A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.4]
________ ___ ___ ________ ___ ___ ________ ___ ___ _____ ______
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\ \ \___| \/ / / \ \ \___| \/ / / \ \ \\ \ \\ \ \\\ \\ \ \ \ \ \
\ \__\ __/ / / \ \__\ __/ / / \ \__\\ \__\\ \_______\\ \__\ \ \__\
\|__| |\___/ / \|__| |\___/ / \|__| \|__| \|_______| \|__| \|__|
\|___|/ \|___|/
Version -> 1.11.0 | PyPI -> https://pypi.org/project/PyPyNum/ | Gitee -> https://www.gitee.com/PythonSJL/PyPyNum | GitHub -> https://github.com/PythonSJL/PyPyNum
PyPIไธๆ ๆณๆพ็คบlogo๏ผๅฏไปฅๅจGiteeๆ่ GitHubไธญๆฅ็ใ
The logo cannot be displayed on PyPI, it can be viewed in Gitee or GitHub.
- ๅคๅ่ฝๆฐๅญฆๅบ๏ผ็ฑปไผผไบnumpyใscipy็ญ๏ผไธไธบPyPy่งฃ้ๅจๅถไฝ๏ผไบฆๆฏๆๅ ถไป็ฑปๅ็Python่งฃ้ๅจ
- Multi functional math library, similar to numpy, scipy, etc., designed specifically for PyPy interpreters and also supports other types of Python interpreters
- ไธๅฎๆๆดๆฐ็ๆฌ๏ผๅขๅ ๆดๅคๅฎ็จๅ่ฝ
- Update versions periodically to add more practical features
- ๅฆ้่็ณป๏ผ่ฏทๆทปๅ QQๅท2261748025 ๏ผPy๐ฟ๐ข๐๐๐๐-ๆฐดๆถๅ ฐ๏ผ
- If you need to contact, please add QQ number 2261748025 (Py๐ฟ๐ข๐๐๐๐-ๆฐดๆถๅ ฐ)
ๅญๆจกๅๅ็งฐ Submodule Name | ๅ่ฝ็ฎไป Function Introduction |
---|---|
pypynum.Array |
ๅค็ปดๆฐ็ป Multidimensional array |
pypynum.chars |
็นๆฎๆฐๅญฆ็ฌฆๅท Special mathematical symbols |
pypynum.cipher |
ๅ ๅฏ่งฃๅฏ็ฎๆณ Encryption and decryption algorithm |
pypynum.constants |
ๆฐๅญฆๅธธๆฐ้ๅ Set of mathematical constants |
pypynum.dists |
ๆฆ็ๅๅธ Probability distribution |
pypynum.equations |
ๆน็จๆฑ่งฃ Solving equations |
pypynum.errors |
ๅผๅธธๅฏน่ฑก Exception object |
pypynum.file |
ๆไปถ่ฏปๅ File read and write |
pypynum.FourierT |
ๅ ้ๅถๅๆข Fourier transform |
pypynum.Geometry |
ๅ ไฝๅฝข็ถ Geometric shape |
pypynum.Graph |
ๅพ่ฎบ็ฎๆณ Graph Theory Algorithm |
pypynum.Group |
็พค่ฎบ็ฎๆณ Group Theory Algorithm |
pypynum.image |
ๅพๅๅค็ Image processing |
pypynum.Logic |
้ป่พ็ต่ทฏ่ฎพ่ฎก Logic circuit design |
pypynum.maths |
้็จๆฐๅญฆๅฝๆฐ General mathematical functions |
pypynum.Matrix |
็ฉ้ต่ฟ็ฎ Matrix operation |
pypynum.NeuralN |
็ฅ็ป็ฝ็ป่ฎญ็ป Neural network training |
pypynum.numbers |
ๆฐๅญๅค็ Number processing |
pypynum.plotting |
ๆฐๆฎๅฏ่งๅ Data visualization |
pypynum.polynomial |
ๅค้กนๅผ่ฟ็ฎ Polynomial operation |
pypynum.Quaternion |
ๅๅ ๆฐ่ฟ็ฎ Quaternion operation |
pypynum.random |
้ๆบๆฐ็ๆ Random number generation |
pypynum.regression |
ๅๅฝๅๆ Regression analysis |
pypynum.sequence |
ๆฐๅ่ฎก็ฎ Sequence calculation |
pypynum.stattest |
็ป่ฎกๆฃ้ช Statistical test |
pypynum.Symbolics |
็ฌฆๅท่ฎก็ฎ Symbol calculation |
pypynum.Tensor |
ๅผ ้่ฟ็ฎ Tensor operation |
pypynum.test |
็ฎๆๆต่ฏ Easy test |
pypynum.this |
้กน็ฎไน็ฆ Zen of Projects |
pypynum.tools |
่พ ๅฉๅฝๆฐ Auxiliary functions |
pypynum.Tree |
ๆ ๅฝขๆฐๆฎ็ปๆ Tree data structure |
pypynum.types |
็นๆฎ็ฑปๅ Special types |
pypynum.ufuncs |
้็จๅฝๆฐ Universal functions |
pypynum.utils |
ๅฎ็จๅทฅๅ ท Utility |
pypynum.Vector |
ๅ้่ฟ็ฎ Vector operation |
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
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
ไปฃ็ ๅขๅ ไบ็บฆ1000่ก
The code has increased by about
1000 lines
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
ๅ ้คไบmathsๆจกๅไธญ็ไธไบๅๅธๅฝๆฐ
Removed some distribution
functions from the math module
ๅ ้คไบprobabilityๆจกๅ
The probability module has been
removed
ๅขๅ ไบdistsๆจกๅ
Added dists module
ๅขๅ ไบstattestๆจกๅ
Added stattest module
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
<<<ๆฐๅข็ๅฝๆฐ>>>
<<<New functions added>>>
PyPyNum
โโโ dists
โ โโโ FUNCTION
โ โโโ beta_pdf(x: Any, a: Any, b: Any) -> Any
โ โโโ binom_pmf(k: Any, n: Any, p: Any) -> Any
โ โโโ cauchy_cdf(x: Any, x0: Any, gamma: Any) -> Any
โ โโโ cauchy_pdf(x: Any, x0: Any, gamma: Any) -> Any
โ โโโ chi2_cdf(k: Any, x: Any) -> Any
โ โโโ chi2_pdf(x: Any, df: Any) -> Any
โ โโโ expon_cdf(x: Any, scale: Any) -> Any
โ โโโ expon_pdf(x: Any, scale: Any) -> Any
โ โโโ f_pdf(x: Any, dfnum: Any, dfden: Any) -> Any
โ โโโ gamma_pdf(x: Any, shape: Any, scale: Any) -> Any
โ โโโ geometric_pmf(k: Any, p: Any) -> Any
โ โโโ hypergeom_pmf(k: Any, mg: Any, n: Any, nt: Any) -> Any
โ โโโ inv_gauss_pdf(x: Any, mu: Any, lambda_: Any, alpha: Any) -> Any
โ โโโ levy_pdf(x: Any, c: Any) -> Any
โ โโโ log_logistic_cdf(x: Any, alpha: Any, beta: Any) -> Any
โ โโโ log_logistic_pdf(x: Any, alpha: Any, beta: Any) -> Any
โ โโโ logistic_cdf(x: Any, mu: Any, s: Any) -> Any
โ โโโ logistic_pdf(x: Any, mu: Any, s: Any) -> Any
โ โโโ lognorm_cdf(x: Any, mu: Any, sigma: Any) -> Any
โ โโโ lognorm_pdf(x: Any, s: Any, scale: Any) -> Any
โ โโโ logser_pmf(k: Any, p: Any) -> Any
โ โโโ multinomial_pmf(k: Any, n: Any, p: Any) -> Any
โ โโโ nbinom_pmf(k: Any, n: Any, p: Any) -> Any
โ โโโ nhypergeom_pmf(k: Any, m: Any, n: Any, r: Any) -> Any
โ โโโ normal_cdf(x: Any, mu: Any, sigma: Any) -> Any
โ โโโ normal_pdf(x: Any, mu: Any, sigma: Any) -> Any
โ โโโ pareto_pdf(x: Any, k: Any, m: Any) -> Any
โ โโโ poisson_pmf(k: Any, mu: Any) -> Any
โ โโโ rayleigh_pdf(x: Any, sigma: Any) -> Any
โ โโโ t_pdf(x: Any, df: Any) -> Any
โ โโโ uniform_cdf(x: Any, loc: Any, scale: Any) -> Any
โ โโโ uniform_pdf(x: Any, loc: Any, scale: Any) -> Any
โ โโโ vonmises_pdf(x: Any, mu: Any, kappa: Any) -> Any
โ โโโ weibull_max_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
โ โโโ weibull_min_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
โ โโโ zipf_pmf(k: Any, s: Any, n: Any) -> Any
โโโ maths
โ โโโ FUNCTION
โ โโโ bessel_i0(x: Any) -> Any
โ โโโ bessel_iv(v: Any, x: Any) -> Any
โ โโโ lower_gamma(s: Any, x: Any) -> Any
โ โโโ upper_gamma(s: Any, x: Any) -> Any
โ โโโ xlogy(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
โโโ stattest
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ chi2_cont(contingency: list, lambda_: float, calc_p: bool, corr: bool) -> tuple
โ โโโ chisquare(observed: list, expected: list) -> tuple
โ โโโ kurttest(data: list, two_tailed: bool) -> tuple
โ โโโ mediantest(samples: Any, ties: Any, lambda_: Any, corr: Any) -> Any
โ โโโ normaltest(data: list) -> tuple
โ โโโ skewtest(data: list, two_tailed: bool) -> tuple
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
Python่งฃ้ๅจ็ๆฌ
Python interpreter version
-
CPython 3.8.10
-
PyPy 3.10.12
็ฉ้ต็จๆถๆต่ฏ Matrix Time Test |
NumPy๏ปฟ+๏ปฟCPython๏ปฟ๏ผ๏ปฟseconds๏ปฟ๏ผ | ๆๅ Ranking |
PyPyNum๏ปฟ+๏ปฟPyPy๏ปฟ๏ผ๏ปฟseconds๏ปฟ๏ผ | ๆๅ Ranking |
Mpmath๏ปฟ_๏ปฟ+๏ปฟ_๏ปฟPyPy๏ปฟ_๏ปฟ๏ผ๏ปฟ_๏ปฟseconds๏ปฟ_๏ปฟ๏ผ | ๆๅ Ranking |
SymPy๏ปฟ_๏ปฟ+๏ปฟ_๏ปฟPyPy๏ปฟ_๏ปฟ๏ผ๏ปฟ_๏ปฟseconds๏ปฟ_๏ปฟ๏ผ | ๆๅ Ranking |
---|---|---|---|---|---|---|---|---|
ๅ๏ปฟๅปบ๏ปฟไธ๏ปฟ็พ๏ปฟ้ถ๏ปฟ้๏ปฟๆบ๏ปฟๆฐ๏ปฟ็ฉ๏ปฟ้ต Create a hundred order random number matrix |
0.000083 | 1 | 0.005374 | 2 | 0.075253 | 3 | 0.230530 | 4 |
ๅๅปบไธๅ้ถ้ๆบๆฐ็ฉ้ต Create a thousand order random number matrix |
0.006740 | 1 | 0.035666 | 2 | 1.200950 | 3 | 4.370265 | 4 |
ไธ็พ้ถ็ฉ้ต็ธๅ Addition of matrices of order one hundred |
0.000029 | 1 | 0.002163 | 2 | 0.045641 | 4 | 0.035700 | 3 |
ไธๅ้ถ็ฉ้ต็ธๅ Adding matrices of order one thousand |
0.002647 | 1 | 0.019111 | 2 | 1.746957 | 4 | 0.771542 | 3 |
ไธ็พ้ถ็ฉ้ต่กๅๅผ Determinant of a hundred order matrix |
0.087209 | 2 | 0.016331 | 1 | 4.354507 | 3 | 5.157206 | 4 |
ไธๅ้ถ็ฉ้ต่กๅๅผ Determinant of a thousand order matrix |
0.616113 | 1 | 3.509747 | 2 | It takes a long time | 3 | It takes a long time | 4 |
ไธ็พ้ถ็ฉ้ตๆฑ้ Finding the inverse of a hundred order matrix |
0.162770 | 2 | 0.015768 | 1 | 8.162948 | 3 | 21.437424 | 4 |
ไธๅ้ถ็ฉ้ตๆฑ้ 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 |
ๆฐ็ป่พๅบๆๆ Array output effect |
[[[[โ-7โ-67] [-78โโ29]] [[-86โ-97] [โ68โโ-3]]] [[[โ11โโ42] [โ24โ-65]] [[-60โโ72] [โ73โโโ2]]]]
|
/ |
[[[[โ37โโ83] [โ40โโโ2]] [[โ-5โ-34] [โ-7โโ72]]] [[[โ13โ-64] [โโ6โโ90]] [[โ68โโ57] [โ78โโ11]]]]
|
/ |
[-80.0โโโ-8.0โโ80.0โโ-88.0] [-99.0โโ-43.0โโ87.0โโโ81.0] [โ20.0โโ-55.0โโ98.0โโโโ8.0] [โโ8.0โโโ44.0โโ64.0โโ-35.0] (ๅชๆฏๆ็ฉ้ต) (Only supports matrices) |
/ |
โกโก16โโโ-56โคโโโกโ8โโโ-28โคโค โขโขโโโโโโโโโฅโโโขโโโโโโโโโฅโฅ โขโฃ-56โโ56โโฆโโโฃ-28โโ28โโฆโฅ โขโโโโโโโโโโโโโโโโโโโโโโโฅ โขโโก-2โโ7โโคโโโโก-18โโ63โโคโฅ โขโโขโโโโโโโฅโโโโขโโโโโโโโโฅโฅ โฃโโฃ7โโโ-7โฆโโโโฃ63โโโ-63โฆโฆ
|
/ |
PyPyNum
โโโ Array
โ โโโ CLASS
โ โ โโโ Array(object)/__init__(self: Any, data: Any, check: Any) -> Any
โ โโโ FUNCTION
โ โโโ array(data: Any) -> Any
โ โโโ asarray(data: Any) -> Any
โ โโโ aslist(data: Any) -> Any
โ โโโ fill(shape: Any, sequence: Any, repeat: Any, pad: Any, rtype: Any) -> Any
โ โโโ full(shape: Any, fill_value: Any, rtype: Any) -> Any
โ โโโ full_like(a: Any, fill_value: Any, rtype: Any) -> Any
โ โโโ get_shape(data: Any) -> Any
โ โโโ is_valid_array(_array: Any, _shape: Any) -> Any
โ โโโ ones(shape: Any, rtype: Any) -> Any
โ โโโ ones_like(a: Any, rtype: Any) -> Any
โ โโโ zeros(shape: Any, rtype: Any) -> Any
โ โโโ zeros_like(a: Any, rtype: Any) -> Any
โโโ FourierT
โ โโโ CLASS
โ โ โโโ FT1D(object)/__init__(self: Any, data: Any) -> Any
โ โโโ FUNCTION
โโโ Geometry
โ โโโ CLASS
โ โ โโโ Circle(object)/__init__(self: Any, center: typing.Union[list, tuple], radius: typing.Union[int, float]) -> Any
โ โ โโโ Line(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple]) -> Any
โ โ โโโ Point(object)/__init__(self: Any, p: typing.Union[list, tuple]) -> Any
โ โ โโโ Polygon(object)/__init__(self: Any, p: typing.Union[list, tuple]) -> Any
โ โ โโโ 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
โ โ โโโ Triangle(object)/__init__(self: Any, a: typing.Union[list, tuple], b: typing.Union[list, tuple], c: typing.Union[list, tuple]) -> Any
โ โโโ FUNCTION
โ โโโ distance(g1: Any, g2: Any, error: typing.Union[int, float]) -> float
โโโ Graph
โ โโโ CLASS
โ โ โโโ BaseGraph(object)/__init__(self: Any) -> Any
โ โ โโโ BaseWeGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any
โ โ โโโ DiGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any
โ โ โโโ UnGraph(pypynum.Graph.BaseGraph)/__init__(self: Any) -> Any
โ โ โโโ WeDiGraph(pypynum.Graph.BaseWeGraph)/__init__(self: Any) -> Any
โ โ โโโ WeUnGraph(pypynum.Graph.BaseWeGraph)/__init__(self: Any) -> Any
โ โโโ FUNCTION
โโโ Group
โ โโโ CLASS
โ โ โโโ Group(object)/__init__(self: Any, data: Any) -> Any
โ โโโ FUNCTION
โ โโโ group(data: Any) -> Any
โโโ Logic
โ โโโ CLASS
โ โ โโโ AND(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ Basic(object)/__init__(self: Any, label: Any) -> Any
โ โ โโโ Binary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ COMP(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ DFF(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any, state: Any) -> Any
โ โ โโโ FullAdder(pypynum.Logic.Ternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any
โ โ โโโ FullSuber(pypynum.Logic.Ternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any
โ โ โโโ HalfAdder(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ HalfSuber(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ JKFF(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, state: Any) -> Any
โ โ โโโ NAND(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ NOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ NOT(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any) -> Any
โ โ โโโ OR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ Quaternary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any
โ โ โโโ TFF(pypynum.Logic.Unary)/__init__(self: Any, label: Any, pin0: Any, state: Any) -> Any
โ โ โโโ Ternary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any) -> Any
โ โ โโโ TwoBDiver(pypynum.Logic.Quaternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any
โ โ โโโ TwoBMuler(pypynum.Logic.Quaternary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any, pin2: Any, pin3: Any) -> Any
โ โ โโโ Unary(pypynum.Logic.Basic)/__init__(self: Any, label: Any, pin0: Any) -> Any
โ โ โโโ XNOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โ โโโ XOR(pypynum.Logic.Binary)/__init__(self: Any, label: Any, pin0: Any, pin1: Any) -> Any
โ โโโ FUNCTION
โ โโโ connector(previous: Any, latter: Any) -> Any
โโโ Matrix
โ โโโ CLASS
โ โ โโโ Matrix(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any
โ โโโ FUNCTION
โ โโโ eigen(matrix: pypynum.Matrix.Matrix) -> tuple
โ โโโ hessenberg(matrix: pypynum.Matrix.Matrix) -> tuple
โ โโโ identity(n: int) -> pypynum.Matrix.Matrix
โ โโโ lu(matrix: pypynum.Matrix.Matrix) -> tuple
โ โโโ mat(data: Any) -> Any
โ โโโ qr(matrix: pypynum.Matrix.Matrix) -> tuple
โ โโโ rotate90(matrix: pypynum.Matrix.Matrix, times: int) -> pypynum.Matrix.Matrix
โ โโโ svd(matrix: pypynum.Matrix.Matrix) -> tuple
โ โโโ tril_indices(n: int, k: int, m: int) -> tuple
โโโ NeuralN
โ โโโ CLASS
โ โ โโโ NeuralNetwork(object)/__init__(self: Any, _input: Any, _hidden: Any, _output: Any) -> Any
โ โโโ FUNCTION
โ โโโ neuraln(_input: Any, _hidden: Any, _output: Any) -> Any
โโโ Quaternion
โ โโโ CLASS
โ โ โโโ Euler(object)/__init__(self: Any, y: typing.Union[int, float], p: typing.Union[int, float], r: typing.Union[int, float]) -> Any
โ โ โโโ 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
โ โโโ FUNCTION
โ โโโ 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]
โ โโโ euler(yaw: typing.Union[int, float], pitch: typing.Union[int, float], roll: typing.Union[int, float]) -> pypynum.Quaternion.Euler
โ โโโ quat(w: typing.Union[int, float], x: typing.Union[int, float], y: typing.Union[int, float], z: typing.Union[int, float]) -> pypynum.Quaternion.Quaternion
โโโ Symbolics
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ parse_expr(expr: str) -> list
โโโ Tensor
โ โโโ CLASS
โ โ โโโ Tensor(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any
โ โโโ FUNCTION
โ โโโ ten(data: list) -> pypynum.Tensor.Tensor
โ โโโ tensor_and_number(tensor: Any, operator: Any, number: Any) -> Any
โ โโโ tensorproduct(tensors: pypynum.Tensor.Tensor) -> pypynum.Tensor.Tensor
โ โโโ zeros(_dimensions: Any) -> Any
โ โโโ zeros_like(_nested_list: Any) -> Any
โโโ Tree
โ โโโ CLASS
โ โ โโโ MultiTree(object)/__init__(self: Any, root: Any) -> Any
โ โ โโโ MultiTreeNode(object)/__init__(self: Any, data: Any) -> Any
โ โโโ FUNCTION
โโโ Vector
โ โโโ CLASS
โ โ โโโ Vector(pypynum.Array.Array)/__init__(self: Any, data: Any, check: Any) -> Any
โ โโโ FUNCTION
โ โโโ vec(data: Any) -> Any
โโโ chars
โ โโโ CLASS
โ โโโ FUNCTION
โโโ cipher
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ atbash(text: str) -> str
โ โโโ base_64(text: str, decrypt: bool) -> str
โ โโโ caesar(text: str, shift: int, decrypt: bool) -> str
โ โโโ hill256(text: bytes, key: list, decrypt: bool) -> bytes
โ โโโ ksa(key: bytes) -> list
โ โโโ morse(text: str, decrypt: bool) -> str
โ โโโ playfair(text: str, key: str, decrypt: bool) -> str
โ โโโ prga(s: list) -> Any
โ โโโ rc4(text: bytes, key: bytes) -> bytes
โ โโโ rot13(text: str) -> str
โ โโโ substitution(text: str, sub_map: dict, decrypt: bool) -> str
โ โโโ vigenere(text: str, key: str, decrypt: bool) -> str
โโโ constants
โ โโโ CLASS
โ โโโ FUNCTION
โโโ dists
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ beta_pdf(x: Any, a: Any, b: Any) -> Any
โ โโโ binom_pmf(k: Any, n: Any, p: Any) -> Any
โ โโโ cauchy_cdf(x: Any, x0: Any, gamma: Any) -> Any
โ โโโ cauchy_pdf(x: Any, x0: Any, gamma: Any) -> Any
โ โโโ chi2_cdf(x: Any, df: Any) -> Any
โ โโโ chi2_pdf(x: Any, df: Any) -> Any
โ โโโ expon_cdf(x: Any, scale: Any) -> Any
โ โโโ expon_pdf(x: Any, scale: Any) -> Any
โ โโโ f_pdf(x: Any, dfnum: Any, dfden: Any) -> Any
โ โโโ gamma_pdf(x: Any, shape: Any, scale: Any) -> Any
โ โโโ geometric_pmf(k: Any, p: Any) -> Any
โ โโโ hypergeom_pmf(k: Any, mg: Any, n: Any, nt: Any) -> Any
โ โโโ inv_gauss_pdf(x: Any, mu: Any, lambda_: Any, alpha: Any) -> Any
โ โโโ levy_pdf(x: Any, c: Any) -> Any
โ โโโ log_logistic_cdf(x: Any, alpha: Any, beta: Any) -> Any
โ โโโ log_logistic_pdf(x: Any, alpha: Any, beta: Any) -> Any
โ โโโ logistic_cdf(x: Any, mu: Any, s: Any) -> Any
โ โโโ logistic_pdf(x: Any, mu: Any, s: Any) -> Any
โ โโโ lognorm_cdf(x: Any, mu: Any, sigma: Any) -> Any
โ โโโ lognorm_pdf(x: Any, s: Any, scale: Any) -> Any
โ โโโ logser_pmf(k: Any, p: Any) -> Any
โ โโโ multinomial_pmf(k: Any, n: Any, p: Any) -> Any
โ โโโ nbinom_pmf(k: Any, n: Any, p: Any) -> Any
โ โโโ nhypergeom_pmf(k: Any, m: Any, n: Any, r: Any) -> Any
โ โโโ normal_cdf(x: Any, mu: Any, sigma: Any) -> Any
โ โโโ normal_pdf(x: Any, mu: Any, sigma: Any) -> Any
โ โโโ pareto_pdf(x: Any, k: Any, m: Any) -> Any
โ โโโ poisson_pmf(k: Any, mu: Any) -> Any
โ โโโ rayleigh_pdf(x: Any, sigma: Any) -> Any
โ โโโ t_pdf(x: Any, df: Any) -> Any
โ โโโ uniform_cdf(x: Any, loc: Any, scale: Any) -> Any
โ โโโ uniform_pdf(x: Any, loc: Any, scale: Any) -> Any
โ โโโ vonmises_pdf(x: Any, mu: Any, kappa: Any) -> Any
โ โโโ weibull_max_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
โ โโโ weibull_min_pdf(x: Any, c: Any, scale: Any, loc: Any) -> Any
โ โโโ zipf_pmf(k: Any, s: Any, n: Any) -> Any
โโโ equations
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ lin_eq(left: list, right: list) -> list
โ โโโ poly_eq(coefficients: list) -> list
โโโ errors
โ โโโ CLASS
โ โโโ FUNCTION
โโโ file
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ read(file: str) -> list
โ โโโ write(file: str, cls: object) -> Any
โโโ image
โ โโโ CLASS
โ โ โโโ PNG(object)/__init__(self: Any) -> None
โ โโโ FUNCTION
โ โโโ crc(data: Any, length: Any, init: Any, xor: Any) -> Any
โโโ maths
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ arrangement(n: int, r: int) -> int
โ โโโ combination(n: int, r: int) -> int
โ โโโ acos(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ acosh(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ acot(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ acoth(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ acsc(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ acsch(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ arrangement(n: int, r: int) -> int
โ โโโ asec(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ asech(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ asin(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ asinh(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ atan(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ atanh(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ average(data: Any, weights: Any, expected: Any) -> Any
โ โโโ bessel_i0(x: Any) -> Any
โ โโโ bessel_iv(v: Any, x: Any) -> Any
โ โโโ beta(p: typing.Union[int, float], q: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ central_moment(data: typing.Union[list, tuple], order: int) -> float
โ โโโ coeff_det(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ combination(n: int, r: int) -> int
โ โโโ corr_coeff(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ cos(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ cosh(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ cot(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ coth(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ cov(x: typing.Union[list, tuple], y: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]
โ โโโ crt(n: typing.Union[list, tuple], a: typing.Union[list, tuple]) -> int
โ โโโ csc(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ csch(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ cumprod(lst: typing.Union[list, tuple]) -> list
โ โโโ cumsum(lst: typing.Union[list, tuple]) -> list
โ โโโ deriv(f: Any, x: typing.Union[int, float], h: typing.Union[int, float], args: Any, kwargs: Any) -> float
โ โโโ erf(x: typing.Union[int, float]) -> float
โ โโโ exgcd(a: int, b: int) -> tuple
โ โโโ exp(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ factorial(n: int) -> int
โ โโโ freq(data: typing.Union[list, tuple]) -> dict
โ โโโ gamma(alpha: typing.Union[int, float]) -> float
โ โโโ gcd(args: int) -> int
โ โโโ geom_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ harm_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ integ(f: Any, x_start: typing.Union[int, float], x_end: typing.Union[int, float], n: int, args: Any, kwargs: Any) -> float
โ โโโ iroot(y: int, n: int) -> int
โ โโโ is_possibly_square(n: int) -> bool
โ โโโ is_square(n: int) -> bool
โ โโโ isqrt(x: int) -> int
โ โโโ kurt(data: typing.Union[list, tuple], fisher: bool) -> float
โ โโโ lcm(args: int) -> int
โ โโโ ln(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ lower_gamma(s: Any, x: Any) -> Any
โ โโโ mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ median(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ mod_order(a: int, n: int, b: int) -> int
โ โโโ mode(data: typing.Union[list, tuple]) -> Any
โ โโโ normalize(data: typing.Union[list, tuple], target: typing.Union[int, float, complex]) -> typing.Union[list, tuple]
โ โโโ parity(x: int) -> int
โ โโโ pi(i: int, n: int, f: Any) -> typing.Union[int, float, complex]
โ โโโ primitive_root(a: int, single: bool) -> typing.Union[int, list]
โ โโโ product(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ ptp(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ raw_moment(data: typing.Union[list, tuple], order: int) -> float
โ โโโ roll(seq: typing.Union[list, tuple, str], shift: int) -> typing.Union[list, tuple, str]
โ โโโ root(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
โ โโโ sec(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ sech(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ sigma(i: int, n: int, f: Any) -> typing.Union[int, float, complex]
โ โโโ sigmoid(x: typing.Union[int, float]) -> float
โ โโโ sign(x: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
โ โโโ sin(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ sinh(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ skew(data: typing.Union[list, tuple]) -> float
โ โโโ square_mean(numbers: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ std(numbers: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]
โ โโโ sumprod(arrays: typing.Union[list, tuple]) -> typing.Union[int, float, complex]
โ โโโ tan(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ tanh(x: typing.Union[int, float]) -> typing.Union[int, float]
โ โโโ totient(n: int) -> int
โ โโโ upper_gamma(s: Any, x: Any) -> Any
โ โโโ var(numbers: typing.Union[list, tuple], dof: int) -> typing.Union[int, float, complex]
โ โโโ xlogy(x: typing.Union[int, float, complex], y: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
โ โโโ zeta(alpha: typing.Union[int, float, complex]) -> typing.Union[int, float, complex]
โโโ numbers
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ float2fraction(number: float, mixed: bool, error: float) -> tuple
โ โโโ int2roman(integer: int, overline: bool) -> str
โ โโโ int2words(integer: int) -> str
โ โโโ roman2int(roman_num: str) -> int
โ โโโ str2int(string: str) -> int
โโโ plotting
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ 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]
โ โโโ 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]
โ โโโ 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]
โ โโโ change(data: typing.Union[list, str]) -> typing.Union[list, str]
โ โโโ color(text: str, rgb: typing.Union[list, tuple]) -> str
โ โโโ 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]
โโโ polynomial
โ โโโ CLASS
โ โ โโโ Polynomial(object)/__init__(self: Any, terms: Any) -> Any
โ โโโ FUNCTION
โ โโโ from_coeffs(coeffs: Any) -> Any
โ โโโ from_coords(coords: Any) -> Any
โ โโโ leggauss(polynomial: Any) -> Any
โ โโโ legpoly(n: Any) -> Any
โ โโโ poly(terms: Any) -> Any
โโโ random
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ choice(seq: typing.Union[list, tuple, str], shape: typing.Union[list, tuple]) -> Any
โ โโโ gauss(mu: typing.Union[int, float], sigma: typing.Union[int, float], shape: typing.Union[list, tuple]) -> typing.Union[float, list]
โ โโโ gauss_error(original: typing.Union[list, tuple], mu: typing.Union[int, float], sigma: typing.Union[int, float]) -> list
โ โโโ rand(shape: typing.Union[list, tuple]) -> typing.Union[float, list]
โ โโโ randint(a: int, b: int, shape: typing.Union[list, tuple]) -> typing.Union[int, list]
โ โโโ uniform(a: typing.Union[int, float], b: typing.Union[int, float], shape: typing.Union[list, tuple]) -> typing.Union[float, list]
โโโ regression
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ lin_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> list
โ โโโ par_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple]) -> list
โ โโโ poly_reg(x: typing.Union[list, tuple], y: typing.Union[list, tuple], n: int) -> list
โโโ sequence
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ 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
โ โโโ bernoulli(n: int, single: bool) -> list
โ โโโ catalan(n: int, single: bool) -> typing.Union[int, list]
โ โโโ farey(n: int) -> list
โ โโโ fibonacci(n: int, single: bool) -> typing.Union[int, list]
โ โโโ 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
โ โโโ recaman(n: int, single: bool) -> typing.Union[int, list]
โโโ stattest
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ chi2_cont(contingency: list, lambda_: float, calc_p: bool, corr: bool) -> tuple
โ โโโ chisquare(observed: list, expected: list) -> tuple
โ โโโ kurttest(data: list, two_tailed: bool) -> tuple
โ โโโ mediantest(samples: Any, ties: Any, lambda_: Any, corr: Any) -> Any
โ โโโ normaltest(data: list) -> tuple
โ โโโ skewtest(data: list, two_tailed: bool) -> tuple
โโโ test
โ โโโ CLASS
โ โโโ FUNCTION
โโโ this
โ โโโ CLASS
โ โโโ FUNCTION
โโโ tools
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ classify(array: typing.Union[list, tuple]) -> dict
โ โโโ dedup(iterable: typing.Union[list, tuple, str]) -> typing.Union[list, tuple, str]
โ โโโ frange(start: typing.Union[int, float], stop: typing.Union[int, float], step: float) -> list
โ โโโ generate_primes(limit: int) -> list
โ โโโ generate_semiprimes(limit: int) -> list
โ โโโ geomspace(start: typing.Union[int, float], stop: typing.Union[int, float], number: int) -> list
โ โโโ interp(data: typing.Union[list, tuple], length: int) -> list
โ โโโ linspace(start: typing.Union[int, float], stop: typing.Union[int, float], number: int) -> list
โ โโโ magic_square(n: Any) -> Any
โ โโโ primality(n: int, iter_num: int) -> bool
โ โโโ prime_factors(integer: int, dictionary: bool, pollard_rho: bool) -> typing.Union[list, dict]
โ โโโ split(iterable: typing.Union[list, tuple, str], key: typing.Union[list, tuple], retain: bool) -> list
โโโ types
โ โโโ CLASS
โ โโโ FUNCTION
โโโ ufuncs
โ โโโ CLASS
โ โโโ FUNCTION
โ โโโ add(x: Any, y: Any) -> Any
โ โโโ base_ufunc(arrays: Any, func: Any, args: Any, rtype: Any) -> Any
โ โโโ divide(x: Any, y: Any) -> Any
โ โโโ floor_divide(x: Any, y: Any) -> Any
โ โโโ modulo(x: Any, y: Any) -> Any
โ โโโ multiply(x: Any, y: Any) -> Any
โ โโโ power(x: Any, y: Any, m: Any) -> Any
โ โโโ subtract(x: Any, y: Any) -> Any
โ โโโ ufunc_helper(x: Any, y: Any, func: Any) -> Any
โโโ utils
โโโ CLASS
โ โโโ InfIterator(object)/__init__(self: Any, start: typing.Union[int, float, complex], mode: str, common: typing.Union[int, float, complex]) -> Any
โ โโโ LinkedList(object)/__init__(self: Any) -> Any
โ โโโ LinkedListNode(object)/__init__(self: Any, value: Any, next_node: Any) -> Any
โ โโโ OrderedSet(object)/__init__(self: Any, sequence: Any) -> Any
โโโ FUNCTION
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))**(-ฮถn1-2.718/mฮฃ99)//9"))
"""
%()*+-./0123456789
ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
ฮฮฮฮฮฮฮฮฮฮฮฮฮฮฮฮ ฮกฮฃฮคฮฅฮฆฮงฮจฮฉฮฑฮฒฮณฮดฮตฮถฮทฮธฮนฮบฮปฮผฮฝฮพฮฟฯฯฯฯฯ
ฯฯฯฯ
[['10', '+', 'a', '-', ['3.14', '+', 'b0'], '*', '-5'], '**', ['-ฮถn1', '-', '2.718', '/', 'mฮฃ99'], '//', '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]
"""
# ๆ็คบ๏ผ
#
# ๆต่ฏๅทฒๆๅ้่ฟๅนถ็ปๆใ
#
# ่ฟไบๆต่ฏๅชๆฏ่ฟไธชๅ
ๅ่ฝ็ไธ้จๅใ
#
# ๆดๅค็ๅ่ฝ้่ฆ่ชๅทฑๆข็ดขๅๅฐ่ฏ๏ผ
#
# 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!