Merge pull request #22 from luca-fiorito-11/sampling_using_njoy

Sampling using njoy

.travis.yml

@@ -1,12 +1,19 @@
 language: python
 
+dist: xenial
+
 git:
   depth: 3
 
 addons:
   apt:
     packages:
       - gfortran
+      - cmake
+      - git
+
+env:
+  - NJOY=${TRAVIS_BUILD_DIR}/NJOY2016/build/njoy
 
 python:
   - "3.6"
@@ -15,25 +22,23 @@ branches:
   except: gh-pages
 
 before_install:
+  - git clone https://github.com/njoy/NJOY2016.git && (cd NJOY2016 && mkdir build && cd build && cmake .. && make) # install NJOY
+  - rm -rf NJOY2016/tests
   - pip install sphinx sphinx_rtd_theme codecov numpydoc
 
 install:
   - python setup.py install
 
 script:
   - pytest
-  - (git clone https://gist.github.com/luca-fiorito-11/ad352e80b01f81fe12599c079f7cb9d7 && cd ad352e80b01f81fe12599c079f7cb9d7 && python  sandy_get_eigenvalues.py ../sandy/data/U5/u235.endf)
+#  - (git clone https://gist.github.com/luca-fiorito-11/ad352e80b01f81fe12599c079f7cb9d7 && cd ad352e80b01f81fe12599c079f7cb9d7 && python  sandy_get_eigenvalues.py ../sandy/data/U5/u235.endf)
 
 after_success:
   - codecov --file coverage/lcov.info --disable search
 
 # Set up sphinx configuration file and produce SANDY documentation in html.
 before_deploy:
-  - sphinx-quickstart --sep --dot _ --language en --suffix .rst --master index --makefile --batchfile --use-make-mode --author "Luca Fiorito" --project "SANDY API" -v 1 --release 0 --ext-autodoc --ext-doctest --ext-githubpages --ext-mathjax --extensions numpydoc api_docs
-  - sed -i '13i\   sandy' api_docs/source/index.rst 
-  - sed -i "s/alabaster/sphinx_rtd_theme/" api_docs/source/conf.py
-  - sphinx-apidoc --separate --force --module-first -o api_docs/source sandy
-  - cd api_docs && make html >/dev/null 2>&1
+  - bash make_docs.sh html
 
 # Deploy SANDY docs to gihub pages
 deploy:

make_docs.sh

@@ -0,0 +1,14 @@
+set -e
+
+# Set up docs folder
+sphinx-quickstart --sep --dot _ --language en --suffix .rst --master index --makefile --batchfile --use-make-mode --author "Luca Fiorito" --project "SANDY API" -v 1 --release 0 --ext-autodoc --ext-doctest --ext-githubpages --ext-mathjax --extensions numpydoc api_docs
+
+# Change configuration file
+sed -i '13i\   sandy' api_docs/source/index.rst
+sed -i "s/alabaster/sphinx_rtd_theme/" api_docs/source/conf.py
+
+# Create rst files
+sphinx-apidoc --separate --force --module-first -o api_docs/source sandy
+
+# Run make
+[[ -n $1 ]] && (cd api_docs && make $1) >/dev/null 2>&1

pytest.ini

@@ -1,7 +1,7 @@
 [pytest]
 python_files =
     tests/*.py
-addopts = -s  -v --basetemp=sandy_tests -m "not slow and not plot and not njoy_exe"
+addopts = -s  -v --basetemp=sandy_tests -m "not slow and not plot"
 filterwarnings =
     ignore::UserWarning
     ignore::DeprecationWarning
@@ -26,3 +26,5 @@ markers =
     errorr:
     sampling:
     slow:
+    njoy:
+    njoy_exe:

sandy/__init__.py

@@ -30,4 +30,6 @@ def filter(self, record):
 logging.basicConfig(format=FORMAT)
 logging.getLogger().setLevel(logging.INFO)
 logging.getLogger().addHandler(ShutdownHandler(level=40))
-#logging.getLogger().addFilter(DuplicateFilter())
+#logging.getLogger().addFilter(DuplicateFilter())
+
+__version__ = '0.9.0'

sandy/formats/__init__.py

@@ -1,5 +1,5 @@
 from .endf6 import *
-from .errorr import *
+# from .errorr import *
 from .groupr import *
 from .utils import *
 from .records import *
@@ -33,7 +33,10 @@ def get_file_format(file):
     elif flag == -1:
         ftype = "gendf"
     else:
-        ftype = "endf6"
+        if C.L1 == 2:
+            ftype = "pendf"
+        else:
+            ftype = "endf6"
     return ftype
 
 
@@ -51,7 +54,7 @@ def read_formatted_file(file, listmat=None, listmf=None, listmt=None):
         return Errorr.from_file(file).filter_by(listmat=listmat, listmf=listmf, listmt=listmt)
     elif ftype is "gendf":
         return Gendf.from_file(file).filter_by(listmat=listmat, listmf=listmf, listmt=listmt)
-    elif ftype is "endf6":
+    elif ftype is "endf6" or ftype is "pendf":
         return Endf6.from_file(file).filter_by(listmat=listmat, listmf=listmf, listmt=listmt)
     else:
         raise SandyError("file '{}' not in a known format".format(file))

sandy/formats/endf6.py

@@ -13,6 +13,7 @@
 import numpy as np
 import pandas as pd
 
+from sandy.formats.records import read_cont
 from sandy.formats import (mf1,
         mf3,
         mf4,
@@ -74,6 +75,11 @@ class _BaseFile(pd.DataFrame):
 
         - TEXT : (`string`) MAT/MF/MT section reported as a single string
     
+    Attributes
+    ----------
+    labels : `list` of `str`
+        index labels MAT, MT and MT
+
     Methods
     -------
     add_sections
@@ -86,6 +92,13 @@ class _BaseFile(pd.DataFrame):
         Create dataframe by reading a endf6 file
     from_text
         Create dataframe from endf6 text in string
+    
+    Raises
+    ------
+    `SandyError`
+        if the tape is empty
+    `SandyError`
+        if the same combination MAT/MF/MT is found more than once
     """
 
     labels = ['MAT', 'MF', 'MT']
@@ -175,7 +188,7 @@ def delete_sections(self, *tuples):
         ----------
         tuples : sequence of `tuple`
             each tuple should have the format (MAT, MF, MT)
-            To delete, say a given MF, independentently from the MAT and MT, assign `None` 
+            To delete, say, a given MF independentently from the MAT and MT, assign `None` 
             to the MAT and MT position in the tuple.
 
         Returns
@@ -209,11 +222,11 @@ def filter_by(self, listmat=None, listmf=None, listmt=None):
         Parameters
         ----------
         listmat : `list` or `None`
-            list of requested MAT values
+            list of requested MAT values (default is `None`: use all MAT)
         listmf : `list` or `None`
-            list of requested MF values
+            list of requested MF values (default is `None`: use all MF)
         listmt : `list` or `None`
-            list of requested MT values
+            list of requested MT values (default is `None`: use all MT)
         
         Returns
         -------
@@ -240,6 +253,32 @@ def mf(self):
     @property
     def mt(self):
         return sorted(self.index.get_level_values("MT").unique())
+
+    def get_file_format(self):
+        """Determine ENDF-6 format type by reading flags "NLIB" and "LRP" of first MAT in file:
+            
+            * `NLIB = -11 | NLIB = -12` : errorr
+            * `NLIB = -1` : gendf
+            * `LRP = 2` : pendf
+            * `LRP != 2` : endf6
+        
+        Returns
+        -------
+        `str`
+            type of ENDF-6 format
+        """
+        lines = self.TEXT.loc[self.mat[0], 1, 451].splitlines()
+        C, i = read_cont(lines, 0)
+        if C.N1 == -11 or C.N1 == -12:
+            ftype = "errorr"
+        elif C.N1 == -1:
+            ftype = "gendf"
+        else:
+            if C.L1 == 2:
+                ftype = "pendf"
+            else:
+                ftype = "endf6"
+        return ftype
 
 
 
@@ -260,6 +299,19 @@ class Endf6(_BaseFile):
     -------
     """
 
+    def get_nsub(self):
+        """Determine ENDF-6 sub-library type by reading flag "NSUB" of first MAT in file:
+            
+            * `NSUB = 10` : Incident-Neutron Data
+            * `NSUB = 11` : Neutron-Induced Fission Product Yields
+        
+        Returns
+        -------
+        `int`
+            NSUB value
+        """
+        return self.read_section(self.mat[0], 1, 451)["NSUB"]
+
     def read_section(self, mat, mf, mt):
         """Parse MAT/MF/MT section.
         """

sandy/formats/mf1.py

@@ -33,7 +33,7 @@ def read_errorr(text):
     out = {"MAT" : MAT, "MF" : MF, "MT" : MT}
     i = 0
     C, i = read_cont(str_list, i)
-    out.update({"ZA" : C.C1, "AWR" : C.C2, "ERRFLAG" :C.N1})
+    out.update({"ZA" : C.C1, "AWR" : C.C2, "LRP" :C.N1})
     L, i = read_list(str_list, i)
     out.update({"EG" : L.B})
     return out
@@ -44,7 +44,7 @@ def read_groupr(text):
     out = {"MAT" : MAT, "MF" : MF, "MT" : MT}
     i = 0
     C, i = read_cont(str_list, i)
-    out.update({"ZA" : C.C1, "AWR" : C.C2, "NZ" : C.L2, "GROUPRFLAG" : C.N1, "NTW" : C.N2})
+    out.update({"ZA" : C.C1, "AWR" : C.C2, "NZ" : C.L2, "LRP" : C.N1, "NTW" : C.N2})
     L, i = read_list(str_list, i)
     out.update({"TEMPIN" : L.C1, "NGN" : L.L1, "NGG" : L.L2})
     out["TITLE"] = L.B[:out["NTW"]]; del L.B[:out["NTW"]]
@@ -77,7 +77,7 @@ def read_info(text):
         TEXT.append(T)
     out.update({ "TEXT" : TEXT })
     # This part is not given in PENDF files
-    if out["LRP"] != 2:
+    if out["LRP"] != 2 and len(TEXT) > 0:
 #        groups = TEXT[0][:11].split("-")
 #        out["Z"] = int(groups[0])
 #        out["SYM"] = groups[1].strip()

sandy/formats/utils.py

@@ -619,10 +619,6 @@ def perturb(self, pert, method=2, **kwargs):
 
 
 
-###############################################################################
-# Nuclear Data Covariance Objects (NDCO)
-###############################################################################
-
 class BaseCov(pd.DataFrame):
     """Base covariance class inheriting from `pandas.DataFrame`.
     Must be used as superclass by all other Nuclear Data Covariance Objects.
@@ -667,7 +663,7 @@ def to_matrix(self):
             covariance matrix as a `numpy` array
         """
         return Cov(self.values)
-    
+
     def eig(self):
         """Extract eigenvalues in descending order.
         
@@ -781,8 +777,8 @@ def _from_list(cls, iterable):
         
         Returns
         -------
-        `XsCov`
-            global cross section/nubar covariance matrix
+        `XsCov` or `pandas.DataFrame`
+            global cross section/nubar covariance matrix (empty dataframe if no covariance matrix was found)
         """
         columns = ["KEYS_ROWS", "KEYS_COLS", "COV"]
         # Reindex the cross-reaction matrices
@@ -804,6 +800,9 @@ def _from_list(cls, iterable):
                 ey = covs[keys_cols,keys_cols].columns.values
                 covs[keys_rows,keys_cols] = cov.change_grid(ex, ey)
         covs.dropna(inplace=True)
+        if covs.empty:
+            logging.warn("covariance matrix is empty")
+            return pd.DataFrame()
         # Create index for global matrix
         rows_levels = covs.index.levels[0]
         indexlist = [(*keys,e) for keys in rows_levels for e in covs[(keys,keys)].index.values]
@@ -816,7 +815,6 @@ def _from_list(cls, iterable):
             matrix[ix.start:ix.stop,ix1.start:ix1.stop] = cov
             if keys_rows != keys_cols:
                 matrix[ix1.start:ix1.stop,ix.start:ix.stop] = cov.T
-#        pdb.set_trace()
         return cls(matrix, index=index, columns=index)
 
 
@@ -867,11 +865,11 @@ def __init__(self, *args, **kwargs):
         self.index.names = self.labels
         self.columns.names = self.labels
 
-    def get_samples(self, nsmp, eig=0):
+    def get_samples(self, nsmp, eig=0, seed=None):
         cov = self.to_matrix()
-        frame = pd.DataFrame(cov.sampling(nsmp) + 1, index=self.index, columns=range(1,nsmp+1))
+        frame = pd.DataFrame(cov.sampling(nsmp, seed=seed) + 1, index=self.index, columns=range(1,nsmp+1))
         frame.columns.name = 'SMP'
-        if eig > 0:
+        if eig > 0 and nsmp > 1:
             eigs = cov.eig()[0]
             idxs = np.abs(eigs).argsort()[::-1]
             dim = min(len(eigs), eig)
@@ -920,11 +918,11 @@ def _change_energy_grid(self, mat, mt, new_grid):
 
     @classmethod
     def from_endf6(cls, endf6):
-        """Extract cross section/nubar covariance from ```Endf6``` instance.
+        """Extract cross section/nubar covariance from `Endf6` instance.
         
         Parameters
         ----------
-        endf6 : `Endf6`
+        endf6 : `sandy.formats.endf6.Endf6`
             `Endf6` instance containing covariance sections
         
         Returns
@@ -985,35 +983,35 @@ def from_errorr(cls, errorr):
         
         Parameters
         ----------
-        errorr : `Errorr`
+        errorr : `sandy.formats.endf6.Errorr`
             `Errorr` instance containing covariance sections
         
         Returns
         -------
         `XsCov`
             global xs/nubar covariance matrix from ERRORR file
         """
-        mat = errorr.mat[0]
-        eg = errorr.read_section(mat,1,451)["EG"]
-        List = []
-        for (mat,mf,mt),text in errorr.TEXT.iteritems():
-            if mf not in [31, 33]:
-                continue
-            X = errorr.read_section(mat,mf,mt)
-            for mt1,y in X["RP"].items():
-                List.append([mat, X["MT"], mat, mt1, y])
-        frame = pd.DataFrame(List, columns=('MAT', 'MT','MAT1', 'MT1', 'COV'))
-        mi = [(mat,mt,e) for mat,mt in sorted(set(zip(frame.MAT, frame.MT))) for e in eg]
-        index = pd.MultiIndex.from_tuples(mi, names=("MAT", "MT", "E"))
-        # initialize union matrix
-        matrix = np.zeros((len(index),len(index)))
-        for i,row in frame.iterrows():
-            ix = index.get_loc((row.MAT,row.MT))
-            ix1 = index.get_loc((row.MAT1,row.MT1))
-            matrix[ix.start:ix.stop-1,ix1.start:ix1.stop-1] = row.COV
-        i_lower = np.tril_indices(len(index), -1)
-        matrix[i_lower] = matrix.T[i_lower]  # make the matrix symmetric
-        return XsCov(matrix, index=index, columns=index)
+        tape = errorr.filter_by(listmf=[31,33])
+        eg = errorr.energy_grid
+        data = []
+        # Loop MF/MT
+        logging.debug("found {} covariance sections".format(len(tape)))
+        for (mat,mf,mt), text in tape.TEXT.iteritems():
+            X = tape.read_section(mat, mf, mt)
+            # Loop subsections
+            logging.debug("reading section MAT={}/MF={}/MT={}".format(mat, mf, mt))
+            logging.debug("found {} subsections".format(len(X["RP"])))
+            for mt1,cov in X["RP"].items():
+                logging.debug("\treading subsection MAT1={}/MT1={}".format(mat, mt1))
+                # add zero row and column at the end of the matrix (this must be done for ERRORR covariance matrices)
+                cov = np.insert(cov, cov.shape[0], [0]*cov.shape[1], axis=0)
+                cov = np.insert(cov, cov.shape[1], [0]*cov.shape[0], axis=1)
+                cov = EnergyCov(cov, index=eg, columns=eg)
+                data.append([(mat, mt), (mat, mt1), cov])
+        if not data:
+            logging.warn("no xs covariance was found")
+            return pd.DataFrame()
+        return cls._from_list(data)