From bd78da135d50a0062819b8252c4fab80ebe0a484 Mon Sep 17 00:00:00 2001
From: bramjanssen <bram.janssen@vito.be>
Date: Thu, 14 Dec 2023 17:03:39 +0100
Subject: [PATCH] feat: added new notebook showcasing the usage of MOGPR using
 a multi band input datacube

---
 .../FuseTS - MOGPR Multi Source Fusion.ipynb  | 962 ++++++++++++++++++
 1 file changed, 962 insertions(+)
 create mode 100644 notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb

diff --git a/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb
new file mode 100644
index 0000000..7a2f988
--- /dev/null
+++ b/notebooks/OpenEO/FuseTS - MOGPR Multi Source Fusion.ipynb	
@@ -0,0 +1,962 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3d58e2f2",
+   "metadata": {
+    "id": "3d58e2f2"
+   },
+   "source": [
+    "# FuseTS - MOGPR Multi Source Fusion openEO Workflow\n",
+    "\n",
+    "In this notebook, we'll demonstrate how to use openEO's diverse capabilities to build a workflow for fusing data. In this particular case, we'll integrate both FuseTS's MOGPR service to fuse a datacube that contains multiple data sources. In this example, we'll be chosing Sentinel 2 NDVI and S1 Coherence data.\n",
+    "\n",
+    "**Prerequisites**\n",
+    "- In this notebook, we utilize openEO to retrieve time series data. To leverage the full range of openEO features, you can create a free trial account on the [openEO Platform](https://docs.openeo.cloud/join/free_trial.html) and receive 1000 free credits, enabling you to execute the functionalities showcased in this notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b7f006d",
+   "metadata": {
+    "id": "9b7f006d"
+   },
+   "source": [
+    "Lets start with importing the different libraries that we need within this notebook.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f517e4f-229f-4d85-9f1e-9f43d614a6cb",
+   "metadata": {
+    "id": "1f517e4f-229f-4d85-9f1e-9f43d614a6cb"
+   },
+   "source": [
+    "## Setting up the environment\n",
+    "\n",
+    "First of all we need to make sure that all our dependencies are correctly installed.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "47aa953d",
+   "metadata": {
+    "id": "47aa953d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: openeo in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (0.22.0)\n",
+      "Requirement already satisfied: xarray>=0.12.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2023.1.0)\n",
+      "Requirement already satisfied: deprecated>=1.2.12 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.2.14)\n",
+      "Requirement already satisfied: pandas>0.20.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.3)\n",
+      "Requirement already satisfied: numpy>=1.17.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (1.23.5)\n",
+      "Requirement already satisfied: shapely>=1.6.4 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.0.1)\n",
+      "Requirement already satisfied: requests>=2.26.0 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from openeo) (2.31.0)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from deprecated>=1.2.12->openeo) (1.15.0)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2023.3)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from pandas>0.20.0->openeo) (2.8.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2.0.4)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (3.2.0)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from requests>=2.26.0->openeo) (2023.7.22)\n",
+      "Requirement already satisfied: packaging>=21.3 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from xarray>=0.12.3->openeo) (23.1)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/bramjanssen/projects/vito/FuseTS/venv_clean_v2/lib/python3.8/site-packages (from python-dateutil>=2.8.2->pandas>0.20.0->openeo) (1.16.0)\n",
+      "\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.3.1\u001B[0m\n",
+      "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install openeo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "35b24fd9",
+   "metadata": {
+    "id": "35b24fd9",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import json\n",
+    "import warnings\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import openeo\n",
+    "from openeo.rest.conversions import timeseries_json_to_pandas\n",
+    "\n",
+    "import xarray\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from ipyleaflet import GeoJSON, Map, basemaps\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69bbf07b-97d9-4873-b8e6-d0eb2b63f6c9",
+   "metadata": {},
+   "source": [
+    "# Exploring the area of interest\n",
+    "In this first part, we will explore the area of interest that will be used for calculating the phenology metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37b1970f",
+   "metadata": {
+    "id": "37b1970f",
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "year = 2023\n",
+    "spat_ext = {\n",
+    "    \"type\": \"Polygon\",\n",
+    "    \"coordinates\": [\n",
+    "        [\n",
+    "            [\n",
+    "                5.170012098271149,\n",
+    "                51.25062964728295\n",
+    "            ],\n",
+    "            [\n",
+    "                5.17085904378298,\n",
+    "                51.24882567194015\n",
+    "            ],\n",
+    "            [\n",
+    "                5.17857421368097,\n",
+    "                51.2468515482926\n",
+    "            ],\n",
+    "            [\n",
+    "                5.178972704726344,\n",
+    "                51.24982704376254\n",
+    "            ],\n",
+    "            [\n",
+    "                5.170012098271149,\n",
+    "                51.25062964728295\n",
+    "            ]\n",
+    "        ]\n",
+    "    ]\n",
+    "}\n",
+    "temp_ext = [f\"{year}-01-01\", f\"{year}-12-01\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ebb96d4",
+   "metadata": {
+    "id": "2ebb96d4"
+   },
+   "source": [
+    "Plot the area to see what we're working with.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b2bf0d05",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 421,
+     "referenced_widgets": [
+      "ae380de71eee4a5db3f93b4d72391c55",
+      "5bde1dfbad6a4c38bdeec7f198292be2",
+      "4dea090226fb4def9909b02d5a79f92a",
+      "1c42eea0ba6f4a00b594b7317991a774",
+      "a6b2219658314282b9714ead2b5bcbb8",
+      "fa72b583a1fd4247911a5500ec07dc62",
+      "7dfc8a56f36447f388cd7450ad0f209e",
+      "075be31fc63941ab8c7f3c0ac8ee27d4",
+      "78e3180acb034052bdfe52ecbcde3dd2"
+     ]
+    },
+    "id": "b2bf0d05",
+    "outputId": "2418e4e3-34fe-4903-e217-9cd4f999dbe6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ebdc33c9af64e0788e83a9f8a7628b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Map(center=[51.249352711712234, 5.173686031746518], controls=(ZoomControl(options=['position', 'zoom_in_text',…"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "center = np.mean(spat_ext[\"coordinates\"][0], axis=0).tolist()[::-1]\n",
+    "zoom = 16\n",
+    "\n",
+    "m = Map(basemap=basemaps.Esri.WorldImagery, center=center, zoom=zoom)\n",
+    "g = GeoJSON(\n",
+    "    data=spat_ext,\n",
+    "    style={\n",
+    "        \"color\": \"red\",\n",
+    "        \"opacity\": 1,\n",
+    "        \"weight\": 1.9,\n",
+    "        \"dashArray\": \"9\",\n",
+    "        \"fillOpacity\": 0.5,\n",
+    "    },\n",
+    ")\n",
+    "m.add(g)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19e8133f-8b24-45df-9c5d-f5bd92f78e46",
+   "metadata": {},
+   "source": [
+    "# Setting up connection with openEO\n",
+    "\n",
+    "The first step in setting up the workflow, is creating a connection with openEO. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "25dd29b3-856b-4b47-b814-834036262ce2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Authenticated using refresh token.\n"
+     ]
+    }
+   ],
+   "source": [
+    "connection = openeo.connect(\"openeo.vito.be\").authenticate_oidc()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "702803f6",
+   "metadata": {
+    "id": "702803f6"
+   },
+   "source": [
+    "# Creating the input data cube\n",
+    "\n",
+    "In the following section, we will leverage the capabilities of openEO to create a datacube that contains multiple data sources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ed09a40-ab57-442b-a256-8b3fc7dfc0c1",
+   "metadata": {},
+   "source": [
+    "First we start creating a datacube that represents the NDVI values to be calculated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dbd5f5dd-69e6-4d16-ad76-51827f262d2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_s2 = connection.load_collection('SENTINEL2_L2A',\n",
+    "                                     spatial_extent=spat_ext,\n",
+    "                                     temporal_extent=temp_ext,\n",
+    "                                     bands=[\"B04\", \"B08\", \"SCL\"])\n",
+    "base_s2 = base_s2.process(\"mask_scl_dilation\", data=base_s2, scl_band_name=\"SCL\")\n",
+    "base_s2 = base_s2.ndvi(red=\"B04\", nir=\"B08\", target_band='NDVI')\n",
+    "base_s2 = base_s2.filter_bands(bands=['NDVI'])\n",
+    "base_s2 = base_s2.mask_polygon(spat_ext)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b02f417-cf53-4fec-8b5a-6b5b71511c57",
+   "metadata": {},
+   "source": [
+    "The second datacube will contain the RVI data for the given temporal and spatial extent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "369304a6-5020-413c-9a0f-162cc242a0ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_s1 = connection.load_collection('SENTINEL1_GRD',\n",
+    "                                     spatial_extent=spat_ext,\n",
+    "                                     temporal_extent=temp_ext,\n",
+    "                                     bands=[\"VH\", \"VV\"])\n",
+    "\n",
+    "VH = base_s1.band(\"VH\")\n",
+    "VV = base_s1.band(\"VV\")\n",
+    "base_s1 = (VH + VH) / (VV + VH)\n",
+    "base_s1 = base_s1.add_dimension(name=\"bands\", label=\"RVI\",type=\"bands\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a93dce7-bf9c-4a38-b044-b4c5784200ef",
+   "metadata": {},
+   "source": [
+    "Next we merge the two datacubes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ffcfafe2-40a1-44ff-a738-fb86b66ce65c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_datacube = base_s2.merge(base_s1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b018170-731c-4170-ae1a-feedb5383fbd",
+   "metadata": {},
+   "source": [
+    "# Downloading the original timeseries values\n",
+    "Before continuing with the data fusion step, we download the time series for both the S1 and S2 data. This would allow us to later compare the raw values with the fused data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "299a18e3-63d3-4e05-83e3-9460fe059705",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_s2 = base_s2.aggregate_spatial(spat_ext, reducer='mean')\n",
+    "base_s2.download('./mogpr-multisource-s2-base.json', format='json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "faec8adf-2a1d-486e-a363-544d2158f56b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_s1 = base_s1.aggregate_spatial(spat_ext, reducer='mean')\n",
+    "base_s1.download('./mogpr-multisource-s1-base.json', format='json')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b83aa34-29f6-482b-b805-a43cb39d30c4",
+   "metadata": {},
+   "source": [
+    "# Executing the MOGPR function\n",
+    "The input data cube is now passed to the MOGPR function. Additionally, we also calculate the mean timeseries values for the given area of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "77b3f539-b919-43fb-b7c5-31c2853c4c7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service = 'mogpr'\n",
+    "namespace = 'u:fusets'\n",
+    "mogpr = connection.datacube_from_process(service,\n",
+    "                                         namespace=f'https://openeo.vito.be/openeo/1.1/processes/{namespace}/{service}',\n",
+    "                                         data=merged_datacube)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bdea476-661e-4271-a118-776a6c8fb6aa",
+   "metadata": {},
+   "source": [
+    "# Downloading the results\n",
+    "Now that we've setup our workflow, we can now download the results by starting an openEO batch job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "632a7088-67c2-4f89-95ff-813e4587f2be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mogpr_output_file = './mogpr-multisource.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "db4ac328-5413-4304-b1a5-b1c6dd6710fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:00:00 Job 'j-2312141e96e747528c195bf8569c05db': send 'start'\n",
+      "0:00:35 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
+      "0:00:40 Job 'j-2312141e96e747528c195bf8569c05db': queued (progress N/A)\n",
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+      "0:19:17 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:20:18 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:21:18 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:22:19 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:23:19 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:24:20 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:25:20 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:26:20 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:27:21 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:28:21 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:29:22 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:30:22 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:31:23 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:32:23 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:33:24 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:34:24 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:35:24 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:36:25 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:37:25 Job 'j-2312141e96e747528c195bf8569c05db': running (progress N/A)\n",
+      "0:38:26 Job 'j-2312141e96e747528c195bf8569c05db': finished (progress N/A)\n"
+     ]
+    }
+   ],
+   "source": [
+    "mogpr_job = mogpr.execute_batch(mogpr_output_file, out_format=\"netcdf\",\n",
+    "                                title=f'FuseTS - MOGPR - Multi Source', job_options={\n",
+    "        'executor-memory': '8g',\n",
+    "        'udf-dependency-archives': [ \n",
+    "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets_venv.zip#tmp/venv',\n",
+    "            'https://artifactory.vgt.vito.be:443/artifactory/auxdata-public/ai4food/fusets.zip#tmp/venv_static'\n",
+    "        ]\n",
+    "    })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3235c845-50fa-484d-85c3-1695d6ef88b5",
+   "metadata": {},
+   "source": [
+    "# Explore the results¶"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ec95ceb1-9027-4305-a40a-6bcf9905c7a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cubes_dfs = []\n",
+    "ds = xarray.load_dataset(mogpr_output_file)\n",
+    "for var in ds.data_vars.items():\n",
+    "    if var[0] != 'crs':\n",
+    "        var_df = var[1].mean(dim=['x', 'y'])\n",
+    "        var_df = var_df.to_dataframe()\n",
+    "        var_df.index = pd.to_datetime(var_df.index)\n",
+    "        var_df['date'] = var_df.index.date\n",
+    "        var_df = var_df.set_index('date')\n",
+    "        cubes_dfs.append(var_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "84dab365-5139-4e62-9f1d-438329614d21",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "cols = ['RVI - Raw', 'NDVI - Raw']\n",
+    "for result in ['mogpr-multisource-s1-base.json', 'mogpr-multisource-s2-base.json']:\n",
+    "    with open(result, 'r') as result_file:\n",
+    "        df = timeseries_json_to_pandas(json.load(result_file))\n",
+    "        df.index = pd.to_datetime(df.index).date\n",
+    "        cubes_dfs.append(df)\n",
+    "        result_file.close()\n",
+    "\n",
+    "joined_df = pd.concat(cubes_dfs, axis=1)\n",
+    "joined_df = joined_df.rename(columns={0: cols[0], 1: cols[1]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8826c305-1f4c-481f-88aa-291d80e38448",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x12769f670>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16, 6))\n",
+    "for col in joined_df.columns. values:\n",
+    "    values = joined_df[~joined_df[col].isna()]\n",
+    "    plt.plot(values.index, values[col], '.' if 'Raw' in col else '-.', label=col)\n",
+    "plt.grid(True)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dd182d1-8865-4d7f-a924-1786bfff7634",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.17"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "5c03b434e59eed88f0de80727dd778a0649e6474303457c247ef449eabdd7cac"
+   }
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "075be31fc63941ab8c7f3c0ac8ee27d4": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
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