paper_runtime(Fig.S9).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T17:30:24.900232Z",
     "start_time": "2019-10-28T17:30:21.722693Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scvelo==0.1.24  scanpy==1.4.5.dev136+gd8f32c0  anndata==0.6.22.post2.dev82+g6310e3e  loompy==3.0.0  numpy==1.17.2  scipy==1.3.1  matplotlib==3.0.3  sklearn==0.21.3  pandas==0.25.1  \n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "from time import time\n",
    "\n",
    "import scvelo as scv\n",
    "scv.logging.print_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T10:26:23.351017Z",
     "start_time": "2019-10-28T10:26:23.315184Z"
    }
   },
   "outputs": [],
   "source": [
    "scv.settings.set_figure_params(style='scvelo')\n",
    "scv.settings.verbosity = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T10:26:25.023691Z",
     "start_time": "2019-10-28T10:26:24.984111Z"
    }
   },
   "outputs": [],
   "source": [
    "def run_scv(adata, mode='steady_state'):\n",
    "    scv.pp.filter_and_normalize(adata, min_counts=20, min_counts_u=10, n_top_genes=1000)\n",
    "    scv.pp.moments(adata, method='hnsw', n_pcs=30, n_neighbors=30)\n",
    "    \n",
    "    if mode is 'dynamical':\n",
    "        scv.tl.velocity(adata, mode=\"steady_state\", filter_genes=True)\n",
    "        scv.tl.recover_dynamics(adata)\n",
    "        scv.tl.velocity(adata, mode=mode)  \n",
    "    else:\n",
    "        scv.tl.velocity(adata, mode=mode, filter_genes=True)\n",
    "    try:\n",
    "        scv.tl.velocity_graph(adata)\n",
    "        scv.tl.velocity_embedding(adata)\n",
    "    except:\n",
    "        print(\"neighbor graph corrupted\")\n",
    "    \n",
    "def run_vcy(vlm):\n",
    "    vlm.score_detection_levels(min_expr_counts=20, min_expr_counts_U=10)\n",
    "    vlm.filter_genes(by_detection_levels=True)\n",
    "    \n",
    "    if vlm.S.shape[0] > 1000:\n",
    "        vlm.score_cv_vs_mean(1000)\n",
    "        vlm.filter_genes(by_cv_vs_mean=True)\n",
    "\n",
    "    vlm._normalize_S(relative_size=vlm.initial_cell_size, target_size=np.median(vlm.initial_cell_size))\n",
    "    vlm._normalize_U(relative_size=vlm.initial_Ucell_size, target_size=np.median(vlm.initial_Ucell_size))\n",
    "\n",
    "    vlm.S_norm = np.log1p(vlm.S_sz)\n",
    "\n",
    "    vlm.perform_PCA(n_components=30)\n",
    "    vlm.knn_imputation(n_pca_dims=30, k=30)\n",
    "    vlm.normalize_median()\n",
    "    \n",
    "    vlm.fit_gammas()\n",
    "    vlm.filter_genes_good_fit()\n",
    "    \n",
    "    vlm.predict_U()\n",
    "    vlm.calculate_velocity()\n",
    "    vlm.calculate_shift()\n",
    "    vlm.extrapolate_cell_at_t()\n",
    "    \n",
    "    vlm.estimate_transition_prob()\n",
    "    vlm.calculate_embedding_shift()\n",
    "    vlm.calculate_grid_arrows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-25T11:31:22.814042Z",
     "start_time": "2019-10-25T11:31:09.377114Z"
    }
   },
   "outputs": [],
   "source": [
    "adata_ref = scv.read('data/pancreas/endocrinogenesis.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-25T12:03:30.733476Z",
     "start_time": "2019-10-25T12:03:28.190319Z"
    }
   },
   "outputs": [],
   "source": [
    "#scvelo's steady-state and stochastic model\n",
    "np.random.seed(42)\n",
    "n_cells = np.array([1e3, 5e3, 10e3, 15e3, 20e3, 25919, 30e3, 35e3, 40e3, 50e3, 60e3], dtype=int)\n",
    "if test: n_cells = np.array([.5e3, 1e3], dtype=int)\n",
    "scv_stoch_time, scv_time = [], []\n",
    "\n",
    "for n in n_cells:\n",
    "    indices = np.random.choice(adata_ref.n_obs, n)\n",
    "    adata = adata_ref[indices]\n",
    "    adata.obs_names_make_unique()\n",
    "    \n",
    "    scv.utils.cleanup(adata, clean='all')\n",
    "    \n",
    "    adata_run = adata.copy()\n",
    "    t_init = time()\n",
    "    run_scv(adata_run)\n",
    "    scv_time.extend([(time() - t_init) / 60])\n",
    "    \n",
    "    adata_run = adata.copy()\n",
    "    t_init = time()\n",
    "    run_scv(adata_run, mode='stochastic')\n",
    "    scv_stoch_time.extend([(time() - t_init) / 60])\n",
    "    \n",
    "    np.save('data/runtime/scv_time' + str(n), scv_time[-1])\n",
    "    np.save('data/runtime/scv_stoch_time' + str(n), scv_stoch_time[-1])\n",
    "\n",
    "scv_time = np.array(scv_time)\n",
    "scv_stoch_time = np.array(scv_stoch_time)\n",
    "\n",
    "np.save('data/runtime/n_cells', n_cells)\n",
    "np.save('data/runtime/scv_time', scv_time)\n",
    "np.save('data/runtime/scv_stoch_time', scv_stoch_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-25T12:03:52.444175Z",
     "start_time": "2019-10-25T12:03:50.026911Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:object does not have the attribute `small_U_pop`, so all the unspliced will be normalized by relative size, this might cause the overinflation the unspliced counts of cells where only few unspliced molecules were detected\n",
      "WARNING:root:Nans encountered in corrcoef and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n",
      "WARNING:root:Nans encountered in corrcoef_random and corrected to 1s. If not identical cells were present it is probably a small isolated cluster converging after imputation.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-17705123488f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mt_init\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mrun_vcy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvlm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m     \u001b[0mvcy_time\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt_init\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-3-90869d0351a5>\u001b[0m in \u001b[0;36mrun_vcy\u001b[0;34m(vlm)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mvlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimate_transition_prob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0mvlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_embedding_shift\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0mvlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalculate_grid_arrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/velocyto.py/velocyto/analysis.py\u001b[0m in \u001b[0;36mcalculate_embedding_shift\u001b[0;34m(self, sigma_corr, expression_scaling, scaling_penalty)\u001b[0m\n\u001b[1;32m   1702\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransition_prob_random\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransition_prob_random\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1704\u001b[0;31m             \u001b[0munitary_vectors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# shape (2,ncells,ncells)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1705\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivide\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1706\u001b[0m                 \u001b[0munitary_vectors\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munitary_vectors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mord\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# divide by L2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#velocyto's steady-state model\n",
    "np.random.seed(42)\n",
    "n_cells = np.array([1e3, 5e3, 10e3, 15e3, 20e3, 25919, 30e3, 35e3, 40e3, 50e3, 60e3], dtype=int)  # run out of 64GB RAM at 40k cells\n",
    "if test: n_cells = np.array([.5e3, 1e3], dtype=int)\n",
    "vcy_time = []\n",
    "\n",
    "for n in n_cells:\n",
    "    indices = np.random.choice(adata_ref.n_obs, n)\n",
    "    adata = adata_ref[indices]\n",
    "    adata.obs_names_make_unique()\n",
    "    \n",
    "    scv.utils.cleanup(adata, clean='all')\n",
    "    adata_run = adata.copy()\n",
    "    vlm = scv.utils.convert_to_loom(adata_run, basis='umap')\n",
    "    \n",
    "    t_init = time()\n",
    "    run_vcy(vlm)\n",
    "    vcy_time.extend([(time() - t_init) / 60])\n",
    "    \n",
    "    np.save('data/runtime/vcy_time' + str(n), vcy_time[-1])\n",
    "\n",
    "vcy_time = np.array(vcy_time)\n",
    "np.save('data/runtime/vcy_time', vcy_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vcy_time = np.array(vcy_time)\n",
    "np.save('data/runtime/vcy_time', vcy_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-25T12:04:46.441284Z",
     "start_time": "2019-10-25T12:04:45.093603Z"
    }
   },
   "outputs": [],
   "source": [
    "#scvelo's dynamical model\n",
    "np.random.seed(42)\n",
    "n_dyn_cells = np.array([1e3, 10e3, 20e3, 25919, 30e3, 40e3, 60e3], dtype=int)\n",
    "if test: n_dyn_cells = np.array([.5e3, 1e3], dtype=int)\n",
    "scv_dyn_time = []\n",
    "\n",
    "for n in n_dyn_cells:\n",
    "    indices = np.random.choice(adata_ref.n_obs, n)\n",
    "    adata = adata_ref[indices]\n",
    "    adata.obs_names_make_unique()\n",
    "    \n",
    "    scv.utils.cleanup(adata, clean='all')\n",
    "    \n",
    "    adata_run = adata.copy()\n",
    "    t_init = time()\n",
    "    run_scv(adata_run, mode='dynamical')\n",
    "    scv_dyn_time.extend([(time() - t_init) / 60])\n",
    "    \n",
    "    np.save('data/runtime/scv_dyn_time' + str(n), scv_dyn_time[-1])\n",
    "    \n",
    "scv_dyn_time = np.array(scv_dyn_time)\n",
    "np.save('data/runtime/n_dyn_cells', n_dyn_cells)\n",
    "np.save('data/runtime/scv_dyn_time', scv_dyn_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-25T12:04:56.681669Z",
     "start_time": "2019-10-25T12:04:54.374635Z"
    }
   },
   "outputs": [],
   "source": [
    "#scvelo's steady-state and stochastic model second run with large cell numbers\n",
    "np.random.seed(2020)\n",
    "n_large_cells = np.array([100e3, 150e3, 200e3, 250e3, 300e3], dtype=int)\n",
    "if test: n_large_cells = np.array([.5e3, 1e3], dtype=int)\n",
    "scv_stoch_large_time, scv_large_time = [], []\n",
    "\n",
    "for n in n_large_cells:\n",
    "    indices = np.random.choice(adata_ref.n_obs, n)\n",
    "    adata = adata_ref[indices]\n",
    "    adata.obs_names_make_unique()\n",
    "    \n",
    "    scv.utils.cleanup(adata, clean='all')\n",
    "    \n",
    "    adata_run = adata.copy()\n",
    "    t_init = time()\n",
    "    run_scv(adata_run)\n",
    "    scv_large_time.extend([(time() - t_init) / 60])\n",
    "    \n",
    "    adata_run = adata.copy()\n",
    "    t_init = time()\n",
    "    run_scv(adata_run, mode='stochastic')\n",
    "    scv_stoch_large_time.extend([(time() - t_init) / 60])\n",
    "    \n",
    "    np.save('data/runtime/scv_large_time' + str(n), scv_large_time[-1])\n",
    "    np.save('data/runtime/scv_stoch_large_time' + str(n), scv_stoch_large_time[-1])\n",
    "\n",
    "scv_large_time = np.array(scv_large_time)\n",
    "scv_stoch_large_time = np.array(scv_stoch_large_time)\n",
    "np.save('data/runtime/n_large_cells', n_large_cells)\n",
    "np.save('data/runtime/scv_large_time', scv_large_time)\n",
    "np.save('data/runtime/scv_stoch_large_time', scv_stoch_large_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T10:27:48.367070Z",
     "start_time": "2019-10-28T10:27:48.328353Z"
    }
   },
   "outputs": [],
   "source": [
    "n_cells = scv.load('data/runtime/n_cells.npy')\n",
    "scv_time = scv.load('data/runtime/scv_time.npy')\n",
    "scv_stoch_time = scv.load('data/runtime/scv_stoch_time.npy')\n",
    "vcy_time = scv.load('data/runtime/vcy_time.npy')\n",
    "\n",
    "n_dyn_cells = scv.load('data/runtime/n_dyn_cells.npy')\n",
    "scv_dyn_time = scv.load('data/runtime/scv_dyn_time.npy')\n",
    "\n",
    "n_large_cells = scv.load('data/runtime/n_large_cells.npy')\n",
    "scv_large_time = scv.load('data/runtime/scv_large_time.npy')\n",
    "scv_stoch_large_time = scv.load('data/runtime/scv_stoch_large_time.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T10:28:21.253574Z",
     "start_time": "2019-10-28T10:28:21.213633Z"
    }
   },
   "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>n_cells</th>\n",
       "      <th>scvelo steady-state</th>\n",
       "      <th>scvelo stochastic</th>\n",
       "      <th>velocyto steady-state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.76</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>25919.0</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.91</td>\n",
       "      <td>8.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.00</td>\n",
       "      <td>14.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.15</td>\n",
       "      <td>27.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.87</td>\n",
       "      <td>1.21</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.54</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_cells  scvelo steady-state  scvelo stochastic  velocyto steady-state\n",
       "0    1000.0                 0.05               0.02                   0.06\n",
       "1    5000.0                 0.10               0.17                   0.41\n",
       "2   10000.0                 0.25               0.38                   1.33\n",
       "3   15000.0                 0.40               0.59                   2.91\n",
       "4   20000.0                 0.53               0.76                   5.10\n",
       "5   25919.0                 0.64               0.91                   8.39\n",
       "6   30000.0                 0.71               1.00                  14.26\n",
       "7   35000.0                 0.82               1.15                  27.13\n",
       "8   40000.0                 0.87               1.21                    NaN\n",
       "9   50000.0                 0.98               1.34                    NaN\n",
       "10  60000.0                 1.15               1.54                    NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcy_time_ext = np.hstack([vcy_time, np.ones(len(n_cells) - len(vcy_time))*np.nan])\n",
    "pd.DataFrame(np.vstack([n_cells, scv_time, scv_stoch_time, vcy_time_ext]).T.round(2), \n",
    "             columns=['n_cells', 'scvelo steady-state', 'scvelo stochastic', 'velocyto steady-state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T10:28:34.713133Z",
     "start_time": "2019-10-28T10:28:34.080419Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 461,
       "width": 632
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kwargs = {'show': False, 'fontsize': 20, 'size': 100, 'linewidth': 3}\n",
    "labels = ['velocyto steady-state', 'scVelo steady-state',\n",
    "          'scVelo stochastic', 'scVelo dynamical']\n",
    "\n",
    "ax = scv.pl.scatter(x=n_cells[:len(vcy_time)], y=vcy_time, **kwargs, show_polyfit=5, label=labels[0])\n",
    "ax = scv.pl.scatter(x=n_cells, y=scv_time, color='lightblue', ax=ax, **kwargs, show_polyfit=True, label=labels[1])\n",
    "ax = scv.pl.scatter(x=n_cells, y=scv_stoch_time, ax=ax, color='royalblue', **kwargs, show_polyfit=True, label=labels[2])\n",
    "ax = scv.pl.scatter(x=n_dyn_cells, y=scv_dyn_time, ax=ax, color='purple', **kwargs, show_polyfit=True, label=labels[3])\n",
    "\n",
    "pl.legend(fontsize=14)\n",
    "pl.xlabel('# cells', fontsize=20);\n",
    "pl.ylabel('runtime (min)', fontsize=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-28T10:29:25.670347Z",
     "start_time": "2019-10-28T10:29:24.806245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complexity:  0.6825109901178577\n"
     ]
    },
    {
     "data": {
      "image/png": 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71SwLXoVju+2+sErQ/klo2gf8Av71Fr9tO8yLUzay/ZC9W69hVARDetbjkiql87FwOR+FKBERkfzi8cCG8TB/BBzZbveFlIPL+sEld4P/v88e7T2SykvTNjJz40EASocE8PRVtbmpeWWcDh1Z4E0KUSIiInnN44HNU2DeCDi8ye4rURra9YUW90HAv++aO5Xh5sP52/no151kZHpwOiz6tKlC38trERF8/mU/KRgKUSIiInnFGNg2E+YOgwPr7L6gCGj7KLR6EALDcnALw7R1+xk+bRMJSWkAtK1ehiE961Or/L9/XgqOQpSIiEheiFsBs16APYvsdkAYtHkIWj8EJUrm6Bab9iczZPIGluw6CkBUyRK80L0uV9WvoNPGfZBClIiIyMU4uhPmDIUNE+y2XxC0+i9c2heCc/bQ9/HUDN6ctZVv/tiDx0Cgn4OHOtbgvx2qEeSvIwt8lUKUiIjIhTiZCAteg+WjwZMJWNDkdug0ACKic3QLt8cwduleXp+5heOpLgC6NqzAwK51iS4VnI/FS17IdYiyLKsNMNIY0+5v/c2B94FMYA3wmDHGnSdVioiI+IqMVPhjFCx8BzJO2H01usAVQ6BCgxzfZumuowyevIFN+5MBqFU+lCE96tO2Rtm8r1nyRa5ClGVZg4CbgPSzXP4YuMMYs8myrE+B64EfL75EERERH+DOhDVjYN7wM6eMV2wCXYZCtQ45vs3+pFOM+Hkzk9ckABAe5MeTV9bm9lYx+DnP/5oX8S25nYnahB2OvsveaVlWGBBujMnax8lUoCvnCFGWZQUCgdm6tN1ARER8kzGwdQbMHgyHN9t9JavA5YOgfq9/fb/dn9JcbkYv3MX7c7dzyuXGsqB3yxie7FKLMqGB/34D8Tm5ClHGmB8sy4o9y6UIIDlb+0RW37kMAAbn5t8tIiJS4P6+465EKWj/NLS4F/xyFnyMMczedIhhUzey92gqAM2rlGJIz/o0iDrfH5Xi6/LqwfJk/jqbFA4cP8/4EcCb2dphQFwe1SIiInJxzrrj7kFo90SOjysA2H4ohaFTN/Lr1sMAlAsLZGDXulzbpJKOLCgC8iREGWOSLctKsSyrbtaSXjdg3nnGp5PtuSr9hyQiIj7hrDvuboNOA3O84w7gRJqLd+ds4/NFu8n0GAKcDu69rCoPd6pBaKA2xhcVF/X/pGVZNwJRxph3gPuB0ZZlOYG1wLg8qE9ERCT/ZaTCHx/AwrcvasfdyfRMJqyK5+3Z20hMsecKLq9Tjue716Nq2X9/1YsULpYxxts1YFlWOJCUlJREeHi4t8sREZHi4qw77hpDl2G52nG3ISGJsUv3MnFVAinpmQBULRvCoO716FSnXH5ULnkoOTmZiIgIgAhjTPK/jf+T5hRFRKT4OeuOuxi4fHCOd9ydTM9kypoExi7dy5q4pNP9sWWCubNNLHe2rkKAn44sKMoUokREpHiJWwGzBsGehXa7RClo/xS0uC9HO+7Wx9uzTpNWn5l18ndaXFW/Are1jKF1tTI4HHrWtzhQiBIRkeLhInbc/TnrNGbpXtb+bdapd8sYbrwkWmc9FUMKUSIiUrSd3nH3GXhc5GbH3fr4JMYs3cukVfGczLDfZObvtLi6QUV6t6xMm2pltMO8GFOIEhGRoukCd9ylZHvWKfusU9WyIfRuWZkbmmnWSWwKUSIiUrR43LD627PsuBsK1Tqe82OadZLcUogSEZGiwRjYNhNmDYbDWa9yLRkDnQdBgxvOuuMuJT2TyavtWad18WdmnaqVDaF3yxh6NYvSrJOck0KUiIgUfrnccbcuzp51mrz6zKxTgNPB1Q0q0LtlDK2rldask/wrhSgRESm8/r7jzhkIrf/vrDvu/px1GrN0D+vjz5yn+Oes0w2XRFM6JKAgq5dCTiFKREQKn5OJ8OtIWDb6zI67xr3tHXclK/9lqD3rtIdJqxNI/dus022tYmhVVbNOcmEUokREpPA46467K+CKF/+y4y4lPZNJq+MZu3TvX2edIkO4rWUMvZpp1kkunkKUiIj4vhzsuDPGsC7baeLZZ52uaWg/66RZJ8lLClEiIuK7crDj7kSai0lZO+w2JGjWSQqOQpSIiPim+BUwM9uOu6CS0OFpaHEfxhnA2jh71mnymn/OOt3WMoaWmnWSfKYQJSIivuXoTpgzDDaMt9vOQGhtv+PuhBXKpOUJjFmyl437z8w6VY/M2mHXLJpSmnWSAqIQJSIivuEcO+5MpwGsPRHOmGn2rNMpV9ask5+DrlnnOmnWSbxBIUpERLzrHDvuUtq/wISEUoz9cs8/Zp1ua1WFXk2jNOskXqUQJSIi3uFxw+oxMO/l0zvuTIVG7GjyDJ/GxTD50wROueIBe9apW8OK9G4ZQ4vYUpp1Ep+gECUiIgXrLDvuPOGVWVTlIUbsq8/GiSnAPgBqlAvNetYpipLBmnUS36IQJSIiBSd+hR2edv8GQGZABL+UvoPn4tuQfMgBpJyedbqtVQzNq2jWSXyXQpSIiOS/v+24czsC+Mm/By8lXUVycihgzzrZ5zpp1kkKB4UoERHJPyePwK+vYZaNxvK48GAxydOekWk3kEBZAvwc9GpYkd6adZJCSCFKRETyXkYqLPkQ89tbWBknsIAF7ka8ktmbTaYKNcuFMkizTlLIKUSJiEje8bgxq7/FNftlAlIPYAHrPbGMyOzNckdjujWtyLCWMVyiWScpAhSiRETk4hnDyQ0/kzF9EKVSthMAxJmyjHTdzKYyV9K7dSyjmmrWSYoWhSgREblgxhg2r1iA/9wh1EhdRQhw3ITwoacXR+v3oU+bGjSL0ayTFE0KUSIikmtJp1zMWfQHpZe8SkeXfVxBuvFnYmAP3G378lDLekQE+3u5SpH8pRAlIiI5Yoxh5d7jTFq0luqbP6S3NZMAy43HWKwoeSWBV77AzfUaaNZJig2FKBEROa+kVBcTVsUxfsk22h35kf5+kwl3nAIgvkxbwnsMp0VsUy9XKVLwFKJEROQf7FmnY4xZso+f18bR3cznY78fqeh/FIDU0vUp0e1loqp38nKlIt6jECUiIqclpboYvyqOsUv3svXgCTo6VjPB7zvqOOx32XnCK+O4YhDBDW4Eh8PL1Yp4l0KUiEgxZ4xhxZ5jjFm6l2lr95Oe6aGhtZNxgWNoZW20xwSVxGr/FI4W94F/kJcrFvENClEiIsXU/qRTTFyVwPiVcWw7lAJAZesgw8Im0NH1qz3IGQit/ot1WT8oUcqL1Yr4HoUoEZFi5GR6JtPXH2D8qjgW7ziCMXZ/Bf8URkbO5NLjk3C4XIAFjW+FTs9BycperVnEVylEiYgUcW6PYfGORMavjGf6+gOccrlPX7u0SihPl5xDo92fYR09YXdWvxy6vAgVGnqpYpHCQSFKRKSI2nLgBONXxjFxdTwHk9NP98eWCaZX0yhuC19D2cXPwpa99oUKDaHLUKje2UsVixQuClEiIkXIoRNpTF6dwPiV8Wzcn3y6P6KEPz0aV6RXs2iaBsRhTe8Hu+2TxgmrBFcMhoY3a8edSC4oRImIFHJpLjczNx5k/Mo4ftuWiNtjP+jk77ToVLscvZpF06lOJIHpx2HeMFjxORgP+AVB28egXV8ICPHytxApfBSiREQKIY/HsHT3UcavjOOXdQc4kZ55+lqTyiW5oVkU3RtVolRIALhdsOxTmD8c0pLsQfWus5fuSlXx0jcQKfwUokRECpEdh1OYsDKeCaviiT9+6nR/VMkS9GoWxXVNo6geGXrmA9tnw/SBkLjFbpdvCNe8ArHtCrhykaJHIUpExMcdO5nBlLX2c06r9x0/3R8a6Ee3hhW5vlkULWNL43Bke/HvkR0wYyBsnW63g8tA5xegWR9wOAv4G4gUTQpRIiI+KD3TzbzNhxi/Mp55Ww7hctvPOTkdFu1rlqVXs2i61CtPkP/fAlFaEvw6Ev74CDwucPhBy/9Ch6ehREkvfBORokshSkTER9gv/T3O+JVxTF27n6RTrtPX6lcKp1ezaHo2rkRkWOA/P+xxw+pvYc5QOHnY7qvRBa4aDpG1CugbiBQvClEiIl6272gq41fGM2FVHLuPpJ7uLx8eyHVNo+jVNJraFcLOfYM9v8P0Z2D/GrtdpgZcNQJqXZnPlYsUbwpRIiJekHTKxc/r9jNhZTxLdx893V/C38k1DSpwfbMo2lYvizP7c05/d3wfzBoEG8bb7cAI6PgMtLgf/ALy+RuIiEKUiEgBcbk9/Lr1MONXxTNr40EyMj0AWBZcWr0s1zeN4uoGFQgJ/JffmjNSYdE79o/MU4AFl9wFnZ6H0Mj8/yIiAihEiYjkK2MM6+OTGb8qjsmrEzhyMuP0tZrlQunVLJrrmlaiYkSJnNwM1v8EswZDcpzdV+VSuPoVqNgon76BiJyLQpSISD5wuT18uXg345btY9uhlNP9ZUMD6Nk4il7NoqhfKRzLOs9yXXYJq+CXZ2HfH3Y7IgauHAb1rrWnskSkwClEiYjksYTjp3hs7CqW7zkGQICfgyvrladXsyguqxmJvzMX76dLOWTvuFv1DWDAPxja9YO2j4B/DmavRCTfKESJiOSheZsP0e/71RxLdREW6MfTV9emZ5MoIkr45+5Gmemw5CNYMBIyTth9DW+GK4ZARFRely0iF0AhSkQkD7jcHt6YuZWPFuwAoEFUOKNua0aVMrl8sa8x9injMwbC0Z12X6WmcPWrENMqj6sWkYuR4xBl2Qv37wAtAQ/Q1xizNNv1VsB7QAawHbjXGOPO23JFRHzP/qRTPDrmzPLdXW2qMLBbXQL9cvl6lUObYcYA2DHXboeUs2eeGvcGRy6WAEWkQORmJqoHEGmMaW1ZVlXgJ6BZtusvAc8aY+ZalvUt0BOYkHelioj4nnlbDtFvnL18Fxrox6s3NKJbo4q5u8mpYzD/FVj6KRg3OAOg9UNw2ZMQFJ4/hYvIRctNiOoATAMwxuyybBWNMfuzrq8CSmfNWIUBrnPcR0Sk0Mt0e3hj1lY+nG8v39WvZC/fxZbNxfKdOxNWfgFzX4ZTWQdu1u5m77orUz3vixaRPJWbEBUBJGVrn8jq+zNEbQM+AYYDR4EZ57qRZVmBQPaXP53nfQYiIr5lf5K9+27Zbnv5rk+bKgzsWvefLwM+n50LYPoAOLTBbkfWgatHQPXO+VCxiOSH3ISoZCD7vHI4cBzAsqwI7PDU2Bizz7Ksl4BBwAvnuNcAYHDuyxUR8a75Ww7R7/s1HD2ZcWHLd0d3wcznYfNUux1UEjo9B83vAaf2+ogUJrn5FfsrcDPwbdYzUX7GmANZ19KAVODPE+XigbrnudcI4M1s7TAgLhe1iIgUqEy3hzdnbeWDC12+Sz8Bv70Jv78P7gywnNDiXug4AIJL52PlIpJfchOiJgGdLcv6Petz/7Us60YgyhjzjmVZjwPTLctKww5Td5/rRsaYdCD9z3aOT+wVEfGCA0lpPDp25enluztbV+G5bjlcvvN4YO04mD0EUrL+3lm1g/2qlvL18q9oEcl3ljHG2zVgWVY4kJSUlER4uHaiiIjv+Pvy3Ss3NKR7o0o5+/C+ZTD9GYhfYbdLxcJVw6F2V72qRcSHJCcnExERARBhjEnO6ee0AC8ichaZbg9vzd7KqHn28l29iuF8cHsOl+9OJsLswVmvagECQqF9f/vYAr/A839WRAoNhSgRkb85kJTGY2NXsXS3fezAHa1jeL5bvX9fvvO4YeWXMPtFSDtu9zW+Da4YDGEV8rlqESloClEiItks2HqYJ8atPr18N6JXQ3o0zsHyXfxKmNYPElbZ7fINodvrENM6fwsWEa9RiBIR4ezLd6Nub0bVf1u+Sz0Kc4fB8s8BA4Hh9pEFLe7TkQUiRZx+hYtIsXcwOY1Hx65i6a5cLN95PLBmDMwaBKlH7L6GN9unjWvpTqRYUIgSkWLt16zluyMnMwgJcDLihkb0/Lflu/1r4ef+sG+J3Y6sA11fh6qX5X/BIuIzFKJEpFjKdHt4e/Y2Rs3fjjFQN2v33XmX79KSYN5wWPoJGA/4h0DHZ6H1/4HTv+CKFxGfoBAlIsXO35fvbm8Vwwvdz7N8Zwys+8F+XUvKQbuv/vVw5csQEVVAVYuIr1GIEpFiJdfLd4cGot+zAAAgAElEQVQ2wbT+sGeh3S5TA7qO1IuCRUQhSkSKh0y3h3fmbOP9eWeW70bd1pRqkaFn/0B6Cix4Bf74EDyZ4FfCPjCz7aM6MFNEAIUoESkGDibbh2cuyVq+u61VDIPOtXxnDGycCNMHwokEu69Od/t1LaWqFGDVIuLrFKJEpEj7bdth+n6Xw+W7xG3w81Owc57dLhUL17wGta4qsHpFpPBQiBKRIsntMbwzeyvv5WT5LiMVfnsdFr0LHhc4A6HdE9CuL/iXKPjiRaRQUIgSkSLnUHIaj323ij92/svynTGw5Wf45VlI2mv31egCXV+D0tUKuGoRKWwUokSkSPltm737LjHFXr4b3qsh1zY5yzEER3fBL0/Dtpl2O6IyXP0K1OkGllWwRYtIoaQQJSJFwt+X7+pUCOOD25v9c/nOlQaL3obf3gR3Ojj87R137ftDwL+8J09EJBuFKBEp9P6+fNe7ZQyDe5xl+W7bLPvB8WO77HbVDvbrWiJrFXDFIlIUKESJSKG2cFsifcetOv/y3fG9MH0AbJ5qt8Mq2kcW1L9eS3cicsEUokSkUHJ7DO/M2cZ7c7edXr4bdXszqmdfvsvMgN/fgwUjIfMUWE77PXcdn4XAMO8VLyJFgkKUiBQ6h5LTePy71fy+8whwjuW7nfPt17Uc2Wa3q1xqL92Vr1fwBYtIkaQQJSKFhjGGyWsSGDZ1I4kpGQQHOBl+fUOua5pt+S45AWY8BxvG2+2QcnDlS9DoZi3diUieUogSkUJh28ETvDBp/emHx/+xfOd2wZKPYP4rkJEClgNa3A+dBkKJkl6sXESKKoUoEfFpJ9MzeXfuNkb/totMjyHI38GjnWty32VVCfTLWr7bvQh+7g+HNtrt6JbQ7Q2o2Mh7hYtIkacQJSI+yRjD9PUHGDp1I/uT0gDoUq88g7rXo3LpYHtQyiGY+QKs/c5uB5eBK16EJreDw+GlykWkuFCIEhGfsyvxJIMnb+DXrYcBqFy6BEN61OfyuuXtAe5MWD4a5r4E6cmABc3vhs4vQHBp7xUuIsWKQpSI+Iw0l5sP5m3nowU7yXB7CHA6eLBjdR7qWP3Mzrv4FTClLxxYa7crNbWX7qIu8V7hIlIsKUSJiE+YvfEgQ6ZsIO7YKQA61IrkxZ71iS2b9SqW9BSY97L98LjxQFBJuHwQXPIfcDjPfWMRkXyiECUiXrXvaCovTtnA7E2HAKgYEcTgHvW4qn4FrD+PJNg6E6b1g6R9drvRLXDlyxAa6aWqRUQUokTES9Iz3XyyYCfvz9tOeqYHP4fFvZdV5bHONQkJzPqt6cRBmP7smTOfSsZA97egxhXeK1xEJItClIgUuF+3Hmbw5A3sSjwJQOtqpRl2bQNqls96FYsxsOprmPk8pCXZr2tp87D9upaAEC9WLiJyhkKUiBSY/UmneGnqJqat2w9AZFggz3erS8/Glc4s3SVuhymPw56FdrtiY+j5nv1PEREfohAlIvnO5fbw2cJdvDNnG6kZbhwW3NU2lie61CI8yN8elJkBi96BX0eCOx38g6HTc9DqQXDqtyoR8T36nUlE8tUfO4/wwsT1bDuUAsAlVUox7NoG1KsUfmbQvqUw+TE4vMlu17gCur0Jpap4oWIRkZxRiBKRfHHoRBrDp21i4uoEAEqHBPDsNXW4sVk0DkfW0l1aEswZCstGAwaCy8I1r0KDG/SyYBHxeQpRIpKnMt0evv5jD2/O3MqJ9EwsC25rGcNTV9WmZHDAmYGbptrvuzthPx9F0zugyzCdOC4ihYZClIjkmRV7jvHCxPVs3J8MQKPoCIZd24DGlUueGZScAD8/BZun2u3S1aDHO1C1vRcqFhG5cApRInLRjp7M4JVfNvH98jgAIkr489RVtendMgbnn0t3Hg+s+Axmv2i/787hB5c+Du2fAv8SXqxeROTCKESJyAXzeAxjl+3ltelbSDrlAuCmS6J59po6lAkNPDPw0Cb72IJ9S+x2VHPo+S6Ur++FqkVE8oZClIhckHVxSTw/cR1r4pIAqFMhjJeua0Dz2GzPNLnS4Lc3YOFb4HFBQChcPhha3Kv33YlIoacQJSK5kpTqYuTMzXy7ZC/GQGigH/261KJPmyr4OR1nBu5eCFP6wpFtdrt2V+g6EiKivVO4iEgeU4gSkRzxeAw/rYzjlV82c+RkBgDXNqnEc13rUi486MzAU8dg1iBY+ZXdDi1vh6e6PXVsgYgUKQpRIvKv9ied4qkf1rJweyIANcqFMvTa+rStXvbMIGNgwwT45Rk4ecjuu+RuuGIIlCj5j3uKiBR2ClEicl6T1yTw/IR1JKdlEuTvoO8Vtbjn0qoE+GVbuju+D6Y9Cdtm2O2yte1jC6q08U7RIiIFQCFKRM4qKdXFC5PWM3mNfeJ44+gI3rylCdUjQ88M8rhh6ScwZxi4ToIzAC57Eto9AX6B57iziEjRoBAlIv+waHsi/X9Yw/6kNJwOi0c61eCRzjXwz/7g+IF19vvuElba7Zg29uxTZG3vFC0iUsAUokTktDSXm9emb+GzRbsAiC0TzFu3NKFpTKkzgzJSYcGrsPg9MG4IjIAuL0Kzu8DhOMedRUSKHoUoEQFgfXwST4xbzbZDKQDc3iqG57rVJTgg228TO+bB1L5wbLfdrnctXPMahFUo+IJFRLxMIUqkmHN7DB8t2MHbs7fichvKhgby2o0N6Vyn/JlBaUkwYyCs+sZuh0dB19ehTlfvFC0i4gMUokSKsb1HUun3/WqW7zkGwFX1yzP8+oZ/fWXLjnkw6RFIjgMsaPkAXP4CBIZ5p2gRER+hECVSDBlj+H75PoZO2cjJDDehgX4M7lGPGy+JxvrzQMz0FPvQzOWj7XapqnDdB1ClrfcKFxHxIQpRIsVMYko6A8avY9bGgwC0jC3NGzc3pnLp4DODdi+CSQ+defapxf32w+MBIQVfsIiIj1KIEilGZm88yLPj15KYkoG/0+LJK2tz/2XVcDqyZp8yUmHuMPjjQ8BARGW49n2o1tGLVYuI+KYchyjLnuN/B2gJeIC+xpil2a7HAh8BwYAF3GmM2Z2HtYrIBTqZnslL0zYyduk+AGqXD+OtW5pQr1L4mUH7lsLE/4Mj2+12sz5w5csQFH6WO4qISG5monoAkcaY1pZlVQV+Applu/4/4CVjzHzLsroAdYDdeVapiFyQFXuO0e/71ew5koplwX3tqvLklbUJ8nfaA1xpMH941rlPHgirCD3fg5pdvFu4iIiPy02I6gBMAzDG7LJsFY0x+y3LKgFEAx0syxoEbAOeONeNLMsKBLK/E0LbfETymMvt4d052xg1bzseA5Uignj95sZ/fWlwwiqY8CAc3my3G90K17wCJUqd/aYiInJabo4XjgCSsrVPZPUBlAZqAyuMMZ2BI8Az57nXgKx7/fkjLhd1iMi/2H7oBL0+WMx7c+0AdX3TKH7p2/5MgMrMgLkvw6eX2wEqJBJuHQO9PlaAEhHJodzMRCUD2R+OCAeOZ/38CJBB1kwVMAF48Tz3GgG8ma0dhoKUyEXzeAxf/b6bEb9sJj3TQ0QJf16+vgHdG1U6M+jAepj4oP3uO4D610PXNyCkjFdqFhEprHITon4Fbga+zXomys8YcwDAGJNmWdYqoAswE+gIbDjXjYwx6UD6n+3T59KIyAU7kJTGUz+u4bdtiQBcVrMsI29sTIWIIHuAOxMWvQXzXwWPC0qUhm5vQINeXqxaRKTwsowxORt4ZndeC+zw1ReoCEQZY96xLKse8CEQgD0zdYcx5vi57ve3e4cDSUlJSYSHayeQSG5NWZPA8xPXk3TKRaCfg+e61eXO1lXO/AXl8Bb72aeElXa7djfo8TaElvNe0SIiPiI5OZmIiAiACGNMck4/l+OZKGOnrcfOc30j9sPnIlJAklJdDJq8nkmrEwBoFB3Bmzc3oUa5UHuAxw2/j4K5L4E7HYIi4JqR0Ohm0AywiMhF0WGbIoXUou2J9P9hDfuT0nBY8EinGjx6eU38nVn7RY7sgIkPwb4/7HaNLtDzXQivdO6biohIjilEiRQyaS43I2dsYfTCXQDElgnmzVua0Cwma1edxwPLPoVZgyHzFASEwdXDoemdmn0SEclDClEihciGhCSeGLearQdTALitVQzPda1LSGDWL+Vje2DSw7D7N7tdtT1cOwpKxnipYhGRokshSqQQcHsMn/y6kzdnbcHlNpQNDeS1GxvSuU55e4AxsOILmPk8ZKSAfzB0GQrN7wVHbo6DExGRnFKIEvFx+46m0u/71SzbfQyAK+uVZ0SvhpQJzTr0PykeJj8KO+bY7Zg29uxTmepeqlhEpHhQiBLxUcYYflgRx4uTN3Ayw01IgJPBPetz0yXR9tEFxsCasfDLs5CeBH5BcPkgaPUgOJzeLl9EpMhTiBLxQUdS0hkwfh0zNx4EoEVsKd68uQmVSwfbA04chCmPw9Zf7HZUc7juQ4is5aWKRUSKH4UoER8zZ9NBnvlpLYkpGfg7Lfp1qc0D7avhdGTNPq3/CX7uD6eOgcMfOg2Eto+BU7+cRUQKkn7XFfERJ9MzeWnaJsYu3QtArfKhvHVLE+pXynrP98lEmNYPNk6y2xUawfUfQfn6XqpYRKR4U4gS8QEr9hyj3/er2XMkFYD72lWl/1W1CfLPerZp0xSY0hdSE8HhB5f1h/b9wenvxapFRIo3hSgRL3K5Pbw7Zxuj5m3HY6BiRBBv3NSYtjXK2gNSj8Ivz8C67+12uXr2s0+VmnivaBERARSiRLxm+6EUnhi3mnXxSQBc16QSL17bgIgSWbNLW2fA5Mcg5QBYDri0L3R8FvwCvVi1iIj8SSFKpIAZY/jq9z0M/3kT6ZkeIkr489J1DejROOuddmlJMH0grP7GbpepaT/7FN3ce0WLiMg/KESJFKCDyWn0/2ENv21LBOCymmUZeWNjKkQE2QN2zINJj0ByHGBBm4eh8/PgX8J7RYuIyFkpRIkUkGlr9zNwwjqSTrkI9HMw4Jo69GkTi8NhQXoKzBoEy0fbg0vF2s8+VWnr1ZpFROTcFKJE8llKeiYvTFzPhFXxADSICuftW5pQo1yYPWD3Ipj0EBzbbbdb3AdXvAiBod4pWEREckQhSiQfJaakc/fny1gXn4TDgoc71eDRzjUJ8HNARirMHQZ/fAgYiKgM174P1Tp6uWoREckJhSiRfLLnyEn6fLaUPUdSKR0SwMd3XkKL2NL2xX1LYeL/wZHtdrtZH7jyZQgK917BIiKSKwpRIvlgfXwS//l8KYkpGUSXKsFX97SkWmQouNJg/nBY/B4YD4RVhJ7vQc0u3i5ZRERySSFKJI8t3JbIf79ezskMN3UrhvPl3S0oFx4ECatgwoNweLM9sNGtcM0rUKKUdwsWEZELohAlkocmr0ngye9X43IbWlcrzSd9mhPuD8wbDr++DsYNIZHQ/W2o293b5YqIyEVQiBLJI6MX7mLY1I0AdGtYkTdvaUzgiTj4+h6IX24PqncddHsTQsp4sVIREckLClEiF8kYwyvTN/Pxgp0A3NWmCoN61Me5eTJMehTSkyAwAnq8BQ1u8HK1IiKSVxSiRC6Cy+3hmZ/WMn6lfQbUU1fV5qFLK2H93A+Wf2YPim4BN4yGUlW8WKmIiOQ1hSiRC3QyPZOHvl3Jgq2HcTosRvRqyM1VTsHoLnBwvT3o0r72a1uc/t4tVkRE8pxClMgFOJKSzj1fLGNNXBJB/g5G9W7K5elz4JP+4EqF4LLQ62OocYW3SxURkXyiECWSS/uOptLns6XsSjxJyWB/vri9Lk1WvwDrvrcHVO0AvT6BsAreLVRERPKVQpRILmxMSOauz5dy+EQ6USVL8F2PElSe1hOO7gTLCZ0GQrsnwOH0dqkiIpLPFKJEcmjxjkT++9UKTqRnUqd8KN83XUv4+KHgzoDwaLjhf1CljbfLFBGRAqIQJZID09bu54lxq8lwe+hcxY+Pwz7Af/50+2LtbvaLg4NLe7dIEREpUApRIv/iq993M3jyBoyBh6sdpv+JV7EOJoAzAK58CVo+AJbl7TJFRKSAKUSJnIMxhjdmbuX9edtx4OHT2Plcvv8zLOOB0tXhps+hYmNvlykiIl6iECVyFpluDwMnrOP75XFEcozx5b+g8oFl9sVGt0C3NyAwzLtFioiIVylEifzNqQw3j4xZyZzNh+joWMNHoZ8SlHQU/IPt8NTkNm+XKCIiPkAhSiSbYyczuPfLZazdm8hzAT9wv2MKZADlG8CNn0NkLW+XKCIiPkIhSiRL/PFT9Bm9hPTEXYwPGkUjttkXWtwHV74M/kHeLVBERHyKQpQIsPlAMnd9tpRmKb/yWuCnhJEKQRHQ832o19Pb5YmIiA9SiJJib8nOIzz81WL6Zn7OHQFz7M7oFnDDaChVxbvFiYiIz1KIkmJt+voDvPPdVL52vENdv712Z7snoNNz4PT3bnEiIuLTFKKk2Prm992snTqKn/y+JNhKx4REYl3/MdS43NuliYhIIaAQJcWOMYb3p68mevFzvOa/CABP1Y44en0CYeW9XJ2IiBQWClFSrGS6PXw4djzdtw6kqvMgHpxYnQfiaNcPHA5vlyciIoWIQpQUG2kZmUz4eBAPJH5MoCOTk0EVCLntS4hp7e3SRESkEFKIkmIh6cgBtn5yF73T/wALDla6gvJ3fArBpb1dmoiIFFIKUVLkJW6Yh+fH+2hhEsnAj4SWzxN7TV+wLG+XJiIihZhClBRdHjeJ00dQaukbOPGwl4p4bvyc2AZtvF2ZiIgUAQpRUjSdOEDymLspu38xADP9OtLggf9RqVyklwsTEZGiQiFKip5ts8n48X7C049y0gQyOvwR7nzwWUqFBHi7MhERKUIUoqTocLtg7jBY9A4BwEZPFb6pPIQX7rqWEgFOb1cnIiJFjEKUFA3HdmN+vBcrfjkAX2Z2YUujpxl6Q3P8nDr/SURE8p5ClBR+GyZiJj+KlZ5MkgnmadcD1Op4Gy93qYWlHXgiIpJPFKKk8HKlwYwBsPwzLGCFpyaPux7hgZ4d6dMm1tvViYhIEZfjEGXZf6V/B2gJeIC+xpilZxl3B/C4MaZFnlUp8nfJ+2Hc7RC/Ag8WH2b2YJS5mddva07XhhW9XZ2IiBQDuZmJ6gFEGmNaW5ZVFfgJaJZ9gGVZLYC7Aa2hSP6JWwHf3QYpB0i2wng4/WFW+zdjdJ/mtKlextvViYhIMZGbENUBmAZgjNll2SoaY/YDWJZVARgGPA58cb4bWZYVCARm6wrLTdFSjK39HiY9Au50djli6HPqCdJDYxh3d0vqVQr3dnUiIlKM5GbbUgSQlK19IqsPy7ICgM+AR4CUHNxrQNa9/vwRl4s6pDjyuGHWYBh/P7jTWeLfkh6pg3CFVeHHB9sqQImISIHLTYhKBrL/SRUOHM/6eXsgBvgf8B1Q27Ksj89zrxHYAezPH9G5qEOKm7RkGNsbFr0NwE8ht3DriccICi3Jt/e3IqZMsJcLFBGR4ig3y3m/AjcD32Y9E+VnjDkAYIyZDTQAsCwrFvjRGPPfc93IGJMOpP/Z1jZ0OacjO+wAlbgF4xfEqPC+vJ7QiPAgP766pxXVI0O9XaGIiBRTuQlRk4DOlmX9nvW5/1qWdSMQZYx5J1+qk+Jtxzz44T+QdhwTVolhoc/x2a5ShAQ4+fIePQMlIiLeleMQZYwxwGM5GLcbaH4RNUlxZwws+RhmDATjxkS1YGDgM4zdmEGgn4PR/2lB05hS3q5SRESKOR22Kb4lMwOm9YNVXwNgGt3KQNe9jF11GH+nxcd3XkLrajrGQEREvE8hSnxHymH4/k7Y+ztYDkyXobx4uBNjl+7B6bB4r3dTOtYu5+0qRUREAIUo8RX719oHaCbtg8AIuPEzRu6I5ovfd2BZ8PpNjbi6gU4iFxER36EQJd63YSJM/D9wpUKZGtD7O0ats/hg/hYAXrquAdc31SkYIiLiW3JzTpRI3vJ4YN5w+OEuO0BVvxzum81nm/0YOcMOUM91rcvtrap4uVAREZF/0kyUeEd6Ckx8EDZNsdttHoErXmTcygSGTt0IQN8ranJ/+2peLFJEROTcFKKk4B3bYz//dHA9OAOg+9vQ9HYmrY7n2fHrAHigfTUev7ymlwsVERE5N4UoKVi7F9k78FKPQEg5uOUbiGnFzA0H6Pf9GoyB21vFMOCaOjrJXkREfJpClBScFV/AtCfBkwkVG8OtYyAimt+2HeaRMatwewy9mkYx7NoGClAiIuLzFKIk/7ld9unjSz+x2/V7wbWjICCYZbuP8sBXK8hwe7i6fgVeu7ERDocClIiI+D6FKMlfqUft3Xe7frXbnV+Ay54Ey2Jt3HHu+XwZp1xuOtaO5N3eTfFzasOoiIgUDgpRkn8ObYKxt8Kx3RAQCr0+gTrdANhy4AR9PlvKifRMWlcrzUd3XEKAnwKUiIgUHgpRkj+2/AI/3QcZKVCyCvQeC+XrA7Ar8SS3/28Jx1NdNKlckv/d1YIgf6eXCxYREckdhSjJW8bAwjdhzjDAQOxlcNOXEGK/NDjuWCq3f/oHiSnp1K0Yzpd3tyQ0UP8ZiohI4aM/vSTvuE7BpEdg/Y92u8V9cPUr4PQH4FByGnf8bwkJSWlUiwzh63tbEhHs78WCRURELpxClOSNpHj7AM39q8HhB9e8Bi3uPX356MkM7hi9hN1HUqlcugRj7mtN2dBALxYsIiJycRSi5OLtWwbjboeUg1CiNNzyNcS2O305Oc1Fn8+WsPVgCuXDAxlzX2sqRAR5sWAREZGLpxAlF2f1GJjyOLgzoFw9+wHyUrGnL6dmZHLP58tYH59MmZAAvr2vNZVLB3uvXhERkTyiECUXxuOGWYPg9/ftdp3ucP1HEBh2ekiay80DX61g+Z5jhAf58dW9LalRLtRLBYuIiOQthSjJvVPH4ad7Yftsu93+aeg4ABxnznlyuT08MmYlC7cnEhLg5It7WlK/UoSXChYREcl7ClGSO4nb7QM0j2wDvxJw3QfQoNdfhrg9hifGrWb2pkME+jn4310taBZTyksFi4iI5A+FKMm57bPhh3sgPQnCo6H3GPtFwtl4PIYB49cyde1+/J0WH91xCW2ql/FSwSIiIvlHIUr+nTHwxwcw83kwHqjcCm75BkLL/W2YYejUjXy/PA6HBe/e2pROdcqd46YiIiKFm0KUnF9mOkx9AlZ/a7eb3AHd3wS/f57x9PrMLXyxeLf985sac03DigVYqIiISMFSiJJzO3EQxt0BcUvBcsBVw6HVg2BZ/xg6at52Rs3bAcCw6xrQq1l0QVcrIiJSoBSi5OwSVsF3t0NyPARFwI2fQ43Lzzr080W7GDljCwADu9bhztZVCrJSERERr1CIkn/aPA1+vBcyT0HZWtD7OyhT/axDv1+2jxenbATg8ctr8kD7s48TEZH/b+/Oo6Mo8zWOf98sJAES9k02wyYowiCQiOAFxVHZFBREBAQELiiOzjje4eLMgePVO+IdxxlREZfI6q4sCu4KMqwxICACssuOLGYxkLXf+0cVEpmsTaeT7n4+5/Sxqrqq+fHrJnmserteCTYKUfJrWxfCwvHgyYNWv4XBSc6ZqEK8v/kIkxduAWD8tfH8/obW/qxURESkQilEyXmb34LFE51v4HUYCrfOhPDCPyKfbTvOQ29twlq4K7EZj/RthylkrJSIiEiwCit5FwkJG+fDoglOgOo0Aga+UGSAWrXrJJNe20iexzKoU2Mev7W9ApSIiIQchSiBr1+B9+8HLHQZCwOehbDwQndN2X+a8fNSyMn3cNMVDfjb4A6EhSlAiYhI6FGICnVrn4dlf3SWr74P+v39V3PgFfTtoTTGzP6as7n59GxTjxnDOhERro+QiIiEJv0GDGX/eho+ecRZ7vEH5z5QRVyW+/5YBiNfXU9Gdh4J8bWZNaIzURGFn60SEREJBRpYHoqshRXT4avpznqvKdBzcpEBat/JTEYkrSf1TC4dm9bk1dFdiamiACUiIqFNISrUWAtfPAqr/uGs954K1/6xyN0Pp55l+MvrOJGRTduGscwd05XqUfrYiIiI6LdhKLEWPvkzrHveWb/pr9BtUpG7/5iexfCX13EkLYsW9aoxf2wiNatW8VOxIiIilZtCVKjweODDhyElyVnv+xQkjC9y958ycxiRtJ79p87QpFYMr41LpF7sv086LCIiEqoUokKBJx8+eBC+mQ8YuGUGXHV3kbunZ+Vy96vJ7Dz+Mw3ionhtXCKNasT4r14REZEAoBAV7PLzYMl9sOUtMGEwcBZ0HFrk7lm5+Yybk8K3h9OoXa0Kr41LpHmdan4sWEREJDAoRAWz/FxnHrzvFoEJh9tfgfa3Fbl7br6HSa9tJHn/aWKjIph3TwKt6sf6sWAREZHAoRAVrPKy4Z0x8P0yCIuEIXOgXf8id/d4LH96dwtf7PiRqIgwkkZ3pX3jwiceFhEREYWo4JR7Ft4aCbs/g/AoGLoA2txY5O7WWh5bto1F3xwmPMwwc/hVJMTX9mPBIiIigUchKtjkZMKbd8HeFRARA8PegJbXFXvIs1/uZvbq/QA8NaQDvds1KP86RUREApxCVDDJzoDXh8IPqyGyGgx/Gy7tUewh89fu5+nPdgIwbcDlDOrUxA+FioiIBD6FqGCRlQYLBsOhZIiKg+HvQrPEYg9ZsukwU9//DoAHerdmTPd4f1QqIiISFBSigsGZ07DgNjjyDUTXhJGLoPFVxR6y/Psf+ePbm7EWRnVrzh9uaO2nYkVERIKDQlSgyzwJ8wbC8W+hah0YuRgadSj2kJT9p7l3wQbyPJZbOl7CtAFXYIqYfFhEREQKpxAVyDKOw7xb4MQOqFYfRr0P9dsVe8j2o+ncM+drsnI99LqsHn+/oyNhYQpQIiIiZaUQFajSj8DcAXBqN8Q2glEfQN3iL8n9cCqTkUnJpGfl0aV5LV4Y3pnI8DA/FSwiItD3BdYAABI2SURBVBJcFKICUeoBJ0D9tB9qNHXOQNVuUewhx9OzGJG0npM/Z9O2YSxJo7sSUyXcP/WKiIgEIYWoQHN6L8y9BdIOQq1LnTNQNZsVe0jamVzuTkrm4OmzNK9TlXljE6gRE+mfekVERIJUqa/lGMcMY8w6Y8waY0zCBc/3cLevMMZ8YozRHRt97eQumN3XCVB1WsGYj0oMUGdy8hgzJ5nvj2dQPzaKBWMTqR8b7aeCRUREgldZBsQMAOpZa68GhgOzLnj+JWCktbYXsBh4xCcViuP4NidAZRyFem1h9IcQd0mxh+TkeZi4YCMbD6QSFx3BvLEJNK1d1U8Fi4iIBLeyhKiewDIAa+0+nJNTjQo8f4O1do+7HAFk+6ZE4egWmNsfMn+EBlfC6GUQW/yJvnyP5aG3N7Fy5wliIsOZPSaBtg3j/FSwiIhI8CvLmKgaQFqB9Qx321EAa+0RAGNML2AScH1RL2SMiQKiCmyKLUMdoeXwBph/G2SlwiWdYMRCqFr85MDWWqYu2crSLUeJDDfMGtmZzs1r+algERGR0FCWM1HpQMFTGXFAasEdjDH3AP8E+pwLVUWYghPIzj0OlaGO0HFgvXMjzaxUaJIAdy8pMUABPP3ZTl5bfwBj4Ok7fkPPNvX8UKyIiEhoKUuIWgn0AzDGxAMR1tpj5540xjyIM1aqp3u5rzhP4JzFOvfQrLcX2r8K5g+C7HRo3h1GLoToGiUelrRqH89+uRuAx25tz4COxY+bEhEREe+U5XLeEuB6Y8xa97gJxpjBQGNgHvAksBVY4k4hsspa+5fCXsham02BMVOacuQCe5bDG8Mg7yy06AV3vgFVSh4Q/t6GQzy2dBsAD9/YhhFXNy/fOkVEREJYqUOUtdYCDxSzi7437ws7P4W3RkB+NrS+Ee6YD5Elt/azbcf503tbABjbI55J17Uq70pFRERCmub8qEy2L4U373ICVNv+MHRBqQLUur2nmPT6RvI9ltuvasKf+7bT2T0REZFyphBVWWx9D96+Gzy5cMUgGDIHIqJKPuxwGuPmppCT5+GGdg148vYrNaGwiIiIHyhEVQab34T3xoHNhw53wm2vQHjJ07LsPfEzo15N5ufsPBLja/PcXZ2I0ITCIiIifqHfuBVt4zxYNBGsBzqNhIEzIbzkoWpHUs8yMimZU5k5tG8cxyujuhAdqQmFRURE/EUhqiIlvwzv/w6w0HUcDJgBYSUHodOZOYxMWs/h1LO0qFuNOWMSiI3WhMIiIiL+pBBVUdY+Dx8+7CxfPQn6PgVhJb8dP2fnMWZ2MntOZNKoRjTzxyVSt3rJY6dERETEt8pynyjxlX/9Hb74H2e5x0PQeyqU4tt02Xn5TJifwuZDadSqGsn8sQk0rhlTzsWKiIhIYRSi/MlaWDEdvprurPeaAj0nlypA5eV7ePCNTazefYpqVcKZMyaBVvU15aCIiEhFUYjyp9XPnA9QvafBtQ+V6jBrLX9etJWPvztGlfAwXrq7Cx2b1izHQkVERKQkClH+8sMa+OJRZ/nGx+Ga35X60Okf7+CtlIOEGZgxrBPdW9UtpyJFRESktDSw3B8yT8K79zi3Meg4DLrdX+pDZ321hxe/2gvA9Ns6cHP7huVVpYiIiJSBQlR583hg0QTIOAp12zjfwivllCxvJh9g+kc7AJjSpy13dG1anpWKiIhIGShElbfV/4Tdn0NEDAyZC1HVS3XYR98e5ZFF3wIwsWdLJvRsWZ5VioiISBkpRJWnH9bCl487y33/DxpcXqrDVu06yYNvbsJjYVhCUybffFk5FikiIiLeUIgqL5mn3HFQ+dBhqDOlSylsOpjKf85PISffQ98rG/L4wCsxpbz8JyIiIv6jEFUePB5YPBEyjkCd1tDv6VKNg9p1PIPRs5M5k5NPj1Z1+cfQ3xAepgAlIiJSGSlElYc1M2DXpxARDUPmlGoc1KGfzjAyKZnUM7l0bFqTF0d2JipCEwqLiIhUVgpRvnZg/fkpXfo8CQ3bl3jIiYxsRiYlcyw9i9b1qzNndFeqRekWXiIiIpWZQpQvnTkN745xxkFdOQSuGlXiIelZuYx6NZl9JzNpXDOG+WMTqVWtih+KFRERkYuhEOUrHg8smgjph6FOK+j/jxLHQWXl5jNubgrbjqZTp1oVFoxLpGGNaD8VLCIiIhdDIcpX1j4Huz6B8Ch3HFTxkwPn5nu4//WNJO87TWxUBHPvSSC+bjX/1CoiIiIXTSHKFw4mn58Xr890aHhlsbt7PJbJ727h8+0/EhURxiujutC+cQ0/FCoiIiK+ohB1sc6cdu4H5cmDK26DzmOK3d1ay2PLtrHwm8OEhxmev+sqElvU8VOxIiIi4isKURfDWlh8H6QdhNotYMAzJY6DevbL3cxevR+Ap4Z04IbLG/ihUBEREfE1fY/+Yqx9HnZ+BOFVnHFQ0XFF7nrg1BlmrtjNm18fBGDagMsZ1KmJnwoVERERX1OI8tahFPh8mrN88xPQqGOhu+04ls4LK/bwweYjeKyz7cHerRnTPd5PhYqIiEh5UIjyxtmf4J0xzjioywdCl7H/tsuGH35i5vLdfLHjx1+29WxTj/t6tdQYKBERkSCgEFVW1sLiSZB2AGrFwy0zfhkHZa1l5a6TzFy+m/X7TgPOU33bN+LeXi31DTwREZEgohBVVutegO+XFRgHVYN8j+WT744xc8Vuth5OByAy3HBbpyZM6NmCFvVKnjtPREREAotCVFkc2gCfTXWWb/orOfU7sDjlILO+2sPeE5kAxESGMyyhGeP/I55GNWIqsFgREREpTwpRpXU2Fd4dDZ5c8tvewryc3rz8t+UcScsCIC46gtHXXMro7vHU1tx3IiIiQU8hqjSshSWTIPUAadGNGbDzdg5s2g5Avdgoxl8bz12JzakepXaKiIiECv3WL4WMlc8Ru2MpuTacEWn3csBG0rR2DBN7tuT2q5oQHRle0SWKiIiInylEFePg6TN88PEyxn0/DQw8njeCnPodeea6lvS7shER4brhu4iISKhSiCrEzuMZvLBiD8s37+L9iClUCctnbdQ1XDvkEaa1a0BYWPFTu4iIiEjwU4gqYOOBn5i5fA+fbz8OWGZGvkSzsBNkVW/C1ZNex8TUqugSRUREpJII+RBlrWXV7pPMXL6HtXtPAc4NMp9osp6+J5IhLJLoYfNAAUpEREQKCNkQ5fFYPt12jJkr9rDlUBoAEWGGQZ0a88DlZ2i6cJaz442PQePOFVipiIiIVEYhF6Jy8z0s2XSEF1bsZo97g8zoyDDu7NqM8f/RgsbROfBiT8jPgbb9IXFiBVcsIiIilVHIhKizOfm8nXKQl1bu5XDqWQBioyMY1e1SxnS/lDrVo5z7Qb0zEX7aBzWawa3P/TIvnoiIiEhBIRGiPt92nMnvbeFUZg4AdatHMe7aeIYnNiM2OvL8jilJsG0xhEXAkNkaByUiIiJFCokQ1axOVU5l5tCkVgwTerZkSOdCbpB5dDN8PMVZvuFRaNLF/4WKiIhIwAiJENWmQSyvj08k4dLahd8gMysd3hntjINq0we6TfJ7jSIiIhJYQiJEAVzTsm7hT1gLHzwIp/dCjaYwcKbGQYmIiEiJNG/Jhtnw3UJnHNTgV6Fq7YquSERERAJAaIeoo1vgo/92lntPg6YJFVuPiIiIBIzQDVHZGe44qGxoczN0u7+iKxIREZEAEpohylr44Pdweg/ENYGBL0BYaLZCREREvBOayWHjXNj6LphwjYMSERERr4ReiDq2FT6a7Cz3ngrNEiu2HhEREQlIoRWisjPgnVGQlwWtb4RrHqjoikRERCRAhU6IshaWPgSndkPsJTBwlsZBiYiIiNdKnSKMY4YxZp0xZo0xJuGC57u4z60yxjxvjAkv6rUqxDfz4du3z4+DqlanoisSERGRAFaWUzEDgHrW2quB4cCsC55/ERhjre0BVAEG+aZEHzj+HXz4X87y9X+B5t0qth4REREJeGUJUT2BZQDW2n04J6ca4SzEAnHW2u3uvkuB3xb1QsaYKGNM3LkHEOtV9aWVetC5I3mrG6D778v1jxIREZHQUJa582oAaQXWM9xtR93/phfyXFGmANPK8GdfnMtuhgkrIbqGxkGJiIiIT5QlUaQDcQXW44DUAs/FFvFcYZ7ACVnnHk3KUId36rSEakVMQiwiIiJSRmUJUSuBfgDGmHggwlp7DMBamw78bIxp5+7bz92/UNbabGtt+rkHzpkrERERkYBRlst5S4DrjTFr3eMmGGMGA42ttc8A44Ek91t5W4C3fF6tiIiISCVR6hBlrbVAkXentNZuAK7xRVEiIiIilZ1GWYuIiIh4QSFKRERExAsKUSIiIiJeUIgSERER8YJClIiIiIgXFKJEREREvKAQJSIiIuIFhSgRERERLyhEiYiIiHhBIUpERETEC2WZO6/cpaenV3QJIiIiEmK8zR/GmRKvYhljGgOHKroOERERCWlNrLWHS7tzZQlRBrgEyPDyJWJxQliTi3gN+TX11LfUT99SP31L/fQt9dO3/NXPWOCILUMwqhSX89yCS538LuRkMAAyrLW6JugD6qlvqZ++pX76lvrpW+qnb/mxn2V+bQ0sFxEREfGCQpSIiIiIF4IlRGUDj7r/Fd9QT31L/fQt9dO31E/fUj99q9L2s1IMLBcREREJNMFyJkpERETErxSiRERERLygECUiIiLihYAPUcYxwxizzhizxhiTUNE1VVbGmG7GmFXucrwx5itjzEpjzJvGmKru9tHGmBS3n+PcbVHuPquMMcuNMa3c7V3c/VYZY543xoRX3N/Of4wxYcaYl4wxq40xXxtjxhtjahtjPjLG/MsYs8wYU8/dt4+7zxpjzFR3W6Gf2aLek2B3QT/WGWOuUz8vnjGmmjFmkzGmv/p5cdy+rTTGrDDGfKp+XhxjzEPGmLXGmA3GmMkB3U9rbUA/gFuAN9zleGBjRddUGR/AVOBbIMVdXwjc5C7/GXgYqAdsB2KAKGAjzp3kHwCecPftDix1lzcA7dzll4HBFf339FMvhwLz3OVoYC/wKjDB3TYceA7nZra73b6GAZ8AXYr6zBb2nlT039VP/ewNLC7Qj++Ap9XPi+qpAea7/4b7q58X1ctw4PsLtqmf3vfzWuAzt18RwP8Gcj8D/kwU0BNYBmCt3YcTVBtVbEmV0nZgUIH1q3E+yABLgd+621Zba89aa7OBr3D6W7DHq4EuxphYIM5au/2C1wgFS4HfucsW54dsJ9wecb4X7YD91toT1loP8KG7vajPbGHvSdCz1n4BDHZXL8W5a/AvPUL99MZfgC+ALe66+um9y4FIY8zH7lmOfqifF6MP8DXwDvAlsIIA7mcwhKgaQFqB9Qx3mxRgrX0HyCuwKcL9YML5nhXVywu357jb0gvZN+hZazOttWnGmCjgdZyzULGc71FZ+3lue2HvSUiw1uYZY54GPgDm8useqZ9lYIy5FahrrZ1TYLP66b08nDMlfYE7gGdQPy9GPZyzUXcBdwKzgNoEaD+DIUSlA3EF1uOA1AqqJZDkG2POvf/nelZULy/cXsXdFlvIviHBGNMA+Bzn8uij/LpHZe3nue2FvSchw1r7EM7l4weBOqif3hoPdDbGrABuBp4AWqJ+emsvkGSt9Vhrj+FcItW/d++dAj52r3gcAXYAzQnQfgZDiFoJ9ANnYBlOGj1WsSUFhPWcP93ZD6eP64Duxpiq7lmWXsAa97n+AMaY7sAW60wC+bMxpt0FrxH0jDG1geXAP621T7ibf+kR53uxHYg3xtR3/3H3cbcX9Zkt7D0JesaY240x093VLJz/81c/vWSt7W+t7WGt7QV8DEzBOXuifnpnGM6YT4wxcUAHYC3qp7dWADcZY8LdfrbDGQMVmP2s6EFmPhikZoAZOB/qr4HuFV1TZX3gjDc5N7C8BU4QWA0sAqq52+92+7gRuNfdFo1z2Wq1+7jM3d4ZJ2Stx/khE17Rf0c/9fFJ4CTOD4Nzjy441+lX4YxFaeDuexOQDKQAj7nbCv3MFvWeBPsD50sM893erQPuAeqqnz7p7RycX07qp/c9jACS3J91a4Bb1c+L7uljbo824lzWC9h+atoXERERES8Ew+U8EREREb9TiBIRERHxgkKUiIiIiBcUokRERES8oBAlIiIi4gWFKBEREREvKESJiIiIeEEhSkRERMQLClEiIiIiXlCIEhEREfGCQpSIiIiIF/4f/rFjTTAb6d8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 211,
       "width": 296
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complexity:  0.8324485537356342\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 211,
       "width": 295
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complexity:  3.9414349162779394\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 211,
       "width": 295
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def expo(x, a, b, c):\n",
    "        o = 1 if np.isscalar(x) else np.ones(len(x))\n",
    "        return a * x ** b + c * o\n",
    "    \n",
    "def fit(x, y, n_extrapol=None):\n",
    "    def do_fit(function, x, y):\n",
    "        popt, pcov = curve_fit(eval(function), x, y, maxfev=10000)\n",
    "        print('complexity: ', popt[1])\n",
    "        #print('Standard error fit: ', np.sqrt(np.diag(pcov)))\n",
    "        return popt, pcov\n",
    "    \n",
    "    ax=pl.figure(dpi=50).gca()\n",
    "    x = np.array(x)\n",
    "    ax.plot(x, y)\n",
    "\n",
    "    popt, pcov = do_fit('expo', x, y)\n",
    "    ax.plot(x, expo(x, popt[0], popt[1], popt[2]))\n",
    "    pl.legend(['Data', 'fit'])\n",
    "    \n",
    "    pl.show()\n",
    "    if n_extrapol is not None: \n",
    "        return expo(n_extrapol, popt[0], popt[1], popt[2])\n",
    "    \n",
    "fit(n_cells, scv_time)\n",
    "fit(n_dyn_cells, scv_dyn_time)\n",
    "fit(n_cells[:len(vcy_time)], vcy_time)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}