ikarus is a stepwise machine learning pipeline that tries to cope with a task of distinguishing tumor cells from normal cells. Leveraging multiple annotated single cell datasets it can be used to define a gene set specific to tumor cells. First, the latter gene set is used to rank cells and then to train a logistic classifier for the robust classification of tumor and normal cells. Finally, sensitivity is increased by propagating the cell labels based on a custom cell-cell network. ikarus is tested on multiple single cell datasets to ascertain that it achieves high sensitivity and specificity in multiple experimental contexts.
Python-daemon is a library that assists a Python program to turn itself into a well-behaved Unix daemon process, as specified in PEP 3143.
This library provides a DaemonContext class that manages the following important tasks for becoming a daemon process:
Detach the process into its own process group.
Set process environment appropriate for running inside a chroot.
Renounce suid and sgid privileges.
Close all open file descriptors.
Change the working directory, uid, gid, and umask.
Set appropriate signal handlers.
Open new file descriptors for stdin, stdout, and stderr.
Manage a specified PID lock file.
Register cleanup functions for at-exit processing.
This package implements functionality for simulating X-ray emission from astrophysical sources.
X-rays probe the high-energy universe, from hot galaxy clusters to compact objects such as neutron stars and black holes and many interesting sources in between. pyXSIM makes it possible to generate synthetic X-ray observations of these sources from a wide variety of models, whether from grid-based simulation codes such as FLASH, Enzo, and Athena, to particle-based codes such as Gadget and AREPO, and even from datasets that have been created 'by hand', such as from NumPy arrays. pyXSIM also provides facilities for manipulating the synthetic observations it produces in various ways, as well as ways to export the simulated X-ray events to other software packages to simulate the end products of specific X-ray observatories.
This package implements sparse arrays of arbitrary dimension on top of numpy and scipy.sparse. Sparse array is a matrix in which most of the elements are zero. python-sparse generalizes the scipy.sparse.coo_matrix and scipy.sparse.dok_matrix layouts, but extends beyond just rows and columns to an arbitrary number of dimensions. Additionally, this project maintains compatibility with the numpy.ndarray interface rather than the numpy.matrix interface used in scipy.sparse. These differences make this project useful in certain situations where scipy.sparse matrices are not well suited, but it should not be considered a full replacement. It lacks layouts that are not easily generalized like compressed sparse row/column(CSR/CSC) and depends on scipy.sparse for some computations.
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Python inotify.
Video editing with Python
Implementation of semantic version
Ed25519 public-key signatures
Python driver for MongoDB.
Awesome List used GitHub stars.
Advanced directory tree synchronisation tool.
Promises/A+ implementation for Python
Multidimensional data visualization across files.
Python client library for Core API.
Function decoration for backoff and retry
Multidimensional data visualization across files.
Python implementation of Jean Meeus astronomical routines
Python Gettext po to mo file compiler.
Embedding of configuration information in Python code.
Utility functions for writing pythonic emacs package.
NFC Data Exchange Format decoder and encoder.
A micro unittest suite harness for Python.
Navigate HTTP resources using WADL files as guides.