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241,000 | SALib/SALib | src/SALib/sample/morris/__init__.py | sample | def sample(problem, N, num_levels=4, optimal_trajectories=None,
local_optimization=True):
"""Generate model inputs using the Method of Morris
Returns a NumPy matrix containing the model inputs required for Method of
Morris. The resulting matrix has :math:`(G+1)*T` rows and :math:`D`
columns, where :math:`D` is the number of parameters, :math:`G` is the
number of groups (if no groups are selected, the number of parameters).
:math:`T` is the number of trajectories :math:`N`,
or `optimal_trajectories` if selected.
These model inputs are intended to be used with
:func:`SALib.analyze.morris.analyze`.
Parameters
----------
problem : dict
The problem definition
N : int
The number of trajectories to generate
num_levels : int, default=4
The number of grid levels
optimal_trajectories : int
The number of optimal trajectories to sample (between 2 and N)
local_optimization : bool, default=True
Flag whether to use local optimization according to Ruano et al. (2012)
Speeds up the process tremendously for bigger N and num_levels.
If set to ``False`` brute force method is used, unless ``gurobipy`` is
available
Returns
-------
sample : numpy.ndarray
Returns a numpy.ndarray containing the model inputs required for Method
of Morris. The resulting matrix has :math:`(G/D+1)*N/T` rows and
:math:`D` columns, where :math:`D` is the number of parameters.
"""
if problem.get('groups'):
sample = _sample_groups(problem, N, num_levels)
else:
sample = _sample_oat(problem, N, num_levels)
if optimal_trajectories:
sample = _compute_optimised_trajectories(problem,
sample,
N,
optimal_trajectories,
local_optimization)
scale_samples(sample, problem['bounds'])
return sample | python | def sample(problem, N, num_levels=4, optimal_trajectories=None,
local_optimization=True):
if problem.get('groups'):
sample = _sample_groups(problem, N, num_levels)
else:
sample = _sample_oat(problem, N, num_levels)
if optimal_trajectories:
sample = _compute_optimised_trajectories(problem,
sample,
N,
optimal_trajectories,
local_optimization)
scale_samples(sample, problem['bounds'])
return sample | [
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Returns a NumPy matrix containing the model inputs required for Method of
Morris. The resulting matrix has :math:`(G+1)*T` rows and :math:`D`
columns, where :math:`D` is the number of parameters, :math:`G` is the
number of groups (if no groups are selected, the number of parameters).
:math:`T` is the number of trajectories :math:`N`,
or `optimal_trajectories` if selected.
These model inputs are intended to be used with
:func:`SALib.analyze.morris.analyze`.
Parameters
----------
problem : dict
The problem definition
N : int
The number of trajectories to generate
num_levels : int, default=4
The number of grid levels
optimal_trajectories : int
The number of optimal trajectories to sample (between 2 and N)
local_optimization : bool, default=True
Flag whether to use local optimization according to Ruano et al. (2012)
Speeds up the process tremendously for bigger N and num_levels.
If set to ``False`` brute force method is used, unless ``gurobipy`` is
available
Returns
-------
sample : numpy.ndarray
Returns a numpy.ndarray containing the model inputs required for Method
of Morris. The resulting matrix has :math:`(G/D+1)*N/T` rows and
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241,001 | SALib/SALib | src/SALib/sample/morris/__init__.py | _sample_oat | def _sample_oat(problem, N, num_levels=4):
"""Generate trajectories without groups
Arguments
---------
problem : dict
The problem definition
N : int
The number of samples to generate
num_levels : int, default=4
The number of grid levels
"""
group_membership = np.asmatrix(np.identity(problem['num_vars'],
dtype=int))
num_params = group_membership.shape[0]
sample = np.zeros((N * (num_params + 1), num_params))
sample = np.array([generate_trajectory(group_membership,
num_levels)
for n in range(N)])
return sample.reshape((N * (num_params + 1), num_params)) | python | def _sample_oat(problem, N, num_levels=4):
group_membership = np.asmatrix(np.identity(problem['num_vars'],
dtype=int))
num_params = group_membership.shape[0]
sample = np.zeros((N * (num_params + 1), num_params))
sample = np.array([generate_trajectory(group_membership,
num_levels)
for n in range(N)])
return sample.reshape((N * (num_params + 1), num_params)) | [
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Arguments
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The problem definition
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The number of samples to generate
num_levels : int, default=4
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241,002 | SALib/SALib | src/SALib/sample/morris/__init__.py | _sample_groups | def _sample_groups(problem, N, num_levels=4):
"""Generate trajectories for groups
Returns an :math:`N(g+1)`-by-:math:`k` array of `N` trajectories,
where :math:`g` is the number of groups and :math:`k` is the number
of factors
Arguments
---------
problem : dict
The problem definition
N : int
The number of trajectories to generate
num_levels : int, default=4
The number of grid levels
Returns
-------
numpy.ndarray
"""
if len(problem['groups']) != problem['num_vars']:
raise ValueError("Groups do not match to number of variables")
group_membership, _ = compute_groups_matrix(problem['groups'])
if group_membership is None:
raise ValueError("Please define the 'group_membership' matrix")
if not isinstance(group_membership, np.ndarray):
raise TypeError("Argument 'group_membership' should be formatted \
as a numpy ndarray")
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
sample = np.zeros((N * (num_groups + 1), num_params))
sample = np.array([generate_trajectory(group_membership,
num_levels)
for n in range(N)])
return sample.reshape((N * (num_groups + 1), num_params)) | python | def _sample_groups(problem, N, num_levels=4):
if len(problem['groups']) != problem['num_vars']:
raise ValueError("Groups do not match to number of variables")
group_membership, _ = compute_groups_matrix(problem['groups'])
if group_membership is None:
raise ValueError("Please define the 'group_membership' matrix")
if not isinstance(group_membership, np.ndarray):
raise TypeError("Argument 'group_membership' should be formatted \
as a numpy ndarray")
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
sample = np.zeros((N * (num_groups + 1), num_params))
sample = np.array([generate_trajectory(group_membership,
num_levels)
for n in range(N)])
return sample.reshape((N * (num_groups + 1), num_params)) | [
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Arguments
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problem : dict
The problem definition
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The number of trajectories to generate
num_levels : int, default=4
The number of grid levels
Returns
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241,003 | SALib/SALib | src/SALib/sample/morris/__init__.py | generate_trajectory | def generate_trajectory(group_membership, num_levels=4):
"""Return a single trajectory
Return a single trajectory of size :math:`(g+1)`-by-:math:`k`
where :math:`g` is the number of groups,
and :math:`k` is the number of factors,
both implied by the dimensions of `group_membership`
Arguments
---------
group_membership : np.ndarray
a k-by-g matrix which notes factor membership of groups
num_levels : int, default=4
The number of levels in the grid
Returns
-------
np.ndarray
"""
delta = compute_delta(num_levels)
# Infer number of groups `g` and number of params `k` from
# `group_membership` matrix
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
# Matrix B - size (g + 1) * g - lower triangular matrix
B = np.tril(np.ones([num_groups + 1, num_groups],
dtype=int), -1)
P_star = generate_p_star(num_groups)
# Matrix J - a (g+1)-by-num_params matrix of ones
J = np.ones((num_groups + 1, num_params))
# Matrix D* - num_params-by-num_params matrix which decribes whether
# factors move up or down
D_star = np.diag([rd.choice([-1, 1]) for _ in range(num_params)])
x_star = generate_x_star(num_params, num_levels)
# Matrix B* - size (num_groups + 1) * num_params
B_star = compute_b_star(J, x_star, delta, B,
group_membership, P_star, D_star)
return B_star | python | def generate_trajectory(group_membership, num_levels=4):
delta = compute_delta(num_levels)
# Infer number of groups `g` and number of params `k` from
# `group_membership` matrix
num_params = group_membership.shape[0]
num_groups = group_membership.shape[1]
# Matrix B - size (g + 1) * g - lower triangular matrix
B = np.tril(np.ones([num_groups + 1, num_groups],
dtype=int), -1)
P_star = generate_p_star(num_groups)
# Matrix J - a (g+1)-by-num_params matrix of ones
J = np.ones((num_groups + 1, num_params))
# Matrix D* - num_params-by-num_params matrix which decribes whether
# factors move up or down
D_star = np.diag([rd.choice([-1, 1]) for _ in range(num_params)])
x_star = generate_x_star(num_params, num_levels)
# Matrix B* - size (num_groups + 1) * num_params
B_star = compute_b_star(J, x_star, delta, B,
group_membership, P_star, D_star)
return B_star | [
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Arguments
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Returns
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241,004 | SALib/SALib | src/SALib/sample/morris/__init__.py | generate_p_star | def generate_p_star(num_groups):
"""Describe the order in which groups move
Arguments
---------
num_groups : int
Returns
-------
np.ndarray
Matrix P* - size (g-by-g)
"""
p_star = np.eye(num_groups, num_groups)
rd.shuffle(p_star)
return p_star | python | def generate_p_star(num_groups):
p_star = np.eye(num_groups, num_groups)
rd.shuffle(p_star)
return p_star | [
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241,005 | SALib/SALib | src/SALib/scripts/salib.py | parse_subargs | def parse_subargs(module, parser, method, opts):
'''Attach argument parser for action specific options.
Arguments
---------
module : module
name of module to extract action from
parser : argparser
argparser object to attach additional arguments to
method : str
name of method (morris, sobol, etc).
Must match one of the available submodules
opts : list
A list of argument options to parse
Returns
---------
subargs : argparser namespace object
'''
module.cli_args(parser)
subargs = parser.parse_args(opts)
return subargs | python | def parse_subargs(module, parser, method, opts):
'''Attach argument parser for action specific options.
Arguments
---------
module : module
name of module to extract action from
parser : argparser
argparser object to attach additional arguments to
method : str
name of method (morris, sobol, etc).
Must match one of the available submodules
opts : list
A list of argument options to parse
Returns
---------
subargs : argparser namespace object
'''
module.cli_args(parser)
subargs = parser.parse_args(opts)
return subargs | [
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Arguments
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module : module
name of module to extract action from
parser : argparser
argparser object to attach additional arguments to
method : str
name of method (morris, sobol, etc).
Must match one of the available submodules
opts : list
A list of argument options to parse
Returns
---------
subargs : argparser namespace object | [
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241,006 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.find_local_maximum | def find_local_maximum(self, input_sample, N, num_params,
k_choices, num_groups=None):
"""Find the most different trajectories in the input sample using a
local approach
An alternative by Ruano et al. (2012) for the brute force approach as
originally proposed by Campolongo et al. (2007). The method should
improve the speed with which an optimal set of trajectories is
found tremendously for larger sample sizes.
Arguments
---------
input_sample : np.ndarray
N : int
The number of trajectories
num_params : int
The number of factors
k_choices : int
The number of optimal trajectories to return
num_groups : int, default=None
The number of groups
Returns
-------
list
"""
distance_matrix = self.compute_distance_matrix(input_sample, N,
num_params, num_groups,
local_optimization=True)
tot_indices_list = []
tot_max_array = np.zeros(k_choices - 1)
# Loop over `k_choices`, i starts at 1
for i in range(1, k_choices):
indices_list = []
row_maxima_i = np.zeros(len(distance_matrix))
row_nr = 0
for row in distance_matrix:
indices = tuple(row.argsort()[-i:][::-1]) + (row_nr,)
row_maxima_i[row_nr] = self.sum_distances(
indices, distance_matrix)
indices_list.append(indices)
row_nr += 1
# Find the indices belonging to the maximum distance
i_max_ind = self.get_max_sum_ind(indices_list, row_maxima_i, i, 0)
# Loop 'm' (called loop 'k' in Ruano)
m_max_ind = i_max_ind
# m starts at 1
m = 1
while m <= k_choices - i - 1:
m_ind = self.add_indices(m_max_ind, distance_matrix)
m_maxima = np.zeros(len(m_ind))
for n in range(0, len(m_ind)):
m_maxima[n] = self.sum_distances(m_ind[n], distance_matrix)
m_max_ind = self.get_max_sum_ind(m_ind, m_maxima, i, m)
m += 1
tot_indices_list.append(m_max_ind)
tot_max_array[i -
1] = self.sum_distances(m_max_ind, distance_matrix)
tot_max = self.get_max_sum_ind(
tot_indices_list, tot_max_array, "tot", "tot")
return sorted(list(tot_max)) | python | def find_local_maximum(self, input_sample, N, num_params,
k_choices, num_groups=None):
distance_matrix = self.compute_distance_matrix(input_sample, N,
num_params, num_groups,
local_optimization=True)
tot_indices_list = []
tot_max_array = np.zeros(k_choices - 1)
# Loop over `k_choices`, i starts at 1
for i in range(1, k_choices):
indices_list = []
row_maxima_i = np.zeros(len(distance_matrix))
row_nr = 0
for row in distance_matrix:
indices = tuple(row.argsort()[-i:][::-1]) + (row_nr,)
row_maxima_i[row_nr] = self.sum_distances(
indices, distance_matrix)
indices_list.append(indices)
row_nr += 1
# Find the indices belonging to the maximum distance
i_max_ind = self.get_max_sum_ind(indices_list, row_maxima_i, i, 0)
# Loop 'm' (called loop 'k' in Ruano)
m_max_ind = i_max_ind
# m starts at 1
m = 1
while m <= k_choices - i - 1:
m_ind = self.add_indices(m_max_ind, distance_matrix)
m_maxima = np.zeros(len(m_ind))
for n in range(0, len(m_ind)):
m_maxima[n] = self.sum_distances(m_ind[n], distance_matrix)
m_max_ind = self.get_max_sum_ind(m_ind, m_maxima, i, m)
m += 1
tot_indices_list.append(m_max_ind)
tot_max_array[i -
1] = self.sum_distances(m_max_ind, distance_matrix)
tot_max = self.get_max_sum_ind(
tot_indices_list, tot_max_array, "tot", "tot")
return sorted(list(tot_max)) | [
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Arguments
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The number of trajectories
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The number of factors
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The number of optimal trajectories to return
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The number of groups
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241,007 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.sum_distances | def sum_distances(self, indices, distance_matrix):
"""Calculate combinatorial distance between a select group of
trajectories, indicated by indices
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Returns
-------
numpy.ndarray
Notes
-----
This function can perhaps be quickened by calculating the sum of the
distances. The calculated distances, as they are right now,
are only used in a relative way. Purely summing distances would lead
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"""
combs_tup = np.array(tuple(combinations(indices, 2)))
# Put indices from tuples into two-dimensional array.
combs = np.array([[i[0] for i in combs_tup],
[i[1] for i in combs_tup]])
# Calculate distance (vectorized)
dist = np.sqrt(
np.sum(np.square(distance_matrix[combs[0], combs[1]]), axis=0))
return dist | python | def sum_distances(self, indices, distance_matrix):
combs_tup = np.array(tuple(combinations(indices, 2)))
# Put indices from tuples into two-dimensional array.
combs = np.array([[i[0] for i in combs_tup],
[i[1] for i in combs_tup]])
# Calculate distance (vectorized)
dist = np.sqrt(
np.sum(np.square(distance_matrix[combs[0], combs[1]]), axis=0))
return dist | [
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numpy.ndarray
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This function can perhaps be quickened by calculating the sum of the
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to the same result, at a perhaps quicker rate. | [
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241,008 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.get_max_sum_ind | def get_max_sum_ind(self, indices_list, distances, i, m):
'''Get the indices that belong to the maximum distance in `distances`
Arguments
---------
indices_list : list
list of tuples
distances : numpy.ndarray
size M
i : int
m : int
Returns
-------
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'''
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raise ValueError(msg.format(
len(indices_list), len(distances), i, m))
max_index = tuple(distances.argsort()[-1:][::-1])
return indices_list[max_index[0]] | python | def get_max_sum_ind(self, indices_list, distances, i, m):
'''Get the indices that belong to the maximum distance in `distances`
Arguments
---------
indices_list : list
list of tuples
distances : numpy.ndarray
size M
i : int
m : int
Returns
-------
list
'''
if len(indices_list) != len(distances):
msg = "Indices and distances are lists of different length." + \
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raise ValueError(msg.format(
len(indices_list), len(distances), i, m))
max_index = tuple(distances.argsort()[-1:][::-1])
return indices_list[max_index[0]] | [
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241,009 | SALib/SALib | src/SALib/sample/morris/local.py | LocalOptimisation.add_indices | def add_indices(self, indices, distance_matrix):
'''Adds extra indices for the combinatorial problem.
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Example
-------
>>> add_indices((1,2), numpy.array((5,5)))
[(1, 2, 3), (1, 2, 4), (1, 2, 5)]
'''
list_new_indices = []
for i in range(0, len(distance_matrix)):
if i not in indices:
list_new_indices.append(indices + (i,))
return list_new_indices | python | def add_indices(self, indices, distance_matrix):
'''Adds extra indices for the combinatorial problem.
Arguments
---------
indices : tuple
distance_matrix : numpy.ndarray (M,M)
Example
-------
>>> add_indices((1,2), numpy.array((5,5)))
[(1, 2, 3), (1, 2, 4), (1, 2, 5)]
'''
list_new_indices = []
for i in range(0, len(distance_matrix)):
if i not in indices:
list_new_indices.append(indices + (i,))
return list_new_indices | [
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241,010 | SALib/SALib | src/SALib/util/results.py | ResultDict.to_df | def to_df(self):
'''Convert dict structure into Pandas DataFrame.'''
return pd.DataFrame({k: v for k, v in self.items() if k is not 'names'},
index=self['names']) | python | def to_df(self):
'''Convert dict structure into Pandas DataFrame.'''
return pd.DataFrame({k: v for k, v in self.items() if k is not 'names'},
index=self['names']) | [
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241,011 | SALib/SALib | src/SALib/plotting/morris.py | horizontal_bar_plot | def horizontal_bar_plot(ax, Si, param_dict, sortby='mu_star', unit=''):
'''Updates a matplotlib axes instance with a horizontal bar plot
of mu_star, with error bars representing mu_star_conf
'''
assert sortby in ['mu_star', 'mu_star_conf', 'sigma', 'mu']
# Sort all the plotted elements by mu_star (or optionally another
# metric)
names_sorted = _sort_Si(Si, 'names', sortby)
mu_star_sorted = _sort_Si(Si, 'mu_star', sortby)
mu_star_conf_sorted = _sort_Si(Si, 'mu_star_conf', sortby)
# Plot horizontal barchart
y_pos = np.arange(len(mu_star_sorted))
plot_names = names_sorted
out = ax.barh(y_pos,
mu_star_sorted,
xerr=mu_star_conf_sorted,
align='center',
ecolor='black',
**param_dict)
ax.set_yticks(y_pos)
ax.set_yticklabels(plot_names)
ax.set_xlabel(r'$\mu^\star$' + unit)
ax.set_ylim(min(y_pos)-1, max(y_pos)+1)
return out | python | def horizontal_bar_plot(ax, Si, param_dict, sortby='mu_star', unit=''):
'''Updates a matplotlib axes instance with a horizontal bar plot
of mu_star, with error bars representing mu_star_conf
'''
assert sortby in ['mu_star', 'mu_star_conf', 'sigma', 'mu']
# Sort all the plotted elements by mu_star (or optionally another
# metric)
names_sorted = _sort_Si(Si, 'names', sortby)
mu_star_sorted = _sort_Si(Si, 'mu_star', sortby)
mu_star_conf_sorted = _sort_Si(Si, 'mu_star_conf', sortby)
# Plot horizontal barchart
y_pos = np.arange(len(mu_star_sorted))
plot_names = names_sorted
out = ax.barh(y_pos,
mu_star_sorted,
xerr=mu_star_conf_sorted,
align='center',
ecolor='black',
**param_dict)
ax.set_yticks(y_pos)
ax.set_yticklabels(plot_names)
ax.set_xlabel(r'$\mu^\star$' + unit)
ax.set_ylim(min(y_pos)-1, max(y_pos)+1)
return out | [
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241,012 | SALib/SALib | src/SALib/plotting/morris.py | sample_histograms | def sample_histograms(fig, input_sample, problem, param_dict):
'''Plots a set of subplots of histograms of the input sample
'''
num_vars = problem['num_vars']
names = problem['names']
framing = 101 + (num_vars * 10)
# Find number of levels
num_levels = len(set(input_sample[:, 1]))
out = []
for variable in range(num_vars):
ax = fig.add_subplot(framing + variable)
out.append(ax.hist(input_sample[:, variable],
bins=num_levels,
normed=False,
label=None,
**param_dict))
ax.set_title('%s' % (names[variable]))
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top='off', # ticks along the top edge are off
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if variable > 0:
ax.tick_params(axis='y', # changes apply to the y-axis
which='both', # both major and minor ticks affected
labelleft='off') # labels along the left edge off)
return out | python | def sample_histograms(fig, input_sample, problem, param_dict):
'''Plots a set of subplots of histograms of the input sample
'''
num_vars = problem['num_vars']
names = problem['names']
framing = 101 + (num_vars * 10)
# Find number of levels
num_levels = len(set(input_sample[:, 1]))
out = []
for variable in range(num_vars):
ax = fig.add_subplot(framing + variable)
out.append(ax.hist(input_sample[:, variable],
bins=num_levels,
normed=False,
label=None,
**param_dict))
ax.set_title('%s' % (names[variable]))
ax.tick_params(axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge off)
if variable > 0:
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which='both', # both major and minor ticks affected
labelleft='off') # labels along the left edge off)
return out | [
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241,013 | SALib/SALib | src/SALib/sample/ff.py | extend_bounds | def extend_bounds(problem):
"""Extends the problem bounds to the nearest power of two
Arguments
=========
problem : dict
The problem definition
"""
num_vars = problem['num_vars']
num_ff_vars = 2 ** find_smallest(num_vars)
num_dummy_variables = num_ff_vars - num_vars
bounds = list(problem['bounds'])
names = problem['names']
if num_dummy_variables > 0:
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names.extend(["dummy_" + str(var)
for var in range(num_dummy_variables)])
problem['bounds'] = bounds
problem['names'] = names
problem['num_vars'] = num_ff_vars
return problem | python | def extend_bounds(problem):
num_vars = problem['num_vars']
num_ff_vars = 2 ** find_smallest(num_vars)
num_dummy_variables = num_ff_vars - num_vars
bounds = list(problem['bounds'])
names = problem['names']
if num_dummy_variables > 0:
bounds.extend([[0, 1] for x in range(num_dummy_variables)])
names.extend(["dummy_" + str(var)
for var in range(num_dummy_variables)])
problem['bounds'] = bounds
problem['names'] = names
problem['num_vars'] = num_ff_vars
return problem | [
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241,014 | SALib/SALib | src/SALib/sample/ff.py | generate_contrast | def generate_contrast(problem):
"""Generates the raw sample from the problem file
Arguments
=========
problem : dict
The problem definition
"""
num_vars = problem['num_vars']
# Find the smallest n, such that num_vars < k
k = [2 ** n for n in range(16)]
k_chosen = 2 ** find_smallest(num_vars)
# Generate the fractional factorial contrast
contrast = np.vstack([hadamard(k_chosen), -hadamard(k_chosen)])
return contrast | python | def generate_contrast(problem):
num_vars = problem['num_vars']
# Find the smallest n, such that num_vars < k
k = [2 ** n for n in range(16)]
k_chosen = 2 ** find_smallest(num_vars)
# Generate the fractional factorial contrast
contrast = np.vstack([hadamard(k_chosen), -hadamard(k_chosen)])
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241,015 | SALib/SALib | src/SALib/sample/ff.py | sample | def sample(problem, seed=None):
"""Generates model inputs using a fractional factorial sample
Returns a NumPy matrix containing the model inputs required for a
fractional factorial analysis.
The resulting matrix has D columns, where D is smallest power of 2 that is
greater than the number of parameters.
These model inputs are intended to be used with
:func:`SALib.analyze.ff.analyze`.
The problem file is padded with a number of dummy variables called
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This algorithm is an implementation of that contained in
[`Saltelli et al. 2008 <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470059974.html>`_]
Arguments
=========
problem : dict
The problem definition
Returns
=======
sample : :class:`numpy.array`
"""
if seed:
np.random.seed(seed)
contrast = generate_contrast(problem)
sample = np.array((contrast + 1.) / 2, dtype=np.float)
problem = extend_bounds(problem)
scale_samples(sample, problem['bounds'])
return sample | python | def sample(problem, seed=None):
if seed:
np.random.seed(seed)
contrast = generate_contrast(problem)
sample = np.array((contrast + 1.) / 2, dtype=np.float)
problem = extend_bounds(problem)
scale_samples(sample, problem['bounds'])
return sample | [
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241,016 | SALib/SALib | src/SALib/sample/ff.py | cli_action | def cli_action(args):
"""Run sampling method
Parameters
----------
args : argparse namespace
"""
problem = read_param_file(args.paramfile)
param_values = sample(problem, seed=args.seed)
np.savetxt(args.output, param_values, delimiter=args.delimiter,
fmt='%.' + str(args.precision) + 'e') | python | def cli_action(args):
problem = read_param_file(args.paramfile)
param_values = sample(problem, seed=args.seed)
np.savetxt(args.output, param_values, delimiter=args.delimiter,
fmt='%.' + str(args.precision) + 'e') | [
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241,017 | SALib/SALib | src/SALib/sample/common_args.py | setup | def setup(parser):
"""Add common sampling options to CLI parser.
Parameters
----------
parser : argparse object
Returns
----------
Updated argparse object
"""
parser.add_argument(
'-p', '--paramfile', type=str, required=True,
help='Parameter Range File')
parser.add_argument(
'-o', '--output', type=str, required=True, help='Output File')
parser.add_argument(
'-s', '--seed', type=int, required=False, default=None,
help='Random Seed')
parser.add_argument(
'--delimiter', type=str, required=False, default=' ',
help='Column delimiter')
parser.add_argument('--precision', type=int, required=False,
default=8, help='Output floating-point precision')
return parser | python | def setup(parser):
parser.add_argument(
'-p', '--paramfile', type=str, required=True,
help='Parameter Range File')
parser.add_argument(
'-o', '--output', type=str, required=True, help='Output File')
parser.add_argument(
'-s', '--seed', type=int, required=False, default=None,
help='Random Seed')
parser.add_argument(
'--delimiter', type=str, required=False, default=' ',
help='Column delimiter')
parser.add_argument('--precision', type=int, required=False,
default=8, help='Output floating-point precision')
return parser | [
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241,018 | SALib/SALib | src/SALib/sample/common_args.py | run_cli | def run_cli(cli_parser, run_sample, known_args=None):
"""Run sampling with CLI arguments.
Parameters
----------
cli_parser : function
Function to add method specific arguments to parser
run_sample: function
Method specific function that runs the sampling
known_args: list [optional]
Additional arguments to parse
Returns
----------
argparse object
"""
parser = create(cli_parser)
args = parser.parse_args(known_args)
run_sample(args) | python | def run_cli(cli_parser, run_sample, known_args=None):
parser = create(cli_parser)
args = parser.parse_args(known_args)
run_sample(args) | [
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Parameters
----------
cli_parser : function
Function to add method specific arguments to parser
run_sample: function
Method specific function that runs the sampling
known_args: list [optional]
Additional arguments to parse
Returns
----------
argparse object | [
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241,019 | SALib/SALib | src/SALib/sample/morris/strategy.py | Strategy.run_checks | def run_checks(number_samples, k_choices):
"""Runs checks on `k_choices`
"""
assert isinstance(k_choices, int), \
"Number of optimal trajectories should be an integer"
if k_choices < 2:
raise ValueError(
"The number of optimal trajectories must be set to 2 or more.")
if k_choices >= number_samples:
msg = "The number of optimal trajectories should be less than the \
number of samples"
raise ValueError(msg) | python | def run_checks(number_samples, k_choices):
assert isinstance(k_choices, int), \
"Number of optimal trajectories should be an integer"
if k_choices < 2:
raise ValueError(
"The number of optimal trajectories must be set to 2 or more.")
if k_choices >= number_samples:
msg = "The number of optimal trajectories should be less than the \
number of samples"
raise ValueError(msg) | [
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241,020 | SALib/SALib | src/SALib/sample/morris/strategy.py | Strategy._make_index_list | def _make_index_list(num_samples, num_params, num_groups=None):
"""Identify indices of input sample associated with each trajectory
For each trajectory, identifies the indexes of the input sample which
is a function of the number of factors/groups and the number of samples
Arguments
---------
num_samples : int
The number of trajectories
num_params : int
The number of parameters
num_groups : int
The number of groups
Returns
-------
list of numpy.ndarray
Example
-------
>>> BruteForce()._make_index_list(num_samples=4, num_params=3,
num_groups=2)
[np.array([0, 1, 2]), np.array([3, 4, 5]), np.array([6, 7, 8]),
np.array([9, 10, 11])]
"""
if num_groups is None:
num_groups = num_params
index_list = []
for j in range(num_samples):
index_list.append(np.arange(num_groups + 1) + j * (num_groups + 1))
return index_list | python | def _make_index_list(num_samples, num_params, num_groups=None):
if num_groups is None:
num_groups = num_params
index_list = []
for j in range(num_samples):
index_list.append(np.arange(num_groups + 1) + j * (num_groups + 1))
return index_list | [
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Arguments
---------
num_samples : int
The number of trajectories
num_params : int
The number of parameters
num_groups : int
The number of groups
Returns
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>>> BruteForce()._make_index_list(num_samples=4, num_params=3,
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241,021 | SALib/SALib | src/SALib/sample/morris/strategy.py | Strategy.compile_output | def compile_output(self, input_sample, num_samples, num_params,
maximum_combo, num_groups=None):
"""Picks the trajectories from the input
Arguments
---------
input_sample : numpy.ndarray
num_samples : int
num_params : int
maximum_combo : list
num_groups : int
"""
if num_groups is None:
num_groups = num_params
self.check_input_sample(input_sample, num_groups, num_samples)
index_list = self._make_index_list(num_samples, num_params, num_groups)
output = np.zeros(
(np.size(maximum_combo) * (num_groups + 1), num_params))
for counter, combo in enumerate(maximum_combo):
output[index_list[counter]] = np.array(
input_sample[index_list[combo]])
return output | python | def compile_output(self, input_sample, num_samples, num_params,
maximum_combo, num_groups=None):
if num_groups is None:
num_groups = num_params
self.check_input_sample(input_sample, num_groups, num_samples)
index_list = self._make_index_list(num_samples, num_params, num_groups)
output = np.zeros(
(np.size(maximum_combo) * (num_groups + 1), num_params))
for counter, combo in enumerate(maximum_combo):
output[index_list[counter]] = np.array(
input_sample[index_list[combo]])
return output | [
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241,022 | SALib/SALib | src/SALib/sample/morris/strategy.py | Strategy.check_input_sample | def check_input_sample(input_sample, num_params, num_samples):
"""Check the `input_sample` is valid
Checks input sample is:
- the correct size
- values between 0 and 1
Arguments
---------
input_sample : numpy.ndarray
num_params : int
num_samples : int
"""
assert type(input_sample) == np.ndarray, \
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assert np.any((input_sample >= 0) | (input_sample <= 1)), \
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assert type(input_sample) == np.ndarray, \
"Input sample is not an numpy array"
assert input_sample.shape[0] == (num_params + 1) * num_samples, \
"Input sample does not match number of parameters or groups"
assert np.any((input_sample >= 0) | (input_sample <= 1)), \
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241,023 | SALib/SALib | src/SALib/sample/morris/strategy.py | Strategy.compute_distance | def compute_distance(m, l):
'''Compute distance between two trajectories
Returns
-------
numpy.ndarray
'''
if np.shape(m) != np.shape(l):
raise ValueError("Input matrices are different sizes")
if np.array_equal(m, l):
# print("Trajectory %s and %s are equal" % (m, l))
distance = 0
else:
distance = np.array(np.sum(cdist(m, l)), dtype=np.float32)
return distance | python | def compute_distance(m, l):
'''Compute distance between two trajectories
Returns
-------
numpy.ndarray
'''
if np.shape(m) != np.shape(l):
raise ValueError("Input matrices are different sizes")
if np.array_equal(m, l):
# print("Trajectory %s and %s are equal" % (m, l))
distance = 0
else:
distance = np.array(np.sum(cdist(m, l)), dtype=np.float32)
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241,024 | SALib/SALib | src/SALib/sample/morris/strategy.py | Strategy.compute_distance_matrix | def compute_distance_matrix(self, input_sample, num_samples, num_params,
num_groups=None,
local_optimization=False):
"""Computes the distance between each and every trajectory
Each entry in the matrix represents the sum of the geometric distances
between all the pairs of points of the two trajectories
If the `groups` argument is filled, then the distances are still
calculated for each trajectory,
Arguments
---------
input_sample : numpy.ndarray
The input sample of trajectories for which to compute
the distance matrix
num_samples : int
The number of trajectories
num_params : int
The number of factors
num_groups : int, default=None
The number of groups
local_optimization : bool, default=False
If True, fills the lower triangle of the distance matrix
Returns
-------
distance_matrix : numpy.ndarray
"""
if num_groups:
self.check_input_sample(input_sample, num_groups, num_samples)
else:
self.check_input_sample(input_sample, num_params, num_samples)
index_list = self._make_index_list(num_samples, num_params, num_groups)
distance_matrix = np.zeros(
(num_samples, num_samples), dtype=np.float32)
for j in range(num_samples):
input_1 = input_sample[index_list[j]]
for k in range(j + 1, num_samples):
input_2 = input_sample[index_list[k]]
# Fills the lower triangle of the matrix
if local_optimization is True:
distance_matrix[j, k] = self.compute_distance(
input_1, input_2)
distance_matrix[k, j] = self.compute_distance(input_1, input_2)
return distance_matrix | python | def compute_distance_matrix(self, input_sample, num_samples, num_params,
num_groups=None,
local_optimization=False):
if num_groups:
self.check_input_sample(input_sample, num_groups, num_samples)
else:
self.check_input_sample(input_sample, num_params, num_samples)
index_list = self._make_index_list(num_samples, num_params, num_groups)
distance_matrix = np.zeros(
(num_samples, num_samples), dtype=np.float32)
for j in range(num_samples):
input_1 = input_sample[index_list[j]]
for k in range(j + 1, num_samples):
input_2 = input_sample[index_list[k]]
# Fills the lower triangle of the matrix
if local_optimization is True:
distance_matrix[j, k] = self.compute_distance(
input_1, input_2)
distance_matrix[k, j] = self.compute_distance(input_1, input_2)
return distance_matrix | [
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The number of groups
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241,025 | nicodv/kmodes | kmodes/kmodes.py | move_point_cat | def move_point_cat(point, ipoint, to_clust, from_clust, cl_attr_freq,
membship, centroids):
"""Move point between clusters, categorical attributes."""
membship[to_clust, ipoint] = 1
membship[from_clust, ipoint] = 0
# Update frequencies of attributes in cluster.
for iattr, curattr in enumerate(point):
to_attr_counts = cl_attr_freq[to_clust][iattr]
from_attr_counts = cl_attr_freq[from_clust][iattr]
# Increment the attribute count for the new "to" cluster
to_attr_counts[curattr] += 1
current_attribute_value_freq = to_attr_counts[curattr]
current_centroid_value = centroids[to_clust][iattr]
current_centroid_freq = to_attr_counts[current_centroid_value]
if current_centroid_freq < current_attribute_value_freq:
# We have incremented this value to the new mode. Update the centroid.
centroids[to_clust][iattr] = curattr
# Decrement the attribute count for the old "from" cluster
from_attr_counts[curattr] -= 1
old_centroid_value = centroids[from_clust][iattr]
if old_centroid_value == curattr:
# We have just removed a count from the old centroid value. We need to
# recalculate the centroid as it may no longer be the maximum
centroids[from_clust][iattr] = get_max_value_key(from_attr_counts)
return cl_attr_freq, membship, centroids | python | def move_point_cat(point, ipoint, to_clust, from_clust, cl_attr_freq,
membship, centroids):
membship[to_clust, ipoint] = 1
membship[from_clust, ipoint] = 0
# Update frequencies of attributes in cluster.
for iattr, curattr in enumerate(point):
to_attr_counts = cl_attr_freq[to_clust][iattr]
from_attr_counts = cl_attr_freq[from_clust][iattr]
# Increment the attribute count for the new "to" cluster
to_attr_counts[curattr] += 1
current_attribute_value_freq = to_attr_counts[curattr]
current_centroid_value = centroids[to_clust][iattr]
current_centroid_freq = to_attr_counts[current_centroid_value]
if current_centroid_freq < current_attribute_value_freq:
# We have incremented this value to the new mode. Update the centroid.
centroids[to_clust][iattr] = curattr
# Decrement the attribute count for the old "from" cluster
from_attr_counts[curattr] -= 1
old_centroid_value = centroids[from_clust][iattr]
if old_centroid_value == curattr:
# We have just removed a count from the old centroid value. We need to
# recalculate the centroid as it may no longer be the maximum
centroids[from_clust][iattr] = get_max_value_key(from_attr_counts)
return cl_attr_freq, membship, centroids | [
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241,026 | nicodv/kmodes | kmodes/kmodes.py | _labels_cost | def _labels_cost(X, centroids, dissim, membship=None):
"""Calculate labels and cost function given a matrix of points and
a list of centroids for the k-modes algorithm.
"""
X = check_array(X)
n_points = X.shape[0]
cost = 0.
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diss = dissim(centroids, curpoint, X=X, membship=membship)
clust = np.argmin(diss)
labels[ipoint] = clust
cost += diss[clust]
return labels, cost | python | def _labels_cost(X, centroids, dissim, membship=None):
X = check_array(X)
n_points = X.shape[0]
cost = 0.
labels = np.empty(n_points, dtype=np.uint16)
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labels[ipoint] = clust
cost += diss[clust]
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241,027 | nicodv/kmodes | kmodes/kmodes.py | _k_modes_iter | def _k_modes_iter(X, centroids, cl_attr_freq, membship, dissim, random_state):
"""Single iteration of k-modes clustering algorithm"""
moves = 0
for ipoint, curpoint in enumerate(X):
clust = np.argmin(dissim(centroids, curpoint, X=X, membship=membship))
if membship[clust, ipoint]:
# Point is already in its right place.
continue
# Move point, and update old/new cluster frequencies and centroids.
moves += 1
old_clust = np.argwhere(membship[:, ipoint])[0][0]
cl_attr_freq, membship, centroids = move_point_cat(
curpoint, ipoint, clust, old_clust, cl_attr_freq, membship, centroids
)
# In case of an empty cluster, reinitialize with a random point
# from the largest cluster.
if not membship[old_clust, :].any():
from_clust = membship.sum(axis=1).argmax()
choices = [ii for ii, ch in enumerate(membship[from_clust, :]) if ch]
rindx = random_state.choice(choices)
cl_attr_freq, membship, centroids = move_point_cat(
X[rindx], rindx, old_clust, from_clust, cl_attr_freq, membship, centroids
)
return centroids, moves | python | def _k_modes_iter(X, centroids, cl_attr_freq, membship, dissim, random_state):
moves = 0
for ipoint, curpoint in enumerate(X):
clust = np.argmin(dissim(centroids, curpoint, X=X, membship=membship))
if membship[clust, ipoint]:
# Point is already in its right place.
continue
# Move point, and update old/new cluster frequencies and centroids.
moves += 1
old_clust = np.argwhere(membship[:, ipoint])[0][0]
cl_attr_freq, membship, centroids = move_point_cat(
curpoint, ipoint, clust, old_clust, cl_attr_freq, membship, centroids
)
# In case of an empty cluster, reinitialize with a random point
# from the largest cluster.
if not membship[old_clust, :].any():
from_clust = membship.sum(axis=1).argmax()
choices = [ii for ii, ch in enumerate(membship[from_clust, :]) if ch]
rindx = random_state.choice(choices)
cl_attr_freq, membship, centroids = move_point_cat(
X[rindx], rindx, old_clust, from_clust, cl_attr_freq, membship, centroids
)
return centroids, moves | [
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241,028 | nicodv/kmodes | kmodes/kmodes.py | k_modes | def k_modes(X, n_clusters, max_iter, dissim, init, n_init, verbose, random_state, n_jobs):
"""k-modes algorithm"""
random_state = check_random_state(random_state)
if sparse.issparse(X):
raise TypeError("k-modes does not support sparse data.")
X = check_array(X, dtype=None)
# Convert the categorical values in X to integers for speed.
# Based on the unique values in X, we can make a mapping to achieve this.
X, enc_map = encode_features(X)
n_points, n_attrs = X.shape
assert n_clusters <= n_points, "Cannot have more clusters ({}) " \
"than data points ({}).".format(n_clusters, n_points)
# Are there more n_clusters than unique rows? Then set the unique
# rows as initial values and skip iteration.
unique = get_unique_rows(X)
n_unique = unique.shape[0]
if n_unique <= n_clusters:
max_iter = 0
n_init = 1
n_clusters = n_unique
init = unique
results = []
seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init)
if n_jobs == 1:
for init_no in range(n_init):
results.append(k_modes_single(X, n_clusters, n_points, n_attrs, max_iter,
dissim, init, init_no, verbose, seeds[init_no]))
else:
results = Parallel(n_jobs=n_jobs, verbose=0)(
delayed(k_modes_single)(X, n_clusters, n_points, n_attrs, max_iter,
dissim, init, init_no, verbose, seed)
for init_no, seed in enumerate(seeds))
all_centroids, all_labels, all_costs, all_n_iters = zip(*results)
best = np.argmin(all_costs)
if n_init > 1 and verbose:
print("Best run was number {}".format(best + 1))
return all_centroids[best], enc_map, all_labels[best], \
all_costs[best], all_n_iters[best] | python | def k_modes(X, n_clusters, max_iter, dissim, init, n_init, verbose, random_state, n_jobs):
random_state = check_random_state(random_state)
if sparse.issparse(X):
raise TypeError("k-modes does not support sparse data.")
X = check_array(X, dtype=None)
# Convert the categorical values in X to integers for speed.
# Based on the unique values in X, we can make a mapping to achieve this.
X, enc_map = encode_features(X)
n_points, n_attrs = X.shape
assert n_clusters <= n_points, "Cannot have more clusters ({}) " \
"than data points ({}).".format(n_clusters, n_points)
# Are there more n_clusters than unique rows? Then set the unique
# rows as initial values and skip iteration.
unique = get_unique_rows(X)
n_unique = unique.shape[0]
if n_unique <= n_clusters:
max_iter = 0
n_init = 1
n_clusters = n_unique
init = unique
results = []
seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init)
if n_jobs == 1:
for init_no in range(n_init):
results.append(k_modes_single(X, n_clusters, n_points, n_attrs, max_iter,
dissim, init, init_no, verbose, seeds[init_no]))
else:
results = Parallel(n_jobs=n_jobs, verbose=0)(
delayed(k_modes_single)(X, n_clusters, n_points, n_attrs, max_iter,
dissim, init, init_no, verbose, seed)
for init_no, seed in enumerate(seeds))
all_centroids, all_labels, all_costs, all_n_iters = zip(*results)
best = np.argmin(all_costs)
if n_init > 1 and verbose:
print("Best run was number {}".format(best + 1))
return all_centroids[best], enc_map, all_labels[best], \
all_costs[best], all_n_iters[best] | [
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241,029 | nicodv/kmodes | kmodes/kmodes.py | KModes.fit | def fit(self, X, y=None, **kwargs):
"""Compute k-modes clustering.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
"""
X = pandas_to_numpy(X)
random_state = check_random_state(self.random_state)
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self.n_clusters,
self.max_iter,
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random_state,
self.n_jobs)
return self | python | def fit(self, X, y=None, **kwargs):
X = pandas_to_numpy(X)
random_state = check_random_state(self.random_state)
self._enc_cluster_centroids, self._enc_map, self.labels_,\
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self.n_jobs)
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241,030 | nicodv/kmodes | kmodes/kmodes.py | KModes.fit_predict | def fit_predict(self, X, y=None, **kwargs):
"""Compute cluster centroids and predict cluster index for each sample.
Convenience method; equivalent to calling fit(X) followed by
predict(X).
"""
return self.fit(X, **kwargs).predict(X, **kwargs) | python | def fit_predict(self, X, y=None, **kwargs):
return self.fit(X, **kwargs).predict(X, **kwargs) | [
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241,031 | nicodv/kmodes | kmodes/util/__init__.py | get_max_value_key | def get_max_value_key(dic):
"""Gets the key for the maximum value in a dict."""
v = np.array(list(dic.values()))
k = np.array(list(dic.keys()))
maxima = np.where(v == np.max(v))[0]
if len(maxima) == 1:
return k[maxima[0]]
else:
# In order to be consistent, always selects the minimum key
# (guaranteed to be unique) when there are multiple maximum values.
return k[maxima[np.argmin(k[maxima])]] | python | def get_max_value_key(dic):
v = np.array(list(dic.values()))
k = np.array(list(dic.keys()))
maxima = np.where(v == np.max(v))[0]
if len(maxima) == 1:
return k[maxima[0]]
else:
# In order to be consistent, always selects the minimum key
# (guaranteed to be unique) when there are multiple maximum values.
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241,032 | nicodv/kmodes | kmodes/util/__init__.py | decode_centroids | def decode_centroids(encoded, mapping):
"""Decodes the encoded centroids array back to the original data
labels using a list of mappings.
"""
decoded = []
for ii in range(encoded.shape[1]):
# Invert the mapping so that we can decode.
inv_mapping = {v: k for k, v in mapping[ii].items()}
decoded.append(np.vectorize(inv_mapping.__getitem__)(encoded[:, ii]))
return np.atleast_2d(np.array(decoded)).T | python | def decode_centroids(encoded, mapping):
decoded = []
for ii in range(encoded.shape[1]):
# Invert the mapping so that we can decode.
inv_mapping = {v: k for k, v in mapping[ii].items()}
decoded.append(np.vectorize(inv_mapping.__getitem__)(encoded[:, ii]))
return np.atleast_2d(np.array(decoded)).T | [
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241,033 | nicodv/kmodes | kmodes/kprototypes.py | move_point_num | def move_point_num(point, to_clust, from_clust, cl_attr_sum, cl_memb_sum):
"""Move point between clusters, numerical attributes."""
# Update sum of attributes in cluster.
for iattr, curattr in enumerate(point):
cl_attr_sum[to_clust][iattr] += curattr
cl_attr_sum[from_clust][iattr] -= curattr
# Update sums of memberships in cluster
cl_memb_sum[to_clust] += 1
cl_memb_sum[from_clust] -= 1
return cl_attr_sum, cl_memb_sum | python | def move_point_num(point, to_clust, from_clust, cl_attr_sum, cl_memb_sum):
# Update sum of attributes in cluster.
for iattr, curattr in enumerate(point):
cl_attr_sum[to_clust][iattr] += curattr
cl_attr_sum[from_clust][iattr] -= curattr
# Update sums of memberships in cluster
cl_memb_sum[to_clust] += 1
cl_memb_sum[from_clust] -= 1
return cl_attr_sum, cl_memb_sum | [
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241,034 | nicodv/kmodes | kmodes/kprototypes.py | _split_num_cat | def _split_num_cat(X, categorical):
"""Extract numerical and categorical columns.
Convert to numpy arrays, if needed.
:param X: Feature matrix
:param categorical: Indices of categorical columns
"""
Xnum = np.asanyarray(X[:, [ii for ii in range(X.shape[1])
if ii not in categorical]]).astype(np.float64)
Xcat = np.asanyarray(X[:, categorical])
return Xnum, Xcat | python | def _split_num_cat(X, categorical):
Xnum = np.asanyarray(X[:, [ii for ii in range(X.shape[1])
if ii not in categorical]]).astype(np.float64)
Xcat = np.asanyarray(X[:, categorical])
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241,035 | nicodv/kmodes | kmodes/kprototypes.py | _labels_cost | def _labels_cost(Xnum, Xcat, centroids, num_dissim, cat_dissim, gamma, membship=None):
"""Calculate labels and cost function given a matrix of points and
a list of centroids for the k-prototypes algorithm.
"""
n_points = Xnum.shape[0]
Xnum = check_array(Xnum)
cost = 0.
labels = np.empty(n_points, dtype=np.uint16)
for ipoint in range(n_points):
# Numerical cost = sum of Euclidean distances
num_costs = num_dissim(centroids[0], Xnum[ipoint])
cat_costs = cat_dissim(centroids[1], Xcat[ipoint], X=Xcat, membship=membship)
# Gamma relates the categorical cost to the numerical cost.
tot_costs = num_costs + gamma * cat_costs
clust = np.argmin(tot_costs)
labels[ipoint] = clust
cost += tot_costs[clust]
return labels, cost | python | def _labels_cost(Xnum, Xcat, centroids, num_dissim, cat_dissim, gamma, membship=None):
n_points = Xnum.shape[0]
Xnum = check_array(Xnum)
cost = 0.
labels = np.empty(n_points, dtype=np.uint16)
for ipoint in range(n_points):
# Numerical cost = sum of Euclidean distances
num_costs = num_dissim(centroids[0], Xnum[ipoint])
cat_costs = cat_dissim(centroids[1], Xcat[ipoint], X=Xcat, membship=membship)
# Gamma relates the categorical cost to the numerical cost.
tot_costs = num_costs + gamma * cat_costs
clust = np.argmin(tot_costs)
labels[ipoint] = clust
cost += tot_costs[clust]
return labels, cost | [
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241,036 | nicodv/kmodes | kmodes/kprototypes.py | _k_prototypes_iter | def _k_prototypes_iter(Xnum, Xcat, centroids, cl_attr_sum, cl_memb_sum, cl_attr_freq,
membship, num_dissim, cat_dissim, gamma, random_state):
"""Single iteration of the k-prototypes algorithm"""
moves = 0
for ipoint in range(Xnum.shape[0]):
clust = np.argmin(
num_dissim(centroids[0], Xnum[ipoint]) +
gamma * cat_dissim(centroids[1], Xcat[ipoint], X=Xcat, membship=membship)
)
if membship[clust, ipoint]:
# Point is already in its right place.
continue
# Move point, and update old/new cluster frequencies and centroids.
moves += 1
old_clust = np.argwhere(membship[:, ipoint])[0][0]
# Note that membship gets updated by kmodes.move_point_cat.
# move_point_num only updates things specific to the k-means part.
cl_attr_sum, cl_memb_sum = move_point_num(
Xnum[ipoint], clust, old_clust, cl_attr_sum, cl_memb_sum
)
cl_attr_freq, membship, centroids[1] = kmodes.move_point_cat(
Xcat[ipoint], ipoint, clust, old_clust,
cl_attr_freq, membship, centroids[1]
)
# Update old and new centroids for numerical attributes using
# the means and sums of all values
for iattr in range(len(Xnum[ipoint])):
for curc in (clust, old_clust):
if cl_memb_sum[curc]:
centroids[0][curc, iattr] = cl_attr_sum[curc, iattr] / cl_memb_sum[curc]
else:
centroids[0][curc, iattr] = 0.
# In case of an empty cluster, reinitialize with a random point
# from largest cluster.
if not cl_memb_sum[old_clust]:
from_clust = membship.sum(axis=1).argmax()
choices = [ii for ii, ch in enumerate(membship[from_clust, :]) if ch]
rindx = random_state.choice(choices)
cl_attr_sum, cl_memb_sum = move_point_num(
Xnum[rindx], old_clust, from_clust, cl_attr_sum, cl_memb_sum
)
cl_attr_freq, membship, centroids[1] = kmodes.move_point_cat(
Xcat[rindx], rindx, old_clust, from_clust,
cl_attr_freq, membship, centroids[1]
)
return centroids, moves | python | def _k_prototypes_iter(Xnum, Xcat, centroids, cl_attr_sum, cl_memb_sum, cl_attr_freq,
membship, num_dissim, cat_dissim, gamma, random_state):
moves = 0
for ipoint in range(Xnum.shape[0]):
clust = np.argmin(
num_dissim(centroids[0], Xnum[ipoint]) +
gamma * cat_dissim(centroids[1], Xcat[ipoint], X=Xcat, membship=membship)
)
if membship[clust, ipoint]:
# Point is already in its right place.
continue
# Move point, and update old/new cluster frequencies and centroids.
moves += 1
old_clust = np.argwhere(membship[:, ipoint])[0][0]
# Note that membship gets updated by kmodes.move_point_cat.
# move_point_num only updates things specific to the k-means part.
cl_attr_sum, cl_memb_sum = move_point_num(
Xnum[ipoint], clust, old_clust, cl_attr_sum, cl_memb_sum
)
cl_attr_freq, membship, centroids[1] = kmodes.move_point_cat(
Xcat[ipoint], ipoint, clust, old_clust,
cl_attr_freq, membship, centroids[1]
)
# Update old and new centroids for numerical attributes using
# the means and sums of all values
for iattr in range(len(Xnum[ipoint])):
for curc in (clust, old_clust):
if cl_memb_sum[curc]:
centroids[0][curc, iattr] = cl_attr_sum[curc, iattr] / cl_memb_sum[curc]
else:
centroids[0][curc, iattr] = 0.
# In case of an empty cluster, reinitialize with a random point
# from largest cluster.
if not cl_memb_sum[old_clust]:
from_clust = membship.sum(axis=1).argmax()
choices = [ii for ii, ch in enumerate(membship[from_clust, :]) if ch]
rindx = random_state.choice(choices)
cl_attr_sum, cl_memb_sum = move_point_num(
Xnum[rindx], old_clust, from_clust, cl_attr_sum, cl_memb_sum
)
cl_attr_freq, membship, centroids[1] = kmodes.move_point_cat(
Xcat[rindx], rindx, old_clust, from_clust,
cl_attr_freq, membship, centroids[1]
)
return centroids, moves | [
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241,037 | nicodv/kmodes | kmodes/kprototypes.py | k_prototypes | def k_prototypes(X, categorical, n_clusters, max_iter, num_dissim, cat_dissim,
gamma, init, n_init, verbose, random_state, n_jobs):
"""k-prototypes algorithm"""
random_state = check_random_state(random_state)
if sparse.issparse(X):
raise TypeError("k-prototypes does not support sparse data.")
if categorical is None or not categorical:
raise NotImplementedError(
"No categorical data selected, effectively doing k-means. "
"Present a list of categorical columns, or use scikit-learn's "
"KMeans instead."
)
if isinstance(categorical, int):
categorical = [categorical]
assert len(categorical) != X.shape[1], \
"All columns are categorical, use k-modes instead of k-prototypes."
assert max(categorical) < X.shape[1], \
"Categorical index larger than number of columns."
ncatattrs = len(categorical)
nnumattrs = X.shape[1] - ncatattrs
n_points = X.shape[0]
assert n_clusters <= n_points, "Cannot have more clusters ({}) " \
"than data points ({}).".format(n_clusters, n_points)
Xnum, Xcat = _split_num_cat(X, categorical)
Xnum, Xcat = check_array(Xnum), check_array(Xcat, dtype=None)
# Convert the categorical values in Xcat to integers for speed.
# Based on the unique values in Xcat, we can make a mapping to achieve this.
Xcat, enc_map = encode_features(Xcat)
# Are there more n_clusters than unique rows? Then set the unique
# rows as initial values and skip iteration.
unique = get_unique_rows(X)
n_unique = unique.shape[0]
if n_unique <= n_clusters:
max_iter = 0
n_init = 1
n_clusters = n_unique
init = list(_split_num_cat(unique, categorical))
init[1], _ = encode_features(init[1], enc_map)
# Estimate a good value for gamma, which determines the weighing of
# categorical values in clusters (see Huang [1997]).
if gamma is None:
gamma = 0.5 * Xnum.std()
results = []
seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init)
if n_jobs == 1:
for init_no in range(n_init):
results.append(k_prototypes_single(Xnum, Xcat, nnumattrs, ncatattrs,
n_clusters, n_points, max_iter,
num_dissim, cat_dissim, gamma,
init, init_no, verbose, seeds[init_no]))
else:
results = Parallel(n_jobs=n_jobs, verbose=0)(
delayed(k_prototypes_single)(Xnum, Xcat, nnumattrs, ncatattrs,
n_clusters, n_points, max_iter,
num_dissim, cat_dissim, gamma,
init, init_no, verbose, seed)
for init_no, seed in enumerate(seeds))
all_centroids, all_labels, all_costs, all_n_iters = zip(*results)
best = np.argmin(all_costs)
if n_init > 1 and verbose:
print("Best run was number {}".format(best + 1))
# Note: return gamma in case it was automatically determined.
return all_centroids[best], enc_map, all_labels[best], \
all_costs[best], all_n_iters[best], gamma | python | def k_prototypes(X, categorical, n_clusters, max_iter, num_dissim, cat_dissim,
gamma, init, n_init, verbose, random_state, n_jobs):
random_state = check_random_state(random_state)
if sparse.issparse(X):
raise TypeError("k-prototypes does not support sparse data.")
if categorical is None or not categorical:
raise NotImplementedError(
"No categorical data selected, effectively doing k-means. "
"Present a list of categorical columns, or use scikit-learn's "
"KMeans instead."
)
if isinstance(categorical, int):
categorical = [categorical]
assert len(categorical) != X.shape[1], \
"All columns are categorical, use k-modes instead of k-prototypes."
assert max(categorical) < X.shape[1], \
"Categorical index larger than number of columns."
ncatattrs = len(categorical)
nnumattrs = X.shape[1] - ncatattrs
n_points = X.shape[0]
assert n_clusters <= n_points, "Cannot have more clusters ({}) " \
"than data points ({}).".format(n_clusters, n_points)
Xnum, Xcat = _split_num_cat(X, categorical)
Xnum, Xcat = check_array(Xnum), check_array(Xcat, dtype=None)
# Convert the categorical values in Xcat to integers for speed.
# Based on the unique values in Xcat, we can make a mapping to achieve this.
Xcat, enc_map = encode_features(Xcat)
# Are there more n_clusters than unique rows? Then set the unique
# rows as initial values and skip iteration.
unique = get_unique_rows(X)
n_unique = unique.shape[0]
if n_unique <= n_clusters:
max_iter = 0
n_init = 1
n_clusters = n_unique
init = list(_split_num_cat(unique, categorical))
init[1], _ = encode_features(init[1], enc_map)
# Estimate a good value for gamma, which determines the weighing of
# categorical values in clusters (see Huang [1997]).
if gamma is None:
gamma = 0.5 * Xnum.std()
results = []
seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init)
if n_jobs == 1:
for init_no in range(n_init):
results.append(k_prototypes_single(Xnum, Xcat, nnumattrs, ncatattrs,
n_clusters, n_points, max_iter,
num_dissim, cat_dissim, gamma,
init, init_no, verbose, seeds[init_no]))
else:
results = Parallel(n_jobs=n_jobs, verbose=0)(
delayed(k_prototypes_single)(Xnum, Xcat, nnumattrs, ncatattrs,
n_clusters, n_points, max_iter,
num_dissim, cat_dissim, gamma,
init, init_no, verbose, seed)
for init_no, seed in enumerate(seeds))
all_centroids, all_labels, all_costs, all_n_iters = zip(*results)
best = np.argmin(all_costs)
if n_init > 1 and verbose:
print("Best run was number {}".format(best + 1))
# Note: return gamma in case it was automatically determined.
return all_centroids[best], enc_map, all_labels[best], \
all_costs[best], all_n_iters[best], gamma | [
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241,038 | nicodv/kmodes | kmodes/kprototypes.py | KPrototypes.fit | def fit(self, X, y=None, categorical=None):
"""Compute k-prototypes clustering.
Parameters
----------
X : array-like, shape=[n_samples, n_features]
categorical : Index of columns that contain categorical data
"""
if categorical is not None:
assert isinstance(categorical, (int, list, tuple)), "The 'categorical' \
argument needs to be an integer with the index of the categorical \
column in your data, or a list or tuple of several of them, \
but it is a {}.".format(type(categorical))
X = pandas_to_numpy(X)
random_state = check_random_state(self.random_state)
# If self.gamma is None, gamma will be automatically determined from
# the data. The function below returns its value.
self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_,\
self.n_iter_, self.gamma = k_prototypes(X,
categorical,
self.n_clusters,
self.max_iter,
self.num_dissim,
self.cat_dissim,
self.gamma,
self.init,
self.n_init,
self.verbose,
random_state,
self.n_jobs)
return self | python | def fit(self, X, y=None, categorical=None):
if categorical is not None:
assert isinstance(categorical, (int, list, tuple)), "The 'categorical' \
argument needs to be an integer with the index of the categorical \
column in your data, or a list or tuple of several of them, \
but it is a {}.".format(type(categorical))
X = pandas_to_numpy(X)
random_state = check_random_state(self.random_state)
# If self.gamma is None, gamma will be automatically determined from
# the data. The function below returns its value.
self._enc_cluster_centroids, self._enc_map, self.labels_, self.cost_,\
self.n_iter_, self.gamma = k_prototypes(X,
categorical,
self.n_clusters,
self.max_iter,
self.num_dissim,
self.cat_dissim,
self.gamma,
self.init,
self.n_init,
self.verbose,
random_state,
self.n_jobs)
return self | [
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241,039 | nicodv/kmodes | kmodes/util/dissim.py | euclidean_dissim | def euclidean_dissim(a, b, **_):
"""Euclidean distance dissimilarity function"""
if np.isnan(a).any() or np.isnan(b).any():
raise ValueError("Missing values detected in numerical columns.")
return np.sum((a - b) ** 2, axis=1) | python | def euclidean_dissim(a, b, **_):
if np.isnan(a).any() or np.isnan(b).any():
raise ValueError("Missing values detected in numerical columns.")
return np.sum((a - b) ** 2, axis=1) | [
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241,040 | nicodv/kmodes | kmodes/util/dissim.py | ng_dissim | def ng_dissim(a, b, X=None, membship=None):
"""Ng et al.'s dissimilarity measure, as presented in
Michael K. Ng, Mark Junjie Li, Joshua Zhexue Huang, and Zengyou He, "On the
Impact of Dissimilarity Measure in k-Modes Clustering Algorithm", IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3,
January, 2007
This function can potentially speed up training convergence.
Note that membship must be a rectangular array such that the
len(membship) = len(a) and len(membship[i]) = X.shape[1]
In case of missing membship, this function reverts back to
matching dissimilarity (e.g., when predicting).
"""
# Without membership, revert to matching dissimilarity
if membship is None:
return matching_dissim(a, b)
def calc_cjr(b, X, memj, idr):
"""Num objects w/ category value x_{i,r} for rth attr in jth cluster"""
xcids = np.where(memj == 1)
return float((np.take(X, xcids, axis=0)[0][:, idr] == b[idr]).sum(0))
def calc_dissim(b, X, memj, idr):
# Size of jth cluster
cj = float(np.sum(memj))
return (1.0 - (calc_cjr(b, X, memj, idr) / cj)) if cj != 0.0 else 0.0
if len(membship) != a.shape[0] and len(membship[0]) != X.shape[1]:
raise ValueError("'membship' must be a rectangular array where "
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"number of rows in 'a' and the number of "
"columns in 'membship' equals the number of rows in 'X'.")
return np.array([np.array([calc_dissim(b, X, membship[idj], idr)
if b[idr] == t else 1.0
for idr, t in enumerate(val_a)]).sum(0)
for idj, val_a in enumerate(a)]) | python | def ng_dissim(a, b, X=None, membship=None):
# Without membership, revert to matching dissimilarity
if membship is None:
return matching_dissim(a, b)
def calc_cjr(b, X, memj, idr):
"""Num objects w/ category value x_{i,r} for rth attr in jth cluster"""
xcids = np.where(memj == 1)
return float((np.take(X, xcids, axis=0)[0][:, idr] == b[idr]).sum(0))
def calc_dissim(b, X, memj, idr):
# Size of jth cluster
cj = float(np.sum(memj))
return (1.0 - (calc_cjr(b, X, memj, idr) / cj)) if cj != 0.0 else 0.0
if len(membship) != a.shape[0] and len(membship[0]) != X.shape[1]:
raise ValueError("'membship' must be a rectangular array where "
"the number of rows in 'membship' equals the "
"number of rows in 'a' and the number of "
"columns in 'membship' equals the number of rows in 'X'.")
return np.array([np.array([calc_dissim(b, X, membship[idj], idr)
if b[idr] == t else 1.0
for idr, t in enumerate(val_a)]).sum(0)
for idj, val_a in enumerate(a)]) | [
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Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3,
January, 2007
This function can potentially speed up training convergence.
Note that membship must be a rectangular array such that the
len(membship) = len(a) and len(membship[i]) = X.shape[1]
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241,041 | Bogdanp/dramatiq | dramatiq/results/backend.py | ResultBackend.store_result | def store_result(self, message, result: Result, ttl: int) -> None:
"""Store a result in the backend.
Parameters:
message(Message)
result(object): Must be serializable.
ttl(int): The maximum amount of time the result may be
stored in the backend for.
"""
message_key = self.build_message_key(message)
return self._store(message_key, result, ttl) | python | def store_result(self, message, result: Result, ttl: int) -> None:
message_key = self.build_message_key(message)
return self._store(message_key, result, ttl) | [
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241,042 | Bogdanp/dramatiq | dramatiq/results/backend.py | ResultBackend.build_message_key | def build_message_key(self, message) -> str:
"""Given a message, return its globally-unique key.
Parameters:
message(Message)
Returns:
str
"""
message_key = "%(namespace)s:%(queue_name)s:%(actor_name)s:%(message_id)s" % {
"namespace": self.namespace,
"queue_name": q_name(message.queue_name),
"actor_name": message.actor_name,
"message_id": message.message_id,
}
return hashlib.md5(message_key.encode("utf-8")).hexdigest() | python | def build_message_key(self, message) -> str:
message_key = "%(namespace)s:%(queue_name)s:%(actor_name)s:%(message_id)s" % {
"namespace": self.namespace,
"queue_name": q_name(message.queue_name),
"actor_name": message.actor_name,
"message_id": message.message_id,
}
return hashlib.md5(message_key.encode("utf-8")).hexdigest() | [
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241,043 | Bogdanp/dramatiq | dramatiq/results/backend.py | ResultBackend._store | def _store(self, message_key: str, result: Result, ttl: int) -> None: # pragma: no cover
"""Store a result in the backend. Subclasses may implement
this method if they want to use the default implementation of
set_result.
"""
raise NotImplementedError("%(classname)r does not implement _store()" % {
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}) | python | def _store(self, message_key: str, result: Result, ttl: int) -> None: # pragma: no cover
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241,044 | Bogdanp/dramatiq | dramatiq/rate_limits/rate_limiter.py | RateLimiter.acquire | def acquire(self, *, raise_on_failure=True):
"""Attempt to acquire a slot under this rate limiter.
Parameters:
raise_on_failure(bool): Whether or not failures should raise an
exception. If this is false, the context manager will instead
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Returns:
bool: Whether or not the slot could be acquired.
"""
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yield acquired
finally:
if acquired:
self._release() | python | def acquire(self, *, raise_on_failure=True):
acquired = False
try:
acquired = self._acquire()
if raise_on_failure and not acquired:
raise RateLimitExceeded("rate limit exceeded for key %(key)r" % vars(self))
yield acquired
finally:
if acquired:
self._release() | [
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241,045 | Bogdanp/dramatiq | dramatiq/middleware/prometheus.py | flock | def flock(path):
"""Attempt to acquire a POSIX file lock.
"""
with open(path, "w+") as lf:
try:
fcntl.flock(lf, fcntl.LOCK_EX | fcntl.LOCK_NB)
acquired = True
yield acquired
except OSError:
acquired = False
yield acquired
finally:
if acquired:
fcntl.flock(lf, fcntl.LOCK_UN) | python | def flock(path):
with open(path, "w+") as lf:
try:
fcntl.flock(lf, fcntl.LOCK_EX | fcntl.LOCK_NB)
acquired = True
yield acquired
except OSError:
acquired = False
yield acquired
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] | a8cc2728478e794952a5a50c3fb19ec455fe91b6 | https://github.com/Bogdanp/dramatiq/blob/a8cc2728478e794952a5a50c3fb19ec455fe91b6/dramatiq/middleware/prometheus.py#L227-L242 |
241,046 | Bogdanp/dramatiq | dramatiq/message.py | Message.copy | def copy(self, **attributes):
"""Create a copy of this message.
"""
updated_options = attributes.pop("options", {})
options = self.options.copy()
options.update(updated_options)
return self._replace(**attributes, options=options) | python | def copy(self, **attributes):
updated_options = attributes.pop("options", {})
options = self.options.copy()
options.update(updated_options)
return self._replace(**attributes, options=options) | [
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241,047 | Bogdanp/dramatiq | dramatiq/message.py | Message.get_result | def get_result(self, *, backend=None, block=False, timeout=None):
"""Get the result associated with this message from a result
backend.
Warning:
If you use multiple result backends or brokers you should
always pass the backend parameter. This method is only able
to infer the result backend off of the default broker.
Parameters:
backend(ResultBackend): The result backend to use to get the
result. If omitted, this method will try to find and use
the result backend on the default broker instance.
block(bool): Whether or not to block while waiting for a
result.
timeout(int): The maximum amount of time, in ms, to block
while waiting for a result.
Raises:
RuntimeError: If there is no result backend on the default
broker.
ResultMissing: When block is False and the result isn't set.
ResultTimeout: When waiting for a result times out.
Returns:
object: The result.
"""
if not backend:
broker = get_broker()
for middleware in broker.middleware:
if isinstance(middleware, Results):
backend = middleware.backend
break
else:
raise RuntimeError("The default broker doesn't have a results backend.")
return backend.get_result(self, block=block, timeout=timeout) | python | def get_result(self, *, backend=None, block=False, timeout=None):
if not backend:
broker = get_broker()
for middleware in broker.middleware:
if isinstance(middleware, Results):
backend = middleware.backend
break
else:
raise RuntimeError("The default broker doesn't have a results backend.")
return backend.get_result(self, block=block, timeout=timeout) | [
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241,048 | Bogdanp/dramatiq | dramatiq/common.py | compute_backoff | def compute_backoff(attempts, *, factor=5, jitter=True, max_backoff=2000, max_exponent=32):
"""Compute an exponential backoff value based on some number of attempts.
Parameters:
attempts(int): The number of attempts there have been so far.
factor(int): The number of milliseconds to multiply each backoff by.
max_backoff(int): The max number of milliseconds to backoff by.
max_exponent(int): The maximum backoff exponent.
Returns:
tuple: The new number of attempts and the backoff in milliseconds.
"""
exponent = min(attempts, max_exponent)
backoff = min(factor * 2 ** exponent, max_backoff)
if jitter:
backoff /= 2
backoff = int(backoff + uniform(0, backoff))
return attempts + 1, backoff | python | def compute_backoff(attempts, *, factor=5, jitter=True, max_backoff=2000, max_exponent=32):
exponent = min(attempts, max_exponent)
backoff = min(factor * 2 ** exponent, max_backoff)
if jitter:
backoff /= 2
backoff = int(backoff + uniform(0, backoff))
return attempts + 1, backoff | [
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241,049 | Bogdanp/dramatiq | dramatiq/common.py | join_all | def join_all(joinables, timeout):
"""Wait on a list of objects that can be joined with a total
timeout represented by ``timeout``.
Parameters:
joinables(object): Objects with a join method.
timeout(int): The total timeout in milliseconds.
"""
started, elapsed = current_millis(), 0
for ob in joinables:
ob.join(timeout=timeout / 1000)
elapsed = current_millis() - started
timeout = max(0, timeout - elapsed) | python | def join_all(joinables, timeout):
started, elapsed = current_millis(), 0
for ob in joinables:
ob.join(timeout=timeout / 1000)
elapsed = current_millis() - started
timeout = max(0, timeout - elapsed) | [
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241,050 | Bogdanp/dramatiq | dramatiq/common.py | dq_name | def dq_name(queue_name):
"""Returns the delayed queue name for a given queue. If the given
queue name already belongs to a delayed queue, then it is returned
unchanged.
"""
if queue_name.endswith(".DQ"):
return queue_name
if queue_name.endswith(".XQ"):
queue_name = queue_name[:-3]
return queue_name + ".DQ" | python | def dq_name(queue_name):
if queue_name.endswith(".DQ"):
return queue_name
if queue_name.endswith(".XQ"):
queue_name = queue_name[:-3]
return queue_name + ".DQ" | [
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241,051 | Bogdanp/dramatiq | dramatiq/common.py | xq_name | def xq_name(queue_name):
"""Returns the dead letter queue name for a given queue. If the
given queue name belongs to a delayed queue, the dead letter queue
name for the original queue is generated.
"""
if queue_name.endswith(".XQ"):
return queue_name
if queue_name.endswith(".DQ"):
queue_name = queue_name[:-3]
return queue_name + ".XQ" | python | def xq_name(queue_name):
if queue_name.endswith(".XQ"):
return queue_name
if queue_name.endswith(".DQ"):
queue_name = queue_name[:-3]
return queue_name + ".XQ" | [
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241,052 | Bogdanp/dramatiq | dramatiq/broker.py | get_broker | def get_broker() -> "Broker":
"""Get the global broker instance. If no global broker is set,
this initializes a RabbitmqBroker and returns it.
Returns:
Broker: The default Broker.
"""
global global_broker
if global_broker is None:
from .brokers.rabbitmq import RabbitmqBroker
set_broker(RabbitmqBroker(
host="127.0.0.1",
port=5672,
heartbeat=5,
connection_attempts=5,
blocked_connection_timeout=30,
))
return global_broker | python | def get_broker() -> "Broker":
global global_broker
if global_broker is None:
from .brokers.rabbitmq import RabbitmqBroker
set_broker(RabbitmqBroker(
host="127.0.0.1",
port=5672,
heartbeat=5,
connection_attempts=5,
blocked_connection_timeout=30,
))
return global_broker | [
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241,053 | Bogdanp/dramatiq | dramatiq/broker.py | Broker.add_middleware | def add_middleware(self, middleware, *, before=None, after=None):
"""Add a middleware object to this broker. The middleware is
appended to the end of the middleware list by default.
You can specify another middleware (by class) as a reference
point for where the new middleware should be added.
Parameters:
middleware(Middleware): The middleware.
before(type): Add this middleware before a specific one.
after(type): Add this middleware after a specific one.
Raises:
ValueError: When either ``before`` or ``after`` refer to a
middleware that hasn't been registered yet.
"""
assert not (before and after), \
"provide either 'before' or 'after', but not both"
if before or after:
for i, m in enumerate(self.middleware): # noqa
if isinstance(m, before or after):
break
else:
raise ValueError("Middleware %r not found" % (before or after))
if before:
self.middleware.insert(i, middleware)
else:
self.middleware.insert(i + 1, middleware)
else:
self.middleware.append(middleware)
self.actor_options |= middleware.actor_options
for actor_name in self.get_declared_actors():
middleware.after_declare_actor(self, actor_name)
for queue_name in self.get_declared_queues():
middleware.after_declare_queue(self, queue_name)
for queue_name in self.get_declared_delay_queues():
middleware.after_declare_delay_queue(self, queue_name) | python | def add_middleware(self, middleware, *, before=None, after=None):
assert not (before and after), \
"provide either 'before' or 'after', but not both"
if before or after:
for i, m in enumerate(self.middleware): # noqa
if isinstance(m, before or after):
break
else:
raise ValueError("Middleware %r not found" % (before or after))
if before:
self.middleware.insert(i, middleware)
else:
self.middleware.insert(i + 1, middleware)
else:
self.middleware.append(middleware)
self.actor_options |= middleware.actor_options
for actor_name in self.get_declared_actors():
middleware.after_declare_actor(self, actor_name)
for queue_name in self.get_declared_queues():
middleware.after_declare_queue(self, queue_name)
for queue_name in self.get_declared_delay_queues():
middleware.after_declare_delay_queue(self, queue_name) | [
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241,054 | Bogdanp/dramatiq | dramatiq/broker.py | Broker.declare_actor | def declare_actor(self, actor): # pragma: no cover
"""Declare a new actor on this broker. Declaring an Actor
twice replaces the first actor with the second by name.
Parameters:
actor(Actor): The actor being declared.
"""
self.emit_before("declare_actor", actor)
self.declare_queue(actor.queue_name)
self.actors[actor.actor_name] = actor
self.emit_after("declare_actor", actor) | python | def declare_actor(self, actor): # pragma: no cover
self.emit_before("declare_actor", actor)
self.declare_queue(actor.queue_name)
self.actors[actor.actor_name] = actor
self.emit_after("declare_actor", actor) | [
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241,055 | Bogdanp/dramatiq | dramatiq/brokers/rabbitmq.py | URLRabbitmqBroker | def URLRabbitmqBroker(url, *, middleware=None):
"""Alias for the RabbitMQ broker that takes a connection URL as a
positional argument.
Parameters:
url(str): A connection string.
middleware(list[Middleware]): The middleware to add to this
broker.
"""
warnings.warn(
"Use RabbitmqBroker with the 'url' parameter instead of URLRabbitmqBroker.",
DeprecationWarning, stacklevel=2,
)
return RabbitmqBroker(url=url, middleware=middleware) | python | def URLRabbitmqBroker(url, *, middleware=None):
warnings.warn(
"Use RabbitmqBroker with the 'url' parameter instead of URLRabbitmqBroker.",
DeprecationWarning, stacklevel=2,
)
return RabbitmqBroker(url=url, middleware=middleware) | [
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241,056 | Bogdanp/dramatiq | dramatiq/brokers/rabbitmq.py | RabbitmqBroker.close | def close(self):
"""Close all open RabbitMQ connections.
"""
# The main thread may keep connections open for a long time
# w/o publishing heartbeats, which means that they'll end up
# being closed by the time the broker is closed. When that
# happens, pika logs a bunch of scary stuff so we want to
# filter that out.
logging_filter = _IgnoreScaryLogs()
logging.getLogger("pika.adapters.base_connection").addFilter(logging_filter)
logging.getLogger("pika.adapters.blocking_connection").addFilter(logging_filter)
self.logger.debug("Closing channels and connections...")
for channel_or_conn in chain(self.channels, self.connections):
try:
channel_or_conn.close()
except pika.exceptions.AMQPError:
pass
except Exception: # pragma: no cover
self.logger.debug("Encountered an error while closing %r.", channel_or_conn, exc_info=True)
self.logger.debug("Channels and connections closed.") | python | def close(self):
# The main thread may keep connections open for a long time
# w/o publishing heartbeats, which means that they'll end up
# being closed by the time the broker is closed. When that
# happens, pika logs a bunch of scary stuff so we want to
# filter that out.
logging_filter = _IgnoreScaryLogs()
logging.getLogger("pika.adapters.base_connection").addFilter(logging_filter)
logging.getLogger("pika.adapters.blocking_connection").addFilter(logging_filter)
self.logger.debug("Closing channels and connections...")
for channel_or_conn in chain(self.channels, self.connections):
try:
channel_or_conn.close()
except pika.exceptions.AMQPError:
pass
except Exception: # pragma: no cover
self.logger.debug("Encountered an error while closing %r.", channel_or_conn, exc_info=True)
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] | a8cc2728478e794952a5a50c3fb19ec455fe91b6 | https://github.com/Bogdanp/dramatiq/blob/a8cc2728478e794952a5a50c3fb19ec455fe91b6/dramatiq/brokers/rabbitmq.py#L147-L168 |
241,057 | Bogdanp/dramatiq | dramatiq/brokers/rabbitmq.py | RabbitmqBroker.declare_queue | def declare_queue(self, queue_name):
"""Declare a queue. Has no effect if a queue with the given
name already exists.
Parameters:
queue_name(str): The name of the new queue.
Raises:
ConnectionClosed: If the underlying channel or connection
has been closed.
"""
attempts = 1
while True:
try:
if queue_name not in self.queues:
self.emit_before("declare_queue", queue_name)
self._declare_queue(queue_name)
self.queues.add(queue_name)
self.emit_after("declare_queue", queue_name)
delayed_name = dq_name(queue_name)
self._declare_dq_queue(queue_name)
self.delay_queues.add(delayed_name)
self.emit_after("declare_delay_queue", delayed_name)
self._declare_xq_queue(queue_name)
break
except (pika.exceptions.AMQPConnectionError,
pika.exceptions.AMQPChannelError) as e: # pragma: no cover
# Delete the channel and the connection so that the next
# caller may initiate new ones of each.
del self.channel
del self.connection
attempts += 1
if attempts > MAX_DECLARE_ATTEMPTS:
raise ConnectionClosed(e) from None
self.logger.debug(
"Retrying declare due to closed connection. [%d/%d]",
attempts, MAX_DECLARE_ATTEMPTS,
) | python | def declare_queue(self, queue_name):
attempts = 1
while True:
try:
if queue_name not in self.queues:
self.emit_before("declare_queue", queue_name)
self._declare_queue(queue_name)
self.queues.add(queue_name)
self.emit_after("declare_queue", queue_name)
delayed_name = dq_name(queue_name)
self._declare_dq_queue(queue_name)
self.delay_queues.add(delayed_name)
self.emit_after("declare_delay_queue", delayed_name)
self._declare_xq_queue(queue_name)
break
except (pika.exceptions.AMQPConnectionError,
pika.exceptions.AMQPChannelError) as e: # pragma: no cover
# Delete the channel and the connection so that the next
# caller may initiate new ones of each.
del self.channel
del self.connection
attempts += 1
if attempts > MAX_DECLARE_ATTEMPTS:
raise ConnectionClosed(e) from None
self.logger.debug(
"Retrying declare due to closed connection. [%d/%d]",
attempts, MAX_DECLARE_ATTEMPTS,
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241,058 | Bogdanp/dramatiq | dramatiq/brokers/rabbitmq.py | RabbitmqBroker.get_queue_message_counts | def get_queue_message_counts(self, queue_name):
"""Get the number of messages in a queue. This method is only
meant to be used in unit and integration tests.
Parameters:
queue_name(str): The queue whose message counts to get.
Returns:
tuple: A triple representing the number of messages in the
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"""
queue_response = self._declare_queue(queue_name)
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return (
queue_response.method.message_count,
dq_queue_response.method.message_count,
xq_queue_response.method.message_count,
) | python | def get_queue_message_counts(self, queue_name):
queue_response = self._declare_queue(queue_name)
dq_queue_response = self._declare_dq_queue(queue_name)
xq_queue_response = self._declare_xq_queue(queue_name)
return (
queue_response.method.message_count,
dq_queue_response.method.message_count,
xq_queue_response.method.message_count,
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241,059 | Bogdanp/dramatiq | dramatiq/rate_limits/barrier.py | Barrier.create | def create(self, parties):
"""Create the barrier for the given number of parties.
Parameters:
parties(int): The number of parties to wait for.
Returns:
bool: Whether or not the new barrier was successfully created.
"""
assert parties > 0, "parties must be a positive integer."
return self.backend.add(self.key, parties, self.ttl) | python | def create(self, parties):
assert parties > 0, "parties must be a positive integer."
return self.backend.add(self.key, parties, self.ttl) | [
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241,060 | Bogdanp/dramatiq | dramatiq/rate_limits/barrier.py | Barrier.wait | def wait(self, *, block=True, timeout=None):
"""Signal that a party has reached the barrier.
Warning:
Barrier blocking is currently only supported by the stub and
Redis backends.
Warning:
Re-using keys between blocking calls may lead to undefined
behaviour. Make sure your barrier keys are always unique
(use a UUID).
Parameters:
block(bool): Whether or not to block while waiting for the
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timeout(int): The maximum number of milliseconds to wait for
the barrier to be cleared.
Returns:
bool: Whether or not the barrier has been reached by all parties.
"""
cleared = not self.backend.decr(self.key, 1, 1, self.ttl)
if cleared:
self.backend.wait_notify(self.key_events, self.ttl)
return True
if block:
return self.backend.wait(self.key_events, timeout)
return False | python | def wait(self, *, block=True, timeout=None):
cleared = not self.backend.decr(self.key, 1, 1, self.ttl)
if cleared:
self.backend.wait_notify(self.key_events, self.ttl)
return True
if block:
return self.backend.wait(self.key_events, timeout)
return False | [
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241,061 | Bogdanp/dramatiq | dramatiq/middleware/threading.py | raise_thread_exception | def raise_thread_exception(thread_id, exception):
"""Raise an exception in a thread.
Currently, this is only available on CPython.
Note:
This works by setting an async exception in the thread. This means
that the exception will only get called the next time that thread
acquires the GIL. Concretely, this means that this middleware can't
cancel system calls.
"""
if current_platform == "CPython":
_raise_thread_exception_cpython(thread_id, exception)
else:
message = "Setting thread exceptions (%s) is not supported for your current platform (%r)."
exctype = (exception if inspect.isclass(exception) else type(exception)).__name__
logger.critical(message, exctype, current_platform) | python | def raise_thread_exception(thread_id, exception):
if current_platform == "CPython":
_raise_thread_exception_cpython(thread_id, exception)
else:
message = "Setting thread exceptions (%s) is not supported for your current platform (%r)."
exctype = (exception if inspect.isclass(exception) else type(exception)).__name__
logger.critical(message, exctype, current_platform) | [
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241,062 | Bogdanp/dramatiq | dramatiq/watcher.py | setup_file_watcher | def setup_file_watcher(path, use_polling=False):
"""Sets up a background thread that watches for source changes and
automatically sends SIGHUP to the current process whenever a file
changes.
"""
if use_polling:
observer_class = watchdog.observers.polling.PollingObserver
else:
observer_class = EVENTED_OBSERVER
file_event_handler = _SourceChangesHandler(patterns=["*.py"])
file_watcher = observer_class()
file_watcher.schedule(file_event_handler, path, recursive=True)
file_watcher.start()
return file_watcher | python | def setup_file_watcher(path, use_polling=False):
if use_polling:
observer_class = watchdog.observers.polling.PollingObserver
else:
observer_class = EVENTED_OBSERVER
file_event_handler = _SourceChangesHandler(patterns=["*.py"])
file_watcher = observer_class()
file_watcher.schedule(file_event_handler, path, recursive=True)
file_watcher.start()
return file_watcher | [
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241,063 | Bogdanp/dramatiq | dramatiq/brokers/stub.py | StubBroker.declare_queue | def declare_queue(self, queue_name):
"""Declare a queue. Has no effect if a queue with the given
name has already been declared.
Parameters:
queue_name(str): The name of the new queue.
"""
if queue_name not in self.queues:
self.emit_before("declare_queue", queue_name)
self.queues[queue_name] = Queue()
self.emit_after("declare_queue", queue_name)
delayed_name = dq_name(queue_name)
self.queues[delayed_name] = Queue()
self.delay_queues.add(delayed_name)
self.emit_after("declare_delay_queue", delayed_name) | python | def declare_queue(self, queue_name):
if queue_name not in self.queues:
self.emit_before("declare_queue", queue_name)
self.queues[queue_name] = Queue()
self.emit_after("declare_queue", queue_name)
delayed_name = dq_name(queue_name)
self.queues[delayed_name] = Queue()
self.delay_queues.add(delayed_name)
self.emit_after("declare_delay_queue", delayed_name) | [
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241,064 | Bogdanp/dramatiq | dramatiq/brokers/stub.py | StubBroker.flush_all | def flush_all(self):
"""Drop all messages from all declared queues.
"""
for queue_name in chain(self.queues, self.delay_queues):
self.flush(queue_name) | python | def flush_all(self):
for queue_name in chain(self.queues, self.delay_queues):
self.flush(queue_name) | [
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241,065 | Bogdanp/dramatiq | dramatiq/composition.py | pipeline.run | def run(self, *, delay=None):
"""Run this pipeline.
Parameters:
delay(int): The minimum amount of time, in milliseconds, the
pipeline should be delayed by.
Returns:
pipeline: Itself.
"""
self.broker.enqueue(self.messages[0], delay=delay)
return self | python | def run(self, *, delay=None):
self.broker.enqueue(self.messages[0], delay=delay)
return self | [
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241,066 | Bogdanp/dramatiq | dramatiq/composition.py | pipeline.get_result | def get_result(self, *, block=False, timeout=None):
"""Get the result of this pipeline.
Pipeline results are represented by the result of the last
message in the chain.
Parameters:
block(bool): Whether or not to block until a result is set.
timeout(int): The maximum amount of time, in ms, to wait for
a result when block is True. Defaults to 10 seconds.
Raises:
ResultMissing: When block is False and the result isn't set.
ResultTimeout: When waiting for a result times out.
Returns:
object: The result.
"""
return self.messages[-1].get_result(block=block, timeout=timeout) | python | def get_result(self, *, block=False, timeout=None):
return self.messages[-1].get_result(block=block, timeout=timeout) | [
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241,067 | Bogdanp/dramatiq | dramatiq/composition.py | pipeline.get_results | def get_results(self, *, block=False, timeout=None):
"""Get the results of each job in the pipeline.
Parameters:
block(bool): Whether or not to block until a result is set.
timeout(int): The maximum amount of time, in ms, to wait for
a result when block is True. Defaults to 10 seconds.
Raises:
ResultMissing: When block is False and the result isn't set.
ResultTimeout: When waiting for a result times out.
Returns:
A result generator.
"""
deadline = None
if timeout:
deadline = time.monotonic() + timeout / 1000
for message in self.messages:
if deadline:
timeout = max(0, int((deadline - time.monotonic()) * 1000))
yield message.get_result(block=block, timeout=timeout) | python | def get_results(self, *, block=False, timeout=None):
deadline = None
if timeout:
deadline = time.monotonic() + timeout / 1000
for message in self.messages:
if deadline:
timeout = max(0, int((deadline - time.monotonic()) * 1000))
yield message.get_result(block=block, timeout=timeout) | [
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241,068 | Bogdanp/dramatiq | dramatiq/composition.py | group.run | def run(self, *, delay=None):
"""Run the actors in this group.
Parameters:
delay(int): The minimum amount of time, in milliseconds,
each message in the group should be delayed by.
"""
for child in self.children:
if isinstance(child, (group, pipeline)):
child.run(delay=delay)
else:
self.broker.enqueue(child, delay=delay)
return self | python | def run(self, *, delay=None):
for child in self.children:
if isinstance(child, (group, pipeline)):
child.run(delay=delay)
else:
self.broker.enqueue(child, delay=delay)
return self | [
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Parameters:
delay(int): The minimum amount of time, in milliseconds,
each message in the group should be delayed by. | [
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241,069 | Bogdanp/dramatiq | dramatiq/composition.py | group.get_results | def get_results(self, *, block=False, timeout=None):
"""Get the results of each job in the group.
Parameters:
block(bool): Whether or not to block until the results are stored.
timeout(int): The maximum amount of time, in milliseconds,
to wait for results when block is True. Defaults to 10
seconds.
Raises:
ResultMissing: When block is False and the results aren't set.
ResultTimeout: When waiting for results times out.
Returns:
A result generator.
"""
deadline = None
if timeout:
deadline = time.monotonic() + timeout / 1000
for child in self.children:
if deadline:
timeout = max(0, int((deadline - time.monotonic()) * 1000))
if isinstance(child, group):
yield list(child.get_results(block=block, timeout=timeout))
else:
yield child.get_result(block=block, timeout=timeout) | python | def get_results(self, *, block=False, timeout=None):
deadline = None
if timeout:
deadline = time.monotonic() + timeout / 1000
for child in self.children:
if deadline:
timeout = max(0, int((deadline - time.monotonic()) * 1000))
if isinstance(child, group):
yield list(child.get_results(block=block, timeout=timeout))
else:
yield child.get_result(block=block, timeout=timeout) | [
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241,070 | Bogdanp/dramatiq | dramatiq/composition.py | group.wait | def wait(self, *, timeout=None):
"""Block until all the jobs in the group have finished or
until the timeout expires.
Parameters:
timeout(int): The maximum amount of time, in ms, to wait.
Defaults to 10 seconds.
"""
for _ in self.get_results(block=True, timeout=timeout): # pragma: no cover
pass | python | def wait(self, *, timeout=None):
for _ in self.get_results(block=True, timeout=timeout): # pragma: no cover
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241,071 | Bogdanp/dramatiq | dramatiq/actor.py | actor | def actor(fn=None, *, actor_class=Actor, actor_name=None, queue_name="default", priority=0, broker=None, **options):
"""Declare an actor.
Examples:
>>> import dramatiq
>>> @dramatiq.actor
... def add(x, y):
... print(x + y)
...
>>> add
Actor(<function add at 0x106c6d488>, queue_name='default', actor_name='add')
>>> add(1, 2)
3
>>> add.send(1, 2)
Message(
queue_name='default',
actor_name='add',
args=(1, 2), kwargs={}, options={},
message_id='e0d27b45-7900-41da-bb97-553b8a081206',
message_timestamp=1497862448685)
Parameters:
fn(callable): The function to wrap.
actor_class(type): Type created by the decorator. Defaults to
:class:`Actor` but can be any callable as long as it returns an
actor and takes the same arguments as the :class:`Actor` class.
actor_name(str): The name of the actor.
queue_name(str): The name of the queue to use.
priority(int): The actor's global priority. If two tasks have
been pulled on a worker concurrently and one has a higher
priority than the other then it will be processed first.
Lower numbers represent higher priorities.
broker(Broker): The broker to use with this actor.
**options(dict): Arbitrary options that vary with the set of
middleware that you use. See ``get_broker().actor_options``.
Returns:
Actor: The decorated function.
"""
def decorator(fn):
nonlocal actor_name, broker
actor_name = actor_name or fn.__name__
if not _queue_name_re.fullmatch(queue_name):
raise ValueError(
"Queue names must start with a letter or an underscore followed "
"by any number of letters, digits, dashes or underscores."
)
broker = broker or get_broker()
invalid_options = set(options) - broker.actor_options
if invalid_options:
invalid_options_list = ", ".join(invalid_options)
raise ValueError((
"The following actor options are undefined: %s. "
"Did you forget to add a middleware to your Broker?"
) % invalid_options_list)
return actor_class(
fn, actor_name=actor_name, queue_name=queue_name,
priority=priority, broker=broker, options=options,
)
if fn is None:
return decorator
return decorator(fn) | python | def actor(fn=None, *, actor_class=Actor, actor_name=None, queue_name="default", priority=0, broker=None, **options):
def decorator(fn):
nonlocal actor_name, broker
actor_name = actor_name or fn.__name__
if not _queue_name_re.fullmatch(queue_name):
raise ValueError(
"Queue names must start with a letter or an underscore followed "
"by any number of letters, digits, dashes or underscores."
)
broker = broker or get_broker()
invalid_options = set(options) - broker.actor_options
if invalid_options:
invalid_options_list = ", ".join(invalid_options)
raise ValueError((
"The following actor options are undefined: %s. "
"Did you forget to add a middleware to your Broker?"
) % invalid_options_list)
return actor_class(
fn, actor_name=actor_name, queue_name=queue_name,
priority=priority, broker=broker, options=options,
)
if fn is None:
return decorator
return decorator(fn) | [
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broker(Broker): The broker to use with this actor.
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241,072 | Bogdanp/dramatiq | dramatiq/actor.py | Actor.message | def message(self, *args, **kwargs):
"""Build a message. This method is useful if you want to
compose actors. See the actor composition documentation for
details.
Parameters:
*args(tuple): Positional arguments to send to the actor.
**kwargs(dict): Keyword arguments to send to the actor.
Examples:
>>> (add.message(1, 2) | add.message(3))
pipeline([add(1, 2), add(3)])
Returns:
Message: A message that can be enqueued on a broker.
"""
return self.message_with_options(args=args, kwargs=kwargs) | python | def message(self, *args, **kwargs):
return self.message_with_options(args=args, kwargs=kwargs) | [
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**kwargs(dict): Keyword arguments to send to the actor.
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241,073 | Bogdanp/dramatiq | dramatiq/actor.py | Actor.message_with_options | def message_with_options(self, *, args=None, kwargs=None, **options):
"""Build a message with an arbitray set of processing options.
This method is useful if you want to compose actors. See the
actor composition documentation for details.
Parameters:
args(tuple): Positional arguments that are passed to the actor.
kwargs(dict): Keyword arguments that are passed to the actor.
**options(dict): Arbitrary options that are passed to the
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Returns:
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"""
for name in ["on_failure", "on_success"]:
callback = options.get(name)
if isinstance(callback, Actor):
options[name] = callback.actor_name
elif not isinstance(callback, (type(None), str)):
raise TypeError(name + " value must be an Actor")
return Message(
queue_name=self.queue_name,
actor_name=self.actor_name,
args=args or (), kwargs=kwargs or {},
options=options,
) | python | def message_with_options(self, *, args=None, kwargs=None, **options):
for name in ["on_failure", "on_success"]:
callback = options.get(name)
if isinstance(callback, Actor):
options[name] = callback.actor_name
elif not isinstance(callback, (type(None), str)):
raise TypeError(name + " value must be an Actor")
return Message(
queue_name=self.queue_name,
actor_name=self.actor_name,
args=args or (), kwargs=kwargs or {},
options=options,
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241,074 | Bogdanp/dramatiq | dramatiq/actor.py | Actor.send | def send(self, *args, **kwargs):
"""Asynchronously send a message to this actor.
Parameters:
*args(tuple): Positional arguments to send to the actor.
**kwargs(dict): Keyword arguments to send to the actor.
Returns:
Message: The enqueued message.
"""
return self.send_with_options(args=args, kwargs=kwargs) | python | def send(self, *args, **kwargs):
return self.send_with_options(args=args, kwargs=kwargs) | [
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241,075 | Bogdanp/dramatiq | dramatiq/actor.py | Actor.send_with_options | def send_with_options(self, *, args=None, kwargs=None, delay=None, **options):
"""Asynchronously send a message to this actor, along with an
arbitrary set of processing options for the broker and
middleware.
Parameters:
args(tuple): Positional arguments that are passed to the actor.
kwargs(dict): Keyword arguments that are passed to the actor.
delay(int): The minimum amount of time, in milliseconds, the
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**options(dict): Arbitrary options that are passed to the
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Returns:
Message: The enqueued message.
"""
message = self.message_with_options(args=args, kwargs=kwargs, **options)
return self.broker.enqueue(message, delay=delay) | python | def send_with_options(self, *, args=None, kwargs=None, delay=None, **options):
message = self.message_with_options(args=args, kwargs=kwargs, **options)
return self.broker.enqueue(message, delay=delay) | [
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241,076 | Bogdanp/dramatiq | dramatiq/worker.py | Worker.start | def start(self):
"""Initialize the worker boot sequence and start up all the
worker threads.
"""
self.broker.emit_before("worker_boot", self)
worker_middleware = _WorkerMiddleware(self)
self.broker.add_middleware(worker_middleware)
for _ in range(self.worker_threads):
self._add_worker()
self.broker.emit_after("worker_boot", self) | python | def start(self):
self.broker.emit_before("worker_boot", self)
worker_middleware = _WorkerMiddleware(self)
self.broker.add_middleware(worker_middleware)
for _ in range(self.worker_threads):
self._add_worker()
self.broker.emit_after("worker_boot", self) | [
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241,077 | Bogdanp/dramatiq | dramatiq/worker.py | Worker.pause | def pause(self):
"""Pauses all the worker threads.
"""
for child in chain(self.consumers.values(), self.workers):
child.pause()
for child in chain(self.consumers.values(), self.workers):
child.paused_event.wait() | python | def pause(self):
for child in chain(self.consumers.values(), self.workers):
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241,078 | Bogdanp/dramatiq | dramatiq/worker.py | Worker.resume | def resume(self):
"""Resumes all the worker threads.
"""
for child in chain(self.consumers.values(), self.workers):
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241,079 | Bogdanp/dramatiq | dramatiq/worker.py | Worker.stop | def stop(self, timeout=600000):
"""Gracefully stop the Worker and all of its consumers and
workers.
Parameters:
timeout(int): The number of milliseconds to wait for
everything to shut down.
"""
self.broker.emit_before("worker_shutdown", self)
self.logger.info("Shutting down...")
# Stop workers before consumers. The consumers are kept alive
# during this process so that heartbeats keep being sent to
# the broker while workers finish their current tasks.
self.logger.debug("Stopping workers...")
for thread in self.workers:
thread.stop()
join_all(self.workers, timeout)
self.logger.debug("Workers stopped.")
self.logger.debug("Stopping consumers...")
for thread in self.consumers.values():
thread.stop()
join_all(self.consumers.values(), timeout)
self.logger.debug("Consumers stopped.")
self.logger.debug("Requeueing in-memory messages...")
messages_by_queue = defaultdict(list)
for _, message in iter_queue(self.work_queue):
messages_by_queue[message.queue_name].append(message)
for queue_name, messages in messages_by_queue.items():
try:
self.consumers[queue_name].requeue_messages(messages)
except ConnectionError:
self.logger.warning("Failed to requeue messages on queue %r.", queue_name, exc_info=True)
self.logger.debug("Done requeueing in-progress messages.")
self.logger.debug("Closing consumers...")
for consumer in self.consumers.values():
consumer.close()
self.logger.debug("Consumers closed.")
self.broker.emit_after("worker_shutdown", self)
self.logger.info("Worker has been shut down.") | python | def stop(self, timeout=600000):
self.broker.emit_before("worker_shutdown", self)
self.logger.info("Shutting down...")
# Stop workers before consumers. The consumers are kept alive
# during this process so that heartbeats keep being sent to
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self.logger.debug("Stopping workers...")
for thread in self.workers:
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join_all(self.workers, timeout)
self.logger.debug("Workers stopped.")
self.logger.debug("Stopping consumers...")
for thread in self.consumers.values():
thread.stop()
join_all(self.consumers.values(), timeout)
self.logger.debug("Consumers stopped.")
self.logger.debug("Requeueing in-memory messages...")
messages_by_queue = defaultdict(list)
for _, message in iter_queue(self.work_queue):
messages_by_queue[message.queue_name].append(message)
for queue_name, messages in messages_by_queue.items():
try:
self.consumers[queue_name].requeue_messages(messages)
except ConnectionError:
self.logger.warning("Failed to requeue messages on queue %r.", queue_name, exc_info=True)
self.logger.debug("Done requeueing in-progress messages.")
self.logger.debug("Closing consumers...")
for consumer in self.consumers.values():
consumer.close()
self.logger.debug("Consumers closed.")
self.broker.emit_after("worker_shutdown", self)
self.logger.info("Worker has been shut down.") | [
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241,080 | Bogdanp/dramatiq | dramatiq/worker.py | Worker.join | def join(self):
"""Wait for this worker to complete its work in progress.
This method is useful when testing code.
"""
while True:
for consumer in self.consumers.values():
consumer.delay_queue.join()
self.work_queue.join()
# If nothing got put on the delay queues while we were
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# This could still miss stuff but the chances are slim.
for consumer in self.consumers.values():
if consumer.delay_queue.unfinished_tasks:
break
else:
if self.work_queue.unfinished_tasks:
continue
return | python | def join(self):
while True:
for consumer in self.consumers.values():
consumer.delay_queue.join()
self.work_queue.join()
# If nothing got put on the delay queues while we were
# joining on the work queue then it shoud be safe to exit.
# This could still miss stuff but the chances are slim.
for consumer in self.consumers.values():
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break
else:
if self.work_queue.unfinished_tasks:
continue
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241,081 | Bogdanp/dramatiq | dramatiq/worker.py | _ConsumerThread.handle_delayed_messages | def handle_delayed_messages(self):
"""Enqueue any delayed messages whose eta has passed.
"""
for eta, message in iter_queue(self.delay_queue):
if eta > current_millis():
self.delay_queue.put((eta, message))
self.delay_queue.task_done()
break
queue_name = q_name(message.queue_name)
new_message = message.copy(queue_name=queue_name)
del new_message.options["eta"]
self.broker.enqueue(new_message)
self.post_process_message(message)
self.delay_queue.task_done() | python | def handle_delayed_messages(self):
for eta, message in iter_queue(self.delay_queue):
if eta > current_millis():
self.delay_queue.put((eta, message))
self.delay_queue.task_done()
break
queue_name = q_name(message.queue_name)
new_message = message.copy(queue_name=queue_name)
del new_message.options["eta"]
self.broker.enqueue(new_message)
self.post_process_message(message)
self.delay_queue.task_done() | [
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241,082 | Bogdanp/dramatiq | dramatiq/worker.py | _ConsumerThread.handle_message | def handle_message(self, message):
"""Handle a message received off of the underlying consumer.
If the message has an eta, delay it. Otherwise, put it on the
work queue.
"""
try:
if "eta" in message.options:
self.logger.debug("Pushing message %r onto delay queue.", message.message_id)
self.broker.emit_before("delay_message", message)
self.delay_queue.put((message.options.get("eta", 0), message))
else:
actor = self.broker.get_actor(message.actor_name)
self.logger.debug("Pushing message %r onto work queue.", message.message_id)
self.work_queue.put((actor.priority, message))
except ActorNotFound:
self.logger.error(
"Received message for undefined actor %r. Moving it to the DLQ.",
message.actor_name, exc_info=True,
)
message.fail()
self.post_process_message(message) | python | def handle_message(self, message):
try:
if "eta" in message.options:
self.logger.debug("Pushing message %r onto delay queue.", message.message_id)
self.broker.emit_before("delay_message", message)
self.delay_queue.put((message.options.get("eta", 0), message))
else:
actor = self.broker.get_actor(message.actor_name)
self.logger.debug("Pushing message %r onto work queue.", message.message_id)
self.work_queue.put((actor.priority, message))
except ActorNotFound:
self.logger.error(
"Received message for undefined actor %r. Moving it to the DLQ.",
message.actor_name, exc_info=True,
)
message.fail()
self.post_process_message(message) | [
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241,083 | Bogdanp/dramatiq | dramatiq/worker.py | _ConsumerThread.post_process_message | def post_process_message(self, message):
"""Called by worker threads whenever they're done processing
individual messages, signaling that each message is ready to
be acked or rejected.
"""
while True:
try:
if message.failed:
self.logger.debug("Rejecting message %r.", message.message_id)
self.broker.emit_before("nack", message)
self.consumer.nack(message)
self.broker.emit_after("nack", message)
else:
self.logger.debug("Acknowledging message %r.", message.message_id)
self.broker.emit_before("ack", message)
self.consumer.ack(message)
self.broker.emit_after("ack", message)
return
# This applies to the Redis broker. The alternative to
# constantly retrying would be to give up here and let the
# message be re-processed after the worker is eventually
# stopped or restarted, but we'd be doing the same work
# twice in that case and the behaviour would surprise
# users who don't deploy frequently.
except ConnectionError as e:
self.logger.warning(
"Failed to post_process_message(%s) due to a connection error: %s\n"
"The operation will be retried in %s seconds until the connection recovers.\n"
"If you restart this worker before this operation succeeds, the message will be re-processed later.",
message, e, POST_PROCESS_MESSAGE_RETRY_DELAY_SECS
)
time.sleep(POST_PROCESS_MESSAGE_RETRY_DELAY_SECS)
continue
# Not much point retrying here so we bail. Most likely,
# the message will be re-run after the worker is stopped
# or restarted (because its ack lease will have expired).
except Exception: # pragma: no cover
self.logger.exception(
"Unhandled error during post_process_message(%s). You've found a bug in Dramatiq. Please report it!\n"
"Although your message has been processed, it will be processed again once this worker is restarted.",
message,
)
return | python | def post_process_message(self, message):
while True:
try:
if message.failed:
self.logger.debug("Rejecting message %r.", message.message_id)
self.broker.emit_before("nack", message)
self.consumer.nack(message)
self.broker.emit_after("nack", message)
else:
self.logger.debug("Acknowledging message %r.", message.message_id)
self.broker.emit_before("ack", message)
self.consumer.ack(message)
self.broker.emit_after("ack", message)
return
# This applies to the Redis broker. The alternative to
# constantly retrying would be to give up here and let the
# message be re-processed after the worker is eventually
# stopped or restarted, but we'd be doing the same work
# twice in that case and the behaviour would surprise
# users who don't deploy frequently.
except ConnectionError as e:
self.logger.warning(
"Failed to post_process_message(%s) due to a connection error: %s\n"
"The operation will be retried in %s seconds until the connection recovers.\n"
"If you restart this worker before this operation succeeds, the message will be re-processed later.",
message, e, POST_PROCESS_MESSAGE_RETRY_DELAY_SECS
)
time.sleep(POST_PROCESS_MESSAGE_RETRY_DELAY_SECS)
continue
# Not much point retrying here so we bail. Most likely,
# the message will be re-run after the worker is stopped
# or restarted (because its ack lease will have expired).
except Exception: # pragma: no cover
self.logger.exception(
"Unhandled error during post_process_message(%s). You've found a bug in Dramatiq. Please report it!\n"
"Although your message has been processed, it will be processed again once this worker is restarted.",
message,
)
return | [
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241,084 | Bogdanp/dramatiq | dramatiq/worker.py | _ConsumerThread.close | def close(self):
"""Close this consumer thread and its underlying connection.
"""
try:
if self.consumer:
self.requeue_messages(m for _, m in iter_queue(self.delay_queue))
self.consumer.close()
except ConnectionError:
pass | python | def close(self):
try:
if self.consumer:
self.requeue_messages(m for _, m in iter_queue(self.delay_queue))
self.consumer.close()
except ConnectionError:
pass | [
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241,085 | Bogdanp/dramatiq | dramatiq/worker.py | _WorkerThread.process_message | def process_message(self, message):
"""Process a message pulled off of the work queue then push it
back to its associated consumer for post processing.
Parameters:
message(MessageProxy)
"""
try:
self.logger.debug("Received message %s with id %r.", message, message.message_id)
self.broker.emit_before("process_message", message)
res = None
if not message.failed:
actor = self.broker.get_actor(message.actor_name)
res = actor(*message.args, **message.kwargs)
self.broker.emit_after("process_message", message, result=res)
except SkipMessage:
self.logger.warning("Message %s was skipped.", message)
self.broker.emit_after("skip_message", message)
except BaseException as e:
# Stuff the exception into the message [proxy] so that it
# may be used by the stub broker to provide a nicer
# testing experience.
message.stuff_exception(e)
if isinstance(e, RateLimitExceeded):
self.logger.warning("Rate limit exceeded in message %s: %s.", message, e)
else:
self.logger.warning("Failed to process message %s with unhandled exception.", message, exc_info=True)
self.broker.emit_after("process_message", message, exception=e)
finally:
# NOTE: There is no race here as any message that was
# processed must have come off of a consumer. Therefore,
# there has to be a consumer for that message's queue so
# this is safe. Probably.
self.consumers[message.queue_name].post_process_message(message)
self.work_queue.task_done() | python | def process_message(self, message):
try:
self.logger.debug("Received message %s with id %r.", message, message.message_id)
self.broker.emit_before("process_message", message)
res = None
if not message.failed:
actor = self.broker.get_actor(message.actor_name)
res = actor(*message.args, **message.kwargs)
self.broker.emit_after("process_message", message, result=res)
except SkipMessage:
self.logger.warning("Message %s was skipped.", message)
self.broker.emit_after("skip_message", message)
except BaseException as e:
# Stuff the exception into the message [proxy] so that it
# may be used by the stub broker to provide a nicer
# testing experience.
message.stuff_exception(e)
if isinstance(e, RateLimitExceeded):
self.logger.warning("Rate limit exceeded in message %s: %s.", message, e)
else:
self.logger.warning("Failed to process message %s with unhandled exception.", message, exc_info=True)
self.broker.emit_after("process_message", message, exception=e)
finally:
# NOTE: There is no race here as any message that was
# processed must have come off of a consumer. Therefore,
# there has to be a consumer for that message's queue so
# this is safe. Probably.
self.consumers[message.queue_name].post_process_message(message)
self.work_queue.task_done() | [
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241,086 | github/octodns | octodns/manager.py | Manager.compare | def compare(self, a, b, zone):
'''
Compare zone data between 2 sources.
Note: only things supported by both sources will be considered
'''
self.log.info('compare: a=%s, b=%s, zone=%s', a, b, zone)
try:
a = [self.providers[source] for source in a]
b = [self.providers[source] for source in b]
except KeyError as e:
raise Exception('Unknown source: {}'.format(e.args[0]))
sub_zones = self.configured_sub_zones(zone)
za = Zone(zone, sub_zones)
for source in a:
source.populate(za)
zb = Zone(zone, sub_zones)
for source in b:
source.populate(zb)
return zb.changes(za, _AggregateTarget(a + b)) | python | def compare(self, a, b, zone):
'''
Compare zone data between 2 sources.
Note: only things supported by both sources will be considered
'''
self.log.info('compare: a=%s, b=%s, zone=%s', a, b, zone)
try:
a = [self.providers[source] for source in a]
b = [self.providers[source] for source in b]
except KeyError as e:
raise Exception('Unknown source: {}'.format(e.args[0]))
sub_zones = self.configured_sub_zones(zone)
za = Zone(zone, sub_zones)
for source in a:
source.populate(za)
zb = Zone(zone, sub_zones)
for source in b:
source.populate(zb)
return zb.changes(za, _AggregateTarget(a + b)) | [
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241,087 | github/octodns | octodns/manager.py | Manager.dump | def dump(self, zone, output_dir, lenient, split, source, *sources):
'''
Dump zone data from the specified source
'''
self.log.info('dump: zone=%s, sources=%s', zone, sources)
# We broke out source to force at least one to be passed, add it to any
# others we got.
sources = [source] + list(sources)
try:
sources = [self.providers[s] for s in sources]
except KeyError as e:
raise Exception('Unknown source: {}'.format(e.args[0]))
clz = YamlProvider
if split:
clz = SplitYamlProvider
target = clz('dump', output_dir)
zone = Zone(zone, self.configured_sub_zones(zone))
for source in sources:
source.populate(zone, lenient=lenient)
plan = target.plan(zone)
if plan is None:
plan = Plan(zone, zone, [], False)
target.apply(plan) | python | def dump(self, zone, output_dir, lenient, split, source, *sources):
'''
Dump zone data from the specified source
'''
self.log.info('dump: zone=%s, sources=%s', zone, sources)
# We broke out source to force at least one to be passed, add it to any
# others we got.
sources = [source] + list(sources)
try:
sources = [self.providers[s] for s in sources]
except KeyError as e:
raise Exception('Unknown source: {}'.format(e.args[0]))
clz = YamlProvider
if split:
clz = SplitYamlProvider
target = clz('dump', output_dir)
zone = Zone(zone, self.configured_sub_zones(zone))
for source in sources:
source.populate(zone, lenient=lenient)
plan = target.plan(zone)
if plan is None:
plan = Plan(zone, zone, [], False)
target.apply(plan) | [
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241,088 | github/octodns | octodns/provider/dyn.py | _CachingDynZone.flush_zone | def flush_zone(cls, zone_name):
'''Flushes the zone cache, if there is one'''
cls.log.debug('flush_zone: zone_name=%s', zone_name)
try:
del cls._cache[zone_name]
except KeyError:
pass | python | def flush_zone(cls, zone_name):
'''Flushes the zone cache, if there is one'''
cls.log.debug('flush_zone: zone_name=%s', zone_name)
try:
del cls._cache[zone_name]
except KeyError:
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] | 65ee60491e22e6bb0a2aa08f7069c6ecf6c3fee6 | https://github.com/github/octodns/blob/65ee60491e22e6bb0a2aa08f7069c6ecf6c3fee6/octodns/provider/dyn.py#L156-L162 |
241,089 | github/octodns | octodns/provider/azuredns.py | AzureProvider._check_zone | def _check_zone(self, name, create=False):
'''Checks whether a zone specified in a source exist in Azure server.
Note that Azure zones omit end '.' eg: contoso.com vs contoso.com.
Returns the name if it exists.
:param name: Name of a zone to checks
:type name: str
:param create: If True, creates the zone of that name.
:type create: bool
:type return: str or None
'''
self.log.debug('_check_zone: name=%s', name)
try:
if name in self._azure_zones:
return name
self._dns_client.zones.get(self._resource_group, name)
self._azure_zones.add(name)
return name
except CloudError as err:
msg = 'The Resource \'Microsoft.Network/dnszones/{}\''.format(name)
msg += ' under resource group \'{}\''.format(self._resource_group)
msg += ' was not found.'
if msg == err.message:
# Then the only error is that the zone doesn't currently exist
if create:
self.log.debug('_check_zone:no matching zone; creating %s',
name)
create_zone = self._dns_client.zones.create_or_update
create_zone(self._resource_group, name,
Zone(location='global'))
return name
else:
return
raise | python | def _check_zone(self, name, create=False):
'''Checks whether a zone specified in a source exist in Azure server.
Note that Azure zones omit end '.' eg: contoso.com vs contoso.com.
Returns the name if it exists.
:param name: Name of a zone to checks
:type name: str
:param create: If True, creates the zone of that name.
:type create: bool
:type return: str or None
'''
self.log.debug('_check_zone: name=%s', name)
try:
if name in self._azure_zones:
return name
self._dns_client.zones.get(self._resource_group, name)
self._azure_zones.add(name)
return name
except CloudError as err:
msg = 'The Resource \'Microsoft.Network/dnszones/{}\''.format(name)
msg += ' under resource group \'{}\''.format(self._resource_group)
msg += ' was not found.'
if msg == err.message:
# Then the only error is that the zone doesn't currently exist
if create:
self.log.debug('_check_zone:no matching zone; creating %s',
name)
create_zone = self._dns_client.zones.create_or_update
create_zone(self._resource_group, name,
Zone(location='global'))
return name
else:
return
raise | [
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241,090 | github/octodns | octodns/provider/azuredns.py | AzureProvider._apply_Create | def _apply_Create(self, change):
'''A record from change must be created.
:param change: a change object
:type change: octodns.record.Change
:type return: void
'''
ar = _AzureRecord(self._resource_group, change.new)
create = self._dns_client.record_sets.create_or_update
create(resource_group_name=ar.resource_group,
zone_name=ar.zone_name,
relative_record_set_name=ar.relative_record_set_name,
record_type=ar.record_type,
parameters=ar.params)
self.log.debug('* Success Create/Update: {}'.format(ar)) | python | def _apply_Create(self, change):
'''A record from change must be created.
:param change: a change object
:type change: octodns.record.Change
:type return: void
'''
ar = _AzureRecord(self._resource_group, change.new)
create = self._dns_client.record_sets.create_or_update
create(resource_group_name=ar.resource_group,
zone_name=ar.zone_name,
relative_record_set_name=ar.relative_record_set_name,
record_type=ar.record_type,
parameters=ar.params)
self.log.debug('* Success Create/Update: {}'.format(ar)) | [
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241,091 | github/octodns | octodns/provider/base.py | BaseProvider.apply | def apply(self, plan):
'''
Submits actual planned changes to the provider. Returns the number of
changes made
'''
if self.apply_disabled:
self.log.info('apply: disabled')
return 0
self.log.info('apply: making changes')
self._apply(plan)
return len(plan.changes) | python | def apply(self, plan):
'''
Submits actual planned changes to the provider. Returns the number of
changes made
'''
if self.apply_disabled:
self.log.info('apply: disabled')
return 0
self.log.info('apply: making changes')
self._apply(plan)
return len(plan.changes) | [
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241,092 | github/octodns | octodns/provider/ovh.py | OvhProvider._is_valid_dkim | def _is_valid_dkim(self, value):
"""Check if value is a valid DKIM"""
validator_dict = {'h': lambda val: val in ['sha1', 'sha256'],
's': lambda val: val in ['*', 'email'],
't': lambda val: val in ['y', 's'],
'v': lambda val: val == 'DKIM1',
'k': lambda val: val == 'rsa',
'n': lambda _: True,
'g': lambda _: True}
splitted = value.split('\\;')
found_key = False
for splitted_value in splitted:
sub_split = map(lambda x: x.strip(), splitted_value.split("=", 1))
if len(sub_split) < 2:
return False
key, value = sub_split[0], sub_split[1]
if key == "p":
is_valid_key = self._is_valid_dkim_key(value)
if not is_valid_key:
return False
found_key = True
else:
is_valid_key = validator_dict.get(key, lambda _: False)(value)
if not is_valid_key:
return False
return found_key | python | def _is_valid_dkim(self, value):
validator_dict = {'h': lambda val: val in ['sha1', 'sha256'],
's': lambda val: val in ['*', 'email'],
't': lambda val: val in ['y', 's'],
'v': lambda val: val == 'DKIM1',
'k': lambda val: val == 'rsa',
'n': lambda _: True,
'g': lambda _: True}
splitted = value.split('\\;')
found_key = False
for splitted_value in splitted:
sub_split = map(lambda x: x.strip(), splitted_value.split("=", 1))
if len(sub_split) < 2:
return False
key, value = sub_split[0], sub_split[1]
if key == "p":
is_valid_key = self._is_valid_dkim_key(value)
if not is_valid_key:
return False
found_key = True
else:
is_valid_key = validator_dict.get(key, lambda _: False)(value)
if not is_valid_key:
return False
return found_key | [
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241,093 | github/octodns | octodns/provider/googlecloud.py | GoogleCloudProvider._get_gcloud_records | def _get_gcloud_records(self, gcloud_zone, page_token=None):
""" Generator function which yields ResourceRecordSet for the managed
gcloud zone, until there are no more records to pull.
:param gcloud_zone: zone to pull records from
:type gcloud_zone: google.cloud.dns.ManagedZone
:param page_token: page token for the page to get
:return: a resource record set
:type return: google.cloud.dns.ResourceRecordSet
"""
gcloud_iterator = gcloud_zone.list_resource_record_sets(
page_token=page_token)
for gcloud_record in gcloud_iterator:
yield gcloud_record
# This is to get results which may be on a "paged" page.
# (if more than max_results) entries.
if gcloud_iterator.next_page_token:
for gcloud_record in self._get_gcloud_records(
gcloud_zone, gcloud_iterator.next_page_token):
# yield from is in python 3 only.
yield gcloud_record | python | def _get_gcloud_records(self, gcloud_zone, page_token=None):
gcloud_iterator = gcloud_zone.list_resource_record_sets(
page_token=page_token)
for gcloud_record in gcloud_iterator:
yield gcloud_record
# This is to get results which may be on a "paged" page.
# (if more than max_results) entries.
if gcloud_iterator.next_page_token:
for gcloud_record in self._get_gcloud_records(
gcloud_zone, gcloud_iterator.next_page_token):
# yield from is in python 3 only.
yield gcloud_record | [
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241,094 | github/octodns | octodns/provider/googlecloud.py | GoogleCloudProvider._get_cloud_zones | def _get_cloud_zones(self, page_token=None):
"""Load all ManagedZones into the self._gcloud_zones dict which is
mapped with the dns_name as key.
:return: void
"""
gcloud_zones = self.gcloud_client.list_zones(page_token=page_token)
for gcloud_zone in gcloud_zones:
self._gcloud_zones[gcloud_zone.dns_name] = gcloud_zone
if gcloud_zones.next_page_token:
self._get_cloud_zones(gcloud_zones.next_page_token) | python | def _get_cloud_zones(self, page_token=None):
gcloud_zones = self.gcloud_client.list_zones(page_token=page_token)
for gcloud_zone in gcloud_zones:
self._gcloud_zones[gcloud_zone.dns_name] = gcloud_zone
if gcloud_zones.next_page_token:
self._get_cloud_zones(gcloud_zones.next_page_token) | [
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241,095 | nabla-c0d3/sslyze | sslyze/plugins/robot_plugin.py | RobotTlsRecordPayloads.get_client_key_exchange_record | def get_client_key_exchange_record(
cls,
robot_payload_enum: RobotPmsPaddingPayloadEnum,
tls_version: TlsVersionEnum,
modulus: int,
exponent: int
) -> TlsRsaClientKeyExchangeRecord:
"""A client key exchange record with a hardcoded pre_master_secret, and a valid or invalid padding.
"""
pms_padding = cls._compute_pms_padding(modulus)
tls_version_hex = binascii.b2a_hex(TlsRecordTlsVersionBytes[tls_version.name].value).decode('ascii')
pms_with_padding_payload = cls._CKE_PAYLOADS_HEX[robot_payload_enum]
final_pms = pms_with_padding_payload.format(
pms_padding=pms_padding, tls_version=tls_version_hex, pms=cls._PMS_HEX
)
cke_robot_record = TlsRsaClientKeyExchangeRecord.from_parameters(
tls_version, exponent, modulus, int(final_pms, 16)
)
return cke_robot_record | python | def get_client_key_exchange_record(
cls,
robot_payload_enum: RobotPmsPaddingPayloadEnum,
tls_version: TlsVersionEnum,
modulus: int,
exponent: int
) -> TlsRsaClientKeyExchangeRecord:
pms_padding = cls._compute_pms_padding(modulus)
tls_version_hex = binascii.b2a_hex(TlsRecordTlsVersionBytes[tls_version.name].value).decode('ascii')
pms_with_padding_payload = cls._CKE_PAYLOADS_HEX[robot_payload_enum]
final_pms = pms_with_padding_payload.format(
pms_padding=pms_padding, tls_version=tls_version_hex, pms=cls._PMS_HEX
)
cke_robot_record = TlsRsaClientKeyExchangeRecord.from_parameters(
tls_version, exponent, modulus, int(final_pms, 16)
)
return cke_robot_record | [
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241,096 | nabla-c0d3/sslyze | sslyze/plugins/robot_plugin.py | RobotTlsRecordPayloads.get_finished_record_bytes | def get_finished_record_bytes(cls, tls_version: TlsVersionEnum) -> bytes:
"""The Finished TLS record corresponding to the hardcoded PMS used in the Client Key Exchange record.
"""
# TODO(AD): The ROBOT poc script uses the same Finished record for all possible client hello (default, GCM,
# etc.); as the Finished record contains a hashes of all previous records, it will be wrong and will cause
# servers to send a TLS Alert 20
# Here just like in the poc script, the Finished message does not match the Client Hello we sent
return b'\x16' + TlsRecordTlsVersionBytes[tls_version.name].value + cls._FINISHED_RECORD | python | def get_finished_record_bytes(cls, tls_version: TlsVersionEnum) -> bytes:
# TODO(AD): The ROBOT poc script uses the same Finished record for all possible client hello (default, GCM,
# etc.); as the Finished record contains a hashes of all previous records, it will be wrong and will cause
# servers to send a TLS Alert 20
# Here just like in the poc script, the Finished message does not match the Client Hello we sent
return b'\x16' + TlsRecordTlsVersionBytes[tls_version.name].value + cls._FINISHED_RECORD | [
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241,097 | nabla-c0d3/sslyze | sslyze/plugins/robot_plugin.py | RobotServerResponsesAnalyzer.compute_result_enum | def compute_result_enum(self) -> RobotScanResultEnum:
"""Look at the server's response to each ROBOT payload and return the conclusion of the analysis.
"""
# Ensure the results were consistent
for payload_enum, server_responses in self._payload_responses.items():
# We ran the check twice per payload and the two responses should be the same
if server_responses[0] != server_responses[1]:
return RobotScanResultEnum.UNKNOWN_INCONSISTENT_RESULTS
# Check if the server acts as an oracle by checking if the server replied differently to the payloads
if len(set([server_responses[0] for server_responses in self._payload_responses.values()])) == 1:
# All server responses were identical - no oracle
return RobotScanResultEnum.NOT_VULNERABLE_NO_ORACLE
# All server responses were NOT identical, server is vulnerable
# Check to see if it is a weak oracle
response_1 = self._payload_responses[RobotPmsPaddingPayloadEnum.WRONG_FIRST_TWO_BYTES][0]
response_2 = self._payload_responses[RobotPmsPaddingPayloadEnum.WRONG_POSITION_00][0]
response_3 = self._payload_responses[RobotPmsPaddingPayloadEnum.NO_00_IN_THE_MIDDLE][0]
# From the original script:
# If the response to the invalid PKCS#1 request (oracle_bad1) is equal to both
# requests starting with 0002, we have a weak oracle. This is because the only
# case where we can distinguish valid from invalid requests is when we send
# correctly formatted PKCS#1 message with 0x00 on a correct position. This
# makes our oracle weak
if response_1 == response_2 == response_3:
return RobotScanResultEnum.VULNERABLE_WEAK_ORACLE
else:
return RobotScanResultEnum.VULNERABLE_STRONG_ORACLE | python | def compute_result_enum(self) -> RobotScanResultEnum:
# Ensure the results were consistent
for payload_enum, server_responses in self._payload_responses.items():
# We ran the check twice per payload and the two responses should be the same
if server_responses[0] != server_responses[1]:
return RobotScanResultEnum.UNKNOWN_INCONSISTENT_RESULTS
# Check if the server acts as an oracle by checking if the server replied differently to the payloads
if len(set([server_responses[0] for server_responses in self._payload_responses.values()])) == 1:
# All server responses were identical - no oracle
return RobotScanResultEnum.NOT_VULNERABLE_NO_ORACLE
# All server responses were NOT identical, server is vulnerable
# Check to see if it is a weak oracle
response_1 = self._payload_responses[RobotPmsPaddingPayloadEnum.WRONG_FIRST_TWO_BYTES][0]
response_2 = self._payload_responses[RobotPmsPaddingPayloadEnum.WRONG_POSITION_00][0]
response_3 = self._payload_responses[RobotPmsPaddingPayloadEnum.NO_00_IN_THE_MIDDLE][0]
# From the original script:
# If the response to the invalid PKCS#1 request (oracle_bad1) is equal to both
# requests starting with 0002, we have a weak oracle. This is because the only
# case where we can distinguish valid from invalid requests is when we send
# correctly formatted PKCS#1 message with 0x00 on a correct position. This
# makes our oracle weak
if response_1 == response_2 == response_3:
return RobotScanResultEnum.VULNERABLE_WEAK_ORACLE
else:
return RobotScanResultEnum.VULNERABLE_STRONG_ORACLE | [
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] | 0fb3ae668453d7ecf616d0755f237ca7be9f62fa | https://github.com/nabla-c0d3/sslyze/blob/0fb3ae668453d7ecf616d0755f237ca7be9f62fa/sslyze/plugins/robot_plugin.py#L135-L164 |
241,098 | nabla-c0d3/sslyze | sslyze/plugins/utils/trust_store/trust_store.py | TrustStore.is_extended_validation | def is_extended_validation(self, certificate: Certificate) -> bool:
"""Is the supplied server certificate EV?
"""
if not self.ev_oids:
raise ValueError('No EV OIDs supplied for {} store - cannot detect EV certificates'.format(self.name))
try:
cert_policies_ext = certificate.extensions.get_extension_for_oid(ExtensionOID.CERTIFICATE_POLICIES)
except ExtensionNotFound:
return False
for policy in cert_policies_ext.value:
if policy.policy_identifier in self.ev_oids:
return True
return False | python | def is_extended_validation(self, certificate: Certificate) -> bool:
if not self.ev_oids:
raise ValueError('No EV OIDs supplied for {} store - cannot detect EV certificates'.format(self.name))
try:
cert_policies_ext = certificate.extensions.get_extension_for_oid(ExtensionOID.CERTIFICATE_POLICIES)
except ExtensionNotFound:
return False
for policy in cert_policies_ext.value:
if policy.policy_identifier in self.ev_oids:
return True
return False | [
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] | 0fb3ae668453d7ecf616d0755f237ca7be9f62fa | https://github.com/nabla-c0d3/sslyze/blob/0fb3ae668453d7ecf616d0755f237ca7be9f62fa/sslyze/plugins/utils/trust_store/trust_store.py#L58-L72 |
241,099 | nabla-c0d3/sslyze | sslyze/synchronous_scanner.py | SynchronousScanner.run_scan_command | def run_scan_command(
self,
server_info: ServerConnectivityInfo,
scan_command: PluginScanCommand
) -> PluginScanResult:
"""Run a single scan command against a server; will block until the scan command has been completed.
Args:
server_info: The server's connectivity information. The test_connectivity_to_server() method must have been
called first to ensure that the server is online and accessible.
scan_command: The scan command to run against this server.
Returns:
The result of the scan command, which will be an instance of the scan command's
corresponding PluginScanResult subclass.
"""
plugin_class = self._plugins_repository.get_plugin_class_for_command(scan_command)
plugin = plugin_class()
return plugin.process_task(server_info, scan_command) | python | def run_scan_command(
self,
server_info: ServerConnectivityInfo,
scan_command: PluginScanCommand
) -> PluginScanResult:
plugin_class = self._plugins_repository.get_plugin_class_for_command(scan_command)
plugin = plugin_class()
return plugin.process_task(server_info, scan_command) | [
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scan_command: The scan command to run against this server.
Returns:
The result of the scan command, which will be an instance of the scan command's
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] | 0fb3ae668453d7ecf616d0755f237ca7be9f62fa | https://github.com/nabla-c0d3/sslyze/blob/0fb3ae668453d7ecf616d0755f237ca7be9f62fa/sslyze/synchronous_scanner.py#L32-L50 |
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