{
"cells": [
{
"cell_type": "markdown",
"id": "b570aefb-f909-4779-ad1d-f37cdc42df78",
"metadata": {},
"source": [
"# Example: Inspecting a NeXus file\n",
"\n",
"To demonstrate that the `h5rdmtoolbox` works with [NeXus](https://manual.nexusformat.org/fileformat.html) files, we download a file and inspect it. Note that by \"works\" we don't mean that the data model of NeXus is understood by the toolbox. Rather, we want to demonstrate that we can identify specific data in the file, plot it, and apply a minimal example of convention and layout validation that we will quickly design here."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ce843cf0-50f9-4f7f-9160-2fcae3425503",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Failed to import module h5tbx\n"
]
}
],
"source": [
"import h5rdmtoolbox as h5tbx"
]
},
{
"cell_type": "markdown",
"id": "c9f2432b-cd6a-40a2-ae7a-9eae5010de7d",
"metadata": {},
"source": [
"## 1. Get the file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4f6cf57c-dfcc-4ebc-9c6f-b84214c724e0",
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import pathlib\n",
"\n",
"if not pathlib.Path('file.nxs').exists():\n",
" r = requests.get('https://github.com/nexusformat/exampledata/raw/master/Soleil/hdf5/file_1.nxs')\n",
" with open('file.nxs', 'wb') as f:\n",
" f.write(r.content)"
]
},
{
"cell_type": "markdown",
"id": "4c3f18be-ce47-4fec-a061-b6f6ba7c27f9",
"metadata": {},
"source": [
"## 2. Visit (Dump) the file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8c16217f-d920-4dc4-b4c5-5b46a2aab5f9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Matthias\\Documents\\programming\\GitHub\\h5RDMtoolbox\\h5rdmtoolbox\\_repr.py:552: RuntimeWarning: Cannot decode attribute value for units\n",
" warnings.warn(f'Cannot decode attribute value for {name}', RuntimeWarning)\n"
]
},
{
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" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C08__EX__TAB.1-MT_TZ.2__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C08__EX__TAB.1-MT_Tx__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__DT__DTC_CCD.A(1) \n",
" \n",
"\n",
" NX_class : NXdetector name : {Name of detector bank} \n",
" \n",
" \n",
" \n",
" Exposure(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__DT__DTC_CCD.A__#1(1) \n",
" \n",
"\n",
" NX_class : NXdetector name : {Name of detector bank} \n",
" \n",
" \n",
" \n",
" Exposure(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__DT__MI_DIODE.8a(5) \n",
" \n",
"\n",
" NX_class : NXintensity_monitor name : {monitor name} \n",
" \n",
" \n",
" \n",
" AverageIntensity(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" Gain(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" bias(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" offset(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__DT__MI_DIODE.8a__#1(4) \n",
" \n",
"\n",
" NX_class : NXintensity_monitor name : {monitor name} \n",
" \n",
" \n",
" \n",
" AverageIntensity(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" Gain(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" bias(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" offset(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__DT__MI_DIODE.9__#1(4) \n",
" \n",
"\n",
" NX_class : NXintensity_monitor name : {monitor name} \n",
" \n",
" \n",
" \n",
" AverageIntensity(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" Gain(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" bias(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" offset(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__BST.3-MT_Tx__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__BST.3-MT_Tz__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__BST.6-MT_Tx__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__BST.6-MT_Tz__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__SPT.1-MT_Ts__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__SPT.2-MT_Tx__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" I11-C-C09__EX__SPT.2-MT_Tz.1__#1(3) \n",
" \n",
"\n",
" NX_class : NXpositioner name : {Name of positioner} \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" Sample(1) \n",
" \n",
"\n",
" NX_class : NXsample name : {name of the sample} \n",
" \n",
" \n",
" \n",
" temperature_env(1) \n",
" \n",
"\n",
" NX_class : NXenvironment name : {Name of sample environment} \n",
" \n",
" \n",
" \n",
" I11-C-C08__EX__THERM_BATH.1__#1(6) \n",
" \n",
"\n",
" NX_class : NXsensor name : {Name of sensor} \n",
" \n",
" \n",
" \n",
" externalTemperature(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" internalTemperature(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" setPointTemperature(2) \n",
" \n",
"\n",
" NX_class : NXtechnical_data name : {Data name} \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" User(7) \n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
"\n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" \n",
" comment_conditions(2) \n",
" \n",
"\n",
" NX_class : NXnote name : {name of note} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" experimental_frame(2) \n",
" \n",
"\n",
" NX_class : NXnote name : {name of note} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#0(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#1(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#2(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#3(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#4(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#5(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#6(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#7(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#8(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" image#9(1) \n",
" \n",
"\n",
" NX_class : NXdata name : {Name of data block} \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" sample_info(1) \n",
" \n",
"\n",
" NX_class : NXsample name : {name of the sample} \n",
" \n",
" \n",
" \n",
" \n",
" comment_sample(2) \n",
" \n",
"\n",
" NX_class : NXnote name : {name of note} \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"h5tbx.dump('file.nxs')"
]
},
{
"cell_type": "markdown",
"id": "029b7205-24c5-4a05-b050-28882d9adf5c",
"metadata": {},
"source": [
"## 3. Find and plot data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b32486e0-b3e2-4973-97fc-c3d8c55b6e55",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obj = h5tbx.database.FileDB('file.nxs').find_one({'$name': {'$basename': 'image#5'}}, \n",
" recursive=True)\n",
"obj"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0622160c-643e-4d6d-b3c5-949300179ace",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"obj.data[()].plot() # note, we can access children (e.g. dataset \"data\") of the (lazy) group"
]
},
{
"cell_type": "markdown",
"id": "5d09cfc4-04db-4695-a007-228be364de6d",
"metadata": {},
"source": [
"## 4. Write a standard attribute for timestamp\n",
"\n",
"If you carefully check the `dump` output, you will find, that all numeric (!) datasets have the attribute \"timestamp\". All of them? Well, let's write a convention, which defines that providing the attribute \"timestamp\" with every numeric dataset is *required*. The timestamp value must have a specific format.\n",
"\n",
"Let's create the standard name first:"
]
},
{
"cell_type": "markdown",
"id": "3f039604-3b60-40cc-8316-34ab68a485f2",
"metadata": {},
"source": [
"### 4.1 Standard Name \"timestamp\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5394bd5c-e6b0-4466-9f8b-fba8f6bd9b85",
"metadata": {},
"outputs": [],
"source": [
"from h5rdmtoolbox.convention import StandardAttribute\n",
"\n",
"from pydantic import BaseModel, ValidationError\n",
"from pydantic.functional_validators import AfterValidator, Annotated\n",
"from datetime import datetime\n",
"from typing import Union\n",
"\n",
"def parse_datetime(v: Union[str, datetime]) -> str:\n",
" \"\"\"validation function used by the pydantic model class\"\"\"\n",
" _format = '%Y-%m-%dT%H-%M-%S.%f'\n",
" if isinstance(v, datetime):\n",
" return v.strftime(_format)\n",
" return datetime.strptime(v, _format).strftime(_format)\n",
"\n",
"class NexusTimeStampObject(BaseModel):\n",
" \"\"\"The standard name table of the convention.\"\"\"\n",
" value: Annotated[str, AfterValidator(parse_datetime)]\n",
"\n",
"NexusTimeStamp = StandardAttribute('timestamp',\n",
" validator=NexusTimeStampObject,\n",
" target_method='create_dataset',\n",
" description='Timestamp of writing the data',\n",
" default_value='$EMPTY')"
]
},
{
"cell_type": "markdown",
"id": "e645e352-c821-461d-bd67-71450e07a1ce",
"metadata": {},
"source": [
"### 4.2 Build the convention"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7376d8e1-ffd8-4cc6-9e12-850c6bc25aa8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"using(\"nexus\")"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from h5rdmtoolbox.convention import Convention\n",
"\n",
"cv = Convention(name='nexus', contact=h5tbx.__author__)\n",
"\n",
"cv.add_standard_attribute(NexusTimeStamp)\n",
"cv.register()\n",
"\n",
"h5tbx.use(cv)"
]
},
{
"cell_type": "markdown",
"id": "672faf53-c581-4b9e-b3e2-bdc227def3d6",
"metadata": {},
"source": [
"First test the convention on a temporary file:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "fefd1e02-3348-4b21-a3a7-a87a1cad7664",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"\n",
"
\n",
" \n",
" \n",
" /(1) \n",
" \n",
"\n",
" \n",
"\n",
" \n",
" \n",
" \n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with h5tbx.File() as h5:\n",
" h5.create_dataset('test', data=4, timestamp='2008-10-23T15-09-39.175')\n",
" h5.dump()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "01d00671-9399-4bb4-8db3-e4066c6d0ee3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Convention \"nexus\" expects standard attribute \"timestamp\" to be provided as an argument during dataset creation.\n"
]
}
],
"source": [
"# should raise an error due to NO timestamp\n",
"with h5tbx.File() as h5:\n",
" try:\n",
" h5.create_dataset('test', data=4)\n",
" except h5tbx.errors.StandardAttributeError as e:\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0c7e628b-71fb-4e63-80c9-a5bd918c8736",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation of \"2008 10 23 15-09-39.175\" for standard attribute \"timestamp\" failed.\n",
"Expected fields: {'value': FieldInfo(annotation=str, required=True, metadata=[AfterValidator(func=)])}\n",
"Pydantic error: 1 validation error for NexusTimeStampObject\n",
"value\n",
" Value error, time data '2008 10 23 15-09-39.175' does not match format '%Y-%m-%dT%H-%M-%S.%f' [type=value_error, input_value='2008 10 23 15-09-39.175', input_type=str]\n",
" For further information visit https://errors.pydantic.dev/2.7/v/value_error\n"
]
}
],
"source": [
"# should raise an error due to a wrong timestamp\n",
"with h5tbx.File() as h5:\n",
" try:\n",
" h5.create_dataset('test', data=4, timestamp='2008 10 23 15-09-39.175')\n",
" except h5tbx.errors.StandardAttributeError as e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"id": "1ab57e0d-7d2b-4996-9c1f-c1e360f19cd6",
"metadata": {},
"source": [
"### 4.3 Validate the NeXus file based on the convention:\n",
"\n",
"We find, that two datasets in fact have no timestamp!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "90d253d5-42aa-4b31-86c7-e66dfa0752f9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[MissingAttribute(/NDX3_00021_ND__DLPC__00001/duration, timestamp),\n",
" MissingAttribute(/NDX3_00021_ND__DLPC__00001/run_number, timestamp)]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"invalid_objs = cv.validate('file.nxs')\n",
"invalid_objs"
]
},
{
"cell_type": "markdown",
"id": "4514f306-fa02-46ac-b7d4-aee844ac2979",
"metadata": {},
"source": [
"Let's convince ourselves:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d980b92c-8f16-4819-be68-a3701f05c8b7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'missing!!!'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obj = h5tbx.database.FileDB('file.nxs').find_one({'$name': invalid_objs[0].object_name})\n",
"obj.attrs.get('timestamp', 'missing!!!')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "9fa5b083-d897-42eb-a706-ba4e00dba8dc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"using(\"h5py\")"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h5tbx.use(None)"
]
},
{
"cell_type": "markdown",
"id": "1a4323d2-94be-467b-bdae-55a3c6c7a5da",
"metadata": {},
"source": [
"## 5. Validating the file structure with a layout definition\n",
"\n",
"The *structure* or *layout* defines which groups, datasets and attributes need to exist and in which arrangement. This cannot be defined/checked during file creation (using standard attributes).\n",
"\n",
"Let's define part of a potential layout for the NeXus file in words first and then write the code:\n",
"1) **Ten datasets** starting with \"image#\" and ending with a number between 0 and 9\n",
"2) The group **user** must exist, which has the datasets \"name\" with the attribute \"description\"\n",
"\n",
"The layout is defined by specifying queries, which correspond to what we are expecting in the file. If it is not found, the layout specification failed:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "87f4c34c-1450-4a56-a1f2-012ef2dc54cd",
"metadata": {},
"outputs": [],
"source": [
"from h5rdmtoolbox import layout\n",
"\n",
"lay = layout.Layout()\n",
"\n",
"# 1.) 10 images:\n",
"spec_10images = lay.add(h5tbx.database.FileDB.find,\n",
" flt={'$name': {'$regex': '.*/image#[0-9]$'}},\n",
" n=10)\n",
"\n",
"# 2.) User group. Identify by attribute \"NX_class\":\n",
"spec_userGroupExists = lay.add(h5tbx.database.FileDB.find,\n",
" flt={'NX_class': b'NXuser'},\n",
" n=None)\n",
"\n",
"# within the User group, find group \"name\":\n",
"spec_UserName = spec_userGroupExists.add(\n",
" h5tbx.database.ObjDB.find_one,\n",
" flt={'$name': {'$basename': 'name'}},\n",
" n=1\n",
")\n",
"\n",
"# within the Name group find dataset \"name\" with a certain \"description\":\n",
"spec_UserNameDescription = spec_UserName.add(\n",
" h5tbx.database.ObjDB.find_one,\n",
" flt={'$name': {'$basename': 'name'}, 'description': b'Name of user responsible for this entry'}, # !binary string!\n",
" n=1\n",
")"
]
},
{
"cell_type": "markdown",
"id": "94421201-9568-4e64-a232-626b2a129253",
"metadata": {},
"source": [
"Validate the file:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "4c2ed0ad-29cc-432a-b2bb-5bc698abd10f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = lay.validate('file.nxs')\n",
"res.is_valid()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8cf5e0d-4b18-4dfb-aaf3-cab0c6a0e486",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
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