Features section and some new links

.gitignore

@@ -1,5 +1,7 @@
+__pycache__/
+
 build
 jupyter_execute
-source/generated
-source/examples
-source/gallery
+source/gallery
+source/gallery_py
+source/pages/api

docs/.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: 0adf304430f2fda758257fe547f78ce0
+config: 4767841221894b33757b012ad1407921
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_downloads/32a4a7c47369d38cf18d67f5346b06ba/plot_geom_boxplot.py → docs/_downloads/121fe74c371c19aa2bda5eee79a4e181/plot_geom_boxplot.py

@@ -1,25 +1,25 @@
-"""
-Geom Boxplot
-============
-
-"""
-
-import numpy as np
-
-from lets_plot import *
-LetsPlot.setup_html()
-
-#%%
-
-# sphinx_gallery_thumbnail_path = "/Users/vdovidaytis/Projects/lets-plot-docs/source/../examples/geom_boxplot.png"
-
-np.random.seed(42)
-n = 100
-data = {'y': np.random.normal(size=n), \
-        'class': np.random.randint(5, size=n)}
-
-# %%
-
-ggplot(data, aes(x='class', y='y')) + \
-    geom_boxplot(aes(fill='class', color='class'), alpha=.5) + \
+"""
+Geom Boxplot
+============
+
+"""
+
+import numpy as np
+
+from lets_plot import *
+LetsPlot.setup_html()
+
+#%%
+
+# sphinx_gallery_thumbnail_path = "E:\Projects\DataPad\Subprojects\LetsPlotDocs\MyFork\lets-plot-docs\source\gallery_py\geom_boxplot.png"
+
+np.random.seed(42)
+n = 100
+data = {'y': np.random.normal(size=n), \
+        'class': np.random.randint(5, size=n)}
+
+# %%
+
+ggplot(data, aes(x='class', y='y')) + \
+    geom_boxplot(aes(fill='class', color='class'), alpha=.5) + \
     scale_color_discrete() + scale_fill_discrete()

docs/_downloads/d8068b43c0e63dfca5e104998181da35/plot_geom_point.py → docs/_downloads/2c4ab1068ca8d344668d5845b4cf1664/plot_geom_point.py

@@ -1,24 +1,24 @@
-"""
-Geometry geom_point()
-=====================
-
-An example with the geom_point() geometry.
-
-"""
-
-import numpy as np
-
-from lets_plot import *
-LetsPlot.setup_html()
-
-# %%
-
-np.random.seed(42)
-n = 100
-x = np.random.uniform(-5, 5, size=n)
-y = x ** 2 + np.random.normal(size=n)
-data = {'x': x, 'y': y}
-
-# %%
-
+"""
+Geometry geom_point()
+=====================
+
+An example with the geom_point() geometry.
+
+"""
+
+import numpy as np
+
+from lets_plot import *
+LetsPlot.setup_html()
+
+# %%
+
+np.random.seed(42)
+n = 100
+x = np.random.uniform(-5, 5, size=n)
+y = x ** 2 + np.random.normal(size=n)
+data = {'x': x, 'y': y}
+
+# %%
+
 ggplot(data, aes(x='x', y='y')) + geom_point()

docs/_downloads/1768ef5cb9cb22524981227733e27762/plot_geopandas_kotlin_isl.py → docs/_downloads/4ac048c5d1070454c7ea27cfd1a42961/plot_geopandas_kotlin_isl.py

@@ -1,133 +1,135 @@
-"""
-Inset Map of Kotlin Island
-==========================
-
-Kotlin island is situated in the Gulf of Finland and is one of the
-districts of the city of Saint Petersburg in Russia.
-
-This example shows how *Lets-Plot* *GeoPandas* integration can help to
-build an inset map of Kotlin island.
-
-"""
-
-# sphinx_gallery_thumbnail_path = "/Users/vdovidaytis/Projects/lets-plot-docs/source/../examples/geopandas_kotlin_isl.png"
-
-# %% [markdown]
-#
-# `The geodata is provided by © OpenStreetMap contributors and is made
-# available here under the Open Database License
-# (ODbL) <https://www.openstreetmap.org/copyright>`__.
-
-# %%
-
-import requests
-import pandas as pd
-import geopandas as gpd
-from shapely.geometry import Point, shape, box
-
-from lets_plot import *
-LetsPlot.setup_html()
-
-# %%
-
-def osm_boundaries(osm_id):
-    requests.get('http://polygons.openstreetmap.fr/?id={0}'.format(osm_id))
-    response = requests.get('http://polygons.openstreetmap.fr/get_geojson.py?id={0}&params=0'.format(osm_id))
-    return gpd.GeoDataFrame(geometry=[ shape(response.json()['geometries'][0]) ])
-
-# %%
-
-# %% [markdown]
-#
-# Load boundaries of St.-Petersburg districts from OSM to GeoPandas *GeoDataFrame*.
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-# %%
-
-spb_districts_ids = [1115082, 1114193, 1114252, 1114354, 1114806, 1114809, 337424, 1114895, 363103, 1115366, 338636, 368287, 1114905, 367375, 1115367, 338635, 369514, 1114902]
-spb_gdf = pd.concat([osm_boundaries(osm_id) for osm_id in spb_districts_ids])
-
-# %%
-
-# %% [markdown]
-#
-# Create a map showing all the districts of St.-Petersburg.
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# This map will become an **inset map**. The red rectangle indicates the
-# bounds of the future **main map**.
-
-# %%
-
-kotlin_bbox = [29.63, 59.965, 29.815, 60.035]
-kotlin_rect = dict(zip(['xmin', 'ymin', 'xmax', 'ymax'], kotlin_bbox))
-
-# %%
-
-spb_plot = ggplot() + \
-    geom_rect(map=gpd.GeoDataFrame(geometry=[box(*spb_gdf.geometry.total_bounds)]), color='black', fill='white') + \
-    geom_polygon(map=spb_gdf, color='#a1d99b', fill='#f7fcf5') + \
-    geom_rect(**kotlin_rect, color='red', alpha=0) + \
-    geom_text(label='Saint Petersburg', x=30.334445, y=59.934294, color='black', size=6) + \
-    theme(legend_position='none', axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank')
-spb_plot
-
-# %%
-
-# %% [markdown]
-#
-# Create the main map with only Kotlin island on it.
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# We use ``xlim`` and ``ylim`` parameters of the coordinate system to crop
-# the entire map containing all the districts of St.-Petersburg.
-
-# %%
-
-# GeoDataFrame containing names and coordinates of some tourist attractions to show on the main map.
-places_gdf = gpd.GeoDataFrame({
-    'name' : [
-        'Kronstadt Naval Cathedral',
-        'Kronstadt History Museum',
-        'Kronstadt Naval Museum',
-        'City Russian Cemetery',
-        'Kronstadt Lutheran Cemetery',
-        'Vladimir Church',
-    ],
-    'type' : ['church', 'museum', 'museum', 'cemetery', 'cemetery', 'church'],
-    'geometry' : [
-        Point(29.777455, 59.991744),
-        Point(29.791317, 59.986777),
-        Point(29.763422, 59.996108),
-        Point(29.706130, 60.019788),
-        Point(29.749861, 60.002212),
-        Point(29.766254, 59.998515),
-    ],
-})
-
-# %%
-
-# Cut-out the Kotlin area and add layes with text and points of interest.
-kotlin_plot = ggplot() + \
-    geom_rect(**kotlin_rect, fill='#aadaff', alpha=0.2) + \
-    geom_polygon(map=spb_gdf, color='#31a354', fill='#e5f5e0') + \
-    geom_point(aes(color='type', shape='type'), data=places_gdf, size=5) + \
-    geom_text(aes(label='name'), data=places_gdf, hjust='right', position=position_nudge(x=-.002)) + \
-    geom_text(label='Kotlin Isl.', x=29.725, y=60.011, color='#31a354', size=13, fontface='italic') + \
-    geom_text(label='Gulf of Finland', x=29.665, y=60.002, color='#578bcc', size=11, fontface='italic') + \
-    coord_cartesian(xlim=kotlin_bbox[0::2], ylim=kotlin_bbox[1::2]) + \
-    ggtitle('Tourist attractions on Kotlin island') + \
-    theme(legend_position=(0.2, 0.2), axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank')
-kotlin_plot
-
-# %%
-
-# %% [markdown]
-#
-# Finally, use *GGBunch* to show these two maps together.
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-# %%
-
-bunch = GGBunch()
-bunch.add_plot(kotlin_plot, 0, 0, 800, 600)
-bunch.add_plot(spb_plot, 600, 25, 200, 150)
+"""
+Inset Map of Kotlin Island
+==========================
+
+Kotlin island is situated in the Gulf of Finland and is one of the
+districts of the city of Saint Petersburg in Russia.
+
+This example shows how *Lets-Plot* *GeoPandas* integration can help to
+build an inset map of Kotlin island.
+
+"""
+
+# sphinx_gallery_thumbnail_path = "E:\Projects\DataPad\Subprojects\LetsPlotDocs\MyFork\lets-plot-docs\source\gallery_py\geopandas_kotlin_isl.png"
+
+# %% [markdown]
+#
+# `The geodata is provided by © OpenStreetMap contributors and is made
+# available here under the Open Database License
+# (ODbL) <https://www.openstreetmap.org/copyright>`__.
+
+# %%
+
+import requests
+import pandas as pd
+import geopandas as gpd
+from shapely.geometry import Point, shape, box
+
+from lets_plot import *
+LetsPlot.setup_html()
+
+# %%
+
+def osm_boundaries(osm_id):
+    requests.get('http://polygons.openstreetmap.fr/?id={0}'.format(osm_id))
+    response = requests.get('http://polygons.openstreetmap.fr/get_geojson.py?id={0}&params=0'.format(osm_id))
+    return gpd.GeoDataFrame(geometry=[ shape(response.json()['geometries'][0]) ])
+
+# %%
+
+# %% [markdown]
+#
+# Load boundaries of St.-Petersburg districts from OSM to GeoPandas *GeoDataFrame*.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# %%
+
+spb_districts_ids = [1115082, 1114193, 1114252, 1114354, 1114806, 1114809, 337424, 1114895, 363103, 1115366, 338636, 368287, 1114905, 367375, 1115367, 338635, 369514, 1114902]
+spb_gdf = pd.concat([osm_boundaries(osm_id) for osm_id in spb_districts_ids])
+
+# %%
+
+# %% [markdown]
+#
+# Create a map showing all the districts of St.-Petersburg.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# 
+# This map will become an **inset map**. The red rectangle indicates the
+# bounds of the future **main map**.
+
+# %%
+
+kotlin_bbox = [29.63, 59.965, 29.815, 60.035]
+kotlin_rect = dict(zip(['xmin', 'ymin', 'xmax', 'ymax'], kotlin_bbox))
+
+# %%
+
+spb_plot = ggplot() + \
+    geom_rect(map=gpd.GeoDataFrame(geometry=[box(*spb_gdf.geometry.total_bounds)]), color='black', fill='white') + \
+    geom_polygon(map=spb_gdf, color='#a1d99b', fill='#f7fcf5') + \
+    geom_rect(**kotlin_rect, color='red', alpha=0) + \
+    geom_text(label='Saint Petersburg', x=30.334445, y=59.934294, color='black', size=6) + \
+    theme(legend_position='none', axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank')
+spb_plot
+
+# %%
+
+# %% [markdown]
+#
+# Create the main map with only Kotlin island on it.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# 
+# We use ``xlim`` and ``ylim`` parameters of the coordinate system to crop
+# the entire map containing all the districts of St.-Petersburg.
+
+# %%
+
+# GeoDataFrame containing names and coordinates of some tourist attractions to show on the main map.
+places_gdf = gpd.GeoDataFrame({
+    'name' : [
+        'Kronstadt Naval Cathedral',
+        'Kronstadt History Museum',
+        'Kronstadt Naval Museum',
+        'City Russian Cemetery',
+        'Kronstadt Lutheran Cemetery',
+        'Vladimir Church',
+    ],
+    'type' : ['church', 'museum', 'museum', 'cemetery', 'cemetery', 'church'],
+    'geometry' : [
+        Point(29.777455, 59.991744),
+        Point(29.791317, 59.986777),
+        Point(29.763422, 59.996108),
+        Point(29.706130, 60.019788),
+        Point(29.749861, 60.002212),
+        Point(29.766254, 59.998515),
+    ],
+})
+
+# %%
+
+# Cut-out the Kotlin area and add layes with text and points of interest.
+kotlin_plot = ggplot() + \
+    geom_rect(**kotlin_rect, fill='#aadaff', alpha=0.2) + \
+    geom_polygon(map=spb_gdf, color='#31a354', fill='#e5f5e0') + \
+    geom_point(aes(color='type', shape='type'), data=places_gdf, size=5) + \
+    geom_text(aes(label='name'), data=places_gdf, hjust='right', position=position_nudge(x=-.002)) + \
+    geom_text(label='Kotlin Isl.', x=29.725, y=60.011, color='#31a354', size=13, fontface='italic') + \
+    geom_text(label='Gulf of Finland', x=29.665, y=60.002, color='#578bcc', size=11, fontface='italic') + \
+    coord_cartesian(xlim=kotlin_bbox[0::2], ylim=kotlin_bbox[1::2]) + \
+    ggtitle('Tourist attractions on Kotlin island') + \
+    theme(legend_position=(0.2, 0.2), axis_title='blank', axis_text='blank', axis_ticks='blank', axis_line='blank')
+kotlin_plot
+
+# %%
+
+# %% [markdown]
+#
+# Finally, use *GGBunch* to show these two maps together.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+# %%
+
+bunch = GGBunch()
+bunch.add_plot(kotlin_plot, 0, 0, 800, 600)
+bunch.add_plot(spb_plot, 600, 25, 200, 150)
 bunch