def process_one(data, axis):
if isinstance(data, xray.DataArray):
name, attrs, coords, dims = meta(data)
if axis is not None:
dims_del = utils.makelist(axis)
else:
# All singleton dimensions
dims_del = np.where(np.array(data.shape) == 1)[0]
squeezed = np.squeeze(data.values, axis=dims_del)
# Update metadata
dims = list(dims)
dimnames_del = [dims[d] for d in dims_del]
for nm in dimnames_del:
attrs[nm] = coords[nm].values
coords = utils.odict_delete(coords, nm)
dims.remove(nm)
# Squeeze and pack into DataArray
squeezed = xray.DataArray(np.squeeze(data.values, axis=dims_del),
dims=dims,
coords=coords,
name=name,
attrs=attrs)
else:
squeezed = np.squeeze(data, axis)
return squeezed
def latlon_labels(vals, latlon='lat', fmt='%.0f', deg_symbol=True,
join_str=None):
"""Return a label string for list of latitudes or longitudes."""
if latlon.lower() == 'lat':
pos, neg = 'N', 'S'
elif latlon.lower() == 'lon':
pos, neg = 'E', 'W'
else:
raise ValueError('Invalid input latlon = ' + latlon)
vals = utils.makelist(vals)
labels = []
for num in vals:
if num >= 0:
suffix = pos
x = fmt % num
else:
suffix = neg
x = fmt % abs(num)
if deg_symbol:
labels.append(x + degree_sign() + suffix)
else:
labels.append(x + suffix)
if len(vals) == 1:
labels = labels[0]
elif join_str is not None:
labels = join_str.join(labels)
return labels
def latlon_labels(vals, latlon='lat', fmt='%.0f', deg_symbol=True,
join_str=None):
"""Return a label string for list of latitudes or longitudes."""
if latlon.lower() == 'lat':
pos, neg = 'N', 'S'
elif latlon.lower() == 'lon':
pos, neg = 'E', 'W'
else:
raise ValueError('Invalid input latlon = ' + latlon)
vals = utils.makelist(vals)
labels = []
for num in vals:
if num >= 0:
suffix = pos
x = fmt % num
else:
suffix = neg
x = fmt % abs(num)
if deg_symbol:
labels.append(x + degree_sign() + suffix)
else:
labels.append(x + suffix)
if len(vals) == 1:
labels = labels[0]
elif join_str is not None:
labels = join_str.join(labels)
return labels
def process_one(data, axis):
if isinstance(data, xray.DataArray):
name, attrs, coords, dims = meta(data)
if axis is not None:
dims_del = utils.makelist(axis)
else:
# All singleton dimensions
dims_del = np.where(np.array(data.shape) == 1)[0]
squeezed = np.squeeze(data.values, axis=dims_del)
# Update metadata
dims = list(dims)
dimnames_del = [dims[d] for d in dims_del]
for nm in dimnames_del:
attrs[nm] = coords[nm].values
coords = utils.odict_delete(coords, nm)
dims.remove(nm)
# Squeeze and pack into DataArray
squeezed = xray.DataArray(np.squeeze(data.values, axis=dims_del),
dims=dims, coords=coords, name=name,
attrs=attrs)
else:
squeezed = np.squeeze(data, axis)
return squeezed
