[image: daytemphilow_line_gap_0.2.png] Thanks for the advice.
Changing line_gap_fraction to 0.2 did the trick. I've tested another values down to 0.03. The 0.04 is the smallest value, when the graph is plotted right. I've noticed in this image a small gap at the start and the end of the data lines (left and right side). None of my other images does have this. But as a workarround the line_gap_fraction trick is reasonable good for me. Although the trick helped, I did the manager.py test. Resulting output is in attachment. Thank you a lot for your help Pavel -- You received this message because you are subscribed to the Google Groups "weewx-user" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/weewx-user/42b71920-100c-45a1-98cf-095b486311b0%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
pi@rpi-meteo:~ $ sudo wee_reports Using configuration file /etc/weewx/weewx.conf Generating for all time timespan=[2019-07-07 06:00:00 CEST (1562472000) -> 2019-07-08 09:00:00 CEST (1562569200)]; sql_type=outTemp; aggregate_type=min; aggregate_interval=3600 start_vec= [1562472000, 1562475600, 1562479200, 1562482800, 1562486400, 1562490000, 1562493600, 1562497200, 1562500800, 1562504400, 1562508000, 1562511600, 1562515200, 1562518800, 1562522400, 1562526000, 1562529600, 1562533200, 1562536800, 1562540400, 1562544000, 1562547600, 1562551200, 1562554800, 1562558400] stop_vec= [1562475600, 1562479200, 1562482800, 1562486400, 1562490000, 1562493600, 1562497200, 1562500800, 1562504400, 1562508000, 1562511600, 1562515200, 1562518800, 1562522400, 1562526000, 1562529600, 1562533200, 1562536800, 1562540400, 1562544000, 1562547600, 1562551200, 1562554800, 1562558400, 1562562000] data_vec= [67.5, 68.97176470588235, 71.52631578947367, 67.54000000000002, 69.5475, 68.20941176470589, 66.64736842105265, 66.44842105263157, 66.15, 67.14, 68.44235294117645, 70.25000000000006, 71.45000000000002, 69.08000000000003, 66.62842105263158, 64.47000000000003, 62.810000000000024, 60.21, 58.78588235294116, 57.77999999999999, 55.79999999999998, 53.748, 51.84, 51.803999999999995, 52.55999999999997] timespan=[2019-07-07 06:00:00 CEST (1562472000) -> 2019-07-08 09:00:00 CEST (1562569200)]; sql_type=outTemp; aggregate_type=max; aggregate_interval=3600 start_vec= [1562472000, 1562475600, 1562479200, 1562482800, 1562486400, 1562490000, 1562493600, 1562497200, 1562500800, 1562504400, 1562508000, 1562511600, 1562515200, 1562518800, 1562522400, 1562526000, 1562529600, 1562533200, 1562536800, 1562540400, 1562544000, 1562547600, 1562551200, 1562554800, 1562558400] stop_vec= [1562475600, 1562479200, 1562482800, 1562486400, 1562490000, 1562493600, 1562497200, 1562500800, 1562504400, 1562508000, 1562511600, 1562515200, 1562518800, 1562522400, 1562526000, 1562529600, 1562533200, 1562536800, 1562540400, 1562544000, 1562547600, 1562551200, 1562554800, 1562558400, 1562562000] data_vec= [68.90210526315792, 75.14, 75.71249999999999, 70.22250000000001, 71.82000000000001, 71.27999999999999, 68.03999999999999, 67.5, 67.19, 68.23000000000002, 70.02, 71.96, 72.1042105263158, 71.27999999999999, 68.9021052631579, 66.42, 64.21, 62.64, 59.940000000000026, 58.64823529411763, 57.77999999999998, 55.98000000000001, 53.52352941176472, 52.45999999999998, 53.279999999999994]
