function stringlengths 11 56k | repo_name stringlengths 5 60 | features list |
|---|---|---|
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat)
self.issymmetric = True
self._inner_arg = self._lu.data | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def apply_constraints(self, b, constraints, axis=0):
if len(constraints) > 0:
assert len(constraints) == 1
assert constraints[0][0] == 0, 'Can only fix first row'
self._lu.diagonal(0)[0] = 1
s = [slice(None)]*len(b.shape)
s[axis] = 0
b[tupl... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, lu):
d = lu[0]
u[:d.shape[0]] /= d | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat)
self.issymmetric = self.mat.issymmetric | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def LU(data):
ld = data[0, :-2]
d = data[1, :]
ud = data[2, 2:]
n = d.shape[0]
for i in range(2, n):
ld[i-2] = ld[i-2]/d[i-2]
d[i] = d[i] - ld[i-2]*ud[i-2] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def perform_lu(self):
if self._inner_arg is None:
self.LU(self._lu.data)
self._inner_arg = self._lu.data
return self._lu | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, data):
ld = data[0, :-2]
d = data[1, :]
ud = data[2, 2:]
n = d.shape[0]
for i in range(2, n):
u[i] -= ld[i-2]*u[i-2]
u[n-1] = u[n-1]/d[n-1]
u[n-2] = u[n-2]/d[n-2]
for i in range(n - 3, -1, -1):
u[i] = (u[i] - ud[... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat) | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def LU(data):
ld = data[0, :-1]
d = data[1, :]
ud = data[2, 1:]
n = d.shape[0]
for i in range(1, n):
ld[i-1] = ld[i-1]/d[i-1]
d[i] -= ld[i-1]*ud[i-1] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, data):
ld = data[0, :-1]
d = data[1, :]
ud = data[2, 1:]
n = d.shape[0]
for i in range(1, n):
u[i] -= ld[i-1]*u[i-1]
u[n-1] = u[n-1]/d[n-1]
for i in range(n-2, -1, -1):
u[i] = (u[i] - ud[i]*u[i+1])/d[i] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat)
assert len(self.mat) == 5 | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def LU(data): # pragma: no cover
"""LU decomposition"""
a = data[0, :-4]
b = data[1, :-2]
d = data[2, :]
e = data[3, 2:]
f = data[4, 4:]
n = d.shape[0]
m = e.shape[0]
k = n - m
for i in range(n-2*k):
lam = b[i]/d[i]
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, data):
a = data[0, :-4]
b = data[1, :-2]
d = data[2, :]
e = data[3, 2:]
f = data[4, 4:]
n = d.shape[0]
u[2] -= b[0]*u[0]
u[3] -= b[1]*u[1]
for k in range(4, n):
u[k] -= (b[k-2]*u[k-2] + a[k-4]*u[k-4])
u[n-1] /... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat) | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def LU(data):
ld = data[0, :-2]
d = data[1, :]
u1 = data[2, 2:]
u2 = data[3, 4:]
n = d.shape[0]
for i in range(2, n):
ld[i-2] = ld[i-2]/d[i-2]
d[i] = d[i] - ld[i-2]*u1[i-2]
if i < n-2:
u1[i] = u1[i] - ld[i-2]*u2[i-2] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, data):
ld = data[0, :-2]
d = data[1, :]
u1 = data[2, 2:]
u2 = data[3, 4:]
n = d.shape[0]
for i in range(2, n):
u[i] -= ld[i-2]*u[i-2]
u[n-1] = u[n-1]/d[n-1]
u[n-2] = u[n-2]/d[n-2]
u[n-3] = (u[n-3] - u1[n-3]*u[n-1])/d[... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat)
self._inner_arg = self._lu.data | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def perform_lu(self):
return self._lu | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, data):
d = data[0, :]
u1 = data[1, 2:]
n = d.shape[0]
u[n-1] = u[n-1]/d[n-1]
u[n-2] = u[n-2]/d[n-2]
for i in range(n - 3, -1, -1):
u[i] = (u[i] - u1[i]*u[i+2])/d[i] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat):
BandedMatrixSolver.__init__(self, mat)
self._inner_arg = self._lu.data | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def perform_lu(self):
return self._lu | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def inner_solve(u, data):
d = data[0, :]
u1 = data[1, 2:]
u2 = data[1, 4:]
n = d.shape[0]
u[n-1] = u[n-1]/d[n-1]
u[n-2] = u[n-2]/d[n-2]
u[n-3] = (u[n-3]-u1[n-3]*u[n-1])/d[n-3]
u[n-4] = (u[n-4]-u1[n-4]*u[n-2])/d[n-4]
for i in range(n - 5, -1, -1):
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def Solve(u, data, axis=0):
raise NotImplementedError('Only optimized version') | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mat, format=None):
format = config['matrix']['sparse']['solve'] if format is None else format
SparseMatrixSolver.__init__(self, mat)
self.mat = self.mat.diags(format) | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, tpmats):
tpmats = get_simplified_tpmatrices(tpmats)
bc_mats = extract_bc_matrices([tpmats])
self.tpmats = tpmats
self.bc_mats = bc_mats
self.T = tpmats[0].space
self.mats2D = {}
self._lu = None | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def get_diagonal_axis(self):
naxes = self.T.get_nondiagonal_axes()
diagonal_axis = np.setxor1d([0, 1, 2], naxes)
assert len(diagonal_axis) == 1
return diagonal_axis[0] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def apply_constraints(self, b, constraints):
"""Apply constraints to matrix and rhs vector `b`
Parameters
----------
b : array
constraints : tuple of 2-tuples
The 2-tuples represent (row, val)
The constraint indents the matrix row and sets b[row] = val
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def perform_lu(self):
if self._lu is not None:
return self._lu
ndim = self.tpmats[0].dimensions
self._lu = {}
if ndim == 2:
self._lu[0] = splu(self.mats2D[0], permc_spec=config['matrix']['sparse']['permc_spec'])
else:
diagonal_axis = self.get_d... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, tpmats):
tpmats = get_simplified_tpmatrices(tpmats)
assert len(tpmats) == 1
self.mat = tpmats[0] | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, tpmats):
bc_mats = extract_bc_matrices([tpmats])
self.tpmats = tpmats
self.bc_mats = bc_mats
self._lu = None
m = tpmats[0]
self.T = T = m.space
assert m._issimplified is False, "Cannot use simplified matrices with this solver"
mat = m.di... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def apply_constraints(A, b, constraints):
"""Apply constraints to matrix `A` and rhs vector `b`
Parameters
----------
A : Sparse matrix
b : array
constraints : tuple of 2-tuples
The 2-tuples represent (row, val)
The constraint indents the matrix r... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, tpmats):
Solver2D.__init__(self, tpmats) | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, tpmats):
Solver2D.__init__(self, tpmats) | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mats):
assert isinstance(mats, list)
mats = get_simplified_tpmatrices(mats)
assert len(mats[0].naxes) == 1
self.naxes = mats[0].naxes[0]
bc_mats = extract_bc_matrices([mats])
self.mats = mats
self.bc_mats = bc_mats
self.solvers1D = None
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def assemble(self):
ndim = self.mats[0].dimensions
shape = self.mats[0].space.shape(True)
self.solvers1D = []
if ndim == 2:
zi = np.ndindex((1, shape[1])) if self.naxes == 0 else np.ndindex((shape[0], 1))
other_axis = (self.naxes+1) % 2
for i in zi:
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def perform_lu(self):
if self._lu is True:
return
if isinstance(self.solvers1D[0], SparseMatrixSolver):
for m in self.solvers1D:
lu = m.perform_lu()
else:
for mi in self.solvers1D:
for mij in mi:
lu = mij.p... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def solve_data(u, data, sol, naxes, is_rank_zero):
s = [0]*u.ndim
s[naxes] = slice(None)
paxes = np.setxor1d(range(u.ndim), naxes)
if u.ndim == 2:
for i in range(u.shape[paxes[0]]):
if i == 0 and is_rank_zero:
continue
s[pax... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def solve(self, u, b, solvers1D, naxes):
if u is not b:
u[:] = b
s = [0]*u.ndim
s[naxes] = slice(None)
paxes = np.setxor1d(range(u.ndim), naxes)
if u.ndim == 2:
for i, sol in enumerate(solvers1D):
s[paxes[0]] = i
s0 = tuple... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def __init__(self, mats):
assert isinstance(mats, (BlockMatrix, list))
self.bc_mat = None
self._lu = None
if isinstance(mats, BlockMatrix):
mats = mats.get_mats()
bc_mats = extract_bc_matrices([mats])
assert len(mats) > 0
self.mat = BlockMatrix(mats)
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def apply_constraint(A, b, offset, i, constraint):
if constraint is None or comm.Get_rank() > 0:
return A, b
if isinstance(i, int):
if i > 0:
return A, b
if isinstance(i, tuple):
if np.sum(np.array(i)) > 0:
return A, b
... | spectralDNS/shenfun | [
148,
38,
148,
24,
1485264542
] |
def custom_import_install():
if __builtin__.__import__ == NATIVE_IMPORTER:
INVALID_MODULES.update(sys.modules.keys())
__builtin__.__import__ = custom_importer | OpenTreeOfLife/opentree | [
105,
24,
105,
279,
1361218666
] |
def is_tracking_changes():
return current.request._custom_import_track_changes | OpenTreeOfLife/opentree | [
105,
24,
105,
279,
1361218666
] |
def custom_importer(name, globals=None, locals=None, fromlist=None, level=-1):
"""
The web2py custom importer. Like the standard Python importer but it
tries to transform import statements as something like
"import applications.app_name.modules.x".
If the import failed, fall back on naive_importer
... | OpenTreeOfLife/opentree | [
105,
24,
105,
279,
1361218666
] |
def __init__(self):
self._import_dates = {} # Import dates of the files of the modules | OpenTreeOfLife/opentree | [
105,
24,
105,
279,
1361218666
] |
def _update_dates(self, name, globals, locals, fromlist, level):
"""
Update all the dates associated to the statement import. A single
import statement may import many modules.
"""
self._reload_check(name, globals, locals, level)
for fromlist_name in fromlist or []:
... | OpenTreeOfLife/opentree | [
105,
24,
105,
279,
1361218666
] |
def _get_module_file(self, module):
"""
Get the absolute path file associated to the module or None.
"""
file = getattr(module, "__file__", None)
if file:
# Make path absolute if not:
file = os.path.splitext(file)[0] + ".py" # Change .pyc for .py
... | OpenTreeOfLife/opentree | [
105,
24,
105,
279,
1361218666
] |
def __init__(
self,
tool_name,
work_root,
test_name=None,
param_types=["plusarg", "vlogdefine", "vlogparam"],
files=None,
tool_options={},
ref_dir=".",
use_vpi=False,
toplevel="top_module", | SymbiFlow/edalize | [
5,
3,
5,
5,
1567527171
] |
def compare_files(self, files, ref_subdir="."):
"""Check some files in the work root match those in the ref directory
The files argument gives the list of files to check. These are
interpreted as paths relative to the work directory and relative to
self.ref_dir / ref_subdir.
Th... | SymbiFlow/edalize | [
5,
3,
5,
5,
1567527171
] |
def make_edalize_test(monkeypatch, tmpdir):
"""A factory fixture to make an edalize backend with work_root directory
The returned factory method takes a `tool_name` (the name of the tool) and
the keyword arguments supported by :class:`TestFixture`. It returns a
:class:`TestFixture` object, whose `work_... | SymbiFlow/edalize | [
5,
3,
5,
5,
1567527171
] |
def param_gen(paramtypes):
"""Generate dictionary of definitions in *paramtypes* list."""
defs = OrderedDict()
for paramtype in paramtypes:
for datatype in ["bool", "int", "str"]:
if datatype == "int":
default = 42
elif datatype == "str":
defa... | SymbiFlow/edalize | [
5,
3,
5,
5,
1567527171
] |
def api_call(self, *args, **kwargs):
if 'ids' in kwargs:
kwargs['group_ids'] = ','.join(map(lambda i: str(i), kwargs.pop('ids')))
return super(GroupRemoteManager, self).api_call(*args, **kwargs) | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def fetch(self, *args, **kwargs):
"""
Add additional fields to parent fetch request
"""
if 'fields' not in kwargs:
kwargs['fields'] = 'members_count'
return super(GroupRemoteManager, self).fetch(*args, **kwargs) | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def check_members_count(self, group, count):
if group.members_count and count > 0:
division = float(group.members_count) / count
if 0.99 > division or 1.01 < division:
raise CheckMembersCountFailed("Suspicious ammount of members fetched for group %s. "
... | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def __str__(self):
return self.name | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def refresh_kwargs(self):
return {'ids': [self.remote_id]} | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def wall_comments(self):
if 'vkontakte_wall' not in settings.INSTALLED_APPS:
raise ImproperlyConfigured("Application 'vkontakte_wall' not in INSTALLED_APPS")
from vkontakte_wall.models import Comment
# TODO: improve schema and queries with using owner_id field
return Comment... | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def topics_comments(self):
if 'vkontakte_board' not in settings.INSTALLED_APPS:
raise ImproperlyConfigured("Application 'vkontakte_board' not in INSTALLED_APPS")
from vkontakte_board.models import Comment
# TODO: improve schema and queries with using owner_id field
return Co... | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def fetch_topics(self, *args, **kwargs):
if 'vkontakte_board' not in settings.INSTALLED_APPS:
raise ImproperlyConfigured("Application 'vkontakte_board' not in INSTALLED_APPS")
from vkontakte_board.models import Topic
return Topic.remote.fetch(group=self, *args, **kwargs) | ramusus/django-vkontakte-groups | [
2,
6,
2,
1,
1355991753
] |
def profile_edit(request): | eldarion/pycon | [
105,
22,
105,
4,
1277157390
] |
def scurve(x, A, mu, sigma):
return 0.5 * A * erf((x - mu) / (np.sqrt(2) * sigma)) + 0.5 * A | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def __init__(self, raw_data_file=None, analyzed_data_file=None, create_pdf=True, scan_parameter_name=None):
'''Initialize the AnalyzeRawData object:
- The c++ objects (Interpreter, Histogrammer, Clusterizer) are constructed
- Create one scan parameter table from all provided raw data fil... | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def __exit__(self, *exc_info):
self.close() | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def end_of_cluster_function(hits, clusters, cluster_size, cluster_hit_indices, cluster_index, cluster_id, charge_correction, noisy_pixels, disabled_pixels, seed_hit_index):
clusters[cluster_index].event_status = hits[seed_hit_index].event_status | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def close(self):
del self.interpreter
del self.histogram
del self.clusterizer
self._close_h5()
self._close_pdf() | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def _close_pdf(self):
if self.output_pdf is not None:
logging.info('Closing output PDF file: %s', str(self.output_pdf._file.fh.name))
self.output_pdf.close()
self.output_pdf = None | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def reset(self):
'''Reset the c++ libraries for new analysis.
'''
self.interpreter.reset()
self.histogram.reset() | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def chunk_size(self):
return self._chunk_size | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def chunk_size(self, value):
self.interpreter.set_hit_array_size(2 * value) # worst case: one raw data word becoming 2 hit words
self._chunk_size = value | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_hit_table(self):
return self._create_hit_table | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_hit_table(self, value):
self._create_hit_table = value | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_empty_event_hits(self):
return self._create_empty_event_hits | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_empty_event_hits(self, value):
self._create_empty_event_hits = value
self.interpreter.create_empty_event_hits(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_occupancy_hist(self):
return self._create_occupancy_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_occupancy_hist(self, value):
self._create_occupancy_hist = value
self.histogram.create_occupancy_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_mean_tot_hist(self):
return self._create_mean_tot_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_mean_tot_hist(self, value):
self._create_mean_tot_hist = value
self.histogram.create_mean_tot_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_source_scan_hist(self):
return self._create_source_scan_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_source_scan_hist(self, value):
self._create_source_scan_hist = value | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tot_hist(self):
return self.create_tot_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tot_hist(self, value):
self._create_tot_hist = value
self.histogram.create_tot_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tdc_hist(self):
return self._create_tdc_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tdc_hist(self, value):
self._create_tdc_hist = value
self.histogram.create_tdc_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tdc_pixel_hist(self):
return self._create_tdc_pixel_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tdc_pixel_hist(self, value):
self._create_tdc_pixel_hist = value
self.histogram.create_tdc_pixel_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tot_pixel_hist(self):
return self._create_tot_pixel_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_tot_pixel_hist(self, value):
self._create_tot_pixel_hist = value
self.histogram.create_tot_pixel_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_rel_bcid_hist(self):
return self._create_rel_bcid_hist | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_rel_bcid_hist(self, value):
self._create_rel_bcid_hist = value
self.histogram.create_rel_bcid_hist(value) | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_threshold_hists(self):
return self._create_threshold_hists | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_threshold_hists(self, value):
self._create_threshold_hists = value | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_threshold_mask(self):
return self._create_threshold_mask | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_threshold_mask(self, value):
self._create_threshold_mask = value | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_fitted_threshold_mask(self):
return self._create_fitted_threshold_mask | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
def create_fitted_threshold_mask(self, value):
self._create_fitted_threshold_mask = value | SiLab-Bonn/pyBAR | [
9,
17,
9,
3,
1422005052
] |
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