"""
Color handling and maps.
"""
from __future__ import division
# Builtins
import warnings
import os
from os import path
import copy
# External libs
import numpy as np
try:
from skimage.transform import resize as imresize
has_skimage = True
except ImportError:
has_skimage = False
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.collections import PatchCollection, LineCollection
from shapely.geometry import MultiPoint, LineString, Polygon
from salem.descartes import PolygonPatch
from matplotlib.transforms import Transform as MPLTranform
from salem import utils, gis, sio, Grid, wgs84, sample_data_dir, GeoTiff
shapefiles = dict()
shapefiles['world_borders'] = path.join(sample_data_dir, 'shapes',
'world_borders', 'world_borders.shp')
shapefiles['oceans'] = path.join(sample_data_dir, 'shapes', 'oceans',
'ne_50m_ocean.shp')
shapefiles['rivers'] = path.join(sample_data_dir, 'shapes', 'rivers',
'ne_50m_rivers_lake_centerlines.shp')
shapefiles['lakes'] = path.join(sample_data_dir, 'shapes', 'lakes',
'ne_50m_lakes.shp')
# Be sure we have the directory
if not os.path.exists(shapefiles['world_borders']):
from salem.utils import get_demo_file
_ = get_demo_file('world_borders.shp')
class ExtendedNorm(mpl.colors.BoundaryNorm):
""" A better BoundaryNorm with an ``extend'' keyword.
TODO: remove this when PR is accepted
See: https://github.com/matplotlib/matplotlib/issues/4850
https://github.com/matplotlib/matplotlib/pull/5034
"""
def __init__(self, boundaries, ncolors, extend='neither'):
_b = np.atleast_1d(boundaries).astype(float)
mpl.colors.BoundaryNorm.__init__(self, _b, ncolors, clip=False)
# 'neither' | 'both' | 'min' | 'max'
if extend == 'both':
_b = np.append(_b, _b[-1]+1)
_b = np.insert(_b, 0, _b[0]-1)
elif extend == 'min':
_b = np.insert(_b, 0, _b[0]-1)
elif extend == 'max':
_b = np.append(_b, _b[-1]+1)
self._b = _b
self._N = len(self._b)
if self._N - 1 == self.Ncmap:
self._interp = False
else:
self._interp = True
def __call__(self, value):
xx, is_scalar = self.process_value(value)
mask = np.ma.getmaskarray(xx)
xx = np.atleast_1d(xx.filled(self.vmax + 1))
iret = np.zeros(xx.shape, dtype=np.int16)
for i, b in enumerate(self._b):
iret[xx >= b] = i
if self._interp:
scalefac = float(self.Ncmap - 1) / (self._N - 2)
iret = (iret * scalefac).astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = self.Ncmap
ret = np.ma.array(iret, mask=mask)
if is_scalar:
ret = int(ret[0]) # assume python scalar
return ret
[docs]
def get_cmap(cmap='viridis'):
"""Get a colormap from mpl, and also those defined by Salem.
Currently we have: topo, dem, nrwc
see https://github.com/fmaussion/salem-sample-data/tree/master/colormaps
"""
try:
return plt.get_cmap(cmap)
except ValueError:
cl = utils.read_colormap(cmap)
return LinearSegmentedColormap.from_list(cmap, cl, N=256)
[docs]
class DataLevels(object):
"""Assigns the right color to your data.
Simple tool that ensures the full compatibility of the plot
colors and the colorbar. It's working principle is best understood
with a few examples, available in the docs.
"""
[docs]
def __init__(
self,
data=None,
levels=None,
nlevels=None,
vmin=None,
vmax=None,
extend=None,
cmap=None,
norm=None,
):
"""Instanciate.
Parameters
----------
see the set_* functions
"""
self.set_data(data)
self.set_levels(levels)
self.set_nlevels(nlevels)
self.set_vmin(vmin)
self.set_vmax(vmax)
self.set_extend(extend)
self.set_cmap(cmap)
self.set_norm(norm)
def update(self, d):
"""
Update the properties of :class:`DataLevels` from the dictionary *d*.
"""
for k, v in d.items():
func = getattr(self, 'set_' + k, None)
if func is None or not callable(func):
raise AttributeError('Unknown property %s' % k)
func(v)
[docs]
def set_data(self, data=None):
"""Any kind of data array (also masked)."""
if data is not None:
self.data = np.ma.masked_invalid(np.atleast_1d(data), copy=False)
else:
self.data = np.ma.asarray([0., 1.])
[docs]
def set_levels(self, levels=None):
"""Levels you define. Must be monotically increasing."""
self._levels = levels
[docs]
def set_nlevels(self, nlevels=None):
"""Automatic N levels. Ignored if set_levels has been set."""
self._nlevels = nlevels
[docs]
def set_vmin(self, val=None):
"""Mininum level value. Ignored if set_levels has been set."""
self._vmin = val
[docs]
def set_vmax(self, val=None):
"""Maximum level value. Ignored if set_levels has been set."""
self._vmax = val
[docs]
def set_cmap(self, cm=None):
"""Set a colormap."""
self.cmap = get_cmap(cm or 'viridis')
def set_norm(self, norm=None):
"""Set a normalization function. Related parameters will be ignored if set.
(e.g., vmin and vmax will be ignored if using LogNorm)"""
self._norm = norm
[docs]
def set_extend(self, extend=None):
"""Colorbar extensions: 'neither' | 'both' | 'min' | 'max'"""
self._extend = extend
[docs]
def set_plot_params(
self,
levels=None,
nlevels=None,
vmin=None,
vmax=None,
extend=None,
cmap=None,
norm=None,
):
"""Shortcut to all parameters related to the plot.
As a side effect, running set_plot_params() without arguments will
reset the default behavior
"""
self.set_vmin(vmin)
self.set_vmax(vmax)
self.set_levels(levels)
self.set_nlevels(nlevels)
self.set_extend(extend)
if cmap is not None:
self.set_cmap(cmap)
self.set_norm(norm)
@property
def levels(self):
"""Clever getter."""
levels = self._levels
nlevels = self._nlevels
if levels is not None:
self.set_vmin(levels[0])
self.set_vmax(levels[-1])
return levels
else:
if nlevels is None:
if self.extend in ['max', 'min']:
nlevels = self.cmap.N - 1
elif self.extend in ['both']:
nlevels = self.cmap.N - 2
else:
nlevels = self.cmap.N
if self.vmax == self.vmin:
return np.linspace(self.vmin, self.vmax+1, nlevels)
return np.linspace(self.vmin, self.vmax, nlevels)
@property
def nlevels(self):
"""Clever getter."""
return len(self.levels)
@property
def vmin(self):
"""Clever getter."""
if self._vmin is None:
return np.min(self.data)
else:
return self._vmin
@property
def vmax(self):
"""Clever getter."""
if self._vmax is None:
return np.max(self.data)
else:
return self._vmax
@property
def extend(self):
"""Clever getter."""
if self._extend is None:
# If the user didnt set it, we decide
maxd, mind = np.max(self.data), np.min(self.data)
if maxd > self.vmax and mind < self.vmin:
out = 'both'
elif maxd > self.vmax:
out = 'max'
elif mind < self.vmin:
out = 'min'
else:
out = 'neither'
return out
else:
return self._extend
@property
def norm(self):
"""Clever getter."""
l = self.levels
e = self.extend
if self._norm is None:
# Warnings
if e not in ["both", "min"] and (np.min(l) > np.min(self.data)):
warnings.warn("Minimum data out of bounds.", RuntimeWarning)
if e not in ["both", "max"] and (np.max(l) < np.max(self.data)):
warnings.warn("Maximum data out of bounds.", RuntimeWarning)
try:
# Added in mpl 3.3.0
return mpl.colors.BoundaryNorm(l, self.cmap.N, extend=e)
except TypeError:
return ExtendedNorm(l, self.cmap.N, extend=e)
else:
return self._norm
def to_rgb(self):
"""Transform the data to RGB triples."""
if np.all(self.data.mask):
# unfortunately the functions below can't handle this one
return np.zeros(self.data.shape + (4, ))
return self.cmap(self.norm(self.data))
def get_colorbarbase_kwargs(self):
"""If you need to make a colorbar based on a given DataLevel state."""
# This is a discutable choice: with more than 60 colors (could be
# less), we assume a continuous colorbar.
if self.nlevels < 60 or self._norm is not None:
norm = self.norm
else:
norm = mpl.colors.Normalize(vmin=self.vmin, vmax=self.vmax)
return dict(extend=self.extend, cmap=self.cmap, norm=norm)
[docs]
def colorbarbase(self, cax, **kwargs):
"""Returns a ColorbarBase to add to the cax axis. All keywords are
passed to matplotlib.colorbar.ColorbarBase
"""
# This is a discutable choice: with more than 60 colors (could be
# less), we assume a continuous colorbar.
if self.nlevels < 60 or self._norm is not None:
norm = self.norm
else:
norm = mpl.colors.Normalize(vmin=self.vmin, vmax=self.vmax)
return mpl.colorbar.ColorbarBase(cax, extend=self.extend,
cmap=self.cmap, norm=norm, **kwargs)
[docs]
def append_colorbar(self, ax, position='right', size='5%', pad=0.5,
**kwargs):
"""Appends a colorbar to existing axes
It uses matplotlib's make_axes_locatable toolkit.
Parameters
----------
ax: the axis to append the colorbar to
position: "left"|"right"|"bottom"|"top"
size: the size of the colorbar (e.g. in % of the ax)
pad: pad between axes given in inches or tuple-like of floats,
(horizontal padding, vertical padding)
"""
orientation = 'horizontal'
if position in ['left', 'right']:
orientation = 'vertical'
cax = make_axes_locatable(ax).append_axes(position, size=size, pad=pad)
return self.colorbarbase(cax, orientation=orientation, **kwargs)
[docs]
def plot(self, ax):
"""Add a kind of plot of the data to an axis.
More useful for child classes.
Returns an imshow primitive
"""
data = np.atleast_2d(self.data)
toplot = self.cmap(self.norm(data))
primitive = ax.imshow(toplot, interpolation='none', origin='lower')
return primitive
[docs]
def visualize(self, ax=None, title=None, orientation='vertical',
add_values=False, addcbar=True, cbar_title=''):
"""Quick plot, useful for debugging.
Parameters
----------
ax: the axis to add the plot to (optinal)
title: the plot title
orientation: the colorbar's orientation
add_values: add the data values as text in the pixels (for testing)
Returns a dict containing the primitives of the various plot calls
"""
# Do we make our own fig?
if ax is None:
ax = plt.gca()
# Plot
out = self.plot(ax)
# Colorbar
addcbar = (self.vmin != self.vmax) and addcbar
if addcbar:
if orientation == 'horizontal':
self.append_colorbar(ax, "top", size=0.2, pad=0.5,
label=cbar_title)
else:
self.append_colorbar(ax, "right", size="5%", pad=0.2,
label=cbar_title)
# Mini add-on
if add_values:
data = np.atleast_2d(self.data)
x, y = np.meshgrid(np.arange(data.shape[1]),
np.arange(data.shape[0]))
for v, i, j in zip(data.flatten(), x.flatten(), y.flatten()):
ax.text(i, j, v, horizontalalignment='center',
verticalalignment='center')
# Details
if title is not None:
ax.set_title(title)
return out
[docs]
class Map(DataLevels):
"""Plotting georeferenced data.
A Map is an implementation of DataLevels that wraps imshow(), by adding
all kinds of geoinformation on the plot. It's primary purpose is to add
country borders or topographical shading. Another purpose is
to be able to plot all kind of georeferenced data on an existing map
while being sure that the plot is accurate.
In short: the Map is a higher-level, less wordy and less flexible
version of cartopy or basemap. It's usefulness is best shown by the
examples in the doc.
For worldwide maps or very flexible plots you'd better use cartopy,
because Salem's Map is constrained by it's primary objective: plotting
regional maps.
"""
[docs]
def __init__(self, grid, nx=500, ny=None, factor=None,
countries=True, **kwargs):
"""Make a new map.
Parameters
----------
grid : salem.Grid
the grid defining the map
nx : int
x resolution (in pixels) of the map
ny : int
y resolution (in pixels) of the map (ignored if nx is set)
factor : float
shortcut to keep the same image resolution as the grid.
1 prevents image resizing
countries : bool
automatically add country borders to the map (you can do
it later with a call to set_shapefile)
kwargs: **
all keywords accepted by DataLevels
"""
if factor is not None:
nx = None
ny = None
self.grid = grid.center_grid.regrid(nx=nx, ny=ny, factor=factor)
self.origin = 'lower' if self.grid.origin == 'lower-left' else 'upper'
DataLevels.__init__(self, **kwargs)
self._collections = []
self._geometries = []
self._text = []
self.set_shapefile(countries=countries)
self.set_lonlat_contours()
self._shading_base()
self._rgb = None
self._contourf_data = None
self._contour_data = None
def _check_data(self, data=None, crs=None, interp='nearest',
overplot=False):
"""Interpolates the data to the map grid."""
if crs is None:
# try xarray
# TODO: note that this might slow down the plotting a bit
# if the data already matches the grid...
try:
crs = data.salem.grid
except:
pass
data = np.ma.fix_invalid(np.squeeze(data))
shp = data.shape
if len(shp) != 2:
raise ValueError('Data should be 2D.')
if crs is None:
# Reform case, but with a sanity check
if not np.isclose(shp[0] / shp[1], self.grid.ny / self.grid.nx,
atol=1e-2):
raise ValueError('Dimensions of data do not match the map.')
# need to resize if not same
if not ((shp[0] == self.grid.ny) and (shp[1] == self.grid.nx)):
# We convert to float for img resizing
if data.dtype not in [np.float32, np.float64]:
data = data.astype(np.float64)
if interp.lower() == 'nearest':
interp = 0
elif interp.lower() == 'linear':
interp = 1
elif interp.lower() == 'spline':
interp = 3
if not has_skimage:
raise ImportError('Needs scikit-image to be installed.')
with warnings.catch_warnings():
mess = "invalid value encountered in reduce"
warnings.filterwarnings("ignore", message=mess)
mess = "All-NaN slice encountered"
warnings.filterwarnings("ignore", message=mess)
mess = ("Possible precision loss when converting from "
"int64 to float64")
warnings.filterwarnings("ignore", message=mess)
mess = "Passing `np.nan` to mean no clipping in np.clip"
warnings.filterwarnings("ignore", message=mess)
try:
data = imresize(data.filled(np.NaN),
(self.grid.ny, self.grid.nx),
order=interp, mode='edge',
anti_aliasing=True)
except RuntimeError:
# For some order anti_aliasing doesnt work with 'edge'
data = imresize(data.filled(np.NaN),
(self.grid.ny, self.grid.nx),
order=interp, mode='edge',
anti_aliasing=False)
return data
crs = gis.check_crs(crs, raise_on_error=True)
if isinstance(crs, Grid):
# Remap
if overplot:
data = self.grid.map_gridded_data(data, crs, interp=interp,
out=self.data)
else:
data = self.grid.map_gridded_data(data, crs, interp=interp)
else:
raise ValueError('crs should be a grid, not a proj')
return data
[docs]
def set_data(self, data=None, crs=None, interp='nearest',
overplot=False):
"""Adds data to the plot. The data has to be georeferenced, i.e. by
setting crs (if omitted the data is assumed to be defined on the
map's grid)
Parameters
----------
data: the data array (2d)
crs: the data coordinate reference system
interp: 'nearest' (default) or 'linear', the interpolation algorithm
overplot: add the data to an existing plot (useful for mosaics for
example)
"""
# Check input
if data is None:
self.data = np.ma.zeros((self.grid.ny, self.grid.nx))
self.data.mask = self.data + 1
return
data = self._check_data(data=data, crs=crs, interp=interp,
overplot=overplot)
DataLevels.set_data(self, data)
[docs]
def set_contourf(self, data=None, crs=None, interp='nearest', **kwargs):
"""Adds data to contourfill on the map.
Parameters
----------
mask: bool array (2d)
crs: the data coordinate reference system
interp: 'nearest' (default) or 'linear', the interpolation algorithm
kwargs: anything accepted by contourf
"""
# Check input
if data is None:
self._contourf_data = None
return
self._contourf_data = self._check_data(data=data, crs=crs,
interp=interp)
kwargs.setdefault('zorder', 1.4)
self._contourf_kw = kwargs
[docs]
def set_contour(self, data=None, crs=None, interp='nearest', **kwargs):
"""Adds data to contour on the map.
Parameters
----------
mask: bool array (2d)
crs: the data coordinate reference system
interp: 'nearest' (default) or 'linear', the interpolation algorithm
kwargs: anything accepted by contour
"""
# Check input
if data is None:
self._contour_data = None
return
self._contour_data = self._check_data(data=data, crs=crs,
interp=interp)
kwargs.setdefault('zorder', 1.4)
self._contour_kw = kwargs
[docs]
def set_geometry(self, geometry=None, crs=wgs84, text=None,
text_delta=(0.01, 0.01), text_kwargs=dict(), **kwargs):
"""Adds any Shapely geometry to the map.
If called without arguments, it removes all previous geometries.
Parameters
----------
geometry: a Shapely gometry object (must be a scalar!)
crs: the associated coordinate reference system (default wgs84)
text: if you want to add a text to the geometry (it's position is
based on the geometry's centroid)
text_delta: it can be useful to shift the text of a certain amount
when annotating points. units are percentage of data coordinates.
text_kwargs: the keyword arguments to pass to the test() function
kwargs: any keyword associated with the geometry's plotting function:
- Point: all keywords accepted by scatter(): marker, s, edgecolor,
facecolor...
- Line: all keywords accepted by plot(): color, linewidth...
- Polygon: all keywords accepted by PathPatch(): color, edgecolor,
facecolor, linestyle, linewidth, alpha...
"""
# Reset?
if geometry is None:
self._geometries = []
return
# Transform
geom = gis.transform_geometry(geometry, crs=crs,
to_crs=self.grid.center_grid)
# Text
if text is not None:
x, y = geom.centroid.xy
x = x[0] + text_delta[0] * self.grid.nx
sign = self.grid.dy / np.abs(self.grid.dy)
y = y[0] + text_delta[1] * self.grid.ny * sign
self.set_text(x, y, text, crs=self.grid.center_grid,
**text_kwargs)
# Save
if 'Multi' in geom.geom_type:
for g in geom.geoms:
self._geometries.append((g, kwargs))
# dirty solution: I should use collections instead
if 'label' in kwargs:
kwargs = kwargs.copy()
del kwargs['label']
else:
self._geometries.append((geom, kwargs))
[docs]
def set_points(self, x, y, **kwargs):
"""Shortcut for set_geometry() accepting coordinates as input."""
x, y = np.atleast_1d(x), np.atleast_1d(y)
self.set_geometry(MultiPoint(np.array([x, y]).T), **kwargs)
[docs]
def set_text(self, x=None, y=None, text='', crs=wgs84, **kwargs):
"""Add a text to the map.
Keyword arguments will be passed to mpl's text() function.
"""
# Reset?
if x is None:
self._text = []
return
kwargs.setdefault('zorder', 5)
# Transform
x, y = self.grid.center_grid.transform(x, y, crs=crs)
self._text.append((x, y, text, kwargs))
[docs]
def set_shapefile(self, shape=None, countries=False, oceans=False,
rivers=False, lakes=False, **kwargs):
"""Add a shapefile to the plot.
Salem is shipped with a few default settings for country borders,
oceans and rivers (set one at the time!)
set_shapefile() without argument will reset the map to zero shapefiles.
Parameters
----------
shape: the path to the shapefile to read
countries: if True, add country borders
oceans: if True, add oceans
rivers: if True, add rivers
lakes: if True, add lakes
kwargs: all keywords accepted by the corresponding collection.
For LineStrings::
linewidths, colors, linestyles, ...
For Polygons::
alpha, edgecolor, facecolor, fill, linestyle, linewidth, color, ...
"""
# See if the user wanted defaults settings
if oceans:
kwargs.setdefault('facecolor', (0.36862745, 0.64313725, 0.8))
kwargs.setdefault('edgecolor', 'none')
kwargs.setdefault('alpha', 1)
return self.set_shapefile(shapefiles['oceans'], **kwargs)
if rivers:
kwargs.setdefault('color', (0.08984375, 0.65625, 0.8515625))
return self.set_shapefile(shapefiles['rivers'], **kwargs)
if lakes:
kwargs.setdefault('color', (0.08984375, 0.65625, 0.8515625))
return self.set_shapefile(shapefiles['lakes'], **kwargs)
if countries:
kwargs.setdefault('zorder', 1.5)
return self.set_shapefile(shapefiles['world_borders'], **kwargs)
# Defaults
if not all(k in kwargs for k in ('facecolor', 'edgecolor')):
kwargs.setdefault('color', 'k')
# Reset?
if shape is None:
self._collections = []
return
# Transform
if isinstance(shape, pd.DataFrame):
shape = gis.transform_geopandas(shape, to_crs=self.grid)
else:
shape = sio.read_shapefile_to_grid(shape, grid=self.grid)
if len(shape) == 0:
return
# Different collection for each type
geomtype = shape.iloc[0].geometry.geom_type
if 'Polygon' in geomtype:
patches = []
for g in shape.geometry:
if 'Multi' in g.geom_type:
for gg in g.geoms:
patches.append(PolygonPatch(gg))
else:
patches.append(PolygonPatch(g))
kwargs.setdefault('facecolor', 'none')
if 'color' in kwargs:
kwargs.setdefault('edgecolor', kwargs['color'])
del kwargs['color']
self._collections.append(PatchCollection(patches, **kwargs))
elif 'LineString' in geomtype:
lines = []
for g in shape.geometry:
if 'Multi' in g.geom_type:
for gg in g.geoms:
lines.append(np.array(gg.coords))
else:
lines.append(np.array(g.coords))
self._collections.append(LineCollection(lines, **kwargs))
else:
raise NotImplementedError(geomtype)
def _find_interval(self, max_nticks):
"""Quick n dirty function to find a suitable lonlat interval."""
candidates = [0.001, 0.002, 0.005,
0.01, 0.02, 0.05,
0.1, 0.2, 0.5,
1, 2, 5, 10, 20]
xx, yy = self.grid.pixcorner_ll_coordinates
for inter in candidates:
_xx = xx / inter
_yy = yy / inter
mm_x = [np.ceil(np.min(_xx)), np.floor(np.max(_xx))]
mm_y = [np.ceil(np.min(_yy)), np.floor(np.max(_yy))]
nx = mm_x[1]-mm_x[0]+1
ny = mm_y[1]-mm_y[0]+1
if np.max([nx, ny]) <= max_nticks:
break
return inter
[docs]
def set_lonlat_contours(self, interval=None, xinterval=None,
yinterval=None, add_tick_labels=True,
add_xtick_labels=True, add_ytick_labels=True,
max_nticks=8, **kwargs):
"""Add longitude and latitude contours to the map.
Calling it with interval=0 will remove all contours.
Parameters
----------
interval : float
interval (in degrees) between the contours (same for lon and lat)
xinterval : float
set a different interval for lons
yinterval : float
set a different interval for lats
add_tick_label : bool
add ticks labels to the axes
add_xtick_label : bool
add ticks labels to the x axis
add_ytick_label : bool
add ticks labels to the y axis
max_nticks : int
maximum number of contour levels (when chosen automatically).
Ignore if ``interval`` is set to a value
kwargs : {}
any keyword accepted by contour()
"""
# Defaults
if interval is None:
interval = self._find_interval(max_nticks)
if xinterval is None:
xinterval = interval
if yinterval is None:
yinterval = interval
if xinterval == 0:
# no contour
self.xtick_levs = []
self.ytick_levs = []
add_tick_labels = False
else:
# Change XY into interval coordinates, and back after rounding
xx, yy = self.grid.pixcorner_ll_coordinates
_xx = xx / xinterval
_yy = yy / yinterval
mm_x = [np.ceil(np.min(_xx)), np.floor(np.max(_xx))]
mm_y = [np.ceil(np.min(_yy)), np.floor(np.max(_yy))]
self.xtick_levs = (mm_x[0] + np.arange(mm_x[1]-mm_x[0]+1)) * \
xinterval
self.ytick_levs = (mm_y[0] + np.arange(mm_y[1]-mm_y[0]+1)) * \
yinterval
# Decide on float format
d = np.array(['4', '3', '2', '1', '0'])
d = d[interval < np.array([0.001, 0.01, 0.1, 1, 10000])][0]
# The labels (quite ugly)
self.xtick_pos = []
self.xtick_val = []
self.ytick_pos = []
self.ytick_val = []
if add_tick_labels:
if add_xtick_labels:
_xx = xx[0 if self.origin == 'lower' else -1, :]
_xi = np.arange(self.grid.nx+1)
for xl in self.xtick_levs:
if (xl > _xx[-1]) or (xl < _xx[0]):
continue
self.xtick_pos.append(np.interp(xl, _xx, _xi))
label = ('{:.' + d + 'f}').format(xl)
label += 'W' if (xl < 0) else 'E'
if xl == 0:
label = '0'
self.xtick_val.append(label)
if add_ytick_labels:
_yy = np.sort(yy[:, 0])
_yi = np.arange(self.grid.ny+1)
if self.origin == 'upper':
_yi = _yi[::-1]
for yl in self.ytick_levs:
if (yl > _yy[-1]) or (yl < _yy[0]):
continue
self.ytick_pos.append(np.interp(yl, _yy, _yi))
label = ('{:.' + d + 'f}').format(yl)
label += 'S' if (yl < 0) else 'N'
if yl == 0:
label = 'Eq.'
self.ytick_val.append(label)
# Done
kwargs.setdefault('colors', 'gray')
kwargs.setdefault('linestyles', 'dashed')
kwargs.setdefault('linewidths', 0.8)
kwargs.setdefault('zorder', 1.5)
self.ll_contour_kw = kwargs
def _shading_base(self, slope=None, relief_factor=0.7):
"""Compute the shading factor out of the slope."""
# reset?
if slope is None:
self.slope = None
return
# I got this formula from D. Scherer. It works and I dont know why
p = np.nonzero(slope > 0)
if len(p[0]) > 0:
temp = np.clip(slope[p] / (2 * np.std(slope)), -1, 1)
slope[p] = 0.4 * np.sin(0.5*np.pi*temp)
self.relief_factor = relief_factor
self.slope = slope
[docs]
def set_topography(self, topo=None, crs=None, relief_factor=0.7, **kwargs):
"""Add topographical shading to the map.
Parameters
----------
topo: path to a geotiff file containing the topography, OR
2d data array
relief_factor: how strong should the shading be?
kwargs: any keyword accepted by salem.Grid.map_gridded_data (interp,ks)
Returns
-------
the topography if needed (bonus)
"""
if topo is None:
self._shading_base()
return
kwargs.setdefault('interp', 'spline')
if isinstance(topo, str):
_, ext = os.path.splitext(topo)
if ext.lower() == '.tif':
g = GeoTiff(topo)
# Spare memory
ex = self.grid.extent_in_crs(crs=wgs84) # l, r, b, t
g.set_subset(corners=((ex[0], ex[2]), (ex[1], ex[3])),
crs=wgs84, margin=10)
z = g.get_vardata()
z[z < -999] = 0
z = self.grid.map_gridded_data(z, g.grid, **kwargs)
else:
raise ValueError('File extension not recognised: {}'
.format(ext))
else:
z = self._check_data(topo, crs=crs, **kwargs)
# Gradient in m m-1
ddx = self.grid.dx
ddy = self.grid.dy
if gis.proj_is_latlong(self.grid.proj):
# we make a coarse approx of the avg dx on a sphere
_, lat = self.grid.ll_coordinates
ddx = np.mean(ddx * 111200 * np.cos(lat * np.pi / 180))
ddy *= 111200
dy, dx = np.gradient(z, ddy, ddx)
self._shading_base(dx - dy, relief_factor=relief_factor)
return z
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def set_rgb(self, img=None, crs=None, interp='nearest',
natural_earth=None):
"""Manually force to a rgb img
Parameters
----------
img : array
the image to plot
crs : Grid
the image reference system
interp : str, default 'nearest'
'nearest', 'linear', or 'spline'
natural_earth : str
'lr', 'mr' or 'hr' (low res, medium or high res)
natural earth background img
"""
if natural_earth is not None:
from matplotlib.image import imread
with warnings.catch_warnings():
# DecompressionBombWarning
warnings.simplefilter("ignore")
img = imread(utils.get_natural_earth_file(natural_earth))
ny, nx = img.shape[0], img.shape[1]
dx, dy = 360. / nx, 180. / ny
grid = Grid(nxny=(nx, ny), dxdy=(dx, -dy), x0y0=(-180., 90.),
pixel_ref='corner').center_grid
return self.set_rgb(img, grid, interp='linear')
if (len(img.shape) != 3) or (img.shape[-1] not in [3, 4]):
raise ValueError('img should be of shape (y, x, 3) or (y, x, 4)')
# Unefficient but by far easiest right now
out = []
for i in range(img.shape[-1]):
out.append(self._check_data(img[..., i], crs=crs, interp=interp))
self._rgb = np.dstack(out)
[docs]
def set_scale_bar(self, location=None, length=None, maxlen=0.25,
add_bbox=False, bbox_dx=1.2, bbox_dy=1.2,
bbox_kwargs=None, **kwargs):
"""Add a legend bar showing the scale to the plot.
Parameters
----------
location : tuple
the location of the bar (in the plot's relative coordinates)
length : float
the length of the bar in proj units (m or deg). Default is to
find the nicest number satisfying ``maxlen``
maxlen : float
the maximum lenght of the bar (in the plot's relative coordinates)
when choosing the length automatically
add_bbox : bool
add a bounding box to the scale bar (WIP: experimental)
bbox_dx : bool, default: 1.2
a multiplicating factor controlling the x size of the bounding box
(trial and error works best for this one)
bbox_dy : bool, default: 1.2
a multiplicating factor controlling the y size of the bounding box
(trial and error works best for this one)
bbox_kwargs : dict
kwarg to pass to set_geometry() for the bounding box (e.g.
facecolor, alpha, etc...)
kwargs : dict
any kwarg accepted by ``set_geometry``. Defaults are put on
``color``, ``linewidth``, ``text``, ``text_kwargs``... But you can
do whatever you want
"""
x0, x1, y0, y1 = self.grid.extent
# Find a sensible length for the scale
if length is None:
length = utils.nice_scale(x1 - x0, maxlen=maxlen)
if location is None:
location = (0.96 - length/2/(x1 - x0), 0.04)
# scalebar center location in proj coordinates
sbcx, sbcy = x0 + (x1 - x0) * location[0], y0 + (y1 - y0) * location[1]
# coordinates for the scalebar
line = LineString(([sbcx - length/2, sbcy], [sbcx + length/2, sbcy]))
# Of the bounding box
bbox = [[sbcx - length / 2 * bbox_dx, sbcy - length / 4 * bbox_dy],
[sbcx - length / 2 * bbox_dx, sbcy + length / 4 * bbox_dy],
[sbcx + length / 2 * bbox_dx, sbcy + length / 4 * bbox_dy],
[sbcx + length / 2 * bbox_dx, sbcy - length / 4 * bbox_dy],
]
# Units
if gis.proj_is_latlong(self.grid.proj):
units = 'deg'
elif length >= 1000.:
length /= 1000
units = 'km'
else:
units = 'm'
# Nice number
if int(length) == length:
length = int(length)
# Defaults
kwargs.setdefault('color', 'k')
kwargs.setdefault('text', '{} '.format(length) + units)
kwargs.setdefault('text_delta', (0.0, 0.015))
kwargs.setdefault('linewidth', 3)
kwargs.setdefault('zorder', 99)
tkw = kwargs.get('text_kwargs', {})
tkw.setdefault('horizontalalignment', 'center')
tkw.setdefault('zorder', 99)
tkw.setdefault('color', kwargs['color'])
kwargs['text_kwargs'] = tkw
if add_bbox:
if bbox_kwargs is None:
bbox_kwargs = {}
bbox_kwargs.setdefault('facecolor', 'w')
bbox_kwargs.setdefault('edgecolor', 'k')
bbox_kwargs.setdefault('zorder', 98)
poly = Polygon(np.asarray(bbox))
self.set_geometry(poly, crs=self.grid.proj, **bbox_kwargs)
self.set_geometry(line, crs=self.grid.proj, **kwargs)
def to_rgb(self):
"""Transform the data to a RGB image and add topographical shading."""
if self._rgb is None:
toplot = DataLevels.to_rgb(self)
else:
toplot = self._rgb
# Shading
if self.slope is not None:
# remove alphas?
try:
pno = np.where(toplot[:, :, 3] == 0.)
for i in [0, 1, 2]:
toplot[pno[0], pno[1], i] = 1
toplot[:, :, 3] = 1
except IndexError:
pass
# Actual shading
level = 1.0 - 0.1 * self.relief_factor
sens = 1 + 0.7 * self.relief_factor * self.slope
for i in [0, 1, 2]:
toplot[:, :, i] = np.clip(level * toplot[:, :, i] * sens, 0, 1)
# OK!
return toplot
[docs]
def plot(self, ax):
"""Add the map plot to an axis.
It first plots the image and then adds all the cartographic
information on top of it.
Returns a dict containing the primitives of the various plot calls
"""
out = {'imshow': None,
'contour': [],
'contourf': [],
}
# Image is the easiest
out['imshow'] = ax.imshow(self.to_rgb(), interpolation='none',
origin=self.origin)
ax.autoscale(enable=False)
# Contour
if self._contour_data is not None:
cs = ax.contour(self._contour_data, **self._contour_kw)
out['contour'].append(cs)
# Contourfill
if self._contourf_data is not None:
cs = ax.contourf(self._contourf_data, **self._contourf_kw)
out['contourf'].append(cs)
# Shapefiles
for col in self._collections:
ax.add_collection(copy.copy(col))
# Lon lat contours
lon, lat = self.grid.pixcorner_ll_coordinates
if len(self.xtick_levs) > 0:
ax.contour(lon, levels=self.xtick_levs,
extent=(-0.5, self.grid.nx-0.5, -0.5, self.grid.ny),
**self.ll_contour_kw)
if len(self.ytick_levs) > 0:
ax.contour(lat, levels=self.ytick_levs,
extent=(-0.5, self.grid.nx, -0.5, self.grid.ny-0.5),
**self.ll_contour_kw)
# Geometries
for g, kwargs in self._geometries:
if g.geom_type == 'Polygon':
kwargs.setdefault('facecolor', 'none')
plot_polygon(ax, g, **kwargs) # was g.buffer(0). Why?
if g.geom_type in ['LineString', 'LinearRing']:
a = np.array(g.coords)
kwargs.setdefault('color', 'k')
ax.plot(a[:, 0], a[:, 1], **kwargs)
if g.geom_type == 'Point':
kwargs.setdefault('zorder', 5)
kwargs.setdefault('marker', 'o')
kwargs.setdefault('s', 60)
kwargs.setdefault('facecolor', 'w')
kwargs.setdefault('edgecolor', 'k')
kwargs.setdefault('linewidths', 1)
if 'markersize' in kwargs:
# For those tempted to use the whole kw
kwargs['s'] = kwargs['markersize']
del kwargs['markersize']
if 'color' in kwargs:
# For those tempted to use the whole kw
kwargs['facecolor'] = kwargs['color']
kwargs['edgecolor'] = kwargs['color']
if 'c' in kwargs:
# For those tempted to use the whole kw
kwargs['facecolor'] = kwargs['c']
kwargs['edgecolor'] = kwargs['c']
del kwargs['c']
ax.scatter(g.x, g.y, **kwargs)
# Texts
for x, y, s, kwargs in self._text:
ax.text(x, y, s, **kwargs)
# Ticks
if (len(self.xtick_pos) > 0) or (len(self.ytick_pos) > 0):
ax.xaxis.set_ticks(np.array(self.xtick_pos)-0.5)
ax.yaxis.set_ticks(np.array(self.ytick_pos)-0.5)
ax.set_xticklabels(self.xtick_val)
ax.set_yticklabels(self.ytick_val)
ax.xaxis.set_zorder(5)
ax.yaxis.set_zorder(5)
else:
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
return out
def plot_polygon(ax, poly, edgecolor='black', **kwargs):
""" Plot a single Polygon geometry """
a = np.asarray(poly.exterior.coords)
# without Descartes, we could make a Patch of exterior
ax.add_patch(PolygonPatch(poly, **kwargs))
ax.plot(a[:, 0], a[:, 1], color=edgecolor)
for p in poly.interiors:
x, y = zip(*p.coords)
ax.plot(x, y, color=edgecolor)
class _SalemTransform(MPLTranform):
"""
A transform class for mpl axes using Grids.
"""
input_dims = 2
output_dims = 2
is_separable = False
has_inverse = False
def __init__(self, target_grid=None, source_crs=None, ax=None):
""" Instanciate.
Parameters
----------
target_grid : salem.Grid
typically, the map grid
source_grid
"""
self.source_crs = source_crs
self.target_grid = target_grid
self.ax = ax
MPLTranform.__init__(self)
def transform_non_affine(self, xy):
xx, yy = xy[:, 0:1], xy[:, 1:2]
xx, yy = self.target_grid.transform(xx, yy, crs=self.source_crs)
ax = plt.gca() if self.ax is None else self.ax
return ax.transData.transform(np.concatenate((xx, yy), 1))
def transform_path_non_affine(self, path):
raise NotImplementedError('path transforms not working yet')
def inverted(self):
raise NotImplementedError('inverted not working yet')