Recipes for timeseriesΒΆ

<TableOfContents(4)>

NOTE: The official documentation and important remarks from the developers can be found at the timseries scikit sourceforge page.

FAQ

General threads

1. time series analysis -http://article.gmane.org/gmane.comp.python.scientific.user/13949\ 1. time series: Python vs. R URL missing!!!\ 1. roadmap/plans for timeseries package -http://permalink.gmane.org/gmane.comp.python.scientific.user/14599

Reading data and creating timeseries objects

masking NoData values

Question

In my original data nodata values are marked with "-999". How can I import the data or create the time series and exclude these no data points from further processing? (flagging no data in timeseries - http://permalink.gmane.org/gmane.comp.python.scientific.user/14455)

Answer

* use masked_where from maskedarray

In [ ]:
#!python
myvalues_ts_hourly = masked_where(myvalues_ts_hourly , -999)

* Use indexing

In [ ]:
#!python
myvalues_ts_hourly[myvalues_ts_hourly==-999] = M.masked

More extensive answer

* START SAMPLE DATA (tmp.txt) *

In [ ]:
date;hour_of_day;value
01.02.2004;1;247
01.02.2004;2;889
01.02.2004;3;914
01.02.2004;4;292
01.02.2004;5;183
01.02.2004;6;251
01.02.2004;7;953
01.02.2004;8;156
01.02.2004;9;991
01.02.2004;10;557
01.02.2004;11;581
01.02.2004;12;354
01.02.2004;13;485
01.02.2004;14;655
01.02.2004;15;-999
01.02.2004;16;-999
01.02.2004;17;-999
01.02.2004;18;744
01.02.2004;19;445
01.02.2004;20;374
01.02.2004;21;168
01.02.2004;22;995
01.02.2004;23;943
01.02.2004;24;326
02.02.2004;1;83.98
02.02.2004;2;302.26
02.02.2004;3;310.76
02.02.2004;4;-999
02.02.2004;5;62.22
02.02.2004;6;85.34
02.02.2004;7;324.02
02.02.2004;8;53.04
02.02.2004;9;336.94
02.02.2004;10;189.38
02.02.2004;11;197.54
02.02.2004;12;120.36
02.02.2004;13;164.9
02.02.2004;14;222.7
02.02.2004;15;34.74
02.02.2004;16;85.34
02.02.2004;17;53.04
02.02.2004;18;252.96
02.02.2004;19;151.3
02.02.2004;20;-999
02.02.2004;21;57.12
02.02.2004;22;338.3
02.02.2004;23;320.62
02.02.2004;24;110.84}}}

'''* END SAMPLE DATA *'''

{{{
#!python
import numpy as N
import maskedarray as M
import timeseries as ts
data = N.loadtxt("tmp.txt", dtype='|S10', skiprows=2)
dates = ts.date_array([ts.Date(freq='H',string="%s %s:00" %
(d[0],int(d[1])-1))
                       for d in data],
                      freq='H')
series = ts.time_series(data[:,-1].astype(N.float_),
                        dates,
                        mask=(data[:,-1]=='-999'))

frequencies

Question

Is there a example data set for at least one year on a high temporal resolution: 15min or at least 1h. Having such a common data set one could set up tutorials examples and debug or ask questions easier because all will have the same (non-confidetial) data on the disk.

Answer

For hours, you have the 'hourly' frequency. For 15min, you have the 'minutely' frequency, from which you can select every other 15th point.

(cf. Re: roadmap/plans for timeseries package - http://permalink.gmane.org/gmane.comp.python.scientific.user/1459)

hour of the day

(cf.: assignment of hours of day in time series - http://permalink.gmane.org/gmane.comp.python.scientific.user/14597) When exchanging agrregated data sets (e.g. with hourly frequency) the data is often presented as follows: desired report output

In [ ]:
date; hour_of_day; value
 1-Feb-2004;1:00;247
 1-Feb-2004;2:00;889
 1-Feb-2004;3:00;914
 1-Feb-2004;4:00;292
 1-Feb-2004;5:00;183
 1-Feb-2004;6:00;251
 1-Feb-2004;7:00;953
 1-Feb-2004;8:00;156
 1-Feb-2004;9:00;991
 1-Feb-2004;10:00;557
 1-Feb-2004;11:00;581
 1-Feb-2004;12:00;354
 1-Feb-2004;13:00;485
 1-Feb-2004;14:00;655
 1-Feb-2004;15:00;862
 1-Feb-2004;16:00;399
 1-Feb-2004;17:00;598
 1-Feb-2004;18:00;744
 1-Feb-2004;19:00;445
 1-Feb-2004;20:00;374
 1-Feb-2004;21:00;168
 1-Feb-2004;22:00;995
 1-Feb-2004;23:00;943
 1-Feb-2004;24:00;326

This formatting may be the result of some logging devices which for instance record 5 minutes averaged values which have been taken with a device using a sample rate of 16 sec. As well, syntetically generated data sets which have been created by scientifc models or from remote sensing information can have such a format. When creating a timeseries object the start hour should be set to zero (0) internally to achieve a correct assignment of the hours (01:00 h is the end of the period 00:00 h - 01:00 h => data for this period starts at 00:00 h). For the output one can be customized as shown below in the answer. The python built-in module datetime can help here.

Question

I have hourly measurements where hour 1 represents the end of the period 0:00-1:00, 2 the end of the period 1:00-2:00, ... , 24 the end of the period 23:00 to 24:00.

When I plot these hourly time series from February to November the curve is continued into December because of that matter. time series then assumes that the value for hour 0:00 of dec, 01 is 0 which then leads do a wrong plotting behaviour.

I want to achieve that hour 24 is accounted as the last measurement period of a day and not as the first measurement of the next day (like 0:00).

Answer

Since the time "24:00" doesn't actually exist (as far as I am aware anyway), you will have to rely on somewhat of a hack to get your desired output. Try this:

In [ ]:
#!python
import timeseries as ts
series = ts.time_series(range(400, 430), start_date=ts.now('hourly'))
hours = ts.time_series(series.hour + 1, dates=series.dates)
hour_fmtfunc = lambda x : '%i:00' % x
ts.Report(hours, series, datefmt='%d-%b-%Y', delim='  ', fmtfunc=[None hour_fmtfunc,])()
In [ ]:
date time; value
06-Jan-2008  23:00;  400
06-Jan-2008  24:00;  401
07-Jan-2008   1:00;  402
07-Jan-2008   2:00;  403
07-Jan-2008   3:00;  404
07-Jan-2008   4:00;  405
07-Jan-2008   5:00;  406
07-Jan-2008   6:00;  407
07-Jan-2008   7:00;  408
07-Jan-2008   8:00;  409
07-Jan-2008   9:00;  410
07-Jan-2008  10:00;  411
07-Jan-2008  11:00;  412
07-Jan-2008  12:00;  413
07-Jan-2008  13:00;  414
07-Jan-2008  14:00;  415
07-Jan-2008  15:00;  416
07-Jan-2008  16:00;  417
07-Jan-2008  17:00;  418
07-Jan-2008  18:00;  419
07-Jan-2008  19:00;  420
07-Jan-2008  20:00;  421
07-Jan-2008  21:00;  422
07-Jan-2008  22:00;  423
07-Jan-2008  23:00;  424
07-Jan-2008  24:00;  425
08-Jan-2008   1:00;  426
08-Jan-2008   2:00;  427
08-Jan-2008   3:00;  428
08-Jan-2008   4:00;  429

Manipulations & Operations with time series

use the datetime information of the time series

(Re: roadmap/plans for timeseries package - http://permalink.gmane.org/gmane.comp.python.scientific.user/14598) A example:

Question

One has to get rainfall intensity during early morning hours. For such a filter the information on the corresponding hours are neccessary.

Answer

In [ ]:
import timeseries as ts
data = ts.time_series(range(100), start_date=ts.today('hourly'))
hours = data.hour
filtered_data = data[(hours < 7) & (hours > 3)]
filtered_data
timeseries([80  6  7  8 30 31 32 54 55 56 78 79],
dates = [07-Jan-2008 04:00 07-Jan-2008 05:00 07-Jan-2008 06:00
08-Jan-2008 04:00 08-Jan-2008 05:00 08-Jan-2008 06:00 09-Jan-2008 04:00
09-Jan-2008 05:00 09-Jan-2008 06:00 10-Jan-2008 04:00 10-Jan-2008 05:00
10-Jan-2008 06:00],
          freq  = H)

using the result of time series operations

Question

How can one save the read the result of time series operations into a array?

For instance, if I convert data in an hourly frequency to daily averages how to I read the daily averages into a array for further processing?

when I print out my daily timeseries converted from hourly data I get something like this:

In [ ]:
#!python
In: myvalues_ts_daily
Out:
timeseries([  1.4   89.4  3.5 ...,  11.5  1.6
     0.        ],
            dates = [01-Dec-2006 01-Feb-1995 ...],
            freq  = D)

What I would like is an array with just the values of the daily averages . Additional a report-like array output with the format day value

In [ ]:
1   3
2   11

Answer

> For instance, if I convert data in an hourly frequency to daily averages > > how to I read the daily averages into a array for further processing?

1. possibility #1: use the keyword func while converting.

In [ ]:
1. possibility #2:
If you don't use the keyword func, you end up with a 2d array, each row being  a day, each column an hour. Just use maskedarray.mean on each row avgdata = convert(data,'D').mean(-1)

If you only want the values, use the .series attribute, it will give you a  view of the array as a MaskedArray.

== Plotting ==
Word of caution... the timeseries plotting stuff does not currently support frequencies higher than daily (eg. hourly, minutely, etc...). Support for these frequencies could be added without too much trouble, but just haven't got around to it yet. (Cf. Re: roadmap/plans for timeseries package - http://permalink.gmane.org/gmane.comp.python.scientific.user/14598)

= About this page =
== Source ==
 * Most information presented here has been compiled from discussions at the scipy mailing list.
== todo ==
 * Use one data set consistently for the examples
 * offer the code for download

Section author: TimMichelsen, MattKnox, Unknown[149]