one with everything

tests/generalTests.py

@@ -254,23 +254,6 @@ def rectify_stimulus_array(stimulus_array: np.ndarray):
 
 
 if __name__ == '__main__':
-    # # X = [0.05, 0.02, 50, 0.1, 0.03]
-    # model = LifacNoiseModel()
-    # # model.set_variable("mem_tau", X[0])
-    # # model.set_variable("noise_strength", X[1])
-    # # model.set_variable("input_scaling", X[2])
-    # # model.set_variable("tau_a", X[3])
-    # # model.set_variable("delta_a", X[4])
-    # stim = SinusoidalStepStimulus(700, 0.2, start_time=1, duration=1)
-    # bf, vs, sc = model.calculate_baseline_markers(700)
-    # print("baseline freq:{:.2f}\nVector strength: {:.3f}\nSerial cor:".format(bf, vs), sc)
-    # contrasts = np.arange(-0.3, 0.31, 0.05)
-    # model.calculate_fi_curve(contrasts, 700)
-    # f_infinities, slope = model.calculate_fi_markers(contrasts, 700)
-    # print("FI-Curve\nSlope: {:.2f}\nValues:".format(slope), f_infinities)
-    # plot_model_during_stimulus(model, stim, 3)
-
-    # quit()
 
     model_parameters = {'v_offset': -15.234375, 'input_scaling': 64.94152780134829, 'step_size': 5e-05, 'a_zero': 2,
                         'threshold': 1, 'v_base': 0, 'delta_a': 0.04763179657857666, 'tau_a': 0.07891848949732623,
@@ -288,7 +271,7 @@ if __name__ == '__main__':
 
     # test_baseline_polar_plot()
     # time_test_function()
-    # test_cell_data()
+    test_cell_data()
     # test_peak_detection()
     # test_simulation_speed()
     # test_parameters()

tests/smallest_error_thrower.py

@@ -0,0 +1,40 @@
+
+import pyrelacs.DataLoader as Dl
+
+
+def main():
+    traces_missing()
+
+
+# Index / Value error depending on cell:
+def throw_error():
+    cell_folder = "../invivo_data/2014-06-06-ah-invivo-1/"
+    repro = "BaselineActivity"
+
+    for info, key, time, x in Dl.iload_traces(cell_folder, repro=repro):
+        continue
+
+
+def traces_missing():
+    cell_folder = "../invivo_data/2011-10-25-ab-invivo-1"
+
+    repro = "BaselineActivity"
+
+    time_traces = []
+    v1_traces = []
+    eod_traces = []
+    local_eod_traces = []
+    stimulus_traces = []
+
+    for info, key, time, x in Dl.iload_traces(cell_folder, repro=repro):
+        time_traces.append(time)
+        v1_traces.append(x[0])
+        eod_traces.append(x[1])
+        local_eod_traces.append(x[2])
+        stimulus_traces.append(x[3])
+
+    print("num of v1 traces: {:}\nnum of eod traces: {:}\nnum of local eod traces: {:}\nnum of stim traces: {:}".format(len(v1_traces), len(eod_traces), len(local_eod_traces), len(stimulus_traces)))
+
+
+if __name__ == '__main__':
+    main()

tests/test.py

@@ -1,27 +0,0 @@
-import pyrelacs.DataLoader as Dl
-import matplotlib.pyplot as plt
-import numpy as np
-from DataParserFactory import get_parser
-import pprint
-from Baseline import get_baseline_class
-from FiCurve import get_fi_curve_class
-from CellData import icelldata_of_dir
-from models.LIFACnoise import LifacNoiseModel
-
-parameter_bursty_model = {'step_size': 5e-05, 'mem_tau': 0.0066693150193490695, 'v_base': 0, 'v_zero': 0,
-                          'threshold': 1, 'v_offset': -45.703125, 'input_scaling': 172.13861987237314,
-                          'delta_a': 0.06148215166012024, 'tau_a': 0.03391674075000068, 'a_zero': 2,
-                          'noise_strength': 0.0684136549210377, 'dend_tau': 0.0013694103932013805,
-                          'refractory_period': 0.001}
-eod = 752
-model = LifacNoiseModel(parameter_bursty_model)
-baseline_model = get_baseline_class(model, 752, trials=2)
-baseline_model.get_burstiness()
-
-quit()
-
-
-for cell_data in icelldata_of_dir("../data/"):
-    baseline = get_baseline_class(cell_data)
-
-    baseline.get_burstiness()