SD (scientific dataset) API (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
-
class
pyhdf.SD.SD(path, mode=1)[source]¶ 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.
-
attr(name_or_index)[source]¶ 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
-
attributes(full=0)[source]¶ 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
-
create(name, data_type, dim_sizes)[source]¶ 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
-
datasets()[source]¶ 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
-
end()[source]¶ End access to the SD interface and close the HDF file.
Args:
no argument
Returns:
NoneThe instance should not be used afterwards. The ‘end()’ method is implicitly called when the SD instance is deleted.
C library equivalent : SDend
-
info()[source]¶ 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
-
nametoindex(sds_name)[source]¶ 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
-
reftoindex(sds_ref)[source]¶ 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
-
select(name_or_index)[source]¶ Locate a dataset.
Args:
name_or_index dataset name or index number
Returns:
SDS instance for the dataset
C library equivalent : SDselect
-
setfillmode(fill_mode)[source]¶ 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
-
-
class
pyhdf.SD.SDAttr(obj, index_or_name)[source]¶ -
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 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”.
-
index()[source]¶ Retrieve the attribute index number.
Args:
no argument
Returns:
attribute index number (starting at 0)
C library equivalent : SDfindattr
-
info()[source]¶ 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
-
set(data_type, values)[source]¶ 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:
NoneC library equivalent : SDsetattr
Attributes can also be written like ordinary python attributes, using the dot notation. See “High level attribute access”.
-
-
class
pyhdf.SD.SDC[source]¶ 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
-
CHAR= 4¶
-
CHAR8= 4¶
-
COMP_DEFLATE= 4¶
-
COMP_NBIT= 2¶
-
COMP_NONE= 0¶
-
COMP_RLE= 1¶
-
COMP_SKPHUFF= 3¶
-
COMP_SZIP= 5¶
-
COMP_SZIP_EC= 4¶
-
COMP_SZIP_NN= 32¶
-
COMP_SZIP_RAW= 128¶
-
CREATE= 4¶
-
FILL= 0¶
-
FLOAT32= 5¶
-
FLOAT64= 6¶
-
INT16= 22¶
-
INT32= 24¶
-
INT8= 20¶
-
NOFILL= 256¶
-
READ= 1¶
-
TRUNC= 256¶
-
UCHAR= 3¶
-
UCHAR8= 3¶
-
UINT16= 23¶
-
UINT32= 25¶
-
UINT8= 21¶
-
UNLIMITED= 0¶
-
WRITE= 2¶
-
equivNumericTypes= [5, 6, 20, 21, 22, 23, 24, 25, 4, 3]¶
-
class
pyhdf.SD.SDS(sd, id)[source]¶ 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”.
-
attr(name_or_index)[source]¶ 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
-
attributes(full=0)[source]¶ 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
-
checkempty()[source]¶ Determine whether the dataset is empty.
Args:
no argument
Returns:
True(1) if dataset is empty, False(0) if not
C library equivalent : SDcheckempty
-
dim(dim_index)[source]¶ 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
-
dimensions(full=0)[source]¶ 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
-
endaccess()[source]¶ 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
-
get(start=None, count=None, stride=None)[source]¶ 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”.
-
getcal()[source]¶ 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()
-
getcompress()[source]¶ 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
-
getdatastrs()[source]¶ 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
-
getfillvalue()[source]¶ 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:
_FillValueC library equivalent: SDgetfillvalue
-
getrange()[source]¶ 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_rangeC library equivalent: SDgetrange
-
info()[source]¶ 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
-
iscoordvar()[source]¶ 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
-
isrecord()[source]¶ 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
-
ref()[source]¶ Get the reference number of the dataset.
Args:
no argument
Returns:
dataset reference number
C library equivalent : SDidtoref
-
set(data, start=None, count=None, stride=None)[source]¶ 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”.
-
setcal(cal, cal_error, offset, offset_err, data_type)[source]¶ 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:
NoneSee 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
-
setcompress(comp_type, value=0, v2=0)[source]¶ 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:
NoneNote
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
-
setdatastrs(label, unit, format, coord_sys)[source]¶ 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:
NoneThose 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
-
setexternalfile(filename, offset=0)[source]¶ 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:
NoneC library equivalent : SDsetexternalfile
-
setfillvalue(fill_val)[source]¶ Set the dataset fill value.
Args:
fill_val dataset fill value (attribute '_FillValue')
Returns:
NoneThe fill value is part of the so-called “standard” SDS attributes. Calling ‘setfillvalue’ is equivalent to setting the following attribute:
_FillValueC library equivalent: SDsetfillvalue
-
setrange(min, max)[source]¶ Set the dataset min and max values.
Args:
min dataset minimum value (attribute 'valid_range') max dataset maximum value (attribute 'valid_range')
Returns:
NoneThe 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_rangeC library equivalent: SDsetrange
-
-
class
pyhdf.SD.SDim(sds, id, index)[source]¶ 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”.
-
attr(name_or_index)[source]¶ 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
-
attributes(full=0)[source]¶ 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
-
getscale()[source]¶ 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
-
getstrs()[source]¶ 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
-
info()[source]¶ 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
-
length()[source]¶ 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
-
setname(dim_name)[source]¶ 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:
NoneC library equivalent : SDsetdimname
-
setscale(data_type, scale)[source]¶ 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.
-