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:

1. Detach the process into its own process group.

2. Set process environment appropriate for running inside a chroot.

3. Renounce suid and sgid privileges.

4. Close all open file descriptors.

5. Change the working directory, uid, gid, and umask.

6. Set appropriate signal handlers.

7. Open new file descriptors for stdin, stdout, and stderr.

8. Manage a specified PID lock file.

9. Register cleanup functions for at-exit processing.

This package provides NASA's Coordinated Data Analysis System Web Service Client Library.

The Coordinated Data Analysis System (CDAS) supports simultaneous multi-mission, multi-instrument selection and comparison of science data among a wide range of current space missions. While CDAWeb provides access to this data through an HTML-based user interface, these Web services provides a (Web) application programmming interface (API) to CDAS. If you are not a software developer and simply want to use the existing web (HTML) interface to CDAS, then return to the main CDAWeb page. If you are developing software that requires science data from any of the CDAWeb datasets, then the CDAS Web services will provide access to the data without having to explicitly find,download, and read the data files.

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.

Aurora is an open-source package that robustly estimates single station and remote reference electromagnetic TFs from MT time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container MTH5, which in turn leverages the general mt-metadata framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt-metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.

pynose is a maintained successor of deprecated nose unittest runner. Changes over nose:

• fixes AttributeError: module 'collections' has no attribute 'Callable'

• fixes AttributeError: module 'inspect' has no attribute 'getargspec'

• fixes ImportError: cannot import name '_TextTestResult' from 'unittest'

• fixes RuntimeWarning: TestResult has no addDuration method

• fixes DeprecationWarning: pkg_resources is deprecated as an API

• fixes all flake8 issues from the original nose

• replaces the imp module with the newer importlib module

• the default logging level now hides INFO logs for less noise

• adds --capture-logs for hiding output from all logging levels

• adds --logging-init to use logging.basicConfig(level)

• the -s option is always active to see the output of print()

• adds --capture-output for hiding the output of print()

• adds --co as a shortcut to using --collect-only

Python driver for MongoDB.

Video editing with Python

Documentation at https://melpa.org/#/pythonic

Multidimensional data visualization across files.

Python client library for Core API.

Efficient coalescent simulation in continuous space.

Utility functions for writing pythonic emacs package.

Embedding of configuration information in Python code.

Python Gettext po to mo file compiler.

Documentation at https://melpa.org/#/python-x

Regions is an Astropy package for region handling.

Dirsync is an advanced directory tree synchronisation tool.

Plotext lets you plot directly to the terminal.

Quantitative trait simulation of tree sequence data

Efficient complex trait analyses from ARG.

Infer tree sequences from genetic variation data.

Forward-time population genetic simulation in Python