multi-roi notebook

notebooks/multi_roi_area_astimate.ipynb

@@ -121,7 +121,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "anual_estimates = run_area_estimates_config(config, show_output_charts=True)"
+    "annual_estimates = run_area_estimates_config(config, show_output_charts=True)"
    ]
   },
   {
@@ -133,7 +133,7 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "\n",
-    "def plot_crop_area_with_confidence(anual_estimates):\n",
+    "def plot_crop_area_with_confidence(annual_estimates):\n",
     "    \"\"\"\n",
     "    This function takes in a dictionary with crop area data and plots a bar chart \n",
     "    with 95% confidence intervals for each region and year.\n",
@@ -142,11 +142,11 @@
     "    data (dict): A dictionary where keys are region names and values contain \n",
     "                 crop area data, confidence intervals, and years.\n",
     "    \"\"\"\n",
-    "    regions = list(anual_estimates.keys())\n",
+    "    regions = list(annual_estimates.keys())\n",
     "    years = [2022, 2023]\n",
     "\n",
-    "    crop_areas = np.array([anual_estimates[region]['crop area'] for region in regions])\n",
-    "    crop_ci = np.array([anual_estimates[region]['95%CI crop'] for region in regions])\n",
+    "    crop_areas = np.array([annual_estimates[region]['crop area'] for region in regions])\n",
+    "    crop_ci = np.array([annual_estimates[region]['95%CI crop'] for region in regions])\n",
     "\n",
     "    fig, ax = plt.subplots(figsize=(10, 6))\n",
     "\n",
@@ -177,7 +177,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plot_crop_area_with_confidence(anual_estimates)"
+    "plot_crop_area_with_confidence(annual_estimates)"
    ]
   }
  ],