xaveros/jar_project
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

23.10

Browse Source
This commit is contained in:
xaver 2020-10-23 17:42:53 +02:00
parent 1797c75a6f
commit ebf365dbe0
7 changed files with 80 additions and 73 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

50
apteronotus_code/figure_apteronotus_gain_plot.py
View File

@ -7,6 +7,8 @@ from matplotlib.mlab import specgram
import os import os
from jar_functions import gain_curve_fit from jar_functions import gain_curve_fit
plt.rcParams.update({'font.size': 10})
identifier = ['2018lepto1', identifier = ['2018lepto1',
#'2018lepto4', #'2018lepto4',
'2018lepto5', '2018lepto5',
@ -52,81 +54,73 @@ for ID in identifier:
sort = sorted(zip(f_cs, identifier)) sort = sorted(zip(f_cs, identifier))
print(sort) print(sort)
# order of plotting: 2018lepto1, 2018lepto5, 2018lepto76, 2018lepto98, 2019lepto24, 2020lepto06
# order of f_c: 2019lepto24, 2020lepto06, 2018lepto98, 2018lepto76, 2018lepto1, 2018lepto5 # order of f_c: 2019lepto24, 2020lepto06, 2018lepto98, 2018lepto76, 2018lepto1, 2018lepto5
fig = plt.figure(figsize=(8.27,11.69)) fig = plt.figure(figsize=(8.27,11.69))
ax0 = fig.add_subplot(321) 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_xlim(0.0007, 1.5)
ax0.set_ylim(0.001, 10)
ax0.plot(amfs[4], gains[4],'o' , label = 'gain') ax0.plot(amfs[4], gains[4],'o' , label = 'gain')
ax0.plot(amfs[4], predicts[4], label = 'fit') 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.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax0.set_xscale('log') ax0.set_xscale('log')
ax0.set_yscale('log') ax0.set_yscale('log')
ax0.set_ylabel('gain [Hz/(mV/cm)]') ax0.axes.xaxis.set_ticklabels([])
#ax0.set_xlabel('envelope_frequency [Hz]')
#ax0.set_title('gaincurve %s' %ID)
ax1 = fig.add_subplot(322) ax1 = fig.add_subplot(322)
ax1.set_xlim(0.0007, 1.5) ax1.set_xlim(0.0007, 1.5)
ax1.get_shared_y_axes().join(ax0, ax1) ax1.set_ylim(0.001, 10)
ax1.axes.yaxis.set_ticklabels([])
ax1.plot(amfs[5], gains[5],'o' , label = 'gain') ax1.plot(amfs[5], gains[5],'o' , label = 'gain')
ax1.plot(amfs[5], predicts[5], label = 'fit') 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.axvline(x=f_cs[5], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax1.set_xscale('log') ax1.set_xscale('log')
ax1.set_yscale('log') ax1.set_yscale('log')
#ax1.set_ylabel('gain [Hz/(mV/cm)]') ax1.axes.yaxis.set_ticklabels([])
#ax1.set_xlabel('envelope_frequency [Hz]') ax1.axes.xaxis.set_ticklabels([])
#ax1.set_title('gaincurve %s' %ID)
ax2 = fig.add_subplot(323) ax2 = fig.add_subplot(323)
ax2.set_xlim(0.0007, 1.5) ax2.set_xlim(0.0007, 1.5)
ax2.plot(amfs[4], gains[4],'o' , label = 'gain') ax2.set_ylim(0.001, 10)
ax2.plot(amfs[4], predicts[4], label = 'fit') ax2.plot(amfs[3], gains[3],'o' , label = 'gain')
ax2.axvline(x=f_cs[4], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency') 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_xscale('log')
ax2.set_yscale('log') ax2.set_yscale('log')
ax2.set_ylabel('gain [Hz/(mV/cm)]') ax2.axes.xaxis.set_ticklabels([])
#ax2.set_xlabel('envelope_frequency [Hz]')
#ax2.set_title('gaincurve %s' %ID)
ax3 = fig.add_subplot(324) ax3 = fig.add_subplot(324)
ax3.set_xlim(0.0007, 1.5) ax3.set_xlim(0.0007, 1.5)
ax3.get_shared_y_axes().join(ax2, ax3) ax3.set_ylim(0.001, 10)
ax3.axes.yaxis.set_ticklabels([])
ax3.plot(amfs[2], gains[2],'o' , label = 'gain') ax3.plot(amfs[2], gains[2],'o' , label = 'gain')
ax3.plot(amfs[2], predicts[2], label = 'fit') 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.axvline(x=f_cs[2], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax3.set_xscale('log') ax3.set_xscale('log')
ax3.set_yscale('log') ax3.set_yscale('log')
#ax3.set_ylabel('gain [Hz/(mV/cm)]') ax3.axes.yaxis.set_ticklabels([])
#ax3.set_xlabel('envelope_frequency [Hz]') ax3.axes.xaxis.set_ticklabels([])
#ax3.set_title('gaincurve %s' %ID)
ax4 = fig.add_subplot(325) ax4 = fig.add_subplot(325)
ax4.set_xlim(0.0007, 1.5) 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], gains[0],'o' , label = 'gain')
ax4.plot(amfs[0], predicts[0], label = 'fit') 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.axvline(x=f_cs[0], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax4.set_xscale('log') ax4.set_xscale('log')
ax4.set_yscale('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 = fig.add_subplot(326)
ax5.set_xlim(0.0007, 1.5) ax5.set_xlim(0.0007, 1.5)
ax5.axes.yaxis.set_ticklabels([]) ax5.set_ylim(0.001, 10)
ax5.get_shared_y_axes().join(ax4, ax5)
ax5.plot(amfs[1], gains[1],'o' , label = 'gain') ax5.plot(amfs[1], gains[1],'o' , label = 'gain')
ax5.plot(amfs[1], predicts[1], label = 'fit') 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.axvline(x=f_cs[1], ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax5.set_xscale('log') ax5.set_xscale('log')
ax5.set_yscale('log') ax5.set_yscale('log')
#ax5.set_ylabel('gain [Hz/(mV/cm)]') ax5.axes.yaxis.set_ticklabels([])
#ax5.set_xlabel('envelope_frequency [Hz]')
#ax5.set_title('gaincurve %s' %ID)
#plt.legend(loc = 'lower left') #plt.legend(loc = 'lower left')
plt.show() plt.show()

4
apteronotus_code/figure_apteronotus_gaincurve_cutofff_tau.py
View File

@ -35,8 +35,8 @@ for ID in identifier:
fig = plt.figure() fig = plt.figure()
ax = fig.add_subplot() ax = fig.add_subplot()
ax.plot(amf, gain,'o' , label = 'gain') ax.plot(amf, gain,'o' , label = 'gain')
#ax.plot(amf, predict, label = 'fit') ax.plot(amf, predict, label = 'fit')
#ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency') ax.axvline(x=f_cutoff, ymin=0, ymax=5, ls='-', alpha=0.5, label = 'cutoff frequency')
ax.set_xscale('log') ax.set_xscale('log')
ax.set_yscale('log') ax.set_yscale('log')
ax.set_ylabel('gain [Hz/(mV/cm)]') 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 mean_noise_cut
from jar_functions import gain_curve_fit 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 def take_second(elem): # function for taking the names out of files
return elem[1] return elem[1]
identifier = [#'2018lepto1', identifier = ['2018lepto1',
#'2018lepto4', '2018lepto4',
#'2018lepto5', '2018lepto5',
#'2018lepto76', '2018lepto76',
'2018lepto98', '2018lepto98',
'2019lepto03', '2019lepto03',
#'2019lepto24', '2019lepto24',
#'2019lepto27', '2019lepto27',
#'2019lepto30', '2019lepto30',
#'2020lepto04', '2020lepto04',
#'2020lepto06', '2020lepto06',
'2020lepto16', '2020lepto16',
'2020lepto19', '2020lepto19',
'2020lepto20' '2020lepto20'

10
apteronotus_code/figure_apteronotus_rms_gaincurve.py
View File

@ -19,7 +19,7 @@ plt.rcParams.update({'font.size': 12})
def take_second(elem): # function for taking the names out of files def take_second(elem): # function for taking the names out of files
return elem[1] return elem[1]
identifier = ['2020lepto16'] identifier = ['2020lepto19']
for ident in identifier: for ident in identifier:
predict = [] predict = []
@ -37,16 +37,16 @@ for ident in identifier:
currf = None currf = None
idxlist = [] 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): for i, d in enumerate(data):
dd = list(d) 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 jm = jar - np.mean(jar) # low-pass filtering by subtracting mean
print(dd) 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] dt = time[1] - time[0]
n = int(1/float(d[1])/dt) n = int(1/float(d[1])/dt)
@ -127,7 +127,7 @@ for ident in identifier:
fig = plt.figure(figsize = (8,14)) fig = plt.figure(figsize = (8,14))
fig.suptitle('gaincurve and RMS %s' %ident) fig.suptitle('gaincurve and RMS %s' %ident)
ax0 = fig.add_subplot(2, 1, 1) 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_yscale('log')
ax0.set_xscale('log') ax0.set_xscale('log')
ax0.set_ylabel('gain [Hz/(mV/cm)]') 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 import matplotlib as mpl
from matplotlib import cm from matplotlib import cm
#plt.rcParams.update({'font.size': 18})
identifier_uniform = ['2018lepto1', identifier_uniform = ['2018lepto1',
#'2018lepto4', #'2018lepto4',
'2018lepto5', '2018lepto5',
@ -32,14 +34,14 @@ identifier = ['2018lepto1',
'2019lepto24', '2019lepto24',
'2019lepto27', '2019lepto27',
'2019lepto30', '2019lepto30',
#'2020lepto04', '2020lepto04',
'2020lepto06', '2020lepto06',
'2020lepto16', '2020lepto16',
#'2020lepto19', '2020lepto19',
'2020lepto20' '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'] #colors = ['dimgray', 'dimgrey', 'gray', 'grey', 'darkgray', 'darkgrey', 'silver', 'lightgray', 'lightgrey', 'gainsboro', 'whitesmoke']
colorss = ['g', 'b', 'r', 'y', 'c', 'm', 'k'] colorss = ['g', 'b', 'r', 'y', 'c', 'm', 'k']
all = [] all = []
@ -54,10 +56,16 @@ for ident in identifier_uniform:
av = avgNestedLists(all) av = avgNestedLists(all)
new_av = avgNestedLists(new_all) new_av = avgNestedLists(new_all)
fig = plt.figure() fig = plt.figure(figsize=(8.27, 11.69/2))
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
ax.plot(amf, av, 'o', color = 'darkorange', label = 'normal') ax.plot(custom_amf, av, 'o', label = 'normal')
ax.plot(amf, new_av, 'o', label = 'uniform') #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 = [] tau = []
f_c = [] f_c = []
@ -76,8 +84,10 @@ for ID in identifier:
f_c.append(f_cutoff) f_c.append(f_cutoff)
fit.append(gain_curve_fit(amf, *sinv)) fit.append(gain_curve_fit(amf, *sinv))
fit_amf.append(amf) 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 = [] tau_uniform = []
f_c_uniform = [] f_c_uniform = []
@ -98,6 +108,7 @@ for ID in identifier_uniform:
fit_uniform.append(gain_curve_fit(amf, *sinv)) fit_uniform.append(gain_curve_fit(amf, *sinv))
fit_amf_uniform.append(amf) fit_amf_uniform.append(amf)
colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform))) colors = plt.cm.flag(np.linspace(0,1,len(fit_uniform)))
#for ff ,f in enumerate(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_xscale('log')
ax.set_yscale('log') ax.set_yscale('log')
ax.set_title('gain average all fish uniform')
ax.set_ylabel('gain [Hz/(mV/cm)]') ax.set_ylabel('gain [Hz/(mV/cm)]')
ax.set_xlabel('envelope_frequency [Hz]') ax.set_xlabel('envelope frequency [Hz]')
ax.set_ylim(0.0008, ) ax.set_xlim(0.0007, 1.5)
ax.plot(f_c, np.full(len(identifier), 0.0015), 'o', alpha = 0.5, c = 'darkorange', label = 'normal cutoff frequencies') ax.set_ylim(0.001, 10)
ax.plot(f_c_uniform, np.full(len(identifier_uniform), 0.002), 'o', alpha = 0.5, c = 'C0', label = 'uniform cutoff frequencies') ax.plot(custom_amf, predict)
ax.legend(loc = 'center left') #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() plt.show()
embed() 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 from jar_functions import avgNestedLists
identifier = [#'2018lepto1', identifier = ['2018lepto1',
#'2018lepto4', '2018lepto4',
#'2018lepto5', '2018lepto5',
#'2018lepto76', '2018lepto76',
'2018lepto98', '2018lepto98',
#'2019lepto03', '2019lepto03',
#'2019lepto24', '2019lepto24',
#'2019lepto27', '2019lepto27',
#'2019lepto30', '2019lepto30',
#'2020lepto04', '2020lepto04',
#'2020lepto06', '2020lepto06',
#'2020lepto16', '2020lepto16',
'2020lepto19', '2020lepto19',
#'2020lepto20' '2020lepto20'
] ]
amf = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1] 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) av = avgNestedLists(all)
embed() embed()
fig = plt.figure() fig = plt.figure(figsize=(8.27,11.69/2))
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
ax.plot(amf, av, 'o', c = 'C0', label = 'gain') 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 jar_functions import import_data_eigen
from scipy.signal import savgol_filter 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' 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.title('spectogram %s, deltaf: %sHz' %tuple(ID_delta_f))
# plt.xlim(times[0],times[-1]) # 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 = 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.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.plot(sub_times[0], jars[0], 'k', label = 'peak detection trace', lw = 2)