def main():
args, kwargs = _parse_args(sys.argv[1:])
if args.profiler or args.line_profiler:
from phy.utils.testing import _enable_profiler, _profile
prof = _enable_profiler(args.line_profiler)
else:
prof = None
import phy
if args.debug:
phy.debug()
if args.cluster_ids:
cluster_ids = list(map(int, args.cluster_ids.split(',')))
else:
cluster_ids = None
if args.command == 'cluster-manual':
cmd = ('run_manual(args.file, clustering=args.clustering, '
'interactive=args.ipython, cluster_ids=cluster_ids)')
elif args.command == 'cluster-auto':
cmd = ('run_auto(args.file, clustering=args.clustering, '
'interactive=args.ipython, **kwargs)')
elif args.command == 'describe':
cmd = 'describe(args.file)'
else:
raise NotImplementedError()
if not prof:
exec_(cmd, globals(), locals())
else:
_profile(prof, cmd, globals(), locals())
def main(args=None):
p = ParserCreator()
if args is None:
args = sys.argv[1:]
elif isinstance(args, string_types):
args = args.split(' ')
args = p.parse(args)
if args is None:
return
if args.profiler or args.line_profiler:
from phy.utils.testing import _enable_profiler, _profile
prof = _enable_profiler(args.line_profiler)
else:
prof = None
import phy
if args.debug:
phy.debug()
# Hide the traceback.
if args.hide_traceback:
def exception_handler(exception_type, exception, traceback):
print("{}: {}".format(exception_type.__name__, exception))
sys.excepthook = exception_handler
# Activate IPython debugger.
if args.pdb:
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(mode='Verbose',
color_scheme='Linux',
call_pdb=1,
)
func = args.func
if func is None:
p.parser.print_help()
return
out = func(args)
if not out:
return
cmd, ns = out
if not cmd:
return
requires_qt = ns.pop('requires_qt', False)
requires_vispy = ns.pop('requires_vispy', False)
# Default variables in namespace.
ns.update(phy=phy, path=args.file)
if 'session' in ns:
ns['model'] = ns['session'].model
# Interactive mode with IPython.
if args.ipython:
print("\nStarting IPython...")
from IPython import start_ipython
args_ipy = ["-i", "-c='{}'".format(cmd)]
if requires_qt or requires_vispy:
# Activate Qt event loop integration with Qt.
args_ipy += ["--gui=qt"]
start_ipython(args_ipy, user_ns=ns)
else:
if not prof:
exec_(cmd, {}, ns)
else:
_profile(prof, cmd, {}, ns)
if requires_qt:
# Launch the Qt app.
from phy.gui import run_qt_app
run_qt_app()
elif requires_vispy:
# Launch the VisPy Qt app.
from vispy.app import use_app, run
use_app('pyqt4')
run()
import os.path as op
import shutil
from pprint import pprint
from timeit import default_timer
import h5py
import numpy as np
from numpy.testing import assert_allclose as ac
import phy
from phy.cluster.manual.store import DiskStore
from phy.io.h5 import open_h5
from phy.cluster.manual._utils import _spikes_per_cluster
from phy.utils.array import _index_of
phy.debug()
_store_path = '_store'
n_spikes = 2000000
n_channels = 300
n_clusters = 500
# Generate the dataset.
def _gen_arr():
arr = np.random.rand(n_spikes, n_channels).astype(np.float32)
with open_h5('test', 'w') as f:
f.write('/test', arr)
def _gen_spike_clusters():
def main(args=None):
p = ParserCreator()
if args is None:
args = sys.argv[1:]
elif isinstance(args, string_types):
args = args.split(' ')
args = p.parse(args)
if args is None:
return
if args.profiler or args.line_profiler:
from phy.utils.testing import _enable_profiler, _profile
prof = _enable_profiler(args.line_profiler)
else:
prof = None
import phy
if args.debug:
phy.debug()
# Hide the traceback.
if args.hide_traceback:
def exception_handler(exception_type, exception, traceback):
print("{}: {}".format(exception_type.__name__, exception))
sys.excepthook = exception_handler
# Activate IPython debugger.
if args.pdb:
from IPython.core import ultratb
sys.excepthook = ultratb.FormattedTB(
mode='Verbose',
color_scheme='Linux',
call_pdb=1,
)
func = args.func
if func is None:
p.parser.print_help()
return
out = func(args)
if not out:
return
cmd, ns = out
if not cmd:
return
requires_qt = ns.pop('requires_qt', False)
requires_vispy = ns.pop('requires_vispy', False)
# Default variables in namespace.
ns.update(phy=phy, path=args.file)
if 'session' in ns:
ns['model'] = ns['session'].model
# Interactive mode with IPython.
if args.ipython:
print("\nStarting IPython...")
from IPython import start_ipython
args_ipy = ["-i", "-c='{}'".format(cmd)]
if requires_qt or requires_vispy:
# Activate Qt event loop integration with Qt.
args_ipy += ["--gui=qt"]
start_ipython(args_ipy, user_ns=ns)
else:
if not prof:
exec_(cmd, {}, ns)
else:
_profile(prof, cmd, {}, ns)
if requires_qt:
# Launch the Qt app.
from phy.gui import run_qt_app
run_qt_app()
elif requires_vispy:
# Launch the VisPy Qt app.
from vispy.app import use_app, run
use_app('pyqt4')
run()
import shutil
from pprint import pprint
from timeit import default_timer
import h5py
import numpy as np
from numpy.testing import assert_allclose as ac
import phy
from phy.cluster.manual.store import DiskStore
from phy.io.h5 import open_h5
from phy.cluster.manual._utils import _spikes_per_cluster
from phy.utils.array import _index_of
phy.debug()
_store_path = '_store'
n_spikes = 2000000
n_channels = 300
n_clusters = 500
# Generate the dataset.
def _gen_arr():
arr = np.random.rand(n_spikes, n_channels).astype(np.float32)
with open_h5('test', 'w') as f:
f.write('/test', arr)
def _gen_spike_clusters():