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|
NumPy 1.9.0 Release Notes
*************************
This release supports Python 2.6 -2.7 and 3.2 - 3.3.
Highlights
==========
Dropped Support
===============
The oldnumeric and numarray modules have been removed.
Future Changes
==============
Compatibility notes
===================
Special scalar float values don't cause upcast to double anymore
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In previous numpy versions operations involving floating point scalars
containing special values `NaN`, `Inf` and `-Inf` caused the result type to be
at least `float64`.
As the special values can be represented in the smallest available floating
point type the upcast not performed anymore.
For example the dtype of:
np.array([1.], dtype=np.float32) * float('nan')
now remains `float32` instead of being cast to `float64`.
Operations involving non-special values have not been changed.
Percentile output changes
~~~~~~~~~~~~~~~~~~~~~~~~~
If given more than one percentile to compute numpy.percentile returns an array
instead of a list. A single percentile still returns a scalar.
The array is equivalent to converting the the list returned in older versions
to an array via `np.array`.
If the `overwrite_input` option is used the input is only partially instead of
fully sorted.
npy_3kcompat.h header change
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The unused ``simple_capsule_dtor`` function has been removed from
``npy_3kcompat.h``. Note that this header is not meant to be used outside of
numpy; other projects should be using their own copy of this file when needed.
Negative indices in C-Api sq_item and sq_ass_item sequence methods
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
When directly accessing the ``sq_item`` or ``sq_ass_item`` PyObject slots
for item getting, negative indices will not be supported anymore.
``PySequence_GetItem`` and ``PySequence_SetItem`` however fix negative
indices so that they can be used there.
ndarray.tofile exception type
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
All tofile exceptions are now IOError, some were previously ValueError.
New Features
============
Percentile supports more interpolation options
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`np.percentile` now has the interpolation keyword argument to specify in which
way points should be interpolated if the percentiles fall between two values.
See the documentation for the available options.
Ufunc and Dot Overrides
~~~~~~~~~~~~~~~~~~~~~~~
For better compatibility with external objects you can now override universal
functions (ufuncs), ``numpy.core._dotblas.dot``, and
``numpy.core.multiarray.dot`` (the numpy.dot functions). By defining a
``__numpy_ufunc__`` method.
Dtype parameter added to `np.linspace` and `np.logspace`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The returned data type from the `linspace` and `logspace` functions
can now be specificed using the dtype parameter.
More general `np.triu` and `np.tril` broadcasting
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For arrays with `ndim` exceeding 2, these functions will now apply to the
final two axes instead of raising an exception.
`tobytes` alias for `tostring` method
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`ndarray.tobytes` and `MaskedArray.tobytes` have been added as aliases for
`tostring` which exports arrays as `bytes`. This is more consistent in Python 3
where `str` and `bytes` are not the same.
Improvements
============
Percentile implemented in terms of `np.partition`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
`np.percentile` has been implemented interms of `np.partition` which only
partially sorts the data via a selection algorithm. This improves the time
complexcity from `O(nlog(n))` to `O(n)`.
Performance improvement for `np.array`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The performance of converting lists containing arrays to arrays using
`np.array` has been improved. It is now equivalent in speed to
`np.vstack(list)`.
Performance improvement for `np.searchsorted`
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For the built-in numeric types, `np.searchsorted` no longer relies on the
data type's `compare` function to perform the search, but is now implemented
by type specific functions. Depending on the size of the inputs, this can
result in performance improvements over 2x.
Changes
=======
Argmin and argmax out argument
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The `out` argument to `np.argmin` and `np.argmax` and their equivalent
C-API functions is now checked to match the desired output shape exactly.
If the check fails a `ValueError` instead of `TypeError` is raised.
Einsum
~~~~~~
Remove unnecessary broadcasting notation restrictions.
np.einsum('ijk,j->ijk', A, B) can also be written as
np.einsum('ij...,j->ij...', A, B) (ellipsis is no longer required on 'j')
Indexing
~~~~~~~~
The NumPy indexing has seen a complete rewrite in this version. This makes
most advanced integer indexing operations much faster and should have no
other implications.
However some subtle changes and deprecations were introduced in advanced
indexing operations:
* Boolean indexing into scalar arrays will always return a new 1-d array.
This means that ``array(1)[array(True)]`` gives ``array([1])`` and
not the original array.
* Advanced indexing into one dimensional arrays used to have (undocumented)
special handling regarding repeating the value array in assignments
when the shape of the value array was too small or did not match.
Code using this will raise an error. For compatibility you can use
``arr.flat[index] = values``, which uses the old code branch.
(for example `a = np.ones(10); a[np.arange(10)] = [1, 2, 3]`)
* The iteration order over advanced indexes used to be always C-order.
In NumPy 1.9. the iteration order adapts to the inputs and is not
guaranteed (with the exception of a *single* advanced index which is
never reversed for compatibility reasons). This means that the result is
undefined if multiple values are assigned to the same element.
An example for this is ``arr[[0, 0], [1, 1]] = [1, 2]``, which may
set ``arr[0, 1]`` to either 1 or 2.
* Equivalent to the iteration order, the memory layout of the advanced
indexing result is adapted for faster indexing and cannot be predicted.
* All indexing operations return a view or a copy. No indexing operation
will return the original array object. (For example `arr[...]`)
* In the future Boolean array-likes (such as lists of python bools)
will always be treated as Boolean indexes and Boolean scalars (including
python `True`) will be a legal *boolean* index. At this time, this is
already the case for scalar arrays to allow the general
``positive = a[a > 0]`` to work when ``a`` is zero dimensional.
* In NumPy 1.8 it was possible to use `array(True)` and `array(False)`
equivalent to 1 and 0 if the result of the operation was a scalar.
This will raise an error in NumPy 1.9 and, as noted above, treated as a
boolean index in the future.
* All non-integer array-likes are deprecated, object arrays of custom
integer like objects may have to be cast explicitly.
* The error reporting for advanced indexing is more informative, however
the error type has changed in some cases. (Broadcasting errors of
indexing arrays are reported as `IndexError`)
* Indexing with more then one ellipsis (`...`) is deprecated.
promote_types and string dtype
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
promote_types function now returns a valid string length when given an
integer or float dtype as one argument and a string dtype as another argument.
Previously it always returned the input string dtype, even if it wasn't
long enough to store the max integer/float value converted to a string.
can_cast and string dtype
~~~~~~~~~~~~~~~~~~~~~~~~~
can_cast function now returns False in "safe" casting mode for integer/float
dtype and string dtype if the string dtype length is not long enough to store
the max integer/float value converted to a string. Previously can_cast in "safe"
mode returned True for integer/float dtype and a string dtype of any length.
astype and string dtype
~~~~~~~~~~~~~~~~~~~~~~~
astype method now returns an error if the string dtype to cast to is not long
enough in "safe" casting mode to hold the max value of integer/float array that
is being casted. Previously the casting was allowed even if the result was
truncated.
C-API
~~~~~
None
Deprecations
============
Non-integer scalars for sequence repetition
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Using non-integer numpy scalars to repeat python sequences is deprecated.
For example `np.float_(2) * [1]` will be an error in the future.
C-API
~~~~~
None
New Features
============
|
