{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CStreet: a computed <ins>C</ins>ell <ins>S</ins>tate <ins>tr</ins>ajectory inf<ins>e</ins>r<ins>e</ins>nce method for <ins>t</ins>ime-series single-cell RNA-seq data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is a tutorial written using Jupyter Notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. CStreet installation following the [tutorial](https://github.com/yw-Hua/CStreet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Input preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CStreet utilizes time-series expression levels in tab-delimited format or AnnData format as input. \n",
    "\n",
    "The cell state information can be generated using the built-in clustering function of CStreet or input by the user. \n",
    "\n",
    "We provided a small [test dataset](https://github.com/yw-Hua/CStreet/tree/master/test/test_data) containing normalized expression levels and the state information at three time points. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Operation of CStreet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Load required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cstreet import *\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Read expression matrix and cell state information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expression data: Expression matrix containing the time-series expression level as reads counts or normalized values in tab delimited format, and anndata format are accepted as the input of CStreet. (For example: ExpressionMatrix_t1.txt ExpressionMatrix_t2.txt ExpressionMatrix_t3.txt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_t1=pd.read_table('ExpressionMatrix_t1.txt',header=0, sep=\"\\t\",index_col=0) \n",
    "data_t2=pd.read_table('ExpressionMatrix_t2.txt',header=0, sep=\"\\t\",index_col=0)\n",
    "data_t3=pd.read_table('ExpressionMatrix_t3.txt',header=0, sep=\"\\t\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cell states info: The cell states information can be inputted by the user or generated using the internal clustering function of CStreet. (For example: CellStates_t1.txt CellStates_t2.txt CellStates_t3.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_t1=pd.read_table('CellStates_t1.txt',header=None, sep=\"\\t\",index_col=0) \n",
    "state_t2=pd.read_table('CellStates_t2.txt',header=None, sep=\"\\t\",index_col=0)\n",
    "state_t3=pd.read_table('CellStates_t3.txt',header=None, sep=\"\\t\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Create a new CStreet object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdata=CStreetData()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Add data into CStreet object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdata.add_new_timepoint_scdata(data_t1,list(state_t1[1]))\n",
    "cdata.add_new_timepoint_scdata(data_t2,list(state_t2[1]))\n",
    "cdata.add_new_timepoint_scdata(data_t3,list(state_t3[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AnnData object can be inputted as well. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cdata.timepoint_scdata_dict[1]=anndata_t1\n",
    "#cdata.timepoint_scdata_dict[2]=anndata_t2\n",
    "#cdata.timepoint_scdata_dict[3]=anndata_t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 200 × 2000\n",
       "    obs: 'scdata_cluster'\n",
       "    uns: 'cluster_flag'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata.timepoint_scdata_dict[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Custom parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "#Step0:basic params# \n",
       "Output_Dir=./\n",
       "Output_Name=CStreet\n",
       "\n",
       "#Step1:cell_cluster# \n",
       "CellClusterParam_PCAn=10\n",
       "CellClusterParam_k=15\n",
       "CellClusterParam_Resolution=0.1\n",
       "\n",
       "#Step2:gene and cell filter#\n",
       "Switch_DeadCellFilter=True\n",
       "Threshold_MitoPercent=0.2\n",
       "Switch_LowCellNumGeneFilter=True\n",
       "Threshold_LowCellNum=3\n",
       "Switch_LowGeneCellsFilter=True\n",
       "Threshold_LowGeneNum=200\n",
       "\n",
       "#Step3:normalize#\n",
       "Switch_Normalize=True\n",
       "Threshold_NormalizeBase=1000000\n",
       "Switch_LogTransform=True\n",
       "\n",
       "#Step4:get_graph#\n",
       "WithinTimePointParam_PCAn=10\n",
       "WithinTimePointParam_k=15\n",
       "BetweenTimePointParam_PCAn=10\n",
       "BetweenTimePointParam_k=15\n",
       "\n",
       "#Step5: calculate probability#\n",
       "ProbParam_RandomSeed=0\n",
       "ProbParam_SamplingSize=5\n",
       "\n",
       "#Step6:plot graph#\n",
       "FigureParam_FigureSize=(6, 7)\n",
       "FigureParam_LabelBoxWidth=10\n",
       "Threshold_MaxOutDegree=10\n",
       "Threshold_MinCellNumofStates=0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdata.params.FigureParam_LabelBoxWidth=8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Run CStreet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6.1 step by step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: cell_clusters start...]\n",
      "\n",
      "timepoint:1\n",
      "clusters have been given\n",
      "timepoint:2\n",
      "clusters have been given\n",
      "timepoint:3\n",
      "clusters have been given\n",
      "\n",
      "[Function: cell_clusters finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.cell_clusters(CellClusterParam_PCAn=10,CellClusterParam_k=15,CellClusterParam_Resolution=1,Switch_Normalize=True,Switch_LogTransform=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: filter_dead_cell start...]\n",
      "\n",
      "timepoint:1\n",
      "filtered out 0 cells that are detected in more than 0.2 mito percent\n",
      "\n",
      "timepoint:2\n",
      "filtered out 0 cells that are detected in more than 0.2 mito percent\n",
      "\n",
      "timepoint:3\n",
      "filtered out 0 cells that are detected in more than 0.2 mito percent\n",
      "\n",
      "\n",
      "[Function: filter_dead_cell finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.filter_dead_cell(Threshold_MitoPercent=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.var` of view, copying.\n",
      "Trying to set attribute `.var` of view, copying.\n",
      "Trying to set attribute `.var` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: filter_lowcell_gene start...]\n",
      "\n",
      "timepoint:1\n",
      "filtered out 31 genes that are detected in less than 3 cells\n",
      "\n",
      "timepoint:2\n",
      "filtered out 30 genes that are detected in less than 3 cells\n",
      "\n",
      "timepoint:3\n",
      "filtered out 37 genes that are detected in less than 3 cells\n",
      "\n",
      "\n",
      "[Function: filter_lowcell_gene finished, spent time: 0.04s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.filter_lowcell_gene(Threshold_LowCellNum=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: filter_lowgene_cells start...]\n",
      "\n",
      "timepoint:1\n",
      "filtered out 0 cells that are detected in less than 200 genes\n",
      "\n",
      "timepoint:2\n",
      "filtered out 0 cells that are detected in less than 200 genes\n",
      "\n",
      "timepoint:3\n",
      "filtered out 0 cells that are detected in less than 200 genes\n",
      "\n",
      "\n",
      "[Function: filter_lowgene_cells finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.filter_lowgene_cells(Threshold_LowGeneNum=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: normalize_data start...]\n",
      "\n",
      "Normalize data to 1000000 count ...\n",
      "\n",
      "[Function: normalize_data finished, spent time: 0.00s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.normalize_data(Threshold_NormalizeBase=1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: log_transform start...]\n",
      "\n",
      "\n",
      "[Function: log_transform finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.log_transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_knn_within start...]\n",
      "\n",
      "timepoint:1\n",
      "\n",
      "timepoint:2\n",
      "\n",
      "timepoint:3\n",
      "\n",
      "\n",
      "[Function: get_knn_within finished, spent time: 2.61s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.get_knn_within(WithinTimePointParam_PCAn=10,WithinTimePointParam_k=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_knn_between start...]\n",
      "\n",
      "timepoint between 1 and 2 \n",
      "timepoint between 2 and 3 \n",
      "\n",
      "[Function: get_knn_between finished, spent time: 3.15s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.get_knn_between(BetweenTimePointParam_PCAn=10,BetweenTimePointParam_k=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_knn_graph start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "timepoint between 1 and 2 \n",
      "timepoint between 2 and 3 \n",
      "\n",
      "[Function: get_knn_graph finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.get_knn_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: filter_knn_graph start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "timepoint between 1 and 2 \n",
      "timepoint between 2 and 3 \n",
      "\n",
      "[Function: filter_knn_graph finished, spent time: 0.05s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.filter_knn_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_state_trajectory start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "\n",
      "[Function: get_state_trajectory finished, spent time: 10.76s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.get_state_trajectory(ProbParam_SamplingSize=5,ProbParam_RandomSeed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_knn_nxG start...]\n",
      "\n",
      "timepoint:1\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 169/2000 [00:00<00:00, 102226.33it/s]\n",
      "  8%|▊         | 157/2000 [00:00<00:00, 113928.33it/s]\n",
      "  8%|▊         | 155/2000 [00:00<00:00, 87299.20it/s]\n",
      "100%|██████████| 2000/2000 [00:00<00:00, 97917.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timepoint:2\n",
      "BarnesHut Approximation  took  0.00  seconds\n",
      "Repulsion forces  took  0.00  seconds\n",
      "Gravitational forces  took  0.00  seconds\n",
      "Attraction forces  took  0.00  seconds\n",
      "AdjustSpeedAndApplyForces step  took  0.00  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:00<00:00, 94050.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timepoint:3\n",
      "BarnesHut Approximation  took  0.00  seconds\n",
      "Repulsion forces  took  0.00  seconds\n",
      "Gravitational forces  took  0.00  seconds\n",
      "Attraction forces  took  0.00  seconds\n",
      "AdjustSpeedAndApplyForces step  took  0.00  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_knn_nxG finished, spent time: 1.10s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.get_knn_nxG(Threshold_MaxOutDegree=10,Threshold_MinCellNumofStates=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: draw_nxG start...]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: draw_nxG finished, spent time: 1.01s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.draw_nxG(FigureParam_FigureSize=(6,7),FigureParam_LabelBoxWidth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: output_results start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "\n",
      "[Function: output_results finished, spent time: 0.03s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.output_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6.2 one step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdata=CStreetData()\n",
    "state_t1=pd.read_table('CellStates_t1.txt',header=None, sep=\"\\t\",index_col=0) \n",
    "state_t2=pd.read_table('CellStates_t2.txt',header=None, sep=\"\\t\",index_col=0)\n",
    "state_t3=pd.read_table('CellStates_t3.txt',header=None, sep=\"\\t\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Trying to set attribute `.var` of view, copying.\n",
      "Trying to set attribute `.var` of view, copying.\n",
      "Trying to set attribute `.var` of view, copying.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: run_cstreet start...]\n",
      "\n",
      "\n",
      "[Function: cell_clusters start...]\n",
      "\n",
      "The result folder ./CStreet exists! CStreet overwrite it. To avoid the overwriting, try the -o parameter.\n",
      "timepoint:1\n",
      "clusters have been given\n",
      "timepoint:2\n",
      "clusters have been given\n",
      "timepoint:3\n",
      "clusters have been given\n",
      "\n",
      "[Function: cell_clusters finished, spent time: 0.01s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: filter_dead_cell start...]\n",
      "\n",
      "timepoint:1\n",
      "filtered out 0 cells that are detected in more than 0.2 mito percent\n",
      "\n",
      "timepoint:2\n",
      "filtered out 0 cells that are detected in more than 0.2 mito percent\n",
      "\n",
      "timepoint:3\n",
      "filtered out 0 cells that are detected in more than 0.2 mito percent\n",
      "\n",
      "\n",
      "[Function: filter_dead_cell finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: filter_lowcell_gene start...]\n",
      "\n",
      "timepoint:1\n",
      "filtered out 0 genes that are detected in less than 3 cells\n",
      "\n",
      "timepoint:2\n",
      "filtered out 0 genes that are detected in less than 3 cells\n",
      "\n",
      "timepoint:3\n",
      "filtered out 0 genes that are detected in less than 3 cells\n",
      "\n",
      "\n",
      "[Function: filter_lowcell_gene finished, spent time: 0.04s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: filter_lowgene_cells start...]\n",
      "\n",
      "timepoint:1\n",
      "filtered out 0 cells that are detected in less than 200 genes\n",
      "\n",
      "timepoint:2\n",
      "filtered out 0 cells that are detected in less than 200 genes\n",
      "\n",
      "timepoint:3\n",
      "filtered out 0 cells that are detected in less than 200 genes\n",
      "\n",
      "\n",
      "[Function: filter_lowgene_cells finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: normalize_data start...]\n",
      "\n",
      "Normalize data to 1000000 count ...\n",
      "\n",
      "[Function: normalize_data finished, spent time: 0.00s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: log_transform start...]\n",
      "\n",
      "WARNING: adata.X seems to be already log-transformed.\n",
      "WARNING: adata.X seems to be already log-transformed.\n",
      "WARNING: adata.X seems to be already log-transformed.\n",
      "\n",
      "[Function: log_transform finished, spent time: 0.03s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: get_knn_within start...]\n",
      "\n",
      "timepoint:1\n",
      "\n",
      "timepoint:2\n",
      "\n",
      "timepoint:3\n",
      "\n",
      "\n",
      "[Function: get_knn_within finished, spent time: 2.74s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: get_knn_between start...]\n",
      "\n",
      "timepoint between 1 and 2 \n",
      "timepoint between 2 and 3 \n",
      "\n",
      "[Function: get_knn_between finished, spent time: 3.12s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: get_knn_graph start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "timepoint between 1 and 2 \n",
      "timepoint between 2 and 3 \n",
      "\n",
      "[Function: get_knn_graph finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: filter_knn_graph start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "timepoint between 1 and 2 \n",
      "timepoint between 2 and 3 \n",
      "\n",
      "[Function: filter_knn_graph finished, spent time: 0.05s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: get_state_trajectory start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "\n",
      "[Function: get_state_trajectory finished, spent time: 10.76s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: get_knn_nxG start...]\n",
      "\n",
      "timepoint:1\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 169/2000 [00:00<00:00, 112890.17it/s]\n",
      "  8%|▊         | 157/2000 [00:00<00:00, 117674.36it/s]\n",
      "  8%|▊         | 155/2000 [00:00<00:00, 88055.96it/s]\n",
      "100%|██████████| 2000/2000 [00:00<00:00, 98465.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timepoint:2\n",
      "BarnesHut Approximation  took  0.00  seconds\n",
      "Repulsion forces  took  0.00  seconds\n",
      "Gravitational forces  took  0.00  seconds\n",
      "Attraction forces  took  0.00  seconds\n",
      "AdjustSpeedAndApplyForces step  took  0.00  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2000/2000 [00:00<00:00, 93624.95it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timepoint:3\n",
      "BarnesHut Approximation  took  0.00  seconds\n",
      "Repulsion forces  took  0.00  seconds\n",
      "Gravitational forces  took  0.00  seconds\n",
      "Attraction forces  took  0.00  seconds\n",
      "AdjustSpeedAndApplyForces step  took  0.00  seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: get_knn_nxG finished, spent time: 1.03s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: draw_nxG start...]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[Function: draw_nxG finished, spent time: 0.86s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: output_results start...]\n",
      "\n",
      "timepoint:1\n",
      "timepoint:2\n",
      "timepoint:3\n",
      "\n",
      "[Function: output_results finished, spent time: 0.02s]\n",
      "\n",
      "================================================================================\n",
      "\n",
      "[Function: run_cstreet finished, spent time: 18.72s]\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "cdata.run_cstreet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pypi_test]",
   "language": "python",
   "name": "conda-env-pypi_test-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}