08.07 analysis step_stimulus not perfect, but works

This commit is contained in:
xaver 2020-07-08 13:36:26 +02:00
parent 85a4b51680
commit f80f6b7583
2 changed files with 46 additions and 36 deletions

View File

@ -73,7 +73,7 @@ def parse_infodataset(dataset_name):
identifier.append((l.split(':')[-1].strip()[1:12])) identifier.append((l.split(':')[-1].strip()[1:12]))
return identifier return identifier
def mean_loops(start, stop, timespan, frequencies, time): def mean_traces(start, stop, timespan, frequencies, time):
minimumt = min(len(time[0]), len(time[1])) minimumt = min(len(time[0]), len(time[1]))
# new time with wished timespan because it varies for different loops # new time with wished timespan because it varies for different loops
tnew = np.arange(start, stop, timespan / minimumt) # 3rd input is stepspacing: tnew = np.arange(start, stop, timespan / minimumt) # 3rd input is stepspacing:
@ -139,6 +139,13 @@ def JAR_eod(frequencies, time, offset_point):
return jar_eod return jar_eod
def sort_values(values):
a = values[:2]
tau = np.array(sorted(values[2:], reverse=False))
values = np.array([a, tau])
values_flat = values.flatten()
return values_flat

View File

@ -7,10 +7,11 @@ from IPython import embed
from scipy.optimize import curve_fit from scipy.optimize import curve_fit
from jar_functions import parse_dataset from jar_functions import parse_dataset
from jar_functions import parse_infodataset from jar_functions import parse_infodataset
from jar_functions import mean_loops from jar_functions import mean_traces
from jar_functions import mean_noise_cut from jar_functions import mean_noise_cut
from jar_functions import norm_function from jar_functions import norm_function
from jar_functions import step_response from jar_functions import step_response
from jar_functions import sort_values
#nicht: 19-aa, 22-ae, 22-ad (?) #nicht: 19-aa, 22-ae, 22-ad (?)
datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')), #-5Hz delta f, horrible fit datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')), #-5Hz delta f, horrible fit
@ -31,9 +32,6 @@ infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')),
time_all = [] time_all = []
freq_all = [] freq_all = []
constant_factors = []
time_constants = []
ID = [] ID = []
for infodataset in infodatasets: for infodataset in infodatasets:
@ -48,9 +46,9 @@ for dataset in datasets:
dm = np.mean(duration) dm = np.mean(duration)
pm = np.mean(pause) pm = np.mean(pause)
timespan = dm + pm timespan = dm + pm
start = -10 start = (time[0][0] + time[1][0]) / 2
stop = 200 stop = (time[0][-1] + time[1][-1]) / 2
mf , tnew = mean_loops(start, stop, timespan, frequency, time) mf , tnew = mean_traces(start, stop, timespan, frequency, time) # maybe fixed timespan/sampling rate
#for i in range(len(mf)): #for i in range(len(mf)):
@ -64,34 +62,40 @@ for dataset in datasets:
freq_all.append(norm.tolist()) freq_all.append(norm.tolist())
time_all.append(ct_arr) time_all.append(ct_arr)
plt.plot(ct_arr, norm) #, label='fish=%s' % ID) plt.plot(ct_arr, norm, color = 'grey', label='fish=%s' % ID)
# fit function
sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm]) #step_values and step_cov
# sorted a and tau
values = sort_values(sv)
'''
plt.plot(ct_arr[ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], color='orange',
label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values))
'''
print('fish: a1, a2, tau1, tau2', values)
sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm], maxfev = 2000) #step_values and step_cov # average over all fish
mf_all , tnew_all = mean_traces(start, stop, timespan, freq_all, time_all)
a = sv[:2] plt.plot(tnew_all, mf_all, color = 'b', label = 'average', ls = 'dashed')
tau = np.array(sorted(sv[2:], reverse=False))
values = np.array([a, tau])
values_flat = values.flatten()
plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_flat)) # fit for average
sv_all, sc_all = curve_fit(step_response, tnew_all[tnew_all < dm], mf_all[tnew_all < dm]) #step_values and step_cov
print('a1, a2, tau1, tau2', values_flat) values_all = sort_values(sv_all)
constant_factors.append(a)
time_constants.append(tau)
fr = []
for j in freq_all:
fr.append(freq_all[j])
embed()
minimumf_all = min(len(freq_all[j]))
f_all = freq_all[j][:minimumf_all]
print(freq_all[0])
print(len((freq_all[j])))
#f_all_arr = np.array([f0_all], [f1_all]) plt.plot(tnew_all[tnew_all < 100], step_response(tnew_all, *sv_all)[tnew_all < 100], color='orange',
#f_mean_all = np.mean(freq_all, axis = 0) label='average_fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_all))
#t_mean_all = np.mean(time_all, axis = 0)
print('average: a1, a2, tau1, tau2', values_all)
const_line = plt.axhline(y = 0.632)
stimulus_duration = plt.hlines(y = -0.25, xmin = 0, xmax = 100, color = 'r', label = 'stimulus_duration')
base_line = plt.axhline(y = 0, color = 'black', ls = 'dotted', linewidth = '1')
const_line = plt.axhline(y=0.632)
plt.xlim([-10,220]) plt.xlim([-10,220])
plt.xlabel('time [s]') plt.xlabel('time [s]')
plt.ylabel('rel. JAR magnitude') plt.ylabel('rel. JAR magnitude')
@ -101,9 +105,8 @@ plt.legend(loc = 'lower right')
plt.show() plt.show()
embed() embed()
# Fragen:
# alle daten einlesen durch große for schleife (auch average über alle fische?) # wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt? oder einfach immer bei 0 onset..?
# für einzelne fische fit kontrollieren
# Fragen: wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt?
# wie zip ich ID liste mit plot (für eine for schleife) zusammen? # wie zip ich ID liste mit plot (für eine for schleife) zusammen?
# welche Stimulusintesität?
# start/stop/timespan ok?