltype == "l"] else: - self.nporll = self.npor[self.ltype == 'l'] + self.nporll = self.npor[self.ltype == "l"] # Storage coefs - self.Saq = np.atleast_1d(Saq).astype('d') - self.Sll = np.atleast_1d(Sll).astype('d') - self.Sll[self.Sll < 1e-20] = 1e-20 # Cannot be zero + self.Saq = np.atleast_1d(Saq).astype("d") + self.Sll = np.atleast_1d(Sll).astype("d") + self.Sll[self.Sll < 1e-20] = 1e-20 # Cannot be zero # what to do with these? self.phreatictop = phreatictop # used in calibration - self.area = 1e200 # Smaller than default of ml.aq so that inhom is found - + self.area = 1e200 # Smaller than default of ml.aq so that inhom is found + def __repr__(self): - return 'Inhom T: ' + str(self.T) - + return "Inhom T: " + str(self.T) + def initialize(self): - ''' + """ eigval[Naq,npval]: Array with eigenvalues lab[Naq,npval]: Array with lambda values lab2[Naq,Nin,npint]: Array with lambda values reorganized per interval eigvec[Naq,Naq,npval]: Array with eigenvector matrices coef[Naq,Naq,npval]: Array with coefficients; coef[ilayers,:,np] are the coefficients if the element is in ilayers belonging to Laplace parameter number np - ''' + """ # Recompute T for when kaq is changed manually self.T = self.kaq * self.Haq - self.Tcol = self.T.reshape(self.Naq,1) + self.Tcol = self.T.reshape(self.Naq, 1) self.D = self.T / self.Saq # - self.eigval = np.zeros((self.Naq,self.model.Np),'D') - self.lab = np.zeros((self.Naq,self.model.Np),'D') - self.eigvec = np.zeros((self.Naq,self.Naq,self.model.Np),'D') - self.coef = np.zeros((self.Naq,self.Naq,self.model.Np),'D') + self.eigval = np.zeros((self.Naq, self.model.Np), "D") + self.lab = np.zeros((self.Naq, self.model.Np), "D") + self.eigvec = np.zeros((self.Naq, self.Naq, self.model.Np), "D") + self.coef = np.zeros((self.Naq, self.Naq, self.model.Np), "D") b = np.diag(np.ones(self.Naq)) for i in range(self.model.Np): - w,v = self.compute_lab_eigvec(self.model.p[i]) # Eigenvectors are columns of v + w, v = self.compute_lab_eigvec( + self.model.p[i] + ) # Eigenvectors are columns of v ## moved to compute_lab_eigvec routine - #index = np.argsort( abs(w) )[::-1] - #w = w[index]; v = v[:,index] - self.eigval[:,i] = w; self.eigvec[:,:,i] = v - self.coef[:,:,i] = np.linalg.solve( v, b ).T + # index = np.argsort( abs(w) )[::-1] + # w = w[index]; v = v[:,index] + self.eigval[:, i] = w + self.eigvec[:, :, i] = v + self.coef[:, :, i] = np.linalg.solve(v, b).T self.lab = 1.0 / np.sqrt(self.eigval) - self.lab2 = self.lab.copy(); self.lab2.shape = (self.Naq,self.model.Nin,self.model.Npin) - self.lababs = np.abs(self.lab2[:,:,0]) # used to check distances - - def compute_lab_eigvec(self, p, returnA = False, B = None): - sqrtpSc = np.sqrt( p * self.Sll * self.c ) + self.lab2 = self.lab.copy() + self.lab2.shape = (self.Naq, self.model.Nin, self.model.Npin) + self.lababs = np.abs(self.lab2[:, :, 0]) # used to check distances + + def compute_lab_eigvec(self, p, returnA=False, B=None): + sqrtpSc = np.sqrt(p * self.Sll * self.c) a, b = np.zeros_like(sqrtpSc), np.zeros_like(sqrtpSc) small = np.abs(sqrtpSc) < 200 a[small] = sqrtpSc[small] / np.tanh(sqrtpSc[small]) b[small] = sqrtpSc[small] / np.sinh(sqrtpSc[small]) - a[~small] = sqrtpSc[~small] / ( (1.0 - np.exp(-2.0*sqrtpSc[~small])) / (1.0 + np.exp(-2.0*sqrtpSc[~small])) ) - b[~small] = sqrtpSc[~small] * 2.0 * np.exp(-sqrtpSc[~small]) / (1.0 - np.exp(-2.0*sqrtpSc[~small])) - if self.ltype[0] == 'l': + a[~small] = sqrtpSc[~small] / ( + (1.0 - np.exp(-2.0 * sqrtpSc[~small])) + / (1.0 + np.exp(-2.0 * sqrtpSc[~small])) + ) + b[~small] = ( + sqrtpSc[~small] + * 2.0 + * np.exp(-sqrtpSc[~small]) + / (1.0 - np.exp(-2.0 * sqrtpSc[~small])) + ) + if self.ltype[0] == "l": if abs(sqrtpSc[0]) < 200: - dzero = sqrtpSc[0] * np.tanh( sqrtpSc[0] ) + dzero = sqrtpSc[0] * np.tanh(sqrtpSc[0]) else: - dzero = sqrtpSc[0] * cmath_tanh( sqrtpSc[0] ) # Bug in complex tanh in numpy + dzero = sqrtpSc[0] * cmath_tanh( + sqrtpSc[0] + ) # Bug in complex tanh in numpy d0 = p / self.D if B is not None: d0 = d0 * B # B is vector of load efficiency paramters d0[:-1] += a[1:] / (self.c[1:] * self.T[:-1]) - d0[1:] += a[1:] / (self.c[1:] * self.T[1:]) + d0[1:] += a[1:] / (self.c[1:] * self.T[1:]) ## Need to make option 'lea' possible somehow - #if self.topboundary[:3] == 'lea': + # if self.topboundary[:3] == 'lea': # d0[0] += dzero / ( self.c[0] * self.T[0] ) - #elif self.topboundary[:3] == 'sem': + # elif self.topboundary[:3] == 'sem': # d0[0] += a[0] / ( self.c[0] * self.T[0] ) - if self.ltype[0] == 'l': - d0[0] += a[0] / ( self.c[0] * self.T[0] ) - + if self.ltype[0] == "l": + d0[0] += a[0] / (self.c[0] * self.T[0]) + dm1 = -b[1:] / (self.c[1:] * self.T[:-1]) dp1 = -b[1:] / (self.c[1:] * self.T[1:]) - A = np.diag(dm1,-1) + np.diag(d0,0) + np.diag(dp1,1) - if returnA: return A - w,v = np.linalg.eig(A) + A = np.diag(dm1, -1) + np.diag(d0, 0) + np.diag(dp1, 1) + if returnA: + return A + w, v = np.linalg.eig(A) # sorting moved here - index = np.argsort( abs(w) )[::-1] - w = w[index]; v = v[:,index] - return w,v - - def headToPotential(self,h,layers): + index = np.argsort(abs(w))[::-1] + w = w[index] + v = v[:, index] + return w, v + + def headToPotential(self, h, layers): return h * self.Tcol[layers] - - def potentialToHead(self,pot,layers): + + def potentialToHead(self, pot, layers): return pot / self.Tcol[layers] - - def isInside(self,x,y): - print('Must overload AquiferData.isInside method') + + def isInside(self, x, y): + print("Must overload AquiferData.isInside method") return True - + def inWhichLayer(self, z): - '''Returns -9999 if above top of system, +9999 if below bottom of system, negative for in leaky layer. - leaky layer -n is on top of aquifer n''' + """Returns -9999 if above top of system, +9999 if below bottom of system, negative for in leaky layer. + leaky layer -n is on top of aquifer n""" if z > self.zt[0]: return -9999 - for i in range(self.Naquifers-1): + for i in range(self.Naquifers - 1): if z >= self.zb[i]: return i - if z > self.zt[i+1]: - return -i-1 - if z >= self.zb[self.Naquifers-1]: + if z > self.zt[i + 1]: + return -i - 1 + if z >= self.zb[self.Naquifers - 1]: return self.Naquifers - 1 return +9999 - + def set_kaq(self, value, layer): self.kaq[layer] = value - + def set_Saq(self, value, layer): self.Saq[layer] = value - + def findlayer(self, z): - ''' - Returns layer-number, layer-type and model-layer-number''' + """ + Returns layer-number, layer-type and model-layer-number""" if z > self.z[0]: - modellayer, ltype = -1, 'above' + modellayer, ltype = -1, "above" layernumber = None elif z < self.z[-1]: - modellayer, ltype = len(self.layernumber), 'below' + modellayer, ltype = len(self.layernumber), "below" layernumber = None else: modellayer = np.argwhere((z <= self.z[:-1]) & (z >= self.z[1:]))[0, 0] layernumber = self.layernumber[modellayer] - ltype = self.ltype[modellayer] + ltype = self.ltype[modellayer] return layernumber, ltype, modellayer - + + class Aquifer(AquiferData): def __init__(self, model, kaq, Haq, c, Saq, Sll, topboundary, phreatictop): - #AquiferData.__init__(self, model, kaq, Haq, c, Saq, Sll, \ + # AquiferData.__init__(self, model, kaq, Haq, c, Saq, Sll, \ # topboundary, phreatictop) - AquiferData.__init__(self, model, kaq, c, z, npor, ltype, \ - Saq, Sll, phreatictop) + AquiferData.__init__(self, model, kaq, c, z, npor, ltype, Saq, Sll, phreatictop) self.inhomList = [] - self.area = 1e300 # Needed to find smallest inhomogeneity - + self.area = 1e300 # Needed to find smallest inhomogeneity + def __repr__(self): - return 'Background Aquifer T: ' + str(self.T) - - + return "Background Aquifer T: " + str(self.T) + def initialize(self): AquiferData.initialize(self) for inhom in self.inhomList: inhom.initialize() - + def find_aquifer_data(self, x, y): rv = self for aq in self.inhomList: if aq.isInside(x, y): if aq.area < rv.area: rv = aq - return rv \ No newline at end of file + return rv diff --git a/ttim/besselnumba.py b/ttim/besselnumba.py index 7f632ba..9611cf0 100644 --- a/ttim/besselnumba.py +++ b/ttim/besselnumba.py @@ -1,5 +1,5 @@ -import numpy as np import numba +import numpy as np """ real(kind=8) :: pi, tiny @@ -29,34 +29,34 @@ b = np.zeros(21, dtype=np.float_) for n in range(1, 21): - fac = n*fac - a[n] = 1.0 / (4.0**nrange[n] * fac**2) - b[n] = b[n-1] + 1 / nrange[n] + fac = n * fac + a[n] = 1.0 / (4.0 ** nrange[n] * fac**2) + b[n] = b[n - 1] + 1 / nrange[n] b = (b - c) * a a = -a / 2.0 gam = np.zeros((21, 21), dtype=np.float_) for n in range(21): - for m in range(n+1): - gam[n, m] = np.prod(nrange[m+1:n+1]) / np.prod(nrange[1:n-m+1]) + for m in range(n + 1): + gam[n, m] = np.prod(nrange[m + 1 : n + 1]) / np.prod(nrange[1 : n - m + 1]) afar = np.zeros(21, dtype=np.float_) -afar[0] = np.sqrt(np.pi / 2.) +afar[0] = np.sqrt(np.pi / 2.0) for n in range(1, 21): - afar[n] = -(2. * n - 1.)**2 / (n * 8) * afar[n-1] + afar[n] = -((2.0 * n - 1.0) ** 2) / (n * 8) * afar[n - 1] fac = 1.0 bot = np.zeros(21, dtype=np.float_) bot[0] = 4.0 for n in range(1, 21): fac = n * fac - bot[n] = fac * (n+1)*fac * 4.0**(n+1) + bot[n] = fac * (n + 1) * fac * 4.0 ** (n + 1) psi = np.zeros(21, dtype=np.float_) for n in range(2, 22): - psi[n-1] = psi[n-2] + 1 / (n-1) + psi[n - 1] = psi[n - 2] + 1 / (n - 1) psi = psi - 0.577215664901532860 a1 = np.empty(21, dtype=np.float_) @@ -64,7 +64,7 @@ twologhalf = 2 * np.log(0.5) for n in range(21): a1[n] = 1 / bot[n] - b1[n] = (twologhalf - (2.0 * psi[n] + 1 / (n+1))) / bot[n] + b1[n] = (twologhalf - (2.0 * psi[n] + 1 / (n + 1))) / bot[n] wg = np.zeros(8, dtype=np.float_) @@ -99,12 +99,12 @@ def besselk0near(z, Nt): complex(kind=8) :: rsq, log1, term integer :: n """ - rsq = z ** 2 + rsq = z**2 term = 1.0 + 0.0j log1 = np.log(rsq) omega = a[0] * log1 + b[0] - for n in range(1, Nt+1): + for n in range(1, Nt + 1): term = term * rsq omega = omega + (a[n] * log1 + b[n]) * term @@ -124,36 +124,36 @@ def besselk0cheb(z, Nt): complex(kind=8) :: z1, z2, S, T """ - cnp1 = complex(1., 0.) - cnp2 = complex(0., 0.) - cnp3 = complex(0., 0.) + cnp1 = complex(1.0, 0.0) + cnp2 = complex(0.0, 0.0) + cnp3 = complex(0.0, 0.0) a = 0.5 - c = 1. - b = 1. + a - c + c = 1.0 + b = 1.0 + a - c - z1 = 2. * z - z2 = 2. * z1 - ts = (-1)**(Nt+1) + z1 = 2.0 * z + z2 = 2.0 * z1 + ts = (-1) ** (Nt + 1) S = ts - T = 1. + T = 1.0 for n in range(Nt, -1, -1): - u = (n+a) * (n+b) + u = (n + a) * (n + b) n2 = 2 * n - A1 = 1. - (z2 + (n2+3.)*(n+a+1.)*(n+b+1.) / (n2+4.)) / u - A2 = 1. - (n2+2.)*(n2+3.-z2) / u - A3 = -(n+1.)*(n+3.-a)*(n+3.-b) / (u*(n+2.)) - cn = (2.*n+2.) * A1 * cnp1 + A2 * cnp2 + A3 * cnp3 + A1 = 1.0 - (z2 + (n2 + 3.0) * (n + a + 1.0) * (n + b + 1.0) / (n2 + 4.0)) / u + A2 = 1.0 - (n2 + 2.0) * (n2 + 3.0 - z2) / u + A3 = -(n + 1.0) * (n + 3.0 - a) * (n + 3.0 - b) / (u * (n + 2.0)) + cn = (2.0 * n + 2.0) * A1 * cnp1 + A2 * cnp2 + A3 * cnp3 ts = -ts S = S + ts * cn T = T + cn cnp3 = cnp2 cnp2 = cnp1 cnp1 = cn - cn = cn / 2. + cn = cn / 2.0 S = S - cn T = T - cn - omega = 1. / np.sqrt(z1) * T / S + omega = 1.0 / np.sqrt(z1) * T / S omega = np.sqrt(np.pi) * np.exp(-z) * omega return omega @@ -171,13 +171,14 @@ def besselk0(x, y, lab): z = np.sqrt(x**2 + y**2) / lab cond = np.abs(z) - if (cond < 6): + if cond < 6: omega = besselk0near(z, 17) else: omega = besselk0cheb(z, 6) return omega + @numba.njit(nogil=True, cache=True) def bessells_int_test1(x, y, z1, z2, lab): """ @@ -193,7 +194,7 @@ def bessells_int_test1(x, y, z1, z2, lab): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) biglab = 2 * biga / L @@ -204,11 +205,13 @@ def bessells_int_test1(x, y, z1, z2, lab): anew = a * exprange bnew = (b - a * complex(0, 2) * ang) * exprange -#zminzbar = np.zeros(21, dtype=np.complex_) + +# zminzbar = np.zeros(21, dtype=np.complex_) exprange = np.zeros(21, dtype=np.complex_) anew = np.zeros(21, dtype=np.complex_) bnew = np.zeros(21, dtype=np.complex_) + @numba.njit(nogil=True, cache=True) def bessells_int_test2(x, y, z1, z2, lab, exprange=exprange, anew=anew, bnew=bnew): """ @@ -223,7 +226,7 @@ def bessells_int_test2(x, y, z1, z2, lab, exprange=exprange, anew=anew, bnew=bne integer :: n """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) biglab = 2 * biga / L @@ -235,7 +238,6 @@ def bessells_int_test2(x, y, z1, z2, lab, exprange=exprange, anew=anew, bnew=bne bnew[i] = (b[i] - a[i] * 2j * ang) * exprange[i] - @numba.njit(nogil=True, cache=True) def bessells_int(x, y, z1, z2, lab): """ @@ -251,7 +253,7 @@ def bessells_int(x, y, z1, z2, lab): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) biglab = 2 * biga / L @@ -262,7 +264,7 @@ def bessells_int(x, y, z1, z2, lab): anew = a * exprange bnew = (b - a * complex(0, 2) * ang) * exprange - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) # #for n in range(21): # # zminzbar[n] = (zeta-zetabar)**(20-n) # Ordered from high power to low power @@ -270,13 +272,13 @@ def bessells_int(x, y, z1, z2, lab): for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -287,17 +289,17 @@ def bessells_int(x, y, z1, z2, lab): alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] omega = 0 - d1minzeta = -1/biglab - zeta - d2minzeta = 1/biglab - zeta + d1minzeta = -1 / biglab - zeta + d2minzeta = 1 / biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) @@ -309,19 +311,24 @@ def bessells_int(x, y, z1, z2, lab): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega = omega + (alpha[n] * log2 - alpha[n] / - (n + 1) + beta[n]) * term2 / (n+1) - omega = omega - (alpha[n] * log1 - alpha[n] / - (n+1) + beta[n]) * term1 / (n+1) - omega = omega + (alpha2[n] * np.conj(log2) - - alpha2[n] / (n+1)) * np.conj(term2) / (n+1) - omega = omega - (alpha2[n] * np.conj(log1) - - alpha2[n] / (n+1)) * np.conj(term1) / (n+1) - - omega = -biga / (2*np.pi) * omega + omega = omega + (alpha[n] * log2 - alpha[n] / (n + 1) + beta[n]) * term2 / ( + n + 1 + ) + omega = omega - (alpha[n] * log1 - alpha[n] / (n + 1) + beta[n]) * term1 / ( + n + 1 + ) + omega = omega + (alpha2[n] * np.conj(log2) - alpha2[n] / (n + 1)) * np.conj( + term2 + ) / (n + 1) + omega = omega - (alpha2[n] * np.conj(log1) - alpha2[n] / (n + 1)) * np.conj( + term1 + ) / (n + 1) + + omega = -biga / (2 * np.pi) * omega return omega - + + @numba.njit(nogil=True, cache=True) def bessells_gauss(x, y, z1, z2, lab): """ @@ -334,15 +341,15 @@ def bessells_gauss(x, y, z1, z2, lab): real(kind=8) :: L, x0 complex(kind=8) :: bigz, biglab """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biglab = 2 * lab / L - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) omega = complex(0, 0) for n in range(1, 9): - x0 = bigz.real - xg[n-1] - omega = omega + wg[n-1] * besselk0(x0, bigz.imag, biglab) + x0 = bigz.real - xg[n - 1] + omega = omega + wg[n - 1] * besselk0(x0, bigz.imag, biglab) - omega = -L/(4*np.pi) * omega + omega = -L / (4 * np.pi) * omega return omega @@ -361,23 +368,23 @@ def bessellsuni(x, y, z1, z2, lab): complex(kind=8) :: z, delz, za, zb """ - Lnear = 3. + Lnear = 3.0 z = complex(x, y) - omega = complex(0., 0.) - L = np.abs(z2-z1) - if (L < Lnear * np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L): # Do integration + omega = complex(0.0, 0.0) + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = bessells_int(x, y, z1, z2, lab) else: omega = bessells_gauss(x, y, z1, z2, lab) else: # Break integral up in parts - Nls = int(np.ceil(L / (Lnear*np.abs(lab)))) + Nls = int(np.ceil(L / (Lnear * np.abs(lab)))) delz = (z2 - z1) / Nls L = np.abs(delz) for n in range(1, Nls + 1): za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L): # integration + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # integration omega = omega + bessells_int(x, y, za, zb, lab) else: omega = omega + bessells_gauss(x, y, za, zb, lab) @@ -404,6 +411,7 @@ def bessellsuniv(x, y, z1, z2, lab, rzero): omega[n] = bessellsuni(x, y, za, zb, lab[n]) return omega + @numba.njit(nogil=True, cache=True) def circle_line_intersection(z1, z2, zc, R): """ @@ -418,24 +426,25 @@ def circle_line_intersection(z1, z2, zc, R): N = 0 za = complex(0, 0) zb = complex(0, 0) - Lover2 = np.abs(z2-z1) / 2 - bigz = (2*zc - (z1+z2)) * Lover2 / (z2-z1) - if (abs(bigz.imag) < R): - d = np.sqrt(R ** 2 - bigz.imag ** 2) + Lover2 = np.abs(z2 - z1) / 2 + bigz = (2 * zc - (z1 + z2)) * Lover2 / (z2 - z1) + if abs(bigz.imag) < R: + d = np.sqrt(R**2 - bigz.imag**2) xa = bigz.real - d xb = bigz.real + d - if ((xa < Lover2) and (xb > -Lover2)): + if (xa < Lover2) and (xb > -Lover2): N = 2 - if (xa < -Lover2): + if xa < -Lover2: za = z1 else: - za = (xa * (z2-z1) / Lover2 + (z1+z2)) / 2.0 - if (xb > Lover2): + za = (xa * (z2 - z1) / Lover2 + (z1 + z2)) / 2.0 + if xb > Lover2: zb = z2 else: - zb = (xb * (z2-z1) / Lover2 + (z1+z2)) / 2.0 + zb = (xb * (z2 - z1) / Lover2 + (z1 + z2)) / 2.0 return za, zb, N + @numba.njit(nogil=True, cache=True) def bessellsv2(x, y, z1, z2, lab, order, R): """ @@ -450,14 +459,15 @@ def bessellsv2(x, y, z1, z2, lab, order, R): integer :: n, nterms """ nlab = len(lab) - nterms = order+1 - omega = np.zeros((order+1, nlab), dtype=np.complex_) + nterms = order + 1 + omega = np.zeros((order + 1, nlab), dtype=np.complex_) # Check if endpoints need to be adjusted using the largest lambda (the first one) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): - omega[:nterms+1, n] = bessells(x, y, z1, z2, lab[n], order, d1, d2) + omega[: nterms + 1, n] = bessells(x, y, z1, z2, lab[n], order, d1, d2) return omega + @numba.njit(nogil=True, cache=True) def find_d1d2(z1, z2, zc, R): """ @@ -468,25 +478,26 @@ def find_d1d2(z1, z2, zc, R): real(kind=8) :: Lover2, d, xa, xb complex(kind=8) :: bigz """ - d1 = -1. - d2 = 1. - Lover2 = np.abs(z2-z1) / 2 - bigz = (2*zc - (z1+z2)) * Lover2 / (z2-z1) - if (np.abs((bigz.imag)) < R): + d1 = -1.0 + d2 = 1.0 + Lover2 = np.abs(z2 - z1) / 2 + bigz = (2 * zc - (z1 + z2)) * Lover2 / (z2 - z1) + if np.abs((bigz.imag)) < R: d = np.sqrt(R**2 - bigz.imag**2) xa = bigz.real - d xb = bigz.real + d - if ((xa < Lover2) and (xb > -Lover2)): - if (xa < -Lover2): - d1 = -1. + if (xa < Lover2) and (xb > -Lover2): + if xa < -Lover2: + d1 = -1.0 else: d1 = xa / Lover2 - if (xb > Lover2): - d2 = 1. + if xb > Lover2: + d2 = 1.0 else: d2 = xb / Lover2 return d1, d2 + @numba.njit(nogil=True, cache=True) def bessells(x, y, z1, z2, lab, order, d1in, d2in): """ @@ -504,35 +515,33 @@ def bessells(x, y, z1, z2, lab, order, d1in, d2in): omega = np.zeros(order + 1, dtype=np.complex_) Lnear = 3 z = complex(x, y) - L = np.abs(z2-z1) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = bessells_int_ho(x, y, z1, z2, lab, order, d1in, d2in) else: - omega = bessells_gauss_ho_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + omega = bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = int(np.ceil(L / (Lnear*np.abs(lab)))) + Nls = int(np.ceil(L / (Lnear * np.abs(lab)))) delta = 2 / Nls - delz = (z2-z1)/Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1 + (n-1) * delta + for n in range(1, Nls + 1): + d1 = -1 + (n - 1) * delta d2 = -1 + n * delta - if ((d2 < d1in) or (d1 > d2in)): + if (d2 < d1in) or (d1 > d2in): continue d1 = np.max(np.array([d1, d1in])) d2 = np.min(np.array([d2, d2in])) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - omega = omega + \ - bessells_int_ho(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + omega = omega + bessells_int_ho(x, y, z1, z2, lab, order, d1, d2) else: - omega = omega + \ - bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) + omega = omega + bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) return omega + @numba.njit(nogil=True, cache=True) def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): """ @@ -549,7 +558,7 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): complex(kind=8), dimension(0:50) :: alphanew, betanew, alphanew2 ! Order fixed to 10 integer :: m, n, p """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) biglab = 2 * biga / L @@ -560,7 +569,7 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): anew = a * exprange bnew = (b - a * complex(0, 2) * ang) * exprange - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) # #for n in range(21): @@ -571,13 +580,13 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -586,17 +595,17 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta # #d1minzeta = -1/biglab - zeta # #d2minzeta = 1/biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) @@ -605,19 +614,21 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - for p in range(order+1): - alphanew[0:40+p+1] = 0 - betanew[0:40+p+1] = 0 - alphanew2[0:40+p+1] = 0 + for p in range(order + 1): + alphanew[0 : 40 + p + 1] = 0 + betanew[0 : 40 + p + 1] = 0 + alphanew2[0 : 40 + p + 1] = 0 - for m in range(0, p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] + for m in range(0, p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) omega[p] = 0 term1 = 1 @@ -625,18 +636,23 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) - - omega = -biga / (2*np.pi) * omega + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) + + omega = -biga / (2 * np.pi) * omega return omega + @numba.njit(nogil=True, cache=True) def bessells_gauss_ho(x, y, z1, z2, lab, order): """ @@ -651,24 +667,25 @@ def bessells_gauss_ho(x, y, z1, z2, lab, order): complex(kind=8) :: bigz, biglab complex(kind=8), dimension(8) :: k0 """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biglab = 2 * lab / L - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) k0 = np.zeros(8, dtype=np.complex_) for n in range(8): x0 = bigz.real - xg[n] k0[n] = besselk0(x0, bigz.imag, biglab) - omega = np.zeros(order+1, dtype=np.complex_) - for p in range(order+1): + omega = np.zeros(order + 1, dtype=np.complex_) + for p in range(order + 1): omega[p] = complex(0, 0) for n in range(8): - omega[p] = omega[p] + wg[n] * xg[n]**p * k0[n] - omega[p] = -L/(4*np.pi) * omega[p] + omega[p] = omega[p] + wg[n] * xg[n] ** p * k0[n] + omega[p] = -L / (4 * np.pi) * omega[p] return omega + @numba.njit(nogil=True, cache=True) def bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2): """ @@ -683,22 +700,23 @@ def bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2): real(kind=8) :: xp, yp, dc, fac complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) bigz1 = complex(d1, 0) bigz2 = complex(d2, 0) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) omegac = bessells_gauss_ho(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - omega[n] = omega[n] + gam[n, m] * dc**(n-m) * omegac[m] - omega[n] = (0.5 * (d2-d1))**n * omega[n] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + omega[n] = omega[n] + gam[n, m] * dc ** (n - m) * omegac[m] + omega[n] = (0.5 * (d2 - d1)) ** n * omega[n] return omega + @numba.njit(nogil=True, cache=True) def isinside(z1, z2, zc, R): - """ Checks whether point zc is within oval with 'radius' R from line element + """Checks whether point zc is within oval with 'radius' R from line element implicit none complex(kind=8), intent(in) :: z1, z2, zc real(kind=8), intent(in) :: R @@ -709,14 +727,15 @@ def isinside(z1, z2, zc, R): irv = 0 Lover2 = np.abs(z2 - z1) / 2 bigz = (2 * zc - (z1 + z2)) * np.abs(z2 - z1) / (2 * (z2 - z1)) - if (np.abs(bigz.imag) < R): - d = np.sqrt(R ** 2 - bigz.imag ** 2) + if np.abs(bigz.imag) < R: + d = np.sqrt(R**2 - bigz.imag**2) xa = bigz.real - d xb = bigz.real + d - if ((xa < Lover2) and (xb > - Lover2)): + if (xa < Lover2) and (xb > -Lover2): irv = 1 return irv + @numba.njit(nogil=True, cache=True) def bessellsqxqyv2(x, y, z1, z2, lab, order, R): """ @@ -732,16 +751,17 @@ def bessellsqxqyv2(x, y, z1, z2, lab, order, R): integer :: n, nterms, nhalf """ nlab = len(lab) - qxqy = np.zeros((2*(order+1), nlab), dtype=np.complex_) - nterms = order+1 - nhalf = nlab*(order+1) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + qxqy = np.zeros((2 * (order + 1), nlab), dtype=np.complex_) + nterms = order + 1 + nhalf = nlab * (order + 1) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): qxqylab = bessellsqxqy(x, y, z1, z2, lab[n], order, d1, d2) - qxqy[:nterms, n] = qxqylab[0:order + 1] - qxqy[nterms:2 * nterms, n] = qxqylab[order + 1:2 * (order + 1)] + qxqy[:nterms, n] = qxqylab[0 : order + 1] + qxqy[nterms : 2 * nterms, n] = qxqylab[order + 1 : 2 * (order + 1)] return qxqy + @numba.njit(nogil=True, cache=True) def bessellsqxqy(x, y, z1, z2, lab, order, d1in, d2in): """ @@ -756,42 +776,41 @@ def bessellsqxqy(x, y, z1, z2, lab, order, d1in, d2in): real(kind=8) :: Lnear, L, d1, d2, delta complex(kind=8) :: z, delz, za, zb """ - Lnear = 3. + Lnear = 3.0 z = complex(x, y) qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) # print *,'Lnear*np.abs(lab) ',Lnear*np.abs(lab) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration qxqy = bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1in, d2in) else: - qxqy = bessells_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + qxqy = bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = int(np.ceil(L / (Lnear*np.abs(lab)))) + Nls = int(np.ceil(L / (Lnear * np.abs(lab)))) # print *,'NLS ',Nls - delta = 2. / Nls - delz = (z2-z1)/Nls + delta = 2.0 / Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1. + (n-1) * delta - d2 = -1. + n * delta - if ((d2 < d1in) or (d1 > d2in)): + for n in range(1, Nls + 1): + d1 = -1.0 + (n - 1) * delta + d2 = -1.0 + n * delta + if (d2 < d1in) or (d1 > d2in): continue d1 = np.max(np.array([d1, d1in])) d2 = np.min(np.array([d2, d2in])) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - qxqy = qxqy + \ - bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + qxqy = qxqy + bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) else: - qxqy = qxqy + \ - bessells_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1, d2) + qxqy = qxqy + bessells_gauss_ho_qxqy_d1d2( + x, y, z1, z2, lab, order, d1, d2 + ) return qxqy + @numba.njit(nogil=True, cache=True) def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): """ @@ -815,13 +834,13 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigx = bigz.real bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - angz = np.arctan2((z2-z1).imag, (z2-z1).real) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) biglab = 2 * biga / L biglabcomplex = 2.0 * lab / L @@ -831,18 +850,18 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): anew = a1 * exprange bnew = (b1 - a1 * complex(0, 2) * ang) * exprange - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) zminzbar[20] = 1 for n in range(1, 21): # Ordered from high power to low po - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -852,16 +871,16 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] d1minzeta = d1 / biglab - zeta d2minzeta = d2 / biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) @@ -870,20 +889,21 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(52, dtype=np.complex_) betanew = np.zeros(52, dtype=np.complex_) - omega = np.zeros(order+2, dtype=np.complex_) - qxqy = np.zeros(2*order+2, dtype=np.complex_) - - for p in range(0, order+2): + omega = np.zeros(order + 2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - alphanew[0:40+p+1] = 0 - betanew[0:40+p+1] = 0 - alphanew2[0:40+p+1] = 0 - for m in range(0, p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] + for p in range(0, order + 2): + alphanew[0 : 40 + p + 1] = 0 + betanew[0 : 40 + p + 1] = 0 + alphanew2[0 : 40 + p + 1] = 0 + for m in range(0, p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) omega[p] = 0 term1 = 1 @@ -891,27 +911,32 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): for n in range(40 + p + 1): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) - - omega = biglab / (2*np.pi*biglabcomplex**2) * omega + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) + + omega = biglab / (2 * np.pi * biglabcomplex**2) * omega omegalap = lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2) # multiplication with 2/L inherently included - qx = -(bigx * omega[0:order+1] - omega[1:order+2] + omegalap.imag) - qy = -(bigy * omega[0:order+1] + omegalap.real) + qx = -(bigx * omega[0 : order + 1] - omega[1 : order + 2] + omegalap.imag) + qy = -(bigy * omega[0 : order + 1] + omegalap.real) - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) - qxqy[order+1:2*order+2] = qx * np.sin(angz) + qy * np.cos(angz) + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + qxqy[order + 1 : 2 * order + 2] = qx * np.sin(angz) + qy * np.cos(angz) return qxqy + @numba.njit(nogil=True, cache=True) def bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2): """ @@ -926,24 +951,26 @@ def bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2): real(kind=8) :: xp, yp, dc, fac complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) qxqyc = bessells_gauss_ho_qxqy(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - qxqy[n] = qxqy[n] + gam[n, m] * dc**(n-m) * qxqyc[m] - qxqy[n+order+1] = qxqy[n+order+1] + \ - gam[n, m] * dc**(n-m) * qxqyc[m+order+1] - qxqy[n] = (0.5*(d2-d1))**n * qxqy[n] - qxqy[n+order+1] = (0.5*(d2-d1))**n * qxqy[n+order+1] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + qxqy[n] = qxqy[n] + gam[n, m] * dc ** (n - m) * qxqyc[m] + qxqy[n + order + 1] = ( + qxqy[n + order + 1] + gam[n, m] * dc ** (n - m) * qxqyc[m + order + 1] + ) + qxqy[n] = (0.5 * (d2 - d1)) ** n * qxqy[n] + qxqy[n + order + 1] = (0.5 * (d2 - d1)) ** n * qxqy[n + order + 1] return qxqy + @numba.njit(nogil=True, cache=True) def lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2): """ @@ -958,21 +985,22 @@ def lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2): real(kind=8) :: xp, yp, dc, fac complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) omegac = lapld_int_ho(x, y, z1p, z2p, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - omega[n] = omega[n] + gam[n, m] * dc**(n-m) * omegac[m] - omega[n] = (0.5*(d2-d1))**n * omega[n] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + omega[n] = omega[n] + gam[n, m] * dc ** (n - m) * omegac[m] + omega[n] = (0.5 * (d2 - d1)) ** n * omega[n] return omega + @numba.njit(nogil=True, cache=True) def lapld_int_ho(x, y, z1, z2, order): """ @@ -987,34 +1015,35 @@ def lapld_int_ho(x, y, z1, z2, order): complex(kind=8) :: z, zplus1, zmin1 """ - omega = np.zeros(order+1, dtype=np.complex_) - qm = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) + qm = np.zeros(order + 1, dtype=np.complex_) - L = np.abs(z2-z1) - z = (2. * complex(x, y) - (z1+z2)) / (z2-z1) - zplus1 = z + 1. - zmin1 = z - 1. + L = np.abs(z2 - z1) + z = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) + zplus1 = z + 1.0 + zmin1 = z - 1.0 # Not sure if this gives correct answer at corner point (z also appears in qm); should really be caught in code that calls this function if np.abs(zplus1) < tiny: zplus1 = tiny if np.abs(zmin1) < tiny: zmin1 = tiny - omega[0] = np.log(zmin1/zplus1) - for n in range(1, order+1): - omega[n] = z * omega[n-1] + omega[0] = np.log(zmin1 / zplus1) + for n in range(1, order + 1): + omega[n] = z * omega[n - 1] if order > 0: - qm[1] = 2. - for m in range(3, order+1, 2): - qm[m] = qm[m-2] * z * z + 2. / m + qm[1] = 2.0 + for m in range(3, order + 1, 2): + qm[m] = qm[m - 2] * z * z + 2.0 / m - for m in range(2, order+1, 2): - qm[m] = qm[m-1] * z + for m in range(2, order + 1, 2): + qm[m] = qm[m - 1] * z - omega = 1. / (complex(0., 2.) * np.pi) * (omega + qm) + omega = 1.0 / (complex(0.0, 2.0) * np.pi) * (omega + qm) return omega + @numba.njit(nogil=True, cache=True) def bessells_gauss_ho_qxqy(x, y, z1, z2, lab, order): """ @@ -1031,36 +1060,37 @@ def bessells_gauss_ho_qxqy(x, y, z1, z2, lab, order): complex(kind=8), dimension(8) :: k1 complex(kind=8), dimension(0:order) :: qx,qy """ - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) xmind = np.zeros(8, dtype=np.complex_) k1 = np.zeros(8, dtype=np.complex_) r = np.zeros(8, dtype=np.complex_) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biglab = 2 * lab / L - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag for n in range(8): xmind[n] = bigz.real - xg[n] - r[n] = np.sqrt(xmind[n]**2 + bigz.imag**2) + r[n] = np.sqrt(xmind[n] ** 2 + bigz.imag**2) k1[n] = besselk1(xmind[n], bigz.imag, biglab) - qx = np.zeros(order+1, dtype=np.complex_) - qy = np.zeros(order+1, dtype=np.complex_) - for p in range(order+1): + qx = np.zeros(order + 1, dtype=np.complex_) + qy = np.zeros(order + 1, dtype=np.complex_) + for p in range(order + 1): for n in range(8): - qx[p] = qx[p] + wg[n] * xg[n]**p * xmind[n] * k1[n] / r[n] - qy[p] = qy[p] + wg[n] * xg[n]**p * bigy * k1[n] / r[n] + qx[p] = qx[p] + wg[n] * xg[n] ** p * xmind[n] * k1[n] / r[n] + qy[p] = qy[p] + wg[n] * xg[n] ** p * bigy * k1[n] / r[n] - qx = -qx * L / (4*np.pi*biglab) * 2/L - qy = -qy * L / (4*np.pi*biglab) * 2/L + qx = -qx * L / (4 * np.pi * biglab) * 2 / L + qy = -qy * L / (4 * np.pi * biglab) * 2 / L - angz = np.arctan2((z2-z1).imag, (z2-z1).real) - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) - qxqy[order+1:2*order+2] = qx * np.sin(angz) + qy * np.cos(angz) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + qxqy[order + 1 : 2 * order + 2] = qx * np.sin(angz) + qy * np.cos(angz) return qxqy + @numba.njit(nogil=True, cache=True) def besselk1cheb(z, Nt): """ @@ -1083,7 +1113,7 @@ def besselk1cheb(z, Nt): z1 = 2 * z z2 = 2 * z1 - ts = (-1)**(Nt+1) + ts = (-1) ** (Nt + 1) S = ts T = 1.0 @@ -1109,6 +1139,7 @@ def besselk1cheb(z, Nt): return omega + @numba.njit(nogil=True, cache=True) def besselk1(x, y, lab): """ @@ -1121,13 +1152,14 @@ def besselk1(x, y, lab): z = np.sqrt(x**2 + y**2) / lab cond = np.abs(z) - if (cond < 6): + if cond < 6: omega = besselk1near(z, 20) else: omega = besselk1cheb(z, 6) return omega + @numba.njit(nogil=True, cache=True) def besselk1near(z, Nt): """ @@ -1141,14 +1173,15 @@ def besselk1near(z, Nt): zsq = z**2 term = z log1 = np.log(zsq) - omega = 1. / z + (a1[0] * log1 + b1[0]) * z + omega = 1.0 / z + (a1[0] * log1 + b1[0]) * z - for n in range(1, Nt+1): + for n in range(1, Nt + 1): term = term * zsq omega = omega + (a1[n] * log1 + b1[n]) * term return omega + @numba.njit(nogil=True, cache=True) def besselldv2(x, y, z1, z2, lab, order, R): """ @@ -1163,16 +1196,17 @@ def besselldv2(x, y, z1, z2, lab, order, R): integer :: n, nterms """ nlab = len(lab) - omega = np.zeros((order+1, nlab), dtype=np.complex_) + omega = np.zeros((order + 1, nlab), dtype=np.complex_) - nterms = order+1 + nterms = order + 1 # Check if endpoints need to be adjusted using the largest lambda (the first one) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): - omega[:nterms+1, n] = besselld(x, y, z1, z2, lab[n], order, d1, d2) + omega[: nterms + 1, n] = besselld(x, y, z1, z2, lab[n], order, d1, d2) return omega + @numba.njit(nogil=True, cache=True) def besselld(x, y, z1, z2, lab, order, d1in, d2in): """ @@ -1188,39 +1222,37 @@ def besselld(x, y, z1, z2, lab, order, d1in, d2in): complex(kind=8) :: z, delz, za, zb """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - Lnear = 3. + Lnear = 3.0 z = complex(x, y) - L = np.abs(z2-z1) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = besselld_int_ho(x, y, z1, z2, lab, order, d1in, d2in) else: - omega = besselld_gauss_ho_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + omega = besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = int(np.ceil(L / (Lnear*np.abs(lab)))) - delta = 2. / Nls - delz = (z2-z1)/Nls + Nls = int(np.ceil(L / (Lnear * np.abs(lab)))) + delta = 2.0 / Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1. + (n-1) * delta - d2 = -1. + n * delta - if ((d2 < d1in) or (d1 > d2in)): + for n in range(1, Nls + 1): + d1 = -1.0 + (n - 1) * delta + d2 = -1.0 + n * delta + if (d2 < d1in) or (d1 > d2in): continue d1 = max(d1, d1in) d2 = min(d2, d2in) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - omega = omega + \ - besselld_int_ho(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + omega = omega + besselld_int_ho(x, y, z1, z2, lab, order, d1, d2) else: - omega = omega + \ - besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) + omega = omega + besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) return omega + @numba.njit(nogil=True, cache=True) def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): """ @@ -1241,14 +1273,14 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - biglab = 2. * biga / L + biglab = 2.0 * biga / L biglabcomplex = 2.0 * lab / L tol = 1e-12 @@ -1257,19 +1289,19 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): anew = a1 * exprange bnew = (b1 - a1 * complex(0, 2) * ang) * exprange - zeta = (2. * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) - zminzbar[20] = 1. + zminzbar[20] = 1.0 for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -1278,18 +1310,18 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] - - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta - #d1minzeta = -1./biglab - zeta - #d2minzeta = 1./biglab - zeta - if (np.abs(d1minzeta) < tol): - d1minzeta = d1minzeta + complex(tol, 0.) - if (np.abs(d2minzeta) < tol): - d2minzeta = d2minzeta + complex(tol, 0.) + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] + + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta + # d1minzeta = -1./biglab - zeta + # d2minzeta = 1./biglab - zeta + if np.abs(d1minzeta) < tol: + d1minzeta = d1minzeta + complex(tol, 0.0) + if np.abs(d2minzeta) < tol: + d2minzeta = d2minzeta + complex(tol, 0.0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) @@ -1297,37 +1329,46 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - for p in range(order+1): - alphanew[0:40+p+1] = 0. - betanew[0:40+p+1] = 0. - alphanew2[0:40+p+1] = 0. - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] - - omega[p] = 0. - term1 = 1. - term2 = 1. + for p in range(order + 1): + alphanew[0 : 40 + p + 1] = 0.0 + betanew[0 : 40 + p + 1] = 0.0 + alphanew2[0 : 40 + p + 1] = 0.0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) + + omega[p] = 0.0 + term1 = 1.0 + term2 = 1.0 for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) # omega = bigy * biglab / (2.*pi*biglabcomplex**2) * omega + real( lapld_int_ho_d1d2(x,y,z1,z2,order,d1,d2) ) - omega = bigy * biglab / (2.*np.pi*biglabcomplex**2) * \ - omega + lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2).real + omega = ( + bigy * biglab / (2.0 * np.pi * biglabcomplex**2) * omega + + lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2).real + ) return omega + @numba.njit(nogil=True, cache=True) def besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2): """ @@ -1342,21 +1383,22 @@ def besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2): complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) omegac = besselld_gauss_ho(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - omega[0:order+1] = 0. - for n in range(order+1): - for m in range(n+1): - omega[n] = omega[n] + gam[n, m] * dc**(n-m) * omegac[m] - omega[n] = (0.5*(d2-d1))**n * omega[n] + dc = (d1 + d2) / (d2 - d1) + omega[0 : order + 1] = 0.0 + for n in range(order + 1): + for m in range(n + 1): + omega[n] = omega[n] + gam[n, m] * dc ** (n - m) * omegac[m] + omega[n] = (0.5 * (d2 - d1)) ** n * omega[n] return omega + @numba.njit(nogil=True, cache=True) def besselld_gauss_ho(x, y, z1, z2, lab, order): """ @@ -1373,22 +1415,23 @@ def besselld_gauss_ho(x, y, z1, z2, lab, order): """ k1overr = np.zeros(8, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - L = np.abs(z2-z1) - biglab = 2. * lab / L - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + biglab = 2.0 * lab / L + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) for n in range(8): x0 = bigz.real - xg[n] r = np.sqrt(x0**2 + bigz.imag**2) k1overr[n] = besselk1(x0, bigz.imag, biglab) / r - for p in range(order+1): - omega[p] = complex(0., 0.) + for p in range(order + 1): + omega[p] = complex(0.0, 0.0) for n in range(8): - omega[p] = omega[p] + wg[n] * xg[n]**p * k1overr[n] - omega[p] = bigz.imag/(2.*np.pi*biglab) * omega[p] + omega[p] = omega[p] + wg[n] * xg[n] ** p * k1overr[n] + omega[p] = bigz.imag / (2.0 * np.pi * biglab) * omega[p] return omega + @numba.njit(nogil=True, cache=True) def besselldqxqyv2(x, y, z1, z2, lab, order, R): """ @@ -1404,16 +1447,17 @@ def besselldqxqyv2(x, y, z1, z2, lab, order, R): integer :: n, nterms, nhalf """ nlab = len(lab) - qxqy = np.zeros((2*(order+1), nlab), dtype=np.complex_) - nterms = order+1 - nhalf = nlab*(order+1) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + qxqy = np.zeros((2 * (order + 1), nlab), dtype=np.complex_) + nterms = order + 1 + nhalf = nlab * (order + 1) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): qxqylab = besselldqxqy(x, y, z1, z2, lab[n], order, d1, d2) - qxqy[:nterms, n] = qxqylab[0:order+1] - qxqy[nterms:2*nterms, n] = qxqylab[order+1:2*order+1+1] + qxqy[:nterms, n] = qxqylab[0 : order + 1] + qxqy[nterms : 2 * nterms, n] = qxqylab[order + 1 : 2 * order + 1 + 1] return qxqy + @numba.njit(nogil=True, cache=True) def besselldqxqy(x, y, z1, z2, lab, order, d1in, d2in): """ @@ -1432,41 +1476,40 @@ def besselldqxqy(x, y, z1, z2, lab, order, d1in, d2in): Lnear = 3 z = complex(x, y) qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - - L = np.abs(z2-z1) + + L = np.abs(z2 - z1) # print *,'Lnear*np.abs(lab) ',Lnear*np.abs(lab) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration qxqy = besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1in, d2in) else: - qxqy = besselld_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + qxqy = besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = int(np.ceil(L / (Lnear*np.abs(lab)))) + Nls = int(np.ceil(L / (Lnear * np.abs(lab)))) # print *,'NLS ',Nls delta = 2 / Nls - delz = (z2-z1)/Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1 + (n-1) * delta + for n in range(1, Nls + 1): + d1 = -1 + (n - 1) * delta d2 = -1 + n * delta - if ((d2 < d1in) or (d1 > d2in)): + if (d2 < d1in) or (d1 > d2in): continue d1 = np.max(np.array([d1, d1in])) d2 = np.min(np.array([d2, d2in])) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - qxqy = qxqy + \ - besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + qxqy = qxqy + besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) else: - qxqy = qxqy + \ - besselld_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1, d2) + qxqy = qxqy + besselld_gauss_ho_qxqy_d1d2( + x, y, z1, z2, lab, order, d1, d2 + ) return qxqy + @numba.njit(nogil=True, cache=True) def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): """ @@ -1489,15 +1532,15 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - omega = np.zeros(order+2, dtype=np.complex_) - qxqy = np.zeros(2*order+2, dtype=np.complex_) + omega = np.zeros(order + 2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - angz = np.arctan2((z2-z1).imag, (z2-z1).real) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) biglab = 2 * biga / L biglabcomplex = 2.0 * lab / L @@ -1509,25 +1552,25 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): azero = anew[0] for n in range(20): - bnew[n] = (n+1)*bnew[n+1] + anew[n+1] - anew[n] = (n+1)*anew[n+1] + bnew[n] = (n + 1) * bnew[n + 1] + anew[n + 1] + anew[n] = (n + 1) * anew[n + 1] anew[20] = 0 # This is a bit lazy bnew[20] = 0 - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) zminzbar[20] = 1 for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) alpha2 = np.zeros(41, dtype=np.complex_) @@ -1537,17 +1580,17 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta # d1minzeta = -1/biglab - zeta # d2minzeta = 1/biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) @@ -1557,17 +1600,18 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(52, dtype=np.complex_) betanew = np.zeros(52, dtype=np.complex_) - for p in range(order+2): - - alphanew[0:40+p+1] = 0 - betanew[0:40+p+1] = 0 - alphanew2[0:40+p+1] = 0 - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] + for p in range(order + 2): + alphanew[0 : 40 + p + 1] = 0 + betanew[0 : 40 + p + 1] = 0 + alphanew2[0 : 40 + p + 1] = 0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) omega[p] = 0 term1 = 1 @@ -1575,24 +1619,29 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) - - omegalap = lapld_int_ho_d1d2( - x, y, z1, z2, order, d1, d2) / complex(0, 1) + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) + + omegalap = lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2) / complex(0, 1) omegaom = besselldpart(x, y, z1, z2, lab, order, d1, d2) wdis = lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2) - rvz = -biglab * bigy / (2*np.pi*biglabcomplex**2) * (omega[1:order+1+1]/biglab - zetabar * omega[0:order+1]) + \ - biglab * omegaom / complex(0, 2) - rvzbar = -biglab * bigy / (2*np.pi*biglabcomplex**2) * (omega[1:order+1+1]/biglab - zeta * omega[0:order+1]) - \ - biglab * omegaom / complex(0, 2) + rvz = -biglab * bigy / (2 * np.pi * biglabcomplex**2) * ( + omega[1 : order + 1 + 1] / biglab - zetabar * omega[0 : order + 1] + ) + biglab * omegaom / complex(0, 2) + rvzbar = -biglab * bigy / (2 * np.pi * biglabcomplex**2) * ( + omega[1 : order + 1 + 1] / biglab - zeta * omega[0 : order + 1] + ) - biglab * omegaom / complex(0, 2) # qxqy[0:order+1] = -2.0 / L * ( rvz + rvzbar ) / biglab # As we need to take derivative w.r.t. z not zeta # qxqy[order+1:2*order+1+1] = -2.0 / L * complex(0,1) * (rvz-rvzbar) / biglab # @@ -1605,24 +1654,28 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): # As we need to take derivative w.r.t. z not zeta qx = -2.0 / L * (rvz + rvzbar) / biglab - qy = -2.0 / L * complex(0, 1) * (rvz-rvzbar) / biglab + qy = -2.0 / L * complex(0, 1) * (rvz - rvzbar) / biglab - qx = qx - 2.0 / L * bigy / biglabcomplex**2 * \ - azero * (omegalap + np.conj(omegalap)) - qy = qy - 2.0 / L * bigy / biglabcomplex**2 * azero * \ - complex(0, 1) * (omegalap - np.conj(omegalap)) + qx = qx - 2.0 / L * bigy / biglabcomplex**2 * azero * ( + omegalap + np.conj(omegalap) + ) + qy = qy - 2.0 / L * bigy / biglabcomplex**2 * azero * complex(0, 1) * ( + omegalap - np.conj(omegalap) + ) # qx = qx + real(wdis * (z2-z1) / L) # qy = qy - aimag(wdis * (z2-z1) / L) # print *,'angz ',angz # wdis already includes the correct rotation - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) + wdis.real - qxqy[order+1:2*order+1+1] = qx * \ - np.sin(angz) + qy * np.cos(angz) - wdis.imag + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + wdis.real + qxqy[order + 1 : 2 * order + 1 + 1] = ( + qx * np.sin(angz) + qy * np.cos(angz) - wdis.imag + ) return qxqy + @numba.njit(nogil=True, cache=True) def besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2): """ @@ -1638,25 +1691,27 @@ def besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2): complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) bigz1 = complex(d1, 0) bigz2 = complex(d2, 0) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) qxqyc = besselld_gauss_ho_qxqy(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - qxqy[n] = qxqy[n] + gam[n, m] * dc**(n-m) * qxqyc[m] - qxqy[n+order+1] = qxqy[n+order+1] + \ - gam[n, m] * dc**(n-m) * qxqyc[m+order+1] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + qxqy[n] = qxqy[n] + gam[n, m] * dc ** (n - m) * qxqyc[m] + qxqy[n + order + 1] = ( + qxqy[n + order + 1] + gam[n, m] * dc ** (n - m) * qxqyc[m + order + 1] + ) - qxqy[n] = (0.5*(d2-d1))**n * qxqy[n] - qxqy[n+order+1] = (0.5*(d2-d1))**n * qxqy[n+order+1] + qxqy[n] = (0.5 * (d2 - d1)) ** n * qxqy[n] + qxqy[n + order + 1] = (0.5 * (d2 - d1)) ** n * qxqy[n + order + 1] return qxqy + @numba.njit(nogil=True, cache=True) def besselld_gauss_ho_qxqy(x, y, z1, z2, lab, order): """ @@ -1678,38 +1733,40 @@ def besselld_gauss_ho_qxqy(x, y, z1, z2, lab, order): r = np.zeros(8, dtype=np.float_) k0 = np.zeros(8, dtype=np.complex_) k1 = np.zeros(8, dtype=np.complex_) - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - L = np.abs(z2-z1) - biglab = 2. * lab / L - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + biglab = 2.0 * lab / L + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag for n in range(8): xmind[n] = bigz.real - xg[n] - r[n] = np.sqrt(xmind[n]**2 + bigz.imag**2) + r[n] = np.sqrt(xmind[n] ** 2 + bigz.imag**2) k0[n] = besselk0(xmind[n], bigz.imag, biglab) k1[n] = besselk1(xmind[n], bigz.imag, biglab) - qx = np.zeros(order+1, dtype=np.complex_) - qy = np.zeros(order+1, dtype=np.complex_) - for p in range(order+1): + qx = np.zeros(order + 1, dtype=np.complex_) + qy = np.zeros(order + 1, dtype=np.complex_) + for p in range(order + 1): for n in range(8): - qx[p] = qx[p] + wg[n] * xg[n]**p * \ - (-bigy) * xmind[n] / r[n]**3 * \ - (r[n]*k0[n]/biglab + 2.*k1[n]) - qy[p] = qy[p] + wg[n] * xg[n]**p * \ - (k1[n]/r[n] - bigy**2 / r[n]**3 * - (r[n]*k0[n]/biglab + 2.*k1[n])) + qx[p] = qx[p] + wg[n] * xg[n] ** p * (-bigy) * xmind[n] / r[n] ** 3 * ( + r[n] * k0[n] / biglab + 2.0 * k1[n] + ) + qy[p] = qy[p] + wg[n] * xg[n] ** p * ( + k1[n] / r[n] + - bigy**2 / r[n] ** 3 * (r[n] * k0[n] / biglab + 2.0 * k1[n]) + ) - qx = -qx / (2*np.pi*biglab) * 2/L - qy = -qy / (2*np.pi*biglab) * 2/L + qx = -qx / (2 * np.pi * biglab) * 2 / L + qy = -qy / (2 * np.pi * biglab) * 2 / L - angz = np.arctan2((z2-z1).imag, (z2-z1).real) - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) - qxqy[order+1:2*order+1+1] = qx * np.sin(angz) + qy * np.cos(angz) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + qxqy[order + 1 : 2 * order + 1 + 1] = qx * np.sin(angz) + qy * np.cos(angz) return qxqy + @numba.njit(nogil=True, cache=True) def besselldpart(x, y, z1, z2, lab, order, d1, d2): """ @@ -1729,12 +1786,12 @@ def besselldpart(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - biglab = 2. * biga / L + biglab = 2.0 * biga / L biglabcomplex = 2.0 * lab / L tol = 1e-12 @@ -1743,18 +1800,18 @@ def besselldpart(x, y, z1, z2, lab, order, d1, d2): anew = a1 * exprange bnew = (b1 - a1 * complex(0, 2) * ang) * exprange - zeta = (2. * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) - zminzbar[-1] = 1. + zminzbar[-1] = 1.0 for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -1763,58 +1820,64 @@ def besselldpart(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta - if (np.abs(d1minzeta) < tol): - d1minzeta = d1minzeta + complex(tol, 0.) - if (np.abs(d2minzeta) < tol): - d2minzeta = d2minzeta + complex(tol, 0.) + if np.abs(d1minzeta) < tol: + d1minzeta = d1minzeta + complex(tol, 0.0) + if np.abs(d2minzeta) < tol: + d2minzeta = d2minzeta + complex(tol, 0.0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) alphanew = np.zeros(51, dtype=np.complex_) alphanew2 = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) - - for p in range(order+1): - - alphanew[0:40+p+1] = 0. - betanew[0:40+p+1] = 0. - alphanew2[0:40+p+1] = 0. - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] - - omega[p] = 0. - term1 = 1. - term2 = 1. - for n in range(40+p+1): + omega = np.zeros(order + 1, dtype=np.complex_) + + for p in range(order + 1): + alphanew[0 : 40 + p + 1] = 0.0 + betanew[0 : 40 + p + 1] = 0.0 + alphanew2[0 : 40 + p + 1] = 0.0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) + + omega[p] = 0.0 + term1 = 1.0 + term2 = 1.0 + for n in range(40 + p + 1): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) # + real( lapld_int_ho(x,y,z1,z2,order) ) - omega = biglab / (2.*np.pi*biglabcomplex**2) * omega + omega = biglab / (2.0 * np.pi * biglabcomplex**2) * omega # omega = real( lapld_int_ho(x,y,z1,z2,order) ) return omega + @numba.njit(nogil=True, cache=True) def lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2): """ @@ -1830,21 +1893,22 @@ def lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2): complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - wdis = np.zeros(order+1, dtype=np.complex_) + wdis = np.zeros(order + 1, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) wdisc = lapld_int_ho_wdis(x, y, z1p, z2p, order) - dc = (d1+d2) / (d2-d1) - wdis[0:order+1] = 0. - for n in range(order+1): - for m in range(n+1): - wdis[n] = wdis[n] + gam[n, m] * dc**(n-m) * wdisc[m] - wdis[n] = (0.5*(d2-d1))**n * wdis[n] + dc = (d1 + d2) / (d2 - d1) + wdis[0 : order + 1] = 0.0 + for n in range(order + 1): + for m in range(n + 1): + wdis[n] = wdis[n] + gam[n, m] * dc ** (n - m) * wdisc[m] + wdis[n] = (0.5 * (d2 - d1)) ** n * wdis[n] return wdis + @numba.njit(nogil=True, cache=True) def lapld_int_ho_wdis(x, y, z1, z2, order): """ @@ -1860,32 +1924,30 @@ def lapld_int_ho_wdis(x, y, z1, z2, order): """ qm = np.zeros(11, dtype=np.complex_) - wdis = np.zeros(order+1, dtype=np.complex_) + wdis = np.zeros(order + 1, dtype=np.complex_) - z = (2. * complex(x, y) - (z1+z2)) / (z2-z1) - zplus1 = z + 1. - zmin1 = z - 1. + z = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) + zplus1 = z + 1.0 + zmin1 = z - 1.0 # Not sure if this gives correct answer at corner point (z also appears in qm); should really be caught in code that calls this function - if (np.abs(zplus1) < tiny): + if np.abs(zplus1) < tiny: zplus1 = tiny - if (np.abs(zmin1) < tiny): + if np.abs(zmin1) < tiny: zmin1 = tiny - qm[0:1] = 0. - for m in range(2, order+1): - qm[m] = 0. - for n in range(1, m//2): - qm[m] = qm[m] + (m-2*n+1) * z**(m-2*n) / (2*n-1) + qm[0:1] = 0.0 + for m in range(2, order + 1): + qm[m] = 0.0 + for n in range(1, m // 2): + qm[m] = qm[m] + (m - 2 * n + 1) * z ** (m - 2 * n) / (2 * n - 1) - term1 = 1. / zmin1 - 1. / zplus1 - term2 = np.log(zmin1/zplus1) + term1 = 1.0 / zmin1 - 1.0 / zplus1 + term2 = np.log(zmin1 / zplus1) wdis[0] = term1 - zterm = complex(1., 0.) - for m in range(1, order+1): - wdis[m] = m * zterm * term2 + z * zterm * term1 + 2. * qm[m] + zterm = complex(1.0, 0.0) + for m in range(1, order + 1): + wdis[m] = m * zterm * term2 + z * zterm * term1 + 2.0 * qm[m] zterm = zterm * z - wdis = - wdis / (np.pi*complex(0., 1.)*(z2-z1)) + wdis = -wdis / (np.pi * complex(0.0, 1.0) * (z2 - z1)) return wdis - - diff --git a/ttim/besselnumba_total.py b/ttim/besselnumba_total.py index 31ce351..e470fc5 100644 --- a/ttim/besselnumba_total.py +++ b/ttim/besselnumba_total.py @@ -1,5 +1,5 @@ -import numpy as np import numba +import numpy as np """ real(kind=8) :: pi, tiny @@ -29,34 +29,34 @@ b = np.zeros(21, dtype=np.float_) for n in range(1, 21): - fac = n*fac - a[n] = 1.0 / (4.0**nrange[n] * fac**2) - b[n] = b[n-1] + 1 / nrange[n] + fac = n * fac + a[n] = 1.0 / (4.0 ** nrange[n] * fac**2) + b[n] = b[n - 1] + 1 / nrange[n] b = (b - c) * a a = -a / 2.0 gam = np.zeros((21, 21), dtype=np.float_) for n in range(21): - for m in range(n+1): - gam[n, m] = np.prod(nrange[m+1:n+1]) / np.prod(nrange[1:n-m+1]) + for m in range(n + 1): + gam[n, m] = np.prod(nrange[m + 1 : n + 1]) / np.prod(nrange[1 : n - m + 1]) afar = np.zeros(21, dtype=np.float_) -afar[0] = np.sqrt(np.pi / 2.) +afar[0] = np.sqrt(np.pi / 2.0) for n in range(1, 21): - afar[n] = -(2. * n - 1.)**2 / (n * 8) * afar[n-1] + afar[n] = -((2.0 * n - 1.0) ** 2) / (n * 8) * afar[n - 1] fac = 1.0 bot = np.zeros(21, dtype=np.float_) bot[0] = 4.0 for n in range(1, 21): fac = n * fac - bot[n] = fac * (n+1)*fac * 4.0**(n+1) + bot[n] = fac * (n + 1) * fac * 4.0 ** (n + 1) psi = np.zeros(21, dtype=np.float_) for n in range(2, 22): - psi[n-1] = psi[n-2] + 1 / (n-1) + psi[n - 1] = psi[n - 2] + 1 / (n - 1) psi = psi - 0.577215664901532860 a1 = np.empty(21, dtype=np.float_) @@ -64,7 +64,7 @@ twologhalf = 2 * np.log(0.5) for n in range(21): a1[n] = 1 / bot[n] - b1[n] = (twologhalf - (2.0 * psi[n] + 1 / (n+1))) / bot[n] + b1[n] = (twologhalf - (2.0 * psi[n] + 1 / (n + 1))) / bot[n] wg = np.zeros(8, dtype=np.float_) @@ -103,7 +103,7 @@ def besselk0far(z, Nt): term = 1.0 omega = afar[0] - for n in range(1, Nt+1): + for n in range(1, Nt + 1): term = term / z omega = omega + afar[n] * term @@ -127,7 +127,7 @@ def besselk0near(z, Nt): log1 = np.log(rsq) omega = a[0] * log1 + b[0] - for n in range(1, Nt+1): + for n in range(1, Nt + 1): term = term * rsq omega = omega + (a[n] * log1 + b[n]) * term @@ -147,9 +147,9 @@ def besselk1near(z, Nt): zsq = z**2 term = z log1 = np.log(zsq) - omega = 1. / z + (a1[0] * log1 + b1[0]) * z + omega = 1.0 / z + (a1[0] * log1 + b1[0]) * z - for n in range(1, Nt+1): + for n in range(1, Nt + 1): term = term * zsq omega = omega + (a1[n] * log1 + b1[n]) * term @@ -169,36 +169,36 @@ def besselk0cheb(z, Nt): complex(kind=8) :: z1, z2, S, T """ - cnp1 = complex(1., 0.) - cnp2 = complex(0., 0.) - cnp3 = complex(0., 0.) + cnp1 = complex(1.0, 0.0) + cnp2 = complex(0.0, 0.0) + cnp3 = complex(0.0, 0.0) a = 0.5 - c = 1. - b = 1. + a - c + c = 1.0 + b = 1.0 + a - c - z1 = 2. * z - z2 = 2. * z1 - ts = (-1)**(Nt+1) + z1 = 2.0 * z + z2 = 2.0 * z1 + ts = (-1) ** (Nt + 1) S = ts - T = 1. + T = 1.0 for n in range(Nt, -1, -1): - u = (n+a) * (n+b) + u = (n + a) * (n + b) n2 = 2 * n - A1 = 1. - (z2 + (n2+3.)*(n+a+1.)*(n+b+1.) / (n2+4.)) / u - A2 = 1. - (n2+2.)*(n2+3.-z2) / u - A3 = -(n+1.)*(n+3.-a)*(n+3.-b) / (u*(n+2.)) - cn = (2.*n+2.) * A1 * cnp1 + A2 * cnp2 + A3 * cnp3 + A1 = 1.0 - (z2 + (n2 + 3.0) * (n + a + 1.0) * (n + b + 1.0) / (n2 + 4.0)) / u + A2 = 1.0 - (n2 + 2.0) * (n2 + 3.0 - z2) / u + A3 = -(n + 1.0) * (n + 3.0 - a) * (n + 3.0 - b) / (u * (n + 2.0)) + cn = (2.0 * n + 2.0) * A1 * cnp1 + A2 * cnp2 + A3 * cnp3 ts = -ts S = S + ts * cn T = T + cn cnp3 = cnp2 cnp2 = cnp1 cnp1 = cn - cn = cn / 2. + cn = cn / 2.0 S = S - cn T = T - cn - omega = 1. / np.sqrt(z1) * T / S + omega = 1.0 / np.sqrt(z1) * T / S omega = np.sqrt(np.pi) * np.exp(-z) * omega return omega @@ -226,7 +226,7 @@ def besselk1cheb(z, Nt): z1 = 2 * z z2 = 2 * z1 - ts = (-1)**(Nt+1) + ts = (-1) ** (Nt + 1) S = ts T = 1.0 @@ -265,7 +265,7 @@ def besselk0(x, y, lab): z = np.sqrt(x**2 + y**2) / lab cond = np.abs(z) - if (cond < 6): + if cond < 6: omega = besselk0near(z, 17) else: omega = besselk0cheb(z, 6) @@ -285,7 +285,7 @@ def besselk1(x, y, lab): z = np.sqrt(x**2 + y**2) / lab cond = np.abs(z) - if (cond < 6): + if cond < 6: omega = besselk1near(z, 20) else: omega = besselk1cheb(z, 6) @@ -303,7 +303,7 @@ def k0bessel(z): """ cond = np.abs(z) - if (cond < 6): + if cond < 6: omega = besselk0near(z, 17) else: omega = besselk0cheb(z, 6) @@ -353,11 +353,11 @@ def besselk0OLD(x, y, lab): z = np.sqrt(x**2 + y**2) / lab cond = np.abs(z) - if (cond < 4): + if cond < 4: omega = besselk0near(z, 12) # Was 10 - elif (cond < 8): + elif cond < 8: omega = besselk0near(z, 18) - elif (cond < 12): + elif cond < 12: omega = besselk0far(z, 11) # was 6 else: omega = besselk0far(z, 8) # was 4 @@ -377,8 +377,8 @@ def besselk0OLD(x, y, lab): # z2 = 2.0 * z # omega = np.sqrt(np.pi) * np.exp(-z) * ucheb(0.5, 1, z2, Nt) # return omega -# -# +# +# # @numba.njit(nogil=True, cache=True) # def ucheb(a, c, z, n0): # """ @@ -387,14 +387,14 @@ def besselk0OLD(x, y, lab): # real(kind=8), intent(in) :: a # complex(kind=8), intent(in) :: z # complex(kind=8) :: ufunc -# +# # integer :: n, n2, ts # real(kind=8) :: A3, u, b # complex(kind=8) :: A1, A2, cn,cnp1,cnp2,cnp3 # complex(kind=8) :: z2, S, T -# +# # """ -# +# # cnp1 = complex(1.) # cnp2 = complex(0.) # cnp3 = complex(0.) @@ -403,7 +403,7 @@ def besselk0OLD(x, y, lab): # T = 1. # z2 = 2. * z # b = 1. + a - c -# +# # for n in range(n0, -1, -1): # u = (n+a) * (n+b) # n2 = 2 * n @@ -443,11 +443,11 @@ def besselk0OLD(x, y, lab): # for n in range(21): # # Ordered from high power to low power # zminzbar[n] = (zeta-zetabar)**(20-n) -# +# # gamnew = np.asarray(gam, dtype=np.complex_) # for n in range(21): # gamnew[n, 0:n+1] = gamnew[n, 0:n+1] * zminzbar[20-n:20+1] -# +# # alpha = np.zeros(41, dtype=np.complex_) # beta = np.zeros(41, dtype=np.complex_) # alpha[0] = a[0] @@ -455,18 +455,18 @@ def besselk0OLD(x, y, lab): # for n in range(1, 21): # alpha[n:2*n+1] = alpha[n:2*n+1] + a[n] * gamnew[n, :n+1] # beta[n:2*n+1] = beta[n:2*n+1] + b[n] * gamnew[n, :n+1] -# +# # omega = complex(0., 0.) # logdminzdminzbar = np.log((d-zeta) * (d-zetabar)) # dminzeta = d - zeta # term = 1. -# +# # for n in range(41): # omega = omega + (alpha[n] * logdminzdminzbar + beta[n]) * term # term = term * dminzeta -# +# # phi = np.real(omega) -# +# # return phi @@ -483,38 +483,39 @@ def lapls_int_ho(x, y, z1, z2, order): real(kind=8) :: L complex(kind=8) :: z, zplus1, zmin1, log1, log2, log3, zpower """ - omega = np.empty((order+1,), dtype=np.complex_) - qm = np.empty((order+2,), dtype=np.complex_) + omega = np.empty((order + 1,), dtype=np.complex_) + qm = np.empty((order + 2,), dtype=np.complex_) - L = np.abs(z2-z1) - z = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + z = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) zplus1 = z + 1 zmin1 = z - 1 # Not sure if this gives correct answer at corner point(z also appears in qm) # should really be caught in code that calls this def - if (np.abs(zplus1) < tiny): + if np.abs(zplus1) < tiny: zplus1 = tiny - if (np.abs(zmin1) < tiny): + if np.abs(zmin1) < tiny: zmin1 = tiny qm[0] = 0 qm[1] = 2 - for m in range(2, order+1, 2): - qm[m+1] = qm[m-1] * z * z + 2 / m + for m in range(2, order + 1, 2): + qm[m + 1] = qm[m - 1] * z * z + 2 / m - for m in range(1, order+1, 2): - qm[m+1] = qm[m] * z + for m in range(1, order + 1, 2): + qm[m + 1] = qm[m] * z log1 = np.log((zmin1) / (zplus1)) log2 = np.log(zmin1) log3 = np.log(zplus1) zpower = 1 - for i in range(1, order+2): + for i in range(1, order + 2): zpower = zpower * z - omega[i-1] = -L/(4*np.pi*i) * (zpower * log1 + - qm[i] - log2 + (-1)**i * log3) + omega[i - 1] = ( + -L / (4 * np.pi * i) * (zpower * log1 + qm[i] - log2 + (-1) ** i * log3) + ) return omega @@ -535,30 +536,30 @@ def bessellsreal(x, y, x1, y1, x2, y2, lab): z1 = complex(x1, y1) z2 = complex(x2, y2) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) biglab = 2 * biga / L - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) for n in range(21): # Ordered from high power to low power - zminzbar[n] = (zeta-zetabar)**(20-n) + zminzbar[n] = (zeta - zetabar) ** (20 - n) gamnew = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) alpha[0] = a[0] beta[0] = b[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + a[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + b[n] * gamnew[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + a[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + b[n] * gamnew[n, 0 : n + 1] omega = 0 - d1minzeta = -1/biglab - zeta - d2minzeta = 1/biglab - zeta + d1minzeta = -1 / biglab - zeta + d2minzeta = 1 / biglab - zeta log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) term1 = 1 @@ -568,12 +569,14 @@ def bessellsreal(x, y, x1, y1, x2, y2, lab): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega = omega + (2 * alpha[n] * log2 - 2 * - alpha[n] / (n+1) + beta[n]) * term2 / (n+1) - omega = omega - (2 * alpha[n] * log1 - 2 * - alpha[n] / (n+1) + beta[n]) * term1 / (n+1) + omega = omega + ( + 2 * alpha[n] * log2 - 2 * alpha[n] / (n + 1) + beta[n] + ) * term2 / (n + 1) + omega = omega - ( + 2 * alpha[n] * log1 - 2 * alpha[n] / (n + 1) + beta[n] + ) * term1 / (n + 1) - phi = -biga / (2*np.pi) * np.real(omega) + phi = -biga / (2 * np.pi) * np.real(omega) return phi @@ -594,48 +597,48 @@ def bessellsrealho(x, y, x1, y1, x2, y2, lab, order): complex(kind=8), dimension(0:50) :: alphanew, betanew ! Maximum programmed order is 10 integer :: n, m, p """ - phi = np.zeros(order+1, dtype=np.float_) + phi = np.zeros(order + 1, dtype=np.float_) zminzbar = np.zeros(21, dtype=np.complex_) z1 = complex(x1, y1) z2 = complex(x2, y2) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) biglab = 2 * biga / L - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) for n in range(21): # Ordered from high power to low power - zminzbar[n] = (zeta-zetabar)**(20-n) - + zminzbar[n] = (zeta - zetabar) ** (20 - n) + gamnew = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) alpha[0] = a[0] beta[0] = b[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + a[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + b[n] * gamnew[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + a[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + b[n] * gamnew[n, 0 : n + 1] - d1minzeta = -1/biglab - zeta - d2minzeta = 1/biglab - zeta + d1minzeta = -1 / biglab - zeta + d2minzeta = 1 / biglab - zeta log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) alphanew = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - for p in range(order+1): - alphanew[0:40+p] = 0 - betanew[0:40+p] = 0 - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] + for p in range(order + 1): + alphanew[0 : 40 + p] = 0 + betanew[0 : 40 + p] = 0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] omega = 0 term1 = 1 @@ -644,14 +647,14 @@ def bessellsrealho(x, y, x1, y1, x2, y2, lab, order): for n in range(40 + p + 1): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega = omega + \ - (2 * alphanew[n] * log2 - 2 * alphanew[n] / - (n+1) + betanew[n]) * term2 / (n+1) - omega = omega - \ - (2 * alphanew[n] * log1 - 2 * alphanew[n] / - (n+1) + betanew[n]) * term1 / (n+1) + omega = omega + ( + 2 * alphanew[n] * log2 - 2 * alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega = omega - ( + 2 * alphanew[n] * log1 - 2 * alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) - phi[p] = -biga / (2*np.pi) * np.real(omega) + phi[p] = -biga / (2 * np.pi) * np.real(omega) return phi @@ -671,7 +674,7 @@ def bessells_int(x, y, z1, z2, lab): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) biglab = 2 * biga / L @@ -682,7 +685,7 @@ def bessells_int(x, y, z1, z2, lab): anew = a * exprange bnew = (b - a * complex(0, 2) * ang) * exprange - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) # #for n in range(21): # # zminzbar[n] = (zeta-zetabar)**(20-n) # Ordered from high power to low power @@ -690,13 +693,13 @@ def bessells_int(x, y, z1, z2, lab): for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -707,17 +710,17 @@ def bessells_int(x, y, z1, z2, lab): alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] omega = 0 - d1minzeta = -1/biglab - zeta - d2minzeta = 1/biglab - zeta + d1minzeta = -1 / biglab - zeta + d2minzeta = 1 / biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) @@ -729,16 +732,20 @@ def bessells_int(x, y, z1, z2, lab): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega = omega + (alpha[n] * log2 - alpha[n] / - (n+1) + beta[n]) * term2 / (n+1) - omega = omega - (alpha[n] * log1 - alpha[n] / - (n+1) + beta[n]) * term1 / (n+1) - omega = omega + (alpha2[n] * np.conj(log2) - - alpha2[n] / (n+1)) * np.conj(term2) / (n+1) - omega = omega - (alpha2[n] * np.conj(log1) - - alpha2[n] / (n+1)) * np.conj(term1) / (n+1) - - omega = -biga / (2*np.pi) * omega + omega = omega + (alpha[n] * log2 - alpha[n] / (n + 1) + beta[n]) * term2 / ( + n + 1 + ) + omega = omega - (alpha[n] * log1 - alpha[n] / (n + 1) + beta[n]) * term1 / ( + n + 1 + ) + omega = omega + (alpha2[n] * np.conj(log2) - alpha2[n] / (n + 1)) * np.conj( + term2 + ) / (n + 1) + omega = omega - (alpha2[n] * np.conj(log1) - alpha2[n] / (n + 1)) * np.conj( + term1 + ) / (n + 1) + + omega = -biga / (2 * np.pi) * omega return omega @@ -759,7 +766,7 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): complex(kind=8), dimension(0:50) :: alphanew, betanew, alphanew2 ! Order fixed to 10 integer :: m, n, p """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) biglab = 2 * biga / L @@ -770,7 +777,7 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): anew = a * exprange bnew = (b - a * complex(0, 2) * ang) * exprange - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) # #for n in range(21): @@ -781,13 +788,13 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -796,17 +803,17 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta # #d1minzeta = -1/biglab - zeta # #d2minzeta = 1/biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) @@ -815,19 +822,21 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - for p in range(order+1): - alphanew[0:40+p+1] = 0 - betanew[0:40+p+1] = 0 - alphanew2[0:40+p+1] = 0 + for p in range(order + 1): + alphanew[0 : 40 + p + 1] = 0 + betanew[0 : 40 + p + 1] = 0 + alphanew2[0 : 40 + p + 1] = 0 - for m in range(0, p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] + for m in range(0, p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) omega[p] = 0 term1 = 1 @@ -835,16 +844,20 @@ def bessells_int_ho(x, y, z1, z2, lab, order, d1, d2): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) - - omega = -biga / (2*np.pi) * omega + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) + + omega = -biga / (2 * np.pi) * omega return omega @@ -872,13 +885,13 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigx = bigz.real bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - angz = np.arctan2((z2-z1).imag, (z2-z1).real) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) biglab = 2 * biga / L biglabcomplex = 2.0 * lab / L @@ -888,18 +901,18 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): anew = a1 * exprange bnew = (b1 - a1 * complex(0, 2) * ang) * exprange - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) zminzbar[20] = 1 for n in range(1, 21): # Ordered from high power to low po - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -909,16 +922,16 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] d1minzeta = d1 / biglab - zeta d2minzeta = d2 / biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) @@ -927,20 +940,21 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(52, dtype=np.complex_) betanew = np.zeros(52, dtype=np.complex_) - omega = np.zeros(order+2, dtype=np.complex_) - qxqy = np.zeros(2*order+2, dtype=np.complex_) - - for p in range(0, order+2): - - alphanew[0:40+p+1] = 0 - betanew[0:40+p+1] = 0 - alphanew2[0:40+p+1] = 0 - for m in range(0, p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] + omega = np.zeros(order + 2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) + + for p in range(0, order + 2): + alphanew[0 : 40 + p + 1] = 0 + betanew[0 : 40 + p + 1] = 0 + alphanew2[0 : 40 + p + 1] = 0 + for m in range(0, p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) omega[p] = 0 term1 = 1 @@ -948,24 +962,28 @@ def bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): for n in range(40 + p + 1): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) - - omega = biglab / (2*np.pi*biglabcomplex**2) * omega + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) + + omega = biglab / (2 * np.pi * biglabcomplex**2) * omega omegalap = lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2) # multiplication with 2/L inherently included - qx = -(bigx * omega[0:order+1] - omega[1:order+2] + omegalap.imag) - qy = -(bigy * omega[0:order+1] + omegalap.real) + qx = -(bigx * omega[0 : order + 1] - omega[1 : order + 2] + omegalap.imag) + qy = -(bigy * omega[0 : order + 1] + omegalap.real) - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) - qxqy[order+1:2*order+2] = qx * np.sin(angz) + qy * np.cos(angz) + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + qxqy[order + 1 : 2 * order + 2] = qx * np.sin(angz) + qy * np.cos(angz) return qxqy @@ -982,15 +1000,15 @@ def bessells_gauss(x, y, z1, z2, lab): real(kind=8) :: L, x0 complex(kind=8) :: bigz, biglab """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biglab = 2 * lab / L - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) omega = complex(0, 0) for n in range(1, 9): - x0 = bigz.real - xg[n-1] - omega = omega + wg[n-1] * besselk0(x0, bigz.imag, biglab) + x0 = bigz.real - xg[n - 1] + omega = omega + wg[n - 1] * besselk0(x0, bigz.imag, biglab) - omega = -L/(4*np.pi) * omega + omega = -L / (4 * np.pi) * omega return omega @@ -1008,21 +1026,21 @@ def bessells_gauss_ho(x, y, z1, z2, lab, order): complex(kind=8) :: bigz, biglab complex(kind=8), dimension(8) :: k0 """ - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biglab = 2 * lab / L - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) k0 = np.zeros(8, dtype=np.complex_) for n in range(8): x0 = bigz.real - xg[n] k0[n] = besselk0(x0, bigz.imag, biglab) - omega = np.zeros(order+1, dtype=np.complex_) - for p in range(order+1): + omega = np.zeros(order + 1, dtype=np.complex_) + for p in range(order + 1): omega[p] = complex(0, 0) for n in range(8): - omega[p] = omega[p] + wg[n] * xg[n]**p * k0[n] - omega[p] = -L/(4*np.pi) * omega[p] + omega[p] = omega[p] + wg[n] * xg[n] ** p * k0[n] + omega[p] = -L / (4 * np.pi) * omega[p] return omega @@ -1041,18 +1059,18 @@ def bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2): real(kind=8) :: xp, yp, dc, fac complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) bigz1 = complex(d1, 0) bigz2 = complex(d2, 0) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) omegac = bessells_gauss_ho(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - omega[n] = omega[n] + gam[n, m] * dc**(n-m) * omegac[m] - omega[n] = (0.5 * (d2-d1))**n * omega[n] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + omega[n] = omega[n] + gam[n, m] * dc ** (n - m) * omegac[m] + omega[n] = (0.5 * (d2 - d1)) ** n * omega[n] return omega @@ -1072,33 +1090,33 @@ def bessells_gauss_ho_qxqy(x, y, z1, z2, lab, order): complex(kind=8), dimension(8) :: k1 complex(kind=8), dimension(0:order) :: qx,qy """ - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) xmind = np.zeros(8, dtype=np.complex_) k1 = np.zeros(8, dtype=np.complex_) r = np.zeros(8, dtype=np.complex_) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) biglab = 2 * lab / L - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag for n in range(8): xmind[n] = bigz.real - xg[n] - r[n] = np.sqrt(xmind[n]**2 + bigz.imag**2) + r[n] = np.sqrt(xmind[n] ** 2 + bigz.imag**2) k1[n] = besselk1(xmind[n], bigz.imag, biglab) - qx = np.zeros(order+1, dtype=np.complex_) - qy = np.zeros(order+1, dtype=np.complex_) - for p in range(order+1): + qx = np.zeros(order + 1, dtype=np.complex_) + qy = np.zeros(order + 1, dtype=np.complex_) + for p in range(order + 1): for n in range(8): - qx[p] = qx[p] + wg[n] * xg[n]**p * xmind[n] * k1[n] / r[n] - qy[p] = qy[p] + wg[n] * xg[n]**p * bigy * k1[n] / r[n] + qx[p] = qx[p] + wg[n] * xg[n] ** p * xmind[n] * k1[n] / r[n] + qy[p] = qy[p] + wg[n] * xg[n] ** p * bigy * k1[n] / r[n] - qx = -qx * L / (4*np.pi*biglab) * 2/L - qy = -qy * L / (4*np.pi*biglab) * 2/L + qx = -qx * L / (4 * np.pi * biglab) * 2 / L + qy = -qy * L / (4 * np.pi * biglab) * 2 / L - angz = np.arctan2((z2-z1).imag, (z2-z1).real) - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) - qxqy[order+1:2*order+2] = qx * np.sin(angz) + qy * np.cos(angz) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + qxqy[order + 1 : 2 * order + 2] = qx * np.sin(angz) + qy * np.cos(angz) return qxqy @@ -1117,21 +1135,22 @@ def bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2): real(kind=8) :: xp, yp, dc, fac complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) qxqyc = bessells_gauss_ho_qxqy(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - qxqy[n] = qxqy[n] + gam[n, m] * dc**(n-m) * qxqyc[m] - qxqy[n+order+1] = qxqy[n+order+1] + \ - gam[n, m] * dc**(n-m) * qxqyc[m+order+1] - qxqy[n] = (0.5*(d2-d1))**n * qxqy[n] - qxqy[n+order+1] = (0.5*(d2-d1))**n * qxqy[n+order+1] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + qxqy[n] = qxqy[n] + gam[n, m] * dc ** (n - m) * qxqyc[m] + qxqy[n + order + 1] = ( + qxqy[n + order + 1] + gam[n, m] * dc ** (n - m) * qxqyc[m + order + 1] + ) + qxqy[n] = (0.5 * (d2 - d1)) ** n * qxqy[n] + qxqy[n + order + 1] = (0.5 * (d2 - d1)) ** n * qxqy[n + order + 1] return qxqy @@ -1150,37 +1169,34 @@ def bessells(x, y, z1, z2, lab, order, d1in, d2in): real(kind=8) :: Lnear, L, d1, d2, delta complex(kind=8) :: z, delz, za, zb """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) Lnear = 3 z = complex(x, y) - L = np.abs(z2-z1) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = bessells_int_ho(x, y, z1, z2, lab, order, d1in, d2in) else: - omega = bessells_gauss_ho_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + omega = bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = np.ceil(L / (Lnear*np.abs(lab))) + Nls = np.ceil(L / (Lnear * np.abs(lab))) delta = 2 / Nls - delz = (z2-z1)/Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1 + (n-1) * delta + for n in range(1, Nls + 1): + d1 = -1 + (n - 1) * delta d2 = -1 + n * delta - if ((d2 < d1in) or (d1 > d2in)): + if (d2 < d1in) or (d1 > d2in): continue d1 = np.max(np.array([d1, d1in])) d2 = np.min(np.array([d2, d2in])) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - omega = omega + \ - bessells_int_ho(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + omega = omega + bessells_int_ho(x, y, z1, z2, lab, order, d1, d2) else: - omega = omega + \ - bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) + omega = omega + bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) return omega @@ -1197,14 +1213,15 @@ def bessellsv(x, y, z1, z2, lab, order, R, nlab): complex(kind=8), dimension(nlab*(order+1)) :: omega integer :: n, nterms """ - nterms = order+1 - omega = np.zeros(nlab*(order+1), dtype=np.complex_) + nterms = order + 1 + omega = np.zeros(nlab * (order + 1), dtype=np.complex_) # Check if endpoints need to be adjusted using # the largest lambda (the first one) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(0, nlab): - omega[n*nterms:(n+1)*nterms] = bessells(x, y, z1, z2, lab[n], - order, d1, d2) + omega[n * nterms : (n + 1) * nterms] = bessells( + x, y, z1, z2, lab[n], order, d1, d2 + ) return omega @@ -1221,12 +1238,12 @@ def bessellsv2(x, y, z1, z2, lab, order, R, nlab): complex(kind=8), dimension(order+1,nlab) :: omega integer :: n, nterms """ - nterms = order+1 - omega = np.zeros((order+1, nlab), dtype=np.complex_) + nterms = order + 1 + omega = np.zeros((order + 1, nlab), dtype=np.complex_) # Check if endpoints need to be adjusted using the largest lambda (the first one) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): - omega[:nterms+1, n] = bessells(x, y, z1, z2, lab[n], order, d1, d2) + omega[: nterms + 1, n] = bessells(x, y, z1, z2, lab[n], order, d1, d2) return omega @@ -1244,40 +1261,38 @@ def bessellsqxqy(x, y, z1, z2, lab, order, d1in, d2in): real(kind=8) :: Lnear, L, d1, d2, delta complex(kind=8) :: z, delz, za, zb """ - Lnear = 3. + Lnear = 3.0 z = complex(x, y) - qxqy = np.zeros(2*order+2, dtype=np.complex_) - L = np.abs(z2-z1) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) + L = np.abs(z2 - z1) # print *,'Lnear*np.abs(lab) ',Lnear*np.abs(lab) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration qxqy = bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1in, d2in) else: - qxqy = bessells_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + qxqy = bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = np.ceil(L / (Lnear*np.abs(lab))) + Nls = np.ceil(L / (Lnear * np.abs(lab))) # print *,'NLS ',Nls - delta = 2. / Nls - delz = (z2-z1)/Nls + delta = 2.0 / Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1. + (n-1) * delta - d2 = -1. + n * delta - if ((d2 < d1in) or (d1 > d2in)): + for n in range(1, Nls + 1): + d1 = -1.0 + (n - 1) * delta + d2 = -1.0 + n * delta + if (d2 < d1in) or (d1 > d2in): continue d1 = np.max(np.array([d1, d1in])) d2 = np.min(np.array([d2, d2in])) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - qxqy = qxqy + \ - bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + qxqy = qxqy + bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) else: - qxqy = qxqy + \ - bessells_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1, d2) + qxqy = qxqy + bessells_gauss_ho_qxqy_d1d2( + x, y, z1, z2, lab, order, d1, d2 + ) return qxqy @@ -1295,15 +1310,16 @@ def bessellsqxqyv(x, y, z1, z2, lab, order, R, nlab): complex(kind=8), dimension(0:2*order+1) :: qxqylab integer :: n, nterms, nhalf """ - qxqy = np.zeros(2*nlab*(order+1), dtype=np.complex_) - nterms = order+1 - nhalf = nlab*(order+1) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + qxqy = np.zeros(2 * nlab * (order + 1), dtype=np.complex_) + nterms = order + 1 + nhalf = nlab * (order + 1) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): qxqylab = bessellsqxqy(x, y, z1, z2, lab[n], order, d1, d2) - qxqy[(n)*nterms:(n+1)*nterms] = qxqylab[0:order+1] - qxqy[(n)*nterms+nhalf:(n+1)*nterms + - nhalf] = qxqylab[order+1:2*order+1+1] + qxqy[(n) * nterms : (n + 1) * nterms] = qxqylab[0 : order + 1] + qxqy[(n) * nterms + nhalf : (n + 1) * nterms + nhalf] = qxqylab[ + order + 1 : 2 * order + 1 + 1 + ] return qxqy @@ -1321,14 +1337,14 @@ def bessellsqxqyv2(x, y, z1, z2, lab, order, R, nlab): complex(kind=8), dimension(0:2*order+1) :: qxqylab integer :: n, nterms, nhalf """ - qxqy = np.zeros((2*(order+1), nlab), dtype=np.complex_) - nterms = order+1 - nhalf = nlab*(order+1) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + qxqy = np.zeros((2 * (order + 1), nlab), dtype=np.complex_) + nterms = order + 1 + nhalf = nlab * (order + 1) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): - qxqylab = bessellsqxqy(x, y, z1, z2, lab[n-1], order, d1, d2) - qxqy[:nterms, n] = qxqylab[0:order+1] - qxqy[nterms:2*nterms, n] = qxqylab[order+1:2*order+1+1] + qxqylab = bessellsqxqy(x, y, z1, z2, lab[n - 1], order, d1, d2) + qxqy[:nterms, n] = qxqylab[0 : order + 1] + qxqy[nterms : 2 * nterms, n] = qxqylab[order + 1 : 2 * order + 1 + 1] return qxqy @@ -1347,23 +1363,23 @@ def bessellsuni(x, y, z1, z2, lab): complex(kind=8) :: z, delz, za, zb """ - Lnear = 3. + Lnear = 3.0 z = complex(x, y) - omega = complex(0., 0.) - L = np.abs(z2-z1) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + omega = complex(0.0, 0.0) + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = bessells_int(x, y, z1, z2, lab) else: omega = bessells_gauss(x, y, z1, z2, lab) else: # Break integral up in parts - Nls = np.ceil(L / (Lnear*np.abs(lab))) - delz = (z2-z1)/Nls + Nls = np.ceil(L / (Lnear * np.abs(lab))) + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - za = z1 + (n-1) * delz + for n in range(1, Nls + 1): + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration omega = omega + bessells_int(x, y, za, zb, lab) else: omega = omega + bessells_gauss(x, y, za, zb, lab) @@ -1403,32 +1419,32 @@ def lapld_int_ho(x, y, z1, z2, order): complex(kind=8) :: z, zplus1, zmin1 """ - omega = np.zeros(order+1, dtype=np.complex_) - qm = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) + qm = np.zeros(order + 1, dtype=np.complex_) - L = np.abs(z2-z1) - z = (2. * complex(x, y) - (z1+z2)) / (z2-z1) - zplus1 = z + 1. - zmin1 = z - 1. + L = np.abs(z2 - z1) + z = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) + zplus1 = z + 1.0 + zmin1 = z - 1.0 # Not sure if this gives correct answer at corner point (z also appears in qm); should really be caught in code that calls this function if np.abs(zplus1) < tiny: zplus1 = tiny if np.abs(zmin1) < tiny: zmin1 = tiny - omega[0] = np.log(zmin1/zplus1) - for n in range(1, order+1): - omega[n] = z * omega[n-1] + omega[0] = np.log(zmin1 / zplus1) + for n in range(1, order + 1): + omega[n] = z * omega[n - 1] if order > 0: - qm[1] = 2. - for m in range(3, order+1, 2): - qm[m] = qm[m-2] * z * z + 2. / m + qm[1] = 2.0 + for m in range(3, order + 1, 2): + qm[m] = qm[m - 2] * z * z + 2.0 / m - for m in range(2, order+1, 2): - qm[m] = qm[m-1] * z + for m in range(2, order + 1, 2): + qm[m] = qm[m - 1] * z - omega = 1. / (complex(0., 2.) * np.pi) * (omega + qm) + omega = 1.0 / (complex(0.0, 2.0) * np.pi) * (omega + qm) return omega @@ -1446,18 +1462,18 @@ def lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2): real(kind=8) :: xp, yp, dc, fac complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) omegac = lapld_int_ho(x, y, z1p, z2p, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - omega[n] = omega[n] + gam[n, m] * dc**(n-m) * omegac[m] - omega[n] = (0.5*(d2-d1))**n * omega[n] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + omega[n] = omega[n] + gam[n, m] * dc ** (n - m) * omegac[m] + omega[n] = (0.5 * (d2 - d1)) ** n * omega[n] return omega @@ -1477,32 +1493,32 @@ def lapld_int_ho_wdis(x, y, z1, z2, order): """ qm = np.zeros(11, dtype=np.complex_) - wdis = np.zeros(order+1, dtype=np.complex_) + wdis = np.zeros(order + 1, dtype=np.complex_) - z = (2. * complex(x, y) - (z1+z2)) / (z2-z1) - zplus1 = z + 1. - zmin1 = z - 1. + z = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) + zplus1 = z + 1.0 + zmin1 = z - 1.0 # Not sure if this gives correct answer at corner point (z also appears in qm); should really be caught in code that calls this function - if (np.abs(zplus1) < tiny): + if np.abs(zplus1) < tiny: zplus1 = tiny - if (np.abs(zmin1) < tiny): + if np.abs(zmin1) < tiny: zmin1 = tiny - qm[0:1] = 0. - for m in range(2, order+1): - qm[m] = 0. - for n in range(1, m//2): - qm[m] = qm[m] + (m-2*n+1) * z**(m-2*n) / (2*n-1) + qm[0:1] = 0.0 + for m in range(2, order + 1): + qm[m] = 0.0 + for n in range(1, m // 2): + qm[m] = qm[m] + (m - 2 * n + 1) * z ** (m - 2 * n) / (2 * n - 1) - term1 = 1. / zmin1 - 1. / zplus1 - term2 = np.log(zmin1/zplus1) + term1 = 1.0 / zmin1 - 1.0 / zplus1 + term2 = np.log(zmin1 / zplus1) wdis[0] = term1 - zterm = complex(1., 0.) - for m in range(1, order+1): - wdis[m] = m * zterm * term2 + z * zterm * term1 + 2. * qm[m] + zterm = complex(1.0, 0.0) + for m in range(1, order + 1): + wdis[m] = m * zterm * term2 + z * zterm * term1 + 2.0 * qm[m] zterm = zterm * z - wdis = - wdis / (np.pi*complex(0., 1.)*(z2-z1)) + wdis = -wdis / (np.pi * complex(0.0, 1.0) * (z2 - z1)) return wdis @@ -1521,19 +1537,19 @@ def lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2): complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - wdis = np.zeros(order+1, dtype=np.complex_) + wdis = np.zeros(order + 1, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) wdisc = lapld_int_ho_wdis(x, y, z1p, z2p, order) - dc = (d1+d2) / (d2-d1) - wdis[0:order+1] = 0. - for n in range(order+1): - for m in range(n+1): - wdis[n] = wdis[n] + gam[n, m] * dc**(n-m) * wdisc[m] - wdis[n] = (0.5*(d2-d1))**n * wdis[n] + dc = (d1 + d2) / (d2 - d1) + wdis[0 : order + 1] = 0.0 + for n in range(order + 1): + for m in range(n + 1): + wdis[n] = wdis[n] + gam[n, m] * dc ** (n - m) * wdisc[m] + wdis[n] = (0.5 * (d2 - d1)) ** n * wdis[n] return wdis @@ -1557,14 +1573,14 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - biglab = 2. * biga / L + biglab = 2.0 * biga / L biglabcomplex = 2.0 * lab / L tol = 1e-12 @@ -1573,19 +1589,19 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): anew = a1 * exprange bnew = (b1 - a1 * complex(0, 2) * ang) * exprange - zeta = (2. * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) - zminzbar[20] = 1. + zminzbar[20] = 1.0 for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -1594,18 +1610,18 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] - - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta - #d1minzeta = -1./biglab - zeta - #d2minzeta = 1./biglab - zeta - if (np.abs(d1minzeta) < tol): - d1minzeta = d1minzeta + complex(tol, 0.) - if (np.abs(d2minzeta) < tol): - d2minzeta = d2minzeta + complex(tol, 0.) + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] + + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta + # d1minzeta = -1./biglab - zeta + # d2minzeta = 1./biglab - zeta + if np.abs(d1minzeta) < tol: + d1minzeta = d1minzeta + complex(tol, 0.0) + if np.abs(d2minzeta) < tol: + d2minzeta = d2minzeta + complex(tol, 0.0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) @@ -1613,35 +1629,43 @@ def besselld_int_ho(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - for p in range(order+1): - alphanew[0:40+p+1] = 0. - betanew[0:40+p+1] = 0. - alphanew2[0:40+p+1] = 0. - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] - - omega[p] = 0. - term1 = 1. - term2 = 1. + for p in range(order + 1): + alphanew[0 : 40 + p + 1] = 0.0 + betanew[0 : 40 + p + 1] = 0.0 + alphanew2[0 : 40 + p + 1] = 0.0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) + + omega[p] = 0.0 + term1 = 1.0 + term2 = 1.0 for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) # omega = bigy * biglab / (2.*pi*biglabcomplex**2) * omega + real( lapld_int_ho_d1d2(x,y,z1,z2,order,d1,d2) ) - omega = bigy * biglab / (2.*np.pi*biglabcomplex**2) * \ - omega + lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2).real + omega = ( + bigy * biglab / (2.0 * np.pi * biglabcomplex**2) * omega + + lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2).real + ) return omega @@ -1661,21 +1685,21 @@ def besselld_gauss_ho(x, y, z1, z2, lab, order): """ k1overr = np.zeros(8, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - L = np.abs(z2-z1) - biglab = 2. * lab / L - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + biglab = 2.0 * lab / L + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) for n in range(8): x0 = bigz.real - xg[n] r = np.sqrt(x0**2 + bigz.imag**2) k1overr[n] = besselk1(x0, bigz.imag, biglab) / r - for p in range(order+1): - omega[p] = complex(0., 0.) + for p in range(order + 1): + omega[p] = complex(0.0, 0.0) for n in range(8): - omega[p] = omega[p] + wg[n] * xg[n]**p * k1overr[n] + omega[p] = omega[p] + wg[n] * xg[n] ** p * k1overr[n] - omega[p] = bigz.imag/(2.*np.pi*biglab) * omega[p] + omega[p] = bigz.imag / (2.0 * np.pi * biglab) * omega[p] return omega @@ -1693,19 +1717,19 @@ def besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2): complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - bigz1 = complex(d1, 0.) - bigz2 = complex(d2, 0.) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + bigz1 = complex(d1, 0.0) + bigz2 = complex(d2, 0.0) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) omegac = besselld_gauss_ho(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - omega[0:order+1] = 0. - for n in range(order+1): - for m in range(n+1): - omega[n] = omega[n] + gam[n, m] * dc**(n-m) * omegac[m] - omega[n] = (0.5*(d2-d1))**n * omega[n] + dc = (d1 + d2) / (d2 - d1) + omega[0 : order + 1] = 0.0 + for n in range(order + 1): + for m in range(n + 1): + omega[n] = omega[n] + gam[n, m] * dc ** (n - m) * omegac[m] + omega[n] = (0.5 * (d2 - d1)) ** n * omega[n] return omega @@ -1724,37 +1748,34 @@ def besselld(x, y, z1, z2, lab, order, d1in, d2in): complex(kind=8) :: z, delz, za, zb """ - omega = np.zeros(order+1, dtype=np.complex_) + omega = np.zeros(order + 1, dtype=np.complex_) - Lnear = 3. + Lnear = 3.0 z = complex(x, y) - L = np.abs(z2-z1) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = besselld_int_ho(x, y, z1, z2, lab, order, d1in, d2in) else: - omega = besselld_gauss_ho_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + omega = besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = np.ceil(L / (Lnear*np.abs(lab))) - delta = 2. / Nls - delz = (z2-z1)/Nls + Nls = np.ceil(L / (Lnear * np.abs(lab))) + delta = 2.0 / Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1. + (n-1) * delta - d2 = -1. + n * delta - if ((d2 < d1in) or (d1 > d2in)): + for n in range(1, Nls + 1): + d1 = -1.0 + (n - 1) * delta + d2 = -1.0 + n * delta + if (d2 < d1in) or (d1 > d2in): continue d1 = max(d1, d1in) d2 = min(d2, d2in) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - omega = omega + \ - besselld_int_ho(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + omega = omega + besselld_int_ho(x, y, z1, z2, lab, order, d1, d2) else: - omega = omega + \ - besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) + omega = omega + besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) return omega @@ -1773,14 +1794,15 @@ def besselldv(x, y, z1, z2, lab, order, R, nlab): integer :: n, nterms """ - omega = np.zeros(nlab*(order+1), dtype=np.complex_) + omega = np.zeros(nlab * (order + 1), dtype=np.complex_) - nterms = order+1 + nterms = order + 1 # Check if endpoints need to be adjusted using the largest lambda (the first one) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): - omega[(n)*nterms:(n+1)*nterms] = besselld(x, y, z1, z2, - lab[n], order, d1, d2) + omega[(n) * nterms : (n + 1) * nterms] = besselld( + x, y, z1, z2, lab[n], order, d1, d2 + ) return omega @@ -1798,13 +1820,13 @@ def besselldv2(x, y, z1, z2, lab, order, R, nlab): integer :: n, nterms """ - omega = np.zeros((order+1, nlab), dtype=np.complex_) + omega = np.zeros((order + 1, nlab), dtype=np.complex_) - nterms = order+1 + nterms = order + 1 # Check if endpoints need to be adjusted using the largest lambda (the first one) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): - omega[:nterms+1, n] = besselld(x, y, z1, z2, lab[n], order, d1, d2) + omega[: nterms + 1, n] = besselld(x, y, z1, z2, lab[n], order, d1, d2) return omega @@ -1828,12 +1850,12 @@ def besselldpart(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - biglab = 2. * biga / L + biglab = 2.0 * biga / L biglabcomplex = 2.0 * lab / L tol = 1e-12 @@ -1842,18 +1864,18 @@ def besselldpart(x, y, z1, z2, lab, order, d1, d2): anew = a1 * exprange bnew = (b1 - a1 * complex(0, 2) * ang) * exprange - zeta = (2. * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) - zminzbar[-1] = 1. + zminzbar[-1] = 1.0 for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) beta = np.zeros(41, dtype=np.complex_) @@ -1862,54 +1884,59 @@ def besselldpart(x, y, z1, z2, lab, order, d1, d2): beta[0] = bnew[0] alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta - if (np.abs(d1minzeta) < tol): - d1minzeta = d1minzeta + complex(tol, 0.) - if (np.abs(d2minzeta) < tol): - d2minzeta = d2minzeta + complex(tol, 0.) + if np.abs(d1minzeta) < tol: + d1minzeta = d1minzeta + complex(tol, 0.0) + if np.abs(d2minzeta) < tol: + d2minzeta = d2minzeta + complex(tol, 0.0) log1 = np.log(d1minzeta) log2 = np.log(d2minzeta) alphanew = np.zeros(51, dtype=np.complex_) alphanew2 = np.zeros(51, dtype=np.complex_) betanew = np.zeros(51, dtype=np.complex_) - omega = np.zeros(order+1, dtype=np.complex_) - - for p in range(order+1): - - alphanew[0:40+p+1] = 0. - betanew[0:40+p+1] = 0. - alphanew2[0:40+p+1] = 0. - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] - - omega[p] = 0. - term1 = 1. - term2 = 1. - for n in range(40+p+1): + omega = np.zeros(order + 1, dtype=np.complex_) + + for p in range(order + 1): + alphanew[0 : 40 + p + 1] = 0.0 + betanew[0 : 40 + p + 1] = 0.0 + alphanew2[0 : 40 + p + 1] = 0.0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) + + omega[p] = 0.0 + term1 = 1.0 + term2 = 1.0 + for n in range(40 + p + 1): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) # + real( lapld_int_ho(x,y,z1,z2,order) ) - omega = biglab / (2.*np.pi*biglabcomplex**2) * omega + omega = biglab / (2.0 * np.pi * biglabcomplex**2) * omega # omega = real( lapld_int_ho(x,y,z1,z2,order) ) return omega @@ -1937,15 +1964,15 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): """ zminzbar = np.zeros(21, dtype=np.complex_) - omega = np.zeros(order+2, dtype=np.complex_) - qxqy = np.zeros(2*order+2, dtype=np.complex_) + omega = np.zeros(order + 2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - L = np.abs(z2-z1) - bigz = (2 * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + bigz = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag biga = np.abs(lab) ang = np.arctan2(lab.imag, lab.real) - angz = np.arctan2((z2-z1).imag, (z2-z1).real) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) biglab = 2 * biga / L biglabcomplex = 2.0 * lab / L @@ -1957,25 +1984,25 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): azero = anew[0] for n in range(20): - bnew[n] = (n+1)*bnew[n+1] + anew[n+1] - anew[n] = (n+1)*anew[n+1] + bnew[n] = (n + 1) * bnew[n + 1] + anew[n + 1] + anew[n] = (n + 1) * anew[n + 1] anew[20] = 0 # This is a bit lazy bnew[20] = 0 - zeta = (2 * complex(x, y) - (z1+z2)) / (z2-z1) / biglab + zeta = (2 * complex(x, y) - (z1 + z2)) / (z2 - z1) / biglab zetabar = np.conj(zeta) zminzbar[20] = 1 for n in range(1, 21): # Ordered from high power to low power - zminzbar[20-n] = zminzbar[21-n] * (zeta-zetabar) + zminzbar[20 - n] = zminzbar[21 - n] * (zeta - zetabar) gamnew = np.zeros((21, 21), dtype=np.complex_) gam2 = np.zeros((21, 21), dtype=np.complex_) for n in range(21): - gamnew[n, 0:n+1] = gam[n, 0:n+1] * zminzbar[20-n:20+1] - gam2[n, 0:n+1] = np.conj(gamnew[n, 0:n+1]) + gamnew[n, 0 : n + 1] = gam[n, 0 : n + 1] * zminzbar[20 - n : 20 + 1] + gam2[n, 0 : n + 1] = np.conj(gamnew[n, 0 : n + 1]) alpha = np.zeros(41, dtype=np.complex_) alpha2 = np.zeros(41, dtype=np.complex_) @@ -1985,17 +2012,17 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): alpha2[0] = anew[0] for n in range(1, 21): - alpha[n:2*n+1] = alpha[n:2*n+1] + anew[n] * gamnew[n, 0:n+1] - beta[n:2*n+1] = beta[n:2*n+1] + bnew[n] * gamnew[n, 0:n+1] - alpha2[n:2*n+1] = alpha2[n:2*n+1] + anew[n] * gam2[n, 0:n+1] + alpha[n : 2 * n + 1] = alpha[n : 2 * n + 1] + anew[n] * gamnew[n, 0 : n + 1] + beta[n : 2 * n + 1] = beta[n : 2 * n + 1] + bnew[n] * gamnew[n, 0 : n + 1] + alpha2[n : 2 * n + 1] = alpha2[n : 2 * n + 1] + anew[n] * gam2[n, 0 : n + 1] - d1minzeta = d1/biglab - zeta - d2minzeta = d2/biglab - zeta + d1minzeta = d1 / biglab - zeta + d2minzeta = d2 / biglab - zeta # d1minzeta = -1/biglab - zeta # d2minzeta = 1/biglab - zeta - if (np.abs(d1minzeta) < tol): + if np.abs(d1minzeta) < tol: d1minzeta = d1minzeta + complex(tol, 0) - if (np.abs(d2minzeta) < tol): + if np.abs(d2minzeta) < tol: d2minzeta = d2minzeta + complex(tol, 0) log1 = np.log(d1minzeta) @@ -2005,17 +2032,18 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): alphanew2 = np.zeros(52, dtype=np.complex_) betanew = np.zeros(52, dtype=np.complex_) - for p in range(order+2): - - alphanew[0:40+p+1] = 0 - betanew[0:40+p+1] = 0 - alphanew2[0:40+p+1] = 0 - for m in range(p+1): - cm = biglab**p * gam[p, m] * zeta**(p-m) - alphanew[m:40+m+1] = alphanew[m:40+m+1] + cm * alpha[0:40+1] - betanew[m:40+m+1] = betanew[m:40+m+1] + cm * beta[0:40+1] - cm = biglab**p * gam[p, m] * zetabar**(p-m) - alphanew2[m:40+m+1] = alphanew2[m:40+m+1] + cm * alpha2[0:40+1] + for p in range(order + 2): + alphanew[0 : 40 + p + 1] = 0 + betanew[0 : 40 + p + 1] = 0 + alphanew2[0 : 40 + p + 1] = 0 + for m in range(p + 1): + cm = biglab**p * gam[p, m] * zeta ** (p - m) + alphanew[m : 40 + m + 1] = alphanew[m : 40 + m + 1] + cm * alpha[0 : 40 + 1] + betanew[m : 40 + m + 1] = betanew[m : 40 + m + 1] + cm * beta[0 : 40 + 1] + cm = biglab**p * gam[p, m] * zetabar ** (p - m) + alphanew2[m : 40 + m + 1] = ( + alphanew2[m : 40 + m + 1] + cm * alpha2[0 : 40 + 1] + ) omega[p] = 0 term1 = 1 @@ -2023,24 +2051,29 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): for n in range(41): term1 = term1 * d1minzeta term2 = term2 * d2minzeta - omega[p] = omega[p] + (alphanew[n] * log2 - - alphanew[n] / (n+1) + betanew[n]) * term2 / (n+1) - omega[p] = omega[p] - (alphanew[n] * log1 - - alphanew[n] / (n+1) + betanew[n]) * term1 / (n+1) - omega[p] = omega[p] + (alphanew2[n] * np.conj(log2) - - alphanew2[n] / (n+1)) * np.conj(term2) / (n+1) - omega[p] = omega[p] - (alphanew2[n] * np.conj(log1) - - alphanew2[n] / (n+1)) * np.conj(term1) / (n+1) - - omegalap = lapld_int_ho_d1d2( - x, y, z1, z2, order, d1, d2) / complex(0, 1) + omega[p] = omega[p] + ( + alphanew[n] * log2 - alphanew[n] / (n + 1) + betanew[n] + ) * term2 / (n + 1) + omega[p] = omega[p] - ( + alphanew[n] * log1 - alphanew[n] / (n + 1) + betanew[n] + ) * term1 / (n + 1) + omega[p] = omega[p] + ( + alphanew2[n] * np.conj(log2) - alphanew2[n] / (n + 1) + ) * np.conj(term2) / (n + 1) + omega[p] = omega[p] - ( + alphanew2[n] * np.conj(log1) - alphanew2[n] / (n + 1) + ) * np.conj(term1) / (n + 1) + + omegalap = lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2) / complex(0, 1) omegaom = besselldpart(x, y, z1, z2, lab, order, d1, d2) wdis = lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2) - rvz = -biglab * bigy / (2*np.pi*biglabcomplex**2) * (omega[1:order+1+1]/biglab - zetabar * omega[0:order+1]) + \ - biglab * omegaom / complex(0, 2) - rvzbar = -biglab * bigy / (2*np.pi*biglabcomplex**2) * (omega[1:order+1+1]/biglab - zeta * omega[0:order+1]) - \ - biglab * omegaom / complex(0, 2) + rvz = -biglab * bigy / (2 * np.pi * biglabcomplex**2) * ( + omega[1 : order + 1 + 1] / biglab - zetabar * omega[0 : order + 1] + ) + biglab * omegaom / complex(0, 2) + rvzbar = -biglab * bigy / (2 * np.pi * biglabcomplex**2) * ( + omega[1 : order + 1 + 1] / biglab - zeta * omega[0 : order + 1] + ) - biglab * omegaom / complex(0, 2) # qxqy[0:order+1] = -2.0 / L * ( rvz + rvzbar ) / biglab # As we need to take derivative w.r.t. z not zeta # qxqy[order+1:2*order+1+1] = -2.0 / L * complex(0,1) * (rvz-rvzbar) / biglab # @@ -2053,21 +2086,24 @@ def besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2): # As we need to take derivative w.r.t. z not zeta qx = -2.0 / L * (rvz + rvzbar) / biglab - qy = -2.0 / L * complex(0, 1) * (rvz-rvzbar) / biglab + qy = -2.0 / L * complex(0, 1) * (rvz - rvzbar) / biglab - qx = qx - 2.0 / L * bigy / biglabcomplex**2 * \ - azero * (omegalap + np.conj(omegalap)) - qy = qy - 2.0 / L * bigy / biglabcomplex**2 * azero * \ - complex(0, 1) * (omegalap - np.conj(omegalap)) + qx = qx - 2.0 / L * bigy / biglabcomplex**2 * azero * ( + omegalap + np.conj(omegalap) + ) + qy = qy - 2.0 / L * bigy / biglabcomplex**2 * azero * complex(0, 1) * ( + omegalap - np.conj(omegalap) + ) # qx = qx + real(wdis * (z2-z1) / L) # qy = qy - aimag(wdis * (z2-z1) / L) # print *,'angz ',angz # wdis already includes the correct rotation - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) + wdis.real - qxqy[order+1:2*order+1+1] = qx * \ - np.sin(angz) + qy * np.cos(angz) - wdis.imag + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + wdis.real + qxqy[order + 1 : 2 * order + 1 + 1] = ( + qx * np.sin(angz) + qy * np.cos(angz) - wdis.imag + ) return qxqy @@ -2093,35 +2129,36 @@ def besselld_gauss_ho_qxqy(x, y, z1, z2, lab, order): r = np.zeros(8, dtype=np.float_) k0 = np.zeros(8, dtype=np.complex_) k1 = np.zeros(8, dtype=np.complex_) - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) - L = np.abs(z2-z1) - biglab = 2. * lab / L - bigz = (2. * complex(x, y) - (z1+z2)) / (z2-z1) + L = np.abs(z2 - z1) + biglab = 2.0 * lab / L + bigz = (2.0 * complex(x, y) - (z1 + z2)) / (z2 - z1) bigy = bigz.imag for n in range(8): xmind[n] = bigz.real - xg[n] - r[n] = np.sqrt(xmind[n]**2 + bigz.imag**2) + r[n] = np.sqrt(xmind[n] ** 2 + bigz.imag**2) k0[n] = besselk0(xmind[n], bigz.imag, biglab) k1[n] = besselk1(xmind[n], bigz.imag, biglab) - qx = np.zeros(order+1, dtype=np.complex_) - qy = np.zeros(order+1, dtype=np.complex_) - for p in range(order+1): + qx = np.zeros(order + 1, dtype=np.complex_) + qy = np.zeros(order + 1, dtype=np.complex_) + for p in range(order + 1): for n in range(8): - qx[p] = qx[p] + wg[n] * xg[n]**p * \ - (-bigy) * xmind[n] / r[n]**3 * \ - (r[n]*k0[n]/biglab + 2.*k1[n]) - qy[p] = qy[p] + wg[n] * xg[n]**p * \ - (k1[n]/r[n] - bigy**2 / r[n]**3 * - (r[n]*k0[n]/biglab + 2.*k1[n])) + qx[p] = qx[p] + wg[n] * xg[n] ** p * (-bigy) * xmind[n] / r[n] ** 3 * ( + r[n] * k0[n] / biglab + 2.0 * k1[n] + ) + qy[p] = qy[p] + wg[n] * xg[n] ** p * ( + k1[n] / r[n] + - bigy**2 / r[n] ** 3 * (r[n] * k0[n] / biglab + 2.0 * k1[n]) + ) - qx = -qx / (2*np.pi*biglab) * 2/L - qy = -qy / (2*np.pi*biglab) * 2/L + qx = -qx / (2 * np.pi * biglab) * 2 / L + qy = -qy / (2 * np.pi * biglab) * 2 / L - angz = np.arctan2((z2-z1).imag, (z2-z1).real) - qxqy[0:order+1] = qx * np.cos(angz) - qy * np.sin(angz) - qxqy[order+1:2*order+1+1] = qx * np.sin(angz) + qy * np.cos(angz) + angz = np.arctan2((z2 - z1).imag, (z2 - z1).real) + qxqy[0 : order + 1] = qx * np.cos(angz) - qy * np.sin(angz) + qxqy[order + 1 : 2 * order + 1 + 1] = qx * np.sin(angz) + qy * np.cos(angz) return qxqy @@ -2141,22 +2178,23 @@ def besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2): complex(kind=8) :: z1p,z2p,bigz1,bigz2 """ - qxqy = np.zeros(2*order+2, dtype=np.complex_) + qxqy = np.zeros(2 * order + 2, dtype=np.complex_) bigz1 = complex(d1, 0) bigz2 = complex(d2, 0) - z1p = 0.5 * (z2-z1) * bigz1 + 0.5 * (z1+z2) - z2p = 0.5 * (z2-z1) * bigz2 + 0.5 * (z1+z2) + z1p = 0.5 * (z2 - z1) * bigz1 + 0.5 * (z1 + z2) + z2p = 0.5 * (z2 - z1) * bigz2 + 0.5 * (z1 + z2) qxqyc = besselld_gauss_ho_qxqy(x, y, z1p, z2p, lab, order) - dc = (d1+d2) / (d2-d1) - for n in range(order+1): - for m in range(n+1): - qxqy[n] = qxqy[n] + gam[n, m] * dc**(n-m) * qxqyc[m] - qxqy[n+order+1] = qxqy[n+order+1] + \ - gam[n, m] * dc**(n-m) * qxqyc[m+order+1] + dc = (d1 + d2) / (d2 - d1) + for n in range(order + 1): + for m in range(n + 1): + qxqy[n] = qxqy[n] + gam[n, m] * dc ** (n - m) * qxqyc[m] + qxqy[n + order + 1] = ( + qxqy[n + order + 1] + gam[n, m] * dc ** (n - m) * qxqyc[m + order + 1] + ) - qxqy[n] = (0.5*(d2-d1))**n * qxqy[n] - qxqy[n+order+1] = (0.5*(d2-d1))**n * qxqy[n+order+1] + qxqy[n] = (0.5 * (d2 - d1)) ** n * qxqy[n] + qxqy[n + order + 1] = (0.5 * (d2 - d1)) ** n * qxqy[n + order + 1] return qxqy @@ -2179,38 +2217,36 @@ def besselldqxqy(x, y, z1, z2, lab, order, d1in, d2in): Lnear = 3 z = complex(x, y) qxqy = complex(0, 0) - L = np.abs(z2-z1) + L = np.abs(z2 - z1) # print *,'Lnear*np.abs(lab) ',Lnear*np.abs(lab) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration qxqy = besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1in, d2in) else: - qxqy = besselld_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1in, d2in) + qxqy = besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1in, d2in) else: # Break integral up in parts - Nls = np.ceil(L / (Lnear*np.abs(lab))) + Nls = np.ceil(L / (Lnear * np.abs(lab))) # print *,'NLS ',Nls delta = 2 / Nls - delz = (z2-z1)/Nls + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - d1 = -1 + (n-1) * delta + for n in range(1, Nls + 1): + d1 = -1 + (n - 1) * delta d2 = -1 + n * delta - if ((d2 < d1in) or (d1 > d2in)): + if (d2 < d1in) or (d1 > d2in): continue d1 = np.max(np.array([d1, d1in])) d2 = np.min(np.array([d2, d2in])) - za = z1 + (n-1) * delz + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration - qxqy = qxqy + \ - besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration + qxqy = qxqy + besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) else: - qxqy = qxqy + \ - besselld_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1, d2) + qxqy = qxqy + besselld_gauss_ho_qxqy_d1d2( + x, y, z1, z2, lab, order, d1, d2 + ) return qxqy @@ -2228,16 +2264,17 @@ def besselldqxqyv(x, y, z1, z2, lab, order, R, nlab): complex(kind=8), dimension(0:2*order+1) :: qxqylab """ - qxqy = np.zeros(2*nlab*(order+1), dtype=np.complex_) + qxqy = np.zeros(2 * nlab * (order + 1), dtype=np.complex_) - nterms = order+1 - nhalf = nlab*(order+1) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + nterms = order + 1 + nhalf = nlab * (order + 1) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): qxqylab = besselldqxqy(x, y, z1, z2, lab[n], order, d1, d2) - qxqy[n*nterms:(n+1)*nterms] = qxqylab[0:order+1] - qxqy[n*nterms+nhalf:(n+1)*nterms + - nhalf] = qxqylab[order+1:2*order+1+1] + qxqy[n * nterms : (n + 1) * nterms] = qxqylab[0 : order + 1] + qxqy[n * nterms + nhalf : (n + 1) * nterms + nhalf] = qxqylab[ + order + 1 : 2 * order + 1 + 1 + ] return qxqy @@ -2256,15 +2293,15 @@ def besselldqxqyv2(x, y, z1, z2, lab, order, R, nlab): complex(kind=8), dimension(0:2*order+1) :: qxqylab integer :: n, nterms, nhalf """ - qxqy = np.zeros((2*(order+1), nlab), dtype=np.complex_) + qxqy = np.zeros((2 * (order + 1), nlab), dtype=np.complex_) - nterms = order+1 - nhalf = nlab*(order+1) - d1, d2 = find_d1d2(z1, z2, complex(x, y), R*np.abs(lab[0])) + nterms = order + 1 + nhalf = nlab * (order + 1) + d1, d2 = find_d1d2(z1, z2, complex(x, y), R * np.abs(lab[0])) for n in range(nlab): qxqylab = besselldqxqy(x, y, z1, z2, lab[n], order, d1, d2) - qxqy[:nterms, n] = qxqylab[0:order+1] - qxqy[nterms:2*nterms, n] = qxqylab[order+1:2*order+1+1] + qxqy[:nterms, n] = qxqylab[0 : order + 1] + qxqy[nterms : 2 * nterms, n] = qxqylab[order + 1 : 2 * order + 1 + 1] return qxqy @@ -2284,29 +2321,28 @@ def bessells_circcheck(x, y, z1in, z2in, lab): Lnear = 3 Lzero = 20 z = complex(x, y) - x1, y1, x2, y2, Npt = circle_line_intersection( - z1in, z2in, z, Lzero*np.abs(lab)) + x1, y1, x2, y2, Npt = circle_line_intersection(z1in, z2in, z, Lzero * np.abs(lab)) z1 = complex(x1, y1) z2 = complex(x2, y2) omega = complex(0, 0) - if (Npt == 2): - L = np.abs(z2-z1) - if (L < Lnear*np.abs(lab)): # No need to break integral up - if (np.abs(z - 0.5*(z1+z2)) < 0.5 * Lnear * L): # Do integration + if Npt == 2: + L = np.abs(z2 - z1) + if L < Lnear * np.abs(lab): # No need to break integral up + if np.abs(z - 0.5 * (z1 + z2)) < 0.5 * Lnear * L: # Do integration omega = bessells_int(x, y, z1, z2, lab) else: omega = bessells_gauss(x, y, z1, z2, lab) else: # Break integral up in parts - Nls = np.ceil(L / (Lnear*np.abs(lab))) - delz = (z2-z1)/Nls + Nls = np.ceil(L / (Lnear * np.abs(lab))) + delz = (z2 - z1) / Nls L = np.abs(delz) - for n in range(1, Nls+1): - za = z1 + (n-1) * delz + for n in range(1, Nls + 1): + za = z1 + (n - 1) * delz zb = z1 + n * delz - if (np.abs(z - 0.5*(za+zb)) < 0.5 * Lnear * L): # Do integration + if np.abs(z - 0.5 * (za + zb)) < 0.5 * Lnear * L: # Do integration omega = omega + bessells_int(x, y, za, zb, lab) else: omega = omega + bessells_gauss(x, y, za, zb, lab) @@ -2319,11 +2355,11 @@ def is_too_far(z1, z2, zc, R): Checks whether zc is more than R away from oval surrounding line element """ - + Lover2 = np.abs(z2 - z1) / 2 bigz = (2 * zc - (z1 + z2)) / (z2 - z1) Radj = R / Lover2 - + rv = False if np.abs(bigz.imag) < Radj: if np.abs(bigz.real) < 1: @@ -2335,6 +2371,7 @@ def is_too_far(z1, z2, zc, R): rv = True return rv + @numba.njit(nogil=True, cache=True) def circle_line_intersection(z1, z2, zc, R): """ @@ -2350,23 +2387,23 @@ def circle_line_intersection(z1, z2, zc, R): za = complex(0, 0) zb = complex(0, 0) - Lover2 = np.abs(z2-z1) / 2 - bigz = (2*zc - (z1+z2)) * Lover2 / (z2-z1) + Lover2 = np.abs(z2 - z1) / 2 + bigz = (2 * zc - (z1 + z2)) * Lover2 / (z2 - z1) - if (abs(bigz.imag) < R): + if abs(bigz.imag) < R: d = np.sqrt(R**2 - bigz.imag**2) xa = bigz.real - d xb = bigz.real + d - if ((xa < Lover2) and (xb > -Lover2)): + if (xa < Lover2) and (xb > -Lover2): N = 2 - if (xa < -Lover2): + if xa < -Lover2: za = z1 else: - za = (xa * (z2-z1) / Lover2 + (z1+z2)) / 2. - if (xb > Lover2): + za = (xa * (z2 - z1) / Lover2 + (z1 + z2)) / 2.0 + if xb > Lover2: zb = z2 else: - zb = (xb * (z2-z1) / Lover2 + (z1+z2)) / 2. + zb = (xb * (z2 - z1) / Lover2 + (z1 + z2)) / 2.0 xouta = za.real youta = za.imag @@ -2386,23 +2423,23 @@ def find_d1d2(z1, z2, zc, R): real(kind=8) :: Lover2, d, xa, xb complex(kind=8) :: bigz """ - d1 = -1. - d2 = 1. + d1 = -1.0 + d2 = 1.0 - Lover2 = np.abs(z2-z1) / 2 - bigz = (2*zc - (z1+z2)) * Lover2 / (z2-z1) + Lover2 = np.abs(z2 - z1) / 2 + bigz = (2 * zc - (z1 + z2)) * Lover2 / (z2 - z1) - if (np.abs((bigz.imag)) < R): + if np.abs((bigz.imag)) < R: d = np.sqrt(R**2 - bigz.imag**2) xa = bigz.real - d xb = bigz.real + d - if ((xa < Lover2) and (xb > -Lover2)): - if (xa < -Lover2): - d1 = -1. + if (xa < Lover2) and (xb > -Lover2): + if xa < -Lover2: + d1 = -1.0 else: d1 = xa / Lover2 - if (xb > Lover2): - d2 = 1. + if xb > Lover2: + d2 = 1.0 else: d2 = xb / Lover2 return d1, d2 @@ -2410,7 +2447,7 @@ def find_d1d2(z1, z2, zc, R): @numba.njit(nogil=True, cache=True) def isinside(z1, z2, zc, R): - """ Checks whether point zc is within oval with 'radius' R from line element + """Checks whether point zc is within oval with 'radius' R from line element implicit none complex(kind=8), intent(in) :: z1, z2, zc real(kind=8), intent(in) :: R @@ -2419,13 +2456,13 @@ def isinside(z1, z2, zc, R): complex(kind=8) :: bigz """ irv = 0 - Lover2 = np.abs(z2-z1) / 2 - bigz = (2*zc - (z1+z2)) * np.abs(z2-z1) / (2*(z2-z1)) + Lover2 = np.abs(z2 - z1) / 2 + bigz = (2 * zc - (z1 + z2)) * np.abs(z2 - z1) / (2 * (z2 - z1)) - if (np.abs(bigz.imag) < R): + if np.abs(bigz.imag) < R: d = np.sqrt(R**2 - bigz.imag**2) xa = bigz.real - d xb = bigz.real + d - if ((xa < Lover2) and (xb > -Lover2)): + if (xa < Lover2) and (xb > -Lover2): irv = 1 return irv diff --git a/ttim/circareasink.py b/ttim/circareasink.py index ed479a2..d4e8e5f 100644 --- a/ttim/circareasink.py +++ b/ttim/circareasink.py @@ -1,16 +1,19 @@ -import numpy as np +import inspect # Used for storing the input + import matplotlib.pyplot as plt -from scipy.special import kv, iv -import inspect # Used for storing the input +import numpy as np +from scipy.special import iv, kv + from .element import Element + class CircAreaSink(Element): """ Create a circular area-sink with uniform infiltration rate in aquifer layer 0. Infiltration rate in length / time, positive for water entering the aquifer. - + Parameters ---------- model : Model object @@ -24,127 +27,150 @@ class CircAreaSink(Element): tuples of starting time and infiltration rate after starting time label : string or None (default: None) label of the area-sink - + """ - - def __init__(self, model, xc=0, yc=0, R=0.1, tsandN=[(0, 1)], - name='CircAreaSink', label=None): + + def __init__( + self, model, xc=0, yc=0, R=0.1, tsandN=[(0, 1)], name="CircAreaSink", label=None + ): self.storeinput(inspect.currentframe()) - Element.__init__(self, model, nparam=1, nunknowns=0, layers=0, - tsandbc=tsandN, type='g', name=name, label=label) - self.xc = float(xc); self.yc = float(yc); self.R = float(R) + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=0, + tsandbc=tsandN, + type="g", + name=name, + label=label, + ) + self.xc = float(xc) + self.yc = float(yc) + self.R = float(R) self.model.addelement(self) - + def __repr__(self): - return self.name + ' at ' + str((self.xc, self.yc)) + return self.name + " at " + str((self.xc, self.yc)) def initialize(self): self.aq = self.model.aq.find_aquifer_data(self.xc, self.yc) self.setbc() self.setflowcoef() # Since recharge is in layer 0, and RHS is -N - self.an = self.aq.coef[0, :] * self.flowcoef + self.an = self.aq.coef[0, :] * self.flowcoef self.an.shape = (self.aq.naq, self.model.nint, self.model.npint) - self.termin = self.aq.lab2 * self.R * self.an - self.termin2 = self.aq.lab2 ** 2 * self.an + self.termin = self.aq.lab2 * self.R * self.an + self.termin2 = self.aq.lab2**2 * self.an self.terminq = self.R * self.an self.termout = self.aq.lab2 * self.R * self.an self.i1R = iv(1, self.R / self.aq.lab2) self.k1R = kv(1, self.R / self.aq.lab2) - self.termoutq= self.R * self.an + self.termoutq = self.R * self.an self.dischargeinf = self.aq.coef[0, :] * self.flowcoef - self.dischargeinflayers = np.sum(self.dischargeinf * - self.aq.eigvec[self.layers, :, :], 1) + self.dischargeinflayers = np.sum( + self.dischargeinf * self.aq.eigvec[self.layers, :, :], 1 + ) def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' + """Separate function so that this can be overloaded for other types""" self.flowcoef = 1.0 / self.model.p # Step function def potinf(self, x, y, aq=None): - '''Can be called with only one x,y value''' + """Can be called with only one x,y value""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + rv = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: r = np.sqrt((x - self.xc) ** 2 + (y - self.yc) ** 2) - pot = np.zeros(self.model.npint, 'D') + pot = np.zeros(self.model.npint, "D") if r < self.R: for i in range(self.aq.naq): for j in range(self.model.nint): - #if r / abs(self.aq.lab2[i,j,0]) < self.rzero: - rv[0, i, j] = -self.termin[i, j] * \ - self.K1RI0r(r, i, j) + self.termin2[i, j] + # if r / abs(self.aq.lab2[i,j,0]) < self.rzero: + rv[0, i, j] = ( + -self.termin[i, j] * self.K1RI0r(r, i, j) + + self.termin2[i, j] + ) else: for i in range(self.aq.naq): for j in range(self.model.nint): - if (r - self.R) / \ - abs(self.aq.lab2[i, j, 0]) < self.rzero: - rv[0, i, j, :] = self.termout[i, j, :] * \ - self.I1RK0r(r, i, j) + if (r - self.R) / abs(self.aq.lab2[i, j, 0]) < self.rzero: + rv[0, i, j, :] = self.termout[i, j, :] * self.I1RK0r( + r, i, j + ) rv.shape = (self.nparam, aq.naq, self.model.npval) return rv - - def disvecinf(self,x,y,aq=None): - '''Can be called with only one x,y value''' + + def disvecinf(self, x, y, aq=None): + """Can be called with only one x,y value""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - qx = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') - qy = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') + qx = np.zeros((self.nparam, aq.naq, self.model.npval), "D") + qy = np.zeros((self.nparam, aq.naq, self.model.npval), "D") if aq == self.aq: - qr = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + qr = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") r = np.sqrt((x - self.xc) ** 2 + (y - self.yc) ** 2) if r < self.R: for i in range(self.aq.naq): for j in range(self.model.nint): - #if r / abs(self.aq.lab2[i,j,0]) < self.rzero: + # if r / abs(self.aq.lab2[i,j,0]) < self.rzero: qr[0, i, j] = self.terminq[i, j] * self.K1RI1r(r, i, j) else: for i in range(self.aq.naq): for j in range(self.model.nint): - if (r - self.R) / \ - abs(self.aq.lab2[i, j, 0]) < self.rzero: - qr[0, i, j] = self.termoutq[i, j, :] * \ - self.I1RK1r(r, i, j) + if (r - self.R) / abs(self.aq.lab2[i, j, 0]) < self.rzero: + qr[0, i, j] = self.termoutq[i, j, :] * self.I1RK1r(r, i, j) qr.shape = (self.nparam, aq.naq, self.model.npval) qx[:] = qr * (x - self.xc) / r qy[:] = qr * (y - self.yc) / r return qx, qy - + def plot(self): - plt.plot(self.xc + self.R * np.cos(np.linspace(0, 2 * np.pi, 100)), \ - self.yc + self.R * np.sin(np.linspace(0, 2 * np.pi, 100)), 'k') - + plt.plot( + self.xc + self.R * np.cos(np.linspace(0, 2 * np.pi, 100)), + self.yc + self.R * np.sin(np.linspace(0, 2 * np.pi, 100)), + "k", + ) + def K1RI0r(self, rin, iaq, ipint): r = rin / self.aq.lab2[iaq, ipint] R = self.R / self.aq.lab2[iaq, ipint] if np.isinf(self.i1R[iaq, ipint]).any(): - rv = np.sqrt(1 / (4 * r * R)) * np.exp(r - R) * \ - (1 + 3 / (8 * R) - 15 / (128 * R ** 2) + 315 / (3072 * R ** 3)) * \ - (1 + 1 / (8 * r) + 9 / (128 * r ** 2) + 225 / (3072 * r ** 3)) + rv = ( + np.sqrt(1 / (4 * r * R)) + * np.exp(r - R) + * (1 + 3 / (8 * R) - 15 / (128 * R**2) + 315 / (3072 * R**3)) + * (1 + 1 / (8 * r) + 9 / (128 * r**2) + 225 / (3072 * r**3)) + ) else: rv = self.k1R[iaq, ipint] * iv(0, r) return rv - + def I1RK0r(self, rin, iaq, ipint): r = rin / self.aq.lab2[iaq, ipint] R = self.R / self.aq.lab2[iaq, ipint] if np.isinf(self.i1R[iaq, ipint]).any(): - rv = np.sqrt(1 / (4 * r * R)) * np.exp(R - r) * \ - (1 - 3 / (8 * R) - 15 / (128 * R ** 2) - 315 / (3072 * R ** 3)) * \ - (1 - 1 / (8 * r) + 9 / (128 * r ** 2) - 225 / (3072 * r ** 3)) + rv = ( + np.sqrt(1 / (4 * r * R)) + * np.exp(R - r) + * (1 - 3 / (8 * R) - 15 / (128 * R**2) - 315 / (3072 * R**3)) + * (1 - 1 / (8 * r) + 9 / (128 * r**2) - 225 / (3072 * r**3)) + ) else: rv = self.i1R[iaq, ipint] * kv(0, r) return rv - + def K1RI1r(self, rin, iaq, ipint): r = rin / self.aq.lab2[iaq, ipint] R = self.R / self.aq.lab2[iaq, ipint] if np.isinf(self.i1R[iaq, ipint]).any(): - rv = np.sqrt(1 / (4 * r * R)) * np.exp(r - R) * \ - (1 + 3 / (8 * R) - 15 / (128 * R ** 2) + 315 / (3072 * R ** 3)) * \ - (1 - 3 / (8 * r) - 15 / (128 * r ** 2) - 315 / (3072 * r ** 3)) + rv = ( + np.sqrt(1 / (4 * r * R)) + * np.exp(r - R) + * (1 + 3 / (8 * R) - 15 / (128 * R**2) + 315 / (3072 * R**3)) + * (1 - 3 / (8 * r) - 15 / (128 * r**2) - 315 / (3072 * r**3)) + ) else: rv = self.k1R[iaq, ipint] * iv(1, r) return rv @@ -153,9 +179,12 @@ def I1RK1r(self, rin, iaq, ipint): r = rin / self.aq.lab2[iaq, ipint] R = self.R / self.aq.lab2[iaq, ipint] if np.isinf(self.i1R[iaq, ipint]).any(): - rv = np.sqrt(1 / (4 * r * R)) * np.exp(R - r) * \ - (1 - 3 / (8 * R) - 15 / (128 * R ** 2) - 315 / (3072 * R ** 3)) * \ - (1 + 3 / (8 * r) - 15 / (128 * r ** 2) + 315 / (3072 * r ** 3)) + rv = ( + np.sqrt(1 / (4 * r * R)) + * np.exp(R - r) + * (1 - 3 / (8 * R) - 15 / (128 * R**2) - 315 / (3072 * R**3)) + * (1 + 3 / (8 * r) - 15 / (128 * r**2) + 315 / (3072 * r**3)) + ) else: rv = self.i1R[iaq, ipint] * kv(1, r) - return rv \ No newline at end of file + return rv diff --git a/ttim/circinhom.py b/ttim/circinhom.py index b44f911..416e3fd 100644 --- a/ttim/circinhom.py +++ b/ttim/circinhom.py @@ -1,133 +1,217 @@ # flake8: noqa import numpy as np -from scipy.special import kv, iv # Needed for K1 in Well class, and in CircInhom +from scipy.special import iv # Needed for K1 in Well class, and in CircInhom +from scipy.special import kv + from .aquifer import AquiferData from .element import Element from .equation import InhomEquation - + + class CircInhomData(AquiferData): - def __init__(self, model, x0=0, y0=0, R=1, kaq=[1], Haq=[1], c=[1], - Saq=[.1], Sll=[.1], topboundary='imp'): - AquiferData.__init__(self, model, kaq, Haq, Hll, c, Saq, Sll, - topboundary, phreatictop) + def __init__( + self, + model, + x0=0, + y0=0, + R=1, + kaq=[1], + Haq=[1], + c=[1], + Saq=[0.1], + Sll=[0.1], + topboundary="imp", + ): + AquiferData.__init__( + self, model, kaq, Haq, Hll, c, Saq, Sll, topboundary, phreatictop + ) self.x0 = float(x0) self.y0 = float(y0) self.R = float(R) - self.Rsq = self.R ** 2 + self.Rsq = self.R**2 self.area = np.pi * self.Rsq self.model.addInhom(self) + def isInside(self, x, y): rv = False - if (x - self.x0) ** 2 + (y - self.y0) ** 2 < self.Rsq: + if (x - self.x0) ** 2 + (y - self.y0) ** 2 < self.Rsq: rv = True return rv + class CircInhomDataMaq(CircInhomData): - def __init__(self, model, x0=0, y0=0, R=1, kaq=[1], z=[1, 0], c=[], - Saq=[0.001], Sll=[0], topboundary='imp', phreatictop=False): - kaq, Haq, Hll, c, Saq, Sll = param_maq(kaq, z, c, Saq, Sll, - topboundary, phreatictop) - CircInhomData.__init__(self, model, x0, y0, R, kaq, Haq, c, Saq, Sll, - topboundary, phreatictop) - + def __init__( + self, + model, + x0=0, + y0=0, + R=1, + kaq=[1], + z=[1, 0], + c=[], + Saq=[0.001], + Sll=[0], + topboundary="imp", + phreatictop=False, + ): + kaq, Haq, Hll, c, Saq, Sll = param_maq( + kaq, z, c, Saq, Sll, topboundary, phreatictop + ) + CircInhomData.__init__( + self, model, x0, y0, R, kaq, Haq, c, Saq, Sll, topboundary, phreatictop + ) + + class CircInhomData3D(CircInhomData): - def __init__(self, model, x0=0, y0=0, R=1, kaq=1, z=[4, 3, 2, 1], - Saq=[0.3, 0.001, 0.001], kzoverkh=0.1, phreatictop=True, - topboundary='conf', topres=0, topthick=0, topSll=0): - kaq, Haq, Hll, c, Saq, Sll = param_3d(kaq, z, Saq, kzoverkh, - phreatictop, topboundary, topres, - topthick, topSll) - CircInhomData.__init__(self, model, x0, y0, R, kaq, Haq, c, Saq, Sll, - 'imp') - + def __init__( + self, + model, + x0=0, + y0=0, + R=1, + kaq=1, + z=[4, 3, 2, 1], + Saq=[0.3, 0.001, 0.001], + kzoverkh=0.1, + phreatictop=True, + topboundary="conf", + topres=0, + topthick=0, + topSll=0, + ): + kaq, Haq, Hll, c, Saq, Sll = param_3d( + kaq, z, Saq, kzoverkh, phreatictop, topboundary, topres, topthick, topSll + ) + CircInhomData.__init__(self, model, x0, y0, R, kaq, Haq, c, Saq, Sll, "imp") + + class BesselRatioApprox: # Never fully debugged def __init__(self, Norder, Nterms): - self.Norder= Norder+1 - self.Nterms = Nterms+1 + self.Norder = Norder + 1 + self.Nterms = Nterms + 1 self.krange = np.arange(self.Nterms) self.minonek = (-np.ones(self.Nterms)) ** self.krange - self.hankeltot = np.ones( (self.Norder,2*self.Nterms), 'd' ) - self.muk = np.ones( (self.Norder,self.Nterms), 'd' ) - self.nuk = np.ones( (self.Norder,self.Nterms), 'd' ) + self.hankeltot = np.ones((self.Norder, 2 * self.Nterms), "d") + self.muk = np.ones((self.Norder, self.Nterms), "d") + self.nuk = np.ones((self.Norder, self.Nterms), "d") for n in range(self.Norder): - mu = 4.0*n**2 - for k in range(1,self.Nterms): - self.hankeltot[n,k] = self.hankeltot[n,k-1] * (mu - (2*k-1)**2) / ( 4.0 * k ) + mu = 4.0 * n**2 + for k in range(1, self.Nterms): + self.hankeltot[n, k] = ( + self.hankeltot[n, k - 1] * (mu - (2 * k - 1) ** 2) / (4.0 * k) + ) for k in range(self.Nterms): - self.muk[n,k] = ( 4.0 * n**2 + 16.0 * k**2 - 1.0 ) / ( 4.0 * n**2 - (4.0*k - 1.0)**2 ) - self.nuk[n,k] = ( 4.0 * n**2 + 4.0 * (2.0*k+1.0)**2 - 1.0 ) / ( 4.0 * n**2 - (4.0*k + 1.0)**2 ) - self.hankelnk = self.hankeltot[:,:self.Nterms] - self.hankeln2k = self.hankeltot[:,::2] - self.hankeln2kp1 = self.hankeltot[:,1::2] - def ivratio( self, rho, R, lab): + self.muk[n, k] = (4.0 * n**2 + 16.0 * k**2 - 1.0) / ( + 4.0 * n**2 - (4.0 * k - 1.0) ** 2 + ) + self.nuk[n, k] = (4.0 * n**2 + 4.0 * (2.0 * k + 1.0) ** 2 - 1.0) / ( + 4.0 * n**2 - (4.0 * k + 1.0) ** 2 + ) + self.hankelnk = self.hankeltot[:, : self.Nterms] + self.hankeln2k = self.hankeltot[:, ::2] + self.hankeln2kp1 = self.hankeltot[:, 1::2] + + def ivratio(self, rho, R, lab): lab = np.atleast_1d(lab) - rv = np.empty((self.Norder,len(lab)),'D') + rv = np.empty((self.Norder, len(lab)), "D") for k in range(len(lab)): - top = np.sum( self.minonek * self.hankelnk / ( 2.0 * rho / lab[k] )**self.krange, 1 ) - bot = np.sum( self.minonek * self.hankelnk / ( 2.0 * R / lab[k] )**self.krange, 1 ) - rv[:,k] = top / bot * np.sqrt ( float(R) / rho ) * np.exp( (rho-R)/ lab[k] ) + top = np.sum( + self.minonek * self.hankelnk / (2.0 * rho / lab[k]) ** self.krange, 1 + ) + bot = np.sum( + self.minonek * self.hankelnk / (2.0 * R / lab[k]) ** self.krange, 1 + ) + rv[:, k] = top / bot * np.sqrt(float(R) / rho) * np.exp((rho - R) / lab[k]) return rv - def kvratio( self, rho, R, lab ): + + def kvratio(self, rho, R, lab): lab = np.atleast_1d(lab) - rv = np.empty((self.Norder,len(lab)),'D') + rv = np.empty((self.Norder, len(lab)), "D") for k in range(len(lab)): - top = np.sum( self.hankelnk / ( 2.0 * rho / lab[k] )**self.krange, 1 ) - bot = np.sum( self.hankelnk / ( 2.0 * R / lab[k] )**self.krange, 1 ) - rv[:,k] = top / bot * np.sqrt ( float(R) / rho ) * np.exp( (R-rho)/ lab[k] ) - return rv - def ivratiop( self, rho, R, lab ): + top = np.sum(self.hankelnk / (2.0 * rho / lab[k]) ** self.krange, 1) + bot = np.sum(self.hankelnk / (2.0 * R / lab[k]) ** self.krange, 1) + rv[:, k] = top / bot * np.sqrt(float(R) / rho) * np.exp((R - rho) / lab[k]) + return rv + + def ivratiop(self, rho, R, lab): lab = np.atleast_1d(lab) - rv = np.empty((self.Norder,len(lab)),'D') + rv = np.empty((self.Norder, len(lab)), "D") for k in range(len(lab)): - top = np.sum( self.muk * self.hankeln2k / ( 2.0 * rho / lab[k] )**(2*self.krange), 1 ) - \ - np.sum( self.nuk * self.hankeln2kp1 / ( 2.0 * rho / lab[k] )**(2*self.krange+1), 1 ) - bot = np.sum( self.minonek * self.hankelnk / ( 2.0 * R / lab[k] )**self.krange, 1 ) - rv[:,k] = top / bot * np.sqrt ( float(R) / rho ) * np.exp( (rho-R)/ lab[k] ) + top = np.sum( + self.muk * self.hankeln2k / (2.0 * rho / lab[k]) ** (2 * self.krange), 1 + ) - np.sum( + self.nuk + * self.hankeln2kp1 + / (2.0 * rho / lab[k]) ** (2 * self.krange + 1), + 1, + ) + bot = np.sum( + self.minonek * self.hankelnk / (2.0 * R / lab[k]) ** self.krange, 1 + ) + rv[:, k] = top / bot * np.sqrt(float(R) / rho) * np.exp((rho - R) / lab[k]) return rv - def kvratiop( self, rho, R, lab ): + + def kvratiop(self, rho, R, lab): lab = np.atleast_1d(lab) - rv = np.empty((self.Norder,len(lab)),'D') + rv = np.empty((self.Norder, len(lab)), "D") for k in range(len(lab)): - top = np.sum( self.muk * self.hankeln2k / ( 2.0 * rho / lab[k] )**(2*self.krange), 1 ) + \ - np.sum( self.nuk * self.hankeln2kp1 / ( 2.0 * rho / lab[k] )**(2*self.krange+1), 1 ) - bot = np.sum( self.hankelnk / ( 2.0 * R / lab[k] )**self.krange, 1 ) - rv[:,k] = -top / bot * np.sqrt ( float(R) / rho ) * np.exp( (R-rho)/ lab[k] ) + top = np.sum( + self.muk * self.hankeln2k / (2.0 * rho / lab[k]) ** (2 * self.krange), 1 + ) + np.sum( + self.nuk + * self.hankeln2kp1 + / (2.0 * rho / lab[k]) ** (2 * self.krange + 1), + 1, + ) + bot = np.sum(self.hankelnk / (2.0 * R / lab[k]) ** self.krange, 1) + rv[:, k] = -top / bot * np.sqrt(float(R) / rho) * np.exp((R - rho) / lab[k]) return rv - + + class CircInhomRadial(Element, InhomEquation): def __init__(self, model, x0=0, y0=0, R=1.0, label=None): - Element.__init__(self, model, nparam=2 * model.aq.naq, - nunknowns=2 * model.aq.naq, - layers=range(model.aq.naq), type='z', - name='CircInhom', label=label) + Element.__init__( + self, + model, + nparam=2 * model.aq.naq, + nunknowns=2 * model.aq.naq, + layers=range(model.aq.naq), + type="z", + name="CircInhom", + label=label, + ) self.x0 = float(x0) self.y0 = float(y0) self.R = float(R) self.model.addElement(self) self.approx = BesselRatioApprox(0, 2) - + def __repr__(self): - return self.name + ' at ' + str((self.x0, self.y0)) - + return self.name + " at " + str((self.x0, self.y0)) + def initialize(self): - self.xc = np.array([self.x0 + self.R]); self.yc = np.array([self.y0]) + self.xc = np.array([self.x0 + self.R]) + self.yc = np.array([self.y0]) self.thetacp = np.zeros(1) self.ncp = 1 self.aqin = self.model.aq.findAquiferData( - self.x0 + (1 - 1e-8) * self.R, self.y0) - assert self.aqin.R == self.R, ( - 'Radius of CircInhom and CircInhomData must be equal') + self.x0 + (1 - 1e-8) * self.R, self.y0 + ) + assert ( + self.aqin.R == self.R + ), "Radius of CircInhom and CircInhomData must be equal" self.aqout = self.model.aq.findAquiferData( - self.x0 + (1 + 1e-8) * self.R, self.y0) + self.x0 + (1 + 1e-8) * self.R, self.y0 + ) self.setbc() self.facin = np.ones_like(self.aqin.lab2) self.facout = np.ones_like(self.aqout.lab2) - # To keep track which circles are small - self.circ_in_small = np.ones((self.aqin.naq, self.model.nin), dtype='i') - self.circ_out_small = np.ones((self.aqout.naq,self.model.nin),dtype='i') + # To keep track which circles are small + self.circ_in_small = np.ones((self.aqin.naq, self.model.nin), dtype="i") + self.circ_out_small = np.ones((self.aqout.naq, self.model.nin), dtype="i") self.Rbig = 700 - #for i in range(self.aqin.Naq): + # for i in range(self.aqin.Naq): # for j in range(self.model.Nin): # assert self.R / abs(self.aqin.lab2[i,j,0]) < self.Rbig, 'TTim input error, Radius too big' # assert self.R / abs(self.aqout.lab2[i,j,0]) < self.Rbig, 'TTim input error, Radius too big' @@ -137,100 +221,113 @@ def initialize(self): # if self.R / abs(self.aqout.lab2[i,j,0]) < self.Rbig: # self.circ_out_small[i,j] = 1 # self.facout[i,j,:] = 1.0 / kv(0, self.R / self.aqout.lab2[i,j,:]) - #for i in range(self.aqin.Naq): + # for i in range(self.aqin.Naq): # for j in range(self.model.Nin): # assert self.R / abs(self.aqin.lab2[i,j,0]) < 900, 'radius too large compared to aqin lab2[i,j,0] '+str((i,j)) # assert self.R / abs(self.aqout.lab2[i,j,0]) < 900, 'radius too large compared to aqin lab2[i,j,0] '+str((i,j)) - #self.facin = 1.0 / iv(0, self.R / self.aqin.lab2) - #self.facout = 1.0 / kv(0, self.R / self.aqout.lab2) - self.parameters = np.zeros((self.model.Ngvbc, self.Nparam, - self.model.Np), 'D') + # self.facin = 1.0 / iv(0, self.R / self.aqin.lab2) + # self.facout = 1.0 / kv(0, self.R / self.aqout.lab2) + self.parameters = np.zeros((self.model.Ngvbc, self.Nparam, self.model.Np), "D") + def potinf(self, x, y, aq=None): - '''Can be called with only one x,y value''' - if aq is None: aq = self.model.aq.findAquiferData(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.nin, - self.model.npin), 'D') + """Can be called with only one x,y value""" + if aq is None: + aq = self.model.aq.findAquiferData(x, y) + rv = np.zeros((self.nparam, aq.naq, self.model.nin, self.model.npin), "D") if aq == self.aqin: r = np.sqrt((x - self.x0) ** 2 + (y - self.y0) ** 2) for i in range(self.aqin.Naq): for j in range(self.model.Nin): if abs(r - self.R) / abs(self.aqin.lab2[i, j, 0]) < self.Rzero: if self.circ_in_small[i, j]: - rv[i, i, j, :] = self.facin[i, j, :] * \ - iv(0, r / self.aqin.lab2[i, j, :]) + rv[i, i, j, :] = self.facin[i, j, :] * iv( + 0, r / self.aqin.lab2[i, j, :] + ) else: - print('using approx') + print("using approx") rv[i, i, j, :] = self.approx.ivratio( - r, self.R, self.aqin.lab2[i, j, :]) + r, self.R, self.aqin.lab2[i, j, :] + ) if aq == self.aqout: - r = np.sqrt( (x - self.x0) ** 2 + (y - self.y0) ** 2) + r = np.sqrt((x - self.x0) ** 2 + (y - self.y0) ** 2) for i in range(self.aqout.Naq): for j in range(self.model.Nin): if abs(r - self.R) / abs(self.aqout.lab2[i, j, 0]) < self.Rzero: if self.circ_out_small[i, j]: - rv[self.aqin.Naq + i, i, j, :] = \ - self.facin[i, j, :] * \ - kv(0, r / self.aqout.lab2[i, j, :]) + rv[self.aqin.Naq + i, i, j, :] = self.facin[i, j, :] * kv( + 0, r / self.aqout.lab2[i, j, :] + ) else: - print('using approx') - rv[self.aqin.Naq + i, i, j, :] = \ - self.approx.kvratio(r, self.R, - self.aqout.lab2[i, j, :]) + print("using approx") + rv[self.aqin.Naq + i, i, j, :] = self.approx.kvratio( + r, self.R, self.aqout.lab2[i, j, :] + ) rv.shape = (self.Nparam, aq.Naq, self.model.Np) return rv - - def disinf(self,x,y,aq=None): - '''Can be called with only one x,y value''' - if aq is None: + + def disinf(self, x, y, aq=None): + """Can be called with only one x,y value""" + if aq is None: aq = self.model.aq.findAquiferData(x, y) - qx = np.zeros((self.nparam, aq.naq, self.model.np), 'D') - qy = np.zeros((self.nparam, aq.naq, self.model.np), 'D') + qx = np.zeros((self.nparam, aq.naq, self.model.np), "D") + qy = np.zeros((self.nparam, aq.naq, self.model.np), "D") if aq == self.aqin: - qr = np.zeros((self.nparam, aq.naq, self.model.nin, - self.model.npin), 'D') + qr = np.zeros((self.nparam, aq.naq, self.model.nin, self.model.npin), "D") r = np.sqrt((x - self.x0) ** 2 + (y - self.y0) ** 2) - if r < 1e-20: + if r < 1e-20: r = 1e-20 # As we divide by that on the return for i in range(self.aqin.Naq): for j in range(self.model.Nin): if abs(r - self.R) / abs(self.aqin.lab2[i, j, 0]) < self.Rzero: if self.circ_in_small[i, j]: - qr[i, i, j, :] = -self.facin[i, j, :] * \ - iv(1, r / self.aqin.lab2[i, j, :] ) / \ - self.aqin.lab2[i, j, :] + qr[i, i, j, :] = ( + -self.facin[i, j, :] + * iv(1, r / self.aqin.lab2[i, j, :]) + / self.aqin.lab2[i, j, :] + ) else: - qr[i, i, j, :] = -self.approx.ivratiop(r, self.R, - self.aqin.lab2[i, j, :]) / \ - self.aqin.lab2[i, j, :] + qr[i, i, j, :] = ( + -self.approx.ivratiop( + r, self.R, self.aqin.lab2[i, j, :] + ) + / self.aqin.lab2[i, j, :] + ) qr.shape = (self.nparam, aq.naq, self.model.np) - qx[:] = qr * (x-self.x0) / r; qy[:] = qr * (y-self.y0) / r + qx[:] = qr * (x - self.x0) / r + qy[:] = qr * (y - self.y0) / r if aq == self.aqout: - qr = np.zeros((self.Nparam, aq.Naq, - self.model.Nin, self.model.Npin), 'D') - r = np.sqrt((x-self.x0) ** 2 + (y - self.y0) ** 2) + qr = np.zeros((self.Nparam, aq.Naq, self.model.Nin, self.model.Npin), "D") + r = np.sqrt((x - self.x0) ** 2 + (y - self.y0) ** 2) for i in range(self.aqout.Naq): for j in range(self.model.Nin): if abs(r - self.R) / abs(self.aqout.lab2[i, j, 0]) < self.Rzero: - if self.circ_out_small[i,j]: - qr[self.aqin.Naq + i, i, j, :] = \ - self.facin[i, j, :] * \ - kv(1, r / self.aqout.lab2[i, j, :]) / \ - self.aqout.lab2[i, j, :] + if self.circ_out_small[i, j]: + qr[self.aqin.Naq + i, i, j, :] = ( + self.facin[i, j, :] + * kv(1, r / self.aqout.lab2[i, j, :]) + / self.aqout.lab2[i, j, :] + ) else: - qr[self.aqin.Naq + i, i, j, :] = \ - self.approx.kvratiop(r, self.R, - self.aqout.lab2[i, j, :]) / \ - self.aqout.lab2[i, j, :] + qr[self.aqin.Naq + i, i, j, :] = ( + self.approx.kvratiop( + r, self.R, self.aqout.lab2[i, j, :] + ) + / self.aqout.lab2[i, j, :] + ) qr.shape = (self.Nparam, aq.Naq, self.model.Np) qx[:] = qr * (x - self.x0) / r qy[:] = qr * (y - self.y0) / r return qx, qy - + def layout(self): alpha = np.linspace(0, 2 * np.pi, 100) - return 'line', self.x0 + self.R * np.cos(alpha), \ - self.y0 + self.R * np.sin(alpha) - + return ( + "line", + self.x0 + self.R * np.cos(alpha), + self.y0 + self.R * np.sin(alpha), + ) + + # class CircInhom(Element,InhomEquation): # def __init__(self,model,x0=0,y0=0,R=1.0,order=0,label=None,test=False): # Element.__init__(self, model, Nparam=2*model.aq.Naq*(2*order+1), Nunknowns=2*model.aq.Naq*(2*order+1), layers=range(model.aq.Naq), type='z', name='CircInhom', label=label) @@ -341,7 +438,7 @@ def layout(self): # qr[i,0,i,j,:] = -pot[1] / self.aqin.lab2[i,j,:] * self.facin[0,i,j,:] # for n in range(1,self.order+1): # qr[i,2*n-1,i,j,:] = -(pot[n-1] + pot[n+1]) / 2 / self.aqin.lab2[i,j,:] * np.cos(n*alpha) * self.facin[n,i,j,:] -# qr[i,2*n ,i,j,:] = -(pot[n-1] + pot[n+1]) / 2 / self.aqin.lab2[i,j,:] * np.sin(n*alpha) * self.facin[n,i,j,:] +# qr[i,2*n ,i,j,:] = -(pot[n-1] + pot[n+1]) / 2 / self.aqin.lab2[i,j,:] * np.sin(n*alpha) * self.facin[n,i,j,:] # qt[i,2*n-1,i,j,:] = pot[n] * np.sin(n*alpha) * n / r * self.facin[n,i,j,:] # qt[i,2*n ,i,j,:] = -pot[n] * np.cos(n*alpha) * n / r * self.facin[n,i,j,:] # else: @@ -388,7 +485,7 @@ def layout(self): # qr.shape = (self.Nparam/2,aq.Naq,self.model.Np) # qt.shape = (self.Nparam/2,aq.Naq,self.model.Np) # qx[self.Nparam/2:,:,:] = qr * np.cos(alpha) - qt * np.sin(alpha); -# qy[self.Nparam/2:,:,:] = qr * np.sin(alpha) + qt * np.cos(alpha); +# qy[self.Nparam/2:,:,:] = qr * np.sin(alpha) + qt * np.cos(alpha); # return qx,qy # def layout(self): # return 'line', self.x0 + self.R * np.cos(np.linspace(0,2*np.pi,100)), self.y0 + self.R * np.sin(np.linspace(0,2*np.pi,100)) @@ -396,33 +493,33 @@ def layout(self): # def CircInhomMaq(model,x0=0,y0=0,R=1,order=1,kaq=[1],z=[1,0],c=[],Saq=[0.001],Sll=[0],topboundary='imp',phreatictop=False,label=None,test=False): # CircInhomDataMaq(model,x0,y0,R,kaq,z,c,Saq,Sll,topboundary,phreatictop) # return CircInhom(model,x0,y0,R,order,label,test) - + # def CircInhom3D(model,x0=0,y0=0,R=1,order=1,kaq=[1,1,1],z=[4,3,2,1],Saq=[0.3,0.001,0.001],kzoverkh=[.1,.1,.1],phreatictop=True,label=None): -# CircInhomData3D(model,x0,y0,R,kaq,z,Saq,kzoverkh,phreatictop) +# CircInhomData3D(model,x0,y0,R,kaq,z,Saq,kzoverkh,phreatictop) # return CircInhom(model,x0,y0,R,order,label) # -#ml = ModelMaq(kaq=[4,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=1,tmax=10,M=20) +# ml = ttim.ModelMaq(kaq=[4,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=1,tmax=10,M=20) ##ls = MscreenLineSinkDitchString(ml,[(-1,0),(0,0),(1,0)],tsandQ=[(0.0,1.0)],layers=[2]) -#e1a = EllipseInhomDataMaq(ml,0,0,along=2.0,bshort=1.0,angle=0.0,kaq=[10,2],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) -#e1 = EllipseInhom(ml,0,0,along=2.0,bshort=1.0,angle=0.0,order=5) -#e1 = EllipseInhomMaq(ml,0,0,along=2.0,bshort=1.0,angle=0.0,order=5,kaq=[10,2],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) +# e1a = EllipseInhomDataMaq(ml,0,0,along=2.0,bshort=1.0,angle=0.0,kaq=[10,2],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) +# e1 = EllipseInhom(ml,0,0,along=2.0,bshort=1.0,angle=0.0,order=5) +# e1 = EllipseInhomMaq(ml,0,0,along=2.0,bshort=1.0,angle=0.0,order=5,kaq=[10,2],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) ## Same inside and outside -#c1 = CircInhomMaq(ml,0,0,2.0,order=5,kaq=[4,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6]) -#c1 = CircInhomMaq(ml,0,0,2.0,order=5,kaq=[10,.1],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) +# c1 = CircInhomMaq(ml,0,0,2.0,order=5,kaq=[4,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6]) +# c1 = CircInhomMaq(ml,0,0,2.0,order=5,kaq=[10,.1],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) ##c2 = CircInhomMaq(ml,0,0,5000.0,order=1,kaq=[10,2],z=[4,2,1,0],c=[200],Saq=[2e-3,2e-4],Sll=[1e-5]) ##ml.initialize() ##c2.circ_in_small[:] = 0 ##c2.circ_out_small[:] = 0 -#w = DischargeWell(ml,xw=.5,yw=0,rw=.1,tsandQ=[0,5.0],layers=1) -#ml.solve() +# w = DischargeWell(ml,xw=.5,yw=0,rw=.1,tsandQ=[0,5.0],layers=1) +# ml.solve() -#ml.solve() -#h1,h2 = np.zeros((2,e1.Ncp)), np.zeros((2,e1.Ncp)) -#qn1,qn2 = np.zeros((2,e1.Ncp)), np.zeros((2,e1.Ncp)) -#for i in range(e1.Ncp): +# ml.solve() +# h1,h2 = np.zeros((2,e1.Ncp)), np.zeros((2,e1.Ncp)) +# qn1,qn2 = np.zeros((2,e1.Ncp)), np.zeros((2,e1.Ncp)) +# for i in range(e1.Ncp): # h1[:,i] = ml.head(e1.xc[i],e1.yc[i],2,aq=e1.aqin)[:,0] # h2[:,i] = ml.head(e1.xc[i],e1.yc[i],2,aq=e1.aqout)[:,0] # qx1,qy1 = ml.discharge(e1.xc[i],e1.yc[i],2,aq=e1.aqin) @@ -432,24 +529,23 @@ def layout(self): # qn2[:,i] = qx2[:,0]*np.cos(a) + qy2[:,0]*np.sin(a) +# ml = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10) +# w1 = Well(ml,0,2,.1,tsandQ=[(0,10)],layers=[1]) +# ls2 = ZeroHeadLineSinkString(ml,xy=[(-10,-2),(0,-4),(4,0)],layers=[1]) +# ls1 = MscreenLineSinkDitchString(ml,xy=[(-10,0),(0,0),(10,10)],tsandQ=[(0.0,7.0)],res=0.0,wh='H',layers=[2],label=None) +# ml.solve() -#ml = ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10) -#w1 = Well(ml,0,2,.1,tsandQ=[(0,10)],layers=[1]) -#ls2 = ZeroHeadLineSinkString(ml,xy=[(-10,-2),(0,-4),(4,0)],layers=[1]) -#ls1 = MscreenLineSinkDitchString(ml,xy=[(-10,0),(0,0),(10,10)],tsandQ=[(0.0,7.0)],res=0.0,wh='H',layers=[2],label=None) -#ml.solve() - -#ml = ModelMaq([1,20,2],[25,20,18,10,8,0],c=[1000,2000],Saq=[0.1,1e-4,1e-4],Sll=[0,0],phreatictop=True,tmin=1e-6,tmax=10,M=30) -#w1 = Well(ml,0,0,.1,tsandQ=[(0,1000)],layers=[2]) -#ls1 = ZeroMscreenLineSink(ml,10,-5,10,5,layers=[1,2,3],res=0.5,wh=1,vres=3,wv=1) -#w2 = ZeroMscreenWell(ml,10,0,res=1.0,layers=[1,2,3],vres=1.0) -#w3 = Well(ml,0,-10,.1,tsandQ=[(0,700)],layers=[2]) -#ml.solve() -##ml1 = ModelMaq([1,20,2],[25,20,18,10,8,0],c=[1000,2000],Saq=[1e-4,1e-4,1e-4],Sll=[0,0],tmin=0.1,tmax=10000,M=30) +# ml = ttim.ModelMaq([1,20,2],[25,20,18,10,8,0],c=[1000,2000],Saq=[0.1,1e-4,1e-4],Sll=[0,0],phreatictop=True,tmin=1e-6,tmax=10,M=30) +# w1 = Well(ml,0,0,.1,tsandQ=[(0,1000)],layers=[2]) +# ls1 = ZeroMscreenLineSink(ml,10,-5,10,5,layers=[1,2,3],res=0.5,wh=1,vres=3,wv=1) +# w2 = ZeroMscreenWell(ml,10,0,res=1.0,layers=[1,2,3],vres=1.0) +# w3 = Well(ml,0,-10,.1,tsandQ=[(0,700)],layers=[2]) +# ml.solve() +##ml1 = ttim.ModelMaq([1,20,2],[25,20,18,10,8,0],c=[1000,2000],Saq=[1e-4,1e-4,1e-4],Sll=[0,0],tmin=0.1,tmax=10000,M=30) ##w1 = Well(ml1,0,0,.1,tsandQ=[(0,1000)],layers=[2],res=0.1) ##ml1.solve() -#t = np.logspace(-1,3,100) -#h0 = ml.head(50,0,t) +# t = np.logspace(-1,3,100) +# h0 = ml.head(50,0,t) ##h1 = ml1.head(50,0,t) ##w = MscreenWell(ml,0,0,.1,tsandQ=[(0,1000),(100,0),(365,1000),(465,0)],layers=[2,3]) ##w2 = HeadWell(ml,50,0,.2,tsandh=[(0,1)],layers=[2]) @@ -460,90 +556,90 @@ def layout(self): ##ml.solve() -#ml = Model3D( kaq=[2,1,5,10,4], z=[10,8,6,4,2,0], Saq=[.1,.0001,.0002,.0002,.0001], phreatictop=True, kzoverkh=0.1, tmin=1e-3, tmax=1e3 ) -#w = MscreenWell(ml,0,-25,rw=.3,tsandQ=[(0,100),(100,50)],layers=[2,3]) -#ml.solve() - -##ml = Model3D(kaq=2.0,z=[10,5,0],Saq=[.002,.001],kzoverkh=0.2,phreatictop=False,tmin=.1,tmax=10,M=15) -#ml = ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=100,tmax=300,M=50) -#w = HeadWellNew(ml,0,0,.1,tsandh=[(0.0,1.0)],layers=1) -#ml.solve() +# ml = ttim.Model3D( kaq=[2,1,5,10,4], z=[10,8,6,4,2,0], Saq=[.1,.0001,.0002,.0002,.0001], phreatictop=True, kzoverkh=0.1, tmin=1e-3, tmax=1e3 ) +# w = MscreenWell(ml,0,-25,rw=.3,tsandQ=[(0,100),(100,50)],layers=[2,3]) +# ml.solve() + +##ml = ttim.Model3D(kaq=2.0,z=[10,5,0],Saq=[.002,.001],kzoverkh=0.2,phreatictop=False,tmin=.1,tmax=10,M=15) +# ml = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=100,tmax=300,M=50) +# w = HeadWellNew(ml,0,0,.1,tsandh=[(0.0,1.0)],layers=1) +# ml.solve() ##L1 = np.sqrt(10**2+5**2) ##ls1 = LineSink(ml,-10,-10,0,-5,tsandQ=[(0,.05*L1),(1,.02*L1)],res=1.0,layers=[1,2],label='mark1') -#w = MscreenWell(ml,-5,-5,.1,[0,5],layers=[1,2]) -#L2 = np.sqrt(10**2+15**2) -#ls2 = LineSink(ml,0,-5,10,10,tsandQ=[(0,.03*L2),(2,.07*L2)],layers=[1],label='mark2') +# w = MscreenWell(ml,-5,-5,.1,[0,5],layers=[1,2]) +# L2 = np.sqrt(10**2+15**2) +# ls2 = LineSink(ml,0,-5,10,10,tsandQ=[(0,.03*L2),(2,.07*L2)],layers=[1],label='mark2') ##ls3a = ZeroHeadLineSink(ml,-10,5,-5,5,res=1.0,layers=[1,2]) ##ls3b = ZeroHeadLineSink(ml,-5,5,0,5,res=1.0,layers=[1,2]) ##ls3c = ZeroHeadLineSink(ml,0,5,5,5,res=1.0,layers=[1,2]) ##lss = HeadLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) -#lss = ZeroHeadLineSinkString(ml,[(-10,5),(-5,5),(0,5),(5,5)],res=1.0,layers=[1,2]) +# lss = ZeroHeadLineSinkString(ml,[(-10,5),(-5,5),(0,5),(5,5)],res=1.0,layers=[1,2]) ##lss = MscreenLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandQ=[(0,0.2),(3,0.1)],res=1.0,layers=[1,2]) ##lss = ZeroMscreenLineSinkString(ml,[(-10,5),(-5,5),(0,5)],res=1.0,layers=[1,2]) ##ml.initialize() -#ml.solve() -#print ml.potential(50,50,[0.5,5]) - -#ml2 = ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) -#L1 = np.sqrt(10**2+5**2) -#ls1b = LineSink(ml2,-10,-10,0,-5,tsandQ=[(0,.05*L1),(1,.02*L1)],res=1.0,layers=[1,2],label='mark1') -#L2 = np.sqrt(10**2+15**2) -#ls2b = LineSink(ml2,0,-5,10,10,tsandQ=[(0,.03*L2),(2,.07*L2)],layers=[1],label='mark2') +# ml.solve() +# print ml.potential(50,50,[0.5,5]) + +# ml2 = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) +# L1 = np.sqrt(10**2+5**2) +# ls1b = LineSink(ml2,-10,-10,0,-5,tsandQ=[(0,.05*L1),(1,.02*L1)],res=1.0,layers=[1,2],label='mark1') +# L2 = np.sqrt(10**2+15**2) +# ls2b = LineSink(ml2,0,-5,10,10,tsandQ=[(0,.03*L2),(2,.07*L2)],layers=[1],label='mark2') ##ls3a = HeadLineSink(ml2,-10,5,-5,5,tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) ##ls3b = HeadLineSink(ml2,-5,5,0,5,tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) ##ls3a = ZeroHeadLineSink(ml2,-10,5,-5,5,res=1.0,layers=[1,2]) ##ls3b = ZeroHeadLineSink(ml2,-5,5,0,5,res=1.0,layers=[1,2]) ##ls3a = MscreenLineSink(ml2,-10,5,-5,5,tsandQ=[(0,0.2),(3,0.1)],res=1.0,layers=[1,2]) ##ls3b = MscreenLineSink(ml2,-5,5,0,5,tsandQ=[(0,0.2),(3,0.1)],res=1.0,layers=[1,2]) -#ls3a = ZeroMscreenLineSink(ml2,-10,5,-5,5,res=1.0,layers=[1,2]) -#ls3b = ZeroMscreenLineSink(ml2,-5,5,0,5,res=1.0,layers=[1,2]) +# ls3a = ZeroMscreenLineSink(ml2,-10,5,-5,5,res=1.0,layers=[1,2]) +# ls3b = ZeroMscreenLineSink(ml2,-5,5,0,5,res=1.0,layers=[1,2]) ##lssb = HeadLineSinkStringOld(ml2,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=0.0,layers=[1,2]) -#ml2.solve() -#print ml2.potential(50,50,[0.5,5]) +# ml2.solve() +# print ml2.potential(50,50,[0.5,5]) -#lss = HeadLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) -#lss = MscreenLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandQ=[(0,.03*5),(2,.07*5)],res=0.5,layers=[1,2]) -#ls3a = MscreenLineSink(ml,-10,5,-5,5,tsandQ=[(0,.03*5),(2,.07*5)],res=0.5,layers=[1,2]) -#ls3b = MscreenLineSink(ml,-5,5,0,5,tsandQ=[(0,.03*5),(2,.07*5)],res=0.5,layers=[1,2]) +# lss = HeadLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) +# lss = MscreenLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandQ=[(0,.03*5),(2,.07*5)],res=0.5,layers=[1,2]) +# ls3a = MscreenLineSink(ml,-10,5,-5,5,tsandQ=[(0,.03*5),(2,.07*5)],res=0.5,layers=[1,2]) +# ls3b = MscreenLineSink(ml,-5,5,0,5,tsandQ=[(0,.03*5),(2,.07*5)],res=0.5,layers=[1,2]) # -#ml2 = ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) -#L1 = np.sqrt(10**2+5**2) -#ls1a = LineSink(ml2,-10,-10,0,-5,tsandQ=[(0,.05*L1),(1,.02*L1)],res=1.0,layers=[1,2],label='mark1') -#L2 = np.sqrt(10**2+15**2) -#ls2a = LineSink(ml2,0,-5,10,10,tsandQ=[(0,.03*L2),(2,.07*L2)],layers=[1],label='mark2') -#ls3a = HeadLineSink(ml2,-10,5,-5,5,tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) -#ls3b = HeadLineSink(ml2,-5,5,0,5,tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) +# ml2 = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) +# L1 = np.sqrt(10**2+5**2) +# ls1a = LineSink(ml2,-10,-10,0,-5,tsandQ=[(0,.05*L1),(1,.02*L1)],res=1.0,layers=[1,2],label='mark1') +# L2 = np.sqrt(10**2+15**2) +# ls2a = LineSink(ml2,0,-5,10,10,tsandQ=[(0,.03*L2),(2,.07*L2)],layers=[1],label='mark2') +# ls3a = HeadLineSink(ml2,-10,5,-5,5,tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) +# ls3b = HeadLineSink(ml2,-5,5,0,5,tsandh=[(0,0.02),(3,0.01)],res=1.0,layers=[1,2]) ##lss = HeadLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=0.0,layers=[1,2]) ##ls3 = ZeroMscreenLineSink(ml,-10,5,0,5,res=1.0,layers=[1,2]) -#ml = ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) -#w1 = Well(ml,0,0,.1,tsandQ=[(0,5),(1,2)],res=1.0,layers=[1,2]) -#w2 = Well(ml,100,0,.1,tsandQ=[(0,3),(2,7)],layers=[1]) +# ml = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) +# w1 = Well(ml,0,0,.1,tsandQ=[(0,5),(1,2)],res=1.0,layers=[1,2]) +# w2 = Well(ml,100,0,.1,tsandQ=[(0,3),(2,7)],layers=[1]) ##w3 = MscreenWell(ml,0,100,.1,tsandQ=[(0,2),(3,1)],res=2.0,layers=[1,2]) -#w3 = ZeroMscreenWell(ml,0,100,.1,res=2.0,layers=[1,2]) +# w3 = ZeroMscreenWell(ml,0,100,.1,res=2.0,layers=[1,2]) ##w3 = ZeroHeadWell(ml,0,100,.1,res=1.0,layers=[1,2]) ##w3 = HeadWell(ml,0,100,.1,tsandh=[(0,2),(3,1)],res=1.0,layers=[1,2]) -#ml.solve() +# ml.solve() ###print ml.potential(2,3,[.5,5]) -#print ml.potential(50,50,[0.5,5]) -#ml2.solve() -#print ml2.potential(50,50,[.5,5]) -#print lss.strength([.5,5]) +# print ml.potential(50,50,[0.5,5]) +# ml2.solve() +# print ml2.potential(50,50,[.5,5]) +# print lss.strength([.5,5]) # -#ml2 = ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=0.1,tmax=10,M=15) -#ls1a = LineSink(ml2,-10,-10,0,-5,tsandsig=[(0,.05),(1,.02)],res=1.0,layers=[1,2],label='mark1') -#ls2a = LineSink(ml2,0,-5,10,10,tsandsig=[(0,.03),(2,.07)],layers=[1],label='mark2') -#ls3a = HeadLineSinkStringOld(ml2,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=0.0,layers=[1,2]) -#ml2.solve() -#print ml2.potential(50,50,[0.5,5]) - -#print 'Q from strength: ',w3.strength(.5) -#print 'Q from head diff: ',(ml.head(w3.xc,w3.yc,.5)-w3.headinside(.5))/w3.res*2*np.pi*w3.rw*ml.aq.Haq[:,np.newaxis] -#print 'Q from head diff: ',(ml.head(w3.xc,w3.yc,.5)-2.0)/w3.res*2*np.pi*w3.rw*ml.aq.Haq[:,np.newaxis] -#print w3.strength([.5,5]) -#print ls3.strength([.5,5]) -#print sum(ls3.strength([.5,5]),0) -#Q = w3.strength([.5,5]) -#print sum(Q,0) -#print ml.potential(w3.xc,w3.yc,[.5,5]) \ No newline at end of file +# ml2 = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=0.1,tmax=10,M=15) +# ls1a = LineSink(ml2,-10,-10,0,-5,tsandsig=[(0,.05),(1,.02)],res=1.0,layers=[1,2],label='mark1') +# ls2a = LineSink(ml2,0,-5,10,10,tsandsig=[(0,.03),(2,.07)],layers=[1],label='mark2') +# ls3a = HeadLineSinkStringOld(ml2,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=0.0,layers=[1,2]) +# ml2.solve() +# print ml2.potential(50,50,[0.5,5]) + +# print 'Q from strength: ',w3.strength(.5) +# print 'Q from head diff: ',(ml.head(w3.xc,w3.yc,.5)-w3.headinside(.5))/w3.res*2*np.pi*w3.rw*ml.aq.Haq[:,np.newaxis] +# print 'Q from head diff: ',(ml.head(w3.xc,w3.yc,.5)-2.0)/w3.res*2*np.pi*w3.rw*ml.aq.Haq[:,np.newaxis] +# print w3.strength([.5,5]) +# print ls3.strength([.5,5]) +# print sum(ls3.strength([.5,5]),0) +# Q = w3.strength([.5,5]) +# print sum(Q,0) +# print ml.potential(w3.xc,w3.yc,[.5,5]) diff --git a/ttim/element.py b/ttim/element.py index 163a927..60f6dee 100644 --- a/ttim/element.py +++ b/ttim/element.py @@ -1,46 +1,61 @@ +import inspect # Used for storing the input + import numpy as np -import inspect # Used for storing the input + from .invlapnumba import invlapcomp + class Element: - def __init__(self, model, nparam=1, nunknowns=0, layers=0, \ - tsandbc=[(0, 0)], type='z', name='', label=None): - '''Types of elements + def __init__( + self, + model, + nparam=1, + nunknowns=0, + layers=0, + tsandbc=[(0, 0)], + type="z", + name="", + label=None, + ): + """Types of elements 'g': strength is given through time 'v': boundary condition is variable through time 'z': boundary condition is zero through time Definition of nlayers, Ncp, Npar, nunknowns: - nlayers: Number of layers that the element is screened in, + nlayers: Number of layers that the element is screened in, as set in Element Ncp: Number of control points along the element nparam: Number of parameters, commonly nlayers * Ncp nunknowns: Number of unknown parameters, commonly zero or Npar - ''' + """ self.model = model - self.aq = None # Set in the initialization function + self.aq = None # Set in the initialization function self.nparam = nparam # Number of parameters self.nunknowns = nunknowns self.layers = np.atleast_1d(layers) self.nlayers = len(self.layers) # - tsandbc = np.atleast_2d(tsandbc).astype('d') - tsandbc_error = "tsandQ or tsandh need to be 2D lists" + \ - " or arrays, like [(0, 1), (2, 5), (8, 0)] " + tsandbc = np.atleast_2d(tsandbc).astype("d") + tsandbc_error = ( + "tsandQ or tsandh need to be 2D lists" + + " or arrays, like [(0, 1), (2, 5), (8, 0)] " + ) assert tsandbc.shape[1] == 2, tsandbc_error - self.tstart, self.bcin = tsandbc[:,0] - self.model.tstart, tsandbc[:,1] + self.tstart, self.bcin = tsandbc[:, 0] - self.model.tstart, tsandbc[:, 1] if self.tstart[0] > 0: self.tstart = np.hstack((np.zeros(1), self.tstart)) self.bcin = np.hstack((np.zeros(1), self.bcin)) # # 'z' boundary condition thru time or 'v' boundary condition thru time - self.type = type + self.type = type self.name = name self.label = label if self.label is not None: - assert self.label not in self.model.elementdict.keys(), \ - "TTim error: label " + self.label + " already exists" + assert self.label not in self.model.elementdict.keys(), ( + "TTim error: label " + self.label + " already exists" + ) self.rzero = 30 - + def setbc(self): if len(self.tstart) > 1: self.bc = np.zeros_like(self.bcin) @@ -49,96 +64,96 @@ def setbc(self): else: self.bc = self.bcin.copy() self.ntstart = len(self.tstart) - + def initialize(self): - '''Initialization of terms that cannot be initialized before other + """Initialization of terms that cannot be initialized before other elements or the aquifer is defined. - As we don't want to require a certain order of entering elements, - these terms are initialized when Model.solve is called - The initialization class needs to be overloaded - by all derived classes''' + As we don't want to require a certain order of entering elements, + these terms are initialized when Model.solve is called + The initialization class needs to be overloaded + by all derived classes""" pass - + def potinf(self, x, y, aq=None): - '''Returns complex array of size (nparam, naq, npval)''' - raise 'Must overload Element.potinf()' - + """Returns complex array of size (nparam, naq, npval)""" + raise "Must overload Element.potinf()" + def potential(self, x, y, aq=None): - '''Returns complex array of size (ngvbc, naq, npval)''' + """Returns complex array of size (ngvbc, naq, npval)""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - return np.sum(self.parameters[:, :, np.newaxis, :] * \ - self.potinf(x, y, aq), 1) - + return np.sum(self.parameters[:, :, np.newaxis, :] * self.potinf(x, y, aq), 1) + def unitpotential(self, x, y, aq=None): - '''Returns complex array of size (naq, npval) - Can be more efficient for given elements''' + """Returns complex array of size (naq, npval) + Can be more efficient for given elements""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) return np.sum(self.potinf(x, y, aq), 0) - + def unitpotentialone(self, x, y, jtime, aq=None): - '''Returns complex array of size (naq, npval) - Can be more efficient for given elements''' + """Returns complex array of size (naq, npval) + Can be more efficient for given elements""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) return np.sum(self.potinfone(x, y, jtime, aq), 0) - + def disvecinf(self, x, y, aq=None): - '''Returns 2 complex arrays of size (nparam, naq, npval)''' - raise 'Must overload Element.disvecinf()' - + """Returns 2 complex arrays of size (nparam, naq, npval)""" + raise "Must overload Element.disvecinf()" + def disvec(self, x, y, aq=None): - '''Returns 2 complex arrays of size (ngvbc, naq, npval)''' + """Returns 2 complex arrays of size (ngvbc, naq, npval)""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) qx, qy = self.disvecinf(x, y, aq) - return np.sum(self.parameters[:, :, np.newaxis, :] * qx, 1), \ - np.sum(self.parameters[:, :, np.newaxis, :] * qy, 1) - + return np.sum(self.parameters[:, :, np.newaxis, :] * qx, 1), np.sum( + self.parameters[:, :, np.newaxis, :] * qy, 1 + ) + def unitdisvec(self, x, y, aq=None): - '''Returns 2 complex arrays of size (naq, npval) - Can be more efficient for given elements''' + """Returns 2 complex arrays of size (naq, npval) + Can be more efficient for given elements""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) qx, qy = self.disvecinf(x, y, aq) return np.sum(qx, 0), np.sum(qy, 0) - + # Functions used to build equations def potinflayers(self, x, y, layers=0, aq=None): - '''layers can be scalar, list, or array. + """layers can be scalar, list, or array. returns array of size (len(layers),nparam,npval) - only used in building equations''' + only used in building equations""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) pot = self.potinf(x, y, aq) rv = np.sum(pot[:, np.newaxis, :, :] * aq.eigvec, 2) # first axis needs to be the number of layers - rv = rv.swapaxes(0, 1) + rv = rv.swapaxes(0, 1) return rv[layers, :] - + def potentiallayers(self, x, y, layers=0, aq=None): - '''Returns complex array of size (ngvbc, len(layers),npval) - only used in building equations''' + """Returns complex array of size (ngvbc, len(layers),npval) + only used in building equations""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) pot = self.potential(x, y, aq) phi = np.sum(pot[:, np.newaxis, :, :] * aq.eigvec, 2) return phi[:, layers, :] - + def unitpotentiallayers(self, x, y, layers=0, aq=None): - '''Returns complex array of size (len(layers), npval) - only used in building equations''' + """Returns complex array of size (len(layers), npval) + only used in building equations""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) pot = self.unitpotential(x, y, aq) phi = np.sum(pot[np.newaxis, :, :] * aq.eigvec, 1) return phi[layers, :] - + def disvecinflayers(self, x, y, layers=0, aq=None): - '''layers can be scalar, list, or array. + """layers can be scalar, list, or array. returns 2 arrays of size (len(layers),nparam,npval) - only used in building equations''' + only used in building equations""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) qx, qy = self.disvecinf(x, y, aq) @@ -148,135 +163,151 @@ def disvecinflayers(self, x, y, layers=0, aq=None): rvx = rvx.swapaxes(0, 1) rvy = rvy.swapaxes(0, 1) return rvx[layers, :], rvy[layers, :] - + def disveclayers(self, x, y, layers=0, aq=None): - '''Returns 2 complex array of size (ngvbc, len(layers), npval) - only used in building equations''' + """Returns 2 complex array of size (ngvbc, len(layers), npval) + only used in building equations""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) qx, qy = self.disvec(x, y, aq) rvx = np.sum(qx[:, np.newaxis, :, :] * aq.eigvec, 2) rvy = np.sum(qy[:, np.newaxis, :, :] * aq.eigvec, 2) return rvx[:, layers, :], rvy[:, layers, :] - + def unitdisveclayers(self, x, y, layers=0, aq=None): - '''Returns complex array of size (len(layers), npval) - only used in building equations''' + """Returns complex array of size (len(layers), npval) + only used in building equations""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) qx, qy = self.unitdisvec(x, y, aq) rvx = np.sum(qx[np.newaxis, :, :] * aq.eigvec, 1) rvy = np.sum(qy[np.newaxis, :, :] * aq.eigvec, 1) return rvx[layers, :], rvy[layers, :] - + def discharge(self, t, derivative=0): """The discharge in each layer - + Parameters ---------- t : scalar, list or array times at which discharge is computed. t must be ordered and tmin <= t <= tmax - + Returns ------- array of discharges (nlayers,len(t)) Discharge in each screen with zeros for layers that are not screened - + """ - # Could potentially be more efficient if s is pre-computed for + # Could potentially be more efficient if s is pre-computed for # all elements, but may not be worthwhile to store as it is quick now - time = np.atleast_1d(t).astype('d') + time = np.atleast_1d(t).astype("d") rv = np.zeros((self.nlayers, len(time))) - if self.type == 'g': - s = self.dischargeinflayers * self.model.p ** derivative - rv = invlapcomp(time, s[np.newaxis, :], self.model.npint, - self.model.M, self.model.tintervals, - np.zeros(self.ntstart, dtype='int'), - self.tstart, self.bc, self.nlayers) + if self.type == "g": + s = self.dischargeinflayers * self.model.p**derivative + rv = invlapcomp( + time, + s[np.newaxis, :], + self.model.npint, + self.model.M, + self.model.tintervals, + np.zeros(self.ntstart, dtype="int"), + self.tstart, + self.bc, + self.nlayers, + ) else: - s = np.sum(self.parameters[:, :, np.newaxis, :] * - self.dischargeinf, 1) + s = np.sum(self.parameters[:, :, np.newaxis, :] * self.dischargeinf, 1) s = np.sum(s[:, np.newaxis, :, :] * self.aq.eigvec, 2) - s = s[:, self.layers, :] * self.model.p ** derivative - rv = invlapcomp(time, s, self.model.npint, self.model.M, - self.model.tintervals, self.model.enumber, - self.model.etstart, self.model.ebc, self.nlayers) + s = s[:, self.layers, :] * self.model.p**derivative + rv = invlapcomp( + time, + s, + self.model.npint, + self.model.M, + self.model.tintervals, + self.model.enumber, + self.model.etstart, + self.model.ebc, + self.nlayers, + ) return rv - + # this function is kept for testing of version 0.6.6 def dischargeold(self, t, derivative=0): """The discharge in each layer - + Parameters ---------- t : scalar, list or array times at which discharge is computed. t must be ordered and tmin <= t <= tmax - + Returns ------- array of discharges (nlayers,len(t)) Discharge in each screen with zeros for layers that are not screened - + """ - # Could potentially be more efficient if s is pre-computed for + # Could potentially be more efficient if s is pre-computed for # all elements, but may not be worthwhile to store as it is quick now - time = np.atleast_1d(t).astype('d') + time = np.atleast_1d(t).astype("d") if (time[0] < self.model.tmin) or (time[-1] > self.model.tmax): - print('Warning, some of the times are smaller than tmin or' + \ - 'larger than tmax; zeros are substituted') + print( + "Warning, some of the times are smaller than tmin or" + + "larger than tmax; zeros are substituted" + ) rv = np.zeros((self.nlayers, np.size(time))) - if self.type == 'g': - s = self.dischargeinflayers * self.model.p ** derivative + if self.type == "g": + s = self.dischargeinflayers * self.model.p**derivative for itime in range(self.ntstart): - t = time - self.tstart[itime] + t = time - self.tstart[itime] for i in range(self.nlayers): - rv[i] += self.bc[itime] * \ - self.model.inverseLapTran(s[i], t) + rv[i] += self.bc[itime] * self.model.inverseLapTran(s[i], t) else: - s = np.sum(self.parameters[:, :, np.newaxis, :] * - self.dischargeinf, 1) + s = np.sum(self.parameters[:, :, np.newaxis, :] * self.dischargeinf, 1) s = np.sum(s[:, np.newaxis, :, :] * self.aq.eigvec, 2) - s = s[:, self.layers, :] * self.model.p ** derivative + s = s[:, self.layers, :] * self.model.p**derivative for k in range(self.model.ngvbc): e = self.model.gvbclist[k] for itime in range(e.ntstart): t = time - e.tstart[itime] if t[-1] >= self.model.tmin: # Otherwise all zero for i in range(self.nlayers): - rv[i] += e.bc[itime] * \ - self.model.inverseLapTran(s[k, i], t) + rv[i] += e.bc[itime] * self.model.inverseLapTran(s[k, i], t) return rv - + def headinside(self, t): print("This function not implemented for this element") return - + def storeinput(self, frame): self.inputargs, _, _, self.inputvalues = inspect.getargvalues(frame) - + def write(self): - rv = self.name + '(' + self.model.modelname + ',\n' + rv = self.name + "(" + self.model.modelname + ",\n" for key in self.inputargs[2:]: # The first two are ignored - if isinstance(self.inputvalues[key],np.ndarray): - rv += key + ' = ' + np.array2string(self.inputvalues[key], - separator=',') + ',\n' - elif isinstance(self.inputvalues[key],str): + if isinstance(self.inputvalues[key], np.ndarray): + rv += ( + key + + " = " + + np.array2string(self.inputvalues[key], separator=",") + + ",\n" + ) + elif isinstance(self.inputvalues[key], str): rv += key + " = '" + self.inputvalues[key] + "',\n" else: - rv += key + ' = ' + str(self.inputvalues[key]) + ',\n' - rv += ')\n' + rv += key + " = " + str(self.inputvalues[key]) + ",\n" + rv += ")\n" return rv - + def run_after_solve(self): - '''function to run after a solution is completed. + """function to run after a solution is completed. for most elements nothing needs to be done, - but for strings of elements some arrays may need to be filled''' + but for strings of elements some arrays may need to be filled""" pass - + def plot(self): pass - \ No newline at end of file diff --git a/ttim/equation.py b/ttim/equation.py index fd89522..aff22eb 100644 --- a/ttim/equation.py +++ b/ttim/equation.py @@ -1,8 +1,9 @@ import numpy as np + class HeadEquation: def equation(self): - '''Mix-in class that returns matrix rows for head-specified conditions. + """Mix-in class that returns matrix rows for head-specified conditions. (really written as constant potential element) Works for nunknowns = 1 Returns matrix part nunknowns,neq,npval, complex @@ -10,213 +11,238 @@ def equation(self): Phi_out - c*T*q_s = Phi_in Well: q_s = Q / (2*pi*r_w*H) LineSink: q_s = sigma / H = Q / (L*H) - ''' - mat = np.empty((self.nunknowns, self.model.neq, - self.model.npval), 'D') + """ + mat = np.empty((self.nunknowns, self.model.neq, self.model.npval), "D") # rhs needs be initialized zero - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") for icp in range(self.ncp): istart = icp * self.nlayers - ieq = 0 + ieq = 0 for e in self.model.elementlist: if e.nunknowns > 0: - mat[istart: istart + self.nlayers, - ieq: ieq + e.nunknowns, :] = e.potinflayers( - self.xc[icp], self.yc[icp], self.layers) + mat[ + istart : istart + self.nlayers, ieq : ieq + e.nunknowns, : + ] = e.potinflayers(self.xc[icp], self.yc[icp], self.layers) if e == self: - for i in range(self.nlayers): - mat[istart + i, ieq + istart + i, :] -= \ - self.resfacp[istart + i] * \ - e.dischargeinflayers[istart + i] + for i in range(self.nlayers): + mat[istart + i, ieq + istart + i, :] -= ( + self.resfacp[istart + i] + * e.dischargeinflayers[istart + i] + ) ieq += e.nunknowns for i in range(self.model.ngbc): - rhs[istart: istart + self.nlayers, i, :] -= \ - self.model.gbclist[i].unitpotentiallayers( - self.xc[icp], self.yc[icp], self.layers) - if self.type == 'v': + rhs[istart : istart + self.nlayers, i, :] -= self.model.gbclist[ + i + ].unitpotentiallayers(self.xc[icp], self.yc[icp], self.layers) + if self.type == "v": iself = self.model.vbclist.index(self) for i in range(self.nlayers): - rhs[istart + i, self.model.ngbc + iself, :] = \ + rhs[istart + i, self.model.ngbc + iself, :] = ( self.pc[istart + i] / self.model.p + ) return mat, rhs - + + class WellBoreStorageEquation: def equation(self): - '''Mix-in class that returns matrix rows for multi-aquifer element with - total given discharge, uniform but unknown head and + """Mix-in class that returns matrix rows for multi-aquifer element with + total given discharge, uniform but unknown head and InternalStorageEquation - ''' - mat = np.zeros((self.nunknowns, self.model.neq, - self.model.npval), 'D') - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + """ + mat = np.zeros((self.nunknowns, self.model.neq, self.model.npval), "D") + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") ieq = 0 for e in self.model.elementlist: if e.nunknowns > 0: - head = e.potinflayers(self.xc[0], self.yc[0], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis, np.newaxis] - mat[:-1, ieq: ieq + e.nunknowns, :] = head[:-1, :] - head[1:, :] - mat[-1, ieq: ieq + e.nunknowns, :] -= np.pi * self.rc**2 * \ - self.model.p * head[0, :] + head = ( + e.potinflayers(self.xc[0], self.yc[0], self.layers) + / self.aq.T[self.layers][:, np.newaxis, np.newaxis] + ) + mat[:-1, ieq : ieq + e.nunknowns, :] = head[:-1, :] - head[1:, :] + mat[-1, ieq : ieq + e.nunknowns, :] -= ( + np.pi * self.rc**2 * self.model.p * head[0, :] + ) if e == self: - disterm = self.dischargeinflayers * self.res / (2 * np.pi * - self.rw * self.aq.Haq[self.layers][:, np.newaxis]) + disterm = ( + self.dischargeinflayers + * self.res + / ( + 2 + * np.pi + * self.rw + * self.aq.Haq[self.layers][:, np.newaxis] + ) + ) if self.nunknowns > 1: # Multiple layers for i in range(self.nunknowns - 1): mat[i, ieq + i, :] -= disterm[i] mat[i, ieq + i + 1, :] += disterm[i + 1] - mat[-1, ieq: ieq + self.nunknowns, :] += \ - self.dischargeinflayers - mat[-1, ieq, :] += \ - np.pi * self.rc ** 2 * self.model.p * disterm[0] + mat[-1, ieq : ieq + self.nunknowns, :] += self.dischargeinflayers + mat[-1, ieq, :] += np.pi * self.rc**2 * self.model.p * disterm[0] ieq += e.nunknowns for i in range(self.model.ngbc): - head = self.model.gbclist[i].unitpotentiallayers( - self.xc[0], self.yc[0], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis] + head = ( + self.model.gbclist[i].unitpotentiallayers( + self.xc[0], self.yc[0], self.layers + ) + / self.aq.T[self.layers][:, np.newaxis] + ) rhs[:-1, i, :] -= head[:-1, :] - head[1:, :] - rhs[-1, i, :] += np.pi * self.rc ** 2 * self.model.p * head[0, :] - if self.type == 'v': + rhs[-1, i, :] += np.pi * self.rc**2 * self.model.p * head[0, :] + if self.type == "v": iself = self.model.vbclist.index(self) rhs[-1, self.model.ngbc + iself, :] += self.flowcoef if self.hdiff is not None: # head[0] - head[1] = hdiff - rhs[:-1, self.model.ngbc + iself, :] += \ - self.hdiff[:, np.newaxis] / self.model.p + rhs[:-1, self.model.ngbc + iself, :] += ( + self.hdiff[:, np.newaxis] / self.model.p + ) return mat, rhs - + + class HeadEquationNores: def equation(self): - '''Mix-in class that returns matrix rows for head-specified conditions. + """Mix-in class that returns matrix rows for head-specified conditions. (really written as constant potential element) Returns matrix part nunknowns, neq, npval, complex Returns rhs part nunknowns, nvbc, npval, complex - ''' - mat = np.empty((self.nunknowns, self.model.neq, - self.model.npval), 'D') - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + """ + mat = np.empty((self.nunknowns, self.model.neq, self.model.npval), "D") + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") for icp in range(self.ncp): istart = icp * self.nlayers - ieq = 0 + ieq = 0 for e in self.model.elementlist: if e.nunknowns > 0: - mat[istart: istart + self.nlayers, - ieq: ieq + e.nunknowns, :] = e.potinflayers( - self.xc[icp], self.yc[icp], self.layers) + mat[ + istart : istart + self.nlayers, ieq : ieq + e.nunknowns, : + ] = e.potinflayers(self.xc[icp], self.yc[icp], self.layers) ieq += e.nunknowns for i in range(self.model.ngbc): - rhs[istart: istart + self.nlayers, i, :] -= \ - self.model.gbclist[i].unitpotentiallayers( - self.xc[icp], self.yc[icp], self.layers) - if self.type == 'v': + rhs[istart : istart + self.nlayers, i, :] -= self.model.gbclist[ + i + ].unitpotentiallayers(self.xc[icp], self.yc[icp], self.layers) + if self.type == "v": iself = self.model.vbclist.index(self) for i in range(self.nlayers): - rhs[istart + i, self.model.ngbc + iself, :] = \ + rhs[istart + i, self.model.ngbc + iself, :] = ( self.pc[istart + i] / self.model.p + ) return mat, rhs - + + class LeakyWallEquation: def equation(self): - '''Mix-in class that returns matrix rows for leaky-wall condition + """Mix-in class that returns matrix rows for leaky-wall condition Returns matrix part nunknowns,neq,npval, complex Returns rhs part nunknowns,nvbc,npval, complex - ''' - mat = np.empty((self.nunknowns, self.model.neq, - self.model.npval), 'D') - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + """ + mat = np.empty((self.nunknowns, self.model.neq, self.model.npval), "D") + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") for icp in range(self.ncp): istart = icp * self.nlayers - ieq = 0 + ieq = 0 for e in self.model.elementlist: if e.nunknowns > 0: - qx, qy = e.disvecinflayers(self.xc[icp], self.yc[icp], - self.layers) - mat[istart: istart + self.nlayers, - ieq: ieq + e.nunknowns, :] = \ + qx, qy = e.disvecinflayers(self.xc[icp], self.yc[icp], self.layers) + mat[istart : istart + self.nlayers, ieq : ieq + e.nunknowns, :] = ( qx * self.cosout[icp] + qy * self.sinout[icp] + ) if e == self: - hmin = e.potinflayers( - self.xcneg[icp], self.ycneg[icp], self.layers) / \ - self.aq.T[self.layers][: ,np.newaxis, np.newaxis] - hplus = e.potinflayers( - self.xc[icp], self.yc[icp], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis, np.newaxis] - mat[istart:istart + self.nlayers, - ieq: ieq + e.nunknowns, :] -= \ - self.resfac[:, np.newaxis, np.newaxis] * \ - (hplus - hmin) + hmin = ( + e.potinflayers( + self.xcneg[icp], self.ycneg[icp], self.layers + ) + / self.aq.T[self.layers][:, np.newaxis, np.newaxis] + ) + hplus = ( + e.potinflayers(self.xc[icp], self.yc[icp], self.layers) + / self.aq.T[self.layers][:, np.newaxis, np.newaxis] + ) + mat[ + istart : istart + self.nlayers, ieq : ieq + e.nunknowns, : + ] -= self.resfac[:, np.newaxis, np.newaxis] * (hplus - hmin) ieq += e.nunknowns for i in range(self.model.ngbc): qx, qy = self.model.gbclist[i].unitdisveclayers( - self.xc[icp], self.yc[icp], self.layers) - rhs[istart: istart + self.nlayers, i, :] -= \ + self.xc[icp], self.yc[icp], self.layers + ) + rhs[istart : istart + self.nlayers, i, :] -= ( qx * self.cosout[icp] + qy * self.sinout[icp] - #if self.type == 'v': + ) + # if self.type == 'v': # iself = self.model.vbclist.index(self) # for i in range(self.nlayers): # rhs[istart+i,self.model.ngbc+iself,:] = \ # self.pc[istart+i] / self.model.p return mat, rhs - + + class MscreenEquation: def equation(self): - '''Mix-in class that returns matrix rows for multi-screen conditions + """Mix-in class that returns matrix rows for multi-screen conditions where total discharge is specified. Works for nunknowns = 1 Returns matrix part nunknowns, neq, npval, complex Returns rhs part nunknowns, nvbc, npval, complex head_out - c * q_s = h_in - Set h_i - h_(i + 1) = 0 and Sum Q_i = Q''' - mat = np.zeros((self.nunknowns, self.model.neq, - self.model.npval), 'D') - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + Set h_i - h_(i + 1) = 0 and Sum Q_i = Q""" + mat = np.zeros((self.nunknowns, self.model.neq, self.model.npval), "D") + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") ieq = 0 for icp in range(self.ncp): istart = icp * self.nlayers - ieq = 0 + ieq = 0 for e in self.model.elementlist: if e.nunknowns > 0: - head = e.potinflayers( - self.xc[icp], self.yc[icp], self.layers) / \ - self.aq.T[self.layers][:,np.newaxis,np.newaxis] - mat[istart: istart + self.nlayers - 1, - ieq: ieq + e.nunknowns, :] = \ - head[:-1,:] - head[1:,:] + head = ( + e.potinflayers(self.xc[icp], self.yc[icp], self.layers) + / self.aq.T[self.layers][:, np.newaxis, np.newaxis] + ) + mat[ + istart : istart + self.nlayers - 1, ieq : ieq + e.nunknowns, : + ] = (head[:-1, :] - head[1:, :]) if e == self: - for i in range(self.nlayers-1): - mat[istart + i, ieq + istart + i, :] -= \ - self.resfach[istart + i] * \ - e.dischargeinflayers[istart + i] - mat[istart + i, ieq + istart + i + 1, :] += \ - self.resfach[istart + i + 1] * \ - e.dischargeinflayers[istart + i + 1] - mat[istart + i, - ieq + istart: ieq + istart + i + 1, :] -= \ - self.vresfac[istart + i] * \ - e.dischargeinflayers[istart + i] - mat[istart + self.nlayers - 1, - ieq + istart: ieq + istart + self.nlayers, :] = 1.0 + for i in range(self.nlayers - 1): + mat[istart + i, ieq + istart + i, :] -= ( + self.resfach[istart + i] + * e.dischargeinflayers[istart + i] + ) + mat[istart + i, ieq + istart + i + 1, :] += ( + self.resfach[istart + i + 1] + * e.dischargeinflayers[istart + i + 1] + ) + mat[istart + i, ieq + istart : ieq + istart + i + 1, :] -= ( + self.vresfac[istart + i] + * e.dischargeinflayers[istart + i] + ) + mat[ + istart + self.nlayers - 1, + ieq + istart : ieq + istart + self.nlayers, + :, + ] = 1.0 ieq += e.nunknowns for i in range(self.model.ngbc): - head = self.model.gbclist[i].unitpotentiallayers( - self.xc[icp], self.yc[icp], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis] - rhs[istart: istart + self.nlayers - 1, i, :] -= \ - head[:-1,:] - head[1:,:] - if self.type == 'v': + head = ( + self.model.gbclist[i].unitpotentiallayers( + self.xc[icp], self.yc[icp], self.layers + ) + / self.aq.T[self.layers][:, np.newaxis] + ) + rhs[istart : istart + self.nlayers - 1, i, :] -= ( + head[:-1, :] - head[1:, :] + ) + if self.type == "v": iself = self.model.vbclist.index(self) - rhs[istart + self.nlayers - 1, self.model.ngbc + iself, :] = 1.0 - # If self.type == 'z', it should sum to zero, + rhs[istart + self.nlayers - 1, self.model.ngbc + iself, :] = 1.0 + # If self.type == 'z', it should sum to zero, # which is the default value of rhs return mat, rhs - + + class MscreenDitchEquation: def equation(self): - '''Mix-in class that returns matrix rows for multi-screen conditions + """Mix-in class that returns matrix rows for multi-screen conditions where total discharge is specified. Returns matrix part nunknowns,neq,npval, complex Returns rhs part nunknowns,nvbc,npval, complex @@ -224,145 +250,180 @@ def equation(self): Set h_i - h_(i+1) = 0 and Sum Q_i = Q I would say headin_i - headin_(i+1) = 0 - headout_i - c*qs_i - headout_(i+1) + c*qs_(i+1) = 0 + headout_i - c*qs_i - headout_(i+1) + c*qs_(i+1) = 0 In case of storage: Sum Q_i - A * p^2 * headin = Q - ''' - mat = np.zeros((self.nunknowns, self.model.neq, - self.model.npval), 'D') - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + """ + mat = np.zeros((self.nunknowns, self.model.neq, self.model.npval), "D") + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") ieq = 0 for icp in range(self.ncp): istart = icp * self.nlayers - ieq = 0 + ieq = 0 for e in self.model.elementlist: if e.nunknowns > 0: - head = e.potinflayers( - self.xc[icp], self.yc[icp], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis, np.newaxis] - if self.nlayers > 1: - mat[istart: istart + self.nlayers - 1, - ieq: ieq + e.nunknowns, :] = \ + head = ( + e.potinflayers(self.xc[icp], self.yc[icp], self.layers) + / self.aq.T[self.layers][:, np.newaxis, np.newaxis] + ) + if self.nlayers > 1: + mat[ + istart : istart + self.nlayers - 1, + ieq : ieq + e.nunknowns, + :, + ] = ( head[:-1, :] - head[1:, :] - # Store head in top layer in 2nd to last equation + ) + # Store head in top layer in 2nd to last equation # of this control point - mat[istart + self.nlayers - 1, - ieq: ieq + e.nunknowns, :] = head[0,:] + mat[istart + self.nlayers - 1, ieq : ieq + e.nunknowns, :] = head[ + 0, : + ] if e == self: - # Correct head in top layer in second to last equation + # Correct head in top layer in second to last equation # to make it head inside - mat[istart + self.nlayers - 1, - ieq + istart, :] -= self.resfach[istart] * \ - e.dischargeinflayers[istart] + mat[istart + self.nlayers - 1, ieq + istart, :] -= ( + self.resfach[istart] * e.dischargeinflayers[istart] + ) if icp == 0: istartself = ieq # Needed to build last equation - for i in range(self.nlayers-1): - mat[istart + i, ieq + istart + i, :] -= \ - self.resfach[istart + i] * \ - e.dischargeinflayers[istart + i] - mat[istart + i, ieq + istart + i + 1, :] += \ - self.resfach[istart + i + 1] * \ - e.dischargeinflayers[istart + i + 1] - #vresfac not yet used here; it is set to zero as - #I don't quite now what is means yet - #mat[istart + i, ieq + istart:ieq+istart+i+1,:] -= \ + for i in range(self.nlayers - 1): + mat[istart + i, ieq + istart + i, :] -= ( + self.resfach[istart + i] + * e.dischargeinflayers[istart + i] + ) + mat[istart + i, ieq + istart + i + 1, :] += ( + self.resfach[istart + i + 1] + * e.dischargeinflayers[istart + i + 1] + ) + # vresfac not yet used here; it is set to zero as + # I don't quite now what is means yet + # mat[istart + i, ieq + istart:ieq+istart+i+1,:] -= \ # self.vresfac[istart + i] * \ # e.dischargeinflayers[istart + i] ieq += e.nunknowns for i in range(self.model.ngbc): - head = self.model.gbclist[i].unitpotentiallayers( - self.xc[icp], self.yc[icp], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis] - if self.nlayers > 1: - rhs[istart: istart + self.nlayers - 1, i, :] -= \ + head = ( + self.model.gbclist[i].unitpotentiallayers( + self.xc[icp], self.yc[icp], self.layers + ) + / self.aq.T[self.layers][:, np.newaxis] + ) + if self.nlayers > 1: + rhs[istart : istart + self.nlayers - 1, i, :] -= ( head[:-1, :] - head[1:, :] - # Store minus the head in top layer in second to last equation + ) + # Store minus the head in top layer in second to last equation # for this control point - rhs[istart + self.nlayers - 1, i, :] -= head[0, :] + rhs[istart + self.nlayers - 1, i, :] -= head[0, :] # Modify last equations for icp in range(self.ncp - 1): ieq = (icp + 1) * self.nlayers - 1 # Head first layer control point icp - Head first layer control # point icp + 1 - mat[ieq, :, :] -= mat[ieq + self.nlayers, :, :] + mat[ieq, :, :] -= mat[ieq + self.nlayers, :, :] rhs[ieq, :, :] -= rhs[ieq + self.nlayers, :, :] # Last equation setting the total discharge of the ditch - mat[-1, :, :] = 0.0 - mat[-1, istartself: istartself + self.nparam, :] = 1.0 + mat[-1, :, :] = 0.0 + mat[-1, istartself : istartself + self.nparam, :] = 1.0 if self.Astorage is not None: # Used to store last equation in case of ditch storage - matlast = np.zeros((self.model.neq, self.model.npval), 'D') - rhslast = np.zeros((self.model.npval), 'D') + matlast = np.zeros((self.model.neq, self.model.npval), "D") + rhslast = np.zeros((self.model.npval), "D") ieq = 0 for e in self.model.elementlist: - head = e.potinflayers(self.xc[0], self.yc[0], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis, np.newaxis] - matlast[ieq: ieq + e.nunknowns] -= \ - self.Astorage * self.model.p ** 2 * head[0, :] + head = ( + e.potinflayers(self.xc[0], self.yc[0], self.layers) + / self.aq.T[self.layers][:, np.newaxis, np.newaxis] + ) + matlast[ieq : ieq + e.nunknowns] -= ( + self.Astorage * self.model.p**2 * head[0, :] + ) if e == self: - # only need to correct first unknown - matlast[ieq] += self.Astorage * self.model.p ** 2 * \ - self.resfach[0] * e.dischargeinflayers[0] + # only need to correct first unknown + matlast[ieq] += ( + self.Astorage + * self.model.p**2 + * self.resfach[0] + * e.dischargeinflayers[0] + ) ieq += e.nunknowns for i in range(self.model.ngbc): - head = self.model.gbclist[i].unitpotentiallayers( - self.xc[0], self.yc[0], self.layers) / \ - self.aq.T[self.layers][:, np.newaxis] - rhslast += self.Astorage * self.model.p ** 2 * head[0] + head = ( + self.model.gbclist[i].unitpotentiallayers( + self.xc[0], self.yc[0], self.layers + ) + / self.aq.T[self.layers][:, np.newaxis] + ) + rhslast += self.Astorage * self.model.p**2 * head[0] mat[-1] += matlast rhs[-1, :, :] = 0.0 - if self.type == 'v': + if self.type == "v": iself = self.model.vbclist.index(self) - rhs[-1, self.model.ngbc + iself, :] = 1.0 - # If self.type == 'z', it should sum to zero, which is the default + rhs[-1, self.model.ngbc + iself, :] = 1.0 + # If self.type == 'z', it should sum to zero, which is the default # value of rhs - if self.Astorage is not None: + if self.Astorage is not None: rhs[-1, self.model.ngbc + iself, :] += rhslast return mat, rhs - + + class InhomEquation: def equation(self): - '''Mix-in class that returns matrix rows for inhomogeneity conditions''' - mat = np.zeros((self.nunknowns, self.model.neq, - self.model.npval), 'D') - rhs = np.zeros((self.nunknowns, self.model.ngvbc, - self.model.npval), 'D') + """Mix-in class that returns matrix rows for inhomogeneity conditions""" + mat = np.zeros((self.nunknowns, self.model.neq, self.model.npval), "D") + rhs = np.zeros((self.nunknowns, self.model.ngvbc, self.model.npval), "D") for icp in range(self.ncp): istart = icp * 2 * self.nlayers - ieq = 0 + ieq = 0 for e in self.model.elementList: if e.nunknowns > 0: - mat[istart: istart + self.nlayers, - ieq: ieq + e.nunknowns, :] = \ - e.potinflayers(self.xc[icp], self.yc[icp], - self.layers, self.aqin) / \ - self.aqin.T[self.layers][:, np.newaxis, np.newaxis] - \ - e.potinflayers(self.xc[icp], self.yc[icp], - self.layers, self.aqout) / \ - self.aqout.T[self.layers][:, np.newaxis, np.newaxis] + mat[istart : istart + self.nlayers, ieq : ieq + e.nunknowns, :] = ( + e.potinflayers( + self.xc[icp], self.yc[icp], self.layers, self.aqin + ) + / self.aqin.T[self.layers][:, np.newaxis, np.newaxis] + - e.potinflayers( + self.xc[icp], self.yc[icp], self.layers, self.aqout + ) + / self.aqout.T[self.layers][:, np.newaxis, np.newaxis] + ) qxin, qyin = e.disinflayers( - self.xc[icp], self.yc[icp], self.layers, self.aqin) + self.xc[icp], self.yc[icp], self.layers, self.aqin + ) qxout, qyout = e.disinflayers( - self.xc[icp], self.yc[icp], self.layers, self.aqout) - mat[istart + self.nlayers: istart + 2 * self.nlayers, - ieq: ieq + e.nunknowns, :] = \ - (qxin - qxout) * np.cos(self.thetacp[icp]) + \ - (qyin - qyout) * np.sin(self.thetacp[icp]) + self.xc[icp], self.yc[icp], self.layers, self.aqout + ) + mat[ + istart + self.nlayers : istart + 2 * self.nlayers, + ieq : ieq + e.nunknowns, + :, + ] = (qxin - qxout) * np.cos(self.thetacp[icp]) + ( + qyin - qyout + ) * np.sin( + self.thetacp[icp] + ) ieq += e.nunknowns for i in range(self.model.ngbc): - rhs[istart: istart + self.nlayers, i, :] -= ( - self.model.gbclist[i].unitpotentiallayers( - self.xc[icp], self.yc[icp], self.layers, self.aqin) / - self.aqin.T[self.layers][:, np.newaxis] - + rhs[istart : istart + self.nlayers, i, :] -= ( self.model.gbclist[i].unitpotentiallayers( - self.xc[icp], self.yc[icp], self.layers, self.aqout) / - self.aqout.T[self.layers][:, np.newaxis]) + self.xc[icp], self.yc[icp], self.layers, self.aqin + ) + / self.aqin.T[self.layers][:, np.newaxis] + - self.model.gbclist[i].unitpotentiallayers( + self.xc[icp], self.yc[icp], self.layers, self.aqout + ) + / self.aqout.T[self.layers][:, np.newaxis] + ) qxin, qyin = self.model.gbclist[i].unitdischargelayers( - self.xc[icp], self.yc[icp], self.layers, self.aqin) - qxout,qyout = self.model.gbclist[i].unitdischargelayers( - self.xc[icp], self.yc[icp], self.layers, self.aqout) - rhs[istart + self.nlayers: istart + 2 * self.nlayers, i, :] -= \ - (qxin - qxout) * np.cos(self.thetacp[icp]) + \ - (qyin - qyout) * np.sin(self.thetacp[icp]) - return mat, rhs \ No newline at end of file + self.xc[icp], self.yc[icp], self.layers, self.aqin + ) + qxout, qyout = self.model.gbclist[i].unitdischargelayers( + self.xc[icp], self.yc[icp], self.layers, self.aqout + ) + rhs[istart + self.nlayers : istart + 2 * self.nlayers, i, :] -= ( + qxin - qxout + ) * np.cos(self.thetacp[icp]) + (qyin - qyout) * np.sin( + self.thetacp[icp] + ) + return mat, rhs diff --git a/ttim/fit.py b/ttim/fit.py old mode 100644 new mode 100755 index fb13d20..d5fdfed --- a/ttim/fit.py +++ b/ttim/fit.py @@ -1,39 +1,42 @@ +import re + +import lmfit import numpy as np import pandas as pd +from scipy.linalg import svd from scipy.optimize import least_squares -import re -import lmfit + class Calibrate: - def __init__(self, model): """initialize Calibration class - + Parameters ---------- model : ttim.Model model to calibrate - + """ self.model = model - self.parameters = pd.DataFrame(columns=[ - 'optimal', 'std', 'perc_std', 'pmin', 'pmax', 'initial', 'parray']) + self.parameters = pd.DataFrame( + columns=["optimal", "std", "perc_std", "pmin", "pmax", "initial", "parray"] + ) self.seriesdict = {} self.seriesinwelldict = {} - + def set_parameter(self, name=None, initial=0, pmin=-np.inf, pmax=np.inf): """set parameter to be optimized - + Parameters ---------- name : str - parameter name, can include layer information. - name can be 'kaq', 'Saq' or 'c'. A number after the parameter - name denotes the layer number, i.e. 'kaq0' refers to the hydraulic - conductivity of layer 0. + parameter name, can include layer information. + name can be 'kaq', 'Saq' or 'c'. A number after the parameter + name denotes the layer number, i.e. 'kaq0' refers to the hydraulic + conductivity of layer 0. name also supports layer ranges, entered by adding a '_' and a - layer number, i.e. 'kaq0_3' denotes conductivity for layers 0 up to + layer number, i.e. 'kaq0_3' denotes conductivity for layers 0 up to and including 3. initial : float, optional initial value for the parameter (the default is 0) @@ -41,76 +44,90 @@ def set_parameter(self, name=None, initial=0, pmin=-np.inf, pmax=np.inf): lower bound for parameter value (the default is -np.inf) pmax : float, optional upper bound for paramater value (the default is np.inf) - + """ assert type(name) == str, "Error: name must be string" # find numbers in name str for support layer ranges - layers_from_name = re.findall(r'\d+', name) + layers_from_name = re.findall(r"\d+", name) p = None if "_" in name: fromlay, tolay = [int(i) for i in layers_from_name] - if name[:3] == 'kaq': - p = self.model.aq.kaq[fromlay:tolay+1] - elif name[:3] == 'Saq': - p = self.model.aq.Saq[fromlay:tolay+1] - elif name[0] == 'c': - p = self.model.aq.c[fromlay:tolay+1] - elif name[:3] == 'Sll': - p = self.model.aq.Sll[fromlay:tolay+1] - elif name[0:8] == 'kzoverkh': - p = self.model.aq.kzoverkh[fromlay:tolay+1] + if name[:3] == "kaq": + p = self.model.aq.kaq[fromlay : tolay + 1] + elif name[:3] == "Saq": + p = self.model.aq.Saq[fromlay : tolay + 1] + elif name[0] == "c": + p = self.model.aq.c[fromlay : tolay + 1] + elif name[:3] == "Sll": + p = self.model.aq.Sll[fromlay : tolay + 1] + elif name[0:8] == "kzoverkh": + p = self.model.aq.kzoverkh[fromlay : tolay + 1] else: layer = int(layers_from_name[0]) # Set, kaq, Saq, c - if name[:3] == 'kaq': - p = self.model.aq.kaq[layer:layer + 1] - elif name[:3] == 'Saq': - p = self.model.aq.Saq[layer:layer + 1] - elif name[0] == 'c': - p = self.model.aq.c[layer:layer + 1] - elif name[:3] == 'Sll': - p = self.model.aq.Sll[layer:layer + 1] - elif name[0:8] == 'kzoverkh': - p = self.model.aq.kzoverkh[layer:layer + 1] + if name[:3] == "kaq": + p = self.model.aq.kaq[layer : layer + 1] + elif name[:3] == "Saq": + p = self.model.aq.Saq[layer : layer + 1] + elif name[0] == "c": + p = self.model.aq.c[layer : layer + 1] + elif name[:3] == "Sll": + p = self.model.aq.Sll[layer : layer + 1] + elif name[0:8] == "kzoverkh": + p = self.model.aq.kzoverkh[layer : layer + 1] if p is None: # no parameter set - print('parameter name not recognized or no parameter ref supplied') + print("parameter name not recognized or no parameter ref supplied") return - self.parameters.loc[name] = {'optimal':initial, 'std':None, - 'perc_std':None, 'pmin':pmin, 'pmax':pmax, - 'initial':initial, 'parray':p[:]} - - def set_parameter_by_reference(self, name=None, parameter=None, initial=0, - pmin=-np.inf, pmax=np.inf): + self.parameters.loc[name] = { + "optimal": initial, + "std": None, + "perc_std": None, + "pmin": pmin, + "pmax": pmax, + "initial": initial, + "parray": p[:], + } + + def set_parameter_by_reference( + self, name=None, parameter=None, initial=0, pmin=-np.inf, pmax=np.inf + ): """set parameter to be optimized - + Parameters ---------- name : str parameter name parameter : np.array - array reference containing the parameter to be optimized. must be - specified as reference, i.e. w.rc[0:] + array reference containing the parameter to be optimized. must be + specified as reference, i.e. w.rc[0:] initial : float, optional initial value for the parameter (the default is 0) pmin : float, optional lower bound for parameter value (the default is -np.inf) pmax : float, optional upper bound for paramater value (the default is np.inf) - + """ assert type(name) == str, "Error: name must be string" if parameter is not None: - assert isinstance(parameter, np.ndarray), \ - "Error: parameter needs to be numpy array" + assert isinstance( + parameter, np.ndarray + ), "Error: parameter needs to be numpy array" p = parameter - self.parameters.loc[name] = {'optimal':initial, 'std':None, - 'perc_std':None, 'pmin':pmin, 'pmax':pmax, - 'initial':initial, 'parray':p[:]} - - def series(self, name, x, y, layer, t, h): + self.parameters.loc[name] = { + "optimal": initial, + "std": None, + "perc_std": None, + "pmin": pmin, + "pmax": pmax, + "initial": initial, + "parray": p[:], + } + + def series(self, name, x, y, layer, t, h, weights=None): """method to add observations to Calibration object - + Parameters ---------- name : str @@ -125,15 +142,15 @@ def series(self, name, x, y, layer, t, h): array containing timestamps of timeseries h : np.array array containing timeseries values, i.e. head observations - + """ - s = Series(x, y, layer, t, h) + s = Series(x, y, layer, t, h, weights=weights) self.seriesdict[name] = s - + def seriesinwell(self, name, element, t, h): """method to add observations to Calibration object - + Parameters ---------- name : str @@ -143,142 +160,163 @@ def seriesinwell(self, name, element, t, h): array containing timestamps of timeseries h : np.array array containing timeseries values, i.e. head observations - + """ e = SeriesInWell(element, t, h) self.seriesinwelldict[name] = e - - def residuals(self, p, printdot=False): + + def residuals(self, p, printdot=False, weighted=True, layers=None, series=None): """method to calculate residuals given certain parameters - + Parameters ---------- p : np.array array containing parameter values printdot : bool, optional print dot for each function call - + Returns ------- np.array array containing all residuals - + """ if printdot: - print('.', end='') + print(".", end="") # set the values of the variables - + if printdot == 7: print(p) - + + if layers is None: + layers = range(self.model.aq.naq) + for i, k in enumerate(self.parameters.index): # [:] needed to do set value in array - self.parameters.loc[k, 'parray'][:] = p[i] - + self.parameters.loc[k, "parray"][:] = p[i] + self.model.solve(silent=True) - + rv = np.empty(0) - for key in self.seriesdict: + cal_series = self.seriesdict.keys() if series is None else series + for key in cal_series: s = self.seriesdict[key] + if s.layer not in layers: + continue h = self.model.head(s.x, s.y, s.t, layers=s.layer) - rv = np.append(rv, s.h - h) + w = s.weights if ((s.weights is not None) and weighted) else np.ones_like(h) + rv = np.append(rv, (s.h - h) * w) for key in self.seriesinwelldict: s = self.seriesinwelldict[key] h = s.element.headinside(s.t)[0] rv = np.append(rv, s.h - h) return rv - + def residuals_lmfit(self, lmfitparams, printdot=False): vals = lmfitparams.valuesdict() p = np.array([vals[k] for k in self.parameters.index]) - #p = np.array([vals[k] for k in vals]) + # p = np.array([vals[k] for k in vals]) return self.residuals(p, printdot) - - def fit_least_squares(self, report=True, diff_step=1e-4, xtol=1e-8, - method='lm'): + + def fit_least_squares(self, report=True, diff_step=1e-4, xtol=1e-8, method="lm"): self.fitresult = least_squares( - self.residuals, self.parameters.initial.values, args=(True,), + self.residuals, + self.parameters.initial.values, + args=(True,), bounds=(self.parameters.pmin.values, self.parameters.pmax.values), - method=method, diff_step=diff_step, xtol=xtol, x_scale="jac") - print('', flush=True) + method=method, + diff_step=diff_step, + xtol=xtol, + x_scale="jac", + ) + print("", flush=True) # Call residuals to specify optimal values for model res = self.residuals(self.fitresult.x) for ipar in self.parameters.index: - self.parameters.loc[ipar, 'optimal'] = \ - self.parameters.loc[ipar, 'parray'][0] + self.parameters.loc[ipar, "optimal"] = self.parameters.loc[ipar, "parray"][ + 0 + ] nparam = len(self.fitresult.x) H = self.fitresult.jac.T @ self.fitresult.jac sigsq = np.var(res, ddof=nparam) - self.covmat = np.linalg.inv(H) * sigsq + self.covmat = np.linalg.inv(H) * sigsq self.sig = np.sqrt(np.diag(self.covmat)) D = np.diag(1 / self.sig) self.cormat = D @ self.covmat @ D - self.parameters['std'] = self.sig - self.parameters['perc_std'] = self.sig / \ - self.parameters['optimal'] * 100 + self.parameters["std"] = self.sig + self.parameters["perc_std"] = self.sig / self.parameters["optimal"] * 100 if report: print(self.parameters) print(self.sig) print(self.covmat) print(self.cormat) - + def fit_lmfit(self, report=True, printdot=True): import lmfit + self.lmfitparams = lmfit.Parameters() for name in self.parameters.index: p = self.parameters.loc[name] - self.lmfitparams.add(name, value=p['initial'], min=p['pmin'], - max=p['pmax']) + self.lmfitparams.add(name, value=p["initial"], min=p["pmin"], max=p["pmax"]) fit_kws = {"epsfcn": 1e-4} - self.fitresult = lmfit.minimize(self.residuals_lmfit, self.lmfitparams, - method="leastsq", - kws={"printdot":printdot}, **fit_kws) - print('', flush=True) + self.fitresult = lmfit.minimize( + self.residuals_lmfit, + self.lmfitparams, + method="leastsq", + kws={"printdot": printdot}, + **fit_kws, + ) + print("", flush=True) print(self.fitresult.message) if self.fitresult.success: for name in self.parameters.index: - self.parameters.loc[name, 'optimal'] = \ - self.fitresult.params.valuesdict()[name] - if hasattr(self.fitresult, 'covar'): - self.parameters['std'] = np.sqrt(np.diag(self.fitresult.covar)) - self.parameters['perc_std'] = 100 * self.parameters['std'] / \ - np.abs(self.parameters['optimal']) + self.parameters.loc[ + name, "optimal" + ] = self.fitresult.params.valuesdict()[name] + if hasattr(self.fitresult, "covar"): + self.parameters["std"] = np.sqrt(np.diag(self.fitresult.covar)) + self.parameters["perc_std"] = ( + 100 * self.parameters["std"] / np.abs(self.parameters["optimal"]) + ) else: - self.parameters['std'] = np.nan - self.parameters['perc_std'] = np.nan + self.parameters["std"] = np.nan + self.parameters["perc_std"] = np.nan if report: print(lmfit.fit_report(self.fitresult)) - + def fit(self, report=True, printdot=True): # current default fitting routine return self.fit_lmfit(report, printdot) - - def rmse(self): + + def rmse(self, weighted=True, layers=None): """calculate root-mean-squared-error - + Returns ------- float return rmse value """ - r = self.residuals(self.parameters['optimal'].values) - return np.sqrt(np.mean(r ** 2)) - + r = self.residuals( + self.parameters["optimal"].values, weighted=weighted, layers=layers + ) + return np.sqrt(np.mean(r**2)) + + class Series: - def __init__(self, x, y, layer, t, h): + def __init__(self, x, y, layer, t, h, weights=None): self.x = x self.y = y self.layer = layer self.t = t self.h = h - + self.weights = weights + + class SeriesInWell: def __init__(self, element, t, h): self.element = element self.t = t self.h = h - - diff --git a/ttim/invlapnumba.py b/ttim/invlapnumba.py index b9e4f13..2a6a50c 100644 --- a/ttim/invlapnumba.py +++ b/ttim/invlapnumba.py @@ -1,25 +1,26 @@ # Copyright 2019 Kristopher L. Kuhlman -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: -# The above copyright notice and this permission notice shall be included in +# The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. -import numpy as np import numba +import numpy as np + @numba.njit(nogil=True, cache=True) def invlap(t, tmax, fp, M, alpha=1e-10, tol=1e-9): @@ -32,23 +33,23 @@ def invlap(t, tmax, fp, M, alpha=1e-10, tol=1e-9): tmax : float maximum time fp : complex array - Laplace transformed solution + Laplace transformed solution M : integer number of terms (number of values of fp) - alpha: is the real part of the rightmost pole or singularity, which - is chosen based on the desired accuracy (assuming the rightmost + alpha: is the real part of the rightmost pole or singularity, which + is chosen based on the desired accuracy (assuming the rightmost singularity is 0), and tol=10α is the desired tolerance Returns ------- - result : array + result : array time domain solution for specified times Reference -------- - de Hoog, F., J. Knight, A. Stokes (1982). An improved method for - numerical inversion of Laplace transforms. SIAM Journal of Scientific + de Hoog, F., J. Knight, A. Stokes (1982). An improved method for + numerical inversion of Laplace transforms. SIAM Journal of Scientific and Statistical Computing 3:357-366, http://dx.doi.org/10.1137/0903022 https://bitbucket.org/klkuhlm/invlap/src/default/invlap.py @@ -57,19 +58,19 @@ def invlap(t, tmax, fp, M, alpha=1e-10, tol=1e-9): # return zeros if all fp equal to zero if np.all(np.abs(fp) == 0): return np.zeros(len(t)) - + NP = 2 * M + 1 scale = 2.0 T = scale * tmax Nt = len(t) # - #tol = alpha * 10.0 + # tol = alpha * 10.0 gamma = alpha - np.log(tol) / (scale * T) # would it be useful to try re-using # space between e&q and A&B? # Kris programmed this as np.complex64 - e = np.empty((2 * M + 1, M+1), dtype=np.complex128) + e = np.empty((2 * M + 1, M + 1), dtype=np.complex128) q = np.empty((2 * M, M), dtype=np.complex128) d = np.empty(2 * M + 1, dtype=np.complex128) A = np.empty((2 * M + 2, Nt), dtype=np.complex128) @@ -85,27 +86,26 @@ def invlap(t, tmax, fp, M, alpha=1e-10, tol=1e-9): for r in range(1, M + 1): # start with e, column 1, 0:2*M-2 mr = 2 * (M - r) + 1 - e[0:mr, r] = q[1:mr + 1, r - 1] - q[0:mr, r - 1] + e[1:mr + 1, r - 1] + e[0:mr, r] = q[1 : mr + 1, r - 1] - q[0:mr, r - 1] + e[1 : mr + 1, r - 1] if not r == M: rq = r + 1 mr = 2 * (M - rq) + 2 for i in range(mr): - q[i, rq - 1] = q[i + 1, rq - 2] * e[i + 1, rq - 1] / \ - e[i, rq - 1] + q[i, rq - 1] = q[i + 1, rq - 2] * e[i + 1, rq - 1] / e[i, rq - 1] # build up continued fraction coefficients (d) d[0] = fp[0] / 2.0 for r in range(1, M + 1): - d[2 * r - 1] = -q[0, r - 1] # even terms - d[2 * r] = -e[0, r] # odd terms + d[2 * r - 1] = -q[0, r - 1] # even terms + d[2 * r] = -e[0, r] # odd terms # seed A and B for recurrence - A[0] = 0.0 + A[0] = 0.0 A[1] = d[0] - B[0: 2] = 1.0 + 0j + B[0:2] = 1.0 + 0j # base of the power series - z = np.exp(1j * np.pi * t / T) + z = np.exp(1j * np.pi * t / T) # coefficients of Pade approximation (A & B) # using recurrence for all but last term @@ -114,8 +114,8 @@ def invlap(t, tmax, fp, M, alpha=1e-10, tol=1e-9): B[i + 1] = B[i] + d[i] * B[i - 1] * z # "improved remainder" to continued fraction - brem = (1.0 + (d[2 * M - 1] - d[2 * M]) * z) / 2.0 - rem = -brem * (1.0 - np.sqrt(1.0 + d[2 * M] * z / brem ** 2)) + brem = (1.0 + (d[2 * M - 1] - d[2 * M]) * z) / 2.0 + rem = -brem * (1.0 - np.sqrt(1.0 + d[2 * M] * z / brem**2)) # last term of recurrence using new remainder A[NP] = A[2 * M] + rem * A[2 * M - 1] @@ -124,18 +124,19 @@ def invlap(t, tmax, fp, M, alpha=1e-10, tol=1e-9): # diagonal Pade approximation # F=A/B represents accelerated trapezoid rule result = np.exp(gamma * t) / T * (A[NP] / B[NP]).real - #print('results:', result) - #print('A', A) - #print('B', B) + # print('results:', result) + # print('A', A) + # print('B', B) return result + @numba.njit(nogil=True, cache=True) def compute_laplace_parameters_numba(tmax, M=20, alpha=1e-10, tol=1e-9): # 2*M+1 terms in approximation # desired tolerance (here simply related to alpha) - #tol = alpha * 10.0 - nump = 2 * M + 1 # number of terms in approximation + # tol = alpha * 10.0 + nump = 2 * M + 1 # number of terms in approximation # scaling factor (likely tune-able, but 2 is typical) scale = 2.0 T = scale * tmax @@ -143,17 +144,18 @@ def compute_laplace_parameters_numba(tmax, M=20, alpha=1e-10, tol=1e-9): p = gamma + 1j * np.pi * np.arange(nump) / T return p + def invlaptest(): p = compute_laplace_parameters_numba(tmax=10, alpha=1e-10) fp = 1 / (p + 1) ** 2 t = np.arange(1.0, 10) ft = invlap(t, 10, fp, 20, alpha=1e-10) - print('approximate from invlap:', ft) - print('exact:', t * np.exp(-t)) + print("approximate from invlap:", ft) + print("exact:", t * np.exp(-t)) + -@numba.njit(nogil=True, cache=True) -def invlapcomp(time, pot, npint, M, tintervals, - enumber, etstart, ebc, nlayers): +@numba.njit(nogil=True, cache=True) +def invlapcomp(time, pot, npint, M, tintervals, enumber, etstart, ebc, nlayers): ''' """Compute time domain solution for given laplace domain solution @@ -172,19 +174,19 @@ def invlapcomp(time, pot, npint, M, tintervals, ebc : array with boundary condition value of element nlayers : integer or None (default) number of layers - + Method ------ - enumber, etstart, and ebc are used because numba cannot deal with a list + enumber, etstart, and ebc are used because numba cannot deal with a list of arrays of different lengths (makes sense, actually) - + Returns ------- - pot[naq, ntimes] if layers=None, + pot[naq, ntimes] if layers=None, otherwise pot[len(layers) ,ntimes] - t must be ordered ''' - - print_tmin_warning = True # set to False if warning is printed once + t must be ordered''' + + print_tmin_warning = True # set to False if warning is printed once print_tmax_warning = True nelements, naq, npval = pot.shape nint = len(tintervals) - 1 @@ -199,51 +201,52 @@ def invlapcomp(time, pot, npint, M, tintervals, continue else: # no effect for any t <= 0 - it = np.argmax(t > 0) # find_first - if (t[it] < tintervals[0]): # there are times before first interval + it = np.argmax(t > 0) # find_first + if t[it] < tintervals[0]: # there are times before first interval if print_tmin_warning: - print('Warning, some of the times are smaller than tmin after') - print('a change in boundary condition. nans are substituted') + print("Warning, some of the times are smaller than tmin after") + print("a change in boundary condition. nans are substituted") print_tmin_warning = False - if t[-1] < tintervals[0]: # all times before first interval + if t[-1] < tintervals[0]: # all times before first interval itnew = len(t) else: - itnew = np.argmax(t >= tintervals[0]) # find_first + itnew = np.argmax(t >= tintervals[0]) # find_first rv[:, it:itnew] = np.nan it = itnew for n in range(nint): if n == 0: - tp = t[(t >= tintervals[n]) & \ - (t <= tintervals[n + 1])] + tp = t[(t >= tintervals[n]) & (t <= tintervals[n + 1])] else: - tp = t[(t > tintervals[n]) & \ - (t <= tintervals[n + 1])] + tp = t[(t > tintervals[n]) & (t <= tintervals[n + 1])] nt = len(tp) - #if nt > 0: # if all zero, don't do the inv transform + # if nt > 0: # if all zero, don't do the inv transform if nt == 0: continue for i in range(nlayers): - # I used to check the first value only, but got to check if + # I used to check the first value only, but got to check if # none of the values are zero - if not np.any(pot[enumber[j], i, - n * npint: (n + 1) * npint] == 0) : - rv[i, it: it + nt] += ebc[j] * \ - invlap(tp, tintervals[n + 1], - pot[enumber[j], i , n * npint: (n + 1) * npint], M) + if not np.any(pot[enumber[j], i, n * npint : (n + 1) * npint] == 0): + rv[i, it : it + nt] += ebc[j] * invlap( + tp, + tintervals[n + 1], + pot[enumber[j], i, n * npint : (n + 1) * npint], + M, + ) it = it + nt if it < len(t): # there are times above tintervals[-1] if print_tmax_warning: - print('Warning, some of the times are larger than tmax after') - print('a change in boundary condition. nans are substituted') + print("Warning, some of the times are larger than tmax after") + print("a change in boundary condition. nans are substituted") print_tmax_warning = False rv[:, it:] = np.nan return rv + # The following general function is currently not used but very useful # to do numerical Laplace transformation @numba.njit(nogil=True, cache=True) def invlapgen(time, pot, M, tintervals, tstart, ebc): - ''' + """ Compute time domain solution for given Laplace domain solution Parameters @@ -255,17 +258,17 @@ def invlapgen(time, pot, M, tintervals, tstart, ebc): tintervals : 1D array of time intervals, length nint + 1 tstart : 1D array with starting times of bc in element, length nstart ebc : 1D array with change in boundary condition value, length nstart - + Method ------ ebc is the difference with the previous value - + Returns ------- rv[ntimes] - ''' - - print_tmin_warning = True # set to False if warning is printed once + """ + + print_tmin_warning = True # set to False if warning is printed once print_tmax_warning = True npint = 2 * M + 1 nint = len(tintervals) - 1 @@ -280,40 +283,38 @@ def invlapgen(time, pot, M, tintervals, tstart, ebc): continue else: # no effect for any t <= 0 - it = np.argmax(t > 0) # find_first - if (t[it] < tintervals[0]): # there are times before first interval + it = np.argmax(t > 0) # find_first + if t[it] < tintervals[0]: # there are times before first interval if print_tmin_warning: - print('Warning, some of the times are smaller than tmin after') - print('a change in boundary condition. nans are substituted') + print("Warning, some of the times are smaller than tmin after") + print("a change in boundary condition. nans are substituted") print_tmin_warning = False - if t[-1] < tintervals[0]: # all times before first interval + if t[-1] < tintervals[0]: # all times before first interval itnew = len(t) else: - itnew = np.argmax(t >= tintervals[0]) # find_first + itnew = np.argmax(t >= tintervals[0]) # find_first rv[it:itnew] = np.nan it = itnew for n in range(nint): if n == 0: - tp = t[(t >= tintervals[n]) & \ - (t <= tintervals[n + 1])] + tp = t[(t >= tintervals[n]) & (t <= tintervals[n + 1])] else: - tp = t[(t > tintervals[n]) & \ - (t <= tintervals[n + 1])] + tp = t[(t > tintervals[n]) & (t <= tintervals[n + 1])] nt = len(tp) - #if nt > 0: # if all zero, don't do the inv transform + # if nt > 0: # if all zero, don't do the inv transform if nt == 0: continue - # I used to check the first value only, but got to check if + # I used to check the first value only, but got to check if # none of the values are zero - if not np.any(pot[n * npint: (n + 1) * npint] == 0) : - rv[it: it + nt] += ebc[j] * \ - invlap(tp, tintervals[n + 1], - pot[n * npint: (n + 1) * npint], M) + if not np.any(pot[n * npint : (n + 1) * npint] == 0): + rv[it : it + nt] += ebc[j] * invlap( + tp, tintervals[n + 1], pot[n * npint : (n + 1) * npint], M + ) it = it + nt if it < len(t): # there are times above tintervals[-1] if print_tmax_warning: - print('Warning, some of the times are larger than tmax after') - print('a change in boundary condition. nans are substituted') + print("Warning, some of the times are larger than tmax after") + print("a change in boundary condition. nans are substituted") print_tmax_warning = False rv[it:] = np.nan - return rv \ No newline at end of file + return rv diff --git a/ttim/kuhlman_invlap.py b/ttim/kuhlman_invlap.py index 16f469d..3a73ff7 100644 --- a/ttim/kuhlman_invlap.py +++ b/ttim/kuhlman_invlap.py @@ -6,8 +6,8 @@ # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -class InverseLaplaceTransform(object): +class InverseLaplaceTransform(object): def __init__(self): self.talbot_cache = {} self.stehfest_cache = {} @@ -16,17 +16,17 @@ def clear(self): self.talbot_cache = {} self.stehfest_cache = {} - def calc_laplace_parameter(self,t,**kwargs): + def calc_laplace_parameter(self, t, **kwargs): raise NotImplementedError - def calc_time_domain_solution(self,fp): + def calc_time_domain_solution(self, fp): raise NotImplementedError -class FixedTalbot(InverseLaplaceTransform): - def calc_laplace_parameter(self,t,**kwargs): +class FixedTalbot(InverseLaplaceTransform): + def calc_laplace_parameter(self, t, **kwargs): import numpy as np - + # required # ------------------------------ # time of desired approximation @@ -36,43 +36,42 @@ def calc_laplace_parameter(self,t,**kwargs): # ------------------------------ # maximum time desired (used for scaling) default is requested # time. - self.tmax = kwargs.get('tmax',self.t) + self.tmax = kwargs.get("tmax", self.t) - self.degree = int(kwargs.get('degree',14)) + self.degree = int(kwargs.get("degree", 14)) M = self.degree # Abate & Valko rule of thumb for r parameter - if 'r' in kwargs: - self.r = kwargs['r'] + if "r" in kwargs: + self.r = kwargs["r"] r_default = False else: - self.r = 2.0/5.0*M + self.r = 2.0 / 5.0 * M r_default = True if r_default and self.degree in self.talbot_cache: - self.theta,self.cot_theta,self.delta = self.talbot_cache[self.degree] + self.theta, self.cot_theta, self.delta = self.talbot_cache[self.degree] else: - self.theta = np.linspace(0.0, np.pi, M+1, dtype=np.float64) + self.theta = np.linspace(0.0, np.pi, M + 1, dtype=np.float64) self.cot_theta = np.empty((M,), dtype=np.float64) self.cot_theta[0] = 0.0 - self.cot_theta[1:] = 1.0/np.tan(self.theta[1:-1]) + self.cot_theta[1:] = 1.0 / np.tan(self.theta[1:-1]) # all but time-dependent part of p self.delta = np.empty((M,), dtype=np.complex64) self.delta[0] = self.r - self.delta[1:] = self.r*self.theta[1:-1]*(self.cot_theta[1:] + 1j) + self.delta[1:] = self.r * self.theta[1:-1] * (self.cot_theta[1:] + 1j) - self.talbot_cache[self.degree] = self.theta,self.cot_theta,self.delta - - self.p = self.delta/self.tmax + self.talbot_cache[self.degree] = self.theta, self.cot_theta, self.delta - # NB: p is complex + self.p = self.delta / self.tmax - def calc_time_domain_solution(self,fp,t): + # NB: p is complex + def calc_time_domain_solution(self, fp, t): import numpy as np - + # required # ------------------------------ self.t = t @@ -89,25 +88,28 @@ def calc_time_domain_solution(self,fp,t): r = self.r ans = np.empty((M,), dtype=np.complex64) - ans[0] = np.exp(delta[0])*fp[0]/2.0 + ans[0] = np.exp(delta[0]) * fp[0] / 2.0 - ans[1:] = np.exp(delta[1:])*fp[1:] - ans[1:] *= (1.0 + 1j*theta[1:-1]*(1.0 + self.cot_theta[1:]**2) - - 1j*self.cot_theta[1:]) + ans[1:] = np.exp(delta[1:]) * fp[1:] + ans[1:] *= ( + 1.0 + + 1j * theta[1:-1] * (1.0 + self.cot_theta[1:] ** 2) + - 1j * self.cot_theta[1:] + ) - result = 2.0/5.0*np.sum(ans)/self.t + result = 2.0 / 5.0 * np.sum(ans) / self.t # ignore any small imaginary part return result.real -# **************************************** -class Stehfest(InverseLaplaceTransform): +# **************************************** - def calc_laplace_parameter(self,t,**kwargs): +class Stehfest(InverseLaplaceTransform): + def calc_laplace_parameter(self, t, **kwargs): import numpy as np - + # required # ------------------------------ # time of desired approximation @@ -116,12 +118,12 @@ def calc_laplace_parameter(self,t,**kwargs): # optional # ------------------------------ - self.degree = int(kwargs.get('degree',16)) + self.degree = int(kwargs.get("degree", 16)) self.ln2 = np.log(2.0) - + # _coeff routine requires even degree - if self.degree%2 > 0: + if self.degree % 2 > 0: self.degree += 1 M = self.degree @@ -129,11 +131,11 @@ def calc_laplace_parameter(self,t,**kwargs): if self.degree in self.stehfest_cache: self.V = self.stehfest_cache[self.degree] else: - self.V = kwargs.get('V',self._coeff()) + self.V = kwargs.get("V", self._coeff()) self.stehfest_cache[self.degree] = self.V - - self.p = np.arange(1,M+1)*self.ln2/self.t - + + self.p = np.arange(1, M + 1) * self.ln2 / self.t + # NB: p is real def _coeff(self): @@ -142,80 +144,81 @@ def _coeff(self): import numpy as np from scipy.misc import factorial - + M = self.degree - M2 = int(M/2.0) # checked earlier that M is even + M2 = int(M / 2.0) # checked earlier that M is even V = np.empty((M,), dtype=np.float64) - fac = lambda x: float(factorial(x,exact=True)) - + fac = lambda x: float(factorial(x, exact=True)) + # Salzer summation weights # get very large in magnitude and oscillate in sign, # if the precision is not high enough, there will be # catastrophic cancellation - for k in range(1,M+1): - z = np.zeros((min(k,M2)+1,), dtype=np.float64) - for j in range(int((k+1)/2.0),min(k,M2)+1): - z[j] = (j**M2*fac(2*j)/ - (fac(M2-j)*fac(j)*fac(j-1)*fac(k-j)*fac(2*j-k))) - V[k-1] = (-1)**(k+M2)*np.sum(z) + for k in range(1, M + 1): + z = np.zeros((min(k, M2) + 1,), dtype=np.float64) + for j in range(int((k + 1) / 2.0), min(k, M2) + 1): + z[j] = ( + j**M2 + * fac(2 * j) + / (fac(M2 - j) * fac(j) * fac(j - 1) * fac(k - j) * fac(2 * j - k)) + ) + V[k - 1] = (-1) ** (k + M2) * np.sum(z) return V - def calc_time_domain_solution(self,fp,t): - + def calc_time_domain_solution(self, fp, t): import numpy as np - + # required self.t = t # assume fp was computed from p matrix returned from # calc_laplace_parameter() - result = np.dot(self.V,fp)*self.ln2/self.t + result = np.dot(self.V, fp) * self.ln2 / self.t # ignore any small imaginary part return result.real -# **************************************** -class deHoog(InverseLaplaceTransform): +# **************************************** - def calc_laplace_parameter(self,t,**kwargs): +class deHoog(InverseLaplaceTransform): + def calc_laplace_parameter(self, t, **kwargs): import numpy as np # NB: too many parameters, and simple p # nothing is cached here. - + self.t = t - self.tmax = kwargs.get('tmax',self.t) + self.tmax = kwargs.get("tmax", self.t) - self.degree = int(kwargs.get('degree',17)) + self.degree = int(kwargs.get("degree", 17)) # 2*M+1 terms in approximation M = self.degree - self.alpha = kwargs.get('alpha',1.0E-16) + self.alpha = kwargs.get("alpha", 1.0e-16) # desired tolerance (here simply related to alpha) - self.tol = kwargs.get('tol',self.alpha*10.0) - self.nump = 2*self.degree+1 # number of terms in approximation + self.tol = kwargs.get("tol", self.alpha * 10.0) + self.nump = 2 * self.degree + 1 # number of terms in approximation # scaling factor (likely tune-able, but 2 is typical) - self.scale = kwargs.get('scale',2.0) - self.T = kwargs.get('T',self.scale*self.tmax) + self.scale = kwargs.get("scale", 2.0) + self.T = kwargs.get("T", self.scale * self.tmax) - self.gamma = self.alpha - np.log(self.tol)/(self.scale*self.T) - self.p = self.gamma + 1j*np.pi*np.arange(self.nump)/self.T - - # NB: p is complex (mpc) + self.gamma = self.alpha - np.log(self.tol) / (self.scale * self.T) + self.p = self.gamma + 1j * np.pi * np.arange(self.nump) / self.T - def calc_time_domain_solution(self,fp,t): + # NB: p is complex (mpc) + def calc_time_domain_solution(self, fp, t): import numpy as np - + M = self.degree NP = self.nump T = self.T @@ -224,63 +227,64 @@ def calc_time_domain_solution(self,fp,t): # would it be useful to try re-using # space between e&q and A&B? - e = np.empty((NP,M+1), dtype=np.complex64) - q = np.empty((NP,M), dtype=np.complex64) + e = np.empty((NP, M + 1), dtype=np.complex64) + q = np.empty((NP, M), dtype=np.complex64) d = np.empty((NP,), dtype=np.complex64) - A = np.empty((NP+2,), dtype=np.complex64) - B = np.empty((NP+2,), dtype=np.complex64) + A = np.empty((NP + 2,), dtype=np.complex64) + B = np.empty((NP + 2,), dtype=np.complex64) # initialize Q-D table - e[0:2*M,0] = 0.0 - q[0,0] = fp[1]/(fp[0]/2.0) - for i in range(1,2*M): - q[i,0] = fp[i+1]/fp[i] + e[0 : 2 * M, 0] = 0.0 + q[0, 0] = fp[1] / (fp[0] / 2.0) + for i in range(1, 2 * M): + q[i, 0] = fp[i + 1] / fp[i] # rhombus rule for filling triangular Q-D table (e & q) - for r in range(1,M+1): + for r in range(1, M + 1): # start with e, column 1, 0:2*M-2 - mr = 2*(M-r) - e[0:mr,r] = q[1:mr+1,r-1] - q[0:mr,r-1] + e[1:mr+1,r-1] + mr = 2 * (M - r) + e[0:mr, r] = q[1 : mr + 1, r - 1] - q[0:mr, r - 1] + e[1 : mr + 1, r - 1] if not r == M: - rq = r+1 - mr = 2*(M-rq)+1 + rq = r + 1 + mr = 2 * (M - rq) + 1 for i in range(mr): - q[i,rq-1] = q[i+1,rq-2]*e[i+1,rq-1]/e[i,rq-1] + q[i, rq - 1] = q[i + 1, rq - 2] * e[i + 1, rq - 1] / e[i, rq - 1] # build up continued fraction coefficients (d) - d[0] = fp[0]/2.0 - for r in range(1,M+1): - d[2*r-1] = -q[0,r-1] # even terms - d[2*r] = -e[0,r] # odd terms + d[0] = fp[0] / 2.0 + for r in range(1, M + 1): + d[2 * r - 1] = -q[0, r - 1] # even terms + d[2 * r] = -e[0, r] # odd terms # seed A and B for recurrence - A[0] = 0.0 + A[0] = 0.0 A[1] = d[0] - B[0:2] = 1.0 + B[0:2] = 1.0 # base of the power series - z = np.exp(1j*np.pi*self.t/T) + z = np.exp(1j * np.pi * self.t / T) # coefficients of Pade approximation (A & B) # using recurrence for all but last term - for i in range(1,2*M): - A[i+1] = A[i] + d[i]*A[i-1]*z - B[i+1] = B[i] + d[i]*B[i-1]*z + for i in range(1, 2 * M): + A[i + 1] = A[i] + d[i] * A[i - 1] * z + B[i + 1] = B[i] + d[i] * B[i - 1] * z # "improved remainder" to continued fraction - brem = (1.0 + (d[2*M-1] - d[2*M])*z)/2.0 - rem = -brem*(1.0 - np.sqrt(1.0 + d[2*M]*z/brem**2)) + brem = (1.0 + (d[2 * M - 1] - d[2 * M]) * z) / 2.0 + rem = -brem * (1.0 - np.sqrt(1.0 + d[2 * M] * z / brem**2)) # last term of recurrence using new remainder - A[NP] = A[2*M] + rem*A[2*M-1] - B[NP] = B[2*M] + rem*B[2*M-1] + A[NP] = A[2 * M] + rem * A[2 * M - 1] + B[NP] = B[2 * M] + rem * B[2 * M - 1] # diagonal Pade approximation # F=A/B represents accelerated trapezoid rule - result = np.exp(self.gamma*self.t)/T*(A[NP]/B[NP]).real + result = np.exp(self.gamma * self.t) / T * (A[NP] / B[NP]).real return result + # **************************************** # initialize classes @@ -288,16 +292,16 @@ def calc_time_domain_solution(self,fp,t): _stehfest = Stehfest() _de_hoog = deHoog() -def invertlaplace(f, t, **kwargs): - rule = kwargs.get('method','dehoog') +def invertlaplace(f, t, **kwargs): + rule = kwargs.get("method", "dehoog") if type(rule) is str: lrule = rule.lower() - if lrule == 'talbot': + if lrule == "talbot": rule = _fixed_talbot - elif lrule == 'stehfest': + elif lrule == "stehfest": rule = _stehfest - elif lrule == 'dehoog': + elif lrule == "dehoog": rule = _de_hoog else: raise ValueError("unknown invlap algorithm: %s" % rule) @@ -306,7 +310,7 @@ def invertlaplace(f, t, **kwargs): # determine the vector of Laplace-space parameter # needed for the requested method and desired time - rule.calc_laplace_parameter(t,**kwargs) + rule.calc_laplace_parameter(t, **kwargs) # compute the Laplace-space function evalutations # at the required abscissa. @@ -314,18 +318,20 @@ def invertlaplace(f, t, **kwargs): # compute the time-domain solution from the # Laplace-space function evaluations - return rule.calc_time_domain_solution(fp,t) + return rule.calc_time_domain_solution(fp, t) + # shortcuts for the above function for specific methods def invlaptalbot(*args, **kwargs): - kwargs['method'] = 'talbot' + kwargs["method"] = "talbot" return invertlaplace(*args, **kwargs) + def invlapstehfest(*args, **kwargs): - kwargs['method'] = 'stehfest' + kwargs["method"] = "stehfest" return invertlaplace(*args, **kwargs) + def invlapdehoog(*args, **kwargs): - kwargs['method'] = 'dehoog' + kwargs["method"] = "dehoog" return invertlaplace(*args, **kwargs) - diff --git a/ttim/linedoublet.py b/ttim/linedoublet.py index b935fa5..5f565cf 100644 --- a/ttim/linedoublet.py +++ b/ttim/linedoublet.py @@ -1,140 +1,191 @@ -import numpy as np +import inspect # Used for storing the input + import matplotlib.pyplot as plt -import inspect # Used for storing the input +import numpy as np + +from . import besselnumba from .element import Element from .equation import LeakyWallEquation -from . import besselnumba + class LineDoubletHoBase(Element): - '''Higher Order LineDoublet Base Class. - All Higher Order Line Doublet elements are derived from this class''' - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandbc= - [(0.0, 0.0)], res='imp', order=0, layers=0, type='', - name='LineDoubletHoBase', label=None, addtomodel=True): - Element.__init__(self, model, nparam=1, nunknowns=0, layers=layers, - tsandbc=tsandbc, type=type, name=name, label=label) + """Higher Order LineDoublet Base Class. + All Higher Order Line Doublet elements are derived from this class""" + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandbc=[(0.0, 0.0)], + res="imp", + order=0, + layers=0, + type="", + name="LineDoubletHoBase", + label=None, + addtomodel=True, + ): + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=layers, + tsandbc=tsandbc, + type=type, + name=name, + label=label, + ) self.order = order self.nparam = (self.order + 1) * len(self.layers) self.x1 = float(x1) self.y1 = float(y1) self.x2 = float(x2) self.y2 = float(y2) - if res == 'imp': + if res == "imp": self.res = np.inf else: self.res = float(res) - if addtomodel: self.model.addelement(self) + if addtomodel: + self.model.addelement(self) def __repr__(self): - return self.name + ' from ' + str((self.x1, self.y1)) +\ - ' to ' + str((self.x2, self.y2)) + return ( + self.name + + " from " + + str((self.x1, self.y1)) + + " to " + + str((self.x2, self.y2)) + ) def initialize(self): self.ncp = self.order + 1 self.z1 = self.x1 + 1j * self.y1 self.z2 = self.x2 + 1j * self.y2 self.L = np.abs(self.z1 - self.z2) - self.thetanormOut = np.arctan2(self.y2 - self.y1, self.x2 - self.x1) \ - - np.pi / 2 + self.thetanormOut = np.arctan2(self.y2 - self.y1, self.x2 - self.x1) - np.pi / 2 self.cosout = np.cos(self.thetanormOut) * np.ones(self.ncp) self.sinout = np.sin(self.thetanormOut) * np.ones(self.ncp) # - thetacp = np.arange(np.pi, 0, -np.pi / self.ncp) \ - - 0.5 * np.pi / self.ncp - Zcp = np.zeros(self.ncp, 'D') + thetacp = np.arange(np.pi, 0, -np.pi / self.ncp) - 0.5 * np.pi / self.ncp + Zcp = np.zeros(self.ncp, "D") Zcp.real = np.cos(thetacp) # control point just on positive site (this is handy later on) - Zcp.imag = 1e-6 + Zcp.imag = 1e-6 zcp = Zcp * (self.z2 - self.z1) / 2 + 0.5 * (self.z1 + self.z2) self.xc = zcp.real self.yc = zcp.imag - # control point just on negative side + # control point just on negative side # (this is needed for building the system of equations) - Zcp.imag = -1e-6 + Zcp.imag = -1e-6 zcp = Zcp * (self.z2 - self.z1) / 2 + 0.5 * (self.z1 + self.z2) self.xcneg = zcp.real - self.ycneg = zcp.imag # control points just on negative side + self.ycneg = zcp.imag # control points just on negative side # self.aq = self.model.aq.find_aquifer_data(self.xc[0], self.yc[0]) self.setbc() coef = self.aq.coef[self.layers, :] self.setflowcoef() # shape (self.nlayers,self.aq.naq,self.model.npvalval) - self.term = self.flowcoef * coef - self.term2 = self.term.reshape(self.nlayers, self.aq.naq, - self.model.nint, self.model.npint) + self.term = self.flowcoef * coef + self.term2 = self.term.reshape( + self.nlayers, self.aq.naq, self.model.nint, self.model.npint + ) self.resfac = self.aq.Haq[self.layers] / self.res self.dischargeinf = self.flowcoef * coef - self.dischargeinflayers = np.sum(self.dischargeinf * - self.aq.eigvec[self.layers, :, :], 1) + self.dischargeinflayers = np.sum( + self.dischargeinf * self.aq.eigvec[self.layers, :, :], 1 + ) def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' + """Separate function so that this can be overloaded for other types""" self.flowcoef = 1.0 / self.model.p # Step function - def potinf(self,x,y,aq=None): - '''Can be called with only one x,y value''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + def potinf(self, x, y, aq=None): + """Can be called with only one x,y value""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + rv = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: - pot = np.zeros((self.order+1,self.model.npint),'D') + pot = np.zeros((self.order + 1, self.model.npint), "D") for i in range(self.aq.naq): for j in range(self.model.nint): - if besselnumba.isinside(self.z1, self.z2, x+y*1j, - self.rzero*self.aq.lababs[i, j]): - pot[:,:] = besselnumba.besselldv2(x, y, - self.z1, self.z2, self.aq.lab2[i, j, :], - self.order, - self.rzero * self.aq.lababs[i, j]) / self.L + if besselnumba.isinside( + self.z1, self.z2, x + y * 1j, self.rzero * self.aq.lababs[i, j] + ): + pot[:, :] = ( + besselnumba.besselldv2( + x, + y, + self.z1, + self.z2, + self.aq.lab2[i, j, :], + self.order, + self.rzero * self.aq.lababs[i, j], + ) + / self.L + ) for k in range(self.nlayers): - rv[k::self.nlayers, i, j, :] = \ + rv[k :: self.nlayers, i, j, :] = ( self.term2[k, i, j, :] * pot + ) rv.shape = (self.nparam, aq.naq, self.model.npval) return rv - def disvecinf(self,x,y,aq=None): - '''Can be called with only one x,y value''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rvx = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') - rvy = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + def disvecinf(self, x, y, aq=None): + """Can be called with only one x,y value""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + rvx = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") + rvy = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: - qxqy = np.zeros((2*(self.order+1),self.model.npint),'D') + qxqy = np.zeros((2 * (self.order + 1), self.model.npint), "D") for i in range(self.aq.naq): for j in range(self.model.nint): - if besselnumba.isinside(self.z1, self.z2, x+y*1j, - self.rzero*self.aq.lababs[i, j]): - qxqy[:,:] = besselnumba.besselldqxqyv2(x, y, - self.z1, self.z2,self.aq.lab2[i, j, :], - self.order, - self.rzero * self.aq.lababs[i, j]) / self.L + if besselnumba.isinside( + self.z1, self.z2, x + y * 1j, self.rzero * self.aq.lababs[i, j] + ): + qxqy[:, :] = ( + besselnumba.besselldqxqyv2( + x, + y, + self.z1, + self.z2, + self.aq.lab2[i, j, :], + self.order, + self.rzero * self.aq.lababs[i, j], + ) + / self.L + ) for k in range(self.nlayers): - rvx[k::self.nlayers, i, j, :] = \ - self.term2[k, i, j, :] * \ - qxqy[:self.order + 1,:] - rvy[k::self.nlayers, i, j, :] = \ - self.term2[k, i, j, :] * \ - qxqy[self.order + 1:,:] - + rvx[k :: self.nlayers, i, j, :] = ( + self.term2[k, i, j, :] * qxqy[: self.order + 1, :] + ) + rvy[k :: self.nlayers, i, j, :] = ( + self.term2[k, i, j, :] * qxqy[self.order + 1 :, :] + ) + rvx.shape = (self.nparam, aq.naq, self.model.npval) rvy.shape = (self.nparam, aq.naq, self.model.npval) return rvx, rvy def plot(self): - plt.plot([self.x1, self.x2], [self.y1, self.y2], 'k') - + plt.plot([self.x1, self.x2], [self.y1, self.y2], "k") + + class LeakyLineDoublet(LineDoubletHoBase, LeakyWallEquation): """ Create a segment of a leaky wall, which is simulated with a line-doublet. The specific discharge through the wall is equal to the head difference across the wall - divided by the resistance of the wall. - + divided by the resistance of the wall. + Parameters ---------- - + model : Model object Model to which the element is added x1 : scalar @@ -154,40 +205,64 @@ class LeakyLineDoublet(LineDoubletHoBase, LeakyWallEquation): layers : scalar, list or array layer(s) in which element is placed if scalar: element is placed in this layer - if list or array: element is placed in all these layers + if list or array: element is placed in all these layers label: str or None label of element - + See Also -------- - + :class:`.LeakyLineDoubletString` - + """ - - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, res='imp', order=0, - layers=0, label=None, addtomodel=True): + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + res="imp", + order=0, + layers=0, + label=None, + addtomodel=True, + ): self.storeinput(inspect.currentframe()) - LineDoubletHoBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, - tsandbc=[(0, 0)], res=res, order=order, - layers=layers, type='z', - name='LeakyLineDoublet', - label=label, addtomodel=addtomodel) + LineDoubletHoBase.__init__( + self, + model, + x1=x1, + y1=y1, + x2=x2, + y2=y2, + tsandbc=[(0, 0)], + res=res, + order=order, + layers=layers, + type="z", + name="LeakyLineDoublet", + label=label, + addtomodel=addtomodel, + ) self.nunknowns = self.nparam def initialize(self): LineDoubletHoBase.initialize(self) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') - + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) + + class LeakyLineDoubletString(Element, LeakyWallEquation): """ Create a string of leaky wall segements consisting of line-doublets - + Parameters ---------- - + model : Model object Model to which the element is added xy : array or list @@ -205,56 +280,73 @@ class LeakyLineDoubletString(Element, LeakyWallEquation): if list or array: element is placed in all these layers label: str or None label of element - + See Also -------- - + :class:`.LeakyLineDoublet` - + """ - - def __init__(self, model, xy=[(-1, 0), (1, 0)], res='imp', order=0, - layers=0, label=None): + + def __init__( + self, model, xy=[(-1, 0), (1, 0)], res="imp", order=0, layers=0, label=None + ): self.storeinput(inspect.currentframe()) - Element.__init__(self, model, nparam=1, nunknowns=0, layers=layers, - tsandbc=[(0, 0)], type='z', - name='LeakyLineDoubletString', label=label) + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=layers, + tsandbc=[(0, 0)], + type="z", + name="LeakyLineDoubletString", + label=label, + ) self.res = res self.order = order self.ldlist = [] - xy = np.atleast_2d(xy).astype('d') - self.x,self.y = xy[:,0], xy[:,1] + xy = np.atleast_2d(xy).astype("d") + self.x, self.y = xy[:, 0], xy[:, 1] self.nld = len(self.x) - 1 for i in range(self.nld): - self.ldlist.append(LeakyLineDoublet(model, - x1=self.x[i], y1=self.y[i], - x2=self.x[i + 1], y2=self.y[i + 1], - res=self.res, order=self.order, - layers=layers, label=label, - addtomodel=False)) + self.ldlist.append( + LeakyLineDoublet( + model, + x1=self.x[i], + y1=self.y[i], + x2=self.x[i + 1], + y2=self.y[i + 1], + res=self.res, + order=self.order, + layers=layers, + label=label, + addtomodel=False, + ) + ) self.model.addelement(self) def __repr__(self): - return self.name + ' with nodes ' + str(zip(self.x,self.y)) + return self.name + " with nodes " + str(zip(self.x, self.y)) def initialize(self): for ld in self.ldlist: ld.initialize() # Same order for all elements in string - self.ncp = self.nld * self.ldlist[0].ncp + self.ncp = self.nld * self.ldlist[0].ncp self.nparam = self.nld * self.ldlist[0].nparam self.nunknowns = self.nparam - self.xld,self.yld = np.empty((self.nld,2)), np.empty((self.nld,2)) - for i,ld in enumerate(self.ldlist): - self.xld[i,:] = [ld.x1,ld.x2] - self.yld[i,:] = [ld.y1,ld.y2] + self.xld, self.yld = np.empty((self.nld, 2)), np.empty((self.nld, 2)) + for i, ld in enumerate(self.ldlist): + self.xld[i, :] = [ld.x1, ld.x2] + self.yld[i, :] = [ld.y1, ld.y2] # Only used for layout when it is a continuous string - self.xldlayout = np.hstack((self.xld[:,0],self.xld[-1,1])) - self.yldlayout = np.hstack((self.yld[:,0],self.yld[-1,1])) - self.aq = self.model.aq.find_aquifer_data(self.ldlist[0].xc, - self.ldlist[0].yc) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') + self.xldlayout = np.hstack((self.xld[:, 0], self.xld[-1, 1])) + self.yldlayout = np.hstack((self.yld[:, 0], self.yld[-1, 1])) + self.aq = self.model.aq.find_aquifer_data(self.ldlist[0].xc, self.ldlist[0].yc) + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) self.setbc() # As parameters are only stored for the element not the list, # we need to combine the following @@ -262,32 +354,34 @@ def initialize(self): self.xc, self.yc = np.zeros(self.ncp), np.zeros(self.ncp) self.xcneg, self.ycneg = np.zeros(self.ncp), np.zeros(self.ncp) self.cosout, self.sinout = np.zeros(self.ncp), np.zeros(self.ncp) - for i,ld in enumerate(self.ldlist): - self.xc[i * ld.ncp: (i + 1) * ld.ncp] = ld.xc - self.yc[i * ld.ncp: (i + 1) * ld.ncp] = ld.yc - self.xcneg[i * ld.ncp: (i + 1) * ld.ncp] = ld.xcneg - self.ycneg[i * ld.ncp: (i + 1) * ld.ncp] = ld.ycneg - self.cosout[i * ld.ncp: (i + 1) * ld.ncp] = ld.cosout - self.sinout[i * ld.ncp: (i + 1) * ld.ncp] = ld.sinout + for i, ld in enumerate(self.ldlist): + self.xc[i * ld.ncp : (i + 1) * ld.ncp] = ld.xc + self.yc[i * ld.ncp : (i + 1) * ld.ncp] = ld.yc + self.xcneg[i * ld.ncp : (i + 1) * ld.ncp] = ld.xcneg + self.ycneg[i * ld.ncp : (i + 1) * ld.ncp] = ld.ycneg + self.cosout[i * ld.ncp : (i + 1) * ld.ncp] = ld.cosout + self.sinout[i * ld.ncp : (i + 1) * ld.ncp] = ld.sinout def potinf(self, x, y, aq=None): - '''Returns array (nunknowns,nperiods)''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') - for i,ld in enumerate(self.ldlist): - rv[i*ld.nparam:(i+1)*ld.nparam,:] = ld.potinf(x,y,aq) + """Returns array (nunknowns,nperiods)""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + rv = np.zeros((self.nparam, aq.naq, self.model.npval), "D") + for i, ld in enumerate(self.ldlist): + rv[i * ld.nparam : (i + 1) * ld.nparam, :] = ld.potinf(x, y, aq) return rv def disvecinf(self, x, y, aq=None): - '''Returns array (nunknowns,nperiods)''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rvx = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') - rvy = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') - for i,ld in enumerate(self.ldlist): - qx,qy = ld.disvecinf(x,y,aq) - rvx[i*ld.nparam:(i+1)*ld.nparam,:] = qx - rvy[i*ld.nparam:(i+1)*ld.nparam,:] = qy - return rvx,rvy - + """Returns array (nunknowns,nperiods)""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + rvx = np.zeros((self.nparam, aq.naq, self.model.npval), "D") + rvy = np.zeros((self.nparam, aq.naq, self.model.npval), "D") + for i, ld in enumerate(self.ldlist): + qx, qy = ld.disvecinf(x, y, aq) + rvx[i * ld.nparam : (i + 1) * ld.nparam, :] = qx + rvy[i * ld.nparam : (i + 1) * ld.nparam, :] = qy + return rvx, rvy + def plot(self): - plt.plot(self.xldlayout, self.yldlayout, 'k') + plt.plot(self.xldlayout, self.yldlayout, "k") diff --git a/ttim/linesink.py b/ttim/linesink.py index 72cebfd..8e5ab21 100644 --- a/ttim/linesink.py +++ b/ttim/linesink.py @@ -1,18 +1,48 @@ -import numpy as np +import inspect # Used for storing the input + import matplotlib.pyplot as plt -import inspect # Used for storing the input -from .element import Element -from .equation import HeadEquation, HeadEquationNores, \ - MscreenEquation, MscreenDitchEquation +import numpy as np + from . import besselnumba +from .element import Element +from .equation import ( + HeadEquation, + HeadEquationNores, + MscreenDitchEquation, + MscreenEquation, +) + class LineSinkBase(Element): - '''LineSink Base Class. All LineSink elements are derived from this class''' - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandbc=[(0, 1)], \ - res=0, wh='H', layers=0, type='', name='LineSinkBase', \ - label=None, addtomodel=True): - Element.__init__(self, model, nparam=1, nunknowns=0, layers=layers, - tsandbc=tsandbc, type=type, name=name, label=label) + """LineSink Base Class. All LineSink elements are derived from this class""" + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandbc=[(0, 1)], + res=0, + wh="H", + layers=0, + type="", + name="LineSinkBase", + label=None, + addtomodel=True, + ): + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=layers, + tsandbc=tsandbc, + type=type, + name=name, + label=label, + ) self.nparam = len(self.layers) self.x1 = float(x1) self.y1 = float(y1) @@ -20,156 +50,200 @@ def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandbc=[(0, 1)], \ self.y2 = float(y2) self.res = np.atleast_1d(res).astype(float) self.wh = wh - if addtomodel: self.model.addelement(self) + if addtomodel: + self.model.addelement(self) def __repr__(self): - return self.name + ' from ' + str((self.x1, self.y1)) + \ - ' to ' + str((self.x2,self.y2)) + return ( + self.name + + " from " + + str((self.x1, self.y1)) + + " to " + + str((self.x2, self.y2)) + ) def initialize(self): self.xc = np.array([0.5 * (self.x1 + self.x2)]) self.yc = np.array([0.5 * (self.y1 + self.y2)]) self.ncp = 1 - self.z1 = self.x1 + 1j*self.y1; self.z2 = self.x2 + 1j*self.y2 - self.L = np.abs(self.z1-self.z2) - self.order = 0 # This is for univform discharge only + self.z1 = self.x1 + 1j * self.y1 + self.z2 = self.x2 + 1j * self.y2 + self.L = np.abs(self.z1 - self.z2) + self.order = 0 # This is for univform discharge only self.aq = self.model.aq.find_aquifer_data(self.xc, self.yc) self.setbc() coef = self.aq.coef[self.layers, :] self.setflowcoef() # self.term shape (self.nparam,self.aq.naq,self.model.npval) - self.term = self.flowcoef * coef + self.term = self.flowcoef * coef self.term2 = self.term.reshape( - self.nparam, self.aq.naq, self.model.nint, self.model.npint) + self.nparam, self.aq.naq, self.model.nint, self.model.npint + ) self.dischargeinf = self.flowcoef * coef self.dischargeinflayers = np.sum( - self.dischargeinf * self.aq.eigvec[self.layers, :, :], 1) + self.dischargeinf * self.aq.eigvec[self.layers, :, :], 1 + ) if type(self.wh) is str: - if self.wh == 'H': + if self.wh == "H": self.wh = self.aq.Haq[self.layers] - elif self.wh == '2H': + elif self.wh == "2H": self.wh = 2.0 * self.aq.Haq[self.layers] else: self.wh = np.atleast_1d(self.wh) * np.ones(self.nlayers) # Q = (h - hls) / resfach - self.resfach = self.res / (self.wh * self.L) + self.resfach = self.res / (self.wh * self.L) # Q = (Phi - Phils) / resfacp - self.resfacp = self.resfach * self.aq.T[self.layers] + self.resfacp = self.resfach * self.aq.T[self.layers] def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' + """Separate function so that this can be overloaded for other types""" self.flowcoef = 1.0 / self.model.p # Step function def potinf(self, x, y, aq=None): - '''Can be called with only one x,y value''' + """Can be called with only one x,y value""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros( - (self.nparam, aq.naq, self.model.nint, self.model.npint), 'D') + rv = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: - pot = np.zeros(self.model.npint, 'D') + pot = np.zeros(self.model.npint, "D") for i in range(self.aq.naq): for j in range(self.model.nint): - pot[:] = besselnumba.bessellsuniv(x, y, - self.z1, self.z2, self.aq.lab2[i, j, :], self.rzero) + pot[:] = besselnumba.bessellsuniv( + x, y, self.z1, self.z2, self.aq.lab2[i, j, :], self.rzero + ) # Divide by L as the parameter is total discharge - rv[:,i,j,:] = self.term2[:, i, j, :] * pot / self.L + rv[:, i, j, :] = self.term2[:, i, j, :] * pot / self.L rv.shape = (self.nparam, aq.naq, self.model.npval) return rv - def disvecinf(self,x,y,aq=None): - '''Can be called with only one x,y value''' + def disvecinf(self, x, y, aq=None): + """Can be called with only one x,y value""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rvx = np.zeros( - (self.nparam, aq.naq, self.model.nint, self.model.npint), 'D') - rvy = np.zeros( - (self.nparam, aq.naq, self.model.nint, self.model.npint), 'D') + rvx = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") + rvy = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: - qxqy = np.zeros((2,self.model.npint), 'D') + qxqy = np.zeros((2, self.model.npint), "D") for i in range(self.aq.naq): for j in range(self.model.nint): - if besselnumba.isinside(self.z1, self.z2, x + y * 1j, - self.rzero * self.aq.lababs[i, j]): - qxqy[:,:] = besselnumba.bessellsqxqyv2(x, y, - self.z1, self.z2, self.aq.lab2[i, j, :], - self.order, - self.rzero * self.aq.lababs[i, j]) / self.L - rvx[:,i,j,:] = self.term2[:,i,j,:] * qxqy[0] - rvy[:,i,j,:] = self.term2[:,i,j,:] * qxqy[1] + if besselnumba.isinside( + self.z1, self.z2, x + y * 1j, self.rzero * self.aq.lababs[i, j] + ): + qxqy[:, :] = ( + besselnumba.bessellsqxqyv2( + x, + y, + self.z1, + self.z2, + self.aq.lab2[i, j, :], + self.order, + self.rzero * self.aq.lababs[i, j], + ) + / self.L + ) + rvx[:, i, j, :] = self.term2[:, i, j, :] * qxqy[0] + rvy[:, i, j, :] = self.term2[:, i, j, :] * qxqy[1] rvx.shape = (self.nparam, aq.naq, self.model.npval) rvy.shape = (self.nparam, aq.naq, self.model.npval) return rvx, rvy - def headinside(self,t): + def headinside(self, t): """The head inside the line-sink - + Parameters ---------- t : array or float time(s) for whih head is computed - + Returns ------- array (length number of layers) - Head inside the line-sink for each layer that + Head inside the line-sink for each layer that the line-sink is screened in - + """ - - return self.model.head(self.xc[0],self.yc[0],t)[self.layers] - \ - self.resfach[:, np.newaxis] * self.discharge(t) + + return self.model.head(self.xc[0], self.yc[0], t)[self.layers] - self.resfach[ + :, np.newaxis + ] * self.discharge(t) def plot(self): - plt.plot([self.x1, self.x2], [self.y1, self.y2], 'k') - + plt.plot([self.x1, self.x2], [self.y1, self.y2], "k") + + class LineSink(LineSinkBase): - '''LineSink with non-zero and potentially variable discharge through time - really only used for testing''' - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandQ=[(0, 1)], - res=0, wh='H', layers=0, label=None, addtomodel=True): + """LineSink with non-zero and potentially variable discharge through time + really only used for testing""" + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandQ=[(0, 1)], + res=0, + wh="H", + layers=0, + label=None, + addtomodel=True, + ): self.storeinput(inspect.currentframe()) - LineSinkBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, - tsandbc=tsandQ, res=res, wh=wh, layers=layers, - type='g', name='LineSink', label=label, - addtomodel=addtomodel) - - -#class ZeroHeadLineSink(LineSinkBase,HeadEquation): - #'''HeadLineSink that remains zero and constant through time''' - #def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, res=0.0, wh='H', \ - # layers=0, label=None, addtomodel=True): - # self.storeinput(inspect.currentframe()) - # LineSinkBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, \ - # tsandbc=[(0, 0)], res=res, wh=wh, layers=layers,\ - # type='z', name='ZeroHeadLineSink', label=label, \ - # addtomodel=addtomodel) - # self.nunknowns = self.nparam - # - #def initialize(self): - # LineSinkBase.initialize(self) - # self.parameters = np.zeros((self.model.ngvbc, self.nparam, - # self.model.npval), 'D') - + LineSinkBase.__init__( + self, + model, + x1=x1, + y1=y1, + x2=x2, + y2=y2, + tsandbc=tsandQ, + res=res, + wh=wh, + layers=layers, + type="g", + name="LineSink", + label=label, + addtomodel=addtomodel, + ) + + +# class ZeroHeadLineSink(LineSinkBase,HeadEquation): +#'''HeadLineSink that remains zero and constant through time''' +# def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, res=0.0, wh='H', \ +# layers=0, label=None, addtomodel=True): +# self.storeinput(inspect.currentframe()) +# LineSinkBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, \ +# tsandbc=[(0, 0)], res=res, wh=wh, layers=layers,\ +# type='z', name='ZeroHeadLineSink', label=label, \ +# addtomodel=addtomodel) +# self.nunknowns = self.nparam +# +# def initialize(self): +# LineSinkBase.initialize(self) +# self.parameters = np.zeros((self.model.ngvbc, self.nparam, +# self.model.npval), 'D') + + class HeadLineSink(LineSinkBase, HeadEquation): """ Create a head-specified line-sink which may optionally have a width and resistance Inflow per unit length of line-sink is computed as - + .. math:: \sigma = w(h_{aq} - h_{ls})/c - + where :math:`c` is the resistance of the bottom of the line-sink, :math:`w` is the width over which water enters the line-sink, :math:`h_{aq}` is the head in the aquifer at the center of the line-sink, :math:`h_{ls}` is the specified head inside the line-sink Note that all that matters is the conductance term :math:`w/c` but both are specified separately - + Parameters ---------- - + model : Model object Model to which the element is added x1 : scalar @@ -187,189 +261,239 @@ class HeadLineSink(LineSinkBase, HeadEquation): resistance of line-sink wh : scalar or str distance over which water enters line-sink - if 'H': (default) distance is equal to the thickness of the aquifer + if 'H': (default) distance is equal to the thickness of the aquifer layer (when flow comes mainly from one side) - if '2H': distance is twice the thickness of the aquifer layer (when + if '2H': distance is twice the thickness of the aquifer layer (when flow comes from both sides) - if scalar: the width of the stream that partially penetrates the + if scalar: the width of the stream that partially penetrates the aquifer layer layers : scalar, list or array layer(s) in which element is placed if scalar: element is placed in this layer - if list or array: element is placed in all these layers + if list or array: element is placed in all these layers label: str or None label of element - + See Also -------- - + :class:`.HeadLineSinkString` - + """ - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandh=[(0, 1)], - res=0, wh='H', layers=0, label=None, addtomodel=True): + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandh=[(0, 1)], + res=0, + wh="H", + layers=0, + label=None, + addtomodel=True, + ): self.storeinput(inspect.currentframe()) - if tsandh == 'fixed': + if tsandh == "fixed": tsandh = [(0, 0)] - etype = 'z' + etype = "z" else: - etype = 'v' - LineSinkBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, - tsandbc=tsandh, res=res, wh=wh, layers=layers, - type=etype, name='HeadLineSink', label=label, - addtomodel=addtomodel) + etype = "v" + LineSinkBase.__init__( + self, + model, + x1=x1, + y1=y1, + x2=x2, + y2=y2, + tsandbc=tsandh, + res=res, + wh=wh, + layers=layers, + type=etype, + name="HeadLineSink", + label=label, + addtomodel=addtomodel, + ) self.nunknowns = self.nparam def initialize(self): LineSinkBase.initialize(self) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) # Needed in solving, solve for a unit head - self.pc = self.aq.T[self.layers] - + self.pc = self.aq.T[self.layers] + + class LineSinkStringBase(Element): - def __init__(self, model, tsandbc=[(0, 1)], layers=0, type='', - name='LineSinkStringBase', label=None): - Element.__init__(self, model, nparam=1, nunknowns=0, layers=layers, - tsandbc=tsandbc, type=type, name=name, label=label) + def __init__( + self, + model, + tsandbc=[(0, 1)], + layers=0, + type="", + name="LineSinkStringBase", + label=None, + ): + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=layers, + tsandbc=tsandbc, + type=type, + name=name, + label=label, + ) self.lslist = [] def __repr__(self): - return self.name + ' with nodes ' + str(zip(self.x,self.y)) + return self.name + " with nodes " + str(zip(self.x, self.y)) def initialize(self): self.ncp = self.nls self.nparam = self.nlayers * self.nls self.nunknowns = self.nparam - self.xls,self.yls = np.empty((self.nls,2)), np.empty((self.nls,2)) - for i,ls in enumerate(self.lslist): + self.xls, self.yls = np.empty((self.nls, 2)), np.empty((self.nls, 2)) + for i, ls in enumerate(self.lslist): ls.initialize() - self.xls[i,:] = [ls.x1,ls.x2] - self.yls[i,:] = [ls.y1,ls.y2] + self.xls[i, :] = [ls.x1, ls.x2] + self.yls[i, :] = [ls.y1, ls.y2] # Only used for layout when it is a continuous string - self.xlslayout = np.hstack((self.xls[:,0],self.xls[-1,1])) - self.ylslayout = np.hstack((self.yls[:,0],self.yls[-1,1])) - self.aq = self.model.aq.find_aquifer_data( - self.lslist[0].xc, self.lslist[0].yc) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') + self.xlslayout = np.hstack((self.xls[:, 0], self.xls[-1, 1])) + self.ylslayout = np.hstack((self.yls[:, 0], self.yls[-1, 1])) + self.aq = self.model.aq.find_aquifer_data(self.lslist[0].xc, self.lslist[0].yc) + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) self.setbc() # As parameters are only stored for the element not the list # we need to combine the following - self.resfach = []; self.resfacp = [] + self.resfach = [] + self.resfacp = [] for ls in self.lslist: ls.initialize() self.resfach.extend(ls.resfach.tolist()) # Needed in solving self.resfacp.extend(ls.resfacp.tolist()) # Needed in solving self.resfach = np.array(self.resfach) self.resfacp = np.array(self.resfacp) - self.dischargeinf = np.zeros((self.nparam, self.aq.naq, - self.model.npval), 'D') - self.dischargeinflayers = np.zeros((self.nparam, self.model.npval), 'D') + self.dischargeinf = np.zeros((self.nparam, self.aq.naq, self.model.npval), "D") + self.dischargeinflayers = np.zeros((self.nparam, self.model.npval), "D") self.xc, self.yc = np.zeros(self.nls), np.zeros(self.nls) for i in range(self.nls): self.dischargeinf[ - i * self.nlayers: (i + 1) * self.nlayers, :] = \ - self.lslist[i].dischargeinf[:] + i * self.nlayers : (i + 1) * self.nlayers, : + ] = self.lslist[i].dischargeinf[:] self.dischargeinflayers[ - i * self.nlayers: (i + 1) * self.nlayers, :] = \ - self.lslist[i].dischargeinflayers + i * self.nlayers : (i + 1) * self.nlayers, : + ] = self.lslist[i].dischargeinflayers self.xc[i] = self.lslist[i].xc self.yc[i] = self.lslist[i].yc - def potinf(self, x, y,aq=None): - '''Returns array (nunknowns, Nperiods)''' - if aq is None: + def potinf(self, x, y, aq=None): + """Returns array (nunknowns, Nperiods)""" + if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') + rv = np.zeros((self.nparam, aq.naq, self.model.npval), "D") for i in range(self.nls): - rv[i * self.nlayers: (i + 1) * self.nlayers, :] = \ - self.lslist[i].potinf(x, y, aq) + rv[i * self.nlayers : (i + 1) * self.nlayers, :] = self.lslist[i].potinf( + x, y, aq + ) return rv - def disvecinf(self,x,y,aq=None): - '''Returns array (nunknowns,Nperiods)''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rvx = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') - rvy = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') + def disvecinf(self, x, y, aq=None): + """Returns array (nunknowns,Nperiods)""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + rvx = np.zeros((self.nparam, aq.naq, self.model.npval), "D") + rvy = np.zeros((self.nparam, aq.naq, self.model.npval), "D") for i in range(self.nls): qx, qy = self.lslist[i].disvecinf(x, y, aq) - rvx[i * self.nlayers: (i + 1) * self.nlayers, :] = qx - rvy[i * self.nlayers: (i + 1) * self.nlayers, :] = qy - return rvx,rvy + rvx[i * self.nlayers : (i + 1) * self.nlayers, :] = qx + rvy[i * self.nlayers : (i + 1) * self.nlayers, :] = qy + return rvx, rvy def headinside(self, t, derivative=0): """The head inside the line-sink string - + Parameters ---------- t : array or float time(s) for whih head is computed - + Returns ------- array size nline-sinks, nlayers, ntimes Head inside the line-sink for each line-sink, each layer that the line-sink is screened in, and each time - + """ - + rv = np.zeros((self.nls, self.nlayers, np.size(t))) Q = self.discharge_list(t, derivative=derivative) for i in range(self.nls): - rv[i, :, :] = self.model.head(self.xc[i], self.yc[i], t, - derivative=derivative)[self.layers] - \ - self.resfach[i * self.nlayers: (i + 1) * self.nlayers, - np.newaxis] * Q[i] + rv[i, :, :] = ( + self.model.head(self.xc[i], self.yc[i], t, derivative=derivative)[ + self.layers + ] + - self.resfach[i * self.nlayers : (i + 1) * self.nlayers, np.newaxis] + * Q[i] + ) return rv - + def plot(self): - plt.plot(self.xlslayout, self.ylslayout, 'k') + plt.plot(self.xlslayout, self.ylslayout, "k") def run_after_solve(self): for i in range(self.nls): - self.lslist[i].parameters[:] = \ - self.parameters[:, i * self.nlayers:(i + 1) * self.nlayers, :] + self.lslist[i].parameters[:] = self.parameters[ + :, i * self.nlayers : (i + 1) * self.nlayers, : + ] - def discharge_list(self,t,derivative=0): + def discharge_list(self, t, derivative=0): """The discharge of each line-sink in the string - + Parameters ---------- t : array or float time(s) for whih discharge is computed - + Returns ------- array size nline-sinks, nlayers, ntimes Discharge for each line-sink, each layer that the line-sink is screened in, and each time - + """ rv = np.zeros((self.nls, self.nlayers, np.size(t))) for i in range(self.nls): - rv[i,:,:] = self.lslist[i].discharge(t,derivative=derivative) + rv[i, :, :] = self.lslist[i].discharge(t, derivative=derivative) return rv - + + class HeadLineSinkString(LineSinkStringBase, HeadEquation): """ Create string of head-specified line-sinks which may optionally have a width and resistance Inflow per unit length of line-sink is computed as - + .. math:: \sigma = w(h_{aq} - h_{ls})/c - + where :math:`c` is the resistance of the bottom of the line-sink, :math:`w` is the width over which water enters the line-sink, :math:`h_{aq}` is the head in the aquifer at the center of the line-sink, :math:`h_{ls}` is the specified head inside the line-sink Note that all that matters is the conductance term :math:`w/c` but both are specified separately - + Parameters ---------- - + model : Model object Model to which the element is added xy : array or list @@ -382,36 +506,51 @@ class HeadLineSinkString(LineSinkStringBase, HeadEquation): resistance of line-sink wh : scalar or str distance over which water enters line-sink - if 'H': (default) distance is equal to the thickness of the aquifer + if 'H': (default) distance is equal to the thickness of the aquifer layer (when flow comes mainly from one side) - if '2H': distance is twice the thickness of the aquifer layer (when + if '2H': distance is twice the thickness of the aquifer layer (when flow comes from both sides) - if scalar: the width of the stream that partially penetrates the + if scalar: the width of the stream that partially penetrates the aquifer layer layers : scalar, list or array layer(s) in which element is placed if scalar: element is placed in this layer - if list or array: element is placed in all these layers + if list or array: element is placed in all these layers label: str or None label of element - + See Also -------- - + :class:`.HeadLineSink` - + """ - def __init__(self, model, xy=[(-1, 0), (1, 0)], tsandh=[(0, 1)], - res=0, wh='H', layers=0, label=None): - if tsandh == 'fixed': + + def __init__( + self, + model, + xy=[(-1, 0), (1, 0)], + tsandh=[(0, 1)], + res=0, + wh="H", + layers=0, + label=None, + ): + if tsandh == "fixed": tsandh = [(0, 0)] - etype = 'z' + etype = "z" else: - etype = 'v' - LineSinkStringBase.__init__(self, model, tsandbc=tsandh, layers=layers, - type=etype, name='HeadLineSinkString', - label=label) - xy = np.atleast_2d(xy).astype('d') + etype = "v" + LineSinkStringBase.__init__( + self, + model, + tsandbc=tsandh, + layers=layers, + type=etype, + name="HeadLineSinkString", + label=label, + ) + xy = np.atleast_2d(xy).astype("d") self.x = xy[:, 0] self.y = xy[:, 1] self.nls = len(self.x) - 1 @@ -423,41 +562,82 @@ def __init__(self, model, xy=[(-1, 0), (1, 0)], tsandh=[(0, 1)], def initialize(self): self.lslist = [] for i in range(self.nls): - self.lslist.append(HeadLineSink( - self.model, x1=self.x[i], y1=self.y[i], x2=self.x[i + 1], - y2=self.y[i + 1], tsandh=self.tsandh, res=self.res, wh=self.wh, - layers=self.layers, label=None, addtomodel=False)) + self.lslist.append( + HeadLineSink( + self.model, + x1=self.x[i], + y1=self.y[i], + x2=self.x[i + 1], + y2=self.y[i + 1], + tsandh=self.tsandh, + res=self.res, + wh=self.wh, + layers=self.layers, + label=None, + addtomodel=False, + ) + ) LineSinkStringBase.initialize(self) self.pc = np.zeros(self.nls * self.nlayers) for i in range(self.nls): - self.pc[i * self.nlayers:(i + 1) * self.nlayers] = self.lslist[i].pc - -class MscreenLineSink(LineSinkBase,MscreenEquation): - '''MscreenLineSink that varies through time. - Must be screened in multiple layers but heads are same - in all screened layers''' - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandQ=[(0.0, 1.0)], - res=0.0, wh='H', layers=[0, 1], vres=0.0, wv=1.0, label=None, - addtomodel=True): - #assert len(layers) > 1, "number of layers must be at least 2" + self.pc[i * self.nlayers : (i + 1) * self.nlayers] = self.lslist[i].pc + + +class MscreenLineSink(LineSinkBase, MscreenEquation): + """MscreenLineSink that varies through time. + Must be screened in multiple layers but heads are same + in all screened layers""" + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandQ=[(0.0, 1.0)], + res=0.0, + wh="H", + layers=[0, 1], + vres=0.0, + wv=1.0, + label=None, + addtomodel=True, + ): + # assert len(layers) > 1, "number of layers must be at least 2" self.storeinput(inspect.currentframe()) - LineSinkBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, - tsandbc=tsandQ, res=res, wh=wh, layers=layers, - type='v', name='MscreenLineSink', label=label, - addtomodel=addtomodel) + LineSinkBase.__init__( + self, + model, + x1=x1, + y1=y1, + x2=x2, + y2=y2, + tsandbc=tsandQ, + res=res, + wh=wh, + layers=layers, + type="v", + name="MscreenLineSink", + label=label, + addtomodel=addtomodel, + ) self.nunknowns = self.nparam # Vertical resistance inside line-sink - self.vres = np.atleast_1d(vres) + self.vres = np.atleast_1d(vres) self.wv = wv - if len(self.vres) == 1: + if len(self.vres) == 1: self.vres = self.vres[0] * np.ones(self.nlayers - 1) + def initialize(self): LineSinkBase.initialize(self) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) # Qv = (hn - hn-1) / vresfac[n - 1] - self.vresfac = self.vres / (self.wv * self.L) - + self.vresfac = self.vres / (self.wv * self.L) + + class LineSinkDitchString(LineSinkStringBase, MscreenDitchEquation): """ Create ditch consisting of a string of line-sink. @@ -465,20 +645,20 @@ class LineSinkDitchString(LineSinkStringBase, MscreenDitchEquation): line-sinks such that the head at the center inside each line-sink is equal. A width and resistance may optionally be specified. Inflow per unit length of line-sink is computed as - + .. math:: \sigma = w(h_{aq} - h_{ls})/c - + where :math:`c` is the resistance of the bottom of the line-sink, :math:`w` is the width over which water enters the line-sink, :math:`h_{aq}` is the head in the aquifer at the center of the line-sink, :math:`h_{ls}` is the specified head inside the line-sink Note that all that matters is the conductance term :math:`w/c` but both are specified separately - + Parameters ---------- - + model : Model object Model to which the element is added xy : array or list @@ -490,43 +670,71 @@ class LineSinkDitchString(LineSinkStringBase, MscreenDitchEquation): resistance of line-sink wh : scalar or str distance over which water enters line-sink - if 'H': (default) distance is equal to the thickness of the aquifer + if 'H': (default) distance is equal to the thickness of the aquifer layer (when flow comes mainly from one side) - if '2H': distance is twice the thickness of the aquifer layer (when + if '2H': distance is twice the thickness of the aquifer layer (when flow comes from both sides) - if scalar: the width of the stream that partially penetrates the + if scalar: the width of the stream that partially penetrates the aquifer layer layers : scalar, list or array layer(s) in which element is placed if scalar: element is placed in this layer - if list or array: element is placed in all these layers + if list or array: element is placed in all these layers label: str or None label of element - - """ - def __init__(self, model, xy=[(-1, 0), (1, 0)], tsandQ=[(0, 1)], res=0, - wh='H', layers=0, Astorage=None, label=None): + + """ + + def __init__( + self, + model, + xy=[(-1, 0), (1, 0)], + tsandQ=[(0, 1)], + res=0, + wh="H", + layers=0, + Astorage=None, + label=None, + ): self.storeinput(inspect.currentframe()) - LineSinkStringBase.__init__(self, model, tsandbc=tsandQ, layers=layers, - type='v', name='LineSinkDitchString', - label=label) - xy = np.atleast_2d(xy).astype('d') - self.x,self.y = xy[:, 0], xy[:, 1] + LineSinkStringBase.__init__( + self, + model, + tsandbc=tsandQ, + layers=layers, + type="v", + name="LineSinkDitchString", + label=label, + ) + xy = np.atleast_2d(xy).astype("d") + self.x, self.y = xy[:, 0], xy[:, 1] self.nls = len(self.x) - 1 for i in range(self.nls): self.lslist.append( - MscreenLineSink(model, x1=self.x[i], y1=self.y[i], - x2=self.x[i + 1], y2=self.y[i + 1], - tsandQ=tsandQ, res=res, wh=wh, layers=layers, - label=None, addtomodel=False)) + MscreenLineSink( + model, + x1=self.x[i], + y1=self.y[i], + x2=self.x[i + 1], + y2=self.y[i + 1], + tsandQ=tsandQ, + res=res, + wh=wh, + layers=layers, + label=None, + addtomodel=False, + ) + ) self.Astorage = Astorage self.model.addelement(self) + def initialize(self): LineSinkStringBase.initialize(self) - # set vresfac to zero, as I don't quite know what it would mean if + # set vresfac to zero, as I don't quite know what it would mean if # it is not zero - self.vresfac = np.zeros_like(self.resfach) - + self.vresfac = np.zeros_like(self.resfach) + + class LineSinkDitchString2(LineSinkStringBase, MscreenDitchEquation): """ Create ditch consisting of a string of line-sink. @@ -534,20 +742,20 @@ class LineSinkDitchString2(LineSinkStringBase, MscreenDitchEquation): line-sinks such that the head at the center inside each line-sink is equal. A width and resistance may optionally be specified. Inflow per unit length of line-sink is computed as - + .. math:: \sigma = w(h_{aq} - h_{ls})/c - + where :math:`c` is the resistance of the bottom of the line-sink, :math:`w` is the width over which water enters the line-sink, :math:`h_{aq}` is the head in the aquifer at the center of the line-sink, :math:`h_{ls}` is the specified head inside the line-sink Note that all that matters is the conductance term :math:`w/c` but both are specified separately - + Parameters ---------- - + model : Model object Model to which the element is added xy : array or list @@ -559,51 +767,103 @@ class LineSinkDitchString2(LineSinkStringBase, MscreenDitchEquation): resistance of line-sink wh : scalar or str distance over which water enters line-sink - if 'H': (default) distance is equal to the thickness of the aquifer + if 'H': (default) distance is equal to the thickness of the aquifer layer (when flow comes mainly from one side) - if '2H': distance is twice the thickness of the aquifer layer (when + if '2H': distance is twice the thickness of the aquifer layer (when flow comes from both sides) - if scalar: the width of the stream that partially penetrates the + if scalar: the width of the stream that partially penetrates the aquifer layer layers : scalar, list or array layer(s) in which element is placed if scalar: element is placed in this layer - if list or array: element is placed in all these layers + if list or array: element is placed in all these layers label: str or None label of element - - """ - def __init__(self, model, xy=[(-1, 0), (1, 0)], tsandQ=[(0, 1)], res=0, - wh='H', layers=0, Astorage=None, label=None): + + """ + + def __init__( + self, + model, + xy=[(-1, 0), (1, 0)], + tsandQ=[(0, 1)], + res=0, + wh="H", + layers=0, + Astorage=None, + label=None, + ): self.storeinput(inspect.currentframe()) - LineSinkStringBase.__init__(self, model, tsandbc=tsandQ, layers=layers, - type='v', name='LineSinkDitchString', - label=label) - xy = np.atleast_2d(xy).astype('d') - self.x,self.y = xy[:, 0], xy[:, 1] + LineSinkStringBase.__init__( + self, + model, + tsandbc=tsandQ, + layers=layers, + type="v", + name="LineSinkDitchString", + label=label, + ) + xy = np.atleast_2d(xy).astype("d") + self.x, self.y = xy[:, 0], xy[:, 1] self.nls = len(self.x) - 1 for i in range(self.nls): self.lslist.append( - MscreenLineSink(model, x1=self.x[i], y1=self.y[i], - x2=self.x[i + 1], y2=self.y[i + 1], - tsandQ=tsandQ, res=res, wh=wh, layers=layers, - label=None, addtomodel=False)) + MscreenLineSink( + model, + x1=self.x[i], + y1=self.y[i], + x2=self.x[i + 1], + y2=self.y[i + 1], + tsandQ=tsandQ, + res=res, + wh=wh, + layers=layers, + label=None, + addtomodel=False, + ) + ) self.Astorage = Astorage self.model.addelement(self) + def initialize(self): LineSinkStringBase.initialize(self) - # set vresfac to zero, as I don't quite know what it would mean if + # set vresfac to zero, as I don't quite know what it would mean if # it is not zero - self.vresfac = np.zeros_like(self.resfach) + self.vresfac = np.zeros_like(self.resfach) + class LineSinkHoBase(Element): - '''Higher Order LineSink Base Class. All Higher Order Line Sink elements - are derived from this class''' - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandbc=[(0.0,1.0)], - res=0.0, wh='H', order=0, layers=0, type='', - name='LineSinkBase', label=None, addtomodel=True): - Element.__init__(self, model, nparam=1, nunknowns=0, layers=layers, - tsandbc=tsandbc, type=type, name=name, label=label) + """Higher Order LineSink Base Class. All Higher Order Line Sink elements + are derived from this class""" + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandbc=[(0.0, 1.0)], + res=0.0, + wh="H", + order=0, + layers=0, + type="", + name="LineSinkBase", + label=None, + addtomodel=True, + ): + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=layers, + tsandbc=tsandbc, + type=type, + name=name, + label=label, + ) self.order = order self.nparam = (self.order + 1) * len(self.layers) self.x1 = float(x1) @@ -612,11 +872,17 @@ def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandbc=[(0.0,1.0)], self.y2 = float(y2) self.res = res self.wh = wh - if addtomodel: self.model.addelement(self) + if addtomodel: + self.model.addelement(self) def __repr__(self): - return self.name + ' from ' + str((self.x1, self.y1)) + \ - ' to ' + str((self.x2, self.y2)) + return ( + self.name + + " from " + + str((self.x1, self.y1)) + + " to " + + str((self.x2, self.y2)) + ) def initialize(self): self.ncp = self.order + 1 @@ -624,12 +890,11 @@ def initialize(self): self.z2 = self.x2 + 1j * self.y2 self.L = np.abs(self.z1 - self.z2) # - thetacp = np.arange(np.pi, 0, -np.pi / self.ncp) - \ - 0.5 * np.pi / self.ncp - Zcp = np.zeros(self.ncp, 'D') + thetacp = np.arange(np.pi, 0, -np.pi / self.ncp) - 0.5 * np.pi / self.ncp + Zcp = np.zeros(self.ncp, "D") Zcp.real = np.cos(thetacp) # control point just on positive site (this is handy later on) - Zcp.imag = 1e-6 + Zcp.imag = 1e-6 zcp = Zcp * (self.z2 - self.z1) / 2 + 0.5 * (self.z1 + self.z2) self.xc = zcp.real self.yc = zcp.imag @@ -639,116 +904,161 @@ def initialize(self): coef = self.aq.coef[self.layers, :] self.setflowcoef() # shape of term (self.nlayers, self.aq.naq, self.model.npval) - self.term = self.flowcoef * coef - self.term2 = self.term.reshape(self.nlayers, self.aq.naq, - self.model.nint, self.model.npint) + self.term = self.flowcoef * coef + self.term2 = self.term.reshape( + self.nlayers, self.aq.naq, self.model.nint, self.model.npint + ) self.dischargeinf = self.flowcoef * coef - self.dischargeinflayers = np.sum(self.dischargeinf * - self.aq.eigvec[self.layers, :, :], 1) - if self.wh == 'H': + self.dischargeinflayers = np.sum( + self.dischargeinf * self.aq.eigvec[self.layers, :, :], 1 + ) + if self.wh == "H": self.wh = self.aq.Haq[self.layers] - elif self.wh == '2H': + elif self.wh == "2H": self.wh = 2.0 * self.aq.Haq[self.layers] else: self.wh = np.atleast_1d(self.wh) * np.ones(self.nlayers) # Q = (h - hls) / resfach - self.resfach = self.res / (self.wh * self.L) + self.resfach = self.res / (self.wh * self.L) # Q = (Phi - Phils) / resfacp - self.resfacp = self.resfach * self.aq.T[self.layers] + self.resfacp = self.resfach * self.aq.T[self.layers] def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' + """Separate function so that this can be overloaded for other types""" self.flowcoef = 1 / self.model.p # Step function - def potinf(self,x,y,aq=None): - '''Can be called with only one x,y value''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + def potinf(self, x, y, aq=None): + """Can be called with only one x,y value""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + rv = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: - pot = np.zeros((self.order + 1, self.model.npint), 'D') + pot = np.zeros((self.order + 1, self.model.npint), "D") for i in range(self.aq.naq): for j in range(self.model.nint): - if besselnumba.isinside(self.z1, self.z2, x + y * 1j, - self.rzero * self.aq.lababs[i, j]): - pot[:,:] = besselnumba.bessellsv2(x, y, - self.z1, self.z2, self.aq.lab2[i, j, :], - self.order, self.rzero * self.aq.lababs[i, j] - ) / self.L + if besselnumba.isinside( + self.z1, self.z2, x + y * 1j, self.rzero * self.aq.lababs[i, j] + ): + pot[:, :] = ( + besselnumba.bessellsv2( + x, + y, + self.z1, + self.z2, + self.aq.lab2[i, j, :], + self.order, + self.rzero * self.aq.lababs[i, j], + ) + / self.L + ) for k in range(self.nlayers): - rv[k::self.nlayers, i, j, :] = \ - self.term2[k, i, j, :] * pot - + rv[k :: self.nlayers, i, j, :] = self.term2[k, i, j, :] * pot + rv.shape = (self.nparam, aq.naq, self.model.npval) return rv def disvecinf(self, x, y, aq=None): - '''Can be called with only one x,y value''' + """Can be called with only one x,y value""" if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rvx = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') - rvy = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + rvx = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") + rvy = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: - qxqy = np.zeros((2 * (self.order + 1), self.model.npint), 'D') + qxqy = np.zeros((2 * (self.order + 1), self.model.npint), "D") for i in range(self.aq.naq): for j in range(self.model.nint): - if besselnumba.isinside(self.z1, self.z2, x + y * 1j, - self.rzero * self.aq.lababs[i, j]): - qxqy[:, :] = besselnumba.bessellsqxqyv2(x, y, - self.z1, self.z2, self.aq.lab2[i, j, :], - self.order, - self.rzero * self.aq.lababs[i, j]) / self.L + if besselnumba.isinside( + self.z1, self.z2, x + y * 1j, self.rzero * self.aq.lababs[i, j] + ): + qxqy[:, :] = ( + besselnumba.bessellsqxqyv2( + x, + y, + self.z1, + self.z2, + self.aq.lab2[i, j, :], + self.order, + self.rzero * self.aq.lababs[i, j], + ) + / self.L + ) for k in range(self.nlayers): - rvx[k::self.nlayers, i, j, :] = \ - self.term2[k, i, j, :] * qxqy[:self.order + 1, :] - rvy[k::self.nlayers, i, j, :] = \ - self.term2[k, i, j, :] * qxqy[self.order + 1:, :] + rvx[k :: self.nlayers, i, j, :] = ( + self.term2[k, i, j, :] * qxqy[: self.order + 1, :] + ) + rvy[k :: self.nlayers, i, j, :] = ( + self.term2[k, i, j, :] * qxqy[self.order + 1 :, :] + ) rvx.shape = (self.nparam, aq.naq, self.model.npval) rvy.shape = (self.nparam, aq.naq, self.model.npval) return rvx, rvy def headinside(self, t): """The head inside the line-sink - + Returns ------- array (length number of screens) Head inside the well for each screen - + """ - - return self.model.head(self.xc, self.yc, t)[self.layers] - \ - self.resfach[:, np.newaxis] * self.discharge(t) + + return self.model.head(self.xc, self.yc, t)[self.layers] - self.resfach[ + :, np.newaxis + ] * self.discharge(t) def plot(self): - plt.plot([self.x1, self.x2], [self.y1, self.y2], 'k') - + plt.plot([self.x1, self.x2], [self.y1, self.y2], "k") + + class HeadLineSinkHo(LineSinkHoBase, HeadEquationNores): - '''HeadLineSink of which the head varies through time. - May be screened in multiple layers but all with the same head''' - - def __init__(self, model, x1=-1, y1=0, x2=1, y2=0, tsandh=[(0.0,1.0)],\ - order=0, layers=0, label=None, addtomodel=True): + """HeadLineSink of which the head varies through time. + May be screened in multiple layers but all with the same head""" + + def __init__( + self, + model, + x1=-1, + y1=0, + x2=1, + y2=0, + tsandh=[(0.0, 1.0)], + order=0, + layers=0, + label=None, + addtomodel=True, + ): self.storeinput(inspect.currentframe()) - if tsandh == 'fixed': + if tsandh == "fixed": tsandh = [(0, 0)] - etype = 'z' + etype = "z" else: - etype = 'v' - LineSinkHoBase.__init__(self, model, x1=x1, y1=y1, x2=x2, y2=y2, - tsandbc=tsandh, res=0.0, wh='H', order=order, - layers=layers, type=etype, - name='HeadLineSinkHo', label=label, - addtomodel=addtomodel) + etype = "v" + LineSinkHoBase.__init__( + self, + model, + x1=x1, + y1=y1, + x2=x2, + y2=y2, + tsandbc=tsandh, + res=0.0, + wh="H", + order=order, + layers=layers, + type=etype, + name="HeadLineSinkHo", + label=label, + addtomodel=addtomodel, + ) self.nunknowns = self.nparam def initialize(self): LineSinkHoBase.initialize(self) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) self.pc = np.empty(self.nparam) for i, T in enumerate(self.aq.T[self.layers]): # Needed in solving; solve for a unit head - self.pc[i::self.nlayers] = T \ No newline at end of file + self.pc[i :: self.nlayers] = T diff --git a/ttim/model.py b/ttim/model.py index f8d2829..d9b128b 100644 --- a/ttim/model.py +++ b/ttim/model.py @@ -1,20 +1,42 @@ -import numpy as np -import matplotlib.pyplot as plt -#from .invlap import * -import inspect # Used for storing the input +# from .invlap import * +import inspect # Used for storing the input import sys -from .aquifer_parameters import param_3d, param_maq + +import matplotlib.pyplot as plt +import numpy as np +from tqdm.auto import trange + from .aquifer import Aquifer -#from .bessel import * +from .aquifer_parameters import param_3d, param_maq + +# from .bessel import * from .invlapnumba import compute_laplace_parameters_numba, invlap, invlapcomp from .util import PlotTtim + class TimModel(PlotTtim): - def __init__(self, kaq=[1, 1], z=[3, 2, 1], Haq=[1, 1], Hll=[0], - c=[1e100, 100], Saq=[1e-4, 1e-4], Sll=[0], - poraq=0.3, porll=0.3, ltype=['a', 'a'], topboundary='conf', - phreatictop=False, tmin=1, tmax=10, tstart=0, M=10, - kzoverkh=None, model3d=False, timmlmodel=None): + def __init__( + self, + kaq=[1, 1], + z=[3, 2, 1], + Haq=[1, 1], + Hll=[0], + c=[1e100, 100], + Saq=[1e-4, 1e-4], + Sll=[0], + poraq=0.3, + porll=0.3, + ltype=["a", "a"], + topboundary="conf", + phreatictop=False, + tmin=1, + tmax=10, + tstart=0, + M=10, + kzoverkh=None, + model3d=False, + timmlmodel=None, + ): self.elementlist = [] self.elementdict = {} self.vbclist = [] # variable boundary condition 'v' elements @@ -25,21 +47,36 @@ def __init__(self, kaq=[1, 1], z=[3, 2, 1], Haq=[1, 1], Hll=[0], self.tmax = tmax self.tstart = tstart self.M = M - self.aq = Aquifer(self, kaq, z, Haq, Hll, c, Saq, Sll, poraq, porll, - ltype, topboundary, phreatictop, kzoverkh, model3d) + self.aq = Aquifer( + self, + kaq, + z, + Haq, + Hll, + c, + Saq, + Sll, + poraq, + porll, + ltype, + topboundary, + phreatictop, + kzoverkh, + model3d, + ) self.compute_laplace_parameters() - self.name = 'TimModel' - self.modelname = 'ml' # Used for writing out input + self.name = "TimModel" + self.modelname = "ml" # Used for writing out input self.timmlmodel = timmlmodel - + def __repr__(self): - return 'Model' - + return "Model" + def initialize(self): self.gvbclist = self.gbclist + self.vbclist self.vzbclist = self.vbclist + self.zbclist # Given elements are first in list - self.elementlist = self.gbclist + self.vbclist + self.zbclist + self.elementlist = self.gbclist + self.vbclist + self.zbclist self.ngbc = len(self.gbclist) self.nvbc = len(self.vbclist) self.nzbc = len(self.zbclist) @@ -59,66 +96,67 @@ def initialize(self): self.enumber = np.array(enumber) self.etstart = np.array(etstart) self.ebc = np.array(ebc) - + def addelement(self, e): - if e.label is not None: self.elementdict[e.label] = e - if e.type == 'g': + if e.label is not None: + self.elementdict[e.label] = e + if e.type == "g": self.gbclist.append(e) - elif e.type == 'v': + elif e.type == "v": self.vbclist.append(e) - elif e.type == 'z': + elif e.type == "z": self.zbclist.append(e) - + def removeelement(self, e): - if e.label is not None: self.elementdict.pop(e.label) - if e.type == 'g': + if e.label is not None: + self.elementdict.pop(e.label) + if e.type == "g": self.gbclist.remove(e) - elif e.type == 'v': + elif e.type == "v": self.vbclist.remove(e) - elif e.type == 'z': + elif e.type == "z": self.zbclist.remove(e) - + def addinhom(self, inhom): self.aq.inhomlist.append(inhom) - + def compute_laplace_parameters(self): - ''' + """ nint: Number of time intervals npint: Number of p values per interval npval: Total number of p values (nint * npint) p[npval]: Array with p values - ''' + """ itmin = np.floor(np.log10(self.tmin)) itmax = np.ceil(np.log10(self.tmax)) - self.tintervals = 10.0 ** np.arange(itmin, itmax+1) - # lower and upper limit are adjusted to prevent any problems from t + self.tintervals = 10.0 ** np.arange(itmin, itmax + 1) + # lower and upper limit are adjusted to prevent any problems from t # exactly at the beginning and end of the interval # also, you cannot count on t >= 10 ** log10(t) for all possible t self.tintervals[0] = self.tintervals[0] * (1 - 1e-8) self.tintervals[-1] = self.tintervals[-1] * (1 + 1e-8) - self.nint = len(self.tintervals) - 1 # number of p-intervals - self.npint = 2 * self.M + 1 # number of p values in an interval + self.nint = len(self.tintervals) - 1 # number of p-intervals + self.npint = 2 * self.M + 1 # number of p values in an interval self.npval = self.nint * self.npint - # numba + # numba self.p = np.zeros((self.nint, self.npint), dtype=np.complex128) for i in range(self.nint): - self.p[i] = compute_laplace_parameters_numba( - self.tintervals[i + 1], self.M) - #TODO: make self.p a 2D array + self.p[i] = compute_laplace_parameters_numba(self.tintervals[i + 1], self.M) + # TODO: make self.p a 2D array self.p = np.ravel(self.p) self.aq.initialize() - - def potential(self, x, y, t, layers=None, aq=None, derivative=0, - returnphi=0): - '''Returns pot[naq, ntimes] if layers=None, + + def potential(self, x, y, t, layers=None, aq=None, derivative=0, returnphi=0): + """Returns pot[naq, ntimes] if layers=None, otherwise pot[len(layers), ntimes] - t must be ordered ''' - if aq is None: aq = self.aq.find_aquifer_data(x, y) + t must be ordered""" + if aq is None: + aq = self.aq.find_aquifer_data(x, y) if layers is None: layers = range(aq.naq) nlayers = len(layers) - time = np.atleast_1d(t) - self.tstart # used to be ).copy() - pot = np.zeros((self.ngvbc, aq.naq, self.npval), 'D') + time = np.atleast_1d(t) - self.tstart # used to be ).copy() + pot = np.zeros((self.ngvbc, aq.naq, self.npval), "D") for i in range(self.ngbc): pot[i, :] += self.gbclist[i].unitpotential(x, y, aq) for e in self.vzbclist: @@ -127,28 +165,36 @@ def potential(self, x, y, t, layers=None, aq=None, derivative=0, pot = np.sum(pot[:, np.newaxis, :, :] * aq.eigvec, 2) else: pot = np.sum(pot[:, np.newaxis, :, :] * aq.eigvec[layers, :], 2) - if derivative > 0: - pot *= self.p ** derivative + if derivative > 0: + pot *= self.p**derivative if returnphi: return pot - rv = invlapcomp(time, pot, self.npint, self.M, self.tintervals, - self.enumber, self.etstart, self.ebc, nlayers) + rv = invlapcomp( + time, + pot, + self.npint, + self.M, + self.tintervals, + self.enumber, + self.etstart, + self.ebc, + nlayers, + ) return rv - - def potentialone(self, x, y, time, layers=None, aq=None, derivative=0, - returnphi=0): - '''Returns pot[naq] if layers=None, + + def potentialone(self, x, y, time, layers=None, aq=None, derivative=0, returnphi=0): + """Returns pot[naq] if layers=None, otherwise pot[len(layers)] - time is one value''' - if aq is None: + time is one value""" + if aq is None: aq = self.aq.find_aquifer_data(x, y) if layers is None: layers = range(aq.naq) nlayers = len(layers) - time = np.atleast_1d(time) - self.tstart # used to be ).copy() + time = np.atleast_1d(time) - self.tstart # used to be ).copy() jtime = np.searchsorted(self.tintervals, time)[0] - 1 - assert 0 <= jtime <= len(self.tintervals), 'time not in tintervals' - pot = np.zeros((self.ngvbc, aq.naq, self.npint), 'D') + assert 0 <= jtime <= len(self.tintervals), "time not in tintervals" + pot = np.zeros((self.ngvbc, aq.naq, self.npint), "D") for i in range(self.ngbc): pot[i, :] += self.gbclist[i].unitpotentialone(x, y, jtime, aq) for e in self.vzbclist: @@ -157,26 +203,35 @@ def potentialone(self, x, y, time, layers=None, aq=None, derivative=0, pot = np.sum(pot[:, np.newaxis, :, :] * aq.eigvec2[:, :, jtime], 2) else: pot = np.sum(pot[:, np.newaxis, :, :] * aq.eigvec2[layers, :, jtime], 2) - if derivative > 0: - pot *= self.p ** derivative + if derivative > 0: + pot *= self.p**derivative if returnphi: return pot - rv = invlapcomp(time, pot[:, :, :], self.npint, self.M, - self.tintervals[jtime: jtime + 2], - self.enumber, self.etstart, self.ebc, nlayers) + rv = invlapcomp( + time, + pot[:, :, :], + self.npint, + self.M, + self.tintervals[jtime : jtime + 2], + self.enumber, + self.etstart, + self.ebc, + nlayers, + ) return rv - + def disvec(self, x, y, t, layers=None, aq=None, derivative=0): - '''Returns qx[naq, ntimes], qy[naq, ntimes] if layers=None, otherwise + """Returns qx[naq, ntimes], qy[naq, ntimes] if layers=None, otherwise qx[len(layers,Ntimes)],qy[len(layers, ntimes)] - t must be ordered ''' - if aq is None: aq = self.aq.find_aquifer_data(x, y) + t must be ordered""" + if aq is None: + aq = self.aq.find_aquifer_data(x, y) if layers is None: layers = range(aq.naq) nlayers = len(layers) time = np.atleast_1d(t) - self.tstart - disx = np.zeros((self.ngvbc, aq.naq, self.npval), 'D') - disy = np.zeros((self.ngvbc, aq.naq, self.npval), 'D') + disx = np.zeros((self.ngvbc, aq.naq, self.npval), "D") + disy = np.zeros((self.ngvbc, aq.naq, self.npval), "D") for i in range(self.ngbc): qx, qy = self.gbclist[i].unitdisvec(x, y, aq) disx[i, :] += qx @@ -192,18 +247,35 @@ def disvec(self, x, y, t, layers=None, aq=None, derivative=0): disx = np.sum(disx[:, np.newaxis, :, :] * aq.eigvec[layers, :], 2) disy = np.sum(disy[:, np.newaxis, :, :] * aq.eigvec[layers, :], 2) if derivative > 0: - disx *= self.p ** derivative - disy *= self.p ** derivative - rvx = invlapcomp(time, disx, self.npint, self.M, self.tintervals, - self.enumber, self.etstart, self.ebc, nlayers) - rvy = invlapcomp(time, disy, self.npint, self.M, self.tintervals, - self.enumber, self.etstart, self.ebc, nlayers) + disx *= self.p**derivative + disy *= self.p**derivative + rvx = invlapcomp( + time, + disx, + self.npint, + self.M, + self.tintervals, + self.enumber, + self.etstart, + self.ebc, + nlayers, + ) + rvy = invlapcomp( + time, + disy, + self.npint, + self.M, + self.tintervals, + self.enumber, + self.etstart, + self.ebc, + nlayers, + ) return rvx, rvy - - def head(self, x, y, t, layers=None, - aq=None, derivative=0, neglect_steady=False): + + def head(self, x, y, t, layers=None, aq=None, derivative=0, neglect_steady=False): """Head at x, y, t where t can be multiple times - + Parameters ---------- x : float @@ -213,14 +285,15 @@ def head(self, x, y, t, layers=None, layers : integer, list or array, optional layers for which grid is returned if None: all layers are returned - + Returns ------- h : array size `nlayers, ntimes` """ - - if aq is None: aq = self.aq.find_aquifer_data(x, y) + + if aq is None: + aq = self.aq.find_aquifer_data(x, y) if layers is None: layers = range(aq.naq) else: @@ -232,10 +305,10 @@ def head(self, x, y, t, layers=None, htimml = self.timmlmodel.head(x, y, layers=layers) h += htimml[:, np.newaxis] return h - + def velocompold(self, x, y, z, t, aq=None, layer_ltype=[0, 0]): # implemented for one layer - if aq is None: + if aq is None: aq = self.aq.find_aquifer_data(x, y) assert z <= aq.z[0] and z >= aq.z[-1], "z value not inside aquifer" if layer_ltype is None: @@ -248,64 +321,84 @@ def velocompold(self, x, y, z, t, aq=None, layer_ltype=[0, 0]): vy = qy[layer] / (aq.Haq[layer] * aq.poraq[layer]) vz = np.zeros_like(vx) return vx, vy, vz - + def velocomp(self, x, y, z, t, aq=None, layer_ltype=None): # compute velocity for one point x, y, z, t - if aq is None: + if aq is None: aq = self.aq.find_aquifer_data(x, y) assert z <= aq.z[0] and z >= aq.z[-1], "z value not inside aquifer" if layer_ltype is None: layer, ltype, dummy = aq.findlayer(z) else: - layer, ltype = layer_ltype - if ltype == 'l': # inside leaky layer + layer, ltype = layer_ltype + if ltype == "l": # inside leaky layer vx = 0.0 vy = 0.0 if layer == 0: h = self.head(x, y, t, layers=layer, aq=aq, neglect_steady=True) qz = (h[0, 0] - 0.0) / aq.c[0] else: - h = self.head(x, y, t, layers=[layer - 1, layer], aq=aq, neglect_steady=True) - qz = (h[1, 0] - h[0, 0]) / aq.c[layer] # TO DO include storage in leaky layer + h = self.head( + x, y, t, layers=[layer - 1, layer], aq=aq, neglect_steady=True + ) + qz = (h[1, 0] - h[0, 0]) / aq.c[ + layer + ] # TO DO include storage in leaky layer vz = qz / aq.porll[layer] - else: # in aquifer layer + else: # in aquifer layer h = self.head(x, y, t, layers=layer, aq=aq, neglect_steady=True) qx, qy = self.disvec(x, y, t, aq=aq) - vx = qx[layer, 0] / (aq.Haq[layer] * - (aq.poraq[layer] + aq.Saq[layer] * h[0, 0])) - vy = qy[layer, 0] / (aq.Haq[layer] * - (aq.poraq[layer] + aq.Saq[layer] * h[0, 0])) + vx = qx[layer, 0] / ( + aq.Haq[layer] * (aq.poraq[layer] + aq.Saq[layer] * h[0, 0]) + ) + vy = qy[layer, 0] / ( + aq.Haq[layer] * (aq.poraq[layer] + aq.Saq[layer] * h[0, 0]) + ) # - h = np.zeros(3) # head above layer, in layer, and below layer + h = np.zeros(3) # head above layer, in layer, and below layer if layer > 0: - if layer < aq.naq - 1: # there is a layer above and below - h[:] = self.head(x, y, t, layers=[layer - 1, layer, layer + 1], aq=aq, neglect_steady=True)[:, 0] + if layer < aq.naq - 1: # there is a layer above and below + h[:] = self.head( + x, + y, + t, + layers=[layer - 1, layer, layer + 1], + aq=aq, + neglect_steady=True, + )[:, 0] else: - h[:2] = self.head(x, y, t, layers=[layer - 1, layer], aq=aq, neglect_steady=True)[:, 0] - else: # layer = 0, so top layer - if aq.naq == 1: # only one layer - h[1] = self.head(x, y, t, layers=[layer], aq=aq, neglect_steady=True)[:, 0] + h[:2] = self.head( + x, y, t, layers=[layer - 1, layer], aq=aq, neglect_steady=True + )[:, 0] + else: # layer = 0, so top layer + if aq.naq == 1: # only one layer + h[1] = self.head( + x, y, t, layers=[layer], aq=aq, neglect_steady=True + )[:, 0] else: - h[1:] = self.head(x, y, t, layers=[layer, layer + 1], aq=aq, neglect_steady=True)[:, 0] + h[1:] = self.head( + x, y, t, layers=[layer, layer + 1], aq=aq, neglect_steady=True + )[:, 0] # this works because c[0] = 1e100 for impermeable top - qztop = (h[1] - h[0]) / self.aq.c[layer] + qztop = (h[1] - h[0]) / self.aq.c[layer] # TO DO modify for infiltration in top aquifer - #if layer == 0: - # qztop += self.qztop(x, y) + # if layer == 0: + # qztop += self.qztop(x, y) if layer < aq.naq - 1: qzbot = (h[2] - h[1]) / self.aq.c[layer + 1] else: qzbot = 0.0 - vz = (qzbot + (z - aq.zaqbot[layer]) / aq.Haq[layer] * \ - (qztop - qzbot)) / aq.poraq[layer] + vz = ( + qzbot + (z - aq.zaqbot[layer]) / aq.Haq[layer] * (qztop - qzbot) + ) / aq.poraq[layer] velo = np.array([vx, vy, vz]) - + if self.timmlmodel is not None: velotimml = self.timmlmodel.velocity(x, y, z) velo += velotimml return velo - + def velo_one(self, x, y, z, t, aq=None, layer_ltype=[0, 0]): # implemented for one layer and one time vx, vy, vz = self.velocomp(x, y, z, t, aq, layer_ltype) @@ -313,13 +406,13 @@ def velo_one(self, x, y, z, t, aq=None, layer_ltype=[0, 0]): def headinside(self, elabel, t): return self.elementdict[elabel].headinside(t - self.tstart) - - def strength(self,elabel,t): + + def strength(self, elabel, t): return self.elementdict[elabel].strength(t - self.tstart) - + def headalongline(self, x, y, t, layers=None): """Head along line or curve - + Parameters ---------- x : 1D array or list @@ -330,13 +423,13 @@ def headalongline(self, x, y, t, layers=None): times for which grid is returned layers : integer, list or array, optional layers for which grid is returned - + Returns ------- h : array size `nlayers, ntimes, nx` """ - + xg = np.atleast_1d(x) yg = np.atleast_1d(y) if layers is None: @@ -347,14 +440,14 @@ def headalongline(self, x, y, t, layers=None): if len(yg) == 1: yg = yg * np.ones(nx) t = np.atleast_1d(t) - h = np.zeros( (Nlayers,len(t),nx) ) + h = np.zeros((Nlayers, len(t), nx)) for i in range(nx): - h[:,:,i] = self.head(xg[i], yg[i], t, layers) + h[:, :, i] = self.head(xg[i], yg[i], t, layers) return h - + def headgrid(self, xg, yg, t, layers=None, printrow=False): """Grid of heads - + Parameters ---------- xg : array @@ -367,18 +460,18 @@ def headgrid(self, xg, yg, t, layers=None, printrow=False): layers for which grid is returned printrow : boolean, optional prints dot to screen for each row of grid if set to `True` - + Returns ------- h : array size `nlayers, ntimes, ny, nx` - + See also -------- - + :func:`~ttim.model.Model.headgrid2` """ - + nx = len(xg) ny = len(yg) if layers is None: @@ -386,17 +479,17 @@ def headgrid(self, xg, yg, t, layers=None, printrow=False): else: nlayers = len(np.atleast_1d(layers)) t = np.atleast_1d(t) - h = np.empty( (nlayers,len(t),ny,nx) ) - for j in range(ny): + h = np.empty((nlayers, len(t), ny, nx)) + for j in trange(ny): if printrow: - print('.', end='', flush=True) - for i in range(nx): + print(".", end="", flush=True) + for i in trange(nx): h[:, :, j, i] = self.head(xg[i], yg[j], t, layers) return h - + def headgrid2(self, x1, x2, nx, y1, y2, ny, t, layers=None, printrow=False): """Grid of heads - + Parameters ---------- xg : array @@ -409,73 +502,74 @@ def headgrid2(self, x1, x2, nx, y1, y2, ny, t, layers=None, printrow=False): layers for which grid is returned printrow : boolean, optional prints dot to screen for each row of grid if set to `True` - + Returns ------- h : array size `nlayers, ntimes, ny, nx` - + See also -------- - + :func:`~ttim.model.Model.headgrid` """ - + xg = np.linspace(x1, x2, nx) yg = np.linspace(y1, y2, ny) return self.headgrid(xg, yg, t, layers, printrow) - + def inverseLapTran(self, pot, t): - '''returns array of potentials of len(t) - t must be ordered and tmin <= t <= tmax''' + """returns array of potentials of len(t) + t must be ordered and tmin <= t <= tmax""" t = np.atleast_1d(t) rv = np.zeros(len(t)) it = 0 if t[-1] >= self.tmin: # Otherwise all zero - if (t[0] < self.tmin): it = np.argmax( t >= self.tmin ) + if t[0] < self.tmin: + it = np.argmax(t >= self.tmin) for n in range(self.nint): - if n == self.nint-1: - tp = t[(t >= self.tintervals[n]) & - (t <= self.tintervals[n+1])] + if n == self.nint - 1: + tp = t[(t >= self.tintervals[n]) & (t <= self.tintervals[n + 1])] else: - tp = t[(t >= self.tintervals[n]) & - (t < self.tintervals[n+1])] + tp = t[(t >= self.tintervals[n]) & (t < self.tintervals[n + 1])] nt = len(tp) if nt > 0: # if all values zero, don't do inverse transform # Not needed anymore: if np.abs(pot[n*self.npint]) > 1e-20: - # If there is a zero item, zero should be returned; - # funky enough this can be done with a + # If there is a zero item, zero should be returned; + # funky enough this can be done with a # straight equal comparison - if not np.any(pot[n*self.npint:(n+1)*self.npint] == 0.0): - rv[it : it + nt] = invlap(tp, - self.tintervals[n + 1], - pot[n * self.npint: (n + 1) * self.npint], - self.M) + if not np.any(pot[n * self.npint : (n + 1) * self.npint] == 0.0): + rv[it : it + nt] = invlap( + tp, + self.tintervals[n + 1], + pot[n * self.npint : (n + 1) * self.npint], + self.M, + ) it = it + nt return rv - + def solve(self, printmat=0, sendback=0, silent=False): - """Compute solution - - """ - + """Compute solution""" + # Initialize elements self.initialize() # Compute number of equations self.neq = np.sum([e.nunknowns for e in self.elementlist]) if silent is False: - print('self.neq ', self.neq) + print("self.neq ", self.neq) if self.neq == 0: if silent is False: - print('No unknowns. Solution complete') + print("No unknowns. Solution complete") return - mat = np.empty((self.neq, self.neq, self.npval), 'D') - rhs = np.empty((self.neq, self.ngvbc, self.npval), 'D') + mat = np.empty((self.neq, self.neq, self.npval), "D") + rhs = np.empty((self.neq, self.ngvbc, self.npval), "D") ieq = 0 for e in self.elementlist: if e.nunknowns > 0: - mat[ieq: ieq + e.nunknowns, :, :], \ - rhs[ieq: ieq + e.nunknowns, :, :] = e.equation() + ( + mat[ieq : ieq + e.nunknowns, :, :], + rhs[ieq : ieq + e.nunknowns, :, :], + ) = e.equation() ieq += e.nunknowns if printmat: return mat, rhs @@ -488,43 +582,48 @@ def solve(self, printmat=0, sendback=0, silent=False): icount += 1 e.run_after_solve() if silent is False: - print('solution complete') - elif (silent == 'dot') or (silent == '.'): - print('.', end='', flush=True) + print("solution complete") + elif (silent == "dot") or (silent == "."): + print(".", end="", flush=True) if sendback: return sol return - - def storeinput(self,frame): + + def storeinput(self, frame): self.inputargs, _, _, self.inputvalues = inspect.getargvalues(frame) - + def write(self): - rv = self.modelname + ' = '+self.name+'(\n' + rv = self.modelname + " = " + self.name + "(\n" for key in self.inputargs[1:]: # The first argument (self) is ignored - if isinstance(self.inputvalues[key],np.ndarray): - rv += key + ' = ' + np.array2string(self.inputvalues[key], - separator=',') + ',\n' - elif isinstance(self.inputvalues[key],str): + if isinstance(self.inputvalues[key], np.ndarray): + rv += ( + key + + " = " + + np.array2string(self.inputvalues[key], separator=",") + + ",\n" + ) + elif isinstance(self.inputvalues[key], str): rv += key + " = '" + self.inputvalues[key] + "',\n" else: - rv += key + ' = ' + str(self.inputvalues[key]) + ',\n' - rv += ')\n' + rv += key + " = " + str(self.inputvalues[key]) + ",\n" + rv += ")\n" return rv - - def writemodel(self,fname): + + def writemodel(self, fname): self.initialize() # So that model can be written without solving first - f = open(fname,'w') - f.write('from ttim import *\n') - f.write( self.write() ) + f = open(fname, "w") + f.write("from ttim import *\n") + f.write(self.write()) for e in self.elementlist: - f.write( e.write() ) + f.write(e.write()) f.close() - + + class ModelMaq(TimModel): """ Create a Model object by specifying a mult-aquifer sequence of aquifer-leakylayer-aquifer-leakylayer-aquifer etc - + Parameters ---------- kaq : float, array or list @@ -569,31 +668,62 @@ class ModelMaq(TimModel): M : integer the number of terms to be used in the numerical inversion algorithm. 10 is usually sufficient. If drawdown curves appear to oscillate, - more terms may be needed, but this seldom happens. - timmlmodel : optional instance of a solved TimML model + more terms may be needed, but this seldom happens. + timmlmodel : optional instance of a solved TimML model a timml model may be included to add steady-state flow - + """ - - def __init__(self, kaq=[1], z=[1,0], c=[], Saq=[0.001], Sll=[0], - poraq=[0.3], porll=[0.3], - topboundary='conf', phreatictop=False, - tmin=1, tmax=10, tstart=0, M=10, timmlmodel=None): + + def __init__( + self, + kaq=[1], + z=[1, 0], + c=[], + Saq=[0.001], + Sll=[0], + poraq=[0.3], + porll=[0.3], + topboundary="conf", + phreatictop=False, + tmin=1, + tmax=10, + tstart=0, + M=10, + timmlmodel=None, + ): self.storeinput(inspect.currentframe()) kaq, Haq, Hll, c, Saq, Sll, poraq, porll, ltype = param_maq( - kaq, z, c, Saq, Sll, poraq, porll, topboundary, phreatictop) - TimModel.__init__(self, kaq, z, Haq, Hll, c, Saq, Sll, - poraq, porll, ltype, - topboundary, phreatictop, tmin, tmax, tstart, M, - timmlmodel=timmlmodel) - self.name = 'ModelMaq' - + kaq, z, c, Saq, Sll, poraq, porll, topboundary, phreatictop + ) + TimModel.__init__( + self, + kaq, + z, + Haq, + Hll, + c, + Saq, + Sll, + poraq, + porll, + ltype, + topboundary, + phreatictop, + tmin, + tmax, + tstart, + M, + timmlmodel=timmlmodel, + ) + self.name = "ModelMaq" + + class Model3D(TimModel): """ Create a multi-layer model object consisting of many aquifer layers. The resistance between the layers is computed from the vertical hydraulic conductivity of the layers. - + Parameters ---------- kaq : float, array or list @@ -637,23 +767,66 @@ class Model3D(TimModel): M : integer (default 10) the number of terms to be used in the numerical inversion algorithm. 10 is usually sufficient. If drawdown curves appear to oscillate, - more terms may be needed, but this seldom happens. - timmlmodel : optional instance of a solved TimML model + more terms may be needed, but this seldom happens. + timmlmodel : optional instance of a solved TimML model a timml model may be included to add steady-state flow - + """ - - def __init__(self, kaq=1, z=[4, 3, 2, 1], Saq=0.001, kzoverkh=0.1, - poraq=0.3, topboundary='conf', phreatictop=False, - topres=0, topthick=0, topSll=0, toppor=0.3, - tmin=1, tmax=10, tstart=0, M=10, timmlmodel=None): - '''z must have the length of the number of layers + 1''' + + def __init__( + self, + kaq=1, + z=[4, 3, 2, 1], + Saq=0.001, + kzoverkh=0.1, + poraq=0.3, + topboundary="conf", + phreatictop=False, + topres=0, + topthick=0, + topSll=0, + toppor=0.3, + tmin=1, + tmax=10, + tstart=0, + M=10, + timmlmodel=None, + ): + """z must have the length of the number of layers + 1""" self.storeinput(inspect.currentframe()) kaq, Haq, Hll, c, Saq, Sll, poraq, porll, ltype, z = param_3d( - kaq, z, Saq, kzoverkh, poraq, phreatictop, topboundary, topres, - topthick, topSll, toppor) - TimModel.__init__(self, kaq, z, Haq, Hll, c, Saq, Sll, - poraq, porll, ltype, - topboundary, phreatictop, tmin, tmax, tstart, M, - kzoverkh, model3d=True, timmlmodel=timmlmodel) - self.name = 'Model3D' \ No newline at end of file + kaq, + z, + Saq, + kzoverkh, + poraq, + phreatictop, + topboundary, + topres, + topthick, + topSll, + toppor, + ) + TimModel.__init__( + self, + kaq, + z, + Haq, + Hll, + c, + Saq, + Sll, + poraq, + porll, + ltype, + topboundary, + phreatictop, + tmin, + tmax, + tstart, + M, + kzoverkh, + model3d=True, + timmlmodel=timmlmodel, + ) + self.name = "Model3D" diff --git a/ttim/src/test_bessel.py b/ttim/src/test_bessel.py index c50846b..2188f9e 100644 --- a/ttim/src/test_bessel.py +++ b/ttim/src/test_bessel.py @@ -1,23 +1,14 @@ import sys -sys.path.insert(1, "C:/github/ttim_db") -from ttim.besselnumba import besselnumba -from ttim.bessel import bessel + import numpy as np +from ttim.bessel import bessel # fortran compiled funcs +from ttim.besselnumba import besselnumba # numba funcs bessel.initialize() def test_variables(): - list_of_vars = ['a', - 'a1', - 'afar', - 'b', - 'b1', - 'gam', - 'nrange', - 'tiny', - 'wg', - 'xg'] + list_of_vars = ["a", "a1", "afar", "b", "b1", "gam", "nrange", "tiny", "wg", "xg"] result = [] for var in list_of_vars: @@ -30,7 +21,7 @@ def test_variables(): def test_besselk0far(): - z = 1+1j + z = 1 + 1j Nt = 11 a = bessel.besselk0far(z, Nt) b = besselnumba.besselk0far(z, Nt) @@ -39,7 +30,7 @@ def test_besselk0far(): def test_besselk0near(): - z = 1+1j + z = 1 + 1j Nt = 17 a = bessel.besselk0near(z, Nt) b = besselnumba.besselk0near(z, Nt) @@ -48,7 +39,7 @@ def test_besselk0near(): def test_besselk1near(): - z = 1+1j + z = 1 + 1j Nt = 20 a = bessel.besselk1near(z, Nt) b = besselnumba.besselk1near(z, Nt) @@ -57,7 +48,7 @@ def test_besselk1near(): def test_besselk0cheb(): - z = 1+1j + z = 1 + 1j Nt = 6 a = bessel.besselk0cheb(z, Nt) b = besselnumba.besselk0cheb(z, Nt) @@ -66,7 +57,7 @@ def test_besselk0cheb(): def test_besselk1cheb(): - z = 1+1j + z = 1 + 1j Nt = 6 a = bessel.besselk1cheb(z, Nt) b = besselnumba.besselk1cheb(z, Nt) @@ -75,9 +66,9 @@ def test_besselk1cheb(): def test_besselk0(): - x = 10. - y = 10. - lab = 100. + x = 10.0 + y = 10.0 + lab = 100.0 a = bessel.besselk0(x, y, lab) b = besselnumba.besselk0(x, y, lab) assert a == b, "not equal" @@ -85,9 +76,9 @@ def test_besselk0(): def test_besselk1(): - x = 10. - y = 10. - lab = 100. + x = 10.0 + y = 10.0 + lab = 100.0 a = bessel.besselk1(x, y, lab) b = besselnumba.besselk1(x, y, lab) assert a == b, "not equal" @@ -95,7 +86,7 @@ def test_besselk1(): def test_k0bessel(): - z = 1.+1.j + z = 1.0 + 1.0j a = bessel.k0bessel(z) b = besselnumba.k0bessel(z) assert a == b, "not equal" @@ -103,20 +94,19 @@ def test_k0bessel(): def test_besselk0v(): - x = 10. - y = 10. - lab = np.array([100.]) + x = 10.0 + y = 10.0 + lab = np.array([100.0]) nlab = 1 omega = np.zeros(1, dtype=np.complex_) bessel.besselk0v(x, y, lab, omega=omega) - b = besselnumba.besselk0v(x, y, lab, nlab, - np.zeros(1, dtype=np.complex_)) + b = besselnumba.besselk0v(x, y, lab, nlab, np.zeros(1, dtype=np.complex_)) assert omega == b, "not equal" return omega, b def test_k0besselv(): - z = np.array([1+1j]) + z = np.array([1 + 1j]) nlab = 1 omega = np.zeros(1, dtype=np.complex_) bessel.k0besselv(z, omega=omega) @@ -125,7 +115,7 @@ def test_k0besselv(): return omega, b -#def test_besselcheb(): +# def test_besselcheb(): # z = 1. + 1.j # Nt = 6 # a = bessel.besselcheb(z, Nt) @@ -134,7 +124,7 @@ def test_k0besselv(): # return a, b -#def test_ucheb(): +# def test_ucheb(): # a = 1. # c = 1. # z = 1.+1.j @@ -145,7 +135,7 @@ def test_k0besselv(): # return a, b -#def test_besselk0complex(): +# def test_besselk0complex(): # x = 10. # y = 10. # a = bessel.besselk0complex(x, y) @@ -155,10 +145,10 @@ def test_k0besselv(): def test_lapls_int_ho(): - x = 10. - y = 10. - z1 = 1. + 1.j - z2 = 2. + 2.j + x = 10.0 + y = 10.0 + z1 = 1.0 + 1.0j + z2 = 2.0 + 2.0j order = 1 a = bessel.lapls_int_ho(x, y, z1, z2, order) b = besselnumba.lapls_int_ho(x, y, z1, z2, order) @@ -167,13 +157,13 @@ def test_lapls_int_ho(): def test_bessellsreal(): - x = 5. - y = 5. - x1 = 0. - y1 = 0. - x2 = 10. - y2 = 10. - lab = 100. + x = 5.0 + y = 5.0 + x1 = 0.0 + y1 = 0.0 + x2 = 10.0 + y2 = 10.0 + lab = 100.0 a = bessel.bessellsreal(x, y, x1, y1, x2, y2, lab) b = besselnumba.bessellsreal(x, y, x1, y1, x2, y2, lab) assert np.allclose(a, b), "not equal" @@ -181,13 +171,13 @@ def test_bessellsreal(): def test_bessellsrealho(): - x = 5. - y = 5. - x1 = 0. - y1 = 0. - x2 = 10. - y2 = 10. - lab = 100. + x = 5.0 + y = 5.0 + x1 = 0.0 + y1 = 0.0 + x2 = 10.0 + y2 = 10.0 + lab = 100.0 order = 1 a = bessel.bessellsrealho(x, y, x1, y1, x2, y2, lab, order) b = besselnumba.bessellsrealho(x, y, x1, y1, x2, y2, lab, order) @@ -196,11 +186,11 @@ def test_bessellsrealho(): def test_bessells_int(): - x = 5. - y = 5. - lab = 100. - z1 = 1. + 1.j - z2 = 5. + 5.j + x = 5.0 + y = 5.0 + lab = 100.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j a = bessel.bessells_int(x, y, z1, z2, lab) b = besselnumba.bessells_int(x, y, z1, z2, lab) assert np.allclose(a, b), "not equal" @@ -208,14 +198,14 @@ def test_bessells_int(): def test_bessells_int_ho(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.bessells_int_ho(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.bessells_int_ho(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -223,14 +213,14 @@ def test_bessells_int_ho(): def test_bessells_int_ho_qxqy(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.bessells_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -238,11 +228,11 @@ def test_bessells_int_ho_qxqy(): def test_bessells_gauss(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 a = bessel.bessells_gauss(x, y, z1, z2, lab) b = besselnumba.bessells_gauss(x, y, z1, z2, lab) assert np.allclose(a, b), "not equal" @@ -250,11 +240,11 @@ def test_bessells_gauss(): def test_bessells_gauss_ho(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 a = bessel.bessells_gauss(x, y, z1, z2, lab) b = besselnumba.bessells_gauss(x, y, z1, z2, lab) assert np.allclose(a, b), "not equal" @@ -262,14 +252,14 @@ def test_bessells_gauss_ho(): def test_bessells_gauss_ho_d1d2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.bessells_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -277,11 +267,11 @@ def test_bessells_gauss_ho_d1d2(): def test_bessells_gauss_ho_qxqy(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 a = bessel.bessells_gauss_ho_qxqy(x, y, z1, z2, lab, order) b = besselnumba.bessells_gauss_ho_qxqy(x, y, z1, z2, lab, order) @@ -290,30 +280,29 @@ def test_bessells_gauss_ho_qxqy(): def test_bessells_gauss_ho_qxqy_d1d2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2) - b = besselnumba.bessells_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1, d2) + b = besselnumba.bessells_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2) # assert np.allclose(a, b), "not equal" return a, b def test_bessells(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1in = 1. - d2in = -1. + d1in = 1.0 + d2in = -1.0 a = bessel.bessells(x, y, z1, z2, lab, order, d1in, d2in) b = besselnumba.bessells(x, y, z1, z2, lab, order, d1in, d2in) assert np.allclose(a, b), "not equal" @@ -321,13 +310,13 @@ def test_bessells(): def test_bessellsv(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - R = 1. + R = 1.0 nlab = 1 a = bessel.bessellsv(x, y, z1, z2, lab, order, R, nlab) b = besselnumba.bessellsv(x, y, z1, z2, lab, order, R, nlab) @@ -336,13 +325,13 @@ def test_bessellsv(): def test_bessellsv2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - R = 1. + R = 1.0 nlab = 1 a = bessel.bessellsv2(x, y, z1, z2, lab, order, R, nlab) b = besselnumba.bessellsv2(x, y, z1, z2, lab, order, R, nlab) @@ -351,14 +340,14 @@ def test_bessellsv2(): def test_bessellsqxqy(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1in = 1. - d2in = -1. + d1in = 1.0 + d2in = -1.0 a = bessel.bessellsqxqy(x, y, z1, z2, lab, order, d1in, d2in) b = besselnumba.bessellsqxqy(x, y, z1, z2, lab, order, d1in, d2in) assert np.allclose(a, b), "not equal" @@ -366,13 +355,13 @@ def test_bessellsqxqy(): def test_bessellsqxqyv(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - R = 1. + R = 1.0 nlab = 1 a = bessel.bessellsqxqyv(x, y, z1, z2, lab, order, R, nlab) b = besselnumba.bessellsqxqyv(x, y, z1, z2, lab, order, R, nlab) @@ -381,13 +370,13 @@ def test_bessellsqxqyv(): def test_bessellsqxqyv2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - R = 1. + R = 1.0 nlab = 1 a = bessel.bessellsqxqyv2(x, y, z1, z2, lab, order, R, nlab) b = besselnumba.bessellsqxqyv2(x, y, z1, z2, lab, order, R, nlab) @@ -396,11 +385,11 @@ def test_bessellsqxqyv2(): def test_bessellsuni(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 a = bessel.bessellsuni(x, y, z1, z2, lab) b = besselnumba.bessellsuni(x, y, z1, z2, lab) assert np.allclose(a, b), "not equal" @@ -408,11 +397,11 @@ def test_bessellsuni(): def test_bessellsuniv(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) nlab = 1 a = bessel.bessellsuniv(x, y, z1, z2, lab) b = besselnumba.bessellsuniv(x, y, z1, z2, lab, nlab) @@ -421,10 +410,10 @@ def test_bessellsuniv(): def test_lapld_int_ho(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j order = 1 a = bessel.lapld_int_ho(x, y, z1, z2, order) b = besselnumba.lapld_int_ho(x, y, z1, z2, order) @@ -433,13 +422,13 @@ def test_lapld_int_ho(): def test_lapld_int_ho_d1d2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2) b = besselnumba.lapld_int_ho_d1d2(x, y, z1, z2, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -447,10 +436,10 @@ def test_lapld_int_ho_d1d2(): def test_lapld_int_ho_wdis(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j order = 1 a = bessel.lapld_int_ho_wdis(x, y, z1, z2, order) b = besselnumba.lapld_int_ho_wdis(x, y, z1, z2, order) @@ -459,13 +448,13 @@ def test_lapld_int_ho_wdis(): def test_lapld_int_ho_wdis_d1d2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2) b = besselnumba.lapld_int_ho_wdis_d1d2(x, y, z1, z2, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -473,14 +462,14 @@ def test_lapld_int_ho_wdis_d1d2(): def test_besselld_int_ho(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.besselld_int_ho(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.besselld_int_ho(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -488,11 +477,11 @@ def test_besselld_int_ho(): def test_besselld_gauss_ho(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 a = bessel.besselld_gauss_ho(x, y, z1, z2, lab, order) b = besselnumba.besselld_gauss_ho(x, y, z1, z2, lab, order) @@ -501,14 +490,14 @@ def test_besselld_gauss_ho(): def test_besselld_gauss_ho_d1d2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.besselld_gauss_ho_d1d2(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -516,14 +505,14 @@ def test_besselld_gauss_ho_d1d2(): def test_besselld(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1in = 1. - d2in = -1. + d1in = 1.0 + d2in = -1.0 a = bessel.besselld(x, y, z1, z2, lab, order, d1in, d2in) b = besselnumba.besselld(x, y, z1, z2, lab, order, d1in, d2in) assert np.allclose(a, b), "not equal" @@ -531,14 +520,14 @@ def test_besselld(): def test_besselldv(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 nlab = 1 - R = 1. + R = 1.0 a = bessel.besselldv(x, y, z1, z2, lab, order, R) b = besselnumba.besselldv(x, y, z1, z2, lab, order, R, nlab) assert np.allclose(a, b), "not equal" @@ -546,14 +535,14 @@ def test_besselldv(): def test_besselldv2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 nlab = 1 - R = 1. + R = 1.0 a = bessel.besselldv2(x, y, z1, z2, lab, order, R) b = besselnumba.besselldv2(x, y, z1, z2, lab, order, R, nlab) assert np.allclose(a, b), "not equal" @@ -561,14 +550,14 @@ def test_besselldv2(): def test_besselldpart(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.besselldpart(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.besselldpart(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -576,14 +565,14 @@ def test_besselldpart(): def test_besselld_int_ho_qxqy(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) b = besselnumba.besselld_int_ho_qxqy(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" @@ -591,11 +580,11 @@ def test_besselld_int_ho_qxqy(): def test_besselld_gauss_ho_qxqy(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 a = bessel.besselld_gauss_ho_qxqy(x, y, z1, z2, lab, order) b = besselnumba.besselld_gauss_ho_qxqy(x, y, z1, z2, lab, order) @@ -604,30 +593,29 @@ def test_besselld_gauss_ho_qxqy(): def test_besselld_gauss_ho_qxqy_d1d2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1 = 1. - d2 = -1. + d1 = 1.0 + d2 = -1.0 a = bessel.besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2) - b = besselnumba.besselld_gauss_ho_qxqy_d1d2( - x, y, z1, z2, lab, order, d1, d2) + b = besselnumba.besselld_gauss_ho_qxqy_d1d2(x, y, z1, z2, lab, order, d1, d2) assert np.allclose(a, b), "not equal" return a, b def test_besselldqxqy(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = 100.0 order = 1 - d1in = 1. - d2in = -1. + d1in = 1.0 + d2in = -1.0 a = bessel.besselldqxqy(x, y, z1, z2, lab, order, d1in, d2in) b = besselnumba.besselldqxqy(x, y, z1, z2, lab, order, d1in, d2in) assert np.allclose(a, b), "not equal" @@ -635,14 +623,14 @@ def test_besselldqxqy(): def test_besselldqxqyv(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 nlab = 1 - R = 1. + R = 1.0 a = bessel.besselldqxqyv(x, y, z1, z2, lab, order, R) b = besselnumba.besselldqxqyv(x, y, z1, z2, lab, order, R, nlab) assert np.allclose(a, b), "not equal" @@ -650,13 +638,13 @@ def test_besselldqxqyv(): def test_besselldqxqyv2(): - x = 5. - y = 5. - z1 = 1. + 1.j - z2 = 5. + 5.j - lab = np.array([100.]) + x = 5.0 + y = 5.0 + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + lab = np.array([100.0]) order = 1 - R = 1. + R = 1.0 nlab = 1 a = bessel.besselldqxqyv2(x, y, z1, z2, lab, order, R) b = besselnumba.besselldqxqyv2(x, y, z1, z2, lab, order, R, nlab) @@ -665,11 +653,11 @@ def test_besselldqxqyv2(): def test_bessells_circcheck(): - x = 5. - y = 5. - z1in = 1. + 1.j - z2in = 5. + 5.j - lab = 100. + x = 5.0 + y = 5.0 + z1in = 1.0 + 1.0j + z2in = 5.0 + 5.0j + lab = 100.0 a = bessel.bessells_circcheck(x, y, z1in, z2in, lab) b = besselnumba.bessells_circcheck(x, y, z1in, z2in, lab) assert np.allclose(a, b), "not equal" @@ -677,14 +665,14 @@ def test_bessells_circcheck(): def test_circle_line_intersection(): - z1 = 1. + 1.j - z2 = 5. + 5.j - zc = 2. + 2.j - R = 10. - xouta = 0. - youta = 0. - xoutb = 1. - youtb = 1. + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + zc = 2.0 + 2.0j + R = 10.0 + xouta = 0.0 + youta = 0.0 + xoutb = 1.0 + youtb = 1.0 N = 0 xyn = bessel.circle_line_intersection_func(z1, z2, zc, R) a = (xyn[0], xyn[1], xyn[2], xyn[3], int(xyn[4])) @@ -694,12 +682,12 @@ def test_circle_line_intersection(): def test_find_d1d2(): - z1 = -1 -2j + z1 = -1 - 2j z2 = 2 + 1j zc = 2 + 0.5j R = 2.0 - d1 = 0. - d2 = 0. + d1 = 0.0 + d2 = 0.0 a = bessel.find_d1d2_func(z1, z2, zc, R) b = besselnumba.find_d1d2(z1, z2, zc, R) assert np.allclose(a, b), "not equal" @@ -707,10 +695,10 @@ def test_find_d1d2(): def test_isinside(): - z1 = 1. + 1.j - z2 = 5. + 5.j - zc = 2. + 2.j - R = 10. + z1 = 1.0 + 1.0j + z2 = 5.0 + 5.0j + zc = 2.0 + 2.0j + R = 10.0 a = bessel.isinside(z1, z2, zc, R) b = besselnumba.isinside(z1, z2, zc, R) assert np.allclose(a, b), "not equal" @@ -729,9 +717,9 @@ def test_isinside(): t8 = test_k0bessel() t9 = test_besselk0v() t10 = test_k0besselv() - #t11 = test_besselcheb() - #t12 = test_ucheb() # fails - #t13 = test_besselk0complex() + # t11 = test_besselcheb() + # t12 = test_ucheb() # fails + # t13 = test_besselk0complex() t14 = test_lapls_int_ho() t15 = test_bessellsreal() t16 = test_bessellsrealho() diff --git a/ttim/trace.py b/ttim/trace.py index 031d014..c93be03 100644 --- a/ttim/trace.py +++ b/ttim/trace.py @@ -1,15 +1,26 @@ import numpy as np -def timtrace(ml, xstart, ystart, zstart, tstartend, tstartoffset, - tstep, nstepmax=100, hstepmax=10, silent=False, - correctionstep=True): + +def timtrace( + ml, + xstart, + ystart, + zstart, + tstartend, + tstartoffset, + tstep, + nstepmax=100, + hstepmax=10, + silent=False, + correctionstep=True, +): """ Compute a pathline by numerical integration of the velocity vector. - Pathline is broken up in sections for which starting times are provided. + Pathline is broken up in sections for which starting times are provided. Pathline is computed from first starting time + offset until second - starting time, then continued from second starting time + offset until - third starting time, etc. - + starting time, then continued from second starting time + offset until + third starting time, etc. + Parameters ---------- model : Model object @@ -23,7 +34,7 @@ def timtrace(ml, xstart, ystart, zstart, tstartend, tstartoffset, tstartend : list list of starting times of pathline. last entry is the ending time. tstartoffset : float or list - time after starting time when pathline is started. value or list. if + time after starting time when pathline is started. value or list. if this value is smaller than tmin it may cause problems after change in boundary conditions tstep : scalar or list @@ -38,25 +49,25 @@ def timtrace(ml, xstart, ystart, zstart, tstartend, tstartoffset, each section of the pathline correctionstep : boolean parameter to indicate if a correction step (Euler's method) should - be taken. Taking a correction step is more accurate, especially for + be taken. Taking a correction step is more accurate, especially for curved pathlines. - + Returns -------- result : dictionary with three entries - + * xyzt : 2D array with four columns: x, y, z, t along pathline * message : list with text messages of each section of the pathline * status : numerical indication of the result. Negative is likely undesirable. - + * -2 : reached maximum number of steps before reaching maximum time * -1 : starting z value not inside aquifer * +1 : reached maximum time * +2 : reached element * +3 : flows out of top of aquifer - + """ xyzt = [np.array([[xstart, ystart, zstart, tstartend[0]]])] messages = [] @@ -67,54 +78,74 @@ def timtrace(ml, xstart, ystart, zstart, tstartend, tstartoffset, tstartoffset = len(tstartend) * [tstartoffset] for itrace in range(len(tstartend) - 1): x0, y0, z0, t0 = xyzt[-1][-1] - trace = timtraceline(ml, x0, y0, z0, t0 + tstartoffset[itrace], - tstep[itrace], tstartend[itrace + 1], - nstepmax=nstepmax, hstepmax=hstepmax, - silent=silent, correctionstep=correctionstep) - xyzt.append(trace['xyzt']) - messages.append(trace['message']) - status.append(trace['status']) - if trace['status'] != 1: + trace = timtraceline( + ml, + x0, + y0, + z0, + t0 + tstartoffset[itrace], + tstep[itrace], + tstartend[itrace + 1], + nstepmax=nstepmax, + hstepmax=hstepmax, + silent=silent, + correctionstep=correctionstep, + ) + xyzt.append(trace["xyzt"]) + messages.append(trace["message"]) + status.append(trace["status"]) + if trace["status"] != 1: break xyzt = np.vstack(xyzt) - result = {"xyzt": np.array(xyzt), "message": messages, - "status": status} - return result - + result = {"xyzt": np.array(xyzt), "message": messages, "status": status} + return result + -def timtraceline(ml, xstart, ystart, zstart, tstart, delt, tmax, - nstepmax=100, hstepmax=10, correctionstep=True, silent=False): +def timtraceline( + ml, + xstart, + ystart, + zstart, + tstart, + delt, + tmax, + nstepmax=100, + hstepmax=10, + correctionstep=True, + silent=False, +): # treating aquifer layers and leaky layers the same way - direction = 1 # forward + direction = 1 # forward terminate = False status = 0 - message = 'no message' + message = "no message" eps = 1e-6 # used to place point just above or below aquifer top or bottom aq = ml.aq.find_aquifer_data(xstart, ystart) if zstart > aq.z[0] or zstart < aq.z[-1]: terminate = True status = -1 - message = 'starting z value not inside aquifer' + message = "starting z value not inside aquifer" # slightly alter starting location not to get stuck in surpring points # starting at time 0 - xyzt = [np.array([xstart * (1 + eps), ystart * (1 + eps), zstart, tstart])] + xyzt = [np.array([xstart * (1 + eps), ystart * (1 + eps), zstart, tstart])] layerlist = [] # to keep track of layers for plotting with colors for istep in range(nstepmax): if terminate: break - do_correction = correctionstep # do correction step, unless do_correction changed to False + do_correction = ( + correctionstep # do correction step, unless do_correction changed to False + ) x0, y0, z0, t0 = xyzt[-1] - #print(x0, y0, z0, t0) - #aq = ml.aq.find_aquifer_data(x0, y0) # find new aquifer + # print(x0, y0, z0, t0) + # aq = ml.aq.find_aquifer_data(x0, y0) # find new aquifer layer, ltype, modellayer = aq.findlayer(z0) layerlist.append(modellayer) v0 = ml.velocomp(x0, y0, z0, t0, aq, [layer, ltype]) vx, vy, vz = v0 - substep = 1 # take max 2 substeps - + substep = 1 # take max 2 substeps + for steps in range(2): if substep <= 2: - # check if max time reached if t0 + delt > tmax: delt0 = tmax - t0 @@ -122,7 +153,7 @@ def timtraceline(ml, xstart, ystart, zstart, tstart, delt, tmax, delt0 = delt # check if horizontal step larger than hstepmax - hstep = np.sqrt(vx ** 2 + vy ** 2) * delt0 + hstep = np.sqrt(vx**2 + vy**2) * delt0 if hstep > hstepmax: delt0 = hstepmax / hstep * delt0 @@ -130,12 +161,12 @@ def timtraceline(ml, xstart, ystart, zstart, tstart, delt, tmax, z1 = z0 + delt0 * vz layer1, ltype1, modellayer1 = aq.findlayer(z1) # print(steps, 'z1', z1, layer1, ltype1, modellayer1) - if modellayer1 < modellayer: # step up to next layer + if modellayer1 < modellayer: # step up to next layer delt0 = (aq.z[modellayer] - z0) / (z1 - z0) * delt0 z1 = aq.z[modellayer] + eps # print('stepping up to next layer, z1= ', z1) do_correction = False - elif modellayer1 > modellayer: # step down to next layer + elif modellayer1 > modellayer: # step down to next layer delt0 = (z0 - aq.z[modellayer + 1]) / (z0 - z1) * delt0 z1 = aq.z[modellayer1] - eps do_correction = False @@ -149,8 +180,15 @@ def timtraceline(ml, xstart, ystart, zstart, tstart, delt, tmax, # check elements if point needs to be changed for e in ml.elementlist: changed, terminate, xyztnew, message = e.changetrace( - xyzt[-1], xyzt1, aq, layer, ltype, modellayer, - direction, hstepmax) + xyzt[-1], + xyzt1, + aq, + layer, + ltype, + modellayer, + direction, + hstepmax, + ) if changed or terminate: x1, y1, z1, t1 = xyztnew do_correction = False @@ -161,41 +199,53 @@ def timtraceline(ml, xstart, ystart, zstart, tstart, delt, tmax, if t1 >= tmax: terminate = True status = 1 - message = 'reached maximum time tmax' + message = "reached maximum time tmax" if istep == nstepmax - 1: terminate = True status = -2 - message = 'reached maximum number of steps' + message = "reached maximum number of steps" if z1 > aq.z[0]: terminate = True - message = 'flows out of top of aquifer' + message = "flows out of top of aquifer" status = 3 - if terminate: + if terminate: xyzt.append(np.array([x1, y1, z1, t1])) break - if substep == 1 and do_correction: # do correction step + if substep == 1 and do_correction: # do correction step vnew = ml.velocomp(x1, y1, z1, t1, aq, [layer, ltype]) - v1 = 0.5 * (v0 + vnew) + v1 = 0.5 * (v0 + vnew) vx, vy, vz = v1 - substep = 2 + substep = 2 else: xyzt.append(np.array([x1, y1, z1, t1])) break if not silent: print(message) - result = {"xyzt": np.array(xyzt), "message": message, - "status": status} + result = {"xyzt": np.array(xyzt), "message": message, "status": status} return result + # test with tmult. didn't improve much -def timtraceline2(ml, xstart, ystart, zstart, tstart, tmax, - delt, deltmin, deltmax, tmult=1.1, - nstepmax=100, hstepmax=10, silent=False, - correct=False): +def timtraceline2( + ml, + xstart, + ystart, + zstart, + tstart, + tmax, + delt, + deltmin, + deltmax, + tmult=1.1, + nstepmax=100, + hstepmax=10, + silent=False, + correct=False, +): # treating aquifer layers and leaky layers the same way - direction = 1 # forward + direction = 1 # forward terminate = False message = "no message" eps = 1e-10 # used to place point just above or below aquifer top or bottom @@ -205,20 +255,20 @@ def timtraceline2(ml, xstart, ystart, zstart, tstart, tmax, message = "starting z value not inside aquifer" # slightly alter starting location not to get stuck in surpring points # starting at time 0 - xyzt = [np.array([xstart * (1 + eps), ystart * (1 + eps), zstart, tstart])] + xyzt = [np.array([xstart * (1 + eps), ystart * (1 + eps), zstart, tstart])] layerlist = [] # to keep track of layers for plotting with colors speednew = 0.0 for istep in range(nstepmax): if terminate: break - do_correction = correct # do correction, unless do_correction changed to False + do_correction = correct # do correction, unless do_correction changed to False x0, y0, z0, t0 = xyzt[-1] - #aq = ml.aq.find_aquifer_data(x0, y0) # find new aquifer + # aq = ml.aq.find_aquifer_data(x0, y0) # find new aquifer layer, ltype, modellayer = aq.findlayer(z0) layerlist.append(modellayer) speedold = speednew v0 = ml.velocomp(x0, y0, z0, t0, aq, [layer, ltype]) - speednew = np.sqrt(np.sum(v0 ** 2)) + speednew = np.sqrt(np.sum(v0**2)) if speednew < speedold: delt *= 1.2 if delt > deltmax: @@ -227,13 +277,12 @@ def timtraceline2(ml, xstart, ystart, zstart, tstart, tmax, delt /= 1.2 if delt < deltmin: delt = deltmin - print('delt:', delt) + print("delt:", delt) vx, vy, vz = v0 - substep = 1 # take max 2 substeps - + substep = 1 # take max 2 substeps + for steps in range(2): if substep <= 2: - # check if max time reached if t0 + delt > tmax: delt0 = tmax - t0 @@ -241,18 +290,18 @@ def timtraceline2(ml, xstart, ystart, zstart, tstart, tmax, delt0 = delt # check if horizontal step larger than hstepmax - hstep = np.sqrt(vx ** 2 + vy ** 2) * delt0 + hstep = np.sqrt(vx**2 + vy**2) * delt0 if hstep > hstepmax: delt0 = hstepmax / hstep * delt0 # check if going to different layer z1 = z0 + delt0 * vz layer1, ltype1, modellayer1 = aq.findlayer(z1) - if modellayer1 < modellayer: # step up to next layer + if modellayer1 < modellayer: # step up to next layer delt0 = (aq.z[modellayer] - z0) / (z1 - z0) * delt0 z1 = aq.z[modellayer] + eps do_correction = False - elif modellayer1 > modellayer: # step down to next layer + elif modellayer1 > modellayer: # step down to next layer delt0 = (z0 - aq.z[modellayer + 1]) / (z0 - z1) * delt0 z1 = aq.z[modellayer1] - eps do_correction = False @@ -266,8 +315,15 @@ def timtraceline2(ml, xstart, ystart, zstart, tstart, tmax, # check elements if point needs to be changed for e in ml.elementlist: changed, terminate, xyztnew, changemessage = e.changetrace( - xyzt[-1], xyzt1, aq, layer, ltype, modellayer, - direction, hstepmax) + xyzt[-1], + xyzt1, + aq, + layer, + ltype, + modellayer, + direction, + hstepmax, + ) if changed or terminate: x1, y1, z1, t1 = xyztnew do_correction = False @@ -276,32 +332,24 @@ def timtraceline2(ml, xstart, ystart, zstart, tstart, tmax, if t1 >= tmax: terminate = True - message = 'reached maximum time tmax' + message = "reached maximum time tmax" if istep == nstepmax - 1: terminate = True - message = 'reached maximum number of steps' - if terminate: + message = "reached maximum number of steps" + if terminate: xyzt.append(np.array([x1, y1, z1, t1])) break - if substep == 1 and do_correction: # do correction step + if substep == 1 and do_correction: # do correction step vnew = ml.velocomp(x1, y1, z1, t1, aq, [layer, ltype]) - v1 = 0.5 * (v0 + vnew) + v1 = 0.5 * (v0 + vnew) vx, vy, vz = v1 - substep = 2 + substep = 2 else: xyzt.append(np.array([x1, y1, z1, t1])) break if not silent: print(message) - result = {"xyzt": np.array(xyzt), "message": message, - "complete": terminate} + result = {"xyzt": np.array(xyzt), "message": message, "complete": terminate} return result - - - - - - - diff --git a/ttim/util.py b/ttim/util.py index a505457..1650de0 100644 --- a/ttim/util.py +++ b/ttim/util.py @@ -1,18 +1,19 @@ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + class PlotTtim: def plot(self, win=None, newfig=True, figsize=None): """Plot layout - + Parameters ---------- - + win : list or tuple [x1, x2, y1, y2] - + """ - + if newfig: plt.figure(figsize=figsize) ax1 = plt.subplot() @@ -23,19 +24,32 @@ def plot(self, win=None, newfig=True, figsize=None): plt.sca(ax1) for e in self.elementlist: e.plot() - plt.axis('scaled') + plt.axis("scaled") if win is not None: plt.axis(win) - - def xsection(self, x1=0, x2=1, y1=0, y2=0, npoints=100, t=1, layers=0, - sstart=0, color=None, lw=1, figsize=None, newfig=True, - legend=True): + + def xsection( + self, + x1=0, + x2=1, + y1=0, + y2=0, + npoints=100, + t=1, + layers=0, + sstart=0, + color=None, + lw=1, + figsize=None, + newfig=True, + legend=True, + ): layers = np.atleast_1d(layers) if newfig: plt.figure(figsize=figsize) x = np.linspace(x1, x2, npoints) y = np.linspace(y1, y2, npoints) - s = np.sqrt((x - x[0]) ** 2 + (y - y[0]) ** 2 ) + sstart + s = np.sqrt((x - x[0]) ** 2 + (y - y[0]) ** 2) + sstart h = self.headalongline(x, y, t, layers) nlayers, ntime, npoints = h.shape for i in range(nlayers): @@ -45,18 +59,30 @@ def xsection(self, x1=0, x2=1, y1=0, y2=0, npoints=100, t=1, layers=0, else: plt.plot(s, h[i, j, :], color, lw=lw) if legend: - legendlist = ['layer ' + str(i) for i in layers] + legendlist = ["layer " + str(i) for i in layers] plt.legend(legendlist) - #plt.draw() - - def contour(self, win, ngr=20, t=1, layers=0, levels=20, layout=True, - labels=False, decimals=0, color=None, newfig=True, - figsize=None, legend=True): + # plt.draw() + + def contour( + self, + win, + ngr=20, + t=1, + layers=0, + levels=20, + layout=True, + labels=False, + decimals=0, + color=None, + newfig=True, + figsize=None, + legend=True, + ): """Contour plot - + Parameters ---------- - + win : list or tuple [x1, x2, y1, y2] ngr : scalar, tuple or list @@ -83,9 +109,9 @@ def contour(self, win, ngr=20, t=1, layers=0, levels=20, layout=True, legend : list or boolean (default True) add legend to figure if list of strings: use strings as names in legend - + """ - + x1, x2, y1, y2 = win if np.isscalar(ngr): nx = ny = ngr @@ -99,15 +125,15 @@ def contour(self, win, ngr=20, t=1, layers=0, levels=20, layout=True, plt.figure(figsize=figsize) # color if color is None: - c = plt.rcParams['axes.prop_cycle'].by_key()['color'] + c = plt.rcParams["axes.prop_cycle"].by_key()["color"] elif type(color) is str: c = len(layers) * [color] elif type(color) is list: c = color if len(c) < len(layers): n = np.ceil(self.aq.naq / len(c)) - c = n * c - + c = n * c + # contour cslist = [] cshandlelist = [] @@ -117,7 +143,7 @@ def contour(self, win, ngr=20, t=1, layers=0, levels=20, layout=True, handles, labels = cs.legend_elements() cshandlelist.append(handles[0]) if labels: - fmt = f'%1.{decimals}f' + fmt = f"%1.{decimals}f" plt.clabel(cs, fmt=fmt) if type(legend) is list: plt.legend(cshandlelist, legend) diff --git a/ttim/version.py b/ttim/version.py index fb3652c..eb8a921 100644 --- a/ttim/version.py +++ b/ttim/version.py @@ -1,2 +1,2 @@ -__version__='0.6.5' -#__build__='4.0.0.0' +__version__ = "0.6.5" +# __build__='4.0.0.0' diff --git a/ttim/well.py b/ttim/well.py index 9f6454a..9cca3e5 100644 --- a/ttim/well.py +++ b/ttim/well.py @@ -1,119 +1,146 @@ -import numpy as np +import inspect # Used for storing the input + import matplotlib.pyplot as plt -from scipy.special import kv,iv # Needed for K1 in Well class, and in CircInhom -import inspect # Used for storing the input +import numpy as np +from scipy.special import iv # Needed for K1 in Well class, and in CircInhom +from scipy.special import kv + from .element import Element from .equation import HeadEquation, WellBoreStorageEquation + class WellBase(Element): - '''Well Base Class. All Well elements are derived from this class''' - def __init__(self, model, xw=0, yw=0, rw=0.1, tsandbc=[(0, 1)], res=0, \ - layers=0, type='', name='WellBase', label=None): - Element.__init__(self, model, nparam=1, nunknowns=0, layers=layers, \ - tsandbc=tsandbc, type=type, name=name, label=label) - # Defined here and not in Element as other elements can have multiple + """Well Base Class. All Well elements are derived from this class""" + + def __init__( + self, + model, + xw=0, + yw=0, + rw=0.1, + tsandbc=[(0, 1)], + res=0, + layers=0, + type="", + name="WellBase", + label=None, + ): + Element.__init__( + self, + model, + nparam=1, + nunknowns=0, + layers=layers, + tsandbc=tsandbc, + type=type, + name=name, + label=label, + ) + # Defined here and not in Element as other elements can have multiple # parameters per layers - self.nparam = len(self.layers) + self.nparam = len(self.layers) self.xw = float(xw) self.yw = float(yw) self.rw = float(rw) self.res = np.atleast_1d(res).astype(np.float64) self.model.addelement(self) - + def __repr__(self): - return self.name + ' at ' + str((self.xw, self.yw)) - + return self.name + " at " + str((self.xw, self.yw)) + def initialize(self): - # Control point to make sure the point is always the same for + # Control point to make sure the point is always the same for # all elements self.xc = np.array([self.xw + self.rw]) - self.yc = np.array([self.yw]) + self.yc = np.array([self.yw]) self.ncp = 1 self.aq = self.model.aq.find_aquifer_data(self.xw, self.yw) self.setbc() coef = self.aq.coef[self.layers, :] - laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw/self.aq.lab)) + laboverrwk1 = self.aq.lab / (self.rw * kv(1, self.rw / self.aq.lab)) self.setflowcoef() # term is shape (self.nparam,self.aq.naq,self.model.npval) - self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef - self.term2 = self.term.reshape(self.nparam, self.aq.naq, - self.model.nint, self.model.npint) + self.term = -1.0 / (2 * np.pi) * laboverrwk1 * self.flowcoef * coef + self.term2 = self.term.reshape( + self.nparam, self.aq.naq, self.model.nint, self.model.npint + ) self.dischargeinf = self.flowcoef * coef - self.dischargeinflayers = np.sum(self.dischargeinf * - self.aq.eigvec[self.layers, :, :], 1) + self.dischargeinflayers = np.sum( + self.dischargeinf * self.aq.eigvec[self.layers, :, :], 1 + ) # Q = (h - hw) / resfach - self.resfach = self.res / (2 * np.pi * self.rw * - self.aq.Haq[self.layers]) + self.resfach = self.res / (2 * np.pi * self.rw * self.aq.Haq[self.layers]) # Q = (Phi - Phiw) / resfacp - self.resfacp = self.resfach * self.aq.T[self.layers] - + self.resfacp = self.resfach * self.aq.T[self.layers] + def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' + """Separate function so that this can be overloaded for other types""" self.flowcoef = 1.0 / self.model.p # Step function - + def potinf(self, x, y, aq=None): - '''Can be called with only one x,y value''' - if aq is None: + """Can be called with only one x,y value""" + if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + rv = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") if aq == self.aq: r = np.sqrt((x - self.xw) ** 2 + (y - self.yw) ** 2) - pot = np.zeros(self.model.npint, 'D') + pot = np.zeros(self.model.npint, "D") if r < self.rw: r = self.rw # If at well, set to at radius for i in range(self.aq.naq): for j in range(self.model.nint): if r / abs(self.aq.lab2[i, j, 0]) < self.rzero: pot[:] = kv(0, r / self.aq.lab2[i, j, :]) - #quicker? - #bessel.k0besselv( r / self.aq.lab2[i,j,:], pot ) + # quicker? + # bessel.k0besselv( r / self.aq.lab2[i,j,:], pot ) rv[:, i, j, :] = self.term2[:, i, j, :] * pot rv.shape = (self.nparam, aq.naq, self.model.npval) return rv - + def potinfone(self, x, y, jtime, aq=None): - '''Can be called with only one x,y value for time interval jtime''' - if aq is None: + """Can be called with only one x,y value for time interval jtime""" + if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - rv = np.zeros((self.nparam, aq.naq, self.model.npint), 'D') + rv = np.zeros((self.nparam, aq.naq, self.model.npint), "D") if aq == self.aq: r = np.sqrt((x - self.xw) ** 2 + (y - self.yw) ** 2) - pot = np.zeros(self.model.npint, 'D') + pot = np.zeros(self.model.npint, "D") if r < self.rw: r = self.rw # If at well, set to at radius for i in range(self.aq.naq): if r / abs(self.aq.lab2[i, jtime, 0]) < self.rzero: pot[:] = kv(0, r / self.aq.lab2[i, jtime, :]) rv[:, i, :] = self.term2[:, i, jtime, :] * pot - #rv.shape = (self.nparam, aq.naq, self.model.npval) + # rv.shape = (self.nparam, aq.naq, self.model.npval) return rv - + def disvecinf(self, x, y, aq=None): - '''Can be called with only one x,y value''' - if aq is None: aq = self.model.aq.find_aquifer_data(x, y) - qx = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') - qy = np.zeros((self.nparam, aq.naq, self.model.npval), 'D') + """Can be called with only one x,y value""" + if aq is None: + aq = self.model.aq.find_aquifer_data(x, y) + qx = np.zeros((self.nparam, aq.naq, self.model.npval), "D") + qy = np.zeros((self.nparam, aq.naq, self.model.npval), "D") if aq == self.aq: - qr = np.zeros((self.nparam, aq.naq, self.model.nint, - self.model.npint), 'D') + qr = np.zeros((self.nparam, aq.naq, self.model.nint, self.model.npint), "D") r = np.sqrt((x - self.xw) ** 2 + (y - self.yw) ** 2) - pot = np.zeros(self.model.npint, 'D') + pot = np.zeros(self.model.npint, "D") if r < self.rw: r = self.rw # If at well, set to at radius for i in range(self.aq.naq): for j in range(self.model.nint): if r / abs(self.aq.lab2[i, j, 0]) < self.rzero: - qr[:, i, j, :] = self.term2[:, i, j, :] * \ - kv(1, r / self.aq.lab2[i, j, :]) / \ - self.aq.lab2[i, j, :] + qr[:, i, j, :] = ( + self.term2[:, i, j, :] + * kv(1, r / self.aq.lab2[i, j, :]) + / self.aq.lab2[i, j, :] + ) qr.shape = (self.nparam, aq.naq, self.model.npval) qx[:] = qr * (x - self.xw) / r qy[:] = qr * (y - self.yw) / r - return qx,qy - + return qx, qy + def headinside(self, t, derivative=0): - """Returns head inside the well for the layers that + """Returns head inside the well for the layers that the well is screened in. Parameters @@ -127,32 +154,31 @@ def headinside(self, t, derivative=0): """ - return self.model.head(self.xc[0], self.yc[0], t, - derivative=derivative)[self.layers] - \ - self.resfach[:, np.newaxis] * \ - self.discharge(t, derivative=derivative) - + return self.model.head(self.xc[0], self.yc[0], t, derivative=derivative)[ + self.layers + ] - self.resfach[:, np.newaxis] * self.discharge(t, derivative=derivative) + def plot(self): - plt.plot(self.xw, self.yw, 'k.') - - def changetrace(self, xyzt1, xyzt2, aq, layer, ltype, modellayer, - direction, hstepmax): + plt.plot(self.xw, self.yw, "k.") + + def changetrace( + self, xyzt1, xyzt2, aq, layer, ltype, modellayer, direction, hstepmax + ): changed = False terminate = False xyztnew = 0 message = None - hdistance = np.sqrt((xyzt1[0] - self.xw) ** 2 + (xyzt1[1] - self.yw) ** 2) + hdistance = np.sqrt((xyzt1[0] - self.xw) ** 2 + (xyzt1[1] - self.yw) ** 2) if hdistance < hstepmax: if ltype == "a": if (layer == self.layers).any(): # in a layer where well is screened - layernumber = np.where(self.layers==layer)[0][0] + layernumber = np.where(self.layers == layer)[0][0] dis = self.discharge(xyzt1[3])[layernumber, 0] - if (dis > 0 and direction > 0) or ( - dis < 0 and direction < 0): + if (dis > 0 and direction > 0) or (dis < 0 and direction < 0): vx, vy, vz = self.model.velocomp(*xyzt1) tstep = np.sqrt( (xyzt1[0] - self.xw) ** 2 + (xyzt1[1] - self.yw) ** 2 - ) / np.sqrt(vx ** 2 + vy ** 2) + ) / np.sqrt(vx**2 + vy**2) xnew = self.xw ynew = self.yw znew = xyzt1[2] + tstep * vz * direction @@ -166,7 +192,8 @@ def changetrace(self, xyzt1, xyzt2, aq, layer, ltype, modellayer, else: message = "reached element of type well: " + str(self) return changed, terminate, xyztnew, message - + + class DischargeWell(WellBase): """ Create a well with a specified discharge for each layer that the well @@ -175,13 +202,13 @@ class DischargeWell(WellBase): must be specified for each screened layer. The resistance of the screen may be specified. The head is computed such that the discharge :math:`Q_i` in layer :math:`i` is computed as - + .. math:: Q_i = 2\pi r_wH_i(h_i - h_w)/c - + where :math:`c` is the resistance of the well screen and :math:`h_w` is - the head inside the well. - + the head inside the well. + Parameters ---------- model : Model object @@ -200,23 +227,36 @@ class DischargeWell(WellBase): layer (int) or layers (list or array) where well is screened label : string or None (default: None) label of the well - + Examples -------- Example of a well that pumps with a discharge of 100 between times 10 and 50, with a discharge of 20 between times 50 and 200, and zero discharge after time 200. - + >>> Well(ml, tsandQ=[(10, 100), (50, 20), (200, 0)]) - + """ - def __init__(self, model, xw=0, yw=0, tsandQ=[(0, 1)], rw=0.1, - res=0, layers=0, label=None): + + def __init__( + self, model, xw=0, yw=0, tsandQ=[(0, 1)], rw=0.1, res=0, layers=0, label=None + ): self.storeinput(inspect.currentframe()) - WellBase.__init__(self, model, xw, yw, rw, tsandbc=tsandQ, res=res, - layers=layers, type='g', name='DischargeWell', - label=label) - + WellBase.__init__( + self, + model, + xw, + yw, + rw, + tsandbc=tsandQ, + res=res, + layers=layers, + type="g", + name="DischargeWell", + label=label, + ) + + class Well(WellBase, WellBoreStorageEquation): """ Create a well with a specified discharge. @@ -226,13 +266,13 @@ class Well(WellBase, WellBoreStorageEquation): Wellbore storage and skin effect may be taken into account. The head is computed such that the discharge :math:`Q_i` in layer :math:`i` is computed as - + .. math:: Q_i = 2\pi r_wH_i(h_i - h_w)/c - + where :math:`c` is the resistance of the well screen and :math:`h_w` is the head inside the well. - + Parameters ---------- model : Model object @@ -257,53 +297,78 @@ class Well(WellBase, WellBoreStorageEquation): 'slug': volume of water instantaneously taken out of the well label : string (default: None) label of the well - + """ - def __init__(self, model, xw=0, yw=0, rw=0.1, tsandQ=[(0, 1)], res=0, - rc=None, layers=0, wbstype='pumping', label=None): + + def __init__( + self, + model, + xw=0, + yw=0, + rw=0.1, + tsandQ=[(0, 1)], + res=0, + rc=None, + layers=0, + wbstype="pumping", + label=None, + ): self.storeinput(inspect.currentframe()) - WellBase.__init__(self, model, xw, yw, rw, tsandbc=tsandQ, res=res, - layers=layers, type='v', name='Well', label=label) + WellBase.__init__( + self, + model, + xw, + yw, + rw, + tsandbc=tsandQ, + res=res, + layers=layers, + type="v", + name="Well", + label=label, + ) if (rc is None) or (rc <= 0): self.rc = np.zeros(1) else: - self.rc = np.atleast_1d(rc).astype('float') + self.rc = np.atleast_1d(rc).astype("float") # hdiff is not used right now, but may be used in the future self.hdiff = None - #if hdiff is not None: + # if hdiff is not None: # self.hdiff = np.atleast_1d(hdiff) - # assert len(self.hdiff) == self.nlayers - 1, 'hdiff needs to + # assert len(self.hdiff) == self.nlayers - 1, 'hdiff needs to # have length len(layers) -1' - #else: + # else: # self.hdiff = hdiff self.nunknowns = self.nparam self.wbstype = wbstype - + def initialize(self): WellBase.initialize(self) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') - + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) + def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' - if self.wbstype == 'pumping': + """Separate function so that this can be overloaded for other types""" + if self.wbstype == "pumping": self.flowcoef = 1.0 / self.model.p # Step function - elif self.wbstype == 'slug': + elif self.wbstype == "slug": self.flowcoef = 1.0 # Delta function - -class HeadWell(WellBase,HeadEquation): + + +class HeadWell(WellBase, HeadEquation): """ Create a well with a specified head inside the well. The well may be screened in multiple layers. The resistance of the screen may be specified. The head is computed such that the discharge :math:`Q_i` in layer :math:`i` is computed as - + .. math:: Q_i = 2\pi r_wH_i(h_i - h_w)/c - + where :math:`c` is the resistance of the well screen and :math:`h_w` is the head inside the well. - + Parameters ---------- model : Model object @@ -322,30 +387,66 @@ class HeadWell(WellBase,HeadEquation): layer (int) or layers (list or array) where well is screened label : string (default: None) label of the well - + """ - def __init__(self, model, xw=0, yw=0, rw=0.1, tsandh=[(0, 1)], res=0, - layers=0, label=None): + + def __init__( + self, model, xw=0, yw=0, rw=0.1, tsandh=[(0, 1)], res=0, layers=0, label=None + ): self.storeinput(inspect.currentframe()) - WellBase.__init__(self, model, xw, yw, rw, tsandbc=tsandh, res=res, - layers=layers, type='v', name='HeadWell', label=label) + WellBase.__init__( + self, + model, + xw, + yw, + rw, + tsandbc=tsandh, + res=res, + layers=layers, + type="v", + name="HeadWell", + label=label, + ) self.nunknowns = self.nparam + def initialize(self): WellBase.initialize(self) - self.parameters = np.zeros((self.model.ngvbc, self.nparam, - self.model.npval), 'D') + self.parameters = np.zeros( + (self.model.ngvbc, self.nparam, self.model.npval), "D" + ) # Needed in solving for a unit head - self.pc = self.aq.T[self.layers] - + self.pc = self.aq.T[self.layers] + + class WellTest(WellBase): - def __init__(self, model, xw=0, yw=0, tsandQ=[(0, 1)], rw=0.1, res=0, - layers=0, label=None, fp=None): + def __init__( + self, + model, + xw=0, + yw=0, + tsandQ=[(0, 1)], + rw=0.1, + res=0, + layers=0, + label=None, + fp=None, + ): self.storeinput(inspect.currentframe()) - WellBase.__init__(self, model, xw, yw, rw, tsandbc=tsandQ, res=res, - layers=layers, type='g', name='DischargeWell', - label=label) + WellBase.__init__( + self, + model, + xw, + yw, + rw, + tsandbc=tsandQ, + res=res, + layers=layers, + type="g", + name="DischargeWell", + label=label, + ) self.fp = fp - + def setflowcoef(self): - '''Separate function so that this can be overloaded for other types''' - self.flowcoef = self.fp \ No newline at end of file + """Separate function so that this can be overloaded for other types""" + self.flowcoef = self.fp diff --git a/ttimman/ttimman.tex b/ttimman/ttimman.tex index 48220b5..39c6244 100755 --- a/ttimman/ttimman.tex +++ b/ttimman/ttimman.tex @@ -513,13 +513,13 @@ \section{References} \item M. Bakker. 2013a. Analytic modeling of transient multi-layer flow. In: {\it Advances in Hydrogeology}, edited by P Mishra and K Kuhlman, Springer, Heidelberg, 95-114. \item M. Bakker. 2013b. Semi-analytic modeling of transient multi-layer flow with TTim. {\it Hydrogeology Journal} 21: 935-943. \item M. Bakker and K.L. Kuhlman. 2011. Computational issues and applications of line-elements to model subsurface flow governed by the modified Helmholtz equation. {\it Advances in Water Resources} 34: 1186-1194. -\item F.R. De Hoog, J.H. Knight, and A.N. Stokes. 1982. An improved method for numerical inversion of Laplace transforms. {\it SIAM Journal on ScientiÞc and Statistical Computing}, 3(3):357-366. -\item C.J. Hemker and C. Maas. 1987. Unsteady ßow to wells in layered and Þssured aquifer systems. +\item F.R. De Hoog, J.H. Knight, and A.N. Stokes. 1982. An improved method for numerical inversion of Laplace transforms. {\it SIAM Journal on Scienti�c and Statistical Computing}, 3(3):357-366. +\item C.J. Hemker and C. Maas. 1987. Unsteady �ow to wells in layered and �ssured aquifer systems. {\it Journal of Hydrology}, 90:231-249. \item G.P. Kruseman and N.A. de Ridder. 1990. Analysis and evaluation of pumping test data. International Institute for Land Reclamation and Improvement (ILRI) Bulletin 11, Wageningen. \item K.L. Kuhlman and S.P. Neuman. 2009. Laplace-transform analytic-element method for transient -porous-media ßow. {\it Journal of Engineering Mathematics}, 64(2):113-130. +porous-media �ow. {\it Journal of Engineering Mathematics}, 64(2):113-130. \item A. Louwyck, A. Vandenbohede, M. Bakker, L. Lebbe. 2012. Simulation of axi-symmetric flow towards wells: A finite-difference approach. {\it Computers \& Geosciences}, 44, 136-145. \item S.P. Neuman. 1972. Theory flow in unconfined aquifers considering delayed response of the water table. {\it Water Resources Research}, 8(4):1031-1045. @@ -640,16 +640,16 @@ \subsection{Running the model}It is explained here how to run the \end{verbatim} At the Python command prompt, import the ttim.py file: \begin{verbatim} -In [1]: from ttim import * +In [1]: import ttim \end{verbatim} Now we can begin to enter model data. First define the model: \begin{verbatim} -In [2]: ml = ModelMaq(kaq=[1.0,5.0],z=[3,2,1,0],c=[10.], +In [2]: ml = ttim.ModelMaq(kaq=[1.0,5.0],z=[3,2,1,0],c=[10.], Saq=[0.3,0.01], Sll=[0.001], tmin=0.001, tmax=1000000.0, M=20) \end{verbatim} Next enter the data for the pumping well: \begin{verbatim} -In [3]: Well(ml,xw=0.,yw=0,rw=1e-5,tsandQ=[(0,1)],layers=[1],label='well 1') +In [3]: ttim.Well(ml,xw=0.,yw=0,rw=1e-5,tsandQ=[(0,1)],layers=[1],label='well 1') \end{verbatim} Finally solve the model: \begin{verbatim} From 64749b9e52c293862d3590d1ccfe41976880496b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dav=C3=ADd=20Brakenhoff?= Date: Sun, 8 Oct 2023 17:14:10 +0300 Subject: [PATCH 2/4] ignore formatting commit in git blame --- .git-blame-ignore-revs | 1 + 1 file changed, 1 insertion(+) diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs index b4ebe8b..5f347dd 100644 --- a/.git-blame-ignore-revs +++ b/.git-blame-ignore-revs @@ -1 +1,2 @@ # Migrate code style to black +a8aa6904034da74b91f8046e2440ebb4359d63e2 \ No newline at end of file From 00348f93f81f2217d919251a07d315a889b47cff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dav=C3=ADd=20Brakenhoff?= Date: Fri, 13 Oct 2023 19:45:31 +0300 Subject: [PATCH 3/4] remove tqdm --- ttim/model.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/ttim/model.py b/ttim/model.py index d9b128b..4f3b034 100644 --- a/ttim/model.py +++ b/ttim/model.py @@ -4,7 +4,6 @@ import matplotlib.pyplot as plt import numpy as np -from tqdm.auto import trange from .aquifer import Aquifer from .aquifer_parameters import param_3d, param_maq @@ -480,10 +479,10 @@ def headgrid(self, xg, yg, t, layers=None, printrow=False): nlayers = len(np.atleast_1d(layers)) t = np.atleast_1d(t) h = np.empty((nlayers, len(t), ny, nx)) - for j in trange(ny): + for j in range(ny): if printrow: print(".", end="", flush=True) - for i in trange(nx): + for i in range(nx): h[:, :, j, i] = self.head(xg[i], yg[j], t, layers) return h From 85b6d0ab9044c5c22167fe93465bd4e2ed42e90c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dav=C3=ADd=20Brakenhoff?= Date: Fri, 13 Oct 2023 19:01:00 +0200 Subject: [PATCH 4/4] fix more ttim star import stuff --- notebooks/aem_ttim_sol.ipynb | 2 +- notebooks/circareasink_example.ipynb | 2 +- pumpingtest_benchmarks/2_test_of_dalem.ipynb | 2 +- pumpingtest_benchmarks/4_test_of_gridley.ipynb | 16 ++++++++-------- .../7_test_of_neveda_double-porosity.ipynb | 2 +- ttim/circinhom.py | 14 +++++++------- 6 files changed, 19 insertions(+), 19 deletions(-) diff --git a/notebooks/aem_ttim_sol.ipynb b/notebooks/aem_ttim_sol.ipynb index 42bef77..374d930 100644 --- a/notebooks/aem_ttim_sol.ipynb +++ b/notebooks/aem_ttim_sol.ipynb @@ -283,7 +283,7 @@ " ml = ttim.ModelMaq(\n", " kaq=k, z=[2, 0, -20], Saq=S, c=c, topboundary=\"semi\", tmin=0.001, tmax=100\n", " )\n", - " w = Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", + " w = ttim.Well(ml, 0, 0, rw=0.3, tsandQ=[(0, 800)])\n", " ml.solve(silent=True)\n", " if returnmodel:\n", " return ml\n", diff --git a/notebooks/circareasink_example.ipynb b/notebooks/circareasink_example.ipynb index da516d8..e638523 100644 --- a/notebooks/circareasink_example.ipynb +++ b/notebooks/circareasink_example.ipynb @@ -118,7 +118,7 @@ "Q = N * np.pi * R**2\n", "ml = ttim.ModelMaq(kaq=5, z=[10, 0], Saq=2e-4, tmin=1e-3, tmax=1e4, M=10)\n", "ca = ttim.CircAreaSink(ml, -200, 0, 100, tsandN=[(0, 0.001)])\n", - "w = Well(ml, 200, 0, rw=0.1, tsandQ=[(0, Q)])\n", + "w = ttim.Well(ml, 200, 0, rw=0.1, tsandQ=[(0, Q)])\n", "ml.solve()" ] }, diff --git a/pumpingtest_benchmarks/2_test_of_dalem.ipynb b/pumpingtest_benchmarks/2_test_of_dalem.ipynb index 2bea0e1..f25f9e2 100755 --- a/pumpingtest_benchmarks/2_test_of_dalem.ipynb +++ b/pumpingtest_benchmarks/2_test_of_dalem.ipynb @@ -1330,7 +1330,7 @@ " tmin=0.001,\n", " tmax=0.5,\n", ")\n", - "w_3 = Well(m_3, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", + "w_3 = ttim.Well(m_3, xw=0, yw=0, tsandQ=[(0, 761), (0.34, 0)], layers=1)\n", "m_3.solve(silent=\"True\")" ] }, diff --git a/pumpingtest_benchmarks/4_test_of_gridley.ipynb b/pumpingtest_benchmarks/4_test_of_gridley.ipynb index 84d19a2..3415b66 100755 --- a/pumpingtest_benchmarks/4_test_of_gridley.ipynb +++ b/pumpingtest_benchmarks/4_test_of_gridley.ipynb @@ -18,7 +18,7 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "from ttim import *" + "import ttim" ] }, { @@ -84,7 +84,7 @@ ], "source": [ "ml = ttim.ModelMaq(kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\")\n", - "w = Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "w = ttim.Well(ml, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml.solve()" ] }, @@ -225,7 +225,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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4YnwFyvw8i7TSbbn8YB0U9SJSUukavYQl5xxTVvzKfyYsZ+OOA5zTpBr/7NGEU6uU8bo0EZGg0zV6KXHMjHObVuPru5P5W7fGzFy3nXOfn85TX67kt0MZXpcnIlJoFPQS1qIj/Nx2Zj2+HXQmPVrW4NXv1nHWs9/x2fw0srLC72yWiMixFPRSIlQrH8PzV7Ri9F87UaNiKe79dBG9Xv2BBZt2eV2aiEhIKeilRGkdX4nRt3XiuctbsmX37/R65Qfu+WQhv+496HVpIiIhoaCXEsfnMy5rW5upg87ktjPrMX7Rz3R99jte+W4thzIyvS5PRCSoFPRSYpWNjuBv3Rrz9d3JdKpflae/XMV5L0zn62W/EI6jUUSkZFLQS4mXULUMb96QyAc3tSfS76P/B/O5Yfgc1mzd53VpIiIFpqAXyZbUIJZJA5N46KKmLNq8m25DU3h47DL2HDjsdWkiIvmmoBc5SqTfx42d6zB10Jlc1S6O92emcuazU/nvrI1kajieiBRDCnqRHFQpG81jvZoz/s4kGlYrxz++WMqFw1KYtX6H16WJiJwUBb1ILprWLM9H/U/n5WvasO9gBle9MYvbP/yRtF0HvC5NRCRPFPQiJ2BmXNiiBt/cewZ3n9OQb1Zu5eznpvH85NUcSNd0uiJStCnoRfIoJtLPwHMa8O29Z3L+adUZ9s0azn5uGmMXbdFwPBEpshT0IiepZsVSDLu6NZ/+pSOVy0QxYOQCrnh9Jkt/2uN1aSIif6KgF8mndgmVGXtHF568tDnrt+3nopdmMPizxWz/7ZDXpYmI/EFBL1IAfp9xVft4vh10Jjd1rsOo+Wl0ffY73kpZT3pGltfliYgo6EWCoUKpSP7Roylf3pVMm/hK/GfCCroNnc53q371ujQRKeEU9CJBVP+Usrx7YzuG903EOej7zlz6vTuX9dt+87o0ESmhFPQiQWZmnNW4Gl/dlczfuzdmzoadnD9kOo9PXMG+g5pOV0QKl4JeJESiInz0T67Ht4POoFfrWryZsp6uz37HJ3M3k6XpdEWkkHgS9GZ2uZktM7MsM0vMpV03M1tlZmvNbHBh1igSLKeUi+Hp3i0Zc3tn4iuX5v7PFnP/kLf4adx/YPMcr8sTkTDnVY9+KXApMP14DczMD7wMXAA0Ba42s6aFU55I8LWoXZHPbuvEe+fCf/b+nWrzniN9eA/2rfne69JEJIx5EvTOuRXOuVUnaNYeWOucW++cSwc+AnqGvjqR0DEzzohaSbRlEmFZ+LIO896I/zJ6QZpm1xORkCjK1+hrAZuPep2WvU2keEtIwvxRYH58EVFsrtCWuz9exHVvz2bD9v1eVyciYSYiVDs2sylA9RzeetA5NyYvu8hh23G7PGbWH+gPEB8fn6caRTwR1x76jIXUFHwJSTxeqx3N5mzi6UkrOX/IdO7sWp9bz6hHVERR/jtcRIqLkAW9c+6cAu4iDYg76nVtYEsux3sDeAMgMTFR50ClaItrH3gAfuD600/l/KbVeGT8cp6bvJoxi7bweK/mtK9T2ds6RaTYK8pdhrlAAzOrY2ZRwFXAWI9rEgmZU8rH8PI1bXinbzt+T8/kitdnMvizxew+kO51aSJSjHk1vK6XmaUBHYEJZvZV9vaaZjYRwDmXAdwBfAWsAD5xzi3zol6RwtS18SlMvieZW5Pr8un8NM5+bhpfLPhJN+uJSL5YOP7jkZiY6ObNm+d1GSIFtnzLXv4+egkLN++mS/2q/OeSZiRULeN1WSJSxJjZfOdcjvPSKOhFirjMLMeI2Rt5+stVHMrM4vHEA1xSaQMRdZP/uM4vIiWbgl4kDGzde5APPvmU2zffQ5RlYP4ofH3HKexFJNegL8o344nIUaqVj2FQo1+Jtgz8ZJGVkc43kz7jQHqG16WJSBGmoBcpThKS8EVE48xPli+SlzdU57wXppOyZpvXlYlIEaWgFylOsifbsbMeJKrfeAb370OU38f1b89h0KeLNBRPRP5E1+hFirmDhzN58ds1vDZtPZVKR/HIxafRvXl1zHKaXFJEwpGu0YuEsZhIP/ed35ixd3SmeoVobh/xI7d+MJ+tew96XZqIFAEKepEwcVrNCnzx1848cEFjpq3exjnPT2PknE2aaEekhFPQi4SRCL+PW8+ox5d3JdO0Rnke+HwJ17w5m1StiidSYinoRcJQnaplGHnL6TzeqzlLf9rD+UOm8/q0dWRkZnldmogUMgW9SJjy+YxrOsQz+Z4zSGoQyxOTVtLrlR9YvmWv16WJSCFS0IuEueoVYnjzhra8dE1rft7zOxe/NINnvlrJwcOZXpcmIoUgZOvRi0jRYWb0aFGTzvWq8u8Jy3l56jo2LvqOB5psp1ar8zSNrkgY0zh6kRJowfdf0WTydUS4DJwvkqzrxxBdt6PXZYlIPmkcvYj8j9ZZS4m2TCIsC8s6zPsffci81J1elyUiIaCgFymJEpIwfxSYH4uIYi5Nufz1mfx7/HJ+T9e1e5Fwomv0IiVR9pz5pKbgT0ji+VPa8OSkFbw9YwPfrvyVp3u3oF1CZa+rFJEgyPM1ejOrBNQEfgdSnXNFdkCurtGL5M8Pa7dz36jFbNnzOzd2qsN95zeiVJTf67JE5ATyfY3ezCqY2d/NbAkwC3gd+ATYaGafmlnX4JcrIl7pVL8qX92dzHUdTmX49xu4YOh05mzQtXuR4uxE1+hHAZuBJOdcI+dcF+dconMuDngS6GlmN4W8ShEpNGWjI/j3Jc0YcUsHMrIcV74xk0fGLeNAeobXpYlIPmh4nYgc1/5DGTz15Uren7mRU6uU5unLWtChbhWvyxKRYwRleJ2ZtTCzi83s0iOP4JUoIkVRmegIHu3ZjJG3nE6Wc1z5xiweHqvevUhxkqe77s1sONACWAYcuQnPAZ+HqC4RKUI61qvClwOTefrLlbz7QypTV/2q3r1IMZGnU/dmttw517QQ6gkKnboXCZ2Z63bwt88Ws2nnAfp2SuD+bo0oHaWRuiJeCsap+5lmVmyCXkRCp2O9Knx5VxJ9OyXw7g+pdBuSwsx1O7wuS0SOI69B/x6BsF9lZovNbImZLQ5lYSJSdJWOiuDhi0/jo/6nA3D1m7N4ZNwyzaonUgTl9XzbcOB6YAn/f41eREq40+tW4eveMcyYPIlXfljNhau28ewVLWkTX8nr0kQkW16DfpNzbmxIKxGR4mfzHGJG9OKczHS6lo7ktkMP0fvV/dx2Zj0Gnt2QqAgtpyHitbwG/UozGwGMAw4d2eic0133IiVZagpkpoPLxJ8Fw7rs55/ba/Py1HV8s+JXnr+iFU1rlve6SpESLa9/bpciEPDnARdlP3rk96BmdrmZLTOzLDPL8S7B7Hap2fcDLDQz3UYvUtQkJEH2Knj4o4ipfwbPXN6St25IZPtv6fR8eQYvT11LRqau+Il4xZOZ8cysCYFr/a8Dg5xzOYa4maUCic657Sezfw2vEylEm+cEevYJSYFV8bLt2p/OP8YsZcLin2kVV5HnrmhJvdiyHhYqEr4KsqjNP8zsuGtVmtlZZnbSPXvn3Arn3KqT/ZyIFEFx7SHp3v8JeYBKZaJ4+Zo2vHh1a1J37Kf70BSGz9hAVlb4TbstUpSd6Br9EmCcmR0EfgS2ATFAA6AVMAV4PIT1OeBrM3PA6865N0J4LBEJgYta1qRDncoM/nwJj45fztfLf+GZ3i2Jq1za69JESoS8zozXAOgM1CCwHv0KYLpz7vdcPjMFqJ7DWw8658Zkt/mO3E/d13TObTGzU4DJwJ3OuenHadsf6A8QHx/fduPGjSf8vUSk8Djn+HReGo+OXw7AP3s04YrEOMzM48pEir/cTt17unrdiYL+mLYPA7855549UVtdoxcputJ2HeC+Txczc/0OujaK5cnLWlCtfIzXZYkUa7kFfV4XtWkIDAISjv6Mc+6sYBR4nGOWAXzOuX3Zz88DHg3V8USkcNSuVJoPb+7A+zNTefLLlZz3wnReSjpMl4iVWJ2kP13rF5GCyes4+k+B14C3gALPcWlmvYAXgVhggpktdM6db2Y1gbecc92BasDo7NN6EcAI59yXBT22iHjP5zP6dq5DcsNY3hjxEYnTHiDLMvBFRGF9xinsRYIor0Gf4Zx7NVgHdc6NBkbnsH0L0D37+XqgZbCOKSJFT93Ysjzeeg98m4GPLDIy0tk4dxL1FPQiQZPXCXPGmdlfzayGmVU+8ghpZSJSIvjqJOGLiMaZnwwiuG9ueR74fAn7D2V4XZpIWMhrj75P9s/7jtrmgLrBLUdESpy49tBnLJaagi+uE+1WVOSN6ev5fu12nruiJe0S1KcQKQhP77oPFd11L1K8zU3dydsjP6Lu/oXUaHkuV/a6TAvkiOQiGHfdpwDTgRTge+fcviDWJyLyP9r515KY+ShZEYdIXzqawZt2ceu1V9OoejmvSxMpdvL6J3IfYBVwGfCDmc0zsxdCV5aIlGipKVhmOn6yiPZlUve3BVz00gzeSlmvKXRFTlKegj77DvjJwDcEevalgSYhrEtESrKjVsXz+aO47uprSW4Qy38mrODat2bz0+7jTsopIsfI6xS464DtwAgCp+8XOueK7LqTukYvEgaOWRXPOccn8zbzyLjl+H3Gv3s2o2ermppCV4QgTIFrZgOBLkAcsBKYRmCu+3XBLDRYFPQi4Wvjjv3c88ki5m/cxYUtavDYJc2oWDrK67JEPBW0ue7NrCxwI4HpcGs75/zBKTG4FPQi4S0zy/HatHW8MHk1VcpG8XTvlpzRMNbrskQ8k+/16I/awXNmNhuYTWB52n8RWKpWRKTQ+X3G7V3r88XtnSkfE0mf4XN4aMxSfk8v8AzdImEnrxPmzAKeds5tDWUxIiIno1mtCoy7swtPf7mK4d9vIGXtdoZc2YoWtSt6XZpIkZHnU/dmdjGQnP1ymnNuXMiqKiCduhcpeb5fu51Bny5i275DPN7udy6rvAF/3WQtkCMlQjBO3T8BDASWZz8GZG8TESkSOtevypcDk7m9wU4uWvgXmPoYWe9dFLh7X6QEy+uEORcC5zrnhjvnhgPdsreJiBQZFUpHcnf9rURbBn6yyDqczuIZ4wnHqb5F8upkJo8++qJXhWAXIiISFAn/vxpepkXy8OJK/OW/89m5P93rykQ8kdeb8Z4AFpjZVMAIXKt/IGRViYjk11Gr4UXGd6Hbxqo889Uqug2ZzjOXaxielDwnczNeDaAdgaCf7Zz7JZSFFYRuxhORoy3fspe7Pl7A6q2/0bdTAoMvaExMZJGcBkQkX/J9M56ZtTnyAGoAacBmoGb2NhGRIq9pzfKMvaMLN3ZO4N0fUrnoxRks27LH67JECkWuPfrsU/UAMUAisIhAj74FgV59l5BXmA/q0YvI8UxfvY1Bny5i14F0Bp3XiJuT6uL3ab58Kd7y3aN3znV1znUFNgJtnHOJzrm2QGtgbfBLFREJreSGsXx1VzJnN67GE5NWcu1bs7QanoS1vN5139g5t+TIC+fcUgJT4YqIFDuVykTx6nVteKZ3C5ak7eFvQ95i+ScPacy9hKW83nW/wszeAv4LOOA6YEXIqhIRCTEz4/LEOJJiNlBp1L/xLztM+opXOXzdF5Sp18nr8kSCJq89+huBZQRmx7uLwOx4N4aqKBGRwlJ911yiLIMIy8KXdZgPRn7InA07vS5LJGjyGvSdgNedc72yHy845w6GsjARkUKRkIT5o8D8+CKiWBzRjKvemMmzX63icGaW19WJFFheT933BV4zsx1ASvZjhnNuV6gKExEpFNkT7JCagi8hiadPaUOZsct4aepaUtZsY8hVralTtYzXVYrkW54nzAEws5pAb2AQUNM5l9c/FAqVhteJSEFNXPIzD3y+hMOZWfyrR1OubBeHpc2F1BRISNKqeFKk5Da8Lk9BbWbXAUlAc2A78BKBXr2ISFjq3rwGreMrcu8nixj8+RI2LprK/VvvxzIPgz8qcBZAYS/FQF575EOAdcBrwFTnXGrIKhIRKSJqVCjFf2/qwNszNrBn8pNk+tOJIAsy0wM9ewW9FAN5uhnPOVcV6EdghrzHzGyOmX0Q0spERIoAn8+4Jbkul156FRlEkOF8HCaC9DgNwZPiIU9Bb2blgXjgVGvzSvAAABcmSURBVCCBwDK1+b4d1cyeMbOVZrbYzEabWcXjtOtmZqvMbK2ZDc7v8URECqpu67OwG8YyteYtXHnwAXqOyWDN1n1elyVyQnm6Gc/MFgMzsh/TnXNpBTqo2XnAt865DDN7CsA597dj2viB1cC5BBbTmQtc7ZxbfqL962Y8EQmlb1Zs5b5Ri9l/KIN/XNiE604/FTPNly/eyfdc90c451o45/7qnBtR0JDP3t/XzrmM7JezgNo5NGsPrHXOrXfOpQMfAT0LemwRkYI6u0k1vrwriQ51q/DPMcu4+b15bP/tkNdlieQor6fuY7NPt080s2+PPIJUQz9gUg7baxFYEveItOxtIiKeO6VcDO/2bce/ejQlZc12ug1J4btVv3pdlsif5HVmvA+BlUAd4BEglcCp9OMysylmtjSHR8+j2jwIZGTv/0+7yGHbca8zmFl/M5tnZvO2bdt24t9IRKSAfD6jX5c6jLmjM5XLRNL3nbk8Mm4ZBw9nel2ayB/yOryuinPubTMb6JybBkwzs2m5fcA5d05u75tZH6AHcLbL+UaBNCDuqNe1gS25HO8N4A0IXKPP7dgiIsHUpEZ5xt7RhScmruCd71PZvep7/t50B7HNz9YQPPFcXoP+cPbPn83sQgKBm9N19Twxs27A34AznHMHjtNsLtDAzOoAPwFXAdfk95giIqEUE+nnkZ7NuLjKT5w25UEiZmeQMe8F/H3HYfEdvC5PSrC8nrr/j5lVAO4lMP3tW8DdBTjuS0A5YLKZLTSz1yAwxa6ZTQTIvlnvDuArAkvifuKcW1aAY4qIhFxbt5RoyyTCsiDzMJ9//hE7dKOeeOiEPfrsYW4NnHPjgT1A14Ie1DlX/zjbtwDdj3o9EZhY0OOJiBSa7NXwXGY6+CL4eHsCTw5N4bnLW5LcMNbr6qQEOmGP3jmXCVxcCLWIiBR/2avh2VkPEnHjeB65/UYqlorkhuFz+M/45RzK0I16UrjyOmHOYwRmw/sY2H9ku3Pux9CVln+aMEdEipKDhzN5bMIKPpi1kaY1yjPs6lbUP6Wc12VJGMltwpy8Bv3U7KdHGhvgnHNnBafE4FLQi0hRNGX5Vu7/bDEH0jP4Z4+mXNM+XjPqSVDkO+jN7J4jTwmE/NHfSOecez5oVQaRgl5Eiqpf9x7k1f+OpNRPM3EJnbn12qupWDrK67KkmCvIevRHzi01AtoBYwiE/UXA9KBVKCJSQpyyZzH/2vkALvIQh9JGc+fz+7np6ivpWK+K16VJmMr1Zjzn3CPOuUeAqkAb59wg59y9QFsKMI5eRKTESk3BMtPxkUWML5MOvuVc89Ysnv1qFYcz870oqMhx5XUcfTyQftTrdALL1YqIyMlISAJ/FJgf80dx3ZXXcnnb2rw0dS1XvD6TTTuON4eYSP7kdWa8D4A5ZjaawLX6XsB7IatKRCRcZQ+/IzUFEpIoFdeep+tBcsNYHvh8Cd2HpfBYr2b0bKU1vCQ48nTXPYCZtQGSsl9Od84tCFlVBaSb8USkONq88wB3fbyQ+Rt3cWnrWjx6STPKRue1PyYlWYGH1xU3CnoRKa4yMrN48du1vPjtGuIql2bYVa1pGVfR67KkiMst6PN6jV5ERApBhN/H3ec25KP+HTmckcVlr/7Aq9+tIysr/DplUjgU9CIiRVD7OpWZNDCZ80+rzlNfruS6t2ezde9Br8uSYkhBLyJSRFUoHclL17Tmqcuas2DTbroNmc7k5Vu9LkuKGQW9iEgRZmZc2S6e8QO6ULNiKW55fx7//GIpBw9rcRzJGwW9iEgxUC+2LJ//tRM3d6nDB7M20vOl71n1yz6vy5JiQEEvIlJMREf4+UePprx7Yzt27D/ERS/N4P2ZqTjnYPMcSHku8FPkKBpeJyJSDG3bd4j7Ri3iu1Xb6F9nOw9sux/LPByYda/P2MDEPFJiaHidiEiYiS0XzfA+7fhnj6ZEp/1AZkY6uEzITA/MuieSTUEvIlJM+XzGTV3q0OvSK8kgggzn47BFkBHf2evSpAhR0IuIFHN1W58FN4xlSvWbufL3B+g9PpPNO7U4jgQo6EVEwkBM3Y50u+0Z+l19Jeu2/Ub3oSmMXbTF67KkCFDQi4iEkR4tajJxQBINqpVlwMgF3PfpIvYfyvC6LPGQgl5EJMzEVS7NJ7d25M6z6jPqxzQuenEGS3/a43VZ4hEFvYhIGIrw+7j3vEaMuPl09qdncOkrP/D2jA2E45BqyZ2CXkQkjHWsV4VJA5NJbhjLv8cvp9+7c9n+2yGvy5JCpKAXEQlzlctE8eYNbXm052l8v24HFwxNYcaa7V6XJYVEQS8iUgKYGTd0TGDM7Z2pUCqS64fP5slJKzmcmeV1aRJiCnoRkRKkSY3yjLujC1e1i+e1aevo/dpMNu7Y73VZEkIKehGREqZUlJ8nLm3OK9e2YcO237hw2Ay+WPCT12VJiHgS9Gb2jJmtNLPFZjbazCoep12qmS0xs4VmplVqRESCqHvzGkwcmETj6uW46+OF3PPJQo25D0Ne9egnA82ccy2A1cADubTt6pxrdbxVeUREJP9qVyrNR/1PZ8BZ9fliwU/00Jj7sONJ0DvnvnbOHfmzcRZQ24s6REQkMOb+nvMaMeKW0/k9PZNer3yvMfdhpChco+8HTDrOew742szmm1n/QqxJRKTEOb1uFSYOTOKM7DH3N703jx0ac1/shSzozWyKmS3N4dHzqDYPAhnAh8fZTWfnXBvgAuB2M0vO5Xj9zWyemc3btm1bUH8XEZGSIjDmPpGHL2rKjDXbuWBoCj+s05j74sy8OjVjZn2AvwBnO+dOuJ6imT0M/Oace/ZEbRMTE928ebp3T0QkzzbPgdQUSEiCuPYALNuyhztHLmDD9v3cfmZ97jqnARH+onAiWI5lZvOPdy+bV3fddwP+Blx8vJA3szJmVu7Ic+A8YGnhVSkiUkJsngPvXQzfPhb4uXkOAKfVrMD4O7twedvavDR1LVe+MYu0XVrnvrjx6k+zl4BywOTsoXOvAZhZTTObmN2mGjDDzBYBc4AJzrkvvSlXRCSMpaZAZjq4zMDP1JQ/3iodFcHTvVsy9KpWrPplH92HpjBpyc8eFisnK8KLgzrn6h9n+xage/bz9UDLwqxLRKRESkgCf1Qg5P1RgdfH6NmqFq3iKjJg5AJu+/BHrukQz796NCUm0u9BwXIyPLtGH0q6Ri8icpJyuEafk/SMLJ6bvIrXp62nYbWyvHh1GxpVL3fS+5Hgyu0avYJeRERO2vTV27jnk4XsO5jBP3s05doO8Vja3MA1/iNnBvqMVdgXkiJ3M56IiBRvyQ1jmTQwmfZ1KvOPL5by1w9/5OCaace91i/eUdCLiEi+xJaL5r0b2/PABY2ZvHwrA2aVIdMXCeY/7rV+KXye3IwnIiLhweczbj2jHh3qVmHAyFJcuf8B7mnwKx269sSv0/ZFgnr0IiJSYK3iKjJhQBdqNjuDa1Z25vqvHVv3HvS6LEFBLyIiQVIuJpKhV7Xi6d4tWLBpNxcMTWHqql+9LqvEU9CLiEjQmBlXJMYx7s7OnFIumhvfmctjE5aTnpHldWklloJeRESCrv4p5fji9s5cf/qpvJmygd6v/cDGHfu9LqtEUtCLiEhIxET6+fclzXjtujakbt/PhcNmMGbhT16XVeIo6EVEJKS6NavBxIFJNKpejoEfLeT+UYs4kJ7hdVklhoJeRERCrnal0nzc/3Tu6FqfT+encdGLM1jx816vyyoRFPQiIlIoIvw+Bp3fiP/e1IG9BzPo+fL3fDBrI+E4FXtRoqAXEZFC1bl+VSYNTKJj3Sr884ul3PbfH9lz4LDXZYUtBb2IiBS6qmWjeadvO/7evTFTVmyl+7AU5m/c6XVZYUlBLyIinvD5jP7J9Rh1Wyf8PuOK12fx8tS1ZGbpVH4wKehFRMRTreIqMn5AFy5oVp1nvlrFDcNn86umzw0aBb2IiHiufEwkL17dmqcua878jbu4YGgK3x2ZPnfzHEh5LvBTTppWrxMRkSLBzLiyXTxt4itxx4gF9H1nLo+03s8NawZgmemBpW/7jAWtindS1KMXEZEipUG1coy5ozPXdohn6+IpZGUcApcJmemQmuJ1ecWOgl5ERIqcmEg/j/VqTtK5l5BOBBnOR6YvEhKSvC6t2NGpexERKbI6ntmdrbGf8fXEUYzeWYdGc2P4V7VMSkX5vS6t2FDQi4hIkVbttGSuatyFn75ezWvT1jF/4y5euqYNDauV87q0YkGn7kVEpMiL9PsYfEFj3u/Xnp3707n4pRl8NGeTps/NAwW9iIgUG8kNY5k4MInEUysz+PMl3DlyAfsOavrc3CjoRUSkWDmlXAzv92vPfec3YtLSX7hw2AwWp+32uqwiS0EvIiLFjs9n3N61Ph/3P52MzCwue/UH3kpZT5amz/0TBb2IiBRbiQmVmTgwia6NTuE/E1Zw03tz2bk/3euyihQFvYiIFGsVS0fx+vVteeTi0/h+7Q4uGDqdWet3/H+DEj6FrobXiYhIsWdm9OmUQGJCJe4csYBr3pzFnWc1YEDDXfg/6BmYVa+ETqHrWY/ezP5tZovNbKGZfW1mNY/Tro+Zrcl+9CnsOkVEpPg4rWYFxt3ZhUta12LoN2v4eNQIXGZ6iZ5C18tT988451o451oB44F/HdvAzCoDDwEdgPbAQ2ZWqXDLFBGR4qRMdATPX9GK5y5vybg99Tjk/GSZP9CjL4FT6Hp26t45t/eol2WAnG6VPB+Y7JzbCWBmk4FuwMjQVygiIsXZZW1r0yq+Hw+8H0ONnfOo2vxsrquRSJTXhRUyT2/GM7PHzGwzcC059OiBWsDmo16nZW8TERE5oXqxZXliwE3sbz+ARxeWpfdrP7Bxx36vyypUIQ16M5tiZktzePQEcM496JyLAz4E7shpFzlsy3GQpJn1N7N5ZjZv27ZtwfslRESkWIuJ9PNIz2a8dl1bUrfv58JhMxi3aIvXZRWakAa9c+4c51yzHB5jjmk6Argsh12kAXFHva4N5Ph/xzn3hnMu0TmXGBsbG5xfQEREwka3ZtWZODCJhtXKcufIBTzw+WJ+T8/0uqyQ8/Ku+wZHvbwYWJlDs6+A88ysUvZNeOdlbxMRETlptSuV5uNbO3LbmfUYOWczPV+ewZqt+7wuK6S8vEb/ZPZp/MUEAnwggJklmtlbANk34f0bmJv9ePTIjXkiIiL5Een38bdujXmvX3t2/JbORS/N4OO54bsSnoXjL5aYmOjmzZvndRkiIlLE/br3IHd/spDv1+7g4pY1eaxXM8rFRHpd1kkzs/nOucSc3tMUuCIiUmKdUj6G9/t1YNB5DRm/eAs9XpzBkrQ9XpcVVAp6EREp0fw+446zGvDxrR1Jz8ji0le/5+0ZG3CbZofFHPk6dS8iIpJt1/507hu1mJ0rU/go5gkiycCKwRz5OnUvIiKSB5XKRPHmDW35e9Md+LIOYy4zMFd+MZ4jX0EvIiJyFDMj8cyL8UVEkYGPg1l+Ru1IIDOreJ4B1zK1IiIix4prj6/vOA6vncYrG2rw4qwYvtg+h+evbMkp5WK8ru6k6Bq9iIhILpxzfDx3Mw+PW0bZ6AheuLIVSQ2K1gysukYvIiKST2bGVe3jGXN7FyqVjuKG4XN45quVZGRmeV1anijoRURE8qBR9XKMvaMLV7SN4+Wp67jqjVls2f2712WdkIJeREQkj0pF+XmqdwuGXtWKFT/vpfuwFCYv3+p1WblS0IuIiJyknq1qMX5AErUqluKW9+fxyLhlHMoomivhKehFRETyoU7VMnz+10707ZTAO9+n0vvVmaRu3+91WX+ioBcREcmn6Ag/D198Gq9f35aNO/bT48UZjF20xeuy/oeCXkREpIDOP606Ewcm0bBaWQaMXMDgzxbze3rROJWvoBcREQmC2pVK8/GtHbntzHp8NHczPV+ewZqt+7wuS0EvIiISLJF+H3/r1pj3+7Vn5/50LnppBp/M3YyXk9Mp6EVERIIsuWEsEwck0Sa+Evd/tpi7Pl7Ib4cyPKlFQS8iIhICp5SP4YObOnDvuQ0Zt2gLPYalsPSnPYH17QtxnXvNdS8iIhJis9fvYOBHC4k/sJQRUY/hd8Fd515z3YuIiHioQ90qTByYxBVVUyEzsM49hbTOvYJeRESkEFQuE8Vll14F/kic+cEfBQlJIT+u1qMXEREpJBbfgYgbxwd68glJQTltfyIKehERkcIU175QAv4InboXEREJYwp6ERGRMKagFxERCWMKehERkTCmoBcREQljCnoREZEwpqAXEREJYwp6ERGRMKagFxERCWNhuXqdmW0DNhZwNxWAPUEopygePxj7Lsg+TvazJ9M+L21P1KYqsD2PxytuvP5eh7KGYO03v/vJz+fy+plgtdN3u3gePy/7PtU5F5vjO845PXJ4AG+E6/GDse+C7ONkP3sy7fPS9kRtgHle/r8P5cPr73UoawjWfvO7n/x8Lq+fCVY7fbeL5/ELum+duj++cWF8/GDsuyD7ONnPnkz7vLT1+v+tl4rC7x6qGoK13/zuJz+fy+tngt0uHHn9uxfZf7PD8tS9SEGY2TznXKLXdYgEm77bJZN69CJ/9obXBYiEiL7bJZB69CIiImFMPXoREZEwpqAXEREJYwp6ERGRMKagFzkJZnaJmb1pZmPM7Dyv6xEJFjOra2Zvm9kor2uR4FLQS4lhZsPN7FczW3rM9m5mtsrM1prZ4Nz24Zz7wjl3C9AXuDKE5YrkWZC+2+udczeFtlLxgu66lxLDzJKB34D3nXPNsrf5gdXAuUAaMBe4GvADTxyzi37OuV+zP/cc8KFz7sdCKl/kuIL83R7lnOtdWLVL6EV4XYBIYXHOTTezhGM2twfWOufWA5jZR0BP59wTQI9j92FmBjwJTFLIS1ERjO+2hC+dupeSrhaw+ajXadnbjudO4Bygt5n9JZSFiRTQSX23zayKmb0GtDazB0JdnBQe9eilpLMcth33epZzbhgwLHTliATNyX63dwD64zUMqUcvJV0aEHfU69rAFo9qEQkmfbcFUNCLzAUamFkdM4sCrgLGelyTSDDouy2Agl5KEDMbCcwEGplZmpnd5JzLAO4AvgJWAJ8455Z5WafIydJ3W3Kj4XUiIiJhTD16ERGRMKagFxERCWMKehERkTCmoBcREQljCnoREZEwpqAXEREJYwp6EcHMKprZX7Of1wzmmuRmdpeZ3ZDD9oQjy6qaWXMzezdYxxSR/6egFxGAisBfAZxzW4K1TKmZRQD9gBG5tXPOLQFqm1l8MI4rIv9Pi9qICASW3q1nZguBNUAT51wzM+sLXEJgDfNmwHNAFHA9cAjo7pzbaWb1gJeBWOAAcItzbiVwFvBj9ixtmFlbYHh2mxnH1DCOwDStT4fyFxUpadSjFxGAwcA651wr4L5j3msGXENgffPHgAPOudYEplw9ckr+DeBO51xbYBDwSvb2zsD8o/b1DjDAOdcxhxrmAUlB+F1E5Cjq0YvIiUx1zu0D9pnZHgI9b4AlQAszKwt0Aj41+2Nl1OjsnzUIzLOOmVUAKjrnpmW/9wFwwVHH+RWoGbLfQqSEUtCLyIkcOup51lGvswj8G+IDdmefDTjW70BM9nMjl/XQs9v9XrBSReRYOnUvIgD7gHL5+aBzbi+wwcwuB7CAltlvrwDqZ7fbDewxsy7Z7117zK4aAkvzU4OIHJ+CXkRwzu0Avs8e7vZMPnZxLXCTmS0ClgE9s7dPApKPancj8LKZzeTPvfeuwIR8HFtEcqFlakUkpMxsNHC/c25NLm2igWlAlyN36ItIcCjoRSSkzKwRUM05Nz2XNg2AWs657wqtMJESQkEvIiISxnSNXkREJIwp6EVERMKYgl5ERCSMKehFRETCmIJeREQkjCnoRUREwtj/AV/0a7qzzlHDAAAAAElFTkSuQmCC", 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" ] @@ -376,7 +376,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -422,7 +422,7 @@ "ml_1 = ttim.ModelMaq(\n", " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", ")\n", - "w_1 = Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", + "w_1 = ttim.Well(ml_1, xw=0, yw=0, rw=rw, tsandQ=[(0, Q)], layers=0)\n", "ml_1.solve()" ] }, @@ -556,7 +556,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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12APOuZ8ktCQiIiLSb7Gu7vasmd1uZqPNbETrT0JLJiIiIr0Wa419YeT1f7bb5oBx8S2OiIiI9Eeso+LHJrogIiIi0n+xjopfB7wOrAP+0zl3MqGlEhERkT6JtY99IbAD+EvgDTPbZGY/TFyxREREpC9ibYp/x8wagObIz2XA5EQWTERERHovphq7me0GfgmcA9wPTHHOXZnIgomIiEjvxdoUvxzYD3we+BtgoZmdn7BSiYiISJ/EFOzOuR855z4DfBLYDHwb2JnAcomIiEgfxDoq/m7gEqAA2AD8H8Ij5EVERCSFxDpBzQbgLufcB4ksjIiIiPRPrKPinzCzT5nZ/Mim15xzzyawXCIiItIHsY6K/x7wNaAm8vM3kW0iIiKSQmJtir8GmOacCwGY2YPAFuAfElUwERER6b1YH3cDGN7u92HxLoiIiIj0X6w19u8BW8zsVcCA+ai2LiIiknJiHTz3qJmtBWYRDvZvOufeT2TBREREpPd6DHYzm9Fp08HI67lmdq5z7veJKZaIiIj0xdlq7HdHXnOAmcAfCdfYLwB+R3jSGhEREUkRPQ6ec85d5py7DNgHzHDOzXTOXQRMB94eiAKKiIhI7GIdFf8R59yfW98457YC0xJTJBEREemrWEfFbzOznwEPAw74ArAtYaUSERGRPok12G8GvkJ49jmA14GfJKREIiIi0mexBvtfAD91zv0wkYURERGR/ok12BcB95pZHeHlWtcB651zRxNVMBEREem9WCeo+W8AZnYucCNwD3BurJ8XERGRgRFTMJvZF4BLgalALbCCcK1dREREUkisNe5/A3YD9wKvOuf2JqxEIiIi0mcxPcfunCsGFhOege67ZrbRzFYntGQiIiLSazEFu5kNBcqB84AKwsu2hhJXLBEREemLWJvi17f7WeGcO3iW40VERCQJYh0Vf0GiCyIiIiL9F+uo+JHA3wNVhPvZAXDOfTxB5RIREZE+iHURmJ8D24GxwD8De4G3ElQmERER6aNYg73IOXc/0OKce805txi4OIHlEhERkT6IdfBcS+T1PTO7BjgEjElMkURERKSvYg32/2tmw4C/A/4dGAr8bcJKJSIiIn1y1mA3My8wwTn3K+A4cFnCSyUiIiJ9ctY+dudcEPjUAJQlysy+amY7zKzazO4ayGuLiIgMZrE2xb9hZiuAx4HTrRudc7+Pd4HM7DLgeuAC51yTmZXE+xoiIiLpKtZg/4vI6z9HXg1wQCKeY/8KcKdzrgnAOfdhAq4hIiKSlnoMdjP7euTXXxEOcmu32yWoTBOBS83su0Aj8A3nnJ6ZFxERicHZauxDIq+TgFnAM4TD/Trg9b5e1MxeAkZ1setbkTIVEn5OfhbwH2Y2zjnX4Q8JM7sNuA2gvLy8r0URERFJK9YpL7s+yOw3wF86505G3g8BnnDOXRn3Apm9QLgpfm3k/W7gYufc4e4+M3PmTLdp06Z4F0VERCRlmdlm59zMzttjnXmuHGhu976Z8PKtifBLIn33ZjYRyAJqE3QtERGRtBLr4LnVwEYze5pw3/oNwIMJKtNKYKWZbSX8B8TCzs3wIiIi0rVYl239rpk9D1wa2XSzc25LIgrknGsGvpCIc4uIiKS7WGvsrc+sx/25dREREYmfWPvYRUREZBBQsIuIiKQRBbuIiEgaUbCLiIikEQW7iIhIGlGwi4iIpBEFu4iISBpRsIuIiKQRBbuIiEgaUbCLiIikEQW7iIhIGlGwi4iIpBEFu4iISBpRsIuIiKQRBbuIiEgaUbCLiIikEQW7iIhIGlGwi4iIpBEFu4iISBpRsIuIiKQRBbuIiEgaUbCLiIikEQW7iIhIGlGwi4iIpBEFu4iISBpRsIuIiKQRBbuIiEgaUbCLiIikEQW7iIhIGlGwi4iIpBEFu4iISBrxJbsAidLS0sLBgwdpbGxMdlEGVE5ODmPGjMHv9ye7KIPSjiM72H9yP1VFVYzOH42ZJbtIIiK9krbBfvDgQYYMGUJFRUXG/M/ZOUddXR0HDx5k7NixyS7OoLT+xfvZu/Y5flJu1J4/gsqiSiqLKqkqrqKqqIpz8s7JmO+TiAxOaRvsjY2NGRXqAGZGUVERhw8fTnZRBqX6LVuY9/3fMLcZnM946e+m8nrD+6zcupKgCwJQlFNEVXFVOOyLwmE/Mm9kkksuItImbYMdyKhQb5WJ9xwv9RvfwjW3YKEQFjT+qvFCvvape2gINLDjyA5q6mqorqumpq6G9e+uJ+RCAJTkllBZHA761sAvyi1K8t2ISKZK62BPpmPHjvHII49w++23s3fvXt544w1uuukmADZt2sRDDz3E8uXLYz7fihUr+Ld/+zd2797N4cOHKS4uTlTRM1be7FlYVhaupQXz+8mbPQuAXF8u00qmMa1kWvTY+pZ6dhzdQXVtNdV14Z/XDryGwwEwKn9UtEbf2pxfmFOYlPsSkcxizrlkl6HfZs6c6TZt2tRh27Zt25g8eXKSSgR79+7l2muvZevWraxdu5alS5fyq1/9qs/n27JlC4WFhXzsYx9j06ZNPQZ7su99MKvfsoX6jW+RN3sWedOn9+qzp5pPse3Itg41+30n9kX3lxaUtjXhF1cxecRkhmUPi/ctiEiGMLPNzrmZnberxp4gS5YsYffu3UybNg2/38/OnTuZNm0aCxcuZPr06dGg//a3v82ePXt477332LlzJ8uWLWPDhg08//zzlJaW8uyzz+L3+5ney5CRvsmbPr3Xgd6qIKuAWaNmMWvUrOi2E80n2FbXFvbVtdX8dt9vo/vLhpRFa/atYV+QVdDv+xCRzKVgT5A777yTrVu38oc//OGMGvvatWs7HLt7925effVVampqmDt3Lk899RR33XUXN9xwA8899xwLFixIwh1IPAzNGsqc0XOYM3pOdNvxpuMdavV/OvwnXtj7QnR/xdCKM2r2ef686P7+tCqISPrLiGD/52erqTl0Iq7nrDx3KP90XVVcznXVVVfh9/uZOnUqwWCQK6+8EoCpU6eyd+/euFxDUsew7GHMPXcuc8+dG912tPFoh1r95g828+s9vwbAMMYOG0tVURWza4cx8duPYC0BLCuL8lUrFe4i0kFGBHuqy87OBsDj8eD3+6Mj2z0eD4FAIJlFkwFSmFPIvNJ5zCudF91W21DbVrOvreHN997E+/KHjG8O4XUQaGrkmSe+C7l/SVVRFZNGTCLbm53EuxCRVJBywW5m04B7gRwgANzunNvYn3PGq2bdG0OGDOHkyZNn/C4Sq+LcYuaPmc/8MfOj294te4XjG+4g1BIg5DVeHHGIzb/7VwB85mN84fgOz9hPKJxAljcrWbcgIkmQcsEO3AX8s3PueTO7OvL+Y8ktUu8VFRUxb948pkyZwuWXX47P5+PCCy9k0aJFfRoIt3z5cu666y7ef/99LrjgAq6++mp+9rOfJaDkkspK536cwgcejPaxr5o2jQ/qP4g+dldTV8Mr+1/hF7t+AYDP42Ni4cQOYT++cDx+j6YcFklXKfe4m5m9CKx0zj1uZp8HrnPO3dTTZ1LxcbdkyuR7l/DUwodOH6K6trqt376umpPN4VajLE8Wk0ZMioZ9ZVEl5w8/H58nFf/OF5HuDKbH3e4AXjSzpYRXn/uLJJdHZFAxM0oLSiktKOWKiiuAcNgfPHkwWquvrqvmuXee4/EdjwOQ483pEPZVRVWMHTYWr8ebzFsRkT5ISrCb2UvAqC52fQv4BPC3zrmnzOy/AvcDn+ziHLcBtwGUl5cnsLQig5+ZUTa0jLKhZVw5NvzURciF2H9if4ewf+btZ3h0+6NAeMa9ySMmd1gIp2JoBR7Tas8iqSwVm+KPA8Odc87Cw8OPO+eG9vQZNcV3lMn3Lv0TDAXZd2Jfh7DfVreNxmB4+eN8fz6TR0xumxe/uIqyIWUxhb2evxeJr8HUFH8I+CiwFvg4sCuppRHJIF6Pl3HDxzFu+DiuO/86AAKhAHuO74k+Y19zpIbHdjxGU7AJgCH+IeFafXFltCl/TMGYDgsS1W/Zwv6bF+Oam/X8vUiCpWKw/zXwIzPzAY1EmttFJDl8Hh8TCicwoXACC8aHZ0FsCbXwzrF32sK+roaHax6mJdQChGfca505r7KokvPX/xHX3AyhEK6lJVxzV7CLJETKBbtzbj1wUbLLISLd83v8TBoxiUkjJvHpCZ8GoCXYwq5juzqE/QNbHyDgAkw44fgnTwgf4LxGzXnGxNPvc07eOVpqWCTOUi7Y0137Vd9i8frrr3PHHXfwpz/9iccee4wbb7wxwSUU6Ru/1x8daPeZiZ8BoCnYxK6ju6iurWbtmNdhy1bWlxxl+wfL4cnlFOUUUVVc1WGJ25F5I5N8JyKDm4I9xZWXl/PAAw+wdOnSZBdFpNeyvdlMKZ7ClOIp8JHPwkL4UqCxw1r2NXU1rH93PSEXAqAkt4TK4soOYV+UW5TkOxEZPBTsCbZs2TJWrlwJwK233sqCBQsIBAIsXLiQLVu2MHHiRB566CHy8vJYsmQJa9aswefzccUVV7B06VIqKiqA8LzxIukgx5fDhSMv5MKRF0a31bfUdwj76rpqXjvwGo7wUzuj8ke1LW8bCfvhOcPbPq8R9yJRCvb2DmyEveug4lIom93v023evJlVq1bxu9/9Duccc+bM4aMf/Sg7duzg/vvvZ968eSxevJgf//jHLF68mKeffprt27djZhw7diwONyQyOOT585heMp3pJW2hfLrlNNvqtkWDvqauhpf3vxzdX1pQSmVRJXNqC5n6L09qxTuRCAV7qwMb4cFPQbAZvFmwcE2/w339+vXccMMN5OfnA/DpT3+adevWUVZWxrx54VW8vvCFL7B8+XLuuOMOcnJyuPXWW7nmmmu49tpr+31LIoNZvj+fmaNmMnNU22O6J5pPsL1uezTsq2uryf/tfqrarXj31OPfIei/PrqWfUFWQRLvQmTgKdhb7V0XDnUXDL/uXdfvYO9u8p/Oo4DNDJ/Px8aNG3n55Zd57LHHWLFiBa+88kq/ri+SboZmDWX26NnMHt323+bhsvUc3nA7oZYAzmusLznKG5vvju6vGFrRNlVuJOzz/HnJKL7IgFCwt6q4NFxTb62xV1za71POnz+fRYsWsWTJEpxzPP3006xevZqvfe1rvPnmm8ydO5dHH32USy65hFOnTlFfX8/VV1/NxRdfzPjx4+NwUyLpb+TsS8hvt+LdT6dP50jjkfDMeZHH7jZ/sJlf7/k1AIYxdtjYaNC3rmWf68tN8p2IxEfKTSnbF3GbUjbOfezQ9eC5q6++mvnz5/PGG28wYcIEVq9ezfHjx7n++utpbGzEOcc3vvENFi5cyFtvvcUNN9zA0aNHycnJYdSoUVRXV/d4TU0pK3Km2obaaNi3NuXXNtQC4DEP44aNo6qoilm1Qzn/nQbGzL+S4TPnJLnUIt3rbkpZBXsayuR7F+mND+s/7BD0DX/4I3c8cBRfEAJeePBLYxl60azoaPwJhRPI8mYlu9giwOCaK15EZECU5JVQUl7CZeWXAVC7+6ccDi0HF8JCRtV+xwMjX+YXu34BhKfXnVg4sW0RnKIqxheOp+WPW/W4naQMBbuISETe7NlYVhaupQWv389NN/0rt0ybxqHThzrU7F/Y+wJP7HwCgMpDXr71SDPegAO/H++//wvjL70Gn0f/e5Xk0DdPRCQib/p0yletPKP2XVpQSmlBKVdUXAGEn3g5ePIg1XXVNK76Od7AJjwOgi0t/PyR/8ULB/4vk0ZMalsIZ0QlY4eNxevxJvP2JEMo2EVE2smbPv2szelmRtnQMsqGllF/wznsX7M4XMv3+fjop/6aISWnqa6t5um3n+aR7Y8AkOvLZfKIydH59KuKq6gYWhHTWvYivaFgFxHph861/MnTp/PJyL5gKMi+E/s6TKjz5M4naQw2AuFJeCaPmNzWZ19cRdmQMoW99IuCXUSkn7qr5Xs9XsYNH8e44eO47vzrAAiEAuw5vqfD8raPbn+U5lAzAEP8Q8K1+uLK6AC9MQVjtLytxEzBniDHjh3jkUce4fbbb2fv3r288cYb3HTTTQBs2rSJhx56iOXLl8d8vltuuYVNmzbhnGPixIk88MADFBRoqkyRwcbn8TGhcC/SGnkAAA8GSURBVAITCiewYPwCAFpCLew+trvDc/ara1YTCAWA8Ix70f76SNiPzh+NmWkBHDmDnmNPkPbrrq9du5alS5fyq1/9qs/nO3HiBEOHDgXg61//OiUlJSxZsqTLY5N97yLSf83BZnYd29VhBr1dR3cRcOGwL8wu5OMnSrlxxVY8gSDmz+K8VavIm6FwzxR6jn2ALVmyhN27dzNt2jT8fj87d+5k2rRpLFy4kOnTp0eD/tvf/jZ79uzhvffeY+fOnSxbtowNGzbw/PPPU1payrPPPovf74+GunOOhoYGNcuJpLksb1Z0YpzPTPwMAE3BJnYe2Rld7a7widegJYA5CDY38aP7b+Pg0dkdnrMfmTcyyXciA03BniB33nknW7du5Q9/+MMZNfa1a9d2OHb37t28+uqr1NTUMHfuXJ566inuuusubrjhBp577jkWLAg319188838+te/prKykrvvvrvzJUUkzWV7s5k6cipTR04FoD5vC/vX3kyouQXzeciZfRGHTh1i/bvrCbkQACW5JVQWV3ZYy74otyiZtyEJlhHB/v2N32f7ke1xPedHRnyEb87+ZlzOddVVV+H3+5k6dSrBYJArr7wSgKlTp7J3797ocatWrSIYDPLVr36Vxx9/nJtvvjku1xeRwSk8In9VtI/9b6dP52+B+pZ6th/ZHm7Gj4zIf+3AazjCXa+j8kdFg7417LO27VFffZrIiGBPddnZ2QB4PB78fn+0md3j8RAIBDoc6/V6+exnP8sPfvADBbuIdDkiP8+fx4xzZjDjnBnRbaeaT7HtyLZo2NfU1fDy/pcBmHDQ8U+PhfAFHM7vo2HZN5l46XUMyx42oPci8ZERwR6vmnVvDBkyhJMnT57xe18459i9ezfjx4/HOcezzz7LRz7ykXgVVUQyQEFWAbNGzWLWqFnRbSeaT7C9bjtH/9/9+AKvR2bPC7Dmie/xy3e/T/mQ8g7P2E8eMZmCLD2Nk+oyItiToaioiHnz5jFlyhQuv/xyfD4fF154IYsWLWJ6L5u5nHMsXLiQEydO4Jzjwgsv5Cc/+UmCSi4imWJo1lBmj55N/XV+9v9iY3T2vOtu/DvOG9VETV0Nfzz8R57f+3z0MxVDK6Lr2FcWVTJ5xGTy/HlJvAvpTI+7paFMvncR6Zuenoc/0nikw2N31XXVfFD/AQCGhdeyb/eM/aQRk8j15SbjNjKKHncTEZFu9TRH/oicEVxSegmXlF4S3VbbUNsh6N849AZrdq8BwGvhGffaD86bNGIS2d7sAbmXTKdgFxGRXivOLWZW7VCqNnpZOPsW8j4xnQ/rP+ywvO3rB1/nl2//EgCf+RhfOL7DM/YTCieQ5c1K8p2kHwW7iIj0Wv2WLey/eTGuuRnLyqJ81UpKpk+npLyEy8ovA8Ljgz6o/6BD2L+0/yWe2vUUEJ5ed2LhxA41+/GF4/F7/Mm8tUFPwS4iIr1Wv/EtXHMzhEK4lpZw/3ynpnwzY1T+KEblj+IT530CCIf9odOHOoT9C3te4ImdTwBQecjL/MMjsBlTGT3nY1QWVXL+8PPxeRRXsdI/KRER6bW82bOwrCxcSwvm95M3e9bZP0Q47EsLSiktKOWKiiuAcNgfPHmQXeufo+TuFVjLe7T85j2+8/mX2TXGyPHmMGnEJKqKqpjxYT4Vu09TOv8KCmZcdMb5tSiOgl1ERPqg8zr0/QlRM6NsaBm5BzwcDjhwkO28fL/gi+y8dEp0ids/rX2Sq1bX0xKEt3/2EI//98kMv2hO9PG7kt1HObD4lg7dAz2VK13/CFCwD7D2q77F4t577+Wee+7B6/VSUFDAfffdR2VlZYJLKSJydj2NpO/T+Tq1ApTOv4IJ46ZzzbhrADi8715qQ/8OLoSFjLJdJ1g15Eke3vYwAP/1d14+3dSEx0GouYlD637L+dOmdbloVldjBNIl3BXsKe6mm27iy1/+MgBr1qzh61//Oi+88EKSSyUiEn9nawXInzOHuqyfhifS8ftZ9MUfcNuFU3nn+DvU1NXwnnctwXW/wQUcAY/jf598iPcf+yWVIyo7LIRTWlAa0xiBwUrBnmDLli1j5cqVANx6660sWLCAQCDAwoUL2bJlCxMnTuShhx4iLy+PJUuWsGbNGnw+H1dccQVLly6NLtcKcPr0aS3XKiJpradWgO6Cf2LhRCYWToTxC6iv2sLJ323g6ORz+cK5weggvdU1qwmEwmtvDMsexicYw2d8hifgwXw+8mbFNkZgMNDMc+3Eu79l8+bNLFq0iA0bNuCcY86cOTz88MPMmDGD9evXM2/ePBYvXkxlZSWLFy9m7ty5bN++HTPj2LFjDB8+HIB77rmHZcuW0dzczCuvvMKECRN6vK5mnhMR6ag52MyuY7s6zKDHn3cwaV+A6nKjbnwRlUWV0Wfsq4qqKMkrSenKVHczzynYIxLR3/KjH/2Iuro6vvOd7wDwj//4j4wcOZKlS5eyf/9+AF555RWWL1/Ok08+yUUXXcTMmTO55ppruPbaa8nK6jhxwyOPPMKLL77Igw8+2ON1FewiImfXFGxix5Ed1NTVRGfQ231sN0EXBMKT8LR/xr6quIri3OIkl7qNppQ9i0T0t3T3R1PnvwDNDJ/Px8aNG3n55Zd57LHHWLFiBa+88kqH4z73uc/xla98pV9lEhGRsGxvNheMvIALRl4Q3dYQaGDHkR3RpW1r6mpY9+46Qi4EQEleCR8/Xsq0g35mXbWIkjmXJqv43VKwR/T1mcyezJ8/n0WLFrFkyRKcczz99NOsXr2ar33ta7z55pvMnTuXRx99lEsuuYRTp05RX1/P1VdfzcUXX8z48eMB2LVrV7Tp/bnnnjtrM7yIiPRdri+XaSXTmFYyLbqtvqWe7Ue2U11XzYcb1/HJH63HG3DUPb2ZggdWpdygOwV7RDyfyWw1Y8YMFi1axOzZs4Hw4LnCwkImT57Mgw8+yJe+9CUmTJjAV77yFY4fP871119PY2Mjzjl++MMfArBixQpeeukl/H4/hYWFZ22GFxGR+Mrz5zHjnBnMOGcGtesaOBz8T3AOAoGUHE2vPvY0lMn3LiKSSNHxWJHW3WQ+/64+dhERkX5KROtuvCnYRUREeiHeM+7FmyfZBRAREZH4SetgT4fxA72VifcsIiJtkhLsZvYZM6s2s5CZzey07x/M7G0z22Fm/6Wv18jJyaGuri6jgs45R11dHTk5OckuioiIJEmy+ti3Ap8Gftp+o5lVAp8DqoBzgZfMbKJzkWmAemHMmDEcPHiQw4cPx6O8g0ZOTg5jxoxJdjFERCRJkhLszrltcOYMbMD1wGPOuSZgj5m9DcwG3uztNfx+P2PHju1vUUVERAaVVOtjLwUOtHt/MLJNREREYpCwGruZvQSM6mLXt5xzz3T3sS62ddlJbma3AbcBlJeX96mMIiIi6SZhwe6c+2QfPnYQKGv3fgxwqJvz3wfcB+GZ5/pwLRERkbSTahPUrAEeMbNlhAfPTQA2nu1DmzdvrjWzff289jDgeD/P0V+JKkO8ztvX8/Tlc7F+Jl7HFQO1MZxnMEr2dzuR1x9s3+3eHB/LsbEco+/24Lx+LOc+r8utzrkB/wFuIFw7bwI+AF5st+9bwG5gB3DVAJbpvmT8sxiIMsTrvH09T18+F+tn4nUcsCnZ//4T9ZPs73Yirz/Yvtu9OT6WY2M8Rt/tQXj9/pw7WaPinwae7mbfd4HvDmyJAHg2CdfsLFFliNd5+3qevnwu1s/E+7h0lOx7T+T1B9t3uzfHx3Jssv/dJluy7z8lv9tpsbqbSH+Z2SbXxSpJIoOdvtuZJ9UedxNJlvuSXQCRBNF3O8Ooxi4iIpJGVGMXERFJIwp2ERGRNKJgFxERSSMKdpGzMLMFZvb/zOwZM7si2eURiQczG2dm95vZk8kui8SXgl3SmpmtNLMPzWxrp+1XmtkOM3vbzJb0dA7n3C+dc38NLAI+m8DiisQkTt/rd5xztyS2pJIMGhUvac3M5gOngIecc1Mi27zATuBywjMgvgV8HvAC3+t0isXOuQ8jn7sb+Llz7vcDVHyRLsX5e/2kc+7GgSq7JF6qzRUvElfOudfNrKLT5tnA2865dwDM7DHgeufc94BrO5/DzAy4E3heoS6pIB7fa0lfaoqXTFQKHGj3/mBkW3e+CnwSuNHMvpzIgon0Q6++12ZWZGb3AtPN7B8SXTgZOKqxSyayLrZ12yflnFsOLE9ccUTiorff6zpAf6imIdXYJRMdBMravR8DHEpSWUTiRd9rARTskpneAiaY2VgzywI+B6xJcplE+kvfawEU7JLmzOxR4E1gkpkdNLNbnHMB4H8ALwLbgP9wzlUns5wivaHvtfREj7uJiIikEdXYRURE0oiCXUREJI0o2EVERNKIgl1ERCSNKNhFRETSiIJdREQkjSjYRTKQmQ03s9sjv58bzzW5zewOM/tvXWyvaF1m1MymmtkD8bqmiLRRsItkpuHA7QDOuUPxWrbTzHzAYuCRno5zzv0ZGGNm5fG4roi00SIwIpnpTuB8M/sDsAuY7JybYmaLgAWE1/CeAtwNZAFfBJqAq51zR8zsfOAeYCRQD/y1c2478HHg95FZ0DCzi4CVkWPWdyrDs4SnPb0rkTcqkmlUYxfJTEuA3c65acD/7LRvCnAT4fW9vwvUO+emE57CtLWJ/T7gq865i4BvAD+ObJ8HbG53rlXA3zjn5nZRhk3ApXG4FxFpRzV2EensVefcSeCkmR0nXLMG+DNwgZkVAH8BPGEWXSk0O/I6mvA85ZjZMGC4c+61yL7VwFXtrvMhcG7C7kIkQynYRaSzpna/h9q9DxH+f4YHOBap7XfWAOREfjd6WA88clxD/4oqIp2pKV4kM50EhvTlg865E8AeM/sMgIVdGNm9DRgfOe4YcNzMLons+6tOp5oIbO1LGUSkewp2kQzknKsD/jPy+NkP+nCKvwJuMbM/AtXA9ZHtzwPz2x13M3CPmb3JmbXzy4Dn+nBtEemBlm0Vkbgys6eBv3fO7erhmGzgNeCS1hH0IhIfCnYRiSszmwSc45x7vYdjJgClzrm1A1YwkQyhYBcREUkj6mMXERFJIwp2ERGRNKJgFxERSSMKdhERkTSiYBcREUkjCnYREZE08v8BWOjgZESoQScAAAAASUVORK5CYII=", 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" ] @@ -605,7 +605,7 @@ "ml_2 = ttim.ModelMaq(\n", " kaq=10, z=[0, b], Saq=0.001, tmin=0.001, tmax=1, topboundary=\"conf\"\n", ")\n", - "w_2 = Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n", + "w_2 = ttim.Well(ml_2, xw=0, yw=0, rw=rw, rc=0.2, res=0.2, tsandQ=[(0, Q)], layers=0)\n", "ml_2.solve()" ] }, @@ -761,7 +761,7 @@ "outputs": [ { "data": { - "image/png": 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Wl073rJTndlsqjrawZX89W/bX8eH+Bh56u4JOt2ccTFZyDLNyUpiVk8qs3GRm5qRov3qRMBUTEwOAw+HA5XL5mtkdDgednZ29znU6ndx44438+te/VrCLRBKHwzApM5FJmYncMD8X6N7CtoEt+xvYdrCBjw7U89qOWrrHvHrC3tNPf8awD/R+9eqvlxALVM16IJKSkmhqavrU88Gw1lJeXs6kSZOw1rJmzRrOOeecgJRTwS4Sxjxb2PZuwm9u6+TjQ41sPdjA1gP1bD3YwGs7anxhn50cy8ycFF/Yz8pJZWxSTOD2q1d/vYxSY8aMYeHChcycOZPLLruMqKgo5syZw9KlSykuLh7Qtay1LFmyhMbGRqy1zJkzh9/+9rcBKaemu4lEgOa2TrYfbGDrQU/NfuvBBiqOtvQb9tPHJzNtXBI5qYNcC3/dXfD6Lz399cYJn/0ZLPofgf1AIv0I9XS3UNJ0N5FRJjEmivMmjOG8CWN8x/oL+541++TYKG/IJzN9XDLTxyczKTORmKgzTL0D9deLhDkFu0iE6i/sW9o62VHdxCeHG/n4cCMfH2rk6Y37OdnRBUCUt5+/O+inj/MEf1pCjzXxNa9eJKwp2EVGkYSYKN8e9N263JaqYy2esD/kCfx3yo/yp7KDvnPGpcT2qtlPGzeDgoXn9p5rPxCn66dX2IsMmYJdZJRzOgwTxyYycWwi18we7zt+tLmNTw43+gL/k8NNvLXrCF3e6XdxLidTshKZmp3E1OxkzslOYmp2EhmJMWe/aX/z6kGD8kQCQMEuIv3KSIxh0eSxLJo81nestaOL3TXNfHy4gR3VTeysbuK1T2p5pvSA75wxCdHesE/yhn0ykzMTSYjp8b+b/vrptYiOSEAo2EXEb7EuJ7NyU5iVm9Lr+JGmNnZWN7GjupFdNZ7A79l3D5CfHt8j7HOZ8zdPM76+FOeExacCfLCD8tSEL+KjYBeRIRubFMPYpBgumpzhO+Z2W/YdP8GO6iZf2O+obuS1T2rwtuYT7ZzBhLGtTMkqY0pWGucuXsWUEx+SPO0SnP4GtObVSwj13PHNHw8++CD3338/TqeTxMREHnroIaZPnx7QMinYRSQoHA5DYUYChRkJXDEz23e8taOLPbXN7KxuYldtE7uqm9i0t44XPjzkPWMWMeuPM3HsOqZkJXp2zstMZEpWEnnp8Tj7DthTE76MIF/72tf4zne+A8ALL7zAbbfdxp///OeA3kPBLiLDKtblZGZOCjNzejfnN7d1srumid01zeyqaWJ3bTMbK4/z3JZDvnNiohxMHJvYK/BnpZaQ7YzGDKQJX033Mkh33303K1euBODmm2/muuuuo7OzkyVLllBWVsaUKVN47LHHiI+PZ8WKFbzwwgtERUVx+eWXc+edd/q2agVoaWkJ2FatPSnYRSQsJMZEUZyfRnF+Wq/jTa0d7KltPmPgnxf1U65M2kNd5nm4dqcxqaGayVmJFKTHE+V09L6Rmu5HlRNlZZzY+AHxC84lfoDLvva1adMmVq1axfvvv4+1lvPOO4+LL76YnTt38vDDD7Nw4UKWL1/OAw88wPLly1m9ejU7duzAGEN9fb3vOvfffz9333037e3tvP7660P9iJ8SdsFujPk18DdAO1AOLLPW1p/5XSISqZJiXacN/N21zeypaWZXTRGv115AeU0zB3fu8p3jchqKMhKYnJnEpMxEJmclsuDAWsZ2tWP8bbpX7X7EOlFWxr5ly7Ht7ZjoaPJXrRxSuK9fv57rr7+ehIQEAL74xS+ybt068vLyWLhwIQA33XQT9957Lz/60Y+IjY3l5ptv5uqrr+aaa67xXed73/se3/ve93jyySf5xS9+waOPPjq0D9pH2AU78Ffgp9baTmPMfwA/BYZ/Gx8RCWtJsS7m5acxr0/gt7R1Un7EU8PfXdvMntomth9q4OVth7EW5pk4noh24jIWN1H8oTqf+E0HmJSZyMTMRBJ7TstT7X5EO7HxA2x7O7jd2I4OT819CMF+ur1V+janG2OIiopi48aNvPbaazz99NPcd999n6qdf+UrX+HWW28ddHlOJ+yC3Vr7lx6/bgBuCFVZRGTkSYiJYnZuKrNzU3sdb+3oouJIC3uOFPPcnlziDm7g9dYpvLIlgY5NH/rOG5cS69s+99qmNczpbMPg1sC8ESh+wbmY6GhsRwfG5SJ+wblDut7ixYtZunQpK1aswFrL6tWrefzxx/nhD3/Ie++9xwUXXMBTTz3FRRddRHNzMydOnOCqq67i/PPPZ9KkSQDs3r2byZMnA/DSSy/5ngdS2AV7H8uBP4S6ECIy8sW6nJ7178cnw5wvA1/mOqCjy83eYyfYU9tM+ZFm9tR6fv7wwX4+7EjniegoXHTSiZP/vTkVd+1Hvtr9pLGJ5KTG4Tj4gZrrw1B8cTH5q1YGrI993rx5LF26lAULPP/GN998M2lpaUybNo1HH32Ub3/720yePJlbb72VhoYGrr32WlpbW7HWcs899wBw3333sXbtWlwuF2lpaQFvhocQbdtqjFkLZPfz0s+stc97z/kZUAJ80fZTSGPMLcAtAPn5+fP37t0bxBKLyGjjdlsON7ZSs/1tuirWUcoM3mgpZM+RZo63tPvOO9+1h0ecv8BFJ26Hi/cXrSJj2iIKxyQQ6zrLTnkyINq2NYy3bbXWXnqm140xS4BrgM/1F+reazwEPASe/dgDXkgRGdUcDkNOahw5Cz8PCz/PuUB3b+jxlnZf7T7rw7dxHezEiRvb1cG7a5/ngVfBGMhLi/fU7scmMHFsovd5Yu/d8kQCLOya4o0xV+AZLHextfZEqMsjItJXekI06QnpnFuYDuO+BI8+Cl3tOKOi+fJ1X2WaY2qvpv139hxlRtcOznd8wv9xT6MqfiYTxyYwISORiZkJvk14ctPiPj09T2SAwi7YgfuAGOCv3pGGG6y13wltkURETqPH/vSmcBFFeQso6nNK1973MY/fAZ3tuB0u/rPwN7xxIpHXdtTwh9JTzfrRTgeFGfG+oO8O/Qlj+4zWFzmDsPumWGsnhboMIiIDkrfgjIPmnPvWQ1cH4MZhO7m18BC3LvoqAPUn2ik/0kL5EU8Nv7y2hZ3VTfzl4xrfFrkA2cmxp4I+I4GJ3mb9cSmxQVm9LFxZa0fV54XTT7M7nbALdhGRiNPfNrVeqfHRzC+IZn5B7/n47Z1u9h1vYU9tj9A/0sLqzQdpauv0nRcf7aQoo7tmf+pxQkYicdGRNXgvNjaWY8eOMWbMmFET7tZajh07RmxsrN/vCcmo+EArKSmxpaWloS6GiMjpBWgFO2stR5raqP74bbrK17HZMYO3Tk6g4kgzB+tP0vN/6Tmpcb6wnzg2gQneJv6s5JgRGYwdHR0cOHCA1tbWUBdlWMXGxpKbm4vL5ep1/HSj4hXsIiIjzWlWxGvt6KLyqKeGX3Gk9+OJ9i7f2xOinRR11+4zTtX0izISIq6WH8nCarqbiIgMwWm2qo11OZk2Lplp45J7nW6tpaaxzRv0zb4+/dKqOp7vsZkO9K7ldzfpTxibMOr68kcyBbuIyEhzhj77/hhjyE6JJTslloWTMnq9drLdU8uvOOqp3XcH/x9L99PSo5Yf53J6gt47eK9n+MdHK0rCiZriRURGoiDvOmetpbapzdec72vaP9rMgbreffnjUmJ71e67w398ahxOh2r5waI+dhERCYjWji72HjtBxZFmKo62UF7bTPlRT22/qfXUiP3oKAdFYxK8Yd87+FPiXGe4g/hDfewiItLbIGv9sS4nU7OTmJqd1Ou4tZZjLe2+Jv0Kb9jvrG7irx/X0NljXn5GYnSPoD8V+nnp8bi0+t6QKNhFREajIOw1b4whIzGGjMQYFhSl93qto8vNvuMnToX+EU+//l8/ruFYj011ohyG/PT4Pv35nhH7GYnRGsDnBwW7iMhodJqR9YPiR83f5XT4lsqFrF6vNZzoODV47+ipPv23dx+lvdPtOy8pNupU2PcIfE3T603BLiIyGg1wZP1pBaDmnxLvojg/jeL83qvvdbkth+pP+pr0K460UHm0hfcrjrG67GCvc7un6RX1Cf2c1Dgco2wAn4JdRGQ06rF5zZBG1gey5t+H02HIS48nLz2ei6eM7fXaifZOKo96gr5nn/6fNh+kuceSuzFRDgp7DOAr6u7Xz0ggNT4yt89VsIuIjFZn2bzGL4Gq+Q9QfHQUM8anMGN8Sq/j1lqONLf5mvMrvU37/W2sk54QzQRvU353DX/i2ATyx8QTEzVym/Y13U1ERIYmyHPqA6V7AF+lty/fs/yu5w+Ao81tvvMcBnLT4k817fdYlCc7OXxW4NN0NxERCY5A1Px7CtIfCmcawNfY2nEq8I+0ePv1W3i/4jgnO3qvwFeUkeBZa9/72N28nxwbHnPzFewiIhI+gjANzx/JsS7m5KUyJy+113FrLdWNrVQeaaH8aIsv/LcdbOCVrYfp0bLvm5tflJHQq7afnx5PdNTwzc1XsIuISPgI4mC8wTDGMC4ljnEpcVzYZ539ts4u9h8/QfmR7kF8nub913bU8IfSU3PznQ5DXlocz956IWMSY4JeZgW7iIiEjxANxhuMmCgnkzKTmJSZ9KnXGk50UHnsVNhXHTtB2jCNwlewi4hI+AjUNLwQD+hLiXcxNz6VuX2a9oeDgl1ERMLLUAfjhaifPlxopX0REYks/fXTjyIKdhERiSzd/fTGGfb99MGgpngREYksQ+mnHyGL7ZyJgl1ERCLPYPrpI6RvXk3xIiIiEDF98wp2ERERiJi+eTXFi4iIQODm0IeYgl1ERKRboDe0CQE1xYuIiATT/o2w7i7P4zBQjV1ERCRYQjDSXjV2ERGRYAnBSHsFu4iISLCEYKS9muJFRESCJQQj7RXsIiIiwTTMI+3VFC8iIhJBFOwiIiIRRMEuIiISQfzuYzfGpAHjgZNAlbXWHbRSiYiIyKCcMdiNMSnA94CvAtHAESAWyDLGbAAesNa+EfRSioiIiF/OVmP/b+AxYJG1tr7nC8aY+cA3jDETrLUPB6uAIiIi4r8zBru19rIzvLYJ2BTwEomIiMigDaSPfTZQ2PM91to/BaFMIiIiMkh+BbsxZiUwG9gOdA+as4CCXUREJIz4W2M/31o7Pagl6cMY84/Ar4Gx1tqjw3lvERGRkcrfeezvGWOGLdiNMXnAZcC+4bqniIhIJPC3xv4onnCvBtoAA1hr7ewglese4MfA80G6voiISETyN9hXAt8AtnKqjz0ojDFfAA5aaz80xgTzViIiIhHH32DfZ619IVA3NcasBbL7eelnwD8Bl/txjVuAWwDy8/MDVTQREZERzVhrz36SMQ8AqcAaPE3xQOCnuxljZgGvASe8h3KBQ8ACa2316d5XUlJiS0tLA1kUERGRsGaM2WStLel73N8aexyeQO9Zkw74dDdr7VYgs/t3Y0wVUKJR8SIiIv7xK9ittcuCXRAREREZujNOdzPG/LMxJv0Mr3/WGHNN4IvlYa0tVG1dRETEf2ersW8F1hhjWoHNnNrdbTIwF1gL/J+gllBERET8drZNYJ4HnjfGTAYWAuOARuC/gFustSeDX0QRERHxl7997LuB3UEui4iIiAyRv5vATAH+kU/v7vbZ4BRLREREBsPf6W5/BB4Efg90Ba84IiIiMhT+Bnuntfa3QS2JiIiIDJm/u7utMcZ81xgzzhiT3v0T1JKJiIjIgPlbY1/iffyfPY5ZYEJgiyMiIiJD4e+o+KJgF0RERESGzt9R8euAt4F1wDvW2qaglkpEREQGxd8+9iXATuBLwLvGmFJjzD3BK5aIiIgMhr9N8RXGmJNAu/fnEmBaMAsmIiIiA+dXjd0YUw48B2QBDwMzrbVXBLNgIiIiMnD+NsXfC+wDvgr8AFhijJkYtFKJiIjIoPgV7Nba31hrvwxcCmwCbgd2BbFcIiIiMgj+joq/C7gISAQ2AP+CZ4S8iIiIhBF/F6jZAPzKWlsTzMKIiIjI0Pg7Kv6PxpgvGGMWew+9Za1dE8RyiYiIyCD4Oyr+34EfAh97f37gPSYiIiJhxN+m+KuBudZaN4Ax5lGgDPhpsAomIiIiA+fvdDeA1B7PUwJdEBERERk6f2vs/w6UGd+OfLMAABeNSURBVGPeAAywGNXWRUREwo6/g+eeMsa8CZyLJ9h/Yq2tDmbBREREZODOGOzGmHl9Dh3wPo43xoy31m4OTrFERERkMM5WY7/L+xgLlAAf4qmxzwbex7NojYiIiISJMw6es9ZeYq29BNgLzLPWllhr5wPFwJ7hKKCIiIj4z99R8edYa7d2/2Kt3QbMDU6RREREZLD8HRX/iTHm98B/ARa4CfgkaKUSERGRQfE32JcBt+JZfQ7gbeC3QSmRiIiIDJq/wX4h8Dtr7T3BLIyIiIgMjb/BvhR40BhzDM92reuA9dbaumAVTERERAbO3wVqvglgjBkP3ADcD4z39/0iIiIyPPwKZmPMTcAiYBZwFLgPT61dREREwoi/Ne7/C5QDDwJvWGurglYiERERGTS/5rFbazOA5XhWoPulMWajMebxoJZMREREBsyvYDfGJAP5QAFQiGfbVnfwiiUiIiKD4W9T/PoeP/dZaw+c5XwREREJAX9Hxc8OdkFERERk6PwdFT8W+DEwA08/OwDW2s8GqVwiIiIyCP5uAvMEsAMoAv4VqAI+CFKZREREZJD8DfYx1tqHgQ5r7VvW2uXA+UEsl4iIiAyCv4PnOryPh40xVwOHgNzgFElEREQGy99g/4UxJgX4H8D/A5KBfwhaqURERGRQzhrsxhgnMNla+yLQAFwS7EIZY74P/D3QCbxkrf1xsO8pIiISCc4a7NbaLmPMF4Bh2bLVGHMJcC0w21rbZozJHI77ioiIRAJ/m+LfNcbcB/wBaOk+aK3dHIQy3QrcYa1t896jNgj3EBERiUj+BvuF3sd/9T4awALBmMc+BVhkjPkl0Ar8o7VWU+tERET8cMZgN8bc5n36Ip4gNz1etoO9qTFmLZDdz0s/85YpDc90unOBZ4wxE6y1ve5njLkFuAUgPz9/sEURERGJKGersSd5H6fiCdnn8YT73wBvD/am1tpLT/eaMeZW4E/eIN9ojHEDGcCRPtd4CHgIoKSkZNB/ZIiIiESSMwa7tfZfAYwxfwHmWWubvL/fDvwxSGV6Dk8T/5vGmClANHA0SPcSERGJKP72secD7T1+b8ezfWswrARWGmO2ee+zpG8zvIiIiPTP32B/HE+z+Go8fevXA48Go0DW2nbgpmBcW0REJNL5u23rL40xrwCLvIeWWWvLglcsERERGQx/a+zdc9aDMW9dREREAsTf3d1ERERkBFCwi4iIRBAFu4iISARRsIuIiEQQBbuIiEgEUbCLiIhEEAW7iIhIBFGwi4iIRBAFu4iISARRsIuIiEQQBbuIiEgEUbCLiIhEEAW7iIhIBFGwi4iIRBAFu4iISARRsIuIiEQQBbuIiEgEUbCLiIhEEAW7iIhIBFGwi4iIRBAFu4iISARRsIuIiEQQBbuIiEgEUbCLiIhEEAW7SA+bazZz/5b7Od56PNRFEREZFAW7SA8fVH/Agx8+yOX/fTm/2PAL9jfuD3WRREQGxFhrQ12GISspKbGlpaWhLoZEiIr6Ch7Z/ggvVrxIl+3ic/mfY/nM5czMmBnqoomI+BhjNllrSz51XMEu0r/aE7U88ckT/HHnH2nqaKIkq4RlM5exKGcRxphQF09ERjkFu8ggNbc38+zuZ3n848epOVHDpNRJLJ2xlKuKrsLldIW6eCIySinYRYaoo6uDP1f9mZXbVrKnfg+Z8Zl8Y9o3uGHKDSRGJ4a6eCIyyijYRQLEWss7h95h1bZVbKzeSKIrkS9P/TI3TbuJzPjMUBdPREYJBbtIEGw/up1V21fx171/xWEcXF10NUtnLGVS2qRQF01EIpyCXcQPJ8rKOLHxA+IXnEt8cbHf79vftJ/Htj/Gc3ueo7WrlcW5i1k2Yxnzs+ZroJ2IBIWCXeQsTpSVsW/Zcmx7OyY6mvxVKwcU7gB1rXU8vfNpnvrkKera6piVMYtlM5fx2bzP4nQ4g1RyERmNThfsWqBGxOvExg+w7e3gdmM7Ojix8YMBXyMtNo1b59zKqze8yj+f98/UtdZx25u38YXnvsAzO5+htbM1CCUXETlFwS7iFb/gXEx0NDidGJeL+AXnDvpacVFx3HjOjbx4/YvcdfFdJEcn828b/o3PP/t5HvzwQepb6wNYchGRU9QUL9LDYPvYz8ZaS2lNKau2rWLdwXXERcVx/aTr+cb0b5CblBuw+4jI6KE+dpEwsaduD49sf4SXKl/Cbd18vuDzLJ25lOljpvv1/mD98SEiI4uCXSTM1LTU8MQnT/DMrmdo6WjhvHHnsWzGMi4cf+FpR9IHYoCfiEQGDZ4TCTNZCVncVnIbf73hr9w2/zYq6yv5ztrvcMOaG1hTvoYOd8en3hOIAX4iEtkU7CIhlhSdxLKZy/jzl/7Mvy38N7rcXfzT+n/iqj9dxWPbH6Olo8V3biAH+IlIZAq7pnhjzFzgQSAW6AS+a63deKb3qCleIonbull/cD2rtq2itKaUpOgkbpx6I1+f9nUy4jLUxy4iwAjqYzfG/AW4x1r7ijHmKuDH1trPnOk9CnaJVFuPbGXV9lWs3buWKEcUX5j4Bb4545tMSJkQ6qKJSIidLtijQlGYs7BAsvd5CnAohGURCalZY2dx92fuZl/jPh772LNk7bO7n2Va+jTmZc1jXuY85mXNIyMuI9RFFZEwEY419mnAq4DBMwbgQmvt3jO9RzV2GS2OnTzGn3b/ifcPv89HRz/iZOdJAPKS8nwhPy9zHgXJBVqjXiTChVVTvDFmLZDdz0s/Az4HvGWtfdYY87fALdbaS/u5xi3ALQD5+fnz9+49Y/aLRJwOdwc7ju1gc+1mNtdspqy2jLq2OgDSY9Mpziz2hf056ecQ5QjHBjoRGaywCvYzMcY0AKnWWms8VY4Ga23ymd6jGruIZ3W7ysZKymrKfGF/oPkA4FnidvbY2b6gn50xm3hX/LCWT4P+RAJrJPWxHwIuBt4EPgvsDmlpREYIYwwTUiYwIWUCX5ryJQBqT9SyuXYzZTVllNWW8buPfofbunEaJ+ekn+Nrui/OLGZM3JiglU0L64gMn3AM9r8DfmOMiQJa8Ta3i8jAZcZnckXhFVxReAUATe1NfHTkIzbVbKKstoxndj7D4x8/DkBhcqGn+d4b9nlJeQHrp+9vYR0Fu0hwhF2wW2vXA/NDXQ6RSJQUncTCnIUszFkIQEdXB9uPbaestozNNZt5ff/rrN6zGoCMuIxe/fRT0qYMup++e2Ed29GhhXVEgizs+tgHQ33sIoHhtm4qGyp9NfrNNZs51OKZcRofFc+csXN8NfpZY2cRFxXn97XVxy4SWCNm8NxgKNhFgqe6pZqy2jJf2O+u243FEmWimD5muq/5vjizmLTYtJCUUX80yGikYBeRgGhsb2RL7RZfjX7r0a2+DWsmpEzo1U+fk5gT9Pn0Gpgno9VIGhUvImEsOTqZxbmLWZy7GIC2rjY+Pvaxr0b/l71/4dndzwKQGZdJcdapfvrJqZNxOpwBLY8G5on0pmAXkSGJccZQnFlMcaYnTN3WzZ76PZTVlLGpdhObazbzatWrgKeffnLaZCanTWZK2hQmp3qep8SkDPr+Gpgn0pua4kUk6A43H2ZT7SY+OvIRu+t2s6tuF43tjb7Xs+KzToW997EouQiX0+XX9dXHLqOR+thFJGxYa6k9Ucuuul3srvcE/e663VQ0VNDp7gQgykRRlFrkq9VPSZvClLQpZMVnaR18EdTHLiJhxBhDVkIWWQlZLMpd5Dve4e6gqqHKV6vfXb+bstoyXq582XdOUnTSp8J+UuokEqMTA1I21f5lpFONXUTCXmN7I3vq9vhq9t21/JaOFt854xPGMzF1IpNSJ/kei1KKBrQmvkbYy0iiGruIjFjJ0cmeKXRZ83zHrLUcbjncK+zL68vZcHiDb/qdwTA+cTyTUicxIXWCL/QnpEzod3GdwY6wVy1fwomCXURGJGM8oT0+cTyfyfuM73inu5P9Tfspry9nT/0e3+M7h97x9d8bDDmJOZ8K/PHz5wx4hL1q+RJuFOwiElGiHFEUpRRRlFLEpQWX+o53ujvZ17SvV+CX15ez/tD6XoG/cGkGCw7Fw7yZ7Erez8Sj0Wds0tc8egk3CnYRGRWiHFG+bW0vK7jMd7zD3cH+xv29avcvFFVQ1fgqnetf8p03LmEcE1I975+YMpGJqRM9ga959BJmFOwiMqq5HC5PYKdO6HW8w93BgaYDVNRXUN5QTkVDBRX1FWyq3kRrV6vvvIy4DC76zmRmH3ASXVJMQ3Y7RSdqGRs3VtPyJCQ0Kl5EZADc1s2h5kNUNFRQXn8q8MsbynuN0k9yJfm6BIpSijytBakTyEnMGfT2tyI9aYEaEZEgstZy5OQRX9BXNFRQ1VBFRUMFR04e8Z3ncrgoSC44FfYpEyhKKaIwpXBA2+B204j80UvT3UREgsgYQ2Z8JpnxmZw/7vxerzW2N1LZUEllQyUVDRVU1leyq24Xr+17Dbd1e97vnZpXmFLoC/zu0D/ddrgakS/9UbCLiARZcnQyc8bOYc7YOb2Ot3e1s7dxryfsu0O/ofJT/fhpMWmeGn7qBIqSi3yD+FwbN2pEvnyKgl1EJESindG+3e56cls3h1sO+5r0u2v7a/eupb6t3nfezPpofuq0OAHrNGzNc5NTt4uC5AJinDHD/GkkXKiPXURkBDneetxXu6+or+Dkli0kbKtiQ1YTu3I9o/C7m/V7Dt4rSvY8psem9ztaX331I4/62EVEIkB6bDrpsenMz5rvObDA83Cy8yT7Gvf5aveVDZVUNlZSWl3aq1k/OTrZM1gvudAX+gX7Wun4+5+prz5CKNhFRCJAXFQcU9OnMjV9aq/jbuumpqXGF/Tdof/uoXd5vvx5AK57182NbW6cFrraW/nr6ntwJ17vC/6UmJRQfCQZJAW7iEgEcxgH4xLHMS5xHBfmXNjrtab2Jqoaqqge8xa89yDuzi46nYYn4j7kk3fLfOelx6b3quF3Px+fOF5z8sOQ+thFRKRXH3v0nFkcaj5EVWNVr6b9qsYqjrce973H5XCRl5TnC/vClEJf6KuWH3xaoEZERIasoa3BF/KVDZVUNVRR1VjFvqZ9vs104FQtvzClkKLkIl/o5yTl4HK4QvgJIoeCXUREAqpvLf9g80Ff0J+ulh9loshLzvtU6BclF5EamxrCTzPyaFS8iIgETH+r3hUUF1OQXMDFXNzr3Ia2Bqoaq3yh3/24/uB6OtwdvvNSY1J7Nel3B35eUh4up2r5/lKwi4jIgA1kH/qUmJR+V97rcndxqPkQlY2eJv3Kxkrat2wlYfufeWncSXZ75+U7jZOcxJxegd/dlz8mdox20etDwS4iIgMWiH3onQ4necl55CXnsTh3sacV4HfPYdvbuS7aRef//V9U5kX3quUf++BdGivbeSrfsDvXkOhKpDC5kIKUAgqTC5lyEMbvOkbWRZ8jreT8sxciAinYRURkwOKLi8lftTKgq9X1bgXoJHvnMWZ+5pZTr5eVse9flmPbwboc7Lr962wf10lVYxWbajax6+0XOf+pLjq7YO/v/4ufLM/Ezpzyqf787IRsHMYRsavtKdhFRGRQ4ouLAxqIZ2sF6Bn8ptNwUW0a111/KvgP//Y+6t2/BevGuA2Lj4zhxfYm1lSsoaWjxXdejDOGhXUZ3PzQfhxdFqKicP/m5xQsvIzk6OSAfZ5QUbCLiEhYOFsrwNmCP+X8hTT87mFsRwdOl4sv/u3/4qbiYqy1HD151NOk723WH/PHNzGdbhwWujo6eObpf+G5fbf3mqZXmFzoa+bPS8obMdP0NN1NRERGjLM1n/vbvO4b1d/RAVFOjv7H99mT6+zVn99zmp7TOMlNyvUEfXIBhSmFXFF4BUnRSUH5nP7QPHYREZEezvZHQENbA3sb97K3cS+VDZXsbdxLVWMVexv30tbVxls3vkV6bHoISu6hYBcREQkAt3VT3VLNuIRxIZ1qd7pgd4SiMCIiIiNV65YPiX7iRU5u2RLqovRLg+dERET81N+Ke+E2VU41dhERET/1t+JeuFGwi4iI+Kl7yh1O56BX3As2NcWLiIj4KRgr7gWagl1ERGQAAr3iXqCpKV5ERCSCKNhFREQiSEiC3RjzZWPMdmOM2xhT0ue1nxpj9hhjdhpjPh+K8omIiIxUoepj3wZ8Efhdz4PGmOnAV4AZwHhgrTFmirW2a/iLKCIiMvKEpMZurf3EWruzn5euBZ621rZZayuBPcCC4S2diIjIyBVufew5wP4evx/wHhMRERE/BK0p3hizFsju56WfWWufP93b+jnW7y41xphbgFsA8vPzB1VGERGRSBO0YLfWXjqItx0A8nr8ngscOs31HwIeAs/uboO4l4iISMQJtwVqXgCeNMbcjWfw3GRg49netGnTpqPGmL1DvHcK0DDEawxVsMoQqOsO9jqDeZ+/7wnUeRnAUT+uMxKF+rsdzPuPtO/2QM7351x/ztF3e2Te359rF/R71Fo77D/A9Xhq521ADfBqj9d+BpQDO4Erh7FMD4Xiv8VwlCFQ1x3sdQbzPn/fE6jzgNJQ//sH6yfU3+1g3n+kfbcHcr4/5/p5jr7bI/D+Q7l2SGrs1trVwOrTvPZL4JfDWyIA1oTgnn0FqwyBuu5grzOY9/n7nkCfF4lC/dmDef+R9t0eyPn+nBvqf9tQC/XnD8vvtvH+ZSAyqhljSq21JWc/U2Rk0Xd79Am36W4iofJQqAsgEiT6bo8yqrGLiIhEENXYRUREIoiCXUREJIIo2EVERCKIgl3kLIwx1xlj/tMY87wx5vJQl0ckEIwxE4wxDxtj/jvUZZHAUrBLRDPGrDTG1BpjtvU5foUxZqcxZo8xZsWZrmGtfc5a+3fAUuDGIBZXxC8B+l5XWGu/FdySSihoVLxENGPMYqAZeMxaO9N7zAnsAi7DswLiB8BXASfw730usdxaW+t9313AE9bazcNUfJF+Bfh7/d/W2huGq+wSfOG2VrxIQFlr3zbGFPY5vADYY62tADDGPA1ca639d+CavtcwxhjgDuAVhbqEg0B8ryVyqSleRqMcYH+P3w94j53O94FLgRuMMd8JZsFEhmBA32tjzBhjzINAsTHmp8EunAwf1dhlNDL9HDttn5S19l7g3uAVRyQgBvq9PgboD9UIpBq7jEYHgLwev+cCh0JUFpFA0fdaAAW7jE4fAJONMUXGmGjgK8ALIS6TyFDpey2Agl0inDHmKeA9YKox5oAx5lvW2k7g74FXgU+AZ6y120NZTpGB0PdazkTT3URERCKIauwiIiIRRMEuIiISQRTsIiIiEUTBLiIiEkEU7CIiIhFEwS4iIhJBFOwio5AxJtUY813v8/GB3JPbGPMjY8w3+zle2L3NqDFmljHmkUDdU0ROUbCLjE6pwHcBrLWHArVtpzEmClgOPHmm86y1W4FcY0x+IO4rIqdoExiR0ekOYKIxZguwG5hmrZ1pjFkKXIdnD++ZwF1ANPANoA24ylp73BgzEbgfGAucAP7OWrsD+Cyw2bsKGsaY+cBK7znr+5RhDZ5lT38VzA8qMtqoxi4yOq0Ayq21c4H/2ee1mcDX8Ozv/UvghLW2GM8Spt1N7A8B37fWzgf+EXjAe3whsKnHtVYBP7DWXtBPGUqBRQH4LCLSg2rsItLXG9baJqDJGNOAp2YNsBWYbYxJBC4E/miMb6fQGO/jODzrlGOMSQFSrbVveV97HLiyx31qgfFB+xQio5SCXUT6auvx3N3jdzee/2c4gHpvbb+vk0Cs97nhDPuBe887ObSiikhfaooXGZ2agKTBvNFa2whUGmO+DGA85nhf/gSY5D2vHmgwxlzkfe3rfS41Bdg2mDKIyOkp2EVGIWvtMeAd7/SzXw/iEl8HvmWM+RDYDlzrPf4KsLjHecuA+40x7/Hp2vklwEuDuLeInIG2bRWRgDLGrAZ+bK3dfYZzYoC3gIu6R9CLSGAo2EUkoIwxU4Esa+3bZzhnMpBjrX1z2AomMkoo2EVERCKI+thFREQiiIJdREQkgijYRUREIoiCXUREJIIo2EVERCKIgl1ERCSC/H8ReN0jEdbLiQAAAABJRU5ErkJggg==\n", + "image/png": 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", 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" ] diff --git a/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb b/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb index bc56217..fb2e3c1 100755 --- a/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb +++ b/pumpingtest_benchmarks/7_test_of_neveda_double-porosity.ipynb @@ -336,7 +336,7 @@ " tmin=1e-5,\n", " tmax=3,\n", ")\n", - "w1 = Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", + "w1 = ttim.Well(ml1, xw=0, yw=0, rw=0.11, rc=0, tsandQ=[0, 3093.12], layers=1)\n", "ml1.solve()" ] }, diff --git a/ttim/circinhom.py b/ttim/circinhom.py index 416e3fd..a946b9e 100644 --- a/ttim/circinhom.py +++ b/ttim/circinhom.py @@ -530,19 +530,19 @@ def layout(self): # ml = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10) -# w1 = Well(ml,0,2,.1,tsandQ=[(0,10)],layers=[1]) +# w1 = ttim.Well(ml,0,2,.1,tsandQ=[(0,10)],layers=[1]) # ls2 = ZeroHeadLineSinkString(ml,xy=[(-10,-2),(0,-4),(4,0)],layers=[1]) # ls1 = MscreenLineSinkDitchString(ml,xy=[(-10,0),(0,0),(10,10)],tsandQ=[(0.0,7.0)],res=0.0,wh='H',layers=[2],label=None) # ml.solve() # ml = ttim.ModelMaq([1,20,2],[25,20,18,10,8,0],c=[1000,2000],Saq=[0.1,1e-4,1e-4],Sll=[0,0],phreatictop=True,tmin=1e-6,tmax=10,M=30) -# w1 = Well(ml,0,0,.1,tsandQ=[(0,1000)],layers=[2]) +# w1 = ttim.Well(ml,0,0,.1,tsandQ=[(0,1000)],layers=[2]) # ls1 = ZeroMscreenLineSink(ml,10,-5,10,5,layers=[1,2,3],res=0.5,wh=1,vres=3,wv=1) # w2 = ZeroMscreenWell(ml,10,0,res=1.0,layers=[1,2,3],vres=1.0) -# w3 = Well(ml,0,-10,.1,tsandQ=[(0,700)],layers=[2]) +# w3 = ttim.Well(ml,0,-10,.1,tsandQ=[(0,700)],layers=[2]) # ml.solve() ##ml1 = ttim.ModelMaq([1,20,2],[25,20,18,10,8,0],c=[1000,2000],Saq=[1e-4,1e-4,1e-4],Sll=[0,0],tmin=0.1,tmax=10000,M=30) -##w1 = Well(ml1,0,0,.1,tsandQ=[(0,1000)],layers=[2],res=0.1) +##w1 = ttim.Well(ml1,0,0,.1,tsandQ=[(0,1000)],layers=[2],res=0.1) ##ml1.solve() # t = np.logspace(-1,3,100) # h0 = ml.head(50,0,t) @@ -552,7 +552,7 @@ def layout(self): ##y = [-500,-300,-200,-100,-50,0,50,100,200,300,500] ##x = 50 * np.ones(len(y)) ##ls = ZeroHeadLineSinkString(ml,xy=zip(x,y),layers=[1]) -##w = Well(ml,0,0,.1,tsandQ=[(0,1000),(100,0)],layers=[2]) +##w = ttim.Well(ml,0,0,.1,tsandQ=[(0,1000),(100,0)],layers=[2]) ##ml.solve() @@ -614,8 +614,8 @@ def layout(self): ##lss = HeadLineSinkString(ml,[(-10,5),(-5,5),(0,5)],tsandh=[(0,0.02),(3,0.01)],res=0.0,layers=[1,2]) ##ls3 = ZeroMscreenLineSink(ml,-10,5,0,5,res=1.0,layers=[1,2]) # ml = ttim.ModelMaq(kaq=[10,5],z=[4,2,1,0],c=[100],Saq=[1e-3,1e-4],Sll=[1e-6],tmin=.1,tmax=10,M=15) -# w1 = Well(ml,0,0,.1,tsandQ=[(0,5),(1,2)],res=1.0,layers=[1,2]) -# w2 = Well(ml,100,0,.1,tsandQ=[(0,3),(2,7)],layers=[1]) +# w1 = ttim.Well(ml,0,0,.1,tsandQ=[(0,5),(1,2)],res=1.0,layers=[1,2]) +# w2 = ttim.Well(ml,100,0,.1,tsandQ=[(0,3),(2,7)],layers=[1]) ##w3 = MscreenWell(ml,0,100,.1,tsandQ=[(0,2),(3,1)],res=2.0,layers=[1,2]) # w3 = ZeroMscreenWell(ml,0,100,.1,res=2.0,layers=[1,2]) ##w3 = ZeroHeadWell(ml,0,100,.1,res=1.0,layers=[1,2])