semvec/pypi-packages huggingface dataset:
requests-openapi 1.0.6
RequestsOpenAPI is a python client library for OpenAPI 3.0
License: MIT

# requests-openapi

[![image](https://img.shields.io/pypi/v/requests-openapi.svg)](https://pypi.org/project/requests-openapi/)
[![image](https://img.shields.io/pypi/l/requests-openapi.svg)](https://pypi.org/project/requests-openapi/)
[![image](https://img.shields.io/pypi/pyversions/requests-openapi.svg)](https://pypi.org/project/requests-openapi/)
[![image](https://raw.githubusercontent.com/wy-z/requests-openapi/master/tests/coverage-badge.svg)](https://github.com/wy-z/requests-openapi)

A lightweight but powerful and easy-to-use Python client library for OpenAPI v3.

## Key Features

- **Lightweight Design**: Experience a minimalistic interface.
- **Focus on Essentials**: Helps you simplify the handling of Paths, Parameters, Headers, Cookies, etc., while inheriting all the capabilities of Requests.
- **Testing and Integration Made Easy**: Whether you're running tests or integrating with other systems, the client simplifies these tasks.

## Usage

```python
import requests_openapi

# load spec from url
c = requests_openapi.Client().load_spec_from_url("https://raw.githubusercontent.com/OAI/OpenAPI-Specification/master/examples/v3.0/petstore.yaml")
# or load from file
c = requests_openapi.Client().load_spec_from_file("openapi.json")
# set server
c.set_server(requests_openapi.Server(url="https://fake.com/api"))

# custom session for auth or others
c.requestor # a instance of requests.Session, see https://requests.readthedocs.io/en/latest/user/advanced/#session-objects
# set update token
c.requestor.headers.update({"Authorization": "token"})

# call api by operation id
resp = c.listPets() # resp: requests.Response
resp.json()
# get by path id
resp = c.showPetById(petId=1)
resp.json()
# post
resp = c.createPets(json={})
resp.json()

#
# Advanced Usage
#

# set req options, 'req_opts' param to custom request options
requests_openapi.Client(req_opts={"timeout": 60}).load_spec_from_file("xx")

# parameters starts with '_' or not found in openapi spec, will be passed through to the requesting
c.createPets(json={}, _headers={}, _params={}, _cookies={})

# parameters
# in: cookie, name: csrftoken
c.createPets(csrftoken="***")
# in: header, name: x-foo
c.createPets(**{"x-foo": "***"})
# in: path, name: userId
c.getUser(userId=1)
# in: query, name: offset
c.listUsers(offset=1)

# http body, just like requests.Session
c.createPets(json={}) or c.createPets(data={})
```

## Installation

```
pip install requests-openapi
```

## License

MIT
multidimensionalks 0.2.21
Multidimensional KS test module in python
License: MIT

# multidimensionalks
Python c extension with method for calculating multidimensional Kolmogorov-Smirnov test

`multidimensionalks.test(rvs, cdf=None, counts_rvs=None, counts_cdf=None, n_jobs=1, permutation_samples=0, draw_samples=False, binomial_significance=False, use_avx=3, max_alpha_beta=True, scale_result=False, deduplicate_data=True, debug=False)`

# Example usage

```python
from multidimensionalks import test
import numpy as np

test(np.array([[1, 2, 3], [1, 3, 2]]), cdf=np.array([[1,2,2]]))
```

# Parameters

* `rvs`: 2-dimensional numpy number array with rows representing `d`-dimensional observations,
* `cdf`: 2-dimensional numpy number array with rows representing second sample`d`-dimensional observations,
* `counts_rvs`: in case of `rvs` having multiple duplicates, an array without duplicates and a separate array of counts can be provided,
* `counts_cdf`: in case of `cdf` having multiple duplicates, an array without duplicates and a separate array of counts can be provided, additionally if `cdf` is not given `counts_cdf` are taken as counts of elements of `rvs` array,
* `n_jobs`: number of threads used during calculation,
* `binomial_significance`: boolean value indicating if statistical significance should be calculated. Defaults to `False`,
* `permutation_samples`: number of times data is shuffled and the statistic value is calculated to estimate pvalue,
* `draw_samples`: boolean value indicating that samples should be drawn with replacement instead of permutating (defaults to permutating),
* `use_avx`: integer value indicating if `AVX` instructions should be used during the calculations. `0` disables avx, `3` means to try the best supported set, `1` will try to use AVX512 instruction set and use no otherwise, `2` will try to use `AVX2`. Defaults to `3`,
* `max_alpha_beta`: boolean value indicating how λ and β values should be combined. Value `True` (default) results in `max(λ, β)`. `(λ+β)/2` is used otherwise.
* `scale_result`: Whether to scale the statistic by $\sqrt{\frac{|rvs|+|cdf|}{|rvs|\times|cdf|}}$ (default False),
* `deduplicate_data`: Whether to deduplicate data points before running the algorithms,
* `debug`: Whether to print debug data to stdout.

# Return value

If no pvalue calculation method is selected returns ks statistic value, otherwise returns a tuple:
* ks statistic,
* pvalue calculated using statistical method if `binomial_significance` is set to `True`,
* pvalue calculated using permutation method if `permutation_samples` is larger than `0`.
paleomix 1.3.10
Bioinformatics pipelines for HTS data
License: MIT
Keywords: pipeline bioinformatics hts phylogeny bam

**********************
The PALEOMIX pipelines
**********************

The PALEOMIX pipelines are a set of pipelines and tools designed to aid the rapid processing of High-Throughput Sequencing (HTS) data: The BAM pipeline processes de-multiplexed reads from one or more samples, through sequence processing and alignment, to generate BAM alignment files useful in downstream analyses; the Phylogenetic pipeline carries out genotyping and phylogenetic inference on BAM alignment files, either produced using the BAM pipeline or generated elsewhere; and the Zonkey pipeline carries out a suite of analyses on low coverage equine alignments, in order to detect the presence of F1-hybrids in archaeological assemblages. In addition, PALEOMIX aids in metagenomic analysis of the extracts.

The pipelines have been designed with ancient DNA (aDNA) in mind, and includes several features especially useful for the analyses of ancient samples, but can all be for the processing of modern samples, in order to ensure consistent data processing.

----------------------
Installation and usage
----------------------

Detailed instructions can be found in the `documentation <http://paleomix.readthedocs.io/>`_ for PALEOMIX. For questions, bug reports, and/or suggestions, please use the `GitHub tracker <https://github.com/MikkelSchubert/paleomix/issues/>`_ or contact Mikkel Schubert at `MikkelSch@gmail.com <mailto:MikkelSch@gmail.com>`_.


---------
Citations
---------

The PALEOMIX pipelines have been published in Nature Protocols; if you make use of PALEOMIX in your work, then please cite

  Schubert M, Ermini L, Sarkissian CD, Jónsson H, Ginolhac A, Schaefer R, Martin MD, Fernández R, Kircher M, McCue M, Willerslev E, and Orlando L. "**Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX**". Nat Protoc. 2014 May;9(5):1056-82. doi: `10.1038/nprot.2014.063 <http://dx.doi.org/10.1038/nprot.2014.063>`_. Epub 2014 Apr 10. PubMed PMID: `24722405 <http://www.ncbi.nlm.nih.gov/pubmed/24722405>`_.

The Zonkey pipeline has been published in Journal of Archaeological Science; if you make use of this pipeline in your work, then please cite

  Schubert M, Mashkour M, Gaunitz C, Fages A, Seguin-Orlando A, Sheikhi S, Alfarhan AH, Alquraishi SA, Al-Rasheid KAS, Chuang R, Ermini L, Gamba C, Weinstock J, Vedat O, and Orlando L. "**Zonkey: A simple, accurate and sensitive pipeline to genetically identify equine F1-hybrids in archaeological assemblages**". Journal of Archaeological Science. 2017 Feb; 78:147-157. doi: `10.1016/j.jas.2016.12.005 <http://dx.doi.org/10.1016/j.jas.2016.12.005>`_.