230 lines
9.6 KiB
Python
230 lines
9.6 KiB
Python
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from matplotlib.colors import LinearSegmentedColormap
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#from plot_eod_chirp import find_times
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import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed
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#embed()
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import matplotlib as matplotlib
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import math
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import scipy.integrate as integrate
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from scipy import signal
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from scipy.interpolate import interp1d
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from scipy.interpolate import CubicSpline
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import scipy as sp
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import pickle
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from scipy.spatial import distance
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from myfunctions import *
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import time
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from matplotlib import gridspec
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from matplotlib_scalebar.scalebar import ScaleBar
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import matplotlib.mlab as ml
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import scipy.integrate as si
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import pandas as pd
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from myfunctions import default_settings
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from functionssimulation import single_stim
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from plot_eod_chirp import rectify,find_dev, conv,global_maxima,integrate_chirp,find_periods,find_lm,pl_eods
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from plot_eod_chirp import find_beats,snip, power_func
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import os
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from thunderfish.tabledata import TableData
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import string
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from plotstyle import plot_style, spines_params
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from myfunctions import remove_tick_ymarks
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def plot_beats(ax, f0, freqs, fexpected, ffirst, fmax, title, ylabel = 'yes'):
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freqs /= f0
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sel = np.abs(freqs - ((freqs+0.1)//0.5)*0.5) < 0.01
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ax.set_title(title)
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ax.plot(freqs, fexpected/f0, color = 'black', linestyle = 'dashed',linewidth = 0.8)
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if ffirst is not None:
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ffirst[sel] = np.nan
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ax.plot(freqs, ffirst/f0, color = 'gold')
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if fmax is not None:
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fmax[sel] = np.nan
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ax.plot(freqs, fmax/f0, color = 'orange')
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ax.set_xlim(0, 5)
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ax.set_ylim(0, 0.7)
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ax.set_xticks_delta(1.0)
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#ax.set_yticks_delta(0.2)
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#ax.set_xlabel('Frequency [f0]')
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if ylabel == 'yes':
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ax.set_ylabel('Frequency [f0]')
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else:
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ax = remove_tick_ymarks(ax)
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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def plot_power(beats, results, win, deviation_s, sigma, sampling, d_ms, beat_corr, size, phase_zero, delta_t, a_fr, a_fe, eod_fr, eod_fe, deviation, show_figure = False, plot_dist = False, save = False,bef_c = 1.1,aft_c =-0.1, share = False):
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#embed()
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plt.rcParams['figure.figsize'] = (6.27, 8)
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plt.rcParams["legend.frameon"] = False
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colors = ['black','blue','purple','magenta','pink','orange', 'brown', 'red', 'green','lime']
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#colors = ['brown']*len(name)
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default_settings([0], intermediate_width=6.29, intermediate_length=8, ts=10, ls=9, fs=9)
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all_max = [[]] * len(results)
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#'threshold>max_a'
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first = ['hilbert>first_f','threshold>first_f','rectified>first_f','square>first_f','threshold cubed>first_f']
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max = ['hilbert>max_a', 'threshold>max_a', 'rectified>max_a', 'square>max_a', 'threshold cubed>max_a']
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name = ['Hilbert','Thresholded','Rectified','Squaring','Threshold cubed']
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colors = ['brown'] * len(name)
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fig, ax = plt.subplots(nrows=len(name), ncols=2)
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nrs1 = [0,2,4,6,8]
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nrs2 = [1,3,5,7,9]
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for i in range(len(name)):
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data = TableData('highbeatspecs10.dat')
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#ax['analytic_threhold'] = plt.subplot(axis[0])
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nr_size = 11
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left = -0.1
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ax[i, 0].text(left, 1.1, string.ascii_uppercase[nrs1[i]], transform=ax[i, 0].transAxes,
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size=nr_size, weight='bold')
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f0 = data[data[:, 'freq>norm'] == 1.0, 'freq>freq']
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f0 = f0[len(f0) // 2]
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#plot_beats(ax[i, 0], f0, data[:, 'freq>freq'], data[:, 'freq>expected'],
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# data[:,first[i]], data[:, max[i]], name[i]+' analytic')
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ax[i, 0].plot(data[:, 'freq>freq'] / f0, data[:, max[i]], color='blue')
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if i != (len(name)-1):
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ax[i, 1] = remove_tick_marks(ax[i, 1])
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ax[i, 0] = remove_tick_marks(ax[i, 0])
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#ax[i, 1].set_ylabel(results['type'][i], rotation=0, labelpad=40, color=colors[i])
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#embed()
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ax[i, 1].plot(beats / eod_fr +1, np.array(results['result_amplitude_max' ][i]) / eod_fr, color = 'navy')
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# plt.title(results['type'][i])
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#ax[i, 1].plot(beats / eod_fr + 1, np.array(results['amp'][i]), color=colors[i])
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ax[i, 0].set_title(name[i] + ' analytical')
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ax[i, 1].set_title(name[i]+' numerical')
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ax[i, 1].set_xticks_delta(1.0)
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ax[i, 0].set_xticks_delta(1.0)
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# ax[i, 1].plot(data[:, 'freq>freq']/ f0, data[:, 'freq>expected'] / f0, color='black', linestyle='dashed',linewidth = 0.8)
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ax[i, 1].set_xlim(0, 5)
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ax[i, 0].set_xlim(0, 5)
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#ax[i, 1].set_ylim(0, 0.7)
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#ax[i, 1] = remove_tick_ymarks(ax[i, 1])
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ax[i, 1].text(left, 1.1, string.ascii_uppercase[nrs2[i]], transform=ax[i, 1].transAxes,
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size=nr_size, weight='bold')
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#ax[i, 1].plot(beats / eod_fr + 1, np.array(results['result_amplitude_max'][i]), color=colors[i])
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#ax[i, 2].plot(beats / eod_fr + 1, np.array(results['amp'][i]), color=colors[i])
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#ax[i,1].set_ylim([1,1.6])
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all_max[i] = np.max(np.array(results['result_amplitude_max'][i]))
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#for i in range(len(results)):
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# ax[i, 2].set_ylim([0, np.max(all_max)])
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plt.subplots_adjust(left=0.13, hspace = 0.45)
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ii, jj = np.shape(ax)
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#ax[0, 0].set_title('Most popular frequency')
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#ax[0, 1].set_title('Modulation depth')
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#ax[0, 2].set_title('Modulation depth (same scale)')
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for i in range(ii):
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for j in range(jj):
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ax[-1, j].set_xlabel('EOD multiples')
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ax[i, j].spines['right'].set_visible(False)
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ax[i, j].spines['top'].set_visible(False)
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def onclick(event):
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#embed()
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eod_fe = [(event.xdata-1)*eod_fr]
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nfft = 4096
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e = 0
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#sampling = 121000
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left_b = [bef_c * sampling] * len(beat_corr)
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right_b = [aft_c * sampling] * len(beat_corr)
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p = 0
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s = 0
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d = 0
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time_fish,sampled, cut, cubed, time_b, conv_b, am_corr_b, peaks_interp_b, maxima_interp_b, am_corr_ds_b, am_df_ds_b, am_df_b, eod_overlayed_chirp = snip(
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left_b, right_b, e, e,
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sampling, deviation_s,
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d, eod_fr, a_fr, eod_fe,
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phase_zero, p, size, s,
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sigma, a_fe, deviation,
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beat_corr, chirp=False)
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#cubed, time_b, conv_b, am_corr_b, peaks_interp_b, maxima_interp_b, am_corr_ds_b, am_df_ds_b, am_df_b, eod_overlayed_chirp = onclick_func(e,nfft,beats, results, disc, win, deviation_s, sigma, d_ms, beat_corr, size, phase_zero, delta_t, a_fr, a_fe, eod_fr, eod_fe, deviation, show_figure = show_figure, plot_dist = plot_dist, save = save,bef_c = bef_c,aft_c =aft_c, sampling = sampling)
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nfft = 4096
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results = [[]] * 1
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name = ['cubed']
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var = [cubed]
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var = [maxima_interp_b]
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samp = [sampling]
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nfft = int((4096 * samp[0] / 10000) * 2)
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i = 0
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pp, f = ml.psd(var[i] - np.mean(var[i]), Fs=samp[i], NFFT=nfft,
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noverlap=nfft / 2)
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plt.figure()
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plt.subplot(1,2,1)
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plt.plot(f, pp)
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plt.xlim([0,2000])
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#plt.subplot(1,3,2)
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#plt.plot(time_b, cubed)
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plt.subplot(1,2,2)
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plt.plot(time_b, maxima_interp_b)
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plt.show()
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if share == True:
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cid = fig.canvas.mpl_connect('button_press_event', onclick)
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plt.savefig('numerical_compar_modulation.pdf')
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plt.show()
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def onclick_func(e,nfft, beats, results, disc, win, deviation_s, sigma, d_ms, beat_corr, size, phase_zero, delta_t, a_fr, a_fe, eod_fr, eod_fe, deviation, show_figure = False, plot_dist = False, save = False,bef_c = 1.1,aft_c =-0.1,sampling = 100000):
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left_b = [bef_c * sampling] * len(beat_corr)
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right_b = [aft_c * sampling] * len(beat_corr)
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p = 0
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s = 0
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time_fish,sampled, cut, cubed, time_b, conv_b, am_corr_b, peaks_interp_b, maxima_interp_b, am_corr_ds_b, am_df_ds_b, am_df_b, eod_overlayed_chirp = snip(
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left_b, right_b, e, e,
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sampling, deviation_s,
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d, eod_fr, a_fr, eod_fe,
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phase_zero, p, size, s,
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sigma, a_fe, deviation,
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beat_corr, chirp=False)
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return pp, f, cubed, cubed, time_b, conv_b, am_corr_b, peaks_interp_b, maxima_interp_b, am_corr_ds_b, am_df_ds_b, am_df_b, eod_overlayed_chirp
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delta_t = 0.014
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interest_interval = delta_t * 1.2
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sigma = delta_t / math.sqrt((2 * math.log(10))) # width of the chirp
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phase_zero = np.arange(0,2*np.pi,2*np.pi/10) #phase_zero = [0] # phase when the chirp occured (vary later) / zero at the peak o a beat cycle
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eod_fr = 637# eod fish reciever
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eod_fr = 537
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eod_fr = 1435
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eod_fr = 734
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factor = 200
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sampling_fish = 500
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step = 500
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win = 'w2'
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d = 1
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x = [ 1.5]#x = [ 1.5, 2.5,0.5,]
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time_range = 200 * delta_t
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sampling = 112345
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deviation_ms, deviation_s, deviation_dp = find_dev(x, sampling)
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start = 5
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end = 3500
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step = 25
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eod_fe, beat_corr, beats = find_beats(start,end,step,eod_fr)
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delta_t = 1
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load = True
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size = [120]
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a_fr = 1
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a_fe = 0.5
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bef_c = -2
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aft_c = -1
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load = False
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restrict = np.array([0,2,3,4,5])
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if (load == True) or (not os.path.exists('numerical.pkl')):
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results = power_func(restrict = restrict, a_fr = a_fr, a_fe = a_fe, eod_fr = eod_fr, eod_fe = eod_fe, win = 'w2', deviation_s = deviation_s, sigma = sigma, sampling = sampling, deviation_ms = deviation_ms, beat_corr = beat_corr, size = size,phase_zero = [phase_zero[0]], delta_t = delta_t,deviation_dp = deviation_dp, show_figure = True, plot_dist = False, save = False,bef_c = bef_c,aft_c = aft_c)
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results = pd.DataFrame(results)
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results.to_pickle('numerical.pkl')
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np.save('numerical.npy', results)
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else:
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results = pd.read_pickle('numerical.pkl')
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#embed()
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plot_power(beats, results,'w2', deviation_s, sigma, sampling, deviation_ms, beat_corr, size, [phase_zero[0]], delta_t, a_fr, a_fe, eod_fr, eod_fe, deviation_dp, show_figure = True, plot_dist = False, save = True,bef_c = bef_c,aft_c =aft_c)
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