prepare for fit on kraken

AnalysisMasterScript.py

@@ -0,0 +1,13 @@
+
+import os
+import Figures_Baseline
+import Figures_Stimuli
+import Figures_Model
+import Figures_results
+
+
+def main():
+    pass
+
+if __name__ == '__main__':
+    main()

Figure_constants.py

@@ -5,6 +5,9 @@ from plottools.axes import labelaxes_params
 
 SAVE_FOLDER = "./thesis/figures/"
 
+SAVE_FOLDER_FIGURES = "./figures/analysis/"
+SAVE_FOLDER_PREANALYSIS = "./temp/"
+
 FIG_SIZE_SMALL = (2, 2)
 FIG_SIZE_MEDIUM = (4, 4)
 FIG_SIZE_LARGE = (6, 6)

run_Fitter.py

@@ -14,7 +14,7 @@ from my_util.helperFunctions import plot_errors
 
 import multiprocessing as mp
 
-SAVE_DIRECTORY = "./results/final_sam2/"
+SAVE_DIRECTORY = "./results/sam_cells/"
 # SAVE_DIRECTORY_BEST = "./results/final_sam2_best/"
 
 # SAVE_DIRECTORY = "./results/ref_and_tau/no_dend_tau/"
@@ -94,7 +94,10 @@ def fit_cell_parallel(cell_data, start_parameters):
 
     print(cell_path)
     core_count = mp.cpu_count()
-    pool = mp.Pool(core_count - 1)
+    # normal
+    # pool = mp.Pool(core_count - 1)
+    # kraken (24 cores with 6 used by dennis atm)
+    pool = mp.Pool(16)
 
     parameters = []
     for i, p in enumerate(start_parameters):
@@ -116,13 +119,13 @@ def iget_start_parameters():
     # mem_tau, input_scaling, noise_strength, dend_tau,
     # expand by tau_a, delta_a ?
 
-    mem_tau_list = [0.001]
-    input_scaling_list = [80]
-    noise_strength_list = [0.01]
-    dend_tau_list = [0.002]
+    mem_tau_list = [0.001, 0.002, 0.004]
+    input_scaling_list = [20, 200]
+    noise_strength_list = [0.01, 0.04]
+    dend_tau_list = [0.002, 0.005]
     delta_a_list = [0.01, 0.03, 0.065]
-    tau_a_list = [0.02, 0.04]
-    ref_time_list = [0.00065, 0.0012]
+    tau_a_list = [0.05, 0.12]
+    ref_time_list = [0.00065, 0.001]
 
     for mem_tau in mem_tau_list:
         for input_scaling in input_scaling_list:

test.py

@@ -4,15 +4,30 @@ import numpy as np
 from fitting.ModelFit import ModelFit, get_best_fit
 # from plottools.axes import labelaxes_params
 import matplotlib.pyplot as plt
-
+from run_Fitter import iget_start_parameters
 colors = ["black", "red", "blue", "orange", "green"]
 
 
 def main():
     # sam_tests()
+    # cells = 40
+    number = len([i for i in iget_start_parameters()])
+    single_core = number * 1400 / 60 / 60
+    print("start parameters:", number)
+    print("single core time:", single_core, "h")
+    print("single core time:", single_core/24, "days")
+
+    cores = 16
+    cells = 40
+
+    print(cores, "core time:", single_core/cores, "h")
+    print(cores, "core time:", single_core / 24 / cores, "days")
+    print(cores, "core time all", cells, "cells:", single_core / 24 / cores * cells, "days")
+
+    print("left over:", number%cores)
 
-    fit = get_best_fit("results/final_sam2/2012-12-20-ae-invivo-1/")
-    fit.generate_master_plot()
+    # fit = get_best_fit("results/final_sam2/2012-12-20-ae-invivo-1/")
+    # fit.generate_master_plot()
 
 
 def sam_tests():