74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
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 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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chirp_eod_plot(df_map, eod, times)
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plt.close()
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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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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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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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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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