def plot_cartopy(lons,
lats,
size,
color,
labels=None,
projection='global',
resolution='110m',
continent_fill_color='0.8',
water_fill_color='1.0',
colormap=None,
colorbar=None,
marker="o",
title=None,
colorbar_ticklabel_format=None,
show=True,
proj_kwargs=None,
**kwargs): # @UnusedVariable
"""
Creates a Cartopy plot with a data point scatter plot.
:type lons: list/tuple of floats
:param lons: Longitudes of the data points.
:type lats: list/tuple of floats
:param lats: Latitudes of the data points.
:type size: float or list/tuple of floats
:param size: Size of the individual points in the scatter plot.
:type color: list/tuple of floats (or objects that can be
converted to floats, like e.g.
:class:`~obspy.core.utcdatetime.UTCDateTime`)
:param color: Color information of the individual data points to be
used in the specified color map (e.g. origin depths,
origin times).
:type labels: list/tuple of str
:param labels: Annotations for the individual data points.
:type projection: str, optional
:param projection: The map projection.
Currently supported are:
* ``"global"`` (Will plot the whole world using
:class:`~cartopy.crs.Mollweide`.)
* ``"ortho"`` (Will center around the mean lat/long using
:class:`~cartopy.crs.Orthographic`.)
* ``"local"`` (Will plot around local events using
:class:`~cartopy.crs.AlbersEqualArea`.)
* Any other Cartopy :class:`~cartopy.crs.Projection`. An instance
of this class will be created using the supplied ``proj_kwargs``.
Defaults to "global"
:type resolution: str, optional
:param resolution: Resolution of the boundary database to use. Will be
passed directly to the Cartopy module. Possible values are:
* ``"110m"``
* ``"50m"``
* ``"10m"``
Defaults to ``"110m"``. For compatibility, you may also specify any of
the Basemap resolutions defined in :func:`plot_basemap`.
:type continent_fill_color: Valid matplotlib color, optional
:param continent_fill_color: Color of the continents. Defaults to
``"0.9"`` which is a light gray.
:type water_fill_color: Valid matplotlib color, optional
:param water_fill_color: Color of all water bodies.
Defaults to ``"white"``.
:type colormap: str, any matplotlib colormap, optional
:param colormap: The colormap for color-coding the events as provided
in `color` kwarg.
The event with the smallest `color` property will have the
color of one end of the colormap and the event with the highest
`color` property the color of the other end with all other events
in between.
Defaults to None which will use the default matplotlib colormap.
:type colorbar: bool, optional
:param colorbar: When left `None`, a colorbar is plotted if more than one
object is plotted. Using `True`/`False` the colorbar can be forced
on/off.
:type title: str
:param title: Title above plot.
:type colorbar_ticklabel_format: str or function or
subclass of :class:`matplotlib.ticker.Formatter`
:param colorbar_ticklabel_format: Format string or Formatter used to format
colorbar tick labels.
:type show: bool
:param show: Whether to show the figure after plotting or not. Can be used
to do further customization of the plot before showing it.
:type proj_kwargs: dict
:param proj_kwargs: Keyword arguments to pass to the Cartopy
:class:`~cartopy.ccrs.Projection`. In this dictionary, you may specify
``central_longitude='auto'`` or ``central_latitude='auto'`` to have
this function calculate the latitude or longitude as it would for other
projections. Some arguments may be ignored if you choose one of the
built-in ``projection`` choices.
"""
import matplotlib.pyplot as plt
if isinstance(color[0], (datetime.datetime, UTCDateTime)):
datetimeplot = True
color = [date2num(getattr(t, 'datetime', t)) for t in color]
else:
datetimeplot = False
fig = plt.figure()
# The colorbar should only be plotted if more then one event is
# present.
if colorbar is not None:
show_colorbar = colorbar
else:
if len(lons) > 1 and hasattr(color, "__len__") and \
not isinstance(color, (str, native_str)):
show_colorbar = True
else:
show_colorbar = False
if projection == "local":
ax_x0, ax_width = 0.10, 0.80
elif projection == "global":
ax_x0, ax_width = 0.01, 0.98
else:
ax_x0, ax_width = 0.05, 0.90
proj_kwargs = proj_kwargs or {}
if projection == 'global':
proj_kwargs['central_longitude'] = np.mean(lons)
proj = ccrs.Mollweide(**proj_kwargs)
elif projection == 'ortho':
proj_kwargs['central_latitude'] = np.mean(lats)
proj_kwargs['central_longitude'] = mean_longitude(lons)
proj = ccrs.Orthographic(**proj_kwargs)
elif projection == 'local':
if min(lons) < -150 and max(lons) > 150:
max_lons = max(np.array(lons) % 360)
min_lons = min(np.array(lons) % 360)
else:
max_lons = max(lons)
min_lons = min(lons)
lat_0 = max(lats) / 2. + min(lats) / 2.
lon_0 = max_lons / 2. + min_lons / 2.
if lon_0 > 180:
lon_0 -= 360
deg2m_lat = 2 * np.pi * 6371 * 1000 / 360
deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi)
if len(lats) > 1:
height = (max(lats) - min(lats)) * deg2m_lat
width = (max_lons - min_lons) * deg2m_lon
margin = 0.2 * (width + height)
height += margin
width += margin
else:
height = 2.0 * deg2m_lat
width = 5.0 * deg2m_lon
# Do intelligent aspect calculation for local projection
# adjust to figure dimensions
w, h = fig.get_size_inches()
aspect = w / h
if show_colorbar:
aspect *= 1.2
if width / height < aspect:
width = height * aspect
else:
height = width / aspect
proj_kwargs['central_latitude'] = lat_0
proj_kwargs['central_longitude'] = lon_0
proj_kwargs['standard_parallels'] = [lat_0, lat_0]
proj = ccrs.AlbersEqualArea(**proj_kwargs)
# User-supplied projection.
elif isinstance(projection, type):
if 'central_longitude' in proj_kwargs:
if proj_kwargs['central_longitude'] == 'auto':
proj_kwargs['central_longitude'] = mean_longitude(lons)
if 'central_latitude' in proj_kwargs:
if proj_kwargs['central_latitude'] == 'auto':
proj_kwargs['central_latitude'] = np.mean(lats)
if 'pole_longitude' in proj_kwargs:
if proj_kwargs['pole_longitude'] == 'auto':
proj_kwargs['pole_longitude'] = np.mean(lons)
if 'pole_latitude' in proj_kwargs:
if proj_kwargs['pole_latitude'] == 'auto':
proj_kwargs['pole_latitude'] = np.mean(lats)
proj = projection(**proj_kwargs)
else:
msg = "Projection '%s' not supported." % projection
raise ValueError(msg)
if show_colorbar:
map_ax = fig.add_axes([ax_x0, 0.13, ax_width, 0.77], projection=proj)
cm_ax = fig.add_axes([ax_x0, 0.05, ax_width, 0.05])
plt.sca(map_ax)
else:
ax_y0, ax_height = 0.05, 0.85
if projection == "local":
ax_y0 += 0.05
ax_height -= 0.05
map_ax = fig.add_axes([ax_x0, ax_y0, ax_width, ax_height],
projection=proj)
if projection == 'local':
x0, y0 = proj.transform_point(lon_0, lat_0, proj.as_geodetic())
map_ax.set_xlim(x0 - width / 2, x0 + width / 2)
map_ax.set_ylim(y0 - height / 2, y0 + height / 2)
else:
map_ax.set_global()
# Pick features at specified resolution.
resolution = _CARTOPY_RESOLUTIONS[resolution]
try:
borders, land, ocean = _CARTOPY_FEATURES[resolution]
except KeyError:
borders = cfeature.NaturalEarthFeature(cfeature.BORDERS.category,
cfeature.BORDERS.name,
resolution,
edgecolor='none',
facecolor='none')
land = cfeature.NaturalEarthFeature(cfeature.LAND.category,
cfeature.LAND.name,
resolution,
edgecolor='face',
facecolor='none')
ocean = cfeature.NaturalEarthFeature(cfeature.OCEAN.category,
cfeature.OCEAN.name,
resolution,
edgecolor='face',
facecolor='none')
_CARTOPY_FEATURES[resolution] = (borders, land, ocean)
# Draw coast lines, country boundaries, fill continents.
if MATPLOTLIB_VERSION >= [2, 0, 0]:
map_ax.set_facecolor(water_fill_color)
else:
map_ax.set_axis_bgcolor(water_fill_color)
map_ax.add_feature(ocean, facecolor=water_fill_color)
map_ax.add_feature(land, facecolor=continent_fill_color)
map_ax.add_feature(borders, edgecolor='0.75')
map_ax.coastlines(resolution=resolution, color='0.4')
# Draw grid lines - TODO: draw_labels=True doesn't work yet.
if projection == 'local':
map_ax.gridlines()
else:
# Draw lat/lon grid lines every 30 degrees.
map_ax.gridlines(xlocs=range(-180, 181, 30), ylocs=range(-90, 91, 30))
# Plot labels
if labels and len(lons) > 0:
with map_ax.hold_limits():
for name, xpt, ypt, _colorpt in zip(labels, lons, lats, color):
map_ax.text(xpt,
ypt,
name,
weight="heavy",
color="k",
zorder=100,
transform=ccrs.Geodetic(),
path_effects=[
patheffects.withStroke(linewidth=3,
foreground="white")
])
scatter = map_ax.scatter(lons,
lats,
marker=marker,
s=size,
c=color,
zorder=10,
cmap=colormap,
transform=ccrs.Geodetic())
if title:
plt.suptitle(title)
# Only show the colorbar for more than one event.
if show_colorbar:
if colorbar_ticklabel_format is not None:
if isinstance(colorbar_ticklabel_format, (str, native_str)):
formatter = FormatStrFormatter(colorbar_ticklabel_format)
elif hasattr(colorbar_ticklabel_format, '__call__'):
formatter = FuncFormatter(colorbar_ticklabel_format)
elif isinstance(colorbar_ticklabel_format, Formatter):
formatter = colorbar_ticklabel_format
locator = MaxNLocator(5)
else:
if datetimeplot:
locator = AutoDateLocator()
formatter = AutoDateFormatter(locator)
# Compat with old matplotlib versions.
if hasattr(formatter, "scaled"):
formatter.scaled[1 / (24. * 60.)] = '%H:%M:%S'
else:
locator = None
formatter = None
cb = Colorbar(cm_ax,
scatter,
cmap=colormap,
orientation='horizontal',
ticks=locator,
format=formatter)
# Compat with old matplotlib versions.
if hasattr(cb, "update_ticks"):
cb.update_ticks()
if show:
plt.show()
return fig
def plot_cartopy(lons, lats, size, color, labels=None, projection='global',
resolution='110m', continent_fill_color='0.8',
water_fill_color='1.0', colormap=None, colorbar=None,
marker="o", title=None, colorbar_ticklabel_format=None,
show=True, proj_kwargs=None, **kwargs): # @UnusedVariable
"""
Creates a Cartopy plot with a data point scatter plot.
:type lons: list/tuple of floats
:param lons: Longitudes of the data points.
:type lats: list/tuple of floats
:param lats: Latitudes of the data points.
:type size: float or list/tuple of floats
:param size: Size of the individual points in the scatter plot.
:type color: list/tuple of floats (or objects that can be
converted to floats, like e.g.
:class:`~obspy.core.utcdatetime.UTCDateTime`)
:param color: Color information of the individual data points to be
used in the specified color map (e.g. origin depths,
origin times).
:type labels: list/tuple of str
:param labels: Annotations for the individual data points.
:type projection: str, optional
:param projection: The map projection.
Currently supported are:
* ``"global"`` (Will plot the whole world using
:class:`~cartopy.crs.Mollweide`.)
* ``"ortho"`` (Will center around the mean lat/long using
:class:`~cartopy.crs.Orthographic`.)
* ``"local"`` (Will plot around local events using
:class:`~cartopy.crs.AlbersEqualArea`.)
* Any other Cartopy :class:`~cartopy.crs.Projection`. An instance
of this class will be created using the supplied ``proj_kwargs``.
Defaults to "global"
:type resolution: str, optional
:param resolution: Resolution of the boundary database to use. Will be
passed directly to the Cartopy module. Possible values are:
* ``"110m"``
* ``"50m"``
* ``"10m"``
Defaults to ``"110m"``. For compatibility, you may also specify any of
the Basemap resolutions defined in :func:`plot_basemap`.
:type continent_fill_color: Valid matplotlib color, optional
:param continent_fill_color: Color of the continents. Defaults to
``"0.9"`` which is a light gray.
:type water_fill_color: Valid matplotlib color, optional
:param water_fill_color: Color of all water bodies.
Defaults to ``"white"``.
:type colormap: str, any matplotlib colormap, optional
:param colormap: The colormap for color-coding the events as provided
in `color` kwarg.
The event with the smallest `color` property will have the
color of one end of the colormap and the event with the highest
`color` property the color of the other end with all other events
in between.
Defaults to None which will use the default matplotlib colormap.
:type colorbar: bool, optional
:param colorbar: When left `None`, a colorbar is plotted if more than one
object is plotted. Using `True`/`False` the colorbar can be forced
on/off.
:type title: str
:param title: Title above plot.
:type colorbar_ticklabel_format: str or function or
subclass of :class:`matplotlib.ticker.Formatter`
:param colorbar_ticklabel_format: Format string or Formatter used to format
colorbar tick labels.
:type show: bool
:param show: Whether to show the figure after plotting or not. Can be used
to do further customization of the plot before showing it.
:type proj_kwargs: dict
:param proj_kwargs: Keyword arguments to pass to the Cartopy
:class:`~cartopy.ccrs.Projection`. In this dictionary, you may specify
``central_longitude='auto'`` or ``central_latitude='auto'`` to have
this function calculate the latitude or longitude as it would for other
projections. Some arguments may be ignored if you choose one of the
built-in ``projection`` choices.
"""
import matplotlib.pyplot as plt
if isinstance(color[0], (datetime.datetime, UTCDateTime)):
datetimeplot = True
color = [date2num(getattr(t, 'datetime', t)) for t in color]
else:
datetimeplot = False
fig = plt.figure()
# The colorbar should only be plotted if more then one event is
# present.
if colorbar is not None:
show_colorbar = colorbar
else:
if len(lons) > 1 and hasattr(color, "__len__") and \
not isinstance(color, (str, native_str)):
show_colorbar = True
else:
show_colorbar = False
if projection == "local":
ax_x0, ax_width = 0.10, 0.80
elif projection == "global":
ax_x0, ax_width = 0.01, 0.98
else:
ax_x0, ax_width = 0.05, 0.90
proj_kwargs = proj_kwargs or {}
if projection == 'global':
proj_kwargs['central_longitude'] = np.mean(lons)
proj = ccrs.Mollweide(**proj_kwargs)
elif projection == 'ortho':
proj_kwargs['central_latitude'] = np.mean(lats)
proj_kwargs['central_longitude'] = mean_longitude(lons)
proj = ccrs.Orthographic(**proj_kwargs)
elif projection == 'local':
if min(lons) < -150 and max(lons) > 150:
max_lons = max(np.array(lons) % 360)
min_lons = min(np.array(lons) % 360)
else:
max_lons = max(lons)
min_lons = min(lons)
lat_0 = max(lats) / 2. + min(lats) / 2.
lon_0 = max_lons / 2. + min_lons / 2.
if lon_0 > 180:
lon_0 -= 360
deg2m_lat = 2 * np.pi * 6371 * 1000 / 360
deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi)
if len(lats) > 1:
height = (max(lats) - min(lats)) * deg2m_lat
width = (max_lons - min_lons) * deg2m_lon
margin = 0.2 * (width + height)
height += margin
width += margin
else:
height = 2.0 * deg2m_lat
width = 5.0 * deg2m_lon
# Do intelligent aspect calculation for local projection
# adjust to figure dimensions
w, h = fig.get_size_inches()
aspect = w / h
if show_colorbar:
aspect *= 1.2
if width / height < aspect:
width = height * aspect
else:
height = width / aspect
proj_kwargs['central_latitude'] = lat_0
proj_kwargs['central_longitude'] = lon_0
proj_kwargs['standard_parallels'] = [lat_0, lat_0]
proj = ccrs.AlbersEqualArea(**proj_kwargs)
# User-supplied projection.
elif isinstance(projection, type):
if 'central_longitude' in proj_kwargs:
if proj_kwargs['central_longitude'] == 'auto':
proj_kwargs['central_longitude'] = mean_longitude(lons)
if 'central_latitude' in proj_kwargs:
if proj_kwargs['central_latitude'] == 'auto':
proj_kwargs['central_latitude'] = np.mean(lats)
if 'pole_longitude' in proj_kwargs:
if proj_kwargs['pole_longitude'] == 'auto':
proj_kwargs['pole_longitude'] = np.mean(lons)
if 'pole_latitude' in proj_kwargs:
if proj_kwargs['pole_latitude'] == 'auto':
proj_kwargs['pole_latitude'] = np.mean(lats)
proj = projection(**proj_kwargs)
else:
msg = "Projection '%s' not supported." % projection
raise ValueError(msg)
if show_colorbar:
map_ax = fig.add_axes([ax_x0, 0.13, ax_width, 0.77], projection=proj)
cm_ax = fig.add_axes([ax_x0, 0.05, ax_width, 0.05])
plt.sca(map_ax)
else:
ax_y0, ax_height = 0.05, 0.85
if projection == "local":
ax_y0 += 0.05
ax_height -= 0.05
map_ax = fig.add_axes([ax_x0, ax_y0, ax_width, ax_height],
projection=proj)
if projection == 'local':
x0, y0 = proj.transform_point(lon_0, lat_0, proj.as_geodetic())
map_ax.set_xlim(x0 - width / 2, x0 + width / 2)
map_ax.set_ylim(y0 - height / 2, y0 + height / 2)
else:
map_ax.set_global()
# Pick features at specified resolution.
resolution = _CARTOPY_RESOLUTIONS[resolution]
try:
borders, land, ocean = _CARTOPY_FEATURES[resolution]
except KeyError:
borders = cfeature.NaturalEarthFeature(cfeature.BORDERS.category,
cfeature.BORDERS.name,
resolution,
edgecolor='none',
facecolor='none')
land = cfeature.NaturalEarthFeature(cfeature.LAND.category,
cfeature.LAND.name, resolution,
edgecolor='face', facecolor='none')
ocean = cfeature.NaturalEarthFeature(cfeature.OCEAN.category,
cfeature.OCEAN.name, resolution,
edgecolor='face',
facecolor='none')
_CARTOPY_FEATURES[resolution] = (borders, land, ocean)
# Draw coast lines, country boundaries, fill continents.
if MATPLOTLIB_VERSION >= [2, 0, 0]:
map_ax.set_facecolor(water_fill_color)
else:
map_ax.set_axis_bgcolor(water_fill_color)
map_ax.add_feature(ocean, facecolor=water_fill_color)
map_ax.add_feature(land, facecolor=continent_fill_color)
map_ax.add_feature(borders, edgecolor='0.75')
map_ax.coastlines(resolution=resolution, color='0.4')
# Draw grid lines - TODO: draw_labels=True doesn't work yet.
if projection == 'local':
map_ax.gridlines()
else:
# Draw lat/lon grid lines every 30 degrees.
map_ax.gridlines(xlocs=range(-180, 181, 30), ylocs=range(-90, 91, 30))
# Plot labels
if labels and len(lons) > 0:
with map_ax.hold_limits():
for name, xpt, ypt, _colorpt in zip(labels, lons, lats, color):
map_ax.text(xpt, ypt, name, weight="heavy", color="k",
zorder=100, transform=ccrs.Geodetic(),
path_effects=[
PathEffects.withStroke(linewidth=3,
foreground="white")])
scatter = map_ax.scatter(lons, lats, marker=marker, s=size, c=color,
zorder=10, cmap=colormap,
transform=ccrs.Geodetic())
if title:
plt.suptitle(title)
# Only show the colorbar for more than one event.
if show_colorbar:
if colorbar_ticklabel_format is not None:
if isinstance(colorbar_ticklabel_format, (str, native_str)):
formatter = FormatStrFormatter(colorbar_ticklabel_format)
elif hasattr(colorbar_ticklabel_format, '__call__'):
formatter = FuncFormatter(colorbar_ticklabel_format)
elif isinstance(colorbar_ticklabel_format, Formatter):
formatter = colorbar_ticklabel_format
locator = MaxNLocator(5)
else:
if datetimeplot:
locator = AutoDateLocator()
formatter = AutoDateFormatter(locator)
# Compat with old matplotlib versions.
if hasattr(formatter, "scaled"):
formatter.scaled[1 / (24. * 60.)] = '%H:%M:%S'
else:
locator = None
formatter = None
cb = Colorbar(cm_ax, scatter, cmap=colormap,
orientation='horizontal',
ticks=locator,
format=formatter)
# Compat with old matplotlib versions.
if hasattr(cb, "update_ticks"):
cb.update_ticks()
if show:
plt.show()
return fig
def _plot_basemap_into_axes(ax,
lons,
lats,
size,
color,
bmap=None,
labels=None,
projection='global',
resolution='l',
continent_fill_color='0.8',
water_fill_color='1.0',
colormap=None,
marker="o",
title=None,
adjust_aspect_to_colorbar=False,
**kwargs): # @UnusedVariable
"""
Creates a (or adds to existing) basemap plot with a data point scatter
plot in given axes.
See :func:`plot_basemap` for details on most args/kwargs.
:type ax: :class:`matplotlib.axes.Axes`
:param ax: Existing matplotlib axes instance, optionally with previous
basemap plot (see `bmap` kwarg).
:type bmap: :class:`mpl_toolkits.basemap.Basemap`
:param bmap: Basemap instance in provided matplotlib Axes `ax` to reuse. If
specified, any kwargs regarding the basemap plot setup will be ignored
(i.e. `projection`, `resolution`, `continent_fill_color`,
`water_fill_color`).
:rtype: :class:`matplotlib.collections.PathCollection`
:returns: Matplotlib path collection (e.g. to reuse for colorbars).
"""
fig = ax.figure
if bmap is None:
if projection == 'global':
bmap = Basemap(projection='moll',
lon_0=round(np.mean(lons), 4),
resolution=_BASEMAP_RESOLUTIONS[resolution],
ax=ax)
elif projection == 'ortho':
bmap = Basemap(projection='ortho',
resolution=_BASEMAP_RESOLUTIONS[resolution],
area_thresh=1000.0,
lat_0=round(np.mean(lats), 4),
lon_0=round(mean_longitude(lons), 4),
ax=ax)
elif projection == 'local':
if min(lons) < -150 and max(lons) > 150:
max_lons = max(np.array(lons) % 360)
min_lons = min(np.array(lons) % 360)
else:
max_lons = max(lons)
min_lons = min(lons)
lat_0 = max(lats) / 2. + min(lats) / 2.
lon_0 = max_lons / 2. + min_lons / 2.
if lon_0 > 180:
lon_0 -= 360
deg2m_lat = 2 * np.pi * 6371 * 1000 / 360
deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi)
if len(lats) > 1:
height = (max(lats) - min(lats)) * deg2m_lat
width = (max_lons - min_lons) * deg2m_lon
margin = 0.2 * (width + height)
height += margin
width += margin
else:
height = 2.0 * deg2m_lat
width = 5.0 * deg2m_lon
# do intelligent aspect calculation for local projection
# adjust to figure dimensions
w, h = fig.get_size_inches()
ax_bbox = ax.get_position()
aspect = (w * ax_bbox.width) / (h * ax_bbox.height)
if adjust_aspect_to_colorbar:
aspect *= 1.2
if width / height < aspect:
width = height * aspect
else:
height = width / aspect
bmap = Basemap(projection='aea',
resolution=_BASEMAP_RESOLUTIONS[resolution],
area_thresh=1000.0,
lat_0=round(lat_0, 4),
lon_0=round(lon_0, 4),
width=width,
height=height,
ax=ax)
# not most elegant way to calculate some round lats/lons
def linspace2(val1, val2, n):
"""
returns around n 'nice' values between val1 and val2
"""
dval = val2 - val1
round_pos = int(round(-np.log10(1. * dval / n)))
# Fake negative rounding as not supported by future as of now.
if round_pos < 0:
factor = 10**(abs(round_pos))
delta = round(2. * dval / n / factor) * factor / 2
else:
delta = round(2. * dval / n, round_pos) / 2
new_val1 = np.ceil(val1 / delta) * delta
new_val2 = np.floor(val2 / delta) * delta
n = int((new_val2 - new_val1) / delta + 1)
return np.linspace(new_val1, new_val2, n)
n_1 = int(np.ceil(height / max(width, height) * 8))
n_2 = int(np.ceil(width / max(width, height) * 8))
parallels = linspace2(lat_0 - height / 2 / deg2m_lat,
lat_0 + height / 2 / deg2m_lat, n_1)
# Old basemap versions have problems with non-integer parallels.
try:
bmap.drawparallels(parallels, labels=[0, 1, 1, 0])
except KeyError:
parallels = sorted(list(set(map(int, parallels))))
bmap.drawparallels(parallels, labels=[0, 1, 1, 0])
if min(lons) < -150 and max(lons) > 150:
lon_0 %= 360
meridians = linspace2(lon_0 - width / 2 / deg2m_lon,
lon_0 + width / 2 / deg2m_lon, n_2)
meridians[meridians > 180] -= 360
bmap.drawmeridians(meridians, labels=[1, 0, 0, 1])
else:
msg = "Projection '%s' not supported." % projection
raise ValueError(msg)
# draw coast lines, country boundaries, fill continents.
if MATPLOTLIB_VERSION >= [2, 0, 0]:
ax.set_facecolor(water_fill_color)
else:
ax.set_axis_bgcolor(water_fill_color)
bmap.drawcoastlines(color="0.4")
bmap.drawcountries(color="0.75")
bmap.fillcontinents(color=continent_fill_color,
lake_color=water_fill_color)
# draw the edge of the bmap projection region (the projection limb)
bmap.drawmapboundary(fill_color=water_fill_color)
# draw lat/lon grid lines every 30 degrees.
bmap.drawmeridians(np.arange(-180, 180, 30))
bmap.drawparallels(np.arange(-90, 90, 30))
fig.bmap = bmap
# compute the native bmap projection coordinates for events.
x, y = bmap(lons, lats)
# plot labels
if labels:
if 100 > len(lons) > 1:
for name, xpt, ypt, _colorpt in zip(labels, x, y, color):
# Check if the point can actually be seen with the current bmap
# projection. The bmap object will set the coordinates to very
# large values if it cannot project a point.
if xpt > 1e25:
continue
ax.text(xpt,
ypt,
name,
weight="heavy",
color="k",
zorder=100,
path_effects=[
patheffects.withStroke(linewidth=3,
foreground="white")
])
elif len(lons) == 1:
ax.text(x[0],
y[0],
labels[0],
weight="heavy",
color="k",
path_effects=[
patheffects.withStroke(linewidth=3, foreground="white")
])
# scatter plot is removing valid x/y points with invalid color value,
# so we plot those points separately.
try:
nan_points = np.isnan(np.array(color, dtype=np.float))
except ValueError:
# `color' was not a list of values, but a list of colors.
pass
else:
if nan_points.any():
x_ = np.array(x)[nan_points]
y_ = np.array(y)[nan_points]
size_ = np.array(size)[nan_points]
bmap.scatter(x_,
y_,
marker=marker,
s=size_,
c="0.3",
zorder=10,
cmap=None)
scatter = bmap.scatter(x,
y,
marker=marker,
s=size,
c=color,
zorder=10,
cmap=colormap)
if title:
ax.set_title(title)
return scatter
def _plot_basemap_into_axes(
ax, lons, lats, size, color, bmap=None, labels=None,
projection='global', resolution='l', continent_fill_color='0.8',
water_fill_color='1.0', colormap=None, marker="o", title=None,
adjust_aspect_to_colorbar=False, **kwargs): # @UnusedVariable
"""
Creates a (or adds to existing) basemap plot with a data point scatter
plot in given axes.
See :func:`plot_basemap` for details on most args/kwargs.
:type ax: :class:`matplotlib.axes.Axes`
:param ax: Existing matplotlib axes instance, optionally with previous
basemap plot (see `bmap` kwarg).
:type bmap: :class:`mpl_toolkits.basemap.Basemap`
:param bmap: Basemap instance in provided matplotlib Axes `ax` to reuse. If
specified, any kwargs regarding the basemap plot setup will be ignored
(i.e. `projection`, `resolution`, `continent_fill_color`,
`water_fill_color`).
:rtype: :class:`matplotlib.collections.PathCollection`
:returns: Matplotlib path collection (e.g. to reuse for colorbars).
"""
fig = ax.figure
if bmap is None:
if projection == 'global':
bmap = Basemap(projection='moll', lon_0=round(np.mean(lons), 4),
resolution=_BASEMAP_RESOLUTIONS[resolution],
ax=ax)
elif projection == 'ortho':
bmap = Basemap(projection='ortho',
resolution=_BASEMAP_RESOLUTIONS[resolution],
area_thresh=1000.0, lat_0=round(np.mean(lats), 4),
lon_0=round(mean_longitude(lons), 4), ax=ax)
elif projection == 'local':
if min(lons) < -150 and max(lons) > 150:
max_lons = max(np.array(lons) % 360)
min_lons = min(np.array(lons) % 360)
else:
max_lons = max(lons)
min_lons = min(lons)
lat_0 = max(lats) / 2. + min(lats) / 2.
lon_0 = max_lons / 2. + min_lons / 2.
if lon_0 > 180:
lon_0 -= 360
deg2m_lat = 2 * np.pi * 6371 * 1000 / 360
deg2m_lon = deg2m_lat * np.cos(lat_0 / 180 * np.pi)
if len(lats) > 1:
height = (max(lats) - min(lats)) * deg2m_lat
width = (max_lons - min_lons) * deg2m_lon
margin = 0.2 * (width + height)
height += margin
width += margin
else:
height = 2.0 * deg2m_lat
width = 5.0 * deg2m_lon
# do intelligent aspect calculation for local projection
# adjust to figure dimensions
w, h = fig.get_size_inches()
ax_bbox = ax.get_position()
aspect = (w * ax_bbox.width) / (h * ax_bbox.height)
if adjust_aspect_to_colorbar:
aspect *= 1.2
if width / height < aspect:
width = height * aspect
else:
height = width / aspect
bmap = Basemap(projection='aea',
resolution=_BASEMAP_RESOLUTIONS[resolution],
area_thresh=1000.0, lat_0=round(lat_0, 4),
lon_0=round(lon_0, 4),
width=width, height=height, ax=ax)
# not most elegant way to calculate some round lats/lons
def linspace2(val1, val2, n):
"""
returns around n 'nice' values between val1 and val2
"""
dval = val2 - val1
round_pos = int(round(-np.log10(1. * dval / n)))
# Fake negative rounding as not supported by future as of now.
if round_pos < 0:
factor = 10 ** (abs(round_pos))
delta = round(2. * dval / n / factor) * factor / 2
else:
delta = round(2. * dval / n, round_pos) / 2
new_val1 = np.ceil(val1 / delta) * delta
new_val2 = np.floor(val2 / delta) * delta
n = int((new_val2 - new_val1) / delta + 1)
return np.linspace(new_val1, new_val2, n)
n_1 = int(np.ceil(height / max(width, height) * 8))
n_2 = int(np.ceil(width / max(width, height) * 8))
parallels = linspace2(lat_0 - height / 2 / deg2m_lat,
lat_0 + height / 2 / deg2m_lat, n_1)
# Old basemap versions have problems with non-integer parallels.
try:
bmap.drawparallels(parallels, labels=[0, 1, 1, 0])
except KeyError:
parallels = sorted(list(set(map(int, parallels))))
bmap.drawparallels(parallels, labels=[0, 1, 1, 0])
if min(lons) < -150 and max(lons) > 150:
lon_0 %= 360
meridians = linspace2(lon_0 - width / 2 / deg2m_lon,
lon_0 + width / 2 / deg2m_lon, n_2)
meridians[meridians > 180] -= 360
bmap.drawmeridians(meridians, labels=[1, 0, 0, 1])
else:
msg = "Projection '%s' not supported." % projection
raise ValueError(msg)
# draw coast lines, country boundaries, fill continents.
if MATPLOTLIB_VERSION >= [2, 0, 0]:
ax.set_facecolor(water_fill_color)
else:
ax.set_axis_bgcolor(water_fill_color)
bmap.drawcoastlines(color="0.4")
bmap.drawcountries(color="0.75")
bmap.fillcontinents(color=continent_fill_color,
lake_color=water_fill_color)
# draw the edge of the bmap projection region (the projection limb)
bmap.drawmapboundary(fill_color=water_fill_color)
# draw lat/lon grid lines every 30 degrees.
bmap.drawmeridians(np.arange(-180, 180, 30))
bmap.drawparallels(np.arange(-90, 90, 30))
fig.bmap = bmap
# compute the native bmap projection coordinates for events.
x, y = bmap(lons, lats)
# plot labels
if labels:
if 100 > len(lons) > 1:
for name, xpt, ypt, _colorpt in zip(labels, x, y, color):
# Check if the point can actually be seen with the current bmap
# projection. The bmap object will set the coordinates to very
# large values if it cannot project a point.
if xpt > 1e25:
continue
ax.text(xpt, ypt, name, weight="heavy",
color="k", zorder=100,
path_effects=[
PathEffects.withStroke(linewidth=3,
foreground="white")])
elif len(lons) == 1:
ax.text(x[0], y[0], labels[0], weight="heavy", color="k",
path_effects=[
PathEffects.withStroke(linewidth=3,
foreground="white")])
# scatter plot is removing valid x/y points with invalid color value,
# so we plot those points separately.
try:
nan_points = np.isnan(np.array(color, dtype=np.float))
except ValueError:
# `color' was not a list of values, but a list of colors.
pass
else:
if nan_points.any():
x_ = np.array(x)[nan_points]
y_ = np.array(y)[nan_points]
size_ = np.array(size)[nan_points]
bmap.scatter(x_, y_, marker=marker, s=size_, c="0.3",
zorder=10, cmap=None)
scatter = bmap.scatter(x, y, marker=marker, s=size, c=color, zorder=10,
cmap=colormap)
if title:
ax.set_title(title)
return scatter
