[tuning_curve_max] updated to final version
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@ -1,26 +1,45 @@
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import glob
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import rlxnix as rlx
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import scipy as sp
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import time
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import useful_functions as f
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from matplotlib.lines import Line2D
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from tqdm import tqdm
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# tatsächliche Power der peaks benutzen
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# plot the tuning curves for all cells y/n
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single_plots = True
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# all files we want to use
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files = glob.glob("../data/2024-10-*.nix")
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#EODf file for either day
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eodf_file_w = glob.glob('../data/EOD_only/*-16*.nix')[0]
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eodf_file_m = glob.glob('../data/EOD_only/*-21*.nix')[0]
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# get only the good and fair filepaths
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new_files = f.remove_poor(files)
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#get the filenames as labels for plotting
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labels = [os.path.splitext(os.path.basename(file))[0] for file in new_files]
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# dict for all the different contrasts
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contrast_files = {20 : {'power' :[], 'freq' : []},
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10 : {'power' :[], 'freq' : []},
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5 : {'power' :[], 'freq' : []}}
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norm_contrast_files = {20 : {'power' :[], 'freq' : []},
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10 : {'power' :[], 'freq' : []},
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5 : {'power' :[], 'freq' : []}}
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# loop over all the good files
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for file in new_files:
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for u, file in tqdm(enumerate(new_files), total = len(new_files)):
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#use correct eodf file
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if "-16" in file:
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orig_eodf = f.true_eodf(eodf_file_w)
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else:
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orig_eodf = f.true_eodf(eodf_file_m)
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#define lists
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contrast_frequencies = []
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contrast_powers = []
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# load a file
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@ -30,79 +49,146 @@ for file in new_files:
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# get arrays for frequnecies and power
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stim_frequencies = np.zeros(len(sams))
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peak_powers = np.zeros_like(stim_frequencies)
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# loop over all sams
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# dictionary for the contrasts
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contrast_sams = {20 : [],
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10 : [],
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5 : []}
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# loop over all sams
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for sam in sams:
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# get the contrast
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avg_dur, contrast, _, _, _, _, _ = f.sam_data(sam)
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# check for valid trails
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if np.isnan(contrast):
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continue
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elif sam.stimulus_count < 3: #aborted trials
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continue
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elif avg_dur < 1.7:
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continue
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else:
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contrast = int(contrast) # get integer of contrast
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# sort them accordingly
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if contrast == 20:
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contrast_sams[20].append(sam)
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if contrast == 10:
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contrast_sams[10].append(sam)
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if contrast == 5:
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contrast_sams[5].append(sam)
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else:
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continue
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contrast_sams = f.contrast_sorting(sams)
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eodfs = []
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# loop over the contrasts
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for key in contrast_sams:
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stim_frequencies = np.zeros(len(contrast_sams[key]))
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norm_stim_frequencies = np.zeros_like(stim_frequencies)
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peak_powers = np.zeros_like(stim_frequencies)
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for i, sam in enumerate(contrast_sams[key]):
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# get stimulus frequency and stimuli
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_, _, _, _, _, _, stim_frequency = f.sam_data(sam)
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stimuli = sam.stimuli
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# lists for the power spectra
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frequencies = []
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powers = []
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# loop over the stimuli
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for stimulus in stimuli:
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# get the powerspectrum for each stimuli
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frequency, power = f.power_spectrum(stimulus)
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# append the power spectrum data
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frequencies.append(frequency)
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powers.append(power)
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#average over the stimuli
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sam_frequency = np.mean(frequencies, axis = 0)
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sam_power = np.mean(powers, axis = 0)
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_, _, _, _, eodf, _, stim_frequency = f.sam_data(sam)
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sam_frequency, sam_power = f.sam_spectrum(sam)
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# detect peaks
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integral, surroundings, peak_power = f.calculate_integral(sam_frequency,
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_, _, peak_powers[i] = f.calculate_integral(sam_frequency,
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sam_power, stim_frequency)
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peak_powers[i] = peak_power
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# add the current stimulus frequency
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stim_frequencies[i] = stim_frequency
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norm_stim_frequencies[i] = stim_frequency - orig_eodf
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eodfs.append(eodf)
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# replae zeros with NaN
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peak_powers = np.where(peak_powers == 0, np.nan, peak_powers)
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contrast_frequencies.append(stim_frequencies)
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contrast_powers.append(peak_powers)
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fig, ax = plt.subplots(layout = 'constrained')
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ax.plot(contrast_frequencies[0], contrast_powers[0])
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ax.plot(contrast_frequencies[1], contrast_powers[1])
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ax.plot(contrast_frequencies[2], contrast_powers[2])
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ax.set_xlabel('stimulus frequency [Hz]')
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ax.set_ylabel(r' power [$\frac{\mathrm{mV^2}}{\mathrm{Hz}}$]')
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ax.set_title(f"{file}")
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if key == 20:
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contrast_files[20]['freq'].append(stim_frequencies)
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contrast_files[20]['power'].append(peak_powers)
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norm_contrast_files[20]['freq'].append(norm_stim_frequencies)
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norm_contrast_files[20]['power'].append(peak_powers)
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elif key == 10:
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contrast_files[10]['freq'].append(stim_frequencies)
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contrast_files[10]['power'].append(peak_powers)
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norm_contrast_files[10]['freq'].append(norm_stim_frequencies)
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norm_contrast_files[10]['power'].append(peak_powers)
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else:
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contrast_files[5]['freq'].append(stim_frequencies)
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contrast_files[5]['power'].append(peak_powers)
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norm_contrast_files[5]['freq'].append(norm_stim_frequencies)
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norm_contrast_files[5]['power'].append(peak_powers)
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curr_eodf = np.mean(eodfs)
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if single_plots == True:
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# one cell with all contrasts in one subplot
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fig, ax = plt.subplots()
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ax.plot(contrast_frequencies[0], contrast_powers[0])
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ax.plot(contrast_frequencies[1], contrast_powers[1])
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if contrast_frequencies and contrast_frequencies[-1].size == 0:
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if contrast_frequencies and contrast_frequencies[-2].size == 0:
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ax.set_xlim(0,2000)
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else:
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ax.set_xlim(0,np.max(contrast_frequencies[-2]))
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else:
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ax.plot(contrast_frequencies[2], contrast_powers[2])
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ax.set_xlim(0,np.max(contrast_frequencies[-1]))
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ax.axvline(orig_eodf, color = 'black',linestyle = 'dashed', alpha = 0.8)
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ax.axvline(2*curr_eodf, color = 'black', linestyle = 'dotted', alpha = 0.8)
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ax.set_ylim(0, 0.00014)
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ax.set_xlabel('stimulus frequency [Hz]')
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ax.set_ylabel(r' power [$\frac{\mathrm{mV^2}}{\mathrm{Hz}}$]')
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ax.set_title(f"{file}")
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fig.legend(labels = ['20 % contrast', '10 % contrast','5 % contrast','EODf of awake fish', '1st harmonic of current EODf' ], loc = 'lower center', ncol = 3)
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plt.tight_layout(rect=[0, 0.06, 1, 1])
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plt.savefig(f'../results/tuning_curve{labels[u]}.svg')
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#one cell with the contrasts in different subplots
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fig, axs = plt.subplots(1, 3, figsize = [10,6], sharex = True, sharey = True)
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for p, key in enumerate(contrast_files):
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ax = axs[p]
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ax.plot(contrast_files[key]['freq'][-1],contrast_files[key]['power'][-1])
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ax.set_title(f"{key}")
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ax.axvline(orig_eodf, color = 'black',linestyle = 'dashed')
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ax.axvline(2*curr_eodf, color = 'darkblue', linestyle = 'dotted', alpha = 0.8)
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if p == 0:
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ax.set_ylabel(r'power [$\frac{\mathrm{mV^2}}{\mathrm{Hz}}$]', fontsize=12)
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fig.supxlabel('stimulus frequency [Hz]', fontsize=12)
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fig.suptitle(f'{labels[u]}')
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fig.legend(labels = ['power of stimulus peak', 'EODf of awake fish','1st harmonic of current EODf'], loc = 'lower center', bbox_to_anchor=(0.5, 0.05), ncol = 3)
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plt.tight_layout(rect=[0, 0.06, 1, 1])
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plt.savefig(f'../results/contrast_tuning{labels[u]}.svg')
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cmap = plt.get_cmap('viridis')
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colors = cmap(np.linspace(0, 1, len(new_files)))
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plt.close('all')
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if len(new_files) < 10:
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lines = []
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labels_legend = []
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fig, axs = plt.subplots(1, 3, figsize = [10,6], sharex = True, sharey = True)
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for p, key in enumerate(contrast_files):
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ax = axs[p]
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for i in range(len(contrast_files[key]['power'])):
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line, = ax.plot(contrast_files[key]['freq'][i],contrast_files[key]['power'][i], label = labels[i], color = colors[i])
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ax.set_title(f"{key}")
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ax.axvline(orig_eodf, color = 'black',linestyle = 'dashed')
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if p == 0:
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lines.append(line)
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labels_legend.append(labels[i])
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fig.supxlabel('stimulus frequency [Hz]', fontsize=12)
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fig.supylabel(r'power [$\frac{\mathrm{mV^2}}{\mathrm{Hz}}$]', fontsize=12)
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# Create a single legend beneath the plots with 3 columns
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lines.append(Line2D([0], [0], color='black', linestyle='--')) # Custom line for the legend
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labels_legend.append("Awake fish EODf") # Custom label
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fig.legend(lines, labels_legend, loc='upper center', ncol=3, fontsize=10)
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plt.tight_layout(rect=[0, 0, 1, 0.85]) # Adjust layout to make space for the legend
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if "-16" in new_files[-1]:
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plt.savefig('../results/tuning_curves_10_16.svg')
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elif "-21" in new_files[0]:
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plt.savefig('../results/tuning_curves_10_21.svg')
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else:
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for o in range(2):
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lines = []
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labels_legend = []
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fig, axs = plt.subplots(1, 3, figsize = [10,6], sharex = True, sharey = True)
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for p, key in enumerate(norm_contrast_files):
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ax = axs[p]
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for i in range(len(norm_contrast_files[key]['power'])):
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line, = ax.plot(norm_contrast_files[key]['freq'][i],norm_contrast_files[key]['power'][i], label = labels[i], color = colors[i])
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ax.set_title(f"{key}")
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ax.axvline(0, color = 'black',linestyle = 'dashed')
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if p == 0:
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lines.append(line)
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labels_legend.append(labels[i])
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fig.supylabel(r'power [$\frac{\mathrm{mV^2}}{\mathrm{Hz}}$]', fontsize=12)
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# Create a single legend beneath the plots with 3 columns
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lines.append(Line2D([0], [0], color='black', linestyle='--')) # Custom line for the legend
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labels_legend.append("Awake fish EODf") # Custom label
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fig.legend(lines, labels_legend, loc='upper center', ncol=3, fontsize=10)
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plt.tight_layout(rect=[0, 0, 1, 0.82]) # Adjust layout to make space for the legend
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if o == 0:
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ax.set_xlim(-600, 2100)
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fig.supxlabel('stimulus frequency [Hz]', fontsize=12)
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plt.savefig('../results/tuning_curves_norm.svg')
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else:
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ax.set_xlim(-600, 600)
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fig.supxlabel(' relative stimulus frequency [Hz]', fontsize=12)
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plt.savefig('../results/tuning_curves_norm_zoom.svg')
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#plt.close('all')
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