sergeyprokudin

def count_steps(n):
    """Determine how many ways there are to walk n steps, if you can jump over 1, 2 or 3 steps at the time
    
    f(-1) = 0
    f(0) = 1 # just stand?
    f(1) = 1
    f(2) = 2 
    f(3) = 4
    f(4) = f(n-1)+f(n-2)+f(n-3)
    

sergeyprokudin

from collections import deque

def check_path(grid, i=0, j=0, failed_points=None, current_path=None):
    """ Find a route between upper-left and lower right corner of the grid,
    if you are only allowed to walk right and down.
    
    Input:
    1 1 1
    0 0 1
    0 0 1

sergeyprokudin

import matplotlib.pyplot as plt
%matplotlib inline


def plot2d(fig_lst, labels=None, figsize=(10, 10), savepath=None):
    """Plots a set of 2d shapes represented as 2d point arrays
    
    Parameters
    ----------
    fig_lst: list

sergeyprokudin

import h5py
import os

def save_h5(savepath, arr_dict):
  
    if os.path.exists(savepath):
        os.remove(savepath)

    h5ds = h5py.File(savepath)

sergeyprokudin

import matplotlib.pyplot as plt
import scipy.stats as stats

def plot_gauss_pdf(mu, sigma, gt):
    ''' plots Guassian density together with some ground truth value gt
    '''
    x = np.linspace(mu - 5*sigma, mu + 5*sigma, 100)
    plt.plot(x, stats.norm.pdf(x, mu, sigma))
    
    plt.axvline(mu, c='blue', label='prediction mean')

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import tensorflow as tf
from keras.layers import Input, Dense, Flatten, Reshape, Dropout
from keras.models import Model, Sequential
from keras.optimizers import Adam
from keras.objectives import binary_crossentropy
from keras.layers.merge import concatenate
from keras.losses import mean_squared_error

def variational_autoencoder(n_input_features, latent_space_size=64, hlayer_size=256, 
                            lr=1.0e-3, kl_weight=0.1):

sergeyprokudin

import open3d as o3d
import trimesh
import numpy as np


def ball_pivoting_reconstruction(xyz, radii=None):
    """Given a 3D point cloud, get unstructured mesh using ball pivoting algorithm
    
    Based on this stack overflow code snippet:
    https://stackoverflow.com/questions/56965268/how-do-i-convert-a-3d-point-cloud-ply-into-a-mesh-with-faces-and-vertices

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"""
Simple point cloud visualization with pyntcloud library

"""
import pyntcloud as pynt
import pandas as pd
import numpy as np


def show_cloud(xyz, rgb=None, initial_point_size=0.01):

sergeyprokudin

# details:
# https://stackoverflow.com/questions/11367736/matplotlib-consistent-font-using-latex/27697390
from matplotlib import rc
import matplotlib
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'

sergeyprokudin

import numpy as np
from sklearn.neighbors import NearestNeighbors


def chamfer_distance(x, y, metric='l2', direction='bi'):
    """Chamfer distance between two point clouds

    Parameters
    ----------
    x: numpy array [n_points_x, n_dims]