A Snakemake executor plugin for running SLURM jobs.

Snakemake aims to reduce the complexity of creating workflows by providing a clean and modern domain specific specification language (DSL) in Python style, together with a fast and comfortable execution environment.

This package implements a functionality to create and manipulate plot legends for matplotlib.

Anndata is a package for simple (functional) high-level APIs for data analysis pipelines. In this context, it provides an efficient, scalable way of keeping track of data together with learned annotations and reduces the code overhead typically encountered when using a mostly object-oriented library such as scikit-learn.

PyZX is a Python tool implementing the theory of ZX-calculus for the creation, visualisation, and automated rewriting of large-scale quantum circuits. PyZX currently allows you to:

• Read in quantum circuits in the file format of QASM, Quipper or Quantomatic;

• Rewrite circuits into a pseudo-normal form using the ZX-calculus;

• Extract new simplified circuits from these reduced graphs;

• Visualise the ZX-graphs and rewrites using either Matplotlib, Quantomatic or as a TikZ file for use in LaTeX documents;

• Output the optimised circuits in QASM, QC or QUIPPER format.

QuTiP is a library for simulating the dynamics of closed and open quantum systems. It aims to provide numerical simulations of a wide variety of quantum mechanical problems, including those with Hamiltonians and/or collapse operators with arbitrary time-dependence, commonly found in a wide range of physics applications.

Vector is a Python library for 2D and 3D spatial vectors, as well as 4D space-time vectors. It is especially intended for performing geometric calculations on arrays of vectors, rather than one vector at a time in a Python for loop.

paramz is a lightweight parameterization framework for parameterized model creation and handling. Its features include:

• Easy model creation with parameters.

• Fast optimized access of parameters for optimization routines.

• Memory efficient storage of parameters (only one copy in memory).

• Renaming of parameters.

• Intuitive printing of models and parameters.

• Gradient saving directly inside parameters.

• Gradient checking of parameters.

• Optimization of parameters.

• Jupyter notebook integration.

• Efficient storage of models, for reloading.

• Efficient caching.

This package contains colormaps for commonly-used oceanographic variables. Most of the colormaps started from matplotlib colormaps, but have now been adjusted using the viscm tool to be perceptually uniform.

This package provides a stable interface for interactions between Snakemake and its report plugins.

This package provides a stable interface for interactions between Snakemake and its software deployment plugins.

Histoprint uses a mix of terminal color codes and Unicode trickery (i.e. combining characters) to plot overlaying histograms.

This package provides a framework for building scientific applications. It aims to bring state of the art software design practices to scientific computing, with the goal of providing a strong skeleton on which to build scientific codes by steering the implementation towards usability and maintainability.

This package provides miscellaneous tools for data analysis and scientific computing.

This package contains public type stubs for python-pandas, following the convention of providing stubs in a separate package, as specified in PEP 561. The stubs cover the most typical use cases of python-pandas. In general, these stubs are narrower than what is possibly allowed by python-pandas, but follow a convention of suggesting best recommended practices for using python-pandas.

Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.

Dvc objects provides a filesystem and object-db level abstractions to use in dvc and dvc-data.

Pandas 0.23 added a simple API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by

• adding support for registering methods as well

• making each of these functions backwards compatible with older versions of Pandas

PyAMG is a Python library of Algebraic Multigrid (AMG) solvers. It features implementations of:

• Ruge-Stuben (RS) or Classical AMG

• AMG based on Smoothed Aggregation (SA)

• Adaptive Smoothed Aggregation (αSA)

• Compatible Relaxation (CR)

• Krylov methods such as CG, GMRES, FGMRES, BiCGStab, MINRES, etc.

This package provides a domain-specific language for modeling convex optimization problems in Python.

Pingouin is a statistical package written in Python 3 and based mostly on Pandas and NumPy. Its features include

• ANOVAs: N-ways, repeated measures, mixed, ancova

• Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations

• Robust, partial, distance and repeated measures correlations

• Linear/logistic regression and mediation analysis

• Bayes Factors

• Multivariate tests

• Reliability and consistency

• Effect sizes and power analysis

• Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient

• Circular statistics

• Chi-squared tests

• Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation, and more

SALib provides tools for global sensitivity analysis. It contains Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent and fractional factorial methods.

iminuit is a Jupyter-friendly Python interface for the Minuit2 C++ library maintained by CERN's ROOT team.

Minuit was designed to optimize statistical cost functions, for maximum-likelihood and least-squares fits. It provides the best-fit parameters and error estimates from likelihood profile analysis.

Optionally, Iminuit supports SciPy minimizers as alternatives to Minuit's MIGRAD algorithm and Numba accelerated functions.

This package provides fast numerical derivatives for analytic functions with arbitrary round-off error and error propagation.