data
¶
Module: data.saved_acquisition_schemes
¶
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dmipy.data.saved_acquisition_schemes.
wu_minn_hcp_acquisition_scheme
()¶ Returns DmipyAcquisitionScheme of Wu-Minn HCP project.
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dmipy.data.saved_acquisition_schemes.
duval_cat_spinal_cord_2d_acquisition_scheme
()¶ Returns 2D DmipyAcquisitionScheme of cat spinal cord data.
Module: data.saved_data
¶
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dmipy.data.saved_data.
wu_minn_hcp_coronal_slice
()¶ Returns example slice of Wu-Minn HCP data subject 100307.
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dmipy.data.saved_data.
duval_cat_spinal_cord_2d
()¶ Returns 2D multi-diffusion time AxCaliber data of cat spinal cord.
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dmipy.data.saved_data.
synthetic_camino_data_parallel
()¶ The parallel data was generated using the Camino Monte-Carlo Diffusion Simulator. See http://camino.cs.ucl.ac.uk/.
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dmipy.data.saved_data.
synthetic_camino_data_dispersed
()¶ The dispersed data was generated by using the parallel Camino data as an described above, and then dispersing it using Watson and Bingham distributions.
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dmipy.data.saved_data.
visualize_correlation_camino_and_estimated_fractions
(estim_fractions_parallel, estim_fractions_dispersed)¶ Function that visualizes Camino estimated results versus ground truth.
acquisition_scheme_from_bvalues¶
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dmipy.data.saved_acquisition_schemes.
acquisition_scheme_from_bvalues
(bvalues, gradient_directions, delta, Delta, TE=None, min_b_shell_distance=50000000.0, b0_threshold=10000000.0)¶ Creates an acquisition scheme object from bvalues, gradient directions, pulse duration \(\delta\) and pulse separation time \(\Delta\).
Parameters: bvalues: 1D numpy array of shape (Ndata) :
bvalues of the acquisition in s/m^2. e.g., a bvalue of 1000 s/mm^2 must be entered as 1000 * 1e6 s/m^2
gradient_directions: 2D numpy array of shape (Ndata, 3) :
gradient directions array of cartesian unit vectors.
delta: float or 1D numpy array of shape (Ndata) :
if float, pulse duration of every measurements in seconds. if array, potentially varying pulse duration per measurement.
Delta: float or 1D numpy array of shape (Ndata) :
if float, pulse separation time of every measurements in seconds. if array, potentially varying pulse separation time per measurement.
min_b_shell_distance : float
minimum bvalue distance between different shells. This parameter is used to separate measurements into different shells, which is necessary for any model using spherical convolution or spherical mean.
b0_threshold : float
bvalue threshold for a measurement to be considered a b0 measurement.
Returns: DmipyAcquisitionScheme: acquisition scheme object :
contains all information of the acquisition scheme to be used in any microstructure model.
acquisition_scheme_from_gradient_strengths¶
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dmipy.data.saved_acquisition_schemes.
acquisition_scheme_from_gradient_strengths
(gradient_strengths, gradient_directions, delta, Delta, TE=None, min_b_shell_distance=50000000.0, b0_threshold=10000000.0)¶ Creates an acquisition scheme object from gradient strengths, gradient directions pulse duration \(\delta\) and pulse separation time \(\Delta\).
Parameters: gradient_strengths: 1D numpy array of shape (Ndata) :
gradient strength of the acquisition in T/m. e.g., a gradient strength of 300 mT/m must be entered as 300 / 1e3 T/m
gradient_directions: 2D numpy array of shape (Ndata, 3) :
gradient directions array of cartesian unit vectors.
delta: float or 1D numpy array of shape (Ndata) :
if float, pulse duration of every measurements in seconds. if array, potentially varying pulse duration per measurement.
Delta: float or 1D numpy array of shape (Ndata) :
if float, pulse separation time of every measurements in seconds. if array, potentially varying pulse separation time per measurement.
min_b_shell_distance : float
minimum bvalue distance between different shells. This parameter is used to separate measurements into different shells, which is necessary for any model using spherical convolution or spherical mean.
b0_threshold : float
bvalue threshold for a measurement to be considered a b0 measurement.
Returns: DmipyAcquisitionScheme: acquisition scheme object :
contains all information of the acquisition scheme to be used in any microstructure model.
acquisition_scheme_from_schemefile¶
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dmipy.data.saved_acquisition_schemes.
acquisition_scheme_from_schemefile
(file_path, min_b_shell_distance=50000000.0, b0_threshold=10000000.0)¶ Created an acquisition scheme object from a Camino scheme file, containing gradient directions, strengths, pulse duration \(\delta\) and pulse separation time \(\Delta\) and TE.
Parameters: file_path: string :
absolute file path to schemefile location
Returns: DmipyAcquisitionScheme: acquisition scheme object :
contains all information of the acquisition scheme to be used in any microstructure model.
de_santis_generated_acquisition_scheme¶
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dmipy.data.saved_acquisition_schemes.
de_santis_generated_acquisition_scheme
()¶ Returns 2D DmipyAcquisitionScheme of de Santis.
duval_cat_spinal_cord_2d_acquisition_scheme¶
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dmipy.data.saved_acquisition_schemes.
duval_cat_spinal_cord_2d_acquisition_scheme
() Returns 2D DmipyAcquisitionScheme of cat spinal cord data.
duval_cat_spinal_cord_3d_acquisition_scheme¶
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dmipy.data.saved_acquisition_schemes.
duval_cat_spinal_cord_3d_acquisition_scheme
()¶ Returns 3D DmipyAcquisitionScheme of cat spinal cord data.
isbi2015_white_matter_challenge_scheme¶
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dmipy.data.saved_acquisition_schemes.
isbi2015_white_matter_challenge_scheme
()¶ Returns 35-shell ISBI 2015 challenge DmipyAcquisitionScheme.
join¶
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dmipy.data.saved_acquisition_schemes.
join
(a, *p)¶ Join two or more pathname components, inserting ‘/’ as needed. If any component is an absolute path, all previous path components will be discarded. An empty last part will result in a path that ends with a separator.
panagiotaki_verdict_acquisition_scheme¶
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dmipy.data.saved_acquisition_schemes.
panagiotaki_verdict_acquisition_scheme
()¶ Returns acquisition scheme for VERDICT tumor characterization.
urlopen¶
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dmipy.data.saved_acquisition_schemes.
urlopen
(url, data=None, timeout=<object object>, cafile=None, capath=None, cadefault=False, context=None)¶
wu_minn_hcp_acquisition_scheme¶
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dmipy.data.saved_acquisition_schemes.
wu_minn_hcp_acquisition_scheme
() Returns DmipyAcquisitionScheme of Wu-Minn HCP project.
de_santis_camino_data¶
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dmipy.data.saved_data.
de_santis_camino_data
()¶ Downloads and returns the 4-shell multi-delta/Delta/G scheme based on acquistion scheme defined in [R13]. Note that acquisition parameters in [R13] used for a STEAM sequence, are used here to generate a PGSE one.
Returns: scheme: DmipyAcquisitionScheme instance, :
acquisition scheme of the generated de Santis data.
data_genu: array of size (50, 54), :
contains 50 repetitions with added rician noise SNR=30.
References
[R13] (1, 2, 3) De Santis, S., Jones, D. K., & Roebroeck, A. (2016). Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter. NeuroImage, 130, 91-103.
duval_cat_spinal_cord_2d¶
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dmipy.data.saved_data.
duval_cat_spinal_cord_2d
() Returns 2D multi-diffusion time AxCaliber data of cat spinal cord.
duval_cat_spinal_cord_3d¶
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dmipy.data.saved_data.
duval_cat_spinal_cord_3d
()¶ Returns 2D multi-diffusion time AxCaliber data of cat spinal cord.
isbi2015_white_matter_challenge¶
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dmipy.data.saved_data.
isbi2015_white_matter_challenge
()¶ Downloads and returns the 35-shell multi-delta/Delta/G scheme and data for the fornix and genu data that was used for the ISBI 2015 white matter challenge [R14].
Returns: scheme: DmipyAcquisitionScheme instance, :
acquisition scheme of the challenge data.
data_genu: array of size (3612, 6), :
contains the DWIs for 6 genu voxels.
data_fornix: array of size (3612, 6), :
contains the DWIs for 6 fornix voxels.
References
[R14] (1, 2) Ferizi, Uran, et al. “Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison.” NMR in Biomedicine 30.9 (2017)
join¶
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dmipy.data.saved_data.
join
(a, *p)¶ Join two or more pathname components, inserting ‘/’ as needed. If any component is an absolute path, all previous path components will be discarded. An empty last part will result in a path that ends with a separator.
panagiotaki_verdict¶
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dmipy.data.saved_data.
panagiotaki_verdict
()¶ Downloads and returns the example VERDICT acquisition scheme and data that is available at the UCL website. The data is an example of [R15].
Returns: scheme: DmipyAcquisitionScheme instance, :
acquisition scheme of the challenge data.
data_verdict: array, :
contains the DWIs for a single tumor voxel.
References
[R15] (1, 2) Panagiotaki, Eletheria, et al. “Noninvasive quantification of solid tumor microstructure using VERDICT MRI.” Cancer research 74.7 (2014): 1902-1912.
pearsonr¶
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dmipy.data.saved_data.
pearsonr
(x, y)¶ Calculate a Pearson correlation coefficient and the p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. Strictly speaking, Pearson’s correlation requires that each dataset be normally distributed, and not necessarily zero-mean. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. The p-values are not entirely reliable but are probably reasonable for datasets larger than 500 or so.
Parameters: x : (N,) array_like
Input
y : (N,) array_like
Input
Returns: r : float
Pearson’s correlation coefficient
p-value : float
2-tailed p-value
Notes
The correlation coefficient is calculated as follows:
\[r_{pb} = \frac{\sum (x - m_x) (y - m_y) }{\sqrt{\sum (x - m_x)^2 (y - m_y)^2}}\]where \(m_x\) is the mean of the vector \(x\) and \(m_y\) is the mean of the vector \(y\).
References
http://www.statsoft.com/textbook/glosp.html#Pearson%20Correlation
Examples
>>> from scipy import stats >>> a = np.array([0, 0, 0, 1, 1, 1, 1]) >>> b = np.arange(7) >>> stats.pearsonr(a, b) (0.8660254037844386, 0.011724811003954654)
>>> stats.pearsonr([1,2,3,4,5], [5,6,7,8,7]) (0.83205029433784372, 0.080509573298498519)
synthetic_camino_data_dispersed¶
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dmipy.data.saved_data.
synthetic_camino_data_dispersed
() The dispersed data was generated by using the parallel Camino data as an described above, and then dispersing it using Watson and Bingham distributions.
synthetic_camino_data_parallel¶
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dmipy.data.saved_data.
synthetic_camino_data_parallel
() The parallel data was generated using the Camino Monte-Carlo Diffusion Simulator. See http://camino.cs.ucl.ac.uk/.
urlopen¶
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dmipy.data.saved_data.
urlopen
(url, data=None, timeout=<object object>, cafile=None, capath=None, cadefault=False, context=None)¶
visualize_correlation_camino_and_estimated_fractions¶
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dmipy.data.saved_data.
visualize_correlation_camino_and_estimated_fractions
(estim_fractions_parallel, estim_fractions_dispersed) Function that visualizes Camino estimated results versus ground truth.
wu_minn_hcp_coronal_slice¶
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dmipy.data.saved_data.
wu_minn_hcp_coronal_slice
() Returns example slice of Wu-Minn HCP data subject 100307.