hdfmap 0.6.1

Map objects within a HDF file and create a dataset namespace

hdfmap

Map objects within an HDF file and create a dataset namespace.

Version 0.6

By Dan Porter
Diamond Light Source
2024

Documentation

diamondlightsource.github.io/hdfmap

TL;DR - Usage

from hdfmap import create_nexus_map, load_hdf

# HdfMap from NeXus file - get dataset paths:
m = create_nexus_map('file.nxs')
m['energy']  # >> '/entry/instrument/monochromator/energy'
m['signal']  # >> '/entry/measurement/sum'
m['axes0']  # >> '/entry/measurement/theta'
m.get_image_path()  # >> '/entry/instrument/pil3_100k/data'

# load dataset data
with load_hdf('file.nxs') as nxs:
    path = m.get_path('scan_command')
    cmd = nxs[path][()]  # returns bytes data direct from file
    cmd = m.get_data(nxs, 'scan_command')  # returns converted str output
    string = m.format_hdf(nxs, "the energy is {energy:.2f} keV")
    d = m.get_dataholder(nxs)  # classic data table, d.scannable, d.metadata

# Shortcuts - single file reloader class
from hdfmap import NexusLoader

scan = NexusLoader('file.hdf')
[data1, data2] = scan.get_data(['dataset_name_1', 'dataset_name_2'])
data = scan.eval('dataset_name_1 * 100 + 2')
string = scan.format('my data is {dataset_name_1:.2f}')

# Shortcuts - multifile load data (generate map from first file)
from hdfmap import hdf_data, hdf_eval, hdf_format, hdf_image

all_data = hdf_data([f'file{n}.nxs' for n in range(100)], 'dataset_name')
normalised_data = hdf_eval(filenames, 'total / Transmission / (rc / 300.)')
descriptions = hdf_format(filenames, 'Energy: {en:5.3f} keV')
image_stack = hdf_image(filenames, index=31)

Installation

Requires: Python >=3.10, Numpy, h5py

from PyPI

python -m pip install hdfmap

from GitHub

python -m pip install --upgrade git+https://github.com/DiamondLightSource/hdfmap.git

Description

Another generic hdf reader but the idea here is to build up a namespace dict of {'name': 'path'} for every dataset, then group them in hopefully a useful way.

Objects within the HDF file are separated into Groups and Datasets. Each object has a defined 'path' and 'name' paramater, as well as other attributes

• path -> '/entry/measurement/data' -> the location of an object within the file
• name -> 'data' -> an path expressed as a simple variable name

Paths are unique locations within the file but can be used to identify similar objects in other files Names may not be unique within a file and are generated from the path.

	name	path
Description	simple identifier of dataset	hdf path built from position in file
Example	'scan_command'	'/entry/scan_command'

Names of different types of datasets are stored for arrays (size > 0) and values (size 0) Names for scannables relate to all arrays of a particular size A combined list of names is provided where scannables > arrays > values

HdfMap Attributes

map.groups	stores attributes of each group by path
map.classes	stores list of group paths by nx_class
map.datasets	stores attributes of each dataset by path
map.arrays	stores array dataset paths by name
map.values	stores value dataset paths by name
map.scannables	stores array dataset paths with given size, by name
map.combined	stores array and value paths (arrays overwrite values)
map.image_data	stores dataset paths of image data

E.G.

map.groups = {'/hdf/group': ('class', 'name', {attrs}, [datasets])}
map.classes = {'class_name': ['/hdf/group1', '/hdf/group2']}
map.datasets = {'/hdf/group/dataset': ('name', size, shape, {attrs})}
map.arrays = {'name': '/hdf/group/dataset'}
map.values = {'name': '/hdf/group/dataset'}
map.scannables = {'name': '/hdf/group/dataset'}
map.image_data = {'name': '/hdf/group/dataset'}

HdfMap Methods

map.populate(h5py.File)	populates the dictionaries using the given file
map.generate_scannables(array_size)	populates scannables namespace with arrays of same size
map.most_common_size()	returns the most common dataset size > 1
map.get_attr('name_or_path', 'attr')	return value of dataset attribute
map.get_path('name_or_group_or_class')	returns path of object with name
map.get_image_path()	returns default path of detector dataset (or largest dataset)
map.get_group_path('name_or_path_or_class')	return path of group with class
map.get_group_datasets('name_or_path_or_class')	return list of dataset paths in class
map.find_groups(*names_or_classes)	return list of group paths matching given group names or classes
map.find_datasets(*names_or_classes)	return list of dataset paths matching given names, classes or attributes
map.find_paths('string')	return list of dataset paths containing string
map.find_names('string')	return list of dataset names containing string
map.find_attr('attr_name')	return list of paths of groups or datasets containing attribute 'attr_name'

HdfMap File Methods

map.get_metadata(h5py.File)	returns dict of value datasets
map.get_scannables(h5py.File)	returns dict of scannable datasets
map.get_scannalbes_array(h5py.File)	returns numpy array of scannable datasets
map.get_dataholder(h5py.File)	returns dict like object with metadata and scannables
map.get_image(h5py.File, index)	returns image data
map.get_data(h5py.File, 'name')	returns data from dataset
map.eval(h5py.File, 'expression')	returns output of expression using dataset names
map.format(h5py.File, 'string {name}')	returns output of str expression

NeXus Files

Files using the NeXus Format can generate special NexusMap objects. These work in the same way as the general HdfMaps but contain additional special names in the namespace:

'axes'	returns path of default NXaxes
'signal'	returns path of default NXsignal

In addition, the map.scannables dict will be populated automatically by the names given in the "scan_fields" dataset or by datasets from the first NXdata group. The default image data will be taken from the first NXdetector dataset.

Examples

scan data & metadata

Separate datasets in a NeXus file into Diamond's classic scannables and metadata, similar to what was in the old '*.dat' files.

from hdfmap import create_nexus_map, load_hdf

# HdfMap from NeXus file:
hmap = create_nexus_map('file.nxs')
with load_hdf('file.nxs') as nxs:
    scannables = hmap.get_scannables_array(nxs)  # creates 2D numpy array
    labels = scannables.dtype.names
    metadata = hmap.get_metadata(nxs)  # {'name': value}
    d = hmap.get_dataholder(nxs)  # classic data table, d.scannable, d.metadata
d.theta == d['theta']  # scannable array 'theta'
d.metadata.scan_command == d.metadata['scan_command']  # single value 'scan_command'

# OR, use the shortcut:
from hdfmap import nexus_data_block

d = nexus_data_block('file.nxs')

# The data loader class removes the need to open the files:
from hdfmap import NexusLoader

scan = NexusLoader('file.nxs')
metadata = scan.get_metadata()
scannables = scan.get_scannables()

automatic default plot axes

If defined in the nexus file, 'axes' and 'signal' will be populated automatically

import matplotlib.pyplot as plt
from hdfmap import create_nexus_map, load_hdf

# HdfMap from NeXus file:
hmap = create_nexus_map('file.nxs')
with load_hdf('file.nxs') as nxs:
    axes = hmap.get_data(nxs, 'axes')
    signal = hmap.get_data(nxs, 'signal')
    title = hmap.format_hdf(nxs, "{entry_identifier}\n{scan_command}")
axes_label = hmap.get_path('axes')
signal_label = hmap.get_path('signal')
# plot the data (e.g. using matplotlib)
plt.figure()
plt.plot(axes, signal)
plt.xlabel(axes_label)
plt.ylabel(signal_label)
plt.title(title)

# Or, using NexusLoader:
from hdfmap import NexusLoader

scan = NexusLoader('file.nxs')
axes, signal = scan('axes, signal')
axes_label, signal_label = scan('_axes, _signal')
title = scan.format("{entry_identifier}\n{scan_command}")

Automatic image data

Get images from the first detector in a NeXus file

from hdfmap import create_nexus_map, load_hdf

# HdfMap from NeXus file:
hmap = create_nexus_map('file.nxs')
image_location = hmap.get_image_path()  # returns the hdf path chosen for the default detector
with load_hdf('file.nxs') as nxs:
    middle_image = hmap.get_image(nxs)  # returns single image from index len(dataset)//2
    first_image = hmap.get_image(nxs, 0)  # returns single image from dataset[0, :, :]
    volume = hmap.get_image(nxs, ())  # returns whole volume as array
    roi = hmap.get_image(nxs, (0, slice(5, 10, 1), slice(5, 10, 1)))  # returns part of dataset

# Or, using NexusLoader:
from hdfmap import NexusLoader

scan = NexusLoader('file.nxs')
image = scan.get_image(index=0)  # using index as defined above

Multi-scan metadata string

Generate a metadata string from every file in a directory very quickly. The HdfMap is only created for the first file, the remaining files are treated as having identical structure.

from hdfmap import list_files, hdf_format

format_string = "#{entry_identifier}: {start_time} : E={incident_energy:.3f} keV : {scan_command}"
files = list_files('/directoy/path', extension='.nxs')
strings_list = hdf_format(files, format_string)
print('\n'.join(strings_list))

# other multi-file readers:
from hdfmap import hdf_data, hdf_image, hdf_eval

data_list = hdf_data(files, 'incident_energy')
image_list = hdf_image(files, index=0)
data_list = hdf_eval(files, 'signal / Transmission')