Initial import

README.md

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+Simple Sequence Segmenting
+==========================
+
+This repository contains Python code I wrote for segmenting 1-D time series. In other words,
+it can be used for transforming a time series into a piecewise linear represenation. 
+The algorithms are Python implementations of the "classical" algorithms, as described in 
+[An Online Algorithm for Segmenting Time Series][keogh], including:
+
+- the sliding window algorithm;
+- the top-down algorithm; and
+- the bottom-up algorithm.
+
+The code is *not* optimized for performance in any way, but I've found it useful for 
+experimenting and data exploration.
+
+Requirements
+------------
+
+The segmenting algorithms use NumPy's least squares fitting routine, so naturally it depends on NumPy.
+
+Example
+-------
+
+You can run the code to see example output by running the example.py script. The script
+requires [matplotlib][mpl] to display the plots.
+
+The example uses ECG data I found on an [ECG data site][ecg].
+
+
+References
+----------
+
+[keogh]: http://www.cs.ucr.edu/~eamonn/icdm-01.pdf
+[numpy]: http://numpy.scipy.org
+[mpl]: http://matplotlib.sourceforge.net
+[ecg]: http://myweb.msoe.edu/~martynsc/signals/ecg/ecg.html

example.py

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+from matplotlib.pylab import gca, figure, plot, subplot, title, xlabel, ylabel, xlim,show
+from matplotlib.lines import Line2D
+import segment
+import fit
+
+def draw_plot(data,plot_title):
+    plot(range(len(data)),data,alpha=0.8,color='red')
+    title(plot_title)
+    xlabel("Samples")
+    ylabel("Signal")
+    xlim((0,len(data)-1))
+
+def draw_segments(segments):
+    ax = gca()
+    for segment in segments:
+        line = Line2D((segment[0],segment[2]),(segment[1],segment[3]))
+        ax.add_line(line)
+
+with open("example_data/16265-normalecg.txt") as f:
+    file_lines = f.readlines()
+
+data = [float(x.split("\t")[2].strip()) for x in file_lines[100:320]]
+
+max_error = 0.005
+
+#sliding window with regression
+figure()
+segments = segment.slidingwindowsegment(data, fit.regression, fit.sumsquared_error, max_error)
+draw_plot(data,"Sliding window with regression")
+draw_segments(segments)
+
+#bottom-up with regression
+figure()
+segments = segment.bottomupsegment(data, fit.regression, fit.sumsquared_error, max_error)
+draw_plot(data,"Bottom-up with regression")
+draw_segments(segments)
+
+#top-down with regression
+figure()
+segments = segment.topdownsegment(data, fit.regression, fit.sumsquared_error, max_error)
+draw_plot(data,"Top-down with regression")
+draw_segments(segments)
+
+
+
+#sliding window with simple interpolation
+figure()
+segments = segment.slidingwindowsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
+draw_plot(data,"Sliding window with simple interpolation")
+draw_segments(segments)
+
+#bottom-up with  simple interpolation
+figure()
+segments = segment.bottomupsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
+draw_plot(data,"Bottom-up with simple interpolation")
+draw_segments(segments)
+
+#top-down with  simple interpolation
+figure()
+segments = segment.topdownsegment(data, fit.interpolate, fit.sumsquared_error, max_error)
+draw_plot(data,"Top-down with simple interpolation")
+draw_segments(segments)
+
+
+show()
+