88 lines
2.5 KiB
Python
88 lines
2.5 KiB
Python
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 matplotlib.pyplot as plt
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import numpy as np
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def chirp_eod_plot(df_map, eod, times):
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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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plt.close()
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def cut_chirps(freq, eod, times):
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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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return(ls_mod, beat_mod)
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def plot_std_chirp(sort_df, df_map, chirp_spikes, ls_mod):
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plt.figure()
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dct_phase = {}
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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.scatter(dct_phase[i], ls_mod[i])
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plt.title('Change of std depending on the phase where the chirp occured')
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plt.close()
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