While not spectacularly ugly, here's an example with a half-dozen -99.0 values at the start of the dataset:
There is a work around for this. If we replace the -99.0 values with the placeholder NaN (short for Not a Number), we then get something better.
- Code: Select all
Timestamp,outdoor humidity
2017-04-03 08:08:40.317000,NaN
2017-04-03 08:23:48.772000,NaN
2017-04-03 08:38:57.276000,NaN
2017-04-03 08:54:06.047000,NaN
2017-04-03 09:09:14.608000,NaN
2017-04-03 09:24:23.878000,83.0
2017-04-03 09:39:32.208000,83.0
2017-04-03 09:54:41.107000,84.0
2017-04-03 10:09:50.003000,86.0
2017-04-03 10:24:58.110000,89.0
2017-04-03 10:40:07.057000,93.0
2017-04-03 10:55:16.087000,94.0
2017-04-03 11:10:25.595000,95.0
2017-04-03 11:25:34.361000,96.0
2017-04-03 11:40:42.778000,96.0
2017-04-03 11:55:51.606000,97.0
2017-04-03 12:11:00.491000,97.0
2017-04-03 12:26:09.192000,97.0
Note: There is a bug in versions of Matplotlib prior to 2.0, which causes things to go haywire with the Y axis range if the dataset starts with NaN. One way to address this issue is to specify a Y axis range for the chart device in the plugin (I set the Y axis range to 0-100 for this example).
I'm mulling how best to address the charting behavior (and the Matplotlib bug) in the plugin, but this is a short-term workaround for those inclined to fix their own data.