Plot Spatial Daylight Autonomy

PlotSpatialDaylightAutonomy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ef9eabb-423d-44bf-aa9b-6f42483ae113",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "68a576a0-ff5a-4e96-9bfe-7502053ea965",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab862a41-60ed-4dcd-9212-4116c10c0e92",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpath = \"Developer/frads/test/Results/grid_two_phase_floor.txt\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507b9549-5701-425a-b07b-a88ff67da7ca",
   "metadata": {},
   "source": [
    "### Read the input file into a pandas dataframe\n",
    "Input file format: tab-separated\n",
    "\n",
    "        x1,y1,z1 x2,y2,z2 x3,y3,z3\n",
    "    0101_0730 v1 v2 v3\n",
    "    0101_0830 v4 v5 v6\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c01ca6f1-8f7d-433f-8652-a20cf735cd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(fpath, sep=\"\\t\", index_col=0, parse_dates=True, date_parser=lambda x: datetime.datetime.strptime(x, \"%m%d_%H%M\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adbfd9d1-0be0-4c32-87a6-ae38bfd0dfe8",
   "metadata": {},
   "source": [
    "#### Calculating fractions of daylit hours the sensor is >= 300 lux. Assuming \"daylight_hours_only\" is turned on when running mrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "40b986d6-41da-400e-a386-c02ee869db13",
   "metadata": {},
   "outputs": [],
   "source": [
    "fractions = df[df>=300].count()/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72df6bce-254a-4463-98e7-54e481b5cb2d",
   "metadata": {},
   "source": [
    "#### Alternatively, you can calculate the fraction of the \"occupied\" 10 hours, say between 08:00 and 18:00, which is divided by 3650 (10 hours/day * 365 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "f030955b-6645-465c-b363-55ad06587e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "fractions = df.between_time(\"08:00\", \"18:00\")[df>=300].count() / 3650"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af5ac2c6-347a-431c-a6aa-c77e5c48e6e4",
   "metadata": {},
   "source": [
    "#### Here we generate a new dataframe with the column names (sensor positions) as new columns\n",
    "    e.g.:\n",
    "    \n",
    "    x1 y1 z1\n",
    "    x2 y2 z2\n",
    "    ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "decd0f69-8077-48d3-ad78-c794fd8a2073",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_grid = pd.DataFrame(fractions.index.format(formatter=lambda x: map(float, x.split(\",\"))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43fd3486-79de-4c63-93e8-86fc11294a0b",
   "metadata": {},
   "source": [
    "#### We then add the fractions values in as the fourth columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "43d20b2f-db6b-4bf8-904d-7c025c919717",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_grid[3] = fractions.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b923a268-f1fa-4f42-8360-922a7ffda735",
   "metadata": {},
   "source": [
    "#### Here we make a mesh grid based on the x and y position of the sensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "1ce6e352-e3d0-4519-a835-fe5f94f1b965",
   "metadata": {},
   "outputs": [],
   "source": [
    "xpos = df_with_grid.iloc[:, 0].unique()\n",
    "ypos = df_with_grid.iloc[:, 1].unique()\n",
    "x, y = np.meshgrid(ypos, xpos)\n",
    "z = df_with_grid.iloc[:, 3].values.reshape(len(xpos), len(ypos))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4bd321-3cd2-49d9-b0d9-0ad473ba7781",
   "metadata": {},
   "source": [
    "### Now we are ready to plot our fraction values\n",
    "#### 1. We first make a contour plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "560fedf7-73b4-4167-979d-cdba38985eab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x414 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We create a plot with the same size ratio as our data so \n",
    "fig, axes = plt.subplots(figsize=(5,5.75))\n",
    "cs = axes.contourf(y, x, z, cmap='turbo', levels=[i/10 for i in range(11)])\n",
    "csbar = plt.colorbar(cs)\n",
    "axes.spines[:].set_visible(False)\n",
    "axes.get_xaxis().set_visible(False)\n",
    "axes.get_yaxis().set_visible(False)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83b676a-5cad-46b5-b5cd-a0750715acf7",
   "metadata": {},
   "source": [
    "#### 2. Next we make a grid with 50% contour line mapped to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "fac3736e-b7ed-414c-96a0-ab07a54a8bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x414 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(5, 5.75))\n",
    "# Use the same x,y mesh grid we created earlier\n",
    "im = axes.pcolormesh(y, x, z, cmap=\"turbo\", shading=\"auto\")\n",
    "\n",
    "# Mapping the 50% contour line over the plot\n",
    "axes.contour(cs, colors=\"w\", linewidths=[0,3,0], levels=[0, .5, 1])\n",
    "\n",
    "# Adding the legend and turn off axis visibility\n",
    "cbar1 = plt.colorbar(im)\n",
    "axes.spines[:].set_visible(False)\n",
    "axes.get_xaxis().set_visible(False)\n",
    "axes.get_yaxis().set_visible(False)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8071f122-5eb8-4041-be04-9ba929cdfbe6",
   "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": "3.8.6rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}