clusterStats_balance_reordered.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import os\n",
    "\n",
    "import scanpy\n",
    "import numpy as np\n",
    "import scipy.sparse as sp\n",
    "\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import gc\n",
    "from skimage import io\n",
    "import umap\n",
    "from sklearn.cluster import MiniBatchKMeans,AgglomerativeClustering\n",
    "from sklearn.decomposition import PCA\n",
    "from util.plot_new import plotembeddingbyCT,plotembeddingbyCT_contrast\n",
    "import cv2\n",
    "import sklearn.metrics \n",
    "\n",
    "from util.plot_new import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "anno=pd.read_csv('/media/xinyi/dcis2idc/data/Supplementary Table 1_v1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "imaged=np.sum(anno.to_numpy()[:,-4:],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(anno['patient_id'][imaged>0]).size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "radius=48\n",
    "datadir='/media/xinyi/dcis2idc1/data'\n",
    "with open(os.path.join(datadir,'processed','train_cnnvae_names'), 'rb') as input:\n",
    "    allImgNames=pickle.load(input)\n",
    "name='exp0'\n",
    "plotsavepath='/media/xinyi/dcis2idc1/plots/cnnvae'+name\n",
    "modelsavepath='/media/xinyi/dcis2idc1/models/cnnvae'+name\n",
    "plottype='umap'\n",
    "sampledir=plotsavepath\n",
    "savedir=os.path.join(sampledir,'embedding_'+plottype+'_reordered')\n",
    "clustersavedir=os.path.join(sampledir,'cluster_reordered')\n",
    "clustersavedir_old=os.path.join(sampledir,'cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(datadir,'processed','train_cnnvae_coord'), 'rb') as output:\n",
    "    coordlist=pickle.load(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(datadir,'processed','train_cnnvae_cellLabels'), 'rb') as output:\n",
    "    cellIDlist=pickle.load(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCTcomp_hist(labels,ctlist,savepath,savenamecluster,byCT,addname=''):\n",
    "    res=np.zeros((np.unique(labels).size,np.unique(ctlist).size))\n",
    "    for li in range(res.shape[0]):\n",
    "        l=np.unique(labels)[li]\n",
    "        nl=np.sum(labels==l)\n",
    "        ctlist_l=ctlist[labels==l]\n",
    "        for ci in range(res.shape[1]):\n",
    "            c=np.unique(ctlist)[ci]\n",
    "            res[li,ci]=np.sum(ctlist_l==c)\n",
    "#             res[li,ci]=np.sum(ctlist_l==c)/nl\n",
    "    if not byCT:\n",
    "        addname+=''\n",
    "        for li in range(res.shape[0]):\n",
    "            l=np.unique(labels)[li]\n",
    "            nl=np.sum(labels==l)\n",
    "            res[li]=res[li]/nl\n",
    "    else:\n",
    "        addname+='_normbyCT'\n",
    "        for ci in range(res.shape[1]):\n",
    "            c=np.unique(ctlist)[ci]\n",
    "            nc=np.sum(ctlist==c)\n",
    "            res[:,ci]=res[:,ci]/nc\n",
    "    \n",
    "    fig, ax = plt.subplots(nrows=res.shape[0], ncols=1,figsize=(10, 10),sharex=True,sharey=True)\n",
    "    for r in range(res.shape[0]):\n",
    "        ax[r].bar(np.arange(res.shape[1]),res[r])\n",
    "        ax[r].set_xticks(np.arange(np.unique(ctlist).size))\n",
    "        ax[r].set_xticklabels(np.unique(ctlist))\n",
    "        ax[r].set_ylim(0,1)\n",
    "        ax[r].set_ylabel(np.unique(labels)[r])\n",
    "        \n",
    "#         ax.set_yticks(np.arange(np.unique(labels).size))\n",
    "#         ax.set_yticklabels(np.unique(labels))\n",
    "#         ax.set_xticks(np.arange(np.unique(ctlist).size))\n",
    "#         ax.set_xticklabels(np.unique(ctlist))\n",
    "#         plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",rotation_mode=\"anchor\")\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(os.path.join(savepath,savenamecluster+addname+'.jpg'))\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCTcomp_hist_cont(labels,ctlist,samplelist,savepath,savenamecluster,addname=''):\n",
    "    \n",
    "    fig, ax = plt.subplots(nrows=np.unique(labels).size, ncols=1,figsize=(10, 10),sharex=True,sharey=True)\n",
    "    for r in range(np.unique(labels).size):\n",
    "        counts=[]\n",
    "        for s in np.unique(samplelist[labels==np.unique(labels)[r]]):\n",
    "            sridx=np.logical_and(labels==np.unique(labels)[r],samplelist==s)\n",
    "            counts.append(np.sum(ctlist[sridx])/np.sum(sridx))\n",
    "        ax[r].hist(counts,bins=10,range=(0,1))\n",
    "        ax[r].set_ylabel(np.unique(labels)[r])\n",
    "        \n",
    "#         ax.set_yticks(np.arange(np.unique(labels).size))\n",
    "#         ax.set_yticklabels(np.unique(labels))\n",
    "#         ax.set_xticks(np.arange(np.unique(ctlist).size))\n",
    "#         ax.set_xticklabels(np.unique(ctlist))\n",
    "#         plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",rotation_mode=\"anchor\")\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(os.path.join(savepath,savenamecluster+addname+'.jpg'))\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getHistMatrix(labels,ctlist):\n",
    "    res=np.zeros((np.unique(labels).size,np.unique(ctlist).size))\n",
    "    for li in range(res.shape[0]):\n",
    "        l=np.unique(labels)[li]\n",
    "        nl=np.sum(labels==l)\n",
    "        ctlist_l=ctlist[labels==l]\n",
    "        for ci in range(res.shape[1]):\n",
    "            c=np.unique(ctlist)[ci]\n",
    "            res[li,ci]=np.sum(ctlist_l==c)\n",
    "#             res[li,ci]=np.sum(ctlist_l==c)/nl\n",
    "        res[li]=res[li]/nl\n",
    "    return res\n",
    "\n",
    "def getHistMatrix_clusters(labels,ctlist,nrow=8,ncol=8):\n",
    "    res=np.zeros((nrow,ncol))\n",
    "    for li in range(res.shape[0]):\n",
    "        l=li\n",
    "        nl=np.sum(labels==l)\n",
    "        ctlist_l=ctlist[labels==l]\n",
    "        for ci in range(res.shape[1]):\n",
    "            c=ci\n",
    "            res[l,c]=np.sum(ctlist_l==c)\n",
    "#             res[li,ci]=np.sum(ctlist_l==c)/nl\n",
    "        if nl!=0:\n",
    "            res[li]=res[li]/nl\n",
    "    return res\n",
    "\n",
    "def plotCTcomp_hist_withRandom(labels,ctlist,sampledlabels,sampledctlist,savepath,savenamecluster,savenamestats,addname='',distMeasure=['euclidean','tv']):\n",
    "    res=getHistMatrix(labels,ctlist)    \n",
    "    nRandomsamples=sampledlabels.shape[0]\n",
    "    resRandom=np.zeros((nRandomsamples,res.shape[0],res.shape[1]))\n",
    "    print(sampledlabels.shape)\n",
    "    print(sampledctlist.shape)\n",
    "    for rand in range(nRandomsamples):\n",
    "        resRandom[rand]=getHistMatrix(sampledlabels[rand],sampledctlist[rand])\n",
    "    resRandomMean=np.mean(resRandom,axis=0,keepdims=True)\n",
    "    resRandomStd=np.std(resRandom,axis=0,keepdims=True)\n",
    "    \n",
    "    fig, ax = plt.subplots(nrows=res.shape[0], ncols=1,figsize=(10, 10),sharex=True,sharey=True)\n",
    "    for r in range(res.shape[0]):\n",
    "        ax[r].bar(np.arange(res.shape[1])-0.2,res[r],0.4,label='observed')\n",
    "        ax[r].bar(np.arange(res.shape[1])+0.2,resRandomMean[0,r],0.4,label='random',yerr=resRandomStd[0,r],capsize=2,ecolor='red')\n",
    "        ax[r].set_xticks(np.arange(np.unique(ctlist).size))\n",
    "        ax[r].set_xticklabels(np.unique(ctlist))\n",
    "        ax[r].set_ylim(0,1)\n",
    "        ax[r].set_ylabel(np.unique(labels)[r])\n",
    "        ax[r].legend()\n",
    "#         ax.set_yticks(np.arange(np.unique(labels).size))\n",
    "#         ax.set_yticklabels(np.unique(labels))\n",
    "#         ax.set_xticks(np.arange(np.unique(ctlist).size))\n",
    "#         ax.set_xticklabels(np.unique(ctlist))\n",
    "#         plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",rotation_mode=\"anchor\")\n",
    "    fig.tight_layout()\n",
    "    plt.savefig(os.path.join(savepath,savenamecluster+addname+'.jpg'))\n",
    "    plt.close()\n",
    "    \n",
    "    #compute significance\n",
    "    significanceRes=pd.DataFrame(np.zeros((np.unique(labels).size,len(distMeasure))),index=np.unique(labels),columns=distMeasure)\n",
    "    for c in range(len(distMeasure)):\n",
    "        dist_c=distMeasure[c]\n",
    "        if dist_c=='euclidean':\n",
    "            distRand=np.linalg.norm(resRandom-resRandomMean,ord=2,axis=2)\n",
    "            distTrue=np.linalg.norm(res-resRandomMean[0],ord=2,axis=1).reshape((1,-1))\n",
    "            distCompare=np.greater(distRand,distTrue)\n",
    "            distCompare=np.sum(distCompare,axis=0)\n",
    "            significanceRes['euclidean']=(distCompare+1)/(nRandomsamples+1)            \n",
    "        if dist_c=='tv': #omitting 1/2 since results do not change\n",
    "            distRand=np.linalg.norm(resRandom-resRandomMean,ord=1,axis=2)\n",
    "            distTrue=np.linalg.norm(res-resRandomMean[0],ord=1,axis=1).reshape((1,-1))\n",
    "            distCompare=np.greater(distRand,distTrue)\n",
    "            distCompare=np.sum(distCompare,axis=0)\n",
    "            significanceRes['tv']=(distCompare+1)/(nRandomsamples+1)\n",
    "    significanceRes.to_csv(os.path.join(savepath,savenamecluster+addname+'_significance.csv'))\n",
    "    #calculate individual p-values\n",
    "    distRand=np.abs(resRandom-resRandomMean)\n",
    "    distTrue=np.abs(res-resRandomMean[0]).reshape((1,res.shape[0],res.shape[1]))\n",
    "    distCompare=np.greater(distRand,distTrue)\n",
    "    distCompare=(np.sum(distCompare,axis=0)+1)/(nRandomsamples+1)\n",
    "    np.savetxt(os.path.join(savepath,savenamecluster+addname+'_significanceSeparated.csv'),distCompare)\n",
    "#     return significanceRes,distCompare\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotCTcomp_hist_withRandom_bonferroni(labels,ctlist,sampledlabels,sampledctlist,savepath,savenamecluster,savenamestats,addname='',distMeasure=['euclidean','tv']):\n",
    "    res=getHistMatrix(labels,ctlist)    \n",
    "#     actualSamples=sampledlabels.shape[0]\n",
    "    resRandom=np.zeros((actualSamples,res.shape[0],res.shape[1]))\n",
    "#     print(sampledlabels.shape)\n",
    "#     print(sampledctlist.shape)\n",
    "    rand=0\n",
    "    for imgNidx in range(len(sampledlabels)):\n",
    "        for randIdx in range(sampledlabels[imgNidx].shape[0]):\n",
    "            resRandom[rand]=getHistMatrix(sampledlabels[imgNidx][randIdx],sampledctlist[imgNidx][randIdx])\n",
    "            rand+=1\n",
    "    print(np.sum(np.sum(resRandom,axis=(1,2))==0))\n",
    "    resRandomMean=np.mean(resRandom,axis=0,keepdims=True)\n",
    "    resRandomStd=np.std(resRandom,axis=0,keepdims=True)\n",
    "    \n",
    "    \n",
    "    #compute significance\n",
    "    significanceRes=pd.DataFrame(np.zeros((np.unique(labels).size,len(distMeasure))),index=np.unique(labels),columns=distMeasure)\n",
    "    for c in range(len(distMeasure)):\n",
    "        dist_c=distMeasure[c]\n",
    "        if dist_c=='euclidean':\n",
    "            distRand=np.linalg.norm(resRandom-resRandomMean,ord=2,axis=2)\n",
    "            distTrue=np.linalg.norm(res-resRandomMean[0],ord=2,axis=1).reshape((1,-1))\n",
    "            distCompare=np.greater(distRand,distTrue)\n",
    "            distCompare=np.sum(distCompare,axis=0)\n",
    "            significanceRes['euclidean']=(distCompare+1)/(actualSamples+1)            \n",
    "        if dist_c=='tv': #omitting 1/2 since results do not change\n",
    "            distRand=np.linalg.norm(resRandom-resRandomMean,ord=1,axis=2)\n",
    "            distTrue=np.linalg.norm(res-resRandomMean[0],ord=1,axis=1).reshape((1,-1))\n",
    "            distCompare=np.greater(distRand,distTrue)\n",
    "            distCompare=np.sum(distCompare,axis=0)\n",
    "            significanceRes['tv']=(distCompare+1)/(actualSamples+1)\n",
    "    significanceRes.to_csv(os.path.join(savepath,savenamecluster+addname+'_significance.csv'))\n",
    "    #calculate individual p-values\n",
    "    distRand=np.abs(resRandom-resRandomMean)\n",
    "    distTrue=np.abs(res-resRandomMean[0]).reshape((1,res.shape[0],res.shape[1]))\n",
    "    distCompare=np.greater(distRand,distTrue)\n",
    "    distCompare=(np.sum(distCompare,axis=0)+1)/(actualSamples+1)\n",
    "    np.savetxt(os.path.join(savepath,savenamecluster+addname+'_significanceSeparated.csv'),distCompare)\n",
    "    np.savetxt(os.path.join(savepath,savenamecluster+addname+'_resRandomMean.csv'),resRandomMean[0])\n",
    "    np.savetxt(os.path.join(savepath,savenamecluster+addname+'_res.csv'),res)\n",
    "    #plot later for same scales across pathologies\n",
    "#     res_plot=np.zeros((res.shape[0],res.shape[0]),figsize=(10,10))\n",
    "#     res_plot=res/resRandomMean[0]\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cluster vs pathology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot by disease progression\n",
    "br1003aSpecs=pd.read_excel('/media/xinyi/dcis2idc1/data/BR1003a specs.xlsx',header=10)\n",
    "br301Specs=pd.read_excel('/media/xinyi/dcis2idc1/data/BR301 specs.xlsx',header=10)\n",
    "br8018aSpecs=pd.read_excel('/media/xinyi/dcis2idc1/data/BR8018a specs.xlsx',header=10)\n",
    "br1003aSpecs.index=br1003aSpecs.loc[:,'Position']\n",
    "br301Specs.index=br301Specs.loc[:,'Position']\n",
    "br8018aSpecs.index=br8018aSpecs.loc[:,'Position']\n",
    "\n",
    "progList=np.copy(allImgNames)\n",
    "for s in np.unique(allImgNames):\n",
    "    ssplit=s.split('_')\n",
    "    if 'br1003a'==ssplit[0]:\n",
    "        prog_s=br1003aSpecs.loc[(ssplit[-1],'Pathology diagnosis')]\n",
    "    elif 'br301'==ssplit[0]:\n",
    "        prog_s=br301Specs.loc[(ssplit[-1],'Pathology diagnosis')]\n",
    "    elif 'br8018a'==ssplit[0]:\n",
    "        prog_s=br8018aSpecs.loc[(ssplit[-1],'Pathology diagnosis')]\n",
    "    progList[allImgNames==s]=prog_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24224"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(progCounts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A1 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A2 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A4 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A5 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A6 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A7 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A8 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A9 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C1 Atypical hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C10 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C2 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C3 Atypical hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C4 Atypical hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C5 Atypical hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C6 Atypical hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C7 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C8 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C9 Hyperplasia\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I1 Breast tissue\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I10 Breast tissue\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I2 Breast tissue\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I3 Breast tissue\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I7 Breast tissue\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I8 Breast tissue\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I9 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_A1 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A2 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A3 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A5 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A6 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A7 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A8 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_A9 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C1 Atypical hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C10 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C2 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C3 Atypical hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C4 Atypical hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C5 Atypical hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C6 Atypical hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C7 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C8 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_C9 Hyperplasia\n",
      "br1003a_3_collagen1_647_hoechst_I1 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_I10 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_I2 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_I3 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_I7 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_I8 Breast tissue\n",
      "br1003a_3_collagen1_647_hoechst_I9 Breast tissue\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A1 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A10 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A2 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A3 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A4 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A5 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A6 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A7 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A8 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A9 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C1 Atypical hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C10 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C2 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C4 Atypical hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C5 Atypical hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C6 Atypical hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C7 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C8 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C9 Hyperplasia\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I1 Breast tissue\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I2 Breast tissue\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I3 Breast tissue\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I7 Breast tissue\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I8 Breast tissue\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I9 Breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A1 Ductal carcinoma in situ and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A3 Ductal carcinoma in situ\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A4 Ductal carcinoma in situ\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A5 Ductal carcinoma in situ and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A6 Ductal carcinoma in situ and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B1 Invasive ductal carcinoma\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B2 Ductal carcinoma in situ and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B3 Ductal carcinoma in situ and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B4 Ductal carcinoma in situ and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B5 Invasive ductal carcinoma and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B6 Invasive ductal carcinoma and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C1 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C3 Micropapillary type ductal carcinoma in situ wi\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C4 Micropapillary type ductal carcinoma in situ wi\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C5 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C6 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D1 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D3 Invasive ductal carcinoma and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D4 Invasive ductal carcinoma\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D5 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D6 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E1 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E3 Invasive ductal carcinoma and breast tissue\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E4 Invasive ductal carcinoma\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E5 Ductal carcinoma in situ with early infiltratio\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E6 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_A1 Ductal carcinoma in situ and breast tissue\n",
      "br301_6_collagen1_647_hoechst_A2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_A3 Ductal carcinoma in situ\n",
      "br301_6_collagen1_647_hoechst_A4 Ductal carcinoma in situ\n",
      "br301_6_collagen1_647_hoechst_A5 Ductal carcinoma in situ and breast tissue\n",
      "br301_6_collagen1_647_hoechst_A6 Ductal carcinoma in situ and breast tissue\n",
      "br301_6_collagen1_647_hoechst_B1 Invasive ductal carcinoma\n",
      "br301_6_collagen1_647_hoechst_B2 Ductal carcinoma in situ and breast tissue\n",
      "br301_6_collagen1_647_hoechst_B3 Ductal carcinoma in situ and breast tissue\n",
      "br301_6_collagen1_647_hoechst_B4 Ductal carcinoma in situ and breast tissue\n",
      "br301_6_collagen1_647_hoechst_B5 Invasive ductal carcinoma and breast tissue\n",
      "br301_6_collagen1_647_hoechst_B6 Invasive ductal carcinoma and breast tissue\n",
      "br301_6_collagen1_647_hoechst_C1 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_C2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_C3 Micropapillary type ductal carcinoma in situ wi\n",
      "br301_6_collagen1_647_hoechst_C4 Micropapillary type ductal carcinoma in situ wi\n",
      "br301_6_collagen1_647_hoechst_C5 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_C6 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_D1 Ductal carcinoma in situ with early infiltratio\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br301_6_collagen1_647_hoechst_D2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_D3 Invasive ductal carcinoma and breast tissue\n",
      "br301_6_collagen1_647_hoechst_D4 Invasive ductal carcinoma\n",
      "br301_6_collagen1_647_hoechst_D5 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_D6 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_E1 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_E2 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_E3 Invasive ductal carcinoma and breast tissue\n",
      "br301_6_collagen1_647_hoechst_E4 Invasive ductal carcinoma\n",
      "br301_6_collagen1_647_hoechst_E5 Ductal carcinoma in situ with early infiltratio\n",
      "br301_6_collagen1_647_hoechst_E6 Ductal carcinoma in situ with early infiltratio\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A1 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A10 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A2 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A3 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A4 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A5 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A6 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A7 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A8 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A9 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B1 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B10 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B2 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B3 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B4 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B5 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B6 Invasive ductal carcinoma (breast tissue)\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B7 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B8 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F1 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F10 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F2 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F3 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F4 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F5 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F6 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F7 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F8 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F9 Invasive ductal carcinoma\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H1 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H2 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H3 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H4 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H5 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H6 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H7 Cancer adjacent normal breast tissue\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H8 Cancer adjacent normal breast tissue\n",
      "br8018a_3_collagen1_647_hoechst_A1 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A2 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A3 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A4 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A6 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A7 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A8 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_A9 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B1 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B10 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B2 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B3 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B4 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B5 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B6 Invasive ductal carcinoma (breast tissue)\n",
      "br8018a_3_collagen1_647_hoechst_B7 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B8 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_B9 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F10 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F3 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F4 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F6 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F7 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F8 Invasive ductal carcinoma\n",
      "br8018a_3_collagen1_647_hoechst_F9 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A2 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A3 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A5 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A7 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A8 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B4 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B5 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B6 Invasive ductal carcinoma (breast tissue)\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B7 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F3 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F4 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F7 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F8 Invasive ductal carcinoma\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H1 Cancer adjacent normal breast tissue\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H3 Cancer adjacent normal breast tissue\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H5 Cancer adjacent normal breast tissue\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H6 Cancer adjacent normal breast tissue\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H8 Cancer adjacent normal breast tissue\n"
     ]
    }
   ],
   "source": [
    "progNames,progCounts=np.unique(progList,return_counts=True)\n",
    "progSampleRate={}\n",
    "for p in range(progNames.size):\n",
    "    progSampleRate[progNames[p]]=np.min(progCounts)/progCounts[p]\n",
    "    \n",
    "plotepoch=311\n",
    "np.random.seed(6)\n",
    "plottingIdx_i=np.array([])\n",
    "n_pcs=50\n",
    "uniqueImgNames,imgNameIdx=np.unique(allImgNames,return_index=True)\n",
    "for i in range(1):\n",
    "    for sidx in range(uniqueImgNames.size):\n",
    "        s=uniqueImgNames[sidx]\n",
    "        p=progList[imgNameIdx[sidx]]\n",
    "        print(s+' '+p)\n",
    "        nsamples=int(np.sum(allImgNames==s)*progSampleRate[p])\n",
    "        plottingIdx_i=np.concatenate((plottingIdx_i,\n",
    "                                    np.random.choice(np.arange(allImgNames.shape[0])[allImgNames==s],nsamples,replace=False)))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotsavenameAdd='_plottingIdx_progBalanced_'+str(i)+'_reordered'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Atypical hyperplasia', 'Breast tissue',\n",
       "       'Cancer adjacent normal breast tissue', 'Ductal carcinoma in situ',\n",
       "       'Ductal carcinoma in situ and breast tissue',\n",
       "       'Ductal carcinoma in situ with early infiltratio', 'Hyperplasia',\n",
       "       'Invasive ductal carcinoma',\n",
       "       'Invasive ductal carcinoma (breast tissue)',\n",
       "       'Invasive ductal carcinoma and breast tissue',\n",
       "       'Micropapillary type ductal carcinoma in situ wi'], dtype='<U47')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(progList[plottingIdx_i.astype(int)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "progList_comb=np.copy(progList)\n",
    "for p in np.unique(progList):\n",
    "    if p=='Ductal carcinoma in situ':\n",
    "        progList_comb[progList==p]='DCIS and breast tissue'\n",
    "        progList[progList==p]='DCIS'\n",
    "    elif p=='Ductal carcinoma in situ and breast tissue':\n",
    "        progList_comb[progList==p]='DCIS and breast tissue'\n",
    "        progList[progList==p]='DCIS and breast tissue'\n",
    "    elif p=='Ductal carcinoma in situ with early infiltratio':\n",
    "        progList_comb[progList==p]='DCIS with early infiltration'\n",
    "        progList[progList==p]='DCIS with early infiltration'\n",
    "    elif p=='Micropapillary type ductal carcinoma in situ wi':\n",
    "        progList_comb[progList==p]='DCIS with early infiltration'\n",
    "        progList[progList==p]='Micropapillary DCIS with early infiltration'\n",
    "    elif p=='Atypical hyperlasia':\n",
    "        progList_comb[progList==p]='Hyperplasia'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctorder=np.array(['Breast tissue','Cancer adjacent normal breast tissue','Invasive ductal carcinoma (breast tissue)','Hyperplasia','Atypical hyperplasia','DCIS and breast tissue','DCIS','DCIS with early infiltration', 'Micropapillary DCIS with early infiltration','Invasive ductal carcinoma and breast tissue','Invasive ductal carcinoma'])\n",
    "nclusterL=[8]\n",
    "for ncluster in nclusterL:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "    plotCTcomp(clusterRes,progList[plottingIdx_i.astype(int)],clusterplotsavepath,'pathologyHeatmap_rename',False,ctorder=ctorder,vmin=0)\n",
    "    plotCTcomp(clusterRes,progList[plottingIdx_i.astype(int)],clusterplotsavepath,'pathologyHeatmap_rename',True,ctorder=ctorder,vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add colorbar; add histogram; add combined pathology\n",
    "ctorder_rename=np.array(['Breast tissue','Cancer adjacent normal breast tissue','Hyperplasia','DCIS and breast tissue','DCIS with early infiltration', 'Invasive ductal carcinoma and breast tissue','Invasive ductal carcinoma'])\n",
    "nclusterL=[8]\n",
    "for ncluster in nclusterL:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "    plotCTcomp(clusterRes,progList_comb[plottingIdx_i.astype(int)],clusterplotsavepath,'pathologyHeatmap_renameComb',False,ctorder=ctorder_rename,vmin=0)\n",
    "    plotCTcomp(clusterRes,progList_comb[plottingIdx_i.astype(int)],clusterplotsavepath,'pathologyHeatmap_renameComb',True,ctorder=ctorder_rename,vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "progNames,progCounts=np.unique(progList,return_counts=True)\n",
    "progCounts_order=np.array([])\n",
    "for p in ctorder:\n",
    "    progCounts_order=np.concatenate((progCounts_order,progCounts[progNames==p]))\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(ctorder,progCounts_order,color='black')\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",rotation_mode=\"anchor\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'pathologyHist.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterNames,clusterCounts=np.unique(clusterRes,return_counts=True)\n",
    "fig, ax = plt.subplots()\n",
    "neworder=np.array([1, 5, 3, 7, 2, 0, 4, 6]).astype(int)\n",
    "ax.bar(clusterNames,clusterCounts,color=np.array(sns.color_palette(\"husl\",8))[neworder])\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",rotation_mode=\"anchor\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'clusterHist.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sUnique,sidx_start=np.unique(allImgNames[plottingIdx_i.astype(int)],return_index=True)\n",
    "\n",
    "#get cluster composition\n",
    "clusterUnique=np.unique(clusterRes)\n",
    "inputCluster=np.zeros((sUnique.size,clusterUnique.size))\n",
    "for i in range(sUnique.size):\n",
    "    clusterRes_i=clusterRes[allImgNames[plottingIdx_i.astype(int)]==sUnique[i]]\n",
    "    for j in range(clusterUnique.size):\n",
    "        inputCluster[i,j]=np.sum(clusterRes_i==clusterUnique[j])\n",
    "inputCluster=inputCluster/np.sum(inputCluster,axis=1,keepdims=True)\n",
    "\n",
    "inputCluster_orderedP=np.zeros((sUnique.size+ctorder.size,clusterUnique.size))\n",
    "progList_orderedP=np.array([])\n",
    "currIdx=0\n",
    "for p in ctorder:\n",
    "    coreIdx=progList[plottingIdx_i.astype(int)][sidx_start]==p\n",
    "    inputCluster_orderedP[currIdx:(currIdx+np.sum(coreIdx))]=inputCluster[coreIdx]\n",
    "    progList_orderedP=np.concatenate((progList_orderedP,np.repeat(p,np.sum(coreIdx))))\n",
    "    currIdx+=np.sum(coreIdx)+1\n",
    "    \n",
    "fig, ax = plt.subplots()\n",
    "bottom=np.zeros(inputCluster_orderedP.shape[0])\n",
    "for c in clusterUnique:\n",
    "    ax.bar(np.arange(inputCluster_orderedP.shape[0]),inputCluster_orderedP[:,c],bottom=bottom,color=np.array(sns.color_palette(\"husl\",8))[neworder[c]],width=1)\n",
    "    bottom+=inputCluster_orderedP[:,c]\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'cluster_cores.pdf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cluster vs proteins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A9\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C10\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C9\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I10\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I9\n",
      "aSMA\n",
      "cytokeratin\n",
      "br1003a_3_collagen1_647_hoechst_A1\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A2\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A3\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A5\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A6\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A7\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A8\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_A9\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C1\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C10\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C2\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C3\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C4\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C5\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C6\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C7\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C8\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_C9\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I1\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I10\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I2\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I3\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I7\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I8\n",
      "collagen1\n",
      "br1003a_3_collagen1_647_hoechst_I9\n",
      "collagen1\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A1\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A10\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A2\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A3\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A4\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A5\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A6\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A7\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A8\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A9\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C1\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C10\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C2\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C4\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C5\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C6\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C7\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C8\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C9\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I1\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I2\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I3\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I7\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I8\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I9\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br301_6_collagen1_647_hoechst_A1\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_A2\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_A3\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_A4\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_A5\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_A6\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_B1\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_B2\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_B3\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_B4\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_B5\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_B6\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_C1\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_C2\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_C3\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_C4\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_C5\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_C6\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_D1\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_D2\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_D3\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_D4\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_D5\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_D6\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_E1\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_E2\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_E3\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_E4\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_E5\n",
      "collagen1\n",
      "br301_6_collagen1_647_hoechst_E6\n",
      "collagen1\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A10\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_A9\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B10\n",
      "aSMA\n",
      "cytokeratin\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_B8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F10\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F9\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H1\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H2\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H3\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H4\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H5\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H6\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H7\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H8\n",
      "aSMA\n",
      "cytokeratin\n",
      "br8018a_3_collagen1_647_hoechst_A1\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A2\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A3\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A4\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A6\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A7\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A8\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_A9\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B1\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B10\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B2\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B3\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B4\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B5\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B6\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B7\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B8\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_B9\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F10\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F3\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F4\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F6\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F7\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F8\n",
      "collagen1\n",
      "br8018a_3_collagen1_647_hoechst_F9\n",
      "collagen1\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A2\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A3\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A5\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A7\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_A8\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B4\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B5\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B6\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_B7\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F3\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F4\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F7\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F8\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H1\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H3\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H5\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H6\n",
      "cytokeratin\n",
      "gh2ax\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H8\n",
      "cytokeratin\n",
      "gh2ax\n"
     ]
    }
   ],
   "source": [
    "stats2plot=['int_median','kurtosis','skewness','int_mode','entropy']\n",
    "stains=['aSMA','cytokeratin','collagen1','gh2ax']\n",
    "uniquenames,nameIdx=np.unique(allImgNames,return_index=True)\n",
    "\n",
    "for ncluster in [8]:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "        \n",
    "    allstats=None\n",
    "    alllabels=None\n",
    "    allvarnames=None\n",
    "    for sidx in range(uniquenames.size):\n",
    "        s=np.unique(allImgNames)[sidx]\n",
    "        plottingIdx_i_s=plottingIdx_i.astype(int)[allImgNames[plottingIdx_i.astype(int)]==s]-nameIdx[sidx]\n",
    "        \n",
    "        print(s)\n",
    "        assert np.min(plottingIdx_i_s)>=0\n",
    "#         ssplit=s.split('_')\n",
    "        slabels=clusterRes[allImgNames[plottingIdx_i.astype(int)]==s]\n",
    "        for stain_s in stains:\n",
    "            if not stain_s in s:\n",
    "                continue\n",
    "            path_s=os.path.join(datadir,'_'.join(s.split('_')[:-1]),'cellular_'+stain_s.lower(),s.split('_')[-1] +'.csv')\n",
    "            if not os.path.exists(path_s):\n",
    "                continue\n",
    "            print(stain_s)\n",
    "            stats_s=pd.read_csv(path_s)\n",
    "            stats_s.index=stats_s.loc[:,'label']\n",
    "            if allstats is None:\n",
    "                allstats=stats_s.loc[cellIDlist[s][plottingIdx_i_s],stats2plot].to_numpy()\n",
    "                alllabels=np.copy(slabels)\n",
    "                allvarnames=np.repeat(stain_s,slabels.size)\n",
    "            else:\n",
    "                allstats=np.concatenate((allstats,stats_s.loc[cellIDlist[s][plottingIdx_i_s],stats2plot].to_numpy()),axis=0)\n",
    "                alllabels=np.concatenate((alllabels,np.copy(slabels)))\n",
    "                allvarnames=np.concatenate((allvarnames,np.repeat(stain_s,slabels.size)))\n",
    "    plotCTcomp_exprs(stats2plot,alllabels,allstats,allvarnames,clusterplotsavepath,'proteinHeatmap',True)\n",
    "    plotCTcomp_exprs(stats2plot,alllabels,allstats,allvarnames,clusterplotsavepath,'proteinHeatmap',False)\n",
    "\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### duct membership histogram\n",
    "subdivide cluster by pathology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I2\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I3\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I7\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I8\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I9\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E6\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F2\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F7\n"
     ]
    }
   ],
   "source": [
    "uniquenames,nameIdx=np.unique(allImgNames,return_index=True)\n",
    "\n",
    "for ncluster in nclusterL:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "        \n",
    "    allstats=None\n",
    "    alllabels=None\n",
    "    allvarnames=None\n",
    "    for sidx in range(uniquenames.size):\n",
    "        s=np.unique(allImgNames)[sidx]\n",
    "        \n",
    "        path_s=os.path.join(datadir,'_'.join(s.split('_')[:-1]),'duct_nuc_membership',s.split('_')[-1] +'.csv')\n",
    "        if not os.path.exists(path_s):\n",
    "            continue\n",
    "        print(s)\n",
    "        plottingIdx_i_s=plottingIdx_i.astype(int)[allImgNames[plottingIdx_i.astype(int)]==s]-nameIdx[sidx]\n",
    "        assert np.min(plottingIdx_i_s)>=0\n",
    "        \n",
    "        stats_s=pd.read_csv(path_s)\n",
    "        stats_s.index=stats_s.loc[:,'label']\n",
    "        stats_s=stats_s.loc[cellIDlist[s][plottingIdx_i_s],'int_median'].to_numpy()\n",
    "\n",
    "        \n",
    "#         ssplit=s.split('_')\n",
    "        slabels=clusterRes[allImgNames[plottingIdx_i.astype(int)]==s]\n",
    "    \n",
    "        if allstats is None:\n",
    "            allstats=stats_s>0\n",
    "            alllabels=np.copy(slabels)\n",
    "        else:\n",
    "            allstats=np.concatenate((allstats,stats_s>0))\n",
    "            alllabels=np.concatenate((alllabels,np.copy(slabels)))\n",
    "    plotCTcomp_hist(alllabels,allstats,clusterplotsavepath,'ductHist',False)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I2\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I3\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I7\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I8\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I9\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E6\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F2\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_F7\n"
     ]
    }
   ],
   "source": [
    "uniquenames,nameIdx=np.unique(allImgNames,return_index=True)\n",
    "\n",
    "for ncluster in nclusterL:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "        \n",
    "    allstats=None\n",
    "    alllabels=None\n",
    "    allprogs=None\n",
    "    for sidx in range(uniquenames.size):\n",
    "        s=np.unique(allImgNames)[sidx]\n",
    "        \n",
    "        path_s=os.path.join(datadir,'_'.join(s.split('_')[:-1]),'duct_nuc_membership',s.split('_')[-1] +'.csv')\n",
    "        if not os.path.exists(path_s):\n",
    "            continue\n",
    "        print(s)\n",
    "        plottingIdx_i_s=plottingIdx_i.astype(int)[allImgNames[plottingIdx_i.astype(int)]==s]-nameIdx[sidx]\n",
    "        assert np.min(plottingIdx_i_s)>=0\n",
    "        \n",
    "        stats_s=pd.read_csv(path_s)\n",
    "        stats_s.index=stats_s.loc[:,'label']\n",
    "        stats_s=stats_s.loc[cellIDlist[s][plottingIdx_i_s],'int_median'].to_numpy()\n",
    "\n",
    "        \n",
    "#         ssplit=s.split('_')\n",
    "        slabels=clusterRes[allImgNames[plottingIdx_i.astype(int)]==s]\n",
    "        sprog=progList[plottingIdx_i.astype(int)][allImgNames[plottingIdx_i.astype(int)]==s]\n",
    "    \n",
    "        if allstats is None:\n",
    "            allstats=stats_s>0\n",
    "            alllabels=np.copy(slabels)\n",
    "            allprogs=np.copy(sprog)\n",
    "        else:\n",
    "            allstats=np.concatenate((allstats,stats_s>0))\n",
    "            alllabels=np.concatenate((alllabels,np.copy(slabels)))\n",
    "            allprogs=np.concatenate((allprogs,np.copy(sprog)))\n",
    "    for p in np.unique(allprogs):\n",
    "        plotCTcomp_hist(alllabels[allprogs==p],allstats[allprogs==p],clusterplotsavepath,'ductHist'+p,False)\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot nuclei in each clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(datadir,'processed','train_cnnvae'), 'rb') as input:\n",
    "    allImg=pickle.load(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sample 10 nuclei/pathology from each cluster\n",
    "ncells=20\n",
    "\n",
    "for ncluster in nclusterL:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "        \n",
    "    progs=np.unique(progList[plottingIdx_i.astype(int)])\n",
    "    for p in range(progs.size):\n",
    "        img_p=allImg[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "        cluster_p=clusterRes[progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "        plotExample=np.zeros((np.unique(cluster_p).size*(radius+1)*2,ncells*(radius+1)*2))\n",
    "        for c in range(np.unique(cluster_p).size):\n",
    "            clusteridx=np.arange(img_p.shape[0])[cluster_p==np.unique(cluster_p)[c]]\n",
    "            np.random.seed(6)\n",
    "            cp_plot=np.random.choice(clusteridx,min(ncells,clusteridx.size),replace=False)\n",
    "            for cp_i in range(cp_plot.size):\n",
    "                plotExample[c*(radius*2+2):c*(radius*2+2)+radius*2,cp_i*(radius*2+2):cp_i*(radius*2+2)+radius*2]=img_p[cp_plot[cp_i]]\n",
    "        fig, ax = plt.subplots(figsize=(20,20))\n",
    "        img = ax.imshow(plotExample)\n",
    "        ax.set_yticks(np.arange(np.unique(cluster_p).size).astype(int)*(radius*2+2)+(radius+1))\n",
    "        ax.set_yticklabels(np.unique(cluster_p))\n",
    "        fig.tight_layout()\n",
    "        plt.savefig(os.path.join(clusterplotsavepath,'plotexamples'+progs[p]+'.jpg'))\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145503"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allImg=None\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot shuffled nuclei samples for annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "progs=np.unique(progList[plottingIdx_i.astype(int)])\n",
    "progs.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncells=20\n",
    "totalNexamples=20*8*11\n",
    "nrow=40\n",
    "ncol=44\n",
    "plotExample=np.zeros((nrow*(radius+1)*2,ncol*(radius+1)*2))\n",
    "\n",
    "exampleClusters=np.zeros((nrow,ncol))\n",
    "exampleProgs=np.zeros((nrow,ncol)).astype(str)\n",
    "\n",
    "imgOrder=np.arange(totalNexamples)\n",
    "np.random.seed(3)\n",
    "np.random.shuffle(imgOrder)\n",
    "\n",
    "ncluster=8\n",
    "savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "    clusterRes=pickle.load(output)\n",
    "clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "if not os.path.exists(clusterplotsavepath):\n",
    "    os.mkdir(clusterplotsavepath)\n",
    "\n",
    "progs=np.unique(progList[plottingIdx_i.astype(int)])\n",
    "imgIdx=0\n",
    "for p in range(progs.size):\n",
    "    img_p=allImg[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    cluster_p=clusterRes[progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    for c in range(np.unique(cluster_p).size):\n",
    "        clusteridx=np.arange(img_p.shape[0])[cluster_p==np.unique(cluster_p)[c]]\n",
    "        np.random.seed(6)\n",
    "        cp_plot=np.random.choice(clusteridx,min(ncells,clusteridx.size),replace=False)\n",
    "        for cp_i in range(cp_plot.size):\n",
    "            rowIdx=int(np.floor(imgOrder[imgIdx]/ncol))\n",
    "            colIdx=imgOrder[imgIdx]%ncol\n",
    "            plotExample[rowIdx*(radius*2+2):rowIdx*(radius*2+2)+radius*2,colIdx*(radius*2+2):colIdx*(radius*2+2)+radius*2]=img_p[cp_plot[cp_i]]\n",
    "            exampleProgs[rowIdx,colIdx]=progs[p]\n",
    "            exampleClusters[rowIdx,colIdx]=c\n",
    "            imgIdx+=1\n",
    "            \n",
    "plt.imsave(os.path.join(clusterplotsavepath,'plotexamples_forAnnotation.jpg'),plotExample)\n",
    "plt.close()\n",
    "\n",
    "np.savetxt(os.path.join(clusterplotsavepath,'plotexamples_trueProg.csv'),exampleProgs,fmt='%s', delimiter=',')\n",
    "np.savetxt(os.path.join(clusterplotsavepath,'plotexamples_trueClusters.csv'),exampleClusters,fmt='%i', delimiter=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4312"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncol*(radius+1)*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A1'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allImgNames[0].split('_')[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(allImgNames[0].split('_')[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'br1003a_1_cytokeratin_555_aSMA_647_hoechst'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allImgNames[0][:-len(allImgNames[0].split('_')[-1])-1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1876734"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allImgNames.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1876734, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordlist.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/media/xinyi/dcis2idc1/data'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datadir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot with surrounding patch\n",
    "radius_patch=radius*5\n",
    "ncells=4\n",
    "ncluster=8\n",
    "totalNexamples=ncells*ncluster*11\n",
    "nrow=16\n",
    "ncol=22\n",
    "plotExample=np.zeros((nrow*(radius_patch+1)*2,ncol*(radius_patch+1)*2))\n",
    "\n",
    "exampleClusters=np.zeros((nrow,ncol))\n",
    "exampleProgs=np.zeros((nrow,ncol)).astype(str)\n",
    "\n",
    "imgOrder=np.arange(totalNexamples)\n",
    "np.random.seed(3)\n",
    "np.random.shuffle(imgOrder)\n",
    "\n",
    "\n",
    "savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "    clusterRes=pickle.load(output)\n",
    "clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "if not os.path.exists(clusterplotsavepath):\n",
    "    os.mkdir(clusterplotsavepath)\n",
    "\n",
    "progs=np.unique(progList[plottingIdx_i.astype(int)])\n",
    "imgIdx=0\n",
    "for p in range(progs.size):\n",
    "    img_p_name=allImgNames[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    coord_p=coordlist[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    cluster_p=clusterRes[progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    for c in range(np.unique(cluster_p).size):\n",
    "        clusteridx=np.arange(img_p_name.shape[0])[cluster_p==np.unique(cluster_p)[c]]\n",
    "        np.random.seed(6)\n",
    "        cp_plot=np.random.choice(clusteridx,min(ncells,clusteridx.size),replace=False)\n",
    "        for cp_i in range(cp_plot.size):\n",
    "            rowIdx=int(np.floor(imgOrder[imgIdx]/ncol))\n",
    "            colIdx=imgOrder[imgIdx]%ncol\n",
    "            img_p_split=img_p_name[cp_plot[cp_i]].split('_')\n",
    "            coreID=img_p_split[-1]\n",
    "            img_p_ID=img_p_name[cp_plot[cp_i]][:-len(coreID)-1] \n",
    "            coord_cp=coord_p[cp_plot[cp_i]]\n",
    "            img_p=io.imread(os.path.join(datadir,img_p_ID,'zproject','dna',coreID+'.tif'))\n",
    "            img_p=img_p[int(coord_cp[0]-radius_patch):int(coord_cp[0]+radius_patch),int(coord_cp[1]-radius_patch):int(coord_cp[1]+radius_patch)]\n",
    "            plotExample[rowIdx*(radius_patch*2+2):rowIdx*(radius_patch*2+2)+img_p.shape[0],colIdx*(radius_patch*2+2):colIdx*(radius_patch*2+2)+img_p.shape[1]]=img_p\n",
    "            exampleProgs[rowIdx,colIdx]=progs[p]\n",
    "            exampleClusters[rowIdx,colIdx]=c\n",
    "            imgIdx+=1\n",
    "            \n",
    "plt.imsave(os.path.join(clusterplotsavepath,'plotexamples_patch_forAnnotation.jpg'),plotExample)\n",
    "plt.close()\n",
    "\n",
    "np.savetxt(os.path.join(clusterplotsavepath,'plotexamples_trueProg_patch.csv'),exampleProgs,fmt='%s', delimiter=',')\n",
    "np.savetxt(os.path.join(clusterplotsavepath,'plotexamples_trueClusters_patch.csv'),exampleClusters,fmt='%i', delimiter=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/media/xinyi/dcis2idc1/plots/cnnvaeexp0/cluster_reordered/minibatchkmean_ncluster8n_pcs50epoch311_plottingIdx_progBalanced_0_reordered_plots'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterplotsavepath\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "plt.imshow(plotExample)\n",
    "# Get the current reference\n",
    "ax = plt.gca()\n",
    "imgIdx=0      \n",
    "for p in range(progs.size):\n",
    "    img_p_name=allImgNames[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    coord_p=coordlist[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    cluster_p=clusterRes[progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    for c in range(np.unique(cluster_p).size):\n",
    "        clusteridx=np.arange(img_p_name.shape[0])[cluster_p==np.unique(cluster_p)[c]]\n",
    "        np.random.seed(6)\n",
    "        cp_plot=np.random.choice(clusteridx,min(ncells,clusteridx.size),replace=False)\n",
    "        for cp_i in range(cp_plot.size):\n",
    "            rowIdx=int(np.floor(imgOrder[imgIdx]/ncol))\n",
    "            colIdx=imgOrder[imgIdx]%ncol\n",
    "            img_p_split=img_p_name[cp_plot[cp_i]].split('_')\n",
    "            coreID=img_p_split[-1]\n",
    "            img_p_ID=img_p_name[cp_plot[cp_i]][:-len(coreID)-1] \n",
    "            coord_cp=coord_p[cp_plot[cp_i]]\n",
    "            img_p=io.imread(os.path.join(datadir,img_p_ID,'zproject','dna',coreID+'.tif'))\n",
    "            img_p=img_p[int(coord_cp[0]-radius_patch):int(coord_cp[0]+radius_patch),int(coord_cp[1]-radius_patch):int(coord_cp[1]+radius_patch)]\n",
    "            rect = Rectangle((int(rowIdx*(radius_patch*2+2)+img_p.shape[0]/2-radius),int(colIdx*(radius_patch*2+2)+img_p.shape[1]/2-radius)),radius*2,radius*2,linewidth=0.1,edgecolor='r',facecolor='none')\n",
    "\n",
    "            # Add the patch to the Axes\n",
    "            ax.add_patch(rect)\n",
    "            \n",
    "            imgIdx+=1\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'plotexamples_patch_forAnnotation_withBoxes.jpg'),dpi=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterplotsavepath='/media/xinyi/dcis2idc1/plots/cnnvaeexp0/cluster_reordered/minibatchkmean_ncluster8n_pcs50epoch311_plottingIdx_progBalanced_0_reordered_plots'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "patchAnno=pd.read_csv(os.path.join(clusterplotsavepath,'nuclearGradeAssigned.csv'),header=None,dtype='str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
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       "     0   1    2    3    4    5    6    7   8    9   ...   12   13   14   15  \\\n",
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       "13    1   2    3    2    3  NaN  NaN  NaN   2    2  ...    3    2  NaN    3   \n",
       "14    2   2    1  NaN    2  NaN    2  NaN   2    1  ...    3    2  NaN  NaN   \n",
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       "\n",
       "     16   17   18   19   20   21  \n",
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       "1   NaN  NaN  NaN  NaN  NaN  NaN  \n",
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       "4   NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "5   NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "6   NaN  NaN  NaN  NaN  NaN  NaN  \n",
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       "14  NaN  NaN  NaN  NaN  NaN  NaN  \n",
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       "\n",
       "[16 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patchAnno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "patchProg=pd.read_csv(os.path.join(clusterplotsavepath,'plotexamples_trueProg_patch.csv'),header=None)\n",
    "patchCluster=pd.read_csv(os.path.join(clusterplotsavepath,'plotexamples_trueClusters_patch.csv'),header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "compCluster=np.zeros((np.unique(patchCluster).size,3))\n",
    "for c in np.unique(patchCluster):\n",
    "    compCluster[c,0]=np.sum(patchAnno.to_numpy()[patchCluster.to_numpy()==c]=='1')\n",
    "    compCluster[c,1]=np.sum(patchAnno.to_numpy()[patchCluster.to_numpy()==c]=='2')\n",
    "    compCluster[c,2]=np.sum(patchAnno.to_numpy()[patchCluster.to_numpy()==c]=='3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compCluster_norm=compCluster/np.sum(compCluster,axis=1,keepdims=True)\n",
    "\n",
    "x = np.arange(compCluster.shape[0])  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "multiplier = 0\n",
    "\n",
    "fig, ax = plt.subplots(layout='constrained',figsize=(4,3))\n",
    "\n",
    "for g in range(3):\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, compCluster_norm[:,g], width, label=g)\n",
    "    ax.bar_label(rects,compCluster[:,g].astype(int), padding=3,fmt='%i')\n",
    "    multiplier += 1\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Nuclear Grade Assignment')\n",
    "ax.set_xticks(x + width, np.arange(8))\n",
    "ax.legend(loc='upper right', ncol=3)\n",
    "ax.set_ylim(0, 1)\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'nuclearGrade2Cluster.pdf'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "nontumor=['Breast tissue','Atypical hyperplasia','Cancer adjacent normal breast ti','Hyperplasia','Invasive ductal carcinoma (breas']\n",
    "dcis=['DCIS','DCIS and breast tissue','DCIS with early infiltration','Micropapillary DCIS with early i']\n",
    "invasive=['Invasive ductal carcinoma','Invasive ductal carcinoma and br']\n",
    "compProg=np.zeros((3,3))\n",
    "for c in np.unique(patchProg):\n",
    "    if c in nontumor:\n",
    "        compProg[0,0]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='1')\n",
    "        compProg[0,1]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='2')\n",
    "        compProg[0,2]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='3')\n",
    "    if c in dcis:\n",
    "        compProg[1,0]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='1')\n",
    "        compProg[1,1]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='2')\n",
    "        compProg[1,2]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='3')\n",
    "    if c in invasive:\n",
    "        compProg[2,0]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='1')\n",
    "        compProg[2,1]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='2')\n",
    "        compProg[2,2]+=np.sum(patchAnno.to_numpy()[patchProg.to_numpy()==c]=='3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compProg_norm=compProg/np.sum(compProg,axis=1,keepdims=True)\n",
    "\n",
    "x = np.arange(compProg.shape[0])  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "multiplier = 0\n",
    "\n",
    "fig, ax = plt.subplots(layout='constrained',figsize=(3,2))\n",
    "\n",
    "for g in range(3):\n",
    "    offset = width * multiplier\n",
    "    rects = ax.bar(x + offset, compProg_norm[:,g], width, label=g)\n",
    "    ax.bar_label(rects,compProg.astype(int)[:,g], padding=3)\n",
    "    multiplier += 1\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Nuclear Grade Assignment')\n",
    "ax.set_xticks(x + width, ['Non-tumor','DCIS','IDC'])\n",
    "ax.legend(loc='upper left', ncol=3)\n",
    "ax.set_ylim(0, 1)\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'nuclearGrade2Prog.pdf'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['nan', '2', '1', 'nan', '1', '1', 'nan', '1', '3', '3', '3', '1',\n",
       "        '2', 'nan', 'nan', '1'],\n",
       "       ['1', '1', '2', '2', '1', '3', 'nan', '1', 'NO', 'NO', 'NO', '1',\n",
       "        '1', '1', 'NO', '1'],\n",
       "       ['2', '1', '2', '2', '1', 'nan', '2', '1', '2', '1', '3', 'nan',\n",
       "        'nan', 'nan', '3', 'NO'],\n",
       "       ['1', 'NO', '2', '1', 'NO', '1', '2', '3', '1', '1', '2', '2',\n",
       "        '1', 'nan', 'nan', '2'],\n",
       "       ['2', '2', '1', '3', '2', 'nan', '2', '2', '3', '1', 'NO', '3',\n",
       "        '3', '2', '1', '1'],\n",
       "       ['1', '3', '3', '2', '2', 'nan', 'NO', '2', '2', '2', '3', '3',\n",
       "        '1', 'NO', 'NO', '1'],\n",
       "       ['1', '1', '1', 'nan', '2', '3', '3', '2', '2', '1', '1', 'nan',\n",
       "        'nan', 'nan', '1', '2'],\n",
       "       ['3', '1', '1', '2', '3', '1', 'nan', 'nan', '2', '2', '2', 'nan',\n",
       "        '1', '1', 'NO', '2'],\n",
       "       ['nan', '1', 'nan', '1', '1', 'NO', '3', '1', '2', 'nan', 'NO',\n",
       "        '1', '2', 'NO', '2', '1'],\n",
       "       ['2', '1', '2', '3', '3', '1', '2', '1', 'NO', 'nan', '3', 'nan',\n",
       "        '2', '1', '2', '2'],\n",
       "       ['1', '2', '1', '1', 'NO', 'NO', '2', '1', '1', '1', '2', 'nan',\n",
       "        '1', '2', '2', '2'],\n",
       "       ['2', '3', '3', '1', 'nan', '2', '2', '3', '2', 'nan', 'nan', '3',\n",
       "        'nan', 'nan', 'nan', '2'],\n",
       "       ['2', '3', '1', '2', 'nan', '2', 'NO', 'nan', '3', '1', '2', 'NO',\n",
       "        '2', 'nan', 'NO', '2'],\n",
       "       ['1', '2', '3', '2', '3', 'nan', 'nan', 'nan', '2', '2', '1', '2',\n",
       "        '3', '2', 'nan', '3'],\n",
       "       ['2', '2', '1', 'nan', '2', 'nan', '2', 'nan', '2', '1', '1', '3',\n",
       "        '3', '2', 'nan', 'nan'],\n",
       "       ['1', 'NO', '2', '2', '1', '2', '3', 'nan', '1', '2', 'nan', '2',\n",
       "        '1', '3', '1', 'nan']], dtype='<U3')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patchAnnoNP=patchAnno.to_numpy()[:,:16].astype(str)\n",
    "patchAnnoNP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "patchCluster_withAnno=np.copy(patchCluster.to_numpy()[:,:16])\n",
    "naCluster=np.zeros(np.unique(patchCluster_withAnno).size)\n",
    "for c in np.unique(patchCluster_withAnno):\n",
    "    naCluster[c]=np.sum(patchAnnoNP[patchCluster_withAnno==c]=='NO')\n",
    "    naCluster[c]+=np.sum(patchAnnoNP[patchCluster_withAnno==c]=='nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterCounts=np.zeros(8)\n",
    "for c in np.unique(patchCluster_withAnno):\n",
    "    clusterCounts[c]=np.sum(patchCluster_withAnno==c)\n",
    "    \n",
    "naCluster_norm=naCluster/clusterCounts\n",
    "\n",
    "x = np.arange(naCluster.shape[0])  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(layout='constrained',figsize=(4,3))\n",
    "\n",
    "rects = ax.bar(x , naCluster_norm, width)\n",
    "ax.bar_label(rects,np.round(naCluster_norm,decimals=2), padding=3,fmt='%i')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('% annotated nuclei')\n",
    "ax.set_xticks(x + width, np.arange(8))\n",
    "# ax.legend(loc='upper right', ncol=2)\n",
    "ax.set_ylim(0, 0.56)\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'nuclearGrade2Cluster_NAs.pdf'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([35., 33., 34., 35., 32., 30., 29., 28.])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterCounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "patchProg_withAnno=np.copy(patchProg.to_numpy()[:,:16])\n",
    "nontumor=['Breast tissue','Atypical hyperplasia','Cancer adjacent normal breast ti','Hyperplasia','Invasive ductal carcinoma (breas']\n",
    "dcis=['DCIS','DCIS and breast tissue','DCIS with early infiltration','Micropapillary DCIS with early i']\n",
    "invasive=['Invasive ductal carcinoma','Invasive ductal carcinoma and br']\n",
    "naProg=np.zeros(3)\n",
    "for c in np.unique(patchProg):\n",
    "    if c in nontumor:\n",
    "        naProg[0]+=np.sum(patchAnnoNP[patchProg_withAnno==c]=='NO')\n",
    "        naProg[0]+=np.sum(patchAnnoNP[patchProg_withAnno==c]=='nan')\n",
    "    if c in dcis:\n",
    "        naProg[1]+=np.sum(patchAnnoNP[patchProg_withAnno==c]=='NO')\n",
    "        naProg[1]+=np.sum(patchAnnoNP[patchProg_withAnno==c]=='nan')\n",
    "    if c in invasive:\n",
    "        naProg[2]+=np.sum(patchAnnoNP[patchProg_withAnno==c]=='NO')\n",
    "        naProg[2]+=np.sum(patchAnnoNP[patchProg_withAnno==c]=='nan')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "progCounts=np.zeros(3)\n",
    "for c in np.unique(patchProg_withAnno):\n",
    "    if c in nontumor:\n",
    "        progCounts[0]+=np.sum(patchProg_withAnno==c)\n",
    "    if c in dcis:\n",
    "        progCounts[1]+=np.sum(patchProg_withAnno==c)\n",
    "    if c in invasive:\n",
    "        progCounts[2]+=np.sum(patchProg_withAnno==c)\n",
    "\n",
    "naProg_norm=naProg/progCounts\n",
    "x = np.arange(naProg.shape[0])  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(layout='constrained',figsize=(3,2))\n",
    "\n",
    "rects = ax.bar(x , naProg_norm, width)\n",
    "ax.bar_label(rects,np.round(naProg_norm,decimals=2), padding=3)\n",
    "multiplier += 1\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('% annotated nuclei')\n",
    "ax.set_xticks(x + width, ['Non-tumor','DCIS','IDC'])\n",
    "# ax.legend(loc='upper left', ncol=3)\n",
    "ax.set_ylim(0, 0.35)\n",
    "# plt.tight_layout()\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'nuclearGrade2Prog_NAs.pdf'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## staging: claudio vs biomax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "stageAnno=pd.read_csv(os.path.join(clusterplotsavepath,'stageAnnotation.csv'),index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "stageAnno[np.logical_not(np.isfinite(stageAnno))]=0\n",
    "stageAnno=stageAnno.iloc[:,:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "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>Non-tumoral breast tissue</th>\n",
       "      <th>Hyperplasia</th>\n",
       "      <th>Atypical hyperplasia</th>\n",
       "      <th>DCIS and breast tissue</th>\n",
       "      <th>DCIS</th>\n",
       "      <th>DCIS with microinvasion and breast tissue</th>\n",
       "      <th>DCIS with microinvasion</th>\n",
       "      <th>DCIS+IDC</th>\n",
       "      <th>IDC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Breast tissue</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cancer adjacent breast tissue</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDC (breast tissue)</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyperplasia</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atypical hyperplasia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCIS and breast tissue</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCIS</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCIS with early infiltration</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Micropapilary DCIS with early infiltration</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDC and breast tissue</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDC</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Non-tumoral breast tissue  \\\n",
       "Breast tissue                                                     3.0   \n",
       "Cancer adjacent breast tissue                                     3.0   \n",
       "IDC (breast tissue)                                               0.0   \n",
       "Hyperplasia                                                       1.0   \n",
       "Atypical hyperplasia                                              0.0   \n",
       "DCIS and breast tissue                                            0.0   \n",
       "DCIS                                                              0.0   \n",
       "DCIS with early infiltration                                      0.0   \n",
       "Micropapilary DCIS with early infiltration                        0.0   \n",
       "IDC and breast tissue                                             0.0   \n",
       "IDC                                                               0.0   \n",
       "\n",
       "                                            Hyperplasia  Atypical hyperplasia  \\\n",
       "Breast tissue                                       0.0                   0.0   \n",
       "Cancer adjacent breast tissue                       0.0                   0.0   \n",
       "IDC (breast tissue)                                 1.0                   1.0   \n",
       "Hyperplasia                                         0.0                   1.0   \n",
       "Atypical hyperplasia                                0.0                   0.0   \n",
       "DCIS and breast tissue                              0.0                   0.0   \n",
       "DCIS                                                0.0                   2.0   \n",
       "DCIS with early infiltration                        0.0                   0.0   \n",
       "Micropapilary DCIS with early infiltration          0.0                   0.0   \n",
       "IDC and breast tissue                               0.0                   0.0   \n",
       "IDC                                                 0.0                   0.0   \n",
       "\n",
       "                                            DCIS and breast tissue  DCIS  \\\n",
       "Breast tissue                                                  0.0   0.0   \n",
       "Cancer adjacent breast tissue                                  0.0   0.0   \n",
       "IDC (breast tissue)                                            0.0   0.0   \n",
       "Hyperplasia                                                    0.0   0.0   \n",
       "Atypical hyperplasia                                           0.0   1.0   \n",
       "DCIS and breast tissue                                         1.0   0.0   \n",
       "DCIS                                                           0.0   0.0   \n",
       "DCIS with early infiltration                                   1.0   1.0   \n",
       "Micropapilary DCIS with early infiltration                     0.0   0.0   \n",
       "IDC and breast tissue                                          0.0   0.0   \n",
       "IDC                                                            0.0   0.0   \n",
       "\n",
       "                                            DCIS with microinvasion and breast tissue  \\\n",
       "Breast tissue                                                                     0.0   \n",
       "Cancer adjacent breast tissue                                                     0.0   \n",
       "IDC (breast tissue)                                                               0.0   \n",
       "Hyperplasia                                                                       0.0   \n",
       "Atypical hyperplasia                                                              0.0   \n",
       "DCIS and breast tissue                                                            2.0   \n",
       "DCIS                                                                              0.0   \n",
       "DCIS with early infiltration                                                      0.0   \n",
       "Micropapilary DCIS with early infiltration                                        1.0   \n",
       "IDC and breast tissue                                                             1.0   \n",
       "IDC                                                                               0.0   \n",
       "\n",
       "                                            DCIS with microinvasion  DCIS+IDC  \\\n",
       "Breast tissue                                                   0.0       0.0   \n",
       "Cancer adjacent breast tissue                                   0.0       0.0   \n",
       "IDC (breast tissue)                                             0.0       0.0   \n",
       "Hyperplasia                                                     0.0       0.0   \n",
       "Atypical hyperplasia                                            0.0       2.0   \n",
       "DCIS and breast tissue                                          0.0       0.0   \n",
       "DCIS                                                            0.0       0.0   \n",
       "DCIS with early infiltration                                    0.0       1.0   \n",
       "Micropapilary DCIS with early infiltration                      0.0       1.0   \n",
       "IDC and breast tissue                                           1.0       1.0   \n",
       "IDC                                                             1.0       0.0   \n",
       "\n",
       "                                            IDC  \n",
       "Breast tissue                               0.0  \n",
       "Cancer adjacent breast tissue               0.0  \n",
       "IDC (breast tissue)                         0.0  \n",
       "Hyperplasia                                 0.0  \n",
       "Atypical hyperplasia                        0.0  \n",
       "DCIS and breast tissue                      0.0  \n",
       "DCIS                                        0.0  \n",
       "DCIS with early infiltration                0.0  \n",
       "Micropapilary DCIS with early infiltration  0.0  \n",
       "IDC and breast tissue                       0.0  \n",
       "IDC                                         2.0  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stageAnno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stagingBg=np.zeros((stageAnno.shape[0],stageAnno.shape[1],3))\n",
    "stagingBg[:5,:3,1]=1\n",
    "stagingBg[:5,3:,0]=1\n",
    "stagingBg[:5,3:,1]=182/255\n",
    "stagingBg[:5,3:,2]=193/255\n",
    "\n",
    "stagingBg[5:9,3:7,1]=1\n",
    "stagingBg[5:9,:3,0]=1\n",
    "stagingBg[5:9,:3,1]=182/255\n",
    "stagingBg[5:9,:3,2]=193/255\n",
    "stagingBg[5:9,7:,0]=1\n",
    "stagingBg[5:9,7:,1]=182/255\n",
    "stagingBg[5:9,7:,2]=193/255\n",
    "\n",
    "stagingBg[9:,7:,1]=1\n",
    "stagingBg[9:,:7,0]=1\n",
    "stagingBg[9:,:7,1]=182/255\n",
    "stagingBg[9:,:7,2]=193/255\n",
    "\n",
    "stagingBg[:5,3:][stageAnno.to_numpy()[:5,3:]>0]=[1,0,0]\n",
    "stagingBg[5:9,:3][stageAnno.to_numpy()[5:9,:3]>0]=[1,0,0]\n",
    "stagingBg[5:9,7:][stageAnno.to_numpy()[5:9,7:]>0]=[1,0,0]\n",
    "stagingBg[9:,:7][stageAnno.to_numpy()[9:,:7]>0]=[1,0,0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5,7))\n",
    "im = ax.imshow(stagingBg)\n",
    "ax.set_xticks(np.arange(stagingBg.shape[1]), labels=stageAnno.columns)\n",
    "ax.set_yticks(np.arange(stagingBg.shape[0]), labels=stageAnno.index)\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",rotation_mode=\"anchor\")\n",
    "for i in range(stagingBg.shape[0]):\n",
    "    for j in range(stagingBg.shape[1]):\n",
    "        text = ax.text(j, i, int(stageAnno.iloc[i, j]),\n",
    "                       ha=\"center\", va=\"center\", color=\"black\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'staging.pdf'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stageAnnoNP=stageAnno.to_numpy()\n",
    "stageCounts=np.zeros(3)\n",
    "stageCounts[0]=np.sum(stageAnnoNP[:5])\n",
    "stageCounts[1]=np.sum(stageAnnoNP[5:9])\n",
    "stageCounts[2]=np.sum(stageAnnoNP[9:])\n",
    "fig, ax = plt.subplots(figsize=(3,4))\n",
    "bottom=np.zeros(3)\n",
    "lessProg=np.zeros(3)\n",
    "lessProg[1]=np.sum(stageAnnoNP[5:9,:3])\n",
    "lessProg[2]=np.sum(stageAnnoNP[9:,:7])\n",
    "lessProg=lessProg/stageCounts\n",
    "ax.bar(np.arange(3),lessProg,bottom=bottom,color='yellow',width=0.5)\n",
    "bottom+=lessProg\n",
    "\n",
    "correctProg=np.zeros(3)\n",
    "correctProg[0]=np.sum(stageAnnoNP[:5,:3])\n",
    "correctProg[1]=np.sum(stageAnnoNP[5:9,3:7])\n",
    "correctProg[2]=np.sum(stageAnnoNP[9:,7:])\n",
    "correctProg=correctProg/stageCounts\n",
    "ax.bar(np.arange(3),correctProg,bottom=bottom,color='green',width=0.5)\n",
    "bottom+=correctProg\n",
    "\n",
    "moreProg=np.zeros(3)\n",
    "moreProg[0]=np.sum(stageAnnoNP[:5,3:])\n",
    "moreProg[1]=np.sum(stageAnnoNP[5:9,7:])\n",
    "moreProg=moreProg/stageCounts\n",
    "ax.bar(np.arange(3),moreProg,bottom=bottom,color='red',width=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(clusterplotsavepath,'stagingBar.pdf'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## neighbor cluster ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import pairwise_distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### distance threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C1\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C3\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C4\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C5\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C6\n",
      "br1003a_3_collagen1_647_hoechst_C1\n",
      "br1003a_3_collagen1_647_hoechst_C3\n",
      "br1003a_3_collagen1_647_hoechst_C4\n",
      "br1003a_3_collagen1_647_hoechst_C5\n",
      "br1003a_3_collagen1_647_hoechst_C6\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C1\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C4\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C5\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_C6\n",
      "1\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I1\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I10\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I2\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I3\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I7\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I8\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_I9\n",
      "br1003a_3_collagen1_647_hoechst_I1\n",
      "br1003a_3_collagen1_647_hoechst_I10\n",
      "br1003a_3_collagen1_647_hoechst_I2\n",
      "br1003a_3_collagen1_647_hoechst_I3\n",
      "br1003a_3_collagen1_647_hoechst_I7\n",
      "br1003a_3_collagen1_647_hoechst_I8\n",
      "br1003a_3_collagen1_647_hoechst_I9\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I1\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I2\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I3\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I7\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I8\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_I9\n",
      "2\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H1\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H2\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H3\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H4\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H5\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H6\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H7\n",
      "br8018a_1_cytokeratin_555_aSMA_647_hoechst_H8\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H1\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H3\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H5\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H6\n",
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_H8\n",
      "3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "br301_6_collagen1_647_hoechst_A3\n",
      "br301_6_collagen1_647_hoechst_A4\n",
      "4\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B3\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B4\n",
      "br301_6_collagen1_647_hoechst_A1\n",
      "br301_6_collagen1_647_hoechst_A5\n",
      "br301_6_collagen1_647_hoechst_A6\n",
      "br301_6_collagen1_647_hoechst_B2\n",
      "br301_6_collagen1_647_hoechst_B3\n",
      "br301_6_collagen1_647_hoechst_B4\n",
      "5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_C6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_D6\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E1\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E2\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E5\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_E6\n",
      "br301_6_collagen1_647_hoechst_A2\n",
      "br301_6_collagen1_647_hoechst_C1\n",
      "br301_6_collagen1_647_hoechst_C2\n",
      "br301_6_collagen1_647_hoechst_C5\n",
      "br301_6_collagen1_647_hoechst_C6\n",
      "br301_6_collagen1_647_hoechst_D1\n",
      "br301_6_collagen1_647_hoechst_D2\n",
      "br301_6_collagen1_647_hoechst_D5\n",
      "br301_6_collagen1_647_hoechst_D6\n",
      "br301_6_collagen1_647_hoechst_E1\n",
      "br301_6_collagen1_647_hoechst_E2\n",
      "br301_6_collagen1_647_hoechst_E5\n",
      "br301_6_collagen1_647_hoechst_E6\n",
      "6\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A1\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A2\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A4\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A5\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A6\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A7\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A8\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_A9\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C10\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C2\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C7\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C8\n",
      "br1003a_1_cytokeratin_555_aSMA_647_hoechst_C9\n",
      "br1003a_3_collagen1_647_hoechst_A1\n",
      "br1003a_3_collagen1_647_hoechst_A2\n",
      "br1003a_3_collagen1_647_hoechst_A3\n",
      "br1003a_3_collagen1_647_hoechst_A5\n",
      "br1003a_3_collagen1_647_hoechst_A6\n",
      "br1003a_3_collagen1_647_hoechst_A7\n",
      "br1003a_3_collagen1_647_hoechst_A8\n",
      "br1003a_3_collagen1_647_hoechst_A9\n",
      "br1003a_3_collagen1_647_hoechst_C10\n",
      "br1003a_3_collagen1_647_hoechst_C2\n",
      "br1003a_3_collagen1_647_hoechst_C7\n",
      "br1003a_3_collagen1_647_hoechst_C8\n",
      "br1003a_3_collagen1_647_hoechst_C9\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A1\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A10\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A2\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A3\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A4\n",
      "br1003a_4_cytokeratin_555_gh2ax_647_hoechst_A5\n",
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      "7\n",
      "br301_4_cytokeratin_555_aSMA_647_hoechst_B1\n",
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      "br301_4_cytokeratin_555_aSMA_647_hoechst_E4\n",
      "br301_6_collagen1_647_hoechst_B1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "br8018a_4_cytokeratin_555_gh2ax_647_hoechst_F4\n",
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     ]
    }
   ],
   "source": [
    "# maxncell=130000\n",
    "\n",
    "neighborhoodSize=16*9\n",
    "#neighborhoodSize=16*9*1.5 #run later\n",
    "for ncluster in [8]:\n",
    "    savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "    with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "        clusterRes=pickle.load(output)\n",
    "    clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "    if not os.path.exists(clusterplotsavepath):\n",
    "        os.mkdir(clusterplotsavepath)\n",
    "    progs=np.unique(progList[plottingIdx_i.astype(int)])\n",
    "    for p in range(progs.size):\n",
    "        print(p)\n",
    "        coord_p=coordlist[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "        cluster_p=clusterRes[progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "        imgNames_p=allImgNames[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "        \n",
    "        selfp=np.array([])\n",
    "        neighborp=np.array([])\n",
    "        for imgN in np.unique(imgNames_p):\n",
    "            print(imgN)\n",
    "#         subsampleIdx=np.arange(coord_p.shape[0])\n",
    "#         if coord_p.shape[0]>maxncell:\n",
    "#             np.random.seed(6)\n",
    "#             subsampleIdx=np.random.choice(subsampleIdx,maxncell,replace=False)\n",
    "        \n",
    "            neighbors=np.tile(cluster_p[imgNames_p==imgN],(np.sum(imgNames_p==imgN),1))\n",
    "            selfs=np.tile(cluster_p[imgNames_p==imgN].reshape((-1,1)),(1,np.sum(imgNames_p==imgN)))\n",
    "            dist=pairwise_distances(coord_p[imgNames_p==imgN],n_jobs=-1)\n",
    "            distIn=np.logical_and(dist<neighborhoodSize,dist>0)\n",
    "            selfp=np.concatenate((selfp,selfs[distIn]))\n",
    "            neighborp=np.concatenate((neighborp,neighbors[distIn]))\n",
    "            \n",
    "        plotCTcomp_hist(selfp,neighborp,clusterplotsavepath,'neighborHist'+progs[p],False)\n",
    "        gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### random samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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   ],
   "source": [
    "#separate comparisons of each core with its random assignments\n",
    "\n",
    "#cluster labels in each sample are randomly reordered\n",
    "\n",
    "nRandomsamplesTotal=40000 #for bonferroni\n",
    "ngroups=80\n",
    "\n",
    "neighborhoodSize=16*9\n",
    "ncluster=8\n",
    "savenamecluster='minibatchkmean_ncluster'+str(ncluster)+'n_pcs'+str(n_pcs)+'epoch'+str(plotepoch)+plotsavenameAdd\n",
    "with open(os.path.join(clustersavedir_old,savenamecluster), 'rb') as output:\n",
    "    clusterRes=pickle.load(output)\n",
    "clusterplotsavepath=os.path.join(clustersavedir,savenamecluster+'_plots')\n",
    "if not os.path.exists(clusterplotsavepath):\n",
    "    os.mkdir(clusterplotsavepath)\n",
    "neighborplotsavepath=os.path.join(clusterplotsavepath,'neighborHist_withRandom_nsamples'+str(nRandomsamplesTotal))\n",
    "if not os.path.exists(neighborplotsavepath):\n",
    "    os.mkdir(neighborplotsavepath)\n",
    "progs=np.unique(progList[plottingIdx_i.astype(int)])\n",
    "\n",
    "for p in range(progs.size)\n",
    "    print(p)\n",
    "    coord_p=coordlist[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    cluster_p=clusterRes[progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "    imgNames_p=allImgNames[plottingIdx_i.astype(int)][progList[plottingIdx_i.astype(int)]==progs[p]]\n",
    "\n",
    "    selfp=np.array([])\n",
    "    neighborp=np.array([])\n",
    "    selfp_random=[None]*np.unique(imgNames_p).size\n",
    "    neighborp_random=[None]*np.unique(imgNames_p).size\n",
    "#     nRandomsamplesTotal=np.ceil(nRandomsamplesTotal_perProg/np.unique(imgNames_p).size).astype(int)\n",
    "    nRandomsamples=np.ceil(nRandomsamplesTotal/ngroups).astype(int)\n",
    "    nRandomsamplesTotal=nRandomsamples*ngroups\n",
    "    actualSamples=nRandomsamples*ngroups*np.unique(imgNames_p).size\n",
    "    \n",
    "    resRandom_sum=np.zeros((ncluster,ncluster))\n",
    "    greaterCount=np.zeros((ncluster,ncluster))\n",
    "    for imgNidx in range(np.unique(imgNames_p).size):\n",
    "        imgN=np.unique(imgNames_p)[imgNidx]\n",
    "        print(imgN)\n",
    "        nsamples=np.sum(imgNames_p==imgN)\n",
    "        cluster_ps=cluster_p[imgNames_p==imgN]\n",
    "        neighbors=np.tile(cluster_ps,(nsamples,1))\n",
    "        selfs=np.tile(cluster_ps.reshape((-1,1)),(1,nsamples))\n",
    "\n",
    "        dist=pairwise_distances(coord_p[imgNames_p==imgN],n_jobs=-1)\n",
    "        distIn=np.logical_and(dist<neighborhoodSize,dist>0)\n",
    "        selfp=np.concatenate((selfp,selfs[distIn]))\n",
    "        neighborp=np.concatenate((neighborp,neighbors[distIn]))\n",
    "        np.random.seed(6)\n",
    "        \n",
    "        res_curr=getHistMatrix_clusters(selfs[distIn],neighbors[distIn])\n",
    "        \n",
    "#         selfp_random_s=np.ones((nRandomsamplesTotal,np.sum(distIn)))\n",
    "#         neighborp_random_s=np.ones((nRandomsamplesTotal,np.sum(distIn)))\n",
    "        resRandom=np.zeros((nRandomsamplesTotal,ncluster,ncluster))\n",
    "        randAll=0\n",
    "        for ng in range(ngroups):\n",
    "            print(ng)\n",
    "            \n",
    "            orderj=np.random.rand(nRandomsamples,nsamples).argsort(axis=-1)\n",
    "            print('sampled')\n",
    "            cluster_psCopied=np.tile(cluster_ps,(nRandomsamples,1))\n",
    "            selfp_random_sn=np.tile(cluster_psCopied[np.arange(nRandomsamples)[:,None],orderj].reshape(nRandomsamples,nsamples,1),(1,1,nsamples))[np.tile(distIn,(nRandomsamples,1,1))]\n",
    "            neighborp_random_sn=np.tile(cluster_psCopied[np.arange(nRandomsamples)[:,None],orderj].reshape(nRandomsamples,1,nsamples),(1,nsamples,1))[np.tile(distIn,(nRandomsamples,1,1))]\n",
    "            selfp_random_s=selfp_random_sn.reshape(nRandomsamples,np.sum(distIn))\n",
    "            neighborp_random_s=neighborp_random_sn.reshape(nRandomsamples,np.sum(distIn))\n",
    "            for rand in range(nRandomsamples):\n",
    "                resRandom[randAll]=getHistMatrix_clusters(selfp_random_s[rand],neighborp_random_s[rand])\n",
    "                randAll+=1\n",
    "            gc.collect()\n",
    "#             selfp_random_s=np.zeros((nRandomsamples,np.sum(distIn)))\n",
    "#             neighborp_random_s=np.zeros((nRandomsamples,np.sum(distIn)))\n",
    "#             for j in range(nRandomsamples):\n",
    "#                 print(j)\n",
    "# #                 orderj=np.random.choice(nsamples,nsamples,replace=False)\n",
    "#                 selfp_random_s[j]=np.tile(cluster_ps[orderj[j]].reshape((-1,1)),(1,nsamples))[distIn]\n",
    "#                 neighborp_random_s[j]=np.tile(cluster_ps[orderj[j]],(nsamples,1))[distIn]\n",
    "\n",
    "#                 if selfp_random_s is None:\n",
    "#                     selfp_random_s=selfp_random_sj\n",
    "#                     neighborp_random_s=neighborp_random_sj\n",
    "#                 else:\n",
    "#                     selfp_random_s=np.vstack((selfp_random_s,selfp_random_sj))\n",
    "#                     neighborp_random_s=np.vstack((neighborp_random_s,neighborp_random_sj))\n",
    "        \n",
    "    \n",
    "        \n",
    "        \n",
    "        resRandomMean=np.mean(resRandom,axis=0,keepdims=True)\n",
    "    \n",
    "        distRand=np.abs(resRandom-resRandomMean)\n",
    "        distTrue=np.abs(res_curr-resRandomMean[0]).reshape((1,ncluster,ncluster))\n",
    "        distCompare=np.greater(distRand,distTrue)\n",
    "        greaterCount=greaterCount+np.sum(distCompare,axis=0)+1\n",
    "    \n",
    "        resRandom_sum=resRandom_sum+np.sum(resRandom,axis=0)\n",
    "    res=getHistMatrix(selfp,neighborp)\n",
    "    #save simulation results\n",
    "    np.savetxt(os.path.join(neighborplotsavepath,'neighborHist_withRandom'+progs[p]+'_significanceSeparated.csv'),greaterCount/(actualSamples+1))\n",
    "    np.savetxt(os.path.join(neighborplotsavepath,'neighborHist_withRandom'+progs[p]+'randMean.csv'),resRandom_sum/actualSamples)\n",
    "    np.savetxt(os.path.join(neighborplotsavepath,'neighborHist_withRandom'+progs[p]+'res.csv'),res)\n",
    "\n",
    "    \n",
    "    gc.collect()"
   ]
  }
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