💻 DIPY Coding Style Guideline#

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.

Coding style#

DIPY uses the standard Python PEP8 style to ensure the readability and consistency across the toolkit. DIPY has adopted an automated style checking framework that uses pre-commit and ruff. Style conformance rules are specified in the ruff.toml configuration file. When requesting to push to DIPY, the compliance to the set of rules is automatically checked using a GitHub Actions workflow.

In order to check the compliance locally, developers should install the [style] dependencies:

pip install -e .[style]

The git hook scripts need to be installed then running:

pre-commit install

pre-commit will then run automatically on git commit.

Most text editors can be configured to check the compliance of your code with the set of rules specified in the ruff.toml file. Beyond the aspects checked, as a contributor to DIPY, you should try to ensure that your code, including comments, conforms to the above principles.

Imports#

DIPY recommends using the following package shorthands to increase consistency and readability across the library:

import nibabel as nib
import numpy as np
import numpy.testing as npt
import scipy as sp

No alias should be used for h5py:

import h5py

Cython coding style#

DIPY recommends the use of the standard Python PEP8 style when writing cython code.

Cython-specific syntax should follow these additional rules:

Imports#

The cimport’s should add the c prefix to the usual Python import package shorthand, e.g.:

cimport numpy as cnp

Adding the c prefix to the import line makes it clear that the Cython/C symbols are being referred to as compared to the Python symbols.

Variable declaration#

Separate cdef, cpdef, and 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 cdef or 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[3]
cdef double d[3]
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[3]
    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[3], d[3]
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 body:

# 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.])

Using C libraries#

The cimport’s should follow the same rules defined in PEP8 for import statements. If a module is both imported and cimported, the cimport should come before the import.

An example of an imported C library:

from libc.stdlib cimport calloc, realloc, free

Do not use include statements.

Error return values#

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 :. Avoid except * unless it is needed for functions returning void:

# Good
cdef void bar() except *
cdef void c_extract(Feature self, Data2D datum, Data2D out) noexcept nogil:
cdef int front(x) except +:
    ...

# Bad
cdef char * hat(x) except *:
cdef int front(x)    except   +  :
    ...

Pointers and references#

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 name:

# Good
cdef int& i
cdef char * s
i = &j

# Bad
cdef int &i
cdef char *s
i = & j

Casting#

When casting a variable there must be no whitespace between the opening < and the type. There must one space between the closing > and the variable:

# Good
<float> i
<void *> s

# Bad
< float >i
<void*>  s

Loops#

Use Python loop syntax:

for i in range(nrows):
  ...

Other for-loop constructs are deprecated and must be avoided.

Documentation#

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 relevant papers.

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 (.rst) located in the doc folder. They contain information about theoretical backgrounds of DIPY, installation instructions, description of the contribution process, etc.

Again, both sets of files use the reStructuredText markup language for comments. Sphinx parses the files to produce the contents that are later rendered in the DIPY website.

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 .rst files, and with the consistency criterion in mind, beyond the numpy docstring standard aspects, contributors are encouraged to observe the following guidelines:

  • 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.

  • Cite the relevant papers. Use the [NameYear] convention for cross-referencing them, such as in [Garyfallidis2014], and put them under the References section.

  • 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 Sphinx syntax :ref:`specific_filename`.

  • 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.

References#

[Garyfallidis2014]

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.

On this page