API

API#

GUI_tool module#

relative_dose_1d.GUI_tool.plot(D_ref, D_eval)#

A function to show a graphical user interface (GUI) to showing 1D dose profiles, gamma analysis and dose difference. Data has to be in 2 columns, corresponding to positions and dose values, respectively.

Parameters:

  • D_ref (ndarray,) – Reference dose profile represented by a (M, 2) numpy array.

  • D_eva (ndarray,) – Dose profile to be evaluated, represented by a (N, 2) numpy array.

Return type:

A GUI showing dose profiles, gamma analysis and dose difference.

Examples

>>> from relative_dose_1d.GUI_tool import plot
>>> from relative_dose_1d.tools import build_from_array_and_step
>>> import numpy as np

>>> a = np.array([0,1,2,3,4,5,6,7,8,9,10])
>>> b = a + np.random.random_sample((11,))

>>> A = build_from_array_and_step(a, 1)
>>> B = build_from_array_and_step(b, 1)

>>> w = plot(A,B)

Tools module#

relative_dose_1d.tools.text_to_list(file_name)#

Convert a text file to a python list. Each element of the list represents a line from the text file.

Parameters:

file_name (string) – Text file name

Returns:

Loaded data as a list.

Return type:

list

relative_dose_1d.tools.identify_format(data_list)#

Identify text format.

Parameters:

data_list (list) – Each element of the list represents a line from the text file.

Returns:

‘varian’ for w2CAD format, identified by the ‘$’ character at the beginning of the file.

’ptw’ for mcc fromat, identified by the word ‘BEGIN_SCAN_DATA’.

’just_numbers’ for data without headers.

’text_file’ for other formats.

Return type:

string

relative_dose_1d.tools.get_data(file_name, start_word=None, end_word=None, delta=None)#

Get and normalize data from a text-file (file that is structured as a sequence of lines). Since w2CAD and mcc formats are automatically detected, it is not necessary to specify start/end words in such cases.

Parameters:

  • file_name (string) – Name of the file

  • start_word (string) – Word to identify the beginning of the data

  • end_word (string) – Word to identify the end of the data

  • delta (float) – Displacement in mm to define the started point

Returns:

Data as a Numpy object

Return type:

ndarray

relative_dose_1d.tools.build_from_array_and_step(array, step)#

Create a new array with the same length but with an additional axis. The first column represents the physical positions of the given values. The second column is a normalization of the given array. The positions are builded with evenly step spacing starting from zero.

Parameters:

  • array (ndarrya,) – Numpy 1D array with the profile values

  • step (float,) – The spacing between samples

Returns:

A new array with shape (M,2), where M is the shape of the array.

Return type:

array, ndarray

Examples

>>> y = np.array([2,4,6,8,10])
>>> A = build_from_array_and_step(y, 0.5)
[
[0.0, 2]
[0.5, 4]
[1.0, 6]
[1.5, 8]
[2.0, 10]]

>>> y = np.arange(6)
>>> B = build_from_array_and_step(y, 3)
[
[0, 0]
[3, 1]
[6, 2]
[9, 3]
[12, 4]]
relative_dose_1d.tools.gamma_1D(ref, eval, dose_t=3, dist_t=2, dose_threshold=0, interpol=1)#

1-dimensional gamma index calculation. Dose profiles have to be normalized (0-100%).

Parameters:

  • ref (ndarray,) – Reference dose profile represented by a (M, 2) numpy array.

  • eva (ndarray,) – Dose profile to be evaluated, represented by a (N, 2) numpy array.

  • dose_t (float, default = 3) – Dose tolerance [%].

  • dist_t (float, default = 2) – Distance to agreement [mm].

  • dose_threshold (float, default = 0) – Dose threshold [%]. Any point in the distribution with a dose value less than the threshold is going to be excluded from the analysis.

  • interpol (float, default = 1) – Number of interpolated points to generate between each two consecutive points in “eval” data.

Returns:

gamma distribution, gamma percent and number of evaluated points

Return type:

ndarray, float