willemolding

from rasterio import features
from shapely.geometry import Polygon
from skimage.segmentation import find_boundaries
from skimage.morphology import binary_dilation, disk
import numpy as np


def undersegmentation_error(gt_segments, superpixels):
    """
    Computes the undersegmentation error metric. An efficient implementation. 

willemolding

class norm:
    def __init__(self, loc, scale):
        self.loc=float(loc)
        self.scale=float(scale)
        
    def pdf(self, x):
        coeff = 1./(np.sqrt(2*np.pi*self.scale**2))
        return coeff * np.exp(-(x - self.loc)**2/(2.*self.scale**2))
    
    def logpdf(self, x):

willemolding

/* eslint no-bitwise: 0 */

import PNGReader from 'png.js';

import TZ1200RLE from './TZ1200RLE';

export function readPNG(imageBytes) {
	const reader = new PNGReader(imageBytes);
	return new Promise((resolve, reject) => {
		reader.parse((err, data) => {

willemolding

def condition(mu, sigma, observations):
    """
    Condition on the observations to produce a new mean and covariance matrix
    Parameters:
    ----------
        mu - the mean vector
        sigma - the covariance matrix
        observations - the observed variables. 
            Variables that are not observed should be Nan in this vector. 
            The resulting distribution will be over these variables in the order they appear

willemolding

Title: Luigi Pipelines for Academia (Part 1) Date: 2017-01-29 Category: Articles

I'm excited to share with you a tool I have been using to streamline the aspects of research that require processing and performing computations on data. I'm sure there are a not of researchers who, like me, have less than ideal data pipelines.

My previous setup looked something like this. I would keep the raw data untouched in a folder called data. Each level of data processing (e.g. reformatting, cleaning, fitting a model, cross-validating, plotting) has its own script or collection of scripts and would write it's output to a folder prefixed by a number. The directory structure might look like this:

Experiment 1

willemolding

def get_first_index(bool_array, axis, nonevalue=-1):
    """
    Returns the first index in each axis that is true. Returns nonevalue if none are found
    """
    return np.choose(np.any(bool_array, axis=axis), [nonevalue, np.argmax(bool_array, axis=axis)])

willemolding

<script>
  function code_toggle() {
    if (code_shown){
      $('div.input').hide('500');
      $('#toggleButton').val('Show Code')
    } else {
      $('div.input').show('500');
      $('#toggleButton').val('Hide Code')
    }
    code_shown = !code_shown

willemolding

def grab_prob(time_idx):
    # make frame__idx an integer to avoid slicing errors
    frame_idx = int(time_idx)
    # get 'frame_time'
    frame_time = grab_sst_time(frame_idx)

    # set month of year
    MoY_val = int(frame_time.month)

    # get list of temperature and salinity values from subset

willemolding

/*==========================================
=            function overrides            =
==========================================*/

agentShortname = App.Agent.String.substr(0, App.Agent.String.indexOf('@')); 

var oldCommit = commit;
commit = function(entryType, entryData) {
  if(entryType.indexOf("private") !== -1) {
    debug(agentShortname + '-->>' + agentShortname +': '+ entryType);

willemolding

[package]
name = "agent-address-generator"
version = "0.1.0"
authors = ["willem <willemolding@gmail.com>"]
edition = "2018"

[dependencies]
holochain_core_types = { git = "https://github.com/holochain/holochain-rust", branch="develop" }