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# File types
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*.svg
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@@ -0,0 +1,171 @@
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{
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"cells": [
|
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{
|
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"cell_type": "code",
|
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"execution_count": 1,
|
||||
"id": "8ea6253d",
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"metadata": {},
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"outputs": [],
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"source": [
|
||||
"# Imports\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
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"import matplotlib.pyplot as plt\n",
|
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"plt.style.use(r\"Z:\\People\\David_Tschan\\code\\Master_Thesis\\.mplstyle\")\n",
|
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"from matplotlib.ticker import MultipleLocator\n",
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"import pandas as pd\n",
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"\n",
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"from matplotlib.dates import DateFormatter\n",
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"\n",
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"# PGF Backend\n",
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"#import matplotlib\n",
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"#matplotlib.use('pgf')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "21b63e45",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def read_co2(file):\n",
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"\n",
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" dates = []\n",
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" times = []\n",
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" values = []\n",
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"\n",
|
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" with open(file, 'r', errors='ignore') as f:\n",
|
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" for i, line in enumerate(f.readlines()):\n",
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"\n",
|
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" if i >= 3:\n",
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" \n",
|
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" items = line.split()\n",
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" dates.append(items[0])\n",
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" times.append(items[1])\n",
|
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" values.append(float(items[2]))\n",
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"\n",
|
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" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
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" datetimes = pd.to_datetime(pre_datetimes, format=r'%d.%m.%Y %H:%M:%S')\n",
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" return pd.DataFrame({'time': datetimes, 'value': values})\n",
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"\n",
|
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"def read_btof(file):\n",
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"\n",
|
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" dates = []\n",
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" times = []\n",
|
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" values = []\n",
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"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
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" for line in f.readlines():\n",
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" \n",
|
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" items = line.split()\n",
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" dates.append(items[0])\n",
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" times.append(items[1])\n",
|
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" values.append(float(items[2]))\n",
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"\n",
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" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
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"\n",
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" datetimes = pd.to_datetime(pre_datetimes, format=r'%Y/%m/%d %H:%M:%S')\n",
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" return pd.DataFrame({'time': datetimes, 'value': values})\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ca7877db",
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"metadata": {},
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"source": [
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"# SET01 EXP01"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "6c3e022f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
|
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"<Figure size 700x400 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"co2_path = Path(r\"Z:\\Data\\T360U_CO2\\2026\\05_Mai\\2026-05-20_09.21.36_PC14750_T360U_CO2.dat\")\n",
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"btof_path = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260520_SET01_EXP01\\noncalibrated_export\\m_Q_333.txt\")\n",
|
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"btof_path_FA = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260520_SET01_EXP01\\noncalibrated_export\\m_Q_125.txt\")\n",
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"\n",
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"titlestr = 'Iodine and CO2 for SET 01 EXP 01'\n",
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"\n",
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"co2_data = read_co2(co2_path)\n",
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"btof_data = read_btof(btof_path)\n",
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"FA_data = read_btof(btof_path_FA)\n",
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"\n",
|
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"#Plotting and saving the data\n",
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"fig = plt.figure(figsize=(7, 4), constrained_layout = True)\n",
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"ax = fig.add_subplot(111)\n",
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"ax_2 = plt.twinx(ax)\n",
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"ax.set_yscale('log')\n",
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"\n",
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"ax.plot(btof_data['time'], btof_data['value'], color='blue', label='I2')\n",
|
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"ax.plot(FA_data['time'], FA_data['value'], color='green', label='FA')\n",
|
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"ax_2.plot(co2_data['time'], co2_data['value'], color='red', label='CO2')\n",
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"\n",
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"ax.set_title(titlestr)\n",
|
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"ax.set_xlabel(r'Time')\n",
|
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"ax.set_ylabel(r'BTOF ion counts $(\\unit{\\per\\second})$')\n",
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"ax_2.set_ylabel(r'CO2 concentration (ppm)')\n",
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"\n",
|
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"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
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"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
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"\n",
|
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"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
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"\n",
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"ax.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
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"\n",
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"ax.set_xlim([pd.Timestamp('2026-05-20 16:45:00'), pd.Timestamp('2026-05-20 18:00:00')])\n",
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"\n",
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"dark_times = [pd.Timestamp('2026-05-20 16:49:00'), pd.Timestamp('2026-05-20 17:17:00')]\n",
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"bright_times = [pd.Timestamp('2026-05-20 17:17:00'), pd.Timestamp('2026-05-20 17:52:00')]\n",
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"\n",
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"ax.axvspan(dark_times[0], dark_times[1], color='grey', alpha=0.5)\n",
|
||||
"ax.axvspan(bright_times[0], bright_times[1], color='yellow', alpha=0.5)\n",
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"\n",
|
||||
"plt.savefig(\"I2_CO2_SET01_EXP01.svg\", format='svg', dpi=400)"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9b3c8c5f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
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||||
}
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@@ -0,0 +1,171 @@
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{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8ea6253d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"plt.style.use(r\"Z:\\People\\David_Tschan\\code\\Master_Thesis\\.mplstyle\")\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"\n",
|
||||
"# PGF Backend\n",
|
||||
"#import matplotlib\n",
|
||||
"#matplotlib.use('pgf')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "21b63e45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def read_co2(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for i, line in enumerate(f.readlines()):\n",
|
||||
"\n",
|
||||
" if i >= 3:\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%d.%m.%Y %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n",
|
||||
"\n",
|
||||
"def read_btof(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for line in f.readlines():\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
"\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%Y/%m/%d %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ab643fbd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# SET01 EXP03"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "1d892704",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"co2_path = Path(r\"Z:\\Data\\T360U_CO2\\2026\\05_Mai\\2026-05-27_11.15.49_PC14750_T360U_CO2.dat\")\n",
|
||||
"btof_path = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260527_SET01_EXP03\\noncalibrated_export\\m_Q_333.txt\")\n",
|
||||
"btof_path_FA = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260527_SET01_EXP03\\noncalibrated_export\\m_Q_125.txt\")\n",
|
||||
"\n",
|
||||
"titlestr = 'Iodine and CO2 for SET 01 EXP 03'\n",
|
||||
"\n",
|
||||
"co2_data = read_co2(co2_path)\n",
|
||||
"btof_data = read_btof(btof_path)\n",
|
||||
"FA_data = read_btof(btof_path_FA)\n",
|
||||
"\n",
|
||||
"#Plotting and saving the data\n",
|
||||
"fig = plt.figure(figsize=(7, 4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"\n",
|
||||
"ax.plot(btof_data['time'], btof_data['value'], color='blue', label='I2')\n",
|
||||
"ax.plot(FA_data['time'], FA_data['value'], color='green', label='FA')\n",
|
||||
"ax_2.plot(co2_data['time'], co2_data['value'], color='red', label='CO2')\n",
|
||||
"\n",
|
||||
"ax.set_title(titlestr)\n",
|
||||
"ax.set_xlabel(r'Time')\n",
|
||||
"ax.set_ylabel(r'BTOF ion counts $(\\unit{\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'CO2 concentration (ppm)')\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"ax.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([pd.Timestamp('2026-05-27 12:45:00'), pd.Timestamp('2026-05-27 16:20:00')])\n",
|
||||
"\n",
|
||||
"dark_times = [pd.Timestamp('2026-05-27 12:55:00'), pd.Timestamp('2026-05-27 13:57:00')]\n",
|
||||
"bright_times = [pd.Timestamp('2026-05-27 13:57:00'), pd.Timestamp('2026-05-27 15:50:00')]\n",
|
||||
"\n",
|
||||
"ax.axvspan(dark_times[0], dark_times[1], color='grey', alpha=0.5)\n",
|
||||
"ax.axvspan(bright_times[0], bright_times[1], color='yellow', alpha=0.5)\n",
|
||||
"\n",
|
||||
"plt.savefig(\"I2_CO2_SET01_EXP03.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c3b772b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,171 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8ea6253d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"plt.style.use(r\"Z:\\People\\David_Tschan\\code\\Master_Thesis\\.mplstyle\")\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"\n",
|
||||
"# PGF Backend\n",
|
||||
"#import matplotlib\n",
|
||||
"#matplotlib.use('pgf')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "21b63e45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def read_co2(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for i, line in enumerate(f.readlines()):\n",
|
||||
"\n",
|
||||
" if i >= 3:\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%d.%m.%Y %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n",
|
||||
"\n",
|
||||
"def read_btof(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for line in f.readlines():\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
"\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%Y/%m/%d %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a7ca1a3a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# SET01 EXP05"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "4cf4407d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"co2_path = Path(r\"Z:\\Data\\T360U_CO2\\2026\\05_Mai\\2026-05-29_16.53.47_PC14750_T360U_CO2.dat\")\n",
|
||||
"btof_path = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260529_SET01_EXP05\\noncalibrated_export\\m_Q_333.txt\")\n",
|
||||
"btof_path_FA = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260529_SET01_EXP05\\noncalibrated_export\\m_Q_125.txt\")\n",
|
||||
"\n",
|
||||
"titlestr = 'Iodine and CO2 for SET 01 EXP 05'\n",
|
||||
"\n",
|
||||
"co2_data = read_co2(co2_path)\n",
|
||||
"btof_data = read_btof(btof_path)\n",
|
||||
"FA_data = read_btof(btof_path_FA)\n",
|
||||
"\n",
|
||||
"#Plotting and saving the data\n",
|
||||
"fig = plt.figure(figsize=(7, 4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"\n",
|
||||
"ax.plot(btof_data['time'], btof_data['value'], color='blue', label='I2')\n",
|
||||
"ax.plot(FA_data['time'], FA_data['value'], color='green', label='FA')\n",
|
||||
"ax_2.plot(co2_data['time'], co2_data['value'], color='red', label='CO2')\n",
|
||||
"\n",
|
||||
"ax.set_title(titlestr)\n",
|
||||
"ax.set_xlabel(r'Time')\n",
|
||||
"ax.set_ylabel(r'BTOF ion counts $(\\unit{\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'CO2 concentration (ppm)')\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"ax.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([pd.Timestamp('2026-05-29 17:13:00'), pd.Timestamp('2026-05-29 19:00:00')])\n",
|
||||
"\n",
|
||||
"dark_times = [pd.Timestamp('2026-05-29 17:17:00'), pd.Timestamp('2026-05-29 18:01:00')]\n",
|
||||
"bright_times = [pd.Timestamp('2026-05-29 18:01:00'), pd.Timestamp('2026-05-29 18:33:00')]\n",
|
||||
"\n",
|
||||
"ax.axvspan(dark_times[0], dark_times[1], color='grey', alpha=0.5)\n",
|
||||
"ax.axvspan(bright_times[0], bright_times[1], color='yellow', alpha=0.5)\n",
|
||||
"\n",
|
||||
"plt.savefig(\"I2_CO2_SET01_EXP05.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6c0c1938",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,172 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "8ea6253d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"plt.style.use(r\"Z:\\People\\David_Tschan\\code\\Master_Thesis\\.mplstyle\")\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"\n",
|
||||
"# PGF Backend\n",
|
||||
"#import matplotlib\n",
|
||||
"#matplotlib.use('pgf')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "21b63e45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def read_co2(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for i, line in enumerate(f.readlines()):\n",
|
||||
"\n",
|
||||
" if i >= 3:\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%d.%m.%Y %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n",
|
||||
"\n",
|
||||
"def read_btof(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for line in f.readlines():\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
"\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%Y/%m/%d %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5ab84502",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# SET01 EXP07/08"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "f2d31202",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"co2_path_1 = Path(r\"Z:\\Data\\T360U_CO2\\2026\\06_Juni\\2026-06-08_10.40.30_PC14750_T360U_CO2.dat\")\n",
|
||||
"co2_path_2 = Path(r\"Z:\\Data\\T360U_CO2\\2026\\06_Juni\\2026-06-08_15.03.04_PC14750_T360U_CO2.dat\")\n",
|
||||
"btof_path = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260608_SET01_EXP07_EXP08\\noncalibrated_export\\m_Q_333.txt\")\n",
|
||||
"btof_path_FA = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260608_SET01_EXP07_EXP08\\noncalibrated_export\\m_Q_125.txt\")\n",
|
||||
"\n",
|
||||
"titlestr = 'Iodine and CO2 for SET 01 EXP 07/08'\n",
|
||||
"\n",
|
||||
"co2_data_1 = read_co2(co2_path_1)\n",
|
||||
"co2_data_2 = read_co2(co2_path_2)\n",
|
||||
"btof_data = read_btof(btof_path)\n",
|
||||
"FA_data = read_btof(btof_path_FA)\n",
|
||||
"\n",
|
||||
"#Plotting and saving the data\n",
|
||||
"fig = plt.figure(figsize=(7, 4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"\n",
|
||||
"ax.plot(btof_data['time'], btof_data['value'], color='blue', label='I2')\n",
|
||||
"ax.plot(FA_data['time'], FA_data['value'], color='green', label='FA')\n",
|
||||
"ax_2.plot(co2_data_1['time'], co2_data_1['value'], color='red', label='CO2')\n",
|
||||
"ax_2.plot(co2_data_2['time'], co2_data_2['value'], color='red')\n",
|
||||
"\n",
|
||||
"ax.set_title(titlestr)\n",
|
||||
"ax.set_xlabel(r'Time')\n",
|
||||
"ax.set_ylabel(r'BTOF ion counts $(\\unit{\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'CO2 concentration (ppm)')\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"ax.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([pd.Timestamp('2026-06-08 12:00:00'), pd.Timestamp('2026-06-08 17:45:00')])\n",
|
||||
"\n",
|
||||
"dark_times_1 = [pd.Timestamp('2026-06-08 12:18:00'), pd.Timestamp('2026-06-08 12:46:00')]\n",
|
||||
"dark_times_2 = [pd.Timestamp('2026-06-08 15:32:00'), pd.Timestamp('2026-06-08 16:10:00')]\n",
|
||||
"\n",
|
||||
"bright_times_1 = [pd.Timestamp('2026-06-08 12:46:00'), pd.Timestamp('2026-06-08 13:46:00')]\n",
|
||||
"bright_times_2 = [pd.Timestamp('2026-06-08 16:10:00'), pd.Timestamp('2026-06-08 17:10:00')]\n",
|
||||
"\n",
|
||||
"ax.axvspan(dark_times_1[0], dark_times_1[1], color='grey', alpha=0.5)\n",
|
||||
"ax.axvspan(bright_times_1[0], bright_times_1[1], color='yellow', alpha=0.5)\n",
|
||||
"\n",
|
||||
"ax.axvspan(dark_times_2[0], dark_times_2[1], color='grey', alpha=0.5)\n",
|
||||
"ax.axvspan(bright_times_2[0], bright_times_2[1], color='yellow', alpha=0.5)\n",
|
||||
"\n",
|
||||
"plt.savefig(\"I2_CO2_SET01_EXP007_08.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,171 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "8ea6253d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"plt.style.use(r\"Z:\\People\\David_Tschan\\code\\Master_Thesis\\.mplstyle\")\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"\n",
|
||||
"# PGF Backend\n",
|
||||
"#import matplotlib\n",
|
||||
"#matplotlib.use('pgf')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "21b63e45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def read_co2(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for i, line in enumerate(f.readlines()):\n",
|
||||
"\n",
|
||||
" if i >= 3:\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%d.%m.%Y %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n",
|
||||
"\n",
|
||||
"def read_btof(file):\n",
|
||||
"\n",
|
||||
" dates = []\n",
|
||||
" times = []\n",
|
||||
" values = []\n",
|
||||
"\n",
|
||||
" with open(file, 'r', errors='ignore') as f:\n",
|
||||
" for line in f.readlines():\n",
|
||||
" \n",
|
||||
" items = line.split()\n",
|
||||
" dates.append(items[0])\n",
|
||||
" times.append(items[1])\n",
|
||||
" values.append(float(items[2]))\n",
|
||||
"\n",
|
||||
" pre_datetimes = [date + ' ' + time for date, time in zip(dates, times)]\n",
|
||||
"\n",
|
||||
" datetimes = pd.to_datetime(pre_datetimes, format=r'%Y/%m/%d %H:%M:%S')\n",
|
||||
" return pd.DataFrame({'time': datetimes, 'value': values})\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7e575da1",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# SET01 EXP09"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "a57af78b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 700x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"co2_path = Path(r\"Z:\\Data\\T360U_CO2\\2026\\07_Juli\\2026-07-16_15.50.28_PC14750_T360U_CO2.dat\")\n",
|
||||
"btof_path = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260716_SET01_EXP09\\noncalibrated_export\\m_Q_333.txt\")\n",
|
||||
"btof_path_FA = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260716_SET01_EXP09\\noncalibrated_export\\m_Q_125.txt\")\n",
|
||||
"\n",
|
||||
"titlestr = 'Iodine and CO2 for SET 01 EXP 09'\n",
|
||||
"\n",
|
||||
"co2_data = read_co2(co2_path)\n",
|
||||
"btof_data = read_btof(btof_path)\n",
|
||||
"FA_data = read_btof(btof_path_FA)\n",
|
||||
"\n",
|
||||
"#Plotting and saving the data\n",
|
||||
"fig = plt.figure(figsize=(7, 4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"\n",
|
||||
"ax.plot(btof_data['time'], btof_data['value'], color='blue', label='I2')\n",
|
||||
"ax.plot(FA_data['time'], FA_data['value'], color='green', label='I2')\n",
|
||||
"ax_2.plot(co2_data['time'], co2_data['value'], color='red', label='CO2')\n",
|
||||
"\n",
|
||||
"ax.set_title(titlestr)\n",
|
||||
"ax.set_xlabel(r'Time')\n",
|
||||
"ax.set_ylabel(r'BTOF ion counts $(\\unit{\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'CO2 concentration (ppm)')\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"ax.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([pd.Timestamp('2026-07-16 17:00:00'), pd.Timestamp('2026-07-16 19:30:00')])\n",
|
||||
"\n",
|
||||
"dark_times = [pd.Timestamp('2026-07-16 17:17:00'), pd.Timestamp('2026-07-16 17:55:00')]\n",
|
||||
"bright_times = [pd.Timestamp('2026-07-16 17:55:00'), pd.Timestamp('2026-07-16 18:55:00')]\n",
|
||||
"\n",
|
||||
"ax.axvspan(dark_times[0], dark_times[1], color='grey', alpha=0.5)\n",
|
||||
"ax.axvspan(bright_times[0], bright_times[1], color='yellow', alpha=0.5)\n",
|
||||
"\n",
|
||||
"plt.savefig(\"I2_CO2_SET01_EXP09.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7a9067df",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,296 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "36f6dc0f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# OFR Analysis: File `20260723_OFR_Analysis_01.ipynb`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "8769766b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"\n",
|
||||
"from defs.OFR import OFRSample, OFRAnalyser\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"plt.style.use(r'C:\\Users\\tschan_d\\Documents\\code\\Master_Thesis\\.mplstyle')\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Specify the source file for this analysis\n",
|
||||
"source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")\n",
|
||||
"flows_source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full_flows.txt\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ee2e9af5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### First we plot a sample spectrum to get a feeling of what the data looks like: Careful: All Plotting requires having a functional LaTeX installation!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "76a9b026",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x600 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# First load and get the data of sample 33 (arbitrary) and create a sample object\n",
|
||||
"sample_number = 33\n",
|
||||
"sample = OFRSample(source_file = source_file, sample_number = sample_number)\n",
|
||||
"\n",
|
||||
"#Plotting and saying the data\n",
|
||||
"fig = plt.figure(figsize=(10,6), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax.set_xscale('log')\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"\n",
|
||||
"ax.plot(np.power(10, sample.Count.var), sample.Count.fct, color='red', label=r'$\\der{N}/\\der{\\log_{10}D}$')\n",
|
||||
"ax_2.plot(np.power(10, sample.Mass.var), sample.Mass.fct / 1E12, color='blue', label=r'$\\der{m}/\\der{\\log_{10}D}$')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Number- and Mass Spectra of Sample Nr. ' + str(sample_number))\n",
|
||||
"ax.set_xlabel(r'Particle Diameter $D$ $(\\unit{\\meter})$')\n",
|
||||
"ax.set_ylabel(r'Log Number Denisty $\\der{N}/\\der{\\log_{10}D}$ $(\\unit{\\per\\meter})$')\n",
|
||||
"ax_2.set_ylabel(r'Mass Denisty $\\der{m}/\\der{\\log_{10}D}$ $(\\unit{\\kilo\\gram\\per\\cubic\\meter})$')\n",
|
||||
"\n",
|
||||
"#ax.set_xlim([20, 1100])\n",
|
||||
"#ax.set_ylim([0, 1200000])\n",
|
||||
"#ax_2.set_ylim([0, 18])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Spectrum_Sample_\" + str(sample_number) + \".svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "041fc7b6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Now let's load the data for the entire sampling experiment and process it!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "8db19b57",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sample_number_list = [int(i) for i in range(1, 157)]\n",
|
||||
"\n",
|
||||
"analyser = OFRAnalyser(data_source_file = source_file, flows_source_file = flows_source_file, sample_number_list = sample_number_list)\n",
|
||||
"analyser.GetData()\n",
|
||||
"analyser.data.to_csv(path_or_buf=r\"C:\\Users\\tschan_d\\Documents\\data_local\\data.csv\", index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3a2ae595",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We first plot the overall timeseries of total aerosol count and mass:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "a4477003",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"\n",
|
||||
"ax.plot(analyser.data['Sample (-)'], analyser.data['Number (#/m^3)'], color='red', label='Number')\n",
|
||||
"ax_2.plot(analyser.data['Sample (-)'], analyser.data['Mass (kg/m^3)'], color='blue', label='Mass')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Time Series of Number- and Mass Densities')\n",
|
||||
"ax.set_xlabel(r'Sample $(-)$')\n",
|
||||
"ax.set_ylabel(r'Number Density $N$ $(\\unit{\\per\\cubic\\meter})$')\n",
|
||||
"ax_2.set_ylabel(r'Mass Density $m$ $(\\unit{\\kilo\\gram\\per\\cubic\\meter})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([1, 156])\n",
|
||||
"#ax.set_ylim([0, 1.2E6])\n",
|
||||
"#ax_2.set_ylim([0, 15])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Timeseries_of_Densities.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9555beb6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Clearly, something is happening at samples 25, 67, and after 105. This corresponds to shifts in the flow rates... These are read from disk and depicted below:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "a5af245c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x250 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,2.5), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax)\n",
|
||||
"\n",
|
||||
"ax.step(analyser.data['Sample (-)'], analyser.data['Ozone Flow (m^3/s)'], color='forestgreen', linestyle='-', label='Ozone')\n",
|
||||
"ax_2.step(analyser.data['Sample (-)'], analyser.data['Species Flow (m^3/s)'], color='magenta', linestyle='-', label=r'$\\alpha$-Pinene')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Time Series of Flow Rates')\n",
|
||||
"ax.set_xlabel(r'Sample $(-)$')\n",
|
||||
"ax.set_ylabel(r'Ozone Flow Rate $(\\unit{\\cubic\\meter\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $(\\unit{\\cubic\\meter\\per\\second})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([1, 156])\n",
|
||||
"#ax.set_ylim([0.6, 2.4])\n",
|
||||
"#ax_2.set_ylim([0.0, 0.05])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Timeseries_of_Flow_Rates.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e6212394",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To refine the analysis, we may also consider the production rates of SOA using the integral of the SOA production rate, obtained by multiplying the concentration with the flow rate with the sampling time for each sample:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "e2ae6dbb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax)\n",
|
||||
"\n",
|
||||
"ax.step(analyser.data['Sample (-)'], analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label='Mass Production Rate')\n",
|
||||
"ax_2.step(analyser.data['Sample (-)'], analyser.data['Cumulative Mass Produced (kg)'], color='magenta', linestyle='-', label=r'Cumulative Mass Produced')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Timesseries of Mass Production')\n",
|
||||
"ax.set_xlabel(r'Sample $(-)$')\n",
|
||||
"ax.set_ylabel(r'Mass Production Rate $(\\unit{\\kilo\\gram\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'Cumulative Mass Produced $(\\unit{\\kilo\\gram})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([1, 156])\n",
|
||||
"#ax.set_ylim([0.6, 2.4])\n",
|
||||
"#ax_2.set_ylim([0.0, 0.05])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Timeseries_of_Mass_Production.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fcde1cd8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
BIN
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@@ -0,0 +1,303 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "36f6dc0f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# OFR Analysis: File `20260723_OFR_Analysis_01.ipynb`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8769766b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Imports\n",
|
||||
"\n",
|
||||
"from defs.OFR import OFRSample, OFRAnalyser\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"# plt.style.use('../../../.mplstyle')\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Specify the source file for this analysis\n",
|
||||
"source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\07_July\\20260709\\20260709_aPinene_SOA_full.txt\")\n",
|
||||
"flows_source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\07_July\\20260709\\20260709_aPinene_SOA_flows.txt\")\n",
|
||||
"\n",
|
||||
"# PGF Backend\n",
|
||||
"import matplotlib\n",
|
||||
"matplotlib.use('pgf')\n",
|
||||
"plt.rcParams.update({\"pgf.texsystem\": \"lualatex\",\n",
|
||||
" \"pgf.preamble\": \"\\usepackage{unicode-math}\\setmainfont{STIX Two Text}\\setmathfont{STIX Two Math}\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ee2e9af5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### First we plot a sample spectrum to get a feeling of what the data looks like: Careful: All Plotting requires having a functional LaTeX installation!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "76a9b026",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x600 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# First load and get the data of sample 33 (arbitrary) and create a sample object\n",
|
||||
"sample_number = 450\n",
|
||||
"sample = OFRSample(source_file = source_file, sample_number = sample_number)\n",
|
||||
"\n",
|
||||
"#Plotting and saying the data\n",
|
||||
"fig = plt.figure(figsize=(10,6), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax.set_xscale('log')\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"\n",
|
||||
"ax.plot(np.power(10, sample.Count.var), sample.Count.fct, color='red', label=r'$\\der{N}/\\der{\\log_{10}D}$')\n",
|
||||
"ax_2.plot(np.power(10, sample.Mass.var), sample.Mass.fct / 1E12, color='blue', label=r'$\\der{m}/\\der{\\log_{10}D}$')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Number- and Mass Spectra of Sample Nr. ' + str(sample_number))\n",
|
||||
"ax.set_xlabel(r'Particle Diameter $D$ $(\\unit{\\meter})$')\n",
|
||||
"ax.set_ylabel(r'Log Number Denisty $\\der{N}/\\der{\\log_{10}D}$ $(\\unit{\\per\\meter})$')\n",
|
||||
"ax_2.set_ylabel(r'Mass Denisty $\\der{m}/\\der{\\log_{10}D}$ $(\\unit{\\kilo\\gram\\per\\cubic\\meter})$')\n",
|
||||
"\n",
|
||||
"#ax.set_xlim([20, 1100])\n",
|
||||
"ax.set_ylim([0, 2.5E13])\n",
|
||||
"ax_2.set_ylim([0, 1.7E-16])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260710_OFR_Analysis_01_Spectrum_Sample_\" + str(sample_number) + \".svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "041fc7b6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Now let's load the data for the entire sampling experiment and process it!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8db19b57",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#sample_number_list = [int(i) for i in range(1, 300)]\n",
|
||||
"sample_number_list = np.linspace(start=1, stop=609, num=609, dtype=int).tolist()\n",
|
||||
"analyser = OFRAnalyser(data_source_file = source_file, flows_source_file = flows_source_file, sample_number_list = sample_number_list)\n",
|
||||
"analyser.GetData()\n",
|
||||
"analyser.data.to_csv(path_or_buf=r\"C:\\Users\\tschan_d\\Documents\\code\\Master_Thesis\\run\\OFR_Analysis\\20260710\\data.csv\", index=False)\n",
|
||||
"#print(analyser.data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3a2ae595",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We first plot the overall timeseries of total aerosol count and mass:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "a4477003",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax)\n",
|
||||
"\n",
|
||||
"ax.plot(analyser.data['Sample (-)'], analyser.data['Number (#/m^3)'], color='red', label='Number')\n",
|
||||
"ax_2.plot(analyser.data['Sample (-)'], analyser.data['Mass (kg/m^3)'], color='blue', label='Mass')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Time Series of Number- and Mass Densities')\n",
|
||||
"ax.set_xlabel(r'Sample $(-)$')\n",
|
||||
"ax.set_ylabel(r'Number Density $N$ $(\\unit{\\per\\cubic\\meter})$')\n",
|
||||
"ax_2.set_ylabel(r'Mass Density $m$ $(\\unit{\\kilo\\gram\\per\\cubic\\meter})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=50, offset=0))\n",
|
||||
"\n",
|
||||
"#ax.set_xlim([1, 156])\n",
|
||||
"#ax.set_ylim([0, 1.2E6])\n",
|
||||
"#ax_2.set_ylim([0, 15])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"plt.savefig(\"20260710_OFR_Analysis_01_Experiment_Timeseries_of_Densities.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9555beb6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Clearly, something is happening at samples 25, 67, and after 105. This corresponds to shifts in the flow rates... These are read from disk and depicted below:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a5af245c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x250 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,2.5), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax)\n",
|
||||
"\n",
|
||||
"ax.step(analyser.data['Sample (-)'], analyser.data['Ozone Flow (m^3/s)'], color='forestgreen', linestyle='-', label='Ozone')\n",
|
||||
"ax_2.step(analyser.data['Sample (-)'], analyser.data['Species Flow (m^3/s)'], color='magenta', linestyle='-', label=r'$\\alpha$-Pinene')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Time Series of Flow Rates')\n",
|
||||
"ax.set_xlabel(r'Sample $(-)$')\n",
|
||||
"ax.set_ylabel(r'Ozone Flow Rate $(\\unit{\\cubic\\meter\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $(\\unit{\\cubic\\meter\\per\\second})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=50, offset=0))\n",
|
||||
"\n",
|
||||
"#ax.set_xlim([1, 156])\n",
|
||||
"#ax.set_ylim([0.6, 2.4])\n",
|
||||
"#ax_2.set_ylim([0.0, 0.05])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260710_OFR_Analysis_01_Experiment_Timeseries_of_Flow_Rates.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e6212394",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To refine the analysis, we may also consider the production rates of SOA using the integral of the SOA production rate, obtained by multiplying the concentration with the flow rate with the sampling time for each sample:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "e2ae6dbb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x400 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax)\n",
|
||||
"\n",
|
||||
"ax.step(analyser.data['Sample (-)'], analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label='Mass Production Rate')\n",
|
||||
"ax_2.step(analyser.data['Sample (-)'], analyser.data['Cumulative Mass Produced (kg)'], color='magenta', linestyle='-', label=r'Cumulative Mass Produced')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Timesseries of Mass Production')\n",
|
||||
"ax.set_xlabel(r'Sample $(-)$')\n",
|
||||
"ax.set_ylabel(r'Mass Production Rate $(\\unit{\\kilo\\gram\\per\\second})$')\n",
|
||||
"ax_2.set_ylabel(r'Cumulative Mass Produced $(\\unit{\\kilo\\gram})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=50, offset=0))\n",
|
||||
"\n",
|
||||
"#ax.set_xlim([1, 156])\n",
|
||||
"#ax.set_ylim([0.6, 2.4])\n",
|
||||
"#ax_2.set_ylim([0.0, 0.05])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260710_OFR_Analysis_01_Experiment_Timeseries_of_Mass_Production.svg\", format='svg', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a8dc2b0a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,610 @@
|
||||
Sample (-),Number (#/m^3),Length (m/m^3),Surface (m^2/m^3),Volume (m^3/m^3),Density (kg/m^3),Mass (kg/m^3),Start (s),Stop (s),Duration (s),Dry Air Flow (m^3/s),Species Flow (m^3/s),Ozone Flow (m^3/s),Total Flow (m^3/s),CPC Flow (m^3/s),Mass Production Rate (kg/s),Mass Produced (kg),Cumulative Mass Produced (kg)
|
||||
1,30473748.549298167,1.0655038770112348,3.03454062387635e-07,1.1501132380604197e-15,1200.0,1.3801358856725036e-12,0.0,180.0,180.0,0.0029999999999999996,0.0,0.0012,0.0042,0.00017999999999999998,5.796570719824515e-15,1.0433827295684126e-12,1.0433827295684126e-12
|
||||
2,271619604.9912712,10.701070299000348,3.3583255073604333e-06,1.2431563947700667e-14,1200.0,1.49178767372408e-11,180.0,360.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,6.301311133810513e-14,1.1342360040858924e-11,1.2385742770427337e-11
|
||||
3,2749917265221.817,654686.5231873773,1.3550286349900742,3.338941120065853e-08,1200.0,4.006729344079023e-05,360.0,540.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.692442474938979e-07,3.0463964548901622e-05,3.0463976934644393e-05
|
||||
4,2652868624518.834,656264.1001732149,1.4777757049234599,4.0722612200852445e-08,1200.0,4.8867134641022925e-05,540.0,720.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,2.064147767236808e-07,3.715465981026254e-05,6.761863674490694e-05
|
||||
5,2190334692106.0571,538055.8070744942,1.223155491718034,3.4621629442773246e-08,1200.0,4.1545955331327896e-05,720.0,900.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.75490115319529e-07,3.1588220757515224e-05,9.920685750242216e-05
|
||||
6,1760019659737.483,408112.57440904074,0.8967637242689219,2.5303132356723486e-08,1200.0,3.0363758828068185e-05,900.0,1080.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.2825651728976e-07,2.30861731121568e-05,0.00012229303061457895
|
||||
7,1334527196353.5903,298428.6234367075,0.637566903772419,1.77314909225092e-08,1200.0,2.1277789107011037e-05,1080.0,1260.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,8.98773811880146e-08,1.617792861384263e-05,0.00013847095922842157
|
||||
8,1235729836016.894,261593.21795268156,0.5348422766110378,1.4440502982431771e-08,1200.0,1.7328603578918122e-05,1260.0,1440.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,7.319602151735013e-08,1.3175283873123025e-05,0.00015164624310154458
|
||||
9,1135957669102.008,230193.86043837306,0.4401986168669609,1.1020176845019727e-08,1200.0,1.3224212214023673e-05,1440.0,1620.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,5.5859072392035985e-08,1.0054633030566477e-05,0.00016170087613211106
|
||||
10,1158898684484.4265,224863.18710129158,0.41319820848309124,9.964951588412869e-09,1200.0,1.1957941906095442e-05,1620.0,1800.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,5.051034661134714e-08,9.091862390042485e-06,0.00017079273852215355
|
||||
11,1176043586314.5059,225756.7758560184,0.40779613742258974,9.603787389971598e-09,1200.0,1.1524544867965916e-05,1800.0,1980.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.867967752228802e-08,8.762341954011844e-06,0.0001795550804761654
|
||||
12,1203435874588.4822,226922.96548691017,0.4012289861423403,9.233460675035122e-09,1200.0,1.1080152810042146e-05,1980.0,2160.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.680256546961802e-08,8.424461784531244e-06,0.00018797954226069664
|
||||
13,1213044745011.384,228717.09694833867,0.3986253266198,8.94753419783532e-09,1200.0,1.0737041037402384e-05,2160.0,2340.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.535326134198766e-08,8.16358704155778e-06,0.00019614312930225442
|
||||
14,1173855666504.4648,223703.08976327942,0.3932772557121522,8.846688904822865e-09,1200.0,1.0616026685787438e-05,2340.0,2520.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.484209672076613e-08,8.071577409737903e-06,0.00020421470671199232
|
||||
15,1111711397386.159,217514.36265534803,0.3925593075462923,9.0369225390716e-09,1200.0,1.084430704688592e-05,2520.0,2700.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.580635296604612e-08,8.245143533888302e-06,0.0002124598502458806
|
||||
16,1123711736598.0298,216406.7584775405,0.388574147185608,8.979309385499793e-09,1200.0,1.0775171262599752e-05,2700.0,2880.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.5514323413221344e-08,8.192578214379843e-06,0.00022065242846026046
|
||||
17,1129553495201.37,216234.68458857754,0.3826368187101604,8.685289343311555e-09,1200.0,1.0422347211973866e-05,2880.0,3060.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.4023994623377606e-08,7.924319032207969e-06,0.00022857674749246842
|
||||
18,1154644610963.7822,221810.90077819396,0.3922460741083902,8.867425255689586e-09,1200.0,1.0640910306827506e-05,3060.0,3240.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.494720513603938e-08,8.090496924487089e-06,0.0002366672444169555
|
||||
19,1112871134631.1863,215783.57097424302,0.38750554641266566,8.89384334128012e-09,1200.0,1.0672612009536147e-05,3240.0,3420.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.508111312828068e-08,8.114600363090522e-06,0.000244781844780046
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||||
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161,1027938160558.1891,199945.77324481844,0.3698642464696633,8.942606156220364e-09,1200.0,1.0731127387464437e-05,28800.0,28980.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.532828208464978e-08,8.15909077523696e-06,0.001427517263785544
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162,1074149343920.1648,205110.15136833163,0.374597361383993,8.954772800875419e-09,1200.0,1.0745727361050504e-05,28980.0,29160.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.5389952373077325e-08,8.170191427153918e-06,0.001435687455212698
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165,1012988302919.2942,183774.21795678695,0.3188800940756095,7.3523634948497e-09,1200.0,8.822836193819639e-06,29520.0,29700.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,3.726766008269415e-08,6.708178814884947e-06,0.001457527529018075
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596,9136832112884.174,1290950.2640315115,1.7572913350336705,3.136325849211906e-08,1200.0,3.763591019054287e-05,107100.0,107280.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.5897408464485306e-07,2.861533523607355e-05,0.007195302413482098
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598,8977574800214.191,1252820.315738219,1.7209306161123963,3.139226132101792e-08,1200.0,3.767071358522151e-05,107460.0,107640.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.5912109418397562e-07,2.864179695311561e-05,0.007252201097816741
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||||
599,6461348121090.877,839696.0532647588,1.0830897145569292,1.8580956708409354e-08,1200.0,2.2297148050091223e-05,107640.0,107820.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,9.418315336358531e-08,1.6952967605445356e-05,0.007269154065422186
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600,3395691239862.585,488092.220563212,0.6649278129401957,1.1887869002833955e-08,1200.0,1.4265442803400748e-05,107820.0,108000.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,6.025723040156475e-08,1.0846301472281655e-05,0.007280000366894467
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601,1731386421749.7966,291498.75227093184,0.4490861871996167,8.540015062325075e-09,1200.0,1.0248018074790089e-05,108000.0,108180.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,4.328762834791333e-08,7.7917731026244e-06,0.007287792139997092
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602,793495269503.2251,125448.39149330044,0.18476311551678876,3.457345132266908e-09,1200.0,4.14881415872029e-06,108180.0,108360.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.75245910064345e-08,3.15442638115821e-06,0.007290946566378249
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||||
603,351443210422.22266,56809.5775418689,0.08446383832656193,1.570957320296972e-09,1200.0,1.8851487843563666e-06,108360.0,108540.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,7.962868465121292e-09,1.4333163237218325e-06,0.007292379882701971
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||||
604,146338567212.42444,22827.850220198787,0.03300028467680259,6.05652809994876e-10,1200.0,7.267833719938512e-07,108540.0,108720.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,3.069932963302027e-09,5.525879333943648e-07,0.007292932470635366
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||||
605,71998229543.39963,11354.689632046586,0.016286354110537554,2.9411360002997324e-10,1200.0,3.529363200359679e-07,108720.0,108900.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.4908030158319282e-09,2.6834454284974704e-07,0.007293200815178215
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||||
606,31977129298.837036,4897.222258249236,0.006838170078708838,1.2051229564977572e-10,1200.0,1.4461475477973084e-07,108900.0,109080.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,6.10852724189583e-10,1.0995349035412494e-07,0.00729331076866857
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||||
607,15200546932.815556,2332.767487094632,0.00326325900260579,5.7375876828213216e-11,1200.0,6.885105219385587e-08,109080.0,109260.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,2.908268444668471e-10,5.234883200403248e-08,0.007293363117500574
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||||
608,6455031715.7379055,995.6110370817089,0.0013981669926106197,2.498662818486573e-11,1200.0,2.998395382183888e-08,109260.0,109440.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.266522209434474e-10,2.279739976982053e-08,0.007293385914900344
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||||
609,51516206.523794174,2.3487310484275516,7.876296970622012e-07,2.676892215902671e-15,1200.0,3.2122706590832057e-12,109440.0,109620.0,180.0,0.0029999999999999996,2.3999999999999997e-05,0.0012,0.0042239999999999995,0.00017999999999999998,1.3568631263967459e-14,2.4423536275141424e-12,0.007293385917342698
|
||||
|
Binary file not shown.
@@ -0,0 +1,166 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "1cc3c304",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Importing custom code\n",
|
||||
"from defs.OFR import OFRSample, OFRAnalyser\n",
|
||||
"from defs.Helper import DATFile\n",
|
||||
"\n",
|
||||
"# Importing required libraries\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Importing libraries required for plotting and exporting\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"\n",
|
||||
"# Setting style of figures and backend\n",
|
||||
"import matplotlib\n",
|
||||
"matplotlib.use('pgf')\n",
|
||||
"plt.style.use(r'C:\\Users\\tschan_d\\Documents\\code\\Master_Thesis\\.mplstyle')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "a67c15f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Configuring backend\n",
|
||||
"\n",
|
||||
"matplotlib.rcParams.update({'text.usetex': True,\n",
|
||||
" 'pgf.rcfonts': False,\n",
|
||||
" 'font.family': 'serif',\n",
|
||||
" 'font.serif': [],\n",
|
||||
" 'font.sans-serif': [],\n",
|
||||
" 'font.size': 8,\n",
|
||||
" 'legend.fontsize': 8,\n",
|
||||
" 'xtick.labelsize': 8,\n",
|
||||
" 'ytick.labelsize': 8,\n",
|
||||
" 'pgf.texsystem': r'lualatex',\n",
|
||||
" 'pgf.preamble': r'\\usepackage{unicode-math}' + \n",
|
||||
" r'\\usepackage{accents}' + \n",
|
||||
" r'\\usepackage{fontspec}' + \n",
|
||||
" r'\\usepackage[T1]{fontenc}' +\n",
|
||||
" r'\\usepackage{unicode-math}' +\n",
|
||||
" r'\\setmainfont{STIX Two Text}' +\n",
|
||||
" r'\\setmathfont{STIX Two Math}' + \n",
|
||||
" r'\\usepackage{siunitx}' + \n",
|
||||
" r'\\usepackage{mhchem}' + \n",
|
||||
" r'\\newcommand{\\der}[1]{\\ensuremath{\\textnormal{d}\\,{#1}}}'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "429e1aed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Linking to the data\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to for SMPS data\n",
|
||||
"smps_analyser = OFRAnalyser(data_source_file = Path(r\"Z:\\Data\\SMPS\\processed\\2026\\09_September\\20260902\\20260902_aPinene_SOA.txt\"),\n",
|
||||
" flow_source_file = Path(r\"Z:\\People\\David_Tschan\\OFR_SMPS\\flow_files\\20260902_SET02_EXP13_flow_file.txt\"),\n",
|
||||
" smpl_number_list = range(1, 60))\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to the API O3 monitor data\n",
|
||||
"ozon_analyser = DATFile(file = Path(r\"Z:\\Data\\API_O3\\2026\\09_September\\2026-09-02_09.01.11_PC14750_API_O3.dat\"), data_name = 'ozone')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "26333625",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reading in SMPS data and writing it to disk as a csv\n",
|
||||
"smps_analyser.GetData()\n",
|
||||
"smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)\n",
|
||||
"\n",
|
||||
"# Reading in the O3 data and writing it to disk as a csv\n",
|
||||
"ozon_analyser.read()\n",
|
||||
"ozon_analyser.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "99c569ff",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Creating figure\n",
|
||||
"\n",
|
||||
"fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54), constrained_layout = True)\n",
|
||||
"ax_1 = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax_1)\n",
|
||||
"\n",
|
||||
"ax_1.plot(ozon_analyser.data['timestamp'], ozon_analyser.data['ozone'], color = 'red', linestyle = '-.',label=r'$\\chi_{\\ce{O3}}$')\n",
|
||||
"ax_1.set_ylabel(r'Ozone mole fraction $\\chi_{\\ce{O3}}$ $(\\text{ppb})$')\n",
|
||||
"\n",
|
||||
"ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label=r'$\\accentset{*}{m}$')\n",
|
||||
"ax_2.set_ylabel(r'Mass flux $\\accentset{*}{m}$ $(\\unit{\\kilo\\gram\\per\\second})$')\n",
|
||||
"\n",
|
||||
"ozone_on_times = [pd.Timestamp('2026-09-02 09:08:00'), pd.Timestamp('2026-09-02 11:22:00')]\n",
|
||||
"voc_on_times = [pd.Timestamp('2026-09-02 09:52:00'), pd.Timestamp('2026-09-02 10:52:00')]\n",
|
||||
"ax_1.vlines(ozone_on_times, ymin=0, ymax=70000, colors='k', linestyles='--', linewidth=0.5)\n",
|
||||
"ax_2.vlines(voc_on_times, ymin=0, ymax=2E-7, colors='k', linestyles='-.', linewidth=0.5)\n",
|
||||
"\n",
|
||||
"ax_1.set_xlabel(r'Time $(-)$')\n",
|
||||
"ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"ax_1.set_xlim([pd.Timestamp('2026-09-02 08:45:00'), pd.Timestamp('2026-09-02 12:15:00')])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax_1.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax_1.set_ylim([0, 70000])\n",
|
||||
"ax_2.set_ylim([0, 2E-7])\n",
|
||||
"\n",
|
||||
"ax_1.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')\n",
|
||||
"\n",
|
||||
"#plt.show()\n",
|
||||
"plt.savefig(\"figure.pgf\", format='pgf', dpi=400)\n",
|
||||
"plt.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "89951350",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,60 @@
|
||||
Sample (-),Starting Time (-),Sampling Time (s),Number (#/m^3),Length (m/m^3),Surface (m^2/m^3),Volume (m^3/m^3),Density (kg/m^3),Mass (kg/m^3),Filter Flow (m^3/s),Mass Production Rate (kg/s),Mass Produced (kg),Cumulative Mass Produced (kg)
|
||||
1,2026-09-02 09:53:07,180.0,55560016.61042881,30.090101264761802,0.00019057019815946726,1.246516017406506e-11,1200.0,1.4958192208878072e-08,0.0026999999999999997,4.038711896397079e-11,7.2696814135147425e-09,7.2696814135147425e-09
|
||||
2,2026-09-02 09:56:10,180.0,201491736862.19382,30199.77810610323,0.034485293343670606,4.326486172360339e-10,1200.0,5.191783406832407e-07,0.0026999999999999997,1.4017815198447498e-09,2.5232067357205496e-07,2.595903549855697e-07
|
||||
3,2026-09-02 09:59:10,180.0,6021275091018.962,1224054.6813494358,2.1204335997285098,4.288982843565784e-08,1200.0,5.14677941227894e-05,0.0026999999999999997,1.3896304413153135e-07,2.5013347943675644e-05,2.5272938298661212e-05
|
||||
4,2026-09-02 10:02:10,180.0,5810509321902.4795,1158695.448510189,2.064763931612662,4.472179072492488e-08,1200.0,5.366614886990986e-05,0.0026999999999999997,1.4489860194875662e-07,2.608174835077619e-05,5.1354686649437406e-05
|
||||
5,2026-09-02 10:05:10,180.0,5372806571153.719,1059896.988791524,1.9162784594048805,4.311603608022426e-08,1200.0,5.173924329626911e-05,0.0026999999999999997,1.396959568999266e-07,2.5145272241986787e-05,7.649995889142419e-05
|
||||
6,2026-09-02 10:08:10,180.0,4742845493501.402,935303.9076773255,1.6880281843812939,3.8064344788366955e-08,1200.0,4.567721374604035e-05,0.0026999999999999997,1.2332847711430894e-07,2.219912588057561e-05,9.86990847719998e-05
|
||||
7,2026-09-02 10:11:10,180.0,4246350893563.627,839710.9455883508,1.538666265443443,3.527435107294562e-08,1200.0,4.232922128753475e-05,0.0026999999999999997,1.1428889747634382e-07,2.057200154574189e-05,0.0001192710863177417
|
||||
8,2026-09-02 10:14:10,180.0,3899227555978.9795,768504.7029800205,1.353865148267341,2.9354838264244556e-08,1200.0,3.5225805917093466e-05,0.0026999999999999997,9.510967597615234e-08,1.7119741675707422e-05,0.00013639082799344913
|
||||
9,2026-09-02 10:17:10,180.0,4148856647402.9453,775862.1061656608,1.3008514116074108,2.716234537722476e-08,1200.0,3.259481445266971e-05,0.0026999999999999997,8.80059990222082e-08,1.5841079823997475e-05,0.0001522319078174466
|
||||
10,2026-09-02 10:20:10,180.0,4369027376805.924,784085.8235757891,1.2762950765454089,2.6068924956022836e-08,1200.0,3.12827099472274e-05,0.0026999999999999997,8.446331685751398e-08,1.5203397034352515e-05,0.00016743530485179912
|
||||
11,2026-09-02 10:23:10,180.0,3820200846960.393,693566.9101453575,1.128209284530461,2.271535889489004e-08,1200.0,2.7258430673868047e-05,0.0026999999999999997,7.359776281944373e-08,1.324759730749987e-05,0.000180682902159299
|
||||
12,2026-09-02 10:26:10,180.0,4236306907951.0586,731951.6861755841,1.1627464537096572,2.3250472024037834e-08,1200.0,2.7900566428845404e-05,0.0026999999999999997,7.533152935788258e-08,1.3559675284418864e-05,0.00019424257744371786
|
||||
13,2026-09-02 10:29:10,180.0,4256002374605.3228,727698.6003622125,1.1182278247017716,2.1520677985950256e-08,1200.0,2.5824813583140306e-05,0.0026999999999999997,6.972699667447882e-08,1.2550859401406188e-05,0.00020679343684512405
|
||||
14,2026-09-02 10:32:10,180.0,4346922223948.9824,727219.2899223289,1.1087244254540782,2.132963041868865e-08,1200.0,2.5595556502426377e-05,0.0026999999999999997,6.910800255655121e-08,1.2439440460179218e-05,0.00021923287730530328
|
||||
15,2026-09-02 10:35:10,180.0,4328578718082.548,716528.5250466033,1.0758812205402093,2.04039051488044e-08,1200.0,2.448468617856528e-05,0.0026999999999999997,6.610865268212625e-08,1.1899557482782726e-05,0.000231132434788086
|
||||
16,2026-09-02 10:38:10,180.0,3968183817700.604,676974.6884308748,1.0297261924555619,1.9476331777536453e-08,1200.0,2.3371598133043742e-05,0.0026999999999999997,6.31033149592181e-08,1.1358596692659259e-05,0.00024249103148074526
|
||||
17,2026-09-02 10:41:10,180.0,4119429579911.478,679210.5604203079,1.0200110409731706,1.9299598083192186e-08,1200.0,2.3159517699830616e-05,0.0026999999999999997,6.253069778954266e-08,1.1255525602117678e-05,0.0002537465570828629
|
||||
18,2026-09-02 10:44:10,180.0,4270093747134.419,697118.7171255238,1.02176135659248,1.8793684747436545e-08,1200.0,2.255242169692385e-05,0.0026999999999999997,6.089153858169439e-08,1.096047694470499e-05,0.0002647070340275679
|
||||
19,2026-09-02 10:47:10,180.0,3826307928958.043,643850.3371685657,0.979006644336897,1.855179122885257e-08,1200.0,2.226214947462308e-05,0.0026999999999999997,6.01078035814823e-08,1.0819404644666814e-05,0.0002755264386722347
|
||||
20,2026-09-02 10:50:10,180.0,4188047080342.7207,681768.6922396016,1.005087992046282,1.8574435579062263e-08,1200.0,2.2289322694874717e-05,0.0026999999999999997,6.018117127616172e-08,1.083261082970911e-05,0.0002863590495019438
|
||||
21,2026-09-02 10:53:10,180.0,4045073876552.9443,657883.9881526339,0.9803202584888272,1.8453658936766348e-08,1200.0,2.2144390724119617e-05,0.0026999999999999997,5.978985495512296e-08,1.0762173891922134e-05,0.0002971212233938659
|
||||
22,2026-09-02 10:56:10,180.0,1385448093023.602,240408.15773651795,0.3698456384918325,6.978146598192597e-09,1200.0,8.373775917831118e-06,0.0026999999999999997,2.2609194978144016e-08,4.069655096065923e-06,0.00030119087848993185
|
||||
23,2026-09-02 10:59:10,180.0,553987352654.5011,108871.25402041085,0.18951297208516768,3.979545306208133e-09,1200.0,4.77545436744976e-06,0.0026999999999999997,1.289372679211435e-08,2.320870822580583e-06,0.00030351174931251244
|
||||
24,2026-09-02 11:02:10,180.0,114572320374.6398,20853.673547398754,0.03331256507262749,6.441024789127433e-10,1200.0,7.72922974695292e-07,0.0026999999999999997,2.0868920316772884e-09,3.756405657019119e-07,0.0003038873898782144
|
||||
25,2026-09-02 11:05:10,180.0,46650316208.775375,8993.831524505087,0.014994053399159078,2.973966282219818e-10,1200.0,3.5687595386637816e-07,0.0026999999999999997,9.63565075439221e-10,1.7344171357905977e-07,0.00030406083159179346
|
||||
26,2026-09-02 11:08:10,180.0,19347294970.962006,3676.3753088918515,0.006059672440307046,1.2115609470129957e-10,1200.0,1.4538731364155948e-07,0.0026999999999999997,3.9254574683221056e-10,7.06582344297979e-08,0.0003041314898262233
|
||||
27,2026-09-02 11:11:10,180.0,8187358312.849644,1590.3632352118498,0.002634594301389583,5.1450696574191767e-11,1200.0,6.174083588903014e-08,0.0026999999999999997,1.6670025690038136e-10,3.000604624206865e-08,0.0003041614958724653
|
||||
28,2026-09-02 11:14:10,180.0,3719925996.8518305,742.8019219435629,0.0012579867518839275,2.526782530576085e-11,1200.0,3.0321390366913016e-08,0.0026999999999999997,8.186775399066513e-11,1.4736195718319723e-08,0.00030417623206818365
|
||||
29,2026-09-02 11:17:10,180.0,1510440476.3997312,310.26705818551966,0.0005308265320017444,1.0478236278928293e-11,1200.0,1.2573883534713951e-08,0.0026999999999999997,3.394948554372766e-11,6.110907397870979e-09,0.00030418234297558154
|
||||
30,2026-09-02 11:20:10,180.0,626131707.8556437,131.37723808155627,0.00022933989405983035,4.584303392370679e-12,1200.0,5.501164070844815e-09,0.0026999999999999997,1.4853142991281e-11,2.67356573843058e-09,0.00030418501654131997
|
||||
31,2026-09-02 11:23:10,180.0,276443494.4698538,58.230037737187715,0.00010626502519132383,2.264368942482436e-12,1200.0,2.717242730978923e-09,0.0026999999999999997,7.336555373643091e-12,1.3205799672557564e-09,0.00030418633712128723
|
||||
32,2026-09-02 11:26:10,180.0,159984510.85068458,27.23152158920079,4.791977942613267e-05,1.0459282793117932e-12,1200.0,1.255113935174152e-09,0.0026999999999999997,3.38880762497021e-12,6.099853724946378e-10,0.0003041869471066597
|
||||
33,2026-09-02 11:29:10,180.0,73767827.28564245,12.481277977443364,2.0828060535188012e-05,3.987360311524629e-13,1200.0,4.784832373829556e-10,0.0026999999999999997,1.29190474093398e-12,2.325428533681164e-10,0.0003041871796495131
|
||||
34,2026-09-02 11:32:10,180.0,41407760.27196593,5.783248493918618,9.318489468861524e-06,1.9570884499753316e-13,1200.0,2.348506139970398e-10,0.0026999999999999997,6.340966577920074e-13,1.1413739840256134e-10,0.0003041872937869115
|
||||
35,2026-09-02 11:35:10,180.0,20852483.129102133,3.1141756237796567,5.455154824284169e-06,1.1902017251334521e-13,1200.0,1.4282420701601425e-10,0.0026999999999999997,3.856253589432384e-13,6.941256460978292e-11,0.0003041873631994761
|
||||
36,2026-09-02 11:38:10,180.0,10365103.333044857,1.499282873411174,2.255241774568566e-06,4.3552322845823175e-14,1200.0,5.226278741498781e-11,0.0026999999999999997,1.4110952602046708e-13,2.5399714683684073e-11,0.0003041873885991908
|
||||
37,2026-09-02 11:41:10,180.0,7189031.970751051,1.2345326867832265,2.5584843529031698e-06,7.166494055077669e-14,1200.0,8.599792866093204e-11,0.0026999999999999997,2.321944073845165e-13,4.179499332921297e-11,0.00030418743039418414
|
||||
38,2026-09-02 11:44:10,180.0,4760452.667658446,1.1905189761285444,3.568825595522147e-06,1.4403836045763965e-13,1200.0,1.728460325491676e-10,0.0026999999999999997,4.666842878827524e-13,8.400317181889544e-11,0.00030418751439735596
|
||||
39,2026-09-02 11:47:10,180.0,2361669.42553466,0.2951465302082522,3.5847500558884937e-07,5.10551507496326e-15,1200.0,6.126618089955912e-12,0.0026999999999999997,1.654186884288096e-14,2.977536391718573e-12,0.00030418751737489237
|
||||
40,2026-09-02 11:50:10,180.0,1445524.6401016556,0.37744798030011906,8.842631770440815e-07,2.373747679368093e-14,1200.0,2.848497215241712e-11,0.0026999999999999997,7.690942481152622e-14,1.384369646607472e-11,0.00030418753121858885
|
||||
41,2026-09-02 11:53:10,180.0,0.0,0.0,0.0,0.0,1200.0,0.0,0.0026999999999999997,0.0,0.0,0.00030418753121858885
|
||||
42,2026-09-02 11:56:10,180.0,2856685.5722636436,0.3795363915617735,8.601883318665738e-07,2.5276260844117496e-14,1200.0,3.033151301294099e-11,0.0026999999999999997,8.189508513494066e-14,1.474111532428932e-11,0.0003041875459597042
|
||||
43,2026-09-02 11:59:11,180.0,2153767.3627035897,0.8561934639525883,4.215723593484976e-06,2.5174705817530456e-13,1200.0,3.0209646981036545e-10,0.0026999999999999997,8.156604684879867e-13,1.468188843278376e-10,0.0003041876927785885
|
||||
44,2026-09-02 12:02:11,180.0,718004.7465160172,0.02311975283781576,5.385234507846335e-09,1.2551439950417724e-17,1200.0,1.506172794050127e-14,0.0026999999999999997,4.0666665439353425e-17,7.319999779083616e-15,0.0003041876927859085
|
||||
45,2026-09-02 12:05:11,180.0,2241794.608558021,0.1295764529882944,6.578334381394865e-08,3.639459446820425e-16,1200.0,4.3673513361845097e-13,0.0026999999999999997,1.1791848607698175e-15,2.1225327493856715e-13,0.00030418769299816176
|
||||
46,2026-09-02 12:08:11,180.0,847771.9741717982,0.09166027188516285,7.49184099738375e-08,6.368286898810447e-16,1200.0,7.641944278572536e-13,0.0026999999999999997,2.0633249552145844e-15,3.713984919386252e-13,0.00030418769336956026
|
||||
47,2026-09-02 12:11:11,180.0,418564.1739148422,0.05102297280021926,4.49919716901824e-08,3.9698289296094697e-16,1200.0,4.763794715531364e-13,0.0026999999999999997,1.286224573193468e-15,2.3152042317482426e-13,0.0003041876936010807
|
||||
48,2026-09-02 12:14:11,180.0,0.0,0.0,0.0,0.0,1200.0,0.0,0.0026999999999999997,0.0,0.0,0.0003041876936010807
|
||||
49,2026-09-02 12:17:11,180.0,1038236.7481297532,0.035854444233644256,9.662700430359127e-09,2.7535643602026425e-17,1200.0,3.3042772322431714e-14,0.0026999999999999997,8.921548527056562e-17,1.605878734870181e-14,0.00030418769361713945
|
||||
50,2026-09-02 12:20:11,180.0,927600.3321879625,0.04123297024586511,1.6004579542000383e-08,6.88072994317121e-17,1200.0,8.256875931805453e-14,0.0026999999999999997,2.229356501587472e-16,4.0128417028574495e-14,0.0003041876936572679
|
||||
51,2026-09-02 12:23:11,180.0,426001.53400822676,0.048308573956532916,3.9628061971533654e-08,3.252736993481141e-16,1200.0,3.90328439217737e-13,0.0026999999999999997,1.0538867858878898e-15,1.8969962145982016e-13,0.0003041876938469675
|
||||
52,2026-09-02 12:26:11,180.0,1763230.8179673427,0.08589682507960526,3.506030992007523e-08,1.568788679986549e-16,1200.0,1.8825464159838588e-13,0.0026999999999999997,5.082875323156418e-16,9.149175581681553e-14,0.00030418769393845925
|
||||
53,2026-09-02 12:29:11,180.0,1796505.6209520656,0.06451075847963256,2.162389650178595e-08,8.891627757001092e-17,1200.0,1.066995330840131e-13,0.0026999999999999997,2.8808873932683535e-16,5.1855973078830364e-14,0.0003041876939903152
|
||||
54,2026-09-02 12:32:11,180.0,571380.9350122605,0.22021021235372512,6.139241638885591e-07,1.7126140345660976e-14,1200.0,2.0551368414793173e-11,0.0026999999999999997,5.548869471994156e-14,9.987965049589482e-12,0.00030418770397828027
|
||||
55,2026-09-02 12:35:11,180.0,1650208.7656837613,0.04591794247245152,9.292525049884406e-09,1.8918116953095185e-17,1200.0,2.2701740343714222e-14,0.0026999999999999997,6.129469892802839e-17,1.103304580704511e-14,0.00030418770398931333
|
||||
56,2026-09-02 12:38:11,180.0,442137.0270085765,0.0349730388363784,2.0011305174281053e-08,1.1457370559303292e-16,1200.0,1.3748844671163947e-13,0.0026999999999999997,3.7121880612142653e-16,6.681938510185678e-14,0.0003041877040561327
|
||||
57,2026-09-02 12:41:11,180.0,467850.2964792327,0.03204774530882744,1.5880113912061937e-08,7.87367067190404e-17,1200.0,9.448404806284848e-14,0.0026999999999999997,2.5510692976969085e-16,4.5919247358544354e-14,0.000304187704102052
|
||||
58,2026-09-02 12:44:11,180.0,1316232.9611504641,0.1435817145731606,1.2300590122109984e-07,1.1303964481847938e-15,1200.0,1.3564757378217527e-12,0.0026999999999999997,3.6624844921187316e-15,6.592472085813716e-13,0.0003041877047612992
|
||||
59,2026-09-02 12:47:11,180.0,425677.73927897844,0.04656914467712025,3.685370412341673e-08,2.9183107017189526e-16,1200.0,3.501972842062743e-13,0.0026999999999999997,9.455326673569406e-16,1.701958801242493e-13,0.0003041877049314951
|
||||
|
@@ -0,0 +1,166 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1cc3c304",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Importing custom code\n",
|
||||
"from defs.OFR import OFRSample, OFRAnalyser\n",
|
||||
"from defs.Helper import DATFile\n",
|
||||
"\n",
|
||||
"# Importing required libraries\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Importing libraries required for plotting and exporting\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"\n",
|
||||
"# Setting style of figures and backend\n",
|
||||
"import matplotlib\n",
|
||||
"matplotlib.use('pgf')\n",
|
||||
"plt.style.use(r'C:\\Users\\tschan_d\\Documents\\code\\Master_Thesis\\.mplstyle')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "a67c15f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Configuring backend\n",
|
||||
"\n",
|
||||
"matplotlib.rcParams.update({'text.usetex': True,\n",
|
||||
" 'pgf.rcfonts': False,\n",
|
||||
" 'font.family': 'serif',\n",
|
||||
" 'font.serif': [],\n",
|
||||
" 'font.sans-serif': [],\n",
|
||||
" 'font.size': 8,\n",
|
||||
" 'legend.fontsize': 8,\n",
|
||||
" 'xtick.labelsize': 8,\n",
|
||||
" 'ytick.labelsize': 8,\n",
|
||||
" 'pgf.texsystem': r'lualatex',\n",
|
||||
" 'pgf.preamble': r'\\usepackage{unicode-math}' + \n",
|
||||
" r'\\usepackage{accents}' + \n",
|
||||
" r'\\usepackage{fontspec}' + \n",
|
||||
" r'\\usepackage[T1]{fontenc}' +\n",
|
||||
" r'\\usepackage{unicode-math}' +\n",
|
||||
" r'\\setmainfont{STIX Two Text}' +\n",
|
||||
" r'\\setmathfont{STIX Two Math}' + \n",
|
||||
" r'\\usepackage{siunitx}' + \n",
|
||||
" r'\\usepackage{mhchem}' + \n",
|
||||
" r'\\newcommand{\\der}[1]{\\ensuremath{\\textnormal{d}\\,{#1}}}'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "429e1aed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Linking to the data\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to for SMPS data\n",
|
||||
"smps_analyser = OFRAnalyser(data_source_file = Path(r\"Z:\\Data\\SMPS\\processed\\2026\\09_September\\20260903\\20260903_aPinene_SOA.txt\"),\n",
|
||||
" flow_source_file = Path(r\"Z:\\People\\David_Tschan\\OFR_SMPS\\flow_files\\20260903_SET02_EXP14_flow_file.txt\"),\n",
|
||||
" smpl_number_list = range(1, 60))\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to the API O3 monitor data\n",
|
||||
"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')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "26333625",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reading in SMPS data and writing it to disk as a csv\n",
|
||||
"smps_analyser.GetData()\n",
|
||||
"smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)\n",
|
||||
"\n",
|
||||
"# Reading in the O3 data and writing it to disk as a csv\n",
|
||||
"ozon_analyser.read()\n",
|
||||
"ozon_analyser.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "99c569ff",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Creating figure\n",
|
||||
"\n",
|
||||
"fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54), constrained_layout = True)\n",
|
||||
"ax_1 = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax_1)\n",
|
||||
"\n",
|
||||
"ax_1.plot(ozon_analyser.data['timestamp'], ozon_analyser.data['ozone'], color = 'red', linestyle = '-.',label=r'$\\chi_{\\ce{O3}}$')\n",
|
||||
"ax_1.set_ylabel(r'Ozone mole fraction $\\chi_{\\ce{O3}}$ $(\\text{ppb})$')\n",
|
||||
"\n",
|
||||
"ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label=r'$\\accentset{*}{m}$')\n",
|
||||
"ax_2.set_ylabel(r'Mass flux $\\accentset{*}{m}$ $(\\unit{\\kilo\\gram\\per\\second})$')\n",
|
||||
"\n",
|
||||
"ozone_on_times = [pd.Timestamp('2026-09-03 14:35:00'), pd.Timestamp('2026-09-03 16:50:00')]\n",
|
||||
"voc_on_times = [pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')]\n",
|
||||
"ax_1.vlines(ozone_on_times, ymin=0, ymax=70000, colors='k', linestyles='--', linewidth=0.5)\n",
|
||||
"ax_2.vlines(voc_on_times, ymin=0, ymax=2E-7, colors='k', linestyles='-.', linewidth=0.5)\n",
|
||||
"\n",
|
||||
"ax_1.set_xlabel(r'Time $(-)$')\n",
|
||||
"ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"ax_1.set_xlim([pd.Timestamp('2026-09-03 14:15:00'), pd.Timestamp('2026-09-03 17:45:00')])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax_1.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax_1.set_ylim([0, 70000])\n",
|
||||
"ax_2.set_ylim([0, 2E-7])\n",
|
||||
"\n",
|
||||
"ax_1.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')\n",
|
||||
"\n",
|
||||
"#plt.show()\n",
|
||||
"plt.savefig(\"figure.pgf\", format='pgf', dpi=400)\n",
|
||||
"plt.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "89951350",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,60 @@
|
||||
Sample (-),Starting Time (-),Sampling Time (s),Number (#/m^3),Length (m/m^3),Surface (m^2/m^3),Volume (m^3/m^3),Density (kg/m^3),Mass (kg/m^3),Filter Flow (m^3/s),Mass Production Rate (kg/s),Mass Produced (kg),Cumulative Mass Produced (kg)
|
||||
1,2026-09-03 14:35:41,180.0,8948762.743072975,1.2710855300211632,4.529687119851957e-06,2.6009314140817793e-13,1200.0,3.1211176968981347e-10,0.0026999999999999997,8.427017781624963e-13,1.5168632006924933e-10,1.5168632006924933e-10
|
||||
2,2026-09-03 14:38:44,180.0,6995592.731997896,2.012645190358432,8.401262908433098e-06,4.2347225842678107e-13,1200.0,5.081667101121373e-10,0.0026999999999999997,1.3720501173027706e-12,2.469690211144987e-10,3.9865534118374805e-10
|
||||
3,2026-09-03 14:41:44,180.0,9157440302.418922,432.9499283557492,0.00017391525893224592,1.610843525040168e-12,1200.0,1.9330122300482014e-09,0.0026999999999999997,5.219133021130143e-12,9.394439438034259e-10,1.338099284987174e-09
|
||||
4,2026-09-03 14:44:44,180.0,3925170315.427129,220.5477411415589,0.00010570904976253256,9.79129967393403e-13,1200.0,1.174955960872084e-09,0.0026999999999999997,3.1723810943546265e-12,5.710285969838327e-10,1.9091278819710064e-09
|
||||
5,2026-09-03 14:47:44,180.0,921891541.7849052,48.97145829443346,2.3941461167045317e-05,3.237994903237253e-13,1200.0,3.885593883884704e-10,0.0026999999999999997,1.0491103486488701e-12,1.8883986275679662e-10,2.097967744727803e-09
|
||||
6,2026-09-03 14:50:44,180.0,212509514.29363117,11.055323997291318,4.640756920858268e-06,2.169288381964398e-14,1200.0,2.6031460583572777e-11,0.0026999999999999997,7.028494357564649e-14,1.2651289843616367e-11,2.1106190345714194e-09
|
||||
7,2026-09-03 14:53:44,180.0,76090156.96498092,4.460837715366054,2.0934345147772643e-06,1.0969047339710725e-14,1200.0,1.316285680765287e-11,0.0026999999999999997,3.553971338066275e-14,6.397148408519294e-12,2.1170161829799386e-09
|
||||
8,2026-09-03 14:56:44,180.0,27198182.379794434,1.0778507939355868,3.668373886683288e-07,1.484622170337401e-15,1200.0,1.7815466044048812e-12,0.0026999999999999997,4.810175831893179e-15,8.658316497407722e-13,2.1178820146296792e-09
|
||||
9,2026-09-03 14:59:44,180.0,13333503.787143884,1.1861074853519462,3.4100836491895487e-06,2.1823713020975078e-13,1200.0,2.618845562517009e-10,0.0026999999999999997,7.070883018795924e-13,1.2727589433832664e-10,2.245157908968006e-09
|
||||
10,2026-09-03 15:02:44,180.0,8711095.18694913,0.43530393242493337,1.7734659382995175e-07,8.003352087699018e-16,1200.0,9.60402250523882e-13,0.0026999999999999997,2.593086076414481e-15,4.667554937546066e-13,2.2456246644617605e-09
|
||||
11,2026-09-03 15:05:44,180.0,22089034.68424354,0.9759307447298684,3.8889115223154766e-07,1.865064210890654e-15,1200.0,2.2380770530687847e-12,0.0026999999999999997,6.042808043285718e-15,1.0877054477914292e-12,2.246712369909552e-09
|
||||
12,2026-09-03 15:08:44,180.0,116398052.25133978,4.49845704158753,1.3599762929180843e-06,4.544624729033911e-15,1200.0,5.453549674840694e-12,0.0026999999999999997,1.4724584122069874e-14,2.6504251419725774e-12,2.2493627950515246e-09
|
||||
13,2026-09-03 15:11:44,180.0,77828597.81266521,2.776405953849628,8.126173988560902e-07,3.025352414281895e-15,1200.0,3.630422897138274e-12,0.0026999999999999997,9.802141822273339e-15,1.764385528009201e-12,2.2511271805795336e-09
|
||||
14,2026-09-03 15:14:44,180.0,29157615.581503555,2.474679118205111,5.445223438406546e-06,2.4548717652806253e-13,1200.0,2.94584611833675e-10,0.0026999999999999997,7.953784519509224e-13,1.4316812135116604e-10,2.3942953019306996e-09
|
||||
15,2026-09-03 15:17:44,180.0,118846832.80084887,5.469743373273912,2.8477572368244912e-06,7.290003596975009e-14,1200.0,8.748004316370011e-11,0.0026999999999999997,2.3619611654199027e-13,4.251530097755825e-11,2.4368106029082576e-09
|
||||
16,2026-09-03 15:20:44,180.0,344108755280.6448,60405.7805063153,0.08420738713742479,1.3098651765379408e-09,1200.0,1.5718382118455288e-06,0.0026999999999999997,4.2439631719829274e-09,7.639133709569269e-07,7.663501815598352e-07
|
||||
17,2026-09-03 15:23:44,180.0,6061632543787.68,1205146.1133101098,2.060491184099607,4.150238673502474e-08,1200.0,4.980286408202968e-05,0.0026999999999999997,1.3446773302148011e-07,2.420419194386642e-05,2.4970542125426256e-05
|
||||
18,2026-09-03 15:26:45,180.0,5824050213677.0205,1138280.2125437164,2.0087775735460873,4.3501690054333764e-08,1200.0,5.220202806520052e-05,0.0026999999999999997,1.409454757760414e-07,2.537018563968745e-05,5.0340727765113705e-05
|
||||
19,2026-09-03 15:29:45,180.0,5815359006425.131,1054550.9205527084,1.7776258813576913,3.825721786984925e-08,1200.0,4.5908661443819104e-05,0.0026999999999999997,1.2395338589831157e-07,2.2311609461696084e-05,7.265233722680979e-05
|
||||
20,2026-09-03 15:32:45,180.0,5181183627829.479,939617.6353446611,1.5656065020919634,3.327656626623004e-08,1200.0,3.993187951947604e-05,0.0026999999999999997,1.078160747025853e-07,1.9406893446465354e-05,9.205923067327514e-05
|
||||
21,2026-09-03 15:35:45,180.0,5054296427138.903,888574.0681300415,1.4480680615461774,3.040798414275195e-08,1200.0,3.648958097130234e-05,0.0026999999999999997,9.85218686225163e-08,1.7733936352052936e-05,0.00010979316702532807
|
||||
22,2026-09-03 15:38:45,180.0,5013986211021.9375,859781.2045109133,1.3748835946471392,2.8392673221070884e-08,1200.0,3.407120786528506e-05,0.0026999999999999997,9.199226123626966e-08,1.655860702252854e-05,0.0001263517740478566
|
||||
23,2026-09-03 15:41:45,180.0,4847689730069.404,810486.5644693417,1.2557212268988749,2.5052749849050476e-08,1200.0,3.0063299818860575e-05,0.0026999999999999997,8.117090951092354e-08,1.4610763711966239e-05,0.00014096253775982285
|
||||
24,2026-09-03 15:44:45,180.0,4901119437390.537,804842.3750049513,1.2105570780656745,2.3333556134446237e-08,1200.0,2.8000267361335487e-05,0.0026999999999999997,7.56007218756058e-08,1.3608129937609044e-05,0.00015457066769743188
|
||||
25,2026-09-03 15:47:45,180.0,4523844402329.684,757915.4822414137,1.1416427846448363,2.173423393507571e-08,1200.0,2.608108072209085e-05,0.0026999999999999997,7.041891794964529e-08,1.2675405230936153e-05,0.00016724607292836803
|
||||
26,2026-09-03 15:50:45,180.0,4095959961230.161,691771.1643514496,1.064477236754862,2.0679542978864808e-08,1200.0,2.4815451574637762e-05,0.0026999999999999997,6.700171925152195e-08,1.2060309465273951e-05,0.000179306382393642
|
||||
27,2026-09-03 15:53:45,180.0,4190589723504.9214,699074.4826264452,1.04932555191224,1.9830026833663146e-08,1200.0,2.3796032200395772e-05,0.0026999999999999997,6.424928694106857e-08,1.1564871649392343e-05,0.00019087125404303434
|
||||
28,2026-09-03 15:56:45,180.0,4412102794125.187,719544.0975675561,1.0615437298202803,1.9775500712299417e-08,1200.0,2.3730600854759297e-05,0.0026999999999999997,6.407262230785009e-08,1.1533072015413016e-05,0.00020240432605844735
|
||||
29,2026-09-03 15:59:45,180.0,4288652589396.031,703728.6207435927,1.0396618363701742,1.9313624354184202e-08,1200.0,2.3176349225021042e-05,0.0026999999999999997,6.25761429075568e-08,1.1263705723360225e-05,0.00021366803178180757
|
||||
30,2026-09-03 16:02:45,180.0,4440118122792.217,710266.8669605446,1.0335226754187665,1.8984218284531582e-08,1200.0,2.2781061941437898e-05,0.0026999999999999997,6.150886724188232e-08,1.1071596103538817e-05,0.0002247396278853464
|
||||
31,2026-09-03 16:05:45,180.0,4435142733987.152,716622.2166571647,1.0401974373580578,1.8944793959926255e-08,1200.0,2.2733752751911504e-05,0.0026999999999999997,6.138113243016105e-08,1.1048603837428989e-05,0.0002357882317227754
|
||||
32,2026-09-03 16:08:45,180.0,4272317660198.4365,682805.0298689228,0.9874777483861477,1.794014267005009e-08,1200.0,2.1528171204060112e-05,0.0026999999999999997,5.81260622509623e-08,1.0462691205173214e-05,0.00024625092292794863
|
||||
33,2026-09-03 16:11:45,180.0,3980799447253.002,656952.0401758219,0.9686977221132977,1.7771025829037324e-08,1200.0,2.132523099484479e-05,0.0026999999999999997,5.757812368608093e-08,1.0364062263494568e-05,0.0002566149851914432
|
||||
34,2026-09-03 16:14:45,180.0,4032658302550.781,662411.354359285,0.9793364737855754,1.809479092831901e-08,1200.0,2.171374911398281e-05,0.0026999999999999997,5.862712260775358e-08,1.0552882069395645e-05,0.00026716786726083884
|
||||
35,2026-09-03 16:17:45,180.0,4454738001663.653,700299.8100121957,0.9997162653702019,1.805082715556807e-08,1200.0,2.1660992586681683e-05,0.0026999999999999997,5.848467998404054e-08,1.0527242397127297e-05,0.00027769510965796614
|
||||
36,2026-09-03 16:20:45,180.0,4273445352466.1943,674297.9033864252,0.982378910752936,1.8253040565753493e-08,1200.0,2.190364867890419e-05,0.0026999999999999997,5.913985143304131e-08,1.0645173257947435e-05,0.00028834028291591356
|
||||
37,2026-09-03 16:23:45,180.0,1597969835414.1206,285033.91475330584,0.4505478319111743,8.727821916207686e-09,1200.0,1.0473386299449222e-05,0.0026999999999999997,2.8278143008512896e-08,5.090065741532321e-06,0.00029343034865744587
|
||||
38,2026-09-03 16:26:45,180.0,536406696505.9314,98978.15128783538,0.16255364877514783,3.2565426233787202e-09,1200.0,3.907851148054465e-06,0.0026999999999999997,1.0551198099747053e-08,1.8992156579544697e-06,0.00029532956431540033
|
||||
39,2026-09-03 16:29:45,180.0,125666834311.81418,22919.01402263673,0.036709526955180316,7.152215522717066e-10,1200.0,8.58265862726048e-07,0.0026999999999999997,2.3173178293603293e-09,4.1711720928485925e-07,0.0002957466815246852
|
||||
40,2026-09-03 16:32:45,180.0,50520135163.672134,9492.990205756909,0.01557640048860085,3.0941957173413173e-10,1200.0,3.71303486080958e-07,0.0026999999999999997,1.0025194124185866e-09,1.804534942353456e-07,0.00029592713501892055
|
||||
41,2026-09-03 16:35:45,180.0,21362109271.93611,4121.5033178282565,0.006829658207481898,1.3471526228857666e-10,1200.0,1.61658314746292e-07,0.0026999999999999997,4.3647744981498833e-10,7.85659409666979e-08,0.00029600570095988726
|
||||
42,2026-09-03 16:38:45,180.0,9124089148.707813,1760.3799370398638,0.002894755596962821,5.638100656237831e-11,1200.0,6.765720787485397e-08,0.0026999999999999997,1.826744612621057e-10,3.2881403027179025e-08,0.00029603858236291444
|
||||
43,2026-09-03 16:41:45,180.0,3714143957.889918,746.4505998674296,0.001261660020819368,2.487176433665143e-11,1200.0,2.9846117203981716e-08,0.0026999999999999997,8.058451645075062e-11,1.4505212961135112e-08,0.0002960530875758756
|
||||
44,2026-09-03 16:44:45,180.0,1605247390.5864286,323.2301732126811,0.0005371766921261041,1.0183385747791848e-11,1200.0,1.2220062897350218e-08,0.0026999999999999997,3.299416982284558e-11,5.938950568112205e-09,0.0002960590265264437
|
||||
45,2026-09-03 16:47:45,180.0,717689869.2626414,154.16614154969386,0.00027534911273274863,5.768685431168621e-12,1200.0,6.922422517402345e-09,0.0026999999999999997,1.869054079698633e-11,3.3642973434575395e-09,0.00029606239082378716
|
||||
46,2026-09-03 16:50:45,180.0,320452558.75893605,67.88851988784799,0.00012523029685878928,2.730575318002441e-12,1200.0,3.276690381602929e-09,0.0026999999999999997,8.847064030327907e-12,1.5924715254590233e-09,0.00029606398329531263
|
||||
47,2026-09-03 16:53:45,180.0,145384991.4070465,28.417248277188055,5.397103816351274e-05,1.3902708522741804e-12,1200.0,1.6683250227290164e-09,0.0026999999999999997,4.5044775613683434e-12,8.108059610463018e-10,0.0002960647941012737
|
||||
48,2026-09-03 16:56:45,180.0,78476056.69811139,13.17795399340295,2.4871457005032266e-05,5.995542972243636e-13,1200.0,7.194651566692364e-10,0.0026999999999999997,1.942555923006938e-12,3.4966006614124883e-10,0.00029606514376133983
|
||||
49,2026-09-03 16:59:45,180.0,40919586.13142923,6.6096112616691345,1.1297369536381903e-05,2.405879173726424e-13,1200.0,2.887055008471709e-10,0.0026999999999999997,7.795048522873614e-13,1.4031087341172505e-10,0.00029606528407221327
|
||||
50,2026-09-03 17:02:45,180.0,19830491.184929296,3.6151659862908834,8.196862878675818e-06,2.653588688964087e-13,1200.0,3.184306426756904e-10,0.0026999999999999997,8.59762735224364e-13,1.5475729234038552e-10,0.0002960654388295056
|
||||
51,2026-09-03 17:05:45,180.0,14371047.803476995,2.2316449894768997,3.352822351801946e-06,5.950922215340346e-14,1200.0,7.141106658408414e-11,0.0026999999999999997,1.9280987977702717e-13,3.4705778359864894e-11,0.00029606547353528393
|
||||
52,2026-09-03 17:08:45,180.0,6634248.10463251,1.3681217387174964,4.469548124670371e-06,2.4095999056004356e-13,1200.0,2.891519886720523e-10,0.0026999999999999997,7.807103694145412e-13,1.405278664946174e-10,0.0002960656140631504
|
||||
53,2026-09-03 17:11:45,180.0,7935745.231847211,1.37595260936397,3.2075170372142867e-06,9.448565332021841e-14,1200.0,1.1338278398426207e-10,0.0026999999999999997,3.0613351675750754e-13,5.5104033016351355e-11,0.0002960656691671834
|
||||
54,2026-09-03 17:14:45,180.0,7509618.775418732,0.7917647006306339,1.3412246078589778e-06,2.856066290722988e-14,1200.0,3.427279548867586e-11,0.0026999999999999997,9.253654781942482e-14,1.665657860749647e-11,0.000296065685823762
|
||||
55,2026-09-03 17:17:45,180.0,2176002.12701365,0.30431757912280266,4.159424960318821e-07,6.437272053709063e-15,1200.0,7.724726464450874e-12,0.0026999999999999997,2.085676145401736e-14,3.754217061723125e-12,0.00029606568957797906
|
||||
56,2026-09-03 17:20:45,180.0,580245.4401459037,0.23180805333828852,6.699013684229109e-07,1.9371386642723006e-14,1200.0,2.324566397126761e-11,0.0026999999999999997,6.276329272242254e-14,1.1297392690036057e-11,0.00029606570087537177
|
||||
57,2026-09-03 17:23:45,180.0,1850177.652648109,0.2306840656034141,2.7603347587744855e-07,4.015302902527584e-15,1200.0,4.818363483033102e-12,0.0026999999999999997,1.3009581404189374e-14,2.3417246527540874e-12,0.0002960657032170964
|
||||
58,2026-09-03 17:26:45,180.0,0.0,0.0,0.0,0.0,1200.0,0.0,0.0026999999999999997,0.0,0.0,0.0002960657032170964
|
||||
59,2026-09-03 17:29:45,180.0,1506187.6684643065,0.40418712706031973,1.1423126723576972e-06,3.7276723757159996e-14,1200.0,4.4732068508591995e-11,0.0026999999999999997,1.2077658497319836e-13,2.1739785295175704e-11,0.0002960657249568817
|
||||
|
@@ -0,0 +1,166 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1cc3c304",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Importing custom code\n",
|
||||
"from defs.OFR import OFRSample, OFRAnalyser\n",
|
||||
"from defs.Helper import DATFile\n",
|
||||
"\n",
|
||||
"# Importing required libraries\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Importing libraries required for plotting and exporting\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"\n",
|
||||
"# Setting style of figures and backend\n",
|
||||
"import matplotlib\n",
|
||||
"matplotlib.use('pgf')\n",
|
||||
"plt.style.use(r'C:\\Users\\tschan_d\\Documents\\code\\Master_Thesis\\.mplstyle')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "a67c15f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Configuring backend\n",
|
||||
"\n",
|
||||
"matplotlib.rcParams.update({'text.usetex': True,\n",
|
||||
" 'pgf.rcfonts': False,\n",
|
||||
" 'font.family': 'serif',\n",
|
||||
" 'font.serif': [],\n",
|
||||
" 'font.sans-serif': [],\n",
|
||||
" 'font.size': 8,\n",
|
||||
" 'legend.fontsize': 8,\n",
|
||||
" 'xtick.labelsize': 8,\n",
|
||||
" 'ytick.labelsize': 8,\n",
|
||||
" 'pgf.texsystem': r'lualatex',\n",
|
||||
" 'pgf.preamble': r'\\usepackage{unicode-math}' + \n",
|
||||
" r'\\usepackage{accents}' + \n",
|
||||
" r'\\usepackage{fontspec}' + \n",
|
||||
" r'\\usepackage[T1]{fontenc}' +\n",
|
||||
" r'\\usepackage{unicode-math}' +\n",
|
||||
" r'\\setmainfont{STIX Two Text}' +\n",
|
||||
" r'\\setmathfont{STIX Two Math}' + \n",
|
||||
" r'\\usepackage{siunitx}' + \n",
|
||||
" r'\\usepackage{mhchem}' + \n",
|
||||
" r'\\newcommand{\\der}[1]{\\ensuremath{\\textnormal{d}\\,{#1}}}'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "429e1aed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Linking to the data\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to for SMPS data\n",
|
||||
"smps_analyser = OFRAnalyser(data_source_file = Path(r\"Z:\\Data\\SMPS\\processed\\2026\\09_September\\20260904\\20260904_aPinene_SOA.txt\"),\n",
|
||||
" flow_source_file = Path(r\"Z:\\People\\David_Tschan\\OFR_SMPS\\flow_files\\20260904_SET02_EXP15_flow_file.txt\"),\n",
|
||||
" smpl_number_list = range(1, 60))\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to the API O3 monitor data\n",
|
||||
"ozon_analyser = DATFile(file = Path(r\"Z:\\Data\\API_O3\\2026\\09_September\\2026-09-04_12.28.13_PC14750_API_O3.dat\"), data_name = 'ozone')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "26333625",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reading in SMPS data and writing it to disk as a csv\n",
|
||||
"smps_analyser.GetData()\n",
|
||||
"smps_analyser.data.to_csv(path_or_buf = 'smps_data.csv', index = False)\n",
|
||||
"\n",
|
||||
"# Reading in the O3 data and writing it to disk as a csv\n",
|
||||
"ozon_analyser.read()\n",
|
||||
"ozon_analyser.data.to_csv(path_or_buf = 'ozone_data.csv', index = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "99c569ff",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Creating figure\n",
|
||||
"\n",
|
||||
"fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54), constrained_layout = True)\n",
|
||||
"ax_1 = fig.add_subplot(111)\n",
|
||||
"ax_2 = plt.twinx(ax=ax_1)\n",
|
||||
"\n",
|
||||
"ax_1.plot(ozon_analyser.data['timestamp'], ozon_analyser.data['ozone'], color = 'red', linestyle = '-.',label=r'$\\chi_{\\ce{O3}}$')\n",
|
||||
"ax_1.set_ylabel(r'Ozone mole fraction $\\chi_{\\ce{O3}}$ $(\\text{ppb})$')\n",
|
||||
"\n",
|
||||
"ax_2.step(smps_analyser.data['Starting Time (-)'], smps_analyser.data['Mass Production Rate (kg/s)'], color='blue', linestyle='-', label=r'$\\accentset{*}{m}$')\n",
|
||||
"ax_2.set_ylabel(r'Mass flux $\\accentset{*}{m}$ $(\\unit{\\kilo\\gram\\per\\second})$')\n",
|
||||
"\n",
|
||||
"ozone_on_times = [pd.Timestamp('2026-09-04 12:37:00'), pd.Timestamp('2026-09-04 14:52:00')]\n",
|
||||
"voc_on_times = [pd.Timestamp('2026-09-04 13:22:00'), pd.Timestamp('2026-09-04 14:22:00')]\n",
|
||||
"ax_1.vlines(ozone_on_times, ymin=0, ymax=70000, colors='k', linestyles='--', linewidth=0.5)\n",
|
||||
"ax_2.vlines(voc_on_times, ymin=0, ymax=2E-7, colors='k', linestyles='-.', linewidth=0.5)\n",
|
||||
"\n",
|
||||
"ax_1.set_xlabel(r'Time $(-)$')\n",
|
||||
"ax_1.xaxis.set_major_formatter(DateFormatter(r'%H:%M'))\n",
|
||||
"ax_1.set_xlim([pd.Timestamp('2026-09-04 12:15:00'), pd.Timestamp('2026-09-04 15:45:00')])\n",
|
||||
"\n",
|
||||
"lines_1, labels_1 = ax_1.get_legend_handles_labels()\n",
|
||||
"lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
|
||||
"\n",
|
||||
"ax_1.set_ylim([0, 70000])\n",
|
||||
"ax_2.set_ylim([0, 2E-7])\n",
|
||||
"\n",
|
||||
"ax_1.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper right')\n",
|
||||
"\n",
|
||||
"#plt.show()\n",
|
||||
"plt.savefig(\"figure.pgf\", format='pgf', dpi=400)\n",
|
||||
"plt.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "89951350",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
File diff suppressed because it is too large.
Load diff
File diff suppressed because it is too large.
Load diff
@@ -0,0 +1,60 @@
|
||||
Sample (-),Starting Time (-),Sampling Time (s),Number (#/m^3),Length (m/m^3),Surface (m^2/m^3),Volume (m^3/m^3),Density (kg/m^3),Mass (kg/m^3),Filter Flow (m^3/s),Mass Production Rate (kg/s),Mass Produced (kg),Cumulative Mass Produced (kg)
|
||||
1,2026-09-04 12:38:04,180.0,10100522.817806289,4.791157120796416,2.7545204119614977e-05,1.6933425969059297e-12,1200.0,2.0320111162871158e-09,0.0026999999999999997,5.486430013975212e-12,9.875574025155382e-10,9.875574025155382e-10
|
||||
2,2026-09-04 12:41:08,180.0,3814143.679071794,3.139706439415847,2.0478872702311325e-05,1.3532155804144244e-12,1200.0,1.623858696497309e-09,0.0026999999999999997,4.384418480542734e-12,7.891953264976921e-10,1.7767527290132302e-09
|
||||
3,2026-09-04 12:44:08,180.0,5682736749.436019,300.3073090499828,0.00012593061261345002,5.873555910552062e-13,1200.0,7.048267092662474e-10,0.0026999999999999997,1.9030321150188678e-12,3.425457807033962e-10,2.1192985097166266e-09
|
||||
4,2026-09-04 12:47:08,180.0,1911086409.7156925,106.41324111330675,4.905040373480027e-05,2.7864919936981754e-13,1200.0,3.34379039243781e-10,0.0026999999999999997,9.028234059582085e-13,1.6250821307247753e-10,2.281806722789104e-09
|
||||
5,2026-09-04 12:50:08,180.0,228194998.97767586,13.231211223430503,6.4724832688751385e-06,3.620579851487508e-14,1200.0,4.344695821785009e-11,0.0026999999999999997,1.1730678718819524e-13,2.1115221693875143e-11,2.302921944482979e-09
|
||||
6,2026-09-04 12:53:08,180.0,39219011.525664404,2.919974865484181,3.2770724632947087e-06,9.52111356720813e-14,1200.0,1.1425336280649758e-10,0.0026999999999999997,3.0848407957754344e-13,5.552713432395782e-11,2.358449078806937e-09
|
||||
7,2026-09-04 12:56:08,180.0,17156971.100489758,1.1676183606945107,6.212179162082776e-07,3.511221224144883e-15,1200.0,4.2134654689738604e-12,0.0026999999999999997,1.1376356766229422e-14,2.047744217921296e-12,2.3604968230248582e-09
|
||||
8,2026-09-04 12:59:08,180.0,10421258.311787004,0.41459375709663737,1.6565738176347592e-07,8.762353092223582e-16,1200.0,1.0514823710668297e-12,0.0026999999999999997,2.83900240188044e-15,5.110204323384791e-13,2.3610078434571966e-09
|
||||
9,2026-09-04 13:02:08,180.0,1650619.7409235938,0.08745602963460569,3.8236460332853164e-08,1.891269162156315e-16,1200.0,2.269522994587578e-13,0.0026999999999999997,6.12771208538646e-16,1.1029881753695627e-13,2.3611181422747337e-09
|
||||
10,2026-09-04 13:05:08,180.0,3946050.718919608,0.1829902937119559,9.475814000997946e-08,6.973399818369074e-16,1200.0,8.36807978204289e-13,0.0026999999999999997,2.25938154115158e-15,4.066886774072844e-13,2.361524830952141e-09
|
||||
11,2026-09-04 13:08:08,180.0,68227872.89208338,2.7153174506597466,8.304094644728934e-07,2.7101640778447688e-15,1200.0,3.2521968934137222e-12,0.0026999999999999997,8.780931612217049e-15,1.5805676901990688e-12,2.36310539864234e-09
|
||||
12,2026-09-04 13:11:08,180.0,4420018.034803477,0.18222200412779854,7.10826081830824e-08,3.802840035130439e-16,1200.0,4.563408042156526e-13,0.0026999999999999997,1.232120171382262e-15,2.2178163084880715e-13,2.363327180273189e-09
|
||||
13,2026-09-04 13:14:08,180.0,64374092.352981955,3.891614253255669,2.154709902749329e-06,1.5816201385857716e-14,1200.0,1.897944166302926e-11,0.0026999999999999997,5.124449249017899e-14,9.224008648232219e-12,2.372551188921421e-09
|
||||
14,2026-09-04 13:17:08,180.0,204378890.9732665,8.76942617650193,3.169945310967301e-06,1.3429086957680755e-14,1200.0,1.6114904349216904e-11,0.0026999999999999997,4.3510241742885634e-14,7.831843513719414e-12,2.3803830324351408e-09
|
||||
15,2026-09-04 13:20:08,180.0,17090139.13807369,0.7215243304668465,3.2574981178499114e-07,2.54628625076007e-15,1200.0,3.0555435009120835e-12,0.0026999999999999997,8.249967452462624e-15,1.4849941414432722e-12,2.381868026576584e-09
|
||||
16,2026-09-04 13:23:08,180.0,4573179162.470583,1061.763696307487,0.002106883638637139,5.161915490754571e-11,1200.0,6.194298588905485e-08,0.0026999999999999997,1.6724606190044807e-10,3.010429114208065e-08,3.2486159168657235e-08
|
||||
17,2026-09-04 13:26:08,180.0,6027124858657.676,1140515.8818395603,1.8652187735188925,3.618561112716292e-08,1200.0,4.342273335259551e-05,0.0026999999999999997,1.1724138005200788e-07,2.1103448409361418e-05,2.1135934568530074e-05
|
||||
18,2026-09-04 13:29:08,180.0,5585624149380.809,1086540.554984612,1.8700637065321315,3.9025342285169825e-08,1200.0,4.683041074220379e-05,0.0026999999999999997,1.2644210900395023e-07,2.275957962071104e-05,4.389551418924111e-05
|
||||
19,2026-09-04 13:32:08,180.0,5185514676692.623,978762.4751514631,1.6870949442107435,3.624744432630748e-08,1200.0,4.349693319156899e-05,0.0026999999999999997,1.1744171961723626e-07,2.1139509531102527e-05,6.503502372034363e-05
|
||||
20,2026-09-04 13:35:08,180.0,5278031814465.66,929303.4172966308,1.5218451882958706,3.199355394441755e-08,1200.0,3.839226473330107e-05,0.0026999999999999997,1.0365911477991288e-07,1.865864066038432e-05,8.369366438072795e-05
|
||||
21,2026-09-04 13:38:08,180.0,4908532272480.57,858229.2185504921,1.3769561881500838,2.8240897086811627e-08,1200.0,3.3889076504173954e-05,0.0026999999999999997,9.150050656126967e-08,1.647009118102854e-05,0.0001001637555617565
|
||||
22,2026-09-04 13:41:08,180.0,4737842155023.135,809718.6261384869,1.2717877256374304,2.5570909044934607e-08,1200.0,3.0685090853921536e-05,0.0026999999999999997,8.284974530558813e-08,1.4912954155005864e-05,0.00011507670971676236
|
||||
23,2026-09-04 13:44:08,180.0,4568451822591.366,765044.5256354911,1.1674288841497227,2.2709270739657927e-08,1200.0,2.7251124887589508e-05,0.0026999999999999997,7.357803719649166e-08,1.32440466953685e-05,0.00012832075641213087
|
||||
24,2026-09-04 13:47:08,180.0,4695548883239.235,772094.0566430758,1.1657419059636784,2.2489967450505872e-08,1200.0,2.6987960940607046e-05,0.0026999999999999997,7.286749453963902e-08,1.3116149017135023e-05,0.0001414369054292659
|
||||
25,2026-09-04 13:50:08,180.0,4263902108199.131,713638.1541499661,1.0761952964448631,2.044132412986305e-08,1200.0,2.452958895583566e-05,0.0026999999999999997,6.622989018075628e-08,1.192138023253613e-05,0.00015335828566180202
|
||||
26,2026-09-04 13:53:08,180.0,4122970637384.531,692701.9836374412,1.0484636719616356,1.988855036176385e-08,1200.0,2.386626043411662e-05,0.0026999999999999997,6.443890317211486e-08,1.1599002570980675e-05,0.0001649572882327827
|
||||
27,2026-09-04 13:56:08,180.0,4171972137203.3677,682348.047583038,1.0170389599982883,1.9066595187040268e-08,1200.0,2.2879914224448324e-05,0.0026999999999999997,6.177576840601048e-08,1.1119638313081885e-05,0.00017607692654586458
|
||||
28,2026-09-04 13:59:08,180.0,4305213531078.9585,692493.6575078857,1.0105447038593938,1.864236251196518e-08,1200.0,2.2370835014358217e-05,0.0026999999999999997,6.040125453876717e-08,1.0872225816978091e-05,0.00018694915236284267
|
||||
29,2026-09-04 14:02:08,180.0,4044766273026.3984,662476.4037691297,0.9860735306508279,1.846319893555658e-08,1200.0,2.2155838722667897e-05,0.0026999999999999997,5.982076455120331e-08,1.0767737619216595e-05,0.00019771688998205926
|
||||
30,2026-09-04 14:05:08,180.0,4060240527573.701,654403.4879496837,0.9601037340697933,1.7754086107456203e-08,1200.0,2.1304903328947442e-05,0.0026999999999999997,5.752323898815809e-08,1.0354183017868457e-05,0.0002080710729999277
|
||||
31,2026-09-04 14:08:08,180.0,4287661865516.731,678852.3093369869,0.9733160367051552,1.7644185468234604e-08,1200.0,2.1173022561881524e-05,0.0026999999999999997,5.7167160917080107e-08,1.0290088965074419e-05,0.00021836116196500213
|
||||
32,2026-09-04 14:11:08,180.0,3928641351577.2124,633173.4354910706,0.9333788855091852,1.7336185078748193e-08,1200.0,2.0803422094497833e-05,0.0026999999999999997,5.616923965514414e-08,1.0110463137925946e-05,0.00022847162510292808
|
||||
33,2026-09-04 14:14:08,180.0,3980438234843.48,636925.2722365389,0.9258160638681968,1.6944506962020335e-08,1200.0,2.0333408354424402e-05,0.0026999999999999997,5.490020255694588e-08,9.88203646025026e-06,0.00023835366156317833
|
||||
34,2026-09-04 14:17:08,180.0,4115740902703.9194,655521.6964019879,0.9453610389743532,1.711077368251646e-08,1200.0,2.0532928419019753e-05,0.0026999999999999997,5.543890673135333e-08,9.9790032116436e-06,0.0002483326647748219
|
||||
35,2026-09-04 14:20:08,180.0,4297427321360.137,679932.9191556494,0.9654642599608028,1.7247430975671457e-08,1200.0,2.0696917170805754e-05,0.0026999999999999997,5.588167636117553e-08,1.0058701745011595e-05,0.00025839136651983354
|
||||
36,2026-09-04 14:23:08,180.0,4007082435116.297,650925.6001108537,0.9637540272954915,1.7966862101182212e-08,1200.0,2.1560234521418653e-05,0.0026999999999999997,5.821263320783036e-08,1.0478273977409465e-05,0.000268869640497243
|
||||
37,2026-09-04 14:26:08,180.0,1392708518599.0408,244457.62444786518,0.3806505640175327,7.282863501043123e-09,1200.0,8.739436201251747e-06,0.0026999999999999997,2.3596477743379714e-08,4.247365993808348e-06,0.00027311700649105137
|
||||
38,2026-09-04 14:29:08,180.0,339614039820.58167,61382.46701682033,0.0968897398577062,1.8449198336524362e-09,1200.0,2.213903800382923e-06,0.0026999999999999997,5.977540261033892e-09,1.0759572469861006e-06,0.0002741929637380375
|
||||
39,2026-09-04 14:32:08,180.0,121260868845.58374,22214.34335127509,0.035690071714589794,6.953059434416999e-10,1200.0,8.343671321300399e-07,0.0026999999999999997,2.2527912567511074e-09,4.0550242621519935e-07,0.0002745984661642527
|
||||
40,2026-09-04 14:35:08,180.0,45986219585.13162,8476.181780068468,0.013483332644615759,2.5659198682668713e-10,1200.0,3.079103841920245e-07,0.0026999999999999997,8.313580373184661e-10,1.496444467173239e-07,0.00027474811061097
|
||||
41,2026-09-04 14:38:08,180.0,17700531544.199352,3299.432077513502,0.005296376399638049,1.0231895445633147e-10,1200.0,1.2278274534759775e-07,0.0026999999999999997,3.315134124385139e-10,5.96724142389325e-08,0.0002748077830252089
|
||||
42,2026-09-04 14:41:08,180.0,6949001176.18897,1329.9960894922228,0.002184366500711576,4.270911243093794e-11,1200.0,5.125093491712553e-08,0.0026999999999999997,1.3837752427623891e-10,2.4907954369723005e-08,0.00027483269097957865
|
||||
43,2026-09-04 14:44:08,180.0,2580735969.152096,525.5435149273407,0.0009092864404578403,1.885350027970313e-11,1200.0,2.2624200335643753e-08,0.0026999999999999997,6.108534090623812e-11,1.0995361363122862e-08,0.00027484368634094175
|
||||
44,2026-09-04 14:47:08,180.0,1160292409.4128022,238.86305013739167,0.0004120214814775626,8.30081243526485e-12,1200.0,9.960974922317822e-09,0.0026999999999999997,2.6894632290258115e-11,4.84103381224646e-09,0.000274848527374754
|
||||
45,2026-09-04 14:50:08,180.0,531243722.9483658,110.80616447708654,0.00019103330965734066,3.7396732560859e-12,1200.0,4.48760790730308e-09,0.0026999999999999997,1.2116541349718315e-11,2.180977442949297e-09,0.000274850708352197
|
||||
46,2026-09-04 14:53:08,180.0,199660454.34875894,42.82358374925328,7.761754330531248e-05,1.5704119677086665e-12,1200.0,1.8844943612503997e-09,0.0026999999999999997,5.088134775376078e-12,9.158642595676941e-10,0.0002748516242164566
|
||||
47,2026-09-04 14:56:08,180.0,105530987.12745008,20.642333140633518,3.838748332286209e-05,8.450647042177795e-13,1200.0,1.0140776450613355e-09,0.0026999999999999997,2.7380096416656054e-12,4.92841735499809e-10,0.0002748521170581921
|
||||
48,2026-09-04 14:59:08,180.0,55789909.09519776,8.226124394314223,1.268419482423675e-05,2.3057166076110343e-13,1200.0,2.766859929133241e-10,0.0026999999999999997,7.47052180865975e-13,1.344693925558755e-10,0.00027485225152758464
|
||||
49,2026-09-04 15:02:08,180.0,39007481.1607303,5.444156017410567,9.22871447754234e-06,2.0051517338878922e-13,1200.0,2.4061820806654703e-10,0.0026999999999999997,6.49669161779677e-13,1.1694044912034186e-10,0.00027485236846803377
|
||||
50,2026-09-04 15:05:08,180.0,16608438.761056408,2.7058610840999915,6.853678245242035e-06,3.0126237891253615e-13,1200.0,3.615148546950433e-10,0.0026999999999999997,9.760901076766167e-13,1.7569621938179102e-10,0.00027485254416425317
|
||||
51,2026-09-04 15:08:08,180.0,9739323.352994898,1.3516659371060928,2.5696358984935546e-06,6.706832877652376e-14,1200.0,8.04819945318285e-11,0.0026999999999999997,2.1730138523593693e-13,3.911424934246865e-11,0.0002748525832785025
|
||||
52,2026-09-04 15:11:08,180.0,4525594.350308625,0.712168522621142,1.066327401578009e-06,1.7299164803908485e-14,1200.0,2.0758997764690184e-11,0.0026999999999999997,5.604929396466349e-14,1.008887291363943e-11,0.00027485259336737543
|
||||
53,2026-09-04 15:14:08,180.0,2530070.017800019,0.3883501644201086,6.383789131768815e-07,1.2662188412712025e-14,1200.0,1.5194626095254432e-11,0.0026999999999999997,4.1025490457186964e-14,7.384588282293653e-12,0.0002748526007519637
|
||||
54,2026-09-04 15:17:08,180.0,2786969.121719135,0.3012088649832265,2.800175128323805e-07,2.839685262121329e-15,1200.0,3.4076223145455947e-12,0.0026999999999999997,9.200580249273105e-15,1.656104444869159e-12,0.00027485260240806816
|
||||
55,2026-09-04 15:20:08,180.0,1498839.100556354,0.349860794550045,7.463713999287623e-07,1.7339030671624013e-14,1200.0,2.0806836805948818e-11,0.0026999999999999997,5.61784593760618e-14,1.0112122687691125e-11,0.00027485261252019084
|
||||
56,2026-09-04 15:23:08,180.0,2476829.904440271,0.6188599763718221,1.3530819392593154e-06,3.272350419074004e-14,1200.0,3.926820502888805e-11,0.0026999999999999997,1.0602415357799772e-13,1.908434764403959e-11,0.0002748526316045385
|
||||
57,2026-09-04 15:26:08,180.0,1262686.800042009,0.05324246591011368,1.838848602852365e-08,7.03524367174472e-17,1200.0,8.442292406093663e-14,0.0026999999999999997,2.279418949645289e-16,4.10295410936152e-14,0.000274852631645568
|
||||
58,2026-09-04 15:29:08,180.0,1336188.2936312014,0.2656506664550545,5.850135786934441e-07,1.4180423230409146e-14,1200.0,1.7016507876490975e-11,0.0026999999999999997,4.5944571266525626e-14,8.270022827974613e-12,0.00027485263991559086
|
||||
59,2026-09-04 15:32:08,180.0,432404.20823329734,0.0424620932485098,3.016327189509372e-08,2.1439911957438253e-16,1200.0,2.57278943489259e-13,0.0026999999999999997,6.946531474209993e-16,1.2503756653577987e-13,0.0002748526400406284
|
||||
|
@@ -0,0 +1,232 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"id": "1cc3c304",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Importing custom code\n",
|
||||
"from defs.OFR import OFRSample, OFRAnalyser\n",
|
||||
"from defs.Helper import DATFile\n",
|
||||
"\n",
|
||||
"# Importing required libraries\n",
|
||||
"import numpy as np\n",
|
||||
"from pathlib import Path\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"# Importing libraries required for plotting and exporting\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib.dates import DateFormatter\n",
|
||||
"from matplotlib.ticker import MultipleLocator\n",
|
||||
"\n",
|
||||
"# Setting style of figures and backend\n",
|
||||
"import matplotlib\n",
|
||||
"matplotlib.use('pgf')\n",
|
||||
"plt.style.use(r'C:\\Users\\tschan_d\\Documents\\code\\Master_Thesis\\.mplstyle')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"id": "a67c15f2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Configuring backend\n",
|
||||
"\n",
|
||||
"matplotlib.rcParams.update({'text.usetex': True,\n",
|
||||
" 'pgf.rcfonts': False,\n",
|
||||
" 'font.family': 'serif',\n",
|
||||
" 'font.serif': [],\n",
|
||||
" 'font.sans-serif': [],\n",
|
||||
" 'font.size': 8,\n",
|
||||
" 'legend.fontsize': 8,\n",
|
||||
" 'xtick.labelsize': 8,\n",
|
||||
" 'ytick.labelsize': 8,\n",
|
||||
" 'pgf.texsystem': r'lualatex',\n",
|
||||
" 'pgf.preamble': r'\\usepackage{unicode-math}' + \n",
|
||||
" r'\\usepackage{accents}' + \n",
|
||||
" r'\\usepackage{fontspec}' + \n",
|
||||
" r'\\usepackage[T1]{fontenc}' +\n",
|
||||
" r'\\usepackage{unicode-math}' +\n",
|
||||
" r'\\setmainfont{STIX Two Text}' +\n",
|
||||
" r'\\setmathfont{STIX Two Math}' + \n",
|
||||
" r'\\usepackage{siunitx}' + \n",
|
||||
" r'\\usepackage{mhchem}' + \n",
|
||||
" r'\\newcommand{\\der}[1]{\\ensuremath{\\textnormal{d}\\,{#1}}}' + \n",
|
||||
" r'\\DeclareSIUnit\\ppb{ppb}' + \n",
|
||||
" r'\\DeclareSIUnit\\ppm{ppm}'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "429e1aed",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Linking to the data\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to for SMPS data for the different filters used\n",
|
||||
"smps_analyser_01 = OFRAnalyser(data_source_file = Path(r\"Z:\\Data\\SMPS\\processed\\2026\\09_September\\20260902\\20260902_aPinene_SOA.txt\"),\n",
|
||||
" flow_source_file = Path(r\"Z:\\People\\David_Tschan\\OFR_SMPS\\flow_files\\20260902_SET02_EXP13_flow_file.txt\"),\n",
|
||||
" smpl_number_list = range(1, 60))\n",
|
||||
"smps_analyser_02 = OFRAnalyser(data_source_file = Path(r\"Z:\\Data\\SMPS\\processed\\2026\\09_September\\20260903\\20260903_aPinene_SOA.txt\"),\n",
|
||||
" flow_source_file = Path(r\"Z:\\People\\David_Tschan\\OFR_SMPS\\flow_files\\20260903_SET02_EXP14_flow_file.txt\"),\n",
|
||||
" smpl_number_list = range(1, 60))\n",
|
||||
"smps_analyser_03 = OFRAnalyser(data_source_file = Path(r\"Z:\\Data\\SMPS\\processed\\2026\\09_September\\20260904\\20260904_aPinene_SOA.txt\"),\n",
|
||||
" flow_source_file = Path(r\"Z:\\People\\David_Tschan\\OFR_SMPS\\flow_files\\20260904_SET02_EXP15_flow_file.txt\"),\n",
|
||||
" smpl_number_list = range(1, 60))\n",
|
||||
"\n",
|
||||
"# Specify the source file paths to the API O3 monitor data for the different filters used\n",
|
||||
"ozon_analyser_01 = DATFile(file = Path(r\"Z:\\Data\\API_O3\\2026\\09_September\\2026-09-02_09.01.11_PC14750_API_O3.dat\"), data_name = 'ozone')\n",
|
||||
"ozon_analyser_02 = DATFile(file = Path(r\"Z:\\Data\\API_O3\\2026\\09_September\\2026-09-03_14.23.44_PC14750_API_O3.dat\"), data_name = 'ozone')\n",
|
||||
"ozon_analyser_03 = DATFile(file = Path(r\"Z:\\Data\\API_O3\\2026\\09_September\\2026-09-04_12.28.13_PC14750_API_O3.dat\"), data_name = 'ozone')\n",
|
||||
"\n",
|
||||
"voc_on_times_01 = [pd.Timestamp('2026-09-02 09:52:00'), pd.Timestamp('2026-09-02 10:52:00')]\n",
|
||||
"voc_on_times_02 = [pd.Timestamp('2026-09-03 15:20:00'), pd.Timestamp('2026-09-03 16:20:00')]\n",
|
||||
"voc_on_times_03 = [pd.Timestamp('2026-09-04 13:22:00'), pd.Timestamp('2026-09-04 14:22:00')]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"id": "39ca7d2b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Defining function to average O3 data over timespans of each SMPS sample\n",
|
||||
"\n",
|
||||
"def ozone_averager(ozon_analyser: DATFile, smps_analyser: OFRAnalyser, voc_on_times, cut_edges = True, subtract_5min_baseline = True) -> np.ndarray:\n",
|
||||
"\n",
|
||||
" average_ozon_values = []\n",
|
||||
" mass_production_rates = []\n",
|
||||
"\n",
|
||||
" 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()\n",
|
||||
"\n",
|
||||
" baseline = None\n",
|
||||
"\n",
|
||||
" for index, data in local_smps_data.iterrows():\n",
|
||||
"\n",
|
||||
" start = data['Starting Time (-)']\n",
|
||||
"\n",
|
||||
" if index == 0:\n",
|
||||
"\n",
|
||||
" if subtract_5min_baseline == True:\n",
|
||||
" baseline_start = start - pd.Timedelta(minutes=5)\n",
|
||||
" baseline_ozone_data = ozon_analyser.data[(ozon_analyser.data['timestamp'] >= baseline_start) & (ozon_analyser.data['timestamp'] <= start)]\n",
|
||||
" baseline = np.average(baseline_ozone_data['ozone'])\n",
|
||||
"\n",
|
||||
" if cut_edges == True:\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
" stop = start + pd.Timedelta(seconds = data['Sampling Time (s)'])\n",
|
||||
"\n",
|
||||
" mass_production_rates.append(data['Mass Production Rate (kg/s)'])\n",
|
||||
"\n",
|
||||
" local_ozone_data = ozon_analyser.data[(ozon_analyser.data['timestamp'] >= start) & (ozon_analyser.data['timestamp'] <= stop)]\n",
|
||||
" average_ozon_values.append(np.average(local_ozone_data['ozone']))\n",
|
||||
"\n",
|
||||
" if cut_edges == True:\n",
|
||||
" del average_ozon_values[-1]\n",
|
||||
" del mass_production_rates[-1]\n",
|
||||
"\n",
|
||||
" if subtract_5min_baseline == True:\n",
|
||||
" output_average_ozone_values = [baseline - value for value in average_ozon_values]\n",
|
||||
" elif subtract_5min_baseline == False:\n",
|
||||
" output_average_ozone_values = [value for value in average_ozon_values]\n",
|
||||
"\n",
|
||||
" return output_average_ozone_values, mass_production_rates\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "f090dba3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reading in SMPS data\n",
|
||||
"for smps_analyser in [smps_analyser_01, smps_analyser_02, smps_analyser_03]:\n",
|
||||
" smps_analyser.GetData()\n",
|
||||
"\n",
|
||||
"# Reading in the O3 data\n",
|
||||
"for ozon_analyser in [ozon_analyser_01, ozon_analyser_02, ozon_analyser_03]:\n",
|
||||
" ozon_analyser.read()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"id": "26333625",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ozone_average_01, mass_production_rates_01 = ozone_averager(ozon_analyser=ozon_analyser_01, smps_analyser=smps_analyser_01, voc_on_times=voc_on_times_01, cut_edges=True, subtract_5min_baseline=True)\n",
|
||||
"\n",
|
||||
"ozone_average_02, mass_production_rates_02 = ozone_averager(ozon_analyser=ozon_analyser_02, smps_analyser=smps_analyser_02, voc_on_times=voc_on_times_02, cut_edges=True, subtract_5min_baseline=True)\n",
|
||||
"\n",
|
||||
"ozone_average_03, mass_production_rates_03 = ozone_averager(ozon_analyser=ozon_analyser_03, smps_analyser=smps_analyser_03, voc_on_times=voc_on_times_03, cut_edges=True, subtract_5min_baseline=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"id": "99c569ff",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Creating figure\n",
|
||||
"\n",
|
||||
"fig = plt.figure(figsize=(8.8 / 2.54, 5 / 2.54), constrained_layout = True)\n",
|
||||
"ax = fig.add_subplot(111)\n",
|
||||
"\n",
|
||||
"ax.plot(np.array(ozone_average_01) / 1000, mass_production_rates_01, color='red', linewidth=0.5, linestyle = ':', marker='o', markersize=2.5, markerfacecolor='red', label=r'Filter 1')\n",
|
||||
"ax.plot(np.array(ozone_average_02) / 1000, mass_production_rates_02, color='blue', linewidth=0.5, linestyle = ':', marker='d', markersize=4, markerfacecolor='blue', label='Filter 2')\n",
|
||||
"ax.plot(np.array(ozone_average_03) / 1000, mass_production_rates_03, color='green', linewidth=0.5, linestyle = ':', marker='s', markersize=2.5, markerfacecolor='green', label='Filter 3')\n",
|
||||
"\n",
|
||||
"ax.set_yscale('log')\n",
|
||||
"\n",
|
||||
"ax.set_xlabel(r'Deviation from $\\ce{O3}$ baseline $\\Delta\\chi_{\\ce{O3}}$ $(\\unit{\\ppm})$')\n",
|
||||
"ax.set_ylabel(r'Mass flux $\\accentset{*}{m}$ $(\\unit{\\kilo\\gram\\per\\second})$')\n",
|
||||
"\n",
|
||||
"ax.legend(loc='upper right')\n",
|
||||
"\n",
|
||||
"#plt.show()\n",
|
||||
"plt.savefig(\"figure.pgf\", format='pgf', dpi=400)\n",
|
||||
"plt.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "89951350",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "MA",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.12.13"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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