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-rw-r--r--scipy/base/convertcode.py2
-rw-r--r--scipy/base/function_base.py171
-rw-r--r--scipy/base/ma.py3
-rw-r--r--scipy/base/mlab.py14
-rw-r--r--scipy/base/oldnumeric.py64
-rw-r--r--scipy/base/shape_base.py5
-rw-r--r--scipy/base/src/arraymethods.c1
-rw-r--r--scipy/base/tests/test_function_base.py10
-rw-r--r--scipy/base/twodim_base.py4
9 files changed, 208 insertions, 66 deletions
diff --git a/scipy/base/convertcode.py b/scipy/base/convertcode.py
index 1d93b94ed..792ed384c 100644
--- a/scipy/base/convertcode.py
+++ b/scipy/base/convertcode.py
@@ -99,7 +99,7 @@ def fromstr(filestr):
filestr, fromall1 = changeimports(filestr, 'Precision', 'scipy.base')
filestr, fromall2 = changeimports(filestr, 'numerix', 'scipy.base')
filestr, fromall3 = changeimports(filestr, 'scipy_base', 'scipy.base')
- filestr, fromall3 = changeimports(filestr, 'MLab', 'scipy.corelinalg')
+ filestr, fromall3 = changeimports(filestr, 'MLab', 'scipy.base.mlab')
filestr, fromall3 = changeimports(filestr, 'LinearAlgebra', 'scipy.corelinalg')
filestr, fromall3 = changeimports(filestr, 'RNG', 'scipy.random')
filestr, fromall3 = changeimports(filestr, 'RandomArray', 'scipy.random')
diff --git a/scipy/base/function_base.py b/scipy/base/function_base.py
index 55f96fd5e..03d02e5c7 100644
--- a/scipy/base/function_base.py
+++ b/scipy/base/function_base.py
@@ -1,22 +1,25 @@
__all__ = ['logspace', 'linspace', 'round_',
- 'select', 'piecewise', 'trim_zeros', 'alen', 'amax', 'amin', 'ptp',
- 'copy', 'iterable', 'base_repr', 'binary_repr', 'prod', 'cumprod',
+ 'select', 'piecewise', 'trim_zeros',
+ 'copy', 'iterable', 'base_repr', 'binary_repr',
'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'disp',
'unique', 'extract', 'insert', 'nansum', 'nanmax', 'nanargmax',
'nanargmin', 'nanmin', 'vectorize', 'asarray_chkfinite', 'average',
- 'histogram', 'bincount', 'digitize']
+ 'histogram', 'bincount', 'digitize', 'cov', 'corrcoef', 'msort', 'median',
+ 'sinc', 'hamming', 'hanning', 'bartlett', 'blackman', 'kaiser', 'trapz'
+ ]
import types
import math, operator
import numeric as _nx
from numeric import ones, zeros, arange, concatenate, array, asarray, empty
-from numeric import ScalarType
+from numeric import ScalarType, dot, where
from umath import pi, multiply, add, arctan2, maximum, minimum, frompyfunc, \
- isnan, absolute
+ isnan, absolute, cos, less_equal, sqrt, sin
from oldnumeric import ravel, nonzero, choose, \
- sometrue, alltrue, reshape, any, all, typecodes, ArrayType
+ sometrue, alltrue, reshape, any, all, typecodes, ArrayType, squeeze
from type_check import ScalarType, isscalar
-from shape_base import squeeze, atleast_1d
+from shape_base import atleast_1d
+from twodim_base import diag
from scipy.base._compiled_base import digitize, bincount, _insert
from ufunclike import sign
@@ -65,7 +68,6 @@ def base_repr (number, base=2, padding=0):
#end Fernando's utilities
-
def linspace(start, stop, num=50, endpoint=True, retstep=False):
"""Return evenly spaced numbers.
@@ -196,12 +198,6 @@ def average(a, axis=0, weights=None, returned=False):
else:
return n/d
-
-def isaltered():
- val = str(type(_nx.array([1])))
- return 'scipy' in val
-
-
def asarray_chkfinite(a):
"""Like asarray, but check that no NaNs or Infs are present.
"""
@@ -329,35 +325,6 @@ def copy(a):
return array(a, copy=True)
# Basic operations
-def amax(a, axis=-1):
- """Return the maximum of 'a' along dimension axis.
- """
- return asarray(a).max(axis)
-
-def amin(a, axis=-1):
- """Return the minimum of a along dimension axis.
- """
- return asarray(a).min(axis)
-
-def alen(a):
- """Return the length of a Python object interpreted as an array
- """
- return len(asarray(a))
-
-def ptp(a, axis=-1):
- """Return maximum - minimum along the the given dimension
- """
- return asarray(a).ptp(axis)
-
-def prod(a, axis=-1):
- """Return the product of the elements along the given axis
- """
- return asarray(a).prod(axis)
-
-def cumprod(a, axis=-1):
- """Return the cumulative product of the elments along the given axis
- """
- return asarray(a).cumprod(axis)
def gradient(f, *varargs):
"""Calculate the gradient of an N-dimensional scalar function.
@@ -722,3 +689,121 @@ def round_(a, decimals=0):
else:
return multiply(a, s)
+
+def cov(m,y=None, rowvar=0, bias=0):
+ """Estimate the covariance matrix.
+
+ If m is a vector, return the variance. For matrices where each row
+ is an observation, and each column a variable, return the covariance
+ matrix. Note that in this case diag(cov(m)) is a vector of
+ variances for each column.
+
+ cov(m) is the same as cov(m, m)
+
+ Normalization is by (N-1) where N is the number of observations
+ (unbiased estimate). If bias is 1 then normalization is by N.
+
+ If rowvar is zero, then each row is a variable with
+ observations in the columns.
+ """
+ if y is None:
+ y = asarray(m)
+ else:
+ y = asarray(y)
+ m = asarray(m)
+ if rowvar:
+ m = m.transpose()
+ y = y.transpose()
+ if (m.shape[0] == 1):
+ m = m.transpose()
+ if (y.shape[0] == 1):
+ y = y.transpose()
+ N = m.shape[0]
+ if (y.shape[0] != N):
+ raise ValueError, "x and y must have the same number of observations."
+ m = m - m.mean(axis=0)
+ y = y - y.mean(axis=0)
+ if bias:
+ fact = N*1.0
+ else:
+ fact = N-1.0
+
+ val = squeeze(dot(m.transpose(),y.conj()) / fact)
+ return val
+
+def corrcoef(x, y=None):
+ """The correlation coefficients
+ """
+ c = cov(x, y)
+ d = diag(c)
+ return c/sqrt(multiply.outer(d,d))
+
+def blackman(M):
+ """blackman(M) returns the M-point Blackman window.
+ """
+ n = arange(0,M)
+ return 0.42-0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1))
+
+def bartlett(M):
+ """bartlett(M) returns the M-point Bartlett window.
+ """
+ n = arange(0,M)
+ return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1))
+
+def hanning(M):
+ """hanning(M) returns the M-point Hanning window.
+ """
+ n = arange(0,M)
+ return 0.5-0.5*cos(2.0*pi*n/(M-1))
+
+def hamming(M):
+ """hamming(M) returns the M-point Hamming window.
+ """
+ n = arange(0,M)
+ return 0.54-0.46*cos(2.0*pi*n/(M-1))
+
+def kaiser(M,beta):
+ """kaiser(M, beta) returns a Kaiser window of length M with shape parameter
+ beta. It depends on scipy.special (in full scipy) for the modified bessel
+ function i0.
+ """
+ from scipy.special import i0
+ n = arange(0,M)
+ alpha = (M-1)/2.0
+ return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(beta)
+
+def sinc(x):
+ """sinc(x) returns sin(pi*x)/(pi*x) at all points of array x.
+ """
+ y = pi* where(x == 0, 1.0e-20, x)
+ return sin(y)/y
+
+def msort(a):
+ return a.sort(0)
+
+def median(m):
+ """median(m) returns a median of m along the first dimension of m.
+ """
+ sorted = msort(m)
+ if sorted.shape[0] % 2 == 1:
+ return sorted[int(sorted.shape[0]/2)]
+ else:
+ sorted = msort(m)
+ index=sorted.shape[0]/2
+ return (sorted[index-1]+sorted[index])/2.0
+
+def trapz(y, x=None, dx=1.0, axis=-1):
+ """Integrate y(x) using samples along the given axis and the composite
+ trapezoidal rule. If x is None, spacing given by dx is assumed.
+ """
+ y = asarray(y)
+ if x is None:
+ d = dx
+ else:
+ d = diff(x,axis=axis)
+ nd = len(y.shape)
+ slice1 = [slice(None)]*nd
+ slice2 = [slice(None)]*nd
+ slice1[axis] = slice(1,None)
+ slice2[axis] = slice(None,-1)
+ return add.reduce(d * (y[slice1]+y[slice2])/2.0,axis)
diff --git a/scipy/base/ma.py b/scipy/base/ma.py
index 874c62319..691de03dd 100644
--- a/scipy/base/ma.py
+++ b/scipy/base/ma.py
@@ -12,9 +12,8 @@ import string, types, sys
import umath
import oldnumeric
import function_base
-from function_base import amax, amin
from numeric import e, pi, newaxis, ndarray
-from oldnumeric import typecodes
+from oldnumeric import typecodes, amax, amin
from numerictypes import *
import numeric
diff --git a/scipy/base/mlab.py b/scipy/base/mlab.py
new file mode 100644
index 000000000..749600d9b
--- /dev/null
+++ b/scipy/base/mlab.py
@@ -0,0 +1,14 @@
+# This module is for compatibility only. All functions are defined elsewhere.
+
+from numeric import *
+
+from twodim_base import eye, tri, diag, fliplr, flipud, rot90, tril, triu
+from oldnumeric import amax as max
+from oldnumeric import amin as min
+from function_base import msort, median, trapz, diff, cov, corrcoef, kaiser, blackman, \
+ bartlett, hanning, hamming, sinc, angle
+from oldnumeric import cumsum, ptp, mean, std, prod, cumprod, squeeze
+from polynomial import roots
+
+from scipy.random import rand, randn
+from scipy.corelinalg import eig, svd
diff --git a/scipy/base/oldnumeric.py b/scipy/base/oldnumeric.py
index 1980b38e6..6e42209bb 100644
--- a/scipy/base/oldnumeric.py
+++ b/scipy/base/oldnumeric.py
@@ -19,11 +19,11 @@ __all__ = ['asarray', 'array', 'concatenate',
# functions that are now methods
'take', 'reshape', 'choose', 'repeat', 'put', 'putmask',
'swapaxes', 'transpose', 'sort', 'argsort', 'argmax', 'argmin',
- 'searchsorted',
+ 'searchsorted', 'alen',
'resize', 'diagonal', 'trace', 'ravel', 'nonzero', 'shape',
- 'compress', 'clip', 'sum', 'product', 'sometrue', 'alltrue',
- 'any', 'all', 'cumsum', 'cumproduct', 'ndim',
- 'rank', 'size', 'around',
+ 'compress', 'clip', 'sum', 'product', 'prod', 'sometrue', 'alltrue',
+ 'any', 'all', 'cumsum', 'cumproduct', 'cumprod', 'ptp', 'ndim',
+ 'rank', 'size', 'around', 'mean', 'std', 'var', 'squeeze', 'amax', 'amin'
]
import multiarray as mu
@@ -260,6 +260,10 @@ def resize(a, new_shape):
return reshape(a, new_shape)
+def squeeze(a):
+ "Returns a with any ones from the shape of a removed"
+ return asarray(a).squeeze()
+
def diagonal(a, offset=0, axis1=0, axis2=1):
"""diagonal(a, offset=0, axis1=0, axis2=1) returns the given diagonals
defined by the last two dimensions of the array.
@@ -356,6 +360,36 @@ def cumproduct (x, axis=0, dtype=None):
"""Sum the array over the given axis."""
return asarray(x).cumprod(axis, dtype)
+def ptp(a, axis=0):
+ """Return maximum - minimum along the the given dimension
+ """
+ return asarray(a).ptp(axis)
+
+def amax(a, axis=0):
+ """Return the maximum of 'a' along dimension axis.
+ """
+ return asarray(a).max(axis)
+
+def amin(a, axis=0):
+ """Return the minimum of a along dimension axis.
+ """
+ return asarray(a).min(axis)
+
+def alen(a):
+ """Return the length of a Python object interpreted as an array
+ """
+ return len(asarray(a))
+
+def prod(a, axis=0):
+ """Return the product of the elements along the given axis
+ """
+ return asarray(a).prod(axis)
+
+def cumprod(a, axis=0):
+ """Return the cumulative product of the elments along the given axis
+ """
+ return asarray(a).cumprod(axis)
+
def ndim(a):
try:
return a.ndim
@@ -368,15 +402,29 @@ def rank (a):
"""
try:
return a.ndim
- except:
+ except AttributeError:
return asarray(a).ndim
def size (a, axis=None):
"Get the number of elements in sequence a, or along a certain axis."
- a = asarray(a)
if axis is None:
- return a.size
+ try:
+ return a.size
+ except AttributeError:
+ return asarray(a).size
else:
- return a.shape[axis]
+ try:
+ return a.shape[axis]
+ except AttributeError:
+ return asarray(a).shape[axis]
from function_base import round_ as around
+
+def mean(a, axis=0, dtype=None):
+ return asarray(a).mean(axis, dtype)
+
+def std(a, axis=0, dtype=None):
+ return asarray(a).std(axis, dtype)
+
+def var(a, axis=0, dtype=None):
+ return asarray(a).var(axis, dtype)
diff --git a/scipy/base/shape_base.py b/scipy/base/shape_base.py
index 4ea8f61a4..e8cc9bcd3 100644
--- a/scipy/base/shape_base.py
+++ b/scipy/base/shape_base.py
@@ -1,6 +1,6 @@
__all__ = ['atleast_1d','atleast_2d','atleast_3d','vstack','hstack',
'column_stack','dstack','array_split','split','hsplit',
- 'vsplit','dsplit','squeeze','apply_over_axes','expand_dims',
+ 'vsplit','dsplit','apply_over_axes','expand_dims',
'apply_along_axis']
import numeric as _nx
@@ -108,9 +108,6 @@ def expand_dims(a, axis):
axis = axis + len(shape) + 1
return a.reshape(shape[:axis] + (1,) + shape[axis:])
-def squeeze(a):
- "Returns a with any ones from the shape of a removed"
- return asarray(a).squeeze()
def atleast_1d(*arys):
""" Force a sequence of arrays to each be at least 1D.
diff --git a/scipy/base/src/arraymethods.c b/scipy/base/src/arraymethods.c
index 0410b78b6..18ccc35ee 100644
--- a/scipy/base/src/arraymethods.c
+++ b/scipy/base/src/arraymethods.c
@@ -1281,7 +1281,6 @@ array_all(PyArrayObject *self, PyObject *args)
return PyArray_All(self, axis);
}
-
static char doc_stddev[] = "a.std(axis=None, dtype=None)";
static PyObject *
diff --git a/scipy/base/tests/test_function_base.py b/scipy/base/tests/test_function_base.py
index e727568a8..77beb14f7 100644
--- a/scipy/base/tests/test_function_base.py
+++ b/scipy/base/tests/test_function_base.py
@@ -114,7 +114,7 @@ class test_ptp(ScipyTestCase):
[4,10.0,5.0],
[8,3.0,2.0]]
assert_equal(ptp(b,axis=0),[5.0,7.0,7.0])
- assert_equal(ptp(b),[6.0,6.0,6.0])
+ assert_equal(ptp(b,axis=-1),[6.0,6.0,6.0])
class test_cumsum(ScipyTestCase):
def check_basic(self):
@@ -148,7 +148,7 @@ class test_prod(ScipyTestCase):
assert_equal(prod(a),26400)
assert_array_equal(prod(a2,axis=0),
array([50,36,84,180],ctype))
- assert_array_equal(prod(a2),array([24, 1890, 600],ctype))
+ assert_array_equal(prod(a2,axis=-1),array([24, 1890, 600],ctype))
class test_cumprod(ScipyTestCase):
def check_basic(self):
@@ -162,15 +162,15 @@ class test_cumprod(ScipyTestCase):
self.failUnlessRaises(ArithmeticError, cumprod, a)
self.failUnlessRaises(ArithmeticError, cumprod, a2, 1)
self.failUnlessRaises(ArithmeticError, cumprod, a)
- else:
- assert_array_equal(cumprod(a),
+ else:
+ assert_array_equal(cumprod(a,axis=-1),
array([1, 2, 20, 220,
1320, 6600, 26400],ctype))
assert_array_equal(cumprod(a2,axis=0),
array([[ 1, 2, 3, 4],
[ 5, 12, 21, 36],
[50, 36, 84, 180]],ctype))
- assert_array_equal(cumprod(a2),
+ assert_array_equal(cumprod(a2,axis=-1),
array([[ 1, 2, 6, 24],
[ 5, 30, 210, 1890],
[10, 30, 120, 600]],ctype))
diff --git a/scipy/base/twodim_base.py b/scipy/base/twodim_base.py
index 3e7e392e8..b21532ea6 100644
--- a/scipy/base/twodim_base.py
+++ b/scipy/base/twodim_base.py
@@ -36,9 +36,9 @@ def rot90(m, k=1):
raise ValueError, "Input must >= 2-d."
k = k % 4
if k == 0: return m
- elif k == 1: return transpose(fliplr(m))
+ elif k == 1: return fliplr(m).transpose()
elif k == 2: return fliplr(flipud(m))
- else: return fliplr(transpose(m)) # k==3
+ else: return fliplr(m.transpose()) # k==3
def eye(N, M=None, k=0, dtype=int_):
""" eye returns a N-by-M 2-d array where the k-th diagonal is all ones,
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