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07.07

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xaver 2020-07-07 15:40:28 +02:00
parent e91b648b5c
commit 85a4b51680
2 changed files with 59 additions and 28 deletions
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15
jar_functions.py
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@ -13,6 +13,7 @@ def parse_dataset(dataset_name):
deltafs = [] deltafs = []
stimulusfs = [] stimulusfs = []
duration = [] duration = []
pause = []
# data itself # data itself
times = [] times = []
@ -34,6 +35,8 @@ def parse_dataset(dataset_name):
stimulusfs.append(float(l.split(':')[-1].strip()[:-2])) stimulusfs.append(float(l.split(':')[-1].strip()[:-2]))
if "#" in l and "Duration" in l: if "#" in l and "Duration" in l:
duration.append(float(l.split(':')[-1].strip()[:-3])) duration.append(float(l.split(':')[-1].strip()[:-3]))
if "#" in l and "Pause" in l:
pause.append(float(l.split(':')[-1].strip()[:-3]))
if '#Key' in l: if '#Key' in l:
if len(time) != 0: #therefore empty in the first round if len(time) != 0: #therefore empty in the first round
@ -55,7 +58,7 @@ def parse_dataset(dataset_name):
amplitudes.append(ampl) #these append the data from the first loop to the final lists, because we overwrite them (?) amplitudes.append(ampl) #these append the data from the first loop to the final lists, because we overwrite them (?)
frequencies.append(freq) frequencies.append(freq)
return frequencies, times, amplitudes, eodfs, deltafs, stimulusfs, duration #output of the function return frequencies, times, amplitudes, eodfs, deltafs, stimulusfs, duration, pause #output of the function
def parse_infodataset(dataset_name): def parse_infodataset(dataset_name):
assert(os.path.exists(dataset_name)) #see if data exists assert(os.path.exists(dataset_name)) #see if data exists
@ -90,13 +93,11 @@ def mean_loops(start, stop, timespan, frequencies, time):
def mean_noise_cut(frequencies, time, n): def mean_noise_cut(frequencies, time, n):
cutf = [] cutf = []
cutt = [] cutt = []
for k in np.arange(0, len(frequencies), n): for k in np.arange(0, len(frequencies), n):
t = time[k] t = time[k]
f = np.mean(frequencies[k:k+n]) f = np.mean(frequencies[k:k+n])
cutf.append(f) cutf.append(f)
cutt.append(t) cutt.append(t)
return cutf, cutt return cutf, cutt
@ -109,11 +110,11 @@ def norm_function(cf_arr, ct_arr, onset_point, offset_point):
onset_end = onset_point - 10 onset_end = onset_point - 10
offset_start = offset_point - 10 offset_start = offset_point - 10
base = np.mean(cf_arr[(ct_arr >= onset_end) & (ct_arr < onset_point)]) base = np.median(cf_arr[(ct_arr >= onset_end) & (ct_arr < onset_point)])
ground = cf_arr - base ground = cf_arr - base
jar = np.mean(ground[(ct_arr >= offset_start) & (ct_arr < offset_point)]) jar = np.median(ground[(ct_arr >= offset_start) & (ct_arr < offset_point)])
norm = ground / jar norm = ground / jar
return norm return norm
@ -123,7 +124,7 @@ def base_eod(frequencies, time, onset_point):
onset_end = onset_point - 10 onset_end = onset_point - 10
base = np.mean(frequencies[(time >= onset_end) & (time < onset_point)]) base = np.median(frequencies[(time >= onset_end) & (time < onset_point)])
base_eod.append(base) base_eod.append(base)
return base_eod return base_eod
@ -133,7 +134,7 @@ def JAR_eod(frequencies, time, offset_point):
offset_start = offset_point - 10 offset_start = offset_point - 10
jar = np.mean(frequencies[(time >= offset_start) & (time < offset_point)]) jar = np.median(frequencies[(time >= offset_start) & (time < offset_point)])
jar_eod.append(jar) jar_eod.append(jar)
return jar_eod return jar_eod

72
second_try.py
View File

@ -12,53 +12,84 @@ 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
datasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')), #nicht: 19-aa, 22-ae, 22-ad (?)
(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat'))] datasets = [#(os.path.join('D:\\jar_project\\JAR\\2020-06-19-aa\\beats-eod.dat')), #-5Hz delta f, horrible fit
infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat'))] #(os.path.join('D:\\jar_project\\JAR\\2020-06-19-ab\\beats-eod.dat')), #-5Hz delta f, bad fit
#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-aa\\beats-eod.dat')), #-5Hz delta f, bad fit
#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ab\\beats-eod.dat')), #-5Hz delta f, bad fit
(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\beats-eod.dat')), #-15Hz delta f, good fit
#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ad\\beats-eod.dat')), #-15Hz delta f, horrible fit
#(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ae\\beats-eod.dat')), #-15Hz delta f, maxfev way to high so horrible
(os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\beats-eod.dat'))] #-15Hz delta f, good fit
#np.array(sorted(glob.glob('D:\\jar_project\\JAR\\2020*\\beats-eod.dat')))
time = [] infodatasets = [(os.path.join('D:\\jar_project\\JAR\\2020-06-22-ac\\info.dat')),
frequency_mean = [] (os.path.join('D:\\jar_project\\JAR\\2020-06-22-af\\info.dat'))]
time_all = []
freq_all = []
constant_factors = [] constant_factors = []
time_constants = [] time_constants = []
start = -10 ID = []
stop = 200
timespan = 210
for infodataset in infodatasets: for infodataset in infodatasets:
i= parse_infodataset(infodataset) i = parse_infodataset(infodataset)
identifier = i[0] identifier = i[0]
ID.append(identifier)
for dataset in datasets: for dataset in datasets:
#input of the function #input of the function
frequency, time, amplitude, eodf, deltaf, stimulusf, duration = parse_dataset(dataset) frequency, time, amplitude, eodf, deltaf, stimulusf, duration, pause = parse_dataset(dataset)
mf , tnew = mean_loops(start, stop, timespan, frequency, time)
dm = np.mean(duration) dm = np.mean(duration)
frequency_mean.append(mf) pm = np.mean(pause)
time.append(tnew) timespan = dm + pm
start = -10
stop = 200
mf , tnew = mean_loops(start, stop, timespan, frequency, time)
#for i in range(len(mf)):
for i in range(len(frequency_mean)): cf, ct = mean_noise_cut(mf, tnew, n=1250)
cf, ct = mean_noise_cut(frequency_mean[i], time[i], n=1000)
cf_arr = np.array(cf) cf_arr = np.array(cf)
ct_arr = np.array(ct) ct_arr = np.array(ct)
norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec norm = norm_function(cf_arr, ct_arr, onset_point = dm - dm, offset_point = dm) #dm-dm funktioniert nur wenn onset = 0 sec
plt.plot(ct_arr, norm) #, label='n=%d' % n) freq_all.append(norm.tolist())
time_all.append(ct_arr)
plt.plot(ct_arr, norm) #, label='fish=%s' % ID)
sv, sc = curve_fit(step_response, ct_arr[ct_arr < dm], norm[ct_arr < dm], maxfev = 2000) #step_values and step_cov
sv, sc = curve_fit(step_response, ct_arr[ct_arr < 100], norm[ct_arr < 100]) #step_values and step_cov
a = sv[:2] a = sv[:2]
tau = np.array(sorted(sv[2:], reverse=False)) tau = np.array(sorted(sv[2:], reverse=False))
values = np.array([a, tau]) values = np.array([a, tau])
values_flat = values.flatten() values_flat = values.flatten()
plt.plot(ct_arr [ct_arr < 100], step_response(ct_arr, *sv)[ct_arr < 100], 'r-', label='fit: a1=%.2f, a2=%.2f, tau1=%.2f, tau2=%.2f' % tuple(values_flat)) 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))
print('a1, a2, tau1, tau2', values_flat) print('a1, a2, tau1, tau2', values_flat)
constant_factors.append(a) constant_factors.append(a)
time_constants.append(tau) 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])
#f_mean_all = np.mean(freq_all, axis = 0)
#t_mean_all = np.mean(time_all, axis = 0)
const_line = plt.axhline(y=0.632) const_line = plt.axhline(y=0.632)
plt.xlim([-10,220]) plt.xlim([-10,220])
@ -74,6 +105,5 @@ embed()
# alle daten einlesen durch große for schleife (auch average über alle fische?) # alle daten einlesen durch große for schleife (auch average über alle fische?)
# für einzelne fische fit kontrollieren # für einzelne fische fit kontrollieren
#Fragen: wie offset point wenn nicht start bei 0 sec? # Fragen: wie offset point wenn nicht start bei 0 sec? über zeitdatenpunkt?
#wie a1, tau1,.. ohne array? (funkt wegen dimensionen wenn ichs nochmal in liste appende) # wie zip ich ID liste mit plot (für eine for schleife) zusammen?