espdev

"""
Прореживание облака 2D-точек по минимальному расстоянию между точками

Реализация "в лоб" с использованием KD-Tree
"""

import numpy as np
import scipy.spatial as spatial

espdev

espdev

import itertools

def slide(seq, n=2):
    """Returns a sliding window (of width n) over data from the sequence
    
    s -> (s0, s1, ..., s[n-1]), (s1, s2, ..., sn), ...
    """
    it = iter(seq)
    result = tuple(itertools.islice(it, n))
    if len(result) == n:

espdev

espdev

from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import matplotlib.pyplot as plt

#    x  y  z
a = (1, 1, 1)  # p1
b = (2, 2, 2)  # p2

x, y, z = 0, 1, 2

espdev

name='laplace' params=LaplaceAlgoParams(gradient_sigma=1.0, laplacian_sigma=2.0)
name='gauss' params=GaussAlgoParams(gaussian_sigma=1.5)
1 validation error for AlgoSettings
name
  Cannot find algorithm 'lol'. Existing algorithms: ['laplace', 'gauss'] (type=value_error)
1 validation error for AlgoSettings
params
  <class '__main__.GaussAlgoParams'> parameters type is invalid for 'laplace' algorithm. Parameters type must be a subclass of <class '__main__.LaplaceAlgoParams'> (type=type_error)

espdev

import numpy as np

def index_offset(a):
    if a.base is None:
        ravel_index = 0
        shape = a.shape
    else:
        byte_offset = np.byte_bounds(a)[0] - np.byte_bounds(a.base)[0]
        ravel_index = byte_offset // a.dtype.itemsize
        shape = a.base.shape

espdev

from typing import Optional
from pydantic import BaseModel, validator


class MyModel(BaseModel):
    name: Optional[str]

    class Config:
        validate_assignment = True

espdev

import time
import numpy as np
from scipy.sparse import diags


def make_matrix(pcount):
    x = np.linspace(0., 10., pcount)
    dx = np.diff(x)

    odx = 1. / dx

espdev

import time
import numpy as np
from scipy.sparse import csr_matrix


def make_matrix(shape, nnz):
    i = np.random.randint(0, shape[0] - 1, nnz)
    j = np.random.randint(0, shape[1] - 1, nnz)
    data = np.random.randn(nnz)