Implement HOGER homogenization

.github/workflows/deploy-pages.yaml

@@ -29,6 +29,7 @@ jobs:
     - name: Copy examples to docs
       working-directory: ${{runner.workspace}}/flasc/
       run: |
+        rsync -av --mkpath  examples_artificial_data/01_raw_data_processing/ docs/examples/01_raw_data_processing
         rsync -av --mkpath  examples_artificial_data/03_energy_ratio/ docs/examples/03_energy_ratio
         rsync -av --mkpath  examples_artificial_data/floris_input_artificial/ docs/examples/floris_input_artificial
         ls docs/examples

docs/_toc.yml

@@ -23,10 +23,12 @@ parts:
   #   - file: contributing
   #   - file: development
   #   - file: testing
+  - caption: Examples Data Processing
+    chapters:
+    - file: examples/01_raw_data_processing/03_northing_calibration_hoger
 
-  - caption: Examples
+  - caption: Examples Energy Ratio
     chapters:
-    # - file: flascdataframe
     - file: examples/03_energy_ratio/00_demo_energy_ratio_syntax
     - file: examples/03_energy_ratio/01_demo_energy_ratio_options
 

examples_artificial_data/01_raw_data_processing/01_northing_calibration.ipynb

@@ -10,18 +10,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n",
-      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# from datetime import timedelta as td\n",
     "import os\n",
@@ -49,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,9 +95,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "UserWarning",
+     "evalue": "Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' for the appropriate wake models first.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUserWarning\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[49], line 13\u001b[0m\n\u001b[1;32m     11\u001b[0m     df_fm_approx \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_feather(fn_approx)\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 13\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mUserWarning\u001b[39;00m(\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease run \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m00_setup_floris_model/02_precalculate_floris_solutions.py\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     15\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor the appropriate wake models first.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     16\u001b[0m     )\n",
+      "\u001b[0;31mUserWarning\u001b[0m: Please run '00_setup_floris_model/02_precalculate_floris_solutions.py' for the appropriate wake models first."
+     ]
+    }
+   ],
    "source": [
     "# Now we calculate a grid of FLORIS solutions. Since our estimated SCADA\n",
     "# data changes as we shift its wind direction, the predicted solutions\n",
@@ -137,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -239,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1316,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2926,7 +2929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2979,7 +2982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3087,7 +3090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -3111,7 +3114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [