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fatiando · Feb 6, 2024 · 8cef205 · 8cef205
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diff --git a/dev/.buildinfo b/dev/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 18603f67385e4801d6d6316f8363f5ec
+config: b4f0410e636063c13cfb7aacffde9565
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/dev/_downloads/03d4f2871f2886a1c2bc97f18bce2c4a/linear_gridder.ipynb b/dev/_downloads/03d4f2871f2886a1c2bc97f18bce2c4a/linear_gridder.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Gridding with a linear interpolator\n\nVerde offers the :class:`verde.Linear` class for piecewise linear gridding.\nIt uses :class:`scipy.interpolate.LinearNDInterpolator` under the hood while\noffering the convenience of Verde's gridder API.\n\nThe interpolation works on Cartesian data, so if we want to grid geographic\ndata (like our Baja California bathymetry) we need to project them into a\nCartesian system. We'll use `pyproj <https://github.com/jswhit/pyproj>`__ to\ncalculate a Mercator projection for the data.\n\nFor convenience, Verde still allows us to make geographic grids by passing the\n``projection`` argument to :meth:`verde.Linear.grid` and the like. When\ndoing so, the grid will be generated using geographic coordinates which will be\nprojected prior to interpolation.\n"
+        "\n# Gridding with a linear interpolator\n\nVerde offers the :class:`verde.Linear` class for piecewise linear gridding.\nIt uses :class:`scipy.interpolate.LinearNDInterpolator` under the hood while\noffering the convenience of Verde's gridder API.\n\nThe interpolation works on Cartesian data, so if we want to grid geographic\ndata (like our Baja California bathymetry) we need to project them into a\nCartesian system. We'll use [pyproj](https://github.com/jswhit/pyproj)_ to\ncalculate a Mercator projection for the data.\n\nFor convenience, Verde still allows us to make geographic grids by passing the\n``projection`` argument to :meth:`verde.Linear.grid` and the like. When\ndoing so, the grid will be generated using geographic coordinates which will be\nprojected prior to interpolation.\n"
       ]
     },
     {
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/053858a0efcae8d25a42f2bcf81f25e4/trends.ipynb b/dev/_downloads/053858a0efcae8d25a42f2bcf81f25e4/trends.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -125,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/0ecb780bdd79a75e89aa13f5b057df28/distance_mask.ipynb b/dev/_downloads/0ecb780bdd79a75e89aa13f5b057df28/distance_mask.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/1595fb684603e36c044b077f6b56bcd3/model_selection.ipynb b/dev/_downloads/1595fb684603e36c044b077f6b56bcd3/model_selection.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -184,7 +173,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Like :func:`verde.cross_val_score`, :class:`~verde.SplineCV` can also run the\ngrid search in parallel using `Dask <https://dask.org/>`__ by specifying the\n``delayed`` attribute:\n\n"
+        "Like :func:`verde.cross_val_score`, :class:`~verde.SplineCV` can also run the\ngrid search in parallel using [Dask](https://dask.org/)_ by specifying the\n``delayed`` attribute:\n\n"
       ]
     },
     {
@@ -301,7 +290,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/16745cb398e8d7436b94804865fe1fac/decimation.ipynb b/dev/_downloads/16745cb398e8d7436b94804865fe1fac/decimation.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -125,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/2070872514282930d7a0cb271e43740d/trend.ipynb b/dev/_downloads/2070872514282930d7a0cb271e43740d/trend.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/20ccfb7485becea704ca8ee5aae7ae9f/model_evaluation.ipynb b/dev/_downloads/20ccfb7485becea704ca8ee5aae7ae9f/model_evaluation.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -152,7 +141,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Scoring\n\nGridders in Verde implement the :meth:`~verde.base.BaseGridder.score` method\nthat calculates the `R\u00b2 coefficient of determination\n<https://en.wikipedia.org/wiki/Coefficient_of_determination>`__ for a given\ncomparison dataset (``test`` in our case). The R\u00b2 score is at most 1, meaning\na perfect prediction, but has no lower bound.\n\n"
+        "## Scoring\n\nGridders in Verde implement the :meth:`~verde.base.BaseGridder.score` method\nthat calculates the [R\u00b2 coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination)_ for a given\ncomparison dataset (``test`` in our case). The R\u00b2 score is at most 1, meaning\na perfect prediction, but has no lower bound.\n\n"
       ]
     },
     {
@@ -188,7 +177,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Cross-validation\n\nA more robust way of scoring the gridders is to use function\n:func:`verde.cross_val_score`, which (by default) uses a `k-fold\ncross-validation\n<https://en.wikipedia.org/wiki/Cross-validation_(statistics)#k-fold_cross-validation>`__\nby default. It will split the data *k* times and return the score on each\n*fold*. We can then take a mean of these scores.\n\n"
+        "## Cross-validation\n\nA more robust way of scoring the gridders is to use function\n:func:`verde.cross_val_score`, which (by default) uses a [k-fold\ncross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)#k-fold_cross-validation)_\nby default. It will split the data *k* times and return the score on each\n*fold*. We can then take a mean of these scores.\n\n"
       ]
     },
     {
@@ -224,7 +213,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Parallel cross-validation\n\nCross-validation involves running several model fit and score operations\nwhich are independent of each other. Because of this, they are prime targets\nfor parallelization. Verde uses the excellent `Dask <https://dask.org/>`__\nlibrary for parallel execution.\n\nTo run :func:`verde.cross_val_score` with Dask, use the ``delayed`` argument:\n\n"
+        "## Parallel cross-validation\n\nCross-validation involves running several model fit and score operations\nwhich are independent of each other. Because of this, they are prime targets\nfor parallelization. Verde uses the excellent [Dask](https://dask.org/)_\nlibrary for parallel execution.\n\nTo run :func:`verde.cross_val_score` with Dask, use the ``delayed`` argument:\n\n"
       ]
     },
     {
@@ -305,7 +294,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/21b786417a2cadc7218662660c3dfac8/convex_hull_mask.ipynb b/dev/_downloads/21b786417a2cadc7218662660c3dfac8/convex_hull_mask.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
     }
   },
   "nbformat": 4,

diff --git a/dev/_downloads/254ff2dc2633890de3bb07e412fe965b/chain.ipynb b/dev/_downloads/254ff2dc2633890de3bb07e412fe965b/chain.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -183,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/30b9d07a4e99de5861ab9cbccdb2b927/train_test_split.ipynb b/dev/_downloads/30b9d07a4e99de5861ab9cbccdb2b927/train_test_split.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
     }
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diff --git a/dev/_downloads/33f59e3a2c271ac75bda1223ac7b1d53/checkerboard.ipynb b/dev/_downloads/33f59e3a2c271ac75bda1223ac7b1d53/checkerboard.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
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diff --git a/dev/_downloads/3f9cd881acef1c5744bb9eb0dfb560db/project_grid.ipynb b/dev/_downloads/3f9cd881acef1c5744bb9eb0dfb560db/project_grid.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Projection of gridded data\n\nSometimes gridded data products need to be projected before they can be used.\nFor example, you might want to project the topography of Antarctica from\ngeographic latitude and longitude to a planar polar stereographic projection\nbefore doing your analysis. When projecting, the data points will likely not\nfall on a regular grid anymore and must be interpolated (re-sampled) onto a new\ngrid.\n\nThe :func:`verde.project_grid` function automates this process using the\ninterpolation methods available in Verde. An input grid\n(:class:`xarray.DataArray`) is interpolated onto a new grid in the given\n`pyproj <https://jswhit.github.io/pyproj/>`__ projection. The function takes\ncare of choosing a default grid spacing and region, running a blocked mean to\navoid spatial aliasing (using :class:`~verde.BlockReduce`), and masking the\npoints in the new grid that aren't constrained by the original data (using\n:func:`~verde.convexhull_mask`).\n\nIn this example, we'll generate a synthetic geographic grid with a checkerboard\npattern around the South pole. We'll project the grid to South Polar\nStereographic, revealing the checkered pattern of the data.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The interpolation methods are limited to what is available in Verde and\n    there is only support for single 2D grids. For more sophisticated use\n    cases, you might want to try\n    `pyresample <https://github.com/pytroll/pyresample>`__ instead.</p></div>\n"
+        "\n# Projection of gridded data\n\nSometimes gridded data products need to be projected before they can be used.\nFor example, you might want to project the topography of Antarctica from\ngeographic latitude and longitude to a planar polar stereographic projection\nbefore doing your analysis. When projecting, the data points will likely not\nfall on a regular grid anymore and must be interpolated (re-sampled) onto a new\ngrid.\n\nThe :func:`verde.project_grid` function automates this process using the\ninterpolation methods available in Verde. An input grid\n(:class:`xarray.DataArray`) is interpolated onto a new grid in the given\n[pyproj](https://jswhit.github.io/pyproj/)_ projection. The function takes\ncare of choosing a default grid spacing and region, running a blocked mean to\navoid spatial aliasing (using :class:`~verde.BlockReduce`), and masking the\npoints in the new grid that aren't constrained by the original data (using\n:func:`~verde.convexhull_mask`).\n\nIn this example, we'll generate a synthetic geographic grid with a checkerboard\npattern around the South pole. We'll project the grid to South Polar\nStereographic, revealing the checkered pattern of the data.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The interpolation methods are limited to what is available in Verde and\n    there is only support for single 2D grids. For more sophisticated use\n    cases, you might want to try\n    [pyresample](https://github.com/pytroll/pyresample)_ instead.</p></div>\n"
       ]
     },
     {
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
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diff --git a/dev/_downloads/4a53c1d8896e7e40f82d2db771321b55/spline_cv.ipynb b/dev/_downloads/4a53c1d8896e7e40f82d2db771321b55/spline_cv.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -46,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
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