Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio
This commit is contained in:
Ramona
commit
96dede4e4d
@ -8,45 +8,50 @@ from IPython import embed
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data_dir = "../data"
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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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dataset = "2018-11-13-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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#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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sort_df = sorted(df_map.keys())
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eods = chirp_eod_plot(df_map, eod, times)
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plt.show()
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plt.close('all')
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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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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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chirp_mods = {}
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beat_mods = []
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for i in sort_df:
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chirp_mods[i] = []
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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[i].append(ls_mod)
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beat_mods.append(beat_mod)
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#Chirps einer Phase zuordnen - zusammen plotten
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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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#plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods)
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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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example = [-50, 200, 400]
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dct_phase = plot_std_chirp(example, df_map, chirp_spikes, chirp_mods)
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plt.show()
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plt.close('all')
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'''
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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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name = str(dataset.replace('-invivo-1', ''))
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print('saving ../results/Chirpcut/Cc_' + name + '.dat')
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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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@ -56,3 +61,4 @@ for dataset in data:
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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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'''
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@ -8,14 +8,22 @@ from IPython import embed #Funktionen importieren
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data_dir = "../data"
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dataset = "2018-11-09-aa-invivo-1"
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#data = ("2018-11-09-aa-invivo-1", "2018-11-09-ab-invivo-1", "2018-11-09-ac-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", "2018-11-09-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-aa-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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time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
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zeit = np.asarray(time)
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plt.plot(zeit[0:1000], eod[0:1000])
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plt.title('A.lepto EOD')#Plottitelk
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plt.xlabel('time [ms]', fontsize = 12)#Achsentitel
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plt.ylabel('amplitude[mv]', fontsize = 12)#Achsentitel
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plt.xticks(fontsize = 12)
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plt.yticks(fontsize = 12)
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inch_factor = 2.54
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.plot(zeit[0:1000], eod[0:1000], color = 'darkblue')
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plt.title('A.lepto EOD', fontsize = 24)#Plottitel
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plt.xlabel('time [ms]', fontsize = 22)#Achsentitel
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plt.ylabel('amplitude[mv]', fontsize = 22)#Achsentitel
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plt.tick_params(axis='both', which='major', labelsize = 22)
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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fig.tight_layout()
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plt.show()
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@ -3,7 +3,7 @@ from read_chirp_data import *
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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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from IPython import embed #Funktionen importieren
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@ -11,77 +11,89 @@ data_dir = "../data"
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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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dataset = "2018-11-13-ad-invivo-1"
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inch_factor = 2.54
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#for dataset in data_base:
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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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x = np.arange(0.001, 0.01, 0.0001)
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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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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.hist(spike_iv,x, color = 'darkblue')
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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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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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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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'''
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plt.title('A.lepto ISI Histogramm', fontsize = 24)
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plt.xlabel('duration ISI[ms]', fontsize = 22)
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plt.ylabel('number of ISI', fontsize = 22)
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plt.tick_params(axis='both', which='major', labelsize = 22)
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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plt.tight_layout()
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plt.show()
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for dataset in data_chirps:
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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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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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plt.figure()
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ls_mean = plot_df_spikes(sort_df, dct_rate)
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dct_rate, over_r = spike_rates(sort_df, df_map, chirp_spikes)
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plt.figure()
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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ls_mean = plot_df_spikes(sort_df, dct_rate)
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plt.show()
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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.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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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.plot(np.arange(0,len(ls_mean),1),ls_mean, color = 'darkblue')
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plt.scatter(np.arange(0,len(ls_mean),1), np.ones(len(ls_mean))*over_r, color = 'green')
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plt.title('Mean firing rate of a cell for a range of frequency differences', fontsize = 24)
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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]', fontsize = 22)
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plt.ylabel('Mean firing rate of the cell', fontsize = 22)
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plt.tick_params(axis='both', which='major', labelsize = 18)
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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plt.tight_layout()
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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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adapt = adaptation_df(sort_df, dct_rate)
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fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
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plt.boxplot(adapt)
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plt.title('Adaptation of cell firing rate during a trial', fontsize = 24)
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plt.xlabel('Cell', fontsize = 22)
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plt.ylabel('Adaptation size [Hz]', fontsize = 22)
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plt.tick_params(axis='both', which='major', labelsize = 18)
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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plt.tight_layout()
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plt.show()
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'''
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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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name = str(dataset.replace('-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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@ -91,3 +103,4 @@ for dataset in data_chirps:
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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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'''
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96
code/eod_chirp_beat.py
Normal file
96
code/eod_chirp_beat.py
Normal file
@ -0,0 +1,96 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import thunderfish.peakdetection as pd
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def create_chirp(eodf):
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stimulusrate = eodf # the eod frequency of the fake fish
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currentchirptimes = [0.0]
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chirpwidth = 0.014 # ms
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chirpsize = 100.
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chirpampl = 0.02
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chirpkurtosis = 1.
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p = 0.
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stepsize = 0.00001
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time = np.arange(-0.05, 0.05, stepsize)
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signal = np.zeros(time.shape)
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ampl = np.ones(time.shape)
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freq = np.ones(time.shape)
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ck = 0
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csig = 0.5 * chirpwidth / np.power(2.0*np.log(10.0), 0.5/chirpkurtosis)
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for k, t in enumerate(time):
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a = 1.
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f = stimulusrate
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if ck < len(currentchirptimes):
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if np.abs(t - currentchirptimes[ck]) < 2.0 * chirpwidth:
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x = t - currentchirptimes[ck]
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g = np.exp(-0.5 * (x/csig)**2)
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f = chirpsize * g + stimulusrate
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a *= 1.0 - chirpampl * g
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elif t > currentchirptimes[ck] + 2.0 * chirpwidth:
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ck += 1
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freq[k] = f
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ampl[k] = a
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p += f * stepsize
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signal[k] = a * np.sin(6.28318530717959 * p)
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return time, signal
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def plot_chirp(eodf, eodf1, phase, axis):
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time, chirp_eod = create_chirp(eodf)
|
||||
eod = np.sin(time * 2 * np.pi * eodf1 + phase)
|
||||
|
||||
y = chirp_eod * 0.4 + eod
|
||||
p, t = pd.detect_peaks(y, 0.1)
|
||||
axis.plot(time*1000, y, color = 'royalblue')
|
||||
axis.plot(time[p]*1000, (y)[p], lw=2, color='k')
|
||||
axis.plot(time[t]*1000, (y)[t], lw=2, color='k')
|
||||
axis.spines["top"].set_visible(False)
|
||||
axis.spines["right"].set_visible(False)
|
||||
|
||||
|
||||
|
||||
inch_factor = 2.54
|
||||
|
||||
fig = plt.figure(figsize=(20 / inch_factor, 10 / inch_factor))
|
||||
ax1 = fig.add_subplot(221)
|
||||
ax2 = fig.add_subplot(222)
|
||||
ax3 = fig.add_subplot(223)
|
||||
ax4 = fig.add_subplot(224)
|
||||
|
||||
plot_chirp(600, 650, 0, ax2)
|
||||
plot_chirp(600, 650, np.pi, ax4)
|
||||
|
||||
plot_chirp(600, 620, 0, ax1)
|
||||
plot_chirp(600, 620, np.pi, ax3)
|
||||
|
||||
ax1.set_ylabel('EOD [mV]', fontsize=22)
|
||||
ax1.set_title('$\Delta$f = 20 Hz', fontsize = 18)
|
||||
ax1.yaxis.set_tick_params(labelsize=18)
|
||||
ax1.set_xticklabels([])
|
||||
|
||||
ax2.set_title('$\Delta$f = 50 Hz', fontsize = 18)
|
||||
ax2.set_xticklabels([])
|
||||
ax2.set_yticklabels([])
|
||||
ax3.set_ylabel('EOD [mV]', fontsize=22)
|
||||
ax3.xaxis.set_tick_params(labelsize=18)
|
||||
ax3.yaxis.set_tick_params(labelsize=18)
|
||||
|
||||
ax3.set_xlabel('time [ms]', fontsize=22)
|
||||
ax4.set_xlabel('time [ms]', fontsize=22)
|
||||
ax4.xaxis.set_tick_params(labelsize=18)
|
||||
ax4.set_yticklabels([])
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
fig.tight_layout()
|
||||
#plt.show()
|
||||
plt.savefig('chirps_while_beat.png')
|
||||
@ -2,38 +2,43 @@ from read_baseline_data import *
|
||||
from IPython import embed
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import thunderfish.peakdetection as pd
|
||||
from IPython import embed
|
||||
|
||||
## beat
|
||||
|
||||
data_dir = '../data'
|
||||
dataset = '2018-11-09-ad-invivo-1'
|
||||
inch_factor = 2.54
|
||||
|
||||
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
||||
eod_norm = eod - np.mean(eod)
|
||||
eod_norm = eod_norm[10000:20000]
|
||||
|
||||
# calculate eod times and indices by zero crossings
|
||||
threshold = 0
|
||||
shift_eod = np.roll(eod_norm, 1)
|
||||
eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
|
||||
x = np.arange(0., len(eod_norm))
|
||||
|
||||
#x = eod_times*40000
|
||||
x = np.arange(0., len(eod_times)-1)
|
||||
y = np.sin(x*2*np.pi*600)
|
||||
eod_freq_beat = 1/(np.diff(eod_times) + y)
|
||||
y = np.sin(time[10000:20000]*2*np.pi*600)*0.5
|
||||
ampl = eod_norm + y
|
||||
p, t = pd.detect_peaks(ampl, 0.1)
|
||||
|
||||
# glätten
|
||||
kernel = np.ones(7)/7
|
||||
smooth_eod_freq_beat = np.convolve(eod_freq_beat, kernel, mode = 'valid')
|
||||
time_axis = np.arange(len(smooth_eod_freq_beat))
|
||||
time_axis = np.arange(len(ampl))
|
||||
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
||||
plt.plot(time[10000:20000], ampl)
|
||||
plt.plot(time[10000:20000][p], ampl[p], lw=2, color='k')
|
||||
plt.plot(time[10000:20000][t], ampl[t], lw=2, color='k')
|
||||
ax.set_xlabel("time [ms]", fontsize = 22)
|
||||
plt.xticks(fontsize = 18)
|
||||
ax.set_ylabel("eod amplitude [mV]", fontsize = 22)
|
||||
plt.yticks(fontsize = 18)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.spines["right"].set_visible(False)
|
||||
fig.tight_layout()
|
||||
|
||||
plt.plot(time_axis, smooth_eod_freq_beat)
|
||||
plt.show()
|
||||
#plt.savefig('beat.png')
|
||||
|
||||
|
||||
|
||||
#eod_freq_beat = eod_freq_normal + y
|
||||
#smooth_eod_freq_beat = np.convolve(eod_freq_beat, kernel, mode = 'valid')
|
||||
#fig = plt.plot(time_axis,smooth_eod_freq_beat)
|
||||
#plt.xlabel("time [ms]")
|
||||
#plt.ylabel("eod frequency [mV]")
|
||||
#plt.show()
|
||||
|
||||
|
||||
@ -2,8 +2,10 @@ from read_baseline_data import *
|
||||
from read_chirp_data import *
|
||||
from utility import *
|
||||
import matplotlib.pyplot as plt
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
inch_factor = 2.54
|
||||
|
||||
def chirp_eod_plot(df_map, eod, times):
|
||||
#die äußere Schleife geht für alle Keys durch und somit durch alle dfs
|
||||
@ -11,7 +13,7 @@ def chirp_eod_plot(df_map, eod, times):
|
||||
|
||||
for i in df_map.keys():
|
||||
freq = list(df_map[i])
|
||||
fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
|
||||
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
||||
|
||||
for idx, k in enumerate(freq):
|
||||
ct = times[k]
|
||||
@ -19,25 +21,29 @@ def chirp_eod_plot(df_map, eod, times):
|
||||
zeit = e1[0]
|
||||
eods = e1[1]
|
||||
|
||||
if idx <= 3:
|
||||
axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
elif 4<= idx <= 7:
|
||||
axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
elif 8<= idx <= 11:
|
||||
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)
|
||||
if idx <= 1:
|
||||
ax.plot(zeit, eods, color= 'darkblue')
|
||||
ax.scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
|
||||
#elif 4<= idx <= 7:
|
||||
# axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
# axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
|
||||
#elif 8<= idx <= 11:
|
||||
# axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
# axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), 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)
|
||||
continue
|
||||
#axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
#axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*np.mean(eods), color = 'green', s= 22)
|
||||
|
||||
ax.set_ylabel('Amplitude [mV]', fontsize = 22)
|
||||
ax.set_xlabel('Time [ms]', fontsize = 22)
|
||||
ax.tick_params(axis='both', which='major', labelsize = 18)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
fig.suptitle('EOD for chirps', fontsize = 24)
|
||||
fig.tight_layout()
|
||||
|
||||
|
||||
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()
|
||||
|
||||
|
||||
|
||||
@ -60,16 +66,18 @@ def cut_chirps(freq, eod, times):
|
||||
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()
|
||||
#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')
|
||||
return(ls_mod, beat_mod)
|
||||
|
||||
|
||||
|
||||
def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
|
||||
plt.figure()
|
||||
|
||||
|
||||
def plot_std_chirp(sort_df, df_map, chirp_spikes, chirp_mods):
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
||||
dct_phase = {}
|
||||
num_bin = 12
|
||||
phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
|
||||
@ -81,7 +89,14 @@ def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
|
||||
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()
|
||||
for i in sort_df:
|
||||
norm = np.asarray(dct_phase[i]) *2*math.pi
|
||||
plt.scatter(norm, chirp_mods[i], label = i, s = 22)
|
||||
plt.title('Change of std depending on the phase where the chirp occured', fontsize = 24)
|
||||
plt.xlabel('Phase', fontsize = 22)
|
||||
plt.ylabel('Standard deviation of the amplitude modulation', fontsize = 22)
|
||||
plt.xticks([0, math.pi/2, math.pi, math.pi*1.5, math.pi*2], ('0', '$\pi$ /2', '$\pi$', '1.5 $\pi$', '2$\pi$'))
|
||||
plt.tick_params(axis='both', which='major', labelsize = 18)
|
||||
plt.legend()
|
||||
return(dct_phase)
|
||||
|
||||
|
||||
@ -43,6 +43,8 @@ def spike_rates(sort_df, df_map, chirp_spikes):
|
||||
|
||||
def plot_df_spikes(sort_df, dct_rate):
|
||||
#gibt die Feuerrate gegen die Frequenz aufgetragen
|
||||
inch_factor = 2.54
|
||||
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
||||
ls_mean = []
|
||||
for h in sort_df:
|
||||
mean = np.mean(dct_rate[h])
|
||||
@ -50,9 +52,13 @@ def plot_df_spikes(sort_df, dct_rate):
|
||||
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')
|
||||
plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 24)
|
||||
plt.xlabel('# of trials', fontsize = 22)
|
||||
plt.ylabel('Instant firing rate of the cell', fontsize = 22)
|
||||
plt.tick_params(axis='both', which='major', labelsize = 18)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
plt.tight_layout()
|
||||
return(ls_mean)
|
||||
|
||||
|
||||
|
||||
48
code/order_eff.py
Normal file
48
code/order_eff.py
Normal file
@ -0,0 +1,48 @@
|
||||
from read_chirp_data import *
|
||||
from func_spike import *
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
|
||||
|
||||
|
||||
data_dir = "../data"
|
||||
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")
|
||||
|
||||
|
||||
|
||||
inch_factor = 2.54
|
||||
data_rate_dict = {}
|
||||
for dataset in data_chirps:
|
||||
|
||||
data_rate_dict[dataset] = []
|
||||
chirp_spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
|
||||
times = read_chirp_times(os.path.join(data_dir, dataset))
|
||||
eod = read_chirp_eod(os.path.join(data_dir, dataset))
|
||||
df_map = map_keys(chirp_spikes)
|
||||
|
||||
|
||||
for i in df_map.keys():
|
||||
freq = list(df_map[i])
|
||||
k = freq[0]
|
||||
phase = list(chirp_spikes[k].keys())[0]
|
||||
|
||||
spikes = chirp_spikes[k][phase]
|
||||
rate = len(spikes)/ 1.2
|
||||
data_rate_dict[dataset].append(rate)
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
||||
for dataset in data_rate_dict:
|
||||
plt.plot(data_rate_dict[dataset])
|
||||
|
||||
plt.title('Test for sequence effects', fontsize = 24)
|
||||
plt.xlabel('Number of stimulus presentations', fontsize = 22)
|
||||
plt.ylabel('Firing rates of cells', fontsize = 22)
|
||||
plt.tick_params(axis='both', which='major', labelsize = 22)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
fig.tight_layout()
|
||||
plt.show()
|
||||
|
||||
|
||||
@ -14,6 +14,9 @@ dataset = '2018-11-14-ad-invivo-1'
|
||||
|
||||
# read eod and time of baseline
|
||||
time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
||||
|
||||
|
||||
|
||||
eod_norm = eod - np.mean(eod)
|
||||
|
||||
# calculate eod times and indices by zero crossings
|
||||
@ -23,6 +26,10 @@ eod_times = time[(eod_norm >= threshold) & (shift_eod < threshold)]
|
||||
|
||||
eod_duration = eod_times[2]- eod_times[1] #time in s
|
||||
|
||||
eod_duration = eod_times[2]- eod_times[1]
|
||||
|
||||
|
||||
|
||||
# read spikes during baseline activity
|
||||
spikes = read_baseline_spikes(os.path.join(data_dir, dataset)) #spikes in s
|
||||
# calculate interpike intervals and plot them
|
||||
@ -37,9 +44,8 @@ plt.yticks(fontsize = 18)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.spines["right"].set_visible(False)
|
||||
fig.tight_layout()
|
||||
plt.show()
|
||||
#plt.show()
|
||||
#plt.savefig('isis.pdf')
|
||||
exit()
|
||||
plt.savefig('isis.png')
|
||||
|
||||
|
||||
@ -93,10 +99,10 @@ plt.yticks(fontsize=18)
|
||||
ax1.spines['top'].set_visible(False)
|
||||
|
||||
ax2 = ax1.twinx()
|
||||
ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='navy', alpha=0.5)
|
||||
ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='royalblue', alpha=0.5)
|
||||
ax2.plot(time_axis, mu_eod, color='black', lw=2)
|
||||
ax2.set_ylabel('voltage [mV]', fontsize=22)
|
||||
ax2.tick_params(axis='y', labelcolor='navy')
|
||||
ax2.tick_params(axis='y', labelcolor='royalblue')
|
||||
ax2.spines['top'].set_visible(False)
|
||||
|
||||
plt.yticks(fontsize=18)
|
||||
|
||||
@ -17,6 +17,7 @@ interspikeintervals = np.diff(spikes)*1000
|
||||
|
||||
fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))
|
||||
plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001), color='darkblue')
|
||||
#Titel fehlt!!
|
||||
plt.xlabel("time [ms]", fontsize = 22)
|
||||
plt.xticks(fontsize = 18)
|
||||
plt.ylabel("Number of \n Interspikeinterval", fontsize = 22)
|
||||
|
||||
@ -35,13 +35,13 @@ for k, t in enumerate(time):
|
||||
p += f * stepsize
|
||||
signal[k] = a * np.sin(6.28318530717959 * p)
|
||||
|
||||
fig = plt.figure(figsize = (20/inch_factor, 15/inch_factor))
|
||||
fig = plt.figure(figsize = (20/inch_factor, 12/inch_factor))
|
||||
ax1 = fig.add_subplot(211)
|
||||
plt.yticks(fontsize=18)
|
||||
ax2 = fig.add_subplot(212, sharex=ax1)
|
||||
plt.setp(ax1.get_xticklabels(), visible=False)
|
||||
ax1.plot(time*1000, signal, color = 'midnightblue', lw = 1)
|
||||
ax2.plot(time*1000, freq, color = 'midnightblue', lw = 3)
|
||||
ax1.plot(time*1000, signal, color = 'royalblue', lw = 1)
|
||||
ax2.plot(time*1000, freq, color = 'royalblue', lw = 3)
|
||||
|
||||
ax1.set_ylabel("field [mV]", fontsize = 22)
|
||||
|
||||
@ -53,5 +53,5 @@ ax2.yaxis.set_label_coords(-0.1, 0.5)
|
||||
plt.xticks(fontsize=18)
|
||||
plt.yticks(fontsize=18)
|
||||
fig.tight_layout()
|
||||
#plt.show()
|
||||
plt.savefig('stimulus_chirp.png')
|
||||
plt.show()
|
||||
#plt.savefig('stimulus_chirp.png')
|
||||
|
||||
@ -1,25 +0,0 @@
|
||||
from read_baseline_data import *
|
||||
from utility import *
|
||||
#import nix_helpers as nh
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
|
||||
#Zeitpunkte einer EOD über Zero-crossings finden, die in einer Steigung liegen
|
||||
data_dir = "../data"
|
||||
dataset = "2018-11-09-ad-invivo-1"
|
||||
time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
||||
spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
|
||||
print(len(spike_times))
|
||||
|
||||
eod_times = zero_crossing(eod,time)
|
||||
eod_durations = np.diff(eod_times)
|
||||
print(len(spike_times))
|
||||
print(len(eod_durations))
|
||||
|
||||
#for st in spike_times:
|
||||
#et = eod_times[eod_times < st]
|
||||
#dt = st - et
|
||||
|
||||
#vs = vector_strength(spike_times, eod_durations)
|
||||
Loading…
Reference in New Issue
Block a user