burndowncharts | 易学教程

Azure DevOps OData Sprint Burndown Chart not showing 0 story point

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问题 I am referring to this MS docs when building Power BI report using OData query. I have slightly modified the query to display Sprint Burndown, sprint-by-sprint and it all works fine until I realize when the team has completed all story points, by right the burndown area should go all the way to zero like we can see in Sprint > Analytics in Azure DevOps UI, but in my OData query, the last day of the data will not shown because it has no story point, resulting my visualization will never go to

Is there easy way in python to extrapolate data points to the future?

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问题 I have a simple numpy array, for every date there is a data point. Something like this: >>> import numpy as np >>> from datetime import date >>> from datetime import date >>> x = np.array( [(date(2008,3,5), 4800 ), (date(2008,3,15), 4000 ), (date(2008,3, 20), 3500 ), (date(2008,4,5), 3000 ) ] ) Is there easy way to extrapolate data points to the future: date(2008,5,1), date(2008, 5, 20) etc? I understand it can be done with mathematical algorithms. But here I am seeking for some low hanging

Burndown chart shows no data in TFS

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问题 I have on my project many PBIs and each PBI has tasks with remaining work filled in. I have finished some of my tasks, others are not started and others are in progress. However, the burndown chart is not showing anything. Not sure exactly what I am missing to configure! My iterations are like this: 回答1: This has to do with the fact that the burn down needs at least 2 days of data to show something. So basically it won't show any data the first day of the sprint but everything works

Burndown chart shows no data in TFS

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I have on my project many PBIs and each PBI has tasks with remaining work filled in. I have finished some of my tasks, others are not started and others are in progress. However, the burndown chart is not showing anything. Not sure exactly what I am missing to configure! My iterations are like this: This has to do with the fact that the burn down needs at least 2 days of data to show something. So basically it won't show any data the first day of the sprint but everything works 'surprisingly' on the second day. This is a longer existing issue as can seen for example on https://connect

How to configure TFS burndown chart to be for effort points not task hours?

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问题 My question is really simple, hopefully the answer will be too. You all know the task burndown in TFS online right ? Here's an example Obviously, the y-axis is the remaining hours of tasks. I want to change that to be the remaining effort points of Product Backlog Items and Bugs. Can I do that ? If so, How ? 回答1: Yes. You have to edit the "Sprint Burndown.rdl" file in your process template (it's in the reports folder). Particularly, you have to change this field: <Field Name="Remaining_Work">

Specific examples of Agile documentation? [closed]

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问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . In an answer to the question Documents for a project? , Chris Ballance replied that "User Stories" and a "burndown chart" are the two most useful types of project documentation for a developer. My question is, do you know of any good example[s], which I can see (for example on the

Is there easy way in python to extrapolate data points to the future?

阅读更多关于 Is there easy way in python to extrapolate data points to the future?

I have a simple numpy array, for every date there is a data point. Something like this: >>> import numpy as np >>> from datetime import date >>> from datetime import date >>> x = np.array( [(date(2008,3,5), 4800 ), (date(2008,3,15), 4000 ), (date(2008,3, 20), 3500 ), (date(2008,4,5), 3000 ) ] ) Is there easy way to extrapolate data points to the future: date(2008,5,1), date(2008, 5, 20) etc? I understand it can be done with mathematical algorithms. But here I am seeking for some low hanging fruit. Actually I like what numpy.linalg.solve does, but it does not look applicable for the