VS (Vdata table) API (`pyhdf.VS`)¶

A module of the pyhdf package implementing the VS (Vdata table) API of the NCSA HDF4 library.

Introduction¶

VS is one of the modules composing pyhdf, a python package implementing the NCSA HDF library and letting one manage HDF files from within a python program. Two versions of the HDF library currently exist, version 4 and version 5. pyhdf only implements version 4 of the library. Many different APIs are to be found inside the HDF4 specification. Currently, pyhdf implements just a few of those: the SD, VS and V APIs. Other APIs should be added in the future (GR, AN, etc).

VS allows the definition of structured data tables inside an HDF file. Those tables are designated as “vdatas” (the name has to do with data associated with the “vertices” of geometrical models, the storage of which the API was originally designed for). A vdata is composed of a fixed number of columns (also called fields), where a column can store a fixed number of data values, all of the same type. The number of values allowed inside a field is called the “order” of the field. A table is composed of a varying number of rows (also called records), a record representing the sequence of values stored in each field of the vdata.

A vdata is associated with a descriptive name, and likewise each field of the vdata. A vdata can also be tagged with a “class” to further describe the vdata purpose. Records and fields are identified by a zero-based index. An arbitrary number of attributes of different types can be attached to a vdata as a whole, or to its individual fields. An attribute is a (name, value) pair, where “value” can be of many types, and be either single or multi-valued. The number of values stored in an attribute is called the “order” of the attribute.

The following example illustrates a simple vdata that could be stored inside an HDF file. See section “Programming models” for an example program implementing this vdata.

INVENTORY (experimental status)

partid

description

qty

wght(lb)

price($)

Q1234

bolt

12

0.01

0.05

B5432

brush

10

0.4

4.25

S7613

scissor

2

0.2

3.75

The vdata is composed of 5 fields. 3 records are shown (of course, a vdata can store much more than that). “INVENTORY” would be the vdata name, and “partid”, “description”, etc, would be the field names. The data type varies between fields. “partid” and “description” would be of “multicharacter” type (aka “string”), “qty” would be a integer, and “wght” and “price” would be floats. The text in parentheses could be stored as attributes. A “status” attribute could be defined for the table as a whole, and given the value “experimental”. Likewise, a “unit” attribute could be associated with fields “wght” and “price”, and given the values “lb” and “$”, resp.

The VS API allows one to create, locate and open a vdata inside an HDF file, update and append records inside it, read records randomly or sequentially, and access and update the vdata and field attributes. Attributes can be read and written using the familiar python “dot notation”, and records can be read and written by indexing and slicing the vdata as if it were a python sequence.

VS module key features¶

VS key features are as follows.

pyhdf implements almost every routine of the original VS API. Only a few have been ignored, most of them being of a rare use:
- VSgetblocksize() / VSsetblocksize()
- VSsetnumblocks()
- VSlone
It is quite straightforward to go from a C version to a python version of a program accessing the VS API, and to learn VS usage by referring to the C API documentation.
A few high-level python methods have been developed to ease programmers task. Of greatest interest are the following:
- Access to attributes through the familiar “dot notation”.
- Indexing and slicing a vdata to read and write its records, similarly to a python sequence.
- Easy retrieval of info on a vdata and its fields.
- Easy creation of vdatas.

Accessing the VS module¶

To access the VS module a python program can say one of:

>>> import pyhdf.VS        # must prefix names with "pyhdf.VS."
>>> from pyhdf import VS   # must prefix names with "VS."
>>> from pyhdf.VS import * # names need no prefix

This document assumes the last import style is used.

VS is not self-contained, and needs functionality provided by another pyhdf module, namely the HDF module. This module must thus be imported also:

>>> from .HDF import *

Package components¶

pyhdf is a proper Python package, eg a collection of modules stored under a directory whose name is that of the package and which stores an __init__.py file. Following the normal installation procedure, this directory will be <python-lib>/site-packages/pyhdf’, where <python-lib> stands for the python installation directory.

For each HDF API exists a corresponding set of modules.

The following modules are related to the VS API.

_hdfext
C extension module responsible for wrapping the HDF C library for all python modules

hdfext
python module implementing some utility functions complementing the _hdfext extension module

error
defines the HDF4Error exception

HDF
python module providing support to the VS module

VS
python module wrapping the VS API routines inside an OOP framework

_hdfext and hdfext were generated using the SWIG preprocessor. SWIG is however not needed to run the package. Those two modules are meant to do their work in the background, and should never be called directly. Only HDF and VS should be imported by the user program.

Prerequisites¶

The following software must be installed in order for VS to work.

HDF (v4) library
pyhdf does not include the HDF4 library, which must be installed separately.

HDF is available at: “https://portal.hdfgroup.org/display/support/Download+HDF4”.

Numeric is also needed by the SD module. See the SD module documentation.

Documentation¶

pyhdf has been written so as to stick as closely as possible to the naming conventions and calling sequences documented inside the “HDF User s Guide” manual. Even if pyhdf gives an OOP twist to the C API, the manual can be easily used as a documentary source for pyhdf, once the class to which a function belongs has been identified, and of course once requirements imposed by the Python language have been taken into account. Consequently, this documentation will not attempt to provide an exhaustive coverage of the HDF VS API. For this, the user is referred to the above manual. The documentation of each pyhdf method will indicate the name of the equivalent routine as it is found inside the C API.

This document (in both its text and html versions) has been completely produced using “pydoc”, the Python documentation generator (which made its debut in the 2.1 Python release). pydoc can also be used as an on-line help tool. For example, to know everything about the VS.VD class, say:

>>> from pydoc import help
>>> from pyhdf.VS import *
>>> help(VD)

To be more specific and get help only for the read() method of the VD class:

>>> help(VD.read)

pydoc can also be called from the command line, as in:

% pydoc pyhdf.VS.VD         # doc for the whole VD class
% pydoc pyhdf.VS.VD.read    # doc for the VD.read method

Summary of differences between the pyhdf and C VS API¶

Most of the differences between the pyhdf and C VS API can be summarized as follows.

In the C API, every function returns an integer status code, and values computed by the function are returned through one or more pointers passed as arguments.
In pyhdf, error statuses are returned through the Python exception mechanism, and values are returned as the method result. When the C API specifies that multiple values are returned, pyhdf returns a sequence of values, which are ordered similarly to the pointers in the C function argument list.

Error handling¶

All errors reported by the C VS API with a SUCCESS/FAIL error code are reported by pyhdf using the Python exception mechanism. When the C library reports a FAIL status, pyhdf raises an HDF4Error exception (a subclass of Exception) with a descriptive message. Unfortunately, the C library is rarely informative about the cause of the error. pyhdf does its best to try to document the error, but most of the time cannot do more than saying “execution error”.

VS needs support from the HDF module¶

The VS module is not self-contained (countrary to the SD module). It requires help from the HDF module, namely:

the HDF.HDF class to open and close the HDF file, and initialize the VS interface
the HDF.HC class to provide different sorts of constants (opening modes, data types, etc).

A program wanting to access HDF vdatas will almost always need to execute the following minimal set of calls:

>>> from pyhdf.HDF import *
>>> from pyhdf.VS import *
>>> hdfFile = HDF(name, HC.xxx)# open HDF file
>>> vs = hdfFile.vstart()      # initialize VS interface on HDF file
>>> ...                        # manipulate vdatas through "vs"
>>> vs.end()                   # terminate VS interface
>>> hdfFile.close()            # close HDF file

Classes summary¶

pyhdf wraps the VS API using different python classes:

VS      HDF VS interface
VD      vdata
VDField vdata field
VDattr  attribute (either at the vdata or field level)

In more detail:

VS     The VS class implements the VS (Vdata) interface applied to an
       HDF file. This class encapsulates the hdf instance, and all
       the top-level functions of the VS API.

       To create a VS instance, call the vstart() method of an
       HDF instance.

       methods:
         constructors:
           attach()       open an existing vdata given its name or
                          reference number, or create a new one,
                          returning a VD instance
           create()       create a new vdata and define its structure,
                          returning a VD instance

         creating and initializing a simple vdata
           storedata()    create a single-field vdata and initialize
                          its values

         closing the interface
           end()          close the VS interface on the HDF file

         searching
           find()         get a vdata reference number given its name
           next()         get the reference number of the vdata following
                          a given one

         inquiry
           vdatainfo()    return info about all the vdatas in the
                          HDF file

VD     The VD class describes a vdata. It encapsulates
       the VS instance to which the vdata belongs, and the vdata
       identifier.

       To instantiate a VD class, call the attach() or create()
       method of a VS class instance.

       methods:
         constructors
           attr()         create a VDAttr instance representing a
                          vdata attribute; "dot notation" can also be
                          used to access a vdata attribute
           field()        return a VDField instance representing a given
                          field of the vdata

         closing vdata
           detach()       end access to the vdata

         defining fields
           fdefine()      define the name, type and order of a new field
           setfields()    define the field names and field order for
                          the read() and write() methods; also used to
                          initialize the structure of a vdata previously
                          created with the VS.attach() method

         reading and writing
                          note: a vdata can be indexed and sliced like a
                          python sequence

           read()         return the values of a number of records
                          starting at the current record position
           seek()         reset the current record position
           seekend()      seek past the last record
           tell()         return the current record position
           write()        write a number of records starting at the
                          current record position

         inquiry
           attrinfo()     return info about all the vdata attributes
           fexist()       check if a vdata contains a given set of fields
           fieldinfo()    return info about all the vdata fields
           findattr()     locate an attribute, returning a VDAttr instance
                          if found
           inquire()      return info about the vdata
           sizeof()       return the size in bytes of one or more fields

VDField  The VDField class represents a vdata field. It encapsulates
         the VD instance to which the field belongs, and the field
         index number.

         To instantiate a VDField, call the field() method of a VD class
         instance.

         methods:
           constructors:
             attr()       return a VDAttr instance representing an
                          attribute of the field; "dot notation"
                          can also be used to get/set an attribute.

           inquiry
             attrinfo()   return info about all the field attributes
             find()       locate an attribute, returning a VDAttr
                          instance if found

VDAttr   The VDAttr class encapsulates methods used to set and query
         attributes defined at the level either of the vdata or the
         vdata field.

         To create an instance of this class, call the attr() or
         findattr() methods of a VD instance (for vdata attributes),
         or call the attr() or find() methods of a VDField instance
         (for field attributes).

         methods:
           get / set
             get()        get the attribute value
             set()        set the attribute value

           info
             info()       retrieve info about the attribute

Data types¶

Data types come into play when first defining vdata fields and attributes, and later when querying the definition of those fields and attributes. Data types are specified using the symbolic constants defined inside the HC class of the HDF module.

CHAR and CHAR8 (equivalent): an 8-bit character.
UCHAR, UCHAR8 and UINT8 (equivalent): unsigned 8-bit values (0 to 255)
INT8: signed 8-bit values (-128 to 127)
INT16: signed 16-bit values
UINT16: unsigned 16 bit values
INT32: signed 32 bit values
UINT32: unsigned 32 bit values
FLOAT32: 32 bit floating point values (C floats)
FLOAT64: 64 bit floating point values (C doubles)

There is no explicit “string” type. To simulate a string, set the field or attribute type to CHAR, and set the field or attribute “order” to a value of ‘n’ > 1. This creates and “array of characters”, close to a string (except that strings will always be of length ‘n’, right-padded with spaces if necessary).

Attribute access: low and high level¶

The VS API allow setting attributes on vdatas and vdata fields. Attributes can be of many types (int, float, char) of different bit lengths (8, 16, 32, 64 bits), and can be single or multi-valued. Values of a multi-valued attribute must all be of the same type.

Attributes can be set and queried in two different ways. First, given a VD instance (describing a vdata object) or a VDField instance (describing a vdata field), the attr() method of that instance is called to create a VDAttr instance representing the wanted attribute (possibly non existent). The set() method of this VDAttr instance is then called to define the attribute value, creating it if it does not already exist. The get() method returns the current attribute value. Here is an example.

>>> from pyhdf.HDF import *
>>> from pyhdf.VS import *
>>> f = HDF('test.hdf', HC.WRITE) # Open file 'test.hdf' in write mode
>>> vs = f.vstart()            # init vdata interface
>>> vd = vs.attach('vtest', 1) # attach vdata 'vtest' in write mode
>>> attr = vd.attr('version')  # prepare to define the 'version' attribute
                               # on the vdata
>>> attr.set(HC.CHAR8,'1.0')   # set attribute 'version' to string '1.0'
>>> print(attr.get())           # get and print attribute value
>>> fld  = vd.field('fld1')    # obtain a field instance for field 'fld1'
>>> attr = fld.attr('range')   # prepare to define attribute 'range' on
                               # this field
>>> attr.set(HC.INT32,(-10, 15)) # set attribute 'range' to a pair of ints
>>> print(attr.get())             # get and print attribute value

>>> vd.detach()                # "close" the vdata
>>> vs.end()                   # terminate the vdata interface
>>> f.close()                  # close the HDF file

The second way consists of setting/querying an attribute as if it were a normal python class attribute, using the usual dot notation. Above example then becomes:

>>> from pyhdf.HDF import *
>>> from pyhdf.VS import *
>>> f = HDF('test.hdf', HC.WRITE) # Open file 'test.hdf' in write mode
>>> vs = f.vstart()            # init vdata interface
>>> vd = vs.attach('vtest', 1) # attach vdata 'vtest' in write mode
>>> vd.version = '1.0'         # create vdata attribute 'version',
                               # setting it to string '1.0'
>>> print(vd.version)           # print attribute value
>>> fld  = vd.field('fld1')    # obtain a field instance for field 'fld1'
>>> fld.range = (-10, 15)      # create field attribute 'range', setting
                               # it to the pair of ints (-10, 15)
>>> print(fld.range)            # print attribute value
>>> vd.detach()                # "close" the vdata
>>> vs.end()                   # terminate the vdata interface
>>> f.close()                  # close the HDF file

Note how the dot notation greatly simplifies and clarifies the code. Some latitude is however lost by manipulating attributes in that way, because the pyhdf package, not the programmer, is then responsible of setting the attribute type. The attribute type is chosen to be one of:

HC.CHAR8

if the attribute value is a string

HC.INT32

if all attribute values are integers

HC.FLOAT64

otherwise

The first way of handling attribute values must be used if one wants to define an attribute of any other type (for ex. 8 or 16 bit integers, signed or unsigned). Also, only a VDAttr instance gives access to attribute info, through its info() method.

However, accessing HDF attributes as if they were python attributes raises an important issue. There must exist a way to assign generic attributes to the python objects without requiring those attributes to be converted to HDF attributes. pyhdf uses the following rule: an attribute whose name starts with an underscore (‘_’) is either a “predefined” attribute (see below) or a standard python attribute. Otherwise, the attribute is handled as an HDF attribute. Also, HDF attributes are not stored inside the object dictionary: the python dir() function will not list them.

Attribute values can be updated, but it is illegal to try to change the value type, or the attribute order (number of values). This is important for attributes holding string values. An attribute initialized with an ‘n’ character string is simply a character attribute of order ‘n’ (eg a character array of length ‘n’). If ‘vd’ is a vdata and we initialize its ‘a1’ attribute as ‘vd.a1 = “abcdef”’, then a subsequent update attempt like ‘vd.a1 = “12”’ will fail, because we then try to change the order of the attribute (from 6 to 2). It is mandatory to keep the length of string attributes constant. Examples below show simple ways how this can be done.

Predefined attributes¶

The VD and VDField classes support predefined attributes to get (and occasionnaly set) attribute values easily, without having to call a class method. The names of predefined attributes all start with an underscore (‘_’).

In the following tables, the RW column holds an X if the attribute is read/write. See the HDF User s guide for details about more “exotic” topics like “class”, “faked vdata” and “tag”.

VD predefined attributes

name

RW

description

C library routine

_class

X

class name

VSgetclass/VSsetclass

_fields

list of field names

VSgetfields

_interlace

X

interlace mode

VSgetinterlace/VSsetinterlace

_isattr

true if vdata is “faked” by HDF to hold attributes

VSisattr

_name

X

name of the vdata

VSgetname/VSsetname

_nattrs

number of attributes

VSfnattrs

_nfields

number of fields

VFnfields

_nrecs

number of records

VSelts

_recsize

record size (bytes)

VSQueryvsize

_refnum

reference number

VSQueryref

_tag

vdata tag

VSQuerytag

_tnattrs

total number of vdata and field attributes

VSnattrs

VDField predefined attributes

name

RW

description

C library routine

_esize

external size (bytes)

VFfieldesize

_index

index number

VSfindex

_isize

internal size (bytes)

VFfieldisize

_name

name

VFfieldname

_nattrs

number of attributes

VSfnattrs

_order

order (number of values)

VFfieldorder

_type

field type (HC.xxx)

VFfieldtype

Record access: low and high level¶

vdata records can be read and written in two different ways. The first one consists of calling the basic I/O methods of the vdata:

seek() to set the current record position, if necessary;
read() to retrieve a given number of records from that position;
write() to write a given number of records starting at that position

A second, higher level way, lets one see a vdata similarly to a python sequence, and access its contents using the familiar indexing and slicing notation in square brackets. Reading and writing a vdata as if it were a python sequence may often look simpler, and improve code legibility.

Here are some examples of how a vdata ‘vd’ holding 3 fields could be read.

>>> print(vd[0])         # print record 0
>>> print(vd[-1])        # print last record
>>> print(vd[2:])        # print records 2 and those that follow
>>> print(vd[:])         # print all records
>>> print(vd[:,0])       # print field 0 of all records
>>> print(vd[:3,:2])     # print first 2 fields of first 3 records

As the above examples show, the usual python rules are obeyed regarding the interpretation of indexing and slicing values. Note that the vdata fields can be indexed and sliced, not only the records. The setfields() method can also be used to select a subset to the vdata fields (setfields() also let you reorder the fields). When the vdata is indexed (as opposed to being sliced), a single record is returned as a list of values. When the vdata is sliced, a list of records is always returned (thus a 2-level list), even if the slice contains only one record.

A vdata can also be written similarly to a python sequence. When indexing the vdata (as opposed to slicing it), a single record must be assigned, and the record must be given as a sequence of values. It is legal to use as an index the current number of records in the vdata: the record is then appended to the vdata. When slicing the vdata, the records assigned to the slice must always be given as a list of records, even if only one record is assigned. Also, the number of records assigned must always match the width of the slice, except if the slice includes or goes past the last record of the vdata. In that case, the number of records assigned can exceed the width of the slice, and the extra records are appended to the vdata. So, to append records to vdata ‘vd’, simply assign records to the slice ‘vd[vd._nrecs:]’. Note that, even if the ‘field’ dimension can be specified in the left-hand side expression, there is no real interest in doing so, since all fields must be specified when assigning a record to the vdata: it is an error to try to assign just a few of the fields.

For example, given a vdata ‘vd’ holding 5 records, and lists ‘reca’, ‘recb’, etc, holding record values:

vd[0] = reca              # updates record 0
vd[0,:] = reca            # specifying fields is OK, but useless
vd[0,1:] = reca[1:]       # error: all fields must be assigned
vd[1] = [recb, recc]      # error: only one record allowed
vd[5] = recc              # append one record
vd[1:3] = [reca,recb]     # updates second and third record
vd[1:4] = [reca, recb]    # error: 3 records needed
vd[5:] = [reca,recb]      # appends 2 records to the vdata
vd[4:] = [reca, recb]     # updates last record, append one

Programming models¶

Creating and initializing a new vdata¶

The following code can serve as a model for the creation and initialization of a new vdata. It implements the INVENTORY example described in the “Introduction” section:

from pyhdf.HDF import *
from pyhdf.VS import *

# Open HDF file and initialize the VS interface
f = HDF('inventory.hdf',    # Open file 'inventory.hdf' in write mode
        HC.WRITE|HC.CREATE) # creating it if it does not exist
vs = f.vstart()             # init vdata interface

# Create vdata and define its structure
vd = vs.create(             # create a new vdata
               'INVENTORY', # name of the vdata
                            # fields of the vdata follow
           (('partid',HC.CHAR8, 5),       # 5 char string
            ('description',HC.CHAR8, 10), # 10 char string field
            ('qty',HC.INT16, 1),          # 1 16 bit int field
            ('wght',HC.FLOAT32, 1),       # 1 32 bit float
            ('price',HC.FLOAT32,1)        # 1 32 bit float
           ))         # 5 fields allocated in the vdata

# Set attributes on the vdata and its fields
vd.field('wght').unit = 'lb'
vd.field('price').unit = '$'
# In order to be able to update a string attribute, it must
# always be set to the same length. This sets 'status' to a 20
# char long, left-justified string, padded with spaces on the right.
vd.status = "%-20s" % 'phase 1 done'

# Store records
vd.write((                # write 3 records
          ('Q1234', 'bolt',12, 0.01, 0.05),   # record 1
          ('B5432', 'brush', 10, 0.4, 4.25),  # record 2
          ('S7613', 'scissor', 2, 0.2, 3.75)  # record 3
          ))
vd.detach()               # "close" the vdata

vs.end()                  # terminate the vdata interface
f.close()                 # close the HDF file

Note that is mandatory to always write whole records to the vdata. Note also the comments about the initialization of the ‘status’ vdata attribute. We want to be able update this attribute (see following examples). However, the VS API prohibits changing an attribute type when updating its value. Since the length (order) of an attribute is part of its type, we make sure of setting the attribute to a length long enough to accommodate the longest possible string we migh want to assign to the attribute.

Appending records to a vdata¶

Appending records requires first seeking to the end of the vdata, to avoid overwriting existing records. The following code can serve as a model. The INVENTORY vdata created before is used:

from pyhdf.HDF import *
from pyhdf.VS import *

f = HDF('inventory.hdf',         # Open 'inventory.hdf' in write mode
        HC.WRITE|HC.CREATE)      # creating it if it does not exist
vs = f.vstart()                  # init vdata interface
vd = vs.attach('INVENTORY', 1)   # attach 'INVENTORY' in write mode

# Update the `status' vdata attribute. The attribute length must not
# change. We call the attribute info() method, which returns a list
# where number of values (eg string length) is stored at index 2.
# We then assign a left justified string of exactly that length.
len = vd.attr('status').info()[2]
vd.status = '%-*s' % (len, 'phase 2 done')

vd[vd._nrecs:] = (                     # append 2 records
      ('A4321', 'axe', 5, 1.5, 25),    # first record
      ('C3214', 'cup', 100, 0.1, 3.25) # second record
                )
vd.detach()               # "close" the vdata

vs.end()                  # terminate the vdata interface
f.close()                 # close the HDF file

Note how, when updating the value of the ‘status’ vdata attribute, we take care of assigning a value of the same length as that of the original value. Otherwise, the assignment would raise an exception. Records are written by assigning the vdata through a slicing expression, like a python sequence. By specifying the number of records as the start of the slice, the records are appended to the vdata.

Updating records in a vdata¶

Updating requires seeking to the record to update before writing the new records. New data will overwrite this record and all records that follow, until a new seek is performed or the vdata is closed. Note that record numbering starts at 0.

The following code can serve as a model. The INVENTORY vdata created before is used:

from pyhdf.HDF import *
from pyhdf.VS import *

f = HDF('inventory.hdf',         # Open 'inventory.hdf' in write mode
        HC.WRITE|HC.CREATE)      # creating it if it does not exist
vs = f.vstart()                  # init vdata interface
vd = vs.attach('INVENTORY', 1)   # attach 'INVENTORY' in write mode

# Update the `status' vdata attribute. The attribute length must not
# change. We call the attribute info() method, which returns a list
# where number of values (eg string length) is stored at index 2.
# We then assign a left justified string of exactly that length.
len = vd.attr('status').info()[2]
vd.status = '%-*s' % (len, 'phase 3 done')

# Update record at index 1 (second record)
vd[1]  = ('Z4367', 'surprise', 10, 3.1, 44.5)
# Update record at index 4, and all those that follow
vd[4:] = (
          ('QR231', 'toy', 12, 2.5, 45),
          ('R3389', 'robot', 3, 45, 2000)
          )
vd.detach()               # "close" the vdata
vs.end()                  # terminate the vdata interface
f.close()                 # close the HDF file

Reading a vdata¶

The following example shows how read the vdata attributes and sequentially maneuver through its records. Note how we use the exception mechanism to break out of the reading loop when we reach the end of the vdata:

from pyhdf.HDF import *
from pyhdf.VS import *

f = HDF('inventory.hdf')         # open 'inventory.hdf' in read mode
vs = f.vstart()                  # init vdata interface
vd = vs.attach('INVENTORY')      # attach 'INVENTORY' in read mode

# Display some vdata attributes
print "status:", vd.status
print "vdata: ", vd._name        # predefined attribute: vdata name
print "nrecs: ", vd._nrecs       # predefined attribute:  num records

# Display value of attribute 'unit' for all fields on which
# this attribute is set
print "units: ",
for fieldName in vd._fields:     # loop over all field names
    try:
        # instantiate field and obtain value of attribute 'unit'
        v = vd.field(fieldName).unit
        print "%s: %s" % (fieldName, v),
    except:                      # no 'unit' attribute: ignore
        pass
print ""
print ""

# Display table header.
header = "%-7s %-12s %3s %4s %8s" % tuple(vd._fields)
print "-" * len(header)
print header
print "-" * len(header)

# Loop over the vdata records, displaying each record as a table row.
# Current record position is 0 after attaching the vdata.
while 1:
    try:
        rec = vd.read()       # read next record
        # equivalent to:
      # rec = vd[vd.tell()]
        print "%-7s %-12s %3d %4.1f %8.2f" % tuple(rec[0])
    except HDF4Error:             # end of vdata reached
        break

vd.detach()               # "close" the vdata
vs.end()                  # terminate the vdata interface
f.close()                 # close the HDF file

In the previous example, the reading/displaying loop can be greatly simplified by rewriting it as follows:

from pyhdf.HDF import *
from pyhdf.VS import *

f = HDF('inventory.hdf')         # open 'inventory.hdf' in read mode
vs = f.vstart()                  # init vdata interface
vd = vs.attach('INVENTORY')      # attach 'INVENTORY' in read mode

....

# Read all records at once, and loop over the sequence.
for rec in vd[:]:
    print "%-7s %-12s %3d %4.1f %8.2f" % tuple(rec)

vd.detach()               # "close" the vdata
...

The indexing expression ‘vd[:]’ returns the complete set of records, which can then be looped over using a ‘for’ statement. This style of loop is quite clean, and should look very familiar to python adepts.

class pyhdf.VS.VS(hinst)[source]¶

The VS class implements the VS (Vdata) interface applied to an HDF file. To instantiate a VS class, call the vstart() method of an HDF instance.

attach(num_name, write=0)[source]¶

Locate an existing vdata or create a new vdata in the HDF file, returning a VD instance.

Args:

num_name  Name or reference number of the vdata. An existing vdata
          can be specified either through its reference number or
          its name. Use -1 to create a new vdata.
          Note that uniqueness is not imposed on vdatas names,
          whereas refnums are guaranteed to be unique. Thus
          knowledge of its reference number may be the only way
          to get at a wanted vdata.

write     Set to 0 to open the vdata in read-only mode,
          set to 1 to open it in write mode

Returns:

VD instance representing the vdata

C library equivalent : VSattach

After creating a new vdata (num_name == -1), fields must be defined using method fdefine() of the VD instance, and those fields must be allocated to the vdata with method setfields(). Same results can be achieved, but more simply, by calling the create() method of the VS instance.

create(name, fields)[source]¶

Create a new vdata, setting its name and allocating its fields.

Args:

name     Name to assign to the vdata
fields   Sequence of field definitions. Each field definition
         is a sequence with the following elements in order:

         - field name
         - field type (one of HC.xxx constants)
         - field order (number of values)

         Fields are allocated to the vdata in the given order

Returns:

VD instance representing the created vdata

Calling the create() method is equivalent to the following calls:

vd = attach(-1,1), to create a new vdata and open it in
write mode
vd._name = name, to set the vdata name
vd.fdefine(…), to define the name, type and order of
each field
vd.setfields(…), to allocate fields to the vdata

C library equivalent : no equivalent

end()[source]¶

Close the VS interface.

Args:

No argument

Returns:

None

C library equivalent : Vend

find(vName)[source]¶

Get the reference number of a vdata given its name. The vdata can then be opened (attached) by passing this reference number to the attach() method.

Args:

vName    Name of the vdata for which the reference number
         is needed. vdatas names are not guaranteed to be
         unique. When more than one vdata bear the same name,
         find() will return the refnum of the first one founmd.

Returns:

vdata reference number. 0 is returned if the vdata does not exist.

C library equivalent : VSfind

next(vRef)[source]¶

Get the reference number of the vdata following a given vdata.

Args:

vRef   Reference number of the vdata preceding the one
       we require. Set to -1 to get the first vdata in
       the HDF file. Knowing its reference number,
       the vdata can then be opened (attached) by passing this
       reference number to the attach() method.

Returns:

Reference number of the vdata following the one given
by argument vref

An exception is raised if no vdata follows the one given by vRef.

C library equivalent : VSgetid

storedata(fieldName, values, data_type, vName, vClass)[source]¶

Create and initialize a single field vdata, returning the vdata reference number.

Args:

fieldName   Name of the single field in the vadata to create
values      Sequence of values to store in the field;. Each value can
            itself be a sequence, in which case the field will be
            multivalued (all second-level sequences must be of
            the same length)
data_type   Values type (one of HC.xxx constants). All values
            must be of the same type
vName       Name of the vdata to create
vClass      Vdata class (string)

Returns:

vdata reference number

C library equivalent : VHstoredata / VHstoredatam

vdatainfo(listAttr=0)[source]¶

Return info about all the file vdatas.

Args:

listAttr   Set to 0 to ignore vdatas used to store attribute
           values, 1 to list them (see the VD._isattr readonly
           attribute)

Returns:

List of vdata descriptions. Each vdata is described as
a 9-element tuple, composed of the following:

- vdata name
- vdata class
- vdata reference number
- vdata number of records
- vdata number of fields
- vdata number of attributes
- vdata record size in bytes
- vdata tag number
- vdata interlace mode

C library equivalent : no equivalent

vend()¶

Close the VS interface.

Args:

No argument

Returns:

None

C library equivalent : Vend

class pyhdf.VS.VD(vsinst, id)[source]¶

The VD class encapsulates the functionality of a vdata. To instantiate a VD class, call the attach() or the create() method of a VS class instance.

attr(name_or_index)[source]¶

Create a VDAttr instance representing a vdata attribute.

Args:

name_or_index   attribute name or index number; if a name is
                given, the attribute may not exist; in that
                case, it will be created when the VSAttr
                instance set() method is called

Returns:

VSAttr instance for the attribute. Call the methods of this
class to query, read or set the attribute.

C library equivalent : no equivalent

attrinfo()[source]¶

Return info about all the vdata attributes.

Args:

no argument

Returns:

dictionary describing each vdata attribute; for each attribute
a (name,data) pair is added to the dictionary, where 'data' is
a tuple holding:
- attribute data type (one of HC.xxx constants)
- attribute order
- attribute value
- attribute size in bytes

C library equivalent : no equivalent

detach()[source]¶

Terminate access to the vdata.

Args:

no argument

Returns:

None

C library equivalent : VSdetach

fdefine(name, type, order)[source]¶

Define a field. To initialize a newly created vdata with fields created with fdefine(), assign a tuple of field names to the _fields attribute or call the setfields() method.

Args:

name     field name
type     field data type (one of HC.xxx)
order    field order (number of values in the field)

Returns:

None

C library equivalent : VSfdefine

fexist(fields)[source]¶

Check if a vdata contains a given set of fields.

Args:

fields   sequence of field names whose presence in the
         vdata must be checked

Returns:

true  (1) if the given fields are present
false (0) otherwise

C library equivalent : VSfexist

field(name_index)[source]¶

Get a VDField instance representing a field of the vdata.

Args:

name_index   name or index number of the field

Returns:

VDfield instance representing the field

C library equivalent : no equivalent

fieldinfo()[source]¶

Retrieve info about all vdata fields.

Args:

no argument

Returns:

list where each element describes a field of the vdata;
each field is described by an 7-element tuple containing
the following elements:

- field name
- field data type (one of HC.xxx constants)
- field order
- number of attributes attached to the field
- field index number
- field external size
- field internal size

C library equivalent : no equivalent

findattr(name)[source]¶

Search the vdata for a given attribute.

Args:

name    attribute name

Returns:

 if found, VDAttr instance describing the attribute
 None otherwise

C library equivalent : VSfindattr

inquire()[source]¶

Retrieve info about the vdata.

Args:

no argument

Returns:

5-element tuple with the following elements:
  -number of records in the vdata
  -interlace mode
  -list of vdata field names
  -size in bytes of the vdata record
  -name of the vdata

C library equivalent : VSinquire

read(nRec=1)[source]¶

Retrieve the values of a number of records, starting at the current record position. The current record position is advanced by the number of records read. Current position is 0 after “opening” the vdata with the attach() method.

Args:

nRec    number of records to read

Returns:

2-level list. First level is a sequence of records,
second level gives the sequence of values for each record.
The values returned for each record are those of the fields
specified in the last call to method setfields(), in that
order. The complete vdata field set is returned if
setfields() has not been called.

An exception is raised if the current record position is already at the end of the vdata when read() is called. This exception can be caught as an “end of vdata” indication to exit a loop which scans each record of the vdata. Otherwise, the number of records to be read is lowered to the number of records remaining in the vdata, if that number is less than the number asked for by parameter ‘nRec’. Setting ‘nRec’ to an arbitrarily large value can thus be used to retrieve the remaining records in the vdata.

C library equivalent : VSread

seek(recIndex)[source]¶

Seek to the beginning of the record identified by its record index. A succeeding read will load this record in memory.

Args:

recIndex  index of the record in the vdata; numbering
          starts at 0. Legal values range from 0
          (start of vdata) to the current number of
          records (at end of vdata).

Returns:

record index

An exception is raised if an attempt is made to seek beyond the last record.

The C API prohibits seeking past the next-to-last record, forcing one to read the last record to advance to the end of the vdata. The python API removes this limitation.

Seeking to the end of the vdata can also be done by calling method seekend().

C library equivalent : VSseek

seekend()[source]¶

Set the current record position past the last vdata record. Subsequent write() calls will append records to the vdata.

Args:

no argument

Returns:

index of the last record plus 1

C library equivalent : no equivalent

setfields(*fldNames)[source]¶

Define the name and order of the fields to access with the read() and write() methods.

Args:

fldNames  variable length argument specifying one or more
          vdata field names

Returns:

None

C library equivalent : VSsetfields

setfields() indicates how to perform the matching between the vdata fields and the values passed to the write() method or returned by the read() method.

For example, if the vdata contains fields ‘a’, ‘b’ and ‘c’ and a “setfields(‘c’,’a’)” call is made, read() will thereafter return for each record the values of field ‘c’ and ‘a’, in that order. Field ‘b’ will be ignored.

When writing to a vdata, setfields() has a second usage. It is used to initialize the structure of the vdata, that is, the name and order of the fields that it will contain. The fields must have been previously defined by calls to the fdefine() method. Following that first call, setfields() can be called again to change the order in which the record values will be passed to the write() method. However, since it is mandatory to write whole records, subsequent calls to setfields() must specify every field name: only the field order can be changed.

sizeof(fields)[source]¶

Retrieve the size in bytes of the given fields.

Args:

fields   sequence of field names to query

Returns:

total size of the fields in bytes

C library equivalent : VSsizeof

tell()[source]¶

Return current record position in the vdata.

Args:

no argument

Returns:

current record position; 0 is at start of vdata.

C library equivalent : no equivalent

write(values)[source]¶

Write records to the vdata. Writing starts at the current record position, which is advanced by the number of records written.

Args:

values: 2-level sequence. First level is a sequence of records.
        A second level gives the sequence of record values.
        It is mandatory to always write whole records. Thus
        every record field must appear at the second level.
        The record values are ordered according the list of
        field names set in the last call to the setfields()
        method. The ordre of the complete vdata field set is
        used if setfields() has not been called.

Returns:

number of records written

To append to a vdata already holding ‘n’ records, it is necessary to first move the current record position to ‘n-1’ with a call to method seek(), then to call method read() for the side effect of advancing the current record position past this last record. Method seekend() does just that.

C library equivalent : VSwrite

class pyhdf.VS.VDField(vdinst, fIndex)[source]¶

The VDField class represents a vdata field. To create a VDField instance, call the field() method of a VD class instance.

attr(name_or_index)[source]¶

Create a VDAttr instance representing a field attribute.

Args:

name_or_index   attribute name or index number; if a name is
                specified, the attribute may not exist; in that
                case, it will be created when the VDAttr
                instance set() method is called; if an
                index number is specified, the attribute
                must exist

Returns:

VSAttr instance for the attribute. Call the methods of this
class to query, read or set the attribute.

C library equivalent : no equivalent

attrinfo()[source]¶

Return info about all the field attributes.

Args:

no argument

Returns:

dictionary describing each vdata attribute; for each attribute
a (name,data) pair is added to the dictionary, where 'data' is
a tuple holding:

- attribute data type (one of HC.xxx constants)
- attribute order
- attribute value
- attribute size in bytes

C library equivalent : no equivalent

find(name)[source]¶

Search the field for a given attribute.

Args:

name    attribute name

Returns:

 if found, VDAttr instance describing the attribute
 None otherwise

C library equivalent : VSfindattr

class pyhdf.VS.VDAttr(obj, name_or_index, fIndex)[source]¶

The VDAttr class encapsulates methods used to set and query attributes defined at the level either of the vdata or of the vdata field. To create an instance of this class, call the attr() method of a VD (vdata) or VDField (vdata field) instance.

get()[source]¶

Retrieve the attribute value.

Args:

no argument

Returns:

attribute value(s); a list is returned if the attribute
is made up of more than one value, except in the case of a
string-valued attribute (data type HC.CHAR8) where the
values are returned as a string

C library equivalent : VSgetattr

info()[source]¶

Retrieve info about the attribute.

Args:

no argument

Returns:

4-element tuple with the following components:
  -attribute name
  -attribute data type (one of HC.xxx constants)
  -attribute order (number of values)
  -attribute size in bytes

C library equivalent : VSattrinfo

set(data_type, values)[source]¶

Set the attribute value.

Args:

data_type    : attribute data type (see constants HC.xxx)
values       : attribute value(s); specify a list to create
               a multi-valued attribute; a string valued
               attribute can be created by setting 'data_type'
               to HC.CHAR8 and 'values' to the corresponding
               string

               If the attribute already exists, it will be
               updated. However, it is illegal to try to change
               its data type or its order (number of values).

Returns:

None

C library equivalent : VSsetattr

VS (Vdata table) API (`pyhdf.VS`)¶

Introduction¶

VS module key features¶

Accessing the VS module¶

Package components¶

Prerequisites¶

Documentation¶

Summary of differences between the pyhdf and C VS API¶

Error handling¶

VS needs support from the HDF module¶

Classes summary¶

Data types¶

Attribute access: low and high level¶

Predefined attributes¶

Record access: low and high level¶

Programming models¶

Creating and initializing a new vdata¶

Appending records to a vdata¶

Updating records in a vdata¶

Reading a vdata¶

Table of Contents

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HC.CHAR8	if the attribute value is a string
HC.INT32	if all attribute values are integers
HC.FLOAT64	otherwise

name	RW	description	C library routine
_class	X	class name	VSgetclass/VSsetclass
_fields		list of field names	VSgetfields
_interlace	X	interlace mode	VSgetinterlace/VSsetinterlace
_isattr		true if vdata is “faked” by HDF to hold attributes	VSisattr
_name	X	name of the vdata	VSgetname/VSsetname
_nattrs		number of attributes	VSfnattrs
_nfields		number of fields	VFnfields
_nrecs		number of records	VSelts
_recsize		record size (bytes)	VSQueryvsize
_refnum		reference number	VSQueryref
_tag		vdata tag	VSQuerytag
_tnattrs		total number of vdata and field attributes	VSnattrs

name	description	C library routine
_esize	external size (bytes)	VFfieldesize
_index	index number	VSfindex
_isize	internal size (bytes)	VFfieldisize
_name	name	VFfieldname
_nattrs	number of attributes	VSfnattrs
_order	order (number of values)	VFfieldorder
_type	field type (HC.xxx)	VFfieldtype

VS (Vdata table) API (pyhdf.VS)¶

Introduction¶

VS module key features¶

Accessing the VS module¶

Package components¶

Prerequisites¶

Documentation¶

Summary of differences between the pyhdf and C VS API¶

Error handling¶

VS needs support from the HDF module¶

Classes summary¶

Data types¶

Attribute access: low and high level¶

Predefined attributes¶

Record access: low and high level¶

Programming models¶

Creating and initializing a new vdata¶

Appending records to a vdata¶

Updating records in a vdata¶

Reading a vdata¶

VS (Vdata table) API (`pyhdf.VS`)¶