def tide_height(t):
if isinstance(t, datetime.datetime):
t = [t]
t = np.asarray(t)
dt = datetime.timedelta(hours=1.5)
t_begin = np.nanmin(t).replace(minute=0, second=0, microsecond=0)
t_end = np.nanmax(t) + dt
# https://tidesandcurrents.noaa.gov/api/
params = dict(
format='json',
units='metric',
time_zone='gmt',
datum='MSL',
product='hourly_height',
station=9454240, # Valdez
begin_date=t_begin.strftime('%Y%m%d %H:%M'),
end_date=t_end.strftime('%Y%m%d %H:%M'))
r = requests.get('https://tidesandcurrents.noaa.gov/api/datagetter',
params=params)
v = [float(item['v']) for item in r.json()['data']]
return np.interp([dti.total_seconds() for dti in t - t_begin],
np.linspace(0, 3600 * len(v[1:]), len(v)), v)
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])
params = glimpse.helpers.read_pickle(
os.path.join(points_path, basename + '.pkl'))
# ---- Load DEM ----
# dem, dem_sigma
dem, dem_sigma = dem_interpolant(t, return_sigma=True)
# Crop DEM (for lower memory use)
box = (glimpse.helpers.bounding_box(params['xy']) +
np.array([-1, -1, 1, 1]) * dem_padding)
dem.crop(xlim=box[0::2], ylim=box[1::2])
dem_sigma.crop(xlim=box[0::2], ylim=box[1::2])
dem.crop_to_data()
dem_sigma.crop_to_data()
# ---- Compute motion models ----
cylindrical = 'vrthz' in params
# motion_models
time_unit = datetime.timedelta(days=1)
m = len(params['xy'])
if cylindrical:
vrthz_sigmas = [compute_vrthz_sigma(
params['vrthz_sigma'][i], params['flotation'][i]) for i in range(m)]
arthz_sigmas = [compute_arthz_sigma(
vrthz_sigmas[i], params['flotation'][i]) for i in range(m)]
motion_models = [glimpse.tracker.CylindricalMotionModel(
n=n, dem=dem, dem_sigma=dem_sigma, time_unit=time_unit,
xy_sigma=xy_sigma, xy=params['xy'][i], vrthz=params['vrthz'][i],
vrthz_sigma=vrthz_sigmas[i], arthz_sigma=arthz_sigmas[i])
for i in range(m)]
else:
vxyz_sigmas = [compute_vxyz_sigma(
params['vxyz_sigma'][i], params['flotation'][i]) for i in range(m)]
axyz_sigmas = [compute_axyz_sigma(
import cg
from cg import glimpse
from glimpse.imports import (os, np, datetime, matplotlib)
root = '/volumes/science/data/columbia'
cg.IMAGE_PATH = os.path.join(root, 'timelapse')
# Stations to include
stations = (
'CG04', 'CG05', 'CG06', 'AK01', 'AK03', 'AK03b', 'AK01b', 'AK10', 'AK09',
'AK09b', 'AKST03A', 'AKST03B', 'AK12', 'AKJNC', 'AK10b')
# HACK: Use same snap as in build_viewdirs.py, etc.
snap = datetime.timedelta(hours=2)
# Max rotations (instantaneous or cumulative) to allow within Observer
max_rotation = 1 # degrees about optical axis
max_pan_tilt = 2 # degrees pan or tilt
# Max temporal gap to allow within Observer
max_gap = datetime.timedelta(days=2)
# Minimum number of images to require within Observer
min_images = 2
# Minimum time span of Observer stack
min_dt = datetime.timedelta(days=1)
# Minimum number of Observers in stack
min_observers = 1
# Nominal Observer time span
nominal_bin_dt = datetime.timedelta(days=3)
# Forward shift between consecutive bins
step_dt = datetime.timedelta(days=1)
# ---- Compute station ranges ----
# station_ranges, station_images, station_datetimes
from glimpse.imports import (sys, datetime, matplotlib, np, os)
import glob
root = '/volumes/science-b/data/columbia'
cg.IMAGE_PATH = os.path.join(root, 'timelapse')
cg.KEYPOINT_PATH = os.path.join(root, 'timelapse-keypoints')
cg.MATCHES_PATH = os.path.join(root, 'timelapse-matches')
# ---- Set script constants ----
STATIONS = ('CG04', 'CG05', 'CG06', 'AK01', 'AK01b', 'AK03', 'AK03b', 'AK09',
'AK09b', 'AK10', 'AK10b', 'AK12', 'AKJNC', 'AKST03A', 'AKST03B')
SKIP_SEQUENCES = ('AK01_20070817', 'AK01_20080616', 'AK01b_20080619',
'AK01b_20090824', 'AK01b_20090825', 'AK01b_20160908',
'AK03_20070817', 'AK12_20100820')
SNAP = datetime.timedelta(hours=2)
MAXDT = datetime.timedelta(days=1)
MATCH_SEQ = np.concatenate((np.arange(12) + 1, (100, 300, 1000, 3000)))
MAX_RATIO = 0.6
MAX_ERROR = 0.03 # fraction of image width
N_MATCHES = 50
MIN_MATCHES = 50
# ---- Functions ----
def write_matches(matcher, **kwargs):
matcher.build_matches(maxdt=MAXDT,
seq=MATCH_SEQ,
path=cg.MATCHES_PATH,
overwrite=False,
# ---- 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)
#-------------------------
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)
