end
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efish
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@ -1,6 +1,6 @@
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from read_chirp_data import *
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from func_chirp import *
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from utility import *
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#import nix_helpers as nh
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import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed
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@ -18,45 +18,13 @@ eod = read_chirp_eod(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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df_map = map_keys(eod)
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chirp_eod_plot(df_map, eod, times)
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plt.close()
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#die äußere Schleife geht für alle Keys durch und somit durch alle dfs
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#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz ab
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for i in df_map.keys():
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freq = list(df_map[i])
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fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
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for idx, k in enumerate(freq):
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ct = times[k]
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e1 = eod[k]
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zeit = e1[0]
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eods = e1[1]
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if idx <= 3:
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axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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elif 4<= idx <= 7:
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axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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elif 8<= idx <= 11:
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axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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else:
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axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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fig.suptitle('EOD for chirps', fontsize = 16)
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axs[0,0].set_ylabel('Amplitude [mV]')
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axs[0,1].set_xlabel('Amplitude [mV]')
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axs[1,0].set_xlabel('Time [ms]')
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axs[1,1].set_xlabel('Time [ms]')
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#for i in df_map.keys():
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freq = list(df_map[-50])
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#ACHTUNG: df für beide Plots anpassen!
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#momentan per Hand durch alle Frequenzen
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freq = list(df_map[-100])
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ls_mod = []
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ls_beat = []
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for k in freq:
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@ -78,11 +46,11 @@ beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
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plt.figure()
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plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
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plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet')
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plt.show()
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plt.close()
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#Chirps einer Phase zuordnen - zusammen plotten?
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#Chirps einer Phase zuordnen - zusammen plotten
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dct_phase = {}
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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@ -98,8 +66,8 @@ for i in sort_df:
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for k in freq:
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for phase in chirp_spikes[k]:
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dct_phase[i].append(phase[1])
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#for idx in np.arange(num_bin):
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#if phase[1] > phase_vec[idx] and phase[1] < phase_vec[idx+1]:
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print(dct_phase)
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plt.figure()
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plt.scatter(dct_phase[-100], ls_mod)
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plt.title('Change of std depending on the phase where the chirp occured')
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plt.show()
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@ -9,20 +9,19 @@ from IPython import embed #Funktionen imposrtieren
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data_dir = "../data"
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dataset = "2018-11-13-ad-invivo-1"
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#data = ("2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1")
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#data = ("2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1", "2018-11-20-aa-invivo-1", "2018-11-20-ab-invivo-1", "2018-11-20-ac-invivo-1", "2018-11-20-ad-invivo-1"," 2018-11-20-ae-invivo-1", "2018-11-20-af-invivo-1", "2018-11-20-ag-invivo-1", "2018-11-20-ah-invivo-1", "2018-11-20-ai-invivo-1") Durchgang für alle Datensets - zwischenspeichern von Daten?
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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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#inst_frequency = 1. / np.diff(spike_times)
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spike_rate = np.diff(spike_times)
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spike_iv = np.diff(spike_times)
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x = np.arange(0.001, 0.01, 0.0001)
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plt.hist(spike_rate,x)
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plt.hist(spike_iv,x)
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mu = np.mean(spike_rate)
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sigma = np.std(spike_rate)
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mu = np.mean(iv)
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sigma = np.std(iv)
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cv = sigma/mu
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plt.title('A.lepto ISI Histogramm', fontsize = 14)
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@ -45,20 +44,28 @@ sort_df = sorted(df_map.keys())
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plt.figure()
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dct_rate = {}
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overall_r = {}
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for i in sort_df:
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freq = list(df_map[i])
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dct_rate[i] = []
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overall_r[i] = []
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for k in freq:
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for phase in chirp_spikes[k]:
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spikes = chirp_spikes[k][phase]
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rate = len(spikes)/ 1.2
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dct_rate[i].append(rate)
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#overall_r[i].extend(rate) #kann man nicht erweitern!
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for h in sort_df:
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ls_mean = []
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for h in sort_df:
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mean = np.mean(dct_rate[h])
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ls_mean.append(mean)
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plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
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#plt.vlines(10, ymin = 190, ymax = 310)
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#Anfang Spur und Endpunkt bestimmen
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#relativ zur mittleren Feuerrate
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#wie hoch ist die Adaption von Zellen
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plt.legend()
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plt.title('Firing rate of the cell for all trials, sorted by df')
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plt.xlabel('# of trials')
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@ -66,18 +73,23 @@ plt.ylabel('Instant firing rate of the cell')
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plt.show()
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#mittlere Feuerrate einer Frequenz auf Frequenz
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#mittlere Feuerrate einer Frequenz auf Frequenz:
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plt.figure()
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ls_mean = []
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for d in sort_df:
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mean = np.mean(dct_rate[d])
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ls_mean.append(mean)
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plt.plot(np.arange(0,len(ls_mean),1),ls_mean)
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#plt.scatter(np.arange(0,len(ls_mean),1), np.mean(int(overall_r)))
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plt.title('Mean firing rate of a cell for a range of frequency differences')
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plt. xticks(np.arange(len(sort_df)), (sort_df))
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plt. xticks(np.arange(1,len(sort_df),1), (sort_df))
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plt.xlabel('Range of frequency differences [Hz]')
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plt.ylabel('Mean firing rate of the cell')
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plt.show()
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#Boxplot
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#wie viel Prozent macht die Adaption von Zellen aus?
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#Reihen-Plot
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#macht die zeitliche Reihenfolge der Präsentation einen Unterschied in der Zellantwort?
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