diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 50 |
1 files changed, 34 insertions, 16 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 1a43da8b0..2992e92bb 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -305,12 +305,17 @@ def average(a, axis=None, weights=None, returned=False): Returns ------- - average, [sum_of_weights] : array_type or double - Return the average along the specified axis. When returned is `True`, + retval, [sum_of_weights] : array_type or double + Return the average along the specified axis. When `returned` is `True`, return a tuple with the average as the first element and the sum - of the weights as the second element. The return type is `Float` - if `a` is of integer type, otherwise it is of the same type as `a`. - `sum_of_weights` is of the same type as `average`. + of the weights as the second element. `sum_of_weights` is of the + same type as `retval`. The result dtype follows a genereal pattern. + If `weights` is None, the result dtype will be that of `a` , or ``float64`` + if `a` is integral. Otherwise, if `weights` is not None and `a` is non- + integral, the result type will be the type of lowest precision capable of + representing values of both `a` and `weights`. If `a` happens to be + integral, the previous rules still applies but the result dtype will + at least be ``float64``. Raises ------ @@ -327,6 +332,8 @@ def average(a, axis=None, weights=None, returned=False): ma.average : average for masked arrays -- useful if your data contains "missing" values + numpy.result_type : Returns the type that results from applying the + numpy type promotion rules to the arguments. Examples -------- @@ -346,10 +353,16 @@ def average(a, axis=None, weights=None, returned=False): >>> np.average(data, axis=1, weights=[1./4, 3./4]) array([ 0.75, 2.75, 4.75]) >>> np.average(data, weights=[1./4, 3./4]) + 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) + >>> print(avg.dtype) + complex256 """ a = np.asanyarray(a) @@ -1309,7 +1322,7 @@ def interp(x, xp, fp, left=None, right=None, period=None): return interp_func(x, xp, fp, left, right) -def angle(z, deg=0): +def angle(z, deg=False): """ Return the angle of the complex argument. @@ -1325,6 +1338,9 @@ def angle(z, deg=0): 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`. See Also -------- @@ -1339,18 +1355,18 @@ def angle(z, deg=0): 45.0 """ - if deg: - fact = 180/pi - else: - fact = 1.0 - z = asarray(z) - if (issubclass(z.dtype.type, _nx.complexfloating)): + z = asanyarray(z) + if issubclass(z.dtype.type, _nx.complexfloating): zimag = z.imag zreal = z.real else: zimag = 0 zreal = z - return arctan2(zimag, zreal) * fact + + a = arctan2(zimag, zreal) + if deg: + a *= 180/pi + return a def unwrap(p, discont=pi, axis=-1): @@ -1766,8 +1782,8 @@ class vectorize(object): Generalized function class. Define a vectorized function which takes a nested sequence of objects or - numpy arrays as inputs and returns an single or tuple of numpy array as - output. The vectorized function evaluates `pyfunc` over successive tuples + numpy arrays as inputs and returns a single numpy array or a tuple of numpy + arrays. The vectorized function evaluates `pyfunc` over successive tuples of the input arrays like the python map function, except it uses the broadcasting rules of numpy. @@ -3989,11 +4005,13 @@ def meshgrid(*xi, **kwargs): `meshgrid` is very useful to evaluate functions on a grid. + >>> import matplotlib.pyplot as plt >>> x = np.arange(-5, 5, 0.1) >>> y = np.arange(-5, 5, 0.1) >>> xx, yy = np.meshgrid(x, y, sparse=True) >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2) >>> h = plt.contourf(x,y,z) + >>> plt.show() """ ndim = len(xi) |