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signal_tracker.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.patches import ConnectionPatch
from plottools.colors import *
from plottools.tag import tag
colors_params(colors_muted, colors_tableau)
import os
import sys
from IPython import embed
from tqdm import tqdm
from PyQt5.QtCore import *
from thunderfish.powerspectrum import decibel
class Emit_progress():
progress = pyqtSignal(float)
def gauss(t, shift, sigma, size, norm = False):
g = np.exp(-((t - shift) / sigma) ** 2 / 2) * size
if norm:
g = g / np.sum(g) / (t[1] - t[0])
# print(np.sum(g) * (t[1] - t[0]))
return g
class Validate():
def __init__(self):
self.a_error_dist = None
self.error_col = {}
self.error_col['hit'] = []
self.error_col['originID'] = []
self.error_col['targetID'] = []
self.error_col['alternID'] = []
self.error_col['target_dfreq'] = []
self.error_col['target_dfield'] = []
self.error_col['target_freq_e'] = []
self.error_col['target_field_e'] = []
self.error_col['target_signal_e'] = []
self.error_col['altern_dfreq'] = []
self.error_col['altern_dfield'] = []
self.error_col['altern_freq_e'] = []
self.error_col['altern_field_e'] = []
self.error_col['altern_signal_e'] = []
def save_dict(self):
np.save('./quantification/error_col.npy', self.error_col)
np.save('./quantification/a_error_dist.npy', self.a_error_dist)
def hist_kde(self, target_param, altern_param, sigma_factor = 1/10):
help_array = np.concatenate((target_param, altern_param))
error_steps = np.linspace(0, np.max(help_array) * 501 / 500, 500)
kde_target = np.zeros(len(error_steps))
for e in tqdm(target_param, desc='target'):
kde_target += gauss(error_steps, e, np.std(target_param) * sigma_factor, 1, norm=True)
kde_altern = np.zeros(len(error_steps))
for e in tqdm(altern_param, desc='altern'):
kde_altern += gauss(error_steps, e, np.std(altern_param) * sigma_factor, 1, norm=True)
bin_edges = np.linspace(0, np.max(help_array), int(5 * (1/sigma_factor)))
n_tar, _ = np.histogram(target_param, bin_edges)
n_tar = n_tar / np.sum(n_tar) / (bin_edges[1] - bin_edges[0])
n_alt, _ = np.histogram(altern_param, bin_edges)
n_alt = n_alt / np.sum(n_alt) / (bin_edges[1] - bin_edges[0])
return error_steps, kde_target, kde_altern, bin_edges, n_tar, n_alt
def roc_analysis(self, error_steps, target_param, altern_param):
true_pos = np.ones(len(error_steps))
false_pos = np.ones(len(error_steps))
for i in tqdm(range(len(error_steps)), desc='ROC'):
true_pos[i] = len(np.array(target_param)[np.array(target_param) < error_steps[i]]) / len(target_param)
false_pos[i] = len(np.array(altern_param)[np.array(altern_param) < error_steps[i]]) / len(altern_param)
auc_value = np.sum(true_pos[:-1] * np.diff(false_pos))
return true_pos, false_pos, auc_value
def error_dist_and_auc_display(self):
fig = plt.figure(figsize=(15/2.54, 10/2.54))
gs = gridspec.GridSpec(2, 2, left=0.15, bottom=0.15, right=0.95, top=0.95, wspace=0.6, hspace=0.6, width_ratios=[2, 1])
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[1, 0]))
ax_auc = []
ax_auc.append(fig.add_subplot(gs[0, 1]))
ax_auc.append(fig.add_subplot(gs[1, 1]))
ax_m = []
ax_m.append(ax[0].twinx())
ax_m.append(ax[1].twinx())
# ax_m = ax[0].twinx()
ax_m[0].plot(np.linspace(0, 2.5, 1000), boltzmann(np.linspace(0, 2.5, 1000), alpha=1, beta=0, x0=.35, dx=.08), color='k')
ax_m[0].set_ylim(bottom=0)
ax_m[0].set_yticks([0, 1])
ax_m[0].set_ylabel(r'$\varepsilon_{f}$', fontsize=10)
ax_m[1].plot(self.a_error_dist[np.argsort(self.a_error_dist)], np.linspace(0, 1, len(self.a_error_dist)), color='k')
ax_m[1].set_ylim(bottom=0)
ax_m[1].set_yticks([0, 1])
ax_m[1].set_ylabel(r'$\varepsilon_{S}$', fontsize=10)
for enu, key0, key1, name in zip(np.arange(2), ['target_dfreq', 'target_dfield'], ['altern_dfreq', 'altern_dfield'], ['dfreq', 'dfield']):
# for enu, key0, key1 in zip(np.arange(2), ['target_freq_e', 'target_field_e'], ['altern_freq_e', 'altern_field_e']):
sigma_factor = 1 / 2 if enu in [0] else 1 / 10
error_steps, kde_target, kde_altern, bin_edges, n_tar, n_alt = \
self.hist_kde(self.error_col[key0], self.error_col[key1], sigma_factor)
true_pos, false_pos, auc_value = self.roc_analysis(error_steps, self.error_col[key0], self.error_col[key1])
np.save('./quantification/error_steps_%s.npy' % name, error_steps)
np.save('./quantification/kde_target_%s.npy' % name, kde_target)
np.save('./quantification/kde_altern_%s.npy' % name, kde_altern)
np.save('./quantification/bin_edges_%s.npy' % name, bin_edges)
np.save('./quantification/n_tar_%s.npy' % name, n_tar)
np.save('./quantification/n_alt_%s.npy' % name, n_alt)
np.save('./quantification/true_pos_%s.npy' % name, true_pos)
np.save('./quantification/false_pos_%s.npy' % name, false_pos)
np.save('./quantification/auc_value_%s.npy' % name, auc_value)
print(len(self.error_col[key0]))
target_handle, = ax[enu].plot(error_steps, kde_target / len(self.error_col[key0]), lw=2)
altern_handle, = ax[enu].plot(error_steps, kde_altern / len(self.error_col[key1]), lw=2)
ax[enu].bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0]) / 2, n_tar, width=(bin_edges[1] - bin_edges[0]) * 0.8, alpha=0.4, color=target_handle.get_color(), align='center')
ax[enu].bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0]) / 2, n_alt, width=(bin_edges[1] - bin_edges[0]) * 0.8, alpha=0.4, color=altern_handle.get_color(), align='center')
ax[enu].set_ylabel('KDE')
ax[enu].set_xlim(error_steps[0], error_steps[-1])
ax[enu].set_ylim(0, np.max(np.concatenate((n_tar, n_alt))) * 1.1)
ax_auc[enu].fill_between(false_pos, np.zeros(len(false_pos)), true_pos, color='#999999')
ax_auc[enu].plot([0, 1], [0, 1], color='k', linestyle='--', linewidth=2)
ax_auc[enu].text(0.95, 0.05, '%.1f' % (auc_value * 100) + ' %', fontsize=9, color='k', ha='right', va='bottom')
ax_auc[enu].set_xlim(0, 1)
ax_auc[enu].set_ylim(0, 1)
ax_auc[enu].set_xticks([0, 1])
ax_auc[enu].set_yticks([0, 1])
ax[0].set_xlabel(r'$\Delta f$ [Hz]', fontsize=10)
ax[1].set_xlabel(r'field difference ($\Delta S$)', fontsize=10)
ax_auc[0].set_ylabel('true positive', fontsize=10)
ax_auc[1].set_ylabel('true positive', fontsize=10)
ax_auc[1].set_xlabel('false positive', fontsize=10)
for a in np.hstack([ax, ax_auc, ax_m]):
a.tick_params(labelsize=9)
fig.tag(axes=[ax], labels=['A', 'B'], fontsize=15, yoffs=2, xoffs=-6)
plt.savefig('freq_field_difference.pdf')
# fig.tag(axes=[ax_auc], labels=['B', 'D'], fontsize=15, yoffs=2, xoffs=-6)
###### ###### ###### ###### ###### ###### ###### ###### ###### ###### ###### ###### ###### ###### ###### ######
fig = plt.figure(figsize=(15/2.54, 14/2.54))
gs = gridspec.GridSpec(3, 2, left=0.15, bottom=0.1, right=0.95, top=0.95, hspace=0.6, wspace=0.4, height_ratios=[4, 4, 4], width_ratios=[2.5, 1])
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[1, 0]))
ax.append(fig.add_subplot(gs[2, 0]))
ax_roc = []
ax_roc.append(fig.add_subplot(gs[0, 1]))
ax_roc.append(fig.add_subplot(gs[1, 1]))
ax_roc.append(fig.add_subplot(gs[2, 1]))
# ax_auc = fig.add_subplot(fig.add_subplot(gs[-1, :]))
for enu, key0, key1, name in zip(np.arange(5),
['target_freq_e', 'target_field_e', 'target_signal_e'],
['altern_freq_e', 'altern_field_e', 'altern_signal_e'],
['freq_e', 'field_e', 'signal_e']):
sigma_factor = 1 / 2 if enu in [0] else 1 / 10
error_steps, kde_target, kde_altern, bin_edges, n_tar, n_alt = \
self.hist_kde(self.error_col[key0], self.error_col[key1], sigma_factor)
true_pos, false_pos, auc_value = self.roc_analysis(error_steps, self.error_col[key0], self.error_col[key1])
# fig = plt.figure(figsize=(17.5/2.54, 7/2.54))
# gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.2, top=0.95, right=0.95, width_ratios=[2, 1])
# ax = fig.add_subplot(gs[0, 0])
# ax_auc = fig.add_subplot(gs[0, 1])
np.save('./quantification/error_steps_%s.npy' % name, error_steps)
np.save('./quantification/kde_target_%s.npy' % name, kde_target)
np.save('./quantification/kde_altern_%s.npy' % name, kde_altern)
np.save('./quantification/bin_edges_%s.npy' % name, bin_edges)
np.save('./quantification/n_tar_%s.npy' % name, n_tar)
np.save('./quantification/n_alt_%s.npy' % name, n_alt)
np.save('./quantification/true_pos_%s.npy' % name, true_pos)
np.save('./quantification/false_pos_%s.npy' % name, false_pos)
np.save('./quantification/auc_value_%s.npy' % name, auc_value)
target_handle, = ax[enu].plot(error_steps, kde_target / len(self.error_col[key0]))
altern_handle, = ax[enu].plot(error_steps, kde_altern / len(self.error_col[key1]))
ax[enu].bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0]) / 2, n_tar, width=(bin_edges[1] - bin_edges[0]) * 0.8, alpha=0.5, color=target_handle.get_color())
ax[enu].bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0]) / 2, n_alt, width=(bin_edges[1] - bin_edges[0]) * 0.8, alpha=0.5, color=altern_handle.get_color())
ax[enu].set_ylabel('KDE', fontsize=10)
# ax.set_xlabel(key0)
help_array = np.concatenate((self.error_col[key0], self.error_col[key1]))
ax[enu].set_xlim(0, np.percentile(help_array, 95))
ax[enu].set_ylim(0, np.max(np.concatenate((n_tar, n_alt))) * 1.1)
ax_roc[enu].fill_between(false_pos, np.zeros(len(false_pos)), true_pos, color='grey')
ax_roc[enu].plot([0, 1], [0, 1], color='k', linestyle='--', linewidth=2)
ax_roc[enu].text(0.95, 0.05, '%.1f' % (auc_value * 100), fontsize=10, color='k', ha='right', va='bottom')
ax_roc[enu].set_xlim(0, 1)
ax_roc[enu].set_ylim(0, 1)
ax_roc[enu].set_xticks([0, 1])
ax_roc[enu].set_yticks([0, 1])
ax_roc[enu].set_ylabel('true positive', fontsize=10)
if enu == 2:
ax_roc[enu].set_xlabel('false positive', fontsize=10)
ax[0].set_xlabel(r'$\varepsilon_{f}$', fontsize=10)
ax[1].set_xlabel(r'$\varepsilon_{S}$', fontsize=10)
ax[2].set_xlabel(r'$\varepsilon$', fontsize=10)
for a in np.concatenate((ax, ax_roc)):
a.tick_params(labelsize=9)
fig.tag(axes=ax, fontsize=15, yoffs=1, xoffs=-6)
plt.savefig('freq_field_signal_error.pdf')
def which_is_best(self):
for enu, key0, key1 in zip(np.arange(5),
['target_dfreq', 'target_dfield', 'target_freq_e', 'target_field_e', 'target_signal_e'],
['altern_dfreq', 'altern_dfield', 'altern_freq_e', 'altern_field_e', 'altern_signal_e']):
fig = plt.figure(figsize=(17.5/2.54, 7/2.54))
gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.2, top=0.95, right=0.95, width_ratios=[2, 1])
ax = fig.add_subplot(gs[0, 0])
ax_auc = fig.add_subplot(gs[0, 1])
help_array = np.concatenate((self.error_col[key0], self.error_col[key1]))
error_steps = np.linspace(0, np.max(help_array)*501/500, 500)
kde_target = np.zeros(len(error_steps))
sigma_factor = 1/2 if enu in [0, 2] else 1/10
for e in self.error_col[key0]:
kde_target += gauss(error_steps, e, np.std(self.error_col[key0]) * sigma_factor, 1, norm=True)
kde_altern = np.zeros(len(error_steps))
for e in self.error_col[key1]:
kde_altern += gauss(error_steps, e, np.std(self.error_col[key1]) * sigma_factor, 1, norm=True)
target_handle, = ax.plot(error_steps, kde_target / len(self.error_col[key0]))
altern_handle, = ax.plot(error_steps, kde_altern / len(self.error_col[key1]))
###########################################################################################################
bin_edges = np.linspace(0, np.percentile(help_array, 100), 50)
n, _ = np.histogram(self.error_col[key0], bin_edges)
n = n / np.sum(n) / (bin_edges[1] - bin_edges[0])
n_alt, _ = np.histogram(self.error_col[key1], bin_edges)
n_alt = n_alt / np.sum(n_alt) / (bin_edges[1] - bin_edges[0])
ax.bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0]) / 2, n, width=(bin_edges[1] - bin_edges[0]) * 0.8, alpha=0.5, color=target_handle.get_color())
ax.bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0]) / 2, n_alt, width=(bin_edges[1] - bin_edges[0]) * 0.8, alpha=0.5, color=altern_handle.get_color())
###########################################################################################################
ax.set_ylabel('kde')
ax.set_xlabel(key0)
ax.set_xlim(left=0)
###
true_pos = np.ones(len(error_steps))
false_pos = np.ones(len(error_steps))
for i in range(len(error_steps)):
true_pos[i] = len(np.array(self.error_col[key0])[np.array(self.error_col[key0]) < error_steps[i]]) / len(self.error_col[key0])
false_pos[i] = len(np.array(self.error_col[key1])[np.array(self.error_col[key1]) < error_steps[i]]) / len(self.error_col[key1])
auc_value = np.sum(true_pos[:-1] * np.diff(false_pos))
# ax_auc.plot(false_pos, true_pos, color='k', lw=1)
ax_auc.fill_between(false_pos, np.zeros(len(false_pos)), true_pos, color='grey')
ax_auc.plot([0, 1], [0, 1], color='k', linestyle='--', linewidth=2)
ax_auc.text(0.95, 0.05, '%.1f' % (auc_value * 100), fontsize=10, color='k', ha='right', va='bottom')
ax_auc.set_xlim(0, 1)
ax_auc.set_ylim(0, 1)
class Display_agorithm():
def __init__(self, fund_v, ident_v, idx_v, sign_v, times, a_error_distribution, error_dist_i0s, error_dist_i1s):
self.fund_v = fund_v
self.sign_v = sign_v
self.ident_v = ident_v
self.tmp_ident_v = None
self.final_ident_v = None
self.idx_v = idx_v
self.times = times
# self.spec = np.load("/home/raab/thesis/code/tracking_display/spec.npy")
self.spec = np.load("/home/raab/writing/2021_tracking/data/2016-04-10-11_12/spec.npy")
self.a_error_dist = a_error_distribution
self.error_dist_i0s = error_dist_i0s
self.error_dist_i1s = error_dist_i1s
self.tmp_ident_v_state = []
self.handles = {}
self.itter_counter = 0
self.tmp_trace_handels = {}
self.trace_handels = {}
self.origin_idx = []
self.target_idx = []
self.alt_idx = []
self.tracking_i = None
self.idx_comp_range = None
def plot_a_error_dist(self):
from plottools.tag import tag
X, Y = np.meshgrid(np.arange(8), np.arange(8))
####
fig = plt.figure(figsize=(17.5 / 2.54, 10 / 2.54))
gs = gridspec.GridSpec(3, 4, left=0.1, bottom=0.15, right=0.95, top=0.95, hspace=0.4, wspace=0.4, height_ratios=[2, 2, 1.5])
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[1, 0]))
ax.append(fig.add_subplot(gs[0, 1]))
ax.append(fig.add_subplot(gs[1, 1]))
ax.append(fig.add_subplot(gs[0, 2]))
ax.append(fig.add_subplot(gs[1, 2]))
ax.append(fig.add_subplot(gs[0, 3]))
ax.append(fig.add_subplot(gs[1, 3]))
# gs2 = gridspec.GridSpec(1, 1, left=0.25, bottom=0.1, right=0.85, top=0.3, hspace=0.3, wspace=0.3)
ax.append(fig.add_subplot(gs[2, :]))
####
mask = np.argsort(self.a_error_dist)
ex = np.array(np.floor(np.linspace(10, len(mask)-1, 4)), dtype=int)
ex[-1] -= 20
ex_color = ['forestgreen', 'gold', 'darkorange', 'firebrick']
ex_i0 = self.error_dist_i0s[mask[ex]]
ex_i1 = self.error_dist_i1s[mask[ex]]
for enu, i0, i1 in zip(np.arange(len(ex_i0)), ex_i0, ex_i1):
s0 = self.sign_v[i0].reshape(8, 8)
#, aspect='auto'
ax[enu*2].imshow(s0[::-1], alpha=0.7, cmap='jet', vmax=1, vmin=0, interpolation='gaussian', zorder=1)
s1 = self.sign_v[i1].reshape(8, 8)
ax[enu*2+1].imshow(s1[::-1], alpha=0.7, cmap='jet', vmax=1, vmin=0, interpolation='gaussian',
zorder=1)
for x, y in zip(X, Y):
ax[enu*2].plot(x, y, '.', color='k', markersize=2)
ax[enu*2+1].plot(x, y, '.', color='k', markersize=2)
y0, y1 = ax[enu * 2].get_ylim()
#ax[enu * 2].arrow(8.5, 3.5, 2, 0, head_width=.7, head_length=.7, clip_on=False, color=ex_color[enu], lw=2.5)
ax[enu * 2].arrow(3.5, 8.25, 0, .8, head_width=.7, head_length=.7, clip_on=False, color=ex_color[enu], lw=2)
ax[enu * 2].set_ylim(y0, y1)
ax[enu*2].set_xticks([])
ax[enu*2+1].set_xticks([])
ax[enu*2].set_yticks([])
ax[enu*2+1].set_yticks([])
ax[-1].plot(self.a_error_dist[mask], np.linspace(0, 1, len(self.a_error_dist)), color='midnightblue', clip_on=False)
for enu in range(4):
ax[-1].plot(self.a_error_dist[mask[ex[enu]]], np.linspace(0, 1, len(self.a_error_dist))[ex[enu]], 'o', color=ex_color[enu], clip_on=False, markersize=6)
ax[-1].set_ylim(0, 1)
ax[-1].set_yticks([0, 1])
ax[-1].set_xlim(0, np.max(self.a_error_dist))
ax[-1].set_ylabel('field error', fontsize=12)
ax[-1].set_xlabel(r'$\Delta$ field amplitude', fontsize=12)
ax[-1].tick_params(labelsize=10)
# fig.tag(axes=[ax[0], ax[8]], labels=['A', 'E'], fontsize=15, yoffs=2, xoffs=-6)
# fig.tag(axes=[ax[2], ax[4], ax[6]], labels=['B', 'C', 'D'], fontsize=15, yoffs=2, xoffs=-3)
plt.savefig('amplitude_error_dist.pdf')
plt.close()
def plot_assign(self, origin_idx, tartget_idx0, alternatives):
#test_alt_idx = alt_target_idx[0] if alt_target_idx[0] != tartget_idx0 else alt_target_idx[1]
alt_idx0 = alternatives[0]
if np.abs(self.fund_v[origin_idx] - self.fund_v[tartget_idx0]) >= np.abs(self.fund_v[origin_idx] - self.fund_v[alt_idx0]):
fig = plt.figure(figsize=(20/2.54, 12/2.54))
gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.15, right=0.75, top=0.75)
ax = fig.add_subplot(gs[0, 0])
ax.imshow(decibel(self.spec)[::-1], extent=[self.times[0], self.times[-1], 0, 2000],
aspect='auto', alpha=0.7, cmap='jet', vmax=-50, vmin=-110, interpolation='gaussian', zorder=1)
ax.plot(self.times[self.idx_v], self.fund_v, '.', color='grey', markersize=3)
ax.plot(self.times[self.idx_v[origin_idx]], self.fund_v[origin_idx], '.', color='k', markersize=10)
ax.plot(self.times[self.idx_v[tartget_idx0]], self.fund_v[tartget_idx0], '.', color='green', markersize=10)
for alt_idx in alternatives:
if alt_idx != tartget_idx0 and alt_idx != alt_idx0:
ax.plot(self.times[self.idx_v[alt_idx]], self.fund_v[alt_idx], '.', color='red', markersize=10)
ax.plot(self.times[self.idx_v[alt_idx0]], self.fund_v[alt_idx0], '.', color='red', markersize=10, markeredgecolor='k')
help_idx = np.concatenate((np.array([origin_idx, tartget_idx0]), np.array(alternatives)))
xs = self.times[self.idx_v[help_idx]]
ys = self.fund_v[help_idx]
ax.set_xlim(np.min(xs) - 5, np.max(xs) + 5)
ax.set_ylim(np.min(ys) - 30, np.max(ys) + 30)
gs2 = gridspec.GridSpec(1, 1, left = 0.6, bottom=0.6, right=0.95, top=0.95)
ax_ins = fig.add_subplot(gs2[0, 0])
ax_ins.plot(np.arange(0, 2.5, 0.001), boltzmann(np.arange(0, 2.5, 0.001), alpha=1, beta=0, x0=.35, dx=.08), color='midnightblue')
ax_ins.set_xlim(-.025, 1)
ax_ins.set_xticks([0, 0.5, 1])
ax_ins.set_ylim(-.025, 1)
ax_ins.set_yticks([0, 1])
df_target = np.abs(self.fund_v[tartget_idx0] - self.fund_v[origin_idx])
f_error = boltzmann(df_target, alpha=1, beta=0, x0=.35, dx=.08)
ax_ins.plot([df_target, df_target], [-.025, f_error], color='green', lw=4)
ax_ins.plot([-.025, df_target], [f_error, f_error], color='green', lw=4)
for alt_idx in alternatives:
if alt_idx != tartget_idx0:
df_target = np.abs(self.fund_v[alt_idx] - self.fund_v[origin_idx])
f_error = boltzmann(df_target, alpha=1, beta=0, x0=.35, dx=.08)
ax_ins.plot([df_target, df_target], [-.025, f_error], color='red', lw=4)
ax_ins.plot([-.025, df_target], [f_error, f_error], color='red', lw=4)
plt.pause(2)
plt.close('all')
def static_tmp_id_tracking(self, min_i0, max_i1):
t0 = self.times[self.idx_v[min_i0]]
self.combo_fig = plt.figure(figsize=(9.5/2.54, 14/2.54))
gs = gridspec.GridSpec(3, 1, left=.2, bottom=.1, right=.95, top=.9, height_ratios=[2, 2, 2], hspace=0.3)
self.combo_ax = []
# self.combo_ax.append(self.combo_fig.add_subplot(gs[0, 0]))
self.combo_ax.append(self.combo_fig.add_subplot(gs[0, 0]))
self.combo_ax.append(self.combo_fig.add_subplot(gs[1, 0], sharex = self.combo_ax[0]))
self.combo_ax.append(self.combo_fig.add_subplot(gs[2, 0], sharex = self.combo_ax[0]))
for i in np.arange(3):
self.combo_ax[i].imshow(decibel(self.spec)[::-1], extent=[self.times[0] - t0, self.times[-1] - t0, 0, 2000],
aspect='auto', alpha=0.7, cmap='Greys', vmax=-50, vmin=-110,
interpolation='gaussian', zorder=1)
self.combo_ax[i].set_xlim(self.times[self.idx_v[min_i0]] - t0, self.times[self.idx_v[max_i1]] - t0)
self.combo_ax[i].set_ylim(905, 930)
# self.combo_ax[i+1].plot([10, 10], [890, 930], '--', lw=1, color='k')
# self.combo_ax[i+1].plot([20, 20], [890, 930], '--', lw=1, color='k')
for id in self.tmp_ident_v_state[i][~np.isnan(self.tmp_ident_v_state[i])]:
# if 880 < self.fund_v[self.idx_v[self.tmp_ident_v_state[i] == id]][0] < 930:
self.combo_ax[i].plot(self.times[self.idx_v[self.tmp_ident_v_state[i] == id]] - t0,
self.fund_v[self.tmp_ident_v_state[i] == id], marker='.', markersize=4)
for ax in self.combo_ax[:-1]:
plt.setp(ax.get_xticklabels(), visible=False)
self.combo_ax[-1].set_xlabel('time [s]', fontsize=10)
self.combo_ax[1].set_ylabel('frequency [Hz]', fontsize=10)
self.combo_ax[0].fill_between([0, 10], [935, 935], [938, 938], color='grey', clip_on=False)
self.combo_ax[0].fill_between([10, 20], [935, 935], [938, 938], color='k', clip_on=False)
self.combo_ax[0].fill_between([20, 30], [935, 935], [938, 938], color='grey', clip_on=False)
for x0 in [0, 10, 20, 30]:
con = ConnectionPatch(xyA=(x0, 930), xyB=(x0, 905), coordsA="data", coordsB="data",
axesA=self.combo_ax[0], axesB=self.combo_ax[-1], color="k", linestyle='-', zorder=10, lw=1)
self.combo_ax[-1].add_artist(con)
self.combo_ax[0].plot([x0, x0], [930, 935], color='k', lw=1, clip_on=False)
self.combo_ax[0].set_xlim(0, 30)
for a in self.combo_ax:
a.tick_params(labelsize=9)
self.combo_fig.tag(axes=self.combo_ax, labels=['A', 'B', 'C'], fontsize=15, yoffs=1.5, xoffs=-7)
plt.savefig('./fig5_tmp_ident_tracking.jpg', dpi=300)
plt.show()
self.tmp_ident_v_state = []
def static_tmp_id_assign_init(self):
t0 = self.times[self.tracking_i]
for oi, ti, ai in zip(self.origin_idx, self.target_idx, self.alt_idx):
self.fig2 = plt.figure(figsize=(17.5/2.54, 12/2.54))
gs = gridspec.GridSpec(2, 2, left=.15, bottom=.1, right=.95, top=.9, hspace=0.3, wspace=0.2)
self.ax2 = []
# self.combo_ax.append(self.combo_fig.add_subplot(gs[0, 0]))
self.ax2.append(self.fig2.add_subplot(gs[0, 0]))
self.ax2.append(self.fig2.add_subplot(gs[0, 1]))
self.ax2.append(self.fig2.add_subplot(gs[1, 0]))
self.ax2.append(self.fig2.add_subplot(gs[1, 1]))
for a in self.ax2:
a.imshow(decibel(self.spec)[::-1], extent=[self.times[0]-t0, self.times[-1]-t0, 0, 2000], aspect='auto',
alpha=0.4, cmap='Greys', vmax=-50, vmin=-110, interpolation='gaussian', zorder=1)
a.set_ylim(905, 930)
# tmp_ident_time0 = self.times[self.idx_v[~np.isnan(self.tmp_ident_v)][0]]
# self.ax2[0].set_xlim(tmp_ident_time0-10, tmp_ident_time0+30)
# tmp_ids
for tmp_id in np.unique(self.tmp_ident_v[~np.isnan(self.tmp_ident_v)]):
Cmask = np.arange(len(self.idx_v))[(self.tmp_ident_v == tmp_id) &
(self.idx_v > self.tracking_i + self.idx_comp_range) &
(self.idx_v <= self.tracking_i + 2*self.idx_comp_range)]
h, = self.ax2[0].plot(self.times[self.idx_v[self.tmp_ident_v == tmp_id]] -t0,
self.fund_v[self.tmp_ident_v == tmp_id], lw=4, alpha=0.4)
for a in self.ax2[:-1]:
c = h.get_color()
a.plot(self.times[self.idx_v[Cmask]] -t0, self.fund_v[Cmask], marker='.', color=c, markersize=4)
# ids before connect
for Cax in self.ax2[1:3]:
for id in np.unique(self.ident_v[~np.isnan(self.ident_v)]):
Cax.plot(self.times[self.idx_v[self.ident_v == id]] -t0, self.fund_v[self.ident_v == id], marker='.', markersize=4)
# ids after connect
for id in np.unique(self.final_ident_v[~np.isnan(self.final_ident_v)]):
self.ax2[3].plot(self.times[self.idx_v[self.final_ident_v == id]]-t0,
self.fund_v[self.final_ident_v == id], marker='.', markersize=4)
# connection points
self.ax2[2].plot(self.times[self.idx_v[oi]] -t0, self.fund_v[oi], marker='o', markersize=6, color='k')
self.ax2[2].plot(self.times[self.idx_v[ti]] -t0, self.fund_v[ti], marker='o', markersize=6, color='forestgreen')
self.ax2[2].plot(self.times[self.idx_v[ai]] -t0, self.fund_v[ai], marker='o', markersize=6, color='firebrick')
self.ax2[2].annotate("", xy=(self.times[self.idx_v[ti]] -t0, self.fund_v[ti]), xycoords='data',
xytext=(self.times[self.idx_v[oi]] -t0, self.fund_v[oi]), textcoords='data',
arrowprops=dict(arrowstyle="->",
connectionstyle="arc3, rad=-0.7",
lw=2),
)
# cosmetics
for Cax, Cax2 in zip(self.ax2[:2], self.ax2[2:]):
Cax.fill_between([0, 10], [932, 932], [934, 934], color='grey', clip_on=False)
Cax.fill_between([10, 20], [932, 932], [934, 934], color='k', clip_on=False)
Cax.fill_between([20, 30], [932, 932], [934, 934], color='grey', clip_on=False)
for x0 in [0, 10, 20, 30]:
con = ConnectionPatch(xyA=(x0, 930), xyB=(x0, 905), coordsA="data", coordsB="data",
axesA=Cax, axesB=Cax2, color="grey", linestyle='-',
zorder=10, lw=1)
Cax2.add_artist(con)
Cax.plot([x0, x0], [930, 932], color='grey', lw=1, clip_on=False)
Cax.set_xticks(np.arange(-10, 31, 10))
Cax.set_xticklabels([])
Cax2.set_xticks(np.arange(-10, 31, 10))
Cax2.set_xticklabels(np.arange(-10, 31, 10))
Cax2.set_xlabel('time [s]', fontsize=10)
Cax.set_xlim(-10, 30)
Cax2.set_xlim(-10, 30)
Cax.tick_params(labelsize=9)
Cax2.tick_params(labelsize=9)
self.ax2[0].set_ylabel('frequency [Hz]', fontsize=10)
self.ax2[2].set_ylabel('frequency [Hz]', fontsize=10)
plt.setp(self.ax2[1].get_yticklabels(), visible=False)
plt.setp(self.ax2[3].get_yticklabels(), visible=False)
try:
self.fig2.tag(axes=[self.ax2[0], self.ax2[2]], labels=['A', 'C'], fontsize=15, yoffs=2, xoffs=-8)
self.fig2.tag(axes=[self.ax2[1], self.ax2[3]], labels=['B', 'D'], fontsize=15, yoffs=2, xoffs=-3)
plt.savefig('fig_6assign_tmp_identities.jpg', dpi=300)
plt.savefig('assign_tmp_identities2.jpg', dpi=300)
plt.show()
except:
pass
def finalize_tmp_id_assign(self, final_ident_v):
for id in np.unique(final_ident_v[~np.isnan(final_ident_v)]):
self.ax2[3].plot(self.times[self.idx_v[final_ident_v == id]], self.fund_v[final_ident_v == id], marker='.',
markersize=4)
plt.show()
def life_tmp_ident_init(self, min_i0, max_i1):
self.fig, self.ax = plt.subplots()
self.ax.imshow(decibel(self.spec)[::-1], extent=[self.times[0], self.times[-1], 0, 2000],
aspect='auto', alpha=0.7, cmap='jet', vmax=-50, vmin=-110, interpolation='gaussian', zorder=1)
self.ax.set_xlim(self.times[self.idx_v[min_i0]], self.times[self.idx_v[max_i1]])
self.ax.set_ylim(880, 950)
# self.fig.canvas.draw()
plt.pause(0.05)
def life_tmp_ident_update(self, tmp_indet_v, new=None, update=None, delete=None):
if new:
self.handles[new], = self.ax.plot(self.times[self.idx_v[tmp_indet_v == new]], self.fund_v[tmp_indet_v == new], marker='.')
if update:
self.handles[update].set_data(self.times[self.idx_v[tmp_indet_v == update]], self.fund_v[tmp_indet_v == update])
if delete:
self.handles[delete].remove()
del self.handles[delete]
# self.fig.canvas.draw()
plt.pause(0.05)
def freq_tracking_v5(fundamentals, signatures, times, freq_tolerance= 2.5, n_channels=64, max_dt=10., ioi_fti=False,
freq_lims=(200, 1200), emit = False, **kwargs):
"""
Sorting algorithm which sorts fundamental EOD frequnecies detected in consecutive powespectra of single or
multielectrode recordings using frequency difference and frequnency-power amplitude difference on the electodes.
Signal tracking and identity assiginment is accomplished in four steps:
1) Extracting possible frequency and amplitude difference distributions.
2) Esitmate relative error between possible datapoint connections (relative amplitude and frequency error based on
frequency and amplitude error distribution).
3) For a data window covering the EOD frequencies detected 10 seconds before the accual datapoint to assigne
identify temporal identities based on overall error between two datapoints from smalles to largest.
4) Form tight connections between datapoints where one datapoint is in the timestep that is currently of interest.
Repeat these steps until the end of the recording.
The temporal identities are only updated when the timestep of current interest reaches the middle (5 sec.) of the
temporal identities. This is because no tight connection shall be made without checking the temporal identities.
The temnporal identities are used to check if the potential connection from the timestep of interest to a certain
datsapoint is the possibly best or if a connection in the futur will be better. If a future connection is better
the thight connection is not made.
Parameters
----------
fundamentals: 2d-arraylike / list
list of arrays of fundemantal EOD frequnecies. For each timestep/powerspectrum contains one list with the
respectivly detected fundamental EOD frequnecies.
signatures: 3d-arraylike / list
same as fundamentals but for each value in fundamentals contains a list of powers of the respective frequency
detected of n electrodes used.
times: array
respective time vector.
freq_tolerance: float
maximum frequency difference between two datapoints to be connected in Hz.
n_channels: int
number of channels/electodes used in the analysis.,
return_tmp_idenities: bool
only returne temporal identities at a certain timestep. Dependent on ioi_fti and only used to check algorithm.
ioi_fti: int
Index Of Interest For Temporal Identities: respective index in fund_v to calculate the temporal identities for.
a_error_distribution: array
possible amplitude error distributions for the dataset.
f_error_distribution: array
possible frequency error distribution for the dataset.
fig: mpl.figure
figure to plot the tracking progress life.
ax: mpl.axis
axis to plot the tracking progress life.
freq_lims: double
minimum/maximum frequency to be tracked.
Returns
-------
fund_v: array
flattened fundamtantals array containing all detected EOD frequencies in the recording.
ident_v: array
respective assigned identites throughout the tracking progress.
idx_v: array
respective index vectro impliing the time of the detected frequency.
sign_v: 2d-array
for each fundamental frequency the power of this frequency on the used electodes.
a_error_distribution: array
possible amplitude error distributions for the dataset.
f_error_distribution: array
possible frequency error distribution for the dataset.
idx_of_origin_v: array
for each assigned identity the index of the datapoint on which basis the assignement was made.
"""
def reshape_data():
detection_time_diff = times[1] - times[0]
dps = 1. / detection_time_diff
fund_v = np.hstack(fundamentals)
ident_v = np.full(len(fund_v), np.nan) # fish identities of frequencies
idx_of_origin_v = np.full(len(fund_v), np.nan) # ToDo: necessary ? lets see
idx_v = [] # temportal indices
sign_v = [] # power of fundamentals on all electrodes
for enu, funds in enumerate(fundamentals):
idx_v.extend(np.ones(len(funds)) * enu)
sign_v.extend(signatures[enu])
idx_v = np.array(idx_v, dtype=int)
sign_v = np.array(sign_v)
original_sign_v = sign_v
if np.shape(sign_v)[1] > 2:
sign_v = (sign_v - np.min(sign_v, axis=1).reshape(len(sign_v), 1)) / (
np.max(sign_v, axis=1).reshape(len(sign_v), 1) - np.min(sign_v, axis=1).reshape(len(sign_v), 1))
idx_comp_range = int(np.floor(dps * max_dt)) # maximum compare range backwards for amplitude signature comparison
return fund_v, ident_v, idx_v, sign_v, original_sign_v, idx_of_origin_v, idx_comp_range, dps
def clean_up(fund_v, ident_v):
"""
deletes/replaces with np.nan those identities only consisting from little data points and thus are tracking
artefacts. Identities get deleted when the proportion of the trace (slope, ratio of detected datapoints, etc.)
does not fit a real fish.
Parameters
----------
fund_v: array
flattened fundamtantals array containing all detected EOD frequencies in the recording.
ident_v: array
respective assigned identites throughout the tracking progress.
idx_v: array
respective index vectro impliing the time of the detected frequency.
times: array
respective time vector.
Returns
-------
ident_v: array
cleaned up identities vector.
"""
# print('clean up')
for ident in np.unique(ident_v[~np.isnan(ident_v)]):
if np.median(np.abs(np.diff(fund_v[ident_v == ident]))) >= 0.25:
ident_v[ident_v == ident] = np.nan
continue
if len(ident_v[ident_v == ident]) <= 10:
ident_v[ident_v == ident] = np.nan
continue
return ident_v
def get_tmp_identities(i0_m, i1_m, error_cube, fund_v, idx_v, i, ioi_fti, idx_comp_range, visualize=False, validate=False, validated_ident_v= None, **kwargs):
"""
extract temporal identities for a datasnippted of 2*index compare range of the original tracking algorithm.
for each data point in the data window finds the best connection within index compare range and, thus connects
the datapoints based on their minimal error value until no connections are left or possible anymore.
Parameters
----------
i0_m: 2d-array
for consecutive timestamps contains for each the indices of the origin EOD frequencies.
i1_m: 2d-array
respectively contains the indices of the target EOD frequencies, laying within index compare range.
error_cube: 3d-array
error values for each combination from i0_m and the respective indices in i1_m.
fund_v: array
flattened fundamtantals array containing all detected EOD frequencies in the recording.
idx_v: array
respective index vectro impliing the time of the detected frequency.
i: int
loop variable and current index of interest for the assignment of tight connections.
ioi_fti: int
index of interest for temporal identities.
dps: float
detections per second. 1. / 'temporal resolution of the tracking'
idx_comp_range: int
index compare range for the assignment of two data points to each other.
Returns
-------
tmp_ident_v: array
for each EOD frequencies within the index compare range for the current time step of interest contains the
temporal identity.
errors_to_v: array
for each assigned temporal identity contains the error value based on which this connection was made.
"""
def collect_validation_data():
origin_idx = i0_m[layer][idx0] + min_i0
target_idx = i1_m[layer][idx1] + min_i0
collect = False
if i + idx_comp_range < idx_v[origin_idx] < i + idx_comp_range * 2:
if i + idx_comp_range < idx_v[target_idx] < i + idx_comp_range * 2:
collect = True
if collect == False:
return
alt_i = np.arange(len(i1_m[layer]))[~np.isnan(error_cube[layer][idx0, :])]
alt_e = error_cube[layer][idx0, :][alt_i]
alt_idxs = i1_m[layer][~np.isnan(error_cube[layer][idx0, :])][np.argsort(alt_e)] + min_i0
alt_idxs = alt_idxs[(alt_e > error_cube[layer][idx0, idx1])] # alternatives with larger error
# embed()
# quit()
if len(np.unique(validated_ident_v[alt_idxs])) > 1:
alternatives = alt_idxs[
validated_ident_v[alt_idxs] != validated_ident_v[target_idx]] # alternative with other id (all)
alt_idx0 = alt_idxs[validated_ident_v[alt_idxs] != validated_ident_v[target_idx]][
0] # alternative with other id (best)
# alternatives = alt_idxs[np.arange(len(alt_idxs))[validated_ident_v[alt_idxs] != validated_ident_v[target_idx]]] # alternatives with othe id (all)
a_error_target = np.sqrt(
np.sum([(sign_v[origin_idx][j] - sign_v[target_idx][j]) ** 2 for j in range(n_channels)]))
f_error_target = np.abs(fund_v[origin_idx] - fund_v[target_idx])
[a_e_target, f_e_target, t_e] = estimate_error(a_error_target, f_error_target, a_error_distribution)
a_error_alt = np.sqrt(
np.sum([(sign_v[origin_idx][j] - sign_v[alt_idx0][j]) ** 2 for j in range(n_channels)]))
f_error_alt = np.abs(fund_v[origin_idx] - fund_v[alt_idx0])
[a_e_alt, f_e_alt, t_e] = estimate_error(a_error_alt, f_error_alt, a_error_distribution)
if validate:
if validated_ident_v[origin_idx] == validated_ident_v[target_idx]:
va.error_col['hit'].append(True)
va.error_col['originID'].append(validated_ident_v[origin_idx])
va.error_col['targetID'].append(validated_ident_v[target_idx])
va.error_col['target_dfreq'].append(f_error_target)
va.error_col['target_dfield'].append(a_error_target)
va.error_col['target_freq_e'].append(f_e_target)
va.error_col['target_field_e'].append(a_e_target)
va.error_col['target_signal_e'].append(np.sum([a_e_target, f_e_target]))
va.error_col['alternID'].append(validated_ident_v[alt_idx0])
va.error_col['altern_dfreq'].append(f_error_alt)
va.error_col['altern_dfield'].append(a_error_alt)
va.error_col['altern_freq_e'].append(f_e_alt)
va.error_col['altern_field_e'].append(a_e_alt)
va.error_col['altern_signal_e'].append(np.sum([a_e_alt, f_e_alt]))
elif validated_ident_v[origin_idx] == validated_ident_v[alt_idx0]:
va.error_col['hit'].append(False)
va.error_col['originID'].append(validated_ident_v[origin_idx])
va.error_col['targetID'].append(validated_ident_v[alt_idx0])
va.error_col['target_dfreq'].append(f_error_alt)
va.error_col['target_dfield'].append(a_error_alt)
va.error_col['target_freq_e'].append(f_e_alt)
va.error_col['target_field_e'].append(a_e_alt)
va.error_col['target_signal_e'].append(np.sum([a_e_alt, f_e_alt]))
va.error_col['alternID'].append(validated_ident_v[target_idx])
va.error_col['altern_dfreq'].append(f_error_target)
va.error_col['altern_dfield'].append(a_error_target)
va.error_col['altern_freq_e'].append(f_e_target)
va.error_col['altern_field_e'].append(a_e_target)
va.error_col['altern_signal_e'].append(np.sum([a_e_target, f_e_target]))
else:
return
return
show = True if (17160 <= times[i] < 17170) and visualize else False
next_tmp_identity = 0
max_shape = np.max([np.shape(layer) for layer in error_cube[1:]], axis=0)
cp_error_cube = np.full((len(error_cube) - 1, max_shape[0], max_shape[1]), np.nan)
for enu, layer in enumerate(error_cube[1:]):
cp_error_cube[enu, :np.shape(error_cube[enu + 1])[0], :np.shape(error_cube[enu + 1])[1]] = layer
min_i0 = np.min(np.hstack(i0_m))
max_i1 = np.max(np.hstack(i1_m))
tmp_ident_v = np.full(max_i1 - min_i0 + 1, np.nan)
errors_to_v = np.full(max_i1 - min_i0 + 1, np.nan)
tmp_idx_v = idx_v[min_i0:max_i1 + 1]
tmp_fund_v = fund_v[min_i0:max_i1 + 1]
i0_m = np.array(i0_m, dtype=object) - min_i0
i1_m = np.array(i1_m, dtype=object) - min_i0
layers, idx0s, idx1s = np.unravel_index(np.argsort(cp_error_cube, axis=None), np.shape(cp_error_cube))
made_connections = np.zeros(np.shape(cp_error_cube))
not_made_connections = np.zeros(np.shape(cp_error_cube))
not_made_connections[~np.isnan(cp_error_cube)] = 1
layers = layers + 1
i_non_nan = len(cp_error_cube[layers - 1, idx0s, idx1s][~np.isnan(cp_error_cube[layers - 1, idx0s, idx1s])])
if show:
da.life_tmp_ident_init(min_i0, max_i1)
for enu, layer, idx0, idx1 in zip(np.arange(i_non_nan), layers[:i_non_nan], idx0s[:i_non_nan], idx1s[:i_non_nan]):
if show:
if enu in np.array(np.floor(np.linspace(0, i_non_nan, 5)), dtype=int)[1:3]:
tmp_ident_v_ret = np.full(len(fund_v), np.nan)
tmp_ident_v_ret[min_i0:max_i1 + 1] = tmp_ident_v
da.tmp_ident_v_state.append(tmp_ident_v_ret)
if np.isnan(cp_error_cube[layer - 1, idx0, idx1]):
break
# _____ some control functions _____ ###
if not ioi_fti:
if tmp_idx_v[i1_m[layer][idx1]] - i > idx_comp_range * 3:
continue
else:
if idx_v[i1_m[layer][idx1]] - idx_v[ioi_fti] > idx_comp_range * 3:
continue
if np.isnan(tmp_ident_v[i0_m[layer][idx0]]):
if np.isnan(tmp_ident_v[i1_m[layer][idx1]]):
tmp_ident_v[i0_m[layer][idx0]] = next_tmp_identity
tmp_ident_v[i1_m[layer][idx1]] = next_tmp_identity
errors_to_v[i1_m[layer][idx1]] = cp_error_cube[layer - 1][idx0, idx1]
not_made_connections[layer - 1, idx0, idx1] = 0
made_connections[layer - 1, idx0, idx1] = 1
next_tmp_identity += 1
tmp_ident_v_ret = np.full(len(fund_v), np.nan)
tmp_ident_v_ret[min_i0:max_i1 + 1] = tmp_ident_v
if show:
if 850 < tmp_fund_v[i0_m[layer][idx0]] < 950:
da.itter_counter += 1
da.life_tmp_ident_update(tmp_ident_v_ret, new = tmp_ident_v[i0_m[layer][idx0]])
else:
mask = np.arange(len(tmp_ident_v))[tmp_ident_v == tmp_ident_v[i1_m[layer][idx1]]] # idxs of target
if tmp_idx_v[i0_m[layer][idx0]] in tmp_idx_v[mask]: # if goal already in target continue
continue
same_id_idx = np.arange(len(tmp_ident_v))[tmp_ident_v == tmp_ident_v[i1_m[layer][idx1]]]
f_after = tmp_fund_v[same_id_idx[same_id_idx > i0_m[layer][idx0]]]
f_before = tmp_fund_v[same_id_idx[same_id_idx < i0_m[layer][idx0]]]
compare_freqs = []
if len(f_after) > 0:
compare_freqs.append(f_after[0])
if len(f_before) > 0:
compare_freqs.append(f_before[-1])
if len(compare_freqs) == 0:
continue
else:
if np.all(np.abs(np.array(compare_freqs) - tmp_fund_v[i0_m[layer][idx0]]) > 0.5):
continue
tmp_ident_v[i0_m[layer][idx0]] = tmp_ident_v[i1_m[layer][idx1]]
errors_to_v[i1_m[layer][idx1]] = cp_error_cube[layer - 1][idx0, idx1]
not_made_connections[layer - 1, idx0, idx1] = 0
made_connections[layer - 1, idx0, idx1] = 1
tmp_ident_v_ret = np.full(len(fund_v), np.nan)
tmp_ident_v_ret[min_i0:max_i1 + 1] = tmp_ident_v
if show:
if 850 < tmp_fund_v[i0_m[layer][idx0]] < 950:
da.itter_counter += 1
da.life_tmp_ident_update(tmp_ident_v_ret, update = tmp_ident_v[i1_m[layer][idx1]])
else:
if np.isnan(tmp_ident_v[i1_m[layer][idx1]]):
mask = np.arange(len(tmp_ident_v))[tmp_ident_v == tmp_ident_v[i0_m[layer][idx0]]]
if tmp_idx_v[i1_m[layer][idx1]] in tmp_idx_v[mask]:
continue
# same_id_idx = np.arange(len(tmp_ident_v))[tmp_ident_v == tmp_ident_v[i0_m[layer][idx0]]]
# f_after = tmp_fund_v[same_id_idx[same_id_idx > i1_m[layer][idx1]]]
# f_before = tmp_fund_v[same_id_idx[same_id_idx < i1_m[layer][idx1]]]
# compare_freqs = []
# if len(f_after) > 0:
# compare_freqs.append(f_after[0])
# if len(f_before) > 0:
# compare_freqs.append(f_before[-1])
# if len(compare_freqs) == 0:
# continue
# else:
# if np.all(np.abs(np.array(compare_freqs) - tmp_fund_v[i1_m[layer][idx1]]) > 0.5):
# continue
tmp_ident_v[i1_m[layer][idx1]] = tmp_ident_v[i0_m[layer][idx0]]
errors_to_v[i1_m[layer][idx1]] = cp_error_cube[layer - 1][idx0, idx1]
not_made_connections[layer - 1, idx0, idx1] = 0
made_connections[layer - 1, idx0, idx1] = 1
tmp_ident_v_ret = np.full(len(fund_v), np.nan)
tmp_ident_v_ret[min_i0:max_i1 + 1] = tmp_ident_v
if show: