{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T17:54:57.930578Z",
     "iopub.status.busy": "2025-07-15T17:54:57.926902Z",
     "iopub.status.idle": "2025-07-15T17:54:57.937203Z",
     "shell.execute_reply": "2025-07-15T17:54:57.936416Z",
     "shell.execute_reply.started": "2025-07-15T17:54:57.930422Z"
    }
   },
   "source": [
    "# Visualizing Climate Change in India\n",
    "\n",
    "In this vignette, we show how to use **`varunayan`** to easily extract annual average temperature data for India and visualize the temperature changes over the period from **1941 to 2024**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Download Temperature Data for India\n",
    "\n",
    "We use `varunayan.era5ify_geojson` to download **yearly average 2m temperature data** for India, using a GeoJSON boundary file from a public URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:03:49.340292Z",
     "iopub.status.busy": "2025-07-15T18:03:49.339724Z",
     "iopub.status.idle": "2025-07-15T18:09:27.454747Z",
     "shell.execute_reply": "2025-07-15T18:09:27.454301Z",
     "shell.execute_reply.started": "2025-07-15T18:03:49.340264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\n",
      "============================================================\u001b[0m\n",
      "\u001b[0m\u001b[0;34mSTARTING ERA5 SINGLE LEVEL PROCESSING\u001b[0m\u001b[0m\n",
      "\u001b[0m============================================================\u001b[0m\n",
      "\u001b[0mRequest ID: temp_india_yearly\u001b[0m\n",
      "\u001b[0mVariables: ['2m_temperature']\u001b[0m\n",
      "\u001b[0mDate Range: 1941-01-01 to 2024-12-31\u001b[0m\n",
      "\u001b[0mFrequency: yearly\u001b[0m\n",
      "\u001b[0mResolution: 0.25°\u001b[0m\n",
      "\u001b[0mGeoJSON File: C:\\Users\\ATHARV~1\\AppData\\Local\\Temp\\temp_india_yearly_temp_geojson.json\u001b[0m\n",
      "\n",
      "\n",
      "--- GeoJSON Mini Map ---\n",
      "\n",
      "\u001b[0;34mMINI MAP (68.18°W to 97.40°E, 7.97°S to 35.49°N):\u001b[0m\n",
      "┌─────────────────────────────────────────┐\n",
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      "└─────────────────────────────────────────┘\n",
      " \u001b[0;32m■\u001b[0m = Inside the shape\n",
      " \u001b[0;31m·\u001b[0m = Outside the shape\n"
     ]
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     "text": [
      "\u001b[0m\n",
      "Saving files to output directory: temp_india_yearly_output\u001b[0m\n",
      "\u001b[0m  Saved final data to: temp_india_yearly_output\\temp_india_yearly_yearly_data.csv\u001b[0m\n",
      "\u001b[0m  Saved unique coordinates to: temp_india_yearly_output\\temp_india_yearly_unique_latlongs.csv\u001b[0m\n",
      "\u001b[0m  Saved raw data to: temp_india_yearly_output\\temp_india_yearly_raw_data.csv\u001b[0m\n",
      "\u001b[0m\n",
      "============================================================\u001b[0m\n",
      "\u001b[0m\u001b[0;32mPROCESSING COMPLETE\u001b[0m\u001b[0m\n",
      "\u001b[0m============================================================\u001b[0m\n",
      "\u001b[0m\n",
      "\u001b[0;36mRESULTS SUMMARY:\u001b[0m\u001b[0m\n",
      "\u001b[0m----------------------------------------\u001b[0m\n",
      "\u001b[0mVariables processed: 1\u001b[0m\n",
      "\u001b[0mTime period:         1941-01-01 to 2024-12-31\u001b[0m\n",
      "\u001b[0mFinal output shape:  (84, 2)\u001b[0m\n",
      "\u001b[0mTotal complete processing time: 447.03 seconds\u001b[0m\n",
      "\u001b[0m\n",
      "First 5 rows of aggregated data:\u001b[0m\n",
      "\u001b[0m          t2m  year\n",
      "0  296.386200  1941\n",
      "1  296.234344  1942\n",
      "2  295.743408  1943\n",
      "3  296.361176  1944\n",
      "4  296.111603  1945\u001b[0m\n",
      "\u001b[0m\n",
      "============================================================\u001b[0m\n",
      "\u001b[0m\u001b[0;34mERA5 SINGLE LEVEL PROCESSING COMPLETED SUCCESSFULLY\u001b[0m\u001b[0m\n",
      "\u001b[0m============================================================\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "import varunayan\n",
    "\n",
    "df = varunayan.era5ify_geojson(\n",
    "    request_id=\"temp_india_yearly\",\n",
    "    variables=[\"2m_temperature\"],\n",
    "    start_date=\"1941-1-1\",\n",
    "    end_date=\"2024-12-31\",\n",
    "    json_file=\"https://gist.githubusercontent.com/JaggeryArray/bf296307132e7d6127e28864c7bea5bf/raw/4bce03beea35d61a93007f54e52ba81f575a7feb/india.json\",\n",
    "    frequency=\"yearly\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Compute Deviation from Long-Term Mean\n",
    "\n",
    "We calculate the deviation of each year's average temperature from the long-term mean. This helps us understand how each year compares to the overall average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:09:27.455453Z",
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     "shell.execute_reply": "2025-07-15T18:09:27.461676Z",
     "shell.execute_reply.started": "2025-07-15T18:09:27.455443Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>t2m</th>\n",
       "      <th>year</th>\n",
       "      <th>dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>296.386200</td>\n",
       "      <td>1941</td>\n",
       "      <td>-0.202301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>296.234344</td>\n",
       "      <td>1942</td>\n",
       "      <td>-0.354156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>295.743408</td>\n",
       "      <td>1943</td>\n",
       "      <td>-0.845093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>296.361176</td>\n",
       "      <td>1944</td>\n",
       "      <td>-0.227325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>296.111603</td>\n",
       "      <td>1945</td>\n",
       "      <td>-0.476898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          t2m  year       dev\n",
       "0  296.386200  1941 -0.202301\n",
       "1  296.234344  1942 -0.354156\n",
       "2  295.743408  1943 -0.845093\n",
       "3  296.361176  1944 -0.227325\n",
       "4  296.111603  1945 -0.476898"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_t2m = df[\"t2m\"].mean()\n",
    "\n",
    "df[\"dev\"] = df[\"t2m\"] - mean_t2m\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Set Up Matplotlib Styling\n",
    "\n",
    "We configure Matplotlib to ensure consistent, high-resolution plots with a clean visual style. This setup improves readability, especially in documentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:11:14.127775Z",
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     "shell.execute_reply.started": "2025-07-15T18:11:14.127700Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def setup_matplotlib():\n",
    "    plt.rcParams[\"figure.dpi\"] = 300\n",
    "    plt.rcParams[\"savefig.dpi\"] = 300\n",
    "    plt.rcParams[\"font.family\"] = \"sans-serif\"\n",
    "    plt.rcParams[\"font.sans-serif\"] = [\"Arial\"]\n",
    "    plt.rcParams[\"axes.labelweight\"] = \"normal\"\n",
    "\n",
    "    plt.rcParams[\"mathtext.fontset\"] = \"custom\"\n",
    "    plt.rcParams[\"mathtext.rm\"] = \"Arial\"\n",
    "    plt.rcParams[\"mathtext.it\"] = \"Arial:italic\"\n",
    "    plt.rcParams[\"mathtext.bf\"] = \"Arial:bold\"\n",
    "\n",
    "\n",
    "setup_matplotlib()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Normalize Deviation Values\n",
    "\n",
    "We normalize the temperature deviation values so we can map them to a color scale for the bar chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:09:27.861841Z",
     "iopub.status.busy": "2025-07-15T18:09:27.861564Z",
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     "shell.execute_reply.started": "2025-07-15T18:09:27.861820Z"
    }
   },
   "outputs": [],
   "source": [
    "norm = mcolors.Normalize(vmin=df[\"dev\"].min(), vmax=df[\"dev\"].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Set Color Map\n",
    "\n",
    "We use the `coolwarm` color map to represent deviation: cool colors for below-average years and warm colors for above-average years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:09:27.867291Z",
     "iopub.status.busy": "2025-07-15T18:09:27.867075Z",
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     "shell.execute_reply": "2025-07-15T18:09:27.871002Z",
     "shell.execute_reply.started": "2025-07-15T18:09:27.867270Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = plt.colormaps[\"coolwarm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:09:27.872643Z",
     "iopub.status.busy": "2025-07-15T18:09:27.872442Z",
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     "shell.execute_reply.started": "2025-07-15T18:09:27.872621Z"
    }
   },
   "outputs": [],
   "source": [
    "colors = cmap(norm(df[\"dev\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Plot Temperature Deviation Over Time\n",
    "\n",
    "We create a color-coded bar plot of yearly temperature deviations. A colorbar is added to indicate the scale of deviation from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-15T18:11:17.332887Z",
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    {
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      "text/plain": [
       "<Figure size 4200x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14, 6))\n",
    "bars = ax.bar(df[\"year\"], df[\"dev\"], color=colors)\n",
    "\n",
    "sm = cm.ScalarMappable(cmap=cmap, norm=norm)\n",
    "sm.set_array(df[\"dev\"].values)\n",
    "\n",
    "cbar = fig.colorbar(sm, ax=ax)\n",
    "cbar.set_label(\"Deviation\")\n",
    "\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Deviation\")\n",
    "ax.set_title(\"Temperature change in India\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This plot shows the **yearly deviation in average temperature** across India from the long-term mean (1941–2024).  \n",
    "> Bars above zero indicate **warmer-than-average years**, while bars below zero indicate **cooler-than-average years**.  \n",
    "> The color gradient reinforces this — with **warm tones** for hotter years and **cool tones** for colder years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This example demonstrates how `varunayan` can be used to easily access and visualize historical climate data for a specific region — in this case, India's average temperature from 1941 to 2024.\n",
    "The visualization clearly illustrates how India’s **average annual temperature** has changed over the last **80+ years**.\n",
    "\n",
    "- There is a noticeable **upward shift** in temperature deviations over time, with recent decades showing **more frequent and intense warmer-than-average years**.\n",
    "- The use of color gradients helps visually track how the **climate signal has intensified** over time.\n",
    "\n",
    "By combining climate data extracted using `varunayan` with simple processing and visualization, we gain a compelling and accessible picture of India's changing climate — one that is both data-driven and intuitive.\n"
   ]
  }
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