numpypy 1.10.0

A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.4]

PyPyNum

A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.4]

 ________   ___    ___  ________   ___    ___  ________    ___  ___   _____ ______
|\   __  \ |\  \  /  /||\   __  \ |\  \  /  /||\   ___  \ |\  \|\  \ |\   _ \  _   \
\ \  \|\  \\ \  \/  / /\ \  \|\  \\ \  \/  / /\ \  \\ \  \\ \  \\\  \\ \  \\\__\ \  \
 \ \   ____\\ \    / /  \ \   ____\\ \    / /  \ \  \\ \  \\ \  \\\  \\ \  \\|__| \  \
  \ \  \___| \/  /  /    \ \  \___| \/  /  /    \ \  \\ \  \\ \  \\\  \\ \  \    \ \  \
   \ \__\  __/  / /       \ \__\  __/  / /       \ \__\\ \__\\ \_______\\ \__\    \ \__\
    \|__| |\___/ /         \|__| |\___/ /         \|__| \|__| \|_______| \|__|     \|__|
          \|___|/                \|___|/

Version -> 1.10.0 | 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.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.probability	概率统计 Probability statistics
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.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

与上一个版本相比新增功能

New features compared to the previous version

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

名称缩写的函数

Functions with abbreviated names

linear_regression -> lin_reg
parabolic_regression -> par_reg
polynomial_regression -> poly_reg
linear_equation -> lin_eq
polynomial_equation -> poly_eq
derivative -> deriv
definite_integral -> integ

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

★ Matrix.inv(self):
★ 速度提高约55000%
★ Speed increased by about 55000%

Matrix.outer(self, other):
速度提高约900%
Speed increased by about 900%

identity(n: int) -> Matrix:
速度提高约630%
Speed increased by about 630%

tril_indices(n: int,
k: int = 0,
m: int | None = None) -> tuple:
速度提高约140%
Speed increased by about 140%

Matrix.kron(self, other):
速度提高约130%
Speed increased by about 130%

Matrix.t(self):
速度提高约70%
Speed increased by about 70%

lu(matrix: Matrix) -> tuple:
速度提高约9%
Speed increased by about 9%

Matrix.inner(self, other):
速度提高约5%
Speed increased by about 5%

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

矩阵支持更好的修改元素方式

Matrix supports better ways
to modify elements

示例 Example

A = [[0, 1, 2, 3],
     [4, 5, 6, 7],
     [8, 9, 10, 11],
     [12, 13, 14, 15]]
matrix = mat(A)
matrix[0:2, 0:2] = [[16, 77],
                    [72, 16]]

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

这个方法添加了是否返回所有主元

This method adds whether to return all main elements

Matrix.rref(self, pivots=True)

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

新增的函数

New functions added

aslist(data)
asarray(data)
roll(seq, shift)

请注意，“roll”函数目前是一个初步实现，将来将被设计为数组绑定方法。
Please note that the "roll" function is currently a preliminary implementation
and will be designed as an array binding method in the future.

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

<<<添加了image模块>>>

<<<Added image module>>>

<<<添加了PNG类>>>

<<<Added PNG class>>>

class PNG(builtins.object)

    Introduction
    ==========
    This is a PNG class written in pure Python,
    supporting the creation, reading, modification, and saving of PNG images.

    Roadmap
    ==========
    This class is currently in development.
    Future updates will expand its functionality to enhance the capabilities of working with PNG images.

    Usage
    ==========

    Creating a new PNG image:
    ----------
    To create a new PNG image, instantiate the PNG class and use the `new` method
    to define the image dimensions, bit depth, and color mode.

    - from png import PNG

    # Create a new image with a width of 200 pixels, a height of 100 pixels,
    an 8-bit depth, and the default RGB color mode.

    - image = PNG()
    - image.new(200, 100, 8)

    # Optionally, you can specify a background color. For example, to create a new image with a blue background:

    - image.new(200, 100, 8, color=(0, 0, 255))

    Reading an existing PNG image:
    ----------
    To read an existing PNG image from a file, use the `read` method.

    - image = PNG()
    - image.read("example.png")

    Modifying a pixel:
    ----------
    To modify the color of a pixel at a specific coordinate, use the `setp` method.

    # Set the pixel at (10, 10) to red (255, 0, 0).

    - image.setp(10, 10, (255, 0, 0))

    Getting a pixel's color:
    ----------
    To retrieve the color of a pixel at a specific coordinate, use the `getp` method.

    - color = image.getp(10, 10)
    - print(color)  # Output: (255, 0, 0) for the example above

    Saving the image:
    ----------
    To save the image to a file, use the `write` method.

    - image.write("output.png")

    Getting image information:
    ----------
    To obtain information about the image, such as its dimensions and color mode, use the `info` method.

    - info = image.info()
    - print(info)  # Output: {'width': 200, 'height': 100, 'bit_depth': 8, 'color_mode': 'RGB'}

    __init__(self) -> None

    __repr__(self) -> str

    getp(self, x: int, y: int) -> tuple

    info(self) -> dict

    new(self, width: int, height: int, bit_depth: int, color: tuple = (), color_mode: str = 'RGB') -> None

    read(self, filename: str) -> None

    setp(self, x: int, y: int, color: tuple) -> None

    write(self, filename: str = None) -> bytes

!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=

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 Matrix Time Test	NumPy+CPython（seconds）	排名 Ranking	PyPyNum+PyPy（seconds）	排名 Ranking	Mpmath_+_PyPy_（_seconds_）	排名 Ranking	SymPy_+_PyPy_（_seconds_）	排名 Ranking
创建一百阶随机数矩阵 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 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 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 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 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 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 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 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 Array output effect	[[[[ -7 -67] [-78  29]] [[-86 -97] [ 68  -3]]] [[[ 11  42] [ 24 -65]] [[-60  72] [ 73   2]]]]	/	[[[[ 37  83] [ 40   2]] [[ -5 -34] [ -7  72]]] [[[ 13 -64] [  6  90]] [[ 68  57] [ 78  11]]]]	/	[-80.0   -8.0  80.0  -88.0] [-99.0  -43.0  87.0   81.0] [ 20.0  -55.0  98.0    8.0] [  8.0   44.0  64.0  -35.0] (只支持矩阵) (Only supports matrices)	/	⎡⎡16   -56⎤  ⎡ 8   -28⎤⎤ ⎢⎢        ⎥  ⎢        ⎥⎥ ⎢⎣-56  56 ⎦  ⎣-28  28 ⎦⎥ ⎢                      ⎥ ⎢ ⎡-2  7 ⎤   ⎡-18  63 ⎤⎥ ⎢ ⎢      ⎥   ⎢        ⎥⎥ ⎣ ⎣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
├── 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
│       ├── 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
│       ├── gaussian(x: typing.Union[int, float], _mu: typing.Union[int, float], _sigma: 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]) -> float
│       ├── lcm(args: int) -> int
│       ├── ln(x: typing.Union[int, float]) -> typing.Union[int, float]
│       ├── 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]
│       ├── poisson(x: int, _lambda: typing.Union[int, float]) -> float
│       ├── 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
│       ├── var(numbers: typing.Union[list, tuple], dof: int) -> 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
├── probability
│   ├── CLASS
│   └── FUNCTION
│       ├── binomial(sample_size: int, successes: int, success_probability: typing.Union[int, float]) -> float
│       ├── chi2_cont(contingency: list, calc_p: bool, corr: bool) -> tuple
│       ├── chi2_pdf(x: typing.Union[int, float], k: typing.Union[int, float]) -> float
│       └── hypergeometric(total_items: int, success_items: int, sample_size: int, successes_in_sample: int) -> float
├── 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]
├── 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

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!