Automatically update date and time column from other date and time column

I have a list of meetings where 2 of the columns are “Start” and “End” of type “Date and time”.

Now I am trying to automatically update the “End” column after I add the date and time of the Start, but I want to still be able to edit it and I want the update to be asynchrone so it would update before submitting the new element form.

Do you have any ideas ?

Thank you for your response.

Datasets and Time values: Filtering and Plotting

After struggling for a few weeks, I have accumulated a few examples of challenging date-related operatations with datasets. Some I have partial solutions; others I cannot yet do. The resources on Datetime objects in datasets is thin on here (both are relatively new to Mathematica, so no surprise), so maybe this will fill in that gap.

For all of my data, we have events that occur at a datetime. We want to understand how the progress on given days compare; for example, when did task A occur on April 4, 2017 vs October 12, 2018? What is the distribution for Task A occurring throughout a day over 100 days?

  1. How can I plot a DateHistogram with a bin of, say, 20 minutes? I can do it by hour: DateHistogram[dataset, "Hour", DateReduction->"Day"]

  2. How can I utilize the DateHistogram plot style using the operator form? A relatively new functionality allows the syntax dataset[plotstyle,"Key"] (see this nice StackExchange thread). For example, I can implement:

    dataset[Groupby[Key["State"]] /* (PieChart[#, ChartLabels->Keys[#]]&), Length]

    I want to use this syntax with DateHistogram; I can implement the most basic version:


    I want to add the DateReduction option, along the lines of:


    Unfortunately, the above example doesn’t work and I can’t find more documentation.

  3. How can I select objects in a certain time window? I figured out a method, but perhaps there is a more elegant solution. In the below example, I can plot the events that occur before 2016:

    eventsBefore2016 = dataset[Select[#Sent<DateObject[{2016,1,1}]&,"Sent"]; DateHistogram[eventsBefore2016,"Hour",DateReduction->"Day"]

    Can I make these two lines into one line of code, for example? Could I do it using the operate form (question 2)?

  4. How can I adapt a DistributionChart for a dataset of Datetime values? I don’t example starter code for this one.

(Apologies for the generic code examples below; I can’t share the actual data and my workplace blocks the tutorial datasets such as titanic. If this is a real sticking issue I can adapt this question with a tutorial dataset later at home.)

How to manage race condition for a certain period of time? [on hold]

A single user may request for going to vacation or mission by a web application over REST and then AMQP . The vacation/mission request times should not overlap(the overlapping requests should be rejected).

In What ways I can avoid the overlap and manage the race condidion?

How to reject the request if it overlaps an earlier request?

How to reduce the time of calculation using ParalleDo instead of Do Loop?

I am trying to use ParalleDo function through ParametricNDSolveValue and NIntegrate.

The question is pretty similar how to apply paralleDo to my code

First I intend to use Do loop and its works fine but It cost a longer time, so to reduce the computational time I want to use ParalleDo, but I get an error message and I don’t understand where it comes from.

Moreover, I intend to write the output data in data file formate by using

  file = OpenWrite[        "file1.dat",         FormatType -> TableForm]; 

But I got the following error OutputStream[file1.dat ,3] is not open.

Below is my minimal working code and some comments:

 l1 = 0.81;         Z = 1500;         x0 = 10;         v0 = 0.02;         \[Epsilon] = $  MachineEpsilon;          l0 = 0.0714`20.;          ps = ParametricNDSolveValue[{y''[r] +                2 y'[r]/r == -4 \[Pi] l k Exp[-y[r]], y[\[Epsilon]] == y0,              y'[\[Epsilon]] == 0, WhenEvent[r == 1, y'[r] -> y'[r] + Z l]}, {y,              y'}, {r, \[Epsilon], R}, {k, l},             Method -> {"StiffnessSwitching"}, AccuracyGoal -> 5,             PrecisionGoal -> 4, WorkingPrecision -> 15];        file = OpenWrite[            "file1.dat",             FormatType -> TableForm];         ParallelDo[x = i x0;           v = i^3 v0;           R = Rationalize[v^(-1/3), 0];           l = Rationalize[l1/(i x0), 0];           nn = FindRoot[Last[ps[y0, l]][R], {y0, -1}, Evaluated -> False][[1,              2]];           Tot = 4 \[Pi] nn NIntegrate[              r^2 Exp[-First[ps[nn, l]][r]], {r, \[Epsilon], R},               PrecisionGoal -> 4];           Print[NumberForm[i*1., 5], "  ", NumberForm[Tot, 5]];, {i, 292/100,             31/10, 1/100}] // Quiet // AbsoluteTiming Close[file]; 

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How many floating point ops were performed worldwide over a time interval

I am looking for information regarding the evolution of computing capability. Specifically I would like to know how many floating point operations were performed worldwide from, say, the deployment of ENIAC to the first moon landing… Or any other time interval before, say, the ’80s. is this a question that I can estimate based on existing data?

How to prevent querying each user each time when having per microservice authorization? [on hold]

I am trying to find a good approach for authentication and authorization of a user in a microservice-application.

Currently I have the following idea:

authentication and authorization using JWT

In regards to the sequence diagram I have the following questions:

  1. [step 12] and [step 16] check for a permission, which is necessary on the server-side. Where are those permissions supposed to be stored?
  2. What about the note (after [step 20])? Possibilities:
    1. Issue an additional signed Token, that contains permissions / roles for the message service. This token can be acquired during [step 13].
    2. Re-issue existing JWT, include permissions / roles for the message service. This token can be acquired during [step 13].
    3. Issue a server-side request, that returns information about whether a certain GUI-area should be displayed.

If in 2.1 or 2.2 the client-token is modified, the user is able to access GUI-areas, which should not be available to him. However: any subsequent service calls will not succeed, because the modified (nor the original) token permits access to corresponding server-calls to the user.

In 2.3 the microservice is queried each time the GUI has to decide whether to display a GUI-area or not.

Calculate Meant Time Between Failures ( SRE Metrics) in Java Stack

We are currently trying to capture / calculate Mean Time Between Failures for all our services running in production. We use K8s and Prometheus to monitor the same. MBTR = Total Uptime / Number of failures How to get total uptime in prometheus ? Is it possible to use JVM Metrics , is it a good idea to use ps -eo ? Also how to get number of failures ?

Effective database design for time card tracking

I’m building a timecard system for a company of ~50 employees. My problem is designing an efficient way of storing this data. Currently, their timecards are done on a table like the one shown below. current timecard style

With the exception of showing non-worked hours, and budget. I’m wondering if the way I’m approaching this currently is an effective solution. My current structure is in the form of two tables: timesheet, and timecard_entries. Every employee will have one timesheet per week. This holds: timesheetid, employeeid, timesheet_status, nation_working_in, datetime_created, week_start, and week_end. My table timecard_entries will be storing an entry for every ‘day’ (every col, row) table cell (along with additional information on the left side of the screen).

timecard_entries stores: timecardid, timesheetid, project,task,hours_worked,non_hours_worked, work_date, in_time,out_time… What I like about this approach is that it is easy to query all the records on an employee for a given week, all I have to know is the timesheetid. What I don’t like about this approach is the redundancy in data storage such as: keeping a time_in and time_out for multiple records on the same work date (non-worked_hours will be the same way).

Is this a good approach to storing this data? The front-end is web-based built with PHP and JavaScript.

Let me know if I can provide more info.