Merge tests

SCRIPTS/make_figures.py

@@ -3382,7 +3382,7 @@ def network_summary_fig(corrmat_file, NodalMeasures_file, GlobalMeasures_file, R
     ax = plt.Subplot(big_fig, grid[0])
     big_fig.add_subplot(ax)
 
-    ax = plot_anatomical_network(G, 
+    ax = plot_anatomical_network(G, # does this need to be here, if we abandon the edges do we need any information from G
                                     NodalMeasures_file, 
                                     measure='module', 
                                     orientation='sagittal', 

SCRIPTS/make_graphs.py

@@ -365,7 +365,6 @@ def calculate_nodal_measures(G, centroids, aparc_names, nodal_partition=None, na
         * 34_name (Desikan Killiany atlas region)
         * 68_name (Desikan Killiany atlas region with hemisphere)
     '''
-    #IS: Currently I'm having trouble with this. When network_analysis_from_corrmat tries to convert the output nodal_dict ino a dataframe it fails because inside the np.arrays we have dict_values objects. I am replacing np.array with list in all relevant instances. Hopefully this works. SUCCESS!
 
     import numpy as np
     import networkx as nx
@@ -679,7 +678,7 @@ def shortest_path(G):
     shortestpl_dict = {}
 
     for node in G.nodes():
-        shortestpl_dict[node] = np.average(list(shortestpl_dict_dict[node].values()))# IS: I've been getting an error here beause numpy functions do not take dict_values objects so I've put a list() around it. 
+        shortestpl_dict[node] = np.average(list(shortestpl_dict_dict[node].values()))
 
     return shortestpl_dict
 

SCRIPTS/useful_functions.py

@@ -96,10 +96,12 @@ def read_in_data(regional_measures_file, names_file, covars_file=None, names_308
 
     # Load the input files
     df = pd.read_csv(regional_measures_file)
-    names = [ line.strip() for line in open(names_file) ]
+    with open(names_file) as f:
+        names = [ line.strip() for line in f ]
 
     if covars_file:
-        covars_list = [ line.strip() for line in open(covars_file) ]
+        with open(covars_file) as f:
+            covars_list = [ line.strip() for line in f ]
     else:
         covars_list = []
 

TESTS/regression_tests.py

@@ -0,0 +1,61 @@
+import unittest
+import filecmp
+import os
+
+class FixturesTest(unittest.TestCase):
+
+    #------------------- setup and teardown ---------------------------
+    @classmethod
+    def setUpClass(cls):
+        print('\nin set up - this takes about 80 secs')
+	    # We should have fixtures (generated by write_fixtures) saved
+	    # in TESTS/test_fixtures. To set up we run write_fixtures again
+	    # to generate new files in a temporary (/tmp) folder.
+	    # Later we will test that these files are identical to the old 
+	    # ones
+        from write_fixtures import write_fixtures
+        write_fixtures('/tmp')
+        # define paths to individual files for checking, relative to 
+        # the folder write_fixtures has saved in
+        cls.corrmat = '/corrmat_file.txt'
+        cls.gm = '/network-analysis/GlobalMeasures_corrmat_file_COST010.csv'
+        cls.lm = '/network-analysis/NodalMeasures_corrmat_file_COST010.csv'
+        cls.rich = '/network-analysis/RICH_CLUB_corrmat_file_COST010.csv'
+
+    @classmethod
+    def tearDownClass(cls):
+        import shutil
+        print('\nin tear down - deleting files')
+        # delete all files generated in setup
+        shutil.rmtree(os.getcwd()+'/tmp')
+
+    #--------------------------- 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.assertTrue(filecmp.cmp(os.getcwd()+'/tmp'+self.corrmat,
+                        os.getcwd()+'/TESTS/test_fixtures'+self.corrmat))
+
+    def test_gm_against_fixture(self):
+        # test new global measures against fixture
+        print('\ntesting new global measures against fixture')
+        self.assertTrue(filecmp.cmp(os.getcwd()+'/tmp'+self.gm,
+                        os.getcwd()+'/TESTS/test_fixtures'+self.gm))
+
+    def test_lm_against_fixture(self):
+        # test new local measures against fixture
+        print('\ntesting new local measures against fixture')
+        self.assertTrue(filecmp.cmp(os.getcwd()+'/tmp'+self.lm,
+                        os.getcwd()+'/TESTS/test_fixtures'+self.lm))
+
+    def test_rich_against_fixture(self):
+        # test rich club against fixture
+        print('\ntesting rich club against fixture')
+        self.assertTrue(filecmp.cmp(os.getcwd()+'/tmp'+self.rich,
+                        os.getcwd()+'/TESTS/test_fixtures'+self.rich))  
+
+if __name__ == '__main__':
+    unittest.main()

TESTS/test_fixtures/network-analysis/GlobalMeasures_corrmat_file_COST010.csv

@@ -0,0 +1,11 @@
+,C,C_rand,E,E_rand,L,L_rand,M,M_rand,a,a_rand,sigma,sigma_rand
+0,0.44501157500471683,0.2396872949320527,0.4304588561073706,0.48411062496129836,2.3985363171030922,2.0993273827150047,0.39148198969632453,0.1249772927086647,0.09839954481630904,-0.0700837697837918,1.6250254433756204,1.0
+1,0.44501157500471683,0.2174227562868005,0.4304588561073706,0.4847265379086393,2.3985363171030922,2.095731629933584,0.39148198969632453,0.12731870848392296,0.09839954481630904,-0.07679807569425844,1.788363015246689,1.0
+2,0.44501157500471683,0.22092106802269246,0.4304588561073706,0.48403281395004144,2.3985363171030922,2.0997927154278946,0.39148198969632453,0.13036605163106316,0.09839954481630904,-0.08266456062740023,1.7634546652489054,1.0
+3,0.44501157500471683,0.21856495556002697,0.4304588561073706,0.48340967256640077,2.3985363171030922,2.1020982275053934,0.39148198969632453,0.12549473019465238,0.09839954481630904,-0.05266971271118482,1.7844216581434573,1.0
+4,0.44501157500471683,0.2276831828667354,0.4304588561073706,0.4838304070463133,2.3985363171030922,2.1006599263928254,0.39148198969632453,0.13131361885790951,0.09839954481630904,-0.07657874897168201,1.7117873214190515,1.0
+5,0.44501157500471683,0.22756787299188863,0.4304588561073706,0.4854780925985942,2.3985363171030922,2.092368543508609,0.39148198969632453,0.12797845829875495,0.09839954481630904,-0.09161575675817406,1.7058947815404288,1.0
+6,0.44501157500471683,0.21978595203448484,0.4304588561073706,0.484643545098188,2.3985363171030922,2.0962604171073225,0.39148198969632453,0.13341693967455034,0.09839954481630904,-0.07528298446703349,1.7695804520474392,1.0
+7,0.44501157500471683,0.22130242377743747,0.4304588561073706,0.4835040403042075,2.3985363171030922,2.1023308938618386,0.39148198969632453,0.13050373916136204,0.09839954481630904,-0.0667199522043918,1.7625437689454024,1.0
+8,0.44501157500471683,0.21647140482862245,0.4304588561073706,0.48430800311722366,2.3985363171030922,2.0987139895934686,0.39148198969632453,0.12302424263054784,0.09839954481630904,-0.08673604011831655,1.7987786763522513,1.0
+9,0.44501157500471683,0.22394406446908866,0.4304588561073706,0.48465418183949754,2.3985363171030922,2.0961335081856256,0.39148198969632453,0.128253340360523,0.09839954481630904,-0.07014799252576139,1.7366183795743748,1.0