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
	#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz ab

	for i in df_map.keys():
		freq = list(df_map[i]) 
		fig, ax = plt.subplots(figsize=(20/inch_factor, 10/inch_factor))

		for idx, k in enumerate(freq):
			ct = times[k]
			e1 = eod[k]
			zeit = e1[0]
			eods = e1[1]
	
			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: 	
				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()





def cut_chirps(freq, eod, times):
	ls_mod = []
	ls_beat = []
	for k in freq: 
		e1 = eod[k]
		zeit = np.asarray(e1[0])
		ampl = np.asarray(e1[1])

		ct = times[k]
		for chirp in ct:
			time_cut = zeit[(zeit > chirp-10) & (zeit < chirp+10)]
			eods_cut = ampl[(zeit > chirp-10) & (zeit < chirp+10)]
			beat_cut = ampl[(zeit > chirp-55) & (zeit < chirp-10)]
		
			chirp_mod = np.std(eods_cut) #Std vom Bereich um den Chirp
			ls_mod.append(chirp_mod)
			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')
	return(ls_mod, beat_mod)





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)

	for i in sort_df:
		freq = list(df_map[i])
		dct_phase[i] = []
		for k in freq:
			for phase in chirp_spikes[k]:
				dct_phase[i].append(phase[1])
	
	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)