【发布时间】：2015-11-26 19:45:45
【问题描述】：
【问题讨论】：
标签： python matplotlib cartopy
【发布时间】：2015-11-26 19:45:45
【问题描述】：
通过在 CartoPy 0.15 中添加 geodesic module，我们现在可以相当轻松地计算地图上的精确长度。弄清楚如何在地图上的直线上找到两个点，这两个点在球面上的距离是正确的，这有点棘手。指定地图上的方向后，我会执行指数搜索以找到足够远的点。然后我执行二分搜索以找到足够接近所需距离的点。
scale_bar 函数很简单，但它有很多选项。基本签名是scale_bar(ax, location, length)。 ax 是任何 CartoPy 轴，location 是轴坐标中条形左侧的位置（因此每个坐标是从 0 到 1），length 是条形的长度（以千米为单位）。支持其他长度，例如 metres_per_unit 和 unit_name 关键字参数。
额外的关键字参数（如color）被简单地传递给text 和plot。但是，特定于text 或plot 的关键字参数（如family 或path_effects）必须作为字典通过text_kwargs 和plot_kwargs 传递。
我已经包含了一些我认为是常见用例的示例。
请分享任何问题、cmets 或批评。
比例尺.py
import numpy as np
import cartopy.crs as ccrs
import cartopy.geodesic as cgeo
def _axes_to_lonlat(ax, coords):
"""(lon, lat) from axes coordinates."""
display = ax.transAxes.transform(coords)
data = ax.transData.inverted().transform(display)
lonlat = ccrs.PlateCarree().transform_point(*data, ax.projection)
return lonlat
def _upper_bound(start, direction, distance, dist_func):
"""A point farther than distance from start, in the given direction.
It doesn't matter which coordinate system start is given in, as long
as dist_func takes points in that coordinate system.
Args:
start: Starting point for the line.
direction Nonzero (2, 1)-shaped array, a direction vector.
distance: Positive distance to go past.
dist_func: A two-argument function which returns distance.
Returns:
Coordinates of a point (a (2, 1)-shaped NumPy array).
"""
if distance <= 0:
raise ValueError(f"Minimum distance is not positive: {distance}")
if np.linalg.norm(direction) == 0:
raise ValueError("Direction vector must not be zero.")
# Exponential search until the distance between start and end is
# greater than the given limit.
length = 0.1
end = start + length * direction
while dist_func(start, end) < distance:
length *= 2
end = start + length * direction
return end
def _distance_along_line(start, end, distance, dist_func, tol):
"""Point at a distance from start on the segment from start to end.
It doesn't matter which coordinate system start is given in, as long
as dist_func takes points in that coordinate system.
Args:
start: Starting point for the line.
end: Outer bound on point's location.
distance: Positive distance to travel.
dist_func: Two-argument function which returns distance.
tol: Relative error in distance to allow.
Returns:
Coordinates of a point (a (2, 1)-shaped NumPy array).
"""
initial_distance = dist_func(start, end)
if initial_distance < distance:
raise ValueError(f"End is closer to start ({initial_distance}) than "
f"given distance ({distance}).")
if tol <= 0:
raise ValueError(f"Tolerance is not positive: {tol}")
# Binary search for a point at the given distance.
left = start
right = end
while not np.isclose(dist_func(start, right), distance, rtol=tol):
midpoint = (left + right) / 2
# If midpoint is too close, search in second half.
if dist_func(start, midpoint) < distance:
left = midpoint
# Otherwise the midpoint is too far, so search in first half.
else:
right = midpoint
return right
def _point_along_line(ax, start, distance, angle=0, tol=0.01):
"""Point at a given distance from start at a given angle.
Args:
ax: CartoPy axes.
start: Starting point for the line in axes coordinates.
distance: Positive physical distance to travel.
angle: Anti-clockwise angle for the bar, in radians. Default: 0
tol: Relative error in distance to allow. Default: 0.01
Returns:
Coordinates of a point (a (2, 1)-shaped NumPy array).
"""
# Direction vector of the line in axes coordinates.
direction = np.array([np.cos(angle), np.sin(angle)])
geodesic = cgeo.Geodesic()
# Physical distance between points.
def dist_func(a_axes, b_axes):
a_phys = _axes_to_lonlat(ax, a_axes)
b_phys = _axes_to_lonlat(ax, b_axes)
# Geodesic().inverse returns a NumPy MemoryView like [[distance,
# start azimuth, end azimuth]].
return geodesic.inverse(a_phys, b_phys).base[0, 0]
end = _upper_bound(start, direction, distance, dist_func)
return _distance_along_line(start, end, distance, dist_func, tol)
def scale_bar(ax, location, length, metres_per_unit=1000, unit_name='km',
tol=0.01, angle=0, color='black', linewidth=3, text_offset=0.005,
ha='center', va='bottom', plot_kwargs=None, text_kwargs=None,
**kwargs):
"""Add a scale bar to CartoPy axes.
For angles between 0 and 90 the text and line may be plotted at
slightly different angles for unknown reasons. To work around this,
override the 'rotation' keyword argument with text_kwargs.
Args:
ax: CartoPy axes.
location: Position of left-side of bar in axes coordinates.
length: Geodesic length of the scale bar.
metres_per_unit: Number of metres in the given unit. Default: 1000
unit_name: Name of the given unit. Default: 'km'
tol: Allowed relative error in length of bar. Default: 0.01
angle: Anti-clockwise rotation of the bar.
color: Color of the bar and text. Default: 'black'
linewidth: Same argument as for plot.
text_offset: Perpendicular offset for text in axes coordinates.
Default: 0.005
ha: Horizontal alignment. Default: 'center'
va: Vertical alignment. Default: 'bottom'
**plot_kwargs: Keyword arguments for plot, overridden by **kwargs.
**text_kwargs: Keyword arguments for text, overridden by **kwargs.
**kwargs: Keyword arguments for both plot and text.
"""
# Setup kwargs, update plot_kwargs and text_kwargs.
if plot_kwargs is None:
plot_kwargs = {}
if text_kwargs is None:
text_kwargs = {}
plot_kwargs = {'linewidth': linewidth, 'color': color, **plot_kwargs,
**kwargs}
text_kwargs = {'ha': ha, 'va': va, 'rotation': angle, 'color': color,
**text_kwargs, **kwargs}
# Convert all units and types.
location = np.asarray(location) # For vector addition.
length_metres = length * metres_per_unit
angle_rad = angle * np.pi / 180
# End-point of bar.
end = _point_along_line(ax, location, length_metres, angle=angle_rad,
tol=tol)
# Coordinates are currently in axes coordinates, so use transAxes to
# put into data coordinates. *zip(a, b) produces a list of x-coords,
# then a list of y-coords.
ax.plot(*zip(location, end), transform=ax.transAxes, **plot_kwargs)
# Push text away from bar in the perpendicular direction.
midpoint = (location + end) / 2
offset = text_offset * np.array([-np.sin(angle_rad), np.cos(angle_rad)])
text_location = midpoint + offset
# 'rotation' keyword argument is in text_kwargs.
ax.text(*text_location, f"{length} {unit_name}", rotation_mode='anchor',
transform=ax.transAxes, **text_kwargs)
demo.py
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from scalebar import scale_bar
fig = plt.figure(1, figsize=(10, 10))
ax = fig.add_subplot(111, projection=ccrs.Mercator())
ax.set_extent([-180, 180, -85, 85])
ax.coastlines(facecolor='black')
ax.add_feature(cfeature.LAND)
# Standard 6,000 km scale bar.
scale_bar(ax, (0.65, 0.4), 6_000)
# Length of the bar reflects its position on the map.
scale_bar(ax, (0.55, 0.7), 6_000, color='green')
# Bar can be placed at any angle. Any units can be used.
scale_bar(ax, (0.4, 0.4), 3_000, metres_per_unit=1609, angle=-90,
unit_name='mi', color='red')
# Text and line can be styled separately. Keywords are simply passed to
# text or plot.
text_kwargs = dict(family='serif', size='xx-large', color='red')
plot_kwargs = dict(linestyle='dashed', color='blue')
scale_bar(ax, (0.05, 0.3), 6_000, text_kwargs=text_kwargs,
plot_kwargs=plot_kwargs)
# Angles between 0 and 90 may result in the text and line plotted at
# slightly different angles for an unknown reason.
scale_bar(ax, (0.45, 0.15), 5_000, color='purple', angle=45, text_offset=0)
# To get around this override the text's angle and fiddle manually.
scale_bar(ax, (0.55, 0.15), 5_000, color='orange', angle=45, text_offset=0,
text_kwargs={'rotation': 41})
plt.show()
【讨论】：
-
我认为这看起来很有希望。在某些时候，画一个标尺类型的比例尺会很好，但这是一个好的开始。是
location参数决定了比例尺的大小（因为距离因您在地图上的位置和投影而异）？ -
@gauteh
location是比例尺的左端。length是它的长度（在地球上，而不是地图上）。angle是钢筋延伸的方向。所有这三个参数都会影响钢筋的长度。无法直接设置条的屏幕长度，尽管用端点之间的距离标记条很简单。 -
@gauteh 我计划添加一个带有额外文本标签的类似尺子的样式，但我现在还有其他事情要做。当我扩展我的代码时，我会编辑这个答案。
-
亲爱的 Mephistolotl，感谢您的回答。我想知道您的代码是否也适用于其他 cartopy 投影。真诚的，
-
@KhalilAlHooti 我的答案中的代码已经超过三年了。我怀疑从那以后 CartoPy 发生了很多变化。我不会重新检查此代码，但欢迎您发布改进。
这是我为自己使用而编写的 Cartopy 比例尺函数，它使用了 pp-mo 答案的更简单版本： 编辑：修改代码以创建一个新的居中投影，以便比例尺平行于许多坐标系的轴，包括一些正交和更大的地图，并且无需指定 utm 系统。 如果未指定，还添加了计算比例尺长度的代码。
import cartopy.crs as ccrs
import numpy as np
def scale_bar(ax, length=None, location=(0.5, 0.05), linewidth=3):
"""
ax is the axes to draw the scalebar on.
length is the length of the scalebar in km.
location is center of the scalebar in axis coordinates.
(ie. 0.5 is the middle of the plot)
linewidth is the thickness of the scalebar.
"""
#Get the limits of the axis in lat long
llx0, llx1, lly0, lly1 = ax.get_extent(ccrs.PlateCarree())
#Make tmc horizontally centred on the middle of the map,
#vertically at scale bar location
sbllx = (llx1 + llx0) / 2
sblly = lly0 + (lly1 - lly0) * location[1]
tmc = ccrs.TransverseMercator(sbllx, sblly)
#Get the extent of the plotted area in coordinates in metres
x0, x1, y0, y1 = ax.get_extent(tmc)
#Turn the specified scalebar location into coordinates in metres
sbx = x0 + (x1 - x0) * location[0]
sby = y0 + (y1 - y0) * location[1]
#Calculate a scale bar length if none has been given
#(Theres probably a more pythonic way of rounding the number but this works)
if not length:
length = (x1 - x0) / 5000 #in km
ndim = int(np.floor(np.log10(length))) #number of digits in number
length = round(length, -ndim) #round to 1sf
#Returns numbers starting with the list
def scale_number(x):
if str(x)[0] in ['1', '2', '5']: return int(x)
else: return scale_number(x - 10 ** ndim)
length = scale_number(length)
#Generate the x coordinate for the ends of the scalebar
bar_xs = [sbx - length * 500, sbx + length * 500]
#Plot the scalebar
ax.plot(bar_xs, [sby, sby], transform=tmc, color='k', linewidth=linewidth)
#Plot the scalebar label
ax.text(sbx, sby, str(length) + ' km', transform=tmc,
horizontalalignment='center', verticalalignment='bottom')
它有一些限制，但相对简单，所以如果你想要不同的东西，我希望你能看看如何改变它。
示例用法：
import matplotlib.pyplot as plt
ax = plt.axes(projection=ccrs.Mercator())
plt.title('Cyprus')
ax.set_extent([31, 35.5, 34, 36], ccrs.Geodetic())
ax.coastlines(resolution='10m')
scale_bar(ax, 100)
plt.show()
【讨论】：
这是@Siyh 答案的精炼版本，其中添加：
- 自动 UTM 区域选择
- 文本/栏后面的缓冲区，以便在背景中显示
- 指北针
注意事项：
- 如果您的轴不使用 UTM，则条形将被绘制成弯曲的
- 指北针假定北向上
代码：
import os
import cartopy.crs as ccrs
from math import floor
import matplotlib.pyplot as plt
from matplotlib import patheffects
import matplotlib
if os.name == 'nt':
matplotlib.rc('font', family='Arial')
else: # might need tweaking, must support black triangle for N arrow
matplotlib.rc('font', family='DejaVu Sans')
def utm_from_lon(lon):
"""
utm_from_lon - UTM zone for a longitude
Not right for some polar regions (Norway, Svalbard, Antartica)
:param float lon: longitude
:return: UTM zone number
:rtype: int
"""
return floor( ( lon + 180 ) / 6) + 1
def scale_bar(ax, proj, length, location=(0.5, 0.05), linewidth=3,
units='km', m_per_unit=1000):
"""
http://stackoverflow.com/a/35705477/1072212
ax is the axes to draw the scalebar on.
proj is the projection the axes are in
location is center of the scalebar in axis coordinates ie. 0.5 is the middle of the plot
length is the length of the scalebar in km.
linewidth is the thickness of the scalebar.
units is the name of the unit
m_per_unit is the number of meters in a unit
"""
# find lat/lon center to find best UTM zone
x0, x1, y0, y1 = ax.get_extent(proj.as_geodetic())
# Projection in metres
utm = ccrs.UTM(utm_from_lon((x0+x1)/2))
# Get the extent of the plotted area in coordinates in metres
x0, x1, y0, y1 = ax.get_extent(utm)
# Turn the specified scalebar location into coordinates in metres
sbcx, sbcy = x0 + (x1 - x0) * location[0], y0 + (y1 - y0) * location[1]
# Generate the x coordinate for the ends of the scalebar
bar_xs = [sbcx - length * m_per_unit/2, sbcx + length * m_per_unit/2]
# buffer for scalebar
buffer = [patheffects.withStroke(linewidth=5, foreground="w")]
# Plot the scalebar with buffer
ax.plot(bar_xs, [sbcy, sbcy], transform=utm, color='k',
linewidth=linewidth, path_effects=buffer)
# buffer for text
buffer = [patheffects.withStroke(linewidth=3, foreground="w")]
# Plot the scalebar label
t0 = ax.text(sbcx, sbcy, str(length) + ' ' + units, transform=utm,
horizontalalignment='center', verticalalignment='bottom',
path_effects=buffer, zorder=2)
left = x0+(x1-x0)*0.05
# Plot the N arrow
t1 = ax.text(left, sbcy, u'\u25B2\nN', transform=utm,
horizontalalignment='center', verticalalignment='bottom',
path_effects=buffer, zorder=2)
# Plot the scalebar without buffer, in case covered by text buffer
ax.plot(bar_xs, [sbcy, sbcy], transform=utm, color='k',
linewidth=linewidth, zorder=3)
if __name__ == '__main__':
ax = plt.axes(projection=ccrs.Mercator())
plt.title('Cyprus')
ax.set_extent([31, 35.5, 34, 36], ccrs.Geodetic())
ax.stock_img()
ax.coastlines(resolution='10m')
scale_bar(ax, ccrs.Mercator(), 100) # 100 km scale bar
# or to use m instead of km
# scale_bar(ax, ccrs.Mercator(), 100000, m_per_unit=1, units='m')
# or to use miles instead of km
# scale_bar(ax, ccrs.Mercator(), 60, m_per_unit=1609.34, units='miles')
plt.show()
【讨论】：
-
看起来不错，代码中的数字
500是做什么的？能否推广到其他领域？我专门与 UPS 合作。 -
@gauteh - 很好的问题，谢谢。代码被硬编码以绘制以公里为单位的条形图，因此 500 是 1 km / 2，以使其居中。但是根据您的评论，我将其设为参数，现在它支持其他单位:-)
我认为对此没有简单的盆栽解决方案：您必须自己使用图形元素将其绘制出来。
几年前，我编写了一些自适应代码来将比例尺添加到任意比例的 OS 网格地图。
我认为这并不是您真正想要的，但它显示了必要的技术：
def add_osgb_scalebar(ax, at_x=(0.1, 0.4), at_y=(0.05, 0.075), max_stripes=5):
"""
Add a scalebar to a GeoAxes of type cartopy.crs.OSGB (only).
Args:
* at_x : (float, float)
target axes X coordinates (0..1) of box (= left, right)
* at_y : (float, float)
axes Y coordinates (0..1) of box (= lower, upper)
* max_stripes
typical/maximum number of black+white regions
"""
# ensure axis is an OSGB map (meaning coords are just metres)
assert isinstance(ax.projection, ccrs.OSGB)
# fetch axes coordinate mins+maxes
x0, x1 = ax.get_xlim()
y0, y1 = ax.get_ylim()
# set target rectangle in-visible-area (aka 'Axes') coordinates
ax0, ax1 = at_x
ay0, ay1 = at_y
# choose exact X points as sensible grid ticks with Axis 'ticker' helper
x_targets = [x0 + ax * (x1 - x0) for ax in (ax0, ax1)]
ll = mpl.ticker.MaxNLocator(nbins=max_stripes, steps=[1,2,4,5,10])
x_vals = ll.tick_values(*x_targets)
# grab min+max for limits
xl0, xl1 = x_vals[0], x_vals[-1]
# calculate Axes Y coordinates of box top+bottom
yl0, yl1 = [y0 + ay * (y1 - y0) for ay in [ay0, ay1]]
# calculate Axes Y distance of ticks + label margins
y_margin = (yl1-yl0)*0.25
# fill black/white 'stripes' and draw their boundaries
fill_colors = ['black', 'white']
i_color = 0
for xi0, xi1 in zip(x_vals[:-1],x_vals[1:]):
# fill region
plt.fill((xi0, xi1, xi1, xi0, xi0), (yl0, yl0, yl1, yl1, yl0),
fill_colors[i_color])
# draw boundary
plt.plot((xi0, xi1, xi1, xi0, xi0), (yl0, yl0, yl1, yl1, yl0),
'black')
i_color = 1 - i_color
# add short tick lines
for x in x_vals:
plt.plot((x, x), (yl0, yl0-y_margin), 'black')
# add a scale legend 'Km'
font_props = mfonts.FontProperties(size='medium', weight='bold')
plt.text(
0.5 * (xl0 + xl1),
yl1 + y_margin,
'Km',
verticalalignment='bottom',
horizontalalignment='center',
fontproperties=font_props)
# add numeric labels
for x in x_vals:
plt.text(x,
yl0 - 2 * y_margin,
'{:g}'.format((x - xl0) * 0.001),
verticalalignment='top',
horizontalalignment='center',
fontproperties=font_props)
虽然很乱，不是吗？
您可能认为可能可以为此添加某种“浮动轴对象”，以提供自动自重缩放图形，但我无法找到这样做的方法（我想我还是做不到）。
HTH
【讨论】：
-
这很有用，但不是我想要的。或许可以以此为灵感。
基于上面提供的先前示例，并从here，我开发了一种使用cartopy 绘制比例尺的替代方法。
该方法已通过 cartopy.crs.PlateCarree() 投影进行验证。然而，该算法在其他投影中无法正常工作。
这是一个例子：
# importing main libraries
import cartopy
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import font_manager as mfonts
import matplotlib.ticker as mticker
import matplotlib.patches as patches
import geopandas as gpd
import pandas as pd
def get_standard_gdf():
""" basic function for getting some geographical data in geopandas GeoDataFrame python's instance:
An example data can be downloaded from Brazilian IBGE:
ref: ftp://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2017/Brasil/BR/br_municipios.zip
"""
gdf_path = r'C:\path_to_shp\shapefile.shp'
return gpd.read_file(gdf_path)
----------
# defining functions for scalebar
def _crs_coord_project(crs_target, xcoords, ycoords, crs_source):
""" metric coordinates (x, y) from cartopy.crs_source"""
axes_coords = crs_target.transform_points(crs_source, xcoords, ycoords)
return axes_coords
def _add_bbox(ax, list_of_patches, paddings={}, bbox_kwargs={}):
'''
Description:
This helper function adds a box behind the scalebar:
Code inspired by: https://stackoverflow.com/questions/17086847/box-around-text-in-matplotlib
'''
zorder = list_of_patches[0].get_zorder() - 1
xmin = min([t.get_window_extent().xmin for t in list_of_patches])
xmax = max([t.get_window_extent().xmax for t in list_of_patches])
ymin = min([t.get_window_extent().ymin for t in list_of_patches])
ymax = max([t.get_window_extent().ymax for t in list_of_patches])
xmin, ymin = ax.transData.inverted().transform((xmin, ymin))
xmax, ymax = ax.transData.inverted().transform((xmax, ymax))
xmin = xmin - ( (xmax-xmin) * paddings['xmin'])
ymin = ymin - ( (ymax-ymin) * paddings['ymin'])
xmax = xmax + ( (xmax-xmin) * paddings['xmax'])
ymax = ymax + ( (ymax-ymin) * paddings['ymax'])
width = (xmax-xmin)
height = (ymax-ymin)
# Setting xmin according to height
rect = patches.Rectangle((xmin,ymin),
width,
height,
facecolor=bbox_kwargs['facecolor'],
edgecolor =bbox_kwargs['edgecolor'],
alpha=bbox_kwargs['alpha'],
transform=ax.projection,
fill=True,
clip_on=False,
zorder=zorder)
ax.add_patch(rect)
return ax
def add_scalebar(ax, metric_distance=100,
at_x=(0.1, 0.4),
at_y=(0.05, 0.075),
max_stripes=5,
ytick_label_margins = 0.25,
fontsize= 8,
font_weight='bold',
rotation = 45,
zorder=999,
paddings = {'xmin':0.3,
'xmax':0.3,
'ymin':0.3,
'ymax':0.3},
bbox_kwargs = {'facecolor':'w',
'edgecolor':'k',
'alpha':0.7}
):
"""
Add a scalebar to a GeoAxes of type cartopy.crs.OSGB (only).
Args:
* at_x : (float, float)
target axes X coordinates (0..1) of box (= left, right)
* at_y : (float, float)
axes Y coordinates (0..1) of box (= lower, upper)
* max_stripes
typical/maximum number of black+white regions
"""
old_proj = ax.projection
ax.projection = ccrs.PlateCarree()
# Set a planar (metric) projection for the centroid of a given axes projection:
# First get centroid lon and lat coordinates:
lon_0, lon_1, lat_0, lat_1 = ax.get_extent(ax.projection.as_geodetic())
central_lon = np.mean([lon_0, lon_1])
central_lat = np.mean([lat_0, lat_1])
# Second: set the planar (metric) projection centered in the centroid of the axes;
# Centroid coordinates must be in lon/lat.
proj=ccrs.EquidistantConic(central_longitude=central_lon, central_latitude=central_lat)
# fetch axes coordinates in meters
x0, x1, y0, y1 = ax.get_extent(proj)
ymean = np.mean([y0, y1])
# set target rectangle in-visible-area (aka 'Axes') coordinates
axfrac_ini, axfrac_final = at_x
ayfrac_ini, ayfrac_final = at_y
# choose exact X points as sensible grid ticks with Axis 'ticker' helper
xcoords = []
ycoords = []
xlabels = []
for i in range(0 , 1+ max_stripes):
dx = (metric_distance * i) + x0
xlabels.append(dx - x0)
xcoords.append(dx)
ycoords.append(ymean)
# Convertin to arrays:
xcoords = np.asanyarray(xcoords)
ycoords = np.asanyarray(ycoords)
# Ensuring that the coordinate projection is in degrees:
x_targets, y_targets, z_targets = _crs_coord_project(ax.projection, xcoords, ycoords, proj).T
x_targets = [x + (axfrac_ini * (lon_1 - lon_0)) for x in x_targets]
# Checking x_ticks in axes projection coordinates
#print('x_targets', x_targets)
#Setting transform for plotting
transform = ax.projection
# grab min+max for limits
xl0, xl1 = x_targets[0], x_targets[-1]
# calculate Axes Y coordinates of box top+bottom
yl0, yl1 = [lat_0 + ay_frac * (lat_1 - lat_0) for ay_frac in [ayfrac_ini, ayfrac_final]]
# calculate Axes Y distance of ticks + label margins
y_margin = (yl1-yl0)*ytick_label_margins
# fill black/white 'stripes' and draw their boundaries
fill_colors = ['black', 'white']
i_color = 0
filled_boxs = []
for xi0, xi1 in zip(x_targets[:-1],x_targets[1:]):
# fill region
filled_box = plt.fill(
(xi0, xi1, xi1, xi0, xi0),
(yl0, yl0, yl1, yl1, yl0),
fill_colors[i_color],
transform=transform,
clip_on=False,
zorder=zorder
)
filled_boxs.append(filled_box[0])
# draw boundary
plt.plot((xi0, xi1, xi1, xi0, xi0),
(yl0, yl0, yl1, yl1, yl0),
'black',
clip_on=False,
transform=transform,
zorder=zorder)
i_color = 1 - i_color
# adding boxes
_add_bbox(ax,
filled_boxs,
bbox_kwargs = bbox_kwargs ,
paddings =paddings)
# add short tick lines
for x in x_targets:
plt.plot((x, x), (yl0, yl0-y_margin), 'black',
transform=transform,
zorder=zorder,
clip_on=False)
# add a scale legend 'Km'
font_props = mfonts.FontProperties(size=fontsize,
weight=font_weight)
plt.text(
0.5 * (xl0 + xl1),
yl1 + y_margin,
'Km',
color='k',
verticalalignment='bottom',
horizontalalignment='center',
fontproperties=font_props,
transform=transform,
clip_on=False,
zorder=zorder)
# add numeric labels
for x, xlabel in zip(x_targets, xlabels):
print('Label set in: ', x, yl0 - 2 * y_margin)
plt.text(x,
yl0 - 2 * y_margin,
'{:g}'.format((xlabel) * 0.001),
verticalalignment='top',
horizontalalignment='center',
fontproperties=font_props,
transform=transform,
rotation=rotation,
clip_on=False,
zorder=zorder+1,
#bbox=dict(facecolor='red', alpha=0.5) # this would add a box only around the xticks
)
# Adjusting figure borders to ensure that the scalebar is within its limits
ax.projection = old_proj
ax.get_figure().canvas.draw()
fig.tight_layout()
----------
定义一些辅助函数来设置坐标轴的样式#plotting
def format_ax(ax, projection):
xlim = ax.get_xlim()
ylim = ax.get_ylim()
ax.set_global()
ax.coastlines()
ax.set_xlim(xlim)
ax.set_ylim(ylim)
def add_grider(ax, nticks=5):
if isinstance(ax.projection, ccrs.PlateCarree):
Grider = ax.gridlines(draw_labels=True)
Grider.xformatter = LONGITUDE_FORMATTER
Grider.yformatter = LATITUDE_FORMATTER
Grider.xlabels_top = False
Grider.ylabels_right = False
Grider.xlocator = mticker.MaxNLocator(nticks)
Grider.ylocator = mticker.MaxNLocator(nticks)
else:
xmin, xmax, ymin, ymax = ax.get_extent()
ax.set_xticks(np.arange(xmin, xmax, nticks))
ax.set_yticks(np.arange(ymin, ymax, nticks))
ax.grid(True)
----------
# Defining a main helper function for plotting:
def main(projection = ccrs.PlateCarree(central_longitude=0),
nticks=4):
fig, ax1 = plt.subplots( figsize=(8, 10), subplot_kw={'projection':projection})
# Label axes of a Plate Carree projection with a central longitude of 180:
#for enum, proj in enumerate(['Mercator, PlateCarree']):
gdf = get_standard_gdf()
if gdf.crs.is_projected:
epsg = gdf.crs.to_epsg()
crs_epsg = ccrs.epsg(epsg)
else:
crs_epsg = ccrs.PlateCarree()
gdf.plot(ax=ax1, transform=projection)
format_ax(ax1, projection)
add_grider(ax1, nticks)
ax1.set_title('Projection {0}'.format(ax1.projection.__class__.__name__))
plt.draw()
return fig, fig.get_axes()
----------
# Example of the case
length = 1000
fig, axes = main(ccrs.PlateCarree())
for ax in axes:
add_scalebar(ax,
metric_distance=200_000 ,
at_x=(1.1, 1.3),
at_y=(0.08, 0.11),
max_stripes=4,
paddings = {'xmin':0.1,
'xmax':0.1,
'ymin':2.8,
'ymax':0.5},
fontsize=9,
font_weight='bold',
bbox_kwargs = {'facecolor':'w',
'edgecolor':'k',
'alpha':0.7})
fig.show()
这是同一地区（帕拉州 - 巴西）的两个数字，在“add_scalebar”函数中具有不同的设置。图 1 正是从上述设置中得出的。图 2 使用了一个变体：
add_scalebar(ax,
metric_distance=200_000 ,
at_x=(0.55, 0.3),
at_y=(0.08, 0.11),
max_stripes=4,
paddings = {'xmin':0.05,
'xmax':0.05,
'ymin':2.2,
'ymax':0.5},
fontsize=7,
font_weight='bold',
bbox_kwargs = {'facecolor':'w',
'edgecolor':'k',
'alpha':0.7})
唯一的问题是，这个提议的解决方案仍然需要扩展到其他 cartopy 投影（除了 PlateCarree）。
【讨论】：
-
您的代码可以正常工作。但是，我注意到实际距离和比例尺中显示的距离存在差异。我建议使用
pyproj而不是函数_crs_coord_project。
由于关于 cartopy 中比例尺的更新不多，我决定创建自己的...
（它是EOmaps 的一部分...我正在开发的基于 matplotlib/cartopy 的交互式地图库）
它的一些特点是：
- 完全可定制（更改比例、颜色、字体、框架等）
- 投影感知（它可以处理任何
cartopy投影） - 交互式 - 使用鼠标和键盘拖动、旋转和调整框架大小！
【讨论】：