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cartoee.py
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cartoee.py
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"""The cartoee module contains functions for creating publication-quality maps with cartopy and Earth Engine data."""
# *******************************************************************************#
# This module contains extra features of the geemap package. #
# The geemap community will maintain the extra features. #
# *******************************************************************************#
import logging
import os
import subprocess
import sys
import warnings
from collections.abc import Iterable
from io import BytesIO
import ee
import matplotlib as mpl
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
import requests
from matplotlib import cm, colors
from matplotlib import font_manager as mfonts
from .basemaps import custom_tiles
try:
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt
from cartopy.mpl.geoaxes import GeoAxes, GeoAxesSubplot
from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
from PIL import Image
except ImportError:
print(
"cartopy is not installed. Please see https://scitools.org.uk/cartopy/docs/latest/installing.html#installing for instructions on how to install cartopy.\n"
)
print(
"The easiest way to install cartopy is using conda: conda install -c conda-forge cartopy"
)
def check_dependencies():
"""Helper function to check dependencies used for cartoee
Dependencies not included in main geemap are: cartopy, PIL, and scipys
raises:
Exception: when conda is not found in path
Exception: when auto install fails to install/import packages
"""
import importlib
# check if conda in in path and available to use
is_conda = os.path.exists(os.path.join(sys.prefix, "conda-meta"))
# raise error if conda not found
if not is_conda:
raise Exception(
"Auto installation requires `conda`. Please install conda using the following instructions before use: https://docs.conda.io/projects/conda/en/latest/user-guide/install/"
)
# list of dependencies to check, ordered in decreasing complexity
# i.e. cartopy install should install PIL
dependencies = ["cartopy", "pillow", "scipy"]
# loop through dependency list and check if we can import module
# if not try to install
# install fail will be silent to continue through others if there is a failure
# correct install will be checked later
for dependency in dependencies:
try:
# see if we can import
importlib.import_module(dependency)
except ImportError:
# change the dependency name if it is PIL
# import vs install names are different for PIL...
# dependency = dependency if dependency is not "PIL" else "pillow"
# print info if not installed
logging.info(
f"The {dependency} package is not installed. Trying install..."
)
logging.info(f"Installing {dependency} ...")
# run the command
cmd = f"conda install -c conda-forge {dependency} -y"
proc = subprocess.Popen(
cmd,
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
)
# send command
out, _ = proc.communicate()
logging.info(out.decode())
# second pass through dependencies to check if everything was installed correctly
failed = []
for dependency in dependencies:
try:
importlib.import_module(dependency)
except ImportError:
# append failed imports
failed.append(dependency)
# check if there were any failed imports after trying install
if len(failed) > 0:
failed_str = ",".join(failed)
raise Exception(
f"Auto installation failed...the following dependencies were not installed '{failed_str}'"
)
else:
logging.info("All dependencies are successfully imported/installed!")
return
# check_dependencies()
def get_map(ee_object, proj=None, basemap=None, zoom_level=2, **kwargs):
"""
Wrapper function to create a new cartopy plot with project and adds Earth
Engine image results
Args:
ee_object (ee.Image | ee.FeatureCollection): Earth Engine image result to plot
proj (cartopy.crs, optional): Cartopy projection that determines the projection of the resulting plot. By default uses an equirectangular projection, PlateCarree
basemap (str, optional): Basemap to use. It can be one of ["ROADMAP", "SATELLITE", "TERRAIN", "HYBRID"] or cartopy.io.img_tiles, such as cimgt.StamenTerrain(). Defaults to None. See https://scitools.org.uk/cartopy/docs/v0.19/cartopy/io/img_tiles.html
zoom_level (int, optional): Zoom level of the basemap. Defaults to 2.
**kwargs: remaining keyword arguments are passed to addLayer()
Returns:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot): cartopy GeoAxesSubplot object with Earth Engine results displayed
"""
if (
isinstance(ee_object, ee.geometry.Geometry)
or isinstance(ee_object, ee.feature.Feature)
or isinstance(ee_object, ee.featurecollection.FeatureCollection)
):
features = ee.FeatureCollection(ee_object)
if "style" in kwargs and kwargs["style"] is not None:
style = kwargs["style"]
else:
style = {}
props = features.first().propertyNames().getInfo()
if "style" in props:
ee_object = features.style(**{"styleProperty": "style"})
else:
ee_object = features.style(**style)
elif isinstance(ee_object, ee.imagecollection.ImageCollection):
ee_object = ee_object.mosaic()
if proj is None:
proj = ccrs.PlateCarree()
if "style" in kwargs:
del kwargs["style"]
ax = mpl.pyplot.axes(projection=proj)
if basemap is not None:
if isinstance(basemap, str):
if basemap.upper() in ["ROADMAP", "SATELLITE", "TERRAIN", "HYBRID"]:
basemap = cimgt.GoogleTiles(
url=custom_tiles["xyz"][basemap.upper()]["url"]
)
try:
ax.add_image(basemap, zoom_level)
except Exception as e:
print("Failed to add basemap: ", e)
add_layer(ax, ee_object, **kwargs)
return ax
def add_layer(
ax, ee_object, dims=1000, region=None, cmap=None, vis_params=None, **kwargs
):
"""Add an Earth Engine image to a cartopy plot.
args:
ee_object (ee.Image | ee.FeatureCollection): Earth Engine image result to plot.
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object to add image overlay to
dims (list | tuple | int, optional): dimensions to request earth engine result as [WIDTH,HEIGHT]. If only one number is passed, it is used as the maximum, and the other dimension is computed by proportional scaling. Default None and infers dimensions
region (list | tuple, optional): geospatial region of the image to render in format [E,S,W,N]. By default, the whole image
cmap (str, optional): string specifying matplotlib colormap to colorize image. If cmap is specified visParams cannot contain 'palette' key
vis_params (dict, optional): visualization parameters as a dictionary. See https://developers.google.com/earth-engine/image_visualization for options
returns:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot): cartopy GeoAxesSubplot object with Earth Engine results displayed
raises:
ValueError: If `dims` is not of type list, tuple, or int
ValueError: If `imgObj` is not of type ee.image.Image
ValueError: If `ax` if not of type cartopy.mpl.geoaxes.GeoAxesSubplot '
"""
if (
isinstance(ee_object, ee.geometry.Geometry)
or isinstance(ee_object, ee.feature.Feature)
or isinstance(ee_object, ee.featurecollection.FeatureCollection)
):
features = ee.FeatureCollection(ee_object)
if "style" in kwargs and kwargs["style"] is not None:
style = kwargs["style"]
else:
style = {}
props = features.first().propertyNames().getInfo()
if "style" in props:
ee_object = features.style(**{"styleProperty": "style"})
else:
ee_object = features.style(**style)
elif isinstance(ee_object, ee.imagecollection.ImageCollection):
ee_object = ee_object.mosaic()
if type(ee_object) is not ee.image.Image:
raise ValueError("provided `ee_object` is not of type ee.Image")
if region is not None:
map_region = ee.Geometry.Rectangle(region).getInfo()["coordinates"]
view_extent = (region[2], region[0], region[1], region[3])
else:
map_region = ee_object.geometry(100).bounds(1).getInfo()["coordinates"]
# get the image bounds
x, y = list(zip(*map_region[0]))
view_extent = [min(x), max(x), min(y), max(y)]
if ee_object.bandNames().getInfo() == ["vis-red", "vis-green", "vis-blue"]:
warnings.warn(
f"The region parameter is not specified. Using the default region {map_region}. Please specify a region if you get a blank image."
)
if type(dims) not in [list, tuple, int]:
raise ValueError("provided dims not of type list, tuple, or int")
if type(ax) not in [GeoAxes, GeoAxesSubplot]:
raise ValueError(
"provided axes not of type cartopy.mpl.geoaxes.GeoAxes "
"or cartopy.mpl.geoaxes.GeoAxesSubplot"
)
args = {"format": "png", "crs": "EPSG:4326"}
args["region"] = map_region
if dims:
args["dimensions"] = dims
if vis_params:
keys = list(vis_params.keys())
if cmap and ("palette" in keys):
raise KeyError(
"cannot provide `palette` in vis_params if `cmap` is specified"
)
elif cmap:
args["palette"] = ",".join(build_palette(cmap))
else:
pass
args = {**args, **vis_params}
url = ee_object.getThumbUrl(args)
response = requests.get(url)
if response.status_code != 200:
error = eval(response.content)["error"]
raise requests.exceptions.HTTPError(f"{error}")
image = np.array(Image.open(BytesIO(response.content)))
if image.shape[-1] == 2:
image = np.concatenate(
[np.repeat(image[:, :, 0:1], 3, axis=2), image[:, :, -1:]], axis=2
)
ax.imshow(
np.squeeze(image),
extent=view_extent,
origin="upper",
transform=ccrs.PlateCarree(),
zorder=1,
)
return
def build_palette(cmap, n=256):
"""Creates hex color code palette from a matplotlib colormap
args:
cmap (str): string specifying matplotlib colormap to colorize image. If cmap is specified visParams cannot contain 'palette' key
n (int, optional): Number of hex color codes to create from colormap. Default is 256
returns:
palette (list[str]): list of hex color codes from matplotlib colormap for n intervals
"""
colormap = cm.get_cmap(cmap, n)
vals = np.linspace(0, 1, n)
palette = list(map(lambda x: colors.rgb2hex(colormap(x)[:3]), vals))
return palette
def add_colorbar(
ax, vis_params, loc=None, cmap="gray", discrete=False, label=None, **kwargs
):
"""
Add a colorbar to the map based on visualization parameters provided
args:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object to add image overlay to
loc (str, optional): string specifying the position
vis_params (dict, optional): visualization parameters as a dictionary. See https://developers.google.com/earth-engine/guides/image_visualization for options.
**kwargs: remaining keyword arguments are passed to colorbar()
raises:
Warning: If 'discrete' is true when "palette" key is not in visParams
ValueError: If `ax` is not of type cartopy.mpl.geoaxes.GeoAxesSubplot
ValueError: If 'cmap' or "palette" key in visParams is not provided
ValueError: If "min" in visParams is not of type scalar
ValueError: If "max" in visParams is not of type scalar
ValueError: If 'loc' or 'cax' keywords are not provided
ValueError: If 'loc' is not of type str or does not equal available options
"""
if type(ax) not in [GeoAxes, GeoAxesSubplot]:
raise ValueError(
"provided axes not of type cartopy.mpl.geoaxes.GeoAxes "
"or cartopy.mpl.geoaxes.GeoAxesSubplot"
)
if loc:
if (type(loc) == str) and (loc in ["left", "right", "bottom", "top"]):
if "posOpts" not in kwargs:
posOpts = {
"left": [0.01, 0.25, 0.02, 0.5],
"right": [0.88, 0.25, 0.02, 0.5],
"bottom": [0.25, 0.15, 0.5, 0.02],
"top": [0.25, 0.88, 0.5, 0.02],
}
else:
posOpts = {
"left": kwargs["posOpts"],
"right": kwargs["posOpts"],
"bottom": kwargs["posOpts"],
"top": kwargs["posOpts"],
}
del kwargs["posOpts"]
cax = ax.figure.add_axes(posOpts[loc])
if loc == "left":
mpl.pyplot.subplots_adjust(left=0.18)
elif loc == "right":
mpl.pyplot.subplots_adjust(right=0.85)
else:
pass
else:
raise ValueError(
'provided loc not of type str. options are "left", '
'"top", "right", or "bottom"'
)
elif "cax" in kwargs:
cax = kwargs["cax"]
kwargs = {key: kwargs[key] for key in kwargs.keys() if key != "cax"}
else:
raise ValueError("loc or cax keywords must be specified")
vis_keys = list(vis_params.keys())
if vis_params:
if "min" in vis_params:
vmin = vis_params["min"]
if type(vmin) not in (int, float):
raise ValueError("provided min value not of scalar type")
else:
vmin = 0
if "max" in vis_params:
vmax = vis_params["max"]
if type(vmax) not in (int, float):
raise ValueError("provided max value not of scalar type")
else:
vmax = 1
if "opacity" in vis_params:
alpha = vis_params["opacity"]
if type(alpha) not in (int, float):
raise ValueError("provided opacity value of not type scalar")
elif "alpha" in kwargs:
alpha = kwargs["alpha"]
else:
alpha = 1
if cmap is not None:
if discrete:
warnings.warn(
'discrete keyword used when "palette" key is '
"supplied with visParams, creating a continuous "
"colorbar..."
)
cmap = mpl.pyplot.get_cmap(cmap)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
if "palette" in vis_keys:
hexcodes = vis_params["palette"]
hexcodes = [i if i[0] == "#" else "#" + i for i in hexcodes]
if discrete:
cmap = mpl.colors.ListedColormap(hexcodes)
vals = np.linspace(vmin, vmax, cmap.N + 1)
norm = mpl.colors.BoundaryNorm(vals, cmap.N)
else:
cmap = mpl.colors.LinearSegmentedColormap.from_list(
"custom", hexcodes, N=256
)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
elif cmap is not None:
if discrete:
warnings.warn(
'discrete keyword used when "palette" key is '
"supplied with visParams, creating a continuous "
"colorbar..."
)
cmap = mpl.pyplot.get_cmap(cmap)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
else:
raise ValueError(
'cmap keyword or "palette" key in visParams must be provided'
)
tick_font_size = None
if "tick_font_size" in kwargs:
tick_font_size = kwargs.pop("tick_font_size")
label_font_family = None
if "label_font_family" in kwargs:
label_font_family = kwargs.pop("label_font_family")
label_font_size = None
if "label_font_size" in kwargs:
label_font_size = kwargs.pop("label_font_size")
cb = mpl.colorbar.ColorbarBase(cax, norm=norm, alpha=alpha, cmap=cmap, **kwargs)
if label is not None:
if label_font_size is not None and label_font_family is not None:
cb.set_label(label, fontsize=label_font_size, family=label_font_family)
elif label_font_size is not None and label_font_family is None:
cb.set_label(label, fontsize=label_font_size)
elif label_font_size is None and label_font_family is not None:
cb.set_label(label, family=label_font_family)
else:
cb.set_label(label)
elif "bands" in vis_keys:
cb.set_label(vis_params["bands"])
if tick_font_size is not None:
cb.ax.tick_params(labelsize=tick_font_size)
def _buffer_box(bbox, interval):
"""Helper function to buffer a bounding box to the nearest multiple of interval
args:
bbox (list[float]): list of float values specifying coordinates, expects order to be [W,E,S,N]
interval (float): float specifying multiple at which to buffer coordianates to
returns:
extent (tuple[float]): returns tuple of buffered coordinates rounded to interval in order of [W,E,S,N]
"""
if bbox[0] % interval != 0:
xmin = bbox[0] - (bbox[0] % interval)
else:
xmin = bbox[0]
if bbox[1] % interval != 0:
xmax = bbox[1] + (interval - (bbox[1] % interval))
else:
xmax = bbox[1]
if bbox[2] % interval != 0:
ymin = bbox[2] - (bbox[2] % interval)
else:
ymin = bbox[2]
if bbox[3] % interval != 0:
ymax = bbox[3] + (interval - (bbox[3] % interval))
else:
ymax = bbox[3]
return (xmin, xmax, ymin, ymax)
def bbox_to_extent(bbox):
"""Helper function to reorder a list of coordinates from [W,S,E,N] to [W,E,S,N]
args:
bbox (list[float]): list (or tuple) or coordinates in the order of [W,S,E,N]
returns:
extent (tuple[float]): tuple of coordinates in the order of [W,E,S,N]
"""
return (bbox[0], bbox[2], bbox[1], bbox[3])
def add_gridlines(
ax,
interval=None,
n_ticks=None,
xs=None,
ys=None,
buffer_out=True,
xtick_rotation="horizontal",
ytick_rotation="horizontal",
**kwargs,
):
"""Helper function to add gridlines and format ticks to map
args:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object to add the gridlines to
interval (float | list[float], optional): float specifying an interval at which to create gridlines, units are decimal degrees. lists will be interpreted a [x_interval, y_interval]. default = None
n_ticks (int | list[int], optional): integer specifying number gridlines to create within map extent. lists will be interpreted a [nx, ny]. default = None
xs (list, optional): list of x coordinates to create gridlines. default = None
ys (list, optional): list of y coordinates to create gridlines. default = None
buffer_out (boolean, optional): boolean option to buffer out the extent to insure coordinates created cover map extent. default=true
xtick_rotation (str | float, optional):
ytick_rotation (str | float, optional):
**kwargs: remaining keyword arguments are passed to gridlines()
raises:
ValueError: if all interval, n_ticks, or (xs,ys) are set to None
"""
view_extent = ax.get_extent()
extent = view_extent
if xs is not None:
xmain = xs
elif interval is not None:
if isinstance(interval, Iterable):
xspace = interval[0]
else:
xspace = interval
if buffer_out:
extent = _buffer_box(extent, xspace)
xmain = np.arange(extent[0], extent[1] + xspace, xspace)
elif n_ticks is not None:
if isinstance(n_ticks, Iterable):
n_x = n_ticks[0]
else:
n_x = n_ticks
xmain = np.linspace(extent[0], extent[1], n_x)
else:
raise ValueError(
"one of variables interval, n_ticks, or xs must be defined. If you would like default gridlines, please use `ax.gridlines()`"
)
if ys is not None:
ymain = ys
elif interval is not None:
if isinstance(interval, Iterable):
yspace = interval[1]
else:
yspace = interval
if buffer_out:
extent = _buffer_box(extent, yspace)
ymain = np.arange(extent[2], extent[3] + yspace, yspace)
elif n_ticks is not None:
if isinstance(n_ticks, Iterable):
n_y = n_ticks[1]
else:
n_y = n_ticks
ymain = np.linspace(extent[2], extent[3], n_y)
else:
raise ValueError(
"one of variables interval, n_ticks, or ys must be defined. If you would like default gridlines, please use `ax.gridlines()`"
)
ax.gridlines(xlocs=xmain, ylocs=ymain, **kwargs)
xin = xmain[(xmain >= view_extent[0]) & (xmain <= view_extent[1])]
yin = ymain[(ymain >= view_extent[2]) & (ymain <= view_extent[3])]
# set tick labels
ax.set_xticks(xin, crs=ccrs.PlateCarree())
ax.set_yticks(yin, crs=ccrs.PlateCarree())
ax.set_xticklabels(xin, rotation=xtick_rotation, ha="center")
ax.set_yticklabels(yin, rotation=ytick_rotation, va="center")
ax.xaxis.set_major_formatter(LONGITUDE_FORMATTER)
ax.yaxis.set_major_formatter(LATITUDE_FORMATTER)
return
def pad_view(ax, factor=0.05):
"""Function to pad area around the view extent of a map, used for visual appeal
args:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object to pad view extent
factor (float | list[float], optional): factor to pad view extent accepts float [0-1] of a list of floats which will be interpreted at [xfactor, yfactor]
"""
view_extent = ax.get_extent()
if isinstance(factor, Iterable):
xfactor, yfactor = factor
else:
xfactor, yfactor = factor, factor
x_diff = view_extent[1] - view_extent[0]
y_diff = view_extent[3] - view_extent[2]
xmin = view_extent[0] - (x_diff * xfactor)
xmax = view_extent[1] + (x_diff * xfactor)
ymin = view_extent[2] - (y_diff * yfactor)
ymax = view_extent[3] + (y_diff * yfactor)
ax.set_ylim(ymin, ymax)
ax.set_xlim(xmin, xmax)
return
def add_north_arrow(
ax,
text="N",
xy=(0.1, 0.1),
arrow_length=0.1,
text_color="black",
arrow_color="black",
fontsize=20,
width=5,
headwidth=15,
ha="center",
va="center",
):
"""Add a north arrow to the map.
Args:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object.
text (str, optional): Text for north arrow. Defaults to "N".
xy (tuple, optional): Location of the north arrow. Each number representing the percentage length of the map from the lower-left cornor. Defaults to (0.1, 0.1).
arrow_length (float, optional): Length of the north arrow. Defaults to 0.1 (10% length of the map).
text_color (str, optional): Text color. Defaults to "black".
arrow_color (str, optional): North arrow color. Defaults to "black".
fontsize (int, optional): Text font size. Defaults to 20.
width (int, optional): Width of the north arrow. Defaults to 5.
headwidth (int, optional): head width of the north arrow. Defaults to 15.
ha (str, optional): Horizontal alignment. Defaults to "center".
va (str, optional): Vertical alignment. Defaults to "center".
"""
ax.annotate(
text,
xy=xy,
xytext=(xy[0], xy[1] - arrow_length),
color=text_color,
arrowprops=dict(facecolor=arrow_color, width=width, headwidth=headwidth),
ha=ha,
va=va,
fontsize=fontsize,
xycoords=ax.transAxes,
)
return
def convert_SI(val, unit_in, unit_out):
"""Unit converter.
Args:
val (float): The value to convert.
unit_in (str): The input unit.
unit_out (str): The output unit.
Returns:
float: The value after unit conversion.
"""
SI = {
"cm": 0.01,
"m": 1.0,
"km": 1000.0,
"inch": 0.0254,
"foot": 0.3048,
"mile": 1609.34,
}
return val * SI[unit_in] / SI[unit_out]
def add_scale_bar(
ax,
metric_distance=4,
unit="km",
at_x=(0.05, 0.5),
at_y=(0.08, 0.11),
max_stripes=5,
ytick_label_margins=0.25,
fontsize=8,
font_weight="bold",
rotation=0,
zorder=999,
paddings={"xmin": 0.05, "xmax": 0.05, "ymin": 1.5, "ymax": 0.5},
bbox_kwargs={"facecolor": "white", "edgecolor": "black", "alpha": 0.5},
):
"""
Add a scale bar to the map.
Args:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object.
metric_distance (int | float, optional): length in meters of each region of the scale bar. Default to 4.
unit (str, optional): scale bar distance unit. Default to "km"
at_x (float, optional): target axes X coordinates (0..1) of box (= left, right). Default to (0.05, 0.2).
at_y (float, optional): axes Y coordinates (0..1) of box (= lower, upper). Default to (0.08, 0.11).
max_stripes (int, optional): typical/maximum number of black+white regions. Default to 5.
ytick_label_margins (float, optional): Location of distance labels on the Y axis. Default to 0.25.
fontsize (int, optional): scale bar text size. Default to 8.
font_weight (str, optional):font weight. Default to 'bold'.
rotation (int, optional): rotation of the length labels for each region of the scale bar. Default to 0.
zorder (float, optional): z order of the text bounding box.
paddings (dict, optional): boundaries of the box that contains the scale bar.
bbox_kwargs (dict, optional): style of the box containing the scale bar.
"""
warnings.filterwarnings("ignore")
# --------------------------------------------------------------------------
# Auxiliary functions
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
# --------------------------------------------------------------------------
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, _, y0, y1 = ax.get_extent(proj)
ymean = np.mean([y0, y1])
# set target rectangle in-visible-area (aka 'Axes') coordinates
axfrac_ini, _ = at_x
ayfrac_ini, ayfrac_final = at_y
# choose exact X points as sensible grid ticks with Axis 'ticker' helper
converted_metric_distance = convert_SI(metric_distance, unit, "m")
xcoords = []
ycoords = []
xlabels = []
for i in range(0, 1 + max_stripes):
dx = (converted_metric_distance * i) + x0
xlabels.append(metric_distance * i)
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, _, _ = _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 unit
font_props = mfonts.FontProperties(size=fontsize, weight=font_weight)
plt.text(
0.5 * (xl0 + xl1),
yl1 + y_margin,
unit,
color="black",
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)),
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()
def add_scale_bar_lite(
ax,
length=None,
xy=(0.5, 0.05),
linewidth=3,
fontsize=20,
color="black",
unit="km",
ha="center",
va="bottom",
):
"""Add a lite version of scale bar to the map. Reference: https://stackoverflow.com/a/50674451/2676166
Args:
ax (cartopy.mpl.geoaxes.GeoAxesSubplot | cartopy.mpl.geoaxes.GeoAxes): required cartopy GeoAxesSubplot object.
length ([type], optional): Length of the scale car. Defaults to None.
xy (tuple, optional): Location of the north arrow. Each number representing the percentage length of the map from the lower-left cornor. Defaults to (0.1, 0.1).
linewidth (int, optional): Line width of the scale bar. Defaults to 3.
fontsize (int, optional): Text font size. Defaults to 20.
color (str, optional): Color for the scale bar. Defaults to "black".
unit (str, optional): Length unit for the scale bar. Defaults to "km".
ha (str, optional): Horizontal alignment. Defaults to "center".
va (str, optional): Vertical alignment. Defaults to "bottom".
"""
allow_units = ["cm", "m", "km", "inch", "foot", "mile"]
if unit not in allow_units:
print(
"The unit must be one of the following: {}".format(", ".join(allow_units))
)
return
num = length
# 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) * xy[1]
tmc = ccrs.TransverseMercator(sbllx, sblly, approx=True)