# -*- coding: utf-8 -*-
"""
Created on Wed Mar 18 15:21:02 2026

@author: chuffard
"""

import numpy as np
from datetime import datetime
import numpy as np
import pandas as pd
import sys
import os.path
import warnings
import glob
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
warnings.filterwarnings("ignore")


atn = pd.read_csv('//thalassa/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/Mooring/Prep analyses/SES_out_atn_no_inf.csv')
atn['extracted'] = atn['file_name'].str.replace('-', ' ').str.extract(r"t_([\d\s]+)")[0].str.split().apply(lambda x: [int(i) for i in x] if x else [])
atn['date'] = pd.to_datetime(atn['extracted'].str[0].astype(str).str.zfill(6), format='%y%m%d').dt.date
# 2. Extract right portion and convert to Time (HHMMSS)
atn['time'] = pd.to_datetime(atn['extracted'].str[1].astype(str).str.zfill(6), format='%H%M%S').dt.time
date_str = atn['extracted'].str[0].astype(str).str.zfill(6)
time_str = atn['extracted'].str[1].astype(str).str.zfill(6)
# 2. Combine and convert to a single datetime object
atn['DateTime'] = pd.to_datetime(date_str + time_str, format='%y%m%d%H%M%S')
#import attenuance
atn = atn.sort_values(by='DateTime')
atn['atn'] = pd.to_numeric(atn['atn'], errors='coerce')
atn['rounded_hour'] = atn['DateTime'].dt.round('H')
numeric_colsatn = atn.select_dtypes(include=['number']).columns
atn = atn.groupby('rounded_hour')['atn'].mean(numeric_only=True).reset_index()
atn['rm150atn'] = atn['atn'].rolling(150, min_periods=23).mean()
atn['Slope_rm_150atn'] = atn['rm150atn'].diff()

#import fluorescence
fluorescence = pd.read_csv('//thalassa/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/Mooring/Prep analyses/TestingTriggerDualChannel_fluorescence.csv')
fluorescence["DateTime"] = pd.to_datetime(fluorescence["SES Timestamp (ISO601)"])
fluorescence = fluorescence.sort_values(by='DateTime')
fluorescence['Fluorescense_690'] = fluorescence['Fluorescense 690 Counts']-fluorescence['Measure Ref 690 Counts']
fluorescence['rounded_hour'] = fluorescence['DateTime'].dt.round('H')
fluorescence = fluorescence.groupby('rounded_hour').mean(numeric_only=True).reset_index()
#Round to nearest hour first. This will make it easier to combine later.
fluorescence['rm150Fluorescense_690'] = fluorescence['Fluorescense_690'].rolling(150, min_periods=23).mean()
fluorescence['Slope_rm_150fluoro'] = fluorescence['rm150Fluorescense_690'].diff()

#triggering values

lowtrigger_atn = 0.007 
hightrigger_atn = 0.011
hightrigger_fluoro= 2.4
lowtrigger_fluoro= 1.8  
use_min_period = 24
nrec=150

# Trigger code
#Combine by floor minute
merged_df = pd.merge(atn[['rounded_hour','rm150atn' ,'Slope_rm_150atn']], fluorescence[['rounded_hour', 'rm150Fluorescense_690','Slope_rm_150fluoro']], on='rounded_hour', how='outer')


#atn trigger base
merged_df["TriggerHigh_atn"] = np.where(merged_df['Slope_rm_150atn'] >= hightrigger_atn, 'Trigger', 'Wait')
merged_df['Rate_HighTrigger_atn'] = (merged_df['TriggerHigh_atn'] == 'Trigger').rolling(144, min_periods=2).sum()
merged_df['Max_HighTrigger_rate_atn'] =  merged_df['Rate_HighTrigger_atn'].cummax()

merged_df["TriggerLow_atn"] = np.where(merged_df['Slope_rm_150atn'] >= lowtrigger_atn, 'Trigger', 'Wait')
merged_df['Rate_LowTrigger_atn'] = (merged_df['TriggerLow_atn'] == 'Trigger').rolling(144, min_periods=2).sum()
merged_df['Max_LowTrigger_rate_atn'] =  merged_df['Rate_LowTrigger_atn'].cummax()

conditionsatn = [
    (merged_df['Rate_LowTrigger_atn']  <= 1), #fire one low, then switch to high trigger
    (merged_df['Rate_HighTrigger_atn'] <= 1), #fire two high, then switch to higher trigger
    (merged_df['Rate_HighTrigger_atn'] ==3), #fire more 3 highs then even higher trigger
    (merged_df['Max_HighTrigger_rate_atn'] >= 4),#fire four or more highs, then use max trigger
    ((merged_df['Rate_HighTrigger_atn']).rolling(window=240, min_periods=1).sum()==0), #more than 1.5 week since last trigger, use the lower gear
    ]


# Define corresponding choices (results)
choicesatn = [np.where(merged_df['Slope_rm_150atn'] >= lowtrigger_atn, 'FireLow', 'Wait'), 
           np.where(merged_df['Slope_rm_150atn'] >= hightrigger_atn, 'FireHigh', 'Wait'), 
           np.where(merged_df['Slope_rm_150atn'] >= (hightrigger_atn*1.1), 'Fire1_1High', 'Wait'),
           np.where(merged_df['Slope_rm_150atn'] >= (hightrigger_atn*1.2), 'Fire1_2High', 'Wait'),
           np.where(merged_df['Slope_rm_150atn'] >= (hightrigger_atn), 'Fire_High', 'Wait')]
           
merged_df["Fire_trigger_atn"] = np.select(conditionsatn, choicesatn, default='Wait')

#fluoro trigger base
merged_df["TriggerHigh_fluoro"] = np.where(merged_df['Slope_rm_150fluoro'] >= hightrigger_fluoro, 'Trigger', 'Wait')
merged_df['Rate_HighTrigger_fluoro'] = (merged_df['TriggerHigh_fluoro'] == 'Trigger').rolling(144, min_periods=2).sum()
merged_df['Max_HighTrigger_rate_fluoro'] =  merged_df['Rate_HighTrigger_fluoro'].cummax()

merged_df["TriggerLow_fluoro"] = np.where(merged_df['Slope_rm_150fluoro'] >= lowtrigger_fluoro, 'Trigger', 'Wait')
merged_df['Rate_LowTrigger_fluoro'] = (merged_df['TriggerLow_fluoro'] == 'Trigger').rolling(144, min_periods=2).sum()
merged_df['Max_LowTrigger_rate_fluoro'] =  merged_df['Rate_LowTrigger_fluoro'].cummax()


conditionsfluoro = [
    (merged_df['Rate_LowTrigger_fluoro']  <= 1), #fire one low, then switch to high trigger
    (merged_df['Rate_HighTrigger_fluoro'] <= 2), #fire two high, then switch to higher trigger
    (merged_df['Rate_HighTrigger_fluoro'] ==3), #fire more 3 highs then even higher trigger
    (merged_df['Max_HighTrigger_rate_fluoro'] >= 4),#fire four or more highs, then use max trigger
    ((merged_df['Rate_HighTrigger_fluoro']).rolling(window=240, min_periods=1).sum()==0), #more than 1.5 week since last trigger, use the lower gear
    ]


# Define corresponding choices (results)
choicesfluoro = [np.where(merged_df['Slope_rm_150fluoro'] >= lowtrigger_fluoro, 'FireLow', 'Wait'), 
           np.where(merged_df['Slope_rm_150fluoro'] >= hightrigger_fluoro, 'FireHigh', 'Wait'), 
           np.where(merged_df['Slope_rm_150fluoro'] >= (hightrigger_fluoro*1.1), 'Fire1_1High', 'Wait'),
           np.where(merged_df['Slope_rm_150fluoro'] >= (hightrigger_fluoro*1.2), 'Fire1_2High', 'Wait'),
           np.where(merged_df['Slope_rm_150fluoro'] >= (hightrigger_fluoro), 'Fire_High', 'Wait')]
           
merged_df["Fire_trigger_fluoro"] = np.select(conditionsfluoro, choicesfluoro, default='Wait')

#Combine both channels
merged_df['combined_fire'] = np.where(merged_df['Fire_trigger_atn'].str.contains('ire')|merged_df['Fire_trigger_fluoro'].str.contains('ire'), 'Fire', 'Wait')

#Send trigger signal on "Get_down == "Fire!"
merged_df["Get_Down"] = np.where(
    (merged_df['combined_fire'] != 'Wait').rolling(window=48, min_periods=1).sum()==1,
    np.where((merged_df['combined_fire'] != 'Wait'), 'Fire!', 'Wait'),
    np.where(((merged_df['combined_fire'] == 'Wait').rolling(336, min_periods=2).sum()==0), 'Fire!', 'Wait'),
)

#save results
merged_df.to_csv('//Thalassa/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/Mooring/Prep analyses/merged_df_noInf.csv')



palette =["#3776ab", "#FF8C00"]
custom_sizes = {"Wait": 50, "Fire!": 400}
fig, axes = plt.subplots(3, 1, figsize=(8, 10), sharex=True)

# 3. Create the first scatterplot (Top)
fig, axes = plt.subplots(2, 1, figsize=(10, 10))

# 2. Plot on the first axis (axes[0])
sns.scatterplot(data=merged_df, x='rounded_hour', y='rm150atn', hue='Get_Down',size="Get_Down",sizes=custom_sizes, ax=axes[0], alpha = 0.3)

# 3. Plot on the second axis (axes[1])
sns.scatterplot(data=merged_df, x='rounded_hour', y='rm150Fluorescense_690', hue='Get_Down',size="Get_Down",sizes=custom_sizes,  ax=axes[1], alpha = 0.3)



# Adjust layout to prevent overlap
plt.tight_layout()
plt.show()

fig.savefig('//Thalassa/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/Mooring/Prep analyses/TriggernoInf.pdf', dpi=300, bbox_inches='tight')