diff --git a/code/tuning_curve_max.py b/code/tuning_curve_max.py
index 64e669e..eb45ac5 100644
--- a/code/tuning_curve_max.py
+++ b/code/tuning_curve_max.py
@@ -1,26 +1,45 @@
import glob
import matplotlib.pyplot as plt
import numpy as np
+import os
import rlxnix as rlx
-import scipy as sp
-import time
import useful_functions as f
+from matplotlib.lines import Line2D
+from tqdm import tqdm
-# tatsächliche Power der peaks benutzen
-
-
-
+# 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 file in new_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
@@ -30,79 +49,146 @@ for file in new_files:
# get arrays for frequnecies and power
stim_frequencies = np.zeros(len(sams))
peak_powers = np.zeros_like(stim_frequencies)
- # loop over all sams
- # dictionary for the contrasts
- contrast_sams = {20 : [],
- 10 : [],
- 5 : []}
- # loop over all sams
- for sam in sams:
- # get the contrast
- avg_dur, contrast, _, _, _, _, _ = f.sam_data(sam)
- # check for valid trails
- if np.isnan(contrast):
- continue
- elif sam.stimulus_count < 3: #aborted trials
- continue
- elif avg_dur < 1.7:
- continue
- else:
- contrast = int(contrast) # get integer of contrast
- # sort them accordingly
- if contrast == 20:
- contrast_sams[20].append(sam)
- if contrast == 10:
- contrast_sams[10].append(sam)
- if contrast == 5:
- contrast_sams[5].append(sam)
- else:
- continue
+ 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
- _, _, _, _, _, _, stim_frequency = f.sam_data(sam)
- stimuli = sam.stimuli
- # lists for the power spectra
- frequencies = []
- powers = []
- # loop over the stimuli
- for stimulus in stimuli:
- # get the powerspectrum for each stimuli
- frequency, power = f.power_spectrum(stimulus)
- # append the power spectrum data
- frequencies.append(frequency)
- powers.append(power)
- #average over the stimuli
- sam_frequency = np.mean(frequencies, axis = 0)
- sam_power = np.mean(powers, axis = 0)
+ _, _, _, _, eodf, _, stim_frequency = f.sam_data(sam)
+ sam_frequency, sam_power = f.sam_spectrum(sam)
# detect peaks
- integral, surroundings, peak_power = f.calculate_integral(sam_frequency,
+ _, _, peak_powers[i] = f.calculate_integral(sam_frequency,
sam_power, stim_frequency)
- peak_powers[i] = peak_power
# 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)
-
- fig, ax = plt.subplots(layout = 'constrained')
- ax.plot(contrast_frequencies[0], contrast_powers[0])
- ax.plot(contrast_frequencies[1], contrast_powers[1])
- ax.plot(contrast_frequencies[2], contrast_powers[2])
- ax.set_xlabel('stimulus frequency [Hz]')
- ax.set_ylabel(r' power [$\frac{\mathrm{mV^2}}{\mathrm{Hz}}$]')
- ax.set_title(f"{file}")
-
+ 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')
+
\ No newline at end of file