| Name | math-pro JSON |
| Version |
1.11.0
JSON |
| download |
| home_page | https://github.com/PythonSJL/PyPyNum |
| Summary | A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.4] (It is an alias for PyPyNum) |
| upload_time | 2024-07-03 12:43:54 |
| maintainer | None |
| docs_url | None |
| author | Shen Jiayi |
| requires_python | >=3.4 |
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| keywords |
math
数学
mathematics
数学计算
numerical
数值
computation
计算
scientific
科学
algebra
代数
calculus
微积分
statistics
统计
linear-algebra
线性代数
optimization
优化
numerical-analysis
数值分析
matrix
矩阵
vector
向量
tensor
张量
numerics
数值计算
library
库
tools
工具
utils
实用程序
algorithms
算法
software
软件
package
包
methods
方法
data-science
数据科学
machine-learning
机器学习
computational
计算的
operations
操作
functions
函数
processing
处理
programming
编程
simulation
仿真
visualization
可视化
physics
物理
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# <font color = blue>PyPyNum</font>
<font color = gree>A multifunctional mathematical calculation package written in pure Python programming language
</font><font color = red>[Python>=3.4]</font>
```
________ ___ ___ ________ ___ ___ ________ ___ ___ _____ ______
|\ __ \ |\ \ / /||\ __ \ |\ \ / /||\ ___ \ |\ \|\ \ |\ _ \ _ \
\ \ \|\ \\ \ \/ / /\ \ \|\ \\ \ \/ / /\ \ \\ \ \\ \ \\\ \\ \ \\\__\ \ \
\ \ ____\\ \ / / \ \ ____\\ \ / / \ \ \\ \ \\ \ \\\ \\ \ \\|__| \ \
\ \ \___| \/ / / \ \ \___| \/ / / \ \ \\ \ \\ \ \\\ \\ \ \ \ \ \
\ \__\ __/ / / \ \__\ __/ / / \ \__\\ \__\\ \_______\\ \__\ \ \__\
\|__| |\___/ / \|__| |\___/ / \|__| \|__| \|_______| \|__| \|__|
\|___|/ \|___|/
```
[](https://pepy.tech/project/pypynum)
[](https://pepy.tech/project/pypynum)
[](https://pepy.tech/project/pypynum)
## Version -> 1.11.0 | PyPI -> https://pypi.org/project/PyPyNum/ | Gitee -> https://www.gitee.com/PythonSJL/PyPyNum | GitHub -> https://github.com/PythonSJL/PyPyNum
PyPI上无法显示logo，可以在Gitee或者GitHub中查看。
The logo cannot be displayed on PyPI, it can be viewed in Gitee or GitHub.
### 介绍
#### Introduction
+ 多功能数学库，类似于numpy、scipy等，专为PyPy解释器制作，亦支持其他类型的Python解释器
+ Multi functional math library, similar to numpy, scipy, etc., designed specifically for PyPy interpreters and also
supports other types of Python interpreters
+ 不定期更新版本，增加更多实用功能
+ Update versions periodically to add more practical features
+ 如需联系，请添加QQ号2261748025 （Py𝙿𝚢𝚝𝚑𝚘𝚗-水晶兰）
+ If you need to contact, please add QQ number 2261748025 (Py𝙿𝚢𝚝𝚑𝚘𝚗-水晶兰)
### 子模块的名称与功能简介
#### Name and Function Introduction of Submodules
| 子模块名称 Submodule Name | 功能简介 Function Introduction |
|:--------------------:|:------------------------------------------:|
| `pypynum.Array` | 多维数组 Multidimensional array |
| `pypynum.chars` | 特殊数学符号 Special mathematical symbols |
| `pypynum.cipher` | 加密解密算法 Encryption and decryption algorithm |
| `pypynum.constants` | 数学常数集合 Set of mathematical constants |
| `pypynum.dists` | 概率分布 Probability distribution |
| `pypynum.equations` | 方程求解 Solving equations |
| `pypynum.errors` | 异常对象 Exception object |
| `pypynum.file` | 文件读写 File read and write |
| `pypynum.FourierT` | 傅里叶变换 Fourier transform |
| `pypynum.Geometry` | 几何形状 Geometric shape |
| `pypynum.Graph` | 图论算法 Graph Theory Algorithm |
| `pypynum.Group` | 群论算法 Group Theory Algorithm |
| `pypynum.image` | 图像处理 Image processing |
| `pypynum.Logic` | 逻辑电路设计 Logic circuit design |
| `pypynum.maths` | 通用数学函数 General mathematical functions |
| `pypynum.Matrix` | 矩阵运算 Matrix operation |
| `pypynum.NeuralN` | 神经网络训练 Neural network training |
| `pypynum.numbers` | 数字处理 Number processing |
| `pypynum.plotting` | 数据可视化 Data visualization |
| `pypynum.polynomial` | 多项式运算 Polynomial operation |
| `pypynum.Quaternion` | 四元数运算 Quaternion operation |
| `pypynum.random` | 随机数生成 Random number generation |
| `pypynum.regression` | 回归分析 Regression analysis |
| `pypynum.sequence` | 数列计算 Sequence calculation |
| `pypynum.stattest` | 统计检验 Statistical test |
| `pypynum.Symbolics` | 符号计算 Symbol calculation |
| `pypynum.Tensor` | 张量运算 Tensor operation |
| `pypynum.test` | 简易测试 Easy test |
| `pypynum.this` | 项目之禅 Zen of Projects |
| `pypynum.tools` | 辅助函数 Auxiliary functions |
| `pypynum.Tree` | 树形数据结构 Tree data structure |
| `pypynum.types` | 特殊类型 Special types |
| `pypynum.ufuncs` | 通用函数 Universal functions |
| `pypynum.utils` | 实用工具 Utility |
| `pypynum.Vector` | 向量运算 Vector operation |
### PyPyNum的Zen（预览）
#### The Zen of PyPyNum (Preview)
```
The Zen of PyPyNum, by Shen Jiayi
This is a math package written purely in Python.
Elegant is superior to clunky.
Clarity trumps obscurity.
Straightforwardness is preferred over convolution.
Sophisticated is better than overcomplicated.
Flat structure beats nested hierarchies.
Sparse code wins over bloated ones.
```
```
...
Do you want to view all the content?
Enter "from pypynum import this" in your
Python interpreter and run it!
```
```
February 27, 2024
```
### 与上一个版本相比功能变化
#### Functional changes compared to the previous version
```
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
代码增加了约1000行
The code has increased by about
1000 lines
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
删除了maths模块中的一些分布函数
Removed some distribution
functions from the math module
删除了probability模块
The probability module has been
removed
增加了dists模块
Added dists module
增加了stattest模块
Added stattest module
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
<<<新增的函数>>>
<<<New functions added>>>
PyPyNum
├── dists
│ └── FUNCTION
│ ├── beta_pdf(x: Any, a: Any, b: Any) -> Any
│ ├── binom_pmf(k: Any, n: Any, p: Any) -> Any
│ ├── cauchy_cdf(x: Any, x0: Any, gamma: Any) -> Any
│ ├── cauchy_pdf(x: Any, x0: Any, gamma: Any) -> Any
│ ├── chi2_cdf(k: Any, x: Any) -> Any
│ ├── chi2_pdf(x: Any, df: Any) -> Any
│ ├── expon_cdf(x: Any, scale: Any) -> Any
│ ├── expon_pdf(x: Any, scale: Any) -> Any
│ ├── f_pdf(x: Any, dfnum: Any, dfden: Any) -> Any
│ ├── gamma_pdf(x: Any, shape: Any, scale: Any) -> Any
│ ├── geometric_pmf(k: Any, p: Any) -> Any
│ ├── hypergeom_pmf(k: Any, mg: Any, n: Any, nt: Any) -> Any
│ ├── inv_gauss_pdf(x: Any, mu: Any, lambda_: Any, alpha: Any) -> Any
│ ├── levy_pdf(x: Any, c: Any) -> Any
│ ├── log_logistic_cdf(x: Any, alpha: Any, beta: Any) -> Any
│ ├── log_logistic_pdf(x: Any, alpha: Any, beta: Any) -> Any
│ ├── logistic_cdf(x: Any, mu: Any, s: Any) -> Any
│ ├── logistic_pdf(x: Any, mu: Any, s: Any) -> Any
│ ├── lognorm_cdf(x: Any, mu: Any, sigma: Any) -> Any
│ ├── lognorm_pdf(x: Any, s: Any, scale: Any) -> Any
│ ├── logser_pmf(k: Any, p: Any) -> Any
│ ├── multinomial_pmf(k: Any, n: Any, p: Any) -> Any
│ ├── nbinom_pmf(k: Any, n: Any, p: Any) -> Any
│ ├── nhypergeom_pmf(k: Any, m: Any, n: Any, r: Any) -> Any
│ ├── normal_cdf(x: Any, mu: Any, sigma: Any) -> Any
│ ├── normal_pdf(x: Any, mu: Any, sigma: Any) -> Any
│ ├── pareto_pdf(x: Any, k: Any, m: Any) -> Any
│ ├── poisson_pmf(k: Any, mu: Any) -> Any
│ ├── rayleigh_pdf(x: Any, sigma: Any) -> Any
│ ├── t_pdf(x: Any, df: Any) -> Any
│ ├── uniform_cdf(x: Any, loc: Any, scale: Any) -> Any
│ ├── uniform_pdf(x: Any, loc: Any, scale: Any) -> Any
│ ├── vonmises_pdf(x: Any, mu: Any, kappa: Any) -> Any
│ ├── weibull_max_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
│ ├── weibull_min_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
│ └── zipf_pmf(k: Any, s: Any, n: Any) -> Any
├── maths
│ └── FUNCTION
│ ├── bessel_i0(x: Any) -> Any
│ ├── bessel_iv(v: Any, x: Any) -> Any
│ ├── lower_gamma(s: Any, x: Any) -> Any
│ ├── upper_gamma(s: Any, x: Any) -> Any
│ ├── xlogy(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
├── stattest
│ ├── CLASS
│ └── FUNCTION
│ ├── chi2_cont(contingency: list, lambda_: float, calc_p: bool, corr: bool) -> tuple
│ ├── chisquare(observed: list, expected: list) -> tuple
│ ├── kurttest(data: list, two_tailed: bool) -> tuple
│ ├── mediantest(samples: Any, ties: Any, lambda_: Any, corr: Any) -> Any
│ ├── normaltest(data: list) -> tuple
│ └── skewtest(data: list, two_tailed: bool) -> tuple
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
```
### 运行用时测试
#### Run Time Test
Python解释器版本
Python interpreter version
+ CPython 3.8.10
+ PyPy 3.10.12
| 矩阵用时测试<br>Matrix Time Test | NumPy+CPython（seconds） | 排名<br>Ranking | PyPyNum+PyPy（seconds） | 排名<br>Ranking | Mpmath_+_PyPy_（_seconds_） | 排名<br>Ranking | SymPy_+_PyPy_（_seconds_） | 排名<br>Ranking |
|:------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|
| 创建一百阶随机数矩阵<br>Create a hundred order random number matrix | 0.000083 | 1 | 0.005374 | 2 | 0.075253 | 3 | 0.230530 | 4 |
| 创建一千阶随机数矩阵<br>Create a thousand order random number matrix | 0.006740 | 1 | 0.035666 | 2 | 1.200950 | 3 | 4.370265 | 4 |
| 一百阶矩阵相加<br>Addition of matrices of order one hundred | 0.000029 | 1 | 0.002163 | 2 | 0.045641 | 4 | 0.035700 | 3 |
| 一千阶矩阵相加<br>Adding matrices of order one thousand | 0.002647 | 1 | 0.019111 | 2 | 1.746957 | 4 | 0.771542 | 3 |
| 一百阶矩阵行列式<br>Determinant of a hundred order matrix | 0.087209 | 2 | 0.016331 | 1 | 4.354507 | 3 | 5.157206 | 4 |
| 一千阶矩阵行列式<br>Determinant of a thousand order matrix | 0.616113 | 1 | 3.509747 | 2 | It takes a long time | 3 | It takes a long time | 4 |
| 一百阶矩阵求逆<br>Finding the inverse of a hundred order matrix | 0.162770 | 2 | 0.015768 | 1 | 8.162948 | 3 | 21.437424 | 4 |
| 一千阶矩阵求逆<br>Finding the inverse of a thousand order matrix | 0.598905 | 1 | 17.072552 | 2 | It takes a long time | 3 | It takes a long time | 4 |
| 数组输出效果<br>Array output effect | ```[[[[ -7 -67]```<br>```[-78 29]]```<br><br>```[[-86 -97]```<br>```[ 68 -3]]]```<br><br><br>```[[[ 11 42]```<br>```[ 24 -65]]```<br><br>```[[-60 72]```<br>```[ 73 2]]]]``` | / | ```[[[[ 37 83]```<br>```[ 40 2]]```<br><br>```[[ -5 -34]```<br>```[ -7 72]]]```<br><br><br>```[[[ 13 -64]```<br>```[ 6 90]]```<br><br>```[[ 68 57]```<br>```[ 78 11]]]]``` | / | ```[-80.0 -8.0 80.0 -88.0]```<br>```[-99.0 -43.0 87.0 81.0]```<br>```[ 20.0 -55.0 98.0 8.0]```<br>```[ 8.0 44.0 64.0 -35.0]```<br>(只支持矩阵)<br>(Only supports matrices) | / | ```⎡⎡16 -56⎤ ⎡ 8 -28⎤⎤```<br>```⎢⎢ ⎥ ⎢ ⎥⎥```<br>```⎢⎣-56 56 ⎦ ⎣-28 28 ⎦⎥```<br>```⎢ ⎥```<br>```⎢ ⎡-2 7 ⎤ ⎡-18 63 ⎤⎥```<br>```⎢ ⎢ ⎥ ⎢ ⎥⎥```<br>```⎣ ⎣7 -7⎦ ⎣63 -63⎦⎦``` | / |
### 基本结构
#### Basic structure
```
PyPyNum
├── Array
│ ├── CLASS
│ │ └── Array(object)/__init__(self: Any, data: Any, check: Any) -> Any
│ └── FUNCTION
│ ├── array(data: Any) -> Any
│ ├── asarray(data: Any) -> Any
│ ├── aslist(data: Any) -> Any
│ ├── fill(shape: Any, sequence: Any, repeat: Any, pad: Any, rtype: Any) -> Any
│ ├── full(shape: Any, fill_value: Any, rtype: Any) -> Any
│ ├── full_like(a: Any, fill_value: Any, rtype: Any) -> Any
│ ├── get_shape(data: Any) -> Any
│ ├── is_valid_array(_array: Any, _shape: Any) -> Any
│ ├── ones(shape: Any, rtype: Any) -> Any
│ ├── ones_like(a: Any, rtype: Any) -> Any
│ ├── zeros(shape: Any, rtype: Any) -> Any
│ └── zeros_like(a: Any, rtype: Any) -> Any
├── FourierT
│ ├── CLASS
│ │ └── FT1D(object)/__init__(self: Any, data: Any) -> Any
│ └── FUNCTION
├── Geometry
│ ├── CLASS
│ │ ├── Circle(object)/__init__(self: Any, center: typing.Union[list, tuple], radius: typing.Union[int, float]) -> Any
│ │ ├── Line(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple]) -> Any
│ │ ├── Point(object)/__init__(self: Any, p: typing.Union[list, tuple]) -> Any
│ │ ├── Polygon(object)/__init__(self: Any, p: typing.Union[list, tuple]) -> Any
│ │ ├── Quadrilateral(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple], c: typing.Union[list, tuple], d: typing.Union[list, tuple]) -> Any
│ │ └── Triangle(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple], c: typing.Union[list, tuple]) -> Any
│ └── FUNCTION
│ └── distance(g1: Any, g2: Any, error: typing.Union[int, float]) -> float
├── Graph
│ ├── CLASS
│ │ ├── BaseGraph(object)/__init__(self: Any) -> Any
│ │ ├── BaseWeGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any
│ │ ├── DiGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any
│ │ ├── UnGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any
│ │ ├── WeDiGraph(pypynum.Graph.BaseWeGraph)/__init__(self: Any) -> Any
│ │ └── WeUnGraph(pypynum.Graph.BaseWeGraph)/__init__(self: Any) -> Any
│ └── FUNCTION
├── Group
│ ├── CLASS
│ │ └── Group(object)/__init__(self: Any, data: Any) -> Any
│ └── FUNCTION
│ └── group(data: Any) -> Any
├── Logic
│ ├── CLASS
│ │ ├── AND(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── Basic(object)/__init__(self: Any, label: Any) -> Any
│ │ ├── Binary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── COMP(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── DFF(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any, state: Any) -> Any
│ │ ├── FullAdder(pypynum.Logic.Ternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any
│ │ ├── FullSuber(pypynum.Logic.Ternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any
│ │ ├── HalfAdder(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── HalfSuber(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── JKFF(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, state: Any) -> Any
│ │ ├── NAND(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── NOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── NOT(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any) -> Any
│ │ ├── OR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ ├── Quaternary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any
│ │ ├── TFF(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any, state: Any) -> Any
│ │ ├── Ternary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any
│ │ ├── TwoBDiver(pypynum.Logic.Quaternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any
│ │ ├── TwoBMuler(pypynum.Logic.Quaternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any
│ │ ├── Unary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any) -> Any
│ │ ├── XNOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ │ └── XOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
│ └── FUNCTION
│ └── connector(previous: Any, latter: Any) -> Any
├── Matrix
│ ├── CLASS
│ │ └── Matrix(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any
│ └── FUNCTION
│ ├── eigen(matrix: pypynum.Matrix.Matrix) -> tuple
│ ├── hessenberg(matrix: pypynum.Matrix.Matrix) -> tuple
│ ├── identity(n: int) -> pypynum.Matrix.Matrix
│ ├── lu(matrix: pypynum.Matrix.Matrix) -> tuple
│ ├── mat(data: Any) -> Any
│ ├── qr(matrix: pypynum.Matrix.Matrix) -> tuple
│ ├── rotate90(matrix: pypynum.Matrix.Matrix, times: int) -> pypynum.Matrix.Matrix
│ ├── svd(matrix: pypynum.Matrix.Matrix) -> tuple
│ └── tril_indices(n: int, k: int, m: int) -> tuple
├── NeuralN
│ ├── CLASS
│ │ └── NeuralNetwork(object)/__init__(self: Any, _input: Any, _hidden: Any, _output: Any) -> Any
│ └── FUNCTION
│ └── neuraln(_input: Any, _hidden: Any, _output: Any) -> Any
├── Quaternion
│ ├── CLASS
│ │ ├── Euler(object)/__init__(self: Any, y: typing.Union[int, float], p: typing.Union[int, float], r: typing.Union[int, float]) -> Any
│ │ └── Quaternion(object)/__init__(self: Any, w: typing.Union[int, float], x: typing.Union[int, float], y: typing.Union[int, float], z: typing.Union[int, float]) -> Any
│ └── FUNCTION
│ ├── change(data: typing.Union[pypynum.Quaternion.Quaternion, pypynum.Matrix.Matrix, pypynum.Quaternion.Euler], to: str) -> typing.Union[pypynum.Quaternion.Quaternion, pypynum.Matrix.Matrix, pypynum.Quaternion.Euler]
│ ├── euler(yaw: typing.Union[int, float], pitch: typing.Union[int, float], roll: typing.Union[int, float]) -> pypynum.Quaternion.Euler
│ └── quat(w: typing.Union[int, float], x: typing.Union[int, float], y: typing.Union[int, float], z: typing.Union[int, float]) -> pypynum.Quaternion.Quaternion
├── Symbolics
│ ├── CLASS
│ └── FUNCTION
│ └── parse_expr(expr: str) -> list
├── Tensor
│ ├── CLASS
│ │ └── Tensor(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any
│ └── FUNCTION
│ ├── ten(data: list) -> pypynum.Tensor.Tensor
│ ├── tensor_and_number(tensor: Any, operator: Any, number: Any) -> Any
│ ├── tensorproduct(tensors: pypynum.Tensor.Tensor) -> pypynum.Tensor.Tensor
│ ├── zeros(_dimensions: Any) -> Any
│ └── zeros_like(_nested_list: Any) -> Any
├── Tree
│ ├── CLASS
│ │ ├── MultiTree(object)/__init__(self: Any, root: Any) -> Any
│ │ └── MultiTreeNode(object)/__init__(self: Any, data: Any) -> Any
│ └── FUNCTION
├── Vector
│ ├── CLASS
│ │ └── Vector(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any
│ └── FUNCTION
│ └── vec(data: Any) -> Any
├── chars
│ ├── CLASS
│ └── FUNCTION
├── cipher
│ ├── CLASS
│ └── FUNCTION
│ ├── atbash(text: str) -> str
│ ├── base_64(text: str, decrypt: bool) -> str
│ ├── caesar(text: str, shift: int, decrypt: bool) -> str
│ ├── hill256(text: bytes, key: list, decrypt: bool) -> bytes
│ ├── ksa(key: bytes) -> list
│ ├── morse(text: str, decrypt: bool) -> str
│ ├── playfair(text: str, key: str, decrypt: bool) -> str
│ ├── prga(s: list) -> Any
│ ├── rc4(text: bytes, key: bytes) -> bytes
│ ├── rot13(text: str) -> str
│ ├── substitution(text: str, sub_map: dict, decrypt: bool) -> str
│ └── vigenere(text: str, key: str, decrypt: bool) -> str
├── constants
│ ├── CLASS
│ └── FUNCTION
├── dists
│ ├── CLASS
│ └── FUNCTION
│ ├── beta_pdf(x: Any, a: Any, b: Any) -> Any
│ ├── binom_pmf(k: Any, n: Any, p: Any) -> Any
│ ├── cauchy_cdf(x: Any, x0: Any, gamma: Any) -> Any
│ ├── cauchy_pdf(x: Any, x0: Any, gamma: Any) -> Any
│ ├── chi2_cdf(x: Any, df: Any) -> Any
│ ├── chi2_pdf(x: Any, df: Any) -> Any
│ ├── expon_cdf(x: Any, scale: Any) -> Any
│ ├── expon_pdf(x: Any, scale: Any) -> Any
│ ├── f_pdf(x: Any, dfnum: Any, dfden: Any) -> Any
│ ├── gamma_pdf(x: Any, shape: Any, scale: Any) -> Any
│ ├── geometric_pmf(k: Any, p: Any) -> Any
│ ├── hypergeom_pmf(k: Any, mg: Any, n: Any, nt: Any) -> Any
│ ├── inv_gauss_pdf(x: Any, mu: Any, lambda_: Any, alpha: Any) -> Any
│ ├── levy_pdf(x: Any, c: Any) -> Any
│ ├── log_logistic_cdf(x: Any, alpha: Any, beta: Any) -> Any
│ ├── log_logistic_pdf(x: Any, alpha: Any, beta: Any) -> Any
│ ├── logistic_cdf(x: Any, mu: Any, s: Any) -> Any
│ ├── logistic_pdf(x: Any, mu: Any, s: Any) -> Any
│ ├── lognorm_cdf(x: Any, mu: Any, sigma: Any) -> Any
│ ├── lognorm_pdf(x: Any, s: Any, scale: Any) -> Any
│ ├── logser_pmf(k: Any, p: Any) -> Any
│ ├── multinomial_pmf(k: Any, n: Any, p: Any) -> Any
│ ├── nbinom_pmf(k: Any, n: Any, p: Any) -> Any
│ ├── nhypergeom_pmf(k: Any, m: Any, n: Any, r: Any) -> Any
│ ├── normal_cdf(x: Any, mu: Any, sigma: Any) -> Any
│ ├── normal_pdf(x: Any, mu: Any, sigma: Any) -> Any
│ ├── pareto_pdf(x: Any, k: Any, m: Any) -> Any
│ ├── poisson_pmf(k: Any, mu: Any) -> Any
│ ├── rayleigh_pdf(x: Any, sigma: Any) -> Any
│ ├── t_pdf(x: Any, df: Any) -> Any
│ ├── uniform_cdf(x: Any, loc: Any, scale: Any) -> Any
│ ├── uniform_pdf(x: Any, loc: Any, scale: Any) -> Any
│ ├── vonmises_pdf(x: Any, mu: Any, kappa: Any) -> Any
│ ├── weibull_max_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
│ ├── weibull_min_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
│ └── zipf_pmf(k: Any, s: Any, n: Any) -> Any
├── equations
│ ├── CLASS
│ └── FUNCTION
│ ├── lin_eq(left: list, right: list) -> list
│ └── poly_eq(coefficients: list) -> list
├── errors
│ ├── CLASS
│ └── FUNCTION
├── file
│ ├── CLASS
│ └── FUNCTION
│ ├── read(file: str) -> list
│ └── write(file: str, cls: object) -> Any
├── image
│ ├── CLASS
│ │ └── PNG(object)/__init__(self: Any) -> None
│ └── FUNCTION
│ └── crc(data: Any, length: Any, init: Any, xor: Any) -> Any
├── maths
│ ├── CLASS
│ └── FUNCTION
│ ├── arrangement(n: int, r: int) -> int
│ ├── combination(n: int, r: int) -> int
│ ├── acos(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── acosh(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── acot(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── acoth(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── acsc(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── acsch(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── arrangement(n: int, r: int) -> int
│ ├── asec(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── asech(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── asin(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── asinh(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── atan(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── atanh(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── average(data: Any, weights: Any, expected: Any) -> Any
│ ├── bessel_i0(x: Any) -> Any
│ ├── bessel_iv(v: Any, x: Any) -> Any
│ ├── beta(p: typing.Union[int, float], q: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── central_moment(data: typing.Union[list, tuple], order: int) -> float
│ ├── coeff_det(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── combination(n: int, r: int) -> int
│ ├── corr_coeff(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── cos(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── cosh(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── cot(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── coth(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── cov(x: typing.Union[list, tuple], y: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]
│ ├── crt(n: typing.Union[list, tuple], a: typing.Union[list, tuple]) -> int
│ ├── csc(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── csch(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── cumprod(lst: typing.Union[list, tuple]) -> list
│ ├── cumsum(lst: typing.Union[list, tuple]) -> list
│ ├── deriv(f: Any, x: typing.Union[int, float], h: typing.Union[int, float], args: Any, kwargs: Any) -> float
│ ├── erf(x: typing.Union[int, float]) -> float
│ ├── exgcd(a: int, b: int) -> tuple
│ ├── exp(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── factorial(n: int) -> int
│ ├── freq(data: typing.Union[list, tuple]) -> dict
│ ├── gamma(alpha: typing.Union[int, float]) -> float
│ ├── gcd(args: int) -> int
│ ├── geom_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── harm_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── integ(f: Any, x_start: typing.Union[int, float], x_end: typing.Union[int, float], n: int, args: Any, kwargs: Any) -> float
│ ├── iroot(y: int, n: int) -> int
│ ├── is_possibly_square(n: int) -> bool
│ ├── is_square(n: int) -> bool
│ ├── isqrt(x: int) -> int
│ ├── kurt(data: typing.Union[list, tuple], fisher: bool) -> float
│ ├── lcm(args: int) -> int
│ ├── ln(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── lower_gamma(s: Any, x: Any) -> Any
│ ├── mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── median(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── mod_order(a: int, n: int, b: int) -> int
│ ├── mode(data: typing.Union[list, tuple]) -> Any
│ ├── normalize(data: typing.Union[list, tuple], target: typing.Union[int, float, complex]) -> typing.Union[list, tuple]
│ ├── parity(x: int) -> int
│ ├── pi(i: int, n: int, f: Any) -> typing.Union[int, float, complex]
│ ├── primitive_root(a: int, single: bool) -> typing.Union[int, list]
│ ├── product(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── ptp(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── raw_moment(data: typing.Union[list, tuple], order: int) -> float
│ ├── roll(seq: typing.Union[list, tuple, str], shift: int) -> typing.Union[list, tuple, str]
│ ├── root(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
│ ├── sec(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── sech(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── sigma(i: int, n: int, f: Any) -> typing.Union[int, float, complex]
│ ├── sigmoid(x: typing.Union[int, float]) -> float
│ ├── sign(x: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
│ ├── sin(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── sinh(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── skew(data: typing.Union[list, tuple]) -> float
│ ├── square_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── std(numbers: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]
│ ├── sumprod(arrays: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
│ ├── tan(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── tanh(x: typing.Union[int, float]) -> typing.Union[int, float]
│ ├── totient(n: int) -> int
│ ├── upper_gamma(s: Any, x: Any) -> Any
│ ├── var(numbers: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]
│ ├── xlogy(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
│ └── zeta(alpha: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
├── numbers
│ ├── CLASS
│ └── FUNCTION
│ ├── float2fraction(number: float, mixed: bool, error: float) -> tuple
│ ├── int2roman(integer: int, overline: bool) -> str
│ ├── int2words(integer: int) -> str
│ ├── roman2int(roman_num: str) -> int
│ └── str2int(string: str) -> int
├── plotting
│ ├── CLASS
│ └── FUNCTION
│ ├── background(right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], string: bool) -> typing.Union[list, str]
│ ├── binary(function: Any, right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], error: Any, compare: Any, string: bool, basic: list, character: str, data: bool, coloration: Any) -> typing.Union[list, str]
│ ├── c_unary(function: Any, projection: str, right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], string: bool, basic: list, character: str, data: bool, coloration: Any) -> typing.Union[list, str]
│ ├── change(data: typing.Union[list, str]) -> typing.Union[list, str]
│ ├── color(text: str, rgb: typing.Union[list, tuple]) -> str
│ └── unary(function: Any, right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], string: bool, basic: list, character: str, data: bool, coloration: Any) -> typing.Union[list, str]
├── polynomial
│ ├── CLASS
│ │ └── Polynomial(object)/__init__(self: Any, terms: Any) -> Any
│ └── FUNCTION
│ ├── from_coeffs(coeffs: Any) -> Any
│ ├── from_coords(coords: Any) -> Any
│ ├── leggauss(polynomial: Any) -> Any
│ ├── legpoly(n: Any) -> Any
│ └── poly(terms: Any) -> Any
├── random
│ ├── CLASS
│ └── FUNCTION
│ ├── choice(seq: typing.Union[list, tuple, str], shape: typing.Union[list, tuple]) -> Any
│ ├── gauss(mu: typing.Union[int, float], sigma: typing.Union[int, float], shape: typing.Union[list, tuple]) -> typing.Union[float, list]
│ ├── gauss_error(original: typing.Union[list, tuple], mu: typing.Union[int, float], sigma: typing.Union[int, float]) -> list
│ ├── rand(shape: typing.Union[list, tuple]) -> typing.Union[float, list]
│ ├── randint(a: int, b: int, shape: typing.Union[list, tuple]) -> typing.Union[int, list]
│ └── uniform(a: typing.Union[int, float], b: typing.Union[int, float], shape: typing.Union[list, tuple]) -> typing.Union[float, list]
├── regression
│ ├── CLASS
│ └── FUNCTION
│ ├── lin_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> list
│ ├── par_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> list
│ └── poly_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple], n: int) -> list
├── sequence
│ ├── CLASS
│ └── FUNCTION
│ ├── arithmetic_sequence(a1: typing.Union[int, float], an: typing.Union[int, float], d: typing.Union[int, float], n: typing.Union[int, float], s: typing.Union[int, float]) -> dict
│ ├── bernoulli(n: int, single: bool) -> list
│ ├── catalan(n: int, single: bool) -> typing.Union[int, list]
│ ├── farey(n: int) -> list
│ ├── fibonacci(n: int, single: bool) -> typing.Union[int, list]
│ ├── geometric_sequence(a1: typing.Union[int, float], an: typing.Union[int, float], r: typing.Union[int, float], n: typing.Union[int, float], s: typing.Union[int, float]) -> dict
│ └── recaman(n: int, single: bool) -> typing.Union[int, list]
├── stattest
│ ├── CLASS
│ └── FUNCTION
│ ├── chi2_cont(contingency: list, lambda_: float, calc_p: bool, corr: bool) -> tuple
│ ├── chisquare(observed: list, expected: list) -> tuple
│ ├── kurttest(data: list, two_tailed: bool) -> tuple
│ ├── mediantest(samples: Any, ties: Any, lambda_: Any, corr: Any) -> Any
│ ├── normaltest(data: list) -> tuple
│ └── skewtest(data: list, two_tailed: bool) -> tuple
├── test
│ ├── CLASS
│ └── FUNCTION
├── this
│ ├── CLASS
│ └── FUNCTION
├── tools
│ ├── CLASS
│ └── FUNCTION
│ ├── classify(array: typing.Union[list, tuple]) -> dict
│ ├── dedup(iterable: typing.Union[list, tuple, str]) -> typing.Union[list, tuple, str]
│ ├── frange(start: typing.Union[int, float], stop: typing.Union[int, float], step: float) -> list
│ ├── generate_primes(limit: int) -> list
│ ├── generate_semiprimes(limit: int) -> list
│ ├── geomspace(start: typing.Union[int, float], stop: typing.Union[int, float], number: int) -> list
│ ├── interp(data: typing.Union[list, tuple], length: int) -> list
│ ├── linspace(start: typing.Union[int, float], stop: typing.Union[int, float], number: int) -> list
│ ├── magic_square(n: Any) -> Any
│ ├── primality(n: int, iter_num: int) -> bool
│ ├── prime_factors(integer: int, dictionary: bool, pollard_rho: bool) -> typing.Union[list, dict]
│ └── split(iterable: typing.Union[list, tuple, str], key: typing.Union[list, tuple], retain: bool) -> list
├── types
│ ├── CLASS
│ └── FUNCTION
├── ufuncs
│ ├── CLASS
│ └── FUNCTION
│ ├── add(x: Any, y: Any) -> Any
│ ├── base_ufunc(arrays: Any, func: Any, args: Any, rtype: Any) -> Any
│ ├── divide(x: Any, y: Any) -> Any
│ ├── floor_divide(x: Any, y: Any) -> Any
│ ├── modulo(x: Any, y: Any) -> Any
│ ├── multiply(x: Any, y: Any) -> Any
│ ├── power(x: Any, y: Any, m: Any) -> Any
│ ├── subtract(x: Any, y: Any) -> Any
│ └── ufunc_helper(x: Any, y: Any, func: Any) -> Any
└── utils
├── CLASS
│ ├── InfIterator(object)/__init__(self: Any, start: typing.Union[int, float, complex], mode: str, common: typing.Union[int, float, complex]) -> Any
│ ├── LinkedList(object)/__init__(self: Any) -> Any
│ ├── LinkedListNode(object)/__init__(self: Any, value: Any, next_node: Any) -> Any
│ └── OrderedSet(object)/__init__(self: Any, sequence: Any) -> Any
└── FUNCTION
```
### 代码测试
#### Code testing
```python
from pypynum import (Array, Geometry, Logic, Matrix, Quaternion, Symbolics, Tensor, Vector,
cipher, constants, equations, maths, plotting, random, regression, tools)
...
print(Array.array())
print(Array.array([1, 2, 3, 4, 5, 6, 7, 8]))
print(Array.array([[1, 2, 3, 4], [5, 6, 7, 8]]))
print(Array.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]))
"""
[]
[1 2 3 4 5 6 7 8]
[[1 2 3 4]
[5 6 7 8]]
[[[1 2]
[3 4]]
[[5 6]
[7 8]]]
"""
triangle = Geometry.Triangle((0, 0), (2, 2), (3, 0))
print(triangle.perimeter())
print(triangle.area())
print(triangle.centroid())
"""
8.06449510224598
3.0
(1.6666666666666667, 0.6666666666666666)
"""
a, b, c = 1, 1, 1
adder0, adder1 = Logic.HalfAdder("alpha", a, b), Logic.HalfAdder("beta", c, None)
xor0 = Logic.XOR("alpha")
ff0, ff1 = Logic.DFF("alpha"), Logic.DFF("beta")
xor0.set_order0(1)
xor0.set_order1(1)
Logic.connector(adder0, adder1)
Logic.connector(adder0, xor0)
Logic.connector(adder1, xor0)
Logic.connector(adder1, ff0)
Logic.connector(xor0, ff1)
print("sum: {}, carry: {}".format(ff0.out(), ff1.out()))
"""
sum: [1], carry: [1]
"""
m0 = Matrix.mat([[1, 2], [3, 4]])
m1 = Matrix.mat([[5, 6], [7, 8]])
print(m0)
print(m1)
print(m0 + m1)
print(m0 @ m1)
print(m0.inv())
print(m1.rank())
"""
[[1 2]
[3 4]]
[[5 6]
[7 8]]
[[ 6 8]
[10 12]]
[[19 22]
[43 50]]
[[ -1.9999999999999996 0.9999999999999998]
[ 1.4999999999999998 -0.49999999999999994]]
2
"""
q0 = Quaternion.quat(1, 2, 3, 4)
q1 = Quaternion.quat(5, 6, 7, 8)
print(q0)
print(q1)
print(q0 + q1)
print(q0 * q1)
print(q0.inverse())
print(q1.conjugate())
"""
(1+2i+3j+4k)
(5+6i+7j+8k)
(6+8i+10j+12k)
(-60+12i+30j+24k)
(0.18257418583505536+-0.3651483716701107i+-0.5477225575051661j+-0.7302967433402214k)
(5+-6i+-7j+-8k)
"""
print(Symbolics.BASIC)
print(Symbolics.ENGLISH)
print(Symbolics.GREEK)
print(Symbolics.parse_expr("-(10+a-(3.14+b0)*(-5))**(-ζn1-2.718/mΣ99)//9"))
"""
%()*+-./0123456789
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩαβγδεζηθικλμνξοπρστυφχψω
[['10', '+', 'a', '-', ['3.14', '+', 'b0'], '*', '-5'], '**', ['-ζn1', '-', '2.718', '/', 'mΣ99'], '//', '9']
"""
t0 = Tensor.ten([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
t1 = Tensor.ten([[[9, 10], [11, 12]], [[13, 14], [15, 16]]])
print(t0)
print(t1)
print(t0 + t1)
print(t0 @ t1)
"""
[[[1 2]
[3 4]]
[[5 6]
[7 8]]]
[[[ 9 10]
[11 12]]
[[13 14]
[15 16]]]
[[[10 12]
[14 16]]
[[18 20]
[22 24]]]
[[[ 31 34]
[ 71 78]]
[[155 166]
[211 226]]]
"""
string = "PyPyNum"
encrypted = cipher.caesar(string, 10)
print(string)
print(encrypted)
print(cipher.caesar(encrypted, 10, decrypt=True))
encrypted = cipher.vigenere(string, "cipher")
print(string)
print(encrypted)
print(cipher.vigenere(encrypted, "cipher", decrypt=True))
encrypted = cipher.morse(string)
print(string)
print(encrypted)
print(cipher.morse(encrypted, decrypt=True))
"""
PyPyNum
ZiZiXew
PyPyNum
PyPyNum
RgEfRlo
PyPyNum
PyPyNum
.--. -.-- .--. -.-- -. ..- --
PYPYNUM
"""
v0 = Vector.vec([1, 2, 3, 4])
v1 = Vector.vec([5, 6, 7, 8])
print(v0)
print(v1)
print(v0 + v1)
print(v0 @ v1)
print(v0.normalize())
print(v1.angles())
"""
[1 2 3 4]
[5 6 7 8]
[ 5 12 21 32]
70
[0.18257418583505536 0.3651483716701107 0.5477225575051661 0.7302967433402214]
[1.1820279130506308, 1.0985826410133916, 1.0114070854293842, 0.9191723423169716]
"""
print(constants.TB)
print(constants.e)
print(constants.h)
print(constants.phi)
print(constants.pi)
print(constants.tera)
"""
1099511627776
2.718281828459045
6.62607015e-34
1.618033988749895
3.141592653589793
1000000000000
"""
p = [1, -2, -3, 4]
m = [
[
[1, 2, 3],
[6, 10, 12],
[7, 16, 9]
],
[-1, -2, -3]
]
print(equations.poly_eq(p))
print(equations.lin_eq(*m))
"""
[(-1.5615528128088307-6.5209667308287455e-24j) (1.0000000000000007+3.241554513744382e-25j) (2.5615528128088294+4.456233626665941e-24j)]
[ 1.6666666666666667 -0.6666666666666666 -0.4444444444444444]
"""
print(maths.cot(constants.pi / 3))
print(maths.gamma(1.5))
print(maths.pi(1, 10, lambda x: x ** 2))
print(maths.product([2, 3, 5, 7, 11, 13, 17, 19, 23, 29]))
print(maths.sigma(1, 10, lambda x: x ** 2))
print(maths.var([2, 3, 5, 7, 11, 13, 17, 19, 23, 29]))
"""
0.577350269189626
0.886226925452758
13168189440000
6469693230
385
73.29
"""
plt = plotting.unary(lambda x: x ** 2, top=10, bottom=0, character="+")
print(plt)
print(plotting.binary(lambda x, y: x ** 2 + y ** 2 - 10, right=10, left=0, compare="<=", basic=plotting.change(plt)))
print(plotting.c_unary(lambda x: x ** x, right=2, left=-2, top=2, bottom=-2, complexity=20, character="-"))
"""
1.00e+01| + +
|
| + +
|
| + +
| + +
|
| + +
5.00e+00|_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
| + +
| + +
| + +
| + +
| + +
| + +
| + +
| +++ +++
0.00e+00|________________________+++________________________
-5.00e+00 0.00e+00 5.00e+00
1.00e+01| + +
|
| + +
|
|......... + +
|............. +
|..............
|................ +
5.00e+00|................_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
|................ +
|................ +
|.............. + +
|............. + +
|......... + +
| + +
| + +
| +++ +++
0.00e+00|________________________+++________________________
-5.00e+00 0.00e+00 5.00e+00
2.00e+00| - - - - - -
| - - - - - - -
| - - - - - -
|- - - - - - -
| - - - - -- - - - -
| - - - - - - - - -
| - - - - -- - --- -- - -- - - - - -
| - - - -- -- - - - -- - - -
| - - - - - - - -- - --- --- - - --- -- - -
| - - - - - -- ----- -- -- --- -- -- --- -- - -
| - - - ------------ ---- - -- -- - --- - - -
| - - - - - ----- - -- ----------------------- -- ---- - -- --
| - - - - - ---- --------------------------------- - - - - - -
0.00e+00|_ _ _ _ _ _ _ _-_-_-_-_---- ------------------------------------_-- _ _ _ _ _ _ _
| - - - - ----------------------------------------- -- - - - -
| - -- - - -- - - --------------------------------- - - -
| - - ---- - - -- --------------------- ----- ---- - -- -
| - - -- --------- -- -- - ----- --- -- - - - -
| - - - - - - - ---- --- --- --- -- -- --- - - -
| - - - - - -- -- -- - - -- -- --
| - - - -- - -- -- - - -- - -
| - - - - - - - -- - - -- - -
| - - - - -- -- - - - - -
| - - - - - - - -
|- - - - - - - -
| - - - - - -
| - - - - -
-2.00e+00|___________-_________________-___________-_____________________-____________-____
-2.00e+00 0.00e+00 2.00e+00
"""
print(random.gauss(0, 1, [2, 3, 4]))
print(random.rand([2, 3, 4]))
print(random.randint(0, 9, [2, 3, 4]))
print(random.uniform(0, 9, [2, 3, 4]))
"""
[[[1.0022026821190488, -0.38242004448759154, -0.23648445523561967, 0.43813038741951754], [-0.3778652198785619, -0.03865603124657112, -1.5186239424691736, -0.7368762975012327], [-0.7580654190380791, -1.3672869759158346, 0.582588816791107, 1.0281649895276377]], [[0.5270622699930536, 0.6132250709048543, 0.9764619731696673, -0.13740454362420268], [-2.0801461607759886, -0.1935521020633617, 0.44420106801354153, 1.4830089202063659], [-0.8790685594194517, 0.45517163054358967, -1.1448643981658326, 0.986414969442009]]]
[[[0.13698864758140294, 0.634190467772759, 0.25683276170297875, 0.9026812741081188], [0.26303437123782614, 0.02477620234532174, 0.9947822450199725, 0.5916822332583692], [0.7523977891797228, 0.6198410071512576, 0.05799276940261333, 0.4181042411131305]], [[0.21564211884049145, 0.30667940527138227, 0.03010277335333611, 0.904264028183912], [0.33977550248572597, 0.042594462434406455, 0.6371061749651907, 0.8639246364627866], [0.009159271907318911, 0.054475512265855563, 0.7109847662274855, 0.9695933487818381]]]
[[[1, 6, 0, 1], [0, 4, 8, 3], [2, 4, 2, 8]], [[9, 7, 0, 6], [6, 2, 4, 6], [2, 2, 0, 1]]]
[[[4.281963231653285, 7.6564706580977155, 2.7831005401808904, 4.69275453971821], [7.731377457312142, 7.026081604862776, 3.1623746844355916, 4.097454457127405], [1.0053860355938644, 8.396390096875859, 5.860124932392565, 0.7556741321519111]], [[3.0505373562186717, 5.846422325897977, 5.79128924014881, 5.322513543793011], [7.97334322055796, 0.4266873959996582, 6.217219949795519, 2.819046997201407], [7.195256735457888, 3.205909055908082, 2.9903485221015123, 6.695032815286013]]]
"""
print(regression.lin_reg(list(range(5)), [2, 4, 6, 7, 8]))
print(regression.par_reg(list(range(5)), [2, 4, 6, 7, 8]))
print(regression.poly_reg(list(range(5)), [2, 4, 6, 7, 8], 4))
"""
[1.5, 2.4000000000000004]
[-0.21428571428571563, 2.3571428571428625, 1.971428571428569]
[0.08333333333320592, -0.666666666666571, 1.4166666666628345, 1.1666666666688208, 1.9999999999999258]
"""
print(tools.classify([1, 2.3, 4 + 5j, "string", list, True, 3.14, False, tuple, tools]))
print(tools.dedup(["Python", 6, "NumPy", int, "PyPyNum", 9, "pypynum", "NumPy", 6, True]))
print(tools.frange(0, 3, 0.4))
print(tools.linspace(0, 2.8, 8))
"""
{<class 'int'>: [1], <class 'float'>: [2.3, 3.14], <class 'complex'>: [(4+5j)], <class 'str'>: ['string'], <class 'type'>: [<class 'list'>, <class 'tuple'>], <class 'bool'>: [True, False], <class 'module'>: [<module 'pypynum.tools' from 'C:\\Users\\Administrator\\PycharmProjects\\pythonProject\\pypynum\\tools.py'>]}
['Python', 6, 'NumPy', <class 'int'>, 'PyPyNum', 9, 'pypynum', True]
[0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0, 2.4000000000000004, 2.8000000000000003]
[0.0, 0.39999999999999997, 0.7999999999999999, 1.2, 1.5999999999999999, 1.9999999999999998, 2.4, 2.8]
"""
# 提示：
#
# 测试已成功通过并结束。
#
# 这些测试只是这个包功能的一部分。
#
# 更多的功能需要自己探索和尝试！
#
# Tip:
#
# The test has been successfully passed and ended.
#
# These tests are only part of the functionality of this package.
#
# More features need to be explored and tried by yourself!
```
Raw data
{
"_id": null,
"home_page": "https://github.com/PythonSJL/PyPyNum",
"name": "math-pro",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.4",
"maintainer_email": null,
"keywords": "math, \u6570\u5b66, mathematics, \u6570\u5b66\u8ba1\u7b97, numerical, \u6570\u503c, computation, \u8ba1\u7b97, scientific, \u79d1\u5b66, algebra, \u4ee3\u6570, calculus, \u5fae\u79ef\u5206, statistics, \u7edf\u8ba1, linear-algebra, \u7ebf\u6027\u4ee3\u6570, optimization, \u4f18\u5316, numerical-analysis, \u6570\u503c\u5206\u6790, matrix, \u77e9\u9635, vector, \u5411\u91cf, tensor, \u5f20\u91cf, numerics, \u6570\u503c\u8ba1\u7b97, library, \u5e93, tools, \u5de5\u5177, utils, \u5b9e\u7528\u7a0b\u5e8f, algorithms, \u7b97\u6cd5, software, \u8f6f\u4ef6, package, \u5305, methods, \u65b9\u6cd5, data-science, \u6570\u636e\u79d1\u5b66, machine-learning, \u673a\u5668\u5b66\u4e60, computational, \u8ba1\u7b97\u7684, operations, \u64cd\u4f5c, functions, \u51fd\u6570, processing, \u5904\u7406, programming, \u7f16\u7a0b, simulation, \u4eff\u771f, visualization, \u53ef\u89c6\u5316, physics, \u7269\u7406",
"author": "Shen Jiayi",
"author_email": "2261748025@qq.com",
"download_url": "https://files.pythonhosted.org/packages/f0/31/cff015e19946ea44295d2d232ac1852b08fbebbb4d0086645b7bfa741ac5/math_pro-1.11.0.tar.gz",
"platform": null,
"description": "\ufeff# <font color = blue>PyPyNum</font>\r\n\r\n<font color = gree>A multifunctional mathematical calculation package written in pure Python programming language\r\n</font><font color = red>[Python>=3.4]</font>\r\n\r\n```\r\n ________ ___ ___ ________ ___ ___ ________ ___ ___ _____ ______\r\n|\\ __ \\ |\\ \\ / /||\\ __ \\ |\\ \\ / /||\\ ___ \\ |\\ \\|\\ \\ |\\ _ \\ _ \\\r\n\\ \\ \\|\\ \\\\ \\ \\/ / /\\ \\ \\|\\ \\\\ \\ \\/ / /\\ \\ \\\\ \\ \\\\ \\ \\\\\\ \\\\ \\ \\\\\\__\\ \\ \\\r\n \\ \\ ____\\\\ \\ / / \\ \\ ____\\\\ \\ / / \\ \\ \\\\ \\ \\\\ \\ \\\\\\ \\\\ \\ \\\\|__| \\ \\\r\n \\ \\ \\___| \\/ / / \\ \\ \\___| \\/ / / \\ \\ \\\\ \\ \\\\ \\ \\\\\\ \\\\ \\ \\ \\ \\ \\\r\n \\ \\__\\ __/ / / \\ \\__\\ __/ / / \\ \\__\\\\ \\__\\\\ \\_______\\\\ \\__\\ \\ \\__\\\r\n \\|__| |\\___/ / \\|__| |\\___/ / \\|__| \\|__| \\|_______| \\|__| \\|__|\r\n \\|___|/ \\|___|/\r\n```\r\n\r\n[](https://pepy.tech/project/pypynum)\r\n[](https://pepy.tech/project/pypynum)\r\n[](https://pepy.tech/project/pypynum)\r\n\r\n## Version -> 1.11.0 | PyPI -> https://pypi.org/project/PyPyNum/ | Gitee -> https://www.gitee.com/PythonSJL/PyPyNum | GitHub -> https://github.com/PythonSJL/PyPyNum\r\n\r\n\r\n\r\nPyPI\u4e0a\u65e0\u6cd5\u663e\u793alogo\uff0c\u53ef\u4ee5\u5728Gitee\u6216\u8005GitHub\u4e2d\u67e5\u770b\u3002\r\n\r\nThe logo cannot be displayed on PyPI, it can be viewed in Gitee or GitHub.\r\n\r\n### \u4ecb\u7ecd\r\n\r\n#### Introduction\r\n\r\n+ \u591a\u529f\u80fd\u6570\u5b66\u5e93\uff0c\u7c7b\u4f3c\u4e8enumpy\u3001scipy\u7b49\uff0c\u4e13\u4e3aPyPy\u89e3\u91ca\u5668\u5236\u4f5c\uff0c\u4ea6\u652f\u6301\u5176\u4ed6\u7c7b\u578b\u7684Python\u89e3\u91ca\u5668\r\n+ Multi functional math library, similar to numpy, scipy, etc., designed specifically for PyPy interpreters and also\r\n supports other types of Python interpreters\r\n+ \u4e0d\u5b9a\u671f\u66f4\u65b0\u7248\u672c\uff0c\u589e\u52a0\u66f4\u591a\u5b9e\u7528\u529f\u80fd\r\n+ Update versions periodically to add more practical features\r\n+ \u5982\u9700\u8054\u7cfb\uff0c\u8bf7\u6dfb\u52a0QQ\u53f72261748025 \uff08Py\ud835\ude7f\ud835\udea2\ud835\ude9d\ud835\ude91\ud835\ude98\ud835\ude97-\u6c34\u6676\u5170\uff09\r\n+ If you need to contact, please add QQ number 2261748025 (Py\ud835\ude7f\ud835\udea2\ud835\ude9d\ud835\ude91\ud835\ude98\ud835\ude97-\u6c34\u6676\u5170)\r\n\r\n### \u5b50\u6a21\u5757\u7684\u540d\u79f0\u4e0e\u529f\u80fd\u7b80\u4ecb\r\n\r\n#### Name and Function Introduction of Submodules\r\n\r\n| \u5b50\u6a21\u5757\u540d\u79f0 Submodule Name | \u529f\u80fd\u7b80\u4ecb Function Introduction |\r\n|:--------------------:|:------------------------------------------:|\r\n| `pypynum.Array` | \u591a\u7ef4\u6570\u7ec4 Multidimensional array |\r\n| `pypynum.chars` | \u7279\u6b8a\u6570\u5b66\u7b26\u53f7 Special mathematical symbols |\r\n| `pypynum.cipher` | \u52a0\u5bc6\u89e3\u5bc6\u7b97\u6cd5 Encryption and decryption algorithm |\r\n| `pypynum.constants` | \u6570\u5b66\u5e38\u6570\u96c6\u5408 Set of mathematical constants |\r\n| `pypynum.dists` | \u6982\u7387\u5206\u5e03 Probability distribution |\r\n| `pypynum.equations` | \u65b9\u7a0b\u6c42\u89e3 Solving equations |\r\n| `pypynum.errors` | \u5f02\u5e38\u5bf9\u8c61 Exception object |\r\n| `pypynum.file` | \u6587\u4ef6\u8bfb\u5199 File read and write |\r\n| `pypynum.FourierT` | \u5085\u91cc\u53f6\u53d8\u6362 Fourier transform |\r\n| `pypynum.Geometry` | \u51e0\u4f55\u5f62\u72b6 Geometric shape |\r\n| `pypynum.Graph` | \u56fe\u8bba\u7b97\u6cd5 Graph Theory Algorithm |\r\n| `pypynum.Group` | \u7fa4\u8bba\u7b97\u6cd5 Group Theory Algorithm |\r\n| `pypynum.image` | \u56fe\u50cf\u5904\u7406 Image processing |\r\n| `pypynum.Logic` | \u903b\u8f91\u7535\u8def\u8bbe\u8ba1 Logic circuit design |\r\n| `pypynum.maths` | \u901a\u7528\u6570\u5b66\u51fd\u6570 General mathematical functions |\r\n| `pypynum.Matrix` | \u77e9\u9635\u8fd0\u7b97 Matrix operation |\r\n| `pypynum.NeuralN` | \u795e\u7ecf\u7f51\u7edc\u8bad\u7ec3 Neural network training |\r\n| `pypynum.numbers` | \u6570\u5b57\u5904\u7406 Number processing |\r\n| `pypynum.plotting` | \u6570\u636e\u53ef\u89c6\u5316 Data visualization |\r\n| `pypynum.polynomial` | \u591a\u9879\u5f0f\u8fd0\u7b97 Polynomial operation |\r\n| `pypynum.Quaternion` | \u56db\u5143\u6570\u8fd0\u7b97 Quaternion operation |\r\n| `pypynum.random` | \u968f\u673a\u6570\u751f\u6210 Random number generation |\r\n| `pypynum.regression` | \u56de\u5f52\u5206\u6790 Regression analysis |\r\n| `pypynum.sequence` | \u6570\u5217\u8ba1\u7b97 Sequence calculation |\r\n| `pypynum.stattest` | \u7edf\u8ba1\u68c0\u9a8c Statistical test |\r\n| `pypynum.Symbolics` | \u7b26\u53f7\u8ba1\u7b97 Symbol calculation |\r\n| `pypynum.Tensor` | \u5f20\u91cf\u8fd0\u7b97 Tensor operation |\r\n| `pypynum.test` | \u7b80\u6613\u6d4b\u8bd5 Easy test |\r\n| `pypynum.this` | \u9879\u76ee\u4e4b\u7985 Zen of Projects |\r\n| `pypynum.tools` | \u8f85\u52a9\u51fd\u6570 Auxiliary functions |\r\n| `pypynum.Tree` | \u6811\u5f62\u6570\u636e\u7ed3\u6784 Tree data structure |\r\n| `pypynum.types` | \u7279\u6b8a\u7c7b\u578b Special types |\r\n| `pypynum.ufuncs` | \u901a\u7528\u51fd\u6570 Universal functions |\r\n| `pypynum.utils` | \u5b9e\u7528\u5de5\u5177 Utility |\r\n| `pypynum.Vector` | \u5411\u91cf\u8fd0\u7b97 Vector operation |\r\n\r\n### PyPyNum\u7684Zen\uff08\u9884\u89c8\uff09\r\n\r\n#### The Zen of PyPyNum (Preview)\r\n\r\n```\r\n The Zen of PyPyNum, by Shen Jiayi\r\n\r\nThis is a math package written purely in Python.\r\n\r\nElegant is superior to clunky.\r\nClarity trumps obscurity.\r\nStraightforwardness is preferred over convolution.\r\nSophisticated is better than overcomplicated.\r\nFlat structure beats nested hierarchies.\r\nSparse code wins over bloated ones.\r\n```\r\n\r\n```\r\n...\r\n\r\nDo you want to view all the content?\r\n\r\nEnter \"from pypynum import this\" in your\r\n\r\nPython interpreter and run it!\r\n```\r\n\r\n```\r\n February 27, 2024\r\n```\r\n\r\n### \u4e0e\u4e0a\u4e00\u4e2a\u7248\u672c\u76f8\u6bd4\u529f\u80fd\u53d8\u5316\r\n\r\n#### Functional changes compared to the previous version\r\n\r\n```\r\n!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=\r\n\r\n\u4ee3\u7801\u589e\u52a0\u4e86\u7ea61000\u884c\r\n\r\nThe code has increased by about\r\n1000 lines\r\n\r\n!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=\r\n\r\n\u5220\u9664\u4e86maths\u6a21\u5757\u4e2d\u7684\u4e00\u4e9b\u5206\u5e03\u51fd\u6570\r\nRemoved some distribution\r\nfunctions from the math module\r\n\r\n\u5220\u9664\u4e86probability\u6a21\u5757\r\nThe probability module has been\r\nremoved\r\n\r\n\u589e\u52a0\u4e86dists\u6a21\u5757\r\nAdded dists module\r\n\r\n\u589e\u52a0\u4e86stattest\u6a21\u5757\r\nAdded stattest module\r\n\r\n!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=\r\n\r\n<<<\u65b0\u589e\u7684\u51fd\u6570>>>\r\n\r\n<<<New functions added>>>\r\n\r\nPyPyNum\r\n\u251c\u2500\u2500 dists\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 beta_pdf(x: Any, a: Any, b: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 binom_pmf(k: Any, n: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 cauchy_cdf(x: Any, x0: Any, gamma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 cauchy_pdf(x: Any, x0: Any, gamma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 chi2_cdf(k: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 chi2_pdf(x: Any, df: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 expon_cdf(x: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 expon_pdf(x: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 f_pdf(x: Any, dfnum: Any, dfden: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 gamma_pdf(x: Any, shape: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 geometric_pmf(k: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 hypergeom_pmf(k: Any, mg: Any, n: Any, nt: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 inv_gauss_pdf(x: Any, mu: Any, lambda_: Any, alpha: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 levy_pdf(x: Any, c: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 log_logistic_cdf(x: Any, alpha: Any, beta: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 log_logistic_pdf(x: Any, alpha: Any, beta: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 logistic_cdf(x: Any, mu: Any, s: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 logistic_pdf(x: Any, mu: Any, s: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 lognorm_cdf(x: Any, mu: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 lognorm_pdf(x: Any, s: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 logser_pmf(k: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 multinomial_pmf(k: Any, n: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 nbinom_pmf(k: Any, n: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 nhypergeom_pmf(k: Any, m: Any, n: Any, r: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 normal_cdf(x: Any, mu: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 normal_pdf(x: Any, mu: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 pareto_pdf(x: Any, k: Any, m: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 poisson_pmf(k: Any, mu: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 rayleigh_pdf(x: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 t_pdf(x: Any, df: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 uniform_cdf(x: Any, loc: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 uniform_pdf(x: Any, loc: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 vonmises_pdf(x: Any, mu: Any, kappa: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 weibull_max_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 weibull_min_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 zipf_pmf(k: Any, s: Any, n: Any) -> Any\r\n\u251c\u2500\u2500 maths\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 bessel_i0(x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 bessel_iv(v: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 lower_gamma(s: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 upper_gamma(s: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 xlogy(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]\r\n\u251c\u2500\u2500 stattest\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 chi2_cont(contingency: list, lambda_: float, calc_p: bool, corr: bool) -> tuple\r\n\u2502 \u251c\u2500\u2500 chisquare(observed: list, expected: list) -> tuple\r\n\u2502 \u251c\u2500\u2500 kurttest(data: list, two_tailed: bool) -> tuple\r\n\u2502 \u251c\u2500\u2500 mediantest(samples: Any, ties: Any, lambda_: Any, corr: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 normaltest(data: list) -> tuple\r\n\u2502 \u2514\u2500\u2500 skewtest(data: list, two_tailed: bool) -> tuple\r\n!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=\r\n```\r\n\r\n### \u8fd0\u884c\u7528\u65f6\u6d4b\u8bd5\r\n\r\n#### Run Time Test\r\n\r\nPython\u89e3\u91ca\u5668\u7248\u672c\r\n\r\nPython interpreter version\r\n\r\n+ CPython 3.8.10\r\n\r\n+ PyPy 3.10.12\r\n\r\n| \u77e9\u9635\u7528\u65f6\u6d4b\u8bd5<br>Matrix Time Test | NumPy\ufeff+\ufeffCPython\ufeff\uff08\ufeffseconds\ufeff\uff09 | \u6392\u540d<br>Ranking | PyPyNum\ufeff+\ufeffPyPy\ufeff\uff08\ufeffseconds\ufeff\uff09 | \u6392\u540d<br>Ranking | Mpmath\ufeff_\ufeff+\ufeff_\ufeffPyPy\ufeff_\ufeff\uff08\ufeff_\ufeffseconds\ufeff_\ufeff\uff09 | \u6392\u540d<br>Ranking | SymPy\ufeff_\ufeff+\ufeff_\ufeffPyPy\ufeff_\ufeff\uff08\ufeff_\ufeffseconds\ufeff_\ufeff\uff09 | \u6392\u540d<br>Ranking |\r\n|:------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------:|\r\n| \u521b\ufeff\u5efa\ufeff\u4e00\ufeff\u767e\ufeff\u9636\ufeff\u968f\ufeff\u673a\ufeff\u6570\ufeff\u77e9\ufeff\u9635<br>Create a hundred order random number matrix | 0.000083 | 1 | 0.005374 | 2 | 0.075253 | 3 | 0.230530 | 4 |\r\n| \u521b\u5efa\u4e00\u5343\u9636\u968f\u673a\u6570\u77e9\u9635<br>Create a thousand order random number matrix | 0.006740 | 1 | 0.035666 | 2 | 1.200950 | 3 | 4.370265 | 4 |\r\n| \u4e00\u767e\u9636\u77e9\u9635\u76f8\u52a0<br>Addition of matrices of order one hundred | 0.000029 | 1 | 0.002163 | 2 | 0.045641 | 4 | 0.035700 | 3 |\r\n| \u4e00\u5343\u9636\u77e9\u9635\u76f8\u52a0<br>Adding matrices of order one thousand | 0.002647 | 1 | 0.019111 | 2 | 1.746957 | 4 | 0.771542 | 3 |\r\n| \u4e00\u767e\u9636\u77e9\u9635\u884c\u5217\u5f0f<br>Determinant of a hundred order matrix | 0.087209 | 2 | 0.016331 | 1 | 4.354507 | 3 | 5.157206 | 4 |\r\n| \u4e00\u5343\u9636\u77e9\u9635\u884c\u5217\u5f0f<br>Determinant of a thousand order matrix | 0.616113 | 1 | 3.509747 | 2 | It takes a long time | 3 | It takes a long time | 4 |\r\n| \u4e00\u767e\u9636\u77e9\u9635\u6c42\u9006<br>Finding the inverse of a hundred order matrix | 0.162770 | 2 | 0.015768 | 1 | 8.162948 | 3 | 21.437424 | 4 |\r\n| \u4e00\u5343\u9636\u77e9\u9635\u6c42\u9006<br>Finding the inverse of a thousand order matrix | 0.598905 | 1 | 17.072552 | 2 | It takes a long time | 3 | It takes a long time | 4 |\r\n| \u6570\u7ec4\u8f93\u51fa\u6548\u679c<br>Array output effect | ```[[[[\u2002-7\u2002-67]```<br>```[-78\u2002\u200229]]```<br><br>```[[-86\u2002-97]```<br>```[\u200268\u2002\u2002-3]]]```<br><br><br>```[[[\u200211\u2002\u200242]```<br>```[\u200224\u2002-65]]```<br><br>```[[-60\u2002\u200272]```<br>```[\u200273\u2002\u2002\u20022]]]]``` | / | ```[[[[\u200237\u2002\u200283]```<br>```[\u200240\u2002\u2002\u20022]]```<br><br>```[[\u2002-5\u2002-34]```<br>```[\u2002-7\u2002\u200272]]]```<br><br><br>```[[[\u200213\u2002-64]```<br>```[\u2002\u20026\u2002\u200290]]```<br><br>```[[\u200268\u2002\u200257]```<br>```[\u200278\u2002\u200211]]]]``` | / | ```[-80.0\u2002\u2002\u2002-8.0\u2002\u200280.0\u2002\u2002-88.0]```<br>```[-99.0\u2002\u2002-43.0\u2002\u200287.0\u2002\u2002\u200281.0]```<br>```[\u200220.0\u2002\u2002-55.0\u2002\u200298.0\u2002\u2002\u2002\u20028.0]```<br>```[\u2002\u20028.0\u2002\u2002\u200244.0\u2002\u200264.0\u2002\u2002-35.0]```<br>(\u53ea\u652f\u6301\u77e9\u9635)<br>(Only supports matrices) | / | ```\u23a1\u23a116\u2002\u2002\u2002-56\u23a4\u2002\u2002\u23a1\u20028\u2002\u2002\u2002-28\u23a4\u23a4```<br>```\u23a2\u23a2\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u23a5\u2002\u2002\u23a2\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u23a5\u23a5```<br>```\u23a2\u23a3-56\u2002\u200256\u2002\u23a6\u2002\u2002\u23a3-28\u2002\u200228\u2002\u23a6\u23a5```<br>```\u23a2\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u23a5```<br>```\u23a2\u2002\u23a1-2\u2002\u20027\u2002\u23a4\u2002\u2002\u2002\u23a1-18\u2002\u200263\u2002\u23a4\u23a5```<br>```\u23a2\u2002\u23a2\u2002\u2002\u2002\u2002\u2002\u2002\u23a5\u2002\u2002\u2002\u23a2\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u2002\u23a5\u23a5```<br>```\u23a3\u2002\u23a37\u2002\u2002\u2002-7\u23a6\u2002\u2002\u2002\u23a363\u2002\u2002\u2002-63\u23a6\u23a6``` | / |\r\n\r\n### \u57fa\u672c\u7ed3\u6784\r\n\r\n#### Basic structure\r\n\r\n```\r\nPyPyNum\r\n\u251c\u2500\u2500 Array\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 Array(object)/__init__(self: Any, data: Any, check: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 array(data: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 asarray(data: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 aslist(data: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 fill(shape: Any, sequence: Any, repeat: Any, pad: Any, rtype: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 full(shape: Any, fill_value: Any, rtype: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 full_like(a: Any, fill_value: Any, rtype: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 get_shape(data: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 is_valid_array(_array: Any, _shape: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 ones(shape: Any, rtype: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 ones_like(a: Any, rtype: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 zeros(shape: Any, rtype: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 zeros_like(a: Any, rtype: Any) -> Any\r\n\u251c\u2500\u2500 FourierT\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 FT1D(object)/__init__(self: Any, data: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 Geometry\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u251c\u2500\u2500 Circle(object)/__init__(self: Any, center: typing.Union[list, tuple], radius: typing.Union[int, float]) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Line(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple]) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Point(object)/__init__(self: Any, p: typing.Union[list, tuple]) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Polygon(object)/__init__(self: Any, p: typing.Union[list, tuple]) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Quadrilateral(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple], c: typing.Union[list, tuple], d: typing.Union[list, tuple]) -> Any\r\n\u2502 \u2502 \u2514\u2500\u2500 Triangle(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple], c: typing.Union[list, tuple]) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 distance(g1: Any, g2: Any, error: typing.Union[int, float]) -> float\r\n\u251c\u2500\u2500 Graph\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u251c\u2500\u2500 BaseGraph(object)/__init__(self: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 BaseWeGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 DiGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 UnGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 WeDiGraph(pypynum.Graph.BaseWeGraph)/__init__(self: Any) -> Any\r\n\u2502 \u2502 \u2514\u2500\u2500 WeUnGraph(pypynum.Graph.BaseWeGraph)/__init__(self: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 Group\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 Group(object)/__init__(self: Any, data: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 group(data: Any) -> Any\r\n\u251c\u2500\u2500 Logic\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u251c\u2500\u2500 AND(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Basic(object)/__init__(self: Any, label: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Binary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 COMP(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 DFF(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any, state: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 FullAdder(pypynum.Logic.Ternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 FullSuber(pypynum.Logic.Ternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 HalfAdder(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 HalfSuber(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 JKFF(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, state: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 NAND(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 NOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 NOT(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 OR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Quaternary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 TFF(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any, state: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Ternary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 TwoBDiver(pypynum.Logic.Quaternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 TwoBMuler(pypynum.Logic.Quaternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 Unary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any) -> Any\r\n\u2502 \u2502 \u251c\u2500\u2500 XNOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2502 \u2514\u2500\u2500 XOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 connector(previous: Any, latter: Any) -> Any\r\n\u251c\u2500\u2500 Matrix\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 Matrix(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 eigen(matrix: pypynum.Matrix.Matrix) -> tuple\r\n\u2502 \u251c\u2500\u2500 hessenberg(matrix: pypynum.Matrix.Matrix) -> tuple\r\n\u2502 \u251c\u2500\u2500 identity(n: int) -> pypynum.Matrix.Matrix\r\n\u2502 \u251c\u2500\u2500 lu(matrix: pypynum.Matrix.Matrix) -> tuple\r\n\u2502 \u251c\u2500\u2500 mat(data: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 qr(matrix: pypynum.Matrix.Matrix) -> tuple\r\n\u2502 \u251c\u2500\u2500 rotate90(matrix: pypynum.Matrix.Matrix, times: int) -> pypynum.Matrix.Matrix\r\n\u2502 \u251c\u2500\u2500 svd(matrix: pypynum.Matrix.Matrix) -> tuple\r\n\u2502 \u2514\u2500\u2500 tril_indices(n: int, k: int, m: int) -> tuple\r\n\u251c\u2500\u2500 NeuralN\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 NeuralNetwork(object)/__init__(self: Any, _input: Any, _hidden: Any, _output: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 neuraln(_input: Any, _hidden: Any, _output: Any) -> Any\r\n\u251c\u2500\u2500 Quaternion\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u251c\u2500\u2500 Euler(object)/__init__(self: Any, y: typing.Union[int, float], p: typing.Union[int, float], r: typing.Union[int, float]) -> Any\r\n\u2502 \u2502 \u2514\u2500\u2500 Quaternion(object)/__init__(self: Any, w: typing.Union[int, float], x: typing.Union[int, float], y: typing.Union[int, float], z: typing.Union[int, float]) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 change(data: typing.Union[pypynum.Quaternion.Quaternion, pypynum.Matrix.Matrix, pypynum.Quaternion.Euler], to: str) -> typing.Union[pypynum.Quaternion.Quaternion, pypynum.Matrix.Matrix, pypynum.Quaternion.Euler]\r\n\u2502 \u251c\u2500\u2500 euler(yaw: typing.Union[int, float], pitch: typing.Union[int, float], roll: typing.Union[int, float]) -> pypynum.Quaternion.Euler\r\n\u2502 \u2514\u2500\u2500 quat(w: typing.Union[int, float], x: typing.Union[int, float], y: typing.Union[int, float], z: typing.Union[int, float]) -> pypynum.Quaternion.Quaternion\r\n\u251c\u2500\u2500 Symbolics\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 parse_expr(expr: str) -> list\r\n\u251c\u2500\u2500 Tensor\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 Tensor(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 ten(data: list) -> pypynum.Tensor.Tensor\r\n\u2502 \u251c\u2500\u2500 tensor_and_number(tensor: Any, operator: Any, number: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 tensorproduct(tensors: pypynum.Tensor.Tensor) -> pypynum.Tensor.Tensor\r\n\u2502 \u251c\u2500\u2500 zeros(_dimensions: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 zeros_like(_nested_list: Any) -> Any\r\n\u251c\u2500\u2500 Tree\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u251c\u2500\u2500 MultiTree(object)/__init__(self: Any, root: Any) -> Any\r\n\u2502 \u2502 \u2514\u2500\u2500 MultiTreeNode(object)/__init__(self: Any, data: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 Vector\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 Vector(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 vec(data: Any) -> Any\r\n\u251c\u2500\u2500 chars\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 cipher\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 atbash(text: str) -> str\r\n\u2502 \u251c\u2500\u2500 base_64(text: str, decrypt: bool) -> str\r\n\u2502 \u251c\u2500\u2500 caesar(text: str, shift: int, decrypt: bool) -> str\r\n\u2502 \u251c\u2500\u2500 hill256(text: bytes, key: list, decrypt: bool) -> bytes\r\n\u2502 \u251c\u2500\u2500 ksa(key: bytes) -> list\r\n\u2502 \u251c\u2500\u2500 morse(text: str, decrypt: bool) -> str\r\n\u2502 \u251c\u2500\u2500 playfair(text: str, key: str, decrypt: bool) -> str\r\n\u2502 \u251c\u2500\u2500 prga(s: list) -> Any\r\n\u2502 \u251c\u2500\u2500 rc4(text: bytes, key: bytes) -> bytes\r\n\u2502 \u251c\u2500\u2500 rot13(text: str) -> str\r\n\u2502 \u251c\u2500\u2500 substitution(text: str, sub_map: dict, decrypt: bool) -> str\r\n\u2502 \u2514\u2500\u2500 vigenere(text: str, key: str, decrypt: bool) -> str\r\n\u251c\u2500\u2500 constants\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 dists\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 beta_pdf(x: Any, a: Any, b: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 binom_pmf(k: Any, n: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 cauchy_cdf(x: Any, x0: Any, gamma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 cauchy_pdf(x: Any, x0: Any, gamma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 chi2_cdf(x: Any, df: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 chi2_pdf(x: Any, df: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 expon_cdf(x: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 expon_pdf(x: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 f_pdf(x: Any, dfnum: Any, dfden: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 gamma_pdf(x: Any, shape: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 geometric_pmf(k: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 hypergeom_pmf(k: Any, mg: Any, n: Any, nt: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 inv_gauss_pdf(x: Any, mu: Any, lambda_: Any, alpha: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 levy_pdf(x: Any, c: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 log_logistic_cdf(x: Any, alpha: Any, beta: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 log_logistic_pdf(x: Any, alpha: Any, beta: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 logistic_cdf(x: Any, mu: Any, s: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 logistic_pdf(x: Any, mu: Any, s: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 lognorm_cdf(x: Any, mu: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 lognorm_pdf(x: Any, s: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 logser_pmf(k: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 multinomial_pmf(k: Any, n: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 nbinom_pmf(k: Any, n: Any, p: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 nhypergeom_pmf(k: Any, m: Any, n: Any, r: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 normal_cdf(x: Any, mu: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 normal_pdf(x: Any, mu: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 pareto_pdf(x: Any, k: Any, m: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 poisson_pmf(k: Any, mu: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 rayleigh_pdf(x: Any, sigma: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 t_pdf(x: Any, df: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 uniform_cdf(x: Any, loc: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 uniform_pdf(x: Any, loc: Any, scale: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 vonmises_pdf(x: Any, mu: Any, kappa: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 weibull_max_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 weibull_min_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 zipf_pmf(k: Any, s: Any, n: Any) -> Any\r\n\u251c\u2500\u2500 equations\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 lin_eq(left: list, right: list) -> list\r\n\u2502 \u2514\u2500\u2500 poly_eq(coefficients: list) -> list\r\n\u251c\u2500\u2500 errors\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 file\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 read(file: str) -> list\r\n\u2502 \u2514\u2500\u2500 write(file: str, cls: object) -> Any\r\n\u251c\u2500\u2500 image\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 PNG(object)/__init__(self: Any) -> None\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u2514\u2500\u2500 crc(data: Any, length: Any, init: Any, xor: Any) -> Any\r\n\u251c\u2500\u2500 maths\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 arrangement(n: int, r: int) -> int\r\n\u2502 \u251c\u2500\u2500 combination(n: int, r: int) -> int\r\n\u2502 \u251c\u2500\u2500 acos(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 acosh(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 acot(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 acoth(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 acsc(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 acsch(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 arrangement(n: int, r: int) -> int\r\n\u2502 \u251c\u2500\u2500 asec(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 asech(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 asin(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 asinh(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 atan(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 atanh(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 average(data: Any, weights: Any, expected: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 bessel_i0(x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 bessel_iv(v: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 beta(p: typing.Union[int, float], q: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 central_moment(data: typing.Union[list, tuple], order: int) -> float\r\n\u2502 \u251c\u2500\u2500 coeff_det(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 combination(n: int, r: int) -> int\r\n\u2502 \u251c\u2500\u2500 corr_coeff(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 cos(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 cosh(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 cot(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 coth(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 cov(x: typing.Union[list, tuple], y: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 crt(n: typing.Union[list, tuple], a: typing.Union[list, tuple]) -> int\r\n\u2502 \u251c\u2500\u2500 csc(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 csch(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 cumprod(lst: typing.Union[list, tuple]) -> list\r\n\u2502 \u251c\u2500\u2500 cumsum(lst: typing.Union[list, tuple]) -> list\r\n\u2502 \u251c\u2500\u2500 deriv(f: Any, x: typing.Union[int, float], h: typing.Union[int, float], args: Any, kwargs: Any) -> float\r\n\u2502 \u251c\u2500\u2500 erf(x: typing.Union[int, float]) -> float\r\n\u2502 \u251c\u2500\u2500 exgcd(a: int, b: int) -> tuple\r\n\u2502 \u251c\u2500\u2500 exp(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 factorial(n: int) -> int\r\n\u2502 \u251c\u2500\u2500 freq(data: typing.Union[list, tuple]) -> dict\r\n\u2502 \u251c\u2500\u2500 gamma(alpha: typing.Union[int, float]) -> float\r\n\u2502 \u251c\u2500\u2500 gcd(args: int) -> int\r\n\u2502 \u251c\u2500\u2500 geom_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 harm_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 integ(f: Any, x_start: typing.Union[int, float], x_end: typing.Union[int, float], n: int, args: Any, kwargs: Any) -> float\r\n\u2502 \u251c\u2500\u2500 iroot(y: int, n: int) -> int\r\n\u2502 \u251c\u2500\u2500 is_possibly_square(n: int) -> bool\r\n\u2502 \u251c\u2500\u2500 is_square(n: int) -> bool\r\n\u2502 \u251c\u2500\u2500 isqrt(x: int) -> int\r\n\u2502 \u251c\u2500\u2500 kurt(data: typing.Union[list, tuple], fisher: bool) -> float\r\n\u2502 \u251c\u2500\u2500 lcm(args: int) -> int\r\n\u2502 \u251c\u2500\u2500 ln(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 lower_gamma(s: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 median(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 mod_order(a: int, n: int, b: int) -> int\r\n\u2502 \u251c\u2500\u2500 mode(data: typing.Union[list, tuple]) -> Any\r\n\u2502 \u251c\u2500\u2500 normalize(data: typing.Union[list, tuple], target: typing.Union[int, float, complex]) -> typing.Union[list, tuple]\r\n\u2502 \u251c\u2500\u2500 parity(x: int) -> int\r\n\u2502 \u251c\u2500\u2500 pi(i: int, n: int, f: Any) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 primitive_root(a: int, single: bool) -> typing.Union[int, list]\r\n\u2502 \u251c\u2500\u2500 product(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 ptp(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 raw_moment(data: typing.Union[list, tuple], order: int) -> float\r\n\u2502 \u251c\u2500\u2500 roll(seq: typing.Union[list, tuple, str], shift: int) -> typing.Union[list, tuple, str]\r\n\u2502 \u251c\u2500\u2500 root(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 sec(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 sech(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 sigma(i: int, n: int, f: Any) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 sigmoid(x: typing.Union[int, float]) -> float\r\n\u2502 \u251c\u2500\u2500 sign(x: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 sin(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 sinh(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 skew(data: typing.Union[list, tuple]) -> float\r\n\u2502 \u251c\u2500\u2500 square_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 std(numbers: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 sumprod(arrays: typing.Union[list, tuple]) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 tan(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 tanh(x: typing.Union[int, float]) -> typing.Union[int, float]\r\n\u2502 \u251c\u2500\u2500 totient(n: int) -> int\r\n\u2502 \u251c\u2500\u2500 upper_gamma(s: Any, x: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 var(numbers: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]\r\n\u2502 \u251c\u2500\u2500 xlogy(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]\r\n\u2502 \u2514\u2500\u2500 zeta(alpha: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]\r\n\u251c\u2500\u2500 numbers\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 float2fraction(number: float, mixed: bool, error: float) -> tuple\r\n\u2502 \u251c\u2500\u2500 int2roman(integer: int, overline: bool) -> str\r\n\u2502 \u251c\u2500\u2500 int2words(integer: int) -> str\r\n\u2502 \u251c\u2500\u2500 roman2int(roman_num: str) -> int\r\n\u2502 \u2514\u2500\u2500 str2int(string: str) -> int\r\n\u251c\u2500\u2500 plotting\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 background(right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], string: bool) -> typing.Union[list, str]\r\n\u2502 \u251c\u2500\u2500 binary(function: Any, right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], error: Any, compare: Any, string: bool, basic: list, character: str, data: bool, coloration: Any) -> typing.Union[list, str]\r\n\u2502 \u251c\u2500\u2500 c_unary(function: Any, projection: str, right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], string: bool, basic: list, character: str, data: bool, coloration: Any) -> typing.Union[list, str]\r\n\u2502 \u251c\u2500\u2500 change(data: typing.Union[list, str]) -> typing.Union[list, str]\r\n\u2502 \u251c\u2500\u2500 color(text: str, rgb: typing.Union[list, tuple]) -> str\r\n\u2502 \u2514\u2500\u2500 unary(function: Any, right: typing.Union[int, float], left: typing.Union[int, float], top: typing.Union[int, float], bottom: typing.Union[int, float], complexity: typing.Union[int, float], ratio: typing.Union[int, float], string: bool, basic: list, character: str, data: bool, coloration: Any) -> typing.Union[list, str]\r\n\u251c\u2500\u2500 polynomial\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2502 \u2514\u2500\u2500 Polynomial(object)/__init__(self: Any, terms: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 from_coeffs(coeffs: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 from_coords(coords: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 leggauss(polynomial: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 legpoly(n: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 poly(terms: Any) -> Any\r\n\u251c\u2500\u2500 random\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 choice(seq: typing.Union[list, tuple, str], shape: typing.Union[list, tuple]) -> Any\r\n\u2502 \u251c\u2500\u2500 gauss(mu: typing.Union[int, float], sigma: typing.Union[int, float], shape: typing.Union[list, tuple]) -> typing.Union[float, list]\r\n\u2502 \u251c\u2500\u2500 gauss_error(original: typing.Union[list, tuple], mu: typing.Union[int, float], sigma: typing.Union[int, float]) -> list\r\n\u2502 \u251c\u2500\u2500 rand(shape: typing.Union[list, tuple]) -> typing.Union[float, list]\r\n\u2502 \u251c\u2500\u2500 randint(a: int, b: int, shape: typing.Union[list, tuple]) -> typing.Union[int, list]\r\n\u2502 \u2514\u2500\u2500 uniform(a: typing.Union[int, float], b: typing.Union[int, float], shape: typing.Union[list, tuple]) -> typing.Union[float, list]\r\n\u251c\u2500\u2500 regression\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 lin_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> list\r\n\u2502 \u251c\u2500\u2500 par_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> list\r\n\u2502 \u2514\u2500\u2500 poly_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple], n: int) -> list\r\n\u251c\u2500\u2500 sequence\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 arithmetic_sequence(a1: typing.Union[int, float], an: typing.Union[int, float], d: typing.Union[int, float], n: typing.Union[int, float], s: typing.Union[int, float]) -> dict\r\n\u2502 \u251c\u2500\u2500 bernoulli(n: int, single: bool) -> list\r\n\u2502 \u251c\u2500\u2500 catalan(n: int, single: bool) -> typing.Union[int, list]\r\n\u2502 \u251c\u2500\u2500 farey(n: int) -> list\r\n\u2502 \u251c\u2500\u2500 fibonacci(n: int, single: bool) -> typing.Union[int, list]\r\n\u2502 \u251c\u2500\u2500 geometric_sequence(a1: typing.Union[int, float], an: typing.Union[int, float], r: typing.Union[int, float], n: typing.Union[int, float], s: typing.Union[int, float]) -> dict\r\n\u2502 \u2514\u2500\u2500 recaman(n: int, single: bool) -> typing.Union[int, list]\r\n\u251c\u2500\u2500 stattest\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 chi2_cont(contingency: list, lambda_: float, calc_p: bool, corr: bool) -> tuple\r\n\u2502 \u251c\u2500\u2500 chisquare(observed: list, expected: list) -> tuple\r\n\u2502 \u251c\u2500\u2500 kurttest(data: list, two_tailed: bool) -> tuple\r\n\u2502 \u251c\u2500\u2500 mediantest(samples: Any, ties: Any, lambda_: Any, corr: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 normaltest(data: list) -> tuple\r\n\u2502 \u2514\u2500\u2500 skewtest(data: list, two_tailed: bool) -> tuple\r\n\u251c\u2500\u2500 test\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 this\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 tools\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 classify(array: typing.Union[list, tuple]) -> dict\r\n\u2502 \u251c\u2500\u2500 dedup(iterable: typing.Union[list, tuple, str]) -> typing.Union[list, tuple, str]\r\n\u2502 \u251c\u2500\u2500 frange(start: typing.Union[int, float], stop: typing.Union[int, float], step: float) -> list\r\n\u2502 \u251c\u2500\u2500 generate_primes(limit: int) -> list\r\n\u2502 \u251c\u2500\u2500 generate_semiprimes(limit: int) -> list\r\n\u2502 \u251c\u2500\u2500 geomspace(start: typing.Union[int, float], stop: typing.Union[int, float], number: int) -> list\r\n\u2502 \u251c\u2500\u2500 interp(data: typing.Union[list, tuple], length: int) -> list\r\n\u2502 \u251c\u2500\u2500 linspace(start: typing.Union[int, float], stop: typing.Union[int, float], number: int) -> list\r\n\u2502 \u251c\u2500\u2500 magic_square(n: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 primality(n: int, iter_num: int) -> bool\r\n\u2502 \u251c\u2500\u2500 prime_factors(integer: int, dictionary: bool, pollard_rho: bool) -> typing.Union[list, dict]\r\n\u2502 \u2514\u2500\u2500 split(iterable: typing.Union[list, tuple, str], key: typing.Union[list, tuple], retain: bool) -> list\r\n\u251c\u2500\u2500 types\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u251c\u2500\u2500 ufuncs\r\n\u2502 \u251c\u2500\u2500 CLASS\r\n\u2502 \u2514\u2500\u2500 FUNCTION\r\n\u2502 \u251c\u2500\u2500 add(x: Any, y: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 base_ufunc(arrays: Any, func: Any, args: Any, rtype: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 divide(x: Any, y: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 floor_divide(x: Any, y: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 modulo(x: Any, y: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 multiply(x: Any, y: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 power(x: Any, y: Any, m: Any) -> Any\r\n\u2502 \u251c\u2500\u2500 subtract(x: Any, y: Any) -> Any\r\n\u2502 \u2514\u2500\u2500 ufunc_helper(x: Any, y: Any, func: Any) -> Any\r\n\u2514\u2500\u2500 utils\r\n \u251c\u2500\u2500 CLASS\r\n \u2502 \u251c\u2500\u2500 InfIterator(object)/__init__(self: Any, start: typing.Union[int, float, complex], mode: str, common: typing.Union[int, float, complex]) -> Any\r\n \u2502 \u251c\u2500\u2500 LinkedList(object)/__init__(self: Any) -> Any\r\n \u2502 \u251c\u2500\u2500 LinkedListNode(object)/__init__(self: Any, value: Any, next_node: Any) -> Any\r\n \u2502 \u2514\u2500\u2500 OrderedSet(object)/__init__(self: Any, sequence: Any) -> Any\r\n \u2514\u2500\u2500 FUNCTION\r\n```\r\n\r\n### \u4ee3\u7801\u6d4b\u8bd5\r\n\r\n#### Code testing\r\n\r\n```python\r\nfrom pypynum import (Array, Geometry, Logic, Matrix, Quaternion, Symbolics, Tensor, Vector,\r\n cipher, constants, equations, maths, plotting, random, regression, tools)\r\n\r\n...\r\n\r\nprint(Array.array())\r\nprint(Array.array([1, 2, 3, 4, 5, 6, 7, 8]))\r\nprint(Array.array([[1, 2, 3, 4], [5, 6, 7, 8]]))\r\nprint(Array.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]))\r\n\r\n\"\"\"\r\n[]\r\n[1 2 3 4 5 6 7 8]\r\n[[1 2 3 4]\r\n [5 6 7 8]]\r\n[[[1 2]\r\n [3 4]]\r\n\r\n [[5 6]\r\n [7 8]]]\r\n\"\"\"\r\n\r\ntriangle = Geometry.Triangle((0, 0), (2, 2), (3, 0))\r\nprint(triangle.perimeter())\r\nprint(triangle.area())\r\nprint(triangle.centroid())\r\n\r\n\"\"\"\r\n8.06449510224598\r\n3.0\r\n(1.6666666666666667, 0.6666666666666666)\r\n\"\"\"\r\n\r\na, b, c = 1, 1, 1\r\nadder0, adder1 = Logic.HalfAdder(\"alpha\", a, b), Logic.HalfAdder(\"beta\", c, None)\r\nxor0 = Logic.XOR(\"alpha\")\r\nff0, ff1 = Logic.DFF(\"alpha\"), Logic.DFF(\"beta\")\r\nxor0.set_order0(1)\r\nxor0.set_order1(1)\r\nLogic.connector(adder0, adder1)\r\nLogic.connector(adder0, xor0)\r\nLogic.connector(adder1, xor0)\r\nLogic.connector(adder1, ff0)\r\nLogic.connector(xor0, ff1)\r\nprint(\"sum: {}, carry: {}\".format(ff0.out(), ff1.out()))\r\n\r\n\"\"\"\r\nsum: [1], carry: [1]\r\n\"\"\"\r\n\r\nm0 = Matrix.mat([[1, 2], [3, 4]])\r\nm1 = Matrix.mat([[5, 6], [7, 8]])\r\nprint(m0)\r\nprint(m1)\r\nprint(m0 + m1)\r\nprint(m0 @ m1)\r\nprint(m0.inv())\r\nprint(m1.rank())\r\n\r\n\"\"\"\r\n[[1 2]\r\n [3 4]]\r\n[[5 6]\r\n [7 8]]\r\n[[ 6 8]\r\n [10 12]]\r\n[[19 22]\r\n [43 50]]\r\n[[ -1.9999999999999996 0.9999999999999998]\r\n [ 1.4999999999999998 -0.49999999999999994]]\r\n2\r\n\"\"\"\r\n\r\nq0 = Quaternion.quat(1, 2, 3, 4)\r\nq1 = Quaternion.quat(5, 6, 7, 8)\r\nprint(q0)\r\nprint(q1)\r\nprint(q0 + q1)\r\nprint(q0 * q1)\r\nprint(q0.inverse())\r\nprint(q1.conjugate())\r\n\r\n\"\"\"\r\n(1+2i+3j+4k)\r\n(5+6i+7j+8k)\r\n(6+8i+10j+12k)\r\n(-60+12i+30j+24k)\r\n(0.18257418583505536+-0.3651483716701107i+-0.5477225575051661j+-0.7302967433402214k)\r\n(5+-6i+-7j+-8k)\r\n\"\"\"\r\n\r\nprint(Symbolics.BASIC)\r\nprint(Symbolics.ENGLISH)\r\nprint(Symbolics.GREEK)\r\nprint(Symbolics.parse_expr(\"-(10+a-(3.14+b0)*(-5))**(-\u03b6n1-2.718/m\u03a399)//9\"))\r\n\r\n\"\"\"\r\n%()*+-./0123456789\r\nABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\r\n\u0391\u0392\u0393\u0394\u0395\u0396\u0397\u0398\u0399\u039a\u039b\u039c\u039d\u039e\u039f\u03a0\u03a1\u03a3\u03a4\u03a5\u03a6\u03a7\u03a8\u03a9\u03b1\u03b2\u03b3\u03b4\u03b5\u03b6\u03b7\u03b8\u03b9\u03ba\u03bb\u03bc\u03bd\u03be\u03bf\u03c0\u03c1\u03c3\u03c4\u03c5\u03c6\u03c7\u03c8\u03c9\r\n[['10', '+', 'a', '-', ['3.14', '+', 'b0'], '*', '-5'], '**', ['-\u03b6n1', '-', '2.718', '/', 'm\u03a399'], '//', '9']\r\n\"\"\"\r\n\r\nt0 = Tensor.ten([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\r\nt1 = Tensor.ten([[[9, 10], [11, 12]], [[13, 14], [15, 16]]])\r\nprint(t0)\r\nprint(t1)\r\nprint(t0 + t1)\r\nprint(t0 @ t1)\r\n\r\n\"\"\"\r\n[[[1 2]\r\n [3 4]]\r\n\r\n [[5 6]\r\n [7 8]]]\r\n[[[ 9 10]\r\n [11 12]]\r\n\r\n [[13 14]\r\n [15 16]]]\r\n[[[10 12]\r\n [14 16]]\r\n\r\n [[18 20]\r\n [22 24]]]\r\n[[[ 31 34]\r\n [ 71 78]]\r\n\r\n [[155 166]\r\n [211 226]]]\r\n\"\"\"\r\n\r\nstring = \"PyPyNum\"\r\nencrypted = cipher.caesar(string, 10)\r\nprint(string)\r\nprint(encrypted)\r\nprint(cipher.caesar(encrypted, 10, decrypt=True))\r\nencrypted = cipher.vigenere(string, \"cipher\")\r\nprint(string)\r\nprint(encrypted)\r\nprint(cipher.vigenere(encrypted, \"cipher\", decrypt=True))\r\nencrypted = cipher.morse(string)\r\nprint(string)\r\nprint(encrypted)\r\nprint(cipher.morse(encrypted, decrypt=True))\r\n\r\n\"\"\"\r\nPyPyNum\r\nZiZiXew\r\nPyPyNum\r\nPyPyNum\r\nRgEfRlo\r\nPyPyNum\r\nPyPyNum\r\n.--. -.-- .--. -.-- -. ..- --\r\nPYPYNUM\r\n\"\"\"\r\n\r\nv0 = Vector.vec([1, 2, 3, 4])\r\nv1 = Vector.vec([5, 6, 7, 8])\r\nprint(v0)\r\nprint(v1)\r\nprint(v0 + v1)\r\nprint(v0 @ v1)\r\nprint(v0.normalize())\r\nprint(v1.angles())\r\n\r\n\"\"\"\r\n[1 2 3 4]\r\n[5 6 7 8]\r\n[ 5 12 21 32]\r\n70\r\n[0.18257418583505536 0.3651483716701107 0.5477225575051661 0.7302967433402214]\r\n[1.1820279130506308, 1.0985826410133916, 1.0114070854293842, 0.9191723423169716]\r\n\"\"\"\r\n\r\nprint(constants.TB)\r\nprint(constants.e)\r\nprint(constants.h)\r\nprint(constants.phi)\r\nprint(constants.pi)\r\nprint(constants.tera)\r\n\r\n\"\"\"\r\n1099511627776\r\n2.718281828459045\r\n6.62607015e-34\r\n1.618033988749895\r\n3.141592653589793\r\n1000000000000\r\n\"\"\"\r\n\r\np = [1, -2, -3, 4]\r\nm = [\r\n [\r\n [1, 2, 3],\r\n [6, 10, 12],\r\n [7, 16, 9]\r\n ],\r\n [-1, -2, -3]\r\n]\r\nprint(equations.poly_eq(p))\r\nprint(equations.lin_eq(*m))\r\n\r\n\"\"\"\r\n[(-1.5615528128088307-6.5209667308287455e-24j) (1.0000000000000007+3.241554513744382e-25j) (2.5615528128088294+4.456233626665941e-24j)]\r\n[ 1.6666666666666667 -0.6666666666666666 -0.4444444444444444]\r\n\"\"\"\r\n\r\nprint(maths.cot(constants.pi / 3))\r\nprint(maths.gamma(1.5))\r\nprint(maths.pi(1, 10, lambda x: x ** 2))\r\nprint(maths.product([2, 3, 5, 7, 11, 13, 17, 19, 23, 29]))\r\nprint(maths.sigma(1, 10, lambda x: x ** 2))\r\nprint(maths.var([2, 3, 5, 7, 11, 13, 17, 19, 23, 29]))\r\n\r\n\"\"\"\r\n0.577350269189626\r\n0.886226925452758\r\n13168189440000\r\n6469693230\r\n385\r\n73.29\r\n\"\"\"\r\n\r\nplt = plotting.unary(lambda x: x ** 2, top=10, bottom=0, character=\"+\")\r\nprint(plt)\r\nprint(plotting.binary(lambda x, y: x ** 2 + y ** 2 - 10, right=10, left=0, compare=\"<=\", basic=plotting.change(plt)))\r\nprint(plotting.c_unary(lambda x: x ** x, right=2, left=-2, top=2, bottom=-2, complexity=20, character=\"-\"))\r\n\r\n\"\"\"\r\n 1.00e+01| + + \r\n | \r\n | + + \r\n | \r\n | + + \r\n | + + \r\n | \r\n | + + \r\n 5.00e+00|_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n | + + \r\n | + + \r\n | + + \r\n | + + \r\n | + + \r\n | + + \r\n | + + \r\n | +++ +++ \r\n 0.00e+00|________________________+++________________________\r\n -5.00e+00 0.00e+00 5.00e+00\r\n 1.00e+01| + + \r\n | \r\n | + + \r\n | \r\n |......... + + \r\n |............. + \r\n |.............. \r\n |................ + \r\n 5.00e+00|................_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _\r\n |................ + \r\n |................ + \r\n |.............. + + \r\n |............. + + \r\n |......... + + \r\n | + + \r\n | + + \r\n | +++ +++ \r\n 0.00e+00|________________________+++________________________\r\n -5.00e+00 0.00e+00 5.00e+00\r\n 2.00e+00| - - - - - - \r\n | - - - - - - - \r\n | - - - - - - \r\n |- - - - - - - \r\n | - - - - -- - - - - \r\n | - - - - - - - - -\r\n | - - - - -- - --- -- - -- - - - - - \r\n | - - - -- -- - - - -- - - - \r\n | - - - - - - - -- - --- --- - - --- -- - - \r\n | - - - - - -- ----- -- -- --- -- -- --- -- - -\r\n | - - - ------------ ---- - -- -- - --- - - - \r\n | - - - - - ----- - -- ----------------------- -- ---- - -- -- \r\n | - - - - - ---- --------------------------------- - - - - - - \r\n 0.00e+00|_ _ _ _ _ _ _ _-_-_-_-_---- ------------------------------------_-- _ _ _ _ _ _ _\r\n | - - - - ----------------------------------------- -- - - - - \r\n | - -- - - -- - - --------------------------------- - - - \r\n | - - ---- - - -- --------------------- ----- ---- - -- - \r\n | - - -- --------- -- -- - ----- --- -- - - - - \r\n | - - - - - - - ---- --- --- --- -- -- --- - - -\r\n | - - - - - -- -- -- - - -- -- -- \r\n | - - - -- - -- -- - - -- - - \r\n | - - - - - - - -- - - -- - - \r\n | - - - - -- -- - - - - -\r\n | - - - - - - - - \r\n |- - - - - - - - \r\n | - - - - - - \r\n | - - - - - \r\n -2.00e+00|___________-_________________-___________-_____________________-____________-____\r\n -2.00e+00 0.00e+00 2.00e+00\r\n\"\"\"\r\n\r\nprint(random.gauss(0, 1, [2, 3, 4]))\r\nprint(random.rand([2, 3, 4]))\r\nprint(random.randint(0, 9, [2, 3, 4]))\r\nprint(random.uniform(0, 9, [2, 3, 4]))\r\n\r\n\"\"\"\r\n[[[1.0022026821190488, -0.38242004448759154, -0.23648445523561967, 0.43813038741951754], [-0.3778652198785619, -0.03865603124657112, -1.5186239424691736, -0.7368762975012327], [-0.7580654190380791, -1.3672869759158346, 0.582588816791107, 1.0281649895276377]], [[0.5270622699930536, 0.6132250709048543, 0.9764619731696673, -0.13740454362420268], [-2.0801461607759886, -0.1935521020633617, 0.44420106801354153, 1.4830089202063659], [-0.8790685594194517, 0.45517163054358967, -1.1448643981658326, 0.986414969442009]]]\r\n[[[0.13698864758140294, 0.634190467772759, 0.25683276170297875, 0.9026812741081188], [0.26303437123782614, 0.02477620234532174, 0.9947822450199725, 0.5916822332583692], [0.7523977891797228, 0.6198410071512576, 0.05799276940261333, 0.4181042411131305]], [[0.21564211884049145, 0.30667940527138227, 0.03010277335333611, 0.904264028183912], [0.33977550248572597, 0.042594462434406455, 0.6371061749651907, 0.8639246364627866], [0.009159271907318911, 0.054475512265855563, 0.7109847662274855, 0.9695933487818381]]]\r\n[[[1, 6, 0, 1], [0, 4, 8, 3], [2, 4, 2, 8]], [[9, 7, 0, 6], [6, 2, 4, 6], [2, 2, 0, 1]]]\r\n[[[4.281963231653285, 7.6564706580977155, 2.7831005401808904, 4.69275453971821], [7.731377457312142, 7.026081604862776, 3.1623746844355916, 4.097454457127405], [1.0053860355938644, 8.396390096875859, 5.860124932392565, 0.7556741321519111]], [[3.0505373562186717, 5.846422325897977, 5.79128924014881, 5.322513543793011], [7.97334322055796, 0.4266873959996582, 6.217219949795519, 2.819046997201407], [7.195256735457888, 3.205909055908082, 2.9903485221015123, 6.695032815286013]]]\r\n\"\"\"\r\n\r\nprint(regression.lin_reg(list(range(5)), [2, 4, 6, 7, 8]))\r\nprint(regression.par_reg(list(range(5)), [2, 4, 6, 7, 8]))\r\nprint(regression.poly_reg(list(range(5)), [2, 4, 6, 7, 8], 4))\r\n\r\n\"\"\"\r\n[1.5, 2.4000000000000004]\r\n[-0.21428571428571563, 2.3571428571428625, 1.971428571428569]\r\n[0.08333333333320592, -0.666666666666571, 1.4166666666628345, 1.1666666666688208, 1.9999999999999258]\r\n\"\"\"\r\n\r\nprint(tools.classify([1, 2.3, 4 + 5j, \"string\", list, True, 3.14, False, tuple, tools]))\r\nprint(tools.dedup([\"Python\", 6, \"NumPy\", int, \"PyPyNum\", 9, \"pypynum\", \"NumPy\", 6, True]))\r\nprint(tools.frange(0, 3, 0.4))\r\nprint(tools.linspace(0, 2.8, 8))\r\n\r\n\"\"\"\r\n{<class 'int'>: [1], <class 'float'>: [2.3, 3.14], <class 'complex'>: [(4+5j)], <class 'str'>: ['string'], <class 'type'>: [<class 'list'>, <class 'tuple'>], <class 'bool'>: [True, False], <class 'module'>: [<module 'pypynum.tools' from 'C:\\\\Users\\\\Administrator\\\\PycharmProjects\\\\pythonProject\\\\pypynum\\\\tools.py'>]}\r\n['Python', 6, 'NumPy', <class 'int'>, 'PyPyNum', 9, 'pypynum', True]\r\n[0.0, 0.4, 0.8, 1.2000000000000002, 1.6, 2.0, 2.4000000000000004, 2.8000000000000003]\r\n[0.0, 0.39999999999999997, 0.7999999999999999, 1.2, 1.5999999999999999, 1.9999999999999998, 2.4, 2.8]\r\n\"\"\"\r\n\r\n# \u63d0\u793a\uff1a\r\n# \r\n# \u6d4b\u8bd5\u5df2\u6210\u529f\u901a\u8fc7\u5e76\u7ed3\u675f\u3002\r\n# \r\n# \u8fd9\u4e9b\u6d4b\u8bd5\u53ea\u662f\u8fd9\u4e2a\u5305\u529f\u80fd\u7684\u4e00\u90e8\u5206\u3002\r\n# \r\n# \u66f4\u591a\u7684\u529f\u80fd\u9700\u8981\u81ea\u5df1\u63a2\u7d22\u548c\u5c1d\u8bd5\uff01\r\n# \r\n# Tip:\r\n# \r\n# The test has been successfully passed and ended.\r\n# \r\n# These tests are only part of the functionality of this package.\r\n# \r\n# More features need to be explored and tried by yourself!\r\n```\r\n",
"bugtrack_url": null,
"license": " GNU AFFERO GENERAL PUBLIC LICENSE Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU Affero General Public License is a free, copyleft license for software and other kinds of works, specifically designed to ensure cooperation with the community in the case of network server software. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, our General Public Licenses are intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. When we speak of free software, we are referring to freedom, not price. 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Also add information on how to contact you by electronic and paper mail. If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source. For example, if your program is a web application, its interface could display a \"Source\" link that leads users to an archive of the code. There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements. You should also get your employer (if you work as a programmer) or school, if any, to sign a \"copyright disclaimer\" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see <http://www.gnu.org/licenses/>. ",
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