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   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/usnistgov/AFL-agent/blob/main/docs/source/tutorials/quickstart.ipynb)\n",
    "\n",
    "# Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to use AFL-agent to analyze measurement data and identify different phases. We'll create a simple pipeline that:\n",
    "\n",
    "1. Calculates derivatives of measurement data using Savitzky-Golay filtering\n",
    "2. Computes similarity between measurements\n",
    "3. Uses spectral clustering to group similar measurements into phases\n",
    "\n",
    "We'll work with synthetic data that simulates two different types of signals - a flat background and a power law decay, both with added noise.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install git+https://github.com/usnistgov/AFL-agent.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AFL.double_agent import *\n",
    "\n",
    "with Pipeline() as clustering_pipeline:\n",
    "\n",
    "        SavgolFilter(\n",
    "            input_variable='measurement', \n",
    "            output_variable='derivative', \n",
    "            dim='x', \n",
    "            derivative=1\n",
    "            )\n",
    "\n",
    "        Similarity(\n",
    "            input_variable='derivative', \n",
    "            output_variable='similarity', \n",
    "            sample_dim='sample',\n",
    "            params={'metric': 'laplacian','gamma':1e-4}\n",
    "            )\n",
    "    \n",
    "        SpectralClustering(\n",
    "            input_variable='similarity',\n",
    "            output_variable='labels',\n",
    "            dim='sample',\n",
    "            params={'n_phases': 2}\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pipeline above consists of three operations:\n",
    "\n",
    "1. `SavgolFilter`: Applies Savitzky-Golay filtering to calculate derivatives of the measurement data along the x-dimension. This helps identify changes in the signal shape.\n",
    "\n",
    "2. `Similarity`: Computes pairwise similarity between measurements using their derivatives. It uses a Laplacian kernel with gamma=1e-4 to quantify how similar each measurement is to every other measurement.\n",
    "\n",
    "3. `SpectralClustering`: Groups measurements into 2 phases based on their similarity scores. Measurements with high similarity will be grouped into the same phase.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Input Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test this pipeline, we need some data to analyze. We'll use a synthetic dataset containing measurements from a two-phase system. Each measurement represents a signal collected from a sample with different compositions of components A and B. For details on how this dataset was created, see the [Building xarray Datasets](../how-to/building_xarray_datasets.ipynb) tutorial.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 164kB\n",
       "Dimensions:              (sample: 100, component: 2, x: 150, grid: 2500)\n",
       "Coordinates:\n",
       "  * component            (component) &lt;U1 8B &#x27;A&#x27; &#x27;B&#x27;\n",
       "  * x                    (x) float64 1kB 0.001 0.001047 0.001097 ... 0.9547 1.0\n",
       "Dimensions without coordinates: sample, grid\n",
       "Data variables:\n",
       "    composition          (sample, component) float64 2kB ...\n",
       "    ground_truth_labels  (sample) int64 800B ...\n",
       "    measurement          (sample, x) float64 120kB ...\n",
       "    composition_grid     (grid, component) float64 40kB ...</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-d23b9d69-51fa-4a3d-9011-da8e1596514c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d23b9d69-51fa-4a3d-9011-da8e1596514c' 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>: 100</li><li><span class='xr-has-index'>component</span>: 2</li><li><span class='xr-has-index'>x</span>: 150</li><li><span>grid</span>: 2500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-285bd44b-ba82-4e9c-bef0-1166085438f5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-285bd44b-ba82-4e9c-bef0-1166085438f5' class='xr-section-summary' >Coordinates: <span>(2)</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-51d90a9c-f8b8-49c0-88f7-a32121c5e37f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51d90a9c-f8b8-49c0-88f7-a32121c5e37f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2940203d-9f48-4649-856e-8776a5507550' class='xr-var-data-in' type='checkbox'><label for='data-2940203d-9f48-4649-856e-8776a5507550' 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><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 ... 0.9547 1.0</div><input id='attrs-315242fc-fdbb-4c2c-bdda-5097dfebf06c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-315242fc-fdbb-4c2c-bdda-5097dfebf06c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88276063-5f31-4c41-ae22-11c80081211d' class='xr-var-data-in' type='checkbox'><label for='data-88276063-5f31-4c41-ae22-11c80081211d' 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, 0.001261, 0.001321,\n",
       "       0.001383, 0.001449, 0.001518, 0.00159 , 0.001665, 0.001744, 0.001827,\n",
       "       0.001914, 0.002005, 0.0021  , 0.002199, 0.002304, 0.002413, 0.002527,\n",
       "       0.002647, 0.002773, 0.002905, 0.003042, 0.003187, 0.003338, 0.003496,\n",
       "       0.003662, 0.003836, 0.004018, 0.004209, 0.004409, 0.004618, 0.004837,\n",
       "       0.005066, 0.005307, 0.005559, 0.005822, 0.006099, 0.006388, 0.006691,\n",
       "       0.007009, 0.007341, 0.00769 , 0.008055, 0.008437, 0.008837, 0.009256,\n",
       "       0.009696, 0.010156, 0.010638, 0.011142, 0.011671, 0.012225, 0.012805,\n",
       "       0.013413, 0.014049, 0.014716, 0.015414, 0.016146, 0.016912, 0.017714,\n",
       "       0.018555, 0.019435, 0.020358, 0.021324, 0.022335, 0.023395, 0.024505,\n",
       "       0.025668, 0.026886, 0.028162, 0.029498, 0.030898, 0.032364, 0.0339  ,\n",
       "       0.035509, 0.037194, 0.038959, 0.040807, 0.042744, 0.044772, 0.046897,\n",
       "       0.049122, 0.051453, 0.053894, 0.056452, 0.059131, 0.061936, 0.064875,\n",
       "       0.067954, 0.071179, 0.074556, 0.078094, 0.0818  , 0.085681, 0.089747,\n",
       "       0.094006, 0.098467, 0.103139, 0.108033, 0.11316 , 0.118529, 0.124154,\n",
       "       0.130045, 0.136216, 0.14268 , 0.14945 , 0.156542, 0.16397 , 0.171751,\n",
       "       0.179901, 0.188438, 0.197379, 0.206746, 0.216556, 0.226832, 0.237596,\n",
       "       0.24887 , 0.26068 , 0.27305 , 0.286006, 0.299578, 0.313794, 0.328684,\n",
       "       0.344281, 0.360618, 0.37773 , 0.395654, 0.414429, 0.434094, 0.454693,\n",
       "       0.476269, 0.498869, 0.522542, 0.547337, 0.57331 , 0.600514, 0.62901 ,\n",
       "       0.658858, 0.690122, 0.72287 , 0.757172, 0.793102, 0.830736, 0.870156,\n",
       "       0.911447, 0.954697, 1.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2a6dcba-daa9-4f5d-a778-3d427d6d0052' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2a6dcba-daa9-4f5d-a778-3d427d6d0052' class='xr-section-summary' >Data variables: <span>(4)</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>composition</span></div><div class='xr-var-dims'>(sample, component)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1af9ffc7-3b2c-4644-9ea8-44cb422e91f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1af9ffc7-3b2c-4644-9ea8-44cb422e91f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-917dff70-8141-41e4-aae3-612cb4e9942e' class='xr-var-data-in' type='checkbox'><label for='data-917dff70-8141-41e4-aae3-612cb4e9942e' 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>[200 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ground_truth_labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e06a08d8-7f4a-4e9d-810f-184be4c59935' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e06a08d8-7f4a-4e9d-810f-184be4c59935' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3f9f76f-de29-40ea-bc39-d2ada724d24b' class='xr-var-data-in' type='checkbox'><label for='data-c3f9f76f-de29-40ea-bc39-d2ada724d24b' 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>[100 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement</span></div><div class='xr-var-dims'>(sample, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c6eab2b-1f55-41be-b5ad-db0389ddd031' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c6eab2b-1f55-41be-b5ad-db0389ddd031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18d56762-1f34-4abd-aeb5-92d2ba5af3c1' class='xr-var-data-in' type='checkbox'><label for='data-18d56762-1f34-4abd-aeb5-92d2ba5af3c1' 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>[15000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>composition_grid</span></div><div class='xr-var-dims'>(grid, component)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1cdb4019-2865-4842-98ea-65f9e314e2b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cdb4019-2865-4842-98ea-65f9e314e2b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7b1d303-4ef4-4703-a344-8273bbea66f8' class='xr-var-data-in' type='checkbox'><label for='data-a7b1d303-4ef4-4703-a344-8273bbea66f8' 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>[5000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-26e90960-2b57-4adf-836d-1ca9f3a03973' class='xr-section-summary-in' type='checkbox'  ><label for='section-26e90960-2b57-4adf-836d-1ca9f3a03973' class='xr-section-summary' >Indexes: <span>(2)</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-157dea1f-1352-46de-bbf7-ffa9de922586' class='xr-index-data-in' type='checkbox'/><label for='index-157dea1f-1352-46de-bbf7-ffa9de922586' 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><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-83601c5d-179c-44d6-a890-cee2a497d8f9' class='xr-index-data-in' type='checkbox'/><label for='index-83601c5d-179c-44d6-a890-cee2a497d8f9' 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, 0.0012608724076806808,\n",
       "        0.001320703622736631, 0.0013833739627296209,  0.001449018150486198,\n",
       "       0.0015177773017322714,\n",
       "       ...\n",
       "          0.6588581861506815,    0.6901224802908528,    0.7228703350949566,\n",
       "            0.75717214883374,    0.7931016603333051,    0.8307361074919352,\n",
       "          0.8701563933188907,    0.9114472598521185,    0.9546974703287516,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=150))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01a43b50-e6a9-4d1c-8a35-2d8956c7a988' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-01a43b50-e6a9-4d1c-8a35-2d8956c7a988' 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: 164kB\n",
       "Dimensions:              (sample: 100, component: 2, x: 150, grid: 2500)\n",
       "Coordinates:\n",
       "  * component            (component) <U1 8B 'A' 'B'\n",
       "  * x                    (x) float64 1kB 0.001 0.001047 0.001097 ... 0.9547 1.0\n",
       "Dimensions without coordinates: sample, grid\n",
       "Data variables:\n",
       "    composition          (sample, component) float64 2kB ...\n",
       "    ground_truth_labels  (sample) int64 800B ...\n",
       "    measurement          (sample, x) float64 120kB ...\n",
       "    composition_grid     (grid, component) float64 40kB ..."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from AFL.double_agent.data import example_dataset1\n",
    "    \n",
    "# Load the example dataset\n",
    "ds = example_dataset1()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the `measurements` data variable to see the two classes of measurements that we're going to try to separate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.measurement.plot.line(x='x',xscale='log',yscale='log',add_legend=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute the Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll execute our clustering pipeline on the dataset. The pipeline will process the measurements, calculate derivatives, compute similarity between samples, and finally assign each measurement to one of the two phases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "010de2ec0613472ea7b35311b2febc30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "  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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 184kB\n",
       "Dimensions:      (sample: 50, x: 150, log_x: 250, sample_i: 50, sample_j: 50)\n",
       "Coordinates:\n",
       "  * x            (x) float64 1kB 0.001 0.001047 0.001097 ... 0.9114 0.9547 1.0\n",
       "  * log_x        (log_x) float64 2kB -3.0 -2.988 -2.976 ... -0.0241 -0.01205 0.0\n",
       "Dimensions without coordinates: sample, sample_i, sample_j\n",
       "Data variables:\n",
       "    measurement  (sample, x) float64 60kB 1.667e+06 1.417e+06 ... 1.736 2.463\n",
       "    derivative   (sample, log_x) float64 100kB -3.262 -3.317 ... -0.2221 -0.2423\n",
       "    similarity   (sample_i, sample_j) float64 20kB 1.0 0.9496 ... 0.9958 1.0\n",
       "    labels       (sample) int64 400B 0 1 1 1 1 1 0 0 0 1 ... 0 0 0 1 1 0 1 1 1 1</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-87b4dc28-e904-46f6-8fa1-e03527defffd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87b4dc28-e904-46f6-8fa1-e03527defffd' 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><li><span class='xr-has-index'>x</span>: 150</li><li><span class='xr-has-index'>log_x</span>: 250</li><li><span>sample_i</span>: 50</li><li><span>sample_j</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-286b2e57-3a21-4467-86af-1b1b46ebaf0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-286b2e57-3a21-4467-86af-1b1b46ebaf0c' class='xr-section-summary' >Coordinates: <span>(2)</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 ... 0.9547 1.0</div><input id='attrs-390f4dac-6d9c-4c11-9349-b5d10e20a558' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-390f4dac-6d9c-4c11-9349-b5d10e20a558' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fb4451d-e02e-4ba7-8b55-d674ed069b91' class='xr-var-data-in' type='checkbox'><label for='data-7fb4451d-e02e-4ba7-8b55-d674ed069b91' 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, 0.001261, 0.001321,\n",
       "       0.001383, 0.001449, 0.001518, 0.00159 , 0.001665, 0.001744, 0.001827,\n",
       "       0.001914, 0.002005, 0.0021  , 0.002199, 0.002304, 0.002413, 0.002527,\n",
       "       0.002647, 0.002773, 0.002905, 0.003042, 0.003187, 0.003338, 0.003496,\n",
       "       0.003662, 0.003836, 0.004018, 0.004209, 0.004409, 0.004618, 0.004837,\n",
       "       0.005066, 0.005307, 0.005559, 0.005822, 0.006099, 0.006388, 0.006691,\n",
       "       0.007009, 0.007341, 0.00769 , 0.008055, 0.008437, 0.008837, 0.009256,\n",
       "       0.009696, 0.010156, 0.010638, 0.011142, 0.011671, 0.012225, 0.012805,\n",
       "       0.013413, 0.014049, 0.014716, 0.015414, 0.016146, 0.016912, 0.017714,\n",
       "       0.018555, 0.019435, 0.020358, 0.021324, 0.022335, 0.023395, 0.024505,\n",
       "       0.025668, 0.026886, 0.028162, 0.029498, 0.030898, 0.032364, 0.0339  ,\n",
       "       0.035509, 0.037194, 0.038959, 0.040807, 0.042744, 0.044772, 0.046897,\n",
       "       0.049122, 0.051453, 0.053894, 0.056452, 0.059131, 0.061936, 0.064875,\n",
       "       0.067954, 0.071179, 0.074556, 0.078094, 0.0818  , 0.085681, 0.089747,\n",
       "       0.094006, 0.098467, 0.103139, 0.108033, 0.11316 , 0.118529, 0.124154,\n",
       "       0.130045, 0.136216, 0.14268 , 0.14945 , 0.156542, 0.16397 , 0.171751,\n",
       "       0.179901, 0.188438, 0.197379, 0.206746, 0.216556, 0.226832, 0.237596,\n",
       "       0.24887 , 0.26068 , 0.27305 , 0.286006, 0.299578, 0.313794, 0.328684,\n",
       "       0.344281, 0.360618, 0.37773 , 0.395654, 0.414429, 0.434094, 0.454693,\n",
       "       0.476269, 0.498869, 0.522542, 0.547337, 0.57331 , 0.600514, 0.62901 ,\n",
       "       0.658858, 0.690122, 0.72287 , 0.757172, 0.793102, 0.830736, 0.870156,\n",
       "       0.911447, 0.954697, 1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>log_x</span></div><div class='xr-var-dims'>(log_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.0 -2.988 -2.976 ... -0.01205 0.0</div><input id='attrs-6b238741-f13b-4ca2-a3aa-fe6d0d855740' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b238741-f13b-4ca2-a3aa-fe6d0d855740' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c4ddf63-92d4-497f-b43f-b93a73985075' class='xr-var-data-in' type='checkbox'><label for='data-5c4ddf63-92d4-497f-b43f-b93a73985075' 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([-3.      , -2.987952, -2.975904, ..., -0.024096, -0.012048,  0.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ff9040a3-b18b-45e8-af52-130b4bae7574' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff9040a3-b18b-45e8-af52-130b4bae7574' class='xr-section-summary' >Data variables: <span>(4)</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</span></div><div class='xr-var-dims'>(sample, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.667e+06 1.417e+06 ... 1.736 2.463</div><input id='attrs-18c8eabf-797c-4f09-af44-0696adf844e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18c8eabf-797c-4f09-af44-0696adf844e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4297d0cc-8aa0-4692-bb0f-45953a018773' class='xr-var-data-in' type='checkbox'><label for='data-4297d0cc-8aa0-4692-bb0f-45953a018773' 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([[1.66681032e+06, 1.41657155e+06, 1.48046864e+06, ...,\n",
       "        1.82545944e+00, 2.08726041e+00, 2.07532316e+00],\n",
       "       [2.04704674e+00, 2.05620273e+00, 1.64341595e+00, ...,\n",
       "        1.77233056e+00, 2.19350969e+00, 1.71313157e+00],\n",
       "       [2.16372403e+00, 2.17089054e+00, 2.21690634e+00, ...,\n",
       "        1.99746317e+00, 2.21361648e+00, 1.89651039e+00],\n",
       "       ...,\n",
       "       [1.97066376e+00, 2.04335416e+00, 2.35637489e+00, ...,\n",
       "        2.27901656e+00, 1.92548698e+00, 2.10348609e+00],\n",
       "       [2.24268521e+00, 1.87774264e+00, 2.52947982e+00, ...,\n",
       "        2.14632797e+00, 2.42341491e+00, 1.83736469e+00],\n",
       "       [2.16278887e+00, 1.62603408e+00, 2.06192476e+00, ...,\n",
       "        1.84670516e+00, 1.73592981e+00, 2.46273598e+00]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>derivative</span></div><div class='xr-var-dims'>(sample, log_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.262 -3.317 ... -0.2221 -0.2423</div><input id='attrs-ebe84868-4ec3-4726-a8b0-985136050429' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebe84868-4ec3-4726-a8b0-985136050429' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2d381fd-a441-4042-b093-7d84d743fd17' class='xr-var-data-in' type='checkbox'><label for='data-f2d381fd-a441-4042-b093-7d84d743fd17' 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'><dt><span>description :</span></dt><dd>Savitsky-Golay filtered data</dd><dt><span>name :</span></dt><dd>SavgolFilter</dd><dt><span>input_variable :</span></dt><dd>measurement</dd><dt><span>output_variable :</span></dt><dd>derivative</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>None</dd><dt><span>dim :</span></dt><dd>x</dd><dt><span>npts :</span></dt><dd>250</dd><dt><span>xlo :</span></dt><dd>None</dd><dt><span>xhi :</span></dt><dd>None</dd><dt><span>xlo_isel :</span></dt><dd>None</dd><dt><span>xhi_isel :</span></dt><dd>None</dd><dt><span>pedestal :</span></dt><dd>None</dd><dt><span>derivative :</span></dt><dd>1</dd><dt><span>apply_log_scale :</span></dt><dd>True</dd><dt><span>polyorder :</span></dt><dd>2</dd><dt><span>window_length :</span></dt><dd>31</dd></dl></div><div class='xr-var-data'><pre>array([[-3.26237381, -3.31739876, -3.37242371, ...,  0.39407098,\n",
       "         0.43000691,  0.46594284],\n",
       "       [ 0.17010102,  0.16139183,  0.15268264, ..., -0.79188182,\n",
       "        -0.84429393, -0.89670605],\n",
       "       [-0.81536196, -0.76446481, -0.71356765, ...,  0.28131717,\n",
       "         0.31055531,  0.33979344],\n",
       "       ...,\n",
       "       [ 0.0843537 ,  0.075217  ,  0.06608031, ..., -0.02111355,\n",
       "        -0.03059186, -0.04007016],\n",
       "       [-0.59787666, -0.56217375, -0.52647083, ...,  0.45569821,\n",
       "         0.5034612 ,  0.55122419],\n",
       "       [-0.00936809, -0.00878607, -0.00820405, ..., -0.20180244,\n",
       "        -0.22205709, -0.24231174]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>similarity</span></div><div class='xr-var-dims'>(sample_i, sample_j)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9496 0.9512 ... 0.9958 1.0</div><input id='attrs-9c88f374-04a9-419d-9686-5619e5819376' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c88f374-04a9-419d-9686-5619e5819376' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-497afdaf-a8fd-4a1d-8758-9875ad590ca1' class='xr-var-data-in' type='checkbox'><label for='data-497afdaf-a8fd-4a1d-8758-9875ad590ca1' 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'><dt><span>metric :</span></dt><dd>laplacian</dd><dt><span>gamma :</span></dt><dd>0.0001</dd><dt><span>name :</span></dt><dd>SimilarityMetric</dd><dt><span>input_variable :</span></dt><dd>derivative</dd><dt><span>output_variable :</span></dt><dd>similarity</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>None</dd><dt><span>sample_dim :</span></dt><dd>sample</dd><dt><span>constrain_same :</span></dt><dd>[]</dd><dt><span>constrain_different :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[1.        , 0.94955571, 0.95123375, ..., 0.95039207, 0.95189277,\n",
       "        0.94995344],\n",
       "       [0.94955571, 1.        , 0.99489543, ..., 0.99547878, 0.99509997,\n",
       "        0.99635144],\n",
       "       [0.95123375, 0.99489543, 1.        , ..., 0.99496338, 0.99743651,\n",
       "        0.99585264],\n",
       "       ...,\n",
       "       [0.95039207, 0.99547878, 0.99496338, ..., 1.        , 0.99589615,\n",
       "        0.99701581],\n",
       "       [0.95189277, 0.99509997, 0.99743651, ..., 0.99589615, 1.        ,\n",
       "        0.99575875],\n",
       "       [0.94995344, 0.99635144, 0.99585264, ..., 0.99701581, 0.99575875,\n",
       "        1.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 1 1 1 1 0 0 ... 0 1 1 0 1 1 1 1</div><input id='attrs-4e595c60-6d11-405a-a67a-8759adb53595' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e595c60-6d11-405a-a67a-8759adb53595' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41aaa580-de63-43b3-aebe-883842e07c17' class='xr-var-data-in' type='checkbox'><label for='data-41aaa580-de63-43b3-aebe-883842e07c17' 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'><dt><span>n_phases :</span></dt><dd>2</dd><dt><span>name :</span></dt><dd>SpectralClustering</dd><dt><span>input_variable :</span></dt><dd>similarity</dd><dt><span>output_variable :</span></dt><dd>labels</dd><dt><span>input_prefix :</span></dt><dd>None</dd><dt><span>output_prefix :</span></dt><dd>None</dd><dt><span>dim :</span></dt><dd>sample</dd><dt><span>use_silhouette :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 1, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f2b5b99-c105-44ea-a05e-2a227d75b52e' class='xr-section-summary-in' type='checkbox'  ><label for='section-6f2b5b99-c105-44ea-a05e-2a227d75b52e' class='xr-section-summary' >Indexes: <span>(2)</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-7eb38331-f111-40f8-8cb8-0be8f0933b97' class='xr-index-data-in' type='checkbox'/><label for='index-7eb38331-f111-40f8-8cb8-0be8f0933b97' 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, 0.0012608724076806808,\n",
       "        0.001320703622736631, 0.0013833739627296209,  0.001449018150486198,\n",
       "       0.0015177773017322714,\n",
       "       ...\n",
       "          0.6588581861506815,    0.6901224802908528,    0.7228703350949566,\n",
       "            0.75717214883374,    0.7931016603333051,    0.8307361074919352,\n",
       "          0.8701563933188907,    0.9114472598521185,    0.9546974703287516,\n",
       "                         1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=150))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>log_x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-13fddf7a-a9a9-4ae9-92d9-a89ac70888b6' class='xr-index-data-in' type='checkbox'/><label for='index-13fddf7a-a9a9-4ae9-92d9-a89ac70888b6' 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([                 -3.0,   -2.9879518072289155,   -2.9759036144578315,\n",
       "          -2.963855421686747,   -2.9518072289156625,   -2.9397590361445785,\n",
       "          -2.927710843373494,   -2.9156626506024095,   -2.9036144578313254,\n",
       "          -2.891566265060241,\n",
       "       ...\n",
       "        -0.10843373493975905,  -0.09638554216867457,  -0.08433734939759008,\n",
       "        -0.07228915662650603,  -0.06024096385542155,  -0.04819277108433706,\n",
       "        -0.03614457831325302,  -0.02409638554216853, -0.012048192771084043,\n",
       "                         0.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;log_x&#x27;, length=250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2edb591e-435b-4fec-ad20-c3802f6d373a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2edb591e-435b-4fec-ad20-c3802f6d373a' 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: 184kB\n",
       "Dimensions:      (sample: 50, x: 150, log_x: 250, sample_i: 50, sample_j: 50)\n",
       "Coordinates:\n",
       "  * x            (x) float64 1kB 0.001 0.001047 0.001097 ... 0.9114 0.9547 1.0\n",
       "  * log_x        (log_x) float64 2kB -3.0 -2.988 -2.976 ... -0.0241 -0.01205 0.0\n",
       "Dimensions without coordinates: sample, sample_i, sample_j\n",
       "Data variables:\n",
       "    measurement  (sample, x) float64 60kB 1.667e+06 1.417e+06 ... 1.736 2.463\n",
       "    derivative   (sample, log_x) float64 100kB -3.262 -3.317 ... -0.2221 -0.2423\n",
       "    similarity   (sample_i, sample_j) float64 20kB 1.0 0.9496 ... 0.9958 1.0\n",
       "    labels       (sample) int64 400B 0 1 1 1 1 1 0 0 0 1 ... 0 0 0 1 1 0 1 1 1 1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_result = clustering_pipeline.calculate(ds)\n",
    "ds_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the resultant `xarray.Dataset`, we see a Data variable called `labels`. This is the pipelines attempt o group the data into two classes: Phase 0 or Phase1. \n",
    "\n",
    "Let's plot the data in two subplots to visualize the data that the pipeline puts in each of the classes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,figsize=(8,3))\n",
    "for label,sub_ds in ds_result.groupby('labels'):\n",
    "    sub_ds.measurement.plot.line(x='x',xscale='log',yscale='log',add_legend=False,ax=axes[label]);\n",
    "    axes[label].set_title(f'Phase {label}')\n",
    "\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the pipeline does an excellent job at separating the data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this quickstart tutorial, we demonstrated how to use the clustering pipeline to automatically classify different phases in a dataset. We:\n",
    " \n",
    "1. Started with a dataset containing multiple measurements with two distinct patterns\n",
    "2. Applied the clustering pipeline to analyze and classify the data\n",
    "3. Successfully separated the measurements into two distinct phases\n",
    " \n",
    "The pipeline was able to automatically detect and group similar measurements, making it a powerful tool for analyzing phase transitions and other classification tasks in scientific data.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}