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AlexHilson/Padding_cubes.ipynb

Created April 6, 2017 20:00
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Padding_cubes.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pad data arrays with fill values to force consistent shapes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading some sample data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import iris\n",
"iris.FUTURE.netcdf_promote = True\n",
"cube = iris.load_cube('ukcp09_gridded-land-obs-daily_5km_maximum-temperature_19600101_19601231.nc')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<iris 'Cube' of air_temperature / (degC) (time: 366; projection_y_coordinate: 290; projection_x_coordinate: 180)>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cube"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"slices = list(cube.slices_over('time'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This sample data has two sets of spatial dimensions, so I'm going to cheat and remove one of them."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"air_temperature / (degC) (projection_y_coordinate: 290; projection_x_coordinate: 180)\n",
" Dimension coordinates:\n",
" projection_y_coordinate x -\n",
" projection_x_coordinate - x\n",
" Scalar coordinates:\n",
" time: 1960-01-01 12:00:00, bound=(1960-01-01 00:00:00, 1960-01-02 00:00:00)\n",
" Attributes:\n",
" Conventions: CF-1.5\n",
" comment: This dataset has been produced by Met Office in support of UKCP09\n",
" institution: Met Office\n",
" references: doi: 10.1002/joc.1161\n",
" short_name: daily_maxtemp\n",
" source: UKCP09\n",
" title: UKCP09: Met Office Gridded land surface climate observations\n",
" Cell methods:\n",
" maximum: time (observing period is 0900 on day D to 0900 on day D+1)\n"
]
}
],
"source": [
"sample_a = slices[0].copy()\n",
"sample_b = slices[1].copy()\n",
"sample_c = slices[2].copy()\n",
"\n",
"sample_a.remove_coord('latitude')\n",
"sample_a.remove_coord('longitude')\n",
"\n",
"sample_b.remove_coord('latitude')\n",
"sample_b.remove_coord('longitude')\n",
"\n",
"sample_c.remove_coord('latitude')\n",
"sample_c.remove_coord('longitude')\n",
"\n",
"print(sample_a)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<iris 'Cube' of air_temperature / (degC) (projection_y_coordinate: 290; projection_x_coordinate: 180)>,\n",
" <iris 'Cube' of air_temperature / (degC) (projection_y_coordinate: 290; projection_x_coordinate: 180)>,\n",
" <iris 'Cube' of air_temperature / (degC) (projection_y_coordinate: 288; projection_x_coordinate: 178)>)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corrupt_slice = sample_c[:-2, :-2]\n",
"\n",
"sample_a, sample_b, corrupt_slice"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<iris 'Cube' of air_temperature / (degC) (projection_y_coordinate: 290; projection_x_coordinate: 180)>,\n",
"<iris 'Cube' of air_temperature / (degC) (projection_y_coordinate: 290; projection_x_coordinate: 180)>,\n",
"<iris 'Cube' of air_temperature / (degC) (projection_y_coordinate: 288; projection_x_coordinate: 178)>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clist = iris.cube.CubeList([sample_a, sample_b, corrupt_slice])\n",
"clist"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"failed to merge into a single cube.\n",
" cube.shape differs: (290, 180) != (288, 178)\n"
]
}
],
"source": [
"try:\n",
" clist = clist.merge_cube()\n",
"except iris.exceptions.MergeError as e:\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((290, 180), (290, 180))"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clist[0].shape, clist[1].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not sure about the best way to find the index of the missing data points, but here are some pointers:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1242500., 1247500.]), array([ 692500., 697500.]))"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy\n",
"\n",
"diffy = numpy.setdiff1d(sample_a.coords()[0].points, corrupt_slice.coords()[0].points, assume_unique=True)\n",
"diffx = numpy.setdiff1d(sample_a.coords()[1].points, corrupt_slice.coords()[1].points, assume_unique=True)\n",
"\n",
"diffy, diffx"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" True, True, True, True, True, True, True, True, True,\n",
" False, False], dtype=bool)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy.in1d(sample_a.coords()[0].points, corrupt_slice.coords()[0].points)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Most important bit\n",
"\n",
"For simplicity we'll cheat again as we know we removed the -1 and -2 indexes from each axis. To do this properly we just need to determine the missing indexes for each axis and use that as the 'obj' argument."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(290, 178)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corrupt_data = corrupt_slice.data\n",
"part_fix = numpy.insert(arr=corrupt_data, obj=[-1, -2], values=corrupt_data.fill_value, axis=0)\n",
"part_fix.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(290, 180)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_fix = numpy.insert(arr=part_fix, obj=[-1, -2], values=corrupt_data.fill_value, axis=1)\n",
"full_fix.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fixed_slice = iris.cube.Cube(\n",
" data = full_fix,\n",
" standard_name = corrupt_slice.standard_name,\n",
" long_name = corrupt_slice.long_name,\n",
" var_name = corrupt_slice.var_name,\n",
" units = corrupt_slice.units,\n",
" attributes = corrupt_slice.attributes,\n",
" cell_methods = corrupt_slice.cell_methods,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"air_temperature / (degC) (-- : 290; -- : 180)\n",
" Attributes:\n",
" Conventions: CF-1.5\n",
" comment: This dataset has been produced by Met Office in support of UKCP09\n",
" institution: Met Office\n",
" references: doi: 10.1002/joc.1161\n",
" short_name: daily_maxtemp\n",
" source: UKCP09\n",
" title: UKCP09: Met Office Gridded land surface climate observations\n",
" Cell methods:\n",
" maximum: time (observing period is 0900 on day D to 0900 on day D+1)\n"
]
}
],
"source": [
"print(fixed_slice)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i, c in enumerate(sample_a.dim_coords):\n",
" fixed_slice.add_dim_coord(c, i)\n",
" \n",
"for c in corrupt_slice.aux_coords:\n",
" fixed_slice.add_aux_coord(c)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"air_temperature / (degC) (projection_y_coordinate: 290; projection_x_coordinate: 180)\n",
" Dimension coordinates:\n",
" projection_y_coordinate x -\n",
" projection_x_coordinate - x\n",
" Scalar coordinates:\n",
" time: 1960-01-03 12:00:00, bound=(1960-01-03 00:00:00, 1960-01-04 00:00:00)\n",
" Attributes:\n",
" Conventions: CF-1.5\n",
" comment: This dataset has been produced by Met Office in support of UKCP09\n",
" institution: Met Office\n",
" references: doi: 10.1002/joc.1161\n",
" short_name: daily_maxtemp\n",
" source: UKCP09\n",
" title: UKCP09: Met Office Gridded land surface climate observations\n",
" Cell methods:\n",
" maximum: time (observing period is 0900 on day D to 0900 on day D+1)\n"
]
}
],
"source": [
"print(fixed_slice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# end Most important bit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yes you could use Dask"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Nnz49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\nkPfqQd4B7InzFAAA8AfnKQCIA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yRGXKlDGcEABuNHXqVPXt21erVq1S8+bNTcfxWWfPnlXt2rVVsWJFxcbGclNuPqHvnoG+A77D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ukYAPBA\nLL8BAMB6LVmyRG3atJGrq6vpKAAAwApwfur5ME/BfnF+6vlwfsp2MU8h/XC+CYA1og88H+Yp2C/6\nwPOhD9inyMhIeXh4aPr06fr0008VFRWlihUrGsnC9ev5cP2CtWD5DZ7Y0KFDtWLFCq1YsULVqlXT\n4cOHTUeyekePHtUrr7yiVatWKTIyUu+9957pSA/k7++v+Ph4jRkzRp9//nnqi5JwbDVq1NC+ffuU\nM2dOVatWTd98843pSFbvxo0b6tevn/7973+rT58+Wr169WM3VML2cD18erZyPQTgmHLkyKF169bp\nvffeU69evRQQEMAm9icQHh6uWrVqKVu2bNq9e7eqV68u6c4Lhj169Eg9xJaUlKSLFy9qxIgRKly4\nMDdt2RD6wNOjD9iuc+fOafDgwSpdurSmTp2qxMTE1EO5ycnJev/991O/lj7w9OgDAKwZfeDZPKwP\nwH7QB54efcA2Xbp0Sf369VPDhg1VtmxZxcXFKTAwUM7OZm6joG88PfoGAABwBL169dJrr72mXr16\n6e+//zYdBwAAIE3ly5dPK1eu1Lfffqsvv/xS3t7e+vnnn03HAgDAKri4uLD8BoDVYvkNAADW6fTp\n09q3b5/at29vOgoAALAinJ96NsxTsH+cn3p6nJ+yXcxTSF+cbwJgzegDz4Z5CvaPPvD06AP26dSp\nU2rVqpVat26tBg0a6OjRo0bnLdzF9evZcP2CNWH5DZ6Kv7+/Dh48qDx58qhWrVqaPn266UhWKzw8\nXNWrV1eWLFm0d+9etWrVynSkR3Jzc1NgYKDi4+PVsmVL9erVSz4+Pjp06JDpaDCoSJEi2rp1q4YO\nHao333yTJ3uPcPToUdWqVUtLly5VZGSkwsLC5O7ubjoW0gnXWSiR5gAAIABJREFUwydna9dDAI7J\n1dVVISEhWrFihdasWaPq1avz5vtD3LhxQ3379lXPnj3Vu3dvbd++XSVKlLjna4KCgpSUlHTPx5KT\nk/XXX39x05aNoQ88OfqAbbp7k1apUqXuu0lLkpydneXh4aEmTZrc8/voA0+OPgDAFtAHntyT9AHY\nD/rAk6MP2KbIyEh5enpq5cqVWrRokSIjI1W4cGHTsegbT4G+AQAAHMnUqVN17do1DR061HQUAACA\ndBEQEKCYmBjdvn1bVatW1bx580xHAgDAOJbfALBmLL8BAMA6RUREKFeuXPLx8TEdBQAAWBnOTz05\n5ik4Fs5PPTnOT9km5imkP843AbAF9IEnxzwFx0IfeHL0AfuTmJiosLAwVapUScePH9f69esVHh6u\n/Pnzm46WiuvXk+P6BWvE8hs8tSJFimjLli0aNGhQ6pOzCxcumI5lNc6fP69u3bqpZ8+eGjRokHbs\n2GFTP+xfeuklTZs2Tbt379atW7fk5eWlgIAA/d///Z/paDDkn0/2atasqb1795qOZTVSUlL09ddf\nq1q1asqRI4cOHDjAi88Oguvho9n69RCAY/Lz89P+/fv14osvqnbt2poyZYpSUlJMx7Iae/bsUbVq\n1bR8+XKtWrXqoS8+ly1bVn5+fg88yPagm7Zu3ryZEfHxjOgDj0YfsE2//PKLBg8erNKlS2vatGn3\n3aT1vz744AM5OTnd93H6wKPRBwDYIvrAoz1pH4B9oQ88Gn3ANv32229q27atWrdurcaNG+vHH39U\nhw4dTMe6B33j0egbAADAEeXLl09Tp07VtGnT9P3335uOAwAAkC48PDy0a9cu9e/fXz169FDHjh11\n6dIl07EAADDG1dX1vuGJAGAtWH4DAIB1WrJkidq0acM9vgAA4KE4P/VozFNwTJyfejTOT9km5imk\nP843AbBF9IFHY56CY6IPPBp9wD5FR0fLy8tL77//vt59910dPnz4voWY1oTr16Nx/YK1YvkNnomb\nm5vGjx+vyMhIbdq0SeXKldOMGTMc+gd/SkqKpk+frnLlymnr1q1avXq1xo0bJ1dXV9PRnkmNGjW0\nY8cOzZo1Sxs2bFC5cuUUFhbGTfMOzM/PTwcOHFCBAgVUu3Zt9e/fX3/99ZfpWEbFxMSoVq1aevvt\nt/XOO+9o69atKlKkiOlYyEBcD+9nb9dDAI6naNGi2rZtmwIDAxUYGKhXXnlF+/btMx3LqAsXLqhv\n376qU6eOXnrpJR04cEAtW7Z85O8ZNmzYQ298kO7ctHXx4kUNHz5c1apVS+vISAf0gfvRB2zTwYMH\nVapUKU2dOlW3b9/W7du3H/q1BQsWVMeOHR/6efrA/egDAGwdfeB+z9IHYH/oA/ejD9gei8Wi6dOn\nq3z58jp06JA2bNig8PBwvfjii6ajPRB94370DQAA4OjatGmjjh07qk+fPrp48aLpOAAAAOkic+bM\nGjdunNauXavt27fLy8tLO3bsMB0LAAAjXFxclJycbDoGADwQy28AALA+586d0969e9W+fXvTUQAA\ngJXj/NT9mKcAifNTD8L5KdvEPIX0xfkmALaOPnA/5ilAog88CH3A/ly8eFGBgYFq2LChChQooAMH\nDigkJESZMmUyHe2xuH7dj+sXrB3Lb/BcWrVqpZ9++kmDBg3SwIEDVbNmTf3www+mY2W4gwcPytvb\nWwMHDlS3bt105MgR+fr6mo713JycnBQQEKATJ05o8ODBCg4OVsWKFbVu3TrT0WBIkSJFtHnzZn33\n3XdatWqVypQpo7CwMId7IfrSpUsKDAxU7dq1lTVrVu3fv1+jR4/mxWcHxvXwDnu9HgJwPK6urhoz\nZozi4uKUI0cO1axZUwEBAbpw4YLpaBnKYrEoPDxcHh4eioyMTF2MWbhw4cf+3jp16qh27dqPfH5k\nsViUkpKirl27pmVspCP6wB30AdtWunRpVa1a9bFf5+LioqFDhz7R/1f6wB30AQD2gj5wx/P0Adgn\n+sAd9AHbdPz4cTVq1EgDBw7UgAEDFBcXp8aNG5uO9UToG3fQNwAAAO6YOnWqnJ2dFRgYaDoKAABA\numratKkOHjwoT09PNWzYUCEhIQz/BwA4HBcXF6WkpMhisZiOAgD3SUxMlJOTk1xcXExHAQAA/7/F\nixcrZ86cNnNvHAAAMIvzU3cwTwH/xPmpOzg/ZduYp5B+ON8EwF7QB+5gngL+iT5wB33A/tz9eVeu\nXDlFRERo1qxZqcstbQnXrzu4fsFWsPwGzy1btmwKCQnR3r17lTlzZtWpU0dvvfWW/vjjD9PR0t1v\nv/2mAQMGqHr16nJ1ddX+/fsVFhamHDlymI6Wpu7+Pz58+LBKly6tFi1ayN/fX6dOnTIdDYb4+/sr\nLi5O3bp107vvvqv69etr165dpmOlu9u3b2v69OkqW7asFi9erNmzZ2vr1q2qUKGC6WiwAlwP7f96\nCMDxlC1bVuvXr9fs2bO1fv16eXp6asaMGUpMTDQdLd3t2LFDdevWVe/evdWlSxcdO3ZMAQEBcnJy\neuLv8f777yspKemRXzN69GgNHz78eeMig9EH6AO27IUXXtCmTZvk7e39yDfUsmXLpt69ez/x96UP\n0AcA2B/6wPP1Adgv+gB9wJYkJiYqNDRUFStW1OXLl7V7926NGzdOmTNnNh3tqdA36BsAAAB35cqV\nSzNnztS8efO0ZMkS03EAAADSVf78+RUZGalPP/1U48aNU7169Ti/AABwKHfvb2MBHABrlJiYKDc3\nN9MxAADA/1iyZIlee+01ubu7m44CAABsCOenmKeAB+P8FOenbBnzFNIe55sA2Cv6APMU8GD0AfqA\nPfnpp5/UpEkT9erVS23atNHRo0cVEBBgOtZz4frF9Qu2geU3SDOVKlVSdHS0vvnmGy1dulQlSpTQ\noEGDdObMGdPR0tyZM2c0YMAAlSxZUitWrNDMmTO1bds2VaxY0XS0dFWmTBlFRkZqw4YNOnnypDw9\nPTVs2DBdvXrVdDQYkDNnToWFhSkmJkbOzs6qU6eOGjdurM2bN5uOluZu3LihL7/8UqVLl9Zbb72l\n119/XfHx8erWrRtP8HAfrof2fz0E4FicnJzUvXt3HT16VB06dNDAgQNVunRpTZ48WTdv3jQdL81t\n2rRJPj4+8vb2lpubm2JiYjRp0qRnesPd399fZcuWlbPzg196mDRpkkaMGPG8kWEIfYA+YMuyZs2q\n77//Xk2aNJGLi8t9n3d1ddXbb7+tF1544am/N32APgDAvtAHnr0PwL7RB+gDtmDnzp2qUqWKPvro\nI40aNUp79+5VtWrVTMd6LvQN+gYAAIAkNWvWTL169VL//v2VkJBgOg4AAEC6cnJyUmBgoPbt26er\nV6/Ky8tLCxcuNB0LAIAMcffeNpbfALBGLL8BAMC6/PLLL9qzZ4/at29vOgoAALBBnJ9ingIejPNT\nnJ+yZcxTSBucbwLgCOgDzFPAg9EH6AO27vr16woJCVHFihV16dIl7d69W9OmTbObn3dcv7h+wfqx\n/AZpysnJSb169dLp06c1bdo0rVu3TmXKlFFAQICOHj1qOt5zO3XqlAIDA1WuXDmtWbNGoaGhOnHi\nhHr06OFQT8iaNGmigwcPauzYsZoyZYrKly+v8PBwWSwW09FgQJUqVRQVFaXo6GhlzZpVjRs3VtWq\nVRUREWHzfyauXbumsLAwlSxZUu+++658fX114sQJffHFF8qVK5fpeLBi/7wefv/99ypVqhTXQwCw\nYbly5dKXX36pM2fOqHPnzho6dKiKFi2qkJAQXb582XS857Zx40a98soratKkiRITE7Vy5Upt27ZN\nlStXfubv6eTkpODg4Ad+PEuWLMqaNevzRIaVcIQ+ULp0aQUFBdEH7IyTk5OcnZ2VJ0+e+27YcnJy\nUv/+/Z/re/P6GADYF0fqAzdv3kyTPgDH8KA+ULFiRfoAjLp27ZqGDRumevXqqWjRovrxxx8VHBz8\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wAu7u7tqzZ4+aNm2qGTNmqGjRonJ2vjO+v2jRoobTwZ78/PPPatGihVq3bi0f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\npmHDhhn/3fpLly5pzJgx6ty5s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efhNwAAAHXwwAMPxNChQ5OOQYEMHTo0Zs6cmXQM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\nn6c+qo8QYf5zex+XtrPA/GfD2efI3veKmkl9qKfZOyMi1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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask.array as da\n",
"\n",
"lazy_arr = da.from_array(corrupt_data.data, chunks=(288, 178))\n",
"part_fix = da.insert(lazy_arr, obj=[-2, -1], values=corrupt_data.fill_value, axis=0)\n",
"full_fix = da.insert(part_fix, obj=[-2, -1], values=corrupt_data.fill_value, axis=1)\n",
"\n",
"full_fix.visualize() # this graph seems kind of funky"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1.00000000e+20 1.00000000e+20 1.00000000e+20 ..., 1.00000000e+20\n",
" 1.00000000e+20 1.00000000e+20]\n",
" [ 1.00000000e+20 1.00000000e+20 1.00000000e+20 ..., 1.00000000e+20\n",
" 1.00000000e+20 1.00000000e+20]\n",
" [ 1.00000000e+20 1.00000000e+20 1.00000000e+20 ..., 1.00000000e+20\n",
" 1.00000000e+20 1.00000000e+20]\n",
" ..., \n",
" [ 1.00000000e+20 1.00000000e+20 1.00000000e+20 ..., 1.00000000e+20\n",
" 1.00000000e+20 1.00000000e+20]\n",
" [ 1.00000000e+20 1.00000000e+20 1.00000000e+20 ..., 1.00000000e+20\n",
" 1.00000000e+20 1.00000000e+20]\n",
" [ 1.00000000e+20 1.00000000e+20 1.00000000e+20 ..., 1.00000000e+20\n",
" 1.00000000e+20 1.00000000e+20]]\n",
"(290, 180)\n"
]
}
],
"source": [
"print(full_fix.compute())\n",
"print(full_fix.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# And now our merge works"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"og_clist = iris.cube.CubeList([sample_a, sample_b, sample_c])\n",
"fixed_clist = iris.cube.CubeList([sample_a, sample_b, fixed_slice])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<iris 'Cube' of air_temperature / (degC) (time: 3; projection_y_coordinate: 290; projection_x_coordinate: 180)>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"og_clist.merge_cube()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<iris 'Cube' of air_temperature / (degC) (time: 3; projection_y_coordinate: 290; projection_x_coordinate: 180)>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fixed_clist.merge_cube()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The original sample was just a bunch of fill values anyway, so these two cubes are actually identical. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"og_clist.merge_cube() == fixed_clist.merge_cube()"
]
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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