def calculateFits(self): #{{{
"""
Pulls the fit values saved along with the data set and draws the fit line.
"""
dataSet = list(
self.collection.find({'expName': self.currentListSelection}))[0]
# t1
t1Data = dtb.dictToNdData('t1PowerODNP', dataSet)
t1Data.sort('power')
t1fits = dataSet.get('data').get('t1PowerODNP').get('fitList')
powerArray = pys.r_[t1Data.getaxis('power').min():t1Data.
getaxis('power').max():100j]
t1Fit = pys.nddata(t1fits[0] + t1fits[1] * powerArray).rename(
'value', 'power').labels('power', powerArray)
# kSigma
t1Fit.other_info = {'fitValues': t1fits}
kSigmaData = dtb.dictToNdData('kSigmaODNP', dataSet, retValue=False)
powerArray = pys.r_[kSigmaData.getaxis('power').min():kSigmaData.
getaxis('power').max():100j]
ksFits = dataSet.get('data').get('kSigmaODNP').get('fitList')
kSigmaFit = pys.nddata(ksFits[0] / (ksFits[1] + powerArray) *
powerArray).rename('value', 'power').labels(
'power', powerArray)
try:
eprData = dtb.dictToNdData('cwEPR', dataSet)
except:
eprData = False
dataToPlot = {
'kSigma': kSigmaData,
'kSigmaFit': kSigmaFit,
't1Set': t1Data,
't1SetFit': t1Fit,
'cwEPR': eprData
}
self.dataDict.update({self.currentListSelection: dataToPlot})
def calculateFits(self):#{{{
""" Calculate the fits to both the T1 set and the kSigma set
"""
dataSet = list(self.collection.find({'expName':self.currentListSelection}))[0]
t1Data = dtb.dictToNdData('t1Power',dataSet)
t1Data.sort('power')
# weighted fit as function of power
out = minimize(residualLinear, params, args=(t1Data.getaxis('power'), t1Data.runcopy(pys.real).data, t1Data.get_error()))
t1Fit = pys.nddata(analyticLinear(out.params,t1Data.getaxis('power'))).rename('value','power').labels('power',t1Data.getaxis('power'))
kSigmaData = dtb.dictToNdData('kSigma',dataSet,retValue = False)
kSigmaData = nmrfit.ksp(kSigmaData)
kSigmaData.fit()
self.dataDict.update({self.currentListSelection:{'kSigma':kSigmaData,'t1Set':t1Data,'t1SetFit':t1Fit}})
figure()
colorlist = ['r', 'g', 'b', 'm', 'c']
for count, dictionary in enumerate(searchDict):
listOfSets = list(
collection.find(dictionary)
) # This will give me a list of dictionaries that satisfy the searchDict constraints.
# An example of pulling the data from a dMTSL concentration series.
dataList = [] # a list for the nddata sets
concentration = [
] # a list for the associated concentration, from metadata
concentrationError = [
] # a list for the associated concentration error, from metadata
for dataSet in listOfSets:
data = dtb.dictToNdData(
dataTag, dataSet
) # return an nddata for the given entry in the list of database entries
concEntry = data.other_info.get('concentrationMeasured').split(' +/- ')
concentration.append(float(concEntry[0]))
concentrationError.append(float(concEntry[1].split(' ')[0]))
dataList.append(data.mean())
# Throw list of data into nddata set along dimension conc
data = concat(dataList, 'conc')
data.labels('conc',
array(concentration)).set_error('conc',
array(concentrationError))
# Sort and fit the data and make a new nddata set with the fit
data.sort('conc')
out = minimize(residualLinear,
params,
args=(data.getaxis('conc'), data.runcopy(real).data,
'setType': 'dnpExp'
} #,{'spinLabel':'MTSL','osmolyte':'urea','osmolyteConcentration':'5M','macroMolecule':'CheY','repeat':'0'}]
dataTag = 'kSigma' # This defines what data I pull from the database#}}}
# Pull the kSigma data from the data base
listOfSets = list(
collection.find(searchDict)
) # this gives me a list of dictionaries from the collection that satisfy the constraints imposed by my search dictionary.
kSigmaList = []
kSigmaListError = []
concentrationList = [
] # will hold list of nddata, data = concentration, error = concentration errr
siteList = [] # dim0 for both kSigmaList and concentrationList
for dataSet in listOfSets:
data = dtb.dictToNdData(
dataTag, dataSet, retValue=True
) # setting retValue to True gives me the fit value instead of the power data. Note this only works for kSigma and nothing else for now.
kSigmaList.append(data.data)
kSigmaListError.append(data.get_error())
siteList.append(float(data.other_info.get('spinLabelSite')[1:-1]))
# Make an nddata with dim0 defined by the site.
kSigma = nddata(array(kSigmaList)).rename('value', 'site').labels(
'site', array(siteList)).set_error(array(kSigmaListError))
kSigma.sort('site')
figure()
plot(kSigma, 'ro')
figure()
t1zpL = []
siteList = []
# Lets pull in a collection from the database
MONGODB_URI = 'mongodb://*****:*****@ds047040.mongolab.com:47040/magresdata' # This is the address to the database hosted at MongoLab.com
# Make the connection to the server as client
conn = pymongo.MongoClient(MONGODB_URI) # Connect to the database that I purchased
db = conn.magresdata
collection = db.hanLabODNPTest # This is my test collection
searchDict = {'setType':'seriesData','macroMolecule':'CheY','bindingPartner':'None','osmolyte':'None'}
dispersionList = []
listOfSets = list(collection.find(searchDict))
dataTag = 'tau'
fig = figure(figsize=(15,8))
for count,dataSet in enumerate(listOfSets[0:1]):
tauNS = dtb.dictToNdData(dataTag,dataSet) # setting retValue to True gives me the fit value instead of the power data. Note this only works for kSigma and nothing else for now.
tauNS.sort('site')
plot(tauNS*1e12,'--.',alpha = 0.6,markersize=20,label='native state')
dispersionList.append(float(std(tauNS.data))/float(average(tauNS.data)))
searchDict = {'setType':'seriesData','macroMolecule':'CheY','bindingPartner':'None','osmolyte':'urea','osmolyteConcentration':'5M'}
fig = figure(figsize=(15,8))
listOfSets = list(collection.find(searchDict))
dataTag = 'tau'
for count,dataSet in enumerate(listOfSets):
tau5MUNS = dtb.dictToNdData(dataTag,dataSet) # setting retValue to True gives me the fit value instead of the power data. Note this only works for kSigma and nothing else for now.
tau5MUNS.sort('site')
plot(tau5MUNS*1e12,'--.',alpha = 0.6,markersize=20,label='native 5M Urea')
dispersionList.append(float(std(tau5MUNS.data))/float(average(tau5MUNS.data)))
import matlablike as pys
pys.close('all')
MONGODB_URI = 'mongodb://*****:*****@ds047040.mongolab.com:47040/magresdata'
#self.conn = pymongo.MongoClient(self.MONGODB_URI) # Connect to the database that I purchased
#db = self.conn.magresdata # 'dynamicalTransition' is the name of my test database
#self.collection = db.hanLabODNPTest # This is my test collection
conn = pymongo.MongoClient('localhost',
27017) # Connect to the database that I purchased
db = conn.homeDB # 'dynamicalTransition' is the name of my test database
collection = db.localDataRevisedDataLayout # This is my test collection#}}}
dataSet = list(collection.find({'expName':
'150529_CheYPep_N62C_5MUrea_ODNP'}))[0]
epr = dtb.dictToNdData('cwEPR', dataSet)
t1Data = dtb.dictToNdData('t1PowerODNP', dataSet)
t1Data.sort('power')
t1fits = dataSet.get('data').get('t1PowerODNP').get('fitList')
powerArray = pys.r_[t1Data.getaxis('power').min():t1Data.getaxis('power').max(
):100j]
t1Fit = pys.nddata(t1fits[0] + t1fits[1] * powerArray).rename(
'value', 'power').labels('power', powerArray)
pys.figure()
pys.plot(t1Data)
pys.plot(t1Fit)
kSigmaData = dtb.dictToNdData('kSigmaODNP', dataSet, retValue=False)
powerArray = pys.r_[kSigmaData.getaxis('power').min():kSigmaData.
import pymongo
import matlablike as pys
import nmrfit
pys.close('all')
MONGODB_URI = 'mongodb://*****:*****@ds047040.mongolab.com:47040/magresdata' # This is the address to the database hosted at MongoLab.com
# Make the connection to the server as client
conn = pymongo.MongoClient(
MONGODB_URI) # Connect to the database that I purchased
db = conn.magresdata
collection = db.hanLabODNPTest # This is my test collection
searchDict = {
'spinLabel': 'MTSL',
'osmolyte': 'None',
'osmolyteConcentration': 'None',
'macroMolecule': 'CheY',
'bindingPartner': 'P2'
} #,'repeat':'0'}#,{'spinLabel':'MTSL','osmolyte':'urea','osmolyteConcentration':'5M','macroMolecule':'CheY','repeat':'0'}]
listOfSets = list(
collection.find(searchDict)
) # this gives me a list of dictionaries from the collection that satisfy the constraints imposed by my search dictionary.
dataTag = 'kSigma' # This defines what data I pull from the database
data = dtb.dictToNdData(dataTag, listOfSets[0], retValue=False)
data = nmrfit.ksp(data)
data.fit()
pys.plot(data)
pys.plot(data.eval(100))
pys.show()