2. Helpers

This script contains helper functions used by the specsscan class

specsscan.helpers.get_scan_path(path, scan, basepath)

Returns the path to the given scan.

Parameters:

  • path (Path | str) – Path under which to search. If empty, the basepath will be queried

  • scan (int) – Scan number

  • basepath (Path | str) – Default base path to search for scans under

Raises:

FileNotFoundError – Raised if the path or scan cannot be found.

Returns:

Path object to the given scan directory

Return type:

Path

specsscan.helpers.load_images(scan_path, df_lut=None, iterations=None, delays=None, tqdm_enable_nested=False)

Loads a 2D/3D numpy array of images for the given scan path with an optional averaging over the given iterations/delays. The function provides functionality to both load_scan and check_scan methods of the SpecsScan class. When iterations/delays is provided, average is performed over the iterations/delays for all delays/iterations.

Parameters:

  • scan_path (Path) – object of class Path pointing to the scan folder

  • df_lut (pd.DataFrame, optional) – Pandas dataframe containing the contents of LUT.txt as obtained from parse_lut_to_df(). Defaults to None.

  • iterations (np.ndarray | slice | Sequence[int] | Sequence[slice], optional) – A 1-D array of the indices of iterations over which the images are to be averaged. The array can be a list, numpy array or a Tuple consisting of slice objects and integers. For ex., np.s_[1:10, 15, -1] would be a valid input. Defaults to None.

  • delays (np.ndarray | slice | int | Sequence[int] | Sequence[slice], optional) – A 1-D array of the indices of delays over which the images are to be averaged. The array can be a list, numpy array or a Tuple consisting of slice objects and integers. For ex., np.s_[1:10, 15, -1] would be a valid input. Defaults to None.

  • tqdm_enable_nested (bool, optional) – Option to enable a nested progress bar. Defaults to False.

Raises:

  • ValueError – Raised if both iterations and delays is provided.

  • IndexError – Raised if no valid dimension for averaging is found.

Returns:

A list of 2-D numpy arrays of raw data

Return type:

list[np.ndarray]

specsscan.helpers.get_raw2d(scan_list, raw_array)

Converts a 1-D array of raw scan names into 2-D based on the number of iterations

Parameters:

  • scan_list (list[str]) – A list of AVG scan names.

  • raw_list (np.ndarray) – 1-D array of RAW scan names.

Returns:

2-D numpy array of size for ex., (total_iterations, delays) for a delay scan.

Return type:

np.ndarray

specsscan.helpers.parse_lut_to_df(scan_path)

Loads the contents of LUT.txt file into a pandas data frame to be used as metadata.

Parameters:

scan_path (Path) – Path object for the scan path

Returns:

A pandas DataFrame

Return type:

pd.DataFrame

specsscan.helpers.get_coords(scan_path, scan_type, scan_info, df_lut=None)

Reads the contents of scanvector.txt file into a numpy array.

Parameters:

  • scan_path (Path) – Path object for the scan path

  • scan_type (str) – Type of scan (delay, mirror etc.)

  • scan_info (dict[Any, Any]) – scan_info class dict

  • df_lut (pd.DataFrame, optional) – Pandas dataframe containing the contents of LUT.txt as obtained from parse_lut_to_df(). Defaults to None.

Raises:

FileNotFoundError – Raised in neither scanvector.txt nor LUT.txt are found.

Returns:

  • coords: 1-D numpy array containing coordinates of the scanned axis.

  • dim: string containing the name of the coordinate

Return type:

tuple[np.ndarray, str]

specsscan.helpers.compare_coords(axis_data)

Identifies the most changing column in a given 2-D numpy array.

Parameters:

axis_data (np.ndarray) – 2-D numpy array containing LUT data

Returns:

  • coords: Maximum changing column as a coordinate

  • index: Index of the coords in the axis_data array

Return type:

tuple[np.ndarray, int]

specsscan.helpers.parse_info_to_dict(path)

Parses the contents of info.txt file into a dictionary

Parameters:

path (Path) – Path object pointing to the scan folder

Returns:

Parsed info_dict dictionary

Return type:

dict

specsscan.helpers.handle_meta(df_lut, scan_info, config, fast_axes, slow_axes, projection, metadata=None, collect_metadata=False)

Helper function for the handling metadata from different files

Parameters:

  • df_lut (pd.DataFrame) – Pandas dataframe containing the contents of LUT.txt as obtained from parse_lut_to_df()

  • scan_info (dict) – scan_info class dict containing containing the contents of info.txt file

  • config (dict) – config dictionary containing the contents of config.yaml file

  • fast_axes (list[str]) – The fast-axis dimensions of the scan

  • slow_axes (list[str]) – The slow-axis dimensions of the scan

  • metadata (dict, optional) – Metadata dictionary with additional metadata for the scan. Defaults to empty dictionary.

  • collect_metadata (bool, optional) – Option to collect further metadata e.g. from EPICS archiver needed for NeXus conversion. Defaults to False.

Returns:

metadata dictionary containing additional metadata from the EPICS archive.

Return type:

dict

specsscan.helpers.get_archiver_data(archiver_url, archiver_channel, ts_from, ts_to)

Extract time stamps and corresponding data from and EPICS archiver instance

Parameters:

  • archiver_url (str) – URL of the archiver data extraction interface

  • archiver_channel (str) – EPICS channel to extract data for

  • ts_from (float) – starting time stamp of the range of interest

  • ts_to (float) – ending time stamp of the range of interest

Returns:

The extracted time stamps and corresponding data

Return type:

tuple[List, List]

specsscan.helpers.find_scan(path, scan)

Search function to locate the scan folder

Parameters:

  • path (Path) – Path object for data from the default config file

  • scan (int) – Scan number of the scan of interest

Returns:

scan_path: Path object pointing to the scan folder

Return type:

List[Path]

specsscan.helpers.find_scan_type(path, scan_type)

Rudimentary function to print scan paths given the scan_type

Parameters:

  • path (Path) – Path object pointing to the year, for ex., Path(“//nap32/topfloor/trARPES/PESData/2020”)

  • scan_type (str) – string containing the scan_type from the list [“delay”,”temperature”,”manipulator”,”mirror”,”single”]