| Name | nxmx JSON |
| Version |
0.0.4
JSON |
| download |
| home_page | None |
| Summary | Read HDF5 data conforming to the NXmx application definition of the NeXus format |
| upload_time | 2024-10-01 13:57:48 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License |
| keywords |
nexus
nxmx
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Read NXmx-flavour NeXus HDF5 data in Python
[](https://pypi.python.org/pypi/nxmx)
[](https://pypi.org/project/nxmx)
[]( https://github.com/ambv/black)
This package provides a neat and tidy Python interface for reading data from [HDF5 files](https://www.hdfgroup.org/solutions/hdf5/) that are structured according to the [NXmx application definition](https://manual.nexusformat.org/classes/applications/NXmx.html) of the [NeXus standard](https://www.nexusformat.org/).
## Installation
`python-nxmx` is available as `nxmx` on PyPI, so you just need Pip.
```Bash
$ pip install nxmx
```
## Getting started
If you have an HDF5 file in NXmx format, inspecting it with `h5ls` will look something like this:
```Bash
$ h5ls -r my-nxmx-file.h5
/ Group
/entry Group
/entry/data Group
/entry/definition Dataset {SCALAR}
/entry/end_time Dataset {SCALAR}
/entry/end_time_estimated Dataset {SCALAR}
/entry/instrument Group
/entry/instrument/beam Group
/entry/instrument/beam/incident_beam_size Dataset {2}
/entry/instrument/beam/incident_wavelength Dataset {SCALAR}
/entry/instrument/beam/total_flux Dataset {SCALAR}
/entry/instrument/detector Group
... etc. ...
```
With `nxmx`, you can access the NXmx data in Python like this:
```Python
import h5py
import nxmx
with h5py.File("my-nxmx-file.h5") as f:
nxmx_data = nxmx.NXmx(f)
```
## A slightly more detailed example
```Python
import h5py
import nxmx
with h5py.File("my-nxmx-file.h5") as f:
nxmx_data = nxmx.NXmx(f)
# Explore the NXmx data structure.
entry, *_ = nxmx_data.entries
print(entry.definition) # Prints "NXmx".
instrument, *_ = entry.instruments
detector, *_ = instrument.detectors
# Get the h5py object underlying an instance of a NX class.
entry_group = entry._handle # entry_group == f["entry"]
# Find instances of a given NX class in a h5py.Group.
beams = nxmx.find_class(instrument._handle, "NXbeam")
# The equivalent for more than one NX class.
beams, detectors = nxmx.find_classes(instrument._handle, "NXbeam", "NXdetector")
# Query attributes of an object in the normal h5py way.
# Suppose out detector has a transformation called "det_z".
transformations, *_ = nxmx.find_class(detector._handle, "NXtransformations")
attrs = transformations["det_z"].attrs # Get the attributes of the "det_z" dataset.
```
Raw data
{
"_id": null,
"home_page": null,
"name": "nxmx",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "NeXus, NXmx",
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/32/b7/6cd51f43dd453fd3084dc631ac83ae3642b3a82b644f871a45f849060f27/nxmx-0.0.4.tar.gz",
"platform": null,
"description": "# Read NXmx-flavour NeXus HDF5 data in Python\n\n[](https://pypi.python.org/pypi/nxmx)\n[](https://pypi.org/project/nxmx)\n[]( https://github.com/ambv/black)\n\nThis package provides a neat and tidy Python interface for reading data from [HDF5 files](https://www.hdfgroup.org/solutions/hdf5/) that are structured according to the [NXmx application definition](https://manual.nexusformat.org/classes/applications/NXmx.html) of the [NeXus standard](https://www.nexusformat.org/).\n\n## Installation\n`python-nxmx` is available as `nxmx` on PyPI, so you just need Pip.\n```Bash\n$ pip install nxmx\n```\n\n## Getting started\n\nIf you have an HDF5 file in NXmx format, inspecting it with `h5ls` will look something like this:\n```Bash\n$ h5ls -r my-nxmx-file.h5 \n/ Group\n/entry Group\n/entry/data Group\n/entry/definition Dataset {SCALAR}\n/entry/end_time Dataset {SCALAR}\n/entry/end_time_estimated Dataset {SCALAR}\n/entry/instrument Group\n/entry/instrument/beam Group\n/entry/instrument/beam/incident_beam_size Dataset {2}\n/entry/instrument/beam/incident_wavelength Dataset {SCALAR}\n/entry/instrument/beam/total_flux Dataset {SCALAR}\n/entry/instrument/detector Group\n\n... etc. ...\n```\nWith `nxmx`, you can access the NXmx data in Python like this:\n```Python\nimport h5py\nimport nxmx\n\nwith h5py.File(\"my-nxmx-file.h5\") as f:\n nxmx_data = nxmx.NXmx(f)\n```\n\n## A slightly more detailed example\n```Python\nimport h5py\nimport nxmx\n\nwith h5py.File(\"my-nxmx-file.h5\") as f:\n nxmx_data = nxmx.NXmx(f)\n # Explore the NXmx data structure.\n entry, *_ = nxmx_data.entries\n print(entry.definition) # Prints \"NXmx\".\n instrument, *_ = entry.instruments\n detector, *_ = instrument.detectors\n \n # Get the h5py object underlying an instance of a NX class.\n entry_group = entry._handle # entry_group == f[\"entry\"]\n\n # Find instances of a given NX class in a h5py.Group.\n beams = nxmx.find_class(instrument._handle, \"NXbeam\")\n # The equivalent for more than one NX class.\n beams, detectors = nxmx.find_classes(instrument._handle, \"NXbeam\", \"NXdetector\")\n\n # Query attributes of an object in the normal h5py way.\n # Suppose out detector has a transformation called \"det_z\".\n transformations, *_ = nxmx.find_class(detector._handle, \"NXtransformations\")\n attrs = transformations[\"det_z\"].attrs # Get the attributes of the \"det_z\" dataset.\n```\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License",
"summary": "Read HDF5 data conforming to the NXmx application definition of the NeXus format",
"version": "0.0.4",
"project_urls": null,
"split_keywords": [
"nexus",
" nxmx"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "3d32f1b43d771f1f6d4772f9fa1bcce7da76f73438b6511d08d1324e64d24e8f",
"md5": "6d544f3e642e4facb147be4b78f21f78",
"sha256": "b9860916cc25926be46ffa8a122f49ec1156ac5cc924633f37995064e208b04d"
},
"downloads": -1,
"filename": "nxmx-0.0.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "6d544f3e642e4facb147be4b78f21f78",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 15906,
"upload_time": "2024-10-01T13:57:47",
"upload_time_iso_8601": "2024-10-01T13:57:47.206357Z",
"url": "https://files.pythonhosted.org/packages/3d/32/f1b43d771f1f6d4772f9fa1bcce7da76f73438b6511d08d1324e64d24e8f/nxmx-0.0.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "32b76cd51f43dd453fd3084dc631ac83ae3642b3a82b644f871a45f849060f27",
"md5": "8aef8aec78db6b02c71c9708cee2ffd8",
"sha256": "baf205d803412675bb613b01217985347704940a774170dfe7114fe1bb5ca7d5"
},
"downloads": -1,
"filename": "nxmx-0.0.4.tar.gz",
"has_sig": false,
"md5_digest": "8aef8aec78db6b02c71c9708cee2ffd8",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 22927,
"upload_time": "2024-10-01T13:57:48",
"upload_time_iso_8601": "2024-10-01T13:57:48.785074Z",
"url": "https://files.pythonhosted.org/packages/32/b7/6cd51f43dd453fd3084dc631ac83ae3642b3a82b644f871a45f849060f27/nxmx-0.0.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-01 13:57:48",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "nxmx"
}
