""" 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 = [ feature_id for feature_id in 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, n) 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 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(geopandas.datasets.get_path("naturalearth_lowres")) >>> 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.") try: import pandas as pd except ImportError: raise ImportError("The 'pandas' package is required.") try: from libpysal.weights import Queen, Rook, W, fuzzy_contiguity except ImportError: raise ImportError("The 'libpysal' package is required.") 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