Statistics package for photometry.
Author:
N.J.G. Cross
Organization:
WFAU, IfA, University of Edinburgh
Requires:
Numpy, math, Scientific
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chiSq(mag,
rms,
model,
nParam)
This returns the reduced chiSquared and number of degrees of freedom. |
source code
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clippedHistogram(xx,
clip=0,
Niter=10,
imean=-999999500.0,
isd=-999999500.0)
Calculates mean and standard deviation of clipped distribution. |
source code
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tuple
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clippedMADHistogram(xx,
clip=0,
Niter=10,
imed=-999999500.0,
imad=-999999500.0,
retValues=' median,psd ' ,
minSd=None)
Calculates mean,median,standard deviation and median absolute
deviation of a clipped distribution. |
source code
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firstMomentDist(x,
y)
This calculates the mean value of x - sum(xy)/sum(y) |
source code
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numpy array
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float
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list
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listCounts(inputList,
minCount=None)
This returns the sorted unique values in a list of objects and the
number of times this value is listed. |
source code
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MAD(xx,
minSd=None)
Median Absolute Deviation |
source code
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polynomialModel(param,
magArray)
Uses numpy to calculate polynomial model |
source code
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numpy array
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probChisq(chiSqArray,
ndofArray,
nFinalPoints=10000,
maxValueY=5.0)
Calculates the probability that the REDUCED chi-squared of a model is
less than a particular value. |
source code
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numpy array
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probGauss(sigmaArray,
nFinalPoints=10000,
maxValueY=5.0)
Calculates the probability that the abs(sigma) of a standard normal
distribution is less than a particular value. |
source code
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Pearson(X,
Y)
Calculates the Pearson Correlation Coefficient. |
source code
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scaleError(diffXY,
dataS,
diffXYErr=[ 1.0] ,
rangeMin=None,
rangeMax=None)
This function divides the data into several bins and finds whether
there is a gradient in the data. |
source code
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clippedWeightedMean(xx,
sig,
clip,
Niter=5) |
source code
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float
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