def _infer_time_period(filename, results_folder):
filenames = sorted(
glob(os.path.join(results_folder,
_variable_name(filename) + '*.nc')))
start = start_date(managed_dataset(filenames[0], 'r'))
end = end_date(managed_dataset(filenames[-1], 'r'))
freq = dataset_frequency(managed_dataset(filenames[0], 'r'))
return ObjectDict(type=freq,
start=start.strftime('%Y-%m-%d'),
end=end.strftime('%Y-%m-%d'),
representation='YYYY-MM-DD')
def __init__(self,filename,variable,preload=False):
#import netCDF4 as nc
self.fh = managed_dataset(filename,'r')
self.extent = self.fh.get_extent()
self.cs = self.extent.to_coords()
if preload:
self.v = self.fh.variables[variable][...]
else:
self.v = self.fh.variables[variable]
def __init__(self, ncfile, mode='r'):
if mode == 'r':
self.f = h5py.File(ncfile, 'r')
self.available = [
m for m in list(self.f) if isinstance(self.f[m], h5py.Group)
]
self._fastget = True
else:
self.f = managed_dataset(ncfile, mode)
self.available = sorted(self.f.groups)
self._fastget = False
def plot_cell_timeseries(file_map, slice, ax=None, figsize=(20, 10)):
if not ax:
ax = plt.figure(figsize=figsize).gca()
for s in file_map:
md = dm.managed_dataset(file_map[s])
d = md.awra_var[slice]
df = pd.DataFrame(d)
df.plot(ax=ax)
return ax
def load_georef_nc(fn, load_mask=True):
from awrams.utils.io.data_mapping import managed_dataset
ds = managed_dataset(fn)
if load_mask:
e = ds.get_extent()
georef = e.parent_ref
mask = e.mask
if hasattr(mask, 'shape'):
if (mask == True).all():
mask = False
else:
georef = latlon_to_georef(ds.variables['latitude'][...],
ds.variables['longitude'][...])
mask = False
return georef, mask
def __init__(self, source, filename, path):
self.filename = filename
self.path = path
pattern = os.path.join(os.path.abspath(path), filename + '*.nc')
files = sorted(glob(pattern))
ref_ds = managed_dataset(files[0])
name = ref_ds.awra_var.name
if name.startswith('/'):
name = name[1:]
units = ref_ds.awra_var.units
ref_ds.close()
VariableInstance.__init__(self, source, name, units)
self._pattern = pattern
self._reader = None
self._dataset = ref_ds
self.agg_method = 'mean' ### temporal aggregate method
def display_ncslice(fn,
idx,
extent=None,
mask_value=None,
cmap=None,
norm=None,
fig=None,
ax=None,
**kwds):
import awrams.utils.io.data_mapping as dm
import os.path
md = dm.managed_dataset(fn)
v = md.get_mapping_var()
shape = md.variables[v.name].shape
md.close()
p, f = os.path.split(fn)
sfm = dm.SplitFileManager.open_existing(p, f, v)
def get_data(idx, extent, sfm):
if not extent:
a = sfm[np.s_[idx, :]]
return a, 'slice', None
else:
try:
s = [idx]
s.extend(x for x in extent)
a = sfm[np.s_[s]]
return a, 'slice', None
except:
#raise
gb = from_boundary_coords(*extent, compute_areas=False)
print(gb.x_size, gb.y_size)
a = sfm[np.s_[idx,
slice(gb.x_min, gb.x_max),
slice(gb.y_min, gb.y_max)]]
extent = (gb.lon_min, gb.lon_max, gb.lat_min, gb.lat_max
) #(111.975,154.025,-44.025,-9.975)
return a, 'geobounds', extent
if type(idx) == int:
if idx < 0:
idx += shape[0]
a, extent_type, extent = get_data(idx, extent, sfm)
else: # idx is dti
_idx = sfm.ref_ds.idx_for_period(idx)
a, extent_type, extent = get_data(int(_idx[0]), extent, sfm)
a = np.ma.masked_invalid(a)
if mask_value is not None:
a = np.ma.masked_equal(a, mask_value)
print(a.min(), a.max(), a.mean(), a.shape)
if fig is None:
fig = plt.figure(figsize=(16, 20))
if ax is None:
ax = fig.add_subplot(1, 1, 1)
if cmap:
kwds['cmap'] = cmap
if norm:
kwds['norm'] = norm
if extent_type == 'geobounds':
cax = ax.imshow(a, interpolation='none', extent=extent, **kwds)
else:
cax = ax.imshow(a, interpolation='none', **kwds)
sfm.close_all()
return fig, ax, cax
def __init__(self, ncfile, mode='r'):
self.f = managed_dataset(ncfile, mode)
self.available = sorted(self.f.groups)
