# $Id: SD.py,v 1.10 2008-08-05 00:20:44 gosselin_a Exp $
# $Log: not supported by cvs2svn $
# Revision 1.9 2008/06/30 02:59:57 gosselin_a
# Fixed definition of equivNumericTypes list.
#
# Revision 1.8 2008/06/30 02:41:44 gosselin_a
# Preleminary check-in of changes leading to the 0.8 revision.
# - switch to numpy, Numeric now unsupported
# - better documentation of the compression features
# - some bug fixes
#
# Revision 1.7 2005/07/14 01:36:41 gosselin_a
# pyhdf-0.7-3
# Ported to HDF4.2r1.
# Support for SZIP compression on SDS datasets.
# All classes are now 'new-style' classes, deriving from 'object'.
# Update documentation.
#
# Revision 1.6 2005/01/25 18:17:53 gosselin_a
# Importer le symbole 'HDF4Error' a partir du module SD.
#
# Revision 1.5 2004/08/02 17:06:20 gosselin
# pyhdf-0.7.2
#
# Revision 1.4 2004/08/02 15:36:04 gosselin
# pyhdf-0.7-1
#
# Revision 1.3 2004/08/02 15:22:59 gosselin
# pyhdf -0.6-1
#
# Revision 1.2 2004/08/02 15:00:34 gosselin
# pyhdf 0.5-2
#
# Author: Andre Gosselin
# Maurice Lamontagne Institute
# Andre.Gosselin@dfo-mpo.gc.ca
"""
SD (scientific dataset) API (:mod:`pyhdf.SD`)
=============================================
A module of the pyhdf package implementing the SD (scientific
dataset) API of the NCSA HDF4 library.
Introduction
------------
SD 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).
The SD module implements the SD API of the HDF4 library, supporting what
are known as "scientific datasets". The HDF SD API has many similarities
with the netCDF API, another popular API for dealing with scientific
datasets. netCDF files can be in fact read and modified using the SD
module (but cannot be created from scratch).
SD module key features
----------------------
SD key features are as follows.
- Almost every routine of the original SD API has been implemented inside
pyhdf. Only a few have been ignored, most of them being of a rare use:
- SDsetnbitdataset()
- All chunking/tiling routines : SDgetchunkinfo(), SDreadchunk(),
SDsetchunk(), SDsetchunkcache(), SDwritechunk()
- SDsetblocksize()
- SDisdimval_bwcomp(), SDsetdimval_comp()
- It is quite straightforward to go from a C version to a python version
of a program accessing the SD API, and to learn SD 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 those allowing access
to SD datasets through familiar python idioms.
- Attributes can be read/written like ordinary python class
attributes.
- Datasets can be read/written like ordinary python lists using
multidimensional indices and so-called "extended slice syntax", with
strides allowed.
See "High level attribute access" and "High level variable access"
sections for details.
- SD offers methods to retrieve a dictionary of the attributes,
dimensions and variables defined on a dataset, and of the attributes
set on a variable and a dimension. Querying a dataset is thus geatly
simplified.
- SD datasets are read/written through "numpy", a sophisticated
python package for efficiently handling multi-dimensional arrays of
numbers. numpy can nicely extend the SD functionality, eg.
adding/subtracting arrays with the '+/-' operators.
Accessing the SD module
-----------------------
To access the SD API a python program can say one of:
>>> import pyhdf.SD # must prefix names with "pyhdf.SD."
>>> from pyhdf import SD # must prefix names with "SD."
>>> from pyhdf.SD import * # names need no prefix
This document assumes the last import style is used.
numpy will also need to be imported:
>>> from numpy 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 SD 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
SD
python module wrapping the SD 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 'pyhdf.SD' should be imported by the user program.
Prerequisites
-------------
The following software must be installed in order for pyhdf release 0.8 to
work.
HDF (v4) library, release 4.2r1
pyhdf does *not* include the HDF4 library, which must
be installed separately.
HDF is available at:
"https://portal.hdfgroup.org/display/support/Download+HDF4".
HDF4.2r1 in turn relies on the following packages :
======= ============== ===========================================
libjpeg (jpeg library) release 6b
libz (zlib library) release 1.1.4 or above
libsz (SZIP library) release 2.0; this package is optional
if pyhdf is installed with NOSZIP macro set
======= ============== ===========================================
The SD module also needs:
numpy python package
SD variables are read/written using the array data type provided
by the python NumPy package. Note that since version 0.8 of
pyhdf, version 1.0.5 or above of NumPy is needed.
numpy is available at:
"http://www.numpy.org".
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 SD
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 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 SD.SDS class, say:
>>> from pydoc import help
>>> from pyhdf.SD import *
>>> help(SDS)
To be more specific and get help only for the get() method of the
SDS class:
>>> help(SDS.get) # or...
>>> help(vinst.get) # if vinst is an SDS instance
pydoc can also be called from the command line, as in::
% pydoc pyhdf.SD.SDS # doc for the whole SDS class
% pydoc pyhdf.SD.SDS.get # doc for the SDS.get method
Summary of differences between the pyhdf and C SD API
-----------------------------------------------------
Most of the differences between the pyhdf and C SD 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
tuple of values, which are ordered similarly to the pointers in the
C function argument list.
Error handling
--------------
All errors that the C SD API reports 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".
Attribute access: low and high level
------------------------------------
In the SD API, attributes can be of many types (integer, float, string,
etc) and can be single or multi-valued. Attributes can be set either at
the dataset, the variable or the dimension level. This can can be achieved
in two ways.
- By calling the get()/set() method of an attribute instance. In the
following example, HDF file 'example.hdf' is created, and string
attribute 'title' is attached to the file and given value
'example'.
>>> from pyhdf.SD import *
>>> d = SD('example.hdf',SDC.WRITE|SDC.CREATE) # create file
>>> att = d.attr('title') # create attribute instance
>>> att.set(SDC.CHAR, 'example') # set attribute type and value
>>> print(att.get()) # get attribute value
>>>
- By handling the attribute like an ordinary Python class attribute.
The above example can then be rewritten as follows:
>>> from pyhdf.SD import *
>>> d = SD('example.hdf',SDC.WRITE|SDC.CREATE) # create dataset
>>> d.title = 'example' # set attribute type and value
>>> print(d.title) # get attribute value
>>>
What has been said above applies as well to multi-valued attributes.
>>> att = d.attr('values') # With an attribute instance
>>> att.set(SDC.INT32, (1,2,3,4,5)) # Assign 5 ints as attribute value
>>> att.get() # Get attribute values
[1, 2, 3, 4, 5]
>>> d.values = (1,2,3,4,5) # As a Python class attribute
>>> d.values # Get attribute values
[1, 2, 3, 4, 5]
When the attribute is known by its name , standard functions 'setattr()'
and 'getattr()' can be used to replace the dot notation.
Above example becomes:
>>> setattr(d, 'values', (1,2,3,4,5))
>>> getattr(d, 'values')
[1, 2, 3, 4, 5]
Handling a SD attribute like a Python class attribute is admittedly
more natural, and also much simpler. Some control is however lost in
doing so.
- Attribute type cannot be specified. pyhdf automatically selects one of
three types according to the value(s) assigned to the attribute:
SDC.CHAR if value is a string, SDC.INT32 if all values are integral,
SDC.DOUBLE if one value is a float.
- Consequently, byte values cannot be assigned.
- Attribute properties (length, type, index number) can only be queried
through methods of an attribute instance.
Variable access: low and high level
-----------------------------------
Similarly to attributes, datasets can be read/written in two ways.
The first way is through the get()/set() methods of a dataset instance.
Those methods accept parameters to specify the starting indices, the count
of values to read/write, and the strides along each dimension. For example,
if 'v' is a 4x4 array:
>>> v.get() # complete array
>>> v.get(start=(0,0),count=(1,4)) # first row
>>> v.get(start=(0,1),count=(2,2), # second and third columns of
... stride=(2,1)) # first and third row
The second way is by indexing and slicing the variable like a Python
sequence. pyhdf here follows most of the rules used to index and slice
numpy arrays. Thus an HDF dataset can be seen almost as a numpy
array, except that data is read from/written to a file instead of memory.
Extended indexing let you access variable elements with the familiar
[i,j,...] notation, with one index per dimension. For example, if 'm' is a
rank 3 dataset, one could write:
>>> m[0,3,5] = m[0,5,3]
When indexing is used to select a dimension in a 'get' operation, this
dimension is removed from the output array, thus reducing its rank by 1. A
rank 0 array is converted to a scalar. Thus, for a 3x3x3 'm' dataset
(rank 3) of integer type :
>>> a = m[0] # a is a 3x3 array (rank 2)
>>> a = m[0,0] # a is a 3 element array (rank 1)
>>> a = m[0,0,0] # a is an integer (rank 0 array becomes a scalar)
Had this rule not be followed, m[0,0,0] would have resulted in a single
element array, which could complicate computations.
Extended slice syntax allows slicing HDF datasets along each of its
dimensions, with the specification of optional strides to step through
dimensions at regular intervals. For each dimension, the slice syntax
is: "i:j[:stride]", the stride being optional. As with ordinary slices,
the starting and ending values of a slice can be omitted to refer to the
first and last element, respectively, and the end value can be negative to
indicate that the index is measured relative to the tail instead of the
beginning. Omitted dimensions are assumed to be sliced from beginning to
end. Thus:
>>> m[0] # treated as 'm[0,:,:]'.
Example above with get()/set() methods can thus be rewritten as follows:
>>> v[:] # complete array
>>> v[:1] # first row
>>> v[::2,1:3] # second and third columns of first and third row
Indexes and slices can be freely mixed, eg:
>>> m[:2,3,1:3:2]
Note that, countrary to indexing, a slice never reduces the rank of the
output array, even if its length is 1. For example, given a 3x3x3 'm'
dataset:
>>> a = m[0] # indexing: a is a 3x3 array (rank 2)
>>> a = m[0:1] # slicing: a is a 1x3x3 array (rank 3)
As can easily be seen, extended slice syntax is much more elegant and
compact, and offers a few possibilities not easy to achieve with the
get()/sett() methods. Negative indices offer a nice example:
>>> v[-2:] # last two rows
>>> v[-3:-1] # second and third row
>>> v[:,-1] # last column
Reading/setting multivalued HDF attributes and variables
--------------------------------------------------------
Multivalued HDF attributes are set using a python sequence (tuple or
list). Reading such an attribute returns a python list. The easiest way to
read/set an attribute is by handling it like a Python class attribute
(see "High level attribute access"). For example:
>>> d=SD('test.hdf',SDC.WRITE|SDC.CREATE) # create file
>>> d.integers = (1,2,3,4) # define multivalued integer attr
>>> d.integers # get the attribute value
[1, 2, 3, 4]
The easiest way to set multivalued HDF datasets is to assign to a
subset of the dataset, using "[:]" to assign to the whole dataset
(see "High level variable access"). The assigned value can be a python
sequence, which can be multi-leveled when assigning to a multdimensional
dataset. For example:
>>> d=SD('test.hdf',SDC.WRITE|SDC.CREATE) # create file
>>> v1=d.create('v1',SDC.INT32,3) # 3-elem vector
>>> v1[:]=[1,2,3] # assign 3-elem python list
>>> v2=d.create('d2',SDC.INT32,(3,3)) # create 3x3 variable
# The list assigned to v2 is composed
# of 3 lists, each representing a row of v2.
>>> v2[:]=[[1,2,3],[11,12,13],[21,22,23]]
The assigned value can also be a numpy array. Rewriting example above:
>>> v1=array([1,2,3])
>>> v2=array([[1,2,3],[11,12,13],[21,22,23]])
Note how we use indexing expressions 'v1[:]' and 'v2[:]' when assigning
using python sequences, and just the variable names when assigning numpy
arrays.
Reading an HDF dataset always returns a numpy array, except if
indexing is used and produces a rank-0 array, in which case a scalar is
returned.
netCDF files
------------
Files written in the popular Unidata netCDF format can be read and updated
using the HDF SD API. However, pyhdf cannot create netCDF formatted
files from scratch. The python 'pycdf' package can be used for that.
When accessing netCDF files through pyhdf, one should be aware of the
following differences between the netCDF and the HDF SD libraries.
- Differences in terminology can be confusing. What netCDF calls a
'dataset' is called a 'file' or 'SD interface' in HDF. What HDF calls
a dataset is called a 'variable' in netCDF parlance.
- In the netCDF API, dimensions are defined at the global (netCDF dataset)
level. Thus, two netCDF variables defined over dimensions X and Y
necessarily have the same rank and shape.
- In the HDF SD API, dimensions are defined at the HDF dataset level,
except when they are named. Dimensions with the same name are considered
to be "shared" between all the file datasets. They must be of the same
length, and they share all their scales and attributes. For example,
setting an attribute on a shared dimension affects all datasets sharing
that dimension.
- When two or more netCDF variables are based on the unlimited dimension,
they automatically grow in sync. If variables A and B use the unlimited
dimension, adding "records" to A along its unlimited dimension
implicitly adds records in B (which are left in an undefined state and
filled with the fill_value when the file is refreshed).
- In HDF, unlimited dimensions behave independently. If HDF datasets A and
B are based on an unlimited dimension, adding records to A does not
affect the number of records to B. This is true even if the unlimited
dimensions bear the same name (they do not appear to be "shared" as is
the case when the dimensions are fixed).
Classes summary
---------------
pyhdf wraps the SD API using different types of python classes::
SD HDF SD interface (almost synonymous with the subset of the
HDF file holding all the SD datasets)
SDS scientific dataset
SDim dataset dimension
SDAttr attribute (either at the file, dataset or dimension level)
SDC constants (opening modes, data types, etc)
In more detail::
SD The SD class implements the HDF SD interface as applied to a given
file. This class encapsulates the "SD interface" identifier
(referred to as "sd_id" in the C API documentation), and all
the SD API top-level functions.
To create an SD instance, call the SD() constructor.
methods:
constructors:
SD() open an existing HDF file or create a new one,
returning an SD instance
attr() create an SDAttr (attribute) instance to access
an existing file attribute or create a new one;
"dot notation" can also be used to get and set
an attribute
create() create a new dataset, returning an SDS instance
select() locate an existing dataset given its name or
index number, returning an SDS instance
file closing
end() end access to the SD interface and close the
HDF file
inquiry
attributes() return a dictionary describing every global
attribute attached to the HDF file
datasets() return a dictionary describing every dataset
stored inside the file
info() get the number of datasets stored in the file
and the number of attributes attached to it
nametoindex() get a dataset index number given the dataset
name
reftoindex() get a dataset index number given the dataset
reference number
misc
setfillmode() set the fill mode for all the datasets in
the file
SDAttr The SDAttr class defines an attribute, either at the file (SD),
dataset (SDS) or dimension (SDim) level. The class encapsulates
the object to which the attribute is attached, and the attribute
name.
To create an SDAttr instance, obtain an instance for an SD (file),
SDS (dataset) or dimension (SDim) object, and call its attr()
method.
NOTE. An attribute can also be read/written like
a python class attribute, using the familiar
dot notation. See "High level attribute access".
methods:
read/write value
get() get the attribute value
set() set the attribute value
inquiry
index() get the attribute index number
info() get the attribute name, type and number of
values
SDC The SDC class holds constants defining file opening modes and
data types. Constants are named after their C API counterparts.
file opening modes:
SDC.CREATE create file if non existent
SDC.READ read-only mode
SDC.TRUNC truncate file if already exists
SDC.WRITE read-write mode
data types:
SDC.CHAR 8-bit character
SDC.CHAR8 8-bit character
SDC.UCHAR unsigned 8-bit integer
SDC.UCHAR8 unsigned 8-bit integer
SDC.INT8 signed 8-bit integer
SDC.UINT8 unsigned 8-bit integer
SDC.INT16 signed 16-bit integer
SDC.UINT16 unsigned 16-bit intege
SDC.INT32 signed 32-bit integer
SDC.UINT32 unsigned 32-bit integer
SDC.FLOAT32 32-bit floating point
SDC.FLOAT64 64-bit floaring point
dataset fill mode:
SDC.FILL
SDC.NOFILL
dimension:
SDC.UNLIMITED dimension can grow dynamically
data compression:
SDC.COMP_NONE
SDC.COMP_RLE
SDC.COMP_NBIT
SDC.COMP_SKPHUFF
SDC.COMP_DEFLATE
SDC.COMP_SZIP
SDC.COMP_SZIP_EC
SDC.COMP_SZIP_NN
SDC.COMP_SZIP_RAW
SDS The SDS class implements an HDF scientific dataset (SDS) object.
To create an SDS instance, call the create() or select() methods
of an SD instance.
methods:
constructors
attr() create an SDAttr (attribute) instance to access
an existing dataset attribute or create a
new one; "dot notation" can also be used to get
and set an attribute
dim() return an SDim (dimension) instance for a given
dataset dimension, given the dimension index
number
dataset closing
endaccess() terminate access to the dataset
inquiry
attributes() return a dictionary describing every
attribute defined on the dataset
checkempty() determine whether the dataset is empty
dimensions() return a dictionary describing all the
dataset dimensions
info() get the dataset name, rank, dimension lengths,
data type and number of attributes
iscoordvar() determine whether the dataset is a coordinate
variable (holds a dimension scale)
isrecord() determine whether the dataset is appendable
(the dataset dimension 0 is unlimited)
ref() get the dataset reference number
reading/writing data values
get() read data from the dataset
set() write data to the dataset
A dataset can also be read/written using the
familiar index and slice notation used to
access python sequences. See "High level
variable access".
reading/writing standard attributes
getcal() get the dataset calibration coefficients:
scale_factor, scale_factor_err, add_offset,
add_offset_err, calibrated_nt
getdatastrs() get the dataset standard string attributes:
long_name, units, format, coordsys
getfillvalue() get the dataset fill value:
_FillValue
getrange() get the dataset min and max values:
valid_range
setcal() set the dataset calibration coefficients
setdatastrs() set the dataset standard string attributes
setfillvalue() set the dataset fill value
setrange() set the dataset min and max values
compression
getcompress() get info about the dataset compression type and mode
setcompress() set the dataset compression type and mode
misc
setexternalfile() store the dataset in an external file
SDim The SDdim class implements a dimension object.
To create an SDim instance, call the dim() method of an SDS
(dataset) instance.
Methods:
constructors
attr() create an SDAttr (attribute) instance to access
an existing dimension attribute or create a
new one; "dot notation" can also be used to
get and set an attribute
inquiry
attributes() return a dictionary describing every
attribute defined on the dimension
info() get the dimension name, length, scale data type
and number of attributes
length() return the current dimension length
reading/writing dimension data
getscale() get the dimension scale values
setname() set the dimension name
setscale() set the dimension scale values
reading/writing standard attributes
getstrs() get the dimension standard string attributes:
long_name, units, format
setstrs() set the dimension standard string attributes
Data types
----------
Data types come into play when first defining datasets and their attributes,
and later when querying the definition of those objects.
Data types are specified using the symbolic constants defined inside the
SDC class of the SD 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
type to CHAR, and set the length 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).
Programming models
------------------
Writing
^^^^^^^
The following code can be used as a model to create an SD dataset.
It shows how to use the most important functionalities
of the SD interface needed to initialize a dataset.
A real program should of course add error handling::
# Import SD and numpy.
from pyhdf.SD import *
from numpy import *
fileName = 'template.hdf'
# Create HDF file.
hdfFile = SD(fileName ,SDC.WRITE|SDC.CREATE)
# Assign a few attributes at the file level
hdfFile.author = 'It is me...'
hdfFile.priority = 2
# Create a dataset named 'd1' to hold a 3x3 float array.
d1 = hdfFile.create('d1', SDC.FLOAT32, (3,3))
# Set some attributes on 'd1'
d1.description = 'Sample 3x3 float array'
d1.units = 'celsius'
# Name 'd1' dimensions and assign them attributes.
dim1 = d1.dim(0)
dim2 = d1.dim(1)
dim1.setname('width')
dim2.setname('height')
dim1.units = 'm'
dim2.units = 'cm'
# Assign values to 'd1'
d1[0] = (14.5, 12.8, 13.0) # row 1
d1[1:] = ((-1.3, 0.5, 4.8), # row 2 and
(3.1, 0.0, 13.8)) # row 3
# Close dataset
d1.endaccess()
# Close file
hdfFile.end()
Reading
^^^^^^^
The following code, which reads the dataset created above, can also serve as
a model for any program which needs to access an SD dataset::
# Import SD and numpy.
from pyhdf.SD import *
from numpy import *
fileName = 'template.hdf'
# Open file in read-only mode (default)
hdfFile = SD(fileName)
# Display attributes.
print "file:", fileName
print "author:", hdfFile.author
print "priority:", hdfFile.priority
# Open dataset 'd1'
d1 = hdfFile.select('d1')
# Display dataset attributes.
print "dataset:", 'd1'
print "description:",d1.description
print "units:", d1.units
# Display dimensions info.
dim1 = d1.dim(0)
dim2 = d1.dim(1)
print "dimensions:"
print "dim1: name=", dim1.info()[0],
print "length=", dim1.length(),
print "units=", dim1.units
print "dim2: name=", dim2.info()[0],
print "length=", dim2.length(),
print "units=", dim2.units
# Show dataset values
print d1[:]
# Close dataset
d1.endaccess()
# Close file
hdfFile.end()
Examples
--------
Example-1
^^^^^^^^^
The following simple example exercises some important pyhdf.SD methods. It
shows how to create an HDF dataset, define attributes and dimensions,
create variables, and assign their contents.
Suppose we have a series of text files each defining a 2-dimensional real-
valued matrix. First line holds the matrix dimensions, and following lines
hold matrix values, one row per line. The following procedure will load
into an HDF dataset the contents of any one of those text files. The
procedure computes the matrix min and max values, storing them as
dataset attributes. It also assigns to the variable the group of
attributes passed as a dictionary by the calling program. Note how simple
such an assignment becomes with pyhdf: the dictionary can contain any
number of attributes, of different types, single or multi-valued. Doing
the same in a conventional language would be a much more challenging task.
Error checking is minimal, to keep example as simple as possible
(admittedly a rather poor excuse ...)::
from numpy import *
from pyhdf.SD import *
import os
def txtToHDF(txtFile, hdfFile, varName, attr):
try: # Catch pyhdf errors
# Open HDF file in update mode, creating it if non existent.
d = SD(hdfFile, SDC.WRITE|SDC.CREATE)
# Open text file and get matrix dimensions on first line.
txt = open(txtFile)
ni, nj = map(int, txt.readline().split())
# Define an HDF dataset of 32-bit floating type (SDC.FLOAT32)
# with those dimensions.
v = d.create(varName, SDC.FLOAT32, (ni, nj))
# Assign attributes passed as argument inside dict 'attr'.
for attrName in attr.keys():
setattr(v, attrName, attr[attrName])
# Load variable with lines of data. Compute min and max
# over the whole matrix.
i = 0
while i < ni:
elems = map(float, txt.readline().split())
v[i] = elems # load row i
minE = min(elems)
maxE = max(elems)
if i:
minVal = min(minVal, minE)
maxVal = max(maxVal, maxE)
else:
minVal = minE
maxVal = maxE
i += 1
# Set variable min and max attributes.
v.minVal = minVal
v.maxVal = maxVal
# Close dataset and file objects (not really necessary, since
# closing is automatic when objects go out of scope.
v.endaccess()
d.end()
txt.close()
except HDF4Error, msg:
print "HDF4Error:", msg
We could now call the procedure as follows::
hdfFile = 'table.hdf'
try: # Delete if exists.
os.remove(hdfFile)
except:
pass
# Load contents of file 'temp.txt' into dataset 'temperature'
# an assign the attributes 'title', 'units' and 'valid_range'.
txtToHDF('temp.txt', hdfFile, 'temperature',
{'title' : 'temperature matrix',
'units' : 'celsius',
'valid_range': (-2.8,27.0)})
# Load contents of file 'depth.txt' into dataset 'depth'
# and assign the same attributes as above.
txtToHDF('depth.txt', hdfFile, 'depth',
{'title' : 'depth matrix',
'units' : 'meters',
'valid_range': (0, 500.0)})
Example 2
^^^^^^^^^
This example shows a useful python program that will display the
structure of the SD component of any HDF file whose name is given on
the command line. After the HDF file is opened, high level inquiry methods
are called to obtain dictionaries describing attributes, dimensions and
datasets. The rest of the program mostly consists in nicely formatting
the contents of those dictionaries::
import sys
from pyhdf.SD import *
from numpy import *
# Dictionary used to convert from a numeric data type to its symbolic
# representation
typeTab = {
SDC.CHAR: 'CHAR',
SDC.CHAR8: 'CHAR8',
SDC.UCHAR8: 'UCHAR8',
SDC.INT8: 'INT8',
SDC.UINT8: 'UINT8',
SDC.INT16: 'INT16',
SDC.UINT16: 'UINT16',
SDC.INT32: 'INT32',
SDC.UINT32: 'UINT32',
SDC.FLOAT32: 'FLOAT32',
SDC.FLOAT64: 'FLOAT64'
}
printf = sys.stdout.write
def eol(n=1):
printf("%s" % chr(10) * n)
hdfFile = sys.argv[1] # Get first command line argument
try: # Catch pyhdf.SD errors
# Open HDF file named on the command line
f = SD(hdfFile)
# Get global attribute dictionary
attr = f.attributes(full=1)
# Get dataset dictionary
dsets = f.datasets()
# File name, number of attributes and number of variables.
printf("FILE INFO"); eol()
printf("-------------"); eol()
printf("%-25s%s" % ("File:", hdfFile)); eol()
printf("%-25s%d" % (" file attributes:", len(attr))); eol()
printf("%-25s%d" % (" datasets:", len(dsets))); eol()
eol();
# Global attribute table.
if len(attr) > 0:
printf("File attributes"); eol(2)
printf(" name idx type len value"); eol()
printf(" -------------------- --- ------- --- -----"); eol()
# Get list of attribute names and sort them lexically
attNames = attr.keys()
attNames.sort()
for name in attNames:
t = attr[name]
# t[0] is the attribute value
# t[1] is the attribute index number
# t[2] is the attribute type
# t[3] is the attribute length
printf(" %-20s %3d %-7s %3d %s" %
(name, t[1], typeTab[t[2]], t[3], t[0])); eol()
eol()
# Dataset table
if len(dsets) > 0:
printf("Datasets (idx:index num, na:n attributes, cv:coord var)"); eol(2)
printf(" name idx type na cv dimension(s)"); eol()
printf(" -------------------- --- ------- -- -- ------------"); eol()
# Get list of dataset names and sort them lexically
dsNames = dsets.keys()
dsNames.sort()
for name in dsNames:
# Get dataset instance
ds = f.select(name)
# Retrieve the dictionary of dataset attributes so as
# to display their number
vAttr = ds.attributes()
t = dsets[name]
# t[0] is a tuple of dimension names
# t[1] is a tuple of dimension lengths
# t[2] is the dataset type
# t[3] is the dataset index number
printf(" %-20s %3d %-7s %2d %-2s " %
(name, t[3], typeTab[t[2]], len(vAttr),
ds.iscoordvar() and 'X' or ''))
# Display dimension info.
n = 0
for d in t[0]:
printf("%s%s(%d)" % (n > 0 and ', ' or '', d, t[1][n]))
n += 1
eol()
eol()
# Dataset info.
if len(dsNames) > 0:
printf("DATASET INFO"); eol()
printf("-------------"); eol(2)
for name in dsNames:
# Access the dataset
dsObj = f.select(name)
# Get dataset attribute dictionary
dsAttr = dsObj.attributes(full=1)
if len(dsAttr) > 0:
printf("%s attributes" % name); eol(2)
printf(" name idx type len value"); eol()
printf(" -------------------- --- ------- --- -----"); eol()
# Get the list of attribute names and sort them alphabetically.
attNames = dsAttr.keys()
attNames.sort()
for nm in attNames:
t = dsAttr[nm]
# t[0] is the attribute value
# t[1] is the attribute index number
# t[2] is the attribute type
# t[3] is the attribute length
printf(" %-20s %3d %-7s %3d %s" %
(nm, t[1], typeTab[t[2]], t[3], t[0])); eol()
eol()
# Get dataset dimension dictionary
dsDim = dsObj.dimensions(full=1)
if len(dsDim) > 0:
printf ("%s dimensions" % name); eol(2)
printf(" name idx len unl type natt");eol()
printf(" -------------------- --- ----- --- ------- ----");eol()
# Get the list of dimension names and sort them alphabetically.
dimNames = dsDim.keys()
dimNames.sort()
for nm in dimNames:
t = dsDim[nm]
# t[0] is the dimension length
# t[1] is the dimension index number
# t[2] is 1 if the dimension is unlimited, 0 if not
# t[3] is the the dimension scale type, 0 if no scale
# t[4] is the number of attributes
printf(" %-20s %3d %5d %s %-7s %4d" %
(nm, t[1], t[0], t[2] and "X" or " ",
t[3] and typeTab[t[3]] or "", t[4])); eol()
eol()
except HDF4Error, msg:
print "HDF4Error", msg
"""
import os, sys, types
from . import hdfext as _C
from .six.moves import xrange
from .error import _checkErr, HDF4Error
# List of names we want to be imported by an "from pyhdf.SD import *"
# statement
__all__ = ['SD', 'SDAttr', 'SDC', 'SDS', 'SDim', 'HDF4Error']
try:
import numpy as _toto
del _toto
except ImportError:
raise HDF4Error("numpy package required but not installed")
[docs]class SDC(object):
"""The SDC class holds constants defining opening modes and data types.
file opening modes:
========== === ===============================
SDC.CREATE 4 create file if non existent
SDC.READ 1 read-only mode
SDC.TRUNC 256 truncate file if already exists
SDC.WRITE 2 read-write mode
========== === ===============================
data types:
=========== === ===============================
SDC.CHAR 4 8-bit character
SDC.CHAR8 4 8-bit character
SDC.UCHAR 3 unsigned 8-bit integer
SDC.UCHAR8 3 unsigned 8-bit integer
SDC.INT8 20 signed 8-bit integer
SDC.UINT8 21 unsigned 8-bit integer
SDC.INT16 22 signed 16-bit integer
SDC.UINT16 23 unsigned 16-bit intege
SDC.INT32 24 signed 32-bit integer
SDC.UINT32 25 unsigned 32-bit integer
SDC.FLOAT32 5 32-bit floating point
SDC.FLOAT64 6 64-bit floaring point
=========== === ===============================
dataset fill mode:
=========== ===
SDC.FILL 0
SDC.NOFILL 256
=========== ===
dimension:
============= === ===============================
SDC.UNLIMITED 0 dimension can grow dynamically
============= === ===============================
data compression:
================= ===
SDC.COMP_NONE 0
SDC.COMP_RLE 1
SDC.COMP_NBIT 2
SDC.COMP_SKPHUFF 3
SDC.COMP_DEFLATE 4
SDC.COMP_SZIP 5
SDC.COMP_SZIP_EC 4
SDC.COMP_SZIP_NN 32
SDC.COMP_SZIP_RAW 128
================= ===
"""
CREATE = _C.DFACC_CREATE
READ = _C.DFACC_READ
TRUNC = 0x100 # specific to pyhdf
WRITE = _C.DFACC_WRITE
CHAR = _C.DFNT_CHAR8
CHAR8 = _C.DFNT_CHAR8
UCHAR = _C.DFNT_UCHAR8
UCHAR8 = _C.DFNT_UCHAR8
INT8 = _C.DFNT_INT8
UINT8 = _C.DFNT_UINT8
INT16 = _C.DFNT_INT16
UINT16 = _C.DFNT_UINT16
INT32 = _C.DFNT_INT32
UINT32 = _C.DFNT_UINT32
FLOAT32 = _C.DFNT_FLOAT32
FLOAT64 = _C.DFNT_FLOAT64
FILL = _C.SD_FILL
NOFILL = _C.SD_NOFILL
UNLIMITED = _C.SD_UNLIMITED
COMP_NONE = _C.COMP_CODE_NONE
COMP_RLE = _C.COMP_CODE_RLE
COMP_NBIT = _C.COMP_CODE_NBIT
COMP_SKPHUFF = _C.COMP_CODE_SKPHUFF
COMP_DEFLATE = _C.COMP_CODE_DEFLATE
COMP_SZIP = _C.COMP_CODE_SZIP
COMP_SZIP_EC = 4
COMP_SZIP_NN = 32
COMP_SZIP_RAW = 128
# Types with an equivalent in the numpy package
# NOTE:
# CHAR8 and INT8 are handled similarly (signed byte -128,...,0,...127)
# UCHAR8 and UINT8 are treated equivalently (unsigned byte: 0,1,...,255)
# UINT16 and UINT32 are supported
# INT64 and UINT64 are not yet supported py pyhdf
equivNumericTypes = [FLOAT32, FLOAT64,
INT8, UINT8,
INT16, UINT16,
INT32, UINT32,
CHAR8, UCHAR8]
[docs]class SDAttr(object):
def __init__(self, obj, index_or_name):
"""Init an SDAttr instance. Should not be called directly by
the user program. An SDAttr instance must be created through
the attr() methods of the SD, SDS or SDim classes.
"""
# Args
# obj object instance to which the attribute refers
# (SD, SDS, SDDim)
# index_or_name attribute index or name
#
# Class private attributes:
# _obj object instance
# _index attribute index or None
# _name attribute name or None
self._obj = obj
# Name is given, may exist or not.
if isinstance(index_or_name, type('')):
self._name = index_or_name
self._index = None
# Index is given. Must exist.
else:
self._index = index_or_name
status, self._name, data_type, n_values = \
_C.SDattrinfo(self._obj._id, self._index)
_checkErr('set', status, 'illegal attribute index')
[docs] def info(self):
"""Retrieve info about the attribute : name, data type and
number of values.
Args::
no argument
Returns::
3-element tuple holding:
- attribute name
- attribute data type (see constants SDC.xxx)
- number of values in the attribute; for a string-valued
attribute (data type SDC.CHAR8), the number of values
corresponds to the string length
C library equivalent : SDattrinfo
"""
if self._index is None:
try:
self._index = self._obj.findattr(self._name)
except HDF4Error:
raise HDF4Error("info: cannot convert name to index")
status, self._name, data_type, n_values = \
_C.SDattrinfo(self._obj._id, self._index)
_checkErr('info', status, 'illegal attribute index')
return self._name, data_type, n_values
[docs] def index(self):
"""Retrieve the attribute index number.
Args::
no argument
Returns::
attribute index number (starting at 0)
C library equivalent : SDfindattr
"""
self._index = _C.SDfindattr(self._obj._id, self._name)
_checkErr('find', self._index, 'illegal attribute name')
return self._index
[docs] def get(self):
"""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 SDC.CHAR8) where the
values are returned as a string
C library equivalent : SDreadattr
Attributes can also be read like ordinary python attributes,
using the dot notation. See "High level attribute access".
"""
if self._index is None:
try:
self._index = self._obj.findattr(self._name)
except HDF4Error:
raise HDF4Error("get: cannot convert name to index")
# Obtain attribute type and the number of values.
status, self._name, data_type, n_values = \
_C.SDattrinfo(self._obj._id, self._index)
_checkErr('read', status, 'illegal attribute index')
# Get attribute value.
convert = _array_to_ret
if data_type == SDC.CHAR8:
buf = _C.array_byte(n_values)
convert = _array_to_str
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf = _C.array_float64(n_values)
else:
raise HDF4Error("read: attribute index %d has an "\
"illegal or unupported type %d" % \
(self._index, data_type))
status = _C.SDreadattr(self._obj._id, self._index, buf)
_checkErr('read', status, 'illegal attribute index')
return convert(buf, n_values)
[docs] def set(self, data_type, values):
"""Update/Create a new attribute and set its value(s).
Args::
data_type : attribute data type (see constants SDC.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 SDC.CHAR8 and 'values' to the corresponding
string
Returns::
None
C library equivalent : SDsetattr
Attributes can also be written like ordinary python attributes,
using the dot notation. See "High level attribute access".
"""
try:
n_values = len(values)
except:
n_values = 1
values = [values]
if data_type == SDC.CHAR8:
buf = _C.array_byte(n_values)
# Allow values to be passed as a string.
# Noop if a list is passed.
values = list(values)
for n in range(n_values):
values[n] = ord(values[n])
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf = _C.array_float64(n_values)
else:
raise HDF4Error("set: illegal or unimplemented data_type")
for n in range(n_values):
buf[n] = values[n]
status = _C.SDsetattr(self._obj._id, self._name,
data_type, n_values, buf)
_checkErr('set', status, 'illegal attribute')
# Init index following attribute creation.
self._index = _C.SDfindattr(self._obj._id, self._name)
_checkErr('find', self._index, 'illegal attribute')
[docs]class SD(object):
"""The SD class implements an HDF SD interface.
To instantiate an SD class, call the SD() constructor.
To set attributes on an SD instance, call the SD.attr()
method to create an attribute instance, then call the methods
of this instance. """
def __init__(self, path, mode=SDC.READ):
"""SD constructor. Initialize an SD interface on an HDF file,
creating the file if necessary.
Args::
path name of the HDF file on which to open the SD interface
mode file opening mode; this mode is a set of binary flags
which can be ored together
SDC.CREATE combined with SDC.WRITE to create file
if it does not exist
SDC.READ open file in read-only access (default)
SDC.TRUNC if combined with SDC.WRITE, overwrite
file if it already exists
SDC.WRITE open file in read-write mode; if file
exists it is updated, unless SDC.TRUNC is
set, in which case it is erased and
recreated; if file does not exist, an
error is raised unless SDC.CREATE is set,
in which case the file is created
Note an important difference in the way CREATE is
handled by the C library and the pyhdf package.
For the C library, CREATE indicates that a new file
should always be created, overwriting an existing one if
any. For pyhdf, CREATE indicates a new file should be
created only if it does not exist, and the overwriting
of an already existing file must be explicitly asked
for by setting the TRUNC flag.
Those differences were introduced so as to harmonize
the way files are opened in the pycdf and pyhdf
packages. Also, this solves a limitation in the
hdf (and netCDF) library, where there is no easy way
to implement the frequent requirement that an existent
file be opened in read-write mode, or created
if it does not exist.
Returns::
an SD instance
C library equivalent : SDstart
"""
# Private attributes:
# _id: file id
# Make sure _id is initialized in case __del__ is called
# when the SD object goes out of scope after failing to
# open file. Failure to do so may put python into an infinite loop
# (thanks to Richard.Andrews@esands.com for reporting this bug).
self._id = None
# See if file exists.
exists = os.path.exists(path)
# We must have either WRITE or READ flag.
if SDC.WRITE & mode:
if exists:
if SDC.TRUNC & mode:
try:
os.remove(path)
except Exception as msg:
raise HDF4Error(msg)
mode = SDC.CREATE|SDC.WRITE
else:
mode = SDC.WRITE
else:
if SDC.CREATE & mode:
mode |= SDC.WRITE
else:
raise HDF4Error("SD: no such file")
elif SDC.READ & mode:
if exists:
mode = SDC.READ
else:
raise HDF4Error("SD: no such file")
else:
raise HDF4Error("SD: bad mode, READ or WRITE must be set")
id = _C.SDstart(path, mode)
_checkErr('SD', id, "cannot open %s" % path)
self._id = id
def __del__(self):
"""Delete the instance, first calling the end() method
if not already done. """
try:
if self._id:
self.end()
except:
pass
def __getattr__(self, name):
# Get value(s) of SD attribute 'name'.
return _getattr(self, name)
def __setattr__(self, name, value):
# Set value(s) of SD attribute 'name'.
# A name starting with an underscore will be treated as
# a standard python attribute, and as an HDF attribute
# otherwise.
_setattr(self, name, value, ['_id'])
[docs] def end(self):
"""End access to the SD interface and close the HDF file.
Args::
no argument
Returns::
None
The instance should not be used afterwards.
The 'end()' method is implicitly called when the
SD instance is deleted.
C library equivalent : SDend
"""
status = _C.SDend(self._id)
_checkErr('end', status, "cannot execute")
self._id = None
[docs] def info(self):
"""Retrieve information about the SD interface.
Args::
no argument
Returns::
2-element tuple holding:
number of datasets inside the file
number of file attributes
C library equivalent : SDfileinfo
"""
status, n_datasets, n_file_attrs = _C.SDfileinfo(self._id)
_checkErr('info', status, "cannot execute")
return n_datasets, n_file_attrs
[docs] def nametoindex(self, sds_name):
"""Return the index number of a dataset given the dataset name.
Args::
sds_name : dataset name
Returns::
index number of the dataset
C library equivalent : SDnametoindex
"""
sds_idx = _C.SDnametoindex(self._id, sds_name)
_checkErr('nametoindex', sds_idx, 'non existent SDS')
return sds_idx
[docs] def reftoindex(self, sds_ref):
"""Returns the index number of a dataset given the dataset
reference number.
Args::
sds_ref : dataset reference number
Returns::
dataset index number
C library equivalent : SDreftoindex
"""
sds_idx = _C.SDreftoindex(self._id, sds_ref)
_checkErr('reftoindex', sds_idx, 'illegal SDS ref number')
return sds_idx
[docs] def setfillmode(self, fill_mode):
"""Set the fill mode for all the datasets in the file.
Args::
fill_mode : fill mode; one of :
SDC.FILL write the fill value to all the datasets
of the file by default
SDC.NOFILL do not write fill values to all datasets
of the file by default
Returns::
previous fill mode value
C library equivalent: SDsetfillmode
"""
if not fill_mode in [SDC.FILL, SDC.NOFILL]:
raise HDF4Error("bad fill mode")
old_mode = _C.SDsetfillmode(self._id, fill_mode)
_checkErr('setfillmode', old_mode, 'cannot execute')
return old_mode
[docs] def create(self, name, data_type, dim_sizes):
"""Create a dataset.
Args::
name dataset name
data_type type of the data, set to one of the SDC.xxx
constants;
dim_sizes lengths of the dataset dimensions; a one-
dimensional array is specified with an integer,
an n-dimensional array with an n-element sequence
of integers; the length of the first dimension can
be set to SDC.UNLIMITED to create an unlimited
dimension (a "record" variable).
IMPORTANT: netCDF and HDF differ in the way
the UNLIMITED dimension is handled. In netCDF,
all variables of a dataset with an unlimited
dimension grow in sync, eg adding a record to
a variable will implicitly extend other record
variables. In HDF, each record variable grows
independently of each other.
Returns::
SDS instance for the dataset
C library equivalent : SDcreate
"""
# Validate args.
if isinstance(dim_sizes, type(1)): # allow k instead of [k]
# for a 1-dim arr
dim_sizes = [dim_sizes]
rank = len(dim_sizes)
buf = _C.array_int32(rank)
for n in range(rank):
buf[n] = dim_sizes[n]
id = _C.SDcreate(self._id, name, data_type, rank, buf)
_checkErr('CREATE', id, "cannot execute")
return SDS(self, id)
[docs] def select(self, name_or_index):
"""Locate a dataset.
Args::
name_or_index dataset name or index number
Returns::
SDS instance for the dataset
C library equivalent : SDselect
"""
if isinstance(name_or_index, type(1)):
idx = name_or_index
else:
try:
idx = self.nametoindex(name_or_index)
except HDF4Error:
raise HDF4Error("select: non-existent dataset")
id = _C.SDselect(self._id, idx)
_checkErr('select', id, "cannot execute")
return SDS(self, id)
[docs] def attr(self, name_or_index):
"""Create an SDAttr instance representing a global
attribute (defined at the level of the SD interface).
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 SDAttr
instance set() method is called
Returns::
SDAttr instance for the attribute. Call the methods of this
class to query, read or set the attribute.
C library equivalent : no equivalent
"""
return SDAttr(self, name_or_index)
[docs] def attributes(self, full=0):
"""Return a dictionary describing every global
attribute attached to the SD interface.
Args::
full true to get complete info about each attribute
false to report only each attribute value
Returns::
Empty dictionary if no global attribute defined
Otherwise, dictionary where each key is the name of a
global attribute. If parameter 'full' is false,
key value is the attribute value. If 'full' is true,
key value is a tuple with the following elements:
- attribute value
- attribute index number
- attribute type
- attribute length
C library equivalent : no equivalent
"""
# Get the number of global attributes.
nsds, natts = self.info()
# Inquire each attribute
res = {}
for n in range(natts):
a = self.attr(n)
name, aType, nVal = a.info()
if full:
res[name] = (a.get(), a.index(), aType, nVal)
else:
res[name] = a.get()
return res
[docs] def datasets(self):
"""Return a dictionary describing all the file datasets.
Args::
no argument
Returns::
Empty dictionary if no dataset is defined.
Otherwise, dictionary whose keys are the file dataset names,
and values are tuples describing the corresponding datasets.
Each tuple holds the following elements in order:
- tuple holding the names of the dimensions defining the
dataset coordinate axes
- tuple holding the dataset shape (dimension lengths);
if a dimension is unlimited, the reported length corresponds
to the dimension current length
- dataset type
- dataset index number
C library equivalent : no equivalent
"""
# Get number of datasets
nDs = self.info()[0]
# Inquire each var
res = {}
for n in range(nDs):
# Get dataset info.
v = self.select(n)
vName, vRank, vLen, vType, vAtt = v.info()
if vRank < 2: # need a sequence
vLen = [vLen]
# Get dimension info.
dimNames = []
dimLengths = []
for dimNum in range(vRank):
d = v.dim(dimNum)
dimNames.append(d.info()[0])
dimLengths.append(vLen[dimNum])
res[vName] = (tuple(dimNames), tuple(dimLengths),
vType, n)
return res
[docs]class SDS(object):
"""The SDS class implements an HDF dataset object.
To create an SDS instance, call the create() or select()
methods of the SD class. To set attributes on an SDS instance,
call the SDS.attr() method to create an attribute instance,
then call the methods of this instance. Attributes can also be
set using the "dot notation". """
def __init__(self, sd, id):
"""This constructor should not be called by the user program.
Call the SD.create() and SD.select() methods instead.
"""
# Args
# sd : SD instance
# id : SDS identifier
# Private attributes
# _sd SD instance
# _id SDS identifier
self._sd = sd
self._id = id
def __del__(self):
# Delete the instance, first calling the endaccess() method
# if not already done.
try:
if self._id:
self.endaccess()
except:
pass
def __getattr__(self, name):
# Get value(s) of SDS attribute 'name'.
return _getattr(self, name)
def __setattr__(self, name, value):
# Set value(s) of SDS attribute 'name'.
_setattr(self, name, value, ['_sd', '_id'])
def __len__(self): # Needed for slices like "-2:" but why ?
return 0
def __getitem__(self, elem):
# This special method is used to index the SDS dataset
# using the "extended slice syntax". The extended slice syntax
# is a perfect match for the "start", "count" and "stride"
# arguments to the SDreaddara() function, and is much more easy
# to use.
# Compute arguments to 'SDreaddata_0()'.
start, count, stride = self.__buildStartCountStride(elem)
# Get elements.
return self.get(start, count, stride)
def __setitem__(self, elem, data):
# This special method is used to assign to the SDS dataset
# using "extended slice syntax". The extended slice syntax
# is a perfect match for the "start", "count" and "stride"
# arguments to the SDwritedata() function, and is much more easy
# to use.
# Compute arguments to 'SDwritedata_0()'.
start, count, stride = self.__buildStartCountStride(elem)
# A sequence type is needed. Convert a single number to a list.
if type(data) in [int, float]:
data = [data]
# Assign.
self.set(data, start, count, stride)
[docs] def endaccess(self):
"""Terminates access to the SDS.
Args::
no argument
Returns::
None.
The SDS instance should not be used afterwards.
The 'endaccess()' method is implicitly called when
the SDS instance is deleted.
C library equivalent : SDendaccess
"""
status = _C.SDendaccess(self._id)
_checkErr('endaccess', status, "cannot execute")
self._id = None # Invalidate identifier
[docs] def dim(self, dim_index):
"""Get an SDim instance given a dimension index number.
Args::
dim_index index number of the dimension (numbering starts at 0)
C library equivalent : SDgetdimid
"""
id = _C.SDgetdimid(self._id, dim_index)
_checkErr('dim', id, 'invalid SDS identifier or dimension index')
return SDim(self, id, dim_index)
[docs] def get(self, start=None, count=None, stride=None):
"""Read data from the dataset.
Args::
start : indices where to start reading in the data array;
default to 0 on all dimensions
count : number of values to read along each dimension;
default to the current length of all dimensions
stride : sampling interval along each dimension;
default to 1 on all dimensions
For n-dimensional datasets, those 3 parameters are entered
using lists. For one-dimensional datasets, integers
can also be used.
Note that, to read the whole dataset contents, one should
simply call the method with no argument.
Returns::
numpy array initialized with the data.
C library equivalent : SDreaddata
The dataset can also be read using the familiar indexing and
slicing notation, like ordinary python sequences.
See "High level variable access".
"""
# Obtain SDS info.
try:
sds_name, rank, dim_sizes, data_type, n_attrs = self.info()
if isinstance(dim_sizes, type(1)):
dim_sizes = [dim_sizes]
except HDF4Error:
raise HDF4Error('get : cannot execute')
# Validate args.
if start is None:
start = [0] * rank
elif isinstance(start, type(1)):
start = [start]
if count is None:
count = dim_sizes
if count[0] == 0:
count[0] = 1
elif isinstance(count, type(1)):
count = [count]
if stride is None:
stride = [1] * rank
elif isinstance(stride, type(1)):
stride = [stride]
if len(start) != rank or len(count) != rank or len(stride) != rank:
raise HDF4Error('get : start, stride or count ' \
'do not match SDS rank')
for n in range(rank):
if start[n] < 0 or start[n] + \
(abs(count[n]) - 1) * stride[n] >= dim_sizes[n]:
raise HDF4Error('get arguments violate ' \
'the size (%d) of dimension %d' \
% (dim_sizes[n], n))
if not data_type in SDC.equivNumericTypes:
raise HDF4Error('get cannot currently deal with '\
'the SDS data type')
return _C._SDreaddata_0(self._id, data_type, start, count, stride)
[docs] def set(self, data, start=None, count=None, stride=None):
"""Write data to the dataset.
Args::
data : array of data to write; can be given as a numpy
array, or as Python sequence (whose elements can be
imbricated sequences)
start : indices where to start writing in the dataset;
default to 0 on all dimensions
count : number of values to write along each dimension;
default to the current length of dataset dimensions
stride : sampling interval along each dimension;
default to 1 on all dimensions
For n-dimensional datasets, those 3 parameters are entered
using lists. For one-dimensional datasets, integers
can also be used.
Note that, to write the whole dataset at once, one has simply
to call the method with the dataset values in parameter
'data', omitting all other parameters.
Returns::
None.
C library equivalent : SDwritedata
The dataset can also be written using the familiar indexing and
slicing notation, like ordinary python sequences.
See "High level variable access".
"""
# Obtain SDS info.
try:
sds_name, rank, dim_sizes, data_type, n_attrs = self.info()
if isinstance(dim_sizes, type(1)):
dim_sizes = [dim_sizes]
except HDF4Error:
raise HDF4Error('set : cannot execute')
# Validate args.
if start is None:
start = [0] * rank
elif isinstance(start, type(1)):
start = [start]
if count is None:
count = dim_sizes
if count[0] == 0:
count[0] = 1
elif isinstance(count, type(1)):
count = [count]
if stride is None:
stride = [1] * rank
elif isinstance(stride, type(1)):
stride = [stride]
if len(start) != rank or len(count) != rank or len(stride) != rank:
raise HDF4Error('set : start, stride or count '\
'do not match SDS rank')
unlimited = self.isrecord()
for n in range(rank):
ok = 1
if start[n] < 0:
ok = 0
elif n > 0 or not unlimited:
if start[n] + (abs(count[n]) - 1) * stride[n] >= dim_sizes[n]:
ok = 0
if not ok:
raise HDF4Error('set arguments violate '\
'the size (%d) of dimension %d' \
% (dim_sizes[n], n))
# ??? Check support for UINT16
if not data_type in SDC.equivNumericTypes:
raise HDF4Error('set cannot currently deal '\
'with the SDS data type')
_C._SDwritedata_0(self._id, data_type, start, count, data, stride)
def __buildStartCountStride(self, elem):
# Create the 'start', 'count', 'slice' and 'stride' tuples that
# will be passed to '_SDreaddata_0'/'_SDwritedata_0'.
# start starting indices along each dimension
# count count of values along each dimension; a value of -1
# indicates that and index, not a slice, was applied to
# the dimension; in that case, the dimension should be
# dropped from the output array.
# stride strides along each dimension
# Make sure the indexing expression does not exceed the variable
# number of dimensions.
dsName, nDims, shape, dsType, nAttr = self.info()
if isinstance(elem, tuple):
if len(elem) > nDims:
raise HDF4Error("get", 0,
"indexing expression exceeds variable "
"number of dimensions")
else: # Convert single index to sequence
elem = [elem]
if isinstance(shape, int):
shape = [shape]
start = []
count = []
stride = []
n = -1
unlimited = self.isrecord()
for e in elem:
n += 1
# See if the dimension is unlimited (always at index 0)
unlim = n == 0 and unlimited
# Simple index
if isinstance(e, int):
isslice = False
if e < 0 :
e += shape[n]
# Respect standard python list behavior: it is illegal to
# specify an out of bound index (except for the
# unlimited dimension).
if e < 0 or (not unlim and e >= shape[n]):
raise IndexError("index out of range")
beg = e
end = e + 1
inc = 1
# Slice index. Respect Python syntax for slice upper bounds,
# which are not included in the resulting slice. Also, if the
# upper bound exceed the dimension size, truncate it.
elif isinstance(e, slice):
isslice = True
# None or 0 means not specified
if e.start:
beg = e.start
if beg < 0:
beg += shape[n]
else:
beg = 0
# None of maxint means not specified
if e.stop and e.stop != sys.maxsize:
end = e.stop
if end < 0:
end += shape[n]
else:
end = shape[n]
# None means not specified
if e.step:
inc = e.step
else:
inc = 1
# Bug
else:
raise ValueError("Bug: unexpected element type to __getitem__")
# Clip end index (except if unlimited dimension)
# and compute number of elements to get.
if not unlim and end > shape[n]:
end = shape[n]
if isslice:
cnt = (end - beg) // inc
if cnt * inc < end - beg:
cnt += 1
else:
cnt = -1
start.append(beg)
count.append(cnt)
stride.append(inc)
# Complete missing dimensions
while n < nDims - 1:
n += 1
start.append(0)
count.append(shape[n])
stride.append(1)
# Done
return start, count, stride
[docs] def info(self):
"""Retrieves information about the dataset.
Args::
no argument
Returns::
5-element tuple holding:
- dataset name
- dataset rank (number of dimensions)
- dataset shape, that is a list giving the length of each
dataset dimension; if the first dimension is unlimited, then
the first value of the list gives the current length of the
unlimited dimension
- data type (one of the SDC.xxx values)
- number of attributes defined for the dataset
C library equivalent : SDgetinfo
"""
buf = _C.array_int32(_C.H4_MAX_VAR_DIMS)
status, sds_name, rank, data_type, n_attrs = \
_C.SDgetinfo(self._id, buf)
_checkErr('info', status, "cannot execute")
dim_sizes = _array_to_ret(buf, rank)
return sds_name, rank, dim_sizes, data_type, n_attrs
[docs] def checkempty(self):
"""Determine whether the dataset is empty.
Args::
no argument
Returns::
True(1) if dataset is empty, False(0) if not
C library equivalent : SDcheckempty
"""
status, emptySDS = _C.SDcheckempty(self._id)
_checkErr('checkempty', status, 'invalid SDS identifier')
return emptySDS
[docs] def ref(self):
"""Get the reference number of the dataset.
Args::
no argument
Returns::
dataset reference number
C library equivalent : SDidtoref
"""
sds_ref = _C.SDidtoref(self._id)
_checkErr('idtoref', sds_ref, 'illegal SDS identifier')
return sds_ref
[docs] def iscoordvar(self):
"""Determine whether the dataset is a coordinate variable
(holds a dimension scale). A coordinate variable is created
when a dimension is assigned a set of scale values.
Args::
no argument
Returns::
True(1) if the dataset represents a coordinate variable,
False(0) if not
C library equivalent : SDiscoordvar
"""
return _C.SDiscoordvar(self._id) # no error status here
[docs] def isrecord(self):
"""Determines whether the dataset is appendable
(contains an unlimited dimension). Note that if true, then
the unlimited dimension is always dimension number 0.
Args::
no argument
Returns::
True(1) if the dataset is appendable, False(0) if not.
C library equivalent : SDisrecord
"""
return _C.SDisrecord(self._id) # no error status here
[docs] def getcal(self):
"""Retrieve the SDS calibration coefficients.
Args::
no argument
Returns::
5-element tuple holding:
- cal: calibration factor (attribute 'scale_factor')
- cal_error : calibration factor error
(attribute 'scale_factor_err')
- offset: calibration offset (attribute 'add_offset')
- offset_err : offset error (attribute 'add_offset_err')
- data_type : type of the data resulting from applying
the calibration formula to the dataset values
(attribute 'calibrated_nt')
An exception is raised if no calibration data are defined.
Original dataset values 'orival' are converted to calibrated
values 'calval' through the formula::
calval = cal * (orival - offset)
The calibration coefficients are part of the so-called
"standard" SDS attributes. The values inside the tuple returned
by 'getcal' are those of the following attributes, in order::
scale_factor, scale_factor_err, add_offset, add_offset_err,
calibrated_nt
C library equivalent: SDgetcal()
"""
status, cal, cal_error, offset, offset_err, data_type = \
_C.SDgetcal(self._id)
_checkErr('getcal', status, 'no calibration record')
return cal, cal_error, offset, offset_err, data_type
[docs] def getdatastrs(self):
"""Retrieve the dataset standard string attributes.
Args::
no argument
Returns::
4-element tuple holding:
- dataset label string (attribute 'long_name')
- dataset unit (attribute 'units')
- dataset output format (attribute 'format')
- dataset coordinate system (attribute 'coordsys')
The values returned by 'getdatastrs' are part of the
so-called "standard" SDS attributes. Those 4 values
correspond respectively to the following attributes::
long_name, units, format, coordsys .
C library equivalent: SDgetdatastrs
"""
status, label, unit, format, coord_system = \
_C.SDgetdatastrs(self._id, 128)
_checkErr('getdatastrs', status, 'cannot execute')
return label, unit, format, coord_system
[docs] def getfillvalue(self):
"""Retrieve the dataset fill value.
Args::
no argument
Returns::
dataset fill value (attribute '_FillValue')
An exception is raised if the fill value is not set.
The fill value is part of the so-called "standard" SDS
attributes, and corresponds to the following attribute::
_FillValue
C library equivalent: SDgetfillvalue
"""
# Obtain SDS data type.
try:
sds_name, rank, dim_sizes, data_type, n_attrs = \
self.info()
except HDF4Error:
raise HDF4Error('getfillvalue : invalid SDS identifier')
n_values = 1 # Fill value stands for 1 value.
convert = _array_to_ret
if data_type == SDC.CHAR8:
buf = _C.array_byte(n_values)
convert = _array_to_str
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf = _C.array_float64(n_values)
else:
raise HDF4Error("getfillvalue: SDS has an illegal type or " \
"unsupported type %d" % data_type)
status = _C.SDgetfillvalue(self._id, buf)
_checkErr('getfillvalue', status, 'fill value not set')
return convert(buf, n_values)
[docs] def getrange(self):
"""Retrieve the dataset min and max values.
Args::
no argument
Returns::
(min, max) tuple (attribute 'valid_range')
Note that those are the values as stored
by the 'setrange' method. 'getrange' does *NOT* compute the
min and max from the current dataset contents.
An exception is raised if the range is not set.
The range returned by 'getrange' is part of the so-called
"standard" SDS attributes. It corresponds to the following
attribute::
valid_range
C library equivalent: SDgetrange
"""
# Obtain SDS data type.
try:
sds_name, rank, dim_sizes, data_type, n_attrs = \
self.info()
except HDF4Error:
raise HDF4Error('getrange : invalid SDS identifier')
n_values = 1
convert = _array_to_ret
if data_type == SDC.CHAR8:
buf1 = _C.array_byte(n_values)
buf2 = _C.array_byte(n_values)
convert = _array_to_str
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf1 = _C.array_byte(n_values)
buf2 = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf1 = _C.array_int8(n_values)
buf2 = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf1 = _C.array_int16(n_values)
buf2 = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf1 = _C.array_uint16(n_values)
buf2 = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf1 = _C.array_int32(n_values)
buf2 = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf1 = _C.array_uint32(n_values)
buf2 = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf1 = _C.array_float32(n_values)
buf2 = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf1 = _C.array_float64(n_values)
buf2 = _C.array_float64(n_values)
else:
raise HDF4Error("getrange: SDS has an illegal or " \
"unsupported type %d" % data)
# Note: The C routine returns the max in buf1 and the min
# in buf2. We swap the values returned by the Python
# interface, since it is more natural to return
# min first, then max.
status = _C.SDgetrange(self._id, buf1, buf2)
_checkErr('getrange', status, 'range not set')
return convert(buf2, n_values), convert(buf1, n_values)
[docs] def setcal(self, cal, cal_error, offset, offset_err, data_type):
"""Set the dataset calibration coefficients.
Args::
cal the calibraton factor (attribute 'scale_factor')
cal_error calibration factor error
(attribute 'scale_factor_err')
offset offset value (attribute 'add_offset')
offset_err offset error (attribute 'add_offset_err')
data_type data type of the values resulting from applying the
calibration formula to the dataset values
(one of the SDC.xxx constants)
(attribute 'calibrated_nt')
Returns::
None
See method 'getcal' for the definition of the calibration
formula.
Calibration coefficients are part of the so-called standard
SDS attributes. Calling 'setcal' is equivalent to setting
the following attributes, which correspond to the method
parameters, in order::
scale_factor, scale_factor_err, add_offset, add_offset_err,
calibrated_nt
C library equivalent: SDsetcal
"""
status = _C.SDsetcal(self._id, cal, cal_error,
offset, offset_err, data_type)
_checkErr('setcal', status, 'cannot execute')
[docs] def setdatastrs(self, label, unit, format, coord_sys):
"""Set the dataset standard string type attributes.
Args::
label dataset label (attribute 'long_name')
unit dataset unit (attribute 'units')
format dataset format (attribute 'format')
coord_sys dataset coordinate system (attribute 'coordsys')
Returns::
None
Those strings are part of the so-called standard
SDS attributes. Calling 'setdatastrs' is equivalent to setting
the following attributes, which correspond to the method
parameters, in order::
long_name, units, format, coordsys
C library equivalent: SDsetdatastrs
"""
status = _C.SDsetdatastrs(self._id, label, unit, format, coord_sys)
_checkErr('setdatastrs', status, 'cannot execute')
[docs] def setfillvalue(self, fill_val):
"""Set the dataset fill value.
Args::
fill_val dataset fill value (attribute '_FillValue')
Returns::
None
The fill value is part of the so-called "standard" SDS
attributes. Calling 'setfillvalue' is equivalent to setting
the following attribute::
_FillValue
C library equivalent: SDsetfillvalue
"""
# Obtain SDS data type.
try:
sds_name, rank, dim_sizes, data_type, n_attrs = self.info()
except HDF4Error:
raise HDF4Error('setfillvalue : cannot execute')
n_values = 1 # Fill value stands for 1 value.
if data_type == SDC.CHAR8:
buf = _C.array_byte(n_values)
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf = _C.array_float64(n_values)
else:
raise HDF4Error("setfillvalue: SDS has an illegal or " \
"unsupported type %d" % data_type)
buf[0] = fill_val
status = _C.SDsetfillvalue(self._id, buf)
_checkErr('setfillvalue', status, 'cannot execute')
[docs] def setrange(self, min, max):
"""Set the dataset min and max values.
Args::
min dataset minimum value (attribute 'valid_range')
max dataset maximum value (attribute 'valid_range')
Returns::
None
The data range is part of the so-called "standard" SDS
attributes. Calling method 'setrange' is equivalent to
setting the following attribute with a 2-element [min,max]
array::
valid_range
C library equivalent: SDsetrange
"""
# Obtain SDS data type.
try:
sds_name, rank, dim_sizes, data_type, n_attrs = self.info()
except HDF4Error:
raise HDF4Error('setrange : cannot execute')
n_values = 1
if data_type == SDC.CHAR8:
buf1 = _C.array_byte(n_values)
buf2 = _C.array_byte(n_values)
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf1 = _C.array_byte(n_values)
buf2 = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf1 = _C.array_int8(n_values)
buf2 = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf1 = _C.array_int16(n_values)
buf2 = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf1 = _C.array_uint16(n_values)
buf2 = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf1 = _C.array_int32(n_values)
buf2 = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf1 = _C.array_uint32(n_values)
buf2 = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf1 = _C.array_float32(n_values)
buf2 = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf1 = _C.array_float64(n_values)
buf2 = _C.array_float64(n_values)
else:
raise HDF4Error("SDsetrange: SDS has an illegal or " \
"unsupported type %d" % data_type)
buf1[0] = max
buf2[0] = min
status = _C.SDsetrange(self._id, buf1, buf2)
_checkErr('setrange', status, 'cannot execute')
[docs] def getcompress(self):
"""Retrieves info about dataset compression type and mode.
Args::
no argument
Returns::
tuple holding:
- compression type (one of the SDC.COMP_xxx constants)
- optional values, depending on the compression type
COMP_NONE 0 value no additional value
COMP_SKPHUFF 1 value : skip size
COMP_DEFLATE 1 value : gzip compression level (1 to 9)
COMP_SZIP 5 values : options mask,
pixels per block (2 to 32)
pixels per scanline,
bits per pixel (number of bits in the SDS datatype)
pixels (number of elements in the SDS)
Note: in the context of an SDS, the word "pixel"
should really be understood as meaning "data element",
eg a cell value inside a multidimensional grid.
Test the options mask against constants SDC.COMP_SZIP_NN
and SDC.COMP_SZIP_EC, eg :
if optionMask & SDC.COMP_SZIP_EC:
print "EC encoding scheme used"
An exception is raised if dataset is not compressed.
.. note::
Starting with v0.8, an exception is always raised if
pyhdf was installed with the NOCOMPRESS macro set.
C library equivalent: SDgetcompress
"""
status, comp_type, value, v2, v3, v4, v5 = _C._SDgetcompress(self._id)
_checkErr('getcompress', status, 'no compression')
if comp_type == SDC.COMP_NONE:
return (comp_type,)
elif comp_type == SDC.COMP_SZIP:
return comp_type, value, v2, v3, v4, v5
else:
return comp_type, value
[docs] def setcompress(self, comp_type, value=0, v2=0):
"""Compresses the dataset using a specified compression method.
Args::
comp_type compression type, identified by one of the
SDC.COMP_xxx constants
value,v2 auxiliary value(s) needed by some compression types
SDC.COMP_SKPHUFF Skipping-Huffman; compression value=data size in bytes, v2 is ignored
SDC.COMP_DEFLATE Gzip compression; value=deflate level (1 to 9), v2 is ignored
SDC.COMP_SZIP Szip compression; value=encoding scheme (SDC.COMP_SZIP_EC or
SDC.COMP_SZIP_NN), v2=pixels per block (2 to 32)
Returns::
None
.. note::
Starting with v0.8, an exception is always raised if
pyhdf was installed with the NOCOMPRESS macro set.
SDC.COMP_DEFLATE applies the GZIP compression to the dataset,
and the value varies from 1 to 9, according to the level of
compression desired.
SDC.COMP_SZIP compresses the dataset using the SZIP algorithm. See the HDF User's Guide
for details about the encoding scheme and the number of pixels per block. SZIP is new
with HDF 4.2.
'setcompress' must be called before writing to the dataset.
The dataset must be written all at once, unless it is
appendable (has an unlimited dimension). Updating the dataset
in not allowed. Refer to the HDF user's guide for more details
on how to use data compression.
C library equivalent: SDsetcompress
"""
status = _C._SDsetcompress(self._id, comp_type, value, v2)
_checkErr('setcompress', status, 'cannot execute')
[docs] def setexternalfile(self, filename, offset=0):
"""Store the dataset data in an external file.
Args::
filename external file name
offset offset in bytes where to start writing in
the external file
Returns::
None
C library equivalent : SDsetexternalfile
"""
status = _C.SDsetexternalfile(self._id, filename, offset)
_checkErr('setexternalfile', status, 'execution error')
[docs] def attr(self, name_or_index):
"""Create an SDAttr instance representing an SDS
(dataset) attribute.
Args::
name_or_index attribute name or index number; if a name is
given, the attribute may not exist
Returns::
SDAttr instance for the attribute. Call the methods of this
class to query, read or set the attribute.
C library equivalent : no equivalent
"""
return SDAttr(self, name_or_index)
[docs] def attributes(self, full=0):
"""Return a dictionary describing every attribute defined
on the dataset.
Args::
full true to get complete info about each attribute
false to report only each attribute value
Returns::
Empty dictionary if no attribute defined.
Otherwise, dictionary where each key is the name of a
dataset attribute. If parameter 'full' is false,
key value is the attribute value. If 'full' is true,
key value is a tuple with the following elements:
- attribute value
- attribute index number
- attribute type
- attribute length
C library equivalent : no equivalent
"""
# Get the number of dataset attributes.
natts = self.info()[4]
# Inquire each attribute
res = {}
for n in range(natts):
a = self.attr(n)
name, aType, nVal = a.info()
if full:
res[name] = (a.get(), a.index(), aType, nVal)
else:
res[name] = a.get()
return res
[docs] def dimensions(self, full=0):
"""Return a dictionary describing every dataset dimension.
Args::
full true to get complete info about each dimension
false to report only each dimension length
Returns::
Dictionary where each key is a dimension name. If no name
has been given to the dimension, the key is set to
'fakeDimx' where 'x' is the dimension index number.
If parameter 'full' is false, key value is the dimension
length. If 'full' is true, key value is a 5-element tuple
with the following elements:
- dimension length; for an unlimited dimension, the reported
length is the current dimension length
- dimension index number
- 1 if the dimension is unlimited, 0 otherwise
- dimension scale type, or 0 if no scale is defined for
the dimension
- number of attributes defined on the dimension
C library equivalent : no equivalent
"""
# Get the number of dimensions and their lengths.
nDims, dimLen = self.info()[1:3]
if isinstance(dimLen, int): # need a sequence
dimLen = [dimLen]
# Check if the dataset is appendable.
unlim = self.isrecord()
# Inquire each dimension
res = {}
for n in range(nDims):
d = self.dim(n)
# The length reported by info() is 0 for an unlimited dimension.
# Rather use the lengths reported by SDS.info()
name, k, scaleType, nAtt = d.info()
length = dimLen[n]
if full:
res[name] = (length, n, unlim and n == 0,
scaleType, nAtt)
else:
res[name] = length
return res
[docs]class SDim(object):
"""The SDim class implements a dimension object.
There can be one dimension object for each dataset dimension.
To create an SDim instance, call the dim() method of an SDS class
instance. To set attributes on an SDim instance, call the
SDim.attr() method to create an attribute instance, then call the
methods of this instance. Attributes can also be set using the
"dot notation". """
def __init__(self, sds, id, index):
"""Init an SDim instance. This method should not be called
directly by the user program. To create an SDim instance,
call the SDS.dim() method.
"""
# Args:
# sds SDS instance
# id dimension identifier
# index index number of the dimension
# SDim private attributes
# _sds sds instance
# _id dimension identifier
# _index dimension index number
self._sds = sds
self._id = id
self._index = index
def __getattr__(self, name):
# Get value(s) of SDim attribute 'name'.
return _getattr(self, name)
def __setattr__(self, name, value):
# Set value(s) of SDim attribute 'name'.
_setattr(self, name, value, ['_sds', '_id', '_index'])
[docs] def info(self):
"""Return info about the dimension instance.
Args::
no argument
Returns::
4-element tuple holding:
- dimension name; 'fakeDimx' is returned if the dimension
has not been named yet, where 'x' is the dimension
index number
- dimension length; 0 is returned if the dimension is unlimited;
call the SDim.length() or SDS.info() methods to obtain the
current dimension length
- scale data type (one of the SDC.xxx constants); 0 is
returned if no scale has been set on the dimension
- number of attributes attached to the dimension
C library equivalent : SDdiminfo
"""
status, dim_name, dim_size, data_type, n_attrs = \
_C.SDdiminfo(self._id)
_checkErr('info', status, 'cannot execute')
return dim_name, dim_size, data_type, n_attrs
[docs] def length(self):
"""Return the dimension length. This method is useful
to quickly retrieve the current length of an unlimited
dimension.
Args::
no argument
Returns::
dimension length; if the dimension is unlimited, the
returned value is the current dimension length
C library equivalent : no equivalent
"""
return self._sds.info()[2][self._index]
[docs] def setname(self, dim_name):
"""Set the dimension name.
Args::
dim_name dimension name; setting 2 dimensions to the same
name make the dimensions "shared"; in order to be
shared, the dimensions must be defined similarly.
Returns::
None
C library equivalent : SDsetdimname
"""
status = _C.SDsetdimname(self._id, dim_name)
_checkErr('setname', status, 'cannot execute')
[docs] def getscale(self):
"""Obtain the scale values along a dimension.
Args::
no argument
Returns::
list with the scale values; the list length is equal to the
dimension length; the element type is equal to the dimension
data type, as set when the 'setdimscale()' method was called.
C library equivalent : SDgetdimscale
"""
# Get dimension info. If data_type is 0, no scale have been set
# on the dimension.
status, dim_name, dim_size, data_type, n_attrs = _C.SDdiminfo(self._id)
_checkErr('getscale', status, 'cannot execute')
if data_type == 0:
raise HDF4Error("no scale set on that dimension")
# dim_size is 0 for an unlimited dimension. The actual length is
# obtained through SDgetinfo.
if dim_size == 0:
dim_size = self._sds.info()[2][self._index]
# Get scale values.
if data_type in [SDC.UCHAR8, SDC.UINT8]:
buf = _C.array_byte(dim_size)
elif data_type == SDC.INT8:
buf = _C.array_int8(dim_size)
elif data_type == SDC.INT16:
buf = _C.array_int16(dim_size)
elif data_type == SDC.UINT16:
buf = _C.array_uint16(dim_size)
elif data_type == SDC.INT32:
buf = _C.array_int32(dim_size)
elif data_type == SDC.UINT32:
buf = _C.array_uint32(dim_size)
elif data_type == SDC.FLOAT32:
buf = _C.array_float32(dim_size)
elif data_type == SDC.FLOAT64:
buf = _C.array_float64(dim_size)
else:
raise HDF4Error("getscale: dimension has an "\
"illegal or unsupported type %d" % data_type)
status = _C.SDgetdimscale(self._id, buf)
_checkErr('getscale', status, 'cannot execute')
return _array_to_ret(buf, dim_size)
[docs] def setscale(self, data_type, scale):
"""Initialize the scale values along the dimension.
Args::
data_type data type code (one of the SDC.xxx constants)
scale sequence holding the scale values; the number of
values must match the current length of the dataset
along that dimension
C library equivalent : SDsetdimscale
Setting a scale on a dimension generates what HDF calls a
"coordinate variable". This is a rank 1 dataset similar to any
other dataset, which is created to hold the scale values. The
dataset name is identical to that of the dimension on which
setscale() is called, and the data type passed in 'data_type'
determines the type of the dataset. To distinguish between such
a dataset and a "normal" dataset, call the iscoordvar() method
of the dataset instance.
"""
try:
n_values = len(scale)
except:
n_values = 1
# Validate args
info = self._sds.info()
if info[1] == 1:
dim_size = info[2]
else:
dim_size = info[2][self._index]
if n_values != dim_size:
raise HDF4Error('number of scale values (%d) does not match ' \
'dimension size (%d)' % (n_values, dim_size))
if data_type == SDC.CHAR8:
buf = _C.array_byte(n_values)
# Allow a string as the scale argument.
# Becomes a noop if already a list.
scale = list(scale)
for n in range(n_values):
scale[n] = ord(scale[n])
elif data_type in [SDC.UCHAR8, SDC.UINT8]:
buf = _C.array_byte(n_values)
elif data_type == SDC.INT8:
buf = _C.array_int8(n_values)
elif data_type == SDC.INT16:
buf = _C.array_int16(n_values)
elif data_type == SDC.UINT16:
buf = _C.array_uint16(n_values)
elif data_type == SDC.INT32:
buf = _C.array_int32(n_values)
elif data_type == SDC.UINT32:
buf = _C.array_uint32(n_values)
elif data_type == SDC.FLOAT32:
buf = _C.array_float32(n_values)
elif data_type == SDC.FLOAT64:
buf = _C.array_float64(n_values)
else:
raise HDF4Error("setscale: illegal or usupported data_type")
if n_values == 1:
buf[0] = scale
else:
for n in range(n_values):
buf[n] = scale[n]
status = _C.SDsetdimscale(self._id, n_values, data_type, buf)
_checkErr('setscale', status, 'cannot execute')
[docs] def getstrs(self):
"""Retrieve the dimension standard string attributes.
Args::
no argument
Returns::
3-element tuple holding:
-dimension label (attribute 'long_name')
-dimension unit (attribute 'units')
-dimension format (attribute 'format')
An exception is raised if the standard attributes have
not been set.
C library equivalent: SDgetdimstrs
"""
status, label, unit, format = _C.SDgetdimstrs(self._id, 128)
_checkErr('getstrs', status, 'cannot execute')
return label, unit, format
[docs] def setstrs(self, label, unit, format):
"""Set the dimension standard string attributes.
Args::
label dimension label (attribute 'long_name')
unit dimension unit (attribute 'units')
format dimension format (attribute 'format')
Returns::
None
C library equivalent: SDsetdimstrs
"""
status = _C.SDsetdimstrs(self._id, label, unit, format)
_checkErr('setstrs', status, 'cannot execute')
[docs] def attr(self, name_or_index):
"""Create an SDAttr instance representing an SDim
(dimension) attribute.
Args::
name_or_index attribute name or index number; if a name is
given, the attribute may not exist; in that
case, the attribute is created when the
instance set() method is called
Returns::
SDAttr instance for the attribute. Call the methods of this
class to query, read or set the attribute.
C library equivalent : no equivalent
"""
return SDAttr(self, name_or_index)
[docs] def attributes(self, full=0):
"""Return a dictionary describing every attribute defined
on the dimension.
Args::
full true to get complete info about each attribute
false to report only each attribute value
Returns::
Empty dictionary if no attribute defined.
Otherwise, dictionary where each key is the name of a
dimension attribute. If parameter 'full' is false,
key value is the attribute value. If 'full' is true,
key value is a tuple with the following elements:
- attribute value
- attribute index number
- attribute type
- attribute length
C library equivalent : no equivalent
"""
# Get the number of dataset attributes.
natts = self.info()[3]
# Inquire each attribute
res = {}
for n in range(natts):
a = self.attr(n)
name, aType, nVal = a.info()
if full:
res[name] = (a.get(), a.index(), aType, nVal)
else:
res[name] = a.get()
return res
###########################
# Support functions
###########################
def _getattr(obj, name):
# Called by the __getattr__ method of the SD, SDS and SDim objects.
# Python will call __getattr__ to see if the class wants to
# define certain missing methods (__str__, __len__, etc).
# Always fail if the name starts with two underscores.
if name[:2] == '__':
raise AttributeError
# See if we deal with an SD attribute.
a = SDAttr(obj, name)
try:
index = a.index()
except HDF4Error:
raise AttributeError("attribute not found")
# Return attribute value(s).
return a.get()
def _setattr(obj, name, value, privAttr):
# Called by the __setattr__ method of the SD, SDS and SDim objects.
# Be careful with private attributes.
#if name in privAttr:
if name[0] == '_':
obj.__dict__[name] = value
return
# Treat everything else as an HDF attribute.
if type(value) not in [list, tuple]:
value = [value]
typeList = []
for v in value:
t = type(v)
# Prohibit mixing numeric types and strings.
if t in [int, float] and \
not str in typeList:
if t not in typeList:
typeList.append(t)
# Prohibit sequence of strings or a mix of numbers and string.
elif t == str and not typeList:
typeList.append(t)
else:
typeList = []
break
if str in typeList:
xtype = SDC.CHAR8
value = value[0]
# double is "stronger" than int
elif float in typeList:
xtype = SDC.FLOAT64
elif int in typeList:
xtype = SDC.INT32
else:
raise HDF4Error("Illegal attribute value")
# Assign value
try:
a = SDAttr(obj, name)
a.set(xtype, value)
except HDF4Error as msg:
raise HDF4Error("cannot set attribute: %s" % msg)
def _array_to_ret(buf, nValues):
# Convert array 'buf' to a scalar or a list.
if nValues == 1:
ret = buf[0]
else:
ret = []
for i in xrange(nValues):
ret.append(buf[i])
return ret
def _array_to_str(buf, nValues):
# Convert array of bytes 'buf' to a string.
# Return empty string if there is no value.
if nValues == 0:
return ""
# When there is just one value, _array_to_ret returns a scalar
# over which we cannot iterate.
if nValues == 1:
chrs = [chr(buf[0])]
else:
chrs = [chr(b) for b in _array_to_ret(buf, nValues)]
return ''.join(chrs)