PyJEM.eftem.analysis¶

PyJEM.eftem.analysis.create_quantitativemap_with_two_window(param)¶

Create a quantitative map using the two-window method.

Parameters:

param (dict) –

Quantitative map creation parameters.

key	type	value
FirstDatasetId	str	The ID of the first dataset.
SecondDatasetId	str	The ID of the second dataset.
AutoDriftCorrection	bool	Whether to use auto drift correction.

Returns:

A dictionary containing the created quantitative map.

key	type	value
Ids	list[str]	List of IDs for the created quantitative maps.

Return type:

dict

Examples

>>> body = {
...     "FirstDatasetId": '3165b2e3-2f22-4028-9dd5-441bff4d702e',
...     "SecondDatasetId": 'dea6b690-e964-47b1-9078-f6ef3e301c3c',
...     "AutoDriftCorrection": False
... }
>>> create_quantitativemap_with_two_window(body)
{
    "Ids": [
        "0c755de8-fe11-470f-a593-242b6b16f5fa",
        "4afc5580-e5d4-4258-8c43-92594381ffe0",
        "0868a2c7-4874-4408-9353-f804f3e9cc6f"
    ]
}

PyJEM.eftem.analysis.create_quantitativemap_with_three_window(param)¶

Create a quantitative map using the three-window method.

Parameters:

param (dict) –

Quantitative map creation parameters.

key	type	value
FirstDatasetId	str	The ID of the first dataset.
SecondDatasetId	str	The ID of the second dataset.
ThirdDatasetId	str	The ID of the third dataset.
BackgroundType	int	The background type (enum value). PowerLaw = 0 Constant = 1 Linear = 2 SecondDegreePolynomial = 3 Log = 4
AutoDriftCorrection	bool	Whether to use auto drift correction.

Returns:

A dictionary containing the created quantitative map.

key	type	value
Ids	list[str]	List of IDs for the created quantitative maps.

Return type:

dict

Examples

>>> body = {
...     "FirstDatasetId": '3165b2e3-2f22-4028-9dd5-441bff4d702e',
...     "SecondDatasetId": 'dea6b690-e964-47b1-9078-f6ef3e301c3c',
...     "ThirdDatasetId": '6fc0313b-cc5d-4a8f-b9b8-0ca85f78d17b',
...     "BackgroundType": 1,
...     "AutoDriftCorrection": False
... }
>>> create_quantitativemap_with_three_window(body)
{
    "Ids": [
        "0c755de8-fe11-470f-a593-242b6b16f5fa",
        "4afc5580-e5d4-4258-8c43-92594381ffe0",
        "792eda59-6465-470f-8d84-510ed9f8e014",
        "3522ee25-e358-4c1e-88da-bbd1bf5240a4"
    ]
}

PyJEM.eftem.analysis.create_thickness_map(param)¶

Create a thickness map.

Parameters:

param (dict) –

Thickness map creation parameters.

key	type	value
FirstDatasetId	str	The ID of the first dataset.
SecondDatasetId	str	The ID of the second dataset.
AutoDriftCorrection	bool	Whether to use auto drift correction.

Returns:

A dictionary containing the created thickness map.

key	type	value
Ids	list[str]	List of IDs for the created thickness maps.

Return type:

dict

Examples

>>> body = {
...     "FirstDatasetId": '3165b2e3-2f22-4028-9dd5-441bff4d702e',
...     "SecondDatasetId": 'dea6b690-e964-47b1-9078-f6ef3e301c3c',
...     "AutoDriftCorrection": False
... }
>>> create_thickness_map(body)
{
    "Ids": [
        "0c755de8-fe11-470f-a593-242b6b16f5fa",
        "4afc5580-e5d4-4258-8c43-92594381ffe0",
        "148e082d-f24d-401e-bce8-21c31e679d52"
    ]
}