Fix loading of data for Analysis.pop_rate_dists, fix filename in network_scaling test

multiarea_model/analysis.py

@@ -23,6 +23,7 @@ Sacha van Albada
 from . import analysis_helpers as ah
 import glob
 import inspect
+from itertools import chain, product
 import json
 import matplotlib.pyplot as plt
 import numpy as np
@@ -279,11 +280,13 @@ class Analysis:
         iterator = ah.model_iter(mode='single',
                                  areas=params['areas'],
                                  pops=params['pops'])
-
+        elements = [('histogram',), ('stats-mu',), ('stats-sigma',)]
+        iter_list = [tuple(chain.from_iterable(prod)) for
+                     prod in product(iterator, elements)]
         # Check if population rates have been stored with the same parameters
         self.pop_rate_dists = ah._check_stored_data(os.path.join(self.output_dir,
                                                                  'pop_rate_dists'),
-                                                    copy(iterator), params)
+                                                    iter_list, params)
 
         if self.pop_rate_dists is None:
             print("Computing population dists")

tests/test_analysis.py

 import os
+import sys
 from multiarea_model import MultiAreaModel
+from io import StringIO
 
 """
 Test analysis class:
@@ -31,3 +33,15 @@ def test_analysis():
     M.analysis.create_pop_LvR(t_min=100.)
 
     M.analysis.save()
+    out = StringIO()
+    sys.stdout = out
+    M.analysis.create_pop_rates(t_min=100.)
+    M.analysis.create_pop_rate_dists(t_min=100.)
+    M.analysis.create_synchrony(t_min=100.)
+    M.analysis.create_rate_time_series(t_min=100.)
+    M.analysis.create_synaptic_input(t_min=100.)
+    M.analysis.create_pop_cv_isi(t_min=100.)
+    M.analysis.create_pop_LvR(t_min=100.)
+    sys.stdout = sys.__stdout__
+    val = out.getvalue()
+    assert(val.count("Loading data from") == 9)

tests/test_network_scaling.py

@@ -25,12 +25,12 @@ def test_network_scaling():
                        M0.area_list,
                        M0.structure)
 
-    with open('fullscale_rates.json', 'w') as f:
+    with open('mf_rates.json', 'w') as f:
         json.dump(d, f)
 
     network_params = {'N_scaling': .1,
                       'K_scaling': .1,
-                      'fullscale_rates': 'fullscale_rates.json'}
+                      'fullscale_rates': 'mf_rates.json'}
     theory_params = {'initial_rates': r0[:, -1]}
     M = MultiAreaModel(network_params, theory=True, theory_spec=theory_params)