PyJEM.femtus.process

PyJEM.femtus.process.binning(datasetid, param)

Perform binning operation.

Parameters:

• datasetid (str) – Unique identifier of the target dataset.

• param (dict) –

  Dictionary of binning parameters.

  ⇒ See Binning param items.

  Binning param items

  key	type	value
  Sizes	list[int]	List of binning sizes to apply.

Return type:

None

Examples

>>> param = {
        "Sizes": [
            1,
            2
        ]
    }
>>> binning('781519d9-9063-4fb3-a392-8f27d837c6d4', param)

PyJEM.femtus.process.extract(datasetid, param)

Extract a range from the target.

Parameters:

• datasetid (str) – Unique identifier of the target dataset.

• param (dict) –

  Dictionary specifying the extraction range.

  ⇒ See Extract param items.

  items

  key	type	value
  Range	list[dict]	List of extraction range entries. ⇒ See Extract range items.

  Range items

  key	type	value
  Start	int	Start energy value (e.g., 0).
  End	int	End energy value (e.g., 255).

Return type:

None

Examples

>>> param = {
        "Range": [
            {
                "End": 255,
                "Start": 0
            },
            {
                "End": 255,
                "Start": 0
            }
        ]
    }
>>> extract('781519d9-9063-4fb3-a392-8f27d837c6d4', param)

PyJEM.femtus.process.integrate(datasetid, param)

Integrate data along a specified axis and range.

Parameters:

• datasetid (str) – The datasetid of the target.

• param (dict) –

  Dictionary specifying integration parameters.

  ⇒ See Integrate param items.

  items

  key	type	value
  Axis	str	Axis along which to integrate. One of "X", "Y", "Z".
  Range	dict	Integration range. ⇒ See Integrate range items.

  Range items

  key	type	value
  Start	int	Start value of the range.
  End	int	End value of the range.

Return type:

None

Examples

>>> param = {
        "Axis": "X",
        "Range": {
            "End": 255,
            "Start": 0
        }
    }
>>> integrate('781519d9-9063-4fb3-a392-8f27d837c6d4', param)

PyJEM.femtus.process.fft(datasetid)

Perform FFT on the target.

Parameters:

datasetid (str) – The datasetid of the target.

Return type:

None

Examples

>>> fft('781519d9-9063-4fb3-a392-8f27d837c6d4')

PyJEM.femtus.process.ifft(datasetid)

Perform inverse FFT on the target.

Parameters:

datasetid (str) – The datasetid of the target.

Return type:

None

Examples

>>> ifft('781519d9-9063-4fb3-a392-8f27d837c6d4')

PyJEM.femtus.process.flip(datasetid, body)

Flip the image horizontally and/or vertically.

Parameters:

• datasetid (str) – The datasetid of the target.

• body (dict) –

  Dictionary specifying flip directions.

  ⇒ See Flip body items.

  items

  key	type	value
  Horizontal	bool	If True, apply horizontal flip.
  Vertical	bool	If True, apply vertical flip.

Return type:

None

Examples

>>> param = {
        "Horizontal": True,
        "Vertical": True
    }
>>> flip('781519d9-9063-4fb3-a392-8f27d837c6d4', param)

PyJEM.femtus.process.rotation(datasetid, body)

Rotate the image.

Parameters:

• datasetid (str) – The datasetid of the target.

• body (dict) –

  Dictionary specifying rotation parameters.

  ⇒ See Rotation body items.

  items

  key	type	value
  Angle	int | float	Rotation angle in degrees.
  IsClockwise	bool	If True, rotate clockwise; otherwise counterclockwise.
  Crop	bool	If True, crop the image to remove empty regions after rotation.

Return type:

None

Examples

>>> param = {'Angle': 30, 'IsClockwise': True, 'Crop': True}
>>> rotation("781519d9-9063-4fb3-a392-8f27d837c6d4", param)

PyJEM.femtus.process.image_filters(datasetid, body)

Apply image filters.

Parameters:

• datasetid (str) – The datasetid of the target.

• body (dict) –

  Dictionary specifying filter parameters.

  ⇒ See Image filters body items.

  items

  key	type	value
  FilterType	str	Type of filter to apply (for example, Gaussian).
  Sigma	float	Standard deviation for the Gaussian kernel. Applicable only when FilterType is "Gaussian".

Return type:

None

Examples

>>> param = {"FilterType": "Gaussian", "Sigma": 1.0}
>>> image_filters("781519d9-9063-4fb3-a392-8f27d837c6d4", param)

PyJEM.femtus.process.get_default_image_filters(datasetid, body)

Get default image filter results.

Parameters:

• datasetid (str) – The datasetid of the target.

• body (dict) –

  Dictionary specifying filter parameters.

  ⇒ See Default image filters body items.

  items

  key	type	value
  FilterType	str	Type of filter to apply (for example, Median).
  Size	int	Kernel or window size for the filter.

Return type:

None

Examples

>>> param = {"FilterType": "Median", "Size": 3}
>>> get_default_image_filters("781519d9-9063-4fb3-a392-8f27d837c6d4", param)

PyJEM.femtus.process.projection(body)

Project data.

Parameters:

body (dict) –

Dictionary specifying projection parameters.

⇒ See Projection body items.

items

key	type	value
Type	str	Type of projection to apply.
Axis	str	Axis along which to project. One of "X", "Y", "Z".

Return type:

None

Examples

>>> param = {"Type": "Sum", "Axis": "Y"}
result = projection(param)

PyJEM.femtus.process.spectrum_first_derivative(body)

Calculate the first derivative of a spectrum.

Parameters:

body (dict) –

Dictionary containing the input data.

⇒ See Spectrum first derivative body items.

items

key	type	value
Data	list[float]	List of numerical values representing the spectrum signal.

Return type:

None

Examples

>>> param = {"Data": [1, 2, 3, 4]}
>>> spectrum_first_derivative(param)

PyJEM.femtus.process.spectrum_second_derivative(body)

Calculate the second derivative of a spectrum.

Parameters:

body (dict) –

Dictionary containing the input data.

⇒ See Spectrum second derivative body items.

items

key	type	value
Data	list[float]	List of numerical values representing the spectrum signal.

Return type:

None

Examples

>>> param = {"Data": [1, 2, 3, 4]}
>>> spectrum_second_derivative(param)