293 lines
12 KiB
Python
293 lines
12 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 IPython import embed
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from matplotlib import pyplot as plt
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from plotstyle import plot_style
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from threefish.core_plot_labels import title_find_cell_add
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from threefish.defaults import default_figsize, default_settings
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from threefish.core_filenames import overlap_cells
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from threefish.core_load import resave_small_files, save_visualization
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from threefish.core_reformat_RAM import get_stack, load_model_susept
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from threefish.core import find_folder_name
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from threefish.utils1_RAM import trial_nrs_ram_model
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import itertools as it
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##from update_project import *
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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 trialnr(nffts=['whole'], powers=[1], 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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# plot_style()#['_RAMscaled']'_RAMscaled'
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duration_noise = '_short',
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formula = 'code' ##'formula'
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# ,int(2 ** 16) int(2 ** 16), int(2 ** 15),
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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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stimulus_type = '_StimulusOrig_' # ,#
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# ,3]#, 3, 1, 1.5, 0.5, ] # ,1,1.5, 0.5] #[1,1.5, 0.5] # 1.5,0.5]3, 1,
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variant = 'sinz'
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mimick = 'no'
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cell_recording_save_name = ''
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trans = 1 # 5
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rep = 1000000 # 500000#0
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repeats = [20, rep] # 250000
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aa = 0
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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) #.254.75 0.75
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#grid = gridspec.GridSpec(1, 1, wspace=0.95, bottom=0.09,
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# hspace=0.25, width_ratios = [1,0,1,1,1], left=0.09, right=0.93, 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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perc05 = []
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perc95 = []
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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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nr = '2'
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# cell_contrasts = ["2013-01-08-aa-invivo-1"]
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# cells_triangl_contrast = np.concatenate([cells_all,cell_contrasts])
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# cells_triangl_contrast = 1
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# cell_contrasts = 1
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rows = len(cells_all) # len(good_data)+len(cell_contrasts)
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perc = 'perc'
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lp = 2
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label_model = r'Nonlinearity $\frac{1}{S}$'
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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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# print(trials_stim,stim_type_noise, power, nfft, a_fe,a_fr, dendrid, var_type, cut_off1,trial_nrs)
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fig = plt.figure()
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hs = 0.45
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##################################
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# model part
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trial_nr = 500000
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cell = '2013-01-08-aa-invivo-1'
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cell = '2012-07-03-ak-invivo-1'
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print('cell'+str(cell))
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cells_given = [cell]
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save_name_rev = find_folder_name(
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'calc_model') + '/' + '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_revQuadrant_'
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# for trial in trials:#.009
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trial_nr = 1000000#1000000
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trial_nrs_here = trial_nrs_ram_model()
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stacks = []
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perc95 = []
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vars = []
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cv_stims = []
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fr_stims = []
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perc05 = []
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median = []
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stacks_wo_norm = []
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perc95_wo_norm = []
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perc05_wo_norm = []
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median_wo_norm = []
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for tr in trial_nrs_here:
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save_names = [
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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(tr)+'_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_5000000_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_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_afe_0.009_RAM_RAM_additiv_cv_adapt_factor_scaled_cNoise_0.1_cSig_0.9_cutoff1_300_cutoff2_300no_sinz_length1_TrialsStim_500000_a_fr_1__trans1s__TrialsNr_1_fft_o_forward_fft_i_forward_Hz_mV',
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nrs_s = [3, 4, 8, 9]#, 10, 11
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#embed()
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tr_name = trial_nr/1000000
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if tr_name == 1:
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tr_name = 1
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ax_model = []
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for s, sav_name in enumerate(save_names):
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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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# stack = get_stack_one_quadrant(cell, cell_add, cells_save, path, save_name)
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# full_matrix = create_full_matrix2(np.array(stack), np.array(stack_rev))
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# stack_final = get_axis_on_full_matrix(full_matrix, stack)
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# im = plt_RAM_perc(ax, perc, np.abs(stack))
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#
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stack = load_model_susept(path, cells_save, save_name.split(r'/')[-1] + cell_add)
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if len(stack)> 0:
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model_show, stack_plot, stack_plot_wo_norm = get_stack(cell, stack)
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stacks.append(stack_plot)
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#em
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cv_stims.append(stack['cv_stim_mean'].iloc[0])
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fr_stims.append(stack['fr_stim_mean'].iloc[0])
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vars.append(stack['var_RAM'].iloc[0])
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perc95.append(np.percentile(stack_plot,99.99))
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perc05.append(np.percentile(stack_plot, 10))
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median.append(np.percentile(stack_plot, 50))
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stacks_wo_norm.append(stack_plot_wo_norm)
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perc95_wo_norm.append(np.percentile(stack_plot_wo_norm,99.99))
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perc05_wo_norm.append(np.percentile(stack_plot_wo_norm, 10))
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median_wo_norm.append(np.percentile(stack_plot_wo_norm, 50))
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else:
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vars.append(float('nan'))
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cv_stims.append(float('nan'))
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fr_stims.append(float('nan'))
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stacks.append([])
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perc95.append(float('nan'))
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perc05.append(float('nan'))
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median.append(float('nan'))
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stacks_wo_norm.append([])
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perc95_wo_norm.append(float('nan'))
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perc05_wo_norm.append(float('nan'))
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median_wo_norm.append(float('nan'))
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#embed()
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test = False
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if test:
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fig, ax = plt.subplots(1,3)
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ax[0].plot(trial_nrs_here,cv_stims/trial_nrs_here)
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ax[1].plot(trial_nrs_here, fr_stims / trial_nrs_here)
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ax[2].plot(trial_nrs_here, vars / trial_nrs_here)
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plt.show()
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# vars, cv_stims, fr_stims
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fig, ax = plt.subplots(1,2)
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#ax.plot(trial_nrs_here, perc05, color = 'grey')
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ax[0].plot(trial_nrs_here, perc95, color = 'grey', clip_on = False, label = '99.99th percentile')
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#ax.plot(trial_nrs_here, median, color = 'black', label = 'median')
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#ax.scatter(trial_nrs_here, perc05, color = 'grey')
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ax[0].scatter(trial_nrs_here, perc95, color = 'black', clip_on = False)
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ax[0].set_xscale('log')
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ax[0].set_yscale('log')
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ax[0].set_xlabel('Trials [$N$]')
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ax[0].set_ylabel('$\chi_{2}$\,[Hz]')
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ax[0].legend()
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############################################
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ax[1].plot(trial_nrs_here, perc05, color='grey', clip_on=False, label = '10th percentile')
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ax[1].scatter(trial_nrs_here, perc05, color='black', clip_on=False)
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ax[1].set_xscale('log')
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ax[1].set_yscale('log')
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ax[1].set_xlabel('Trials [$N$]')
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ax[1].set_ylabel('$\chi_{2}$\,[Hz]')
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ax[1].legend()
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''' ax = plt.subplot(1,3,2)
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ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
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ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
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ax.plot(trial_nrs_here, median_wo_norm, color = 'black', label = 'median')
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ax.fill_between(trial_nrs_here, perc05_wo_norm, perc95_wo_norm, color='grey')
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#ax.scatter(trial_nrs_here, median, color='black', label='measured points')
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ax = plt.subplot(1,3,3)
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ax.plot(trial_nrs_here, perc05_wo_norm, color = 'grey')
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ax.plot(trial_nrs_here, perc95_wo_norm, color = 'grey')
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ax.plot(trial_nrs_here, median_wo_norm, color = 'black', label = 'median')
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ax.fill_between(trial_nrs_here, perc05_wo_norm, perc95_wo_norm, color='grey')
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#ax.scatter(trial_nrs_here, median, color='black', label='measured points')
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ax.legend()
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ax.set_xscale('log')
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ax.set_yscale('log')
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'''
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plt.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.2, top = 0.95)
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#plt.set_scale
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#embed()
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#embed()
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#fig.tag([axes[0:3]], xoffs=-3, yoffs=1.6) # ax_ams[3],
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#fig.tag([[axes[4]]], xoffs=-3, yoffs=1.6, minor_index=0) # ax_ams[3],
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#fig.tag([axes[3:6]], xoffs=-3, yoffs=1.6) #, major_index = 1, minor_index = 2 ax_ams[3],
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#fig.tag([[axes[7]]], xoffs=-3, yoffs=1.6, major_index=2,minor_index=0) # ax_ams[3],
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#fig.tag([axes[8::]], xoffs=-3, yoffs=1.6, major_index=2, minor_index=2) # ax_ams[3],
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#fig.tag([axes[7::]], xoffs=-3, yoffs=1.6) # ax_ams[3],
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#fig.tag([ax_ams[0],ax_data[0],axes[2], axes[3]], xoffs=-3, yoffs=1.6)#ax_ams[3],
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save_visualization(pdf=True)
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def start_pos_modeldata():
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return 1.03
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def signal_component_name():
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return r'$\xi_{signal}$'#signal noise'
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def noise_component_name():#$\xi_{noise}$noise_name =
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return r'$\xi_{noise}$'#'Noise component'#'intrinsic noise'
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def ypos_x_modelanddata():
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return -0.45
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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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trialnr(D_extraction_method=D_extraction_method) #r'$\frac{1}{mV^2S}$'
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