notebooks/memory_benchmark/MK_2020-10-16_memory_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-16T14:41:41.541364Z",
     "start_time": "2020-10-16T14:41:41.534341Z"
    }
   },
   "source": [
    "Memory consumption benchmark\n",
    "----\n",
    "\n",
    "In this notebook, we plot the multi-core memory performance, as well as produce Suppl. Tab. 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Please consult the [analysis_files/README.md](analysis_files/README.md) on how to run the memory performance benchmarks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:05.435957Z",
     "start_time": "2021-11-09T21:01:04.257247Z"
    }
   },
   "outputs": [],
   "source": [
    "# import standard packages\n",
    "from pathlib import Path\n",
    "from collections import defaultdict\n",
    "import os\n",
    "import sys\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:05.442327Z",
     "start_time": "2021-11-09T21:01:05.438393Z"
    }
   },
   "outputs": [],
   "source": [
    "sys.path.insert(0, \"../..\")  # this depends on the notebook depth and must be adapted per notebook\n",
    "\n",
    "from paths import DATA_DIR, FIG_DIR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set global parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:05.447016Z",
     "start_time": "2021-11-09T21:01:05.444539Z"
    }
   },
   "outputs": [],
   "source": [
    "root = DATA_DIR / 'benchmarking' / 'memory_analysis'\n",
    "alg = \"algorithm\"\n",
    "\n",
    "pretty_names = {\n",
    "    'fateid': 'FateID (fate probs.)',\n",
    "    'stemnet': 'STEMNET (fate probs.)',\n",
    "    'palantir': 'Palantir (fate probs.)',\n",
    "    'ap': 'CellRank (fate probs.)',\n",
    "    'macro': 'CellRank (initial/terminal)',\n",
    "    'run_markov': 'Velocyto (initial/terminal)',\n",
    "}\n",
    "\n",
    "save_figure = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.757610Z",
     "start_time": "2021-11-09T21:01:05.449326Z"
    }
   },
   "outputs": [],
   "source": [
    "res = {\n",
    "    'fateid': defaultdict(dict),\n",
    "    'ap': defaultdict(dict),\n",
    "    'macro': defaultdict(dict),\n",
    "    'palantir': defaultdict(dict),\n",
    "    'stemnet': defaultdict(dict),\n",
    "    'kernel': defaultdict(dict),\n",
    "    'estimate_transition_probability': defaultdict(dict),\n",
    "    'calculate_embedding_shift': defaultdict(dict),\n",
    "    'prepare_markov': defaultdict(dict),\n",
    "    'run_markov': defaultdict(dict),\n",
    "}\n",
    "velo_cats = ['estimate_transition_probability', 'calculate_embedding_shift',\n",
    "             'prepare_markov', 'run_markov']\n",
    "\n",
    "for key in ['fateid', 'gpcca', 'palantir', 'stemnet', 'velocyto']:\n",
    "    for fname in os.listdir(root / key):\n",
    "        if not fname.endswith('.txt') and not fname.endswith('.pickle'):\n",
    "            continue\n",
    "        name = fname.split('.')[0]\n",
    "        rest = name.split('_')\n",
    "        if len(rest) == 3:\n",
    "            rest = rest[1:]\n",
    "            \n",
    "        size, split = map(int, rest)\n",
    "        path = root / key / fname\n",
    "                \n",
    "        if fname.endswith('.pickle'):\n",
    "            with open(path, 'rb') as fin:\n",
    "                data = pickle.load(fin)\n",
    "                if 'ap_mem' in data:  # CellRank\n",
    "                    assert key == 'gpcca'\n",
    "                    res['kernel'][size][split] = max(data['kernel_mem'])\n",
    "                    res['macro'][size][split] = max(data['macro_mem'])\n",
    "                    res['ap'][size][split] = max(data['ap_mem'])\n",
    "                elif \"run_markov\" in data:  # Velocyto\n",
    "                    for k in velo_cats:\n",
    "                        res[k][size][split] = max(data[k])\n",
    "                else:\n",
    "                    assert key != 'gpcca'  # anything else\n",
    "                    res[key][size][split] = max(data)\n",
    "        else:\n",
    "            with open(path, 'r') as fin:\n",
    "                data = fin.read().strip().split('\\n')[-1]\n",
    "                res[key][size][split] = float(data.split(' ')[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.778180Z",
     "start_time": "2021-11-09T21:01:06.759241Z"
    }
   },
   "outputs": [],
   "source": [
    "dfs = []\n",
    "for key in res.keys():\n",
    "    df = pd.DataFrame(res[key])\n",
    "    df.sort_index(axis=0, inplace=True)\n",
    "    df.sort_index(axis=1, inplace=True)\n",
    "    df['algorithm'] = key\n",
    "    dfs.append(df)\n",
    "    \n",
    "df = pd.concat(dfs, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum memory for CellRank macrostates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.787867Z",
     "start_time": "2021-11-09T21:01:06.779310Z"
    }
   },
   "outputs": [],
   "source": [
    "km = pd.DataFrame(\n",
    "    np.max([df[df[alg] == name].select_dtypes(exclude='category').values\n",
    "            for name in ['macro', 'kernel']], axis=0), columns=df.columns\n",
    ")\n",
    "km['algorithm'] = 'macro'\n",
    "\n",
    "df = df[df[alg] != 'kernel']\n",
    "df[df[alg] == \"macro\"] = km.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum memory for Velocyto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.798972Z",
     "start_time": "2021-11-09T21:01:06.789290Z"
    }
   },
   "outputs": [],
   "source": [
    "vm = pd.DataFrame(\n",
    "    np.max([df[df[alg] == name].select_dtypes(exclude='category').values\n",
    "            for name in velo_cats], axis=0), columns=df.columns\n",
    ")\n",
    "\n",
    "vm['algorithm'] = 'run_markov'\n",
    "\n",
    "df = df[~np.isin(df[alg], list(set(velo_cats) - {'run_markov'}))]\n",
    "df[df[alg] == \"run_markov\"] = vm.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rename categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.807965Z",
     "start_time": "2021-11-09T21:01:06.801800Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/marius/miniconda3/envs/cellrank/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2630: FutureWarning: The `inplace` parameter in pandas.Categorical.rename_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  res = method(*args, **kwargs)\n",
      "/Users/marius/miniconda3/envs/cellrank/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2630: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.\n",
      "  res = method(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "df[alg] = df[alg].astype(\"category\")\n",
    "df[alg].cat.rename_categories(pretty_names, inplace=True)\n",
    "df[alg].cat.reorder_categories([pretty_names[c] for c in\n",
    "                                ['ap', 'macro', 'fateid', 'palantir', 'stemnet', 'run_markov']], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to GiB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.813520Z",
     "start_time": "2021-11-09T21:01:06.809970Z"
    }
   },
   "outputs": [],
   "source": [
    "tmp = df.select_dtypes(exclude='category').divide(1024)  # work in GiB instead of MiB\n",
    "tmp[alg] = df[alg]\n",
    "df = tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show the resulting table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.838511Z",
     "start_time": "2021-11-09T21:01:06.815065Z"
    }
   },
   "outputs": [
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       "      <td>2.547559</td>\n",
       "      <td>10.010449</td>\n",
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       "      <td>125.328516</td>\n",
       "      <td>157.385059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FateID (fate probs.)</td>\n",
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       "      <td>2.548047</td>\n",
       "      <td>10.009961</td>\n",
       "      <td>18.499316</td>\n",
       "      <td>34.896191</td>\n",
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       "      <td>159.361133</td>\n",
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       "      <td>FateID (fate probs.)</td>\n",
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       "      <td>FateID (fate probs.)</td>\n",
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       "      <th>9</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>FateID (fate probs.)</td>\n",
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       "      <td>32.612919</td>\n",
       "      <td>35.522289</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.062252</td>\n",
       "      <td>12.644039</td>\n",
       "      <td>15.605682</td>\n",
       "      <td>18.291260</td>\n",
       "      <td>21.135040</td>\n",
       "      <td>23.841854</td>\n",
       "      <td>26.595779</td>\n",
       "      <td>29.606895</td>\n",
       "      <td>32.405560</td>\n",
       "      <td>35.461449</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.873753</td>\n",
       "      <td>12.700035</td>\n",
       "      <td>15.505363</td>\n",
       "      <td>18.320271</td>\n",
       "      <td>21.071911</td>\n",
       "      <td>23.811172</td>\n",
       "      <td>26.633286</td>\n",
       "      <td>29.668568</td>\n",
       "      <td>32.489876</td>\n",
       "      <td>35.661961</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.850586</td>\n",
       "      <td>12.476894</td>\n",
       "      <td>15.470444</td>\n",
       "      <td>18.192894</td>\n",
       "      <td>21.015621</td>\n",
       "      <td>23.868050</td>\n",
       "      <td>26.650265</td>\n",
       "      <td>29.760723</td>\n",
       "      <td>32.752056</td>\n",
       "      <td>35.411335</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.852631</td>\n",
       "      <td>12.591633</td>\n",
       "      <td>15.434055</td>\n",
       "      <td>18.259438</td>\n",
       "      <td>20.898941</td>\n",
       "      <td>23.890087</td>\n",
       "      <td>26.772430</td>\n",
       "      <td>29.837692</td>\n",
       "      <td>32.494507</td>\n",
       "      <td>35.515324</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.783054</td>\n",
       "      <td>12.772835</td>\n",
       "      <td>15.575436</td>\n",
       "      <td>18.178986</td>\n",
       "      <td>21.127518</td>\n",
       "      <td>23.770527</td>\n",
       "      <td>26.715965</td>\n",
       "      <td>29.401337</td>\n",
       "      <td>32.600727</td>\n",
       "      <td>35.615456</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.802605</td>\n",
       "      <td>12.582394</td>\n",
       "      <td>15.596725</td>\n",
       "      <td>18.301334</td>\n",
       "      <td>20.941631</td>\n",
       "      <td>23.925133</td>\n",
       "      <td>26.709976</td>\n",
       "      <td>29.632725</td>\n",
       "      <td>32.601315</td>\n",
       "      <td>35.470341</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.960964</td>\n",
       "      <td>12.554340</td>\n",
       "      <td>15.555279</td>\n",
       "      <td>18.210033</td>\n",
       "      <td>20.922523</td>\n",
       "      <td>23.722759</td>\n",
       "      <td>26.594051</td>\n",
       "      <td>29.463581</td>\n",
       "      <td>32.692955</td>\n",
       "      <td>35.216610</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.010059</td>\n",
       "      <td>12.629162</td>\n",
       "      <td>15.530094</td>\n",
       "      <td>18.214363</td>\n",
       "      <td>20.984203</td>\n",
       "      <td>23.988422</td>\n",
       "      <td>26.720764</td>\n",
       "      <td>29.606361</td>\n",
       "      <td>32.229023</td>\n",
       "      <td>35.632607</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.843887</td>\n",
       "      <td>12.633450</td>\n",
       "      <td>15.410801</td>\n",
       "      <td>18.206371</td>\n",
       "      <td>21.176380</td>\n",
       "      <td>23.723717</td>\n",
       "      <td>26.746029</td>\n",
       "      <td>29.567711</td>\n",
       "      <td>32.581272</td>\n",
       "      <td>35.699188</td>\n",
       "      <td>CellRank (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.202049</td>\n",
       "      <td>10.608837</td>\n",
       "      <td>13.750217</td>\n",
       "      <td>17.179554</td>\n",
       "      <td>20.393246</td>\n",
       "      <td>23.556778</td>\n",
       "      <td>26.870956</td>\n",
       "      <td>30.127693</td>\n",
       "      <td>33.379108</td>\n",
       "      <td>36.649357</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.151718</td>\n",
       "      <td>10.584091</td>\n",
       "      <td>13.798119</td>\n",
       "      <td>17.160847</td>\n",
       "      <td>20.433270</td>\n",
       "      <td>23.615788</td>\n",
       "      <td>26.870312</td>\n",
       "      <td>30.155857</td>\n",
       "      <td>33.415794</td>\n",
       "      <td>36.729362</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.194393</td>\n",
       "      <td>10.585506</td>\n",
       "      <td>13.798489</td>\n",
       "      <td>17.190605</td>\n",
       "      <td>20.400173</td>\n",
       "      <td>23.623066</td>\n",
       "      <td>26.887985</td>\n",
       "      <td>30.121769</td>\n",
       "      <td>33.407101</td>\n",
       "      <td>36.640907</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.989883</td>\n",
       "      <td>10.586407</td>\n",
       "      <td>13.818623</td>\n",
       "      <td>17.213104</td>\n",
       "      <td>20.384781</td>\n",
       "      <td>23.646809</td>\n",
       "      <td>26.896183</td>\n",
       "      <td>30.157028</td>\n",
       "      <td>33.421349</td>\n",
       "      <td>36.690662</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.035038</td>\n",
       "      <td>10.590126</td>\n",
       "      <td>13.768742</td>\n",
       "      <td>17.176121</td>\n",
       "      <td>20.364429</td>\n",
       "      <td>23.579941</td>\n",
       "      <td>26.914791</td>\n",
       "      <td>30.086304</td>\n",
       "      <td>33.424210</td>\n",
       "      <td>36.687382</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.781567</td>\n",
       "      <td>10.598209</td>\n",
       "      <td>13.853798</td>\n",
       "      <td>17.155041</td>\n",
       "      <td>20.368736</td>\n",
       "      <td>23.600555</td>\n",
       "      <td>26.866615</td>\n",
       "      <td>30.159962</td>\n",
       "      <td>33.460094</td>\n",
       "      <td>36.689049</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.866096</td>\n",
       "      <td>10.585037</td>\n",
       "      <td>13.815777</td>\n",
       "      <td>17.179173</td>\n",
       "      <td>20.340706</td>\n",
       "      <td>23.619137</td>\n",
       "      <td>26.887337</td>\n",
       "      <td>30.134361</td>\n",
       "      <td>33.421959</td>\n",
       "      <td>36.712910</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.205921</td>\n",
       "      <td>10.590378</td>\n",
       "      <td>13.797649</td>\n",
       "      <td>17.131809</td>\n",
       "      <td>20.418839</td>\n",
       "      <td>23.579132</td>\n",
       "      <td>26.877274</td>\n",
       "      <td>30.114807</td>\n",
       "      <td>33.393360</td>\n",
       "      <td>36.735619</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.941372</td>\n",
       "      <td>10.606098</td>\n",
       "      <td>13.888046</td>\n",
       "      <td>17.208130</td>\n",
       "      <td>20.363754</td>\n",
       "      <td>23.620853</td>\n",
       "      <td>26.867397</td>\n",
       "      <td>30.152420</td>\n",
       "      <td>33.376038</td>\n",
       "      <td>36.673405</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.001259</td>\n",
       "      <td>10.591946</td>\n",
       "      <td>13.851833</td>\n",
       "      <td>17.168873</td>\n",
       "      <td>20.457500</td>\n",
       "      <td>23.622566</td>\n",
       "      <td>26.870785</td>\n",
       "      <td>30.157913</td>\n",
       "      <td>33.447983</td>\n",
       "      <td>36.722061</td>\n",
       "      <td>CellRank (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.839134</td>\n",
       "      <td>14.100594</td>\n",
       "      <td>20.190571</td>\n",
       "      <td>27.104958</td>\n",
       "      <td>36.958870</td>\n",
       "      <td>47.382782</td>\n",
       "      <td>53.557552</td>\n",
       "      <td>67.931644</td>\n",
       "      <td>80.794216</td>\n",
       "      <td>97.832241</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.697010</td>\n",
       "      <td>13.953705</td>\n",
       "      <td>20.113358</td>\n",
       "      <td>27.630524</td>\n",
       "      <td>36.147858</td>\n",
       "      <td>46.388081</td>\n",
       "      <td>53.728580</td>\n",
       "      <td>65.880535</td>\n",
       "      <td>79.810719</td>\n",
       "      <td>98.725491</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.712502</td>\n",
       "      <td>14.159554</td>\n",
       "      <td>19.956238</td>\n",
       "      <td>27.713154</td>\n",
       "      <td>37.048836</td>\n",
       "      <td>47.481247</td>\n",
       "      <td>51.716202</td>\n",
       "      <td>65.541977</td>\n",
       "      <td>80.892876</td>\n",
       "      <td>98.512798</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.632271</td>\n",
       "      <td>14.168446</td>\n",
       "      <td>19.685036</td>\n",
       "      <td>26.688244</td>\n",
       "      <td>37.011677</td>\n",
       "      <td>47.612774</td>\n",
       "      <td>53.680122</td>\n",
       "      <td>67.096920</td>\n",
       "      <td>81.648239</td>\n",
       "      <td>97.386101</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.686970</td>\n",
       "      <td>14.196148</td>\n",
       "      <td>20.301609</td>\n",
       "      <td>27.638138</td>\n",
       "      <td>37.145233</td>\n",
       "      <td>46.360134</td>\n",
       "      <td>52.136871</td>\n",
       "      <td>65.797375</td>\n",
       "      <td>81.989887</td>\n",
       "      <td>98.633186</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.694248</td>\n",
       "      <td>14.225803</td>\n",
       "      <td>20.035282</td>\n",
       "      <td>27.559483</td>\n",
       "      <td>36.949924</td>\n",
       "      <td>47.908257</td>\n",
       "      <td>53.662071</td>\n",
       "      <td>65.118721</td>\n",
       "      <td>80.218494</td>\n",
       "      <td>99.296864</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.767300</td>\n",
       "      <td>14.196133</td>\n",
       "      <td>19.829868</td>\n",
       "      <td>27.671993</td>\n",
       "      <td>36.899956</td>\n",
       "      <td>47.700054</td>\n",
       "      <td>53.705658</td>\n",
       "      <td>64.941994</td>\n",
       "      <td>83.689167</td>\n",
       "      <td>98.855030</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.809479</td>\n",
       "      <td>14.226452</td>\n",
       "      <td>20.104103</td>\n",
       "      <td>27.509640</td>\n",
       "      <td>37.041740</td>\n",
       "      <td>47.697048</td>\n",
       "      <td>53.934505</td>\n",
       "      <td>67.654861</td>\n",
       "      <td>80.800030</td>\n",
       "      <td>97.009544</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.782085</td>\n",
       "      <td>14.056778</td>\n",
       "      <td>20.148449</td>\n",
       "      <td>27.644222</td>\n",
       "      <td>36.816193</td>\n",
       "      <td>47.735085</td>\n",
       "      <td>52.446434</td>\n",
       "      <td>65.111137</td>\n",
       "      <td>80.865646</td>\n",
       "      <td>99.138912</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.774555</td>\n",
       "      <td>14.256641</td>\n",
       "      <td>19.837788</td>\n",
       "      <td>27.126671</td>\n",
       "      <td>36.878845</td>\n",
       "      <td>47.493382</td>\n",
       "      <td>53.788265</td>\n",
       "      <td>67.751244</td>\n",
       "      <td>81.178638</td>\n",
       "      <td>97.226650</td>\n",
       "      <td>Palantir (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.229687</td>\n",
       "      <td>0.457227</td>\n",
       "      <td>0.684766</td>\n",
       "      <td>0.912305</td>\n",
       "      <td>1.139844</td>\n",
       "      <td>1.359961</td>\n",
       "      <td>1.585352</td>\n",
       "      <td>1.810742</td>\n",
       "      <td>2.036230</td>\n",
       "      <td>2.261523</td>\n",
       "      <td>STEMNET (fate probs.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.293682</td>\n",
       "      <td>57.660305</td>\n",
       "      <td>118.795212</td>\n",
       "      <td>206.656090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.184498</td>\n",
       "      <td>57.564194</td>\n",
       "      <td>118.801468</td>\n",
       "      <td>206.663849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.289005</td>\n",
       "      <td>57.556355</td>\n",
       "      <td>118.877754</td>\n",
       "      <td>206.747879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.183334</td>\n",
       "      <td>57.558884</td>\n",
       "      <td>118.782940</td>\n",
       "      <td>206.661770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.231007</td>\n",
       "      <td>57.593563</td>\n",
       "      <td>118.771454</td>\n",
       "      <td>206.703243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.238998</td>\n",
       "      <td>57.548656</td>\n",
       "      <td>118.774067</td>\n",
       "      <td>203.116390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.216972</td>\n",
       "      <td>57.558575</td>\n",
       "      <td>118.824356</td>\n",
       "      <td>206.643925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.321915</td>\n",
       "      <td>57.645828</td>\n",
       "      <td>118.828945</td>\n",
       "      <td>206.667171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.234562</td>\n",
       "      <td>57.536617</td>\n",
       "      <td>118.798061</td>\n",
       "      <td>206.703465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.189034</td>\n",
       "      <td>57.564209</td>\n",
       "      <td>118.845100</td>\n",
       "      <td>206.654648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Velocyto (initial/terminal)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       10000      20000       30000       40000      50000      60000  \\\n",
       "0   2.546875   9.995703   18.478613   34.937598  52.908105  74.603906   \n",
       "1   2.543652  10.012305   18.499707   34.933203  52.912793  71.765332   \n",
       "2   2.545508  10.008691   18.503223   34.910645  52.901758  71.855078   \n",
       "3   2.547559  10.010449   18.503711   34.905371  52.911816  74.246289   \n",
       "4   2.542578   8.055762   18.498633   34.903418  52.497461  69.254590   \n",
       "5   2.548047  10.009961   18.499316   34.896191  52.862988  74.674316   \n",
       "6   2.542578  10.012598   18.501562   34.529883  52.534961  73.953223   \n",
       "7   2.543164  10.001074   18.496582   34.902637  52.936914  74.673242   \n",
       "8   2.546875   9.998340   18.480957   34.927051  52.897266  74.619727   \n",
       "9   2.548242  10.009570   18.500098   34.942285  51.855664  74.625684   \n",
       "0   9.950592  12.793457   15.661877   18.409195  20.923187  23.795105   \n",
       "1  10.062252  12.644039   15.605682   18.291260  21.135040  23.841854   \n",
       "2   9.873753  12.700035   15.505363   18.320271  21.071911  23.811172   \n",
       "3   9.850586  12.476894   15.470444   18.192894  21.015621  23.868050   \n",
       "4   9.852631  12.591633   15.434055   18.259438  20.898941  23.890087   \n",
       "5   9.783054  12.772835   15.575436   18.178986  21.127518  23.770527   \n",
       "6   9.802605  12.582394   15.596725   18.301334  20.941631  23.925133   \n",
       "7   9.960964  12.554340   15.555279   18.210033  20.922523  23.722759   \n",
       "8  10.010059  12.629162   15.530094   18.214363  20.984203  23.988422   \n",
       "9   9.843887  12.633450   15.410801   18.206371  21.176380  23.723717   \n",
       "0   7.202049  10.608837   13.750217   17.179554  20.393246  23.556778   \n",
       "1   7.151718  10.584091   13.798119   17.160847  20.433270  23.615788   \n",
       "2   7.194393  10.585506   13.798489   17.190605  20.400173  23.623066   \n",
       "3   6.989883  10.586407   13.818623   17.213104  20.384781  23.646809   \n",
       "4   7.035038  10.590126   13.768742   17.176121  20.364429  23.579941   \n",
       "5   6.781567  10.598209   13.853798   17.155041  20.368736  23.600555   \n",
       "6   6.866096  10.585037   13.815777   17.179173  20.340706  23.619137   \n",
       "7   7.205921  10.590378   13.797649   17.131809  20.418839  23.579132   \n",
       "8   6.941372  10.606098   13.888046   17.208130  20.363754  23.620853   \n",
       "9   7.001259  10.591946   13.851833   17.168873  20.457500  23.622566   \n",
       "0   9.839134  14.100594   20.190571   27.104958  36.958870  47.382782   \n",
       "1   9.697010  13.953705   20.113358   27.630524  36.147858  46.388081   \n",
       "2   9.712502  14.159554   19.956238   27.713154  37.048836  47.481247   \n",
       "3   9.632271  14.168446   19.685036   26.688244  37.011677  47.612774   \n",
       "4   9.686970  14.196148   20.301609   27.638138  37.145233  46.360134   \n",
       "5   9.694248  14.225803   20.035282   27.559483  36.949924  47.908257   \n",
       "6   9.767300  14.196133   19.829868   27.671993  36.899956  47.700054   \n",
       "7   9.809479  14.226452   20.104103   27.509640  37.041740  47.697048   \n",
       "8   9.782085  14.056778   20.148449   27.644222  36.816193  47.735085   \n",
       "9   9.774555  14.256641   19.837788   27.126671  36.878845  47.493382   \n",
       "0   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "1   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "2   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "3   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "4   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "5   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "6   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "7   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "8   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "9   0.229687   0.457227    0.684766    0.912305   1.139844   1.359961   \n",
       "0  16.293682  57.660305  118.795212  206.656090        NaN        NaN   \n",
       "1  16.184498  57.564194  118.801468  206.663849        NaN        NaN   \n",
       "2  16.289005  57.556355  118.877754  206.747879        NaN        NaN   \n",
       "3  16.183334  57.558884  118.782940  206.661770        NaN        NaN   \n",
       "4  16.231007  57.593563  118.771454  206.703243        NaN        NaN   \n",
       "5  16.238998  57.548656  118.774067  203.116390        NaN        NaN   \n",
       "6  16.216972  57.558575  118.824356  206.643925        NaN        NaN   \n",
       "7  16.321915  57.645828  118.828945  206.667171        NaN        NaN   \n",
       "8  16.234562  57.536617  118.798061  206.703465        NaN        NaN   \n",
       "9  16.189034  57.564209  118.845100  206.654648        NaN        NaN   \n",
       "\n",
       "        70000       80000       90000     100000                    algorithm  \n",
       "0   96.169336  124.579785  155.448926        NaN         FateID (fate probs.)  \n",
       "1   99.785449  124.610742  149.610547        NaN         FateID (fate probs.)  \n",
       "2  100.024316  128.125293  156.439258        NaN         FateID (fate probs.)  \n",
       "3  100.044531  122.123926  155.517480        NaN         FateID (fate probs.)  \n",
       "4   97.660938  125.328516  157.385059        NaN         FateID (fate probs.)  \n",
       "5   99.302539  124.271191  159.361133        NaN         FateID (fate probs.)  \n",
       "6   99.826465  125.832617  150.316797        NaN         FateID (fate probs.)  \n",
       "7   99.693164  123.488965  155.308887        NaN         FateID (fate probs.)  \n",
       "8   98.147559  126.562402  154.169434        NaN         FateID (fate probs.)  \n",
       "9   98.507324  121.618457  157.422461        NaN         FateID (fate probs.)  \n",
       "0   26.693268   29.731873   32.612919  35.522289       CellRank (fate probs.)  \n",
       "1   26.595779   29.606895   32.405560  35.461449       CellRank (fate probs.)  \n",
       "2   26.633286   29.668568   32.489876  35.661961       CellRank (fate probs.)  \n",
       "3   26.650265   29.760723   32.752056  35.411335       CellRank (fate probs.)  \n",
       "4   26.772430   29.837692   32.494507  35.515324       CellRank (fate probs.)  \n",
       "5   26.715965   29.401337   32.600727  35.615456       CellRank (fate probs.)  \n",
       "6   26.709976   29.632725   32.601315  35.470341       CellRank (fate probs.)  \n",
       "7   26.594051   29.463581   32.692955  35.216610       CellRank (fate probs.)  \n",
       "8   26.720764   29.606361   32.229023  35.632607       CellRank (fate probs.)  \n",
       "9   26.746029   29.567711   32.581272  35.699188       CellRank (fate probs.)  \n",
       "0   26.870956   30.127693   33.379108  36.649357  CellRank (initial/terminal)  \n",
       "1   26.870312   30.155857   33.415794  36.729362  CellRank (initial/terminal)  \n",
       "2   26.887985   30.121769   33.407101  36.640907  CellRank (initial/terminal)  \n",
       "3   26.896183   30.157028   33.421349  36.690662  CellRank (initial/terminal)  \n",
       "4   26.914791   30.086304   33.424210  36.687382  CellRank (initial/terminal)  \n",
       "5   26.866615   30.159962   33.460094  36.689049  CellRank (initial/terminal)  \n",
       "6   26.887337   30.134361   33.421959  36.712910  CellRank (initial/terminal)  \n",
       "7   26.877274   30.114807   33.393360  36.735619  CellRank (initial/terminal)  \n",
       "8   26.867397   30.152420   33.376038  36.673405  CellRank (initial/terminal)  \n",
       "9   26.870785   30.157913   33.447983  36.722061  CellRank (initial/terminal)  \n",
       "0   53.557552   67.931644   80.794216  97.832241       Palantir (fate probs.)  \n",
       "1   53.728580   65.880535   79.810719  98.725491       Palantir (fate probs.)  \n",
       "2   51.716202   65.541977   80.892876  98.512798       Palantir (fate probs.)  \n",
       "3   53.680122   67.096920   81.648239  97.386101       Palantir (fate probs.)  \n",
       "4   52.136871   65.797375   81.989887  98.633186       Palantir (fate probs.)  \n",
       "5   53.662071   65.118721   80.218494  99.296864       Palantir (fate probs.)  \n",
       "6   53.705658   64.941994   83.689167  98.855030       Palantir (fate probs.)  \n",
       "7   53.934505   67.654861   80.800030  97.009544       Palantir (fate probs.)  \n",
       "8   52.446434   65.111137   80.865646  99.138912       Palantir (fate probs.)  \n",
       "9   53.788265   67.751244   81.178638  97.226650       Palantir (fate probs.)  \n",
       "0    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "1    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "2    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "3    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "4    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "5    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "6    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "7    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "8    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "9    1.585352    1.810742    2.036230   2.261523        STEMNET (fate probs.)  \n",
       "0         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "1         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "2         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "3         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "4         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "5         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "6         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "7         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "8         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  \n",
       "9         NaN         NaN         NaN        NaN  Velocyto (initial/terminal)  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:06.845746Z",
     "start_time": "2021-11-09T21:01:06.839792Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot(df):\n",
    "\n",
    "    tmp = pd.melt(df, id_vars=[alg], var_name='size', value_name='memory')\n",
    "    tmp[alg] = tmp[alg].astype('category')\n",
    "    tmp[\"memory\"] = tmp[\"memory\"].astype(np.float64)\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(6, 4), dpi=400)\n",
    "    colors = [sns.palettes.color_palette('colorblind')[i] for i in range(len(tmp[alg].cat.categories))]\n",
    "    colors[-1] = sns.palettes.color_palette('colorblind')[len(tmp[alg].cat.categories) + 3]\n",
    "    \n",
    "    flierprops = dict(marker='o', markersize=2, markerfacecolor='none', linestyle='none')\n",
    "    \n",
    "    sns.boxplot(x='size', y='memory', hue=alg, data=tmp, ax=ax, width=0.25,\n",
    "                saturation=1,\n",
    "                dodge=False,\n",
    "                flierprops=flierprops,\n",
    "                linewidth=1,\n",
    "                palette=colors)\n",
    "            \n",
    "    data = tmp.groupby([\"algorithm\", \"size\"]).median()\n",
    "    for i, cat in enumerate(tmp[alg].cat.categories):\n",
    "        med = data.loc[cat].values.squeeze()\n",
    "        med = med[med == med]\n",
    "        ax.plot(np.arange(len(med)), med, c=colors[i], zorder=-1, ls='--', lw=1.25)\n",
    "        \n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1.25, 0.5), ncol=1)\n",
    "    \n",
    "    ax.set_title(\"comparison of peak memory usage\")\n",
    "    ax.set_xlabel('number of cells in thousands')\n",
    "    ax.set_xticklabels(np.linspace(10, 100, num=10, dtype='int'))\n",
    "    ax.set_ylabel('memory (GiB)')\n",
    "    ax.margins(0.025)\n",
    "    \n",
    "    fig.show()\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:57.973573Z",
     "start_time": "2021-11-09T21:01:56.542486Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-08c3d20bd990>:34: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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dNm1alk7n+btBaGiow/04MzPTq7i4WPH19a1256w//vhDr6qO08PCwuq8k56ISPPmzUvsQ3qpqakO4Qc0focOHfI+evSoT9kxrVarfvTRRyeqE9ArLCxUTCaTR1rpJScnex08eLBcR8Dhw4fneqIWe82bN3d4PbMPHwN1aeXKlUE2W/mHdJs2bUyvvfZalQN6IiLnzp1rcPff06dPe9mPOXvswZHn+p8CAADUwGdHisRSwa+tt3T0E41CFz0AAIDLiaqqsjR5l3T+8gW3A3o6RSP/6Ha97BnzVwJ6AAAAAAAAAACgQZoxY0aW/Vhqaqr+u+++87/48cqVK4Oys7MdNnHfeeedDnOdCQkJsTVp0sRsP378+PFGFRbq0qWLw5WlMjIyvNLS0mq0wX337t2+9mNRUVGl/v7+TsNhPXr0cLi6+OHDh32Ki4s9smEpNDTUNnbs2HIdFUtLS5V33333UvfF/fv367dv3x5Q9hytVit33313Zn3VWZGrrrrKobOc1WpVdu7c6ePsfHft2LHD4Xvr7e2tdurUqaQm67qrY8eODp/X0aNHaxwsRcOzbds2h6v19e7duyAuLs7hudcdW7dudbjv1peVK1cG249NmDAht/4rcdSpUyeH14EzZ840qtcyNG67d+92eKxPmDAhu7qBd2c/g3jakSNHHC484Oz1DI4I6QEAgEbnRJ5ZNp9xfSX3ruFe0j2c37kAAAAuJycLcmTEhvfljl8/lZwS1x2Vy+oVHi07xtwvT8cPF4PO4SJfAAAAAAAAAAAADcKUKVPygoODHTqz/ec//7kUsPrwww/D7Y83adLEPHbs2Hx3b6dnz56F9mM//fRTgLNzG6pBgwYVabVah/H169fX6PP45ZdfHOY7+3pdNHTo0AL7MYvFoqxbt85jX8/58+dn2I8tXbr00v3mrbfeCrfvKDdw4MC86gaIFEWpdnc7Zzp06FDqrEPhjz/+WKOv6caNGx3md+rUqdhgMNRq/a706tWryH7s4MGDNQoe4gKlgV3E/+zZsw4JnXbt2rne6FgJTz4/r1mzJrjsx1FRUaUJCQnVCujU9nPFoEGDHJ6bU1JS9MeOHWNTAOqFsy7EzsKj7tq6dWuD+1ls//79Dq9Tzl7P4IiQHgAAaHQ+PlQoFf3WNq2Df4P7BRwAAADVY7XZ5PUDv0q3r16U788cdmuOj9ZLXrh6lGwZeY90D42q4woBAAAAAAAAAABqRq/Xq+PHj8+xH//2229DCgoKNOnp6bpNmzYF2R+fNGlSlrPAmisjRozIsx/btGlTkNFobDQbbYKCgmzt2rVzuKLj8uXLQ6u7Zmpqqm7z5s2B9uN9+/Z1uRk9JibG0rp1a4cN+e+8805Edeuoqf79+xu7detWruYTJ04YvvnmG/+SkhJlxYoVDkHP2bNnV7uLnrOQm8ViqdF9yVkwcsWKFWHVXc9kMilr164NsR/v1auXywBmbevXr5+zrosNrmtSY2QwGGz2Y2az2WPPZ3l5eQ5PyP7+/g41usNms8nHH3/s8JitD9nZ2dqdO3eWCw3dcMMNudVdT6/XOzxX1OT7FB8fb2revHmp/fgnn3zi8FgH6kJ+fr7DYz0gIKBaj/WjR496b9u2zeFnEE9z9jpV0c9F+B9CegAAoFFJzCyRfVmuL940IEovrYO4IAoAAMDl4MD5szJo3VvywI7VUmRxeI/dqeuaxUniuAflr10Gi07j/h+lAQCoLQaDwdMl1JrL6XMBAAAAAABo6ObOnZtlP1ZUVKT573//G/L++++HOgs/zZ07N7sqtzFt2rTzvr6+5TaRZ2Zmer388sseC5ZVx5gxY87bj/38889B+/bt01dnveeff76J1Wot9/XVarXqlClTHG6nrKlTpzp8z3766afgzZs3eyyANWvWLIfQ3XvvvRexbNmy4JycnHKdf5o0aWKeNGmSQ3DTXUFBQVb7seLi4hrtTR8/frzD1/zw4cM+a9asqVaXobfeeissNzfXoeNRZd/b2tS3b9/iwMDAcl+r7Oxs3dGjR73rq4bLVXBwsEMoxmg0eiwf4ewxkZ6eXq3NjMuXLw8+depUtZ7Taurzzz8Psn/Nuemmm3Kru15AQIDD16WoqKhG36eJEyc6vP698cYbTYuLixtN6ByNl/1zuojI6dOnq/VYf/bZZ5tYrQ7LeVRycrJXdnZ2udfOgIAAa0JCgkPoHI4I6QEAgEbDpqry8SHXF2LQaUQmt/evx4oAAABQF0qsFnly73qJ//oV+S3zlFtzgrwNsqj/RPl+2FxpE+iRCwoCACAiItHR0Z4uodZcTp8LAAAAAABAQ9e7d29j586dHTY/f/TRR2HOuin16tWrsHPnziVVuY3Q0FDb1KlTHUJc//73v6P27NnTaK7YdO+992Z5e3uX68xksViUu+++O7aqa+3bt0//3nvvNbEfv+GGG3LbtGnj+kriIvLXv/4109/fv9zOepvNJjNmzGjlqaDIzJkzc+yDQuvXrw95/fXXHT7HadOmZel0Dvk1t4WHhzukCtLS0moUPLvjjjvOh4eHO3zdH3zwwRizucJvh4OzZ89qn3nmmeb24126dCm+/vrr660bkE6nk4EDB+bbj69fv75awUP8T2BgoM3Ly6vcc0FhYaE2KyvLI1dzbd68ucOddPPmzYHVue8++OCDVX4+qy1ff/11cNmPAwMDrcOHDy+o7nohISEOzxUpKSk1eq544IEHMuw7KWZkZHjNmjXLY183XDmaNm3q8KBev369Q8fjyqxfv97/v//9b2TtVFV7vvvuO4fOfgMGDMj38qKBijsI6QEAgEZjy5kSOZlvcXl8WAsfifSlWwoAAEBj9/YfW+Sp3zeI2ebe1cJuatFVDtz0sMxs10cUhQvjAQAAAAAAAACAxunWW2916My2ffv2gEOHDvnYj992220O57pj4cKF6cHBweU24BQUFGhHjx7dtjaCeklJSfo333wzrKbrVCQqKsoybtw4hy5KmzdvDrznnnscQlmupKWl6caOHRtnNpsd/sD0wAMPnKtsfmhoqG3mzJkZ9uPHjx83DBkypO358+ertU+7sLBQqW7IyNfXV500aVK5+0ZpaamSlJTkV3ZMq9XK3Xff7RDYrIqWLVua7TtkFRQUaGtyP9Lr9eodd9zhUNfRo0d9Jk6c2Mpmc2ic5lRBQYFm5MiRcc666M2fP7/S721tu/HGGx06Fv7888+E9GpB69atTfZjGzdu9HN2bl0bMmRIof3fqzMyMryee+45t0M4OTk5mpEjR8ZlZmZ6JA1jMpmUX375pVxA59prr82rSTinW7duRvuxxMREP3cfz87ExsZa7r777rP245988kn43Xff3bymnclKSkqUd955J/TgwYN0vISDQYMGFdqPrV+/PqQqnXR37dplmD59euuaPA7qyk8//eTw+jRs2LBqd9690hDSAwAAjUKpVZVPjzj8XHuJn06R8XEe+d0aAAAAteyuDgnSLjCi0vOa+gTIymtnyOfXzZBmvg4X8gIAwCPi4uKka9euFf7rGBshcb5FotfU7I/E1eHj41NpfRf/xcXF1Xt9AAAAAAAAV7JZs2bl6PV6tbLzfH19bTNmzDhfndto0qSJ9fXXXz9lP56Wlubdv3//jk899VRkQUFBlfYX5+XlaT744IOQ6667Lq579+5dPvvss9Dq1FYVr7322umIiAiHTjZvvvlm0xkzZsTk5+dX+Dls2bLFZ9CgQe1PnjzpECi75ZZbMocOHepWp7XnnnsuvWvXrg7nbtu2LaBPnz4d1qxZ43YQKyUlRffoo482jY2N7fbbb7+5vdHf3r333ptZ2YUtBw0alFdZp0B3OOv++MQTTzSrSUDmySefPNu2bVuHUM/q1atDR40a1To9Pb3C9n8HDx70Hjx4cNs9e/b42x+75ppr8ubOnZtT7eKqafLkybn2Hd9++eWXwIYYzmhsunXr5vD4e/HFF5sZjcZ6v7prVFSUpUePHg6bHJ988slod8LLmzdv9k1ISOhQ9r6r1dZv04JvvvkmoLCwsNyNjh07Nrcma8bHx5vsu59mZWV5vfjii5VvCqjAwoULz3bp0sXhOejtt99u2q9fv/ZbtmxxCLhXZufOnYYHH3ywWUxMTNd58+a1Sk9Pp3UYHIwZMybfx8en3BO41WqV8ePHx/3666+Vvn6///77IUOHDm1/MYxb34/zithsNvn111/LbcDR6XTq5MmTcz1UUqNT/R7FAAAA9Wj9SaNkGV2/KTE2zlf8vbn+AAAAwOXAoPOS9/rfLNd++47Lc2a26yP/7jVSQvTV/vskAAA1plotUnTwR8nbskwiJz4rXmExsmDBgkrnWfIz5Mh9UfL4kdaSXFy/Fx2Ki4uTpUuX1uttAgAAAAAAwD3h4eHWoUOHnl+7dm2FIbeRI0fmBAYGVjvdM3369NykpKS0F154IarsuMlk0jz++OMxL730UtTo0aNzBg4cWNi7d+/ipk2bWsLDwy1Go1Fz/vx5bWZmpjYpKcknMTHRZ8+ePX7bt28PKC0trddATNOmTa3vvPPOyYkTJ7ZV1fK5xqVLl0Zu2LAhePLkydnjx4/PjYuLKw0ODraePn3aa9euXT6ffvpp6DfffBNitVodam7ZsqXpnXfeOe1uHQaDQf3888+P9+7du1NeXl65XfZHjx71GTNmTLv4+PjCkSNH5g4ZMqQgNjbW3LRpU4vJZFIyMjJ0+/fvN+zYscN306ZNgTt37gyojdBWly5dSvr165e/detWl1e5nD17do266F00bty487/99lu5IOKaNWtCu3btahg/fnxO586dTSEhIVb7gJqISEJCQrGPj4/DuI+Pj/rRRx+dGDhwYEf7+9W3334b0qlTp4CJEydmT5w48Xz79u1LIiMjrenp6bp9+/YZPvvss5Avv/wyzGQyOWwkCwkJsSxbtuxkLXzaVRYREWG99tpr877//vvgi2MZGRleGzdu9BsyZIhbgVA4d/PNN59fuXJleNmxnTt3+nfs2LHzzTffnN29e3djeHi4xT4kJiLSo0cPY2hoaK0mJf/xj3+k33zzzW3LjlmtVuWee+5p+fHHH4fdfvvtmYMHDy6KiYkxm81mJTU11Wvz5s1+X375ZfCGDRtCyj4HjBs3LnvHjh0BaWlp9dbN7csvvwwu+7G3t7c6fvz4GnXQ0uv16pAhQ3LXrVsXUnb873//e+x3330XNGzYsLy4uLiSwMBAq0ZT/qFrMBhs/fv3dwjtXlz366+/Tu7fv3+H9PT0cl+jnTt3+g8YMKBTfHx84bBhw/IGDhxY2LZt25KIiAirj4+PLScnR3v+/HntyZMnvffu3euTlJTku3nz5oDU1FR9TT5XXBnCwsKsd9xxR8bbb7/dtOx4Zmam13XXXddh7NixOVOnTs3p27dvcXh4uDU/P19z6tQpr/Xr1weuXLkyNDExsdwfB+fPn5/+2muvNavfz8K5jRs3+mVkZJQLpw4ePDivadOm9X/l0UaKkB4AAGjwCktt8mWy6/ciwgwaGd6SzdkAAACXk0FN28jc9v3kvcPbyo23DgiT9xJuluui2rqYCQBA3TOlJErulqWSv+1jseSdFRERfXQXCR/1iFvzdYGR4t91mESnJImIiKL1Eq1/uGgDI0Tj7fo9jtOnT4vJZCo3ZjAYJDo62u3a6Y4HAAAAAADQsN15553ZlYX0Zs2alVXT23n++efTNRqN+u9//7u5/bH8/Hzt8uXLI5YvX16jLkd1bcKECflPPvlk6oIFC2Lsj6Wnp3u/+uqrzV599VW3N71HRESY16xZkxwQEFCl0E67du1Kv/766yNjxoxpZx/UExHZvXu3/+7du/2feuqpqixbI3Pnzs10FdJr2rRp6cSJE2sUurlo9uzZ2c8991xUbm5uuT3pf/zxh+/ChQsr3NB16NChpPbt25c6O9a7d2/je++9d3zWrFmt7cOUubm5usWLFzdZvHhxE3fr9PPzs61cuTI5JibG4u6c2jZ9+vTssiE9EZEVK1aEENKrmQkTJuS3atXKdOLEiXJdMU+dOqV/6aWXolzNExFZs2bNkVGjRhXUdj1jx47NWb16tcPz+LZt2wK2bdvmVnfNTp06FS9ZsiSlS5cunWuzvorYbDbZsGFDcNmxvn375gcFBdU4yHjfffed+/bbb0PsQ9WbNm0K2rRpU5CreVFRUaVnzpxJcnW8VatW5k2bNh2+8cYb2zrrjHrx+bdGxQNOPPPMM+nr168PPnbsWLn7ncViUVatWhW2atWqSrtniohMmTIl66677spqKCG9FStWhNiPTZ8+PdsTtTRWhPQAAECD99WxYimyOFzI5pLJ7f3EW1vv3ekBAABQx57rNVLWph6UM8V5olEUeaDzIHmixzDx1dXbhQIBALjEfD5N8rYtl7wty6TktOPfg/O2LJOwkX8XRXHvPYrgQbPkQZ+VEpQwXfy7DBVFy59sAAAAAACoD5aCHDn+/C2eLgNVYCnI8XQJ9WrMmDH5zZo1K7XvCnRRixYtSm644YZaCfU899xzZ6+66irj/PnzW2ZnZ9faG1TOuqbVlX/+858ZYWFhlgcffLCFs+5p7urYsWPxV199daxdu3ZOQ2OVGTRoUPHWrVsPTpo0qc2BAwc8frXxqVOn5v7tb38z23fDERGZNm1alk5XO9/u0NBQ27vvvnvylltuibNaa7fJzu23354bFBSUfOedd7ayDwFWRfPmzUtXrlyZ3K9fP6fduOrL5MmTcx966CHzuXPnLn1P1qxZE2K1Wk9rtQ7ZTrhJq9XKsmXLjg8ZMqRDTZ4DatMnn3xy8vrrr9dt27bNZTfLinTv3r1o3bp1ySEhIbXa5a8yv/76q6/9c8bo0aNza2PtG264oeiuu+46a995rDa0b9++dNeuXYfuvPPO2K+//rrCkHtV1efrGRqXkJAQ29dff3106NCh7avb7XLq1KlZS5cuPXXs2LEGsQnGarXKmjVryoX0IiIizJMnT66VYP+VokG8EAEAALiSWWyV704WuzweG6CTgc0dLoACAACAy0Cgt0He6jdeuodGybZR98rzV48moAcAqFc2U6Hkblkmp56/QY4+ECMZnz3sNKAnIlKSdlBMp/a6vXZgr5sk+q6PJaD7cAJ6AAAAAADUJ9Umlvws/jWif6LWa0bB47RarUyaNMllx5KpU6fWuIteWVOmTMk7cuRI0j333JMeFBRU7ZSVTqdTBw0alLdo0aLja9euPVabNVZm3rx5Obt27Tp4/fXX57p7Ea2LAgMDrY888siZvXv3HqpuQO+iTp06lf7+++9//Otf/0pp0qSJuTprBAYGWm+//faM+Pj4GgXKvLy85NZbb820H9dqtTJv3rxavQ9Nnjw5b926dYdatmxpqs11RURuuumm/H379h2YMGFCtlarrVJYxmAw2ObMmXPuwIEDBzwd0BO58D2ZNWvWubJjZ8+e9V65cqXLLmJwT//+/Y2bN2/+o2vXrg2iK6GPj4/6yy+/HJ03b95ZnU7n9v3Wy8tLnT9//tlt27YdjoqKqveuj6tWrQou+7GiKFJbXTdFRN56660zL7zwwqmavNa4EhYWZl29evWJtWvXHr766qsLa7JWs2bNSufNm3d2z549BwYNGuR68yqueJ06dSrdvXv3weuuuy63KvNCQ0Mtb7311omPP/74VG2F5mvDypUrg86ePVtuU87MmTMz9Ho9YdUqUOxbhgK48iiK0k9EtpYd27p1q/Tr189DFQHA/7z5e778esb1+zePXh0kV0Xq67EiAAAAVIdNtcmiw7/JNU3bSMfgJlWaa7XZRKvhWlMAgPqh2qxSdHCj5G1ZJvm7vxC1xP2/6YfecJ80veXVuisOAABUy7Zt2yQhIcF+OEFV1W2eqAcAADi3e/fuD0Skq4iIwWDo+Od/jW3atDlZG+tPmDChY25urkNHKTQewcHB5lWrVv3h6Trqw7Fjx7zeeuutCGfH7r///ozY2Ng6CW8UFhYqK1euDF67dm3Qnj17/FJSUgw2m/OQZEREhLlNmzambt26FV9//fX5N954Y2FgYKDHE5V79+41LFmyJOynn34KPHTokI/FYnFI7QUHB1t69OhRNHr06Nzbb789py66VZWUlCiff/554BdffBGyc+dO/9OnT+ud7dn28fGxtWrVytSrV6/C4cOH548ZMybf19e3VjZ333HHHTEffvhhZNmx6667LvfHH3+ssxDlt99+6//1118HJSUl+Z44ccJQVFSkLSoq0jj7Phw6dCipffv2bgcjjx496r1kyZLQ77//PujgwYO+zrqm+fn52bp37144fPjwvJkzZ+Y0a9as3oNOFcnKytK2aNGiW3Fx8aXar7322ryNGzcme7Kuy8nmzZt9V65cGZyUlOSbnJxsKCgo0BYVFWnNZrPDfXDNmjVHRo0aVVCX9Rw7dszrxRdfjNy4cWPQ0aNHfeyfB/R6vdq5c+eiG264IW/evHlZMTEx5e6zb775Zlh+fn65+/pdd92VXRfPW23btu2cnJx8qWNCt27dihITEw/V9u0YjUblk08+Cd60aVPAwYMHfU6fPq0vLi7WGI1GrX1HzqioqNIzZ844v3piBfbs2WP49NNPQ3755ZeAgwcP+hYUFDhtV2kwGGyxsbEl7dq1MyYkJBTeeOONBT169Kj1wDEufz///LPvm2++Gblly5YAZ92QAwMDrfHx8YVjx449P3PmzBx/f/9LTwbnz5/XvPPOO2F259vmz5/v8qINdeH6669vs3HjxuCLHxsMBtvJkyf3NWnSpNaDtVV17NixliaTyUdExGQyXfx9ICk+Pv4OD5blFCE9AIT0ADRYJ/PM8sjm8+Lqp5UuYV7yf32CpapXwAIAAED9OpyXIXO3rJRfz52Q/pEt5acR80SjELoDADQsptQkyduyVPK2fSyW3LRqraENjJR2r56hMx4AAA0MIT0AABqHug7pAai6kpIS5eTJk145OTna0tJSxc/PzxYUFGRr0qSJpSEE8ipjsVjk+PHj3llZWVqz2az4+vraoqOjLZ4IbhUXFyvHjx/3zs3N1aqqKoGBgdaQkBBrdHS0RVMHF6ssLi5WmjVr1j0/P79cMOWTTz5JnjJlSq11xvIUm80mJ06c8MrIyNCVlpYqBoNBjYqKMtsHnBqiuXPnRi9atOjSVU21Wq0cOHCgSoFFNE55eXmaM2fO6LKzs3Xe3t5qWFiYpVWrVmat1ml+rF4dOHBA36VLly5lxx599NEz//rXv856qqbadO7cOe3p06e9iouLNRqNRg0KCrKFhIRYmzZtamkIX39cXs6ePas9d+6cLi8vT+vr62tr1qyZpXnz5g369enIkSPenTp16lo2KDtz5sxz77///mkPlnVJYwrp8VdiAADQYC0/VOQyoCcicktHfwJ6AAAADZjZZpUXk36SpxM3SIn1wvuNWzJOyruHtsm8jv09WxwAACJizk2X/G0fS+7WZVKSkljj9WzGPDGl7hOflj1roToAAAAAAADAs/R6vdqYg0M6nU7atWtX2q5dO0+XIr6+vmqXLl1K6uv2PvzwwxD7gF6zZs1KJ06c2OgDeiIiGo1G2rRpY27Tpo3Z07VU1RNPPHF2+fLlEUVFRRoREavVKgsXLmy6dOnSFE/XhroVFBRkCwoKKhWRBve8unLlymD7sZtvvjm3/iupG02aNLE2hG5guDI0bdrU2rRp00Z1f1u4cGGTsgE9X19f21NPPXVZhHTrG5csBwAADZKqqtKribcEejsP4fWP0kvrIK96rgoAAADu2p11WvqseU3+b8+3lwJ6Fz26a52kFJ73UGUAgCudraRI8rYul1Mv3ihH74+Wc58+VOOAnm+Ha6TZne9Lu9fPEdADAAAAAAAA4HHvv/9+pP3YrbfemkXHKM9r1qyZZfbs2efKjq1cuTIsLS2N5jvwmLVr1waX/bhFixYlPXv2NHmoHAD1KC0tTbdixYrwsmOzZs06FxUV1aC7/zVUvJgDAIAGSVEUGdbSVwZFG2TNsWJZe6J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\n",
      "text/plain": [
       "<Figure size 2400x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot(df)\n",
    "if save_figure:\n",
    "    fig.savefig(FIG_DIR / \"fig_5_benchmarking\" / \"memory_benchmark.pdf\", bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract the data for the table creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:08.277841Z",
     "start_time": "2021-11-09T21:01:08.267767Z"
    }
   },
   "outputs": [],
   "source": [
    "mean = df.groupby(alg).mean().T\n",
    "mean.index.name = 'size'\n",
    "mean.columns = [f\"{c} mean\" for c in mean.columns]\n",
    "\n",
    "std = df.groupby(alg).std().T\n",
    "std.index.name = 'size'\n",
    "std.columns = [f\"{c} std\" for c in std.columns]\n",
    "\n",
    "stats = pd.concat([mean, std], axis=1)\n",
    "stats.index = stats.index.astype(int)\n",
    "stats.index = np.array(stats.index / 1000, dtype='int')\n",
    "stats.index.name = '#cells (thousands)'\n",
    "stats = stats.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reorder the table columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:08.295483Z",
     "start_time": "2021-11-09T21:01:08.279116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CellRank (initial/terminal) mean</th>\n",
       "      <th>CellRank (initial/terminal) std</th>\n",
       "      <th>CellRank (fate probs.) mean</th>\n",
       "      <th>CellRank (fate probs.) std</th>\n",
       "      <th>STEMNET (fate probs.) mean</th>\n",
       "      <th>STEMNET (fate probs.) std</th>\n",
       "      <th>Palantir (fate probs.) mean</th>\n",
       "      <th>Palantir (fate probs.) std</th>\n",
       "      <th>FateID (fate probs.) mean</th>\n",
       "      <th>FateID (fate probs.) std</th>\n",
       "      <th>Velocyto (initial/terminal) mean</th>\n",
       "      <th>Velocyto (initial/terminal) std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>#cells (thousands)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>9.90</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.06</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.24</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.59</td>\n",
       "      <td>0.01</td>\n",
       "      <td>12.64</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.15</td>\n",
       "      <td>0.09</td>\n",
       "      <td>9.81</td>\n",
       "      <td>0.62</td>\n",
       "      <td>57.58</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>13.81</td>\n",
       "      <td>0.04</td>\n",
       "      <td>15.53</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.02</td>\n",
       "      <td>0.19</td>\n",
       "      <td>18.50</td>\n",
       "      <td>0.01</td>\n",
       "      <td>118.81</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>17.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>18.26</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.43</td>\n",
       "      <td>0.34</td>\n",
       "      <td>34.88</td>\n",
       "      <td>0.12</td>\n",
       "      <td>206.32</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>20.39</td>\n",
       "      <td>0.04</td>\n",
       "      <td>21.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.89</td>\n",
       "      <td>0.28</td>\n",
       "      <td>52.72</td>\n",
       "      <td>0.35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>23.61</td>\n",
       "      <td>0.03</td>\n",
       "      <td>23.83</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.38</td>\n",
       "      <td>0.55</td>\n",
       "      <td>73.43</td>\n",
       "      <td>1.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>26.88</td>\n",
       "      <td>0.02</td>\n",
       "      <td>26.68</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>98.92</td>\n",
       "      <td>1.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>30.14</td>\n",
       "      <td>0.02</td>\n",
       "      <td>29.63</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.28</td>\n",
       "      <td>1.20</td>\n",
       "      <td>124.65</td>\n",
       "      <td>1.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>33.41</td>\n",
       "      <td>0.03</td>\n",
       "      <td>32.55</td>\n",
       "      <td>0.15</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81.19</td>\n",
       "      <td>1.08</td>\n",
       "      <td>155.10</td>\n",
       "      <td>3.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>36.69</td>\n",
       "      <td>0.03</td>\n",
       "      <td>35.52</td>\n",
       "      <td>0.14</td>\n",
       "      <td>2.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.26</td>\n",
       "      <td>0.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    CellRank (initial/terminal) mean  \\\n",
       "#cells (thousands)                                     \n",
       "10                                              7.04   \n",
       "20                                             10.59   \n",
       "30                                             13.81   \n",
       "40                                             17.18   \n",
       "50                                             20.39   \n",
       "60                                             23.61   \n",
       "70                                             26.88   \n",
       "80                                             30.14   \n",
       "90                                             33.41   \n",
       "100                                            36.69   \n",
       "\n",
       "                    CellRank (initial/terminal) std  \\\n",
       "#cells (thousands)                                    \n",
       "10                                             0.15   \n",
       "20                                             0.01   \n",
       "30                                             0.04   \n",
       "40                                             0.02   \n",
       "50                                             0.04   \n",
       "60                                             0.03   \n",
       "70                                             0.02   \n",
       "80                                             0.02   \n",
       "90                                             0.03   \n",
       "100                                            0.03   \n",
       "\n",
       "                    CellRank (fate probs.) mean  CellRank (fate probs.) std  \\\n",
       "#cells (thousands)                                                            \n",
       "10                                         9.90                        0.09   \n",
       "20                                        12.64                        0.10   \n",
       "30                                        15.53                        0.08   \n",
       "40                                        18.26                        0.07   \n",
       "50                                        21.02                        0.10   \n",
       "60                                        23.83                        0.09   \n",
       "70                                        26.68                        0.06   \n",
       "80                                        29.63                        0.13   \n",
       "90                                        32.55                        0.15   \n",
       "100                                       35.52                        0.14   \n",
       "\n",
       "                    STEMNET (fate probs.) mean  STEMNET (fate probs.) std  \\\n",
       "#cells (thousands)                                                          \n",
       "10                                        0.23                        0.0   \n",
       "20                                        0.46                        0.0   \n",
       "30                                        0.68                        0.0   \n",
       "40                                        0.91                        0.0   \n",
       "50                                        1.14                        0.0   \n",
       "60                                        1.36                        0.0   \n",
       "70                                        1.59                        0.0   \n",
       "80                                        1.81                        0.0   \n",
       "90                                        2.04                        0.0   \n",
       "100                                       2.26                        0.0   \n",
       "\n",
       "                    Palantir (fate probs.) mean  Palantir (fate probs.) std  \\\n",
       "#cells (thousands)                                                            \n",
       "10                                         9.74                        0.06   \n",
       "20                                        14.15                        0.09   \n",
       "30                                        20.02                        0.19   \n",
       "40                                        27.43                        0.34   \n",
       "50                                        36.89                        0.28   \n",
       "60                                        47.38                        0.55   \n",
       "70                                        53.24                        0.81   \n",
       "80                                        66.28                        1.20   \n",
       "90                                        81.19                        1.08   \n",
       "100                                       98.26                        0.83   \n",
       "\n",
       "                    FateID (fate probs.) mean  FateID (fate probs.) std  \\\n",
       "#cells (thousands)                                                        \n",
       "10                                       2.55                      0.00   \n",
       "20                                       9.81                      0.62   \n",
       "30                                      18.50                      0.01   \n",
       "40                                      34.88                      0.12   \n",
       "50                                      52.72                      0.35   \n",
       "60                                      73.43                      1.85   \n",
       "70                                      98.92                      1.28   \n",
       "80                                     124.65                      1.96   \n",
       "90                                     155.10                      3.07   \n",
       "100                                       NaN                       NaN   \n",
       "\n",
       "                    Velocyto (initial/terminal) mean  \\\n",
       "#cells (thousands)                                     \n",
       "10                                             16.24   \n",
       "20                                             57.58   \n",
       "30                                            118.81   \n",
       "40                                            206.32   \n",
       "50                                               NaN   \n",
       "60                                               NaN   \n",
       "70                                               NaN   \n",
       "80                                               NaN   \n",
       "90                                               NaN   \n",
       "100                                              NaN   \n",
       "\n",
       "                    Velocyto (initial/terminal) std  \n",
       "#cells (thousands)                                   \n",
       "10                                             0.05  \n",
       "20                                             0.04  \n",
       "30                                             0.03  \n",
       "40                                             1.13  \n",
       "50                                              NaN  \n",
       "60                                              NaN  \n",
       "70                                              NaN  \n",
       "80                                              NaN  \n",
       "90                                              NaN  \n",
       "100                                             NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order = ['CellRank (initial/terminal)', 'CellRank (fate probs.)', 'STEMNET (fate probs.)',\n",
    "         'Palantir (fate probs.)', 'FateID (fate probs.)', \"Velocyto (initial/terminal)\"]\n",
    "stats = stats[[f\"{c} {s}\" for c in order for s in ('mean', 'std')]]\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-09T21:01:08.300790Z",
     "start_time": "2021-11-09T21:01:08.296741Z"
    }
   },
   "outputs": [],
   "source": [
    "stats.to_csv(DATA_DIR / \"benchmarking_results\" / \"suppl_tab_memory_benchmark\" / \"statistics.csv\")"
   ]
  }
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