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Add the option to ExomeReadCounts to change what kind of count to generatate. RAW or PCOVs
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It turns out that when weights are provided for parameters, the fitting process greatly overestimates parameter errors.
Example from the tests:
``` python
t_data = np.array([1.4, 2.1, 2.6, 3.0, 3.3]…
tBuLi updated
9 years ago
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**Issue by [natduca](https://github.com/natduca)**
_Monday Sep 22, 2014 at 20:08 GMT_
_Originally opened as https://github.com/google/trace-viewer/issues/245_
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_From [cawar...@google.com](https:…
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The presence of NaNs in the xdata or ydata of scipy.optimize.curve_fit(f, xdata, ydata) causes all parameters to be returned at 1.0. This behavior makes it easy to accidentally accept a useless fit. O…
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Inspired by the Stack Overflow question:
http://stackoverflow.com/q/19713689/249341
A minimal working example of the potential pitfall:
```
import numpy as np
import scipy.optimize
x = [1,2,3,4]
y…
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_From [cawar...@google.com](https://code.google.com/u/109490550355908215689/) on June 09, 2013 21:39:55_
Chrome Version: 28.0.1489.4
Operating System: Chrome OS
URL (if applicable) where crash occur…
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see data in 2014-07-01, J1600
```
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
C:\Anaconda\e…
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If you scale the errors fed into curve_fit, the error returned does not change. This is incorrect behavior. The error (variance and covariance) for a final fit parameter should increase given an inc…
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I wrote some code with scipy.optimize curve_fit. It works perfectly on my computer:
Windows 7 Home Premium with Service Pack 1, 64bit Dell Studio 1558 Intel Core i3 cpu M330@2.13GHz 2.13GHz, 3.86 GB …