Updating the Helper.py file and integrating the averager function into the OzonMonitor submodule, which now includes a class OzoneAnalyser.
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@@ -3,7 +3,7 @@
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"configurations": [
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{
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"name": "Python Debugger: Current File",
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"name": "Debug Current File",
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"type": "debugpy",
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"request": "launch",
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"program": "${file}",
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+38
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@@ -1,9 +1,40 @@
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# Author: David Tschan
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# Institute: Paul-Scherrer Institute, Laboratory for Atmospheric Chemistry, Multiphase Chemistry Group,
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# Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
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# Role: Master Student
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# License: n/a
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# Date: 2026
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### SUBMODULE DOCSTRING
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"""The docstring for this submodule
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"""
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### IMPORTS AND CONFIGURATIONS
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import pandas as pd
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### Helper.py
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class DATFile():
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# hekko
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"""The docstring for this class
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Description: Bla bla bla
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Attributes
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----------
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word: str
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A word
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Methods
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-------
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__init__: HelloWorld
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The class constructor
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"""
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def __init__(self, file, data_name = 'value'):
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@@ -29,4 +60,8 @@ class DATFile():
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pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]
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datetimes = pd.to_datetime(pre_datetimes, format=r'%d.%m.%Y %H:%M:%S')
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self.data = pd.DataFrame({'timestamp': datetimes, self.data_name: values})
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self.data = pd.DataFrame({'timestamp': datetimes, self.data_name: values})
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def exclude_outliers():
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pass
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@@ -1,20 +0,0 @@
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class HelloWorld():
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"""The docstring for this class
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Description: Bla bla bla
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Attributes
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----------
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word: str
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A word
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Methods
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-------
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__init__: HelloWorld
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The class constructor
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"""
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def __init__(self, word = "Hellow"):
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self.word = word
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@@ -0,0 +1,96 @@
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# Author: David Tschan
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# Institute: Paul-Scherrer Institute, Laboratory for Atmospheric Chemistry, Multiphase Chemistry Group,
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# Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
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# Role: Master Student
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# License: n/a
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# Date: 2026
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### SUBMODULE DOCSTRING
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"""The docstring for this submodule
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"""
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### IMPORTS AND CONFIGURATIONS
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import numpy as np
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import pandas as pd
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from defs.Helper import DATFile
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from defs.SMPS import OFRAnalyser
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### OzoneAnalyser.py
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class OzoneAnalyser():
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"""The docstring for this class
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Description: Bla bla bla
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Attributes
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----------
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word: str
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A word
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Methods
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-------
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__init__: HelloWorld
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The class constructor
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"""
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def __init__(self, file: DATFile, on_time_interval):
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self.file = file
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self.on_time_interval = on_time_interval
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self.averaged_data = None
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def GetData(self):
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self.file.read()
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def AverageWithSMPS(self, smps_analyser: OFRAnalyser, cut_edges: int, subtract_baseline = ['yes', 5]):
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avg_ozone = []
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mprr_data = []
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local_smps_data = smps_analyser.data[(smps_analyser.data['Starting Time (-)'] >= smps_analyser.on_time_interval[0]) &
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(smps_analyser.data['Starting Time (-)'] <= smps_analyser.on_time_interval[1])].reset_index()
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baseline = None
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for index, data in local_smps_data.iterrows():
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start = data['Starting Time (-)']
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if index == 0:
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if subtract_baseline[0] == 'yes':
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baseline_start = start - pd.Timedelta(minutes = int(subtract_baseline[1]))
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baseline_ozone_data = self.file.data[(self.file.data['timestamp'] >= baseline_start) &
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(self.file.data['timestamp'] <= start)]
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baseline = np.average(baseline_ozone_data['ozone'])
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if index in list(range(cut_edges)):
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continue
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stop = start + pd.Timedelta(seconds = data['Sampling Time (s)'])
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mprr_data.append(data['Mass Production Rate (kg/s)'])
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local_ozone_data = self.file.data[(self.file.data['timestamp'] >= start) &
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(self.file.data['timestamp'] <= stop)]
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avg_ozone.append(np.average(local_ozone_data['ozone']))
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for sub_index in list(range(cut_edges)):
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del avg_ozone[-(sub_index + 1)]
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del mprr_data[-(sub_index + 1)]
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out_mprr_data = np.array(mprr_data)
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if subtract_baseline[0] == 'yes':
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out_avg_ozone = np.array([baseline - value for value in avg_ozone])
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elif subtract_baseline[0] != 'yes':
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out_avg_ozone = np.array([value for value in avg_ozone])
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self.averaged_data = pd.DataFrame({'Ozone (ppb)': out_avg_ozone, 'Mass Production Rate (kg/s)': out_mprr_data})
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@@ -1,3 +1,13 @@
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# Author: David Tschan
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# Institute: Paul-Scherrer Institute, Laboratory for Atmospheric Chemistry, Multiphase Chemistry Group,
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# Forschungsstrasse 111, 5232 Villigen PSI, Switzerland
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# Role: Master Student
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# License: n/a
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# Date: 2026
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### SUBMODULE DOCSTRING
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"""The docstring for this submodule
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"""
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@@ -10,7 +20,7 @@ import math
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import warnings
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### OFR ANALYSIS
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### OFR.py
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class Distribution():
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@@ -305,19 +315,23 @@ class OFRAnalyser:
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A string pointing to the flow data source file
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sample_number_list: [int]
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A list of integers indicating which samples to consider
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on_time_interval: [pd.Timestamp]
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A list with two elements containing the pd.Timestamp objects when the VOC flow was switched on and off, respectively.
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max_samples: int
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The highest sample available in the data
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data: pandas.DataFrame
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A dataframe containing all the data
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"""
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def __init__(self, data_source_file, flow_source_file, smpl_number_list, data_separator = ',') -> None:
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def __init__(self, data_source_file, flow_source_file, smpl_number_list, on_time_interval, data_separator = ',') -> None:
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self.data_source_file = data_source_file
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self.flow_source_file = flow_source_file
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self.smpl_number_list = smpl_number_list
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self.on_time_interval = on_time_interval
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self.__data_separator = data_separator
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with open(self.data_source_file, 'r', errors = 'ignore') as file:
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for i, line in enumerate(file.readlines()):
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if i == 17:
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@@ -1,5 +1,6 @@
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# Importing custom code
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from defs.OFR import OFRSample, OFRAnalyser
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from defs.SMPS import OFRAnalyser
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from defs.OzoneMonitor import OzoneAnalyser
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from defs.Helper import DATFile
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# Importing required libraries
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@@ -7,62 +8,18 @@ import numpy as np
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from pathlib import Path
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import pandas as pd
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def ozone_averager(ozon_analyser: DATFile, smps_analyser: OFRAnalyser, voc_on_times, cut_edges = True, subtract_5min_baseline = True) -> np.ndarray:
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average_ozon_values = []
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mass_production_rates = []
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local_smps_data = smps_analyser.data[(smps_analyser.data['Starting Time (-)'] >= voc_on_times[0]) & (smps_analyser.data['Starting Time (-)'] >= voc_on_times[0])].reset_index()
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baseline = None
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for index, data in local_smps_data.iterrows():
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start = data['Starting Time (-)']
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if index == 0:
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if subtract_5min_baseline == True:
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baseline_start = start - pd.Timedelta(minutes=5)
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baseline_ozone_data = ozon_analyser.data[(ozon_analyser.data['timestamp'] >= baseline_start) & (ozon_analyser.data['timestamp'] <= start)]
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baseline = np.average(baseline_ozone_data['ozone'])
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if cut_edges == True:
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continue
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stop = start + pd.Timedelta(seconds = data['Sampling Time (s)'])
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mass_production_rates.append(data['Mass Production Rate (kg/s)'])
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local_ozone_data = ozon_analyser.data[(ozon_analyser.data['timestamp'] >= start) & (ozon_analyser.data['timestamp'] <= stop)]
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average_ozon_values.append(np.average(local_ozone_data['ozone']))
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if cut_edges == True:
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del average_ozon_values[-1]
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del mass_production_rates[-1]
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if subtract_5min_baseline == True:
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output_average_ozone_values = [baseline - value for value in average_ozon_values]
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elif subtract_5min_baseline == False:
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output_average_ozone_values = [value for value in average_ozon_values]
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return output_average_ozone_values, mass_production_rates
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# Linking to the data
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# Specify the source file paths to for SMPS data for the different filters used
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smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260903\20260903_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260903_SET02_EXP14_flow_file.txt"),
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smpl_number_list = range(1, 60))
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260903_SET02_EXP14_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval = [pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')])
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# Specify the source file paths to the API O3 monitor data for the different filters used
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ozon_analyser =DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-03_14.23.44_PC14750_API_O3.dat"), data_name = 'ozone')
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voc_on_times = [pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')]
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ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-03_14.23.44_PC14750_API_O3.dat"), data_name = 'ozone'),
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on_time_interval=[pd.Timestamp('2026-09-03 14:35:00'), pd.Timestamp('2026-09-03 16:50:00')])
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smps_analyser.GetData()
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ozon_analyser.read()
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ozon_analyser.GetData()
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ozone_average, mass_production_rates = ozone_averager(ozon_analyser=ozon_analyser, smps_analyser=smps_analyser, voc_on_times = voc_on_times, cut_edges=False, subtract_5min_baseline=True)
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ozon_analyser.AverageWithSMPS(smps_analyser = smps_analyser, cut_edges = 2, subtract_baseline = ['yes', 5])
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print('hello')
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print(len(ozon_analyser.averaged_data))
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