semvec/pypi-packages huggingface dataset:
BhagwanTopsis 1.1.3
A Topsis package developed by BHAGWAN BANSAL

# Topsis Package
It takes an Datafile(.csv file) as an input along with 2 more arguments(weights ,impacts) along with name for output file(.csv format) as 4th argument.
Pass as a refrences for the DataFile.
Weights and Impacts as List.

## Installation
To proudly pip install your module, fire up a terminal and run:
``Syntax :``
``pip install BhagwanTopsis``



## Importing the Package
``Syntax :``
``import BhagwanTopsis as BT``

## License

Â© 2022 Bhagwan Bansal

This repository is licensed under the MIT license. See LICENSE for details.touch
cupfromriver 0.1.0
xueqiu api python client | 集成雪球API
Keywords: Financial Data,XueQiu,Snow Ball,雪球,Python Api

# pysnowball
===============

## quick start

```python
>>> import pysnowball as ball
>>> ball.set_token('xq_a_token=651af***************031c96a315c;')
'xq_a_token=651af***************031c96a315c;'
>>> ball.cash_flow('SH600000')
```

```json
{
    'data': {
        'quote_name': '浦发银行',
        'currency_name': '人民币',
        'org_type': 2,
        'last_report_name': '2018三季报',
        'currency': 'CNY',
        'list': [{
                    'report_date': 1514649600000,
                    'report_name': '2017年报',
                    'ncf_from_oa': [-140673000000.0, 0.26730141203064695],
                    'ncf_from_ia': [37800000000.0, 1.590486604702023],
                    'ncf_from_fa': [4820000000.0, -0.9794317706599756
                    ]
                }, {
                    'report_date': 1483113600000,
                    'report_name': '2016年报',
                    'ncf_from_oa': [-191993000000.0, -1.5350677219775932],
                    'ncf_from_ia': [-64015000000.0, 0.8794943714468582
                    ],
                    'ncf_from_fa': [234342000000.0, -0.042650194867269654]
                }, {
                    'report_date': 1451491200000,
                    'report_name': '2015年报',
                    'ncf_from_oa': [358820000000.0, 0.8770859707676373],
                    'ncf_from_ia': [-531220000000.0, -0.5512111080287921],
                    'ncf_from_fa': [244782000000.0, 2.357915963619902]
                }, {
                    'report_date': 1419955200000,
                    'report_name': '2014年报',
                    'ncf_from_oa': [191158000000.0, -0.3801741859756295],
                    'ncf_from_ia': [-342455000000.0, -0.08702069578466226],
                    'ncf_from_fa ': [72897000000.0, 4.966967784066173]
                },  {
                    'report_date ': 1388419200000,
                    'report_name ': '2013 年报 ',
                    'ncf_from_oa ': [308406000000.0, 2.3313098144267537], 
                    'ncf_from_ia ': [-315040000000.0, -1.2059925355889953],
                    'ncf_from_fa ': [-18376000000.0, -1.6123496284447998]
                }
            ]}, 
            'error_code ': 0, 
            'error_description': ''
        }
```
origamipy 1.1.2
Python package for analysis, visualization, and support for the LatticeDNAOrigami simulation program.
Requires Python: >=3.6

# Supporting python modules for the LatticeDNAOrigami simulation program

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6414184.svg)](https://doi.org/10.5281/zenodo.6414184)

Python package for analysis, visualization, and support for the LatticeDNAOrigami simulation program.
This package is part of the replication package for Ref. 1.

Some example scripts for creating plots are provided in the [`scripts`](scripts/) directory.

## Installation

This package was developed and used on Linux.
[It is available on the PyPI respository](https://pypi.org/project/origamipy/).
It can be installed by running
```
pip install origamipy
```
If you do not have root access and it does not automatically default to installing locally, the `--user` flag may be used.
To install directly from this repository, run
```
python -m build
pip install dist/origamipy-[current version]-py3-none-any.whl
```
To run the above, it may be necessary to update a few packages:
```
python3 -m pip install --upgrade pip setuptools wheel
```

For more information on building and installing python packages, see the documentation from the [Python Packaging Authority](https://packaging.python.org/en/latest/).

## References

[1] A. Cumberworth, D. Frenkel, and A. Reinhardt, Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers (2022).

## Links

[Paper](https://doi.org/10.48550/arXiv.2204.02499)

[Replication package](https://doi.org/10.5281/zenodo.6414264)

[LatticeDNAOrigami](https://github.com/cumberworth/LatticeDNAOrigami)

[Python Packaging Authority](https://packaging.python.org/en/latest/)
tri-py 0.9.0
A package for efficient loglikelihood evaluation with structured covariance matrices
Requires Python: >=3.6

# tripy
A package for efficient[^1] likelihood evaluation and sampling for Multivariate Normal distributions where the covariance matrix:

* Is Separable, i.e. can be expressed as the [Kronecker product](https://en.wikipedia.org/wiki/Kronecker_product) of the covariance over different dimensions (e.g. space and time);
* May have Exponential correlation (i.e. [(block-) tridiagonal](https://en.wikipedia.org/wiki/Tridiagonal_matrix) precision matrix) in one or more dimensions;
* Is polluted with uncorrelated scalar or vector noise.

[^1]: In the general case, exact likelihood evaluation has *O(N<sup>3</sup>)* computational complexity and *O(N<sup>2</sup>)* memory requirements. The term "efficient" is used here to refer to the reduction of complexity and memory usage by utilizing the sparsity and Kronecker product structure of the covariance matrix.

## Structure
**base**: Base class for problem formulation, taken from [taralli](https://gitlab.com/tno-bim/taralli). Likely to be removed in a future update.

**utils**: Utility functions for efficient linear algebra invovling tridiagonal and Kronecker product matrices.

**loglikelihood**: Functions for efficient loglikelihood evaluation.

**kernels**: Formulation of commonly used kernels.

**sampling**: Functions for efficient sampling.

## Usage
![Usage flowchart](docs/figures/loglikelihood_selection_flowchart.png "Loglikelihood function selection flowchart")

## TODOs
* Validation of all functions against reference implementations.
* Documentation, including examples and timing tests.
* Unit and integration testing.
* Improve this README by including mathematical notation and references.