Use generated git version in configure.ac. More slight cleanups to do…

…cs and examples.

configure.ac

@@ -4,7 +4,7 @@ dnl
 dnl +------------------------
 dnl | Initialize package info
 dnl +------------------------
-AC_INIT([TIDAS], [0.1.0], [[email protected]], [tidas], [https://github.com/hpc4cmb/tidas])
+AC_INIT([TIDAS], m4_esyscmd_s([m4/git-version.sh]), [[email protected]], [tidas], [https://github.com/hpc4cmb/tidas])
 AC_CONFIG_SRCDIR([Makefile.am])
 AM_INIT_AUTOMAKE([foreign])
 AC_CONFIG_HEADERS(config.h)

docs/sphinx/group.rst

@@ -6,15 +6,16 @@ Groups
 
 A "group" contains a collection of timestreams with consistent sampling.  In other words, each timestream in the group has the same number of samples.  Each timestream has a name, a data type, and a string describing the units.  This metadata for one timestream is called a "field".  The collection of fields used in a group is called the "schema" of the group.  A group's schema is fixed at creation time, but the number of samples may be changed as needed.  A group is associated with a parent block.  Read and write operations on the data in a group is passed off to the "backend" class in use.  A group may also have a dictionary of scalar properties associated with it.
 
-C++
-~~~~~~~~~
-
-.. doxygenclass:: tidas::group
-        :members:
 
 Python
 ~~~~~~~~~
 
 .. autoclass:: tidas.Group
     :members:
 
+
+C++
+~~~~~~~~~
+
+.. doxygenclass:: tidas::group
+        :members:

docs/sphinx/intervals.rst

@@ -6,14 +6,6 @@ Intervals
 
 When dealing with time domain data, it is very common to specify ranges of time that have some meaning.  These might be spans of good or bad data, or time ranges that enclose some important event or feature.  This use case is so common that TIDAS has a built in object for this.  An "intervals" object represents a list of time ranges.  Each span can also optionally have a range of sample indices, which is useful if the intervals are associated with a particular group.
 
-C++
-~~~~~~~~~
-
-.. doxygenclass:: tidas::intrvl
-        :members:
-
-.. doxygenclass:: tidas::intervals
-        :members:
 
 Python
 ~~~~~~~~~
@@ -25,3 +17,13 @@ Python
     :members:
 
 
+C++
+~~~~~~~~~
+
+.. doxygenclass:: tidas::intrvl
+        :members:
+
+.. doxygenclass:: tidas::intervals
+        :members:
+
+

docs/sphinx/volume.rst

@@ -6,15 +6,20 @@ Volumes
 
 A volume is the top-level TIDAS object.  It defines the "backend" data format and holds a metadata database for fast(er) data selection and query operations.  It also contains the root of a hierarchy (a tree) of "blocks".
 
+
+Planning the Layout of a Volume
+--------------------------------------
+
+An important feature of TIDAS volumes is the ability to open / copy / link only a subset of the data.  This selection operation is performed with a regular expression match on the tree of block names.  This means that it is critical to plan out the organization of blocks to make it possible to do any desired data selections.
+
+Most data collected from monitoring systems, experimental apparatuses, etc, have natural ways that the data might be split up into pieces.  For example, if the data collection is started fresh each day, you might have a block for each day.  If there are different sorts of data within one day which you might want to frequently split up, then you could have sub-blocks in each day for the different data types.  Going the other direction up the hierarchy, you might organize the days into blocks for each month, each quarter, each year, etc. 
+
+
 Interface
 ---------------
 
-C++
-~~~~~~~~~
+Here is a basic reference for the volume interface.
 
-.. doxygenclass:: tidas::volume
-    :members:
-    :no-link:
 
 Python
 ~~~~~~~~~
@@ -23,17 +28,19 @@ Python
     :members:
 
 
+C++
+~~~~~~~~~
+
+.. doxygenclass:: tidas::volume
+    :members:
+    :no-link:
+
 
 Blocks
 ----------------
 
 A "block" is simply a logical collection of data which has a "name" (a string) associated with it.  A block can contain data itself, other blocks, or both.  A volume contains a special "root" block, which is the top of the tree / hierarchy of blocks in the volume.
 
-C++
-~~~~~~~~~
-
-.. doxygenclass:: tidas::block
-    :members:
 
 Python
 ~~~~~~~~~
@@ -42,12 +49,11 @@ Python
     :members:
 
 
-Planning the Layout of a Volume
---------------------------------------
-
-An important feature of TIDAS volumes is the ability to open / copy / link only a subset of the data.  This selection operation is performed with a regular expression match on the tree of block names.  This means that it is critical to plan out the organization of blocks to make it possible to do any desired data selections.
+C++
+~~~~~~~~~
 
-Most data collected from monitoring systems, experimental apparatuses, etc, have natural ways that the data might be split up into pieces.  For example, if the data collection is started fresh each day, you might have a block for each day.  If there are different sorts of data within one day which you might want to frequently split up, then you could have sub-blocks in each day for the different data types.  Going the other direction up the hierarchy, you might organize the days into blocks for each month, each quarter, each year, etc. 
+.. doxygenclass:: tidas::block
+    :members:
 
 
 MPI Volumes
@@ -64,16 +70,17 @@ see those changes until the synchronization method is called.
 	Multiple processes writing to the same group or intervals object is not supported, and may corrupt the data files for those objects!
 
 
-C++
+Python
 ~~~~~~~~~
 
-.. doxygenclass:: tidas::mpi_volume
+.. autoclass:: tidas.mpi_volume.MPIVolume
     :members:
-    :no-link:
 
-Python
+
+C++
 ~~~~~~~~~
 
-.. autoclass:: tidas.mpi_volume.MPIVolume
+.. doxygenclass:: tidas::mpi_volume
     :members:
+    :no-link:
 

examples/demo_weather.py

@@ -74,8 +74,8 @@
         # Add a block for this year
         yb = root.block_add(year)
 
-        for monthnum in range(1, 13):
-            # Add a block for the month
+        for monthnum in range(1, 2):
+            # Add a block for the month.  Just use January for now.
             month = calendar.month_abbr[monthnum]
             mb = yb.block_add(month)
 
@@ -155,8 +155,6 @@
 # interested in the speed, not the direction.  Let's extract just the wind
 # speed data for the month of January, 2019.
 
-print("Small demo")
-
 file = "demo_weather_small"
 if os.path.isdir(file):
     shutil.rmtree(file)
@@ -167,6 +165,7 @@
 
 # Take a quick peek at the small data volume:
 
+print("\nSmall volume with just wind speed:\n")
 with tidas.Volume(file) as vol:
     vol.info()
 
@@ -176,8 +175,6 @@
 # new derived data products.  We can make a volume which links to the original
 # data and then add new groups to this.
 
-print("Link demo")
-
 file = "demo_weather_link"
 if os.path.isdir(file):
     shutil.rmtree(file)
@@ -222,5 +219,6 @@
 # in this volume.  However, note that trying to write to these would fail
 # if the filesystem permissions were read-only.
 
+print("\nVolume linked to original with new local Intervals:\n")
 with tidas.Volume(file) as vol:
     vol.info()