[97942] trunk/dports/python/py-h5py/Portfile

[eborisch at macports.org]

Revision: 97942
          http://trac.macports.org//changeset/97942
Author:   eborisch at macports.org
Date:     2012-09-20 07:47:24 -0700 (Thu, 20 Sep 2012)
Log Message:
-----------
py-h5py: Add -devel versions. (Currently 2.1.0b2)

Modified Paths:
--------------
    trunk/dports/python/py-h5py/Portfile

Modified: trunk/dports/python/py-h5py/Portfile
===================================================================
--- trunk/dports/python/py-h5py/Portfile	2012-09-20 13:21:11 UTC (rev 97941)
+++ trunk/dports/python/py-h5py/Portfile	2012-09-20 14:47:24 UTC (rev 97942)
@@ -19,30 +19,44 @@
                     openmaintainer
 description         Python Module for working with HDF5 files
 
-long_description    HDF5 for Python (h5py) is a general-purpose Python \
-                    interface to the Hierarchical Data Format library, \
-                    version 5. HDF5 is a versatile, mature scientific \
-                    software library designed for the fast, flexible \
-                    storage of enormous amounts of data. \n\n\
-                    From a Python programmer's perspective, HDF5 provides \
-                    a robust way to store data, organized by name in a \
-                    tree-like fashion. You can create datasets (arrays on \
-                    disk) hundreds of gigabytes in size, and perform \
-                    random-access I/O on desired sections. Datasets are \
-                    organized in a filesystem-like hierarchy using \
-                    containers called 'groups', and accessed using the \
-                    tradional POSIX /path/to/resource syntax.
+long_description  \
+    HDF5 for Python (h5py) is a general-purpose Python interface to the\
+    Hierarchical Data Format library, version 5. HDF5 is a versatile, mature\
+    scientific software library designed for the fast, flexible storage of\
+    enormous amounts of data.  \
+    \
+    \n\nFrom a Python programmer's perspective, HDF5 provides a robust way to\
+    store data, organized by name in a tree-like fashion. You can create\
+    datasets (arrays on disk) hundreds of gigabytes in size, and perform\
+    random-access I/O on desired sections. Datasets are organized in a\
+    filesystem-like hierarchy using containers called 'groups', and accessed\
+    using the tradional POSIX /path/to/resource syntax.
 
-
 homepage            http://code.google.com/p/h5py/
 master_sites        http://h5py.googlecode.com/files/
 distname            h5py-${version}
 
-checksums           md5     ea271f5cc8a78a531316918906aacdd0 \
-                    sha1    beddbfadb6f9fab651aeb8bede40b74fc2aeb889 \
-                    rmd160  75170ff5de1f7fb0eeda02525343285fb6213ced
+checksums \
+    rmd160  75170ff5de1f7fb0eeda02525343285fb6213ced \
+    sha256  cc5242c8ede616af9d8781c6d06603ff5a1f0de3044877176cc31a00cc581c40
 
-if {$subport != $name } {
+# Support for -devel
+set DEV_VERSION     0
+
+subport             py26-h5py-devel {set DEV_VERSION 26}
+subport             py27-h5py-devel {set DEV_VERSION 27}
+subport             py31-h5py-devel {set DEV_VERSION 31}
+subport             py32-h5py-devel {set DEV_VERSION 32}
+
+if {${DEV_VERSION}} {
+    version         2.1.0b2
+    python.version  ${DEV_VERSION}
+    checksums \
+        rmd160  20a9bc4acd0ba624019130c117118250a2c3f1c7 \
+        sha256  fedd992225fa5afb25c6cc14fcdaa246733b09863821a5e4547d2f65df623e3d
+}
+
+if {$subport != $name} {
     depends_lib-append  port:py${python.version}-numpy \
                         port:hdf5-18
 }
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