neu
This commit is contained in:
efish
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@ -8,66 +8,51 @@ from IPython import embed
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data_dir = "../data"
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dataset = "2018-11-09-ad-invivo-1"
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#data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-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-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-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", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-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-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-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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#for dataset in data:
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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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for dataset in data:
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print(dataset)
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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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sort_df = sorted(df_map.keys())
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chirp_eod_plot(df_map, eod, times)
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plt.close()
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chirp_eod_plot(df_map, eod, times)
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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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e1 = eod[k]
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zeit = np.asarray(e1[0])
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ampl = np.asarray(e1[1])
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ct = times[k]
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for chirp in ct:
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time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)]
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eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)]
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beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)]
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chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp
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ls_mod.append(chirp_mod)
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ls_beat.extend(beat_cut)
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chirp_mods = []
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beat_mods = []
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for i in sort_df:
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freq = list(df_map[i])
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ls_mod, beat_mod = cut_chirps(freq, eod, times)
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chirp_mods.append(ls_mod)
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beat_mods.append(beat_mod)
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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.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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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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num_bin = 12
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phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
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#plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
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for i in sort_df:
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freq = list(df_map[i])
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dct_phase[i] = []
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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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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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#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
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name = str(dataset.strip('invivo-1'))
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f = open('../results/Chirpcut/Cc_' + name + '.dat' , 'w')
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f.write(str(sort_df))
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f.write(str(df_map))
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f.write(str(chirp_spikes))
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f.write(str(eod))
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f.write(str(times))
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#f.write(str(chirp_mods))
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#f.write(str(beat_mods))
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f.close()
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@ -1,95 +1,93 @@
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from read_baseline_data import *
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from read_chirp_data import *
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from utility import *
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#import nix_helpers as nh
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from func_spike import *
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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 #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") Durchgang für alle Datensets - zwischenspeichern von Daten?
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data_base = ("2018-11-09-ab-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-af-invivo-1", "2018-11-13-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-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-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-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_chirps = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-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-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-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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'''
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for dataset in data_base:
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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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spike_iv = np.diff(spike_times)
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print(dataset)
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spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
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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_iv,x)
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mu = np.mean(spike_iv)
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sigma = np.std(spike_iv)
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cv = sigma/mu
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x = np.arange(0.001, 0.01, 0.0001)
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plt.hist(spike_iv,x)
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plt.title('A.lepto ISI Histogramm', fontsize = 14)
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plt.xlabel('duration ISI[ms]', fontsize = 12)
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plt.ylabel('number of ISI', fontsize = 12)
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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.xticks(fontsize = 12)
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plt.yticks(fontsize = 12)
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'''
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plt.title('A.lepto ISI Histogramm', fontsize = 14)
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plt.xlabel('duration ISI[ms]', fontsize = 12)
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plt.ylabel('number of ISI', fontsize = 12)
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plt.xticks(fontsize = 12)
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plt.yticks(fontsize = 12)
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plt.show()
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for dataset in data_chirps:
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#Nyquist-Theorem Plot:
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print(dataset)
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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sort_df = sorted(df_map.keys())
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dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
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#Nyquist-Theorem Plot:
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plt.figure()
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ls_mean = plot_df_spikes(sort_df, dct_rate)
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chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
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df_map = map_keys(chirp_spikes)
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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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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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#mittlere Feuerrate einer Frequenz auf Frequenz:
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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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plt.ylabel('Instant firing rate of the cell')
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plt.show()
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plt.figure()
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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.ones(len(ls_mean))*over_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(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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#mittlere Feuerrate einer Frequenz auf Frequenz:
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plt.figure()
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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(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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#Adaption der Zellen:
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#wie viel Prozent der Anfangsrate macht die Adaption von Zellen aus?
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adapt = adaptation_df(sort_df, dct_rate)
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plt.figure()
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plt.boxplot(adapt)
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plt.title('Adaptation of cell firing rate during a trial')
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plt.xlabel('Cell')
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plt.ylabel('Adaptation size [Hz]')
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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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#Vatriablen speichern, die man für die Übersicht aller Zellen braucht
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name = str(dataset.strip('invivo-1'))
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f = open('../results/Nyquist/Ny_' + name + '.txt' , 'w')
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f.write(str(sort_df))
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f.write(str(df_map))
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f.write(str(chirp_spikes))
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f.write(str(times))
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f.write(str(ls_mean))
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f.write(str(over_r))
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f.write(str(adapt))
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f.close()
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@ -10,32 +10,78 @@ def chirp_eod_plot(df_map, eod, times):
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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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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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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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plt.show()
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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)
|
||||
axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
else:
|
||||
axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
|
||||
fig.suptitle('EOD for chirps', fontsize = 16)
|
||||
axs[0,0].set_ylabel('Amplitude [mV]')
|
||||
axs[0,1].set_xlabel('Amplitude [mV]')
|
||||
axs[1,0].set_xlabel('Time [ms]')
|
||||
axs[1,1].set_xlabel('Time [ms]')
|
||||
plt.close()
|
||||
|
||||
|
||||
|
||||
def cut_chirps(freq, eod, times):
|
||||
ls_mod = []
|
||||
ls_beat = []
|
||||
for k in freq:
|
||||
e1 = eod[k]
|
||||
zeit = np.asarray(e1[0])
|
||||
ampl = np.asarray(e1[1])
|
||||
|
||||
ct = times[k]
|
||||
for chirp in ct:
|
||||
time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)]
|
||||
eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)]
|
||||
beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)]
|
||||
|
||||
chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp
|
||||
ls_mod.append(chirp_mod)
|
||||
ls_beat.extend(beat_cut)
|
||||
|
||||
beat_mod = np.std(ls_beat) #Std vom Bereich vor dem Chirp
|
||||
plt.figure()
|
||||
plt.scatter(np.arange(0,len(ls_mod),1), ls_mod)
|
||||
plt.scatter(np.arange(0,len(ls_mod),1), np.ones(len(ls_mod))*beat_mod, color = 'violet')
|
||||
plt.close()
|
||||
return(ls_mod, beat_mod)
|
||||
|
||||
|
||||
|
||||
def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
|
||||
plt.figure()
|
||||
dct_phase = {}
|
||||
num_bin = 12
|
||||
phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
|
||||
|
||||
for i in sort_df:
|
||||
freq = list(df_map[i])
|
||||
dct_phase[i] = []
|
||||
for k in freq:
|
||||
for phase in chirp_spikes[k]:
|
||||
dct_phase[i].append(phase[1])
|
||||
|
||||
plt.scatter(dct_phase[i], ls_mod[i])
|
||||
plt.title('Change of std depending on the phase where the chirp occured')
|
||||
plt.close()
|
||||
|
||||
|
||||
@ -5,3 +5,65 @@ import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
|
||||
|
||||
def map_keys(input):
|
||||
#gibt ein Dict mit den Keys eines Dict aus, aber als Int
|
||||
df_map = {}
|
||||
for k in input.keys():
|
||||
freq = k[1]
|
||||
df = int(freq.strip('Hz'))
|
||||
if df in df_map.keys():
|
||||
df_map[df].append(k)
|
||||
else:
|
||||
df_map[df] = [k]
|
||||
return df_map
|
||||
|
||||
|
||||
|
||||
def spike_rates(sort_df, df_map, chirp_spikes):
|
||||
#damit wird sowohl die individuelle Rate pro Trial, als auch die Gesamt-Feuerrate berechnet
|
||||
dct_rate = {}
|
||||
over_spikes = []
|
||||
for i in sort_df:
|
||||
freq = list(df_map[i])
|
||||
dct_rate[i] = []
|
||||
for k in freq:
|
||||
for phase in chirp_spikes[k]:
|
||||
spikes = chirp_spikes[k][phase]
|
||||
rate = len(spikes)/ 1.2
|
||||
dct_rate[i].append(rate)
|
||||
over_spikes.extend(spikes)
|
||||
|
||||
duration = 1.2 *1600 #1200ms für 16 Trials
|
||||
overall_r = len(over_spikes)/ duration
|
||||
over_r = int(overall_r)
|
||||
return(dct_rate, over_r)
|
||||
|
||||
|
||||
|
||||
def plot_df_spikes(sort_df, dct_rate):
|
||||
#gibt die Feuerrate gegen die Frequenz aufgetragen
|
||||
ls_mean = []
|
||||
for h in sort_df:
|
||||
mean = np.mean(dct_rate[h])
|
||||
ls_mean.append(mean)
|
||||
plt.plot(np.arange(0,len(dct_rate[h]),1),dct_rate[h], label = h)
|
||||
|
||||
plt.legend()
|
||||
plt.title('Firing rate of the cell for all trials, sorted by df')
|
||||
plt.xlabel('# of trials')
|
||||
plt.ylabel('Instant firing rate of the cell')
|
||||
return(ls_mean)
|
||||
|
||||
|
||||
|
||||
def adaptation_df(sort_df, dct_rate):
|
||||
adapt = []
|
||||
for d in sort_df:
|
||||
spur = dct_rate[d]
|
||||
start = spur[0:-1:10]
|
||||
stop = spur[9:len(spur):10]
|
||||
diff = np.asarray(start) - np.asarray(stop)
|
||||
adapt.extend(diff)
|
||||
|
||||
return(adapt)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user