{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c68003ae",
   "metadata": {},
   "source": [
    "Appending Data to xarray.Datasets\n",
    "================================"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bda8da5",
   "metadata": {},
   "source": [
    "When working with scientific data, you'll often need to combine or append datasets from different experiments or measurements. This tutorial demonstrates various methods to append data to existing xarray.Dataset objects, with a focus on scattering and composition data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8ef101",
   "metadata": {},
   "source": [
    "\n",
    "Setup\n",
    "-----\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e0776d",
   "metadata": {},
   "source": [
    "## Google Colab Setup\n",
    "\n",
    "Only uncomment and run the next cell if you are running this notebook in Google Colab or if don't already have the AFL-agent package installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aedb2ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install git+https://github.com/usnistgov/AFL-agent.git"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f3daf4",
   "metadata": {},
   "source": [
    "Next, let's import the necessary support modules and load data from AFL.double_agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d71bef10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xarray version: 2025.1.2\n",
      "Dataset dimensions: {'sample': 100, 'component': 2, 'x': 150, 'grid': 2500}\n",
      "Dataset variables: ['composition', 'ground_truth_labels', 'measurement', 'composition_grid']\n",
      "Dataset coordinates: ['component', 'x']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(f\"xarray version: {xr.__version__}\")\n",
    "\n",
    "# Import the example dataset from AFL.double_agent.data\n",
    "from AFL.double_agent.data import example_dataset1\n",
    "\n",
    "# Load the example dataset\n",
    "ds = example_dataset1()\n",
    "\n",
    "# Print basic information about the dataset\n",
    "print(f\"Dataset dimensions: {dict(ds.sizes)}\")\n",
    "print(f\"Dataset variables: {list(ds.data_vars)}\")\n",
    "print(f\"Dataset coordinates: {list(ds.coords)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8418da99",
   "metadata": {},
   "source": [
    "Understanding the Dataset\n",
    "------------------------\n",
    "\n",
    "Let's first understand the structure of our example dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49a69bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Composition data shape: (100, 2)\n",
      "Sample of composition data:\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;composition&#x27; (sample: 3, component: 2)&gt; Size: 48B\n",
       "[6 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * component  (component) &lt;U1 8B &#x27;A&#x27; &#x27;B&#x27;\n",
       "Dimensions without coordinates: sample</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'composition'</div><ul class='xr-dim-list'><li><span>sample</span>: 3</li><li><span class='xr-has-index'>component</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d1d1df0e-7c70-43f3-9033-4ea0f225a614' class='xr-array-in' type='checkbox' checked><label for='section-d1d1df0e-7c70-43f3-9033-4ea0f225a614' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[6 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-b0910f45-7ae3-480f-8125-dcc90c8c24ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0910f45-7ae3-480f-8125-dcc90c8c24ce' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;B&#x27;</div><input id='attrs-2f923f5d-a62e-4a4a-8efc-065ef3c8b40d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f923f5d-a62e-4a4a-8efc-065ef3c8b40d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c75e7f9-d6b8-4ef1-95f0-87e4447a529a' class='xr-var-data-in' type='checkbox'><label for='data-1c75e7f9-d6b8-4ef1-95f0-87e4447a529a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;B&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff20a9e9-0392-4fc4-ad72-89a6252d32fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-ff20a9e9-0392-4fc4-ad72-89a6252d32fc' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>component</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d5e7cc44-0e45-4e48-8938-c7dd9d691a5a' class='xr-index-data-in' type='checkbox'/><label for='index-d5e7cc44-0e45-4e48-8938-c7dd9d691a5a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;A&#x27;, &#x27;B&#x27;], dtype=&#x27;object&#x27;, name=&#x27;component&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e89fe50-94b0-4a6e-a9fc-9b796074171a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2e89fe50-94b0-4a6e-a9fc-9b796074171a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'composition' (sample: 3, component: 2)> Size: 48B\n",
       "[6 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * component  (component) <U1 8B 'A' 'B'\n",
       "Dimensions without coordinates: sample"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the composition data\n",
    "print(\"Composition data shape:\", ds.composition.shape)\n",
    "print(\"Sample of composition data:\")\n",
    "ds.composition.isel(sample=slice(0, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ce2b35",
   "metadata": {},
   "source": [
    "The composition data has dimensions ('sample', 'component') with 100 samples and 2 components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "284a19b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Measurement data shape: (100, 150)\n",
      "Sample of measurement data:\n"
     ]
    },
    {
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       "Coordinates:\n",
       "  * x        (x) float64 40B 0.001 0.001047 0.001097 0.001149 0.001204\n",
       "Dimensions without coordinates: sample</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'measurement'</div><ul class='xr-dim-list'><li><span>sample</span>: 2</li><li><span class='xr-has-index'>x</span>: 5</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1d28ca4f-f1f2-4207-aa62-b7627aef4aaa' class='xr-array-in' type='checkbox' checked><label for='section-1d28ca4f-f1f2-4207-aa62-b7627aef4aaa' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[10 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-ffbca2ac-1a3c-4d30-bc92-36cfd91ced63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ffbca2ac-1a3c-4d30-bc92-36cfd91ced63' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001 0.001047 ... 0.001204</div><input id='attrs-ed6e155e-8f4f-494d-bfcc-77962925a57e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed6e155e-8f4f-494d-bfcc-77962925a57e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed1fec57-63e9-48ed-ab97-fbf7507ee690' class='xr-var-data-in' type='checkbox'><label for='data-ed1fec57-63e9-48ed-ab97-fbf7507ee690' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.001   , 0.001047, 0.001097, 0.001149, 0.001204])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8721eb0-d40c-4260-89af-668fe3932d2b' class='xr-section-summary-in' type='checkbox'  ><label for='section-a8721eb0-d40c-4260-89af-668fe3932d2b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-31711361-8d56-4795-aca9-1363d8401763' class='xr-index-data-in' type='checkbox'/><label for='index-31711361-8d56-4795-aca9-1363d8401763' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                0.001, 0.0010474522360006332, 0.0010971561867027272,\n",
       "       0.0011492187010036998, 0.0012037516980200685],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35a2f906-b8e5-4e09-abea-6a917f7218d4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-35a2f906-b8e5-4e09-abea-6a917f7218d4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'measurement' (sample: 2, x: 5)> Size: 80B\n",
       "[10 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * x        (x) float64 40B 0.001 0.001047 0.001097 0.001149 0.001204\n",
       "Dimensions without coordinates: sample"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the measurement data\n",
    "print(\"Measurement data shape:\", ds.measurement.shape)\n",
    "print(\"Sample of measurement data:\")\n",
    "ds.measurement.isel(sample=slice(0, 2), x=slice(0, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9b0276e",
   "metadata": {},
   "source": [
    "The measurement data has dimensions ('sample', 'x') with 100 samples and 150 x-values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33fa9c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Measurement Data for First 3 Samples')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot measurements for the first 3 samples using xarray's built-in plotting\n",
    "ds.measurement.isel(sample=slice(0, 3)).plot.line(x='x', hue='sample', xscale='log',yscale='log')\n",
    "\n",
    "plt.title('Measurement Data for First 3 Samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2ebc84",
   "metadata": {},
   "source": [
    "Creating Subsets of the Dataset\n",
    "------------------------------\n",
    "\n",
    "To demonstrate appending data, let's first create subsets of our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81f8210c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch 1: Frozen({'sample': 50, 'component': 2, 'x': 150, 'grid': 2500})\n",
      "Batch 2: Frozen({'sample': 50, 'component': 2, 'x': 150, 'grid': 2500})\n"
     ]
    }
   ],
   "source": [
    "# Create two subsets of the data\n",
    "ds_batch1 = ds.isel(sample=slice(0, 50))  # First 50 samples\n",
    "ds_batch2 = ds.isel(sample=slice(50, 100))  # Last 50 samples\n",
    "\n",
    "print(f\"Batch 1: {ds_batch1.sizes}\")\n",
    "print(f\"Batch 2: {ds_batch2.sizes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655ba7e8",
   "metadata": {},
   "source": [
    "Method 1: Concatenating Along the Sample Dimension\n",
    "-------------------------------------------------\n",
    "\n",
    "The most common way to append datasets is using ``xr.concat()`` to combine along a dimension. Here, we'll combine our two batches along the sample dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9dc896a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined dataset dimensions: Frozen({'sample': 100, 'component': 2, 'x': 150, 'grid': 2500})\n",
      "Original samples: 100\n",
      "Combined samples: 100\n",
      "Data is identical: True\n"
     ]
    }
   ],
   "source": [
    "# Concatenate along the sample dimension\n",
    "combined_ds = xr.concat([ds_batch1, ds_batch2], dim='sample')\n",
    "\n",
    "print(\"Combined dataset dimensions:\", combined_ds.sizes)\n",
    "\n",
    "# Verify that the combined dataset has the same number of samples as the original\n",
    "print(f\"Original samples: {ds.sizes['sample']}\")\n",
    "print(f\"Combined samples: {combined_ds.sizes['sample']}\")\n",
    "\n",
    "# Check if the data is the same\n",
    "print(\"Data is identical:\", np.allclose(ds.measurement.values, combined_ds.measurement.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "147abec2",
   "metadata": {},
   "source": [
    "The combined dataset has the same dimensions as our original dataset, and the data is identical.\n",
    "\n",
    "Let's visualize the combined data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8742fd87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Samples from Combined Dataset')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot using xarray's built-in plotting functionality\n",
    "combined_ds.measurement.isel(sample=0).plot( label=\"First sample (Batch 1)\",xscale='log',yscale='log')\n",
    "combined_ds.measurement.isel(sample=50).plot( label=\"First sample (Batch 2)\",xscale='log',yscale='log')\n",
    "\n",
    "plt.title('Samples from Combined Dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f6efaf",
   "metadata": {},
   "source": [
    "Method 2: Adding New Variables to Existing Datasets\n",
    "--------------------------------------------------\n",
    "\n",
    "Sometimes you might want to add new variables to an existing dataset, such as adding derived data or analysis results.\n",
    "\n",
    "Let's create a new variable by calculating the mean of each measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eee3522e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
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       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "html[theme=\"dark\"],\n",
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       "\n",
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       "  display: block !important;\n",
       "  min-width: 300px;\n",
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       "\n",
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       "\n",
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       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "}\n",
       "\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       ".xr-array-wrap {\n",
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       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 400B\n",
       "Dimensions:           (sample: 50)\n",
       "Dimensions without coordinates: sample\n",
       "Data variables:\n",
       "    measurement_mean  (sample) float64 400B 8.046e+04 7.622e+04 ... 7.786e+04</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-109b9697-b3a1-4903-9daf-7d10fd799ae7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-109b9697-b3a1-4903-9daf-7d10fd799ae7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>sample</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-92346e36-da11-4203-abe9-c1b6072a6021' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92346e36-da11-4203-abe9-c1b6072a6021' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d73706d3-dcdb-4087-afc2-92de089c17ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d73706d3-dcdb-4087-afc2-92de089c17ed' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>measurement_mean</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.046e+04 7.622e+04 ... 7.786e+04</div><input id='attrs-9e8d4d6f-bc8d-4f2f-aa8f-25baf59e3772' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e8d4d6f-bc8d-4f2f-aa8f-25baf59e3772' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee66a81d-00e6-418c-a41c-3c527cb57f9a' class='xr-var-data-in' type='checkbox'><label for='data-ee66a81d-00e6-418c-a41c-3c527cb57f9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([8.04634193e+04, 7.62245627e+04, 8.03681262e+04, 8.12010674e+04,\n",
       "       8.66154521e+04, 1.98495120e+00, 8.00705091e+04, 7.62392654e+04,\n",
       "       7.25434639e+04, 7.04823790e+04, 8.51165150e+04, 7.99786718e+04,\n",
       "       7.63419439e+04, 8.22973226e+04, 7.50148255e+04, 8.13864440e+04,\n",
       "       7.30837957e+04, 7.34164304e+04, 7.97679532e+04, 8.30021013e+04,\n",
       "       7.68726310e+04, 8.01320113e+04, 8.62397040e+04, 2.01268064e+00,\n",
       "       7.53637641e+04, 8.15839007e+04, 8.09450433e+04, 7.41579697e+04,\n",
       "       2.01842952e+00, 7.61629649e+04, 7.77720815e+04, 8.24302370e+04,\n",
       "       7.20249342e+04, 7.75500459e+04, 7.84772354e+04, 1.99126396e+00,\n",
       "       8.11185989e+04, 8.02697263e+04, 7.18084941e+04, 2.00540539e+00,\n",
       "       7.94692791e+04, 7.84937202e+04, 8.31813044e+04, 7.82302336e+04,\n",
       "       7.81176634e+04, 7.66772532e+04, 1.98864482e+00, 7.70726096e+04,\n",
       "       1.99615992e+00, 7.78562887e+04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-31f5257d-407a-4cf9-a677-fed210d4a88d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31f5257d-407a-4cf9-a677-fed210d4a88d' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-52215441-52af-4c20-941b-11244e0f2b73' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-52215441-52af-4c20-941b-11244e0f2b73' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 400B\n",
       "Dimensions:           (sample: 50)\n",
       "Dimensions without coordinates: sample\n",
       "Data variables:\n",
       "    measurement_mean  (sample) float64 400B 8.046e+04 7.622e+04 ... 7.786e+04"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the mean of each measurement\n",
    "measurement_mean = ds_batch1.measurement.mean(dim='x')\n",
    "\n",
    "\n",
    "# Create a new dataset with this information\n",
    "mean_ds = xr.Dataset()\n",
    "mean_ds['measurement_mean'] = ('sample', measurement_mean.values)\n",
    "mean_ds\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f72d0d",
   "metadata": {},
   "source": [
    "Now we can merge this new dataset with our original batch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f2046d8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Merged dataset variables: ['composition', 'ground_truth_labels', 'measurement', 'composition_grid', 'measurement_mean']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Measurements and Their Means')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Merge the mean dataset with batch1\n",
    "merged_ds = xr.merge([ds_batch1, mean_ds])\n",
    "\n",
    "print(\"Merged dataset variables:\", list(merged_ds.data_vars))\n",
    "\n",
    "# Plot the original measurements and their means\n",
    "merged_ds.measurement.isel(sample=[0,1,2]).plot.line(x='x', hue='sample', xscale='log',yscale='log')\n",
    "for i in range(3):\n",
    "    plt.axhline(y=merged_ds.measurement_mean.isel(sample=i), linestyle='--', color=f'C{i}', label=f\"Mean of Sample {i}\")\n",
    "\n",
    "plt.title('Measurements and Their Means')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d03da3a9",
   "metadata": {},
   "source": [
    "Method 3: Combining Datasets with Different X Ranges\n",
    "---------------------------------------------------\n",
    "\n",
    "Sometimes you need to combine datasets with different x ranges. Let's create a subset with a different x range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5108936",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a subset with a different x range\n",
    "x_subset = ds.x.values[::2]  # Take every other x value\n",
    "\n",
    "# Create a new dataset with this subset\n",
    "ds_subset_x = ds.isel(sample=slice(0, 10)).copy()  # First 10 samples\n",
    "\n",
    "# Interpolate the data to the new x values\n",
    "new_measurement = np.zeros((10, len(x_subset)))\n",
    "\n",
    "for i in range(10):\n",
    "    new_measurement[i] = np.interp(\n",
    "        x_subset, \n",
    "        ds.x.values, \n",
    "        ds.measurement.isel(sample=i).values\n",
    "    )\n",
    "\n",
    "# Create the new dataset\n",
    "ds_different_x = xr.Dataset(\n",
    "    data_vars={\n",
    "        'measurement': (('sample', 'x'), new_measurement),\n",
    "        'composition': ds_subset_x.composition.values,\n",
    "    },\n",
    "    coords={\n",
    "        'sample': ds_subset_x.sample,\n",
    "        'x': x_subset,\n",
    "        'component': ds.component,\n",
    "    }\n",
    ")\n",
    "\n",
    "print(\"Original x length:\", len(ds.x))\n",
    "print(\"New x length:\", len(ds_different_x.x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d85070b",
   "metadata": {},
   "source": [
    "To combine datasets with different x coordinates, we need to interpolate onto a common grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9da7816f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a sample from each dataset\n",
    "sample_original = ds.isel(sample=0)\n",
    "sample_different_x = ds_different_x.isel(sample=0)\n",
    "\n",
    "# Plot to show the different x grids\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(sample_original.x, sample_original.measurement, \n",
    "         'o-', label=\"Original x grid\")\n",
    "plt.plot(sample_different_x.x, sample_different_x.measurement, \n",
    "         'x-', label=\"Different x grid\")\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Measurement')\n",
    "plt.title('Comparison of Different X Grids')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6d6a3b",
   "metadata": {},
   "source": [
    "To combine these datasets, we need to interpolate one onto the grid of the other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ea37de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a combined x grid (union of both)\n",
    "combined_x = np.sort(np.unique(np.concatenate([\n",
    "    ds.x.values, \n",
    "    ds_different_x.x.values\n",
    "])))\n",
    "\n",
    "# Interpolate both datasets to this new grid\n",
    "# For demonstration, we'll just use one sample from each\n",
    "\n",
    "# Interpolate original data\n",
    "original_interp = np.interp(\n",
    "    combined_x, \n",
    "    ds.x.isel(sample=0), \n",
    "    ds.measurement.isel(sample=0)\n",
    ")\n",
    "\n",
    "# Interpolate different_x data\n",
    "different_x_interp = np.interp(\n",
    "    combined_x, \n",
    "    ds_different_x.x.isel(sample=0), \n",
    "    ds_different_x.measurement.isel(sample=0)\n",
    ")\n",
    "\n",
    "# Create a new dataset with the combined x grid\n",
    "combined_x_ds = xr.Dataset(\n",
    "    data_vars={\n",
    "        'measurement_original': ('x', original_interp),\n",
    "        'measurement_different_x': ('x', different_x_interp),\n",
    "    },\n",
    "    coords={\n",
    "        'x': combined_x,\n",
    "    }\n",
    ")\n",
    "\n",
    "print(\"Combined x grid length:\", len(combined_x_ds.x))\n",
    "\n",
    "# Plot the interpolated data\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(combined_x_ds.x, combined_x_ds.measurement_original, \n",
    "         label=\"Original data (interpolated)\")\n",
    "plt.plot(combined_x_ds.x, combined_x_ds.measurement_different_x, \n",
    "         label=\"Different x data (interpolated)\")\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Measurement')\n",
    "plt.title('Data Interpolated to Common X Grid')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937acb22",
   "metadata": {},
   "source": [
    "Method 4: Filling Missing Data\n",
    "-------------------------------\n",
    "\n",
    "Sometimes you might have incomplete data that needs to be filled from another dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4a705a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataset with some missing values\n",
    "ds_with_nans = ds.isel(sample=slice(0, 10)).copy()\n",
    "\n",
    "# Set some measurement values to NaN\n",
    "measurement_with_nans = ds_with_nans.measurement.values.copy()\n",
    "measurement_with_nans[2:5, 30:60] = np.nan  # Set a block to NaN\n",
    "\n",
    "ds_with_nans['measurement'] = (('sample', 'x'), measurement_with_nans)\n",
    "\n",
    "# Visualize the dataset with missing values\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "for i in range(3):\n",
    "    plt.plot(ds_with_nans.x, ds_with_nans.measurement.isel(sample=i), \n",
    "             label=f\"Sample {i}\")\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Measurement')\n",
    "plt.title('Dataset with Missing Values')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "509dad49",
   "metadata": {},
   "source": [
    "We can use the ``combine_first()`` method to fill missing values from another dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0308644c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataset to fill the missing values\n",
    "# We'll use the original dataset for this\n",
    "ds_fill = ds.isel(sample=slice(0, 10))\n",
    "\n",
    "# Fill the missing values\n",
    "ds_filled = ds_with_nans.combine_first(ds_fill)\n",
    "\n",
    "# Check if all NaNs are filled\n",
    "print(\"NaNs in original:\", np.isnan(ds_with_nans.measurement.values).sum())\n",
    "print(\"NaNs after filling:\", np.isnan(ds_filled.measurement.values).sum())\n",
    "\n",
    "# Visualize the filled dataset\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "for i in range(3):\n",
    "    plt.plot(ds_filled.x, ds_filled.measurement.isel(sample=i), \n",
    "             label=f\"Sample {i} (filled)\")\n",
    "    \n",
    "    # Also plot the original data with NaNs for comparison\n",
    "    if i == 2:  # Sample 2 had NaNs\n",
    "        plt.plot(ds_with_nans.x, ds_with_nans.measurement.isel(sample=i), \n",
    "                 'r--', label=f\"Sample {i} (with NaNs)\")\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('Measurement')\n",
    "plt.title('Dataset After Filling Missing Values')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceab9f6f",
   "metadata": {},
   "source": [
    "Method 5: Updating Metadata When Combining Datasets\n",
    "----------------------------------------------------\n",
    "\n",
    "When combining datasets, you might want to update the metadata (attributes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24ca9e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine datasets and update attributes\n",
    "combined_ds = xr.concat([ds_batch1, ds_batch2], dim='sample')\n",
    "\n",
    "# Update attributes\n",
    "combined_ds.attrs = {\n",
    "    'description': 'Combined dataset from two batches',\n",
    "    'samples': f\"{combined_ds.sizes['sample']} samples\",\n",
    "    'x_range': f\"{combined_ds.x.values[0]:.3f} to {combined_ds.x.values[-1]:.3f}\",\n",
    "    'components': ', '.join([str(c.values) for c in combined_ds.component]),\n",
    "    'created_date': pd.Timestamp.now().strftime('%Y-%m-%d'),\n",
    "}\n",
    "\n",
    "print(\"Combined Dataset Attributes:\")\n",
    "for key, value in combined_ds.attrs.items():\n",
    "    print(f\"{key}: {value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13167c96",
   "metadata": {},
   "source": [
    "Best Practices and Considerations\n",
    "-------------------------------\n",
    "\n",
    "When appending data to xarray Datasets, keep these tips in mind:\n",
    "\n",
    "1. **Dimension Alignment**: Ensure that dimensions you're not concatenating along have the same values.\n",
    "2. **Data Types**: Check that variables have compatible data types before combining.\n",
    "3. **Metadata**: Decide how to handle metadata (attributes) when combining datasets.\n",
    "4. **Interpolation**: When combining data with different coordinate values, consider interpolation to a common grid.\n",
    "5. **Units**: Ensure that data being combined has consistent units.\n",
    "6. **Performance**: For very large datasets, consider using dask for parallel processing.\n",
    "\n",
    "Conclusion\n",
    "---------\n",
    "\n",
    "In this tutorial, we've explored various methods to append data to xarray Datasets:\n",
    "\n",
    "1. Using ``xr.concat()`` to combine datasets along a dimension\n",
    "2. Using ``xr.merge()`` to add new variables to existing datasets\n",
    "3. Combining datasets with different coordinate values through interpolation\n",
    "4. Using ``combine_first()`` to fill missing data\n",
    "5. Handling metadata when combining datasets\n",
    "\n",
    "These techniques are essential for working with multiple batches of data, combining data from different sources, or extending your dataset with new samples or derived properties.\n",
    "\n",
    "Further Reading\n",
    "-------------\n",
    "\n",
    "- `xarray Documentation on Combining Data <http://xarray.pydata.org/en/stable/combining.html>`_\n",
    "- `Dask Integration with xarray <http://xarray.pydata.org/en/stable/dask.html>`_\n",
    "- `xarray API Reference <http://xarray.pydata.org/en/stable/api.html>`_"
   ]
  }
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