1.1 - Booking Demand Density Generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Booking Demand Density Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lx5l52Vth4tl"
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   "outputs": [],
   "source": [
    "import math\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0ZCHYfvSh4tr"
   },
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nw3xanyYh4tt"
   },
   "source": [
    "### Load the preprocessed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 18792,
     "status": "ok",
     "timestamp": 1559719786470,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "TcOeYQoeh4tu",
    "outputId": "dded2fb2-5ab6-44a5-d53d-5eb632b1d8bf"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geohash6</th>\n",
       "      <th>day</th>\n",
       "      <th>demand</th>\n",
       "      <th>in_raw</th>\n",
       "      <th>weekday</th>\n",
       "      <th>hour</th>\n",
       "      <th>minutes</th>\n",
       "      <th>ds</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>qp09du</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.336609</td>\n",
       "      <td>90.829468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>qp09jm</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.419006</td>\n",
       "      <td>90.939331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>qp09sf</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.347595</td>\n",
       "      <td>90.917358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>qp09vd</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.303650</td>\n",
       "      <td>90.950317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>qp03y4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1900-01-01 00:00:00</td>\n",
       "      <td>-5.303650</td>\n",
       "      <td>90.620728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  geohash6  day    demand  in_raw  weekday  hour  minutes  \\\n",
       "0   qp09du    1  0.000000   False        1     0        0   \n",
       "1   qp09jm    1  0.000000   False        1     0        0   \n",
       "2   qp09sf    1  0.000000   False        1     0        0   \n",
       "3   qp09vd    1  0.000000   False        1     0        0   \n",
       "4   qp03y4    1  0.002732    True        1     0        0   \n",
       "\n",
       "                    ds  latitude  longitude  \n",
       "0  1900-01-01 00:00:00 -5.336609  90.829468  \n",
       "1  1900-01-01 00:00:00 -5.419006  90.939331  \n",
       "2  1900-01-01 00:00:00 -5.347595  90.917358  \n",
       "3  1900-01-01 00:00:00 -5.303650  90.950317  \n",
       "4  1900-01-01 00:00:00 -5.303650  90.620728  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('full_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yV31aoeOh4uA"
   },
   "source": [
    "### Get info on the coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1188,
     "status": "ok",
     "timestamp": 1559719806170,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "eCAjcnZdh4uB",
    "outputId": "a6a3ac69-6b3f-4f42-9fd4-a17659456d83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46 36\n"
     ]
    }
   ],
   "source": [
    "n_lat  = len(df['latitude'].unique())\n",
    "n_long = len(df['longitude'].unique())\n",
    "print(n_lat, n_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4648,
     "status": "ok",
     "timestamp": 1559719787505,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "qCRR3JPBh4uG",
    "outputId": "24e64c88-6b5c-49db-80ba-066f249e71ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.48492431640625 90.5877685546875\n"
     ]
    }
   ],
   "source": [
    "lat_min  = df['latitude'].min()\n",
    "long_min = df['longitude'].min()\n",
    "print(lat_min, long_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PYGKJsBUh4uM"
   },
   "outputs": [],
   "source": [
    "lat_sorted  = np.sort(df['latitude'].unique())\n",
    "long_sorted = np.sort(df['longitude'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V6aY76LOh4uQ"
   },
   "outputs": [],
   "source": [
    "lat_gap  = (lat_sorted[1:] - lat_sorted[:-1]).min()\n",
    "long_gap = (long_sorted[1:] - long_sorted[:-1]).min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cgoSBB3uh4uU"
   },
   "source": [
    "## Generate Demand Density Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CD4QI_UMh4uW"
   },
   "outputs": [],
   "source": [
    "images = np.zeros((4*24*61, 48, 40))\n",
    "mask = np.zeros((48, 40))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dxFJNWwSh4ub"
   },
   "outputs": [],
   "source": [
    "latitude_i  = ((df['latitude'].values  - lat_min)  / lat_gap).astype(int)\n",
    "longitude_j = ((df['longitude'].values - long_min) / long_gap).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oUhH5yYqh4up"
   },
   "outputs": [],
   "source": [
    "timesteps = 4*24*(df['day'].values-1) + 4*df['hour'].values + df['minutes'].values//15\n",
    "demands   = df['demand'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 24550,
     "status": "ok",
     "timestamp": 1559720513349,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "cs1iP8X_h4uv",
    "outputId": "d7bf3aa8-8b77-4064-a4bf-fb1beb8aa0a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 57 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for i in range(len(timesteps)):\n",
    "    t = timesteps[i]\n",
    "    d = demands[i]\n",
    "    x = latitude_i[i]\n",
    "    y = longitude_j[i]\n",
    "    images[t][x+1][y+2] = d\n",
    "    mask[x+1][y+2] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 581
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 10829,
     "status": "ok",
     "timestamp": 1559719852662,
     "user": {
      "displayName": "Franz Louis Cesista",
      "photoUrl": "https://lh3.googleusercontent.com/-NquKeLt1770/AAAAAAAAAAI/AAAAAAAAAMo/RHnx0T1cGIk/s64/photo.jpg",
      "userId": "08273419339046628946"
     },
     "user_tz": -480
    },
    "id": "0GQoExpnh4u0",
    "outputId": "6c966c81-73bc-40b7-9aab-cde5e7429f39"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (8, 8))\n",
    "sns.heatmap(images[0], vmin=0.0, vmax=1.0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qv4LLhTDh4u6"
   },
   "source": [
    "### Save images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1a-lHxVYh4u7"
   },
   "outputs": [],
   "source": [
    "np.save('density_images.npy', images)\n",
    "np.save('density_mask.npy', mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pM1ArnXkh4u_"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "2.5 Generate Booking Demand Density.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python [conda env:py36]",
   "language": "python",
   "name": "conda-env-py36-py"
  },
  "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
}