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-rw-r--r--numpy/lib/function_base.py138
-rw-r--r--numpy/lib/tests/test_function_base.py81
2 files changed, 162 insertions, 57 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 3b8ddf099..3f293ae4c 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -117,15 +117,16 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
if weights is not None:
weights = asarray(weights)
if np.any(weights.shape != a.shape):
- raise ValueError, 'weights should have the same shape as a.'
+ raise ValueError(
+ 'weights should have the same shape as a.')
weights = weights.ravel()
a = a.ravel()
if (range is not None):
mn, mx = range
if (mn > mx):
- raise AttributeError, \
- 'max must be larger than min in range parameter.'
+ raise AttributeError(
+ 'max must be larger than min in range parameter.')
if not iterable(bins):
if range is None:
@@ -138,7 +139,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None):
else:
bins = asarray(bins)
if (np.diff(bins) < 0).any():
- raise AttributeError, 'bins must increase monotonically.'
+ raise AttributeError(
+ 'bins must increase monotonically.')
# Histogram is an integer or a float array depending on the weights.
if weights is None:
@@ -244,8 +246,9 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
try:
M = len(bins)
if M != D:
- raise AttributeError, 'The dimension of bins must be equal ' \
- 'to the dimension of the sample x.'
+ raise AttributeError(
+ 'The dimension of bins must be equal'\
+ ' to the dimension of the sample x.')
except TypeError:
bins = D*[bins]
@@ -334,12 +337,13 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
s = hist.sum()
for i in arange(D):
shape = ones(D, int)
- shape[i] = nbin[i]-2
+ shape[i] = nbin[i] - 2
hist = hist / dedges[i].reshape(shape)
hist /= s
- if (hist.shape != nbin-2).any():
- raise RuntimeError('Internal Shape Error')
+ if (hist.shape != nbin - 2).any():
+ raise RuntimeError(
+ "Internal Shape Error")
return hist, edges
@@ -429,23 +433,30 @@ def average(a, axis=None, weights=None, returned=False):
# Sanity checks
if a.shape != wgt.shape :
if axis is None :
- raise TypeError, "Axis must be specified when shapes of a and weights differ."
+ raise TypeError(
+ "Axis must be specified when shapes of a "\
+ "and weights differ.")
if wgt.ndim != 1 :
- raise TypeError, "1D weights expected when shapes of a and weights differ."
+ raise TypeError(
+ "1D weights expected when shapes of a and "\
+ "weights differ.")
if wgt.shape[0] != a.shape[axis] :
- raise ValueError, "Length of weights not compatible with specified axis."
+ raise ValueError(
+ "Length of weights not compatible with "\
+ "specified axis.")
# setup wgt to broadcast along axis
- wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1,axis)
+ wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1, axis)
scl = wgt.sum(axis=axis)
if (scl == 0.0).any():
- raise ZeroDivisionError, "Weights sum to zero, can't be normalized"
+ raise ZeroDivisionError(
+ "Weights sum to zero, can't be normalized")
- avg = np.multiply(a,wgt).sum(axis)/scl
+ avg = np.multiply(a, wgt).sum(axis)/scl
if returned:
- scl = np.multiply(avg,0) + scl
+ scl = np.multiply(avg, 0) + scl
return avg, scl
else:
return avg
@@ -513,7 +524,8 @@ def asarray_chkfinite(a):
a = asarray(a)
if (a.dtype.char in typecodes['AllFloat']) \
and (_nx.isnan(a).any() or _nx.isinf(a).any()):
- raise ValueError, "array must not contain infs or NaNs"
+ raise ValueError(
+ "array must not contain infs or NaNs")
return a
def piecewise(x, condlist, funclist, *args, **kw):
@@ -612,8 +624,8 @@ def piecewise(x, condlist, funclist, *args, **kw):
condlist.append(~totlist)
n += 1
if (n != n2):
- raise ValueError, "function list and condition list " \
- "must be the same"
+ raise ValueError(
+ "function list and condition list must be the same")
zerod = False
# This is a hack to work around problems with NumPy's
# handling of 0-d arrays and boolean indexing with
@@ -683,7 +695,8 @@ def select(condlist, choicelist, default=0):
n = len(condlist)
n2 = len(choicelist)
if n2 != n:
- raise ValueError, "list of cases must be same length as list of conditions"
+ raise ValueError(
+ "list of cases must be same length as list of conditions")
choicelist = [default] + choicelist
S = 0
pfac = 1
@@ -791,7 +804,8 @@ def gradient(f, *varargs):
elif n == N:
dx = list(varargs)
else:
- raise SyntaxError, "invalid number of arguments"
+ raise SyntaxError(
+ "invalid number of arguments")
# use central differences on interior and first differences on endpoints
@@ -885,7 +899,8 @@ def diff(a, n=1, axis=-1):
if n == 0:
return a
if n < 0:
- raise ValueError, 'order must be non-negative but got ' + repr(n)
+ raise ValueError(
+ "order must be non-negative but got " + repr(n))
a = asanyarray(a)
nd = len(a.shape)
slice1 = [slice(None)]*nd
@@ -1596,22 +1611,49 @@ def disp(mesg, device=None, linefeed=True):
# return number of input arguments and
# number of default arguments
-import re
+
def _get_nargs(obj):
+ import re
+
+ terr = re.compile(r'.*? takes (exactly|at least) (?P<exargs>(\d+)|(\w+))' +
+ r' argument(s|) \((?P<gargs>(\d+)|(\w+)) given\)')
+ def _convert_to_int(strval):
+ try:
+ result = int(strval)
+ except ValueError:
+ if strval=='zero':
+ result = 0
+ elif strval=='one':
+ result = 1
+ elif strval=='two':
+ result = 2
+ # How high to go? English only?
+ else:
+ raise
+ return result
+
if not callable(obj):
- raise TypeError, "Object is not callable."
+ raise TypeError(
+ "Object is not callable.")
if sys.version_info[0] >= 3:
+ # inspect currently fails for binary extensions
+ # like math.cos. So fall back to other methods if
+ # it fails.
import inspect
- spec = inspect.getargspec(obj)
- nargs = len(spec.args)
- if spec.defaults:
- ndefaults = len(spec.defaults)
- else:
- ndefaults = 0
- if inspect.ismethod(obj):
- nargs -= 1
- return nargs, ndefaults
- elif hasattr(obj,'func_code'):
+ try:
+ spec = inspect.getargspec(obj)
+ nargs = len(spec.args)
+ if spec.defaults:
+ ndefaults = len(spec.defaults)
+ else:
+ ndefaults = 0
+ if inspect.ismethod(obj):
+ nargs -= 1
+ return nargs, ndefaults
+ except:
+ pass
+
+ if hasattr(obj,'func_code'):
fcode = obj.func_code
nargs = fcode.co_argcount
if obj.func_defaults is not None:
@@ -1621,19 +1663,21 @@ def _get_nargs(obj):
if isinstance(obj, types.MethodType):
nargs -= 1
return nargs, ndefaults
- terr = re.compile(r'.*? takes exactly (?P<exargs>\d+) argument(s|) \((?P<gargs>\d+) given\)')
+
try:
obj()
return 0, 0
except TypeError, msg:
m = terr.match(str(msg))
if m:
- nargs = int(m.group('exargs'))
- ndefaults = int(m.group('gargs'))
+ nargs = _convert_to_int(m.group('exargs'))
+ ndefaults = _convert_to_int(m.group('gargs'))
if isinstance(obj, types.MethodType):
nargs -= 1
return nargs, ndefaults
- raise ValueError, 'failed to determine the number of arguments for %s' % (obj)
+
+ raise ValueError(
+ "failed to determine the number of arguments for %s" % (obj))
class vectorize(object):
@@ -1717,11 +1761,13 @@ class vectorize(object):
self.otypes = otypes
for char in self.otypes:
if char not in typecodes['All']:
- raise ValueError, "invalid otype specified"
+ raise ValueError(
+ "invalid otype specified")
elif iterable(otypes):
self.otypes = ''.join([_nx.dtype(x).char for x in otypes])
else:
- raise ValueError, "output types must be a string of typecode characters or a list of data-types"
+ raise ValueError(
+ "Invalid otype specification")
self.lastcallargs = 0
def __call__(self, *args):
@@ -1730,8 +1776,8 @@ class vectorize(object):
nargs = len(args)
if self.nin:
if (nargs > self.nin) or (nargs < self.nin_wo_defaults):
- raise ValueError, "mismatch between python function inputs"\
- " and received arguments"
+ raise ValueError(
+ "Invalid number of arguments")
# we need a new ufunc if this is being called with more arguments.
if (self.lastcallargs != nargs):
@@ -3052,7 +3098,8 @@ def delete(arr, obj, axis=None):
if isinstance(obj, (int, long, integer)):
if (obj < 0): obj += N
if (obj < 0 or obj >=N):
- raise ValueError, "invalid entry"
+ raise ValueError(
+ "invalid entry")
newshape[axis]-=1;
new = empty(newshape, arr.dtype, arr.flags.fnc)
slobj[axis] = slice(None, obj)
@@ -3197,8 +3244,9 @@ def insert(arr, obj, values, axis=None):
if isinstance(obj, (int, long, integer)):
if (obj < 0): obj += N
if obj < 0 or obj > N:
- raise ValueError, "index (%d) out of range (0<=index<=%d) "\
- "in dimension %d" % (obj, N, axis)
+ raise ValueError(
+ "index (%d) out of range (0<=index<=%d) "\
+ "in dimension %d" % (obj, N, axis))
newshape[axis] += 1;
new = empty(newshape, arr.dtype, arr.flags.fnc)
slobj[axis] = slice(None, obj)
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index 330a1a51f..6381c0a72 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -23,6 +23,7 @@ class TestAny(TestCase):
assert_array_equal(sometrue(y1, axis=0), [1, 1, 0])
assert_array_equal(sometrue(y1, axis=1), [0, 1, 1])
+
class TestAll(TestCase):
def test_basic(self):
y1 = [0, 1, 1, 0]
@@ -39,6 +40,7 @@ class TestAll(TestCase):
assert_array_equal(alltrue(y1, axis=0), [0, 0, 1])
assert_array_equal(alltrue(y1, axis=1), [0, 0, 1])
+
class TestAverage(TestCase):
def test_basic(self):
y1 = array([1, 2, 3])
@@ -64,31 +66,30 @@ class TestAverage(TestCase):
def test_weights(self):
y = arange(10)
w = arange(10)
- assert_almost_equal(average(y, weights=w), (arange(10) ** 2).sum()*1. / arange(10).sum())
-
+ actual = average(y, weights=w)
+ desired = (arange(10) ** 2).sum()*1. / arange(10).sum()
+ assert_almost_equal(actual, desired)
+
y1 = array([[1, 2, 3], [4, 5, 6]])
w0 = [1, 2]
actual = average(y1, weights=w0, axis=0)
desired = array([3., 4., 5.])
assert_almost_equal(actual, desired)
-
w1 = [0, 0, 1]
+ actual = average(y1, weights=w1, axis=1)
desired = array([3., 6.])
- assert_almost_equal(average(y1, weights=w1, axis=1), desired)
+ assert_almost_equal(actual, desired)
# This should raise an error. Can we test for that ?
# assert_equal(average(y1, weights=w1), 9./2.)
-
# 2D Case
w2 = [[0, 0, 1], [0, 0, 2]]
desired = array([3., 6.])
assert_array_equal(average(y1, weights=w2, axis=1), desired)
-
assert_equal(average(y1, weights=w2), 5.)
-
def test_returned(self):
y = array([[1, 2, 3], [4, 5, 6]])
@@ -136,6 +137,7 @@ class TestSelect(TestCase):
assert_equal(len(choices), 3)
assert_equal(len(conditions), 3)
+
class TestInsert(TestCase):
def test_basic(self):
a = [1, 2, 3]
@@ -143,6 +145,7 @@ class TestInsert(TestCase):
assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
+
class TestAmax(TestCase):
def test_basic(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
@@ -153,6 +156,7 @@ class TestAmax(TestCase):
assert_equal(amax(b, axis=0), [8.0, 10.0, 9.0])
assert_equal(amax(b, axis=1), [9.0, 10.0, 8.0])
+
class TestAmin(TestCase):
def test_basic(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
@@ -163,6 +167,7 @@ class TestAmin(TestCase):
assert_equal(amin(b, axis=0), [3.0, 3.0, 2.0])
assert_equal(amin(b, axis=1), [3.0, 4.0, 2.0])
+
class TestPtp(TestCase):
def test_basic(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
@@ -173,6 +178,7 @@ class TestPtp(TestCase):
assert_equal(ptp(b, axis=0), [5.0, 7.0, 7.0])
assert_equal(ptp(b, axis= -1), [6.0, 6.0, 6.0])
+
class TestCumsum(TestCase):
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
@@ -189,6 +195,7 @@ class TestCumsum(TestCase):
[5, 11, 18, 27],
[10, 13, 17, 22]], ctype))
+
class TestProd(TestCase):
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
@@ -207,6 +214,7 @@ class TestProd(TestCase):
array([50, 36, 84, 180], ctype))
assert_array_equal(prod(a2, axis= -1), array([24, 1890, 600], ctype))
+
class TestCumprod(TestCase):
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
@@ -232,6 +240,7 @@ class TestCumprod(TestCase):
[ 5, 30, 210, 1890],
[10, 30, 120, 600]], ctype))
+
class TestDiff(TestCase):
def test_basic(self):
x = [1, 4, 6, 7, 12]
@@ -253,6 +262,7 @@ class TestDiff(TestCase):
assert_array_equal(diff(x, axis=0), out3)
assert_array_equal(diff(x, n=2, axis=0), out4)
+
class TestGradient(TestCase):
def test_basic(self):
x = array([[1, 1], [3, 4]])
@@ -271,6 +281,7 @@ class TestGradient(TestCase):
x = np.ma.array([[1, 1], [3, 4]])
assert_equal(type(gradient(x)[0]), type(x))
+
class TestAngle(TestCase):
def test_basic(self):
x = [1 + 3j, sqrt(2) / 2.0 + 1j * sqrt(2) / 2, 1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
@@ -282,6 +293,7 @@ class TestAngle(TestCase):
assert_array_almost_equal(y, yo, 11)
assert_array_almost_equal(z, zo, 11)
+
class TestTrimZeros(TestCase):
""" only testing for integer splits.
"""
@@ -289,10 +301,12 @@ class TestTrimZeros(TestCase):
a = array([0, 0, 1, 2, 3, 4, 0])
res = trim_zeros(a)
assert_array_equal(res, array([1, 2, 3, 4]))
+
def test_leading_skip(self):
a = array([0, 0, 1, 0, 2, 3, 4, 0])
res = trim_zeros(a)
assert_array_equal(res, array([1, 0, 2, 3, 4]))
+
def test_trailing_skip(self):
a = array([0, 0, 1, 0, 2, 3, 0, 4, 0])
res = trim_zeros(a)
@@ -304,10 +318,12 @@ class TestExtins(TestCase):
a = array([1, 3, 2, 1, 2, 3, 3])
b = extract(a > 1, a)
assert_array_equal(b, [3, 2, 2, 3, 3])
+
def test_place(self):
a = array([1, 4, 3, 2, 5, 8, 7])
place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
+
def test_both(self):
a = rand(10)
mask = a > 0.5
@@ -317,6 +333,7 @@ class TestExtins(TestCase):
place(a, mask, c)
assert_array_equal(a, ac)
+
class TestVectorize(TestCase):
def test_simple(self):
def addsubtract(a, b):
@@ -327,6 +344,7 @@ class TestVectorize(TestCase):
f = vectorize(addsubtract)
r = f([0, 3, 6, 9], [1, 3, 5, 7])
assert_array_equal(r, [1, 6, 1, 2])
+
def test_scalar(self):
def addsubtract(a, b):
if a > b:
@@ -336,12 +354,45 @@ class TestVectorize(TestCase):
f = vectorize(addsubtract)
r = f([0, 3, 6, 9], 5)
assert_array_equal(r, [5, 8, 1, 4])
+
def test_large(self):
x = linspace(-3, 2, 10000)
f = vectorize(lambda x: x)
y = f(x)
assert_array_equal(y, x)
+ def test_ufunc(self):
+ import math
+ f = vectorize(math.cos)
+ args = array([0, 0.5*pi, pi, 1.5*pi, 2*pi])
+ r1 = f(args)
+ r2 = cos(args)
+ assert_array_equal(r1, r2)
+
+ def test_keywords(self):
+ import math
+ def foo(a, b=1):
+ return a + b
+ f = vectorize(foo)
+ args = array([1,2,3])
+ r1 = f(args)
+ r2 = array([2,3,4])
+ assert_array_equal(r1, r2)
+ r1 = f(args, 2)
+ r2 = array([3,4,5])
+ assert_array_equal(r1, r2)
+
+ def test_keywords_no_func_code(self):
+ # This needs to test a function that has keywords but
+ # no func_code attribute, since otherwise vectorize will
+ # inspect the func_code.
+ import random
+ try:
+ f = vectorize(random.randrange)
+ except:
+ raise AssertionError()
+
+
class TestDigitize(TestCase):
def test_forward(self):
x = arange(-6, 5)
@@ -358,6 +409,7 @@ class TestDigitize(TestCase):
bin = linspace(x.min(), x.max(), 10)
assert all(digitize(x, bin) != 0)
+
class TestUnwrap(TestCase):
def test_simple(self):
#check that unwrap removes jumps greather that 2*pi
@@ -447,6 +499,7 @@ class TestSinc(TestCase):
#check symmetry
assert_array_almost_equal(w, flipud(w), 7)
+
class TestHistogram(TestCase):
def setUp(self):
pass
@@ -486,7 +539,6 @@ class TestHistogram(TestCase):
area = sum(a * diff(b))
assert_almost_equal(area, 1)
-
def test_outliers(self):
# Check that outliers are not tallied
a = arange(10) + .5
@@ -511,7 +563,6 @@ class TestHistogram(TestCase):
h, b = histogram(a, bins=8, range=[1, 9], weights=w)
assert_equal(h, w[1:-1])
-
def test_type(self):
# Check the type of the returned histogram
a = arange(10) + .5
@@ -527,7 +578,6 @@ class TestHistogram(TestCase):
h, b = histogram(a, weights=ones(10, float))
assert(issubdtype(h.dtype, float))
-
def test_weights(self):
v = rand(100)
w = ones(100) * 5
@@ -550,6 +600,7 @@ class TestHistogram(TestCase):
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True)
assert_array_equal(wa, array([4, 5, 0, 1]) / 10. / 3. * 4)
+
class TestHistogramdd(TestCase):
def test_simple(self):
x = array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], \
@@ -622,6 +673,7 @@ class TestHistogramdd(TestCase):
hist, edges = histogramdd(x, bins=2)
assert_array_equal(edges[0], array([-0.5, 0. , 0.5]))
+
class TestUnique(TestCase):
def test_simple(self):
x = array([4, 3, 2, 1, 1, 2, 3, 4, 0])
@@ -642,6 +694,7 @@ class TestCheckFinite(TestCase):
assert_raises(ValueError, numpy.lib.asarray_chkfinite, b)
assert_raises(ValueError, numpy.lib.asarray_chkfinite, c)
+
class TestNaNFuncts(TestCase):
def setUp(self):
self.A = array([[[ nan, 0.01319214, 0.01620964],
@@ -728,7 +781,6 @@ class TestNaNFuncts(TestCase):
assert_equal(np.isinf(a), np.zeros((2, 4), dtype=bool))
-
class TestCorrCoef(TestCase):
def test_simple(self):
A = array([[ 0.15391142, 0.18045767, 0.14197213],
@@ -756,7 +808,6 @@ class TestCorrCoef(TestCase):
- 0.66173113, 0.98317823, 1. ]]))
-
class Test_i0(TestCase):
def test_simple(self):
assert_almost_equal(i0(0.5), array(1.0634833707413234))
@@ -775,6 +826,7 @@ class Test_i0(TestCase):
[ 1.03352052, 1.13557954],
[ 1.0588429 , 1.06432317]]))
+
class TestKaiser(TestCase):
def test_simple(self):
assert_almost_equal(kaiser(0, 1.0), array([]))
@@ -790,6 +842,7 @@ class TestKaiser(TestCase):
def test_int_beta(self):
kaiser(3, 4)
+
class TestMsort(TestCase):
def test_simple(self):
A = array([[ 0.44567325, 0.79115165, 0.5490053 ],
@@ -800,6 +853,7 @@ class TestMsort(TestCase):
[ 0.44567325, 0.52929049, 0.5490053 ],
[ 0.64864341, 0.79115165, 0.96098397]]))
+
class TestMeshgrid(TestCase):
def test_simple(self):
[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
@@ -857,6 +911,7 @@ class TestPiecewise(TestCase):
assert y.ndim == 0
assert y == 0
+
class TestBincount(TestCase):
def test_simple(self):
y = np.bincount(np.arange(4))
@@ -878,9 +933,11 @@ class TestBincount(TestCase):
y = np.bincount(x, w)
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
+
def compare_results(res, desired):
for i in range(len(desired)):
assert_array_equal(res[i], desired[i])
+
if __name__ == "__main__":
run_module_suite()
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