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
warnings.filterwarnings("ignore")


path = '//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Analyses/2025 MARS deployment/2025July_Nov'

#import attenuance
atn_files = glob.glob(path+'/*sip*')
atn = pd.concat((pd.read_csv(f) for f in atn_files), ignore_index=True)
atn["DateTime"] = pd.to_datetime(atn["Datetime"])
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('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/2026 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['Dark 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('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/2026 Mooring/Prep analyses/merged_df.csv')