Minor docs fixes

docs/advanced_guide/geodedetic_image_overlay.md

@@ -28,7 +28,7 @@ lon -= 180
 
 import matplotlib
 
-cm = matplotlib.cm.get_cmap("cubehelix")
+cm = matplotlib.colormaps["cubehelix"]
 
 normed_data = (data - data.min()) / (data.max() - data.min())
 colored_data = cm(normed_data)

docs/conf.py

@@ -47,8 +47,11 @@
 master_doc = "index"
 
 # General information about the project.
+import datetime
+
+year = datetime.datetime.now(tz=datetime.timezone.utc).date().year
 project = "Folium"
-copyright = "2013, Rob Story"
+copyright = f"2013-{year}, Rob Story"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the

docs/user_guide/geojson/geojson_popup_and_tooltip.md

@@ -35,13 +35,14 @@ states.head()
 ```
 
 ```{code-cell} ipython3
+import io
 import requests
 
 response = requests.get(
     "https://gist.githubusercontent.com/tvpmb/4734703/raw/"
     "b54d03154c339ed3047c66fefcece4727dfc931a/US%2520State%2520List"
 )
-abbrs = pd.read_json(response.text)
+abbrs = pd.read_json(io.StringIO(response.text))
 
 abbrs.head(3)
 ```

docs/user_guide/plugins/WmsTimeDimension.md

@@ -2,12 +2,9 @@
 
 Add a time dimension to a WMS tile layer.
 
-Note that we don't render the output in this example, because the web service
-used here is not always available.
-
 ### Exploring the WMS with OWSLib
 
-```
+```{code-cell} ipython3
 from owslib.wms import WebMapService
 
 
@@ -20,7 +17,7 @@ print("\n".join(web_map_services.contents.keys()))
 
 ### Layer metadata
 
-```
+```{code-cell} ipython3
 layer = "CRW_SST"
 wms = web_map_services.contents[layer]
 
@@ -41,7 +38,10 @@ if style not in wms.styles:
 
 ### Map with WmsTileLayer and TimestampedWmsTileLayers
 
-```
+```{code-cell} ipython3
+import folium
+import folium.plugins
+
 m = folium.Map(location=[-40, -50], zoom_start=5)
 
 wms_tile_layer = folium.WmsTileLayer(

docs/user_guide/plugins/timeslider_choropleth.md

@@ -7,7 +7,8 @@ The class needs at least two arguments to be instantiated.
 1. A string-serielized geojson containing all the features (i.e., the areas)
 2. A dictionary with the following structure:
 
-```
+
+```ipython3
 styledict = {
     '0': {
         '2017-1-1': {'color': 'ffffff', 'opacity': 1},
@@ -32,11 +33,11 @@ Using both color and opacity gives us the ability to simultaneously visualize tw
 We use `geopandas` to load a dataset containing the boundaries of all the countries in the world.
 
 ```{code-cell} ipython3
-import geopandas as gpd
+import geodatasets
+import geopandas
 
-assert "naturalearth_lowres" in gpd.datasets.available
-datapath = gpd.datasets.get_path("naturalearth_lowres")
-gdf = gpd.read_file(datapath)
+datapath = geodatasets.get_path("naturalearth land")
+gdf = geopandas.read_file(datapath)
 ```
 
 ```{code-cell} ipython3
@@ -66,7 +67,7 @@ n_periods, n_sample = 48, 40
 
 assert n_sample < n_periods
 
-datetime_index = pd.date_range("2016-1-1", periods=n_periods, freq="M")
+datetime_index = pd.date_range("2016-1-1", periods=n_periods, freq="ME")
 dt_index_epochs = datetime_index.astype("int64") // 10 ** 9
 dt_index = dt_index_epochs.astype("U10")
 

requirements-dev.txt

@@ -8,6 +8,7 @@ flake8-comprehensions
 flake8-import-order
 flake8-mutable
 flake8-nb>=0.2.5
+geodatasets
 geographiclib
 geopandas
 gpxpy