Weather-Project/Project Final/Transformation/Data_Transformation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting a list of files in raw data folder\n",
    "filenames = os.listdir('./processed_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "header_wanted = [\n",
    " 'HOURLYVISIBILITY',\n",
    " 'HOURLYDRYBULBTEMPC',\n",
    " 'HOURLYWETBULBTEMPC',\n",
    " 'HOURLYDewPointTempC',\n",
    " 'HOURLYRelativeHumidity',\n",
    " 'HOURLYWindSpeed',\n",
    " #'HOURLYWindGustSpeed',\n",
    " 'HOURLYStationPressure',\n",
    " #'HOURLYPressureTendency',\n",
    " #'HOURLYPressureChange',\n",
    " #'HOURLYSeaLevelPressure',\n",
    " #'HOURLYPrecip',\n",
    " #'HOURLYAltimeterSetting'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "usecols = ['DATE','STATION'] + header_wanted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 372/372 [00:34<00:00, 10.78it/s]\n"
     ]
    }
   ],
   "source": [
    "#Loading all files into a pandas Dataframe\n",
    "tqdm.pandas()\n",
    "df = pd.concat([pd.read_csv('./processed_data/{}'.format(x), usecols=usecols, low_memory=False) for x in tqdm(filenames)])\n",
    "df[header_wanted] = (df[header_wanted] - df[header_wanted].min()) / (df[header_wanted].max() - df[header_wanted].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point all the data has been loaded into a single dataframe and any data changes have been made. The next step is to break the data up by WBAN and place in a 2D array at the appropriate grid cell. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations = pd.read_csv(\"./stations_mask.csv\", usecols = ['STATION_ID', 'LON_SCALED', 'LAT_SCALED'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "height = 20\n",
    "width = 40\n",
    "depth = 24 * 365 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = [([0] * width) for i in range(height)]\n",
    "\n",
    "wban_loc = dict(zip(stations.STATION_ID,zip(stations.LON_SCALED,stations.LAT_SCALED)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = [([pd.DataFrame()] * width) for i in range(height)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 372/372 [03:56<00:00,  1.57it/s]\n"
     ]
    }
   ],
   "source": [
    "for key, value in tqdm(wban_loc.items()):\n",
    "    mask[value[1]][value[0]] = 1\n",
    "    grid[value[1]][value[0]] = df.loc[df.STATION == key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16d8d6de978>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16dc639f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_frames(data,height, width, depth):\n",
    "    days = []\n",
    "    frames = []\n",
    "    for i in tqdm(range(depth)):\n",
    "        frame = np.zeros((height,width,7))\n",
    "        frame[:,:,:] = np.nan\n",
    "        for y in range(height):\n",
    "            for x in range(width):\n",
    "                if(not data[y][x].empty):\n",
    "                    frame[y][x] = data[y][x].iloc[[i],1:8].values.flatten()\n",
    "        if((i+1)%24 != 0):\n",
    "            frames.append(frame)\n",
    "        else:\n",
    "            frames.append(frame)\n",
    "            days.append(frames)\n",
    "            frames = []\n",
    "    return np.array(days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average_grid(mask,data, height, width):\n",
    "    temp_frame = [([0] * width) for i in range(height)]\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(mask[i][j] != 1):\n",
    "                neighbors = get_neighbors(j,i,data)\n",
    "                temp_frame[i][j] = np.nanmean(neighbors, axis=0)\n",
    "    merge_frames(temp_frame, mask, data, height, width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_neighbors(x,y,g):\n",
    "    neighbors = []\n",
    "    for i in [y-1,y,y+1]:\n",
    "        for j in [x-1,x,x+1]:\n",
    "            if(i >= 0 and j >= 0):\n",
    "                if(i != y or j != x ):\n",
    "                    try:\n",
    "                        neighbors.append(g[i][j])\n",
    "                    except:\n",
    "                        pass\n",
    "    return np.array(neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_frames(average, mask, data, height, width):\n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            if(mask[i][j] != 1):\n",
    "                data[i][j] = average[i][j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def store_sequence(frames):\n",
    "    import hickle as hkl\n",
    "    train_frames = []\n",
    "    train_sources = []\n",
    "    validation_frames = []\n",
    "    validation_sources = []\n",
    "    test_frames = []\n",
    "    test_sources = []\n",
    "    \n",
    "    for day in range(len(frames)):\n",
    "        for hour in range(len(frames[day])):\n",
    "            if(day/len(frames) < .6):\n",
    "                train_sources.append(\"{}\".format(day))\n",
    "                train_frames.append(frames[day][hour])\n",
    "            elif(day/len(frames) >= .6 and day/len(frames) < .8):\n",
    "                validation_sources.append(\"{}\".format(day))\n",
    "                validation_frames.append(frames[day][hour])\n",
    "            else:\n",
    "                test_sources.append(\"{}\".format(day))\n",
    "                test_frames.append(frames[day][hour])\n",
    "    \n",
    "    print(\"There are {} frames and {} sources in training set\".format(len(train_frames), len(train_sources)))\n",
    "    print(\"There are {} frames and {} sources in validation set\".format(len(validation_frames), len(validation_sources)))\n",
    "    print(\"There are {} frames and {} sources in testing set\".format(len(test_frames), len(test_sources)))\n",
    "    \n",
    "    hkl.dump(np.array(train_frames), '../data/x_train.hkl')\n",
    "    hkl.dump(train_sources, '../data/sources_train.hkl')\n",
    "    hkl.dump(np.array(validation_frames), '../data/x_val.hkl')\n",
    "    hkl.dump(validation_sources, '../data/sources_val.hkl')\n",
    "    hkl.dump(np.array(test_frames), '../data/x_test.hkl')\n",
    "    hkl.dump(test_sources, '../data/sources_test.hkl')\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splits is a dictionary holding train, test, val\n",
    "the values for train, test, and val are lists of tuples holding category and folder name\n",
    "in the end each image gets a source associated with it\n",
    "there is only one data and one source hickle dump for each of train, test, and val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 17520/17520 [1:12:34<00:00,  4.02it/s]\n"
     ]
    }
   ],
   "source": [
    "frames = create_frames(grid, height, width,depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16de006bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,10))\n",
    "columns = 3\n",
    "rows = 4\n",
    "for i in range(1,columns+rows +1):\n",
    "    fig.add_subplot(rows,columns,i)\n",
    "    plt.imshow(frames[0,0,:,:,i-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                          | 0/730 [00:00<?, ?it/s]c:\\users\\dasputer\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:7: RuntimeWarning: Mean of empty slice\n",
      "  import sys\n",
      "100%|████████████████████████████████████████████████████████████████████████████████| 730/730 [12:59<00:00,  1.07s/it]\n"
     ]
    }
   ],
   "source": [
    "for x in tqdm(range(len(frames))):\n",
    "    for y in range(len(frames[0])):\n",
    "        average_grid(mask, frames[x][y], height, width )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16d8d853cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(15,10))\n",
    "columns = 3\n",
    "rows = 4\n",
    "for i in range(1,columns+rows +1):\n",
    "    fig.add_subplot(rows,columns,i)\n",
    "    plt.imshow(frames[0,0,:,:,i-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730, 24, 20, 40, 7)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 10512 frames and 10512 sources in training set\n",
      "There are 3504 frames and 3504 sources in validation set\n",
      "There are 3504 frames and 3504 sources in testing set\n"
     ]
    }
   ],
   "source": [
    "store_sequence(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point the data has been processed and made into discrete frames and it is time to run it through the PredNet architecture for training."
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}