corrected notebook issue

tobac-project · Oct 12, 2024 · 65b0b1b · 65b0b1b
1 parent bca9cf0
commit 65b0b1b
Showing 1 changed file with 36 additions and 50 deletions.
diff --git a/doc/bulk_statistics/notebooks/compute_statistics_postprocessing_example.ipynb b/doc/bulk_statistics/notebooks/compute_statistics_postprocessing_example.ipynb
@@ -33,9 +33,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/glade/u/home/kukulies/tobac/tobac/utils/decorators.py:498: UserWarning: Numba not able to be imported; periodic boundary calculations will be slower.Exception raised: ModuleNotFoundError(\"No module named 'numba'\")\n",
+      "  warnings.warn(\n",
+      "/glade/u/home/kukulies/tobac/tobac/utils/decorators.py:498: UserWarning: Numba not able to be imported; periodic boundary calculations will be slower.Exception raised: ModuleNotFoundError(\"No module named 'numba'\")\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -52,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,19 +98,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "start downloading data\n",
-      "start extracting data\n",
-      "data extracted\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_out=Path('../')\n",
     "# Download the data: This only has to be done once for all tobac examples and can take a while\n",
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -159,13 +159,6 @@
       "feature detection done\n",
       "features saved\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "features saved\n"
-     ]
     }
    ],
    "source": [
@@ -186,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -212,13 +205,6 @@
       "segmentation based on surface precipitation performed, start saving results to files\n",
       "segmentation surface precipitation performed and saved\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "segmentation surface precipitation performed and saved\n"
-     ]
     }
    ],
    "source": [
@@ -242,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,11 +279,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "features_with_stats = get_statistics_from_mask(features, Mask_Precip, precip, statistic=statistics)"
+    "features_with_stats = get_statistics_from_mask(Features, Mask_Precip, Precip, statistic=statistics)"
    ]
   },
   {
@@ -309,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +309,7 @@
        "Name: mean_precip, dtype: object"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,21 +320,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0    16.296951\n",
+       "0    16.296949\n",
        "1    14.095468\n",
        "2    26.856783\n",
        "3    36.831512\n",
-       "4    49.737709\n",
+       "4    49.737713\n",
        "Name: total_precip, dtype: object"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -359,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -373,7 +359,7 @@
        "Name: percentiles, dtype: object"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,9 +378,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "tobac-interactive",
    "language": "python",
-   "name": "python3"
+   "name": "tobac-interactive"
   },
   "language_info": {
    "codemirror_mode": {
@@ -406,7 +392,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.5"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,