def readoutresults(reslall, resnall, pickthese, DeltaOmegaParametersBoundaries,
savstatdir, namresults, conditions, k23max):
"""
reads global fitting results from file.
reslall: list of all residues possibly used for the results output, with a letter
in front indicating whether this is referring to the folded (A), random
coil (B) or dimer(C) peak.
resnall: same list as reslall, without the letter
pickthese: list pointing to the residues in reslall, resnall actually used
for the output
DeltaOmegaParametersBoundaries: boundaries for delta omegas, this is a dummy
variable in most case. format: [residue1, residue2, ...]; residue1=
[site A, site B, site C]; site A: [min, max boundary]
namresults: name of results file. typically has an err_ suffix when referring
to the error calculations based on a corresponding file without the suffix
results files have the .dill file extension.
DEL? conditions: list of fitting conditions, not important (dummy)
format will me explained later. subject to removal
First, a collection of Property Axes is triggered. Containing informations
about possible magnetic fields, temperatures, sites, concentrations,
B1 fields, experiment types, TROSY type, residues
Then a ParameterCollection object based on the params class, using the
PropertyAxesColl object, is generated.
All relaxation dispersion fitting data are loaded into a variety
of variables.
The resultcoll variable contains the actual results. costs are extracted.
The resulting parameters are transferred to the ParameterCollection object.
The ParameterCollection object is re-sorted in order of increasing fitting cost.
Important variables including a list of fitting conditions saved in the
results file are returned. Also resultcoll, cost list, the resorted
Parameter Collection and Property Axes Collection.
"""
PropAxesColl=mainfuncts.GeneratePropertyAxesCollection([x for y,x in \
enumerate(reslall) if y in pickthese],[x for y,x in enumerate(resnall)\
if y in pickthese])
ParameterColl=mainfuncts.parammake(PropAxesColl,0,[x for y,x in enumerate\
(DeltaOmegaParametersBoundaries) if y in pickthese],1,10000,100,900,1,k23max,0.005,0.5) #2 or 900
#DEL?#setparameters2=['combo10l.dat','C:\\Users\\Hans\\Desktop\\TRANSFER\\2020Feb\\',[[x for y,x in enumerate(reslall) if y in pickthese]],conditions,namresults]
#DEL?#namresults=namresults+str(resnall[0])+'_'+str(nn)+'_'
#A variety of pre-calculated data is loaded with this command. Not all of
#them are in use, so no need to understand every sirngle variable here.
poscoll,resnam,allsetcoll,resultcoll,relaxrat0,relaxrat,lookatratio,\
results,relaxrat1,relaxrat2,relaxrat_1,relaxrat_2,intdiffcorr, intcorr,\
intmin,ac,oc,rateconstpre,cond=hkio2.loadeverything(savstatdir,[namresults],0,decoupl=0)
costcoll = [i.cost for i in resultcoll]
#resultcoll=[resultcoll[np.argsort(costcoll)[0]]]
ParameterColl2 = mainfuncts.resc2param(PropAxesColl, ParameterColl,
resultcoll, 2)
ParameterColl2 = [ParameterColl2[i] for i in np.argsort(costcoll)]
costcoll = [costcoll[i] for i in np.argsort(costcoll)]
return PropAxesColl, ParameterColl2, costcoll, resultcoll, cond
#namresults='multix13_NEW_pfr_M2_err_'
#%%
namresultslist = [
'FINAL_stage3_indiv_', 'FINAL_stage3_23_indiv_',
'FINAL_stage3_indiv_restrkp_', 'FINAL_stage3_indiv_restrall_'
]
zx = []
for x in namresultslist:
z = []
for y in np.arange(15):
namresults = x + str(y) + '_0_'
#print namresults
poscoll,resnam,allsetcoll,resultcoll,relaxrat0,relaxrat,lookatratio,\
results,relaxrat1,relaxrat2,relaxrat_1,relaxrat_2,intdiffcorr, intcorr,\
intmin,ac,oc,rateconstpre,cond=hkio2.loadeverything(savstatdir,[namresults],0,decoupl=0)
z.append(np.round(resultcoll[0].cost, 2))
zx.append(z)
print np.average(z)
from matplotlib import pyplot as plt
reslall = [
'A29', 'A30', 'A31', 'A32', 'A33', 'A34', 'A52', 'A53', 'A54', 'A78',
'A81', 'A82', 'A83', 'A84', 'A86'
]
#fullnamlist=['Ser','Gly', 'Trp', 'Val', 'Trp', 'Asn', 'Gln', 'Phe', 'Phe', 'Val', 'Ile', 'Glu', 'Glu', 'Tyr', 'Thr', 'Gly', 'Pro', 'Asp', 'Pro', 'Val', 'Leu', 'Val', 'Gly', 'Arg', 'Leu', 'His', 'Ser', 'Asp', 'Ile', 'Asp', 'Ser', 'Gly', 'Asp', 'Gly', 'Asn', 'Ile', 'Lys', 'Tyr', 'Ile', 'Leu', 'Ser', 'Gly', 'Glu', 'Gly', 'Ala', 'Gly', 'Thr', 'Ile', 'Phe', 'Val', 'Ile', 'Asp', 'Asp', 'Lys', 'Ser', 'Gly', 'Asn', 'Ile', 'His', 'Ala', 'Thr', 'Lys', 'Thr', 'Leu', 'Asp', 'Arg', 'Glu', 'Glu', 'Arg', 'Ala', 'Gln', 'Tyr', 'Thr', 'Leu', 'Met', 'Ala', 'Gln', 'Ala', 'Val', 'Asp', 'Arg', 'Asp', 'Thr', 'Asn', 'Arg', 'Pro', 'Leu', 'Glu', 'Pro', 'Pro', 'Ser', 'Glu', 'Phe', 'Ile', 'Val', 'Lys', 'Val', 'Gln', 'Asp']
plt.rcParams.update({'font.size': 14})
lab = [
'Asp29', 'Ser30', 'Gly31', 'Asp32', 'Gly33', 'Asn34', 'Asp52', 'Lys53',
'Ser54', 'Val78', 'Asp81', 'Thr82', 'Asn83', 'Arg84', 'Leu86'
]
def produceindivplots(path2020, savstatdir, datafile, namresults, reslallx,
resnallx, parbdslistaxallfx, namout, mode, k121, k122,
k131, k132, k231, k232, errruns):
#print 'a'
for pickthis in np.arange(len(reslallx)):
# print 'b'
reslall = [reslallx[pickthis]]
resnall = [resnallx[pickthis]]
pickthese = [0]
# print 'c'
PropAxesColl = mainfuncts.GeneratePropertyAxesCollection(
[x for y, x in enumerate(reslall) if y in pickthese],
[x for y, x in enumerate(resnall) if y in pickthese])
parbdslistaxallf = [parbdslistaxallfx[pickthis]]
#paramsxx=mainfuncts.parammake(PropAxesColl,0,[x for y,x in enumerate(parbdslistaxallf) if y in pickthese],1,8000,100,900,k231,k232,0.005,0.1)
#paramsxx=mainfuncts.parammake(PropAxesColl,0,[x for y,x in enumerate(parbdslistaxallf) if y in pickthese],3000,4000,600,700,k231,k232,0.01,0.02)
paramsxx = mainfuncts.parammake(
PropAxesColl, 0,
[x for y, x in enumerate(parbdslistaxallf) if y in pickthese],
k121, k122, k131, k132, k231, k232, 0.01, 0.02)
##conditions=[0,[1,0,0,5,5]] #individual nitrogen fits
conditions = [0, [1, 0, 0, 10, 5]] #individualproton fits
#setparameters2=['combo10l.dat','C:\\Users\\Hans\\Desktop\\TRANSFER\\2020Feb\\',[[x for y,x in enumerate(reslall) if y in pickthese]],conditions,namresults]
if namresults == []:
ParameterColl6 = paramsxx
else:
poscoll, resnam, allsetcoll, resultcoll, relaxrat0, relaxrat, lookatratio, results, relaxrat1, relaxrat2, relaxrat_1, relaxrat_2, intdiffcorr, intcorr, intmin, ac, oc, rateconstpre, cond = hkio2.loadeverything(
savstatdir, [namresults], 0, decoupl=0)
costcoll = [i.cost for i in resultcoll]
resultcoll = [resultcoll[np.argsort(costcoll)[0]]]
ParameterColl6 = mainfuncts.resc2param(PropAxesColl, paramsxx,
resultcoll, mode)
for nn in [0]: #np.arange(3):
namresultsOUT = namout + str(nn) + '_'
setparameters3 = [
datafile, path2020,
[[x for y, x in enumerate(reslall) if y in pickthese]],
conditions, namresultsOUT, 0
]
hkfit2.parallelfit3(path2020, savstatdir, setparameters3, 0,
ParameterColl6[nn], PropAxesColl)
dataname = datafile
setparameters2 = [
dataname, path2020,
[x for y, x in enumerate(reslall) if y in pickthese],
conditions, namresultsOUT
] #,'A34' #'/home/hanskoss/data/Cadherin/nmrCad/procandcoll/TSnewsort/2020Feb/
selset = 0
ss = hkfit2.evaluaterdfit(path2020, savstatdir, PropAxesColl,
setparameters2, 1,
ParameterColl6[selset], 0)
hkio2.savss(savstatdir, ss, namresultsOUT)
for errc in np.arange(errruns):
setparameters3 = [
datafile, path2020,
[[x for y, x in enumerate(reslall) if y in pickthese]],
conditions, namresultsOUT + 'err_' + str(errc) + '_',
namresultsOUT
]
hkfit2.parallelfit3(path2020, savstatdir, setparameters3, 0,
ParameterColl6[nn], PropAxesColl)
setparameters3 = [
'combo10l.dat', path2020,
[[x for y, x in enumerate(reslall) if y in pickthese]], conditions,
'FINAL_stage4_23_', 'FINAL_stage3_23x_' + str(runnum)
]
p = multiprocessing.Process(target=hkfit2.parallelfit3,
args=(path2020, savstatdir, setparameters3,
outnum * numrep + runnum,
ParameterColl6[runnum],
PropAxesColl))