# -*- coding: utf-8 -*- """ Created on Wed Feb 25 17:05:42 2026 @author: chuffard """ import seaborn as sns import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from datetime import datetime import numpy as np import pandas as pd import sys import os.path import warnings import glob from pathlib import Path #find 97.1 and 99.1th percentiles for fluorescence slope at MARS and also at Station Mprint(percentilefull_991) #print(percentilefull_991) #1.7495666666666636 #print(percentilefull_971) # 1.0466666666666242 #from full Station M dataset #now see how it is for the full MARS fluormenter dataset # path = Path(r'//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Deployments') # # Set the directory to search ('.' for current directory) # # Find all files with 'fluorometer' in the name recursively # fluoro_files = list(path.rglob('*fluorometer*')) # fluoro_filesdf = pd.DataFrame(fluoro_files) path = '//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Analyses/Explore/all_fluoro' # #%% Run atn # ##before deployment fix inf in atn code # # Get all matching file paths as a list # 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['Fluorescense_690'] = fluorescence['Fluorescense 690 Counts']-fluorescence['Measure Ref 690 Counts'] # fluorescence['Fluorescense_590'] = fluorescence['Fluorescense 590 Counts']-fluorescence['Measure Ref 590 Counts'] # fluorescence_sorted = fluorescence.sort_values(by='Datetime') # fluorescence_sorted.drop_duplicates(inplace=True) # fluorescence_sorted.to_csv(path+'/Full_MARS_fluoro.csv') Full_MARS_fluoro = r"//atlas/ProjectLibrary/902305_Event_Detection_with_SES/Analyses/Explore/all_fluoro/Full_MARS_fluoro.csv" Full_MARS_fluoro = pd.read_csv(Full_MARS_fluoro) Full_MARS_fluoro.columns # ndex(['Unnamed: 0', 'SES Timestamp (ISO601)', 'Dark Ref Integration Time', # 'Dark Ref 690 Counts', 'Dark Ref 590 Counts', # 'Measure Ref Integration Time', 'Measure Ref 690 Counts', # 'Measure Ref 590 Counts', 'Luminescense Integration Time', # 'Luminescense 690 Counts', 'Luminescense 590 Counts', # 'Fluorescense Integration Time', 'Fluorescense 690 Counts', # 'Fluorescense 590 Counts', 'Phosphorescense Integration Time', # 'Phosphorescense 690 Counts', 'Phosphorescense 590 Counts', # 'Constant Target Fluorescense Integration Time', # 'Constant Target Fluorescense 690 Counts', # 'Constant Target Fluorescense 590 Counts', 'Code', # 'Fluorescence690DATA', 'Fluorescence590DATA', 'Unnamed: 21', 'Datetime', # 'Fluorescense_690', 'Fluorescense_590'], # dtype='str') Full_MARS_fluoro["DateTime"] = pd.to_datetime(Full_MARS_fluoro['SES Timestamp (ISO601)']) Full_MARS_fluoro = Full_MARS_fluoro.sort_values(by='DateTime') Full_MARS_fluoro = Full_MARS_fluoro[Full_MARS_fluoro['Fluorescense_690'] != 0] Full_MARS_fluoro['rm150Fluorescense_690'] = Full_MARS_fluoro['Fluorescense_690'].rolling(150, min_periods=23).mean() Full_MARS_fluoro['Slope_rm_150Fluorescense_690'] = Full_MARS_fluoro['rm150Fluorescense_690'].diff() positive_Full_MARS_fluoro = Full_MARS_fluoro[Full_MARS_fluoro['Slope_rm_150Fluorescense_690'] > 0] percentiles_995 = positive_Full_MARS_fluoro['Slope_rm_150Fluorescense_690'].quantile(0.995) percentiles_99MARS = positive_Full_MARS_fluoro['Slope_rm_150Fluorescense_690'].quantile(0.99) #np.float64(1.4267333333333445) #Station m #np.float64(1.686666666666619) percentiles_991 = positive_Full_MARS_fluoro['Slope_rm_150Fluorescense_690'].quantile(0.991) percentiles_971 = positive_Full_MARS_fluoro['Slope_rm_150Fluorescense_690'].quantile(0.971) print(percentiles_99MARS)#(1.4267333333333445) print(percentiles_995)#(2.5462000000000207) print(percentiles_991)#1.9377066666666227 print(percentiles_971)#0.6666666666666479 print("hello") #Station M # print(percentilefull_991) # #1.7495666666666636 # print(percentilefull_971) # # 1.0466666666666242 #print(percentilefull_995)# np.float64(2.331500000000036) hightriggerFluoro= 2.4 lowtriggerFluoro= 1.8 a = percentiles_995-percentiles_991 print(a) numeric_colsfluoro = Full_MARS_fluoro.select_dtypes(include=['number']).columns datetime_column_name = Full_MARS_fluoro['DateTime'] with PdfPages('all_sensor_plots MARS fluoro.pdf') as pdf: for col in numeric_colsfluoro: plt.figure(figsize=(10, 6)) # Create a new figure plt.scatter(datetime_column_name, Full_MARS_fluoro[col], label=col, alpha = 0.3) 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 Full_MARS_fluoro["trigger150slopefluoro995"] = np.where(Full_MARS_fluoro['Slope_rm_150Fluorescense_690'] >= percentiles_995, 'TriggerSlope', 'WaitSlope') Full_MARS_fluoro["trigger150slopefluoro991"] = np.where(Full_MARS_fluoro['Slope_rm_150Fluorescense_690'] >= percentiles_991, 'TriggerSlope', 'WaitSlope') Full_MARS_fluoro["trigger150slopefluoro99"] = np.where(Full_MARS_fluoro['Slope_rm_150Fluorescense_690'] >= percentiles_99MARS, 'TriggerSlope', 'WaitSlope') Full_MARS_fluoro['test'] = np.where(Full_MARS_fluoro['Slope_rm_150Fluorescense_690'] >= 0, 'TriggerSlope', 'WaitSlope') palette =["#3776ab", "#FF8C00"] 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=Full_MARS_fluoro, x='DateTime', y='Slope_rm_150Fluorescense_690', hue='trigger150slopefluoro99', ax=axes[0], alpha = 0.3) axes[0].set_title('99') # 3. Plot on the second axis (axes[1]) sns.scatterplot(data=Full_MARS_fluoro, x='DateTime', y='rm150Fluorescense_690', hue='trigger150slopefluoro99', ax=axes[1], alpha = 0.3) axes[1].set_title('99') # Adjust layout to prevent overlap plt.tight_layout() plt.show() fig.savefig(path+'/AllMarsFluoro.pdf', dpi=300, bbox_inches='tight')