confidence_intervals Namespace Reference

This modules contains routines to find the theoretical distribution of power spectrum at each pixel, given the property of tapers used to generate it. More...

Classes
interface	gcf
interface	gser
interface	gammln
interface	gammp
interface	arth
Functions
real(dp)	PhiInv (p)
	Finds the point related to p percentage point.
subroutine	ReturnCI (nu, p, estpowspec, lowerr, higherr)
	Returns Confidence Intervals for the True Power Spectrum Using eqn. (255b) Percival & Walden.
subroutine	ReturnQ (nu, p, Q)
	Calculates the Q factor for p% points for a ChiSqr Distribution.
REAL(DP)	gammp_s (a, x)
REAL(DP), dimension(size(x)	gammp_v (a, x)
REAL(DP)	gammq_s (a, x)
REAL(DP), dimension(size(a)	gammq_v (a, x)
REAL(DP)	gcf_s (a, x, gln)
REAL(DP), dimension(size(a)	gcf_v (a, x, gln)
REAL(DP)	gser_s (a, x, gln)
REAL(DP), dimension(size(a)	gser_v (a, x, gln)
REAL(DP)	gammln_s (xx)
REAL(DP), dimension(size(xx)	gammln_v (xx)
REAL(SP), dimension(n)	arth_r (first, increment, n)
REAL(DP), dimension(n)	arth_d (first, increment, n)
INTEGER(I4B), dimension(n)	arth_i (first, increment, n)
Variables
INTEGER(I4B), parameter	NPAR_ARTH = 16
INTEGER(I4B), parameter	NPAR2_ARTH = 8
INTEGER	iarrgen = 0
integer(I4B), parameter	N = 801
real(dp), dimension(n)	x
real(dp), dimension(n)	percentage
logical	datafileread = .false.

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Detailed Description

This modules contains routines to find the theoretical distribution of power spectrum at each pixel, given the property of tapers used to generate it.

Author:

Sudeep Das and Amir Hajian, Princeton University

Version:

Time-stamp: <2008-06-17 15:50:26 sudeep>

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Function Documentation

REAL(DP),dimension(n) confidence_intervals::arth_d	(	REAL(DP),intent(in)	first,
		REAL(DP),intent(in)	increment,
		INTEGER(I4B),intent(in)	n
	)

INTEGER(I4B),dimension(n) confidence_intervals::arth_i	(	INTEGER(I4B),intent(in)	first,
		INTEGER(I4B),intent(in)	increment,
		INTEGER(I4B),intent(in)	n
	)

REAL(SP),dimension(n) confidence_intervals::arth_r	(	REAL(SP),intent(in)	first,
		REAL(SP),intent(in)	increment,
		INTEGER(I4B),intent(in)	n
	)

REAL(DP) confidence_intervals::gammln_s

(

REAL(DP),intent(in)

xx

)

REAL(DP),dimension(size(xx) confidence_intervals::gammln_v

(

REAL(DP),dimension(:),intent(in)

xx

)

REAL(DP) confidence_intervals::gammp_s	(	REAL(DP),intent(in)	a,
		REAL(DP),intent(in)	x
	)

REAL(DP),dimension(size(x) confidence_intervals::gammp_v	(	REAL(DP),dimension(:),intent(in)	a,
		REAL(DP),dimension(:),intent(in)	x
	)

REAL(DP) confidence_intervals::gammq_s	(	REAL(DP),intent(in)	a,
		REAL(DP),intent(in)	x
	)

REAL(DP),dimension(size(a) confidence_intervals::gammq_v	(	REAL(DP),dimension(:),intent(in)	a,
		REAL(DP),dimension(:),intent(in)	x
	)

REAL(DP) confidence_intervals::gcf_s	(	REAL(DP),intent(in)	a,
		REAL(DP),intent(in)	x,
		REAL(DP),intent(out),optional	gln
	)

REAL(DP),dimension(size(a) confidence_intervals::gcf_v	(	REAL(DP),dimension(:),intent(in)	a,
		REAL(DP),dimension(:),intent(in)	x,
		REAL(DP),dimension(:),intent(out),optional	gln
	)

REAL(DP) confidence_intervals::gser_s	(	REAL(DP),intent(in)	a,
		REAL(DP),intent(in)	x,
		REAL(DP),intent(out),optional	gln
	)

REAL(DP),dimension(size(a) confidence_intervals::gser_v	(	REAL(DP),dimension(:),intent(in)	a,
		REAL(DP),dimension(:),intent(in)	x,
		REAL(DP),dimension(:),intent(out),optional	gln
	)

real(dp) confidence_intervals::PhiInv

(

real(dp)

p

)

Finds the point related to p percentage point.

subroutine confidence_intervals::ReturnCI	(	real(dp)	nu,
		real(dp)	p,
		real(dp)	estpowspec,
		real(dp)	lowerr,
		real(dp)	higherr
	)

Returns Confidence Intervals for the True Power Spectrum Using eqn. (255b) Percival & Walden.

subroutine confidence_intervals::ReturnQ	(	real(dp)	nu,
		real(dp)	p,
		real(dp)	Q
	)

Calculates the Q factor for p% points for a ChiSqr Distribution.

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Variable Documentation

logical confidence_intervals::datafileread = .false.

INTEGER confidence_intervals::iarrgen = 0

integer(I4B),parameter confidence_intervals::N = 801

INTEGER(I4B),parameter confidence_intervals::NPAR2_ARTH = 8

INTEGER(I4B),parameter confidence_intervals::NPAR_ARTH = 16

real(dp),dimension(n) confidence_intervals::percentage

real(dp),dimension(n) confidence_intervals::x

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