semvec/pypi-packages huggingface dataset:
aowniereweav 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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aowniereweau 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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cj7 0.0.1
A simple way for TCP and UDP communication.
Requires Python: >=3.6

# cj7

A simple way to send TCP and UDP packets in Python without the need for sockets.

### Examples

Sending packets in UDP `sendPacketUDP(ip, port, data, showSent)`

Put the target IP as the first variable, the target port as the second, the data to be sent in the third and either True or False in the last variable for whether or not to print a message when the packet has been sent.

Sending packets in TCP `sendPacketTCP(ip, port, data, showSent)`

Put the target IP as the first variable, the target port as the second, the data to be sent in the third
and either True or False in the last variable for whether or not to print a message when the packet has b
een sent.
aowniereweat 2.0.10
aownierewe
Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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TNQMetro 1.0.0
Tensor-network based package for efficient quantum metrology computations.
Requires Python: >=3.6

# TNQMetro

TNQMetro is a numerical package written in Python 3 for calculations of fundamental quantum bounds on measurement precision. Thanks to the usage of the tensor-network formalism it can beat the curse of dimensionality and provides an efficient framework to calculate bounds for finite size system as well as determine the asymptotic scaling of precision in systems where quantum enhancement amounts to a constant factor improvement over the Standard Quantum Limit. It is written in a user-friendly way so that the basic functions do not require any knowledge of tensor networks.

Introduction to the package alongside simple examples can be found in the paper [arXiv:2107.07644](https://arxiv.org/abs/2107.07644).  
Documentation to the package can be found on the [GitHub wiki](https://github.com/kchabuda/TNQMetro/wiki).  
In-depth explanation of the tensor-network based approach to calculations of fundamental quantum bounds on measurement precision can be found in the paper [Nature Communications **11**, 250 (2020)](https://doi.org/10.1038/s41467-019-13735-9).

## Dependencies

TNQMetro requires [NumPy](https://github.com/numpy/numpy) and [ncon](https://github.com/mhauru/ncon) package.

## Installation

`pip install tnqmetro`

## Example of usage

Example of optimization of QFI using TNQMetro for N=1000 qubits with OBC and in the asymptotic regime for the problem of phase estimation with uncorrelated dephasing noise.

```
import numpy as np
import scipy.linalg
import tnqmetro

N = 1000 # number of sites in tensor-network (in this example one site = one qubit)
d = 2 # dimension of local Hilbert space (dimension of physical index)
h = np.arange(d)
h = np.diag(h) # local generator ("Hamiltonian")
c1 = 1. # uncorrelated noise strength parameter
aux = np.kron(h, np.eye(d)) - np.kron(np.eye(d), h)
Y = scipy.linalg.expm(-c1 * aux @ aux / 2) # local superoperator for uncorrelated dephasing noise

F_f, F_m_f, L_MPO_f, psi_MPS_f = tnqmetro.fin(N, [], h, [Y]) # finite appraoch
F_i, F_m_i, L_MPO_i, psi_MPS_i = tnqmetro.inf([], h, [Y]) # infinite appraoch
```
cmdcolorprint 1.1.1
cmd color print
Requires Python: >=3.9.5

<h1>
cmdcolorprint
</h1>

使い方 How to use

cmdでは

cmdcolorprint test greenで緑色でtestが出力でき、

python内では

import cmdcolorprint

cmdcolorprint.color_print.Color.output_color.cprint_name("test","green")

または

import cmdcolorprint

cmdcolorprint.color_print.Color.output_color.color_print("test",0x0002)

で緑色でtestを出力出来ます。

説明以上!
cj7-jack-debug 0.0.1
A simple way for TCP and UDP communication.
Requires Python: >=3.6

# cj7

A simple way to send TCP and UDP packets in Python without the need for sockets.

### Examples

Sending packets in UDP `sendPacketUDP(ip, port, data, showSent)`
Put the target IP as the first variable, the target port as the second, the data to be sent in the third and either True or False in the last variable for whether or not to print a message when the packet has been sent.

Sending packets in TCP `sendPackettcp(ip, port, data, showSent)`
Put the target IP as the first variable, the target port as the second, the data to be sent in the third
and either True or False in the last variable for whether or not to print a message when the packet has b
een sent.
IPcalculation 1.0.7
Pacote para calculos matematicos
Requires Python: >=3.6

IPcalculation
==============

## Esse é um pacote que faz calculos matemáticos, desmostra a fórmula passo a passo até o resultado.

## Veja o exemplo de resultado do pacote:

##IPcalculation.juros_composto(1000.00, 10, 3)

## saida:
##
## O juros composto é calculado de forma que cada parcela contem o valor do capital
## mais um taxa, onde essa taxa é calculada 1 mais a poncentagem do juros, onde o resultado é
## elevado ao número da parcela atual
##
##
## O montante será a soma de todas as parcelas
## M = C + (1+I)^t ---- 1100.00 = 1000.0 * (1+0.1)^1
## M = C + (1+I)^t ---- 1210.00 = 1000.0 * (1+0.1)^2
## M = C + (1+I)^t ---- 1331.00 = 1000.0 * (1+0.1)^3
## Montante final: 3641.00


## Instalação:

    pip install IPcalculation

## Uso:
 ...

    from IPcalculation import IPcalculation 

    IPcalculation.juros_composto(1000.00, 10, 3)
    IPcalculation.pressao(v,n,r,t)
    IPcalculation.calorimetria(m,c,D0)
validatorPCT3 0.0.1
pacote para validar campos - nome, cpf e telefone
Requires Python: >=3.6

validatorPCT3
============

#### Esse é um pacote que faz a validação de três campos comuns vistos em formulários de cadastro ou de login.


## Instalação:

    pip install validatorPCT3

## Uso:

    import validatorPCT3 as vt
    obj = vt()
    a = obj.cpf(06565436398)
    print(a)
validatorPCT2 0.0.1
pacote para validar campos - nome, cpf e telefone
Requires Python: >=3.6

validatorPCT2
============

#### Esse é um pacote que faz a validação de três campos comuns vistos em formulários de cadastro ou de login.


## Instalação:

    pip install validatorPCT2

## Uso:

    import validatorPCT2 as vt
    obj = vt()
    a = obj.cpf(06565436398)
    print(a)
statistic 0.1.6
projeto contendo varios metodos e funções estatisticos
Requires Python: >=3.6

# Statistic

Esta biblioteca foi desenvolvida com o intuito de unir um grande acerto de cálculos estatísticos

## Desenvolvedores:
- Darice da Rocha Sousa
- Weliton de Sousa Araujo

## cálculos inclusos até o presente momento
- Media
- Media ponderada
- Mediana
- Moda
- Desvio padrão
- Desvio médio
- Variância
- Covariância
- Amplitude
- Erro padrão
- Coeficiente de variação
- Coeficiente de correlação
- Soma dos quadrados dos desvios de dados

[Veja a documentação](https://welitonsousa.vercel.app/post?id=lib-statistic-59a2ce35-a7c9-4aa3-8f5e-a5d632b21d6e)
ridge-significance 1.0
Ridge regression with signficance test through either permutation or t-test

# ridge_significance
Ridge regression with a fast implementation of statistical significance test.

Prerequisite:  
1, python >= 3.6 developer version. We suggest install anaconda (https://www.anaconda.com) to include all required packages.  
2, numpy >= 1.19;  
3, pandas >= 1.1.4;  
4, gcc >= 4.2;  
5, gsl-2.6: https://ftp.gnu.org/gnu/gsl  

Install:  
python setup.py install  

Test:  
python -m unittest tests.regression  

Usage:  
Call the regression function in python code as follows:  
beta, se, zscore, pvalue = ridge_significance.fit(X, Y, alpha, alternative, nrand, verbose). 

Input:  
X: explanatory matrix, numpy matrix in C-contiguous order (last-index varies the fastest).  
Y: response variable, numpy matrix in C-contiguous order (last-index varies the fastest).  

The row dimension of X and Y should be the same. Y input allows multiple columns, with each column as one input response variable. The function will return the same number of output variables in output matrices (beta, se, zscore, pvalue).

alpha: penalty factor in ridge regression (>= 0). If alpha is 0, we will use regular ordinary least square.  
alternative: one-tailed or two-tailed statistical test, with three options: 1, two-sided; 2, greater; 3, less.  
nrand: number of randomizations (>=0). If nrand = 0, we will use student t-test for the statistical test. Otherwise, we will use permutation test.  
verbose: 1 or 0. Report intermediate results.  


Output:  
beta: regression coefficient.  
se: standard error.  
zscore: beta/se.  
pvalue: statistical significance from permutation test (nrand>0) or student t-test (nrand=0).