Merge ab8c8db into d47704a

bindings/python/.gitignore → bindings/python/pydeck/.gitignore

@@ -154,3 +154,4 @@ $RECYCLE.BIN/
 **/pydeck/nbextension/static/index.js.map
 **/pydeck/nbextension/static/extensionRequires.js
 **/pydeck/io/templates/requirejs_dependencies.json
+**/_build

bindings/python/pydeck/README.md

@@ -42,7 +42,7 @@ UK_ACCIDENTS_DATA = 'https://raw.githubusercontent.com/uber-common/deck.gl-data/
 layer = pdk.Layer(
     'HexagonLayer',
     UK_ACCIDENTS_DATA,
-    get_position='[lng,lat]',
+    get_position='[lng, lat]',
     auto_highlight=True,
     elevation_scale=50,
     pickable=True,

bindings/python/pydeck/docs/Makefile

@@ -0,0 +1,19 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

bindings/python/pydeck/docs/conf.py

@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+# Point Sphinx autodoc at our source
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath('../'))
+
+project = u'pydeck'
+copyright = u'2019, Uber Technologies, Inc.'
+author = u'Andrew Duberstein'
+# The short X.Y version
+version = u''
+# The full version, including alpha/beta/rc tags
+release = u'0.1.dev4'
+# Extensions to generate documents from our docstrings here
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon'
+]
+templates_path = ['_templates']
+source_suffix = '.rst'
+master_doc = 'index'
+language = None
+exclude_patterns = [u'_build']
+pygments_style = None
+html_theme = 'alabaster'
+html_static_path = ['_static']
+htmlhelp_basename = 'pydeckdoc'
+man_pages = [
+    (master_doc, 'pydeck', u'pydeck Documentation',
+     [author], 1)
+]
+texinfo_documents = [
+    (master_doc, 'pydeck', u'pydeck Documentation',
+     author, 'pydeck', 'One line description of project.',
+     'Miscellaneous'),
+]
+epub_title = project
+epub_exclude_files = ['search.html']
+autoclass_content = "both"

bindings/python/pydeck/docs/data_utils.rst

@@ -0,0 +1,74 @@
+Data utilities
+==============
+
+These items are convenience functions for common data processing work done for pydeck maps.
+
+.. autofunction:: pydeck.data_utils.viewport_helpers.compute_view
+
+For example, suppose you have 100 points, most of which are centered
+around London and ten of which are distributed a few 100 kilometers away
+from it. If you set ``view_proportion=0.9``, pydeck will attempt to fit
+to the middle 90% of the data, aiming to exclude the points furthest
+from the core of the visualization. Suppose your ten points
+are in the Americas in this example--they would be excluded.
+
+.. autofunction:: pydeck.data_utils.color_scales.assign_random_colors
+
+
+Examples
+--------
+
+Below is an illustration of :func:`pydeck.data_utils.color_scales.assign_random_colors`,
+using a small data set of two classes (here, 'Palace' and 'Clock Tower)
+and three obervations ('Big Ben', 'Kensington Palace', and 'Buckingham Palace').
+Our goal is to quickly color the data by category.
+
+>>> import pydeck
+>>> import pandas
+>>> data = pandas.DataFrame([
+>>>     {
+>>>         'site': 'Big Ben',
+>>>         'attraction_type': 'Clock Tower',
+>>>         'lat': 51.5006958,
+>>>         'lng': -0.1266639
+>>>     },
+>>>     {
+>>>         'site': 'Kensington Palace',
+>>>         'attraction_type': 'Palace':
+>>>         'lat': 51.5046188,
+>>>         'lng': -0.1839472
+>>>     },
+>>>     {
+>>>         'attraction_type': 'Palace',
+>>>         'site': 'Buckingham Palace',
+>>>         'lat': 51.501364,
+>>>         'lng': -0.14189
+>>>     }
+>>> ])
+>>> color_lookup = pydeck.data_utils.assign_random_colors(data['attraction_type'])
+>>> # Assign a color based on attraction_type
+>>> data['color'] = data.apply(lambda row: color_lookup.get(row['attraction_type']), axis=1)
+>>> # Data now has an RGB color by attraction type:
+[
+  {
+        'site': 'Big Ben',
+        'attraction_type': 'Clock Tower',
+        'lat': 51.5006958,
+        'lng': -0.1266639,
+        'color': [0, 10, 35]
+    },
+    {
+        'site': 'Kensington Palace',
+        'attraction_type': 'Palace':
+        'lat': 51.5046188,
+        'lng': -0.1839472,
+        'color': [53, 243, 130]
+    },
+  {
+        'attraction_type': 'Palace',
+        'site': 'Buckingham Palace',
+        'lat': 51.501364,
+        'lng': -0.14189,
+        'color': [53, 243, 130]
+    }
+]