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RutgerK/GDAL_ReadAsArray_resampling.ipynb

Last active January 4, 2018 15:58
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GDAL_ReadAsArray_resampling
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GDAL_ReadAsArray_resampling.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'2020300'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import gdal\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"%matplotlib inline\n",
"\n",
"driver = gdal.GetDriverByName('MEM')\n",
"gdal.VersionInfo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The images generated below show different resampling methods applied to different grids. The grids get incrementally more nodata values. A factor is used to set the amount of upsampling, the output grid has size (ysize \\* factor, xsize \\* factor). \n",
"\n",
"Nodata values are shown as white.\n",
"\n",
"Some observations:\n",
"- numerical nodata values in the input result in less nodata in the output compared to a np.nan nodata value\n",
"- For certain grids (not all), an uneven factor seems to cause issues when using Lanczos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Helper functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def get_data(data_raw, nodata, factor=2, resample_alg=gdal.GRIORA_Bilinear):\n",
" \"\"\"Function which uses GDAL ReadAsArray() with resampling\n",
" \"\"\"\n",
" \n",
" ys, xs = data_raw.shape\n",
" \n",
" ds = driver.Create('', xs, ys, 1, gdal.GDT_Float32)\n",
" b1 = ds.GetRasterBand(1)\n",
" b1.WriteArray(data_raw)\n",
"\n",
" b1.SetNoDataValue(nodata)\n",
"\n",
" data_original = ds.ReadAsArray()\n",
" data_resampled = ds.ReadAsArray(buf_xsize=xs*factor, buf_ysize=ys*factor, resample_alg=resample_alg)\n",
"\n",
" ds = None\n",
" \n",
" return data_original, data_resampled\n",
"\n",
"def plot_result(nodata, methods, factor, size, nodata_idxs):\n",
" \"\"\"Function to plot the results\n",
" Different resampling algorithms are on the columns, different 'test grids' on the rows.\n",
" \"\"\"\n",
" \n",
" n_methods = len(methods)\n",
" n_rows = len(nodata_idxs)\n",
" \n",
" cmap = plt.cm.viridis\n",
" cmap.set_bad(alpha=0)\n",
"\n",
" \n",
"\n",
" fig, axs = plt.subplots(n_rows, n_methods+1, figsize=(11,3*n_rows), subplot_kw=dict(xticks=[], yticks=[]))\n",
" fig.subplots_adjust(left=0, right=1, bottom=0, top=1, wspace=0)\n",
" axs = axs.flat \n",
"\n",
" data_raw = np.arange(size**2).reshape(size,size).astype(np.float32)\n",
"# np.random.seed(1)\n",
"# data_raw = np.random.randn(size, size).astype(np.float32)\n",
"\n",
" props = dict(vmin=data_raw.min(), vmax=data_raw.max(), cmap=cmap)\n",
"\n",
" for n, nodata_idx in enumerate(nodata_idxs):\n",
"\n",
" # incrementally add more nodata pixels\n",
" for idx in nodata_idx:\n",
" if idx:\n",
" data_raw[idx] = nodata\n",
"\n",
" for i, method in enumerate(methods):\n",
"\n",
" data_orig, data_int = get_data(data_raw, nodata, factor=factor, resample_alg=getattr(gdal, method))\n",
"\n",
" data_orig = np.ma.masked_where(data_orig==nodata, data_orig)\n",
" data_int = np.ma.masked_where(data_int==nodata, data_int)\n",
" \n",
"# if method == 'GRIORA_Lanczos':\n",
"# # seems buggy for certain grids, but only if factor is uneven\n",
"# print(data_int.min(), data_int.max()) \n",
"\n",
" axs[n*(n_methods+1) ].imshow(data_orig, **props)\n",
" axs[n*(n_methods+1)].set_title('Input array')\n",
"\n",
" axs[n*(n_methods+1)+1+i].set_title(f'{method} (factor: {factor})') \n",
" axs[n*(n_methods+1)+1+i].imshow(data_int, **props)\n",
"\n",
" return fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nodata: -999, factor 3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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bj1PcMXqj4jx8kuJO31sUd+Rek+p5TbpS4FJJH5B0oOL26FLbB1b6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\nJfZjXgzYVqF5kdeX+jLzksRzH3Xy4cf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fYGY3mtlGC0dqn9XSt2+b2XvM7D5J57Z57Qgz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\nCM76dKNK3ZskHdam7+cr7EhMH+T4iMLlm9PvP0fhfpt5H//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AAACKIggBAAAAUBRBCAAA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Om6jg+Qq1Kf6yX9s6SFju6fIelBSa8xs9EentrPpL6PlXSQpAt6+Nuqunywv1HSZY1/b5T0gxDC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\n7zKvb0g6s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"text/plain": [
"<matplotlib.figure.Figure at 0x1a84ce77b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# settings\n",
"methods = ['GRIORA_Bilinear', 'GRIORA_Cubic', 'GRIORA_Lanczos']\n",
"factor = 3 # upsample factor (ysize * factor, xsize * factor)\n",
"size = 7 # test grid size (size,size)\n",
"nodata = -999\n",
"\n",
"nodata_idxs = [[None], \n",
" [(2,3),(2,2),(3,2),(4,2)],\n",
" [(4,3),(4,4),(3,4)],\n",
" [(2,4)],\n",
" [(3,3)]]\n",
"\n",
"fig = plot_result(nodata, methods, factor, size, nodata_idxs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nodata -999, factor 4"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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eX7kTAgAAAKAoJiEAAAAAimISAgAAAKAoJiEAAAAAimISAgAAAKAoJiEAAAAA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\nu/ZOx6qmvTxOlXQ5+UcNvqA96VNflXRiWHaa/C3qLfJfZj6oPSl1x6g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XAz+3Pz/6+Y1WZ2UqRpX9Tej8BUjclfQLtN0rT5hMhqFPAFks4ys1PCPjrS9sTF3ir/HHt1Xfcx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\nGE5vouDKU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Mw+NXGfOyt/knpDj36Ple047yPrXSSNuPOufe37VUk/klLaN7Fp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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a84db06dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# settings\n",
"methods = ['GRIORA_Bilinear', 'GRIORA_Cubic', 'GRIORA_Lanczos']\n",
"factor = 4 # upsample factor (ysize * factor, xsize * factor)\n",
"size = 7 # test grid size (size,size)\n",
"nodata = -999\n",
"\n",
"nodata_idxs = [[None], \n",
" [(2,3),(2,2),(3,2),(4,2)],\n",
" [(4,3),(4,4),(3,4)],\n",
" [(2,4)],\n",
" [(3,3)]]\n",
"\n",
"fig = plot_result(nodata, methods, factor, size, nodata_idxs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nodata np.nan, factor 5"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ydHLbue9ry6BOxkXL9UvLtlm/IFi6/Xfo4OOiONmDFSQyGXWwc8snpld7YnnF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\nKJwkO3OWfv1A0lGFf1+nWMmxMD9uUbhF43yFxOTifH2mwhWVzQq3tDw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ArAhCAAAAAGRV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\nM2l+evgKKS52KiBRRchFq2NRn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"text/plain": [
"<matplotlib.figure.Figure at 0x1a84dfd81d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# settings\n",
"methods = ['GRIORA_Bilinear', 'GRIORA_Cubic', 'GRIORA_Lanczos']\n",
"factor = 5 # upsample factor (ysize * factor, xsize * factor)\n",
"size = 7 # test grid size (size,size)\n",
"nodata = np.nan\n",
"\n",
"nodata_idxs = [[None], \n",
" [(2,3),(2,2),(3,2),(4,2)],\n",
" [(4,3),(4,4),(3,4)],\n",
" [(2,4)],\n",
" [(3,3)]]\n",
"\n",
"fig = plot_result(nodata, methods, factor, size, nodata_idxs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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