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smalldata

Tasks for working with SmallData HDF5 files.

Classes defined in this module provide an interface to extracting data from SmallData files and analyzing it.

Classes:

Name Description
AnalyzeSmallDataXSS

Analyze scattering data for a single detector in a SmallData file.
AnalyzeSmallDataXAS

Analyze absorption data for a single detector in a SmallData file.
AnalyzeSmallDataXES

Analyze emission data for a single detector in a SmallData file.

AnalyzeSmallDataXAS

Bases: AnalyzeSmallData

Task to analyze XAS data stored in a SmallData HDF5 file.

Source code in lute/tasks/smalldata.py 
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class AnalyzeSmallDataXAS(AnalyzeSmallData):
    """Task to analyze XAS data stored in a SmallData HDF5 file."""

    def __init__(self, *, params: TaskParameters, use_mpi: bool = True) -> None:
        super().__init__(params=params, use_mpi=use_mpi)

    def _pre_run(self) -> None:
        # Currently scattering data is extracted as standard since its used
        # for all analysis types (XSS, XAS, XES,...)
        self._extract_standard_data()
        self._extract_xas(self._task_parameters.xas_detname)

    def _run(self) -> None:
        # XAS returns two sets of binned data
        # Bins raw TR-XAS first, then bins by scan
        diff: Optional[np.ndarray[np.float64]]
        ccm_bins: Optional[np.ndarray[np.float64]]
        laser_on: Optional[np.ndarray[np.float64]]
        laser_off: Optional[np.ndarray[np.float64]]
        ccm_bins, diff, laser_on, laser_off = self._calc_binned_difference_xas()

        # We check None on ccm_bins because the monochromator is not always
        # scanned -> We return None if there aren't enough bins to be worth
        # plotting
        if self._mpi_size > 1 and ccm_bins is not None:
            diff = self._mpi_comm.reduce(diff, op=MPI.SUM)
            laser_on = self._mpi_comm.reduce(laser_on, op=MPI.SUM)
            laser_off = self._mpi_comm.reduce(laser_off, op=MPI.SUM)

        all_plots: List[ElogSummaryPlots] = []
        run: int
        try:
            run = int(self._task_parameters.lute_config.run)
        except ValueError:
            run = 0
        plot_display_name: str
        exp_run: str
        if self._mpi_rank == 0 and ccm_bins is not None:
            # Check None again
            diff /= self._mpi_size
            laser_on /= self._mpi_size
            laser_off /= self._mpi_size
            plots: pn.Tabs = self.plot_all_xas(laser_on, laser_off, ccm_bins, diff)
            exp_run = f"{run:04d}_XAS"
            plot_display_name = f"XAS/{exp_run}"
            all_plots.append(ElogSummaryPlots(plot_display_name, plots))

        scan_bins: Optional[np.ndarray[np.float64]]
        scan_bins, diff, laser_on, laser_off = self._calc_scan_binned_difference_xas()
        if self._mpi_size > 1 and scan_bins is not None:
            diff = self._mpi_comm.reduce(diff, op=MPI.SUM)
            laser_on = self._mpi_comm.reduce(laser_on, op=MPI.SUM)
            laser_off = self._mpi_comm.reduce(laser_off, op=MPI.SUM)

        if self._mpi_rank == 0 and scan_bins is not None:
            plots: pn.Tabs = self.plot_xas_scan_hv(laser_on, laser_off, scan_bins, diff)
            name: str = self._scan_var_name if self._scan_var_name else "By_Event"
            exp_run = f"{run:04d}_{name}_XAS"
            if "lens" in name:
                plot_display_name = f"lens_scans/{exp_run}"
            elif "lxe_opa" in name:
                plot_display_name = f"power_scans/{exp_run}"
            else:
                plot_display_name = f"time_scans/{exp_run}"

            all_plots.append(ElogSummaryPlots(plot_display_name, plots))
        self._result.payload = all_plots

    def _post_run(self) -> None: ...

AnalyzeSmallDataXES

Bases: AnalyzeSmallData

Task to analyze XES data stored in a SmallData HDF5 file.

Source code in lute/tasks/smalldata.py 
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class AnalyzeSmallDataXES(AnalyzeSmallData):
    """Task to analyze XES data stored in a SmallData HDF5 file."""

    def __init__(self, *, params: TaskParameters, use_mpi: bool = True) -> None:
        super().__init__(params=params, use_mpi=use_mpi)

    def _pre_run(self) -> None:
        # Currently scattering data is extracted as standard since its used
        # for all analysis types (XSS, XAS, XES,...)
        self._extract_standard_data()
        self._extract_xes(self._task_parameters.xes_detname)

    def _run(self) -> None:
        # XES returns two sets of data
        # Average TR-XES first, then bins by scan variable
        diff: np.ndarray[np.float64]
        laser_on: np.ndarray[np.float64]
        laser_off: np.ndarray[np.float64]
        diff, laser_on, laser_off = self._calc_avg_difference_xes()

        if self._mpi_size > 1:
            diff = self._mpi_comm.reduce(diff, op=MPI.SUM)
            laser_on = self._mpi_comm.reduce(laser_on, op=MPI.SUM)
            laser_off = self._mpi_comm.reduce(laser_off, op=MPI.SUM)

        all_plots: List[ElogSummaryPlots] = []
        run: int
        try:
            run = int(self._task_parameters.lute_config.run)
        except ValueError:
            run = 0
        plot_display_name: str
        exp_run: str
        if self._mpi_rank == 0:
            diff /= self._mpi_size
            laser_on /= self._mpi_size
            laser_off /= self._mpi_size
            energy_bins: Optional[np.ndarray[np.float64]] = None
            plots: pn.Tabs = self.plot_xes_hv(laser_on, laser_off, energy_bins, diff)
            exp_run = f"{run:04d}_XES"
            plot_display_name = f"XES/{exp_run}"
            all_plots.append(ElogSummaryPlots(plot_display_name, plots))

        scan_bins: np.ndarray[np.float64]
        scan_bins, diff, laser_on, laser_off = self._calc_scan_binned_difference_xes()
        if self._mpi_size > 1:
            diff = self._mpi_comm.reduce(diff, op=MPI.SUM)
            laser_on = self._mpi_comm.reduce(laser_on, op=MPI.SUM)
            laser_off = self._mpi_comm.reduce(laser_off, op=MPI.SUM)

        if self._mpi_rank == 0:
            plots: pn.Tabs = self.plot_xes_scan_hv(laser_on, laser_off, scan_bins, diff)
            name: str = self._scan_var_name if self._scan_var_name else "By_Event"
            exp_run = f"{run:04d}_{name}_XES"
            if "lens" in name:
                plot_display_name = f"lens_scans/{exp_run}"
            elif "lxe_opa" in name:
                plot_display_name = f"power_scans/{exp_run}"
            else:
                plot_display_name = f"time_scans/{exp_run}"

            all_plots.append(ElogSummaryPlots(plot_display_name, plots))
        self._result.payload = all_plots

    def _post_run(self) -> None: ...

AnalyzeSmallDataXSS

Bases: AnalyzeSmallData

Task to analyze XSS profiles stored in a SmallData HDF5 file.

Source code in lute/tasks/smalldata.py 
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class AnalyzeSmallDataXSS(AnalyzeSmallData):
    """Task to analyze XSS profiles stored in a SmallData HDF5 file."""

    def __init__(self, *, params: TaskParameters, use_mpi: bool = True) -> None:
        super().__init__(params=params, use_mpi=use_mpi)

    def _pre_run(self) -> None:
        # Currently scattering data is extracted as standard since its used
        # for all analysis types (XSS, XAS, XES,...)
        self._extract_standard_data()

    def _run(self) -> None:
        diff: np.ndarray[np.float64]
        bins: np.ndarray[np.float64]
        laser_on: np.ndarray[np.float64]
        bins, diff, laser_on = self._calc_scan_binned_difference_xss()

        if self._mpi_size > 1:
            diff = self._mpi_comm.reduce(diff, op=sum_diff)
            laser_on = self._mpi_comm.reduce(laser_on, op=laser_on_mean)
        else:
            laser_on = np.nansum(laser_on, axis=0)

        if self._mpi_rank == 0:
            diff /= self._mpi_size
            laser_on /= self._total_num_events
            name: str = self._scan_var_name if self._scan_var_name else "By_Event"
            plots: pn.Tabs = self.plot_all_xss(laser_on, bins, diff, name)
            plot_display_name: str
            run: int
            try:
                run = int(self._task_parameters.lute_config.run)
            except ValueError:
                run = 0
            exp_run: str = f"{run:04d}_{name}_XSS"
            if "lens" in name:
                plot_display_name = f"lens_scans/{exp_run}"
            else:
                plot_display_name = f"time_scans/{exp_run}"

            self._result.payload = ElogSummaryPlots(plot_display_name, plots)