from __future__ import division
import operator
import sys
from types import BuiltinFunctionType, BuiltinMethodType, FunctionType, MethodType
import numpy as np
import param
from ..core.data import PandasInterface
from ..core.dimension import Dimension
from ..core.util import basestring, pd, resolve_dependent_value, unique_iterator
def _maybe_map(numpy_fn):
def fn(values, *args, **kwargs):
series_like = hasattr(values, 'index') and not isinstance(values, list)
map_fn = (getattr(values, 'map_partitions', None) or
getattr(values, 'map_blocks', None))
if map_fn:
if series_like:
return map_fn(
lambda s: type(s)(numpy_fn(s, *args, **kwargs),
index=s.index))
else:
return map_fn(lambda s: numpy_fn(s, *args, **kwargs))
else:
if series_like:
return type(values)(
numpy_fn(values, *args, **kwargs),
index=values.index,
)
else:
return numpy_fn(values, *args, **kwargs)
return fn
[docs]def norm(values, min=None, max=None):
"""Unity-based normalization to scale data into 0-1 range.
(values - min) / (max - min)
Args:
values: Array of values to be normalized
min (float, optional): Lower bound of normalization range
max (float, optional): Upper bound of normalization range
Returns:
Array of normalized values
"""
min = np.min(values) if min is None else min
max = np.max(values) if max is None else max
return (values - min) / (max-min)
[docs]def lognorm(values, min=None, max=None):
"""Unity-based normalization on log scale.
Apply the same transformation as matplotlib.colors.LogNorm
Args:
values: Array of values to be normalized
min (float, optional): Lower bound of normalization range
max (float, optional): Upper bound of normalization range
Returns:
Array of normalized values
"""
min = np.log(np.min(values)) if min is None else np.log(min)
max = np.log(np.max(values)) if max is None else np.log(max)
return (np.log(values) - min) / (max-min)
[docs]class iloc(object):
"""Implements integer array indexing for dim expressions.
"""
__name__ = 'iloc'
def __init__(self, dim_expr):
self.expr = dim_expr
self.index = slice(None)
def __getitem__(self, index):
self.index = index
return dim(self.expr, self)
def __call__(self, values):
return values[self.index]
@_maybe_map
def bin(values, bins, labels=None):
"""Bins data into declared bins
Bins data into declared bins. By default each bin is labelled
with bin center values but an explicit list of bin labels may be
defined.
Args:
values: Array of values to be binned
bins: List or array containing the bin boundaries
labels: List of labels to assign to each bin
If the bins are length N the labels should be length N-1
Returns:
Array of binned values
"""
bins = np.asarray(bins)
if labels is None:
labels = (bins[:-1] + np.diff(bins)/2.)
else:
labels = np.asarray(labels)
dtype = 'float' if labels.dtype.kind == 'f' else 'O'
binned = np.full_like(values, (np.nan if dtype == 'f' else None), dtype=dtype)
for lower, upper, label in zip(bins[:-1], bins[1:], labels):
condition = (values > lower) & (values <= upper)
binned[np.where(condition)[0]] = label
return binned
@_maybe_map
def categorize(values, categories, default=None):
"""Maps discrete values to supplied categories.
Replaces discrete values in input array with a fixed set of
categories defined either as a list or dictionary.
Args:
values: Array of values to be categorized
categories: List or dict of categories to map inputs to
default: Default value to assign if value not in categories
Returns:
Array of categorized values
"""
uniq_cats = list(unique_iterator(values))
cats = []
for c in values:
if isinstance(categories, list):
cat_ind = uniq_cats.index(c)
if cat_ind < len(categories):
cat = categories[cat_ind]
else:
cat = default
else:
cat = categories.get(c, default)
cats.append(cat)
result = np.asarray(cats)
# Convert unicode to object type like pandas does
if result.dtype.kind in ['U', 'S']:
result = result.astype('object')
return result
digitize = _maybe_map(np.digitize)
isin = _maybe_map(np.isin)
astype = _maybe_map(np.asarray)
round_ = _maybe_map(np.round)
def _python_isin(array, values):
return [v in values for v in array]
python_isin = _maybe_map(_python_isin)
function_types = (
BuiltinFunctionType, BuiltinMethodType, FunctionType,
MethodType, np.ufunc, iloc
)
[docs]class dim(object):
"""
dim transform objects are a way to express deferred transforms on
Datasets. dim transforms support all mathematical and bitwise
operators, NumPy ufuncs and methods, and provide a number of
useful methods for normalizing, binning and categorizing data.
"""
_binary_funcs = {
operator.add: '+', operator.and_: '&', operator.eq: '==',
operator.floordiv: '//', operator.ge: '>=', operator.gt: '>',
operator.le: '<=', operator.lshift: '<<', operator.lt: '<',
operator.mod: '%', operator.mul: '*', operator.ne: '!=',
operator.or_: '|', operator.pow: '**', operator.rshift: '>>',
operator.sub: '-', operator.truediv: '/'}
_builtin_funcs = {abs: 'abs', round_: 'round'}
_custom_funcs = {
norm: 'norm',
lognorm: 'lognorm',
bin: 'bin',
categorize: 'categorize',
digitize: 'digitize',
isin: 'isin',
python_isin: 'isin',
astype: 'astype',
round_: 'round',
iloc: 'iloc',
}
_numpy_funcs = {
np.any: 'any', np.all: 'all',
np.cumprod: 'cumprod', np.cumsum: 'cumsum', np.max: 'max',
np.mean: 'mean', np.min: 'min',
np.sum: 'sum', np.std: 'std', np.var: 'var', np.log: 'log',
np.log10: 'log10'}
_unary_funcs = {operator.pos: '+', operator.neg: '-', operator.not_: '~'}
_all_funcs = [_binary_funcs, _builtin_funcs, _custom_funcs,
_numpy_funcs, _unary_funcs]
_namespaces = {'numpy': 'np'}
namespace = 'numpy'
_accessor = None
def __init__(self, obj, *args, **kwargs):
ops = []
self._ns = np.ndarray
self.coerce = kwargs.get('coerce', True)
if isinstance(obj, basestring):
self.dimension = Dimension(obj)
elif isinstance(obj, Dimension):
self.dimension = obj
else:
self.dimension = obj.dimension
ops = obj.ops
if args:
fn = args[0]
else:
fn = None
if fn is not None:
if not (isinstance(fn, function_types+(basestring,)) or
any(fn in funcs for funcs in self._all_funcs)):
raise ValueError('Second argument must be a function, '
'found %s type' % type(fn))
ops = ops + [{'args': args[1:], 'fn': fn, 'kwargs': kwargs,
'reverse': kwargs.pop('reverse', False)}]
self.ops = ops
def __getstate__(self):
return self.__dict__
def __setstate__(self, state):
self.__dict__.update(state)
@property
def _current_accessor(self):
if self.ops and self.ops[-1]['kwargs'].get('accessor'):
return self.ops[-1]['fn']
def __call__(self, *args, **kwargs):
if (not self.ops or not isinstance(self.ops[-1]['fn'], basestring) or
'accessor' not in self.ops[-1]['kwargs']):
raise ValueError("Cannot call method on %r expression. "
"Only methods accessed via namspaces, "
"e.g. dim(...).df or dim(...).xr), "
"can be called. " % self)
op = self.ops[-1]
if op['fn'] == 'str':
new_op = dict(op, fn=astype, args=(str,), kwargs={})
else:
new_op = dict(op, args=args, kwargs=kwargs)
return self.clone(self.dimension, self.ops[:-1]+[new_op])
def __getattr__(self, attr):
if attr in dir(self):
return type(self)(self, attr, accessor=True)
raise AttributeError("%r object has no attribute %r" %
(type(self).__name__, attr))
def __dir__(self):
ns = self._ns
if self._current_accessor:
ns = getattr(ns, self._current_accessor)
extras = {attr for attr in dir(ns) if not attr.startswith('_')}
try:
return sorted(set(super(dim, self).__dir__()) | extras)
except Exception:
return sorted(set(dir(type(self))) | set(self.__dict__) | extras)
def __hash__(self):
return hash(repr(self))
[docs] def clone(self, dimension=None, ops=None, dim_type=None):
"""
Creates a clone of the dim expression optionally overriding
the dim and ops.
"""
dim_type = dim_type or type(self)
if dimension is None:
dimension = self.dimension
new_dim = dim_type(dimension)
if ops is None:
ops = list(self.ops)
new_dim.ops = ops
return new_dim
[docs] @classmethod
def register(cls, key, function):
"""
Register a custom dim transform function which can from then
on be referenced by the key.
"""
cls._custom_funcs[key] = function
@property
def params(self):
params = {}
for op in self.ops:
op_args = list(op['args'])+list(op['kwargs'].values())
for op_arg in op_args:
if 'panel' in sys.modules:
from panel.widgets.base import Widget
if isinstance(op_arg, Widget):
op_arg = op_arg.param.value
if (isinstance(op_arg, param.Parameter) and
isinstance(op_arg.owner, param.Parameterized)):
params[op_arg.name+str(id(op))] = op_arg
return params
# Namespace properties
@property
def df(self):
return self.clone(dim_type=df_dim)
@property
def np(self):
return self.clone(dim_type=dim)
@property
def xr(self):
return self.clone(dim_type=xr_dim)
# Builtin functions
def __abs__(self): return type(self)(self, abs)
def __round__(self, ndigits=None):
args = () if ndigits is None else (ndigits,)
return type(self)(self, round_, *args)
# Unary operators
def __neg__(self): return type(self)(self, operator.neg)
def __not__(self): return type(self)(self, operator.not_)
def __invert__(self): return type(self)(self, operator.inv)
def __pos__(self): return type(self)(self, operator.pos)
# Binary operators
def __add__(self, other): return type(self)(self, operator.add, other)
def __and__(self, other): return type(self)(self, operator.and_, other)
def __div__(self, other): return type(self)(self, operator.div, other)
def __eq__(self, other): return type(self)(self, operator.eq, other)
def __floordiv__(self, other): return type(self)(self, operator.floordiv, other)
def __ge__(self, other): return type(self)(self, operator.ge, other)
def __gt__(self, other): return type(self)(self, operator.gt, other)
def __le__(self, other): return type(self)(self, operator.le, other)
def __lt__(self, other): return type(self)(self, operator.lt, other)
def __lshift__(self, other): return type(self)(self, operator.lshift, other)
def __mod__(self, other): return type(self)(self, operator.mod, other)
def __mul__(self, other): return type(self)(self, operator.mul, other)
def __ne__(self, other): return type(self)(self, operator.ne, other)
def __or__(self, other): return type(self)(self, operator.or_, other)
def __rshift__(self, other): return type(self)(self, operator.rshift, other)
def __pow__(self, other): return type(self)(self, operator.pow, other)
def __sub__(self, other): return type(self)(self, operator.sub, other)
def __truediv__(self, other): return type(self)(self, operator.truediv, other)
# Reverse binary operators
def __radd__(self, other): return type(self)(self, operator.add, other, reverse=True)
def __rand__(self, other): return type(self)(self, operator.and_, other)
def __rdiv__(self, other): return type(self)(self, operator.div, other, reverse=True)
def __rfloordiv__(self, other): return type(self)(self, operator.floordiv, other, reverse=True)
def __rlshift__(self, other): return type(self)(self, operator.rlshift, other)
def __rmod__(self, other): return type(self)(self, operator.mod, other, reverse=True)
def __rmul__(self, other): return type(self)(self, operator.mul, other, reverse=True)
def __ror__(self, other): return type(self)(self, operator.or_, other, reverse=True)
def __rpow__(self, other): return type(self)(self, operator.pow, other, reverse=True)
def __rrshift__(self, other): return type(self)(self, operator.rrshift, other)
def __rsub__(self, other): return type(self)(self, operator.sub, other, reverse=True)
def __rtruediv__(self, other): return type(self)(self, operator.truediv, other, reverse=True)
## NumPy operations
def __array_ufunc__(self, *args, **kwargs):
ufunc = args[0]
kwargs = {k: v for k, v in kwargs.items() if v is not None}
return type(self)(self, ufunc, **kwargs)
def clip(self, min=None, max=None):
if min is None and max is None:
raise ValueError('One of max or min must be given.')
return type(self)(self, np.clip, a_min=min, a_max=max)
def any(self, *args, **kwargs): return type(self)(self, np.any, *args, **kwargs)
def all(self, *args, **kwargs): return type(self)(self, np.all, *args, **kwargs)
def cumprod(self, *args, **kwargs): return type(self)(self, np.cumprod, *args, **kwargs)
def cumsum(self, *args, **kwargs): return type(self)(self, np.cumsum, *args, **kwargs)
def max(self, *args, **kwargs): return type(self)(self, np.max, *args, **kwargs)
def mean(self, *args, **kwargs): return type(self)(self, np.mean, *args, **kwargs)
def min(self, *args, **kwargs): return type(self)(self, np.min, *args, **kwargs)
def sum(self, *args, **kwargs): return type(self)(self, np.sum, *args, **kwargs)
def std(self, *args, **kwargs): return type(self)(self, np.std, *args, **kwargs)
def var(self, *args, **kwargs): return type(self)(self, np.var, *args, **kwargs)
def log(self, *args, **kwargs): return type(self)(self, np.log, *args, **kwargs)
def log10(self, *args, **kwargs): return type(self)(self, np.log10, *args, **kwargs)
## Custom functions
def astype(self, dtype): return type(self)(self, astype, dtype=dtype)
def round(self, decimals=0): return type(self)(self, round_, decimals=decimals)
def digitize(self, *args, **kwargs): return type(self)(self, digitize, *args, **kwargs)
def isin(self, *args, **kwargs):
if kwargs.pop('object', None):
return type(self)(self, python_isin, *args, **kwargs)
return type(self)(self, isin, *args, **kwargs)
@property
def iloc(self):
return iloc(self)
[docs] def bin(self, bins, labels=None):
"""Bins continuous values.
Bins continuous using the provided bins and assigns labels
either computed from each bins center point or from the
supplied labels.
Args:
bins: List or array containing the bin boundaries
labels: List of labels to assign to each bin
If the bins are length N the labels should be length N-1
"""
return type(self)(self, bin, bins, labels=labels)
[docs] def categorize(self, categories, default=None):
"""Replaces discrete values with supplied categories
Replaces discrete values in input array into a fixed set of
categories defined either as a list or dictionary.
Args:
categories: List or dict of categories to map inputs to
default: Default value to assign if value not in categories
"""
return type(self)(self, categorize, categories=categories, default=default)
[docs] def lognorm(self, limits=None):
"""Unity-based normalization log scale.
Apply the same transformation as matplotlib.colors.LogNorm
Args:
limits: tuple of (min, max) defining the normalization range
"""
kwargs = {}
if limits is not None:
kwargs = {'min': limits[0], 'max': limits[1]}
return type(self)(self, lognorm, **kwargs)
[docs] def norm(self, limits=None):
"""Unity-based normalization to scale data into 0-1 range.
(values - min) / (max - min)
Args:
limits: tuple of (min, max) defining the normalization range
"""
kwargs = {}
if limits is not None:
kwargs = {'min': limits[0], 'max': limits[1]}
return type(self)(self, norm, **kwargs)
[docs] @classmethod
def pipe(cls, func, *args, **kwargs):
"""
Wrapper to give multidimensional transforms a more intuitive syntax.
For a custom function 'func' with signature (*args, **kwargs), call as
dim.pipe(func, *args, **kwargs).
"""
args = list(args) # make mutable
for k, arg in enumerate(args):
if isinstance(arg, basestring):
args[k] = cls(arg)
return cls(args[0], func, *args[1:], **kwargs)
@property
def str(self):
"Casts values to strings or provides str accessor."
return type(self)(self, 'str', accessor=True)
# Other methods
[docs] def applies(self, dataset, strict=False):
"""
Determines whether the dim transform can be applied to the
Dataset, i.e. whether all referenced dimensions can be
resolved.
"""
from ..element import Graph
if isinstance(self.dimension, dim):
applies = self.dimension.applies(dataset)
elif self.dimension.name == '*':
applies = True
else:
lookup = self.dimension if strict else self.dimension.name
applies = dataset.get_dimension(lookup) is not None
if isinstance(dataset, Graph) and not applies:
applies = dataset.nodes.get_dimension(lookup) is not None
for op in self.ops:
args = op.get('args')
if not args:
continue
for arg in args:
if isinstance(arg, dim):
applies &= arg.applies(dataset)
kwargs = op.get('kwargs')
for kwarg in kwargs.values():
if isinstance(kwarg, dim):
applies &= kwarg.applies(dataset)
return applies
def interface_applies(self, dataset, coerce):
return True
def _resolve_op(self, op, dataset, data, flat, expanded, ranges,
all_values, keep_index, compute, strict):
args = op['args']
fn = op['fn']
kwargs = dict(op['kwargs'])
fn_name = self._numpy_funcs.get(fn)
if fn_name and hasattr(data, fn_name):
if 'axis' not in kwargs and not isinstance(fn, np.ufunc):
kwargs['axis'] = None
fn = fn_name
if isinstance(fn, basestring):
accessor = kwargs.pop('accessor', None)
fn_args = []
else:
accessor = False
fn_args = [data]
for arg in args:
if isinstance(arg, dim):
arg = arg.apply(
dataset, flat, expanded, ranges, all_values,
keep_index, compute, strict
)
arg = resolve_dependent_value(arg)
fn_args.append(arg)
fn_kwargs = {}
for k, v in kwargs.items():
if isinstance(v, dim):
v = v.apply(
dataset, flat, expanded, ranges, all_values,
keep_index, compute, strict
)
fn_kwargs[k] = resolve_dependent_value(v)
args = tuple(fn_args[::-1] if op['reverse'] else fn_args)
kwargs = dict(fn_kwargs)
return fn, fn_name, args, kwargs, accessor
def _apply_fn(self, dataset, data, fn, fn_name, args, kwargs, accessor, drange):
if (((fn is norm) or (fn is lognorm)) and drange != {} and
not ('min' in kwargs and 'max' in kwargs)):
data = fn(data, *drange)
elif isinstance(fn, basestring):
method = getattr(data, fn, None)
if method is None:
mtype = 'attribute' if accessor else 'method'
raise AttributeError(
"%r could not be applied to '%r', '%s' %s "
"does not exist on %s type."
% (self, dataset, fn, mtype, type(data).__name__)
)
if accessor:
data = method
else:
try:
data = method(*args, **kwargs)
except Exception as e:
if 'axis' in kwargs:
kwargs.pop('axis')
data = method(*args, **kwargs)
else:
raise e
else:
data = fn(*args, **kwargs)
return data
def _compute_data(self, data, drop_index, compute):
"""
Implements conversion of data from namespace specific object,
e.g. pandas Series to NumPy array.
"""
if hasattr(data, 'compute') and compute:
data = data.compute()
return data
def _coerce(self, data):
"""
Implements coercion of data from current data format to the
namespace specific datatype.
"""
return data
[docs] def apply(self, dataset, flat=False, expanded=None, ranges={}, all_values=False,
keep_index=False, compute=True, strict=False):
"""Evaluates the transform on the supplied dataset.
Args:
dataset: Dataset object to evaluate the expression on
flat: Whether to flatten the returned array
expanded: Whether to use the expanded expand values
ranges: Dictionary for ranges for normalization
all_values: Whether to evaluate on all values
Whether to evaluate on all available values, for some
element types, such as Graphs, this may include values
not included in the referenced column
keep_index: For data types that support indexes, whether the index
should be preserved in the result.
compute: For data types that support lazy evaluation, whether
the result should be computed before it is returned.
strict: Whether to strictly check for dimension matches
(if False, counts any dimensions with matching names as the same)
Returns:
values: NumPy array computed by evaluating the expression
"""
from ..element import Graph
dimension = self.dimension
if expanded is None:
expanded = not ((dataset.interface.gridded and dimension in dataset.kdims) or
(dataset.interface.multi and dataset.interface.isunique(dataset, dimension, True)))
if not self.applies(dataset) and (not isinstance(dataset, Graph) or not self.applies(dataset.nodes)):
raise KeyError("One or more dimensions in the expression %r "
"could not resolve on '%s'. Ensure all "
"dimensions referenced by the expression are "
"present on the supplied object." % (self, dataset))
if not self.interface_applies(dataset, coerce=self.coerce):
if self.coerce:
raise ValueError("The expression %r assumes a %s-like "
"API but the dataset contains %s data "
"and cannot be coerced." %
(self, self.namespace, dataset.interface.datatype))
else:
raise ValueError("The expression %r assumes a %s-like "
"API but the dataset contains %s data "
"and coercion is disabled." %
(self, self.namespace, dataset.interface.datatype))
if isinstance(dataset, Graph):
if dimension in dataset.kdims and all_values:
dimension = dataset.nodes.kdims[2]
dataset = dataset if dimension in dataset else dataset.nodes
dataset = self._coerce(dataset)
if self.namespace != 'numpy':
compute_for_compute = False
keep_index_for_compute = True
else:
compute_for_compute = compute
keep_index_for_compute = keep_index
if dimension.name == '*':
data = dataset.data
eldim = None
else:
lookup = dimension if strict else dimension.name
eldim = dataset.get_dimension(lookup).name
data = dataset.interface.values(
dataset, lookup, expanded=expanded, flat=flat,
compute=compute_for_compute, keep_index=keep_index_for_compute
)
for op in self.ops:
fn, fn_name, args, kwargs, accessor = self._resolve_op(
op, dataset, data, flat, expanded, ranges, all_values,
keep_index_for_compute, compute_for_compute, strict
)
drange = ranges.get(eldim, {})
drange = drange.get('combined', drange)
data = self._apply_fn(dataset, data, fn, fn_name, args,
kwargs, accessor, drange)
drop_index = keep_index_for_compute and not keep_index
compute = not compute_for_compute and compute
if (drop_index or compute):
data = self._compute_data(data, drop_index, compute)
return data
def __repr__(self):
op_repr = "'%s'" % self.dimension
accessor = False
for i, o in enumerate(self.ops):
if i == 0:
prev = 'dim({repr}'
elif accessor:
prev = '{repr}'
else:
prev = '({repr}'
fn = o['fn']
ufunc = isinstance(fn, np.ufunc)
args = ', '.join([repr(r) for r in o['args']]) if o['args'] else ''
kwargs = o['kwargs']
prev_accessor = accessor
accessor = kwargs.pop('accessor', None)
kwargs = sorted(kwargs.items(), key=operator.itemgetter(0))
kwargs = '%s' % ', '.join(['%s=%r' % item for item in kwargs]) if kwargs else ''
if fn in self._binary_funcs:
fn_name = self._binary_funcs[o['fn']]
if o['reverse']:
format_string = '{args}{fn}'+prev
else:
format_string = prev+'){fn}{args}'
if any(isinstance(a, dim) for a in o['args']):
format_string = format_string.replace('{args}', '({args})')
elif fn in self._unary_funcs:
fn_name = self._unary_funcs[fn]
format_string = '{fn}' + prev
else:
if isinstance(fn, basestring):
fn_name = fn
else:
fn_name = fn.__name__
if fn in self._builtin_funcs:
fn_name = self._builtin_funcs[fn]
format_string = '{fn}'+prev
elif isinstance(fn, basestring):
if accessor:
sep = '' if op_repr.endswith(')') or prev_accessor else ')'
format_string = prev+sep+'.{fn}'
else:
format_string = prev+').{fn}('
elif fn in self._numpy_funcs:
fn_name = self._numpy_funcs[fn]
format_string = prev+').{fn}('
elif isinstance(fn, iloc):
format_string = prev+').iloc[{0}]'.format(repr(fn.index))
elif fn in self._custom_funcs:
fn_name = self._custom_funcs[fn]
format_string = prev+').{fn}('
elif ufunc:
fn_name = str(fn)[8:-2]
if not (prev.startswith('dim') or prev.endswith(')')):
format_string = '{fn}' + prev
else:
format_string = '{fn}(' + prev
if fn_name in dir(np):
format_string = '.'.join([self._namespaces['numpy'], format_string])
else:
format_string = prev+', {fn}'
if accessor:
pass
elif args:
if not format_string.endswith('('):
format_string += ', '
format_string += '{args}'
if kwargs:
format_string += ', {kwargs}'
elif kwargs:
if not format_string.endswith('('):
format_string += ', '
format_string += '{kwargs}'
# Insert accessor
if i == 0 and self._accessor:
idx = format_string.index(')')
format_string = ''.join([
format_string[:idx], ').', self._accessor,
format_string[idx+1:]
])
op_repr = format_string.format(fn=fn_name, repr=op_repr,
args=args, kwargs=kwargs)
if op_repr.count('(') - op_repr.count(')') > 0:
op_repr += ')'
if not self.ops:
op_repr = 'dim({repr})'.format(repr=op_repr)
if op_repr.count('(') - op_repr.count(')') > 0:
op_repr += ')'
return op_repr
[docs]class df_dim(dim):
"""
A subclass of dim which provides access to the DataFrame namespace
along with tab-completion and type coercion allowing the expression
to be applied on any columnar dataset.
"""
namespace = 'dataframe'
_accessor = 'pd'
def __init__(self, obj, *args, **kwargs):
super(df_dim, self).__init__(obj, *args, **kwargs)
self._ns = pd.Series
def interface_applies(self, dataset, coerce):
return (not dataset.interface.gridded and
(coerce or isinstance(dataset.interface, PandasInterface)))
def _compute_data(self, data, drop_index, compute):
if hasattr(data, 'compute') and compute:
data = data.compute()
if not drop_index:
return data
if compute and hasattr(data, 'to_numpy'):
return data.to_numpy()
return data.values
def _coerce(self, dataset):
if self.interface_applies(dataset, coerce=False):
return dataset
pandas_interfaces = param.concrete_descendents(PandasInterface)
datatypes = [intfc.datatype for intfc in pandas_interfaces.values()
if dataset.interface.multi == intfc.multi]
return dataset.clone(datatype=datatypes)
[docs]class xr_dim(dim):
"""
A subclass of dim which provides access to the xarray DataArray
namespace along with tab-completion and type coercion allowing
the expression to be applied on any gridded dataset.
"""
namespace = 'xarray'
_accessor = 'xr'
def __init__(self, obj, *args, **kwargs):
try:
import xarray as xr
except ImportError:
raise ImportError("XArray could not be imported, dim().xr "
"requires the xarray to be available.")
super(xr_dim, self).__init__(obj, *args, **kwargs)
self._ns = xr.DataArray
def interface_applies(self, dataset, coerce):
return (dataset.interface.gridded and
(coerce or dataset.interface.datatype == 'xarray'))
def _compute_data(self, data, drop_index, compute):
if drop_index:
data = data.data
if hasattr(data, 'compute') and compute:
data = data.compute()
return data
def _coerce(self, dataset):
if self.interface_applies(dataset, coerce=False):
return dataset
return dataset.clone(datatype=['xarray'])
