Updates for maplibre maps

doc/python/axes.md

@@ -41,7 +41,7 @@ Other kinds of subplots and axes are described in other tutorials:
 - [Polar axes](/python/polar-chart/). The axis object is [`go.layout.Polar`](/python/reference/layout/polar/)
 - [Ternary axes](/python/ternary-plots). The axis object is [`go.layout.Ternary`](/python/reference/layout/ternary/)
 - [Geo axes](/python/map-configuration/). The axis object is [`go.layout.Geo`](/python/reference/layout/geo/)
-- [Mapbox axes](/python/mapbox-layers/). The axis object is [`go.layout.Mapbox`](/python/reference/layout/mapbox/)
+- [Map axes](/python/map-layers/). The axis object is [`go.layout.Map`](/python/reference/layout/map/)
 - [Color axes](/python/colorscales/). The axis object is [`go.layout.Coloraxis`](/python/reference/layout/coloraxis/).
 
 **See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

doc/python/bubble-maps.md

@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.3'
+      jupytext_version: 1.16.3
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.10.0
   plotly:
     description: How to make bubble maps in Python with Plotly.
     display_as: maps

doc/python/choropleth-maps.md

@@ -6,9 +6,9 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.3'
-      jupytext_version: 1.14.1
+      jupytext_version: 1.16.3
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.8.8
+    version: 3.10.0
   plotly:
     description: How to make choropleth maps in Python with Plotly.
     display_as: maps
@@ -33,7 +33,7 @@ jupyter:
     thumbnail: thumbnail/choropleth.jpg
 ---
 
-A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps** using our Mapbox trace types](/python/mapbox-county-choropleth).
+A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build **outline** choropleth maps, but you can also build [choropleth **tile maps**](/python/tile-county-choropleth).
 
 Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` function or the lower-level `go.Choropleth` graph object.
 

doc/python/county-choropleth.md

@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.3.1
+      format_version: '1.3'
+      jupytext_version: 1.16.3
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.10.0
   plotly:
     description: How to create colormaped representations of USA counties by FIPS
       values in Python.
@@ -37,7 +37,7 @@ jupyter:
 ### Deprecation warning
 
 
-This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [Mapbox tile-based choropleth maps](/python/mapbox-county-choropleth).
+This page describes a [legacy "figure factory" method](/python/figure-factories/) for creating map-like figures using [self-filled scatter traces](/python/shapes). **This is no longer the recommended way to make county-level choropleth maps**, instead we recommend using a [GeoJSON-based approach to making outline choropleth maps](/python/choropleth-maps/) or the alternative [tile-based choropleth maps](/python/tile-county-choropleth).
 
 
 #### Required Packages
@@ -274,7 +274,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plotly.com/python/mapbox-county-choropleth/](https://plotly.com/python/mapbox-county-choropleth/)
+Also see tile county choropleths made in Python: [https://plotly.com/python/tile-county-choropleth/](https://plotly.com/python/tile-county-choropleth/)
 
 ### Reference
 

doc/python/datashader.md

@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.3.0
+      format_version: '1.3'
+      jupytext_version: 1.16.3
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.3
+    version: 3.10.0
   plotly:
     description: How to use datashader to rasterize large datasets, and visualize
       the generated raster data with plotly.
@@ -36,10 +36,10 @@ jupyter:
 
 [datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
 
-### Passing datashader rasters as a mapbox image layer
+### Passing datashader rasters as a tile map image layer
 
 We visualize here the spatial distribution of taxi rides in New York City. A higher density
-is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
+is observed on major avenues. For more details about tile-based maps, see [the tile map layers tutorial](/python/tile-map-layers).
 
 ```python
 import pandas as pd
@@ -51,7 +51,7 @@ cvs = ds.Canvas(plot_width=1000, plot_height=1000)
 agg = cvs.points(dff, x='Lon', y='Lat')
 # agg is an xarray object, see http://xarray.pydata.org/en/stable/ for more details
 coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
-# Corners of the image, which need to be passed to mapbox
+# Corners of the image
 coordinates = [[coords_lon[0], coords_lat[0]],
                [coords_lon[-1], coords_lat[0]],
                [coords_lon[-1], coords_lat[-1]],
@@ -62,16 +62,12 @@ import datashader.transfer_functions as tf
 img = tf.shade(agg, cmap=fire)[::-1].to_pil()
 
 import plotly.express as px
-# Trick to create rapidly a figure with mapbox axes
-fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
-# Add the datashader image as a mapbox layer image
-fig.update_layout(mapbox_style="carto-darkmatter",
-                 mapbox_layers = [
-                {
-                    "sourcetype": "image",
-                    "source": img,
-                    "coordinates": coordinates
-                }]
+# Trick to create rapidly a figure with map axes
+fig = px.scatter_map(dff[:1], lat='Lat', lon='Lon', zoom=12)
+# Add the datashader image as a tile map layer image
+fig.update_layout(
+    map_style="carto-darkmatter",
+    map_layers=[{"sourcetype": "image", "source": img, "coordinates": coordinates}],
 )
 fig.show()
 ```
@@ -113,7 +109,3 @@ fig.update_traces(hoverongaps=False)
 fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))
 fig.show()
 ```
-
-```python
-
-```

doc/python/figure-structure.md

@@ -97,7 +97,7 @@ The second of the three top-level attributes of a figure is `layout`, whose valu
   * Subplots of various types on which can be drawn multiple traces and which are positioned in paper coordinates:
     * `xaxis`, `yaxis`, `xaxis2`, `yaxis3` etc: X and Y cartesian axes, the intersections of which are cartesian subplots
     * `scene`, `scene2`, `scene3` etc: 3d scene subplots
-    * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `mapbox`, `mapbox2`, `mabox3`, `smith`, `smith2` etc: ternary, polar, geo, mapbox or smith subplots
+    * `ternary`, `ternary2`, `ternary3`, `polar`, `polar2`, `polar3`, `geo`, `geo2`, `geo3`, `map`, `map2`, `map3`, `smith`, `smith2` etc: ternary, polar, geo, map or smith subplots
   * Non-data marks which can be positioned in paper coordinates, or in data coordinates linked to 2d cartesian subplots:
     * `annotations`: [textual annotations with or without arrows](/python/text-and-annotations/)
     * `shapes`: [lines, rectangles, ellipses or open or closed paths](/python/shapes/)
@@ -181,18 +181,18 @@ The following trace types are compatible with smith subplots via the `smith` att
 
 ### Map Trace Types and Subplots
 
-Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [mapbox subplots for tile maps](/python/mapbox-layers/). The following trace types support attributes named `geo` or `mapbox`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc.  Note that attributes such as `layout.geo2` and `layout.mapbox` etc do not have to be explicitly defined, in which case default values will be inferred. Multiple traces of a compatible type can be placed on the same subplot.
+Figures can include two different types of map subplots: [geo subplots for outline maps](/python/map-configuration/) and [tile-based maps](/python/tile-map-layers/). The following trace types support attributes named `geo` or `map`, whose values must refer to corresponding objects in the layout i.e. `geo="geo2"` etc.  Note that attributes such as `layout.geo2` and `layout.map` etc do not have to be explicitly defined, in which case default values will be inferred. Multiple traces of a compatible type can be placed on the same subplot.
 
 The following trace types are compatible with geo subplots via the `geo` attribute:
 
 * [`scattergeo`](/python/scatter-plots-on-maps/), which can be used to draw [individual markers](/python/scatter-plots-on-maps/), [line and curves](/python/lines-on-maps/) and filled areas on outline maps
 * [`choropleth`](/python/choropleth-maps/): [colored polygons](/python/choropleth-maps/) on outline maps
 
-The following trace types are compatible with mapbox subplots via the `mapbox` attribute:
+The following trace types are compatible with tile map subplots via the `map` attribute:
 
-* [`scattermapbox`](/python/scattermapbox/), which can be used to draw [individual markers](/python/scattermapbox/), [lines and curves](/python/lines-on-mapbox/) and [filled areas](/python/filled-area-on-mapbox/) on tile maps
-* [`choroplethmapbox`](/python/mapbox-county-choropleth/): colored polygons on tile maps
-* [`densitymapbox`](/python/mapbox-density-heatmaps/): density heatmaps on tile maps
+* [`scattermap`](/python/tile-scatter-maps/), which can be used to draw [individual markers](/python/tile-scatter-maps/), [lines and curves](/python/lines-on-tile-maps/) and [filled areas](/python/filled-area-tile-maps/) on tile maps
+* [`choroplethmap`](/python/tile-county-choropleth/): colored polygons on tile maps
+* [`densitymap`](/python/tile-density-heatmaps/): density heatmaps on tile maps
 
 ### Traces Which Are Their Own Subplots
 

doc/python/filled-area-on-mapbox.md

@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.3'
+      jupytext_version: 1.16.3
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,68 +20,63 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.10.0
   plotly:
-    description: How to make an area on Map in Python with Plotly.
+    description: How to make an area on tile-based maps in Python with Plotly.
     display_as: maps
     language: python
     layout: base
     name: Filled Area on Maps
     order: 3
     page_type: example_index
-    permalink: python/filled-area-on-mapbox/
+    permalink: python/filled-area-tile-maps/
+    redirect_from: python/filled-area-on-mapbox/
     thumbnail: thumbnail/area.jpg
 ---
 
-<!-- #region -->
-
-### Mapbox Access Token and Base Map Configuration
-
-To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.
+There are three different ways to show a filled area on a tile-based map:
 
-There are three different ways to show a filled area in a Mapbox map:
+- Using a [Scattermap](https://plotly.com/python/reference/scattermap/) trace and setting the `fill` attribute to 'toself'
+- Using a map layout (i.e. by minimally using an empty [Scattermap](https://plotly.com/python/reference/scattermap/) trace) and adding a GeoJSON layer
+- Using the [Choroplethmap](https://plotly.com/python/tile-county-choropleth/) trace type
 
-1. Use a [Scattermapbox](https://plotly.com/python/reference/scattermapbox/) trace and set `fill` attribute to 'toself'
-2. Use a Mapbox layout (i.e. by minimally using an empty [Scattermapbox](https://plotly.com/python/reference/scattermapbox/) trace) and add a GeoJSON layer
-3. Use the [Choroplethmapbox](https://plotly.com/python/mapbox-county-choropleth/) trace type
-   <!-- #endregion -->
+## Filled `Scattermap` Trace
 
-### Filled `Scattermapbox` Trace
-
-The following example uses `Scattermapbox` and sets `fill = 'toself'`
+The following example uses `Scattermap` and sets `fill = 'toself'`
 
 ```python
 import plotly.graph_objects as go
 
-fig = go.Figure(go.Scattermapbox(
+fig = go.Figure(go.Scattermap(
     fill = "toself",
     lon = [-74, -70, -70, -74], lat = [47, 47, 45, 45],
     marker = { 'size': 10, 'color': "orange" }))
 
 fig.update_layout(
-    mapbox = {
+    map = {
         'style': "open-street-map",
         'center': {'lon': -73, 'lat': 46 },
         'zoom': 5},
     showlegend = False)
 
 fig.show()
+
 ```
 
-### Multiple Filled Areas with a `Scattermapbox` trace
+### Multiple Filled Areas with a `Scattermap` trace
 
-The following example shows how to use `None` in your data to draw multiple filled areas. Such gaps in trace data are unconnected by default, but this can be controlled via the [connectgaps](https://plotly.com/python/reference/scattermapbox/#scattermapbox-connectgaps) attribute.
+The following example shows how to use `None` in your data to draw multiple filled areas. Such gaps in trace data are unconnected by default, but this can be controlled via the [connectgaps](https://plotly.com/python/reference/scattermap/#scattermap-connectgaps) attribute.
 
 ```python
 import plotly.graph_objects as go
 
-fig = go.Figure(go.Scattermapbox(
+fig = go.Figure(go.Scattermap(
     mode = "lines", fill = "toself",
     lon = [-10, -10, 8, 8, -10, None, 30, 30, 50, 50, 30, None, 100, 100, 80, 80, 100],
     lat = [30, 6, 6, 30, 30,    None, 20, 30, 30, 20, 20, None, 40, 50, 50, 40, 40]))
 
 fig.update_layout(
-    mapbox = {'style': "open-street-map", 'center': {'lon': 30, 'lat': 30}, 'zoom': 2},
+    map = {'style': "open-street-map", 'center': {'lon': 30, 'lat': 30}, 'zoom': 2},
     showlegend = False,
     margin = {'l':0, 'r':0, 'b':0, 't':0})
 
@@ -95,13 +90,13 @@ In this map we add a GeoJSON layer.
 ```python
 import plotly.graph_objects as go
 
-fig = go.Figure(go.Scattermapbox(
+fig = go.Figure(go.Scattermap(
     mode = "markers",
     lon = [-73.605], lat = [45.51],
     marker = {'size': 20, 'color': ["cyan"]}))
 
 fig.update_layout(
-    mapbox = {
+    map = {
         'style': "open-street-map",
         'center': { 'lon': -73.6, 'lat': 45.5},
         'zoom': 12, 'layers': [{
@@ -139,6 +134,36 @@ fig.update_layout(
 fig.show()
 ```
 
+<!-- #region -->
+### Mapbox Maps
+
+> Mapbox traces are deprecated and may be removed in a future version of Plotly.py.
+
+The earlier examples using `go.Scattermap` use [Maplibre](https://maplibre.org/maplibre-gl-js/docs/) for rendering. This trace was introduced in Plotly.py 5.24 and is now the recommended way to draw filled areas on tile-based maps. There is also a trace that uses [Mapbox](https://docs.mapbox.com), called `go.Scattermapbox`.
+
+To use the `Scattermapbox` trace type, in some cases you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.
+
+Here's one of the earlier examples rewritten to use `Scattermapbox`.
+
+```python
+import plotly.graph_objects as go
+
+fig = go.Figure(go.Scattermapbox(
+    fill = "toself",
+    lon = [-74, -70, -70, -74], lat = [47, 47, 45, 45],
+    marker = { 'size': 10, 'color': "orange" }))
+
+fig.update_layout(
+    mapbox = {
+        'style': "open-street-map",
+        'center': {'lon': -73, 'lat': 46 },
+        'zoom': 5},
+    showlegend = False)
+
+fig.show()
+```
+<!-- #endregion -->
+
 #### Reference
 
-See https://plotly.com/python/reference/scattermapbox/ for more information about mapbox and their attribute options.
+See  https://plotly.com/python/reference/scattermap/ for available attribute options, or for `go.Scattermapbox`, see https://plotly.com/python/reference/scattermapbox/.

doc/python/heatmaps.md

@@ -34,7 +34,7 @@ jupyter:
     thumbnail: thumbnail/heatmap.jpg
 ---
 
-The term "heatmap" usually refers to a cartesian plot with data visualized as colored rectangular tiles, which is the subject of this page. It is also sometimes used to refer to [actual maps with density data displayed as color intensity](/python/mapbox-density-heatmaps/).
+The term "heatmap" usually refers to a Cartesian plot with data visualized as colored rectangular tiles, which is the subject of this page. It is also sometimes used to refer to [actual maps with density data displayed as color intensity](/python/tile-density-heatmaps/).
 
 Plotly supports two different types of colored-tile heatmaps: