Config and Workflow

Config and Workflow#

The config and workflow modules work together to build, run and save the analysis. The config object is a state object that stores the input parameters and output results from the analyzer class. For analyzer to be properly interact with the workflow, the input and output parameters should be picklable. The config content follows the TOML format, with sections named session, reader, roi, analyzer, task, and result. Partial config content are allowed and can be updated, which allows configuration templates. The workflow object is a builder object that builds the readers, ROI, and analyzer objects from the config object. The workflow object also runs the analyzer and saves the result back to the config object.

Config sections#

Session#

In session section, the reader, roi, and analyzer classes are defined by name. The classes are accessed through the respective registries.

Name

Explanation

version

Records the PyLEEM version used to write the config file.

reader

Reader class name.

roi

ROI class name.

analyzer

Analyzer class name.

Reader#

The reader section defines the reader class inputs. For large data stacks, path_pattern can be used to access the data files through a glob pattern. Only one of paths or path_pattern should be provided.

For added metadata, metadata_list can be used to add per-reader metadata entries. For shared metadata, metadata can be used to add the same metadata entry to every reader. When both are provided, the metadata_list overrides the metadata.

Name

Explanation

paths

Lists the data files to read, resolved relative to the workflow root.

path_pattern

Glob pattern used to find data files when paths is not provided.

metadata_list

Adds per-reader metadata entries; these override shared metadata.

metadata

Adds the same metadata entry to every reader.

ROI#

The roi section defines the ROI class inputs. The inputs can be a roi file or a manually defined ROI.

Analyzer#

The analyzer section passes settings to the analyzer constructor.

Task#

The task section passes parameters to analyze().

Example#

We start with a calibration template file xps_calibration_template.toml. Here we define the necessary general settings for the calibration:

  1. necessary classes in the session.

  2. a standardized ROI.

  3. reference peaks for C1s.

[session]
version = "0.3.0"
reader = "UViewReader"
roi = "LineROI"
analyzer = "XPSCalibration"

[roi]
src = [0, 0]
dst = [0, 127]
linewidth = 10

[task]
num_peaks = 1
ref_index = 0
ref_value = 285.0
peak_prominence = 0.1

from pyleem.analysis.xps import XPSCalibration
from pyleem.config import load_config, save_config
from pyleem.workflow import Workflow

config = load_config("xps_calibration_template.toml")

workflow = Workflow(
    config,
    root=".",
    reader={
        "paths": ["data_0eV.dat", "data_1eV.dat", "data_2eV.dat"],
        "metadata": {"Beam Energy": [400, "eV"]},
    },
)

# Run and access the result directly
result = workflow.run(
    baselines=[[197, 100], [197, 100], [197, 100]],
    ref_value=285.0,
    peak_prominence=0.2,
)

# get the updated config
config = workflow.config

# save the result
workflow.save("xps_calibration_result.toml")

The saved result file xps_calibration_result.toml is as follows, note the added/updated input parameters and result values. For example, the peak prominence is updated to 0.2.

[session]
version = "0.3.0"
reader = "UViewReader"
roi = "LineROI"
analyzer = "XPSCalibration"

[reader]
paths = ["data_0eV.dat", "data_1eV.dat", "data_2eV.dat"]
metadata = {"Beam Energy" = [400, "eV"]}

[roi]
src = [0, 0]
dst = [0, 127]
linewidth = 10

[task]
num_peaks = 1
baselines = [[197, 100], [197, 100], [197, 100]]
ref_index = 0
ref_value = 285.0
peak_prominence = 0.2

[result]
pixel_per_ev = 165.8
peak_shift = 3.72

API and Reference#