def extend(aggs, xr_ds, vt, bounds):
# Convert from bin centers to interval edges
x_breaks = xr_ds[x_name].values
x_breaks = infer_interval_breaks(x_breaks, axis=1)
x_breaks = infer_interval_breaks(x_breaks, axis=0)
y_breaks = xr_ds[y_name].values
y_breaks = infer_interval_breaks(y_breaks, axis=1)
y_breaks = infer_interval_breaks(y_breaks, axis=0)
# Scale x and y vertices into integer canvas coordinates
x0, x1, y0, y1 = bounds
xspan = x1 - x0
yspan = y1 - y0
xscaled = (x_mapper(x_breaks) - x0) / xspan
yscaled = (y_mapper(y_breaks) - y0) / yspan
plot_height, plot_width = aggs[0].shape[:2]
xs = (xscaled * plot_width).astype(int)
ys = (yscaled * plot_height).astype(int)
coord_dims = xr_ds.coords[x_name].dims
aggs_and_cols = aggs + info(xr_ds.transpose(*coord_dims))
_extend(plot_height, plot_width, xs, ys, *aggs_and_cols)
def extend(aggs, xr_ds, vt, bounds):
# Convert from bin centers to interval edges
x_breaks = infer_interval_breaks(xr_ds[x_name].values)
y_breaks = infer_interval_breaks(xr_ds[y_name].values)
x0, x1, y0, y1 = bounds
xspan = x1 - x0
yspan = y1 - y0
xscaled = (x_mapper(x_breaks) - x0) / xspan
yscaled = (y_mapper(y_breaks) - y0) / yspan
xmask = np.where((xscaled >= 0) & (xscaled <= 1))
ymask = np.where((yscaled >= 0) & (yscaled <= 1))
xm0, xm1 = max(xmask[0].min() - 1, 0), xmask[0].max() + 1
ym0, ym1 = max(ymask[0].min() - 1, 0), ymask[0].max() + 1
plot_height, plot_width = aggs[0].shape[:2]
# Downselect xs and ys and convert to int
xs = (xscaled[xm0:xm1 + 1] * plot_width).astype(int).clip(0, plot_width)
ys = (yscaled[ym0:ym1 + 1] * plot_height).astype(int).clip(0, plot_height)
# For input "column", down select to valid range
cols_full = info(xr_ds.transpose(y_name, x_name))
cols = tuple([c[ym0:ym1, xm0:xm1] for c in cols_full])
aggs_and_cols = aggs + cols
_extend(xs, ys, *aggs_and_cols)
def extend(aggs, xr_ds, vt, bounds):
from datashader.core import LinearAxis
use_cuda = cupy and isinstance(xr_ds[name].data, cupy.ndarray)
xs = xr_ds[x_name].values
ys = xr_ds[y_name].values
if use_cuda:
xs = cupy.array(xs)
ys = cupy.array(ys)
x_mapper2 = _cuda_mapper(x_mapper)
y_mapper2 = _cuda_mapper(y_mapper)
else:
x_mapper2 = x_mapper
y_mapper2 = y_mapper
# Convert from bin centers to interval edges
x_breaks = infer_interval_breaks(xs)
y_breaks = infer_interval_breaks(ys)
x0, x1, y0, y1 = bounds
xspan = x1 - x0
yspan = y1 - y0
if x_mapper is LinearAxis.mapper:
xscaled = (x_breaks - x0) / xspan
else:
xscaled = (x_mapper2(x_breaks) - x0) / xspan
if y_mapper is LinearAxis.mapper:
yscaled = (y_breaks - y0) / yspan
else:
yscaled = (y_mapper2(y_breaks) - y0) / yspan
xmask = np.where((xscaled >= 0) & (xscaled <= 1))
ymask = np.where((yscaled >= 0) & (yscaled <= 1))
xm0, xm1 = max(xmask[0].min() - 1, 0), xmask[0].max() + 1
ym0, ym1 = max(ymask[0].min() - 1, 0), ymask[0].max() + 1
plot_height, plot_width = aggs[0].shape[:2]
# Downselect xs and ys and convert to int
xs = (xscaled[xm0:xm1 + 1] * plot_width).astype(int).clip(
0, plot_width)
ys = (yscaled[ym0:ym1 + 1] * plot_height).astype(int).clip(
0, plot_height)
# For input "column", down select to valid range
cols_full = info(xr_ds.transpose(y_name, x_name))
cols = tuple([c[ym0:ym1, xm0:xm1] for c in cols_full])
aggs_and_cols = aggs + cols
if use_cuda:
do_extend = extend_cuda[cuda_args(xr_ds[name].shape)]
else:
do_extend = extend_cpu
do_extend(xs, ys, *aggs_and_cols)
def extend(aggs, xr_ds, vt, bounds):
from datashader.core import LinearAxis
use_cuda = cupy and isinstance(xr_ds[name].data, cupy.ndarray)
x_breaks = xr_ds[x_name].values
y_breaks = xr_ds[y_name].values
if use_cuda:
x_breaks = cupy.array(x_breaks)
y_breaks = cupy.array(y_breaks)
x_mapper2 = _cuda_mapper(x_mapper)
y_mapper2 = _cuda_mapper(y_mapper)
else:
x_mapper2 = _cuda_mapper(x_mapper)
y_mapper2 = _cuda_mapper(y_mapper)
# Convert from bin centers to interval edges
x_breaks = infer_interval_breaks(x_breaks, axis=1)
x_breaks = infer_interval_breaks(x_breaks, axis=0)
y_breaks = infer_interval_breaks(y_breaks, axis=1)
y_breaks = infer_interval_breaks(y_breaks, axis=0)
# Scale x and y vertices into integer canvas coordinates
x0, x1, y0, y1 = bounds
xspan = x1 - x0
yspan = y1 - y0
if x_mapper is LinearAxis.mapper:
xscaled = (x_breaks - x0) / xspan
else:
xscaled = (x_mapper2(x_breaks) - x0) / xspan
if y_mapper is LinearAxis.mapper:
yscaled = (y_breaks - y0) / yspan
else:
yscaled = (y_mapper2(y_breaks) - y0) / yspan
plot_height, plot_width = aggs[0].shape[:2]
xs = (xscaled * plot_width).astype(int)
ys = (yscaled * plot_height).astype(int)
coord_dims = xr_ds.coords[x_name].dims
aggs_and_cols = aggs + info(xr_ds.transpose(*coord_dims))
if use_cuda:
do_extend = extend_cuda[cuda_args(xr_ds[name].shape)]
else:
do_extend = extend_cpu
do_extend(plot_height, plot_width, xs, ys, *aggs_and_cols)
def compute_y_bounds(self, xr_ds):
ys = xr_ds[self.y].values
ys = infer_interval_breaks(ys, axis=1)
ys = infer_interval_breaks(ys, axis=0)
bounds = Glyph._compute_bounds_2d(ys)
return self.maybe_expand_bounds(bounds)
def compute_x_bounds(self, xr_ds):
xs = xr_ds[self.x].values
xs = infer_interval_breaks(xs, axis=1)
xs = infer_interval_breaks(xs, axis=0)
bounds = Glyph._compute_bounds_2d(xs)
return self.maybe_expand_bounds(bounds)
def infer_interval_breaks(self, centers):
# Infer breaks for 1D array of centers
breaks = infer_interval_breaks(centers, axis=1)
breaks = infer_interval_breaks(breaks, axis=0)
return breaks
def infer_interval_breaks(self, centers):
# Infer breaks for 1D array of centers
return infer_interval_breaks(centers)
