PAData

class patato.PAData(scan_reader: ReaderInterface, scan_writer: WriterInterface | None = None)

Bases: object

A class that contains the interface to access data from a single scan. Any source of scans (e.g. iThera/HDF5/IPASC) can be linked to this.

__init__(scan_reader: ReaderInterface, scan_writer: WriterInterface | None = None) → None

Parameters:

• scan_reader –

• scan_writer –

Methods

__init__(scan_reader[, scan_writer])	param scan_reader:
add_roi(roi_data[, generated])	Add a region of interest to the hdf5 file.
clear_dso2()
close()
copy([cls])	Copy the pa data with changes given.
delete_recons([name, recon_groups])	Delete the reconstructions.
delete_rois([name_position, number])	Delete a roi with name and number.
from_hdf5(filename[, mode])
get_impulse_response()	Return the time-domain impulse response function.
get_n_samples()	Get the number of time samples in the dataset.
get_overall_correction_factor()	Return the energy correction factors for the dataset.
get_recon_types()	Get the list of different reconstruction types.
get_repetition_numbers()	Get the scan repetition numbers for each frame.
get_responding_pixels([nsigma])	param nsigma:
get_rois([filter_rois, interpolate, ...])	Get the regions of interest from the dataset.
get_run_number()	Get the run number for each of the frames.
get_sampling_frequency()	Get the scan's sampling frequency.
get_scan_baseline_icg()
get_scan_baseline_so2()
get_scan_baseline_standard_deviation_icg()
get_scan_baseline_standard_deviation_so2()
get_scan_datetime()
get_scan_dicg()
get_scan_dso2()
get_scan_geometry()	Get the scan detector geometry.
get_scan_images(group[, ignore_default, suffix])	Get the scan images, e.g. reconstructions or so2 etc.
get_scan_mean(dataset[, operation])	param dataset:
get_scan_name()	Get the scan name.
get_scan_reconstructions()
get_scan_so2()
get_scan_so2_frequency_components([...])	param do_detrend:
get_scan_so2_frequency_peak([fnum])	param fnum:
get_scan_so2_frequency_sum([fnum])	param fnum:
get_scan_so2_time_mean()
get_scan_so2_time_standard_deviation()
get_scan_thb()
get_scan_thb_time_mean()
get_scan_unmixed()
get_segmentation()
get_speed_of_sound()	Get the speed of sound of the data if it has been set.
get_time_series()
get_timestamps()	Get the scan timestamps in seconds.
get_ultrasound()
get_wavelengths()	Get the wavelengths used in the scan.
get_z_positions()	Get the z-positions of the sensor.
is_clinical()
rename_roi(old_name, new_name, new_position)	Rename a region of interest.
save_hdf5(filename)
save_to_hdf5(filename)
set_default_recon([rec_name])	Make all returned data be of a particular reconstruction type.
set_speed_of_sound(c)	Change the speed of sound for the dataset.
summary_measurements([metrics, ...])	param metrics:

Attributes

dataset
shape	Returns the shape of the dataset, minus the image size.

add_roi(roi_data: ROI, generated: bool = False) → None

Add a region of interest to the hdf5 file.

Parameters:

• roi_data (ROI) –

• generated (bool, default False) –

copy(cls: Type[PAData] | None = None) → PAData

Copy the pa data with changes given.

Parameters:

cls (Type[PAData]) –

Returns:

Copy of the dataset.

Return type:

PAData

property dataset

delete_recons(name=None, recon_groups: Sequence[str] | None = None)

Delete the reconstructions.

Parameters:

• name (str or None) –

• recon_groups ((iterable of str) or None) –

delete_rois(name_position: str | None = None, number: str | None = None) → None

Delete a roi with name and number. If number is None, will delete all. If name and number is None, delete all.

Parameters:

• name_position – str or None

• number – str or none

get_impulse_response() → np.ndarray | Array

Return the time-domain impulse response function.

Returns:

Impulse response function.

Return type:

np.ndarray or pyopencl.array.Array

get_n_samples() → int

Get the number of time samples in the dataset.

Returns:

Number of samples.

Return type:

int

get_overall_correction_factor() → np.ndarray | Array | xarray.DataArray

Return the energy correction factors for the dataset.

Returns:

Overall correction factor.

Return type:

np.ndarray

get_recon_types() → list

Get the list of different reconstruction types.

Returns:

List of different reconstruction types that we have.

Return type:

list

get_repetition_numbers() → ndarray

Get the scan repetition numbers for each frame.

Returns:

Scan repetition numbers.

Return type:

np.ndarray

get_responding_pixels(nsigma=2)

Parameters:

nsigma –

get_rois(filter_rois=None, interpolate: bool = False, get_rim_cores=None, rim_core_distance=None) → Dict[Tuple[str, str], ROI]

Get the regions of interest from the dataset.

Parameters:

• rim_core_distance –

• get_rim_cores –

• filter_rois (dict or None) –

• interpolate (bool) –

Returns:

dict of {(tuple of (str, str) – Return all the rois.

Return type:

ROI}

get_run_number() → ndarray

Get the run number for each of the frames.

Returns:

Get the run numbers of each of the frames.

Return type:

np.ndarray

get_sampling_frequency() → float

Get the scan’s sampling frequency.

Returns:

Sampling Frequency

Return type:

float

get_scan_baseline_icg()

get_scan_baseline_so2()

get_scan_baseline_standard_deviation_icg()

get_scan_baseline_standard_deviation_so2()

get_scan_dicg()

get_scan_dso2()

get_scan_geometry() → np.ndarray | Array

Get the scan detector geometry.

Return type:

np.ndarray or pyopencl.array.Array

get_scan_images(group: str, ignore_default=False, suffix='') → Dict[Tuple[str, str], ImageSequence] | ImageSequence

Get the scan images, e.g. reconstructions or so2 etc.

Parameters:

• group (str) – Group to get images from.

• ignore_default (bool) – Ignore the default reconstruction.

• suffix (str) – Suffix to add to the image number (e.g. for ICG unmixing).

Returns:

(dict of {tuple of (str, str) – Images of certain type if default recon has been set, or dict or images for all reconstructions.

Return type:

ImageSequence}) or ImageSequence

get_scan_mean(dataset: ImageSequence, operation=<function mean>)

Parameters:

• dataset –

• operation –

get_scan_name() → str

Get the scan name.

Returns:

Scan name.

Return type:

str

get_scan_so2_frequency_components(do_detrend=True, fmin=1e-05, fmax=1000, fnum=1000)

Parameters:

• do_detrend –

• fmin –

• fmax –

• fnum –

get_scan_so2_frequency_peak(fnum=1000)

Parameters:

fnum –

get_scan_so2_frequency_sum(fnum=1000)

Parameters:

fnum –

get_scan_so2_time_mean()

get_scan_so2_time_standard_deviation()

get_scan_thb()

get_scan_thb_time_mean()

get_segmentation()

get_speed_of_sound() → float | None

Get the speed of sound of the data if it has been set.

Returns:

Speed of sound

Return type:

float or None

get_time_series() → PATimeSeries

get_timestamps() → ndarray

Get the scan timestamps in seconds.

Returns:

Timestamps in seconds.

Return type:

np.ndarray

get_wavelengths() → ndarray

Get the wavelengths used in the scan.

Returns:

Scan Wavelengths.

Return type:

np.ndarray

get_z_positions() → ndarray

Get the z-positions of the sensor.

Returns:

Z-positions array.

Return type:

np.ndarray

rename_roi(old_name: str | Tuple, new_name: str, new_position: str) → None

Rename a region of interest.

Parameters:

• old_name (str or tuple) – Old roi name e.g. “tumour_left/0” or (“tumour_left”, “0”)

• new_name (str) – New roi name e.g. “brain”

• new_position (str) – New roi position e.g. “left”

set_default_recon(rec_name: str | None = None) → None

Make all returned data be of a particular reconstruction type. It is recommended to run this at the start of analysis scripts.

Parameters:

rec_name (tuple of str, optional) –

set_speed_of_sound(c: float) → None

Change the speed of sound for the dataset.

Parameters:

c (float) – Speed of sound.

property shape: Tuple[int]

Returns the shape of the dataset, minus the image size.

Returns:

Shape of the dataset.

Return type:

tuple of int

summary_measurements(metrics=None, include_rois=None, roi_kwargs=None, just_summary=True, return_masks=False)

Parameters:

• metrics –

• include_rois –

• roi_kwargs –

• just_summary –