[amp] 06/18: Lintian clean.

[Muammar El Khatib]

This is an automated email from the git hooks/post-receive script.

muammar pushed a commit to branch master
in repository amp.

commit 5a7141a0853eebb8cfb59f85e5f4c0ff695b5171
Author: Muammar El Khatib <muammarelkhatib at gmail.com>
Date:   Sat Jul 29 21:34:40 2017 -0400

    Lintian clean.
---
 debian/changelog |  2 +-
 debian/control   |  8 ++++----
 debian/copyright | 12 ++++++------
 debian/rules     |  4 ++++
 4 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/debian/changelog b/debian/changelog
index f0615cb..48e64a0 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -2,4 +2,4 @@ amp (0.6~20170729-1) unstable; urgency=medium
 
   * Initial release (Closes: #790803)
 
- -- Muammar El Khatib <muammar at debian.org>  Thu, 15 Dec 2016 18:27:27 -0500
+ -- Muammar El Khatib <muammar at debian.org>  Sat, 29 Jul 2017 21:12:31 -0400
diff --git a/debian/control b/debian/control
index f31a264..4fb68c6 100644
--- a/debian/control
+++ b/debian/control
@@ -37,7 +37,7 @@ Depends: python-ase (>= 3.9.0~),
          ${shlibs:Depends}
 Recommends: python-matplotlib
 Breaks: ${python:Breaks}
-Description: Atomistic Machine-learning Package (Python2)
+Description: Atomistic Machine-learning Package (python 2)
  Amp is an open-source package designed to easily bring machine-learning to
  atomistic calculations. This project is being developed at Brown University in
  the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi,
@@ -52,7 +52,7 @@ Description: Atomistic Machine-learning Package (Python2)
  calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem,
  and Gaussian ─ can easily be used as the parent method.
  .
- This package provides the Python 2 modules.
+ This package provides the python 2 modules.
 
 Package: python3-amp
 Architecture: any
@@ -63,7 +63,7 @@ Depends: python3-ase (>= 3.9.0~),
          ${shlibs:Depends}
 Recommends: python3-matplotlib
 Breaks: ${python3:Breaks}
-Description: Atomistic Machine-learning Package (Python3)
+Description: Atomistic Machine-learning Package (python 3)
  Amp is an open-source package designed to easily bring machine-learning to
  atomistic calculations. This project is being developed at Brown University in
  the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi,
@@ -78,4 +78,4 @@ Description: Atomistic Machine-learning Package (Python3)
  calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem,
  and Gaussian ─ can easily be used as the parent method.
  .
- This package provides the Python 3 modules.
+ This package provides the python 3 modules.
diff --git a/debian/copyright b/debian/copyright
index 8c66dd8..f89e013 100644
--- a/debian/copyright
+++ b/debian/copyright
@@ -1,15 +1,15 @@
 Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
-Upstream-Name: neural
-Upstream-Contact: Andrew Peterson <andrew_peterson at brown.edu>
-Source: https://bitbucket.org/andrewpeterson/neural/
+Upstream-Name: amp
+Upstream-Contact: Andrew A. Peterson <andrew_peterson at brown.edu>
+Source: https://bitbucket.org/andrewpeterson/amp
 
 Files: *
-Copyright: 2014-2015 Andrew Peterson <andrew_peterson at brown.edu>
-           2014-2015 Alireza Khorshidi <alireza_khorshidi at brown.edu>
+Copyright: 2014-2017 Andrew A. Peterson <andrew_peterson at brown.edu>
+           2014-2017 Alireza Khorshidi <alireza_khorshidi at brown.edu>
 License: GPL-3.0+
 
 Files: debian/*
-Copyright: 2015 Graham Inggs <graham at nerve.org.za>
+Copyright: 2017 Muammar El Khatib <muammar at debian.org>
 License: GPL-3.0+
 
 License: GPL-3.0+
diff --git a/debian/rules b/debian/rules
index 941ec75..01582c8 100755
--- a/debian/rules
+++ b/debian/rules
@@ -3,6 +3,10 @@
 export DEB_FFLAGS_MAINT_APPEND=-fPIC
 export DEB_LDFLAGS_MAINT_APPEND=-shared
 
+export DEB_BUILD_MAINT_OPTIONS = hardening=+all
+DPKG_EXPORT_BUILDFLAGS = 1
+include /usr/share/dpkg/buildflags.mk
+
 GIT = 'https://bitbucket.org/andrewpeterson/amp'
 
 # The full changelog version number, used for the orig.tar.gz

-- 
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