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# SciPy 0.11.0 Release Notes

Contents

- SciPy 0.11.0 Release Notes
  - New features
    - Sparse Graph Submodule
    - `scipy.optimize` improvements
      - Unified interfaces to minimizers
      - Unified interface to root finding algorithms
    - `scipy.linalg` improvements
      - New matrix equation solvers
      - QZ and QR Decomposition
      - Pascal matrices
    - Sparse matrix construction and operations
    - LSMR iterative solver
    - Discrete Sine Transform
    - `scipy.interpolate` improvements
    - Binned statistics (`scipy.stats`)
  - Deprecated features
  - Backwards incompatible changes
    - Removal of `scipy.maxentropy`
    - Minor change in behavior of `splev`
    - Behavior of `scipy.integrate.complex_ode`
    - Minor change in behavior of T-tests
  - Other changes
  - Authors

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, embeding 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 decompostion) 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.

## Authors

This release contains work by the following people (contributed at least one patch to this release, names in alphabetical order):

- Jeff Armstrong
- Chad Baker
- Brandon Beacher +
- behrisch +
- borishim +
- Matthew Brett
- Lars Buitinck
- Luis Pedro Coelho +
- Johann Cohen-Tanugi
- David Cournapeau
- dougal +
- Ali Ebrahim +
- endolith +
- Bjørn Forsman +
- Robert Gantner +
- Sebastian Gassner +
- Christoph Gohlke
- Ralf Gommers
- Yaroslav Halchenko
- Charles Harris
- Jonathan Helmus +
- Andreas Hilboll +
- Marc Honnorat +
- Jonathan Hunt +
- Maxim Ivanov +
- Thouis (Ray) Jones
- Christopher Kuster +
- Josh Lawrence +
- Denis Laxalde +
- Travis Oliphant
- Joonas Paalasmaa +
- Fabian Pedregosa
- Josef Perktold
- Gavin Price +
- Jim Radford +
- Andrew Schein +
- Skipper Seabold
- Jacob Silterra +
- Scott Sinclair
- Alexis Tabary +
- Martin Teichmann
- Matt Terry +
- Nicky van Foreest +
- Jacob Vanderplas
- Patrick Varilly +
- Pauli Virtanen
- Nils Wagner +
- Darryl Wally +
- Stefan van der Walt
- Liming Wang +
- David Warde-Farley +
- Warren Weckesser
- Sebastian Werk +
- Mike Wimmer +
- Tony S Yu +

A total of 55 people contributed to this release. People with a “+” by their names contributed a patch for the first time.

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