protoclass.data_management.DCEModality

class protoclass.data_management.DCEModality(path_data=None)

Class to handle DCE-MRI modality.

Parameters:

path_data : str or None, optional (default=None)

Path where the data are located. Can also be specified when reading the data. It will be overidden with this function.

Attributes

path_data_	(str) Location of the data.
data_	(ndarray, shape (T, Y, X, Z)) The different volume of the DCE serie. The data are saved in T, Y, X, Z ordered.
metadata_	(dict) A dictionary containing the metadata related to the DCE serie.
pdf_series_	(list of ndarray, length (n_serie)) List of the PDF for each serie.
bin_series_	(list of ndarray, length (n_serie)) List of the bins used to plot the pdfs.
max_series_	(float) Maximum intensity of all the DCE series.
min_series_	(float) Minimum intensity of all the DCE series.
n_serie_	(int) Number of serie in this DCE sequence.
max_series_list_	(list of float) List of the maximum intensity for each DCE serie.
min_series_list_	(list of float) List of the minimum intensity for each DCE serie.
time_info_	(ndarray, shape (n_serie, )) Array containing the time information of the acquisition time in seconds

Methods

build_heatmap([roi_data, nb_bins])	Function which return a heatmap using the pdf of each serie.
get_pdf_list([roi_data, nb_bins])	Extract the a list of pdf related with the data.
is_read()	Function to know if the data have been read.
read_data_from_path([path_data])	Function to read temporal images which represent a 3D volume over time.
update_histogram([nb_bins])	Update the histogram of each serie and first-order statistics.

__init__(path_data=None)

Methods

__init__([path_data])
build_heatmap([roi_data, nb_bins])	Function which return a heatmap using the pdf of each serie.
get_pdf_list([roi_data, nb_bins])	Extract the a list of pdf related with the data.
is_read()	Function to know if the data have been read.
read_data_from_path([path_data])	Function to read temporal images which represent a 3D volume over time.
update_histogram([nb_bins])	Update the histogram of each serie and first-order statistics.

build_heatmap(roi_data=None, nb_bins='auto')

Function which return a heatmap using the pdf of each serie.

Parameters:

roi_data : tuple

Indices of elements to consider while computing the histogram. The ROI is a 3D volume which will be used for each time serie.

nb_bins : int, str or None, optional (default=None)

The numbers of bins to use to compute the histogram. The possibilities are: - If ‘None’, the number of bins found at reading will be used. - If ‘auto’, the number of bins is found at fitting time. - Otherwise, an integer needs to be given.

Returns:

heatmap : ndarray, shape (n_serie, intensity_range)

Return an heatmap of the different pdfs. This equivalent to pdf_series_ but properly shifted inside an array.

bins_heatmap : ndarray, shape (intensity_range, )

Returns the bins associated with the heatmap.

get_pdf_list(roi_data=None, nb_bins='auto')

Extract the a list of pdf related with the data.

Parameters:

roi_data : tuple

Indices of elements to consider while computing the histogram. The ROI is a 3D volume which will be used for each time serie.

nb_bins : list of int or str, optional (default=’auto’)

The numbers of bins to use to compute the histogram. The possibilities are: - If ‘auto’, the number of bins is found at fitting time. - Otherwise, a list of integer needs to be given.

Returns:

pdf_list : list of ndarray, length (n_serie)

List of the pdf with the associated series.

bin_list : list of ndarray, length (n_series + 1)

List of the bins associated with the list of pdf.

is_read()

Function to know if the data have been read.

Returns:

is_read : bool

If True, the data have been read at least once.

read_data_from_path(path_data=None)

Function to read temporal images which represent a 3D volume over time.

Parameters:

path_data : str, list of str, or None, optional (default=None)

Path to the temporal data. It will overrides the path given in the constructor.

Returns:

self : object

Returns self.

update_histogram(nb_bins=None)

Update the histogram of each serie and first-order statistics.

Parameters:

nb_bins : list of int, str, or None, optional (default=None)

The numbers of bins to use to compute the histogram. The possibilities are: - If None, the number of bins found at reading will be used. - If ‘auto’, the number of bins is found at fitting time. - Otherwise, a list of integer needs to be given.

Returns:

self : object

Returns self.