Author: tlinnet
Date: Mon Mar 17 18:19:01 2014
New Revision: 22499
URL: http://svn.gna.org/viewcvs/relax?rev=22499&view=rev
Log:
Replaced how a global analysis average results from a previous run to instead
take the median.
This is to prevent averaging extreme outliers, and instead take the median of
the previous result.
This was discussed in:
https://mail.gna.org/public/relax-devel/2013-10/msg00009.html.
Modified:
trunk/specific_analyses/relax_disp/parameters.py
Modified: trunk/specific_analyses/relax_disp/parameters.py
URL:
http://svn.gna.org/viewcvs/relax/trunk/specific_analyses/relax_disp/parameters.py?rev=22499&r1=22498&r2=22499&view=diff
==============================================================================
--- trunk/specific_analyses/relax_disp/parameters.py (original)
+++ trunk/specific_analyses/relax_disp/parameters.py Mon Mar 17 18:19:01
2014
@@ -25,7 +25,7 @@
# Python module imports.
from copy import deepcopy
-from numpy import array, float64, identity, zeros
+from numpy import array, float64, identity, median, zeros
import sys
# relax module imports.
@@ -123,7 +123,8 @@
def copy(pipe_from=None, pipe_to=None):
- """Copy dispersion parameters from one data pipe to another, averaging
values for clusters.
+ """Copy dispersion parameters from one data pipe to another, taking the
median of previous values to a start value for clusters.
+ Taking the median prevent averaging extreme outliers.
@param pipe_from: The data pipe to copy the value from. This defaults
to the current data pipe.
@type pipe_from: str
@@ -161,23 +162,23 @@
for spin_ids in loop_cluster():
# Initialise some variables.
model = None
- pA = 0.0
- pB = 0.0
- pC = 0.0
- kex = 0.0
- kex_AB = 0.0
- kex_AC = 0.0
- kex_BC = 0.0
- k_AB = 0.0
- kB = 0.0
- kC = 0.0
- tex = 0.0
+ pA = [0.0]
+ pB = [0.0]
+ pC = [0.0]
+ kex = [0.0]
+ kex_AB = [0.0]
+ kex_AC = [0.0]
+ kex_BC = [0.0]
+ k_AB = [0.0]
+ kB = [0.0]
+ kC = [0.0]
+ tex = [0.0]
count = 0
spins_from = []
spins_to = []
selected_cluster = False
- # Loop over the spins, summing the parameters to be averaged.
+ # Loop over the spins, adding parameters to a list, which in the end
will be used to find the median.
for id in spin_ids:
# Get the spins, then store them.
spin_from = return_spin(id, pipe=pipe_from)
@@ -208,27 +209,27 @@
# Sum the source parameters.
if 'pA' in spin_from.params:
- pA += spin_from.pA
+ pA.append(spin_from.pA)
if 'pB' in spin_from.params:
- pB += spin_from.pB
+ pB.append(spin_from.pB)
if 'pC' in spin_from.params:
- pC += spin_from.pC
+ pC.append(spin_from.pC)
if 'kex' in spin_from.params:
- kex += spin_from.kex
+ kex.append(spin_from.kex)
if 'kex_AB' in spin_from.params:
- kex_AB += spin_from.kex_AB
+ kex_AB.append(spin_from.kex_AB)
if 'kex_AC' in spin_from.params:
- kex_AC += spin_from.kex_AC
+ kex_AC.append(spin_from.kex_AC)
if 'kex_BC' in spin_from.params:
- kex_BC += spin_from.kex_BC
+ kex_BC.append(spin_from.kex_BC)
if 'k_AB' in spin_from.params:
- k_AB += spin_from.k_AB
+ k_AB.append(spin_from.k_AB)
if 'kB' in spin_from.params:
- kB += spin_from.kB
+ kB.append(spin_from.kB)
if 'kC' in spin_from.params:
- kC += spin_from.kC
+ kC.append(spin_from.kC)
if 'tex' in spin_from.params:
- tex += spin_from.tex
+ tex.append(spin_from.tex)
# Increment the spin count.
count += 1
@@ -237,40 +238,40 @@
if not selected_cluster:
continue
- # Average parameters.
- if pA != 0.0:
- pA = pA / count
- print("Averaged pA value: %.15f" % pA)
- if pB != 0.0:
- pB = pB / count
- print("Averaged pA value: %.15f" % pA)
- if pC != 0.0:
- pC = pC / count
- print("Averaged pC value: %.15f" % pC)
- if kex != 0.0:
- kex = kex / count
- print("Averaged kex value: %.15f" % kex)
- if kex_AB != 0.0:
- kex_AB = kex_AB / count
- print("Averaged k_AB value: %.15f" % kex_AB)
- if kex_AC != 0.0:
- kex_AC = kex_AC / count
- print("Averaged k_AC value: %.15f" % kex_AC)
- if kex_BC != 0.0:
- kex_BC = kex_BC / count
- print("Averaged k_BC value: %.15f" % kex_BC)
- if k_AB != 0.0:
- k_AB = k_AB / count
- print("Averaged k_AB value: %.15f" % k_AB)
- if kB != 0.0:
- kB = kB / count
- print("Averaged kB value: %.15f" % kB)
- if kC != 0.0:
- kC = kC / count
- print("Averaged kC value: %.15f" % kC)
- if tex != 0.0:
- tex = tex / count
- print("Averaged tex value: %.15f" % tex)
+ # Take median of parameters.
+ if len(pA) > 1:
+ pA = [median(pA)]
+ print("Median pA value: %.15f" % pA[0])
+ if len(pB) > 1:
+ pB = [median(pB)]
+ print("Median pA value: %.15f" % pA[0])
+ if len(pC) > 1:
+ pC = [median(pC)]
+ print("Median pC value: %.15f" % pC[0])
+ if len(kex) > 1:
+ kex = [median(kex)]
+ print("Median kex value: %.15f" % kex[0])
+ if len(kex_AB) > 1:
+ kex_AB = [median(kex_AB)]
+ print("Median k_AB value: %.15f" % kex_AB[0])
+ if len(kex_AC) > 1:
+ kex_AC = [median(kex_AC)]
+ print("Median k_AC value: %.15f" % kex_AC[0])
+ if len(kex_BC) > 1:
+ kex_BC = [median(kex_BC)]
+ print("Median k_BC value: %.15f" % kex_BC[0])
+ if len(k_AB) > 1:
+ k_AB = [median(k_AB)]
+ print("Median k_AB value: %.15f" % k_AB[0])
+ if len(kB) > 1:
+ kB = [median(kB)]
+ print("Median kB value: %.15f" % kB[0])
+ if len(kC) > 1:
+ kC = [median(kC)]
+ print("Median kC value: %.15f" % kC[0])
+ if len(tex) > 1:
+ tex = [median(tex)]
+ print("Median tex value: %.15f" % tex[0])
# Loop over the spins, this time copying the parameters.
for i in range(len(spin_ids)):
@@ -290,30 +291,30 @@
if 'r2b' in spin_from.params:
spin_to.r2b = deepcopy(spin_from.r2b)
- # The averaged parameters.
+ # The median parameters.
if 'pB' in spin_from.params and 'pC' not in spin_from.params:
- spin_to.pA = pA
- spin_to.pB = pB
- spin_to.pC = 1.0 - pA - pB
+ spin_to.pA = pA[0]
+ spin_to.pB = pB[0]
+ spin_to.pC = 1.0 - pA[0] - pB[0]
elif 'pA' in spin_from.params:
- spin_to.pA = pA
- spin_to.pB = 1.0 - pA
+ spin_to.pA = pA[0]
+ spin_to.pB = 1.0 - pA[0]
if 'kex' in spin_from.params:
- spin_to.kex = kex
+ spin_to.kex = kex[0]
if 'kex_AB' in spin_from.params:
- spin_to.kex_AB = kex_AB
+ spin_to.kex_AB = kex_AB[0]
if 'kex_AC' in spin_from.params:
- spin_to.kex_AC = kex_AC
+ spin_to.kex_AC = kex_AC[0]
if 'kex_BC' in spin_from.params:
- spin_to.kex_BC = kex_BC
+ spin_to.kex_BC = kex_BC[0]
if 'k_AB' in spin_from.params:
- spin_to.k_AB = k_AB
+ spin_to.k_AB = k_AB[0]
if 'kB' in spin_from.params:
- spin_to.kB = kB
+ spin_to.kB = kB[0]
if 'kC' in spin_from.params:
- spin_to.kC = kC
+ spin_to.kC = kC[0]
if 'tex' in spin_from.params:
- spin_to.tex = tex
+ spin_to.tex = tex[0]
# All other spin specific parameters.
for param in spin_from.params:
r22499 - /trunk/specific_analyses/relax_disp/parameters.py