106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
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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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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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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#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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#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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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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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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#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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num_bin = 12
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phase_vec = np.arange(0, 1+1/num_bin, 1/num_bin)
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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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#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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