{
 "cells": [
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flow cytometry simulation. \n",
    "Using ``NBNode`` as basis enables the simulation of (flow) cytometry data. Cytometry data consists of an unordered matrix of n ``cells`` and p ``features``. The ordering of cells in the matrix does not hold any significant information, except in cases where data corruption has occurred during the experiment.\n",
    "\n",
    "As an example, we previously utilized a cell matrix originating from the Beckman Coulter ``DURAClone IM T Cell Subsets Antibody Panel``. This particular panel measures 10 markers in fluorochrome combinations that provide robust population identification, including CD3, CD4, CD8, CD27, CD28, CD45, CD45RA, CD57, CD197 (CCR7), CD279 (PD-1). Additionally, forward scatter and side scatter are usually measured, increasing the number of ``features`` per cell to 13. \n",
    "\n",
    "According to their ``feature`` values, ``cells`` can be classified (\"gated\") into cell types, e.g. ``CD4+ central memory T cells`` or ``CD27+ CD28+ CD4+ T cells``. Within one cell type, ``features`` do still vary. In this package, we use ``cell population`` for any defined set of cells. \n",
    "\n",
    "Our class ``FlowSimulationTree`` uses an existing gating applied to multiple samples and simulates two major parts: \n",
    "\n",
    " 1. How many ``cells`` are of which ``cell population``\n",
    " 2. How are cell ``features`` distributed _within_ one ``cell population``\n",
    " \n",
    "In this example, we will use data from 48 healthy human peripheral blood samples stained with the DURAClone IM T Cell Subsets Tube (Beckman Coulter GmbH). \n",
    "The data is available under [zenodo 7883353](https://doi.org/10.5281/zenodo.7883353). \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthetic data based on previous samples\n",
    "\n",
    "To enable the analysis also with data supplied with package, I supply the final result ``flowsim_tree.pickle`` which has done downloading gating and initiating the simulation. \n",
    "\n",
    "First, download the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%script false --no-raise-error\n",
    "\n",
    "# This downloading takes about 1.5 minutes\n",
    "\n",
    "# From https://github.com/zenodo/zenodo/issues/1888\n",
    "import os\n",
    "\n",
    "import requests\n",
    "\n",
    "# params = {\"access_token\": \"ZENODO_ACCESS_TOKEN\"}  # only necessary until public\n",
    "params = {}\n",
    "\n",
    "record_id = \"7883353\"\n",
    "\n",
    "r = requests.get(\n",
    "    f\"https://zenodo.org/api/records/{record_id}\", params=params\n",
    ")\n",
    "print(r.json())\n",
    "download_urls = [f[\"links\"][\"self\"] for f in r.json()[\"files\"]]\n",
    "filenames = [f[\"key\"] for f in r.json()[\"files\"]]\n",
    "\n",
    "print(r.status_code)\n",
    "print(download_urls)\n",
    "\n",
    "outdir = \"example_data/asinh.align_manual.CD3_Gate\"\n",
    "os.makedirs(outdir, exist_ok=True)\n",
    "\n",
    "for filename, url in zip(filenames, download_urls):\n",
    "    print(\"Downloading:\", filename)\n",
    "    r = requests.get(url, params=params)\n",
    "    with open(os.path.join(outdir, filename), \"wb\") as f:\n",
    "        f.write(r.content)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The (pre-defined) tree for this dataset is ``nbtree.tree_complete_aligned_v2()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%script false --no-raise-error\n",
    "\n",
    "import nbnode.nbnode_trees as nbtree\n",
    "complete_tree = nbtree.tree_complete_aligned_v2()\n",
    "complete_tree.pretty_print()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``nbtree.tree_complete_aligned_v2`` has specific ideas about the naming of \n",
    "the markers, so we need to use the same names here.\n",
    "\n",
    "``new_colnames`` replaces the column names in all CSV files, regardless of how \n",
    "they were named before, so use with caution!\n",
    "\n",
    "``gate_csv()`` now places all cells from all samples in the ``NBNode``. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%script false --no-raise-error\n",
    "\n",
    "# This gate_csv takes about a minute\n",
    "from nbnode.apply.gate_csv import gate_csv\n",
    "all_files = [\n",
    "        os.path.join(outdir, file_x)\n",
    "        for file_x in os.listdir(outdir)\n",
    "    ]\n",
    "new_colnames = [\n",
    "        \"FS\",\n",
    "        \"FS.0\",\n",
    "        \"SS\",\n",
    "        \"CD45RA\",\n",
    "        \"CCR7\",\n",
    "        \"CD28\",\n",
    "        \"PD1\",\n",
    "        \"CD27\",\n",
    "        \"CD4\",\n",
    "        \"CD8\",\n",
    "        \"CD3\",\n",
    "        \"CD57\",\n",
    "        \"CD45\",\n",
    "    ]\n",
    "celltree_gated = gate_csv(\n",
    "        csv=all_files,\n",
    "        celltree=complete_tree,\n",
    "        new_colnames=new_colnames,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%script false --no-raise-error\n",
    "\n",
    "celltree_gated.pretty_print()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we have the number of cells per node, we can generate a Dirichlet-based simulation. In particular, the number of cells per node are described by a single Dirichlet distribution containing a concentration parameter for every leaf node. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%script false --no-raise-error\n",
    "\n",
    "from nbnode.simulation.FlowSimulationTree import FlowSimulationTreeDirichlet\n",
    "\n",
    "flowsim_tree = FlowSimulationTreeDirichlet(\n",
    "    rootnode=celltree_gated,\n",
    "    include_features=new_colnames,\n",
    ")\n",
    "\n",
    "from nbnode.io.pickle_open_dump import pickle_open_dump\n",
    "\n",
    "pickle_open_dump(flowsim_tree, \"flowsim_tree.pickle\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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   ],
   "source": [
    "import os \n",
    "import pickle\n",
    "with open(os.path.join(os.pardir, os.pardir, \"tests\", \"testdata\", \"flowcytometry\", \"flowsim_tree.pickle\"), \"rb\") as f:\n",
    "    flowsim_tree = pickle.load(f)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it. We can now simulate synthetic flow cytometry samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\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>FS</th>\n",
       "      <th>FS.0</th>\n",
       "      <th>SS</th>\n",
       "      <th>CD45RA</th>\n",
       "      <th>CCR7</th>\n",
       "      <th>CD28</th>\n",
       "      <th>PD1</th>\n",
       "      <th>CD27</th>\n",
       "      <th>CD4</th>\n",
       "      <th>CD8</th>\n",
       "      <th>CD3</th>\n",
       "      <th>CD57</th>\n",
       "      <th>CD45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.100577</td>\n",
       "      <td>5.251694</td>\n",
       "      <td>-0.287347</td>\n",
       "      <td>0.065961</td>\n",
       "      <td>0.659882</td>\n",
       "      <td>0.955657</td>\n",
       "      <td>1.205756</td>\n",
       "      <td>0.564757</td>\n",
       "      <td>0.998757</td>\n",
       "      <td>0.028991</td>\n",
       "      <td>0.049317</td>\n",
       "      <td>0.044155</td>\n",
       "      <td>-0.168126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.092387</td>\n",
       "      <td>5.286552</td>\n",
       "      <td>-0.061012</td>\n",
       "      <td>0.007936</td>\n",
       "      <td>0.567971</td>\n",
       "      <td>1.060524</td>\n",
       "      <td>0.914231</td>\n",
       "      <td>0.944080</td>\n",
       "      <td>1.109905</td>\n",
       "      <td>-0.007508</td>\n",
       "      <td>-0.070442</td>\n",
       "      <td>0.025814</td>\n",
       "      <td>0.349142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.159748</td>\n",
       "      <td>5.295267</td>\n",
       "      <td>-0.008783</td>\n",
       "      <td>-0.022918</td>\n",
       "      <td>0.773974</td>\n",
       "      <td>1.353215</td>\n",
       "      <td>0.590003</td>\n",
       "      <td>0.732128</td>\n",
       "      <td>1.040836</td>\n",
       "      <td>0.065331</td>\n",
       "      <td>-0.086695</td>\n",
       "      <td>-0.058003</td>\n",
       "      <td>0.352973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.036379</td>\n",
       "      <td>5.286429</td>\n",
       "      <td>-0.355162</td>\n",
       "      <td>0.022301</td>\n",
       "      <td>0.555761</td>\n",
       "      <td>1.093256</td>\n",
       "      <td>1.016961</td>\n",
       "      <td>0.729056</td>\n",
       "      <td>0.925360</td>\n",
       "      <td>0.085008</td>\n",
       "      <td>0.100495</td>\n",
       "      <td>-0.051198</td>\n",
       "      <td>-0.029892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.211879</td>\n",
       "      <td>5.305992</td>\n",
       "      <td>-0.224904</td>\n",
       "      <td>-0.010840</td>\n",
       "      <td>0.883872</td>\n",
       "      <td>1.357975</td>\n",
       "      <td>1.194074</td>\n",
       "      <td>1.329266</td>\n",
       "      <td>0.886225</td>\n",
       "      <td>0.041453</td>\n",
       "      <td>-0.734879</td>\n",
       "      <td>0.003398</td>\n",
       "      <td>0.009937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.037094</td>\n",
       "      <td>5.235038</td>\n",
       "      <td>-0.212276</td>\n",
       "      <td>0.514944</td>\n",
       "      <td>0.924698</td>\n",
       "      <td>0.973519</td>\n",
       "      <td>0.950437</td>\n",
       "      <td>0.984918</td>\n",
       "      <td>1.038688</td>\n",
       "      <td>0.063404</td>\n",
       "      <td>-0.441071</td>\n",
       "      <td>-0.025791</td>\n",
       "      <td>-0.398935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.236324</td>\n",
       "      <td>5.226253</td>\n",
       "      <td>-0.254201</td>\n",
       "      <td>0.772118</td>\n",
       "      <td>1.087614</td>\n",
       "      <td>0.953043</td>\n",
       "      <td>0.559132</td>\n",
       "      <td>1.006041</td>\n",
       "      <td>0.903030</td>\n",
       "      <td>0.079715</td>\n",
       "      <td>0.196283</td>\n",
       "      <td>-0.033585</td>\n",
       "      <td>-0.073690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.044698</td>\n",
       "      <td>5.232621</td>\n",
       "      <td>-0.174454</td>\n",
       "      <td>0.569029</td>\n",
       "      <td>1.018878</td>\n",
       "      <td>0.909895</td>\n",
       "      <td>0.714083</td>\n",
       "      <td>1.076313</td>\n",
       "      <td>1.044681</td>\n",
       "      <td>0.078261</td>\n",
       "      <td>0.222197</td>\n",
       "      <td>-0.030222</td>\n",
       "      <td>0.158465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.274186</td>\n",
       "      <td>5.271703</td>\n",
       "      <td>0.091757</td>\n",
       "      <td>0.426830</td>\n",
       "      <td>-0.053408</td>\n",
       "      <td>-0.130320</td>\n",
       "      <td>0.889216</td>\n",
       "      <td>-0.187032</td>\n",
       "      <td>0.013959</td>\n",
       "      <td>1.186677</td>\n",
       "      <td>-0.058428</td>\n",
       "      <td>0.947983</td>\n",
       "      <td>-0.061106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.368384</td>\n",
       "      <td>5.290500</td>\n",
       "      <td>-0.148017</td>\n",
       "      <td>-0.016317</td>\n",
       "      <td>-0.096373</td>\n",
       "      <td>0.959208</td>\n",
       "      <td>0.616525</td>\n",
       "      <td>0.484474</td>\n",
       "      <td>0.039499</td>\n",
       "      <td>-0.003198</td>\n",
       "      <td>0.573195</td>\n",
       "      <td>0.695084</td>\n",
       "      <td>-0.030929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
       "0  0.100577  5.251694 -0.287347  0.065961  0.659882  0.955657  1.205756  \\\n",
       "1 -0.092387  5.286552 -0.061012  0.007936  0.567971  1.060524  0.914231   \n",
       "2 -0.159748  5.295267 -0.008783 -0.022918  0.773974  1.353215  0.590003   \n",
       "3  0.036379  5.286429 -0.355162  0.022301  0.555761  1.093256  1.016961   \n",
       "4 -0.211879  5.305992 -0.224904 -0.010840  0.883872  1.357975  1.194074   \n",
       "5  0.037094  5.235038 -0.212276  0.514944  0.924698  0.973519  0.950437   \n",
       "6 -0.236324  5.226253 -0.254201  0.772118  1.087614  0.953043  0.559132   \n",
       "7 -0.044698  5.232621 -0.174454  0.569029  1.018878  0.909895  0.714083   \n",
       "8  0.274186  5.271703  0.091757  0.426830 -0.053408 -0.130320  0.889216   \n",
       "9  0.368384  5.290500 -0.148017 -0.016317 -0.096373  0.959208  0.616525   \n",
       "\n",
       "       CD27       CD4       CD8       CD3      CD57      CD45  \n",
       "0  0.564757  0.998757  0.028991  0.049317  0.044155 -0.168126  \n",
       "1  0.944080  1.109905 -0.007508 -0.070442  0.025814  0.349142  \n",
       "2  0.732128  1.040836  0.065331 -0.086695 -0.058003  0.352973  \n",
       "3  0.729056  0.925360  0.085008  0.100495 -0.051198 -0.029892  \n",
       "4  1.329266  0.886225  0.041453 -0.734879  0.003398  0.009937  \n",
       "5  0.984918  1.038688  0.063404 -0.441071 -0.025791 -0.398935  \n",
       "6  1.006041  0.903030  0.079715  0.196283 -0.033585 -0.073690  \n",
       "7  1.076313  1.044681  0.078261  0.222197 -0.030222  0.158465  \n",
       "8 -0.187032  0.013959  1.186677 -0.058428  0.947983 -0.061106  \n",
       "9  0.484474  0.039499 -0.003198  0.573195  0.695084 -0.030929  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flowsim_tree.sample(n_cells=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells per population in the synthetic sample\n",
      "\n",
      "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        0.0\n",
      "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-        0.0\n",
      "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           298.0\n",
      "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+        0.0\n",
      "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-             0.0\n",
      "                                                    ...  \n",
      "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+      0.0\n",
      "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-      0.0\n",
      "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-           0.0\n",
      "/AllCells/DN                                         84.0\n",
      "/AllCells/DP                                          0.0\n",
      "Name: 0, Length: 96, dtype: float64\n",
      "\n",
      "\n",
      "\n",
      "Synthetic sample\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FS</th>\n",
       "      <th>FS.0</th>\n",
       "      <th>SS</th>\n",
       "      <th>CD45RA</th>\n",
       "      <th>CCR7</th>\n",
       "      <th>CD28</th>\n",
       "      <th>PD1</th>\n",
       "      <th>CD27</th>\n",
       "      <th>CD4</th>\n",
       "      <th>CD8</th>\n",
       "      <th>CD3</th>\n",
       "      <th>CD57</th>\n",
       "      <th>CD45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.199053</td>\n",
       "      <td>5.234598</td>\n",
       "      <td>-0.088241</td>\n",
       "      <td>-0.034998</td>\n",
       "      <td>0.924329</td>\n",
       "      <td>1.131150</td>\n",
       "      <td>1.082737</td>\n",
       "      <td>0.809666</td>\n",
       "      <td>1.125293</td>\n",
       "      <td>0.004234</td>\n",
       "      <td>0.046163</td>\n",
       "      <td>-0.045944</td>\n",
       "      <td>0.179582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.008096</td>\n",
       "      <td>5.277367</td>\n",
       "      <td>0.184243</td>\n",
       "      <td>0.056715</td>\n",
       "      <td>0.681012</td>\n",
       "      <td>0.935260</td>\n",
       "      <td>0.782681</td>\n",
       "      <td>1.024696</td>\n",
       "      <td>1.124938</td>\n",
       "      <td>-0.051580</td>\n",
       "      <td>-0.774571</td>\n",
       "      <td>-0.036563</td>\n",
       "      <td>0.035795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.031465</td>\n",
       "      <td>5.147636</td>\n",
       "      <td>0.214739</td>\n",
       "      <td>0.009059</td>\n",
       "      <td>0.498950</td>\n",
       "      <td>1.033221</td>\n",
       "      <td>0.657734</td>\n",
       "      <td>0.879537</td>\n",
       "      <td>0.999712</td>\n",
       "      <td>0.055606</td>\n",
       "      <td>-0.289094</td>\n",
       "      <td>-0.055068</td>\n",
       "      <td>0.119610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.205881</td>\n",
       "      <td>5.244811</td>\n",
       "      <td>-0.124324</td>\n",
       "      <td>-0.077907</td>\n",
       "      <td>0.594776</td>\n",
       "      <td>1.187281</td>\n",
       "      <td>0.837427</td>\n",
       "      <td>0.892707</td>\n",
       "      <td>0.987820</td>\n",
       "      <td>0.037409</td>\n",
       "      <td>0.041813</td>\n",
       "      <td>-0.041016</td>\n",
       "      <td>-0.032496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.009315</td>\n",
       "      <td>5.210594</td>\n",
       "      <td>-0.185055</td>\n",
       "      <td>0.002598</td>\n",
       "      <td>0.657654</td>\n",
       "      <td>1.181162</td>\n",
       "      <td>0.950851</td>\n",
       "      <td>1.259253</td>\n",
       "      <td>1.112355</td>\n",
       "      <td>0.025556</td>\n",
       "      <td>-0.008730</td>\n",
       "      <td>-0.000847</td>\n",
       "      <td>-0.078750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>-0.159198</td>\n",
       "      <td>5.304853</td>\n",
       "      <td>0.272190</td>\n",
       "      <td>-0.230301</td>\n",
       "      <td>0.271408</td>\n",
       "      <td>0.809975</td>\n",
       "      <td>0.973990</td>\n",
       "      <td>0.149426</td>\n",
       "      <td>-0.036235</td>\n",
       "      <td>-0.036501</td>\n",
       "      <td>0.595494</td>\n",
       "      <td>0.777754</td>\n",
       "      <td>-0.411414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>-0.209859</td>\n",
       "      <td>5.266154</td>\n",
       "      <td>0.135413</td>\n",
       "      <td>0.382660</td>\n",
       "      <td>0.265313</td>\n",
       "      <td>1.075741</td>\n",
       "      <td>1.255181</td>\n",
       "      <td>0.844357</td>\n",
       "      <td>-0.004307</td>\n",
       "      <td>-0.002984</td>\n",
       "      <td>-0.164177</td>\n",
       "      <td>-0.372914</td>\n",
       "      <td>-0.011075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>-0.077003</td>\n",
       "      <td>5.277108</td>\n",
       "      <td>0.339912</td>\n",
       "      <td>0.050631</td>\n",
       "      <td>0.568426</td>\n",
       "      <td>0.513111</td>\n",
       "      <td>0.259577</td>\n",
       "      <td>0.814484</td>\n",
       "      <td>0.058262</td>\n",
       "      <td>-0.021971</td>\n",
       "      <td>0.919959</td>\n",
       "      <td>0.512811</td>\n",
       "      <td>-0.023790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.015126</td>\n",
       "      <td>5.278076</td>\n",
       "      <td>0.214904</td>\n",
       "      <td>0.111095</td>\n",
       "      <td>0.130813</td>\n",
       "      <td>0.388185</td>\n",
       "      <td>0.078628</td>\n",
       "      <td>0.316861</td>\n",
       "      <td>-0.033330</td>\n",
       "      <td>0.009760</td>\n",
       "      <td>0.600114</td>\n",
       "      <td>0.737948</td>\n",
       "      <td>0.521447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.144214</td>\n",
       "      <td>5.218433</td>\n",
       "      <td>0.261650</td>\n",
       "      <td>0.407119</td>\n",
       "      <td>0.179927</td>\n",
       "      <td>0.320780</td>\n",
       "      <td>0.522724</td>\n",
       "      <td>0.805193</td>\n",
       "      <td>0.035672</td>\n",
       "      <td>0.024522</td>\n",
       "      <td>0.070480</td>\n",
       "      <td>-0.048929</td>\n",
       "      <td>-0.143095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
       "0   -0.199053  5.234598 -0.088241 -0.034998  0.924329  1.131150  1.082737  \\\n",
       "1   -0.008096  5.277367  0.184243  0.056715  0.681012  0.935260  0.782681   \n",
       "2    0.031465  5.147636  0.214739  0.009059  0.498950  1.033221  0.657734   \n",
       "3    0.205881  5.244811 -0.124324 -0.077907  0.594776  1.187281  0.837427   \n",
       "4    0.009315  5.210594 -0.185055  0.002598  0.657654  1.181162  0.950851   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "995 -0.159198  5.304853  0.272190 -0.230301  0.271408  0.809975  0.973990   \n",
       "996 -0.209859  5.266154  0.135413  0.382660  0.265313  1.075741  1.255181   \n",
       "997 -0.077003  5.277108  0.339912  0.050631  0.568426  0.513111  0.259577   \n",
       "998  0.015126  5.278076  0.214904  0.111095  0.130813  0.388185  0.078628   \n",
       "999  0.144214  5.218433  0.261650  0.407119  0.179927  0.320780  0.522724   \n",
       "\n",
       "         CD27       CD4       CD8       CD3      CD57      CD45  \n",
       "0    0.809666  1.125293  0.004234  0.046163 -0.045944  0.179582  \n",
       "1    1.024696  1.124938 -0.051580 -0.774571 -0.036563  0.035795  \n",
       "2    0.879537  0.999712  0.055606 -0.289094 -0.055068  0.119610  \n",
       "3    0.892707  0.987820  0.037409  0.041813 -0.041016 -0.032496  \n",
       "4    1.259253  1.112355  0.025556 -0.008730 -0.000847 -0.078750  \n",
       "..        ...       ...       ...       ...       ...       ...  \n",
       "995  0.149426 -0.036235 -0.036501  0.595494  0.777754 -0.411414  \n",
       "996  0.844357 -0.004307 -0.002984 -0.164177 -0.372914 -0.011075  \n",
       "997  0.814484  0.058262 -0.021971  0.919959  0.512811 -0.023790  \n",
       "998  0.316861 -0.033330  0.009760  0.600114  0.737948  0.521447  \n",
       "999  0.805193  0.035672  0.024522  0.070480 -0.048929 -0.143095  \n",
       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_sample, cell_numbers = flowsim_tree.sample(n_cells=1000, return_sampled_cell_numbers=True)\n",
    "print(\"Number of cells per population in the synthetic sample\\n\")\n",
    "print(cell_numbers)\n",
    "\n",
    "print(\"\\n\\n\\nSynthetic sample\")\n",
    "synthetic_sample"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you do not need the actual cell ``features`` but only the number of cells per ``cell population``, you can do that with ``sample_populations()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+       11.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-        3.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           305.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+        3.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-             0.0\n",
       "                                                    ...  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+      0.0\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-      0.0\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-           0.0\n",
       "/AllCells/DN                                         68.0\n",
       "/AllCells/DP                                         12.0\n",
       "Name: 0, Length: 96, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_numbers = flowsim_tree.sample_populations(n_cells=1000)\n",
    "cell_numbers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Internally: \n",
    "\n",
    " 1. ``sample_populations()`` is called to generate the number of cells per leaf \n",
    " 2. ``multivariate_normal()`` generates the cell ``features`` for each cell\n",
    "\n",
    " You can also set a seed for reproducibility. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
      "0  0.033016  5.212589  0.027994 -0.042323  1.068687  1.230491  0.727421  \\\n",
      "1 -0.034233  5.219806  0.041332  0.009111  0.016646  1.218523  0.445606   \n",
      "2  0.395116  5.366544  0.085398  0.737223  0.762656  0.641986  1.200716   \n",
      "\n",
      "       CD27       CD4       CD8       CD3      CD57      CD45  \n",
      "0  0.667649  1.017606 -0.012887 -0.232470 -0.015077  0.049724  \n",
      "1  0.971163  1.167465  0.033722  0.081410  0.019712 -0.191248  \n",
      "2  1.028306 -0.014907  0.892293 -0.686076 -0.011778  0.000069  \n",
      "         FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
      "0  0.033016  5.212589  0.027994 -0.042323  1.068687  1.230491  0.727421  \\\n",
      "1 -0.034233  5.219806  0.041332  0.009111  0.016646  1.218523  0.445606   \n",
      "2  0.395116  5.366544  0.085398  0.737223  0.762656  0.641986  1.200716   \n",
      "\n",
      "       CD27       CD4       CD8       CD3      CD57      CD45  \n",
      "0  0.667649  1.017606 -0.012887 -0.232470 -0.015077  0.049724  \n",
      "1  0.971163  1.167465  0.033722  0.081410  0.019712 -0.191248  \n",
      "2  1.028306 -0.014907  0.892293 -0.686076 -0.011778  0.000069  \n"
     ]
    }
   ],
   "source": [
    "flowsim_tree.set_seed(42)\n",
    "synthetic_sample = flowsim_tree.sample(n_cells=3)\n",
    "print(synthetic_sample)\n",
    "\n",
    "flowsim_tree.set_seed(42)\n",
    "synthetic_sample = flowsim_tree.sample(n_cells=3)\n",
    "print(synthetic_sample)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per default, the normal distribution is based on a diagonal covariance matrix estimated on the cells per leaf node. However, you can enable the full covariance matrix!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
      "0  0.033016  5.212589  0.027994 -0.042323  1.068687  1.230491  0.727421  \\\n",
      "1 -0.034233  5.219806  0.041332  0.009111  0.016646  1.218523  0.445606   \n",
      "2  0.395116  5.366544  0.085398  0.737223  0.762656  0.641986  1.200716   \n",
      "\n",
      "       CD27       CD4       CD8       CD3      CD57      CD45  \n",
      "0  0.667649  1.017606 -0.012887 -0.232470 -0.015077  0.049724  \n",
      "1  0.971163  1.167465  0.033722  0.081410  0.019712 -0.191248  \n",
      "2  1.028306 -0.014907  0.892293 -0.686076 -0.011778  0.000069  \n",
      "         FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
      "0 -0.014040  5.287691  0.113469 -0.056042  0.963664  1.316469  0.788263  \\\n",
      "1  0.077410  5.221344  0.198341 -0.028692  0.049742  1.251280  0.402444   \n",
      "2 -0.093948  5.399214  0.514198  1.252213  1.269626  0.644032  0.538064   \n",
      "\n",
      "       CD27       CD4       CD8       CD3      CD57      CD45  \n",
      "0  1.189001  1.073660  0.097426 -0.041899  0.009045 -0.067505  \n",
      "1  0.653476  0.899301  0.023174 -0.265431  0.006454 -0.508050  \n",
      "2  1.183130  0.016804  1.130555 -0.224201  0.028751  0.131174  \n"
     ]
    }
   ],
   "source": [
    "flowsim_tree.set_seed(42)\n",
    "# default\n",
    "synthetic_sample = flowsim_tree.sample(n_cells=3, use_only_diagonal_covmat=True) \n",
    "print(synthetic_sample)\n",
    "\n",
    "flowsim_tree.set_seed(42)\n",
    "synthetic_sample = flowsim_tree.sample(n_cells=3, use_only_diagonal_covmat=False)\n",
    "print(synthetic_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthetic data with effects\n",
    "\n",
    "Previously, our ability was limited to mimicing the original dataset. However, we can also introduce effects in any ``cell population``! This means we can modify the proportion of cells derived from a particular ``cell population`` in a synthesized sample. \n",
    "Specifically, we defined the proportion of cells in a population using a Dirichlet distribution and its corresponding concentration parameters. By altering these parameters, we can adjust the number of cells in the population.\n",
    "\n",
    "Note that we are not changing how the cells are samples _within_ a leaf node!\n",
    "\n",
    "\n",
    "\n",
    "We show two ways to simulate different effects: \n",
    "\n",
    "1. ``TreeMeanRelative()``: Change any ``cell population`` by a relative amount. \n",
    "2. ``TreeMeanDistributionSampler()``: Force any distribution on a single ``cell population``\n",
    "\n",
    "``TreeMeanRelative`` changes the concentration of a ``cell population`` by a certain factor (e.g. old = 173.2, new = old*2) and all other concentration parameters decrease (by a factor) such that the overall sum of concentration parameters remains equal. \n",
    "\n",
    "``TreeMeanDistributionSampler()`` first samples a _target_ proportion from a given distribution and then sets the concentration parameter such that on average the cell population contains this proportion. Again, the sum of concentration parameters remains equal."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ``TreeMeanRelative``\n",
    "\n",
    "The following sets up a ``TreeMeanRelative`` where the cell population ``/AllCells/DN`` is changed by the factor 1 (therefore remains equal). \n",
    "``n_samples`` is set to 2 and ``n_cells`` to 100. \n",
    "\n",
    "Therefore, calling ``tmr.sample()`` generates two samples with the original distribution, 100 cells each. \n",
    "``tmr.sample()`` returns the number of cells per leaf population for both samples, the parameters after changing (the original tree is not changed) and the actually sampled samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<nbnode.simulation.TreeMeanRelative.TreeMeanRelative object at 0x7f937d9c8b50>\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_0</th>\n",
       "      <th>sample_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-</th>\n",
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       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-</th>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/DN</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/DP</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sample_0  sample_1\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             31.0      29.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              1.0       0.0\n",
       "...                                                    ...       ...\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       1.0\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0\n",
       "/AllCells/DN                                           6.0       6.0\n",
       "/AllCells/DP                                           0.0       0.0\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nbnode.simulation.TreeMeanRelative import TreeMeanRelative\n",
    "tmr = TreeMeanRelative(\n",
    "    flowsim_tree,\n",
    "    change_pop_mean_proportional={\"/AllCells/DN\": 1},\n",
    "    n_samples=2,\n",
    "    n_cells=100,\n",
    "    verbose=False\n",
    ")\n",
    "print(tmr)\n",
    "true_popcounts, changed_parameters, sampled_samples = tmr.sample()\n",
    "\n",
    "true_popcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+       0.901580\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-       0.295210\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           48.203633\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+       0.091623\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-            0.096534\n",
       "                                                      ...    \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+     0.159252\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-     0.153660\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-          0.125163\n",
       "/AllCells/DN                                        15.218566\n",
       "/AllCells/DP                                         1.755718\n",
       "Name: 0, Length: 96, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "changed_parameters[\"alpha\"]  # these are the concentration parameters of the Dirichlet distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 synthetic samples, showing only the first\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FS</th>\n",
       "      <th>FS.0</th>\n",
       "      <th>SS</th>\n",
       "      <th>CD45RA</th>\n",
       "      <th>CCR7</th>\n",
       "      <th>CD28</th>\n",
       "      <th>PD1</th>\n",
       "      <th>CD27</th>\n",
       "      <th>CD4</th>\n",
       "      <th>CD8</th>\n",
       "      <th>CD3</th>\n",
       "      <th>CD57</th>\n",
       "      <th>CD45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.062235</td>\n",
       "      <td>5.241828</td>\n",
       "      <td>0.167840</td>\n",
       "      <td>-0.084488</td>\n",
       "      <td>0.462969</td>\n",
       "      <td>1.327377</td>\n",
       "      <td>1.020327</td>\n",
       "      <td>0.735205</td>\n",
       "      <td>0.919547</td>\n",
       "      <td>-0.024936</td>\n",
       "      <td>-0.459670</td>\n",
       "      <td>-0.024190</td>\n",
       "      <td>-0.155744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.137097</td>\n",
       "      <td>5.211038</td>\n",
       "      <td>0.006071</td>\n",
       "      <td>-0.013674</td>\n",
       "      <td>0.631770</td>\n",
       "      <td>1.354644</td>\n",
       "      <td>0.924415</td>\n",
       "      <td>1.071354</td>\n",
       "      <td>0.909430</td>\n",
       "      <td>-0.011694</td>\n",
       "      <td>-0.594609</td>\n",
       "      <td>-0.015247</td>\n",
       "      <td>-0.368281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.049543</td>\n",
       "      <td>5.226160</td>\n",
       "      <td>-0.092377</td>\n",
       "      <td>0.032866</td>\n",
       "      <td>0.720488</td>\n",
       "      <td>1.218761</td>\n",
       "      <td>0.788295</td>\n",
       "      <td>1.015296</td>\n",
       "      <td>1.006380</td>\n",
       "      <td>0.065456</td>\n",
       "      <td>-0.138280</td>\n",
       "      <td>0.082154</td>\n",
       "      <td>0.100491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.161587</td>\n",
       "      <td>5.303274</td>\n",
       "      <td>-0.203429</td>\n",
       "      <td>-0.063234</td>\n",
       "      <td>0.272455</td>\n",
       "      <td>1.249179</td>\n",
       "      <td>0.712787</td>\n",
       "      <td>1.112724</td>\n",
       "      <td>1.045490</td>\n",
       "      <td>0.031186</td>\n",
       "      <td>-0.482921</td>\n",
       "      <td>0.040894</td>\n",
       "      <td>-0.401152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.153456</td>\n",
       "      <td>5.171440</td>\n",
       "      <td>0.188800</td>\n",
       "      <td>0.084201</td>\n",
       "      <td>0.742456</td>\n",
       "      <td>1.389247</td>\n",
       "      <td>0.397038</td>\n",
       "      <td>0.904283</td>\n",
       "      <td>1.021047</td>\n",
       "      <td>0.004681</td>\n",
       "      <td>-0.283981</td>\n",
       "      <td>-0.004825</td>\n",
       "      <td>-0.402051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.315077</td>\n",
       "      <td>5.217177</td>\n",
       "      <td>0.494209</td>\n",
       "      <td>-0.770656</td>\n",
       "      <td>-0.407820</td>\n",
       "      <td>0.650297</td>\n",
       "      <td>1.125016</td>\n",
       "      <td>0.144094</td>\n",
       "      <td>-0.011654</td>\n",
       "      <td>0.032542</td>\n",
       "      <td>0.723826</td>\n",
       "      <td>1.004760</td>\n",
       "      <td>0.156519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.534101</td>\n",
       "      <td>5.270793</td>\n",
       "      <td>0.460711</td>\n",
       "      <td>0.347915</td>\n",
       "      <td>0.157224</td>\n",
       "      <td>0.784490</td>\n",
       "      <td>1.131765</td>\n",
       "      <td>0.869825</td>\n",
       "      <td>0.019530</td>\n",
       "      <td>-0.018556</td>\n",
       "      <td>0.217103</td>\n",
       "      <td>0.367984</td>\n",
       "      <td>0.458230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.226989</td>\n",
       "      <td>5.252685</td>\n",
       "      <td>-0.189814</td>\n",
       "      <td>0.154086</td>\n",
       "      <td>-0.156006</td>\n",
       "      <td>0.536826</td>\n",
       "      <td>1.165235</td>\n",
       "      <td>0.703251</td>\n",
       "      <td>0.030829</td>\n",
       "      <td>-0.052641</td>\n",
       "      <td>0.138474</td>\n",
       "      <td>0.634439</td>\n",
       "      <td>0.154382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>-0.001238</td>\n",
       "      <td>5.322612</td>\n",
       "      <td>0.424035</td>\n",
       "      <td>0.193686</td>\n",
       "      <td>0.277735</td>\n",
       "      <td>1.124228</td>\n",
       "      <td>0.598610</td>\n",
       "      <td>0.624192</td>\n",
       "      <td>0.010864</td>\n",
       "      <td>-0.030898</td>\n",
       "      <td>0.231010</td>\n",
       "      <td>-0.259944</td>\n",
       "      <td>0.003429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.108700</td>\n",
       "      <td>5.239704</td>\n",
       "      <td>-0.103004</td>\n",
       "      <td>0.554299</td>\n",
       "      <td>0.218544</td>\n",
       "      <td>0.567261</td>\n",
       "      <td>0.524959</td>\n",
       "      <td>0.422115</td>\n",
       "      <td>-0.107181</td>\n",
       "      <td>-0.006251</td>\n",
       "      <td>0.420655</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>-0.431249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          FS      FS.0        SS    CD45RA      CCR7      CD28       PD1   \n",
       "0   0.062235  5.241828  0.167840 -0.084488  0.462969  1.327377  1.020327  \\\n",
       "1  -0.137097  5.211038  0.006071 -0.013674  0.631770  1.354644  0.924415   \n",
       "2   0.049543  5.226160 -0.092377  0.032866  0.720488  1.218761  0.788295   \n",
       "3  -0.161587  5.303274 -0.203429 -0.063234  0.272455  1.249179  0.712787   \n",
       "4   0.153456  5.171440  0.188800  0.084201  0.742456  1.389247  0.397038   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "95  0.315077  5.217177  0.494209 -0.770656 -0.407820  0.650297  1.125016   \n",
       "96  0.534101  5.270793  0.460711  0.347915  0.157224  0.784490  1.131765   \n",
       "97 -0.226989  5.252685 -0.189814  0.154086 -0.156006  0.536826  1.165235   \n",
       "98 -0.001238  5.322612  0.424035  0.193686  0.277735  1.124228  0.598610   \n",
       "99  0.108700  5.239704 -0.103004  0.554299  0.218544  0.567261  0.524959   \n",
       "\n",
       "        CD27       CD4       CD8       CD3      CD57      CD45  \n",
       "0   0.735205  0.919547 -0.024936 -0.459670 -0.024190 -0.155744  \n",
       "1   1.071354  0.909430 -0.011694 -0.594609 -0.015247 -0.368281  \n",
       "2   1.015296  1.006380  0.065456 -0.138280  0.082154  0.100491  \n",
       "3   1.112724  1.045490  0.031186 -0.482921  0.040894 -0.401152  \n",
       "4   0.904283  1.021047  0.004681 -0.283981 -0.004825 -0.402051  \n",
       "..       ...       ...       ...       ...       ...       ...  \n",
       "95  0.144094 -0.011654  0.032542  0.723826  1.004760  0.156519  \n",
       "96  0.869825  0.019530 -0.018556  0.217103  0.367984  0.458230  \n",
       "97  0.703251  0.030829 -0.052641  0.138474  0.634439  0.154382  \n",
       "98  0.624192  0.010864 -0.030898  0.231010 -0.259944  0.003429  \n",
       "99  0.422115 -0.107181 -0.006251  0.420655  0.294118 -0.431249  \n",
       "\n",
       "[100 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"There are {len(sampled_samples)} synthetic samples, showing only the first\\n\")\n",
    "sampled_samples[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing a specific proportion on average increases the respective cell population's number of cells: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmr_base = TreeMeanRelative(\n",
    "    flowsim_tree,\n",
    "    change_pop_mean_proportional={\"/AllCells/DN\": 1},\n",
    "    n_samples=10,\n",
    "    n_cells=100,\n",
    "    verbose=False\n",
    ")\n",
    "tmr_ridiculously_changed = TreeMeanRelative(\n",
    "    flowsim_tree,\n",
    "    change_pop_mean_proportional={\"/AllCells/DN\": 10},\n",
    "    n_samples=10,\n",
    "    n_cells=100,\n",
    "    verbose=False\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check the changed parameters when sampling from the distribution: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+       0.901580\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-       0.295210\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           48.203633\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+       0.091623\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-            0.096534\n",
       "                                                      ...    \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+     0.159252\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-     0.153660\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-          0.125163\n",
       "/AllCells/DN                                        15.218566\n",
       "/AllCells/DP                                         1.755718\n",
       "Name: 0, Length: 96, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_popcounts, changed_parameters, sampled_samples = tmr_base.sample()\n",
    "r_true_popcounts, r_changed_parameters, r_sampled_samples = tmr_ridiculously_changed.sample()\n",
    "\n",
    "changed_parameters[\"alpha\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        0.169073\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-        0.055361\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             9.039610\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+        0.017182\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-             0.018103\n",
       "                                                       ...    \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+      0.029865\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-      0.028816\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-           0.023472\n",
       "/AllCells/DN                                        152.185662\n",
       "/AllCells/DP                                          0.329249\n",
       "Name: 0, Length: 96, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_changed_parameters[\"alpha\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe that the concentration parameter of ``/AllCells/DN`` strongly increased! Therefore also the number of cells from that population changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>sample_0</th>\n",
       "      <th>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>sample_3</th>\n",
       "      <th>sample_4</th>\n",
       "      <th>sample_5</th>\n",
       "      <th>sample_6</th>\n",
       "      <th>sample_7</th>\n",
       "      <th>sample_8</th>\n",
       "      <th>sample_9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-</th>\n",
       "      <td>31.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/DN</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/DP</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sample_0  sample_1   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             31.0      29.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              1.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       1.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                           6.0       6.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_2  sample_3   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         1.0       1.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             36.0      31.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                           7.0       7.0   \n",
       "/AllCells/DP                                           1.0       2.0   \n",
       "\n",
       "                                                  sample_4  sample_5   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             22.0      30.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                          17.0       9.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_6  sample_7   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         1.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             29.0      24.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                           8.0      13.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_8  sample_9  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-             29.0      31.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0  \n",
       "...                                                    ...       ...  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0  \n",
       "/AllCells/DN                                          12.0       5.0  \n",
       "/AllCells/DP                                           2.0       0.0  \n",
       "\n",
       "[96 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_popcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample_0</th>\n",
       "      <th>sample_1</th>\n",
       "      <th>sample_2</th>\n",
       "      <th>sample_3</th>\n",
       "      <th>sample_4</th>\n",
       "      <th>sample_5</th>\n",
       "      <th>sample_6</th>\n",
       "      <th>sample_7</th>\n",
       "      <th>sample_8</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+</th>\n",
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       "      <th>/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+</th>\n",
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       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>/AllCells/DN</th>\n",
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       "<p>96 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sample_0  sample_1   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-              4.0       4.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                          86.0      90.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_2  sample_3   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-              6.0       4.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                          90.0      90.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_4  sample_5   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-              3.0       4.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                          83.0      85.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_6  sample_7   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-              5.0       3.0   \n",
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       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                          82.0      89.0   \n",
       "/AllCells/DP                                           0.0       0.0   \n",
       "\n",
       "                                                  sample_8  sample_9  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-              6.0       5.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0  \n",
       "...                                                    ...       ...  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0  \n",
       "/AllCells/DN                                          85.0      85.0  \n",
       "/AllCells/DP                                           0.0       0.0  \n",
       "\n",
       "[96 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_true_popcounts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ``TreeMeanDistributionSampler``\n",
    "\n",
    "The following sets up a ``TreeMeanDistributionSampler`` where the cell population ``/AllCells/DN`` is sampled from a normal distribution with mean of the original data and a standard deviation of 1 (default).  \n",
    "``n_samples`` is set to 2 and ``n_cells`` to 100. \n",
    "\n",
    "Therefore, calling ``tmr.sample()`` generates two samples with the values from a normal distribution with a mean of 8.28% being in that population, a standard deviation of 1. The value from that normal distribution is then set as target for the Dirichlet distribution, and 100 cells are synthesized. \n",
    "\n",
    "\n",
    "``tmr.sample()`` returns the number of cells per leaf population for both samples, the parameters after changing (the original tree is not changed) and the actually sampled samples. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08279977774316377\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "      <th>sample_1</th>\n",
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       "      <th>/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-</th>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>/AllCells/DN</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                                                  sample_0  sample_1\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+         0.0       0.0\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0\n",
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       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0\n",
       "...                                                    ...       ...\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       1.0\n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0\n",
       "/AllCells/DN                                           6.0       8.0\n",
       "/AllCells/DP                                           0.0       0.0\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nbnode.simulation.TreeMeanDistributionSampler import TreeMeanDistributionSampler\n",
    "\n",
    "tmds = TreeMeanDistributionSampler(\n",
    "    flowsim_tree,\n",
    "    population_name_to_change=\"/AllCells/DN\",\n",
    "    n_samples=2,\n",
    "    n_cells=100,\n",
    "    verbose=False,\n",
    ")\n",
    "(\n",
    "    all_true_popcounts,\n",
    "    all_changed_parameters,\n",
    "    all_sampled_samples,\n",
    "    all_targets,\n",
    ") = tmds.sample()\n",
    "\n",
    "\n",
    "\n",
    "print(flowsim_tree.pop_mean(\"/AllCells/DN\"))\n",
    "all_true_popcounts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can insert any population distribution with a function ``mean_distribution`` taking one argument (``original_mean``) and returning a class with a ``.sample()`` method which can be casted to ``float``. \n",
    "\n",
    "Internally, the ``target_mean_distribution`` is set to ``mean_distribution(original_mean)`` of the original population. \n",
    "Then for every of the ``n_samples``, a single value is sampled by ``float(target_mean_distribution.sample())`` and this value is set as mean of the Dirichlet distribution for the ``population_name_to_change``. \n",
    "\n",
    "We included a ``PseudoTorchDistributionNormal`` with the relevant method, but you can use any distribution from [pytorch-distributions](https://pytorch.org/docs/stable/distributions.html). \n",
    "\n",
    "In the following example, we introduce a much higher variance on one population by basing it on a normal distribution with scale 10. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n",
      "/home/gugl/.conda_envs/nbnode_pyscaffold/lib/python3.8/site-packages/nbnode/simulation/sim_target.py:90: UserWarning: Changing more than 1 population is NOT setting the means to the specifiedvalues - only the LAST value will be exactly the set percentage\n",
      "  warnings.warn(\n"
     ]
    },
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       "</table>\n",
       "<p>96 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sample_0  sample_1   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        23.0      40.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           2671.0    2698.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0      32.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-             27.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0      13.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0     165.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-           44.0       4.0   \n",
       "/AllCells/DN                                         529.0     111.0   \n",
       "/AllCells/DP                                          96.0      86.0   \n",
       "\n",
       "                                                  sample_2  sample_3   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        50.0     125.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           2865.0    2693.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+        32.0       2.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       4.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                        2215.0    1092.0   \n",
       "/AllCells/DP                                          28.0     102.0   \n",
       "\n",
       "                                                  sample_4  sample_5   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        23.0      81.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-        22.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           2262.0    2362.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       4.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       3.0      14.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-      24.0       6.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       1.0   \n",
       "/AllCells/DN                                         524.0    1056.0   \n",
       "/AllCells/DP                                           3.0     119.0   \n",
       "\n",
       "                                                  sample_6  sample_7   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        32.0      50.0  \\\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         0.0      23.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           3006.0    2262.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       0.0   \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-             14.0       0.0   \n",
       "...                                                    ...       ...   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       9.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       1.0       0.0   \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0   \n",
       "/AllCells/DN                                           0.0    1677.0   \n",
       "/AllCells/DP                                          59.0      70.0   \n",
       "\n",
       "                                                  sample_8  sample_9  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+        29.0      81.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-         1.0       4.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-           2621.0    2837.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+         0.0       3.0  \n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-              0.0       0.0  \n",
       "...                                                    ...       ...  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1+       0.0       0.0  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57+/PD1-       0.0       0.0  \n",
       "/AllCells/CD4-/CD8+/naive/CD27-/CD28-/CD57-            0.0       0.0  \n",
       "/AllCells/DN                                         954.0     497.0  \n",
       "/AllCells/DP                                         242.0      19.0  \n",
       "\n",
       "[96 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nbnode.simulation.TreeMeanDistributionSampler import PseudoTorchDistributionNormal\n",
    "\n",
    "tmds_custom = TreeMeanDistributionSampler(\n",
    "    flowsim_tree,\n",
    "    population_name_to_change=\"/AllCells/DN\",\n",
    "    mean_distribution=lambda original_mean: PseudoTorchDistributionNormal(\n",
    "        loc=original_mean, scale=10\n",
    "    ),\n",
    "    n_samples=10,\n",
    "    n_cells=10000,\n",
    "    verbose=False,\n",
    ")\n",
    "(\n",
    "    all_true_popcounts,\n",
    "    all_changed_parameters,\n",
    "    all_sampled_samples,\n",
    "    all_targets,\n",
    ") = tmds_custom.sample()\n",
    "\n",
    "all_true_popcounts\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FlowSimulationTree methods and attributes\n",
    "\n",
    "``FlowSimulationTree`` has a number of interesting methods and attributes. \n",
    "\n",
    "``flowsim_tree.estimate_population_distribution()``\n",
    "    Given node percentages per sample for all lefa nodes estimate their corresponding dirichlet parameters\n",
    "\n",
    "``flowsim_tree.remove_population()``\n",
    "    You can remove a population from the Dirichlet distribution. No cells are going to be sampled from that one and the corresponding concentration parameter is removed (not redistributed). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pickle\n",
    "with open(os.path.join(os.pardir, os.pardir, \"tests\", \"testdata\", \"flowcytometry\", \"flowsim_tree.pickle\"), \"rb\") as f:\n",
    "    flowsim_tree = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183.79960241689196"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision is equal to the total sum of concentration parameters\n",
    "flowsim_tree.precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1+      0.901580\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57+/PD1-      0.295210\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28+/CD57-          48.203633\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57+/PD1+      0.091623\n",
       "/AllCells/CD4+/CD8-/Tcm/CD27+/CD28-/CD57-           0.096534\n",
       "                                                     ...    \n",
       "/AllCells/CD4+/CD8-/Tem/CD27-/CD28+                 3.228699\n",
       "/AllCells/CD4+/CD8-/Tem/CD27-/CD28-                 0.530434\n",
       "/AllCells/CD4+/CD8-/Tem                            11.384744\n",
       "/AllCells/CD4+/CD8-                               120.537038\n",
       "/AllCells                                         183.799602\n",
       "Name: 0, Length: 139, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concentration parameters for all populations, also intermediate populations\n",
    "flowsim_tree.alpha_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/AllCells/CD4+/CD8-/naive/CD27+/CD28+/CD57+/PD1+', '/AllCells/CD4+/CD8-/naive/CD27+/CD28+/CD57+/PD1-', '/AllCells/CD4+/CD8-/naive/CD27+/CD28+/CD57-', '/AllCells/CD4+/CD8-/naive/CD27+/CD28-/CD57+/PD1+', '/AllCells/CD4+/CD8-/naive/CD27+/CD28-/CD57+/PD1-', '/AllCells/CD4+/CD8-/naive/CD27+/CD28-/CD57-', '/AllCells/CD4+/CD8-/naive/CD27-/CD28+/CD57+/PD1+', '/AllCells/CD4+/CD8-/naive/CD27-/CD28+/CD57+/PD1-', '/AllCells/CD4+/CD8-/naive/CD27-/CD28+/CD57-', '/AllCells/CD4+/CD8-/naive/CD27-/CD28-/CD57+/PD1+', '/AllCells/CD4+/CD8-/naive/CD27-/CD28-/CD57+/PD1-', '/AllCells/CD4+/CD8-/naive/CD27-/CD28-/CD57-']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['/AllCells/CD4+/CD8-/naive/CD27+/CD28+/CD57+/PD1+',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27+/CD28+/CD57+/PD1-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27+/CD28+/CD57-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27+/CD28-/CD57+/PD1+',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27+/CD28-/CD57+/PD1-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27+/CD28-/CD57-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27-/CD28+/CD57+/PD1+',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27-/CD28+/CD57+/PD1-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27-/CD28+/CD57-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27-/CD28-/CD57+/PD1+',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27-/CD28-/CD57+/PD1-',\n",
       " '/AllCells/CD4+/CD8-/naive/CD27-/CD28-/CD57-']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All leaf node names (i.e. populations) corresponding to the given population\n",
    "print(flowsim_tree.pop_leafnode_names(\"/AllCells/CD4+/CD8-/naive\"))\n",
    "\n",
    "# Also works directly with NBNodes\n",
    "flowsim_tree.pop_leafnode_names(flowsim_tree.rootnode_structure[\"/AllCells/CD4+/CD8-/naive\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AllCells (counter:0)\n",
      "├── DN (counter:0)\n",
      "├── DP (counter:0)\n",
      "├── CD4-/CD8+ (counter:0)\n",
      "│   ├── naive (counter:0)\n",
      "│   │   ├── CD27+/CD28+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   ├── CD27+/CD28- (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   ├── CD27-/CD28+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   └── CD27-/CD28- (counter:0)\n",
      "│   │       ├── CD57+/PD1+ (counter:0)\n",
      "│   │       ├── CD57+/PD1- (counter:0)\n",
      "│   │       └── CD57- (counter:0)\n",
      "│   ├── Tcm (counter:0)\n",
      "│   │   ├── CD27+/CD28+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   ├── CD27+/CD28- (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   ├── CD27-/CD28+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   └── CD27-/CD28- (counter:0)\n",
      "│   │       ├── CD57+/PD1+ (counter:0)\n",
      "│   │       ├── CD57+/PD1- (counter:0)\n",
      "│   │       └── CD57- (counter:0)\n",
      "│   ├── Temra (counter:0)\n",
      "│   │   ├── CD27+/CD28+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   ├── CD27+/CD28- (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   ├── CD27-/CD28+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1+ (counter:0)\n",
      "│   │   │   ├── CD57+/PD1- (counter:0)\n",
      "│   │   │   └── CD57- (counter:0)\n",
      "│   │   └── CD27-/CD28- (counter:0)\n",
      "│   │       ├── CD57+/PD1+ (counter:0)\n",
      "│   │       ├── CD57+/PD1- (counter:0)\n",
      "│   │       └── CD57- (counter:0)\n",
      "│   └── Tem (counter:0)\n",
      "│       ├── CD27+/CD28+ (counter:0)\n",
      "│       │   ├── CD57+/PD1+ (counter:0)\n",
      "│       │   ├── CD57+/PD1- (counter:0)\n",
      "│       │   └── CD57- (counter:0)\n",
      "│       ├── CD27+/CD28- (counter:0)\n",
      "│       │   ├── CD57+/PD1+ (counter:0)\n",
      "│       │   ├── CD57+/PD1- (counter:0)\n",
      "│       │   └── CD57- (counter:0)\n",
      "│       ├── CD27-/CD28+ (counter:0)\n",
      "│       │   ├── CD57+/PD1+ (counter:0)\n",
      "│       │   ├── CD57+/PD1- (counter:0)\n",
      "│       │   └── CD57- (counter:0)\n",
      "│       └── CD27-/CD28- (counter:0)\n",
      "│           ├── CD57+/PD1+ (counter:0)\n",
      "│           ├── CD57+/PD1- (counter:0)\n",
      "│           └── CD57- (counter:0)\n",
      "└── CD4+/CD8- (counter:0)\n",
      "    ├── naive (counter:0)\n",
      "    │   ├── CD27+/CD28+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   ├── CD27+/CD28- (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   ├── CD27-/CD28+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   └── CD27-/CD28- (counter:0)\n",
      "    │       ├── CD57+/PD1+ (counter:0)\n",
      "    │       ├── CD57+/PD1- (counter:0)\n",
      "    │       └── CD57- (counter:0)\n",
      "    ├── Tcm (counter:0)\n",
      "    │   ├── CD27+/CD28+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   ├── CD27+/CD28- (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   ├── CD27-/CD28+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   └── CD27-/CD28- (counter:0)\n",
      "    │       ├── CD57+/PD1+ (counter:0)\n",
      "    │       ├── CD57+/PD1- (counter:0)\n",
      "    │       └── CD57- (counter:0)\n",
      "    ├── Temra (counter:0)\n",
      "    │   ├── CD27+/CD28+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   ├── CD27+/CD28- (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   ├── CD27-/CD28+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1+ (counter:0)\n",
      "    │   │   ├── CD57+/PD1- (counter:0)\n",
      "    │   │   └── CD57- (counter:0)\n",
      "    │   └── CD27-/CD28- (counter:0)\n",
      "    │       ├── CD57+/PD1+ (counter:0)\n",
      "    │       ├── CD57+/PD1- (counter:0)\n",
      "    │       └── CD57- (counter:0)\n",
      "    └── Tem (counter:0)\n",
      "        ├── CD27+/CD28+ (counter:0)\n",
      "        │   ├── CD57+/PD1+ (counter:0)\n",
      "        │   ├── CD57+/PD1- (counter:0)\n",
      "        │   └── CD57- (counter:0)\n",
      "        ├── CD27+/CD28- (counter:0)\n",
      "        │   ├── CD57+/PD1+ (counter:0)\n",
      "        │   ├── CD57+/PD1- (counter:0)\n",
      "        │   └── CD57- (counter:0)\n",
      "        ├── CD27-/CD28+ (counter:0)\n",
      "        │   ├── CD57+/PD1+ (counter:0)\n",
      "        │   ├── CD57+/PD1- (counter:0)\n",
      "        │   └── CD57- (counter:0)\n",
      "        └── CD27-/CD28- (counter:0)\n",
      "            ├── CD57+/PD1+ (counter:0)\n",
      "            ├── CD57+/PD1- (counter:0)\n",
      "            └── CD57- (counter:0)\n"
     ]
    }
   ],
   "source": [
    "flowsim_tree.rootnode_structure.pretty_print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.5458005721906"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concentration parameter for a single population\n",
    "flowsim_tree.pop_alpha(\"/AllCells/CD4+/CD8-/naive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2967677832537989"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean for a single population\n",
    "flowsim_tree.pop_mean(\"/AllCells/CD4+/CD8-/naive\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.89980120844596\n",
      "0.5000000000000001\n"
     ]
    }
   ],
   "source": [
    "# Set the new mean of the Dirichlet distribution for a single population\n",
    "flowsim_tree.new_pop_mean(\"/AllCells/CD4+/CD8-/naive\", 0.5)\n",
    "print(flowsim_tree.pop_alpha(\"/AllCells/CD4+/CD8-/naive\"))\n",
    "print(flowsim_tree.pop_mean(\"/AllCells/CD4+/CD8-/naive\"))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous command ``new_pop_mean`` actively changes the whole dirichlet distribution. \n",
    "It is usually important to reset the changed parameters to the original values. You can do this easily with \n",
    "``reset_populations``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.5458005721906\n",
      "0.2967677832537989\n"
     ]
    }
   ],
   "source": [
    "flowsim_tree.reset_populations()\n",
    "print(flowsim_tree.pop_alpha(\"/AllCells/CD4+/CD8-/naive\"))\n",
    "print(flowsim_tree.pop_mean(\"/AllCells/CD4+/CD8-/naive\"))"
   ]
  }
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