# [Scipy-svn] r4556 - branches/Interpolate1D

scipy-svn@scip... scipy-svn@scip...
Mon Jul 21 14:02:33 CDT 2008

```Author: fcady
Date: 2008-07-21 14:02:32 -0500 (Mon, 21 Jul 2008)
New Revision: 4556

branches/Interpolate1D/regression_test.py
Modified:
branches/Interpolate1D/Interpolate1D.py
branches/Interpolate1D/TODO.txt
branches/Interpolate1D/__init__.py
branches/Interpolate1D/interpolate1d.py
Log:
various changes, mostly to docstrings, and adding a simple regression test

Modified: branches/Interpolate1D/Interpolate1D.py
===================================================================
--- branches/Interpolate1D/Interpolate1D.py	2008-07-21 16:35:30 UTC (rev 4555)
+++ branches/Interpolate1D/Interpolate1D.py	2008-07-21 19:02:32 UTC (rev 4556)
@@ -11,7 +11,7 @@

Classes provided include:

-        Interpolate1D  :   an object for interpolation of
+        interpolate1d  :   an object for interpolation of
various kinds.  interp1d is a wrapper
around this class.

@@ -35,16 +35,21 @@
from numpy import array, arange, empty, float64, NaN

def make_array_safe(ary, typecode=np.float64):
+    """Used to make sure that inputs and outputs are
+    properly formatted.
+    """
ary = np.atleast_1d(np.asarray(ary, typecode))
if not ary.flags['CONTIGUOUS']:
ary = ary.copy()
return ary

-def interp1d(x, y, new_x, kind='linear', low=np.NaN, high=np.NaN, kindkw={}, lowkw={}, highkw={}, \
+def interp1d(x, y, new_x, kind='linear', low=np.NaN, high=np.NaN, \
+                    kindkw={}, lowkw={}, highkw={}, \
""" A function for interpolation of 1D data.

-        REQUIRED ARGUMENTS:
+        Parameters
+        -----------

x -- list or NumPy array
x includes the x-values for the data set to
@@ -60,7 +65,8 @@
points whose value is to be interpolated from x and y.
new_x must be in sorted order, lowest to highest.

-        OPTIONAL ARGUMENTS:
+        Optional Arguments
+        -------------------

kind -- Usu. function or string.  But can be any type.
Specifies the type of extrapolation to use for values within
@@ -89,7 +95,9 @@

numpy.NaN is always considered bad data.

-        SAMPLE ACCEPTABLE ARGUMENTS:
+        Acceptable Input Strings
+        ------------------------
+
"linear" -- linear interpolation : default
"logarithmic" -- logarithmic interpolation : linear in log space?
"block" --
@@ -98,7 +106,9 @@
indicates order of spline
numpy.NaN -- return numpy.NaN

-        EXAMPLES:
+        Examples
+        ---------
+
>>> import numpy
>>> from Interpolate1D import interp1d
>>> x = range(5)        # note list is permitted
@@ -107,14 +117,16 @@
>>> interp1d(x, y, new_x)
array([.2, 2.3, 5.6, NaN])
"""
-    return Interpolate1D(x, y, kind=kind, low=low, high=high, kindkw=kindkw, lowkw=lowkw, highkw=highkw, \
+    return Interpolate1D(x, y, kind=kind, low=low, high=high, \
+                                    kindkw=kindkw, lowkw=lowkw, highkw=highkw, \

-class interpolate1d(object):
-    """ An object for interpolation of 1D data.
+class Interpolate1d(object):
+    """ A class for interpolation of 1D data.
+
+        Parameters
+        -----------

-        REQUIRED ARGUMENTS:
-
x -- list or NumPy array
x includes the x-values for the data set to
interpolate from.  It must be sorted in
@@ -125,7 +137,8 @@
interpolate from.  Note that y must be
one-dimensional.

-        OPTIONAL ARGUMENTS:
+        Optional Arguments
+        -------------------

kind -- Usu. function or string.  But can be any type.
Specifies the type of extrapolation to use for values within
@@ -154,7 +167,9 @@

numpy.NaN is always considered bad data.

-        SAMPLE ACCEPTABLE ARGUMENTS:
+        Acceptable Input Strings
+        ------------------------
+
"linear" -- linear interpolation : default
"logarithmic" -- logarithmic interpolation : linear in log space?
"block" --
@@ -163,21 +178,22 @@
indicates order of spline
numpy.NaN -- return numpy.NaN

-        EXAMPLES:
+        Examples
+        ---------
+
>>> import numpy
-            >>> from Interpolate1D import Interpolate1D
+            >>> from Interpolate1D import interp1d
>>> x = range(5)        # note list is permitted
>>> y = numpy.arange(5.)
-            >>> interp = Interpolate1D(x, y)
>>> new_x = [.2, 2.3, 5.6]
-            >>> interp(new_x)
+            >>> interp1d(x, y, new_x)
array([.2, 2.3, 5.6, NaN])
-
"""
# FIXME: examples in doc string

-    def __init__(self, x, y, kind='linear', low=np.NaN, high=np.NaN, kindkw={}, lowkw={}, highkw={}, \
+    def __init__(self, x, y, kind='linear', low=np.NaN, high=np.NaN, \
+                        kindkw={}, lowkw={}, highkw={}, \

@@ -187,15 +203,15 @@
self.low = self._init_interp_method(self._x, self._y, low, lowkw)
self.high = self._init_interp_method(self._x, self._y, high, highkw)

-    def _format_array(self, x, y, remove_bad_data = False, bad_data = []):#=[None, np.NaN]):#=[None, np.NaN]):
"""
-        Assigns properly formatted versions of x and y to self._x and self._y.
-        Also records data types.
+            Assigns properly formatted versions of x and y to self._x and self._y.
+            Also records data types.
+
+            Formatting includes removal of all points whose x or y coordinate
+            is in missing_data.  This is the primary difference from
+            make_array_safe.

-        Formatting includes removal of all points whose x or y coordinate
-        is in missing_data.  This is the primary difference from
-        make_array_safe.
-
"""
# FIXME: don't allow copying multiple times.

@@ -208,11 +224,9 @@
x = np.array(x)
y = np.array(y)
-            mask = np.array([  (xi not in bad_data) and (not np.isnan(xi)) and (y[i] not in bad_data) and (not np.isnan(y[i])) \
-                    for i, xi in enumerate(x) ])
-            print 'x equals: ', x
+            mask = np.array([  (xi not in bad_data) and (not np.isnan(xi)) and \
+                                        (y[i] not in bad_data) and (not np.isnan(y[i])) \
+                                    for i, xi in enumerate(x) ])

@@ -228,15 +242,14 @@

def _init_interp_method(self, x, y, interp_arg, kw):
"""
-        User provides interp_arg and dictionary kw.  _init_interp_method
-        returns the interpolating function from x and y specified by interp_arg,
-        possibly with extra keyword arguments given in kw.
+            User provides interp_arg and dictionary kw.  _init_interp_method
+            returns the interpolating function from x and y specified by interp_arg,
+            possibly with extra keyword arguments given in kw.

"""
# FIXME : error checking specific to interpolation method.  x and y long
#   enough for order-3 spline if that's indicated, etc.  Functions should throw
-        #   errors themselves when Interpolate1D is called, but errors at instantiation
-        #   would be nice.
+        #   errors themselves, but errors at instantiation would be nice.

from inspect import isclass, isfunction

@@ -257,7 +270,9 @@

def __call__(self, x):
"""
-
+            Input x must be in sorted order.
+            Breaks x into pieces in-range, below-range, and above range.
+            Performs appropriate operation on each and concatenates results.
"""

x = make_array_safe(x)
@@ -277,6 +292,8 @@

result = np.concatenate((new_low, new_interp, new_high)) # FIXME : deal with mixed datatypes
+                                                                                          # Would be nice to say result = zeros(dtype=?)
+                                                                                          # and fill in

return result

@@ -288,25 +305,31 @@
self.assert_(np.allclose(make_array_safe(x), make_array_safe(y)))

def test__interpolate_wrapper(self):
+        """ run unit test contained in interpolate_wrapper.py
+        """
print "\n\nTESTING _interpolate_wrapper MODULE"
from interpolate_wrapper import Test
T = Test()
T.runTest()

def test__fitpack_wrapper(self):
+        """ run unit test contained in fitpack_wrapper.py
+        """
print "\n\nTESTING _fitpack_wrapper MODULE"
from fitpack_wrapper import Test
T = Test()
T.runTest()

def test_spline1_defaultExt(self):
-        # make sure : spline order 1 (linear) interpolation works correctly
-        # make sure : default extrapolation works
+        """
+            make sure : spline order 1 (linear) interpolation works correctly
+            make sure : default extrapolation works
+        """
print "\n\nTESTING LINEAR (1st ORDER) SPLINE"
N = 7 # must be > 5
x = np.arange(N)
y = np.arange(N)
-        interp_func = Interpolate1D(x, y, kind='Spline', kindkw={'k':1}, low=None, high=599.73)
+        interp_func = Interpolate1d(x, y, kind='Spline', kindkw={'k':1}, low=None, high=599.73)
new_x = np.arange(N+1)-0.5
new_y = interp_func(new_x)

@@ -315,13 +338,16 @@
self.assert_(new_y[-1] == 599.73)

def test_spline2(self):
+        """
+            make sure : order-2 splines work on linear data
+            make sure : order-2 splines work on non-linear data
+        """
print "\n\nTESTING 2nd ORDER SPLINE"
-        # make sure : order-2 splines work on linear data
N = 7 #must be > 5
x = np.arange(N)
y = np.arange(N)
T1 = time.clock()
-        interp_func = Interpolate1D(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
+        interp_func = Interpolate1d(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
T2 = time.clock()
print "time to create 2nd order spline interp function with N = %i: " % N, T2 - T1
new_x = np.arange(N+1)-0.5
@@ -335,22 +361,24 @@
N = 7
x = np.arange(N)
y = x**2
-        interp_func = Interpolate1D(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
+        interp_func = Interpolate1d(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
new_x = np.arange(N+1)-0.5
new_y = interp_func(new_x)
self.assertAllclose(new_x**2, new_y)

def test_linear(self):
-        # make sure : linear interpolation works
-        # make sure : linear extrapolation works
+        """
+            make sure : linear interpolation works
+            make sure : linear extrapolation works
+        """
print "\n\nTESTING LINEAR INTERPOLATION"
N = 7
x = arange(N)
y = arange(N)
new_x = arange(N+1)-0.5
T1 = time.clock()
-        interp_func = Interpolate1D(x, y, kind='linear', low='linear', high='linear')
+        interp_func = Interpolate1d(x, y, kind='linear', low='linear', high='linear')
T2 = time.clock()
print "time to create linear interp function with N = %i: " % N, T2 - T1
t1 = time.clock()
@@ -361,18 +389,22 @@
self.assertAllclose(new_x, new_y)

def test_noLow(self):
-        # make sure : having no out-of-range elements in new_x is fine
-        # There was a bug with this earlier.
+        """
+            make sure : having no out-of-range elements in new_x is fine
+            There was a bug with this earlier.
+        """
N = 5
x = arange(N)
y = arange(N)
new_x = arange(1,N-1)+.2
-        interp_func = Interpolate1D(x, y, kind='linear', low='linear', high=np.NaN)
+        interp_func = Interpolate1d(x, y, kind='linear', low='linear', high=np.NaN)
new_y = interp_func(new_x)
self.assertAllclose(new_x, new_y)

def test_intper1d(self):
-        # make sure : interp1d works, at least in the linear case
+        """
+            make sure : interp1d works, at least in the linear case
+        """
N = 7
x = arange(N)
y = arange(N)

Modified: branches/Interpolate1D/TODO.txt
===================================================================
--- branches/Interpolate1D/TODO.txt	2008-07-21 16:35:30 UTC (rev 4555)
+++ branches/Interpolate1D/TODO.txt	2008-07-21 19:02:32 UTC (rev 4556)
@@ -6,15 +6,29 @@

**comment interpolate1d

**doc strings for interpolate1d and its members
+There's docstrings there already, but they should be
+made better.  In particular, it must be ensured that
+they are of the proper format and include examples.

+The doc strings for __init__.py, interpolate1d.py,
+Interpolate1d, and interp1d are virtually identical
+and very long; perhaps a master string can be stored
+somewhere that they all reference.  This would make

+
**more strings user can pass ('cubic', etc)
+User can specify interpolation type as a string argument
+to interpolate1d at initialization.  More strings should work.

**figure out NumPy version stuff with vectorize.
+In function interpolate1d._format_array.
It would be nice to remove the hack I used.
I believe vectorize is supposed to handle arrays of
length 0, but it's not working on my computer.
@@ -53,6 +67,13 @@
code out there too.  Figure out what is best and incorporate it.

+when the module is more established, there should be a page on
+the wiki which describes the big-picture of the module; what
+the capabilities are and which should be added, large-scale
+architecture of the module, etc.
+
+
**update for 2D and ND
This will probably take the form of two additional
classes both based on interpolate1d.  Thus it probably

Modified: branches/Interpolate1D/__init__.py
===================================================================
--- branches/Interpolate1D/__init__.py	2008-07-21 16:35:30 UTC (rev 4555)
+++ branches/Interpolate1D/__init__.py	2008-07-21 19:02:32 UTC (rev 4556)
@@ -1,32 +1,32 @@
#FIXME : better docstring
"""
-Interpolation of 1D data
+    Interpolation of 1D data

-This module provides several functions and classes for interpolation
-and extrapolation of 1D data (1D in both input and output).  The
-primary function provided is:
+    This module provides several functions and classes for interpolation
+    and extrapolation of 1D data (1D in both input and output).  The
+    primary function provided is:

-    interp1d(x, y, new_x) : from data points x and y, interpolates
-                                    values for points in new_x and
-                                    returns them as an array.
+        interp1d(x, y, new_x) : from data points x and y, interpolates
+                                        values for points in new_x and
+                                        returns them as an array.

-Classes provided include:
+    Classes provided include:

-    Interpolate1D  :   an object for interpolation of
-                            various kinds.  interp1d is a wrapper
-                            around this class.
-
-    Spline : an object for spline interpolation
-
-Functions provided include:
+        Interpolate1d  :   an object for interpolation of
+                                various kinds.  interp1d is a wrapper
+                                around this class.
+
+        Spline : an object for spline interpolation
+
+    Functions provided include:

-    linear : linear interpolation
-    logarithmic :  logarithmic interpolation
-    block : block interpolation
-    block_average_above : block average above interpolation
+        linear : linear interpolation
+        logarithmic :  logarithmic interpolation
+        block : block interpolation
+        block_average_above : block average above interpolation

"""

from interpolate_wrapper import linear, logarithmic, block, block_average_above
from fitpack_wrapper import Spline
-from interpolate1d import interpolate1d, interp1d
\ No newline at end of file
+from interpolate1d import Interpolate1d, interp1d
\ No newline at end of file

Modified: branches/Interpolate1D/interpolate1d.py
===================================================================
--- branches/Interpolate1D/interpolate1d.py	2008-07-21 16:35:30 UTC (rev 4555)
+++ branches/Interpolate1D/interpolate1d.py	2008-07-21 19:02:32 UTC (rev 4556)
@@ -11,7 +11,7 @@

Classes provided include:

-        Interpolate1D  :   an object for interpolation of
+        interpolate1d  :   an object for interpolation of
various kinds.  interp1d is a wrapper
around this class.

@@ -35,16 +35,21 @@
from numpy import array, arange, empty, float64, NaN

def make_array_safe(ary, typecode=np.float64):
+    """Used to make sure that inputs and outputs are
+    properly formatted.
+    """
ary = np.atleast_1d(np.asarray(ary, typecode))
if not ary.flags['CONTIGUOUS']:
ary = ary.copy()
return ary

-def interp1d(x, y, new_x, kind='linear', low=np.NaN, high=np.NaN, kindkw={}, lowkw={}, highkw={}, \
+def interp1d(x, y, new_x, kind='linear', low=np.NaN, high=np.NaN, \
+                    kindkw={}, lowkw={}, highkw={}, \
""" A function for interpolation of 1D data.

-        REQUIRED ARGUMENTS:
+        Parameters
+        -----------

x -- list or NumPy array
x includes the x-values for the data set to
@@ -60,7 +65,8 @@
points whose value is to be interpolated from x and y.
new_x must be in sorted order, lowest to highest.

-        OPTIONAL ARGUMENTS:
+        Optional Arguments
+        -------------------

kind -- Usu. function or string.  But can be any type.
Specifies the type of extrapolation to use for values within
@@ -89,7 +95,9 @@

numpy.NaN is always considered bad data.

-        SAMPLE ACCEPTABLE ARGUMENTS:
+        Acceptable Input Strings
+        ------------------------
+
"linear" -- linear interpolation : default
"logarithmic" -- logarithmic interpolation : linear in log space?
"block" --
@@ -98,7 +106,9 @@
indicates order of spline
numpy.NaN -- return numpy.NaN

-        EXAMPLES:
+        Examples
+        ---------
+
>>> import numpy
>>> from Interpolate1D import interp1d
>>> x = range(5)        # note list is permitted
@@ -107,14 +117,16 @@
>>> interp1d(x, y, new_x)
array([.2, 2.3, 5.6, NaN])
"""
-    return Interpolate1D(x, y, kind=kind, low=low, high=high, kindkw=kindkw, lowkw=lowkw, highkw=highkw, \
+    return Interpolate1D(x, y, kind=kind, low=low, high=high, \
+                                    kindkw=kindkw, lowkw=lowkw, highkw=highkw, \

-class interpolate1d(object):
-    """ An object for interpolation of 1D data.
+class Interpolate1d(object):
+    """ A class for interpolation of 1D data.
+
+        Parameters
+        -----------

-        REQUIRED ARGUMENTS:
-
x -- list or NumPy array
x includes the x-values for the data set to
interpolate from.  It must be sorted in
@@ -125,7 +137,8 @@
interpolate from.  Note that y must be
one-dimensional.

-        OPTIONAL ARGUMENTS:
+        Optional Arguments
+        -------------------

kind -- Usu. function or string.  But can be any type.
Specifies the type of extrapolation to use for values within
@@ -154,7 +167,9 @@

numpy.NaN is always considered bad data.

-        SAMPLE ACCEPTABLE ARGUMENTS:
+        Acceptable Input Strings
+        ------------------------
+
"linear" -- linear interpolation : default
"logarithmic" -- logarithmic interpolation : linear in log space?
"block" --
@@ -163,21 +178,22 @@
indicates order of spline
numpy.NaN -- return numpy.NaN

-        EXAMPLES:
+        Examples
+        ---------
+
>>> import numpy
-            >>> from Interpolate1D import Interpolate1D
+            >>> from Interpolate1D import interp1d
>>> x = range(5)        # note list is permitted
>>> y = numpy.arange(5.)
-            >>> interp = Interpolate1D(x, y)
>>> new_x = [.2, 2.3, 5.6]
-            >>> interp(new_x)
+            >>> interp1d(x, y, new_x)
array([.2, 2.3, 5.6, NaN])
-
"""
# FIXME: examples in doc string

-    def __init__(self, x, y, kind='linear', low=np.NaN, high=np.NaN, kindkw={}, lowkw={}, highkw={}, \
+    def __init__(self, x, y, kind='linear', low=np.NaN, high=np.NaN, \
+                        kindkw={}, lowkw={}, highkw={}, \

@@ -187,15 +203,15 @@
self.low = self._init_interp_method(self._x, self._y, low, lowkw)
self.high = self._init_interp_method(self._x, self._y, high, highkw)

-    def _format_array(self, x, y, remove_bad_data = False, bad_data = []):#=[None, np.NaN]):#=[None, np.NaN]):
"""
-        Assigns properly formatted versions of x and y to self._x and self._y.
-        Also records data types.
+            Assigns properly formatted versions of x and y to self._x and self._y.
+            Also records data types.
+
+            Formatting includes removal of all points whose x or y coordinate
+            is in missing_data.  This is the primary difference from
+            make_array_safe.

-        Formatting includes removal of all points whose x or y coordinate
-        is in missing_data.  This is the primary difference from
-        make_array_safe.
-
"""
# FIXME: don't allow copying multiple times.

@@ -208,11 +224,9 @@
x = np.array(x)
y = np.array(y)
-            mask = np.array([  (xi not in bad_data) and (not np.isnan(xi)) and (y[i] not in bad_data) and (not np.isnan(y[i])) \
-                    for i, xi in enumerate(x) ])
-            print 'x equals: ', x
+            mask = np.array([  (xi not in bad_data) and (not np.isnan(xi)) and \
+                                        (y[i] not in bad_data) and (not np.isnan(y[i])) \
+                                    for i, xi in enumerate(x) ])

@@ -228,15 +242,14 @@

def _init_interp_method(self, x, y, interp_arg, kw):
"""
-        User provides interp_arg and dictionary kw.  _init_interp_method
-        returns the interpolating function from x and y specified by interp_arg,
-        possibly with extra keyword arguments given in kw.
+            User provides interp_arg and dictionary kw.  _init_interp_method
+            returns the interpolating function from x and y specified by interp_arg,
+            possibly with extra keyword arguments given in kw.

"""
# FIXME : error checking specific to interpolation method.  x and y long
#   enough for order-3 spline if that's indicated, etc.  Functions should throw
-        #   errors themselves when Interpolate1D is called, but errors at instantiation
-        #   would be nice.
+        #   errors themselves, but errors at instantiation would be nice.

from inspect import isclass, isfunction

@@ -257,7 +270,9 @@

def __call__(self, x):
"""
-
+            Input x must be in sorted order.
+            Breaks x into pieces in-range, below-range, and above range.
+            Performs appropriate operation on each and concatenates results.
"""

x = make_array_safe(x)
@@ -277,6 +292,8 @@

result = np.concatenate((new_low, new_interp, new_high)) # FIXME : deal with mixed datatypes
+                                                                                          # Would be nice to say result = zeros(dtype=?)
+                                                                                          # and fill in

return result

@@ -288,25 +305,31 @@
self.assert_(np.allclose(make_array_safe(x), make_array_safe(y)))

def test__interpolate_wrapper(self):
+        """ run unit test contained in interpolate_wrapper.py
+        """
print "\n\nTESTING _interpolate_wrapper MODULE"
from interpolate_wrapper import Test
T = Test()
T.runTest()

def test__fitpack_wrapper(self):
+        """ run unit test contained in fitpack_wrapper.py
+        """
print "\n\nTESTING _fitpack_wrapper MODULE"
from fitpack_wrapper import Test
T = Test()
T.runTest()

def test_spline1_defaultExt(self):
-        # make sure : spline order 1 (linear) interpolation works correctly
-        # make sure : default extrapolation works
+        """
+            make sure : spline order 1 (linear) interpolation works correctly
+            make sure : default extrapolation works
+        """
print "\n\nTESTING LINEAR (1st ORDER) SPLINE"
N = 7 # must be > 5
x = np.arange(N)
y = np.arange(N)
-        interp_func = Interpolate1D(x, y, kind='Spline', kindkw={'k':1}, low=None, high=599.73)
+        interp_func = Interpolate1d(x, y, kind='Spline', kindkw={'k':1}, low=None, high=599.73)
new_x = np.arange(N+1)-0.5
new_y = interp_func(new_x)

@@ -315,13 +338,16 @@
self.assert_(new_y[-1] == 599.73)

def test_spline2(self):
+        """
+            make sure : order-2 splines work on linear data
+            make sure : order-2 splines work on non-linear data
+        """
print "\n\nTESTING 2nd ORDER SPLINE"
-        # make sure : order-2 splines work on linear data
N = 7 #must be > 5
x = np.arange(N)
y = np.arange(N)
T1 = time.clock()
-        interp_func = Interpolate1D(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
+        interp_func = Interpolate1d(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
T2 = time.clock()
print "time to create 2nd order spline interp function with N = %i: " % N, T2 - T1
new_x = np.arange(N+1)-0.5
@@ -335,22 +361,24 @@
N = 7
x = np.arange(N)
y = x**2
-        interp_func = Interpolate1D(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
+        interp_func = Interpolate1d(x, y, kind='Spline', kindkw={'k':2}, low='spline', high='spline')
new_x = np.arange(N+1)-0.5
new_y = interp_func(new_x)
self.assertAllclose(new_x**2, new_y)

def test_linear(self):
-        # make sure : linear interpolation works
-        # make sure : linear extrapolation works
+        """
+            make sure : linear interpolation works
+            make sure : linear extrapolation works
+        """
print "\n\nTESTING LINEAR INTERPOLATION"
N = 7
x = arange(N)
y = arange(N)
new_x = arange(N+1)-0.5
T1 = time.clock()
-        interp_func = Interpolate1D(x, y, kind='linear', low='linear', high='linear')
+        interp_func = Interpolate1d(x, y, kind='linear', low='linear', high='linear')
T2 = time.clock()
print "time to create linear interp function with N = %i: " % N, T2 - T1
t1 = time.clock()
@@ -361,18 +389,22 @@
self.assertAllclose(new_x, new_y)

def test_noLow(self):
-        # make sure : having no out-of-range elements in new_x is fine
-        # There was a bug with this earlier.
+        """
+            make sure : having no out-of-range elements in new_x is fine
+            There was a bug with this earlier.
+        """
N = 5
x = arange(N)
y = arange(N)
new_x = arange(1,N-1)+.2
-        interp_func = Interpolate1D(x, y, kind='linear', low='linear', high=np.NaN)
+        interp_func = Interpolate1d(x, y, kind='linear', low='linear', high=np.NaN)
new_y = interp_func(new_x)
self.assertAllclose(new_x, new_y)

def test_intper1d(self):
-        # make sure : interp1d works, at least in the linear case
+        """
+            make sure : interp1d works, at least in the linear case
+        """
N = 7
x = arange(N)
y = arange(N)

===================================================================
--- branches/Interpolate1D/regression_test.py	2008-07-21 16:35:30 UTC (rev 4555)
+++ branches/Interpolate1D/regression_test.py	2008-07-21 19:02:32 UTC (rev 4556)
@@ -0,0 +1,32 @@
+""" regression test:
+
+    This script runs a simple regression test on the functionality of
+    the interpolation module.  Currently, when run, it times each
+    unit test in interpolate1d.py and stores those times in a dict
+    of dicts; outer keys are time test was performed, and inner
+    keys are names of tests run.
+
+"""
+
+import shelve, time
+from interpolate1d import Test
+
+# name of log file to which all data is stored.
+filename = 'regression_test.dbm'
+
+log_total = shelve.open(filename)
+current_time = str(time.localtime()[0:5]) # specified up to minute
+
+# run all tests in interpolate1d's test class
+test_list = [name for name in dir(Test) if name.find('test_') == 0]
+log_now = {}
+
+# record time taken for each test
+for test_name in test_list:
+    t1 = time.clock()
+    eval('Test.%s' % test_name)
+    t2 = time.clock()
+    log_now[test_name] = t2-t1
+
+log_total[current_time] = log_now
+log_total.close()

```