supp fig 3

Author: katarinabraun

data_raw/.ipynb_checkpoints/supplementary_figure_3-checkpoint.ipynb

@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A little python script to plot collection date of each sample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary packages\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "from matplotlib.dates import DateFormatter\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "# import earthpy as et\n",
+    "\n",
+    "# Handle date time conversions between pandas and matplotlib\n",
+    "from pandas.plotting import register_matplotlib_converters\n",
+    "register_matplotlib_converters()\n",
+    "\n",
+    "# Use white grid plot background from seaborn\n",
+    "sns.set(font_scale=1.5, style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#load data and drop all but sample name + collection date\n",
+    "DF = pd.read_csv('../builds/Wisco-Build/data/attributes.csv')\n",
+    "DF = DF[['name', 'Collection Data']]\n",
+    "DF = DF.rename(columns={'Collection Data': 'date'})\n",
+    "DF['name'] = 1\n",
+    "DF.index = DF['date']\n",
+    "DF['Date'] = pd.to_datetime(DF.date)\n",
+    "DF = DF[['name', 'Date']]\n",
+    "DF.sort_values(by='Date', inplace=True)\n",
+    "DF['datetime'] = pd.to_datetime(DF['Date'])\n",
+    "DF.index = DF['datetime']\n",
+    "\n",
+    "DF1 = DF.resample('W').sum()\n",
+    "\n",
+    "DF1 = DF1.reset_index()\n",
+    "\n",
+    "DF1\n",
+    "\n",
+    "# # Create figure and plot space\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "\n",
+    "# # Add x-axis and y-axis\n",
+    "ax.scatter(DF1.datetime.values, DF1.name.values, color='purple')\n",
+    "\n",
+    "# Set title and labels for axes\n",
+    "ax.set(xlabel=\"\\n Date\",\n",
+    "       ylabel=\"sample count \\n\")\n",
+    "\n",
+    "# plt.show()\n",
+    "\n",
+    "plt.savefig('../figures/supplementaryfigure3.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}