analyze¶

Module author: Adam Gagorik <adam.gagorik@gmail.com>

analyze.create_panel(frames, index=None)[source]¶

Turn a list of pandas.DataFrame into a pandas.Panel.

Parameters:	frames (list) – list of pandas.DataFrame index (list) – major axis labels

>>> data1 = lm.common.load_pkl('run.0/calculated.pkl.gz')
>>> data2 = lm.common.load_pkl('run.1/calculated.pkl.gz')
>>> panel = create_panel([data1, data2], index=[0, 1])

analyze.combine(objs)[source]¶

Combine a set of panda’s DataFrames into a single DataFrame. The idea is that each DataFrame holds data from a part of some series of data. The index of each part should be the simulation:time.

Parameters:	objs (list) – list of pandas.DataFrame

>>> data1 = lm.datfile.load('part.0/out.dat.gz')
>>> data2 = lm.datfile.load('part.1/out.dat.gz')
>>> combined = lm.analyze.combine([data1, data2])

analyze.calculate(obj)[source]¶

Compute all flux statistics. Calculates current using, for example, the number of carriers exiting a drain.

Parameters:	obj (pandas.DataFrame) – data

>>> data = lm.common.load_pkl('combined.pkl.gz')
>>> data = lm.analyze.calculate(data)

analyze.equilibrate(obj, last, equil=None)[source]¶

Get the difference between two steps.

Parameters:	obj (pandas.DataFrame) – data last (int) – index of last step equil (int) – index of first step

>>> data = lm.common.load_pkl('combined.pkl.gz')
>>> data = lm.analyze.equilibrate(data, last=-1, equil=-1000)

class analyze.Stats(array, prefix='')[source]¶

Compute various statistics of an array like object.

Attr	Description
max	max
min	min
rng	range
avg	average
std	standard deviation

>>> s = Stats([1, 2, 3, 4, 5])

Parameters:	array (list) – array like object

to_dict()[source]¶

Get summary of stats.