EDA_Sevir_&_Storm_Events.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "informational-surrey",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fleet-compatibility",
   "metadata": {},
   "source": [
    "Import the required modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "great-angola",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'BlobServiceClient' from 'azure.storage.blob' (/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/azure/storage/blob/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-f3cefe4ef0ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0msnowflake\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msnowflake\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpandas_tools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwrite_pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/snowflake/connector/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mlogging\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNullHandler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSnowflakeConnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDictCursor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m from .dbapi import (\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/snowflake/connector/connection.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     46\u001b[0m )\n\u001b[1;32m     47\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSnowflakeConverter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcursor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLOG_MAX_QUERY_LENGTH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSnowflakeCursor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdescription\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCLIENT_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCLIENT_VERSION\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPLATFORM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPYTHON_VERSION\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSNOWFLAKE_CONNECTOR_VERSION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m from .errorcode import (\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/snowflake/connector/cursor.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m )\n\u001b[1;32m     29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDatabaseError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mInterfaceError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotSupportedError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mProgrammingError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfile_transfer_agent\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSnowflakeFileTransferAgent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mjson_result\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDictJsonResult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mJsonResult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqlstate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSQLSTATE_FEATURE_NOT_SUPPORTED\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/snowflake/connector/file_transfer_agent.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbotocore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mazure_util\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSnowflakeAzureUtil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGET_CWD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIS_WINDOWS\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconstants\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSHA256_DIGEST\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mResultStatus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/snowflake/connector/azure_util.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mazure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHttpResponseError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mResourceNotFoundError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mazure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblob\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBlobServiceClient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mContentSettings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExponentialRetry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconstants\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTTP_HEADER_VALUE_OCTET_STREAM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSHA256_DIGEST\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFileHeader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mResultStatus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'BlobServiceClient' from 'azure.storage.blob' (/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/azure/storage/blob/__init__.py)"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import h5py \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import snowflake.connector\n",
    "from snowflake.connector.pandas_tools import write_pandas\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "weighted-native",
   "metadata": {},
   "source": [
    "Set the local path directory for all downloaded data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "positive-railway",
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR='../data/'\n",
    "SEVIR_DIR=DATA_DIR + 'sevir/'\n",
    "STORM_EVENT_DIR = DATA_DIR + 'storm_event/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-minute",
   "metadata": {},
   "source": [
    "a. Read and clean the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "blessed-participant",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(76004, 21)\n"
     ]
    }
   ],
   "source": [
    "# Read catalog csv and saved in dataframe\n",
    "\n",
    "event_catalog_df = pd.read_csv(SEVIR_DIR + 'CATALOG.csv', low_memory=False)\n",
    "print(event_catalog_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "subjective-screening",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename dataframe columns name\n",
    "\n",
    "event_catalog_df.columns = [x.upper() for x in event_catalog_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "closed-turning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13341, 21)\n"
     ]
    }
   ],
   "source": [
    "# Drop NaN values\n",
    "\n",
    "event_catalog_df = event_catalog_df.dropna()\n",
    "print(event_catalog_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fifty-attitude",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metadata processed and changed into a dataframe\n"
     ]
    }
   ],
   "source": [
    "# Change the primary column datatype\n",
    "\n",
    "event_catalog_df['EVENT_ID'] = event_catalog_df['EVENT_ID'].astype(int)\n",
    "print(\"Metadata processed and changed into a dataframe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "religious-dialogue",
   "metadata": {},
   "source": [
    "b. Read and plot the data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aquatic-drove",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dirs = [x[0] for x in os.walk(DATA_DIR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "familiar-federation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a define a dataframe for data files. Also creating a new column img_type\n",
    "\n",
    "event_image_df = pd.DataFrame(columns = ['event_id', 'img_type', 'image'])\n",
    "img_types = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "computational-murray",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vil ['../data/sevir/vil/SEVIR_VIL_STORMEVENTS_2017_0101_0630.h5', '../data/sevir/vil/SEVIR_VIL_STORMEVENTS_2019_0701_1231.h5', '../data/sevir/vil/SEVIR_VIL_STORMEVENTS_2017_0701_1231.h5']\n",
      "vis ['../data/sevir/vis/SEVIR_VIS_STORMEVENTS_2018_0101_0131.h5', '../data/sevir/vis/SEVIR_VIS_STORMEVENTS_2019_0101_0131.h5', '../data/sevir/vis/SEVIR_VIS_STORMEVENTS_2019_0201_0228.h5', '../data/sevir/vis/SEVIR_VIS_STORMEVENTS_2018_1201_1231.h5']\n",
      "ir107 ['../data/sevir/ir107/SEVIR_IR107_STORMEVENTS_2018_0101_0630.h5', '../data/sevir/ir107/SEVIR_IR107_STORMEVENTS_2019_0101_0630.h5', '../data/sevir/ir107/SEVIR_IR107_RANDOMEVENTS_2019_0101_0430.h5', '../data/sevir/ir107/SEVIR_IR107_RANDOMEVENTS_2018_0101_0430.h5']\n",
      "ir069 ['../data/sevir/ir069/SEVIR_IR069_STORMEVENTS_2018_0101_0630.h5', '../data/sevir/ir069/SEVIR_IR069_STORMEVENTS_2019_0101_0630.h5', '../data/sevir/ir069/SEVIR_IR069_RANDOMEVENTS_2019_0101_0430.h5', '../data/sevir/ir069/SEVIR_IR069_RANDOMEVENTS_2018_0101_0430.h5']\n"
     ]
    }
   ],
   "source": [
    "# Read and store the data files into a dataframe\n",
    "\n",
    "for d in data_dirs:\n",
    "    img_type = d[d.rindex(\"/\") + 1:]\n",
    "    filenames = glob.glob(d + \"/*.h5\")\n",
    "    if not filenames:\n",
    "        continue\n",
    "\n",
    "    img_types.append(img_type)\n",
    "    print(img_type, filenames)\n",
    "    \n",
    "    for file in filenames:\n",
    "        hf = h5py.File(file, 'r')\n",
    "        for idx in range(hf['id'].shape[0]):\n",
    "            event_id = float(hf['id'][idx].decode('UTF-8')[1:])\n",
    "            event_image_df = event_image_df.append({'EVENT_ID' : event_id, 'img_type' : img_type, 'image' : hf[img_type][idx]}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "attached-stylus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7ea89c5550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Changing dataframe row into a 1D and plot a row of ir069 data images through Matplotlib\n",
    "\n",
    "arr = event_image_df.iloc[0]['image']\n",
    "\n",
    "#arr = arr.ravel()\n",
    "#arr = arr.reshape(192, 192, 49)\n",
    "\n",
    "fig,axs=plt.subplots(1,4,figsize=(10,5))\n",
    "axs[0].imshow(arr[:,:,10])\n",
    "axs[1].imshow(arr[:,:,20])\n",
    "axs[2].imshow(arr[:,:,30])\n",
    "axs[3].imshow(arr[:,:,40])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tender-brother",
   "metadata": {},
   "source": [
    "c. Read and clean the storm events data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "peaceful-italic",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unzip all storm events files and store in a data frame\n",
    "\n",
    "column_set = set()\n",
    "df_list = []\n",
    "for f in glob.glob(STORM_EVENT_DIR + '*.csv.gz'):\n",
    "    df = pd.read_csv(f, compression='gzip', error_bad_lines=False, low_memory=False)\n",
    "    if len(column_set) == 0:\n",
    "        column_set.update(set(df.columns))\n",
    "        df_list.append(df)\n",
    "    elif column_set == set(df.columns):\n",
    "        df_list.append(df)\n",
    "\n",
    "storm_events_df = pd.concat(df_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bottom-filename",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop index from the dataframe\n",
    "\n",
    "storm_events_df.drop(storm_events_df.columns[0], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fuzzy-airfare",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename dataframe column name\n",
    "\n",
    "storm_events_df.columns = [x.upper() for x in storm_events_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "chicken-district",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1602920, 50)\n"
     ]
    }
   ],
   "source": [
    "# Reset dataframe index\n",
    "\n",
    "storm_events_df.reset_index(drop=True, inplace=True)\n",
    "print(storm_events_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "executed-choice",
   "metadata": {},
   "source": [
    "d. Join the sevir data frame and catalog data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "daily-shannon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15229, 24)\n"
     ]
    }
   ],
   "source": [
    "# Join both the dataframe on Event_Id\n",
    "\n",
    "data_df = pd.merge(event_catalog_df, event_image_df,  how='inner', on=['EVENT_ID'])\n",
    "print(data_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incoming-photograph",
   "metadata": {},
   "source": [
    "e. Now, join the new data frame with storm events data frmae to get all weather information together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "independent-procurement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15229, 73)\n"
     ]
    }
   ],
   "source": [
    "joined_data_df = pd.merge(data_df, storm_events_df,  how='inner', on=['EVENT_ID'])\n",
    "print(joined_data_df.shape)"
   ]
  }
 ],
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