def get_items(self, snls=None, snlgroups=None, ncols=None):
"""iterator over same-composition groups of SNLGroups rev-sorted by size
:param snls: 'snl' collection in 'snl_mp_prod' DB
:type snls: QueryEngine
:param snlgroups: 'snlgroups' collection in 'snl_mp_prod' DB
:type snlgroups: QueryEngine
:param ncols: number of columns for 2D plotly
:type ncols: int
"""
self._matcher = StructureMatcher(
ltol=0.2, stol=0.3, angle_tol=5, primitive_cell=True, scale=True,
attempt_supercell=False, comparator=ElementComparator()
)
self._lock = self._mgr.Lock() if not self._seq else None
self._ncols = ncols if not self._seq else 1
self._nrows = div_plus_mod(self._ncores, self._ncols) if not self._seq else 1
self._counter = self.shared_list()
self._counter.extend([[0]*self._ncols for i in range(self._nrows)])
self._counter_total = multiprocessing.Value('d', 0)
self._mismatch_dict = self.shared_dict()
self._mismatch_dict.update(dict((k,[]) for k in categories[self.checker_name]))
self._mismatch_counter = self.shared_list()
self._mismatch_counter.extend([0]*len(self._mismatch_dict.keys()))
if py is not None:
self._streams = [ py.Stream(stream_id) for stream_id in stream_ids ]
for s in self._streams: s.open()
self._snls = snls
self._snlgroups = snlgroups
if 'SNLGroup' in self.checker_name:
_log.info('analyzing %d SNLGroups', self._snlgroups.collection.count())
# start pipeline to prepare aggregation of items
pipeline = [{ '$project': {
'reduced_cell_formula_abc': 1, 'snlgroup_id': 1, '_id': 0
}}]
group_expression = {
'_id': '$reduced_cell_formula_abc',
'num_snlgroups': { '$sum': 1 },
'snlgroup_ids': { '$addToSet': "$snlgroup_id" }
}
pipeline.append({ '$group': group_expression })
pipeline.append({ '$match': { 'num_snlgroups': { '$gt': 1 } } })
pipeline.append({ '$sort': { 'num_snlgroups': -1 } })
pipeline.append({ '$project': { 'snlgroup_ids': 1 } })
return self._snlgroups.collection.aggregate(pipeline, cursor={})
else:
_log.info('analyzing %d SNLs', snls.collection.count())
return self._snls.query(distinct_key='snl_id')
def get_items(self, snls=None, snlgroups=None, ncols=None):
"""iterator over same-composition groups of SNLGroups rev-sorted by size
:param snls: 'snl' collection in 'snl_mp_prod' DB
:type snls: QueryEngine
:param snlgroups: 'snlgroups' collection in 'snl_mp_prod' DB
:type snlgroups: QueryEngine
:param ncols: number of columns for 2D plotly
:type ncols: int
"""
self._matcher = StructureMatcher(ltol=0.2,
stol=0.3,
angle_tol=5,
primitive_cell=True,
scale=True,
attempt_supercell=False,
comparator=ElementComparator())
self._lock = self._mgr.Lock() if not self._seq else None
self._ncols = ncols if not self._seq else 1
self._nrows = div_plus_mod(self._ncores,
self._ncols) if not self._seq else 1
self._counter = self.shared_list()
self._counter.extend([[0] * self._ncols for i in range(self._nrows)])
self._counter_total = multiprocessing.Value('d', 0)
self._mismatch_dict = self.shared_dict()
self._mismatch_dict.update(
dict((k, []) for k in categories[self.checker_name]))
self._mismatch_counter = self.shared_list()
self._mismatch_counter.extend([0] * len(self._mismatch_dict.keys()))
if py is not None:
self._streams = [py.Stream(stream_id) for stream_id in stream_ids]
for s in self._streams:
s.open()
self._snls = snls
self._snlgroups = snlgroups
if 'SNLGroup' in self.checker_name:
_log.info('analyzing %d SNLGroups',
self._snlgroups.collection.count())
# start pipeline to prepare aggregation of items
pipeline = [{
'$project': {
'reduced_cell_formula_abc': 1,
'snlgroup_id': 1,
'_id': 0
}
}]
group_expression = {
'_id': '$reduced_cell_formula_abc',
'num_snlgroups': {
'$sum': 1
},
'snlgroup_ids': {
'$addToSet': "$snlgroup_id"
}
}
pipeline.append({'$group': group_expression})
pipeline.append({'$match': {'num_snlgroups': {'$gt': 1}}})
pipeline.append({'$sort': {'num_snlgroups': -1}})
pipeline.append({'$project': {'snlgroup_ids': 1}})
return self._snlgroups.collection.aggregate(pipeline, cursor={})
else:
_log.info('analyzing %d SNLs', snls.collection.count())
return self._snls.query(distinct_key='snl_id')
matcher = StructureMatcher(ltol=0.2,
stol=0.3,
angle_tol=5,
primitive_cell=True,
scale=True,
attempt_supercell=False,
comparator=ElementComparator())
num_ids_per_stream = 20000
num_ids_per_stream_k = num_ids_per_stream / 1000
num_snls = sma.snl.count()
num_snlgroups = sma.snlgroups.count()
num_pairs_per_job = 1000 * num_ids_per_stream
num_pairs_max = num_snlgroups * (num_snlgroups - 1) / 2
num_snl_streams = div_plus_mod(num_snls, num_ids_per_stream)
num_snlgroup_streams = div_plus_mod(num_snlgroups, num_ids_per_stream)
num_jobs = div_plus_mod(num_pairs_max, num_pairs_per_job)
print num_snl_streams, num_snlgroup_streams, num_jobs
checks = ['spacegroups', 'groupmembers', 'canonicals']
categories = ['SG Change', 'SG Default', 'PybTeX', 'Others']
num_categories = len(categories)
category_colors = ['red', 'blue', 'green', 'orange']
def _get_filename(day=True):
filename = 'snl_group_check_'
filename += datetime.datetime.now().strftime(
'%Y-%m-%d') if day else 'stream'
return filename
min_sleep = 0.052
sma = SNLMongoAdapter.auto_load()
matcher = StructureMatcher(
ltol=0.2, stol=0.3, angle_tol=5, primitive_cell=True, scale=True,
attempt_supercell=False, comparator=ElementComparator()
)
num_ids_per_stream = 20000
num_ids_per_stream_k = num_ids_per_stream/1000
num_snls = sma.snl.count()
num_snlgroups = sma.snlgroups.count()
num_pairs_per_job = 1000 * num_ids_per_stream
num_pairs_max = num_snlgroups*(num_snlgroups-1)/2
num_snl_streams = div_plus_mod(num_snls, num_ids_per_stream)
num_snlgroup_streams = div_plus_mod(num_snlgroups, num_ids_per_stream)
num_jobs = div_plus_mod(num_pairs_max, num_pairs_per_job)
print num_snl_streams, num_snlgroup_streams, num_jobs
checks = ['spacegroups', 'groupmembers', 'canonicals']
categories = [ 'SG Change', 'SG Default', 'PybTeX', 'Others' ]
num_categories = len(categories)
category_colors = ['red', 'blue', 'green', 'orange']
def _get_filename(day=True):
filename = 'snl_group_check_'
filename += datetime.datetime.now().strftime('%Y-%m-%d') if day else 'stream'
return filename
def _get_shades_of_gray(num_colors):
