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xaver 2020-10-23 17:42:53 +02:00
parent 1797c75a6f
commit ebf365dbe0
7 changed files with 80 additions and 73 deletions
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50
apteronotus_code/figure_apteronotus_gain_plot.py
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@ -7,6 +7,8 @@ from matplotlib.mlab import specgram
import os
from jar_functions import gain_curve_fit
plt.rcParams.update({'font.size': 10})
identifier = ['2018lepto1',
#'2018lepto4',
'2018lepto5',
@ -52,81 +54,73 @@ for ID in identifier:
sort = sorted(zip(f_cs, identifier))
print(sort)
# order of plotting: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2019lepto24, 2020lepto06
# order of f_c: 2019lepto24, 2020lepto06, 2018lepto98, 2018lepto76, 2018lepto1, 2018lepto5
fig = plt.figure(figsize=(8.27,11.69))
ax0 = fig.add_subplot(321)
fig.text(0.05, 0.5, 'gain [Hz/(mV/cm)]', ha='center', va='center', rotation='vertical')
fig.text(0.5, 0.04, 'envelope frequency [Hz]', ha='center', va='center')
ax0.set_xlim(0.0007, 1.5)
ax0.set_ylim(0.001, 10)
ax0.plot(amfs[4], gains[4],'o' , label = 'gain')
ax0.plot(amfs[4], predicts[4], label = 'fit')
ax0.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax0.set_xscale('log')
ax0.set_yscale('log')
ax0.set_ylabel('gain [Hz/(mV/cm)]')
#ax0.set_xlabel('envelope_frequency [Hz]')
#ax0.set_title('gaincurve %s' %ID)
ax0.axes.xaxis.set_ticklabels([])
ax1 = fig.add_subplot(322)
ax1.set_xlim(0.0007, 1.5)
ax1.get_shared_y_axes().join(ax0, ax1)
ax1.axes.yaxis.set_ticklabels([])
ax1.set_ylim(0.001, 10)
ax1.plot(amfs[5], gains[5],'o' , label = 'gain')
ax1.plot(amfs[5], predicts[5], label = 'fit')
ax1.axvline(x=f_cs[5], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax1.set_xscale('log')
ax1.set_yscale('log')
#ax1.set_ylabel('gain [Hz/(mV/cm)]')
#ax1.set_xlabel('envelope_frequency [Hz]')
#ax1.set_title('gaincurve %s' %ID)
ax1.axes.yaxis.set_ticklabels([])
ax1.axes.xaxis.set_ticklabels([])
ax2 = fig.add_subplot(323)
ax2.set_xlim(0.0007, 1.5)
ax2.plot(amfs[4], gains[4],'o' , label = 'gain')
ax2.plot(amfs[4], predicts[4], label = 'fit')
ax2.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax2.set_ylim(0.001, 10)
ax2.plot(amfs[3], gains[3],'o' , label = 'gain')
ax2.plot(amfs[3], predicts[3], label = 'fit')
ax2.axvline(x=f_cs[3], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax2.set_xscale('log')
ax2.set_yscale('log')
ax2.set_ylabel('gain [Hz/(mV/cm)]')
#ax2.set_xlabel('envelope_frequency [Hz]')
#ax2.set_title('gaincurve %s' %ID)
ax2.axes.xaxis.set_ticklabels([])
ax3 = fig.add_subplot(324)
ax3.set_xlim(0.0007, 1.5)
ax3.get_shared_y_axes().join(ax2, ax3)
ax3.axes.yaxis.set_ticklabels([])
ax3.set_ylim(0.001, 10)
ax3.plot(amfs[2], gains[2],'o' , label = 'gain')
ax3.plot(amfs[2], predicts[2], label = 'fit')
ax3.axvline(x=f_cs[2], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax3.set_xscale('log')
ax3.set_yscale('log')
#ax3.set_ylabel('gain [Hz/(mV/cm)]')
#ax3.set_xlabel('envelope_frequency [Hz]')
#ax3.set_title('gaincurve %s' %ID)
ax3.axes.yaxis.set_ticklabels([])
ax3.axes.xaxis.set_ticklabels([])
ax4 = fig.add_subplot(325)
ax4.set_xlim(0.0007, 1.5)
#ax4.get_shared_y_axes().join(ax0, ax4)
ax4.set_ylim(0.001, 10)
ax4.plot(amfs[0], gains[0],'o' , label = 'gain')
ax4.plot(amfs[0], predicts[0], label = 'fit')
ax4.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax4.set_xscale('log')
ax4.set_yscale('log')
ax4.set_ylabel('gain [Hz/(mV/cm)]')
#ax4.set_xlabel('envelope_frequency [Hz]')
#ax4.set_title('gaincurve %s' %ID)
ax5 = fig.add_subplot(326)
ax5.set_xlim(0.0007, 1.5)
ax5.axes.yaxis.set_ticklabels([])
ax5.get_shared_y_axes().join(ax4, ax5)
ax5.set_ylim(0.001, 10)
ax5.plot(amfs[1], gains[1],'o' , label = 'gain')
ax5.plot(amfs[1], predicts[1], label = 'fit')
ax5.axvline(x=f_cs[1], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax5.set_xscale('log')
ax5.set_yscale('log')
#ax5.set_ylabel('gain [Hz/(mV/cm)]')
#ax5.set_xlabel('envelope_frequency [Hz]')
#ax5.set_title('gaincurve %s' %ID)
ax5.axes.yaxis.set_ticklabels([])
#plt.legend(loc = 'lower left')
plt.show()

4
apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
View File

@ -35,8 +35,8 @@ for ID in identifier:
fig = plt.figure()
ax = fig.add_subplot()
ax.plot(amf, gain,'o' , label = 'gain')
#ax.plot(amf, predict, label = 'fit')
#ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax.plot(amf, predict, label = 'fit')
ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylabel('gain [Hz/(mV/cm)]')

20
apteronotus_code/figure_apteronotus_jar_plot.py
View File

@ -13,22 +13,22 @@ from jar_functions import sin_response
from jar_functions import mean_noise_cut
from jar_functions import gain_curve_fit
plt.rcParams.update({'font.size': 10})
#plt.rcParams.update({'font.size': 10})
def take_second(elem): # function for taking the names out of files
return elem[1]
identifier = [#'2018lepto1',
#'2018lepto4',
#'2018lepto5',
#'2018lepto76',
identifier = ['2018lepto1',
'2018lepto4',
'2018lepto5',
'2018lepto76',
'2018lepto98',
'2019lepto03',
#'2019lepto24',
#'2019lepto27',
#'2019lepto30',
#'2020lepto04',
#'2020lepto06',
'2019lepto24',
'2019lepto27',
'2019lepto30',
'2020lepto04',
'2020lepto06',
'2020lepto16',
'2020lepto19',
'2020lepto20'

10
apteronotus_code/figure_apteronotus_rms_gaincurve.py
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@ -19,7 +19,7 @@ plt.rcParams.update({'font.size': 12})
def take_second(elem): # function for taking the names out of files
return elem[1]
identifier = ['2020lepto16']
identifier = ['2020lepto19']
for ident in identifier:
predict = []
@ -37,16 +37,16 @@ for ident in identifier:
currf = None
idxlist = []
data = sorted(np.load('5Hz_%s files.npy' %ident), key = take_second) # list with filenames in it
data = sorted(np.load('%s files.npy' %ident), key = take_second) # list with filenames in it
for i, d in enumerate(data):
dd = list(d)
jar = np.load('5Hz_%s.npy' %dd) # load data for every file name
jar = np.load('%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('5Hz_%s time.npy' %dd) # time file
time = np.load('%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:
fig = plt.figure(figsize = (8,14))
fig.suptitle('gaincurve and RMS %s' %ident)
ax0 = fig.add_subplot(2, 1, 1)
ax0.plot(amfreq_arr, mgain_arr, 'o')
ax0.plot(amfreq_arr[idx_arr], mgain_arr[idx_arr], 'o')
ax0.set_yscale('log')
ax0.set_xscale('log')
ax0.set_ylabel('gain [Hz/(mV/cm)]')

39
apteronotus_code/sin_all_uniform.py → apteronotus_code/sin_all.py
View File

@ -8,6 +8,8 @@ from jar_functions import avgNestedLists
import matplotlib as mpl
from matplotlib import cm
#plt.rcParams.update({'font.size': 18})
identifier_uniform = ['2018lepto1',
#'2018lepto4',
'2018lepto5',
@ -32,14 +34,14 @@ identifier = ['2018lepto1',
'2019lepto24',
'2019lepto27',
'2019lepto30',
#'2020lepto04',
'2020lepto04',
'2020lepto06',
'2020lepto16',
#'2020lepto19',
'2020lepto19',
'2020lepto20'
]
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
custom_amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
#colors = ['dimgray', 'dimgrey', 'gray', 'grey', 'darkgray', 'darkgrey', 'silver', 'lightgray', 'lightgrey', 'gainsboro', 'whitesmoke']
colorss = ['g', 'b', 'r', 'y', 'c', 'm', 'k']
all = []
@ -54,10 +56,16 @@ for ident in identifier_uniform:
av = avgNestedLists(all)
new_av = avgNestedLists(new_all)
fig = plt.figure()
fig = plt.figure(figsize=(8.27, 11.69/2))
ax = fig.add_subplot(111)
ax.plot(amf, av, 'o', color = 'darkorange', label = 'normal')
ax.plot(amf, new_av, 'o', label = 'uniform')
ax.plot(custom_amf, av, 'o', label = 'normal')
#ax.plot(amf, new_av, 'o', label = 'uniform')
sinv, sinc = curve_fit(gain_curve_fit, custom_amf, av, [2, 3])
predict = []
for f in custom_amf:
G = np.max(av) / np.sqrt(1 + (2 * ((np.pi * f * sinv[0]) ** 2)))
predict.append(G)
tau = []
f_c = []
@ -76,8 +84,10 @@ for ID in identifier:
f_c.append(f_cutoff)
fit.append(gain_curve_fit(amf, *sinv))
fit_amf.append(amf)
ax.axvline(x=f_cutoff, ymin=0, ymax=5, color='C0', ls='-', alpha=0.5, label='cutoff frequency')
# uniformed: 2018lepto5, 2018lepto1, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto4
# uniformed: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2020lepto06, 2019lepto24, 2020lepto06
tau_uniform = []
f_c_uniform = []
@ -98,6 +108,7 @@ for ID in identifier_uniform:
fit_uniform.append(gain_curve_fit(amf, *sinv))
fit_amf_uniform.append(amf)
colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
#for ff ,f in enumerate(fit_uniform):
@ -106,13 +117,15 @@ colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
ax.set_xscale('log')
ax.set_yscale('log')
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.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')
ax.set_xlabel('envelope frequency [Hz]')
ax.set_xlim(0.0007, 1.5)
ax.set_ylim(0.001, 10)
ax.plot(custom_amf, predict)
#ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', color = 'C0', alpha = 0.5, 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()

26
apteronotus_code/sin_all_normal.py → apteronotus_code/sin_all_model.py
View File

@ -7,20 +7,20 @@ 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',
#'2020lepto16',
'2019lepto03',
'2019lepto24',
'2019lepto27',
'2019lepto30',
'2020lepto04',
'2020lepto06',
'2020lepto16',
'2020lepto19',
#'2020lepto20'
'2020lepto20'
]
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1]
@ -62,7 +62,7 @@ for ident in identifier:
av = avgNestedLists(all)
embed()
fig = plt.figure()
fig = plt.figure(figsize=(8.27,11.69/2))
ax = fig.add_subplot(111)
ax.plot(amf, av, 'o', c = 'C0', label = 'gain')

4
eigenmannia_code/eigenmannia_jar_subplot.py
View File

@ -12,7 +12,7 @@ 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})
#plt.rcParams.update({'font.size': 18})
base_path = 'D:\\jar_project\\JAR\\eigenmannia\\deltaf'
@ -99,7 +99,7 @@ for ID in identifier:
# plt.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
# plt.xlim(times[0],times[-1])
fig = plt.figure(figsize = (20,20))
fig = plt.figure(figsize = (8.27, 11.69/2))
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)