409 lines
19 KiB
Python
409 lines
19 KiB
Python
#from utils_suseptibility import default_settings
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#from plt_RAM import model_and_data_isi, model_cells
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#from utils_suseptibility import model_and_data, remove_yticks
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#from utils_suseptibility import *
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#from utils_susept import nonlin_title, plt_data_susept, plt_single_square_modl, set_clim_same_here, set_xlabel_arrow, \
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# set_ylabel_arrow, \
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# xpos_y_modelanddata
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#from utils_all import default_settings, find_cell_add, get_flowchart_params, load_folder_name, load_model_susept, \
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# overlap_cells, \
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# plot_lowpass2, plt_time_arrays, remove_xticks, remove_yticks, resave_small_files, save_visualization, set_same_ylim
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from utils_suseptibility import *#model_and_data
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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(width=0.005, nffts=['whole'], powers=[1], cells=["2013-01-08-aa-invivo-1"], show=False,
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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],
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label=r'$\frac{1}{mV^2S}$'): # ['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=4.75) # 0.75
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grid = gridspec.GridSpec(3, 4, wspace=0.85, bottom=0.07,
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hspace=0.18, left=0.09, right=0.93, top=0.94)
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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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# embed()
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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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# data cells
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# embed()
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grid_data = gridspec.GridSpecFromSubplotSpec(1, 1, grid[0, 1],
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hspace=hs)
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#ypos_x_modelanddata()
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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, nr = nr, amp_given = 1,xlabel = False, lp=lp, title=True)
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for ax_external in ax_data:
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# ax.set_ylabel(F2_xlabel())
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# remove_xticks(ax)
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ax_external.set_xticks_delta(100)
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set_ylabel_arrow(ax_external, xpos=xpos_y_modelanddata(), ypos=0.87)
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#embed()
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set_xlabel_arrow(ax_external, ypos=ypos_x_modelanddata())
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# ax.text(-0.42, 0.87, F2_xlabel(), ha='center', va='center',
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# transform=ax.transAxes, rotation = 90)
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# ax.text(1.66, 0.5, nonlin_title(), rotation=90, ha='center', va='center',
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# transform=ax.transAxes)
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ax_external.arrow_spines('lb')
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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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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 = load_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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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_500000_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_500000_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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]
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nrs_s = [2, 3, 6, 7, 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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titles = ['Model\n$N=11$ \n $c=1\,\%$', 'Model\n$N=%s $' % (tr_name) +'\,million \n $c=1\,\%$',
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'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=0\,\%$',
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'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million \n $c=0\,\%$',
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'Model\,('+noise_name().lower()+')' + '\n' + '$N=11$\n $c=1\,\%$',
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'Model\,('+noise_name().lower()+')' + '\n' + '$N=%s$' % (tr_name) + '\,million\n $c=1\,\%$ ']#%
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ax_model = []
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for s, sav_name in enumerate(save_names):
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try:
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ax_external = plt.subplot(grid[nrs_s[s]])
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except:
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print('vers something')
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embed()
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ax_model.append(ax_external)
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save_name = load_folder_name('calc_model') + '/' + sav_name
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cell_add, cells_save = 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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# embed()
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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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add_nonlin_title, cbar, fig, stack_plot, im = plt_single_square_modl(ax_external, cell, stack, perc, titles[s],
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width, titles_plot=True,
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resize=True, nr = nr)
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# if s in [1,3,5]:
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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 = 3
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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[0].set_label(nonlin_title(add_nonlin_title)) # , labelpad=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=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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# ax.set_xlabel(F1_xlabel(), labelpad=20)
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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_here(ims, mats=mats, lim_type='up', nr_clim='perc', clims='', percnr=95)
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#################################################
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# Flowcharts
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var_types = ['', 'additiv_cv_adapt_factor_scaled', 'additiv_cv_adapt_factor_scaled']
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##additiv_cv_adapt_factor_scaled
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a_fes = [0.009, 0, 0.009]
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eod_fe = [750, 750, 750]
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ylim = [-0.5, 0.5]
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c_sigs = [0, 0.9, 0.9]
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grid_left = [[], grid[1, 0], grid[2, 0]]
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ax_ams = []
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for g, grid_here in enumerate([grid[0, 0], grid[1, 0], grid[2, 0]]):
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grid_lowpass = gridspec.GridSpecFromSubplotSpec(4, 1,
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subplot_spec=grid_here, hspace=0.3,
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height_ratios=[1, 1,1, 0.1])
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models = resave_small_files("models_big_fit_d_right.csv", load_folder='calc_model_core')
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model_params = models[models['cell'] == '2012-07-03-ak-invivo-1'].iloc[0]
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cell = model_params.pop('cell') # .iloc[0]# Werte für das Paper nachschauen
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eod_fr = model_params['EODf'] # .iloc[0]
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deltat = model_params.pop("deltat") # .iloc[0]
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v_offset = model_params.pop("v_offset") # .iloc[0]
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# embed()eod_fr = stack.eod_fr.iloc[0]
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print(var_types[g] + ' a_fe ' + str(a_fes[g]))
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noise_final_c, spike_times, stimulus, stimulus_here, time, v_dent_output, v_mem_output, frame = get_flowchart_params(
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a_fes, a_fr, g, c_sigs[g], cell, deltat, eod_fr, model_params, stimulus_length, v_offset, var_types,
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eod_fe=eod_fe)
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# embed()
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if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
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grid_lowpass2 = gridspec.GridSpecFromSubplotSpec(4, 1,
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subplot_spec=grid_here,height_ratios=[1, 1,1, 0.1], hspace=0.2)
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# if (np.unique(frame.RAM_afe) != 0):grid_left[g]
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ax_external = plt_time_arrays('red', grid_lowpass2, 1, (frame.RAM_afe)*100, time=time, nr=0)
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# if (np.unique(frame.RAM_noise) != 0):
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remove_xticks(ax_external)
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ax_intrinsic = plt_time_arrays('purple', grid_lowpass2, 1, (frame.RAM_noise)*100, time=time, nr=1)
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ax_intrinsic.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=ax_intrinsic.transAxes)
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ax_intrinsic.show_spines('l')
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ax_external.show_spines('l')
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# ax_ams.append(axt_p2)
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#color_timeseries = 'black'
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#axt_p2.set_xlabel('Time [ms]')
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#axt_p2.text(-0.6, 0.5, '$\%$', rotation=90, va='center', transform=axt_p2.transAxes)
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#ax_ams.append(axt_p2)
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vers = 'all'
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elif (len(np.unique(frame.RAM_afe)) > 1):
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color_timeseries = 'red'
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nr_plot = 0
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print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
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try:
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ax_external, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time,
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(frame.RAM_afe + frame.RAM_noise)*100,
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deltat, eod_fr,
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color1=color_timeseries, lw=1, extract=False)
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except:
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print('add up thing')
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embed()
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ax_external.show_spines('l')
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ax_intrinsic = plt.subplot(grid_lowpass[1])
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ax_intrinsic.show_spines('l')
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ax_intrinsic.axhline(0, color='black', lw=0.5)
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ax_intrinsic.axhline(0, color='purple', lw=0.5)
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remove_xticks(ax_external)
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remove_xticks(ax_intrinsic)
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join_x([ax_intrinsic, ax_external])
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join_y([ax_intrinsic, ax_external])
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vers = 'first'
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elif (len(np.unique(frame.RAM_noise)) > 1):
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color_timeseries = 'purple'
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nr_plot = 1
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print(str(g) + ' afevar ' + str(np.var(frame.RAM_afe)) + ' afenoise ' + str(np.var(frame.RAM_noise)))
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try:
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ax_intrinsic, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[nr_plot]], time, (frame.RAM_afe + frame.RAM_noise)*100,
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deltat, eod_fr,
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color1=color_timeseries, lw=1, extract=False)
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except:
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print('add up thing')
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embed()
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ax_external = plt.subplot(grid_lowpass[0])
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ax_external.show_spines('l')
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ax_intrinsic.show_spines('l')
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ax_external.axhline(0, color='black', lw=0.5)
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ax_external.axhline(0, color='red', lw=0.5)
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join_x([ax_intrinsic,ax_external])
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join_y([ax_intrinsic, ax_external])
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vers = 'second'
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ax_external.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_external.transAxes)
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ax_intrinsic.text(-0.6, 0.5, '$\%$', va='center', rotation=90, transform=ax_intrinsic.transAxes)
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remove_xticks(ax_intrinsic)
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# if (len(np.unique(frame.RAM_afe)) > 1) & (len(np.unique(frame.RAM_noise)) > 1):
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ax_external.set_xlabel('')
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# remove_yticks(ax)
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ax_ams.append(ax_external)
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remove_xticks(ax_external)
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# embed()
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ax_n, ff, pp, ff_am, pp_am = plot_lowpass2([grid_lowpass[2]], time, noise_final_c, deltat, eod_fr,
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extract=False, color1='grey', lw=1)
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remove_yticks(ax_n)
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if g == 2:
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ax_n.set_xlabel('Time [ms]', labelpad = -0.5)
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else:
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remove_xticks(ax_n)
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ax_n.set_ylim(ylim)
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if vers == 'first':
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ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
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ax_n.text(start_pos_modeldata(), 1.1, noise_component_name(), ha='right', color='gray',
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transform=ax_n.transAxes)
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elif vers == 'second':
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ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
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ax_intrinsic.text(start_pos_modeldata(), 1, signal_component_name(), ha='right', color='purple',
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transform=ax_intrinsic.transAxes)
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ax_n.text(start_pos_modeldata(), 0.8, noise_component_name(), ha='right', color='gray',
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transform=ax_n.transAxes)
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else:
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ax_n.text(start_pos_modeldata(), 0.8, noise_component_name(), ha='right', color='gray',
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transform=ax_n.transAxes)
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ax_external.text(1, 1, 'RAM', ha='right', color='red', transform=ax_external.transAxes)
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ax_intrinsic.text(start_pos_modeldata(), 1, signal_component_name(), ha='right', color='purple',
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transform=ax_intrinsic.transAxes)
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set_same_ylim(ax_ams, up='up')
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# embed()
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axes = np.concatenate([ax_data, ax_model])
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axes = [ax_ams[0], axes[0], axes[1], axes[2], ax_ams[1], axes[3], axes[4], ax_ams[2], axes[5],
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axes[6], ]
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axd1 = plt.subplot(grid[1, 1])
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axd2 = plt.subplot(grid[2, 1])
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#ax_data.extend([,])
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axd1.show_spines('')
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axd2.show_spines('')
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#embed()
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#axes = [[ax_ams[0],ax_data[0],axes[2], axes[3]],[ax_ams[1],axd1,axes[4], axes[5]],[axd2,axd2, axes[6], axes[7]]]
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fig.tag([axes[0:4]], 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[5:7]], 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 'Signal component'#'signal noise'
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def noise_component_name():
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return '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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# embed()
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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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table = pd.read_csv('print_table_suscept-model_params_suscept_table.csv')
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table_printen(table)
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table = pd.read_csv('print_table_all-model_params_suscept_table.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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model_and_data2(width=0.005, show=show, D_extraction_method=D_extraction_method,
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label=r'$\frac{1}{mV^2S}$') #r'$\frac{1}{mV^2S}$'
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