Some changes to mplstyle for scatter plots and default settings for markers.

.mplstyle

@@ -4,6 +4,11 @@ text.usetex: True
 text.latex.preamble:    "\usepackage{arev}\usepackage{accents}\usepackage{siunitx}\usepackage{mhchem}\newcommand{\der}[1]{\ensuremath{\textnormal{d}\,{#1}}}"
 lines.linewidth: 1.00               # line width in points
 lines.linestyle: -                  # solid line
+lines.marker:          None        # the default marker
+lines.markerfacecolor: black        # the default marker face color
+lines.markeredgecolor: None        # the default marker edge color
+lines.markeredgewidth: 0         # the line width around the marker symbol
+lines.markersize:      2           # marker size, in points
 
 # AXES AND BOX
 axes.facecolor:     white   # axes background color

main.py

@@ -1,11 +1,68 @@
-# Imports
+# Importing custom code
 from defs.OFR import OFRSample, OFRAnalyser
+from defs.Helper import DATFile
+
+# Importing required libraries
+import numpy as np
 from pathlib import Path
+import pandas as pd
+
+def ozone_averager(ozon_analyser: DATFile, smps_analyser: OFRAnalyser, voc_on_times, cut_edges = True, subtract_5min_baseline = True) -> np.ndarray:
+
+    average_ozon_values = []
+    mass_production_rates = []
+
+    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()
+
+    baseline = None
+
+    for index, data in local_smps_data.iterrows():
+
+        start = data['Starting Time (-)']
+
+        if index == 0:
+
+            if subtract_5min_baseline == True:
+                baseline_start = start - pd.Timedelta(minutes=5)
+                baseline_ozone_data = ozon_analyser.data[(ozon_analyser.data['timestamp'] >= baseline_start) & (ozon_analyser.data['timestamp'] <= start)]
+                baseline = np.average(baseline_ozone_data['ozone'])
+
+            if cut_edges == True:
+                    continue
+
+        stop = start + pd.Timedelta(seconds = data['Sampling Time (s)'])
+
+        mass_production_rates.append(data['Mass Production Rate (kg/s)'])
+
+        local_ozone_data = ozon_analyser.data[(ozon_analyser.data['timestamp'] >= start) & (ozon_analyser.data['timestamp'] <= stop)]
+        average_ozon_values.append(np.average(local_ozone_data['ozone']))
+
+    if cut_edges == True:
+            del average_ozon_values[-1]
+            del mass_production_rates[-1]
+
+    if subtract_5min_baseline == True:
+            output_average_ozone_values = [baseline - value for value in average_ozon_values]
+    elif subtract_5min_baseline == False:
+            output_average_ozone_values = [value for value in average_ozon_values]
+
+    return output_average_ozone_values, mass_production_rates
 
 # Linking to the data
 
-# Specify the source file paths to for SMPS data
-smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260902\20260902_aPinene_SOA.txt"),
-                            flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260902_SET02_EXP13_flow_file.txt"),
-                            smpl_number_list = range(1, 60))
+# Specify the source file paths to for SMPS data for the different filters used
+smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260903\20260903_aPinene_SOA.txt"),
+                               flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260903_SET02_EXP14_flow_file.txt"),
+                               smpl_number_list = range(1, 60))
+
+# Specify the source file paths to the API O3 monitor data for the different filters used
+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')
+
+voc_on_times = [pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')]
+
 smps_analyser.GetData()
+ozon_analyser.read()
+
+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)
+
+print('hello')