225 lines
9.2 KiB
Python
225 lines
9.2 KiB
Python
import os
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import numpy as np
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import pandas as pd
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from matplotlib import gridspec, pyplot as plt
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from threefish.plot_subplots import plt_model_flowcharts
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from threefish.values import ypos_x_modelanddata
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try:
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from plotstyle import plot_style, spines_params
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except:
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print('plotstyle not installed')
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from threefish.RAM.plot_subplots import plt_data_susept, plt_single_square_modl
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from threefish.RAM.values import overlap_cells, perc_model_full
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from threefish.load import resave_small_files, save_visualization
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from threefish.RAM.reformat_matrix import load_model_susept
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from threefish.core import find_folder_name
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from threefish.RAM.plot_labels import label_noise_name, nonlin_title, remove_xticks, remove_yticks, set_xlabel_arrow, \
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set_ylabel_arrow, title_find_cell_add, xlabel_xpos_y_modelanddata
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import itertools as it
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from threefish.defaults import default_figsize, default_settings
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from threefish.plot.limits import set_clim_same, set_same_ylim
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#from plt_RAM import model_and_data, model_and_data_sheme, model_and_data_vertical2
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def table_printen(table):
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print(table.keys())
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for l in range(len(table)):
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list_here = np.array(table.iloc[l])
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l1 = "& ".join(list_here)
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print(l1)
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def model_and_data2(eod_metrice=False, width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"],
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contrasts=[0], noises_added=[''], D_extraction_method=['additiv_cv_adapt_factor_scaled'],
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internal_noise=['RAM'], external_noise=['RAM'], level_extraction=[''], receiver_contrast=[1],
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dendrids=[''], ref_types=[''], adapt_types=[''], c_noises=[0.1], c_signal=[0.9], cut_offs1=[300]): # ['eRAM']
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stimulus_length = 1 # 20#550 # 30 # 15#45#0.5#1.5 15 45 100
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trials_nrs = [1] # [100, 500, 1000, 3000, 10000, 100000, 1000000] # 500
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good_data, remaining = overlap_cells()
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cells_all = [good_data[0]]
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plot_style()
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default_figsize(column=2, length=3.1) #2.9.254.75 0.75# bottom=0.07, top=0.94,
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grid = gridspec.GridSpec(2, 5, wspace=0.95, bottom=0.13, hspace=0.60, top=0.88,
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width_ratios=[2, 0, 2, 2, 2], left=0.09, right=0.93, ) #bottom=0.09, hspace=0.25, top=0.9,
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a = 0
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maxs = []
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mins = []
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mats = []
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ims = []
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iternames = [D_extraction_method, external_noise,
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internal_noise, powers, nffts, dendrids, cut_offs1, trials_nrs, c_signal,
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c_noises,
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ref_types, adapt_types, noises_added, level_extraction, receiver_contrast, contrasts, ]
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lp = 2
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for all in it.product(*iternames):
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var_type, stim_type_afe, stim_type_noise, power, nfft, dendrid, cut_off1, trial_nrs, c_sig, c_noise, ref_type, adapt_type, noise_added, extract, a_fr, a_fe = all
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fig = plt.figure()
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hs = 0.45
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#################################
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# data cells panel A
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grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 0],
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hspace=hs)
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fr_print = False
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nr = 1
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ax_data, stack_spikes_all, eod_frs = plt_data_susept(fig, grid_data, cells_all, cell_type='p-unit', width=width,
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cbar_label=True, fr_print=fr_print,
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eod_metrice=eod_metrice, nr=nr, amp_given=1, xlabel=False,
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lp=lp, title=True)
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for ax_external in ax_data:
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ax_external.set_xticks_delta(100)
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set_ylabel_arrow(ax_external, xpos=xlabel_xpos_y_modelanddata(), ypos=0.87)
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set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
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#embed()
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#plt.show()
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##################################
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# model part
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##########
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# Erklärung
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# Ich habe hier 0.009 und nicht 0.25 weil das Modell sensitiver ist
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# ich habe das mit dem CV verifiziert dass das so stimmen sollte
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trial_nr = 100000
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cell = '2012-07-03-ak-invivo-1'
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cells_given = [cell]
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trial_nr = 1000000 #1000000
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save_names = [
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'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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'calc_RAM_model-2__nfft_whole_power_1_afe_0.009_RAM_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
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trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_11_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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'calc_RAM_model-2__nfft_whole_power_1_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_' + str(
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trial_nr) + '_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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]
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nrs_s = [3, 4, 8, 9] #, 10, 11
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c = 2.5
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cs = ['$c=%.1f$' % c + r'\,\%', r'$c=0$\,\%']
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titles = ['Model\n$N=11$', 'Model\n' + '$N=10^6$',
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'Model\,(' + label_noise_name().lower() + ')' + '\n' + '$N=11$',
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'Model\,(' + label_noise_name().lower() + ')' + '\n' + '$N=10^6$'
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] #%#%s$' % (tr_name) + '\,million'
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ax_model = []
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for s, sav_name in enumerate(save_names):
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ax_external = plt.subplot(grid[nrs_s[s]])
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ax_model.append(ax_external)
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save_name = find_folder_name('calc_model') + '/' + sav_name
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cell_add, cells_save = title_find_cell_add(cells_given)
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perc = 'perc'
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path = save_name + '.pkl' # '../'+
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model = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
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#embed()
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if len(model) > 0:
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# panels Bii, Biii, Cii, Ciii
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add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, model, perc,
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titles[s],
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width, eod_metrice=eod_metrice,
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titles_plot=True,
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resize=True,
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fr_print=fr_print, nr=nr)
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ims.append(im)
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mats.append(stack_plot)
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maxs.append(np.max(np.array(stack_plot)))
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mins.append(np.min(np.array(stack_plot)))
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col = 2
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row = 2
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ax_external.set_xticks_delta(100)
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ax_external.set_yticks_delta(100)
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cbar.set_label(nonlin_title(' [' + add_nonlin_title), labelpad=lp) # rotation=270,
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if s in np.arange(col - 1, 100, col): # | (s == 0)
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remove_yticks(ax_external)
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else:
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set_ylabel_arrow(ax_external, xpos=xlabel_xpos_y_modelanddata(), ypos=0.87)
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if s >= row * col - col:
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set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
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else:
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remove_xticks(ax_external)
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if len(cells) > 1:
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a += 1
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set_clim_same(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=perc_model_full())#
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#################################################
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# plot Flowcharts panels Bi, Ci
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ax_ams, ax_external = plt_model_flowcharts( a_fr, ax_external, c, cs, grid, model, stimulus_length)
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set_same_ylim(ax_ams, up='up')
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axes = np.concatenate([ax_data, ax_model])
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axes = [ax_ams[0], axes[1], axes[2], ax_ams[1], axes[3], axes[4], ]
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fig.tag(ax_data, xoffs=-3, yoffs=1.6)
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fig.tag([axes[0:3]], xoffs=-3, yoffs=1.6)
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fig.tag([axes[3:6]], xoffs=-3, yoffs=1.6)
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save_visualization(pdf=True)
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if __name__ == '__main__':
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model = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
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cells = model.cell.unique()
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params = {'cells': cells}
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show = True
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# if show == False:
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# low CV: cells = ['2012-07-03-ak-invivo-1']
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plot_style()
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default_settings(lw=0.5, column=2, length=3.35) #8.5
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redo = False
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D_extraction_method = ['additiv_cv_adapt_factor_scaled']
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# D_extraction_method = ['additiv_visual_d_4_scaled']
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##########################
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# hier printen wir die table Werte zum kopieren in den Text
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path = 'print_table_suscept-model_params_suscept_table.csv'
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if os.path.exists(path):
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table = pd.read_csv(path)
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table_printen(table)
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path = 'print_table_all-model_params_suscept_table.csv'
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if os.path.exists(path):
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table = pd.read_csv()
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print('model big')
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table_printen(table)
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#embed()
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##########################
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#embed()
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#print('hi')
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model_and_data2(eod_metrice=False, width=0.005, D_extraction_method=D_extraction_method) #r'$\frac{1}{mV^2S}$'
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