"""
greedy - Greedy (topological) coloring for GeoPandas
Copyright (C) 2019 Martin Fleischmann, 2017 Nyall Dawson
"""
import operator
__all__ = ["greedy"]
def _balanced(features, sw, balance="count", min_colors=4):
"""
Strategy to color features in a way which is visually balanced.
Algorithm ported from QGIS to be used with GeoDataFrames
and libpysal weights objects.
Original algorithm:
Date : February 2017
Copyright : (C) 2017 by Nyall Dawson
Email : nyall dot dawson at gmail dot com
Parameters
----------
features : geopandas.GeoDataFrame
GeoDataFrame.
sw : libpysal.weights.W
Spatial weights object denoting adjacency of features.
balance : str (default 'count')
The method of color balancing.
min_colors : int (default 4)
The minimal number of colors to be used.
Returns
-------
feature_colors : dict
Dictionary with assigned color codes.
"""
feature_colors = {}
# start with minimum number of colors in pool
color_pool = set(range(min_colors))
# calculate count of neighbours
neighbour_count = sw.cardinalities
# sort features by neighbour count - handle those with more neighbours first
sorted_by_count = sorted(
neighbour_count.items(), key=operator.itemgetter(1), reverse=True
)
# counts for each color already assigned
color_counts = {}
color_areas = {}
for c in color_pool:
color_counts[c] = 0
color_areas[c] = 0
if balance == "centroid":
features = features.copy()
features.geometry = features.geometry.centroid
balance = "distance"
for feature_id, _ in sorted_by_count:
# first work out which already assigned colors are adjacent to this feature
adjacent_colors = set()
for neighbour in sw.neighbors[feature_id]:
if neighbour in feature_colors:
adjacent_colors.add(feature_colors[neighbour])
# from the existing colors, work out which are available (ie non-adjacent)
available_colors = color_pool.difference(adjacent_colors)
feature_color = -1
if len(available_colors) == 0:
# no existing colors available for this feature; add new color and repeat
min_colors += 1
return _balanced(features, sw, balance, min_colors)
else:
if balance == "count":
# choose least used available color
counts = [
(c, v) for c, v in color_counts.items() if c in available_colors
]
feature_color = sorted(counts, key=operator.itemgetter(1))[0][0]
color_counts[feature_color] += 1
elif balance == "area":
areas = [
(c, v) for c, v in color_areas.items() if c in available_colors
]
feature_color = sorted(areas, key=operator.itemgetter(1))[0][0]
color_areas[feature_color] += features.loc[feature_id].geometry.area
elif balance == "distance":
min_distances = {c: float("inf") for c in available_colors}
this_feature = features.loc[feature_id].geometry
# find features for all available colors
other_features = {
f_id: c
for (f_id, c) in feature_colors.items()
if c in available_colors
}
distances = features.loc[other_features.keys()].distance(this_feature)
# calculate the min distance from this feature to the nearest
# feature with each assigned color
for other_feature_id, c in other_features.items():
distance = distances.loc[other_feature_id]
if distance < min_distances[c]:
min_distances[c] = distance
# choose color such that min distance is maximised!
# - ie we want MAXIMAL separation between features with the same color
feature_color = sorted(
min_distances, key=min_distances.__getitem__, reverse=True
)[0]
feature_colors[feature_id] = feature_color
return feature_colors
[docs]
def greedy(
gdf,
strategy="balanced",
balance="count",
min_colors=4,
sw="queen",
min_distance=None,
silence_warnings=True,
interchange=False,
):
"""
Color GeoDataFrame using various strategies of greedy (topological) colouring.
Attempts to color a GeoDataFrame using as few colors as possible, where no
neighbours can have same color as the feature itself. Offers various strategies
ported from QGIS or implemented within NetworkX for greedy graph coloring.
``greedy`` will return ``pandas.Series`` representing assigned color codes.
Parameters
----------
gdf : GeoDataFrame
GeoDataFrame
strategy : str (default 'balanced')
Determine coloring strategy. Options are ``'balanced'`` for
algorithm based on QGIS Topological coloring. It is aiming
for a visual balance, defined by the balance parameter. Other
options are those supported by ``networkx.greedy_color``:
* ``'largest_first'``
* ``'random_sequential'``
* ``'smallest_last'``
* ``'independent_set'``
* ``'connected_sequential_bfs'``
* ``'connected_sequential_dfs'``
* ``'connected_sequential'`` (alias for the previous strategy)
* ``'saturation_largest_first'``
* ``'DSATUR'`` (alias for the previous strategy)
For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html
balance : str (default 'count')
If strategy is ``'balanced'``, determine the method of color balancing.
* ``'count'`` attempts to balance the number of features per each color.
* ``'area'`` attempts to balance the area covered by each color.
* ``'centroid'`` attempts to balance the distance between colors based
on the distance between centroids.
* ``'distance'`` attempts to balance the distance between colors based
on the distance between geometries. Slower than ``'centroid'``,
but more precise.
Both ``'centroid'`` and ``'distance'`` are significantly slower than other
especially for larger GeoDataFrames. Apart from ``'count'``, all require
CRS to be projected (not in degrees) to ensure metric values are correct.
min_colors: int (default 4)
If strategy is ``'balanced'``, define the minimal number of colors to be used.
sw : 'queen', 'rook' or libpysal.weights.W (default 'queen')
If min_distance is None, one can pass ``'libpysal.weights.W'``
object denoting neighbors or let greedy generate one based on
``'queen'`` or ``'rook'`` contiguity.
min_distance : float (default None)
Set minimal distance between colors. If ``min_distance`` is not ``None``,
slower algorithm for generating spatial weghts is used based on
intersection between geometries. ``'min_distance'`` is then used as a
tolerance of intersection.
silence_warnings : bool (default True)
Silence libpysal warnings when creating spatial weights.
interchange : bool (default False)
Use the color interchange algorithm (applicable for NetworkX strategies).
For details see https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.coloring.greedy_color.html
Returns
-------
color : pandas.Series
``pandas.Series`` representing assinged color codes.
Examples
--------
>>> from mapclassify import greedy
>>> import geopandas
>>> world = geopandas.read_file(
... "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
... )
>>> africa = world.loc[world.CONTINENT == "Africa"].copy()
>>> africa = africa.to_crs("ESRI:102022").reset_index(drop=True)
Default:
>>> africa["greedy_colors"] = greedy(africa)
>>> africa["greedy_colors"].head()
0 1
1 0
2 0
3 1
4 4
Name: greedy_colors, dtype: int64
Balanced by area:
>>> africa["balanced_area"] = greedy(africa, strategy="balanced", balance="area")
>>> africa["balanced_area"].head()
0 1
1 2
2 0
3 1
4 3
Name: balanced_area, dtype: int64
Using rook adjacency:
>>> africa["rook_adjacency"] = greedy(africa, sw="rook")
>>> africa["rook_adjacency"].tail()
46 3
47 0
48 2
49 3
50 1
Name: rook_adjacency, dtype: int64
Adding minimal distance between colors:
>>> africa["min_distance"] = greedy(africa, min_distance=1000000)
>>> africa["min_distance"].head()
0 1
1 9
2 0
3 7
4 4
Name: min_distance, dtype: int64
Using different coloring strategy:
>>> africa["smallest_last"] = greedy(africa, strategy="smallest_last")
>>> africa["smallest_last"].head()
0 3
1 1
2 1
3 3
4 1
Name: smallest_last, dtype: int64
""" # noqa
if strategy != "balanced":
try:
import networkx as nx
STRATEGIES = nx.algorithms.coloring.greedy_coloring.STRATEGIES.keys()
except ImportError:
raise ImportError("The 'networkx' package is required.") from None
try:
import pandas as pd
except ImportError:
raise ImportError("The 'pandas' package is required.") from None
try:
from libpysal.weights import Queen, Rook, W, fuzzy_contiguity
except ImportError:
raise ImportError("The 'libpysal' package is required.") from None
if min_distance is not None:
sw = fuzzy_contiguity(
gdf,
tolerance=0.0,
buffering=True,
buffer=min_distance / 2.0,
silence_warnings=silence_warnings,
)
if not isinstance(sw, W):
if sw == "queen":
sw = Queen.from_dataframe(gdf, silence_warnings=silence_warnings)
elif sw == "rook":
sw = Rook.from_dataframe(gdf, silence_warnings=silence_warnings)
if strategy == "balanced":
color = pd.Series(_balanced(gdf, sw, balance=balance, min_colors=min_colors))
elif strategy in STRATEGIES:
color = nx.greedy_color(
sw.to_networkx(), strategy=strategy, interchange=interchange
)
else:
raise ValueError(f"'{strategy}' is not a valid strategy.")
color = pd.Series(color).sort_index()
color.index = gdf.index
return color