import os
import json
import re
import subprocess
import nibabel
import shutil
import sys
import numpy

shome=os.path.expanduser('~nixUser')
sys.path.insert(1,shome+'/software/src/labkeyInterface')
import labkeyInterface
import labkeyDatabaseBrowser


fhome=os.path.expanduser('~')
fconfig=os.path.join(fhome,'.labkey','network.json')

net=labkeyInterface.labkeyInterface()
net.init(fconfig)
db=labkeyDatabaseBrowser.labkeyDB(net)


hi=0
project='iPNUMMretro/Study'
#project='Orthanc/Database'

labkeyBase='/data/labkey'
#tempBase=os.path.join(fhome,'temp')

#all images from database
imageDataset='Imaging1'
clinicalDataset='ClinicalData'
anonymousClinicalDataset='AnonymousClinicalData'
anonymousImagingDataset='AnonymousImaging'


ds=db.selectRows(project,'study',clinicalDataset,[])


fieldMatches={
        'SequenceNum':'SequenceNum',
        'datumRojstva':'birthDate',
        'DatumDiagnozeMetaM':'mmDiagnosisDate',
        'StarostObZacetkuIT':'itStartAge',
        'Spol':'sex',
        'Origo':'origo',
        'OrigoCode':'origoCode',
        'MStadij':'mStage',
        'MStadijCode':'mStageCode',
        'MetastatskeLokalizacije':'metastaticLocalization',
        'SteviloMetaLokalizacija':'metastaticLocalizationCount',
        'Genetika':'mutations',
        'DolocitevMut':'mutationDetermination',
        'PSObUvedbiIT':'psAtITIntroduction',
        'PridruzeneKronicneBolezni':'assocatedCronicDiseases',
        'PricetekIT':'itStart',
        'ZakljucekIT':'itEnd',
        'VzrokPrenehanje':'itEndCause',
        'StAplikacij':'applicationCount',
        'NajboljsiOdgovor':'bora',
        'DatumPD_PFS':'DatumPD_PFS',
        'VitalnoStanje':'vitalState',
        'DatumSmrti':'deathDate',
        'KozniIzpuscaj':'skinRash',
        'Vitiligo':'vitiligo',
        'Puritus':'puritus',
        'Hipotiroza':'hypotirosis',
        'Osteoartritis':'osteoartritis',
        'Diareja':'diarrhea',
        'Pnevmonitis':'pneumonitis',
        'Hepatitis':'hepatitis',
        'Fatigue':'fatigue',
        'DrugaTox':'otherTox',
        'LDH':'ldh',
        'S100':'s100',
        'KSSistemsko':'sistemicKS',
        'KSvzrokNiPrejemal':'ksNotAdministeredCause',
        'PETopazovanNU':'petMonitoredAE',
        'PETObelezilNU':'petDetectedAE',
        'PETPredSimZnaki':'earlyPETAEDetection',
        'UkrepanjeZaradiPET':'petRelatedAction',
        'DatumIzhodiscnegaPETPredIT':'initialPETDate',
        'noPETCT':'petCTCount',
        'DatumPrvegaPETZNU':'firstAEPETCTDate',
        'DatumPrvegaSimOzLab':'aeIdentificationDate',
        'DodatnePreiskavePolegPET':'aeAdditionalExams',
        'IzidNU':'aeOutcome'
        }



#randomize patientIDs
patientList=[row['PatientId'] for row in ds['rows']]
patientList=list(set(patientList))

patientCodes={}
perm=numpy.random.permutation(len(patientList))
for i in numpy.arange(len(perm)):
    code='A{:03d}'.format(perm[i])
    patientCodes[patientList[i]]=code


#anonymize clinical data
for row in ds['rows']:
    outRow={}
    for f in fieldMatches:
        outRow[fieldMatches[f]]=row[f]
    #mask patientId
    outRow['PatientId']=patientCodes[row['PatientId']]

    db.modifyRows('insert',project,'study',anonymousClinicalDataset,[outRow])

#anonymize image data

ds=db.selectRows(project,'study',imageDataset,[])
fields=['SequenceNum','studyDate']

#for links
projectAnonymousBase=os.path.join(labkeyBase,'files',project,'@files/anonymous')

for row in ds['rows']:
    outRow={}
    for f in fields:
        outRow[f]=row[f]
    outRow['PatientId']=patientCodes[row['PatientId']]

    #copy links

    for f in ['CT','PETWB']:
        #idealy we should use series uuid from dicom, this is a cludge
        anonSeriesId='{}_{}_{:03.0f}'.\
                format(f,outRow['PatientId'],row['SequenceNum'])
        origFile=os.path.join(projectAnonymousBase,row[f]+'.zip')
        modFile=os.path.join(projectAnonymousBase,anonSeriesId+'.zip')
        subprocess.run(['cp','-d',origFile,modFile])
        outRow[f+'_UUID']=anonSeriesId
        outRow[f]='[DICOM]'

    db.modifyRows('insert',project,'study',anonymousImagingDataset,[outRow])

print('Done')