def select_track_points(xy, images, polygon, dem, max_distance):
"""
Return track points mask for a set of starting images.
Returns:
array: Coordinates of points to track (n, 2)
array: Visibility mask for each image (n, m)
"""
# In DEM, in polygon, and DEM not NaN
z = dem.sample(xy, bounds_error=False, fill_value=np.nan)
mask = ~np.isnan(z) & glimpse.helpers.points_in_polygon(xy, polygon)
# Visible in one or more images
xyz = np.column_stack((xy, z))[mask]
visible = np.tile(mask.reshape(-1, 1), reps=(1, len(images)))
for i, img in enumerate(images):
uv = img.cam.project(xyz, correction=True)
# In image frame
visible[mask, i] &= img.cam.inframe(uv)
# In range
distance = np.linalg.norm(xyz[:, 0:2] - img.cam.xyz[0:2], axis=1)
visible[mask, i] &= distance < max_distance
# In DEM viewshed
viewshed = glimpse.Raster(Z=dem.viewshed(img.cam.xyz),
x=dem.xlim,
y=dem.ylim)
visible[mask, i] &= viewshed.sample(xyz[:, 0:2], order=1) > 0.99
# Not in land mask
land_mask = glimpse.Raster(load_masks([img])[0])
visible[mask, i] &= land_mask.sample(uv,
order=1,
bounds_error=False,
fill_value=1.0) == 0
return visible
def flatten_tracks_doug(runs):
# Join together second forward and backward runs
f, r = runs['fv'], runs['rv']
means = np.column_stack((f.means[..., 3:], r.means[..., 3:]))
sigmas = np.column_stack((f.sigmas[..., 3:], r.sigmas[..., 3:]))
# Flatten joined runs
# Mean: Inverse-variance weighted mean
# Sigma: Linear combination of weighted correlated random variables
# (approximation using the weighted mean of the variances)
weights = sigmas**-2
weights *= 1 / np.nansum(weights, axis=1, keepdims=True)
allnan = np.isnan(means).all(axis=1, keepdims=True)
means = np.nansum(weights * means, axis=1, keepdims=True)
sigmas = np.sqrt(np.nansum(weights * sigmas**2, axis=1, keepdims=True))
# np.nansum interprets sum of nans as 0
means[allnan] = np.nan
sigmas[allnan] = np.nan
return means.squeeze(axis=1), sigmas.squeeze(axis=1)
iranges = glimpse.helpers.cut_ranges(iranges, cuts)
# Sudden very large motions
viewdirs = np.array([img.cam.viewdir for img in images])
dtheta = np.diff(viewdirs, axis=0)
pan_tilt = np.linalg.norm(dtheta[:, 0:2], axis=1)
rotation = np.abs(dtheta[:, 2])
breaks = (pan_tilt > max_pan_tilt) | (rotation > max_rotation)
cuts = [i + 1 for i in np.nonzero(breaks)[0]]
iranges = glimpse.helpers.cut_ranges(iranges, cuts)
# NOTE: Switch to index-based ranges (from range-based)
ranges = iranges - (0, 1)
# Periods of excessive motion
# matplotlib.pyplot.figure()
for i, j in iranges:
viewdirs = np.array([img.cam.viewdir for img in images[i:j]])
bins = np.column_stack((datetimes[i:j], datetimes[i:j] + datetime.timedelta(days=3)))
ibins = np.searchsorted(datetimes[i:j], bins)
pan_tilt = [np.linalg.norm(viewdirs[m, 0:2] - viewdirs[m:n, 0:2], axis=1).max()
for m, n in ibins]
rotation = [np.abs(viewdirs[m, 2] - viewdirs[m:n, 2]).max() for m, n in ibins]
peaks = (np.array(pan_tilt) > max_pan_tilt) | (np.array(rotation) > max_rotation)
# # (plot)
# matplotlib.pyplot.plot(datetimes[i:j], pan_tilt, color='grey')
# matplotlib.pyplot.plot(datetimes[i:j], rotation, color='black')
# matplotlib.pyplot.plot(datetimes[i:j], peaks, color='red')
# # (print)
# print(i, j)
# print(''.join(np.array(['.', 'o'])[peaks.astype(int)]))
# (cut)
cutouts = [(i, i + 1) for i in np.nonzero(peaks)[0]]
ranges = glimpse.helpers.cut_out_ranges(ranges, cutouts)
rows = np.searchsorted(ids, points['ids'])
means[rows, col], sigmas[rows, col] = flatten_function(runs)
flotation[rows, col] = points['flotation']
nobservers[rows, col] = points['observer_mask'].sum(axis=1)
if mean_midtimes:
# Midtime as mean
midtime = origin + (runs['f'].datetimes - origin).mean()
else:
# Midtime as middle
midtime = origin + (runs['f'].datetimes[[0, -1]] - origin).mean()
ti[col] = runs['f'].datetimes[0], runs['f'].datetimes[
-1], midtime, track_ids[col]
# Precompute spatial neighborhoods and masks
ncols = template.shape[1]
neighbor_ids = np.column_stack(
(ids, ids - 1, ids + 1, ids - ncols, ids + ncols))
if diagonal_neighbors:
neighbor_ids = np.column_stack(
(neighbor_ids,
np.column_stack((ids - 1 - cols, ids + 1 - cols, ids - 1 + cols,
ids + 1 + cols))))
neighbor_rows = np.searchsorted(ids, neighbor_ids)
missing = ~np.isin(neighbor_ids, ids)
neighbor_rows[missing] = 0
few_cams = (
(nobservers[neighbor_rows, :] < min_observers) &
(nobservers[neighbor_rows, :].max(axis=1, keepdims=True) >= min_observers))
isnan = np.isnan(means)
# Apply spatial median filter
fmeans = np.full(means.shape, np.nan, dtype=np.float32)
vy[..., lmask] = lvy
# Plot
matplotlib.pyplot.figure()
# glimpse.Raster(np.sum(bad, axis=2), template.x, template.y).plot()
glimpse.Raster(np.sum(~np.isnan(vx), axis=2) > 1, template.x,
template.y).plot()
# matplotlib.pyplot.colorbar()
matplotlib.pyplot.plot(cg.Glacier()[:, 0], cg.Glacier()[:, 1])
# ---- Compute weights ----
# Use sum of distances to neighbors
# Saturate at 0.5 years because of seasonal variability
dts = np.column_stack(
(np.concatenate(([datetime.timedelta(0)], np.diff(datetimes))),
np.concatenate(
(np.diff(datetimes), [datetime.timedelta(0)])))).sum(axis=1)
weights = np.array([dt.total_seconds() / (3600 * 24 * 365) for dt in dts])
weights[weights > 0.5] = 0.5
# Lower weights for observations before 2004
dyears = np.array([
dt.total_seconds() / (3600 * 24 * 365)
for dt in datetime.datetime(2004, 6, 18) - datetimes
])
weights[dyears > 0] *= (1 - dyears[dyears > 0] / max(dyears))
# ---- Compute summary statistics (cartesian) ----
w = glimpse.helpers.tile_axis(weights, vx.shape, axis=(0, 1))
vx_mean = glimpse.helpers.weighted_nanmean(vx, weights=w, axis=2)
vy_mean = glimpse.helpers.weighted_nanmean(vy, weights=w, axis=2)