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A common question you may have when integrating MATLAB Grader into your LMS using the LTI standard is what information is being sent to MATLAB Grader from your LMS?

First, please familiarize yourself with the LTI specification on the IMS Global website: http://www.imsglobal.org/specs/ltiv1p1/implementation-guide

Next, take a look at the documentation we provide on LMS integration that is specific to your platform/vendor: https://www.mathworks.com/help/matlabgrader/lms-integration.html

MathWorks does not require personally identifiable information. More specifically, here are the standard LTI fields that we DO NOT want nor collect, as opposed to what fields we DO collect.

We do NOT want your LMS to send us: - user_image - lis_person_name_given - lis_person_name_family - lis_person_name_full - lis_person_contact_email

We DO require from your LMS: - roles

The other LTI fields listed in the specification are not related to personally identifiable information, and may be required for the LTI session to be launched successfully. For further questions about what is contained in the LTI specification, please refer to the specification and implementation guide provided by IMS, or contact the vendor of your LMS.

Adam Danz
Adam Danz
最後のアクティビティ: 2024 年 5 月 23 日

Starting in r2020a, AppDesigner buttons and TreeNodes can display animated GIFs, SVG, and truecolor image arrays.

Every component in the App above is either a Button or a TreeNode!

Prior to r2020a the icon property of buttons and TreeNodes in AppDesigner supported JPEG, PNG, or GIF image files specified by a character vector or string array but did not support animation.

Here's how to display an animated GIF, SVG, or truecolor image in an App button or TreeNode starting in r2020a. And for the record, "GIF" is pronounced with a hard-g .

Display an animated GIF

Select the button or TreeNode from within AppDesigner > Design View and navigate to Component Browser > Inspector > Button dropdown list of properties (shown below). Select an animated GIF file and set the text and icon alignment properties.

To set the icon property programmatically,

app.Button.Icon = 'launch.gif';  % or "launch.gif"

The filename can be an image file on the Matlab path (see addpath ) or a full path to an image file.

Display SVG

Use “scalable vector graphics” files for high-resolution images that are scaled to different sizes while preserving their shape and retaining their clarity. A quick and easy way to remember which plotting function is assigned to each button in an app is to assign an image of the plot to the button.

After creating the figure, expand the axes by setting the position or outerposition property to [0 0 1 1] in normalized units and save the figure using File > Save as and select svg format. Save the image to the folder containing your app. Then follow the same procedure as animated GIFs.

Display truecolor image

A truecolor image comes in the form of an [m x n x 3] array where each m x n pixel color is specified by an RGB triplet (read more) . This feature allows you to dynamically create a digital image or to upload an image from a mat file rather than an image file.

In this example, a progress bar is created within the uibutton callback function and it’s updated within a loop. For a complete demo of this feature see this comment .

% Button pushed function: ProcessDataButton
function ProcessDataButtonPushed(app, event)
    % Change button name to "Processing"
    app.ProcessDataButton.Text = 'Processing...';
    % Put text on top of icon
    app.ProcessDataButton.IconAlignment = 'bottom';
    % Create waitbar with same color as button
    wbar = permute(repmat(app.ProcessDataButton.BackgroundColor,15,1,200),[1,3,2]);
    % Black frame around waitbar
    wbar([1,end],:,:) = 0;
    wbar(:,[1,end],:) = 0;
    % Load the empty waitbar to the button
    app.ProcessDataButton.Icon = wbar;
    % Loop through something and update waitbar
    n = 10;
    for i = 1:n
        % Update image data (royalblue)
        % if mod(i,10)==0 % update every 10 cycles; improves efficiency
         currentProg = min(round((size(wbar,2)-2)*(i/n)),size(wbar,2)-2);
         RGB = app.ProcessDataButton.Icon;
         RGB(2:end-1, 2:currentProg+1, 1) = 0.25391; % (royalblue)
         RGB(2:end-1, 2:currentProg+1, 2) = 0.41016;
         RGB(2:end-1, 2:currentProg+1, 3) = 0.87891;
           app.ProcessDataButton.Icon = RGB;
          % Pause to slow down animation
          pause(.3)
          % end
      end
      % remove waitbar
      app.ProcessDataButton.Icon = '';
      % Change button name
      app.ProcessDataButton.Text = 'Process Data';
  end

The for-loop above was improved on Feb-11-2022.

Credit for the black & teal GIF icons: lordicon.com

Even
Even
最後のアクティビティ: 2023 年 7 月 23 日

If a large number of fair N-sided dice are rolled, the average of the simulated rolls is likely to be close to the mean of 1,2,...N i.e. the expected value of one die. For example, the expected value of a 6-sided die is 3.5.
Given N, simulate 1e8 N-sided dice rolls by creating a vector of 1e8 uniformly distributed random integers. Return the difference between the mean of this vector and the mean of integers from 1 to N.
function dice_diff = loln(N)
A=randi([1,N],1e8,1)
M=mean(A)
B=1:N
m=mean(B)
dice_diff =abs(M-m);
end
Here is my code, but it can't work out as it needs too long time to creat A.
Amin Zarbakhsh
Amin Zarbakhsh
最後のアクティビティ: 2020 年 4 月 27 日

can anyone advise which Matlab code I can add to the below codes to have Spectrogram Plot?

OptimalValuesx1y1z1 = [dataArray{1:end-1}];
%% Clear temporary variables
clearvars filename delimiter formatSpec fileID dataArray ans;
re=1;
fs=20e3/re;
datatable=OptimalValuesx1y1z1;
datatable=resample(OptimalValuesx1y1z1,1,re);
%datatable=lowpass(OptimalValuesx1y1z1,10,fs);
datatable(:,2)=datatable(:,2).*0.01;
figure
t=1/fs:1/fs:length(datatable)/fs;
plot(t, rms(datatable(:,2:4)*9.81,3));
ylim([0 10])  
xlim([0 10])
%ylim([0 1])
hold on
%plot(t,ones(1,length(datatable(:,2:4)*9.81))*12,'r--')
xlabel('Time [s]')
ylabel('Amplitude [m/s^2]')
legend('axis X','axis Y','axis Z','limit')
out_mean = mean(rms(datatable(:,2:4),3))
std_mean = std(rms(datatable(:,2:4),3))
% %PSD analysis
figure
x=datatable(:,2:4)*9.81;
nbar = 4;
sll = -30;
win = taylorwin(length(x),nbar,sll);
periodogram(x,win,[],fs);
xlim([0 1.624])
legend('axis X','axis Y','axis Z')
Hafsa Asif
Hafsa Asif
最後のアクティビティ: 2020 年 4 月 24 日

plz tell me the web site where i can easily install mathlab.

G Behery
G Behery
最後のアクティビティ: 2020 年 4 月 25 日

I want to use the Image fusions and deep neural network to detect the Corona-virus (COVID-19)

Hello, I want to generate Sinusoidal PWM for 3 level inverter. But I can not finding the way of connect these blocks togather. Is it possible to conenct them? Is there any other way to generate SPWM in simulink?

Richa Gupta
Richa Gupta
最後のアクティビティ: 2023 年 10 月 12 日

The File Exchange team is excited to announce that File Exchange now supports GitHub Releases!

Contributors can now develop software projects in GitHub without having to manually sync and maintain the same code in File Exchange.

To start using this feature, choose 'GitHub Releases' option when you update your existing File Exchange submission or link a new repository to File Exchange.

When you link your GitHub repository to File Exchange using GitHub Releases, your File Exchange submission will automatically update when you create a new release in GitHub that is compatible with File Exchange. In addition, if you package your code as a toolbox (.mltbx) and attach the toolbox package to your latest GitHub release, File Exchange will provide the toolbox to your users for download. If you do not attach a toolbox to the release, File Exchange will provide the zip release asset for download.

See this page for more details.

We encourage you to try out this feature and let us know of any feedback you have by replying below.

Artur Iskhakov
Artur Iskhakov
最後のアクティビティ: 2020 年 4 月 23 日

Hello everyone! I'm trying to find an optimal placement for a recloser using the optimization toolbox. The best place to set a recloser is defined by minimal SAIFI (system average interruption frequency index) value. I've created a little function where Nt - total number of customers, G - are some weights characterizing power lines, Xi - number of interrupted customers (if interruption happens in i-th line AND it has a recloser in it), Mi - row of 1 and 0 (that genetic algorithm should use as a gene I guess...) Here's the code of function:

function [S] = SAIFI_sum (M)
Nt=270;
G = [1.1 1.2 1.3 1.4 1.5 1.6];
X = [270 30 220 180 60 70];
  for i=1:length(M)
      if  M(i)== 1
      N(i) = X(i);
  else
          N(i) = Nt;
      end
  end
  S = 0;
  for j=1:length(M)
      SAIFI(j) = N(j)*G(j);
      S = S + SAIFI(j);
  end
   S
end
As a result I have 51 same results: S = 297 and following message:
"Optimization running.
Objective function value: 297.0
Optimization terminated: average change in the fitness value less than options.FunctionTolerance".
I couldn't understand how to solve this problem.
Adam Danz
Adam Danz
最後のアクティビティ: 2020 年 4 月 21 日

This week is National Volunteer Week in the USA and Canada and to celebrate, I’d like to pay tribute to the volunteers in the Matlab Central Answers forum who have given countless hours to help total strangers make progress in their education, careers, and hobbies.

As of April 20, 2020, there have been 375,869 [1,2] questions asked by 183,968 [3] contributors dating back to the earliest existing question on January 4, 2011.

41,890 volunteers have contributed at least one answer leading up to 68% of the questions answered.

There is no contribution too small for earning well-deserved recognition and appreciation. A single answer or comment may benefit countless individuals who finally find the ideal solution to a problem that kept them up at night.

A number of volunteers in the forum have contributed far and beyond the imaginable and have shared so much of their time and expertise that it’s difficult to fathom. The bar graph below shows the top 10 volunteers in the forum by the number of answers provided. It’s hard to believe that Walter Roberson , a single individual ( we think ), has contributed a portion of answers equal to more than 18% of answered questions in the forum [4]. The top two volunteers, adding Image Analyst , contributed enough answers to equal almost 30% of the answered questions. These folks along with many others not listed in the bar graph who can be found on the contributors page are the foundation of so many Matlab users’ success including my own from June 2014, when I asked my first question.

Whether you’ve come to the forum to look for an answer or to write an answer, you’re undoubtedly standing on the shoulders of giants.

Footnotes

  1. Based on the number of answered and unanswered questions listed in the ‘Status’ table in recently added questions .
  2. Questions and answers posted by the MathWorks Support Team are not included in the data presented here, though much appreciated.
  3. The number of people who provided an answer is based on sorting the contributors page by ‘answers given’ in descending order.
  4. Since a contributor can write more than one answer to a question, we can’t easily measure the number of questions answered by a contributor.

Created and tested in the university classroom, the Introduction to MATLAB zyBook includes automated assessment using MATLAB Grader. Learn how to create custom MATLAB assignments using zyLabs and provide feedback.

Join this webinar running April 21st and 23rd

Here is a great video from Gartner in response to COVID-19 and trends they are seeing for schools transitioning to Distance Learning. We hope this is helpful! See here: Gartner Research

Christophe
Christophe
最後のアクティビティ: 2020 年 4 月 22 日

I organized in September 2019 a one-week workshop in a hybrid set-up (students could attend the workshop either on-site or fully on-line). Beyond the hybrid nature of the workshop, the workshop was "flipped": the workshop attendees had to study some handbook specifically written for the workshop, watch some short videos summarizing the main concepts and answer online quizzes. During the workshop, most of the time was spent on hands-on coding exercises and assignments, during which the workshop attendees had to apply the concepts presented in the handbook and videos.

All coding exercises and assignments were carried out with MATLAB Grader. The platform provided the same coding environment to all workshop attendees, irrespective of whether they attended the workshop on-site or remotely. Furthermore, by providing a coding template and rigorous assessment tests along each exercise, having all students converging to the right "solution" was painless. It allowed me, as a teacher, to entirely focus on helping the students in solving those exercises, which was extremely rewarding.

This teaching set-up, combined with smart IT solutions as e.g MATLAB Grader, favors deep student learning, since the students learn by doing (active learning) and are continuously supported by the teacher in their learning.

I will soon teach in another course along the same principles. The course was supposed to be given on-site only, but because of the outbreak of Covid-19, it will be given entirely online. Thanks to MATLAB Grader, the migration to a online set-up is straightforward.

In case you want to read more about some of my past efforts in the development and application of student-centred pedagogical approaches, you can read more about those at: https://www.chalmers.se/en/departments/physics/news/Pages/Teaching-the-algorithms-that-are-crucial-for-nuclear-reactor-modelling.aspx and https://www.chalmers.se/en/departments/physics/news/Pages/Online-educational-efforts-to-ensure-nuclear-safety.aspx

I will also be glad to answer questions and provide help to those of you who want to get started with online teaching.

Christophe

Adam Danz
Adam Danz
最後のアクティビティ: 2022 年 11 月 29 日

New to r2020a, leapseconds() creates a table showing all leap seconds recognized by the datetime data type.

>> leapseconds()
ans =
  27×2 timetable
       Date        Type    CumulativeAdjustment
    ___________    ____    ____________________
    30-Jun-1972     +              1 sec       
    31-Dec-1972     +              2 sec       
    31-Dec-1973     +              3 sec       
    31-Dec-1974     +              4 sec       
    31-Dec-1975     +              5 sec       
             << 21 rows removed >>
    31-Dec-2016     +             27 sec   

Leap seconds can covertly sneak into your data and cause errors that are difficult to resolve if the leap seconds go undetected. A leap second was responsible for crashing Reddit , Mozilla, Yelp, LinkedIn and other sites in 2012.

Detect leap seconds present in a vector of years

% Define a vector of years
t = 2005:2008; 
allLeapSeconds = leapseconds(); 
isYearWithLeapSecond = ismember(t,year(allLeapSeconds.Date));
% Show years that contain a leap second
t(isYearWithLeapSecond)
ans =
        2005        2008

Detect leap seconds present in a vector of months

% Define a vector of months 
t = datetime(1972, 1, 1) + calmonths(0:11);
t.Format = 'MMM-yyyy';
allLeapSeconds = leapseconds(); 
[tY,tM] = ymd(t); 
[leapSecY, leapSecM] = ymd(allLeapSeconds.Date); 
isMonthWithLeapSecond = ismember([tY(:),tM(:)], [leapSecY, leapSecM], 'rows');
% Show months that contain a leap second
t(isMonthWithLeapSecond)
ans = 
  1×2 datetime array
   Jun-1972   Dec-1972

List all leap seconds in your lifetime

% Enter your birthday in mm/dd/yyyy hh:mm:ss format
yourBirthday = '01/15/1988 14:30:00'; 
yourTimeRange = timerange(datetime(yourBirthday), datetime('now')); 
allLeapSeconds = leapseconds(); 
lifeLeapSeconds = allLeapSeconds(yourTimeRange,:);
lifeLeapSeconds.YourAge = lifeLeapSeconds.Date - datetime(yourBirthday);
lifeLeapSeconds.YourAge.Format = 'y';
% Show table
fprintf('\n Leap seconds in your lifetime:\n')
disp(lifeLeapSeconds)
Leap seconds in your lifetime:
       Date        Type    CumulativeAdjustment     YourAge  
    ___________    ____    ____________________    __________
    31-Dec-1989     +             15 sec           1.9587 yrs
    31-Dec-1990     +             16 sec            2.958 yrs
                        << 8 rows removed >>
    30-Jun-2012     +             25 sec           24.456 yrs
    30-Jun-2015     +             26 sec           27.454 yrs
    31-Dec-2016     +             27 sec            28.96 yrs

What is a leap second?

A second is defined as the time it takes a cesium-133 atom to oscillate 9,192,631,770 times under controlled conditions. The transition frequency is so precise that it takes 100 million years to gain 1 second of error [1]. If the earth’s rotation were perfectly synchronized to the atomic second, a day would be 86,400 seconds. But the earth’s rate of rotation is affected by climate, winds, atmospheric pressure, and the rate of rotation is gradually decreasing due to tidal friction [2,3]. Several months before the expected difference between the atomic clock-based time (UTC) and universal time (UT1) reaches +/- 0.9 seconds the IERS authorizes the addition (or subtraction) of 1 leap second (see plot below). Since the first leap second in 1972, all leap second adjustments have been made on the last day of June or December and all adjustments have been +1 second which explains the + signs in the type column of the leapseconds() table.

[ Image source ]

How to reference the leap second schedule in your code

Since leap second adjustments are not regularly timed, you can record the official IERS Bulletin C version used at the time of your analysis by accessing the 2nd output to leapseconds().

[T,vers] = leapseconds

What do leap seconds look like in datetime values?

A minute typically has 60 seconds spanning from 0:59. A minute containing a leap second has 61 seconds spanning from 0:60.

December 30, 2016 was a normal day. If we add the usual 86400 seconds to the start of that day, the result is the start of the next day.

d = datetime(2016, 12, 30, 'TimeZone','UTCLeapSeconds') + seconds(86400)
d = 
  datetime
   2016-12-31T00:00:00.000Z

The next day, December 31, 2016, had a leap second. If we add 86400 seconds to the start of that day, the result is not the start of the next day.

d = datetime(2016, 12, 31, 'TimeZone','UTCLeapSeconds') + seconds(86400)
d = 
  datetime
   2016-12-31T23:59:60.000Z

When will the next leap second be?

As of the current date (April 2020) the timing of the next leap second is unknown. Based on the data from the plot above, what's your guess?

References

  1. https://www.timeanddate.com/time/how-do-atomic-clocks-work.html
  2. https://www.nasa.gov/centers/goddard/news/topstory/2003/0210rotation.html
  3. https://en.wikipedia.org/wiki/%CE%94T#Universal_time

This Coursera course teaches computer programming to those with little to no previous experience: https://www.coursera.org/learn/matlab

Your students might find this course a useful introduction to programming.

I took a course on Medical Image Processing from one of the instructors, Prof Mike Fitzpatrick, when I was a graduate student at Vanderbilt University a few decades ago. He's a great instructor who helped get me hooked on MATLAB for learning, teaching and researching!

Jiro Doke
Jiro Doke
最後のアクティビティ: 2023 年 4 月 10 日

There’s a webinar on online learning tools with MATLAB this Friday. It’s for time zones in Asia Pacific.

Register if you’re interested!

Boby S
Boby S
最後のアクティビティ: 2020 年 4 月 13 日

Hi I want to track a animal in my recorded video. I tried computer vision toolbox but it is not very accurate for this type of tracking. The recording is from top and the animal runs in a maze. I want to track body and head. The next step is classifying the movements in video using deep learning but again we do not have a trained network.

LAXMI PRIYANKA
LAXMI PRIYANKA
最後のアクティビティ: 2020 年 4 月 14 日

iam working with rgb fundus images my task is to convert the rgb image to hsv space and extract the channels and perform brightness correction in the image now i am little confused on how to extract the channels of the corrected image the corrected image is not saved as jpg format could anyone help me to solve this issue i have also attached the coding thank you in advance

Adam Danz
Adam Danz
最後のアクティビティ: 2020 年 4 月 10 日

Do date ranges from two different timetables intersect?

Is a specific datetime value within the range of a timetable?

Is the range of row times in a timetable within the limits of a datetime array?

Three new functions in r2020a will help to answer these questions.

In these function inputs, TT is a timetable and input #2 is one of the following:

The tf output is a logical scalar indicating pass|fail and the whichRows output is a logical vector identifying the rows of TT that are within the specified time range.

How do these functions differ?

Let's test all 3 functions with different time ranges and a timetable of electric utility outages in the United States, provided by Matlab. The first few rows of outages.csv are shown below in a timetable. You can see that the row times are not sorted which won't affect the behavior of these functions.

8×5 timetable
     OutageTime          Region         Loss     Customers     RestorationTime            Cause       
  ________________    _____________    ______    __________    ________________    ___________________
  2002-02-01 12:18    {'SouthWest'}    458.98    1.8202e+06    2002-02-07 16:50    {'winter storm'   }
  2003-01-23 00:49    {'SouthEast'}    530.14    2.1204e+05                 NaT    {'winter storm'   }
  2003-02-07 21:15    {'SouthEast'}     289.4    1.4294e+05    2003-02-17 08:14    {'winter storm'   }
  2004-04-06 05:44    {'West'     }    434.81    3.4037e+05    2004-04-06 06:10    {'equipment fault'}
  2002-03-16 06:18    {'MidWest'  }    186.44    2.1275e+05    2002-03-18 23:23    {'severe storm'   }
  2003-06-18 02:49    {'West'     }         0             0    2003-06-18 10:54    {'attack'         }
  2004-06-20 14:39    {'West'     }    231.29           NaN    2004-06-20 19:16    {'equipment fault'}
  2002-06-06 19:28    {'West'     }    311.86           NaN    2002-06-07 00:51    {'equipment fault'}

The range of times in utility.csv is depicted by the gray timeline bar in the plot below labeled "Timetable row times". The timeline bars above it are various time ranges or scalar datetime values used to test the three new functions.

The three columns of checkboxes on the right of the plot show the results of the first output of each function, tf, for each time range.

The time ranges were created by the timerange function using the syntax timerange(startTime,endTime). The default intervalType in timerange() is 'openright' which means that datetimes are matched when they are equal to or greater than startTime and less than but not equal to endTime. The scalar datetime values were created with datetime().

The colorbar along with the colored points at the bottom of each timeline bar show the row numbers of timetable TT that were selected by the whichRows output of the three functions.

The containsrange() function returns true when all of the time range values are within the timetable including the timetable's minimum and maximum datetime.

The overlapsrange() function returns true when any of the time range values are with the timetable's range.

The withinrange() function returns true only when all of the timetable's datetime values are within the time range values. A careful observer may see that comparison number 1 is false even though that time range is exactly equal to the timetable's row time range. This is because the default value for intervalType in the timerange() function is 'openright' which does not match the end values if they are equal. If you change the intervalType to 'closed' the withinrange result for comparison 1 would be true.

The scalar datetime values for comparisons 8, 9 and 10 are all exact matches of datetimes within the timetable and result in a single match in the whichRows output. The datetime values for comparisons 7 and 11 do not match any of the timetable row times and the values in whichRows are all false even though comparison 7 is within the timetable range. For all three tests, the whichRows outputs are identical.

---------------------------------------------------------------

Here is the code used to generate this data, test the functions, and produce the plot.

% Read in the outage data as a table and convert it to a timetable TT.
T = readtable('outages.csv');
TT = table2timetable(T);
% Look at the first few rows.
head(TT)
% Show that row time vector is not sorted.
issorted(TT)
% Get the earliest and latest row time.
outageTimeLims = [min(TT.OutageTime), max(TT.OutageTime)];
% Define time ranges to test [start,end] or scalar times [datetime, NaT]
% The scalar times must be listed after time ranges.
dateRanges = [ % start, end
    outageTimeLims;                                 % original data
    outageTimeLims;                                 % equal time range
    datetime(2005,2,1), datetime(2011,2,1);         % all within
    datetime(1998,3,16), datetime(2018,4,11);       % starts before, ends after
    datetime(2000,1,1), datetime(2010,4,11);        % starts before, ends within
    datetime(2009,1,15), datetime(2019, 4,7);       % starts within, ends after
    datetime(2015,6,15), datetime(2019,12,31);      % all outside
    datetime(2008,6,6), NaT;                        % 1-value, inside, not a member
    [TT.OutageTime(find(year(TT.OutageTime)==2010,1)), NaT] % 1 value, inside, is a member
    outageTimeLims(1), NaT;                         % 1-value, on left edge
    outageTimeLims(2), NaT;                         % 1-value, on right edge
    datetime(2000,6,6), NaT;                        % 1-value, outside
    ];
nRanges = size(dateRanges,1);
dateRangeLims = [min(dateRanges,[],'all'), max(dateRanges,[],'all')];
% Set up the figure and axes
uifig = uifigure('Name', 'Timetable_intersection_demo', 'Resize', 'off');
uifig.Position(3:4) = [874,580];
movegui(uifig,'center')
uiax = uiaxes(uifig, 'Position', [0,0,uifig.Position(3:4)], 'box', 'on', 'YAxisLocation', 'right',...
    'ytick', -.5:1:nRanges, 'YTickLabel', [], 'ylim', [-3.5, nRanges], 'FontSize', 18);
hold(uiax, 'on')
grid(uiax, 'on')
uiax.Toolbar.Visible = 'off';
% Add axes labels & title
title(uiax, strrep(uifig.Name,'_',' '))
xlabel(uiax, 'Timeline')
ylab = ylabel(uiax, 'Comparison number');
set(ylab, 'Rotation', -90, 'VerticalAlignment', 'Bottom')
% Add the timetable frame
fill(uiax, outageTimeLims([1,2,2,1]), [-.7,-.7,nRanges-.3,nRanges-.3] , 0.85938*[1,1,1], ... %gainsboro
    'EdgeColor', 0.41016*[1,1,1], 'LineStyle', '--', 'LineWidth', 1.5, 'FaceAlpha', .25)     %dimgray
% Set xtick & xlim after x-axis is converted to datetime
range = @(x)max(x)-min(x); 
uiax.XLim = dateRangeLims + range(dateRangeLims).*[-.01, .40];
uiax.XTick = dateshift(dateRangeLims(1),'start','Year') : calyears(2) : dateshift(dateRangeLims(2),'start','Year','next');
xtickformat(uiax,'yyyy')
% Set up timeline plot
lineColors = [0.41016*[1,1,1]; lines(nRanges-1)]; %dimGray
uiax.Colormap = parula(size(TT,1));
tfUniCodes = {char(09745),char(09746)}; %{true, false} checkbox characters
barHeight = 0.8;
rightMargin = [max(dateRangeLims),max(uiax.XLim)];
tfCenters = linspace(rightMargin(1),rightMargin(2),5);
tfCenters([1,end]) = [];
intervalType = 'openright';  % open, closed, openleft openright(def);  see https://www.mathworks.com/help/matlab/ref/timerange.html#bvdk6vh-intervalType
% Loop through each row of dateRanges
for i = 0:nRanges-1
    % Plot timeline bar
    pObj = fill(uiax, dateRanges(i+1,[1,2,2,1]), i+[-barHeight,-barHeight,barHeight,barHeight]/2, lineColors(i+1,:), 'FaceAlpha', .4);
      % Evaluate date ranges and single values differently
      if any(isnat(dateRanges(i+1,:)))
          % Test single datetime
          tr = dateRanges(i+1,~isnat(dateRanges(i+1,:)));
          set(pObj, 'LineWidth', 3, 'EdgeAlpha', .6, 'EdgeColor', lineColors(i+1,:))
      else
          % Test date range
          tr = timerange(dateRanges(i+1,1), dateRanges(i+1,2),intervalType);
      end
      % Create timerange obj and test for intersections
      [tf(1), whichRows{1}] = containsrange(TT,tr);
      [tf(2), whichRows{2}] = overlapsrange(TT,tr);
      [tf(3), whichRows{3}] = withinrange(TT,tr);
      % Confirm that all 'whichRows' are equal
      assert(isequal(whichRows{1},whichRows{2},whichRows{3}), 'Unequal whichRows outputs.')
      if i>0
          % Add pass/fail checkboxes
          text(uiax, tfCenters(tf), repmat(i,1,sum(tf)), repmat(tfUniCodes(1),1,sum(tf)), ...
              'HorizontalAlignment', 'Center', 'Color', [0 .5 0], 'FontSize', 36, 'FontWeight', 'Bold')
          % Fail checkboxes
          text(uiax, tfCenters(~tf), repmat(i,1,sum(~tf)), repmat(tfUniCodes(2),1,sum(~tf)), ...
              'HorizontalAlignment', 'Center', 'Color', [1 0 0], 'FontSize', 36, 'FontWeight', 'Bold')
          % Plot the TT row number matches
          scatter(uiax, TT.OutageTime(whichRows{1}), repmat(i-barHeight/2+.1,1,sum(whichRows{1})), ...
              10, uiax.Colormap(whichRows{1},:), 'filled', 'MarkerFaceAlpha', 0.2)
      else
          % add stripes to reference bar
          xHatch = linspace(dateRanges(i+1,1)-days(2), dateRanges(i+1,2)+days(2), 20);
          yHatch = repmat(unique(pObj.YData), 1, 19);
          plot(uiax, [xHatch(1:end-1);xHatch(2:end)], yHatch, '-', 'Color', [1 1 1 .5], 'LineWidth', 4)
          pObj.FaceAlpha = .9;
          text(uiax, mean(outageTimeLims), 0, 'Timetable row times', 'FontSize', 20, ...
              'HorizontalAlignment', 'Center', 'VerticalAlignment', 'middle', 'FontWeight', 'Bold')
      end
  end
% Draw frame around checkboxs for duration comparisons and label intervalType
rectEdges = linspace(rightMargin(1),rightMargin(2),33);
nDurations = sum(~isnat(dateRanges(:,2)))-1;
fill(uiax, rectEdges([6,end-5,end-5,6]), [.4 .4 nDurations+[.4,.4]], 'w', ...
    'FaceAlpha', 0, 'LineWidth', 1.5, 'EdgeColor', 0.41016*[1,1,1]) % dimgray
text(uiax, rectEdges(6), nDurations/2+.5, sprintf('intervalType: %s', intervalType), 'FontSize', 16, ...
    'HorizontalAlignment', 'Center', 'VerticalAlignment', 'Bottom', 'Rotation', 90)
% Add text labels for checkboxes and comparison number
text(uiax, tfCenters, [.5 .5 .5], {'containsrange ', 'overlapsrange ', 'withinrange '}, 'Fontsize', 22, 'Rotation', 90, ...
    'HorizontalAlignment', 'right', 'FontWeight', 'b')
text(uiax, repmat(rightMargin(2)-range(xlim(uiax))*.02,1,nRanges-1), 1:nRanges-1, cellstr(num2str((1:nRanges-1)')), ...
    'HorizontalAlignment', 'Right', 'FontSize', 16)
% Add color bar; position it under the timetable bar (must be done after all other axes properties are set)
% requires coordinate tranformation from data units to fig position units.
cb = colorbar(uiax, 'Location', 'South', 'TickDirection', 'Out', 'YAxisLocation', 'Bottom', 'FontSize', 11);
caxis(uiax, [1,size(TT,1)])
cb.Position(3) = range(outageTimeLims)/range(xlim(uiax))  * (uiax.InnerPosition(3)/uifig.Position(3));
cb.Position(1) = ((outageTimeLims(1)-min(xlim(uiax)))/range(xlim(uiax)) * uiax.InnerPosition(3) + uiax.InnerPosition(1)) / uifig.Position(3); 
cb.Position(4) = 0.008;
cb.Position(2) = ((-barHeight-min(ylim(uiax))-.5)/range(ylim(uiax)) * uiax.InnerPosition(4) + uiax.InnerPosition(2)) / uifig.Position(4);
ylabel(cb, 'Timetable row number')
David
David
最後のアクティビティ: 2021 年 4 月 26 日

The following is a list of updates and new features for File Exchange and Cody.

New Features

  • (Coming soon) Support for GitHub releases added to File Exchange - Stay tuned for an upcoming announcement later this month.
  • 2 new Cody problem groups - Visit Cody to see the new MATLAB Basics and Beyond the Basics problem groups.

Let us know what you think about these new features by replying below.

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