diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 68 |
1 files changed, 37 insertions, 31 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index fb40ef927..e9908d1ef 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -377,7 +377,7 @@ def average(a, axis=None, weights=None, returned=False): Traceback (most recent call last): ... TypeError: Axis must be specified when shapes of a and weights differ. - + >>> a = np.ones(5, dtype=np.float128) >>> w = np.ones(5, dtype=np.complex64) >>> avg = np.average(a, weights=w) @@ -1431,7 +1431,7 @@ def angle(z, deg=False): angle : ndarray or scalar The counterclockwise angle from the positive real axis on the complex plane, with dtype as numpy.float64. - + ..versionchanged:: 1.16.0 This function works on subclasses of ndarray like `ma.array`. @@ -1929,6 +1929,30 @@ class vectorize(object): vectorized : callable Vectorized function. + See Also + -------- + frompyfunc : Takes an arbitrary Python function and returns a ufunc + + Notes + ----- + The `vectorize` function is provided primarily for convenience, not for + performance. The implementation is essentially a for loop. + + If `otypes` is not specified, then a call to the function with the + first argument will be used to determine the number of outputs. The + results of this call will be cached if `cache` is `True` to prevent + calling the function twice. However, to implement the cache, the + original function must be wrapped which will slow down subsequent + calls, so only do this if your function is expensive. + + The new keyword argument interface and `excluded` argument support + further degrades performance. + + References + ---------- + .. [1] NumPy Reference, section `Generalized Universal Function API + <https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>`_. + Examples -------- >>> def myfunc(a, b): @@ -1988,8 +2012,8 @@ class vectorize(object): >>> import scipy.stats >>> pearsonr = np.vectorize(scipy.stats.pearsonr, - ... signature='(n),(n)->(),()') - >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]]) + ... signature='(n),(n)->(),()') + >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]]) (array([ 1., -1.]), array([ 0., 0.])) Or for a vectorized convolution: @@ -2001,31 +2025,7 @@ class vectorize(object): [0., 0., 1., 2., 1., 0.], [0., 0., 0., 1., 2., 1.]]) - See Also - -------- - frompyfunc : Takes an arbitrary Python function and returns a ufunc - - Notes - ----- - The `vectorize` function is provided primarily for convenience, not for - performance. The implementation is essentially a for loop. - - If `otypes` is not specified, then a call to the function with the - first argument will be used to determine the number of outputs. The - results of this call will be cached if `cache` is `True` to prevent - calling the function twice. However, to implement the cache, the - original function must be wrapped which will slow down subsequent - calls, so only do this if your function is expensive. - - The new keyword argument interface and `excluded` argument support - further degrades performance. - - References - ---------- - .. [1] NumPy Reference, section `Generalized Universal Function API - <https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html>`_. """ - def __init__(self, pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None): self.pyfunc = pyfunc @@ -2618,7 +2618,9 @@ def blackman(M): >>> 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) + >>> with np.errstate(divide='ignore', invalid='ignore'): + ... response = 20 * np.log10(mag) + ... >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [<matplotlib.lines.Line2D object at 0x...>] @@ -2725,7 +2727,9 @@ def bartlett(M): >>> 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) + >>> with np.errstate(divide='ignore', invalid='ignore'): + ... response = 20 * np.log10(mag) + ... >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [<matplotlib.lines.Line2D object at 0x...>] @@ -2826,7 +2830,9 @@ def hanning(M): >>> 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) + >>> with np.errstate(divide='ignore', invalid='ignore'): + ... response = 20 * np.log10(mag) + ... >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [<matplotlib.lines.Line2D object at 0x...>] |