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from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
from glue.external.six import string_types
__all__ = ['unique', 'shape_to_string', 'view_shape', 'stack_view',
'coerce_numeric', 'check_sorted', 'broadcast_to']
def unique(array):
"""
Return the unique elements of the array U, as well as
the index array I such that U[I] == array
Parameters
----------
array : `numpy.ndarray`
The array to use
Returns
-------
U : `numpy.ndarray`
The unique elements of the array
I : `numpy.ndarray`
The indices such that ``U[I] == array``
"""
# numpy.unique doesn't handle mixed-types on python3,
# so we use pandas
U, I = pd.factorize(array, sort=True)
return I, U
def shape_to_string(shape):
"""
On Windows, shape tuples use long ints which results in formatted shapes
such as (2L, 3L). This function ensures that the shape is always formatted
without the Ls.
"""
return "({0})".format(", ".join(str(int(item)) for item in shape))
def view_shape(shape, view):
"""
Return the shape of a view of an array.
Returns equivalent of ``np.zeros(shape)[view].shape``
Parameters
----------
shape : tuple
The shape of the array
view : slice
A valid index into a Numpy array, or None
"""
if view is None:
return shape
shp = tuple(slice(0, s, 1) for s in shape)
xy = np.broadcast_arrays(*np.ogrid[shp])
assert xy[0].shape == shape
return xy[0][view].shape
def stack_view(shape, *views):
shp = tuple(slice(0, s, 1) for s in shape)
result = np.broadcast_arrays(*np.ogrid[shp])
for v in views:
if isinstance(v, string_types) and v == 'transpose':
result = [r.T for r in result]
continue
result = [r[v] for r in result]
return tuple(result)
def coerce_numeric(arr):
"""
Coerce an array into a numeric array, replacing non-numeric elements with
nans.
If the array is already a numeric type, it is returned unchanged
Parameters
----------
arr : `numpy.ndarray`
The array to coerce
"""
# already numeric type
if np.issubdtype(arr.dtype, np.number):
return arr
if np.issubdtype(arr.dtype, np.bool_):
return arr.astype(np.int)
# a string dtype, or anything else
try:
return pd.to_numeric(arr, errors='coerce')
except AttributeError: # older versions of pandas
return pd.Series(arr).convert_objects(convert_numeric=True).values
def check_sorted(array):
"""
Return `True` if the array is sorted, `False` otherwise.
"""
# this ignores NANs, and does the right thing if nans
# are concentrated at beginning or end of array
# otherwise, it will miss things at nan/finite boundaries
array = np.asarray(array)
return not (array[:-1] > array[1:]).any()
def pretty_number(numbers):
"""
Convert a list/array of numbers into a nice list of strings
Parameters
----------
numbers : list
The numbers to convert
"""
try:
return [pretty_number(n) for n in numbers]
except TypeError:
pass
n = numbers
if n == 0:
result = '0'
elif (abs(n) < 1e-3) or (abs(n) > 1e3):
result = "%0.3e" % n
elif abs(int(n) - n) < 1e-3 and int(n) != 0:
result = "%i" % n
else:
result = "%0.3f" % n
if result.find('.') != -1:
result = result.rstrip('0')
return result
def broadcast_to(array, shape):
"""
Compatibility function - can be removed once we support only Numpy 1.10
and above
"""
try:
return np.broadcast_to(array, shape)
except AttributeError:
return array * np.ones(shape, array.dtype)
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