# -*- coding: utf-8 -*- """ Created on Wed Feb 25 14:16:18 2026 @author: chuffard """ #Python simple two-channel trigger fluorescence and attenuance: either or, not and # -*- coding: utf-8 -*- """ Created on Thu Oct 3 13:10:10 2024 @author: chuffard """ # -*- coding: utf-8 -*- """ Created on Mon Jun 10 13:32:29 2024 @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 warnings.filterwarnings("ignore") #%% path = '//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Analyses/2025 MARS deployment/2025July_Nov' #%% bring in atn data ##before deployment fix inf in atn code and prep for triggering 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() #%% Bring in fluoro data and prep for triggering # fluoro_files = glob.glob(path+'/*fluorom*') # fluorescence = pd.concat((pd.read_csv(f) for f in fluoro_files), ignore_index=True) # fluorescence["DateTime"] = pd.to_datetime(fluorescence["SES Timestamp (ISO601)"]) 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 #>97th percentile hightrigger_atn = 0.011# = 99.1th percentile- find percentiles from all MARS atn hightrigger_fluoro= 2.4 lowtrigger_fluoro= 1.8 use_min_period = 24 # filename = sys.argv[1] # nrec = int(sys.argv[2]) nrec=150 #Find diff in slope #trim first 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') # #Start with high triggers 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() # #If starting trigger is high, and trigger rate is low then lower threshold # #If starting trigger rate is low and trigger rate is too high then raise threshold 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') 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') merged_df['combined_fire'] = np.where(merged_df['Fire_trigger_atn'].str.contains('ire')|merged_df['Fire_trigger_fluoro'].str.contains('ire'), 'Fire', 'Wait') 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'), ) merged_df.to_csv('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/2026 Mooring/Prep analyses/merged_df.csv') #%% plotting to check. This yields 10 triggers from the last deployment, with a mix of triggers from atn vs fluoro, # import matplotlib.pyplot as plt import seaborn as sns palette =["#3776ab", "#FF8C00"] merged_df['size_val'] = merged_df['Get_Down'].map({'Wait': 50, 'Fire!': 300}) 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", # Color by 'sex' size="size_val", # Size by the new 'size_val' column sizes=(50, 250), ax=axes[0],# Optional: control the min/max point size range alpha=0.6 # Optional: adjust transparency for overlap ) # 3. Plot on the second axis (axes[1]) sns.scatterplot( data=merged_df, x="rounded_hour", y="rm150Fluorescense_690", hue="Get_Down", # Color by 'sex' size="size_val", # Size by the new 'size_val' column sizes=(50, 250), ax=axes[1],# Optional: control the min/max point size range alpha=0.6 # Optional: adjust transparency for overlap ) # Adjust layout to prevent overlap plt.tight_layout() plt.show() fig.savefig('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/2026 Mooring/Prep analyses/triggerplots.pdf', dpi=300, bbox_inches='tight') #%% #old stuff # # 1. Create a new 'size_val' column based on the 'sex' (hue) # # Assign different size values for each category ('Male' = 100, 'Female' = 200) # # 2. Create the scatter plot # plt.figure(figsize=(8, 6)) # sns.scatterplot( # data=merged_df, # x="rounded_hour", # y="rm150atn", # hue="Get_Down", # Color by 'sex' # size="size_val", # Size by the new 'size_val' column # sizes=(50, 250), # Optional: control the min/max point size range # alpha=0.6 # Optional: adjust transparency for overlap # ) # plt.title("Get vs down") # plt.show() # # #If starting trigger is high, and trigger rate is low then lower threshold # # #If starting trigger rate is low and trigger rate is too high then raise threshold # conditionsfluoro = [ # (merged_df['Max_LowTrigger_rate_fluoro'] <= 2), # (merged_df['Rate_HighTrigger_fluoro'] <= 2), # (merged_df['Rate_HighTrigger_fluoro'] > 2)&(merged_df['Max_HighTrigger_rate_fluoro'] < 4), # (merged_df['Max_HighTrigger_rate_fluoro'] >= 4)] # # 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')] # merged_df["Fire_trigger_Fluoro"] = np.select(conditionsfluoro, choicesfluoro, default='Wait') # ############# # ########## # merged_df['combined_fire'] = np.where(merged_df['Fire_trigger_atn'].str.contains('ire')|merged_df['Fire_trigger_Fluoro'].str.contains('ire'), 'Fire', 'Wait') # 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'), # ) # # Conditions_merged = [ # # (merged_df['Fire_trigger_atn'] =='*Fire*'), # # ((merged_df['Fire_trigger_atn'] != 'Wait').rolling(window=48, min_periods=1).sum()==1), # # ((merged_df['Fire_trigger_atn'] == 'Wait').rolling(336, min_periods=2).sum()==0), # # (merged_df['Rate_HighTrigger_fluoro'] <= 2), # # (merged_df['Rate_HighTrigger_fluoro'] > 2)&(merged_df['Max_HighTrigger_rate_fluoro'] < 4), # # (merged_df['Max_HighTrigger_rate_fluoro'] >= 4)] # # # Define corresponding choices (results) # # choicesfluoro = [(np.where((merged_df['Fire_trigger_atn'] != 'Wait'))|(np.where(((merged_df['Fire_trigger_Fluoro'] == 'Wait').rolling(336, min_periods=2).sum()==0))), 'Fire_atn', 'Wait'), # # np.where(merged_df['Slope_rm_150_fluoro'] >= hightrigger_Fluoro, 'FireHigh', 'Wait'), # # np.where(merged_df['Slope_rm_150_fluoro'] >= (hightrigger_Fluoro*1.1), 'Fire1_1High', 'Wait'), # # np.where(merged_df['Slope_rm_150_fluoro'] >= (hightrigger_Fluoro*1.2), 'Fire1_2High', 'Wait')] # # merged_df["Fire_trigger_Fluoro"] = np.select(conditionsfluoro, choicesfluoro, default='Wait') # # ########### # # merged_df["Get_Down"] = np.where( # # (merged_df['Fire_trigger_atn'] != 'Wait').rolling(window=48, min_periods=1).sum()==1, # # np.where((merged_df['Fire_trigger_atn'] != 'Wait'), 'Fire_atn', 'Wait'), # # np.where(((merged_df['Fire_trigger_atn'] == 'Wait').rolling(336, min_periods=2).sum()==0), 'Fire!', 'Wait'), # # ) # # merged_df["Get_Way_Down"] = np.where( # # (merged_df['Get_Down'] != 'Wait').rolling(window=48, min_periods=1).sum()==1, # # np.where((merged_df['Fire_trigger_Fluoro'] != 'Wait'), 'Fire_atn', 'Wait'), # # np.where(((merged_df['Fire_trigger_Fluoro'] == 'Wait').rolling(336, min_periods=2).sum()==0), 'Fire!', 'Wait'), # # ) # # import timeit # # def my_function(): # # """The function to be timed.""" # # total = 0 # # for i in range(1000): # # total += i # # return total # # # Time the function call (runs the function 10,000 times by default) # # # Use a lambda to pass the function with arguments if needed # # execution_time = timeit.timeit(lambda: my_function(), number=10000) # # # Print the total time and average time per execution # # print(f"Total time for 10,000 runs: {execution_time:.4f} seconds") # # print(f"Average time per run: {execution_time / 10000:.6f} seconds") # # # try: # # # # Ensure 'max_rate' column is not empty and the values are valid # # # if 'max_rate' in StationMcup.columns and not StationMcup['max_rate'].isnull().all(): # # # # Apply condition element-wise to the 'max_rate' column # # # StationMcup["Fire_slope_trigger"] = np.where( # # # StationMcup['max_rate'].abs() >= 3, # # # np.where(StationMcup['slope'] >= hightrigger, 'Fire', 'Wait'), # # # np.where(StationMcup['slope'] >= lowtrigger, 'Fire', 'Wait') # # # ) # # # else: # # # print("The 'max_rate' column is missing or contains all NaN values.") # # # except Exception as e: # # # print(f"An error occurred: {e}") # # #result = np.where(((arr > 2) & (arr < 6)) | (arr == 7), -1, 100) # # #atn.to_csv('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/2026 Mooring/Prep analyses/TestingTriggerDualChannel_atn.csv') # # #%% # # fluorescence["TriggerHigh_Fluoro"] = np.where(fluorescence['Slope_rm_150Fluoro'] >= hightrigger_Fluoro, 'Trigger', 'Wait') # # fluorescence['Rate_HighTrigger_Fluoro'] = (fluorescence['TriggerHigh_Fluoro'] == 'Trigger').rolling(144, min_periods=2).sum() # # fluorescence['Max_HighTrigger_rate_Fluoro'] = fluorescence['Rate_HighTrigger_Fluoro'].cummax() # # fluorescence["TriggerLow_Fluoro"] = np.where(fluorescence['Slope_rm_150Fluoro'] >= lowtrigger_Fluoro, 'Trigger', 'Wait') # # fluorescence['Rate_LowTrigger_Fluoro'] = (fluorescence['TriggerLow_Fluoro'] == 'Trigger').rolling(144, min_periods=2).sum() # # fluorescence['Max_LowTrigger_rate_Fluoro'] = fluorescence['Rate_LowTrigger_Fluoro'].cummax() # # # #If starting trigger is high, and trigger rate is low then lower threshold # # # #If starting trigger rate is low and trigger rate is too high then raise threshold # # conditions = [ # # (fluorescence['Max_LowTrigger_rate_Fluoro'] <= 2), # # (fluorescence['Rate_HighTrigger_Fluoro'] <= 2), # # (fluorescence['Rate_HighTrigger_Fluoro'] > 2)&(fluorescence['Max_HighTrigger_rate_Fluoro'] < 4), # # (fluorescence['Max_HighTrigger_rate_Fluoro'] >= 4)] # # #add no fires for two days after a trigger # # # Define corresponding choices (results) # # choices = [np.where(fluorescence['Slope_rm_150Fluoro'] >= lowtrigger_Fluoro, 'FireLow', 'Wait'), # # np.where(fluorescence['Slope_rm_150Fluoro'] >= hightrigger_Fluoro, 'FireHigh', 'Wait'), # # np.where(fluorescence['Slope_rm_150Fluoro'] >= (hightrigger_Fluoro*1.1), 'Fire1_1High', 'Wait'), # # np.where(fluorescence['Slope_rm_150Fluoro'] >= (hightrigger_Fluoro*1.2), 'Fire1_2High', 'Wait')] # # fluorescence["Fire_trigger"] = np.select(conditions, choices, default='Wait') # # #fluorescence.to_csv('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments/2026 Mooring/Prep analyses/TestingTriggerDualChannel_fluorescence.csv') # # # #%% OG # # # #### # # # use_min_period = 24 # # # atn = atn.sort_values(by='Datetime') # # # atn['rm150atn'] = atn['atn'].rolling(150, min_periods=23).mean() # # # atn['Slope_rm_150atn'] = atn['rm150atn'].diff() # # # ##what percentil are these values # # # atn['Percentile_Rank'] = atn['Slope_rm_150atn'].rank(pct=True) * 100 # # # lowtrigger = 0.007 #97.05 th percentile (1 away from 97.1th) # # # hightrigger = 0.011# = 99.1th percentile # # # atn["triggerNrecslope_atn"] = np.where(atn['Slope_rm_150atn'] >= lowtrigger, 'LowTriggeratn', 'WaitSlopeatn') # # # nsamples_6weeks = 32 # # # if (len(atn) > nsamples_6weeks): # # # #find the rolling rate low triggers in past ~1.5 months # # # atn['low_trigger_rate'] = (atn['triggerNrecslope_atn'] == 'LowTriggeratn').rolling(nsamples_6weeks, min_periods=2).sum() # # # #Line to help keep tabs on whether the switch has been flipped and we're now using the high trigger # # # atn['max_rate'] = atn['low_trigger_rate'].cummax() # # # try: # # # # Ensure 'max_rate' column is not empty and the values are valid # # # if 'max_rate' in atn.columns and not atn['max_rate'].isnull().all(): # # # # Apply condition element-wise to the 'max_rate' column # # # atn["Fire_slope_trigger"] = np.where( # # # atn['max_rate'].abs() > 3, # # # np.where(atn['Slope_rm_150atn'] >= hightrigger, 'Fire', 'Wait'), # # # np.where(atn['Slope_rm_150atn'] >= lowtrigger, 'Fire', 'Wait') # # # ) # # # else: # # # print("The 'max_rate' column is missing or contains all NaN values.") # # # except Exception as e: # # # print(f"An error occurred: {e}") # # # #if doesn't fire in ~21 days, fire anyway # # # matchlist = ['Wait'] * 30 # # # matchlist.insert(0,'Fire') # # # def check_sequence(df, col_name, seq): # # # """ # # # Checks if a sequence of values in a column matches a given list. # # # Args: # # # df: The DataFrame. # # # col_name: The name of the column to check. # # # seq: The sequence of strings to match. # # # Returns: # # # A new DataFrame with a 'match' column indicating if the sequence was found. # # # """ # # # df['match'] = False # # # for i in range(len(df) - len(seq) + 1): # # # if df[col_name].iloc[i:i + len(seq)].tolist() == seq: # # # df.loc[i + len(seq) - 1, 'match'] = True # # # return df # # # # Example usage: # # # df = atn # # # seq_to_match = matchlist # # # df = check_sequence(df, 'Fire_slope_trigger', seq_to_match) # # # df['match'] = df['match'].astype(str) # # # df["Fire_anyway"] = np.where(df['match'].str.contains('T'), 'Fire', 'Wait') # # # if (len(df) > nsamples_6weeks): # # # df['SendTrigger'] = np.where((df['Fire_anyway'] == 'Fire') | # # # (df['Fire_slope_trigger'] =='Fire'), 'SendTrigger', 'Wait') # # # result = np.where((df['Fire_anyway'] == 'Fire') | # # # (df['Fire_slope_trigger'] =='Fire'), 'Event detected', 'Wait') # # # else: # # # df['SendTrigger'] = np.where((df['Fire_anyway'] == 'Fire') | # # # False, 'SendTrigger', 'Wait') # # # result = np.where((df['Fire_anyway'] == 'Fire') | # # # False, 'Event detected', 'Wait') # # # # Emit result # # # if (len(result) > 0): # # # print(result[len(result)-1]) # # # df.to_csv('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Analyses/2025 MARS deployment/2025July_Nov/SES_triggered.csv', index=False) # # df['Slope_rm_150atn'].hist(bins=100) # # df['z_score690slopeatn'] = stats.zscore(df['Slope_rm_150atn'], nan_policy='omit') # # numeric_colsfluoro = df.select_dtypes(include=['number']).columns # # datetime_column_name = df['Datetime'] # # is_datetime(df['Datetime']) # # # 3. Save plots to a multi-page PDF # # with PdfPages('all_sensor_plotsatn.pdf') as pdf: # # for col in numeric_colsfluoro: # # plt.figure(figsize=(10, 6)) # Create a new figure # # plt.plot(datetime_column_name, df[col], label=col) # # plt.title(f'Line Plot of {col}') # # # plt.xlabel('DateTime_Hour') # # plt.ylabel('Value') # # plt.legend() # # plt.xticks(rotation=45) # # plt.grid(True) # # # Save the current figure to the PDF # # pdf.savefig() # # plt.close() # Close to free up memory # # print("PDF generated successfully: all_sensor_plots.pdf") # # #%% # # #! /usr/bin/env python # # use_min_period = 24 # # fluoro = fluorescence.sort_values(by='DateTime') # # fluoro.columns # # fluoro['rm150fluoro'] = fluoro['Fluorescense_690'].rolling(150, min_periods=23).mean() # # fluoro['Slope_rm_150fluoro'] = fluoro['rm150fluoro'].diff() # # fluoro['z_score690slopefluoro'] = stats.zscore(df['Slope_rm_150fluoro'], nan_policy='omit') # # threshold5 = fluoro['Slope_rm_150fluoro'].quantile(0.95) # # threshold3 = fluoro['Slope_rm_150fluoro'].quantile(0.97) # # lowtrigger =threshold5 # # hightrigger = threshold3 # # fluoro["triggerNrecslope_fluoro"] = np.where(fluoro['Slope_rm_150fluoro'] >= lowtrigger, 'LowTriggerfluoro', 'WaitSlopefluoro') # # nsamples_6weeks = 32 # # if (len(fluoro) > nsamples_6weeks): # # #find the rolling rate low triggers in past ~1.5 months # # fluoro['low_trigger_rate'] = (fluoro['triggerNrecslope_fluoro'] == 'LowTriggerfluoro').rolling(nsamples_6weeks, min_periods=2).sum() # # #Line to help keep tabs on whether the switch has been flipped and we're now using the high trigger # # fluoro['max_rate'] = fluoro['low_trigger_rate'].cummax() # # try: # # # Ensure 'max_rate' column is not empty and the values are valid # # if 'max_rate' in fluoro.columns and not fluoro['max_rate'].isnull().all(): # # # Apply condition element-wise to the 'max_rate' column # # fluoro["Fire_slope_trigger"] = np.where( # # fluoro['max_rate'].abs() > 3, # # np.where(fluoro['Slope_rm_150fluoro'] >= hightrigger, 'Fire', 'Wait'), # # np.where(fluoro['Slope_rm_150fluoro'] >= lowtrigger, 'Fire', 'Wait') # # ) # # else: # # print("The 'max_rate' column is missing or contains all NaN values.") # # except Exception as e: # # print(f"An error occurred: {e}") # # #if doesn't fire in ~21 days, fire anyway # # matchlist = ['Wait'] * 30 # # matchlist.insert(0,'Fire') # # def check_sequence(df, col_name, seq): # # """ # # Checks if a sequence of values in a column matches a given list. # # Args: # # df: The DataFrame. # # col_name: The name of the column to check. # # seq: The sequence of strings to match. # # Returns: # # A new DataFrame with a 'match' column indicating if the sequence was found. # # """ # # df['match'] = False # # for i in range(len(df) - len(seq) + 1): # # if df[col_name].iloc[i:i + len(seq)].tolist() == seq: # # df.loc[i + len(seq) - 1, 'match'] = True # # return df # # # Example usage: # # df = fluoro # # seq_to_match = matchlist # # df = check_sequence(df, 'Fire_slope_trigger', seq_to_match) # # df['match'] = df['match'].astype(str) # # df["Fire_anyway"] = np.where(df['match'].str.contains('T'), 'Fire', 'Wait') # # if (len(df) > nsamples_6weeks): # # df['SendTrigger'] = np.where((df['Fire_anyway'] == 'Fire') | # # (df['Fire_slope_trigger'] =='Fire'), 'SendTrigger', 'Wait') # # result = np.where((df['Fire_anyway'] == 'Fire') | # # (df['Fire_slope_trigger'] =='Fire'), 'Event detected', 'Wait') # # else: # # df['SendTrigger'] = np.where((df['Fire_anyway'] == 'Fire') | # # False, 'SendTrigger', 'Wait') # # result = np.where((df['Fire_anyway'] == 'Fire') | # # False, 'Event detected', 'Wait') # # # Emit result # # if (len(result) > 0): # # print(result[len(result)-1]) # # df.to_csv('//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Analyses/2025 MARS deployment/2025July_Nov/SES_triggeredFluoro.csv', index=False)