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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.patches import Ellipse
import math

# Kalman

def lkf_predict(x, P, A, B, u, Q):

cheind

import numpy as np
import matplotlib.pyplot as plt


o3d = np.array([-100, -100, 50])
d3d = np.array([1, 1, 5.])
d3d /= np.linalg.norm(d3d)

t3d = np.arange(0, 100, 1.)
p3d = o3d + d3d * t3d[:, None]

cheind

import numpy as np
import matplotlib.pyplot as plt

# Relation of SVD to PCA and eigen-problems
# A = USV'
# A'A = VSU'USV' = VS^2V'
# A'AV = VS^2V'V
# A'AV = VS^2 
# which is an eigenvector problem. Means V are the eigenvectors of A'A.
# A similar argument leads to U being the eigenvectors AA'.

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import cv2
import json
import pandas as pd
import numpy as np

def convert_to_pandas(content):
    events = []
    for obj in content:
        for f in obj['frames']:
            events.append({

cheind

cheind

import numpy as np
from itertools import count

def perm_matrix(perm_indices):
    '''Returns the permutation matrix corresponding to given permutation indices

    Here `perm_indices` defines the permutation order in the following sense: 
    value `j` at index `i` will move row/column `j` of the original matrix to 
    row/column `i`in the permuated matrix P*M/M*P^T.
    

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__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017'
__license__ = 'BSD'

"""Trains a HMM based on gradient descent optimization. 

The parameters (theta) of the model are transition and 
emission probabilities, as well as the initial state probabilities. 

Given a start solution, the negative log likelihood of data given the 

cheind

from scipy.optimize import linprog
import numpy as np
import pandas as pd


def print_metrics(df):
    print('Total staff costs', df.to_numpy().sum())
    print('Management cost ratio')
    print(df.MgtStaffCosts / df.to_numpy().sum())
    print('Partner cost ratio')

cheind

import torch

def sample_entropy(
    x: torch.Tensor, m: int = 2, r: float = None, stride: int = 1, subsample: int = 1
):
    """Returns the (batched) sample entropy of the given time series.

    Sample entropy is a measure of complexity of sequences that can be related
    to predictability. Sample entropy (SE) is defined as the negative logarithm of
    the following ratio:

cheind

import numpy as np
import matplotlib.pyplot as plt
import dataclasses


@dataclasses.dataclass
class MotionEstimate:
    coeffs: np.ndarray  # 3x1
    t0: float
    degree: int = dataclasses.field(init=False)