Adding all necessary figures for aPinene to git.
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@@ -15,7 +15,7 @@ os.chdir(pathlib.Path(__file__).parents[0])
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# Imports from own modules
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from defs.SMPS import OFRAnalyser, OFRSample
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from defs.Helper import DATFile, quick_plot_setup, final_plot_setup
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from defs.Helper import DATFile, final_plot_setup
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from defs.OzoneMonitor import OzoneAnalyser
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# Imports from other modueles
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@@ -32,59 +32,63 @@ if __name__ == '__main__':
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# Setting the output format
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format = final_plot_setup()
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# Linking to the data
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# Specify the source file paths to for SMPS data
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smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260904\20260904_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260904_SET02_EXP15_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval=[pd.Timestamp('2026-09-04 13:22:00'), pd.Timestamp('2026-09-04 14:22:00')])
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# Specify the source file paths to for SMPS data
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smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260904\20260904_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260904_SET02_EXP15_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval=[pd.Timestamp('2026-09-04 13:22:00'), pd.Timestamp('2026-09-04 14:22:00')])
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# Specify the source file paths to the API O3 monitor data
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ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-04_12.28.13_PC14750_API_O3.dat"), data_name = 'ozone'),
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on_time_interval = [pd.Timestamp('2026-09-04 12:37:00'), pd.Timestamp('2026-09-04 14:52:00')])
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# Specify the source file paths to the API O3 monitor data
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ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-04_12.28.13_PC14750_API_O3.dat"), data_name = 'ozone'),
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on_time_interval = [pd.Timestamp('2026-09-04 12:37:00'), pd.Timestamp('2026-09-04 14:52:00')])
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# Reading in SMPS data and writing it to disk as a csv
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smps_analyser.GetData()
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smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)
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# Reading in SMPS data and writing it to disk as a csv
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smps_analyser.GetData()
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smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)
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# Reading in the O3 data and writing it to disk as a csv
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ozon_analyser.GetData()
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ozon_analyser.file.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)
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# Reading in the O3 data and writing it to disk as a csv
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ozon_analyser.GetData()
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ozon_analyser.file.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)
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# Creating figure
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fig = plt.figure(figsize=(9/ 2.54, 6 / 2.54))
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ax_1 = plt.axes([0.15, 0.15, 0.7, 0.7], frameon=True)
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ax_2 = plt.twinx(ax=ax_1)
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ax_1.set_zorder(0)
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ax_2.set_zorder(ax_1.get_zorder())
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# Creating figure
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# Plotting ozone data
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ax_1.plot(ozon_analyser.file.data['timestamp'], ozon_analyser.file.data['ozone'], color = 'black', linewidth = 0.5, linestyle = '-',label=r'$\chi_{\ce{O3}}$')
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fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54))
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ax_1 = plt.axes([0.15, 0.2, 0.7, 0.6], frameon=True)
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ax_2 = plt.twinx(ax=ax_1)
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ax_1.set_zorder(0)
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ax_2.set_zorder(ax_1.get_zorder())
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# Plotting SMPS data
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ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label=r'$\accentset{*}{m}$')
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ax_1.plot(ozon_analyser.file.data['timestamp'], ozon_analyser.file.data['ozone'], color = 'black', linewidth = 0.5, linestyle = '-',label=r'$\chi_{\ce{O3}}$')
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ax_1.set_ylabel(r'\ce{O3} mole fraction $\chi_{\ce{O3}}$ $(\text{ppb})$')
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# Adding lines indicating when the different components were switched on and off, respectively
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ax_1.vlines(ozon_analyser.on_time_interval, ymin=0, ymax=120000, colors='k', linestyles='--', linewidth=0.5)
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ax_2.vlines(smps_analyser.on_time_interval, ymin=0, ymax=5E-09, colors='k', linestyles='-.', linewidth=0.5)
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ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label=r'$\accentset{*}{m}$')
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ax_2.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
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# Axes labels
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ax_1.set_xlabel(r'Time $(-)$')
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ax_1.set_ylabel(r'\ce{O3} mole fraction $\chi_{\ce{O3}}$ $(\text{ppb})$')
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ax_2.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
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ax_1.vlines(ozon_analyser.on_time_interval, ymin=0, ymax=120000, colors='k', linestyles='--', linewidth=0.5)
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ax_2.vlines(smps_analyser.on_time_interval, ymin=0, ymax=5E-09, colors='k', linestyles='-.', linewidth=0.5)
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# Axes formats
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ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))
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ax_1.xaxis.set_major_locator(HourLocator())
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ax_1.set_xlabel(r'Time $(-)$')
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ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))
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ax_1.xaxis.set_major_locator(HourLocator())
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ax_1.set_xlim([pd.Timestamp('2026-09-04 12:15:00'), pd.Timestamp('2026-09-04 15:15:00')])
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# Axes limits
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ax_1.set_xlim([pd.Timestamp('2026-09-04 12:15:00'), pd.Timestamp('2026-09-04 15:15:00')])
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ax_1.set_ylim([0, 120000])
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ax_2.set_ylim([0, 5E-09])
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lines_1, labels_1 = ax_1.get_legend_handles_labels()
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lines_2, labels_2 = ax_2.get_legend_handles_labels()
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# Legend
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lines_1, labels_1 = ax_1.get_legend_handles_labels()
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lines_2, labels_2 = ax_2.get_legend_handles_labels()
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legend = plt.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')
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legend.get_frame().set_alpha(None)
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legend.get_frame().set_facecolor('white')
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legend.set_zorder(100)
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ax_1.set_ylim([0, 120000])
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ax_2.set_ylim([0, 5E-09])
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legend = plt.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')
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legend.get_frame().set_alpha(None)
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legend.get_frame().set_facecolor('white')
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legend.set_zorder(100)
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#plt.show()
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plt.savefig('figure' + format, dpi=400)
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plt.close()
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# Export
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plt.savefig('figure' + format, dpi=400)
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plt.close()
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@@ -8,118 +8,133 @@
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### IMPORTS AND CONFIGURATIONS
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# Importing custom code
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# Bookkeeping to store figure in correct place
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import pathlib
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import os
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os.chdir(pathlib.Path(__file__).parents[0])
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# Importing from own modules
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from defs.SMPS import OFRAnalyser
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from defs.Helper import DATFile
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from defs.Helper import DATFile, final_plot_setup
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from defs.OzoneMonitor import OzoneAnalyser
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# Importing required libraries
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import numpy as np
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# Importing from other modules
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from pathlib import Path
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import pandas as pd
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# Importing libraries required for plotting and exporting
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import matplotlib.pyplot as plt
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from defs.Helper import quick_plot_setup
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format = quick_plot_setup()
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# Linking to the data
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# Avoid executing this script if it's being imported
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if __name__ == '__main__':
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# Specify the source file paths to for SMPS data for the different filters used
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smps_analyser_01 = 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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on_time_interval=[pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')])
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smps_analyser_02 = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260904\20260904_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260904_SET02_EXP15_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval = [pd.Timestamp('2026-09-04 13:22:00'), pd.Timestamp('2026-09-04 14:22:00')])
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smps_analyser_03 = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260910\20260910_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260910_SET02_EXP16_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval=[pd.Timestamp('2026-09-10 15:42:00'), pd.Timestamp('2026-09-10 16:42:00')])
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# Specify the source file paths to for SMPS data
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smps_analyser_04 = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260914\20260914_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260914_SET02_EXP17_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval=[pd.Timestamp('2026-09-14 09:45:00'), pd.Timestamp('2026-09-14 10:45:00')])
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# Setting the output format
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format = final_plot_setup()
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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_01 = 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=None)
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ozon_analyser_02 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-04_12.28.13_PC14750_API_O3.dat"), data_name = 'ozone'),
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on_time_interval=None)
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ozon_analyser_03 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-10_14.43.47_PC14750_API_O3.dat"), data_name = 'ozone'),
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on_time_interval = [pd.Timestamp('2026-09-10 15:00:00'), pd.Timestamp('2026-09-04 17:12:00')])
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ozon_analyser_04 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-14_08.48.31_PC14750_API_O3.dat"), data_name = 'ozone'),
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on_time_interval = [pd.Timestamp('2026-09-14 09:00:00'), pd.Timestamp('2026-09-14 11:15:00')])
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# Specify the source file paths to for SMPS data for the different filters used
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smps_analyser_01 = 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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on_time_interval=[pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')])
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smps_analyser_02 = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260904\20260904_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260904_SET02_EXP15_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval = [pd.Timestamp('2026-09-04 13:22:00'), pd.Timestamp('2026-09-04 14:22:00')])
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smps_analyser_03 = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260910\20260910_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260910_SET02_EXP16_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval=[pd.Timestamp('2026-09-10 15:42:00'), pd.Timestamp('2026-09-10 16:42:00')])
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smps_analyser_04 = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260914\20260914_aPinene_SOA.txt"),
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flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260914_SET02_EXP17_flow_file.txt"),
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smpl_number_list = range(1, 60),
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on_time_interval=[pd.Timestamp('2026-09-14 09:45:00'), pd.Timestamp('2026-09-14 10:45:00')])
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# Reading in SMPS data
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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_01 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-03_14.23.44_PC14750_API_O3.dat"),
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data_name = 'ozone'),
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on_time_interval=None)
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ozon_analyser_02 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-04_12.28.13_PC14750_API_O3.dat"),
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data_name = 'ozone'),
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on_time_interval=None)
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ozon_analyser_03 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-10_14.43.47_PC14750_API_O3.dat"),
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data_name = 'ozone'),
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on_time_interval = [pd.Timestamp('2026-09-10 15:00:00'), pd.Timestamp('2026-09-04 17:12:00')])
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ozon_analyser_04 = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-14_08.48.31_PC14750_API_O3.dat"),
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data_name = 'ozone'),
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on_time_interval = [pd.Timestamp('2026-09-14 09:00:00'), pd.Timestamp('2026-09-14 11:15:00')])
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for smps_analyser, ozon_analyser in zip([smps_analyser_01, smps_analyser_02, smps_analyser_03, smps_analyser_04], [ozon_analyser_01, ozon_analyser_02, ozon_analyser_03, ozon_analyser_04]):
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# Reading in the data and averaging it so that a correlation diagram with ozone becomes feasible
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for smps_analyser, ozon_analyser in zip([smps_analyser_01, smps_analyser_02, smps_analyser_03, smps_analyser_04],
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[ozon_analyser_01, ozon_analyser_02, ozon_analyser_03, ozon_analyser_04]):
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smps_analyser.GetData()
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ozon_analyser.GetData()
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smps_analyser.GetData()
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ozon_analyser.GetData()
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ozon_analyser.AverageWithSMPS(smps_analyser = smps_analyser, cut_edges = 2, subtract_baseline = ['yes', 1], normalise_flow=False)
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ozon_analyser.AverageWithSMPS(smps_analyser = smps_analyser, cut_edges = 2, subtract_baseline = ['yes', 1], normalise_flow=False)
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# Creating figure
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# space for figure: 18 cm (text width), therefore we can use 9 cm for a figure.
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# Creating figure
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fig = plt.figure(figsize=(9/ 2.54, 6 / 2.54))
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ax = plt.axes([0.15, 0.15, 0.7, 0.7], frameon=True)
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fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54))
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ax = plt.axes([0.15, 0.2, 0.7, 0.6], frameon=True)
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# Plotting data for first filter
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ax.plot(ozon_analyser_01.averaged_data.values[:, 0] / 1000,
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ozon_analyser_01.averaged_data.values[:, 1],
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color = 'red',
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linewidth = 0.5,
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linestyle = ':',
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marker = 'o',
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markersize = 2.5,
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markerfacecolor = 'red',
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label = r'Set 2 Exp. 14')
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ax.plot(ozon_analyser_01.averaged_data.values[:, 0] / 1000,
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ozon_analyser_01.averaged_data.values[:, 1],
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color = 'red',
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linewidth = 0.5,
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linestyle = ':',
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marker = 'o',
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markersize = 2.5,
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markerfacecolor = 'red',
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label = r'Set 2 Exp. 14')
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# Plotting data for second filter
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ax.plot(ozon_analyser_02.averaged_data.values[:, 0] / 1000,
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ozon_analyser_02.averaged_data.values[:, 1],
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color = 'blue',
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linewidth = 0.5,
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linestyle = ':',
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marker = 'd',
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markersize = 4,
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markerfacecolor = 'blue',
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label = 'Set 2 Exp. 15')
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ax.plot(ozon_analyser_02.averaged_data.values[:, 0] / 1000,
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ozon_analyser_02.averaged_data.values[:, 1],
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color = 'blue',
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linewidth = 0.5,
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linestyle = ':',
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marker = 'd',
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markersize = 4,
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markerfacecolor = 'blue',
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label = 'Set 2 Exp. 15')
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# Plotting data for third filter
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ax.plot(ozon_analyser_03.averaged_data.values[:, 0] / 1000,
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ozon_analyser_03.averaged_data.values[:, 1],
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color = 'green',
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linewidth = 0.5,
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linestyle = ':',
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marker = 's',
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markersize = 2.5,
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markerfacecolor = 'green',
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label = 'Set 2 Exp. 16')
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ax.plot(ozon_analyser_03.averaged_data.values[:, 0] / 1000,
|
||||
ozon_analyser_03.averaged_data.values[:, 1],
|
||||
color = 'green',
|
||||
linewidth = 0.5,
|
||||
linestyle = ':',
|
||||
marker = 's',
|
||||
markersize = 2.5,
|
||||
markerfacecolor = 'green',
|
||||
label = 'Set 2 Exp. 16')
|
||||
ax.plot(ozon_analyser_04.averaged_data.values[:, 0] / 1000,
|
||||
ozon_analyser_04.averaged_data.values[:, 1],
|
||||
color = 'darkorange',
|
||||
linewidth = 0.5,
|
||||
linestyle = ':',
|
||||
marker = '^',
|
||||
markersize = 4,
|
||||
markerfacecolor = 'darkorange',
|
||||
label = 'Set 2 Exp. 17')
|
||||
# Plotting data for forth filter
|
||||
ax.plot(ozon_analyser_04.averaged_data.values[:, 0] / 1000,
|
||||
ozon_analyser_04.averaged_data.values[:, 1],
|
||||
color = 'darkorange',
|
||||
linewidth = 0.5,
|
||||
linestyle = ':',
|
||||
marker = '^',
|
||||
markersize = 4,
|
||||
markerfacecolor = 'darkorange',
|
||||
label = 'Set 2 Exp. 17')
|
||||
|
||||
#ax.set_yscale('log')
|
||||
# Axes labels
|
||||
ax.set_xlabel(r'Deviation from $\ce{O3}$ baseline $\Delta\chi_{\ce{O3}}$ $(\unit{\ppm})$')
|
||||
ax.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
|
||||
|
||||
ax.set_xlabel(r'Deviation from $\ce{O3}$ baseline $\Delta\chi_{\ce{O3}}$ $(\unit{\ppm})$')
|
||||
ax.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
|
||||
# Axes limits
|
||||
ax.set_ylim([1E-9, 5E-09])
|
||||
ax.set_xlim([2, 14])
|
||||
|
||||
#ax.set_ylim([0, 70000])
|
||||
ax.set_ylim([1E-9, 5E-09])
|
||||
#ax.set_xscale('log')
|
||||
ax.set_xlim([2, 14])
|
||||
# Legend
|
||||
ax.legend(loc='upper right')
|
||||
|
||||
ax.legend(loc='upper right')
|
||||
|
||||
#plt.show()
|
||||
plt.savefig('figure' + format, dpi=400)
|
||||
plt.close()
|
||||
# Export
|
||||
plt.savefig('figure' + format, dpi=400)
|
||||
plt.close()
|
||||
Binary file not shown.
@@ -8,75 +8,85 @@
|
||||
### ----------------------------------------------------------------------------------------------------------------------------------- ###
|
||||
### IMPORTS AND CONFIGURATIONS
|
||||
|
||||
# Importing custom code
|
||||
# Bookkeeping to store figure in correct place
|
||||
import pathlib
|
||||
import os
|
||||
os.chdir(pathlib.Path(__file__).parents[0])
|
||||
|
||||
# Imports from own modules
|
||||
from defs.SMPS import OFRAnalyser, OFRSample
|
||||
from defs.Helper import DATFile
|
||||
from defs.Helper import DATFile, final_plot_setup
|
||||
from defs.OzoneMonitor import OzoneAnalyser
|
||||
|
||||
# Importing required libraries
|
||||
import numpy as np
|
||||
# Imports from other modueles
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
|
||||
# Importing libraries required for plotting and exporting
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.dates import DateFormatter, HourLocator
|
||||
from defs.Helper import quick_plot_setup, final_plot_setup
|
||||
format = quick_plot_setup()
|
||||
|
||||
# Linking to the data
|
||||
if __name__ == '__main__':
|
||||
|
||||
# Specify the source file paths to for SMPS data
|
||||
smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260910\20260910_aPinene_SOA.txt"),
|
||||
flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260910_SET02_EXP16_flow_file.txt"),
|
||||
smpl_number_list = range(1, 60),
|
||||
on_time_interval=[pd.Timestamp('2026-09-10 15:42:00'), pd.Timestamp('2026-09-10 16:42:00')])
|
||||
# Setting up the output format
|
||||
format = final_plot_setup()
|
||||
|
||||
# Specify the source file paths to the API O3 monitor data
|
||||
ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-10_14.43.47_PC14750_API_O3.dat"), data_name = 'ozone'),
|
||||
on_time_interval = [pd.Timestamp('2026-09-10 15:00:00'), pd.Timestamp('2026-09-04 17:12:00')])
|
||||
# Specify the source file paths to for SMPS data
|
||||
smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260910\20260910_aPinene_SOA.txt"),
|
||||
flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260910_SET02_EXP16_flow_file.txt"),
|
||||
smpl_number_list = range(1, 60),
|
||||
on_time_interval=[pd.Timestamp('2026-09-10 15:42:00'), pd.Timestamp('2026-09-10 16:42:00')])
|
||||
|
||||
# Reading in SMPS data and writing it to disk as a csv
|
||||
smps_analyser.GetData()
|
||||
smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)
|
||||
# Specify the source file paths to the API O3 monitor data
|
||||
ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-10_14.43.47_PC14750_API_O3.dat"),
|
||||
data_name = 'ozone'),
|
||||
on_time_interval = [pd.Timestamp('2026-09-10 15:00:00'), pd.Timestamp('2026-09-04 17:12:00')])
|
||||
|
||||
# Reading in the O3 data and writing it to disk as a csv
|
||||
ozon_analyser.GetData()
|
||||
ozon_analyser.file.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)
|
||||
# Reading in SMPS data and writing it to disk as a csv
|
||||
smps_analyser.GetData()
|
||||
smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)
|
||||
|
||||
# Creating figure
|
||||
# Reading in the O3 data and writing it to disk as a csv
|
||||
ozon_analyser.GetData()
|
||||
ozon_analyser.file.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)
|
||||
|
||||
fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54))
|
||||
ax_1 = plt.axes([0.15, 0.2, 0.7, 0.6], frameon=True)
|
||||
ax_2 = plt.twinx(ax=ax_1)
|
||||
ax_1.set_zorder(0)
|
||||
ax_2.set_zorder(ax_1.get_zorder())
|
||||
# Creating figure
|
||||
fig = plt.figure(figsize=(9/ 2.54, 6 / 2.54))
|
||||
ax_1 = plt.axes([0.15, 0.15, 0.7, 0.7], frameon=True)
|
||||
ax_2 = plt.twinx(ax=ax_1)
|
||||
ax_1.set_zorder(0)
|
||||
ax_2.set_zorder(ax_1.get_zorder())
|
||||
|
||||
ax_1.plot(ozon_analyser.file.data['timestamp'], ozon_analyser.file.data['ozone'], color = 'black', linewidth = 0.5, linestyle = '-',label=r'$\chi_{\ce{O3}}$')
|
||||
ax_1.set_ylabel(r'\ce{O3} mole fraction $\chi_{\ce{O3}}$ $(\text{ppb})$')
|
||||
# Plotting ozone data
|
||||
ax_1.plot(ozon_analyser.file.data['timestamp'], ozon_analyser.file.data['ozone'], color = 'black', linewidth = 0.5, linestyle = '-',label=r'$\chi_{\ce{O3}}$')
|
||||
|
||||
ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='green', linestyle='-', label=r'$\accentset{*}{m}$')
|
||||
ax_2.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
|
||||
# Plotting SMPS data
|
||||
ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='green', linestyle='-', label=r'$\accentset{*}{m}$')
|
||||
|
||||
ax_1.vlines(ozon_analyser.on_time_interval, ymin=0, ymax=120000, colors='k', linestyles='--', linewidth=0.5)
|
||||
ax_2.vlines(smps_analyser.on_time_interval, ymin=0, ymax=5E-09, colors='k', linestyles='-.', linewidth=0.5)
|
||||
# Adding lines indicating when the different components were switched on and off, respectively
|
||||
ax_1.vlines(ozon_analyser.on_time_interval, ymin=0, ymax=120000, colors='k', linestyles='--', linewidth=0.5)
|
||||
ax_2.vlines(smps_analyser.on_time_interval, ymin=0, ymax=5E-09, colors='k', linestyles='-.', linewidth=0.5)
|
||||
|
||||
ax_1.set_xlabel(r'Time $(-)$')
|
||||
ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))
|
||||
ax_1.xaxis.set_major_locator(HourLocator())
|
||||
ax_1.set_xlim([pd.Timestamp('2026-09-10 14:45:00'), pd.Timestamp('2026-09-10 17:45:00')])
|
||||
# Axes labels
|
||||
ax_1.set_xlabel(r'Time $(-)$')
|
||||
ax_1.set_ylabel(r'\ce{O3} mole fraction $\chi_{\ce{O3}}$ $(\text{ppb})$')
|
||||
ax_2.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
|
||||
|
||||
lines_1, labels_1 = ax_1.get_legend_handles_labels()
|
||||
lines_2, labels_2 = ax_2.get_legend_handles_labels()
|
||||
# Axes formats
|
||||
ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))
|
||||
ax_1.xaxis.set_major_locator(HourLocator())
|
||||
|
||||
ax_1.set_ylim([0, 120000])
|
||||
ax_2.set_ylim([0, 5E-09])
|
||||
# Axes limits
|
||||
ax_1.set_xlim([pd.Timestamp('2026-09-10 14:45:00'), pd.Timestamp('2026-09-10 17:45:00')])
|
||||
ax_1.set_ylim([0, 120000])
|
||||
ax_2.set_ylim([0, 5E-09])
|
||||
|
||||
legend = plt.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')
|
||||
legend.get_frame().set_alpha(None)
|
||||
legend.get_frame().set_facecolor('white')
|
||||
legend.set_zorder(100)
|
||||
# Legend
|
||||
lines_1, labels_1 = ax_1.get_legend_handles_labels()
|
||||
lines_2, labels_2 = ax_2.get_legend_handles_labels()
|
||||
legend = plt.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')
|
||||
legend.get_frame().set_alpha(None)
|
||||
legend.get_frame().set_facecolor('white')
|
||||
legend.set_zorder(100)
|
||||
|
||||
#plt.show()
|
||||
plt.savefig('figure' + format, dpi=400)
|
||||
plt.close()
|
||||
# Export
|
||||
plt.savefig('figure' + format, dpi=400)
|
||||
plt.close()
|
||||
Binary file not shown.
@@ -8,74 +8,88 @@
|
||||
### ----------------------------------------------------------------------------------------------------------------------------------- ###
|
||||
### IMPORTS AND CONFIGURATIONS
|
||||
|
||||
# Importing custom code
|
||||
# Bookkeeping to store figure in correct place
|
||||
import pathlib
|
||||
import os
|
||||
os.chdir(pathlib.Path(__file__).parents[0])
|
||||
|
||||
# Imports from own modules
|
||||
from defs.SMPS import OFRAnalyser, OFRSample
|
||||
from defs.Helper import DATFile
|
||||
from defs.Helper import DATFile, final_plot_setup
|
||||
from defs.OzoneMonitor import OzoneAnalyser
|
||||
|
||||
# Importing required libraries
|
||||
import numpy as np
|
||||
# Imports from other modueles
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
|
||||
# Importing libraries required for plotting and exporting
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.dates import DateFormatter, HourLocator
|
||||
from defs.Helper import quick_plot_setup, final_plot_setup
|
||||
format = quick_plot_setup()
|
||||
|
||||
# Linking to the data
|
||||
### ----------------------------------------------------------------------------------------------------------------------------------- ###
|
||||
### analysis.py
|
||||
|
||||
# Specify the source file paths to for SMPS data
|
||||
smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260914\20260914_aPinene_SOA.txt"),
|
||||
flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260914_SET02_EXP17_flow_file.txt"),
|
||||
smpl_number_list = range(1, 60),
|
||||
on_time_interval=[pd.Timestamp('2026-09-14 09:45:00'), pd.Timestamp('2026-09-14 10:45:00')])
|
||||
if __name__ == '__main__':
|
||||
|
||||
# Specify the source file paths to the API O3 monitor data
|
||||
ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-14_08.48.31_PC14750_API_O3.dat"), data_name = 'ozone'),
|
||||
on_time_interval = [pd.Timestamp('2026-09-14 09:00:00'), pd.Timestamp('2026-09-14 11:15:00')])
|
||||
# Reading in SMPS data and writing it to disk as a csv
|
||||
smps_analyser.GetData()
|
||||
smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)
|
||||
# Setting the output format
|
||||
format = final_plot_setup()
|
||||
|
||||
# Reading in the O3 data and writing it to disk as a csv
|
||||
ozon_analyser.GetData()
|
||||
ozon_analyser.file.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)
|
||||
# Specify the source file paths to for SMPS data
|
||||
smps_analyser = OFRAnalyser(data_source_file = Path(r"Z:\Data\SMPS\processed\2026\09_September\20260914\20260914_aPinene_SOA.txt"),
|
||||
flow_source_file = Path(r"Z:\People\David_Tschan\OFR_SMPS\flow_files\20260914_SET02_EXP17_flow_file.txt"),
|
||||
smpl_number_list = range(1, 60),
|
||||
on_time_interval=[pd.Timestamp('2026-09-14 09:45:00'), pd.Timestamp('2026-09-14 10:45:00')])
|
||||
|
||||
# Creating figure
|
||||
# Specify the source file paths to the API O3 monitor data
|
||||
ozon_analyser = OzoneAnalyser(file = DATFile(file = Path(r"Z:\Data\API_O3\2026\09_September\2026-09-14_08.48.31_PC14750_API_O3.dat"),
|
||||
data_name = 'ozone'),
|
||||
on_time_interval = [pd.Timestamp('2026-09-14 09:00:00'), pd.Timestamp('2026-09-14 11:15:00')])
|
||||
|
||||
# Reading in SMPS data and writing it to disk as a csv
|
||||
smps_analyser.GetData()
|
||||
smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)
|
||||
|
||||
fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54))
|
||||
ax_1 = plt.axes([0.15, 0.2, 0.7, 0.6], frameon=True)
|
||||
ax_2 = plt.twinx(ax=ax_1)
|
||||
ax_1.set_zorder(0)
|
||||
ax_2.set_zorder(ax_1.get_zorder())
|
||||
# Reading in the O3 data and writing it to disk as a csv
|
||||
ozon_analyser.GetData()
|
||||
ozon_analyser.file.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)
|
||||
|
||||
ax_1.plot(ozon_analyser.file.data['timestamp'], ozon_analyser.file.data['ozone'], color = 'black', linestyle = '-', linewidth = 0.5,label=r'$\chi_{\ce{O3}}$')
|
||||
ax_1.set_ylabel(r'\ce{O3} mole fraction $\chi_{\ce{O3}}$ $(\text{ppb})$')
|
||||
# Creating figure
|
||||
fig = plt.figure(figsize=(9/ 2.54, 6 / 2.54))
|
||||
ax_1 = plt.axes([0.15, 0.15, 0.7, 0.7], frameon=True)
|
||||
ax_2 = plt.twinx(ax=ax_1)
|
||||
ax_1.set_zorder(0)
|
||||
ax_2.set_zorder(ax_1.get_zorder())
|
||||
|
||||
ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='darkorange', linestyle='-', label=r'$\accentset{*}{m}$')
|
||||
ax_2.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
|
||||
# Plotting ozone data
|
||||
ax_1.plot(ozon_analyser.file.data['timestamp'], ozon_analyser.file.data['ozone'], color = 'black', linestyle = '-', linewidth = 0.5,label=r'$\chi_{\ce{O3}}$')
|
||||
|
||||
ax_1.vlines(ozon_analyser.on_time_interval, ymin=0, ymax=120000, colors='k', linestyles='--', linewidth=0.5)
|
||||
ax_2.vlines(smps_analyser.on_time_interval, ymin=0, ymax=5E-09, colors='k', linestyles='-.', linewidth=0.5)
|
||||
# Plotting SMPS data
|
||||
ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='darkorange', linestyle='-', label=r'$\accentset{*}{m}$')
|
||||
|
||||
ax_1.set_xlabel(r'Time $(-)$')
|
||||
ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))
|
||||
ax_1.xaxis.set_major_locator(HourLocator())
|
||||
ax_1.set_xlim([pd.Timestamp('2026-09-14 08:45:00'), pd.Timestamp('2026-09-14 11:45:00')])
|
||||
# Adding lines indicating when the different components were switched on and off, respectively
|
||||
ax_1.vlines(ozon_analyser.on_time_interval, ymin=0, ymax=120000, colors='k', linestyles='--', linewidth=0.5)
|
||||
ax_2.vlines(smps_analyser.on_time_interval, ymin=0, ymax=5E-09, colors='k', linestyles='-.', linewidth=0.5)
|
||||
|
||||
lines_1, labels_1 = ax_1.get_legend_handles_labels()
|
||||
lines_2, labels_2 = ax_2.get_legend_handles_labels()
|
||||
# Axes labels
|
||||
ax_1.set_xlabel(r'Time $(-)$')
|
||||
ax_1.set_ylabel(r'\ce{O3} mole fraction $\chi_{\ce{O3}}$ $(\text{ppb})$')
|
||||
ax_2.set_ylabel(r'Mass flux $\accentset{*}{m}$ $(\unit{\kilo\gram\per\second})$')
|
||||
|
||||
ax_1.set_ylim([0, 120000])
|
||||
ax_2.set_ylim([0, 5E-09])
|
||||
# Axes formats
|
||||
ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))
|
||||
ax_1.xaxis.set_major_locator(HourLocator())
|
||||
|
||||
legend = plt.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')
|
||||
legend.get_frame().set_alpha(None)
|
||||
legend.get_frame().set_facecolor('white')
|
||||
legend.set_zorder(100)
|
||||
# Axes limits
|
||||
ax_1.set_xlim([pd.Timestamp('2026-09-14 08:45:00'), pd.Timestamp('2026-09-14 11:45:00')])
|
||||
ax_1.set_ylim([0, 120000])
|
||||
ax_2.set_ylim([0, 5E-09])
|
||||
|
||||
#plt.show()
|
||||
plt.savefig('figure' + format, dpi=400)
|
||||
plt.close()
|
||||
# Legend
|
||||
lines_1, labels_1 = ax_1.get_legend_handles_labels()
|
||||
lines_2, labels_2 = ax_2.get_legend_handles_labels()
|
||||
legend = plt.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')
|
||||
legend.get_frame().set_alpha(None)
|
||||
legend.get_frame().set_facecolor('white')
|
||||
legend.set_zorder(100)
|
||||
|
||||
# Export
|
||||
plt.savefig('figure' + format, dpi=400)
|
||||
plt.close()
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