def glue(self, nch=64, fs=100):
sortkeys = sorted(self.data.keys())
if len(sortkeys):
files = [self.data[sortkey]["filename"] for sortkey in sortkeys]
fulldata = get_raw_data(files[0])
lastvalue = fulldata[-64:]
for ind, _file in enumerate(files[1:]):
data = get_raw_data(_file)
bufferdata = np.tile(lastvalue, int(fs * self.buffer[ind]))
fulldata = np.concatenate((fulldata, bufferdata, data))
if len(sortkeys) == 1:
if fulldata.shape[0] / nch < 360000:
print("Adding some constant data at the end")
bufferdata = np.tile(lastvalue,
360000 - int(fulldata.shape[0] / nch))
fulldata = np.concatenate((fulldata, bufferdata))
### CHECK LENGTHS
print(self.totallen, " == ", fulldata.shape[0] / nch, "?")
# saving file
asksave = messagebox.askquestion(
"Saving glued data",
"Do you want to save glued data into file?",
icon='warning')
if asksave == 'yes':
savefile = filedialog.asksaveasfilename(
title="Save datafile as...",
defaultextension="",
initialdir=dirn(files[0]),
initialfile="GLUED" + base(files[0]))
write_data(savefile, fulldata)
def process_geojson(self,
input_path='./data/geolocation_sample.json',
output_path='./data/geolocation_sample.geojson'):
# format as geojson
# `coordinates` format as [longitude, latitude]
geo_mappings = self.process_json(input_path)
geojson = {
"type":
"FeatureCollection",
"features": [{
"type": "Feature",
"geometry": {
"type":
"Point",
"coordinates": [
float(d['coordinates'].split(',')[1]),
float(d['coordinates'].split(',')[0])
]
},
"properties": {
"name": d['host']
},
} for d in geo_mappings]
}
write_data(output_path, geojson)
def main():
starttime = datetime.datetime.now()
for i in range(1):
# --- Preparation ---
pdb_obj = PDB('./FAcD-FA-ASP.pdb', wk_dir='./FAcD_test'+str(i)) # Initiate with the file from protein data bank (self.path)
pdb_obj.rm_wat() # Remove water and ion in original crystal file (self.path)
pdb_obj.get_protonation() # For most crystal files, add hydrogens (self.path)
# --- Operation ---
# Mutation
Muta_tag = pdb_obj.Add_MutaFlag('r') # Generate a target "Flag" for mutation
if pdb_obj.MutaFlags[0][2] == '108': # Keep the key residue
continue
pdb_obj.PDB2PDBwLeap() # Deploy mutation (self.path)
# protonation modification
pdb_obj.rm_allH() # Remove all hydrogens after mutation (residues only) (self.path)
pdb_obj.get_protonation() # Determine protonation state again (self.path)
# use minimization to relax each mutated PDB
pdb_obj.PDB2FF() # Generate parameter files for MD simulation
pdb_obj.PDBMin(engine='Amber_pmemd_gpu') # Minimization (self.path)
# --- Sample with MD ---
pdb_obj.rm_wat() # Remove water from the minimization (self.path)
pdb_obj.PDB2FF(ifsavepdb=1) # Generate parameter files *savepdb save the exact structure use in MD for future analysis (self.path)
pdb_obj.PDBMD(tag=Muta_tag, engine='Amber_pmemd_gpu', equi_cpu=1) # Run MD (self.nc)
# sample
pdb_obj.nc2mdcrd(point=100) # Sample from trajactory (self.mdcrd)
# --- QM cluster ---
atom_mask = ':108,298' # Define QM cluster / can also use some presets: ligand; residues within a distance using a Layer object
g_route = '# hf/6-31G(d) pop=cm5' # QM keywords
pdb_obj.PDB2QMCluster(atom_mask, g_route=g_route, ifchk=1) # Run QM cluster calculation (self.qm_cluster_out, self.qm_cluster_chk)
pdb_obj.get_fchk(keep_chk=0) # Save fchk files for analysis (self.qm_cluster_fchk)
# --- Analysis ---
# targeting C-F bond
a1 = int(pdb_obj.stru.ligands[0].CH3)
a2 = int(pdb_obj.stru.ligands[0].F)
a1qm = pdb_obj.qm_cluster_map[str(a1)]
a2qm = pdb_obj.qm_cluster_map[str(a2)]
# Field Strength (MM)
E_atom_mask = ':1-107,109-297' # Define atoms for field strength calculation
Es = pdb_obj.get_field_strength(E_atom_mask, a1=a1 ,a2=a2 ,bond_p1='center') # Run Field Strength analysis
# Bond Dipole Moment (QM)
Dipoles = PDB.get_bond_dipole(pdb_obj.qm_cluster_fchk, a1qm, a2qm) # Run Bond Dipole Moment analysis
# Mutation distance
r1 = pdb_obj.stru.ligands[0]
r2 = pdb_obj.stru.chains[ord(pdb_obj.MutaFlags[0][1])-65][int(pdb_obj.MutaFlags[0][2])-1]
Dist = pdb_obj.stru.get_resi_dist(r1, r2)
# write to csv or plot
write_data(pdb_obj.MutaFlags, {'E': Es, 'Bond Dipole': Dipoles, 'Distance': Dist}, data_output_path) # Current data: Mutation - MD geometry - QM cluster wavefunction = Field strength at bond - Bond dipole moment
endtime = datetime.datetime.now()
print(endtime - starttime)
def extract_benchmarkportfolio(db, output):
# def qry_get_investment_name():
# return '''
# select InvestmentListID, InvestmentListName from tblInvestmentList
# where isActive=1 and parentID is null and
# (investmentListName like '%Lonsec (Traditional) SAA Benchmark%')
# '''
def qry_get_investment_name():
return '''
select InvestmentListID, InvestmentListName from tblInvestmentList
where isActive=1 and parentID is null and
(investmentListName like '%Lonsec (Traditional) SAA Benchmark%')
'''
def qry_get_investment_list_benchmark_weight_details(listid):
return '''
With T(InvestmentListID, InvestmentListName, RiskCategoryNo) as (
select InvestmentListID, InvestmentListName, RiskCategoryNo from tblInvestmentList
where investmentListid={listid}
union all
select il.InvestmentListID, il.InvestmentListName, il.RiskCategoryNo
from tblInvestmentList il
inner join T on il.parentID = T.investmentListID
)
select T.InvestmentListName, 'Risk Profile '+ convert(varchar, T.RiskCategoryNo) as Strategy
, ve.AlternativeCode as SecurityCode
, 'CASH' as Exchange
, ve.BenchmarkName
, ilb.[Weight], ilb.DateFrom
from T
left join tblInvestmentListBenchmark ilb on ilb.investmentListID = T.InvestmentListID and ilb.DateTo = '2079-06-06'
left join tblBenchmark ve on ilb.BenchmarkID = ve.BenchmarkID
order by T.investmentListID, T.RiskCategoryNo
'''.format(listid=listid)
def replace_with_parent(row, parent):
row[0] = parent
return row
rows = db.get_data(qry_get_investment_name())
header = ['Portfolio', 'Strategy', 'Security Code', 'Exchange', 'Security Name', 'Weight', 'Effective Date']
report_data = []
for row in rows:
listid, listname = row
logger.info('Processing {} - {}'.format(listname, listid))
data = db.get_data(qry_get_investment_list_benchmark_weight_details(listid))
data = [replace_with_parent(row, listname) for row in data if row.Strategy] # don't count first row
report_data += data
helper.write_data(output, report_data, header)
def process_json(self, output_path):
geo_mappings = []
for record in self.data['hits']['hits']:
try:
mapping = {}
geolocation = record['_source']['geolocation']
host = record['_source']['host']
mapping['host'] = host
mapping['coordinates'] = geolocation
geo_mappings.append(mapping)
except:
print("Oops!", sys.exc_info()[0], "occured.")
write_data(output_path, geo_mappings)
return geo_mappings
def main():
pdb_obj = PDB(glob('*_ff.pdb')[0])
pdb_obj.Add_MutaFlag('XXX')
pdb_obj.prmtop_path = glob('*prmtop')[0]
pdb_obj.prepi_path = {
'SAH': '../ligands/ligand_SAH.prepin',
'MET': '../ligands/ligand_MET.prepin'
}
pdb_obj.mdcrd = glob('./MD/*mdcrd')[0]
# --- QM cluster ---
atom_mask = ':217,218'
pdb_obj.get_stru()
sele_lines, pdb_obj.qm_cluster_map = pdb_obj.stru.get_sele_list(
atom_mask, fix_end='H', prepi_path=pdb_obj.prepi_path)
pdb_obj.qm_cluster_fchk = glob('./QMCluster/*fchk')
pdb_obj.qm_cluster_fchk.sort(
key=lambda file_name: int(file_name.split('.')[-2].split('_')[-1]))
# pdb_obj.get_fchk(keep_chk=0)
# --- Analysis ---
# targeting C-I bond
a1 = int(pdb_obj.stru.ligands[1].C1)
a2 = int(pdb_obj.stru.ligands[1].I1)
a1qm = pdb_obj.qm_cluster_map[str(a1)]
a2qm = pdb_obj.qm_cluster_map[str(a2)]
# Field Strength (MM)
E_atom_mask = ':1-216'
Es = pdb_obj.get_field_strength(E_atom_mask,
a1=a1,
a2=a2,
bond_p1='center')
# Bond Dipole Moment (QM)
print(a1qm, a2qm)
print(pdb_obj.qm_cluster_fchk)
Dipoles = PDB.get_bond_dipole(pdb_obj.qm_cluster_fchk, a1qm, a2qm)
# write to csv or plot
write_data(pdb_obj.MutaFlags, {
'E': Es,
'Bond Dipole': Dipoles
}, data_output_path)
tgt_results = []
pbar = ProgressBar()
for i in pbar(range(len(orig_data))):
en_point = i - batch_size
de_point = i + batch_size
if en_point < 0:
en_point = 0
if de_point > len(simi_src_data) - 1:
de_point = len(simi_src_data) - 1
max_bleu, bleu[0], bleu[1] = get_max_bleu(
orig_data[i], simi_src_data[en_point:de_point])
tgt_results.append(' '.join(
simi_tar_data[(en_point + bleu[0]):(en_point + bleu[1])]))
print_result(orig_data, tgt_results)
return tgt_results
helper.log_w("Reading data. Please wait...")
orig_data = helper.read_data(ORIG_FILE)
simi_src_data = helper.read_data(SIMI_FILE_SRC_LANG)
simi_tar_data = helper.read_data(SIMI_FILE_TAR_LANG)
helper.log_w("Compute similarity...")
out_tgt_data = similarize(orig_data, simi_src_data, simi_tar_data, 10)
helper.log_w("Writing new data...")
helper.write_data(DEST_FILE, out_tgt_data)
helper.log_w("Done.")
bleu[0], bleu[1] = get_max_bleu(orig_data[i], simi_src_data[en_point:de_point])
count= count + 1
if(bleu[0] > 0.45)
print(" ")
print("origin en : ",orig_data[i])
print("translate en : ",simi_src_data[en_point + bleu[1]])
print("target vi: ",simi_tar_data[en_point + bleu[1]])
print("cosine point: ",bleu[0])
print(" ")
tgt_results_vi.append(simi_tar_data[en_point + bleu[1]])
tgt_results_en.append(orig_data[i])
print("GOAL SETENCES",count)
return tgt_results_vi,tgt_results_en
helper.log_w("Reading data. Please wait...")
orig_data = helper.read_data(ORIG_FILE)
simi_src_data = helper.read_data(SIMI_FILE_SRC_LANG)
simi_tar_data = helper.read_data(SIMI_FILE_TAR_LANG)
helper.log_w("Compute similarity...")
out_tgt_data_vi,out_tgt_data_vi_en = similarize(orig_data, simi_src_data, simi_tar_data, 1500)
helper.log_w("Writing new data...")
helper.write_data(DEST_FILE_VI, out_tgt_data_vi)
helper.write_data(DEST_FILE_EN, out_tgt_data_vi_en)
helper.log_w("Done.")
import maxSum as ms
import helper as hp
for i in range(1,4):
print 'test '+str(i)+':'
matrix,m,n = hp.read_data('../data/test'+str(i)+'.txt')
print 'finish reading test data'
T = ms.maxSum(matrix,m,n)
print 'finish computing T'
a,b,score = ms.findMaxScore(T,m,n)
print 'finish finding max score'
stack = ms.backTrack(T,matrix,a,b)
print 'finish backTrack'
output = hp.write_data(stack,score,'../data/test'+str(i)+'grp12.txt')
print 'finish writing output'
def extract_modelportfolio(db, output):
# def qry_get_investment_name():
# return '''
# select InvestmentListID, InvestmentListName from tblInvestmentList
# where isActive=1 and parentID is null and
# (investmentListName like '%Aon%Model Portfolios'
# or investmentListName like '%ASET%Model Portfolio%'
# or investmentListName like '%Aylesbury%Portfolio%'
# or investmentListName like '%BFP%Phase%'
# or investmentListName like '%Camerons%Portfolios%'
# or investmentListName like '%FSS%Portfolios%'
# or investmentListName like '%BT Panorama%Portfolios%'
# or investmentListName like '%DAC objective%Portfolio%'
# or investmentListName like '%Lonsec Retirement%Portfolios%'
# or investmentListName like '%LFG Model Portfolios%'
# or investmentListName like '%AssetChoice Ess%Portfolios%'
# )
# '''
def qry_get_investment_name():
return '''
select InvestmentListID, InvestmentListName from tblInvestmentList
where isActive=1 and parentID is null and
(investmentListName like 'UniSuper%Portfolio%'
)
'''
def qry_get_investment_list_investment_weight_details(listid):
return '''
With T(InvestmentListID, InvestmentListName, RiskCategoryNo) as (
select InvestmentListID, InvestmentListName, RiskCategoryNo from tblInvestmentList
where investmentListid={listid}
union all
select il.InvestmentListID, il.InvestmentListName, il.RiskCategoryNo
from tblInvestmentList il
inner join T on il.parentID = T.investmentListID
)
select T.InvestmentListName, rc.RiskCategory as Strategy, coalesce(ve.stockCode, isff.ApirCode
, ic.investmentCode) as SecurityCode
, case
when ve.Exchange is not null then ve.Exchange
when isff.InstrumentID is not null then 'FND'
else 'CASH'
end
, coalesce(ve.stockName, isff.InvestmentName, 'CASH') as SecurityName
, ili.[Weight], ili.DateFrom, ili.InvestmentID
from T
left join tblRiskCategory rc on T.RiskCategoryNo = rc.RiskCategoryNo
left join tblInvestmentListInvestment ili on ili.investmentListID = T.InvestmentListID
and ili.DateTo = '2079-06-06'
left join vewEquities ve on ili.InvestmentID = ve.StockID
left join vewISF_Fund isff on isff.InvestmentID = ili.InvestmentID
left join tblInvestmentCode ic on ic.InvestmentID = ili.InvestmentID and ic.IsUsedForGrowthSeries=1
order by T.investmentListID, T.RiskCategoryNo
'''.format(listid=listid)
def replace_with_parent(row, parent):
row[0] = parent
return row
rows = db.get_data(qry_get_investment_name())
header = ['Portfolio', 'Strategy', 'Security Code', 'Exchange', 'Security Name', 'Weight', 'Effective Date', 'InvestmentID']
report_data = []
for row in rows:
listid, listname = row
logger.info('Processing {} - {}'.format(listname, listid))
data = db.get_data(qry_get_investment_list_investment_weight_details(listid))
logger.info(data)
data = [replace_with_parent(row, listname) for row in data[1:]] # don't count first row
report_data += data
helper.write_data(output, report_data, header)
