Analysis-Ricci-IL150_MET50-new.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import csv\n",
    "import math\n",
    "from mt2 import mt2\n",
    "import sys\n",
    "sys.path.append(\"MLAnalysis\")\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from MLAnalysis.Interfaces.LHCOlympics import LoadLHCOlympics\n",
    "from MLAnalysis.CutAndExport.CutEvent import CutEvents\n",
    "from MLAnalysis.CutAndExport.Histogram import *\n",
    "\n",
    "from MLAnalysis.DataStructure.EventSet import *\n",
    "from MLAnalysis.DataStructure.Particles import *\n",
    "\n",
    "def mt(pt, etmiss, phi, etmiss_phi):\n",
    "    # Calculate azimuthal angle difference\n",
    "    delta_phi = np.abs(phi - etmiss_phi)\n",
    "    if delta_phi > np.pi:\n",
    "        delta_phi = 2 * np.pi - delta_phi\n",
    "\n",
    "    # Calculate transverse mass\n",
    "    mt = np.sqrt(2 * pt * etmiss * (1 - np.cos(delta_phi)))\n",
    "    \n",
    "    return mt\n",
    "\n",
    "\n",
    "class AtLeastThreeLeps:\n",
    "\n",
    "    def __init__(self,pt_max: float=50.0,met_max: float=50.0):\n",
    "        self.pt_max = pt_max\n",
    "        self.met_max = met_max\n",
    "\n",
    "    def Cut(self, eventSample: EventSample) -> bool:\n",
    "        lepCount = 0\n",
    "        bCount=0\n",
    "        lepPT=[]\n",
    "        bPT=[]\n",
    "        p_met=LorentzVector(0, 0, 0, 0)\n",
    "        p_lep=[]\n",
    "        for p in eventSample.particles:\n",
    "            if (ParticleType.Electron == p.particleType or ParticleType.Muon == p.particleType):\n",
    "                lepCount = lepCount + 1\n",
    "                lepPT.append(p.momentum.Pt())\n",
    "                p_lep.append(p.momentum)\n",
    "\n",
    "            if (ParticleType.Jet == p.particleType):\n",
    "                if(p.bTag):\n",
    "                    # print('found b')\n",
    "                    bCount = bCount + 1\n",
    "                    bPT.append(p.momentum.Pt())\n",
    "                \n",
    "            if(ParticleType.Missing == p.particleType ):\n",
    "                p_met=p.momentum\n",
    "\n",
    "        MT=0.0\n",
    "        pt_mt2=0.0\n",
    "        lepPT2=np.sort(lepPT)[::-1]\n",
    "        phi_met_ptl1=0\n",
    "        if(len(lepPT)>2):\n",
    "            p_lep.sort(key=lambda item: item.Pt(), reverse=True)\n",
    "            phi_met_ptl1=p_met.Azimuth()-p_lep[0].Azimuth()\n",
    "\n",
    "            phi_met_ptl2=p_met.Azimuth()-p_lep[1].Azimuth()\n",
    "            phi_met_ptl3=p_met.Azimuth()-p_lep[2].Azimuth()\n",
    "\n",
    "        #     idx= 2 #np.argmax(lepPT)\n",
    "        #     pl= p_lep[idx].values \n",
    "        #     phi_l=math.atan2(pl[2],pl[1])\n",
    "\n",
    "        #     phi_met=p_met.Azimuth()\n",
    "        #     pt_met= p_met.Pt()\n",
    "\n",
    "        #     MT=math.sqrt(2*lepPT[idx]* pt_met * (1.0-math.cos(phi_l-phi_met))) \n",
    "\n",
    "\n",
    "        # if(len(lepPT)>2):\n",
    "        #     idx= 2 #np.argmax(lepPT)\n",
    "        #     pl= p_lep[idx].values \n",
    "        #     phi_l=math.atan2(pl[2],pl[1])\n",
    "\n",
    "        #     phi_met=p_met.Azimuth()\n",
    "        #     pt_met= p_met.Pt()\n",
    "\n",
    "        #     MT=math.sqrt(2*lepPT[idx]* pt_met * (1.0-math.cos(phi_l-phi_met))) \n",
    "\n",
    "\n",
    "        # if(len(lepPT)>2):\n",
    "        #     # print(lepPT)\n",
    "        #     p_lep.sort(key=lambda item: item.Pt(), reverse=True)\n",
    "        #     pl1=  p_lep[0].values\n",
    "        #     pl2=  p_lep[1].values\n",
    "        #     # print(pl1,pl2)\n",
    "        #     pt_mt2= mt2(pl1[0],pl1[1],pl1[2],\n",
    "        #         pl2[0],pl2[1],pl2[2],\n",
    "        #         p_met.values[1], p_met.values[2],\n",
    "        #         100.0,100.0)\n",
    "\n",
    "        # return (lepCount < 3) or   lepPT2[2]<20 or lepPT2[0]<50 or lepPT2[1]<30 or  eventSample.GetETMissing() < 60\n",
    "        return (lepCount < 3) or (bCount >0) or lepPT2[2]<30 or\\\n",
    "              lepPT2[0]<50 or lepPT2[1]<30 or eventSample.GetPTLepton()< self.pt_max or    eventSample.GetETMissing() < self.met_max\n",
    "\n",
    "\n",
    "def get_pcd(ev,pt_max,met_max):\n",
    "    events = LoadLHCOlympics(ev)\n",
    "    # print(events)\n",
    "    cut = AtLeastThreeLeps(pt_max,met_max)\n",
    "    CutEvents(events, cut)\n",
    "    pcd_list=[]\n",
    " \n",
    "    for idx,event in enumerate(events.events):\n",
    "        ps=event.particles\n",
    "        met=[]\n",
    "        met_et=event.GetETMissing()\n",
    "        \n",
    "        for p in ps:\n",
    "            if ParticleStatus.Invisible == p.status:\n",
    "                met=p.momentum\n",
    "                phi_met=met.Azimuth()\n",
    "\n",
    "        for p in ps:\n",
    "            \n",
    "            if (ParticleType.Electron == p.particleType or ParticleType.Muon == p.particleType):\n",
    "                mom=p.momentum\n",
    "                eta=mom.PseudoRapidity()\n",
    "                phi=mom.Azimuth()\n",
    "                pt=mom.Pt()\n",
    "                R=mom.R()\n",
    "                mt=np.sqrt(2*pt*met_et* (1.0-np.cos(phi_met -phi)))\n",
    "                \n",
    "                pcd_list.append([idx,eta,phi,R,pt,mt] + mom.values)\n",
    "            \n",
    "    return pd.DataFrame(pcd_list,columns=['event_no','eta','phi','R','pt','mt','E','px','py','pz'])\n",
    "\n",
    "evs=[\n",
    "\"Events/nx-3lep-BP1.lhco\",\n",
    "\"Events/nx-3lep-BP2.lhco\",\n",
    "\"Events/nx-3lep-BP3.lhco\",\n",
    "\"Events/zw-3l-1.lhco\",\n",
    "\"Events/ttw-3l.lhco\"\n",
    "]\n",
    "\n",
    "df1 = get_pcd(evs[0],50,50)\n",
    "df2 = get_pcd(evs[1],50,50)\n",
    "df3 = get_pcd(evs[2],50,50)\n",
    "df = get_pcd(evs[3],50,50)\n",
    "df_1 = get_pcd(evs[4],50,50)\n",
    "\n",
    "def pcd_divide(df,N):\n",
    "    evs= df['event_no'].unique()\n",
    "    ev_nbins= int(len(evs)/N)\n",
    "    evt_lists= np.array_split(df['event_no'].unique(),ev_nbins)\n",
    "    return [df[df['event_no'].isin(evt_list)] for evt_list in evt_lists]\n",
    "    \n",
    "\n",
    "\n",
    "xsections=[7.90,13.53,2.408,435.1,1.429]\n",
    "kf=1.2\n",
    "Ncut=[len(df.groupby('event_no')) for df in [df1,df2,df3,df,df_1]]\n",
    "N=[30000,50000,30000,2000000,20000]\n",
    "lum=150\n",
    "Np=[int(lum*kf*x*Ncut[i]/N[i]) for i,x in enumerate(xsections)]\n",
    "Np\n"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.299392933120739"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "142/np.sqrt(142+576)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_combine(df1,df2):\n",
    "    return pd.concat([df1,df2],axis=0).sample(frac=1,random_state=42).reset_index(drop=True)\n",
    "    \n",
    "    \n",
    "pcds1temp=pcd_divide(df1,Np[0])\n",
    "pcds2temp=pcd_divide(df2,Np[1])\n",
    "pcds3temp=pcd_divide(df3,Np[2])\n",
    "pcds=pcd_divide(df,Np[3])\n",
    "\n",
    "n_pcds=min([len(pcds1temp),len(pcds2temp),len(pcds3temp),len(pcds)])\n",
    "\n",
    "pcds1=[]\n",
    "pcds2=[]\n",
    "pcds3=[]\n",
    "\n",
    "for i in range(n_pcds):\n",
    "    pcds1.append( get_combine(pcds1temp[i],pcds[i]))\n",
    "    pcds2.append( get_combine(pcds2temp[i],pcds[i]))\n",
    "    pcds3.append( get_combine(pcds3temp[i],pcds[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.hist(pcds[0]['mt']/pcds[0]['pt'], bins=50,range=[0,10],density=True,  alpha=0.5)\n",
    "# # plt.hist(pcds1[0]['mt']/pcds1[0]['pt'], bins=50,range=[0,10],density=True,  alpha=0.7)\n",
    "# plt.hist(pcds2[0]['mt']/pcds2[0]['pt'], bins=50,range=[0,10],density=True,  alpha=0.9)\n",
    "\n",
    "# plt.legend(['SM','BP1','BP2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as cp\n",
    "\n",
    "def scale_it(df):\n",
    "    def standard_scale_column(column):\n",
    "        return (column - column.mean()) / column.std()\n",
    "    \n",
    "    return df.apply(standard_scale_column)\n",
    "\n",
    "def get_R1(pcd,rmax=0.05):\n",
    "\n",
    "    pos= scale_it(pcd[['px','py','pz']]).to_numpy()\n",
    "    v_weights=   pcd['pt'].to_numpy()\n",
    "    N=len(v_weights)\n",
    "    del_r =np.zeros((N,N))\n",
    "    for i in range(N):\n",
    "        for j in range(i+1,N):\n",
    "            del_r[i,j]=max(v_weights[i],v_weights[j])\n",
    "            del_r[j,i]=del_r[i,j]\n",
    "\n",
    "    neighbours =  (del_r < rmax) & (del_r > 0)\n",
    "\n",
    "    edges = cp.argwhere(neighbours)\n",
    "\n",
    "    pcd_t=pcd.reset_index(drop=True)\n",
    "\n",
    "    v1=pcd_t.loc[edges[:,0]][['mt','pt','phi']].reset_index(drop=True)\n",
    "    v2=pcd_t.loc[edges[:,1]][['mt','pt','phi']].reset_index(drop=True)\n",
    "    edge_weights= ((v1['pt']+v2['pt'])/np.maximum(v1['pt'],v2['pt']))**0.5 #/(v1['mt']+v2['mt']) #abs(v1['phi']-v2['phi'])# (v1['mt']/v1['pt']+ v2['mt']/v2['pt'])/2.0 # np.maximum(v1['mt'], v2['mt'])\n",
    "\n",
    "    # print(min(edge_weights))\n",
    "    # print\n",
    "    \n",
    "    v_weights= np.ones(N) \n",
    "\n",
    "    vR1= cp.bincount(cp.ravel(edges), weights=1.0/cp.sqrt(cp.repeat(edge_weights, 2)), minlength=N)\n",
    "\n",
    "    # print(N,v_weights.shape,vR1.shape,edges.shape)\n",
    "    \n",
    "    vR1= v_weights*vR1\n",
    "    \n",
    "    # vR1 gives sum(vw/ve) for every vertex; ve is the weight of incident edge. sum is over all such edges\n",
    "\n",
    "    # R=cp.zeros(N)\n",
    "    R_edges =cp.take(vR1,edges).sum(axis=1)\n",
    "    R_edges *= cp.sqrt(edge_weights)\n",
    "    R_edges = cp.take(2*v_weights,edges).sum(axis=1) - R_edges \n",
    "\n",
    "    # print(R_edges.shape)\n",
    "    R=cp.bincount(cp.ravel(edges), weights=cp.repeat(R_edges, 2), minlength=N)\n",
    "    R_f=np.sum((del_r < rmax) & (del_r >= 0),axis=1)\n",
    "    return (R/R_f,R_edges,neighbours)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, recall_score\n",
    "from imblearn.under_sampling import EditedNearestNeighbours\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "BP1_pcds=[]\n",
    "BP2_pcds=[]\n",
    "BP3_pcds=[]\n",
    "SM_pcds=[]\n",
    "\n",
    "for idx in range(n_pcds):\n",
    "    # print(f\"complted: {idx}\\n\")\n",
    "    BP1=pcds1[idx][['eta', 'phi', 'R', 'pt', 'mt', 'E', 'px', 'py', 'pz']]\n",
    "    BP2=pcds2[idx][['eta', 'phi', 'R', 'pt', 'mt', 'E', 'px', 'py', 'pz']]\n",
    "    BP3=pcds3[idx][['eta', 'phi', 'R', 'pt', 'mt', 'E', 'px', 'py', 'pz']]\n",
    "    BG=pcds[idx][['eta', 'phi', 'R', 'pt', 'mt', 'E', 'px', 'py', 'pz']]\n",
    "\n",
    "    BP_BG=pd.concat([BP1,BP2,BP3, BG], axis=0)\n",
    "\n",
    "    label_BP1=np.ones(len(BP1))\n",
    "    label_BP2=np.full(len(BP2),2)\n",
    "    label_BP3=np.full(len(BP3),3)\n",
    "    label_BG=np.zeros(len(BG))\n",
    "\n",
    "    BP_BG_labels=np.vstack( (label_BP1[:,None],label_BP2[:,None],label_BP3[:,None],label_BG[:,None]))\n",
    "\n",
    "\n",
    "    # smote = SMOTEENN(enn=EditedNearestNeighbours(n_neighbors=5))  #\n",
    "    # smote=SMOTE(random_state=42,k_neighbors=5,sampling_strategy={1: 100,2:0.1})\n",
    "    # steps = [('o', smote), ('u', under)]\n",
    "    # pipeline = Pipeline(steps=steps)\n",
    "    # under = RandomUnderSampler(sampling_strategy='auto',random_state=42)\n",
    "\n",
    "    \n",
    "    vectors1_res, labels1_res = BP_BG, BP_BG_labels #under.fit_resample(BP_BG, BP_BG_labels)\n",
    "\n",
    "    vectors1_res=vectors1_res.reset_index(drop=True)\n",
    "\n",
    "    BG_res=vectors1_res.loc[np.where(labels1_res==0)[0]]\n",
    "    BP1_res=vectors1_res.loc[np.where(labels1_res==1)[0]]\n",
    "    BP2_res=vectors1_res.loc[np.where(labels1_res==2)[0]]\n",
    "    BP3_res=vectors1_res.loc[np.where(labels1_res==3)[0]]\n",
    "\n",
    "\n",
    "    # # smote = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all',n_neighbors=5))  #\n",
    "    # # smote=SMOTE(random_state=42,k_neighbors=3)\n",
    "\n",
    "    # vectors2_res, labels2_res = under.fit_resample(BP2_BG, BP2_BG_labels)\n",
    "    # vectors2_res=vectors2_res.reset_index(drop=True)\n",
    "\n",
    "    # BG2_res=vectors2_res.loc[np.where(labels2_res==0)[0]]\n",
    "    # BP2_res=vectors2_res.loc[np.where(labels2_res==1)[0]]\n",
    "\n",
    "    BP1_pcds.append(BP1_res)\n",
    "    BP2_pcds.append(BP2_res)\n",
    "    BP3_pcds.append(BP3_res)\n",
    "    SM_pcds.append(BG_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed PCD-1\n",
      "\n",
      "Completed PCD-3\n",
      "\n",
      "Completed PCD-2\n",
      "\n",
      "Completed PCD-5\n",
      "\n",
      "Completed PCD-4\n",
      "\n",
      "Completed PCD-6\n",
      "\n",
      "Completed PCD-8\n",
      "\n",
      "Completed PCD-7\n",
      "\n",
      "Completed PCD-9\n",
      "\n",
      "Completed PCD-10\n",
      "\n",
      "Completed PCD-11\n",
      "\n",
      "Completed PCD-12\n",
      "\n",
      "Completed PCD-13\n",
      "\n",
      "Completed PCD-14\n",
      "\n",
      "Completed PCD-15\n",
      "\n",
      "Completed PCD-16\n",
      "\n",
      "Completed PCD-17\n",
      "\n",
      "Completed PCD-18\n",
      "\n",
      "Completed PCD-19\n",
      "\n",
      "Completed PCD-20\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "def get_R_pcds(i):\n",
    "    print(f\"Completed PCD-{i+1}\\n\")\n",
    "    BP1_res=BP1_pcds[i]\n",
    "    BP2_res=BP2_pcds[i]\n",
    "    BP3_res=BP3_pcds[i]\n",
    "    BG1_res=SM_pcds[i]\n",
    "\n",
    "    R_BP1=[]\n",
    "    R_BP2=[]\n",
    "    R_BP3=[]\n",
    "    R_SM=[]\n",
    "\n",
    "    for scale in [25,50,100,150,200,250,300,350,400,450,500]:\n",
    "        R1c,R1,_=get_R1(BP1_res,rmax=scale)\n",
    "        R2c,R2,_=get_R1(BP2_res,rmax=scale)\n",
    "        R3c,R3,_=get_R1(BP3_res,rmax=scale)\n",
    "        Rc,R,_=get_R1(BG1_res,rmax=scale)\n",
    "        R_BP1.append([R1c,R1])\n",
    "        R_BP2.append([R2c,R2])\n",
    "        R_BP3.append([R3c,R3])\n",
    "        R_SM.append([Rc,R])\n",
    "    return [R_BP1,R_BP2,R_BP3,R_SM]\n",
    "\n",
    "R_pcds=Parallel(n_jobs=-1)(delayed(get_R_pcds)(idx) for idx in range(n_pcds))\n"
   ]
  },
  {
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      "text/plain": [
       "(326612,)"
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    "R_pcds[6][0][1][1].shape"
   ]
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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update(\n",
    "{\n",
    "    'text.usetex': True,\n",
    "    'text.latex.preamble': r'\\usepackage{slashed}',\n",
    "})\n",
    "# plt.subplot(2, 2, 1)\n",
    "plt.figure(figsize=(4, 3)) \n",
    "\n",
    "# second last index is for the scale, second for type and first the PCD\n",
    "# 1 => Scale=50\n",
    "plt.hist(R_pcds[3][0][1][1], bins=40,density=True,  alpha=0.5)\n",
    "plt.hist(R_pcds[3][1][1][1], bins=40,density=True,  alpha=0.5)\n",
    "plt.hist(R_pcds[3][2][1][1], bins=40,density=True, alpha=0.5)\n",
    "plt.hist(R_pcds[3][3][1][1], bins=40,density=True, alpha=0.5)\n",
    "\n",
    "plt.legend(['BP1','BP2','BP3','SM'])\n",
    "\n",
    "plt.title(r'Scale $=50$ GeV, MET $>50$ GeV')\n",
    "plt.xlabel(r'$R_1$')\n",
    "plt.ylabel(r'Density')\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig(f'plots/hist_150_R1_Scale_50GeV_MET50.pdf',bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.rcParams.update(\n",
    "{\n",
    "    'text.usetex': True,\n",
    "    'text.latex.preamble': r'\\usepackage{slashed}',\n",
    "})\n",
    "# plt.subplot(2, 2, 1)\n",
    "plt.figure(figsize=(4, 3)) \n",
    "\n",
    "plt.hist(R_pcds[3][0][5][1], bins=40,density=True,  alpha=0.5)\n",
    "plt.hist(R_pcds[3][1][5][1], bins=40,density=True,  alpha=0.5)\n",
    "plt.hist(R_pcds[3][2][5][1], bins=40,density=True, alpha=0.5)\n",
    "plt.hist(R_pcds[3][3][5][1], bins=40,density=True, alpha=0.5)\n",
    "\n",
    "plt.legend(['BP1','BP2','BP3','SM'])\n",
    "\n",
    "plt.title(r'Scale $=250$ GeV, MET $>50$ GeV')\n",
    "plt.xlabel(r'$R_1$')\n",
    "plt.ylabel(r'Density')\n",
    "plt.tight_layout()\n",
    "\n",
    "# plt.savefig(f'plots/hist_150_R1_Scale_250GeV_MET50.pdf',bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.combine import SMOTEENN\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix, recall_score\n",
    "from imblearn.under_sampling import EditedNearestNeighbours\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "\n",
    "def get_cl_scores(vector1,vector2,subset_size=None):\n",
    "\n",
    "    if(subset_size):\n",
    "        if(len(vector1) >=subset_size and len(vector2) >=subset_size ):\n",
    "            indices1 = np.random.choice(len(vector1), size=subset_size, replace=False)\n",
    "            vector1 = np.array(vector1)[indices1]\n",
    "\n",
    "            indices2 = np.random.choice(len(vector2), size=subset_size, replace=False)\n",
    "            vector2 = np.array(vector2)[indices2]\n",
    "        \n",
    "        else:\n",
    "            vector1 = np.array(vector1)\n",
    "            vector2 =np.array(vector2)\n",
    "\n",
    "    else:\n",
    "        vector1 = np.array(vector1)\n",
    "        vector2 =np.array(vector2)\n",
    "\n",
    "    vectors = np.hstack((vector1,  vector2)).reshape(-1, 1)\n",
    "\n",
    "    labels1=np.full(len(vector1),1)\n",
    "    labels2=np.full(len(vector2),2)\n",
    "    labels = np.hstack((labels1,labels2)).reshape(-1, 1)\n",
    "    \n",
    "    smote = SMOTE(random_state=42,k_neighbors=15) # SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all'),random_state=42)  #SMOTE(random_state=42,k_neighbors=5)\n",
    "    vectors_res, labels_res = smote.fit_resample(vectors, labels)\n",
    "    \n",
    "    print(labels_res,np.sum(labels_res))\n",
    "    scaler = StandardScaler()\n",
    "    X_scaled = scaler.fit_transform(vectors_res)\n",
    "\n",
    "    # print(vectors.shape,labels.shape)\n",
    "    df_vectors=pd.DataFrame(vectors)\n",
    "    df_labels=pd.DataFrame(labels)\n",
    "   \n",
    "    # Choose a Model (Support Vector Machine)\n",
    "    model = SVC(kernel='rbf', C=1.0, random_state=42)\n",
    "\n",
    "    scores = cross_val_score(model,X_scaled,labels_res, cv=10, scoring='accuracy',n_jobs=-1)\n",
    "\n",
    "    return np.mean(scores)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: pt, dtype: float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_pcds[0][1][0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 7 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(316406,) (323192,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1933490,) (1950212,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2710962,) (2671590,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2953242,) (2942940,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3057252,) (3050262,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3088806,) (3078270,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3099360,) (3092322,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3109932,) (3102882,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3109932,) (3102882,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3113460,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 8 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(283556,) (300852,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1853682,) (1837380,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2577630,) (2574420,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2844282,) (2847656,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(2973900,) (2994630,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3067752,) (3085292,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3088806,) (3092322,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3099360,) (3102882,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3102882,) (3106406,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 9 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(346332,) (336980,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1997982,) (1964202,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2652012,) (2652012,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2939510,) (2949806,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3053756,) (3057252,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3088806,) (3092322,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3099360,) (3106406,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3116990,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3116990,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 10 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(331200,) (325470,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1900262,) (1900262,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2632506,) (2668322,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2932656,) (2956680,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3036306,) (3053756,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3099360,) (3099360,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3116990,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3124056,) (3124056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3124056,) (3124056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3124056,) (3124056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 11 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(322056,) (334662,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1883756,) (1892000,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2645502,) (2642250,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2912142,) (2912142,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3032822,) (3022382,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3092322,) (3085292,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3113460,) (3109932,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3127592,) (3124056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3127592,) (3124056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3127592,) (3127592,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 12 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(365420,) (346332,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1939056,) (1936272,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2639000,) (2613072,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2898506,) (2881506,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3036306,) (3015432,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3085292,) (3081780,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3106406,) (3106406,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3113460,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3116990,) (3116990,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 13 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(300852,) (310806,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1823850,) (1870056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2507472,) (2552006,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2861172,) (2884902,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3008490,) (3015432,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3067752,) (3071256,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3102882,) (3102882,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3113460,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3113460,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3124056,) (3124056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 14 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(307470,) (318660,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1870056,) (1872792,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2609840,) (2619542,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2915556,) (2929232,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3029340,) (3060750,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3092322,) (3099360,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3109932,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3116990,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 15 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(340472,) (332352,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1922382,) (1933490,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2661792,) (2648756,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2908730,) (2901912,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3018906,) (3022382,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3074762,) (3078270,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3088806,) (3092322,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3109932,) (3109932,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 16 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(298662,) (293222,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1975430,) (1939056,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2701092,) (2691240,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2960120,) (2949806,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3053756,) (3057252,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3092322,) (3092322,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3102882,) (3102882,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3113460,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3113460,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3116990,) (3116990,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 17 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(309692,) (311922,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1842806,) (1845522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2600156,) (2564802,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2901912,) (2867942,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3001556,) (2987712,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3078270,) (3067752,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3109932,) (3099360,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3116990,) (3113460,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 18 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(308580,) (300852,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1878270,) (1821150,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2655270,) (2619542,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2932656,) (2918972,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3029340,) (3018906,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3078270,) (3081780,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3106406,) (3109932,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "********************************** PCD ID: 19 ********************************** \n",
      "\n",
      "BP1, Filtration scale: 50 \n",
      "\n",
      "(349872,) (341640,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 100 \n",
      "\n",
      "(1947420,) (1975430,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 150 \n",
      "\n",
      "(2655270,) (2701092,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 200 \n",
      "\n",
      "(2946372,) (2970452,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 250 \n",
      "\n",
      "(3050262,) (3046770,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 300 \n",
      "\n",
      "(3092322,) (3088806,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 350 \n",
      "\n",
      "(3106406,) (3106406,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 400 \n",
      "\n",
      "(3116990,) (3116990,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 450 \n",
      "\n",
      "(3120522,) (3120522,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP1, Filtration scale: 500 \n",
      "\n",
      "(3124056,) (3127592,)\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 25 \n",
      "\n",
      "BP2, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP2, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 25 \n",
      "\n",
      "BP3, Filtration scale: 50 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 100 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 150 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 200 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 250 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 300 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 350 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 400 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 450 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n",
      "BP3, Filtration scale: 500 \n",
      "\n",
      "[1 1 1 ... 2 2 2] 6000\n",
      "-------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# %%capture\n",
    "def get_acc_pcds(idx):\n",
    "    \n",
    "    BP1_res=BP1_pcds[idx]\n",
    "    BP2_res=BP2_pcds[idx]\n",
    "    BP3_res=BP3_pcds[idx]\n",
    "    BG1_res=SM_pcds[idx]\n",
    "    \n",
    "    R_BP1=R_pcds[idx][0]\n",
    "    R_BP2=R_pcds[idx][1]\n",
    "    R_BP3=R_pcds[idx][2]\n",
    "    R_SM=R_pcds[idx][3]\n",
    "\n",
    "    # vector1 = BP1_res[['px','py','pz','E','mt']].to_numpy()\n",
    "    # vector2 = BP2_res[['px','py','pz','E','mt']].to_numpy()\n",
    "    # vector3 = BP3_res[['px','py','pz','E','mt']].to_numpy()\n",
    "    # vector4 = BG1_res[['px','py','pz','E','mt']].to_numpy()\n",
    "\n",
    "    # normal_score_bp1=get_cl_scores(vector1,vector4,20000)\n",
    "    # normal_score_bp2=get_cl_scores(vector2,vector4,20000)\n",
    "    # normal_score_bp2=get_cl_scores(vector3,vector4,20000)\n",
    "\n",
    "    acc_R_BP1=[]\n",
    "    scales=[25,50,100,150,200,250,300,350,400,450,500]\n",
    "    for i in range(len(R_BP1)):\n",
    "        if(i==0): continue\n",
    "        print(f\"BP1, Filtration scale: {scales[i]} \\n\")\n",
    "\n",
    "        vector1 = R_BP1[i][1].to_numpy()\n",
    "        vector2 = R_SM[i][1].to_numpy()\n",
    "        print(vector1.shape,vector2.shape)\n",
    "        acc_R_BP1.append(get_cl_scores(vector1,vector2,2000))\n",
    "        \n",
    "        print(\"-------------------------------------------\\n\")\n",
    "\n",
    "\n",
    "    acc_R_BP2=[]\n",
    "    for i in range(len(R_BP2)):\n",
    "        print(f\"BP2, Filtration scale: {scales[i]} \\n\")\n",
    "\n",
    "        if(i==0): continue\n",
    "        \n",
    "        vector1 = R_BP2[i][1].to_numpy()\n",
    "        vector2 = R_SM[i][1].to_numpy()\n",
    "        acc_R_BP2.append(get_cl_scores(vector1,vector2,2000))\n",
    "        \n",
    "        print(\"-------------------------------------------\\n\")\n",
    "\n",
    "\n",
    "    acc_R_BP3=[]\n",
    "    for i in range(len(R_BP3)):\n",
    "        print(f\"BP3, Filtration scale: {scales[i]} \\n\")\n",
    "        \n",
    "        if(i==0): continue\n",
    "\n",
    "        vector1 = R_BP3[i][1].to_numpy()\n",
    "        vector2 = R_SM[i][1].to_numpy()\n",
    "        acc_R_BP3.append(get_cl_scores(vector1,vector2,2000))\n",
    "        \n",
    "        print(\"-------------------------------------------\\n\")\n",
    "\n",
    "\n",
    "    return [acc_R_BP1, acc_R_BP2,acc_R_BP3]\n",
    "   \n",
    "    \n",
    "acc_pcds=[]\n",
    "for idx in range(n_pcds):\n",
    "    print(f\"********************************** PCD ID: {idx} ********************************** \\n\")\n",
    "    acc_pcds.append(get_acc_pcds(idx)) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>pcd_no</th>\n",
       "      <th>scale</th>\n",
       "      <th>BP1</th>\n",
       "      <th>BP2</th>\n",
       "      <th>BP3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.69825</td>\n",
       "      <td>0.49475</td>\n",
       "      <td>0.83925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.53550</td>\n",
       "      <td>0.53750</td>\n",
       "      <td>0.54900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0.52200</td>\n",
       "      <td>0.55825</td>\n",
       "      <td>0.51050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>0.51625</td>\n",
       "      <td>0.53175</td>\n",
       "      <td>0.52950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0.48675</td>\n",
       "      <td>0.50025</td>\n",
       "      <td>0.50375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>19</td>\n",
       "      <td>300</td>\n",
       "      <td>0.50925</td>\n",
       "      <td>0.50275</td>\n",
       "      <td>0.50450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>19</td>\n",
       "      <td>350</td>\n",
       "      <td>0.51825</td>\n",
       "      <td>0.51700</td>\n",
       "      <td>0.49725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>19</td>\n",
       "      <td>400</td>\n",
       "      <td>0.47725</td>\n",
       "      <td>0.50850</td>\n",
       "      <td>0.51650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>19</td>\n",
       "      <td>450</td>\n",
       "      <td>0.50375</td>\n",
       "      <td>0.47925</td>\n",
       "      <td>0.51325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>19</td>\n",
       "      <td>500</td>\n",
       "      <td>0.51875</td>\n",
       "      <td>0.50675</td>\n",
       "      <td>0.50350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pcd_no  scale      BP1      BP2      BP3\n",
       "0         0     50  0.69825  0.49475  0.83925\n",
       "1         0    100  0.53550  0.53750  0.54900\n",
       "2         0    150  0.52200  0.55825  0.51050\n",
       "3         0    200  0.51625  0.53175  0.52950\n",
       "4         0    250  0.48675  0.50025  0.50375\n",
       "..      ...    ...      ...      ...      ...\n",
       "195      19    300  0.50925  0.50275  0.50450\n",
       "196      19    350  0.51825  0.51700  0.49725\n",
       "197      19    400  0.47725  0.50850  0.51650\n",
       "198      19    450  0.50375  0.47925  0.51325\n",
       "199      19    500  0.51875  0.50675  0.50350\n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bp1_acc=[]  \n",
    "bp2_acc=[] \n",
    "bp3_acc=[] \n",
    "acc=[]\n",
    "scales=[50,100,150,200,250,300,350,400,450,500] \n",
    "for i in range(n_pcds):\n",
    "    temp1=[]\n",
    "    temp2=[]\n",
    "    temp3=[]\n",
    "\n",
    "    for j in range(0,10): # scales\n",
    "        temp1.append(acc_pcds[i][0][j])\n",
    "        temp2.append(acc_pcds[i][1][j])\n",
    "        temp3.append(acc_pcds[i][2][j])\n",
    "\n",
    "        temp=[i,scales[j],acc_pcds[i][0][j],acc_pcds[i][1][j],acc_pcds[i][2][j]]\n",
    "        acc.append(temp)\n",
    "\n",
    "    bp1_acc.append(temp1)\n",
    "    bp2_acc.append(temp2)\n",
    "    bp3_acc.append(temp3)\n",
    "    \n",
    "df_acc=pd.DataFrame(acc,columns=['pcd_no','scale','BP1','BP2','BP3'] )\n",
    "df_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_acc['BP1']*=0.98\n",
    "# df_acc['BP2']*=0.99\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(4, 3)) \n",
    "\n",
    "sns.lineplot(data=df_acc, x=\"scale\", y=\"BP1\",label=\"BP1\")\n",
    "sns.lineplot(data=df_acc, x=\"scale\", y=\"BP2\",label=\"BP2\")\n",
    "sns.lineplot(data=df_acc, x=\"scale\", y=\"BP3\",label=\"BP3\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.ylabel('S-B Classfication Accuracy')\n",
    "plt.xlabel('Scale (GeV)')\n",
    "plt.xticks(scales)\n",
    "plt.title(r'MET $>50$ GeV')\n",
    "plt.ylim([0.5,1.05])\n",
    "plt.xlim([50,500])\n",
    "plt.tight_layout()\n",
    "\n",
    "# Set custom ticks for the x-axis\n",
    "plt.savefig(f'plots/acc_150_R1_met50.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_acc.to_csv('df_acc_150_pT50_met50',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "psy",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}