The main principles behind DIPY development are:
Robustness: the results of a piece of code must be verified systematically, and hence stability and robustness of the code must be ensured, reducing code redundancies.
Readability: the code is written and read by humans, and it is read much more frequently than it is written.
Consistency: following these guidelines will ease reading the code, and will make it less error-prone.
Documentation: document the code. Documentation is essential as it is one of the key points for the adoption of DIPY as the toolkit of choice in diffusion by the scientific community. Documenting helps to clarify certain choices, helps to avoid obscure places, and is a way to allow other members decode it with less effort.
Language: the code must be written in English. Norms and spelling should be abided by.
DIPY uses the standard Python PEP8 style to ensure the readability and consistency across the toolkit. Conformance to the PEP8 syntax is checked automatically when requesting to push to DIPY. There are software systems that will check your code for PEP8 compliance, and most text editors can be configured to check the compliance of your code with PEP8. Beyond the aspects checked, as a contributor to DIPY, you should try to ensure that your code, including comments, conforms to the above principles.
Cython-specific syntax should follow these additional rules:
cimport’s should add the
c prefix to the usual Python import package
cimport numpy as cnp
c prefix to the import line makes it clear that the Cython/C
symbols are being referred to as compared to the Python symbols.
ctypedef statements from the following type by
exactly one space. In turn, separate the type from the variable name by exactly
one space. Declare only one
ctypedef variable per line. You may
cpdef multiple variables per line as long as these are simple declarations;
note that multiple assignment, references, or pointers are not allowed on the
same line. Grouping
cdef statements is allowed. For example:
# Good cdef int n cdef char * s cdef double Xf cdef double d cpdef int i, j, k cdef ClusterMapCentroid clusters = ClusterMapCentroid() cdef: double *ps = <double *> cnp.PyArray_DATA(seed) cnp.npy_intp *pstr = <cnp.npy_intp *> qa.strides cnp.npy_intp d, i, j, cnt double direction double tmp, ftmp cdef int get_direction_c(self, double* point, double* direction): return 1 # Bad cdef char *s cdef char * s, * t, * u, * v cdef double Xf, d cdef double x=42, y=x+1, z=x*y cdef ClusterMapCentroid clusters = ClusterMapCentroid() cdef int get_direction_c(self, double* point, double* direction): return 0
Inside of a function, place all
cdef statements at the top of the function
# Good cdef void estimate_kernel_size(self, verbose=True): cdef: double [:] x double [:] y # Bad cdef void estimate_kernel_size(self, verbose=True): cdef double [:] x self.kernelmax = self.k2(x, y, r, v) cdef double [:] y x = np.array([0., 0., 0.])
cimport’s should follow the same rules defined in PEP8 for
statements. If a module is both imported and cimported, the
should come before the
An example of an imported C library:
from libc.stdlib cimport calloc, realloc, free
Do not use
When declaring an error return value with the
except keyword, use one space on
both sides of the
except. If in a function definition, there should be no
spaces between the error return value and the colon
unless it is needed for functions returning
# Good cdef void bar() except * cdef void c_extract(Feature self, Data2D datum, Data2D out) nogil except *: cdef int front(x) except +: ... # Bad cdef char * hat(x) except *: cdef int front(x) except + : ...
Pointers and references may be either zero or one space away from the type name.
If followed by a variable name, they must be one space away from the variable
name. Do not put any spaces between the reference operator
& and the variable
# Good cdef int& i cdef char * s i = &j # Bad cdef int &i cdef char *s i = & j
When casting a variable there must be no whitespace between the opening
the type. There must one space between the closing
> and the variable:
# Good <float> i <void *> s # Bad < float >i <void*> s
DIPY uses Sphinx to generate documentation. We welcome contributions of examples, and suggestions for changes in the documentation, but please make sure that changes that are introduced render properly into the HTML format that is used for the DIPY website.
DIPY follows the numpy docstring standard for documenting modules, classes, functions, and examples.
The documentation includes an extensive library of
examples. These are Python files that
are stored in the
doc/examples folder and contain code to execute the
example, interleaved with multi-line comments that contain explanations of the
blocks of code. Examples demonstrate how to perform processing (segmentation,
tracking, etc.) on diffusion files using the DIPY classes. The code is
intermixed with generous comments that describe the former, and the rationale
and aim of it. If you are contributing a new feature to DIPY, please provide
an extended example, with explanations of this feature, and references to the
If the feature that you are working on integrates well into one of the
existing examples, please edit the
.py file of that example. Otherwise,
create a new
.py file in that directory. Please also add the name of this
file into the
doc/examples/valid_examples.txt file (which controls the
rendering of these examples into the documentation).
Additionally, DIPY relies on a set of reStructuredText files (
located in the
doc folder. They contain information about theoretical
backgrounds of DIPY, installation instructions, description of the
contribution process, etc.
The Python examples are compiled, output images produced and corresponding
.rst files produced so that the comments can be appropriately displayed
in a web page enriched with images.
Particularly, in order to ease the contribution of examples and
files, and with the consistency criterion in mind, beyond the numpy
docstring standard aspects, contributors are encouraged to observe the
The acronym for the Diffusion Imaging in Python toolkit should be written as DIPY.
The classes, objects, and any other construct referenced from the code should be written with inverted commas, such as in In DIPY, we use an object called ``GradientTable`` which holds all the acquisition specific parameters, e.g. b-values, b-vectors, timings, and others.
Cross-reference related examples and files. Use the
.. _specific_filename: convention to label a file at the top of it.
Thus, other pages will be able to reference the file using the standard
Use an all-caps scheme for acronyms, and capitalize the first letters of the long names, such as in Constrained Spherical Deconvolution (CSD), except in those cases where the most common convention has been to use lowercase, such as in superior longitudinal fasciculus (SLF).
As customary in Python, use lowercase and separate words with underscores for filenames, labels for references, etc.
When including figures, use the regular font for captions (i.e. do not use bold faces) unless otherwise required for a specific text part (e.g. a DIPY object, etc.).
When referring to relative paths, use the backquote inline markup
the convention, such as in
doc/devel. Do not add the
greater-than/less-than signs to enclose the path.
Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I and Dipy Contributors (2014). Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, vol.8, no.8.