README.md

chrony

Timeseries analysis tools with specific focus on timespans. Built on top of pandas.

tldr

This library provides helpers for timespans manipulation using pandas objects. Let's consider the following DataFrame df.

In [1]: df
Out[1]: 
         beg        end
0 2015-01-01 2015-01-02
1 2015-01-02 2015-01-03
2 2015-01-04 2015-01-05

Chart timespans

from chrony.charting import plot_events
plot_events(
    categories=np.array(['a', 'b']),
    xmin=np.array([2, 4], dtype='d'),
    xmax=np.array([6, 8], dtype='d'),
    xlim=(0, 10),
    labels=['RED', 'BLUE']
)

df = pd.DataFrame({})
df0 = pd.DataFrame({
    'ts_beg': pd.date_range('2015-10-02', freq='2h', periods=12),
    'ts_end': pd.date_range('2015-10-02T02:00:00', freq='2h', periods=12),
    'job': ['A.%.2d' % i for i in range(12)],
    'category': 'A'
})
df1 = pandas.DataFrame({
    'ts_beg': pd.date_range('2015-10-02T01:00:00', freq='2h', periods=12),
    'ts_end': pd.date_range('2015-10-02T03:00:00', freq='2h', periods=12),
    'job': ['B.%.2d' % i for i in range(12)],
    'category': 'B'
})
df = df.append(df0)
df = df.append(df1)

plot_events(
    categories=df['category'].values,
    xmin=df['ts_beg'].values,
    xmax=df['ts_end'].values,
    labels=df['job'].values
)

Describe the timespan

This following method gives a quick overview of your timespans:

In [2]: describe_timespan(df['beg'], df['end'])
Out[2]: 
beg                           2015-01-01 00:00:00
count                                           3
contiguous transitions                          1
not contiguous transitions                      1
coverage                                     0.75
end                           2015-01-05 00:00:00
dtype: object

Audit the timespan

The library gives you a audit_timespan function which raises an error if:

• beg or end columns have a timezone (due to pandas-dev/pandas#11410)
• Lengths of beg or end columns does not match
• Columns are not sorted
• There is overlap between timespans.

Check out tests for examples.

The audit_timespan_print function makes the exact same verifications as audit_timespan, except it does not raise an error but prints the list of problems it detected.

Example:

>>> import chrony.chrony.timespans as chrony
>>> chrony.audit_timespan_print(events.beg,events.end)

At row 99334 end 2015-01-22 14:27:24 is posterior to 2015-01-22 14:17:43 by 0 days 00:09:41

>>> events.iloc[[99334,99335]]
	                      beg                 end  produced_count  state_id  \
	99334 2015-01-22 14:17:43 2015-01-22 14:27:24              12       208   
	99335 2015-01-22 14:17:43 2015-01-22 14:17:43              12       216   

      state_name  
99334    Stopped  
99335      Ready

Terminology

A timespan is a row of a pandas.DataFrame which represents a period of time between two fixed points. These are represented using a beg and a end column.

Development

Tests

nosetests chrony --with-coverage --cover-package chrony