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