def warn_other_module(): # Apply along axis is implemented in python; stacklevel=2 means # we end up inside its module, not ours. def warn(arr): warnings.warn("Some warning", stacklevel=2) return arr np.apply_along_axis(warn, 0, [0])
def test_apply_along_axis_matrix(): # this test is particularly malicious because matrix # refuses to become 1d # 2018-04-29: moved here from core.tests.test_shape_base. def double(row): return row * 2 m = np.matrix([[0, 1], [2, 3]]) expected = np.matrix([[0, 2], [4, 6]]) result = np.apply_along_axis(double, 0, m) assert_(isinstance(result, np.matrix)) assert_array_equal(result, expected) result = np.apply_along_axis(double, 1, m) assert_(isinstance(result, np.matrix)) assert_array_equal(result, expected)
def test_with_iterable_object(self): # from issue 5248 d = np.array([[set([1, 11]), set([2, 22]), set([3, 33])], [set([4, 44]), set([5, 55]), set([6, 66])]]) actual = np.apply_along_axis(lambda a: set.union(*a), 0, d) expected = np.array([{1, 11, 4, 44}, {2, 22, 5, 55}, {3, 33, 6, 66}]) assert_equal(actual, expected) # issue 8642 - assert_equal doesn't detect this! for i in np.ndindex(actual.shape): assert_equal(type(actual[i]), type(expected[i]))
def test_empty(self): # can't apply_along_axis when there's no chance to call the function def never_call(x): assert_(False) # should never be reached a = np.empty((0, 0)) assert_raises(ValueError, np.apply_along_axis, never_call, 0, a) assert_raises(ValueError, np.apply_along_axis, never_call, 1, a) # but it's sometimes ok with some non-zero dimensions def empty_to_1(x): assert_(len(x) == 0) return 1 a = np.empty((10, 0)) actual = np.apply_along_axis(empty_to_1, 1, a) assert_equal(actual, np.ones(10)) assert_raises(ValueError, np.apply_along_axis, empty_to_1, 0, a)
def pad(array, pad_width, mode, **kwargs): """ Pads an array. Parameters ---------- array : array_like of rank N Input array pad_width : {sequence, array_like, int} Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. mode : str or function One of the following string values or a user supplied function. 'constant' Pads with a constant value. 'edge' Pads with the edge values of array. 'linear_ramp' Pads with the linear ramp between end_value and the array edge value. 'maximum' Pads with the maximum value of all or part of the vector along each axis. 'mean' Pads with the mean value of all or part of the vector along each axis. 'median' Pads with the median value of all or part of the vector along each axis. 'minimum' Pads with the minimum value of all or part of the vector along each axis. 'reflect' Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. 'symmetric' Pads with the reflection of the vector mirrored along the edge of the array. 'wrap' Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning. <function> Padding function, see Notes. stat_length : sequence or int, optional Used in 'maximum', 'mean', 'median', and 'minimum'. Number of values at edge of each axis used to calculate the statistic value. ((before_1, after_1), ... (before_N, after_N)) unique statistic lengths for each axis. ((before, after),) yields same before and after statistic lengths for each axis. (stat_length,) or int is a shortcut for before = after = statistic length for all axes. Default is ``None``, to use the entire axis. constant_values : sequence or int, optional Used in 'constant'. The values to set the padded values for each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad constants for each axis. ((before, after),) yields same before and after constants for each axis. (constant,) or int is a shortcut for before = after = constant for all axes. Default is 0. end_values : sequence or int, optional Used in 'linear_ramp'. The values used for the ending value of the linear_ramp and that will form the edge of the padded array. ((before_1, after_1), ... (before_N, after_N)) unique end values for each axis. ((before, after),) yields same before and after end values for each axis. (constant,) or int is a shortcut for before = after = end value for all axes. Default is 0. reflect_type : {'even', 'odd'}, optional Used in 'reflect', and 'symmetric'. The 'even' style is the default with an unaltered reflection around the edge value. For the 'odd' style, the extended part of the array is created by subtracting the reflected values from two times the edge value. Returns ------- pad : ndarray Padded array of rank equal to `array` with shape increased according to `pad_width`. Notes ----- .. versionadded:: 1.7.0 For an array with rank greater than 1, some of the padding of later axes is calculated from padding of previous axes. This is easiest to think about with a rank 2 array where the corners of the padded array are calculated by using padded values from the first axis. The padding function, if used, should return a rank 1 array equal in length to the vector argument with padded values replaced. It has the following signature:: padding_func(vector, iaxis_pad_width, iaxis, kwargs) where vector : ndarray A rank 1 array already padded with zeros. Padded values are vector[:pad_tuple[0]] and vector[-pad_tuple[1]:]. iaxis_pad_width : tuple A 2-tuple of ints, iaxis_pad_width[0] represents the number of values padded at the beginning of vector where iaxis_pad_width[1] represents the number of values padded at the end of vector. iaxis : int The axis currently being calculated. kwargs : dict Any keyword arguments the function requires. Examples -------- >>> a = [1, 2, 3, 4, 5] >>> np.pad(a, (2,3), 'constant', constant_values=(4, 6)) array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6]) >>> np.pad(a, (2, 3), 'edge') array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5]) >>> np.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4)) array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4]) >>> np.pad(a, (2,), 'maximum') array([5, 5, 1, 2, 3, 4, 5, 5, 5]) >>> np.pad(a, (2,), 'mean') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> np.pad(a, (2,), 'median') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> a = [[1, 2], [3, 4]] >>> np.pad(a, ((3, 2), (2, 3)), 'minimum') array([[1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [3, 3, 3, 4, 3, 3, 3], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1]]) >>> a = [1, 2, 3, 4, 5] >>> np.pad(a, (2, 3), 'reflect') array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2]) >>> np.pad(a, (2, 3), 'reflect', reflect_type='odd') array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8]) >>> np.pad(a, (2, 3), 'symmetric') array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3]) >>> np.pad(a, (2, 3), 'symmetric', reflect_type='odd') array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7]) >>> np.pad(a, (2, 3), 'wrap') array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3]) >>> def pad_with(vector, pad_width, iaxis, kwargs): ... pad_value = kwargs.get('padder', 10) ... vector[:pad_width[0]] = pad_value ... vector[-pad_width[1]:] = pad_value ... return vector >>> a = np.arange(6) >>> a = a.reshape((2, 3)) >>> np.pad(a, 2, pad_with) array([[10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 0, 1, 2, 10, 10], [10, 10, 3, 4, 5, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10]]) >>> np.pad(a, 2, pad_with, padder=100) array([[100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100], [100, 100, 0, 1, 2, 100, 100], [100, 100, 3, 4, 5, 100, 100], [100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100]]) """ if not np.asarray(pad_width).dtype.kind == 'i': raise TypeError('`pad_width` must be of integral type.') narray = np.array(array) pad_width = _validate_lengths(narray, pad_width) allowedkwargs = { 'constant': ['constant_values'], 'edge': [], 'linear_ramp': ['end_values'], 'maximum': ['stat_length'], 'mean': ['stat_length'], 'median': ['stat_length'], 'minimum': ['stat_length'], 'reflect': ['reflect_type'], 'symmetric': ['reflect_type'], 'wrap': [], } kwdefaults = { 'stat_length': None, 'constant_values': 0, 'end_values': 0, 'reflect_type': 'even', } if isinstance(mode, np.compat.basestring): # Make sure have allowed kwargs appropriate for mode for key in kwargs: if key not in allowedkwargs[mode]: raise ValueError('%s keyword not in allowed keywords %s' % (key, allowedkwargs[mode])) # Set kwarg defaults for kw in allowedkwargs[mode]: kwargs.setdefault(kw, kwdefaults[kw]) # Need to only normalize particular keywords. for i in kwargs: if i == 'stat_length': kwargs[i] = _validate_lengths(narray, kwargs[i]) if i in ['end_values', 'constant_values']: kwargs[i] = _normalize_shape(narray, kwargs[i], cast_to_int=False) else: # Drop back to old, slower np.apply_along_axis mode for user-supplied # vector function function = mode # Create a new padded array rank = list(range(narray.ndim)) total_dim_increase = [np.sum(pad_width[i]) for i in rank] offset_slices = tuple( slice(pad_width[i][0], pad_width[i][0] + narray.shape[i]) for i in rank) new_shape = np.array(narray.shape) + total_dim_increase newmat = np.zeros(new_shape, narray.dtype) # Insert the original array into the padded array newmat[offset_slices] = narray # This is the core of pad ... for iaxis in rank: np.apply_along_axis(function, iaxis, newmat, pad_width[iaxis], iaxis, kwargs) return newmat # If we get here, use new padding method newmat = narray.copy() # API preserved, but completely new algorithm which pads by building the # entire block to pad before/after `arr` with in one step, for each axis. if mode == 'constant': for axis, ((pad_before, pad_after), (before_val, after_val)) \ in enumerate(zip(pad_width, kwargs['constant_values'])): newmat = _prepend_const(newmat, pad_before, before_val, axis) newmat = _append_const(newmat, pad_after, after_val, axis) elif mode == 'edge': for axis, (pad_before, pad_after) in enumerate(pad_width): newmat = _prepend_edge(newmat, pad_before, axis) newmat = _append_edge(newmat, pad_after, axis) elif mode == 'linear_ramp': for axis, ((pad_before, pad_after), (before_val, after_val)) \ in enumerate(zip(pad_width, kwargs['end_values'])): newmat = _prepend_ramp(newmat, pad_before, before_val, axis) newmat = _append_ramp(newmat, pad_after, after_val, axis) elif mode == 'maximum': for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \ in enumerate(zip(pad_width, kwargs['stat_length'])): newmat = _prepend_max(newmat, pad_before, chunk_before, axis) newmat = _append_max(newmat, pad_after, chunk_after, axis) elif mode == 'mean': for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \ in enumerate(zip(pad_width, kwargs['stat_length'])): newmat = _prepend_mean(newmat, pad_before, chunk_before, axis) newmat = _append_mean(newmat, pad_after, chunk_after, axis) elif mode == 'median': for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \ in enumerate(zip(pad_width, kwargs['stat_length'])): newmat = _prepend_med(newmat, pad_before, chunk_before, axis) newmat = _append_med(newmat, pad_after, chunk_after, axis) elif mode == 'minimum': for axis, ((pad_before, pad_after), (chunk_before, chunk_after)) \ in enumerate(zip(pad_width, kwargs['stat_length'])): newmat = _prepend_min(newmat, pad_before, chunk_before, axis) newmat = _append_min(newmat, pad_after, chunk_after, axis) elif mode == 'reflect': for axis, (pad_before, pad_after) in enumerate(pad_width): if narray.shape[axis] == 0: # Axes with non-zero padding cannot be empty. if pad_before > 0 or pad_after > 0: raise ValueError("There aren't any elements to reflect" " in axis {} of `array`".format(axis)) # Skip zero padding on empty axes. continue # Recursive padding along any axis where `pad_amt` is too large # for indexing tricks. We can only safely pad the original axis # length, to keep the period of the reflections consistent. if ((pad_before > 0) or (pad_after > 0)) and newmat.shape[axis] == 1: # Extending singleton dimension for 'reflect' is legacy # behavior; it really should raise an error. newmat = _prepend_edge(newmat, pad_before, axis) newmat = _append_edge(newmat, pad_after, axis) continue method = kwargs['reflect_type'] safe_pad = newmat.shape[axis] - 1 while ((pad_before > safe_pad) or (pad_after > safe_pad)): pad_iter_b = min(safe_pad, safe_pad * (pad_before // safe_pad)) pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad)) newmat = _pad_ref(newmat, (pad_iter_b, pad_iter_a), method, axis) pad_before -= pad_iter_b pad_after -= pad_iter_a safe_pad += pad_iter_b + pad_iter_a newmat = _pad_ref(newmat, (pad_before, pad_after), method, axis) elif mode == 'symmetric': for axis, (pad_before, pad_after) in enumerate(pad_width): # Recursive padding along any axis where `pad_amt` is too large # for indexing tricks. We can only safely pad the original axis # length, to keep the period of the reflections consistent. method = kwargs['reflect_type'] safe_pad = newmat.shape[axis] while ((pad_before > safe_pad) or (pad_after > safe_pad)): pad_iter_b = min(safe_pad, safe_pad * (pad_before // safe_pad)) pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad)) newmat = _pad_sym(newmat, (pad_iter_b, pad_iter_a), method, axis) pad_before -= pad_iter_b pad_after -= pad_iter_a safe_pad += pad_iter_b + pad_iter_a newmat = _pad_sym(newmat, (pad_before, pad_after), method, axis) elif mode == 'wrap': for axis, (pad_before, pad_after) in enumerate(pad_width): # Recursive padding along any axis where `pad_amt` is too large # for indexing tricks. We can only safely pad the original axis # length, to keep the period of the reflections consistent. safe_pad = newmat.shape[axis] while ((pad_before > safe_pad) or (pad_after > safe_pad)): pad_iter_b = min(safe_pad, safe_pad * (pad_before // safe_pad)) pad_iter_a = min(safe_pad, safe_pad * (pad_after // safe_pad)) newmat = _pad_wrap(newmat, (pad_iter_b, pad_iter_a), axis) pad_before -= pad_iter_b pad_after -= pad_iter_a safe_pad += pad_iter_b + pad_iter_a newmat = _pad_wrap(newmat, (pad_before, pad_after), axis) return newmat
def test_tuple_func1d(self): def sample_1d(x): return x[1], x[0] res = np.apply_along_axis(sample_1d, 1, np.array([[1, 2], [3, 4]])) assert_array_equal(res, np.array([[2, 1], [4, 3]]))