76 lines
1.8 KiB
Python
76 lines
1.8 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 map_keys(input):
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#gibt ein Dict mit den Keys eines Dict aus, aber als Int
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df_map = {}
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for k in input.keys():
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freq = k[1]
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df = int(freq.strip('Hz'))
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if df in df_map.keys():
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df_map[df].append(k)
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else:
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df_map[df] = [k]
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return df_map
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def spike_rates(sort_df, df_map, chirp_spikes):
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#damit wird sowohl die individuelle Rate pro Trial, als auch die Gesamt-Feuerrate berechnet
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dct_rate = {}
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over_spikes = []
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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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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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over_spikes.extend(spikes)
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duration = 1.2 *1600 #1200ms für 16 Trials
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overall_r = len(over_spikes)/ duration
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over_r = int(overall_r)
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return(dct_rate, over_r)
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def plot_df_spikes(sort_df, dct_rate):
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#gibt die Feuerrate gegen die Frequenz aufgetragen
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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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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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plt.legend()
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plt.title('Firing rate of the cell for all trials, sorted by df', fontsize = 24)
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plt.xlabel('# of trials', fontsize = 22)
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plt.ylabel('Instant 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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return(ls_mean)
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def adaptation_df(sort_df, dct_rate):
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adapt = []
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for d in sort_df:
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spur = dct_rate[d]
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start = spur[0:-1:10]
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stop = spur[9:len(spur):10]
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diff = np.asarray(start) - np.asarray(stop)
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adapt.extend(diff)
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return(adapt)
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