matplotlib
diff --git a/‎TODO
Lines changed: 3 additions & 1 deletion b/‎TODO
Lines changed: 3 additions & 1 deletion
diff --git a/‎examples/anscombe.py
Lines changed: 57 additions & 0 deletions b/‎examples/anscombe.py
Lines changed: 57 additions & 0 deletions
diff --git a/‎examples/specgram_demo.py
Lines changed: 13 additions & 5 deletions b/‎examples/specgram_demo.py
Lines changed: 13 additions & 5 deletions
@@ -587,4 +587,6 @@ ZeroDivisionError: SeparableTransformation::eval_scalars yin interval is zero; c
 
 --  Humufr errobar
 
--- why is tkagg loading init 2x
+-- why is tkagg loading init 2x
+
+-- add ishold and spy
@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+"""
+Edward Tufte uses this example from Anscombe to show 4 datasets of x
+and y that have the same mean, standard deviation, and regression
+line, but which are qualitatively different.
+
+matplotlib fun for a rainy day
+"""
+
+from matplotlib.matlab import *
+
+x =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])
+y1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])
+y2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])
+y3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])
+x4 = array([8,8,8,8,8,8,8,19,8,8,8])
+y4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])
+
+def fit(x):
+    return 3+0.5*x
+
+
+
+xfit = array( [min(x), max(x) ] )
+
+subplot(221)
+plot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)
+axis([2,20,2,14])       
+set(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))
+text(3,12, 'I', fontsize=20)
+
+subplot(222)
+plot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)
+axis([2,20,2,14])
+set(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))
+text(3,12, 'II', fontsize=20)
+
+subplot(223)
+plot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)
+axis([2,20,2,14])
+text(3,12, 'IIII', fontsize=20)
+set(gca(), yticks=(4,8,12), xticks=(0,10,20))
+
+subplot(224)
+
+xfit = array([min(x4),max(x4)])
+plot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)
+axis([2,20,2,14])
+set(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))
+text(3,12, 'IV', fontsize=20)
+
+#verify the stats
+pairs = (x,y1), (x,y2), (x,y3), (x4,y4)
+for x,y in pairs:
+    print 'mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1])
+
+show()
@@ -1,18 +1,26 @@
 #!/usr/bin/env python
 from matplotlib.matlab import *
 
-
 dt = 0.0005
 t = arange(0.0, 20.0, dt)
 s1 = sin(2*pi*100*t)
 s2 = 2*sin(2*pi*400*t)
+
+# create a transient "chirp"
 mask = where(logical_and(t>10, t<12), 1.0, 0.0)
 s2 = s2 * mask
+
+# add some noise into the mix
 nse = 0.01*randn(len(t))
-x = s1 + s2 + nse
-NFFT = 1024
-Fs = int(1.0/dt)
-Noverlap = 0
+
+x = s1 + s2 + nse # the signal
+NFFT = 1024       # the length of the windowing segments
+Fs = int(1.0/dt)  # the sampling frequency
+
+# Pxx is the segments x freqs array of instantaneous power, freqs is
+# the frequency vector, bins are the centers of the time bins in which
+# the power is computed, and im is the matplotlib.image.AxesImage
+# instance
 Pxx, freqs, bins, im = specgram(x, NFFT=NFFT, Fs=Fs, noverlap=900)
 colorbar()
 show()