smd

Models for smalldata_tools Tasks.

Classes:

Name	Description
SubmitSMDParameters	Parameters to run smalldata_tools to produce a smalldata HDF5 file.
AnalyzeSmallDataXSSParameters	Parameter model for the AnalyzeSmallDataXSS Task. Used to determine spatial/temporal overlap based on XSS difference signal and provide basic XSS feedback.
AnalyzeSmallDataXASParameters	Parameter model for the AnalyzeSmallDataXAS Task. Used to determine spatial/temporal overlap based on XAS difference signal and provide basic XAS feedback.
AnalyzeSmallDataXESParameters	Parameter model for the AnalyzeSmallDataXES Task. Used to determine spatial/temporal overlap based on XES difference signal and provide basic XES feedback.

AnalyzeSmallDataXASParameters

Bases: TaskParameters

TaskParameter model for AnalyzeSmallDataXAS Task.

This Task does basic analysis of XAS data based on a SmallData HDF5 output file. It calculates difference absorption and signal binned by various scanned motors.

Source code in lute/io/models/smd.py

class AnalyzeSmallDataXASParameters(TaskParameters):
    """TaskParameter model for AnalyzeSmallDataXAS Task.

    This Task does basic analysis of XAS data based on a SmallData HDF5 output
    file. It calculates difference absorption and signal binned by various
    scanned motors.
    """

    class Thresholds(BaseModel):
        min_Iscat: float = Field(
            10.0, description="Minimum scattering intensity to use for filtering."
        )
        min_ipm: float = Field(
            1000.0, description="Minimum X-ray intensity to use for filtering."
        )

    _find_smd_path = validate_smd_path("smd_path")

    smd_path: str = Field(
        "", description="Path to the Small Data HDF5 file to analyze."
    )
    xas_detname: str = Field(description="Name of the detector with absorption data.")
    xss_detname: Optional[str] = Field(
        None,
        description="Name of the detector with scattering data, for normalization.",
    )
    ipm_var: str = Field(
        description="Name of the IPM to use for X-Ray intensity filtering."
    )
    scan_var: Optional[Union[List[str], str]] = Field(
        None,
        description=(
            "Name of a scan variable or a list of scan variables to analyze. "
            "E.g. lxt, lens_h, etc."
        ),
    )
    ccm: str = Field(description="Name of the PV for CCM position readback.")
    ccm_set: Optional[str] = Field(
        None, description="Name of the PV for the setpoint of the CCM."
    )
    thresholds: Thresholds = Field(Thresholds(min_Iscat=10.0, min_ipm=1000.0))
    element: Optional[str] = Field(
        None,
        description="Element under investigation. Currently unused. For future EXAFS.",
    )

AnalyzeSmallDataXESParameters

Bases: TaskParameters

TaskParameter model for AnalyzeSmallDataXES Task.

This Task does basic analysis of XES data based on a SmallData HDF5 output file. It calculates difference emission and signal binned by various scanned motors.

Source code in lute/io/models/smd.py

class AnalyzeSmallDataXESParameters(TaskParameters):
    """TaskParameter model for AnalyzeSmallDataXES Task.

    This Task does basic analysis of XES data based on a SmallData HDF5 output
    file. It calculates difference emission and signal binned by various
    scanned motors.
    """

    class Thresholds(BaseModel):
        min_Iscat: float = Field(
            10.0, description="Minimum scattering intensity to use for filtering."
        )
        min_ipm: float = Field(
            1000.0, description="Minimum X-ray intensity to use for filtering."
        )

    _find_smd_path = validate_smd_path("smd_path")

    smd_path: str = Field(
        "", description="Path to the Small Data HDF5 file to analyze."
    )
    xes_detname: str = Field(description="Name of the detector with absorption data.")
    xss_detname: Optional[str] = Field(
        None,
        description="Name of the detector with scattering data, for normalization.",
    )
    ipm_var: str = Field(
        description="Name of the IPM to use for X-Ray intensity filtering."
    )
    scan_var: Optional[Union[List[str], str]] = Field(
        None,
        description=(
            "Name of a scan variable or a list of scan variables to analyze. "
            "E.g. lxt, lens_h, etc."
        ),
    )
    thresholds: Thresholds = Field(Thresholds(min_Iscat=10.0, min_ipm=1000.0))
    invert_xes_axes: bool = Field(
        False,
        description=(
            "Flip the projection axes depending on detector orientation. "
            "Default is that projection along axis 1 is spectrum."
        ),
    )
    rot_angle: Optional[float] = Field(
        None,
        description="Optionally rotate the ROIs by a small amount before projection.",
    )
    batch_size: int = Field(
        0,
        description="If non-zero load ROIs in batches. Slower but may help OOM errors.",
    )

AnalyzeSmallDataXSSParameters

Bases: TaskParameters

TaskParameter model for AnalyzeSmallDataXSS Task.

This Task does basic analysis of XSS data based on a SmallData HDF5 output file. It calculates difference scattering and signal binned by various scanned motors.

Source code in lute/io/models/smd.py

class AnalyzeSmallDataXSSParameters(TaskParameters):
    """TaskParameter model for AnalyzeSmallDataXSS Task.

    This Task does basic analysis of XSS data based on a SmallData HDF5 output
    file. It calculates difference scattering and signal binned by various
    scanned motors.
    """

    class Thresholds(BaseModel):
        min_Iscat: float = Field(
            10.0, description="Minimum scattering intensity to use for filtering."
        )
        min_ipm: float = Field(
            1000.0, description="Minimum X-ray intensity to use for filtering."
        )

    class AnalysisFlags(BaseModel):
        use_pyfai: bool = True
        use_asymls: bool = False

    _find_smd_path = validate_smd_path("smd_path")

    smd_path: str = Field(
        "", description="Path to the Small Data HDF5 file to analyze."
    )
    xss_detname: Optional[str] = Field(
        None, description="Name of the detector with scattering data."
    )
    ipm_var: str = Field(
        description="Name of the IPM to use for X-Ray intensity filtering."
    )
    scan_var: Optional[Union[List[str], str]] = Field(
        None,
        description=(
            "Name of a scan variable or a list of scan variables to analyze. "
            "E.g. lxt, lens_h, etc."
        ),
    )
    thresholds: Thresholds = Field(Thresholds(min_Iscat=10.0, min_ipm=1000.0))

SubmitSMDParameters

Bases: ThirdPartyParameters

Parameters for running smalldata to produce reduced HDF5 files.

Source code in lute/io/models/smd.py

class SubmitSMDParameters(ThirdPartyParameters):
    """Parameters for running smalldata to produce reduced HDF5 files."""

    class Config(ThirdPartyParameters.Config):
        """Identical to super-class Config but includes a result."""

        set_result: bool = True
        """Whether the Executor should mark a specified parameter as a result."""

        result_from_params: str = ""
        """Defines a result from the parameters. Use a validator to do so."""

    class ProducerParameters(BaseModel):
        class ROIParams(BaseModel):
            ROIs: List[List[List[int]]] = Field(
                description="Definition of ROIs, can define multiple."
            )

            writeArea: bool = Field(
                False, description="Whether to write out the area image of the ROI."
            )

            thresADU: Optional[float] = Field(
                None, description="Optional threshold on ADU."
            )

        class AzIntParams(BaseModel):
            eBeam: float = Field(description="Beam energy in keV.")

            center: List[float] = Field(description="Beam center in micrometers")

            dis_to_sam: float = Field(description="Detector distance in millimeters.")

            tx: float = Field(0, description="Tilt in x, degrees")

            ty: float = Field(0, description="Tilt in y, degress")

        class AzIntPyFAIParams(BaseModel):
            class AiKwargs(BaseModel):
                dist: float = Field(description="Detector distance.")
                poni1: float = Field(description="First poni.")
                poni2: float = Field(description="Second poni.")

            poni_file: Optional[str] = Field(
                None,
                description="Path to a poni file. Must provide poni_file or ai_kwargs.",
            )

            ai_kwargs: Optional[AiKwargs] = Field(
                None, description="Integration paramters if not using a poni file."
            )

            npts: int = Field(512, description="Number of q points/bins.")

            npts_az: int = Field(13, description="Number of phi bins.")

            int_units: str = Field("2th_deg", description="Integration units")

            return2d: bool = Field(
                False, description="Whether to return the 2D q/phi integration."
            )

        class PhotonParams(BaseModel):
            ADU_per_photon: float = Field(9.5, description="Number of ADU per photon.")

            thresADU: float = Field(
                0.8, description="Threshold in fraction of ADU_per_photon."
            )

        class DropletParams(BaseModel):
            name: str = Field(
                "droplet", description="HDF5 key name for storing droplet data."
            )

            # mask: Optional[np.ndarray] = Field(None, description="Optionally pass a separate mask.")

            threshold: float = Field(
                5,
                description="Threshold for pixel to be part of a droplet. Sigma or ADU depending on useRms.",
            )

            thresholdLow: float = Field(
                5, description="Lower threshold to make spectrum sharper."
            )

            thresADU: float = Field(
                60, description="Threshold on droplet ADU. Rejects droplets below this."
            )

            useRms: bool = Field(
                True,
                description="If True, threshold/thresholdLow are RMS of data, otherwise in ADU.",
            )

            nData: Optional[int] = Field(1e5, description="(float,int or None).")

            relabel: bool = Field(
                True, description="After initial finding, relabel image."
            )

        class Droplet2PhotonParams(BaseModel):
            class DropletParams(BaseModel):
                threshold: float = Field(
                    5,
                    description="Threshold for pixel to be part of a droplet. Sigma or ADU depending on useRms.",
                )

                thresholdLow: float = Field(
                    5, description="Lower threshold to make spectrum sharper."
                )

                thresADU: float = Field(
                    60,
                    description="Threshold on droplet ADU. Rejects droplets below this.",
                )

                useRms: bool = Field(
                    True,
                    description="If True, threshold/thresholdLow are RMS of data, otherwise in ADU.",
                )

            droplet: DropletParams = Field(
                DropletParams(threshold=5, thresholdLow=5, thresADU=60, useRms=True),
                description="Droplet finding parameters.",
            )

            aduspphot: int = Field(162, description="")

            cputime: bool = Field(True, description="")

            nData: float = Field(3e4, description="")

        class SvdParams(BaseModel):
            name: str = Field("svdFit", description="DetObject name.")

            n_components: int = Field(
                2, description="Number of components to use. Max is 25."
            )

            basis_file: Optional[str] = Field(None, description="")

            n_pulse: int = Field(1, description="Number of pulses to fit.")

            delay: Optional[List[float]] = Field(
                [0], description="Delay between pulses."
            )

            mode: str = Field(
                "max",
                description="Method to calculate pulse amplitudes. max, norm, or both.",
            )
            return_reconstructed: bool = Field(
                False, description="Return the reconstructed waveforms."
            )

        class AutocorrParams(BaseModel):
            class IlluminationParams(BaseModel):
                correction: str = Field(
                    description="Path to correction arrays. One per mask/ROI."
                )

                kernel: int = Field(
                    description="Kernel size used in the creation of the correction."
                )

            name: str = Field("autocorr", description="DetObject name.")

            threshADU: List[float] = Field(
                [-1e6, 1e6], description="Low and high pixel intensity thresholds."
            )

            mask: Optional[str] = Field(
                None, description="Mask to define a non-rectangular ROI."
            )

            save_lineout: bool = Field(
                False,
                description="Save autocorr image or only vertical/horizontal lineouts.",
            )

            save_range: Tuple[int, int] = Field(
                (
                    50,
                    50,
                ),
                description="Size of the autocorr image to save.",
            )

            illumination_correction: Optional[IlluminationParams] = Field(
                None, description="Corrections for each mask/ROI."
            )

        detnames: Optional[List[str]] = Field(
            None, description="List of detectors to process."
        )

        epicsPV: Optional[List[Union[str, Tuple[str, str]]]] = Field(
            None, description="List of PVs to save once per event."
        )

        epicsOncePV: Optional[List[Union[str, Tuple[str, str]]]] = Field(
            None, description="List of PVs to save once per run."
        )

        ttCalib: Optional[List[float]] = Field(
            None, description="Alternative calibration parameters for the timetool."
        )

        aioParams: Optional[List[List[Union[str, int, float]]]] = Field(
            None,
            description="Save analog inputs and give them nice names. [[inp],['name']]",
        )

        getROIs: Optional[Dict[str, ROIParams]] = Field(
            None, description="Dictionary of ROI parameters by detector."
        )

        getAzIntParams: Optional[Dict[str, AzIntParams]] = Field(
            None,
            description="Dictionary of azimuthal integration parameters by detector.",
        )

        getAzIntPyFAIParams: Optional[Dict[str, AzIntPyFAIParams]] = Field(
            None,
            description="Dictionary of azimuthal integration with PyFAI parameters.",
        )

        getPhotonParams: Optional[Dict[str, PhotonParams]] = Field(
            None,
            description="Dictionary of photon counting parameters by detector.",
        )

        getDropletParams: Optional[Dict[str, DropletParams]] = Field(
            None,
            description="Dictionary of droplet finding parameters by detector.",
        )

        getDroplet2Photons: Optional[Dict[str, Droplet2PhotonParams]] = Field(
            None,
            description="Dictionary of droplet2photon parameters by detector.",
        )

        getSvdParams: Optional[Dict[str, SvdParams]] = Field(
            None,
            description="Dictionary of SVD parameters by detector.",
        )

        getAutocorrParams: Optional[Dict[str, AutocorrParams]] = Field(
            None,
            description="Dictionary of auto-correlation parameters by detector.",
        )

    _set_producer_template_parameters = template_parameter_validator(
        "producer_parameters"
    )

    executable: str = Field("mpirun", description="MPI executable.", flag_type="")
    np: PositiveInt = Field(
        max(int(os.environ.get("SLURM_NPROCS", len(os.sched_getaffinity(0)))) - 1, 1),
        description="Number of processes",
        flag_type="-",
    )
    p_arg1: str = Field(
        "python", description="Executable to run with mpi (i.e. python).", flag_type=""
    )
    u: str = Field(
        "", description="Python option for unbuffered output.", flag_type="-"
    )
    m: str = Field(
        "mpi4py.run",
        description="Python option to execute a module's contents as __main__ module.",
        flag_type="-",
    )
    producer: str = Field(
        "", description="Path to the SmallData producer Python script.", flag_type=""
    )
    run: str = Field(
        os.environ.get("RUN_NUM", ""), description="DAQ Run Number.", flag_type="--"
    )
    experiment: str = Field(
        os.environ.get("EXPERIMENT", ""),
        description="LCLS Experiment Number.",
        flag_type="--",
    )
    stn: NonNegativeInt = Field(0, description="Hutch endstation.", flag_type="--")
    nevents: int = Field(
        int(1e9), description="Number of events to process.", flag_type="--"
    )
    directory: Optional[str] = Field(
        None,
        description="Optional output directory. If None, will be in ${EXP_FOLDER}/hdf5/smalldata.",
        flag_type="--",
    )
    ## Need mechanism to set result_from_param=True ...
    gather_interval: PositiveInt = Field(
        25, description="Number of events to collect at a time.", flag_type="--"
    )
    norecorder: bool = Field(
        False, description="Whether to ignore recorder streams.", flag_type="--"
    )
    url: HttpUrl = Field(
        "https://pswww.slac.stanford.edu/ws-auth/lgbk",
        description="Base URL for eLog posting.",
        flag_type="--",
    )
    epicsAll: bool = Field(
        False,
        description="Whether to store all EPICS PVs. Use with care.",
        flag_type="--",
    )
    full: bool = Field(
        False,
        description="Whether to store all data. Use with EXTRA care.",
        flag_type="--",
    )
    fullSum: bool = Field(
        False,
        description="Whether to store sums for all area detector images.",
        flag_type="--",
    )
    default: bool = Field(
        False,
        description="Whether to store only the default minimal set of data.",
        flag_type="--",
    )
    image: bool = Field(
        False,
        description="Whether to save everything as images. Use with care.",
        flag_type="--",
    )
    tiff: bool = Field(
        False,
        description="Whether to save all images as a single TIFF. Use with EXTRA care.",
        flag_type="--",
    )
    centerpix: bool = Field(
        False,
        description="Whether to mask center pixels for Epix10k2M detectors.",
        flag_type="--",
    )
    postRuntable: bool = Field(
        False,
        description="Whether to post run tables. Also used as a trigger for summary jobs.",
        flag_type="--",
    )
    wait: bool = Field(
        False, description="Whether to wait for a file to appear.", flag_type="--"
    )
    xtcav: bool = Field(
        False,
        description="Whether to add XTCAV processing to the HDF5 generation.",
        flag_type="--",
    )
    noarch: bool = Field(
        False, description="Whether to not use archiver data.", flag_type="--"
    )

    lute_template_cfg: TemplateConfig = TemplateConfig(
        template_name="smd_producer_template.py", output_path=""
    )

    producer_parameters: Optional[ProducerParameters] = Field(
        None,
        description="Optional parameters to fill in a producer file.",
        flag_type="",  # Does nothing since always None by time it's seen by Task
    )

    @validator("producer", always=True)
    def validate_producer_path(cls, producer: str, values: Dict[str, Any]) -> str:
        if producer == "":
            exp: str = values["lute_config"].experiment
            hutch: str = exp[:3]
            base_path: str = f"/sdf/data/lcls/ds/{hutch}/{exp}/results/smalldata_tools"
            path: str
            if hutch.lower() in ("cxi", "mec", "mfx", "xcs", "xpp"):
                path = f"{base_path}/lcls1_producers/smd_producer.py"
            else:
                path = f"{base_path}/lcls2_producers/smd_producer.py"
            return path
        return producer

    @validator("lute_template_cfg", always=True)
    def use_producer(
        cls, lute_template_cfg: TemplateConfig, values: Dict[str, Any]
    ) -> TemplateConfig:
        if not lute_template_cfg.output_path:
            lute_template_cfg.output_path = values["producer"]
        return lute_template_cfg

    @root_validator(pre=False)
    def define_result(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        exp: str = values["lute_config"].experiment
        hutch: str = exp[:3]
        run: int = int(values["lute_config"].run)
        directory: Optional[str] = values["directory"]
        if directory is None:
            directory = f"/sdf/data/lcls/ds/{hutch}/{exp}/hdf5/smalldata"
        fname: str = f"{exp}_Run{run:04d}.h5"

        cls.Config.result_from_params = f"{directory}/{fname}"
        return values

Config

Bases: Config

Identical to super-class Config but includes a result.

Source code in lute/io/models/smd.py

class Config(ThirdPartyParameters.Config):
    """Identical to super-class Config but includes a result."""

    set_result: bool = True
    """Whether the Executor should mark a specified parameter as a result."""

    result_from_params: str = ""
    """Defines a result from the parameters. Use a validator to do so."""

result_from_params: str = '' class-attribute instance-attribute

Defines a result from the parameters. Use a validator to do so.

set_result: bool = True class-attribute instance-attribute

Whether the Executor should mark a specified parameter as a result.