{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# MaldiAMRKit - Differential Analysis\n",
    "\n",
    "This notebook demonstrates the `maldiamrkit.differential` module for identifying\n",
    "m/z bins that differ significantly between **resistant (R)** and **susceptible (S)**\n",
    "groups, and for comparing significant peaks across multiple drugs.\n",
    "\n",
    "It covers:\n",
    "- `DifferentialAnalysis` on a binned feature matrix (Mann-Whitney U or Welch's t-test)\n",
    "- Multiple-testing correction (FDR-BH, FDR-BY, Bonferroni), log2 fold change, Cohen's d\n",
    "- Ranking peaks with `top_peaks()` and filtering with `significant_peaks()`\n",
    "- AMR-aware plots: `plot_volcano`, `plot_manhattan`\n",
    "- Multi-drug comparison via `compare_drugs` + `plot_drug_comparison` (heatmap / UpSet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "These notebooks use the **MALDI-Kleb-AI** dataset (Rocchi *et al.*, 2026; [Zenodo DOI 10.5281/zenodo.17405072](https://zenodo.org/records/17405072)), a curated archive of MALDI-TOF spectra of *Klebsiella* clinical isolates from three Italian centres (Rome, Milan, Catania) with Amikacin / Meropenem resistance annotations. For simplicity we restrict the demo to the **Rome sub-cohort** (single site, no batch correction needed). The helper in [`notebooks/_demo.py`](_demo.py) caches the 370 MB tarball under `~/.cache/maldiamrkit/` (or `$MALDIAMRKIT_CACHE_DIR`) on first use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Dataset\n",
    "\n",
    "We load the Rome cohort and use `LabelEncoder(intermediate='drop')` to\n",
    "build a binary label vector. The Zenodo dataset also ships a Meropenem\n",
    "annotation, which we will use later for the multi-drug comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:44.037053Z",
     "iopub.status.busy": "2026-05-13T10:10:44.036954Z",
     "iopub.status.idle": "2026-05-13T10:10:53.843069Z",
     "shell.execute_reply": "2026-05-13T10:10:53.842190Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ettore/.venvs/maldiamrkit/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Processing spectra:   0%|          | 0/472 [00:00<?, ?spectrum/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Processing spectra:  98%|█████████▊| 464/472 [00:00<00:00, 4632.35spectrum/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Processing spectra: 100%|██████████| 472/472 [00:00<00:00, 4600.71spectrum/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (470, 6000)\n",
      "Class counts: {1: 273, 0: 197}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from maldiamrkit.susceptibility import LabelEncoder\n",
    "\n",
    "sys.path.insert(0, str(pathlib.Path.cwd()))  # _demo.py sits next to this notebook\n",
    "from _demo import load_maldi_kleb_ai\n",
    "\n",
    "ds = load_maldi_kleb_ai(antibiotic=\"Amikacin\", verbose=True)\n",
    "encoder = LabelEncoder(intermediate=\"nan\")  # map 'I' to NaN so lengths stay aligned\n",
    "y = (\n",
    "    pd.Series(\n",
    "        encoder.fit_transform(ds.meta[\"Amikacin\"]),\n",
    "        index=ds.meta.index,\n",
    "        name=\"Amikacin\",\n",
    "    )\n",
    "    .dropna()\n",
    "    .astype(int)\n",
    ")\n",
    "X = ds.X.loc[y.index]\n",
    "print(f\"X shape: {X.shape}\")\n",
    "print(f\"Class counts: {y.value_counts().to_dict()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the Differential Analysis\n",
    "\n",
    "Instantiate `DifferentialAnalysis` with `X` and `y` and call `.run()`.\n",
    "The default test is Mann-Whitney U (non-parametric, recommended for MALDI peak\n",
    "intensities) with Benjamini-Hochberg FDR correction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:53.844451Z",
     "iopub.status.busy": "2026-05-13T10:10:53.844304Z",
     "iopub.status.idle": "2026-05-13T10:10:55.904719Z",
     "shell.execute_reply": "2026-05-13T10:10:55.904021Z"
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mz_bin</th>\n",
       "      <th>mean_r</th>\n",
       "      <th>mean_s</th>\n",
       "      <th>fold_change</th>\n",
       "      <th>p_value</th>\n",
       "      <th>adjusted_p_value</th>\n",
       "      <th>effect_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>4712.0</td>\n",
       "      <td>0.000492</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>1.611970</td>\n",
       "      <td>1.031418e-07</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.472830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>4715.0</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>1.642962</td>\n",
       "      <td>3.811623e-08</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.546662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>4703.0</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.241332</td>\n",
       "      <td>7.021650e-08</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.522023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3038</th>\n",
       "      <td>11114.0</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.652812</td>\n",
       "      <td>8.531360e-08</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.589519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037</th>\n",
       "      <td>11111.0</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.604420</td>\n",
       "      <td>1.692839e-07</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.545698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mz_bin    mean_r    mean_s  fold_change       p_value  \\\n",
       "904    4712.0  0.000492  0.000161     1.611970  1.031418e-07   \n",
       "905    4715.0  0.000506  0.000162     1.642962  3.811623e-08   \n",
       "901    4703.0  0.000284  0.000120     1.241332  7.021650e-08   \n",
       "3038  11114.0  0.000125  0.000080     0.652812  8.531360e-08   \n",
       "3037  11111.0  0.000102  0.000067     0.604420  1.692839e-07   \n",
       "\n",
       "      adjusted_p_value  effect_size  \n",
       "904           0.000155     0.472830  \n",
       "905           0.000155     0.546662  \n",
       "901           0.000155     0.522023  \n",
       "3038          0.000155     0.589519  \n",
       "3037          0.000197     0.545698  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from maldiamrkit.differential import DifferentialAnalysis\n",
    "\n",
    "analysis = DifferentialAnalysis(X, y).run(\n",
    "    test=\"mann_whitney\",\n",
    "    correction=\"fdr_bh\",\n",
    ")\n",
    "analysis.results.sort_values(\"adjusted_p_value\", ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `results` table has one row per m/z bin with:\n",
    "\n",
    "- `mz_bin` - the bin label (m/z value as stored in `X.columns`)\n",
    "- `mean_r`, `mean_s` - mean intensity in each group\n",
    "- `fold_change` - `log2((mean_r + eps) / (mean_s + eps))`\n",
    "- `p_value` - raw test p-value\n",
    "- `adjusted_p_value` - p-value after multiple-testing correction\n",
    "- `effect_size` - Cohen's d between the two groups\n",
    "\n",
    "### Construct directly from a `MaldiSet`\n",
    "\n",
    "When the antibiotic column already stores numeric binary labels\n",
    "(`0` / `1`), you can skip the manual `X` / `y` extraction:\n",
    "\n",
    "```python\n",
    "analysis = DifferentialAnalysis.from_maldi_set(\n",
    "    ds.maldi_set, antibiotic='Amikacin'\n",
    ").run()\n",
    "```\n",
    "\n",
    "In our case the labels are the strings `'R'` / `'S'`, so we keep the\n",
    "explicit `LabelEncoder` step above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:55.906485Z",
     "iopub.status.busy": "2026-05-13T10:10:55.906360Z",
     "iopub.status.idle": "2026-05-13T10:10:55.909860Z",
     "shell.execute_reply": "2026-05-13T10:10:55.909214Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000 bins tested\n",
      "Smallest adjusted p-value: 1.547e-04\n",
      "#adj <= 0.05: 22\n"
     ]
    }
   ],
   "source": [
    "print(f\"{len(analysis.results)} bins tested\")\n",
    "print(f\"Smallest adjusted p-value: {analysis.results['adjusted_p_value'].min():.3e}\")\n",
    "print(f\"#adj <= 0.05: {(analysis.results['adjusted_p_value'] <= 0.05).sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Peaks and Significance Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:55.911183Z",
     "iopub.status.busy": "2026-05-13T10:10:55.911063Z",
     "iopub.status.idle": "2026-05-13T10:10:55.917710Z",
     "shell.execute_reply": "2026-05-13T10:10:55.917133Z"
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   "outputs": [
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       "      <td>4703.0</td>\n",
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       "      <td>7.021650e-08</td>\n",
       "      <td>0.000155</td>\n",
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       "      <td>4712.0</td>\n",
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       "      <td>0.000155</td>\n",
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       "    <tr>\n",
       "      <th>3036</th>\n",
       "      <td>11108.0</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.562401</td>\n",
       "      <td>2.293709e-07</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.518235</td>\n",
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       "    <tr>\n",
       "      <th>3037</th>\n",
       "      <td>11111.0</td>\n",
       "      <td>0.000102</td>\n",
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       "      <td>0.604420</td>\n",
       "      <td>1.692839e-07</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.545698</td>\n",
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       "    <tr>\n",
       "      <th>3039</th>\n",
       "      <td>11117.0</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.604224</td>\n",
       "      <td>2.099257e-07</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.558619</td>\n",
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       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>4706.0</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>1.430031</td>\n",
       "      <td>4.248146e-07</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.491018</td>\n",
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       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>4700.0</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.017319</td>\n",
       "      <td>7.629639e-07</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.442172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>4709.0</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>1.470420</td>\n",
       "      <td>2.874043e-06</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.464538</td>\n",
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      "text/plain": [
       "       mz_bin    mean_r    mean_s  fold_change       p_value  \\\n",
       "901    4703.0  0.000284  0.000120     1.241332  7.021650e-08   \n",
       "904    4712.0  0.000492  0.000161     1.611970  1.031418e-07   \n",
       "905    4715.0  0.000506  0.000162     1.642962  3.811623e-08   \n",
       "3038  11114.0  0.000125  0.000080     0.652812  8.531360e-08   \n",
       "3036  11108.0  0.000123  0.000083     0.562401  2.293709e-07   \n",
       "3037  11111.0  0.000102  0.000067     0.604420  1.692839e-07   \n",
       "3039  11117.0  0.000127  0.000083     0.604224  2.099257e-07   \n",
       "902    4706.0  0.000376  0.000140     1.430031  4.248146e-07   \n",
       "900    4700.0  0.000243  0.000120     1.017319  7.629639e-07   \n",
       "903    4709.0  0.000417  0.000150     1.470420  2.874043e-06   \n",
       "\n",
       "      adjusted_p_value  effect_size  \n",
       "901           0.000155     0.522023  \n",
       "904           0.000155     0.472830  \n",
       "905           0.000155     0.546662  \n",
       "3038          0.000155     0.589519  \n",
       "3036          0.000197     0.518235  \n",
       "3037          0.000197     0.545698  \n",
       "3039          0.000197     0.558619  \n",
       "902           0.000319     0.491018  \n",
       "900           0.000509     0.442172  \n",
       "903           0.001724     0.464538  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis.top_peaks(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:55.919033Z",
     "iopub.status.busy": "2026-05-13T10:10:55.918896Z",
     "iopub.status.idle": "2026-05-13T10:10:55.925341Z",
     "shell.execute_reply": "2026-05-13T10:10:55.924774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 peaks survive |log2FC| >= 1 and adj.p <= 0.05\n"
     ]
    },
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       "      <td>0.000097</td>\n",
       "      <td>2.282285</td>\n",
       "      <td>4.044768e-05</td>\n",
       "      <td>0.014276</td>\n",
       "      <td>0.394177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4700.0</td>\n",
       "      <td>0.000243</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.017319</td>\n",
       "      <td>7.629639e-07</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.442172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4703.0</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.241332</td>\n",
       "      <td>7.021650e-08</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.522023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4706.0</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>1.430031</td>\n",
       "      <td>4.248146e-07</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.491018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4709.0</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>1.470420</td>\n",
       "      <td>2.874043e-06</td>\n",
       "      <td>0.001724</td>\n",
       "      <td>0.464538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4712.0</td>\n",
       "      <td>0.000492</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>1.611970</td>\n",
       "      <td>1.031418e-07</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.472830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4715.0</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>1.642962</td>\n",
       "      <td>3.811623e-08</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.546662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4718.0</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>1.388156</td>\n",
       "      <td>1.361776e-05</td>\n",
       "      <td>0.006809</td>\n",
       "      <td>0.524848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mz_bin    mean_r    mean_s  fold_change       p_value  adjusted_p_value  \\\n",
       "0  4526.0  0.000472  0.000097     2.282285  4.044768e-05          0.014276   \n",
       "1  4700.0  0.000243  0.000120     1.017319  7.629639e-07          0.000509   \n",
       "2  4703.0  0.000284  0.000120     1.241332  7.021650e-08          0.000155   \n",
       "3  4706.0  0.000376  0.000140     1.430031  4.248146e-07          0.000319   \n",
       "4  4709.0  0.000417  0.000150     1.470420  2.874043e-06          0.001724   \n",
       "5  4712.0  0.000492  0.000161     1.611970  1.031418e-07          0.000155   \n",
       "6  4715.0  0.000506  0.000162     1.642962  3.811623e-08          0.000155   \n",
       "7  4718.0  0.000366  0.000140     1.388156  1.361776e-05          0.006809   \n",
       "\n",
       "   effect_size  \n",
       "0     0.394177  \n",
       "1     0.442172  \n",
       "2     0.522023  \n",
       "3     0.491018  \n",
       "4     0.464538  \n",
       "5     0.472830  \n",
       "6     0.546662  \n",
       "7     0.524848  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig = analysis.significant_peaks(fc_threshold=1.0, p_threshold=0.05)\n",
    "print(f\"{len(sig)} peaks survive |log2FC| >= 1 and adj.p <= 0.05\")\n",
    "sig.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volcano Plot\n",
    "\n",
    "`plot_volcano` shows log2 fold change on the x axis and -log10(adjusted p-value)\n",
    "on the y axis. Points are coloured by direction:\n",
    "\n",
    "- red: up in resistant (`fold_change > fc_threshold` and `adj.p <= p_threshold`)\n",
    "- blue: up in susceptible (`fold_change < -fc_threshold` and `adj.p <= p_threshold`)\n",
    "- grey: non-significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:55.926611Z",
     "iopub.status.busy": "2026-05-13T10:10:55.926494Z",
     "iopub.status.idle": "2026-05-13T10:10:56.440317Z",
     "shell.execute_reply": "2026-05-13T10:10:56.438844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from maldiamrkit.differential import plot_volcano\n",
    "\n",
    "_ = plot_volcano(\n",
    "    analysis.results,\n",
    "    fc_threshold=1.0,\n",
    "    p_threshold=0.05,\n",
    "    title=\"Volcano - Amikacin R vs S (Rome)\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manhattan Plot along the m/z Axis\n",
    "\n",
    "`plot_manhattan` places each bin at its m/z position and plots\n",
    "-log10(adjusted p-value). Points above the dashed line pass the\n",
    "significance threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:56.442482Z",
     "iopub.status.busy": "2026-05-13T10:10:56.442143Z",
     "iopub.status.idle": "2026-05-13T10:10:56.567928Z",
     "shell.execute_reply": "2026-05-13T10:10:56.567273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from maldiamrkit.differential import plot_manhattan\n",
    "\n",
    "_ = plot_manhattan(\n",
    "    analysis.results,\n",
    "    p_threshold=0.05,\n",
    "    title=\"Manhattan - Amikacin R vs S (Rome)\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare across Multiple Drugs\n",
    "\n",
    "`DifferentialAnalysis.compare_drugs` returns a boolean matrix whose rows are\n",
    "the **union of significant m/z bins** across all drugs and whose columns are\n",
    "drug names. `True` means the peak is significant for that drug.\n",
    "\n",
    "We use the two antimicrobials annotated in MALDI-Kleb-AI - Amikacin and\n",
    "Meropenem - which share isolates but have different resistance mechanisms,\n",
    "so the overlap between their significant-peak sets is interesting on its\n",
    "own merits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:10:56.569985Z",
     "iopub.status.busy": "2026-05-13T10:10:56.569839Z",
     "iopub.status.idle": "2026-05-13T10:11:09.474644Z",
     "shell.execute_reply": "2026-05-13T10:11:09.473803Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Union of significant bins: 14\n",
      "Per-drug counts:\n",
      "Amikacin      8\n",
      "Meropenem    13\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "ds_mero = load_maldi_kleb_ai(antibiotic=\"Meropenem\", verbose=False)\n",
    "encoder_nan = LabelEncoder(intermediate=\"nan\")\n",
    "y_mero = (\n",
    "    pd.Series(\n",
    "        encoder_nan.fit_transform(ds_mero.meta[\"Meropenem\"]),\n",
    "        index=ds_mero.meta.index,\n",
    "        name=\"Meropenem\",\n",
    "    )\n",
    "    .dropna()\n",
    "    .astype(int)\n",
    ")\n",
    "X_mero = ds_mero.X.loc[y_mero.index]\n",
    "\n",
    "analysis_amk = DifferentialAnalysis(X, y).run()\n",
    "analysis_mer = DifferentialAnalysis(X_mero, y_mero).run()\n",
    "\n",
    "comparison = DifferentialAnalysis.compare_drugs(\n",
    "    {\"Amikacin\": analysis_amk, \"Meropenem\": analysis_mer},\n",
    "    fc_threshold=1.0,\n",
    "    p_threshold=0.05,\n",
    ")\n",
    "print(f\"Union of significant bins: {len(comparison)}\")\n",
    "print(\"Per-drug counts:\")\n",
    "print(comparison.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean Heatmap\n",
    "\n",
    "`plot_drug_comparison(..., kind='heatmap')` renders a compact peaks x drugs\n",
    "matrix - useful for spotting drug-specific peaks at a glance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:11:09.476069Z",
     "iopub.status.busy": "2026-05-13T10:11:09.475919Z",
     "iopub.status.idle": "2026-05-13T10:11:09.562910Z",
     "shell.execute_reply": "2026-05-13T10:11:09.562420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZYmNjtWXLFvXp00fbtm3TtGnT1K1bN9WvX7/YqTu/9e677yo6OlqtW7e22mrUqKFBgwaV2D88PNw67UY6f8pi8+bN9d1331XIsfz73/9WzZo1dffddxdb53A4rH/7+flZ//7ll1+Uk5Ojjh07auvWrcW269q1q5o2bWotR0ZGKjAw8HfX7HQ6NXLkSGvZx8dHI0eOVHZ2tjIyMiRJK1asUMuWLdWiRQsdPXrUelx//fWSpA8++KDEY8rJydHRo0fVuXNnfffdd8rJySm2/2nTpmnMmDGaOnWqJk6c6LYuODhYJ0+eVGpqapnHsH///jJP5fy15cuXy8vLS/369bPabrnlFr3zzjv65ZdfivWPiYlxO03xmmuukST169dP1apVK9Zentfj7NmzuvHGG7V27Vq9/fbbiouLc1v/6+fwzJkzOnr0qK699lpJst4bhYWFeuONN9S7d2/r+z6/9uv3WUmqVq2qW2+91Vr28fHR1VdffcH6L7/8crVu3Vqvvfaa1VZQUKCVK1eqd+/eVu3lfe3KUr16dXXv3l2vvvqqJGnZsmVq3769GjZsWKxvZmamvv32Ww0cOFA///yz9R49efKkYmJitHHjxmJX6xw1apTb8ttvvy1JGjdunFv7P//5T0nSW2+95dZer149/eMf/7CWAwMDlZCQoC+++EJZWVmSzv/sdOzYUdWrV3f72enatasKCgq0ceNGtzFvuukmVa9e3Vou+j1V9LocO3ZM77//vgYMGKD//ve/1ng///yzunXrpm+//bbYFROHDRvm9n645pprZIzRsGHDrDZvb2+1a9euXO/fn3/+2a3G3+ratatq1aqlsLAw9e/fXwEBAVqzZo0aNGggSTp06JAyMzN1++23q0aNGtZ2kZGRio2NtV6HX/v1FT2Lav3tMQQHBxf7PX4xv7skWcfF1UFRWXCREAAXdNVVV2nVqlU6e/astm3bptWrV2vmzJnq37+/MjMzFR4eXuJ233//vaKjo4u1//pKi7922WWXFWurXr16iX+gX4q9e/eqefPmcjrL/tW3du1aTZ48WZmZmW7fhyjpj+s/quZ69eoVuzjC5ZdfLul88Ln22mv17bff6quvvlKtWrVKHOPXF3H5+OOP9cgjj2jLli06deqUW7+cnBwFBQVZy2lpaXrrrbc0YcIEt++dFbnrrrv0+uuvq0ePHqpfv77i4uI0YMAAde/e/ZKPd8mSJbr66qv1888/W9/patOmjc6ePasVK1ZoxIgRbv1/+7wX1R8WFlZie3lej+TkZOXm5uqdd94p8f55x44d02OPPably5cXu0BOUcg9cuSITpw4Uep3sS6kQYMGxd5n1atX1/bt2y+47U033aR//etf+vHHH1W/fn19+OGHys7O1k033WT1qajXbuDAgbrtttt04MABvfHGG6V+T+7bb7+VJA0ePLjUsXJyctyCRePGjd3Wf//99/Ly8ir2eyM0NFTBwcH6/vvv3dqbNWtW7Dn89c9OaGiovv32W23fvr1cPztS8fdbUb1F76s9e/bIGKOHHnpIDz30UKlj1q9fv9Qxy3oPl/f3iSnju2pz587V5ZdfrpycHC1evFgbN250u7BT0fPYvHnzYtu2bNlS69atK3bRlpKOwdfXt9gVPIOCgty+q3kxv7t+fVwX+g8O4K+CgAag3Hx8fHTVVVfpqquu0uWXX64hQ4ZoxYoV1kU6fi9vb+8S28v6o6OiffTRR+rTp486deqkefPmqW7duqpSpYpeeOGFYhdGkDxbc2FhoVq1aqWnn366xPVFf+jt3btXMTExatGihZ5++mmFhYXJx8dHb7/9tmbOnFlsBuOKK67Q8ePH9corr2jkyJHF/mCuXbu2MjMztW7dOr3zzjt655139MILLyghIUEvvfTSRR/Ht99+a11w4W9/+1ux9UuXLi0W0Ep73n/P69GtWze9++67mjZtmrp06VLsKosDBgzQ5s2bNX78eLVu3VpVq1ZVYWGhunfvXmH3bPs99d90001KSkrSihUrNHbsWL3++usKCgpyC18V9dr16dNHLpdLgwcPVl5eXqlXASx6XqZPn+42k/5rVatWdVv+9Uzlr1XkH+eFhYWKjY3V/fffX+L6okBX5EKvS9Fx3nffferWrVuJfX8bMC/mPVye1z8kJKTMIHf11Vdbs7o33HCDrrvuOg0cOFC7d+8u9hqUV0m1luc9XN7fXUWKjotbN6CyIKABuCRFH/SHDh0qtU/Dhg3d7pNWpKS2P0PTpk316aef6ty5c6Veav3f//63fH19tW7dOrf/XX7hhRcueb+X8oflTz/9VOx/q7/55htJsk7ta9q0qbZt26aYmJgy9/Hmm28qLy9Pa9ascfsf79+eRlSkZs2aWrlypa677jrFxMRo06ZNqlevnlsfHx8f9e7dW71791ZhYaHuuusuLViwQA899FCpM6SlWbp0qapUqaJXXnml2B93mzZt0uzZs3XgwIESZysr0rXXXqtRo0apV69euvHGG7V69WprtvWXX37Rhg0b9Nhjj+nhhx+2timaISpSq1YtBQYGaufOnX9orSVp3Lixrr76ar322msaPXq0Vq1apRtuuKHY7S8q4rXz8/PTDTfcoCVLlqhHjx6l/uFcdPpvYGDgJd8yoWHDhiosLNS3336rli1bWu2HDx/W8ePHi51aWTSb9eufiZJ+dnJzcyvsNg5NmjSRdP4WDn/ErSHKo0WLFtq3b1+5+np7eys5OVl///vfNWfOHD3wwAPW87h79+5i/b/++mvVrFmzwm55UN7fXUWKjuvXrz/wV8Z30ACU6YMPPijxf2+Lvo9Q0ukwRbp166YtW7YoMzPTajt27JiWLl1a4XWWR79+/XT06FHNmTOn2LqiY/T29pbD4VBBQYG1bv/+/XrjjTcueb9Ff9QcP3683Nvk5+e73Wz27NmzWrBggWrVqqW2bdtKOj+j8+OPP2rRokXFtj99+rROnjwp6f/9j/avX8ecnJwyQ2eDBg303nvv6fTp04qNjXU7Pem3l5X38vJSZGSkJLmdElrey+wvXbpUHTt21E033aT+/fu7PYpOsSz6vtMfrWvXrlq+fLneffdd3XbbbdbMSEnPoSTNmjXLbdnLy0s33HCD3nzzTX3++efFxv+jZ1ZvuukmffLJJ1q8eLGOHj3qdnqjVP7Xrjzuu+8+PfLII6We0idJbdu2VdOmTTVjxgzl5uYWW3/kyJEL7qdnz56Sij/XRbMv8fHxbu0//fSTVq9ebS2fOHFCL7/8slq3bq3Q0FBJ5392tmzZonXr1hXb3/Hjx5Wfn3/Bun6tdu3a6tKlixYsWFDif1qV5zh/r+joaO3cubPcr2OXLl109dVXa9asWTpz5ozq1q2r1q1b66WXXnL7XbVz506tX7/eeh0qQnl/dxXJyMiQw+Eo8ZR54K+IGTQAZbr77rt16tQp/eMf/1CLFi109uxZbd68Wa+99poaNWqkIUOGlLrt/fffryVLlig2NlZ33323AgIC9Pzzz+uyyy7TsWPH/vTvEyQkJOjll1/WuHHj9Nlnn6ljx446efKk3nvvPd11113q27ev4uPj9fTTT6t79+4aOHCgsrOzNXfuXDVr1qxc3wMqSVGgevDBB3XzzTerSpUq6t27d5n/G12vXj1NnTpV+/fv1+WXX67XXntNmZmZWrhwoTX7d9ttt+n111/XqFGj9MEHH6hDhw4qKCjQ119/rddff13r1q1Tu3btFBcXZ82ajBw5Urm5uVq0aJFq165d5gxos2bNtH79enXp0kXdunXT+++/r8DAQN1xxx06duyYrr/+ejVo0EDff/+9nn32WbVu3drtf7hjYmIkqcwLhXz66afas2ePRo8eXeL6+vXrKyoqSkuXLtWECRNKHaci3XDDDdZpf4GBgVqwYIECAwPVqVMnTZs2TefOnVP9+vW1fv36EmcsnnzySa1fv16dO3fWiBEj1LJlSx06dEgrVqzQpk2bSrynYEUZMGCA7rvvPt13332qUaNGsdmc8r525XHllVfqyiuvLLOPl5eXnn/+efXo0UNXXHGFhgwZovr16+vHH3/UBx98oMDAQL355psX3M/gwYO1cOFCHT9+XJ07d9Znn32ml156STfccEOx+79dfvnlGjZsmNLT01WnTh0tXrxYhw8fdvsPifHjx2vNmjXq1auXbr/9drVt21YnT57Ujh07tHLlSu3fv/+iT6ebO3eurrvuOrVq1UrDhw9XkyZNdPjwYW3ZskU//PCDtm3bdlHjXay+ffvq8ccfV1paWrEL3JRm/PjxuvHGG/Xiiy9q1KhRmj59unr06KHo6GgNGzZMp0+f1rPPPqugoCDr/pIVoby/u4qkpqaqQ4cOCgkJqbAaAFv7sy8bCeB/yzvvvGOGDh1qWrRoYapWrWp8fHxMs2bNzN13320OHz7s1ve3l9k3xpgvvvjCdOzY0bhcLtOgQQOTnJxsZs+ebSSZrKwst21/ff+tIr+9VP7vucy+Mecv7/3ggw+axo0bmypVqpjQ0FDTv39/s3fvXqtPSkqK+dvf/mZcLpdp0aKFeeGFF6x9/Jr+/8uO/1ZJz8Pjjz9u6tevb7y8vC54yf3OnTubK664wnz++ecmOjra+Pr6moYNGxa7Z5kx528BMHXqVHPFFVcYl8tlqlevbtq2bWsee+wxk5OTY/Vbs2aNiYyMNL6+vqZRo0Zm6tSp1m0Hfnu7g9++Dp9++qmpVq2a6dSpkzl16pRZuXKliYuLM7Vr1zY+Pj7msssuMyNHjix2D67yXGb/7rvvNpLcnv/fevTRR40k6/52JT3vv72XWZGi90vRfcCMufB90IrMmzfPSDL33XefMeb8ff3+8Y9/mODgYBMUFGRuvPFG6/LfjzzyiNu233//vUlISLAuZd6kSROTmJho8vLy3Or67WX2r7jiimLH/9t6L6TovlO/vSy9Mabcr11JSnu//1ppP4tffPGF+b//+z8TEhJiXC6XadiwoRkwYIDZsGHDBbc15vy91h577DHr5zYsLMwkJSW5XdLemP/3/l23bp2JjIy0foZ//foX+e9//2uSkpJMs2bNjI+Pj6lZs6Zp3769mTFjhnUri9LeG0XPx29f971795qEhAQTGhpqqlSpYurXr2969eplVq5cafUp7b5kpR3/4MGDTUBAQLH9lyQyMtIMGzbMra20/Rlz/n5kTZs2NU2bNrVuvfDee++ZDh06GD8/PxMYGGh69+5tvvzyy99Va0nv7fL+7jp+/Ljx8fExzz//fLmeA+CvwGHMn/jtewCQNHbsWC1YsEC5ubmlfqG8MuvSpYuOHj3qke8xAf/LGjVqpIiICK1du9bTpXjEK6+8osTERB04cOAPnan9M82aNUvTpk3T3r17S72ADPBXw3fQAPyhTp8+7bb8888/65VXXtF1111HOAOACjRo0CBddtllmjt3rqdLqRDnzp3T008/rYkTJxLOUKnwHTQAf6jo6Gh16dJFLVu21OHDh5WSkqITJ06UeWEBAMDF8/Ly+kvNvFepUkUHDhzwdBnAn46ABuAP1bNnT61cuVILFy6Uw+FQVFSUUlJS1KlTJ0+XBgAAYDt8Bw0AAAAAbILvoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCa7i+CfIPXn6wp0AAH9JVQPO37/peGyshysBAHhScGpqufoxgwYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAnbBLQpU6bI4XBo7NixxdYZY9SjRw85HA698cYbbus2bNig9u3bq1q1agoNDdWECROUn59fbPsZM2bo8ssvl8vlUv369fXEE0+UWc+xY8c0aNAgBQYGKjg4WMOGDVNubu7vPUwAAAAAKJXT0wVIUnp6uhYsWKDIyMgS18+aNUsOh6NY+7Zt29SzZ089+OCDevnll/Xjjz9q1KhRKigo0IwZM6x+Y8aM0fr16zVjxgy1atVKx44d07Fjx8qsadCgQTp06JBSU1N17tw5DRkyRCNGjNCyZct+38ECAAAAQCkcxhjjyQJyc3MVFRWlefPmafLkyWrdurVmzZplrc/MzFSvXr30+eefq27dulq9erVuuOEGSdK//vUvpaamKj093er/5ptvasCAAcrOzla1atX01VdfKTIyUjt37lTz5s3LVdNXX32l8PBwpaenq127dpKkd999Vz179tQPP/ygevXqXdwxnjx9Uf0BAH8dVQP8JEnHY2M9XAkAwJOCU1PL1c/jpzgmJiYqPj5eXbt2Lbbu1KlTGjhwoObOnavQ0NBi6/Py8uTr6+vW5ufnpzNnzigjI0PS+cDWpEkTrV27Vo0bN1ajRo10xx13lDmDtmXLFgUHB1vhTJK6du0qLy8vffrpp5d6qAAAAABQJo8GtOXLl2vr1q1KTk4ucf29996r9u3bq2/fviWu79atmzZv3qxXX31VBQUF+vHHHzVp0iRJ0qFDhyRJ3333nb7//nutWLFCL7/8sl588UVlZGSof//+pdaVlZWl2rVru7U5nU7VqFFDWVlZl3KoAAAAAHBBHvsO2sGDBzVmzBilpqYWmwWTpDVr1uj999/XF198UeoYcXFxmj59ukaNGqXbbrtNLpdLDz30kD766CN5eZ3PnoWFhcrLy9PLL7+syy+/XJKUkpKitm3bavfu3eU+7bE88vLylJeX59bmcrkqbHwAAAAAf20em0HLyMhQdna2oqKi5HQ65XQ6lZaWptmzZ8vpdCo1NVV79+5VcHCwtV6S+vXrpy5duljjjBs3TsePH9eBAwd09OhRa7atSZMmkqS6devK6XRa4UySWrZsKUk6cOBAibWFhoYqOzvbrS0/P1/Hjh0r8VTLIsnJyQoKCnJ7lDY7CAAAAAC/5bEZtJiYGO3YscOtbciQIWrRooUmTJigmjVrauTIkW7rW7VqpZkzZ6p3795u7Q6Hw7pwx6uvvqqwsDBFRUVJkjp06KD8/Hzt3btXTZs2lSR98803kqSGDRuWWFt0dLSOHz+ujIwMtW3bVpL0/vvvq7CwUNdcc02px5SUlKRx48a5tblcLp3LLyzzuQAAAAAAyYMBrVq1aoqIiHBrCwgIUEhIiNVe0mzVZZddpsaNG1vL06dPV/fu3eXl5aVVq1ZpypQpev311+Xt7S3p/MU9oqKiNHToUM2aNUuFhYVKTExUbGysNav22WefKSEhQRs2bFD9+vXVsmVLde/eXcOHD9f8+fN17tw5jR49WjfffHOZV3B0uVwlntJ4Lp+rOAIAAAC4MI9fxfH3euedd9SxY0e1a9dOb731lv7zn/9Yl+GXJC8vL7355puqWbOmOnXqpPj4eLVs2VLLly+3+pw6dUq7d+/WuXPnrLalS5eqRYsWiomJUc+ePXXddddp4cKFf+ahAQAAAKhkPH4ftMqA+6ABQOXFfdAAANL/0H3QAAAAAADnEdAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAm3AYY4yniwAAAAAAMIMGAAAAALbh9HQBlUHuydOeLgEA4CFVA/wkScdjYz1cCQDAk4JTU8vVjxk0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBN2CagTZkyRQ6HQ2PHji22zhijHj16yOFw6I033nBbt2HDBrVv317VqlVTaGioJkyYoPz8fLc+r7/+ulq3bi1/f381bNhQ06dPv2A9x44d06BBgxQYGKjg4GANGzZMubm5v+cQAQAAAKBMtgho6enpWrBggSIjI0tcP2vWLDkcjmLt27ZtU8+ePdW9e3d98cUXeu2117RmzRo98MADVp933nlHgwYN0qhRo7Rz507NmzdPM2fO1Jw5c8qsadCgQdq1a5dSU1O1du1abdy4USNGjPh9BwoAAAAAZXAYY4wnC8jNzVVUVJTmzZunyZMnq3Xr1po1a5a1PjMzU7169dLnn3+uunXravXq1brhhhskSf/617+Umpqq9PR0q/+bb76pAQMGKDs7W9WqVdPAgQN17tw5rVixwurz7LPPatq0aTpw4ECJwe+rr75SeHi40tPT1a5dO0nSu+++q549e+qHH35QvXr1Lu4YT56+qP4AgL+OqgF+kqTjsbEergQA4EnBqanl6ufxGbTExETFx8era9euxdadOnVKAwcO1Ny5cxUaGlpsfV5ennx9fd3a/Pz8dObMGWVkZJTZ54cfftD3339fYk1btmxRcHCwFc4kqWvXrvLy8tKnn3560ccIAAAAAOXh0YC2fPlybd26VcnJySWuv/fee9W+fXv17du3xPXdunXT5s2b9eqrr6qgoEA//vijJk2aJEk6dOiQ1WfVqlXasGGDCgsL9c033+ipp55y6/NbWVlZql27tlub0+lUjRo1lJWVdUnHCgAAAAAX4rGAdvDgQY0ZM0ZLly4tNsMlSWvWrNH777/vdrrjb8XFxWn69OkaNWqUXC6XLr/8cvXs2VOS5OV1/tCGDx+u0aNHq1evXvLx8dG1116rm2++2a1PRcnLy9OJEyfcHnl5eRW6DwAAAAB/XR4LaBkZGcrOzlZUVJScTqecTqfS0tI0e/ZsOZ1Opaamau/evQoODrbWS1K/fv3UpUsXa5xx48bp+PHjOnDggI4ePWrNtjVp0kSS5HA4NHXqVOXm5ur7779XVlaWrr76arc+vxUaGqrs7Gy3tvz8fB07dqzEUy2LJCcnKygoyO1R2uwgAAAAAPyWxy4S8t///rfYd8CGDBmiFi1aaMKECapZs6aOHj3qtr5Vq1Z65pln1Lt3bzVu3LjEcR9++GG9+OKL2rdvn7y9vUvsk5CQoD179mjz5s0lri+6SMjnn3+utm3bSpLWr1+v7t27l3mRkLy8vGIzZi6XS+fyC0vsDwD46+MiIQAAqfwXCXH+wXWUqlq1aoqIiHBrCwgIUEhIiNVe0mzVZZdd5hbOpk+fru7du8vLy0urVq3SlClT9Prrr1vh7OjRo1q5cqW6dOmiM2fO6IUXXtCKFSuUlpZmjfHZZ58pISFBGzZsUP369dWyZUt1795dw4cP1/z583Xu3DmNHj1aN998c5lXcHS5XHK5XMXaz+VzFUcAAAAAF+bxqzj+Xu+88446duyodu3a6a233tJ//vMf6zL8RV566SW1a9dOHTp00K5du/Thhx9apzlK568WuXv3bp07d85qW7p0qVq0aKGYmBj17NlT1113nRYuXPhnHRYAAACASsjj90GrDLgPGgBUXpziCACQ/ofugwYAAAAAOI+ABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANiEwxhjPF0EAAAAAIAZNAAAAACwDaenC6gMck+e9nQJAAAPqRrgJ0k6Hhvr4UoAAJ4UnJparn7MoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbMI2AW3KlClyOBwaO3as1dalSxc5HA63x6hRo9y2O3DggOLj4+Xv76/atWtr/Pjxys/Pd+uzdOlSXXnllfL391fdunU1dOhQ/fzzz2XWU55xAQAAAKAiOT1dgCSlp6drwYIFioyMLLZu+PDhmjRpkrXs7+9v/bugoEDx8fEKDQ3V5s2bdejQISUkJKhKlSp68sknJUkff/yxEhISNHPmTPXu3Vs//vijRo0apeHDh2vVqlUl1lOecQEAAACgonl8Bi03N1eDBg3SokWLVL169WLr/f39FRoaaj0CAwOtdevXr9eXX36pJUuWqHXr1urRo4cef/xxzZ07V2fPnpUkbdmyRY0aNdI999yjxo0b67rrrtPIkSP12WeflVpTecYFAAAAgIrm8YCWmJio+Ph4de3atcT1S5cuVc2aNRUREaGkpCSdOnXKWrdlyxa1atVKderUsdq6deumEydOaNeuXZKk6OhoHTx4UG+//baMMTp8+LBWrlypnj17llpTecYFAAAAgIrm0VMcly9frq1btyo9Pb3E9QMHDlTDhg1Vr149bd++XRMmTNDu3butUxOzsrLcQpQkazkrK0uS1KFDBy1dulQ33XSTzpw5o/z8fPXu3Vtz584tta7yjAsAAAAAFc1jAe3gwYMaM2aMUlNT5evrW2KfESNGWP9u1aqV6tatq5iYGO3du1dNmzYt136+/PJLjRkzRg8//LC6deumQ4cOafz48Ro1apRSUlIq5FiK5OXlKS8vz63N5XJV6D4AAAAA/HV57BTHjIwMZWdnKyoqSk6nU06nU2lpaZo9e7acTqcKCgqKbXPNNddIkvbs2SNJCg0N1eHDh936FC2HhoZKkpKTk9WhQweNHz9ekZGR6tatm+bNm6fFixfr0KFDJdZWnnFLkpycrKCgILdHcnJyeZ4OAAAAAPBcQIuJidGOHTuUmZlpPdq1a6dBgwYpMzNT3t7exbbJzMyUJNWtW1fS+e+X7dixQ9nZ2Vaf1NRUBQYGKjw8XJJ06tQpeXm5H2bR2MaYEmsrz7glSUpKUk5OjtsjKSmpHM8GAAAAAHjwFMdq1aopIiLCrS0gIEAhISGKiIjQ3r17tWzZMvXs2VMhISHavn277r33XnXq1Mm6HH9cXJzCw8N12223adq0acrKytLEiROVmJhonVrYu3dvDR8+XM8995x1iuPYsWN19dVXq169epKk1atXKykpSV9//XW5xy2Jy+Uqcf25/NMV8pwBAAAA+GuzxX3QSuLj46P33ntPs2bN0smTJxUWFqZ+/fpp4sSJVh9vb2+tXbtWd955p6KjoxUQEKDBgwe73Tft9ttv13//+1/NmTNH//znPxUcHKzrr79eU6dOtfrk5ORo9+7dFzUuAAAAAFQ0hyntPD9UmNyTzKABQGVVNcBPknQ8NtbDlQAAPCk4NbVc/Tx+HzQAAAAAwHkENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmHMYY4+kiAAAAAADMoAEAAACAbTg9XUBlkHvytKdLAAB4SNUAP0nS8dhYD1cCAPCk4NTUcvVjBg0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBO2CWhTpkyRw+HQ2LFjrbYuXbrI4XC4PUaNGuW23T333KO2bdvK5XKpdevWxcY9c+aMbr/9drVq1UpOp1M33HBDueo5duyYBg0apMDAQAUHB2vYsGHKzc39HUcIAAAAAGWzRUBLT0/XggULFBkZWWzd8OHDdejQIesxbdq0Yn2GDh2qm266qcSxCwoK5Ofnp3vuuUddu3Ytd02DBg3Srl27lJqaqrVr12rjxo0aMWJE+Q8KAAAAAC6S09MF5ObmatCgQVq0aJEmT55cbL2/v79CQ0NL3X727NmSpCNHjmj79u3F1gcEBOi5556TJH388cc6fvz4BWv66quv9O677yo9PV3t2rWTJD377LPq2bOnZsyYoXr16pXn0AAAAADgonh8Bi0xMVHx8fGlzm4tXbpUNWvWVEREhJKSknTq1Kk/vKYtW7YoODjYCmeS1LVrV3l5eenTTz/9w/cPAAAAoHLy6Aza8uXLtXXrVqWnp5e4fuDAgWrYsKHq1aun7du3a8KECdq9e7dWrVr1h9aVlZWl2rVru7U5nU7VqFFDWVlZf+i+AQAAAFReHgtoBw8e1JgxY5SamipfX98S+/z6O1+tWrVS3bp1FRMTo71796pp06Z/VqnllpeXp7y8PLc2l8vloWoAAAAA/K/x2CmOGRkZys7OVlRUlJxOp5xOp9LS0jR79mw5nU4VFBQU2+aaa66RJO3Zs+cPrS00NFTZ2dlubfn5+Tp27FiZ34dLTk5WUFCQ2yM5OfkPrRUAAADAX4fHZtBiYmK0Y8cOt7YhQ4aoRYsWmjBhgry9vYttk5mZKUmqW7fuH1pbdHS0jh8/royMDLVt21aS9P7776uwsNAKiSVJSkrSuHHj3NpcLpfO5Rf+ofUCAAAA+GvwWECrVq2aIiIi3NoCAgIUEhKiiIgI7d27V8uWLVPPnj0VEhKi7du3695771WnTp3cLse/Z88e5ebmKisrS6dPn7ZCXHh4uHx8fCRJX375pc6ePatjx47pv//9r9Wn6L5pn332mRISErRhwwbVr19fLVu2VPfu3TV8+HDNnz9f586d0+jRo3XzzTeXeQVHl8tV4imN5/JP/45nCgAAAEBl4fHL7JfGx8dH7733nmbNmqWTJ08qLCxM/fr108SJE9363XHHHUpLS7OW27RpI0nat2+fGjVqJEnq2bOnvv/++2J9jDGSpFOnTmn37t06d+6c1Wfp0qUaPXq0YmJi5OXlpX79+lmX9AcAAACAP4LDFKUU/GFyTzKDBgCVVdUAP0nS8dhYD1cCAPCk4NTUcvXz+H3QAAAAAADnEdAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAm3AYY4yniwAAAAAAMIMGAAAAALbh9HQBlUHuydOeLgEA4CFVA/wkScdjYz1cCQDAk4JTU8vVjxk0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBN2CagTZkyRQ6HQ2PHjrXaunTpIofD4fYYNWqU23b33HOP2rZtK5fLpdatW5e5jz179qhatWoKDg6+YD0HDhxQfHy8/P39Vbt2bY0fP175+fmXcGQAAAAAUD5OTxcgSenp6VqwYIEiIyOLrRs+fLgmTZpkLfv7+xfrM3ToUH366afavn17qfs4d+6cbrnlFnXs2FGbN28us56CggLFx8crNDRUmzdv1qFDh5SQkKAqVaroySefvIgjAwAAAIDy8/gMWm5urgYNGqRFixapevXqxdb7+/srNDTUegQGBrqtnz17thITE9WkSZMy9zNx4kS1aNFCAwYMuGBN69ev15dffqklS5aodevW6tGjhx5//HHNnTtXZ8+evbgDBAAAAIBy8nhAS0xMVHx8vLp27Vri+qVLl6pmzZqKiIhQUlKSTp06ddH7eP/997VixQrNnTu3XP23bNmiVq1aqU6dOlZbt27ddOLECe3ateui9w8AAAAA5eHRUxyXL1+urVu3Kj09vcT1AwcOVMOGDVWvXj1t375dEyZM0O7du7Vq1apy7+Pnn3/W7bffriVLlhSbfStNVlaWWziTZC1nZWWVe98AAAAAcDE8FtAOHjyoMWPGKDU1Vb6+viX2GTFihPXvVq1aqW7duoqJidHevXvVtGnTcu1n+PDhGjhwoDp16lQhdZclLy9PeXl5bm0ul+sP3y8AAACAvwaPneKYkZGh7OxsRUVFyel0yul0Ki0tTbNnz5bT6VRBQUGxba655hpJ56/GWF7vv/++ZsyYYe1j2LBhysnJkdPp1OLFi0vcJjQ0VIcPH3ZrK1oODQ0tdV/JyckKCgpyeyQnJ5e7VgAAAACVm8dm0GJiYrRjxw63tiFDhqhFixaaMGGCvL29i22TmZkpSapbt26597Nlyxa3sPef//xHU6dO1ebNm1W/fv0St4mOjtYTTzyh7Oxs1a5dW5KUmpqqwMBAhYeHl7qvpKQkjRs3zq3N5XLpXH5huesFAAAAUHl5LKBVq1ZNERERbm0BAQEKCQlRRESE9u7dq2XLlqlnz54KCQnR9u3bde+996pTp05ul+Pfs2ePcnNzlZWVpdOnT1shLjw8XD4+PmrZsqXbPj7//HN5eXm57Xv16tVKSkrS119/LUmKi4tTeHi4brvtNk2bNk1ZWVmaOHGiEhMTyzxl0eVylbj+XP7pi35+AAAAAFQ+trgPWkl8fHz03nvvadasWTp58qTCwsLUr18/TZw40a3fHXfcobS0NGu5TZs2kqR9+/apUaNG5dpXTk6Odu/ebS17e3tr7dq1uvPOOxUdHa2AgAANHjzY7X5sAAAAAFDRHMYY4+ki/upyTzKDBgCVVdUAP0nS8dhYD1cCAPCk4NTUcvXz+H3QAAAAAADnEdAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAm3AYY4yniwAAAAAAMIMGAAAAALbh9HQBlUHuydOeLgEA4CFVA/wkScdjYz1cCQDAk4JTU8vVjxk0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBN2CagTZkyRQ6HQ2PHjrXaunTpIofD4fYYNWqUtX7btm265ZZbFBYWJj8/P7Vs2VLPPPOM27ibNm1Shw4dFBISIj8/P7Vo0UIzZ868YD3bt29Xx44d5evrq7CwME2bNq3CjhUAAAAASuL0dAGSlJ6ergULFigyMrLYuuHDh2vSpEnWsr+/v/XvjIwM1a5dW0uWLFFYWJg2b96sESNGyNvbW6NHj5YkBQQEaPTo0YqMjFRAQIA2bdqkkSNHKiAgQCNGjCixnhMnTiguLk5du3bV/PnztWPHDg0dOlTBwcGlbgMAAAAAv5fHA1pubq4GDRqkRYsWafLkycXW+/v7KzQ0tMRthw4d6rbcpEkTbdmyRatWrbICWps2bdSmTRurT6NGjbRq1Sp99NFHpYatpUuX6uzZs1q8eLF8fHx0xRVXKDMzU08//TQBDQAAAMAfxuOnOCYmJio+Pl5du3Ytcf3SpUtVs2ZNRUREKCkpSadOnSpzvJycHNWoUaPU9V988YU2b96szp07l9pny5Yt6tSpk3x8fKy2bt26affu3frll18ucEQAAAAAcGk8OoO2fPlybd26Venp6SWuHzhwoBo2bKh69epp+/btmjBhgnbv3q1Vq1aV2H/z5s167bXX9NZbbxVb16BBAx05ckT5+fl69NFHdccdd5RaV1ZWlho3buzWVqdOHWtd9erVy3uIAAAAAFBuHgtoBw8e1JgxY5SamipfX98S+/z6dMJWrVqpbt26iomJ0d69e9W0aVO3vjt37lTfvn31yCOPKC4urthYH330kXJzc/XJJ5/ogQceULNmzXTLLbdU6DHl5eUpLy/Prc3lclXoPgAAAAD8dXnsFMeMjAxlZ2crKipKTqdTTqdTaWlpmj17tpxOpwoKCoptc80110iS9uzZ49b+5ZdfKiYmRiNGjNDEiRNL3F/jxo3VqlUrDR8+XPfee68effTRUmsLDQ3V4cOH3dqKlkv7PpwkJScnKygoyO2RnJxcan8AAAAA+DWPzaDFxMRox44dbm1DhgxRixYtNGHCBHl7exfbJjMzU5JUt25dq23Xrl26/vrrNXjwYD3xxBPl2ndhYWGxma5fi46O1oMPPqhz586pSpUqkqTU1FQ1b968zNMbk5KSNG7cOLc2l8ulc/mF5aoLAAAAQOXmsYBWrVo1RUREuLUFBAQoJCREERER2rt3r5YtW6aePXsqJCRE27dv17333qtOnTpZl+PfuXOnrr/+enXr1k3jxo1TVlaWJMnb21u1atWSJM2dO1eXXXaZWrRoIUnauHGjZsyYoXvuucfa75w5c7R69Wpt2LBB0vnvvj322GMaNmyYJkyYoJ07d+qZZ5654P3TXC5Xiac0nss/fYnPEgAAAIDKxOOX2S+Nj4+P3nvvPc2aNUsnT55UWFiY+vXr53YK48qVK3XkyBEtWbJES5YssdobNmyo/fv3Szo/W5aUlKR9+/bJ6XSqadOmmjp1qkaOHGn1P3r0qPbu3WstBwUFaf369UpMTFTbtm1Vs2ZNPfzww1xiHwAAAMAfymGMMZ4u4q8u9yQzaABQWVUN8JMkHY+N9XAlAABPCk5NLVc/j98HDQAAAABwHgENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAmHMcZ4uggAAAAAADNoAAAAAGAbTk8XUBkcj431dAkAAA8JTk2VxGcBAFR2RZ8HF8IMGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJmwT0KZMmSKHw6GxY8dabV26dJHD4XB7jBo1ylq/bds23XLLLQoLC5Ofn59atmypZ555ptjYeXl5evDBB9WwYUO5XC41atRIixcvLrOeAwcOKD4+Xv7+/qpdu7bGjx+v/Pz8CjteAAAAAPgtp6cLkKT09HQtWLBAkZGRxdYNHz5ckyZNspb9/f2tf2dkZKh27dpasmSJwsLCtHnzZo0YMULe3t4aPXq01W/AgAE6fPiwUlJS1KxZMx06dEiFhYWl1lNQUKD4+HiFhoZq8+bNOnTokBISElSlShU9+eSTFXTUAAAAAODO4wEtNzdXgwYN0qJFizR58uRi6/39/RUaGlritkOHDnVbbtKkibZs2aJVq1ZZAe3dd99VWlqavvvuO9WoUUOS1KhRozJrWr9+vb788ku99957qlOnjlq3bq3HH39cEyZM0KOPPiofH59LOFIAAAAAKJvHT3FMTExUfHy8unbtWuL6pUuXqmbNmoqIiFBSUpJOnTpV5ng5OTlWEJOkNWvWqF27dpo2bZrq16+vyy+/XPfdd59Onz5d6hhbtmxRq1atVKdOHautW7duOnHihHbt2nWRRwgAAAAA5XPJM2iFhYXas2ePsrOzi50u2KlTp3KNsXz5cm3dulXp6eklrh84cKAaNmyoevXqafv27ZowYYJ2796tVatWldh/8+bNeu211/TWW29Zbd999502bdokX19frV69WkePHtVdd92ln3/+WS+88EKJ42RlZbmFM0nWclZWVrmODQAAAAAu1iUFtE8++UQDBw7U999/L2OM2zqHw6GCgoILjnHw4EGNGTNGqamp8vX1LbHPiBEjrH+3atVKdevWVUxMjPbu3aumTZu69d25c6f69u2rRx55RHFxcVZ7YWGhHA6Hli5dqqCgIEnS008/rf79+2vevHny8/Mr93FfSF5envLy8tzaXC5XhY0PAAAA4K/tkk5xHDVqlNq1a6edO3fq2LFj+uWXX6zHsWPHyjVGRkaGsrOzFRUVJafTKafTqbS0NM2ePVtOp7PEkHfNNddIkvbs2ePW/uWXXyomJkYjRozQxIkT3dbVrVtX9evXt8KZJLVs2VLGGP3www8l1hYaGqrDhw+7tRUtl/Z9OElKTk5WUFCQ2yM5ObmMZwEAAAAA/p9LmkH79ttvtXLlSjVr1uySdxwTE6MdO3a4tQ0ZMkQtWrTQhAkT5O3tXWybzMxMSedDV5Fdu3bp+uuv1+DBg/XEE08U26ZDhw5asWKFcnNzVbVqVUnSN998Iy8vLzVo0KDE2qKjo/XEE08oOztbtWvXliSlpqYqMDBQ4eHhpR5TUlKSxo0b59bmcrl0ulevUrcBAAAAgCKXNIN2zTXXFJvFuljVqlVTRESE2yMgIEAhISGKiIjQ3r179fjjjysjI0P79+/XmjVrlJCQoE6dOlmX49+5c6f+/ve/Ky4uTuPGjVNWVpaysrJ05MgRaz8DBw5USEiIhgwZoi+//FIbN27U+PHjNXToUOv0xtWrV6tFixbWNnFxcQoPD9dtt92mbdu2ad26dZo4caISExPLPGXR5XIpMDDQ7cEpjgAAAADK65Jm0O6++27985//VFZWllq1aqUqVaq4rS/pfmYXy8fHR++9955mzZqlkydPKiwsTP369XM7hXHlypU6cuSIlixZoiVLlljtDRs21P79+yVJVatWVWpqqu6++261a9dOISEhGjBggNsl/XNycrR7925r2dvbW2vXrtWdd96p6OhoBQQEaPDgwW73YwMAAACAiuYwv73KRzl4eRWfeHM4HDLGlPsiIZXJ8dhYT5cAAPCQ4NRUSXwWAEBlV/R5cCGXNIO2b9++S9kMAAAAAFCGSwpoDRs2rOg6AAAAAKDSK3dAW7NmjXr06KEqVapozZo1Zfbt06fP7y4MAAAAACqbcge0G264QVlZWapdu7ZuuOGGUvvxHTQAAAAAuDTlDmiFhYUl/hsAAAAAUDEu6T5oAAAAAICKd8kBbcOGDerVq5eaNm2qpk2bqlevXnrvvfcqsjYAAAAAqFQuKaDNmzdP3bt3V7Vq1TRmzBiNGTNGgYGB6tmzp+bOnVvRNQIAAABApXBJN6pu0KCBHnjgAY0ePdqtfe7cuXryySf1448/VliBfwXcnBQAKi9uVA0AkMp/o+pLmkE7fvy4unfvXqw9Li5OOTk5lzIkAAAAAFR6lxTQ+vTpo9WrVxdr/89//qNevXr97qIAAAAAoDIq92X2Z8+ebf07PDxcTzzxhD788ENFR0dLkj755BN9/PHH+uc//1nxVQIAAABAJVDu76A1bty4fAM6HPruu+9+V1F/NXzvAAAqL76DBgCQyv8dtHLPoO3bt++SiwEAAAAAXBg3qgYAAAAAmyCgAQAAAIBNXNJ90AAAAAAAFY8ZNAAAAACwiXJfJASXjit3AUDlVXTVrtyTpz1cCQDAk6oG+JWr3yXNoDVp0kRDhgxRXl6eW/vRo0fVpEmTSxkSAAAAACq9Swpo+/fv18cff6yOHTsqKyvLai8oKND3339fYcUBAAAAQGVySQHN4XDo3XffVYMGDdS2bVulp6dXdF0AAAAAUOlcUkAzxqhq1apatWqVEhIS1LlzZy1ZsqSiawMAAACASuWSLhLicDisfycnJ+uKK67Q8OHDdcstt1RYYQAAAABQ2VxSQPvtrdNuvfVWNW3aVP/4xz8qpCgAAAAAqIwuKqCdOnVK/v7+KiwsLLYuOjpa27Zt09dff11hxQEAAABAZXJR30GrWbOmevXqpUWLFrldvbFInTp11Llz5worDgAAAAAqk4sKaF9//bW6deum1157TY0aNdI111yjJ554Qjt27Pij6gMAAACASsNhfvuFsnLKycnR22+/rf/85z969913VaNGDfXp00d9+vRR586d5e3tXdG1/s86Hhvr6RIAAB4SnJoqSco9edrDlQAAPKlqgF+5+l3SZfYlKSgoSLfccouWL1+uI0eOaP78+SooKNCQIUNUq1YtLV269FKHBgAAAIBK6ZJn0MryxRdfKD8/X1dddVVFD/0/iRk0AKi8mEEDAEjln0G7pMvsS9KZM2e0fft2ZWdnu13V0eFwqHfv3pc6LAAAAABUWpcU0N59910lJCTo6NGjxdY5HA4VFBT87sIAAAAAoLK5pO+g3X333brxxht16NAhFRYWuj0uNZxNmTJFDodDY8eOlSTt379fDoejxMeKFSus7Q4cOKD4+Hj5+/urdu3aGj9+vPLz893G/vDDDxUVFSWXy6VmzZrpxRdfvGA927dvV8eOHeXr66uwsDBNmzbtko4LAAAAAMrrkgLa4cOHNW7cONWpU6dCikhPT9eCBQsUGRlptYWFhenQoUNuj8cee0xVq1ZVjx49JEkFBQWKj4/X2bNntXnzZr300kt68cUX9fDDD1vj7Nu3T/Hx8fr73/+uzMxMjR07VnfccYfWrVtXaj0nTpxQXFycGjZsqIyMDE2fPl2PPvqoFi5cWCHHCwAAAAAluaSA1r9/f3344YcVUkBubq4GDRqkRYsWqXr16la7t7e3QkND3R6rV6/WgAEDVLVqVUnS+vXr9eWXX2rJkiVq3bq1evTooccff1xz587V2bNnJUnz589X48aN9dRTT6lly5YaPXq0+vfvr5kzZ5Za09KlS3X27FktXrxYV1xxhW6++Wbdc889evrppyvkmAEAAACgJJf0HbQ5c+boxhtv1EcffaRWrVqpSpUqbuvvueeeco+VmJio+Ph4de3aVZMnTy61X0ZGhjIzMzV37lyrbcuWLWrVqpXbTF63bt105513ateuXWrTpo22bNmirl27uo3VrVs361TKkmzZskWdOnWSj4+P2zZTp07VL7/84hYkAQAAAKCiXFJAe/XVV7V+/Xr5+vrqww8/lMPhsNY5HI5yB7Tly5dr69atSk9Pv2DflJQUtWzZUu3bt7fasrKyip1mWbSclZVVZp8TJ07o9OnT8vMrfrnLrKwsNW7cuNRxCWgAAAAA/giXFNAefPBBPfbYY3rggQfk5XVp97o+ePCgxowZo9TUVPn6+pbZ9/Tp01q2bJkeeuihS9rXnyUvL095eXlubS6Xy0PVAAAAAPhfc0np6uzZs7rpppsuOZxJ509ZzM7OVlRUlJxOp5xOp9LS0jR79mw5nU63q0GuXLlSp06dUkJCgtsYoaGhOnz4sFtb0XJoaGiZfQIDA0ucPSvvuCVJTk5WUFCQ2yM5ObmspwEAAAAALJeUsAYPHqzXXnvtd+04JiZGO3bsUGZmpvVo166dBg0apMzMTHl7e1t9U1JS1KdPH9WqVcttjOjoaO3YsUPZ2dlWW2pqqgIDAxUeHm712bBhg9t2qampio6OLrW26Ohobdy4UefOnXPbpnnz5mWe3piUlKScnBy3R1JSUvmeEAAAAACV3iWd4lhQUKBp06Zp3bp1ioyMLHaRkPJc7bBatWqKiIhwawsICFBISIhb+549e7Rx40a9/fbbxcaIi4tTeHi4brvtNk2bNk1ZWVmaOHGiEhMTrVMLR40apTlz5uj+++/X0KFD9f777+v111/XW2+9ZY0zZ84crV692gpyAwcO1GOPPaZhw4ZpwoQJ2rlzp5555pkyr/wonT+dsaRTGk9f8NkAAAAAgEsMaDt27FCbNm0kSTt37nRb9+sLhlSExYsXq0GDBoqLiyu2ztvbW2vXrtWdd96p6OhoBQQEaPDgwZo0aZLVp3Hjxnrrrbd077336plnnlGDBg30/PPPq1u3blafo0ePau/evdZyUFCQ1q9fr8TERLVt21Y1a9bUww8/rBEjRlTosQEAAADArzmMMcbTRfzVHY+N9XQJAAAPCU5NlSTlnuR8CgCozKoGlHz9i9+69Kt8AAAAAAAqFAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAmHMcZ4uggAAAAAADNoAAAAAGAbTk8XUBkcj431dAkAAA8JTk2VxGcBAFR2RZ8HF8IMGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJmwT0KZMmSKHw6GxY8dKkvbv3y+Hw1HiY8WKFdZ2Bw4cUHx8vPz9/VW7dm2NHz9e+fn51vpNmzapQ4cOCgkJkZ+fn1q0aKGZM2desJ7t27erY8eO8vX1VVhYmKZNm1bhxwwAAAAAv+b0dAGSlJ6ergULFigyMtJqCwsL06FDh9z6LVy4UNOnT1ePHj0kSQUFBYqPj1doaKg2b96sQ4cOKSEhQVWqVNGTTz4pSQoICNDo0aMVGRmpgIAAbdq0SSNHjlRAQIBGjBhRYj0nTpxQXFycunbtqvnz52vHjh0aOnSogoODS90GAAAAAH4vhzHGeLKA3NxcRUVFad68eZo8ebJat26tWbNmldi3TZs2ioqKUkpKiiTpnXfeUa9evfTTTz+pTp06kqT58+drwoQJOnLkiHx8fEoc5//+7/8UEBCgV155pcT1zz33nB588EFlZWVZYzzwwAN644039PXXX1/0MR6Pjb3obQAAfw3BqamS+CwAgMqu6PPgQjx+imNiYqLi4+PVtWvXMvtlZGQoMzNTw4YNs9q2bNmiVq1aWeFMkrp166YTJ05o165dJY7zxRdfaPPmzercuXOp+9qyZYs6derkFvC6deum3bt365dffinvoQEAAADARfHoKY7Lly/X1q1blZ6efsG+KSkpatmypdq3b2+1ZWVluYUzSdZyVlaWW3uDBg105MgR5efn69FHH9Udd9xR6r6ysrLUuHHjUsetXr36BesFAAAAgIvlsYB28OBBjRkzRqmpqfL19S2z7+nTp7Vs2TI99NBDl7y/jz76SLm5ufrkk0/0wAMPqFmzZrrlllsuebyS5OXlKS8vz63N5XJV6D4AAAAA/HV57BTHjIwMZWdnKyoqSk6nU06nU2lpaZo9e7acTqcKCgqsvitXrtSpU6eUkJDgNkZoaKgOHz7s1la0HBoa6tbeuHFjtWrVSsOHD9e9996rRx99tNTaLmbcX0tOTlZQUJDbIzk5ufQnAQAAAAB+xWMBLSYmRjt27FBmZqb1aNeunQYNGqTMzEx5e3tbfVNSUtSnTx/VqlXLbYzo6Gjt2LFD2dnZVltqaqoCAwMVHh5e6r4LCwuLzXT9dtyNGzfq3LlzbuM2b968zNMbk5KSlJOT4/ZISkoq83kAAAAAgCIeO8WxWrVqioiIcGsLCAhQSEiIW/uePXu0ceNGvf3228XGiIuLU3h4uG677TZNmzZNWVlZmjhxohITE61TC+fOnavLLrtMLVq0kCRt3LhRM2bM0D333GONM2fOHK1evVobNmyQJA0cOFCPPfaYhg0bpgkTJmjnzp165plnLnj/NJfLVeIpjafL+ZwAAAAAqNxscR+0sixevFgNGjRQXFxcsXXe3t5au3at7rzzTkVHRysgIECDBw/WpEmTrD6FhYVKSkrSvn375HQ61bRpU02dOlUjR460+hw9elR79+61loOCgrR+/XolJiaqbdu2qlmzph5++GHugQYAAADgD+Xx+6BVBtz7BgAqL+6DBgCQ/ofugwYAAAAAOI+ABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANiEwxhjPF0EAAAAAIAZNAAAAACwDaenC6gMjsfGeroEAICHBKemSuKzAAAqu6LPgwthBg0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBMENAAAAACwCQIaAAAAANgEAQ0AAAAAbIKABgAAAAA2QUADAAAAAJsgoAEAAACATRDQAAAAAMAmCGgAAAAAYBO2CWhTpkyRw+HQ2LFjJUn79++Xw+Eo8bFixQpruwMHDig+Pl7+/v6qXbu2xo8fr/z8fLex8/Ly9OCDD6phw4ZyuVxq1KiRFi9eXGY95RkXAAAAACqS09MFSFJ6eroWLFigyMhIqy0sLEyHDh1y67dw4UJNnz5dPXr0kCQVFBQoPj5eoaGh2rx5sw4dOqSEhARVqVJFTz75pLXdgAEDdPjwYaWkpKhZs2Y6dOiQCgsLS62nvOMCAAAAQEVyGGOMJwvIzc1VVFSU5s2bp8mTJ6t169aaNWtWiX3btGmjqKgopaSkSJLeeecd9erVSz/99JPq1KkjSZo/f74mTJigI0eOyMfHR++++65uvvlmfffdd6pRo0a5airPuBfjeGzsRfUHAPx1BKemSuKzAAAqu6LPgwvx+CmOiYmJio+PV9euXcvsl5GRoczMTA0bNsxq27Jli1q1amWFKEnq1q2bTpw4oV27dkmS1qxZo3bt2mnatGmqX7++Lr/8ct133306ffp0qfsqz7gAAAAAUNE8eorj8uXLtXXrVqWnp1+wb0pKilq2bKn27dtbbVlZWW4hSpK1nJWVJUn67rvvtGnTJvn6+mr16tU6evSo7rrrLv3888964YUXStxXecYFAAAAgIrmsYB28OBBjRkzRqmpqfL19S2z7+nTp7Vs2TI99NBDF72fwsJCORwOLV26VEFBQZKkp59+Wv3799e8efPk5+d3SfWXJC8vT3l5eW5tLperwsYHAAAA8NfmsVMcMzIylJ2draioKDmdTjmdTqWlpWn27NlyOp0qKCiw+q5cuVKnTp1SQkKC2xihoaE6fPiwW1vRcmhoqCSpbt26ql+/vhXOJKlly5YyxuiHH34osbbyjFuS5ORkBQUFuT2Sk5Mv9FQAAAAAgCQPBrSYmBjt2LFDmZmZ1qNdu3YaNGiQMjMz5e3tbfVNSUlRnz59VKtWLbcxoqOjtWPHDmVnZ1ttqampCgwMVHh4uCSpQ4cO+umnn5Sbm2v1+eabb+Tl5aUGDRqUWFt5xi1JUlKScnJy3B5JSUkX98QAAAAAqLQ8FtCqVaumiIgIt0dAQIBCQkIUERFh9duzZ482btyoO+64o9gYcXFxCg8P12233aZt27Zp3bp1mjhxohITE61TCwcOHKiQkBANGTJEX375pTZu3Kjx48dr6NCh1umNq1evVosWLS5q3JK4XC4FBga6PTjFEQAAAEB5efwqjheyePFiNWjQQHFxccXWeXt7a+3atfL29lZ0dLRuvfVWJSQkaNKkSVafqlWrKjU1VcePH7dm6Hr37q3Zs2dbfXJycrR79+6LGhcAAAAAKprH74NWGXDvGwCovLgPGgBA+h+6DxoAAAAA4DwCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATDmOM8XQRAAAAAABm0AAAAADANpyeLqAyOB4b6+kSAAAeEpyaKonPAgCo7Io+Dy6EGTQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE3YJqBNmTJFDodDY8eOlSTt379fDoejxMeKFSus7Q4cOKD4+Hj5+/urdu3aGj9+vPLz893Gnjt3rlq2bCk/Pz81b95cL7/88gXrKc+4AAAAAFCRnJ4uQJLS09O1YMECRUZGWm1hYWE6dOiQW7+FCxdq+vTp6tGjhySpoKBA8fHxCg0N1ebNm3Xo0CElJCSoSpUqevLJJyVJzz33nJKSkrRo0SJdddVV+uyzzzR8+HBVr15dvXv3LrGe8owLAAAAABXNYYwxniwgNzdXUVFRmjdvniZPnqzWrVtr1qxZJfZt06aNoqKilJKSIkl655131KtXL/3000+qU6eOJGn+/PmaMGGCjhw5Ih8fH7Vv314dOnTQ9OnTrXH++c9/6tNPP9WmTZtK3E95xr0Yx2NjL6o/AOCvIzg1VRKfBQBQ2RV9HlyIx09xTExMVHx8vLp27Vpmv4yMDGVmZmrYsGFW25YtW9SqVSsrRElSt27ddOLECe3atUuSlJeXJ19fX7ex/Pz89Nlnn+ncuXMl7qs84wIAAABARfNoQFu+fLm2bt2q5OTkC/ZNSUlRy5Yt1b59e6stKyvLLURJspazsrIknQ9Wzz//vDIyMmSM0eeff67nn39e586d09GjR0vcV3nGBQAAAICK5rHvoB08eFBjxoxRampqsRmu3zp9+rSWLVumhx566KL389BDDykrK0vXXnutjDGqU6eOBg8erGnTpsnLq2LzaV5envLy8tzaXC5Xhe4DAAAAwF+Xx2bQMjIylJ2draioKDmdTjmdTqWlpWn27NlyOp0qKCiw+q5cuVKnTp1SQkKC2xihoaE6fPiwW1vRcmhoqKTzpzMuXrxYp06d0v79+3XgwAE1atRI1apVU61atUqsrTzjliQ5OVlBQUFuj/LMDgIAAACA5MGAFhMTox07digzM9N6tGvXToMGDVJmZqa8vb2tvikpKerTp0+xQBUdHa0dO3YoOzvbaktNTVVgYKDCw8Pd+lapUkUNGjSQt7e3li9frl69epU6g3Yx4/5aUlKScnJy3B5JSUkX9bwAAAAAqLw8dopjtWrVFBER4dYWEBCgkJAQt/Y9e/Zo48aNevvtt4uNERcXp/DwcN12222aNm2asrKyNHHiRCUmJlqnFn7zzTf67LPPdM011+iXX37R008/rZ07d+qll16yxlm9erWSkpL09ddfl3vckrhcrhLXn764pwYAAABAJeXxqzheyOLFi9WgQQPFxcUVW+ft7a21a9fK29tb0dHRuvXWW5WQkKBJkyZZfQoKCvTUU0/pyiuvVGxsrM6cOaPNmzerUaNGVp+cnBzt3r37osYFAAAAgIrm8fugVQbc+wYAKi/ugwYAkP6H7oMGAAAAADiPgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYhMMYYzxdBAAAAACAGTQAAAAAsA2npwuoDI7Hxnq6BACAhwSnpkriswAAKruiz4MLYQYNAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATtgloU6ZMkcPh0NixYyVJ+/fvl8PhKPGxYsUKa7t77rlHbdu2lcvlUuvWrYuN++GHH6pv376qW7euAgIC1Lp1ay1duvSC9Rw4cEDx8fHy9/dX7dq1NX78eOXn51fU4QIAAABAMU5PFyBJ6enpWrBggSIjI622sLAwHTp0yK3fwoULNX36dPXo0cOtfejQofr000+1ffv2YmNv3rxZkZGRmjBhgurUqaO1a9cqISFBQUFB6tWrV4n1FBQUKD4+XqGhodq8ebMOHTqkhIQEValSRU8++WQFHDEAAAAAFOcwxhhPFpCbm6uoqCjNmzdPkydPVuvWrTVr1qwS+7Zp00ZRUVFKSUkptu7RRx/VG2+8oczMzAvuMz4+XnXq1NHixYtLXP/OO++oV69e+umnn1SnTh1J0vz58zVhwgQdOXJEPj4+5T4+SToeG3tR/QEAfx3BqamS+CwAgMqu6PPgQjx+imNiYqLi4+PVtWvXMvtlZGQoMzNTw4YN+937zMnJUY0aNUpdv2XLFrVq1coKZ5LUrVs3nThxQrt27frd+wcAAACAknj0FMfly5dr69atSk9Pv2DflJQUtWzZUu3bt/9d+3z99detUypLk5WV5RbOJFnLWVlZv2v/AAAAAFAajwW0gwcPasyYMUpNTZWvr2+ZfU+fPq1ly5bpoYce+l37/OCDDzRkyBAtWrRIV1xxxe8aqyR5eXnKy8tza3O5XBW+HwAAAAB/TR47xTEjI0PZ2dmKioqS0+mU0+lUWlqaZs+eLafTqYKCAqvvypUrderUKSUkJFzy/tLS0tS7d2/NnDnzguOEhobq8OHDbm1Fy6GhoaVul5ycrKCgILdHcnLyJdcMAAAAoHLxWECLiYnRjh07lJmZaT3atWunQYMGKTMzU97e3lbflJQU9enTR7Vq1bqkfX344YeKj4/X1KlTNWLEiAv2j46O1o4dO5SdnW21paamKjAwUOHh4aVul5SUpJycHLdHUlLSJdUMAAAAoPLx2CmO1apVU0REhFtbQECAQkJC3Nr37NmjjRs36u233y5xnD179ig3N1dZWVk6ffq0dRXH8PBw+fj46IMPPlCvXr00ZswY9evXz/oOmY+Pj3WhkNWrVyspKUlff/21JCkuLk7h4eG67bbbNG3aNGVlZWnixIlKTEws85RFl8tV4vrT5X9aAAAAAFRitrgPWlkWL16sBg0aKC4ursT1d9xxh9LS0qzlNm3aSJL27dunRo0a6aWXXtKpU6eUnJzsdrph586d9eGHH0o6f1XH3bt3W+u8vb21du1a3XnnnYqOjlZAQIAGDx6sSZMm/QFHCAAAAADnefw+aJUB974BgMqL+6ABAKT/ofugAQAAAADOI6ABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANuEwxhhPFwEAAAAAYAYNAAAAAGzD6ekCKoPjsbGeLgEA4CHBqamS+CwAgMqu6PPgQphBAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYhG0C2pQpU+RwODR27FhJ0v79++VwOEp8rFixwtrunnvuUdu2beVyudS6deti45Y2zieffFJmPQcOHFB8fLz8/f1Vu3ZtjR8/Xvn5+RV5yAAAAADgxunpAiQpPT1dCxYsUGRkpNUWFhamQ4cOufVbuHChpk+frh49eri1Dx06VJ9++qm2b99e6j7ee+89XXHFFdZySEhIqX0LCgoUHx+v0NBQbd68WYcOHVJCQoKqVKmiJ5988mIPDwAAAADKxeMBLTc3V4MGDdKiRYs0efJkq93b21uhoaFufVevXq0BAwaoatWqVtvs2bMlSUeOHCkzoIWEhBQbrzTr16/Xl19+qffee0916tRR69at9fjjj2vChAl69NFH5ePjczGHCAAAAADl4vFTHBMTExUfH6+uXbuW2S8jI0OZmZkaNmzYJe2nT58+ql27tq677jqtWbOmzL5btmxRq1atVKdOHautW7duOnHihHbt2nVJ+wcAAACAC/HoDNry5cu1detWpaenX7BvSkqKWrZsqfbt21/UPqpWraqnnnpKHTp0kJeXl/7973/rhhtu0BtvvKE+ffqUuE1WVpZbOJNkLWdlZV3U/gEAAACgvDwW0A4ePKgxY8YoNTVVvr6+ZfY9ffq0li1bpoceeuii91OzZk2NGzfOWr7qqqv0008/afr06aUGtEuVl5envLw8tzaXy1Wh+wAAAADw1+WxUxwzMjKUnZ2tqKgoOZ1OOZ1OpaWlafbs2XI6nSooKLD6rly5UqdOnVJCQkKF7Puaa67Rnj17Sl0fGhqqw4cPu7UVLZf1Pbbk5GQFBQW5PZKTkyukZgAAAAB/fR4LaDExMdqxY4cyMzOtR7t27TRo0CBlZmbK29vb6puSkqI+ffqoVq1aFbLvzMxM1a1bt9T10dHR2rFjh7Kzs6221NRUBQYGKjw8vNTtkpKSlJOT4/ZISkqqkJoBAAAA/PV57BTHatWqKSIiwq0tICBAISEhbu179uzRxo0b9fbbb5c4zp49e5Sbm6usrCydPn1amZmZkqTw8HD5+PjopZdeko+Pj9q0aSNJWrVqlRYvXqznn3/eGmP16tVKSkrS119/LUmKi4tTeHi4brvtNk2bNk1ZWVmaOHGiEhMTyzxl0eVylbj+dPmeEgAAAACVnMcvs38hixcvVoMGDRQXF1fi+jvuuENpaWnWclEQ27dvnxo1aiRJevzxx/X999/L6XSqRYsWeu2119S/f39rm5ycHO3evdta9vb21tq1a3XnnXcqOjpaAQEBGjx4sCZNmvQHHCEAAAAAnOcwxhhPF/FXdzw21tMlAAA8JDg1VRKfBQBQ2RV9HlyIx++DBgAAAAA4j4AGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2ITDGGM8XQQAAAAAgBk0AAAAALANp6cLqAyOx8Z6ugQAgIcEp6ZK4rMAACq7os+DC2EGDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgEwQ0AAAAALAJAhoAAAAA2AQBDQAAAABsgoAGAAAAADZBQAMAAAAAmyCgAQAAAIBNENAAAAAAwCYIaAAAAABgE7YJaFOmTJHD4dDYsWMlSfv375fD4SjxsWLFCmu7e+65R23btpXL5VLr1q1LHHvdunW69tprVa1aNdWqVUv9+vXT/v37y6zn2LFjGjRokAIDAxUcHKxhw4YpNze3go4WAAAAAIqzRUBLT0/XggULFBkZabWFhYXp0KFDbo/HHntMVatWVY8ePdy2Hzp0qG666aYSx963b5/69u2r66+/XpmZmVq3bp2OHj2q//u//yuzpkGDBmnXrl1KTU3V2rVrtXHjRo0YMeL3HywAAAAAlMLp6QJyc3M1aNAgLVq0SJMnT7bavb29FRoa6tZ39erVGjBggKpWrWq1zZ49W5J05MgRbd++vdj4GRkZKigo0OTJk+XldT6P3nffferbt6/OnTunKlWqFNvmq6++0rvvvqv09HS1a9dOkvTss8+qZ8+emjFjhurVq/f7DxwAAAAAfsPjM2iJiYmKj49X165dy+yXkZGhzMxMDRs27KLGb9u2rby8vPTCCy+ooKBAOTk5euWVV9S1a9cSw5kkbdmyRcHBwVY4k6SuXbvKy8tLn3766UXtHwAAAADKy6MzaMuXL9fWrVuVnp5+wb4pKSlq2bKl2rdvf1H7aNy4sdavX68BAwZo5MiRKigoUHR0tN5+++1St8nKylLt2rXd2pxOp2rUqKGsrKyL2j8AAAAAlJfHZtAOHjyoMWPGaOnSpfL19S2z7+nTp7Vs2bKLnj2Tzoet4cOHa/DgwUpPT1daWpp8fHzUv39/GWMutfwS5eXl6cSJE26PvLy8Ct0HAAAAgL8ujwW0jIwMZWdnKyoqSk6nU06nU2lpaZo9e7acTqcKCgqsvitXrtSpU6eUkJBw0fuZO3eugoKCNG3aNLVp00adOnXSkiVLtGHDhlJPVwwNDVV2drZbW35+vo4dO1bse3G/lpycrKCgILdHcnLyRdcMAAAAoHLy2CmOMTEx2rFjh1vbkCFD1KJFC02YMEHe3t5We0pKivr06aNatWpd9H5OnTplXRykSNHYhYWFJW4THR2t48ePKyMjQ23btpUkvf/++yosLNQ111xT6r6SkpI0btw4tzaXy6XTvXpddN0AAAAAKh+PBbRq1aopIiLCrS0gIEAhISFu7Xv27NHGjRtL/c7Ynj17lJubq6ysLJ0+fVqZmZmSpPDwcPn4+Cg+Pl4zZ87UpEmTdMstt+i///2v/vWvf6lhw4Zq06aNJOmzzz5TQkKCNmzYoPr166tly5bq3r27hg8frvnz5+vcuXMaPXq0br755jKv4OhyueRyuYq1n77YJwcAAABApeTxy+xfyOLFi9WgQQPFxcWVuP6OO+5QWlqatVwUuvbt26dGjRrp+uuv17JlyzRt2jRNmzZN/v7+io6O1rvvvis/Pz9J52fZdu/erXPnzlnjLF26VKNHj1ZMTIy8vLzUr18/65L+AAAAAPBHcJiKvlIGijkeG+vpEgAAHhKcmiqJzwIAqOyKPg8uxOP3QQMAAAAAnEdAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGyCgAYAAAAANkFAAwAAAACbIKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAAAGzCYYwxni4CAAAAAMAMGoA/UF5enh599FHl5eV5uhQAgAfxeQCUHzNoAP4wJ06cUFBQkHJychQYGOjpcgAAHsLnAVB+zKABAAAAgE0Q0AAAAADAJghoAAAAAGATBDQAfxiXy6VHHnlELpfL06UAADyIzwOg/LhICAAAAADYBDNoAAAAAGATBDQAAAAAsAkCGgAAAADYBAENAAAA/5N+/vln1a5dW/v37/d0KWX68ssv1aBBA508edLTpeB/AAENqCS6dOmisWPHWsuNGjXSrFmz/rT9XSo7fPiePXtWjRo10ueff+6xGgD8b7r99tvlcDg0atSoYusSExPlcDh0++23//mF/UU88cQT6tu3rxo1avSH7WPVqlWKi4tTSEiIHA6HMjMzi/UZOXKkmjZtKj8/P9WqVUt9+/bV119/ba0PDw/Xtddeq6effvoPqxN/HQQ0wIa2bNkib29vxcfHV9iYq1at0uOPP15h4/1Z+/szPnxzc3M1evRoNWjQQH5+fgoPD9f8+fOt9T4+Prrvvvs0YcKEP6wGAH9dYWFhWr58uU6fPm21nTlzRsuWLdNll132u8Y2xig/P//3lvg/6dSpU0pJSdGwYcP+0P2cPHlS1113naZOnVpqn7Zt2+qFF17QV199pXXr1skYo7i4OBUUFFh9hgwZoueee67Svl4oPwIaYEMpKSm6++67tXHjRv30008VMmaNGjVUrVq1Chnrz9rfn/XhO27cOL377rtasmSJvvrqK40dO1ajR4/WmjVrrD6DBg3Spk2btGvXrj+0FgB/PVFRUQoLC9OqVaustlWrVumyyy5TmzZt3PoWFhYqOTlZjRs3lp+fn6688kqtXLnSWv/hhx/K4XDonXfeUdu2beVyubRp0ybl5eXpnnvuUe3ateXr66vrrrtO6enpxbZ76623FBkZKV9fX1177bXauXOn2/43bdqkjh07ys/PT2FhYbrnnnvcTstr1KiRnnzySQ0dOlTVqlXTZZddpoULF7qNcfDgQQ0YMEDBwcGqUaOG+vbt63YWxO23364bbrhBTz75pOrUqaPg4GBNmjRJ+fn5Gj9+vGrUqKEGDRrohRdeKPN5ffvtt+VyuXTttdcWO84NGzaoXbt28vf3V/v27bV79+4yxyrLbbfdpocfflhdu3Yttc+IESPUqVMnNWrUSFFRUZo8ebIOHjzodtyxsbE6duyY0tLSLrkWVA4ENMBmcnNz9dprr+nOO+9UfHy8XnzxRbf1RR8+69atU5s2beTn56frr79e2dnZeuedd9SyZUsFBgZq4MCBOnXqlLXdhU45fP755xUcHKwNGzZIkp5++mm1atVKAQEBCgsL01133aXc3Fy3bT7++GN16dJF/v7+ql69urp166ZffvmlxP2V50P9t/6sD9/Nmzdr8ODB6tKlixo1aqQRI0boyiuv1GeffWb1qV69ujp06KDly5df8n4AVF5Dhw51CxyLFy/WkCFDivVLTk7Wyy+/rPnz52vXrl269957deuttxb7o/6BBx7QlClT9NVXXykyMlL333+//v3vf+ull17S1q1b1axZM3Xr1k3Hjh1z2278+PF66qmnlJ6erlq1aql37946d+6cJGnv3r3q3r27+vXrp+3bt+u1117Tpk2bNHr0aLcxnnrqKbVr105ffPGF7rrrLt15553W7+Bz586pW7duqlatmj766CN9/PHHqlq1qrp3766zZ89aY7z//vv66aeftHHjRj399NN65JFH1KtXL1WvXl2ffvqpRo0apZEjR+qHH34o9Tn96KOP1LZt2xLXPfjgg3rqqaf0+eefy+l0aujQoW7bVa1atczH0qVLS93vhZw8eVIvvPCCGjdurLCwMKvdx8dHrVu31kcffXTJY6OSMABsJSUlxbRr184YY8ybb75pmjZtagoLC631H3zwgZFkrr32WrNp0yazdetW06xZM9O5c2cTFxdntm7dajZu3GhCQkLMlClTrO06d+5sxowZYy03bNjQzJw50xhjzNSpU01ISIj59NNPrfUzZ84077//vtm3b5/ZsGGDad68ubnzzjut9V988YVxuVzmzjvvNJmZmWbnzp3m2WefNUeOHCl1fzVq1DBz58413377rUlOTjZeXl7m66+/LvW5uOeee0z37t3d2oqO/5prrjEffvih2bVrl+nYsaNp37691Wfjxo0mICCgzMeSJUus/sOHDzft2rUzP/zwgyksLDTvv/++qVq1qklLS3Pb94QJE0znzp1LrRcAfmvw4MGmb9++Jjs727hcLrN//36zf/9+4+vra44cOWL69u1rBg8ebIwx5syZM8bf399s3rzZbYxhw4aZW265xRjz/34HvvHGG9b63NxcU6VKFbN06VKr7ezZs6ZevXpm2rRpbtstX77c6vPzzz8bPz8/89prr1n7GTFihNu+P/roI+Pl5WVOnz5tjDn/u/zWW2+11hcWFpratWub5557zhhjzCuvvGKaN2/u9rmVl5dn/Pz8zLp166znpGHDhqagoMDq07x5c9OxY0drOT8/3wQEBJhXX3211Oe2b9++ZujQoW5tRcf53nvvWW1vvfWWkWQdw6lTp8y3335b5uPEiRPF9rdv3z4jyXzxxRcl1jN37lwTEBBgJJnmzZubPXv2FOvzj3/8w9x+++2lHhNgjDFOz0VDACVJSUnRrbfeKknq3r27cnJylJaWpi5durj1mzx5sjp06CBJGjZsmJKSkrR37141adJEktS/f3998MEHF/ze1IQJE/TKK68oLS1NV1xxhdX+29mvyZMna9SoUZo3b54kadq0aWrXrp21LMlt+5L07NlTd911l7XfmTNn6oMPPlDz5s1L7P/999+rXr16Ja574okn1LlzZ0nn/yc5Pj5eZ86cka+vr9q1a1fil7h/rU6dOta/n332WY0YMUINGjSQ0+mUl5eXFi1apE6dOrltU69ePX3//fdljgsAJalVq5Z1VoQxRvHx8apZs6Zbnz179ujUqVOKjY11az979myxUyHbtWtn/Xvv3r06d+6c9ZkgSVWqVNHVV1+tr776ym276Oho6981atRQ8+bNrT7btm3T9u3b3WaPjDEqLCzUvn371LJlS0lSZGSktd7hcCg0NFTZ2dnWGHv27Cl2ivuZM2e0d+9ea/mKK66Ql9f/O5GrTp06ioiIsJa9vb0VEhJijVuS06dPy9fXt8R1v66xbt26kqTs7Gxddtll8vPzU7NmzUod91INGjRIsbGxOnTokGbMmKEBAwbo448/dqvRz8/P7ewWoCQENMBGdu/erc8++0yrV6+WJDmdTt10001KSUkpFtB+/eFTp04d+fv7W+GsqO3Xp+iV5KmnntLJkyf1+eefu20rSe+9956Sk5P19ddf68SJE8rPz9eZM2d06tQp+fv7KzMzUzfeeONFHV9ZH+ol+bM+fJ999ll98sknWrNmjRo2bKiNGzcqMTFR9erVc/vOAR+sAH6PoUOHWqcLzp07t9j6otPI33rrLdWvX99tncvlclsOCAio8Ppyc3M1cuRI3fP/tXf/MVXVfxzHXzeQX3fC1eSH6bDERrUhdmPoWNqsyUXDH0nNilkUttKZPzY0wSZkMekHzamb9ofpUMfgD83yV5kpIi7nnKZMZzZFl7P+4a5hGEL38/3Ddb4efgn+4J7s+fjvfD7n876fc//g8L6fz3mfefM69N1czKRfv362PpfLpUAgYMV46qmnOt0iGBsb222M7uJ2ZtCgQda2+vZujuVyuSTJilVbW6uJEyd2GVeSvvjiC+Xm5nZ7TnsxMTGKiYnRo48+qjFjxmjAgAHatm2bXnnlFeucxsZGJSUl9Sou/ntI0AAHWb9+vdra2myrRsYYhYeHa82aNYqJibHa2998entjk6SxY8dq586dqq6u1pIlS6z2hoYGZWdna/bs2SotLdXAgQN16NAh5efn6/r164qKilJkZGSvr8+JN99r166pqKhI27Zts6pmjhw5UidOnNBnn31mS9AaGxtt/2AAQG/88xyWy+WSz+fr0P/EE08oPDxcly5dsnYI9ERSUpLCwsJUV1enYcOGSbrxLNjRo0c7PHv8448/WsmW3+/Xzz//bK2Meb1enT59+o5Wl7xer6qqqhQXF6fo6OjbjtMTTz75pDZv3tzrcb3dZXE7jDEyxqilpcXWXl9frxdffPGOYuP+R4IGOERbW5sqKipUXl6uzMxMW9+0adNUWVnZ6Xt07kR6errmzp2rrKwshYaGqqCgQJJ07NgxBQIBlZeXW1tQqqurbWNHjhypffv26YMPPrirc7pZX9x8W1tb1draattqI93YXtM+eayvr++wzQgAeiokJMTaThgSEtKhv3///iooKNDChQsVCAT09NNP648//lBdXZ2io6P1+uuvdxrX7XZr9uzZVgXExMREffLJJ2pubu5QBXf58uV68MEHFR8fr6VLl2rQoEGaNm2apBtbz8eMGaO5c+dq1qxZcrvdOn36tPbu3as1a9b06Bpzc3P16aefaurUqVq+fLmGDh2qixcvauvWrVq8eLGGDh3ai2+sez6fT4WFhfL7/RowYECPx/V2l0VjY6MuXbpkVVX+pyBKQkKCEhISdP78eVVVVSkzM1OxsbH69ddfVVZWpsjISE2aNMmK09DQoMuXL3dbDRKQSNAAx9ixY4f8fr/y8/NtK2WSlJOTo/Xr19/1BE2SMjIytGvXLk2cOFGhoaFasGCBRowYodbWVq1evVqTJ09WXV2d7b1gklRYWKiUlBTNmTNH77zzjsLCwrR//3699NJLHZ6ruF19cfONjo7WM888o0WLFikyMlLDhg1TTU2NKioqOrxQtLa2tk/fJQfg/nOrVaUPP/xQsbGxWrFihc6fPy+PxyOv16uioqJux5WVlSkQCGjmzJlqampSWlqavv322w5/O8vKyjR//nydO3dOo0aN0jfffKOwsDBJN354q6mp0dKlSzV27FgZY5SUlKQZM2b0+PqioqJ08OBBvffee5o+fbqampo0ZMgQPffcc3d9RS0lJUVer1fV1dV6++2372rsm3399de2ipsvv/yyJKm4uFglJSWKiIhQbW2tVq5cKb/fr/j4eI0bN06HDx9WXFycNa6yslKZmZnWKifQpWBWKAHwf9nZ2WbSpEmd9h05csRIMj/99JNVocrv91v9GzZsMDExMbYxxcXFJjU11TruroqjMcbU1NQYt9ttVq1aZYwx5vPPPzeDBw82kZGRxufzmYqKig6fe+DAAZORkWHCw8ONx+MxPp/P6r/V5xljTGpqqikuLu72e0lPTzfr1q2zjju7/uPHjxtJ5sKFC93G6sqVK1dMXl6eeeihh0xERIRJTk425eXltipkhw8fNh6PxzQ3N9/WZwBAMHX2t/N+sGPHDvP444/bKkI6UUtLi0lMTDSHDh0K9lTwL+AyxphgJogA0J2dO3dq0aJFqq+v77ANsS/NmDFDqampt/wVGwCc6MCBAxo/frz8fr88Hk+wp3NXrVy5Ujk5ObZ3jjnNL7/8on379t3TlT7cP9jiCMDRnn/+eZ07d06XL18O2s33+vXrSklJ0cKFC4Py+QCArrUvhOJEI0aMuCel/XF/YgUNAAAAABwiePuFAAAAAAA2JGgAAAAA4BAkaAAAAADgECRoAAAAAOAQJGgAAAAA4BAkaAAAAADgECRoAADcQl5enlwul1wul/r166f4+HhNmDBBX375pQKBQLCnBwC4j5CgAQDQA1lZWbpy5YoaGhq0e/dujR8/XvPnz1d2drba2to6HdPa2trHswQA/NuRoAEA0APh4eFKSEjQkCFD5PV6VVRUpO3bt2v37t3auHGjJMnlcmnt2rWaMmWK3G63SktLtXHjRnk8Hlusr776Si6Xy9b20UcfKS4uTv3799esWbO0ZMkSjRo1qm8uDgDgGCRoAADcpmeffVapqanaunWr1VZSUqIXXnhBp06d0ptvvtmjOFu2bFFpaak+/vhjHTt2TImJiVq7du29mjYAwMFCgz0BAAD+zR577DGdPHnSOn711Vf1xhtv9CrG6tWrlZ+fb41btmyZvvvuO129evWuzhUA4HysoAEAcAeMMbbtimlpab2OcfbsWaWnp9va2h8DAP4bSNAAALgDZ86c0SOPPGIdu91uW/8DDzwgY4ytjeIhAICukKABAHCbfvjhB506dUo5OTldnhMbG6umpib9+eefVtuJEyds5yQnJ+vo0aO2tvbHAID/Bp5BAwCgB1paWvTbb7/p77//1u+//649e/ZoxYoVys7O1muvvdbluNGjRysqKkpFRUWaN2+ejhw5YlV9/Me7776rt956S2lpacrIyFBVVZVOnjyp4cOH3+OrAgA4DStoAAD0wJ49ezR48GA9/PDDysrK0v79+7Vq1Spt375dISEhXY4bOHCgNm/erF27diklJUWVlZUqKSmxnZObm6vCwkIVFBTI6/XqwoULysvLU0RExD2+KgCA07hM+43xAAAg6CZMmKCEhARt2rQp2FMBAPQhtjgCABBkzc3NWrdunXw+n0JCQlRZWanvv/9ee/fuDfbUAAB9jBU0AACC7Nq1a5o8ebKOHz+uv/76S8nJyXr//fc1ffr0YE8NANDHSNAAAAAAwCEoEgIAAAAADkGCBgAAAAAOQYIGAAAAAA5BggYAAAAADkGCBgAAAAAOQYIGAAAAAA5BggYAAAAADkGCBgAAAAAO8T9rHEG8h9zzQgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from maldiamrkit.differential import plot_drug_comparison\n",
    "\n",
    "_ = plot_drug_comparison(\n",
    "    comparison,\n",
    "    kind=\"heatmap\",\n",
    "    title=\"Significant peaks: Amikacin vs Meropenem (Rome)\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UpSet-style Intersection Plot\n",
    "\n",
    "With more drugs, a Venn diagram stops scaling - an UpSet plot shows\n",
    "the size of every intersection of significant-peak sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:11:09.564314Z",
     "iopub.status.busy": "2026-05-13T10:11:09.564080Z",
     "iopub.status.idle": "2026-05-13T10:11:09.655688Z",
     "shell.execute_reply": "2026-05-13T10:11:09.654928Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_drug_comparison(\n",
    "    comparison,\n",
    "    kind=\"upset\",\n",
    "    title=\"Peak-set intersections (Rome)\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing a Test and Correction\n",
    "\n",
    "`DifferentialAnalysis.run()` accepts both plain strings and the `StatisticalTest` / `CorrectionMethod`.\n",
    "\n",
    "`StatisticalTest` enums:\n",
    "\n",
    "| `test`           | When to use                                                          |\n",
    "| ---------------- | -------------------------------------------------------------------- |\n",
    "| `mann_whitney`   | Default. Non-parametric, robust to MALDI intensity distributions.    |\n",
    "| `t_test`         | Welch's t-test (unequal variances). Use for approximately-Gaussian data. |\n",
    "\n",
    "`CorrectionMethod` enums:\n",
    "\n",
    "| `correction`   | Meaning                                                          |\n",
    "| -------------- | ---------------------------------------------------------------- |\n",
    "| `fdr_bh`       | Benjamini-Hochberg FDR. Good default.                             |\n",
    "| `fdr_by`       | Benjamini-Yekutieli FDR. More conservative under dependence.      |\n",
    "| `bonferroni`   | Family-wise error control. Most conservative.                     |\n",
    "\n",
    "Example of a conservative configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:11:09.656997Z",
     "iopub.status.busy": "2026-05-13T10:11:09.656858Z",
     "iopub.status.idle": "2026-05-13T10:11:14.091382Z",
     "shell.execute_reply": "2026-05-13T10:11:14.090717Z"
    }
   },
   "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>mz_bin</th>\n",
       "      <th>mean_r</th>\n",
       "      <th>mean_s</th>\n",
       "      <th>fold_change</th>\n",
       "      <th>p_value</th>\n",
       "      <th>adjusted_p_value</th>\n",
       "      <th>effect_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3038</th>\n",
       "      <td>11114.0</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.652812</td>\n",
       "      <td>2.290201e-11</td>\n",
       "      <td>1.374120e-07</td>\n",
       "      <td>0.589519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>4715.0</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>1.642962</td>\n",
       "      <td>9.656653e-11</td>\n",
       "      <td>5.793992e-07</td>\n",
       "      <td>0.546662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3039</th>\n",
       "      <td>11117.0</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.604224</td>\n",
       "      <td>2.661767e-10</td>\n",
       "      <td>1.597060e-06</td>\n",
       "      <td>0.558619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>4703.0</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>1.241332</td>\n",
       "      <td>4.082946e-10</td>\n",
       "      <td>2.449768e-06</td>\n",
       "      <td>0.522023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037</th>\n",
       "      <td>11111.0</td>\n",
       "      <td>0.000102</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.604420</td>\n",
       "      <td>4.436762e-10</td>\n",
       "      <td>2.662057e-06</td>\n",
       "      <td>0.545698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mz_bin    mean_r    mean_s  fold_change       p_value  \\\n",
       "3038  11114.0  0.000125  0.000080     0.652812  2.290201e-11   \n",
       "905    4715.0  0.000506  0.000162     1.642962  9.656653e-11   \n",
       "3039  11117.0  0.000127  0.000083     0.604224  2.661767e-10   \n",
       "901    4703.0  0.000284  0.000120     1.241332  4.082946e-10   \n",
       "3037  11111.0  0.000102  0.000067     0.604420  4.436762e-10   \n",
       "\n",
       "      adjusted_p_value  effect_size  \n",
       "3038      1.374120e-07     0.589519  \n",
       "905       5.793992e-07     0.546662  \n",
       "3039      1.597060e-06     0.558619  \n",
       "901       2.449768e-06     0.522023  \n",
       "3037      2.662057e-06     0.545698  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_strict = DifferentialAnalysis(X, y).run(\n",
    "    test=\"t_test\",\n",
    "    correction=\"bonferroni\",\n",
    ")\n",
    "analysis_strict.top_peaks(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional: Scoping Filters\n",
    "\n",
    "When you already know which m/z regions matter, or you want to limit the\n",
    "hypothesis burden, pass `mz_ranges` and/or `peak_detector` to `run()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-13T10:11:14.092739Z",
     "iopub.status.busy": "2026-05-13T10:11:14.092628Z",
     "iopub.status.idle": "2026-05-13T10:11:14.710753Z",
     "shell.execute_reply": "2026-05-13T10:11:14.710045Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bins tested (scoped):   1994\n",
      "Bins tested (full run): 6000\n",
      "#adj <= 0.05 (scoped):  23\n"
     ]
    }
   ],
   "source": [
    "from maldiamrkit.detection import MaldiPeakDetector\n",
    "\n",
    "scoped = DifferentialAnalysis(X, y).run(\n",
    "    mz_ranges=[(3000, 6000), (9000, 12000)],\n",
    "    peak_detector=MaldiPeakDetector(method=\"local\", binary=True, prominence=1e-4),\n",
    ")\n",
    "print(f\"Bins tested (scoped):   {len(scoped.results)}\")\n",
    "print(f\"Bins tested (full run): {len(analysis.results)}\")\n",
    "print(f\"#adj <= 0.05 (scoped):  {(scoped.results['adjusted_p_value'] <= 0.05).sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See Also\n",
    "\n",
    "- [API Reference - Differential](https://maldiamrkit.readthedocs.io/en/latest/api/differential.html)\n",
    "- [Exploration notebook](05_exploration.ipynb) for PCA, t-SNE, UMAP, and spectral similarity"
   ]
  }
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