[h5py] 244/455: Update examples

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to annotated tag 1.3.0
in repository h5py.

commit fd4e9e77835a6148f9cbb62f4bd6f6309df59c13
Author: andrewcollette <andrew.collette at gmail.com>
Date:   Fri May 22 21:14:23 2009 +0000

    Update examples
---
 examples/groups.py | 76 +++++++++++++++++++++++++++++++++---------------------
 examples/simple.py | 65 ++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 112 insertions(+), 29 deletions(-)

diff --git a/examples/groups.py b/examples/groups.py
index 6bf1778..435119d 100644
--- a/examples/groups.py
+++ b/examples/groups.py
@@ -1,50 +1,68 @@
 
 """
-    Example demonstrating some features of groups in HDF5, and how to
-    use them from h5py.  
+    HDF5 for Python (h5py) is a Python interface to the HDF5 library.  Built
+    on a near-complete Python wrapping of the HDF5 C API, it exposes a simple,
+    NumPy-like interface for interacting with HDF5 files, datasets, attributes
+    and groups.
+
+    This module demonstrates the use of HDF5 groups from h5py.  HDF5 groups
+    are analagous to directories in a filesystem; they even use the UNIX-style
+    /path/to/resource syntax.  In h5py, groups act like dictionaries.  They
+    also provide the necessary methods to create subgroups, datasets, and
+    attributes.
+
+    HDF5 for Python is available at h5py.alfven.org.
 """
 
+import numpy as np
 import h5py
 
-groups = ['/foo', '/foo/bar', '/foo/bar/baz',
-          '/grp1', '/grp1/grp2', '/mygroup']
+f = h5py.File('myfile.hdf5','w')
 
-f = h5py.File('group_test.hdf5','w')
+# The file object is also the "root group" ("/") in HDF5.  It's currently
+# empty:
+print "Number of items in the root group: %d" % len(f)
 
-for grpname in groups:
-    f.create_group(grpname)
+# Create some groups
+g1 = f.create_group('Group1')
+g2 = f.create_group('Another Group')
+g3 = f.create_group('Yet another group')
 
-print "Root group names:"
+# All groups, including the root group, support a basic dictionary-style
+# interface
+print "There are now %d items in the root group" % len(f)
+print "They are: %s" % ", ".join(f)  # iterating yields member names
 
-for name in f:
-    print "   ", name
+# Groups can contain subgroups themselves
+sub1 = g1.create_group("Subgroup1")
 
-print "Root group info:"
+# Prints "/Group1/Subgroup1"
+print "Full name of subgroup is %s" % sub1.name
 
-for name, grp in f.iteritems():
-    print "    %s: %s items" % (name, len(grp))
+# You can retrieve them using __getitem__ syntax
+sub2 = g1['Subgroup1']
 
-if h5py.version.api_version_tuple >= (1,8):
+# You can attach attributes to groups, just like datasets, containing just
+# about anything NumPy can handle.
+g1.attrs['purpose'] = "A demonstration group"
+g1.attrs['Life, universe, everything'] = 42
+g1.attrs['A numpy array'] = np.ones((3,), dtype='>i2')
 
-    def treewalker(name):
-        """ Callback function for visit() """
-        print "    Called for %s" % name
+# Create datasets using group methods.  (See other examples for a more in-
+# depth introduction to datasets).
 
-    print "Walking..."
-    f.visit(treewalker)
+data = np.arange(100*100).reshape((100,100))
+
+dset = sub1.create_dataset("My dataset", data=data)
+
+print "The new dataset has full name %s, shape %s and type %s" % \
+        (dset.name, dset.shape, dset.dtype) 
+
+# Closing the file closes all open objects
+f.close()
 
-    print "Copying /foo to /mygroup/newfoo..."
-    f.copy("/foo", "/mygroup/newfoo")
 
-    print "Walking again..."
-    f.visit(treewalker)
 
-    g = f['/grp1']
 
-    print "Walking from /grp1..."
-    g.visit(treewalker)
 
-else:
-    print "HDF5 1.8 is needed for extra demos"
 
-f.close()
diff --git a/examples/simple.py b/examples/simple.py
new file mode 100644
index 0000000..d43c8a5
--- /dev/null
+++ b/examples/simple.py
@@ -0,0 +1,65 @@
+
+"""
+    HDF5 for Python (h5py) is a Python interface to the HDF5 library.  Built
+    on a near-complete Python wrapping of the HDF5 C API, it exposes a simple,
+    NumPy-like interface for interacting with HDF5 files, datasets, attributes
+    and groups.
+
+    This is a simple module which demonstrates some of the features of HDF5,
+    including the ability to interact with large on-disk datasets in a
+    NumPy-like fashion.
+
+    In this example, we create a file containing a 1 GB dataset, populate it
+    from NumPy, and then slice into it.  HDF5 attributes are also demonstrated.
+
+    HDF5 for Python is available at h5py.alfven.org.
+"""
+
+import numpy as np
+import h5py
+
+f = h5py.File('myfile.hdf5','w')
+
+# Create a new, empty dataset to hold 1GB of floats
+dset = f.create_dataset('MyDataset', (256, 1024, 1024), dtype='f')
+
+# Datasets have some of the same properties as NumPy arrays
+print "The new dataset has shape %s and type %s" % (dset.shape, dset.dtype)
+
+# Attach some attributes
+dset.attrs['purpose'] = "Demonstration dataset for floats"
+dset.attrs['original size'] = (256, 1024, 1024)  # This tuple is auto-
+                                                 # converted to an HDF5 array.
+dset.attrs['constant'] = 42
+
+# Populate the file in a loop.  Note that you can use NumPy-style slicing
+# on datasets directly, including the row-like selection demonstrated here.
+
+base = np.arange(1024*1024, dtype='f').reshape((1024,1024))
+for idx in xrange(256):
+    if(idx%16==0): print 'Populating row %d' % idx
+
+    base += idx*(1024*1024)
+    dset[idx] = base
+
+
+# Perform some operations requiring random access.  Note these operations use
+# HDF5 "dataspaces" for efficient read/write.
+
+print "Resetting some indices to one"
+dset[15, 24, 100:200] = np.ones((100,), dtype='f')
+
+print 'Retrieving every 64th element... '
+subarray = dset[...,::64]
+print 'Retrived array has shape %s' % (subarray.shape,)
+
+# We can also access attributes using dictionary-style syntax
+for name, value in dset.attrs.iteritems():
+    print 'Attribute "%s" has value: %r' % (name, value)
+
+# When finished, close the file.  The dataset (and all other open objects)
+# are closed automatically.
+f.close()
+
+
+

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