louismullie

{
  
  // The type of resource
  "resourceType" : "Observation",
  
  // The status of the observation
  "status": "final",
  
  // Time of the observation
  "effectiveDateTime": "YYYY-MM-DDThh:mm:ss+zz:zz",

louismullie

{
  
  // The type of resource
  "resourceType" : "Encounter",
  
  // Identifier. No special coding system.
  "id": "some-encounter-id",
    
  // The status of the encounter
  "status": "in-progress",

louismullie

{
  
  // The type of resource
  "resourceType" : "Observation",
  
  // The status of the observation
  "status": "final",
  
  // Time of the observation
  "effectiveDateTime": "YYYY-MM-DDThh:mm:ss+zz:zz",

louismullie

{
  
  // The type of resource
  "resourceType" : "Patient",
  
  // An identifier for this patient
  "identifier" : [{
    // Indicate that this ID is a medical record number
    "type": {
      "coding": [

louismullie

{ 
  "system" : "http://loinc.org",
  "version" : "2.62",
  "code" : "55423-8",
  "display" : "Number of steps in unspecified time Pedometer"
}

louismullie

#Step 1. Add the Analytics dependency to your Podfile
# The recommended way to install Analytics for iOS is via Cocoapods, since it means you can create a build with specific integrations, and because it makes it dead simple to install and upgrade.

pod 'Analytics', '~> 3.0'

# Step 2. Initialize the Client
# Add this snippet to your application delegate’s - application:didFinishLaunchingWithOptions: method.
SEGAnalyticsConfiguration *configuration = [SEGAnalyticsConfiguration configurationWithWriteKey:@"fvOmVqVIrSuOiJclgSWOFtUtD36Vksqd"];

# Enable this to record certain application events automatically!

louismullie

<script>
  !function(){var analytics=window.analytics=window.analytics||[];if(!analytics.initialize)if(analytics.invoked)window.console&&console.error&&console.error("Segment snippet included twice.");else{analytics.invoked=!0;analytics.methods=["trackSubmit","trackClick","trackLink","trackForm","pageview","identify","reset","group","track","ready","alias","debug","page","once","off","on","addSourceMiddleware","addIntegrationMiddleware","setAnonymousId","addDestinationMiddleware"];analytics.factory=function(e){return function(){var t=Array.prototype.slice.call(arguments);t.unshift(e);analytics.push(t);return analytics}};for(var e=0;e<analytics.methods.length;e++){var t=analytics.methods[e];analytics[t]=analytics.factory(t)}analytics.load=function(e,t){var n=document.createElement("script");n.type="text/javascript";n.async=!0;n.src="https://cdn.segment.com/analytics.js/v1/"+e+"/analytics.min.js";var a=document.getElementsByTagName("script")[0];a.parentNode.insertBefore(n,a);analytics._loadOptions=t};analytics.

louismullie

import numpy as np

def image_histogram_equalization(image, number_bins=256):
    # from http://www.janeriksolem.net/2009/06/histogram-equalization-with-python-and.html

    # get image histogram
    image_histogram, bins = np.histogram(image.flatten(), number_bins, density=True)
    cdf = image_histogram.cumsum() # cumulative distribution function
    cdf = 255 * cdf / cdf[-1] # normalize

louismullie

from scipy import ndimage
from geometric_utils import find_largest_object

def extract_body_mask(im):

  im_thresholded = im > 90
  im_filled = ndimage.binary_erosion(im_thresholded, iterations=1)
  im_filled = ndimage.binary_fill_holes(im_filled)
  im_filled = ndimage.binary_erosion(im_filled, iterations=3)

louismullie

block_header1:        pageList[pageNameText, pageActionUrl] buttonActionText  buttonActionUrl

block_footer1:        companyNameText contactInformationText

                      socialIcons: 
                        imageList[imageSourceUrl, imageActionUrl] 
                        
                      websiteSections: 
                        sectionList[sectionNameText, pageList: [pageNameText, pageActionUrl]]