GID Class

The GID class provides a framework for analyzing Grazing Incidence Diffraction data taken with a linear detector and Soller collimator. The class manages data loading, normalization, and transformation into reciprocal space, providing an interface for simple analysis. This is complimented by a simple peak fitting procedure to quickly estimate peak parameters.

Overview

Grazing Incidence Diffraction (GID) involves directing X-rays at a surface at a very shallow angle, typically below the critical angle for total external reflection. This configuration maximizes surface sensitivity and minimizes background signal from the bulk substrate. The scattered intensity is recorded as a function of the in-plane scattering angle and the out-of-plane angle, providing information about the lateral organization of molecules or atoms at the interface.

The GID class is designed to facilitate analysis of these measurements at the ID10-SURF beamline at ESRF. It automates the following steps:

  • Data Loading: Reads raw data from HDF5 files generated by the beamline control system. It handles single or multiple scans, concatenating them as needed.

  • Normalization: Corrects raw counts for monitor intensity and filter transmission.

  • Reciprocal Space Conversion: Converts detector pixel coordinates and motor angles into scattering vectors (\(q_{xy}\) and \(q_z\)). This conversion takes into account a gap between two modules of the Mythen detector, typically used for these experiments.

  • Visualization: Provides built-in methods to plot 2D maps of intensity in reciprocal space, as well as 1D cuts (profiles) along specific directions (\(q_{xy}\) or \(q_z\)).

  • Analysis: Includes methods for fitting peaks using various lineshapes (Gaussian, Lorentzian, Voigt) to extract structural parameters like lattice spacing and coherence length.

Key Features

  • Flexible ROI Integration: Easily extract 1D intensity profiles by integrating over a region of interest (ROI) in the q-space.

  • Automated Plotting: Quick “standard analysis” plots allow for immediate data inspection during or after an experiment.

  • Data Export: Save processed data (cuts, 2D maps) in standard formats (ASCII .dat, HDF5, images) for publication or further analysis with external tools.

class ESRF_ID10_SURF.GID.GID(file, scans, alpha_i_name='chi', detector_name='mythen2', monitor_name='mon', transmission_name='autof_eh1_transm', att_name='autof_eh1_curratt', cnttime_name='sec', PX0=50, mythen_gap=120, PPD=198.5, pixel_size_qxz=0.055, angle_name='delta', energy_name='monoe', I0=1000000000000.0, saving_dir=None, *args, **kwargs)[source]

Bases: object

Main class for processing GID (Grazing Incidence Diffraction) data.

This class handles loading data from HDF5 files, processing it (normalization, conversion to reciprocal space), and visualizing/saving the results.

file

Path to the HDF5 data file.

Type:

str

scans

Array of scan numbers to be processed.

Type:

np.ndarray

alpha_i_name

Name of the incident angle motor/counter.

Type:

str

detector_name

Name of the detector dataset.

Type:

str

monitor_name

Name of the monitor counter.

Type:

str

transmission_name

Name of the transmission counter.

Type:

str

att_name

Name of the attenuator counter.

Type:

str

cnttime_name

Name of the counting time counter.

Type:

str

angle_name

Name of the angle motor (e.g., ‘delta’).

Type:

str

energy_name

Name of the energy motor.

Type:

str

PX0

Pixel index of the direct beam (or reference pixel).

Type:

float

mythen_gap

Gap size in pixels between detector modules.

Type:

int

PPD

Pixels per degree calibration factor.

Type:

float

I0

Incident intensity normalization factor.

Type:

float

saving_dir

Directory where output files will be saved.

Type:

str

data

Raw detector data.

Type:

np.ndarray

angle

Array of angles corresponding to the data.

Type:

np.ndarray

alpha_i

Incident angle values.

Type:

np.ndarray

monitor

Monitor counts.

Type:

np.ndarray

transmission

Transmission values.

Type:

np.ndarray

attenuator

Attenuator values.

Type:

np.ndarray

cnttime

Counting times.

Type:

np.ndarray

energy

Beam energy in keV.

Type:

float

sample_name

Name of the sample extracted from metadata.

Type:

str

Pi

Surface pressure (Pi) value if available.

Type:

int or str

data_gap

Processed 2D data map with gap handling.

Type:

np.ndarray

qz

Array of qz values.

Type:

np.ndarray

qxy

Array of qxy values.

Type:

np.ndarray

static calibrate_mythen(filename, scanN, plot=True)[source]
static line_step(x, slope, intercept, step_pos, step)[source]
static line_step_fitting(pars, x, data=None)[source]
analyze_peak(x, y, model='voigt', background='linear', limits=None, save=False, filename_prefix='fit_result', **kwargs)[source]

Wrapper for fit_profile to perform analysis, plotting, and saving.

Parameters:

  • x (np.ndarray) – The independent variable array.

  • y (np.ndarray) – The dependent variable array.

  • model (str, optional) – Fitting model. Defaults to ‘voigt’.

  • background (str, optional) – Background model. Defaults to ‘linear’.

  • limits (tuple, optional) – Fitting limits (min, max). Defaults to None.

  • save (bool, optional) – Whether to save plots and reports. Defaults to False.

  • filename_prefix (str, optional) – Prefix for saved files. Defaults to ‘fit_result’.

  • **kwargs – Extra arguments for fit_profile or lmfit.

Returns:

The result object from fit_profile.

Return type:

lmfit.model.ModelResult

fit_profile(x, y, model='gaussian', background='linear', limits=None, **kwargs)[source]

Fit a profile to the specified model with background using lmfit.

Parameters:

  • x (np.ndarray) – The independent variable array (e.g., q).

  • y (np.ndarray) – The dependent variable array (e.g., Intensity).

  • model (str, optional) – The peak model to use. Options are ‘gaussian’, ‘lorentzian’, ‘voigt’, ‘pseudo_voigt’. Defaults to ‘gaussian’.

  • background (str, optional) – The background model. Options are ‘linear’, ‘constant’, or None. Defaults to ‘linear’.

  • limits (tuple, optional) – A tuple (min, max) to restrict the fitting range. Defaults to None (use full range).

  • **kwargs – Additional keyword arguments passed to mod.fit.

Returns:

The result of the fit.

Return type:

lmfit.model.ModelResult

Raises:

ValueError – If an unknown model is specified.

get_qxy(angle)[source]

Calculate the horizontal scattering vector qxy.

Parameters:

angle (float or np.ndarray) – In-plane scattering angle in degrees.

Returns:

qxy value(s) in inverse Angstroms.

Return type:

float or np.ndarray

get_qxy_cut(qz_min, qz_max)[source]

Extract a 1D cut along qxy by integrating over a range of qz.

Parameters:

  • qz_min (float) – Minimum qz value.

  • qz_max (float) – Maximum qz value.

Returns:

A list containing [qxy, intensity_profile].

intensity_profile is sum of counts in the qz range.

Return type:

list

get_qz(pixels)[source]

Calculate the vertical scattering vector qz.

Parameters:

pixels (np.ndarray) – Array of pixel indices (vertical position on detector).

Returns:

Array of qz values in inverse Angstroms.

Return type:

np.ndarray

get_qz_cut(qxy_min, qxy_max)[source]

Extract a 1D cut along qz by integrating over a range of qxy.

Parameters:

  • qxy_min (float) – Minimum qxy value.

  • qxy_max (float) – Maximum qxy value.

Returns:

A list containing [qz, intensity_profile].

Return type:

list

plot_2D_image(ax=None, save=False, **kwargs)[source]

Plot the 2D GID map in reciprocal space (qxy vs qz).

Parameters:

  • ax (matplotlib.axes.Axes, optional) – Axes to plot on. If None, a new figure is created.

  • save (bool, optional) – Whether to save the figure to disk. Defaults to False.

  • **kwargs – Additional arguments passed to imshow.

Returns:

The axes containing the plot.

Return type:

matplotlib.axes.Axes

plot_quick_analysis(save=False, fig=None)[source]

Perform a quick standard analysis plot including the 2D map and representative qxy cuts.

Parameters:

save (bool, optional) – Whether to save the figure. Defaults to False.

plot_qxy_cut(qz_min, qz_max, ax=None, save=False, **kwargs)[source]

Plot the qxy cut.

Parameters:

  • qz_min (float) – Minimum qz value.

  • qz_max (float) – Maximum qz value.

  • ax (matplotlib.axes.Axes, optional) – Axes to plot on.

  • save (bool, optional) – Whether to save the figure. Defaults to False.

  • **kwargs – Additional arguments.

plot_qz_cut(qxy_min, qxy_max, ax=None, save=False, **kwargs)[source]

Plot the qz cut.

Parameters:

  • qxy_min (float) – Minimum qxy value.

  • qxy_max (float) – Maximum qxy value.

  • ax (matplotlib.axes.Axes, optional) – Axes to plot on.

  • save (bool, optional) – Whether to save the figure. Defaults to False.

  • **kwargs – Additional arguments.

save_image_dat(filename=None)[source]

Save the intensity map, qxy axis, and qz axis.

Parameters:

filename (str, optional) – The name of the file to save. If None, it is auto-generated.

save_image_h5(filename=None)[source]

Save the 2D image data to an HDF5 file in q-coordinates.

This saves the intensity map, qxy axis, and qz axis, along with metadata.

Parameters:

filename (str, optional) – The name of the file to save. If None, it is auto-generated.

save_qxy_cut(qz_min, qz_max, **kwargs)[source]

Save the qxy cut data to a text file.

Parameters:

  • qz_min (float) – Minimum qz value.

  • qz_max (float) – Maximum qz value.

  • **kwargs – Additional keyword arguments.

save_qz_cut(qxy_min, qxy_max, **kwargs)[source]

Save the qz cut data to a text file.

Parameters:

  • qxy_min (float) – Minimum qxy value.

  • qxy_max (float) – Maximum qxy value.

  • **kwargs – Additional keyword arguments.