diff --git a/modules/base/pymod/table.py b/modules/base/pymod/table.py
index 8795799ce95aa0aa1a0e5d1579cfd20790732cf7..3bd8c750e8bb7653cf693e8815673f8bd3674687 100644
--- a/modules/base/pymod/table.py
+++ b/modules/base/pymod/table.py
@@ -90,6 +90,9 @@ class BinaryColExpr:
def __mul__(self, rhs):
return BinaryColExpr(operator.mul, self, rhs)
+ def __div__(self, rhs):
+ return BinaryColExpr(operator.div, self, rhs)
+
class TableCol:
def __init__(self, table, col):
self._table=table
@@ -119,6 +122,7 @@ class TableCol:
def __mul__(self, rhs):
return BinaryColExpr(operator.mul, self, rhs)
+
def __div__(self, rhs):
return BinaryColExpr(operator.div, self, rhs)
@@ -411,6 +415,31 @@ class Table(object):
def __str__(self):
return self.ToString()
+ def Stats(self, col):
+ idx = self.GetColIndex(col)
+ text ='''
+Statistics for column %(col)s
+
+ Number of Rows : %(num)d
+ Number of Rows Not None: %(num_non_null)d
+ Mean : %(mean)f
+ Median : %(median)f
+ Standard Deviation : %(stddev)f
+ Min : %(min)f
+ Max : %(max)f
+'''
+ data = {
+ 'col' : col,
+ 'num' : len(self.rows),
+ 'num_non_null' : self.Count(col),
+ 'median' : self.Median(col),
+ 'mean' : self.Mean(col),
+ 'stddev' : self.StdDev(col),
+ 'min' : self.Min(col),
+ 'max' : self.Max(col),
+ }
+ return text % data
+
def _AddRowsFromDict(self, d, overwrite=None):
'''
Add one or more rows from a :class:`dictionary <dict>`.
@@ -469,6 +498,27 @@ class Table(object):
if not overwrite or not added:
self.rows.append(new_row)
+ def PairedTTest(self, col_a, col_b):
+ """
+ Two-sided test for the null-hypothesis that two related samples
+ have the same average (expected values)
+
+ :param col_a: First column
+ :param col_b: Second column
+
+ :returns: P-value between 0 and 1 that the two columns have the
+ same average. The smaller the value, the less related the two
+ columns are.
+ """
+ from scipy.stats import ttest_rel
+ xs = []
+ ys = []
+ for x, y in self.Zip(col_a, col_b):
+ if x!=None and y!=None:
+ xs.append(x)
+ ys.append(y)
+ result = ttest_rel(xs, ys)
+ return result[1]
def AddRow(self, data, overwrite=None):
"""
@@ -645,6 +695,10 @@ class Table(object):
As a special case, if there are no previous rows, and data is not
None, rows are added for every item in data.
"""
+
+ if col_name in self.col_names:
+ raise ValueError('Column with name %s already exists'%col_name)
+
col_type = self._ParseColTypes(col_type, exp_num=1)[0]
self.col_names.append(col_name)
self.col_types.append(col_type)
@@ -654,10 +708,15 @@ class Table(object):
for row in self.rows:
row.append(data)
else:
+ if hasattr(data, '__len__') and len(data)!=len(self.rows):
+ self.col_names.pop()
+ self.col_types.pop()
+ raise ValueError('Length of data (%i) must correspond to number of '%len(data) +\
+ 'existing rows (%i)'%len(self.rows))
for row, d in zip(self.rows, data):
row.append(d)
- elif data!=None and len(self.col_names)==0:
+ elif data!=None and len(self.col_names)==1:
if IsScalar(data):
self.AddRow({col_name : data})
else:
@@ -1506,6 +1565,45 @@ class Table(object):
self.AddCol(mean_col_name, 'f', mean_rows)
+ def Percentiles(self, col, nths):
+ """
+ returns the percentiles of column *col* given in *nths*.
+
+ The percentils are calculated as
+
+ .. code-block:: python
+
+ values[min(len(values), int(round(len(values)*p/100+0.5)-1))]
+
+ where values are the sorted values of *col* not equal to none
+ :param: nths: list of percentiles to be calculated. Each percentil is a number
+ between 0 and 100.
+
+ :raises: :class:`TypeError` if column type is ``string``
+ :returns: List of percentils in the same order as given in *nths*
+ """
+ idx = self.GetColIndex(col)
+ col_type = self.col_types[idx]
+ if col_type!='int' and col_type!='float' and col_type!='bool':
+ raise TypeError("Median can only be used on numeric column types")
+
+ for nth in nths:
+ if nth < 0 or nth > 100:
+ raise ValueError("percentiles must be between 0 and 100")
+ vals=[]
+ for v in self[col]:
+ if v!=None:
+ vals.append(v)
+ vals=sorted(vals)
+ if len(vals)==0:
+ return [None]*len(nths)
+ percentiles=[]
+
+ for nth in nths:
+ p=vals[min(len(vals)-1, int(round(len(vals)*nth/100.0+0.5)-1))]
+ percentiles.append(p)
+ return percentiles
+
def Median(self, col):
"""
Returns the median of the given column. Cells with None are ignored. Returns
diff --git a/modules/base/tests/test_table.py b/modules/base/tests/test_table.py
index 7a10f4ee131ac625e24d33a4cca634ca53d57803..7337dfbe63baf40cb74e85de3448887ecff020db 100644
--- a/modules/base/tests/test_table.py
+++ b/modules/base/tests/test_table.py
@@ -196,7 +196,14 @@ class TestTable(unittest.TestCase):
self.assertEquals(type(z[0][1]),int)
self.assertEquals(type(z[1][1]),int)
self.assertRaises(ValueError, tab.Zip, 'col5', 'col3')
-
+ def testPercentiles(self):
+ tab = Table(['nums'], 'i')
+ self.assertEqual(tab.Percentiles('nums', [0,100]), [None, None])
+ self.assertRaises(ValueError, tab.Percentiles, 'nums', [101])
+ self.assertRaises(ValueError, tab.Percentiles, 'nums', [-1])
+ for i in (35,15,50,40,20):
+ tab.AddRow([i])
+ self.assertEqual(tab.Percentiles('nums', [0,30,40,100]), [15,20,35,50])
def testTableInitEmpty(self):
'''
empty table
@@ -592,6 +599,15 @@ class TestTable(unittest.TestCase):
'foo': [True, None, True],
'bar': [1, 2, 3]})
+ def testRaiseErrorOnWrongDataLengthAddCol(self):
+ tab = Table()
+ tab.AddCol('a','f',[4.2,4.2,4.2])
+ self.assertRaises(ValueError, tab.AddCol, 'b', 'f', [4.2,4.2])
+
+ def testRaiseErrorColNameAlreadyExists(self):
+ tab = Table()
+ tab.AddCol('awesome','f')
+ self.assertRaises(ValueError, tab.AddCol, 'awesome', 'f')
def testRaiseErrorOnWrongColumnTypes(self):
# wrong columns types in init
@@ -1371,7 +1387,52 @@ class TestTable(unittest.TestCase):
self.assertRaises(RuntimeError, tab.GetOptimalPrefactors, 'c','a','b',weight='d')
self.assertRaises(RuntimeError, tab.GetOptimalPrefactors, 'c',weights='d')
-
+
+ def testGaussianSmooth(self):
+ tab = Table(['a','b','c','d','e','f'],'fffffi',
+ a=[0.5,1.0,2.0,3.0,2.5,1.0,0.5,2.3,1.0],
+ b=[0.5,1.0,2.0,3.0,2.5,1.0,0.5,2.3,1.0],
+ c=[0.5,1.0,2.0,3.0,2.5,1.0,0.5,2.3,1.0],
+ d=[0.5,1.0,2.0,3.0,2.5,1.0,0.5,2.3,1.0],
+ e=[0.5,1.0,2.0,3.0,2.5,1.0,0.5,2.3,1.0],
+ f=[2,6,5,3,8,7,4,4,4])
+
+ tab.GaussianSmooth('a')
+ tab.GaussianSmooth('b', std=2.0)
+ tab.GaussianSmooth('c', padding='wrap')
+ tab.GaussianSmooth('d', padding='constant')
+ tab.GaussianSmooth('e', padding='constant',c=3.0)
+ tab.GaussianSmooth('f')
+
+ ref_list=[]
+
+ ref_list.append([0.74729766,1.20875404,1.93459464,2.39849076,2.11504816,
+ 1.42457403,1.20524937,1.41025075,1.3557406])
+ ref_list.append([1.23447249,1.41295267,1.65198705,1.79959835,1.78131778,
+ 1.64501718,1.49728102,1.40589715,1.37147629])
+ ref_list.append([0.9315564,1.24131027,1.93698455,2.39855767,2.11504816,
+ 1.42450711,1.20285946,1.37769451,1.17148186])
+ ref_list.append([0.5630556,1.17705895,1.93224488,2.39842384,2.11504816,
+ 1.4244402,1.2005097,1.34599942,0.9872398 ])
+ ref_list.append([1.46464039,1.35272941,1.94594196,2.39882533,2.11504816,
+ 1.42484169,1.21420677,1.52166988,1.88882459])
+ ref_list.append([3,4,4,5,6,6,4,4,4])
+
+ tab_list=[[],[],[],[],[],[]]
+
+ for row in tab.rows:
+ for i,v in enumerate(row):
+ tab_list[i].append(v)
+
+ for i in range(len(ref_list[0])):
+ self.assertAlmostEquals(tab_list[0][i],ref_list[0][i])
+ self.assertAlmostEquals(tab_list[1][i],ref_list[1][i])
+ self.assertAlmostEquals(tab_list[2][i],ref_list[2][i])
+ self.assertAlmostEquals(tab_list[3][i],ref_list[3][i])
+ self.assertAlmostEquals(tab_list[4][i],ref_list[4][i])
+ self.assertAlmostEquals(tab_list[5][i],ref_list[5][i])
+
+
def testIsEmpty(self):
tab = Table()
self.assertTrue(tab.IsEmpty())
diff --git a/modules/img/base/src/image_state/image_state_algorithm.hh b/modules/img/base/src/image_state/image_state_algorithm.hh
index b601345196af9c1daec20fc6ec486846aa2d015a..e5546583a2208bd600c66d73667783aa3acb8338 100644
--- a/modules/img/base/src/image_state/image_state_algorithm.hh
+++ b/modules/img/base/src/image_state/image_state_algorithm.hh
@@ -34,7 +34,7 @@ namespace ost { namespace img { namespace image_state {
/*
one-time definition of the constructor adapters, allowing
- zero to 10 ctor parameters to be automagically used. There
+ zero to 12 ctor parameters to be automagically used. There
is probably a recursive way to do this more elegantly...
this version includes a call to a base class to allow a name
@@ -52,14 +52,14 @@ namespace ost { namespace img { namespace image_state {
template <class P0, \
class P1> \
CLASS (const P0& p0, \
- const P1& p1): \
+ const P1& p1): \
FNC(p0,p1), BASE (FNC::GetAlgorithmName()) {} \
/* 3 params */ \
template <class P0, \
class P1, \
class P2> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2): \
FNC(p0,p1,p2), BASE (FNC::GetAlgorithmName()) {} \
/* 4 params */ \
@@ -68,7 +68,7 @@ namespace ost { namespace img { namespace image_state {
class P2, \
class P3> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3): \
FNC(p0,p1,p2,p3), BASE (FNC::GetAlgorithmName()) {} \
@@ -79,7 +79,7 @@ namespace ost { namespace img { namespace image_state {
class P3, \
class P4> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4): \
@@ -92,7 +92,7 @@ namespace ost { namespace img { namespace image_state {
class P4, \
class P5> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -107,7 +107,7 @@ namespace ost { namespace img { namespace image_state {
class P5, \
class P6> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -124,7 +124,7 @@ namespace ost { namespace img { namespace image_state {
class P6, \
class P7> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -143,7 +143,7 @@ namespace ost { namespace img { namespace image_state {
class P7, \
class P8> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -164,7 +164,7 @@ namespace ost { namespace img { namespace image_state {
class P8, \
class P9> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -187,7 +187,7 @@ namespace ost { namespace img { namespace image_state {
class P9, \
class PA> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -212,7 +212,7 @@ namespace ost { namespace img { namespace image_state {
class PA, \
class PB> \
CLASS (const P0& p0, \
- const P1& p1, \
+ const P1& p1, \
const P2& p2, \
const P3& p3, \
const P4& p4, \
@@ -221,8 +221,8 @@ namespace ost { namespace img { namespace image_state {
const P7& p7, \
const P8& p8, \
const P9& p9, \
- const P9& pa, \
- const PA& pb): \
+ const PA& pa, \
+ const PB& pb): \
FNC(p0,p1,p2,p3,p4,p5,p6,p7,p8,p9,pa,pb), BASE (FNC::GetAlgorithmName()) {}
@@ -356,8 +356,8 @@ private:
*/
template <class FNC>
class TEMPLATE_EXPORT ImageStateConstModIPAlgorithm: public FNC,
- public ConstModIPAlgorithm,
- public ImageStateConstModIPVisitorBase {
+ public ConstModIPAlgorithm,
+ public ImageStateConstModIPVisitorBase {
public:
IMAGE_STATE_VISITOR_CTOR_ADAPTERS(ImageStateConstModIPAlgorithm, ConstModIPAlgorithm)
@@ -409,8 +409,8 @@ private:
//! out-of-place modifying image state const visitor plus op algorithm
template <class FNC>
class TEMPLATE_EXPORT ImageStateConstModOPAlgorithm: public FNC,
- public ConstModOPAlgorithm,
- public ImageStateConstModOPVisitorBase {
+ public ConstModOPAlgorithm,
+ public ImageStateConstModOPVisitorBase {
public:
IMAGE_STATE_VISITOR_CTOR_ADAPTERS(ImageStateConstModOPAlgorithm, ConstModOPAlgorithm)