Revert "Feature 345 develop gha node20"

.github/workflows/unit_tests.yml

@@ -64,7 +64,6 @@ jobs:
         pytest test_utils.py
         pytest test_validate_mv_python.py
         pytest test_future_warnings.py
-        pytest test_sl1l2.py
         coverage run -m pytest test_agg_eclv.py test_agg_stats_and_boot.py test_agg_stats_with_groups.py test_calc_difficulty_index.py test_convert_lon_indices.py test_event_equalize.py test_event_equalize_against_values.py test_lon_360_to_180.py test_statistics.py test_tost_paired.py test_utils.py test_future_warnings.py
         coverage html
 

docs/Users_Guide/index.rst

@@ -64,7 +64,6 @@ National Center for Atmospheric Research (NCAR) is sponsored by NSF.
 
    installation
    vertical_interpolation
-   difficulty_index
    release-notes
 
 **Indices and tables**

docs/Users_Guide/release-notes.rst

@@ -9,35 +9,33 @@ METcalcpy Release Notes
 =======================
 
 
-METcalcpy Velsion 3.0.0-beta2 release notes (20231114)
-------------------------------------------------------
+METcalcpy Version 2.1.0 release notes (20230728)
+----------------------------------------------------
 
   .. dropdown:: New Functionality
 
-  .. dropdown:: Enhancements
-
-  .. dropdown:: Internal
-
-     * Change second person references to third (`#315 <https://github.com/dtcenter/METcalcpy/issues/315>`_)
-     * Enhanced documentation for Difficulty index (`#332 <https://github.com/dtcenter/METcalcpy/issues/332>`_)
-
-  .. dropdown:: Bugfixes
-
-     * Add missing reliability statistics (`#330 <https://github.com/dtcenter/METcalcpy/issues/330>`_)
-
-METcalcpy Version 3.0.0-beta1 release notes (20230915)
-------------------------------------------------------
-
-  .. dropdown:: New Functionality
+     * **Convert plot_tcmpr.R calculations to Python** (`#189 <https://github.com/dtcenter/METcalcpy/issues/189>`_)
 
   .. dropdown:: Enhancements
 
+     * **Upgrade to using Python 3.10.4** (`#270 <https://github.com/dtcenter/METcalcpy/issues/270>`_)
+     * Enhance the Release Notes by adding dropdown menus(`#292 <https://github.com/dtcenter/METcalcpy/issues/292>`_)
+     * **Updated Analysis tools to handle TC-RMW functionality** (`#308 <https://github.com/dtcenter/METcalcpy/issues/308>`_)
+
   .. dropdown:: Internal
 
-  .. dropdown:: Bugfixes
+     * Add 'License.txt' to the METcalcpy repo (`#279 <https://github.com/dtcenter/METcalcpy/issues/279>`_)
+     * Update cloud python to 3.10(`#170 <https://github.com/dtcenter/METcalcpy/issues/170>`_)
+     * Update cloud python to 3.8(`#169 <https://github.com/dtcenter/METcalcpy/issues/169>`_)
+     * Changed Project to Cycle assignment in github(`#301 <https://github.com/dtcenter/METcalcpy/issues/301>`_)
+     * Partial completion of logging for STIGS(`#46 <https://github.com/dtcenter/METplus-internal/issues/46>`_)
 
-     * Remove reset_index from various calculations (`#322 <https://github.com/dtcenter/METcalcpy/issues/322>`_)
+  .. dropdown:: Bugfixes
 
+     * **Remaining reference to ARIMA in utils.py compute_std_err_from_median_variance_inflation_factor()** (`#254 <https://github.com/dtcenter/METcalcpy/issues/254>`_)
+     * **Replace frame.append method to pandas.concat in agg_stat.py** (`#296 <https://github.com/dtcenter/METcalcpy/issues/296>`_)
+     * **Fixed Ratio Derived curve not generating** (`#302 <https://github.com/dtcenter/METcalcpy/issues/302>`_)
+     * **Fixed PSTD_ROC_AUC calculation** (`#306 <https://github.com/dtcenter/METcalcpy/issues/306>`_)
 
 METcalcpy Upgrade Instructions
 ==============================

docs/index.rst

@@ -1,14 +1,14 @@
-===========================
+###########################
 METcalcpy version |version|
-===========================
+###########################
 
 Developed by the `Developmental Testbed Center <https://dtcenter.org/>`_,
 Boulder, CO
 
 .. image:: _static/METplus_banner_photo_web.png
 
-History
--------
+**History**
+
 
 The Model Evaluation Tools (MET) were developed by the Developmental Testbed
 Center (DTC)  and released in January 2008. The goal of the tools was to
@@ -37,8 +37,7 @@ diagnostics capability for NOAA's UFS and a component of NCAR's SIMA
 modeling frameworks.  It is being actively developed by NCAR, ESRL, EMC
 and is open to community contributions.
 
-METplus Concept
----------------
+**METplus Concept**
 
 METplus is the overarching, or umbrella, repository and hence framework for
 the Unified Forecast System verification capability.  It is intended to be
@@ -77,8 +76,7 @@ was developed because CESM is comprised of a number of different components
 that are developed and managed independently. Each component also may have
 additional "external" dependencies that need to be maintained independently.
 
-Acronyms
---------
+**Acronyms**
 
 * **MET** - Model Evaluation Tools
 * **DTC** - Developmental Testbed Center
@@ -95,8 +93,7 @@ Acronyms
 * **GSD** - Global Systems Division
 
 
-Authors
--------
+**Authors**
 
 Many authors, listed below in alphabetical order, have contributed to the documentation of METplus.
 To cite this documentation in publications, please refer to the METcalcpy User's Guide :ref:`Citation Instructions<citations>`.
@@ -124,8 +121,7 @@ To cite this documentation in publications, please refer to the METcalcpy User's
 
 
 
-Indices
-=======
+**Indices**
 
 * :ref:`genindex`
 * :ref:`modindex`

docs/version

@@ -1 +1 @@
-__version__="3.0.0-beta3"
+__version__="2.1.0"

internal/scripts/installation/modulefiles/3.0.0_wcoss2 → internal/scripts/installation/modulefiles/2.1.0_wcoss2

@@ -3,7 +3,7 @@
 ##      METcalcpy
 ##
 proc ModulesHelp { } {
-  puts stderr "Sets up the paths and environment variables to use the METcalcpy-3.0.0.
+  puts stderr "Sets up the paths and environment variables to use the METcalcpy-2.1.0.
      *** For help see the official MET webpage at http://www.dtcenter.org/met/users ***"
 }
 
@@ -12,4 +12,4 @@ module load intel
 module use /apps/dev/modulefiles/
 module load ve/evs/2.0
 
-prepend-path PYTHONPATH /apps/sw_review/emc/METcalcpy/3.0.0
+prepend-path PYTHONPATH /apps/sw_review/emc/METcalcpy/2.1.0

metcalcpy/util/pstd_statistics.py

@@ -41,13 +41,16 @@ def calculate_pstd_brier(input_data, columns_names):
         t_table = df_pct_perm['n_i'].sum()
         o_bar_table = df_pct_perm['oy_i'].sum() / t_table
         o_bar = input_data[0, get_column_index_by_name(columns_names, 'o_bar')]
+
+        t_table.reset_index(inplace=True, drop=True)
+
         reliability = calc_reliability(t_table, df_pct_perm)
         resolution = calc_resolution(t_table, df_pct_perm, o_bar)
         uncertainty = calc_uncertainty(o_bar_table)
 
         brier = reliability - resolution + uncertainty
         result = round_half_up(brier, PRECISION)
-    except (TypeError, ZeroDivisionError, Warning, ValueError, AttributeError):
+    except (TypeError, ZeroDivisionError, Warning, ValueError):
         result = None
     warnings.filterwarnings('ignore')
     return result
@@ -72,6 +75,8 @@ def calculate_pstd_bss_smpl(input_data, columns_names):
         t_table = df_pct_perm['n_i'].sum()
         o_bar_table = df_pct_perm['oy_i'].sum() / t_table
         o_bar = input_data[0, get_column_index_by_name(columns_names, 'o_bar')]
+
+        t_table.reset_index(inplace=True, drop=True)
         reliability = calc_reliability(t_table, df_pct_perm)
         resolution = calc_resolution(t_table, df_pct_perm, o_bar)
         uncertainty = calc_uncertainty(o_bar_table)
@@ -156,6 +161,7 @@ def calculate_pstd_resolution(input_data, columns_names):
         df_pct_perm = _calc_common_stats(columns_names, input_data)
         o_bar = input_data[0, get_column_index_by_name(columns_names, 'o_bar')]
         t_table = df_pct_perm['n_i'].sum()
+        t_table.reset_index(inplace=True, drop=True)
         resolution = calc_resolution(t_table, df_pct_perm, o_bar)
         result = round_half_up(resolution, PRECISION)
     except (TypeError, ZeroDivisionError, Warning, ValueError):

metcalcpy/util/sl1l2_statistics.py

@@ -524,13 +524,10 @@ def calculate_bcmse(input_data, columns_names, aggregation=False):
     """
     warnings.filterwarnings('error')
     try:
-
         mse = calculate_mse(input_data, columns_names, aggregation)
         me = calculate_me(input_data, columns_names, aggregation)
         result = mse - me ** 2
         result = round_half_up(result, PRECISION)
-        if result < 0:
-            return 0.
     except (TypeError, Warning):
         result = None
     warnings.filterwarnings('ignore')

metcalcpy/util/utils.py

@@ -691,14 +691,7 @@ def equalize_axis_data(fix_vals_keys, fix_vals_permuted, params, input_data, axi
 
     for fcst_var, fcst_var_stats in fcst_var_val.items():
         series_data_for_ee = pd.DataFrame()
-        fcst_var_stats_current = fcst_var_stats
-        # if the data comes from agg_stat workflow it doesn't contain statistics values.
-        # They will be calculated later. In this case The code should not loop over all
-        # requested statistics but instead should do it only ones to avoid data multiplication
-        if 'stat_name' not in input_data.keys():
-            fcst_var_stats_current = [fcst_var_stats[0]]
-
-        for fcst_var_stat in fcst_var_stats_current:
+        for fcst_var_stat in fcst_var_stats:
             # for each series for the specified axis
             if len(params['series_val_' + axis]) == 0:
                 series_data_for_ee = input_data