Docstring fixes: make some examples to work properly

2 files changed, 32 insertions, 22 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 00dc92f1f..1b84c864d 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -2070,7 +2070,7 @@ def blackman(M):
 
     Plot the window and the frequency response:
 
-    >>> from numpy import clip, log10, array, bartlett
+    >>> from numpy import clip, log10, array, bartlett, linspace
     >>> from scipy.fftpack import fft, fftshift
     >>> import matplotlib.pyplot as plt
 
@@ -2080,17 +2080,19 @@ def blackman(M):
     >>> plt.ylabel("Amplitude")
     >>> plt.xlabel("Sample")
     >>> plt.show()
-
+    
+    >>> plt.figure()
     >>> A = fft(window, 2048) / 25.5
     >>> mag = abs(fftshift(A))
     >>> freq = linspace(-0.5,0.5,len(A))
     >>> response = 20*log10(mag)
     >>> response = clip(response,-100,100)
     >>> plt.plot(freq, response)
-    >>> plt.title("Frequency response of Bartlett window")
+    >>> plt.title("Frequency response of Blackman window")
     >>> plt.ylabel("Magnitude [dB]")
     >>> plt.xlabel("Normalized frequency [cycles per sample]")
-    >>> plt.axis('tight'); plt.show()
+    >>> plt.axis('tight')
+    >>> plt.show()
 
     """
     if M < 1:
@@ -2167,8 +2169,8 @@ def bartlett(M):
 
     Plot the window and its frequency response (requires SciPy and matplotlib):
 
-    >>> from numpy import clip, log10, array, bartlett
-    >>> from numpy.fft import fft
+    >>> from numpy import clip, log10, array, bartlett, linspace
+    >>> from numpy.fft import fft, fftshift
     >>> import matplotlib.pyplot as plt
 
     >>> window = bartlett(51)
@@ -2177,7 +2179,8 @@ def bartlett(M):
     >>> plt.ylabel("Amplitude")
     >>> plt.xlabel("Sample")
     >>> plt.show()
-
+    
+    >>> plt.figure()
     >>> A = fft(window, 2048) / 25.5
     >>> mag = abs(fftshift(A))
     >>> freq = linspace(-0.5,0.5,len(A))
@@ -2187,7 +2190,8 @@ def bartlett(M):
     >>> plt.title("Frequency response of Bartlett window")
     >>> plt.ylabel("Magnitude [dB]")
     >>> plt.xlabel("Normalized frequency [cycles per sample]")
-    >>> plt.axis('tight'); plt.show()
+    >>> plt.axis('tight')
+    >>> plt.show()
 
     """
     if M < 1:
@@ -2261,23 +2265,24 @@ def hanning(M):
     >>> import matplotlib.pyplot as plt
 
     >>> window = np.hanning(51)
-    >>> plt.subplot(121)
     >>> plt.plot(window)
     >>> plt.title("Hann window")
     >>> plt.ylabel("Amplitude")
     >>> plt.xlabel("Sample")
+    >>> plt.show()
 
+    >>> plt.figure()
     >>> A = fft(window, 2048) / 25.5
     >>> mag = abs(fftshift(A))
     >>> freq = np.linspace(-0.5,0.5,len(A))
     >>> response = 20*np.log10(mag)
     >>> response = np.clip(response,-100,100)
-    >>> plt.subplot(122)
     >>> plt.plot(freq, response)
     >>> plt.title("Frequency response of the Hann window")
     >>> plt.ylabel("Magnitude [dB]")
     >>> plt.xlabel("Normalized frequency [cycles per sample]")
-    >>> plt.axis('tight'); plt.show()
+    >>> plt.axis('tight')
+    >>> plt.show()
 
     """
     if M < 1:
@@ -2346,7 +2351,7 @@ def hamming(M):
 
     Plot the window and the frequency response:
 
-    >>> from numpy import clip, log10, array, hamming
+    >>> from numpy import clip, log10, array, hamming, linspace
     >>> from scipy.fftpack import fft, fftshift
     >>> import matplotlib.pyplot as plt
 
@@ -2357,6 +2362,7 @@ def hamming(M):
     >>> plt.xlabel("Sample")
     >>> plt.show()
 
+    >>> plt.figure()
     >>> A = fft(window, 2048) / 25.5
     >>> mag = abs(fftshift(A))
     >>> freq = linspace(-0.5,0.5,len(A))
@@ -2366,7 +2372,8 @@ def hamming(M):
     >>> plt.title("Frequency response of Hamming window")
     >>> plt.ylabel("Magnitude [dB]")
     >>> plt.xlabel("Normalized frequency [cycles per sample]")
-    >>> plt.axis('tight'); plt.show()
+    >>> plt.axis('tight')
+    >>> plt.show()
 
     """
     if M < 1:
@@ -2591,7 +2598,7 @@ def kaiser(M,beta):
 
     Plot the window and the frequency response:
 
-    >>> from numpy import clip, log10, array, kaiser
+    >>> from numpy import clip, log10, array, kaiser, linspace
     >>> from scipy.fftpack import fft, fftshift
     >>> import matplotlib.pyplot as plt
 
@@ -2602,6 +2609,7 @@ def kaiser(M,beta):
     >>> plt.xlabel("Sample")
     >>> plt.show()
 
+    >>> plt.figure()
     >>> A = fft(window, 2048) / 25.5
     >>> mag = abs(fftshift(A))
     >>> freq = linspace(-0.5,0.5,len(A))
@@ -2611,7 +2619,8 @@ def kaiser(M,beta):
     >>> plt.title("Frequency response of Kaiser window")
     >>> plt.ylabel("Magnitude [dB]")
     >>> plt.xlabel("Normalized frequency [cycles per sample]")
-    >>> plt.axis('tight'); plt.show()
+    >>> plt.axis('tight')
+    >>> plt.show()
 
     """
     from numpy.dual import i0
@@ -2676,7 +2685,7 @@ def sinc(x):
             -4.92362781e-02,  -3.89804309e-17])
 
     >>> import matplotlib.pyplot as plt
-    >>> plt.plot(x, sinc(x))
+    >>> plt.plot(x, np.sinc(x))
     >>> plt.title("Sinc Function")
     >>> plt.ylabel("Amplitude")
     >>> plt.xlabel("X")
@@ -2686,7 +2695,7 @@ def sinc(x):
 
     >>> x = np.arange(-200., 201.)/50.
     >>> xx = np.outer(x, x)
-    >>> plt.imshow(sinc(xx))
+    >>> plt.imshow(np.sinc(xx))
 
     """
     y = pi* where(x == 0, 1.0e-20, x)
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index b3b676b9c..2bc2652fe 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -1279,8 +1279,8 @@ cdef class RandomState:
         >>> import matplotlib.pyplot as plt
         >>> import scipy.special as sps
         >>> count, bins, ignored = plt.hist(s, 50, normed=True)
-        >>> y = bins**(shape-1)*((exp(-bins/scale))/\\
-            (sps.gamma(shape)*scale**shape))
+        >>> y = bins**(shape-1)*(exp(-bins/scale) /
+        ...                      (sps.gamma(shape)*scale**shape))
         >>> plt.plot(bins, y, linewidth=2, color='r')
         >>> plt.show()
 
@@ -1672,8 +1672,8 @@ cdef class RandomState:
         >>> import matplotlib.pyplot as plt
         >>> import scipy.special as sps
         >>> count, bins, ignored = plt.hist(s, 50, normed=True)
-        >>> x = arange(-pi, pi, 2*pi/50.)
-        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*pi*sps.jn(0,kappa))
+        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)
+        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))
         >>> plt.plot(x, y/max(y), linewidth=2, color='r')
         >>> plt.show()
 
@@ -1858,6 +1858,7 @@ cdef class RandomState:
         the probability density function:
 
         >>> import matplotlib.pyplot as plt
+        >>> x = np.arange(1,100.)/50.
         >>> def weib(x,n,a):
         ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)
 
@@ -2619,7 +2620,7 @@ cdef class RandomState:
         >>> import scipy.special as sps
         Truncate s values at 50 so plot is interesting
         >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)
-        >>> x = arange(1., 50.)
+        >>> x = np.arange(1., 50.)
         >>> y = x**(-a)/sps.zetac(a)
         >>> plt.plot(x, y/max(y), linewidth=2, color='r')
         >>> plt.show()