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05.10

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xaver
2020-10-05 15:25:49 +02:00
parent 90d9b19d9a
commit d231c75806
7 changed files with 112 additions and 188 deletions
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4
apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
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@@ -7,7 +7,7 @@ from matplotlib.mlab import specgram
import os
from jar_functions import gain_curve_fit
identifier = ['2018lepto1', '2018lepto4', '2018lepto5', '2018lepto76']
identifier = ['2020lepto06']
tau = []
f_c = []
@@ -42,7 +42,7 @@ for ID in identifier:
ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('envelope_frequency [Hz]')
ax.set_title('gaincurve %s' %ID)
plt.legend()
plt.legend(loc = 'lower left')
plt.show()

111
apteronotus_code/figure_apteronotus_jar_filter_fit.py
View File

@@ -17,7 +17,7 @@ from jar_functions import gain_curve_fit
def take_second(elem): # function for taking the names out of files
return elem[1]
identifier = ['2018lepto1']
identifier = ['2018lepto98']
for ident in identifier:
predict = []
@@ -35,16 +35,16 @@ for ident in identifier:
currf = None
idxlist = []
data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second) # list with filenames in it
for i, d in enumerate(data):
dd = list(d)
if dd[1] == '0.005':
jar = np.load('%s.npy' %dd) # load data for every file name
if dd[1] == '0.5':
jar = np.load('5Hz_%s.npy' %dd) # load data for every file name
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
print(dd)
time = np.load('%s time.npy' %dd) # time file
time = np.load('5Hz_%s time.npy' %dd) # time file
dt = time[1] - time[0]
n = int(1/float(d[1])/dt)
@@ -54,10 +54,11 @@ for ident in identifier:
sinv, sinc = curve_fit(sin_response, time, jm - cutf, [float(d[1]), 2, 0.5]) # fitting
print('frequency, phaseshift, amplitude:', sinv)
p = sinv[1]
A = np.sqrt(sinv[2] ** 2)
f = float(d[1])
if sinv[2] < 0:
A = sinv[2]
if A < 0:
p = p + np.pi
A = -A
f = float(d[1])
phaseshift.append(p)
gain.append(A)
if f not in amfreq:
@@ -66,7 +67,7 @@ for ident in identifier:
# jar trace
plt.plot(time, jar, color = 'C0')
#plt.hlines(y=np.min(jar) - 2, xmin=0, xmax=400, lw=2.5, color='r', label='stimulus duration')
plt.title('JAR trace 2018lepto1, AM-frequency:%sHz' % float(d[1]))
plt.title('JAR trace 2018lepto98, AM-frequency:%sHz, deltaf = -5Hz' % float(d[1]))
plt.xlabel('time[s]')
plt.ylabel('frequency[Hz]')
plt.show()
@@ -81,7 +82,7 @@ for ident in identifier:
# filter by running average
plt.plot(time, jm, color = 'C0', label = 'JAR: subtracted by mean')
plt.plot(time, jm - cutf, color = 'darkorange', label = 'JAR: subtracted by mean and step response')
plt.title('JAR trace spectogram 2018lepto1: subtraction of mean and step response')
plt.title('JAR trace spectogram 2018lepto98: subtraction of mean and step response, deltaf = -5Hz')
plt.xlabel('time[s]')
plt.ylabel('frequency[Hz]')
plt.legend()
@@ -89,96 +90,12 @@ for ident in identifier:
# jar trace and fit
plt.plot(time, jm - cutf, color = 'darkorange', label = 'JAR: subtracted by mean and step response')
phase_gain = [(((sinv[1] % (2 * np.pi)) * 360) / (2 * np.pi)), sinv[2]]
phase_gain = [(((p % (2 * np.pi)) * 360) / (2 * np.pi)), A]
plt.plot(time, sin_response(time, *sinv), color = 'limegreen', label='fit: phaseshift=%.2f°, gain=%.2f[Hz/(mV/cm)]' % tuple(phase_gain))
plt.title('JAR trace spectogram 2018lepto1 with fit')
plt.title('JAR trace spectogram 2018lepto98 with fit, deltaf = -5Hz')
plt.xlabel('time[s]')
plt.ylabel('frequency[Hz]')
plt.legend()
plt.show()
# root mean square
RMS = np.sqrt(np.mean(((jm - cutf) - sin_response(cutt, sinv[0], sinv[1], sinv[2]))**2))
thresh = A / np.sqrt(2)
# mean over same amfreqs for phase and gain
if currf is None or currf == d[1]:
currf = d[1]
idxlist.append(i)
else: # currf != f
meanf = [] # lists to make mean of
meanp = []
meanrms = []
meanthresh = []
for x in idxlist:
meanf.append(gain[x])
meanp.append(phaseshift[x])
meanrms.append(RMS)
meanthresh.append(thresh)
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
meanedrms = np.mean(meanrms)
meanedthresh = np.mean(meanthresh)
mgain.append(meanedf)
mphaseshift.append(meanedp)
rootmeansquare.append(meanedrms)
threshold.append(meanedthresh)
currf = d[1] # set back for next loop
idxlist = [i]
meanf = []
meanp = []
meanrms = []
meanthresh = []
for y in idxlist:
meanf.append(gain[y])
meanp.append(phaseshift[y])
meanrms.append(RMS)
meanthresh.append(thresh)
meanedf = np.mean(meanf)
meanedp = np.mean(meanp)
meanedrms = np.mean(meanrms)
meanedthresh = np.mean(meanthresh)
mgain.append(meanedf)
mphaseshift.append(meanedp)
rootmeansquare.append(meanedrms)
threshold.append(meanedthresh)
# as arrays
mgain_arr = np.array(mgain)
mphaseshift_arr = np.array(mphaseshift)
amfreq_arr = np.array(amfreq)
rootmeansquare_arr = np.array(rootmeansquare)
threshold_arr = np.array(threshold)
# condition needed to be fulfilled: RMS < threshold or RMS < mean(RMS)
idx_arr = (rootmeansquare_arr < threshold_arr) | (rootmeansquare_arr < np.mean(rootmeansquare_arr))
fig = plt.figure()
ax0 = fig.add_subplot(2, 1, 1)
ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
ax0.set_yscale('log')
ax0.set_xscale('log')
ax0.set_title('%s' % data[0][0])
ax0.set_ylabel('gain [Hz/(mV/cm)]')
ax0.set_xlabel('envelope_frequency [Hz]')
#plt.savefig('%s gain' % data[0][0])
ax1 = fig.add_subplot(2, 1, 2, sharex = ax0)
ax1.plot(amfreq, threshold, 'o-', label = 'threshold', color = 'b')
ax1.set_xscale('log')
ax1.plot(amfreq, rootmeansquare, 'o-', label = 'RMS', color ='orange')
ax1.set_xscale('log')
ax1.set_xlabel('envelope_frequency [Hz]')
ax1.set_ylabel('RMS [Hz]')
plt.legend()
pylab.show()
#np.save('phaseshift_%s' % ident, mphaseshift_arr[idx_arr])
#np.save('gain_%s' %ident, mgain_arr[idx_arr])
#np.save('amf_%s' %ident, amfreq_arr[idx_arr])
embed()
embed()

10
apteronotus_code/figure_apteronotus_rms_gaincurve.py
View File

@@ -17,7 +17,7 @@ from jar_functions import gain_curve_fit
def take_second(elem): # function for taking the names out of files
return elem[1]
identifier = ['2018lepto1']
identifier = ['2019lepto03']
for ident in identifier:
predict = []
@@ -35,16 +35,16 @@ for ident in identifier:
currf = None
idxlist = []
data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second) # list with filenames in it
for i, d in enumerate(data):
dd = list(d)
jar = np.load('%s.npy' %dd) # load data for every file name
jar = np.load('5Hz_%s.npy' %dd) # load data for every file name
jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
print(dd)
time = np.load('%s time.npy' %dd) # time file
time = np.load('5Hz_%s time.npy' %dd) # time file
dt = time[1] - time[0]
n = int(1/float(d[1])/dt)
@@ -127,7 +127,7 @@ for ident in identifier:
ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
ax0.set_yscale('log')
ax0.set_xscale('log')
ax0.set_title('gaincurve 2018lepto1')
ax0.set_title('gaincurve 2019lepto03, deltaf = -5Hz')
ax0.set_ylabel('gain [Hz/(mV/cm)]')
ax0.set_xlabel('envelope_frequency [Hz]')
#plt.savefig('%s gain' % data[0][0])

40
apteronotus_code/sin_all_normal.py
View File

@@ -7,17 +7,17 @@ from jar_functions import gain_curve_fit
from jar_functions import avgNestedLists
identifier = ['2018lepto1',
'2018lepto4',
'2018lepto5',
'2018lepto76',
identifier = [#'2018lepto1',
#'2018lepto4',
#'2018lepto5',
#'2018lepto76',
'2018lepto98',
'2019lepto03',
'2019lepto24',
'2019lepto27',
'2019lepto30',
'2020lepto04',
'2020lepto06',
#'2019lepto03',
#'2019lepto24',
#'2019lepto27',
#'2019lepto30',
#'2020lepto04',
#'2020lepto06',
'2020lepto16',
'2020lepto19',
'2020lepto20'
@@ -28,14 +28,14 @@ amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
all = []
for ident in identifier:
data = np.load('gain_%s.npy' %ident)
data = np.load('5Hz_gain_%s.npy' %ident)
all.append(data)
av = avgNestedLists(all)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(amf, av, 'o')
ax.plot(amf, av, 'o', c = 'C0', label = 'gain')
#plt.show()
@@ -45,8 +45,8 @@ fit = []
fit_amf = []
for ID in identifier:
print(ID)
amf = np.load('amf_%s.npy' %ID)
gain = np.load('gain_%s.npy' %ID)
amf = np.load('5Hz_amf_%s.npy' %ID)
gain = np.load('5Hz_gain_%s.npy' %ID)
sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
#print('tau:', sinv[0])
@@ -56,17 +56,19 @@ for ID in identifier:
f_c.append(f_cutoff)
fit.append(gain_curve_fit(amf, *sinv))
fit_amf.append(amf)
col = plt.cm.magma(np.linspace(0,0.8,len(fit)))
for ff ,f in enumerate(fit):
ax.plot(fit_amf[ff], fit[ff], c = col[ff])
ax.axvline(x=f_c[ff], ymin=0, ymax=5, alpha=0.8, c = col[ff]) # colors_uniform[ff])
#for ff ,f in enumerate(fit):
# ax.plot(fit_amf[ff], fit[ff])
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_title('gaincurve_average_allfish')
ax.set_title('gain average all fish, deltaf: -5Hz')
ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('envelope_frequency [Hz]')
ax.set_ylim(0.0008, )
ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', label = 'cutoff frequencies')
ax.legend()
#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'cutoff frequencies')
ax.legend(loc = 'center left')
plt.show()

63
apteronotus_code/sin_all_uniform.py
View File

@@ -9,19 +9,19 @@ import matplotlib as mpl
from matplotlib import cm
identifier_uniform = ['2018lepto1',
# '2018lepto4',
# '2018lepto5',
#'2018lepto76',
#'2018lepto4',
'2018lepto5',
'2018lepto76',
'2018lepto98',
# '2019lepto03',
#'2019lepto03',
'2019lepto24',
#'2019lepto27',
# '2019lepto30',
'2020lepto04',
# '2020lepto06',
# '2020lepto16',
'2020lepto19',
# '2020lepto20'
#'2019lepto30',
#'2020lepto04',
'2020lepto06',
#'2020lepto16',
#'2020lepto19',
#'2020lepto20'
]
identifier = ['2018lepto1',
'2018lepto4',
@@ -32,10 +32,10 @@ identifier = ['2018lepto1',
'2019lepto24',
'2019lepto27',
'2019lepto30',
'2020lepto04',
#'2020lepto04',
'2020lepto06',
'2020lepto16',
'2020lepto19',
#'2020lepto19',
'2020lepto20'
]
@@ -56,15 +56,15 @@ new_av = avgNestedLists(new_all)
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.plot(amf, av, 'o', color = 'orange', label = 'normal')
ax.plot(amf, new_av, 'o', label = 'uniformed')
"""
ax.plot(amf, av, 'o', color = 'darkorange', label = 'normal')
ax.plot(amf, new_av, 'o', label = 'uniform')
tau = []
f_c = []
fit = []
fit_amf = []
for ID in identifier:
#print(ID)
print(ID)
amf = np.load('amf_%s.npy' %ID)
gain = np.load('gain_%s.npy' %ID)
@@ -72,47 +72,46 @@ for ID in identifier:
#print('tau:', sinv[0])
tau.append(sinv[0])
f_cutoff = abs(1 / (2*np.pi*sinv[0]))
#print('f_cutoff:', f_cutoff)
print('f_cutoff:', f_cutoff)
f_c.append(f_cutoff)
fit.append(gain_curve_fit(amf, *sinv))
fit_amf.append(amf)
"""
# uniformed: 2018lepto5, 2018lepto1, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto4
tau_uniform = []
f_c_uniform = []
fit_uniform = []
fit_amf_uniform = []
for ID in identifier_uniform:
#print(ID)
print(ID)
amf = np.load('amf_%s.npy' %ID)
gain = np.load('gain_%s.npy' %ID)
sinv, sinc = curve_fit(gain_curve_fit, amf, gain)
#print('tau:', sinv[0])
print('tau:', sinv[0])
tau_uniform.append(sinv[0])
f_cutoff = abs(1 / (2*np.pi*sinv[0]))
#print('f_cutoff:', f_cutoff)
print('f_cutoff:', f_cutoff)
f_c_uniform.append(f_cutoff)
fit_uniform.append(gain_curve_fit(amf, *sinv))
fit_amf_uniform.append(amf)
colors_uniform = plt.cm.flag(np.linspace(0.2,0.8,len(fit_uniform)))
#colors = plt.cm.flag(np.linspace(0.2,0.8,len(fit)))
colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
# for ff ,f in enumerate(fit):
# ax.plot(fit_amf[ff], fit[ff],color = colors[ff])
# ax.axvline(x=f_c[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colors[ff])#colors_uniform[ff])
for ff, f in enumerate(fit_uniform):
ax.plot(fit_amf_uniform[ff], fit_uniform[ff], color = colorss[ff]) #colors_uniform[ff])
ax.axvline(x=f_c_uniform[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colorss[ff])#colors_uniform[ff])
#for ff ,f in enumerate(fit_uniform):
# ax.plot(fit_amf_uniform[ff], fit_uniform[ff],color = colorss[ff])
# ax.axvline(x=f_c_uniform[ff], ymin=0, ymax=5, ls = '-', alpha = 0.5, color= colorss[ff])#colors_uniform[ff])
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_title('gaincurve_average_allfish')
ax.set_title('gain average all fish uniform')
ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('envelope_frequency [Hz]')
ax.set_ylim(0.0008, )
ax.legend()
ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'normal cutoff frequencies')
ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform cutoff frequencies')
ax.legend(loc = 'center left')
plt.show()
embed()