pept.scanners.parallel_screens¶

pept.scanners.parallel_screens(filepath_or_array, screen_separation, sample_size=None, overlap=None, verbose=True, **kwargs)[source]¶

Initialise PEPT LoRs for parallel screens PET/PEPT detectors from an input CSV file or array.

The expected data columns in the file are `[time, x1, y1, x2, y2]`. This is automatically transformed into the standard Lines format with columns being [time, x1, y1, z1, x2, y2, z2], where z1 = 0 and z2 = screen_separation.

ParallelScreens can be initialised with a predefined numpy array of LoRs or read data from a .csv.

Parameters

filepath_or_array[str, pathlib.Path, IO] or numpy.ndarray (N, 5): A path to a file to be read from or an array for initialisation. A path is a string with the (absolute or relative) path to the data file or a URL from which the PEPT data will be read. It should include the full file name, along with its extension (.csv, .a01, etc.).
screen_separationfloat: The separation (in mm) between the two PEPT screens corresponding to the z coordinate of the second point defining each line. The attribute lines, with columns [time, x1, y1, z1, x2, y2, z2], will have z1 = 0 and z2 = screen_separation.
sample_sizeint, default 0: An int that defines the number of lines that should be returned when iterating over lines. A sample_size of 0 yields all the data as one single sample. A good starting value would be 200 times the maximum number of tracers that would be tracked.
overlapint, default 0: An int that defines the overlap between two consecutive samples that are returned when iterating over lines. An overlap of 0 implies consecutive samples, while an overlap of (sample_size - 1) means incrementing the samples by one. A negative overlap means skipping values between samples. An error is raised if overlap is larger than or equal to sample_size.
verbosebool, default True: An option that enables printing the time taken for the initialisation of an instance of the class. Useful when reading large files (10gb files for PEPT data is not unheard of).
kwargsother keyword arguments: Other keyword arguments to be passed to pept.read_csv, e.g. “skiprows” or “max_rows”. See the pept.read_csv documentation for other arguments.

Returns

LineData: The initialised LoRs.

Raises

ValueError: If overlap >= sample_size. Overlap has to be smaller than sample_size. Note that it can also be negative.
ValueError: If the data file does not have the (N, M >= 5) shape.

See also

pept.LineData: Encapsulate LoRs for ease of iteration and plotting.
pept.PointData: Encapsulate points for ease of iteration and plotting.
pept.read_csv: Fast CSV file reading into numpy arrays.
PlotlyGrapher: Easy, publication-ready plotting of PEPT-oriented data.

Examples

Initialise a LineData array for three LoRs on a parallel screens PEPT scanner (i.e. each line is defined by two points each) with a head separation of 500 mm:

>>> lors_raw = np.array([
>>>     [2, 100, 150, 200, 250],
>>>     [4, 350, 250, 100, 150],
>>>     [6, 450, 350, 250, 200]
>>> ])

>>> screen_separation = 500
>>> lors = pept.scanners.parallel_screens(lors_raw, screen_separation)
Initialised PEPT data in 0.001 s.

>>> lors
LineData
--------
sample_size = 0
overlap =     0
samples =     1
lines =
  [[  2. 100. 150.   0. 200. 250. 500.]
   [  4. 350. 250.   0. 100. 150. 500.]
   [  6. 450. 350.   0. 250. 200. 500.]]
lines.shape = (3, 7)
columns = ['t', 'x1', 'y1', 'z1', 'x2', 'y2', 'z2']

pept.scanners.adac_forte pept.scanners.ADACGeometricEfficiency