{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Short description of the problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider a vacuum vessel embodied as a toroidal volume defined by a 2D arbitrary polygon representing in cross-section in (R,Z) coordinates.\n",
    "We want to efficiently mesh both the volume and the 3D surface of the vessel.\n",
    "\n",
    "As input, we provide the cross-section polygon and a list dV defining the three resolutions dV = [dR,dZ,dRphi], where each resolution is a distance in meters in the corresponding direction (note the use of dRphi instead of dphi for that purpose).\n",
    "Indeed, we do not want a simple cylindrical mesh, as we want to capture details in the toroidal (Rphi) direction with a similar accuracy for every value of R.\n",
    "\n",
    "In most uses, we will only need a fraction of the total mesh. Hence, the routine should not compute the total mesh but only the fraction we need.\n",
    "For a given resolution dV (i.e. a given tuple of dR,dZ,dRphi), there should be only one possible mesh.\n",
    "This way, the fraction of interest can be defined either by its physical limits (DR,DZ,DPhi) or by the indices of the mesh elements which are required. \n",
    "\n",
    "Hence, we need a methods to \n",
    "* Compute the mesh fraction from its limits and return it with its indices\n",
    "* Compute the mesh fraction from its indices and return it with its limits\n",
    "\n",
    "Not returning the whole mesh makes sense since it may well be a huge array (memory-expensive). The computation should be as fast as possible.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given input cross-section polygon VPoly, provided with the required resolutions dR, dZ and dRphi, the total mesh is a torus of rectangular cross-section (R goes from a minimum to a maximum, as Z, and phi goes from -pi to pi). This simplifies the computation of the indices without making the result heavier since only the relevant mesh elements are returned.\n",
    "The limits of the desired submesh can be provided as DR, DZ and DPhi (note that the toroidal limit is angular as opposed to the toroidal resolution).\n",
    "\n",
    "The minimum and maximim boundaries of R and Z are also provided. They could be deduced from VPOly but they are explicitly provided for two reasons:\n",
    "* In ToFu these limits are stored in the Ves object, so it is already available and there is no need to compute it again\n",
    "* It may happen that a VPoly is slightly modified with no desire to modify the mesh, so it should remain possible to force the boundaries.\n",
    "\n",
    "The indices are return in numeric form (not boolean), and computed as follows:\n",
    "* The numbering occurs anti-clockwise from -pi to pi for phi\n",
    "* Then, it occurs along Z, from its minimum to its maximum value\n",
    "* Finally it is incremented along R, from its minimum to its maximum value\n",
    "\n",
    "Hence, we first describe the innermost column (starting from the lowest horizontal ring), and we gradually moved towards the outermost column.\n",
    "\n",
    "This is necessary since due the desired quasi-constant toroidal accuracy, the number of mesh elements in the toroidal direction is a function of R. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Surface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The strategy for meshing the surface is similar, expect that here there are only two resolutions, dL and dRPhi, where dL is the resolution in the poloidal direction along the segments of VPoly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volume meshing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 44.64 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "10000000 loops, best of 3: 90.8 ns per loop\n",
      "The slowest run took 36.53 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "10000000 loops, best of 3: 111 ns per loop\n"
     ]
    }
   ],
   "source": [
    "A = np.random.random((100,))\n",
    "%timeit len(A)\n",
    "%timeit A.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Preliminary function to get optimal resolution from input resolution\n",
    "def _Ves_mesh_dlfromL_numpy(LMinMax, dL, DL=None, margin=1.e-9):\n",
    "    \"\"\" Get the actual reolution from the desired resolution and MinMax and limits \"\"\"\n",
    "    # Get the number of mesh elements in LMinMax\n",
    "    N = np.ceil(np.diff(LMinMax)/dL)[0]\n",
    "    # Derive the real (effective) resolution\n",
    "    dLr = (np.diff(LMinMax)/N)[0]\n",
    "    # Get desired limits if any\n",
    "    DL = LMinMax if DL is None else DL\n",
    "    # Get the extreme indices of the mesh elements that really need to be created within those limits\n",
    "    abs0 = np.abs(DL[0]-LMinMax[0])\n",
    "    if abs0-dLr*np.floor(abs0/dLr)<margin*dLr:\n",
    "        nL0 = np.round((DL[0]-LMinMax[0])/dLr)\n",
    "    else:\n",
    "        nL0 = np.floor((DL[0]-LMinMax[0])/dLr)\n",
    "    abs1 = np.abs(DL[1]-LMinMax[0])\n",
    "    if abs1-dLr*np.floor(abs1/dLr)<margin*dLr:\n",
    "        nL1 = np.round((DL[1]-LMinMax[0])/dLr)-1\n",
    "    else:\n",
    "        nL1 = np.floor((DL[1]-LMinMax[0])/dLr)\n",
    "    # Get the corresponding indices\n",
    "    indL = np.arange(nL0,nL1+1,1).astype(int)\n",
    "    # Get the centers of the mesh elements\n",
    "    L = LMinMax[0] + (0.5 + indL)*dLr\n",
    "    return L, dLr, indL, N\n",
    "\n",
    "\n",
    "def _Ves_Vmesh_Phi_numpy(DPhi, dPhir, NRPhi, margin=1.e-9):\n",
    "    \"\"\" Get the min and max indices of the relevant Phi for each R (to be finished) \"\"\"\n",
    "    nPhi0 = np.nan*np.ones((dPhir.size,))\n",
    "    nPhi1 = np.nan*np.ones((dPhir.size,))\n",
    "    abs0 = np.abs(DPhi[0]+np.pi)\n",
    "    ind0 = abs0-dPhir*np.floor(abs0/dPhir)<margin*dPhir\n",
    "    nPhi0[ind0] = np.round((DPhi[0]+np.pi)/dPhir[ind0])\n",
    "    nPhi0[~ind0] = np.floor((DPhi[0]+np.pi)/dPhir[~ind0])\n",
    "    abs1 = np.abs(DPhi[1]+np.pi)\n",
    "    ind1 = abs1-dPhir*np.floor(abs1/dPhir)<margin*dPhir\n",
    "    nPhi1[ind1] = np.round((DPhi[1]+np.pi)/dPhir[ind1])-1\n",
    "    nPhi1[~ind1] = np.floor((DPhi[1]+np.pi)/dPhir[~ind1])\n",
    "    assert np.all(nPhi0>=0) and np.all(nPhi0<=NRPhi)\n",
    "    assert np.all(nPhi1>=0) and np.all(nPhi1<=NRPhi)\n",
    "    return nPhi0, nPhi1\n",
    "\n",
    "\n",
    "def _Ves_Vmesh_Tor_SubFromD_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax, DR=None, DZ=None, DPhi=None, VPoly=None, Out='(X,Y,Z)', margin=1.e-9):\n",
    "    \"\"\" Return the desired submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \"\"\"\n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    R0, dRr0, indR0, NR0 = _Ves_mesh_dlfromL_numpy(RMinMax, dR, None, margin=margin)\n",
    "    R, dRr, indR, NR = _Ves_mesh_dlfromL_numpy(RMinMax, dR, DR, margin=margin)\n",
    "    Z, dZr, indZ, NZ = _Ves_mesh_dlfromL_numpy(ZMinMax, dZ, DZ, margin=margin)\n",
    "    Rn, Zn = len(R), len(Z)\n",
    "\n",
    "    # Get the actual RPhi resolution and Phi mesh elements (! depends on R !)\n",
    "    NRPhi0 = np.ceil(2.*np.pi*R0/dRPhi)\n",
    "    NRPhi = np.ceil(2.*np.pi*R/dRPhi)\n",
    "    dPhir = 2.*np.pi/NRPhi\n",
    "    # Get the limits if any (and make sure to replace them in the proper quadrants)\n",
    "    DPhi = [-np.pi,np.pi] if DPhi is None else np.arctan2(np.sin(DPhi),np.cos(DPhi))\n",
    "    # Later we'll have to remember that the lower limit may be greater than the upper limit due to 2pi modulo\n",
    "    # Get the two extreme indices of the Phi elements inside the limits for each R\n",
    "    nPhi0, nPhi1 = _Ves_Vmesh_Phi_numpy(DPhi, dPhir, NRPhi, margin=margin)\n",
    "\n",
    "    # Compute the centers and indices, using a for loop (no choice) on R\n",
    "    NRZPhi_cum0 = np.concatenate(([0],NZ*np.cumsum(NRPhi0[:-1])))\n",
    "    PtsRZP, dV, ind = [], [], []\n",
    "    for ii in range(0,Rn):\n",
    "        # Get the phi values\n",
    "        if DPhi[0]<DPhi[1]:\n",
    "            indPhi = np.arange(nPhi0[ii],nPhi1[ii]+1)\n",
    "        else:\n",
    "            indPhi = np.concatenate((np.arange(nPhi0[ii],NRPhi[ii]),np.arange(0,nPhi1[ii]+1)))\n",
    "        Phin = len(indPhi)\n",
    "        phi = -np.pi + (0.5 + indPhi)*dPhir[ii]\n",
    "        # Get the (R,Z,phi) points\n",
    "        PtsRZP.append( np.array([R[ii]*np.ones((Phin*Zn,)), np.repeat(Z,Phin), np.tile(phi,Zn)]) )\n",
    "        # Get the volume\n",
    "        dV.append( dRr*dZr*dPhir[ii]*R[ii]*np.ones((Phin*Zn,))  )\n",
    "        # Get the indices\n",
    "        indii = R0==R[ii]\n",
    "        ind.append( NRZPhi_cum0[indii] + (indZ[:,np.newaxis]*NRPhi[ii] + indPhi[np.newaxis,:]).flatten() )\n",
    "        \n",
    "    PtsRZP = np.concatenate(tuple(PtsRZP),axis=1)\n",
    "    dV = np.concatenate(tuple(dV))\n",
    "    ind = np.concatenate(tuple(ind))\n",
    "    \n",
    "    # To be commented out once debugged\n",
    "    assert ind.size==PtsRZP.shape[1]\n",
    "    assert ind.size==dV.size\n",
    "    assert np.unique(ind).size==ind.size \n",
    "\n",
    "    dRPhir = (dPhir*R)\n",
    "    \"\"\"\n",
    "    if not VPoly is None:\n",
    "        indin = _Ves_isInside(VPoly, 'Tor', None, PtsRZP[:-1,:], In='(R,Z)')\n",
    "        PtsRZP, dV, ind = PtsRZP[:,indin], dV[indin], ind[indin]\n",
    "        Ru = np.unique(PtsRZP[0,:])\n",
    "        if not Ru==R:\n",
    "            indii = np.array([R[ii] in Ru for ii in range(0,len(R))], dtype=bool)\n",
    "            dRPhir = dRPhir[indii]\n",
    "    \"\"\"\n",
    "\n",
    "    if Out.lower()=='(x,y,z)':\n",
    "        Pts = np.array([PtsRZP[0,:]*np.cos(PtsRZP[2,:]), PtsRZP[0,:]*np.sin(PtsRZP[2,:]), PtsRZP[1,:]])\n",
    "    else:\n",
    "        Pts = PtsRZP\n",
    "        \n",
    "    return Pts, dV, ind, dRr, dZr, dRPhir\n",
    "\n",
    "def _Ves_Vmesh_Tor_SubFromInd_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax, ind, Out='(X,Y,Z)', margin=1.e-9):\n",
    "    \"\"\" Return the desired submesh indicated by the (numerical) indices, for the desired resolution (dR,dZ,dRphi) \"\"\"\n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    R, dRr, indR, NR = _Ves_mesh_dlfromL_numpy(RMinMax, dR, None, margin=margin)\n",
    "    Z, dZr, indZ, NZ = _Ves_mesh_dlfromL_numpy(ZMinMax, dZ, None, margin=margin)\n",
    "    Rn, Zn = len(R), len(Z)\n",
    "\n",
    "    # Get the actual RPhi resolution and Phi mesh elements (! depends on R !)\n",
    "    NRPhi = np.ceil(2.*np.pi*R/dRPhi)\n",
    "    dPhir = 2.*np.pi/NRPhi\n",
    "    NRZPhi_cum = np.concatenate(([0],NZ*np.cumsum(NRPhi[:-1])))\n",
    "    \n",
    "    # Get R, Z, and Phi indices\n",
    "    Nind = ind.size\n",
    "    indR = ind[:,np.newaxis]-NRZPhi_cum[np.newaxis,:]<0\n",
    "    indRtemp = np.tile(np.arange(0,NR),(Nind,1))\n",
    "    indRtemp[indR] = np.nan\n",
    "    indR = np.nanmax(indRtemp, axis=1).astype(int)\n",
    "    indZ = ((ind - NRZPhi_cum[indR]) // NRPhi[indR]).astype(int)\n",
    "    indPhi = ind - NRZPhi_cum[indR] - indZ*NRPhi[indR]\n",
    "    \n",
    "    # Derive R, Z and phi values\n",
    "    PtsRZP = np.array([R[indR], Z[indZ], np.nan*np.ones((Nind,))])\n",
    "    dV = np.nan*np.ones((Nind,))\n",
    "    Ru = np.unique(PtsRZP[0,:])\n",
    "    dRPhir = np.nan*np.ones((len(Ru),))\n",
    "    for ii in range(0,len(Ru)):\n",
    "        indRii = (R==Ru[ii]).nonzero()[0]\n",
    "        indRu = (PtsRZP[0,:]==Ru[ii]).nonzero()[0]\n",
    "        PtsRZP[2,indRu] = -np.pi + (0.5+indPhi[indRu])*dPhir[indRii]\n",
    "        dRPhir[ii] = dPhir[indRii]*Ru[ii]\n",
    "        dV[indRu] = dRr*dZr*dRPhir[ii]\n",
    "        \n",
    "    if Out.lower()=='(x,y,z)':\n",
    "        Pts = np.array([PtsRZP[0,:]*np.cos(PtsRZP[2,:]), PtsRZP[0,:]*np.sin(PtsRZP[2,:]), PtsRZP[1,:]])\n",
    "    else:\n",
    "        Pts = PtsRZP\n",
    "\n",
    "    return Pts, dV, dRr, dZr, dRPhir\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check and debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check 0 [True, True, True, True, True]\n",
      "Check 1 [True, True, True, True, True]\n"
     ]
    }
   ],
   "source": [
    "# Check and debug\n",
    "[dR, dZ, dRPhi], RMinMax, ZMinMax = [0.03]*3, [2.,3.], [-1.,1.]\n",
    "DR, DZ, DPhi = [2.15,2.6], [-0.21,0.51], [7.*np.pi/4.,5.*np.pi/4.]\n",
    "\n",
    "Pts0, dV0, ind0, dRr0, dZr0, dRPhir0 = _Ves_Vmesh_Tor_SubFromD_numpy(dR,dZ,dRPhi, RMinMax,ZMinMax, Out='(X,Y,Z)')\n",
    "PtsRZP0 = np.array([np.hypot(Pts0[0,:],Pts0[1,:]), Pts0[2,:],np.arctan2(Pts0[1,:],Pts0[0,:])])\n",
    "Pts0b, dV0b, dRr0b, dZr0b, dRPhir0b = _Ves_Vmesh_Tor_SubFromInd_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax, ind0, Out='(X,Y,Z)')\n",
    "print \"Check 0\", [np.allclose(Pts0,Pts0b,atol=1.e-14,rtol=0.,equal_nan=True) for (A,B) in [(Pts0,Pts0b),(dV0,dV0b),(dRr0,dRr0b),(dZr0,dZr0b),(dRPhir0,dRPhir0b)]]\n",
    "\n",
    "Pts, dV, ind, dRr, dZr, dRPhir = _Ves_Vmesh_Tor_SubFromD_numpy(dR,dZ,dRPhi, RMinMax,ZMinMax, DR=DR,DZ=DZ,DPhi=DPhi, Out='(X,Y,Z)')\n",
    "PtsRZP = np.array([np.hypot(Pts[0,:],Pts[1,:]), Pts[2,:],np.arctan2(Pts[1,:],Pts[0,:])])\n",
    "Ptsb, dVb, dRrb, dZrb, dRPhirb = _Ves_Vmesh_Tor_SubFromInd_numpy(dR,dZ,dRPhi, RMinMax,ZMinMax, ind, Out='(X,Y,Z)')\n",
    "print \"Check 1\", [np.allclose(A,B,atol=1.e-14,rtol=0.,equal_nan=True) for (A,B) in [(Pts,Ptsb),(dV,dVb),(dRr,dRrb),(dZr,dZrb),(dRPhir,dRPhirb)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d3EvZ+P+27P9xe13n47xjOjPw6ePLZIi7HZMQJ8v6AXEisOO5iWsJLGceVVCN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\nte+OwAeBRwBXsMckqiRpZv0I+AtPpv/42eay2/jZ9mW3m27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l6gFbZzsxoK2hsuXaNF9p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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f58234b2d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for check and debug\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(16,10))\n",
    "plt.subplot(1,2,1, aspect=\"equal\", adjustable=\"datalim\")\n",
    "plt.plot(PtsRZP0[0,:],PtsRZP0[1,:],'.b', PtsRZP[0,:],PtsRZP[1,:],'.r', markersize=2)\n",
    "plt.plot([DR[0],DR[1],DR[1],DR[0],DR[0]], [DZ[0],DZ[0],DZ[1],DZ[1],DZ[0]],'--k')\n",
    "\n",
    "plt.subplot(1,2,2, aspect=\"equal\", adjustable=\"datalim\")\n",
    "plt.plot(Pts0[0,:],Pts0[1,:],'.b', Pts[0,:],Pts[1,:],'.r', markersize=2)\n",
    "thet = np.linspace(DPhi[0],DPhi[1],100) if DPhi[0]<DPhi[1] else np.linspace(DPhi[0],DPhi[1]+2.*np.pi,100)\n",
    "X = np.concatenate((DR[0]*np.cos(thet),DR[1]*np.cos(thet[::-1]),[DR[0]*np.cos(thet[0])]))\n",
    "Y = np.concatenate((DR[0]*np.sin(thet),DR[1]*np.sin(thet[::-1]),[DR[0]*np.sin(thet[0])]))\n",
    "plt.plot(X,Y,'--k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loop, best of 3: 773 ms per loop\n",
      "1 loop, best of 3: 6.31 s per loop\n"
     ]
    }
   ],
   "source": [
    "# Check and debug\n",
    "[dR, dZ, dRPhi], RMinMax, ZMinMax = [0.02]*3, [2.,3.], [-1.,1.]\n",
    "DR, DZ, DPhi = [2.15,2.6], [-0.21,0.51], [7.*np.pi/4.,5.*np.pi/4.]\n",
    "Pts0, dV0, ind0, dRr0, dZr0, dRPhir0 = _Ves_Vmesh_Tor_SubFromD_numpy(dR,dZ,dRPhi, RMinMax,ZMinMax, Out='(X,Y,Z)')\n",
    "\n",
    "%timeit out = _Ves_Vmesh_Tor_SubFromD_numpy(dR,dZ,dRPhi, RMinMax,ZMinMax, Out='(X,Y,Z)')\n",
    "%timeit out = _Ves_Vmesh_Tor_SubFromInd_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax, ind0, Out='(X,Y,Z)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">009</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">010</span>: <span class=\"c\"># Preliminary function to get optimal resolution from input resolution</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">011</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">cdivision</span><span class=\"p\">(</span><span class=\"bp\">True</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">012</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">013</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-88\" onclick='toggleDiv(this)'>+<span class=\"\">014</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_mesh_dlfromL_cython</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">LMinMax</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">DL</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-88 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_1_Ves_mesh_dlfromL_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static char __pyx_doc_46_cython_magic_ffb7650808bb7830760fff8d6d047f13__Ves_mesh_dlfromL_cython[] = \" Get the actual reolution from the desired resolution and MinMax and limits \";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_1_Ves_mesh_dlfromL_cython = {\"_Ves_mesh_dlfromL_cython\", (PyCFunction)__pyx_pw_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_1_Ves_mesh_dlfromL_cython, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_ffb7650808bb7830760fff8d6d047f13__Ves_mesh_dlfromL_cython};\n",
       "static PyObject *__pyx_pw_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_1_Ves_mesh_dlfromL_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  __Pyx_memviewslice __pyx_v_LMinMax = { 0, 0, { 0 }, { 0 }, { 0 } };\n",
       "  double __pyx_v_dL;\n",
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       "    PyObject* values[4] = {0,0,0,0};\n",
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       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
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       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_10);\n",
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       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_12);\n",
       "  __PYX_XDEC_MEMVIEW(&amp;__pyx_t_13, 1);\n",
       "  __PYX_XDEC_MEMVIEW(&amp;__pyx_t_26, 1);\n",
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       "  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_34);\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ffb7650808bb7830760fff8d6d047f13._Ves_mesh_dlfromL_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">015</span>:     <span class=\"sd\">&quot;&quot;&quot; Get the actual reolution from the desired resolution and MinMax and limits &quot;&quot;&quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">016</span>:     <span class=\"c\"># Get the number of mesh elements in LMinMax</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">017</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">N</span> <span class=\"o\">=</span> <span class=\"n\">Cceil</span><span class=\"p\">((</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dL</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = 1;\n",
       "  __pyx_t_2 = 0;\n",
       "  __pyx_v_N = ceil((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_1)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_2)) )))) / __pyx_v_dL));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">018</span>:     <span class=\"c\"># Derive the real (effective) resolution</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">019</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dLr</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">N</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_3 = 1;\n",
       "  __pyx_t_4 = 0;\n",
       "  __pyx_v_dLr = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_3)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_4)) )))) / __pyx_v_N);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">020</span>:     <span class=\"c\"># Get desired limits if any</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">021</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">DLc</span><span class=\"p\">,</span> <span class=\"n\">L</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">022</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">long</span> [<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indL</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">023</span>:     <span class=\"c\">#cdef cnp.ndarray[double,ndim=1] indL, L</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">024</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">abs0</span><span class=\"p\">,</span> <span class=\"nf\">abs1</span><span class=\"p\">,</span> <span class=\"nf\">A</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">025</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">nL0</span><span class=\"p\">,</span> <span class=\"nf\">nL1</span><span class=\"p\">,</span> <span class=\"nf\">Nind</span><span class=\"p\">,</span> <span class=\"nf\">ii</span><span class=\"p\">,</span> <span class=\"nf\">jj</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">026</span>:     <span class=\"k\">if</span> <span class=\"n\">DL</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_5 = (__pyx_v_DL == Py_None);\n",
       "  __pyx_t_6 = (__pyx_t_5 != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L3;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">027</span>:         <span class=\"n\">DLc</span> <span class=\"o\">=</span> <span class=\"n\">LMinMax</span></pre>\n",
       "<pre class='cython code score-0 '>    __PYX_INC_MEMVIEW(&amp;__pyx_v_LMinMax, 0);\n",
       "    __pyx_v_DLc = __pyx_v_LMinMax;\n",
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       "<pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">029</span>:         <span class=\"k\">if</span> <span class=\"n\">DL</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-3 '>  /*else*/ {\n",
       "    __pyx_t_7 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_DL, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 29, __pyx_L1_error)\n",
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       "    __pyx_t_5 = (__pyx_t_6 != 0);\n",
       "    if (__pyx_t_5) {\n",
       "/* … */\n",
       "    }\n",
       "</pre><pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">030</span>:             <span class=\"n\">DL</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-8 '>      __pyx_t_8 = 0;\n",
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       "<pre class='cython code score-8 '>      __pyx_t_9 = 1;\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">034</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">035</span>:     <span class=\"c\"># Get the extreme indices of the mesh elements that really need to be created within those limits</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">036</span>:     <span class=\"n\">abs0</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_14 = 0;\n",
       "  __pyx_t_15 = 0;\n",
       "  __pyx_v_abs0 = fabs(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_14)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_15)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">037</span>:     <span class=\"k\">if</span> <span class=\"n\">abs0</span><span class=\"o\">-</span><span class=\"n\">dLr</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs0</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">)</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dLr</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = (((__pyx_v_abs0 - (__pyx_v_dLr * floor((__pyx_v_abs0 / __pyx_v_dLr)))) &lt; (__pyx_v_margin * __pyx_v_dLr)) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L6;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">038</span>:         <span class=\"n\">nL0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_16 = 0;\n",
       "    __pyx_t_17 = 0;\n",
       "    __pyx_v_nL0 = ((int)round((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_16)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_17)) )))) / __pyx_v_dLr)));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">039</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">040</span>:         <span class=\"n\">nL0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_18 = 0;\n",
       "    __pyx_t_19 = 0;\n",
       "    __pyx_v_nL0 = ((int)floor((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_18)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_19)) )))) / __pyx_v_dLr)));\n",
       "  }\n",
       "  __pyx_L6:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">041</span>:     <span class=\"n\">abs1</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_20 = 1;\n",
       "  __pyx_t_21 = 0;\n",
       "  __pyx_v_abs1 = fabs(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_20)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_21)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">042</span>:     <span class=\"k\">if</span> <span class=\"n\">abs1</span><span class=\"o\">-</span><span class=\"n\">dLr</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs1</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">)</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dLr</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = (((__pyx_v_abs1 - (__pyx_v_dLr * floor((__pyx_v_abs1 / __pyx_v_dLr)))) &lt; (__pyx_v_margin * __pyx_v_dLr)) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L7;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">043</span>:         <span class=\"n\">nL1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">)</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_22 = 1;\n",
       "    __pyx_t_23 = 0;\n",
       "    __pyx_v_nL1 = ((int)(round((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_22)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_23)) )))) / __pyx_v_dLr)) - 1.0));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">044</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">045</span>:         <span class=\"n\">nL1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_24 = 1;\n",
       "    __pyx_t_25 = 0;\n",
       "    __pyx_v_nL1 = ((int)floor((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_24)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_25)) )))) / __pyx_v_dLr)));\n",
       "  }\n",
       "  __pyx_L7:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">046</span>:     <span class=\"c\"># Get the corresponding indices</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">047</span>:     <span class=\"n\">Nind</span> <span class=\"o\">=</span> <span class=\"n\">nL1</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"o\">-</span><span class=\"n\">nL0</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_Nind = ((__pyx_v_nL1 + 1) - __pyx_v_nL0);\n",
       "</pre><pre class=\"cython line score-37\" onclick='toggleDiv(this)'>+<span class=\"\">048</span>:     <span class=\"n\">indL</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">((</span><span class=\"n\">Nind</span><span class=\"p\">,),</span><span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">int</span><span class=\"p\">)</span><span class=\"c\">#np.linspace(nL0,nL1,Nind,endpoint=True)</span></pre>\n",
       "<pre class='cython code score-37 '>  __pyx_t_7 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 48, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">057</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">058</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
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       "<pre class='cython code score-47 '>/* Python wrapper */\n",
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       "static char __pyx_doc_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_2_Ves_Vmesh_Tor_SubFromD_cython[] = \" Return the desired submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \";\n",
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       "<pre class='cython code score-145 '>    values[5] = ((PyObject *)Py_None);\n",
       "    values[6] = ((PyObject *)Py_None);\n",
       "    values[7] = ((PyObject *)Py_None);\n",
       "    values[8] = ((PyObject *)Py_None);\n",
       "    values[9] = ((PyObject*)__pyx_kp_s_X_Y_Z);\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
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       "      switch (pos_args) {\n",
       "        case 11: values[10] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 10);\n",
       "        case 10: values[9] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 9);\n",
       "        case  9: values[8] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 8);\n",
       "        case  8: values[7] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 7);\n",
       "        case  7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n",
       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dR)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dZ)) != 0)) kw_args--;\n",
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       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DR);\n",
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       "        if (kw_args &gt; 0) {\n",
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       "        case  7:\n",
       "        if (kw_args &gt; 0) {\n",
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       "          if (value) { values[7] = value; kw_args--; }\n",
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       "        case  8:\n",
       "        if (kw_args &gt; 0) {\n",
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       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_Out);\n",
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       "        case 10: values[9] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 9);\n",
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       "        case  8: values[7] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 7);\n",
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       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
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       "    __pyx_v_dR = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[0]); if (unlikely((__pyx_v_dR == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 59, __pyx_L3_error)\n",
       "    __pyx_v_dZ = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_dZ == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 59, __pyx_L3_error)\n",
       "    __pyx_v_dRPhi = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_dRPhi == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 59, __pyx_L3_error)\n",
       "    __pyx_v_RMinMax = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[3]); if (unlikely(!__pyx_v_RMinMax.memview)) __PYX_ERR(0, 60, __pyx_L3_error)\n",
       "    __pyx_v_ZMinMax = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[4]); if (unlikely(!__pyx_v_ZMinMax.memview)) __PYX_ERR(0, 60, __pyx_L3_error)\n",
       "    __pyx_v_DR = values[5];\n",
       "    __pyx_v_DZ = values[6];\n",
       "    __pyx_v_DPhi = values[7];\n",
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       "    __pyx_v_Out = ((PyObject*)values[9]);\n",
       "    if (values[10]) {\n",
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       "  __pyx_r = __pyx_pf_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_2_Ves_Vmesh_Tor_SubFromD_cython(__pyx_self, __pyx_v_dR, __pyx_v_dZ, __pyx_v_dRPhi, __pyx_v_RMinMax, __pyx_v_ZMinMax, __pyx_v_DR, __pyx_v_DZ, __pyx_v_DPhi, __pyx_v_VPoly, __pyx_v_Out, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">062</span>:                                    <span class=\"nb\">str</span> <span class=\"n\">Out</span><span class=\"o\">=</span><span class=\"s\">&#39;(X,Y,Z)&#39;</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">063</span>:     <span class=\"s\">&quot; Return the desired submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">064</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">065</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R0</span><span class=\"p\">,</span> <span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">Z</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span><span class=\"p\">,</span> <span class=\"n\">dPhir</span><span class=\"p\">,</span> <span class=\"n\">NRPhi</span><span class=\"c\">#, dPhi, NRZPhi_cum0, indPhi, phi</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">066</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dRr0</span><span class=\"p\">,</span> <span class=\"nf\">dRr</span><span class=\"p\">,</span> <span class=\"nf\">dZr</span><span class=\"p\">,</span> <span class=\"nf\">DPhi0</span><span class=\"p\">,</span> <span class=\"nf\">DPhi1</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">067</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">abs0</span><span class=\"p\">,</span> <span class=\"nf\">abs1</span><span class=\"p\">,</span> <span class=\"nf\">phi</span><span class=\"p\">,</span> <span class=\"nf\">indiijj</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">068</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">long</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indR0</span><span class=\"p\">,</span> <span class=\"n\">indR</span><span class=\"p\">,</span> <span class=\"n\">indZ</span><span class=\"p\">,</span> <span class=\"n\">Phin</span><span class=\"p\">,</span> <span class=\"n\">NRPhi0</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">069</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NR0</span><span class=\"p\">,</span> <span class=\"nf\">NR</span><span class=\"p\">,</span> <span class=\"nf\">NZ</span><span class=\"p\">,</span> <span class=\"nf\">Rn</span><span class=\"p\">,</span> <span class=\"nf\">Zn</span><span class=\"p\">,</span> <span class=\"nf\">nRPhi0</span><span class=\"p\">,</span> <span class=\"nf\">indR0ii</span><span class=\"p\">,</span> <span class=\"nf\">ii</span><span class=\"p\">,</span> <span class=\"nf\">jj</span><span class=\"p\">,</span> <span class=\"nf\">nPhi0</span><span class=\"p\">,</span> <span class=\"nf\">nPhi1</span><span class=\"p\">,</span> <span class=\"nf\">zz</span><span class=\"p\">,</span> <span class=\"nf\">NP</span><span class=\"p\">,</span> <span class=\"nf\">NRPhi_int</span><span class=\"p\">,</span> <span class=\"nf\">Rratio</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">070</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">indI</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">071</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">dV</span><span class=\"p\">,</span> <span class=\"n\">ind</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">072</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">073</span>:     <span class=\"c\"># Get the actual R and Z resolutions and mesh elements</span></pre>\n",
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       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "  __pyx_t_10 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_long</span>(__pyx_t_2);\n",
       "  if (unlikely(!__pyx_t_10.memview)) __PYX_ERR(0, 89, __pyx_L1_error)\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
       "  __pyx_v_NRPhi0 = __pyx_t_10;\n",
       "  __pyx_t_10.memview = NULL;\n",
       "  __pyx_t_10.data = NULL;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">090</span>:     <span class=\"n\">nRPhi0</span><span class=\"p\">,</span> <span class=\"n\">indR0ii</span> <span class=\"o\">=</span> <span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_11 = 0;\n",
       "  __pyx_t_19 = 0;\n",
       "  __pyx_v_nRPhi0 = __pyx_t_11;\n",
       "  __pyx_v_indR0ii = __pyx_t_19;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">091</span>:     <span class=\"n\">NP</span><span class=\"p\">,</span> <span class=\"n\">NPhimax</span> <span class=\"o\">=</span> <span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_19 = 0;\n",
       "  __pyx_t_20 = 0;\n",
       "  __pyx_v_NP = __pyx_t_19;\n",
       "  __pyx_v_NPhimax = __pyx_t_20;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">092</span>:     <span class=\"n\">Rratio</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">Rn</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_21 = (__pyx_v_Rn - 1);\n",
       "  __pyx_t_22 = 0;\n",
       "  __pyx_v_Rratio = ((int)ceil(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_21)) ))) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_22)) ))))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">093</span>:     <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Rn</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_19 = __pyx_v_Rn;\n",
       "  for (__pyx_t_11 = 0; __pyx_t_11 &lt; __pyx_t_19; __pyx_t_11+=1) {\n",
       "    __pyx_v_ii = __pyx_t_11;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">094</span>:         <span class=\"c\"># Get the actual RPhi resolution and Phi mesh elements (! depends on R !)</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">095</span>:         <span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">*</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_23 = __pyx_v_ii;\n",
       "    __pyx_t_24 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_24)) )) = ceil((((2. * M_PI) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_23)) )))) / __pyx_v_dRPhi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">096</span>:         <span class=\"n\">NRPhi_int</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_25 = __pyx_v_ii;\n",
       "    __pyx_v_NRPhi_int = ((int)(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_25)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">097</span>:         <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">/</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_26 = __pyx_v_ii;\n",
       "    __pyx_t_27 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_27)) )) = ((2. * M_PI) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_26)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">098</span>:         <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_28 = __pyx_v_ii;\n",
       "    __pyx_t_29 = __pyx_v_ii;\n",
       "    __pyx_t_30 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhir.data) + __pyx_t_30)) )) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_28)) ))) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_29)) ))));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">099</span>:         <span class=\"c\"># Get index and cumulated indices from background</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">100</span>:         <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">indR0ii</span><span class=\"p\">,</span><span class=\"n\">NR0</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_31 = __pyx_v_NR0;\n",
       "    for (__pyx_t_32 = __pyx_v_indR0ii; __pyx_t_32 &lt; __pyx_t_31; __pyx_t_32+=1) {\n",
       "      __pyx_v_jj = __pyx_t_32;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">101</span>:             <span class=\"k\">if</span> <span class=\"n\">R0</span><span class=\"p\">[</span><span class=\"n\">jj</span><span class=\"p\">]</span><span class=\"o\">==</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_33 = __pyx_v_jj;\n",
       "      __pyx_t_34 = __pyx_v_ii;\n",
       "      __pyx_t_14 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R0.data) + __pyx_t_33)) ))) == (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_34)) )))) != 0);\n",
       "      if (__pyx_t_14) {\n",
       "/* … */\n",
       "      }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">102</span>:                 <span class=\"n\">indR0ii</span> <span class=\"o\">=</span> <span class=\"n\">jj</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_v_indR0ii = __pyx_v_jj;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">103</span>:                 <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>        goto __pyx_L13_break;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">104</span>:             <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">105</span>:                 <span class=\"n\">nRPhi0</span> <span class=\"o\">+=</span> <span class=\"p\">&lt;</span><span class=\"kt\">long</span><span class=\"p\">&gt;</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">*</span><span class=\"n\">R0</span><span class=\"p\">[</span><span class=\"n\">jj</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      /*else*/ {\n",
       "        __pyx_t_35 = __pyx_v_jj;\n",
       "        __pyx_v_nRPhi0 = (__pyx_v_nRPhi0 + ((long)ceil((((2. * M_PI) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R0.data) + __pyx_t_35)) )))) / __pyx_v_dRPhi))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">106</span>:                 <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">nRPhi0</span><span class=\"o\">*</span><span class=\"n\">NZ</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_36 = __pyx_v_ii;\n",
       "        *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_36)) )) = (__pyx_v_nRPhi0 * __pyx_v_NZ);\n",
       "      }\n",
       "    }\n",
       "    __pyx_L13_break:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">107</span>:         <span class=\"c\"># Get indices of phi</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">108</span>:         <span class=\"c\"># Get the extreme indices of the mesh elements that really need to be created within those limits</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">109</span>:         <span class=\"n\">abs0</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DPhi0</span><span class=\"o\">+</span><span class=\"n\">Cpi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_abs0 = fabs((__pyx_v_DPhi0 + M_PI));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">110</span>:         <span class=\"k\">if</span> <span class=\"n\">abs0</span><span class=\"o\">-</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs0</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_37 = __pyx_v_ii;\n",
       "    __pyx_t_38 = __pyx_v_ii;\n",
       "    __pyx_t_39 = __pyx_v_ii;\n",
       "    __pyx_t_14 = (((__pyx_v_abs0 - ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_37)) ))) * floor((__pyx_v_abs0 / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_38)) ))))))) &lt; (__pyx_v_margin * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_39)) ))))) != 0);\n",
       "    if (__pyx_t_14) {\n",
       "/* … */\n",
       "      goto __pyx_L15;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">111</span>:             <span class=\"n\">nPhi0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DPhi0</span><span class=\"o\">+</span><span class=\"n\">Cpi</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_40 = __pyx_v_ii;\n",
       "      __pyx_v_nPhi0 = ((int)round(((__pyx_v_DPhi0 + M_PI) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_40)) ))))));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">112</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">113</span>:             <span class=\"n\">nPhi0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DPhi0</span><span class=\"o\">+</span><span class=\"n\">Cpi</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-0 '>    /*else*/ {\n",
       "      __pyx_t_41 = __pyx_v_ii;\n",
       "      __pyx_v_nPhi0 = ((int)floor(((__pyx_v_DPhi0 + M_PI) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_41)) ))))));\n",
       "    }\n",
       "    __pyx_L15:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">114</span>:         <span class=\"n\">abs1</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DPhi1</span><span class=\"o\">+</span><span class=\"n\">Cpi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_abs1 = fabs((__pyx_v_DPhi1 + M_PI));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">115</span>:         <span class=\"k\">if</span> <span class=\"n\">abs1</span><span class=\"o\">-</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs1</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_42 = __pyx_v_ii;\n",
       "    __pyx_t_43 = __pyx_v_ii;\n",
       "    __pyx_t_44 = __pyx_v_ii;\n",
       "    __pyx_t_14 = (((__pyx_v_abs1 - ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_42)) ))) * floor((__pyx_v_abs1 / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_43)) ))))))) &lt; (__pyx_v_margin * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_44)) ))))) != 0);\n",
       "    if (__pyx_t_14) {\n",
       "/* … */\n",
       "      goto __pyx_L16;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">116</span>:             <span class=\"n\">nPhi1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DPhi1</span><span class=\"o\">+</span><span class=\"n\">Cpi</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_45 = __pyx_v_ii;\n",
       "      __pyx_v_nPhi1 = ((int)(round(((__pyx_v_DPhi1 + M_PI) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_45)) ))))) - 1.0));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">117</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">118</span>:             <span class=\"n\">nPhi1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DPhi1</span><span class=\"o\">+</span><span class=\"n\">Cpi</span><span class=\"p\">)</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-0 '>    /*else*/ {\n",
       "      __pyx_t_46 = __pyx_v_ii;\n",
       "      __pyx_v_nPhi1 = ((int)floor(((__pyx_v_DPhi1 + M_PI) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_46)) ))))));\n",
       "    }\n",
       "    __pyx_L16:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">119</span>: </pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">120</span>:         <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;</span><span class=\"n\">DPhi1</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_14 = ((__pyx_v_DPhi0 &lt; __pyx_v_DPhi1) != 0);\n",
       "    if (__pyx_t_14) {\n",
       "/* … */\n",
       "      goto __pyx_L17;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">121</span>:             <span class=\"c\">#indI.append(list(range(nPhi0,nPhi1+1)))</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">122</span>:             <span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">nPhi1</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"o\">-</span><span class=\"n\">nPhi0</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_47 = __pyx_v_ii;\n",
       "      *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Phin.data) + __pyx_t_47)) )) = ((__pyx_v_nPhi1 + 1) - __pyx_v_nPhi0);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">123</span>:             <span class=\"k\">if</span> <span class=\"n\">ii</span><span class=\"o\">==</span><span class=\"mf\">0</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_14 = ((__pyx_v_ii == 0) != 0);\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">149</span>:                     <span class=\"n\">phi</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"n\">Cpi</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"n\">indiijj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_69 = __pyx_v_ii;\n",
       "          __pyx_v_phi = ((-M_PI) + ((0.5 + __pyx_v_indiijj) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_69)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">150</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">151</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_73 = __pyx_v_ii;\n",
       "          __pyx_t_74 = 1;\n",
       "          __pyx_t_75 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_74, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_75, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_73)) ))) * sin(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">152</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Z</span><span class=\"p\">[</span><span class=\"n\">zz</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_76 = __pyx_v_zz;\n",
       "          __pyx_t_77 = 2;\n",
       "          __pyx_t_78 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_77, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_78, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Z.data) + __pyx_t_76)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">153</span>:                     <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indZ</span><span class=\"p\">[</span><span class=\"n\">zz</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indiijj</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_79 = __pyx_v_ii;\n",
       "          __pyx_t_80 = __pyx_v_zz;\n",
       "          __pyx_t_81 = __pyx_v_ii;\n",
       "          __pyx_t_82 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_ind.rcbuffer-&gt;pybuffer.buf, __pyx_t_82, __pyx_pybuffernd_ind.diminfo[0].strides) = (((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_79)) ))) + ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_indZ.data) + __pyx_t_80)) ))) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_81)) ))))) + __pyx_v_indiijj);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">154</span>:                     <span class=\"n\">dV</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRr</span><span class=\"o\">*</span><span class=\"n\">dZr</span><span class=\"o\">*</span><span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_83 = __pyx_v_ii;\n",
       "          __pyx_t_84 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dV.rcbuffer-&gt;pybuffer.buf, __pyx_t_84, __pyx_pybuffernd_dV.diminfo[0].strides) = ((__pyx_v_dRr * __pyx_v_dZr) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhir.data) + __pyx_t_83)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">155</span>:                     <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_NP = (__pyx_v_NP + 1);\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">156</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">157</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Rn</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_19 = __pyx_v_Rn;\n",
       "    for (__pyx_t_11 = 0; __pyx_t_11 &lt; __pyx_t_19; __pyx_t_11+=1) {\n",
       "      __pyx_v_ii = __pyx_t_11;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">158</span>:             <span class=\"k\">for</span> <span class=\"n\">zz</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Zn</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_31 = __pyx_v_Zn;\n",
       "      for (__pyx_t_32 = 0; __pyx_t_32 &lt; __pyx_t_31; __pyx_t_32+=1) {\n",
       "        __pyx_v_zz = __pyx_t_32;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">159</span>:                 <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]):</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_85 = __pyx_v_ii;\n",
       "        __pyx_t_20 = (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Phin.data) + __pyx_t_85)) )));\n",
       "        for (__pyx_t_66 = 0; __pyx_t_66 &lt; __pyx_t_20; __pyx_t_66+=1) {\n",
       "          __pyx_v_jj = __pyx_t_66;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">160</span>:                     <span class=\"n\">indiijj</span> <span class=\"o\">=</span> <span class=\"n\">indI</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">,</span><span class=\"n\">jj</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_86 = __pyx_v_ii;\n",
       "          __pyx_t_87 = __pyx_v_jj;\n",
       "          __pyx_v_indiijj = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_indI.rcbuffer-&gt;pybuffer.buf, __pyx_t_86, __pyx_pybuffernd_indI.diminfo[0].strides, __pyx_t_87, __pyx_pybuffernd_indI.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">161</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_88 = __pyx_v_ii;\n",
       "          __pyx_t_89 = 0;\n",
       "          __pyx_t_90 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_89, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_90, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_88)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">162</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Z</span><span class=\"p\">[</span><span class=\"n\">zz</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_91 = __pyx_v_zz;\n",
       "          __pyx_t_92 = 1;\n",
       "          __pyx_t_93 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_92, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_93, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Z.data) + __pyx_t_91)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">163</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"n\">Cpi</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"n\">indiijj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_94 = __pyx_v_ii;\n",
       "          __pyx_t_95 = 2;\n",
       "          __pyx_t_96 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_95, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_96, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((-M_PI) + ((0.5 + __pyx_v_indiijj) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_94)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">164</span>:                     <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indZ</span><span class=\"p\">[</span><span class=\"n\">zz</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indiijj</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_97 = __pyx_v_ii;\n",
       "          __pyx_t_98 = __pyx_v_zz;\n",
       "          __pyx_t_99 = __pyx_v_ii;\n",
       "          __pyx_t_100 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_ind.rcbuffer-&gt;pybuffer.buf, __pyx_t_100, __pyx_pybuffernd_ind.diminfo[0].strides) = (((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_97)) ))) + ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_indZ.data) + __pyx_t_98)) ))) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_99)) ))))) + __pyx_v_indiijj);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">165</span>:                     <span class=\"n\">dV</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRr</span><span class=\"o\">*</span><span class=\"n\">dZr</span><span class=\"o\">*</span><span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_101 = __pyx_v_ii;\n",
       "          __pyx_t_102 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dV.rcbuffer-&gt;pybuffer.buf, __pyx_t_102, __pyx_pybuffernd_dV.diminfo[0].strides) = ((__pyx_v_dRr * __pyx_v_dZr) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhir.data) + __pyx_t_101)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">166</span>:                     <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_NP = (__pyx_v_NP + 1);\n",
       "        }\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  __pyx_L26:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">167</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">168</span>:     <span class=\"sd\">&quot;&quot;&quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">169</span>: <span class=\"sd\">    #if not VPoly is None:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">170</span>: <span class=\"sd\">    #    indin = _Ves_isInside(VPoly, &#39;Tor&#39;, None, PtsRZP[:-1,:], In=&#39;(R,Z)&#39;)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">171</span>: <span class=\"sd\">    #    PtsRZP, dV, ind = PtsRZP[:,indin], dV[indin], ind[indin]</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">172</span>: <span class=\"sd\">    #    Ru = np.unique(PtsRZP[0,:])</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">173</span>: <span class=\"sd\">    #    if not Ru==R:</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">174</span>: <span class=\"sd\">    #        indii = np.array([R[ii] in Ru for ii in range(0,len(R))], dtype=bool)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">175</span>: <span class=\"sd\">    #        dRPhir = dRPhir[indii]</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">176</span>: <span class=\"sd\">    &quot;&quot;&quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">177</span>: </pre>\n",
       "<pre class=\"cython line score-107\" onclick='toggleDiv(this)'>+<span class=\"\">178</span>:     <span class=\"k\">return</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">dV</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">),</span> <span class=\"n\">dRr</span><span class=\"p\">,</span> <span class=\"n\">dZr</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">asarray</span><span class=\"p\">(</span><span class=\"n\">dRPhir</span><span class=\"p\">)</span></pre>\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">182</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-165\" onclick='toggleDiv(this)'>+<span class=\"\">183</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_Vmesh_Tor_SubFromInd_cython</span><span class=\"p\">(</span><span class=\"n\">double</span> <span class=\"n\">dR</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dZ</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dRPhi</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-165 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_5_Ves_Vmesh_Tor_SubFromInd_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static char __pyx_doc_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_4_Ves_Vmesh_Tor_SubFromInd_cython[] = \" Return the desired submesh indicated by the (numerical) indices, for the desired resolution (dR,dZ,dRphi) \";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_5_Ves_Vmesh_Tor_SubFromInd_cython = {\"_Ves_Vmesh_Tor_SubFromInd_cython\", (PyCFunction)__pyx_pw_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_5_Ves_Vmesh_Tor_SubFromInd_cython, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_4_Ves_Vmesh_Tor_SubFromInd_cython};\n",
       "static PyObject *__pyx_pw_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_5_Ves_Vmesh_Tor_SubFromInd_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  double __pyx_v_dR;\n",
       "  double __pyx_v_dZ;\n",
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       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
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       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
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       "        else goto __pyx_L5_argtuple_error;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dZ)) != 0)) kw_args--;\n",
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       "        if (likely((values[2] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dRPhi)) != 0)) kw_args--;\n",
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       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"_Ves_Vmesh_Tor_SubFromInd_cython\") &lt; 0)) __PYX_ERR(0, 183, __pyx_L3_error)\n",
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       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Vmesh_Tor_SubFromInd_cython\", 0, 6, 8, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 183, __pyx_L3_error)\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ffb7650808bb7830760fff8d6d047f13._Ves_Vmesh_Tor_SubFromInd_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
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       "  if (unlikely(!<span class='pyx_c_api'>__Pyx_ArgTypeTest</span>(((PyObject *)__pyx_v_Out), (&amp;PyString_Type), 1, \"Out\", 1))) __PYX_ERR(0, 185, __pyx_L1_error)\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_ffb7650808bb7830760fff8d6d047f13_4_Ves_Vmesh_Tor_SubFromInd_cython(__pyx_self, __pyx_v_dR, __pyx_v_dZ, __pyx_v_dRPhi, __pyx_v_RMinMax, __pyx_v_ZMinMax, __pyx_v_ind, __pyx_v_Out, __pyx_v_margin);\n",
       "\n",
       "  /* function exit code */\n",
       "  goto __pyx_L0;\n",
       "  __pyx_L1_error:;\n",
       "  __pyx_r = NULL;\n",
       "  __pyx_L0:;\n",
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       "  return __pyx_r;\n",
       "}\n",
       "\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">205</span>:     <span class=\"n\">Rratio</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">NR</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_18 = (__pyx_v_NR - 1);\n",
       "  __pyx_t_19 = 0;\n",
       "  __pyx_v_Rratio = ((long)ceil(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_18)) ))) / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_19)) ))))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">206</span>:     <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NR</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_14 = __pyx_v_NR;\n",
       "  for (__pyx_t_20 = 0; __pyx_t_20 &lt; __pyx_t_14; __pyx_t_20+=1) {\n",
       "    __pyx_v_ii = __pyx_t_20;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">207</span>:         <span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">&lt;</span><span class=\"kt\">long</span><span class=\"p\">&gt;(</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">*</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_21 = __pyx_v_ii;\n",
       "    __pyx_t_22 = __pyx_v_ii;\n",
       "    *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_22)) )) = ((long)ceil((((2. * M_PI) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_21)) )))) / __pyx_v_dRPhi)));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">208</span>:         <span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">*</span><span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"p\">&lt;</span><span class=\"kt\">double</span><span class=\"p\">&gt;(</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_23 = __pyx_v_ii;\n",
       "    __pyx_t_24 = __pyx_v_ii;\n",
       "    __pyx_t_25 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_25)) )) = (((2. * M_PI) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_23)) )))) / ((double)(*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_24)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">209</span>:         <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">/</span><span class=\"p\">&lt;</span><span class=\"kt\">double</span><span class=\"p\">&gt;(</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_26 = __pyx_v_ii;\n",
       "    __pyx_t_27 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_27)) )) = ((2. * M_PI) / ((double)(*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_26)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">210</span>:         <span class=\"k\">if</span> <span class=\"n\">ii</span><span class=\"o\">==</span><span class=\"mf\">0</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_28 = ((__pyx_v_ii == 0) != 0);\n",
       "    if (__pyx_t_28) {\n",
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       "      goto __pyx_L9;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">211</span>:             <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">0</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_29 = __pyx_v_ii;\n",
       "      *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_29)) )) = 0;\n",
       "</pre><pre class=\"cython line score-58\" onclick='toggleDiv(this)'>+<span class=\"\">212</span>:             <span class=\"n\">Phi</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">((</span><span class=\"n\">NR</span><span class=\"p\">,</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Rratio</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">))</span></pre>\n",
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       "        }\n",
       "      }\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "      __pyx_t_31 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(__pyx_t_4);\n",
       "      if (unlikely(!__pyx_t_31.memview)) __PYX_ERR(0, 212, __pyx_L1_error)\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n",
       "      __PYX_XDEC_MEMVIEW(&amp;__pyx_v_Phi, 1);\n",
       "      __pyx_v_Phi = __pyx_t_31;\n",
       "      __pyx_t_31.memview = NULL;\n",
       "      __pyx_t_31.data = NULL;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">213</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">214</span>:             <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">NZ</span></pre>\n",
       "<pre class='cython code score-0 '>    /*else*/ {\n",
       "      __pyx_t_32 = (__pyx_v_ii - 1);\n",
       "      __pyx_t_33 = (__pyx_v_ii - 1);\n",
       "      __pyx_t_34 = __pyx_v_ii;\n",
       "      *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_34)) )) = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_32)) ))) + ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_33)) ))) * __pyx_v_NZ));\n",
       "    }\n",
       "    __pyx_L9:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">215</span>:         <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_35 = __pyx_v_ii;\n",
       "    __pyx_t_36 = (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_35)) )));\n",
       "    for (__pyx_t_37 = 0; __pyx_t_37 &lt; __pyx_t_36; __pyx_t_37+=1) {\n",
       "      __pyx_v_jj = __pyx_t_37;\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">216</span>:             <span class=\"n\">Phi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">,</span><span class=\"n\">jj</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"n\">Cpi</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"p\">&lt;</span><span class=\"kt\">double</span><span class=\"p\">&gt;</span><span class=\"n\">jj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-2 '>      __pyx_t_38 = __pyx_v_ii;\n",
       "      if (unlikely(!__pyx_v_Phi.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"Phi\"); __PYX_ERR(0, 216, __pyx_L1_error) }\n",
       "      __pyx_t_39 = __pyx_v_ii;\n",
       "      __pyx_t_40 = __pyx_v_jj;\n",
       "      *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_39 * __pyx_v_Phi.strides[0]) )) + __pyx_t_40)) )) = ((-M_PI) + ((0.5 + ((double)__pyx_v_jj)) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_38)) )))));\n",
       "    }\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">217</span>: </pre>\n",
       "<pre class=\"cython line score-21\" onclick='toggleDiv(this)'>+<span class=\"\">218</span>:     <span class=\"k\">if</span> <span class=\"n\">Out</span><span class=\"o\">.</span><span class=\"n\">lower</span><span class=\"p\">()</span><span class=\"o\">==</span><span class=\"s\">&#39;(x,y,z)&#39;</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-21 '>  __pyx_t_9 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_v_Out, __pyx_n_s_lower); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 218, __pyx_L1_error)\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_9);\n",
       "  __pyx_t_5 = NULL;\n",
       "  if (CYTHON_UNPACK_METHODS &amp;&amp; likely(<span class='py_c_api'>PyMethod_Check</span>(__pyx_t_9))) {\n",
       "    __pyx_t_5 = <span class='py_macro_api'>PyMethod_GET_SELF</span>(__pyx_t_9);\n",
       "    if (likely(__pyx_t_5)) {\n",
       "      PyObject* function = <span class='py_macro_api'>PyMethod_GET_FUNCTION</span>(__pyx_t_9);\n",
       "      <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_5);\n",
       "      <span class='pyx_macro_api'>__Pyx_INCREF</span>(function);\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_t_9, function);\n",
       "    }\n",
       "  }\n",
       "  if (__pyx_t_5) {\n",
       "    __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_t_9, __pyx_t_5); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 218, __pyx_L1_error)\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "  } else {\n",
       "    __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_CallNoArg</span>(__pyx_t_9); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 218, __pyx_L1_error)\n",
       "  }\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_4);\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "  __pyx_t_28 = (<span class='pyx_c_api'>__Pyx_PyString_Equals</span>(__pyx_t_4, __pyx_kp_s_x_y_z, Py_EQ)); if (unlikely(__pyx_t_28 &lt; 0)) __PYX_ERR(0, 218, __pyx_L1_error)\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n",
       "  if (__pyx_t_28) {\n",
       "/* … */\n",
       "    goto __pyx_L12;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">219</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_14 = __pyx_v_NP;\n",
       "    for (__pyx_t_20 = 0; __pyx_t_20 &lt; __pyx_t_14; __pyx_t_20+=1) {\n",
       "      __pyx_v_ii = __pyx_t_20;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">220</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NR</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_36 = (__pyx_v_NR + 1);\n",
       "      for (__pyx_t_37 = 0; __pyx_t_37 &lt; __pyx_t_36; __pyx_t_37+=1) {\n",
       "        __pyx_v_jj = __pyx_t_37;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">221</span>:                 <span class=\"k\">if</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">jj</span><span class=\"p\">]</span><span class=\"o\">&lt;</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_41 = __pyx_v_ii;\n",
       "        __pyx_t_42 = __pyx_v_jj;\n",
       "        __pyx_t_28 = ((((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_41)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_42)) )))) &lt; 0.) != 0);\n",
       "        if (__pyx_t_28) {\n",
       "/* … */\n",
       "        }\n",
       "      }\n",
       "      __pyx_L16_break:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">222</span>:                     <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>          goto __pyx_L16_break;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">223</span>:             <span class=\"n\">iiR</span> <span class=\"o\">=</span> <span class=\"n\">jj</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_v_iiR = (__pyx_v_jj - 1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">224</span>:             <span class=\"n\">iiZ</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">])</span><span class=\"o\">//</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_43 = __pyx_v_ii;\n",
       "      __pyx_t_44 = __pyx_v_iiR;\n",
       "      __pyx_t_45 = __pyx_v_iiR;\n",
       "      __pyx_v_iiZ = (((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_43)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_44)) )))) / (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_45)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">225</span>:             <span class=\"n\">iiphi</span> <span class=\"o\">=</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">iiZ</span><span class=\"o\">*</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_46 = __pyx_v_ii;\n",
       "      __pyx_t_47 = __pyx_v_iiR;\n",
       "      __pyx_t_48 = __pyx_v_iiR;\n",
       "      __pyx_v_iiphi = (((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_46)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_47)) )))) - (__pyx_v_iiZ * (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_48)) )))));\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">226</span>:             <span class=\"n\">phi</span> <span class=\"o\">=</span> <span class=\"n\">Phi</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">,</span><span class=\"n\">iiphi</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-2 '>      if (unlikely(!__pyx_v_Phi.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"Phi\"); __PYX_ERR(0, 226, __pyx_L1_error) }\n",
       "      __pyx_t_49 = __pyx_v_iiR;\n",
       "      __pyx_t_50 = __pyx_v_iiphi;\n",
       "      __pyx_v_phi = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_49 * __pyx_v_Phi.strides[0]) )) + __pyx_t_50)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">227</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_51 = __pyx_v_iiR;\n",
       "      __pyx_t_52 = 0;\n",
       "      __pyx_t_53 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_52, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_53, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_51)) ))) * cos(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">228</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_54 = __pyx_v_iiR;\n",
       "      __pyx_t_55 = 1;\n",
       "      __pyx_t_56 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_55, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_56, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_54)) ))) * sin(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">229</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Z</span><span class=\"p\">[</span><span class=\"n\">iiZ</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_57 = __pyx_v_iiZ;\n",
       "      __pyx_t_58 = 2;\n",
       "      __pyx_t_59 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_58, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_59, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Z.data) + __pyx_t_57)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">230</span>:             <span class=\"n\">dV</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRr</span><span class=\"o\">*</span><span class=\"n\">dZr</span><span class=\"o\">*</span><span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_60 = __pyx_v_iiR;\n",
       "      __pyx_t_61 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dV.rcbuffer-&gt;pybuffer.buf, __pyx_t_61, __pyx_pybuffernd_dV.diminfo[0].strides) = ((__pyx_v_dRr * __pyx_v_dZr) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_60)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">231</span>:             <span class=\"k\">if</span> <span class=\"n\">Ru</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span><span class=\"o\">==</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_62 = __pyx_v_iiR;\n",
       "      __pyx_t_28 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Ru.data) + __pyx_t_62)) ))) == 0.) != 0);\n",
       "      if (__pyx_t_28) {\n",
       "/* … */\n",
       "      }\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">232</span>:                 <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_63 = __pyx_v_iiR;\n",
       "        __pyx_t_64 = __pyx_v_iiR;\n",
       "        *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhir.data) + __pyx_t_64)) )) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_63)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">233</span>:                 <span class=\"n\">Ru</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_65 = __pyx_v_iiR;\n",
       "        *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Ru.data) + __pyx_t_65)) )) = 1.;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">234</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">235</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_14 = __pyx_v_NP;\n",
       "    for (__pyx_t_20 = 0; __pyx_t_20 &lt; __pyx_t_14; __pyx_t_20+=1) {\n",
       "      __pyx_v_ii = __pyx_t_20;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">236</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NR</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_36 = (__pyx_v_NR + 1);\n",
       "      for (__pyx_t_37 = 0; __pyx_t_37 &lt; __pyx_t_36; __pyx_t_37+=1) {\n",
       "        __pyx_v_jj = __pyx_t_37;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">237</span>:                 <span class=\"k\">if</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">jj</span><span class=\"p\">]</span><span class=\"o\">&lt;</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_66 = __pyx_v_ii;\n",
       "        __pyx_t_67 = __pyx_v_jj;\n",
       "        __pyx_t_28 = ((((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_66)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_67)) )))) &lt; 0.) != 0);\n",
       "        if (__pyx_t_28) {\n",
       "/* … */\n",
       "        }\n",
       "      }\n",
       "      __pyx_L22_break:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">238</span>:                     <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>          goto __pyx_L22_break;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">239</span>:             <span class=\"n\">iiR</span> <span class=\"o\">=</span> <span class=\"n\">jj</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_v_iiR = (__pyx_v_jj - 1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">240</span>:             <span class=\"n\">iiZ</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">])</span><span class=\"o\">//</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_68 = __pyx_v_ii;\n",
       "      __pyx_t_69 = __pyx_v_iiR;\n",
       "      __pyx_t_70 = __pyx_v_iiR;\n",
       "      __pyx_v_iiZ = (((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_68)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_69)) )))) / (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_70)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">241</span>:             <span class=\"n\">iiphi</span> <span class=\"o\">=</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">iiZ</span><span class=\"o\">*</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_71 = __pyx_v_ii;\n",
       "      __pyx_t_72 = __pyx_v_iiR;\n",
       "      __pyx_t_73 = __pyx_v_iiR;\n",
       "      __pyx_v_iiphi = (((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_71)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_72)) )))) - (__pyx_v_iiZ * (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_73)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">242</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">R</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_74 = __pyx_v_iiR;\n",
       "      __pyx_t_75 = 0;\n",
       "      __pyx_t_76 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_75, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_76, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_R.data) + __pyx_t_74)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">243</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Z</span><span class=\"p\">[</span><span class=\"n\">iiZ</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_77 = __pyx_v_iiZ;\n",
       "      __pyx_t_78 = 1;\n",
       "      __pyx_t_79 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_78, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_79, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Z.data) + __pyx_t_77)) )));\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">244</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">,</span><span class=\"n\">iiphi</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-2 '>      if (unlikely(!__pyx_v_Phi.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"Phi\"); __PYX_ERR(0, 244, __pyx_L1_error) }\n",
       "      __pyx_t_80 = __pyx_v_iiR;\n",
       "      __pyx_t_81 = __pyx_v_iiphi;\n",
       "      __pyx_t_82 = 2;\n",
       "      __pyx_t_83 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_82, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_83, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_80 * __pyx_v_Phi.strides[0]) )) + __pyx_t_81)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">245</span>:             <span class=\"n\">dV</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRr</span><span class=\"o\">*</span><span class=\"n\">dZr</span><span class=\"o\">*</span><span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_84 = __pyx_v_iiR;\n",
       "      __pyx_t_85 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dV.rcbuffer-&gt;pybuffer.buf, __pyx_t_85, __pyx_pybuffernd_dV.diminfo[0].strides) = ((__pyx_v_dRr * __pyx_v_dZr) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_84)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">246</span>:             <span class=\"k\">if</span> <span class=\"n\">Ru</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span><span class=\"o\">==</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_86 = __pyx_v_iiR;\n",
       "      __pyx_t_28 = (((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Ru.data) + __pyx_t_86)) ))) == 0.) != 0);\n",
       "      if (__pyx_t_28) {\n",
       "/* … */\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  __pyx_L12:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">247</span>:                 <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_87 = __pyx_v_iiR;\n",
       "        __pyx_t_88 = __pyx_v_iiR;\n",
       "        *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhir.data) + __pyx_t_88)) )) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_87)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">248</span>:                 <span class=\"n\">Ru</span><span class=\"p\">[</span><span class=\"n\">iiR</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mf\">1.</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_89 = __pyx_v_iiR;\n",
       "        *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_Ru.data) + __pyx_t_89)) )) = 1.;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">249</span>: </pre>\n",
       "<pre class=\"cython line score-124\" onclick='toggleDiv(this)'>+<span class=\"\">250</span>:     <span class=\"k\">return</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">dV</span><span class=\"p\">,</span> <span class=\"n\">dRr</span><span class=\"p\">,</span> <span class=\"n\">dZr</span><span class=\"p\">,</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">asarray</span><span class=\"p\">(</span><span class=\"n\">dRPhir</span><span class=\"p\">)[</span><span class=\"o\">~</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">isnan</span><span class=\"p\">(</span><span class=\"n\">dRPhir</span><span class=\"p\">)]</span></pre>\n",
       "<pre class='cython code score-124 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
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       "        case  7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n",
       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        case  0: break;\n",
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       "        if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dX)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dY)) != 0)) kw_args--;\n",
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       "        if (kw_args &gt; 0) {\n",
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       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DZ);\n",
       "          if (value) { values[8] = value; kw_args--; }\n",
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       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
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       "    __pyx_v_dZ = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_dZ == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 255, __pyx_L3_error)\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">258</span>:                                    <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">259</span>:     <span class=\"s\">&quot; Return the desired submesh indicated by the limits (DX,DY,DZ), for the desired resolution (dX,dY,dZ) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">260</span>: </pre>\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">262</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dXr</span><span class=\"p\">,</span> <span class=\"nf\">dYr</span><span class=\"p\">,</span> <span class=\"nf\">dZr</span><span class=\"p\">,</span> <span class=\"nf\">dV</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">263</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indX</span><span class=\"p\">,</span> <span class=\"n\">indY</span><span class=\"p\">,</span> <span class=\"n\">indZ</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">264</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NX</span><span class=\"p\">,</span> <span class=\"nf\">NY</span><span class=\"p\">,</span> <span class=\"nf\">NZ</span><span class=\"p\">,</span> <span class=\"nf\">Xn</span><span class=\"p\">,</span> <span class=\"nf\">Yn</span><span class=\"p\">,</span> <span class=\"nf\">Zn</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">265</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">Pts</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">266</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">ind</span></pre>\n",
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    "%%cython -a\n",
    "cimport cython\n",
    "import numpy as np\n",
    "cimport numpy as cnp\n",
    "from cpython cimport bool\n",
    "from libc.math cimport ceil as Cceil, abs as Cabs, floor as Cfloor, round as Cround\n",
    "from libc.math cimport cos as Ccos, sin as Csin, atan2 as Catan2, pi as Cpi\n",
    "from matplotlib.path import Path\n",
    "\n",
    "import datetime as dtm\n",
    "\n",
    "# Preliminary function to get optimal resolution from input resolution\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_mesh_dlfromL_cython(double[::1] LMinMax, double dL, DL=None, double margin=1.e-9):\n",
    "    \"\"\" Get the actual reolution from the desired resolution and MinMax and limits \"\"\"\n",
    "    # Get the number of mesh elements in LMinMax\n",
    "    cdef double N = Cceil((LMinMax[1]-LMinMax[0])/dL)\n",
    "    # Derive the real (effective) resolution\n",
    "    cdef double dLr = (LMinMax[1]-LMinMax[0])/N\n",
    "    # Get desired limits if any\n",
    "    cdef double[::1] DLc, L\n",
    "    cdef long [::1] indL\n",
    "    #cdef cnp.ndarray[double,ndim=1] indL, L\n",
    "    cdef double abs0, abs1, A\n",
    "    cdef int nL0, nL1, Nind, ii, jj\n",
    "    if DL is None:\n",
    "        DLc = LMinMax\n",
    "    else:\n",
    "        if DL[0] is None:\n",
    "            DL[0] = LMinMax[0]\n",
    "        if DL[1] is None:\n",
    "            DL[1] = LMinMax[1]\n",
    "        DLc = np.array(DL)\n",
    "        \n",
    "    # Get the extreme indices of the mesh elements that really need to be created within those limits\n",
    "    abs0 = Cabs(DLc[0]-LMinMax[0])\n",
    "    if abs0-dLr*Cfloor(abs0/dLr)<margin*dLr:\n",
    "        nL0 = int(Cround((DLc[0]-LMinMax[0])/dLr))\n",
    "    else:\n",
    "        nL0 = int(Cfloor((DLc[0]-LMinMax[0])/dLr))\n",
    "    abs1 = Cabs(DLc[1]-LMinMax[0])\n",
    "    if abs1-dLr*Cfloor(abs1/dLr)<margin*dLr:\n",
    "        nL1 = int(Cround((DLc[1]-LMinMax[0])/dLr)-1)\n",
    "    else:\n",
    "        nL1 = int(Cfloor((DLc[1]-LMinMax[0])/dLr))\n",
    "    # Get the corresponding indices\n",
    "    Nind = nL1+1-nL0\n",
    "    indL = np.empty((Nind,),dtype=int)#np.linspace(nL0,nL1,Nind,endpoint=True)\n",
    "    L = np.empty((Nind,))\n",
    "    for ii in range(0,Nind):\n",
    "        jj = nL0+ii\n",
    "        indL[ii] = jj\n",
    "        L[ii] = LMinMax[0] + (0.5 + (<double>jj))*dLr\n",
    "    return np.asarray(L), dLr, np.asarray(indL), N\n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Vmesh_Tor_SubFromD_cython(double dR, double dZ, double dRPhi, \n",
    "                                   double[::1] RMinMax, double[::1] ZMinMax,\n",
    "                                   DR=None, DZ=None, DPhi=None, VPoly=None,\n",
    "                                   str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \" Return the desired submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \"\n",
    "    \n",
    "    cdef double[::1] R0, R, Z, dRPhir, dPhir, NRPhi#, dPhi, NRZPhi_cum0, indPhi, phi\n",
    "    cdef double dRr0, dRr, dZr, DPhi0, DPhi1\n",
    "    cdef double abs0, abs1, phi, indiijj\n",
    "    cdef long[::1] indR0, indR, indZ, Phin, NRPhi0\n",
    "    cdef int NR0, NR, NZ, Rn, Zn, nRPhi0, indR0ii, ii, jj, nPhi0, nPhi1, zz, NP, NRPhi_int, Rratio\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts, indI\n",
    "    cdef cnp.ndarray[double,ndim=1] dV, ind\n",
    "    \n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    R0, dRr0, indR0, NR0 = _Ves_mesh_dlfromL_cython(RMinMax, dR, None, margin=margin)\n",
    "    R, dRr, indR, NR = _Ves_mesh_dlfromL_cython(RMinMax, dR, DR, margin=margin)\n",
    "    Z, dZr, indZ, NZ = _Ves_mesh_dlfromL_cython(ZMinMax, dZ, DZ, margin=margin)\n",
    "    Rn = len(R)\n",
    "    Zn = len(Z)\n",
    "    \n",
    "    # Get the limits if any (and make sure to replace them in the proper quadrants)\n",
    "    if DPhi is None:\n",
    "        DPhi0, DPhi1 = -Cpi, Cpi\n",
    "    else:\n",
    "        DPhi0, DPhi1 = Catan2(Csin(DPhi[0]),Ccos(DPhi[0])), Catan2(Csin(DPhi[1]),Ccos(DPhi[1]))\n",
    "    \n",
    "    dRPhir, dPhir = np.empty((Rn,)), np.empty((Rn,))\n",
    "    Phin = np.empty((Rn,),dtype=int)\n",
    "    NRPhi = np.empty((Rn,))\n",
    "    NRPhi0 = np.zeros((Rn,),dtype=int)\n",
    "    nRPhi0, indR0ii = 0, 0\n",
    "    NP, NPhimax = 0, 0\n",
    "    Rratio = int(Cceil(R[Rn-1]/R[0]))\n",
    "    for ii in range(0,Rn):\n",
    "        # Get the actual RPhi resolution and Phi mesh elements (! depends on R !)\n",
    "        NRPhi[ii] = Cceil(2.*Cpi*R[ii]/dRPhi)\n",
    "        NRPhi_int = int(NRPhi[ii])\n",
    "        dPhir[ii] = 2.*Cpi/NRPhi[ii]\n",
    "        dRPhir[ii] = dPhir[ii]*R[ii]\n",
    "        # Get index and cumulated indices from background\n",
    "        for jj in range(indR0ii,NR0):\n",
    "            if R0[jj]==R[ii]:\n",
    "                indR0ii = jj\n",
    "                break\n",
    "            else:\n",
    "                nRPhi0 += <long>Cceil(2.*Cpi*R0[jj]/dRPhi)\n",
    "                NRPhi0[ii] = nRPhi0*NZ\n",
    "        # Get indices of phi\n",
    "        # Get the extreme indices of the mesh elements that really need to be created within those limits\n",
    "        abs0 = Cabs(DPhi0+Cpi)\n",
    "        if abs0-dPhir[ii]*Cfloor(abs0/dPhir[ii])<margin*dPhir[ii]:\n",
    "            nPhi0 = int(Cround((DPhi0+Cpi)/dPhir[ii]))\n",
    "        else:\n",
    "            nPhi0 = int(Cfloor((DPhi0+Cpi)/dPhir[ii]))\n",
    "        abs1 = Cabs(DPhi1+Cpi)\n",
    "        if abs1-dPhir[ii]*Cfloor(abs1/dPhir[ii])<margin*dPhir[ii]:\n",
    "            nPhi1 = int(Cround((DPhi1+Cpi)/dPhir[ii])-1)\n",
    "        else:\n",
    "            nPhi1 = int(Cfloor((DPhi1+Cpi)/dPhir[ii]))\n",
    "            \n",
    "        if DPhi0<DPhi1:\n",
    "            #indI.append(list(range(nPhi0,nPhi1+1)))\n",
    "            Phin[ii] = nPhi1+1-nPhi0\n",
    "            if ii==0:\n",
    "                indI = np.ones((Rn,Phin[ii]*Rratio+1))\n",
    "            for jj in range(0,Phin[ii]):\n",
    "                indI[ii,jj] = <double>( nPhi0+jj )\n",
    "        else:\n",
    "            #indI.append(list(range(nPhi0,NRPhi_int)+list(range(0,nPhi1+1))))\n",
    "            Phin[ii] = nPhi1+1+NRPhi_int-nPhi0\n",
    "            if ii==0:\n",
    "                indI = np.ones((Rn,Phin[ii]*Rratio+1))\n",
    "            for jj in range(0,NRPhi_int-nPhi0):\n",
    "                indI[ii,jj] = <double>( nPhi0+jj )\n",
    "            for jj in range(NRPhi_int-nPhi0,Phin[ii]):\n",
    "                indI[ii,jj] = <double>( jj- (NRPhi_int-nPhi0) )\n",
    "        NP += Zn*Phin[ii]   \n",
    "    \n",
    "    Pts = np.empty((3,NP))\n",
    "    ind = np.empty((NP,))\n",
    "    dV = np.empty((NP,))\n",
    "    # Compute Pts, dV and ind\n",
    "    # This triple loop is the longest part, it takes ~90% of the CPU time\n",
    "    NP = 0\n",
    "    if Out.lower()=='(x,y,z)':\n",
    "        for ii in range(0,Rn):\n",
    "            for zz in range(0,Zn):\n",
    "                for jj in range(0,Phin[ii]):\n",
    "                    indiijj = indI[ii,jj]\n",
    "                    phi = -Cpi + (0.5+indiijj)*dPhir[ii]\n",
    "                    Pts[0,NP] = R[ii]*Ccos(phi)\n",
    "                    Pts[1,NP] = R[ii]*Csin(phi)\n",
    "                    Pts[2,NP] = Z[zz]\n",
    "                    ind[NP] = NRPhi0[ii] + indZ[zz]*NRPhi[ii] + indiijj\n",
    "                    dV[NP] = dRr*dZr*dRPhir[ii]\n",
    "                    NP += 1\n",
    "    else:\n",
    "        for ii in range(0,Rn):\n",
    "            for zz in range(0,Zn):\n",
    "                for jj in range(0,Phin[ii]):\n",
    "                    indiijj = indI[ii,jj]\n",
    "                    Pts[0,NP] = R[ii]\n",
    "                    Pts[1,NP] = Z[zz]\n",
    "                    Pts[2,NP] = -Cpi + (0.5+indiijj)*dPhir[ii]\n",
    "                    ind[NP] = NRPhi0[ii] + indZ[zz]*NRPhi[ii] + indiijj\n",
    "                    dV[NP] = dRr*dZr*dRPhir[ii]\n",
    "                    NP += 1\n",
    "\n",
    "    if not VPoly is None:\n",
    "        indin = Path(VPoly.T).contains_points(PtsRZP[:-1,:].T, transform=None, radius=0.0)\n",
    "        PtsRZP, dV, ind = PtsRZP[:,indin], dV[indin], ind[indin]\n",
    "        Ru = np.unique(PtsRZP[0,:])\n",
    "        if not Ru==R:\n",
    "            indii = np.array([R[ii] in Ru for ii in range(0,len(R))], dtype=bool)\n",
    "            dRPhir = dRPhir[indii]\n",
    "    return Pts, dV, ind.astype(int), dRr, dZr, np.asarray(dRPhir)\n",
    "    \n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Vmesh_Tor_SubFromInd_cython(double dR, double dZ, double dRPhi, \n",
    "                                     double[::1] RMinMax, double[::1] ZMinMax, long[::1] ind, \n",
    "                                     str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \"\"\" Return the desired submesh indicated by the (numerical) indices, for the desired resolution (dR,dZ,dRphi) \"\"\"\n",
    "    cdef double[::1] R, Z, dRPhirRef, dPhir, Ru, dRPhir\n",
    "    cdef double dRr, dZr, phi\n",
    "    cdef long[::1] indR, indZ, NRPhi0, NRPhi\n",
    "    cdef long NR, NZ, Rn, Zn, NP=len(ind), Rratio\n",
    "    cdef int ii, jj, iiR, iiZ, iiphi\n",
    "    cdef double[:,::1] Phi\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts=np.empty((3,NP))\n",
    "    cdef cnp.ndarray[double,ndim=1] dV=np.empty((NP,))\n",
    "    \n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    R, dRr, indR, NR = _Ves_mesh_dlfromL_cython(RMinMax, dR, None, margin=margin)\n",
    "    Z, dZr, indZ, NZ = _Ves_mesh_dlfromL_cython(ZMinMax, dZ, None, margin=margin)\n",
    "    Rn, Zn = len(R), len(Z)\n",
    "    \n",
    "    # Number of Phi per R\n",
    "    dRPhirRef, dPhir = np.empty((NR,)), np.empty((NR,))\n",
    "    Ru, dRPhir = np.zeros((NR,)), np.nan*np.ones((NR,))\n",
    "    NRPhi, NRPhi0 = np.empty((NR,),dtype=int), np.empty((NR+1,),dtype=int)\n",
    "    Rratio = int(Cceil(R[NR-1]/R[0]))\n",
    "    for ii in range(0,NR):\n",
    "        NRPhi[ii] = <long>(Cceil(2.*Cpi*R[ii]/dRPhi))\n",
    "        dRPhirRef[ii] = 2.*Cpi*R[ii]/<double>(NRPhi[ii])\n",
    "        dPhir[ii] = 2.*Cpi/<double>(NRPhi[ii])\n",
    "        if ii==0:\n",
    "            NRPhi0[ii] = 0\n",
    "            Phi = np.empty((NR,NRPhi[ii]*Rratio+1))\n",
    "        else:\n",
    "            NRPhi0[ii] = NRPhi0[ii-1] + NRPhi[ii-1]*NZ\n",
    "        for jj in range(0,NRPhi[ii]):\n",
    "            Phi[ii,jj] = -Cpi + (0.5+<double>jj)*dPhir[ii]\n",
    "            \n",
    "    if Out.lower()=='(x,y,z)':\n",
    "        for ii in range(0,NP):\n",
    "            for jj in range(0,NR+1):\n",
    "                if ind[ii]-NRPhi0[jj]<0.:\n",
    "                    break\n",
    "            iiR = jj-1\n",
    "            iiZ = (ind[ii] - NRPhi0[iiR])//NRPhi[iiR]\n",
    "            iiphi = ind[ii] - NRPhi0[iiR] - iiZ*NRPhi[iiR]\n",
    "            phi = Phi[iiR,iiphi]\n",
    "            Pts[0,ii] = R[iiR]*Ccos(phi)\n",
    "            Pts[1,ii] = R[iiR]*Csin(phi)\n",
    "            Pts[2,ii] = Z[iiZ]\n",
    "            dV[ii] = dRr*dZr*dRPhirRef[iiR]\n",
    "            if Ru[iiR]==0.:\n",
    "                dRPhir[iiR] = dRPhirRef[iiR]\n",
    "                Ru[iiR] = 1.\n",
    "    else: \n",
    "        for ii in range(0,NP):\n",
    "            for jj in range(0,NR+1):\n",
    "                if ind[ii]-NRPhi0[jj]<0.:\n",
    "                    break\n",
    "            iiR = jj-1\n",
    "            iiZ = (ind[ii] - NRPhi0[iiR])//NRPhi[iiR]\n",
    "            iiphi = ind[ii] - NRPhi0[iiR] - iiZ*NRPhi[iiR]\n",
    "            Pts[0,ii] = R[iiR]\n",
    "            Pts[1,ii] = Z[iiZ]\n",
    "            Pts[2,ii] = Phi[iiR,iiphi]\n",
    "            dV[ii] = dRr*dZr*dRPhirRef[iiR]\n",
    "            if Ru[iiR]==0.:\n",
    "                dRPhir[iiR] = dRPhirRef[iiR]\n",
    "                Ru[iiR] = 1.\n",
    "    \n",
    "    return Pts, dV, dRr, dZr, np.asarray(dRPhir)[~np.isnan(dRPhir)]\n",
    "   \n",
    "    \n",
    "    \n",
    "    \n",
    "def _Ves_Vmesh_Lin_SubFromD_cython(double dX, double dY, double dZ, \n",
    "                                   double[::1] XMinMax, double[::1] YMinMax, double[::1] ZMinMax,\n",
    "                                   DX=None, DY=None, DZ=None, VPoly=None,\n",
    "                                   double margin=1.e-9):\n",
    "    \" Return the desired submesh indicated by the limits (DX,DY,DZ), for the desired resolution (dX,dY,dZ) \"\n",
    "    \n",
    "    cdef double[::1] X, Y, Z\n",
    "    cdef double dXr, dYr, dZr, dV\n",
    "    cdef cnp.ndarray[long,ndim=1] indX, indY, indZ\n",
    "    cdef int NX, NY, NZ, Xn, Yn, Zn\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts\n",
    "    cdef cnp.ndarray[long,ndim=1] ind\n",
    "    \n",
    "    # Get the actual X, Y and Z resolutions and mesh elements\n",
    "    X, dXr, indX, NX = _Ves_mesh_dlfromL_cython(XMinMax, dX, DX, margin=margin)\n",
    "    Y, dYr, indY, NY = _Ves_mesh_dlfromL_cython(YMinMax, dY, DY, margin=margin)\n",
    "    Z, dZr, indZ, NZ = _Ves_mesh_dlfromL_cython(ZMinMax, dZ, DZ, margin=margin)\n",
    "    Xn, Yn, Zn = len(X), len(Y), len(Z)\n",
    "        \n",
    "    Pts = np.array([np.tile(X,(Yn*Zn,1)).flatten(), np.tile(np.repeat(Y,Xn),(Zn,1)).flatten(), np.repeat(Z,Xn*Yn)])\n",
    "    ind = np.repeat(NX*NY*indZ,Xn*Yn) + np.tile(np.repeat(NX*indY,Xn),(Zn,1)).flatten() + np.tile(indX,(Yn*Zn,1)).flatten()\n",
    "    dV = dXr*dYr*dZr\n",
    "    \n",
    "    #if VPoly is not None:\n",
    "    #    indin = _Ves_isInside(Pts[:-1,:], VPoly=VPoly, VType='Lin', In='(X,Y)')\n",
    "    #    Pts, dV, ind = Pts[:,indin], dV[indin], ind[indin]\n",
    "    \n",
    "    return Pts, dV, ind.astype(int), dXr, dYr, dZr\n",
    "    \n",
    "\n",
    "def _Ves_Vmesh_Lin_SubFromInd_cython(double dX, double dY, double dZ, \n",
    "                                     double[::1] XMinMax, double[::1] YMinMax, double[::1] ZMinMax,\n",
    "                                     cnp.ndarray[long,ndim=1] ind, double margin=1.e-9):\n",
    "    \" Return the desired submesh indicated by the limits (DX,DY,DZ), for the desired resolution (dX,dY,dZ) \"\n",
    "    \n",
    "    cdef cnp.ndarray[double,ndim=1] X, Y, Z\n",
    "    cdef double dXr, dYr, dZr, dV\n",
    "    cdef long[::1] bla\n",
    "    cdef cnp.ndarray[long,ndim=1] indX, indY, indZ\n",
    "    cdef int NX, NY, NZ, Xn, Yn, Zn\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts\n",
    "    \n",
    "    # Get the actual X, Y and Z resolutions and mesh elements\n",
    "    X, dXr, bla, NX = _Ves_mesh_dlfromL_cython(XMinMax, dX, None, margin=margin)\n",
    "    Y, dYr, bla, NY = _Ves_mesh_dlfromL_cython(YMinMax, dY, None, margin=margin)\n",
    "    Z, dZr, bla, NZ = _Ves_mesh_dlfromL_cython(ZMinMax, dZ, None, margin=margin)\n",
    "    \n",
    "    #print NX, NY, NZ\n",
    "    indZ = ind // (NX*NY)\n",
    "    indY = (ind - NX*NY*indZ) // NX\n",
    "    indX = ind - NX*NY*indZ - NX*indY\n",
    "    #print ind\n",
    "    #print indZ\n",
    "    #print indY\n",
    "    #print indX\n",
    "    Pts = np.array([X[indX.astype(int)], Y[indY.astype(int)], Z[indZ.astype(int)]])\n",
    "    dV = dXr*dYr*dZr\n",
    "    \n",
    "    return Pts, dV, dXr, dYr, dZr\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46,) (46,) (46,)\n",
      "[ 0.0099981   0.00999282  0.00999493  0.00999702  0.0099991   0.00999391\n",
      "  0.00999598  0.00999803  0.00999291  0.00999496  0.00999699  0.009999\n",
      "  0.00999397  0.00999598  0.00999797  0.00999994  0.00999499  0.00999696\n",
      "  0.00999891  0.00999403  0.00999598  0.00999791  0.00999982  0.00999502\n",
      "  0.00999693  0.00999882  0.00999409  0.00999598  0.00999785  0.00999971\n",
      "  0.00999505  0.0099969   0.00999874  0.00999414  0.00999598  0.0099978\n",
      "  0.00999961  0.00999507  0.00999688  0.00999867  0.00999419  0.00999598\n",
      "  0.00999775  0.00999951  0.0099951   0.00999685]\n",
      "[ 0.0099981   0.00999282  0.00999493  0.00999702  0.0099991   0.00999391\n",
      "  0.00999598  0.00999803  0.00999291  0.00999496  0.00999699  0.009999\n",
      "  0.00999397  0.00999598  0.00999797  0.00999994  0.00999499  0.00999696\n",
      "  0.00999891  0.00999403  0.00999598  0.00999791  0.00999982  0.00999502\n",
      "  0.00999693  0.00999882  0.00999409  0.00999598  0.00999785  0.00999971\n",
      "  0.00999505  0.0099969   0.00999874  0.00999414  0.00999598  0.0099978\n",
      "  0.00999961  0.00999507  0.00999688  0.00999867  0.00999419  0.00999598\n",
      "  0.00999775  0.00999951  0.0099951   0.00999685]\n"
     ]
    }
   ],
   "source": [
    "[dR, dZ, dRPhi], RMinMax, ZMinMax = np.asarray([0.01]*3), np.array([2.,3.]), np.array([-1.,1.])\n",
    "DR, DZ, DPhi = [2.15,2.6], [-0.21,0.51], [7.*np.pi/4.,5.*np.pi/4.]\n",
    "\n",
    "Ptsc, dVc, indc, dRrc, dZrc, dRPhirc = _Ves_Vmesh_Tor_SubFromD_cython(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                      DR=DR, DZ=DZ, DPhi=DPhi, VPoly=None,\n",
    "                                                                      Out='(R,Z,phi)', margin=1.e-9)\n",
    "PtsnI, dVnI, dRrnI, dZrnI, dRPhirnI = _Ves_Vmesh_Tor_SubFromInd_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                      ind=indc, Out='(R,Z,Phi)', margin=1.e-9)\n",
    "PtscI, dVcI, dRrcI, dZrcI, dRPhircI = _Ves_Vmesh_Tor_SubFromInd_cython(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                       ind=indc, Out='(R,Z,phi)', margin=1.e-9)\n",
    "\n",
    "print dRPhirc.shape, dRPhirnI.shape, dRPhircI.shape\n",
    "print dRPhirnI\n",
    "print dRPhircI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5940900 points\n",
      "1000 loops, best of 3: 909 µs per loop\n",
      "1000 loops, best of 3: 550 µs per loop\n",
      "[True, True, True, True]\n",
      "1 loop, best of 3: 1.01 s per loop\n",
      "1 loop, best of 3: 541 ms per loop\n",
      "[True, True, True, True]\n",
      "1 loop, best of 3: 25.2 s per loop\n",
      "1 loop, best of 3: 1.31 s per loop\n",
      "[True, True, True]\n",
      "[True, True, True]\n"
     ]
    }
   ],
   "source": [
    "LMinMax, dL, DL, margin = np.array([0.,10.]), 0.0001, [1.,9.], 1.e-9\n",
    "Ln, dLrn, indLn, NLn = _Ves_mesh_dlfromL_numpy(LMinMax, dL, DL=DL, margin=margin)\n",
    "Lc, dLrc, indLc, NLc = _Ves_mesh_dlfromL_cython(LMinMax, dL, DL=DL, margin=margin)\n",
    "\n",
    "# Check and debug Tor\n",
    "[dR, dZ, dRPhi], RMinMax, ZMinMax = np.asarray([0.02]*3), np.array([4.,8.]), np.array([-4.,4.])\n",
    "DR, DZ, DPhi = np.array([5.,6.5]), np.array([-1.5,2.]), np.array([-np.pi/4.,np.pi/4.])\n",
    "Ptsn, dVn, indn, dRrn, dZrn, dRPhirn = _Ves_Vmesh_Tor_SubFromD_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                     DR=DR, DZ=DZ, DPhi=DPhi, VPoly=None,\n",
    "                                                                     Out='(R,Z,Phi)', margin=1.e-9)\n",
    "Ptsc, dVc, indc, dRrc, dZrc, dRPhirc = _Ves_Vmesh_Tor_SubFromD_cython(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                      DR=DR, DZ=DZ, DPhi=DPhi, VPoly=None,\n",
    "                                                                      Out='(R,Z,phi)', margin=1.e-9)\n",
    "\n",
    "PtsnI, dVnI, dRrnI, dZrnI, dRPhirnI = _Ves_Vmesh_Tor_SubFromInd_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                      ind=indn, Out='(R,Z,Phi)', margin=1.e-9)\n",
    "PtscI, dVcI, dRrcI, dZrcI, dRPhircI = _Ves_Vmesh_Tor_SubFromInd_cython(dR, dZ, dRPhi, RMinMax, ZMinMax,\n",
    "                                                                       ind=indc, Out='(R,Z,Phi)', margin=1.e-9)\n",
    "\n",
    "print Ptsn.shape[1], 'points'\n",
    "%timeit Ln, dLrn, indLn, NLn = _Ves_mesh_dlfromL_numpy(LMinMax, dL, DL=DL, margin=margin)\n",
    "%timeit Lc, dLrc, indLc, NLc = _Ves_mesh_dlfromL_cython(LMinMax, dL, DL=DL, margin=margin)\n",
    "print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(Ln,Lc),(dLrn,dLrc),(indLn,indLc),(NLn,NLc)]]\n",
    "\n",
    "%timeit out = _Ves_Vmesh_Tor_SubFromD_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax, DR=DR, DZ=DZ, DPhi=DPhi, VPoly=None)\n",
    "%timeit out = _Ves_Vmesh_Tor_SubFromD_cython(dR, dZ, dRPhi, RMinMax, ZMinMax, DR=DR, DZ=DZ, DPhi=DPhi, VPoly=None, Out='(X,Y,Z)')\n",
    "print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(Ptsn,Ptsc),(dVn,dVc),(indn,indc),(dRPhirn,dRPhirc)]]\n",
    "\n",
    "%timeit out = _Ves_Vmesh_Tor_SubFromInd_numpy(dR, dZ, dRPhi, RMinMax, ZMinMax, ind=indn, Out='(R,Z,Phi)', margin=1.e-9)\n",
    "%timeit out = _Ves_Vmesh_Tor_SubFromInd_cython(dR, dZ, dRPhi, RMinMax, ZMinMax, ind=indc, Out='(R,Z,Phi)', margin=1.e-9)\n",
    "print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(PtsnI,PtscI),(dVnI,dVcI),(dRPhirnI,dRPhircI)]]\n",
    "print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(Ptsc,PtscI),(dVc,dVcI),(dRPhirc,dRPhircI)]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000000 points\n",
      "10 loops, best of 3: 86.7 ms per loop\n",
      "10 loops, best of 3: 235 ms per loop\n",
      "[True, True]\n"
     ]
    }
   ],
   "source": [
    "# Check and debug Lin\n",
    "[dX, dY, dZ], XMinMax, YMinMax, ZMinMax = np.asarray([0.01]*3), np.array([-1,1.]), np.array([-1.,1]), np.array([0.,4.])\n",
    "DX, DY, DZ = [-0.5,0.5], [-0.5,0.5], [1.,3.]\n",
    "Ptsn, dVn, indn, dXrn, dYrn, dZrn = _Ves_Vmesh_Lin_SubFromD_cython(dX, dY, dZ, XMinMax, YMinMax, ZMinMax,\n",
    "                                                                   DX=DX, DY=DY, DZ=DZ, VPoly=None, margin=1.e-9)\n",
    "Ptsc, dVc, dXrc, dYrc, dZrc = _Ves_Vmesh_Lin_SubFromInd_cython(dX, dY, dZ, XMinMax, YMinMax, ZMinMax,\n",
    "                                                                     indn, margin=1.e-9)\n",
    "\n",
    "print Ptsn.shape[1], 'points'\n",
    "%timeit out = _Ves_Vmesh_Lin_SubFromD_cython(dX, dY, dZ, XMinMax, YMinMax, ZMinMax, DX=DX, DY=DY, DZ=DZ, VPoly=None, margin=1.e-9)\n",
    "%timeit out = _Ves_Vmesh_Lin_SubFromInd_cython(dX, dY, dZ, XMinMax, YMinMax, ZMinMax, indn, margin=1.e-9)\n",
    "print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(Ptsn,Ptsc),(dVn,dVc)]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Surface meshing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "<pre class='cython code score-11 '>  __pyx_t_2 = <span class='py_c_api'>PyDict_New</span>(); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">004</span>: <span class=\"k\">from</span> <span class=\"nn\">cpython</span> <span class=\"k\">cimport</span> <span class=\"nb\">bool</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">005</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.math</span> <span class=\"k\">cimport</span> <span class=\"n\">sqrt</span> <span class=\"k\">as</span> <span class=\"n\">Csqrt</span><span class=\"p\">,</span> <span class=\"n\">ceil</span> <span class=\"k\">as</span> <span class=\"n\">Cceil</span><span class=\"p\">,</span> <span class=\"nb\">round</span> <span class=\"k\">as</span> <span class=\"n\">Cround</span><span class=\"p\">,</span> <span class=\"n\">floor</span> <span class=\"k\">as</span> <span class=\"n\">Cfloor</span><span class=\"p\">,</span> <span class=\"nb\">abs</span> <span class=\"k\">as</span> <span class=\"n\">Cabs</span></pre>\n",
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       "<pre class=\"cython line score-19\" onclick='toggleDiv(this)'>+<span class=\"\">007</span>: <span class=\"k\">from</span> <span class=\"nn\">matplotlib.path</span> <span class=\"k\">import</span> <span class=\"n\">Path</span></pre>\n",
       "<pre class='cython code score-19 '>  __pyx_t_1 = <span class='py_c_api'>PyList_New</span>(1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-89\" onclick='toggleDiv(this)'>+<span class=\"\">014</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_mesh_dlfromL_cython</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">LMinMax</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">DL</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-89 '>/* Python wrapper */\n",
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       "static char __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995__Ves_mesh_dlfromL_cython[] = \" Get the actual reolution from the desired resolution and MinMax and limits \";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_8152fc40fb05daa7217c00b66474f995_1_Ves_mesh_dlfromL_cython = {\"_Ves_mesh_dlfromL_cython\", (PyCFunction)__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_1_Ves_mesh_dlfromL_cython, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995__Ves_mesh_dlfromL_cython};\n",
       "static PyObject *__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_1_Ves_mesh_dlfromL_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  __Pyx_memviewslice __pyx_v_LMinMax = { 0, 0, { 0 }, { 0 }, { 0 } };\n",
       "  double __pyx_v_dL;\n",
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       "  double __pyx_v_margin;\n",
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       "    PyObject* values[4] = {0,0,0,0};\n",
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       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
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       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_LMinMax)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dL)) != 0)) kw_args--;\n",
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       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_mesh_dlfromL_cython\", 0, 2, 4, 1); __PYX_ERR(0, 14, __pyx_L3_error)\n",
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       "        case  2:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DL);\n",
       "          if (value) { values[2] = value; kw_args--; }\n",
       "        }\n",
       "        case  3:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_margin);\n",
       "          if (value) { values[3] = value; kw_args--; }\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"_Ves_mesh_dlfromL_cython\") &lt; 0)) __PYX_ERR(0, 14, __pyx_L3_error)\n",
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       "      switch (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)) {\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "    }\n",
       "    __pyx_v_LMinMax = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[0]); if (unlikely(!__pyx_v_LMinMax.memview)) __PYX_ERR(0, 14, __pyx_L3_error)\n",
       "    __pyx_v_dL = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_dL == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 14, __pyx_L3_error)\n",
       "    __pyx_v_DL = values[2];\n",
       "    if (values[3]) {\n",
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       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_mesh_dlfromL_cython\", 0, 2, 4, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 14, __pyx_L3_error)\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_8152fc40fb05daa7217c00b66474f995._Ves_mesh_dlfromL_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
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       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995__Ves_mesh_dlfromL_cython(__pyx_self, __pyx_v_LMinMax, __pyx_v_dL, __pyx_v_DL, __pyx_v_margin);\n",
       "\n",
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       "\n",
       "static PyObject *__pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995__Ves_mesh_dlfromL_cython(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_LMinMax, double __pyx_v_dL, PyObject *__pyx_v_DL, double __pyx_v_margin) {\n",
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       "<pre class='cython code score-0 '>  __pyx_t_1 = 1;\n",
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       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">019</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dLr</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">N</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_3 = 1;\n",
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       "  __pyx_v_abs0 = fabs(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_14)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_15)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">038</span>:     <span class=\"k\">if</span> <span class=\"n\">abs0</span><span class=\"o\">-</span><span class=\"n\">dLr</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs0</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">)</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dLr</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = (((__pyx_v_abs0 - (__pyx_v_dLr * floor((__pyx_v_abs0 / __pyx_v_dLr)))) &lt; (__pyx_v_margin * __pyx_v_dLr)) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L6;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">039</span>:         <span class=\"n\">nL0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_16 = 0;\n",
       "    __pyx_t_17 = 0;\n",
       "    __pyx_v_nL0 = ((int)round((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_16)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_17)) )))) / __pyx_v_dLr)));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">040</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">041</span>:         <span class=\"n\">nL0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_18 = 0;\n",
       "    __pyx_t_19 = 0;\n",
       "    __pyx_v_nL0 = ((int)floor((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_18)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_19)) )))) / __pyx_v_dLr)));\n",
       "  }\n",
       "  __pyx_L6:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">042</span>:     <span class=\"n\">abs1</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_20 = 1;\n",
       "  __pyx_t_21 = 0;\n",
       "  __pyx_v_abs1 = fabs(((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_20)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_21)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">043</span>:     <span class=\"k\">if</span> <span class=\"n\">abs1</span><span class=\"o\">-</span><span class=\"n\">dLr</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs1</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">)</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dLr</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_6 = (((__pyx_v_abs1 - (__pyx_v_dLr * floor((__pyx_v_abs1 / __pyx_v_dLr)))) &lt; (__pyx_v_margin * __pyx_v_dLr)) != 0);\n",
       "  if (__pyx_t_6) {\n",
       "/* … */\n",
       "    goto __pyx_L7;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">044</span>:         <span class=\"n\">nL1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">)</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_22 = 1;\n",
       "    __pyx_t_23 = 0;\n",
       "    __pyx_v_nL1 = ((int)(round((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_22)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_23)) )))) / __pyx_v_dLr)) - 1.0));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">045</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">046</span>:         <span class=\"n\">nL1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DLc</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dLr</span><span class=\"p\">))</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_24 = 1;\n",
       "    __pyx_t_25 = 0;\n",
       "    __pyx_v_nL1 = ((int)floor((((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_DLc.data) + __pyx_t_24)) ))) - (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_25)) )))) / __pyx_v_dLr)));\n",
       "  }\n",
       "  __pyx_L7:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">047</span>:     <span class=\"c\"># Get the corresponding indices</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">048</span>:     <span class=\"n\">Nind</span> <span class=\"o\">=</span> <span class=\"n\">nL1</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"o\">-</span><span class=\"n\">nL0</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_Nind = ((__pyx_v_nL1 + 1) - __pyx_v_nL0);\n",
       "</pre><pre class=\"cython line score-37\" onclick='toggleDiv(this)'>+<span class=\"\">049</span>:     <span class=\"n\">indL</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">((</span><span class=\"n\">Nind</span><span class=\"p\">,),</span><span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">int</span><span class=\"p\">)</span><span class=\"c\">#np.linspace(nL0,nL1,Nind,endpoint=True)</span></pre>\n",
       "<pre class='cython code score-37 '>  __pyx_t_7 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_7)) __PYX_ERR(0, 49, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-55\" onclick='toggleDiv(this)'>+<span class=\"\">050</span>:     <span class=\"n\">L</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">((</span><span class=\"n\">Nind</span><span class=\"p\">,))</span></pre>\n",
       "<pre class='cython code score-55 '>  __pyx_t_12 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 50, __pyx_L1_error)\n",
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       "  __pyx_t_13.data = NULL;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">051</span>:     <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Nind</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_28 = __pyx_v_Nind;\n",
       "  for (__pyx_t_29 = 0; __pyx_t_29 &lt; __pyx_t_28; __pyx_t_29+=1) {\n",
       "    __pyx_v_ii = __pyx_t_29;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">052</span>:         <span class=\"n\">jj</span> <span class=\"o\">=</span> <span class=\"n\">nL0</span><span class=\"o\">+</span><span class=\"n\">ii</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_jj = (__pyx_v_nL0 + __pyx_v_ii);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">053</span>:         <span class=\"n\">indL</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">jj</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_30 = __pyx_v_ii;\n",
       "    *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_indL.data) + __pyx_t_30)) )) = __pyx_v_jj;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">054</span>:         <span class=\"n\">L</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span> <span class=\"o\">+</span> <span class=\"p\">(&lt;</span><span class=\"kt\">double</span><span class=\"p\">&gt;</span><span class=\"n\">jj</span><span class=\"p\">))</span><span class=\"o\">*</span><span class=\"n\">dLr</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_31 = 0;\n",
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       "<pre class=\"cython line score-154\" onclick='toggleDiv(this)'>+<span class=\"\">061</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_mesh_CrossPoly</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:,::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">VPoly</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">D1</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">D2</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">DIn</span><span class=\"o\">=</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"n\">VIn</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-154 '>/* Python wrapper */\n",
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       "static PyMethodDef __pyx_mdef_46_cython_magic_8152fc40fb05daa7217c00b66474f995_3_Ves_mesh_CrossPoly = {\"_Ves_mesh_CrossPoly\", (PyCFunction)__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_3_Ves_mesh_CrossPoly, METH_VARARGS|METH_KEYWORDS, 0};\n",
       "static PyObject *__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_3_Ves_mesh_CrossPoly(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
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       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
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       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
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       "        case  1:\n",
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       "</pre><pre class=\"cython line score-54\" onclick='toggleDiv(this)'>+<span class=\"\">072</span>:     <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">empty</span><span class=\"p\">((</span><span class=\"n\">NP</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">,),</span><span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">int</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-54 '>  __pyx_t_4 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 72, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">073</span>:     <span class=\"k\">if</span> <span class=\"n\">DIn</span><span class=\"o\">==</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_11 = ((__pyx_v_DIn == 0.) != 0);\n",
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       "/* … */\n",
       "    goto __pyx_L3;\n",
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       "<pre class='cython code score-0 '>    __pyx_t_12 = (__pyx_v_NP - 1);\n",
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       "<pre class='cython code score-0 '>      __pyx_t_13 = 0;\n",
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       "      __pyx_v_v1 = __pyx_t_22;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">076</span>:             <span class=\"n\">LMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Csqrt</span><span class=\"p\">(</span><span class=\"n\">v0</span><span class=\"o\">**</span><span class=\"mf\">2</span> <span class=\"o\">+</span> <span class=\"n\">v1</span><span class=\"o\">**</span><span class=\"mf\">2</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_23 = 1;\n",
       "      *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_LMinMax.data) + __pyx_t_23)) )) = sqrt((pow(__pyx_v_v0, 2.0) + pow(__pyx_v_v1, 2.0)));\n",
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       "<pre class='cython code score-20 '>        __pyx_t_42 = 0;\n",
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       "<pre class='cython code score-0 '>      __pyx_t_56 = 0;\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">122</span>:                                    <span class=\"n\">double</span><span class=\"p\">[:,::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">VPoly</span><span class=\"p\">,</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">123</span>:                                    <span class=\"n\">DR</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">DZ</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">DPhi</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-0 '>    values[3] = ((PyObject *)Py_None);\n",
       "    values[4] = ((PyObject *)Py_None);\n",
       "    values[5] = ((PyObject *)Py_None);\n",
       "</pre><pre class=\"cython line score-139\" onclick='toggleDiv(this)'>+<span class=\"\">124</span>:                                    <span class=\"n\">double</span> <span class=\"n\">DIn</span><span class=\"o\">=</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"n\">VIn</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">PhiMinMax</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-139 '>    values[7] = ((PyObject *)Py_None);\n",
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       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DZ);\n",
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       "        case  5:\n",
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       "          if (value) { values[5] = value; kw_args--; }\n",
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       "        case 11: values[10] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 10);\n",
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       "        break;\n",
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       "    }\n",
       "    __pyx_v_dL = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[0]); if (unlikely((__pyx_v_dL == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 121, __pyx_L3_error)\n",
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       "    __pyx_v_DPhi = values[5];\n",
       "    if (values[6]) {\n",
       "      __pyx_v_DIn = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[6]); if (unlikely((__pyx_v_DIn == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 124, __pyx_L3_error)\n",
       "    } else {\n",
       "      __pyx_v_DIn = ((double)0.);\n",
       "    }\n",
       "    __pyx_v_VIn = values[7];\n",
       "    __pyx_v_PhiMinMax = values[8];\n",
       "    __pyx_v_Out = ((PyObject*)values[9]);\n",
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       "    }\n",
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       "  goto __pyx_L4_argument_unpacking_done;\n",
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       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
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       "  __pyx_L4_argument_unpacking_done:;\n",
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       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995_4_Ves_Smesh_Tor_SubFromD_cython(__pyx_self, __pyx_v_dL, __pyx_v_dRPhi, __pyx_v_VPoly, __pyx_v_DR, __pyx_v_DZ, __pyx_v_DPhi, __pyx_v_DIn, __pyx_v_VIn, __pyx_v_PhiMinMax, __pyx_v_Out, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">125</span>:                                    <span class=\"nb\">str</span> <span class=\"n\">Out</span><span class=\"o\">=</span><span class=\"s\">&#39;(X,Y,Z)&#39;</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">126</span>:     <span class=\"s\">&quot; Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">127</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">Z</span><span class=\"p\">,</span> <span class=\"n\">dPhir</span><span class=\"p\">,</span> <span class=\"n\">NRPhi</span><span class=\"c\">#, dPhi, NRZPhi_cum0, indPhi, phi</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">128</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dRr0</span><span class=\"p\">,</span> <span class=\"nf\">dRr</span><span class=\"p\">,</span> <span class=\"nf\">dZr</span><span class=\"p\">,</span> <span class=\"nf\">DPhi0</span><span class=\"p\">,</span> <span class=\"nf\">DPhi1</span><span class=\"p\">,</span> <span class=\"nf\">DPhiMinMax</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">129</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">abs0</span><span class=\"p\">,</span> <span class=\"nf\">abs1</span><span class=\"p\">,</span> <span class=\"nf\">phi</span><span class=\"p\">,</span> <span class=\"nf\">indiijj</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">130</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">long</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indR0</span><span class=\"p\">,</span> <span class=\"n\">indR</span><span class=\"p\">,</span> <span class=\"n\">indZ</span><span class=\"p\">,</span> <span class=\"n\">Phin</span><span class=\"p\">,</span> <span class=\"n\">NRPhi0</span><span class=\"p\">,</span> <span class=\"n\">Indin</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">131</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NR0</span><span class=\"p\">,</span> <span class=\"nf\">NR</span><span class=\"p\">,</span> <span class=\"nf\">NZ</span><span class=\"p\">,</span> <span class=\"nf\">Rn</span><span class=\"p\">,</span> <span class=\"nf\">Zn</span><span class=\"p\">,</span> <span class=\"nf\">nRPhi0</span><span class=\"p\">,</span> <span class=\"nf\">indR0ii</span><span class=\"p\">,</span> <span class=\"nf\">ii</span><span class=\"p\">,</span> <span class=\"nf\">jj0</span><span class=\"p\">,</span> <span class=\"nf\">jj</span><span class=\"p\">,</span> <span class=\"nf\">nPhi0</span><span class=\"p\">,</span> <span class=\"nf\">nPhi1</span><span class=\"p\">,</span> <span class=\"nf\">zz</span><span class=\"p\">,</span> <span class=\"nf\">NP</span><span class=\"p\">,</span> <span class=\"nf\">NRPhi_int</span><span class=\"p\">,</span> <span class=\"nf\">Rratio</span><span class=\"p\">,</span> <span class=\"nf\">Ln</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">132</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">indI</span><span class=\"p\">,</span> <span class=\"n\">PtsCross</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">133</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R0</span><span class=\"p\">,</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">134</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indL</span><span class=\"p\">,</span> <span class=\"n\">NL</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">135</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">136</span>:     <span class=\"c\"># Pre-format input</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">137</span>:     <span class=\"k\">if</span> <span class=\"n\">PhiMinMax</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = (__pyx_v_PhiMinMax == Py_None);\n",
       "  __pyx_t_2 = (__pyx_t_1 != 0);\n",
       "  if (__pyx_t_2) {\n",
       "/* … */\n",
       "    goto __pyx_L3;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-18\" onclick='toggleDiv(this)'>+<span class=\"\">138</span>:         <span class=\"n\">PhiMinMax</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"o\">-</span><span class=\"n\">Cpi</span><span class=\"p\">,</span><span class=\"n\">Cpi</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-18 '>    __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>((-M_PI)); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 138, __pyx_L1_error)\n",
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       "    __pyx_t_5 = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">139</span>:         <span class=\"n\">DPhiMinMax</span> <span class=\"o\">=</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_DPhiMinMax = (2. * M_PI);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">140</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-70\" onclick='toggleDiv(this)'>+<span class=\"\">141</span>:         <span class=\"n\">PhiMinMax</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])),</span> <span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]))]</span></pre>\n",
       "<pre class='cython code score-70 '>  /*else*/ {\n",
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       "</pre><pre class=\"cython line score-14\" onclick='toggleDiv(this)'>+<span class=\"\">142</span>:         <span class=\"k\">if</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">&gt;=</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-14 '>    __pyx_t_3 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 142, __pyx_L1_error)\n",
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       "/* … */\n",
       "      goto __pyx_L4;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-22\" onclick='toggleDiv(this)'>+<span class=\"\">143</span>:             <span class=\"n\">DPhiMinMax</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-22 '>      __pyx_t_5 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 143, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">144</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-34\" onclick='toggleDiv(this)'>+<span class=\"\">145</span>:             <span class=\"n\">DPhiMinMax</span> <span class=\"o\">=</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span> <span class=\"o\">+</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-34 '>    /*else*/ {\n",
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       "    __pyx_L4:;\n",
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       "  __pyx_L3:;\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">148</span>:     <span class=\"c\"># Get the actual R and Z resolutions and mesh elements</span></pre>\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">174</span>:     <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_24 = __pyx_v_Ln;\n",
       "  for (__pyx_t_15 = 0; __pyx_t_15 &lt; __pyx_t_24; __pyx_t_15+=1) {\n",
       "    __pyx_v_ii = __pyx_t_15;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">175</span>:         <span class=\"c\"># Get the actual RPhi resolution and Phi mesh elements (! depends on R !)</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">176</span>:         <span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">DPhiMinMax</span><span class=\"o\">*</span><span class=\"n\">Rref</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_26 = __pyx_v_ii;\n",
       "    __pyx_t_27 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_27)) )) = ceil(((__pyx_v_DPhiMinMax * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_Rref.rcbuffer-&gt;pybuffer.buf, __pyx_t_26, __pyx_pybuffernd_Rref.diminfo[0].strides))) / __pyx_v_dRPhi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">177</span>:         <span class=\"n\">NRPhi_int</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_28 = __pyx_v_ii;\n",
       "    __pyx_v_NRPhi_int = ((int)(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_28)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">178</span>:         <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">DPhiMinMax</span><span class=\"o\">/</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_29 = __pyx_v_ii;\n",
       "    __pyx_t_30 = __pyx_v_ii;\n",
       "    *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_30)) )) = (__pyx_v_DPhiMinMax / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_29)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">179</span>:         <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Rref</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_31 = __pyx_v_ii;\n",
       "    __pyx_t_32 = __pyx_v_ii;\n",
       "    __pyx_t_33 = __pyx_v_ii;\n",
       "    *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dRPhir.rcbuffer-&gt;pybuffer.buf, __pyx_t_33, __pyx_pybuffernd_dRPhir.diminfo[0].strides) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_31)) ))) * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_Rref.rcbuffer-&gt;pybuffer.buf, __pyx_t_32, __pyx_pybuffernd_Rref.diminfo[0].strides)));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">180</span>:         <span class=\"c\"># Get index and cumulated indices from background</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">181</span>:         <span class=\"k\">for</span> <span class=\"n\">jj0</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">indR0ii</span><span class=\"p\">,</span><span class=\"n\">NR0</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_34 = __pyx_v_NR0;\n",
       "    for (__pyx_t_35 = __pyx_v_indR0ii; __pyx_t_35 &lt; __pyx_t_34; __pyx_t_35+=1) {\n",
       "      __pyx_v_jj0 = __pyx_t_35;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">182</span>:             <span class=\"k\">if</span> <span class=\"n\">jj0</span><span class=\"o\">==</span><span class=\"n\">Indin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_36 = __pyx_v_ii;\n",
       "      __pyx_t_1 = ((__pyx_v_jj0 == (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Indin.data) + __pyx_t_36)) )))) != 0);\n",
       "      if (__pyx_t_1) {\n",
       "/* … */\n",
       "      }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">183</span>:                 <span class=\"n\">indR0ii</span> <span class=\"o\">=</span> <span class=\"n\">jj0</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_v_indR0ii = __pyx_v_jj0;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">184</span>:                 <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>        goto __pyx_L13_break;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">185</span>:             <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">186</span>:                 <span class=\"n\">nRPhi0</span> <span class=\"o\">+=</span> <span class=\"p\">&lt;</span><span class=\"kt\">long</span><span class=\"p\">&gt;</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">DPhiMinMax</span><span class=\"o\">*</span><span class=\"n\">R0</span><span class=\"p\">[</span><span class=\"n\">jj0</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      /*else*/ {\n",
       "        __pyx_t_37 = __pyx_v_jj0;\n",
       "        __pyx_v_nRPhi0 = (__pyx_v_nRPhi0 + ((long)ceil(((__pyx_v_DPhiMinMax * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_R0.rcbuffer-&gt;pybuffer.buf, __pyx_t_37, __pyx_pybuffernd_R0.diminfo[0].strides))) / __pyx_v_dRPhi))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">187</span>:                 <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">nRPhi0</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_38 = __pyx_v_ii;\n",
       "        *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_38)) )) = __pyx_v_nRPhi0;\n",
       "      }\n",
       "    }\n",
       "    __pyx_L13_break:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">188</span>:         <span class=\"c\"># Get indices of phi</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">189</span>:         <span class=\"c\"># Get the extreme indices of the mesh elements that really need to be created within those limits</span></pre>\n",
       "<pre class=\"cython line score-31\" onclick='toggleDiv(this)'>+<span class=\"\">190</span>:         <span class=\"n\">abs0</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DPhi0</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-31 '>    __pyx_t_11 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi0); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 190, __pyx_L1_error)\n",
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       "    __pyx_v_abs0 = __pyx_t_7;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">191</span>:         <span class=\"k\">if</span> <span class=\"n\">abs0</span><span class=\"o\">-</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs0</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_39 = __pyx_v_ii;\n",
       "    __pyx_t_40 = __pyx_v_ii;\n",
       "    __pyx_t_41 = __pyx_v_ii;\n",
       "    __pyx_t_1 = (((__pyx_v_abs0 - ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_39)) ))) * floor((__pyx_v_abs0 / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_40)) ))))))) &lt; (__pyx_v_margin * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_41)) ))))) != 0);\n",
       "    if (__pyx_t_1) {\n",
       "/* … */\n",
       "      goto __pyx_L15;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-34\" onclick='toggleDiv(this)'>+<span class=\"\">192</span>:             <span class=\"n\">nPhi0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DPhi0</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-34 '>      __pyx_t_8 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi0); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 192, __pyx_L1_error)\n",
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       "      __pyx_t_7 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(__pyx_t_8); if (unlikely((__pyx_t_7 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 192, __pyx_L1_error)\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n",
       "      __pyx_v_nPhi0 = ((int)round(__pyx_t_7));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">193</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-34\" onclick='toggleDiv(this)'>+<span class=\"\">194</span>:             <span class=\"n\">nPhi0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">((</span><span class=\"n\">DPhi0</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-34 '>    /*else*/ {\n",
       "      __pyx_t_8 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi0); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 194, __pyx_L1_error)\n",
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       "      __pyx_t_11 = <span class='py_c_api'>PyNumber_Subtract</span>(__pyx_t_8, __pyx_t_9); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 194, __pyx_L1_error)\n",
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       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "      __pyx_t_43 = __pyx_v_ii;\n",
       "      __pyx_t_9 = <span class='py_c_api'>PyFloat_FromDouble</span>((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_43)) )))); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 194, __pyx_L1_error)\n",
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       "      __pyx_t_8 = <span class='pyx_c_api'>__Pyx_PyNumber_Divide</span>(__pyx_t_11, __pyx_t_9); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 194, __pyx_L1_error)\n",
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       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_11); __pyx_t_11 = 0;\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "      __pyx_t_7 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(__pyx_t_8); if (unlikely((__pyx_t_7 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 194, __pyx_L1_error)\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n",
       "      __pyx_v_nPhi0 = ((int)floor(__pyx_t_7));\n",
       "    }\n",
       "    __pyx_L15:;\n",
       "</pre><pre class=\"cython line score-31\" onclick='toggleDiv(this)'>+<span class=\"\">195</span>:         <span class=\"n\">abs1</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">DPhi1</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-31 '>    __pyx_t_8 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi1); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 195, __pyx_L1_error)\n",
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       "    __pyx_t_9 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 195, __pyx_L1_error)\n",
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       "    __pyx_t_7 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(__pyx_t_9); if (unlikely((__pyx_t_7 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 195, __pyx_L1_error)\n",
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       "    __pyx_v_abs1 = __pyx_t_7;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">196</span>:         <span class=\"k\">if</span> <span class=\"n\">abs1</span><span class=\"o\">-</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs1</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span><span class=\"o\">&lt;</span><span class=\"n\">margin</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_44 = __pyx_v_ii;\n",
       "    __pyx_t_45 = __pyx_v_ii;\n",
       "    __pyx_t_46 = __pyx_v_ii;\n",
       "    __pyx_t_1 = (((__pyx_v_abs1 - ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_44)) ))) * floor((__pyx_v_abs1 / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_45)) ))))))) &lt; (__pyx_v_margin * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_46)) ))))) != 0);\n",
       "    if (__pyx_t_1) {\n",
       "/* … */\n",
       "      goto __pyx_L16;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-34\" onclick='toggleDiv(this)'>+<span class=\"\">197</span>:             <span class=\"n\">nPhi1</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cround</span><span class=\"p\">((</span><span class=\"n\">DPhi1</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-34 '>      __pyx_t_9 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 197, __pyx_L1_error)\n",
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       "      __pyx_t_11 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 197, __pyx_L1_error)\n",
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       "      __pyx_t_11 = <span class='py_c_api'>PyFloat_FromDouble</span>((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_47)) )))); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 197, __pyx_L1_error)\n",
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       "      <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n",
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       "      __pyx_t_7 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(__pyx_t_9); if (unlikely((__pyx_t_7 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 197, __pyx_L1_error)\n",
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       "<pre class='cython code score-0 '>      __pyx_t_34 = (__pyx_v_NRPhi_int - __pyx_v_nPhi0);\n",
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       "<pre class='cython code score-0 '>        __pyx_t_56 = __pyx_v_ii;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">212</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">NRPhi_int</span><span class=\"o\">-</span><span class=\"n\">nPhi0</span><span class=\"p\">,</span><span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_58 = __pyx_v_ii;\n",
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       "<pre class='cython code score-0 '>        __pyx_t_59 = __pyx_v_ii;\n",
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       "    __pyx_L17:;\n",
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       "<pre class='cython code score-0 '>    __pyx_t_61 = __pyx_v_ii;\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">216</span>:     <span class=\"c\"># Finish counting to get total number of points</span></pre>\n",
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       "<pre class='cython code score-0 '>  __pyx_t_1 = ((__pyx_v_jj0 &lt;= (__pyx_v_NR0 - 1)) != 0);\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">219</span>:             <span class=\"n\">nRPhi0</span> <span class=\"o\">+=</span> <span class=\"p\">&lt;</span><span class=\"kt\">long</span><span class=\"p\">&gt;</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">DPhiMinMax</span><span class=\"o\">*</span><span class=\"n\">R0</span><span class=\"p\">[</span><span class=\"n\">jj0</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
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       "  } else {\n",
       "    __pyx_t_9 = <span class='pyx_c_api'>__Pyx_PyObject_CallNoArg</span>(__pyx_t_11); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 227, __pyx_L1_error)\n",
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       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_9);\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_11); __pyx_t_11 = 0;\n",
       "  __pyx_t_1 = (<span class='pyx_c_api'>__Pyx_PyUnicode_Equals</span>(__pyx_t_9, __pyx_kp_u_x_y_z, Py_EQ)); if (unlikely(__pyx_t_1 &lt; 0)) __PYX_ERR(0, 227, __pyx_L1_error)\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "  if (__pyx_t_1) {\n",
       "/* … */\n",
       "    goto __pyx_L29;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">228</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_24 = __pyx_v_Ln;\n",
       "    for (__pyx_t_15 = 0; __pyx_t_15 &lt; __pyx_t_24; __pyx_t_15+=1) {\n",
       "      __pyx_v_ii = __pyx_t_15;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">229</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_63 = __pyx_v_ii;\n",
       "      __pyx_t_25 = (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Phin.data) + __pyx_t_63)) )));\n",
       "      for (__pyx_t_34 = 0; __pyx_t_34 &lt; __pyx_t_25; __pyx_t_34+=1) {\n",
       "        __pyx_v_jj = __pyx_t_34;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">230</span>:                 <span class=\"n\">indiijj</span> <span class=\"o\">=</span> <span class=\"n\">indI</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">,</span><span class=\"n\">jj</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_64 = __pyx_v_ii;\n",
       "        __pyx_t_65 = __pyx_v_jj;\n",
       "        __pyx_v_indiijj = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_indI.rcbuffer-&gt;pybuffer.buf, __pyx_t_64, __pyx_pybuffernd_indI.diminfo[0].strides, __pyx_t_65, __pyx_pybuffernd_indI.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-25\" onclick='toggleDiv(this)'>+<span class=\"\">231</span>:                 <span class=\"n\">phi</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"n\">indiijj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-25 '>        __pyx_t_9 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 231, __pyx_L1_error)\n",
       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_9);\n",
       "        __pyx_t_66 = __pyx_v_ii;\n",
       "        __pyx_t_11 = <span class='py_c_api'>PyFloat_FromDouble</span>(((0.5 + __pyx_v_indiijj) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_66)) ))))); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 231, __pyx_L1_error)\n",
       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_11);\n",
       "        __pyx_t_8 = <span class='py_c_api'>PyNumber_Add</span>(__pyx_t_9, __pyx_t_11); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 231, __pyx_L1_error)\n",
       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_8);\n",
       "        <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "        <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_11); __pyx_t_11 = 0;\n",
       "        __pyx_t_7 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(__pyx_t_8); if (unlikely((__pyx_t_7 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 231, __pyx_L1_error)\n",
       "        <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n",
       "        __pyx_v_phi = __pyx_t_7;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">232</span>:                 <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_67 = 0;\n",
       "        __pyx_t_68 = __pyx_v_ii;\n",
       "        __pyx_t_69 = 0;\n",
       "        __pyx_t_70 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_69, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_70, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_67, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_68, __pyx_pybuffernd_PtsCross.diminfo[1].strides)) * cos(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">233</span>:                 <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_71 = 0;\n",
       "        __pyx_t_72 = __pyx_v_ii;\n",
       "        __pyx_t_73 = 1;\n",
       "        __pyx_t_74 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_73, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_74, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_71, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_72, __pyx_pybuffernd_PtsCross.diminfo[1].strides)) * sin(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">234</span>:                 <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_75 = 1;\n",
       "        __pyx_t_76 = __pyx_v_ii;\n",
       "        __pyx_t_77 = 2;\n",
       "        __pyx_t_78 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_77, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_78, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_75, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_76, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">235</span>:                 <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indiijj</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_79 = __pyx_v_ii;\n",
       "        __pyx_t_80 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_ind.rcbuffer-&gt;pybuffer.buf, __pyx_t_80, __pyx_pybuffernd_ind.diminfo[0].strides) = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_79)) ))) + __pyx_v_indiijj);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">236</span>:                 <span class=\"n\">dS</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dLr</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_81 = __pyx_v_ii;\n",
       "        __pyx_t_82 = __pyx_v_ii;\n",
       "        __pyx_t_83 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dS.rcbuffer-&gt;pybuffer.buf, __pyx_t_83, __pyx_pybuffernd_dS.diminfo[0].strides) = ((*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dLr.rcbuffer-&gt;pybuffer.buf, __pyx_t_81, __pyx_pybuffernd_dLr.diminfo[0].strides)) * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dRPhir.rcbuffer-&gt;pybuffer.buf, __pyx_t_82, __pyx_pybuffernd_dRPhir.diminfo[0].strides)));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">237</span>:                 <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_v_NP = (__pyx_v_NP + 1);\n",
       "      }\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">238</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">239</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_24 = __pyx_v_Ln;\n",
       "    for (__pyx_t_15 = 0; __pyx_t_15 &lt; __pyx_t_24; __pyx_t_15+=1) {\n",
       "      __pyx_v_ii = __pyx_t_15;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">240</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_84 = __pyx_v_ii;\n",
       "      __pyx_t_25 = (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Phin.data) + __pyx_t_84)) )));\n",
       "      for (__pyx_t_34 = 0; __pyx_t_34 &lt; __pyx_t_25; __pyx_t_34+=1) {\n",
       "        __pyx_v_jj = __pyx_t_34;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">241</span>:                 <span class=\"n\">indiijj</span> <span class=\"o\">=</span> <span class=\"n\">indI</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">,</span><span class=\"n\">jj</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_85 = __pyx_v_ii;\n",
       "        __pyx_t_86 = __pyx_v_jj;\n",
       "        __pyx_v_indiijj = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_indI.rcbuffer-&gt;pybuffer.buf, __pyx_t_85, __pyx_pybuffernd_indI.diminfo[0].strides, __pyx_t_86, __pyx_pybuffernd_indI.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">242</span>:                 <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_87 = 0;\n",
       "        __pyx_t_88 = __pyx_v_ii;\n",
       "        __pyx_t_89 = 0;\n",
       "        __pyx_t_90 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_89, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_90, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_87, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_88, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">243</span>:                 <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_91 = 1;\n",
       "        __pyx_t_92 = __pyx_v_ii;\n",
       "        __pyx_t_93 = 1;\n",
       "        __pyx_t_94 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_93, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_94, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_91, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_92, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-25\" onclick='toggleDiv(this)'>+<span class=\"\">244</span>:                 <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"n\">indiijj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-25 '>        __pyx_t_8 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_8)) __PYX_ERR(0, 244, __pyx_L1_error)\n",
       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_8);\n",
       "        __pyx_t_95 = __pyx_v_ii;\n",
       "        __pyx_t_11 = <span class='py_c_api'>PyFloat_FromDouble</span>(((0.5 + __pyx_v_indiijj) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_95)) ))))); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 244, __pyx_L1_error)\n",
       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_11);\n",
       "        __pyx_t_9 = <span class='py_c_api'>PyNumber_Add</span>(__pyx_t_8, __pyx_t_11); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 244, __pyx_L1_error)\n",
       "        <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_9);\n",
       "        <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n",
       "        <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_11); __pyx_t_11 = 0;\n",
       "        __pyx_t_7 = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(__pyx_t_9); if (unlikely((__pyx_t_7 == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 244, __pyx_L1_error)\n",
       "        <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
       "        __pyx_t_96 = 2;\n",
       "        __pyx_t_97 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_96, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_97, __pyx_pybuffernd_Pts.diminfo[1].strides) = __pyx_t_7;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">245</span>:                 <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indiijj</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_98 = __pyx_v_ii;\n",
       "        __pyx_t_99 = __pyx_v_NP;\n",
       "        *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_ind.rcbuffer-&gt;pybuffer.buf, __pyx_t_99, __pyx_pybuffernd_ind.diminfo[0].strides) = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_98)) ))) + __pyx_v_indiijj);\n",
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       "<pre class='cython code score-0 '>        __pyx_t_100 = __pyx_v_ii;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">247</span>:                 <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
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       "<pre class=\"cython line score-70\" onclick='toggleDiv(this)'>+<span class=\"\">256</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_Smesh_Tor_SubFromInd_cython</span><span class=\"p\">(</span><span class=\"n\">double</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dRPhi</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-70 '>/* Python wrapper */\n",
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       "static char __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995_6_Ves_Smesh_Tor_SubFromInd_cython[] = \" Return the desired submesh indicated by the (numerical) indices, for the desired resolution (dR,dZ,dRphi) \";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_8152fc40fb05daa7217c00b66474f995_7_Ves_Smesh_Tor_SubFromInd_cython = {\"_Ves_Smesh_Tor_SubFromInd_cython\", (PyCFunction)__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_7_Ves_Smesh_Tor_SubFromInd_cython, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995_6_Ves_Smesh_Tor_SubFromInd_cython};\n",
       "static PyObject *__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_7_Ves_Smesh_Tor_SubFromInd_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  double __pyx_v_dL;\n",
       "  double __pyx_v_dRPhi;\n",
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       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995_6_Ves_Smesh_Tor_SubFromInd_cython(__pyx_self, __pyx_v_dL, __pyx_v_dRPhi, __pyx_v_VPoly, __pyx_v_ind, __pyx_v_DIn, __pyx_v_VIn, __pyx_v_PhiMinMax, __pyx_v_Out, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">259</span>:                                      <span class=\"nb\">str</span> <span class=\"n\">Out</span><span class=\"o\">=</span><span class=\"s\">&#39;(X,Y,Z)&#39;</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">260</span>:     <span class=\"sd\">&quot;&quot;&quot; Return the desired submesh indicated by the (numerical) indices, for the desired resolution (dR,dZ,dRphi) &quot;&quot;&quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">261</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">dRPhirRef</span><span class=\"p\">,</span> <span class=\"n\">dPhir</span></pre>\n",
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       "<pre class='cython code score-5 '>    __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>((2. * M_PI)); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 273, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-70\" onclick='toggleDiv(this)'>+<span class=\"\">275</span>:         <span class=\"n\">PhiMinMax</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])),</span> <span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]))]</span></pre>\n",
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       "<pre class='cython code score-19 '>    __pyx_t_32 = __pyx_v_ii;\n",
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       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">297</span>:             <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>    /*else*/ {\n",
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       "    __pyx_L9:;\n",
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       "<pre class='cython code score-0 '>    __pyx_t_40 = __pyx_v_ii;\n",
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       "<pre class='cython code score-27 '>      __pyx_t_6 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 299, __pyx_L1_error)\n",
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       "      __pyx_t_44 = __pyx_v_ii;\n",
       "      __pyx_t_45 = __pyx_v_jj;\n",
       "      *((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_44 * __pyx_v_Phi.strides[0]) )) + __pyx_t_45)) )) = __pyx_t_11;\n",
       "    }\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">300</span>:     <span class=\"n\">nRPhi0</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">Ln</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">+</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">Ln</span><span class=\"o\">-</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_46 = (__pyx_v_Ln - 1);\n",
       "  __pyx_t_47 = (__pyx_v_Ln - 1);\n",
       "  __pyx_v_nRPhi0 = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_46)) ))) + (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi.data) + __pyx_t_47)) ))));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">301</span>: </pre>\n",
       "<pre class=\"cython line score-21\" onclick='toggleDiv(this)'>+<span class=\"\">302</span>:     <span class=\"k\">if</span> <span class=\"n\">Out</span><span class=\"o\">.</span><span class=\"n\">lower</span><span class=\"p\">()</span><span class=\"o\">==</span><span class=\"s\">&#39;(x,y,z)&#39;</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-21 '>  __pyx_t_14 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_v_Out, __pyx_n_s_lower); if (unlikely(!__pyx_t_14)) __PYX_ERR(0, 302, __pyx_L1_error)\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_14);\n",
       "  __pyx_t_6 = NULL;\n",
       "  if (CYTHON_UNPACK_METHODS &amp;&amp; likely(<span class='py_c_api'>PyMethod_Check</span>(__pyx_t_14))) {\n",
       "    __pyx_t_6 = <span class='py_macro_api'>PyMethod_GET_SELF</span>(__pyx_t_14);\n",
       "    if (likely(__pyx_t_6)) {\n",
       "      PyObject* function = <span class='py_macro_api'>PyMethod_GET_FUNCTION</span>(__pyx_t_14);\n",
       "      <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_6);\n",
       "      <span class='pyx_macro_api'>__Pyx_INCREF</span>(function);\n",
       "      <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_t_14, function);\n",
       "    }\n",
       "  }\n",
       "  if (__pyx_t_6) {\n",
       "    __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_t_14, __pyx_t_6); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 302, __pyx_L1_error)\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;\n",
       "  } else {\n",
       "    __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_CallNoArg</span>(__pyx_t_14); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 302, __pyx_L1_error)\n",
       "  }\n",
       "  <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_4);\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_14); __pyx_t_14 = 0;\n",
       "  __pyx_t_10 = (<span class='pyx_c_api'>__Pyx_PyUnicode_Equals</span>(__pyx_t_4, __pyx_kp_u_x_y_z, Py_EQ)); if (unlikely(__pyx_t_10 &lt; 0)) __PYX_ERR(0, 302, __pyx_L1_error)\n",
       "  <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n",
       "  if (__pyx_t_10) {\n",
       "/* … */\n",
       "    goto __pyx_L12;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">303</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_41 = __pyx_v_NP;\n",
       "    for (__pyx_t_18 = 0; __pyx_t_18 &lt; __pyx_t_41; __pyx_t_18+=1) {\n",
       "      __pyx_v_ii = __pyx_t_18;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">304</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_48 = (__pyx_v_Ln + 1);\n",
       "      for (__pyx_t_26 = 0; __pyx_t_26 &lt; __pyx_t_48; __pyx_t_26+=1) {\n",
       "        __pyx_v_jj = __pyx_t_26;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">305</span>:                 <span class=\"k\">if</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">jj</span><span class=\"p\">]</span><span class=\"o\">&lt;</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_49 = __pyx_v_ii;\n",
       "        __pyx_t_50 = __pyx_v_jj;\n",
       "        __pyx_t_10 = ((((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_49)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_50)) )))) &lt; 0.) != 0);\n",
       "        if (__pyx_t_10) {\n",
       "/* … */\n",
       "        }\n",
       "      }\n",
       "      __pyx_L16_break:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">306</span>:                     <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>          goto __pyx_L16_break;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">307</span>:             <span class=\"n\">iiL</span> <span class=\"o\">=</span> <span class=\"n\">jj</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_v_iiL = (__pyx_v_jj - 1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">308</span>:             <span class=\"n\">iiphi</span> <span class=\"o\">=</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_51 = __pyx_v_ii;\n",
       "      __pyx_t_52 = __pyx_v_iiL;\n",
       "      __pyx_v_iiphi = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_51)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_52)) ))));\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">309</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">iiL</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">Phi</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">,</span><span class=\"n\">iiphi</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-2 '>      __pyx_t_53 = 0;\n",
       "      __pyx_t_54 = __pyx_v_iiL;\n",
       "      if (unlikely(!__pyx_v_Phi.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"Phi\"); __PYX_ERR(0, 309, __pyx_L1_error) }\n",
       "      __pyx_t_55 = __pyx_v_iiL;\n",
       "      __pyx_t_56 = __pyx_v_iiphi;\n",
       "      __pyx_t_57 = 0;\n",
       "      __pyx_t_58 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_57, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_58, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_53, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_54, __pyx_pybuffernd_PtsCross.diminfo[1].strides)) * cos((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_55 * __pyx_v_Phi.strides[0]) )) + __pyx_t_56)) )))));\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">310</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">iiL</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">Phi</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">,</span><span class=\"n\">iiphi</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-2 '>      __pyx_t_59 = 0;\n",
       "      __pyx_t_60 = __pyx_v_iiL;\n",
       "      if (unlikely(!__pyx_v_Phi.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"Phi\"); __PYX_ERR(0, 310, __pyx_L1_error) }\n",
       "      __pyx_t_61 = __pyx_v_iiL;\n",
       "      __pyx_t_62 = __pyx_v_iiphi;\n",
       "      __pyx_t_63 = 1;\n",
       "      __pyx_t_64 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_63, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_64, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_59, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_60, __pyx_pybuffernd_PtsCross.diminfo[1].strides)) * sin((*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_61 * __pyx_v_Phi.strides[0]) )) + __pyx_t_62)) )))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">311</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_65 = 1;\n",
       "      __pyx_t_66 = __pyx_v_iiL;\n",
       "      __pyx_t_67 = 2;\n",
       "      __pyx_t_68 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_67, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_68, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_65, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_66, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">312</span>:             <span class=\"n\">dS</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dLr</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_69 = __pyx_v_iiL;\n",
       "      __pyx_t_70 = __pyx_v_iiL;\n",
       "      __pyx_t_71 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dS.rcbuffer-&gt;pybuffer.buf, __pyx_t_71, __pyx_pybuffernd_dS.diminfo[0].strides) = ((*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dLr.rcbuffer-&gt;pybuffer.buf, __pyx_t_69, __pyx_pybuffernd_dLr.diminfo[0].strides)) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_70)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">313</span>:             <span class=\"k\">if</span> <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span><span class=\"o\">==-</span><span class=\"mf\">1.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_72 = __pyx_v_iiL;\n",
       "      __pyx_t_10 = (((*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dRPhir.rcbuffer-&gt;pybuffer.buf, __pyx_t_72, __pyx_pybuffernd_dRPhir.diminfo[0].strides)) == -1.) != 0);\n",
       "      if (__pyx_t_10) {\n",
       "/* … */\n",
       "      }\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">314</span>:                 <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_73 = __pyx_v_iiL;\n",
       "        __pyx_t_74 = __pyx_v_iiL;\n",
       "        *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dRPhir.rcbuffer-&gt;pybuffer.buf, __pyx_t_74, __pyx_pybuffernd_dRPhir.diminfo[0].strides) = (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_73)) )));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">315</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">316</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">317</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>  /*else*/ {\n",
       "    __pyx_t_41 = __pyx_v_NP;\n",
       "    for (__pyx_t_18 = 0; __pyx_t_18 &lt; __pyx_t_41; __pyx_t_18+=1) {\n",
       "      __pyx_v_ii = __pyx_t_18;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">318</span>:             <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"o\">+</span><span class=\"mf\">1</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_48 = (__pyx_v_Ln + 1);\n",
       "      for (__pyx_t_26 = 0; __pyx_t_26 &lt; __pyx_t_48; __pyx_t_26+=1) {\n",
       "        __pyx_v_jj = __pyx_t_26;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">319</span>:                 <span class=\"k\">if</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">jj</span><span class=\"p\">]</span><span class=\"o\">&lt;</span><span class=\"mf\">0.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_75 = __pyx_v_ii;\n",
       "        __pyx_t_76 = __pyx_v_jj;\n",
       "        __pyx_t_10 = ((((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_75)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_76)) )))) &lt; 0.) != 0);\n",
       "        if (__pyx_t_10) {\n",
       "/* … */\n",
       "        }\n",
       "      }\n",
       "      __pyx_L22_break:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">320</span>:                     <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>          goto __pyx_L22_break;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">321</span>:             <span class=\"n\">iiL</span> <span class=\"o\">=</span> <span class=\"n\">jj</span><span class=\"o\">-</span><span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_v_iiL = (__pyx_v_jj - 1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">322</span>:             <span class=\"n\">iiphi</span> <span class=\"o\">=</span> <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_77 = __pyx_v_ii;\n",
       "      __pyx_t_78 = __pyx_v_iiL;\n",
       "      __pyx_v_iiphi = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_ind.data) + __pyx_t_77)) ))) - (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_78)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">323</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_79 = 0;\n",
       "      __pyx_t_80 = __pyx_v_iiL;\n",
       "      __pyx_t_81 = 0;\n",
       "      __pyx_t_82 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_81, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_82, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_79, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_80, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">324</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_83 = 1;\n",
       "      __pyx_t_84 = __pyx_v_iiL;\n",
       "      __pyx_t_85 = 1;\n",
       "      __pyx_t_86 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_85, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_86, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_83, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_84, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">325</span>:             <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">,</span><span class=\"n\">iiphi</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-2 '>      if (unlikely(!__pyx_v_Phi.memview)) { <span class='pyx_c_api'>__Pyx_RaiseUnboundLocalError</span>(\"Phi\"); __PYX_ERR(0, 325, __pyx_L1_error) }\n",
       "      __pyx_t_87 = __pyx_v_iiL;\n",
       "      __pyx_t_88 = __pyx_v_iiphi;\n",
       "      __pyx_t_89 = 2;\n",
       "      __pyx_t_90 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_89, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_90, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*((double *) ( /* dim=1 */ ((char *) (((double *) ( /* dim=0 */ (__pyx_v_Phi.data + __pyx_t_87 * __pyx_v_Phi.strides[0]) )) + __pyx_t_88)) )));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">326</span>:             <span class=\"n\">dS</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dLr</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">dRPhirRef</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_91 = __pyx_v_iiL;\n",
       "      __pyx_t_92 = __pyx_v_iiL;\n",
       "      __pyx_t_93 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dS.rcbuffer-&gt;pybuffer.buf, __pyx_t_93, __pyx_pybuffernd_dS.diminfo[0].strides) = ((*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dLr.rcbuffer-&gt;pybuffer.buf, __pyx_t_91, __pyx_pybuffernd_dLr.diminfo[0].strides)) * (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dRPhirRef.data) + __pyx_t_92)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">327</span>:             <span class=\"k\">if</span> <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">iiL</span><span class=\"p\">]</span><span class=\"o\">==-</span><span class=\"mf\">1.</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_94 = __pyx_v_iiL;\n",
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       "<pre class='cython code score-0 '>        __pyx_t_95 = __pyx_v_iiL;\n",
       "        __pyx_t_96 = __pyx_v_iiL;\n",
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       "</pre><pre class=\"cython line score-33\" onclick='toggleDiv(this)'>+<span class=\"\">329</span>:     <span class=\"k\">return</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">NL</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">dRPhir</span><span class=\"o\">&gt;-</span><span class=\"mf\">1.</span><span class=\"p\">],</span> <span class=\"p\">&lt;</span><span class=\"kt\">long</span><span class=\"p\">&gt;</span><span class=\"n\">nRPhi0</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span></pre>\n",
       "<pre class='cython code score-33 '>  <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">331</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">332</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">cdivision</span><span class=\"p\">(</span><span class=\"bp\">True</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">333</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">334</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
       "<pre class=\"cython line score-135\" onclick='toggleDiv(this)'>+<span class=\"\">335</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_Smesh_TorStruct_SubFromD_cython</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dRPhi</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-135 '>/* Python wrapper */\n",
       "static PyObject *__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_9_Ves_Smesh_TorStruct_SubFromD_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
       "static char __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995_8_Ves_Smesh_TorStruct_SubFromD_cython[] = \" Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_8152fc40fb05daa7217c00b66474f995_9_Ves_Smesh_TorStruct_SubFromD_cython = {\"_Ves_Smesh_TorStruct_SubFromD_cython\", (PyCFunction)__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_9_Ves_Smesh_TorStruct_SubFromD_cython, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995_8_Ves_Smesh_TorStruct_SubFromD_cython};\n",
       "static PyObject *__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_9_Ves_Smesh_TorStruct_SubFromD_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  __Pyx_memviewslice __pyx_v_PhiMinMax = { 0, 0, { 0 }, { 0 }, { 0 } };\n",
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       "  PyObject *__pyx_v_VIn = 0;\n",
       "  PyObject *__pyx_v_Out = 0;\n",
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       "  int __pyx_v_NZ0;\n",
       "  int __pyx_v_R0n;\n",
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       "  PyArrayObject *__pyx_v_R0 = 0;\n",
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       "  PyArrayObject *__pyx_v_Rref = 0;\n",
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       "  PyArrayObject *__pyx_v_indR0 = 0;\n",
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       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_PhiMinMax)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dL)) != 0)) kw_args--;\n",
       "        else {\n",
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       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_TorStruct_SubFromD_cython\", 0, 4, 11, 2); __PYX_ERR(0, 335, __pyx_L3_error)\n",
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       "        case  3:\n",
       "        if (likely((values[3] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_VPoly)) != 0)) kw_args--;\n",
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       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DR);\n",
       "          if (value) { values[4] = value; kw_args--; }\n",
       "        }\n",
       "        case  5:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DZ);\n",
       "          if (value) { values[5] = value; kw_args--; }\n",
       "        }\n",
       "        case  6:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DPhi);\n",
       "          if (value) { values[6] = value; kw_args--; }\n",
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       "        case  7:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DIn);\n",
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       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_VIn);\n",
       "          if (value) { values[8] = value; kw_args--; }\n",
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       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_Out);\n",
       "          if (value) { values[9] = value; kw_args--; }\n",
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       "        case 10:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_margin);\n",
       "          if (value) { values[10] = value; kw_args--; }\n",
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       "        case 11: values[10] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 10);\n",
       "        case 10: values[9] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 9);\n",
       "        case  9: values[8] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 8);\n",
       "        case  8: values[7] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 7);\n",
       "        case  7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n",
       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "    }\n",
       "    __pyx_v_PhiMinMax = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[0]); if (unlikely(!__pyx_v_PhiMinMax.memview)) __PYX_ERR(0, 335, __pyx_L3_error)\n",
       "    __pyx_v_dL = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_dL == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 335, __pyx_L3_error)\n",
       "    __pyx_v_dRPhi = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_dRPhi == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 335, __pyx_L3_error)\n",
       "    __pyx_v_VPoly = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[3]); if (unlikely(!__pyx_v_VPoly.memview)) __PYX_ERR(0, 336, __pyx_L3_error)\n",
       "    __pyx_v_DR = values[4];\n",
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       "    __pyx_v_DPhi = values[6];\n",
       "    if (values[7]) {\n",
       "      __pyx_v_DIn = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[7]); if (unlikely((__pyx_v_DIn == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 338, __pyx_L3_error)\n",
       "    } else {\n",
       "      __pyx_v_DIn = ((double)0.);\n",
       "    }\n",
       "    __pyx_v_VIn = values[8];\n",
       "    __pyx_v_Out = ((PyObject*)values[9]);\n",
       "    if (values[10]) {\n",
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       "    }\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_TorStruct_SubFromD_cython\", 0, 4, 11, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 335, __pyx_L3_error)\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_8152fc40fb05daa7217c00b66474f995._Ves_Smesh_TorStruct_SubFromD_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
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       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995_8_Ves_Smesh_TorStruct_SubFromD_cython(__pyx_self, __pyx_v_PhiMinMax, __pyx_v_dL, __pyx_v_dRPhi, __pyx_v_VPoly, __pyx_v_DR, __pyx_v_DZ, __pyx_v_DPhi, __pyx_v_DIn, __pyx_v_VIn, __pyx_v_Out, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">339</span>:                                          <span class=\"nb\">str</span> <span class=\"n\">Out</span><span class=\"o\">=</span><span class=\"s\">&#39;(X,Y,Z)&#39;</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">340</span>:     <span class=\"s\">&quot; Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">341</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">Dphi</span><span class=\"p\">,</span> <span class=\"nf\">dR0r</span><span class=\"p\">,</span> <span class=\"nf\">dZ0r</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">342</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NR0</span><span class=\"p\">,</span> <span class=\"nf\">NZ0</span><span class=\"p\">,</span> <span class=\"nf\">R0n</span><span class=\"p\">,</span> <span class=\"nf\">Z0n</span><span class=\"p\">,</span> <span class=\"nf\">NRPhi0</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">343</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span> <span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R0</span><span class=\"p\">,</span> <span class=\"n\">Z0</span><span class=\"p\">,</span> <span class=\"n\">dsF</span><span class=\"p\">,</span> <span class=\"n\">dSM</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span><span class=\"p\">,</span> <span class=\"n\">dS</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">344</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indR0</span><span class=\"p\">,</span> <span class=\"n\">indZ0</span><span class=\"p\">,</span> <span class=\"n\">iind</span><span class=\"p\">,</span> <span class=\"n\">iindF</span><span class=\"p\">,</span> <span class=\"n\">indM</span><span class=\"p\">,</span> <span class=\"n\">NL</span><span class=\"p\">,</span> <span class=\"n\">ind</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">345</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">ptsrz</span><span class=\"p\">,</span> <span class=\"n\">pts</span><span class=\"p\">,</span> <span class=\"n\">PtsM</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span><span class=\"p\">,</span> <span class=\"n\">Pts</span></pre>\n",
       "<pre class=\"cython line score-15\" onclick='toggleDiv(this)'>+<span class=\"\">346</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">list</span> <span class=\"nf\">LPts</span><span class=\"o\">=</span><span class=\"p\">[],</span> <span class=\"n\">LdS</span><span class=\"o\">=</span><span class=\"p\">[],</span> <span class=\"n\">Lind</span><span class=\"o\">=</span><span class=\"p\">[]</span></pre>\n",
       "<pre class='cython code score-15 '>  __pyx_t_1 = <span class='py_c_api'>PyList_New</span>(0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 346, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">348</span>:     <span class=\"c\"># Pre-format input</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">349</span>:     <span class=\"k\">if</span> <span class=\"n\">DPhi</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = (__pyx_v_DPhi != Py_None);\n",
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       "<pre class='cython code score-17 '>    __pyx_t_1 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_DPhi, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 350, __pyx_L1_error)\n",
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       "<pre class='cython code score-2 '>      if (unlikely(<span class='pyx_c_api'>__Pyx_SetItemInt</span>(__pyx_v_DPhi, 0, Py_None, long, 1, __Pyx_PyInt_From_long, 0, 0, 0) &lt; 0)) __PYX_ERR(0, 351, __pyx_L1_error)\n",
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       "<pre class='cython code score-17 '>    __pyx_t_6 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_DPhi, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 352, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">353</span>:             <span class=\"n\">DPhi</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">None</span></pre>\n",
       "<pre class='cython code score-2 '>      if (unlikely(<span class='pyx_c_api'>__Pyx_SetItemInt</span>(__pyx_v_DPhi, 1, Py_None, long, 1, __Pyx_PyInt_From_long, 0, 0, 0) &lt; 0)) __PYX_ERR(0, 353, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-20\" onclick='toggleDiv(this)'>+<span class=\"\">384</span>:                                                                                            <span class=\"n\">DR</span><span class=\"o\">=</span><span class=\"n\">DR</span><span class=\"p\">,</span> <span class=\"n\">DZ</span><span class=\"o\">=</span><span class=\"n\">DZ</span><span class=\"p\">,</span> <span class=\"n\">DPhi</span><span class=\"o\">=</span><span class=\"n\">DPhi</span><span class=\"p\">,</span></pre>\n",
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       "<pre class='cython code score-22 '>  __pyx_t_12 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DIn); if (unlikely(!__pyx_t_12)) __PYX_ERR(0, 385, __pyx_L1_error)\n",
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       "<pre class='cython code score-16 '>  if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_t_1, __pyx_n_s_Out, __pyx_v_Out) &lt; 0) __PYX_ERR(0, 384, __pyx_L1_error)\n",
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       "<pre class='cython code score-11 '>    __pyx_t_15 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>((__pyx_v_NR0 * __pyx_v_NZ0)); if (unlikely(!__pyx_t_15)) __PYX_ERR(0, 393, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">425</span>:                                            <span class=\"nb\">str</span> <span class=\"n\">Out</span><span class=\"o\">=</span><span class=\"s\">&#39;(X,Y,Z)&#39;</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">426</span>:     <span class=\"s\">&quot; Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">427</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">Dphi</span><span class=\"p\">,</span> <span class=\"nf\">dR0r</span><span class=\"p\">,</span> <span class=\"nf\">dZ0r</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">428</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NR0</span><span class=\"p\">,</span> <span class=\"nf\">NZ0</span><span class=\"p\">,</span> <span class=\"nf\">R0n</span><span class=\"p\">,</span> <span class=\"nf\">Z0n</span><span class=\"p\">,</span> <span class=\"nf\">NRPhi0</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">429</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span> <span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R0</span><span class=\"p\">,</span> <span class=\"n\">Z0</span><span class=\"p\">,</span> <span class=\"n\">dsF</span><span class=\"p\">,</span> <span class=\"n\">dSM</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span><span class=\"p\">,</span> <span class=\"n\">dS</span></pre>\n",
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       "<pre class=\"cython line score-15\" onclick='toggleDiv(this)'>+<span class=\"\">432</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">list</span> <span class=\"nf\">LPts</span><span class=\"o\">=</span><span class=\"p\">[],</span> <span class=\"n\">LdS</span><span class=\"o\">=</span><span class=\"p\">[],</span> <span class=\"n\">Lind</span><span class=\"o\">=</span><span class=\"p\">[]</span></pre>\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">498</span>:                                    <span class=\"n\">double</span><span class=\"p\">[:,::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">VPoly</span><span class=\"p\">,</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">499</span>:                                    <span class=\"n\">DX</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">DY</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">DZ</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-0 '>    values[4] = ((PyObject *)Py_None);\n",
       "    values[5] = ((PyObject *)Py_None);\n",
       "    values[6] = ((PyObject *)Py_None);\n",
       "</pre><pre class=\"cython line score-134\" onclick='toggleDiv(this)'>+<span class=\"\">500</span>:                                    <span class=\"n\">double</span> <span class=\"n\">DIn</span><span class=\"o\">=</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"n\">VIn</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-134 '>    values[8] = ((PyObject *)Py_None);\n",
       "    if (unlikely(__pyx_kwds)) {\n",
       "      Py_ssize_t kw_args;\n",
       "      const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
       "      switch (pos_args) {\n",
       "        case 10: values[9] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 9);\n",
       "        case  9: values[8] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 8);\n",
       "        case  8: values[7] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 7);\n",
       "        case  7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n",
       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        case  2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        case  1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        case  0: break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "      kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
       "      switch (pos_args) {\n",
       "        case  0:\n",
       "        if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_XMinMax)) != 0)) kw_args--;\n",
       "        else goto __pyx_L5_argtuple_error;\n",
       "        case  1:\n",
       "        if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dL)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_Lin_SubFromD_cython\", 0, 4, 10, 1); __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "        }\n",
       "        case  2:\n",
       "        if (likely((values[2] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_dX)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_Lin_SubFromD_cython\", 0, 4, 10, 2); __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "        }\n",
       "        case  3:\n",
       "        if (likely((values[3] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_VPoly)) != 0)) kw_args--;\n",
       "        else {\n",
       "          <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_Lin_SubFromD_cython\", 0, 4, 10, 3); __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "        }\n",
       "        case  4:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DX);\n",
       "          if (value) { values[4] = value; kw_args--; }\n",
       "        }\n",
       "        case  5:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DY);\n",
       "          if (value) { values[5] = value; kw_args--; }\n",
       "        }\n",
       "        case  6:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DZ);\n",
       "          if (value) { values[6] = value; kw_args--; }\n",
       "        }\n",
       "        case  7:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_DIn);\n",
       "          if (value) { values[7] = value; kw_args--; }\n",
       "        }\n",
       "        case  8:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_VIn);\n",
       "          if (value) { values[8] = value; kw_args--; }\n",
       "        }\n",
       "        case  9:\n",
       "        if (kw_args &gt; 0) {\n",
       "          PyObject* value = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_margin);\n",
       "          if (value) { values[9] = value; kw_args--; }\n",
       "        }\n",
       "      }\n",
       "      if (unlikely(kw_args &gt; 0)) {\n",
       "        if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"_Ves_Smesh_Lin_SubFromD_cython\") &lt; 0)) __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "      }\n",
       "    } else {\n",
       "      switch (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)) {\n",
       "        case 10: values[9] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 9);\n",
       "        case  9: values[8] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 8);\n",
       "        case  8: values[7] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 7);\n",
       "        case  7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n",
       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "    }\n",
       "    __pyx_v_XMinMax = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dc_double</span>(values[0]); if (unlikely(!__pyx_v_XMinMax.memview)) __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "    __pyx_v_dL = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_dL == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "    __pyx_v_dX = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[2]); if (unlikely((__pyx_v_dX == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "    __pyx_v_VPoly = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[3]); if (unlikely(!__pyx_v_VPoly.memview)) __PYX_ERR(0, 498, __pyx_L3_error)\n",
       "    __pyx_v_DX = values[4];\n",
       "    __pyx_v_DY = values[5];\n",
       "    __pyx_v_DZ = values[6];\n",
       "    if (values[7]) {\n",
       "      __pyx_v_DIn = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[7]); if (unlikely((__pyx_v_DIn == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 500, __pyx_L3_error)\n",
       "    } else {\n",
       "      __pyx_v_DIn = ((double)0.);\n",
       "    }\n",
       "    __pyx_v_VIn = values[8];\n",
       "    if (values[9]) {\n",
       "      __pyx_v_margin = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[9]); if (unlikely((__pyx_v_margin == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 500, __pyx_L3_error)\n",
       "    } else {\n",
       "      __pyx_v_margin = ((double)1.e-9);\n",
       "    }\n",
       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_Lin_SubFromD_cython\", 0, 4, 10, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 497, __pyx_L3_error)\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_8152fc40fb05daa7217c00b66474f995._Ves_Smesh_Lin_SubFromD_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
       "  return NULL;\n",
       "  __pyx_L4_argument_unpacking_done:;\n",
       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995_12_Ves_Smesh_Lin_SubFromD_cython(__pyx_self, __pyx_v_XMinMax, __pyx_v_dL, __pyx_v_dX, __pyx_v_VPoly, __pyx_v_DX, __pyx_v_DY, __pyx_v_DZ, __pyx_v_DIn, __pyx_v_VIn, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">501</span>:     <span class=\"s\">&quot; Return the desired surfacic submesh indicated by the limits (DX,DY,DZ), for the desired resolution (dX,dL) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">502</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">X</span><span class=\"p\">,</span> <span class=\"n\">Y0</span><span class=\"p\">,</span> <span class=\"n\">Z0</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">503</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dXr</span><span class=\"p\">,</span> <span class=\"nf\">dY0r</span><span class=\"p\">,</span> <span class=\"nf\">dZ0r</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">504</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NY0</span><span class=\"p\">,</span> <span class=\"nf\">NZ0</span><span class=\"p\">,</span> <span class=\"nf\">Y0n</span><span class=\"p\">,</span> <span class=\"nf\">Z0n</span><span class=\"p\">,</span> <span class=\"nf\">NX</span><span class=\"p\">,</span> <span class=\"nf\">Xn</span><span class=\"p\">,</span> <span class=\"nf\">Ln</span><span class=\"p\">,</span> <span class=\"nf\">NR0</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">505</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">PtsCross</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">506</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">507</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indX</span><span class=\"p\">,</span> <span class=\"n\">indY0</span><span class=\"p\">,</span> <span class=\"n\">indZ0</span><span class=\"p\">,</span> <span class=\"n\">indL</span><span class=\"p\">,</span> <span class=\"n\">NL</span><span class=\"p\">,</span> <span class=\"n\">ind</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">508</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">509</span>:     <span class=\"c\"># Preformat</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">510</span>:     <span class=\"k\">if</span> <span class=\"n\">DX</span> <span class=\"ow\">is</span> <span class=\"ow\">not</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = (__pyx_v_DX != Py_None);\n",
       "  __pyx_t_2 = (__pyx_t_1 != 0);\n",
       "  if (__pyx_t_2) {\n",
       "/* … */\n",
       "  }\n",
       "</pre><pre class=\"cython line score-17\" onclick='toggleDiv(this)'>+<span class=\"\">511</span>:         <span class=\"k\">if</span> <span class=\"n\">DX</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">&lt;=</span><span class=\"n\">XMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-17 '>    __pyx_t_3 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_DX, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 511, __pyx_L1_error)\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
       "    __pyx_t_4 = 0;\n",
       "    __pyx_t_5 = <span class='py_c_api'>PyFloat_FromDouble</span>((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_XMinMax.data) + __pyx_t_4)) )))); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 511, __pyx_L1_error)\n",
       "    <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
       "    __pyx_t_6 = <span class='py_c_api'>PyObject_RichCompare</span>(__pyx_t_3, __pyx_t_5, Py_LE); <span class='refnanny'>__Pyx_XGOTREF</span>(__pyx_t_6); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 511, __pyx_L1_error)\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_IsTrue</span>(__pyx_t_6); if (unlikely(__pyx_t_2 &lt; 0)) __PYX_ERR(0, 511, __pyx_L1_error)\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;\n",
       "    if (__pyx_t_2) {\n",
       "/* … */\n",
       "    }\n",
       "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">512</span>:             <span class=\"n\">DX</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"bp\">None</span></pre>\n",
       "<pre class='cython code score-2 '>      if (unlikely(<span class='pyx_c_api'>__Pyx_SetItemInt</span>(__pyx_v_DX, 0, Py_None, long, 1, __Pyx_PyInt_From_long, 0, 0, 0) &lt; 0)) __PYX_ERR(0, 512, __pyx_L1_error)\n",
       "</pre><pre class=\"cython line score-17\" onclick='toggleDiv(this)'>+<span class=\"\">513</span>:         <span class=\"k\">if</span> <span class=\"n\">DX</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">&gt;=</span><span class=\"n\">XMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-17 '>    __pyx_t_6 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_DX, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 513, __pyx_L1_error)\n",
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       "    __pyx_t_5 = <span class='py_c_api'>PyFloat_FromDouble</span>((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_XMinMax.data) + __pyx_t_7)) )))); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 513, __pyx_L1_error)\n",
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       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_6); __pyx_t_6 = 0;\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "    __pyx_t_2 = <span class='pyx_c_api'>__Pyx_PyObject_IsTrue</span>(__pyx_t_3); if (unlikely(__pyx_t_2 &lt; 0)) __PYX_ERR(0, 513, __pyx_L1_error)\n",
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       "    if (__pyx_t_2) {\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">582</span>:                                      <span class=\"n\">double</span><span class=\"p\">[:,::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">VPoly</span><span class=\"p\">,</span> <span class=\"n\">cnp</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">[</span><span class=\"nb\">long</span><span class=\"p\">,</span><span class=\"n\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">ind</span><span class=\"p\">,</span></pre>\n",
       "<pre class=\"cython line score-124\" onclick='toggleDiv(this)'>+<span class=\"\">583</span>:                                      <span class=\"n\">double</span> <span class=\"n\">DIn</span><span class=\"o\">=</span><span class=\"mf\">0.</span><span class=\"p\">,</span> <span class=\"n\">VIn</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
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       "    __pyx_v_VPoly = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[3]); if (unlikely(!__pyx_v_VPoly.memview)) __PYX_ERR(0, 582, __pyx_L3_error)\n",
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       "    __pyx_v_VIn = values[6];\n",
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       "      __pyx_v_margin = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[7]); if (unlikely((__pyx_v_margin == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 583, __pyx_L3_error)\n",
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       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_Lin_SubFromInd_cython\", 0, 5, 8, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 581, __pyx_L3_error)\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_8152fc40fb05daa7217c00b66474f995._Ves_Smesh_Lin_SubFromInd_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
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       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995_14_Ves_Smesh_Lin_SubFromInd_cython(__pyx_self, __pyx_v_XMinMax, __pyx_v_dL, __pyx_v_dX, __pyx_v_VPoly, __pyx_v_ind, __pyx_v_DIn, __pyx_v_VIn, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">584</span>:     <span class=\"s\">&quot; Return the desired surfacic submesh indicated by ind, for the desired resolution (dX,dL) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">585</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dXr</span><span class=\"p\">,</span> <span class=\"nf\">dY0r</span><span class=\"p\">,</span> <span class=\"nf\">dZ0r</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">586</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NX</span><span class=\"p\">,</span> <span class=\"nf\">NY0</span><span class=\"p\">,</span> <span class=\"nf\">NZ0</span><span class=\"p\">,</span> <span class=\"nf\">Ln</span><span class=\"p\">,</span> <span class=\"nf\">NR0</span><span class=\"p\">,</span> <span class=\"nf\">nii</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">587</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">list</span> <span class=\"nf\">LPts</span><span class=\"p\">,</span> <span class=\"nf\">LdS</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">588</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">PtsCross</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">589</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">X</span><span class=\"p\">,</span> <span class=\"n\">Y0</span><span class=\"p\">,</span> <span class=\"n\">Z0</span><span class=\"p\">,</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">590</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indX</span><span class=\"p\">,</span> <span class=\"n\">indY0</span><span class=\"p\">,</span> <span class=\"n\">indZ0</span><span class=\"p\">,</span> <span class=\"n\">indL</span><span class=\"p\">,</span> <span class=\"n\">NL</span><span class=\"p\">,</span> <span class=\"n\">ii</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">591</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">592</span>:     <span class=\"c\"># Get the mesh for the faces</span></pre>\n",
       "<pre class=\"cython line score-280\" onclick='toggleDiv(this)'>+<span class=\"\">593</span>:     <span class=\"n\">Y0</span><span class=\"p\">,</span> <span class=\"n\">dY0r</span><span class=\"p\">,</span> <span class=\"n\">bla</span><span class=\"p\">,</span> <span class=\"n\">NY0</span> <span class=\"o\">=</span> <span class=\"n\">_Ves_mesh_dlfromL_cython</span><span class=\"p\">(</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">min</span><span class=\"p\">(</span><span class=\"n\">VPoly</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,:]),</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">max</span><span class=\"p\">(</span><span class=\"n\">VPoly</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,:])]),</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">DL</span><span class=\"o\">=</span><span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"n\">margin</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-280 '>  __pyx_t_1 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_Ves_mesh_dlfromL_cython); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 593, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-64\" onclick='toggleDiv(this)'>+<span class=\"\">660</span>:         <span class=\"n\">Bounds</span> <span class=\"o\">=</span> <span class=\"p\">[[</span><span class=\"n\">DPhi0</span><span class=\"p\">,</span><span class=\"n\">DPhi1</span><span class=\"p\">]]</span> <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">DPhi1</span> <span class=\"k\">else</span> <span class=\"p\">[[</span><span class=\"o\">-</span><span class=\"n\">Cpi</span><span class=\"p\">,</span><span class=\"n\">DPhi1</span><span class=\"p\">],[</span><span class=\"n\">DPhi0</span><span class=\"p\">,</span><span class=\"n\">Cpi</span><span class=\"p\">]]</span></pre>\n",
       "<pre class='cython code score-64 '>    if (((__pyx_v_DPhi0 &lt;= __pyx_v_DPhi1) != 0)) {\n",
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       "      __pyx_t_4 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
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       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_5, 1, __pyx_t_4);\n",
       "      __pyx_t_3 = 0;\n",
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       "      <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_5);\n",
       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_4, 0, __pyx_t_5);\n",
       "      __pyx_t_5 = 0;\n",
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       "      __pyx_t_4 = 0;\n",
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       "      __pyx_t_4 = <span class='py_c_api'>PyFloat_FromDouble</span>((-M_PI)); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
       "      <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_4);\n",
       "      __pyx_t_5 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi1); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
       "      <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_5);\n",
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       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_3, 0, __pyx_t_4);\n",
       "      <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_5);\n",
       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_3, 1, __pyx_t_5);\n",
       "      __pyx_t_4 = 0;\n",
       "      __pyx_t_5 = 0;\n",
       "      __pyx_t_5 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi0); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
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       "      __pyx_t_4 = <span class='py_c_api'>PyFloat_FromDouble</span>(M_PI); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
       "      <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_4);\n",
       "      __pyx_t_6 = <span class='py_c_api'>PyList_New</span>(2); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
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       "      <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_5);\n",
       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_6, 0, __pyx_t_5);\n",
       "      <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_4);\n",
       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_6, 1, __pyx_t_4);\n",
       "      __pyx_t_5 = 0;\n",
       "      __pyx_t_4 = 0;\n",
       "      __pyx_t_4 = <span class='py_c_api'>PyList_New</span>(2); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 660, __pyx_L1_error)\n",
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       "      <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_3);\n",
       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_4, 0, __pyx_t_3);\n",
       "      <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_6);\n",
       "      <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_4, 1, __pyx_t_6);\n",
       "      __pyx_t_3 = 0;\n",
       "      __pyx_t_6 = 0;\n",
       "      __pyx_t_2 = __pyx_t_4;\n",
       "      __pyx_t_4 = 0;\n",
       "    }\n",
       "    __pyx_v_Bounds = __pyx_t_2;\n",
       "    __pyx_t_2 = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">661</span>:         <span class=\"n\">Inter</span> <span class=\"o\">=</span> <span class=\"bp\">True</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_Inter = 1;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">662</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">663</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-1\" onclick='toggleDiv(this)'>+<span class=\"\">664</span>:         <span class=\"n\">Inter</span><span class=\"p\">,</span> <span class=\"n\">Bounds</span> <span class=\"o\">=</span> <span class=\"bp\">False</span><span class=\"p\">,</span> <span class=\"bp\">None</span></pre>\n",
       "<pre class='cython code score-1 '>  /*else*/ {\n",
       "    __pyx_t_1 = 0;\n",
       "    __pyx_t_2 = Py_None;\n",
       "    <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_2);\n",
       "    __pyx_v_Inter = __pyx_t_1;\n",
       "    __pyx_v_Bounds = __pyx_t_2;\n",
       "    __pyx_t_2 = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">665</span>:         <span class=\"k\">if</span> <span class=\"n\">Phi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi1</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_1 = ((__pyx_v_Phi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "    if (__pyx_t_1) {\n",
       "/* … */\n",
       "      goto __pyx_L4;\n",
       "    }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">666</span>:             <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">DPhi1</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_1 = ((__pyx_v_DPhi0 &lt;= __pyx_v_DPhi1) != 0);\n",
       "      if (__pyx_t_1) {\n",
       "/* … */\n",
       "        goto __pyx_L5;\n",
       "      }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">667</span>:                 <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi1</span> <span class=\"ow\">and</span> <span class=\"n\">DPhi1</span><span class=\"o\">&gt;=</span><span class=\"n\">Phi0</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_7 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "        if (__pyx_t_7) {\n",
       "        } else {\n",
       "          __pyx_t_1 = __pyx_t_7;\n",
       "          goto __pyx_L7_bool_binop_done;\n",
       "        }\n",
       "        __pyx_t_7 = ((__pyx_v_DPhi1 &gt;= __pyx_v_Phi0) != 0);\n",
       "        __pyx_t_1 = __pyx_t_7;\n",
       "        __pyx_L7_bool_binop_done:;\n",
       "        if (__pyx_t_1) {\n",
       "/* … */\n",
       "        }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">668</span>:                     <span class=\"n\">Inter</span> <span class=\"o\">=</span> <span class=\"bp\">True</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_Inter = 1;\n",
       "</pre><pre class=\"cython line score-16\" onclick='toggleDiv(this)'>+<span class=\"\">669</span>:                     <span class=\"n\">Bounds</span> <span class=\"o\">=</span> <span class=\"p\">[[</span><span class=\"bp\">None</span><span class=\"p\">,</span><span class=\"bp\">None</span><span class=\"p\">]]</span></pre>\n",
       "<pre class='cython code score-16 '>          __pyx_t_2 = <span class='py_c_api'>PyList_New</span>(2); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 669, __pyx_L1_error)\n",
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       "          <span class='pyx_macro_api'>__Pyx_INCREF</span>(Py_None);\n",
       "          <span class='refnanny'>__Pyx_GIVEREF</span>(Py_None);\n",
       "          <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_2, 0, Py_None);\n",
       "          <span class='pyx_macro_api'>__Pyx_INCREF</span>(Py_None);\n",
       "          <span class='refnanny'>__Pyx_GIVEREF</span>(Py_None);\n",
       "          <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_2, 1, Py_None);\n",
       "          __pyx_t_4 = <span class='py_c_api'>PyList_New</span>(1); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 669, __pyx_L1_error)\n",
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       "          <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n",
       "          <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_4, 0, __pyx_t_2);\n",
       "          __pyx_t_2 = 0;\n",
       "          <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_v_Bounds, __pyx_t_4);\n",
       "          __pyx_t_4 = 0;\n",
       "</pre><pre class=\"cython line score-16\" onclick='toggleDiv(this)'>+<span class=\"\">670</span>:                     <span class=\"n\">Bounds</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">][</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi0</span> <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi0</span> <span class=\"k\">else</span> <span class=\"n\">DPhi0</span></pre>\n",
       "<pre class='cython code score-16 '>          if (((__pyx_v_DPhi0 &lt;= __pyx_v_Phi0) != 0)) {\n",
       "            __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_Phi0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 670, __pyx_L1_error)\n",
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       "          __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_Bounds, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 670, __pyx_L1_error)\n",
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       "          <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n",
       "</pre><pre class=\"cython line score-16\" onclick='toggleDiv(this)'>+<span class=\"\">671</span>:                     <span class=\"n\">Bounds</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">][</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi1</span> <span class=\"k\">if</span> <span class=\"n\">DPhi1</span><span class=\"o\">&gt;=</span><span class=\"n\">Phi1</span> <span class=\"k\">else</span> <span class=\"n\">DPhi1</span></pre>\n",
       "<pre class='cython code score-16 '>          if (((__pyx_v_DPhi1 &gt;= __pyx_v_Phi1) != 0)) {\n",
       "            __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_Phi1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 671, __pyx_L1_error)\n",
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       "            __pyx_t_4 = __pyx_t_2;\n",
       "            __pyx_t_2 = 0;\n",
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       "            __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 671, __pyx_L1_error)\n",
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       "            __pyx_t_4 = __pyx_t_2;\n",
       "            __pyx_t_2 = 0;\n",
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       "          __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_Bounds, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 671, __pyx_L1_error)\n",
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       "          if (unlikely(<span class='pyx_c_api'>__Pyx_SetItemInt</span>(__pyx_t_2, 1, __pyx_t_4, long, 1, __Pyx_PyInt_From_long, 0, 0, 1) &lt; 0)) __PYX_ERR(0, 671, __pyx_L1_error)\n",
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       "          <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_4); __pyx_t_4 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">672</span>:             <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">673</span>:                 <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi1</span> <span class=\"ow\">or</span> <span class=\"n\">DPhi1</span><span class=\"o\">&gt;=</span><span class=\"n\">Phi0</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>      /*else*/ {\n",
       "        __pyx_t_7 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "        if (!__pyx_t_7) {\n",
       "        } else {\n",
       "          __pyx_t_1 = __pyx_t_7;\n",
       "          goto __pyx_L10_bool_binop_done;\n",
       "        }\n",
       "        __pyx_t_7 = ((__pyx_v_DPhi1 &gt;= __pyx_v_Phi0) != 0);\n",
       "        __pyx_t_1 = __pyx_t_7;\n",
       "        __pyx_L10_bool_binop_done:;\n",
       "        if (__pyx_t_1) {\n",
       "/* … */\n",
       "        }\n",
       "      }\n",
       "      __pyx_L5:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">674</span>:                     <span class=\"n\">Inter</span> <span class=\"o\">=</span> <span class=\"bp\">True</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_Inter = 1;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">675</span>:                     <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi1</span> <span class=\"ow\">and</span> <span class=\"n\">DPhi1</span><span class=\"o\">&gt;=</span><span class=\"n\">Phi0</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_7 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "          if (__pyx_t_7) {\n",
       "          } else {\n",
       "            __pyx_t_1 = __pyx_t_7;\n",
       "            goto __pyx_L13_bool_binop_done;\n",
       "          }\n",
       "          __pyx_t_7 = ((__pyx_v_DPhi1 &gt;= __pyx_v_Phi0) != 0);\n",
       "          __pyx_t_1 = __pyx_t_7;\n",
       "          __pyx_L13_bool_binop_done:;\n",
       "          if (__pyx_t_1) {\n",
       "/* … */\n",
       "            goto __pyx_L12;\n",
       "          }\n",
       "</pre><pre class=\"cython line score-42\" onclick='toggleDiv(this)'>+<span class=\"\">676</span>:                         <span class=\"n\">Bounds</span> <span class=\"o\">=</span> <span class=\"p\">[[</span><span class=\"n\">Phi0</span><span class=\"p\">,</span><span class=\"n\">DPhi1</span><span class=\"p\">],[</span><span class=\"n\">DPhi0</span><span class=\"p\">,</span><span class=\"n\">Phi1</span><span class=\"p\">]]</span></pre>\n",
       "<pre class='cython code score-42 '>            __pyx_t_4 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_Phi0); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 676, __pyx_L1_error)\n",
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       "            __pyx_t_6 = <span class='py_c_api'>PyList_New</span>(2); if (unlikely(!__pyx_t_6)) __PYX_ERR(0, 676, __pyx_L1_error)\n",
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       "            <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n",
       "            <span class='py_macro_api'>PyList_SET_ITEM</span>(__pyx_t_6, 1, __pyx_t_2);\n",
       "            __pyx_t_4 = 0;\n",
       "            __pyx_t_2 = 0;\n",
       "            __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 676, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-16\" onclick='toggleDiv(this)'>+<span class=\"\">678</span>:                         <span class=\"n\">Bounds</span> <span class=\"o\">=</span> <span class=\"p\">[[</span><span class=\"bp\">None</span><span class=\"p\">,</span><span class=\"bp\">None</span><span class=\"p\">]]</span></pre>\n",
       "<pre class='cython code score-16 '>          /*else*/ {\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">679</span>:                         <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi1</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>            __pyx_t_1 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
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       "              goto __pyx_L15;\n",
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       "<pre class='cython code score-16 '>              if (((__pyx_v_DPhi0 &lt;= __pyx_v_Phi0) != 0)) {\n",
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       "</pre><pre class=\"cython line score-11\" onclick='toggleDiv(this)'>+<span class=\"\">681</span>:                             <span class=\"n\">Bounds</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">][</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi1</span></pre>\n",
       "<pre class='cython code score-11 '>              __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_Phi1); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 681, __pyx_L1_error)\n",
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       "<pre class=\"cython line score-11\" onclick='toggleDiv(this)'>+<span class=\"\">683</span>:                             <span class=\"n\">Bounds</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">][</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi0</span></pre>\n",
       "<pre class='cython code score-11 '>            /*else*/ {\n",
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       "<pre class='cython code score-16 '>              if (((__pyx_v_DPhi1 &gt;= __pyx_v_Phi1) != 0)) {\n",
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       "          __pyx_L12:;\n",
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       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">686</span>:             <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">DPhi1</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>    /*else*/ {\n",
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       "      if (__pyx_t_1) {\n",
       "/* … */\n",
       "        goto __pyx_L16;\n",
       "      }\n",
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       "<pre class='cython code score-0 '>        __pyx_t_7 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "        if (!__pyx_t_7) {\n",
       "        } else {\n",
       "          __pyx_t_1 = __pyx_t_7;\n",
       "          goto __pyx_L18_bool_binop_done;\n",
       "        }\n",
       "        __pyx_t_7 = ((__pyx_v_DPhi1 &gt;= __pyx_v_Phi0) != 0);\n",
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       "<pre class='cython code score-0 '>          __pyx_v_Inter = 1;\n",
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       "<pre class='cython code score-0 '>          __pyx_t_7 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "          if (__pyx_t_7) {\n",
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       "            goto __pyx_L21_bool_binop_done;\n",
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       "          __pyx_t_1 = __pyx_t_7;\n",
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       "            goto __pyx_L20;\n",
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       "<pre class='cython code score-42 '>            __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_Phi0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 690, __pyx_L1_error)\n",
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       "<pre class='cython code score-0 '>            __pyx_t_1 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
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       "<pre class='cython code score-11 '>              __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 694, __pyx_L1_error)\n",
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       "<pre class='cython code score-16 '>              if (((__pyx_v_DPhi1 &gt;= __pyx_v_Phi1) != 0)) {\n",
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       "<pre class=\"cython line score-16\" onclick='toggleDiv(this)'>+<span class=\"\">697</span>:                             <span class=\"n\">Bounds</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">][</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Phi0</span> <span class=\"k\">if</span> <span class=\"n\">DPhi0</span><span class=\"o\">&lt;=</span><span class=\"n\">Phi0</span> <span class=\"k\">else</span> <span class=\"n\">DPhi0</span></pre>\n",
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       "<pre class='cython code score-11 '>              __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_DPhi1); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 698, __pyx_L1_error)\n",
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       "          __pyx_L20:;\n",
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       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">700</span>:                 <span class=\"n\">Inter</span> <span class=\"o\">=</span> <span class=\"bp\">True</span></pre>\n",
       "<pre class='cython code score-0 '>      /*else*/ {\n",
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       "<pre class='cython code score-0 '>        __pyx_t_7 = ((__pyx_v_DPhi0 &gt;= __pyx_v_Phi0) != 0);\n",
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       "          goto __pyx_L25_bool_binop_done;\n",
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       "        }\n",
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       "<pre class='cython code score-60 '>          __pyx_t_2 = <span class='py_c_api'>PyFloat_FromDouble</span>(__pyx_v_Phi0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 702, __pyx_L1_error)\n",
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       "<pre class='cython code score-0 '>        __pyx_t_7 = ((__pyx_v_DPhi0 &lt;= __pyx_v_Phi1) != 0);\n",
       "        if (__pyx_t_7) {\n",
       "        } else {\n",
       "          __pyx_t_1 = __pyx_t_7;\n",
       "          goto __pyx_L27_bool_binop_done;\n",
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       "        __pyx_t_1 = __pyx_t_7;\n",
       "        __pyx_L27_bool_binop_done:;\n",
       "        if (__pyx_t_1) {\n",
       "/* … */\n",
       "          goto __pyx_L24;\n",
       "        }\n",
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       "<pre class=\"cython line score-96\" onclick='toggleDiv(this)'>+<span class=\"\">718</span>: <span class=\"k\">def</span> <span class=\"nf\">_Ves_Smesh_Tor_SubFromD_bis</span><span class=\"p\">(</span><span class=\"n\">double</span> <span class=\"n\">dL</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">dRPhi</span><span class=\"p\">,</span></pre>\n",
       "<pre class='cython code score-96 '>/* Python wrapper */\n",
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       "static char __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995_18_Ves_Smesh_Tor_SubFromD_bis[] = \" Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \";\n",
       "static PyMethodDef __pyx_mdef_46_cython_magic_8152fc40fb05daa7217c00b66474f995_19_Ves_Smesh_Tor_SubFromD_bis = {\"_Ves_Smesh_Tor_SubFromD_bis\", (PyCFunction)__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_19_Ves_Smesh_Tor_SubFromD_bis, METH_VARARGS|METH_KEYWORDS, __pyx_doc_46_cython_magic_8152fc40fb05daa7217c00b66474f995_18_Ves_Smesh_Tor_SubFromD_bis};\n",
       "static PyObject *__pyx_pw_46_cython_magic_8152fc40fb05daa7217c00b66474f995_19_Ves_Smesh_Tor_SubFromD_bis(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
       "  double __pyx_v_dL;\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">719</span>:                                    <span class=\"n\">double</span><span class=\"p\">[:,::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">VPoly</span><span class=\"p\">,</span></pre>\n",
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       "        case 11: values[10] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 10);\n",
       "        case 10: values[9] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 9);\n",
       "        case  9: values[8] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 8);\n",
       "        case  8: values[7] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 7);\n",
       "        case  7: values[6] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 6);\n",
       "        case  6: values[5] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 5);\n",
       "        case  5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
       "        case  4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
       "        case  3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
       "        values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
       "        values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
       "        break;\n",
       "        default: goto __pyx_L5_argtuple_error;\n",
       "      }\n",
       "    }\n",
       "    __pyx_v_dL = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[0]); if (unlikely((__pyx_v_dL == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 718, __pyx_L3_error)\n",
       "    __pyx_v_dRPhi = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[1]); if (unlikely((__pyx_v_dRPhi == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 718, __pyx_L3_error)\n",
       "    __pyx_v_VPoly = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_d_dc_double</span>(values[2]); if (unlikely(!__pyx_v_VPoly.memview)) __PYX_ERR(0, 719, __pyx_L3_error)\n",
       "    __pyx_v_DR = values[3];\n",
       "    __pyx_v_DZ = values[4];\n",
       "    __pyx_v_DPhi = values[5];\n",
       "    if (values[6]) {\n",
       "      __pyx_v_DIn = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[6]); if (unlikely((__pyx_v_DIn == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 721, __pyx_L3_error)\n",
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       "      __pyx_v_DIn = ((double)0.);\n",
       "    }\n",
       "    __pyx_v_VIn = values[7];\n",
       "    __pyx_v_PhiMinMax = values[8];\n",
       "    __pyx_v_Out = ((PyObject*)values[9]);\n",
       "    if (values[10]) {\n",
       "      __pyx_v_margin = __pyx_<span class='py_c_api'>PyFloat_AsDouble</span>(values[10]); if (unlikely((__pyx_v_margin == (double)-1) &amp;&amp; <span class='py_c_api'>PyErr_Occurred</span>())) __PYX_ERR(0, 722, __pyx_L3_error)\n",
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       "  }\n",
       "  goto __pyx_L4_argument_unpacking_done;\n",
       "  __pyx_L5_argtuple_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"_Ves_Smesh_Tor_SubFromD_bis\", 0, 3, 11, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 718, __pyx_L3_error)\n",
       "  __pyx_L3_error:;\n",
       "  <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_8152fc40fb05daa7217c00b66474f995._Ves_Smesh_Tor_SubFromD_bis\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
       "  <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
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       "  __pyx_r = __pyx_pf_46_cython_magic_8152fc40fb05daa7217c00b66474f995_18_Ves_Smesh_Tor_SubFromD_bis(__pyx_self, __pyx_v_dL, __pyx_v_dRPhi, __pyx_v_VPoly, __pyx_v_DR, __pyx_v_DZ, __pyx_v_DPhi, __pyx_v_DIn, __pyx_v_VIn, __pyx_v_PhiMinMax, __pyx_v_Out, __pyx_v_margin);\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">722</span>:                                    <span class=\"nb\">str</span> <span class=\"n\">Out</span><span class=\"o\">=</span><span class=\"s\">&#39;(X,Y,Z)&#39;</span><span class=\"p\">,</span> <span class=\"n\">double</span> <span class=\"n\">margin</span><span class=\"o\">=</span><span class=\"mf\">1.e-9</span><span class=\"p\">):</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">723</span>:     <span class=\"s\">&quot; Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) &quot;</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">724</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R</span><span class=\"p\">,</span> <span class=\"n\">Z</span><span class=\"p\">,</span> <span class=\"n\">dPhir</span><span class=\"p\">,</span> <span class=\"n\">NRPhi</span><span class=\"c\">#, dPhi, NRZPhi_cum0, indPhi, phi</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">725</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">dRr0</span><span class=\"p\">,</span> <span class=\"nf\">dRr</span><span class=\"p\">,</span> <span class=\"nf\">dZr</span><span class=\"p\">,</span> <span class=\"nf\">DPhi0</span><span class=\"p\">,</span> <span class=\"nf\">DPhi1</span><span class=\"p\">,</span> <span class=\"nf\">DDPhi</span><span class=\"p\">,</span> <span class=\"nf\">DPhiMinMax</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">726</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">double</span> <span class=\"nf\">abs0</span><span class=\"p\">,</span> <span class=\"nf\">abs1</span><span class=\"p\">,</span> <span class=\"nf\">phi</span><span class=\"p\">,</span> <span class=\"nf\">indiijj</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">727</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">long</span>[<span class=\"p\">::</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indR0</span><span class=\"p\">,</span> <span class=\"n\">indR</span><span class=\"p\">,</span> <span class=\"n\">indZ</span><span class=\"p\">,</span> <span class=\"n\">Phin</span><span class=\"p\">,</span> <span class=\"n\">NRPhi0</span><span class=\"p\">,</span> <span class=\"n\">Indin</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">728</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">NR0</span><span class=\"p\">,</span> <span class=\"nf\">NR</span><span class=\"p\">,</span> <span class=\"nf\">NZ</span><span class=\"p\">,</span> <span class=\"nf\">Rn</span><span class=\"p\">,</span> <span class=\"nf\">Zn</span><span class=\"p\">,</span> <span class=\"nf\">nRPhi0</span><span class=\"p\">,</span> <span class=\"nf\">indR0ii</span><span class=\"p\">,</span> <span class=\"nf\">ii</span><span class=\"p\">,</span> <span class=\"nf\">jj0</span><span class=\"o\">=</span><span class=\"mf\">0</span><span class=\"p\">,</span> <span class=\"n\">jj</span><span class=\"p\">,</span> <span class=\"n\">nPhi0</span><span class=\"p\">,</span> <span class=\"n\">nPhi1</span><span class=\"p\">,</span> <span class=\"n\">zz</span><span class=\"p\">,</span> <span class=\"n\">NP</span><span class=\"p\">,</span> <span class=\"n\">NRPhi_int</span><span class=\"p\">,</span> <span class=\"n\">Rratio</span><span class=\"p\">,</span> <span class=\"n\">Ln</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_v_jj0 = 0;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">729</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">2</span><span class=\"p\">]</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">indI</span><span class=\"p\">,</span> <span class=\"n\">PtsCross</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">730</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">double</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">R0</span><span class=\"p\">,</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">731</span>:     <span class=\"k\">cdef</span> <span class=\"kt\">cnp</span>.<span class=\"kt\">ndarray</span>[<span class=\"nf\">long</span><span class=\"p\">,</span><span class=\"nf\">ndim</span><span class=\"o\">=</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"n\">indL</span><span class=\"p\">,</span> <span class=\"n\">NL</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">732</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">733</span>:     <span class=\"c\"># Pre-format input</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">734</span>:     <span class=\"k\">if</span> <span class=\"n\">PhiMinMax</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_1 = (__pyx_v_PhiMinMax == Py_None);\n",
       "  __pyx_t_2 = (__pyx_t_1 != 0);\n",
       "  if (__pyx_t_2) {\n",
       "/* … */\n",
       "    goto __pyx_L3;\n",
       "  }\n",
       "</pre><pre class=\"cython line score-18\" onclick='toggleDiv(this)'>+<span class=\"\">735</span>:         <span class=\"n\">PhiMinMax</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"o\">-</span><span class=\"n\">Cpi</span><span class=\"p\">,</span><span class=\"n\">Cpi</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-18 '>    __pyx_t_3 = <span class='py_c_api'>PyFloat_FromDouble</span>((-M_PI)); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 735, __pyx_L1_error)\n",
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       "    __pyx_t_5 = <span class='py_c_api'>PyList_New</span>(2); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 735, __pyx_L1_error)\n",
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       "    <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_4);\n",
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       "    __pyx_t_4 = 0;\n",
       "    <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_v_PhiMinMax, __pyx_t_5);\n",
       "    __pyx_t_5 = 0;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">736</span>:         <span class=\"n\">DPhiMinMax</span> <span class=\"o\">=</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_DPhiMinMax = (2. * M_PI);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">737</span>:         <span class=\"n\">Full</span> <span class=\"o\">=</span> <span class=\"bp\">True</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_v_Full = 1;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">738</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-70\" onclick='toggleDiv(this)'>+<span class=\"\">739</span>:         <span class=\"n\">PhiMinMax</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])),</span> <span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]))]</span></pre>\n",
       "<pre class='cython code score-70 '>  /*else*/ {\n",
       "    __pyx_t_5 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 739, __pyx_L1_error)\n",
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       "    __pyx_t_5 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 739, __pyx_L1_error)\n",
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       "    <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_5); __pyx_t_5 = 0;\n",
       "    __pyx_t_5 = <span class='py_c_api'>PyFloat_FromDouble</span>(atan2(sin(__pyx_t_6), cos(__pyx_t_7))); if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 739, __pyx_L1_error)\n",
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       "    __pyx_t_4 = <span class='py_c_api'>PyFloat_FromDouble</span>(atan2(sin(__pyx_t_7), cos(__pyx_t_6))); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 739, __pyx_L1_error)\n",
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       "    <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_v_PhiMinMax, __pyx_t_3);\n",
       "    __pyx_t_3 = 0;\n",
       "</pre><pre class=\"cython line score-70\" onclick='toggleDiv(this)'>+<span class=\"\">740</span>:         <span class=\"n\">DPhiMinMax</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"k\">if</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span><span class=\"o\">&gt;=</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"k\">else</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span> <span class=\"o\">+</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span> <span class=\"o\">-</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-70 '>    __pyx_t_3 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 740, __pyx_L1_error)\n",
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       "<pre class='cython code score-0 '>    __pyx_v_Full = 0;\n",
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       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">744</span>:     <span class=\"k\">if</span> <span class=\"n\">DPhi</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n",
       "<pre class='cython code score-0 '>  __pyx_t_2 = (__pyx_v_DPhi == Py_None);\n",
       "  __pyx_t_1 = (__pyx_t_2 != 0);\n",
       "  if (__pyx_t_1) {\n",
       "/* … */\n",
       "    goto __pyx_L4;\n",
       "  }\n",
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       "<pre class='cython code score-26 '>    __pyx_t_3 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 745, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">746</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-42\" onclick='toggleDiv(this)'>+<span class=\"\">747</span>:         <span class=\"n\">DPhi0</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"k\">if</span> <span class=\"n\">DPhi</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span> <span class=\"k\">else</span> <span class=\"n\">Catan2</span><span class=\"p\">(</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">DPhi</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]),</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">DPhi</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-42 '>  /*else*/ {\n",
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       "<pre class='cython code score-42 '>    __pyx_t_3 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_DPhi, 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 748, __pyx_L1_error)\n",
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       "      __pyx_t_7 = atan2(sin(__pyx_t_8), cos(__pyx_t_6));\n",
       "    }\n",
       "    __pyx_v_DPhi1 = __pyx_t_7;\n",
       "  }\n",
       "  __pyx_L4:;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">749</span>:     <span class=\"n\">DDPhi</span> <span class=\"o\">=</span> <span class=\"n\">DPhi1</span><span class=\"o\">-</span><span class=\"n\">DPhi0</span> <span class=\"k\">if</span> <span class=\"n\">DPhi1</span><span class=\"o\">&gt;</span><span class=\"n\">DPhi0</span> <span class=\"k\">else</span> <span class=\"mf\">2.</span><span class=\"o\">*</span><span class=\"n\">Cpi</span><span class=\"o\">+</span><span class=\"n\">DPhi1</span><span class=\"o\">-</span><span class=\"n\">DPhi0</span></pre>\n",
       "<pre class='cython code score-0 '>  if (((__pyx_v_DPhi1 &gt; __pyx_v_DPhi0) != 0)) {\n",
       "    __pyx_t_7 = (__pyx_v_DPhi1 - __pyx_v_DPhi0);\n",
       "  } else {\n",
       "    __pyx_t_7 = (((2. * M_PI) + __pyx_v_DPhi1) - __pyx_v_DPhi0);\n",
       "  }\n",
       "  __pyx_v_DDPhi = __pyx_t_7;\n",
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       "<pre class=\"cython line score-113\" onclick='toggleDiv(this)'>+<span class=\"\">751</span>:     <span class=\"n\">Inter</span><span class=\"p\">,</span> <span class=\"n\">Bounds</span> <span class=\"o\">=</span> <span class=\"n\">_getBoundsInter2AngSeg</span><span class=\"p\">(</span><span class=\"n\">Full</span><span class=\"p\">,</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">],</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">],</span> <span class=\"n\">DPhi0</span><span class=\"p\">,</span> <span class=\"n\">DPhi1</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-113 '>  __pyx_t_4 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_getBoundsInter2AngSeg); if (unlikely(!__pyx_t_4)) __PYX_ERR(0, 751, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">784</span>:         <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    __pyx_t_27 = __pyx_v_Ln;\n",
       "    for (__pyx_t_14 = 0; __pyx_t_14 &lt; __pyx_t_27; __pyx_t_14+=1) {\n",
       "      __pyx_v_ii = __pyx_t_14;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">785</span>:             <span class=\"c\"># Get the actual RPhi resolution and Phi mesh elements (! depends on R !)</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">786</span>:             <span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">DPhiMinMax</span><span class=\"o\">*</span><span class=\"n\">Rref</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_29 = __pyx_v_ii;\n",
       "      __pyx_t_30 = __pyx_v_ii;\n",
       "      *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_30)) )) = ceil(((__pyx_v_DPhiMinMax * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_Rref.rcbuffer-&gt;pybuffer.buf, __pyx_t_29, __pyx_pybuffernd_Rref.diminfo[0].strides))) / __pyx_v_dRPhi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">787</span>:             <span class=\"n\">NRPhi_int</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_31 = __pyx_v_ii;\n",
       "      __pyx_v_NRPhi_int = ((int)(*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_31)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">788</span>:             <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">DPhiMinMax</span><span class=\"o\">/</span><span class=\"n\">NRPhi</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_32 = __pyx_v_ii;\n",
       "      __pyx_t_33 = __pyx_v_ii;\n",
       "      *((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_33)) )) = (__pyx_v_DPhiMinMax / (*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_NRPhi.data) + __pyx_t_32)) ))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">789</span>:             <span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Rref</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_34 = __pyx_v_ii;\n",
       "      __pyx_t_35 = __pyx_v_ii;\n",
       "      __pyx_t_36 = __pyx_v_ii;\n",
       "      *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dRPhir.rcbuffer-&gt;pybuffer.buf, __pyx_t_36, __pyx_pybuffernd_dRPhir.diminfo[0].strides) = ((*((double *) ( /* dim=0 */ ((char *) (((double *) __pyx_v_dPhir.data) + __pyx_t_34)) ))) * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_Rref.rcbuffer-&gt;pybuffer.buf, __pyx_t_35, __pyx_pybuffernd_Rref.diminfo[0].strides)));\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">790</span>:             <span class=\"c\"># Get index and cumulated indices from background</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">791</span>:             <span class=\"k\">for</span> <span class=\"n\">jj0</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">indR0ii</span><span class=\"p\">,</span><span class=\"n\">NR0</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_37 = __pyx_v_NR0;\n",
       "      for (__pyx_t_38 = __pyx_v_indR0ii; __pyx_t_38 &lt; __pyx_t_37; __pyx_t_38+=1) {\n",
       "        __pyx_v_jj0 = __pyx_t_38;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">792</span>:                 <span class=\"k\">if</span> <span class=\"n\">jj0</span><span class=\"o\">==</span><span class=\"n\">Indin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]:</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_39 = __pyx_v_ii;\n",
       "        __pyx_t_1 = ((__pyx_v_jj0 == (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Indin.data) + __pyx_t_39)) )))) != 0);\n",
       "        if (__pyx_t_1) {\n",
       "/* … */\n",
       "        }\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">793</span>:                     <span class=\"n\">indR0ii</span> <span class=\"o\">=</span> <span class=\"n\">jj0</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_indR0ii = __pyx_v_jj0;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">794</span>:                     <span class=\"k\">break</span></pre>\n",
       "<pre class='cython code score-0 '>          goto __pyx_L19_break;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">795</span>:                 <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">796</span>:                     <span class=\"n\">nRPhi0</span> <span class=\"o\">+=</span> <span class=\"p\">&lt;</span><span class=\"kt\">long</span><span class=\"p\">&gt;</span><span class=\"n\">Cceil</span><span class=\"p\">(</span><span class=\"n\">DPhiMinMax</span><span class=\"o\">*</span><span class=\"n\">R0</span><span class=\"p\">[</span><span class=\"n\">jj0</span><span class=\"p\">]</span><span class=\"o\">/</span><span class=\"n\">dRPhi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>        /*else*/ {\n",
       "          __pyx_t_40 = __pyx_v_jj0;\n",
       "          __pyx_v_nRPhi0 = (__pyx_v_nRPhi0 + ((long)ceil(((__pyx_v_DPhiMinMax * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_R0.rcbuffer-&gt;pybuffer.buf, __pyx_t_40, __pyx_pybuffernd_R0.diminfo[0].strides))) / __pyx_v_dRPhi))));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">797</span>:                     <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">nRPhi0</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_41 = __pyx_v_ii;\n",
       "          *((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_41)) )) = __pyx_v_nRPhi0;\n",
       "        }\n",
       "      }\n",
       "      __pyx_L19_break:;\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">798</span>:             <span class=\"c\"># Get indices of phi</span></pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">799</span>:             <span class=\"c\"># Get the extreme indices of the mesh elements that really need to be created within those limits</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">800</span>:             <span class=\"k\">for</span> <span class=\"n\">kk</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">nBounds</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_17 = __pyx_v_nBounds;\n",
       "      for (__pyx_t_18 = 0; __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) {\n",
       "        __pyx_v_kk = __pyx_t_18;\n",
       "</pre><pre class=\"cython line score-29\" onclick='toggleDiv(this)'>+<span class=\"\">801</span>:                 <span class=\"n\">abs0</span> <span class=\"o\">=</span> <span class=\"n\">Cabs</span><span class=\"p\">(</span><span class=\"n\">BC</span><span class=\"p\">[</span><span class=\"n\">kk</span><span class=\"p\">][</span><span class=\"mf\">0</span><span class=\"p\">]</span><span class=\"o\">-</span><span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">])</span></pre>\n",
       "<pre class='cython code score-29 '>        __pyx_t_10 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_BC, __pyx_v_kk), 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 801, __pyx_L1_error)\n",
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       "<pre class='cython code score-0 '>        __pyx_t_42 = __pyx_v_ii;\n",
       "        __pyx_t_43 = __pyx_v_ii;\n",
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       "<pre class='cython code score-0 '>          __pyx_t_45 = __pyx_v_ii;\n",
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       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">805</span>:                     <span class=\"n\">nPhi0</span> <span class=\"o\">=</span> <span class=\"nb\">int</span><span class=\"p\">(</span><span class=\"n\">Cfloor</span><span class=\"p\">(</span><span class=\"n\">abs0</span><span class=\"o\">/</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]))</span></pre>\n",
       "<pre class='cython code score-0 '>        /*else*/ {\n",
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       "<pre class='cython code score-29 '>        __pyx_t_11 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(<span class='py_macro_api'>PyList_GET_ITEM</span>(__pyx_v_BC, __pyx_v_kk), 1, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 806, __pyx_L1_error)\n",
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       "<pre class='cython code score-0 '>        __pyx_t_47 = __pyx_v_ii;\n",
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       "<pre class='cython code score-22 '>        __pyx_t_9 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_nPhi1); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 812, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">821</span>:                     <span class=\"n\">jj</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_jj = (__pyx_v_jj + 1);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">822</span>:             <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>      __pyx_t_58 = __pyx_v_ii;\n",
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       "    }\n",
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       "<pre class='cython code score-0 '>        __pyx_t_59 = __pyx_v_jj0;\n",
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       "      }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">828</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">829</span>:         <span class=\"c\"># Compute Pts, dV and ind</span></pre>\n",
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       "<pre class='cython code score-0 '>          __pyx_t_61 = __pyx_v_ii;\n",
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       "</pre><pre class=\"cython line score-25\" onclick='toggleDiv(this)'>+<span class=\"\">839</span>:                     <span class=\"n\">phi</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"n\">indiijj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-25 '>          __pyx_t_11 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_11)) __PYX_ERR(0, 839, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">840</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Ccos</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_64 = 0;\n",
       "          __pyx_t_65 = __pyx_v_ii;\n",
       "          __pyx_t_66 = 0;\n",
       "          __pyx_t_67 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_66, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_67, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_64, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_65, __pyx_pybuffernd_PtsCross.diminfo[1].strides)) * cos(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">841</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">Csin</span><span class=\"p\">(</span><span class=\"n\">phi</span><span class=\"p\">)</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_68 = 0;\n",
       "          __pyx_t_69 = __pyx_v_ii;\n",
       "          __pyx_t_70 = 1;\n",
       "          __pyx_t_71 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_70, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_71, __pyx_pybuffernd_Pts.diminfo[1].strides) = ((*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_68, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_69, __pyx_pybuffernd_PtsCross.diminfo[1].strides)) * sin(__pyx_v_phi));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">842</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_72 = 1;\n",
       "          __pyx_t_73 = __pyx_v_ii;\n",
       "          __pyx_t_74 = 2;\n",
       "          __pyx_t_75 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_74, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_75, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_72, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_73, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">843</span>:                     <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indiijj</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_76 = __pyx_v_ii;\n",
       "          __pyx_t_77 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_ind.rcbuffer-&gt;pybuffer.buf, __pyx_t_77, __pyx_pybuffernd_ind.diminfo[0].strides) = ((*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_NRPhi0.data) + __pyx_t_76)) ))) + __pyx_v_indiijj);\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">844</span>:                     <span class=\"n\">dS</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dLr</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_78 = __pyx_v_ii;\n",
       "          __pyx_t_79 = __pyx_v_ii;\n",
       "          __pyx_t_80 = __pyx_v_NP;\n",
       "          *__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dS.rcbuffer-&gt;pybuffer.buf, __pyx_t_80, __pyx_pybuffernd_dS.diminfo[0].strides) = ((*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dLr.rcbuffer-&gt;pybuffer.buf, __pyx_t_78, __pyx_pybuffernd_dLr.diminfo[0].strides)) * (*__Pyx_BufPtrStrided1d(double *, __pyx_pybuffernd_dRPhir.rcbuffer-&gt;pybuffer.buf, __pyx_t_79, __pyx_pybuffernd_dRPhir.diminfo[0].strides)));\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">845</span>:                     <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_NP = (__pyx_v_NP + 1);\n",
       "        }\n",
       "      }\n",
       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">846</span>:         <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">847</span>:             <span class=\"k\">for</span> <span class=\"n\">ii</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Ln</span><span class=\"p\">):</span></pre>\n",
       "<pre class='cython code score-0 '>    /*else*/ {\n",
       "      __pyx_t_27 = __pyx_v_Ln;\n",
       "      for (__pyx_t_14 = 0; __pyx_t_14 &lt; __pyx_t_27; __pyx_t_14+=1) {\n",
       "        __pyx_v_ii = __pyx_t_14;\n",
       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">848</span>:                 <span class=\"k\">for</span> <span class=\"n\">jj</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">Phin</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]):</span></pre>\n",
       "<pre class='cython code score-0 '>        __pyx_t_81 = __pyx_v_ii;\n",
       "        __pyx_t_28 = (*((long *) ( /* dim=0 */ ((char *) (((long *) __pyx_v_Phin.data) + __pyx_t_81)) )));\n",
       "        for (__pyx_t_37 = 0; __pyx_t_37 &lt; __pyx_t_28; __pyx_t_37+=1) {\n",
       "          __pyx_v_jj = __pyx_t_37;\n",
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       "<pre class='cython code score-0 '>          __pyx_t_82 = __pyx_v_ii;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">850</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_84 = 0;\n",
       "          __pyx_t_85 = __pyx_v_ii;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">851</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PtsCross</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">,</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_88 = 1;\n",
       "          __pyx_t_89 = __pyx_v_ii;\n",
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       "          *__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_Pts.rcbuffer-&gt;pybuffer.buf, __pyx_t_90, __pyx_pybuffernd_Pts.diminfo[0].strides, __pyx_t_91, __pyx_pybuffernd_Pts.diminfo[1].strides) = (*__Pyx_BufPtrStrided2d(double *, __pyx_pybuffernd_PtsCross.rcbuffer-&gt;pybuffer.buf, __pyx_t_88, __pyx_pybuffernd_PtsCross.diminfo[0].strides, __pyx_t_89, __pyx_pybuffernd_PtsCross.diminfo[1].strides));\n",
       "</pre><pre class=\"cython line score-25\" onclick='toggleDiv(this)'>+<span class=\"\">852</span>:                     <span class=\"n\">Pts</span><span class=\"p\">[</span><span class=\"mf\">2</span><span class=\"p\">,</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">PhiMinMax</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"mf\">0.5</span><span class=\"o\">+</span><span class=\"n\">indiijj</span><span class=\"p\">)</span><span class=\"o\">*</span><span class=\"n\">dPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-25 '>          __pyx_t_9 = <span class='pyx_c_api'>__Pyx_GetItemInt</span>(__pyx_v_PhiMinMax, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 0); if (unlikely(!__pyx_t_9)) __PYX_ERR(0, 852, __pyx_L1_error)\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">853</span>:                     <span class=\"n\">ind</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">NRPhi0</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">indiijj</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_95 = __pyx_v_ii;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">854</span>:                     <span class=\"n\">dS</span><span class=\"p\">[</span><span class=\"n\">NP</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dLr</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span><span class=\"o\">*</span><span class=\"n\">dRPhir</span><span class=\"p\">[</span><span class=\"n\">ii</span><span class=\"p\">]</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_t_97 = __pyx_v_ii;\n",
       "          __pyx_t_98 = __pyx_v_ii;\n",
       "          __pyx_t_99 = __pyx_v_NP;\n",
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       "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">855</span>:                     <span class=\"n\">NP</span> <span class=\"o\">+=</span> <span class=\"mf\">1</span></pre>\n",
       "<pre class='cython code score-0 '>          __pyx_v_NP = (__pyx_v_NP + 1);\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">856</span>:     <span class=\"k\">else</span><span class=\"p\">:</span></pre>\n",
       "<pre class=\"cython line score-102\" onclick='toggleDiv(this)'>+<span class=\"\">857</span>:         <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"p\">,</span> <span class=\"n\">NL</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span><span class=\"p\">,</span> <span class=\"n\">nRPhi0</span> <span class=\"o\">=</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"bp\">None</span><span class=\"p\">,</span> <span class=\"bp\">None</span></pre>\n",
       "<pre class='cython code score-102 '>  /*else*/ {\n",
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       "</pre><pre class=\"cython line score-63\" onclick='toggleDiv(this)'>+<span class=\"\">858</span>:     <span class=\"k\">return</span> <span class=\"n\">Pts</span><span class=\"p\">,</span> <span class=\"n\">dS</span><span class=\"p\">,</span> <span class=\"n\">ind</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">int</span><span class=\"p\">),</span> <span class=\"n\">NL</span><span class=\"p\">,</span> <span class=\"n\">dLr</span><span class=\"p\">,</span> <span class=\"n\">Rref</span><span class=\"p\">,</span> <span class=\"n\">dRPhir</span><span class=\"p\">,</span> <span class=\"n\">nRPhi0</span><span class=\"p\">,</span> <span class=\"n\">VPbis</span></pre>\n",
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       "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">859</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">860</span>: </pre>\n",
       "<pre class=\"cython line score-0\">&#xA0;<span class=\"\">861</span>: </pre>\n",
       "</div></body></html>"
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       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%cython -a\n",
    "\n",
    "cimport cython\n",
    "cimport numpy as cnp\n",
    "from cpython cimport bool\n",
    "from libc.math cimport sqrt as Csqrt, ceil as Cceil, round as Cround, floor as Cfloor, abs as Cabs\n",
    "from libc.math cimport cos as Ccos, sin as Csin, atan2 as Catan2, pi as Cpi\n",
    "from matplotlib.path import Path\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_mesh_dlfromL_cython(double[::1] LMinMax, double dL, DL=None, double margin=1.e-9):\n",
    "    \"\"\" Get the actual reolution from the desired resolution and MinMax and limits \"\"\"\n",
    "    # Get the number of mesh elements in LMinMax\n",
    "    cdef double N = Cceil((LMinMax[1]-LMinMax[0])/dL)\n",
    "    # Derive the real (effective) resolution\n",
    "    cdef double dLr = (LMinMax[1]-LMinMax[0])/N\n",
    "    # Get desired limits if any\n",
    "    cdef double[::1] DLc, L\n",
    "    cdef long [::1] indL\n",
    "    #cdef cnp.ndarray[double,ndim=1] indL, L\n",
    "    cdef double abs0, abs1, A\n",
    "    cdef int nL0, nL1, Nind, ii, jj\n",
    "    cdef list dl\n",
    "    if DL is None:\n",
    "        DLc = LMinMax\n",
    "    else:\n",
    "        dl = list(DL)\n",
    "        if dl[0] is None:\n",
    "            dl[0] = LMinMax[0]\n",
    "        if dl[1] is None:\n",
    "            dl[1] = LMinMax[1]\n",
    "        DLc = np.array(dl)\n",
    "    # Get the extreme indices of the mesh elements that really need to be created within those limits\n",
    "    abs0 = Cabs(DLc[0]-LMinMax[0])\n",
    "    if abs0-dLr*Cfloor(abs0/dLr)<margin*dLr:\n",
    "        nL0 = int(Cround((DLc[0]-LMinMax[0])/dLr))\n",
    "    else:\n",
    "        nL0 = int(Cfloor((DLc[0]-LMinMax[0])/dLr))\n",
    "    abs1 = Cabs(DLc[1]-LMinMax[0])\n",
    "    if abs1-dLr*Cfloor(abs1/dLr)<margin*dLr:\n",
    "        nL1 = int(Cround((DLc[1]-LMinMax[0])/dLr)-1)\n",
    "    else:\n",
    "        nL1 = int(Cfloor((DLc[1]-LMinMax[0])/dLr))\n",
    "    # Get the corresponding indices\n",
    "    Nind = nL1+1-nL0\n",
    "    indL = np.empty((Nind,),dtype=int)#np.linspace(nL0,nL1,Nind,endpoint=True)\n",
    "    L = np.empty((Nind,))\n",
    "    for ii in range(0,Nind):\n",
    "        jj = nL0+ii\n",
    "        indL[ii] = jj\n",
    "        L[ii] = LMinMax[0] + (0.5 + (<double>jj))*dLr\n",
    "    return np.asarray(L), dLr, np.asarray(indL), N\n",
    "\n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_mesh_CrossPoly(double[:,::1] VPoly, double dL, D1=None, D2=None, double margin=1.e-9, double DIn=0., VIn=None):\n",
    "    cdef int ii, jj, nn=0, NP=VPoly.shape[1]\n",
    "    cdef double[::1] LMinMax, L\n",
    "    cdef double v0, v1, dlr\n",
    "    cdef long[::1] indL\n",
    "    cdef cnp.ndarray[long,ndim=1] N, ind\n",
    "    cdef cnp.ndarray[double,ndim=1] dLr, Rref\n",
    "    cdef cnp.ndarray[double,ndim=2] PtsCross\n",
    "    cdef list LPtsCross=[], LdLr=[], Lind=[], LRref=[], VPolybis=[]\n",
    "    \n",
    "    LMinMax = np.array([0.,1.],dtype=float)\n",
    "    N = np.empty((NP-1,),dtype=int)\n",
    "    if DIn==0.:\n",
    "        for ii in range(0,NP-1):\n",
    "            v0, v1 = VPoly[0,ii+1]-VPoly[0,ii], VPoly[1,ii+1]-VPoly[1,ii]\n",
    "            LMinMax[1] = Csqrt(v0**2 + v1**2)\n",
    "            L, dlr, indL, N[ii] = _Ves_mesh_dlfromL_cython(LMinMax, dL, DL=None, margin=margin)\n",
    "            VPolybis.append((VPoly[0,ii],VPoly[1,ii]))\n",
    "            v0, v1 = v0/LMinMax[1], v1/LMinMax[1]\n",
    "            for jj in range(0,N[ii]):\n",
    "                LdLr.append(dlr)\n",
    "                LRref.append(VPoly[0,ii] + L[jj]*v0)\n",
    "                LPtsCross.append((VPoly[0,ii] + L[jj]*v0, VPoly[1,ii] + L[jj]*v1))\n",
    "                Lind.append(nn)\n",
    "                nn += 1\n",
    "                VPolybis.append((VPoly[0,ii] + jj*dlr*v0, VPoly[1,ii] + jj*dlr*v1))\n",
    "        VPolybis.append((VPoly[0,0],VPoly[1,0]))\n",
    "    else:\n",
    "        for ii in range(0,NP-1):\n",
    "            v0, v1 = VPoly[0,ii+1]-VPoly[0,ii], VPoly[1,ii+1]-VPoly[1,ii]\n",
    "            LMinMax[1] = Csqrt(v0**2 + v1**2)\n",
    "            L, dlr, indL, N[ii] = _Ves_mesh_dlfromL_cython(LMinMax, dL, DL=None, margin=margin)\n",
    "            VPolybis.append((VPoly[0,ii],VPoly[1,ii]))\n",
    "            v0, v1 = v0/LMinMax[1], v1/LMinMax[1]\n",
    "            for jj in range(0,N[ii]):\n",
    "                LdLr.append(dlr)\n",
    "                LRref.append(VPoly[0,ii] + L[jj]*v0)\n",
    "                LPtsCross.append((VPoly[0,ii] + L[jj]*v0 + DIn*VIn[0,ii], VPoly[1,ii] + L[jj]*v1 + DIn*VIn[1,ii]))\n",
    "                Lind.append(nn)\n",
    "                nn += 1\n",
    "                VPolybis.append((VPoly[0,ii] + jj*dlr*v0, VPoly[1,ii] + jj*dlr*v1))\n",
    "        VPolybis.append((VPoly[0,0],VPoly[1,0]))\n",
    "    \n",
    "    PtsCross, dLr, ind, Rref = np.array(LPtsCross).T, np.array(LdLr), np.array(Lind,dtype=int), np.array(LRref)\n",
    "    if D1 is not None:\n",
    "        indin = (PtsCross[0,:]>=D1[0]) & (PtsCross[0,:]<=D1[1])\n",
    "        PtsCross = PtsCross[:,indin]\n",
    "        dLr, ind = dLr[indin], ind[indin]\n",
    "    if D2 is not None:\n",
    "        indin = (PtsCross[1,:]>=D2[0]) & (PtsCross[1,:]<=D2[1])\n",
    "        PtsCross = PtsCross[:,indin]\n",
    "        dLr, ind = dLr[indin], ind[indin]   \n",
    "    \n",
    "    return PtsCross, dLr, ind, N, Rref, np.array(VPolybis).T\n",
    "    \n",
    "    \n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_Tor_SubFromD_cython(double dL, double dRPhi, \n",
    "                                   double[:,::1] VPoly,\n",
    "                                   DR=None, DZ=None, DPhi=None,\n",
    "                                   double DIn=0., VIn=None, PhiMinMax=None,\n",
    "                                   str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \" Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \"\n",
    "    cdef double[::1] R, Z, dPhir, NRPhi#, dPhi, NRZPhi_cum0, indPhi, phi\n",
    "    cdef double dRr0, dRr, dZr, DPhi0, DPhi1, DPhiMinMax\n",
    "    cdef double abs0, abs1, phi, indiijj\n",
    "    cdef long[::1] indR0, indR, indZ, Phin, NRPhi0, Indin\n",
    "    cdef int NR0, NR, NZ, Rn, Zn, nRPhi0, indR0ii, ii, jj0, jj, nPhi0, nPhi1, zz, NP, NRPhi_int, Rratio, Ln\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts, indI, PtsCross, VPbis\n",
    "    cdef cnp.ndarray[double,ndim=1] R0, dS, ind, dLr, Rref, dRPhir\n",
    "    cdef cnp.ndarray[long,ndim=1] indL, NL\n",
    "    \n",
    "    # Pre-format input\n",
    "    if PhiMinMax is None:\n",
    "        PhiMinMax = [-Cpi,Cpi]\n",
    "        DPhiMinMax = 2.*Cpi\n",
    "    else:\n",
    "        PhiMinMax = [Catan2(Csin(PhiMinMax[0]),Ccos(PhiMinMax[0])), Catan2(Csin(PhiMinMax[1]),Ccos(PhiMinMax[1]))]\n",
    "        if PhiMinMax[1]>=PhiMinMax[0]:\n",
    "            DPhiMinMax = PhiMinMax[1]-PhiMinMax[0]\n",
    "        else:\n",
    "            DPhiMinMax = 2.*Cpi + PhiMinMax[1] - PhiMinMax[0]\n",
    "        \n",
    "    \n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    PtsCross, dLr, indL, NL, Rref, VPbis = _Ves_mesh_CrossPoly(VPoly, dL, D1=None, D2=None, margin=margin, DIn=DIn, VIn=VIn)\n",
    "    R0 = np.copy(Rref)\n",
    "    NR0 = R0.size\n",
    "    indin = np.ones((PtsCross.shape[1],),dtype=bool)\n",
    "    if DR is not None:\n",
    "        indin = indin & (R0>=DR[0]) & (R0<=DR[1])\n",
    "    if DZ is not None:\n",
    "        indin = indin & (PtsCross[1,:]>=DZ[0]) & (PtsCross[1,:]<=DZ[1])\n",
    "    PtsCross, dLr, indL, Rref = PtsCross[:,indin], dLr[indin], indL[indin], Rref[indin]\n",
    "    Ln = indin.sum()\n",
    "    Indin = indin.nonzero()[0]\n",
    "    \n",
    "    # Get the limits if any (and make sure to replace them in the proper quadrants)\n",
    "    if DPhi is None:\n",
    "        DPhi0, DPhi1 = PhiMinMax[0], PhiMinMax[1]\n",
    "    else:\n",
    "        DPhi0 = PhiMinMax[0] if DPhi[0] is None else Catan2(Csin(DPhi[0]),Ccos(DPhi[0]))\n",
    "        DPhi1 = PhiMinMax[1] if DPhi[1] is None else Catan2(Csin(DPhi[1]),Ccos(DPhi[1]))\n",
    "    dRPhir, dPhir = np.empty((Ln,)), np.empty((Ln,))\n",
    "    Phin = np.empty((Ln,),dtype=int)\n",
    "    NRPhi = np.empty((Ln,))\n",
    "    NRPhi0 = np.zeros((Ln,),dtype=int)\n",
    "    nRPhi0, indR0ii = 0, 0\n",
    "    NP, NPhimax = 0, 0\n",
    "    Rratio = int(Cceil(np.max(Rref)/np.min(Rref)))\n",
    "    for ii in range(0,Ln):\n",
    "        # Get the actual RPhi resolution and Phi mesh elements (! depends on R !)\n",
    "        NRPhi[ii] = Cceil(DPhiMinMax*Rref[ii]/dRPhi)\n",
    "        NRPhi_int = int(NRPhi[ii])\n",
    "        dPhir[ii] = DPhiMinMax/NRPhi[ii]\n",
    "        dRPhir[ii] = dPhir[ii]*Rref[ii]\n",
    "        # Get index and cumulated indices from background\n",
    "        for jj0 in range(indR0ii,NR0):\n",
    "            if jj0==Indin[ii]:\n",
    "                indR0ii = jj0\n",
    "                break\n",
    "            else:\n",
    "                nRPhi0 += <long>Cceil(DPhiMinMax*R0[jj0]/dRPhi)\n",
    "                NRPhi0[ii] = nRPhi0\n",
    "        # Get indices of phi\n",
    "        # Get the extreme indices of the mesh elements that really need to be created within those limits\n",
    "        abs0 = Cabs(DPhi0-PhiMinMax[0])\n",
    "        if abs0-dPhir[ii]*Cfloor(abs0/dPhir[ii])<margin*dPhir[ii]:\n",
    "            nPhi0 = int(Cround((DPhi0-PhiMinMax[0])/dPhir[ii]))\n",
    "        else:\n",
    "            nPhi0 = int(Cfloor((DPhi0-PhiMinMax[0])/dPhir[ii]))\n",
    "        abs1 = Cabs(DPhi1-PhiMinMax[0])\n",
    "        if abs1-dPhir[ii]*Cfloor(abs1/dPhir[ii])<margin*dPhir[ii]:\n",
    "            nPhi1 = int(Cround((DPhi1-PhiMinMax[0])/dPhir[ii])-1)\n",
    "        else:\n",
    "            nPhi1 = int(Cfloor((DPhi1-PhiMinMax[0])/dPhir[ii]))\n",
    "        if DPhi0<DPhi1:\n",
    "            Phin[ii] = nPhi1+1-nPhi0\n",
    "            if ii==0:\n",
    "                indI = np.ones((Ln,Phin[ii]*Rratio+1))\n",
    "            for jj in range(0,Phin[ii]):\n",
    "                indI[ii,jj] = <double>( nPhi0+jj )\n",
    "        else:\n",
    "            Phin[ii] = nPhi1+1+NRPhi_int-nPhi0\n",
    "            if ii==0:\n",
    "                indI = np.ones((Ln,Phin[ii]*Rratio+1))\n",
    "            for jj in range(0,NRPhi_int-nPhi0):\n",
    "                indI[ii,jj] = <double>( nPhi0+jj )\n",
    "            for jj in range(NRPhi_int-nPhi0,Phin[ii]):\n",
    "                indI[ii,jj] = <double>( jj- (NRPhi_int-nPhi0) )\n",
    "        NP += Phin[ii]\n",
    "    \n",
    "    # Finish counting to get total number of points\n",
    "    if jj0<=NR0-1:\n",
    "        for jj0 in range(indR0ii,NR0):\n",
    "            nRPhi0 += <long>Cceil(DPhiMinMax*R0[jj0]/dRPhi)\n",
    "        \n",
    "    # Compute Pts, dV and ind\n",
    "    Pts = np.empty((3,NP))\n",
    "    ind = np.empty((NP,))\n",
    "    dS = np.empty((NP,))\n",
    "    # This triple loop is the longest part, it takes ~90% of the CPU time\n",
    "    NP = 0\n",
    "    if Out.lower()=='(x,y,z)':\n",
    "        for ii in range(0,Ln):\n",
    "            for jj in range(0,Phin[ii]):\n",
    "                indiijj = indI[ii,jj]\n",
    "                phi = PhiMinMax[0] + (0.5+indiijj)*dPhir[ii]\n",
    "                Pts[0,NP] = PtsCross[0,ii]*Ccos(phi)\n",
    "                Pts[1,NP] = PtsCross[0,ii]*Csin(phi)\n",
    "                Pts[2,NP] = PtsCross[1,ii]\n",
    "                ind[NP] = NRPhi0[ii] + indiijj\n",
    "                dS[NP] = dLr[ii]*dRPhir[ii]\n",
    "                NP += 1\n",
    "    else:\n",
    "        for ii in range(0,Ln):\n",
    "            for jj in range(0,Phin[ii]):\n",
    "                indiijj = indI[ii,jj]\n",
    "                Pts[0,NP] = PtsCross[0,ii]\n",
    "                Pts[1,NP] = PtsCross[1,ii]\n",
    "                Pts[2,NP] = PhiMinMax[0] + (0.5+indiijj)*dPhir[ii]\n",
    "                ind[NP] = NRPhi0[ii] + indiijj\n",
    "                dS[NP] = dLr[ii]*dRPhir[ii]\n",
    "                NP += 1\n",
    "    return Pts, dS, ind.astype(int), NL, dLr, Rref, dRPhir, nRPhi0, VPbis\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_Tor_SubFromInd_cython(double dL, double dRPhi, \n",
    "                                     double[:,::1] VPoly, long[::1] ind, \n",
    "                                     double DIn=0., VIn=None, PhiMinMax=None,\n",
    "                                     str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \"\"\" Return the desired submesh indicated by the (numerical) indices, for the desired resolution (dR,dZ,dRphi) \"\"\"\n",
    "    cdef double[::1] dRPhirRef, dPhir\n",
    "    cdef long[::1] indL, NRPhi0, NRPhi\n",
    "    cdef long NR, NZ, Rn, Zn, NP=len(ind), Rratio\n",
    "    cdef int ii, jj0, jj, iiL, iiphi, Ln, nn=0, kk=0, nRPhi0\n",
    "    cdef double[:,::1] Phi\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts=np.empty((3,NP)), indI, PtsCross, VPbis\n",
    "    cdef cnp.ndarray[double,ndim=1] R0, dS=np.empty((NP,)), dLr, dRPhir, Rref\n",
    "    cdef cnp.ndarray[long,ndim=1] NL\n",
    "\n",
    "    # Pre-format input\n",
    "    if PhiMinMax is None:\n",
    "        PhiMinMax = [-Cpi,Cpi]\n",
    "        DPhiMinMax = 2.*Cpi\n",
    "    else:\n",
    "        PhiMinMax = [Catan2(Csin(PhiMinMax[0]),Ccos(PhiMinMax[0])), Catan2(Csin(PhiMinMax[1]),Ccos(PhiMinMax[1]))]\n",
    "        if PhiMinMax[1]>=PhiMinMax[0]:\n",
    "            DPhiMinMax = PhiMinMax[1]-PhiMinMax[0]\n",
    "        else:\n",
    "            DPhiMinMax = 2.*Cpi + PhiMinMax[1] - PhiMinMax[0]\n",
    "    \n",
    "    \n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    PtsCross, dLr, indL, NL, Rref, VPbis = _Ves_mesh_CrossPoly(VPoly, dL, D1=None, D2=None, margin=margin, DIn=DIn, VIn=VIn)\n",
    "    Ln = dLr.size\n",
    "    # Number of Phi per R\n",
    "    dRPhirRef, dPhir, dRPhir = np.empty((Ln,)), np.empty((Ln,)), -np.ones((Ln,))\n",
    "    NRPhi, NRPhi0 = np.empty((Ln,),dtype=int), np.empty((Ln,),dtype=int)\n",
    "    Rratio = int(Cceil(np.max(Rref)/np.min(Rref)))\n",
    "    for ii in range(0,Ln):\n",
    "        NRPhi[ii] = <long>(Cceil(DPhiMinMax*Rref[ii]/dRPhi))\n",
    "        dRPhirRef[ii] = DPhiMinMax*Rref[ii]/<double>(NRPhi[ii])\n",
    "        dPhir[ii] = DPhiMinMax/<double>(NRPhi[ii])\n",
    "        if ii==0:\n",
    "            NRPhi0[ii] = 0\n",
    "            Phi = np.empty((Ln,NRPhi[ii]*Rratio+1))\n",
    "        else:\n",
    "            NRPhi0[ii] = NRPhi0[ii-1] + NRPhi[ii-1]\n",
    "        for jj in range(0,NRPhi[ii]):\n",
    "            Phi[ii,jj] = PhiMinMax[0] + (0.5+<double>jj)*dPhir[ii]\n",
    "    nRPhi0 = NRPhi0[Ln-1]+NRPhi[Ln-1]\n",
    "            \n",
    "    if Out.lower()=='(x,y,z)':\n",
    "        for ii in range(0,NP):\n",
    "            for jj in range(0,Ln+1):\n",
    "                if ind[ii]-NRPhi0[jj]<0.:\n",
    "                    break\n",
    "            iiL = jj-1\n",
    "            iiphi = ind[ii] - NRPhi0[iiL]\n",
    "            Pts[0,ii] = PtsCross[0,iiL]*Ccos(Phi[iiL,iiphi])\n",
    "            Pts[1,ii] = PtsCross[0,iiL]*Csin(Phi[iiL,iiphi])\n",
    "            Pts[2,ii] = PtsCross[1,iiL]\n",
    "            dS[ii] = dLr[iiL]*dRPhirRef[iiL]\n",
    "            if dRPhir[iiL]==-1.:\n",
    "                dRPhir[iiL] = dRPhirRef[iiL]\n",
    "            \n",
    "    else: \n",
    "        for ii in range(0,NP):\n",
    "            for jj in range(0,Ln+1):\n",
    "                if ind[ii]-NRPhi0[jj]<0.:\n",
    "                    break\n",
    "            iiL = jj-1\n",
    "            iiphi = ind[ii] - NRPhi0[iiL]\n",
    "            Pts[0,ii] = PtsCross[0,iiL]\n",
    "            Pts[1,ii] = PtsCross[1,iiL]\n",
    "            Pts[2,ii] = Phi[iiL,iiphi]\n",
    "            dS[ii] = dLr[iiL]*dRPhirRef[iiL]\n",
    "            if dRPhir[iiL]==-1.:\n",
    "                dRPhir[iiL] = dRPhirRef[iiL]\n",
    "    return Pts, dS, NL, dLr, Rref, dRPhir[dRPhir>-1.], <long>nRPhi0, VPbis\n",
    "\n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_TorStruct_SubFromD_cython(double[::1] PhiMinMax, double dL, double dRPhi, \n",
    "                                         double[:,::1] VPoly,\n",
    "                                         DR=None, DZ=None, DPhi=None,\n",
    "                                         double DIn=0., VIn=None,\n",
    "                                         str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \" Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \"\n",
    "    cdef double Dphi, dR0r, dZ0r\n",
    "    cdef int NR0, NZ0, R0n, Z0n, NRPhi0\n",
    "    cdef cnp.ndarray[double, ndim=1] R0, Z0, dsF, dSM, dLr, Rref, dRPhir, dS\n",
    "    cdef cnp.ndarray[long,ndim=1] indR0, indZ0, iind, iindF, indM, NL, ind\n",
    "    cdef cnp.ndarray[double,ndim=2] ptsrz, pts, PtsM, VPbis, Pts\n",
    "    cdef list LPts=[], LdS=[], Lind=[] \n",
    "\n",
    "    # Pre-format input\n",
    "    if DPhi is not None:\n",
    "        if DPhi[0]<=PhiMinMax[0]:\n",
    "            DPhi[0] = None\n",
    "        if DPhi[1]>=PhiMinMax[1]:\n",
    "            DPhi[1] = None\n",
    "            \n",
    "    Dphi = DIn/np.max(VPoly[0,:]) if DIn!=0. else 0. # Required distance effective at max R\n",
    "\n",
    "    # Get the mesh for the faces\n",
    "    Faces = False\n",
    "    if DPhi is None or DPhi[0] is None or DPhi[1] is None:\n",
    "        R0, dR0r, indR0, NR0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[0,:]),np.max(VPoly[0,:])]), dL, DL=DR, margin=margin)\n",
    "        Z0, dZ0r, indZ0, NZ0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[1,:]),np.max(VPoly[1,:])]), dL, DL=DZ, margin=margin)\n",
    "        R0n, Z0n = len(R0), len(Z0)\n",
    "        ptsrz = np.array([np.tile(R0,Z0n),np.repeat(Z0,R0n)])\n",
    "        iind = NR0*np.repeat(indZ0,R0n) + np.tile(indR0,Z0n)\n",
    "        indin = Path(VPoly.T).contains_points(ptsrz.T, transform=None, radius=0.0)\n",
    "        if np.any(indin):\n",
    "            ptsrz = ptsrz[:,indin] if indin.sum()>1 else ptsrz[:,indin].reshape((2,1))\n",
    "            iindF = iind[indin]\n",
    "            dsF = dR0r*dZ0r*np.ones((indin.sum(),))\n",
    "            Faces = True\n",
    "\n",
    "    # First face\n",
    "    if (DPhi is None or DPhi[0] is None) and Faces:\n",
    "        if Out.lower()=='(x,y,z)':\n",
    "            pts = np.array([ptsrz[0,:]*Ccos(PhiMinMax[0]+Dphi), ptsrz[0,:]*Csin(PhiMinMax[0]+Dphi), ptsrz[1,:]])\n",
    "        else:\n",
    "            pts = np.array([ptsrz[0,:],ptsrz[1,:],(PhiMinMax[0]+Dphi)*np.ones((indin.sum(),))])\n",
    "        LPts.append( pts )\n",
    "        Lind.append( iindF )\n",
    "        LdS.append( dsF )\n",
    "  \n",
    "    # Main body\n",
    "    PtsM, dSM, indM, NL, dLr, Rref, dRPhir, nRPhi0, VPbis = _Ves_Smesh_Tor_SubFromD_cython(dL, dRPhi, VPoly, \n",
    "                                                                                           DR=DR, DZ=DZ, DPhi=DPhi,\n",
    "                                                                                           DIn=DIn, VIn=VIn, PhiMinMax=PhiMinMax,\n",
    "                                                                                           Out=Out, margin=margin)      \n",
    "\n",
    "    if PtsM.shape[1]>=1:\n",
    "        if PtsM.shape[1]==1:\n",
    "            LPts.append(PtsM.reshape((3,1)))\n",
    "        else:\n",
    "            LPts.append(PtsM)\n",
    "        Lind.append( indM + NR0*NZ0 )\n",
    "        LdS.append( dSM )\n",
    "\n",
    "    # Second face\n",
    "    if (DPhi is None or DPhi[1] is None) and Faces:\n",
    "        if Out.lower()=='(x,y,z)':\n",
    "            pts = np.array([ptsrz[0,:]*Ccos(PhiMinMax[1]-Dphi), ptsrz[0,:]*Csin(PhiMinMax[1]-Dphi), ptsrz[1,:]])\n",
    "        else:\n",
    "            pts = np.array([ptsrz[0,:],ptsrz[1,:],(PhiMinMax[1]-Dphi)*np.ones((indin.sum(),))])\n",
    "        LPts.append( pts )\n",
    "        Lind.append( iindF + NR0*NZ0 + nRPhi0 )\n",
    "        LdS.append( dsF )\n",
    "\n",
    "    # Aggregate\n",
    "    if len(LPts)==1:\n",
    "        Pts = LPts[0]\n",
    "        ind = Lind[0]\n",
    "        dS = LdS[0]\n",
    "    else:\n",
    "        Pts = np.concatenate(tuple(LPts),axis=1)\n",
    "        ind = np.concatenate(tuple(Lind)).astype(int)\n",
    "        dS = np.concatenate(tuple(LdS))\n",
    "        \n",
    "    return Pts, dS, ind, NL, dLr, Rref, dR0r, dZ0r, dRPhir, VPbis \n",
    "      \n",
    "    \n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_TorStruct_SubFromInd_cython(double[::1] PhiMinMax, double dL, double dRPhi, \n",
    "                                           double[:,::1] VPoly, cnp.ndarray[long,ndim=1] ind,\n",
    "                                           double DIn=0., VIn=None,\n",
    "                                           str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \" Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \"\n",
    "    cdef double Dphi, dR0r, dZ0r\n",
    "    cdef int NR0, NZ0, R0n, Z0n, NRPhi0\n",
    "    cdef cnp.ndarray[double, ndim=1] R0, Z0, dsF, dSM, dLr, Rref, dRPhir, dS\n",
    "    cdef cnp.ndarray[long,ndim=1] bla, indR0, indZ0, iind, iindF, indM, NL\n",
    "    cdef cnp.ndarray[double,ndim=2] ptsrz, pts, PtsM, VPbis, Pts\n",
    "    cdef list LPts=[], LdS=[], Lind=[] \n",
    "\n",
    "    # Pre-format input\n",
    "    Dphi = DIn/np.max(VPoly[0,:]) if DIn!=0. else 0. # Required distance effective at max R\n",
    "\n",
    "    # Get the basic meshes for the faces\n",
    "    R0, dR0r, bla, NR0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[0,:]),np.max(VPoly[0,:])]), dL, DL=None, margin=margin)\n",
    "    Z0, dZ0r, bla, NZ0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[1,:]),np.max(VPoly[1,:])]), dL, DL=None, margin=margin)\n",
    "    \n",
    "    PtsM, dSM, indM, NL, dLr, Rref, dRPhir, nRPhi0, VPbis = _Ves_Smesh_Tor_SubFromD_cython(dL, dRPhi, VPoly, \n",
    "                                                                                           DR=None, DZ=None, DPhi=None,\n",
    "                                                                                           DIn=DIn, VIn=VIn, PhiMinMax=PhiMinMax,\n",
    "                                                                                           Out=Out, margin=margin)\n",
    "    # First face\n",
    "    ii = (ind<NR0*NZ0).nonzero()[0]\n",
    "    nii = len(ii)\n",
    "    if nii>0:\n",
    "        indZ0 = ind[ii] // NR0\n",
    "        indR0 = (ind[ii]-indZ0*NR0)\n",
    "        if Out.lower()=='(x,y,z)':\n",
    "            pts = np.array([R0[indR0]*Ccos(PhiMinMax[0]+Dphi), R0[indR0]*Csin(PhiMinMax[0]+Dphi), Z0[indZ0]])\n",
    "        else:\n",
    "            pts = np.array([R0[indR0], Z0[indZ0], (PhiMinMax[0]+Dphi)*np.ones((nii,))])\n",
    "        pts = pts if nii>1 else pts.reshape((3,1))\n",
    "        LPts.append( pts )\n",
    "        LdS.append( dR0r*dZ0r*np.ones((nii,)) )\n",
    " \n",
    "    # Main body\n",
    "    ii = (ind>=NR0*NZ0) & (ind<NR0*NZ0+PtsM.shape[1])\n",
    "    nii = len(ii)\n",
    "    if nii>0:\n",
    "        pts = PtsM[:,ind[ii]-NR0*NZ0] if nii>1 else PtsM[:,ind[ii]-NR0*NZ0].reshape((3,1))\n",
    "        LPts.append( PtsM[:,ind[ii]-NR0*NZ0] )\n",
    "        LdS.append( dSM[ind[ii]-NR0*NZ0] )\n",
    "        \n",
    "    # Second face\n",
    "    ii = (ind >= NR0*NZ0+PtsM.shape[1] ).nonzero()[0]\n",
    "    nii = len(ii)\n",
    "    if nii>0:\n",
    "        indZ0 = (ind[ii]-(NR0*NZ0+PtsM.shape[1])) // NR0\n",
    "        indR0 = ind[ii]-(NR0*NZ0+PtsM.shape[1]) - indZ0*NR0\n",
    "        if Out.lower()=='(x,y,z)':\n",
    "            pts = np.array([R0[indR0]*Ccos(PhiMinMax[1]-Dphi), R0[indR0]*Csin(PhiMinMax[1]-Dphi), Z0[indZ0]])\n",
    "        else:\n",
    "            pts = np.array([R0[indR0], Z0[indZ0], (PhiMinMax[1]-Dphi)*np.ones((nii,))])\n",
    "        pts = pts if nii>1 else pts.reshape((3,1))\n",
    "        LPts.append( pts )\n",
    "        LdS.append( dR0r*dZ0r*np.ones((nii,)) )\n",
    "        \n",
    "    # Aggregate\n",
    "    if len(LPts)==1:\n",
    "        Pts = LPts[0]\n",
    "        dS = LdS[0]\n",
    "    elif len(LPts)>1:\n",
    "        Pts = np.concatenate(tuple(LPts),axis=1)\n",
    "        dS = np.concatenate(tuple(LdS))\n",
    "        \n",
    "    return Pts, dS, NL, dLr, Rref, dR0r, dZ0r, dRPhir, VPbis\n",
    "    \n",
    "\n",
    "    \n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_Lin_SubFromD_cython(double[::1] XMinMax, double dL, double dX, \n",
    "                                   double[:,::1] VPoly,\n",
    "                                   DX=None, DY=None, DZ=None,\n",
    "                                   double DIn=0., VIn=None, double margin=1.e-9):\n",
    "    \" Return the desired surfacic submesh indicated by the limits (DX,DY,DZ), for the desired resolution (dX,dL) \"\n",
    "    cdef cnp.ndarray[double,ndim=1] X, Y0, Z0\n",
    "    cdef double dXr, dY0r, dZ0r\n",
    "    cdef int NY0, NZ0, Y0n, Z0n, NX, Xn, Ln, NR0\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts, PtsCross, VPbis\n",
    "    cdef cnp.ndarray[double,ndim=1] dS, dLr, Rref\n",
    "    cdef cnp.ndarray[long,ndim=1] indX, indY0, indZ0, indL, NL, ind\n",
    "    \n",
    "    # Preformat\n",
    "    if DX is not None:\n",
    "        if DX[0]<=XMinMax[0]:\n",
    "            DX[0] = None\n",
    "        if DX[1]>=XMinMax[1]:\n",
    "            DX[1] = None\n",
    "    if DY is not None:\n",
    "        if DY[0]<=np.min(VPoly[0,:]):\n",
    "            DY[0] = None\n",
    "        if DY[1]>=np.max(VPoly[0,:]):\n",
    "            DY[1] = None\n",
    "    if DZ is not None:\n",
    "        if DZ[0]<=np.min(VPoly[1,:]):\n",
    "            DZ[0] = None\n",
    "        if DZ[1]>=np.max(VPoly[1,:]):\n",
    "            DZ[1] = None\n",
    "    \n",
    "    # Get the mesh for the faces\n",
    "    Y0, dY0r, indY0, NY0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[0,:]),np.max(VPoly[0,:])]), dL, DL=DY, margin=margin)\n",
    "    Z0, dZ0r, indZ0, NZ0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[1,:]),np.max(VPoly[1,:])]), dL, DL=DZ, margin=margin)\n",
    "    Y0n, Z0n = len(Y0), len(Z0)\n",
    "    \n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    X, dXr, indX, NX = _Ves_mesh_dlfromL_cython(XMinMax, dX, DL=DX, margin=margin)\n",
    "    Xn = len(X)\n",
    "    PtsCross, dLr, indL, NL, Rref, VPbis = _Ves_mesh_CrossPoly(VPoly, dL, D1=None, D2=None, margin=margin, DIn=DIn, VIn=VIn)\n",
    "    NR0 = Rref.size\n",
    "    indin = np.ones((PtsCross.shape[1],),dtype=bool)\n",
    "    if DY is not None:\n",
    "        if DY[0] is not None:\n",
    "            indin = indin & (PtsCross[0,:]>=DY[0])\n",
    "        if DY[1] is not None:\n",
    "            indin = indin & (PtsCross[0,:]<=DY[1])\n",
    "    if DZ is not None:\n",
    "        if DZ[0] is not None:\n",
    "            indin = indin & (PtsCross[1,:]>=DZ[0])\n",
    "        if DZ[1] is not None:\n",
    "            indin = indin & (PtsCross[1,:]<=DZ[1])\n",
    "    PtsCross, dLr, indL, Rref = PtsCross[:,indin], dLr[indin], indL[indin], Rref[indin]\n",
    "    Ln = indin.sum() \n",
    "    # Agregating\n",
    "    Pts = np.array([np.repeat(X,Ln), np.tile(PtsCross[0,:],Xn), np.tile(PtsCross[1,:],Xn)])\n",
    "    ind = NY0*NZ0 + np.repeat(indX*NR0,Ln) + np.tile(indL,Xn)\n",
    "    dS = np.tile(dLr*dXr,Xn)\n",
    "    if DX is None or DX[0] is None:\n",
    "        pts = np.array([(XMinMax[0]+DIn)*np.ones((Y0n*Z0n,)), np.tile(Y0,Z0n), np.repeat(Z0,Y0n)])\n",
    "        iind = NY0*np.repeat(indZ0,Y0n) + np.tile(indY0,Z0n)\n",
    "        indin = Path(VPoly.T).contains_points(pts[1:,:].T, transform=None, radius=0.0)\n",
    "        if np.any(indin):\n",
    "            pts = pts[:,indin].reshape((3,1)) if indin.sum()==1 else pts[:,indin]\n",
    "            Pts = np.concatenate((pts,Pts),axis=1)\n",
    "            ind = np.concatenate((iind[indin], ind))\n",
    "            dS = np.concatenate((dY0r*dZ0r*np.ones((indin.sum(),)),dS))\n",
    "    if DX is None or DX[1] is None:\n",
    "        pts = np.array([(XMinMax[1]-DIn)*np.ones((Y0n*Z0n,)), np.tile(Y0,Z0n), np.repeat(Z0,Y0n)])\n",
    "        iind = NY0*NZ0 + NX*NR0 + NY0*np.repeat(indZ0,Y0n) + np.tile(indY0,Z0n)\n",
    "        indin = Path(VPoly.T).contains_points(pts[1:,:].T, transform=None, radius=0.0)\n",
    "        if np.any(indin):\n",
    "            pts = pts[:,indin].reshape((3,1)) if indin.sum()==1 else pts[:,indin]\n",
    "            Pts = np.concatenate((Pts,pts),axis=1)\n",
    "            ind = np.concatenate((ind,iind[indin]))\n",
    "            dS = np.concatenate((dS,dY0r*dZ0r*np.ones((indin.sum(),))))\n",
    "    return Pts, dS, ind, NL, dLr, Rref, dXr, dY0r, dZ0r, VPbis\n",
    "    \n",
    "  \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_Lin_SubFromInd_cython(double[::1] XMinMax, double dL, double dX, \n",
    "                                     double[:,::1] VPoly, cnp.ndarray[long,ndim=1] ind,\n",
    "                                     double DIn=0., VIn=None, double margin=1.e-9):\n",
    "    \" Return the desired surfacic submesh indicated by ind, for the desired resolution (dX,dL) \"\n",
    "    cdef double dXr, dY0r, dZ0r\n",
    "    cdef int NX, NY0, NZ0, Ln, NR0, nii\n",
    "    cdef list LPts, LdS\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts, PtsCross, VPbis\n",
    "    cdef cnp.ndarray[double,ndim=1] X, Y0, Z0, dS, dLr, Rref\n",
    "    cdef cnp.ndarray[long,ndim=1] indX, indY0, indZ0, indL, NL, ii\n",
    "    \n",
    "    # Get the mesh for the faces\n",
    "    Y0, dY0r, bla, NY0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[0,:]),np.max(VPoly[0,:])]), dL, DL=None, margin=margin)\n",
    "    Z0, dZ0r, bla, NZ0 = _Ves_mesh_dlfromL_cython(np.array([np.min(VPoly[1,:]),np.max(VPoly[1,:])]), dL, DL=None, margin=margin)\n",
    "    \n",
    "    # Get the actual R and Z resolutions and mesh elements\n",
    "    X, dXr, bla, NX = _Ves_mesh_dlfromL_cython(XMinMax, dX, DL=None, margin=margin)\n",
    "    PtsCross, dLr, bla, NL, Rref, VPbis = _Ves_mesh_CrossPoly(VPoly, dL, D1=None, D2=None, margin=margin, DIn=DIn, VIn=VIn)\n",
    "    Ln = PtsCross.shape[1] \n",
    "\n",
    "    LPts, LdS = [], []\n",
    "    # First face\n",
    "    ii = (ind<NY0*NZ0).nonzero()[0]\n",
    "    nii = len(ii)\n",
    "    if nii>0:\n",
    "        indZ0 = ind[ii] // NY0\n",
    "        indY0 = (ind[ii]-indZ0*NY0)\n",
    "        if nii==1:\n",
    "            LPts.append( np.array([[XMinMax[0]+DIn], [Y0[indY0]], [Z0[indZ0]]]) )\n",
    "        else:\n",
    "            LPts.append( np.array([(XMinMax[0]+DIn)*np.ones((nii,)), Y0[indY0], Z0[indZ0]]) )\n",
    "        LdS.append( dY0r*dZ0r*np.ones((nii,)) )\n",
    "\n",
    "    # Cylinder\n",
    "    ii = ((ind>=NY0*NZ0) & (ind<NY0*NZ0+NX*Ln)).nonzero()[0]\n",
    "    nii = len(ii)\n",
    "    if nii>0:\n",
    "        indX = (ind[ii]-NY0*NZ0) // Ln\n",
    "        indL = (ind[ii]-NY0*NZ0 - Ln*indX)\n",
    "        if nii==1:\n",
    "            LPts.append( np.array([[X[indX]], [PtsCross[0,indL]], [PtsCross[1,indL]]]) )\n",
    "            LdS.append( np.array([dXr*dLr[indL]]) )\n",
    "        else:\n",
    "            LPts.append( np.array([X[indX], PtsCross[0,indL], PtsCross[1,indL]]) )\n",
    "            LdS.append( dXr*dLr[indL] )\n",
    "\n",
    "    # End face\n",
    "    ii = (ind >= NY0*NZ0+NX*Ln).nonzero()[0]\n",
    "    nii = len(ii)\n",
    "    if nii>0:\n",
    "        indZ0 = (ind[ii]-NY0*NZ0-NX*Ln) // NY0\n",
    "        indY0 = ind[ii]-NY0*NZ0-NX*Ln - NY0*indZ0\n",
    "        if nii==1:\n",
    "            LPts.append( np.array([[XMinMax[1]-DIn], [Y0[indY0]], [Z0[indZ0]]]) )\n",
    "        else:\n",
    "            LPts.append( np.array([(XMinMax[1]-DIn)*np.ones((nii,)), Y0[indY0], Z0[indZ0]]) )\n",
    "        LdS.append( dY0r*dZ0r*np.ones((nii,)) )\n",
    "\n",
    "    # Format output\n",
    "    if len(LPts)==1:\n",
    "        Pts, dS = LPts[0], LdS[0]\n",
    "    else:\n",
    "        Pts = np.concatenate(tuple(LPts),axis=1)\n",
    "        dS = np.concatenate(tuple(LdS))\n",
    "        \n",
    "    return Pts, dS, NL, dLr, Rref, dXr, dY0r, dZ0r, VPbis\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "##################### New version\n",
    "\n",
    "def _getBoundsInter2AngSeg(bool Full, double Phi0, double Phi1, double DPhi0, double DPhi1):\n",
    "    \"\"\" Return Inter=True if an intersection exist (all angles in radians in [-pi;pi])\n",
    "\n",
    "    If Inter, return Bounds, a list of tuples indicating the segments defining the intersection, with\n",
    "    The intervals are ordered from lowest index to highst index (with respect to [Phi0,Phi1])\n",
    "    \"\"\"\n",
    "    if Full:\n",
    "        Bounds = [[DPhi0,DPhi1]] if DPhi0<=DPhi1 else [[-Cpi,DPhi1],[DPhi0,Cpi]]\n",
    "        Inter = True\n",
    "\n",
    "    else:\n",
    "        Inter, Bounds = False, None\n",
    "        if Phi0<=Phi1:\n",
    "            if DPhi0<=DPhi1:\n",
    "                if DPhi0<=Phi1 and DPhi1>=Phi0:\n",
    "                    Inter = True\n",
    "                    Bounds = [[None,None]]\n",
    "                    Bounds[0][0] = Phi0 if DPhi0<=Phi0 else DPhi0\n",
    "                    Bounds[0][1] = Phi1 if DPhi1>=Phi1 else DPhi1\n",
    "            else:\n",
    "                if DPhi0<=Phi1 or DPhi1>=Phi0:\n",
    "                    Inter = True\n",
    "                    if DPhi0<=Phi1 and DPhi1>=Phi0:\n",
    "                        Bounds = [[Phi0,DPhi1],[DPhi0,Phi1]]\n",
    "                    else:\n",
    "                        Bounds = [[None,None]]\n",
    "                        if DPhi0<=Phi1:\n",
    "                            Bounds[0][0] = Phi0 if DPhi0<=Phi0 else DPhi0\n",
    "                            Bounds[0][1] = Phi1\n",
    "                        else:\n",
    "                            Bounds[0][0] = Phi0\n",
    "                            Bounds[0][1] = Phi1 if DPhi1>=Phi1 else DPhi1\n",
    "        else:\n",
    "            if DPhi0<=DPhi1:\n",
    "                if DPhi0<=Phi1 or DPhi1>=Phi0:\n",
    "                    Inter = True\n",
    "                    if DPhi0<=Phi1 and DPhi1>=Phi0:\n",
    "                        Bounds = [[Phi0,DPhi1],[DPhi0,Phi1]]\n",
    "                    else:\n",
    "                        Bounds = [[None,None]]\n",
    "                        if DPhi0<=Phi1:\n",
    "                            Bounds[0][0] = DPhi0\n",
    "                            Bounds[0][1] = Phi1 if DPhi1>=Phi1 else DPhi1\n",
    "                        else:\n",
    "                            Bounds[0][0] = Phi0 if DPhi0<=Phi0 else DPhi0\n",
    "                            Bounds[0][1] = DPhi1\n",
    "            else:\n",
    "                Inter = True\n",
    "                if DPhi0>=Phi0 and DPhi1>=Phi0:\n",
    "                    Bounds = [[Phi0,DPhi1],[DPhi0,Cpi],[-Cpi,Phi1]]\n",
    "                elif DPhi0<=Phi1 and DPhi1<=Phi1:\n",
    "                    Bounds = [[Phi0,Cpi],[-Cpi,DPhi1],[DPhi0,Phi1]]\n",
    "                else:\n",
    "                    Bounds = [[None,Cpi],[-Cpi,None]]\n",
    "                    Bounds[0][0] = Phi0 if DPhi0<=Phi0 else DPhi0\n",
    "                    Bounds[1][1] = Phi1 if DPhi1>=Phi1 else DPhi1\n",
    "    return Inter, Bounds\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "@cython.cdivision(True)\n",
    "@cython.wraparound(False)\n",
    "@cython.boundscheck(False)\n",
    "def _Ves_Smesh_Tor_SubFromD_bis(double dL, double dRPhi,\n",
    "                                   double[:,::1] VPoly,\n",
    "                                   DR=None, DZ=None, DPhi=None,\n",
    "                                   double DIn=0., VIn=None, PhiMinMax=None,\n",
    "                                   str Out='(X,Y,Z)', double margin=1.e-9):\n",
    "    \" Return the desired surfacic submesh indicated by the limits (DR,DZ,DPhi), for the desired resolution (dR,dZ,dRphi) \"\n",
    "    cdef double[::1] R, Z, dPhir, NRPhi#, dPhi, NRZPhi_cum0, indPhi, phi\n",
    "    cdef double dRr0, dRr, dZr, DPhi0, DPhi1, DDPhi, DPhiMinMax\n",
    "    cdef double abs0, abs1, phi, indiijj\n",
    "    cdef long[::1] indR0, indR, indZ, Phin, NRPhi0, Indin\n",
    "    cdef int NR0, NR, NZ, Rn, Zn, nRPhi0, indR0ii, ii, jj0=0, jj, nPhi0, nPhi1, zz, NP, NRPhi_int, Rratio, Ln\n",
    "    cdef cnp.ndarray[double,ndim=2] Pts, indI, PtsCross, VPbis\n",
    "    cdef cnp.ndarray[double,ndim=1] R0, dS, ind, dLr, Rref, dRPhir\n",
    "    cdef cnp.ndarray[long,ndim=1] indL, NL\n",
    "\n",
    "    # Pre-format input\n",
    "    if PhiMinMax is None:\n",
    "        PhiMinMax = [-Cpi,Cpi]\n",
    "        DPhiMinMax = 2.*Cpi\n",
    "        Full = True\n",
    "    else:\n",
    "        PhiMinMax = [Catan2(Csin(PhiMinMax[0]),Ccos(PhiMinMax[0])), Catan2(Csin(PhiMinMax[1]),Ccos(PhiMinMax[1]))]\n",
    "        DPhiMinMax = PhiMinMax[1]-PhiMinMax[0] if PhiMinMax[1]>=PhiMinMax[0] else 2.*Cpi + PhiMinMax[1] - PhiMinMax[0]\n",
    "        Full = False\n",
    "\n",
    "    # Get the limits if any (and make sure to replace them in the proper quadrants)\n",
    "    if DPhi is None:\n",
    "        DPhi0, DPhi1 = PhiMinMax[0], PhiMinMax[1]\n",
    "    else:\n",
    "        DPhi0 = PhiMinMax[0] if DPhi[0] is None else Catan2(Csin(DPhi[0]),Ccos(DPhi[0]))\n",
    "        DPhi1 = PhiMinMax[1] if DPhi[1] is None else Catan2(Csin(DPhi[1]),Ccos(DPhi[1]))\n",
    "    DDPhi = DPhi1-DPhi0 if DPhi1>DPhi0 else 2.*Cpi+DPhi1-DPhi0\n",
    "\n",
    "    Inter, Bounds = _getBoundsInter2AngSeg(Full, PhiMinMax[0], PhiMinMax[1], DPhi0, DPhi1)\n",
    "    BC = list(Bounds)\n",
    "    nBounds = len(Bounds)\n",
    "    for ii in range(0,nBounds):\n",
    "        if BC[ii][0]<PhiMinMax[0]:\n",
    "            BC[ii][0] += 2.*Cpi\n",
    "        if BC[ii][1]<=PhiMinMax[0]:\n",
    "            BC[ii][1] += 2.*Cpi\n",
    "            \n",
    "    \n",
    "    if Inter:\n",
    "\n",
    "        # Get the actual R and Z resolutions and mesh elements\n",
    "        PtsCross, dLr, indL, NL, Rref, VPbis = _Ves_mesh_CrossPoly(VPoly, dL, D1=None, D2=None, margin=margin, DIn=DIn, VIn=VIn)\n",
    "        R0 = np.copy(Rref)\n",
    "        NR0 = R0.size\n",
    "        indin = np.ones((PtsCross.shape[1],),dtype=bool)\n",
    "        if DR is not None:\n",
    "            indin = indin & (R0>=DR[0]) & (R0<=DR[1])\n",
    "        if DZ is not None:\n",
    "            indin = indin & (PtsCross[1,:]>=DZ[0]) & (PtsCross[1,:]<=DZ[1])\n",
    "        PtsCross, dLr, indL, Rref = PtsCross[:,indin], dLr[indin], indL[indin], Rref[indin]\n",
    "        Ln = indin.sum()\n",
    "        Indin = indin.nonzero()[0]\n",
    "\n",
    "        dRPhir, dPhir = np.empty((Ln,)), np.empty((Ln,))\n",
    "        Phin = np.zeros((Ln,),dtype=int)\n",
    "        NRPhi = np.empty((Ln,))\n",
    "        NRPhi0 = np.zeros((Ln,),dtype=int)\n",
    "        nRPhi0, indR0ii = 0, 0\n",
    "        NP, NPhimax = 0, 0\n",
    "        Rratio = int(Cceil(np.max(Rref)/np.min(Rref)))\n",
    "        indBounds = np.empty((2,nBounds),dtype=int)\n",
    "        for ii in range(0,Ln):\n",
    "            # Get the actual RPhi resolution and Phi mesh elements (! depends on R !)\n",
    "            NRPhi[ii] = Cceil(DPhiMinMax*Rref[ii]/dRPhi)\n",
    "            NRPhi_int = int(NRPhi[ii])\n",
    "            dPhir[ii] = DPhiMinMax/NRPhi[ii]\n",
    "            dRPhir[ii] = dPhir[ii]*Rref[ii]\n",
    "            # Get index and cumulated indices from background\n",
    "            for jj0 in range(indR0ii,NR0):\n",
    "                if jj0==Indin[ii]:\n",
    "                    indR0ii = jj0\n",
    "                    break\n",
    "                else:\n",
    "                    nRPhi0 += <long>Cceil(DPhiMinMax*R0[jj0]/dRPhi)\n",
    "                    NRPhi0[ii] = nRPhi0\n",
    "            # Get indices of phi\n",
    "            # Get the extreme indices of the mesh elements that really need to be created within those limits\n",
    "            for kk in range(0,nBounds):\n",
    "                abs0 = Cabs(BC[kk][0]-PhiMinMax[0])\n",
    "                if abs0-dPhir[ii]*Cfloor(abs0/dPhir[ii])<margin*dPhir[ii]:\n",
    "                    nPhi0 = int(Cround(abs0/dPhir[ii]))\n",
    "                else:\n",
    "                    nPhi0 = int(Cfloor(abs0/dPhir[ii]))\n",
    "                abs1 = Cabs(BC[kk][1]-PhiMinMax[0])\n",
    "                if abs1-dPhir[ii]*Cfloor(abs1/dPhir[ii])<margin*dPhir[ii]:\n",
    "                    nPhi1 = int(Cround(abs1/dPhir[ii]))\n",
    "                else:\n",
    "                    nPhi1 = int(Cfloor(abs1/dPhir[ii]))\n",
    "                indBounds[0,kk] = nPhi0\n",
    "                indBounds[1,kk] = nPhi1\n",
    "                Phin[ii] += nPhi1+1-nPhi0\n",
    "                \n",
    "            if ii==0:\n",
    "                indI = np.ones((Ln,Phin[ii]*Rratio+1))\n",
    "            jj = 0\n",
    "            for kk in range(0,nBounds):\n",
    "                for kkb in range(indBounds[0,kk],indBounds[1,kk]+1):\n",
    "                    indI[ii,jj] = <double>( kkb )\n",
    "                    jj += 1\n",
    "            NP += Phin[ii]\n",
    "\n",
    "        # Finish counting to get total number of points\n",
    "        if jj0<=NR0-1:\n",
    "            for jj0 in range(indR0ii,NR0):\n",
    "                nRPhi0 += <long>Cceil(DPhiMinMax*R0[jj0]/dRPhi)\n",
    "\n",
    "        # Compute Pts, dV and ind\n",
    "        Pts = np.empty((3,NP))\n",
    "        ind = np.empty((NP,))\n",
    "        dS = np.empty((NP,))\n",
    "        # This triple loop is the longest part, it takes ~90% of the CPU time\n",
    "        NP = 0\n",
    "        if Out.lower()=='(x,y,z)':\n",
    "            for ii in range(0,Ln):\n",
    "                for jj in range(0,Phin[ii]):\n",
    "                    indiijj = indI[ii,jj]\n",
    "                    phi = PhiMinMax[0] + (0.5+indiijj)*dPhir[ii]\n",
    "                    Pts[0,NP] = PtsCross[0,ii]*Ccos(phi)\n",
    "                    Pts[1,NP] = PtsCross[0,ii]*Csin(phi)\n",
    "                    Pts[2,NP] = PtsCross[1,ii]\n",
    "                    ind[NP] = NRPhi0[ii] + indiijj\n",
    "                    dS[NP] = dLr[ii]*dRPhir[ii]\n",
    "                    NP += 1\n",
    "        else:\n",
    "            for ii in range(0,Ln):\n",
    "                for jj in range(0,Phin[ii]):\n",
    "                    indiijj = indI[ii,jj]\n",
    "                    Pts[0,NP] = PtsCross[0,ii]\n",
    "                    Pts[1,NP] = PtsCross[1,ii]\n",
    "                    Pts[2,NP] = PhiMinMax[0] + (0.5+indiijj)*dPhir[ii]\n",
    "                    ind[NP] = NRPhi0[ii] + indiijj\n",
    "                    dS[NP] = dLr[ii]*dRPhir[ii]\n",
    "                    NP += 1\n",
    "    else:\n",
    "        Pts, dS, ind, NL, dRPhir, nRPhi0 = None, None, None, None, None, None\n",
    "    return Pts, dS, ind.astype(int), NL, dLr, Rref, dRPhir, nRPhi0, VPbis\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True, True, True, True, True, True, True, True]\n"
     ]
    }
   ],
   "source": [
    "VPoly = np.ascontiguousarray([[1,10.,20,20,10,1,1],[0,-10.,0,10,20,10,0.]])\n",
    "VIn = VPoly[:,1:]-VPoly[:,:-1]\n",
    "VIn = np.array([-VIn[1,:],VIn[0,:]])\n",
    "VIn = VIn/np.sqrt(np.sum(VIn**2,axis=0))[np.newaxis,:]\n",
    "DIn = -0.1\n",
    "Ptsm, dLm, indm, Nm, Rrefm, VPbism = _Ves_mesh_CrossPoly(VPoly, 1., D1=[0,25], D2=[-25,25], DIn=DIn, VIn=VIn)\n",
    "\n",
    "#%timeit out = _Ves_mesh_CrossPoly(VPoly, 0.1)\n",
    "#print np.all(indm==np.unique(indm))\n",
    "\n",
    "Pts, dS, ind, NL, dLr, Rref, dRPhir, nRPhi0, VPbis = _Ves_Smesh_Tor_SubFromD_cython(0.5, 0.5, VPoly, DR=[0,25], DZ=[-25,25], DPhi=[0,np.pi/2.], Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "Ptsi, dSi, NLi, dLri, Rrefi, dRPhiri, nRPhi0i, VPbisi = _Ves_Smesh_Tor_SubFromInd_cython(0.5, 0.5, VPoly, ind, Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "#%timeit out = _Ves_Smesh_Tor_SubFromD_cython(0.1, 0.1, VPoly, DR=None, DZ=None, DPhi=None, Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "#%timeit out = _Ves_Smesh_Tor_SubFromInd_cython(0.5, 0.5, VPoly, ind, Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "print([np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(Pts,Ptsi),(dS,dSi),(NL,NLi),(dLr,dLri),(Rref,Rrefi),(dRPhir,dRPhiri),(nRPhi0,nRPhi0i),(VPbis,VPbisi)]])\n",
    "\n",
    "\n",
    "Ptsb, dSb, indb, NLb, dLrb, Rrefb, dRPhirb, nRPhi0b, VPbisb = _Ves_Smesh_Tor_SubFromD_bis(0.5, 0.5, VPoly, DR=[0,25], DZ=[-25,25], DPhi=[0,np.pi/2.], Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "print([np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(Pts,Ptsb),(dS,dSb),(ind,indb),(NL,NLb),(dLr,dLrb),(Rref,Rrefb),(dRPhir,dRPhirb),(nRPhi0,nRPhi0b),(VPbis,VPbisb)]])\n",
    "PhiMinMax = np.array([-np.pi/2.,0.])\n",
    "#Ptsb, dSb, indb, NLb, dLrb, Rrefb, dRPhirb, nRPhi0b, VPbisb = _Ves_Smesh_Tor_SubFromD_bis(0.5, 0.5, VPoly, PhiMinMax=PhiMinMax, DR=[0,25], DZ=[-25,25], DPhi=[0,np.pi/2.], Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "\n",
    "\n",
    "\n",
    "#PhiMinMax = np.array([-np.pi/2.,0.])\n",
    "#PtsS, dSS, indS, NL, dLr, Rref, dR0r, dZ0r, dRPhir, VPbis = _Ves_Smesh_TorStruct_SubFromD_cython(PhiMinMax, 0.5, 0.5, VPoly, DR=[0.,25.], DZ=[-25.,25.], DPhi=[-np.pi/8.,0.], Out='(x,y,z)', DIn=DIn, VIn=VIn)\n",
    "#PtsSi, dSSi, NLi, dLri, Rrefi, dR0ri, dZ0ri, dRPhiri, VPbisi = _Ves_Smesh_TorStruct_SubFromInd_cython(PhiMinMax, 0.5, 0.5, VPoly, indS, Out='(x,y,z)', DIn=DIn, VIn=VIn)\n",
    "#%timeit out = _Ves_Smesh_TorStruct_SubFromD_cython(PhiMinMax, 0.1, 0.1, VPoly, DR=None, DZ=None, DPhi=None, Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "#%timeit out = _Ves_Smesh_TorStruct_SubFromInd_cython(PhiMinMax, 0.5, 0.5, VPoly, indS, Out='(X,Y,Z)', DIn=DIn, VIn=VIn)\n",
    "#print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(PtsS,PtsSi),(dSS,dSSi),(NL,NLi),(dLr,dLri),(Rref,Rrefi),(dR0r,dR0ri),(dZ0r,dZ0ri),(dRPhir,dRPhiri),(nRPhi0,nRPhi0i),(VPbis,VPbisi)]]\n",
    "\n",
    "\n",
    "#PtsL, dS, ind, NL, dLr, Rref, dXr, dY0r, dZ0r, VPbis = _Ves_Smesh_Lin_SubFromD_cython(np.array([0.,10.]), 0.5, 0.01, VPoly, DY=[0.,25.], DZ=[-25.,25.], DX=None, DIn=DIn, VIn=VIn)\n",
    "#Ptsi, dSi, NLi, dLri, Rrefi, dXri, dY0ri, dZ0ri, VPbisi = _Ves_Smesh_Lin_SubFromInd_cython(np.array([0.,10.]), 0.5, 0.01, VPoly, ind, DIn=DIn, VIn=VIn)\n",
    "#%timeit out = _Ves_Smesh_Lin_SubFromD_cython(np.array([0.,10.]), 0.5, 0.01, VPoly, DX=None, DY=None, DZ=None, DIn=DIn, VIn=VIn)\n",
    "#%timeit out = _Ves_Smesh_Lin_SubFromInd_cython(np.array([0.,10.]), 0.5, 0.01, VPoly, ind, DIn=DIn, VIn=VIn)\n",
    "#print [np.allclose(A,B,atol=1.e-9,rtol=0.,equal_nan=True) for (A,B) in [(PtsL,Ptsi),(dS,dSi),(NL,NLi),(dLr,dLri),(Rref,Rrefi)]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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tG9XV1b5aIF3XhflbI8oTUcQjiZ3gFNoclVNdXU3NUYkpg8QOQZQhtm27IgfIfZetnBE9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MBn7YODg4mC8EGYbhKVZpaWmjcu6hrq4OlZWVNnfK3Wkzc8d9EcIPDh3qwaFDLFJT6zF+\nfBMf9kjaD7/9Vu5w387mdPbsMSAzU4+dO6vx2GOTYTJRVi4PTVvmZUgL2b59tnAAAg9QKCyPY5je\nNrRly8xYtUqBd96Rw3JDjINcDnz5pS3sQerfnhC52dDS0uJ2yxNxfoR5J76+vvx7MFzoh+R7Jj4+\nftRBFsxmM1paWlBaWgqGYSCXy/n3IDAwEAEBASOaxglYz1elpaUN+Q2xy5cvY8WKFcjKysKf//xn\nmM1mfPfdd/jmm2/wzTffoLS0FPHx8XjttdeQnJzskeeUcnbef/99vP322zhw4AB8fX2d7uPZZ5+F\nv78/nnjiCY8ck1deScjbxubV6BHHcWhvb8c333zDz/20tLQgMzOTL34mTJhgd6HT09OD8vJy9PT0\nIC0tbVCgAAMpjuOg1+uh0+nQ2tqKzs5OUBQFg8GAmJgYJCQkjMiWH0fq6elBYWEhVCoVxo8fLzlM\n7cri3B1SmqPChbQfHjrUgyNHKKSl1WHKlB5UVo7BhQthmD1bjquvtv48OpvTWbCgFf7+rbj11gBo\nNBqbQFAhEU0cDnrzzSr09PTCA4TbClvWTp2S4YYbVDCZLNvJZByeflr6HHhaJFcmPDzcI3N6hH4o\nnHkYysBNZwGho0FkdiUxMRFRUVE8dUz4HhAAiNCBGykiIbYymQwpKSlD+j3KcRz++9//4tlnn8WG\nDRtwww03SBbOHMfh4sWLCAsL89j8jrjY2b9/Px577DEcOXLEbjtmZ2cnWJaFRqNBZ2cnrr/+ejz9\n9NM8udUrrwZA3mLHq9Eto9GIH374gae+lZSUIDExETNnzsSsWbMwdepUKBQKbNq0CW+//TY++eST\nPuGkh7t6enqg1WpBURRCQ0Oh1+vR1tbGLzgI+GAktNFIibh8NTU1mDBhAh/s547cRUsDrhVF4oJo\n164OTJjQzLsPLMsiICAA5eWR+PHHYEREyPG73yklnR2ZjAVFUTCbKUnYgHCWh2GkUdFCeABxgRjL\nSA5UKosTRIhshLImRFMPlIS5MmlpaVbZVZ4WwzC889PW1gaGYeDv788XQJ4GgBB1dHSgoKCAd41H\n2/eMO7MrJpOJ/xtoa2uzcuCI+zMcHDixdDodtFotEhISEBUVNaTHYjAY8PTTT6O0tBTvv//+oIbq\nLl68GIcPH0ZTUxMiIyPx3HPPYf369TAYDAgNDQUAzJw5E2+99RZqa2uxfPly7N27F+Xl5Zg/fz4A\ny2fgzjvvxLp16wbtuL36n5S32PHqf0ssy6K0tJRvfTt16hT0ej3i4+OxYsUKzJ49G4GBgaNmESIs\nAsaPH89fhIjIgoO0vjEMY9X65ippaShFcoH8/f2RnJzcp/YYKYcGcA0t7awoclYQmc1mHDrUgzvu\nCIXRSEGhYLFqVSUqK4OhUqmwdKllgP3AATOMxihs3+5rAw84dUqG7dtpcBywZIkZgG3+jb1tt2+n\nraADAwEZcKbOzk4UFBQgODh4SAbYWZblbwDodDro9Xor5HJ/s6/6GxA6EtTZ2Yn8/HxERERg7Nix\nfc4fEztwQvdnKINjOY5DRUUFmpubMXHixCF35IqLi7Fy5Ur86le/wuOPPz4sC0OvvBom8hY7Xv1v\nymQy4bXXXsOOHTuwbt06cByHY8eO4eTJkzAYDJg+fTpmzpyJq6++GrGxscN+wS+lzs5OFBYWIiAg\nAOPGjXOpCJDK2fDx8eGdn+F0t5W0ZVy+fLnfmST9aVsDHBdFwvBPVwuiu+7qxr//7QOj0ZKbc8MN\nl7BwYQ80Gg0WL46SxD+7g4qWChl1ZT7J0+I4DtXV1fx7ONRzD0IJkcttbW1WQ/fuhD2OdhKZMEAz\nPT3do44cceDI+2A0GuHv788XP/7+/oNyPnt6enDhwgUEBwcjMTFxSN9DjuOwbds2/POf/8Tbb7+N\nGTNmDNmxeOXVCJGXxubV/57OnDmDRx55BHP+v717j2v6vvcH/goEJEBAkJsSBeSaoLVyB1vnb9Xp\n8Tg7aY+3ebR1DmvnKa2t1tYzW0+vtnbtjjrbbVV0a/Vh11UfdWlrZ8dMUGF02grhqoDc/jBd/QAA\nIABJREFUETEEAiThm+/vD873u0RAbrn7fj4efTwKfAOfEMDP+/t5X5YsQUFBAX+3MDs7G8BAkFBU\nVASFQoGnnnqK7yTEtbyWyWQOXetiNBr5eRbx8fFj2kC6ubnxd1JnzJjBd1q6ffs2mpqaUFFRYdaN\nyd/f3y6pb93d3SgrK0NAQABSU1MnXOw8f74Rnp4YsquYqaE6qQ3Vze1uwzOHCiTu/PpCoQf0egGM\nRgGMRjd88cV0fPMNiw8+uIYlS5rx+edTwbIC9Pez+PRT8Gvq6xuYccN1Qlu/vp8/tenvZ3Hq1NDd\n6Mw7zdkm0Ont7YVKpYJYLEZKSorDFax7eXnBy8uLTw0ybfdbWVmJ3t5eeHt78xtvbiI8h5vPVV9f\nb5cBobag0+mgUqkgEoms8hp6eHggKCiIn8fC1SF2dnbixo0b6O7uHjR41tJ/j7jW9QkJCXz3M3vR\naDR45pln4O7ujvz8fIe6OUCIs6OTHeJSDh48iIceeggJCQmjup5hGFy9epVPfVOpVJg2bRpf95OS\nkjKqrjO20NnZifLycj6VxBp3IA0GA3+3W61Wo7+/f1xDBsfDNJBLSEiAn5+fxT73UClbQ3Ues2Qt\nj+nnPnzYHadOuWPp0l54eVVj27Y50OsFZs0ENm7sx/33G/laGk9PFk89VYN3342EwTDwWg+sqwdZ\nWYJRdXazVXDDMU2tjI+Pt/sGcrxMi+652VfczQIfHx+0trbCy8vLoQaEWhLX8j42NtauwyG5wbOm\nf4/uPP0Zz98jhmH4tuBSqXTIZie29O233+Kpp55Cbm4uNmzY4JTZBoTYCaWxETJW3CRwpVKJgoIC\nFBcXw8PDw6zldVBQkE3/MWIYBtXV1eju7kZCQoJNp5NzqW9c3U9PT49VWv1qNBqUl5cjKCgIkZGR\nVk8lGS6wGU0dy0hB0XBBiE4HeHgY8cknt+Hr64uPPnLHsWNCvoGAh8dAw4E7W0U/9ZQHDh/+V93N\n449fx7p1DfD398f166G4cmUy/t//E1itVfRocd3yRCIRYmNjHe40Z6IMBgNu3LiB+vp6eHl5gWVZ\nvvHBRDbejoRhGFRWVkKn00EmkzlcU5Oh6q88PT3NTn9GCly6urpQWlqK6dOnY9q0aXZ9zYxGIw4c\nOIDTp08jLy8P8fHxdlsLIU6Kgh0yel9++SVyc3PBMAw2bdqEnTt3mn2cZVnk5uZCLpfD29sbeXl5\nSEpKstNqbYdlWajValy4cIFvea3RaHD//ffzpz/R0dFW25y3t7ejuroaEokE4eHhdt9McYXGXOOD\nrq4uCIVCs9S3sdwlZRgGNTU1uH37NqRSqc2Ku0dbx8MZy/yZOz/3gw/qcf6856BmAgCQm+vBp6KZ\ntoo2XdNQwVVSkm7YjmOTJ0+22ADe0TCdfRQXFzeoUYYrMB0QKpVK4enpadb+vbOzE11dXfD09OR/\nD8b6u2BvGo0GKpXKYf7WjJZOZ/67wDAMxGIx/zpwvwtcHWBraysSExNtetNoKK2trdiyZQsSEhKw\nd+9euzZoIMSJUbBDRodhGMTFxeHrr7+GRCJBamoqjh8/DplMxl8jl8uxf/9+yOVyFBYWIjc3F4WF\nhXZctf3odDoUFxfzpz/Xr19HTEwMX/czZ86cCW9y9Ho9KisrwTAM4uPjHXpOhV6vN0t949otcxvv\n4abcc21ep06davNWvWMdLDqWIn/T6z08WDz5ZCUOHIiDXj/w/ExbQA/VVpobGGr6dUY6seHueHNp\nV1qtFpMmTTILQq1x0qLX61FWVgahUIi4uDin2tyP1lgGhA618bZVGuh4cafZ7e3tYx7y6oi402ju\nNdBqtfDw8EBf30ATEC5YtReWZfG3v/0NL774Il577TUsW7bM4X4mCHEiFOyQ0bl48SJefvllfPXV\nVwCAN954AwDwwgsv8Nds3rwZCxYswJo1awAA8fHxyM/Px9SpU22/YAdjNBpRXl4OpVIJpVKJ77//\nHgEBAUhPT0dWVhbS0tIgFotH9Q+a6V3ymTNn2nS2gqUwDMOnvnFT7n18fPiNt0gkwvXr19Hd3Q2p\nVGq3mqjRpnyNtUYHAI4eZXH79m088ogWK1aEYds2L7MTHNPTnTsfa6k0tN7eXrONt6UHPXLF3TEx\nMcMOGXRmlhgQajQaodFo+Negp6cHIpGIfw38/Pzsmu7n6t3kgIHT8YqKCoSGhoJhGL77nunpj62C\nUL1ej1dffRVXrlzB0aNHER4ebvWvSYiLo25sZHQaGxsxffp0/m2JRDLo1GaoaxobGynYwUCXM5lM\nBplMhpycHD5gUSgU+Otf/4rXX38dDMMgOTkZWVlZyMrKGvIOcVVVFerq6jBt2jSkpKQ47V1yboI9\n16GKZVlotVqo1WpUV1fj9u3bfCes3t5eeHp62qXIm+tqdqc7g6CRurndeToDsDAYAHf3MCxaZIC7\nO4O1axkcOyaEXs+CZYE//lGIn/6U4ddgug5L1dqIRCKIRCJ+OCI3d0mtVqOpqQk6nW5cNSfchHmW\nZZGcnOxwdR2WYDogNCkpadwbYdPuhgD4DohqtRotLS2orKzkg1Bu422rU9zm5mbU1dW5bDc5o9GI\nqqoq9PT0IDU11eznlLshw/1N6unpgZeXl1kKoqWD0NraWuTk5ODf/u3f8NVXX1nk82/cuBFnzpxB\nSEgISkpKAAAdHR1YtWoVamtrERkZiZMnTw7ZKGSk1HVCXAkFO4RYmEAgwNSpU7Fy5UqsXLmSz+0v\nLCyEQqHAsWPH0NrailmzZiEzMxNpaWn4/PPP8cknn2D//v1m6YOuQCAQYNKkSdBoNBAIBJg3bx4E\nAgHUajXa29tx7do1sCzLb/gsceowXsOlrN2tfbNpy2qj0QiWFQAYaAf9zDOeSEzUDdkq+vx5N5s2\nERAKhZgyZQpfU2Nac1JXV2dWczLcsM329nZUVVU57anjSEwHhCYmJlq8hkwgEPBBKHejyDQIbWxs\nhE6nMzsJtfS8GS5YFQgESElJccluct3d3XywGhcXNyhYHeqGDBeEtrW1obq6GgDg5+dndhI6nqCX\nZVl8+umneOedd3Dw4EE88MADE3+C/+exxx7D1q1bsX79ev59b775Jh566CHs3LkTb775Jt58803s\n3bvX7HEMw+AXv/iFWer68uXLXe7fHkI4rvdXjoxZeHg46uvr+bcbGhoGHa+P5hoyNIFAALFYjIUL\nF2LhwoUABjYc3333HT755BMsX74cEokE0dHRKCwshJubG5KTkx26TmcsuDa2ERERZidaoaGhZnNO\nuBazEzl1mKihZu3cefpy58lPYCALgWDgPw8PgGEAhmEBCGA0/utzrF3L4KOPhCPO+7EV7udSLBbz\np7Y6nW5QEOrn5wc/Pz/cunULDMMgKSnJJYupTVO6UlJSbJbSNVQQyp2Ems6b4QLQyZMnj/vUt6Oj\nAxUVFYiKiuJP/FwJ1/q8sbFxTENQhwpCTf8mlZeXo6+v766zl4ai1Wqxfft2dHd3429/+xsCAwMn\n/BxNzZ8/H7W1tWbvO336NPLz8wEAGzZswIIFCwYFO0VFRYiJicHMmTMBAKtXr8bp06cp2CEui4Id\ngtTUVFRVVaGmpgbh4eE4ceIEPv74Y7Nrli9fjgMHDmD16tUoLCyEv78/pbBNgNFoxOeffw6FQoGz\nZ8/ivvvuQ01NDRQKBU6ePInnn38eIpEIGRkZ/H+BgYFOVciq1+tRUVEBo9E44gbZ3d0dgYGBCAwM\nRFRU1JCnDrYouB9LypqnJ/DKK13YtWsSjEYB3N2Bd97RA8D/zcphMWnSvz7HSCdEjmDSpEmDgtCG\nhgZUV1fzaUAVFRVmGz5nr/NwtAGhAoEAvr6+8PX1hUQiAfCvJiBqtRq1tbVmHccmT548Ys2Jaf3R\n3LlzXeZGiimDwQCVSgVPT0+LDEE1/ZsEmHeibGpqQldXl1kKYl9fHyIiIvjHX716FU8++SRycnLw\n85//3Ga/J62trfy/zWFhYWhtbR10zWhS1wlxJRTsEAiFQhw4cACLFy8GwzDYuHEjEhMT8f777wMA\nnnjiCSxduhRyuRwxMTHw9vbGkSNH7Lxq51VeXo5169Zh9erVOH/+PJ9GEh0djejoaDz22GNgWRa3\nbt3iW16/99576OnpQVJSEt/1zVqDRSeKZVm0tbXh+vXr4053GurUoa+vD52dnWZpJqapb5Y4bRhL\nyppez+LECQP6+934ttK3bgmwfXs/EhOH/hzD1Qk5IoZh+JqH9PR0frYMd+rQ0NBg1np8oqcO9qDX\n66FSqTBp0iSkpqY67GwgT09PhISEICQkBIB5x7Fr165Bq9UOW3Oi1WpRWlqKkJCQCdUfOTLuxCo6\nOpr/HlmaQCCAj48PfHx8MG3aNAD/SkG8efMmfvGLX6CtrQ1RUVEIDAxESUkJPvroI8ydO9cq6xnt\nml3x9SZkrKgbGyE2plarcevWLURHR4/pcX19fSgqKuJbXtfV1SE+Pp4PfmbPnm33/HudTofy8nK4\nu7sjPj7eqhtf01oHtVoNvV5vdrfbGrNmCgvdsHTpJOj1Aylrb7+tx/PPj74ttbPg2oKPZubKna3H\nnaHdMgC0tbXh2rVriI2NRVBQkL2XMyFczQn3Gmg0GrAsC3d3d/T19SEhIcHpn+NQjEYjrl+/js7O\nTiQmJtr9xKqtrQ25ubnQ6XQICwvD1atX4evry3fmzMzMtHitW21tLZYtW8Y3KDDtlNrc3IwFCxag\noqLC7DGj6cBKiJOg1tOEuDKGYaBSqaBQKFBQUICSkhIEBwcjMzMTGRkZSE1NtdlwSZZl+e5O9to8\nDjVrhrvbbYk2v1zx+jff9KKpKRaLFnmOOGDU2XDpTp2dnZDJZONqC27ablmtVqO3txcikcjsdbDn\niSQ3IJRhGCQkJLhkNzmdTofS0lK4ublBLBZDo9GYtYD39/d3+hTEnp4elJaWIigoCJGRkXYPqBUK\nBbZv347du3fjkUce4ddz+/ZtFBYW4sKFC7h48SJ++tOf4rHHHrPY170z2Nm+fTumTJnCNyjo6OjA\nW2+9ZfaY/v5+xMXF4dy5cwgPD0dqaio+/vhjJCYmWmxdhNgIBTvEddTX12P9+vVobW2FQCBATk4O\ncnNzza7Jz8/Hww8/jKioKABAdnY2du/ebY/l2gVXnKtQKKBUKlFUVASBQIC0tDRkZmbydxUtvSno\n7e1FWVkZRCIRYmNj7X66ZKq3t5ffdHPd4LhN9+TJk0e90dVqtVCpVAgICHDZeSQajQZlZWUICwuz\n6JBXlmUHvQ7u7u5mKYi2Cji4AaEREREICwuz+wbZGriGIHfedOBSELnTHy4F0bTttbMEftyNFalU\nCn9/f7uupb+/H2+++SYKCgpw7Ngxs7oda1uzZg3y8/PR3t6O0NBQ7NmzBz/5yU+wcuVK3LhxAxER\nETh58iQCAwPR1NSETZs2QS6XAxgYFP7000/zqeu7du2y2boJsSAKdojraG5uRnNzM5KSktDV1YXk\n5GScOnXKrHtMfn4+9u3bhzNnzthxpY6DZVloNBpcvHiRT33r6OjA7Nmz+eAnLi5u3Bt3lmXR0NCA\nxsZGxMXFWbzTkDUYDAazQZsGg+Guhd7caU5rayukUin8/PzsuHrrMBqNqKmpQUdHB2QyGXx8fKz+\nNQ0Gg1nqW39/P8RiMb/xtvSJpNFoRHV1Nbq6uhwi3ckaGIZBZWUldDodZDLZqAIXvV7Pdxyzxesw\nUf39/SgvLwcAJCQk2P3GSkNDA3JycvDggw/ipZdesvt6CLkHUbBDXNfDDz+MrVu3YtGiRfz7KNgZ\nmV6vx+XLl/nUt6qqKkRFRSEjIwOZmZmYO3fuqAr9tVotysrK4Ofnh+joaIct7B6JaaG3Wq3mJ9xz\nDQ/q6+sRGBjosqc53DyS4OBguza84F4HbtN9t4L7seIGhE6dOhXTp093qM27pWg0GqhUqlHVWN3N\nUKmgXBdE7nWw14a+s7MTZWVlfAt7e2JZFp9//jlef/11vPfee/jhD39o1/UQcg+jYIe4ptraWsyf\nPx8lJSVmd9rz8/ORnZ3N/4O/b98+ykEeAXfHm0t9u3z5MsRiMd/0ID09Hf7+/vzmqb+/H3v37kV6\nejpSU1PtnkJiaVx72WvXrqGjowOenp7w8PAwa3ntLKk+d2M6PFMqlY56HomtmA55NE1BHEv3PZZl\nUVtbi5s3b0Imk1l8QKgj4J5je3u71U7lTBsfdHZ28rOXTAcAWzOANH2OiYmJ46ojs6Te3l68+OKL\naG5uxocffojg4GC7roeQexwFO8T1dHd34wc/+AF27dqF7Oxss49pNBq4ubnB19cXcrkcubm5qKqq\nstNKnRPXNrqgoABKpRKXLl2CTqdDcnIyIiIicPz4cTzwwAN47bXXIBKJ7L1cixuqNodLueI2e/39\n/WabPZFI5FSnBc5afzRU973hBs+aDgh1puc4FvZ6jgzDmDWg4IZtcjcDLNmAoq+vD6WlpfD393eI\n11GlUmHLli34z//8T2zdutXu6yGEULBDXIzBYMCyZcuwePFibNu2bcTrIyMjUVxc7JItV23p9u3b\n2LZtG86dO4dZs2ahvr4eUqmUP/2RyWROn6s+ltqcobqNcZs9Rx60ybIs6uvr0dTU5BCF3RNlOnhW\nrVaju7sbnp6ecHd3R3d3N6RSKaZMmWLvZVoFV6DvCENQTYdtDtWAwt/ff1wzsLjW4PHx8XavBzQa\njTh69CgOHz6M3//+93adnUMIMUPBDnEdLMtiw4YNCAwMxHvvvTfkNS0tLXy3saKiIjz66KOoq6tz\nqrvujubbb7/Fk08+iezsbDz77LMQCoVgGAZXr17lU99UKhWmTZvG1/2kpKTYPdVkLLq7u1FWVobA\nwEBERUWNOVDhNnvcptt00Ca32bP3oM3e3l6oVCqIxWKnrrG6G51Ox7ff9fb2RldXF1iW5WtNuFM4\nZ2YwGFBeXg6BQOAQBfrDubMRCDcDi3sdTE/h7sQwDCoqKmAwGCCTyez+u6NWq/HUU09BLBZj//79\nLpkOSYgTo2CHuA6lUokHH3wQs2fP5jejr7/+Om7cuAEAeOKJJ3DgwAEcOnQIQqEQIpEIv/rVr5CV\nlWXPZTu1kydP4je/+Q0++OADxMfHD3sddyqiVCqhVCpRXFwMDw8Ps5bXQUFBDhd0mtatJCQkWLTT\nGjdok9vsGY1Gm9Y5cLh25A0NDYiPj0dAQIDVv6Y9DDcglGEYs0236awZbtPtiKdwQ+no6EBFRQWi\noqIQFhZm7+WMidFohFarHXQKZ3pDQCgU8s0kwsPDJ9RowVIKCwvxzDPPYPv27Vi7dq3d10MIGYSC\nHULI+HEdscZ6CsCyLNRqNS5cuACFQoELFy5Ao9Hg/vvv509/oqOj7brJnOhpzlgNVedg7U13X18f\nysrK4OXl5XDzjyxlrANCuVkzpptuDw8Ps5Qre58k3Ikb9KrRaFyqbbZOpzO7IaDT6WA0GjFjxgyE\nhobatRaOYRi8++67OHv2LPLy8hATE2OXdRBCRkTBDiG2FhkZCbFYDHd3dwiFQhQXF5t9nGVZ5Obm\nQi6Xw9vbG3l5eUhKSrLTam1Hp9OhuLiYn/dz/fp1xMTE8HU/c+bMsckm02g0oq6uDjdv3rT4ac5Y\nDLfpvvNO93g/d0tLC2praxEXF+eydSsdHR2orKyc8IBQnU5ndvrDMIzDNKDQarUoLS1FSEgIIiIi\nXPJkQa/Xo7S0FCKRCCEhIfxNgd7eXr4NPFcLZ4v0y5aWFuTk5CApKQmvvvqqS3RfJMSFUbBDiK2N\n1BRBLpdj//79kMvlKCwsRG5uLgoLC228SvszGo0oLy/nU9++//57BAQEID09HVlZWUhLS4NYLLbo\n5s7Wpzljdeedbq7exDT1bSR6vR5lZWUQCoWIi4tzuFMKS2AYBteuXbPagFCGYcxmL3ENKLjXwpLd\nxobDDextamqCTCZzuNbgltLe3o6qqqpB6YfAwPegt7eX/30YT/vxsWBZFmfPnsVLL72Et99+Gz/6\n0Y8sHlxWVFRg1apV/NvXr1/H//zP/+Dpp5/m35efn4+HH34YUVFRAIDs7Gzs3r3bousgxIVQsEOI\nrY0U7GzevBkLFizAmjVrAADx8fHIz8+3+5A8e+NOI7imB0VFRWAYBsnJycjKykJWVhamTp06rs2H\n6WmOI86UGY5pvYlarYZOpxu21TIAtLa28idmrjr7wx4DQk0bUHCbbnd3d6vNXtLpdFCpVBCJRIiN\njXXJZhJGoxFVVVXQarVITEwcddBi2n6cS33jfif8/f3HnQ6q0+nw0ksvobKyEnl5eTapiWIYBuHh\n4SgsLERERAT/fhqOTciYjOofAddL4ibEjgQCARYuXAh3d3ds3rwZOTk5Zh9vbGzE9OnT+bclEgka\nGxvv+WBHIBBg6tSpWLlyJVauXMm3FS4sLIRCocCxY8fQ1taGxMREZGZmIisrC/Hx8SNuBE1Pc1JS\nUhzuNOdu3N3dERgYyLfdNW21XFtbi+7ubkyaNAlisRidnZ3w8PBAcnKyS6bdmA4ITUxMtGlHLIFA\nAB8fH/j4+CA8PBzAwAkat+muq6tDf38/xGIxHwB5e3uPKxC7efMmqqurhzzpcBVcal5oaCji4uLG\n9H0SCoWYMmUKn5pp+jtx48aNcdVgVVVVYfPmzcjOzsa7775rs+Dy3LlziI6ONgt0CCHWQcEOIRak\nVCoRHh6OtrY2LFq0CAkJCZg/f769l+V0BAIBxGIxFi5ciIULFwIYuKv73XffQaFQYO/evSgrK8P0\n6dP5jm/Jycl8SpNer8eePXswc+ZMrFy50mlOc+6G+56IxWI+YG5qakJ1dTV8fX2h0+lw5coVq6X5\n2EtPTw8/BNVRAlZPT08EBwfzJ2hGo5FPfauurkZPTw+8vLz418HPz++um2iGYVBZWckP8HXVgLWp\nqQn19fWQyWQWqZcb6neCq8Hq6OhATU0NX4Pl6emJnp4ezJo1C25ubmBZFsePH8fBgwfxwQcfIC0t\nbcLrGYsTJ07wJ/x3unDhAu677z6Eh4dj3759SExMtOnaCHE1lMZGiJW8/PLL8PX1xXPPPce/j9LY\nLMdoNKKmpgYKhQIFBQX45z//CS8vL8TFxeHixYuYP38+3njjDaefrTKU/v5+VFZWQq/XQyqV8kGN\naZqPWq3m55twm24fHx+nKXI3bZvtbENQWZZFX18f/zrcrd5Eo9FApVJBIpE4RLtlazAYDHwt2WhO\nZC2JGwJcWlrKjyvg0tS8vb3xhz/8weatvPV6PaZNm8afcJnSaDRwc3ODr68v5HI5cnNzUVVVZdP1\nEeJEqGaHEFvSarUwGo0Qi8XQarVYtGgRdu/ejSVLlvDX/OUvf8GBAwf4BgVPPfUUioqK7Lhq12Ew\nGLBnzx588sknePDBB1FWVoaenh4kJSXxXd8iIiIc4mRgIrguZDNmzBixjsloNPJpPmq1mm8nPtoT\nB3vh6la44NUR1zhW3KBNLhjV6/UQCATo7+9HXFwcgoODXTLQuX37NsrLyzFz5sxBG3t7KC4uxo4d\nOzBnzhy+Y6a7uzvfHCUrK4tPV7SW06dP4+DBgzh79uyI145UB0rIPY5qdgixpdbWVqxYsQLAwB32\ntWvXYsmSJXj//fcBDAw+Xbp0KeRyOWJiYuDt7Y0jR47Yc8kuo6SkBJs3b8bixYtRUlLC5+n39fWh\nqKgISqUSO3bsQF1dHeLj4/ngZ/bs2U4zf4ZhGFRVVaGnpwf333//qLqQubm5wc/PD35+fpgxYwYA\n8B2uWlpaUFlZCTc3N7MTB3unUA03INTZeXh4ICgoCEFBQejt7UVJSQm8vb3h6+uLlpYWXLt2DZMm\nTTJrfODMQR538nr79m3MnTvX7vOBjEYjDh48iM8++wx5eXlISEjgP9bd3Y2ioiJcuHABR44cwY4d\nO7BgwQKrreX48ePDprC1tLQgNDQUAoEARUVFMBqNLts+nhBboZMdQohTq6iowIYNG/DBBx9gzpw5\nd72WYRioVCp+2OnVq1cRHByMzMxMZGRkIDU11SFTvdRqNcrLy62S6sSdOHCnP5Yqth8r0wGhUqnU\nJdtmA0BzczMfdAcEBJh97M7UNwBmM3/sHTCMFhfMTZkyBVFRUXb/fWpra8OWLVsQFxeHvXv32vX7\nqNVqMWPGDFy/fp1PzTS9IXbgwAEcOnQIQqEQIpEIv/rVr5CVlWW39RLi4CiNjRByb+jv7x/XCQ1X\nF8K1vP7HP/4BAEhLS+MbH3B3We3BaDTi2rVr6OzshEwmg7e3t02+pumcmZ6eHrPhjtaYM2M6INRV\n69cMBgPKy8shEAgQHx8/qmCuv7+fH7LJtR/38fExaz/uaGmZ3EDbhIQETJ482a5rYVkW+fn5eOGF\nF/Dqq6/ixz/+sd0DL0KIRVGwQwgZjAbbDY9lWWg0Gly6dIlvfNDR0YHZs2fzwU9cXJxNNpgajQZl\nZWUICwvDjBkz7LZJMx3uyJ04mM6ZmTx58rhPYbgBod3d3ZDJZE5zcjFWXN1KVFTUhIrhuVbL3Elc\nV1cXPD09+bS30bRathbuZM5oNCIhIcHuJ3MGgwGvvvoq/vnPf+Lo0aOQSCR2XQ8hxCoo2CGE3B0N\nthuZXq/H5cuX+dS3yspKREVFISMjA5mZmZg7d65FWzxztQ4dHR2QyWTw8fGx2Oe2FIPBwAc/nZ2d\n6O/vN0u3EolEIwZn9hgQamvcyZxGo0FiYqJVgjmdTmf2WrAsOyj1zdrf287OTpSVlY2qaYYt1NbW\nIicnB4sXL8YLL7zgNHV5hJAxo2CHEHJ3Z8+exZ49e1BQUGD2fgp2hmc0GlFdXc2nvl2+fBl+fn58\nN6f09HT4+/uPa8PX3d0NlUqF4OBgp+ocx7X35Tbdvb298Pb25jfcYrGYfy53Dgh1xGDOErjhmSEh\nIYiIiLBZAMAwjNlr0dfXN+xrMVEsy6Kuro5/LW2RZjnSev785z9j3759OHDgAB4qFMyvAAAd+ElE\nQVR88EG7rocQYnUU7BBC7m7jxo1ISkrC1q1bzd6fn5+P7OxsviCeBtsNj2VZ3Lx5EwUFBVAoFLh0\n6RI/HDIjIwPz5s2DRCK562aX2zS2tbVBKpU6/RBUlmXR09PDb7i7urogFArh4+MDtVqNoKAgREdH\nO00wNxYsy6KhoQFNTU2QyWR2fy2Hey1MO/CNJ+Wsr68PpaWl8PPzc4jXUqvV4vnnn0dnZyd+97vf\nITAw0K7rIYTYBAU7hJDh0WA769FqtSgqKuJT3xobGyGVSvmW1zKZjE+tuXr1Kl566SW88cYbiI2N\ntfum0Rq4YK6+vh6TJ09GX18fjEajzdOtrE2v16O0tBQikQixsbEO2zpar9ebdeBjGAZ+fn58ADRS\nBz6uPXh8fLxDBBUlJSV48skn8bOf/QybN292yd8hQsiQKNghhAyPBtvZDsMwuHr1Kp/6xtWq+Pj4\n4OrVq9i3bx9+9KMf2XuZVsENCL0zABgq3crRO43dzc2bN1FdXe2U84FM0xA7OzsHdeATi8Vwd3cH\nwzCorKyETqeDTCaz+0wmo9GIDz/8EH/84x9x+PBhzJ49267rIYTYHAU7hJDhrV69GosXL8bjjz8+\n6GN3DrZ79NFHUVdX5/R33h1FbW0t1q1bBz8/P0gkEvzzn/+Eh4eHWcvroKAgp/9+t7a24vr164iL\nixtxMCLLstBqtXzw093dDQ8PD7Mhm45YaO5oAYAlDNWBDxgIXIODgxEdHW3RphzjcevWLWzduhVh\nYWF499137V4vRAixCwp2CCFDo8F29sGyLI4cOYL9+/fjf//3f/kCapZloVarceHCBSiVSly4cAEa\njQZz5szhu745Ql3EaBkMBr4N8UQGhA7Vacy01sTerao1Gg1UKpVVhr06CpZlUV9fj6amJoSHh0On\n06GzsxMGgwG+vr78a2HLYbxKpRLbt2/Hf//3f+PRRx91ye87IWRUKNghhBBH8vbbb6OmpgZvvfUW\nfH1973qtTqdDcXExlEolCgoKcP36dcTExPB1P3PmzLH7LJOhdHR0oKKiApGRkRYfEMowjFmtiU6n\nM9tw+/r62mTjy3WUa29vd9j24Jag1+uhUqng5eU1qAbJaDSiu7ubfy20Wi28vLz4YNTf39/iNUv9\n/f146623oFAocPToUURGRlr083MiIyP51D2hUIji4mKzj7Msi9zcXMjlcnh7eyMvLw9JSUlWWQsh\n5K4o2CGEEEfCMMy4N4BGoxEVFRV83c/333+PgIAAvuV1WloaxGKx3e5yMwyD6upqaLVamw0I5YZs\nmqa+TZo0ySz1zdIb7t7eXqhUKvj7+2PmzJlOc9o2Vrdu3UJlZSViYmIQHBw8qsdwqW+dnZ3o7OyE\nQCAwC34m8jPR2NiInJwczJs3Dy+99JJVA/2RahTlcjn2798PuVyOwsJC5ObmorCw0GrrIYQMi4Id\nQojz2bhxI86cOYOQkBCUlJQAGDgtWLVqFWpraxEZGYmTJ08iICBg0GO//PJL5ObmgmEYbNq0CTt3\n7rT18m2GZVm0tLRAoVCgoKAAhYWFYBgGycnJyMrKQlZWls0GPGo0GpSVlTnEgNC+vr5BtSamqW8T\nqTVpbm5GXV0d4uPjh/z5cwXcHKnu7m4kJiZO6PvV39/Pn8R1dnaO6ySOZVmcOXMGr732Gt599108\n9NBD417PaI0U7GzevBkLFizAmjVrAADx8fHIz8+3+EkmIWREFOwQQpzP+fPn4evri/Xr1/PBzo4d\nOxAYGIidO3fizTffxO3bt7F3716zxzEMg7i4OHz99deQSCRITU3F8ePHIZPJ7PE0bI475SgsLOQD\noLa2NiQmJiIzMxNZWVmIj4+36EmH0WhEXV2dQ6dzmW641Wr1uGpNDAYDysvLIRAIEB8f75Dpg5Zg\n7UGoQ53EeXp68q+Ft7c3RCIRf31vby927dqFxsZGHD58eNQnTBMVFRXFnwpu3rwZOTk5Zh9ftmwZ\ndu7ciQceeAAA8NBDD2Hv3r1ISUmxyfoIIbxR/ZFyvNY2hJB72vz581FbW2v2vtOnTyM/Px8AsGHD\nBixYsGBQsFNUVISYmBjMnDkTwEC3udOnT98zwY5AIIBYLMbChQuxcOFCAAMb/e+++w4KhQJ79+5F\nWVkZpk+fznd8S05OHndqUU9PD0pLSxEYGIjk5GSHTecSCoWYMmUK3w3OtNbk+vXrfK0Jt+H28/Mz\nCwhv377N1yCFhYXZ62lYFcuyaG5uxo0bNyCTyeDn52eVr8P9jIrFYkyfPh3AwElcZ2cn2tvb8dxz\nz6GpqQmzZ8+GVCrFZ599hscffxy/+c1vbPrzpVQqER4ejra2NixatAgJCQmYP3++zb4+IcSyKNgh\nhDi81tZWPkUkLCwMra2tg65pbGzkN1AAIJFI7vk8eqFQiOTkZCQnJ+Ppp5+G0WhETU0NlEolPvnk\nE+zcuRNeXl7IyMjg/wsMDLzrHX2WZdHY2IiGhgZIpVK+m5+zcHNzg5+fH/z8/DBjxgywLMunvrW0\ntKCyspK/pq+vD3q9Hvfff7/dO79Zi8FgQFlZGdzd3ZGSkmLz9t5eXl7w8vJCaGgoPv/8c2g0Grz7\n7rs4c+YMJk+ejLy8PFy8eBHz5s3DvHnzbNKYIzw8HAAQEhKCFStWoKioyCzYCQ8PR319Pf92Q0MD\n/xhCiOOhYIcQ4lQEAgG1mh0nNzc3REdHIzo6Ghs2bADLsrh16xYuXLgAhUKBX//61+jp6cHcuXP5\nrm8RERH8XfW6ujq8+OKL2LlzJ1JTUy1e/G8PAoEAIpEIIpGID6g7OztRUlICLy8vCAQCXLlyBWKx\n2CzdyhV+BtVqNcrLyx3m1EqtViM3Nxc+Pj74+9//Dl9fX74xR0FBAQ4ePIjvvvsOaWlpfKt8S9Nq\ntTAajRCLxdBqtTh79ix2795tds3y5ctx4MABrF69GoWFhfD396d6HUIcGAU7hBCHFxoaiubmZkyd\nOhXNzc0ICQkZdA3dbR07gUCAoKAgLF++HMuXLwcwkFZUVFQEpVKJHTt28AX5kydPxjfffIM9e/a4\n7KR6lmXR0NCApqYm3HfffRCLxQAGUt+6urqgVqtRXV2Nnp4eiEQis9Q3R03jGwp3wnf79m3MmTPH\nrE7GXoqKivD0009j+/btWLt2LR9Murm5QSqVQiqVYtOmTQAGanmspbW1FStWrAAwkAa6du1aLFmy\nxGwO2dKlSyGXyxETEwNvb28cOXLEaushhEwcNSgghDic2tpaLFu2jG9QsH37dkyZMoVvUNDR0YG3\n3nrL7DH9/f2Ii4vDuXPnEB4ejtTUVHz88cdITEy0x1NwGR0dHdiwYQNaWloQGxuL0tJSBAcHIzMz\nExkZGUhNTbXpQElr0ev1KC0thUgkGjRT5k4sy/Jtlrmub0Kh0Kzrm6M2Mejt7UVpaSkCAgIQFRVl\n9yCNYRi89957+PLLL5GXl4fY2Fi7rocQ4lSoGxshxPmsWbMG+fn5aG9vR2hoKPbs2YOf/OQnWLly\nJW7cuIGIiAicPHkSgYGBaGpqwqZNmyCXywEMzL94+umnwTAMNm7ciF27dtn52Ti3b775Btu2bcPz\nzz/Pt9nlana4eT//+Mc/AABpaWl844PQ0FCnCn5u3ryJ6upqxMbGDttueCQGg4EPftRqNRiGgZ+f\nHx/8iEQiu39PWltbUVNTg4SEBEyePNmuawGAlpYWbN68GXPmzMHrr78OT09Pey+JEOJcKNghhBAy\nPh9//DGOHTuG3//+95BIJMNex7IsNBoNLl26xLe87ujowOzZs/ngJy4uzu4nCENhGAaVlZXQ6XSQ\nyWQW3WwbjUZoNBo++Ont7YW3tzcf/IjFYpt9T/r7+1FRUQGGYSCVSu1+6sSyLL7++mvs3r0be/fu\nxZIlS+weCBJCnBIFO4QQQsant7cXkyZNGteGXK/X4/Lly1AqlSgoKEBlZSWioqKQkZGBzMxMzJ07\nd0LDKi1Bo9FApVJBIpEgPDzc6pttlmXR09PDBz9dXV0QCoV88OPv72+VIIR7njNmzLDZkNm70el0\nePnll1FeXo6jR486RGMEQojTomCHEEKsZePGjThz5gxCQkLMaos+//xzeHp6Ijo6GkeOHBkyXSgy\nMhJisRju7u4QCoUoLi629fJtymg0orq6mk9947qbpaenIysrC+np6fD397fJRpxlWdTW1jrEIFS9\nXs8HP52dnTAajYNS38aLZVnU1dXxg2UdYeBrdXU1Nm/ejJ/85Cd47rnnXKKbHyHErijYIYQQazl/\n/jx8fX2xfv16Ptg5e/YsfvjDH0IoFOL5558HgEHDT4GBYKe4uHjc9SHOjmVZ3Lx5EwUFBVAoFLh0\n6RJ0Oh2Sk5ORkZGBefPmQSKRWDz46e3thUqlgr+/P2bOnOlwqXUMw5ilvvX19cHHx8cs9W003xOd\nTofS0lL4+voiJibG7s+TZVmcOHEC+/fvxwcffID09HS7rocQ4jIo2CGEEGu6s2ucqc8++wx/+tOf\n8NFHHw362L0e7AxFq9XyLa8LCgrQ2NgIqVTKz/uRyWQTGnjZ3NzMt9EOCAiw4Mqth2VZaLVaPvjp\n7u6Gh4eHWerbnd8TrtlCXFwcpkyZYqeV/0tXVxeeffZZMAyDQ4cOOURjBEKIy6BghxBCrOluwc6P\nf/xjrFq1CuvWrRv0saioKPj7+8Pd3R2bN29GTk6OLZbrVBiGwdWrV/nUt7KyMkydOpWv+0lJSYG3\nt/eIn8dgMKC8vBwCgQDx8fF2L86fKJ1OZ5b6BgB+fn7w8/NDR0cHDAYDEhMTHaKz2eXLl/Ff//Vf\n2Lp1Kx5//HG71wsRQlwOBTuEEGJNwwU7r732GoqLi/HnP/95yA1eY2MjwsPD0dbWhkWLFmH//v2Y\nP3++rZbtlLgaFKVSCaVSieLiYnh4eJi1vA4KCjL7fn/xxReor6/H8uXLXbYQnmEYtLS04Nq1axAK\nhRAIBPD19eVPf3x9fW0eZBiNRvzmN7/Bp59+iry8PEilUpt+fULIPWNUf9zGnxNACCFkkLy8PJw5\ncwbnzp0bdpMZHh4OAAgJCcGKFStQVFREwc4IBAIBIiMjERkZiXXr1oFlWajValy4cAFKpRKHDh2C\nRqPBnDlzkJKSgosXL6KiogJHjhxx2UCHZVk0NTWhqakJc+fOhVgsBsuy6O7uhlqtRm1tLbq7uzFp\n0iSz1DdrNga4efMmtmzZgujoaPz973+Hl5eXRT9/fX091q9fj9bWVggEAuTk5CA3N9fsmvz8fDz8\n8MOIiooCAGRnZ2P37t0WXQchxHnQyQ4hhIzTnSc7X375JbZt24a///3vCA4OHvIxWq0WRqMRYrEY\nWq0WixYtwu7du7FkyRJbLt0l6XQ6fPrpp3jhhRcwbdo0dHV1ISYmhq/7mTNnjtOnsXH0ej1UKhW8\nvLwQGxt71wCmr6+PT33TaDQAAH9/fz4AslQb8Pz8fOzcuROvvPIKli9fbpUTpebmZjQ3NyMpKQld\nXV1ITk7GqVOnIJPJzNaxb98+nDlzxuJfnxDiUOhkhxBCrGXNmjXIz89He3s7JBIJ9uzZgzfeeAM6\nnQ6LFi0CAGRkZOD9999HU1MTNm3aBLlcjtbWVqxYsQLAwLDHtWvXUqBjASzL4siRI/jwww9x6tQp\nzJ07F0ajERUVFVAoFPj973+P77//HgEBAXzL67S0tFF3OHMkHR0dqKioQHR0NEJCQka83svLC2Fh\nYfwJV39/Pzo7O6FWq9HQ0ACDwWCW+ubj4zOm74nBYOBTN+Vy+V2H0E7U1KlTMXXqVACAWCyGVCpF\nY2OjWbBDCCGm6GSHEEKIU2MYBtnZ2ZgxYwb27t07bOMClmXR0tLC1/0UFhaCYRgkJycjKysLWVlZ\nDjF4czhGoxHXrl2DRqNBYmKixVLEjEYjn/qmVquh1Wrh5eXFBz9+fn7DnhzV1dUhJycHixYtwosv\nvjihjnljVVtbi/nz56OkpAR+fn78+/Pz85Gdnc0PjN23bx8SExNtti5CiM1QgwJCCCH3hvLyciQk\nJIzpMVx9S2FhIRQKBQoKCvghnJmZmcjKykJ8fLxDDL/s6elBaWkpgoODERERYdWAjGXZQalvbm5u\n+OqrrxAZGYmHHnoIYWFh+Oyzz/D222/bpcFGd3c3fvCDH2DXrl3Izs42+xi3Xl9fX8jlcuTm5qKq\nqsqm6yOE2AQFO4QQQoCNGzfizJkzCAkJ4euLXn75Zfzud7/ja4tef/11LF26dNBjv/zyS+Tm5oJh\nGGzatAk7d+606dptrb+/H9999x0f/JSVlWH69Ol8x7fk5GSLF93fDcuyaG5uxo0bNyCVSuHv72+z\nr23KYDDgL3/5C/Lz81FcXIz29na4ubnh2WefxeLFixEbG2uzEzGDwYBly5Zh8eLF2LZt24jX01wr\nQlwWBTuEEEKA8+fPw9fXF+vXrzcLdnx9ffHcc88N+ziGYRAXF4evv/4aEokEqampOH78+D1VH2E0\nGlFTU8MPO/3222/h5eWFjIwM/r/AwECrbPS5GUFubm6Ij4+3aYrYcEpKSrBlyxY89thjSE1NxcWL\nF6FUKlFdXY2ZM2di3rx5+MEPfoDU1FSrfH2WZbFhwwYEBgbivffeG/KalpYWhIaGQiAQoKioCI8+\n+ijq6uocNj2REDJu1KCAEEIIMH/+fNTW1o75cUVFRYiJicHMmTMBAKtXr8bp06fvqWDHzc0N0dHR\niI6OxoYNG8CyLG7duoULFy5AoVDg17/+NXp6ejB37ly+61tERATc3Nwm9HXVajXKy8sRGRnpEK2z\njUYjPvzwQ/zhD3/AkSNHcN999wEYaMLxzDPPgGVZXL9+HUqlEvn5+VYLdgoKCvCHP/wBs2fPxv33\n3w9g4FTyxo0bAIAnnngCf/rTn3Do0CEIhUKIRCKcOHGCAh1C7mEU7BBCyD1q//79OHbsGFJSUvDO\nO+8gICDA7OONjY2YPn06/7ZEIkFhYaGtl+lQBAIBgoKCsHz5cixfvhzAQGvnoqIiKJVK7NixA3V1\ndYiPj+eDn9mzZ4/6VIZlWdTU1ODWrVuYM2cORCKRNZ/OqHR0dGDr1q0ICQnB+fPnh2wAIRAI+KDQ\nmh544AGMlJGydetWbN261arrIIQ4Dwp2CCHkHrRlyxb88pe/hEAgwC9/+Us8++yzOHz4sL2X5ZS8\nvLwwf/58vkifYRiUlZVBoVDg0KFDuHr1KoKDg5GZmYmMjAykpqYO2d752rVrqK2tRWRkJJKTkyd8\nOmQJBQUFeO6557Br1y78x3/8B52QEEKcDgU7hBByDwoNDeX//+c//zmWLVs26Jrw8HDU19fzbzc0\nNCA8PNwm63Nm7u7umDVrFmbNmoUtW7aAZVk0NjZCoVBALpdjz549AIC0tDS+8cFXX32Fd955B4cO\nHbL66cho9Pf34+2338b58+dx+vRpREZG2ntJhBAyLhTsEELIPai5uZkfzvjZZ59h1qxZg65JTU1F\nVVUVampqEB4ejhMnTuDjjz+29VKdnkAggEQiwZo1a7BmzRqwLAuNRoNLly7h3Llz2LFjB8RiMdLS\n0lBZWYkpU6YgLi7Obic7jY2NyMnJQVZWFv7617/Cw8PDLusghBBLoGCHEEJc3Jo1a5Cfn4/29nZI\nJBLs2bMH+fn5uHLlCgQCASIjI/HBBx8AAJqamrBp0ybI5XIIhUIcOHAAixcvBsMw2LhxIw1ntACB\nQAB/f3+Ehobim2++wSuvvIJ169bhypUrUCgUePXVV1FZWYmoqChkZGQgMzMTc+fOxaRJk6y6LpZl\nIZfL8corr+BXv/oVFi5caNWvRwghtkCtpwkhhBAbMhqN+PWvf41PPvkER44cQXx8/JDXVFdXQ6FQ\nQKlU4sqVKxCLxUhPT0dWVhbS09Ph7+9vsRqavr4+7Nq1C/X19Th8+DBCQkIs8nkJIcSKaM4OIYQQ\n4mgMBgMOHjyILVu2jPq0hmVZ3Lx5EwUFBVAoFLh06RJ0Oh2Sk5ORkZGBefPmQSKRjCv4KS8vxxNP\nPIE1a9YgNzfXIRojEELIKFCwQwghhLgqrVaLf/zjH1AoFCgoKEBjYyOkUinf8lomk9215bXRaMQf\n//hH/Pa3v8Xvfvc7JCcn23D1hBAyYRTsEEIIcT4bN27EmTNnEBISgpKSEgDAqlWrUFFRAWBg4Obk\nyZNx5cqVQY+NjIyEWCyGu7s7hEIhiouLbbp2e2IYBlevXuWDH5VKhalTp/J1PykpKfyMnM7OTuTm\n5kIkEuHgwYPw9fW18+oJIWTMKNghhBDifM6fPw9fX1+sX7+eD3ZMPfvss/D398fu3bsHfSwyMhLF\nxcUICgqyxVIdGsuyqKurg1KphFKpxLfffguhUIjIyEhcvnwZu3btwrp162h2DiHEWVGwQwghxDnV\n1tZi2bJlg4IdlmUxY8YMfPPNN4iNjR30OAp2hseyLNRqNU6cOIG4uDg89NBDVvk6X375JXJzc8Ew\nDDZt2oSdO3cOWkdubi7kcjm8vb2Rl5eHpKQkq6yFEOLSRhXsUBUiIYQQp6FQKBAaGjpkoAMMtHVe\nuHAhkpOT8dvf/tbGq3NsAoEAAQEB2LJli9UCHYZh8Itf/AJffPEFVCoVjh8/DpVKZXbNF198gaqq\nKlRVVeG3v/0ttmzZYpW1EEIIQHN2CCGEOJHjx49jzZo1w35cqVQiPDwcbW1tWLRoERISEjB//nwb\nrvDeVlRUhJiYGMycORMAsHr1apw+fRoymYy/5vTp01i/fj0EAgEyMjKgVqvNhtwSQogl0ckOIYQQ\np9Df348///nPWLVq1bDXhIeHAwBCQkKwYsUKFBUV2Wp5BEBjYyOmT5/Ovy2RSNDY2DjmawghxFIo\n2CGEEOIU/vrXvyIhIQESiWTIj2u1WnR1dfH/f/bsWcyaNcuWSySEEOJgKNghhBDiUNasWYPMzExU\nVFRAIpHgww8/BACcOHFiUApbU1MTli5dCgBobW3FAw88gDlz5iAtLQ3//u//jiVLlth8/fey8PBw\n1NfX8283NDTwp21juYYQQiyFurERQgghxCL6+/sRFxeHc+fOITw8HKmpqfj444+RmJjIX/OXv/wF\nBw4cgFwuR2FhIZ566ilKNySEjMeourFRgwJCCCGEWIRQKMSBAwewePFiMAyDjRs3IjExEe+//z4A\n4IknnsDSpUshl8sRExMDb29vHDlyxM6rJoS4MjrZIYQQQsapvr4e69evR2trKwQCAXJycpCbm4uO\njg6sWrUKtbW1iIyMxMmTJxEQEDDo8SPNpCGEEDIsGipKCCGEWFNzczOam5uRlJSErq4uJCcn49Sp\nU8jLy0NgYCB27tyJN998E7dv38bevXvNHsswDOLi4vD1119DIpEgNTUVx48fN2vTTAghZFg0VJQQ\nQgixpqlTpyIpKQkAIBaLIZVK0djYiNOnT2PDhg0AgA0bNuDUqVODHms6k8bT05OfSUMIIcRyKNgh\nhBBCLKC2thaXL19Geno6Wltb+SGZYWFhaG1tHXQ9zZshhBDro2CHEEIImaDu7m488sgjeO+99+Dn\n52f2MYFAAIFgVNkWhBBCLIyCHUIIIWQCDAYDHnnkEfz0pz9FdnY2ACA0NBTNzc0ABup6QkJCBj2O\n5s0QQoj1UbBDCCGEjBPLsvjZz34GqVSKbdu28e9fvnw5jh49CgA4evQoHn744UGPTU1NRVVVFWpq\naqDX63HixAksX77cZmsnhJB7AXVjI4QQQsZJqVTiwQcfxOzZs+HmNnD/8PXXX0d6ejpWrlyJGzdu\nICIiAidPnkRgYCCampqwadMmyOVyAIBcLsfTTz/Nz6TZtWuXPZ8OIYQ4E2o9TQghhBBCCHFJ1Hqa\nEEIIIYQQcu+iYIcQQgghhBDikijYIYQQQgghhLgkCnYIIYQQQgghLomCHUIIIYQQQohLomCHEEII\nIYQQ4pIo2CGEEEIIIYS4JAp2CCGEEEIIIS6Jgh1CCCGEEEKIS6JghxBCCCGEEOKSKNghhBBCCCGE\nuCQKdgghhBBCCCEuiYIdQgghhBBCiEuiYIcQQgghhBDikijYIYQQQgghhLgkCnYIIYQQQgghLomC\nHUIIIYQQQohLomCHEEIIIYQQ4pKEY7xeYJVVEEIIIYQQQoiF0ckOIYQQQgghxCVRsEMIIYQQQghx\nSRTsEEIIIYQQQlwSBTuEEEIIIYQQl0TBDiGEEEIIIcQlUbBDCCGEEEIIcUkU7BBCCCGEEEJcEgU7\nhBBCCCGEEJdEwQ4hhBBCCCHEJVGwQwghhBBCCHFJ/x8FjCds7KRTiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21386fc518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CFnKA97ZdBW1cTtF1HUtLS9jc3MTVq1cxOTnp6r/ViW1sTowhnU4jnU7j7Nmz9uOVSsXe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\n2GFH0zQsLi6iUCjg2rVrOH/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bm67rWFlZwfr6et0y0kHfGlYtHA6jVCr5PQwAQLFYxOzs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\nERERuaX/B3xYULFP8ol/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2138a000f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\"\"\"\n",
    "f = plt.figure(figsize=(14,10))\n",
    "ax = f.add_axes([0.1,0.1,0.8,0.8])\n",
    "ax.plot(VPoly[0,:],VPoly[1,:],'-k')\n",
    "ax.plot(Ptsm[0,:],Ptsm[1,:], 'xb')\n",
    "ax.plot(VPbism[0,:], VPbism[1,:],'sr')\n",
    "\"\"\"\n",
    "f = plt.figure(figsize=(14,10))\n",
    "ax = f.add_axes([0.1,0.1,0.8,0.8],projection='3d')\n",
    "ax.plot(Pts[0,:],Pts[1,:], Pts[2,:],'.b')\n",
    "\n",
    "f = plt.figure(figsize=(14,10))\n",
    "ax = f.add_axes([0.1,0.1,0.8,0.8],projection='3d')\n",
    "ax.plot(Ptsb[0,:],Ptsb[1,:], Ptsb[2,:],'.b')\n",
    "\"\"\"\n",
    "f = plt.figure(figsize=(14,10))\n",
    "ax = f.add_axes([0.1,0.1,0.8,0.8],projection='3d')\n",
    "ax.plot(PtsS[0,:],PtsS[1,:], PtsS[2,:],'.b')\n",
    "\n",
    "f = plt.figure(figsize=(14,10))\n",
    "ax = f.add_axes([0.1,0.1,0.8,0.8],projection='3d')\n",
    "ax.plot(PtsL[0,:],PtsL[1,:], PtsL[2,:],'.b')\n",
    "\"\"\"\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
