# SciPy 0.11.0 Release Notes#

SciPy 0.11.0 is the culmination of 8 months of hard work. It contains many new features, numerous bug-fixes, improved test coverage and better documentation. Highlights of this release are:

A new module has been added which provides a number of common sparse graph algorithms.

New unified interfaces to the existing optimization and root finding functions have been added.

All users are encouraged to upgrade to this release, as there are a large number of bug-fixes and optimizations. Our development attention will now shift to bug-fix releases on the 0.11.x branch, and on adding new features on the master branch.

This release requires Python 2.4-2.7 or 3.1-3.2 and NumPy 1.5.1 or greater.

## New features#

### Sparse Graph Submodule#

The new submodule `scipy.sparse.csgraph`

implements a number of efficient
graph algorithms for graphs stored as sparse adjacency matrices. Available
routines are:

`connected_components`

- determine connected components of a graph

`laplacian`

- compute the laplacian of a graph

`shortest_path`

- compute the shortest path between points on a positive graph

`dijkstra`

- use Dijkstra’s algorithm for shortest path

`floyd_warshall`

- use the Floyd-Warshall algorithm for shortest path

`breadth_first_order`

- compute a breadth-first order of nodes

`depth_first_order`

- compute a depth-first order of nodes

`breadth_first_tree`

- construct the breadth-first tree from a given node

`depth_first_tree`

- construct a depth-first tree from a given node

`minimum_spanning_tree`

- construct the minimum spanning tree of a graph

`scipy.optimize`

improvements#

The optimize module has received a lot of attention this release. In addition to added tests, documentation improvements, bug fixes and code clean-up, the following improvements were made:

A unified interface to minimizers of univariate and multivariate functions has been added.

A unified interface to root finding algorithms for multivariate functions has been added.

The L-BFGS-B algorithm has been updated to version 3.0.

#### Unified interfaces to minimizers#

Two new functions `scipy.optimize.minimize`

and
`scipy.optimize.minimize_scalar`

were added to provide a common interface
to minimizers of multivariate and univariate functions respectively.
For multivariate functions, `scipy.optimize.minimize`

provides an
interface to methods for unconstrained optimization (`fmin`

, `fmin_powell`

,
`fmin_cg`

, `fmin_ncg`

, `fmin_bfgs`

and *anneal*) or constrained
optimization (`fmin_l_bfgs_b`

, `fmin_tnc`

, `fmin_cobyla`

and `fmin_slsqp`

).
For univariate functions, `scipy.optimize.minimize_scalar`

provides an
interface to methods for unconstrained and bounded optimization (`brent`

,
`golden`

, `fminbound`

).
This allows for easier comparing and switching between solvers.

#### Unified interface to root finding algorithms#

The new function `scipy.optimize.root`

provides a common interface to
root finding algorithms for multivariate functions, embedding `fsolve`

,
`leastsq`

and `nonlin`

solvers.

`scipy.linalg`

improvements#

#### New matrix equation solvers#

Solvers for the Sylvester equation (`scipy.linalg.solve_sylvester`

, discrete
and continuous Lyapunov equations (`scipy.linalg.solve_lyapunov`

,
`scipy.linalg.solve_discrete_lyapunov`

) and discrete and continuous algebraic
Riccati equations (`scipy.linalg.solve_continuous_are`

,
`scipy.linalg.solve_discrete_are`

) have been added to `scipy.linalg`

.
These solvers are often used in the field of linear control theory.

#### QZ and QR Decomposition#

It is now possible to calculate the QZ, or Generalized Schur, decomposition
using `scipy.linalg.qz`

. This function wraps the LAPACK routines sgges,
dgges, cgges, and zgges.

The function `scipy.linalg.qr_multiply`

, which allows efficient computation
of the matrix product of Q (from a QR decomposition) and a vector, has been
added.

#### Pascal matrices#

A function for creating Pascal matrices, `scipy.linalg.pascal`

, was added.

### Sparse matrix construction and operations#

Two new functions, `scipy.sparse.diags`

and `scipy.sparse.block_diag`

, were
added to easily construct diagonal and block-diagonal sparse matrices
respectively.

`scipy.sparse.csc_matrix`

and `csr_matrix`

now support the operations
`sin`

, `tan`

, `arcsin`

, `arctan`

, `sinh`

, `tanh`

, `arcsinh`

,
`arctanh`

, `rint`

, `sign`

, `expm1`

, `log1p`

, `deg2rad`

, `rad2deg`

,
`floor`

, `ceil`

and `trunc`

. Previously, these operations had to be
performed by operating on the matrices’ `data`

attribute.

### LSMR iterative solver#

LSMR, an iterative method for solving (sparse) linear and linear
least-squares systems, was added as `scipy.sparse.linalg.lsmr`

.

### Discrete Sine Transform#

Bindings for the discrete sine transform functions have been added to
`scipy.fftpack`

.

`scipy.interpolate`

improvements#

For interpolation in spherical coordinates, the three classes
`scipy.interpolate.SmoothSphereBivariateSpline`

,
`scipy.interpolate.LSQSphereBivariateSpline`

, and
`scipy.interpolate.RectSphereBivariateSpline`

have been added.

### Binned statistics (`scipy.stats`

)#

The stats module has gained functions to do binned statistics, which are a
generalization of histograms, in 1-D, 2-D and multiple dimensions:
`scipy.stats.binned_statistic`

, `scipy.stats.binned_statistic_2d`

and
`scipy.stats.binned_statistic_dd`

.

## Deprecated features#

`scipy.sparse.cs_graph_components`

has been made a part of the sparse graph
submodule, and renamed to `scipy.sparse.csgraph.connected_components`

.
Calling the former routine will result in a deprecation warning.

`scipy.misc.radon`

has been deprecated. A more full-featured radon transform
can be found in scikits-image.

`scipy.io.save_as_module`

has been deprecated. A better way to save multiple
Numpy arrays is the `numpy.savez`

function.

The *xa* and *xb* parameters for all distributions in
`scipy.stats.distributions`

already weren’t used; they have now been
deprecated.

## Backwards incompatible changes#

### Removal of `scipy.maxentropy`

#

The `scipy.maxentropy`

module, which was deprecated in the 0.10.0 release,
has been removed. Logistic regression in scikits.learn is a good and modern
alternative for this functionality.

### Minor change in behavior of `splev`

#

The spline evaluation function now behaves similarly to `interp1d`

for size-1 arrays. Previous behavior:

```
>>> from scipy.interpolate import splev, splrep, interp1d
>>> x = [1,2,3,4,5]
>>> y = [4,5,6,7,8]
>>> tck = splrep(x, y)
>>> splev([1], tck)
4.
>>> splev(1, tck)
4.
```

Corrected behavior:

```
>>> splev([1], tck)
array([ 4.])
>>> splev(1, tck)
array(4.)
```

This affects also the `UnivariateSpline`

classes.

### Behavior of `scipy.integrate.complex_ode`

#

The behavior of the `y`

attribute of `complex_ode`

is changed.
Previously, it expressed the complex-valued solution in the form:

```
z = ode.y[::2] + 1j * ode.y[1::2]
```

Now, it is directly the complex-valued solution:

```
z = ode.y
```

### Minor change in behavior of T-tests#

The T-tests `scipy.stats.ttest_ind`

, `scipy.stats.ttest_rel`

and
`scipy.stats.ttest_1samp`

have been changed so that 0 / 0 now returns NaN
instead of 1.

## Other changes#

The SuperLU sources in `scipy.sparse.linalg`

have been updated to version 4.3
from upstream.

The function `scipy.signal.bode`

, which calculates magnitude and phase data
for a continuous-time system, has been added.

The two-sample T-test `scipy.stats.ttest_ind`

gained an option to compare
samples with unequal variances, i.e. Welch’s T-test.

`scipy.misc.logsumexp`

now takes an optional `axis`

keyword argument.