def extract_foreground(self, **kwargs):
img = self.image
if self._initialized:
bg, fg = self.draw_bg.element, self.draw_fg.element
else:
self._initialized = True
bg, fg = self.bg_paths, self.fg_paths
bg, fg = (gv.project(g, projection=img.crs) for g in (bg, fg))
if not len(bg) or not len(fg):
return gv.Path([], img.kdims, crs=img.crs)
if self.downsample != 1:
kwargs = {'dynamic': False}
h, w = img.interface.shape(img, gridded=True)
kwargs['width'] = int(w * self.downsample)
kwargs['height'] = int(h * self.downsample)
img = regrid(img, **kwargs)
foreground = extract_foreground(img,
background=bg,
foreground=fg,
iterations=self.iterations)
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
foreground = gv.Path(
[contours(foreground, filled=True, levels=1).split()[0].data],
kdims=foreground.kdims,
crs=foreground.crs)
self.result = gv.project(foreground, projection=self.crs)
return foreground
def contour(self, x=None, y=None, z=None, data=None, filled=False):
from holoviews.operation import contours
if 'projection' in self._plot_opts:
import cartopy.crs as ccrs
t = self._plot_opts['projection']
if isinstance(t, ccrs.CRS) and not isinstance(t, ccrs.Projection):
raise ValueError('invalid transform:'
' Spherical contouring is not supported - '
' consider using PlateCarree/RotatedPole.')
opts = dict(plot=self._plot_opts,
style=self._style_opts,
norm=self._norm_opts)
qmesh = self.quadmesh(x, y, z, data)
if self.geo:
# Apply projection before rasterizing
import cartopy.crs as ccrs
from geoviews import project
projection = self._plot_opts.get('projection',
ccrs.GOOGLE_MERCATOR)
qmesh = project(qmesh, projection=projection)
if filled:
opts['style']['line_alpha'] = 0
if opts['plot']['colorbar']:
opts['plot']['show_legend'] = False
levels = self.kwds.get('levels', 5)
if isinstance(levels, int):
opts['plot']['color_levels'] = levels
return contours(qmesh, filled=filled, levels=levels).opts(**opts)
def geoview_flat_plot(region_da, time_sel=0, level_sel=False, wide_chile=True):
if type(level_sel) is int:
da = region_da.isel(level=level_sel)
else:
da = region_da
gv_ds = gv.Dataset(da.isel(time=time_sel))
plot = gv_ds.to(gv.Image, ['lon', 'lat'], 't', 'time').opts(cmap='viridis',
colorbar=True)
extras = []
if wide_chile:
easter_island_text = gv.Text(-104.360481, -24.104671, 'Easter Island').opts(color='white')
easter_island_point = gv.Points([(-109.360481, -27.104671)]).opts(color='red')
easter = easter_island_point * easter_island_text
falkland_islands_text = gv.Text(-49.563412, -56.820557, 'Falklands').opts(color='white')
falkland_islands_point = gv.Points([(-51.563412, -59.820557)]).opts(color='red')
falkland = falkland_islands_point * falkland_islands_text
extras.append(easter * falkland)
plot = contours(plot, filled=True, overlaid=True)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
final_plot = plot * gf.coastline
for extra in extras:
final_plot *= extra
gv.output(final_plot)
def plot_qmesh_contours(t,
freqs,
tfr,
l=None,
filled=True,
overlaid=True,
img_args={},
contour_args={}):
'''plot an interpolated QuadMesh as an Image overlaid with contours'''
from holoviews.operation import contours
ti, freqsi, tfri = interpolated_scalogram(t, freqs, tfr, 'cubic')
img = hv.Image( (ti[0,:], freqsi[:,0], tfri), ["Time", "Frequency"] )\
.opts(cmap="Viridis", width=400,show_grid=False)\
.opts( **img_args )
if l is None: l = estimate_levels(tfr)
return contours(img, levels=l, filled=filled,
overlaid=overlaid).opts("Contours", **contour_args)
def extract_foreground(self, **kwargs):
img = self.image
bg, fg = self.bg_path_view(), self.fg_path_view()
self._initialized = True
if not len(bg) or not len(fg):
return gv.Path([], img.kdims, crs=img.crs)
if self.downsample != 1:
kwargs = {'dynamic': False}
h, w = img.interface.shape(img, gridded=True)
kwargs['width'] = int(w*self.downsample)
kwargs['height'] = int(h*self.downsample)
img = regrid(img, **kwargs)
foreground = extract_foreground(img, background=bg, foreground=fg,
iterations=self.iterations)
foreground = gv.Path([contours(foreground, filled=True, levels=1).split()[0].data],
kdims=foreground.kdims, crs=foreground.crs)
self.result = gv.project(foreground, projection=self.crs)
return foreground