jampekka

def project_to_ground(pos, camera, height=0.0):
    matWorldInverse = np.array(camera['matrixWorldInverse']).reshape(4, 4).T
    matWorld = np.linalg.inv(matWorldInverse)
    proj = np.array(camera['projectionMatrix']).reshape(4, 4).T
    
    origin = matWorld[:,3][:-1]
    deproj = np.dot(matWorld, np.linalg.inv(proj))
    direction = np.dot(deproj, (pos[0], pos[1], 0.5, 1.0))
    direction = direction[:-1]/direction[-1]
    direction -= origin

jampekka

import numpy as np
import scipy.interpolate
import scipy.signal



def match_sampled_brutal(ts1, haystack, ts2, needle, minlen=100):
    dt = np.median(np.diff(ts1))
    ts1_base = ts1[0]
    ts1 = ts1 - ts1_base

jampekka

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
import pyximport; pyximport.install()
from pixcount import pixcount
import scipy.ndimage

data = np.load('diffs.npy')
ts = data[:,0]
frames = 1000

jampekka

gistup

jampekka

/*
 * GStreamer
 * Copyright (C) 2006 Stefan Kost <ensonic@users.sf.net>
 * Copyright (C) 2017 Jami Pekkanen <jami.pekkanen@gmail.com>
 *
 * This library is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Library General Public
 * License as published by the Free Software Foundation; either
 * version 2 of the License, or (at your option) any later version.
 *

jampekka

#include <opencv2/opencv.hpp>
#include <string>
using namespace cv;

int main(int argc, char **argv) {
	int camno = 0;
	if(argc > 2) {
		camno = std::atoi(argv[1]);
	}

jampekka

Loppuiko historian loppu:

Historian loppuun (1992)
Lätkän MM-kultaan (1995)
Sokalin tapaus (1996)
Dot-com-romahdukseen (1995)
Dianan kuolema (1997)
WTO-iskuihin (1999)
WTC-iskuihin (2001)
Pentagon-iskuihin (2001)

jampekka

import numpy as np
import matplotlib.pyplot as plt

def compensatory_radius(estimate_dispersion, R):
    cosarg = estimate_dispersion/R**2
    # Cos will go negative when arg > pi/2. arg < 0 makes no sense, so no need to worry about that.
    # Trigonometry breaks down for this at pi/2? Could probably have some sort of analytical
    # continuation if needed, however pi/2 dispersion leading to center seems implausible for a
    # model.
    r_2 = R*np.cos(cosarg)

jampekka

import pandas as pd

hs_poll_perc = {
    'kok': 19.6,
    'ps': 18.0,
    'sdp': 17.9,
    'kesk': 12.7,
    'vihr': 11.1,
    'vas': 8.2,
    'rkp': 4.6,

jampekka

import random
import selectors
import sys

total_cards = 100
cards_per_player = 3

deck = list(range(1, total_cards + 1))
random.shuffle(deck)