From f3dccb92895a9130231d9852cb96d2705a5b45df Mon Sep 17 00:00:00 2001
From: Tschan <david.tschan@psi.ch>
Date: Mon, 7 Sep 2026 14:25:08 +0200
Subject: [PATCH] Initial commit to set up the repository...

---
 .gitignore                                    |    3 +
 .../20260520_SET01_EXP01/analysis.ipynb       |  171 +
 .../20260527_SET01_EXP03/analysis.ipynb       |  171 +
 .../20260529_SET01_EXP05/analysis.ipynb       |  171 +
 .../20260608_SET01_EXP07_EXP08/analysis.ipynb |  172 +
 .../20260716_SET01_EXP09/analysis.ipynb       |  171 +
 .../202607023_OFR_Analysis_01.ipynb           |  296 ++
 ..._01_Experiment_Timeseries_of_Densities.pdf |  Bin 0 -> 496459 bytes
 ...01_Experiment_Timeseries_of_Flow_Rates.pdf |  Bin 0 -> 494593 bytes
 ...periment_Timeseries_of_Mass_Production.pdf |  Bin 0 -> 266978 bytes
 ...723_OFR_Analysis_01_Spectrum_Sample_33.pdf |  Bin 0 -> 494669 bytes
 ...710_OFR_Analysis_01_Spectrum_Sample_33.pdf |  Bin 0 -> 43808 bytes
 ...710_OFR_Analysis_01_Spectrum_Sample_33.pgf | 2431 +++++++++
 .../20260710_OFR_Analysis_02.ipynb            |  303 ++
 OFR_Analysis/20260710_SET02_EXP01/data.csv    |  610 +++
 OFR_Analysis/20260710_SET02_EXP01/data.xlsx   |  Bin 0 -> 114925 bytes
 .../20260902_SET02_EXP13/analysis.ipynb       |  166 +
 OFR_Analysis/20260902_SET02_EXP13/figure.pgf  | 2958 +++++++++++
 .../20260902_SET02_EXP13/ozone_data.csv       | 4565 +++++++++++++++++
 .../20260902_SET02_EXP13/smps_data.csv        |   60 +
 .../20260903_SET02_EXP14/analysis.ipynb       |  166 +
 OFR_Analysis/20260903_SET02_EXP14/figure.pgf  | 2943 +++++++++++
 .../20260903_SET02_EXP14/ozone_data.csv       | 3834 ++++++++++++++
 .../20260903_SET02_EXP14/smps_data.csv        |   60 +
 .../20260904_SET02_EXP15/analysis.ipynb       |  166 +
 OFR_Analysis/20260904_SET02_EXP15/figure.pgf  | 2863 +++++++++++
 .../20260904_SET02_EXP15/ozone_data.csv       | 3790 ++++++++++++++
 .../20260904_SET02_EXP15/smps_data.csv        |   60 +
 .../20260906_SET02_EXP13-14-15/analysis.ipynb |  232 +
 .../20260906_SET02_EXP13-14-15/figure.pgf     | 2646 ++++++++++
 README.md                                     |    0
 31 files changed, 29008 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 CWFTR_Analysis/20260520_SET01_EXP01/analysis.ipynb
 create mode 100644 CWFTR_Analysis/20260527_SET01_EXP03/analysis.ipynb
 create mode 100644 CWFTR_Analysis/20260529_SET01_EXP05/analysis.ipynb
 create mode 100644 CWFTR_Analysis/20260608_SET01_EXP07_EXP08/analysis.ipynb
 create mode 100644 CWFTR_Analysis/20260716_SET01_EXP09/analysis.ipynb
 create mode 100644 OFR_Analysis/20260612_SET02_EXP03/202607023_OFR_Analysis_01.ipynb
 create mode 100644 OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Densities.pdf
 create mode 100644 OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Flow_Rates.pdf
 create mode 100644 OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Mass_Production.pdf
 create mode 100644 OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Spectrum_Sample_33.pdf
 create mode 100644 OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_01_Spectrum_Sample_33.pdf
 create mode 100644 OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_01_Spectrum_Sample_33.pgf
 create mode 100644 OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_02.ipynb
 create mode 100644 OFR_Analysis/20260710_SET02_EXP01/data.csv
 create mode 100644 OFR_Analysis/20260710_SET02_EXP01/data.xlsx
 create mode 100644 OFR_Analysis/20260902_SET02_EXP13/analysis.ipynb
 create mode 100644 OFR_Analysis/20260902_SET02_EXP13/figure.pgf
 create mode 100644 OFR_Analysis/20260902_SET02_EXP13/ozone_data.csv
 create mode 100644 OFR_Analysis/20260902_SET02_EXP13/smps_data.csv
 create mode 100644 OFR_Analysis/20260903_SET02_EXP14/analysis.ipynb
 create mode 100644 OFR_Analysis/20260903_SET02_EXP14/figure.pgf
 create mode 100644 OFR_Analysis/20260903_SET02_EXP14/ozone_data.csv
 create mode 100644 OFR_Analysis/20260903_SET02_EXP14/smps_data.csv
 create mode 100644 OFR_Analysis/20260904_SET02_EXP15/analysis.ipynb
 create mode 100644 OFR_Analysis/20260904_SET02_EXP15/figure.pgf
 create mode 100644 OFR_Analysis/20260904_SET02_EXP15/ozone_data.csv
 create mode 100644 OFR_Analysis/20260904_SET02_EXP15/smps_data.csv
 create mode 100644 OFR_Analysis/20260906_SET02_EXP13-14-15/analysis.ipynb
 create mode 100644 OFR_Analysis/20260906_SET02_EXP13-14-15/figure.pgf
 create mode 100644 README.md

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..4b02af9
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,3 @@
+# File types
+
+*.svg
\ No newline at end of file
diff --git a/CWFTR_Analysis/20260520_SET01_EXP01/analysis.ipynb b/CWFTR_Analysis/20260520_SET01_EXP01/analysis.ipynb
new file mode 100644
index 0000000..2b01102
--- /dev/null
+++ b/CWFTR_Analysis/20260520_SET01_EXP01/analysis.ipynb
@@ -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": "ca7877db",
+   "metadata": {},
+   "source": [
+    "# SET01 EXP01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6c3e022f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "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\\2026-05-20_09.21.36_PC14750_T360U_CO2.dat\")\n",
+    "btof_path = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260520_SET01_EXP01\\noncalibrated_export\\m_Q_333.txt\")\n",
+    "btof_path_FA = Path(r\"Z:\\People\\David_Tschan\\BTOF_analysis_IGOR\\20260520_SET01_EXP01\\noncalibrated_export\\m_Q_125.txt\")\n",
+    "\n",
+    "titlestr = 'Iodine and CO2 for SET 01 EXP 01'\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('2026-05-20 16:45:00'), pd.Timestamp('2026-05-20 18:00:00')])\n",
+    "\n",
+    "dark_times = [pd.Timestamp('2026-05-20 16:49:00'), pd.Timestamp('2026-05-20 17:17:00')]\n",
+    "bright_times = [pd.Timestamp('2026-05-20 17:17:00'), pd.Timestamp('2026-05-20 17:52:00')]\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_EXP01.svg\", format='svg', dpi=400)"
+   ]
+  },
+  {
+   "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
+}
diff --git a/CWFTR_Analysis/20260527_SET01_EXP03/analysis.ipynb b/CWFTR_Analysis/20260527_SET01_EXP03/analysis.ipynb
new file mode 100644
index 0000000..dca87cf
--- /dev/null
+++ b/CWFTR_Analysis/20260527_SET01_EXP03/analysis.ipynb
@@ -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": "ab643fbd",
+   "metadata": {},
+   "source": [
+    "# SET01 EXP03"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1d892704",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "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
+}
diff --git a/CWFTR_Analysis/20260529_SET01_EXP05/analysis.ipynb b/CWFTR_Analysis/20260529_SET01_EXP05/analysis.ipynb
new file mode 100644
index 0000000..f1771e4
--- /dev/null
+++ b/CWFTR_Analysis/20260529_SET01_EXP05/analysis.ipynb
@@ -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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",
+      "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
+}
diff --git a/CWFTR_Analysis/20260608_SET01_EXP07_EXP08/analysis.ipynb b/CWFTR_Analysis/20260608_SET01_EXP07_EXP08/analysis.ipynb
new file mode 100644
index 0000000..3b08e9e
--- /dev/null
+++ b/CWFTR_Analysis/20260608_SET01_EXP07_EXP08/analysis.ipynb
@@ -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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",
+      "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
+}
diff --git a/CWFTR_Analysis/20260716_SET01_EXP09/analysis.ipynb b/CWFTR_Analysis/20260716_SET01_EXP09/analysis.ipynb
new file mode 100644
index 0000000..cdfe450
--- /dev/null
+++ b/CWFTR_Analysis/20260716_SET01_EXP09/analysis.ipynb
@@ -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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",
+      "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
+}
diff --git a/OFR_Analysis/20260612_SET02_EXP03/202607023_OFR_Analysis_01.ipynb b/OFR_Analysis/20260612_SET02_EXP03/202607023_OFR_Analysis_01.ipynb
new file mode 100644
index 0000000..41022ea
--- /dev/null
+++ b/OFR_Analysis/20260612_SET02_EXP03/202607023_OFR_Analysis_01.ipynb
@@ -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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",
+      "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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",
+      "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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",
+      "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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+      "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
+}
diff --git a/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Densities.pdf b/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Densities.pdf
new file mode 100644
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diff --git a/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Flow_Rates.pdf b/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Flow_Rates.pdf
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diff --git a/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Mass_Production.pdf b/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Experiment_Timeseries_of_Mass_Production.pdf
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diff --git a/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Spectrum_Sample_33.pdf b/OFR_Analysis/20260612_SET02_EXP03/20260723_OFR_Analysis_01_Spectrum_Sample_33.pdf
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diff --git a/OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_01_Spectrum_Sample_33.pdf b/OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_01_Spectrum_Sample_33.pdf
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new file mode 100644
index 0000000..b9bd41b
--- /dev/null
+++ b/OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_01_Spectrum_Sample_33.pgf
@@ -0,0 +1,2431 @@
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diff --git a/OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_02.ipynb b/OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_02.ipynb
new file mode 100644
index 0000000..a2f500f
--- /dev/null
+++ b/OFR_Analysis/20260710_SET02_EXP01/20260710_OFR_Analysis_02.ipynb
@@ -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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",
+      "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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",
+      "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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",
+      "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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",
+      "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": []
+  }
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diff --git a/OFR_Analysis/20260710_SET02_EXP01/data.csv b/OFR_Analysis/20260710_SET02_EXP01/data.csv
new file mode 100644
index 0000000..0f74688
--- /dev/null
+++ b/OFR_Analysis/20260710_SET02_EXP01/data.csv
@@ -0,0 +1,610 @@
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diff --git a/OFR_Analysis/20260710_SET02_EXP01/data.xlsx b/OFR_Analysis/20260710_SET02_EXP01/data.xlsx
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diff --git a/OFR_Analysis/20260902_SET02_EXP13/analysis.ipynb b/OFR_Analysis/20260902_SET02_EXP13/analysis.ipynb
new file mode 100644
index 0000000..8745a31
--- /dev/null
+++ b/OFR_Analysis/20260902_SET02_EXP13/analysis.ipynb
@@ -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
+}
diff --git a/OFR_Analysis/20260902_SET02_EXP13/figure.pgf b/OFR_Analysis/20260902_SET02_EXP13/figure.pgf
new file mode 100644
index 0000000..1b7e48a
--- /dev/null
+++ b/OFR_Analysis/20260902_SET02_EXP13/figure.pgf
@@ -0,0 +1,2958 @@
+%% Creator: Matplotlib, PGF backend
+%%
+%% To include the figure in your LaTeX document, write
+%%   \input{<filename>.pgf}
+%%
+%% Make sure the required packages are loaded in your preamble
+%%   \usepackage{pgf}
+%%
+%% Also ensure that all the required font packages are loaded; for instance,
+%% the lmodern package is sometimes necessary when using math font.
+%%   \usepackage{lmodern}
+%%
+%% Figures using additional raster images can only be included by \input if
+%% they are in the same directory as the main LaTeX file. For loading figures
+%% from other directories you can use the `import` package
+%%   \usepackage{import}
+%%
+%% and then include the figures with
+%%   \import{<path to file>}{<filename>.pgf}
+%%
+%% Matplotlib used the following preamble
+%%   \def\mathdefault#1{#1}
+%%   \everymath=\expandafter{\the\everymath\displaystyle}
+%%   \IfFileExists{scrextend.sty}{
+%%     \usepackage[fontsize=8.000000pt]{scrextend}
+%%   }{
+%%     \renewcommand{\normalsize}{\fontsize{8.000000}{9.600000}\selectfont}
+%%     \normalsize
+%%   }
+%%   \usepackage{unicode-math}\usepackage{accents}\usepackage{fontspec}\usepackage[T1]{fontenc}\usepackage{unicode-math}\setmainfont{STIX Two Text}\setmathfont{STIX Two Math}\usepackage{siunitx}\usepackage{mhchem}\newcommand{\der}[1]{\ensuremath{\textnormal{d}\,{#1}}}
+%%   \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother
+%%
+\begingroup%
+\makeatletter%
+\begin{pgfpicture}%
+\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{3.464567in}{1.968504in}}%
+\pgfusepath{use as bounding box, clip}%
+\begin{pgfscope}%
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diff --git a/OFR_Analysis/20260902_SET02_EXP13/ozone_data.csv b/OFR_Analysis/20260902_SET02_EXP13/ozone_data.csv
new file mode 100644
index 0000000..8ae813b
--- /dev/null
+++ b/OFR_Analysis/20260902_SET02_EXP13/ozone_data.csv
@@ -0,0 +1,4565 @@
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diff --git a/OFR_Analysis/20260902_SET02_EXP13/smps_data.csv b/OFR_Analysis/20260902_SET02_EXP13/smps_data.csv
new file mode 100644
index 0000000..7d6461b
--- /dev/null
+++ b/OFR_Analysis/20260902_SET02_EXP13/smps_data.csv
@@ -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)
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diff --git a/OFR_Analysis/20260903_SET02_EXP14/analysis.ipynb b/OFR_Analysis/20260903_SET02_EXP14/analysis.ipynb
new file mode 100644
index 0000000..d45ab8e
--- /dev/null
+++ b/OFR_Analysis/20260903_SET02_EXP14/analysis.ipynb
@@ -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
+}
diff --git a/OFR_Analysis/20260903_SET02_EXP14/figure.pgf b/OFR_Analysis/20260903_SET02_EXP14/figure.pgf
new file mode 100644
index 0000000..3a088a9
--- /dev/null
+++ b/OFR_Analysis/20260903_SET02_EXP14/figure.pgf
@@ -0,0 +1,2943 @@
+%% Creator: Matplotlib, PGF backend
+%%
+%% To include the figure in your LaTeX document, write
+%%   \input{<filename>.pgf}
+%%
+%% Make sure the required packages are loaded in your preamble
+%%   \usepackage{pgf}
+%%
+%% Also ensure that all the required font packages are loaded; for instance,
+%% the lmodern package is sometimes necessary when using math font.
+%%   \usepackage{lmodern}
+%%
+%% Figures using additional raster images can only be included by \input if
+%% they are in the same directory as the main LaTeX file. For loading figures
+%% from other directories you can use the `import` package
+%%   \usepackage{import}
+%%
+%% and then include the figures with
+%%   \import{<path to file>}{<filename>.pgf}
+%%
+%% Matplotlib used the following preamble
+%%   \def\mathdefault#1{#1}
+%%   \everymath=\expandafter{\the\everymath\displaystyle}
+%%   \IfFileExists{scrextend.sty}{
+%%     \usepackage[fontsize=8.000000pt]{scrextend}
+%%   }{
+%%     \renewcommand{\normalsize}{\fontsize{8.000000}{9.600000}\selectfont}
+%%     \normalsize
+%%   }
+%%   \usepackage{unicode-math}\usepackage{accents}\usepackage{fontspec}\usepackage[T1]{fontenc}\usepackage{unicode-math}\setmainfont{STIX Two Text}\setmathfont{STIX Two Math}\usepackage{siunitx}\usepackage{mhchem}\newcommand{\der}[1]{\ensuremath{\textnormal{d}\,{#1}}}
+%%   \makeatletter\@ifpackageloaded{underscore}{}{\usepackage[strings]{underscore}}\makeatother
+%%
+\begingroup%
+\makeatletter%
+\begin{pgfpicture}%
+\pgfpathrectangle{\pgfpointorigin}{\pgfqpoint{3.464567in}{1.968504in}}%
+\pgfusepath{use as bounding box, clip}%
+\begin{pgfscope}%
+\pgfsetbuttcap%
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+\definecolor{currentfill}{rgb}{1.000000,1.000000,1.000000}%
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+\pgfpathclose%
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+}%
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diff --git a/OFR_Analysis/20260903_SET02_EXP14/ozone_data.csv b/OFR_Analysis/20260903_SET02_EXP14/ozone_data.csv
new file mode 100644
index 0000000..93b6563
--- /dev/null
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diff --git a/OFR_Analysis/20260903_SET02_EXP14/smps_data.csv b/OFR_Analysis/20260903_SET02_EXP14/smps_data.csv
new file mode 100644
index 0000000..00590ba
--- /dev/null
+++ b/OFR_Analysis/20260903_SET02_EXP14/smps_data.csv
@@ -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)
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diff --git a/OFR_Analysis/20260904_SET02_EXP15/analysis.ipynb b/OFR_Analysis/20260904_SET02_EXP15/analysis.ipynb
new file mode 100644
index 0000000..a73b8d8
--- /dev/null
+++ b/OFR_Analysis/20260904_SET02_EXP15/analysis.ipynb
@@ -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
+}
diff --git a/OFR_Analysis/20260904_SET02_EXP15/figure.pgf b/OFR_Analysis/20260904_SET02_EXP15/figure.pgf
new file mode 100644
index 0000000..2d857bd
--- /dev/null
+++ b/OFR_Analysis/20260904_SET02_EXP15/figure.pgf
@@ -0,0 +1,2863 @@
+%% Creator: Matplotlib, PGF backend
+%%
+%% To include the figure in your LaTeX document, write
+%%   \input{<filename>.pgf}
+%%
+%% Make sure the required packages are loaded in your preamble
+%%   \usepackage{pgf}
+%%
+%% Also ensure that all the required font packages are loaded; for instance,
+%% the lmodern package is sometimes necessary when using math font.
+%%   \usepackage{lmodern}
+%%
+%% Figures using additional raster images can only be included by \input if
+%% they are in the same directory as the main LaTeX file. For loading figures
+%% from other directories you can use the `import` package
+%%   \usepackage{import}
+%%
+%% and then include the figures with
+%%   \import{<path to file>}{<filename>.pgf}
+%%
+%% Matplotlib used the following preamble
+%%   \def\mathdefault#1{#1}
+%%   \everymath=\expandafter{\the\everymath\displaystyle}
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new file mode 100644
index 0000000..03ed963
--- /dev/null
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diff --git a/OFR_Analysis/20260904_SET02_EXP15/smps_data.csv b/OFR_Analysis/20260904_SET02_EXP15/smps_data.csv
new file mode 100644
index 0000000..dc533ef
--- /dev/null
+++ b/OFR_Analysis/20260904_SET02_EXP15/smps_data.csv
@@ -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)
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diff --git a/OFR_Analysis/20260906_SET02_EXP13-14-15/analysis.ipynb b/OFR_Analysis/20260906_SET02_EXP13-14-15/analysis.ipynb
new file mode 100644
index 0000000..b60d770
--- /dev/null
+++ b/OFR_Analysis/20260906_SET02_EXP13-14-15/analysis.ipynb
@@ -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",
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new file mode 100644
index 0000000..0cf0779
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e69de29