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xaver
2020-10-19 12:29:04 +02:00
parent d231c75806
commit b53890c7fc
11 changed files with 311 additions and 64 deletions
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42
eigenmannia_code/eigenmannia_jar_stacked.py
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@@ -12,10 +12,14 @@ from jar_functions import get_time_zeros
from jar_functions import import_data_eigen
from scipy.signal import savgol_filter
plt.rcParams.update({'font.size': 18})
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
#2015eigen8 no nix files
identifier = ['2015eigen16', '2013eigen13','2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
identifier = [#'2013eigen13',
'2015eigen16','2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
response = []
deltaf = []
@@ -28,8 +32,8 @@ for ID in identifier:
delta_f, duration = parse_stimuli_dat(stimuli_dat)
dur = int(duration[0][0:2])
print(delta_f)
if delta_f ==[-2.0]:
print('HANDLE WITH CARE -2Hz:', datapath)
if delta_f != [4.0]:
continue
data, pre_data, dt = import_data_eigen(datapath)
#hstack concatenate: 'glue' pre_data and data
@@ -49,7 +53,7 @@ for ID in identifier:
eodf4 = eodf * 4
lim0 = eodf4 - 40
lim1 = eodf4 + 40
lim1 = eodf4 + 60
df = freqs[1] - freqs[0]
ix0 = int(np.floor(lim0/df)) # back to index
@@ -60,16 +64,6 @@ for ID in identifier:
cut_time_jar = times[:len(jar4)]
ID_delta_f = [ID, str(delta_f[0]).split('.')[0]]
plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0] - 10, times[-1] - 10, lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
plt.plot((cut_time_jar - 10), jar4, 'k', label = 'jar trace', lw = 2)
plt.hlines(y=lim0 + 5, xmin=0, xmax=60, lw=2.5, color='gold', label='stimulus duration')
plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
plt.xlim(right=times[-1] - 10)
plt.legend()
#plt.show()
delta_f_ID = [str(delta_f[0]).split('.')[0], ID]
plt.savefig('%sHz_specgram_jar_%s' %tuple(delta_f_ID))
plt.close()
b = []
for idx, i in enumerate(times):
@@ -81,10 +75,28 @@ for ID in identifier:
j.append(jar4[idx])
r = np.median(j) - np.median(b)
print(r)
print('response:', r)
deltaf.append(delta_f[0])
response.append(r)
plt.figure(figsize = (14,8))
plt.imshow(spec4, cmap='jet', origin='lower', extent=(times[0], times[-1], lim0, lim1), aspect='auto', vmin=-80, vmax=-10)
plt.plot(cut_time_jar, jar4, 'k', label = 'peak detection trace', lw = 2)
plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='yellow', label='stimulus duration')
plt.hlines(y=lim0 + 5, xmin=0, xmax=10, lw=4, color='red', label='pause')
plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
plt.xlim(times[0],times[-1])
#embed()
#plt.xticks((times[0], 10, 20, 30, 40, 50, 60, times[-1]), [0, 10, 20, 30 ,40, 50, 60, 70])
plt.xlabel('time [s]')
plt.ylabel('frequency [Hz]')
plt.legend(loc = 'best')
plt.show()
delta_f_ID = [str(delta_f[0]).split('.')[0], ID]
plt.close()
res_df = sorted(zip(deltaf,response))
#np.save('res_df_%s_new' %ID, res_df)

138
eigenmannia_code/eigenmannia_jar_subplot.py Normal file
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@@ -0,0 +1,138 @@
import matplotlib.pyplot as plt
import numpy as np
import os
import nix_helpers as nh
from IPython import embed
from matplotlib.mlab import specgram
#from tqdm import tqdm
from jar_functions import parse_stimuli_dat
from jar_functions import norm_function_eigen
from jar_functions import mean_noise_cut_eigen
from jar_functions import get_time_zeros
from jar_functions import import_data_eigen
from scipy.signal import savgol_filter
plt.rcParams.update({'font.size': 18})
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
#2015eigen8 no nix files
identifier = [#'2013eigen13',
'2015eigen16'] #,'2015eigen17', '2015eigen19', '2020eigen22','2020eigen32']
response = []
deltaf = []
specs = []
jars = []
sub_times = []
sub_lim0 = []
sub_lim1 = []
for ID in identifier:
for dataset in os.listdir(os.path.join(base_path, ID)):
datapath = os.path.join(base_path, ID, dataset, '%s.nix' % dataset)
#print(datapath)
stimuli_dat = os.path.join(base_path, ID, dataset, 'manualjar-eod.dat')
#print(stimuli_dat)
delta_f, duration = parse_stimuli_dat(stimuli_dat)
dur = int(duration[0][0:2])
if delta_f == [-2.0] or delta_f == [2.0] or delta_f == [-10.0] or delta_f == [10.0]:
print(delta_f)
data, pre_data, dt = import_data_eigen(datapath)
# hstack concatenate: 'glue' pre_data and data
dat = np.hstack((pre_data, data))
# data
nfft = 2 ** 17
spec, freqs, times = specgram(dat[0], Fs=1 / dt, detrend='mean', NFFT=nfft, noverlap=nfft * 0.95)
dbspec = 10.0 * np.log10(spec) # in dB
power = dbspec[:, 25]
fish_p = power[(freqs > 200) & (freqs < 1000)]
fish_f = freqs[(freqs > 200) & (freqs < 1000)]
index = np.argmax(fish_p)
eodf = fish_f[index]
eodf4 = eodf * 4
lim0 = eodf4 - 40
lim1 = eodf4 + 40
df = freqs[1] - freqs[0]
ix0 = int(np.floor(lim0 / df)) # back to index
ix1 = int(np.ceil(lim1 / df)) # back to index
spec4 = dbspec[ix0:ix1, :]
freq4 = freqs[ix0:ix1]
jar4 = freq4[np.argmax(spec4, axis=0)] # all freqs at max specs over axis 0
cut_time_jar = times[:len(jar4)]
ID_delta_f = [ID, str(delta_f[0]).split('.')[0]]
b = []
for idx, i in enumerate(times):
if i > 0 and i < 10:
b.append(jar4[idx])
j = []
for idx, i in enumerate(times):
if i > 15 and i < 55:
j.append(jar4[idx])
r = np.median(j) - np.median(b)
print('response:', r)
deltaf.append(delta_f[0])
response.append(r)
specs.append(spec4)
jars.append(jar4)
sub_times.append(cut_time_jar)
sub_lim0.append(lim0)
sub_lim1.append(lim1)
if len(specs) == 4:
break
# plt.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
# plt.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
# plt.hlines(y=lim0 + 5, xmin=10, xmax=70, lw=4, color='yellow', label='stimulus duration')
# plt.hlines(y=lim0 + 5, xmin=0, xmax=10, lw=4, color='red', label='pause')
# plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
# plt.xlim(times[0],times[-1])
fig = plt.figure(figsize = (20,20))
ax0 = fig.add_subplot(221)
ax0.imshow(specs[0], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[0], sub_lim1[0]), aspect='auto', vmin=-80, vmax=-10)
ax0.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)
ax0.set_xlim(times[0],times[-1])
ax0.set_ylabel('frequency [Hz]')
ax0.axes.xaxis.set_ticklabels([])
ax0.set_title('∆F -2 Hz')
ax1 = fig.add_subplot(222)
ax1.imshow(specs[1], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[1], sub_lim1[1]), aspect='auto', vmin=-80, vmax=-10)
ax1.plot(sub_times[1], jars[1], 'k', label = 'peak detection trace', lw = 2)
ax1.set_xlim(times[0],times[-1])
ax1.axes.xaxis.set_ticklabels([])
ax1.axes.yaxis.set_ticklabels([])
ax1.set_title('∆F -10 Hz')
ax2 = fig.add_subplot(223)
ax2.imshow(specs[2], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[2], sub_lim1[2]), aspect='auto', vmin=-80, vmax=-10)
ax2.plot(sub_times[2], jars[2], 'k', label = 'peak detection trace', lw = 2)
ax2.set_xlim(times[0],times[-1])
ax2.set_ylabel('frequency [Hz]')
ax2.set_xlabel('time [s]')
ax2.set_title('∆F 2 Hz')
ax3 = fig.add_subplot(224)
ax3.imshow(specs[3], cmap='jet', origin='lower', extent=(times[0], times[-1], sub_lim0[3], sub_lim1[3]), aspect='auto', vmin=-80, vmax=-10)
ax3.plot(sub_times[3], jars[3], 'k', label = 'peak detection trace', lw = 2)
ax3.set_xlim(times[0],times[-1])
ax3.set_xlabel('time [s]')
ax3.axes.yaxis.set_ticklabels([])
ax3.set_title('∆F 10 Hz')
plt.show()
embed()