{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c000",
   "metadata": {},
   "source": [
    "# Normalize, select, aggregate\n",
    "\n",
    "Plates differ in imaging day, reagent lot and microscope state, and a CellProfiler run gives a few thousand\n",
    "features, most of them redundant. This page puts the cells of a JUMP plate on the scale of its own controls and\n",
    "aggregates them to wells, then reduces the features of a whole screen to the informative ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:19:43.043830Z",
     "iopub.status.busy": "2026-10-22T03:19:43.043409Z",
     "iopub.status.idle": "2026-10-22T03:19:59.120979Z",
     "shell.execute_reply": "2026-10-22T03:19:59.120373Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import mantispy as mt\n",
    "\n",
    "cells = mt.ds.jump_cells()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c002",
   "metadata": {},
   "source": [
    "## Normalization\n",
    "\n",
    "Plates differ in imaging day, reagent lot and microscope state. The standard fix is to express\n",
    "every cell relative to the negative controls on its own plate, which is what\n",
    "`reference=\"negcon\"` together with `by=\"Metadata_Plate\"` does.\n",
    "\n",
    "The default method, `mad_robustize`, subtracts the control median and divides by a robust\n",
    "spread, so a few extreme cells do not set the scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:19:59.124811Z",
     "iopub.status.busy": "2026-10-22T03:19:59.124336Z",
     "iopub.status.idle": "2026-10-22T03:20:03.826890Z",
     "shell.execute_reply": "2026-10-22T03:20:03.825332Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:1: UserWarning: 285 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon'. Adding epsilon=1e-18 to their scale multiplies them by up to 1e18, so they dominate every distance downstream. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n",
      "  mt.pp.normalize(cells, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\", keep_raw=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.pp.normalize(cells, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\", keep_raw=True)\n",
    "\n",
    "controls = cells[cells.obs[\"Metadata_Control\"].to_numpy()]\n",
    "np.round(np.nanmedian(np.asarray(controls.X), axis=0)[:5], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004",
   "metadata": {},
   "source": [
    "The controls now sit at zero on every plate by construction. `keep_raw=True` kept the\n",
    "original values in `layers[\"raw\"]` for the before-and-after plot. It is off by default\n",
    "because the extra layer doubles memory and the original is already on disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:03.830094Z",
     "iopub.status.busy": "2026-10-22T03:20:03.829750Z",
     "iopub.status.idle": "2026-10-22T03:20:05.387889Z",
     "shell.execute_reply": "2026-10-22T03:20:05.386786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.feature_distributions(\n",
    "    cells, features=[\"Cells_AreaShape_Area\", \"Nuclei_Intensity_MeanIntensity_DNA\", \"Cells_Intensity_MeanIntensity_Mito\"]\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c006",
   "metadata": {},
   "source": [
    "### Features normalization cannot scale\n",
    "\n",
    "`normalize` warned about features in `var[\"degenerate_scale\"]`. They have no spread among the control cells of\n",
    "some plate, so `mad_robustize` divides them by `epsilon = 1e-18` instead of by zero, as pycytominer\n",
    "{cite:p}`Serrano_2025` does, and multiplies them by up to 1e18."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c007",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:05.393352Z",
     "iopub.status.busy": "2026-10-22T03:20:05.392921Z",
     "iopub.status.idle": "2026-10-22T03:20:05.400107Z",
     "shell.execute_reply": "2026-10-22T03:20:05.399112Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flagged degenerate_scale': 285, 'of': 5839}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\"flagged degenerate_scale\": int(cells.var[\"degenerate_scale\"].sum()), \"of\": cells.n_vars}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c008",
   "metadata": {},
   "source": [
    "A variance threshold does not catch a feature that varies across the plate and is constant among the control\n",
    "wells: it measures variance across all wells, keeps that feature, and the feature then dominates every distance\n",
    "computed afterwards. The flag catches it. {func}`~mantispy.pp.feature_select` drops flagged features first by\n",
    "default and judges the rest without them. Anything that reads the object before selection needs them gone by\n",
    "hand:\n",
    "\n",
    "```python\n",
    "adata = adata[:, ~adata.var[\"degenerate_scale\"].to_numpy()].copy()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c009",
   "metadata": {},
   "source": [
    "## Aggregating to wells\n",
    "\n",
    "Each well's profile is the median across its cells. The median is used instead of the mean so\n",
    "that a few extreme cells do not move the profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22ad68c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:05.402211Z",
     "iopub.status.busy": "2026-10-22T03:20:05.401877Z",
     "iopub.status.idle": "2026-10-22T03:20:05.407095Z",
     "shell.execute_reply": "2026-10-22T03:20:05.406102Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13,578 single cells x 5839 features, resolution=object\n",
      "across 24 wells on 1 plate(s)\n"
     ]
    }
   ],
   "source": [
    "print(f\"{cells.n_obs:,} single cells x {cells.n_vars} features, resolution={cells.uns['mantispy']['resolution']}\")\n",
    "print(f\"across {cells.obs['Metadata_Well'].nunique()} wells on {cells.obs['Metadata_Plate'].nunique()} plate(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:05.408980Z",
     "iopub.status.busy": "2026-10-22T03:20:05.408826Z",
     "iopub.status.idle": "2026-10-22T03:20:06.949768Z",
     "shell.execute_reply": "2026-10-22T03:20:06.948576Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 24 × 5839\n",
       "    obs: 'Metadata_Plate', 'Metadata_Well', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_BinningX', 'Metadata_BinningY', 'Metadata_ChannelID', 'Metadata_ChannelName', 'Metadata_Col', 'Metadata_ExposureTime', 'Metadata_ImageResolutionX', 'Metadata_ImageResolutionY', 'Metadata_ImageSizeX', 'Metadata_ImageSizeY', 'Metadata_MainEmissionWavelength', 'Metadata_MainExcitationWavelength', 'Metadata_MaxIntensity', 'Metadata_ObjectiveMagnification', 'Metadata_ObjectiveNA', 'Metadata_PlaneID', 'Metadata_PositionZ', 'Metadata_Row', 'Metadata_ObjectType', 'Metadata_Source', 'Metadata_JCP2022', 'Metadata_InChIKey', 'Metadata_Perturbation', 'Metadata_Perturbation_Type', 'Metadata_Control_Type', 'Metadata_Control'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind', 'selected', 'degenerate_scale'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells = mt.tl.aggregate(cells, by=(\"Metadata_Plate\", \"Metadata_Well\"), func=\"median\")\n",
    "wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca661e82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:06.952876Z",
     "iopub.status.busy": "2026-10-22T03:20:06.952704Z",
     "iopub.status.idle": "2026-10-22T03:20:07.070733Z",
     "shell.execute_reply": "2026-10-22T03:20:07.069094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aggregated 13,578 cells -> 24 well profiles (median), resolution=well\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_Plate</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>Metadata_CellCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>A02</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>A21</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>B01</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>B04</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>B09</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Metadata_Plate Metadata_Well  Metadata_CellCount\n",
       "0     BR00121438           A02                 702\n",
       "1     BR00121438           A21                 582\n",
       "2     BR00121438           B01                 401\n",
       "3     BR00121438           B04                 555\n",
       "4     BR00121438           B09                 498"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\n",
    "    f\"aggregated {cells.n_obs:,} cells -> {wells.n_obs} well profiles (median), \"\n",
    "    f\"resolution={wells.uns['mantispy']['resolution']}\"\n",
    ")\n",
    "wells.obs[[\"Metadata_Plate\", \"Metadata_Well\", \"Metadata_CellCount\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c011",
   "metadata": {},
   "source": [
    "The result keeps every metadata column that is constant within a\n",
    "well. Columns that vary within a well, such as a per-cell measurement, are dropped because\n",
    "they have no single value per well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c013",
   "metadata": {},
   "source": [
    "`Metadata_CellCount` is added as well. Cell count is a common confounder, and `mt.pp.regress_out` can remove its\n",
    "influence later.\n",
    "\n",
    "## Feature selection\n",
    "\n",
    "Most of a CellProfiler run's features are redundant. `AreaShape_Area` and `AreaShape_Perimeter` carry much the\n",
    "same information, and so does a texture feature at scale 3 and the same texture at scale 5. Others are constant,\n",
    "or blow up because they are ratios with a near-zero denominator. mantispy reproduces pycytominer's operations\n",
    "{cite:p}`Serrano_2025`, verified feature by feature, because published pipelines depend on their precise behavior.\n",
    "\n",
    "Selection needs a screen rather than one plate's 24 wells. `pki` is the JUMP pilot's kinase inhibitors: eight\n",
    "plates, 3,072 wells {cite:p}`Chandrasekaran_2023`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c014",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:07.073651Z",
     "iopub.status.busy": "2026-10-22T03:20:07.073233Z",
     "iopub.status.idle": "2026-10-22T03:20:07.865275Z",
     "shell.execute_reply": "2026-10-22T03:20:07.864423Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:2: UserWarning: 133 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon'. Adding epsilon=1e-18 to their scale multiplies them by up to 1e18, so they dominate every distance downstream. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n",
      "  mt.pp.normalize(screen, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3072 × 5839\n",
       "    obs: 'Metadata_plate_map_name', 'Metadata_broad_sample', 'Metadata_mg_per_ml', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_Plate', 'Metadata_Well', 'Metadata_Site_Count', 'Metadata_Count_Cells', 'Metadata_Count_CellsIncludingEdges', 'Metadata_Count_Cytoplasm', 'Metadata_Count_Nuclei', 'Metadata_Count_NucleiIncludingEdges', 'Metadata_Object_Count', 'Metadata_Barcode', 'Metadata_Supplier', 'Metadata_Supplier_Catalog', 'Metadata_pert_type', 'Metadata_control_type', 'Metadata_Row', 'Metadata_Col', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_Control_Type', 'Metadata_Control', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationUnit', 'Metadata_MOA', 'Metadata_Perturbation', 'Metadata_Perturbation_Type'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind', 'degenerate_scale'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "screen = mt.ds.pki()\n",
    "mt.pp.normalize(screen, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\")\n",
    "screen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c015",
   "metadata": {},
   "source": [
    "## The operations\n",
    "\n",
    "Each operation tests one criterion, and `feature_select` combines them. It does not drop\n",
    "anything: it writes a boolean column to `var`, and only `subset_features` changes the shape,\n",
    "so you can inspect what would be removed first.\n",
    "\n",
    "| operation | drops a feature when |\n",
    "| --- | --- |\n",
    "| `drop_degenerate` | `normalize` could not scale it and flagged it in `var[\"degenerate_scale\"]` |\n",
    "| `variance_threshold` | its variance is below `min_variance` |\n",
    "| `frequency_threshold` | one value dominates it, or it takes very few distinct values |\n",
    "| `correlation_threshold` | it is highly correlated with another feature |\n",
    "| `drop_na_columns` | too much of it is missing |\n",
    "| `blocklist` | it is on the CellProfiler blocklist of known-unreliable features |\n",
    "| `drop_outliers` | its magnitude has blown up |\n",
    "| `noise_removal` | it varies too much between replicates of the same perturbation |\n",
    "\n",
    "Two of these are easy to get wrong.\n",
    "\n",
    "`variance_threshold` is a plain variance cut. The frequency-and-uniqueness rule often\n",
    "described under that name is a separate operation, `frequency_threshold`, and its two\n",
    "criteria combine with OR.\n",
    "\n",
    "`correlation_threshold` uses the signed correlation, not the absolute value, so two\n",
    "features correlated at −1.0 are both kept. pycytominer does the same, and mantispy matches\n",
    "it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:07.866953Z",
     "iopub.status.busy": "2026-10-22T03:20:07.866790Z",
     "iopub.status.idle": "2026-10-22T03:20:11.875083Z",
     "shell.execute_reply": "2026-10-22T03:20:11.874248Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drop_degenerate          keeps  5706 of 5839\n",
      "variance_threshold       keeps  5828 of 5839\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation_threshold    keeps   928 of 5839\n",
      "drop_na_columns          keeps  5839 of 5839\n",
      "blocklist                keeps  5784 of 5839\n"
     ]
    }
   ],
   "source": [
    "for operation in (\"drop_degenerate\", \"variance_threshold\", \"correlation_threshold\", \"drop_na_columns\", \"blocklist\"):\n",
    "    trial = screen.copy()\n",
    "    mt.pp.feature_select(trial, operations=(operation,))\n",
    "    print(f\"{operation:24s} keeps {int(trial.var['selected'].sum()):5d} of {trial.n_vars}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c017",
   "metadata": {},
   "source": [
    "Each operation is judged on its own here. `correlation_threshold` does the most, because most of a CellProfiler\n",
    "run is near-duplicates of other features.\n",
    "\n",
    "## Running the default pipeline\n",
    "\n",
    "The default runs five of the eight operations: `drop_degenerate`, which runs first, and pycytominer's own default\n",
    "of four. `frequency_threshold` and `drop_outliers` are off because both can remove an informative feature on a\n",
    "screen with few conditions, and `noise_removal` is off because it needs replicate structure, which not every\n",
    "object has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c018",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:11.876767Z",
     "iopub.status.busy": "2026-10-22T03:20:11.876592Z",
     "iopub.status.idle": "2026-10-22T03:20:15.565582Z",
     "shell.execute_reply": "2026-10-22T03:20:15.564830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'drop_degenerate': 133,\n",
       " 'variance_threshold': 0,\n",
       " 'correlation_threshold': 4836,\n",
       " 'drop_na_columns': 0,\n",
       " 'blocklist': 55}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.pp.feature_select(screen)\n",
    "screen.uns[\"mantispy\"][\"feature_select\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c019",
   "metadata": {},
   "source": [
    "### Before and after\n",
    "\n",
    "The correlation heatmap is ordered by feature group, with a line at each group boundary. Here it is for the shape\n",
    "measurements of the whole cell, before selection and after. Blocks along the diagonal are features that measure\n",
    "nearly the same thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:15.567662Z",
     "iopub.status.busy": "2026-10-22T03:20:15.567462Z",
     "iopub.status.idle": "2026-10-22T03:20:23.850105Z",
     "shell.execute_reply": "2026-10-22T03:20:23.848818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script>\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        </script>\n",
       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"05b41ca7-7eb3-4e9d-ac44-e3ee9f1f8f1d\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"05b41ca7-7eb3-4e9d-ac44-e3ee9f1f8f1d\")) {                    Plotly.newPlot(                        \"05b41ca7-7eb3-4e9d-ac44-e3ee9f1f8f1d\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"0\",\"x\":[\"Cells_AreaShape_Area\",\"Cells_AreaShape_BoundingBoxArea\",\"Cells_AreaShape_Compactness\",\"Cells_AreaShape_Eccentricity\",\"Cells_AreaShape_EquivalentDiameter\",\"Cells_AreaShape_EulerNumber\",\"Cells_AreaShape_Extent\",\"Cells_AreaShape_FormFactor\",\"Cells_AreaShape_MajorAxisLength\",\"Cells_AreaShape_MaxFeretDiameter\",\"Cells_AreaShape_MaximumRadius\",\"Cells_AreaShape_MeanRadius\",\"Cells_AreaShape_MedianRadius\",\"Cells_AreaShape_MinFeretDiameter\",\"Cells_AreaShape_MinorAxisLength\",\"Cells_AreaShape_Orientation\",\"Cells_AreaShape_Perimeter\",\"Cells_AreaShape_Solidity\",\"Cells_AreaShape_Zernike_0_0\",\"Cells_AreaShape_Zernike_1_1\",\"Cells_AreaShape_Zernike_2_0\",\"Cells_AreaShape_Zernike_2_2\",\"Cells_AreaShape_Zernike_3_1\",\"Cells_AreaShape_Zernike_3_3\",\"Cells_AreaShape_Zernike_4_0\",\"Cells_AreaShape_Zernike_4_2\",\"Cells_AreaShape_Zernike_4_4\",\"Cells_AreaShape_Zernike_5_1\",\"Cells_AreaShape_Zernike_5_3\",\"Cells_AreaShape_Zernike_5_5\",\"Cells_AreaShape_Zernike_6_0\",\"Cells_AreaShape_Zernike_6_2\",\"Cells_AreaShape_Zernike_6_4\",\"Cells_AreaShape_Zernike_6_6\",\"Cells_AreaShape_Zernike_7_1\",\"Cells_AreaShape_Zernike_7_3\",\"Cells_AreaShape_Zernike_7_5\",\"Cells_AreaShape_Zernike_7_7\",\"Cells_AreaShape_Zernike_8_0\",\"Cells_AreaShape_Zernike_8_2\",\"Cells_AreaShape_Zernike_8_4\",\"Cells_AreaShape_Zernike_8_6\",\"Cells_AreaShape_Zernike_8_8\",\"Cells_AreaShape_Zernike_9_1\",\"Cells_AreaShape_Zernike_9_3\",\"Cells_AreaShape_Zernike_9_5\",\"Cells_AreaShape_Zernike_9_7\",\"Cells_AreaShape_Zernike_9_9\"],\"y\":[\"Cells_AreaShape_Area\",\"Cells_AreaShape_BoundingBoxArea\",\"Cells_AreaShape_Compactness\",\"Cells_AreaShape_Eccentricity\",\"Cells_AreaShape_EquivalentDiameter\",\"Cells_AreaShape_EulerNumber\",\"Cells_AreaShape_Extent\",\"Cells_AreaShape_FormFactor\",\"Cells_AreaShape_MajorAxisLength\",\"Cells_AreaShape_MaxFeretDiameter\",\"Cells_AreaShape_MaximumRadius\",\"Cells_AreaShape_MeanRadius\",\"Cells_AreaShape_MedianRadius\",\"Cells_AreaShape_MinFeretDiameter\",\"Cells_AreaShape_MinorAxisLength\",\"Cells_AreaShape_Orientation\",\"Cells_AreaShape_Perimeter\",\"Cells_AreaShape_Solidity\",\"Cells_AreaShape_Zernike_0_0\",\"Cells_AreaShape_Zernike_1_1\",\"Cells_AreaShape_Zernike_2_0\",\"Cells_AreaShape_Zernike_2_2\",\"Cells_AreaShape_Zernike_3_1\",\"Cells_AreaShape_Zernike_3_3\",\"Cells_AreaShape_Zernike_4_0\",\"Cells_AreaShape_Zernike_4_2\",\"Cells_AreaShape_Zernike_4_4\",\"Cells_AreaShape_Zernike_5_1\",\"Cells_AreaShape_Zernike_5_3\",\"Cells_AreaShape_Zernike_5_5\",\"Cells_AreaShape_Zernike_6_0\",\"Cells_AreaShape_Zernike_6_2\",\"Cells_AreaShape_Zernike_6_4\",\"Cells_AreaShape_Zernike_6_6\",\"Cells_AreaShape_Zernike_7_1\",\"Cells_AreaShape_Zernike_7_3\",\"Cells_AreaShape_Zernike_7_5\",\"Cells_AreaShape_Zernike_7_7\",\"Cells_AreaShape_Zernike_8_0\",\"Cells_AreaShape_Zernike_8_2\",\"Cells_AreaShape_Zernike_8_4\",\"Cells_AreaShape_Zernike_8_6\",\"Cells_AreaShape_Zernike_8_8\",\"Cells_AreaShape_Zernike_9_1\",\"Cells_AreaShape_Zernike_9_3\",\"Cells_AreaShape_Zernike_9_5\",\"Cells_AreaShape_Zernike_9_7\",\"Cells_AreaShape_Zernike_9_9\"],\"z\":{\"dtype\":\"f8\",\"bdata\":\"AAAAAAAA8D+qkQkuJJTvP3ojyI3pbu0\\u002fbPuxH2TUw784Ied2F83vP3JVmZDcwH8\\u002fCIl291Djx7\\u002fZjNWv6VPsv4An\\u002fu5yd+8\\u002fRvh1qMZv7z+wtDRM32\\u002fvP8D8Ic1UT+8\\u002fcWBfgro+7z8tD6D3GLPvP4+3+Hkvn+8\\u002fVWlZ0yZjrb\\u002fVSF8GSn7vP2qOP68wgOG\\u002f\\u002fkbzyl\\u002fwy7+n5LJ9iQ\\u002fLP1khkzYfBcK\\u002fJ8tFwm2b6b\\u002fmBK8sOjzPP6Q7UmQj0uG\\u002fpVMJe5aSwT8JnoEx+l2+v\\u002fx0Z718Nem\\u002fE\\u002fTsR98y3D8w+h5SJTy1Py5fVv9PoOO\\u002fx0RlUYoi0D\\u002fL\\u002fORmaorKP5NgaZW4h5K\\u002fCPBwKkwS6r8swQ2X9XzXP44n7jrn8Mo\\u002fGbXZLFKtbL855WHfL3Tmv0x\\u002f2RheFcI\\u002fl22FuefqyT\\u002fU2gMUKk\\u002fCP66K8CCn3Iq\\u002fKoTsO4EV6r9udwncMEfRP7NLc6+lkMM\\u002fxlK3WEo6wD8OEe6zyTqkv96MbMNH3Oe\\u002fqpEJLiSU7z8AAAAAAADwP9\\u002f7xI1DQe4\\u002fzS1ejZpSv7+63jenaS7vP7WnxGimdIw\\u002fol04QKwUz7\\u002fWfs\\u002feiwjtvwbhYxTgfu8\\u002fwvaBi\\u002faq7z95MJymhvPuPxyirQERu+4\\u002fILuC+DSm7j8JKME795jvP3BwZYk\\u002flu8\\u002fRQ9jPGbrqr\\u002fO1XDv2+fvP+tqcrAlIOO\\u002ftMITxGHY0L8P3MDcIxvNPy0g7kIiJcm\\u002fXiqOFMWz6b8T9uekDW7SP2gvOWDQ8eG\\u002fG4Im4kSrvT\\u002fkkwDME2K3v9NyZMtaNum\\u002fWG5SDIfo3D9EkMnLoMu6P\\u002f6XW67XBeS\\u002fCZ67vJlb0D\\u002fKDC1mE97JPxq0PHCthnU\\u002fnv3Bwtvp6b8o9VD11cXXP9CYSps+WM0\\u002f7r27mo\\u002fkij\\u002faMp9e3czmvzwipE2aMMI\\u002fAqbIB2Luyz8WI98N30vDP8b9HSqUaXc\\u002f6H4FYw+T6r\\u002flcwMIU9TRP4B\\u002fF5+T\\u002fsQ\\u002f7JSvsjzfwj9E93i6ghefv\\u002f\\u002fAdLe2aui\\u002feiPIjelu7T\\u002ff+8SNQ0HuPwAAAAAAAPA\\u002f0eYh5M+4tT8l8x+KukjtPxlWFqWTo48\\u002fD6vErNlL3b9d37fQQDPvv4uIQgsV4u0\\u002fBAxe6HtK7j8hvvb9OxLsP0UkOfxHoes\\u002frxSLaZdu6z9ooPu9y\\u002fLtP\\u002fpoknwXgu0\\u002fyVNHP8fqrb8ImPe694TuP5XiCJYSEum\\u002fzYEL6RwW3r8FGJRoMGnGP+tdYCQo9de\\u002fEQZcMmPz6L8cANNevLDdP1Gp5IQzYuK\\u002fqmzB2hbygr+7zug8oca1PyB+VUtLdua\\u002fxrdSSUF53T9v5O4yIMfQP2kPgQ2JfOS\\u002fiHJOLG\\u002f7yj8dkcCNL27CP2A\\u002f5HYIo8g\\u002fID6EnHN557+kX\\u002fRx+QLXP62HhFO40tQ\\u002fyFQyAA73xD\\u002fF2EeRVxLnv2PEJZc9SsA\\u002fLsQUso7b0T\\u002f0sOCBeh3MP+dOdD9ZScc\\u002fXtXAK8El6L9KyvUB9IHRPxlXEexnO88\\u002f2actYSJZ0T+qnFphaEy5PxglhAirfOi\\u002fbPuxH2TUw7\\u002fNLV6NmlK\\u002fv9HmIeTPuLU\\u002fAAAAAAAA8D9UKiuEQ7vBv4nRi6yOiaC\\u002fIcQjaDrY6b+\\u002fnZvtXVW+v6C6OjEyeLm\\u002fngRku9O\\u002ftL+jzuyHb77Rv5\\u002f73f\\u002fWwdK\\u002fYbETh\\u002fII07\\u002fngg7G4VXEvzZt\\u002fBTizsm\\u002fx32Rgltkt78UWPPpjvy9v5\\u002fRZEjh4OC\\u002fljCmsbUy7L\\u002f94ECzwk3Pv4sshfmMPOG\\u002fLOqRXrBK1T++IubJXrDnPxC58Nv46bS\\u002fDU3fVdzg4r9Z3IDWo2btP3FT4b0hG94\\u002f1tUmRH31or\\u002fx6OLj6sPhP7znp4KJ0bS\\u002f5Nb0kjev0L\\u002fdfpJMy6nFv8I9Y4rNt+o\\u002fuvjdMxe83D8g6dl4Wzikv2klrphbRdc\\u002fE+UusY3a4T8SF9HAUp6xv8peasGJ8cG\\u002fkExv+iHP0z+YIRmjHPDTP065ebHZyeg\\u002fVHpAwZDI2D+g5+CeVomyv0tq7Mp7cdc\\u002fJUtJ\\u002faWn3T90p\\u002f45KN3eP\\u002f04X25PorW\\u002fOCHndhfN7z+63jenaS7vPyXzH4q6SO0\\u002fVCorhEO7wb8AAAAAAADwP1+nyYzKPW8\\u002fTWMP06lbyb9AkbLDfIrsvxuaviN1hu8\\u002f9PN2ED9x7z9QX9P8KHzvP0Y9vZ9iW+8\\u002f8U5CZ4BL7z+ODCefCK7vPw+rtNGqle8\\u002fN7okyFAJrr9Ui28uM03vP\\u002fzc\\u002fmVjwuG\\u002fqxhJKilkzr\\u002fWqsYgJNbHP4IibWPyvcC\\u002f+maxZvvY6b\\u002fH+MiXoD3QP1taZKguT+K\\u002f\\u002fuO5hF\\u002fGwD9IGPeA1+q4v8r1zGUiIum\\u002fnECneiZd2z\\u002fpinl89qq3Px77UwbzBOS\\u002f+boiytxjzz+mzDrDWHzJP3CeE29mboE\\u002fzavL34TO6b82WYDyMD7XPyh8seDd4cs\\u002flFPUUgnFjT+0WXFgL7Lmv5ZhJ2EZlsI\\u002fVoEf4HiAyj8nFiqqqyDDPzy2yEIohJI\\u002f8RB9dg3C6b\\u002f4X5\\u002f24PjQP7pFhBVCJ8Q\\u002fOgdIAXADwj+vnhy3ki2QvxML3wGgEei\\u002fclWZkNzAfz+1p8RopnSMPxlWFqWTo48\\u002fidGLrI6JoL9fp8mMyj1vPwAAAAAAAPA\\u002fRo8v6mpekj9o\\u002f90IApyAv6cqLAL+UHY\\u002f9mlcPjKleT+i4+pe3XmIP+3HFk9owog\\u002fy6cD175siD\\u002f\\u002fgNnGkm2LP9cFiuSWyY4\\u002fdNbk9Syofj+cjtzZr8KPP8jmkIBXL1i\\u002f2YB5DwftmT9GWfbq9z2WP4EB3c4HgJy\\u002frdCDomK8gb9+DYMb4AWZv2ZrtqpEOWQ\\u002f\\u002fkzc\\u002fBn+lz9TqJtbLYufv1lmcj0miqe\\u002fvIi2xhMNgj+x\\u002f6CcjihsP+lz\\u002fzo\\u002f8X4\\u002fkH8Cmm8OiT+uoaKHBhpmP8jeelBYvqK\\u002fx7vKeu16pr+X0ZYeMDR\\u002fvxBR0ZaJAZG\\u002fKYPIyOJdk7\\u002fVELN\\u002fUYyKv9IWRBf+lpY\\u002fisQBXyfNkL\\u002fp0OKt\\u002fxGHv7X+edt6zae\\u002f4TTbp2VOpr+d7xAy14OCvx+Xdn84cTA\\u002fNeHGbkLxe7\\u002fTkuyMniehvzPknYH0\\u002fpC\\u002fCIl291Djx7+iXThArBTPvw+rxKzZS92\\u002fIcQjaDrY6b9NYw\\u002fTqVvJv0aPL+pqXpI\\u002fAAAAAAAA8D9V0c27nBHdP7w4zCtjVM2\\u002fhYasgwSm0L86RGfHehWuv5XX6pz1Y5+\\u002fBWBIR+1Olr93pvwdzCjLvz0w4PtAx8W\\u002fPkmwg0nKtj9OrwO+Iw7Qv+nayLyZkus\\u002fh2dB7xOA7j8vSBszNgihP6EL3RuMa+c\\u002f8n6ZPzFewj+oil1Budfrv9hSafRrasE\\u002fNORa2w9L4T9nROqxF2bov9GjfAmSNLe\\u002fMG5QRl0Jy7\\u002fCpcsst7rlv0tDq5jMis4\\u002fmOkLLP74tT+AdqrbCm7CPxznydt56Oi\\u002fNlccppNDtr9h5xR4NlrBvymDUHbbX96\\u002fPmrNr1mW47\\u002fYTTAF+sDPP3nY9EzGDac\\u002fc7uEqzcR2L8C4br4YBnVv8LlqrrfIue\\u002fnj5PLHJkj7+hOWHdgoSrv3lOq\\u002fsz\\u002f9u\\u002fBNB6yTvq4L\\u002f8g4H1b2\\u002fgv2wRMrarudM\\u002f2YzVr+lT7L\\u002fWfs\\u002feiwjtv13ft9BAM++\\u002fv52b7V1Vvr9AkbLDfIrsv2j\\u002f3QgCnIC\\u002fVdHNu5wR3T8AAAAAAADwP1PmwsbFXO2\\u002fIPB4xo+67b84v2phJlvrv2dT5ED6AOu\\u002fYVT4T43e6r\\u002f60sUXF+rsv9squc9Xk+y\\u002fBg9Upmaurj+ipd47B5ntv\\u002fC8dT4Tbeg\\u002f8czL455R3z8q8nkjGEy\\u002fvyr1q80h99I\\u002fJ\\u002fGKxr\\u002fM5z\\u002fMgCqtsgDev6u09EY1r+I\\u002fsrbOjQXWoD8hobRwHpLAv4QpCzgVMeU\\u002fU3R1CIBG2r8xTk8ugqjRvzaGszxdbOQ\\u002fc470+gBVxb+9VpH0ZEu\\u002fv5W4qeiH7c6\\u002f71Weye\\u002fj5T\\u002fwIi35J3LVvxEY9Ue8atS\\u002fBPXwmgkxyb9u\\u002fsrt\\u002fdjmP2ARD+8fMre\\u002fE+84O3sp0b8kpNj4vXnMv+0qO5xrM86\\u002fLNiXXfrh5j97+qzmYHfPv9sX\\u002fqaShc2\\u002fgZhLsvEK079xTpgYGZLCv7ynd6Ey3uc\\u002fgCf+7nJ37z8G4WMU4H7vP4uIQgsV4u0\\u002foLo6MTJ4ub8bmr4jdYbvP6cqLAL+UHY\\u002fvDjMK2NUzb9T5sLGxVztvwAAAAAAAPA\\u002fDN7ZVqbo7z8NVYVV1jnvP6mdanCpFO8\\u002fujjTw4cH7z\\u002fUN89641TvP7P\\u002f5sATWe8\\u002fYbOySheRrb9lnz1UxZXvPwkB53xxZeK\\u002fuWt\\u002fC6qr0b8GTul4JrzEPwlreEqe1cS\\u002fqeoplS2F6b9fnLDqfrHRP9LD6xRMWuO\\u002fI0t\\u002fglKfwT+RBttdO1yxvwpqnqEX6ui\\u002fBjRofb312z8DcCRYZoW4PzlKj6bG7uS\\u002f8Qt\\u002foA\\u002fGzz+u7BiAgz7MP8la9gtoTaM\\u002fuBbbNyxQ6b9SuJFaQy3XPw628\\u002fTk0c0\\u002fsM+s\\u002fHXsmD\\u002f+ccocBnXnv7nXOvkAvME\\u002fA7Zor9iyyz9JxkBVpCDGPwCBGPgT5ak\\u002fSUxbP6mc6b8cCPhMmyDRP3H8UtJkosQ\\u002fgZB\\u002fXehNxT8D9dHX1IV4v5AmJROrx+i\\u002fRvh1qMZv7z\\u002fC9oGL9qrvPwQMXuh7Su4\\u002fngRku9O\\u002ftL\\u002f083YQP3HvP\\u002fZpXD4ypXk\\u002fhYasgwSm0L8g8HjGj7rtvwze2Vam6O8\\u002fAAAAAAAA8D8P2QQVsQfvP0rCQce92u4\\u002fXWJveg7L7j9\\u002fi0ET03rvP7R6mgcZdO8\\u002fn7CAq\\u002fLArr\\u002fOZcA9UMTvP3ak063ZTeO\\u002fh+xZREZH07\\u002fEGKJ0r0vGPxs7GxQYNce\\u002fqoQDLJHG6b\\u002fSewSWAofTP9DaDdWrP+O\\u002f1YUCqo0UvT9nKnqJDgusv+GtfDyHwei\\u002fPhCCMW\\u002fs2z9uOF3FVQO9P\\u002f7gBQ7XBuW\\u002fysmHQ8Rbzz8omUrNhqDKP9n5YWo36ao\\u002ftBTET4dH6b9d1EdzqHbXP6R+ZnFNMs8\\u002foOiR+s2aoz9Tu2kAeJTnv2r\\u002fYJwz2sE\\u002fLLabGPMbzT8lr2eEWUbGP5hh05qm\\u002fK8\\u002fC5f3Euvg6b9roahz5lnRP+t9ouXguMU\\u002fFQjlh4aWxj+RkFUjn7JqP2OS0Ohr8ui\\u002fsLQ0TN9v7z95MJymhvPuPyG+9v07Euw\\u002fo87sh2++0b9QX9P8KHzvP6Lj6l7deYg\\u002fOkRnx3oVrr84v2phJlvrvw1VhVXWOe8\\u002fD9kEFbEH7z8AAAAAAADwPytepjno+O8\\u002fXjHDHbLx7z\\u002f3YXbV\\u002fmXvP4huN2vmnu8\\u002f33e\\u002fa9ykoL+IcfgqCQ3vPx5M0ERriNy\\u002fAO9BBdlPuL9sdwyCPG7MP4\\u002fLw94aI6W\\u002f1Trptwgd6r\\u002f0kLApBPrAP0EXxEzBIeK\\u002f6yqXQiOTzT\\u002ftG\\u002f0EUCfNv9bFBapKx+q\\u002f+LrZRdfi2j\\u002fuo6fxfGSLvxqYlH2ek+O\\u002fuqkrxgrB0T8L3z52pJLMPwtV2qCyd7+\\u002fY9uv1AJc67+5dWZ8yrTWP5GzYtlp4cI\\u002f3wOO6pO1tr8LjfnpOljmv+1LELE9ksQ\\u002fUXmeVoJcwz+\\u002fOoJlSEW3P8rNF8Qpxbu\\u002fjntQV8Yo679WlO6z52XRP4eM923JwLY\\u002fxnMeMwalrT+aM09FTG68v0QY\\u002fjLpdOe\\u002fwPwhzVRP7z8coq0BEbvuP0UkOfxHoes\\u002fn\\u002fvd\\u002f9bB0r9GPb2fYlvvP+3HFk9owog\\u002fldfqnPVjn79nU+RA+gDrv6mdanCpFO8\\u002fSsJBx73a7j8rXqY56PjvPwAAAAAAAPA\\u002fJsPrgC3+7z8RT4T5OzTvP8D+Tdy2de8\\u002fEeb523\\u002fgn79c035JZtHuP+RNt5Mvqdq\\u002fijERz+Ewsr+8Bya\\u002fhQXMP3iTLEmiK5K\\u002f+F1Hh73J6b823x738Ci8P\\u002flxw20yIeK\\u002fm6bovY9\\u002fzz+bfyPlMTTPv1snB57Isuq\\u002f9qPXEFBs2j8kEi13pKegv+z3xRLGb+O\\u002fQpAEMlWz0T\\u002fjzDFzBEfNP2h1MVJD5cG\\u002f0lHyR+Jb67+3pvRaRVvWP+CxTH+hIME\\u002f8QCIwcT8ur8OTXdxDz7mv8DAWNBDOsQ\\u002fs40TSZYBwj+HNH5Ll4C1P\\u002fBuTObtBsC\\u002fhPXX7NEd67\\u002ft7SDyjRnRPyn3HUj8FLM\\u002fx90eYYBlpj\\u002fhIfWlhhjAv2nuy6c7Qee\\u002fcWBfgro+7z8gu4L4NKbuP68Ui2mXbus\\u002fYbETh\\u002fII07\\u002fxTkJngEvvP8unA9e+bIg\\u002fBWBIR+1Olr9hVPhPjd7qv7o408OHB+8\\u002fXWJveg7L7j9eMcMdsvHvPybD64At\\u002fu8\\u002fAAAAAAAA8D\\u002fHdggZxhzvPyVrl8GGZO8\\u002fDuXMLuBrn7\\u002ff20ljy7vuP5g5ca3D9Nm\\u002fMHHIt8ZZsL+Yjx0X99HLPzfTjjd91YK\\u002fOYLkYSSW6b\\u002fEuPPSMVu6P81bNYnGFOK\\u002fYeNluP0I0D\\u002fGrQdwc4zPv5A42DokoOq\\u002fbWdPW3I12j9plILT7oSjv1vM3mVJW+O\\u002f++BLehuh0T+2JSWau43NP9BEM+oJbsK\\u002frBkhQb1G67+xCP6ebDDWP5HZfiWGd8A\\u002fXl8BitNFvL+fsR4EGyzmv5aN5RKX4MM\\u002fn5leJh6RwT8tV8\\u002fOJt60P2JAxQVrncC\\u002fIG\\u002fngKMW678osvGV\\u002fOnQP4F8z\\u002fQ\\u002fqbE\\u002f+\\u002fUf0Nf7oz9EOunboqPAv0oYbXbtLee\\u002fLQ+g9xiz7z8JKME795jvP2ig+73L8u0\\u002f54IOxuFVxL+ODCefCK7vP\\u002f+A2caSbYs\\u002fd6b8Hcwoy7\\u002f60sUXF+rsv9Q3z3rjVO8\\u002ff4tBE9N67z\\u002f3YXbV\\u002fmXvPxFPhPk7NO8\\u002fx3YIGcYc7z8AAAAAAADwP19QfOyW7e8\\u002fqUFJZ6CUqL8Az0a\\u002fhLrvPw6YK43KwOK\\u002fE5ztmny2zb9aShBE0y3OP5xuP\\u002fLRdMS\\u002f8sXCFDOL6r\\u002fbtnG6OCDRPwXWWtQeqOG\\u002fnEhQA18kvj8B6PMOZPq+v99Xrr5PxOm\\u002fXihR6QUx3D+ezCCiwCu6P1gMiUh+weO\\u002fr\\u002fYrRawh0D9Xq55aWH7HP+0hGcmVI4q\\u002f9HvWh5qu6r+CfG4g5tHXP4H1Zh3PaMw\\u002fEtaSDNgnhj8g4h3BHp\\u002fmv+lStTEGqsM\\u002fFWTbLspQyj9pmEqhBEDBPxQOK12UBoe\\u002fvtkabm7+6r+iLM38\\u002fMTRP\\u002fKvwYyiK8Q\\u002fjI2yWDpUwT8yE7JQJYKevwMETS7JHei\\u002fj7f4eS+f7z9wcGWJP5bvP\\u002fpoknwXgu0\\u002fNm38FOLOyb8Pq7TRqpXvP9cFiuSWyY4\\u002fPTDg+0DHxb\\u002fbKrnPV5Psv7P\\u002f5sATWe8\\u002ftHqaBxl07z+Ibjdr5p7vP8D+Tdy2de8\\u002fJWuXwYZk7z9fUHzslu3vPwAAAAAAAPA\\u002f83wJcCmkpL9kgL5qr7fvP64PI+GeneG\\u002fv7ymhhhUyL\\u002fxpnuQrEvPP1m\\u002fLTlbXMC\\u002fK0vTY9yI6r9gaMPfMn\\u002fNP5Yy3uwSleG\\u002ftszvAh0Rwz+Yp46rhxLEvxP1Zt5jSuq\\u002fh7Bw4A3u2z96PAxC4IOxP95RcsYPneO\\u002fhMpfoZOx0D+usHnJAvDIP7j7hO2\\u002fQ6q\\u002fKGx1EYoB679twMhS3ZnXP+iR6Y7Clck\\u002fvP7dhMVjk78\\u002f3IjJPnTmv\\u002f9McLL57sM\\u002fF\\u002fPfB91LyD\\u002fPJYZz11q+P2dEEVl3Bqi\\u002fR7A1FvBt679K491vH73RP0uYhAF8WME\\u002fhlsbFUKnvD85ZIxlte+rv+gFtJKk8+e\\u002fVWlZ0yZjrb9FD2M8Zuuqv8lTRz\\u002fH6q2\\u002fx32Rgltkt783uiTIUAmuv3TW5PUsqH4\\u002fPkmwg0nKtj8GD1SmZq6uP2GzskoXka2\\u002fn7CAq\\u002fLArr\\u002ffd79r3KSgvxHm+dt\\u002f4J+\\u002fDuXMLuBrn7+pQUlnoJSov\\u002fN8CXAppKS\\u002fAAAAAAAA8D+Zqp5dit2pv5gY8RzQUbM\\u002fdp6O8eYNuz8grxOFcCKzP+o6Zrg9Bok\\u002fUUwZuCEdoD9MDD8L49Owv3aS324GDKQ\\u002fB4VQqcwxsj\\u002f74ie3A5u1v1k1BlYuHJW\\u002fX1klyg6Gdj8oC2sYXXmqv+byoowH56Q\\u002ft7Rf7XgdoT\\u002fGxWJV6OSNv4RgdNi5iLa\\u002fkWRsa6vokL9yn+FIU+KBv+1SNwZqaK2\\u002f+Vuo5rQ4rL\\u002f8PFdUQ5WnP5Bq9Qw6dJ0\\u002ffF6l2Kdzrr9LCzaPI363v\\u002fcK08Mhs76\\u002f8H37XwHoj79CBgsw7Zt2v1oec\\u002fVfOKq\\u002fAphdONSjqL9X60ZzdDu2vxy3OvDc9qA\\u002f1UhfBkp+7z\\u002fO1XDv2+fvPwiY97r3hO4\\u002fFFjz6Y78vb9Ui28uM03vP5yO3Nmvwo8\\u002fTq8DviMO0L+ipd47B5ntv2WfPVTFle8\\u002fzmXAPVDE7z+IcfgqCQ3vP1zTfklm0e4\\u002f39tJY8u77j8Az0a\\u002fhLrvP2SAvmqvt+8\\u002fmaqeXYrdqb8AAAAAAADwP+CR9ruwk+O\\u002fU140gLKS0b\\u002fy65YzUhbMPzQRGERPVci\\u002f\\u002fatzVr8P6r9CX\\u002fFIYOjSP7urTyB4VuK\\u002f2n8LcyV3vD8C38RZmSa1vyk7cXaTbum\\u002fbZn3HheF3D\\u002fsWMv4xUC9P4FUrTJsVeS\\u002fF7CrtZgY0D9tmj\\u002ffi8\\u002fIP+GXMscPOZM\\u002flE79RTIQ6r\\u002fizioKPrnXP5t6YNUKws0\\u002fKNEFWLkdmD8iaCM4\\u002fBLnvws+dfLMd8I\\u002fPmcxVaYUzD+zjaBixl7DPw0I7f9i1pM\\u002fyE\\u002fX9y7G6r8QjyML\\u002ftLRP0JmbMQfDcU\\u002fjoyWXeTwwz8MkTuk7yiTvxVVH9cVqOi\\u002fao4\\u002frzCA4b\\u002franKwJSDjv5XiCJYSEum\\u002fn9FkSOHg4L\\u002f83P5lY8Lhv8jmkIBXL1i\\u002f6drIvJmS6z\\u002fwvHU+E23oPwkB53xxZeK\\u002fdqTTrdlN478eTNBEa4jcv+RNt5Mvqdq\\u002fmDlxrcP02b8OmCuNysDiv64PI+GeneG\\u002fmBjxHNBRsz\\u002fgkfa7sJPjvwAAAAAAAPA\\u002f1WUx37dd6j9VqsOeOW64vzPyXv9G0+U\\u002fCgcHPsmG3z9N9jFGwGPpv+kP5t1mbtQ\\u002fCb+s7Wek2T\\u002fTxx1gjh3gv2MkQHPP3tI\\u002fQt6TOqV+17\\u002fXRNovNWfjv5wLdz+MBNs\\u002f5+xMoo3Rl7\\u002f4q0NKYjKmPzMG9GlSi+K\\u002f6xfYEXmS0z+obW+TqHnRv0Z8NGemAuC\\u002fHwOwIhN+4L\\u002f59KfXHHndP1PLR3a8IKK\\u002f1J0bkWDY17\\u002fVDC1+QhTVv\\u002fFLCy6tc+G\\u002fGy8ZSNTv1j96Lxc2mqbGv1YgAWpok9u\\u002fejPGoJhj4L\\u002fvNlIxrozbv\\u002fqZji3z++A\\u002f\\u002fkbzyl\\u002fwy7+0whPEYdjQv82BC+kcFt6\\u002fljCmsbUy7L+rGEkqKWTOv9mAeQ8H7Zk\\u002fh2dB7xOA7j\\u002fxzMvjnlHfP7lrfwuqq9G\\u002fh+xZREZH078A70EF2U+4v4oxEc\\u002fhMLK\\u002fMHHIt8ZZsL8TnO2afLbNv7+8poYYVMi\\u002fdp6O8eYNuz9TXjSAspLRv9VlMd+3Xeo\\u002fAAAAAAAA8D\\u002fw2y9jj7y\\u002fP7QJyBUuxeQ\\u002fN8CXCIPhvD9qc7wdSezrv4eZhxihOc4\\u002fQkCB8woV4T\\u002fCzEDNO6jqv9XOgjOHsL6\\u002fqJZO5YEbxb8VYl5SA+Lkv7m4H5\\u002fscNQ\\u002fuab6v\\u002fL5uj85P8fADk7BP4BJegOP8Oq\\u002f+gAo526avL9F3\\u002f9i8MG\\u002fv6\\u002fRc0WQ992\\u002flu+RI4QN47\\u002frBBuFhNnUP0egHwOvc6s\\u002fmvjwM0862b9+AIipvZzWvwZFUghhXem\\u002ft93aYvhQpb9I4v510BOqv4pdpC1vUNy\\u002fkBncAhYz4b93OLD0hVTgv14jZMcNm9c\\u002fp+SyfYkPyz8P3MDcIxvNPwUYlGgwacY\\u002f\\u002feBAs8JNz7\\u002fWqsYgJNbHP0ZZ9ur3PZY\\u002fL0gbMzYIoT8q8nkjGEy\\u002fvwZO6XgmvMQ\\u002fxBiidK9Lxj9sdwyCPG7MP7wHJr+FBcw\\u002fmI8dF\\u002ffRyz9aShBE0y3OP\\u002fGme5CsS88\\u002fIK8ThXAisz\\u002fy65YzUhbMP1Wqw545bri\\u002f8NsvY4+8vz8AAAAAAADwP7y3tzYROcW\\u002fFvVkWEC70b\\u002fQvxmv1KXPP0aIC+YVo6w\\u002faajaqKwGv7\\u002fpw8jW3zPSv+DplNbfTdS\\u002fwFQ6PD+c3D\\u002fOvzp99buiP6YiEknEyKM\\u002fV+n\\u002fm1ydrL8F9wMbRR+hv23BDoZc\\u002fM+\\u002fBxfip5gF1b\\u002fMBApFFUfPPxbfuG44YLM\\u002fKjDhBnwemL\\u002fUxJwF5NiTv18LB3TyYWu\\u002f+ONOuRPQkL9qC8QblOjAv\\u002fk6KZo8A9K\\u002fCqmaNNl01r+xlbB1LuvAP9IknC4YM6Y\\u002fxNNdsFuEmL9xQWx1l+q0v9j9D7P9dbG\\u002fWSGTNh8Fwr8tIO5CIiXJv+tdYCQo9de\\u002fiyyF+Yw84b+CIm1j8r3Av4EB3c4HgJy\\u002foQvdG4xr5z8q9avNIffSPwlreEqe1cS\\u002fGzsbFBg1x7+Py8PeGiOlv3iTLEmiK5K\\u002fN9OON33Vgr+cbj\\u002fy0XTEv1m\\u002fLTlbXMC\\u002f6jpmuD0GiT80ERhET1XIvzPyXv9G0+U\\u002ftAnIFS7F5D+8t7c2ETnFvwAAAAAAAPA\\u002fb6ZUqcZhwD\\u002fhtGzppKflv5tI\\u002fQxyu8E\\u002fVjy1wvwAyD9+Y\\u002f4DshPdv8\\u002fxi\\u002fiGHJc\\u002fou8Ixcqz1b\\u002fJPv25+ILbv1\\u002fzn2rNoMo\\u002fdWfRWoQ2tr8okmr0pdutP6qEGKDa3dy\\u002fNsT2tuqkqD\\u002fS6C9muhqxvynihowKytS\\u002fDNjKAO7C1b9TwXWyOaPLP575LP1zeqq\\u002fzJlaNf2izr94c\\u002fsIuUHIvzsgrmzmyNe\\u002fex4qyoE8tT97xCueujunvy+e2KsZ09C\\u002fHXQN7P5N0r9ty0SF9VbLv+7OD2X2CdI\\u002fJ8tFwm2b6b9eKo4UxbPpvxEGXDJj8+i\\u002fLOqRXrBK1T\\u002f6ZrFm+9jpv63Qg6JivIG\\u002f8n6ZPzFewj8n8YrGv8znP6nqKZUthem\\u002fqoQDLJHG6b\\u002fVOum3CB3qv\\u002fhdR4e9yem\\u002fOYLkYSSW6b\\u002fyxcIUM4vqvytL02PciOq\\u002fUUwZuCEdoD\\u002f9q3NWvw\\u002fqvwoHBz7Jht8\\u002fN8CXCIPhvD8W9WRYQLvRv2+mVKnGYcA\\u002fAAAAAAAA8D\\u002fZqH6J7z3HvwcbsRLe5do\\u002fpDt2yRHyzb+Z7gUKTRHVP1CH5muIy+g\\u002fO+uIVPz22r9dQ58TWFenv1Ogwl9NeuE\\u002fC5obqJDg1b\\u002fJ\\u002fhganmi7vyklGiC9\\u002f7Y\\u002fFVivZnGf6D\\u002fkMh2vul3Xv6IQPodGZcG\\u002foz4jhQB0pT9fNl84GfPiP7+cvwBQYMy\\u002f4GQ8wltmwL\\u002fNS+cIYeagv\\u002fOjxV0Tw6k\\u002f05VRHpKy5z+VX6bkidTRv7jAfMwL\\u002fbW\\u002fLwwwBEF6r78iNJTb38upPwh1HCW2rOM\\u002f5gSvLDo8zz8T9uekDW7SPxwA0168sN0\\u002fviLmyV6w5z\\u002fH+MiXoD3QP34NgxvgBZm\\u002fqIpdQbnX67\\u002fMgCqtsgDev1+csOp+sdE\\u002f0nsElgKH0z\\u002f0kLApBPrAPzbfHvfwKLw\\u002fxLjz0jFbuj\\u002fbtnG6OCDRP2Bow98yf80\\u002fTAw\\u002fC+PTsL9CX\\u002fFIYOjSP032MUbAY+m\\u002fanO8HUns67\\u002fQvxmv1KXPP+G0bOmkp+W\\u002f2ah+ie89x78AAAAAAADwP0HZTLTh6sS\\u002fMgb0FdbU4r\\u002fPyRWBqDzlP20crWign6Y\\u002fhx3dZHSh0T+kSUixSjLkP2JR7eKmkNC\\u002fXBUIi98zyb\\u002fJ6kQgc+DMv9w460BQoeY\\u002f3H+SvR45mD\\u002fwHexEGLXQPwl2afMFu9w\\u002fPkeZr+jy4T+TrqFY+PjRvy82tB+77bS\\u002f93VP9Jsk1T\\u002fhEhUE4CbQPwyY77emeuQ\\u002foQiLsSjzqL+dpZbrFWvAP\\u002fgxhJlt7ts\\u002f\\u002fwca0mcy3z8+uf2gC7\\u002fdP2\\u002flN7tjy9W\\u002fpDtSZCPS4b9oLzlg0PHhv1Gp5IQzYuK\\u002fELnw2\\u002fjptL9bWmSoLk\\u002fiv2ZrtqpEOWQ\\u002f2FJp9GtqwT+rtPRGNa\\u002fiP9LD6xRMWuO\\u002f0NoN1as\\u002f479BF8RMwSHiv\\u002flxw20yIeK\\u002fzVs1icYU4r8F1lrUHqjhv5Yy3uwSleG\\u002fdpLfbgYMpD+7q08geFbiv+kP5t1mbtQ\\u002fh5mHGKE5zj9GiAvmFaOsP5tI\\u002fQxyu8E\\u002fBxuxEt7l2j9B2Uy04erEvwAAAAAAAPA\\u002fsxNK47eJ0b9ZYxHkHKKev1qHlsq8Zts\\u002fLDL29lUjzr+PWeJTxCrRP558pFSum+U\\u002fMjgM59MNzr85dreqxB\\u002fJvx0Pvf3w5rW\\u002fH83UJf2e3D9WDdQI8s\\u002fHv7CtJzPMyoy\\u002fKC3dUkQSvT+ViYVV6kHlP8kgOQMsd8e\\u002fPFH55DOTw7\\u002fSzu2Gq7mxv6oMjTTYAbe\\u002fJpcErwFf3D8tYlAUwhLKv7jqmhlmF46\\u002fzwOd4c0Rgr9MLwYfJnLAP6tXeWuVa+Q\\u002fpVMJe5aSwT8bgibiRKu9P6pswdoW8oK\\u002fDU3fVdzg4r\\u002f+47mEX8bAP\\u002f5M3PwZ\\u002fpc\\u002fNORa2w9L4T+yts6NBdagPyNLf4JSn8E\\u002f1YUCqo0UvT\\u002frKpdCI5PNP5um6L2Pf88\\u002fYeNluP0I0D+cSFADXyS+P7bM7wIdEcM\\u002fB4VQqcwxsj\\u002fafwtzJXe8Pwm\\u002frO1npNk\\u002fQkCB8woV4T9pqNqorAa\\u002fv1Y8tcL8AMg\\u002fpDt2yRHyzb8yBvQV1tTiv7MTSuO3idG\\u002fAAAAAAAA8D+efhhiud3ivzhqJFc7JNe\\u002fLnfLgcW+hT+p0ilQU23kvwSYab31sMi\\u002f2NY4fX4A1j\\u002fYgar8N\\u002ffNP+qILZgdjOG\\u002f9xMZDxQ11b\\u002fLHH2nStq\\u002fv4xoSOeX3Na\\u002fmS+wEsiO4r\\u002flHHlGZ9\\u002fHv5kR+JlWrco\\u002fjYipx7P+z7\\u002fB5oselqnOvwABifSnFuC\\u002fhF6\\u002f5AGP0b+S3+2\\u002f9UG2P\\u002faWtzyrI9i\\u002fGh7XLN6U3L8tF0Rm6dvgv3HJT5xVs8C\\u002fCZ6BMfpdvr\\u002fkkwDME2K3v7vO6DyhxrU\\u002fWdyA1qNm7T9IGPeA1+q4v1Oom1sti5+\\u002fZ0TqsRdm6L8hobRwHpLAv5EG2107XLG\\u002fZyp6iQ4LrL\\u002ftG\\u002f0EUCfNv5t\\u002fI+UxNM+\\u002fxq0HcHOMz78B6PMOZPq+v5injquHEsS\\u002f++IntwObtb8C38RZmSa1v9PHHWCOHeC\\u002fwsxAzTuo6r\\u002fpw8jW3zPSv35j\\u002fgOyE92\\u002fme4FCk0R1T\\u002fPyRWBqDzlP1ljEeQcop6\\u002fnn4YYrnd4r8AAAAAAADwPxG\\u002foWp3Ets\\u002fei9VqlJcuL\\u002f6YEqNQrnhPxvCYSwpn62\\u002f1BWaIxYQ0L8Bc+XiMvbMv6PXuXKwT+o\\u002ff08f\\u002fGi+2j+28wMqaPizv3UV6sVJ5NU\\u002fhfTGbjnl4T\\u002fZ8P5\\u002fC66ov0MbQzAC3cO\\u002fbvEnBmNy1D86HUama4nTP06HZ2kJGug\\u002fOB\\u002finFRm1j\\u002fBI8usHHa4v9WyWlTbUNc\\u002fZBgcm8hR3D9fCo8jY\\u002fbeP6CNhduEybS\\u002f\\u002fHRnvXw16b\\u002fTcmTLWjbpvyB+VUtLdua\\u002fcVPhvSEb3j\\u002fK9cxlIiLpv1lmcj0miqe\\u002f0aN8CZI0t7+EKQs4FTHlPwpqnqEX6ui\\u002f4a18PIfB6L\\u002fWxQWqSsfqv1snB57Isuq\\u002fkDjYOiSg6r\\u002ffV66+T8TpvxP1Zt5jSuq\\u002fWTUGVi4clb8pO3F2k27pv2MkQHPP3tI\\u002f1c6CM4ewvr\\u002fg6ZTW303Uv8\\u002fxi\\u002fiGHJc\\u002fUIfma4jL6D9tHK1ooJ+mP1qHlsq8Zts\\u002fOGokVzsk178Rv6FqdxLbPwAAAAAAAPA\\u002fMXuAgi322r8OsKbBdXS9PzQg8\\u002fbj79s\\u002fycy8bxK21L9eZMr7CsLQv70zXP0XV9s\\u002fDuUHpRx56z8zEU73\\u002fGnUv6env6uQhKu\\u002fKWDphAWNyD8bkJDgXK7hPxhh3E+svMi\\u002fmLDM4vqBrb9UX\\u002fSRVzGsPxeSOJf8vNM\\u002f4NBUYt5h6j+iMFBIIqPRv3aViqmk6Wg\\u002fpnj1u7Dysz8idr5bn9rOP0G\\u002fQ81ZZeI\\u002fE\\u002fTsR98y3D9YblIMh+jcP8a3UklBed0\\u002f1tUmRH31or+cQKd6Jl3bP7yItsYTDYI\\u002fMG5QRl0Jy79TdHUIgEbavwY0aH299ds\\u002fPhCCMW\\u002fs2z\\u002f4utlF1+LaP\\u002faj1xBQbNo\\u002fbWdPW3I12j9eKFHpBTHcP4ewcOAN7ts\\u002fX1klyg6Gdj9tmfceF4XcP0Lekzqlfte\\u002fqJZO5YEbxb\\u002fAVDo8P5zcP6LvCMXKs9W\\u002fO+uIVPz22r+HHd1kdKHRPywy9vZVI86\\u002fLnfLgcW+hT96L1WqUly4vzF7gIIt9tq\\u002fAAAAAAAA8D+xOK5wn+zBPyNmimeB1c6\\u002fMY5T3GZURL+78LCT6hy5P9aQz+5iMaO\\u002fHovrK7N12r\\u002fA2YB8R5HRPyJcYZeD2sw\\u002fK7W1ETzYsj9Boyi+HOHSv2zHNoIprby\\u002fCCMSCAP3aD\\u002fuog5ber+4P4l5LMsItZ6\\u002fSJpA+p712b9ILaewKlvSP9nT6z6uKLo\\u002fBxJ4CWX0vz\\u002fsNpeF8+t+v9cZcmWnzdS\\u002fMPoeUiU8tT9EkMnLoMu6P2\\u002fk7jIgx9A\\u002f8eji4+rD4T\\u002fpinl89qq3P7H\\u002foJyOKGw\\u002fwqXLLLe65b8xTk8ugqjRvwNwJFhmhbg\\u002fbjhdxVUDvT\\u002fuo6fxfGSLvyQSLXekp6C\\u002faZSC0+6Eo7+ezCCiwCu6P3o8DELgg7E\\u002fKAtrGF15qr\\u002fsWMv4xUC9P9dE2i81Z+O\\u002fFWJeUgPi5L\\u002fOvzp99buiP8k+\\u002fbn4gtu\\u002fXUOfE1hXp7+kSUixSjLkP49Z4lPEKtE\\u002fqdIpUFNt5L\\u002f6YEqNQrnhPw6wpsF1dL0\\u002fsTiucJ\\u002fswT8AAAAAAADwP75UYCoZzps\\u002fUgPdHMN2yr\\u002f35onpXe3JvwbT+hgsWeI\\u002fKt6xYdKivz+RnVvmOgO3P1Sr2Lm+4t0\\u002fj729aaSj4z\\u002faHRdlHKSVv1vp0IevecO\\u002fk1IiKymMzj\\u002ff7Q6Dy\\u002ffOPwTUMREx1+E\\u002f2OsQylKPtD9Vl4hLz5uWv2\\u002fmubGYcds\\u002fGhmIWNSC3z9GdTTs8iLgP3EhGNf7Are\\u002fLl9W\\u002f0+g47\\u002f+l1uu1wXkv2kPgQ2JfOS\\u002fvOengonRtL8e+1MG8wTkv+lz\\u002fzo\\u002f8X4\\u002fS0OrmMyKzj82hrM8XWzkPzlKj6bG7uS\\u002f\\u002fuAFDtcG5b8amJR9npPjv+z3xRLGb+O\\u002fW8zeZUlb479YDIlIfsHjv95RcsYPneO\\u002f5vKijAfnpD+BVK0ybFXkv5wLdz+MBNs\\u002fubgfn+xw1D+mIhJJxMijP1\\u002fzn2rNoMo\\u002fU6DCX0164T9iUe3ippDQv558pFSum+U\\u002fBJhpvfWwyL8bwmEsKZ+tvzQg8\\u002fbj79s\\u002fI2aKZ4HVzr++VGAqGc6bPwAAAAAAAPA\\u002fDIUDRyW+0L+nXoIkJx\\u002fBv6JKool33cO\\u002fZfo41MG+3T\\u002fF5YOL8FrMv8dVAewPY8G\\u002frXKWqD5jzj+1IGWcff3nP79UAA2aLMO\\u002fFc3yWn4ryL9EFjq62WHAv024O5x9C8O\\u002fN3SDleCT3z8sKN2dVd3Iv0Kf\\u002f52M57S\\u002fsKt63dsMrr+dx3qqrWG6PwKpuM3gMec\\u002fx0RlUYoi0D8Jnru8mVvQP4hyTixv+8o\\u002f5Nb0kjev0L\\u002f5uiLK3GPPP5B\\u002fAppvDok\\u002fmOkLLP74tT9zjvT6AFXFv\\u002fELf6APxs8\\u002fysmHQ8Rbzz+6qSvGCsHRP0KQBDJVs9E\\u002f++BLehuh0T+v9itFrCHQP4TKX6GTsdA\\u002ft7Rf7XgdoT8XsKu1mBjQP+fsTKKN0Ze\\u002fuab6v\\u002fL5uj9X6f+bXJ2sv3Vn0VqENra\\u002fC5obqJDg1b9cFQiL3zPJvzI4DOfTDc6\\u002f2NY4fX4A1j\\u002fUFZojFhDQv8nMvG8SttS\\u002fMY5T3GZURL9SA90cw3bKvwyFA0clvtC\\u002fAAAAAAAA8D9dpQ2una7FP4G\\u002fftG8sce\\u002fhafdPyNy1L83E87peE+nvxuE5Kflpse\\u002fb6IepwnQzr+EhH3L7xbQv38Pv+KZF80\\u002fi0uAu69+pD83K0kZYfW5v5j8D7w7use\\u002fZHx+JXxG078218gh1Zakv9iV4hWs78C\\u002fmkL9hXZQy7\\u002fpctbRORHQvxbuSetoU9C\\u002fy\\u002fzkZmqKyj\\u002fKDC1mE97JPx2RwI0vbsI\\u002f3X6STMupxb+mzDrDWHzJP66hoocGGmY\\u002fgHaq2wpuwj+9VpH0ZEu\\u002fv67sGICDPsw\\u002fKJlKzYagyj8L3z52pJLMP+PMMXMER80\\u002ftiUlmruNzT9Xq55aWH7HP66weckC8Mg\\u002fxsViVejkjb9tmj\\u002ffi8\\u002fIP\\u002firQ0piMqY\\u002fOT\\u002fHwA5OwT8F9wMbRR+hvyiSavSl260\\u002fyf4YGp5ou7\\u002fJ6kQgc+DMvzl2t6rEH8m\\u002f2IGq\\u002fDf3zT8Bc+XiMvbMv15kyvsKwtC\\u002fu\\u002fCwk+ocuT\\u002f35onpXe3Jv6degiQnH8G\\u002fXaUNrp2uxT8AAAAAAADwPz1MGwtyu8m\\u002foSxIGAnHy798RDnwRd6Fv1ftt+kQnrC\\u002fyywhCBkww7+0aq1eUUPBv3aSCgLcq9K+5qLCouoekj9CFf9mn0u9PxW1q2CYU8G\\u002f3L4dSgNVyr\\u002ffXpDAhaGqv36uFqnFZbm\\u002frO7Wa\\u002fxppL8icF5evuLAv83sIajKlsG\\u002fk2BplbiHkr8atDxwrYZ1P2A\\u002f5HYIo8g\\u002fwj1jis236j9wnhNvZm6BP8jeelBYvqK\\u002fHOfJ23no6L+VuKnoh+3Ov8la9gtoTaM\\u002f2flhajfpqj8LVdqgsne\\u002fv2h1MVJD5cG\\u002f0EQz6gluwr\\u002ftIRnJlSOKv7j7hO2\\u002fQ6q\\u002fhGB02LmItr\\u002fhlzLHDzmTPzMG9GlSi+K\\u002fgEl6A4\\u002fw6r9twQ6GXPzPv6qEGKDa3dy\\u002fKSUaIL3\\u002ftj\\u002fcOOtAUKHmPx0Pvf3w5rW\\u002f6ogtmB2M4b+j17lysE\\u002fqP70zXP0XV9s\\u002f1pDP7mIxo78G0\\u002foYLFniP6JKool33cO\\u002fgb9+0byxx789TBsLcrvJvwAAAAAAAPA\\u002fDXcN+YWO1T98C95P31mCvwLcDwFdp9U\\u002fKBytO3Oy4T\\u002fVQ7iM1ba9v6gQ+dybybu\\u002flpE4CTtN0j+3O1cGs7rUPxjPNZ7FyOk\\u002fEoC2ruDS0T\\u002f4xhDqnwawv9RjWk\\u002fCFNY\\u002fG6yu+3Zw3j\\u002fEcWTBHfzgP8uIIXmo\\u002f8K\\u002fCPBwKkwS6r+e\\u002fcHC2+npvyA+hJxzeee\\u002fuvjdMxe83D\\u002fNq8vfhM7pv8e7ynrteqa\\u002fNlccppNDtr\\u002fvVZ7J7+PlP7gW2zcsUOm\\u002ftBTET4dH6b9j26\\u002fUAlzrv9JR8kfiW+u\\u002frBkhQb1G67\\u002f0e9aHmq7qvyhsdRGKAeu\\u002fkWRsa6vokL+UTv1FMhDqv+sX2BF5ktM\\u002f+gAo526avL8HF+KnmAXVvzbE9rbqpKg\\u002fFVivZnGf6D\\u002fcf5K9HjmYPx\\u002fN1CX9ntw\\u002f9xMZDxQ11b9\\u002fTx\\u002f8aL7aPw7lB6Ucees\\u002fHovrK7N12r8q3rFh0qK\\u002fP2X6ONTBvt0\\u002fhafdPyNy1L+hLEgYCcfLvw13DfmFjtU\\u002fAAAAAAAA8D+3+h\\u002fEqnnUv4x++4o5l7C\\u002fjOH1SluhyD\\u002f89HFBJiThP2CGsa4UWsm\\u002f8l+pTG5prL9HLuBz8Q9Wv8gbsJ43HNo\\u002fVsWeiWwH7D9AQiAT\\u002fvTRvzOB9tMrQow\\u002fDIBc6eWAsD86HsCe\\u002fZvNPy9v\\u002f1xR4uI\\u002fLMENl\\u002fV81z8o9VD11cXXP6Rf9HH5Atc\\u002fIOnZeFs4pL82WYDyMD7XP5fRlh4wNH+\\u002fYecUeDZawb\\u002fwIi35J3LVv1K4kVpDLdc\\u002fXdRHc6h21z+5dWZ8yrTWP7em9FpFW9Y\\u002fsQj+nmww1j+CfG4g5tHXP23AyFLdmdc\\u002fcp\\u002fhSFPigb\\u002fizioKPrnXP6htb5OoedG\\u002fRd\\u002f\\u002fYvDBv7\\u002fMBApFFUfPP9LoL2a6GrG\\u002f5DIdr7pd17\\u002fwHexEGLXQP1YN1Ajyz8e\\u002fyxx9p0rav7+28wMqaPizvzMRTvf8adS\\u002fwNmAfEeR0T+RnVvmOgO3P8Xlg4vwWsy\\u002fNxPO6XhPp798RDnwRd6Fv3wL3k\\u002ffWYK\\u002ft\\u002fofxKp51L8AAAAAAADwP+RtrBXrOsU\\u002ffFDa07JfrD8pzKsm+QvQvwJg3EmRFLK\\u002fnMkZPLBPhj+XXcLOXKN2v+7zjrO\\u002fjmU\\u002fHptTXTM\\u002f1L9yfvwslQ7OPy7\\u002frEc8P8I\\u002fbex+6\\u002f1Jtj8veD\\u002fS50ubPxbkCGBSf9K\\u002fjifuOufwyj\\u002fQmEqbPljNP62HhFO40tQ\\u002faSWumFtF1z8ofLHg3eHLPxBR0ZaJAZG\\u002fKYNQdttf3r8RGPVHvGrUvw628\\u002fTk0c0\\u002fpH5mcU0yzz+Rs2LZaeHCP+CxTH+hIME\\u002fkdl+JYZ3wD+B9WYdz2jMP+iR6Y7Clck\\u002f7VI3Bmporb+bemDVCsLNP0Z8NGemAuC\\u002fr9FzRZD33b8W37huOGCzPynihowKytS\\u002fohA+h0Zlwb8JdmnzBbvcP7CtJzPMyoy\\u002fjGhI55fc1r91FerFSeTVP6env6uQhKu\\u002fIlxhl4PazD9Uq9i5vuLdP8dVAewPY8G\\u002fG4Tkp+Wmx79X7bfpEJ6wvwLcDwFdp9U\\u002fjH77ijmXsL\\u002fkbawV6zrFPwAAAAAAAPA\\u002fHIdNosGd2T++lSyFxdzCv9vXVRTTjb2\\u002fxCxX0uLwvj8\\u002fBM6IRvTHP8c\\u002flYYfo9U\\u002fktddD4Rdtb8ZAUEmNaGdP+CKBQt6g9c\\u002fpfBHdZ\\u002f32z8mPU1Q16rVP8pin7S91MW\\u002fGbXZLFKtbL\\u002fuvbuaj+SKP8hUMgAO98Q\\u002fE+UusY3a4T+UU9RSCcWNPymDyMjiXZO\\u002fPmrNr1mW478E9fCaCTHJv7DPrPx17Jg\\u002foOiR+s2aoz\\u002ffA47qk7W2v\\u002fEAiMHE\\u002fLq\\u002fXl8BitNFvL8S1pIM2CeGP7z+3YTFY5O\\u002f+Vuo5rQ4rL8o0QVYuR2YPx8DsCITfuC\\u002flu+RI4QN478qMOEGfB6YvwzYygDuwtW\\u002foz4jhQB0pT8+R5mv6PLhPygt3VJEEr0\\u002fmS+wEsiO4r+F9MZuOeXhPylg6YQFjcg\\u002fK7W1ETzYsj+Pvb1ppKPjP61ylqg+Y84\\u002fb6IepwnQzr\\u002fLLCEIGTDDvygcrTtzsuE\\u002fjOH1SluhyD98UNrTsl+sPxyHTaLBndk\\u002fAAAAAAAA8D\\u002farXdqYn60P+SQLHGFVMC\\u002fUHpELpwuyj+g2seFvaHOP7SD2Lhe5uA\\u002fpOHq5OJBxj9ni7Xje9Kjv7WjFsHDq9Y\\u002f3vVSdSQG4T\\u002f5Kufls\\u002fviP2SwnAH3QJg\\u002fOeVh3y905r\\u002faMp9e3czmv8XYR5FXEue\\u002fEhfRwFKesb+0WXFgL7Lmv9UQs39RjIq\\u002f2E0wBfrAzz9u\\u002fsrt\\u002fdjmP\\u002f5xyhwGdee\\u002fU7tpAHiU578LjfnpOljmvw5Nd3EPPua\\u002fn7EeBBss5r8g4h3BHp\\u002fmvz\\u002fciMk+dOa\\u002f\\u002fDxXVEOVpz8iaCM4\\u002fBLnv\\u002fn0p9cced0\\u002f6wQbhYTZ1D\\u002fUxJwF5NiTv1PBdbI5o8s\\u002fXzZfOBnz4j+TrqFY+PjRv5WJhVXqQeU\\u002f5Rx5Rmffx7\\u002fZ8P5\\u002fC66ovxuQkOBcruE\\u002fQaMovhzh0r\\u002faHRdlHKSVv7UgZZx9\\u002fec\\u002fhIR9y+8W0L+0aq1eUUPBv9VDuIzVtr2\\u002f\\u002fPRxQSYk4T8pzKsm+QvQv76VLIXF3MK\\u002f2q13amJ+tD8AAAAAAADwP\\u002ft0SemwBcS\\u002fEFAp7zz0yr+6YMuF6pe5vyyr+GCHDsG\\u002fouRr0GLR4T9TqoHmSbvNv7fUoyjh57u\\u002fYq+ql5P3tr+lUhEjPTvQP4QngIKIpuk\\u002fTH\\u002fZGF4Vwj88IqRNmjDCP2PEJZc9SsA\\u002fyl5qwYnxwb+WYSdhGZbCP9IWRBf+lpY\\u002fedj0TMYNpz9gEQ\\u002fvHzK3v7nXOvkAvME\\u002fav9gnDPawT\\u002ftSxCxPZLEP8DAWNBDOsQ\\u002flo3lEpfgwz\\u002fpUrUxBqrDP\\u002f9McLL57sM\\u002fkGr1DDp0nT8LPnXyzHfCP1PLR3a8IKK\\u002fR6AfA69zqz9fCwd08mFrv575LP1zeqq\\u002fv5y\\u002fAFBgzL8vNrQfu+20v8kgOQMsd8e\\u002fmRH4mVatyj9DG0MwAt3Dvxhh3E+svMi\\u002fbMc2gimtvL9b6dCHr3nDv79UAA2aLMO\\u002ffw+\\u002f4pkXzT92kgoC3KvSvqgQ+dybybu\\u002fYIaxrhRayb8CYNxJkRSyv9vXVRTTjb2\\u002f5JAscYVUwL\\u002f7dEnpsAXEvwAAAAAAAPA\\u002fuWzSYtgAvT\\u002fk1Nx5QPyov5eEbByGsbi\\u002f2jY\\u002fFaIuxr\\u002fI5KHhK1Cev91k32fZNr2\\u002f86QYbbADwL\\u002fmarN+9ym\\u002fv1P\\u002fkdwC3cS\\u002fl22FuefqyT8CpsgHYu7LPy7EFLKO29E\\u002fkExv+iHP0z9WgR\\u002fgeIDKP4rEAV8nzZC\\u002fc7uEqzcR2L8T7zg7eynRvwO2aK\\u002fYsss\\u002fLLabGPMbzT9ReZ5WglzDP7ONE0mWAcI\\u002fn5leJh6RwT8VZNsuylDKPxfz3wfdS8g\\u002ffF6l2Kdzrr8+ZzFVphTMP9SdG5Fg2Ne\\u002fmvjwM0862b\\u002f44065E9CQv8yZWjX9os6\\u002f4GQ8wltmwL\\u002f3dU\\u002f0myTVPzxR+eQzk8O\\u002fjYipx7P+z79u8ScGY3LUP5iwzOL6ga2\\u002fCCMSCAP3aD+TUiIrKYzOPxXN8lp+K8i\\u002fi0uAu69+pD\\u002fmosKi6h6SP5aROAk7TdI\\u002f8l+pTG5prL+cyRk8sE+GP8QsV9Li8L4\\u002fUHpELpwuyj8QUCnvPPTKv7ls0mLYAL0\\u002fAAAAAAAA8D8M7+5VE5bKP0SmV+PFqtI\\u002f1WLQN5Qnub\\u002fLFcH+4fuov\\u002febzU7wyrw\\u002f2kq5Wlj\\u002fwz827bvOdbnCP1LdnCh3ZM+\\u002f1NoDFCpPwj8WI98N30vDP\\u002fSw4IF6Hcw\\u002fmCEZoxzw0z8nFiqqqyDDP+nQ4q3\\u002fEYe\\u002fAuG6+GAZ1b8kpNj4vXnMv0nGQFWkIMY\\u002fJa9nhFlGxj+\\u002fOoJlSEW3P4c0fkuXgLU\\u002fLVfPzibetD9pmEqhBEDBP88lhnPXWr4\\u002fSws2jyN+t7+zjaBixl7DP9UMLX5CFNW\\u002ffgCIqb2c1r9qC8QblOjAv3hz+wi5Qci\\u002fzUvnCGHmoL\\u002fhEhUE4CbQP9LO7YarubG\\u002fweaLHpapzr86HUama4nTP1Rf9JFXMaw\\u002f7qIOW3q\\u002fuD\\u002ff7Q6Dy\\u002ffOP0QWOrrZYcC\\u002fNytJGWH1ub9CFf9mn0u9P7c7VwazutQ\\u002fRy7gc\\u002fEPVr+XXcLOXKN2vz8EzohG9Mc\\u002foNrHhb2hzj+6YMuF6pe5v+TU3HlA\\u002fKi\\u002fDO\\u002fuVROWyj8AAAAAAADwP0sl+rjX1dQ\\u002fXYuEEuB9bT8Glp3dQdOqv42VRAX\\u002f3sM\\u002fL9c7TPhH0D+uGyIK7JzQP2ou+rCReMC\\u002frorwIKfcir\\u002fG\\u002fR0qlGl3P+dOdD9ZScc\\u002fTrl5sdnJ6D88tshCKISSP7X+edt6zae\\u002fwuWqut8i57\\u002ftKjucazPOvwCBGPgT5ak\\u002fmGHTmqb8rz\\u002fKzRfEKcW7v\\u002fBuTObtBsC\\u002fYkDFBWudwL8UDitdlAaHv2dEEVl3Bqi\\u002f9wrTwyGzvr8NCO3\\u002fYtaTP\\u002fFLCy6tc+G\\u002fBkVSCGFd6b\\u002f5OimaPAPSvzsgrmzmyNe\\u002f86PFXRPDqT8MmO+3pnrkP6oMjTTYAbe\\u002fAAGJ9KcW4L9Oh2dpCRroPxeSOJf8vNM\\u002fiXksywi1nr8E1DERMdfhP024O5x9C8O\\u002fmPwPvDu6x78VtatgmFPBvxjPNZ7FyOk\\u002fyBuwnjcc2j\\u002fu846zv45lP8c\\u002flYYfo9U\\u002ftIPYuF7m4D8sq\\u002fhghw7Bv5eEbByGsbi\\u002fRKZX48Wq0j9LJfq419XUPwAAAAAAAPA\\u002f2ZlRws+z0z8UMHMOIs2xv8UOm\\u002fzym9U\\u002fR+TzJD2M3D+1\\u002f+HzGsvfP314B+wJIr+\\u002fKoTsO4EV6r\\u002fofgVjD5Pqv17VwCvBJei\\u002fVHpAwZDI2D\\u002fxEH12DcLpv+E026dlTqa\\u002fnj5PLHJkj78s2Jdd+uHmP0lMWz+pnOm\\u002fC5f3Euvg6b+Oe1BXxijrv4T11+zRHeu\\u002fIG\\u002fngKMW67++2Rpubv7qv0ewNRbwbeu\\u002f8H37XwHoj7\\u002fIT9f3LsbqvxsvGUjU79Y\\u002ft93aYvhQpb8KqZo02XTWv3seKsqBPLU\\u002f05VRHpKy5z+hCIuxKPOovyaXBK8BX9w\\u002fhF6\\u002f5AGP0b84H+KcVGbWP+DQVGLeYeo\\u002fSJpA+p712b\\u002fY6xDKUo+0Pzd0g5Xgk98\\u002fZHx+JXxG07\\u002fcvh1KA1XKvxKAtq7g0tE\\u002fVsWeiWwH7D8em1NdMz\\u002fUv5LXXQ+EXbW\\u002fpOHq5OJBxj+i5GvQYtHhP9o2PxWiLsa\\u002f1WLQN5Qnub9di4QS4H1tP9mZUcLPs9M\\u002fAAAAAAAA8D8lDKTYnz\\u002fRv8Hgejd+342\\u002fzT6nYPHXqD8o5l6BelfJPwuhgXCJwOI\\u002fbncJ3DBH0T\\u002flcwMIU9TRP0rK9QH0gdE\\u002foOfgnlaJsr\\u002f4X5\\u002f24PjQP53vEDLXg4K\\u002foTlh3YKEq797+qzmYHfPvxwI+EybINE\\u002fa6Goc+ZZ0T9WlO6z52XRP+3tIPKNGdE\\u002fKLLxlfzp0D+iLM38\\u002fMTRP0rj3W8fvdE\\u002fQgYLMO2bdr8QjyML\\u002ftLRP3ovFzaapsa\\u002fSOL+ddATqr+xlbB1LuvAP3vEK566O6e\\u002flV+m5InU0b+dpZbrFWvAPy1iUBTCEsq\\u002fkt\\u002ftv\\u002fVBtj\\u002fBI8usHHa4v6IwUEgio9G\\u002fSC2nsCpb0j9Vl4hLz5uWvywo3Z1V3ci\\u002fNtfIIdWWpL\\u002ffXpDAhaGqv\\u002fjGEOqfBrC\\u002fQEIgE\\u002f700b9yfvwslQ7OPxkBQSY1oZ0\\u002fZ4u143vSo79TqoHmSbvNv8jkoeErUJ6\\u002fyxXB\\u002fuH7qL8Glp3dQdOqvxQwcw4izbG\\u002fJQyk2J8\\u002f0b8AAAAAAADwPwBuczjZLro\\u002f94s6Bmrxmz9Qtr1cnFarv0poPKVk\\u002fs6\\u002fs0tzr6WQwz+Afxefk\\u002f7EPxlXEexnO88\\u002fS2rsyntx1z+6RYQVQifEPx+Xdn84cTA\\u002feU6r+zP\\u002f27\\u002fbF\\u002f6mkoXNv3H8UtJkosQ\\u002f632i5eC4xT+HjPdtycC2Pyn3HUj8FLM\\u002fgXzP9D+psT\\u002fyr8GMoivEP0uYhAF8WME\\u002fWh5z9V84qr9CZmzEHw3FP1YgAWpok9u\\u002fil2kLW9Q3L\\u002fSJJwuGDOmPy+e2KsZ09C\\u002fuMB8zAv9tb\\u002f4MYSZbe7bP7jqmhlmF46\\u002f9pa3PKsj2L\\u002fVslpU21DXP3aViqmk6Wg\\u002f2dPrPq4ouj9v5rmxmHHbP0Kf\\u002f52M57S\\u002f2JXiFazvwL9+rhapxWW5v9RjWk\\u002fCFNY\\u002fM4H20ytCjD8u\\u002f6xHPD\\u002fCP+CKBQt6g9c\\u002ftaMWwcOr1j+31KMo4ee7v91k32fZNr2\\u002f95vNTvDKvD+NlUQF\\u002f97DP8UOm\\u002fzym9U\\u002fweB6N37fjb8AbnM42S66PwAAAAAAAPA\\u002fe3jE65HD1j8QDoy\\u002fkx\\u002fUP+ekknQadsK\\u002fxlK3WEo6wD\\u002fslK+yPN\\u002fCP9mnLWEiWdE\\u002fJUtJ\\u002faWn3T86B0gBcAPCPzXhxm5C8Xu\\u002fBNB6yTvq4L+BmEuy8QrTv4GQf13oTcU\\u002fFQjlh4aWxj\\u002fGcx4zBqWtP8fdHmGAZaY\\u002f+\\u002fUf0Nf7oz+MjbJYOlTBP4ZbGxVCp7w\\u002fAphdONSjqL+OjJZd5PDDP3ozxqCYY+C\\u002fkBncAhYz4b\\u002fE012wW4SYvx10Dez+TdK\\u002fLwwwBEF6r7\\u002f\\u002fBxrSZzLfP88DneHNEYK\\u002fGh7XLN6U3L9kGBybyFHcP6Z49buw8rM\\u002fBxJ4CWX0vz8aGYhY1ILfP7Cret3bDK6\\u002fmkL9hXZQy7+s7tZr\\u002fGmkvxusrvt2cN4\\u002fDIBc6eWAsD9t7H7r\\u002fUm2P6XwR3Wf99s\\u002f3vVSdSQG4T9ir6qXk\\u002fe2v\\u002fOkGG2wA8C\\u002f2kq5Wlj\\u002fwz8v1ztM+EfQP0fk8yQ9jNw\\u002fzT6nYPHXqD\\u002f3izoGavGbP3t4xOuRw9Y\\u002fAAAAAAAA8D9gZtCHF8DeP63cQlwICMC\\u002fDhHus8k6pL9E93i6ghefv6qcWmFoTLk\\u002fdKf+OSjd3j+vnhy3ki2Qv9OS7IyeJ6G\\u002f\\u002fIOB9W9v4L9xTpgYGZLCvwP10dfUhXi\\u002fkZBVI5+yaj+aM09FTG68v+Eh9aWGGMC\\u002fRDrp26KjwL8yE7JQJYKevzlkjGW176u\\u002fV+tGc3Q7tr8MkTuk7yiTv+82UjGujNu\\u002fdziw9IVU4L9xQWx1l+q0v23LRIX1Vsu\\u002fIjSU29\\u002fLqT8+uf2gC7\\u002fdP0wvBh8mcsA\\u002fLRdEZunb4L9fCo8jY\\u002fbePyJ2vluf2s4\\u002f7DaXhfPrfr9GdTTs8iLgP53HeqqtYbo\\u002f6XLW0TkR0L8icF5evuLAv8RxZMEd\\u002fOA\\u002fOh7Anv2bzT8veD\\u002fS50ubPyY9TVDXqtU\\u002f+Srn5bP74j+lUhEjPTvQP+Zqs373Kb+\\u002fNu27znW5wj+uGyIK7JzQP7X\\u002f4fMay98\\u002fKOZegXpXyT9Qtr1cnFarvxAOjL+TH9Q\\u002fYGbQhxfA3j8AAAAAAADwP7pAOwdL\\u002fbo\\u002f3oxsw0fc57\\u002f\\u002fwHS3tmrovxglhAirfOi\\u002f\\u002fThfbk+itb8TC98BoBHovzPknYH0\\u002fpC\\u002fbBEytqu50z+8p3ehMt7nP5AmJROrx+i\\u002fY5LQ6Gvy6L9EGP4y6XTnv2nuy6c7Qee\\u002fShhtdu0t578DBE0uyR3ov+gFtJKk8+e\\u002fHLc68Nz2oD8VVR\\u002fXFajov\\u002fqZji3z++A\\u002fXiNkxw2b1z\\u002fY\\u002fQ+z\\u002fXWxv+7OD2X2CdI\\u002fCHUcJbas4z9v5Te7Y8vVv6tXeWuVa+Q\\u002fcclPnFWzwL+gjYXbhMm0v0G\\u002fQ81ZZeI\\u002f1xlyZafN1L9xIRjX+wK3vwKpuM3gMec\\u002fFu5J62hT0L\\u002fN7CGoypbBv8uIIXmo\\u002f8K\\u002fL2\\u002f\\u002fXFHi4j8W5AhgUn\\u002fSv8pin7S91MW\\u002fZLCcAfdAmD+EJ4CCiKbpP1P\\u002fkdwC3cS\\u002fUt2cKHdkz79qLvqwkXjAv314B+wJIr+\\u002fC6GBcInA4j9KaDylZP7Ov+ekknQadsK\\u002frdxCXAgIwL+6QDsHS\\u002f26PwAAAAAAAPA\\u002f\",\"shape\":\"48, 48\"},\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"x: %{x}\\u003cbr\\u003ey: %{y}\\u003cbr\\u003ecorrelation: %{z}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\"},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"correlation\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"title\":{\"text\":\"feature correlation (48 features)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('05b41ca7-7eb3-4e9d-ac44-e3ee9f1f8f1d');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script>\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        </script>\n",
       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"a182251a-96fd-469e-8c9b-a3df59245309\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"a182251a-96fd-469e-8c9b-a3df59245309\")) {                    Plotly.newPlot(                        \"a182251a-96fd-469e-8c9b-a3df59245309\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"0\",\"x\":[\"Cells_AreaShape_Extent\",\"Cells_AreaShape_FormFactor\",\"Cells_AreaShape_Solidity\",\"Cells_AreaShape_Zernike_1_1\",\"Cells_AreaShape_Zernike_2_0\",\"Cells_AreaShape_Zernike_2_2\",\"Cells_AreaShape_Zernike_3_1\",\"Cells_AreaShape_Zernike_4_0\",\"Cells_AreaShape_Zernike_4_2\",\"Cells_AreaShape_Zernike_5_1\",\"Cells_AreaShape_Zernike_5_3\",\"Cells_AreaShape_Zernike_6_0\",\"Cells_AreaShape_Zernike_6_2\",\"Cells_AreaShape_Zernike_7_1\",\"Cells_AreaShape_Zernike_7_3\",\"Cells_AreaShape_Zernike_8_0\",\"Cells_AreaShape_Zernike_8_2\",\"Cells_AreaShape_Zernike_8_4\",\"Cells_AreaShape_Zernike_9_1\",\"Cells_AreaShape_Zernike_9_3\",\"Cells_AreaShape_Zernike_9_5\"],\"y\":[\"Cells_AreaShape_Extent\",\"Cells_AreaShape_FormFactor\",\"Cells_AreaShape_Solidity\",\"Cells_AreaShape_Zernike_1_1\",\"Cells_AreaShape_Zernike_2_0\",\"Cells_AreaShape_Zernike_2_2\",\"Cells_AreaShape_Zernike_3_1\",\"Cells_AreaShape_Zernike_4_0\",\"Cells_AreaShape_Zernike_4_2\",\"Cells_AreaShape_Zernike_5_1\",\"Cells_AreaShape_Zernike_5_3\",\"Cells_AreaShape_Zernike_6_0\",\"Cells_AreaShape_Zernike_6_2\",\"Cells_AreaShape_Zernike_7_1\",\"Cells_AreaShape_Zernike_7_3\",\"Cells_AreaShape_Zernike_8_0\",\"Cells_AreaShape_Zernike_8_2\",\"Cells_AreaShape_Zernike_8_4\",\"Cells_AreaShape_Zernike_9_1\",\"Cells_AreaShape_Zernike_9_3\",\"Cells_AreaShape_Zernike_9_5\"],\"z\":{\"dtype\":\"f8\",\"bdata\":\"AAAAAAAA8D9V0c27nBHdP+nayLyZkus\\u002fL0gbMzYIoT+hC90bjGvnP\\u002fJ+mT8xXsI\\u002fqIpdQbnX67805FrbD0vhP2dE6rEXZui\\u002fMG5QRl0Jy7\\u002fCpcsst7rlv5jpCyz++LU\\u002fgHaq2wpuwj9h5xR4NlrBvymDUHbbX96\\u002fedj0TMYNpz90u4SrNxHYvwDhuvhgGdW\\u002fpTlh3YKEq792Tqv7M\\u002f\\u002fbvwTQesk76uC\\u002fVdHNu5wR3T8AAAAAAADwP\\u002fC8dT4Tbeg\\u002fKvJ5IxhMv78q9avNIffSPyfxisa\\u002fzOc\\u002fzIAqrbIA3r+yts6NBdagPyGhtHAeksC\\u002fU3R1CIBG2r8xTk8ugqjRv3OO9PoAVcW\\u002fvVaR9GRLv7\\u002fwIi35J3LVvxEY9Ue8atS\\u002fYBEP7x8yt78U7zg7eynRvyGk2Pi9ecy\\u002ff\\u002fqs5mB3z7\\u002fXF\\u002f6mkoXNv4OYS7LxCtO\\u002f6drIvJmS6z\\u002fwvHU+E23oPwAAAAAAAPA\\u002fVarDnjluuL8z8l7\\u002fRtPlPwoHBz7Jht8\\u002fTfYxRsBj6b8Jv6ztZ6TZP9PHHWCOHeC\\u002fQt6TOqV+17\\u002fXRNovNWfjv+fsTKKN0Ze\\u002f+KtDSmIypj+obW+TqHnRv0Z8NGemAuC\\u002fU8tHdrwgor\\u002fXnRuRYNjXv9UMLX5CFNW\\u002fey8XNpqmxr9TIAFqaJPbv3gzxqCYY+C\\u002fL0gbMzYIoT8q8nkjGEy\\u002fv1Wqw545bri\\u002fAAAAAAAA8D+8t7c2ETnFvxb1ZFhAu9G\\u002f0L8Zr9Slzz9pqNqorAa\\u002fv+nDyNbfM9K\\u002fwFQ6PD+c3D\\u002fOvzp99buiP1fp\\u002f5tcnay\\u002fBfcDG0Ufob\\u002fMBApFFUfPPxbfuG44YLM\\u002fXwsHdPJha7\\u002f54065E9CQv2oLxBuU6MC\\u002fr5WwdS7rwD\\u002fPJJwuGDOmP8TTXbBbhJi\\u002foQvdG4xr5z8q9avNIffSPzPyXv9G0+U\\u002fvLe3NhE5xb8AAAAAAADwP2+mVKnGYcA\\u002f4bRs6aSn5b9WPLXC\\u002fADIP35j\\u002fgOyE92\\u002fou8Ixcqz1b\\u002fJPv25+ILbv3Vn0VqENra\\u002fKJJq9KXbrT\\u002fS6C9muhqxvynihowKytS\\u002fnvks\\u002fXN6qr\\u002fSmVo1\\u002faLOv3pz+wi5Qci\\u002ffsQrnro7p78yntirGdPQvxx0Dez+TdK\\u002f8n6ZPzFewj8n8YrGv8znPwoHBz7Jht8\\u002fFvVkWEC70b9vplSpxmHAPwAAAAAAAPA\\u002f2ah+ie89x7+kO3bJEfLNv5nuBQpNEdU\\u002fO+uIVPz22r9dQ58TWFenvwuaG6iQ4NW\\u002fyf4YGp5ou7\\u002fkMh2vul3Xv6IQPodGZcG\\u002fv5y\\u002fAFBgzL\\u002fiZDzCW2bAv89L5whh5qC\\u002fll+m5InU0b+6wHzMC\\u002f21vzUMMARBeq+\\u002fqIpdQbnX67\\u002fMgCqtsgDev032MUbAY+m\\u002f0L8Zr9Slzz\\u002fhtGzppKflv9mofonvPce\\u002fAAAAAAAA8D8yBvQV1tTiv8\\u002fJFYGoPOU\\u002fhx3dZHSh0T+kSUixSjLkP1wVCIvfM8m\\u002fyepEIHPgzL\\u002fwHexEGLXQPwl2afMFu9w\\u002fLza0H7vttL\\u002f3dU\\u002f0myTVP+ESFQTgJtA\\u002foKWW6xVrwD\\u002f4MYSZbe7bP\\u002f8HGtJnMt8\\u002fNORa2w9L4T+yts6NBdagPwm\\u002frO1npNk\\u002faajaqKwGv79WPLXC\\u002fADIP6Q7dskR8s2\\u002fMgb0FdbU4r8AAAAAAADwP55+GGK53eK\\u002fLnfLgcW+hT+p0ilQU23kv9jWOH1+ANY\\u002f2IGq\\u002fDf3zT\\u002fLHH2nStq\\u002fv4xoSOeX3Na\\u002fmRH4mVatyj+IiKnHs\\u002f7Pv8Hmix6Wqc6\\u002flt\\u002ftv\\u002fVBtj\\u002f3lrc8qyPYvxce1yzelNy\\u002fZ0TqsRdm6L8hobRwHpLAv9PHHWCOHeC\\u002f6cPI1t8z0r9+Y\\u002f4DshPdv5nuBQpNEdU\\u002fz8kVgag85T+efhhiud3ivwAAAAAAAPA\\u002fei9VqlJcuL\\u002f6YEqNQrnhP9QVmiMWENC\\u002fAXPl4jL2zL+28wMqaPizv3UV6sVJ5NU\\u002fQxtDMALdw79v8ScGY3LUPzodRqZridM\\u002fvCPLrBx2uL\\u002fUslpU21DXP2IYHJvIUdw\\u002fMG5QRl0Jy79TdHUIgEbav0Lekzqlfte\\u002fwFQ6PD+c3D+i7wjFyrPVvzvriFT89tq\\u002fhx3dZHSh0T8ud8uBxb6FP3ovVapSXLi\\u002fAAAAAAAA8D+xOK5wn+zBPzGOU9xmVES\\u002fu\\u002fCwk+ocuT\\u002fA2YB8R5HRPyJcYZeD2sw\\u002fbMc2gimtvL9mIxIIA\\u002fdoP+yiDlt6v7g\\u002fRy2nsCpb0j\\u002fZ0+s+rii6PwkSeAll9L8\\u002fwqXLLLe65b8xTk8ugqjRv9dE2i81Z+O\\u002fzr86ffW7oj\\u002fJPv25+ILbv11DnxNYV6e\\u002fpElIsUoy5D+p0ilQU23kv\\u002fpgSo1CueE\\u002fsTiucJ\\u002fswT8AAAAAAADwP1ID3RzDdsq\\u002f9+aJ6V3tyb+RnVvmOgO3P1Sr2Lm+4t0\\u002fW+nQh695w7+VUiIrKYzOP9\\u002ftDoPL984\\u002fUZeIS8+blr9v5rmxmHHbPxoZiFjUgt8\\u002fmOkLLP74tT9zjvT6AFXFv+fsTKKN0Ze\\u002fV+n\\u002fm1ydrL91Z9FahDa2vwuaG6iQ4NW\\u002fXBUIi98zyb\\u002fY1jh9fgDWP9QVmiMWENC\\u002fMY5T3GZURL9SA90cw3bKvwAAAAAAAPA\\u002fXaUNrp2uxT83E87peE+nvxuE5Kflpse\\u002ffw+\\u002f4pkXzT+KS4C7r36kPzcrSRlh9bm\\u002fNtfIIdWWpL\\u002fXleIVrO\\u002fAv5pC\\u002fYV2UMu\\u002fgHaq2wpuwj+9VpH0ZEu\\u002fv\\u002firQ0piMqY\\u002fBfcDG0Ufob8okmr0pdutP8n+GBqeaLu\\u002fyepEIHPgzL\\u002fYgar8N\\u002ffNPwFz5eIy9sy\\u002fu\\u002fCwk+ocuT\\u002f35onpXe3Jv12lDa6drsU\\u002fAAAAAAAA8D98RDnwRd6Fv1ftt+kQnrC\\u002fdpIKAtyr0r7mosKi6h6SP0IV\\u002f2afS70\\u002f316QwIWhqr9+rhapxWW5v6zu1mv8aaS\\u002fYecUeDZawb\\u002fwIi35J3LVv6htb5OoedG\\u002fzAQKRRVHzz\\u002fS6C9muhqxv+QyHa+6Xde\\u002f8B3sRBi10D\\u002fLHH2nStq\\u002fv7bzAypo+LO\\u002fwNmAfEeR0T+RnVvmOgO3PzcTzul4T6e\\u002ffEQ58EXehb8AAAAAAADwP+RtrBXrOsU\\u002fAmDcSZEUsr+cyRk8sE+GP5ddws5co3a\\u002fcn78LJUOzj8u\\u002f6xHPD\\u002fCP23sfuv9SbY\\u002fKYNQdttf3r8RGPVHvGrUv0Z8NGemAuC\\u002fFt+4bjhgsz8p4oaMCsrUv6IQPodGZcG\\u002fCXZp8wW73D+MaEjnl9zWv3UV6sVJ5NU\\u002fIlxhl4PazD9Uq9i5vuLdPxuE5Kflpse\\u002fV+236RCesL\\u002fkbawV6zrFPwAAAAAAAPA\\u002f29dVFNONvb\\u002fELFfS4vC+Pz8EzohG9Mc\\u002fGQFBJjWhnT\\u002fgigULeoPXP6XwR3Wf99s\\u002fedj0TMYNpz9gEQ\\u002fvHzK3v1PLR3a8IKK\\u002fXwsHdPJha7+e+Sz9c3qqv7+cvwBQYMy\\u002fLza0H7vttL+ZEfiZVq3KP0MbQzAC3cO\\u002fbMc2gimtvL9b6dCHr3nDv38Pv+KZF80\\u002fdpIKAtyr0r4CYNxJkRSyv9vXVRTTjb2\\u002fAAAAAAAA8D+5bNJi2AC9P+TU3HlA\\u002fKi\\u002fyOSh4StQnr\\u002fdZN9n2Ta9v\\u002fOkGG2wA8C\\u002fdLuEqzcR2L8U7zg7eynRv9edG5Fg2Ne\\u002f+eNOuRPQkL\\u002fSmVo1\\u002faLOv+JkPMJbZsC\\u002f93VP9Jsk1T+IiKnHs\\u002f7Pv2\\u002fxJwZjctQ\\u002fZiMSCAP3aD+VUiIrKYzOP4pLgLuvfqQ\\u002f5qLCouoekj+cyRk8sE+GP8QsV9Li8L4\\u002fuWzSYtgAvT8AAAAAAADwPwzv7lUTlso\\u002fyxXB\\u002fuH7qL\\u002f3m81O8Mq8P9pKuVpY\\u002f8M\\u002fAOG6+GAZ1b8hpNj4vXnMv9UMLX5CFNW\\u002fagvEG5TowL96c\\u002fsIuUHIv89L5whh5qC\\u002f4RIVBOAm0D\\u002fB5oselqnOvzodRqZridM\\u002f7KIOW3q\\u002fuD\\u002ff7Q6Dy\\u002ffOPzcrSRlh9bm\\u002fQhX\\u002fZp9LvT+XXcLOXKN2vz8EzohG9Mc\\u002f5NTceUD8qL8M7+5VE5bKPwAAAAAAAPA\\u002fBpad3UHTqr+NlUQF\\u002f97DPy\\u002fXO0z4R9A\\u002fpTlh3YKEq79\\u002f+qzmYHfPv3svFzaapsa\\u002fr5WwdS7rwD9+xCueujunv5ZfpuSJ1NG\\u002foKWW6xVrwD+W3+2\\u002f9UG2P7wjy6wcdri\\u002fRy2nsCpb0j9Rl4hLz5uWvzbXyCHVlqS\\u002f316QwIWhqr9yfvwslQ7OPxkBQSY1oZ0\\u002fyOSh4StQnr\\u002fLFcH+4fuovwaWnd1B06q\\u002fAAAAAAAA8D8AbnM42S66P\\u002feLOgZq8Zs\\u002fdk6r+zP\\u002f27\\u002fXF\\u002f6mkoXNv1MgAWpok9u\\u002fzyScLhgzpj8yntirGdPQv7rAfMwL\\u002fbW\\u002f+DGEmW3u2z\\u002f3lrc8qyPYv9SyWlTbUNc\\u002f2dPrPq4ouj9v5rmxmHHbP9eV4hWs78C\\u002ffq4WqcVlub8u\\u002f6xHPD\\u002fCP+CKBQt6g9c\\u002f3WTfZ9k2vb\\u002f3m81O8Mq8P42VRAX\\u002f3sM\\u002fAG5zONkuuj8AAAAAAADwP3t4xOuRw9Y\\u002fBNB6yTvq4L+DmEuy8QrTv3gzxqCYY+C\\u002fxNNdsFuEmL8cdA3s\\u002fk3SvzUMMARBeq+\\u002f\\u002fwca0mcy3z8XHtcs3pTcv2IYHJvIUdw\\u002fCRJ4CWX0vz8aGYhY1ILfP5pC\\u002fYV2UMu\\u002frO7Wa\\u002fxppL9t7H7r\\u002fUm2P6XwR3Wf99s\\u002f86QYbbADwL\\u002faSrlaWP\\u002fDPy\\u002fXO0z4R9A\\u002f94s6Bmrxmz97eMTrkcPWPwAAAAAAAPA\\u002f\",\"shape\":\"21, 21\"},\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"x: %{x}\\u003cbr\\u003ey: %{y}\\u003cbr\\u003ecorrelation: %{z}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\"},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"correlation\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"title\":{\"text\":\"feature correlation (21 features)\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a182251a-96fd-469e-8c9b-a3df59245309');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shape = ((screen.var[\"object\"] == \"Cells\") & (screen.var[\"feature_group\"] == \"AreaShape\")).to_numpy()\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "mt.pl.feature_correlation(screen[:, shape].copy(), key=None, ax=axes[0])\n",
    "axes[0].set_title(f\"before: {int(shape.sum())} features\")\n",
    "kept = shape & screen.var[\"selected\"].to_numpy()\n",
    "mt.pl.feature_correlation(screen[:, kept].copy(), key=None, ax=axes[1])\n",
    "axes[1].set_title(f\"after: {int(kept.sum())} features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c021",
   "metadata": {},
   "source": [
    "The panel titles give the shape measurements of the whole cell before and after selection. The large block of features that move together\n",
    "collapses to a few, and what remains correlates below the cutoff. Strongly negative pairs survive, because the cut\n",
    "uses the signed correlation, as pycytominer's does. `pl.feature_groups` shows which feature families lost\n",
    "features to selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c022",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:23.852264Z",
     "iopub.status.busy": "2026-10-22T03:20:23.851837Z",
     "iopub.status.idle": "2026-10-22T03:20:25.993595Z",
     "shell.execute_reply": "2026-10-22T03:20:25.992448Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script>\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        </script>\n",
       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"07531533-2f1e-43f3-8ae8-38f5685aa25c\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"07531533-2f1e-43f3-8ae8-38f5685aa25c\")) {                    Plotly.newPlot(                        \"07531533-2f1e-43f3-8ae8-38f5685aa25c\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=none\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"none\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"none\",\"offsetgroup\":\"none\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"kAAAAAAAAAAVAAAAAAAAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=AGP\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"AGP\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"AGP\",\"offsetgroup\":\"AGP\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=BFHigh\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"BFHigh\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"BFHigh\",\"offsetgroup\":\"BFHigh\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=BFLow\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"BFLow\",\"marker\":{\"color\":\"#ab63fa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"BFLow\",\"offsetgroup\":\"BFLow\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=Brightfield\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Brightfield\",\"marker\":{\"color\":\"#FFA15A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Brightfield\",\"offsetgroup\":\"Brightfield\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=DNA\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"DNA\",\"marker\":{\"color\":\"#19d3f3\",\"pattern\":{\"shape\":\"\"}},\"name\":\"DNA\",\"offsetgroup\":\"DNA\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=ER\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"ER\",\"marker\":{\"color\":\"#FF6692\",\"pattern\":{\"shape\":\"\"}},\"name\":\"ER\",\"offsetgroup\":\"ER\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=Mito\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Mito\",\"marker\":{\"color\":\"#B6E880\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Mito\",\"offsetgroup\":\"Mito\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=RNA\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"RNA\",\"marker\":{\"color\":\"#FF97FF\",\"pattern\":{\"shape\":\"\"}},\"name\":\"RNA\",\"offsetgroup\":\"RNA\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AAAAADAALQAAACQA1AEAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=mito\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"mito\",\"marker\":{\"color\":\"#FECB52\",\"pattern\":{\"shape\":\"\"}},\"name\":\"mito\",\"offsetgroup\":\"mito\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAAAAAAAAQ=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=multiple\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"multiple\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"multiple\",\"offsetgroup\":\"multiple\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AACgAgAAAAAAAN4AAAAAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"features\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"feature_group\"}},\"legend\":{\"title\":{\"text\":\"channel\"},\"tracegroupgap\":0},\"title\":{\"text\":\"feature groups\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('07531533-2f1e-43f3-8ae8-38f5685aa25c');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script>\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        </script>\n",
       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style=\"height:525px; width:100%;\">            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.2/es5/tex-svg.min.js\"></script><script>if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-4.1.1.min.js\" integrity=\"sha256-O24V1F27f8pb0glCkelh3cVHLNiHAJ5gCaVtq2aNch8=\" crossorigin=\"anonymous\"></script>                <div id=\"4df88a60-5161-463d-99f8-865d15dd2869\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script>                window.PLOTLYENV=window.PLOTLYENV || {};                                if (document.getElementById(\"4df88a60-5161-463d-99f8-865d15dd2869\")) {                    Plotly.newPlot(                        \"4df88a60-5161-463d-99f8-865d15dd2869\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=none\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"none\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"none\",\"offsetgroup\":\"none\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"TgAAAAUAAAA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=AGP\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"AGP\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"AGP\",\"offsetgroup\":\"AGP\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAgCAAUFQA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=BFHigh\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"BFHigh\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"BFHigh\",\"offsetgroup\":\"BFHigh\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAYAgAUKQA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=BFLow\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"BFLow\",\"marker\":{\"color\":\"#ab63fa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"BFLow\",\"offsetgroup\":\"BFLow\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAYAgAKIgA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=Brightfield\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Brightfield\",\"marker\":{\"color\":\"#FFA15A\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Brightfield\",\"offsetgroup\":\"Brightfield\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAWAgAQIAA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=DNA\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"DNA\",\"marker\":{\"color\":\"#19d3f3\",\"pattern\":{\"shape\":\"\"}},\"name\":\"DNA\",\"offsetgroup\":\"DNA\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAUBgAVDgA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=ER\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"ER\",\"marker\":{\"color\":\"#FF6692\",\"pattern\":{\"shape\":\"\"}},\"name\":\"ER\",\"offsetgroup\":\"ER\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAANAwACCgA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=Mito\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"Mito\",\"marker\":{\"color\":\"#B6E880\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Mito\",\"offsetgroup\":\"Mito\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAYBwATHQA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=RNA\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"RNA\",\"marker\":{\"color\":\"#FF97FF\",\"pattern\":{\"shape\":\"\"}},\"name\":\"RNA\",\"offsetgroup\":\"RNA\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAQAgARDQA=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=mito\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"mito\",\"marker\":{\"color\":\"#FECB52\",\"pattern\":{\"shape\":\"\"}},\"name\":\"mito\",\"offsetgroup\":\"mito\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i1\",\"bdata\":\"AAAAAAAAAAE=\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"channel=multiple\\u003cbr\\u003efeatures=%{x}\\u003cbr\\u003efeature_group=%{y}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"legendgroup\":\"multiple\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"multiple\",\"offsetgroup\":\"multiple\",\"orientation\":\"h\",\"showlegend\":true,\"textposition\":\"auto\",\"x\":{\"dtype\":\"i2\",\"bdata\":\"AADPAAAAAAAAACEAAAAAAA==\"},\"xaxis\":\"x\",\"y\":[\"AreaShape\",\"Correlation\",\"Granularity\",\"Intensity\",\"Neighbors\",\"RadialDistribution\",\"Texture\",\"ObjectSkeleton\"],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermap\":[{\"type\":\"scattermap\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"features\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"feature_group\"}},\"legend\":{\"title\":{\"text\":\"channel\"},\"tracegroupgap\":0},\"title\":{\"text\":\"feature groups\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4df88a60-5161-463d-99f8-865d15dd2869');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "mt.pl.feature_groups(screen, ax=axes[0])\n",
    "axes[0].set_title(\"before\")\n",
    "mt.pl.feature_groups(screen, key=\"selected\", ax=axes[1])\n",
    "axes[1].set_title(\"after\")\n",
    "axes[0].get_legend().remove()  # both panels share the channels; one legend on the right is enough\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c022a",
   "metadata": {},
   "source": [
    "### Removing multivariate redundancy\n",
    "\n",
    "`correlation_threshold` is pairwise: it drops one feature from each pair correlated above the cutoff. A\n",
    "feature that is a linear combination of *several* others, a texture that tracks the sum of two neighbouring\n",
    "scales, say, need not correlate strongly with any single one, so the pairwise cut keeps it even though it\n",
    "carries no new information.\n",
    "\n",
    "`decorrelate` removes exactly that. A rank-revealing, column-pivoted QR {cite:p}`Businger_1965,Golub_2013`\n",
    "orders the features so that each is the one least explained by those already kept, and drops a feature once\n",
    "the features kept before it predict it at multiple correlation `decorr_threshold`. One decomposition does it,\n",
    "cheap enough to run at several strengths, and it keeps original features rather than projecting to components.\n",
    "It is experimental and not part of pycytominer, so it is opt-in via `decorrelate=True`.\n",
    "\n",
    "How hard to decorrelate is a judgement call, so rather than trust one number, run it at a few thresholds and\n",
    "score each against the biological outcome you care about. Below: information retained, technical replicate\n",
    "retrieval, and biological activity retrieval (perturbation vs. control) {cite:p}`Kalinin_2025`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c022b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:20:25.997275Z",
     "iopub.status.busy": "2026-10-22T03:20:25.996845Z",
     "iopub.status.idle": "2026-10-22T03:21:18.249658Z",
     "shell.execute_reply": "2026-10-22T03:21:18.248009Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features</th>\n",
       "      <th>information kept (R2)</th>\n",
       "      <th>replicate mAP</th>\n",
       "      <th>activity mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exact</th>\n",
       "      <td>850</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorrelate 0.99</th>\n",
       "      <td>515</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorrelate 0.95</th>\n",
       "      <td>272</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorrelate 0.9</th>\n",
       "      <td>182</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  features  information kept (R2)  replicate mAP  activity mAP\n",
       "exact                  850                  1.000          0.206         0.622\n",
       "decorrelate 0.99       515                  0.980          0.203         0.604\n",
       "decorrelate 0.95       272                  0.932          0.199         0.596\n",
       "decorrelate 0.9        182                  0.860          0.200         0.587"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "selected = mt.pp.subset_features(screen)  # the pycytominer set\n",
    "reference = np.nan_to_num(np.asarray(selected.X, dtype=np.float64))\n",
    "reference = (reference - reference.mean(0)) / (reference.std(0) + 1e-9)\n",
    "rng = np.random.default_rng(0)\n",
    "\n",
    "\n",
    "def information_kept(kept, n=30):\n",
    "    \"\"\"Cross-validated R2 predicting held-out pycytominer features from the kept subset.\"\"\"\n",
    "    mask = selected.var_names.isin(kept.var_names)\n",
    "    block = reference[:, mask]\n",
    "    scores = []\n",
    "    for c in rng.choice(selected.n_vars, size=n, replace=False):\n",
    "        y = reference[:, c]\n",
    "        pred = cross_val_predict(RidgeCV(), block, y, cv=5)\n",
    "        scores.append(1.0 - ((y - pred) ** 2).sum() / ((y - y.mean()) ** 2).sum())\n",
    "    return round(float(np.mean(scores)), 3)\n",
    "\n",
    "\n",
    "def activity(kept, mode):\n",
    "    \"\"\"Mean mAP over the treated wells: replicate (technical) or activity (biological) retrieval.\"\"\"\n",
    "    adata = kept.copy()\n",
    "    mt.tl.map(adata, mode=mode, null_size=1000, seed=0, **({\"reference\": None} if mode == \"replicability\" else {}))\n",
    "    treated = ~adata.obs[\"Metadata_Control\"].to_numpy()\n",
    "    return round(float(adata.obs[\"map\"].to_numpy()[treated].mean()), 3)\n",
    "\n",
    "\n",
    "# Sweep decorrelate at a few thresholds and score each selected set against the metric that matters.\n",
    "sweep = mt.pp.decorr_threshold_sweep(\n",
    "    selected,\n",
    "    [0.99, 0.95, 0.9],\n",
    "    {\n",
    "        \"information kept (R2)\": information_kept,\n",
    "        \"replicate mAP\": lambda kept: activity(kept, \"replicability\"),\n",
    "        \"activity mAP\": lambda kept: activity(kept, \"activity\"),\n",
    "    },\n",
    "    operations=(),\n",
    ")\n",
    "\n",
    "baseline = {\n",
    "    \"n_kept\": selected.n_vars,\n",
    "    \"information kept (R2)\": 1.0,\n",
    "    \"replicate mAP\": activity(selected, \"replicability\"),\n",
    "    \"activity mAP\": activity(selected, \"activity\"),\n",
    "}\n",
    "table = pd.concat([pd.DataFrame([baseline]), sweep.drop(columns=\"threshold\")], ignore_index=True)\n",
    "table.index = [\"exact\", \"decorrelate 0.99\", \"decorrelate 0.95\", \"decorrelate 0.9\"]\n",
    "table = table.rename(columns={\"n_kept\": \"features\"})[\n",
    "    [\"features\", \"information kept (R2)\", \"replicate mAP\", \"activity mAP\"]\n",
    "]\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c022c",
   "metadata": {},
   "source": [
    "`decorrelate` is a dial, and there is no free lunch: a lower threshold removes more features, but because the\n",
    "redundant ones still carry a sliver of unique signal, retrieval falls with them. The technical replicate\n",
    "retrieval is nearly flat here; the biological activity retrieval is the one that pays, gently at 0.99, where\n",
    "the set shrinks by about 40%, and more by 0.9. On a screen with real mechanism-of-action structure the trade is often\n",
    "kinder still, because dropping redundant, noisy features can tighten same-MOA neighbourhoods, but pki has too\n",
    "few compounds per MOA to show that here.\n",
    "\n",
    "So the recommendation is the workflow above rather than a fixed cutoff: sweep a few thresholds, compare against\n",
    "the metric that matters for your screen, and keep the smallest set that still holds it. `decorr_threshold=0.99`\n",
    "is a sensible place to start."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c023",
   "metadata": {},
   "source": [
    "### The blocklist\n",
    "\n",
    "The default blocklist is pycytominer's, copied unchanged with its source recorded in the file. It lists features\n",
    "known to be unreliable: twenty Manders and twenty rank-weighted colocalization coefficients, plus the three\n",
    "highest nuclear granularity bands in each channel. `MeasureColocalization` writes Manders, RWC and Costes\n",
    "coefficients, and only the first two are on the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c024",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:21:18.252691Z",
     "iopub.status.busy": "2026-10-22T03:21:18.252494Z",
     "iopub.status.idle": "2026-10-22T03:21:18.259694Z",
     "shell.execute_reply": "2026-10-22T03:21:18.258561Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'on the list': 55,\n",
       " 'in this screen': 55,\n",
       " 'examples': ['Nuclei_Correlation_Manders_AGP_DNA',\n",
       "  'Nuclei_Correlation_Manders_AGP_ER',\n",
       "  'Nuclei_Correlation_Manders_AGP_Mito']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocked = mt.pp.feature_blocklist()\n",
    "{\"on the list\": len(blocked), \"in this screen\": int(screen.var_names.isin(blocked).sum()), \"examples\": blocked[:3]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cscale0",
   "metadata": {},
   "source": [
    "## Scaling correlation to large screens\n",
    "\n",
    "`correlation_threshold` compares every pair of features, so its cost grows with the square of the feature count;\n",
    "on a screen with tens of thousands of features the exact pass takes minutes.\n",
    "\n",
    "mantispy keeps the exact pass as the default, matching pycytominer {cite:p}`Serrano_2025`. Passing `corr_window`\n",
    "switches to a faster two-pass approximation: pass one sorts features by name (a CellProfiler family sorts\n",
    "together) and prunes redundancy only within a sliding window, which is cheap and whittles the list down; pass two\n",
    "runs the exact all-pairs correlation on the survivors, a much smaller set, so it catches the cross-family\n",
    "redundancy the windows could not see while its quadratic cost stays small. A window of 500 is a good default.\n",
    "\n",
    "Be clear about the deviation from the standard: the fast pass does not reproduce the exact pass feature for\n",
    "feature. Correlated features are near-duplicates, so which member survives is close to arbitrary, and the fast\n",
    "pass keeps a different set. What matters is whether the reduced space keeps the same information and the same\n",
    "biology. The next cells measure exactly that, across strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cscale1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:21:18.261555Z",
     "iopub.status.busy": "2026-10-22T03:21:18.261399Z",
     "iopub.status.idle": "2026-10-22T03:23:54.546887Z",
     "shell.execute_reply": "2026-10-22T03:23:54.545716Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:8: UserWarning: 133 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon'. Adding epsilon=1e-18 to their scale multiplies them by up to 1e18, so they dominate every distance downstream. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n",
      "  mt.pp.normalize(base, method=\"mad_robustize\", by=\"Metadata_Plate\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>features kept</th>\n",
       "      <th>time (s)</th>\n",
       "      <th>overlap with exact (Jaccard)</th>\n",
       "      <th>information kept (R2 vs exact)</th>\n",
       "      <th>replicate mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exact</th>\n",
       "      <td>870</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast (window 500)</th>\n",
       "      <td>1002</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast (window 1500)</th>\n",
       "      <td>1010</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    features kept  time (s)  overlap with exact (Jaccard)  \\\n",
       "exact                         870       3.5                         1.000   \n",
       "fast (window 500)            1002       0.7                         0.560   \n",
       "fast (window 1500)           1010       1.0                         0.561   \n",
       "\n",
       "                    information kept (R2 vs exact)  replicate mAP  \n",
       "exact                                        1.000          0.362  \n",
       "fast (window 500)                            0.995          0.363  \n",
       "fast (window 1500)                           0.992          0.363  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Normalize a copy so the downstream mAP is meaningful, then compare selection strategies on it.\n",
    "base = screen.copy()\n",
    "mt.pp.normalize(base, method=\"mad_robustize\", by=\"Metadata_Plate\")\n",
    "matrix = np.nan_to_num(np.asarray(base.X, dtype=np.float64))\n",
    "matrix = (matrix - matrix.mean(0)) / (matrix.std(0) + 1e-9)\n",
    "rng = np.random.default_rng(0)\n",
    "\n",
    "\n",
    "def reconstruct_r2(target, predictors, n=30):\n",
    "    \"\"\"Cross-validated R2 predicting each target feature from the predictor feature set.\"\"\"\n",
    "    # cross-validated R2 predicting each target feature from the predictor set: is the information kept\n",
    "    cols = rng.choice(target, size=min(n, len(target)), replace=False)\n",
    "    block = matrix[:, predictors]\n",
    "    scores = []\n",
    "    for c in cols:\n",
    "        y = matrix[:, c]\n",
    "        tot = ((y - y.mean()) ** 2).sum()\n",
    "        if tot == 0:\n",
    "            scores.append(1.0)\n",
    "            continue\n",
    "        pred = cross_val_predict(RidgeCV(), block, y, cv=5)\n",
    "        scores.append(1.0 - ((y - pred) ** 2).sum() / tot)\n",
    "    return float(np.mean(scores))\n",
    "\n",
    "\n",
    "def select(**kwargs):\n",
    "    \"\"\"Run feature_select with these kwargs; return the boolean selection mask and the runtime.\"\"\"\n",
    "    adata = base.copy()\n",
    "    start = time.perf_counter()\n",
    "    mt.pp.feature_select(adata, operations=(\"drop_degenerate\", \"correlation_threshold\"), **kwargs)\n",
    "    return adata.var[\"selected\"].to_numpy(dtype=bool), time.perf_counter() - start\n",
    "\n",
    "\n",
    "def replicate_map(mask):\n",
    "    \"\"\"Mean replicate-retrieval mAP on the subset of features flagged by mask.\"\"\"\n",
    "    # downstream biology: do a compound's replicate wells retrieve each other in the reduced space\n",
    "    adata = base[:, mask].copy()\n",
    "    mt.tl.map(adata, mode=\"replicability\", null_size=1000, reference=None, seed=0)\n",
    "    return float(adata.obs[\"map\"].mean())\n",
    "\n",
    "\n",
    "strategies = {\"exact\": {}, \"fast (window 500)\": {\"corr_window\": 500}, \"fast (window 1500)\": {\"corr_window\": 1500}}\n",
    "masks = {name: select(**kw) for name, kw in strategies.items()}\n",
    "exact_mask = masks[\"exact\"][0]\n",
    "exact_idx = np.flatnonzero(exact_mask)\n",
    "\n",
    "rows = []\n",
    "for name, (mask, seconds) in masks.items():\n",
    "    idx = np.flatnonzero(mask)\n",
    "    rows.append(\n",
    "        {\n",
    "            \"features kept\": int(mask.sum()),\n",
    "            \"time (s)\": round(seconds, 1),\n",
    "            \"overlap with exact (Jaccard)\": round((exact_mask & mask).sum() / (exact_mask | mask).sum(), 3),\n",
    "            \"information kept (R2 vs exact)\": 1.0 if name == \"exact\" else round(reconstruct_r2(exact_idx, idx), 3),\n",
    "            \"replicate mAP\": round(replicate_map(mask), 3),\n",
    "        }\n",
    "    )\n",
    "pd.DataFrame(rows, index=list(strategies))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cscale2",
   "metadata": {},
   "source": [
    "In the table above, the fast selections overlap the exact one only about half at the feature level\n",
    "(this is the deviation from pycytominer, and it is real), yet they reconstruct the exact features at an R2 near 1\n",
    "and reach the same replicate mAP. Different features, same information, same biology. Window size trades a little\n",
    "speed for a slightly larger, still-equivalent set. The exact row here keeps 870 features and a replicate\n",
    "mAP of 0.362, unlike the 850 and 0.219 of the decorrelate table, because that table normalizes against the\n",
    "negative controls and this one does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cscale3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:23:54.554113Z",
     "iopub.status.busy": "2026-10-22T03:23:54.553910Z",
     "iopub.status.idle": "2026-10-22T03:24:17.402318Z",
     "shell.execute_reply": "2026-10-22T03:24:17.401565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'features': 20000,\n",
       " 'exact': '20.4s, 1500 kept',\n",
       " 'fast (window 500)': '1.4s, 1500 kept',\n",
       " 'speedup': '14.5x'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from anndata import AnnData\n",
    "\n",
    "rng = np.random.default_rng(0)\n",
    "n_cells, n_features, n_drivers = 2000, 20_000, 1500\n",
    "drivers = rng.standard_normal((n_cells, n_drivers), dtype=np.float32)\n",
    "assign = np.sort(rng.integers(0, n_drivers, size=n_features))\n",
    "X = drivers[:, assign] + rng.standard_normal((n_cells, n_features), dtype=np.float32) * 0.05\n",
    "names = [f\"Cells_Texture_Driver{assign[i]:05d}_{i:06d}\" for i in range(n_features)]\n",
    "big = AnnData(X, var=pd.DataFrame(index=names))\n",
    "\n",
    "\n",
    "def timed(**kwargs):\n",
    "    \"\"\"Run feature_select on the synthetic screen; return the runtime and the features kept.\"\"\"\n",
    "    adata = big.copy()\n",
    "    start = time.perf_counter()\n",
    "    mt.pp.feature_select(adata, operations=(\"correlation_threshold\",), **kwargs)\n",
    "    return time.perf_counter() - start, int(adata.var[\"selected\"].sum())\n",
    "\n",
    "\n",
    "exact_time, exact_kept = timed()\n",
    "fast_time, fast_kept = timed(corr_window=500)\n",
    "{\n",
    "    \"features\": n_features,\n",
    "    \"exact\": f\"{exact_time:.1f}s, {exact_kept} kept\",\n",
    "    \"fast (window 500)\": f\"{fast_time:.1f}s, {fast_kept} kept\",\n",
    "    \"speedup\": f\"{exact_time / fast_time:.1f}x\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cscale4",
   "metadata": {},
   "source": [
    "Guidance: use the exact default for faithful pycytominer parity, and reach for `corr_window=500` when a screen has\n",
    "many thousands of features and the correlation step dominates. The saving grows with the feature count, and the\n",
    "reduced space carries the same information and the same replicate signal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c025",
   "metadata": {},
   "source": [
    "## Looking at the plates\n",
    "\n",
    "Plate layout artifacts are easiest to see on a plate map. `pl.plate` draws any feature or `obs` column; here, how\n",
    "many cells each well of the eight plates held."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c026",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:24:17.403943Z",
     "iopub.status.busy": "2026-10-22T03:24:17.403765Z",
     "iopub.status.idle": "2026-10-22T03:24:18.749361Z",
     "shell.execute_reply": "2026-10-22T03:24:18.748070Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x640 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.plate(screen, color=\"Metadata_CellCount\", groupby=\"Metadata_Plate\", ncols=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c027",
   "metadata": {},
   "source": [
    "## Handing off\n",
    "\n",
    "`get.to_dataframe` returns the flat, metadata-first table that pycytominer {cite:p}`Serrano_2025`, cytominer-eval\n",
    "and most published scripts expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c028",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-22T03:24:18.755404Z",
     "iopub.status.busy": "2026-10-22T03:24:18.754944Z",
     "iopub.status.idle": "2026-10-22T03:24:18.816649Z",
     "shell.execute_reply": "2026-10-22T03:24:18.815719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Metadata_plate_map_name</th>\n",
       "      <th>Metadata_broad_sample</th>\n",
       "      <th>Metadata_mg_per_ml</th>\n",
       "      <th>Metadata_mmoles_per_liter</th>\n",
       "      <th>Metadata_solvent</th>\n",
       "      <th>Metadata_Plate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Metadata_plate_map_name Metadata_broad_sample  Metadata_mg_per_ml  \\\n",
       "0        C-7210-01-CMP-014                   NaN                 NaN   \n",
       "1        C-7210-01-CMP-014                   NaN                 NaN   \n",
       "10       C-7210-01-CMP-014                   NaN                 NaN   \n",
       "\n",
       "    Metadata_mmoles_per_liter Metadata_solvent Metadata_Plate  \n",
       "0                         NaN             DMSO     BR00122970  \n",
       "1                         NaN             DMSO     BR00122970  \n",
       "10                        NaN             DMSO     BR00122970  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = mt.get.to_dataframe(mt.pp.subset_features(screen))\n",
    "frame.iloc[:3, :6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c029",
   "metadata": {},
   "source": [
    "Next: [Plate artifacts and corrections](artifacts.ipynb)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.13.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}