A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.5]
Project description
PyPyNum
A multifunctional mathematical calculation package written in pure Python programming language [Python>=3.5]
________ ___ ___ ________ ___ ___ ________ ___ ___ _____ ______
|\ __ \ |\ \ / /||\ __ \ |\ \ / /||\ ___ \ |\ \|\ \ |\ _ \ _ \
\ \ \|\ \\ \ \/ / /\ \ \|\ \\ \ \/ / /\ \ \\ \ \\ \ \\\ \\ \ \\\__\ \ \
\ \ ____\\ \ / / \ \ ____\\ \ / / \ \ \\ \ \\ \ \\\ \\ \ \\|__| \ \
\ \ \___| \/ / / \ \ \___| \/ / / \ \ \\ \ \\ \ \\\ \\ \ \ \ \ \
\ \__\ __/ / / \ \__\ __/ / / \ \__\\ \__\\ \_______\\ \__\ \ \__\
\|__| |\___/ / \|__| |\___/ / \|__| \|__| \|_______| \|__| \|__|
\|___|/ \|___|/
Version -> 1.7.0 | PyPI -> https://pypi.org/project/PyPyNum/ | Gitee -> https://www.gitee.com/PythonSJL/PyPyNum
PyPI上无法显示logo，可以在Gitee中查看。
The logo cannot be displayed on PyPI, it can be viewed in Gitee.
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)
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
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
新增代码行数：
约八百行
New code lines:
approximately 800 lines
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
部分函数再次优化，性能提高一倍。
Partial functions were optimized
again, resulting in a doubling
of performance.
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
新增的好玩功能举例。
Examples of newly added fun
features.
新增的有序集合包含绝大多数内置集合的运算
，并且另有几个新函数。请注意它不是从内置
集合继承的。
The newly added ordered set
contains the majority of
built-in set operations, and
there are also several new
functions. Please note that it
does not inherit from the
built-in set.
新增的无穷迭代器支持迭代多种数列，默认的
空字符串即为自增数列。目前支持的数列有等
差数列、等比数列、斐波那契数列与卡特兰数
列。
The newly added infinite
iterator supports iterating
multiple sequences, and the
default empty string is the self
increasing sequence. The
currently supported sequences
include arithmetic sequences,
proportional sequences,
Fibonacci sequences, and Catalan
sequences.
!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=!=
<<<以下结构中的省略号表示原有的部分>>>
<<<The ellipsis in the following structure represents the original part>>>
PyPyNum
★ Tree [Various trees]
CLASSES
MultiTree
MultiTreeNode
★ maths [Mathematical functions]
FUNCTIONS
...
totient(n: int) -> int
mod_order(a: int, n: int, b: int) -> int
primitive_root(a: int, single: bool = False) -> Union[int, list]
normalize(data: arr, target: num = 1) -> arr
average(data, weights, expected=False)
exgcd(a: int, b: int) -> tuple
crt(n: arr, a: arr) -> int
★ sequence [Various sequences]
FUNCTIONS
farey(n: int) -> list
...
★ tools [Other useful tools]
FUNCTIONS
...
primality(n: int, iter_num: int = 10) -> bool
generate_primes(limit: int) -> list
prime_factors(integer: int, dictionary: bool = False, pollard_rho: bool = True) -> Union[list, dict]
★ utils [Other useful tools]
CLASSES
InfIterator
OrderedSet
运行用时测试
Run Time Test
| 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 | 速度极慢 | 3 | 无法计算 | 4 |
| 一百阶矩阵求逆 Finding the inverse of a hundred order matrix |
0.162770 | 1 | 31.088849 | 4 | 8.162948 | 2 | 21.437424 | 3 |
| 一千阶矩阵求逆 Finding the inverse of a thousand order matrix |
0.598905 | 1 | 速度较慢 | 4 | 速度较慢 | 2 | 速度较慢 | 3 |
| 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
★ __init__
[Import some features from other modules in this package]
★ errors [Special errors]
CLASSES
LogicError
RandomError
ShapeError
★ file [Reading and saving instance data]
FUNCTIONS
read(file: str) -> list
write(file: str, *cls: object)
★ test
[A code test file]
★ this
[The Zen of PyPyNum]
★ types [Special types]
DATA
arr = typing.Union[list, tuple]
ite = typing.Union[list, tuple, str]
num = typing.Union[int, float, complex]
real = typing.Union[int, float]
★ Array [N-dimensional array]
CLASSES
Array
FUNCTIONS
array(data=None)
fill(shape, sequence=None)
function(_array, _function, args=None)
get_shape(data)
is_valid_array(_array, _shape)
zeros(shape)
zeros_like(_nested_list)
★ FourierT [Fourier transform and inverse Fourier transform]
CLASSES
FT1D
★ Geometry [Planar geometry]
CLASSES
Circle
Line
Point
Polygon
Quadrilateral
Triangle
FUNCTIONS
distance(g1, g2, error: int | float = 0) -> float
★ Group [Group theory]
CLASSES
Group
FUNCTIONS
add(x, y)
divide(x, y)
group(data)
multiply(x, y)
subtract(x, y)
★ Logic [Logic circuit simulation]
CLASSES
Basic
Binary
AND
COMP
HalfAdder
HalfSuber
JKFF
NAND
NOR
OR
XNOR
XOR
Quaternary
TwoBDiver
TwoBMuler
Ternary
FullAdder
FullSuber
Unary
DFF
NOT
TFF
★ Matrix [Matrix calculation]
CLASSES
Matrix
FUNCTIONS
eig(matrix)
identity(n)
lu(matrix)
mat(data)
qr(matrix)
same(rows, cols, value=0)
svd(matrix)
tril_indices(n, k=0, m=None)
zeros(_dimensions)
zeros_like(_nested_list)
★ NeuralN [A simple neural network model]
CLASSES
NeuralNetwork
FUNCTIONS
neuraln(_input, _hidden, _output)
★ Quaternion [Quaternion calculation]
CLASSES
Euler
Quaternion
FUNCTIONS
change(data: Union[pypynum.Quaternion.Quaternion, pypynum.Matrix.Matrix, pypynum.Quaternion.Euler], to: str) -> Union[pypynum.Quaternion.Quaternion, pypynum.Matrix.Matrix, pypynum.Quaternion.Euler]
euler(yaw: Union[int, float] = 0, pitch: Union[int, float] = 0, roll: Union[int, float] = 0) -> pypynum.Quaternion.Euler
quat(w: Union[int, float] = 0, x: Union[int, float] = 0, y: Union[int, float] = 0, z: Union[int, float] = 0) -> pypynum.Quaternion.Quaternion
★ Symbolics [Symbol calculation]
FUNCTIONS
interpreter(expr: str) -> list
DATA
basic = "%()*+-./0123456789"
english = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
greek = "ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩαβγδεζηθικλμνξοπρστυφχψω"
operators = ["**", "*", "//", "/", "%", "+", "-"]
valid = "%()*+-./0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcd...zΑΒΓΔΕΖΗΘΙ...
★ Tensor [Tensor calculation]
CLASSES
Tensor
FUNCTIONS
ten(data)
tensor_and_number(tensor, operator, number)
tolist(_nested_list)
zeros(_dimensions)
zeros_like(_nested_list)
★ Tree [Various trees]
CLASSES
MultiTree
MultiTreeNode
★ Vector [Vector calculation]
CLASSES
Vector
FUNCTIONS
same(length, value=0)
vec(data)
zeros(_dimensions)
zeros_like(_nested_list)
★ chars [Special mathematical symbols]
DATA
arrow = [["↖", "↑", "↗"], ["←", "⇌", "→"], ["↙", "↓", "↘"], ["↔", "⇋",...
div = "÷"
mul = "×"
others = "¬°‰‱′″∀∂∃∅∆∇∈∉∏∐∑∝∞∟∠∣∥∧∨∩∪∫∬∭∮∯∰∴∵∷∽≈≌≒≠≡≢≤≥≪≫≮≯≰≱≲≳⊕⊙⊥⊿⌒㏑㏒...
overline = "̄"
pi = "Ππ𝜫𝝅𝝥𝝿𝞟𝞹Пп∏ϖ∐ℼㄇ兀"
sgn = "±"
strikethrough = "̶"
subscript = "₀₁₂₃₄₅₆₇₈₉₊₋₌₍₎ₐₑₕᵢⱼₖₗₘₙₒₚᵣₛₜᵤᵥₓ"
superscript = "⁰¹²³⁴⁵⁶⁷⁸⁹⁺⁻⁼⁽⁾ᴬᴮᴰᴱᴳᴴᴵᴶᴷᴸᴹᴺᴼᴾᴿᵀᵁⱽᵂᵃᵇᶜᵈᵉᶠᵍʰⁱʲᵏˡᵐⁿᵒᵖʳˢᵗᵘᵛ...
tab = [["┌", "┬", "┐"], ["├", "┼", "┤"], ["└", "┴", "┘"], ["─", "╭", "...
underline = "_"
★ cipher [String encryption and decryption algorithms]
FUNCTIONS
dna(string: str, decrypt: bool = False) -> str
★ constants [Constants in mathematics and science]
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
e = 2.718281828459045
exa = 1000000000000000000
femto = 1e-15
gamma = 0.5772156649015329
giga = 1000000000
h = 6.62607015e-34
hecto = 100
inf = inf
kilo = 1000
mega = 1000000
micro = 1e-06
milli = 0.001
nan = nan
nano = 1e-09
peta = 1000000000000000
phi = 1.618033988749895
pi = 3.141592653589793
pico = 1e-12
qe = 1.60217733e-19
tera = 1000000000000
yocto = 1e-24
yotta = 1000000000000000000000000
zepto = 1e-21
zetta = 1000000000000000000000
★ equations [Solving specific forms of equations]
FUNCTIONS
linear_equation(left: list, right: list) -> list
polynomial_equation(coefficients: list) -> list
★ maths [Mathematical functions]
FUNCTIONS
root(x: num, y: num) -> num
exp(x: real) -> real
ln(x: real) -> real
gcd(*args: int) -> int
lcm(*args: int) -> int
sin(x: real) -> real
cos(x: real) -> real
tan(x: real) -> real
csc(x: real) -> real
sec(x: real) -> real
cot(x: real) -> real
asin(x: real) -> real
acos(x: real) -> real
atan(x: real) -> real
acsc(x: real) -> real
asec(x: real) -> real
acot(x: real) -> real
sinh(x: real) -> real
cosh(x: real) -> real
tanh(x: real) -> real
csch(x: real) -> real
sech(x: real) -> real
coth(x: real) -> real
asinh(x: real) -> real
acosh(x: real) -> real
atanh(x: real) -> real
acsch(x: real) -> real
asech(x: real) -> real
acoth(x: real) -> real
ptp(numbers: arr) -> num
median(numbers: arr) -> num
freq(data: arr) -> dict
mode(data: arr)
mean(numbers: arr) -> num
geom_mean(numbers: arr) -> num
square_mean(numbers: arr) -> num
harm_mean(numbers: arr) -> num
raw_moment(data: arr, order: int) -> float
central_moment(data: arr, order: int) -> float
var(numbers: arr, ddof: int = 0) -> num
skew(data: arr) -> float
kurt(data: arr) -> float
std(numbers: arr, ddof: int = 0) -> num
cov(x: arr, y: arr, ddof: int = 0) -> num
corr_coeff(x: arr, y: arr) -> num
coeff_det(x: arr, y: arr) -> num
product(numbers: arr) -> num
sigma(i: int, n: int, f) -> num
pi(i: int, n: int, f) -> num
derivative(f, x: real, h: real = 1e-7) -> float
definite_integral(f, x_start: real, x_end: real, n: int = 10000000) -> float
beta(p: real, q: real) -> real
gamma(alpha: real) -> float
factorial(n: int) -> int
arrangement(n: int, r: int) -> int
combination(n: int, r: int) -> int
zeta(alpha: real) -> float
gaussian(x: real, _mu: real = 0, _sigma: real = 1) -> float
poisson(x: int, _lambda: real) -> float
erf(x: real) -> float
sigmoid(x: real) -> float
sign(x: real) -> int
parity(x: int) -> int
cumsum(lst: arr) -> list
cumprod(lst: arr) -> list
iroot(y: int, n: int) -> int
totient(n: int) -> int
mod_order(a: int, n: int, b: int) -> int
primitive_root(a: int, single: bool = False) -> Union[int, list]
normalize(data: arr, target: num = 1) -> arr
average(data, weights, expected=False)
exgcd(a: int, b: int) -> tuple
crt(n: arr, a: arr) -> int
★ numbers [Conversion of various numbers]
FUNCTIONS
float2fraction(number: float, mixed: bool = False, error: float = 1e-15) -> tuple
int2roman(integer: int, overline: bool = True) -> str
roman2int(roman_num: str) -> int
str2int(string: str) -> int
DATA
roman_symbols = ["M", "CM", "D", "CD", "C", "XC", "L", "XL", "X", "IX", "V", "IV", "I"]
roman_values = [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1]
★ plotting [Draw a graph of equations using characters]
FUNCTIONS
color(text: str, rgb: arr) -> str
change(data: thing) -> thing
background(right: real = 5, left: real = -5, top: real = 5, bottom: real = -5...
unary(function, right: real = 5, left: real = -5, top: real = 5, bottom: real = -5, complexity: real = 5...
binary(function, right: real = 5, left: real = -5, top: real = 5, bottom: real = -5, complexity: real = 5...
c_unary(function, start: real, end: real, interval: real = 5, projection: str = "ri", right: real = 5...
★ probability [Probability function]
FUNCTIONS
binomial(sample_size: int, successes: int, success_probability: Union[int, float]) -> float
hypergeometric(total_items: int, success_items: int, sample_size: int, successes_in_sample: int) -> float
★ random [Generate random numbers or random arrays]
FUNCTIONS
choice(seq: Union[list, tuple, str], shape: Union[list, tuple] = None)
gauss(mu: Union[int, float] = 0, sigma: Union[int, float] = 1, shape: Union[list, tuple] = None) -> Union[float, list]
gauss_error(original: Union[list, tuple], mu: Union[int, float] = 0, sigma: Union[int, float] = 1) -> list
rand(shape: Union[list, tuple] = None) -> Union[float, list]
randint(a: int, b: int, shape: Union[list, tuple] = None) -> Union[int, list]
uniform(a: Union[int, float], b: Union[int, float], shape: Union[list, tuple] = None) -> Union[float, list]
★ regression [Formula based polynomial regression]
FUNCTIONS
linear_regression(x: Union[list, tuple], y: Union[list, tuple]) -> list
parabolic_regression(x: Union[list, tuple], y: Union[list, tuple]) -> list
polynomial_regression(x: Union[list, tuple], y: Union[list, tuple], n: int = None) -> list
★ sequence [Various sequences]
FUNCTIONS
farey(n: int) -> list
fibonacci(n: int, single: bool = True) -> Union[int, list]
catalan(n: int, single: bool = True) -> Union[int, list]
bernoulli(n: int, single: bool = True) -> list
recaman(n: int, single: bool = True) -> Union[int, list]
arithmetic_sequence(*, a1: real = None, an: real = None, d: real = None, n: real = None, s: real = None) -> dict
geometric_sequence(*, a1: real = None, an: real = None, r: real = None, n: real = None, s: real = None) -> dict
★ tools [Other useful tools]
FUNCTIONS
frange(start: real, stop: real, step: float = 1.0) -> list
linspace(start: real, stop: real, number: int) -> list
geomspace(start: real, stop: real, number: int) -> list
deduplicate(iterable: ite) -> ite
classify(array: arr) -> dict
split(iterable: ite, key: arr, retain: bool = False) -> list
interpolation(data: arr, length: int) -> list
primality(n: int, iter_num: int = 10) -> bool
generate_primes(limit: int) -> list
prime_factors(integer: int, dictionary: bool = False, pollard_rho: bool = True) -> Union[list, dict]
★ utils [Other useful tools]
CLASSES
InfIterator
OrderedSet
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]]
[[-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)
>>> print(Symbolics.basic)
>>> print(Symbolics.english)
>>> print(Symbolics.greek)
>>> print(Symbolics.interpreter("-(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.dna(string)
>>> print(string)
>>> print(encrypted)
>>> print(cipher.dna(encrypted, decrypt=True))
PyPyNum
CCCTAGACCCTCGTCCCGCTAAACCCTG
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
1000000000000.0
>>> p = [1, -2, -3, 4]
>>> m = [
[
[1, 2, 3],
[6, 10, 12],
[7, 16, 9]
],
[-1, -2, -3]
]
>>> print(equations.polynomial_equation(p))
>>> print(equations.linear_equation(*m))
[(-1.5615528128088307-6.5209667308287455e-24j) (2.5615528128088294+4.456233626665941e-24j) (1.0000000000000007+3.241554513744382e-25j)]
[ 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, 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(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]))
[[[0.4334341920363395, 0.055711784711422116, -1.0235500373980284, 0.30031229336738374], [-0.2650367914670356, 0.5513398538865067, -0.9735921328831166, 0.41345578602104827], [-0.11598957920080871, -0.9044539791933183, 1.6448227575237069, -0.26304156924843813]], [[0.27363898507271256, -0.5897181011789576, 1.5120937498473583, 2.1302709742844694], [1.9743293887616236, 0.4117207260898469, 0.5809554193110543, -1.8456249006764007], [1.274481044612177, -0.30645083457981553, -1.3285606156236818, 0.33473439037886943]]]
[[[0.5269441534226782, 0.36498666932667356, 0.7363066388832684, 0.5878544826035406], [0.5684721009896431, 0.9009577979323332, 0.036288112799501615, 0.18351641818419884], [0.24258369409385339, 0.09354340906140202, 0.4856203412285762, 0.783031677244552]], [[0.8777465681935882, 0.6406910705155251, 0.10275292827025073, 0.01295823682977526], [0.3898500974345528, 0.6216248983423127, 0.3179425906177036, 0.012870877167621808], [0.2660481991211192, 0.09872041627158801, 0.3681944568198672, 0.494087114885137]]]
[[[5, 9, 5, 6], [6, 7, 6, 1], [1, 3, 2, 4]], [[5, 8, 8, 3], [3, 2, 3, 9], [3, 0, 7, 1]]]
[[[8.610851610963957, 1.3747433091161905, 1.3831050577679438, 4.715182178697273], [0.8765517657148284, 4.809554825684029, 2.7557819856736137, 5.938765584746821], [6.088739464744903, 4.627722536295625, 0.6116370455995369, 5.875683438664389]], [[7.7228845997304845, 5.428461366109726, 8.02712172516869, 5.9319006090345425], [5.726626482636939, 7.978329508380601, 1.114307478513796, 6.236721706167868], [1.4123245528031072, 5.327811122183013, 7.324213082306745, 1.5016363011868927]]]
>>> print(regression.linear_regression(list(range(5)), [2, 4, 6, 7, 8]))
>>> print(regression.parabolic_regression(list(range(5)), [2, 4, 6, 7, 8]))
>>> print(regression.polynomial_regression(list(range(5)), [2, 4, 6, 7, 8], 4))
[1.5, 2.4000000000000004]
[-0.21428571428571183, 2.3571428571428474, 1.9714285714285764]
[0.08333333334800574, -0.6666666668092494, 1.4166666678382942, 1.1666666648311956, 2.0000000002900613]
>>> 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]
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!
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