data

Simulate the signal for a Sticks & Ball model.

References

Return arr as native byteordered array

If arr is already native byte ordered, return unchanged. If it is opposite endian, then make a native byte ordered copy and return that

Returns the directory component of a pathname

Download atlas tractogram from the hcp842 dataset with 80 bundles

Download map of FA within two bundles in oneof the hcp dataset subjects

Download 2 subjects from the SNAIL dataset with their bundles

Fetch ‘HCP-like’ data, collected at multiple b-values

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

Download CFIN multi b-value diffusion data

Download a 2-shell software phantom dataset

Download IVIM dataset

fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files Notes —– The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

Copyright (C) 1993-2004, Louis Collins McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies. The authors and McGill University make no representations about the suitability of this software for any purpose. It is provided “as is” without express or implied warranty. The authors are not responsible for any data loss, equipment damage, property loss, or injury to subjects or patients resulting from the use or misuse of this software package.

Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T)

Download a 3shell HARDI dataset with 192 gradient direction

Download a HARDI dataset with 160 gradient directions

Download reduced freesurfer aparc image from stanford web site

Download t1 and b0 volumes from the same session

Download a DSI dataset with 203 gradient directions

Download tractogram of one of the hcp dataset subjects

Download images to be used for tissue classification

Makes a callable, similar to maptlotlib.pyplot.get_cmap

Deprecate function.

provides filenames of some test datasets or other useful parametrisations

Examples

>>> import numpy as np
>>> from dipy.data import get_fnames
>>> fimg,fbvals,fbvecs=get_fnames('small_101D')
>>> bvals=np.loadtxt(fbvals)
>>> bvecs=np.loadtxt(fbvecs).T
>>> import nibabel as nib
>>> img=nib.load(fimg)
>>> data=img.get_data()
>>> data.shape == (6, 10, 10, 102)
True
>>> bvals.shape == (102,)
True
>>> bvecs.shape == (102, 3)
True

provide some simulated voxel data

Notes

These sim voxels were provided by M.M. Correia using Rician noise.

Examples

>>> from dipy.data import get_sim_voxels
>>> sv=get_sim_voxels('fib1')
>>> sv['data'].shape == (100, 102)
True
>>> sv['fibres']
'1'
>>> sv['gradients'].shape == (102, 3)
True
>>> sv['bvals'].shape == (102,)
True
>>> sv['snr']
'60'
>>> sv2=get_sim_voxels('fib2')
>>> sv2['fibres']
'2'
>>> sv2['snr']
'80'

provide skeletons generated from Local Skeleton Clustering (LSC)

Examples

>>> from dipy.data import get_skeleton
>>> C=get_skeleton('C1')
>>> len(C.keys())
117
>>> for c in C: break
>>> sorted(C[c].keys())
['N', 'hidden', 'indices', 'most']

provide triangulated spheres

Examples

>>> import numpy as np
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric362')
>>> verts, faces = sphere.vertices, sphere.faces
>>> verts.shape == (362, 3)
True
>>> faces.shape == (720, 3)
True
>>> verts, faces = get_sphere('not a sphere name') 
Traceback (most recent call last):
    ...
DataError: No sphere called "not a sphere name"

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
2. We assume that the minimum number of b-values is 7.
3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt = gradient_table(bvals, bvecs)
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt = gradient_table(bvals, bvecs.T)
>>> gt.bvecs.shape == bvecs.T.shape
False

Load file given filename, guessing at file type

Spherical functions represented by spherical harmonic coefficients and evaluated on a discrete sphere.

Notes

These coefficients were obtained by using the dwi2SH command of mrtrix.

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.

Read images and streamlines from 2 subjects of the SNAIL dataset

Notes

If you are using these datasets please cite the following publications.

References

K. Whittingstall, “Morphology of thalamus, LGN and optic radiation do not influence EEG alpha waves”, Plos One (under submission), 2015.

M. Descoteaux. Robust and efficient linear registration of fascicles in the space of streamlines , Neuroimage, 117:124-140, 2015.

Read CENIR multi b-value data

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

Load CFIN multi b-value DWI data

Load CFIN T1-weighted data.

Load ISBI 2013 2-shell synthetic dataset

Load IVIM dataset

Read the MNI template from disk

Notes

The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

Copyright (C) 1993-2004, Louis Collins McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies. The authors and McGill University make no representations about the suitability of this software for any purpose. It is provided “as is” without express or implied warranty. The authors are not responsible for any data loss, equipment damage, property loss, or injury to subjects or patients resulting from the use or misuse of this software package.

Examples

Get only the T1 file for version c: >>> T1 = read_mni_template(“c”, contrast = “T1”) # doctest: +SKIP Get both files in this order for version a: >>> T1, T2 = read_mni_template(contrast = [“T1”, “T2”]) # doctest: +SKIP

Load GE 3T b0 image form the scil b0 dataset.

Load Sherbrooke 3-shell HARDI dataset

Load Stanford HARDI dataset

Read stanford hardi data and label map

Load t1 and b0 volumes from the same session

Load Taiwan NTU dataset

Load images to be used for tissue classification

Given a representation of a set of streamlines as a large array and an offsets array return the streamlines as a list of shorter arrays.

Computes the md5 of filename and check if it matches with the supplied string md5

copy data from file-like object fsrc to file-like object fdst

Download atlas tractogram from the hcp842 dataset with 80 bundles

Download map of FA within two bundles in oneof the hcp dataset subjects

Download 2 subjects from the SNAIL dataset with their bundles

Fetch ‘HCP-like’ data, collected at multiple b-values

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

Download CFIN multi b-value diffusion data

Downloads files to folder and checks their md5 checksums

Download a 2-shell software phantom dataset

Download IVIM dataset

fetch the MNI 2009a T1 and T2, and 2009c T1 and T1 mask files Notes —– The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

Copyright (C) 1993-2004, Louis Collins McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies. The authors and McGill University make no representations about the suitability of this software for any purpose. It is provided “as is” without express or implied warranty. The authors are not responsible for any data loss, equipment damage, property loss, or injury to subjects or patients resulting from the use or misuse of this software package.

Downloads test-retest qt-dMRI acquisitions of two C57Bl6 mice.

Download b=0 datasets from multiple MR systems (GE, Philips, Siemens) and different magnetic fields (1.5T and 3T)

Download a 3shell HARDI dataset with 192 gradient direction

Download a HARDI dataset with 160 gradient directions

Download reduced freesurfer aparc image from stanford web site

Download t1 and b0 volumes from the same session

Download a DSI dataset with 203 gradient directions

Download tractogram of one of the hcp dataset subjects

Download images to be used for tissue classification

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
2. We assume that the minimum number of b-values is 7.
3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import gradient_table
>>> bvals = 1500 * np.ones(7)
>>> bvals[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt = gradient_table(bvals, bvecs)
>>> gt.bvecs.shape == bvecs.shape
True
>>> gt = gradient_table(bvals, bvecs.T)
>>> gt.bvecs.shape == bvecs.T.shape
False

A general function for creating diffusion MR gradients.

It reads, loads and prepares scanner parameters like the b-values and b-vectors so that they can be useful during the reconstruction process.

Notes

1. Often b0s (b-values which correspond to images without diffusion weighting) have 0 values however in some cases the scanner cannot provide b0s of an exact 0 value and it gives a bit higher values e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
2. We assume that the minimum number of b-values is 7.
3. B-vectors should be unit vectors.

Examples

>>> from dipy.core.gradients import (
...    gradient_table_from_gradient_strength_bvecs)
>>> gradient_strength = .03e-3 * np.ones(7)  # clinical strength at 30 mT/m
>>> big_delta = .03  # pulse separation of 30ms
>>> small_delta = 0.01  # pulse duration of 10ms
>>> gradient_strength[0] = 0
>>> sq2 = np.sqrt(2) / 2
>>> bvecs = np.array([[0, 0, 0],
...                   [1, 0, 0],
...                   [0, 1, 0],
...                   [0, 0, 1],
...                   [sq2, sq2, 0],
...                   [sq2, 0, sq2],
...                   [0, sq2, sq2]])
>>> gt = gradient_table_from_gradient_strength_bvecs(
...     gradient_strength, bvecs, big_delta, small_delta)

Returns a md5 hash object; optionally initialized with a string

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.

Read images and streamlines from 2 subjects of the SNAIL dataset

Notes

If you are using these datasets please cite the following publications.

References

K. Whittingstall, “Morphology of thalamus, LGN and optic radiation do not influence EEG alpha waves”, Plos One (under submission), 2015.

M. Descoteaux. Robust and efficient linear registration of fascicles in the space of streamlines , Neuroimage, 117:124-140, 2015.

Read b-values and b-vectors from disk

Notes

Files can be either ‘.bvals’/’.bvecs’ or ‘.txt’ or ‘.npy’ (containing arrays stored with the appropriate values).

Read CENIR multi b-value data

Notes

Details of the acquisition and processing, and additional meta-data are available through UW researchworks:

https://digital.lib.washington.edu/researchworks/handle/1773/33311

Load CFIN multi b-value DWI data

Load CFIN T1-weighted data.

Load ISBI 2013 2-shell synthetic dataset

Load IVIM dataset

Read the MNI template from disk

Notes

The templates were downloaded from the MNI (McGill University) website in July 2015.

The following publications should be referenced when using these templates:

Copyright (C) 1993-2004, Louis Collins McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University. Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies. The authors and McGill University make no representations about the suitability of this software for any purpose. It is provided “as is” without express or implied warranty. The authors are not responsible for any data loss, equipment damage, property loss, or injury to subjects or patients resulting from the use or misuse of this software package.

Examples

Get only the T1 file for version c: >>> T1 = read_mni_template(“c”, contrast = “T1”) # doctest: +SKIP Get both files in this order for version a: >>> T1, T2 = read_mni_template(contrast = [“T1”, “T2”]) # doctest: +SKIP

Load test-retest qt-dMRI acquisitions of two C57Bl6 mice. These datasets were used to study test-retest reproducibility of time-dependent q-space indices (q:math:` au`-indices) in the corpus callosum of two mice [1]. The data itself and its details are publicly available and can be cited at [2].

The test-retest diffusion MRI spin echo sequences were acquired from two C57Bl6 wild-type mice on an 11.7 Tesla Bruker scanner. The test and retest acquisition were taken 48 hours from each other. The (processed) data consists of 80x160x5 voxels of size 110x110x500μm. Each data set consists of 515 Diffusion-Weighted Images (DWIs) spread over 35 acquisition shells. The shells are spread over 7 gradient strength shells with a maximum gradient strength of 491 mT/m, 5 pulse separation shells between [10.8 - 20.0]ms, and a pulse length of 5ms. We manually created a brain mask and corrected the data from eddy currents and motion artifacts using FSL’s eddy. A region of interest was then drawn in the middle slice in the corpus callosum, where the tissue is reasonably coherent.

References

Load GE 3T b0 image form the scil b0 dataset.

Load Sherbrooke 3-shell HARDI dataset

Load Siemens 1.5T b0 image form the scil b0 dataset.

Load Stanford HARDI dataset

Read stanford hardi data and label map

Load t1 and b0 volumes from the same session

Load Taiwan NTU dataset

Load images to be used for tissue classification

Show progressbar

Takes a number between 0 and 1 to indicate progress from 0 to 100%.

Open the URL url, which can be either a string or a Request object.

data must be an object specifying additional data to be sent to the server, or None if no such data is needed. See Request for details.

urllib.request module uses HTTP/1.1 and includes a “Connection:close” header in its HTTP requests.

The optional timeout parameter specifies a timeout in seconds for blocking operations like the connection attempt (if not specified, the global default timeout setting will be used). This only works for HTTP, HTTPS and FTP connections.

If context is specified, it must be a ssl.SSLContext instance describing the various SSL options. See HTTPSConnection for more details.

The optional cafile and capath parameters specify a set of trusted CA certificates for HTTPS requests. cafile should point to a single file containing a bundle of CA certificates, whereas capath should point to a directory of hashed certificate files. More information can be found in ssl.SSLContext.load_verify_locations().

The cadefault parameter is ignored.

This function always returns an object which can work as a context manager and has methods such as

• geturl() - return the URL of the resource retrieved, commonly used to determine if a redirect was followed
• info() - return the meta-information of the page, such as headers, in the form of an email.message_from_string() instance (see Quick Reference to HTTP Headers)
• getcode() - return the HTTP status code of the response. Raises URLError on errors.

For HTTP and HTTPS URLs, this function returns a http.client.HTTPResponse object slightly modified. In addition to the three new methods above, the msg attribute contains the same information as the reason attribute — the reason phrase returned by the server — instead of the response headers as it is specified in the documentation for HTTPResponse.

For FTP, file, and data URLs and requests explicitly handled by legacy URLopener and FancyURLopener classes, this function returns a urllib.response.addinfourl object.

Note that None may be returned if no handler handles the request (though the default installed global OpenerDirector uses UnknownHandler to ensure this never happens).

In addition, if proxy settings are detected (for example, when a *_proxy environment variable like http_proxy is set), ProxyHandler is default installed and makes sure the requests are handled through the proxy.