A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.4]
Project description
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!
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
numpypy-1.10.0.tar.gz
(89.7 kB
view hashes)
Built Distribution
numpypy-1.10.0-py3-none-any.whl
(50.8 kB
view hashes)
