silx.io.spech5: h5py-like API to SpecFile

This module provides a h5py-like API to access SpecFile data.

API description

Specfile data structure exposed by this API:

/
    1.1/
        title = "…"
        start_time = "…"
        instrument/
            specfile/
                file_header = ["…", "…", …]
                scan_header = ["…", "…", …]
            positioners/
                motor_name = value
                …
            mca_0/
                data = …
                calibration = …
                channels = …
                preset_time = …
                elapsed_time = …
                live_time = …

            mca_1/
                …
            …
        measurement/
            colname0 = …
            colname1 = …
            …
            mca_0/
                 data -> /1.1/instrument/mca_0/data
                 info -> /1.1/instrument/mca_0/
            …
    2.1/
        …

file_header and scan_header are numpy arrays of fixed-length strings containing raw header lines relevant to the scan.

The title is the content of the #S scan header line without the leading #S (e.g "1  ascan  ss1vo -4.55687 -0.556875  40 0.2").

The start time is in ISO8601 format ("2016-02-23T22:49:05Z")

All numeric datasets store values in float32 format.

Motor positions (e.g. /1.1/instrument/positioners/motor_name) can be 1D numpy arrays if they are measured as scan data, or else scalars as defined on #P scan header lines. A simple test is done to check if the motor name is also a data column header defined in the #L scan header line.

Scan data (e.g. /1.1/measurement/colname0) is accessed by column, the dataset name colname0 being the column label as defined in the #L scan header line.

MCA data is exposed as a 2D numpy array containing all spectra for a given analyser. The number of analysers is calculated as the number of MCA spectra per scan data line. Demultiplexing is then performed to assign the correct spectra to a given analyser.

MCA calibration is an array of 3 scalars, from the #@CALIB header line. It is identical for all MCA analysers, as there can be only one #@CALIB line per scan.

MCA channels is an array containing all channel numbers. This information is computed from the #@CHANN scan header line (if present), or computed from the shape of the first spectrum in a scan ([0, … len(first_spectrum] - 1]).

Accessing data

Data and groups are accessed in h5py fashion:

from silx.io.spech5 import SpecH5

# Open a SpecFile
sfh5 = SpecH5("test.dat")

# using SpecH5 as a regular group to access scans
scan1group = sfh5["1.1"]
instrument_group = scan1group["instrument"]

# altenative: full path access
measurement_group = sfh5["/1.1/measurement"]

# accessing a scan data column by name as a 1D numpy array
data_array = measurement_group["Pslit HGap"]

# accessing all mca-spectra for one MCA device
mca_0_spectra = measurement_group["mca_0/data"]

SpecH5 and SpecH5Group provide a SpecH5Group.keys() method:

>>> sfh5.keys()
['96.1', '97.1', '98.1']
>>> sfh5['96.1'].keys()
['title', 'start_time', 'instrument', 'measurement']

They can also be treated as iterators:

for scan_group in SpecH5("test.dat"):
    dataset_names = [item.name in scan_group["measurement"] if
                     isinstance(item, SpecH5Dataset)]
    print("Found data columns in scan " + scan_group.name)
    print(", ".join(dataset_names))

You can test for existence of data or groups:

>>> "/1.1/measurement/Pslit HGap" in sfh5
True
>>> "positioners" in sfh5["/2.1/instrument"]
True
>>> "spam" in sfh5["1.1"]
False

Classes

• SpecH5
• SpecH5Group
• SpecH5Dataset
• SpecH5LinkToGroup
• SpecH5LinkToDataset

silx.io.spech5.is_group(name)

Check if name matches a valid group name pattern in a SpecH5.

Parameters:name (str) – Full name of member

For example:

• is_group("/123.456/instrument/") returns True.
• is_group("spam") returns False because "spam" is not at all a valid group name.
• is_group("/1.2/instrument/positioners/xyz") returns False because this key would point to a motor position, which is a dataset and not a group.

silx.io.spech5.is_dataset(name)

Check if name matches a valid dataset name pattern in a SpecH5.

Parameters:name (str) – Full name of member

For example:

• is_dataset("/1.2/instrument/positioners/xyz") returns True because this name could be the key to the dataset recording motor positions for motor xyz in scan 1.2.
• is_dataset("/123.456/instrument/") returns False because this name points to a group.
• is_dataset("spam") returns False because "spam" is not at all a valid dataset name.

silx.io.spech5.is_link_to_group(name)

Check if name is a valid link to a group in a SpecH5. Return True or False

Parameters:name (str) – Full name of member

silx.io.spech5.is_link_to_dataset(name)

Check if name is a valid link to a dataset in a SpecH5. Return True or False

Parameters:name (str) – Full name of member

silx.io.spech5.spec_date_to_iso8601(date, zone=None)

Convert SpecFile date to Iso8601.

Parameters:

• date (str) – Date (see supported formats below)
• zone – Time zone as it appears in a ISO8601 date

Supported formats:

• DDD MMM dd hh:mm:ss YYYY
• DDD YYYY/MM/dd hh:mm:ss YYYY

where DDD is the abbreviated weekday, MMM is the month abbreviated name, MM is the month number (zero padded), dd is the weekday number (zero padded) YYYY is the year, hh the hour (zero padded), mm the minute (zero padded) and ss the second (zero padded). All names are expected to be in english.

Examples:

>>> spec_date_to_iso8601("Thu Feb 11 09:54:35 2016")
'2016-02-11T09:54:35'

>>> spec_date_to_iso8601("Sat 2015/03/14 03:53:50")
'2015-03-14T03:53:50'

class silx.io.spech5.SpecH5Dataset

Bases: numpy.ndarray

Emulate h5py.Dataset for a SpecFile object

Parameters:

• array_like – Input dataset in a type that can be digested by numpy.array() (str, list, numpy.ndarray…)
• name (str) – Dataset full name (posix path format, starting with /)
• file – Parent SpecH5
• parent – Parent SpecH5Group which contains this dataset

This class inherits from numpy.ndarray and adds name and value attributes for HDF5 compatibility. value is a reference to the class instance (value = self).

Data is stored in float32 format, unless it is a string.

class silx.io.spech5.SpecH5LinkToDataset

Bases: silx.io.spech5.SpecH5Dataset

Special SpecH5Dataset representing a link to a dataset. It works exactly like a regular dataset, but SpecH5Group.visit() and SpecH5Group.visititems() methods will recognize that it is a link and will ignore it.

class silx.io.spech5.SpecH5Group(name, specfileh5)

Bases: object

Emulate h5py.Group for a SpecFile object

Parameters:

• name (str) – Group full name (posix path format, starting with /)
• specfileh5 – parent SpecH5 instance

name = None

Full name/path of group

file = None

Parent SpecH5 object

attrs = None

Attributes dictionary

parent

Parent group (group that contains this group)

__contains__(key)

Parameters:key – Path to child element (e.g. "mca_0/info") or full name of group or dataset (e.g. "/2.1/instrument/positioners") Returns:True if key refers to a valid member of this group, else False

__getitem__(key)

Return a SpecH5Group or a SpecH5Dataset if key is a valid name of a group or dataset.

key can be a member of self.keys(), i.e. an immediate child of the group, or a path reaching into subgroups (e.g. "instrument/positioners")

In the special case were this group is the root group, key can start with a / character.

Parameters:key (str) – Name of member Raise:KeyError if key is not a known member of this group.

__len__()

Return number of members,subgroups and datasets, attached to this group.

keys()

Returns:List of all names of members attached to this group

visit(func)

Recursively visit all names in this group and subgroups.

Parameters:func (function) – Callable (function, method or callable object)

You supply a callable (function, method or callable object); it will be called exactly once for each link in this group and every group below it. Your callable must conform to the signature:

func(<member name>) => <None or return value>

Returning None continues iteration, returning anything else stops and immediately returns that value from the visit method. No particular order of iteration within groups is guaranteed.

Example:

# Get a list of all contents (groups and datasets) in a SpecFile
mylist = []
f = File('foo.dat')
f.visit(mylist.append)

visititems(func)

Recursively visit names and objects in this group.

Parameters:func (function) – Callable (function, method or callable object)

You supply a callable (function, method or callable object); it will be called exactly once for each link in this group and every group below it. Your callable must conform to the signature:

func(<member name>, <object>) => <None or return value>

Returning None continues iteration, returning anything else stops and immediately returns that value from the visit method. No particular order of iteration within groups is guaranteed.

Example:

# Get a list of all datasets in a specific scan
mylist = []
def func(name, obj):
    if isinstance(obj, SpecH5Dataset):
        mylist.append(name)

f = File('foo.dat')
f["1.1"].visititems(func)

class silx.io.spech5.SpecH5LinkToGroup(name, specfileh5)

Bases: silx.io.spech5.SpecH5Group

Special SpecH5Group representing a link to a group.

It works exactly like a regular group but SpecH5Group.visit() and SpecH5Group.visititems() methods will recognize it as a link and will ignore it.

keys()

Returns:List of all names of members attached to the target group

class silx.io.spech5.SpecH5(filename)

Bases: silx.io.spech5.SpecH5Group

Special SpecH5Group representing the root of a SpecFile.

Parameters:filename (str) – Path to SpecFile in filesystem

In addition to all generic SpecH5Group attributes, this class also keeps a reference to the original SpecFile object and has a filename attribute.

Its immediate children are scans, but it also gives access to any group or dataset in the entire SpecFile tree by specifying the full path.

keys()

Returns:List of all scan keys in this SpecFile (e.g. ["1.1", "2.1"…])