def __init__(self, *args, **kwargs):
""" initialize a dataset from a set of objects of varying dimensions
data : dict of DimArrays or list of named DimArrays or Axes object
keys : keys to order data if provided as dict, or to name data if list
"""
assert not {'axes', 'keys'}.issubset(
kwargs.keys()
) # just to check bugs due to back-compat ==> TO BE REMOVED AFTER DEBUGGING
# check input arguments: same init as odict
kwargs = odict(*args, **kwargs)
# Basic initialization
self.axes = Axes()
# initialize an ordered dictionary
super(Dataset, self).__init__()
values = kwargs.values()
keys = kwargs.keys()
# Check everything is a DimArray
#for key, value in zip(keys, values):
for i, key in enumerate(keys):
value = values[i]
if not isinstance(value, DimArray):
if np.isscalar(value):
values[i] = DimArray(value)
else:
raise TypeError(
"A Dataset can only store DimArray instances, got {}: {}"
.format(key, value))
# Align objects
values = align_axes(*values)
# Append object (will automatically update self.axes)
for key, value in zip(keys, values):
self[key] = value
def __init__(self, *args, **kwargs):
""" initialize a dataset from a set of objects of varying dimensions
data : dict of DimArrays or list of named DimArrays or Axes object
keys : keys to order data if provided as dict, or to name data if list
"""
assert not {'axes','keys'}.issubset(kwargs.keys()) # just to check bugs due to back-compat ==> TO BE REMOVED AFTER DEBUGGING
# check input arguments: same init as odict
data = odict(*args, **kwargs)
# Basic initialization
self.axes = Axes()
# initialize an ordered dictionary
super(Dataset, self).__init__()
#self.data = odict()
values = data.values()
keys = data.keys()
# Check everything is a DimArray
#for key, value in zip(keys, values):
for i, key in enumerate(keys):
value = values[i]
if not isinstance(value, DimArray):
if np.isscalar(value):
values[i] = self._constructor(value)
else:
raise TypeError("A Dataset can only store DimArray instances, got {}: {}".format(key, value))
# Align objects
values = align_axes(*values)
# Append object (will automatically update self.axes)
for key, value in zip(keys, values):
self[key] = value
class Dataset(odict):
""" Container for a set of aligned objects
"""
_metadata = MetadataDesc(exclude=['axes'])
def __init__(self, *args, **kwargs):
""" initialize a dataset from a set of objects of varying dimensions
data : dict of DimArrays or list of named DimArrays or Axes object
keys : keys to order data if provided as dict, or to name data if list
"""
assert not {'axes', 'keys'}.issubset(
kwargs.keys()
) # just to check bugs due to back-compat ==> TO BE REMOVED AFTER DEBUGGING
# check input arguments: same init as odict
kwargs = odict(*args, **kwargs)
# Basic initialization
self.axes = Axes()
# initialize an ordered dictionary
super(Dataset, self).__init__()
values = kwargs.values()
keys = kwargs.keys()
# Check everything is a DimArray
#for key, value in zip(keys, values):
for i, key in enumerate(keys):
value = values[i]
if not isinstance(value, DimArray):
if np.isscalar(value):
values[i] = DimArray(value)
else:
raise TypeError(
"A Dataset can only store DimArray instances, got {}: {}"
.format(key, value))
# Align objects
values = align_axes(*values)
# Append object (will automatically update self.axes)
for key, value in zip(keys, values):
self[key] = value
@property
def dims(self):
""" list of dimensions contained in the Dataset, consistently with DimArray's `dims`
"""
return [ax.name for ax in self.axes]
def __repr__(self):
""" string representation
"""
lines = []
header = "Dataset of %s variables" % (len(self))
if len(self) == 1: header = header.replace('variables', 'variable')
lines.append(header)
axes = repr(self.axes)
lines.append(axes)
for nm in self.keys():
dims = self[nm].dims
shape = self[nm].shape
#print nm,":", ", ".join(dims)
repr_dims = repr(dims)
if repr_dims == "()": repr_dims = self[nm].values
lines.append("{}: {}".format(nm, repr_dims))
return "\n".join(lines)
def __delitem__(self, item):
"""
"""
axes = self[item].axes
super(Dataset, self).__delitem__(item)
# update axes
for ax in axes:
found = False
for k in self:
if ax.name in self[k].dims:
found = True
continue
if not found:
self.axes.remove(ax)
def __setitem__(self, key, val):
""" Make sure the object is a DimArray with appropriate axes
Tests:
-----
# >>> axes = da.Axes.from_tuples(('time',[1, 2, 3]))
>>> ds = Dataset()
>>> ds
Dataset of 0 variables
dimensions:
<BLANKLINE>
>>> a = DimArray([0, 1, 2], dims=('time',))
>>> ds['yo'] = a
>>> ds['yo']
dimarray: 3 non-null elements (0 null)
dimensions: 'time'
0 / time (3): 0 to 2
array([0, 1, 2])
"""
if not isinstance(val, DimArray):
if np.isscalar(val):
val = DimArray(val)
else:
raise TypeError("can only append DimArray instances")
# Check dimensions
for axis in val.axes:
# Check dimensions if already existing axis
if axis.name in [ax.name for ax in self.axes]:
if not axis == self.axes[axis.name]:
raise ValueError(
"axes values do not match, align data first.\
\nDataset: {}, \nGot: {}".format(
self.axes[axis.name], axis))
# Append new axis
else:
self.axes.append(axis)
# update name
val.name = key
super(Dataset, self).__setitem__(key, val)
def write_nc(self, f, *args, **kwargs):
""" Saves dataset in netCDF file.
parameters:
-----------
f : netCDF file name.
understood keyword arguments:
zlib : Enable zlib compression if True. Default is False (no compression).
complevel : integer between 1 and 9 describing the level of compression desired. Ignored if zlib=False.
mode : File creation mode. Default is 'w-'. Set to 'w' to overwrite any existing file.
format : netCDF file format. Default is 'NETCDF4'.
See the netCDF4-python module documentation for more information about the use
of keyword arguments to write_nc.
"""
import io.nc as ncio
ncio.write_dataset(f, self, *args, **kwargs)
write = write_nc
@classmethod
def read_nc(cls, f, *args, **kwargs):
import io.nc as ncio
return ncio.read_dataset(f, *args, **kwargs)
read = read_nc
def to_array(self, axis=None, keys=None, _constructor=None):
""" Convert to DimArray
axis : axis name, by default "unnamed"
"""
#if names is not None or dims is not None:
# return self.subset(names=names, dims=dims).to_array()
if axis is None:
axis = "unnamed"
if axis in self.dims:
i = 1
while "unnamed_{}".format(i) in self.dims:
i += 1
axis = "unnamed_{}".format(i)
if axis in self.dims:
raise ValueError("please provide an axis name which does not \
already exist in Dataset")
if keys is None:
keys = self.keys()
# align all variables to the same dimensions
data = odict()
for k in keys:
data[k] = self[k].reshape(self.dims).broadcast(self.axes)
# make it a numpy array
data = [data[k].values for k in keys]
data = np.array(data)
# determine axes
axes = [Axis(keys, axis)] + self.axes
if _constructor is None: _constructor = DimArray
return _constructor(data, axes)
def take(self, indices, axis=0, raise_error=True, **kwargs):
""" analogous to DimArray's take, but for each DimArray of the Dataset
parameters:
-----------
indices: scalar, or array-like, or slice
axis: axis name (str)
raise_error: raise an error if a variable does not have the desired dimension
**kwargs: arguments passed to the axis locator, similar to `take`, such as `indexing` or `keepdims`
parameters:
-----------
Examples:
---------
>>> a = DimArray([1,2,3], axes=('time', [1950, 1951, 1952]))
>>> b = DimArray([11,22,33], axes=('time', [1951, 1952, 1953]))
>>> ds = Dataset(a=a, b=b)
>>> ds
Dataset of 2 variables
dimensions: 'time'
0 / time (4): 1950 to 1953
a: ('time',)
b: ('time',)
>>> ds.take(1951, axis='time')
Dataset of 2 variables
dimensions:
<BLANKLINE>
a: 2.0
b: 11.0
>>> ds.take(0, axis='time', indexing='position')
Dataset of 2 variables
dimensions:
<BLANKLINE>
a: 1.0
b: nan
"""
assert isinstance(axis, str), "axis must be a string"
ii = self.axes[axis].loc(indices, **kwargs)
newdata = self.copy() # copy the dict
for k in self.keys():
if axis not in self[k].dims:
if raise_error:
raise ValueError(
"{} does not have dimension {} ==> set raise_error=False to keep this variable unchanged"
.format(k, axis))
else:
continue
a = self[k].take(ii, axis=axis, indexing='position')
if not isinstance(a, DimArray):
a = DimArray(a)
odict.__setitem__(newdata, k, a)
# update the axis
newaxis = self.axes[axis][ii]
if type(axis) is not int: axis = self.dims.index(axis) # axis is int
# remove if axis collapsed
if not isinstance(newaxis, Axis):
del newdata.axes[axis]
# otherwise update
else:
newdata.axes[axis] = newaxis
return newdata
def _apply_dimarray_axis(self, funcname, *args, **kwargs):
""" Apply a function on every Dataset variable.
If the 'axis=' parameter is passed, only the variables with the required axis are called.
"""
axis = kwargs.pop('axis', None)
if axis is not None: axis = self.axes[axis].name
kwargs['axis'] = axis
d = odict(self)
for k in self.keys():
if axis is not None and axis not in self[k].dims:
continue
#d[k] = self[k].apply(func, *args, **kwargs)
d[k] = getattr(self[k], funcname)(*args, **kwargs)
return Dataset(d)
def mean(self, axis=0, **kwargs):
""" Apply transformantion on every variable of the Dataset
Examples:
---------
>>> a = DimArray([1,2,3], axes=('time', [1950, 1951, 1952]))
>>> b = DimArray([[11,22,33],[44,55,66]], axes=[('items',['a','b']), ('time', [1950, 1951, 1952])])
>>> ds = Dataset(a=a, b=b)
>>> ds.mean(axis='time')
Dataset of 2 variables
dimensions: 'items'
0 / items (2): a to b
a: 2.0
b: ('items',)
>>> ds.mean(axis='items')
Dataset of 2 variables
dimensions: 'time'
0 / time (3): 1950 to 1952
a: ('time',)
b: ('time',)
"""
return self._apply_dimarray_axis('mean', axis=axis, **kwargs)
def std(self, axis=0, **kwargs):
return self._apply_dimarray_axis('std', axis=axis, **kwargs)
def var(self, axis=0, **kwargs):
return self._apply_dimarray_axis('var', axis=axis, **kwargs)
def median(self, axis=0, **kwargs):
return self._apply_dimarray_axis('median', axis=axis, **kwargs)
def __getattr__(self, att):
""" allow access of dimensions
"""
# check for dimensions
if att in self.dims:
ax = self.axes[att]
return ax.values # return numpy array
else:
raise AttributeError("{} object has no attribute {}".format(
self.__class__.__name__, att))
def to_dict(self):
""" export to dict
"""
return dict(self)
def to_odict(self):
""" export to ordered dict
"""
return odict(self)
class Dataset(odict):
""" Container for a set of aligned objects
"""
_metadata = MetadataDesc(exclude=['axes'])
def __init__(self, *args, **kwargs):
""" initialize a dataset from a set of objects of varying dimensions
data : dict of DimArrays or list of named DimArrays or Axes object
keys : keys to order data if provided as dict, or to name data if list
"""
assert not {'axes','keys'}.issubset(kwargs.keys()) # just to check bugs due to back-compat ==> TO BE REMOVED AFTER DEBUGGING
# check input arguments: same init as odict
kwargs = odict(*args, **kwargs)
# Basic initialization
self.axes = Axes()
# initialize an ordered dictionary
super(Dataset, self).__init__()
values = kwargs.values()
keys = kwargs.keys()
# Check everything is a DimArray
#for key, value in zip(keys, values):
for i, key in enumerate(keys):
value = values[i]
if not isinstance(value, DimArray):
if np.isscalar(value):
values[i] = DimArray(value)
else:
raise TypeError("A Dataset can only store DimArray instances, got {}: {}".format(key, value))
# Align objects
values = align_axes(*values)
# Append object (will automatically update self.axes)
for key, value in zip(keys, values):
self[key] = value
@property
def dims(self):
""" list of dimensions contained in the Dataset, consistently with DimArray's `dims`
"""
return [ax.name for ax in self.axes]
def __repr__(self):
""" string representation
"""
lines = []
header = "Dataset of %s variables" % (len(self))
if len(self) == 1: header = header.replace('variables','variable')
lines.append(header)
axes = repr(self.axes)
lines.append(axes)
for nm in self.keys():
dims = self[nm].dims
shape = self[nm].shape
#print nm,":", ", ".join(dims)
repr_dims = repr(dims)
if repr_dims == "()": repr_dims = self[nm].values
lines.append("{}: {}".format(nm,repr_dims))
return "\n".join(lines)
def __delitem__(self, item):
"""
"""
axes = self[item].axes
super(Dataset, self).__delitem__(item)
# update axes
for ax in axes:
found = False
for k in self:
if ax.name in self[k].dims:
found = True
continue
if not found:
self.axes.remove(ax)
def __setitem__(self, key, val):
""" Make sure the object is a DimArray with appropriate axes
Tests:
-----
# >>> axes = da.Axes.from_tuples(('time',[1, 2, 3]))
>>> ds = Dataset()
>>> ds
Dataset of 0 variables
dimensions:
<BLANKLINE>
>>> a = DimArray([0, 1, 2], dims=('time',))
>>> ds['yo'] = a
>>> ds['yo']
dimarray: 3 non-null elements (0 null)
dimensions: 'time'
0 / time (3): 0 to 2
array([0, 1, 2])
"""
if not isinstance(val, DimArray):
if np.isscalar(val):
val = DimArray(val)
else:
raise TypeError("can only append DimArray instances")
# Check dimensions
for axis in val.axes:
# Check dimensions if already existing axis
if axis.name in [ax.name for ax in self.axes]:
if not axis == self.axes[axis.name]:
raise ValueError("axes values do not match, align data first.\
\nDataset: {}, \nGot: {}".format(self.axes[axis.name], axis))
# Append new axis
else:
self.axes.append(axis)
# update name
val.name = key
super(Dataset, self).__setitem__(key, val)
def write_nc(self, f, *args, **kwargs):
""" Saves dataset in netCDF file.
parameters:
-----------
f : netCDF file name.
understood keyword arguments:
zlib : Enable zlib compression if True. Default is False (no compression).
complevel : integer between 1 and 9 describing the level of compression desired. Ignored if zlib=False.
mode : File creation mode. Default is 'w-'. Set to 'w' to overwrite any existing file.
format : netCDF file format. Default is 'NETCDF4'.
See the netCDF4-python module documentation for more information about the use
of keyword arguments to write_nc.
"""
import io.nc as ncio
ncio.write_dataset(f, self, *args, **kwargs)
write = write_nc
@classmethod
def read_nc(cls, f, *args, **kwargs):
import io.nc as ncio
return ncio.read_dataset(f, *args, **kwargs)
read = read_nc
def to_array(self, axis=None, keys=None, _constructor=None):
""" Convert to DimArray
axis : axis name, by default "unnamed"
"""
#if names is not None or dims is not None:
# return self.subset(names=names, dims=dims).to_array()
if axis is None:
axis = "unnamed"
if axis in self.dims:
i = 1
while "unnamed_{}".format(i) in self.dims:
i+=1
axis = "unnamed_{}".format(i)
if axis in self.dims:
raise ValueError("please provide an axis name which does not \
already exist in Dataset")
if keys is None:
keys = self.keys()
# align all variables to the same dimensions
data = odict()
for k in keys:
data[k] = self[k].reshape(self.dims).broadcast(self.axes)
# make it a numpy array
data = [data[k].values for k in keys]
data = np.array(data)
# determine axes
axes = [Axis(keys, axis)] + self.axes
if _constructor is None: _constructor = DimArray
return _constructor(data, axes)
def take(self, indices, axis=0, raise_error=True, **kwargs):
""" analogous to DimArray's take, but for each DimArray of the Dataset
parameters:
-----------
indices: scalar, or array-like, or slice
axis: axis name (str)
raise_error: raise an error if a variable does not have the desired dimension
**kwargs: arguments passed to the axis locator, similar to `take`, such as `indexing` or `keepdims`
parameters:
-----------
Examples:
---------
>>> a = DimArray([1,2,3], axes=('time', [1950, 1951, 1952]))
>>> b = DimArray([11,22,33], axes=('time', [1951, 1952, 1953]))
>>> ds = Dataset(a=a, b=b)
>>> ds
Dataset of 2 variables
dimensions: 'time'
0 / time (4): 1950 to 1953
a: ('time',)
b: ('time',)
>>> ds.take(1951, axis='time')
Dataset of 2 variables
dimensions:
<BLANKLINE>
a: 2.0
b: 11.0
>>> ds.take(0, axis='time', indexing='position')
Dataset of 2 variables
dimensions:
<BLANKLINE>
a: 1.0
b: nan
"""
assert isinstance(axis, str), "axis must be a string"
ii = self.axes[axis].loc(indices, **kwargs)
newdata = self.copy() # copy the dict
for k in self.keys():
if axis not in self[k].dims:
if raise_error:
raise ValueError("{} does not have dimension {} ==> set raise_error=False to keep this variable unchanged".format(k, axis))
else:
continue
a = self[k].take(ii, axis=axis, indexing='position')
if not isinstance(a, DimArray):
a = DimArray(a)
odict.__setitem__(newdata, k, a)
# update the axis
newaxis = self.axes[axis][ii]
if type(axis) is not int: axis = self.dims.index(axis) # axis is int
# remove if axis collapsed
if not isinstance(newaxis, Axis):
del newdata.axes[axis]
# otherwise update
else:
newdata.axes[axis] = newaxis
return newdata
def _apply_dimarray_axis(self, funcname, *args, **kwargs):
""" Apply a function on every Dataset variable.
If the 'axis=' parameter is passed, only the variables with the required axis are called.
"""
axis = kwargs.pop('axis',None)
if axis is not None: axis = self.axes[axis].name
kwargs['axis'] = axis
d = odict(self)
for k in self.keys():
if axis is not None and axis not in self[k].dims:
continue
#d[k] = self[k].apply(func, *args, **kwargs)
d[k] = getattr(self[k], funcname)(*args, **kwargs)
return Dataset(d)
def mean(self, axis=0, **kwargs):
""" Apply transformantion on every variable of the Dataset
Examples:
---------
>>> a = DimArray([1,2,3], axes=('time', [1950, 1951, 1952]))
>>> b = DimArray([[11,22,33],[44,55,66]], axes=[('items',['a','b']), ('time', [1950, 1951, 1952])])
>>> ds = Dataset(a=a, b=b)
>>> ds.mean(axis='time')
Dataset of 2 variables
dimensions: 'items'
0 / items (2): a to b
a: 2.0
b: ('items',)
>>> ds.mean(axis='items')
Dataset of 2 variables
dimensions: 'time'
0 / time (3): 1950 to 1952
a: ('time',)
b: ('time',)
"""
return self._apply_dimarray_axis('mean', axis=axis, **kwargs)
def std(self, axis=0, **kwargs): return self._apply_dimarray_axis('std', axis=axis, **kwargs)
def var(self, axis=0, **kwargs): return self._apply_dimarray_axis('var', axis=axis, **kwargs)
def median(self, axis=0, **kwargs): return self._apply_dimarray_axis('median', axis=axis, **kwargs)
def __getattr__(self, att):
""" allow access of dimensions
"""
# check for dimensions
if att in self.dims:
ax = self.axes[att]
return ax.values # return numpy array
else:
raise AttributeError("{} object has no attribute {}".format(self.__class__.__name__, att))
def to_dict(self):
""" export to dict
"""
return dict(self)
def to_odict(self):
""" export to ordered dict
"""
return odict(self)
class Dataset(odict, MetadataBase):
""" Container for a set of aligned objects
"""
@property
def axes(self):
return self._axes
@axes.setter
def axes(self, axes):
self._axes = axes
_constructor = DimArray
def __init__(self, *args, **kwargs):
""" initialize a dataset from a set of objects of varying dimensions
data : dict of DimArrays or list of named DimArrays or Axes object
keys : keys to order data if provided as dict, or to name data if list
"""
assert not {'axes','keys'}.issubset(kwargs.keys()) # just to check bugs due to back-compat ==> TO BE REMOVED AFTER DEBUGGING
# check input arguments: same init as odict
data = odict(*args, **kwargs)
# Basic initialization
self.axes = Axes()
# initialize an ordered dictionary
super(Dataset, self).__init__()
#self.data = odict()
values = data.values()
keys = data.keys()
# Check everything is a DimArray
#for key, value in zip(keys, values):
for i, key in enumerate(keys):
value = values[i]
if not isinstance(value, DimArray):
if np.isscalar(value):
values[i] = self._constructor(value)
else:
raise TypeError("A Dataset can only store DimArray instances, got {}: {}".format(key, value))
# Align objects
values = align_axes(*values)
# Append object (will automatically update self.axes)
for key, value in zip(keys, values):
self[key] = value
@property
def dims(self):
""" tuple of dimensions contained in the Dataset, consistently with DimArray's `dims`
"""
return tuple([ax.name for ax in self.axes])
@dims.setter
def dims(self, newdims):
""" rename all axis names at once
"""
if not np.iterable(newdims):
raise TypeError("new dims must be iterable")
if not len(newdims) == len(self.axes):
raise ValueError("dimension mistmatch")
# update every element's dimension
for i, newname in enumerate(newdims):
oldname = self.axes[i].name
self.axes[i].name = newname
# axes in individual items will be updated automatically
# since they are all references of the central axes
def _repr(self, metadata=True):
""" string representation
"""
lines = []
header = "Dataset of %s variables" % (len(self))
if len(self) == 1: header = header.replace('variables','variable')
lines.append(header)
lines.append(self.axes._repr(metadata=metadata))
# display single variables
for nm in self.keys():
v = self[nm]
repr_dims = repr(v.dims)
if repr_dims == "()": repr_dims = v.values
vlines = []
vlines.append("{}".format(repr_dims))
if metadata and len(v._metadata()) > 0:
vlines.append(v._metadata_summary())
lines.append(nm+': '+"\n".join(vlines))
if metadata and len(self._metadata()) > 0:
lines.append('//global attributes:')
lines.append(self._metadata_summary())
return "\n".join(lines)
def summary(self):
return self._repr(metadata=True)
def __repr__(self):
return self._repr(metadata=False)
#
# overload dictionary methods
#
def __delitem__(self, item):
"""
"""
axes = self[item].axes
#del self.data[item]
super(Dataset, self).__delitem__(item)
#del super(Dataset, self)[item]
# update axes
for ax in axes:
found = False
for k in self:
if ax.name in self[k].dims:
found = True
continue
if not found:
self.axes.remove(ax)
def __setitem__(self, key, val):
""" Make sure the object is a DimArray with appropriate axes
Examples
--------
>>> ds = Dataset()
>>> ds
Dataset of 0 variables
<BLANKLINE>
>>> a = DimArray([0, 1, 2], dims=('time',))
>>> ds['yo'] = a
>>> ds['yo']
dimarray: 3 non-null elements (0 null)
0 / time (3): 0 to 2
array([0, 1, 2])
>>> ds['ya'] = a.values # also accepts numpy array if shape matches
>>> ds['ya']
dimarray: 3 non-null elements (0 null)
0 / time (3): 0 to 2
array([0, 1, 2])
"""
if not isinstance(val, DimArray):
if np.isscalar(val):
val = self._constructor(val)
elif hasattr(val, '__array__'):
if np.shape(val) == tuple([ax.size for ax in self.axes]):
val = self._constructor(val, axes=self.axes) # make a dimarray with same axes
else:
raise ValueError("array_like shape does not match, use DimArray if dimensions vary within the dataset")
else:
raise TypeError("can only append DimArray instances")
# shallow copy of the DimArray so that its axes attribute can be
# modified without affecting the original array
val = copy.copy(val)
val.axes = copy.deepcopy(val.axes)
# Check dimensions
# make sure axes match those of the dataset
for i, newaxis in enumerate(val.axes):
# Check dimensions if already existing axis
if newaxis.name in [ax.name for ax in self.axes]:
existing_axis = self.axes[newaxis.name]
if not newaxis == existing_axis:
raise ValueError("axes values do not match, align data first.\
\nDataset: {}, \nGot: {}".format(existing_axis, newaxis))
# assign the Dataset axis : they all must share the same axis
val.axes[i] = existing_axis
# Append new axis
else:
self.axes.append(newaxis)
assert val.axes[i] is self.axes[newaxis.name]
super(Dataset, self).__setitem__(key, val)
# now just checking
test_internal = super(Dataset, self).__getitem__(key)
for ax in test_internal.axes:
assert self.axes[ax.name] is ax
def copy(self):
ds2 = super(Dataset, self).copy() # odict method, copy axes but not metadata
ds2._metadata(self._metadata())
return ds2
def __eq__(self, other):
""" test equality but bypass annoying numpy's __eq__ method
"""
return isinstance(other, Dataset) and self.keys() == other.keys() \
and self.axes == other.axes \
and np.all([np.all(self[k] == other[k]) for k in self.keys()])
#
#
#
def write_nc(self, f, *args, **kwargs):
""" Save dataset in netCDF file.
If you see this documentation, it means netCDF4 is not installed on your system
and you will not be able to use this functionality.
"""
import io.nc as ncio
ncio._write_dataset(f, self, *args, **kwargs)
write = write_nc
@classmethod
def read_nc(cls, f, *args, **kwargs):
""" Read dataset from netCDF file.
If you see this documentation, it means netCDF4 is not installed on your system
and you will not be able to use this functionality.
"""
import io.nc as ncio
return ncio._read_dataset(f, *args, **kwargs)
#read = read_nc
def to_array(self, axis=None, keys=None):
""" Convert to DimArray
axis : axis name, by default "unnamed"
"""
#if names is not None or dims is not None:
# return self.subset(names=names, dims=dims).to_array()
if axis is None:
axis = "unnamed"
if axis in self.dims:
i = 1
while "unnamed_{}".format(i) in self.dims:
i+=1
axis = "unnamed_{}".format(i)
if axis in self.dims:
raise ValueError("please provide an axis name which does not \
already exist in Dataset")
if keys is None:
keys = self.keys()
# align all variables to the same dimensions
data = odict()
for k in keys:
data[k] = self[k].reshape(self.dims).broadcast(self.axes)
# make it a numpy array
data = [data[k].values for k in keys]
data = np.array(data)
# determine axes
axes = [Axis(keys, axis)] + self.axes
return self._constructor(data, axes)
#
# REMOVE THESE FUNCTIONS AS NON-ESSENTIAL ???
#
def take(self, indices, axis=0, raise_error=False, **kwargs):
""" analogous to DimArray's take, but for each DimArray of the Dataset
Parameters
----------
indices : scalar, or array-like, or slice
axis : axis name (str)
raise_error : raise an error if a variable does not have the desired dimension
**kwargs : arguments passed to the axis locator, similar to `take`, such as `indexing` or `keepdims`
Examples
--------
>>> a = DimArray([1,2,3], axes=('time', [1950, 1951, 1952]))
>>> b = DimArray([11,22,33], axes=('time', [1951, 1952, 1953]))
>>> ds = Dataset(a=a, b=b)
>>> ds
Dataset of 2 variables
0 / time (4): 1950 to 1953
a: ('time',)
b: ('time',)
>>> ds.take(1951, axis='time')
Dataset of 2 variables
<BLANKLINE>
a: 2.0
b: 11.0
>>> ds.take(0, axis='time', indexing='position')
Dataset of 2 variables
<BLANKLINE>
a: 1.0
b: nan
>>> ds['c'] = DimArray([[1,2],[11,22],[111,222],[3,4]], axes=[('time', [1950,1951,1952,1953]),('item',['a','b'])])
>>> ds.take({'time':1950})
Dataset of 3 variables
0 / item (2): a to b
a: 1.0
b: nan
c: ('item',)
>>> ds.take({'time':1950})['c']
dimarray: 2 non-null elements (0 null)
0 / item (2): a to b
array([1, 2])
>>> ds.take({'item':'b'})
Dataset of 3 variables
0 / time (4): 1950 to 1953
a: ('time',)
b: ('time',)
c: ('time',)
"""
# first find the index for the shared axes
kw_indices = {self.axes[i].name:ind for i,ind in enumerate(self.axes.loc(indices, axis=axis, **kwargs))}
# then apply take in 'position' mode
newdata = self.__class__()
# loop over variables
for k in self.keys():
v = self[k]
# loop over axes to index on
for axis in kw_indices.keys():
if np.ndim(v) == 0 or axis not in v.dims:
if raise_error:
raise ValueError("{} does not have dimension {} ==> set raise_error=False to keep this variable unchanged".format(k, axis))
else:
continue
# slice along one axis
v = v.take({axis:kw_indices[axis]}, indexing='position')
newdata[k] = v
return newdata
def _apply_dimarray_axis(self, funcname, *args, **kwargs):
""" Apply a function on every Dataset variable.
If the 'axis=' parameter is passed, only the variables with the required axis are called.
"""
axis = kwargs.pop('axis',None)
if axis is not None: axis = self.axes[axis].name
kwargs['axis'] = axis
d = self.to_odict()
for k in self.keys():
if axis is not None and axis not in self[k].dims:
continue
#d[k] = self[k].apply(func, *args, **kwargs)
d[k] = getattr(self[k], funcname)(*args, **kwargs)
return Dataset(d)
def mean(self, axis=0, **kwargs):
""" Apply transformantion on every variable of the Dataset
Examples
--------
>>> a = DimArray([1,2,3], axes=('time', [1950, 1951, 1952]))
>>> b = DimArray([[11,22,33],[44,55,66]], axes=[('items',['a','b']), ('time', [1950, 1951, 1952])])
>>> ds = Dataset(a=a, b=b)
>>> ds.mean(axis='time')
Dataset of 2 variables
0 / items (2): a to b
a: 2.0
b: ('items',)
>>> ds.mean(axis='items')
Dataset of 2 variables
0 / time (3): 1950 to 1952
a: ('time',)
b: ('time',)
"""
return self._apply_dimarray_axis('mean', axis=axis, **kwargs)
def std(self, axis=0, **kwargs): return self._apply_dimarray_axis('std', axis=axis, **kwargs)
def var(self, axis=0, **kwargs): return self._apply_dimarray_axis('var', axis=axis, **kwargs)
def median(self, axis=0, **kwargs): return self._apply_dimarray_axis('median', axis=axis, **kwargs)
def sum(self, axis=0, **kwargs): return self._apply_dimarray_axis('sum', axis=axis, **kwargs)
def __getattr__(self, att):
""" allow access of dimensions
"""
# check for dimensions
if att in self.dims:
ax = self.axes[att]
return ax.values # return numpy array
else:
raise AttributeError("{} object has no attribute {}".format(self.__class__.__name__, att))
def to_dict(self):
""" export to dict
"""
return dict({nm:self[nm] for nm in self.keys()})
def to_odict(self):
""" export to ordered dict
"""
return odict([(nm, self[nm]) for nm in self.keys()])
@format_doc(**_doc_reset_axis)
def set_axis(self, values=None, axis=0, inplace=False, **kwargs):
""" (re)set axis values and attributes in all dimarrays present in the dataset
Parameters
----------
{values}
{axis}
{inplace}
{kwargs}
Returns
-------
Dataset instance, or None if inplace is True
Examples
--------
>>> ds = Dataset()
>>> ds['a'] = da.zeros(shape=(3,)) # some dimarray with dimension 'x0'
>>> ds['b'] = da.zeros(shape=(3,4)) # dimensions 'x0', 'x1'
>>> ds.set_axis(['a','b','c'], axis='x0')
Dataset of 2 variables
0 / x0 (3): a to c
1 / x1 (4): 0 to 3
a: ('x0',)
b: ('x0', 'x1')
"""
if inplace is False:
self = self.copy()
## update every dimarray in the dict
#axis_name = self.axes[axis].name
#for nm in self.keys():
# if not axis_name in self[nm].dims:
# continue
# super(Dataset, self).__setitem__(nm, self[nm].set_axis(values, axis, inplace=False, **kwargs) )
# update the main axis instance
self.axes = self.axes.set_axis(values, axis, inplace=False, **kwargs)
if inplace is False:
return self
@format_doc(**_doc_reset_axis)
def reset_axis(self, axis=0, inplace=False, **kwargs):
""" (re)set axis values and attributes in all dimarrays present in the dataset
Parameters
----------
{axis}
{inplace}
{kwargs}
Returns
-------
Dataset instance, or None if inplace is True
Examples
--------
>>> ds = Dataset()
>>> ds['a'] = da.zeros(axes=[['a','b','c']]) # some dimarray with dimension 'x0'
>>> ds['b'] = da.zeros(axes=[['a','b','c'], [11,22,33,44]]) # dimensions 'x0', 'x1'
>>> ds.reset_axis(axis='x0')
Dataset of 2 variables
0 / x0 (3): 0 to 2
1 / x1 (4): 11 to 44
a: ('x0',)
b: ('x0', 'x1')
"""
if inplace is False:
self = self.copy()
## update every dimarray in the dict
#axis_name = self.axes[axis].name
#for nm in self.keys():
# if not axis_name in self[nm].dims:
# continue
# super(Dataset, self).__setitem__(nm, self[nm].reset_axis(axis, inplace=False, **kwargs) )
# update the main axis instance
self.axes = self.axes.reset_axis(axis, inplace=False, **kwargs)
if inplace is False:
return self