def makejobconfig(catalog):
logging.info('Seeding 1 job')
settings = systemsettings()
# Generate new set of params/coords
pdbfile, dcdfile = deshaw.getHistoricalTrajectory_prot(0)
traj = md.load(dcdfile, top=pdbfile, frame=0)
jcID, params = generateNewJC(traj)
dcdfreq = catalog.get('dcdfreq')
runtime = catalog.get('runtime')
sim_step_size = catalog.get('sim_step_size')
# Update Additional JC Params and Decision History, as needed
config = dict(params,
name = jcID,
runtime = runtime,
dcdfreq = dcdfreq,
interval = dcdfreq * sim_step_size,
temp = 310,
timestep = 0,
gc = 1,
origin = 'deshaw',
src_index = 0,
src_bin = (0, 0),
application = settings.name)
logging.info("New Simulation Job Created: %s", jcID)
for k, v in config.items():
logging.debug(" %s: %s", k, str(v))
catalog.rpush('jcqueue', jcID)
catalog.hmset(wrapKey('jc', jcID), config)
TBIN10 = ['%s%d'%(a,b) for a in ['W', 'T'] for b in range(5)]
# ['W0', 'W1', 'W2', 'W3', 'W4', 'T0', 'T1', 'T2', 'T3', 'T4']
REF =
afilt,hfilt = DE.FILTER['alpha'], DE.FILTER['heavy']
cent = np.load('../data/bpti-alpha-dist-centroid.npy')
topo = md.load(home+'/work/jc/serial2/de0_0/de0_0.pdb')
topoa = topo.atom_slice(topo.top.select_atom_indices('alpha'))
deL = DE.loadlabels_aslist()
bL = [deL[int(i/22.09)] for i in range(91116)]
traj, rmsd = [], []
for tnum in range(42):
p, d = DE.getHistoricalTrajectory_prot(tnum)
traj.append(md.load(d, top=p).atom_slice(afilt))
resrms = []
ccent = np.load('../data/bpti-alpha-cartesian-centroid.npy')
for tnum, tr in enumerate(detraj):
minlist = TS.TimeScape.read_log(home+'/work/timescape/desh_%02d_minima.log'%tnum)
minima = tr.slice(minlist)
minima.superpose(topoa)
for m in minima.xyz:
resrms.append(LA.norm(ref - m, axis=1))
ds = DR.distance_space(minima)
state = np.array([np.argmin(LA.norm(cent-i, axis=1)) for i in ds])
resrms = [np.zeros(58)]
def backProjection(self, index_list):
"""Perform back projection function for a list of indices. Return a list
of high dimensional points (one per index). Check cache for each point and
condolidate file I/O for all cache misses.
"""
logging.debug('-------- BACK PROJECTION: %d POINTS ---', len(index_list))
bench = microbench('bkproj', self.seqNumFromID())
# reverse_index = {index_list[i]: i for i in range(len(index_list))}
source_points = []
cache_miss = []
self.trajlist_async = deque()
# DEShaw topology is assumed here
bench.start()
# Derefernce indices to file, frame tuple:
historical_framelist = []
generated_framelist = []
if self.xidreference is None:
self.xidreference = self.catalog.lrange('xid:reference', 0, -1)
# pipe = self.catalog.pipeline()
logging.debug('Select Index List size = %d', len(index_list))
for idx in index_list:
# Negation indicates historical index:
index = int(idx)
if index < 0:
file_index, frame = deshaw.refFromIndex(-idx)
historical_framelist.append((file_index, frame))
# logging.debug('[BP] DEShaw: file #%d, frame#%d', file_index, frame)
else:
generated_framelist.append(self.xidreference[index])
# pipe.lindex('xid:reference', index)
# Load higher dim point indices from catalog
# logging.debug('Exectuting...')
# start = dt.datetime.now()
# generated_framelist = pipe.execute()
# logging.debug('...Exectuted in %4.1f sec', ((dt.datetime.now()-start).total_seconds()))
# start = dt.datetime.now()
# all_idx = self.catalog.lrange('xid:reference', 0, -1)
# logging.debug('Got ALL pts in %4.1f sec', ((dt.datetime.now()-start).total_seconds()))
bench.mark('BP:LD:Redis:xidlist')
ref = deshaw.topo_prot # Hard coded for now
# Group all Historical indidces by file number and add to frame Mask
logging.debug('Group By file idx (DEshaw)')
historical_frameMask = {}
for i, idx in enumerate(historical_framelist):
file_index, frame = idx
if file_index not in historical_frameMask:
historical_frameMask[file_index] = []
historical_frameMask[file_index].append(frame)
for k, v in historical_frameMask.items():
logging.debug('[BP] Deshaw lookups: %d, %s', k, str(v))
# Group all Generated indidces by file index
logging.debug('Group By file idx (Gen data)')
groupbyFileIdx = {}
for i, idx in enumerate(generated_framelist):
file_index, frame = eval(idx)
if file_index not in groupbyFileIdx:
groupbyFileIdx[file_index] = []
groupbyFileIdx[file_index].append(frame)
# Dereference File index to filenames
logging.debug('Deref fileidx -> file names')
generated_frameMask = {}
generated_filemap = {}
for file_index in groupbyFileIdx.keys():
filename = self.catalog.lindex('xid:filelist', file_index)
if filename is None:
logging.error('Error file not found in catalog: %s', filename)
else:
key = os.path.splitext(os.path.basename(filename))[0]
generated_frameMask[key] = groupbyFileIdx[file_index]
generated_filemap[key] = filename
bench.mark('BP:GroupBy:Files')
# Ensure the cache is alive an connected
logging.debug('Check Cache client')
self.cacheclient.connect()
# Check cache for historical data points
logging.debug('Checking cache for %d DEShaw points to back-project', len(historical_frameMask.keys()))
for fileno, frames in historical_frameMask.items():
# handle 1 frame case (to allow follow on multi-frame, mix cache hit/miss)
if len(frames) == 1:
datapt = self.cacheclient.get(fileno, frames[0], 'deshaw')
dataptlist = [datapt] if datapt is not None else None
else:
dataptlist = self.cacheclient.get_many(fileno, frames, 'deshaw')
if dataptlist is None:
self.cache_miss += 1
# logging.debug('[BP] Cache MISS on: %d', fileno)
cache_miss.append(('deshaw', fileno, frames))
else:
self.cache_hit += 1
# logging.debug('[BP] Cache HIT on: %d', fileno)
source_points.extend(dataptlist)
# Check cache for generated data points
logging.debug('Checking cache for %d Generated points to back-project', len(generated_frameMask.keys()))
for filename, frames in generated_frameMask.items():
# handle 1 frame case (to allow follow on multi-frame, mix cache hit/miss)
if len(frames) == 1:
datapt = self.cacheclient.get(filename, frames[0], 'sim')
dataptlist = [datapt] if datapt is not None else None
else:
dataptlist = self.cacheclient.get_many(filename, frames, 'sim')
if dataptlist is None:
self.cache_miss += 1
# logging.debug('[BP] Cache MISS on: %s', filename)
cache_miss.append(('sim', generated_filemap[filename], frames))
else:
self.cache_hit += 1
# logging.debug('[BP] Cache HIT on: %s', filename)
source_points.extend(dataptlist)
# Package all cached points into one trajectory
logging.debug('Cache hits: %d points.', len(source_points))
if len(source_points) > 0:
source_traj_cached = md.Trajectory(source_points, ref.top)
else:
source_traj_cached = None
# All files were cached. Return back-projected points
if len(cache_miss) == 0:
return source_traj_cached
# Add high-dim points to list of source points in a trajectory
# Optimized for parallel file loading
source_points_uncached = []
logging.debug('Sequentially Loading all trajectories')
for miss in cache_miss:
ftype, fileno, framelist = miss
if ftype == 'deshaw':
pdb, dcd = deshaw.getHistoricalTrajectory_prot(fileno)
traj = md.load(dcd, top=pdb)
elif ftype == 'sim':
traj = datareduce.load_trajectory(fileno)
selected_frames = traj.slice(framelist)
source_points_uncached.extend(selected_frames.xyz)
bench.mark('BP:LD:File')
logging.debug('All Uncached Data collected Total # points = %d', len(source_points_uncached))
source_traj_uncached = md.Trajectory(np.array(source_points_uncached), ref.top)
bench.mark('BP:Build:Traj')
# bench.show()
logging.info('-------- Back Projection Complete ---------------')
if source_traj_cached is None:
return source_traj_uncached
else:
return source_traj_cached.join(source_traj_uncached)
def seedJob_Uniform(catalog, num=1, exact=None):
"""
Seeds jobs into the JCQueue -- pulled from DEShaw
Selects equal `num` of randomly start frames from each bin
to seed as job candidates
"""
logging.info('Seeding %d jobs per transtion bin', num)
settings = systemsettings()
numLabels = int(catalog.get('numLabels'))
binlist = [(A, B) for A in range(numLabels) for B in range(numLabels)]
dcdfreq = int(catalog.get('dcdfreq'))
runtime = int(catalog.get('runtime'))
sim_step_size = int(catalog.get('sim_step_size'))
if catalog.exists('label:deshaw'):
rmslabel = [eval(x) for x in catalog.lrange('label:deshaw', 0, -1)]
elif os.path.exists(DESHAW_LABEL_FILE):
logging.info('Loading DEShaw Points From File....')
with open(DESHAW_LABEL_FILE) as lfile:
rmslabel = [eval(label) for label in lfile.read().strip().split('\n')]
logging.info('Loaded DEShaw %d Labels from file, %s', len(rmslabel), DESHAW_LABEL_FILE)
pipe = catalog.pipeline()
for rms in rmslabel:
pipe.rpush('label:deshaw', rms)
pipe.execute()
logging.info('DEShaw Labels stored in the catalog.')
else:
rmslabel = labelDEShaw_rmsd(store_to_disk=True)
pipe = catalog.pipeline()
for rms in rmslabel:
pipe.rpush('label:deshaw', rms)
pipe.execute()
logging.info('DEShaw Labels stored in the catalog.')
logging.info('Grouping all prelabeled Data:')
groupby = {b:[] for b in binlist}
for i, b in enumerate(rmslabel):
groupby[b].append(i)
for k in sorted(groupby.keys()):
v = groupby[k]
logging.info('%s %7d %4.1f', str(k), len(v), (100*len(v)/len(rmslabel)))
if exact is None:
source_list = sorted(groupby.keys())
else:
bin_list = list(groupby.keys())
if exact <= 25:
idx_list = np.random.choice(len(bin_list), exact, replace=False)
else:
idx_list = np.random.choice(len(bin_list), exact, replace=True)
source_list = [bin_list[i] for i in idx_list]
for binlabel in source_list:
clist = groupby[binlabel]
A, B = binlabel
# No candidates
if len(clist) == 0:
logging.info('NO Candidates for %s', str(binlabel))
if binlabel == (1, 3):
logging.info('Swapping (1,2) for (1,3)')
clist = groupby[(1,2)]
B = 2
elif binlabel == (3, 1):
logging.info('Swapping (3,0) for (3,1)')
clist = groupby[(3,0)]
B = 0
else:
logging.info('Not sampling this bin')
continue
for k in range(num):
logging.debug('\nSeeding Job #%d for bin (%d,%d) ', k, A, B)
index = np.random.choice(clist)
src, frame = deshaw.refFromIndex(index)
logging.debug(" Selected: BPTI %s, frame: %s", src, frame)
pdbfile, dcdfile = deshaw.getHistoricalTrajectory_prot(int(src))
traj = md.load(dcdfile, top=pdbfile, frame=int(frame))
# Generate new set of params/coords
jcID, params = generateNewJC(traj)
# Update Additional JC Params and Decision History, as needed
config = dict(params,
name = jcID,
runtime = runtime,
dcdfreq = dcdfreq,
interval = dcdfreq * sim_step_size,
temp = 310,
timestep = 0,
gc = 1,
origin = 'deshaw',
src_index = index,
src_bin = (A, B),
src_hcube = 'D',
application = settings.APPL_LABEL)
logging.info("New Simulation Job Created: %s", jcID)
for k, v in config.items():
logging.debug(" %s: %s", k, str(v))
catalog.rpush('jcqueue', jcID)
catalog.hmset(wrapKey('jc', jcID), config)
