#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Fri Jun 29 09:20:28 2018

@author: william

"""

import obspy

from obspy import read

import numpy as np

from numpy import genfromtxt

import matplotlib.pyplot as plt

from obspy import Stream

from obspy.signal.trigger import classic_sta_lta, recursive_sta_lta

from obspy.signal.trigger import plot_trigger, trigger_onset

from obspy import UTCDateTime

#%% import data

stre = read("/Users/william/Documents/scanner/output_data/EXP_all_data_stream_month_1.mseed")

per_day = genfromtxt("/Users/william/Documents/scanner/all_stations/Explosions_per_day_v2.csv", delimiter=',',skip_header=1,skip_footer=1)

data = genfromtxt("/Users/william/Documents/scanner/all_stations/EXP_all_coincidence_month_1.csv", delimiter=',',skip_header=1)

#%% Events per day and per week

plt.figure(10001)

plt.plot(per_day[:,1],per_day[:,0])

plt.ylim([0,max(per_day[:,0])+10])

plt.xlabel('Day Number')

plt.ylabel('Number of Explosions')

plt.title('Explosions per Day')

epw=np.zeros(shape=(1,2))

day=0

week=0

nepw=0

for x in range(0,len(per_day)):

nepw += per_day[x,0]

day += 1

if day == 7:

epw[week][0] = nepw

epw[week][1] = week

week +=1

day=0

nepw=0

if len(per_day)-x > 7:

epw = np.lib.pad(epw, ((0,1),(0,0)), 'constant', constant_values=(0))

plt.figure(10002)

plt.plot(epw[:,1],epw[:,0])

plt.ylim([0,max(epw[:,0])+50])

plt.xlabel('Week Number')

plt.ylabel('Number of Explosions')

plt.title('Explosions per Week')

#

#%% "Energy" of each event

event_stream = Stream()

event_list=np.zeros(shape=(1,1))

event_count=0

event_stream.append(stre[0])

event_list[event_count]=stre[0].stats.starttime.timestamp

event_list = np.lib.pad(event_list, ((0,1),(0,0)), 'constant', constant_values=(0))

event_count +=1

for x in range(1,len(stre)):

if stre[x].stats.station == "LB01":

event_stream.append(stre[x])

event_list[event_count]=stre[x].stats.starttime.timestamp

event_list = np.lib.pad(event_list, ((0,1),(0,0)), 'constant', constant_values=(0))

event_count +=1

else:

rt=stre[x].stats.starttime.timestamp

near,ix=find_nearest(event_list[:,0], rt)

if abs(near-rt) > 60:

event_stream.append(stre[x])

event_list[event_count]=stre[x].stats.starttime.timestamp

event_list = np.lib.pad(event_list, ((0,1),(0,0)), 'constant', constant_values=(0))

event_count +=1

#print(len(data_stream))

sr = 100

nsta=int(1*sr)

nlta=int(10*sr)

trig_on=2.5

trig_off=0.05

#for x in range(0,len(data_stream)):

for x in range(0,10):

data_s=event_stream[x].data

max_a = data_s.max()

min_a = data_s.min()

p2p= max_a-min_a

cft=recursive_sta_lta(data_s, nsta, nlta)

# plot_trigger(sq_stream[x], cft, trig_on, trig_off)

on_off = trigger_onset(cft,trig_on,trig_off)

start = event_stream[x].stats.starttime

tr = event_stream[x].slice(starttime=start+(on_off[0,0]/sr) , endtime=start+(on_off[0,1]/sr))

print('event:',event_stream[x].stats.starttime ,'from station: ',stre[x].stats.station,', has energy: ',sum(np.square(tr.data)),' and peak to peak:', p2p)

plt.figure(x)

plt.plot(tr)

plt.figure(x+20)

plt.plot(np.square(tr.data))

#%% energy information

day_one = 1416787200.0

day_energy=0

day_count=0

e_count=0

days=0

energy_each_event = av_energy_list =np.zeros(shape=(1,1))

av_energy_list =np.zeros(shape=(1,1))

total_energy_list =np.zeros(shape=(1,1))

for x in range(0,len(per_day)):#len(per_day)

day_start = day_one + x*24*60*60

day_end = day_start + 24*60*60 - 0.01

# print(UTCDateTime(day_start),' to', UTCDateTime(day_end))

for p in range(0,len(event_stream)):

if day_start < event_stream[p].stats.starttime.timestamp < day_end :

# ADD IN CALIBRATIONS ########################################################

day_count += 1

data_s=event_stream[p].data

max_a = data_s.max()

min_a = data_s.min()

p2p= max_a-min_a

cft=recursive_sta_lta(data_s, nsta, nlta)

# plot_trigger(sq_stream[x], cft, trig_on, trig_off)

on_off = trigger_onset(cft,trig_on,trig_off)

start = event_stream[p].stats.starttime

tr = event_stream[p].slice(starttime=start+(on_off[0,0]/sr) , endtime=start+(on_off[0,1]/sr))

event_energy = sum(np.square(tr.data))

day_energy += event_energy

# print('event:',event_stream[p].stats.starttime ,'station: ',event_stream[p].stats.station,', energy: ',event_energy,', peak to peak:', p2p)

energy_each_event[e_count] = event_energy

energy_each_event = np.lib.pad(energy_each_event, ((0,1),(0,0)), 'constant', constant_values=(0))

e_count += 1

# if event_energy > 1e16:

# tr.plot()

print('total energy in the day:',UTCDateTime(day_start),'=', day_energy)

av_day_energy = day_energy/day_count

print('average energy in day:',UTCDateTime(day_start),'=', av_day_energy)

av_energy_list[days]= av_day_energy

total_energy_list[days] = day_energy

av_energy_list = np.lib.pad(av_energy_list, ((0,1),(0,0)), 'constant', constant_values=(0))

total_energy_list = np.lib.pad(total_energy_list, ((0,1),(0,0)), 'constant', constant_values=(0))

day_count = 0

day_energy=0

days += 1

plt.figure(2001)

plt.plot(av_energy_list)

plt.figure(2002)

plt.plot(total_energy_list)

plt.figure(2003)

plt.plot(energy_each_event)