Fix typos (#260)

* Fix typos

* fix RTD

---------

Co-authored-by: Martin Fleischmann <[email protected]>

Author: nathanjmcdougall

contextily/tile.py

@@ -567,7 +567,7 @@ def _sm2ll(x, y):
 def _calculate_zoom(w, s, e, n):
     """Automatically choose a zoom level given a desired number of tiles.
 
-    .. note:: all values are interpreted as latitude / longitutde.
+    .. note:: all values are interpreted as latitude / longitude.
 
     Parameters
     ----------

notebooks/friends_cenpy_osmnx.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Warping tiles\n",
     "\n",
-    "Most basemap tiles provided through the web are expressed in the Web Mercator coordinate reference syste (`EPSG:3857`). However, often our data is not expressed in the same CRS. In those cases, we have two options if we want to plot them on top of a basemap: a) reproject our data to Web Mercator, or b) reproject (warp) the tiles to conform with our data. Which one is best depends on many things but mostly on the size of your own data. If you have a large and/or detailed dataset (e.g. high resolution polygons), reprojecting it might be expensive; if you have a small one, it might be the easiest as you can plot on top of the native tile. For the case where you don't want to transform your dataset, an alternative is to change the tiles to its CRS. In raster parlance, this is called \"warping\", and `contextily` can help you do that."
+    "Most basemap tiles provided through the web are expressed in the Web Mercator coordinate reference system (`EPSG:3857`). However, often our data is not expressed in the same CRS. In those cases, we have two options if we want to plot them on top of a basemap: a) reproject our data to Web Mercator, or b) reproject (warp) the tiles to conform with our data. Which one is best depends on many things but mostly on the size of your own data. If you have a large and/or detailed dataset (e.g. high resolution polygons), reprojecting it might be expensive; if you have a small one, it might be the easiest as you can plot on top of the native tile. For the case where you don't want to transform your dataset, an alternative is to change the tiles to its CRS. In raster parlance, this is called \"warping\", and `contextily` can help you do that."
    ]
   },
   {
@@ -135,7 +135,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The result here is a map expresed in Web Mercator."
+    "The result here is a map expressed in Web Mercator."
    ]
   },
   {
@@ -144,7 +144,7 @@
    "source": [
     "## Convert the tiles to your data's CRS\n",
     "\n",
-    "The same journey can be travelled in the oposite direction by leaving your data untouched and warping the tiles coming from the web. To do this in `add_basemap`, all you have to do is express the CRS your data are in:"
+    "The same journey can be travelled in the opposite direction by leaving your data untouched and warping the tiles coming from the web. To do this in `add_basemap`, all you have to do is express the CRS your data are in:"
    ]
   },
   {

notebooks/warping_guide.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Working with local files\n",
     "\n",
-    "At heart, `contextily` is a package to work with data from the web. Its main functionality allows you to access tilesets exposed through the popular XYZ format and include them in your workflow through `matplotlib`. However, a little hidden gem in the pacakge is also how it is useful to work with local files. For all this functionality, `contextily` relies on `rasterio` so, in the name of showing how a streamlined workflow could look like, we will switch back and forth between the two in this notebook. For good measure, we will also use `geopandas` as it'll show how they are all a family that works great together!"
+    "At heart, `contextily` is a package to work with data from the web. Its main functionality allows you to access tilesets exposed through the popular XYZ format and include them in your workflow through `matplotlib`. However, a little hidden gem in the package is also how it is useful to work with local files. For all this functionality, `contextily` relies on `rasterio` so, in the name of showing how a streamlined workflow could look like, we will switch back and forth between the two in this notebook. For good measure, we will also use `geopandas` as it'll show how they are all a family that works great together!"
    ]
   },
   {

notebooks/working_with_local_files.ipynb

@@ -9,4 +9,7 @@ python:
     path: .
 conda:
   environment: docs/environment.yml
-formats: []
+formats: []
+sphinx:
+  # Path to your Sphinx configuration file.
+  configuration: docs/conf.py