test

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+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/Raw_data/2023.01.09_PI_LowIntensity_Control.csv filter=lfs diff=lfs merge=lfs -text
+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/Raw_data/2023.01.30_PI_HighIntensity.csv filter=lfs diff=lfs merge=lfs -text
+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/Raw_data/2023.08.28_PICurve_37°C_3%CO2_Part1_3%.csv filter=lfs diff=lfs merge=lfs -text
+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/Raw_data/2023.08.28_PICurve_37°C_3%CO2_Part2_5%.csv filter=lfs diff=lfs merge=lfs -text
+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/Raw_data/2024.04.17_PI_Curve_UTEX2973_37°C_3%CO2.csv filter=lfs diff=lfs merge=lfs -text
+assays/Growth[[:space:]]in[[:space:]]Multi-Cultivator/dataset/Raw_data/2024.04.23_PI_UTEX2973_75and150_uE.csv filter=lfs diff=lfs merge=lfs -text
+assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:space:]]growth[[:space:]]light[[:space:]]and[[:space:]]CO2[[:space:]]control/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
+assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:space:]]growth[[:space:]]light[[:space:]]and[[:space:]]day[[:space:]]night[[:space:]]cycle/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
+assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]gradient[[:space:]]growth[[:space:]]light/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text
+assays/Microfluidic[[:space:]]cultivation[[:space:]]with[[:space:]]homogeneous[[:space:]]growth[[:space:]]light/dataset/.gitkeep filter=lfs diff=lfs merge=lfs -text

assays/Growth in Multi-Cultivator/README.md

+The script was used to plot and analyse the raw data from the MC 1000-OD cultivation system. Growth rates from multiple runs were then selected. Mean values and standart deviations were calculated in Origin2020 Pro. Then the data was plotted.
\ No newline at end of file

assays/Growth in Multi-Cultivator/protocols/Multi-Cultivator_Analysis.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "553eba3a-cb48-4186-b5c4-59ca3194554a",
+   "metadata": {},
+   "source": [
+    "# Multi-Cultivator MC-1000 OD Analysis Workbook\n",
+    "\n",
+    "This skript was desinged by Lennart Ole Witting to parse, plot and analyse data from the MC-1000 OD cultivation system for photoautotropic cultivation.\n",
+    "Data is exported by the software OD-View as .csv file. \n",
+    "First .csv file is read and subsequntly parsed. The parsed data is then plotted and finally growth rate and doubling time are calculated.\n",
+    "\n",
+    "Find more information about the cultivation system at: https://photo-bio-reactors.com/products/multi-cultivators/#info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "895aa22a-e480-48dd-bad5-719453a7c3de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Read raw data from csv\n",
+    "\n",
+    "df = pd.read_csv('Example.csv', sep = ',', encoding = 'utf8', header = 0, index_col=False, skiprows= 4)\n",
+    "\n",
+    "# Separate OD720 from OD680\n",
+    "\n",
+    "df_od720 = df.iloc[:,[3,5,7,9,11,13,15,17]]\n",
+    "df_od720.columns = [1,2,3,4,5,6,7,8]\n",
+    "\n",
+    "df_od680 = df.iloc[:,[2,4,6,8,10,12,14,16]]\n",
+    "df_od680.columns = [1,2,3,4,5,6,7,8]\n",
+    "\n",
+    "df_time = df.iloc[:,0]/60/60 # Convert time from sec to h\n",
+    "\n",
+    "df_temp = df.iloc[:,1] # Grab temperature\n",
+    "\n",
+    "Tubes = ['Tube 1', 'Tube 2', 'Tube 3', 'Tube 4', 'Tube 5', 'Tube 6', 'Tube 7', 'Tube 8'] # Specify names of tubes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89d20c1f-1ea0-4e71-9191-95ce4228ec09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Check which tubes are in use\n",
+    "    \n",
+    "last_od720 = df_od720.iloc[len(df_od720)-1,:] # extract last line from last_od720\n",
+    "\n",
+    "status = []\n",
+    "\n",
+    "for od in last_od720:\n",
+    "    if od == 'Overflow':\n",
+    "        status.append(0)\n",
+    "    elif od > 0.005:\n",
+    "        status.append(1)\n",
+    "    else:\n",
+    "        status.append(0) \n",
+    "        \n",
+    "# status = [1, 1, 1, 1, 1, 1, 1, 1] # In case status in altered manually\n",
+    "        \n",
+    "print(status) # 1 means tube is active, 0 means tube is inactive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5c99c70-2eb9-48de-917c-48e82365f428",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Plot the results\n",
+    "\n",
+    "corperate_idendity = ['#023d6b', '#adbde3', '#faeb5a', '#eb5f73', '#b9d25f', '#af82b9', '#fab45a', '#ebebeb'] # Fz Juelich corperate identity\n",
+    "\n",
+    "fig, ax = plt.subplots(1,3, figsize=(12, 4),facecolor='white')\n",
+    "fig.tight_layout(pad = 2)\n",
+    "\n",
+    "for n in range(0,8):\n",
+    "    if status[n] == 1:\n",
+    "        ax[0].plot(df_time,df_od720[n+1], label = Tubes[n] ,color=corperate_idendity[n])\n",
+    "        ax[1].plot(df_time,df_od680[n+1], color=corperate_idendity[n])\n",
+    "\n",
+    "ax[2].plot(df_time, df_temp, label='Temperature [°C]', color='k')\n",
+    "    \n",
+    "ax[0].set_xlabel(f'Time [h]')\n",
+    "ax[1].set_xlabel(f'Time [h]')\n",
+    "ax[2].set_xlabel(f'Time [h]')\n",
+    "\n",
+    "ax[0].set_xlim(0, df_time.iat[-1])\n",
+    "ax[1].set_xlim(0, df_time.iat[-1])\n",
+    "ax[2].set_xlim(0, df_time.iat[-1])\n",
+    "\n",
+    "# find max value in dataframe\n",
+    "ax[0].set_ylim(0, )\n",
+    "ax[1].set_ylim(0, )\n",
+    "ax[2].set_ylim(0, 45)\n",
+    "\n",
+    "ax[0].set_ylabel('OD 720 [-]')\n",
+    "ax[1].set_ylabel('OD 680 [-]')\n",
+    "ax[2].set_ylabel('Temperature [°C]')\n",
+    "\n",
+    "ax[0].set_title('OD$_{720}$')\n",
+    "ax[1].set_title('OD$_{680}$')\n",
+    "ax[2].set_title('Temperature')\n",
+    "\n",
+    "# ax[0].set_yscale('log')\n",
+    "\n",
+    "plt.figlegend(loc='lower center', bbox_to_anchor=(0.5, -0.12), ncol=9)\n",
+    "\n",
+    "plt.savefig('MC-1000_Plot.png', bbox_inches='tight', transparent=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b99f7f9-c268-4efc-bf4e-1918f8f6155f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "dfs_fitting = []\n",
+    "counter = 0\n",
+    "\n",
+    "for n in range(0,8): # Select range within linear measurment for each tube\n",
+    "    if status[n] == 1:\n",
+    "        od720 = df_od720[n+1]\n",
+    "        od720_cutoff = od720[(od720 < 0.4) & (od720 > 0.05)]\n",
+    "        df = pd.concat([df_time, od720_cutoff], axis=1, join='inner')\n",
+    "        df.columns = ['time[s]','OD']\n",
+    "        dfs_fitting.append(df)\n",
+    "        counter = counter + 1\n",
+    "\n",
+    "m = []\n",
+    "b = []\n",
+    "doubling_time = []\n",
+    "Tubes_cut = []\n",
+    "\n",
+    "for n in range(0, counter): # fit a linear model to  N=m*t+b\n",
+    "    df = dfs_fitting[n]\n",
+    "    m_current, b_current = np.polyfit(df['time[s]'], np.log(df['OD']), 1)\n",
+    "    doubling_time_current = np.log(2) / m_current\n",
+    "    m.append(m_current)\n",
+    "    b.append(b_current)\n",
+    "    doubling_time.append(doubling_time_current)\n",
+    "    Tubes_cut.append(Tubes[n])\n",
+    "\n",
+    "df_Results = pd.DataFrame({'Tube': Tubes_cut,\n",
+    "                           'Growth rate [1/h]': m,\n",
+    "                           'Doubling Time [h]': doubling_time}).round(4)\n",
+    "\n",
+    "print(df_Results) # Finally print results\n",
+    "df_Results.to_csv(str('Results.csv'),  sep=';', decimal=',')  # Export results to .csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a45dcaf-7a3b-428a-9d7a-7da40a37f34f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Optional: Create PI_Curve\n",
+    "\n",
+    "fig, ax1 = plt.subplots(facecolor='white')\n",
+    "Intensities = pd.DataFrame({'Intensity [µE/(m$^2$$\\cdot$s)]': [20, 75, 450, 600, 750, 900]})\n",
+    "\n",
+    "ax1.plot(Intensities['Intensity [µE/(m$^2$$\\cdot$s)]'], df_Results['Growth rate [1/h]'], lw = 0, marker = 'x')\n",
+    "\n",
+    "ax1.set_ylabel('Growth rate 1/h')\n",
+    "ax1.set_xlabel('Intensity [µE/(m$^2$$\\cdot$s)]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbd22207-9dda-4eb0-9bb1-3493ec6360a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd5bc356-794d-4f59-b20b-eae54f017e64",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}