/usr/lib/python3/dist-packages/geopandas/plotting.py is in python3-geopandas 0.1.1-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 | from __future__ import print_function
import numpy as np
from six import next
from six.moves import xrange
def plot_polygon(ax, poly, facecolor='red', edgecolor='black', alpha=0.5):
""" Plot a single Polygon geometry """
from descartes.patch import PolygonPatch
a = np.asarray(poly.exterior)
# without Descartes, we could make a Patch of exterior
ax.add_patch(PolygonPatch(poly, facecolor=facecolor, alpha=alpha))
ax.plot(a[:, 0], a[:, 1], color=edgecolor)
for p in poly.interiors:
x, y = zip(*p.coords)
ax.plot(x, y, color=edgecolor)
def plot_multipolygon(ax, geom, facecolor='red', alpha=0.5):
""" Can safely call with either Polygon or Multipolygon geometry
"""
if geom.type == 'Polygon':
plot_polygon(ax, geom, facecolor=facecolor, alpha=alpha)
elif geom.type == 'MultiPolygon':
for poly in geom.geoms:
plot_polygon(ax, poly, facecolor=facecolor, alpha=alpha)
def plot_linestring(ax, geom, color='black', linewidth=1):
""" Plot a single LineString geometry """
a = np.array(geom)
ax.plot(a[:,0], a[:,1], color=color, linewidth=linewidth)
def plot_multilinestring(ax, geom, color='red', linewidth=1):
""" Can safely call with either LineString or MultiLineString geometry
"""
if geom.type == 'LineString':
plot_linestring(ax, geom, color=color, linewidth=linewidth)
elif geom.type == 'MultiLineString':
for line in geom.geoms:
plot_linestring(ax, line, color=color, linewidth=linewidth)
def plot_point(ax, pt, marker='o', markersize=2):
""" Plot a single Point geometry """
ax.plot(pt.x, pt.y, marker=marker, markersize=markersize, linewidth=0)
def gencolor(N, colormap='Set1'):
"""
Color generator intended to work with one of the ColorBrewer
qualitative color scales.
Suggested values of colormap are the following:
Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3
(although any matplotlib colormap will work).
"""
from matplotlib import cm
# don't use more than 9 discrete colors
n_colors = min(N, 9)
cmap = cm.get_cmap(colormap, n_colors)
colors = cmap(range(n_colors))
for i in xrange(N):
yield colors[i % n_colors]
def plot_series(s, colormap='Set1', alpha=0.5, axes=None):
""" Plot a GeoSeries
Generate a plot of a GeoSeries geometry with matplotlib.
Parameters
----------
Series
The GeoSeries to be plotted. Currently Polygon,
MultiPolygon, LineString, MultiLineString and Point
geometries can be plotted.
colormap : str (default 'Set1')
The name of a colormap recognized by matplotlib. Any
colormap will work, but categorical colormaps are
generally recommended. Examples of useful discrete
colormaps include:
Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3
alpha : float (default 0.5)
Alpha value for polygon fill regions. Has no effect for
lines or points.
axes : matplotlib.pyplot.Artist (default None)
axes on which to draw the plot
Returns
-------
matplotlib axes instance
"""
import matplotlib.pyplot as plt
if axes == None:
fig = plt.gcf()
fig.add_subplot(111, aspect='equal')
ax = plt.gca()
else:
ax = axes
color = gencolor(len(s), colormap=colormap)
for geom in s:
if geom.type == 'Polygon' or geom.type == 'MultiPolygon':
plot_multipolygon(ax, geom, facecolor=next(color), alpha=alpha)
elif geom.type == 'LineString' or geom.type == 'MultiLineString':
plot_multilinestring(ax, geom, color=next(color))
elif geom.type == 'Point':
plot_point(ax, geom)
plt.draw()
return ax
def plot_dataframe(s, column=None, colormap=None, alpha=0.5,
categorical=False, legend=False, axes=None, scheme=None,
k=5):
""" Plot a GeoDataFrame
Generate a plot of a GeoDataFrame with matplotlib. If a
column is specified, the plot coloring will be based on values
in that column. Otherwise, a categorical plot of the
geometries in the `geometry` column will be generated.
Parameters
----------
GeoDataFrame
The GeoDataFrame to be plotted. Currently Polygon,
MultiPolygon, LineString, MultiLineString and Point
geometries can be plotted.
column : str (default None)
The name of the column to be plotted.
categorical : bool (default False)
If False, colormap will reflect numerical values of the
column being plotted. For non-numerical columns (or if
column=None), this will be set to True.
colormap : str (default 'Set1')
The name of a colormap recognized by matplotlib.
alpha : float (default 0.5)
Alpha value for polygon fill regions. Has no effect for
lines or points.
legend : bool (default False)
Plot a legend (Experimental; currently for categorical
plots only)
axes : matplotlib.pyplot.Artist (default None)
axes on which to draw the plot
scheme : pysal.esda.mapclassify.Map_Classifier
Choropleth classification schemes
k : int (default 5)
Number of classes (ignored if scheme is None)
Returns
-------
matplotlib axes instance
"""
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.colors import Normalize
from matplotlib import cm
if column is None:
return plot_series(s.geometry, colormap=colormap, alpha=alpha, axes=axes)
else:
if s[column].dtype is np.dtype('O'):
categorical = True
if categorical:
if colormap is None:
colormap = 'Set1'
categories = list(set(s[column].values))
categories.sort()
valuemap = dict([(k, v) for (v, k) in enumerate(categories)])
values = [valuemap[k] for k in s[column]]
else:
values = s[column]
if scheme is not None:
values = __pysal_choro(values, scheme, k=k)
cmap = norm_cmap(values, colormap, Normalize, cm)
if axes == None:
fig = plt.gcf()
fig.add_subplot(111, aspect='equal')
ax = plt.gca()
else:
ax = axes
for geom, value in zip(s.geometry, values):
if geom.type == 'Polygon' or geom.type == 'MultiPolygon':
plot_multipolygon(ax, geom, facecolor=cmap.to_rgba(value), alpha=alpha)
elif geom.type == 'LineString' or geom.type == 'MultiLineString':
plot_multilinestring(ax, geom, color=cmap.to_rgba(value))
# TODO: color point geometries
elif geom.type == 'Point':
plot_point(ax, geom)
if legend:
if categorical:
patches = []
for value, cat in enumerate(categories):
patches.append(Line2D([0], [0], linestyle="none",
marker="o", alpha=alpha,
markersize=10, markerfacecolor=cmap.to_rgba(value)))
ax.legend(patches, categories, numpoints=1, loc='best')
else:
# TODO: show a colorbar
raise NotImplementedError
plt.draw()
return ax
def __pysal_choro(values, scheme, k=5):
""" Wrapper for choropleth schemes from PySAL for use with plot_dataframe
Parameters
----------
values
Series to be plotted
scheme
pysal.esda.mapclassify classificatin scheme ['Equal_interval'|'Quantiles'|'Fisher_Jenks']
k
number of classes (2 <= k <=9)
Returns
-------
values
Series with values replaced with class identifier if PySAL is available, otherwise the original values are used
"""
try:
from pysal.esda.mapclassify import Quantiles, Equal_Interval, Fisher_Jenks
schemes = {}
schemes['equal_interval'] = Equal_Interval
schemes['quantiles'] = Quantiles
schemes['fisher_jenks'] = Fisher_Jenks
s0 = scheme
scheme = scheme.lower()
if scheme not in schemes:
scheme = 'quantiles'
print('Unrecognized scheme: ', s0)
print('Using Quantiles instead')
if k<2 or k>9:
print('Invalid k: ', k)
print('2<=k<=9, setting k=5 (default)')
k = 5
binning = schemes[scheme](values, k)
values = binning.yb
except ImportError:
print('PySAL not installed, setting map to default')
return values
def norm_cmap(values, cmap, normalize, cm):
""" Normalize and set colormap
Parameters
----------
values
Series or array to be normalized
cmap
matplotlib Colormap
normalize
matplotlib.colors.Normalize
cm
matplotlib.cm
Returns
-------
n_cmap
mapping of normalized values to colormap (cmap)
"""
mn, mx = min(values), max(values)
norm = normalize(vmin=mn, vmax=mx)
n_cmap = cm.ScalarMappable(norm=norm, cmap=cmap)
return n_cmap
|