fixed broken links to xarray-spatial.readthedocs.io (#298)

Co-authored-by: Marc Lichtman <[email protected]>

Author: 777arc

datasets/nasadem/nasadem-example.ipynb

@@ -167,7 +167,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can generate a shaded relief using [xarray-spatial](https://xarray-spatial.org/user_guide/surface.html)."
+    "Now we can generate a shaded relief using [xarray-spatial](https://xarray-spatial.readthedocs.io/en/latest/user_guide/surface.html)."
    ]
   },
   {

quickstarts/storage.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "### Compute NDVI\n",
     "\n",
-    "We need something to write. To simulate a workload, let's load in a single [NAIP image](https://planetarycomputer.microsoft.com/dataset/naip#Example-Notebook) from the Planetary Computer's data catalog using [rioxarray](https://corteva.github.io/rioxarray/html/rioxarray.html) and compute the NDVI using [xrspatial](https://xarray-spatial.org/reference/_autosummary/xrspatial.multispectral.ndvi.html)."
+    "We need something to write. To simulate a workload, let's load in a single [NAIP image](https://planetarycomputer.microsoft.com/dataset/naip#Example-Notebook) from the Planetary Computer's data catalog using [rioxarray](https://corteva.github.io/rioxarray/html/rioxarray.html) and compute the NDVI using [xrspatial](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.multispectral.ndvi.html)."
    ]
   },
   {

tutorials/coregistration.ipynb

@@ -15,7 +15,7 @@
     "- Reprojecting and resampling [Sentinel-2 Level-2A](https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a) and [Landsat Collection 2 Level-2\n",
     "](https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2) data to use a common resolution and coordinate system\n",
     "- Croping to the region of interest and aligning Sentinel and Landsat data into a single dataset\n",
-    "- Calculating and comparing NDVIs with [xarray-spatial](https://xarray-spatial.org/index.html)"
+    "- Calculating and comparing NDVIs with [xarray-spatial](https://xarray-spatial.readthedocs.io/en/latest/index.html)"
    ]
   },
   {
@@ -306,7 +306,7 @@
    "id": "226cfa5d",
    "metadata": {},
    "source": [
-    "To display a preview of the two data sets, use the [xrspatial.multispectral.true_color](https://xarray-spatial.org/reference/_autosummary/xrspatial.multispectral.true_color.html) function with `sentinel_data` and `landsat_data`."
+    "To display a preview of the two data sets, use the [xrspatial.multispectral.true_color](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.multispectral.true_color.html) function with `sentinel_data` and `landsat_data`."
    ]
   },
   {
@@ -958,7 +958,7 @@
    "source": [
     "### Wrap-up: compare NDVI\n",
     "\n",
-    "Now let's compute NDVI on the coregistered dataset using [xrspatial.multispectral.ndvi](https://xarray-spatial.org/reference/_autosummary/xrspatial.multispectral.ndvi.html). We'll use the near-infrared band from Sentinel and the red band from Landsat. Because the original datasets both contain Red and near-infrared bands, we can compare the NDVI computed on the co-registered datasets to the NDVIs computed on the original datasets.\n",
+    "Now let's compute NDVI on the coregistered dataset using [xrspatial.multispectral.ndvi](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.multispectral.ndvi.html). We'll use the near-infrared band from Sentinel and the red band from Landsat. Because the original datasets both contain Red and near-infrared bands, we can compare the NDVI computed on the co-registered datasets to the NDVIs computed on the original datasets.\n",
     "\n",
     "First, compute the NVDIs for `sentinel_ndvi` and `landsat_ndvi`."
    ]

tutorials/local-tools.ipynb

@@ -26,7 +26,7 @@
    "id": "ea55ffed",
    "metadata": {},
    "source": [
-    "In this notebook, we'll demonstrate how to use the [Xarray-spatial](http://xarray-spatial.org/) local tools functions supported by [Numpy](https://numpy.org/). The spatial functions available are:\n",
+    "In this notebook, we'll demonstrate how to use the [Xarray-spatial](http://xarray-spatial.readthedocs.io/en/latest/index.html) local tools functions supported by [Numpy](https://numpy.org/). The spatial functions available are:\n",
     "- [Cell Statistics](#Cell-Statistics)\n",
     "- [Combine](#Combine)\n",
     "- [Lesser Frequency](#Lesser-Frequency)\n",
@@ -143,7 +143,7 @@
    "id": "01ab159c",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.cell_stats`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.cell_stats.html) calculates statistics from a raster dataset on a cell-by-cell basis."
+    "[`xrspatial.local.cell_stats`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.cell_stats.html) calculates statistics from a raster dataset on a cell-by-cell basis."
    ]
   },
   {
@@ -211,7 +211,7 @@
    "id": "87c34a83",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.combine`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.combine.html) combines multiple arrays from a raster dataset, assigning a unique output value to each unique combination of raster values."
+    "[`xrspatial.local.combine`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.combine.html) combines multiple arrays from a raster dataset, assigning a unique output value to each unique combination of raster values."
    ]
   },
   {
@@ -261,7 +261,7 @@
    "id": "4f6da50e",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.lesser_frequency`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.lesser_frequency.html) calculates, given a raster dataset, the number of times the data variables values are lower than the values of a given reference data variable on a cell-by-cell basis."
+    "[`xrspatial.local.lesser_frequency`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.lesser_frequency.html) calculates, given a raster dataset, the number of times the data variables values are lower than the values of a given reference data variable on a cell-by-cell basis."
    ]
   },
   {
@@ -313,7 +313,7 @@
    "id": "intelligent-philadelphia",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.equal_frequency`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.equal_frequency.html) calculates, given a raster dataset, the number of times the data variables values are equal than the values of a given reference data variable on a cell-by-cell basis."
+    "[`xrspatial.local.equal_frequency`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.equal_frequency.html) calculates, given a raster dataset, the number of times the data variables values are equal than the values of a given reference data variable on a cell-by-cell basis."
    ]
   },
   {
@@ -365,7 +365,7 @@
    "id": "vocational-inside",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.greater_frequency`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.greater_frequency.html) calculates, given a raster dataset, the number of times the data variables values are greater than the values of a given reference data variable on a cell-by-cell basis."
+    "[`xrspatial.local.greater_frequency`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.greater_frequency.html) calculates, given a raster dataset, the number of times the data variables values are greater than the values of a given reference data variable on a cell-by-cell basis."
    ]
   },
   {
@@ -417,7 +417,7 @@
    "id": "8d7235ec",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.lowest_position`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.lowest_position.html) calculates the data variable index of the lowest value on a cell-by-cell basis."
+    "[`xrspatial.local.lowest_position`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.lowest_position.html) calculates the data variable index of the lowest value on a cell-by-cell basis."
    ]
   },
   {
@@ -468,7 +468,7 @@
    "id": "a17c6e93",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.highest_position`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.highest_position.html) calculates the data variable index of the highest value on a cell-by-cell basis."
+    "[`xrspatial.local.highest_position`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.highest_position.html) calculates the data variable index of the highest value on a cell-by-cell basis."
    ]
   },
   {
@@ -519,7 +519,7 @@
    "id": "e5408cea",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.popularity`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.popularity.html) calculates the number of occurrences of each value of a raster dataset, on a cell-by-cell basis. The output value is assigned based on the reference data variable nth most popular."
+    "[`xrspatial.local.popularity`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.popularity.html) calculates the number of occurrences of each value of a raster dataset, on a cell-by-cell basis. The output value is assigned based on the reference data variable nth most popular."
    ]
   },
   {
@@ -572,7 +572,7 @@
    "id": "385dac65",
    "metadata": {},
    "source": [
-    "[`xrspatial.local.rank`](https://xarray-spatial.org/reference/_autosummary/xrspatial.local.rank.html) calculates the rank of each value of a raster dataset, on a cell-by-cell basis. The output value is assigned based on the rank of the reference data variable rank."
+    "[`xrspatial.local.rank`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.local.rank.html) calculates the rank of each value of a raster dataset, on a cell-by-cell basis. The output value is assigned based on the rank of the reference data variable rank."
    ]
   },
   {

tutorials/ndvi_hotspots.ipynb

@@ -15,7 +15,7 @@
     "- Calculate focal statistics of the values within a specified focal neighborhood for each pixel in an input data array\n",
     "- Identify hot and cold spots in a image, neighborhoods that are significantly different from the rest of the image\n",
     "\n",
-    "The focus of this notebook is to analyse information for each pixel based on its focal neighborhood kernel. The [xrspatial.focal](https://xarray-spatial.org/user_guide/focal.html) module from `xarray-spatial` provides a set of analysis tools performing neighborhood operations that will be used through this tutorial."
+    "The focus of this notebook is to analyse information for each pixel based on its focal neighborhood kernel. The [xrspatial.focal](https://xarray-spatial.readthedocs.io/en/latest/user_guide/focal.html) module from `xarray-spatial` provides a set of analysis tools performing neighborhood operations that will be used through this tutorial."
    ]
   },
   {
@@ -740,7 +740,7 @@
    "source": [
     "### True color\n",
     "\n",
-    "Let's see how the data actually looks by visualizing them with `true_color` function from [xrspatial.multispectral](https://xarray-spatial.org/reference/multispectral.html). To hide the label for x and y axes from sub-plots, we can update `rcParams` for `matplotlib.pyplot` as below:"
+    "Let's see how the data actually looks by visualizing them with `true_color` function from [xrspatial.multispectral](https://xarray-spatial.readthedocs.io/en/latest/reference/multispectral.html). To hide the label for x and y axes from sub-plots, we can update `rcParams` for `matplotlib.pyplot` as below:"
    ]
   },
   {
@@ -831,7 +831,7 @@
    "source": [
     "### NDVI\n",
     "\n",
-    "NDVI can be calculated with [xarray-spatial](https://xarray-spatial.org/reference/_autosummary/xrspatial.multispectral.ndvi.html). We'll compute the NDVI for each year's image."
+    "NDVI can be calculated with [xarray-spatial](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.multispectral.ndvi.html). We'll compute the NDVI for each year's image."
    ]
   },
   {
@@ -868,7 +868,7 @@
    "source": [
     "### Smoothing Images with Focal Mean\n",
     "\n",
-    "[`focal.mean`](https://xarray-spatial.org/reference/_autosummary/xrspatial.focal.mean.html) can be used to smooth or reduce noises in an image by a mean reduction to each 3x3 window in an image."
+    "[`focal.mean`](https://xarray-spatial.readthedocs.io/en/latest/reference/_autosummary/xrspatial.focal.mean.html) can be used to smooth or reduce noises in an image by a mean reduction to each 3x3 window in an image."
    ]
   },
   {
@@ -905,7 +905,7 @@
    "source": [
     "### Focal statistics\n",
     "\n",
-    "In this step, we calculate [focal statistics](https://xarray-spatial.org/user_guide/focal.html#) using a circular kernel for the NDVI data arrays computed above. By default, seven statistics are computed:\n",
+    "In this step, we calculate [focal statistics](https://xarray-spatial.readthedocs.io/en/latest/user_guide/focal.html#) using a circular kernel for the NDVI data arrays computed above. By default, seven statistics are computed:\n",
     "\n",
     "1. Mean\n",
     "2. Max\n",
@@ -1234,7 +1234,7 @@
     "\n",
     "### Next steps\n",
     "\n",
-    "To find out more about xarray-spatial focal statistics and other toolsets provided by the library, please visit: https://xarray-spatial.org/index.html"
+    "To find out more about xarray-spatial focal statistics and other toolsets provided by the library, please visit: https://xarray-spatial.readthedocs.io/en/latest/index.html"
    ]
   }
  ],