def __init__(self, ratios, args):
self.__membership = None
self.__organism = None
self.config_params = args
self.ratios = ratios
if args['resume']:
self.row_seeder = memb.make_db_row_seeder(args['out_database'])
self.column_seeder = memb.make_db_column_seeder(args['out_database'])
else:
self.row_seeder = memb.make_kmeans_row_seeder(args['num_clusters'])
self.column_seeder = microarray.seed_column_members
self.__conn = None
today = date.today()
logging.info('Input matrix has # rows: %d, # columns: %d',
ratios.num_rows, ratios.num_columns)
logging.info("# clusters/row: %d", args['memb.clusters_per_row'])
logging.info("# clusters/column: %d", args['memb.clusters_per_col'])
logging.info("# CLUSTERS: %d", args['num_clusters'])
logging.info("use operons: %d", args['use_operons'])
if args['MEME']['version']:
logging.info('using MEME version %s', args['MEME']['version'])
else:
logging.error('MEME not detected - please check')
def __init__(self, ratios, args_in):
self.__membership = None
self.__organism = None
self.config_params = args_in
self.ratios = ratios
if args_in['resume']:
self.row_seeder = memb.make_db_row_seeder(args_in['out_database'])
if args_in['new_data_file'] == True: #data file has changed
self.column_seeder = microarray.seed_column_members
else:
self.column_seeder = memb.make_db_column_seeder(args_in['out_database'])
else:
self.row_seeder = memb.make_kmeans_row_seeder(args_in['num_clusters'])
self.column_seeder = microarray.seed_column_members
self.__conn = None
today = date.today()
logging.info('Input matrix has # rows: %d, # columns: %d',
ratios.num_rows, ratios.num_columns)
logging.info("# clusters/row: %d", args_in['memb.clusters_per_row'])
logging.info("# clusters/column: %d", args_in['memb.clusters_per_col'])
logging.info("# CLUSTERS: %d", args_in['num_clusters'])
logging.info("use operons: %d", args_in['use_operons'])
if args_in['MEME']['version']:
logging.info('using MEME version %s', args_in['MEME']['version'])
else:
logging.error('MEME not detected - please check')
def __init__(self, organism_code, ratio_matrix,
string_file=None,
num_clusters=None):
logging.basicConfig(format=LOG_FORMAT,
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.DEBUG)
self.__membership = None
self.__organism = None
self.config_params = {}
self.ratio_matrix = ratio_matrix.sorted_by_row_name()
# membership update default parameters
# these come first, since a lot depends on clustering numbers
self['memb.clusters_per_row'] = 2
if num_clusters == None:
num_clusters = int(round(self.ratio_matrix.num_rows *
self['memb.clusters_per_row'] / 20.0))
#self['memb.clusters_per_col'] = int(round(num_clusters * 2.0 / 3.0))
self['memb.clusters_per_col'] = int(round(num_clusters / 2.0))
self['memb.prob_row_change'] = 0.5
self['memb.prob_col_change'] = 1.0
self['memb.max_changes_per_row'] = 1
self['memb.max_changes_per_col'] = 5
self['organism_code'] = organism_code
self['num_clusters'] = num_clusters
logging.info("# CLUSTERS: %d", self['num_clusters'])
# defaults
self.row_seeder = memb.make_kmeans_row_seeder(num_clusters)
self.column_seeder = microarray.seed_column_members
self['row_scaling'] = 6.0
self['string_file'] = None
self['cache_dir'] = CACHE_DIR
self['output_dir'] = 'out'
self['start_iteration'] = 1
self['num_iterations'] = 2000
self['multiprocessing'] = True
# Quantile normalization is false by default in cMonkey-R
self['quantile_normalize'] = True
# used to select sequences and MEME
self['sequence_types'] = ['upstream']
self['search_distances'] = {'upstream': (-20, 150)}
# used for background distribution and MAST
self['scan_distances'] = {'upstream': (-30, 250)}
# membership default parameters
self['memb.min_cluster_rows_allowed'] = 3
self['memb.max_cluster_rows_allowed'] = 70
self['string_file'] = string_file
self['out_database'] = self['output_dir'] + '/cmonkey_run.db'
today = date.today()
self.CHECKPOINT_INTERVAL = None
self.__checkpoint_basename = "cmonkey-checkpoint-%s-%d%d%d" % (
organism_code, today.year, today.month, today.day)
def __init__(self, organism_code, ratio_matrix, num_clusters=None):
self.__membership = None
self.__organism = None
self.config_params = {}
self.ratio_matrix = ratio_matrix.sorted_by_row_name()
# membership update default parameters
# these come first, since a lot depends on clustering numbers
self['memb.clusters_per_row'] = 2
if num_clusters == None:
num_clusters = int(round(self.ratio_matrix.num_rows() *
self['memb.clusters_per_row'] / 20.0))
self['memb.clusters_per_col'] = int(round(num_clusters * 2.0 / 3.0))
self['memb.prob_row_change'] = 0.5
self['memb.prob_col_change'] = 1.0
self['memb.max_changes_per_row'] = 1
self['memb.max_changes_per_col'] = 5
self['organism_code'] = organism_code
self['num_clusters'] = num_clusters
#logging.info("# CLUSTERS: %d", self['num_clusters'])
# defaults
self.row_seeder = memb.make_kmeans_row_seeder(num_clusters)
self.column_seeder = microarray.seed_column_members
self['row_scaling'] = 6.0
self['string_file'] = None
self['cache_dir'] = CACHE_DIR
self['output_dir'] = 'out'
self['start_iteration'] = 1
self['num_iterations'] = 2000
self['multiprocessing'] = True
# used to select sequences and MEME
self['sequence_types'] = ['upstream']
self['search_distances'] = {'upstream': (-20, 150)}
# used for background distribution and MAST
self['scan_distances'] = {'upstream': (-30, 250)}
# membership default parameters
self['memb.min_cluster_rows_allowed'] = 3
self['memb.max_cluster_rows_allowed'] = 70
today = date.today()
self.CHECKPOINT_INTERVAL = None
self.__checkpoint_basename = "cmonkey-checkpoint-%s-%d%d%d" % (
organism_code, today.year, today.month, today.day)
print "inited the bare bones main cMonkey instance"
def __init__(self, organism_code, ratio_matrix, num_clusters=None):
cmonkey_run.CMonkeyRun.__init__(self, organism_code, ratio_matrix, num_clusters)
self.__membership = None
self.__organism = None
self.config_params = {}
self.ratio_matrix = ratio_matrix.sorted_by_row_name()
# membership update default parameters
# these come first, since a lot depends on clustering numbers
self['memb.clusters_per_row'] = 2
if num_clusters == None:
num_clusters = int(round(self.ratio_matrix.num_rows() *
self['memb.clusters_per_row'] / 20.0))
self['memb.clusters_per_col'] = int(round(num_clusters * 2.0 / 3.0))
self['memb.prob_row_change'] = 0.5
self['memb.prob_col_change'] = 1.0
self['memb.max_changes_per_row'] = 1
self['memb.max_changes_per_col'] = 5
self['organism_code'] = organism_code
self['num_clusters'] = num_clusters
logging.info("\x1b[31mMain:\t\x1b[0m# CLUSTERS: %d", self['num_clusters'])
# defaults
self.row_seeder = memb.make_kmeans_row_seeder(num_clusters)
self.column_seeder = microarray.seed_column_members
self['row_scaling'] = 6.0
self['string_file'] = None
self['cache_dir'] = 'cache'
self['output_dir'] = 'out'
self['start_iteration'] = 1
self['num_iterations'] = 2000
self['multiprocessing'] = True
# membership default parameters
self['memb.min_cluster_rows_allowed'] = 10
self['memb.max_cluster_rows_allowed'] = 70
self['sequence_types'] = ['Promoter', '3pUTR']
self['search_distances'] = {'Promoter': (-1000, 200), '3pUTR': (0, 500)}
# used for background distribution and MAST
self['scan_distances'] = {'Promoter': (-2000, 750), '3pUTR': (0, 750)}
logging.info("\x1b[31mMain:\t\x1b[0mcM object initialized")
today = date.today()
self.CHECKPOINT_INTERVAL = None
self.__checkpoint_basename = "cmonkey-checkpoint-%s-%d%d%d" % (
organism_code, today.year, today.month, today.day)
def __init__(self,
organism_code,
ratio_matrix,
string_file=None,
num_clusters=None,
rsat_organism=None,
log_filename=None,
remap_network_nodes=False,
ncbi_code=None,
operon_file=None,
rsat_dir=None):
logging.basicConfig(format=LOG_FORMAT,
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.DEBUG,
filename=log_filename)
self.__membership = None
self.__organism = None
self.config_params = {}
self.ratio_matrix = ratio_matrix
# membership update default parameters
# these come first, since a lot depends on clustering numbers
self['memb.clusters_per_row'] = 2
if num_clusters is None:
num_clusters = int(
round(self.ratio_matrix.num_rows *
self['memb.clusters_per_row'] / 20.0))
if ratio_matrix.num_columns >= 60:
self['memb.clusters_per_col'] = int(round(num_clusters / 2.0))
else:
self['memb.clusters_per_col'] = int(round(num_clusters * 2.0 /
3.0))
logging.info("# clusters/row: %d", self['memb.clusters_per_row'])
logging.info("# clusters/column: %d", self['memb.clusters_per_col'])
self['organism_code'] = organism_code
self['num_clusters'] = num_clusters
self['use_operons'] = True
self['use_string'] = True
self['global_background'] = True
self['ncbi_code'] = ncbi_code
self['remap_network_nodes'] = remap_network_nodes
logging.info("# CLUSTERS: %d", self['num_clusters'])
logging.info("use operons: %d", self['use_operons'])
# defaults
self.row_seeder = memb.make_kmeans_row_seeder(num_clusters)
self.column_seeder = microarray.seed_column_members
# file overrides
self['string_file'] = string_file
self['operon_file'] = operon_file
self['rsat_organism'] = rsat_organism
self['rsat_dir'] = rsat_dir
# which scoring functions should be active
self['donetworks'] = True
self['domotifs'] = True
today = date.today()
self.__checkpoint_basename = "cmonkey-checkpoint-%d%d%d" % (
today.year, today.month, today.day)
self['meme_version'] = meme.check_meme_version()
if self['meme_version']:
logging.info('using MEME version %s', self['meme_version'])
else:
logging.error('MEME not detected - please check')
if os.path.exists(USER_DEFAULT_PIPELINE_PATH):
with open(USER_DEFAULT_PIPELINE_PATH) as infile:
self['pipeline'] = json.load(infile)
def __init__(self, organism_code, ratio_matrix,
string_file=None,
num_clusters=None,
rsat_organism=None,
log_filename=None,
remap_network_nodes=False,
ncbi_code=None,
operon_file=None,
rsat_dir=None):
logging.basicConfig(format=LOG_FORMAT,
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.DEBUG,
filename=log_filename)
self.__membership = None
self.__organism = None
self.config_params = {}
self.ratio_matrix = ratio_matrix
# membership update default parameters
# these come first, since a lot depends on clustering numbers
self['memb.clusters_per_row'] = 2
if num_clusters is None:
num_clusters = int(round(self.ratio_matrix.num_rows *
self['memb.clusters_per_row'] / 20.0))
if ratio_matrix.num_columns >= 60:
self['memb.clusters_per_col'] = int(round(num_clusters / 2.0))
else:
self['memb.clusters_per_col'] = int(round(num_clusters * 2.0 / 3.0))
logging.info("# clusters/row: %d", self['memb.clusters_per_row'])
logging.info("# clusters/column: %d", self['memb.clusters_per_col'])
self['organism_code'] = organism_code
self['num_clusters'] = num_clusters
self['use_operons'] = True
self['use_string'] = True
self['global_background'] = True
self['ncbi_code'] = ncbi_code
self['remap_network_nodes'] = remap_network_nodes
logging.info("# CLUSTERS: %d", self['num_clusters'])
logging.info("use operons: %d", self['use_operons'])
# defaults
self.row_seeder = memb.make_kmeans_row_seeder(num_clusters)
self.column_seeder = microarray.seed_column_members
# file overrides
self['string_file'] = string_file
self['operon_file'] = operon_file
self['rsat_organism'] = rsat_organism
self['rsat_dir'] = rsat_dir
# which scoring functions should be active
self['donetworks'] = True
self['domotifs'] = True
today = date.today()
self.__checkpoint_basename = "cmonkey-checkpoint-%d%d%d" % (
today.year, today.month, today.day)
self['meme_version'] = meme.check_meme_version()
if self['meme_version']:
logging.info('using MEME version %s', self['meme_version'])
else:
logging.error('MEME not detected - please check')
if os.path.exists(USER_DEFAULT_PIPELINE_PATH):
with open(USER_DEFAULT_PIPELINE_PATH) as infile:
self['pipeline'] = json.load(infile)
