def make_prop_lines(gdf, field_name, color='red', normalize_values=False,
norm_min=None, norm_max=None, axes=None):
"""
Display a GeoDataFrame collection of (Multi)LineStrings,
proportionaly to numerical field.
Parameters
----------
gdf: GeoDataFrame
The collection of linestring/multilinestring to be displayed
field_name: String
The name of the field containing values to scale the line width of
each border.
color: String
The color to render the lines with.
normalize_values: Boolean, default False
Normalize the value of the 'field_name' column between norm_min and
norm_max.
norm_min: float, default None
The linewidth for the minimum value to plot.
norm_max: float, default None
The linewidth for the maximum value to plot.
axes:
Axes on which to draw the plot.
Return
------
axes: matplotlib.axes._subplots.AxesSubplot
"""
from geopandas.plotting import plot_linestring, plot_multilinestring
from shapely.geometry import MultiLineString
import matplotlib.pyplot as plt
if normalize_values and norm_max and norm_min:
vals = (norm_max - norm_min) * (normalize(gdf[field_name].astype(float))).T + norm_min
elif normalize_values:
print('Warning : values where not normalized '
'(norm_max or norm_min is missing)')
vals = gdf[field_name].values
else:
vals = gdf[field_name].values
if not axes:
axes = plt.gca()
for nbi, line in enumerate(gdf.geometry.values):
if isinstance(line, MultiLineString):
plot_multilinestring(axes, gdf.geometry.iloc[nbi],
linewidth=vals[nbi], color=color)
else:
plot_linestring(axes, gdf.geometry.iloc[nbi],
linewidth=vals[nbi], color=color)
return axes
def make_prop_lines(gdf, field_name, color="red", normalize_values=False, norm_min=None, norm_max=None, axes=None):
"""
Display a GeoDataFrame collection of (Multi)LineStrings,
proportionaly to numerical field.
Parameters
----------
gdf: GeoDataFrame
The collection of linestring/multilinestring to be displayed
field_name: String
The name of the field containing values to scale the line width of
each border.
color: String
The color to render the lines with.
normalize_values: Boolean, default False
Normalize the value of the 'field_name' column between norm_min and
norm_max.
norm_min: float, default None
The linewidth for the minimum value to plot.
norm_max: float, default None
The linewidth for the maximum value to plot.
axes:
Axes on which to draw the plot.
Return
------
axes: matplotlib.axes._subplots.AxesSubplot
"""
from geopandas.plotting import plot_linestring, plot_multilinestring
from shapely.geometry import MultiLineString
import matplotlib.pyplot as plt
if normalize_values and norm_max and norm_min:
vals = (norm_max - norm_min) * (normalize(gdf[field_name].astype(float))).T + norm_min
elif normalize_values:
print("Warning : values where not normalized " "(norm_max or norm_min is missing)")
vals = gdf[field_name].values
else:
vals = gdf[field_name].values
if not axes:
axes = plt.gca()
for nbi, line in enumerate(gdf.geometry.values):
if isinstance(line, MultiLineString):
plot_multilinestring(axes, gdf.geometry.iloc[nbi], linewidth=vals[nbi], color=color)
else:
plot_linestring(axes, gdf.geometry.iloc[nbi], linewidth=vals[nbi], color=color)
return axes
def m_plot_dataframe(s,
column=None,
colormap=None,
alpha=0.5,
edgecolor=None,
categorical=False,
legend=False,
axes=None,
scheme=None,
contour_poly_width=0.5,
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([(key, v) for (v, key) in enumerate(categories)])
values = [valuemap[key] for key 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 not axes:
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':
m_plot_multipolygon(ax,
geom,
facecolor=cmap.to_rgba(value),
edgecolor=edgecolor,
linewidth=contour_poly_width,
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 m_plot_dataframe(s, column=None, colormap=None, alpha=0.5, edgecolor=None,
categorical=False, legend=False, axes=None, scheme=None,
contour_poly_width=0.5,
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([(key, v) for (v, key) in enumerate(categories)])
values = [valuemap[key] for key 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 not axes:
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':
m_plot_multipolygon(ax, geom, facecolor=cmap.to_rgba(value),
edgecolor=edgecolor,
linewidth=contour_poly_width, 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