def tortoise_from_scratch():
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Building all matrices.")
schedule_workers(lambda x: force_create_matrix(x, force=True), cluster_sets)
empty_tortoise_results_table()
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Starting disambiguation.")
schedule_workers(wedge, cluster_sets)
def tortoise_from_scratch():
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Building all matrices.")
schedule_workers(lambda x: force_create_matrix(x, force=True), cluster_sets)
empty_tortoise_results_table()
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Starting disambiguation.")
schedule_workers(wedge, cluster_sets)
def tortoise_from_scratch():
logger.log("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
logger.log("Building all matrices.")
cluster_sets = [(s, ) for s in cluster_sets]
schedule_workers(lambda x: force_create_matrix(x, force=True),
cluster_sets)
empty_tortoise_results_table()
logger.log("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
cluster_sets = [(s(), ) for s in cluster_sets]
logger.log("Starting disambiguation.")
schedule_workers(wedge_and_store, cluster_sets)
def tortoise_from_scratch():
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Building all matrices.")
exit_statuses = schedule_create_matrix(cluster_sets, sizes, force=True)
assert len(exit_statuses) == len(cluster_sets)
assert all(stat == os.EX_OK for stat in exit_statuses)
empty_results_table()
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Starting disambiguation.")
exit_statuses = schedule_wedge_and_store(cluster_sets, sizes)
assert len(exit_statuses) == len(cluster_sets)
assert all(stat == os.EX_OK for stat in exit_statuses)
def tortoise_from_scratch():
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Building all matrices.")
exit_statuses = schedule_create_matrix(cluster_sets, sizes, force=True)
assert len(exit_statuses) == len(cluster_sets)
assert all(stat == os.EX_OK for stat in exit_statuses)
empty_results_table()
bibauthor_print("Preparing cluster sets.")
cluster_sets, _lnames, sizes = delayed_cluster_sets_from_marktables()
bibauthor_print("Starting disambiguation.")
exit_statuses = schedule_wedge_and_store(cluster_sets, sizes)
assert len(exit_statuses) == len(cluster_sets)
assert all(stat == os.EX_OK for stat in exit_statuses)
def tortoise_last_name(name, wedge_threshold=None, from_mark=True, pure=False):
logger.log('Start working on %s' % name)
assert not (from_mark and pure)
lname = generate_last_name_cluster_str(name)
if from_mark:
logger.log(' ... from mark!')
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
logger.log(' ... delayed done')
else:
logger.log(' ... from pid, pure=%s' % str(pure))
clusters, lnames, sizes = delayed_cluster_sets_from_personid(pure)
logger.log(' ... delayed pure done!')
try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
cluster_set = cluster()
logger.log("Found, %s(%s). Total number of bibs: %d." %
(name, lname, size))
create_matrix(cluster_set, False)
wedge_and_store(cluster_set)
except (IndexError, ValueError):
logger.log("Sorry, %s not found in the last name clusters" % (lname))
def _create_matrix(lname):
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, True)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
bibauthor_print("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d"
% (cluster_set.last_name, bibs, expected))
cluster_set.store()
def _create_matrix(lname):
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, True)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
bibauthor_print("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d" %
(cluster_set.last_name, bibs, expected))
cluster_set.store()
def _create_matrix(lname):
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, False)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
bibauthor_print("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d"
% (cluster_set.last_name, bibs, expected))
cluster_set.store()
except (IndexError, ValueError):
bibauthor_print("Sorry, %s not found in the last name clusters, not creating matrix" % (lname))
def _create_matrix(lname):
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, False)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
bibauthor_print("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d"
% (cluster_set.last_name, bibs, expected))
cluster_set.store()
except (IndexError, ValueError):
bibauthor_print("Sorry, %s not found in the last name clusters, not creating matrix" % (lname))
def _collect_statistics_lname_coeff(params):
lname = params[0]
coeff = params[1]
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, False)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
bibauthor_print("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d"
% (cluster_set.last_name, bibs, expected))
wedge(cluster_set, True, coeff)
remove_result_cluster(cluster_set.last_name)
def _collect_statistics_lname_coeff(params):
lname = params[0]
coeff = params[1]
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, False)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
bibauthor_print("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d" %
(cluster_set.last_name, bibs, expected))
wedge(cluster_set, True, coeff)
remove_result_cluster(cluster_set.last_name)
def tortoise_last_name(name, from_mark=False, pure=False):
assert not (from_mark and pure)
lname = generate_last_name_cluster_str(name)
if from_mark:
clusters, lnames, sizes = delayed_cluster_sets_from_marktables()
else:
clusters, lnames, sizes = delayed_cluster_sets_from_personid(pure)
try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
bibauthor_print("Found, %s(%s). Total number of bibs: %d." % (name, lname, size))
cluster_set = cluster()
create_matrix(cluster_set, True)
wedge_and_store(cluster_set)
except IndexError:
bibauthor_print("Sorry, %s(%s) not found in the last name clusters" % (name, lname))
def tortoise_last_name(name, from_mark=False, pure=False):
bibauthor_print('Start working on %s' % name)
assert not(from_mark and pure)
lname = generate_last_name_cluster_str(name)
if from_mark:
bibauthor_print(' ... from mark!')
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
bibauthor_print(' ... delayed done')
else:
bibauthor_print(' ... from pid, pure')
clusters, lnames, sizes = delayed_cluster_sets_from_personid(pure)
bibauthor_print(' ... delayed pure done!')
# try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
cluster_set = cluster()
bibauthor_print("Found, %s(%s). Total number of bibs: %d." % (name, lname, size))
create_matrix(cluster_set, True)
wedge_and_store(cluster_set)
def tortoise_last_name(name, from_mark=True, pure=False):
bibauthor_print('Start working on %s' % name)
assert not(from_mark and pure)
lname = generate_last_name_cluster_str(name)
if from_mark:
bibauthor_print(' ... from mark!')
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
bibauthor_print(' ... delayed done')
else:
bibauthor_print(' ... from pid, pure=%s'%str(pure))
clusters, lnames, sizes = delayed_cluster_sets_from_personid(pure)
bibauthor_print(' ... delayed pure done!')
# try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
cluster_set = cluster()
bibauthor_print("Found, %s(%s). Total number of bibs: %d." % (name, lname, size))
create_matrix(cluster_set, False)
wedge_and_store(cluster_set)
def _collect_statistics_lname_coeff(params):
lname = params[0]
coeff = params[1]
clusters, lnames, sizes = delayed_cluster_sets_from_marktables([lname])
try:
idx = lnames.index(lname)
cluster = clusters[idx]
size = sizes[idx]
logger.log("Found, %s. Total number of bibs: %d." % (lname, size))
cluster_set = cluster()
create_matrix(cluster_set, False)
bibs = cluster_set.num_all_bibs
expected = bibs * (bibs - 1) / 2
logger.log("Start working on %s. Total number of bibs: %d, "
"maximum number of comparisons: %d" %
(cluster_set.last_name, bibs, expected))
wedge(cluster_set, True, coeff)
remove_clusters_by_name(cluster_set.last_name)
except (IndexError, ValueError):
logger.log("Sorry, %s not found in the last name clusters," % (lname))
