diff options
Diffstat (limited to 'numpy/oldnumeric')
| -rw-r--r-- | numpy/oldnumeric/arrayfns.py | 4 | ||||
| -rw-r--r-- | numpy/oldnumeric/random_array.py | 14 | ||||
| -rw-r--r-- | numpy/oldnumeric/rng.py | 10 |
3 files changed, 14 insertions, 14 deletions
diff --git a/numpy/oldnumeric/arrayfns.py b/numpy/oldnumeric/arrayfns.py index 683ed309d..54992de57 100644 --- a/numpy/oldnumeric/arrayfns.py +++ b/numpy/oldnumeric/arrayfns.py @@ -20,7 +20,7 @@ def array_set(vals1, indices, vals2): vals1 = asarray(vals1) vals2 = asarray(vals2) if vals1.ndim != vals2.ndim or vals1.ndim < 1: - raise error, "vals1 and vals2 must have same number of dimensions (>=1)" + raise error("vals1 and vals2 must have same number of dimensions (>=1)") vals1[indices] = vals2 from numpy import digitize @@ -38,7 +38,7 @@ def interp(y, x, z, typ=None): if typ == 'f': return res.astype('f') - raise error, "incompatible typecode" + raise error("incompatible typecode") def nz(x): x = asarray(x,dtype=np.ubyte) diff --git a/numpy/oldnumeric/random_array.py b/numpy/oldnumeric/random_array.py index 777e2a645..7141e564b 100644 --- a/numpy/oldnumeric/random_array.py +++ b/numpy/oldnumeric/random_array.py @@ -201,20 +201,20 @@ def test(): mt.set_state(obj) obj2 = mt.get_state() if (obj2[1] - obj[1]).any(): - raise SystemExit, "Failed seed test." + raise SystemExit("Failed seed test.") print "First random number is", random() print "Average of 10000 random numbers is", np.sum(random(10000),axis=0)/10000. x = random([10,1000]) if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: - raise SystemExit, "random returned wrong shape" + raise SystemExit("random returned wrong shape") x.shape = (10000,) print "Average of 100 by 100 random numbers is", np.sum(x,axis=0)/10000. y = uniform(0.5,0.6, (1000,10)) if len(y.shape) !=2 or y.shape[0] != 1000 or y.shape[1] != 10: - raise SystemExit, "uniform returned wrong shape" + raise SystemExit("uniform returned wrong shape") y.shape = (10000,) if np.minimum.reduce(y) <= 0.5 or np.maximum.reduce(y) >= 0.6: - raise SystemExit, "uniform returned out of desired range" + raise SystemExit("uniform returned out of desired range") print "randint(1, 10, shape=[50])" print randint(1, 10, shape=[50]) print "permutation(10)", permutation(10) @@ -224,18 +224,18 @@ def test(): s = 3.0 x = normal(2.0, s, [10, 1000]) if len(x.shape) != 2 or x.shape[0] != 10 or x.shape[1] != 1000: - raise SystemExit, "standard_normal returned wrong shape" + raise SystemExit("standard_normal returned wrong shape") x.shape = (10000,) mean_var_test(x, "normally distributed numbers with mean 2 and variance %f"%(s**2,), 2, s**2, 0) x = exponential(3, 10000) mean_var_test(x, "random numbers exponentially distributed with mean %f"%(s,), s, s**2, 2) x = multivariate_normal(np.array([10,20]), np.array(([1,2],[2,4]))) print "\nA multivariate normal", x - if x.shape != (2,): raise SystemExit, "multivariate_normal returned wrong shape" + if x.shape != (2,): raise SystemExit("multivariate_normal returned wrong shape") x = multivariate_normal(np.array([10,20]), np.array([[1,2],[2,4]]), [4,3]) print "A 4x3x2 array containing multivariate normals" print x - if x.shape != (4,3,2): raise SystemExit, "multivariate_normal returned wrong shape" + if x.shape != (4,3,2): raise SystemExit("multivariate_normal returned wrong shape") x = multivariate_normal(np.array([-100,0,100]), np.array([[3,2,1],[2,2,1],[1,1,1]]), 10000) x_mean = np.sum(x,axis=0)/10000. print "Average of 10000 multivariate normals with mean [-100,0,100]" diff --git a/numpy/oldnumeric/rng.py b/numpy/oldnumeric/rng.py index 28d3f16df..38b182eae 100644 --- a/numpy/oldnumeric/rng.py +++ b/numpy/oldnumeric/rng.py @@ -36,7 +36,7 @@ class Distribution(object): class ExponentialDistribution(Distribution): def __init__(self, lambda_): if (lambda_ <= 0): - raise error, "parameter must be positive" + raise error("parameter must be positive") Distribution.__init__(self, 'exponential', lambda_) def density(x): @@ -51,7 +51,7 @@ class LogNormalDistribution(Distribution): m = float(m) s = float(s) if (s <= 0): - raise error, "standard deviation must be positive" + raise error("standard deviation must be positive") Distribution.__init__(self, 'lognormal', m, s) sn = math.log(1.0+s*s/(m*m)); self._mn = math.log(m)-0.5*sn @@ -69,7 +69,7 @@ class NormalDistribution(Distribution): m = float(m) s = float(s) if (s <= 0): - raise error, "standard deviation must be positive" + raise error("standard deviation must be positive") Distribution.__init__(self, 'normal', m, s) self._fac = 1.0/math.sqrt(2*math.pi)/s @@ -84,7 +84,7 @@ class UniformDistribution(Distribution): b = float(b) width = b-a if (width <=0): - raise error, "width of uniform distribution must be > 0" + raise error("width of uniform distribution must be > 0") Distribution.__init__(self, 'uniform', a, b) self._fac = 1.0/width @@ -106,7 +106,7 @@ class CreateGenerator(object): if dist is None: dist = default_distribution if not isinstance(dist, Distribution): - raise error, "Not a distribution object" + raise error("Not a distribution object") self._dist = dist def ranf(self): |