Package: pydiagnostics
Diagnosticsis a Python module designed to make analysis of diagnostic data easier.
It comes with a couple of clear data-structures with automatic quality checks, easy
Boolean logic operators and built-in bookkeeping. To top that off, it’s built onnumpy!InstallationThe diagnostics library is tested on python 3.7. However, it should run
on python 3.6 and 3.5 as well.You can install the library usingpip:pipinstallpydiagnosticsAlternatively, you can clone the repository and usesetup.pyto
install:gitclonehttps://github.com/tim00w/diagnostics.gitcddiagnosticspythonsetup.pyinstallOpen SourceThis project strives to abide by generally accepted best practices in open-source software development:DocumentationThe docs are hosted onReadTheDocs.QuickstartEager to begin and no time to read the documentation? Here is a quickstart!TimeSeriesDiagnostic events are derived from from real occurances. For instance,
your phone will probably generate a message (event) if your battery is
running low (percentage below threshold value). The diagnostics library
has aTimeSerieclass that can capture these occurances.For example, aTimeSerierepresenting your battery life, which
drains 0.01% each second:importnumpyasnpimportdiagnosticsasdsbattery_life=ds.TimeSerie(np.arange(100,0,-0.01),fs=1)the first argument is consists of a data array (bothlist()andnumpy.array()are supported), and additionally you can provide some
keyword parameters. Here we’ve provided the sample frequency (fs)
which is 1 Hz, because we said our battery drains 0.01% each second. In
this particular case we could’ve leftfsout, since the default
value offsis also 1.Now that we’ve got our data, we can easily visualize this:battery_life.plot(show=True)There are other keyword parameters that we can use as well, such as t0
(start time ofTimeSeriein posixtime or adatetimeobject), and
a name (default is an empty string).fromdatetimeimportdatetimebattery_life=ds.TimeSerie(np.arange(100,0,-0.01),fs=1,t0=datetime(2019,1,1,8,5),# 2019-01-01 08:05name='battery life')Now we’ve got our battery life set to a specific (start-)datetime, and gave it a
name. Both will come in handy later.BooleanTimeSeriesLet’s be honest, the battery percentage of your phone does not really
matter to you, unless it goes below a certain threshold. Luckily for us,
ourTimeSeriecan easily be converted to aBooleanTimeSerie,
which only contains boolean values of when the percentage reaches below
25%:battery_below25=battery_life<=25battery_below25.plot(show=True)Now that’s easy! We can see that our battery goes below 25% at HH:MM:SS.StateChangeArrayYou could argue that ourBooleanTimeSeriecontains a lot of data
points with the same value. I’d agree with you, and therefore introduce
a class that only keeps track of the changes in data points, theStateChangeArray:battery_low_state=battery_below25.to_statechangearray()Alternatively, we can create aStateChangeArray(orBooleanStateChangeArray, you can probably guess the difference
:smile:) from scratch:s=ds.StateChangeArray([1,4,8,13],t=[1,2,4,8],name='my state')b=ds.BooleanStateChangeArray([True,False,True,False],t=[1,3,6,9],name='b')Both the data array as the values for time (t) can belist()ornp.array(). The time is considered as posixtime. For now it is not
possible to give a datetimearray or list of datetimes as an input, but
this wil be implemented in the near future.Comparing TimeSeries and StateChangeArraysThere are more classes besides TimeSeries and StateChangearrays, each
with their own advantages and disadvantages. The power of this module
lies in clear transformations from one class to another (we’ve already
shown theTimeSerie.to_statechangearray()method), and the
comparison of multiple classes.To start with TimeSeries, if two (or more) have the same array_length,t0andfs, we can easily do calculations with them!# create two TimeSerie objects that we'll combinea=ds.TimeSerie(np.sin(np.linspace(0,2*np.pi,100)),t0=0,fs=1,name='a')b=ds.TimeSerie(np.sin(2*np.linspace(0,2*np.pi,100)),t0=0,fs=1,name='b')# It's this easy!c=a+b# We're interested in the more extreme values, lets create TimeSeries for these:d=c<=-1e=c>=1# we'll name them to keep our bookkeeping up to dated.name='c <= -1'e.name='c >= 1'# and find when one of the above conditions is True!f=d|e# when performing boolean operators ('~', '^', '&', '|'), the library# does it's own bookkeeping:print(f.name)f.plot(show=True)Comparing StateChangeArrays would normally be a bit tricky, since the
data is most likely non-linearly spaced. This means that we can’t just
perform vectorized boolean operations, but we’ll need to combine both
data values as well as their respective points in time.Luckily for us, theStateChangeArrayhas this built in:a=StateChangeArray([True,False,True,False],t=[2,4,6,8],name='a')b=StateChangeArray([True,False,True,False],t=[3,5,7,9],name='b')c=a|bd=a&be=~af=a^ag=a^eThat’s pretty great right?Reports & EventsWIP