I have been playing around with a package that uses a linear scipy.interpolate.interp1d to create a history function for the ode solver in scipy, described here.
The relevant bit of code goes something like
def update(self, ti, Y):
""" Add one new (ti, yi) to the interpolator """
self.itpr.x = np.hstack([self.itpr.x, [ti]])
yi = np.array([Y]).T
self.itpr.y = np.hstack([self.itpr.y, yi])
#self.itpr._y = np.hstack([self.itpr.y, yi])
self.itpr.fill_value = Y
Where "self.itpr" is initialized in __init__:
def __init__(self, g, tc=0):
""" g(t) = expression of Y(t) for t<tc """
self.g = g
self.tc = tc
# We must fill the interpolator with 2 points minimum
self.itpr = scipy.interpolate.interp1d(
np.array([tc-1, tc]), # X
np.array([self.g(tc), self.g(tc)]).T, # Y
kind='linear', bounds_error=False,
fill_value = self.g(tc))
Where g
is some function that returns an array of values that are solutions to a set of differential equations and tc
is the current time.
This seems nice to me because a new interpolator object doesn't have to be re-created every time I want to update the ranges of values (which happens at each explicit time step during a simulation). This method of updating the interpolator works well under scipy v 0.11.0. However, after updating to v 0.12.0 I ran into issues. I see that the new interpolator now includes an array _y
that seems to just be another copy of the original. Is it safe and/or sane to just update _y
as outlined above as well? Is there a simpler, more pythonic way to address this that would hopefully be more robust to future updates in scipy? Again, in v 0.11 everything works well and expected results are produced, and in v 0.12 I get an IndexError
when _y
is referenced as it isn't updated in my function while y itself is.
Any help/pointers would be appreciated!
It looks like _y
is just a copy of y
that has been reshaped by interp1d._reshape_yi()
. It should therefore be safe to just update it using:
self.itpr._y = self.itpr._reshape_yi(self.itpr.y)
In fact, as far as I can tell it's only _y
that gets used internally by the interpolator, so I think you could get away without actually updating y
at all.
A much more elegant solution would be to make _y
a property of the interpolator that returns a suitably reshaped copy of y
. It's possible to achieve this by monkey-patching your specific instance of interp1d
after it has been created (see Alex Martelli's answer here for more explanation):
x = np.arange(100)
y = np.random.randn(100)
itpr = interp1d(x,y)
# method to get self._y from self.y
def get_y(self):
return self._reshape_yi(self.y)
meth = property(get_y,doc='reshaped version of self.y')
# make this a method of this interp1d instance only
basecls = type(itpr)
cls = type(basecls.__name__, (basecls,), {})
setattr(cls, '_y', meth)
itpr.__class__ = cls
# itpr._y is just a reshaped version of itpr.y
print itpr.y.shape,itpr._y.shape
>>> (100,) (100, 1)
Now itpr._y
gets updated when you update itpr.y
itpr.x = np.arange(110)
itpr.y = np.random.randn(110)
print itpr._y.shape
>>> (110,) (110, 1)
This is all quite fiddly and not very Pythonic - it's much easier to fix the scipy source code (scipy/interpolate/interpolate.py). All you'd need to do is remove the last line from interp1d.__init__()
where it sets:
self._y = y
and add these lines:
@property
def _y(self):
return self._reshape_yi(self.y)
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