def test_append(ax0, ax1):
# test copy/ref behaviour when appending an object
axes = Axes()
axes.append(ax0)
assert ax0 is axes[0]
axes.append(ax1)
assert ax1 is axes[1]
def __init__(self, values=None, axes=None, time=None, lat=None, lon=None, **kwargs):
"""
"""
keyword = (time is not None or lat is not None or lon is not None)
assert not (axes is not None and keyword), "can't input both `axes=` and keyword arguments!"
# construct the axes
if keyword:
axes = Axes()
if time is not None: axes.append(Axis(time, 'time'))
if lat is not None: axes.append(Axis(lat, 'lat'))
if lon is not None: axes.append(Axis(lon, 'lon'))
super(GeoArray, self).__init__(values, axes, **kwargs) # order dimensions
# add weight on latitude
for ax in self.axes:
if is_latitude(ax.name):
ax.weights = lambda x: np.cos(np.radians(x))
if is_longitude(ax.name):
ax.modulo = 360
def __init__(self,
values=None,
axes=None,
time=None,
lat=None,
lon=None,
**kwargs):
"""
"""
keyword = (time is not None or lat is not None or lon is not None)
assert not (axes is not None and
keyword), "can't input both `axes=` and keyword arguments!"
# construct the axes
if keyword:
axes = Axes()
if time is not None: axes.append(Axis(time, 'time'))
if lat is not None: axes.append(Axis(lat, 'lat'))
if lon is not None: axes.append(Axis(lon, 'lon'))
super(GeoArray, self).__init__(values, axes,
**kwargs) # order dimensions
# add weight on latitude
for ax in self.axes:
if is_latitude(ax.name):
ax.weights = lambda x: np.cos(np.radians(x))
if is_longitude(ax.name):
ax.modulo = 360
def getaxes_broadcast(obj, indices):
""" broadcast array-indices & integers, numpy's classical
Examples
--------
>>> import dimarray as da
>>> a = da.zeros(shape=(3,4,5,6))
>>> a.take((slice(None),[0, 1],slice(None),2), broadcast=True).shape
(2, 3, 5)
>>> a.take((slice(None),[0, 1],2,slice(None)), broadcast=True).shape
(3, 2, 6)
"""
from dimarray import Axis, Axes
# new axes: broacast indices (should do the same as above, since integers are just broadcast)
indices2 = broadcast_indices(indices)
# assert np.all(newval == obj.values[indices2])
# make a multi-axis with tuples
is_array2 = np.array([np.iterable(ix) for ix in indices2])
nb_array2 = is_array2.sum()
# If none or one array is present, easy
if nb_array2 <= 1:
newaxes = [obj.axes[i][ix] for i, ix in enumerate(indices) if not np.isscalar(ix)] # indices or indices2, does not matter
# else, finer check needed
else:
# same stats but on original indices
is_array = np.array([np.iterable(ix) for ix in indices])
array_ix_pos = np.where(is_array)[0]
# Determine where the axis will be inserted
# - need to consider the integers as well (broadcast as arrays)
# - if two indexed dimensions are not contiguous, new axis placed at first position...
# obj = zeros((3,4,5,6))
# obj[:,[1,2],:,0].shape ==> (2, 3, 5)
# obj[:,[1,2],0,:].shape ==> (3, 2, 6)
array_ix_pos2 = np.where(is_array2)[0]
if np.any(np.diff(array_ix_pos2) > 1): # that mean, if two indexed dimensions are not contiguous
insert = 0
else:
insert = array_ix_pos2[0]
# Now determine axis value
# ...if originally only one array was provided, use these values correspondingly
if len(array_ix_pos) == 1:
i = array_ix_pos[0]
values = obj.axes[i].values[indices[i]]
name = obj.axes[i].name
# ...else use a list of tuples
else:
values = list(zip(*[obj.axes[i].values[indices2[i]] for i in array_ix_pos]))
name = ",".join([obj.axes[i].name for i in array_ix_pos])
broadcastaxis = Axis(values, name)
newaxes = Axes()
for i, ax in enumerate(obj.axes):
# axis is already part of the broadcast axis: skip
if is_array2[i]:
continue
else:
newaxis = ax[indices2[i]]
## do not append axis if scalar
#if np.isscalar(newaxis):
# continue
newaxes.append(newaxis)
# insert the right new axis at the appropriate position
newaxes.insert(insert, broadcastaxis)
return newaxes
def __init__(
self,
values=None,
axes=None,
time=None,
z=None,
y=None,
x=None,
lat=None,
lon=None,
dims=None,
standard_name=None,
long_name=None,
units=None,
**kwargs
):
"""
Parameters
----------
values : array-like
axes : list of array-like or Axis objects, optional
time, z, y, x, lat, lon: spatiotemporal coordinates, optional
These keyword arguments are provided for convenience. They
can be used instead of (but not in addition to) the `axes`
parameter. See notes below about implied array shape.
dims : sequence, optional
sequence of axis names (dimensions)
This is needed when axes are defined via keywords but
array-shape does not conform with CF-conventions.
standard_name, long_name, units : str, optional
standard attributes according to the CF-1.4 netCDF
conventions. Will be added to metadata.
**kwargs : keyword arguments, optional
Passed to dimarray.DimArray
Notes
-----
When coordinate axes are passed via keyword arguments
it is assumed that the array shape follows the CF-conventions:
time, vertical coordinate (z), northing coordinate (y or lat),
easting coordinate (x or lon).
If it is not the case, please indicate the `dims` parameters or
pass axes via the `axes=` parameter instead of keyword arguments.
See Also
--------
dimarray.DimArray : base class with no "geo" specificities
dimarray.geo.Coordinate : base class for geo-axes
dimarray.geo.Time
dimarray.geo.Latitude, dimarray.geo.Longitude
dimarray.geo.Z, dimarray.geo.Y, dimarray.geo.X
"""
keyword = (
time is not None or lat is not None or lon is not None or x is not None or y is not None or z is not None
)
if axes is not None and keyword:
msg = "Axes can be provided EITHER via `axes=` OR keyword arguments"
raise ValueError(msg)
# construct the axes
if keyword:
axes = Axes()
if time is not None:
axes.append(Time(time, "time"))
if z is not None:
axes.append(Z(z, "z"))
if y is not None:
axes.append(Y(y, "y"))
if lat is not None:
axes.append(Latitude(lat, "lat"))
if x is not None:
axes.append(X(x, "x"))
if lon is not None:
axes.append(Longitude(lon, "lon"))
if dims is not None:
if len(dims) != len(axes):
msg = "dims ({}) and axes ({}) lengths \
do not match".format(
len(dims), len(axes)
)
raise ValueError(msg)
axes = [axes[nm] for nm in dims]
# if metadata is not None:
# for k in metadata.keys():
# assert k not in kwargs, "{} was provided multiple times".format(k)
# kwargs.update(metadata) # TODO: make metadata a parameter in DimArray as well
self._grid_mapping = None
super(GeoArray, self).__init__(values, axes, **kwargs) # order dimensions
# add metadata
if units:
self.units = units
if standard_name:
self.standard_name = standard_name
if long_name:
self.long_name = long_name
# Do some guessing to define coordinates
for i, ax in enumerate(self.axes):
if isinstance(ax, Coordinate):
continue
elif is_latitude(ax.name) or (hasattr(ax, "standard_name") and ax.standard_name == "latitude"):
self.axes[i] = Latitude.from_axis(ax)
elif is_longitude(ax.name) or (hasattr(ax, "standard_name") and ax.standard_name == "longitude"):
self.axes[i] = Longitude.from_axis(ax)
elif is_time(ax.name):
self.axes[i] = Time.from_axis(ax)
# 'x', 'y', 'z' are too general to be used.
elif ax.name == "x" or (hasattr(ax, "standard_name") and ax.standard_name == "projection_x_coordinate"):
self.axes[i] = X.from_axis(ax)
elif ax.name == "y" or (hasattr(ax, "standard_name") and ax.standard_name == "projection_y_coordinate"):
self.axes[i] = Y.from_axis(ax)
elif ax.name in ("z", "height", "depth"):
self.axes[i] = Z.from_axis(ax)
# Check unicity of coordinates.
time_coords = filter(lambda ax: isinstance(ax, Time), self.axes)
x_coords = filter(lambda ax: isinstance(ax, X), self.axes)
y_coords = filter(lambda ax: isinstance(ax, Y), self.axes)
z_coords = filter(lambda ax: isinstance(ax, Z), self.axes)
if len(time_coords) > 1:
raise ValueError("More than one time coordinate found")
if len(x_coords) > 1:
raise ValueError("More than one x coordinate found")
if len(y_coords) > 1:
raise ValueError("More than one y coordinate found")
if len(z_coords) > 1:
raise ValueError("More than one z coordinate found")
