PyJEM.eds.analysis¶

PyJEM.eds.analysis.get_analysis_settings()¶

Gets the settings for analyzing.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
UserCategories	list[str]	List of standard data categories created by the user.
DisableElements	dict	Dictionary containing a list of disabled element atomic numbers.
QualitativeSensitivity	str	Sensitivity for qualitative analysis. One of `"Low"`, `"Middle"`, or `"High"`.
ZAFEnable	bool	`True`: ZAF correction enabled.
PRZEnable	bool	`True`: PRZ correction enabled.
CliffLorimer	bool	`True`: Cliff-Lorimer correction enabled.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_analysis_settings()
{
    "CliffLorimer": True,
    "DisableElements": {
        "ElementalNumber": [
            1,
            2,
            3,
            4,
            10,
            18,
            36,
            43,
            54,
            61,
            84,
            85,
            86,
            87,
            88,
            89,
            91,
            93,
            94,
            95,
            96,
            97,
            98,
            99,
            100,
            101,
            102,
            103
        ]
    },
    "PRZEnable": True,
    "QualitativeSensitivity": "Middle",
    "UserCategories": [],
    "Version": "2.0",
    "ZAFEnable": True
}

PyJEM.eds.analysis.get_analysis_status()¶

Gets the status of execution.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
ExecutingAnalysis	bool	`True` if analysis is currently in progress.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_analysis_status()
{'Version': '2.0', 'ExecutingAnalysis': False}

PyJEM.eds.analysis.execute_autoqualitative_analysis(param)¶

Executes auto qualitative analysis.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
Elements	dict	Dictionary containing a list of detected atomic numbers under the key `"ElementalNumber"`.
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.execute_autoqualitative_analysis({
...     "TargetDataID": "49402239-824b-41e4-b7a5-ebe18b658465",
...     "TargetDetector": 0
... })
{
    "Elements": {
        "ElementalNumber": [
            31,
            78,
            82
        ]
    },
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.get_quantitative_spectrum_analysis_settings()¶

Gets the settings of quantitative spectrum analysis.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is `4` (USER).
CorrectionType	int	Correction type. `0`: None, `1`: ZAF, `2`: PRZ, `3`: Cliff-Lorimer, `4`: ZETA. ZAF, PRZ and Cliff-Lorimer are supported.
EnableAbsorptionCorrection	bool	`True`: Enable absorption correction. Available in Cliff-Lorimer mode.
Thickness	int	Sample thickness in nm. Available when `CorrectionType` is Cliff-Lorimer and `EnableAbsorptionCorrection` is `True`.
Density	float	Sample density in g/cm³. Available when `CorrectionType` is Cliff-Lorimer and `EnableAbsorptionCorrection` is `True`.
EnableFluorescenceCorrection	bool	`True`: Enable fluorescence correction. Available in Cliff-Lorimer mode.
ConversionType	int	Conversion type. `0`: None, `1`: Metal, `2`: Oxide, `3`: Compound. Metal and Oxide are available.
OxideCation	int	Cation number of the oxide. Available in oxide conversion mode.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_quantitative_spectrum_analysis_settings()
{
    "ConversionType": 1,
    "CorrectionType": 3,
    "Density": 2.3299999237060547,
    "EnableAbsorptionCorrection": False,
    "EnableFluorescenceCorrection": False,
    "OxideCation": 24,
    "StandardDataType": 2,
    "Thickness": 100,
    "Version": "2.0"
}

PyJEM.eds.analysis.set_quantitative_spectrum_analysis_settings(param)¶

Sets the settings of quantitative spectrum analysis.

Parameters:

param (dict) –

Quantitative spectrum analysis settings payload.

key	type	value
Version	str	API version string.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is `4` (USER).
CorrectionType	int	Correction type. `0`: None, `1`: ZAF, `2`: PRZ, `3`: Cliff-Lorimer, `4`: ZETA. ZAF, PRZ and Cliff-Lorimer are supported.
EnableAbsorptionCorrection	bool	`True`: Enable absorption correction. Available in Cliff-Lorimer mode.
Thickness	int	Sample thickness in nm. Available when `CorrectionType` is Cliff-Lorimer and `EnableAbsorptionCorrection` is `True`.
Density	float	Sample density in g/cm³. Available when `CorrectionType` is Cliff-Lorimer and `EnableAbsorptionCorrection` is `True`.
EnableFluorescenceCorrection	bool	`True`: Enable fluorescence correction. Available in Cliff-Lorimer mode.
ConversionType	int	Conversion type. `0`: None, `1`: Metal, `2`: Oxide, `3`: Compound. Metal and Oxide are available.
OxideCation	int	Cation number of the oxide. Available in oxide conversion mode.

Returns:

Corresponds to get_quantitative_spectrum_analysis_settings().

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.set_quantitative_spectrum_analysis_settings({
...     "ConversionType": 1,
...     "CorrectionType": 3,
...     "Density": 2.3299999237060547,
...     "EnableAbsorptionCorrection": False,
...     "EnableFluorescenceCorrection": False,
...     "OxideCation": 24,
...     "StandardDataType": 2,
...     "Thickness": 100,
...     "Version": "2.0"
... })
{
    "ConversionType": 1,
    "CorrectionType": 3,
    "Density": 2.3299999237060547,
    "EnableAbsorptionCorrection": False,
    "EnableFluorescenceCorrection": False,
    "OxideCation": 24,
    "StandardDataType": 2,
    "Thickness": 100,
    "Version": "2.0"
}

PyJEM.eds.analysis.execute_quantitative_spectrum_analysis(param)¶

Executes quantitative spectrum analysis.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
Quantities	list[float]	Array of elemental quantities in Mass%.
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.execute_quantitative_spectrum_analysis({
...     "TargetDataID": "xxxx-xxxx-xxxx-xxxx",
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDetector": 1
... })
{
    "Message": "",
    "Quantities": [
        100,
        0,
        0
    ],
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.extract_line_spectrum(param)¶

Extracts the ID of spectrum data of line analysis.

Parameters:

param (dict) –

Request payload.

key	type	value
TargetDataID	str	Dataset ID.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
SpectrumDataID	str	ID of the extracted line spectrum.
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.extract_line_spectrum({"TargetDataID": "ed8c008a-ee32-4e03-a01e-e586c21f2591"})
{
    "Message": "",
    "Result": "OK",
    "SpectrumDataID": "bbbda4db-bd52-4bf0-9e35-c2819c86bad0",
    "Version": "2.0"
}

PyJEM.eds.analysis.extract_area_spectrum(param)¶

Extracts the ID of spectrum data of area analysis.

Parameters:

param (dict) –

Request payload.

key	type	value
TargetDataID	str	Dataset ID.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
SpectrumDataID	str	ID of the extracted area spectrum.
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.extract_area_spectrum({"TargetDataID": "ed8c008a-ee32-4e03-a01e-e586c21f2591"})
{
    "Message": "",
    "Result": "OK",
    "SpectrumDataID": "bbbda4db-bd52-4bf0-9e35-c2819c86bad0",
    "Version": "2.0"
}

PyJEM.eds.analysis.execute_count_line_analysis(param)¶

Executes gross count data of line analysis.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
DataIDs	list[str]	List of result data IDs. Example: `["aaaa-aaaa-aaaa-aaaa", "bbbb-bbbb-bbbb-bbbb"]`
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.execute_count_line_analysis({
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDataID": "ed8c008a-ee32-4e03-a01e-e586c21f2591",
...     "TargetDetector": 1
... })
{
    "DataIDs": [
        "636b94cb-0a8e-45d8-9870-e682324ffd39",
        "9df5b300-4418-4933-a9e0-6396f71ab857",
        "318df351-ab6a-47c5-b5d6-d422b24beee9",
        "702c9aea-84d6-4adb-a4ec-2b029943a8b5"
    ],
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.get_net_count_line_analysis_settings()¶

Gets the settings of net count line analysis.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of standard data. If `StandardDataType` is NORM, returns nothing; if COMMON, returns `"Common"`; if USER, returns the configured string.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_net_count_line_analysis_settings()
{
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.set_net_count_line_analysis_settings(param)¶

Sets the settings of net count line analysis.

Parameters:

param (dict) –

Net count line analysis settings payload.

key	type	value
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available exclusively when `StandardDataType` is USER.

Returns:

Corresponds to get_net_count_line_analysis_settings().

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.set_net_count_line_analysis_settings({
...     "ProcessingMethod": 1,
...     "StandardDataType": 2,
...     "Version": "2.0"
... })
{
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.execute_net_count_line_analysis(param)¶

Executes net count data of line analysis.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
DataIDs	list[str]	List of result data IDs. Example: `["aaaa-aaaa-aaaa-aaaa", "bbbb-bbbb-bbbb-bbbb"]`
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.execute_net_count_line_analysis({
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDataID": "ed8c008a-ee32-4e03-a01e-e586c21f2591",
...     "TargetDetector": 1
... })
{
    "DataIDs": [
        "636b94cb-0a8e-45d8-9870-e682324ffd39"
    ],
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.get_quantitative_line_analysis_settings()¶

Gets the settings of quantitative line analysis.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is `4` (USER).
CorrectionType	int	Correction type. `0`: None, `1`: ZAF, `2`: PRZ, `3`: Cliff-Lorimer, `4`: ZETA. ZAF, PRZ and Cliff-Lorimer are supported.
ConversionType	int	Conversion type. `0`: None, `1`: Metal, `2`: Oxide, `3`: Compound. Metal and Oxide are available.
OxideCation	int	Cation number of the oxide. Available in oxide conversion mode.
EnableSumPeakRemoval	bool	`True`: Enable sum peak removal.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_quantitative_line_analysis_settings()
{
    "ConversionType": 1,
    "CorrectionType": 3,
    "EnableSumPeakRemoval": False,
    "OxideCation": 24,
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.set_quantitative_line_analysis_settings(param)¶

Sets the settings of quantitative line analysis.

Parameters:

param (dict) –

Quantitative line analysis settings payload.

key	type	value
Version	str	API version string.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is `4` (USER).
CorrectionType	int	Correction type. `0`: None, `1`: ZAF, `2`: PRZ, `3`: Cliff-Lorimer, `4`: ZETA. ZAF, PRZ and Cliff-Lorimer are supported.
ConversionType	int	Conversion type. `0`: None, `1`: Metal, `2`: Oxide, `3`: Compound. Metal and Oxide are available.
OxideCation	int	Cation number of the oxide. Available in oxide conversion mode.
EnableSumPeakRemoval	bool	`True`: Enable sum peak removal.

Returns:

Corresponds to get_quantitative_line_analysis_settings().

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.set_quantitative_line_analysis_settings({
...     "ConversionType": 1,
...     "CorrectionType": 3,
...     "EnableSumPeakRemoval": False,
...     "OxideCation": 24,
...     "ProcessingMethod": 1,
...     "StandardDataType": 2,
...     "Version": "2.0"
... })
{
    "ConversionType": 1,
    "CorrectionType": 3,
    "EnableSumPeakRemoval": False,
    "OxideCation": 24,
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.execute_quantitative_line_analysis(param)¶

Executes quantitative data of line analysis.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
DataIDs	list[str]	List of result data IDs. Example: `["aaaa-aaaa-aaaa-aaaa", "bbbb-bbbb-bbbb-bbbb"]`
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.execute_quantitative_line_analysis({
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDataID": "ed8c008a-ee32-4e03-a01e-e586c21f2591",
...     "TargetDetector": 1
... })
{
    "DataIDs": [
        "636b94cb-0a8e-45d8-9870-e682324ffd39"
    ],
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.create_count_map(param)¶

Creates gross count mapping data.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
DataIDs	list[str]	List of result data IDs. Example: `["aaaa-aaaa-aaaa-aaaa", "bbbb-bbbb-bbbb-bbbb"]`
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.create_count_map({
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDataID": "630a8daa-08f2-4298-bec9-92ce2e89ea7d",
...     "TargetDetector": 1
... })
{
    "DataIDs": [
        "3a04ea36-d080-46b3-8d2d-c8b4300d54b1",
        "6fee5578-6986-4f34-9445-1c89a598f97a",
        "268d43a8-4f64-4ed5-a75b-a960c42d6e37",
        "1a4206b3-317c-4cd7-9093-82557ac4c0c3",
        "134b497a-c9f9-40e2-be63-28983597ca21",
        "f9cb63cf-2c7e-420f-87b0-7b1321ad4dae"
    ],
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.get_net_count_map_analysis_settings()¶

Gets the settings of net count mapping.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
PixelWidth	int	Width in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image width.
PixelHeight	int	Height in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image height.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
Smoothing	int	Gaussian filter range. `0`: None, `1`: 3×3, `2`: 5×5, `3`: 7×7.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is USER.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_net_count_map_analysis_settings()
{
    "PixelHeight": 0,
    "PixelWidth": 0,
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.set_net_count_map_analysis_settings(param)¶

Sets the settings of net count mapping.

Parameters:

param (dict) –

Net count map analysis settings payload.

key	type	value
PixelWidth	int	Width in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image width.
PixelHeight	int	Height in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image height.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External.
Smoothing	int	Gaussian filter range. `0`: None, `1`: 3×3, `2`: 5×5, `3`: 7×7.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is USER.

Returns:

Corresponds to get_net_count_map_analysis_settings().

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.set_net_count_map_analysis_settings({
...     "PixelHeight": 0,
...     "PixelWidth": 0,
...     "ProcessingMethod": 1,
...     "StandardDataType": 2,
...     "Version": "2.0"
... })
{
    "PixelHeight": 0,
    "PixelWidth": 0,
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.create_net_count_map(param)¶

Creates net count mapping data.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
DataIDs	list[str]	List of result data IDs. Example: `["aaaa-aaaa-aaaa-aaaa", "bbbb-bbbb-bbbb-bbbb"]`
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.create_net_count_map({
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDataID": "630a8daa-08f2-4298-bec9-92ce2e89ea7d",
...     "TargetDetector": 1
... })
{
    "DataIDs": [
        "00520849-3e58-4c6c-9baa-ed3e1ca78161",
        "70ee93fa-b69f-4b54-9d92-a958d7d1d16a",
        "aa11153a-80b4-46b1-acd5-3dd209992a33",
        "d3b85b80-c8fd-43fe-a9c7-22c36f68d2d7"
    ],
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.get_quantitative_map_analysis_settings()¶

Gets the settings of quantitative mapping.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is `4` (USER).
CorrectionType	int	Correction type. `0`: None, `1`: ZAF, `2`: PRZ, `3`: Cliff-Lorimer, `4`: ZETA. ZAF, PRZ and Cliff-Lorimer are supported.
ConversionType	int	Conversion type. `0`: None, `1`: Metal, `2`: Oxide, `3`: Compound. Metal and Oxide are available.
OxideCation	int	Cation number of the oxide. Available in oxide conversion mode.
EnableSumPeakRemoval	bool	`True`: Enable sum peak removal.
PixelWidth	int	Width in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image width.
PixelHeight	int	Height in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image height.
Smoothing	int	Gaussian filter range. `0`: None, `1`: 3×3, `2`: 5×5, `3`: 7×7.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_quantitative_map_analysis_settings()
{
    "ConversionType": 1,
    "CorrectionType": 3,
    "EnableSumPeakRemoval": False,
    "PixelHeight": 0,
    "PixelWidth": 0,
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.set_quantitative_map_analysis_settings(param)¶

Sets the settings of quantitative mapping.

Parameters:

param (dict) –

Quantitative map analysis settings payload.

key	type	value
Version	str	API version string.
ProcessingMethod	int	Processing method. `0`: None, `1`: Speed, `2`: Precise. Speed and Precise are supported.
StandardDataType	int	Standard data type. `0`: None, `1`: DEFSPC, `2`: NORM, `3`: COMMON, `4`: USER, `5`: External. NORM, COMMON and USER are supported.
UserCategoryName	str	Category name of the standard data. Available when `StandardDataType` is `4` (USER).
CorrectionType	int	Correction type. `0`: None, `1`: ZAF, `2`: PRZ, `3`: Cliff-Lorimer, `4`: ZETA. ZAF, PRZ and Cliff-Lorimer are supported.
ConversionType	int	Conversion type. `0`: None, `1`: Metal, `2`: Oxide, `3`: Compound. Metal and Oxide are available.
OxideCation	int	Cation number of the oxide. Available in oxide conversion mode.
EnableSumPeakRemoval	bool	`True`: Enable sum peak removal.
PixelWidth	int	Width in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image width.
PixelHeight	int	Height in pixels. Select from `[64, 128, 256, 512, 1024, 2048, 4096]`. Must be less than or equal to the original image height.
Smoothing	int	Gaussian filter range. `0`: None, `1`: 3×3, `2`: 5×5, `3`: 7×7.

Returns:

Corresponds to get_quantitative_map_analysis_settings().

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.set_quantitative_map_analysis_settings({
...     "ConversionType": 1,
...     "CorrectionType": 3,
...     "EnableSumPeakRemoval": False,
...     "PixelHeight": 0,
...     "PixelWidth": 0,
...     "ProcessingMethod": 1,
...     "StandardDataType": 2,
...     "Version": "2.0"
... })
{
    "ConversionType": 1,
    "CorrectionType": 3,
    "EnableSumPeakRemoval": False,
    "PixelHeight": 0,
    "PixelWidth": 0,
    "ProcessingMethod": 1,
    "StandardDataType": 2,
    "Version": "2.0"
}

PyJEM.eds.analysis.create_quantitative_map(param)¶

Creates quantitative mapping data.

Parameters:

param (dict) –

Analysis request payload.

key	type	value
TargetDataID	str	Dataset ID.
Elements	list[dict]	List of elements for analysis. ⇒ See Elements items.
TargetDetector	int	Target detector. `0`: Total, `1`: FirstDetector, `2`: SecondDetector, `3`: ThirdDetector, `4`: FourthDetector.

Elements items

key	type	value
ElementalNumber	int	Atomic number.
Line	str	One of `"None"`, `"K"`, `"L"`, `"M"`, `"N"`, `"O"`, or `"Default"`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Message	str	Free text message from the EDS system.
DataIDs	list[str]	List of result data IDs. Example: `["aaaa-aaaa-aaaa-aaaa", "bbbb-bbbb-bbbb-bbbb"]`
Result	str	Execution status. One of `"OK"` or `"Fail"`.

Return type:

dict

Notes

If the error "The target data ID is invalid." is displayed, verify the type of the dataset specified by TargetDataID.

Examples

>>> from PyJEM import eds
>>> eds.create_quantitative_map({
...     "Elements": [
...         {"ElementalNumber": 8, "Line": "K"},
...         {"ElementalNumber": 14, "Line": "L"},
...         {"ElementalNumber": 21, "Line": "L"}
...     ],
...     "TargetDataID": "630a8daa-08f2-4298-bec9-92ce2e89ea7d",
...     "TargetDetector": 1
... })
{
    "DataIDs": [
        "ef4bd1fc-60c9-4b25-9963-4fba5d156f0c",
        "99876b2e-dcb0-4fb1-b7ce-b846fed31b09",
        "01cf6319-352c-435a-8409-ce5650c692a4"
    ],
    "Message": "",
    "Result": "OK",
    "Version": "2.0"
}

PyJEM.eds.analysis.get_map_image_size(param)¶

Gets width and height of an acquired map image.

Parameters:

param (dict) –

Request payload.

key	type	value
TargetDataID	str	An ID returned as `DataIDs` by `create_count_map()`, `create_net_count_map()`, or `create_quantitative_map()`.

Returns:

The returned value is a dictionary. A description of each item is provided below.

key	type	value
Version	str	API version string.
Width	int	Width of the acquired map image in pixels.
Height	int	Height of the acquired map image in pixels.

Return type:

dict

Examples

>>> from PyJEM import eds
>>> eds.get_map_image_size({"TargetDataID": "630a8daa-08f2-4298-bec9-92ce2e89ea7d"})
{
    "Height": 204,
    "Version": "2.0",
    "Width": 176
}