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Monthly totals do not display correctly

Posted By Jakub Rehacek 8 Months Ago
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Jakub Rehacek
Problem Posted 8 Months Ago
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I have a Line chart where I am trying to display  monthly totals.  The values passed to it are correct (first day of each month for X axis). The X axis is set to DatetimeScale. But the  data pints are displayed incorrectly. It shows all points posted on January of relevant year. Data passed in is below.
What am I doing wrong? Axis is formatted correctly for the date span used. Just the points do not go where they are supposed to.
Same chart and code work fine when I pass it daily numbers.
                 X axis                  Y axis
1/7/2021 41
1/9/2021 7
1/10/2021 8
1/1/2022 8
1/2/2022 33


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Posted 8 Months Ago
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Last Active: 5 hours ago
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Hi Jakub,

Given this data, the chart displays correctly. The first three dates are close to each other as are the last two. The distance between them is one year, which is correctly reflected in the chart. Let us know if you have any questions. 


Best Regards,
Nevron Support Team





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