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03.07

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xaver 2020-07-03 13:27:21 +02:00
parent b7bc739f37
commit 9a6d29073e
2 changed files with 43 additions and 37 deletions
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33
jar_functions.py
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@ -58,11 +58,10 @@ def parse_dataset(dataset_name):
def mean_noise_cut(frequencies, time, n):
cutf = []
cutt = []
for k in np.arange(0, len(time), n):
f = frequencies[k:k+n]
for k in np.arange(0, len(frequencies), n):
t = time[k]
mean = np.mean(f)
cutf.append(mean)
f = np.mean(frequencies[k:k+n])
cutf.append(f)
cutt.append(t)
return cutf, cutt
@ -74,19 +73,21 @@ def step_response(t, a1, a2, tau1, tau2):
def normalized_JAR(frequencies, time, onset=0, offset=100):
onset_point = onset - 10
offset_point = offset - 10
embed()
def base_eod(frequencies, time, onset_point):
base_eod = []
step_eod = []
np.mean(f[(time >= onset_point) & time < onset])
for i in range(len(frequencies)):
if time < onset and time > onset_point:
onset_end = onset_point - 10
base = np.mean(frequencies[(time >= onset_end) & (time < onset_point)])
base_eod.append(base)
return base_eod
def JAR_eod(frequencies, time, offset_point):
jar_eod = []
offset_start = offset_point - 10
base_eod.append(frequencies[i])
jar = np.mean(frequencies[(time >= offset_start) & (time < offset_point)])
jar_eod.append(jar)
if time[i] < offset and time[i] > offset_range:
step_eod.append(frequencies[i])
return jar_eod

47
second_try.py
View File

@ -7,6 +7,8 @@ from IPython import embed
from jar_functions import parse_dataset
from jar_functions import mean_noise_cut
from jar_functions import step_response
from jar_functions import JAR_eod
from jar_functions import base_eod
datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))]
@ -49,40 +51,43 @@ for dataset in datasets:
frequency_mean.append(mf)
time.append(tnew)
"""
for a in [0, 1, 2]:
for b in [0, 1, 2]:
r_step = step_response(t = ct_arr, a1 = a, a2 = b, tau1 = 30, tau2 = 60)
"""
for i in range(len(frequency_mean)):
for n in [10, 50, 100, 1000, 10000, 20000, 30000]:
for n in [100, 500, 1000]:
cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
#plt.plot(ct, cf, label='n=%d' % n)
ct_array = np.array(ct) +10
r_step = step_response(t=ct_array, a1=0.58, a2=0, tau1=100, tau2=100)
#plt.plot(r_step)
ct_arr = np.array(ct)
cf_arr = np.array(cf)
base = base_eod(cf_arr, ct_arr, onset_point = 0)
ground = cf_arr - base
jar = JAR_eod(ground, ct_arr, offset_point = 100)
norm = ground / jar
for a in [0, 1, 2]:
for b in [0, 1, 2]:
r_step = step_response(t = ct_array, a1 = a, a2 = b, tau1 = 30, tau2 = 60)
plt.plot(ct_arr, norm, label='n=%d' % n)
plt.plot(time[0], frequency_mean[0])
plt.show()
embed()
for n in [1480]:
cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=n)
ct_arr = np.array(ct)
cf_arr = np.array(cf)
r_step = step_response(t=ct_arr + 10, a1=0.55, a2=0.89, tau1=11.2, tau2= 280)
plt.plot(r_step, label='fit: n=%d' % n)
'plotting'
plt.xlim([-10,200])
plt.xlim([-10,220])
#plt.ylim([400, 1000])
plt.xlabel('time [s]')
#plt.ylabel('rel. JAR magnitude')
plt.ylabel('rel. JAR magnitude')
#plt.title('fit_function(a1=0)')
#plt.savefig('fit_function(a1=0)')
plt.legend()
plt.show()
# normiert darstellen (frequency / mean von baseline frequency?)?
# Zeitkonstante: von sec. 0 bis 63%? relative JAR
embed()
# Zeitkonstante: von sec. 0 bis 63%? relative JAR