Merge pull request #25 from VisheshSaluja/Model_Training

added code- Prediction File

book/chapters/Predictions.ipynb

@@ -18,6 +18,80 @@
         "Finally, we are at the final Chapter where we see the end-product of the model created. As we venture into this critical phase, the model_predict script emerges, guiding the way toward understanding and anticipating the future of snow water equivalent (SWE) through the ExtraTree model. This chapter delves into the intricacies of this script, unraveling the processes that transforms raw, unprocessed data into precise predictions that illuminate the path forward."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "import pandas as pd\n",
+        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import random\n",
+        "import string\n",
+        "import shutil\n",
+        "\n",
+        "\n",
+        "def generate_random_string(length):\n",
+        "    # Define the characters that can be used in the random string\n",
+        "    characters = string.ascii_letters + string.digits  # You can customize this to include other characters if needed\n",
+        "\n",
+        "    # Generate a random string of the specified length\n",
+        "    random_string = ''.join(random.choice(characters) for _ in range(length))\n",
+        "\n",
+        "    return random_string\n",
+        "  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "homedir = os.path.expanduser('~')\n",
+        "work_dir = f\"{homedir}/gridmet_test_run\"\n",
+        "\n",
+        "test_start_date = ''\n",
+        "\n",
+        "selected_columns = [\n",
+        "  'swe_value',\n",
+        "  'SWE',\n",
+        "  'cumulative_SWE',\n",
+        "#   'cumulative_relative_humidity_rmin',\n",
+        "#   'cumulative_air_temperature_tmmx', \n",
+        "#   'cumulative_air_temperature_tmmn',\n",
+        "#   'cumulative_relative_humidity_rmax',\n",
+        "#   'cumulative_potential_evapotranspiration',\n",
+        "#   'cumulative_wind_speed',\n",
+        "  #'cumulative_fsca',\n",
+        "  'fsca',\n",
+        "  'air_temperature_tmmx', \n",
+        "  'air_temperature_tmmn', \n",
+        "  'potential_evapotranspiration', \n",
+        "  'relative_humidity_rmax', \n",
+        "  'Elevation',\t\n",
+        "  'Slope',\t\n",
+        "  'Curvature',\t\n",
+        "  'Aspect',\t\n",
+        "  'Eastness',\t\n",
+        "  'Northness',\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def month_to_season(month):\n",
+        "    if 3 <= month <= 5:\n",
+        "        return 1\n",
+        "    elif 6 <= month <= 8:\n",
+        "        return 2\n",
+        "    elif 9 <= month <= 11:\n",
+        "        return 3\n",
+        "    else:\n",
+        "        return 4"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -43,7 +117,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 35,
       "metadata": {
         "id": "LkzaZTCtgJ5R"
       },
@@ -71,7 +145,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 36,
       "metadata": {
         "id": "wXQ-4JBdgjdG"
       },
@@ -125,7 +199,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 37,
       "metadata": {
         "id": "d7EsL7aVhltv"
       },
@@ -157,7 +231,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 38,
       "metadata": {
         "id": "Uv3nAmw-h3EO"
       },
@@ -202,7 +276,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 39,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -279,7 +353,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 40,
       "metadata": {
         "id": "aa_il7YriZlE"
       },
@@ -339,6 +413,13 @@
         "## 9.5 Results"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![SWE MAP](../img/SWE_Map.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -384,7 +465,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 42,
       "metadata": {
         "id": "qMeBEobUoJcW"
       },
@@ -435,7 +516,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 43,
       "metadata": {
         "id": "7h9706LXVba2"
       },
@@ -589,15 +670,6 @@
         "\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "JHTfd_L9UkBt"
-      },
-      "source": [
-        "![SWE MAP](../img/SWE_Map.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {

book/chapters/ghcnd.ipynb

@@ -448,6 +448,14 @@
     "#     result_df.to_csv(csv_file, index=False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Active Stations <br /> \n",
+    "![](../img/ghcnd/Stations.png)\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,