save more data after Fit

run_Fitter.py

@@ -4,11 +4,13 @@ from CellData import icelldata_of_dir, CellData
 from Baseline import get_baseline_class
 from FiCurve import get_fi_curve_class
 from Fitter import Fitter
+from ModelFit import ModelFit
 
 import time
 import os
 import copy
 import argparse
+import numpy as np
 
 import multiprocessing as mp
 
@@ -32,12 +34,15 @@ def main():
     # quit()
 
     start_parameters = [p for p in iget_start_parameters()]
-    start_data = 8
+    # start_data = 8
-    count = 0
+    # count = 0
-    for cell_data in icelldata_of_dir("./invivo_data/"):
+    # for cell_data in icelldata_of_dir("./invivo_data/"):
-        count += 1
+    #     count += 1
-        if count < start_data:
+    #     if count < start_data:
-            continue
+    #         continue
+    #     fit_cell_parallel(cell_data, start_parameters)
+
+    cell_data = CellData("invivo_data/2012-04-20-ab-invivo-1/")
     fit_cell_parallel(cell_data, start_parameters)
 
 
@@ -94,13 +99,15 @@ def fit_all_cells_parallel_sync(cells, start_parameters, thread_pool, results_ba
 
 def fit_cell_parallel(cell_data, start_parameters):
     cell_path = os.path.basename(cell_data.get_data_path())
+    save_directory = "./results/invivo_results/"
+    save_path_cell = os.path.join(save_directory, cell_data.get_cell_name())
     print(cell_path)
     core_count = mp.cpu_count()
     pool = mp.Pool(core_count - 1)
 
     parameters = []
     for i, p in enumerate(start_parameters):
-        parameters.append((cell_data, i, p, "./results/invivo_results/"))
+        parameters.append((cell_data, i, p, save_directory))
 
     time1 = time.time()
     pool.map(fit_cell_base, parameters)
@@ -109,6 +116,17 @@ def fit_cell_parallel(cell_data, start_parameters):
     del pool
     del cell_data
 
+    best_fit = None
+    min_err = np.inf
+    for fit in os.listdir(save_path_cell):
+        cur_fit = ModelFit(os.path.join(save_path_cell, fit))
+        if cur_fit.comparable_error() < min_err:
+            min_err = cur_fit.comparable_error()
+            best_fit = cur_fit
+
+    best_fit.generate_master_plot("./results/invivo_best/singles/")
+
+
 
 def test_fit_routines():
     fitter = Fitter()
@@ -238,6 +256,9 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
         if not os.path.exists(save_path):
             os.makedirs(save_path)
 
+    if save_path is None:
+        return
+
     with open(save_path + "parameters_info.txt", "w") as file:
         file.writelines(["start_parameters:\t" + str(start_parameters),
                          "\nfinal_parameters:\t" + str(parameters)])
@@ -245,6 +266,24 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
     model = LifacNoiseModel(parameters)
     eod_frequency = cell_data.get_eod_frequency()
 
+    data_baseline = get_baseline_class(cell_data)
+    c_bf = data_baseline.get_baseline_frequency()
+    c_vs = data_baseline.get_vector_strength()
+    c_sc = data_baseline.get_serial_correlation(1)
+    c_cv = data_baseline.get_coefficient_of_variation()
+    c_burst = data_baseline.get_burstiness()
+
+    data_fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
+    c_f_inf_slope = data_fi_curve.get_f_inf_slope()
+    c_f_inf_values = data_fi_curve.f_inf_frequencies
+
+    c_f_zero_slope = data_fi_curve.get_f_zero_fit_slope_at_straight()
+    c_f_zero_values = data_fi_curve.f_zero_frequencies
+
+    if c_f_inf_slope < 0:
+        contrasts = np.array(cell_data.get_fi_contrasts()) * -1
+    else:
+        contrasts = np.array(cell_data.get_fi_contrasts())
     model_baseline = get_baseline_class(model, eod_frequency)
     m_bf = model_baseline.get_baseline_frequency()
     m_vs = model_baseline.get_vector_strength()
@@ -258,19 +297,18 @@ def save_fitting_run_info(cell_data, parameters, start_parameters, plot=False, s
     m_f_infinities_slope = model_ficurve.get_f_inf_slope()
     m_f_zero_slope = model_ficurve.get_f_zero_fit_slope_at_straight()
 
-    data_baseline = get_baseline_class(cell_data)
+    np.save(os.path.join(save_path, "model_fi_inf_values.npy"), np.array(m_f_infinities))
-    c_bf = data_baseline.get_baseline_frequency()
+    np.save(os.path.join(save_path, "cell_fi_inf_values.npy"), np.array(c_f_inf_values))
-    c_vs = data_baseline.get_vector_strength()
+    np.save(os.path.join(save_path, "model_fi_zero_values.npy"), np.array(m_f_zeros))
-    c_sc = data_baseline.get_serial_correlation(1)
+    np.save(os.path.join(save_path, "cell_fi_zero_values.npy"), np.array(c_f_zero_values))
-    c_cv = data_baseline.get_coefficient_of_variation()
-    c_burst = data_baseline.get_burstiness()
 
-    data_fi_curve = get_fi_curve_class(cell_data, cell_data.get_fi_contrasts())
+    with open(os.path.join(save_path, "cell_data_path.txt"), "w") as f:
-    c_f_inf_slope = data_fi_curve.get_f_inf_slope()
+        path = cell_data.get_data_path() + "\n"
-    c_f_inf_values = data_fi_curve.f_inf_frequencies
+        f.write(path)
+
+    if c_f_inf_slope < 0:
+        model_ficurve.stimulus_values = contrasts * -1
 
-    c_f_zero_slope = data_fi_curve.get_f_zero_fit_slope_at_straight()
-    c_f_zero_values = data_fi_curve.f_zero_frequencies
     # print("EOD-frequency: {:.2f}".format(cell_data.get_eod_frequency()))
     # print("bf: cell - {:.2f} vs model {:.2f}".format(c_bf, m_bf))
     # print("vs: cell - {:.2f} vs model {:.2f}".format(c_vs, m_vs))