def get_dynamic_datasets_in_timerange(self, datasets, time_min, time_max):
assert (isinstance(time_min, np.datetime64))
assert (isinstance(time_max, np.datetime64))
# find valid time stamps in range
mask = np.logical_and(time_min <= self._valid_times,
self._valid_times <= time_max)
idxs = np.argwhere(mask).flatten().astype(np.int32)
# remove duplicate time stamps
valid_times = self._valid_times[idxs]
valid_times, valid_idxs = np.unique(valid_times, return_index=True)
idxs = idxs[valid_idxs]
result = []
# find all grids contained in dynamic variables
grids_contained = [
d for d in datasets if d in self._dynamic_datasets.keys()
]
if len(grids_contained) > 0:
progress = ProgressBar(len(grids_contained))
print(
"[INFO]: Load dynamic grids <{}> for <{}> in time range ({} - {}) ..."
.format(grids_contained, self.area, time_min, time_max))
progress.proceed(0)
for i, grid in enumerate(grids_contained):
# if grid in self._dynamic_datasets.keys():
selected_grids = self._dynamic_datasets[grid]
result += [selected_grids[idxs]]
progress.proceed(i + 1)
return np.array(result), self._valid_times[idxs], grids_contained
def get_static_datasets(self, datasets, raw=False):
assert isinstance(datasets, (list, str))
if not isinstance(datasets, list):
datasets = [datasets]
result = []
grids_contained = [
d for d in datasets if d in self._static_datasets.keys()
]
if len(grids_contained) > 0:
progress = ProgressBar(len(grids_contained))
print("[INFO]: Load static grids <{}> for <{}> ...".format(
grids_contained, self.area))
progress.proceed(0)
for i, grid_name in enumerate(grids_contained):
if grid_name == 'seaMask':
lsm = self._static_datasets[grid_name]
if not raw:
lsm = (np.array(lsm) > 0.5).astype(np.float32)
result += [lsm]
else:
result += [self._static_datasets[grid_name]]
progress.proceed(i + 1)
return np.array(result), grids_contained