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SciPy 0.9.0 is the culmination of 6 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. There have been a number of deprecations and API changes in this release, which are documented below. All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Moreover, our development attention will now shift to bug-fix releases on the 0.9.x branch, and on adding new features on the development trunk.

This release requires Python 2.4 - 2.7 or 3.1 - and NumPy 1.5 or greater.

Please note that SciPy is still considered to have “Beta” status, as we work toward a SciPy 1.0.0 release. The 1.0.0 release will mark a major milestone in the development of SciPy, after which changing the package structure or API will be much more difficult. Whilst these pre-1.0 releases are considered to have “Beta” status, we are committed to making them as bug-free as possible.

However, until the 1.0 release, we are aggressively reviewing and refining the functionality, organization, and interface. This is being done in an effort to make the package as coherent, intuitive, and useful as possible. To achieve this, we need help from the community of users. Specifically, we need feedback regarding all aspects of the project - everything - from which algorithms we implement, to details about our function’s call signatures.

Scipy 0.9.0 is the first SciPy release to support Python 3. The only module
that is not yet ported is `scipy.weave`.

Soon after this release, Scipy will stop using SVN as the version control system, and move to Git. The development source code for Scipy can from then on be found at

Scipy now includes routines for computing Delaunay tesselations in N
dimensions, powered by the Qhull computational geometry library. Such
calculations can now make use of the new `scipy.spatial.Delaunay`
interface.

Support for scattered data interpolation is now significantly
improved. This version includes a `scipy.interpolate.griddata`
function that can perform linear and nearest-neighbour interpolation
for N-dimensional scattered data, in addition to cubic spline
(C1-smooth) interpolation in 2D and 1D. An object-oriented interface
to each interpolator type is also available.

Scipy includes new routines for large-scale nonlinear equation solving
in `scipy.optimize`. The following methods are implemented:

- Newton-Krylov (
`scipy.optimize.newton_krylov`) - (Generalized) secant methods:
- Limited-memory Broyden methods (
`scipy.optimize.broyden1`,`scipy.optimize.broyden2`) - Anderson method (
`scipy.optimize.anderson`)

- Limited-memory Broyden methods (
- Simple iterations (
`scipy.optimize.diagbroyden`,`scipy.optimize.excitingmixing`,`scipy.optimize.linearmixing`)

The `scipy.optimize.nonlin` module was completely rewritten, and
some of the functions were deprecated (see above).

Scipy now contains routines for effectively solving triangular
equation systems (`scipy.linalg.solve_triangular`).

The function `scipy.signal.firwin` was enhanced to allow the
design of highpass, bandpass, bandstop and multi-band FIR filters.

The function `scipy.signal.firwin2` was added. This function
uses the window method to create a linear phase FIR filter with
an arbitrary frequency response.

The functions `scipy.signal.kaiser_atten` and `scipy.signal.kaiser_beta`
were added.

A new function `scipy.stats.fisher_exact` was added, that provides Fisher’s
exact test for 2x2 contingency tables.

The function `scipy.stats.kendalltau` was rewritten to make it much faster
(O(n log(n)) vs O(n^2)).

The following nonlinear solvers from `scipy.optimize` are
deprecated:

`broyden_modified`(bad performance)`broyden1_modified`(bad performance)`broyden_generalized`(equivalent to`anderson`)`anderson2`(equivalent to`anderson`)`broyden3`(obsoleted by new limited-memory broyden methods)`vackar`(renamed to`diagbroyden`)

The deprecated modules `helpmod`, `pexec` and `ppimport` were removed
from `scipy.misc`.

The `output_type` keyword in many `scipy.ndimage` interpolation functions
has been removed.

The `econ` keyword in `scipy.linalg.qr` has been removed. The same
functionality is still available by specifying `mode='economic'`.

The old behavior for `scipy.signal.convolve`, `scipy.signal.convolve2d`,
`scipy.signal.correlate` and `scipy.signal.correlate2d` was deprecated in
0.8.0 and has now been removed. Convolve and correlate used to swap their
arguments if the second argument has dimensions larger than the first one, and
the mode was relative to the input with the largest dimension. The current
behavior is to never swap the inputs, which is what most people expect, and is
how correlation is usually defined.

Many functions in `scipy.stats` that are either available from numpy or have
been superseded, and have been deprecated since version 0.7, have been removed:
*std*, *var*, *mean*, *median*, *cov*, *corrcoef*, *z*, *zs*, *stderr*,
*samplestd*, *samplevar*, *pdfapprox*, *pdf_moments* and *erfc*. These changes
are mirrored in `scipy.stats.mstats`.

Several methods of the sparse matrix classes in `scipy.sparse` which had
been deprecated since version 0.7 were removed: *save*, *rowcol*, *getdata*,
*listprint*, *ensure_sorted_indices*, *matvec*, *matmat* and *rmatvec*.

The functions `spkron`, `speye`, `spidentity`, `lil_eye` and
`lil_diags` were removed from `scipy.sparse`. The first three functions
are still available as `scipy.sparse.kron`, `scipy.sparse.eye` and
`scipy.sparse.identity`.

The *dims* and *nzmax* keywords were removed from the sparse matrix
constructor. The *colind* and *rowind* attributes were removed from CSR and CSC
matrices respectively.

A duplicated interface to the ARPACK library was removed.

The interface to the ARPACK eigenvalue routines in
`scipy.sparse.linalg` was changed for more robustness.

The eigenvalue and SVD routines now raise `ArpackNoConvergence` if
the eigenvalue iteration fails to converge. If partially converged results
are desired, they can be accessed as follows:

```
import numpy as np
from scipy.sparse.linalg import eigs, ArpackNoConvergence
m = np.random.randn(30, 30)
try:
w, v = eigs(m, 6)
except ArpackNoConvergence, err:
partially_converged_w = err.eigenvalues
partially_converged_v = err.eigenvectors
```

Several bugs were also fixed.

The routines were moreover renamed as follows:

- eigen –> eigs
- eigen_symmetric –> eigsh
- svd –> svds