Coverage for /usr/local/lib/python3.7/dist-packages/grond/clustering/__init__.py : 83%

import optparse 

from pyrocko.guts import Object, Int, Float 

from pyrocko import guts 

from grond.version import __version__ 

from grond.meta import GrondError 

guts_prefix = 'grond' 

class Clustering(Object): 

'''Base class for clustering method configuration objects.''' 

def perform(self): 

raise NotImplementedError('should be implemented in subclass') 

@classmethod 

def _cli_setup(cls, parser): 

pmap = { 

Float.T: float, 

Int.T: int} 

group = optparse.OptionGroup( 

parser, cls.name, 

'Options specific for the "%s" clustering method' % cls.name) 

for prop in cls.T.properties: 

if isinstance(prop, tuple(pmap.keys())): 

group.add_option( 

'--%s' % u2d(prop.name), 

dest=prop.name, 

type=pmap[prop.__class__], 

default=prop.default(), 

help=prop.help + ' (default: %default)') 

parser.add_option_group(group) 

@staticmethod 

def cli_setup(name, setup): 

if name in Clustering.name_to_class: 

def setup_(parser): 

setup(parser) 

Clustering.name_to_class[name]._cli_setup(parser) 

return setup_ 

else: 

return setup 

@classmethod 

def _cli_instantiate(cls, options): 

pmap = { 

Float.T: float, 

Int.T: int} 

kwargs = {} 

for prop in cls.T.properties: 

if isinstance(prop, tuple(pmap.keys())): 

kwargs[prop.name] = getattr(options, prop.name) 

return cls(**kwargs) 

@staticmethod 

def cli_instantiate(name, options): 

return Clustering.name_to_class[name]._cli_instantiate(options) 

def u2d(u): 

return u.replace('_', '-') 

class DBScan(Clustering): 

'''DBSCAN clustering algorithm.''' 

name = 'dbscan' 

nmin = Int.T( 

default=10, 

help='Minimum number of neighbours to define a cluster.') 

eps = Float.T( 

default=0.1, 

help='Maximum distance to search for neighbors.') 

ncluster_limit = Int.T( 

default=None, 

help='Limit maximum number of clusters created to N.') 

def perform(self, similarity_matrix): 

from .dbscan import dbscan 

return dbscan( 

similarity_matrix, 

nmin=self.nmin, 

eps=self.eps, 

ncluster_limit=self.ncluster_limit) 

def read_config(path): 

try: 

config = guts.load(filename=path) 

except OSError: 

raise GrondError( 

'cannot read Grond clustering configuration file: %s' % path) 

if not isinstance(config, Clustering): 

raise GrondError( 

'invalid Grond clustering configuration in file "%s"' % path) 

return config 

def write_config(config, path): 

try: 

guts.dump( 

config, 

filename=path, 

header='Grond clustering configuration file, version %s' 

% __version__) 

except OSError: 

raise GrondError( 

'cannot write Grond report configuration file: %s' % path) 

Clustering.name_to_class = dict((cls.name, cls) for cls in [DBScan]) 

methods = sorted(Clustering.name_to_class.keys()) 

__all__ = [ 

'Clustering', 

'DBScan', 

]