def test_any():
out = xr.DataArray(np.array([[True, True], [True, True]]),
coords=coords, dims=dims)
assert_eq(c.points(df, 'x', 'y', ds.any('i64')), out)
assert_eq(c.points(df, 'x', 'y', ds.any('f64')), out)
assert_eq(c.points(df, 'x', 'y', ds.any()), out)
out = xr.DataArray(np.array([[True, True], [True, False]]),
coords=coords, dims=dims)
assert_eq(c.points(df, 'x', 'y', ds.any('empty_bin')), out)
def test_any():
out = xr.DataArray(np.array([[True, True], [True, True]]),
coords=coords, dims=dims)
assert_eq(c.points(ddf, 'x', 'y', ds.any('i64')), out)
assert_eq(c.points(ddf, 'x', 'y', ds.any('f64')), out)
assert_eq(c.points(ddf, 'x', 'y', ds.any()), out)
out = xr.DataArray(np.array([[True, True], [True, False]]),
coords=coords, dims=dims)
assert_eq(c.points(ddf, 'x', 'y', ds.any('empty_bin')), out)
def _get_aggregator(self, element, add_field=True):
agg = self.p.aggregator
if not element.vdims and agg.column is None and not isinstance(
agg, (rd.count, rd.any)):
return ds.any()
return super(contours_rasterize,
self)._get_aggregator(element, add_field)
def agg_count_cat(
data, x, y, z, scale_paras,
clip_max=0,
reduce=False,
sharp_boundary=True,
):
"""count categorical data
"""
# collect aggdata and ps
agg = ds.count_cat(z)
aggdata, ps = agg_data_ps(data, x, y, agg, scale_paras)
zlabels = aggdata[z].values
if clip_max:
aggdata = aggdata.clip(max=clip_max)
if reduce:
aggdata = aggdata.argmax(z)
if sharp_boundary:
# normalize by any (set no cells to nan)
agg = ds.any()
aggdata_any = agg_data(data, x, y, ps.npxlx, ps.npxly, agg)
aggdata_any = aggdata_any.astype(int)
aggdata = aggdata/aggdata_any
return aggdata, ps, zlabels
def agg_data_count(
data,
x, y,
npxlx, npxly,
):
agg = ds.count()
aggdata = agg_data(data, x, y, npxlx, npxly, agg)
agg = ds.any()
aggdata_any = agg_data(data, x, y, npxlx, npxly, agg)
aggdata = aggdata/aggdata_any
return aggdata
def _process(self, element, key=None):
try:
import cv2 as cv
except ImportError:
raise ImportError('GrabCut algorithm requires openCV')
if isinstance(self.p.foreground, hv.Polygons):
rasterize_op = rasterize_polygon
else:
rasterize_op = rasterize.instance(aggregator=ds.any())
kwargs = {'dynamic': False, 'target': element}
fg_mask = rasterize_op(self.p.foreground, **kwargs)
bg_mask = rasterize_op(self.p.background, **kwargs)
fg_mask = fg_mask.dimension_values(2, flat=False)
bg_mask = bg_mask.dimension_values(2, flat=False)
if fg_mask[np.isfinite(fg_mask)].sum() == 0 or bg_mask[np.isfinite(
bg_mask)].sum() == 0:
return element.clone([],
vdims=['Foreground'],
new_type=gv.Image,
crs=element.crs)
mask = np.where(fg_mask, fg_mask, 2)
mask = np.where(bg_mask, 0, mask).copy()
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
if isinstance(element, hv.RGB):
img = np.dstack([
element.dimension_values(d, flat=False) for d in element.vdims
])
else:
img = element.dimension_values(2, flat=False)
mask, _, _ = cv.grabCut(img, mask.astype('uint8'), None, bgdModel,
fgdModel, self.p.iterations,
cv.GC_INIT_WITH_MASK)
fg_mask = np.where((mask == 2) | (mask == 0), 0, 1).astype('bool')
xs, ys = (element.dimension_values(d, expanded=False)
for d in element.kdims)
return element.clone((xs, ys, fg_mask),
vdims=['Foreground'],
new_type=gv.Image,
crs=element.crs)
linkNext,
)
simTime += time.time() - tic
# Rendering
# ============================
if it == 0:
df = pd.DataFrame(np.array([x, y]).T, columns=('x', 'y'))
refineFac = 4.0
cvs = ds.Canvas(plot_width=int(nx * refineFac),
plot_height=int(ny * refineFac),
x_range=(xmin, xmax),
y_range=(ymin, ymax),
x_axis_type='linear',
y_axis_type='linear')
agg = cvs.points(df, 'x', 'y', ds.any())
im = plt.imshow(agg.variable,
extent=[xmin, xmax, ymin, ymax],
origin='lower')
if it % 5 == 0:
tic = time.time()
df = pd.DataFrame(np.array([x, y]).T, columns=('x', 'y'))
agg = cvs.points(df, 'x', 'y', ds.any())
im.set_array(agg.variable)
# markers.set_data(x,y)
# plt.clf()
# plt.plot(gridX,gridY,'k',linewidth=0.5)
# plt.plot(gridX.T,gridY.T,'k',linewidth=0.5)
## plt.fill([xmin,xmax,xmax,xmin],[ymin,ymin,ymax,ymax],color=[.9,.9,.9],linewidth=0.0)
#
