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Fills missing data with zonal periodicity and Neumann conditions applied at poles and land boundaries
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.sparse\n",
"import scipy.sparse.linalg\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f7da2379cc0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qy8wso4mPhMmW1+rPWdfgnM34izkz6yP9s0QtY1XSCzvU/XjdXHQAlCMGKEcc\nZYgByhFHGWKAZnFIF3Y3jHzWwoV7ZTx3sMGxLHltHZuXN2pZkblTSbhlVdKIyFSJ1MysHSJiRs4m\nslRbXgScDXxX0hHAM2nduaY6koSbVSXtRF9mZt2QpdpyRPxI0gmSfgX8ETizVbuKiM5GbmZmTXXs\nZo2xFHUjh6Q1ku6RtELS7emxHSUtlvSQpJskbd+Bfq+UNCjp3ppjTfuVNC9d7P2ApGM7GMN8Sb+R\ndFe6Hd/hGHaXtFTS/ZJWSjonPd7ta1EfxyfS4127HpK2kXRb+rO4UtL89Hi3r0WzOLr6s5G2u1Xa\n16L0dVevRWEioqsbSeL/FbAXsDVwN3BAl/p+GNix7tglwGfT/fOAizvQ79uAWcC9rfoFDgJWkEwV\nzUivlToUw3zgUw3OPbBDMewKzEr3twMeAg4o4Fo0i6Pb1+NP0v+fAtxKsg61q9dijDi6ei3Stj8J\nfBtYVMTfkaK2IkbCRd7IIbYc/c8BFqT7C4CT2t1pRCwHns7Y74nAdRExHBFrgNUk16wTMUByTerN\n6VAM6yPi7nT/OeABkm+Pu30tGsUxspazm9fj+XR3G5KEEnT5WowRB3TxWkjaHTgB+EZdX129FkUo\nIgkXeSNHAD+RdIekv0mPTY/028uIWA/s0qVYdmnS77gXe+f08fQe92/U/HOv4zFImkEyMr+V5n8G\n3YzjtvRQ165H+s/vFcB64CcRcQcFXIsmcUB3fzYuAz7Dy78AoMCfi24qZE64QEdGxGEkv3HPlvR2\nNv9Dp8Hrbimi38uBmRExi+Qv4KXd6FTSdsANwLnpSLSQP4MGcXT1ekTEpoj4HyT/Gjhc0sEUcC0a\nxHEQXbwWkt4DDKb/Ohlr6WpPriIoIglnvJGj/SLit+n//w64keSfMINK7+2WtCvweDdiGaPfcS/2\nnqiI+F2kk2zAv/LyP+k6FoOkqSSJ71sRsTA93PVr0SiOIq5H2u+zQBU4ngJ/Lmrj6PK1OBI4UdLD\nwHeAd0r6FrC+6L8j3VBEEh5d8CxpGsmC50Wd7lTSn6QjHyS9EjgWWJn2fUZ62unAwoYNtCEENv8t\n36zfRcAHJU2TtDewL3B7J2JIf7BHvB+4rwsxfBNYFRFfqTlWxLXYIo5uXg9JO4/8E1/StsC7Seam\nu3otmsTxYDevRURcEBF7RsRMknywNCJOA35I938uuq+IbwNJfuM/RDKhfn6X+tybZCXGCpLke356\n/DXAkjSQce6mAAAAmUlEQVSexcAOHej7WuAxYAPwCMkC7h2b9QvMI/nG9wHg2A7GcDVwb3pdbiSZ\ng+tkDEcCG2v+HO5Kfxaa/hl0OY6uXQ/gDWm/d6d9/n2rn8cOXYtmcXT1Z6Om7aN4eXVEV69FUZtv\n1jAzK1C/fTFnZlYqTsJmZgVyEjYzK5CTsJlZgZyEzcwK5CRsZlYgJ2EzswI5CZuZFej/A7Vo+jwk\nTj6MAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7da48d4d68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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eGPDewYwXYNTA+J9sMGeH0zoP5BmeZxcA9uSP5cYedSIbzhQrgEdhvHZi5hui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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7da9b97630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nj,ni = 180,360\n",
"x,y = numpy.meshgrid(numpy.arange(ni) / ni, numpy.arange(nj) / nj)\n",
"numpy.random.seed(1) # Ensure same numbers are generated while testing\n",
"ocean_mask = numpy.ones((nj,ni))\n",
"ocean_mask[(x<.3) & (y>.4) & (y<x+.5)] = 0 # Fake continent\n",
"ocean_mask[(numpy.abs(x-.35)<.15) & (numpy.abs(y-.4)<.15)] = 0 # Fake continent\n",
"ocean_mask[(numpy.abs(x-.8)<.1) & (numpy.abs(y-.7)<.1)] = 0 # Fake continent\n",
"ocean_mask[(y<.05)] = 0 # Antartica\n",
"fake_data = numpy.cos(6.3*x*2+.1) * numpy.cos(y*3.+.1) # Large scale signal\n",
"fake_data += 0.05*(numpy.random.rand(nj,ni)-0.5) # Add noise\n",
"missing_mask = numpy.zeros((nj,ni))\n",
"missing_mask[ocean_mask==0] = 1 # No data on land\n",
"missing_mask[(numpy.abs(x-.8)<.05) & (numpy.abs(y-.7)<.05)] = 0 # Except on fake lake\n",
"missing_mask[y>.9] = 1 # Missing Arctic\n",
"missing_mask[(y<.1)] = 1 # Missing Antartica\n",
"missing_mask[numpy.random.rand(nj,ni)<0.95] = 1 # Random missing data\n",
"fake_data = numpy.ma.array(fake_data, mask=(missing_mask>0))\n",
"ocean_mask[int(nj/3),int(ni/3)-2] = 1 # Single point isolated basin\n",
"ocean_mask[int(nj/3),int(ni/3):int(ni/3)+2] = 1 # Two point isolated basin\n",
"ocean_mask[int(nj/3):int(nj/3)+2,int(ni/3)+3] = 1 # Two point isolated basin\n",
"\n",
"plt.figure(); plt.pcolormesh(ocean_mask); plt.title('ocean_mask'); plt.colorbar();\n",
"#plt.figure(); plt.pcolormesh(missing_mask); plt.title('missing_mask'); plt.colorbar();\n",
"plt.figure(); plt.pcolormesh(fake_data); plt.title('fake_data'); plt.colorbar();\n",
"#plt.figure(); plt.pcolormesh(fake_data.filled(2.)); plt.title('filled data'); plt.colorbar();"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def fill_missing_data(idata, mask, fast=True, verbose=True, maxiter=0, debug=False, stabilizer=1.e-14):\n",
" \"\"\"\n",
" Returns data with masked values objectively interpolated except where mask==0.\n",
" \n",
" Arguments:\n",
" data - numpy.ma.array with mask==True where there is missing data or land.\n",
" mask - numpy.array of 0 or 1, 0 for land, 1 for ocean.\n",
" \n",
" Returns a numpy.ma.array.\n",
" \"\"\"\n",
" nj,ni = idata.shape\n",
" fdata = idata.filled(0.) # Working with an ndarray is faster than working with a masked array\n",
" if debug:\n",
" plt.figure(); plt.pcolormesh(mask); plt.title('mask'); plt.colorbar();\n",
" plt.figure(); plt.pcolormesh(idata.mask); plt.title('idata.mask'); plt.colorbar();\n",
" plt.figure(); plt.pcolormesh(idata); plt.title('idata'); plt.colorbar();\n",
" plt.figure(); plt.pcolormesh(idata.filled(3.)); plt.title('idata.filled'); plt.colorbar();\n",
" plt.figure(); plt.pcolormesh(idata.filled(3.)); plt.title('fdata'); plt.colorbar();\n",
" missing_j, missing_i = numpy.where( idata.mask & (mask>0) )\n",
" n_missing = missing_i.size\n",
" if verbose:\n",
" print('Data shape: %i x %i = %i with %i missing values'%(nj, ni, nj*ni, numpy.count_nonzero(idata.mask)))\n",
" print('Mask shape: %i x %i = %i with %i land cells'%(mask.shape[0], mask.shape[1],\n",
" numpy.prod(mask.shape), numpy.count_nonzero(1-mask)))\n",
" print('Data has %i missing values in ocean'%(n_missing))\n",
" print('Data range: %g .. %g '%(idata.min(),idata.max()))\n",
" # ind contains column of matrix/row of vector corresponding to point [j,i]\n",
" ind = numpy.zeros( fdata.shape, dtype=int ) - int(1e6)\n",
" ind[missing_j,missing_i] = numpy.arange( n_missing )\n",
" if verbose: print('Building matrix')\n",
" A = scipy.sparse.lil_matrix( (n_missing, n_missing) )\n",
" b = numpy.zeros( (n_missing) )\n",
" ld = numpy.zeros( (n_missing) )\n",
" if fast:\n",
" if verbose: print('Constructing diagonals')\n",
" # North\n",
" jm,im = numpy.minimum(missing_j+1,nj-1),missing_i\n",
" si = ind[jm,im]\n",
" A[(si>=0) & (missing_j<nj-1),si[(si>=0) & (missing_j<nj-1)]] = 1.\n",
" ll = (si<0) & (missing_j<nj-1)\n",
" b[ll] -= fdata[jm, im][ll] * mask[jm,im][ll]\n",
" ld[(missing_j<nj-1)] -= mask[jm, im][(missing_j<nj-1)]\n",
" # South\n",
" jm,im = numpy.maximum(missing_j-1,0),missing_i\n",
" si = ind[jm,im]\n",
" A[(si>=0) & (missing_j>0),si[(si>=0) & (missing_j>0)]] = 1.\n",
" ll = (si<0) & (missing_j>0)\n",
" b[ll] -= fdata[jm, im][ll] * mask[jm,im][ll]\n",
" ld[(missing_j>0)] -= mask[jm, im][(missing_j>0)]\n",
" # East\n",
" jm,im = missing_j,numpy.mod(missing_i+1, ni)\n",
" si = ind[jm,im]\n",
" A[si>=0,si[si>=0]] = 1.\n",
" ll = (si<0)\n",
" b[ll] -= fdata[jm, im][ll] * mask[jm,im][ll]\n",
" ld[:] -= mask[jm, im]\n",
" # West\n",
" jm,im = missing_j,numpy.mod(missing_i+ni-1, ni)\n",
" si = ind[jm,im]\n",
" A[si>=0,si[si>=0]] = 1.\n",
" ll = (si<0)\n",
" b[ll] -= fdata[jm, im][ll] * mask[jm,im][ll]\n",
" ld[:] -= mask[jm, im]\n",
" else:\n",
" A[range(n_missing),range(n_missing)] = 0.\n",
" if verbose: print('Looping over cells')\n",
" for n in range(n_missing):\n",
" j,i = missing_j[n],missing_i[n]\n",
" im1 = ( i + ni - 1 ) % ni\n",
" ip1 = ( i + 1 ) % ni\n",
" jm1 = max( j-1, 0)\n",
" jp1 = min( j+1, nj-1)\n",
" if j>0 and mask[jm1,i]>0:\n",
" ld[n] -= 1.\n",
" ij = ind[jm1,i]\n",
" if ij>=0:\n",
" A[n,ij] = 1.\n",
" else:\n",
" b[n] -= fdata[jm1,i]\n",
" if mask[j,im1]>0:\n",
" ld[n] -= 1.\n",
" ij = ind[j,im1]\n",
" if ij>=0:\n",
" A[n,ij] = 1.\n",
" else:\n",
" b[n] -= fdata[j,im1]\n",
" if mask[j,ip1]>0:\n",
" ld[n] -= 1.\n",
" ij = ind[j,ip1]\n",
" if ij>=0:\n",
" A[n,ij] = 1.\n",
" else:\n",
" b[n] -= fdata[j,ip1]\n",
" if j<nj-1 and mask[jp1,i]>0:\n",
" ld[n] -= 1.\n",
" ij = ind[jp1,i]\n",
" if ij>=0:\n",
" A[n,ij] = 1.\n",
" else:\n",
" b[n] -= fdata[jp1,i]\n",
" if debug:\n",
" tmp = numpy.zeros((nj,ni)); tmp[ missing_j, missing_i ] = b\n",
" plt.figure(); plt.pcolormesh(tmp); plt.title('b (initial)'); plt.colorbar();\n",
" # Set leading diagonal\n",
" b[ld>=0] = 0.\n",
" A[range(n_missing),range(n_missing)] = ld - stabilizer\n",
" if debug:\n",
" tmp = numpy.zeros((nj,ni)); tmp[ missing_j, missing_i ] = b\n",
" plt.figure(); plt.pcolormesh(tmp); plt.title('b (final)'); plt.colorbar();\n",
" tmp = numpy.ones((nj,ni)); tmp[ missing_j, missing_i ] = A.diagonal()\n",
" plt.figure(); plt.pcolormesh(tmp); plt.title('A[i,i]'); plt.colorbar();\n",
" if verbose: print('Matrix constructed')\n",
" A = scipy.sparse.csr_matrix(A)\n",
" if verbose: print('Matrix converted')\n",
" new_data = numpy.ma.array( fdata, mask=(mask==0))\n",
" if maxiter is None:\n",
" x,info = scipy.sparse.linalg.bicg(A, b)\n",
" elif maxiter==0:\n",
" x = scipy.sparse.linalg.spsolve(A, b)\n",
" else:\n",
" x,info = scipy.sparse.linalg.bicg(A, b, maxiter=maxiter)\n",
" if verbose: print('Matrix inverted')\n",
" new_data[missing_j,missing_i] = x\n",
" return new_data\n",
"#test = fill_missing_data(fake_data, ocean_mask, fast=True, debug=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data shape: 180 x 360 = 64800 with 62682 missing values\n",
"Mask shape: 180 x 360 = 64800 with 15369 land cells\n",
"Data has 47349 missing values in ocean\n",
"Data range: -0.950258 .. 0.95355 \n",
"Building matrix\n",
"Constructing diagonals\n",
"Matrix constructed\n",
"Matrix converted\n",
"Matrix inverted\n"
]
},
{
"data": {
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RwoSh41ERHrXrXkv6Wnxjdc0t+p1yLTqwnQ4MENBGO66lhpyEY7mn8Pk6zuQ6zizZvg2r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\nRBrM/wPmAQtFJZgaPwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7daa044550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MKLgITviNy2cswyjjI+1ILTBzPvQVNbWsaEwt3enCHeSEs067MNeWkWeMwtU2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\nloKCgoKTY0mTjHPuWSQ8rZ1z3wXQI9DOuT8B8CeLpL0ZdtLM7Co3bbm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4AAAH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7da2231b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test = fill_missing_data(fake_data, ocean_mask, fast=True)\n",
"plt.pcolormesh( fake_data ); plt.colorbar(); plt.title('Original data with holes');\n",
"plt.figure();\n",
"plt.pcolormesh( test ); plt.colorbar(); plt.title('Filled data - MAGIC!');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%timeit test = fill_missing_data(fake_data, ocean_mask, verbose=False, fast=False)\n",
"%timeit test = fill_missing_data(fake_data, ocean_mask, verbose=False, fast=True)\n",
"%timeit test = fill_missing_data(fake_data, ocean_mask, verbose=False, fast=True, maxiter=None)\n",
"%timeit test = fill_missing_data(fake_data, ocean_mask, verbose=False, fast=True, maxiter=20)"
]
}
],
"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": "3.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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