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robotpy/pyfrc | lib/pyfrc/physics/drivetrains.py | mecanum_drivetrain | def mecanum_drivetrain(
lr_motor,
rr_motor,
lf_motor,
rf_motor,
x_wheelbase=2,
y_wheelbase=3,
speed=5,
deadzone=None,
):
"""
.. deprecated:: 2018.2.0
Use :class:`MecanumDrivetrain` instead
"""
return MecanumDrivetrain(x_wheelbase, y_wheelbase, speed, deadzo... | python | def mecanum_drivetrain(
lr_motor,
rr_motor,
lf_motor,
rf_motor,
x_wheelbase=2,
y_wheelbase=3,
speed=5,
deadzone=None,
):
"""
.. deprecated:: 2018.2.0
Use :class:`MecanumDrivetrain` instead
"""
return MecanumDrivetrain(x_wheelbase, y_wheelbase, speed, deadzo... | [
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robotpy/pyfrc | lib/pyfrc/physics/drivetrains.py | four_motor_swerve_drivetrain | def four_motor_swerve_drivetrain(
lr_motor,
rr_motor,
lf_motor,
rf_motor,
lr_angle,
rr_angle,
lf_angle,
rf_angle,
x_wheelbase=2,
y_wheelbase=2,
speed=5,
deadzone=None,
):
"""
Four motors that can be rotated in any direction
If any motors are i... | python | def four_motor_swerve_drivetrain(
lr_motor,
rr_motor,
lf_motor,
rf_motor,
lr_angle,
rr_angle,
lf_angle,
rf_angle,
x_wheelbase=2,
y_wheelbase=2,
speed=5,
deadzone=None,
):
"""
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If any motors are i... | [
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:param rr_motor: Right rear motor value (-1 to 1); 1 is forward
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robotpy/pyfrc | lib/pyfrc/physics/drivetrains.py | TwoMotorDrivetrain.get_vector | def get_vector(self, l_motor: float, r_motor: float) -> typing.Tuple[float, float]:
"""
Given motor values, retrieves the vector of (distance, speed) for your robot
:param l_motor: Left motor value (-1 to 1); -1 is forward
:param r_motor: Right motor value (-1 ... | python | def get_vector(self, l_motor: float, r_motor: float) -> typing.Tuple[float, float]:
"""
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robotpy/pyfrc | lib/pyfrc/physics/drivetrains.py | FourMotorDrivetrain.get_vector | def get_vector(
self, lr_motor: float, rr_motor: float, lf_motor: float, rf_motor: float
) -> typing.Tuple[float, float]:
"""
:param lr_motor: Left rear motor value (-1 to 1); -1 is forward
:param rr_motor: Right rear motor value (-1 to 1); 1 is forward
:param... | python | def get_vector(
self, lr_motor: float, rr_motor: float, lf_motor: float, rf_motor: float
) -> typing.Tuple[float, float]:
"""
:param lr_motor: Left rear motor value (-1 to 1); -1 is forward
:param rr_motor: Right rear motor value (-1 to 1); 1 is forward
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robotpy/pyfrc | lib/pyfrc/physics/drivetrains.py | MecanumDrivetrain.get_vector | def get_vector(
self, lr_motor: float, rr_motor: float, lf_motor: float, rf_motor: float
) -> typing.Tuple[float, float, float]:
"""
Given motor values, retrieves the vector of (distance, speed) for your robot
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openstack/monasca-common | docker/mysql_check.py | connect_mysql | def connect_mysql(host, port, user, password, database):
"""Connect to MySQL with retries."""
return pymysql.connect(
host=host, port=port,
user=user, passwd=password,
db=database
) | python | def connect_mysql(host, port, user, password, database):
"""Connect to MySQL with retries."""
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host=host, port=port,
user=user, passwd=password,
db=database
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openstack/monasca-common | docker/mysql_check.py | main | def main():
"""Start main part of the wait script."""
logger.info('Waiting for database: `%s`', MYSQL_DB)
connect_mysql(
host=MYSQL_HOST, port=MYSQL_PORT,
user=MYSQL_USER, password=MYSQL_PASSWORD,
database=MYSQL_DB
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logger.info('Database `%s` found', MYSQL_DB) | python | def main():
"""Start main part of the wait script."""
logger.info('Waiting for database: `%s`', MYSQL_DB)
connect_mysql(
host=MYSQL_HOST, port=MYSQL_PORT,
user=MYSQL_USER, password=MYSQL_PASSWORD,
database=MYSQL_DB
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partofthething/ace | ace/ace.py | unsort_vector | def unsort_vector(data, indices_of_increasing):
"""Upermutate 1-D data that is sorted by indices_of_increasing."""
return numpy.array([data[indices_of_increasing.index(i)] for i in range(len(data))]) | python | def unsort_vector(data, indices_of_increasing):
"""Upermutate 1-D data that is sorted by indices_of_increasing."""
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"""Plot the transforms."""
if not plt:
raise ImportError('Cannot plot without the matplotlib package')
plt.rcParams.update({'font.size': 8})
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num_cols = len(ace_model.x) / 2 + 1
for i in range(len(ace_model.x)):
... | python | def plot_transforms(ace_model, fname='ace_transforms.png'):
"""Plot the transforms."""
if not plt:
raise ImportError('Cannot plot without the matplotlib package')
plt.rcParams.update({'font.size': 8})
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partofthething/ace | ace/ace.py | ACESolver.specify_data_set | def specify_data_set(self, x_input, y_input):
"""
Define input to ACE.
Parameters
----------
x_input : list
list of iterables, one for each independent variable
y_input : array
the dependent observations
"""
self.x = x_input
... | python | def specify_data_set(self, x_input, y_input):
"""
Define input to ACE.
Parameters
----------
x_input : list
list of iterables, one for each independent variable
y_input : array
the dependent observations
"""
self.x = x_input
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partofthething/ace | ace/ace.py | ACESolver.solve | def solve(self):
"""Run the ACE calculational loop."""
self._initialize()
while self._outer_error_is_decreasing() and self._outer_iters < MAX_OUTERS:
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self._initialize()
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partofthething/ace | ace/ace.py | ACESolver._initialize | def _initialize(self):
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self.y_transform = self.y - numpy.mean(self.y)
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partofthething/ace | ace/ace.py | ACESolver._compute_sorted_indices | def _compute_sorted_indices(self):
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partofthething/ace | ace/ace.py | ACESolver._outer_error_is_decreasing | def _outer_error_is_decreasing(self):
"""True if outer iteration error is decreasing."""
is_decreasing, self._last_outer_error = self._error_is_decreasing(self._last_outer_error)
return is_decreasing | python | def _outer_error_is_decreasing(self):
"""True if outer iteration error is decreasing."""
is_decreasing, self._last_outer_error = self._error_is_decreasing(self._last_outer_error)
return is_decreasing | [
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partofthething/ace | ace/ace.py | ACESolver._error_is_decreasing | def _error_is_decreasing(self, last_error):
"""True if current error is less than last_error."""
current_error = self._compute_error()
is_decreasing = current_error < last_error
return is_decreasing, current_error | python | def _error_is_decreasing(self, last_error):
"""True if current error is less than last_error."""
current_error = self._compute_error()
is_decreasing = current_error < last_error
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partofthething/ace | ace/ace.py | ACESolver._compute_error | def _compute_error(self):
"""Compute unexplained error."""
sum_x = sum(self.x_transforms)
err = sum((self.y_transform - sum_x) ** 2) / len(sum_x)
return err | python | def _compute_error(self):
"""Compute unexplained error."""
sum_x = sum(self.x_transforms)
err = sum((self.y_transform - sum_x) ** 2) / len(sum_x)
return err | [
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partofthething/ace | ace/ace.py | ACESolver._iterate_to_update_x_transforms | def _iterate_to_update_x_transforms(self):
"""Perform the inner iteration."""
self._inner_iters = 0
self._last_inner_error = float('inf')
while self._inner_error_is_decreasing():
print(' Starting inner iteration {0:03d}. Current err = {1:12.5E}'
''.format(s... | python | def _iterate_to_update_x_transforms(self):
"""Perform the inner iteration."""
self._inner_iters = 0
self._last_inner_error = float('inf')
while self._inner_error_is_decreasing():
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partofthething/ace | ace/ace.py | ACESolver._update_x_transforms | def _update_x_transforms(self):
"""
Compute a new set of x-transform functions phik.
phik(xk) = theta(y) - sum of phii(xi) over i!=k
This is the first of the eponymous conditional expectations. The conditional
expectations are computed using the SuperSmoother.
"""
... | python | def _update_x_transforms(self):
"""
Compute a new set of x-transform functions phik.
phik(xk) = theta(y) - sum of phii(xi) over i!=k
This is the first of the eponymous conditional expectations. The conditional
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"""
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partofthething/ace | ace/ace.py | ACESolver._update_y_transform | def _update_y_transform(self):
"""
Update the y-transform (theta).
y-transform theta is forced to have mean = 0 and stddev = 1.
This is the second conditional expectation
"""
# sort all phis wrt increasing y.
sorted_data_indices = self._yi_sorted
sorted_... | python | def _update_y_transform(self):
"""
Update the y-transform (theta).
y-transform theta is forced to have mean = 0 and stddev = 1.
This is the second conditional expectation
"""
# sort all phis wrt increasing y.
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partofthething/ace | ace/ace.py | ACESolver.write_input_to_file | def write_input_to_file(self, fname='ace_input.txt'):
"""Write y and x values used in this run to a space-delimited txt file."""
self._write_columns(fname, self.x, self.y) | python | def write_input_to_file(self, fname='ace_input.txt'):
"""Write y and x values used in this run to a space-delimited txt file."""
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partofthething/ace | ace/ace.py | ACESolver.write_transforms_to_file | def write_transforms_to_file(self, fname='ace_transforms.txt'):
"""Write y and x transforms used in this run to a space-delimited txt file."""
self._write_columns(fname, self.x_transforms, self.y_transform) | python | def write_transforms_to_file(self, fname='ace_transforms.txt'):
"""Write y and x transforms used in this run to a space-delimited txt file."""
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partofthething/ace | ace/samples/smoother_friedman82.py | build_sample_smoother_problem_friedman82 | def build_sample_smoother_problem_friedman82(N=200):
"""Sample problem from supersmoother publication."""
x = numpy.random.uniform(size=N)
err = numpy.random.standard_normal(N)
y = numpy.sin(2 * math.pi * (1 - x) ** 2) + x * err
return x, y | python | def build_sample_smoother_problem_friedman82(N=200):
"""Sample problem from supersmoother publication."""
x = numpy.random.uniform(size=N)
err = numpy.random.standard_normal(N)
y = numpy.sin(2 * math.pi * (1 - x) ** 2) + x * err
return x, y | [
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partofthething/ace | ace/samples/smoother_friedman82.py | run_friedman82_basic | def run_friedman82_basic():
"""Run Friedman's test of fixed-span smoothers from Figure 2b."""
x, y = build_sample_smoother_problem_friedman82()
plt.figure()
# plt.plot(x, y, '.', label='Data')
for span in smoother.DEFAULT_SPANS:
smooth = smoother.BasicFixedSpanSmoother()
smooth.speci... | python | def run_friedman82_basic():
"""Run Friedman's test of fixed-span smoothers from Figure 2b."""
x, y = build_sample_smoother_problem_friedman82()
plt.figure()
# plt.plot(x, y, '.', label='Data')
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robotpy/pyfrc | lib/pyfrc/sim/field/field.py | RobotField.add_moving_element | def add_moving_element(self, element):
"""Add elements to the board"""
element.initialize(self.canvas)
self.elements.append(element) | python | def add_moving_element(self, element):
"""Add elements to the board"""
element.initialize(self.canvas)
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robotpy/pyfrc | lib/pyfrc/sim/field/field.py | RobotField.on_key_pressed | def on_key_pressed(self, event):
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"""
return
# TODO
if event.keysym == "Up":
self.manager... | python | def on_key_pressed(self, event):
"""
likely to take in a set of parameters to treat as up, down, left,
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"""
return
# TODO
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robotpy/pyfrc | lib/pyfrc/configloader.py | _load_config | def _load_config(robot_path):
"""
Used internally by pyfrc, don't call this directly.
Loads a json file from sim/config.json and makes the information available
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"""
from . import config
config_obj = config.config_obj
s... | python | def _load_config(robot_path):
"""
Used internally by pyfrc, don't call this directly.
Loads a json file from sim/config.json and makes the information available
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"""
from . import config
config_obj = config.config_obj
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partofthething/ace | ace/smoother.py | perform_smooth | def perform_smooth(x_values, y_values, span=None, smoother_cls=None):
"""
Convenience function to run the basic smoother.
Parameters
----------
x_values : iterable
List of x value observations
y_ values : iterable
list of y value observations
span : float, optional
F... | python | def perform_smooth(x_values, y_values, span=None, smoother_cls=None):
"""
Convenience function to run the basic smoother.
Parameters
----------
x_values : iterable
List of x value observations
y_ values : iterable
list of y value observations
span : float, optional
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partofthething/ace | ace/smoother.py | Smoother.add_data_point_xy | def add_data_point_xy(self, x, y):
"""Add a new data point to the data set to be smoothed."""
self.x.append(x)
self.y.append(y) | python | def add_data_point_xy(self, x, y):
"""Add a new data point to the data set to be smoothed."""
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partofthething/ace | ace/smoother.py | Smoother.specify_data_set | def specify_data_set(self, x_input, y_input, sort_data=False):
"""
Fully define data by lists of x values and y values.
This will sort them by increasing x but remember how to unsort them for providing results.
Parameters
----------
x_input : iterable
list o... | python | def specify_data_set(self, x_input, y_input, sort_data=False):
"""
Fully define data by lists of x values and y values.
This will sort them by increasing x but remember how to unsort them for providing results.
Parameters
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x_input : iterable
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partofthething/ace | ace/smoother.py | Smoother.plot | def plot(self, fname=None):
"""
Plot the input data and resulting smooth.
Parameters
----------
fname : str, optional
name of file to produce. If none, will show interactively.
"""
plt.figure()
xy = sorted(zip(self.x, self.smooth_result))
... | python | def plot(self, fname=None):
"""
Plot the input data and resulting smooth.
Parameters
----------
fname : str, optional
name of file to produce. If none, will show interactively.
"""
plt.figure()
xy = sorted(zip(self.x, self.smooth_result))
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partofthething/ace | ace/smoother.py | Smoother._store_unsorted_results | def _store_unsorted_results(self, smooth, residual):
"""Convert sorted smooth/residual back to as-input order."""
if self._original_index_of_xvalue:
# data was sorted. Unsort it here.
self.smooth_result = numpy.zeros(len(self.y))
self.cross_validated_residual = numpy.... | python | def _store_unsorted_results(self, smooth, residual):
"""Convert sorted smooth/residual back to as-input order."""
if self._original_index_of_xvalue:
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother.compute | def compute(self):
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self._compute_window_size()
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._compute_window_size | def _compute_window_size(self):
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._update_values_in_window | def _update_values_in_window(self):
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._update_mean_in_window | def _update_mean_in_window(self):
"""
Compute mean in window the slow way. useful for first step.
Considers all values in window
See Also
--------
_add_observation_to_means : fast update of mean for single observation addition
_remove_observation_from_means : fa... | python | def _update_mean_in_window(self):
"""
Compute mean in window the slow way. useful for first step.
Considers all values in window
See Also
--------
_add_observation_to_means : fast update of mean for single observation addition
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._update_variance_in_window | def _update_variance_in_window(self):
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See Also
--------
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Compute variance and covariance in window using all values in window (slow).
See Also
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._advance_window | def _advance_window(self):
"""Update values in current window and the current window means and variances."""
x_to_remove, y_to_remove = self._x_in_window[0], self._y_in_window[0]
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"""Update values in current window and the current window means and variances."""
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._remove_observation | def _remove_observation(self, x_to_remove, y_to_remove):
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self._remove_observation_from_variances(x_to_remove, y_to_remove)
self._remove_observation_from_means(x_to_remove, y_to_remove)
self.window_size -= 1 | python | def _remove_observation(self, x_to_remove, y_to_remove):
"""Remove observation from window, updating means/variance efficiently."""
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._add_observation | def _add_observation(self, x_to_add, y_to_add):
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self._add_observation_to_means(x_to_add, y_to_add)
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._add_observation_to_means | def _add_observation_to_means(self, xj, yj):
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._remove_observation_from_means | def _remove_observation_from_means(self, xj, yj):
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._add_observation_to_variances | def _add_observation_to_variances(self, xj, yj):
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See Also
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_update_variance_in_window : compute variance considering full window
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Quickly update the variance and co-variance for the addition of one observation.
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_update_variance_in_window : compute variance considering full window
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._compute_smooth_during_construction | def _compute_smooth_during_construction(self, xi):
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partofthething/ace | ace/smoother.py | BasicFixedSpanSmoother._compute_cross_validated_residual_here | def _compute_cross_validated_residual_here(self, xi, yi, smooth_here):
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partofthething/ace | ace/samples/breiman85.py | build_sample_ace_problem_breiman85 | def build_sample_ace_problem_breiman85(N=200):
"""Sample problem from Breiman 1985."""
x_cubed = numpy.random.standard_normal(N)
x = scipy.special.cbrt(x_cubed)
noise = numpy.random.standard_normal(N)
y = numpy.exp((x ** 3.0) + noise)
return [x], y | python | def build_sample_ace_problem_breiman85(N=200):
"""Sample problem from Breiman 1985."""
x_cubed = numpy.random.standard_normal(N)
x = scipy.special.cbrt(x_cubed)
noise = numpy.random.standard_normal(N)
y = numpy.exp((x ** 3.0) + noise)
return [x], y | [
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partofthething/ace | ace/samples/breiman85.py | build_sample_ace_problem_breiman2 | def build_sample_ace_problem_breiman2(N=500):
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x = numpy.linspace(0, 1, N)
# x = numpy.random.uniform(0, 1, size=N)
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return [x], y | python | def build_sample_ace_problem_breiman2(N=500):
"""Build sample problem y(x) = exp(sin(x))."""
x = numpy.linspace(0, 1, N)
# x = numpy.random.uniform(0, 1, size=N)
noise = numpy.random.standard_normal(N)
y = numpy.exp(numpy.sin(2 * numpy.pi * x)) + 0.0 * noise
return [x], y | [
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partofthething/ace | ace/samples/breiman85.py | run_breiman85 | def run_breiman85():
"""Run Breiman 85 sample."""
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ace_solver.specify_data_set(x, y)
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except ImportError:
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ret... | python | def run_breiman85():
"""Run Breiman 85 sample."""
x, y = build_sample_ace_problem_breiman85(200)
ace_solver = ace.ACESolver()
ace_solver.specify_data_set(x, y)
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try:
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partofthething/ace | ace/samples/breiman85.py | run_breiman2 | def run_breiman2():
"""Run Breiman's other sample problem."""
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ace_solver = ace.ACESolver()
ace_solver.specify_data_set(x, y)
ace_solver.solve()
try:
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except ImportError:
pass
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"""Run Breiman's other sample problem."""
x, y = build_sample_ace_problem_breiman2(500)
ace_solver = ace.ACESolver()
ace_solver.specify_data_set(x, y)
ace_solver.solve()
try:
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except ImportError:
pass
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openstack/monasca-common | monasca_common/kafka/producer.py | KafkaProducer.publish | def publish(self, topic, messages, key=None):
"""Takes messages and puts them on the supplied kafka topic
"""
if not isinstance(messages, list):
messages = [messages]
first = True
success = False
if key is None:
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"""Takes messages and puts them on the supplied kafka topic
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | create_gzip_message | def create_gzip_message(payloads, key=None, compresslevel=None):
"""
Construct a Gzipped Message containing multiple Messages
The given payloads will be encoded, compressed, and sent as a single atomic
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Arguments:
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"""
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol._encode_message | def _encode_message(cls, message):
"""
Encode a single message.
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The only supported magic number right now is zero
Format
======
Message => Crc MagicByte Attributes Key Value
Crc => int32
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"""
Encode a single message.
The magic number of a message is a format version number.
The only supported magic number right now is zero
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======
Message => Crc MagicByte Attributes Key Value
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol._decode_message_set_iter | def _decode_message_set_iter(cls, data):
"""
Iteratively decode a MessageSet
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"""
Iteratively decode a MessageSet
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol._decode_message | def _decode_message(cls, data, offset):
"""
Decode a single Message
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"""
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.encode_produce_request | def encode_produce_request(cls, client_id, correlation_id,
payloads=None, acks=1, timeout=1000):
"""
Encode some ProduceRequest structs
Arguments:
client_id: string
correlation_id: int
payloads: list of ProduceRequest
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Encode some ProduceRequest structs
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client_id: string
correlation_id: int
payloads: list of ProduceRequest
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.decode_produce_response | def decode_produce_response(cls, data):
"""
Decode bytes to a ProduceResponse
Arguments:
data: bytes to decode
"""
((correlation_id, num_topics), cur) = relative_unpack('>ii', data, 0)
for _ in range(num_topics):
((strlen,), cur) = relative_unpa... | python | def decode_produce_response(cls, data):
"""
Decode bytes to a ProduceResponse
Arguments:
data: bytes to decode
"""
((correlation_id, num_topics), cur) = relative_unpack('>ii', data, 0)
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.encode_fetch_request | def encode_fetch_request(cls, client_id, correlation_id, payloads=None,
max_wait_time=100, min_bytes=4096):
"""
Encodes some FetchRequest structs
Arguments:
client_id: string
correlation_id: int
payloads: list of FetchRequest
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Encodes some FetchRequest structs
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client_id: string
correlation_id: int
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.decode_fetch_response | def decode_fetch_response(cls, data):
"""
Decode bytes to a FetchResponse
Arguments:
data: bytes to decode
"""
((correlation_id, num_topics), cur) = relative_unpack('>ii', data, 0)
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(topic, cur) = read_short_string(data... | python | def decode_fetch_response(cls, data):
"""
Decode bytes to a FetchResponse
Arguments:
data: bytes to decode
"""
((correlation_id, num_topics), cur) = relative_unpack('>ii', data, 0)
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.decode_offset_response | def decode_offset_response(cls, data):
"""
Decode bytes to an OffsetResponse
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data: bytes to decode
"""
((correlation_id, num_topics), cur) = relative_unpack('>ii', data, 0)
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"""
Decode bytes to an OffsetResponse
Arguments:
data: bytes to decode
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.encode_metadata_request | def encode_metadata_request(cls, client_id, correlation_id, topics=None,
payloads=None):
"""
Encode a MetadataRequest
Arguments:
client_id: string
correlation_id: int
topics: list of strings
"""
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"""
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client_id: string
correlation_id: int
topics: list of strings
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.decode_metadata_response | def decode_metadata_response(cls, data):
"""
Decode bytes to a MetadataResponse
Arguments:
data: bytes to decode
"""
((correlation_id, numbrokers), cur) = relative_unpack('>ii', data, 0)
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brokers = []
for _ in range(numbrokers):
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"""
Decode bytes to a MetadataResponse
Arguments:
data: bytes to decode
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.encode_offset_commit_request | def encode_offset_commit_request(cls, client_id, correlation_id,
group, payloads):
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Encode some OffsetCommitRequest structs
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client_id: string
correlation_id: int
group: string, the consumer group you are comm... | python | def encode_offset_commit_request(cls, client_id, correlation_id,
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Encode some OffsetCommitRequest structs
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openstack/monasca-common | monasca_common/kafka_lib/protocol.py | KafkaProtocol.decode_offset_commit_response | def decode_offset_commit_response(cls, data):
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Decode bytes to an OffsetCommitResponse
Arguments:
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openstack/monasca-common | monasca_common/kafka_lib/consumer/base.py | Consumer.commit | def commit(self, partitions=None):
"""Commit stored offsets to Kafka via OffsetCommitRequest (v0)
Keyword Arguments:
partitions (list): list of partitions to commit, default is to commit
all of them
Returns: True on success, False on failure
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partofthething/ace | ace/model.py | read_column_data_from_txt | def read_column_data_from_txt(fname):
"""
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x_values : list
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y_values : list
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partofthething/ace | ace/model.py | Model.build_model_from_xy | def build_model_from_xy(self, x_values, y_values):
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partofthething/ace | ace/model.py | Model.build_interpolators | def build_interpolators(self):
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partofthething/ace | ace/model.py | Model.eval | def eval(self, x_values):
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a float x-value for each independent variable, e.g. (1.5, 2.5)
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openstack/monasca-common | docker/kafka_wait_for_topics.py | retry | def retry(retries=KAFKA_WAIT_RETRIES, delay=KAFKA_WAIT_INTERVAL,
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"""Retry decorator."""
def decorator(func):
"""Decorator."""
def f_retry(*args, **kwargs):
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openstack/monasca-common | docker/kafka_wait_for_topics.py | check_topics | def check_topics(client, req_topics):
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client.update_cluster()
logger.debug('Found topics: %r', client.topics.keys())
for req_topic in req_topics:
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"""Check for existence of provided topics in Kafka."""
client.update_cluster()
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openstack/monasca-common | docker/kafka_wait_for_topics.py | main | def main():
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openstack/monasca-common | monasca_common/kafka_lib/conn.py | collect_hosts | def collect_hosts(hosts, randomize=True):
"""
Collects a comma-separated set of hosts (host:port) and optionally
randomize the returned list.
"""
if isinstance(hosts, six.string_types):
hosts = hosts.strip().split(',')
result = []
for host_port in hosts:
res = host_port.sp... | python | def collect_hosts(hosts, randomize=True):
"""
Collects a comma-separated set of hosts (host:port) and optionally
randomize the returned list.
"""
if isinstance(hosts, six.string_types):
hosts = hosts.strip().split(',')
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openstack/monasca-common | monasca_common/kafka_lib/conn.py | KafkaConnection.send | def send(self, request_id, payload):
"""
Send a request to Kafka
Arguments::
request_id (int): can be any int (used only for debug logging...)
payload: an encoded kafka packet (see KafkaProtocol)
"""
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"""
Send a request to Kafka
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request_id (int): can be any int (used only for debug logging...)
payload: an encoded kafka packet (see KafkaProtocol)
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openstack/monasca-common | monasca_common/kafka_lib/conn.py | KafkaConnection.recv | def recv(self, request_id):
"""
Get a response packet from Kafka
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request_id: can be any int (only used for debug logging...)
Returns:
str: Encoded kafka packet response from server
"""
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Get a response packet from Kafka
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openstack/monasca-common | monasca_common/kafka_lib/conn.py | KafkaConnection.copy | def copy(self):
"""
Create an inactive copy of the connection object, suitable for
passing to a background thread.
The returned copy is not connected; you must call reinit() before
using.
"""
c = copy.deepcopy(self)
# Python 3 doesn't copy custom attribut... | python | def copy(self):
"""
Create an inactive copy of the connection object, suitable for
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The returned copy is not connected; you must call reinit() before
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"""
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openstack/monasca-common | monasca_common/kafka_lib/conn.py | KafkaConnection.close | def close(self):
"""
Shutdown and close the connection socket
"""
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# Call shutdown to be a good TCP client
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"""
Shutdown and close the connection socket
"""
log.debug("Closing socket connection for %s:%d" % (self.host, self.port))
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openstack/monasca-common | monasca_common/kafka_lib/conn.py | KafkaConnection.reinit | def reinit(self):
"""
Re-initialize the socket connection
close current socket (if open)
and start a fresh connection
raise ConnectionError on error
"""
log.debug("Reinitializing socket connection for %s:%d" % (self.host, self.port))
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"""
Re-initialize the socket connection
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.configure | def configure(self, **configs):
"""Configure the consumer instance
Configuration settings can be passed to constructor,
otherwise defaults will be used:
Keyword Arguments:
bootstrap_servers (list): List of initial broker nodes the consumer
should contact to ... | python | def configure(self, **configs):
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Configuration settings can be passed to constructor,
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.set_topic_partitions | def set_topic_partitions(self, *topics):
"""
Set the topic/partitions to consume
Optionally specify offsets to start from
Accepts types:
* str (utf-8): topic name (will consume all available partitions)
* tuple: (topic, partition)
* dict:
- { topic: ... | python | def set_topic_partitions(self, *topics):
"""
Set the topic/partitions to consume
Optionally specify offsets to start from
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* str (utf-8): topic name (will consume all available partitions)
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.next | def next(self):
"""Return the next available message
Blocks indefinitely unless consumer_timeout_ms > 0
Returns:
a single KafkaMessage from the message iterator
Raises:
ConsumerTimeout after consumer_timeout_ms and no message
Note:
This is ... | python | def next(self):
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.fetch_messages | def fetch_messages(self):
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Returns:
Generator that yields KafkaMessage structs
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Note:
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.get_partition_offsets | def get_partition_offsets(self, topic, partition, request_time_ms, max_num_offsets):
"""Request available fetch offsets for a single topic/partition
Keyword Arguments:
topic (str): topic for offset request
partition (int): partition for offset request
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topic (str): topic for offset request
partition (int): partition for offset request
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.offsets | def offsets(self, group=None):
"""Get internal consumer offset values
Keyword Arguments:
group: Either "fetch", "commit", "task_done", or "highwater".
If no group specified, returns all groups.
Returns:
A copy of internal offsets struct
"""
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"""Get internal consumer offset values
Keyword Arguments:
group: Either "fetch", "commit", "task_done", or "highwater".
If no group specified, returns all groups.
Returns:
A copy of internal offsets struct
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.task_done | def task_done(self, message):
"""Mark a fetched message as consumed.
Offsets for messages marked as "task_done" will be stored back
to the kafka cluster for this consumer group on commit()
Arguments:
message (KafkaMessage): the message to mark as complete
Returns:
... | python | def task_done(self, message):
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message (KafkaMessage): the message to mark as complete
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openstack/monasca-common | monasca_common/kafka_lib/consumer/kafka.py | KafkaConsumer.commit | def commit(self):
"""Store consumed message offsets (marked via task_done())
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Returns:
True on success, or False if no offsets were found for commit
Note:
this functionality requires server version >=0.8.1.1
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openstack/monasca-common | monasca_common/rest/utils.py | as_json | def as_json(data, **kwargs):
"""Writes data as json.
:param dict data: data to convert to json
:param kwargs kwargs: kwargs for json dumps
:return: json string
:rtype: str
"""
if 'sort_keys' not in kwargs:
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kwa... | python | def as_json(data, **kwargs):
"""Writes data as json.
:param dict data: data to convert to json
:param kwargs kwargs: kwargs for json dumps
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:rtype: str
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openstack/monasca-common | monasca_common/rest/utils.py | read_body | def read_body(payload, content_type=JSON_CONTENT_TYPE):
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Note:
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robotpy/pyfrc | lib/pyfrc/sim/ui.py | SimUI.__process_idle_events | def __process_idle_events(self):
"""This should never be called directly, it is called via an
event, and should always be on the GUI thread"""
while True:
try:
callable, args = self.queue.get(block=False)
except queue.Empty:
break
... | python | def __process_idle_events(self):
"""This should never be called directly, it is called via an
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while True:
try:
callable, args = self.queue.get(block=False)
except queue.Empty:
break
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robotpy/pyfrc | lib/pyfrc/sim/ui.py | SimUI.timer_fired | def timer_fired(self):
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openstack/monasca-common | monasca_common/kafka_lib/codec.py | snappy_encode | def snappy_encode(payload, xerial_compatible=False, xerial_blocksize=32 * 1024):
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... | python | def snappy_encode(payload, xerial_compatible=False, xerial_blocksize=32 * 1024):
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robotpy/pyfrc | lib/pyfrc/mains/cli_deploy.py | relpath | def relpath(path):
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) | python | def relpath(path):
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robotpy/pyfrc | lib/pyfrc/sim/field/user_renderer.py | UserRenderer.draw_pathfinder_trajectory | def draw_pathfinder_trajectory(
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**kwargs
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robotpy/pyfrc | lib/pyfrc/sim/field/user_renderer.py | UserRenderer.draw_line | def draw_line(
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arrow=True,
scale=(1, 1),
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