From 5165046f2021ac95c952017e93f5d4a7317f61a1 Mon Sep 17 00:00:00 2001
From: "a.ott" <a.ott@student.uni-tuebingen.de>
Date: Mon, 27 Jan 2020 13:43:42 +0100
Subject: [PATCH] delete out of date code

---
 generalTests.py |  5 -----
 main.py         | 42 +-----------------------------------------
 2 files changed, 1 insertion(+), 46 deletions(-)

diff --git a/generalTests.py b/generalTests.py
index 452662e..3f1aa74 100644
--- a/generalTests.py
+++ b/generalTests.py
@@ -1,8 +1,3 @@
-import numpy as np
-import matplotlib.pyplot as plt
-from models.LeakyIntegrateFireModel import LIFModel
-
-
 g = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
 print(g[3:])
 print(g[:-3])
diff --git a/main.py b/main.py
index 244ae25..98f905a 100644
--- a/main.py
+++ b/main.py
@@ -4,58 +4,18 @@ from CellData import icelldata_of_dir
 import os
 import helperFunctions as hf
 from AdaptionCurrent import Adaption
-from models.NeuronModel import NeuronModel
 from functionalityTests import *
 # TODO command line interface needed/nice ?
 
 
 def main():
-    run_tests()
-    quit()
+
     for cell_data in icelldata_of_dir("./data/"):
         print()
         print(cell_data.get_data_path())
 
-        model = NeuronModel(cell_data)
-
-        x_values = np.arange(0, 1000, 0.01)
-        stimulus = [0]*int(200/0.01)
-        stimulus.extend([0.19]*int(400/0.01))
-        stimulus.extend([0]*int(400/0.01))
-
-        v, spikes = model.simulate(0, 1000, stimulus)
-
-        # plt.plot(x_values, v)
-
-        spikes = [s/1000 for s in spikes]
-        time, freq = hf.calculate_isi_frequency(spikes, 0, 0.01/1000)
-
-        plt.plot(time, freq)
-        plt.show()
-
         quit()
-        continue
-
-        figures_save_path = "./figures/" + os.path.basename(cell_data.get_data_path()) + "/"
-        ficurve = FICurve(cell_data)
-        ficurve.plot_fi_curve(figures_save_path)
-
-        adaption = Adaption(cell_data, ficurve)
-        adaption.plot_exponential_fits(figures_save_path + "exponential_fits/", delete_previous=True)
-
-        for i in range(len(adaption.exponential_fit_vars)):
-            if len(adaption.exponential_fit_vars[i]) == 0:
-                continue
-            tau = round(adaption.exponential_fit_vars[i][1]*1000, 2)
-            contrast = round(ficurve.stimulus_value[i], 3)
-            # print(tau, "ms - tau_eff at", contrast, "contrast")
-
-        # test_plot_inverses(ficurve)
-        print("Chosen tau [ms]:", adaption.tau_real)
-
 
-def run_tests():
-    test_lifac()
 
 if __name__ == '__main__':
     main()