A Python math package written in pure Python programming language
Project description
PyPyNum
Version -> 1.0.0
Gitee -> https://www.gitee.com/PythonSJL/PyPyNum
介绍
Introduction
- DIY数学库，类似于numpy、scipy等，专为PyPy解释器制作
- DIY math library, similar to numpy, scipy, etc., specifically designed for PyPy 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，QQ 2261748025 （Py𝙿𝚢𝚝𝚑𝚘𝚗-水晶兰）
- If you need to contact, QQ 2261748025 (Py𝙿𝚢𝚝𝚑𝚘𝚗-水晶兰)
基本结构
Basic structure
PyPyNum
test
[A Code Test File]
Geometry
CLASSES
Circle
Line
Point
Polygon
Quadrilateral
Triangle
FUNCTIONS
distance(g1: <built-in function any>, g2: <built-in function any>, error: int | float = 0) -> float
Matrix
CLASSES
Matrix
FUNCTIONS
eig(matrix)
hypot(a, b)
identity(n)
lu(matrix)
mat(data)
qr(matrix)
svd(matrix)
tril_indices(n, k=0, m=None)
zeros(_dimensions)
zeros_like(_nested_list)
Quaternion
CLASSES
Euler
Quaternion
FUNCTIONS
change(data: Euler | Quaternion) -> Quaternion | Euler
euler(yaw: int | float = 0, pitch: int | float = 0, roll: int | float = 0) -> Euler
quat(w: int | float = 0, x: int | float = 0, y: int | float = 0, z: int | float = 0) -> Quaternion
Tensor
CLASSES
Tensor
FUNCTIONS
get_shape(_tensor)
is_valid_tensor(_tensor, _shape)
ten(data)
tensor_and_number(tensor, operator, number)
tolist(_nested_list)
constants
DATA
AMU = 1.6605402e-27
EB = 1152921504606846976
G = 6.6743e-11
GB = 1073741824
KB = 1024
MB = 1048576
NA = 6.02214076e+23
PB = 1125899906842624
TB = 1099511627776
YB = 1208925819614629174706176
ZB = 1180591620717411303424
atto = 1e-18
c = 299792458
centi = 0.01
deci = 0.1
deka = 10.0
e = 2.718281828459045
exa = 1e+18
femto = 1e-15
gamma = 0.5772156649015329
giga = 1000000000.0
h = 6.62607015e-34
hecto = 100.0
inf = inf
kilo = 1000.0
mega = 1000000.0
micro = 1e-06
milli = 0.001
nan = nan
nano = 1e-09
peta = 1000000000000000.0
phi = 1.618033988749895
pi = 3.141592653589793
pico = 1e-12
qe = 1.60217733e-19
tera = 1000000000000.0
yocto = 1e-24
yotta = 1e+24
zepto = 1e-21
zetta = 1e+21
equations
FUNCTIONS
mles = multivariate_linear_equation_system(left: list, right: list) -> None | list
multivariate_linear_equation_system(left: list, right: list) -> None | list
pe = polynomial_equation(coefficients: list) -> list
polynomial_equation(coefficients: list) -> list
mathematics
FUNCTIONS
A = arrangement(n: int, r: int) -> int
C = combination(n: int, r: int) -> int
acos(x: int | float) -> int | float
acosh(x: int | float) -> int | float
acot(x: int | float) -> int | float
acoth(x: int | float) -> int | float
acsc(x: int | float) -> int | float
acsch(x: int | float) -> int | float
arrangement(n: int, r: int) -> int
asec(x: int | float) -> int | float
asech(x: int | float) -> int | float
asin(x: int | float) -> int | float
asinh(x: int | float) -> int | float
atan(x: int | float) -> int | float
atanh(x: int | float) -> int | float
beta(p: int | float, q: int | float) -> int | float
combination(n: int, r: int) -> int
cos(x: int | float) -> int | float
cosh(x: int | float) -> int | float
cot(x: int | float) -> int | float
coth(x: int | float) -> int | float
csc(x: int | float) -> int | float
csch(x: int | float) -> int | float
definite_integral(f, x_start: int | float, x_end: int | float, n: int = 10000000) -> float
derivative(f, x: int | float, h: int | float = 1e-07) -> float
erf(x: int | float) -> float
exp(x: int | float) -> int | float
factorial(n: int) -> int
freq(data: list | tuple) -> dict
gamma(alpha: int | float) -> float
gaussian(x: int | float, _mu: int | float = 0, _sigma: int | float = 1) -> float
ln(x: int | float) -> int | float
mean(numbers: list | tuple) -> int | float | complex
median(numbers: list | tuple) -> int | float | complex
mode(data: list | tuple) -> <built-in function any>
pi(i: int, n: int, f) -> int | float | complex
product(numbers: list | tuple) -> int | float | complex
ptp(numbers: list | tuple) -> int | float | complex
root(x: int | float | complex, y: int | float | complex) -> int | float | complex
sec(x: int | float) -> int | float
sech(x: int | float) -> int | float
sigma(i: int, n: int, f) -> int | float | complex
sigmoid(x: int | float) -> float
sign(x: int | float) -> int
sin(x: int | float) -> int | float
sinh(x: int | float) -> int | float
std(numbers: list | tuple) -> int | float | complex
tan(x: int | float) -> int | float
tanh(x: int | float) -> int | float
var(numbers: list | tuple) -> int | float | complex
zeta(alpha: int | float) -> float
plotting
FUNCTIONS
background(right: int | float = 5, left: int | float = -5, top: int | float = 5, bottom: int | float = -5, complexity: int | float = 5, ratio: int | float = 3, merge: bool = False) -> list | str
binary(function, right: int | float = 5, left: int | float = -5, top: int | float = 5, bottom: int | float = -5, complexity: int | float = 5, ratio: int | float = 3, error=0, compare='==', merge: bool = True, basic: list = None, character: str = '.', data: bool = False) -> list | str
c_unary(function, start: int | float, end: int | float, interval: int | float = 5, projection: str = 'ri', right: int | float = 5, left: int | float = -5, top: int | float = 5, bottom: int | float = -5, complexity: int | float = 5, ratio: int | float = 3, merge: bool = True, basic: list = None, character: str = '.', data: bool = False) -> list | str
change(data: list | str) -> list | str
unary(function, right: int | float = 5, left: int | float = -5, top: int | float = 5, bottom: int | float = -5, complexity: int | float = 5, ratio: int | float = 3, merge: bool = True, basic: list = None, character: str = '.', data: bool = False) -> list | str
regression
FUNCTIONS
__covariance_matrix(_x, _y)
linear_regression(x_values, y_values)
parabolic_regression(x, y)
__solve_equations(a1, b1, c1, a2, b2, c2)
tools
FUNCTIONS
classify(array: list | tuple) -> dict
deduplicate(iterable: list | tuple | str) -> list | tuple | str
frange(start: int | float, stop: int | float, step: float = 1.0) -> list
linspace(start: int | float, stop: int | float, number: int) -> list
代码测试
Code testing
>>> from pypynum import Geometry, Matrix, Quaternion, Tensor, constants, equations, mathematics, regression, plotting, tools
...
>>> 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)
>>> 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]]
[[-2.0 1.0]
[ 1.5 -0.5]]
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)
>>> 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]]]
>>> 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
1000000000000.0
>>> p = [1, -2, -3, 4]
>>> m = [
[
[1, 2, 3],
[6, 10, 12],
[7, 16, 9]
],
[-1, -2, -3]
]
>>> print(equations.pe(p))
>>> print(equations.mles(*m))
[2.561552812809, -1.561552812809, 1.0]
[1.666666666667, -0.666666666667, -0.444444444444]
>>> print(mathematics.cot(constants.pi / 3))
>>> print(mathematics.gamma(1.5))
>>> print(mathematics.pi(1, 10, lambda x: x ** 2))
>>> print(mathematics.product([2, 3, 5, 7, 11, 13, 17, 19, 23, 29]))
>>> print(mathematics.sigma(1, 10, lambda x: x ** 2))
>>> print(mathematics.var([2, 3, 5, 7, 11, 13, 17, 19, 23, 29]))
0.577350269189626
0.886226925452758
13168189440000
6469693230
385
73.29
>>> print(regression.linear_regression(range(5), [2, 4, 6, 7, 8]))
>>> print(regression.parabolic_regression(range(5), [2, 4, 6, 7, 8]))
f(x) = 1.5 * x + 2.4
[1.5, 2.4]
f(x) = -0.214285714 * x ** 2 + 2.357142857 * x + 1.971428571
[-0.214285714, 2.357142857, 1.971428571]
>>> 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, start=-10, end=10, interval=100, 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(tools.classify([1, 2.3, 4 + 5j, "string", list, True, 3.14, False, tuple, tools]))
>>> print(tools.deduplicate(["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 'F:\PyPyproject\PyPyproject1\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]
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