When I last visited I was given excellent advice about Gaussian and other
bell-shaped curves. Upon further reflection I realized that the Gaussian
curves will not do; the curve does need to have y=0.0 at each end.
I tried to apply a Beta distribution, but I cannot correlate the alpha and
2008/5/2 Rich Shepard [EMAIL PROTECTED]:
When I last visited I was given excellent advice about Gaussian and other
bell-shaped curves. Upon further reflection I realized that the Gaussian
curves will not do; the curve does need to have y=0.0 at each end.
I tried to apply a Beta
On Fri, 2 May 2008, Angus McMorland wrote:
How about multiplying two Boltzmann terms together, ala:
f(x) = 1/(1+exp(-(x-flex1)/tau1)) * 1/(1+exp((x-flex2)/tau2))
You'll find if your two flexion points get too close together, the peak
will drop below the maximum for each individual curve,
Rich,
this could use some serious vectorization/numpyification! Poke around
the scipy Wiki and whatever other tutorials you can find -- you'll be
glad you did. A hint:
When you are writing a loop like:
for i in xL:
x.append(xL[i])
You should be doing array operations!
On Fri, 2 May 2008, Christopher Barker wrote:
this could use some serious vectorization/numpyification! Poke around the
scipy Wiki and whatever other tutorials you can find -- you'll be glad you
did. A hint:
When you are writing a loop like:
for i in xL:
x.append(xL[i])
2008/5/2 Rich Shepard [EMAIL PROTECTED]:
What will work (I call it a pi curve) is a matched pair of sigmoid curves,
the ascending curve on the left and the descending curve on the right. Using
the Boltzmann function for these I can calculate and plot each individually,
but I'm having
On Fri, 2 May 2008, Anne Archibald wrote:
It's better not to work point-by-point, appending things, when working
with numpy. Ideally you could find a formula which just produced the right
curve, and then you'd apply it to the input vector and get the output
vector all at once.
Anne,
2008/5/2 Rich Shepard [EMAIL PROTECTED]:
On Fri, 2 May 2008, Anne Archibald wrote:
It's better not to work point-by-point, appending things, when working
with numpy. Ideally you could find a formula which just produced the right
curve, and then you'd apply it to the input vector and get
Anne Archibald wrote:
2008/5/2 Rich Shepard [EMAIL PROTECTED]:
No, no. You *want* scaled_x to range from -1 to 1.
Why not just scale to -pi to pi right there?
(The 0.998 is because you didn't include the endpoint, 100.)
Which is why you want linspace, rather than arange. Really, trust me on
On Fri, 2 May 2008, Christopher Barker wrote:
Why not just scale to -pi to pi right there?
Dunno, Chris. As I wrote to Anne (including a couple of files and the
resulting plot), it's been almost three decades since I dealt with the math
underlying distribution functions.
Which is why you
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