[Scipy-svn] r6612 - in trunk/scipy/stats: . tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Sat Jul 17 00:29:07 EDT 2010
Author: rgommers
Date: 2010-07-16 23:29:06 -0500 (Fri, 16 Jul 2010)
New Revision: 6612
Modified:
trunk/scipy/stats/stats.py
trunk/scipy/stats/tests/test_stats.py
Log:
ENH: Change to signature of stats.linregress to something saner. Closes #1164.
Modified: trunk/scipy/stats/stats.py
===================================================================
--- trunk/scipy/stats/stats.py 2010-07-15 11:19:52 UTC (rev 6611)
+++ trunk/scipy/stats/stats.py 2010-07-17 04:29:06 UTC (rev 6612)
@@ -1469,7 +1469,7 @@
if extrapoints > 0 and printextras:
# fixme: warnings.warn()
print '\nPoints outside given histogram range =',extrapoints
- return (hist, defaultlimits[0], binsize, extrapoints)
+ return (hist, defaultlimits[0], binsize, extrapoints)
def cumfreq(a, numbins=10, defaultreallimits=None, weights=None):
@@ -1576,8 +1576,8 @@
"""
Calculates the signal-to-noise ratio, defined as the ratio between the mean
and the standard deviation.
-
+
Parameters
----------
a: array-like
@@ -1587,8 +1587,8 @@
integer, this is the axis over which to operate. Defaults to None???0
ddof : integer, optional, default 0
degrees of freedom correction for standard deviation
-
+
Returns
-------
array containing the value of the ratio of the mean to the standard
@@ -1646,12 +1646,12 @@
def sem(a, axis=0, ddof=1):
"""
Calculates the standard error of the mean (or standard error of
- measurement) of the values in the passed array.
+ measurement) of the values in the passed array.
Parameters
----------
a: array like
- An array containing the values for which
+ An array containing the values for which
axis: int or None, optional.
if equal None, ravel array first. If equal to an integer, this will be
the axis over which to operate. Defaults to 0.
@@ -1661,7 +1661,7 @@
Returns
-------
- The standard error of the mean in the sample(s), along the input axis
+ The standard error of the mean in the sample(s), along the input axis
"""
a, axis = _chk_asarray(a, axis)
@@ -1702,8 +1702,8 @@
def zscore(a, axis=0, ddof=0):
"""
Calculates the z score of each value in the sample, relative to the sample
- mean and standard deviation.
-
+ mean and standard deviation.
+
Parameters
----------
a: array_like
@@ -1711,7 +1711,7 @@
axis: int or None, optional
If axis is equal to None, the array is first ravel'd. If axis is an
integer, this is the axis over which to operate. Defaults to 0.
-
+
Returns
-------
zscore: array_like
@@ -1732,9 +1732,9 @@
np.expand_dims(sstd,axis=axis)))
else:
return (a - mns) / sstd
-
+
def zmap(scores, compare, axis=0, ddof=0):
"""
Calculates the zscores relative to the mean and standard deviation
@@ -1743,7 +1743,7 @@
Returns an array of z-scores, i.e. scores that are standardized to zero
mean and unit variance, where mean and variance are calculated from the
comparison array.
-
+
Parameters
----------
scores : array-like
@@ -1805,10 +1805,10 @@
def sigmaclip(a, low=4., high=4.):
"""Iterative sigma-clipping of array elements.
-
+
The output array contains only those elements of the input array `c`
that satisfy the conditions ::
-
+
mean(c) - std(c)*low < c < mean(c) + std(c)*high
Parameters
@@ -1828,8 +1828,8 @@
lower threshold value use for clipping
critlupper : float
upper threshold value use for clipping
-
-
+
+
Examples
--------
>>> a = np.concatenate((np.linspace(9.5,10.5,31),np.linspace(0,20,5)))
@@ -1843,7 +1843,7 @@
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
>>> upp, c.mean() + fact*c.std(), c.max()
(10.035355339059327, 10.035355339059327, 10.033333333333333)
-
+
>>> a = np.concatenate((np.linspace(9.5,10.5,11),
np.linspace(-100,-50,3)))
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
@@ -2134,7 +2134,7 @@
observations. Each column of m represents a variable, and each row
entry a single observation of those variables. Also see axis below.
Both arrays need to have the same length in the `axis` dimension.
-
+
axis : int or None, optional
If axis=0 (default), then each column represents a variable, with
observations in the rows. If axis=0, the relationship is transposed:
@@ -2197,7 +2197,7 @@
(0.10816770419260482, 0.1273562188027364)
>>> spearmanr(x2n.ravel(), y2n.ravel())
(0.10816770419260482, 0.1273562188027364)
-
+
>>> xint = np.random.randint(10,size=(100,2))
>>> spearmanr(xint)
(0.052760927029710199, 0.60213045837062351)
@@ -2205,7 +2205,7 @@
"""
a, axisout = _chk_asarray(a, axis)
ar = np.apply_along_axis(rankdata,axisout,a)
-
+
br = None
if not b is None:
b, axisout = _chk_asarray(b, axis)
@@ -2215,7 +2215,7 @@
t = rs * np.sqrt((n-2) / ((rs+1.0)*(1.0-rs)))
prob = distributions.t.sf(np.abs(t),n-2)*2
-
+
if rs.shape == (2,2):
return rs[1,0], prob[1,0]
else:
@@ -2332,7 +2332,7 @@
return tau, prob
-def linregress(*args):
+def linregress(x, y=None):
"""
Calculate a regression line
@@ -2342,9 +2342,9 @@
----------
x, y : array_like
two sets of measurements. Both arrays should have the same length.
- If only x is given, then it must be a two-dimensional array where one
- dimension has length 2. The two sets of measurements are then found
- by splitting the array along the length-2 dimension.
+ If only x is given (and y=None), then it must be a two-dimensional
+ array where one dimension has length 2. The two sets of measurements
+ are then found by splitting the array along the length-2 dimension.
Returns
-------
@@ -2376,17 +2376,19 @@
"""
TINY = 1.0e-20
- if len(args) == 1: # more than 1D array?
- args = asarray(args[0])
- if len(args) == 2:
- x = args[0]
- y = args[1]
+ if y is None: # x is a (2, N) or (N, 2) shaped array_like
+ x = asarray(x)
+ if x.shape[0] == 2:
+ x, y = x
+ elif x.shape[1] == 2:
+ x, y = x.T
else:
- x = args[:,0]
- y = args[:,1]
+ msg = "If only `x` is given as input, it has to be of shape (2, N) \
+ or (N, 2), provided shape was %s" % str(x.shape)
+ raise ValueError(msg)
else:
- x = asarray(args[0])
- y = asarray(args[1])
+ x = asarray(x)
+ y = asarray(y)
n = len(x)
xmean = np.mean(x,None)
ymean = np.mean(y,None)
@@ -3021,10 +3023,10 @@
if T == 0:
raise ValueError, 'All numbers are identical in amannwhitneyu'
sd = np.sqrt(T*n1*n2*(n1+n2+1)/12.0)
-
+
if use_continuity:
# normal approximation for prob calc with continuity correction
- z = abs((bigu-0.5-n1*n2/2.0) / sd)
+ z = abs((bigu-0.5-n1*n2/2.0) / sd)
else:
z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc
return smallu, distributions.norm.sf(z) #(1.0 - zprob(z))
Modified: trunk/scipy/stats/tests/test_stats.py
===================================================================
--- trunk/scipy/stats/tests/test_stats.py 2010-07-15 11:19:52 UTC (rev 6611)
+++ trunk/scipy/stats/tests/test_stats.py 2010-07-17 04:29:06 UTC (rev 6612)
@@ -497,6 +497,35 @@
res = stats.linregress(x, y)
assert_almost_equal(res[4], 2.3957814497838803e-3) #4.3609875083149268e-3)
+ def test_regress_simple_onearg_rows(self):
+ """Regress a line with sinusoidal noise, with a single input of shape
+ (2, N).
+ """
+ x = np.linspace(0, 100, 100)
+ y = 0.2 * np.linspace(0, 100, 100) + 10
+ y += np.sin(np.linspace(0, 20, 100))
+ rows = np.vstack((x, y))
+
+ res = stats.linregress(rows)
+ assert_almost_equal(res[4], 2.3957814497838803e-3) #4.3609875083149268e-3)
+
+ def test_regress_simple_onearg_cols(self):
+ """Regress a line with sinusoidal noise, with a single input of shape
+ (N, 2).
+ """
+ x = np.linspace(0, 100, 100)
+ y = 0.2 * np.linspace(0, 100, 100) + 10
+ y += np.sin(np.linspace(0, 20, 100))
+ cols = np.hstack((np.expand_dims(x, 1), np.expand_dims(y, 1)))
+
+ res = stats.linregress(cols)
+ assert_almost_equal(res[4], 2.3957814497838803e-3) #4.3609875083149268e-3)
+
+ def test_regress_shape_error(self):
+ """Check that a single input argument to linregress with wrong shape
+ results in a ValueError."""
+ assert_raises(ValueError, stats.linregress, np.ones((3, 3)))
+
def test_linregress(self):
'''compared with multivariate ols with pinv'''
x = np.arange(11)
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