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-rw-r--r--numpy/lib/function_base.py84
1 files changed, 72 insertions, 12 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 76ea08dfb..7253740be 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -112,7 +112,6 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, new=None):
(array([ 0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))
- ]), array([0, 1, 2, 3]))
>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, normed=True)
@@ -147,7 +146,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, new=None):
if mn == mx:
mn -= 0.5
mx += 0.5
- bins = linspace(mn, mx, bins, endpoint=False)
+ bins = np.linspace(mn, mx, bins, endpoint=False)
else:
if normed:
raise ValueError, 'Use new=True to pass bin edges explicitly.'
@@ -293,7 +292,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
>>> r = np.random.randn(100,3)
>>> H, edges = np.histogramdd(r, bins = (5, 8, 4))
>>> H.shape, edges[0].size, edges[1].size, edges[2].size
- ((5,8,4), 6, 9, 5)
+ ((5, 8, 4), 6, 9, 5)
"""
@@ -791,7 +790,7 @@ def copy(a):
-----
This is equivalent to
- >>> np.array(a, copy=True)
+ >>> np.array(a, copy=True) #doctest: +SKIP
Examples
--------
@@ -1019,7 +1018,7 @@ def interp(x, xp, fp, left=None, right=None):
>>> np.interp(2.5, xp, fp)
1.0
>>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
- array([ 3. , 3. , 2.5, 0.56, 0. ])
+ array([ 3. , 3. , 2.5 , 0.56, 0. ])
>>> UNDEF = -99.0
>>> np.interp(3.14, xp, fp, right=UNDEF)
-99.0
@@ -1032,7 +1031,9 @@ def interp(x, xp, fp, left=None, right=None):
>>> yinterp = np.interp(xvals, x, y)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'o')
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(xvals, yinterp, '-x')
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.show()
"""
@@ -1161,7 +1162,7 @@ def sort_complex(a):
array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
- array([ 1.+2.j, 2.-1.j, 3.-5.j, 3.-3.j, 3.+2.j])
+ array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a,copy=True)
@@ -2041,28 +2042,38 @@ def blackman(M):
Plot the window and the frequency response:
- >>> from numpy import clip, log10, array, bartlett, linspace
- >>> from scipy.fftpack import fft, fftshift
+ >>> from numpy import clip, log10, array, blackman, linspace
+ >>> from numpy.fft import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = blackman(51)
>>> plt.plot(window)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Blackman window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
+ <matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
+ <matplotlib.figure.Figure object at 0x...>
>>> 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)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Blackman window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
+ (-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
@@ -2146,22 +2157,32 @@ def bartlett(M):
>>> window = bartlett(51)
>>> plt.plot(window)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Bartlett window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
+ <matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
+ <matplotlib.figure.Figure object at 0x...>
>>> 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 Blackman window")
+ [<matplotlib.lines.Line2D object at 0x...>]
+ >>> plt.title("Frequency response of Bartlett window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
+ (-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
@@ -2237,25 +2258,39 @@ def hanning(M):
>>> window = np.hanning(51)
>>> plt.plot(window)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Hann window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
+ <matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
+ <matplotlib.figure.Figure object at 0x...>
>>> 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.plot(freq, response)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of the Hann window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
+ (-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
+ # XXX: this docstring is inconsistent with other filter windows, e.g.
+ # Blackman and Bartlett - they should all follow the same convention for
+ # clarity. Either use np. for all numpy members (as above), or import all
+ # numpy members (as in Blackman and Bartlett examples)
if M < 1:
return array([])
if M == 1:
@@ -2321,27 +2356,37 @@ def hamming(M):
Plot the window and the frequency response:
- >>> from scipy.fftpack import fft, fftshift
+ >>> from numpy.fft import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = np.hamming(51)
>>> plt.plot(window)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Hamming window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
+ <matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
+ <matplotlib.figure.Figure object at 0x...>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Hamming window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
+ (-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
@@ -2587,27 +2632,37 @@ def kaiser(M,beta):
Plot the window and the frequency response:
>>> from numpy import clip, log10, array, kaiser, linspace
- >>> from scipy.fftpack import fft, fftshift
+ >>> from numpy.fft import fft, fftshift
>>> import matplotlib.pyplot as plt
>>> window = kaiser(51, 14)
>>> plt.plot(window)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Kaiser window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Sample")
+ <matplotlib.text.Text object at 0x...>
>>> plt.show()
>>> plt.figure()
+ <matplotlib.figure.Figure object at 0x...>
>>> 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)
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Kaiser window")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Magnitude [dB]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("Normalized frequency [cycles per sample]")
+ <matplotlib.text.Text object at 0x...>
>>> plt.axis('tight')
+ (-0.5, 0.5, -100.0, ...)
>>> plt.show()
"""
@@ -2674,9 +2729,13 @@ def sinc(x):
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, np.sinc(x))
+ [<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Sinc Function")
+ <matplotlib.text.Text object at 0x...>
>>> plt.ylabel("Amplitude")
+ <matplotlib.text.Text object at 0x...>
>>> plt.xlabel("X")
+ <matplotlib.text.Text object at 0x...>
>>> plt.show()
It works in 2-D as well:
@@ -2684,6 +2743,7 @@ def sinc(x):
>>> x = np.arange(-200., 201.)/50.
>>> xx = np.outer(x, x)
>>> plt.imshow(np.sinc(xx))
+ <matplotlib.image.AxesImage object at 0x...>
"""
y = pi* where(x == 0, 1.0e-20, x)
@@ -3277,7 +3337,7 @@ def append(arr, values, axis=None):
>>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
Traceback (most recent call last):
...
- ValueError: arrays must have same number of dimension
+ ValueError: arrays must have same number of dimensions
"""
arr = asanyarray(arr)
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