for code refer to package threefish

ampullary.py

@@ -1,8 +1,9 @@
-from utils_suseptibility import ampullary_punit
+##from update_project import *ampullary_punit, p_units_to_show, update_cell_names#, load_folder_name
-from utils_all import p_units_to_show, update_cell_names, load_folder_name
+#from threefish.utils0 import load_folder_name
-#from utils_all import load_folder_name
 #from plt_RAM import plt_punit
-from IPython import embed
+
+from threefish.utils0 import p_units_to_show, update_cell_names
+from threefish.utils1_suscept import ampullary_punit
 
 if __name__ == '__main__':
 

cells_suscept.py

@@ -1,8 +1,7 @@
-from utils_suseptibility import ampullary_punit, fr_name_rm
+from threefish.utils0 import p_units_to_show
-from utils_all import p_units_to_show
+from threefish.utils1_suscept import ampullary_punit, fr_name_rm
-#from utils_all import load_folder_name
+#from threefish.utils0 import load_folder_name
 #from plt_RAM import plt_punit
-from IPython import embed
 
 if __name__ == '__main__':
 

cells_suscept_high_CV.py

@@ -1,8 +1,7 @@
-from utils_suseptibility import ampullary_punit, fr_name_rm
+from threefish.utils0 import p_units_to_show
-from utils_all import p_units_to_show
+from threefish.utils1_suscept import ampullary_punit, fr_name_rm
-#from utils_all import load_folder_name
+#from threefish.utils0 import load_folder_name
 #from plt_RAM import plt_punit
-from IPython import embed
 
 if __name__ == '__main__':
 

data_overview_mod.py

@@ -1,15 +1,12 @@
 #sys.path.insert(0, '..')
 #from plt_RAM import plt_RAM_overview_nice
-#from utils_susept import
+#from threefish.utils1 import
 from IPython import embed
 from matplotlib import gridspec as gridspec, pyplot as plt
 import numpy as np
-from utils_all_down import default_settings
+from threefish.utils0 import colors_overview,default_figsize, update_cell_names, load_overview_susept, make_log_ticks, p_units_to_show, save_visualization, setting_overview_score
-from utils_suseptibility import colors_overview
+from threefish.utils1_suscept import basename_small, exclude_nans_for_corr, kernel_scatter, pearson_label, \
-from utils_suseptibility import default_figsize, NLI_scorename2_small,pearson_label, exclude_nans_for_corr, kernel_scatter, \
+    scatter_with_marginals_colorcoded, stimname_small, version_final, NLI_scorename2_small
-    scatter_with_marginals_colorcoded, \
-    version_final, basename_small, stimname_small
-from utils_all import update_cell_names, load_overview_susept, make_log_ticks, p_units_to_show, save_visualization, setting_overview_score
 from scipy import stats
 try:
     from plotstyle import plot_style, spines_params

flowchart.py

@@ -1,12 +1,11 @@
-#from utils_suseptibility import default_settings
+#from utils0import default_settings
 #from plt_RAM import model_and_data_isi, model_cells
 from IPython import embed
 import numpy as np
 import pandas as pd
 from plotstyle import plot_style, spines_params
-from utils_suseptibility import model_sheme_in_one
+from threefish.utils0 import default_figsize, model_sheme_split2, save_visualization
-from utils_all import default_figsize, model_sheme_split2, remove_yticks, save_visualization
+#from threefish.utils0 import default_settings, resave_small_files
-#from utils_all import default_settings, resave_small_files
 #from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
 import matplotlib.gridspec as gridspec
 from matplotlib import pyplot as plt

model_and_data.py

@@ -1,16 +1,25 @@
-#from utils_suseptibility import default_settings
+##from update_project import **#model_and_data
-#from plt_RAM import model_and_data_isi, model_cells
+import os
+
+import numpy as np
+import pandas as pd
+from IPython import embed
+from matplotlib import gridspec, pyplot as plt
+
+from plotstyle import plot_style
+from threefish.utils0 import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
+    noise_name, overlap_cells, \
+    plot_lowpass2, plt_time_arrays, \
+    remove_xticks, \
+    remove_yticks, \
+    resave_small_files, \
+    save_visualization, set_same_ylim
+import itertools as it
+from threefish.utils0 import default_figsize
+from threefish.utils1_suscept import nonlin_title, perc_model_full, plt_data_susept, plt_single_square_modl, \
+    xpos_y_modelanddata
+from threefish.utils0 import join_x, join_y, set_clim_same, set_xlabel_arrow, set_ylabel_arrow
 
-#from utils_suseptibility import model_and_data, remove_yticks
-#from utils_suseptibility import *
-#from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \
-#    set_ylabel_arrow, \
-#    xpos_y_modelanddata
-
-#from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
-#    overlap_cells, \
-#    plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim
-from utils_suseptibility import *#model_and_data
 
 #from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
 
@@ -323,6 +332,7 @@ def model_and_data2(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1
                                                              color1=color_timeseries, lw=1, extract=False)
                 except:
                     print('add up thing')
+
                     embed()
 
                 ax_external = plt.subplot(grid_lowpass[0])

model_full.py

@@ -1,4 +1,29 @@
-from utils_suseptibility import *
+##from update_project import **
+import numpy as np
+import pandas as pd
+from IPython import embed
+from matplotlib import gridspec, pyplot as plt
+import matplotlib.mlab as ml
+from scipy.ndimage import gaussian_filter
+
+from threefish.utils0 import find_code_vs_not, load_folder_name
+from plotstyle import plot_style
+from threefish.utils0 import resave_small_files, ISI_frequency, colorbar_outside, cr_spikes_mat, create_stimulus_SAM, default_figsize, \
+    eod_fish_r_generation, extract_am, \
+    find_base_fr, find_base_fr2, plt_RAM_perc, \
+    remove_xticks, remove_yticks, save_visualization, simulate, time
+from threefish.utils1_suscept import all_damping_variants, calc_ps, check_var_substract_method, chose_certain_group, \
+    colors_suscept_paper_dots, convert_csv_str_to_float, create_full_matrix2, default_model0, deltaf1_label, \
+    deltaf2_label, deltat_choice, diagonal_points, diff_label, exclude_file_name_short, \
+    extract_waves, fbasename_small, find_all_dir_cells, gaussian_intro, get_arrays_for_three, get_axis_on_full_matrix, \
+    get_cutoffs_nr, get_file_names_exclude, get_mat_susept, get_phaseshifts, get_psds_ROC, labels_didactic2, \
+    load_cells_three, log_calc_psd, nonlin_title, outputmodel, perc_model_full, plt_model_big, \
+    plt_peaks_several, plt_psds_ROC, predefine_grouping_frame, rescale_colorbar_and_values, restrict_cell_type, \
+    save_arrays_susept, sum_label, title_motivation, \
+    two_deltaf1_label, two_deltaf2_label, version_final
+from threefish.utils0 import eod_fish_e_generation, join_y, load_b_public, set_clim_same, set_xlabel_arrow, set_ylabel_arrow, chose_mat_max_value
+from threefish.utils1_suscept import  load_data_susept, restrict_punits #test_spikes_clusters,
+
 
 def model_full(c1=10, mult_type='_multsorted2_', devs=['05'], end='all', chose_score='mean_nrs',
                detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm', dfs = ['m1', 'm2']):
@@ -285,7 +310,8 @@ def plt_model_full_model(axp, min=0.2, cells=[], a_f2 = 0.1, perc = 0.05, alpha
                         base_cut, mat_base = find_base_fr(spike_adapted, deltat, stimulus_length, time_array)
                         fr = np.mean(base_cut)
                         frate, isis_diff = ISI_frequency(time_array, spike_adapted[0], fill=0.0)
-                        isi = np.diff(spike_adapted[0])
+                        isi = (
+                            np.diff(spike_adapted[0]))
                         cv0 = np.std(isi) / np.mean(isi)
                         cv1 = np.std(frate) / np.mean(frate)
                     
@@ -445,7 +471,9 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled', af_2 = 0.1, dev=0.0005
                           fish_jammer='Alepto', markers = [], clip_on = True,
                           ms = 14, us_name='', dev_spikes='original', log =''):
     plot_style()
-    model_cells = pd.read_csv(load_folder_name('calc_model_core') + "/models_big_fit_d_right.csv")
+    #model_cells = pd.read_csv(load_folder_name('calc_model_core') + "/models_big_fit_d_right.csv")
+    model_cells = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core', index_col = True)
+    #embed()
 
     if len(cells) < 1:
         cells = len(model_cells)
@@ -465,6 +493,7 @@ def plt_model_full_model2(grid0, reshuffled='reshuffled', af_2 = 0.1, dev=0.0005
             trials_nr = array_len[g]
         except:
             print('array nr something')
+
             embed()
 
         for cell_here in cells:

motivation.py

@@ -1,21 +1,16 @@
-#from matplotlib import gridspec as gridspec, pyplot as plt
+##from update_project import **
-
+import numpy as np
-#from plotstyle import plot_style
+from matplotlib import gridspec, pyplot as plt
-#from utils_all import chose_mat_max_value, default_settings, find_code_vs_not, save_visualization
+from scipy.ndimage import gaussian_filter
-#from utils_susept import check_var_substract_method, chose_certain_group, divergence_title_add_on, extract_waves, \
+
-#    find_all_dir_cells, \
+from plotstyle import plot_style
-#    load_b_public, \
+from threefish.utils0 import chose_mat_max_value, cr_spikes_mat, default_figsize, find_code_vs_not, save_visualization
-#    load_cells_three, \
+import time
-#    plot_arrays_ROC_psd_single3, plot_shemes4, plt_coherences, predefine_grouping_frame, \
+from threefish.utils1_suscept import check_var_substract_method, chose_certain_group, circle_plot, colors_suscept_paper_dots, \
-#    restrict_cell_type, save_arrays_susept
+    extract_waves, find_all_dir_cells, load_cells_three, plot_arrays_ROC_psd_single3, plot_shemes4, \
-from utils_suseptibility import *
+    plt_coherences, predefine_grouping_frame, restrict_cell_type, save_arrays_susept, title_motivation, \
-
+    ws_nonlin_systems
-
+from threefish.utils0 import load_b_public
-#sys.path.insert(0, '..')
-#from utils_suseptibility import motivation_small
-
-#from utils import divergence_title_add_on,chose_certain_group,predefine_grouping_frame,check_nix_fish,find_all_dir_cells,load_cells_three,restrict_cell_type,plot_shemes2,plot_arrays_ROC_psd_single
-
 
 
 def motivation_all_small(dev_desired = '1', ylim=[-1.25, 1.25], c1=10, dfs=['m1', 'm2'], mult_type='_multsorted2_', top=0.94, devs=['2'],

motivation_stim.py

@@ -1,371 +0,0 @@
-#from matplotlib import gridspec as gridspec, pyplot as plt
-
-#from plotstyle import plot_style
-#from utils_all import chose_mat_max_value, default_settings, find_code_vs_not, save_visualization
-#from utils_susept import check_var_substract_method, chose_certain_group, divergence_title_add_on, extract_waves, \
-#    find_all_dir_cells, \
-#    load_b_public, \
-#    load_cells_three, \
-#    plot_arrays_ROC_psd_single3, plot_shemes4, plt_coherences, predefine_grouping_frame, \
-#    restrict_cell_type, save_arrays_susept
-from utils_suseptibility import *
-
-
-#sys.path.insert(0, '..')
-#from utils_suseptibility import motivation_small
-
-#from utils import divergence_title_add_on,chose_certain_group,predefine_grouping_frame,check_nix_fish,find_all_dir_cells,load_cells_three,restrict_cell_type,plot_shemes2,plot_arrays_ROC_psd_single
-
-
-
-def motivation_all_small_stim(dev_desired = '1',ylim=[-1.25, 1.25], c1=10, dfs=['m1', 'm2'], mult_type='_multsorted2_', top=0.94, devs=['2'],
-                   figsize=None, redo=False, save=True, end='0', cut_matrix='malefemale', chose_score='mean_nrs',
-                   a_fr=1, restrict='modulation', adapt='adaptoffsetallall2', step=str(30),
-                   detections=['AllTrialsIndex'], variant='no', sorted_on='LocalReconst0.2Norm'):
-    autodefines = [
-        'triangle_diagonal_fr']  # ['triangle_fr', 'triangle_diagonal_fr', 'triangle_df2_fr','triangle_df2_eodf''triangle_df1_eodf', ]  # ,'triangle_df2_fr''triangle_df1_fr','_triangle_diagonal__fr',]
-    cells = ['2021-08-03-ac-invivo-1']  ##'2021-08-03-ad-invivo-1',,[10, ][5 ]
-    c1s = [10]  # 1, 10,
-    c2s = [10]
-
-    plot_style()
-    default_figsize(column=2, length=3.3)  #6.7 ts=12, ls=12, fs=12
-    show = True
-    DF2_desired = [0.8]
-    DF1_desired = [0.87]
-
-    DF2_desired = [-0.23]
-    DF1_desired = [0.94]
-
-    # mean_type = '_MeanTrialsIndexPhaseSort_Min0.25sExcluded_'
-    extract = ''
-    datasets, data_dir = find_all_dir_cells()
-    cells = ['2022-01-28-ah-invivo-1']  # , '2022-01-28-af-invivo-1', '2022-01-28-ab-invivo-1',
-    # '2022-01-27-ab-invivo-1', ]  # ,'2022-01-28-ah-invivo-1',  '2022-01-28-af-invivo-1', ]
-    append_others = 'apend_others'  # '#'apend_others'#'apend_others'#'apend_others'##'apend_others'
-    autodefine = '_DFdesired_'
-    autodefine = 'triangle_diagonal_fr'  # ['triangle_fr', 'triangle_diagonal_fr', 'triangle_df2_fr','triangle_df2_eodf''triangle_df1_eodf', ]  # ,'triangle_df2_fr''triangle_df1_fr','_triangle_diagonal__fr',]
-    DF2_desired = [-33]
-    DF1_desired = [133]
-    autodefine = '_dfchosen_closest_'
-    autodefine = '_dfchosen_closest_first_'
-    cells = ['2021-08-03-ac-invivo-1']  ##'2021-08-03-ad-invivo-1',,[10, ][5 ]
-    # c1s = [10]  # 1, 10,
-    # c2s = [10]
-    minsetting = 'Min0.25sExcluded'
-    c2 = 10
-
-    # detections = ['MeanTrialsIndexPhaseSort']  # ['AllTrialsIndex']  # ,'MeanTrialsIndexPhaseSort''DetectionAnalysis''_MeanTrialsPhaseSort'
-    # detections = ['AllTrialsIndex']  # ['_MeanTrialsIndexPhaseSort_Min0.25sExcluded_extended_eod_loc_synch']
-
-    extend_trials = ''  # 'extended'#''#'extended'#''#'extended'#''#'extended'#''#'extended'#''#'extended'# ok kein Plan was das hier ist
-    #    phase_sorting = ''#'PhaseSort'
-    eodftype = '_psdEOD_'
-    concat = ''  # 'TrialsConcat'
-
-    indices = ['_allindices_']
-    chirps = [
-        '']  # '_ChirpsDelete3_',,'_ChirpsDelete3_'','','',''#'_ChirpsDelete3_'#''#'_ChirpsDelete3_'#'#'_ChirpsDelete2_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsDelete_'#''#'_ChirpsCache_'
-    extract = ''  # '_globalmax_'
-    devs_savename = ['original', '05']  # ['05']#####################
-
-    # control = pd.read_pickle(
-    #    load_folder_name(
-    #        'calc_model') + '/modell_all_cell_no_sinz3_afe0.1__afr1__afj0.1__length1.5_adaptoffsetallall2___stepefish' + step + 'Hz_ratecorrrisidual35__modelbigfit_nfft4096.pkl')
-
-    if len(cells) < 1:
-        data_dir, cells = load_cells_three(end, data_dir=data_dir, datasets=datasets)
-        cells, p_units_cells, pyramidals = restrict_cell_type(cells, 'p-units')
-
-    # default_settings(fs=8)
-
-    start = 'min'  #
-    cells = ['2021-08-03-ac-invivo-1']
-    tag_cells = []
-    for c, cell in enumerate(cells):
-        counter_pic = 0
-        contrasts = [c2]
-        tag_cell = []
-        for c, contrast in enumerate(contrasts):
-            contrast_small = 'c2'
-            contrast_big = 'c1'
-            contrasts1 = [c1]
-            for contrast1 in contrasts1:
-                for devname_orig in devs:
-                    datapoints = [1000]
-                    for d in datapoints:
-                        ################################
-                        # prepare DF1 desired
-
-                        # chose_score = 'auci02_012-auci_base_01'
-
-                        # hier muss das halt stimmen mit der auswahl
-                        # hier wollen wir eigntlich kein autodefine
-                        # sondern wir wollen so ein diagonal ding haben
-
-                        extra_f_calculatoin = False
-                        if extra_f_calculatoin:
-                            divergnce, fr, pivot_chosen, max_val, max_x, max_y, mult, DF1_desired, DF2_desired, min_y, min_x, min_val, diff_cut = chose_mat_max_value(
-                                DF1_desired, DF2_desired, '', mult_type, eodftype, indices, cell, contrast_small,
-                                contrast_big, contrast1, dfs, start, devname_orig, contrast, autodefine=autodefine,
-                                cut_matrix='cut', chose_score=chose_score)  # chose_score = 'auci02_012-auci_base_01'
-                        DF1_desired = [1.2]#DF1_desired  # [::-1]
-                        DF2_desired = [0.95]#DF2_desired  # [::-1]
-                        #embed()
-
-                        #######################################
-                        # ROC part
-                        # fr, celltype = get_fr_from_info(cell, data_dir[c])
-
-                        version_comp, subfolder, mod_name_slash, mod_name, subfolder_path = find_code_vs_not()
-                        b = load_b_public(c, cell, data_dir)
-
-                        mt_sorted = predefine_grouping_frame(b, eodftype=eodftype, cell_name=cell)
-                        counter_waves = 0
-
-                        mt_sorted = mt_sorted[(mt_sorted['c2'] == c2) & (mt_sorted['c1'] == c1)]
-                        for gg in range(len(DF1_desired)):
-
-
-                            # try:
-                            t3 = time.time()
-                            # except:
-                            #    print('time thing')
-                            #    embed()
-                            ax_w = []
-
-                            ###################
-                            # all trials in one
-                            grouped = mt_sorted.groupby(
-                                ['c1', 'c2', 'm1, m2'],
-                                as_index=False)
-                            # try:
-                            grouped_mean = chose_certain_group(DF1_desired[gg],
-                                                               DF2_desired[gg], grouped,
-                                                               several=True, emb=False,
-                                                               concat=True)
-                            # except:
-                            #    print('grouped thing')
-                            #    embed()
-                            ###################
-                            # groups sorted by repro tag
-                            # todo: evnetuell die tuples gleich hier umspeichern vom csv ''
-
-                            grouped = mt_sorted.groupby(
-                                ['c1', 'c2', 'm1, m2', 'repro_tag_id'],
-                                as_index=False)
-                            grouped_orig = chose_certain_group(DF1_desired[gg],
-                                                               DF2_desired[gg],
-                                                               grouped,
-                                                               several=True)
-                            gr_trials = len(grouped_orig)
-                            ###################
-
-                            groups_variants = [grouped_mean]
-                            group_mean = [grouped_orig[0][0], grouped_mean]
-
-                            for d, detection in enumerate(detections):
-                                mean_type = '_' + detection  # + '_' + minsetting + '_' + extend_trials + concat
-
-                                ##############################################################
-                                # load plotting arrays
-                                arrays, arrays_original, spikes_pure = save_arrays_susept(
-                                    data_dir, cell, c, chirps, devs, extract, group_mean, mean_type, plot_group=0,
-                                    rocextra=False, sorted_on=sorted_on, dev_desired = dev_desired)
-                                ####################################################
-
-                                ####################################################
-                                # hier checken wir ob für diesen einen Punkt das funkioniert mit der standardabweichung
-
-                                try:
-                                    check_var_substract_method(spikes_pure)
-                                except:
-                                    print('var checking not possible')
-                                # fig = plt.figure()
-                                # grid = gridspec.GridSpec(2, 1, wspace=0.7, left=0.05, top=0.95, bottom=0.15,
-                                #                         right=0.98)
-                                if figsize:
-                                    fig = plt.figure(figsize=figsize)
-                                else:
-                                    fig = plt.figure()
-                                grid = gridspec.GridSpec(1, 1, wspace=0.7, hspace=0.35, left=0.055, top=top,
-                                                         bottom=0.15,
-                                                         right=0.935)  # height_ratios=[1, 2], height_ratios = [1,6]bottom=0.25, top=0.8,
-                                hr = [1, 0.35, 1.2, 0, 3, ]  # 1
-                                ##########################################################################
-                                # several coherence plot
-
-                                # frame_psd = pd.read_pickle(load_folder_name('calc_RAM')+'/noise_data11_nfft1sec_original__StimPreSaved4__first__CutatBeginning_0.05_s_NeurDelay_0.005_s_2021-08-03-ab-invivo-1.pkl')
-                                # frame_psd = pd.read_pickle(load_folder_name('calc_RAM') + '/noise_data11_nfft1sec_original__StimPreSaved4__first__CutatBeginning_0.05_s_NeurDelay_0.005_s_2021-08-03-ab-invivo-1.pkl')
-
-                                coh = False
-                                if coh:
-                                    ax_w, d, data_dir, devs = plt_coherences(ax_w, d, devs, grid)
-
-                                # ax_cohs = plt.subplot(grid[0,1])
-
-                                ##########################################################################
-                                # part with the power spectra
-                                grid0 = gridspec.GridSpecFromSubplotSpec(5, 4, wspace=0.15, hspace=0.35,
-                                                                         subplot_spec=grid[:, :],
-                                                                         height_ratios=hr)
-
-                                xlim = [0, 100]
-                                ###########################################
-                                stimulus_length = 0.3
-                                deltat = 1 / 40000
-                                eodf = np.mean(group_mean[1].eodf)
-                                eod_fr = eodf
-
-                                a_fr = 1
-
-                                eod_fe = eodf + np.mean(
-                                    group_mean[1].DF2)  # data.eodf.iloc[0] + 10  # cell_model.eode.iloc[0]
-                                a_fe = group_mean[0][1] / 100
-                                eod_fj = eodf + np.mean(
-                                    group_mean[1].DF1)  # data.eodf.iloc[0] + 50  # cell_model.eodj.iloc[0]
-                                a_fj = group_mean[0][0] / 100
-                                variant_cell = 'no'  # 'receiver_emitter_jammer'
-                                print('f0' + str(eod_fr))
-                                print('f1'+str(eod_fe))
-                                print('f2' + str(eod_fj))
-                                eod_fish_j, time_array, time_fish_r, eod_fish_r, time_fish_e, eod_fish_e, time_fish_sam, eod_fish_sam, stimulus_am, stimulus_sam = extract_waves(
-                                    variant_cell, '',
-                                    stimulus_length, deltat, eod_fr, a_fr, a_fe, [eod_fe], 0, eod_fj, a_fj)
-
-                                jammer_name = 'female'
-                                cocktail_names = False
-                                if cocktail_names:
-                                    titles = ['receiver ',
-                                              '+' + 'intruder ',
-                                              '+' + jammer_name,
-
-                                              '+' + jammer_name + '+intruder',
-                                              []]  ##'receiver  + ' + 'receiver  + receiver
-                                else:
-                                    titles = title_motivation()
-                                gs = [0, 1, 2, 3, 4]
-                                waves_presents = [['receiver', '', '', 'all'],
-                                                  ['receiver', 'emitter', '', 'all'],
-                                                  ['receiver', '', 'jammer', 'all'],
-
-                                                  ['receiver', 'emitter', 'jammer', 'all'],
-                                                  ]  # ['', '', '', ''],['receiver', '', '', 'all'],
-                                # ['receiver', '', 'jammer', 'all'],
-                                # ['receiver', 'emitter', '', 'all'],'receiver', 'emitter', 'jammer',
-                                symbols = ['']  # '$+$', '$-$', '$-$', '$=$',
-                                symbols = ['', '', '', '', '']
-
-                                time_array = time_array * 1000
-
-                                color01, color012, color01_2, color02, color0_burst, color0 = colors_suscept_paper_dots()
-                                colors_am = ['black', 'black', 'black', 'black']  # color01, color02, color012]
-                                extracted = [False, True, True, True]
-                                extracted2 = [False, False, False, False]
-                                for i in range(len(waves_presents)):
-                                    ax = plot_shemes4(eod_fish_r, eod_fish_e, eod_fish_j, grid0, time_array,
-                                                      g=gs[i], title_top=True, eod_fr=eod_fr,
-                                                      waves_present=waves_presents[i], ylim=ylim,
-                                                      xlim=xlim, color_am=colors_am[i],
-                                                      color_am2 = color01_2, extracted=extracted[i], extracted2=extracted2[i],
-                                                      title=titles[i])  # 'intruder','receiver'#jammer_name
-
-                                    ax_w.append(ax)
-                                    if ax != []:
-                                        ax.text(1.1, 0.45, symbols[i], fontsize=35, transform=ax.transAxes)
-                                    bar = False
-                                    if bar:
-                                        if i == 0:
-                                            ax.plot([0, 20], [ylim[0] + 0.01, ylim[0] + 0.01], color='black')
-                                            ax.text(0, -0.16, '20 ms', va='center', fontsize=10,
-                                                    transform=ax.transAxes)
-
-                                printing = True
-                                if printing:
-                                    print(time.time() - t3)
-
-
-
-                                ##########################################
-                                # spike response
-                                array_chosen = 1
-                                if d == 0:  #
-
-                                    #embed()
-
-                                    frs = []
-                                    for i in range(len(spikes_pure['base_0'])):
-                                        #duration = spikes_pure['base_0'][i][-1] / 1000
-                                        duration = 0.5
-                                        fr = len(spikes_pure['base_0'][i])/duration
-                                        frs.append(fr)
-                                    fr = np.mean(frs)
-                                    #embed()
-
-                                    base_several = False
-                                    if base_several:
-                                        spikes_new = []
-                                        for i in range(len(spikes_pure['base_0'])):
-                                            duration = 100
-                                            duration_full = 101#501
-                                            dur = np.arange(0, duration_full, duration)
-                                            for d_nr in range(len(dur) - 1):
-                                                #embed()
-                                                spikes_new.append(np.array(spikes_pure['base_0'][i][
-                                                                      (spikes_pure['base_0'][i] > dur[d_nr]) & (
-                                                                                  spikes_pure['base_0'][i] < dur[
-                                                                              d_nr + 1])])/1000-dur[d_nr]/1000)
-                                                # spikes_pure['base_0'] = spikes_new
-
-                                        sampling_rate = 1/np.diff(time_array)
-                                        sampling_rate = int(sampling_rate[0]*1000)
-                                        spikes_mats = []
-                                        smoothed05 = []
-                                        for i in range(len(spikes_new)):
-                                            spikes_mat = cr_spikes_mat(spikes_new[i], sampling_rate, int(sampling_rate*duration/1000))
-                                            spikes_mats.append(spikes_mat)
-                                            smoothed05.append(gaussian_filter(spikes_mat, sigma=(float(dev_desired)/1000) * sampling_rate))
-                                        smoothed_base = np.mean(smoothed05, axis=0)
-                                        mat_base = np.mean(spikes_mats, axis=0)
-                                    else:
-                                        smoothed_base = arrays[0][0]
-                                        mat_base = arrays_original[0][0]
-                                    #embed()#arrays[0]v
-                                    fr_isi, ax_ps, ax_as = plot_arrays_ROC_psd_single4([[smoothed_base], arrays[2], arrays[1], arrays[3]],
-                                        [[smoothed_base], arrays[2], arrays[1], arrays[3]],
-                                        [[mat_base], arrays_original[2], arrays_original[1],
-                                         arrays_original[3]], spikes_pure, cell, grid0, mean_type,
-                                        group_mean, xlim=xlim, row=1 + d * 3,
-                                        array_chosen=array_chosen,
-                                        color0_burst=color0_burst, color01=color01, color02=color02,ylim_log=(-15, 3),
-                                        color012=color012,color012_minus = color01_2,color0=color0)
-
-                            ##########################################################################
-
-                            individual_tag = 'DF1' + str(DF1_desired[gg]) + 'DF2' + str(
-                                DF2_desired[gg]) + cell + '_c1_' + str(c1) + '_c2_' + str(c2) + mean_type
-
-                            # save_all(individual_tag, show, counter_contrast=0, savename='')
-                            # print('individual_tag')
-
-                            axes = []
-                            axes.append(ax_w)
-                            # axes.extend(np.transpose(ax_as))
-                            # axes.append(np.transpose(ax_ps))
-                            # np.transpose(axes)
-
-                            #fig.tag(ax_w[0:3], xoffs=-2.3, yoffs=1.7)
-                            #fig.tag(ax_w[3::], xoffs=-1.9, yoffs=1.4)
-                            fig.tag(ax_w, xoffs=-1.9, yoffs=1.4)
-                            # ax_w, np.transpose(ax_as), ax_ps
-                            if save:
-                                save_visualization(individual_tag=individual_tag, show=show, pdf=True)
-                            # fig = plt.gcf()
-                            # fig.savefig
-                            # plt.show()
-
-
-#if __name__ == '__main__':#2.5
-#    motivation_all_small_stim(dev_desired = '1', c1=10, mult_type='_multsorted2_', devs=['05'], redo=True, save=True, end='all',
-#                   cut_matrix='malefemale', chose_score='mean_nrs', restrict='modulation_no_classes', step='50',
-#                   detections=['MeanTrialsIndexPhaseSort'], sorted_on='LocalReconst0.2NormAm')#

trialnr.py

@@ -1,18 +1,17 @@
-#from utils_suseptibility import default_settings
+import os
-#from plt_RAM import model_and_data_isi, model_cells
 
-#from utils_suseptibility import model_and_data, remove_yticks
+import numpy as np
-#from utils_suseptibility import *
+import pandas as pd
-#from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \
+from matplotlib import pyplot as plt
-#    set_ylabel_arrow, \
+from plotstyle import plot_style
-#    xpos_y_modelanddata
+from threefish.utils0 import default_figsize, default_settings, find_cell_add, load_folder_name, load_model_susept, \
+    overlap_cells, \
+    resave_small_files, save_visualization #utils0_project
+from threefish.utils1_suscept import get_stack, trial_nrs_ram_model
+import itertools as it
 
-#from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
+##from update_project import *
-#    overlap_cells, \
-#    plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim
-from utils_suseptibility import *#model_and_data
 
-#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
 
 def table_printen(table):
     print(table.keys())
@@ -22,11 +21,9 @@ def table_printen(table):
         print(l1)
 
 
-def trialnr(eod_metrice = False, width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
+def trialnr(nffts=['whole'], powers=[1], contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
-            contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
             internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1],
-            dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300],
+            dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300]):  # ['eRAM']
-            label=r'$\frac{1}{mV^2S}$'):  # ['eRAM']
     # plot_style()#['_RAMscaled']'_RAMscaled'
 
     duration_noise = '_short',
@@ -258,7 +255,6 @@ if __name__ == '__main__':
 
     ##########################
     #embed()
-    trialnr(eod_metrice = False, width=0.005, show=show, D_extraction_method=D_extraction_method,
+    trialnr(D_extraction_method=D_extraction_method)  #r'$\frac{1}{mV^2S}$'
-            label=r'$\frac{1}{mV^2S}$')  #r'$\frac{1}{mV^2S}$'
 
 

update_project.py

@@ -1,4 +1,4 @@
-import os
+'''import os
 
 try:
     from numba import jit
@@ -22,18 +22,19 @@ from IPython import embed
 
 
 # utils susept wird in utils paper copiert und von utisl_susepitbility gestartet
-utils_suseptibility_name = 'utils_susept'
+utils_suseptibility_name = 'utils1'
-utils_susept_name = 'utils_paper'
+utils_susept_name = 'utils1_project'
 
 utils_suseptibility_name2 = 'utils_susept2'
 utils_susept_name2 = 'utils_paper2'
 
-utils_suseptibility_name_all = 'utils_all'
+utils_suseptibility_name_all = 'utils0'
-utils_susept_name_all = 'utils_all_down'#_down
+utils_susept_name_all = 'utils0_project'#_down
+
 
 
 try:# this will not load but I want this to be reference for the refractoring in pycharm
-    from utils_susept import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+    from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
     cont_other_dir = True#
 except:
     cont_other_dir = False#then I know that I am on alexandras PC and I can update my code
@@ -45,18 +46,18 @@ if cont_other_dir == False:
 
 
     if not os.path.exists('../'+utils_suseptibility_name+'.py'):
-        from utils_paper import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+        from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
         #from utils_paper2 import *  # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
 
     else:
         #embed()
         if filecmp.cmp('../'+utils_suseptibility_name+'.py', utils_susept_name+'.py'):
-            from utils_paper import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+            from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
             #from utils_paper2 import *  # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
 
             if os.path.exists('../'+utils_suseptibility_name+'.py'):# das mache ich um in dem richtigen embed zu arbeiten
                 sys.path.insert(0, '..')
-                from utils_susept import *#resave_small_files,remove_yticks, unify_cell_names,load_cv_table, colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+                from threefish.utils1 import *#resave_small_files,remove_yticks, unify_cell_names,load_cv_table, colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
 
         else:
             # wir schauen erstmal ohne sys dass das immer zu teilen da ist
@@ -70,15 +71,15 @@ if cont_other_dir == False:
         # wenn wir auf meinem Computer sind ziehen wir es aber immer vom code
         # damit das refractors und wenn wir wo anders sind von dem extra kopierten file
         if not os.path.exists('../'+utils_suseptibility_name+'.py'):
-            from utils_paper import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+            from utils1_project import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
             #from utils_paper2 import *  # resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
 
             sys.path.insert(0, '..')
-            from utils_susept import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+            from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
         else:
 
             sys.path.insert(0, '..')
-            from utils_susept import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
+            from threefish.utils1 import *#resave_small_files,plt_cv_part,RAM_norm_data, remove_yticks, unify_cell_names,load_cv_table, calc_base_reclassification,colorbar_outside_right2, find_code_vs_not, load_folder_name, save_visualization
 
     ##############################
     # find out if we are in the code or develop mode (alexandra) or the public mode!
@@ -177,11 +178,11 @@ def plt_scatter_four(grid, frame, cell_types, cell_type_type, annotate, colors):
         # frame_all = frame[(frame[cell_type_type] == cell_type)]
         frame_g = frame[
             (frame[cell_type_type] == cell_type_it) & ((frame.gwn == True) | (frame.fs == True))]
-    
+
         plt_cv_fr(annotate, ax2, add[1], frame_g, colors, cell_type_it)
 
 
 
-    return ax0, ax1, ax2,
+    return ax0, ax1, ax2,'''