The Trendy application is being retired in the new year. The new web application, ThingSpeak, offers similar functionality. We recommend that active Trendy users who are looking to create new trends use ThingSpeak. To begin collecting your data in ThingSpeak, please read the tutorial : ThingSpeak for Trendy users. Although Trendy no longer allows the creation of new trends, the application will still remain accessible until January 13, 2016. Until that date, you can view your current trends and download your data. After that date, your data will no longer be accessible. Thanks for using Trendy and we encourage you to check out ThingSpeak.


Snow Predictor: Portes du Soleil (Red Channel)

This trend is broken.

Warning: JPEG library error: Invalid component ID 255 in SOS.
Error using jpeg_depth
JPEG library error: Invalid component ID 255 in SOS.

Error in readjpg (line 13)
depth = jpeg_depth(filename);

Error in imread (line 434)
    [X, map] = feval(, filename, extraArgs{:});


data points



I am interested in tracking the snow season of my favourite ski resort in the Alps, to help get a good idea of when are the best times to go. MATLAB Trendy is a good way of tracking this kind of information on a day by day basis. However, in order to achieve this I would like to be able to analyse webcam data to measure snow coverage.

Image data is obtained from

Recently Collected Data (last 5 of 81) Show All Data

Time Recorded (time1821) Data (data1821)
15 Dec 2012 08:01:33 NaN
14 Dec 2012 08:01:24 [130.414608442306]
13 Dec 2012 08:01:27 [119.412935106795]
12 Dec 2012 08:01:36 [121.072657062997]
11 Dec 2012 08:01:36 [125.410628529878]
%% Read in the website which does screen caps of webcams
a = urlread('');

%% Find the image which corresponds to Avoriaz
[~, ~, ~, matches] = regexp(a, '/grabs/[0-9]+/[0-9]+\.jpg');
uniqueMatches = unique(matches);
urls = cellfun(...
    @(x)['' x], ...
    uniqueMatches, ...
    'UniformOutput', false ...

%% Read that image
red = zeros(length(urls),1);
green = zeros(length(urls),1);
blue = zeros(length(urls),1);
for i = 1:length(urls)
    myUrl = urls{i};
    im = imread(myUrl);
    red(i) = mean(mean(im(:,:,1)));

redPredictor = mean(red);


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