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11.0
12.0
12.1
12.2
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Isaac Yu
242648acbf
academic/scipy3: Updated for version 1.8.0. New maintainer.
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2 years ago | |
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| .. | ||
| README | 3f5005bf95 academic/scipy3: Wrap README at 72 columns. | 2 years ago |
| scipy3.SlackBuild | 242648acbf academic/scipy3: Updated for version 1.8.0. New maintainer. | 2 years ago |
| scipy3.info | 242648acbf academic/scipy3: Updated for version 1.8.0. New maintainer. | 2 years ago |
| slack-desc | 2c477bc3a9 academic/scipy3: Update HOMEPAGE. | 7 years ago |
README
SciPy (pronounced "Sigh Pie") is open-source software for mathematics,
science, and engineering.
The SciPy library is built to work with NumPy arrays, and provides
many user-friendly and efficient numerical routines such as routines
for numerical integration and optimization. Together, they run on
all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be
depended upon by some of the world's leading scientists and engineers.
matplotlib and ipython are nice to have in connection with scipy, but
scipy does not depend on them.
Since ver. 0.14.0 scipy uses SuperLU instead of UMFPACK for solving
unsymmetric sparse linear systems. If you want to use UMFPACK you
must install it in advance of numpy and build numpy accordingly (see
the README to numpy's SlackBuild). Moreover, after installing scipy,
you have to install scikit-umfpack, which presently is not available
on SlackBuilds.org.
If you need to build scipy for debugging, set DEBUG=y.
NOTE: this is for Python 3. If you need Python 2 support, install
scipy.
science, and engineering.
The SciPy library is built to work with NumPy arrays, and provides
many user-friendly and efficient numerical routines such as routines
for numerical integration and optimization. Together, they run on
all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be
depended upon by some of the world's leading scientists and engineers.
matplotlib and ipython are nice to have in connection with scipy, but
scipy does not depend on them.
Since ver. 0.14.0 scipy uses SuperLU instead of UMFPACK for solving
unsymmetric sparse linear systems. If you want to use UMFPACK you
must install it in advance of numpy and build numpy accordingly (see
the README to numpy's SlackBuild). Moreover, after installing scipy,
you have to install scikit-umfpack, which presently is not available
on SlackBuilds.org.
If you need to build scipy for debugging, set DEBUG=y.
NOTE: this is for Python 3. If you need Python 2 support, install
scipy.