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@crusaderky
Created April 24, 2018 22:44
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from functools import partial\n",
"import dask\n",
"import pandas\n",
"import xarray\n",
"import numpy as np\n",
"import scipy.interpolate\n",
"\n",
"\n",
"def interp1d(a, dim, func=scipy.interpolate.interp1d, **kwargs):\n",
" \"\"\"One-dimensional interpolation of an N-dimensional array\n",
" \n",
" :param a:\n",
" any `xarray.DataArray`\n",
" :param dim:\n",
" dimension of a to be interpolated\n",
" :param func:\n",
" Any of:\n",
"\n",
" - :func:`scipy.interpolate.interp1d`\n",
" - :func:`scipy.interpolate.CubicSpline`\n",
" - :func:`scipy.interpolate.Akima1DInterpolator`\n",
" - :func:`scipy.interpolate.PchipInterpolator`\n",
" - Any other callable with parameters ``x, y, axis`` which\n",
" returns a callable with parameters ``x_new``.\n",
"\n",
" :param kwargs:\n",
" Parameters passed verbatim to func\n",
" :returns:\n",
" `xarray.DataArray` with the same shape and coords\n",
" as the input, but without ``dim``.\n",
" The output has all data variables replaced with dask arrays\n",
" that cannot be computed and an \"eval\" method which takes as an input\n",
" the new coordinate (along the original dimension or on new ones;\n",
" can also be N-dimensional).\n",
"\n",
" The method output will be a dask-backed DataArray if the original\n",
" input was dask-backed, otherwise it will be computed on the fly.\n",
"\n",
" **Example**\n",
" \n",
" >>> x = np.arange(0, 120, 20)\n",
" >>> x = xarray.DataArray(x, dims=['x'], coords={'x': x})\n",
" >>> s = xarray.DataArray(np.linspace(1, 20, 5), dims=['s'])\n",
" >>> y = np.exp(-x / s)\n",
" >>> x_new = np.arange(0, 120, 1)\n",
" >>> interpolator = interp1d(y, dim='t', kind='cubic', fill_value='extrapolate')\n",
" >>> y_new = interpolator.eval(x_new)\n",
" \n",
" **Features**\n",
"\n",
" - Interpolate a ND array on any arbitrary dimension\n",
" - Nearest-neighbour, linear, quadratic, cubic, Akima, PCHIP,\n",
" and custom interpolators are supported\n",
" - dask supported on both on the interpolated array and x_new\n",
" - Supports ND x_new arrays\n",
" - The CPU-heavy interpolator generation (splrep) is executed only\n",
" once and then can be applied to multiple x_new (splev)\n",
" - Pickleable and distributed-friendly \n",
" \n",
" **Design hacks**\n",
"\n",
" - Depends on dask module, even when all inputs are based on plain numpy.\n",
" - Abuses attrs and the ability to invoke a.attrname to get\n",
" the user experience of a new DataArray method.\n",
" - Abuses the fact that the chunks of a :class:`dask.array.Array` can contain\n",
" anything and you won't notice until you compute them.\n",
" \n",
" **Limitations**\n",
" \n",
" - Can't dump to netcdf (but pickle works).\n",
" Not solvable without hacking into the implementation details of scipy.\n",
" - Datasets are not supported.\n",
" Trivial to fix after solving `https://github.com/pydata/xarray/issues/1699`_.\n",
" - Chunks are not supported on x_new.\n",
" Trivial to fix after solving `https://github.com/pydata/xarray/issues/1995`_.\n",
" - Chunks are not supported along dim on the interpolated dimension.\n",
" This is very complicated to solve. If x and x_new were always monotonic ascending,\n",
" it would be (not trivially) solvable with dask.array.ghost.ghost.\n",
" If you make no assumptions about monotonicity, things become way more complicated.\n",
" A solution would need to go in the dask module, and then be invoked\n",
" trivially from here with dask='allowed'.\n",
" \"\"\"\n",
" kwargs['func'] = func\n",
" rep = xarray.apply_ufunc(\n",
" _interp1d_rep,\n",
" a.coords[dim], a, kwargs=kwargs,\n",
" input_core_dims=[[dim], [dim]],\n",
" dask='allowed')\n",
" \n",
" rep.attrs['eval'] = partial(_interp1d_ev, rep, dim,\n",
" isinstance(a.data, dask.array.Array))\n",
" return rep\n",
"\n",
"\n",
"def _interp1d_rep(x, y, func, **kwargs):\n",
" \"\"\"Helper of :func:`interp1d`.\n",
"\n",
" dask-allowed kernel of :class:`apply_ufunc`.\n",
" \"\"\"\n",
" if isinstance(y, dask.array.Array):\n",
" # Delay generating the interpolator\n",
" return dask.array.map_blocks(func, x, y, axis=-1, **kwargs,\n",
" dtype=y.dtype, drop_axis=y.ndim - 1)\n",
" # Calculate the interpolator straight away,\n",
" # then build a dask array on top of the result\n",
" name = 'interp1d-' + dask.base.tokenize(x, y, func, kwargs)\n",
" key = (name, ) + (0, ) * (y.ndim - 1)\n",
" dsk = {key: func(x, y, axis=-1, **kwargs)}\n",
" chunks = [(s, ) for s in y.shape[:-1]]\n",
" return dask.array.Array(dsk, name=name, chunks=chunks, dtype=y.dtype)\n",
"\n",
"\n",
"def _interp1d_ev(rep, dim, use_dask, x_new):\n",
" \"\"\"Helper of :func:`interp1d`.\n",
" \n",
" ``interp1d(...).eval()`` method.\n",
" ``rep, dim, use_dask`` are pre-set through :func:`partial`.\n",
" \"\"\"\n",
" # Pre-process x_new into a DataArray\n",
" if not isinstance(x_new, xarray.DataArray):\n",
" if not isinstance(x_new, dask.array.Array):\n",
" x_new = np.array(x_new)\n",
" if x_new.ndim == 0:\n",
" dims = []\n",
" elif x_new.ndim == 1:\n",
" dims = [dim]\n",
" else:\n",
" raise ValueError(\"N-dimensional x_new is only supported with DataArrays\")\n",
" x_new = xarray.DataArray(x_new, dims=dims, coords={dim: x_new})\n",
"\n",
" res = xarray.apply_ufunc(\n",
" _interp1d_ev_kernel,\n",
" rep, x_new,\n",
" input_core_dims=[[], x_new.dims],\n",
" output_core_dims=[x_new.dims],\n",
" dask='parallelized',\n",
" # This is the only reason why Datasets aren't supported\n",
" # See https://github.com/pydata/xarray/issues/1699\n",
" output_dtypes=[rep.dtype])\n",
" if not use_dask and not isinstance(x_new.data, dask.array.Array):\n",
" res.load(get=dask.local.get_sync)\n",
" return res\n",
"\n",
"\n",
"def _interp1d_ev_kernel(interpolator, x_new):\n",
" \"\"\"Helper of :func:`interp1d`.\n",
"\n",
" dask kernel executed by ``interp1d(...).eval()``\n",
" \"\"\"\n",
" return interpolator(x_new)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<xarray.DataArray (s: 5)>\n",
"dask.array<shape=(5,), dtype=float64, chunksize=(5,)>\n",
"Dimensions without coordinates: s\n",
"Attributes:\n",
" eval: functools.partial(<function _interp1d_ev at 0x7fba95e3b598>, <x...\n",
"<xarray.DataArray (s: 5, x: 120)>\n",
"array([[ 2.270833e+00, 2.113737e+00, 1.963733e+00, ..., -1.233750e-02,\n",
" -1.493333e-02, -1.776250e-02],\n",
" [ 2.194178e+00, 2.047253e+00, 1.906822e+00, ..., -9.694249e-03,\n",
" -1.173409e-02, -1.395732e-02],\n",
" [ 1.932033e+00, 1.819550e+00, 1.711602e+00, ..., -4.656727e-03,\n",
" -5.656245e-03, -6.747153e-03],\n",
" [ 1.709441e+00, 1.625666e+00, 1.544897e+00, ..., -2.426815e-03,\n",
" -3.207291e-03, -4.055575e-03],\n",
" [ 1.556914e+00, 1.492414e+00, 1.429968e+00, ..., 1.988456e-03,\n",
" 1.144342e-03, 2.478885e-04]])\n",
"Coordinates:\n",
" * x (x) int64 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 ...\n",
"Dimensions without coordinates: s\n"
]
}
],
"source": [
"x = np.arange(0, 120, 20)\n",
"x = xarray.DataArray(x, dims=['x'], coords={'x': x})\n",
"s = xarray.DataArray(np.linspace(1, 20, 5), dims=['s'])\n",
"y = np.exp(-x / s)\n",
"x_new = np.arange(-10, 110, 1)\n",
"interpolator = interp1d(y, dim='x', kind='cubic', fill_value='extrapolate')\n",
"y_new = interpolator.eval(x_new)\n",
"print(interpolator)\n",
"print(y_new)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y.to_pandas().plot(style='.')\n",
"for xa in [\n",
" interp1d(y, dim='x', kind='nearest', fill_value='extrapolate').eval(x_new),\n",
" interp1d(y, dim='x', kind='linear', fill_value='extrapolate').eval(x_new),\n",
" interp1d(y, dim='x', kind='cubic', fill_value='extrapolate').eval(x_new),\n",
" interp1d(y, dim='x', func=scipy.interpolate.Akima1DInterpolator).eval(x_new),\n",
" interp1d(y, dim='x', func=scipy.interpolate.PchipInterpolator).eval(x_new),\n",
"]:\n",
" xa.T.to_pandas().plot()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (s: 5, foo: 2, bar: 3)>\n",
"array([[[ 2.958333e-01, 2.061154e-09, -4.583333e-02],\n",
" [ 6.938894e-18, 1.250000e-02, -8.673617e-19]],\n",
"\n",
" [[ 3.265473e-01, 3.086104e-02, -3.001765e-02],\n",
" [ 9.524035e-04, 9.990611e-03, 2.939216e-05]],\n",
"\n",
" [[ 4.369146e-01, 1.488581e-01, 4.191224e-02],\n",
" [ 2.215873e-02, 1.252213e-02, 3.298506e-03]],\n",
"\n",
" [[ 5.391936e-01, 2.694223e-01, 1.330869e-01],\n",
" [ 7.258835e-02, 3.918396e-02, 1.955692e-02]],\n",
"\n",
" [[ 6.157901e-01, 3.678794e-01, 2.198629e-01],\n",
" [ 1.353353e-01, 8.272410e-02, 4.978707e-02]]])\n",
"Coordinates:\n",
" * foo (foo) <U2 'f1' 'f2'\n",
" * bar (bar) int64 1 2 3\n",
"Dimensions without coordinates: s"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_new = xarray.DataArray(\n",
" [[10, 20, 30], [40, 50, 60]], \n",
" dims=['foo', 'bar'], \n",
" coords={'foo': ['f1', 'f2'], 'bar': [1, 2, 3]})\n",
"interpolator.eval(x_new)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (s: 5, foo: 2, bar: 3)>\n",
"dask.array<shape=(5, 2, 3), dtype=float64, chunksize=(1, 2, 3)>\n",
"Coordinates:\n",
" * foo (foo) <U2 'f1' 'f2'\n",
" * bar (bar) int64 1 2 3\n",
"Dimensions without coordinates: s"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interpolator = interp1d(y.chunk({'s': 1}), dim='x', kind='cubic', fill_value='extrapolate')\n",
"y_new = interpolator.eval(x_new.chunk())\n",
"y_new"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (s: 5, foo: 2, bar: 3)>\n",
"array([[[ 2.958333e-01, 2.061154e-09, -4.583333e-02],\n",
" [ 6.938894e-18, 1.250000e-02, -8.673617e-19]],\n",
"\n",
" [[ 3.265473e-01, 3.086104e-02, -3.001765e-02],\n",
" [ 9.524035e-04, 9.990611e-03, 2.939216e-05]],\n",
"\n",
" [[ 4.369146e-01, 1.488581e-01, 4.191224e-02],\n",
" [ 2.215873e-02, 1.252213e-02, 3.298506e-03]],\n",
"\n",
" [[ 5.391936e-01, 2.694223e-01, 1.330869e-01],\n",
" [ 7.258835e-02, 3.918396e-02, 1.955692e-02]],\n",
"\n",
" [[ 6.157901e-01, 3.678794e-01, 2.198629e-01],\n",
" [ 1.353353e-01, 8.272410e-02, 4.978707e-02]]])\n",
"Coordinates:\n",
" * foo (foo) <U2 'f1' 'f2'\n",
" * bar (bar) int64 1 2 3\n",
"Dimensions without coordinates: s"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_new.compute()"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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