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for code refer to package threefish

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saschuta 2024-05-02 23:12:20 +02:00
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from utils_suseptibility import ampullary_punit
from utils_all import p_units_to_show, update_cell_names, load_folder_name
#from utils_all import load_folder_name
##from update_project import *ampullary_punit, p_units_to_show, update_cell_names#, load_folder_name
#from threefish.utils0 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__':

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

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cells_suscept_high_CV.py
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from utils_suseptibility import ampullary_punit, fr_name_rm
from utils_all import p_units_to_show
#from utils_all import load_folder_name
from threefish.utils0 import p_units_to_show
from threefish.utils1_suscept import ampullary_punit, fr_name_rm
#from threefish.utils0 import load_folder_name
#from plt_RAM import plt_punit
from IPython import embed
if __name__ == '__main__':

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data_overview_mod.py
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#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 utils_suseptibility import colors_overview
from utils_suseptibility import default_figsize, NLI_scorename2_small,pearson_label, exclude_nans_for_corr, kernel_scatter, \
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 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 threefish.utils1_suscept import basename_small, exclude_nans_for_corr, kernel_scatter, pearson_label, \
scatter_with_marginals_colorcoded, stimname_small, version_final, NLI_scorename2_small
from scipy import stats
try:
from plotstyle import plot_style, spines_params

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#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 utils_all import default_figsize, model_sheme_split2, remove_yticks, save_visualization
#from utils_all import default_settings, resave_small_files
from threefish.utils0 import default_figsize, model_sheme_split2, save_visualization
#from threefish.utils0 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

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#from utils_suseptibility import default_settings
#from plt_RAM import model_and_data_isi, model_cells
##from update_project import **#model_and_data
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])

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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:

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#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
##from update_project import **
import numpy as np
from matplotlib import gridspec, pyplot as plt
from scipy.ndimage import gaussian_filter
from plotstyle import plot_style
from threefish.utils0 import chose_mat_max_value, cr_spikes_mat, default_figsize, find_code_vs_not, save_visualization
import time
from threefish.utils1_suscept import check_var_substract_method, chose_certain_group, circle_plot, colors_suscept_paper_dots, \
extract_waves, find_all_dir_cells, load_cells_three, plot_arrays_ROC_psd_single3, plot_shemes4, \
plt_coherences, predefine_grouping_frame, restrict_cell_type, save_arrays_susept, title_motivation, \
ws_nonlin_systems
from threefish.utils0 import load_b_public
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'],

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#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')#

0
test_project.py
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BIN
trialnr.pdf
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32
trialnr.py
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@ -1,18 +1,17 @@
#from utils_suseptibility import default_settings
#from plt_RAM import model_and_data_isi, model_cells
import os
#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
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from plotstyle import plot_style
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, \
# 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 update_project import *
#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,
contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
def trialnr(nffts=['whole'], powers=[1], 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],
label=r'$\frac{1}{mV^2S}$'): # ['eRAM']
dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300]): # ['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,
label=r'$\frac{1}{mV^2S}$') #r'$\frac{1}{mV^2S}$'
trialnr(D_extraction_method=D_extraction_method) #r'$\frac{1}{mV^2S}$'

27
utils_suseptibility.py → update_project.py
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@ -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_susept_name = 'utils_paper'
utils_suseptibility_name = 'utils1'
utils_susept_name = 'utils1_project'
utils_suseptibility_name2 = 'utils_susept2'
utils_susept_name2 = 'utils_paper2'
utils_suseptibility_name_all = 'utils_all'
utils_susept_name_all = 'utils_all_down'#_down
utils_suseptibility_name_all = 'utils0'
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!
@ -182,6 +183,6 @@ def plt_scatter_four(grid, frame, cell_types, cell_type_type, annotate, colors):
return ax0, ax1, ax2,
return ax0, ax1, ax2,'''

14170
utils_all_down.py
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35777
utils_paper.py
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