import numpy as np
import pandas as pd
from .pycompat import basestring, iteritems, suppress
from . import formatting
from .utils import SortedKeysDict
class ImplementsArrayReduce(object):
@classmethod
def _reduce_method(cls, func, include_skipna, numeric_only):
if include_skipna:
def wrapped_func(self, dim=None, axis=None, skipna=None,
keep_attrs=False, **kwargs):
return self.reduce(func, dim, axis, keep_attrs=keep_attrs,
skipna=skipna, allow_lazy=True, **kwargs)
else:
def wrapped_func(self, dim=None, axis=None, keep_attrs=False,
**kwargs):
return self.reduce(func, dim, axis, keep_attrs=keep_attrs,
allow_lazy=True, **kwargs)
return wrapped_func
_reduce_extra_args_docstring = \
"""dim : str or sequence of str, optional
Dimension(s) over which to apply `{name}`.
axis : int or sequence of int, optional
Axis(es) over which to apply `{name}`. Only one of the 'dim'
and 'axis' arguments can be supplied. If neither are supplied, then
`{name}` is calculated over axes."""
class ImplementsDatasetReduce(object):
@classmethod
def _reduce_method(cls, func, include_skipna, numeric_only):
if include_skipna:
def wrapped_func(self, dim=None, keep_attrs=False, skipna=None,
**kwargs):
return self.reduce(func, dim, keep_attrs, skipna=skipna,
numeric_only=numeric_only, allow_lazy=True,
**kwargs)
else:
def wrapped_func(self, dim=None, keep_attrs=False, **kwargs):
return self.reduce(func, dim, keep_attrs,
numeric_only=numeric_only, allow_lazy=True,
**kwargs)
return wrapped_func
_reduce_extra_args_docstring = \
"""dim : str or sequence of str, optional
Dimension(s) over which to apply `func`. By default `func` is
applied over all dimensions."""
class AbstractArray(ImplementsArrayReduce):
def __bool__(self):
return bool(self.values)
# Python 3 uses __bool__, Python 2 uses __nonzero__
__nonzero__ = __bool__
def __float__(self):
return float(self.values)
def __int__(self):
return int(self.values)
def __complex__(self):
return complex(self.values)
def __long__(self):
return long(self.values)
def __array__(self, dtype=None):
return np.asarray(self.values, dtype=dtype)
def __repr__(self):
return formatting.array_repr(self)
def _iter(self):
for n in range(len(self)):
yield self[n]
def __iter__(self):
if self.ndim == 0:
raise TypeError('iteration over a 0-d array')
return self._iter()
@property
def T(self):
return self.transpose()
def get_axis_num(self, dim):
"""Return axis number(s) corresponding to dimension(s) in this array.
Parameters
----------
dim : str or iterable of str
Dimension name(s) for which to lookup axes.
Returns
-------
int or tuple of int
Axis number or numbers corresponding to the given dimensions.
"""
if isinstance(dim, basestring):
return self._get_axis_num(dim)
else:
return tuple(self._get_axis_num(d) for d in dim)
def _get_axis_num(self, dim):
try:
return self.dims.index(dim)
except ValueError:
raise ValueError("%r not found in array dimensions %r" %
(dim, self.dims))
class AttrAccessMixin(object):
"""Mixin class that allows getting keys with attribute access
"""
_initialized = False
@property
def _attr_sources(self):
"""List of places to look-up items for attribute-style access"""
return [self, self.attrs]
def __getattr__(self, name):
if name != '__setstate__':
# this avoids an infinite loop when pickle looks for the
# __setstate__ attribute before the xarray object is initialized
for source in self._attr_sources:
with suppress(KeyError):
return source[name]
raise AttributeError("%r object has no attribute %r" %
(type(self).__name__, name))
def __setattr__(self, name, value):
if self._initialized:
try:
# Allow setting instance variables if they already exist
# (e.g., _attrs). We use __getattribute__ instead of hasattr
# to avoid key lookups with attribute-style access.
self.__getattribute__(name)
except AttributeError:
raise AttributeError(
"cannot set attribute %r on a %r object. Use __setitem__ "
"style assignment (e.g., `ds['name'] = ...`) instead to "
"assign variables." % (name, type(self).__name__))
object.__setattr__(self, name, value)
def __dir__(self):
"""Provide method name lookup and completion. Only provide 'public'
methods.
"""
extra_attrs = [item for sublist in self._attr_sources
for item in sublist]
return sorted(set(dir(type(self)) + extra_attrs))
class BaseDataObject(AttrAccessMixin):
def _calc_assign_results(self, kwargs):
results = SortedKeysDict()
for k, v in kwargs.items():
if callable(v):
results[k] = v(self)
else:
results[k] = v
return results
def assign_coords(self, **kwargs):
"""Assign new coordinates to this object, returning a new object
with all the original data in addition to the new coordinates.
Parameters
----------
kwargs : keyword, value pairs
keywords are the variables names. If the values are callable, they
are computed on this object and assigned to new coordinate
variables. If the values are not callable, (e.g. a DataArray,
scalar, or array), they are simply assigned.
Returns
-------
assigned : same type as caller
A new object with the new coordinates in addition to the existing
data.
Notes
-----
Since ``kwargs`` is a dictionary, the order of your arguments may not
be preserved, and so the order of the new variables is not well
defined. Assigning multiple variables within the same ``assign_coords``
is possible, but you cannot reference other variables created within
the same ``assign_coords`` call.
See also
--------
Dataset.assign
"""
data = self.copy(deep=False)
results = self._calc_assign_results(kwargs)
data.coords.update(results)
return data
def pipe(self, func, *args, **kwargs):
"""
Apply func(self, *args, **kwargs)
This method replicates the pandas method of the same name.
Parameters
----------
func : function
function to apply to this xarray object (Dataset/DataArray).
``args``, and ``kwargs`` are passed into ``func``.
Alternatively a ``(callable, data_keyword)`` tuple where
``data_keyword`` is a string indicating the keyword of
``callable`` that expects the xarray object.
args : positional arguments passed into ``func``.
kwargs : a dictionary of keyword arguments passed into ``func``.
Returns
-------
object : the return type of ``func``.
Notes
-----
Use ``.pipe`` when chaining together functions that expect
xarray or pandas objects, e.g., instead of writing
>>> f(g(h(ds), arg1=a), arg2=b, arg3=c)
You can write
>>> (ds.pipe(h)
... .pipe(g, arg1=a)
... .pipe(f, arg2=b, arg3=c)
... )
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose ``f`` takes its data as ``arg2``:
>>> (ds.pipe(h)
... .pipe(g, arg1=a)
... .pipe((f, 'arg2'), arg1=a, arg3=c)
... )
See Also
--------
pandas.DataFrame.pipe
"""
if isinstance(func, tuple):
func, target = func
if target in kwargs:
msg = '%s is both the pipe target and a keyword argument' % target
raise ValueError(msg)
kwargs[target] = self
return func(*args, **kwargs)
else:
return func(self, *args, **kwargs)
def groupby(self, group, squeeze=True):
"""Returns a GroupBy object for performing grouped operations.
Parameters
----------
group : str, DataArray or Coordinate
Array whose unique values should be used to group this array. If a
string, must be the name of a variable contained in this dataset.
squeeze : boolean, optional
If "group" is a dimension of any arrays in this dataset, `squeeze`
controls whether the subarrays have a dimension of length 1 along
that dimension or if the dimension is squeezed out.
Returns
-------
grouped : GroupBy
A `GroupBy` object patterned after `pandas.GroupBy` that can be
iterated over in the form of `(unique_value, grouped_array)` pairs.
"""
if isinstance(group, basestring):
group = self[group]
return self.groupby_cls(self, group, squeeze=squeeze)
def resample(self, freq, dim, how='mean', skipna=None, closed=None,
label=None, base=0):
"""Resample this object to a new temporal resolution.
Handles both downsampling and upsampling. Upsampling with filling is
not yet supported; if any intervals contain no values in the original
object, they will be given the value ``NaN``.
Parameters
----------
freq : str
String in the '#offset' to specify the step-size along the
resampled dimension, where '#' is an (optional) integer multipler
(default 1) and 'offset' is any pandas date offset alias. Examples
of valid offsets include:
* 'AS': year start
* 'QS-DEC': quarterly, starting on December 1
* 'MS': month start
* 'D': day
* 'H': hour
* 'Min': minute
The full list of these offset aliases is documented in pandas [1]_.
dim : str
Name of the dimension to resample along (e.g., 'time').
how : str or func, optional
Used for downsampling. If a string, ``how`` must be a valid
aggregation operation supported by xarray. Otherwise, ``how`` must be
a function that can be called like ``how(values, axis)`` to reduce
ndarray values along the given axis. Valid choices that can be
provided as a string include all the usual Dataset/DataArray
aggregations (``all``, ``any``, ``argmax``, ``argmin``, ``max``,
``mean``, ``median``, ``min``, ``prod``, ``sum``, ``std`` and
``var``), as well as ``first`` and ``last``.
skipna : bool, optional
Whether to skip missing values when aggregating in downsampling.
closed : 'left' or 'right', optional
Side of each interval to treat as closed.
label : 'left or 'right', optional
Side of each interval to use for labeling.
base : int, optionalt
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '24H' frequency, base could
range from 0 through 23.
Returns
-------
resampled : same type as caller
This object resampled.
References
----------
.. [1] http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
"""
from .dataarray import DataArray
RESAMPLE_DIM = '__resample_dim__'
if isinstance(dim, basestring):
dim = self[dim]
group = DataArray(dim, [(RESAMPLE_DIM, dim)], name=RESAMPLE_DIM)
time_grouper = pd.TimeGrouper(freq=freq, how=how, closed=closed,
label=label, base=base)
gb = self.groupby_cls(self, group, grouper=time_grouper)
if isinstance(how, basestring):
f = getattr(gb, how)
if how in ['first', 'last']:
result = f(skipna=skipna)
else:
result = f(dim=dim.name, skipna=skipna)
else:
result = gb.reduce(how, dim=dim.name)
result = result.rename({RESAMPLE_DIM: dim.name})
return result
def where(self, cond):
"""Return an object of the same shape with all entries where cond is
True and all other entries masked.
This operation follows the normal broadcasting and alignment rules that
xarray uses for binary arithmetic.
Parameters
----------
cond : boolean DataArray or Dataset
Returns
-------
same type as caller
Examples
--------
>>> import numpy as np
>>> a = xr.DataArray(np.arange(25).reshape(5, 5), dims=('x', 'y'))
>>> a.where((a > 6) & (a < 18))
<xarray.DataArray (x: 5, y: 5)>
array([[ nan, nan, nan, nan, nan],
[ nan, nan, 7., 8., 9.],
[ 10., 11., 12., 13., 14.],
[ 15., 16., 17., nan, nan],
[ nan, nan, nan, nan, nan]])
Coordinates:
* y (y) int64 0 1 2 3 4
* x (x) int64 0 1 2 3 4
"""
return self._where(cond)
def squeeze(xarray_obj, dims, dim=None):
"""Squeeze the dims of an xarray object."""
if dim is None:
dim = [d for d, s in iteritems(dims) if s == 1]
else:
if isinstance(dim, basestring):
dim = [dim]
if any(dims[k] > 1 for k in dim):
raise ValueError('cannot select a dimension to squeeze out '
'which has length greater than one')
return xarray_obj.isel(**dict((d, 0) for d in dim))
def _maybe_promote(dtype):
"""Simpler equivalent of pandas.core.common._maybe_promote"""
# N.B. these casting rules should match pandas
if np.issubdtype(dtype, float):
fill_value = np.nan
elif np.issubdtype(dtype, int):
# convert to floating point so NaN is valid
dtype = float
fill_value = np.nan
elif np.issubdtype(dtype, np.datetime64):
fill_value = np.datetime64('NaT')
elif np.issubdtype(dtype, np.timedelta64):
fill_value = np.timedelta64('NaT')
else:
dtype = object
fill_value = np.nan
return np.dtype(dtype), fill_value
def _possibly_convert_objects(values):
"""Convert arrays of datetime.datetime and datetime.timedelta objects into
datetime64 and timedelta64, according to the pandas convention.
"""
return np.asarray(pd.Series(values.ravel())).reshape(values.shape)
|