DATA_DIR = "/home/dunbar/Research/helheim/data/observations"
DEM_DIR = os.path.join(DATA_DIR, 'dem')
MAX_DEPTH = 30e3
# ---- Prepare Observers ----
observerpath = ['stardot1','stardot2']
observers = []
for observer in observerpath:
path = join(DATA_DIR,observer)
campaths = glob.glob(join(path,"*.JSON"))
images = [glimpse.Image(path=campath.replace(".JSON",".jpg"),cam=campath) for campath in campaths]
images.sort(key= lambda img: img.datetime)
datetimes = np.array([img.datetime for img in images])
for n, delta in enumerate(np.diff(datetimes)):
if delta <= datetime.timedelta(seconds=0):
secs = datetime.timedelta(seconds= n%5 + 1)
images[n+1].datetime = images[n+1].datetime + secs
diffs = np.array([dt.total_seconds() for dt in np.diff(np.array([img.datetime for img in images]))])
negate = diffs[diffs <= 1].astype(np.int)
[images.pop(_) for _ in negate]
images = images[:int(len(images)/2)]
print("Image set {} \n".format(len(images)))
obs = glimpse.Observer(list(np.array(images)),cache=False)
observers.append(obs)
#-------------------------
uv = observer[1].images[0].cam.project(( 7361411.0,533528.0,180))
observer[1].images[0].cam.plot()
matplotlib.pyplot.scatter(uv[:, 0], uv[:, 1])
station_datetimes = dict()
for station in stations:
print(station)
services = sequences[sequences.station == station].service.values
images = cg.load_images(
station=station, services=services, snap=snap,
service_exif=True, anchors=False, viewdir=True, viewdir_as_anchor=True,
file_errors=False)
images = [img for img in images if img.anchor]
images.sort(key=lambda x: x.datetime)
datetimes = np.array([img.datetime for img in images])
# Endpoints
iranges = np.atleast_2d((0, len(images)))
# Changes in image size
width = [img.cam.imgsz[0] for img in images]
cuts = [i + 1 for i in np.nonzero(np.diff(width))[0]]
iranges = glimpse.helpers.cut_ranges(iranges, cuts)
# Camera changes
# HACK: Use k1 as a proxy
k = [img.cam.k[0] for img in images]
cuts = [i + 1 for i in np.nonzero(np.diff(k))[0]]
iranges = glimpse.helpers.cut_ranges(iranges, cuts)
# Gaps in coverage
dt = np.diff(datetimes)
cuts = [i + 1 for i in np.nonzero(dt > max_gap)[0]]
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])
# ---- Build DEM template ----
json = glimpse.helpers.read_json('observers.json',
object_pairs_hook=collections.OrderedDict)
stations = set([station for x in json for station in x])
station_xy = np.vstack([f['geometry']['coordinates'][:, 0:2]
for station, f in cg.Stations().items()
if station in stations])
box = glimpse.helpers.bounding_box(cg.Glacier())
XY = glimpse.helpers.box_to_grid(box, step=(grid_size, grid_size),
snap=(0, 0), mode='grid')
xy = glimpse.helpers.grid_to_points(XY)
distances = glimpse.helpers.pairwise_distance(xy, station_xy, metric='euclidean')
selected = distances.min(axis=1) < max_distance
box = glimpse.helpers.bounding_box(xy[selected]) + 0.5 * np.array([-1, -1, 1, 1]) * grid_size
shape = np.diff([box[1::2], box[0::2]], axis=1) / grid_size
dem_template = glimpse.Raster(np.ones(shape.astype(int).ravel(), dtype=bool),
x=box[0::2], y=box[1::2][::-1])
dem_points = glimpse.helpers.grid_to_points((dem_template.X, dem_template.Y))
# ---- Select DEMs ----
dem_sigmas = {
'aerometric': 1.5,
'ifsar': 1.5 + 0.5, # additional time uncertainty
'arcticdem': 3,
'tandem': 3 # after bulk corrections
}
dem_keys = [
('20040618', 'aerometric'),
('20040707', 'aerometric'),
# ---- Prepare Observers ----
observerpath = ['stardot1', 'stardot2']
observers = []
for observer in observerpath:
path = join(DATA_DIR, observer)
campaths = glob.glob(join(path, "*.JSON"))
images = [
glimpse.Image(path=campath.replace(".JSON", ".jpg"), cam=campath)
for campath in campaths
]
print("Image set {} \n".format(len(images)))
images.sort(key=lambda img: img.datetime)
datetimes = np.array([img.datetime for img in images])
for n, delta in enumerate(np.diff(datetimes)):
if delta <= datetime.timedelta(seconds=0):
secs = datetime.timedelta(seconds=n % 5 + 1)
images[n + 1].datetime = images[n + 1].datetime + secs
diffs = np.array([
dt.total_seconds()
for dt in np.diff(np.array([img.datetime for img in images]))
])
negate = diffs[diffs <= 1].astype(np.int)
[images.pop(_) for _ in negate]
images = images[:int(len(images) / 2)]
obs = glimpse.Observer(list(np.array(images)), cache=False)
observers.append(obs)
observers[1].animate(interval=100).save("stardot2animation.mp4",
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)