"Functions that operate on larrys."
import numpy as np
from la.deflarry import larry
from la.flabel import flattenlabel, listmap, listmap_fill
from la.farray import covMissing
from la.missing import missing_marker, ismissing
__all__ = ['align', 'align_axis', 'align_raw', 'lrange', 'empty', 'ones',
'zeros', 'isaligned', 'union', 'intersection', 'binaryop', 'add',
'sortby', 'subtract', 'multiply', 'divide', 'unique', 'stack',
'panel', 'cov', 'rand', 'randn']
# Alignment -----------------------------------------------------------------
def align(lar1, lar2, join='inner', cast=True):
"""
Align two larrys using one of five join methods.
Parameters
----------
lar1 : larry
One of the input larrys. Must have the same number of dimensions as
`lar2`.
lar2 : larry
One of the input larrys. Must have the same number of dimensions as
`lar1`.
join : {'inner', 'outer', 'left', 'right', 'skip', list}, optional
The join method used to align the two larrys. The default join method
along each axis is 'inner', i.e., the intersection of the labels. If
`join` is a list of strings then the length of the list should be the
same as the number of dimensions of the two larrys. The first element
in the list is the join method for axis=0, the second element is the
join method for axis=1, and so on. The 'skip' join method means to
not align the specified axis.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
Returns
-------
lar3 : larry
A copy of the aligned version of `lar1`.
lar4 : larry
A copy of the aligned version of `lar2`.
See Also
--------
la.isaligned: Return True if two larrys are aligned along specified axis.
la.align_raw: Low level version of la.align.
Examples
--------
Create two larrys:
>>> lar1 = larry([1, 2])
>>> lar2 = larry([1, 2, 3])
The default join method is an inner join:
>>> lar3, lar4 = la.align(lar1, lar2)
>>> lar3
label_0
0
1
x
array([1, 2])
>>> lar4
label_0
0
1
x
array([1, 2])
An outer join adds a missing value (NaN) to lar1, therefore the the dtype
of lar1 is changed from int to float:
>>> lar3, lar4 = la.align(lar1, lar2, join='outer')
>>> lar3
label_0
0
1
2
x
array([ 1., 2., NaN])
>>> lar4
label_0
0
1
2
x
array([1, 2, 3])
"""
# Align
x1, x2, label, x1isview, x2isview = align_raw(lar1, lar2, join=join,
cast=cast)
# Convert x1 array to larry
label1 = []
for j, lab in enumerate(label):
if lab is None:
label1.append(list(lar1.label[j]))
else:
label1.append(list(lab))
if x1isview:
x1 = x1.copy()
lar3 = larry(x1, label1, validate=False)
# Convert x2 array to larry
label2 = []
for j, lab in enumerate(label):
if lab is None:
label2.append(list(lar2.label[j]))
else:
label2.append(list(lab))
if x2isview:
x2 = x2.copy()
lar4 = larry(x2, label2, validate=False)
return lar3, lar4
def align_raw(lar1, lar2, join='inner', cast=True):
"""
Align two larrys but return Numpy arrays and label instead of larrys.
This function is the same as la.align() except that instead of returning
two larrys, the components of the two larrys are returned (two Numpy
arrays, a label, and flags for whether the two Numpy arrays are views of
the data arrays of the corresponding input larrys).
Parameters
----------
lar1 : larry
One of the input larrys. Must have the same number of dimensions as
`lar2`.
lar2 : larry
One of the input larrys. Must have the same number of dimensions as
`lar1`.
join : {'inner', 'outer', 'left', 'right', 'skip', list}, optional
The join method used to align the two larrys. The default join method
along each axis is 'inner', i.e., the intersection of the labels. If
`join` is a list of strings then the length of the list should be the
same as the number of dimensions of the two larrys. The first element
in the list is the join method for axis=0, the second element is the
join method for axis=1, and so on. The 'skip' join method means to
not align the specified axis.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
Returns
-------
x1 : ndarray
The aligned version of `lar1`.
x2 : ndarray
The aligned version of `lar2`.
label : list of lists
The label of the joined larrys. If join method along any axis is
'skip', then the corresponding entry of label is `None`.
x1isview : bool
True if x1 is a view of lar1.x; False otherwise. A view of lar1.x is
retuned if the labels of `lar1` and `lar2` are the same along all
axes; otherwise a copy is returned.
x2isview : bool
True if x2 is a view of lar2.x; False otherwise. A view of lar2.x is
retuned if the labels of `lar1` and `lar2` are the same along all
axes; otherwise a copy is returned.
See Also
--------
la.align: Align two larrys using one of five join methods.
la.isaligned: Return True if two larrys are aligned along specified axis.
Notes
-----
The returned Numpy arrays are views of the corresponding input larrys if
the labels of the two input larrys are the same along all axes. If the
labels are not the same along any axis then a copy is returned.
Examples
--------
Create two larrys:
>>> y1 = larry([1, 2])
>>> y2 = larry([1, 2, 3])
The default join method is an inner join:
>>> x1, x2, label, x1isview, x2isview = la.flarry._align_raw(lar1, lar2)
>>> x1
array([1, 2])
>>> x2
array([1, 2])
>>> label
[[0, 1]]
>>> x1isview
False
>>> x2isview
False
An outer join adds a missing value (NaN) to lar1, therefore the the dtype
of lar1 is changed from int to float:
>>> x1, x2, label, x1isview, x2isview = la.flarry._align_raw(lar1, lar2, join='outer')
>>> x1
array([ 1., 2., NaN])
>>> x2
array([1, 2, 3])
>>> label
[[0, 1, 2]]
>>> x1isview
False
>>> x2isview
False
If the labels are already aligned, then a view of the data array is
returned:
>>> lar1 = larry([1, 2])
>>> lar2 = larry([3, 4])
>>> x1, x2, label, x1isview, x2isview = la.flarry._align_raw(lar1, lar2)
>>> x1isview
True
>>> x2isview
True
"""
# Check number of dimensions
ndim = lar2.ndim
if lar1.ndim != ndim:
msg = "'lar1' and 'lar2' must have the same number of dimensions."
raise ValueError(msg)
# Check join type
typejoin = type(join)
if typejoin is str:
join = [join] * ndim
elif typejoin is list:
if len(join) != ndim:
msg = "Length of `join` list equal number of dimension of `lar1`."
raise ValueError(msg)
else:
raise TypeError("`join` must be a string or a list.")
# Initialize missing markers, set value later (in loop) only if needed.
# The weird initialization value ensures a user would never pick the same
undefined = 'aB!@12#E~=-'
miss1 = undefined
miss2 = undefined
# For loop initialization
label = []
x1 = lar1.x
x2 = lar2.x
label1 = lar1.label
label2 = lar2.label
x1isview = True
x2isview = True
# Loop: align one axis at a time
msg = "`fill` type not compatible with larry dtype"
for ax in range(ndim):
list1 = label1[ax]
list2 = label2[ax]
joinax = join[ax]
if joinax == 'inner':
if list1 == list2:
list3 = list(list1)
else:
list3 = list(set(list1).intersection(list2))
list3.sort()
idx1 = listmap(list1, list3)
idx2 = listmap(list2, list3)
x1 = x1.take(idx1, ax)
x2 = x2.take(idx2, ax)
x1isview = False
x2isview = False
elif joinax == 'outer':
if list1 == list2:
list3 = list(list1)
else:
list3 = list(set(list1).union(list2))
list3.sort()
idx1, idx1_miss = listmap_fill(list1, list3, fill=0)
idx2, idx2_miss = listmap_fill(list2, list3, fill=0)
if x1.size == 0:
shape = list(x1.shape)
shape[ax] = len(idx1)
x1 = np.ones(shape, dtype=x1.dtype)
else:
x1 = x1.take(idx1, ax)
if x2.size == 0:
shape = list(x2.shape)
shape[ax] = len(idx2)
x2 = np.ones(shape, dtype=x2.dtype)
else:
x2 = x2.take(idx2, ax)
if len(idx1_miss) > 0:
if miss1 == undefined:
miss1 = missing_marker(lar1)
if miss1 == NotImplemented:
if cast:
x1 = x1.astype(float)
miss1 = missing_marker(x1)
else:
raise TypeError(msg)
index1 = [slice(None)] * ndim
index1[ax] = idx1_miss
x1[index1] = miss1
if len(idx2_miss) > 0:
if miss2 == undefined:
miss2 = missing_marker(lar2)
if miss2 == NotImplemented:
if cast:
x2 = x2.astype(float)
miss2 = missing_marker(x2)
else:
raise TypeError(msg)
index2 = [slice(None)] * ndim
index2[ax] = idx2_miss
x2[index2] = miss2
x1isview = False
x2isview = False
elif joinax == 'left':
list3 = list(list1)
if list1 != list2:
idx2, idx2_miss = listmap_fill(list2, list3, fill=0)
if x2.size == 0:
shape = list(x2.shape)
shape[ax] = len(idx2)
x2 = np.ones(shape, dtype=x2.dtype)
else:
x2 = x2.take(idx2, ax)
if len(idx2_miss) > 0:
if miss2 == undefined:
miss2 = missing_marker(lar2)
if miss2 == NotImplemented:
if miss2 is None:
miss2 = missing_marker(lar2)
if miss2 is None:
miss2 = missing_marker(lar2)
if cast:
x2 = x2.astype(float)
miss2 = missing_marker(x2)
else:
raise TypeError(msg)
index2 = [slice(None)] * ndim
index2[ax] = idx2_miss
x2[index2] = miss2
x2isview = False
elif joinax == 'right':
list3 = list(list2)
if list1 != list2:
idx1, idx1_miss = listmap_fill(list1, list3, fill=0)
if x1.size == 0:
shape = list(x1.shape)
shape[ax] = len(idx1)
x1 = np.ones(shape, dtype=x1.dtype)
else:
x1 = x1.take(idx1, ax)
if len(idx1_miss) > 0:
if miss1 == undefined:
miss1 = missing_marker(lar1)
if miss1 == NotImplemented:
if cast:
x1 = x1.astype(float)
miss1 = missing_marker(x1)
else:
raise TypeError(msg)
index1 = [slice(None)] * ndim
index1[ax] = idx1_miss
x1[index1] = miss1
x1isview = False
elif joinax == 'skip':
list3 = None
else:
raise ValueError('join type not recognized')
label.append(list3)
return x1, x2, label, x1isview, x2isview
def align_axis(lars, axis=0, join='inner'):
"""
Align many larrys along potentially different axes.
Parameters
----------
lars : array_like
A collection (list, tuple, set, array, etc.) of larrys to align.
axis : {int, array_like}, optional
An integer indicating which axis along which to align the larrys in
`lars`, or a sequence of integers of the same length as `lars`
indicating which axis to use for each entry in `lars`.
join : {'inner', 'outer', 'left', 'right'}, optional
If 'inner', then labels present in every larry will be kept. If
'outer', all labels appearing in any array are kept, and additional
entries are added to larrys containing fewer labels. See la.morph() for
rules on how this is done. If 'right' or 'left' then the labels of the
output will match those of either the first or last entry of `lars`,
respectively
Returns
-------
(lar1, lar2, ...) : tuple
Tuple of larrys, one corresponding to each entry of lars. None of the
output refer to input, and the labels of the output do not refer to one
another.
Examples
--------
Create three larrys:
>>> l1 = la.larry([1, 2, 3, 4], [['a', 'b', 'c', 'd']])
>>> l2 = la.larry([[4, 5], [6, 7]], [['x', 'y'], ['c', 'd']])
>>> l3 = la.larry([8, 9, 10], [['c', 'd', 'e']])
Align the first axis of the first larry with the second axis of the
second larry using an inner join:
>>> a1, a2 = la.align_axis([l1, l2], axis=[0, 1])
>>> a1
label_0
c
d
x
array([3, 4])
>>> a2
label_0
x
y
label_1
c
d
x
array([[4, 5],
[6, 7]])
Align the first axis of two larrys with an outer join:
>>> a1, a2 = la.align_axis([l1, l3], join='outer')
>>> a1
label_0
a
b
c
d
e
x
array([ 1., 2., 3., 4., nan])
>>> a2
label_0
a
b
c
d
e
x
array([ nan, nan, 8., 9., 10.])
Align multiple larrys with an inner join:
>>> a1, a2, a3 = la.align_axis([l1, l2, l3], axis=[0, 1, 0])
>>> a1
label_0
c
d
x
array([3, 4])
>>> a2
label_0
x
y
label_1
c
d
x
array([[4, 5],
[6, 7]])
>>> a3
label_0
c
d
x
array([8, 9])
"""
# Input checks and preprocessing
nlar = len(lars)
if isinstance(axis, int):
axis = [axis for j in range(nlar)]
for j, lar in enumerate(lars):
if not isinstance(lar, larry):
raise ValueError("Inputs must be larry.")
if (axis[j] >= lar.ndim) or (axis[j] < -lar.ndim):
raise ValueError("Axis out of range for input larry %d" % j)
if join not in ['inner', 'outer', 'left', 'right']:
raise ValueError("Value of `join` not recognized.")
# Alignment
if join == 'left':
label = lars[0].label[axis[0]]
elif join == 'right':
label = lars[-1].label[axis[-1]]
else:
label = frozenset(lars[0].label[axis[0]])
if join == 'inner':
for i, lar in enumerate(lars[1:]):
label = label.intersection(lar.label[axis[i]])
elif join == 'outer':
for i, lar in enumerate(lars[1:]):
label = label.union(lar.label[axis[i]])
label = list(label)
label.sort()
lars_out = []
# Create output
for j, lar in enumerate(lars):
lab = list(label)
lars_out.append(lar.morph(lab, axis[j]))
return tuple(lars_out)
def isaligned(lar1, lar2, axis=None):
"""
Return True if labels of two given larrys are aligned along specified axis.
Parameters
----------
lar1 : larry
Input.
lar2 : larry
Input
axis : {int, None}, optional
The axis along which to check for aligment of labels. By default
(axis=None) all axes are checked.
Returns
-------
y : bool
Returns True if labels are aligned; False if labels are not aligned.
See Also
--------
la.align: Align two larrys using one of five join methods.
Examples
--------
Make two 2d larrys that are aligned along columns but not rows:
>>> lar1 = larry([[1, 2], [3, 4]], [['row1', 'row2'], ['col1', 'col2']])
>>> lar2 = larry([[1, 2], [3, 4]], [['row2', 'row1'], ['col1', 'col2']])
The two larrys are not aligned:
>>> la.isaligned(lar1, lar2)
False
>>> la.isaligned(lar1, lar2, axis=0)
False
But the columns of the two larrys are aligned:
>>> la.isaligned(lar1, lar2, axis=1)
True
"""
if axis is None:
return lar1.label == lar2.label
else:
return lar1.label[axis] == lar2.label[axis]
def union(axis, *args):
"""
Union of labels along specified axis.
Parameters
----------
axis : int
The axis along which to take the union of the labels.
args : larrys
The larrys (separated by commas) over which the union is taken.
Returns
-------
out : list
A list containing the union of the labels.
See Also
--------
la.intersection : Intersection of labels along specified axis.
Examples
--------
>>> import la
>>> y1 = larry([[1, 2], [3, 4]], [['a', 'b'], ['c', 'd']])
>>> y2 = larry([[1, 2], [3, 4]], [['e', 'b'], ['f', 'd']])
>>> la.union(0, y1, y2)
['a', 'b', 'e']
>>> la.union(1, y1, y2)
['c', 'd', 'f']
"""
rc = frozenset([])
for arg in args:
if isinstance(arg, larry):
rc = rc.union(arg.label[axis])
else:
raise TypeError('One or more input is not a larry')
rc = list(rc)
rc.sort()
return rc
def intersection(axis, *args):
"""
Sorted list containing the intersection of labels along specified axis.
Parameters
----------
axis : int
The axis along which to take the intersection of the labels.
args : larrys
The larrys (separated by commas) over which the intersection is taken.
Returns
-------
out : list
A sorted list containing the intersection of the labels.
See Also
--------
la.union : Union of labels along specified axis.
Examples
--------
>>> import la
>>> y1 = larry([[1, 2], [3, 4]], [['a', 'b'], ['c', 'd']])
>>> y2 = larry([[1, 2], [3, 4]], [['e', 'b'], ['f', 'd']])
>>> la.intersection(0, y1, y2)
['b']
>>> la.intersection(1, y1, y2)
['d']
"""
rc = frozenset(args[0].label[axis])
for i in range(1, len(args)):
arg = args[i]
if isinstance(arg, larry):
rc = rc.intersection(arg.label[axis])
else:
raise TypeError('One or more input is not a larry')
rc = list(rc)
rc.sort()
return rc
# Binary-- -----------------------------------------------------------------
def binaryop(func, lar1, lar2, join='inner', cast=True, missone='ignore',
misstwo='ignore', **kwargs):
"""
Binary operation on two larrys using given function and join method.
Parameters
----------
func : function
A function that takes two Numpy arrays as input and returns a Numpy
array as output. For example: np.add. You can also pass keyword
arguments to the function; see `**kwargs`.
lar1 : larry
The larry on the left-hand side of the binary operation. Must have
the same number of dimensions as `lar2`.
lar2 : larry
The larry on the right-hand side of the binary operation. Must have
the same number of dimensions as `lar1`.
join : {'inner', 'outer', 'left', 'right', list}, optional
The method used to join the two larrys. The default join method along
all axes is 'inner', i.e., the intersection of the labels. If `join`
is a list of strings then the length of the list should be the number
of dimensions of the two larrys. The first element in the list is the
join method for axis=0, the second element is the join method for
axis=1, and so on.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
missone : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `missone` is set to something other than 'ignore', such
as 0, then all elements that are missing in one larry but not missing
in the other larry are replaced by `missone`. For example, if an
element is in one larry but missing in the other larry then you may
want to set the missing value to zero when summing two larrys.
misstwo : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `misstwo` is set to something other than 'ignore', such
as 0, then all elements that are missing in both larrys are replaced
by `misstwo`.
**kwargs : Keyword arguments, optional
Keyword arguments to pass to `func`. The keyword arguments passed to
`func` cannot have the following keys: join, cast, missone, misstwo.
Returns
-------
lar3 : larry
The result of the binary operation.
See Also
--------
la.align: Align two larrys using one of five join methods.
Examples
--------
Create two larrys:
>>> from la import nan
>>> lar1 = larry([1, 2, nan], [['a', 'b', 'c']])
>>> lar2 = larry([1, nan, nan], [['a', 'b', 'dd']])
The default is an inner join (note that lar1 and lar2 have two labels in
common):
>>> la.binaryop(np.add, lar1, lar2)
label_0
a
b
x
array([ 2., NaN])
If one data element is missing in one larry but not in the other, then you
can replace the missing value with `missone` (here 0):
>>> la.binaryop(np.add, lar1, lar2, missone=0)
label_0
a
b
x
array([ 2., 2.])
An outer join:
>>> la.binaryop(np.add, lar1, lar2, join='outer')
label_0
a
b
c
dd
x
array([ 2., NaN, NaN, NaN])
An outer join with single and double missing values replaced by zero:
>>> la.binaryop(np.add, lar1, lar2, join='outer', missone=0, misstwo=0)
label_0
a
b
c
dd
x
array([ 2., 2., 0., 0.])
"""
# Align
x1, x2, label, ign1, ign2 = align_raw(lar1, lar2, join=join, cast=cast)
# Replacing missing values is slow, so only do if requested
if missone != 'ignore' or misstwo != 'ignore':
miss1 = ismissing(x1)
miss2 = ismissing(x2)
if missone != 'ignore':
missone1 = miss1 & ~miss2
if missone1.any():
np.putmask(x1, missone1, missone)
missone2 = miss2 & ~miss1
if missone2.any():
np.putmask(x2, missone2, missone)
if misstwo != 'ignore':
misstwo12 = miss1 & miss2
if misstwo12.any():
np.putmask(x1, misstwo12, misstwo)
np.putmask(x2, misstwo12, misstwo)
# Binary function
x = func(x1, x2, **kwargs)
return larry(x, label, validate=False)
def add(lar1, lar2, join='inner', cast=True, missone='ignore',
misstwo='ignore'):
"""
Sum of two larrys using given join and fill methods.
Parameters
----------
lar1 : larry
The larry on the left-hand side of the sum. Must have the same number
of dimensions as `lar2`.
lar2 : larry
The larry on the right-hand side of the sum. Must have the same number
of dimensions as `lar1`.
join : {'inner', 'outer', 'left', 'right', list}, optional
The method used to join the two larrys. The default join method along
all axes is 'inner', i.e., the intersection of the labels. If `join`
is a list of strings then the length of the list should be the number
of dimensions of the two larrys. The first element in the list is the
join method for axis=0, the second element is the join method for
axis=1, and so on.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
missone : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `missone` is set to something other than 'ignore', such
as 0, then all elements that are missing in one larry but not missing
in the other larry are replaced by `missone`. For example, if an
element is in one larry but missing in the other larry then you may
want to set the missing value to zero when summing two larrys.
misstwo : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `misstwo` is set to something other than 'ignore', such
as 0, then all elements that are missing in both larrys are replaced
by `misstwo`.
Returns
-------
y : larry
The sum of the two larrys, `lar1` and `lar2`.
See Also
--------
la.larry.__add__: Sum a larry with another larry, Numpy array, or scalar.
la.binaryop: Binary operation on two larrys using given function.
Notes
-----
This is a convenience function that calls la.binaryop() with `func` set
to numpy.add.
Examples
--------
Create two larrys:
>>> from la import nan
>>> lar1 = larry([1, 2, nan], [['a', 'b', 'c']])
>>> lar2 = larry([1, nan, nan], [['a', 'b', 'dd']])
The default is an inner join (note that lar1 and lar2 have two labels in
common):
>>> la.add(lar1, lar2)
label_0
a
b
x
array([ 2., NaN])
which is the same result you get with lar1 + lar2:
>>> lar1 + lar2
label_0
a
b
x
array([ 2., NaN])
If one data element is missing in one larry but not in the other, then you
can replace the missing value with `missone` (here 0):
>>> la.add(lar1, lar2, missone=0)
label_0
a
b
x
array([ 2., 2.])
An outer join:
>>> la.add(lar1, lar2, join='outer')
label_0
a
b
c
dd
x
array([ 2., NaN, NaN, NaN])
An outer join with single and double missing values replaced by zero:
>>> la.add(lar1, lar2, join='outer', missone=0, misstwo=0)
label_0
a
b
c
dd
x
array([ 2., 2., 0., 0.])
"""
return binaryop(np.add, lar1, lar2, join=join, cast=cast, missone=missone,
misstwo=misstwo)
def subtract(lar1, lar2, join='inner', cast=True, missone='ignore',
misstwo='ignore'):
"""
Difference of two larrys using given join and fill methods.
Parameters
----------
lar1 : larry
The larry on the left-hand side of the difference. Must have the same
number of dimensions as `lar2`.
lar2 : larry
The larry on the right-hand side of the difference. Must have the same
number of dimensions as `lar1`.
join : {'inner', 'outer', 'left', 'right', list}, optional
The method used to join the two larrys. The default join method along
all axes is 'inner', i.e., the intersection of the labels. If `join`
is a list of strings then the length of the list should be the number
of dimensions of the two larrys. The first element in the list is the
join method for axis=0, the second element is the join method for
axis=1, and so on.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
missone : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `missone` is set to something other than 'ignore', such
as 0, then all elements that are missing in one larry but not missing
in the other larry are replaced by `missone`. For example, if an
element is in one larry but missing in the other larry then you may
want to set the missing value to zero when subtracting two larrys.
misstwo : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `misstwo` is set to something other than 'ignore', such
as 0, then all elements that are missing in both larrys are replaced
by `misstwo`.
Returns
-------
y : larry
The difference of the two larrys, `lar1` and `lar2`.
See Also
--------
la.larry.__sub__: Subtract a larry from another larry, array, or scalar.
la.binaryop: Binary operation on two larrys using given function.
Notes
-----
This is a convenience function that calls la.binaryop() with `func` set
to numpy.subtract.
Examples
--------
Create two larrys:
>>> from la import nan
>>> lar1 = larry([1, 2, nan], [['a', 'b', 'c']])
>>> lar2 = larry([1, nan, nan], [['a', 'b', 'dd']])
The default is an inner join (note that lar1 and lar2 have two labels in
common):
>>> la.subtract(lar1, lar2)
label_0
a
b
x
array([ 0., NaN])
which is the same result you get with lar1 - lar2:
>>> lar1 - lar2
label_0
a
b
x
array([ 0., NaN])
If one data element is missing in one larry but not in the other, then you
can replace the missing value with `missone` (here 0):
>>> la.subtract(lar1, lar2, missone=0)
label_0
a
b
x
array([ 0., 2.])
An outer join:
>>> la.subtract(lar1, lar2, join='outer')
label_0
a
b
c
dd
x
array([ 0., NaN, NaN, NaN])
An outer join with single and double missing values replaced by zero:
>>> la.subtract(lar1, lar2, join='outer', missone=0, misstwo=0)
label_0
a
b
c
dd
x
array([ 0., 2., 0., 0.])
"""
return binaryop(np.subtract, lar1, lar2, join=join, cast=cast,
missone=missone, misstwo=misstwo)
def multiply(lar1, lar2, join='inner', cast=True, missone='ignore',
misstwo='ignore'):
"""
Multiply two larrys element-wise using given join and fill methods.
Parameters
----------
lar1 : larry
The larry on the left-hand side of the product. Must have the same
number of dimensions as `lar2`.
lar2 : larry
The larry on the right-hand side of the product. Must have the same
number of dimensions as `lar1`.
join : {'inner', 'outer', 'left', 'right', list}, optional
The method used to join the two larrys. The default join method along
all axes is 'inner', i.e., the intersection of the labels. If `join`
is a list of strings then the length of the list should be the number
of dimensions of the two larrys. The first element in the list is the
join method for axis=0, the second element is the join method for
axis=1, and so on.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
missone : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `missone` is set to something other than 'ignore', such
as 0, then all elements that are missing in one larry but not missing
in the other larry are replaced by `missone`. For example, if an
element is in one larry but missing in the other larry then you may
want to set the missing value to one when multiplying two larrys.
misstwo : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `misstwo` is set to something other than 'ignore', such
as 0, then all elements that are missing in both larrys are replaced
by `misstwo`.
Returns
-------
y : larry
The element-wise product of the two larrys, `lar1` and `lar2`.
See Also
--------
la.larry.__mul__: Multiply a larry with another larry, array, or scalar.
la.binaryop: Binary operation on two larrys using given function.
Notes
-----
This is a convenience function that calls la.binaryop() with `func` set
to numpy.multiply.
Examples
--------
Create two larrys:
>>> from la import nan
>>> lar1 = larry([1, 2, nan], [['a', 'b', 'c']])
>>> lar2 = larry([1, nan, nan], [['a', 'b', 'dd']])
The default is an inner join (note that lar1 and lar2 have two labels in
common):
>>> la.multiply(lar1, lar2)
label_0
a
b
x
array([ 1., NaN])
which is the same result you get with lar1 * lar2:
>>> lar1 * lar2
label_0
a
b
x
array([ 1., NaN])
If one data element is missing in one larry but not in the other, then you
can replace the missing value with `missone` (here 1):
>>> la.multiply(lar1, lar2, missone=1)
label_0
a
b
x
array([ 1., 2.])
An outer join:
>>> la.multiply(lar1, lar2, join='outer')
label_0
a
b
c
dd
x
array([ 1., NaN, NaN, NaN])
An outer join with single and double missing values replaced by one:
>>> la.multiply(lar1, lar2, join='outer', missone=1, misstwo=1)
label_0
a
b
c
dd
x
array([ 1., 2., 1., 1.])
"""
return binaryop(np.multiply, lar1, lar2, join=join, cast=cast,
missone=missone, misstwo=misstwo)
def divide(lar1, lar2, join='inner', cast=True, missone='ignore',
misstwo='ignore'):
"""
Divide two larrys element-wise using given join and fill methods.
Parameters
----------
lar1 : larry
The larry on the left-hand side of the division. Must have the same
number of dimensions as `lar2`.
lar2 : larry
The larry on the right-hand side of the division. Must have the same
number of dimensions as `lar1`.
join : {'inner', 'outer', 'left', 'right', list}, optional
The method used to join the two larrys. The default join method along
all axes is 'inner', i.e., the intersection of the labels. If `join`
is a list of strings then the length of the list should be the number
of dimensions of the two larrys. The first element in the list is the
join method for axis=0, the second element is the join method for
axis=1, and so on.
cast : bool, optional
Only float, str, and object dtypes have missing value markers (la.nan,
'', and None, respectively). Other dtypes, such as int and bool, do
not have missing value markers. If `cast` is set to True (default)
then int and bool dtypes, for example, will be cast to float if any
new rows, columns, etc are created. If cast is set to False, then a
TypeError will be raised for int and bool dtype input if the join
introduces new rows, columns, etc. An inner join will never introduce
new rows, columns, etc.
missone : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `missone` is set to something other than 'ignore', such
as 0, then all elements that are missing in one larry but not missing
in the other larry are replaced by `missone`. For example, if an
element is in one larry but missing in the other larry then you may
want to set the missing value to one when dividing two larrys.
misstwo : {scalar, 'ignore'}, optional
By default ('ignore') no special treatment of missing values is made.
If, however, `misstwo` is set to something other than 'ignore', such
as 0, then all elements that are missing in both larrys are replaced
by `misstwo`.
Returns
-------
y : larry
The element-wise quotient of the two larrys, `lar1` and `lar2`.
See Also
--------
la.larry.__div__: Multiply a larry with another larry, array, or scalar.
la.binaryop: Binary operation on two larrys using given function.
Notes
-----
This is a convenience function that calls la.binaryop() with `func` set
to numpy.divide.
Examples
--------
Create two larrys:
>>> from la import nan
>>> lar1 = larry([1, 2, nan], [['a', 'b', 'c']])
>>> lar2 = larry([1, nan, nan], [['a', 'b', 'dd']])
The default is an inner join (note that lar1 and lar2 have two labels in
common):
>>> la.divide(lar1, lar2)
label_0
a
b
x
array([ 1., NaN])
which is the same result you get with lar1 / lar2:
>>> lar1 / lar2
label_0
a
b
x
array([ 1., NaN])
If one data element is missing in one larry but not in the other, then you
can replace the missing value with `missone` (here 1):
>>> la.divide(lar1, lar2, missone=1)
label_0
a
b
x
array([ 1., 2.])
An outer join:
>>> la.divide(lar1, lar2, join='outer')
label_0
a
b
c
dd
x
array([ 1., NaN, NaN, NaN])
An outer join with single and double missing values replaced by one:
>>> la.divide(lar1, lar2, join='outer', missone=1, misstwo=1)
label_0
a
b
c
dd
x
array([ 1., 2., 1., 1.])
"""
return binaryop(np.divide, lar1, lar2, join=join, cast=cast,
missone=missone, misstwo=misstwo)
# Misc ----------------------------------------------------------------------
def unique(lar, return_index=False, return_inverse=False):
"""
Find the unique elements of a larry.
Returns the sorted unique elements of a larry as a Numpy array. There are
two optional outputs in addition to the unique elements: the indices of the
input larry that give the unique values, and the indices of the unique array
that reconstruct the input array.
Parameters
----------
ar : larry
Input larry. This will be flattened if it is not already 1-D.
return_index : bool, optional
If True, also return the indices of `lar` that result in the unique
larry.
return_inverse : bool, optional
If True, also return the indices of the unique larry that can be used
to reconstruct `lar`.
Returns
-------
unique : ndarray
The sorted unique values.
unique_indices : ndarray, optional
The indices of the unique values in the (flattened) original larry.
Only provided if `return_index` is True.
unique_inverse : ndarray, optional
The indices to reconstruct the (flattened) original larry from the
unique array. Only provided if `return_inverse` is True.
Examples
--------
>>> la.unique(larry([1, 1, 2, 2, 3, 3]))
array([1, 2, 3])
>>> lar = larry([[1, 1], [2, 3]])
>>> la.unique(lar)
array([1, 2, 3])
Return the indices of the original larry that give the unique values:
>>> lar = larry(['a', 'b', 'b', 'c', 'a'])
>>> u, indices = la.unique(lar, return_index=True)
>>> u
array(['a', 'b', 'c'],
dtype='|S1')
>>> indices
array([0, 1, 3])
>>> lar[indices]
array(['a', 'b', 'c'],
dtype='|S1')
Reconstruct the input array (the data portion of the larry, not the label
portion) from the unique values:
>>> lar = larry([1, 2, 6, 4, 2, 3, 2])
>>> u, indices = la.unique(lar, return_inverse=True)
>>> u
array([1, 2, 3, 4, 6])
>>> indices
array([0, 1, 4, 3, 1, 2, 1])
>>> u[indices]
array([1, 2, 6, 4, 2, 3, 2])
"""
return np.unique(lar.x, return_index, return_inverse)
def stack(mode, **kwargs):
"""Stack 2d larrys to make a 3d larry.
Parameters
----------
mode : {'union', 'intersection'}
Should the 3d larry be made from the union or intersection of all the
rows and all the columns?
kwargs : name=larry
Variable length input listing the z axis name and larry. For example,
stack('union', distance=x, temperature=y, pressure=z)
Returns
-------
out : larry
Returns a 3d larry. The labels along all 3 dimenstions will be sorted.
Raises
------
ValueError
If mode is not union or intersection or if any of the input larrys are
not 2d.
Examples
--------
>>> import la
>>> y1 = la.larry([[1, 2], [3, 4]])
>>> y2 = la.larry([[5, 6], [7, 8]])
>>> la.stack('union', name1=y1, othername=y2)
label_0
name1
othername
label_1
0
1
label_2
0
1
x
array([[[ 1., 2.],
[ 3., 4.]],
.
[[ 5., 6.],
[ 7., 8.]]])
"""
if not np.all([kwargs[key].ndim == 2 for key in kwargs]):
raise ValueError('All input larrys must be 2d')
if mode == 'union':
logic = union
elif mode == 'intersection':
logic = intersection
else:
raise ValueError('mode must be union or intersection')
row = logic(0, *kwargs.values())
col = logic(1, *kwargs.values())
x = np.zeros((len(kwargs), len(row), len(col)))
zlabel = []
keys = sorted(kwargs.keys())
for i, key in enumerate(keys):
y = kwargs[key]
y = y.morph(row, 0)
y = y.morph(col, 1)
x[i] = y.x
zlabel.append(key)
label = [zlabel, row, col]
return larry(x, label)
def panel(lar):
"""
Convert a 3d larry of shape (n, m, k) to a 2d larry of shape (m*k, n).
Parameters
----------
lar : 3d larry
The input must be a 3d larry.
Returns
-------
y : 2d larry
If the input larry has shape (n, m, k) then a larry of shape (m*k, n)
is returned.
See Also
--------
la.larry.swapaxes : Swap the two specified axes.
la.larry.flatten : Collapsing into one dimension.
Examples
--------
First make a 3d larry:
>>> x = np.ones((2,2)).cumsum(0) - 1
>>> lar = la.larry(x, [['r1', 'r2'], ['c1', 'c2']])
>>> lar = lar.insertaxis(0, "name")
>>> lar
label_0
name
label_1
r1
r2
label_2
c1
c2
x
array([[[ 0., 0.],
[ 1., 1.]]])
Then make a panel:
>>> la.panel(lar)
label_0
('r1', 'c1')
('r1', 'c2')
('r2', 'c1')
('r2', 'c2')
label_1
name
x
array([[ 0.],
[ 0.],
[ 1.],
[ 1.]])
"""
if lar.ndim != 3:
raise ValueError("lar must be 3d.")
y = lar.copy()
y.label = [flattenlabel([y.label[1], y.label[2]])[0], y.label[0]]
y.x = y.x.T.reshape(-1, y.shape[0], order="F")
return y
def cov(lar):
"""
Covariance matrix adjusted for missing (NaN) values.
Note: Only works on 2d larrys.
The mean of each row is assumed to be zero. So rows are not demeaned
and therefore the covariance is normalized by the number of columns,
not by the number of columns minus 1.
Parameters
----------
lar : larry
The larry you want to find the covariance of.
Returns
-------
out : larry
For 2d input of shape (N, T), for example, returns a NxN covariance
matrix.
Raises
------
ValueError
If input is not 2d
"""
if lar.ndim != 2:
raise ValueError('This function only works on 2d larrys')
label = [list(lar.label[0]), list(lar.label[0])]
x = covMissing(lar.x)
return larry(x, label, validate=False)
# Random -----------------------------------------------------------
def rand(*args, **kwargs):
"""
Random samples from a uniform distribution in a given shape.
The random samples are from a uniform distribution over ``[0, 1)``.
Parameters
----------
args : `n` ints, optional
The dimensions of the returned larry, should be all positive. These
may be omitted if you pass in a label as a keyword argument.
kwargs : keyword arguments, optional
Keyword arguments to use in the construction of the larry such as
label and validate. If a label is passed then its dimensions must
match the `n` integers passed in or, optionally, you can pass in the
label without the `n` shape integers. If rand is passed in then that
will be used to generate the random numbers. In that way you can set
the state of the random number generator outside of this function.
Returns
-------
Z : larry or float
A ``(d1, ..., dn)``-shaped larry of floating-point samples from
a uniform distribution, or a single such float if no parameters were
supplied.
See Also
--------
la.randn : Random samples from the "standard normal" distribution.
Examples
--------
A single random sample:
>>> la.rand()
0.64323350463488804
A shape (2, 2) random larry:
>>> la.rand(2, 2)
label_0
0
1
label_1
0
1
x
array([[ 0.09277439, 0.94194077],
[ 0.72887997, 0.41124147]])
A shape (2, 2) random larry with given labels:
>>> la.rand(label=[['row1', 'row2'], ['col1', 'col2']])
label_0
row1
row2
label_1
col1
col2
x
array([[ 0.3449072 , 0.40397174],
[ 0.7791279 , 0.86084403]])
Results are repeatable if you set the state of the random number generator
outside of la.rand:
>>> import numpy as np
>>> rs = np.random.RandomState([1, 2, 3])
>>> la.randn(randn=rs.randn)
0.89858244820995015
>>> la.randn(randn=rs.randn)
0.25528876596298244
>>> rs = np.random.RandomState([1, 2, 3])
>>> la.randn(randn=rs.randn)
0.89858244820995015
>>> la.randn(randn=rs.randn)
0.25528876596298244
"""
if 'rand' in kwargs:
randfunc = kwargs['rand']
kwargs = dict(kwargs)
del kwargs['rand']
else:
randfunc = np.random.rand
if len(args) > 0:
return larry(randfunc(*args), **kwargs)
elif 'label' in kwargs:
n = [len(z) for z in kwargs['label']]
return larry(randfunc(*n), **kwargs)
elif (len(args) == 0) and (len(kwargs) == 0):
return randfunc()
elif (len(args) == 0) and (len(kwargs) == 1) and ('rand' in kwargs):
return randfunc()
else:
raise ValueError('Input parameters not recognized')
def randn(*args, **kwargs):
"""
Random samples from the "standard normal" distribution in a given shape.
The random samples are from a "normal" (Gaussian) distribution of mean 0
and variance 1.
Parameters
----------
args : `n` ints, optional
The dimensions of the returned larry, should be all positive. These
may be omitted if you pass in a label as a keyword argument.
kwargs : keyword arguments, optional
Keyword arguments to use in the construction of the larry such as
label and validate. If a label is passed then its dimensions must
match the `n` integers passed in or, optionally, you can pass in the
label without the `n` shape integers. If randn is passed in then that
will be used to generate the random numbers. In that way you can set
the state of the random number generator outside of this function.
Returns
-------
Z : larry or float
A ``(d1, ..., dn)``-shaped larry of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied.
See Also
--------
la.rand : Random values from a uniform distribution in a given shape.
Examples
--------
A single random sample:
>>> la.randn()
0.33086946957034052
A shape (2, 2) random larry:
>>> la.randn(2, 2)
label_0
0
1
label_1
0
1
x
array([[-0.08182341, 0.79768108],
[-0.23584547, 1.80118376]])
A shape (2, 2) random larry with given labels:
>>> la.randn(label=[['row1', 'row2'], ['col1', 'col2']])
label_0
row1
row2
label_1
col1
col2
x
array([[ 0.10737701, -0.24947824],
[ 1.51021208, 1.00280387]])
Results are repeatable if you set the state of the random number generator
outside of la.rand:
>>> import numpy as np
>>> rs = np.random.RandomState([1, 2, 3])
>>> la.randn(randn=rs.randn)
0.89858244820995015
>>> la.randn(randn=rs.randn)
0.25528876596298244
>>> rs = np.random.RandomState([1, 2, 3])
>>> la.randn(randn=rs.randn)
0.89858244820995015
>>> la.randn(randn=rs.randn)
0.25528876596298244
"""
if 'randn' in kwargs:
randnfunc = kwargs['randn']
kwargs = dict(kwargs)
del kwargs['randn']
else:
randnfunc = np.random.randn
if len(args) > 0:
return larry(randnfunc(*args), **kwargs)
elif 'label' in kwargs:
n = [len(z) for z in kwargs['label']]
return larry(randnfunc(*n), **kwargs)
elif (len(args) == 0) and (len(kwargs) == 0):
return randnfunc()
elif (len(args) == 0) and (len(kwargs) == 1) and ('randn' in kwargs):
return randnfunc()
else:
raise ValueError('Input parameters not recognized')
def sortby(lar, element, axis, reverse=False):
"""
Sort 2d larry by the row or column corresponding to given label element.
Parameters
----------
lar : larry
A 2d input larry to be sorted.
element : str
The label element specifying the row or column by which to sort.
axis : int
The axis along which the element is located. Sorting takes place along
the opposite axis.
reverse : bool, optional
Keyword indicating whether to sort in ascending order (False) or
descending order (True). The default is to sort in ascending order.
Returns
-------
lar2 : larry
A sorted copy of the larry.
Examples
--------
Create a larry:
>>> label = [['a','b'], ['c','d','e']]
>>> x = np.array([[1, 2, 3], [3, 1, 2]])
>>> lar = la.larry(x, label)
Sort larry by row 'b' along the first axis:
>>> la.sortby(lar, 'b', axis=0)
label_0
a
b
label_1
d
e
c
x
array([[2, 3, 1],
[1, 2, 3]])
"""
if lar.ndim != 2:
raise ValueError("`lar` must be 2d.")
if lar.size == 0:
if element in lar.label[axis]:
return lar.copy()
if axis < 0:
axis += lar.ndim
if axis not in (0, 1):
raise ValueError("`axis` must be 0 or 1.")
idx = lar.labelindex(element, axis)
if axis == 0:
idx = lar.x[idx,:].argsort()
if reverse:
idx = idx[::-1]
return lar[:,idx]
else:
idx = lar.x[:,idx].argsort()
if reverse:
idx = idx[::-1]
return lar[idx,:]
# Instantiation shortcuts---------------------------------------------------
def lrange(shape=None, label=None, start=0, step=1, dtype=None):
"""
Return a new larry of sequential integers, shaped according to input.
Parameters
----------
shape : {int, tuple}, optional
If `shape` is not given, then `label` must be supplied. If `shape` is
an int, output will be one-dimensional.
label : list, optional
List of lists, a label for the larry produced.
start : int, optional
First integer appearing. Defaults to 0
step : int, optional
Difference between successive integers. Defaults to 1.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
Returns
-------
lar : larry
a ``(d1, ..., dn)``-shaped larry of consecutive integers (or spaced
`step` apart if such a keyword argument is used), labeled either by
integers or by the labels supplied.
See Also
--------
la.empty, la.ones, la.zeros
Examples
--------
A basic, 1d lrange using the 'dtype' argument:
>>> la.lrange(3, dtype='f4')
label_0
0
1
2
x
array([0.0, 1.0, 2.0], dtype=np.float32)
A multi-dimensional lrange:
>>> la.lrange((2,3,3))
label_0
0
1
label_1
0
1
2
label_2
0
1
2
x
array([[[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8]],
.
[[ 9, 10, 11],
[12, 13, 14],
[15, 16, 17]]])
Using the `label` keyword:
>>> la.lrange(label=[['a', 'b']])
label_0
a
b
x
array([0, 1])
"""
if label is not None:
shape = [len(lab) for lab in label]
else:
if shape is None:
raise ValueError("Either `label` or `shape` must be supplied.")
total = np.product(shape)
data = np.arange(start=start, stop=step*total+start, step=step,
dtype=dtype).reshape(shape)
return larry(data, label)
def empty(shape=None, label=None, dtype=None, order='C'):
"""
Return a new larry of given shape and type, without initializing entries.
Parameters
----------
shape : {int, tuple}, optional
If `shape` is not given, then `label` must be supplied. If `shape` is
an int, output will be one-dimensional.
label : list, optional
List of lists, a label for the larry produced.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. The default is C order.
Returns
-------
lar : larry
a ``(d1, ..., dn)``-shaped larry of uninitialized values, labeled either
by integers or by the labels supplied.
See Also
--------
la.zeros, la.ones, la.lrange
Examples
--------
A basic, 1d larry using the 'dtype' argument:
>>> la.empty(3, dtype='i4')
label_0
0
1
2
3
4
x
array([0, -7, 987], dtype=np.int32)
A multi-dimensional larry:
>>> la.empty(2, 3, 3)
label_0
0
1
label_1
0
1
2
label_2
0
1
2
x
array([[[ 0e0, 0e0, 0e0],
[ 0e0, 0e0, 0e0],
[ 0e0, 0e0, 0e0]],
[[ 3.2e-254, 0e0, 0e0],
[ 0e0., 0e0, 0e0],
[ 0e0, 0e0, 0e0]]])
Using the 'label' argument:
>>> la.empty(label=[['a', 'b']])
label_0
a
b
x
array([0e0, -3.2e-256])
"""
if label is not None:
shape = [len(lab) for lab in label]
else:
if shape is None:
raise ValueError("Either `label` or `shape` must be supplied.")
data = np.empty(shape, dtype, order)
return larry(data, label)
def ones(shape=None, label=None, dtype=None, order='C'):
"""
Return a new larry of given shape and type, filled with ones.
Parameters
----------
shape : {int, tuple}, optional
If `shape` is not given, then `label` must be supplied. If `shape` is
an int, output will be one-dimensional.
label : list, optional
List of lists, a label for the larry produced.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory. The default is C order.
Returns
-------
lar : larry
a ``(d1, ..., dn)``-shaped larry of ones, labeled either by
integers or by the labels supplied.
See Also
--------
la.empty, la.zeros, la.lrange
Examples
--------
A basic, 1d larry using the 'dtype' argument:
>>> la.ones(3, dtype='i4')
label_0
0
1
2
x
array([1, 1, 1], dtype=np.int32)
A multi-dimensional larry:
>>> la.ones(2, 3, 3)
label_0
0
1
label_1
0
1
2
label_2
0
1
2
x
array([[[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]],
.
[[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]]])
Using the 'label' argument:
>>> la.ones(label=[['a', 'b']])
label_0
a
b
x
array([1., 1.])
"""
lar = empty(shape, label, dtype, order)
lar.x.fill(1)
return lar
def zeros(shape=None, label=None, dtype=None, order='C'):
"""
Return a new larry of given shape and type, filled with zeros.
Parameters
----------
shape : {int, tuple}, optional
If `shape` is not given, then `label` must be supplied. If `shape` is
an int, output will be one-dimensional.
label : list, optional
List of lists, a label for the larry produced.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
Returns
-------
lar : larry
a ``(d1, ..., dn)``-shaped larry of zeros, labeled either by
integers or by the labels supplied.
See Also
--------
la.empty, la.ones, la.lrange
Examples
--------
A basic, 1d larry using the 'dtype' argument:
>>> la.zeros(3, dtype='i4')
label_0
0
1
2
x
array([0, 0, 0], dtype=np.int32)
A multi-dimensional larry:
>>> la.zeros(2, 3, 3)
label_0
0
1
label_1
0
1
2
label_2
0
1
2
x
array([[[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]],
.
[[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]]])
Using the 'label' argument:
>>> la.zeros(label=[['a', 'b']])
label_0
a
b
x
array([0., 0.])
"""
lar = empty(shape, label, dtype, order)
lar.x.fill(0)
return lar
