Add regression testing

Author: trallard

04_Testing.ipynb

@@ -378,6 +378,17 @@
     "```"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TDD and tests first\n",
+    "\n",
+    "Have you ever heard about test-driven-development? It is a commonly used practice in which you write the tests for your scripts before or at the time of writing the actual code. \n",
+    "\n",
+    "Some of the advantages include early bug detection, better test coverage, and generally a higher code quality. It also helps you ensure you know what your code is doing at all times and makes it easier to add new features without major risk of breakdown. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

05_SharingAnalysis.ipynb

@@ -88,7 +88,7 @@
     "  - source activate testenv\n",
     "  \n",
     "script:\n",
-    "  - pytest\n",
+    "  - python3 -m pytest tests/\n",
     "  - pytest --nbval notebooks/00_explore-data.ipynb\n",
     "```"
    ]

solutions/tests/regression_test.py

@@ -0,0 +1,50 @@
+import unittest
+import os
+import sys
+
+class FixturesTest(unittest.TestCase):
+
+    #------------------- setup and teardown ---------------------------
+    @classmethod
+    def setUpClass(cls):
+        print('\nin set up - this takes about 80 secs')
+
+        from tests.write_fixtures import generate_fixture_hashes, unpickle_hash
+        cls.hash_dict_new = generate_fixture_hashes()
+        cls.hash_dict_original = unpickle_hash()
+        # define dictionary keys for individual files for checking
+        folder = 'temporary_test_fixtures'
+        cls.corrmat = folder + '/corrmat_file.txt'
+        cls.gm = folder + '/network-analysis/GlobalMeasures_corrmat_file_COST010.csv'
+        cls.lm = folder + '/network-analysis/NodalMeasures_corrmat_file_COST010.csv'
+        cls.rich = folder + '/network-analysis/RICH_CLUB_corrmat_file_COST010.csv'
+        
+    #--------------------------- Tests --------------------------------
+    # Each of these tests checks that ourly newly generated version of
+    # file_x matches the fixture version
+    
+    def test_corrmat_matches_fixture(self):
+        # test new correlation matrix against fixture
+        print('\ntesting new correlation matrix against fixture')
+        self.assertEqual(self.hash_dict_new[self.corrmat],
+                        self.hash_dict_original[self.corrmat])
+
+    def test_lm_against_fixture(self):
+        # test new local measures against fixture
+        print('\ntesting new nodal measures against fixture')
+        self.assertEqual(self.hash_dict_new[self.lm],
+                        self.hash_dict_original[self.lm])
+    
+    def test_gm_against_fixture(self):
+        # test new global measures against fixture
+        print('\ntesting new global measures against fixture')
+        self.assertEqual(self.hash_dict_new[self.gm],
+                        self.hash_dict_original[self.gm])
+    
+    def test_rich_against_fixture(self):
+        # test rich club against fixture
+        print('\ntesting rich club against fixture')
+        self.assertEqual(self.hash_dict_new[self.rich],
+                        self.hash_dict_original[self.rich])
+
+if __name__ == '__main__':

solutions/tests/write_fixtures.py

@@ -0,0 +1,124 @@
+#--------------------------- Write fixtures ---------------------------
+# To regression test our wrappers we need examples. This script 
+# generates files. We save these files once, and regression_test.py
+# re-generates these files to tests them for identicality with the
+# presaved examples (fixtures). If they are found not to be identical 
+# it throws up an error. 
+#
+# The point of this is to check that throughout the changes we make to 
+# our package the functionality of this script stays the same
+#------------------------------------------------------------------------
+
+import os
+import sys
+import networkx as nx
+             
+def recreate_correlation_matrix_fixture(folder):
+    ##### generate a correlation matrix in the given folder using #####
+    ##### the Whitaker_Vertes dataset                             ##### 
+    import BrainNetworksInPython.datasets.NSPN_WhitakerVertes_PNAS2016.data as data
+    centroids, regionalmeasures, names, covars, names_308_style = data._get_data()
+    from BrainNetworksInPython.wrappers.corrmat_from_regionalmeasures import corrmat_from_regionalmeasures
+    corrmat_path = os.getcwd()+folder+'/corrmat_file.txt'
+    corrmat_from_regionalmeasures(
+        regionalmeasures,
+        names, 
+        corrmat_path,
+        names_308_style=names_308_style)
+     
+def recreate_network_analysis_fixture(folder, corrmat_path):
+    ##### generate network analysis in the given folder using the #####
+    ##### data in example_data and the correlation matrix given   #####
+    ##### by corrmat_path                                         #####  
+    import BrainNetworksInPython.datasets.NSPN_WhitakerVertes_PNAS2016.data as data
+    centroids, regionalmeasures, names, covars, names_308_style = data._get_data()
+    # It is necessary to specify a random seed because 
+    # network_analysis_from_corrmat generates random graphs to 
+    # calculate global measures
+    import random
+    random.seed(2984)
+    from BrainNetworksInPython.wrappers.network_analysis_from_corrmat import network_analysis_from_corrmat
+    network_analysis_from_corrmat(corrmat_path,
+                              names,
+                              centroids,
+                              os.getcwd()+folder+'/network-analysis',
+                              cost=10,
+                              n_rand=10, # this is not a reasonable 
+                              # value for n, we generate only 10 random
+                              # graphs to save time
+                              names_308_style=names_308_style)
+    
+def write_fixtures(folder='/temporary_test_fixtures'): 
+    ## Run functions corrmat_from_regionalmeasures and               ##
+    ## network_analysis_from_corrmat to save corrmat in given folder ##
+    ##---------------------------------------------------------------##
+    # if the folder does not exist, create it
+    if not os.path.isdir(os.getcwd()+folder):
+        os.makedirs(os.getcwd()+folder)
+    # generate and save the correlation matrix
+    print("generating new correlation matrix") 
+    recreate_correlation_matrix_fixture(folder)
+    # generate and save the network analysis
+    print("generating new network analysis") 
+    corrmat_path = 'temporary_test_fixtures/corrmat_file.txt'
+    recreate_network_analysis_fixture(folder, corrmat_path)
+    
+def delete_fixtures(folder):
+        import shutil
+        print('\ndeleting temporary files')
+        shutil.rmtree(os.getcwd()+folder)
+    
+def hash_folder(folder='temporary_test_fixtures'):
+    hashes = {}
+    for path, directories, files in os.walk(folder):
+        for file in sorted(files):
+            hashes[os.path.join(path, file)] = hash_file(os.path.join(path, file))
+        for dir in sorted(directories):
+            hashes.update(hash_folder(os.path.join(path, dir)))
+        break 
+    return hashes
+    
+
+def hash_file(filename):
+    import hashlib
+    m = hashlib.sha256()
+    with open(filename, 'rb') as f:
+        while True:
+            b = f.read(2**10) 
+            if not b: break
+            m.update(b)
+    return m.hexdigest()
+
+def generate_fixture_hashes(folder='temporary_test_fixtures'):
+    # generate the fixtures
+    write_fixtures("/"+folder)
+    # calculate the hash
+    hash_dict = hash_folder(folder)
+    # delete the new files
+    delete_fixtures("/"+folder)
+    # return hash
+    return hash_dict
+
+def current_fixture_name():
+    # returns the fixture name appropriate the current versions 
+    # of python and networkx
+    return "tests/.fixture_hash"+str(sys.version_info[:2])+'networkx_version'+str(nx.__version__)
+    
+def pickle_hash(hash_dict):
+    import pickle
+    # when we save we record the python and networkx versions
+    with open(current_fixture_name(), 'wb') as f:
+        pickle.dump(hash_dict, f)
+    
+def unpickle_hash():
+    import pickle
+    # import fixture relevant to the current python, networkx versions
+    print('loading fixtures for python version {}, networkx version {}'.format(sys.version_info[:2], nx.__version__))
+    with open(current_fixture_name(), "rb" ) as f:
+        pickle_file = pickle.load( f )
+    return pickle_file
+
+if __name__ == '__main__':
+    if input("Are you sure you want to update Brain Networks In Python's test fixtures? (y/n)") == 'y':
+        hash_dict = generate_fixture_hashes()
+        pickle_hash(hash_dict)