def backProjection(db, index_list):
"""Perform OFFLINE back projection function for a list of indices using
given DB. Return a list of high dimensional points (one per index).
Assumes NO CACHE or DESHAW.
"""
logging.debug('-------- BACK PROJECTION: %d POINTS ---', len(index_list))
# Derefernce indices to file, frame tuple:
pipe = db.pipeline()
for idx in index_list:
pipe.lindex('xid:reference', int(idx))
generated_framelist = pipe.execute()
# Group all Generated indidces by file index
groupbyFileIdx = {}
for i, idx in enumerate(generated_framelist):
try:
file_index, frame = eval(idx)
except TypeError as e:
print('Bad Index:', str(idx))
continue
if file_index not in groupbyFileIdx:
groupbyFileIdx[file_index] = []
groupbyFileIdx[file_index].append(frame)
# Dereference File index to filenames
generated_frameMask = {}
generated_filemap = {}
for file_index in groupbyFileIdx.keys():
filename = db.lindex('xid:filelist', file_index)
if filename is None:
logging.warning('Error file not found in catalog: %s', filename)
if not os.path.exists(filename):
logging.warning('DCD File not found: %s', filename)
else:
key = os.path.splitext(os.path.basename(filename))[0]
generated_frameMask[key] = groupbyFileIdx[file_index]
generated_filemap[key] = filename
# Add high-dim points to list of source points in a trajectory
# Optimized for parallel file loading
logging.debug('Sequentially Loading all trajectories')
source_points = []
for key, framelist in generated_frameMask.items():
traj = datareduce.load_trajectory(generated_filemap[key])
traj = datareduce.filter_alpha(traj)
selected_frames = traj.slice(framelist)
source_points.extend(selected_frames.xyz)
return np.array(source_points)
def backProjection(db, index_list):
"""Perform OFFLINE back projection function for a list of indices using
given DB. Return a list of high dimensional points (one per index).
Assumes NO CACHE or DESHAW.
"""
logging.debug('-------- BACK PROJECTION: %d POINTS ---', len(index_list))
# Derefernce indices to file, frame tuple:
pipe = db.pipeline()
for idx in index_list:
pipe.lindex('xid:reference', int(idx))
generated_framelist = pipe.execute()
# Group all Generated indidces by file index
groupbyFileIdx = {}
for i, idx in enumerate(generated_framelist):
try:
file_index, frame = eval(idx)
except TypeError as e:
print('Bad Index:', str(idx))
continue
if file_index not in groupbyFileIdx:
groupbyFileIdx[file_index] = []
groupbyFileIdx[file_index].append(frame)
# Dereference File index to filenames
generated_frameMask = {}
generated_filemap = {}
for file_index in groupbyFileIdx.keys():
filename = db.lindex('xid:filelist', file_index)
if filename is None:
logging.warning('Error file not found in catalog: %s', filename)
if not os.path.exists(filename):
logging.warning('DCD File not found: %s', filename)
else:
key = os.path.splitext(os.path.basename(filename))[0]
generated_frameMask[key] = groupbyFileIdx[file_index]
generated_filemap[key] = filename
# Add high-dim points to list of source points in a trajectory
# Optimized for parallel file loading
logging.debug('Sequentially Loading all trajectories')
source_points = []
for key, framelist in generated_frameMask.items():
traj = datareduce.load_trajectory(generated_filemap[key])
traj = datareduce.filter_alpha(traj)
selected_frames = traj.slice(framelist)
source_points.extend(selected_frames.xyz)
return np.array(source_points)
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 backProjection(r, 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))
# reverse_index = {index_list[i]: i for i in range(len(index_list))}
source_points = []
pipe = r.pipeline()
for idx in index_list:
# Negation indicates historical index:
index = int(idx)
if index < 0:
continue
else:
pipe.lindex('xid:reference', index)
# Load higher dim point indices from catalog
generated_framelist = [i for i in pipe.execute() if i is not None]
ref = deshaw.topo_prot # Hard coded for now
# Group all Generated indidces by file index
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
generated_frameMask = {}
generated_filemap = {}
for file_index in groupbyFileIdx.keys():
filename = r.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
# Check cache for generated data points
bplist = []
for filename, frames in generated_frameMask.items():
bplist.append(('sim', generated_filemap[filename], frames))
source_points = []
logging.debug('Sequentially Loading %d trajectories', len(bplist))
for ftype, fileno, framelist in bplist:
traj = datareduce.load_trajectory(fileno)
selected_frames = traj.slice(framelist)
source_points.extend(selected_frames.xyz)
logging.debug('All Uncached Data collected Total # points = %d', len(source_points))
source_traj = md.Trajectory(np.array(source_points), ref.top)
logging.info('-------- Back Projection Complete ---------------')
return source_traj
