[Scipy-svn] r4555 - branches/Interpolate1D

scipy-svn@scip... scipy-svn@scip...
Mon Jul 21 11:35:30 CDT 2008


Author: fcady
Date: 2008-07-21 11:35:30 -0500 (Mon, 21 Jul 2008)
New Revision: 4555

Added:
   branches/Interpolate1D/TODO.txt
   branches/Interpolate1D/interpolate1d.py
Modified:
   branches/Interpolate1D/Interpolate1D.py
   branches/Interpolate1D/__init__.py
Log:
added a TODO.txt file discussing areas for improvement, removed CamelCase from main file name, and made minor changes

Modified: branches/Interpolate1D/Interpolate1D.py
===================================================================
--- branches/Interpolate1D/Interpolate1D.py	2008-07-21 03:17:18 UTC (rev 4554)
+++ branches/Interpolate1D/Interpolate1D.py	2008-07-21 16:35:30 UTC (rev 4555)
@@ -34,9 +34,7 @@
 import numpy as np
 from numpy import array, arange, empty, float64, NaN
 
-# FIXME: use this to ensure proper type of all inputs and outputs in Interpolate1D
 def make_array_safe(ary, typecode=np.float64):
-    # FIXME: could pick correct typecode
     ary = np.atleast_1d(np.asarray(ary, typecode))
     if not ary.flags['CONTIGUOUS']:
         ary = ary.copy()
@@ -112,90 +110,79 @@
     return Interpolate1D(x, y, kind=kind, low=low, high=high, kindkw=kindkw, lowkw=lowkw, highkw=highkw, \
                         remove_bad_data = remove_bad_data, bad_data=bad_data)(new_x)
 
-class Interpolate1D(object):
+class interpolate1d(object):
     """ An object for interpolation of 1D data.
+            
+        REQUIRED ARGUMENTS:
+            
+        x -- list or NumPy array
+            x includes the x-values for the data set to
+            interpolate from.  It must be sorted in
+            ascending order
+                
+        y -- list or NumPy array
+            y includes the y-values for the data set  to
+            interpolate from.  Note that y must be
+            one-dimensional.
+                
+        OPTIONAL ARGUMENTS:
         
-    REQUIRED ARGUMENTS:
+        kind -- Usu. function or string.  But can be any type.
+            Specifies the type of extrapolation to use for values within
+            the range of x.  If a string is passed, it will look for an object
+            or function with that name and call it when evaluating.  If 
+            a function or object is passed, it will be called when interpolating.
+            If nothing else, assumes the argument is intended as a value
+            to be returned for all arguments.  Defaults to linear interpolation.
+            
+        kindkw -- dictionary
+            If kind is a class, function or string, additional keyword arguments
+            may be needed (example: if you want a 2nd order spline, kind = 'spline'
+            and kindkw = {'k' : 2}.
+            
+        low (high) -- same as for kind
+            Same options as for 'kind'.  Defaults to returning numpy.NaN ('not 
+            a number') for all values outside the range of x.
         
-    x -- list or NumPy array
-        x includes the x-values for the data set to
-        interpolate from.  It must be sorted in
-        ascending order
+        remove_bad_data -- bool
+            indicates whether to remove bad data.
             
-    y -- list or NumPy array
-        y includes the y-values for the data set  to
-        interpolate from.  Note that y must be
-        one-dimensional.
+        bad_data -- list
+            List of values (in x or y) which indicate unacceptable data. All points
+            that have x or y value in missing_data will be removed before
+            any interpolatin is performed if remove_bad_data is true.
             
-    OPTIONAL ARGUMENTS:
-    
-    kind -- Usu. function or string.  But can be any type.
-        Specifies the type of extrapolation to use for values within
-        the range of x.  If a string is passed, it will look for an object
-        or function with that name and call it when evaluating.  If 
-        a function or object is passed, it will be called when interpolating.
-        If nothing else, assumes the argument is intended as a value
-        to be returned for all arguments.  Defaults to linear interpolation.
+            numpy.NaN is always considered bad data.
+            
+        SAMPLE ACCEPTABLE ARGUMENTS:
+            "linear" -- linear interpolation : default
+            "logarithmic" -- logarithmic interpolation : linear in log space?
+            "block" --
+            "block_average_above' -- block average above
+            "Spline" -- spline interpolation.  keyword k (defaults to 3) 
+                indicates order of spline
+            numpy.NaN -- return numpy.NaN
         
-    kindkw -- dictionary
-        If kind is a class, function or string, additional keyword arguments
-        may be needed (example: if you want a 2nd order spline, kind = 'spline'
-        and kindkw = {'k' : 2}.
+        EXAMPLES:
+            >>> import numpy
+            >>> from Interpolate1D import Interpolate1D
+            >>> 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)
+            array([.2, 2.3, 5.6, NaN])
         
-    low (high) -- same as for kind
-        Same options as for 'kind'.  Defaults to returning numpy.NaN ('not 
-        a number') for all values outside the range of x.
-    
-    remove_bad_data -- bool
-        indicates whether to remove bad data.
-        
-    bad_data -- list
-        List of values (in x or y) which indicate unacceptable data. All points
-        that have x or y value in missing_data will be removed before
-        any interpolatin is performed if remove_bad_data is true.
-        
-        numpy.NaN is always considered bad data.
-        
-    SAMPLE ACCEPTABLE ARGUMENTS:
-        "linear" -- linear interpolation : default
-        "logarithmic" -- logarithmic interpolation : linear in log space?
-        "block" --
-        "block_average_above' -- block average above
-        "Spline" -- spline interpolation.  keyword k (defaults to 3) 
-            indicates order of spline
-        numpy.NaN -- return numpy.NaN
-    
-    EXAMPLES:
-        >>> import numpy
-        >>> from Interpolate1D import Interpolate1D
-        >>> 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)
-        array([.2, 2.3, 5.6, NaN])
-        
     """
     # FIXME: more informative descriptions of sample arguments
     # FIXME: examples in doc string
     
     def __init__(self, x, y, kind='linear', low=np.NaN, high=np.NaN, kindkw={}, lowkw={}, highkw={}, \
                         remove_bad_data = False, bad_data=[]):
-        
-        #print "size of bad_data: ", len(bad_data)
-        
+                
         self._format_array(x, y, remove_bad_data = remove_bad_data, bad_data = bad_data)
-        # FIXME: Handle checking if they are the correct size.
-        #self._x = make_array_safe(x).copy()
-        #self._y = make_array_safe(y).copy()
+
         
-        #assert len(x) == len(y) , "x and y must be of the same length"
-        #assert x.ndim == 1 , "x must be one-dimensional"
-        #assert y.ndim == 1 , "y must be one-dimensional"
-        # FIXME: let y be 2-dimensional.  Involves reworking of Interpolate1D.__call__
-        #   because Spline enumerates y along the last, rather then first, axis,
-        #   while concatenate works along first axis
-        
         self.kind = self._init_interp_method(self._x, self._y, kind, kindkw)
         self.low = self._init_interp_method(self._x, self._y, low, lowkw)
         self.high = self._init_interp_method(self._x, self._y, high, highkw)
@@ -216,10 +203,10 @@
         assert len(x) > 0 and len(y) > 0 , "Interpolate1D does not support\
                                         arrays of length 0"
         assert len(x) == len(y) , "x and y must be of the same length"
-        x = np.array(x)
-        y = np.array(y)
         
         # remove bad data
+        x = np.array(x)
+        y = np.array(y)
         if remove_bad_data:
             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) ])
@@ -253,6 +240,7 @@
         
         from inspect import isclass, isfunction
         
+        # FIXME : more string options available ('cubic', etc)
         if interp_arg in ['linear', 'logarithmic', 'block', 'block_average_above']:
             func = {'linear':linear, 'logarithmic':logarithmic, 'block':block, \
                         'block_average_above':block_average_above}[interp_arg]
@@ -277,9 +265,10 @@
         high_mask = x>self._x[-1]
         interp_mask = (~low_mask) & (~high_mask)
         
-        if len(x[low_mask]) == 0: new_low=np.array([]) # hack, since vectorize is failing
-                                                                            # work on lists/arrays of length 0
-                                                                            # on computer where this is being
+        if len(x[low_mask]) == 0: new_low=np.array([])  # FIXME : remove need for if/else.
+                                                                            # if/else is a hack, since vectorize is failing
+                                                                            # to work on lists/arrays of length 0
+                                                                            # on the computer where this is being
                                                                             # developed
         else: new_low = self.low(x[low_mask])
         if len(x[interp_mask])==0: new_interp=np.array([])

Added: branches/Interpolate1D/TODO.txt
===================================================================
--- branches/Interpolate1D/TODO.txt	2008-07-21 03:17:18 UTC (rev 4554)
+++ branches/Interpolate1D/TODO.txt	2008-07-21 16:35:30 UTC (rev 4555)
@@ -0,0 +1,59 @@
+TODO
+This list contains a number of things which need fixing and/or
+improvement.  There's also a small explanatory
+note with ideas.  More info is often contained in FIXMEs
+at appropriate places in the code.
+
+
+**comment interpolate1d
+
+
+**doc strings for interpolate1d and its members
+
+
+**more strings user can pass ('cubic', etc)
+
+
+**figure out NumPy version stuff with vectorize.
+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.
+
+
+**better handling of variable types
+Currently everything is cast to a float64 if it is not already
+a float32.  Is this the best way to do it?
+
+Also, for the future, code should be added for record arrays,
+which mix real values with strings.  This is, I believe already
+largely supported, but that's not because the code was written
+with that in mind.  I haven't thought through the details.
+
+Perhaps this should be done as another function/class which 
+wraps interpolate1d.
+
+
+**allow y to be 2-dimensional
+That way the interpolated function is from R1 -> Rn, and
+not just R1 -> R1.  This requires some thinking about axes.
+
+
+**write regression tests
+desired for fitpack_wrapper and _interpolate_wrapper
+as well as interpolate1d.  Recommend using the
+shelve module
+
+
+**pick best spline
+Under-the-hood machinery currently comes from _interpolate.cpp
+(used in enthought.interpolate) and FITPACK (Fortran, used in 
+scipy.interpolate).  This isn't necessarily the best (for example,
+speed of FITPACK is highly sensitive to parameter k).  Other code
+is used in scipy.ndimage and scipy.signal.  There is surely other
+code out there too.  Figure out what is best and incorporate it.
+
+
+**update for 2D and ND
+This will probably take the form of two additional
+classes both based on interpolate1d.  Thus it probably
+shouldn't be done until interpolate1d is more settled.

Modified: branches/Interpolate1D/__init__.py
===================================================================
--- branches/Interpolate1D/__init__.py	2008-07-21 03:17:18 UTC (rev 4554)
+++ branches/Interpolate1D/__init__.py	2008-07-21 16:35:30 UTC (rev 4555)
@@ -29,4 +29,4 @@
 
 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

Added: branches/Interpolate1D/interpolate1d.py
===================================================================
--- branches/Interpolate1D/interpolate1d.py	2008-07-21 03:17:18 UTC (rev 4554)
+++ branches/Interpolate1D/interpolate1d.py	2008-07-21 16:35:30 UTC (rev 4555)
@@ -0,0 +1,384 @@
+"""
+    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:
+
+        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:
+
+        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
+
+"""
+
+# FIXME: information strings giving mathematical descriptions of the actions
+#     of the functions.
+
+from interpolate_wrapper import linear, logarithmic, block, block_average_above
+from fitpack_wrapper import Spline
+import numpy as np
+from numpy import array, arange, empty, float64, NaN
+
+def make_array_safe(ary, typecode=np.float64):
+    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={}, \
+                        remove_bad_data = False, bad_data=[]):
+    """ A function for interpolation of 1D data.
+        
+        REQUIRED ARGUMENTS:
+            
+        x -- list or NumPy array
+            x includes the x-values for the data set to
+            interpolate from.  It must be sorted in
+            ascending order
+                
+        y -- list or NumPy array
+            y includes the y-values for the data set  to
+            interpolate from.  Note that y must be
+            one-dimensional.
+            
+        new_x -- list or NumPy array
+            points whose value is to be interpolated from x and y.
+            new_x must be in sorted order, lowest to highest.
+                
+        OPTIONAL ARGUMENTS:
+        
+        kind -- Usu. function or string.  But can be any type.
+            Specifies the type of extrapolation to use for values within
+            the range of x.  If a string is passed, it will look for an object
+            or function with that name and call it when evaluating.  If 
+            a function or object is passed, it will be called when interpolating.
+            If nothing else, assumes the argument is intended as a value
+            to be returned for all arguments.  Defaults to linear interpolation.
+            
+        kindkw -- dictionary
+            If kind is a class, function or string, additional keyword arguments
+            may be needed (example: if you want a 2nd order spline, kind = 'spline'
+            and kindkw = {'k' : 2}.
+            
+        low (high) -- same as for kind
+            Same options as for 'kind'.  Defaults to returning numpy.NaN ('not 
+            a number') for all values outside the range of x.
+        
+        remove_bad_data -- bool
+            indicates whether to remove bad data.
+            
+        bad_data -- list
+            List of values (in x or y) which indicate unacceptable data. All points
+            that have x or y value in missing_data will be removed before
+            any interpolatin is performed if remove_bad_data is true.
+            
+            numpy.NaN is always considered bad data.
+            
+        SAMPLE ACCEPTABLE ARGUMENTS:
+            "linear" -- linear interpolation : default
+            "logarithmic" -- logarithmic interpolation : linear in log space?
+            "block" --
+            "block_average_above' -- block average above
+            "Spline" -- spline interpolation.  keyword k (defaults to 3) 
+                indicates order of spline
+            numpy.NaN -- return numpy.NaN
+        
+        EXAMPLES:
+            >>> import numpy
+            >>> from Interpolate1D import interp1d
+            >>> x = range(5)        # note list is permitted
+            >>> y = numpy.arange(5.)
+            >>> new_x = [.2, 2.3, 5.6]
+            >>> 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, \
+                        remove_bad_data = remove_bad_data, bad_data=bad_data)(new_x)
+
+class interpolate1d(object):
+    """ An object for interpolation of 1D data.
+            
+        REQUIRED ARGUMENTS:
+            
+        x -- list or NumPy array
+            x includes the x-values for the data set to
+            interpolate from.  It must be sorted in
+            ascending order
+                
+        y -- list or NumPy array
+            y includes the y-values for the data set  to
+            interpolate from.  Note that y must be
+            one-dimensional.
+                
+        OPTIONAL ARGUMENTS:
+        
+        kind -- Usu. function or string.  But can be any type.
+            Specifies the type of extrapolation to use for values within
+            the range of x.  If a string is passed, it will look for an object
+            or function with that name and call it when evaluating.  If 
+            a function or object is passed, it will be called when interpolating.
+            If nothing else, assumes the argument is intended as a value
+            to be returned for all arguments.  Defaults to linear interpolation.
+            
+        kindkw -- dictionary
+            If kind is a class, function or string, additional keyword arguments
+            may be needed (example: if you want a 2nd order spline, kind = 'spline'
+            and kindkw = {'k' : 2}.
+            
+        low (high) -- same as for kind
+            Same options as for 'kind'.  Defaults to returning numpy.NaN ('not 
+            a number') for all values outside the range of x.
+        
+        remove_bad_data -- bool
+            indicates whether to remove bad data.
+            
+        bad_data -- list
+            List of values (in x or y) which indicate unacceptable data. All points
+            that have x or y value in missing_data will be removed before
+            any interpolatin is performed if remove_bad_data is true.
+            
+            numpy.NaN is always considered bad data.
+            
+        SAMPLE ACCEPTABLE ARGUMENTS:
+            "linear" -- linear interpolation : default
+            "logarithmic" -- logarithmic interpolation : linear in log space?
+            "block" --
+            "block_average_above' -- block average above
+            "Spline" -- spline interpolation.  keyword k (defaults to 3) 
+                indicates order of spline
+            numpy.NaN -- return numpy.NaN
+        
+        EXAMPLES:
+            >>> import numpy
+            >>> from Interpolate1D import Interpolate1D
+            >>> 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)
+            array([.2, 2.3, 5.6, NaN])
+        
+    """
+    # FIXME: more informative descriptions of sample arguments
+    # FIXME: examples in doc string
+    
+    def __init__(self, x, y, kind='linear', low=np.NaN, high=np.NaN, kindkw={}, lowkw={}, highkw={}, \
+                        remove_bad_data = False, bad_data=[]):
+                
+        self._format_array(x, y, remove_bad_data = remove_bad_data, bad_data = bad_data)
+
+        
+        self.kind = self._init_interp_method(self._x, self._y, kind, kindkw)
+        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.
+        
+        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.
+         
+        # check acceptable lengths for x and y
+        assert len(x) > 0 and len(y) > 0 , "Interpolate1D does not support\
+                                        arrays of length 0"
+        assert len(x) == len(y) , "x and y must be of the same length"
+        
+        # remove bad data
+        x = np.array(x)
+        y = np.array(y)
+        if remove_bad_data:
+            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 'mask equals: ', mask, type(mask)
+            print 'x equals: ', x
+            print 'x[mask] is: ', x[mask]
+            x = x[mask]
+            y = y[mask]
+            
+        # collect dataypes and make arrays
+        self._xdtype = {np.float32 : np.float32}.setdefault(type(x[0]), np.float64) # unless data is float32,  cast to float64
+        self._ydtype = {np.float32 : np.float32}.setdefault(type(y[0]), np.float64)
+        self._x = make_array_safe(x, self._xdtype).copy()
+        self._y = make_array_safe(y, self._ydtype).copy()
+            
+        # check dimensionality
+        assert self._x.ndim == 1 , "x must be one-dimensional"
+        assert self._y.ndim == 1 , "y must be one-dimensional"    
+    
+    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.
+        
+        """
+        # 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.
+        
+        from inspect import isclass, isfunction
+        
+        # FIXME : more string options available ('cubic', etc)
+        if interp_arg in ['linear', 'logarithmic', 'block', 'block_average_above']:
+            func = {'linear':linear, 'logarithmic':logarithmic, 'block':block, \
+                        'block_average_above':block_average_above}[interp_arg]
+            result = lambda new_x : func(self._x, self._y, new_x, **kw)
+        elif interp_arg in ['Spline', Spline, 'spline']:
+            result = Spline(self._x, self._y, **kw)
+        elif isfunction(interp_arg):
+            result = lambda new_x : interp_arg(new_x, **kw)
+        elif isclass(interp_arg):
+            result = interp_arg(x, y, **kw)
+        else:
+            result = np.vectorize(lambda new_x : interp_arg)
+        return result
+
+    def __call__(self, x):
+        """
+            
+        """
+        
+        x = make_array_safe(x)
+        low_mask = x<self._x[0]
+        high_mask = x>self._x[-1]
+        interp_mask = (~low_mask) & (~high_mask)
+        
+        if len(x[low_mask]) == 0: new_low=np.array([])  # FIXME : remove need for if/else.
+                                                                            # if/else is a hack, since vectorize is failing
+                                                                            # to work on lists/arrays of length 0
+                                                                            # on the computer where this is being
+                                                                            # developed
+        else: new_low = self.low(x[low_mask])
+        if len(x[interp_mask])==0: new_interp=np.array([])
+        else: new_interp = self.kind(x[interp_mask])
+        if len(x[high_mask]) == 0: new_high = np.array([])
+        else: new_high = self.high(x[high_mask])
+        
+        result = np.concatenate((new_low, new_interp, new_high)) # FIXME : deal with mixed datatypes
+        
+        return result
+        
+# unit testing
+import unittest, time
+class Test(unittest.TestCase):
+    
+    def assertAllclose(self, x, y):
+        self.assert_(np.allclose(make_array_safe(x), make_array_safe(y)))
+        
+    def test__interpolate_wrapper(self):
+        print "\n\nTESTING _interpolate_wrapper MODULE"
+        from interpolate_wrapper import Test
+        T = Test()
+        T.runTest()
+        
+    def test__fitpack_wrapper(self):
+        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
+        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)
+        new_x = np.arange(N+1)-0.5
+        new_y = interp_func(new_x)
+        
+        self.assertAllclose(new_y[1:5], [0.5, 1.5, 2.5, 3.5])
+        self.assert_(new_y[0] == None)
+        self.assert_(new_y[-1] == 599.73)
+        
+    def test_spline2(self):
+        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')
+        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
+        t1 = time.clock()
+        new_y = interp_func(new_x)
+        t2 = time.clock()
+        print "time to evaluate 2nd order spline interp function with N = %i: " % N, t2 - t1
+        self.assertAllclose(new_x, new_y)
+        
+        # make sure for non-linear data
+        N = 7
+        x = np.arange(N)
+        y = x**2
+        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
+        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')
+        T2 = time.clock()
+        print "time to create linear interp function with N = %i: " % N, T2 - T1
+        t1 = time.clock()
+        new_y = interp_func(new_x)
+        t2 = time.clock()
+        print "time to create linear interp function with N = %i: " % N, t2 - t1
+        
+        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.
+        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)
+        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
+        N = 7
+        x = arange(N)
+        y = arange(N)
+        new_x = arange(N+1)-0.5
+        new_y = interp1d(x, y, new_x, kind='linear', low='linear', high='linear')        
+        self.assertAllclose(new_x, new_y)
+        
+if __name__ == '__main__':
+    unittest.main()                 
\ No newline at end of file



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