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stbraun/fuzzing | features/steps/ft_fuzzer.py | step_impl11 | def step_impl11(context, runs):
"""Execute multiple runs.
:param runs: number of test runs to perform.
:param context: test context.
"""
executor = context.fuzz_executor
executor.run_test(runs)
stats = executor.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: stats available." | python | def step_impl11(context, runs):
"""Execute multiple runs.
:param runs: number of test runs to perform.
:param context: test context.
"""
executor = context.fuzz_executor
executor.run_test(runs)
stats = executor.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: stats available." | [
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stbraun/fuzzing | features/steps/ft_fuzzer.py | step_impl12 | def step_impl12(context, runs):
"""Check called apps / files.
:param runs: expected number of records.
:param context: test context.
"""
executor_ = context.fuzz_executor
stats = executor_.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: Number of recorded runs." | python | def step_impl12(context, runs):
"""Check called apps / files.
:param runs: expected number of records.
:param context: test context.
"""
executor_ = context.fuzz_executor
stats = executor_.stats
count = stats.cumulated_counts()
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stbraun/fuzzing | features/steps/ft_fuzzer.py | step_impl13 | def step_impl13(context, runs):
"""Check called apps / files.
:param runs: expected number of records.
:param context: test context.
"""
executor_ = context.fuzz_executor
stats = executor_.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: Number of recorded runs."
successful_runs = stats.cumulated_counts_for_status(Status.SUCCESS)
assert successful_runs == runs | python | def step_impl13(context, runs):
"""Check called apps / files.
:param runs: expected number of records.
:param context: test context.
"""
executor_ = context.fuzz_executor
stats = executor_.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: Number of recorded runs."
successful_runs = stats.cumulated_counts_for_status(Status.SUCCESS)
assert successful_runs == runs | [
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stbraun/fuzzing | features/steps/ft_fuzzer.py | step_impl14 | def step_impl14(context, runs):
"""Check called apps / files.
:param runs: expected number of records.
:param context: test context.
"""
executor_ = context.fuzz_executor
stats = executor_.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: Number of recorded runs."
failed_runs = stats.cumulated_counts_for_status(Status.FAILED)
assert failed_runs == runs | python | def step_impl14(context, runs):
"""Check called apps / files.
:param runs: expected number of records.
:param context: test context.
"""
executor_ = context.fuzz_executor
stats = executor_.stats
count = stats.cumulated_counts()
assert count == runs, "VERIFY: Number of recorded runs."
failed_runs = stats.cumulated_counts_for_status(Status.FAILED)
assert failed_runs == runs | [
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stbraun/fuzzing | features/steps/ft_fuzzer.py | number_of_modified_bytes | def number_of_modified_bytes(buf, fuzzed_buf):
"""Determine the number of differing bytes.
:param buf: original buffer.
:param fuzzed_buf: fuzzed buffer.
:return: number of different bytes.
:rtype: int
"""
count = 0
for idx, b in enumerate(buf):
if b != fuzzed_buf[idx]:
count += 1
return count | python | def number_of_modified_bytes(buf, fuzzed_buf):
"""Determine the number of differing bytes.
:param buf: original buffer.
:param fuzzed_buf: fuzzed buffer.
:return: number of different bytes.
:rtype: int
"""
count = 0
for idx, b in enumerate(buf):
if b != fuzzed_buf[idx]:
count += 1
return count | [
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all-umass/graphs | graphs/construction/msg.py | flesh_out | def flesh_out(X, W, embed_dim, CC_labels, dist_mult=2.0, angle_thresh=0.2,
min_shortcircuit=4, max_degree=5, verbose=False):
'''Given a connected graph adj matrix (W), add edges to flesh it out.'''
W = W.astype(bool)
assert np.all(W == W.T), 'graph given to flesh_out must be symmetric'
D = pairwise_distances(X, metric='sqeuclidean')
# compute average edge lengths for each point
avg_edge_length = np.empty(X.shape[0])
for i,nbr_mask in enumerate(W):
avg_edge_length[i] = D[i,nbr_mask].mean()
# candidate edges must satisfy edge length for at least one end point
dist_thresh = dist_mult * avg_edge_length
dist_mask = (D < dist_thresh) | (D < dist_thresh[:,None])
# candidate edges must connect points >= min_shortcircuit hops away
hops_mask = np.isinf(dijkstra(W, unweighted=True, limit=min_shortcircuit-1))
# candidate edges must not already be connected, or in the same initial CC
CC_mask = CC_labels != CC_labels[:,None]
candidate_edges = ~W & dist_mask & hops_mask & CC_mask
if verbose: # pragma: no cover
print('before F:', candidate_edges.sum(), 'potentials')
# calc subspaces
subspaces, _ = cluster_subspaces(X, embed_dim, CC_labels.max()+1, CC_labels)
# upper triangular avoids p,q <-> q,p repeats
ii,jj = np.where(np.triu(candidate_edges))
# Get angles
edge_dirs = X[ii] - X[jj]
ssi = subspaces[CC_labels[ii]]
ssj = subspaces[CC_labels[jj]]
F = edge_cluster_angle(edge_dirs, ssi, ssj)
mask = F < angle_thresh
edge_ii = ii[mask]
edge_jj = jj[mask]
edge_order = np.argsort(F[mask])
if verbose: # pragma: no cover
print('got', len(edge_ii), 'potential edges')
# Prevent any one node from getting a really high degree
degree = W.sum(axis=0)
sorted_edges = np.column_stack((edge_ii, edge_jj))[edge_order]
for e in sorted_edges:
if degree[e].max() < max_degree:
W[e[0],e[1]] = True
W[e[1],e[0]] = True
degree[e] += 1
return Graph.from_adj_matrix(np.where(W, np.sqrt(D), 0)) | python | def flesh_out(X, W, embed_dim, CC_labels, dist_mult=2.0, angle_thresh=0.2,
min_shortcircuit=4, max_degree=5, verbose=False):
'''Given a connected graph adj matrix (W), add edges to flesh it out.'''
W = W.astype(bool)
assert np.all(W == W.T), 'graph given to flesh_out must be symmetric'
D = pairwise_distances(X, metric='sqeuclidean')
# compute average edge lengths for each point
avg_edge_length = np.empty(X.shape[0])
for i,nbr_mask in enumerate(W):
avg_edge_length[i] = D[i,nbr_mask].mean()
# candidate edges must satisfy edge length for at least one end point
dist_thresh = dist_mult * avg_edge_length
dist_mask = (D < dist_thresh) | (D < dist_thresh[:,None])
# candidate edges must connect points >= min_shortcircuit hops away
hops_mask = np.isinf(dijkstra(W, unweighted=True, limit=min_shortcircuit-1))
# candidate edges must not already be connected, or in the same initial CC
CC_mask = CC_labels != CC_labels[:,None]
candidate_edges = ~W & dist_mask & hops_mask & CC_mask
if verbose: # pragma: no cover
print('before F:', candidate_edges.sum(), 'potentials')
# calc subspaces
subspaces, _ = cluster_subspaces(X, embed_dim, CC_labels.max()+1, CC_labels)
# upper triangular avoids p,q <-> q,p repeats
ii,jj = np.where(np.triu(candidate_edges))
# Get angles
edge_dirs = X[ii] - X[jj]
ssi = subspaces[CC_labels[ii]]
ssj = subspaces[CC_labels[jj]]
F = edge_cluster_angle(edge_dirs, ssi, ssj)
mask = F < angle_thresh
edge_ii = ii[mask]
edge_jj = jj[mask]
edge_order = np.argsort(F[mask])
if verbose: # pragma: no cover
print('got', len(edge_ii), 'potential edges')
# Prevent any one node from getting a really high degree
degree = W.sum(axis=0)
sorted_edges = np.column_stack((edge_ii, edge_jj))[edge_order]
for e in sorted_edges:
if degree[e].max() < max_degree:
W[e[0],e[1]] = True
W[e[1],e[0]] = True
degree[e] += 1
return Graph.from_adj_matrix(np.where(W, np.sqrt(D), 0)) | [
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all-umass/graphs | graphs/construction/msg.py | edge_cluster_angle | def edge_cluster_angle(edge_dirs, subspaces1, subspaces2):
'''edge_dirs is a (n,D) matrix of edge vectors.
subspaces are (n,D,d) or (D,d) matrices of normalized orthogonal subspaces.
Result is an n-length array of angles.'''
QG = edge_dirs / np.linalg.norm(edge_dirs, ord=2, axis=1)[:,None]
X1 = np.einsum('...ij,...i->...j', subspaces1, QG)
X2 = np.einsum('...ij,...i->...j', subspaces2, QG)
# TODO: check the math on this for more cases
# angles = np.maximum(1-np.sum(X1**2, axis=1), 1-np.sum(X2**2, axis=1))
C1 = np.linalg.svd(X1[:,:,None], compute_uv=False)
C2 = np.linalg.svd(X2[:,:,None], compute_uv=False)
angles = np.maximum(1-C1**2, 1-C2**2)[:,0]
angles[np.isnan(angles)] = 0.0 # nan when edge length == 0
return angles | python | def edge_cluster_angle(edge_dirs, subspaces1, subspaces2):
'''edge_dirs is a (n,D) matrix of edge vectors.
subspaces are (n,D,d) or (D,d) matrices of normalized orthogonal subspaces.
Result is an n-length array of angles.'''
QG = edge_dirs / np.linalg.norm(edge_dirs, ord=2, axis=1)[:,None]
X1 = np.einsum('...ij,...i->...j', subspaces1, QG)
X2 = np.einsum('...ij,...i->...j', subspaces2, QG)
# TODO: check the math on this for more cases
# angles = np.maximum(1-np.sum(X1**2, axis=1), 1-np.sum(X2**2, axis=1))
C1 = np.linalg.svd(X1[:,:,None], compute_uv=False)
C2 = np.linalg.svd(X2[:,:,None], compute_uv=False)
angles = np.maximum(1-C1**2, 1-C2**2)[:,0]
angles[np.isnan(angles)] = 0.0 # nan when edge length == 0
return angles | [
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jesford/cluster-lensing | clusterlensing/nfw.py | SurfaceMassDensity.sigma_nfw | def sigma_nfw(self):
"""Calculate NFW surface mass density profile.
Generate the surface mass density profiles of each cluster halo,
assuming a spherical NFW model. Optionally includes the effect of
cluster miscentering offsets, if the parent object was initialized
with offsets.
Returns
----------
Quantity
Surface mass density profiles (ndarray, in astropy.units of
Msun/pc/pc). Each row corresponds to a single cluster halo.
"""
def _centered_sigma(self):
# perfectly centered cluster case
# calculate f
bigF = np.zeros_like(self._x)
f = np.zeros_like(self._x)
numerator_arg = ((1. / self._x[self._x_small]) +
np.sqrt((1. / (self._x[self._x_small]**2)) - 1.))
denominator = np.sqrt(1. - (self._x[self._x_small]**2))
bigF[self._x_small] = np.log(numerator_arg) / denominator
bigF[self._x_big] = (np.arccos(1. / self._x[self._x_big]) /
np.sqrt(self._x[self._x_big]**2 - 1.))
f = (1. - bigF) / (self._x**2 - 1.)
f[self._x_one] = 1. / 3.
if np.isnan(np.sum(f)) or np.isinf(np.sum(f)):
print('\nERROR: f is not all real\n')
# calculate & return centered profiles
if f.ndim == 2:
sigma = 2. * self._rs_dc_rcrit * f
else:
rs_dc_rcrit_4D = self._rs_dc_rcrit.T.reshape(1, 1,
f.shape[2],
f.shape[3])
sigma = 2. * rs_dc_rcrit_4D * f
return sigma
def _offset_sigma(self):
# size of "x" arrays to integrate over
numRoff = self._numRoff
numTh = self._numTh
numRbins = self._nbins
maxsig = self._sigmaoffset.value.max()
# inner/outer bin edges
roff_1D = np.linspace(0., 4. * maxsig, numRoff)
theta_1D = np.linspace(0., 2. * np.pi, numTh)
rMpc_1D = self._rbins.value
# reshape for broadcasting: (numTh,numRoff,numRbins)
theta = theta_1D.reshape(numTh, 1, 1)
roff = roff_1D.reshape(1, numRoff, 1)
rMpc = rMpc_1D.reshape(1, 1, numRbins)
r_eq13 = np.sqrt(rMpc ** 2 + roff ** 2 -
2. * rMpc * roff * np.cos(theta))
# 3D array r_eq13 -> 4D dimensionless radius (nlens)
_set_dimensionless_radius(self, radii=r_eq13, integration=True)
sigma = _centered_sigma(self)
inner_integrand = sigma.value / (2. * np.pi)
# INTEGRATE OVER theta
sigma_of_RgivenRoff = simps(inner_integrand, x=theta_1D, axis=0,
even='first')
# theta is gone, now dimensions are: (numRoff,numRbins,nlens)
sig_off_3D = self._sigmaoffset.value.reshape(1, 1, self._nlens)
roff_v2 = roff_1D.reshape(numRoff, 1, 1)
PofRoff = (roff_v2 / (sig_off_3D**2) *
np.exp(-0.5 * (roff_v2 / sig_off_3D)**2))
dbl_integrand = sigma_of_RgivenRoff * PofRoff
# INTEGRATE OVER Roff
# (integration axis=0 after theta is gone).
sigma_smoothed = simps(dbl_integrand, x=roff_1D, axis=0,
even='first')
# reset _x to correspond to input rbins (default)
_set_dimensionless_radius(self)
sigma_sm = np.array(sigma_smoothed.T) * units.solMass / units.pc**2
return sigma_sm
if self._sigmaoffset is None:
finalsigma = _centered_sigma(self)
elif np.abs(self._sigmaoffset).sum() == 0:
finalsigma = _centered_sigma(self)
else:
finalsigma = _offset_sigma(self)
self._sigma_sm = finalsigma
return finalsigma | python | def sigma_nfw(self):
"""Calculate NFW surface mass density profile.
Generate the surface mass density profiles of each cluster halo,
assuming a spherical NFW model. Optionally includes the effect of
cluster miscentering offsets, if the parent object was initialized
with offsets.
Returns
----------
Quantity
Surface mass density profiles (ndarray, in astropy.units of
Msun/pc/pc). Each row corresponds to a single cluster halo.
"""
def _centered_sigma(self):
# perfectly centered cluster case
# calculate f
bigF = np.zeros_like(self._x)
f = np.zeros_like(self._x)
numerator_arg = ((1. / self._x[self._x_small]) +
np.sqrt((1. / (self._x[self._x_small]**2)) - 1.))
denominator = np.sqrt(1. - (self._x[self._x_small]**2))
bigF[self._x_small] = np.log(numerator_arg) / denominator
bigF[self._x_big] = (np.arccos(1. / self._x[self._x_big]) /
np.sqrt(self._x[self._x_big]**2 - 1.))
f = (1. - bigF) / (self._x**2 - 1.)
f[self._x_one] = 1. / 3.
if np.isnan(np.sum(f)) or np.isinf(np.sum(f)):
print('\nERROR: f is not all real\n')
# calculate & return centered profiles
if f.ndim == 2:
sigma = 2. * self._rs_dc_rcrit * f
else:
rs_dc_rcrit_4D = self._rs_dc_rcrit.T.reshape(1, 1,
f.shape[2],
f.shape[3])
sigma = 2. * rs_dc_rcrit_4D * f
return sigma
def _offset_sigma(self):
# size of "x" arrays to integrate over
numRoff = self._numRoff
numTh = self._numTh
numRbins = self._nbins
maxsig = self._sigmaoffset.value.max()
# inner/outer bin edges
roff_1D = np.linspace(0., 4. * maxsig, numRoff)
theta_1D = np.linspace(0., 2. * np.pi, numTh)
rMpc_1D = self._rbins.value
# reshape for broadcasting: (numTh,numRoff,numRbins)
theta = theta_1D.reshape(numTh, 1, 1)
roff = roff_1D.reshape(1, numRoff, 1)
rMpc = rMpc_1D.reshape(1, 1, numRbins)
r_eq13 = np.sqrt(rMpc ** 2 + roff ** 2 -
2. * rMpc * roff * np.cos(theta))
# 3D array r_eq13 -> 4D dimensionless radius (nlens)
_set_dimensionless_radius(self, radii=r_eq13, integration=True)
sigma = _centered_sigma(self)
inner_integrand = sigma.value / (2. * np.pi)
# INTEGRATE OVER theta
sigma_of_RgivenRoff = simps(inner_integrand, x=theta_1D, axis=0,
even='first')
# theta is gone, now dimensions are: (numRoff,numRbins,nlens)
sig_off_3D = self._sigmaoffset.value.reshape(1, 1, self._nlens)
roff_v2 = roff_1D.reshape(numRoff, 1, 1)
PofRoff = (roff_v2 / (sig_off_3D**2) *
np.exp(-0.5 * (roff_v2 / sig_off_3D)**2))
dbl_integrand = sigma_of_RgivenRoff * PofRoff
# INTEGRATE OVER Roff
# (integration axis=0 after theta is gone).
sigma_smoothed = simps(dbl_integrand, x=roff_1D, axis=0,
even='first')
# reset _x to correspond to input rbins (default)
_set_dimensionless_radius(self)
sigma_sm = np.array(sigma_smoothed.T) * units.solMass / units.pc**2
return sigma_sm
if self._sigmaoffset is None:
finalsigma = _centered_sigma(self)
elif np.abs(self._sigmaoffset).sum() == 0:
finalsigma = _centered_sigma(self)
else:
finalsigma = _offset_sigma(self)
self._sigma_sm = finalsigma
return finalsigma | [
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jesford/cluster-lensing | clusterlensing/nfw.py | SurfaceMassDensity.deltasigma_nfw | def deltasigma_nfw(self):
"""Calculate NFW differential surface mass density profile.
Generate the differential surface mass density profiles of each cluster
halo, assuming a spherical NFW model. Optionally includes the effect of
cluster miscentering offsets, if the parent object was initialized
with offsets.
Returns
----------
Quantity
Differential surface mass density profiles (ndarray, in
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cluster halo.
"""
def _centered_dsigma(self):
# calculate g
firstpart = np.zeros_like(self._x)
secondpart = np.zeros_like(self._x)
g = np.zeros_like(self._x)
small_1a = 4. / self._x[self._x_small]**2
small_1b = 2. / (self._x[self._x_small]**2 - 1.)
small_1c = np.sqrt(1. - self._x[self._x_small]**2)
firstpart[self._x_small] = (small_1a + small_1b) / small_1c
big_1a = 8. / (self._x[self._x_big]**2 *
np.sqrt(self._x[self._x_big]**2 - 1.))
big_1b = 4. / ((self._x[self._x_big]**2 - 1.)**1.5)
firstpart[self._x_big] = big_1a + big_1b
small_2a = np.sqrt((1. - self._x[self._x_small]) /
(1. + self._x[self._x_small]))
secondpart[self._x_small] = np.log((1. + small_2a) /
(1. - small_2a))
big_2a = self._x[self._x_big] - 1.
big_2b = 1. + self._x[self._x_big]
secondpart[self._x_big] = np.arctan(np.sqrt(big_2a / big_2b))
both_3a = (4. / (self._x**2)) * np.log(self._x / 2.)
both_3b = 2. / (self._x**2 - 1.)
g = firstpart * secondpart + both_3a - both_3b
g[self._x_one] = (10. / 3.) + 4. * np.log(0.5)
if np.isnan(np.sum(g)) or np.isinf(np.sum(g)):
print('\nERROR: g is not all real\n', g)
# calculate & return centered profile
deltasigma = self._rs_dc_rcrit * g
return deltasigma
def _offset_dsigma(self):
original_rbins = self._rbins.value
# if offset sigma was already calculated, use it!
try:
sigma_sm_rbins = self._sigma_sm
except AttributeError:
sigma_sm_rbins = self.sigma_nfw()
innermost_sampling = 1.e-10 # stable for anything below 1e-5
inner_prec = self._numRinner
r_inner = np.linspace(innermost_sampling,
original_rbins.min(),
endpoint=False, num=inner_prec)
outer_prec = self._factorRouter * self._nbins
r_outer = np.linspace(original_rbins.min(),
original_rbins.max(),
endpoint=False, num=outer_prec + 1)[1:]
r_ext_unordered = np.hstack([r_inner, r_outer, original_rbins])
r_extended = np.sort(r_ext_unordered)
# set temporary extended rbins, nbins, x, rs_dc_rcrit array
self._rbins = r_extended * units.Mpc
self._nbins = self._rbins.shape[0]
_set_dimensionless_radius(self) # uses _rbins, _nlens
rs_dc_rcrit = self._rs * self._delta_c * self._rho_crit
self._rs_dc_rcrit = rs_dc_rcrit.reshape(self._nlens,
1).repeat(self._nbins, 1)
sigma_sm_extended = self.sigma_nfw()
mean_inside_sigma_sm = np.zeros([self._nlens,
original_rbins.shape[0]])
for i, r in enumerate(original_rbins):
index_of_rbin = np.where(r_extended == r)[0][0]
x = r_extended[0:index_of_rbin + 1]
y = sigma_sm_extended[:, 0:index_of_rbin + 1] * x
integral = simps(y, x=x, axis=-1, even='first')
# average of sigma_sm at r < rbin
mean_inside_sigma_sm[:, i] = (2. / r**2) * integral
mean_inside_sigma_sm = mean_inside_sigma_sm * (units.Msun /
units.pc**2)
# reset original rbins, nbins, x
self._rbins = original_rbins * units.Mpc
self._nbins = self._rbins.shape[0]
_set_dimensionless_radius(self)
rs_dc_rcrit = self._rs * self._delta_c * self._rho_crit
self._rs_dc_rcrit = rs_dc_rcrit.reshape(self._nlens,
1).repeat(self._nbins, 1)
self._sigma_sm = sigma_sm_rbins # reset to original sigma_sm
dsigma_sm = mean_inside_sigma_sm - sigma_sm_rbins
return dsigma_sm
if self._sigmaoffset is None:
finaldeltasigma = _centered_dsigma(self)
elif np.abs(self._sigmaoffset).sum() == 0:
finaldeltasigma = _centered_dsigma(self)
else:
finaldeltasigma = _offset_dsigma(self)
return finaldeltasigma | python | def deltasigma_nfw(self):
"""Calculate NFW differential surface mass density profile.
Generate the differential surface mass density profiles of each cluster
halo, assuming a spherical NFW model. Optionally includes the effect of
cluster miscentering offsets, if the parent object was initialized
with offsets.
Returns
----------
Quantity
Differential surface mass density profiles (ndarray, in
astropy.units of Msun/pc/pc). Each row corresponds to a single
cluster halo.
"""
def _centered_dsigma(self):
# calculate g
firstpart = np.zeros_like(self._x)
secondpart = np.zeros_like(self._x)
g = np.zeros_like(self._x)
small_1a = 4. / self._x[self._x_small]**2
small_1b = 2. / (self._x[self._x_small]**2 - 1.)
small_1c = np.sqrt(1. - self._x[self._x_small]**2)
firstpart[self._x_small] = (small_1a + small_1b) / small_1c
big_1a = 8. / (self._x[self._x_big]**2 *
np.sqrt(self._x[self._x_big]**2 - 1.))
big_1b = 4. / ((self._x[self._x_big]**2 - 1.)**1.5)
firstpart[self._x_big] = big_1a + big_1b
small_2a = np.sqrt((1. - self._x[self._x_small]) /
(1. + self._x[self._x_small]))
secondpart[self._x_small] = np.log((1. + small_2a) /
(1. - small_2a))
big_2a = self._x[self._x_big] - 1.
big_2b = 1. + self._x[self._x_big]
secondpart[self._x_big] = np.arctan(np.sqrt(big_2a / big_2b))
both_3a = (4. / (self._x**2)) * np.log(self._x / 2.)
both_3b = 2. / (self._x**2 - 1.)
g = firstpart * secondpart + both_3a - both_3b
g[self._x_one] = (10. / 3.) + 4. * np.log(0.5)
if np.isnan(np.sum(g)) or np.isinf(np.sum(g)):
print('\nERROR: g is not all real\n', g)
# calculate & return centered profile
deltasigma = self._rs_dc_rcrit * g
return deltasigma
def _offset_dsigma(self):
original_rbins = self._rbins.value
# if offset sigma was already calculated, use it!
try:
sigma_sm_rbins = self._sigma_sm
except AttributeError:
sigma_sm_rbins = self.sigma_nfw()
innermost_sampling = 1.e-10 # stable for anything below 1e-5
inner_prec = self._numRinner
r_inner = np.linspace(innermost_sampling,
original_rbins.min(),
endpoint=False, num=inner_prec)
outer_prec = self._factorRouter * self._nbins
r_outer = np.linspace(original_rbins.min(),
original_rbins.max(),
endpoint=False, num=outer_prec + 1)[1:]
r_ext_unordered = np.hstack([r_inner, r_outer, original_rbins])
r_extended = np.sort(r_ext_unordered)
# set temporary extended rbins, nbins, x, rs_dc_rcrit array
self._rbins = r_extended * units.Mpc
self._nbins = self._rbins.shape[0]
_set_dimensionless_radius(self) # uses _rbins, _nlens
rs_dc_rcrit = self._rs * self._delta_c * self._rho_crit
self._rs_dc_rcrit = rs_dc_rcrit.reshape(self._nlens,
1).repeat(self._nbins, 1)
sigma_sm_extended = self.sigma_nfw()
mean_inside_sigma_sm = np.zeros([self._nlens,
original_rbins.shape[0]])
for i, r in enumerate(original_rbins):
index_of_rbin = np.where(r_extended == r)[0][0]
x = r_extended[0:index_of_rbin + 1]
y = sigma_sm_extended[:, 0:index_of_rbin + 1] * x
integral = simps(y, x=x, axis=-1, even='first')
# average of sigma_sm at r < rbin
mean_inside_sigma_sm[:, i] = (2. / r**2) * integral
mean_inside_sigma_sm = mean_inside_sigma_sm * (units.Msun /
units.pc**2)
# reset original rbins, nbins, x
self._rbins = original_rbins * units.Mpc
self._nbins = self._rbins.shape[0]
_set_dimensionless_radius(self)
rs_dc_rcrit = self._rs * self._delta_c * self._rho_crit
self._rs_dc_rcrit = rs_dc_rcrit.reshape(self._nlens,
1).repeat(self._nbins, 1)
self._sigma_sm = sigma_sm_rbins # reset to original sigma_sm
dsigma_sm = mean_inside_sigma_sm - sigma_sm_rbins
return dsigma_sm
if self._sigmaoffset is None:
finaldeltasigma = _centered_dsigma(self)
elif np.abs(self._sigmaoffset).sum() == 0:
finaldeltasigma = _centered_dsigma(self)
else:
finaldeltasigma = _offset_dsigma(self)
return finaldeltasigma | [
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pairs : integer array-like with shape (num_edges, 2)
'''
if not symmetric:
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row, col = np.asarray(pairs).T
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shape = None if num_vertices is None else (num_vertices, num_vertices)
adj = ss.coo_matrix((weights, (row, col)), shape=shape)
return SparseAdjacencyMatrixGraph(adj)
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G = SymmEdgePairGraph(pairs, num_vertices=num_vertices)
if weights is None:
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s = G.matrix('coo').astype(float)
# shenanigans to assign edge weights in the right order
flat_idx = np.ravel_multi_index(s.nonzero(), s.shape)
r, c = np.transpose(pairs)
rc_idx = np.ravel_multi_index((r,c), s.shape)
cr_idx = np.ravel_multi_index((c,r), s.shape)
order = np.argsort(flat_idx)
flat_idx = flat_idx[order]
s.data[order[np.searchsorted(flat_idx, rc_idx)]] = weights
s.data[order[np.searchsorted(flat_idx, cr_idx)]] = weights
return SparseAdjacencyMatrixGraph(s) | python | def from_edge_pairs(pairs, num_vertices=None, symmetric=False, weights=None):
'''Constructor for Graph objects based on edges given as pairs of vertices.
pairs : integer array-like with shape (num_edges, 2)
'''
if not symmetric:
if weights is None:
return EdgePairGraph(pairs, num_vertices=num_vertices)
row, col = np.asarray(pairs).T
row, weights = np.broadcast_arrays(row, weights)
shape = None if num_vertices is None else (num_vertices, num_vertices)
adj = ss.coo_matrix((weights, (row, col)), shape=shape)
return SparseAdjacencyMatrixGraph(adj)
# symmetric case
G = SymmEdgePairGraph(pairs, num_vertices=num_vertices)
if weights is None:
return G
# Convert to sparse adj graph with provided edge weights
s = G.matrix('coo').astype(float)
# shenanigans to assign edge weights in the right order
flat_idx = np.ravel_multi_index(s.nonzero(), s.shape)
r, c = np.transpose(pairs)
rc_idx = np.ravel_multi_index((r,c), s.shape)
cr_idx = np.ravel_multi_index((c,r), s.shape)
order = np.argsort(flat_idx)
flat_idx = flat_idx[order]
s.data[order[np.searchsorted(flat_idx, rc_idx)]] = weights
s.data[order[np.searchsorted(flat_idx, cr_idx)]] = weights
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limix/limix-core | limix_core/mean/mean_base.py | MeanBase.W | def W(self,value):
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assert value.shape[0]==self._N, 'Dimension mismatch'
self._K = value.shape[1]
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | obfuscate | def obfuscate(
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"""
An example, barebone name obfuscation ruleset
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If true, identifier names on the global scope will also be
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A tuple of strings that should not be generated as obfuscated
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Scope.global_symbols_in_children | def global_symbols_in_children(self):
"""
This is based on all children referenced symbols that have not
been declared.
The intended use case is to ban the symbols from being used as
remapped symbol values.
"""
result = set()
for child in self.children:
result |= (
child.global_symbols |
child.global_symbols_in_children)
return result | python | def global_symbols_in_children(self):
"""
This is based on all children referenced symbols that have not
been declared.
The intended use case is to ban the symbols from being used as
remapped symbol values.
"""
result = set()
for child in self.children:
result |= (
child.global_symbols |
child.global_symbols_in_children)
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Scope.close | def close(self):
"""
Mark the scope as closed, i.e. all symbols have been declared,
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"""
if self._closed:
raise ValueError('scope is already marked as closed')
# By letting parent know which symbols this scope has leaked, it
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if self.parent:
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self.parent.reference(symbol, c)
self._closed = True | python | def close(self):
"""
Mark the scope as closed, i.e. all symbols have been declared,
and no further declarations should be done.
"""
if self._closed:
raise ValueError('scope is already marked as closed')
# By letting parent know which symbols this scope has leaked, it
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Scope._reserved_symbols | def _reserved_symbols(self):
"""
Helper property for the build_remap_symbols method. This
property first resolves _all_ local references from parents,
skipping all locally declared symbols as the goal is to generate
a local mapping for them, but in a way not to shadow over any
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"""
# In practice, and as a possible optimisation, the parent's
# remapped symbols table can be merged into this instance, but
# this bloats memory use and cause unspecified reservations that
# may not be applicable this or any child scope. So for clarity
# and purity of references made, this somewhat more involved way
# is done instead.
remapped_parents_symbols = {
self.resolve(v) for v in self.non_local_symbols}
return (
# block implicit children globals.
self.global_symbols_in_children |
# also not any global symbols
self.global_symbols |
# also all remapped parent symbols referenced here
remapped_parents_symbols
) | python | def _reserved_symbols(self):
"""
Helper property for the build_remap_symbols method. This
property first resolves _all_ local references from parents,
skipping all locally declared symbols as the goal is to generate
a local mapping for them, but in a way not to shadow over any
already declared symbols from parents, and also the implicit
globals in all children.
This is marked "private" as there are a number of computations
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"""
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# remapped symbols table can be merged into this instance, but
# this bloats memory use and cause unspecified reservations that
# may not be applicable this or any child scope. So for clarity
# and purity of references made, this somewhat more involved way
# is done instead.
remapped_parents_symbols = {
self.resolve(v) for v in self.non_local_symbols}
return (
# block implicit children globals.
self.global_symbols_in_children |
# also not any global symbols
self.global_symbols |
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remapped_parents_symbols
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Scope.build_remap_symbols | def build_remap_symbols(self, name_generator, children_only=True):
"""
This builds the replacement table for all the defined symbols
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"""
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self.remapped_symbols[symbol] = next(replacement)
for child in self.children:
child.build_remap_symbols(name_generator, False) | python | def build_remap_symbols(self, name_generator, children_only=True):
"""
This builds the replacement table for all the defined symbols
for all the children, and this scope, if the children_only
argument is False.
"""
if not children_only:
replacement = name_generator(skip=(self._reserved_symbols))
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Scope.nest | def nest(self, node, cls=None):
"""
Create a new nested scope that is within this instance, binding
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"""
if cls is None:
cls = type(self)
nested_scope = cls(node, self)
self.children.append(nested_scope)
return nested_scope | python | def nest(self, node, cls=None):
"""
Create a new nested scope that is within this instance, binding
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"""
if cls is None:
cls = type(self)
nested_scope = cls(node, self)
self.children.append(nested_scope)
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | CatchScope.declare | def declare(self, symbol):
"""
Nothing gets declared here - it's the parents problem, except
for the case where the symbol is the one we have here.
"""
if symbol != self.catch_symbol:
self.parent.declare(symbol) | python | def declare(self, symbol):
"""
Nothing gets declared here - it's the parents problem, except
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | CatchScope.reference | def reference(self, symbol, count=1):
"""
However, if referenced, ensure that the counter is applied to
the catch symbol.
"""
if symbol == self.catch_symbol:
self.catch_symbol_usage += count
else:
self.parent.reference(symbol, count) | python | def reference(self, symbol, count=1):
"""
However, if referenced, ensure that the counter is applied to
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"""
if symbol == self.catch_symbol:
self.catch_symbol_usage += count
else:
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | CatchScope.build_remap_symbols | def build_remap_symbols(self, name_generator, children_only=None):
"""
The children_only flag is inapplicable, but this is included as
the Scope class is defined like so.
Here this simply just place the catch symbol with the next
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"""
replacement = name_generator(skip=(self._reserved_symbols))
self.remapped_symbols[self.catch_symbol] = next(replacement)
# also to continue down the children.
for child in self.children:
child.build_remap_symbols(name_generator, False) | python | def build_remap_symbols(self, name_generator, children_only=None):
"""
The children_only flag is inapplicable, but this is included as
the Scope class is defined like so.
Here this simply just place the catch symbol with the next
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"""
replacement = name_generator(skip=(self._reserved_symbols))
self.remapped_symbols[self.catch_symbol] = next(replacement)
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Obfuscator.register_reference | def register_reference(self, dispatcher, node):
"""
Register this identifier to the current scope, and mark it as
referenced in the current scope.
"""
# the identifier node itself will be mapped to the current scope
# for the resolve to work
# This should probably WARN about the node object being already
# assigned to an existing scope that isn't current_scope.
self.identifiers[node] = self.current_scope
self.current_scope.reference(node.value) | python | def register_reference(self, dispatcher, node):
"""
Register this identifier to the current scope, and mark it as
referenced in the current scope.
"""
# the identifier node itself will be mapped to the current scope
# for the resolve to work
# This should probably WARN about the node object being already
# assigned to an existing scope that isn't current_scope.
self.identifiers[node] = self.current_scope
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Obfuscator.shadow_reference | def shadow_reference(self, dispatcher, node):
"""
Only simply make a reference to the value in the current scope,
specifically for the FuncBase type.
"""
# as opposed to the previous one, only add the value of the
# identifier itself to the scope so that it becomes reserved.
self.current_scope.reference(node.identifier.value) | python | def shadow_reference(self, dispatcher, node):
"""
Only simply make a reference to the value in the current scope,
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"""
# as opposed to the previous one, only add the value of the
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self.current_scope.reference(node.identifier.value) | [
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Obfuscator.resolve | def resolve(self, dispatcher, node):
"""
For the given node, resolve it into the scope it was declared
at, and if one was found, return its value.
"""
scope = self.identifiers.get(node)
if not scope:
return node.value
return scope.resolve(node.value) | python | def resolve(self, dispatcher, node):
"""
For the given node, resolve it into the scope it was declared
at, and if one was found, return its value.
"""
scope = self.identifiers.get(node)
if not scope:
return node.value
return scope.resolve(node.value) | [
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Obfuscator.walk | def walk(self, dispatcher, node):
"""
Walk through the node with a custom dispatcher for extraction of
details that are required.
"""
deferrable_handlers = {
Declare: self.declare,
Resolve: self.register_reference,
}
layout_handlers = {
PushScope: self.push_scope,
PopScope: self.pop_scope,
PushCatch: self.push_catch,
# should really be different, but given that the
# mechanism is within the same tree, the only difference
# would be sanity check which should have been tested in
# the first place in the primitives anyway.
PopCatch: self.pop_scope,
}
if not self.shadow_funcname:
layout_handlers[ResolveFuncName] = self.shadow_reference
local_dispatcher = Dispatcher(
definitions=dict(dispatcher),
token_handler=None,
layout_handlers=layout_handlers,
deferrable_handlers=deferrable_handlers,
)
return list(walk(local_dispatcher, node)) | python | def walk(self, dispatcher, node):
"""
Walk through the node with a custom dispatcher for extraction of
details that are required.
"""
deferrable_handlers = {
Declare: self.declare,
Resolve: self.register_reference,
}
layout_handlers = {
PushScope: self.push_scope,
PopScope: self.pop_scope,
PushCatch: self.push_catch,
# should really be different, but given that the
# mechanism is within the same tree, the only difference
# would be sanity check which should have been tested in
# the first place in the primitives anyway.
PopCatch: self.pop_scope,
}
if not self.shadow_funcname:
layout_handlers[ResolveFuncName] = self.shadow_reference
local_dispatcher = Dispatcher(
definitions=dict(dispatcher),
token_handler=None,
layout_handlers=layout_handlers,
deferrable_handlers=deferrable_handlers,
)
return list(walk(local_dispatcher, node)) | [
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calmjs/calmjs.parse | src/calmjs/parse/handlers/obfuscation.py | Obfuscator.finalize | def finalize(self):
"""
Finalize the run - build the name generator and use it to build
the remap symbol tables.
"""
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self.global_scope.build_remap_symbols(
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"""
Finalize the run - build the name generator and use it to build
the remap symbol tables.
"""
self.global_scope.close()
name_generator = NameGenerator(skip=self.reserved_keywords)
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"take out the trash"
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if src_dir is None:
src_dir = 'src' if easy.path('src').exists() else '.'
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | Generate._set_grid_info | def _set_grid_info(self, which, low, high, num, scale, name):
"""Set the grid values for x or y.
Create information for the grid of x and y values.
Args:
which (str): `x` or `y`.
low/high (float): Lowest/highest value for the axis.
num (int): Number of points on axis.
scale (str): Scale of the axis. Choices are 'log' or 'lin'.
name (str): Name representing the axis. See GenerateContainer documentation
for options for the name.
unit (str): Unit for this axis quantity. See GenerateContainer documentation
for options for the units.
Raises:
ValueError: If scale is not 'log' or 'lin'.
"""
setattr(self.generate_info, which + '_low', low)
setattr(self.generate_info, which + '_high', high)
setattr(self.generate_info, 'num_' + which, num)
setattr(self.generate_info, which + 'val_name', name)
if scale not in ['lin', 'log']:
raise ValueError('{} scale must be lin or log.'.format(which))
setattr(self.generate_info, which + 'scale', scale)
return | python | def _set_grid_info(self, which, low, high, num, scale, name):
"""Set the grid values for x or y.
Create information for the grid of x and y values.
Args:
which (str): `x` or `y`.
low/high (float): Lowest/highest value for the axis.
num (int): Number of points on axis.
scale (str): Scale of the axis. Choices are 'log' or 'lin'.
name (str): Name representing the axis. See GenerateContainer documentation
for options for the name.
unit (str): Unit for this axis quantity. See GenerateContainer documentation
for options for the units.
Raises:
ValueError: If scale is not 'log' or 'lin'.
"""
setattr(self.generate_info, which + '_low', low)
setattr(self.generate_info, which + '_high', high)
setattr(self.generate_info, 'num_' + which, num)
setattr(self.generate_info, which + 'val_name', name)
if scale not in ['lin', 'log']:
raise ValueError('{} scale must be lin or log.'.format(which))
setattr(self.generate_info, which + 'scale', scale)
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | Generate.set_y_grid_info | def set_y_grid_info(self, y_low, y_high, num_y, yscale, yval_name):
"""Set the grid values for y.
Create information for the grid of y values.
Args:
num_y (int): Number of points on axis.
y_low/y_high (float): Lowest/highest value for the axis.
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return | python | def set_y_grid_info(self, y_low, y_high, num_y, yscale, yval_name):
"""Set the grid values for y.
Create information for the grid of y values.
Args:
num_y (int): Number of points on axis.
y_low/y_high (float): Lowest/highest value for the axis.
yscale (str): Scale of the axis. Choices are 'log' or 'lin'.
yval_name (str): Name representing the axis. See GenerateContainer documentation
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"""
self._set_grid_info('y', y_low, y_high, num_y, yscale, yval_name)
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | Generate.set_x_grid_info | def set_x_grid_info(self, x_low, x_high, num_x, xscale, xval_name):
"""Set the grid values for x.
Create information for the grid of x values.
Args:
num_x (int): Number of points on axis.
x_low/x_high (float): Lowest/highest value for the axis.
xscale (str): Scale of the axis. Choices are 'log' or 'lin'.
xval_name (str): Name representing the axis. See GenerateContainer documentation
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"""
self._set_grid_info('x', x_low, x_high, num_x, xscale, xval_name)
return | python | def set_x_grid_info(self, x_low, x_high, num_x, xscale, xval_name):
"""Set the grid values for x.
Create information for the grid of x values.
Args:
num_x (int): Number of points on axis.
x_low/x_high (float): Lowest/highest value for the axis.
xscale (str): Scale of the axis. Choices are 'log' or 'lin'.
xval_name (str): Name representing the axis. See GenerateContainer documentation
for options for the name.
"""
self._set_grid_info('x', x_low, x_high, num_x, xscale, xval_name)
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | SensitivityInput.add_noise_curve | def add_noise_curve(self, name, noise_type='ASD', is_wd_background=False):
"""Add a noise curve for generation.
This will add a noise curve for an SNR calculation by appending to the sensitivity_curves
list within the sensitivity_input dictionary.
The name of the noise curve prior to the file extension will appear as its
label in the final output dataset. Therefore, it is recommended prior to
running the generator that file names are renamed to simple names
for later reference.
Args:
name (str): Name of noise curve including file extension inside input_folder.
noise_type (str, optional): Type of noise. Choices are `ASD`, `PSD`, or `char_strain`.
Default is ASD.
is_wd_background (bool, optional): If True, this sensitivity is used as the white dwarf
background noise. Default is False.
"""
if is_wd_background:
self.sensitivity_input.wd_noise = name
self.sensitivity_input.wd_noise_type_in = noise_type
else:
if 'sensitivity_curves' not in self.sensitivity_input.__dict__:
self.sensitivity_input.sensitivity_curves = []
if 'noise_type_in' not in self.sensitivity_input.__dict__:
self.sensitivity_input.noise_type_in = []
self.sensitivity_input.sensitivity_curves.append(name)
self.sensitivity_input.noise_type_in.append(noise_type)
return | python | def add_noise_curve(self, name, noise_type='ASD', is_wd_background=False):
"""Add a noise curve for generation.
This will add a noise curve for an SNR calculation by appending to the sensitivity_curves
list within the sensitivity_input dictionary.
The name of the noise curve prior to the file extension will appear as its
label in the final output dataset. Therefore, it is recommended prior to
running the generator that file names are renamed to simple names
for later reference.
Args:
name (str): Name of noise curve including file extension inside input_folder.
noise_type (str, optional): Type of noise. Choices are `ASD`, `PSD`, or `char_strain`.
Default is ASD.
is_wd_background (bool, optional): If True, this sensitivity is used as the white dwarf
background noise. Default is False.
"""
if is_wd_background:
self.sensitivity_input.wd_noise = name
self.sensitivity_input.wd_noise_type_in = noise_type
else:
if 'sensitivity_curves' not in self.sensitivity_input.__dict__:
self.sensitivity_input.sensitivity_curves = []
if 'noise_type_in' not in self.sensitivity_input.__dict__:
self.sensitivity_input.noise_type_in = []
self.sensitivity_input.sensitivity_curves.append(name)
self.sensitivity_input.noise_type_in.append(noise_type)
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | SensitivityInput.set_wd_noise | def set_wd_noise(self, wd_noise):
"""Add White Dwarf Background Noise
This adds the White Dwarf (WD) Background noise. This can either do calculations with,
without, or with and without WD noise.
Args:
wd_noise (bool or str, optional): Add or remove WD background noise. First option is to
have only calculations with the wd_noise. For this, use `yes` or True.
Second option is no WD noise. For this, use `no` or False. For both calculations
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Raises:
ValueError: Input value is not one of the options.
"""
if isinstance(wd_noise, bool):
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elif wd_noise.lower() == 'no' or wd_noise.lower() == 'false':
wd_noise = 'False'
elif wd_noise.lower() == 'both':
wd_noise = 'Both'
else:
raise ValueError('wd_noise must be yes, no, True, False, or Both.')
self.sensitivity_input.add_wd_noise = wd_noise
return | python | def set_wd_noise(self, wd_noise):
"""Add White Dwarf Background Noise
This adds the White Dwarf (WD) Background noise. This can either do calculations with,
without, or with and without WD noise.
Args:
wd_noise (bool or str, optional): Add or remove WD background noise. First option is to
have only calculations with the wd_noise. For this, use `yes` or True.
Second option is no WD noise. For this, use `no` or False. For both calculations
with and without WD noise, use `both`.
Raises:
ValueError: Input value is not one of the options.
"""
if isinstance(wd_noise, bool):
wd_noise = str(wd_noise)
if wd_noise.lower() == 'yes' or wd_noise.lower() == 'true':
wd_noise = 'True'
elif wd_noise.lower() == 'no' or wd_noise.lower() == 'false':
wd_noise = 'False'
elif wd_noise.lower() == 'both':
wd_noise = 'Both'
else:
raise ValueError('wd_noise must be yes, no, True, False, or Both.')
self.sensitivity_input.add_wd_noise = wd_noise
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | ParallelInput.set_generation_type | def set_generation_type(self, num_processors=-1, num_splits=1000, verbose=-1):
"""Change generation type.
Choose weather to generate the data in parallel or on a single processor.
Args:
num_processors (int or None, optional): Number of parallel processors to use.
If ``num_processors==-1``, this will use multiprocessing module and use
available cpus. If single generation is desired, num_processors is set
to ``None``. Default is -1.
num_splits (int, optional): Number of binaries to run during each process.
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verbose (int, optional): Describes the notification of when parallel processes
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If ``verbose == -1``, no notifications are given. Default is -1.
"""
self.parallel_input.num_processors = num_processors
self.parallel_input.num_splits = num_splits
self.parallel_input.verbose = verbose
return | python | def set_generation_type(self, num_processors=-1, num_splits=1000, verbose=-1):
"""Change generation type.
Choose weather to generate the data in parallel or on a single processor.
Args:
num_processors (int or None, optional): Number of parallel processors to use.
If ``num_processors==-1``, this will use multiprocessing module and use
available cpus. If single generation is desired, num_processors is set
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num_splits (int, optional): Number of binaries to run during each process.
Default is 1000.
verbose (int, optional): Describes the notification of when parallel processes
are finished. Value describes cadence of process completion notifications.
If ``verbose == -1``, no notifications are given. Default is -1.
"""
self.parallel_input.num_processors = num_processors
self.parallel_input.num_splits = num_splits
self.parallel_input.verbose = verbose
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | SNRInput.set_signal_type | def set_signal_type(self, sig_type):
"""Set the signal type of interest.
Sets the signal type for which the SNR is calculated.
This means inspiral, merger, and/or ringdown.
Args:
sig_type (str or list of str): Signal type desired by user.
Choices are `ins`, `mrg`, `rd`, `all` for circular waveforms created with PhenomD.
If eccentric waveforms are used, must be `all`.
"""
if isinstance(sig_type, str):
sig_type = [sig_type]
self.snr_input.signal_type = sig_type
return | python | def set_signal_type(self, sig_type):
"""Set the signal type of interest.
Sets the signal type for which the SNR is calculated.
This means inspiral, merger, and/or ringdown.
Args:
sig_type (str or list of str): Signal type desired by user.
Choices are `ins`, `mrg`, `rd`, `all` for circular waveforms created with PhenomD.
If eccentric waveforms are used, must be `all`.
"""
if isinstance(sig_type, str):
sig_type = [sig_type]
self.snr_input.signal_type = sig_type
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Sets the signal type for which the SNR is calculated.
This means inspiral, merger, and/or ringdown.
Args:
sig_type (str or list of str): Signal type desired by user.
Choices are `ins`, `mrg`, `rd`, `all` for circular waveforms created with PhenomD.
If eccentric waveforms are used, must be `all`. | [
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mikekatz04/BOWIE | snr_calculator_folder/gwsnrcalc/genconutils/forminput.py | MainContainer.return_dict | def return_dict(self):
"""Output dictionary for :mod:`gwsnrcalc.generate_contour_data` input.
Iterates through the entire MainContainer class turning its contents
into dictionary form. This dictionary becomes the input for
:mod:`gwsnrcalc.generate_contour_data`.
If `print_input` attribute is True, the entire dictionary will be printed
prior to returning the dicitonary.
Returns:
- output_dict: Dicitonary for input into
:mod:`gwsnrcalc.generate_contour_data`.
"""
output_dict = {}
output_dict['general'] = self._iterate_through_class(self.general.__dict__)
output_dict['generate_info'] = self._iterate_through_class(self.generate_info.__dict__)
output_dict['sensitivity_input'] = (self._iterate_through_class(
self.sensitivity_input.__dict__))
output_dict['snr_input'] = self._iterate_through_class(self.snr_input.__dict__)
output_dict['parallel_input'] = self._iterate_through_class(self.parallel_input.__dict__)
output_dict['output_info'] = self._iterate_through_class(self.output_info.__dict__)
if self.print_input:
print(output_dict)
return output_dict | python | def return_dict(self):
"""Output dictionary for :mod:`gwsnrcalc.generate_contour_data` input.
Iterates through the entire MainContainer class turning its contents
into dictionary form. This dictionary becomes the input for
:mod:`gwsnrcalc.generate_contour_data`.
If `print_input` attribute is True, the entire dictionary will be printed
prior to returning the dicitonary.
Returns:
- output_dict: Dicitonary for input into
:mod:`gwsnrcalc.generate_contour_data`.
"""
output_dict = {}
output_dict['general'] = self._iterate_through_class(self.general.__dict__)
output_dict['generate_info'] = self._iterate_through_class(self.generate_info.__dict__)
output_dict['sensitivity_input'] = (self._iterate_through_class(
self.sensitivity_input.__dict__))
output_dict['snr_input'] = self._iterate_through_class(self.snr_input.__dict__)
output_dict['parallel_input'] = self._iterate_through_class(self.parallel_input.__dict__)
output_dict['output_info'] = self._iterate_through_class(self.output_info.__dict__)
if self.print_input:
print(output_dict)
return output_dict | [
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calmjs/calmjs.parse | src/calmjs/parse/unparsers/es5.py | pretty_print | def pretty_print(ast, indent_str=' '):
"""
Simple pretty print function; returns a string rendering of an input
AST of an ES5 Program.
arguments
ast
The AST to pretty print
indent_str
The string used for indentations. Defaults to two spaces.
"""
return ''.join(chunk.text for chunk in pretty_printer(indent_str)(ast)) | python | def pretty_print(ast, indent_str=' '):
"""
Simple pretty print function; returns a string rendering of an input
AST of an ES5 Program.
arguments
ast
The AST to pretty print
indent_str
The string used for indentations. Defaults to two spaces.
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calmjs/calmjs.parse | src/calmjs/parse/unparsers/es5.py | minify_printer | def minify_printer(
obfuscate=False,
obfuscate_globals=False,
shadow_funcname=False,
drop_semi=False):
"""
Construct a minimum printer.
Arguments
obfuscate
If True, obfuscate identifiers nested in each scope with a
shortened identifier name to further reduce output size.
Defaults to False.
obfuscate_globals
Also do the same to identifiers nested on the global scope; do
not enable unless the renaming of global variables in a not
fully deterministic manner into something else is guaranteed to
not cause problems with the generated code and other code that
in the same environment that it will be executed in.
Defaults to False for the reason above.
drop_semi
Drop semicolons whenever possible (e.g. the final semicolons of
a given block).
"""
active_rules = [rules.minify(drop_semi=drop_semi)]
if obfuscate:
active_rules.append(rules.obfuscate(
obfuscate_globals=obfuscate_globals,
shadow_funcname=shadow_funcname,
reserved_keywords=(Lexer.keywords_dict.keys())
))
return Unparser(rules=active_rules) | python | def minify_printer(
obfuscate=False,
obfuscate_globals=False,
shadow_funcname=False,
drop_semi=False):
"""
Construct a minimum printer.
Arguments
obfuscate
If True, obfuscate identifiers nested in each scope with a
shortened identifier name to further reduce output size.
Defaults to False.
obfuscate_globals
Also do the same to identifiers nested on the global scope; do
not enable unless the renaming of global variables in a not
fully deterministic manner into something else is guaranteed to
not cause problems with the generated code and other code that
in the same environment that it will be executed in.
Defaults to False for the reason above.
drop_semi
Drop semicolons whenever possible (e.g. the final semicolons of
a given block).
"""
active_rules = [rules.minify(drop_semi=drop_semi)]
if obfuscate:
active_rules.append(rules.obfuscate(
obfuscate_globals=obfuscate_globals,
shadow_funcname=shadow_funcname,
reserved_keywords=(Lexer.keywords_dict.keys())
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return Unparser(rules=active_rules) | [
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calmjs/calmjs.parse | src/calmjs/parse/unparsers/es5.py | minify_print | def minify_print(
ast,
obfuscate=False,
obfuscate_globals=False,
shadow_funcname=False,
drop_semi=False):
"""
Simple minify print function; returns a string rendering of an input
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Arguments
ast
The AST to minify print
obfuscate
If True, obfuscate identifiers nested in each scope with a
shortened identifier name to further reduce output size.
Defaults to False.
obfuscate_globals
Also do the same to identifiers nested on the global scope; do
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fully deterministic manner into something else is guaranteed to
not cause problems with the generated code and other code that
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Defaults to False for the reason above.
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Drop semicolons whenever possible (e.g. the final semicolons of
a given block).
"""
return ''.join(chunk.text for chunk in minify_printer(
obfuscate, obfuscate_globals, shadow_funcname, drop_semi)(ast)) | python | def minify_print(
ast,
obfuscate=False,
obfuscate_globals=False,
shadow_funcname=False,
drop_semi=False):
"""
Simple minify print function; returns a string rendering of an input
AST of an ES5 program
Arguments
ast
The AST to minify print
obfuscate
If True, obfuscate identifiers nested in each scope with a
shortened identifier name to further reduce output size.
Defaults to False.
obfuscate_globals
Also do the same to identifiers nested on the global scope; do
not enable unless the renaming of global variables in a not
fully deterministic manner into something else is guaranteed to
not cause problems with the generated code and other code that
in the same environment that it will be executed in.
Defaults to False for the reason above.
drop_semi
Drop semicolons whenever possible (e.g. the final semicolons of
a given block).
"""
return ''.join(chunk.text for chunk in minify_printer(
obfuscate, obfuscate_globals, shadow_funcname, drop_semi)(ast)) | [
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calmjs/calmjs.parse | src/calmjs/parse/vlq.py | encode_vlq | def encode_vlq(i):
"""
Encode integer `i` into a VLQ encoded string.
"""
# shift in the sign to least significant bit
raw = (-i << 1) + 1 if i < 0 else i << 1
if raw < VLQ_MULTI_CHAR:
# short-circuit simple case as it doesn't need continuation
return INT_B64[raw]
result = []
while raw:
# assume continue
result.append(raw & VLQ_BASE_MASK | VLQ_CONT)
# shift out processed bits
raw = raw >> VLQ_SHIFT
# discontinue the last unit
result[-1] &= VLQ_BASE_MASK
return ''.join(INT_B64[i] for i in result) | python | def encode_vlq(i):
"""
Encode integer `i` into a VLQ encoded string.
"""
# shift in the sign to least significant bit
raw = (-i << 1) + 1 if i < 0 else i << 1
if raw < VLQ_MULTI_CHAR:
# short-circuit simple case as it doesn't need continuation
return INT_B64[raw]
result = []
while raw:
# assume continue
result.append(raw & VLQ_BASE_MASK | VLQ_CONT)
# shift out processed bits
raw = raw >> VLQ_SHIFT
# discontinue the last unit
result[-1] &= VLQ_BASE_MASK
return ''.join(INT_B64[i] for i in result) | [
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calmjs/calmjs.parse | src/calmjs/parse/vlq.py | decode_vlqs | def decode_vlqs(s):
"""
Decode str `s` into a list of integers.
"""
ints = []
i = 0
shift = 0
for c in s:
raw = B64_INT[c]
cont = VLQ_CONT & raw
i = ((VLQ_BASE_MASK & raw) << shift) | i
shift += VLQ_SHIFT
if not cont:
sign = -1 if 1 & i else 1
ints.append((i >> 1) * sign)
i = 0
shift = 0
return tuple(ints) | python | def decode_vlqs(s):
"""
Decode str `s` into a list of integers.
"""
ints = []
i = 0
shift = 0
for c in s:
raw = B64_INT[c]
cont = VLQ_CONT & raw
i = ((VLQ_BASE_MASK & raw) << shift) | i
shift += VLQ_SHIFT
if not cont:
sign = -1 if 1 & i else 1
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i = 0
shift = 0
return tuple(ints) | [
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inveniosoftware/invenio-config | invenio_config/default.py | InvenioConfigDefault.init_app | def init_app(self, app):
"""Initialize Flask application."""
# Ensure SECRET_KEY is set.
SECRET_KEY = app.config.get('SECRET_KEY')
if SECRET_KEY is None:
app.config['SECRET_KEY'] = 'CHANGE_ME'
warnings.warn(
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"""Initialize Flask application."""
# Ensure SECRET_KEY is set.
SECRET_KEY = app.config.get('SECRET_KEY')
if SECRET_KEY is None:
app.config['SECRET_KEY'] = 'CHANGE_ME'
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pyroscope/pyrobase | src/pyrobase/pyutil.py | require_json | def require_json():
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# Fails when "json" is missing and "simplejson" is not installed either
try:
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return json
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""" Load the best available json library on demand.
"""
# Fails when "json" is missing and "simplejson" is not installed either
try:
import json # pylint: disable=F0401
return json
except ImportError:
try:
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ubccr/pinky | pinky/canonicalization/primes.py | Primes._findNextPrime | def _findNextPrime(self, N):
"""Generate the first N primes"""
primes = self.primes
nextPrime = primes[-1]+1
while(len(primes)<N):
maximum = nextPrime * nextPrime
prime = 1
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break
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primes.append(nextPrime)
nextPrime+=1 | python | def _findNextPrime(self, N):
"""Generate the first N primes"""
primes = self.primes
nextPrime = primes[-1]+1
while(len(primes)<N):
maximum = nextPrime * nextPrime
prime = 1
for i in primes:
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break
if nextPrime % i == 0:
prime = 0
break
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primes.append(nextPrime)
nextPrime+=1 | [
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pyroscope/pyrobase | src/pyrobase/paver/support.py | venv_bin | def venv_bin(name=None): # pylint: disable=inconsistent-return-statements
""" Get the directory for virtualenv stubs, or a full executable path
if C{name} is provided.
"""
if not hasattr(sys, "real_prefix"):
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bindir = os.path.join(sys.prefix, bindir)
if os.path.exists(bindir):
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return os.path.join(bindir, name + bin_ext)
else:
return bindir
easy.error("ERROR: Scripts directory not found in '%s'" % (sys.prefix,))
sys.exit(1) | python | def venv_bin(name=None): # pylint: disable=inconsistent-return-statements
""" Get the directory for virtualenv stubs, or a full executable path
if C{name} is provided.
"""
if not hasattr(sys, "real_prefix"):
easy.error("ERROR: '%s' is not a virtualenv" % (sys.executable,))
sys.exit(1)
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bindir = os.path.join(sys.prefix, bindir)
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bin_ext = os.path.splitext(sys.executable)[1] if sys.platform == 'win32' else ''
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else:
return bindir
easy.error("ERROR: Scripts directory not found in '%s'" % (sys.prefix,))
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pyroscope/pyrobase | src/pyrobase/paver/support.py | vsh | def vsh(cmd, *args, **kw):
""" Execute a command installed into the active virtualenv.
"""
args = '" "'.join(i.replace('"', r'\"') for i in args)
easy.sh('"%s" "%s"' % (venv_bin(cmd), args)) | python | def vsh(cmd, *args, **kw):
""" Execute a command installed into the active virtualenv.
"""
args = '" "'.join(i.replace('"', r'\"') for i in args)
easy.sh('"%s" "%s"' % (venv_bin(cmd), args)) | [
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pyroscope/pyrobase | src/pyrobase/paver/support.py | install_tools | def install_tools(dependencies):
""" Install a required tool before using it, if it's missing.
Note that C{dependencies} can be a distutils requirement,
or a simple name from the C{tools} task configuration, or
a (nested) list of such requirements.
"""
tools = getattr(easy.options, "tools", {})
for dependency in iterutil.flatten(dependencies):
dependency = tools.get(dependency, dependency)
try:
pkg_resources.require(dependency)
except pkg_resources.DistributionNotFound:
vsh("pip", "install", "-q", dependency)
dependency = pkg_resources.require(dependency)
easy.info("Installed required tool %s" % (dependency,)) | python | def install_tools(dependencies):
""" Install a required tool before using it, if it's missing.
Note that C{dependencies} can be a distutils requirement,
or a simple name from the C{tools} task configuration, or
a (nested) list of such requirements.
"""
tools = getattr(easy.options, "tools", {})
for dependency in iterutil.flatten(dependencies):
dependency = tools.get(dependency, dependency)
try:
pkg_resources.require(dependency)
except pkg_resources.DistributionNotFound:
vsh("pip", "install", "-q", dependency)
dependency = pkg_resources.require(dependency)
easy.info("Installed required tool %s" % (dependency,)) | [
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pyroscope/pyrobase | src/pyrobase/paver/support.py | task_requires | def task_requires(*dependencies):
""" A task decorator that ensures a distutils dependency (or a list thereof) is met
before that task is executed.
"""
def entangle(task):
"Decorator wrapper."
if not isinstance(task, tasks.Task):
task = tasks.Task(task)
def tool_task(*args, **kw):
"Install requirements, then call original task."
install_tools(dependencies)
return task_body(*args, **kw)
# Apply our wrapper to original task
task_body, task.func = task.func, tool_task
return task
return entangle | python | def task_requires(*dependencies):
""" A task decorator that ensures a distutils dependency (or a list thereof) is met
before that task is executed.
"""
def entangle(task):
"Decorator wrapper."
if not isinstance(task, tasks.Task):
task = tasks.Task(task)
def tool_task(*args, **kw):
"Install requirements, then call original task."
install_tools(dependencies)
return task_body(*args, **kw)
# Apply our wrapper to original task
task_body, task.func = task.func, tool_task
return task
return entangle | [
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pyroscope/pyrobase | src/pyrobase/paver/support.py | toplevel_packages | def toplevel_packages():
""" Get package list, without sub-packages.
"""
packages = set(easy.options.setup.packages)
for pkg in list(packages):
packages -= set(p for p in packages if str(p).startswith(pkg + '.'))
return list(sorted(packages)) | python | def toplevel_packages():
""" Get package list, without sub-packages.
"""
packages = set(easy.options.setup.packages)
for pkg in list(packages):
packages -= set(p for p in packages if str(p).startswith(pkg + '.'))
return list(sorted(packages)) | [
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jbm950/pygame_toolbox | pygame_toolbox/graphics/__init__.py | Button.set_position | def set_position(self, position, midpoint=False, surface=None):
"""This method allows the button to be moved manually and keep the click
on functionality.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Inputs:
position - This is the x, y position of the top left corner of the
button. (defining point can be changed to midpoint)
midpoint - If true is passed to midpoint the button will be blitted
to a surface, either automatically if a surface is passed or
manually, such that the position input is the center of the
button rather than the top left corner.
(doc string updated ver 0.1)"""
# Find the image size and midpoint of the image
imagesize = self.image.get_size()
imagemidp = (int(imagesize[0] * 0.5), int(imagesize[1] * 0.5))
# if a midpoint arguement is passed, set the pos to the top left pixel
# such that the position passed in is in the middle of the button
if midpoint:
self.pos = (position[0] - imagemidp[0], position[1] - imagemidp[1])
else:
self.pos = position
# set the rectangle to be used for collision detection
self.rect = pygame.Rect(self.pos, self.image.get_size())
# Set up the information that is needed to blit the image to the surface
self.blitinfo = (self.image, self.pos)
# automatically blit the button onto an input surface
if surface:
surface.blit(*self.blitinfo) | python | def set_position(self, position, midpoint=False, surface=None):
"""This method allows the button to be moved manually and keep the click
on functionality.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Inputs:
position - This is the x, y position of the top left corner of the
button. (defining point can be changed to midpoint)
midpoint - If true is passed to midpoint the button will be blitted
to a surface, either automatically if a surface is passed or
manually, such that the position input is the center of the
button rather than the top left corner.
(doc string updated ver 0.1)"""
# Find the image size and midpoint of the image
imagesize = self.image.get_size()
imagemidp = (int(imagesize[0] * 0.5), int(imagesize[1] * 0.5))
# if a midpoint arguement is passed, set the pos to the top left pixel
# such that the position passed in is in the middle of the button
if midpoint:
self.pos = (position[0] - imagemidp[0], position[1] - imagemidp[1])
else:
self.pos = position
# set the rectangle to be used for collision detection
self.rect = pygame.Rect(self.pos, self.image.get_size())
# Set up the information that is needed to blit the image to the surface
self.blitinfo = (self.image, self.pos)
# automatically blit the button onto an input surface
if surface:
surface.blit(*self.blitinfo) | [
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jbm950/pygame_toolbox | pygame_toolbox/graphics/__init__.py | BaseScreen.set_offset | def set_offset(self, offset, mid=None):
"""This method will allow the menu to be placed anywhere in the open
window instead of just the upper left corner.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Inputs:
offset - This is the x,y tuple of the position that you want to
move the screen to.
mid - The offset will be treated as the value passed in instead of
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'x' (the x point in offset will be treated as the middle of the
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'y' (the y point in offset will be treated as the middle of the
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'c' (the offset will be treated as the center of the menu image)
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"""
if mid:
imagesize = self.image.get_size()
imagemidp = (int(imagesize[0] * 0.5), int(imagesize[1] * 0.5))
if mid == 'x':
offset = (offset[0] - imagemidp[0], offset[1])
if mid == 'y':
offset = (offset[0], offset[1] - imagemidp[1])
if mid == 'c':
offset = (offset[0] - imagemidp[0], offset[1] - imagemidp[1])
self.pos = offset
for i in self.buttonlist:
i.rect[0] += offset[0]
i.rect[1] += offset[1]
try:
for i in self.widgetlist:
i.rect[0] += offset[0]
i.rect[1] += offset[1]
except AttributeError:
pass | python | def set_offset(self, offset, mid=None):
"""This method will allow the menu to be placed anywhere in the open
window instead of just the upper left corner.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Inputs:
offset - This is the x,y tuple of the position that you want to
move the screen to.
mid - The offset will be treated as the value passed in instead of
the top left pixel.
'x' (the x point in offset will be treated as the middle of the
menu image)
'y' (the y point in offset will be treated as the middle of the
menu image)
'c' (the offset will be treated as the center of the menu image)
(doc string updated ver 0.1)
"""
if mid:
imagesize = self.image.get_size()
imagemidp = (int(imagesize[0] * 0.5), int(imagesize[1] * 0.5))
if mid == 'x':
offset = (offset[0] - imagemidp[0], offset[1])
if mid == 'y':
offset = (offset[0], offset[1] - imagemidp[1])
if mid == 'c':
offset = (offset[0] - imagemidp[0], offset[1] - imagemidp[1])
self.pos = offset
for i in self.buttonlist:
i.rect[0] += offset[0]
i.rect[1] += offset[1]
try:
for i in self.widgetlist:
i.rect[0] += offset[0]
i.rect[1] += offset[1]
except AttributeError:
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jbm950/pygame_toolbox | pygame_toolbox/graphics/__init__.py | Menu.widget_status | def widget_status(self):
"""This method will return the status of all of the widgets in the
widget list"""
widget_status_list = []
for i in self.widgetlist:
widget_status_list += [[i.name, i.status]]
return widget_status_list | python | def widget_status(self):
"""This method will return the status of all of the widgets in the
widget list"""
widget_status_list = []
for i in self.widgetlist:
widget_status_list += [[i.name, i.status]]
return widget_status_list | [
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jbm950/pygame_toolbox | pygame_toolbox/graphics/__init__.py | Menu.update | def update(self, screen, clock):
"""Event handling loop for the menu"""
# If a music file was passed, start playing it on repeat
if self.music is not None:
pygame.mixer.music.play(-1)
while True:
clock.tick(30)
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
# Check if any of the buttons were clicked
for i in self.buttonlist:
if (event.type == pygame.MOUSEBUTTONUP and
i.rect.collidepoint(pygame.mouse.get_pos())):
if self.music is not None:
pygame.mixer.music.stop()
if self.widgetlist:
return [i(), self.widget_status()]
else:
return i()
# If there is a widget list, check to see if any were clicked
if self.widgetlist:
for i in self.widgetlist:
if (event.type == pygame.MOUSEBUTTONDOWN and
i.rect.collidepoint(pygame.mouse.get_pos())):
# Call the widget and give it the menu information
i(self)
screen.blit(self.image, self.pos)
pygame.display.flip() | python | def update(self, screen, clock):
"""Event handling loop for the menu"""
# If a music file was passed, start playing it on repeat
if self.music is not None:
pygame.mixer.music.play(-1)
while True:
clock.tick(30)
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
# Check if any of the buttons were clicked
for i in self.buttonlist:
if (event.type == pygame.MOUSEBUTTONUP and
i.rect.collidepoint(pygame.mouse.get_pos())):
if self.music is not None:
pygame.mixer.music.stop()
if self.widgetlist:
return [i(), self.widget_status()]
else:
return i()
# If there is a widget list, check to see if any were clicked
if self.widgetlist:
for i in self.widgetlist:
if (event.type == pygame.MOUSEBUTTONDOWN and
i.rect.collidepoint(pygame.mouse.get_pos())):
# Call the widget and give it the menu information
i(self)
screen.blit(self.image, self.pos)
pygame.display.flip() | [
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jbm950/pygame_toolbox | pygame_toolbox/graphics/__init__.py | Textscreens.Screens | def Screens(self, text, prog, screen, clock):
"""Prog = 0 for first page, 1 for middle pages, 2 for last page"""
# Initialize the screen class
BaseScreen.__init__(self, self.size, self.background)
# Determine the mid position of the given screen size and the
# y button height
xmid = self.size[0]//2
# Create the header text
Linesoftext(text, (xmid, 40), xmid=True, surface=self.image,
fontsize=30)
# Create the buttons
self.buttonlist = []
if prog == 0:
self.buttonlist += [self.nextbutton]
elif prog == 1:
self.buttonlist += [self.nextbutton]
self.buttonlist += [self.backbutton]
elif prog == 2:
self.buttonlist += [self.lastbutton]
self.buttonlist += [self.backbutton]
# Draw the buttons to the screen
for i in self.buttonlist:
self.image.blit(*i.blitinfo)
# Use the menu update method to run the screen and process button clicks
return Menu.update(self, screen, clock) | python | def Screens(self, text, prog, screen, clock):
"""Prog = 0 for first page, 1 for middle pages, 2 for last page"""
# Initialize the screen class
BaseScreen.__init__(self, self.size, self.background)
# Determine the mid position of the given screen size and the
# y button height
xmid = self.size[0]//2
# Create the header text
Linesoftext(text, (xmid, 40), xmid=True, surface=self.image,
fontsize=30)
# Create the buttons
self.buttonlist = []
if prog == 0:
self.buttonlist += [self.nextbutton]
elif prog == 1:
self.buttonlist += [self.nextbutton]
self.buttonlist += [self.backbutton]
elif prog == 2:
self.buttonlist += [self.lastbutton]
self.buttonlist += [self.backbutton]
# Draw the buttons to the screen
for i in self.buttonlist:
self.image.blit(*i.blitinfo)
# Use the menu update method to run the screen and process button clicks
return Menu.update(self, screen, clock) | [
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pedroburon/tbk | tbk/webpay/commerce.py | Commerce.create_commerce | def create_commerce():
"""
Creates commerce from environment variables ``TBK_COMMERCE_ID``, ``TBK_COMMERCE_KEY``
or for testing purposes ``TBK_COMMERCE_TESTING``.
"""
commerce_id = os.getenv('TBK_COMMERCE_ID')
commerce_key = os.getenv('TBK_COMMERCE_KEY')
commerce_testing = os.getenv('TBK_COMMERCE_TESTING') == 'True'
if not commerce_testing:
if commerce_id is None:
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if commerce_key is None:
raise ValueError("create_commerce needs TBK_COMMERCE_KEY environment variable")
return Commerce(
id=commerce_id or Commerce.TEST_COMMERCE_ID,
key=commerce_key,
testing=commerce_testing
) | python | def create_commerce():
"""
Creates commerce from environment variables ``TBK_COMMERCE_ID``, ``TBK_COMMERCE_KEY``
or for testing purposes ``TBK_COMMERCE_TESTING``.
"""
commerce_id = os.getenv('TBK_COMMERCE_ID')
commerce_key = os.getenv('TBK_COMMERCE_KEY')
commerce_testing = os.getenv('TBK_COMMERCE_TESTING') == 'True'
if not commerce_testing:
if commerce_id is None:
raise ValueError("create_commerce needs TBK_COMMERCE_ID environment variable")
if commerce_key is None:
raise ValueError("create_commerce needs TBK_COMMERCE_KEY environment variable")
return Commerce(
id=commerce_id or Commerce.TEST_COMMERCE_ID,
key=commerce_key,
testing=commerce_testing
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pedroburon/tbk | tbk/webpay/commerce.py | Commerce.get_config_tbk | def get_config_tbk(self, confirmation_url):
'''
Returns a string with the ``TBK_CONFIG.dat``.
:param confirmation_url: URL where callback is made.
'''
config = (
"IDCOMERCIO = {commerce_id}\n"
"MEDCOM = 1\n"
"TBK_KEY_ID = 101\n"
"PARAMVERIFCOM = 1\n"
"URLCGICOM = {confirmation_path}\n"
"SERVERCOM = {confirmation_host}\n"
"PORTCOM = {confirmation_port}\n"
"WHITELISTCOM = ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz 0123456789./:=&?_\n"
"HOST = {confirmation_host}\n"
"WPORT = {confirmation_port}\n"
"URLCGITRA = /filtroUnificado/bp_revision.cgi\n"
"URLCGIMEDTRA = /filtroUnificado/bp_validacion.cgi\n"
"SERVERTRA = {webpay_server}\n"
"PORTTRA = {webpay_port}\n"
"PREFIJO_CONF_TR = HTML_\n"
"HTML_TR_NORMAL = http://127.0.0.1/notify\n"
)
confirmation_uri = six.moves.urllib.parse.urlparse(confirmation_url)
webpay_server = "https://certificacion.webpay.cl" if self.testing else "https://webpay.transbank.cl"
webpay_port = 6443 if self.testing else 443
return config.format(commerce_id=self.id,
confirmation_path=confirmation_uri.path,
confirmation_host=confirmation_uri.hostname,
confirmation_port=confirmation_uri.port,
webpay_port=webpay_port,
webpay_server=webpay_server) | python | def get_config_tbk(self, confirmation_url):
'''
Returns a string with the ``TBK_CONFIG.dat``.
:param confirmation_url: URL where callback is made.
'''
config = (
"IDCOMERCIO = {commerce_id}\n"
"MEDCOM = 1\n"
"TBK_KEY_ID = 101\n"
"PARAMVERIFCOM = 1\n"
"URLCGICOM = {confirmation_path}\n"
"SERVERCOM = {confirmation_host}\n"
"PORTCOM = {confirmation_port}\n"
"WHITELISTCOM = ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz 0123456789./:=&?_\n"
"HOST = {confirmation_host}\n"
"WPORT = {confirmation_port}\n"
"URLCGITRA = /filtroUnificado/bp_revision.cgi\n"
"URLCGIMEDTRA = /filtroUnificado/bp_validacion.cgi\n"
"SERVERTRA = {webpay_server}\n"
"PORTTRA = {webpay_port}\n"
"PREFIJO_CONF_TR = HTML_\n"
"HTML_TR_NORMAL = http://127.0.0.1/notify\n"
)
confirmation_uri = six.moves.urllib.parse.urlparse(confirmation_url)
webpay_server = "https://certificacion.webpay.cl" if self.testing else "https://webpay.transbank.cl"
webpay_port = 6443 if self.testing else 443
return config.format(commerce_id=self.id,
confirmation_path=confirmation_uri.path,
confirmation_host=confirmation_uri.hostname,
confirmation_port=confirmation_uri.port,
webpay_port=webpay_port,
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calmjs/calmjs.parse | src/calmjs/parse/handlers/indentation.py | indent | def indent(indent_str=None):
"""
An example indentation ruleset.
"""
def indentation_rule():
inst = Indentator(indent_str)
return {'layout_handlers': {
Indent: inst.layout_handler_indent,
Dedent: inst.layout_handler_dedent,
Newline: inst.layout_handler_newline,
OptionalNewline: inst.layout_handler_newline_optional,
OpenBlock: layout_handler_openbrace,
CloseBlock: layout_handler_closebrace,
EndStatement: layout_handler_semicolon,
}}
return indentation_rule | python | def indent(indent_str=None):
"""
An example indentation ruleset.
"""
def indentation_rule():
inst = Indentator(indent_str)
return {'layout_handlers': {
Indent: inst.layout_handler_indent,
Dedent: inst.layout_handler_dedent,
Newline: inst.layout_handler_newline,
OptionalNewline: inst.layout_handler_newline_optional,
OpenBlock: layout_handler_openbrace,
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EndStatement: layout_handler_semicolon,
}}
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mozilla/funfactory | funfactory/middleware.py | LocaleURLMiddleware._is_lang_change | def _is_lang_change(self, request):
"""Return True if the lang param is present and URL isn't exempt."""
if 'lang' not in request.GET:
return False
return not any(request.path.endswith(url) for url in self.exempt_urls) | python | def _is_lang_change(self, request):
"""Return True if the lang param is present and URL isn't exempt."""
if 'lang' not in request.GET:
return False
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chbrown/argv | argv/parsers/boolean.py | BooleanParser.add | def add(self, *matches, **kw): # kw=default=None, boolean=False
'''Add an argument; this is optional, and mostly useful for setting up aliases or setting boolean=True
Apparently `def add(self, *matches, default=None, boolean=False):` is invalid syntax in Python. Not only is this absolutely ridiculous, but the alternative `def add(self, default=None, boolean=False, *matches):` does not do what you would expect. This syntax works as intended in Python 3.
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'''
# python syntax hack
default = kw.get('default', None)
boolean = kw.get('boolean', False)
del kw
# do not use kw after this line! It's a hack; it should never have been there in the first place.
positional = None
names = []
for match in matches:
if match.startswith('--'):
names.append(match[2:])
elif match.startswith('-'):
names.append(match[1:])
elif positional:
# positional has already been filled
names.append(match)
else:
# first positional: becomes canonical positional
positional = match
names.append(match)
argument = BooleanArgument(names, default, boolean, positional)
self.arguments.append(argument)
# chainable
return self | python | def add(self, *matches, **kw): # kw=default=None, boolean=False
'''Add an argument; this is optional, and mostly useful for setting up aliases or setting boolean=True
Apparently `def add(self, *matches, default=None, boolean=False):` is invalid syntax in Python. Not only is this absolutely ridiculous, but the alternative `def add(self, default=None, boolean=False, *matches):` does not do what you would expect. This syntax works as intended in Python 3.
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Specifying any positional arguments and then using `boolean=True` is just weird, and their will be no special consideration for boolean=True in that case for the position-enabled argument.
'''
# python syntax hack
default = kw.get('default', None)
boolean = kw.get('boolean', False)
del kw
# do not use kw after this line! It's a hack; it should never have been there in the first place.
positional = None
names = []
for match in matches:
if match.startswith('--'):
names.append(match[2:])
elif match.startswith('-'):
names.append(match[1:])
elif positional:
# positional has already been filled
names.append(match)
else:
# first positional: becomes canonical positional
positional = match
names.append(match)
argument = BooleanArgument(names, default, boolean, positional)
self.arguments.append(argument)
# chainable
return self | [
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sobolevn/jinja2-git | jinja2_git.py | GitExtension.parse | def parse(self, parser):
"""Main method to render data into the template."""
lineno = next(parser.stream).lineno
if parser.stream.skip_if('name:short'):
parser.stream.skip(1)
short = parser.parse_expression()
else:
short = nodes.Const(False)
result = self.call_method('_commit_hash', [short], [], lineno=lineno)
return nodes.Output([result], lineno=lineno) | python | def parse(self, parser):
"""Main method to render data into the template."""
lineno = next(parser.stream).lineno
if parser.stream.skip_if('name:short'):
parser.stream.skip(1)
short = parser.parse_expression()
else:
short = nodes.Const(False)
result = self.call_method('_commit_hash', [short], [], lineno=lineno)
return nodes.Output([result], lineno=lineno) | [
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pyroscope/pyrobase | src/pyrobase/iterutil.py | flatten | def flatten(nested, containers=(list, tuple)):
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"""
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""" Flatten a nested list by yielding its scalar items.
"""
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mozilla/funfactory | funfactory/settings_base.py | get_template_context_processors | def get_template_context_processors(exclude=(), append=(),
current={'processors': TEMPLATE_CONTEXT_PROCESSORS}):
"""
Returns TEMPLATE_CONTEXT_PROCESSORS without the processors listed in
exclude and with the processors listed in append.
The use of a mutable dict is intentional, in order to preserve the state of
the TEMPLATE_CONTEXT_PROCESSORS tuple across multiple settings files.
"""
current['processors'] = tuple(
[p for p in current['processors'] if p not in exclude]
) + tuple(append)
return current['processors'] | python | def get_template_context_processors(exclude=(), append=(),
current={'processors': TEMPLATE_CONTEXT_PROCESSORS}):
"""
Returns TEMPLATE_CONTEXT_PROCESSORS without the processors listed in
exclude and with the processors listed in append.
The use of a mutable dict is intentional, in order to preserve the state of
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"""
current['processors'] = tuple(
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mozilla/funfactory | funfactory/settings_base.py | get_middleware | def get_middleware(exclude=(), append=(),
current={'middleware': MIDDLEWARE_CLASSES}):
"""
Returns MIDDLEWARE_CLASSES without the middlewares listed in exclude and
with the middlewares listed in append.
The use of a mutable dict is intentional, in order to preserve the state of
the MIDDLEWARE_CLASSES tuple across multiple settings files.
"""
current['middleware'] = tuple(
[m for m in current['middleware'] if m not in exclude]
) + tuple(append)
return current['middleware'] | python | def get_middleware(exclude=(), append=(),
current={'middleware': MIDDLEWARE_CLASSES}):
"""
Returns MIDDLEWARE_CLASSES without the middlewares listed in exclude and
with the middlewares listed in append.
The use of a mutable dict is intentional, in order to preserve the state of
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"""
current['middleware'] = tuple(
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mozilla/funfactory | funfactory/settings_base.py | get_apps | def get_apps(exclude=(), append=(), current={'apps': INSTALLED_APPS}):
"""
Returns INSTALLED_APPS without the apps listed in exclude and with the apps
listed in append.
The use of a mutable dict is intentional, in order to preserve the state of
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"""
current['apps'] = tuple(
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) + tuple(append)
return current['apps'] | python | def get_apps(exclude=(), append=(), current={'apps': INSTALLED_APPS}):
"""
Returns INSTALLED_APPS without the apps listed in exclude and with the apps
listed in append.
The use of a mutable dict is intentional, in order to preserve the state of
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limix/limix-core | limix_core/covar/cov2kronSum.py | Cov2KronSum.solve_t | def solve_t(self, Mt):
"""
Mt is dim_r x dim_c x d tensor
"""
if len(Mt.shape)==2: _Mt = Mt[:, :, sp.newaxis]
else: _Mt = Mt
LMt = vei_CoR_veX(_Mt, R=self.Lr(), C=self.Lc())
DLMt = self.D()[:, :, sp.newaxis] * LMt
RV = vei_CoR_veX(DLMt, R=self.Lr().T, C=self.Lc().T)
if len(Mt.shape)==2: RV = RV[:, :, 0]
return RV | python | def solve_t(self, Mt):
"""
Mt is dim_r x dim_c x d tensor
"""
if len(Mt.shape)==2: _Mt = Mt[:, :, sp.newaxis]
else: _Mt = Mt
LMt = vei_CoR_veX(_Mt, R=self.Lr(), C=self.Lc())
DLMt = self.D()[:, :, sp.newaxis] * LMt
RV = vei_CoR_veX(DLMt, R=self.Lr().T, C=self.Lc().T)
if len(Mt.shape)==2: RV = RV[:, :, 0]
return RV | [
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ubccr/pinky | pinky/fingerprints/ecfp.py | invariants | def invariants(mol):
"""Generate initial atom identifiers using atomic invariants"""
atom_ids = {}
for a in mol.atoms:
components = []
components.append(a.number)
components.append(len(a.oatoms))
components.append(a.hcount)
components.append(a.charge)
components.append(a.mass)
if len(a.rings) > 0:
components.append(1)
atom_ids[a.index] = gen_hash(components)
return atom_ids | python | def invariants(mol):
"""Generate initial atom identifiers using atomic invariants"""
atom_ids = {}
for a in mol.atoms:
components = []
components.append(a.number)
components.append(len(a.oatoms))
components.append(a.hcount)
components.append(a.charge)
components.append(a.mass)
if len(a.rings) > 0:
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atom_ids[a.index] = gen_hash(components)
return atom_ids | [
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ubccr/pinky | pinky/fingerprints/ecfp.py | ecfp | def ecfp(mol, radius=2):
"""Compute the Extended-Connectivity fingerprint for a molecule.
:param mol: molecule object parsed from SMILES string
:param radius: The number of iterations to perform. Defaults to 2 which is equivilent to ECFP4.
:rtype: dictionary representing the molecular fingprint (atom identifiers and their counts).
"""
atom_ids = invariants(mol)
fp = {}
for i in atom_ids.values():
fp[i] = fp.get(i, 0) + 1
neighborhoods = []
atom_neighborhoods = [ len(mol.bonds) * bitarray('0') for a in mol.atoms]
dead_atoms = len(mol.atoms) * bitarray('0')
for layer in xrange(1, radius+1):
round_ids = {}
round_atom_neighborhoods = copy.deepcopy(atom_neighborhoods)
neighborhoods_this_round = []
for a in mol.atoms:
if dead_atoms[a.index]: continue
nbsr = []
for b in a.bonds:
round_atom_neighborhoods[a.index][b.index] = True
oidx = b.xatom(a).index
round_atom_neighborhoods[a.index] |= atom_neighborhoods[oidx]
nbsr.append((b.bondtype, atom_ids[oidx]))
nbsr = sorted(nbsr)
nbsr = [item for sublist in nbsr for item in sublist]
nbsr.insert(0, atom_ids[a.index])
nbsr.insert(0, layer)
round_ids[a.index] = gen_hash(nbsr)
neighborhoods_this_round.append(
(round_atom_neighborhoods[a.index], round_ids[a.index], a.index)
)
for lst in neighborhoods_this_round:
if lst[0] not in neighborhoods:
fp[lst[1]] = fp.get(lst[1], 0) + 1
neighborhoods.append(lst[0])
else:
dead_atoms[lst[2]] = True
atom_ids = round_ids
atom_neighborhoods = copy.deepcopy(round_atom_neighborhoods)
return fp | python | def ecfp(mol, radius=2):
"""Compute the Extended-Connectivity fingerprint for a molecule.
:param mol: molecule object parsed from SMILES string
:param radius: The number of iterations to perform. Defaults to 2 which is equivilent to ECFP4.
:rtype: dictionary representing the molecular fingprint (atom identifiers and their counts).
"""
atom_ids = invariants(mol)
fp = {}
for i in atom_ids.values():
fp[i] = fp.get(i, 0) + 1
neighborhoods = []
atom_neighborhoods = [ len(mol.bonds) * bitarray('0') for a in mol.atoms]
dead_atoms = len(mol.atoms) * bitarray('0')
for layer in xrange(1, radius+1):
round_ids = {}
round_atom_neighborhoods = copy.deepcopy(atom_neighborhoods)
neighborhoods_this_round = []
for a in mol.atoms:
if dead_atoms[a.index]: continue
nbsr = []
for b in a.bonds:
round_atom_neighborhoods[a.index][b.index] = True
oidx = b.xatom(a).index
round_atom_neighborhoods[a.index] |= atom_neighborhoods[oidx]
nbsr.append((b.bondtype, atom_ids[oidx]))
nbsr = sorted(nbsr)
nbsr = [item for sublist in nbsr for item in sublist]
nbsr.insert(0, atom_ids[a.index])
nbsr.insert(0, layer)
round_ids[a.index] = gen_hash(nbsr)
neighborhoods_this_round.append(
(round_atom_neighborhoods[a.index], round_ids[a.index], a.index)
)
for lst in neighborhoods_this_round:
if lst[0] not in neighborhoods:
fp[lst[1]] = fp.get(lst[1], 0) + 1
neighborhoods.append(lst[0])
else:
dead_atoms[lst[2]] = True
atom_ids = round_ids
atom_neighborhoods = copy.deepcopy(round_atom_neighborhoods)
return fp | [
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pyroscope/pyrobase | src/pyrobase/io/http.py | HttpPost.send | def send(self):
""" Post fields and files to an HTTP server as multipart/form-data.
Return the server's response.
"""
scheme, location, path, query, _ = urlparse.urlsplit(self.url)
assert scheme in ("http", "https"), "Unsupported scheme %r" % scheme
content_type, body = self._encode_multipart_formdata()
handle = getattr(httplib, scheme.upper() + "Connection")(location)
if self.mock_http:
# Don't actually send anything, print to stdout instead
handle.sock = parts.Bunch(
sendall=lambda x: sys.stdout.write(fmt.to_utf8(
''.join((c if 32 <= ord(c) < 127 or ord(c) in (8, 10) else u'\u27ea%02X\u27eb' % ord(c)) for c in x)
)),
makefile=lambda dummy, _: StringIO.StringIO("\r\n".join((
"HTTP/1.0 204 NO CONTENT",
"Content-Length: 0",
"",
))),
close=lambda: None,
)
handle.putrequest('POST', urlparse.urlunsplit(('', '', path, query, '')))
handle.putheader('Content-Type', content_type)
handle.putheader('Content-Length', str(len(body)))
for key, val in self.headers.items():
handle.putheader(key, val)
handle.endheaders()
handle.send(body)
#print handle.__dict__
return handle.getresponse() | python | def send(self):
""" Post fields and files to an HTTP server as multipart/form-data.
Return the server's response.
"""
scheme, location, path, query, _ = urlparse.urlsplit(self.url)
assert scheme in ("http", "https"), "Unsupported scheme %r" % scheme
content_type, body = self._encode_multipart_formdata()
handle = getattr(httplib, scheme.upper() + "Connection")(location)
if self.mock_http:
# Don't actually send anything, print to stdout instead
handle.sock = parts.Bunch(
sendall=lambda x: sys.stdout.write(fmt.to_utf8(
''.join((c if 32 <= ord(c) < 127 or ord(c) in (8, 10) else u'\u27ea%02X\u27eb' % ord(c)) for c in x)
)),
makefile=lambda dummy, _: StringIO.StringIO("\r\n".join((
"HTTP/1.0 204 NO CONTENT",
"Content-Length: 0",
"",
))),
close=lambda: None,
)
handle.putrequest('POST', urlparse.urlunsplit(('', '', path, query, '')))
handle.putheader('Content-Type', content_type)
handle.putheader('Content-Length', str(len(body)))
for key, val in self.headers.items():
handle.putheader(key, val)
handle.endheaders()
handle.send(body)
#print handle.__dict__
return handle.getresponse() | [
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pyroscope/pyrobase | src/pyrobase/io/http.py | HttpPost._encode_multipart_formdata | def _encode_multipart_formdata(self):
""" Encode POST body.
Return (content_type, body) ready for httplib.HTTP instance
"""
def get_content_type(filename):
"Helper to get MIME type."
return mimetypes.guess_type(filename)[0] or 'application/octet-stream'
boundary = '----------ThIs_Is_tHe_b0uNdaRY_%d$' % (time.time())
logical_lines = []
for name, value in self.fields:
if value is None:
continue
logical_lines.append('--' + boundary)
if hasattr(value, "read"):
filename = getattr(value, "name", str(id(value))+".dat")
logical_lines.append('Content-Disposition: form-data; name="%s"; filename="%s"' % (
name,
os.path.basename(filename).replace("'", '_').replace('"', '_')
))
logical_lines.append('Content-Type: %s' % get_content_type(filename))
logical_lines.append('Content-Transfer-Encoding: binary')
value = value.read()
else:
logical_lines.append('Content-Disposition: form-data; name="%s"' % name)
logical_lines.append('Content-Type: text/plain; charset="UTF-8"')
value = fmt.to_utf8(value)
#logical_lines.append('Content-Length: %d' % len(value))
logical_lines.append('')
logical_lines.append(value)
logical_lines.append('--' + boundary + '--')
logical_lines.append('')
body = '\r\n'.join(logical_lines)
content_type = 'multipart/form-data; boundary=%s' % boundary
return content_type, body | python | def _encode_multipart_formdata(self):
""" Encode POST body.
Return (content_type, body) ready for httplib.HTTP instance
"""
def get_content_type(filename):
"Helper to get MIME type."
return mimetypes.guess_type(filename)[0] or 'application/octet-stream'
boundary = '----------ThIs_Is_tHe_b0uNdaRY_%d$' % (time.time())
logical_lines = []
for name, value in self.fields:
if value is None:
continue
logical_lines.append('--' + boundary)
if hasattr(value, "read"):
filename = getattr(value, "name", str(id(value))+".dat")
logical_lines.append('Content-Disposition: form-data; name="%s"; filename="%s"' % (
name,
os.path.basename(filename).replace("'", '_').replace('"', '_')
))
logical_lines.append('Content-Type: %s' % get_content_type(filename))
logical_lines.append('Content-Transfer-Encoding: binary')
value = value.read()
else:
logical_lines.append('Content-Disposition: form-data; name="%s"' % name)
logical_lines.append('Content-Type: text/plain; charset="UTF-8"')
value = fmt.to_utf8(value)
#logical_lines.append('Content-Length: %d' % len(value))
logical_lines.append('')
logical_lines.append(value)
logical_lines.append('--' + boundary + '--')
logical_lines.append('')
body = '\r\n'.join(logical_lines)
content_type = 'multipart/form-data; boundary=%s' % boundary
return content_type, body | [
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all-umass/graphs | graphs/construction/spanning_tree.py | perturbed_mst | def perturbed_mst(X, num_perturbations=20, metric='euclidean', jitter=None):
'''Builds a graph as the union of several MSTs on perturbed data.
Reference: http://ecovision.mit.edu/~sloop/shao.pdf, page 8
jitter refers to the scale of the gaussian noise added for each perturbation.
When jitter is None, it defaults to the 5th percentile interpoint distance.
Note that metric cannot be 'precomputed', as multiple MSTs are computed.'''
assert metric != 'precomputed'
D = pairwise_distances(X, metric=metric)
if jitter is None:
jitter = np.percentile(D[D>0], 5)
W = minimum_spanning_tree(D)
W = W + W.T
W.data[:] = 1.0 # binarize
for i in range(num_perturbations):
pX = X + np.random.normal(scale=jitter, size=X.shape)
pW = minimum_spanning_tree(pairwise_distances(pX, metric=metric))
pW = pW + pW.T
pW.data[:] = 1.0
W = W + pW
# final graph is the average over all pertubed MSTs + the original
W.data /= (num_perturbations + 1.0)
return Graph.from_adj_matrix(W) | python | def perturbed_mst(X, num_perturbations=20, metric='euclidean', jitter=None):
'''Builds a graph as the union of several MSTs on perturbed data.
Reference: http://ecovision.mit.edu/~sloop/shao.pdf, page 8
jitter refers to the scale of the gaussian noise added for each perturbation.
When jitter is None, it defaults to the 5th percentile interpoint distance.
Note that metric cannot be 'precomputed', as multiple MSTs are computed.'''
assert metric != 'precomputed'
D = pairwise_distances(X, metric=metric)
if jitter is None:
jitter = np.percentile(D[D>0], 5)
W = minimum_spanning_tree(D)
W = W + W.T
W.data[:] = 1.0 # binarize
for i in range(num_perturbations):
pX = X + np.random.normal(scale=jitter, size=X.shape)
pW = minimum_spanning_tree(pairwise_distances(pX, metric=metric))
pW = pW + pW.T
pW.data[:] = 1.0
W = W + pW
# final graph is the average over all pertubed MSTs + the original
W.data /= (num_perturbations + 1.0)
return Graph.from_adj_matrix(W) | [
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all-umass/graphs | graphs/construction/spanning_tree.py | disjoint_mst | def disjoint_mst(X, num_spanning_trees=3, metric='euclidean'):
'''Builds a graph as the union of several spanning trees,
each time removing any edges present in previously-built trees.
Reference: http://ecovision.mit.edu/~sloop/shao.pdf, page 9.'''
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mst = minimum_spanning_tree(D)
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for i in range(1, num_spanning_trees):
ii,jj = mst.nonzero()
D[ii,jj] = np.inf
D[jj,ii] = np.inf
mst = minimum_spanning_tree(D)
W = W + mst
# MSTs are all one-sided, so we symmetrize here
return Graph.from_adj_matrix(W + W.T) | python | def disjoint_mst(X, num_spanning_trees=3, metric='euclidean'):
'''Builds a graph as the union of several spanning trees,
each time removing any edges present in previously-built trees.
Reference: http://ecovision.mit.edu/~sloop/shao.pdf, page 9.'''
D = pairwise_distances(X, metric=metric)
if metric == 'precomputed':
D = D.copy()
mst = minimum_spanning_tree(D)
W = mst.copy()
for i in range(1, num_spanning_trees):
ii,jj = mst.nonzero()
D[ii,jj] = np.inf
D[jj,ii] = np.inf
mst = minimum_spanning_tree(D)
W = W + mst
# MSTs are all one-sided, so we symmetrize here
return Graph.from_adj_matrix(W + W.T) | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.kernelize | def kernelize(self, kernel):
'''Re-weight according to a specified kernel function.
kernel : str, {none, binary, rbf}
none -> no reweighting
binary -> all edges are given weight 1
rbf -> applies a gaussian function to edge weights
'''
if kernel == 'none':
return self
if kernel == 'binary':
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return self
if kernel == 'rbf':
w = self.edge_weights()
r = np.exp(-w / w.std())
return self._update_edges(r, copy=True)
raise ValueError('Invalid kernel type: %r' % kernel) | python | def kernelize(self, kernel):
'''Re-weight according to a specified kernel function.
kernel : str, {none, binary, rbf}
none -> no reweighting
binary -> all edges are given weight 1
rbf -> applies a gaussian function to edge weights
'''
if kernel == 'none':
return self
if kernel == 'binary':
if self.is_weighted():
return self._update_edges(1, copy=True)
return self
if kernel == 'rbf':
w = self.edge_weights()
r = np.exp(-w / w.std())
return self._update_edges(r, copy=True)
raise ValueError('Invalid kernel type: %r' % kernel) | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.barycenter_edge_weights | def barycenter_edge_weights(self, X, copy=True, reg=1e-3):
'''Re-weight such that the sum of each vertex's edge weights is 1.
The resulting weighted graph is suitable for locally linear embedding.
reg : amount of regularization to keep the problem well-posed
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new_weights = []
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w /= w.sum()
new_weights.extend(w.tolist())
return self.reweight(new_weights, copy=copy) | python | def barycenter_edge_weights(self, X, copy=True, reg=1e-3):
'''Re-weight such that the sum of each vertex's edge weights is 1.
The resulting weighted graph is suitable for locally linear embedding.
reg : amount of regularization to keep the problem well-posed
'''
new_weights = []
for i, adj in enumerate(self.adj_list()):
C = X[adj] - X[i]
G = C.dot(C.T)
trace = np.trace(G)
r = reg * trace if trace > 0 else reg
G.flat[::G.shape[1] + 1] += r
w = solve(G, np.ones(G.shape[0]), sym_pos=True,
overwrite_a=True, overwrite_b=True)
w /= w.sum()
new_weights.extend(w.tolist())
return self.reweight(new_weights, copy=copy) | [
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The resulting weighted graph is suitable for locally linear embedding.
reg : amount of regularization to keep the problem well-posed | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.connected_subgraphs | def connected_subgraphs(self, directed=True, ordered=False):
'''Generates connected components as subgraphs.
When ordered=True, subgraphs are ordered by number of vertices.
'''
num_ccs, labels = self.connected_components(directed=directed)
# check the trivial case first
if num_ccs == 1:
yield self
raise StopIteration
if ordered:
# sort by descending size (num vertices)
order = np.argsort(np.bincount(labels))[::-1]
else:
order = range(num_ccs)
# don't use self.subgraph() here, because we can reuse adj
adj = self.matrix('dense', 'csr', 'csc')
for c in order:
mask = labels == c
sub_adj = adj[mask][:,mask]
yield self.__class__.from_adj_matrix(sub_adj) | python | def connected_subgraphs(self, directed=True, ordered=False):
'''Generates connected components as subgraphs.
When ordered=True, subgraphs are ordered by number of vertices.
'''
num_ccs, labels = self.connected_components(directed=directed)
# check the trivial case first
if num_ccs == 1:
yield self
raise StopIteration
if ordered:
# sort by descending size (num vertices)
order = np.argsort(np.bincount(labels))[::-1]
else:
order = range(num_ccs)
# don't use self.subgraph() here, because we can reuse adj
adj = self.matrix('dense', 'csr', 'csc')
for c in order:
mask = labels == c
sub_adj = adj[mask][:,mask]
yield self.__class__.from_adj_matrix(sub_adj) | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.minimum_spanning_subtree | def minimum_spanning_subtree(self):
'''Returns the (undirected) minimum spanning tree subgraph.'''
dist = self.matrix('dense', copy=True)
dist[dist==0] = np.inf
np.fill_diagonal(dist, 0)
mst = ssc.minimum_spanning_tree(dist)
return self.__class__.from_adj_matrix(mst + mst.T) | python | def minimum_spanning_subtree(self):
'''Returns the (undirected) minimum spanning tree subgraph.'''
dist = self.matrix('dense', copy=True)
dist[dist==0] = np.inf
np.fill_diagonal(dist, 0)
mst = ssc.minimum_spanning_tree(dist)
return self.__class__.from_adj_matrix(mst + mst.T) | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.neighborhood_subgraph | def neighborhood_subgraph(self, start_idx, radius=1, weighted=True,
directed=True, return_mask=False):
'''Returns a subgraph containing only vertices within a given
geodesic radius of start_idx.'''
adj = self.matrix('dense', 'csr', 'csc')
dist = ssc.dijkstra(adj, directed=directed, indices=start_idx,
unweighted=(not weighted), limit=radius)
mask = np.isfinite(dist)
sub_adj = adj[mask][:,mask]
g = self.__class__.from_adj_matrix(sub_adj)
if return_mask:
return g, mask
return g | python | def neighborhood_subgraph(self, start_idx, radius=1, weighted=True,
directed=True, return_mask=False):
'''Returns a subgraph containing only vertices within a given
geodesic radius of start_idx.'''
adj = self.matrix('dense', 'csr', 'csc')
dist = ssc.dijkstra(adj, directed=directed, indices=start_idx,
unweighted=(not weighted), limit=radius)
mask = np.isfinite(dist)
sub_adj = adj[mask][:,mask]
g = self.__class__.from_adj_matrix(sub_adj)
if return_mask:
return g, mask
return g | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.isograph | def isograph(self, min_weight=None):
'''Remove short-circuit edges using the Isograph algorithm.
min_weight : float, optional
Minimum weight of edges to consider removing. Defaults to max(MST).
From "Isograph: Neighbourhood Graph Construction Based On Geodesic Distance
For Semi-Supervised Learning" by Ghazvininejad et al., 2011.
Note: This uses the non-iterative algorithm which removes edges
rather than reweighting them.
'''
W = self.matrix('dense')
# get candidate edges: all edges - MST edges
tree = self.minimum_spanning_subtree()
candidates = np.argwhere((W - tree.matrix('dense')) > 0)
cand_weights = W[candidates[:,0], candidates[:,1]]
# order by increasing edge weight
order = np.argsort(cand_weights)
cand_weights = cand_weights[order]
# disregard edges shorter than a threshold
if min_weight is None:
min_weight = tree.edge_weights().max()
idx = np.searchsorted(cand_weights, min_weight)
cand_weights = cand_weights[idx:]
candidates = candidates[order[idx:]]
# check each candidate edge
to_remove = np.zeros_like(cand_weights, dtype=bool)
for i, (u,v) in enumerate(candidates):
W_uv = np.where(W < cand_weights[i], W, 0)
len_uv = ssc.dijkstra(W_uv, indices=u, unweighted=True, limit=2)[v]
if len_uv > 2:
to_remove[i] = True
ii, jj = candidates[to_remove].T
return self.remove_edges(ii, jj, copy=True) | python | def isograph(self, min_weight=None):
'''Remove short-circuit edges using the Isograph algorithm.
min_weight : float, optional
Minimum weight of edges to consider removing. Defaults to max(MST).
From "Isograph: Neighbourhood Graph Construction Based On Geodesic Distance
For Semi-Supervised Learning" by Ghazvininejad et al., 2011.
Note: This uses the non-iterative algorithm which removes edges
rather than reweighting them.
'''
W = self.matrix('dense')
# get candidate edges: all edges - MST edges
tree = self.minimum_spanning_subtree()
candidates = np.argwhere((W - tree.matrix('dense')) > 0)
cand_weights = W[candidates[:,0], candidates[:,1]]
# order by increasing edge weight
order = np.argsort(cand_weights)
cand_weights = cand_weights[order]
# disregard edges shorter than a threshold
if min_weight is None:
min_weight = tree.edge_weights().max()
idx = np.searchsorted(cand_weights, min_weight)
cand_weights = cand_weights[idx:]
candidates = candidates[order[idx:]]
# check each candidate edge
to_remove = np.zeros_like(cand_weights, dtype=bool)
for i, (u,v) in enumerate(candidates):
W_uv = np.where(W < cand_weights[i], W, 0)
len_uv = ssc.dijkstra(W_uv, indices=u, unweighted=True, limit=2)[v]
if len_uv > 2:
to_remove[i] = True
ii, jj = candidates[to_remove].T
return self.remove_edges(ii, jj, copy=True) | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.circle_tear | def circle_tear(self, spanning_tree='mst', cycle_len_thresh=5, spt_idx=None,
copy=True):
'''Circular graph tearing.
spanning_tree: one of {'mst', 'spt'}
cycle_len_thresh: int, length of longest allowable cycle
spt_idx: int, start vertex for shortest_path_subtree, random if None
From "How to project 'circular' manifolds using geodesic distances?"
by Lee & Verleysen, ESANN 2004.
See also: shortest_path_subtree, minimum_spanning_subtree
'''
# make the initial spanning tree graph
if spanning_tree == 'mst':
tree = self.minimum_spanning_subtree().matrix()
elif spanning_tree == 'spt':
if spt_idx is None:
spt_idx = np.random.choice(self.num_vertices())
tree = self.shortest_path_subtree(spt_idx, directed=False).matrix()
# find edges in self but not in the tree
potential_edges = np.argwhere(ss.triu(self.matrix() - tree))
# remove edges that induce large cycles
ii, jj = _find_cycle_inducers(tree, potential_edges, cycle_len_thresh)
return self.remove_edges(ii, jj, symmetric=True, copy=copy) | python | def circle_tear(self, spanning_tree='mst', cycle_len_thresh=5, spt_idx=None,
copy=True):
'''Circular graph tearing.
spanning_tree: one of {'mst', 'spt'}
cycle_len_thresh: int, length of longest allowable cycle
spt_idx: int, start vertex for shortest_path_subtree, random if None
From "How to project 'circular' manifolds using geodesic distances?"
by Lee & Verleysen, ESANN 2004.
See also: shortest_path_subtree, minimum_spanning_subtree
'''
# make the initial spanning tree graph
if spanning_tree == 'mst':
tree = self.minimum_spanning_subtree().matrix()
elif spanning_tree == 'spt':
if spt_idx is None:
spt_idx = np.random.choice(self.num_vertices())
tree = self.shortest_path_subtree(spt_idx, directed=False).matrix()
# find edges in self but not in the tree
potential_edges = np.argwhere(ss.triu(self.matrix() - tree))
# remove edges that induce large cycles
ii, jj = _find_cycle_inducers(tree, potential_edges, cycle_len_thresh)
return self.remove_edges(ii, jj, symmetric=True, copy=copy) | [
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all-umass/graphs | graphs/mixins/transformation.py | TransformMixin.cycle_cut | def cycle_cut(self, cycle_len_thresh=12, directed=False, copy=True):
'''CycleCut algorithm: removes bottleneck edges.
Paper DOI: 10.1.1.225.5335
'''
symmetric = not directed
adj = self.kernelize('binary').matrix('csr', 'dense', copy=True)
if symmetric:
adj = adj + adj.T
removed_edges = []
while True:
c = _atomic_cycle(adj, cycle_len_thresh, directed=directed)
if c is None:
break
# remove edges in the cycle
ii, jj = c.T
adj[ii,jj] = 0
if symmetric:
adj[jj,ii] = 0
removed_edges.extend(c)
#XXX: if _atomic_cycle changes, may need to do this on each loop
if ss.issparse(adj):
adj.eliminate_zeros()
# select only the necessary cuts
ii, jj = _find_cycle_inducers(adj, removed_edges, cycle_len_thresh,
directed=directed)
# remove the bad edges
return self.remove_edges(ii, jj, symmetric=symmetric, copy=copy) | python | def cycle_cut(self, cycle_len_thresh=12, directed=False, copy=True):
'''CycleCut algorithm: removes bottleneck edges.
Paper DOI: 10.1.1.225.5335
'''
symmetric = not directed
adj = self.kernelize('binary').matrix('csr', 'dense', copy=True)
if symmetric:
adj = adj + adj.T
removed_edges = []
while True:
c = _atomic_cycle(adj, cycle_len_thresh, directed=directed)
if c is None:
break
# remove edges in the cycle
ii, jj = c.T
adj[ii,jj] = 0
if symmetric:
adj[jj,ii] = 0
removed_edges.extend(c)
#XXX: if _atomic_cycle changes, may need to do this on each loop
if ss.issparse(adj):
adj.eliminate_zeros()
# select only the necessary cuts
ii, jj = _find_cycle_inducers(adj, removed_edges, cycle_len_thresh,
directed=directed)
# remove the bad edges
return self.remove_edges(ii, jj, symmetric=symmetric, copy=copy) | [
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stbraun/fuzzing | fuzzing/fuzzer.py | fuzz_string | def fuzz_string(seed_str, runs=100, fuzz_factor=50):
"""A random fuzzer for a simulated text viewer application.
It takes a string as seed and generates <runs> variant of it.
:param seed_str: the string to use as seed for fuzzing.
:param runs: number of fuzzed variants to supply.
:param fuzz_factor: degree of fuzzing = 1 / fuzz_factor.
:return: list of fuzzed variants of seed_str.
:rtype: [str]
"""
buf = bytearray(seed_str, encoding="utf8")
variants = []
for _ in range(runs):
fuzzed = fuzzer(buf, fuzz_factor)
variants.append(''.join([chr(b) for b in fuzzed]))
logger().info('Fuzzed strings: {}'.format(variants))
return variants | python | def fuzz_string(seed_str, runs=100, fuzz_factor=50):
"""A random fuzzer for a simulated text viewer application.
It takes a string as seed and generates <runs> variant of it.
:param seed_str: the string to use as seed for fuzzing.
:param runs: number of fuzzed variants to supply.
:param fuzz_factor: degree of fuzzing = 1 / fuzz_factor.
:return: list of fuzzed variants of seed_str.
:rtype: [str]
"""
buf = bytearray(seed_str, encoding="utf8")
variants = []
for _ in range(runs):
fuzzed = fuzzer(buf, fuzz_factor)
variants.append(''.join([chr(b) for b in fuzzed]))
logger().info('Fuzzed strings: {}'.format(variants))
return variants | [
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stbraun/fuzzing | fuzzing/fuzzer.py | fuzzer | def fuzzer(buffer, fuzz_factor=101):
"""Fuzz given buffer.
Take a buffer of bytes, create a copy, and replace some bytes
with random values. Number of bytes to modify depends on fuzz_factor.
This code is taken from Charlie Miller's fuzzer code.
:param buffer: the data to fuzz.
:type buffer: byte array
:param fuzz_factor: degree of fuzzing.
:type fuzz_factor: int
:return: fuzzed buffer.
:rtype: byte array
"""
buf = deepcopy(buffer)
num_writes = number_of_bytes_to_modify(len(buf), fuzz_factor)
for _ in range(num_writes):
random_byte = random.randrange(256)
random_position = random.randrange(len(buf))
buf[random_position] = random_byte
return buf | python | def fuzzer(buffer, fuzz_factor=101):
"""Fuzz given buffer.
Take a buffer of bytes, create a copy, and replace some bytes
with random values. Number of bytes to modify depends on fuzz_factor.
This code is taken from Charlie Miller's fuzzer code.
:param buffer: the data to fuzz.
:type buffer: byte array
:param fuzz_factor: degree of fuzzing.
:type fuzz_factor: int
:return: fuzzed buffer.
:rtype: byte array
"""
buf = deepcopy(buffer)
num_writes = number_of_bytes_to_modify(len(buf), fuzz_factor)
for _ in range(num_writes):
random_byte = random.randrange(256)
random_position = random.randrange(len(buf))
buf[random_position] = random_byte
return buf | [
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stbraun/fuzzing | fuzzing/fuzzer.py | number_of_bytes_to_modify | def number_of_bytes_to_modify(buf_len, fuzz_factor):
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:param buf_len: len of data buffer to fuzz.
:param fuzz_factor: degree of fuzzing.
:return: number of bytes to change.
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return random.randrange(math.ceil((float(buf_len) / fuzz_factor))) + 1 | python | def number_of_bytes_to_modify(buf_len, fuzz_factor):
"""Calculate number of bytes to modify.
:param buf_len: len of data buffer to fuzz.
:param fuzz_factor: degree of fuzzing.
:return: number of bytes to change.
"""
return random.randrange(math.ceil((float(buf_len) / fuzz_factor))) + 1 | [
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stbraun/fuzzing | fuzzing/fuzzer.py | FuzzExecutor._fuzz_data_file | def _fuzz_data_file(self, data_file):
"""Generate fuzzed variant of given file.
:param data_file: path to file to fuzz.
:type data_file: str
:return: path to fuzzed file.
:rtype: str
"""
buf = bytearray(open(os.path.abspath(data_file), 'rb').read())
fuzzed = fuzzer(buf, self.fuzz_factor)
try:
_, fuzz_output = mkstemp(prefix='fuzzed_')
open(fuzz_output, 'wb').write(fuzzed)
finally:
pass
return fuzz_output | python | def _fuzz_data_file(self, data_file):
"""Generate fuzzed variant of given file.
:param data_file: path to file to fuzz.
:type data_file: str
:return: path to fuzzed file.
:rtype: str
"""
buf = bytearray(open(os.path.abspath(data_file), 'rb').read())
fuzzed = fuzzer(buf, self.fuzz_factor)
try:
_, fuzz_output = mkstemp(prefix='fuzzed_')
open(fuzz_output, 'wb').write(fuzzed)
finally:
pass
return fuzz_output | [
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stbraun/fuzzing | fuzzing/fuzzer.py | FuzzExecutor._execute | def _execute(self, app_, file_):
"""Run app with file as input.
:param app_: application to run.
:param file_: file to run app with.
:return: success True, else False
:rtype: bool
"""
app_name = os.path.basename(app_)
args = [app_]
args.extend(self.args[app_])
args.append(file_)
process = subprocess.Popen(args)
time.sleep(1)
status = {True: Status.SUCCESS, False: Status.FAILED}
crashed = process.poll()
result = status[crashed is None]
self.stats_.add(app_name, result)
if result is Status.SUCCESS:
# process did not crash, so just terminate it
process.terminate() | python | def _execute(self, app_, file_):
"""Run app with file as input.
:param app_: application to run.
:param file_: file to run app with.
:return: success True, else False
:rtype: bool
"""
app_name = os.path.basename(app_)
args = [app_]
args.extend(self.args[app_])
args.append(file_)
process = subprocess.Popen(args)
time.sleep(1)
status = {True: Status.SUCCESS, False: Status.FAILED}
crashed = process.poll()
result = status[crashed is None]
self.stats_.add(app_name, result)
if result is Status.SUCCESS:
# process did not crash, so just terminate it
process.terminate() | [
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stbraun/fuzzing | fuzzing/fuzzer.py | FuzzExecutor.__parse_app_list | def __parse_app_list(app_list):
"""Parse list of apps for arguments.
:param app_list: list of apps with optional arguments.
:return: list of apps and assigned argument dict.
:rtype: [String], {String: [String]}
"""
args = {}
apps = []
for app_str in app_list:
parts = app_str.split("&")
app_path = parts[0].strip()
apps.append(app_path)
if len(parts) > 1:
args[app_path] = [arg.strip() for arg in parts[1].split()]
else:
args[app_path] = []
return apps, args | python | def __parse_app_list(app_list):
"""Parse list of apps for arguments.
:param app_list: list of apps with optional arguments.
:return: list of apps and assigned argument dict.
:rtype: [String], {String: [String]}
"""
args = {}
apps = []
for app_str in app_list:
parts = app_str.split("&")
app_path = parts[0].strip()
apps.append(app_path)
if len(parts) > 1:
args[app_path] = [arg.strip() for arg in parts[1].split()]
else:
args[app_path] = []
return apps, args | [
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wangsix/vmo | vmo/VMO/utility/distances/tonnetz.py | _make_tonnetz_matrix | def _make_tonnetz_matrix():
"""Return the tonnetz projection matrix."""
pi = np.pi
chroma = np.arange(12)
# Define each row of the transform matrix
fifth_x = r_fifth*(np.sin((7*pi/6) * chroma))
fifth_y = r_fifth*(np.cos((7*pi/6) * chroma))
minor_third_x = r_minor_thirds*(np.sin(3*pi/2 * chroma))
minor_third_y = r_minor_thirds*(np.cos(3*pi/2 * chroma))
major_third_x = r_major_thirds*(np.sin(2*pi/3 * chroma))
major_third_y = r_major_thirds*(np.cos(2*pi/3 * chroma))
# Return the tonnetz matrix
return np.vstack((fifth_x, fifth_y,
minor_third_x, minor_third_y,
major_third_x, major_third_y)) | python | def _make_tonnetz_matrix():
"""Return the tonnetz projection matrix."""
pi = np.pi
chroma = np.arange(12)
# Define each row of the transform matrix
fifth_x = r_fifth*(np.sin((7*pi/6) * chroma))
fifth_y = r_fifth*(np.cos((7*pi/6) * chroma))
minor_third_x = r_minor_thirds*(np.sin(3*pi/2 * chroma))
minor_third_y = r_minor_thirds*(np.cos(3*pi/2 * chroma))
major_third_x = r_major_thirds*(np.sin(2*pi/3 * chroma))
major_third_y = r_major_thirds*(np.cos(2*pi/3 * chroma))
# Return the tonnetz matrix
return np.vstack((fifth_x, fifth_y,
minor_third_x, minor_third_y,
major_third_x, major_third_y)) | [
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wangsix/vmo | vmo/VMO/utility/distances/tonnetz.py | _to_tonnetz | def _to_tonnetz(chromagram):
"""Project a chromagram on the tonnetz.
Returned value is normalized to prevent numerical instabilities.
"""
if np.sum(np.abs(chromagram)) == 0.:
# The input is an empty chord, return zero.
return np.zeros(6)
_tonnetz = np.dot(__TONNETZ_MATRIX, chromagram)
one_norm = np.sum(np.abs(_tonnetz)) # Non-zero value
_tonnetz = _tonnetz / float(one_norm) # Normalize tonnetz vector
return _tonnetz | python | def _to_tonnetz(chromagram):
"""Project a chromagram on the tonnetz.
Returned value is normalized to prevent numerical instabilities.
"""
if np.sum(np.abs(chromagram)) == 0.:
# The input is an empty chord, return zero.
return np.zeros(6)
_tonnetz = np.dot(__TONNETZ_MATRIX, chromagram)
one_norm = np.sum(np.abs(_tonnetz)) # Non-zero value
_tonnetz = _tonnetz / float(one_norm) # Normalize tonnetz vector
return _tonnetz | [
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wangsix/vmo | vmo/VMO/utility/distances/tonnetz.py | distance | def distance(a, b):
"""Compute tonnetz-distance between two chromagrams.
----
>>> C = np.zeros(12)
>>> C[0] = 1
>>> D = np.zeros(12)
>>> D[2] = 1
>>> G = np.zeros(12)
>>> G[7] = 1
The distance is zero on equivalent chords
>>> distance(C, C) == 0
True
The distance is symetric
>>> distance(C, D) == distance(D, C)
True
>>> distance(C, D) > 0
True
>>> distance(C, G) < distance(C, D)
True
"""
[a_tonnetz, b_tonnetz] = [_to_tonnetz(x) for x in [a, b]]
return np.linalg.norm(b_tonnetz - a_tonnetz) | python | def distance(a, b):
"""Compute tonnetz-distance between two chromagrams.
----
>>> C = np.zeros(12)
>>> C[0] = 1
>>> D = np.zeros(12)
>>> D[2] = 1
>>> G = np.zeros(12)
>>> G[7] = 1
The distance is zero on equivalent chords
>>> distance(C, C) == 0
True
The distance is symetric
>>> distance(C, D) == distance(D, C)
True
>>> distance(C, D) > 0
True
>>> distance(C, G) < distance(C, D)
True
"""
[a_tonnetz, b_tonnetz] = [_to_tonnetz(x) for x in [a, b]]
return np.linalg.norm(b_tonnetz - a_tonnetz) | [
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gautammishra/lyft-rides-python-sdk | examples/utils.py | fail_print | def fail_print(error):
"""Print an error in red text.
Parameters
error (HTTPError)
Error object to print.
"""
print(COLORS.fail, error.message, COLORS.end)
print(COLORS.fail, error.errors, COLORS.end) | python | def fail_print(error):
"""Print an error in red text.
Parameters
error (HTTPError)
Error object to print.
"""
print(COLORS.fail, error.message, COLORS.end)
print(COLORS.fail, error.errors, COLORS.end) | [
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gautammishra/lyft-rides-python-sdk | examples/utils.py | import_oauth2_credentials | def import_oauth2_credentials(filename=STORAGE_FILENAME):
"""Import OAuth 2.0 session credentials from storage file.
Parameters
filename (str)
Name of storage file.
Returns
credentials (dict)
All your app credentials and information
imported from the configuration file.
"""
with open(filename, 'r') as storage_file:
storage = safe_load(storage_file)
# depending on OAuth 2.0 grant_type, these values may not exist
client_secret = storage.get('client_secret')
refresh_token = storage.get('refresh_token')
credentials = {
'access_token': storage['access_token'],
'client_id': storage['client_id'],
'client_secret': client_secret,
'expires_in_seconds': storage['expires_in_seconds'],
'grant_type': storage['grant_type'],
'refresh_token': refresh_token,
'scopes': storage['scopes'],
}
return credentials | python | def import_oauth2_credentials(filename=STORAGE_FILENAME):
"""Import OAuth 2.0 session credentials from storage file.
Parameters
filename (str)
Name of storage file.
Returns
credentials (dict)
All your app credentials and information
imported from the configuration file.
"""
with open(filename, 'r') as storage_file:
storage = safe_load(storage_file)
# depending on OAuth 2.0 grant_type, these values may not exist
client_secret = storage.get('client_secret')
refresh_token = storage.get('refresh_token')
credentials = {
'access_token': storage['access_token'],
'client_id': storage['client_id'],
'client_secret': client_secret,
'expires_in_seconds': storage['expires_in_seconds'],
'grant_type': storage['grant_type'],
'refresh_token': refresh_token,
'scopes': storage['scopes'],
}
return credentials | [
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gautammishra/lyft-rides-python-sdk | examples/utils.py | create_lyft_client | def create_lyft_client(credentials):
"""Create an LyftRidesClient from OAuth 2.0 credentials.
Parameters
credentials (dict)
Dictionary of OAuth 2.0 credentials.
Returns
(LyftRidesClient)
An authorized LyftRidesClient to access API resources.
"""
oauth2credential = OAuth2Credential(
client_id=credentials.get('client_id'),
access_token=credentials.get('access_token'),
expires_in_seconds=credentials.get('expires_in_seconds'),
scopes=credentials.get('scopes'),
grant_type=credentials.get('grant_type'),
client_secret=credentials.get('client_secret'),
refresh_token=credentials.get('refresh_token'),
)
session = Session(oauth2credential=oauth2credential)
return LyftRidesClient(session) | python | def create_lyft_client(credentials):
"""Create an LyftRidesClient from OAuth 2.0 credentials.
Parameters
credentials (dict)
Dictionary of OAuth 2.0 credentials.
Returns
(LyftRidesClient)
An authorized LyftRidesClient to access API resources.
"""
oauth2credential = OAuth2Credential(
client_id=credentials.get('client_id'),
access_token=credentials.get('access_token'),
expires_in_seconds=credentials.get('expires_in_seconds'),
scopes=credentials.get('scopes'),
grant_type=credentials.get('grant_type'),
client_secret=credentials.get('client_secret'),
refresh_token=credentials.get('refresh_token'),
)
session = Session(oauth2credential=oauth2credential)
return LyftRidesClient(session) | [
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markfinger/python-js-host | js_host/bin.py | spawn_managed_host | def spawn_managed_host(config_file, manager, connect_on_start=True):
"""
Spawns a managed host, if it is not already running
"""
data = manager.request_host_status(config_file)
is_running = data['started']
# Managed hosts run as persistent processes, so it may already be running
if is_running:
host_status = json.loads(data['host']['output'])
logfile = data['host']['logfile']
else:
data = manager.start_host(config_file)
host_status = json.loads(data['output'])
logfile = data['logfile']
host = JSHost(
status=host_status,
logfile=logfile,
config_file=config_file,
manager=manager
)
if not is_running and settings.VERBOSITY >= verbosity.PROCESS_START:
print('Started {}'.format(host.get_name()))
if connect_on_start:
host.connect()
return host | python | def spawn_managed_host(config_file, manager, connect_on_start=True):
"""
Spawns a managed host, if it is not already running
"""
data = manager.request_host_status(config_file)
is_running = data['started']
# Managed hosts run as persistent processes, so it may already be running
if is_running:
host_status = json.loads(data['host']['output'])
logfile = data['host']['logfile']
else:
data = manager.start_host(config_file)
host_status = json.loads(data['output'])
logfile = data['logfile']
host = JSHost(
status=host_status,
logfile=logfile,
config_file=config_file,
manager=manager
)
if not is_running and settings.VERBOSITY >= verbosity.PROCESS_START:
print('Started {}'.format(host.get_name()))
if connect_on_start:
host.connect()
return host | [
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mpetazzoni/tslib | tslib/__init__.py | parse_input | def parse_input(s):
"""Parse the given input and intelligently transform it into an absolute,
non-naive, timezone-aware datetime object for the UTC timezone.
The input can be specified as a millisecond-precision UTC timestamp (or
delta against Epoch), with or without a terminating 'L'. Alternatively, the
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segments, like '24d6h4m500' (24 days, 6 hours, 4 minutes and 500ms), as
long as the segments are in descending unit span order."""
if isinstance(s, six.integer_types):
s = str(s)
elif not isinstance(s, six.string_types):
raise ValueError(s)
original = s
if s[-1:] == 'L':
s = s[:-1]
sign = {'-': -1, '=': 0, '+': 1}.get(s[0], None)
if sign is not None:
s = s[1:]
ts = 0
for unit in _SORTED_UNITS:
pos = s.find(unit[0])
if pos == 0:
raise ValueError(original)
elif pos > 0:
# If we find a unit letter, we're dealing with an offset. Default
# to positive offset if a sign wasn't specified.
if sign is None:
sign = 1
ts += int(s[:pos]) * __timedelta_millis(unit[1])
s = s[min(len(s), pos + 1):]
if s:
ts += int(s)
return date_from_utc_ts(ts) if not sign else \
utc() + sign * delta(milliseconds=ts) | python | def parse_input(s):
"""Parse the given input and intelligently transform it into an absolute,
non-naive, timezone-aware datetime object for the UTC timezone.
The input can be specified as a millisecond-precision UTC timestamp (or
delta against Epoch), with or without a terminating 'L'. Alternatively, the
input can be specified as a human-readable delta string with unit-separated
segments, like '24d6h4m500' (24 days, 6 hours, 4 minutes and 500ms), as
long as the segments are in descending unit span order."""
if isinstance(s, six.integer_types):
s = str(s)
elif not isinstance(s, six.string_types):
raise ValueError(s)
original = s
if s[-1:] == 'L':
s = s[:-1]
sign = {'-': -1, '=': 0, '+': 1}.get(s[0], None)
if sign is not None:
s = s[1:]
ts = 0
for unit in _SORTED_UNITS:
pos = s.find(unit[0])
if pos == 0:
raise ValueError(original)
elif pos > 0:
# If we find a unit letter, we're dealing with an offset. Default
# to positive offset if a sign wasn't specified.
if sign is None:
sign = 1
ts += int(s[:pos]) * __timedelta_millis(unit[1])
s = s[min(len(s), pos + 1):]
if s:
ts += int(s)
return date_from_utc_ts(ts) if not sign else \
utc() + sign * delta(milliseconds=ts) | [
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mpetazzoni/tslib | tslib/__init__.py | render_delta | def render_delta(d):
"""Render the given delta (in milliseconds) as a human-readable delta."""
s = '' if d >= 0 else '-'
d = abs(d)
for unit in _SORTED_UNITS:
span = __timedelta_millis(unit[1])
if d >= span:
count = int(d // span)
s += '{0}{1}'.format(count, unit[0])
d -= count * span
if d or not s:
s += str(d)
return s | python | def render_delta(d):
"""Render the given delta (in milliseconds) as a human-readable delta."""
s = '' if d >= 0 else '-'
d = abs(d)
for unit in _SORTED_UNITS:
span = __timedelta_millis(unit[1])
if d >= span:
count = int(d // span)
s += '{0}{1}'.format(count, unit[0])
d -= count * span
if d or not s:
s += str(d)
return s | [
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mpetazzoni/tslib | tslib/__init__.py | render_date | def render_date(date, tz=pytz.utc, fmt=_FULL_OUTPUT_FORMAT):
"""Format the given date for output. The local time render of the given
date is done using the given timezone."""
local = date.astimezone(tz)
ts = __date_to_millisecond_ts(date)
return fmt.format(
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millis=ts % 1000,
utc_tz=date.strftime(_TZ_FORMAT),
local=local.strftime(_DATE_FORMAT),
local_tz=local.strftime(_TZ_FORMAT),
delta=render_delta_from_now(date)) | python | def render_date(date, tz=pytz.utc, fmt=_FULL_OUTPUT_FORMAT):
"""Format the given date for output. The local time render of the given
date is done using the given timezone."""
local = date.astimezone(tz)
ts = __date_to_millisecond_ts(date)
return fmt.format(
ts=ts,
utc=date.strftime(_DATE_FORMAT),
millis=ts % 1000,
utc_tz=date.strftime(_TZ_FORMAT),
local=local.strftime(_DATE_FORMAT),
local_tz=local.strftime(_TZ_FORMAT),
delta=render_delta_from_now(date)) | [
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chbrown/argv | argv/parsers/descriptive.py | DescriptiveParser.parse | def parse(self, complete=True, args=None):
'''Parse a list of arguments, returning a dict
See BooleanParser.parse for `args`-related documentation.
If `complete` is True and there are values in `args` that don't have corresponding arguments,
or there are required arguments that don't have args, then raise an error.
'''
opts = dict()
positions = [argument.positional for argument in self.arguments if argument.positional]
if args is None:
import sys
# skip over the program name with the [1:] slice
args = sys.argv[1:]
# arglist is a tuple of (is_flag, name) pairs
arglist = peekable(parse_tokens(args))
for is_flag, name in arglist:
if is_flag is True:
argument = self.find_argument(name)
# .peek will return the default argument iff there are no more entries
next_is_flag, next_name = arglist.peek(default=(None, None))
# next_is_flag will be None if there are no more items, but True/False if there is a next item
# if this argument looks for a subsequent (is set as boolean), and the subsequent is not a flag, consume it
if argument.boolean is False and next_is_flag is False:
opts[name] = next_name
# finally, advance our iterator, but since we already have the next values, just discard it
arglist.next()
else:
# if there is no next, or the next thing is a flag all the boolean=False's in the world can't save you then
opts[name] = True
else:
# add positional argument
if len(positions) > 0:
# we pop the positions off from the left
position = positions.pop(0)
opts[position] = name
else:
# the rest of the args now end up as a list in '_'
opts.setdefault('_', []).append(name)
# propagate aliases and defaults:
for argument in self.arguments:
# merge provided value from aliases
for name in argument.names:
if name in opts:
value = opts[name]
# we simply break on the first match.
break
else:
# if we iterate through all names and fine none in opts, use the default
value = argument.default
for name in argument.names:
opts[name] = value
return opts | python | def parse(self, complete=True, args=None):
'''Parse a list of arguments, returning a dict
See BooleanParser.parse for `args`-related documentation.
If `complete` is True and there are values in `args` that don't have corresponding arguments,
or there are required arguments that don't have args, then raise an error.
'''
opts = dict()
positions = [argument.positional for argument in self.arguments if argument.positional]
if args is None:
import sys
# skip over the program name with the [1:] slice
args = sys.argv[1:]
# arglist is a tuple of (is_flag, name) pairs
arglist = peekable(parse_tokens(args))
for is_flag, name in arglist:
if is_flag is True:
argument = self.find_argument(name)
# .peek will return the default argument iff there are no more entries
next_is_flag, next_name = arglist.peek(default=(None, None))
# next_is_flag will be None if there are no more items, but True/False if there is a next item
# if this argument looks for a subsequent (is set as boolean), and the subsequent is not a flag, consume it
if argument.boolean is False and next_is_flag is False:
opts[name] = next_name
# finally, advance our iterator, but since we already have the next values, just discard it
arglist.next()
else:
# if there is no next, or the next thing is a flag all the boolean=False's in the world can't save you then
opts[name] = True
else:
# add positional argument
if len(positions) > 0:
# we pop the positions off from the left
position = positions.pop(0)
opts[position] = name
else:
# the rest of the args now end up as a list in '_'
opts.setdefault('_', []).append(name)
# propagate aliases and defaults:
for argument in self.arguments:
# merge provided value from aliases
for name in argument.names:
if name in opts:
value = opts[name]
# we simply break on the first match.
break
else:
# if we iterate through all names and fine none in opts, use the default
value = argument.default
for name in argument.names:
opts[name] = value
return opts | [
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limix/limix-core | limix_core/covar/covar_base.py | Covariance.setRandomParams | def setRandomParams(self):
"""
set random hyperparameters
"""
params = sp.randn(self.getNumberParams())
self.setParams(params) | python | def setRandomParams(self):
"""
set random hyperparameters
"""
params = sp.randn(self.getNumberParams())
self.setParams(params) | [
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limix/limix-core | limix_core/covar/covar_base.py | Covariance.perturbParams | def perturbParams(self, pertSize=1e-3):
"""
slightly perturbs the values of the parameters
"""
params = self.getParams()
self.setParams(params+pertSize*sp.randn(params.shape[0])) | python | def perturbParams(self, pertSize=1e-3):
"""
slightly perturbs the values of the parameters
"""
params = self.getParams()
self.setParams(params+pertSize*sp.randn(params.shape[0])) | [
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