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sjsrey/xarray.ipynb

Created August 23, 2020 00:18
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xarray for pysal w indexing and compute
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xarray.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import libpysal\n",
"import xarray\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## W Class Prototype\n",
"\n",
"This is to explore what from the old api would break if we go with xarray"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# import warnings\n",
"import pandas\n",
"import xarray\n",
"\n",
"\n",
"class W(object):\n",
" \"\"\"\n",
" Spatial weights.\n",
" \n",
" \"\"\"\n",
" \n",
" def __init__(self, neighbors, weights=None, silence_warning=False):\n",
" \n",
" self.transformations = {}\n",
" self.neighbors = neighbors\n",
" if not weights:\n",
" weights = {}\n",
" for neighbor in neighbors:\n",
" weights[neighbor] = [1] * len(neighbors[neighbor])\n",
" self.weights = weights\n",
" self.silence_warnings = silence_warning\n",
" self._n = len(self.weights)\n",
" self._reset()\n",
" \n",
" \n",
" def _reset(self):\n",
" \"\"\"Reset properties.\"\"\"\n",
" self._cache = {}\n",
"\n",
" @property\n",
" def cardinalities(self):\n",
" nb = self.neighbors\n",
" self._cardinalities = dict([(focal,len(nb[focal])) for focal in nb])\n",
" self._cache['cardinalities'] = self._cardinalities\n",
" return self._cardinalities\n",
" \n",
" @property \n",
" def islands(self):\n",
" \"\"\"List of ids without any neighbors.\n",
" \"\"\"\n",
" if \"islands\" not in self._cache:\n",
" nb = self.neighbors\n",
" self._islands = [i for i, c in list(self.cardinalities.items()) if c == 0]\n",
" self._cache[\"islands\"] = self._islands\n",
" return self._islands\n",
" \n",
" @property\n",
" def n(self):\n",
" \"\"\"Number of units.\n",
" \"\"\"\n",
" if \"n\" not in self._cache:\n",
" self._n = len(self.neighbors)\n",
" self._cache[\"n\"] = self._n\n",
" return self._n\n",
" \n",
" \n",
" \n",
" def __iter__(self):\n",
" \"\"\"\n",
" Support iteration over weights.\n",
"\n",
" Examples\n",
" --------\n",
" >>> from libpysal.weights import lat2W\n",
" >>> w=lat2W(3,3)\n",
" >>> for i,wi in enumerate(w):\n",
" ... print(i,wi[0])\n",
" ...\n",
" 0 0\n",
" 1 1\n",
" 2 2\n",
" 3 3\n",
" 4 4\n",
" 5 5\n",
" 6 6\n",
" 7 7\n",
" 8 8\n",
" >>>\n",
" \"\"\"\n",
" for i in self.neighbors:\n",
" yield i, dict(list(zip(self.neighbors[i], self.weights[i])))\n",
"\n",
"\n",
" \n",
" def to_adjlist(\n",
" self,\n",
" remove_symmetric=False,\n",
" focal_col=\"focal\",\n",
" neighbor_col=\"neighbor\",\n",
" weight_col=\"weight\",\n",
" ):\n",
" \"\"\"\n",
" Compute an adjacency list representation of a weights object.\n",
"\n",
" Parameters\n",
" ----------\n",
" remove_symmetric : bool\n",
" Whether or not to remove symmetric entries. If the ``W``\n",
" is symmetric, a standard directed adjacency list will contain\n",
" both the forward and backward links by default because adjacency\n",
" lists are a directed graph representation. If this is ``True``,\n",
" a ``W`` created from this adjacency list **MAY NOT BE THE SAME**\n",
" as the original ``W``. If you would like to consider (1,2) and\n",
" (2,1) as distinct links, leave this as ``False``.\n",
" focal_col : str\n",
" Name of the column in which to store \"source\" node ids.\n",
" neighbor_col : str\n",
" Name of the column in which to store \"destination\" node ids.\n",
" weight_col : str\n",
" Name of the column in which to store weight information.\n",
" \"\"\"\n",
" try:\n",
" import pandas as pd\n",
" except ImportError:\n",
" raise ImportError(\"pandas must be installed to use this method\")\n",
" n_islands = len(self.islands)\n",
" if n_islands > 0 and (not self.silence_warnings):\n",
" warnings.warn(\n",
" \"{} islands in this weights matrix. Conversion to an \"\n",
" \"adjacency list will drop these observations!\"\n",
" )\n",
" adjlist = pd.DataFrame(\n",
" ((idx, n, w) for idx, neighb in self for n, w in list(neighb.items())),\n",
" columns=(\"focal\", \"neighbor\", \"weight\"),\n",
" )\n",
" return adjtools.filter_adjlist(adjlist) if remove_symmetric else adjlist\n",
" \n",
" \n",
" @property\n",
" def xarray(self):\n",
" if \"xarray\" not in self._cache:\n",
" adj_list = self.to_adjlist()\n",
" adj_list = adj_list.set_index(['focal','neighbor'])\n",
" self._xarray = xarray.DataArray.from_series(adj_list.weight,\n",
" sparse=True).fillna(0)\n",
" self._cache['xarray'] = self._xarray\n",
" return self._xarray\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"w = W({0: [3, 1],\n",
" 1: [0, 4, 2],\n",
" 2: [1, 5],\n",
" 3: [0, 6, 4],\n",
" 4: [1, 3, 7, 5],\n",
" 5: [2, 4, 8],\n",
" 6: [3, 7],\n",
" 7: [4, 6, 8],\n",
" 8: [5, 7]})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;weight&#x27; (focal: 9, neighbor: 9)&gt;\n",
"&lt;COO: shape=(9, 9), dtype=float64, nnz=24, fill_value=0.0&gt;\n",
"Coordinates:\n",
" * focal (focal) int64 0 1 2 3 4 5 6 7 8\n",
" * neighbor (neighbor) int64 0 1 2 3 4 5 6 7 8</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'weight'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>focal</span>: 9</li><li><span class='xr-has-index'>neighbor</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9e127063-73ac-47fd-8bde-e66b5162f2b1' class='xr-array-in' type='checkbox' checked><label for='section-9e127063-73ac-47fd-8bde-e66b5162f2b1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>&lt;COO: nnz=24, fill_value=0.0&gt;</span></div><div class='xr-array-data'><table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">coo</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(9, 9)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">24</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.2962962962962963</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">576</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.9</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-10c4d76b-b22a-481a-9d55-ac5a84f88c6a' class='xr-section-summary-in' type='checkbox' checked><label for='section-10c4d76b-b22a-481a-9d55-ac5a84f88c6a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>focal</span></div><div class='xr-var-dims'>(focal)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8</div><input id='attrs-858d49df-a461-4ca2-bfa5-a6a963b07a7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-858d49df-a461-4ca2-bfa5-a6a963b07a7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9faec0f-9a3b-4a63-8457-7e28fe345269' class='xr-var-data-in' type='checkbox'><label for='data-b9faec0f-9a3b-4a63-8457-7e28fe345269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>neighbor</span></div><div class='xr-var-dims'>(neighbor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8</div><input id='attrs-c97ad471-6527-4a0d-a5db-c6827bd4f436' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c97ad471-6527-4a0d-a5db-c6827bd4f436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f6c545-6926-44b3-b910-98ac1273d2b9' class='xr-var-data-in' type='checkbox'><label for='data-99f6c545-6926-44b3-b910-98ac1273d2b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a037a9c-1d4f-4665-ab6f-304f812f4de9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a037a9c-1d4f-4665-ab6f-304f812f4de9' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'weight' (focal: 9, neighbor: 9)>\n",
"<COO: shape=(9, 9), dtype=float64, nnz=24, fill_value=0.0>\n",
"Coordinates:\n",
" * focal (focal) int64 0 1 2 3 4 5 6 7 8\n",
" * neighbor (neighbor) int64 0 1 2 3 4 5 6 7 8"
]
},
"execution_count": 4,
"metadata": {},
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"w.xarray"
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def lag_spat(w, y):\n",
" return (w.xarray * y).sum(axis=1).data.todense()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6., 9., 8., 13., 20., 17., 12., 21., 14.])"
]
},
"execution_count": 6,
"metadata": {},
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"source": [
"y = np.arange(1, w.n+1)\n",
"lag_spat(w, y)"
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},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 1., 0., 1., 0., 0., 0., 0., 0.],\n",
" [1., 0., 1., 0., 1., 0., 0., 0., 0.],\n",
" [0., 1., 0., 0., 0., 1., 0., 0., 0.],\n",
" [1., 0., 0., 0., 1., 0., 1., 0., 0.],\n",
" [0., 1., 0., 1., 0., 1., 0., 1., 0.],\n",
" [0., 0., 1., 0., 1., 0., 0., 0., 1.],\n",
" [0., 0., 0., 1., 0., 0., 0., 1., 0.],\n",
" [0., 0., 0., 0., 1., 0., 1., 0., 1.],\n",
" [0., 0., 0., 0., 0., 1., 0., 1., 0.]])"
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},
"execution_count": 7,
"metadata": {},
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"source": [
"w.xarray.data.todense()"
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{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;weight&#x27; (focal: 9, neighbor: 9)&gt;\n",
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"Coordinates:\n",
" * focal (focal) int64 0 1 2 3 4 5 6 7 8\n",
" * neighbor (neighbor) int64 0 1 2 3 4 5 6 7 8</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'weight'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>focal</span>: 9</li><li><span class='xr-has-index'>neighbor</span>: 9</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cd7da210-5dde-424a-8499-c12362244c7f' class='xr-array-in' type='checkbox' checked><label for='section-cd7da210-5dde-424a-8499-c12362244c7f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>&lt;COO: nnz=24, fill_value=0.0&gt;</span></div><div class='xr-array-data'><table><tbody><tr><th style=\"text-align: left\">Format</th><td style=\"text-align: left\">coo</td></tr><tr><th style=\"text-align: left\">Data Type</th><td style=\"text-align: left\">float64</td></tr><tr><th style=\"text-align: left\">Shape</th><td style=\"text-align: left\">(9, 9)</td></tr><tr><th style=\"text-align: left\">nnz</th><td style=\"text-align: left\">24</td></tr><tr><th style=\"text-align: left\">Density</th><td style=\"text-align: left\">0.2962962962962963</td></tr><tr><th style=\"text-align: left\">Read-only</th><td style=\"text-align: left\">True</td></tr><tr><th style=\"text-align: left\">Size</th><td style=\"text-align: left\">576</td></tr><tr><th style=\"text-align: left\">Storage ratio</th><td style=\"text-align: left\">0.9</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-d4ee64c1-80d9-4a71-b72a-0f5f37f27cc5' class='xr-section-summary-in' type='checkbox' checked><label for='section-d4ee64c1-80d9-4a71-b72a-0f5f37f27cc5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>focal</span></div><div class='xr-var-dims'>(focal)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8</div><input id='attrs-8f24947b-bb47-41b1-ae94-f305413114c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f24947b-bb47-41b1-ae94-f305413114c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bc0da9-7268-4820-a70f-99ae15f98999' class='xr-var-data-in' type='checkbox'><label for='data-20bc0da9-7268-4820-a70f-99ae15f98999' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>neighbor</span></div><div class='xr-var-dims'>(neighbor)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8</div><input id='attrs-76343e60-43dc-487c-8cda-ffd6580aa0e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76343e60-43dc-487c-8cda-ffd6580aa0e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d757787d-02e4-41df-9b61-bca92679bdad' class='xr-var-data-in' type='checkbox'><label for='data-d757787d-02e4-41df-9b61-bca92679bdad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d3525b4-634c-463c-b41a-4375d29df20a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d3525b4-634c-463c-b41a-4375d29df20a' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'weight' (focal: 9, neighbor: 9)>\n",
"<COO: shape=(9, 9), dtype=float64, nnz=24, fill_value=0.0>\n",
"Coordinates:\n",
" * focal (focal) int64 0 1 2 3 4 5 6 7 8\n",
" * neighbor (neighbor) int64 0 1 2 3 4 5 6 7 8"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w.xarray"
]
}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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