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dhermes/bezier | src/bezier/_geometric_intersection.py | prune_candidates | def prune_candidates(candidates):
"""Reduce number of candidate intersection pairs.
.. note::
This is a helper for :func:`_all_intersections`.
Uses more strict bounding box intersection predicate by forming the
actual convex hull of each candidate curve segment and then checking
if those convex hulls collide.
Args:
candidates (List): An iterable of pairs of curves (or
linearized curves).
Returns:
List: A pruned list of curve pairs.
"""
pruned = []
# NOTE: In the below we replace ``isinstance(a, B)`` with
# ``a.__class__ is B``, which is a 3-3.5x speedup.
for first, second in candidates:
if first.__class__ is Linearization:
nodes1 = first.curve.nodes
else:
nodes1 = first.nodes
if second.__class__ is Linearization:
nodes2 = second.curve.nodes
else:
nodes2 = second.nodes
if convex_hull_collide(nodes1, nodes2):
pruned.append((first, second))
return pruned | python | def prune_candidates(candidates):
"""Reduce number of candidate intersection pairs.
.. note::
This is a helper for :func:`_all_intersections`.
Uses more strict bounding box intersection predicate by forming the
actual convex hull of each candidate curve segment and then checking
if those convex hulls collide.
Args:
candidates (List): An iterable of pairs of curves (or
linearized curves).
Returns:
List: A pruned list of curve pairs.
"""
pruned = []
# NOTE: In the below we replace ``isinstance(a, B)`` with
# ``a.__class__ is B``, which is a 3-3.5x speedup.
for first, second in candidates:
if first.__class__ is Linearization:
nodes1 = first.curve.nodes
else:
nodes1 = first.nodes
if second.__class__ is Linearization:
nodes2 = second.curve.nodes
else:
nodes2 = second.nodes
if convex_hull_collide(nodes1, nodes2):
pruned.append((first, second))
return pruned | [
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Uses more strict bounding box intersection predicate by forming the
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Args:
candidates (List): An iterable of pairs of curves (or
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dhermes/bezier | src/bezier/_geometric_intersection.py | make_same_degree | def make_same_degree(nodes1, nodes2):
"""Degree-elevate a curve so two curves have matching degree.
Args:
nodes1 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
nodes2 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]: The potentially degree-elevated
nodes passed in.
"""
_, num_nodes1 = nodes1.shape
_, num_nodes2 = nodes2.shape
for _ in six.moves.xrange(num_nodes2 - num_nodes1):
nodes1 = _curve_helpers.elevate_nodes(nodes1)
for _ in six.moves.xrange(num_nodes1 - num_nodes2):
nodes2 = _curve_helpers.elevate_nodes(nodes2)
return nodes1, nodes2 | python | def make_same_degree(nodes1, nodes2):
"""Degree-elevate a curve so two curves have matching degree.
Args:
nodes1 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
nodes2 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]: The potentially degree-elevated
nodes passed in.
"""
_, num_nodes1 = nodes1.shape
_, num_nodes2 = nodes2.shape
for _ in six.moves.xrange(num_nodes2 - num_nodes1):
nodes1 = _curve_helpers.elevate_nodes(nodes1)
for _ in six.moves.xrange(num_nodes1 - num_nodes2):
nodes2 = _curve_helpers.elevate_nodes(nodes2)
return nodes1, nodes2 | [
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nodes1 (numpy.ndarray): Set of control points for a
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nodes2 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
Returns:
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dhermes/bezier | src/bezier/_geometric_intersection.py | coincident_parameters | def coincident_parameters(nodes1, nodes2):
r"""Check if two B |eacute| zier curves are coincident.
Does so by projecting each segment endpoint onto the other curve
.. math::
B_1(s_0) = B_2(0)
B_1(s_m) = B_2(1)
B_1(0) = B_2(t_0)
B_1(1) = B_2(t_n)
and then finding the "shared interval" where both curves are defined.
If such an interval can't be found (e.g. if one of the endpoints can't be
located on the other curve), returns :data:`None`.
If such a "shared interval" does exist, then this will specialize
each curve onto that shared interval and check if the new control points
agree.
Args:
nodes1 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
nodes2 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
Returns:
Optional[Tuple[Tuple[float, float], ...]]: A ``2 x 2`` array of
parameters where the two coincident curves meet. If they are not
coincident, returns :data:`None`.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-return-statements,too-many-branches
nodes1, nodes2 = make_same_degree(nodes1, nodes2)
s_initial = _curve_helpers.locate_point(
nodes1, nodes2[:, 0].reshape((2, 1), order="F")
)
s_final = _curve_helpers.locate_point(
nodes1, nodes2[:, -1].reshape((2, 1), order="F")
)
if s_initial is not None and s_final is not None:
# In this case, if the curves were coincident, then ``curve2``
# would be "fully" contained in ``curve1``, so we specialize
# ``curve1`` down to that interval to check.
specialized1 = _curve_helpers.specialize_curve(
nodes1, s_initial, s_final
)
if _helpers.vector_close(
specialized1.ravel(order="F"), nodes2.ravel(order="F")
):
return ((s_initial, 0.0), (s_final, 1.0))
else:
return None
t_initial = _curve_helpers.locate_point(
nodes2, nodes1[:, 0].reshape((2, 1), order="F")
)
t_final = _curve_helpers.locate_point(
nodes2, nodes1[:, -1].reshape((2, 1), order="F")
)
if t_initial is None and t_final is None:
# An overlap must have two endpoints and since at most one of the
# endpoints of ``curve2`` lies on ``curve1`` (as indicated by at
# least one of the ``s``-parameters being ``None``), we need (at least)
# one endpoint of ``curve1`` on ``curve2``.
return None
if t_initial is not None and t_final is not None:
# In this case, if the curves were coincident, then ``curve1``
# would be "fully" contained in ``curve2``, so we specialize
# ``curve2`` down to that interval to check.
specialized2 = _curve_helpers.specialize_curve(
nodes2, t_initial, t_final
)
if _helpers.vector_close(
nodes1.ravel(order="F"), specialized2.ravel(order="F")
):
return ((0.0, t_initial), (1.0, t_final))
else:
return None
if s_initial is None and s_final is None:
# An overlap must have two endpoints and since exactly one of the
# endpoints of ``curve1`` lies on ``curve2`` (as indicated by exactly
# one of the ``t``-parameters being ``None``), we need (at least)
# one endpoint of ``curve1`` on ``curve2``.
return None
# At this point, we know exactly one of the ``s``-parameters and exactly
# one of the ``t``-parameters is not ``None``.
if s_initial is None:
if t_initial is None:
# B1(s_final) = B2(1) AND B1(1) = B2(t_final)
start_s = s_final
end_s = 1.0
start_t = 1.0
end_t = t_final
else:
# B1(0) = B2(t_initial) AND B1(s_final) = B2(1)
start_s = 0.0
end_s = s_final
start_t = t_initial
end_t = 1.0
else:
if t_initial is None:
# B1(s_initial) = B2(0) AND B1(1 ) = B2(t_final)
start_s = s_initial
end_s = 1.0
start_t = 0.0
end_t = t_final
else:
# B1(0) = B2(t_initial) AND B1(s_initial) = B2(0)
start_s = 0.0
end_s = s_initial
start_t = t_initial
end_t = 0.0
width_s = abs(start_s - end_s)
width_t = abs(start_t - end_t)
if width_s < _MIN_INTERVAL_WIDTH and width_t < _MIN_INTERVAL_WIDTH:
return None
specialized1 = _curve_helpers.specialize_curve(nodes1, start_s, end_s)
specialized2 = _curve_helpers.specialize_curve(nodes2, start_t, end_t)
if _helpers.vector_close(
specialized1.ravel(order="F"), specialized2.ravel(order="F")
):
return ((start_s, start_t), (end_s, end_t))
else:
return None | python | def coincident_parameters(nodes1, nodes2):
r"""Check if two B |eacute| zier curves are coincident.
Does so by projecting each segment endpoint onto the other curve
.. math::
B_1(s_0) = B_2(0)
B_1(s_m) = B_2(1)
B_1(0) = B_2(t_0)
B_1(1) = B_2(t_n)
and then finding the "shared interval" where both curves are defined.
If such an interval can't be found (e.g. if one of the endpoints can't be
located on the other curve), returns :data:`None`.
If such a "shared interval" does exist, then this will specialize
each curve onto that shared interval and check if the new control points
agree.
Args:
nodes1 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
nodes2 (numpy.ndarray): Set of control points for a
B |eacute| zier curve.
Returns:
Optional[Tuple[Tuple[float, float], ...]]: A ``2 x 2`` array of
parameters where the two coincident curves meet. If they are not
coincident, returns :data:`None`.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-return-statements,too-many-branches
nodes1, nodes2 = make_same_degree(nodes1, nodes2)
s_initial = _curve_helpers.locate_point(
nodes1, nodes2[:, 0].reshape((2, 1), order="F")
)
s_final = _curve_helpers.locate_point(
nodes1, nodes2[:, -1].reshape((2, 1), order="F")
)
if s_initial is not None and s_final is not None:
# In this case, if the curves were coincident, then ``curve2``
# would be "fully" contained in ``curve1``, so we specialize
# ``curve1`` down to that interval to check.
specialized1 = _curve_helpers.specialize_curve(
nodes1, s_initial, s_final
)
if _helpers.vector_close(
specialized1.ravel(order="F"), nodes2.ravel(order="F")
):
return ((s_initial, 0.0), (s_final, 1.0))
else:
return None
t_initial = _curve_helpers.locate_point(
nodes2, nodes1[:, 0].reshape((2, 1), order="F")
)
t_final = _curve_helpers.locate_point(
nodes2, nodes1[:, -1].reshape((2, 1), order="F")
)
if t_initial is None and t_final is None:
# An overlap must have two endpoints and since at most one of the
# endpoints of ``curve2`` lies on ``curve1`` (as indicated by at
# least one of the ``s``-parameters being ``None``), we need (at least)
# one endpoint of ``curve1`` on ``curve2``.
return None
if t_initial is not None and t_final is not None:
# In this case, if the curves were coincident, then ``curve1``
# would be "fully" contained in ``curve2``, so we specialize
# ``curve2`` down to that interval to check.
specialized2 = _curve_helpers.specialize_curve(
nodes2, t_initial, t_final
)
if _helpers.vector_close(
nodes1.ravel(order="F"), specialized2.ravel(order="F")
):
return ((0.0, t_initial), (1.0, t_final))
else:
return None
if s_initial is None and s_final is None:
# An overlap must have two endpoints and since exactly one of the
# endpoints of ``curve1`` lies on ``curve2`` (as indicated by exactly
# one of the ``t``-parameters being ``None``), we need (at least)
# one endpoint of ``curve1`` on ``curve2``.
return None
# At this point, we know exactly one of the ``s``-parameters and exactly
# one of the ``t``-parameters is not ``None``.
if s_initial is None:
if t_initial is None:
# B1(s_final) = B2(1) AND B1(1) = B2(t_final)
start_s = s_final
end_s = 1.0
start_t = 1.0
end_t = t_final
else:
# B1(0) = B2(t_initial) AND B1(s_final) = B2(1)
start_s = 0.0
end_s = s_final
start_t = t_initial
end_t = 1.0
else:
if t_initial is None:
# B1(s_initial) = B2(0) AND B1(1 ) = B2(t_final)
start_s = s_initial
end_s = 1.0
start_t = 0.0
end_t = t_final
else:
# B1(0) = B2(t_initial) AND B1(s_initial) = B2(0)
start_s = 0.0
end_s = s_initial
start_t = t_initial
end_t = 0.0
width_s = abs(start_s - end_s)
width_t = abs(start_t - end_t)
if width_s < _MIN_INTERVAL_WIDTH and width_t < _MIN_INTERVAL_WIDTH:
return None
specialized1 = _curve_helpers.specialize_curve(nodes1, start_s, end_s)
specialized2 = _curve_helpers.specialize_curve(nodes2, start_t, end_t)
if _helpers.vector_close(
specialized1.ravel(order="F"), specialized2.ravel(order="F")
):
return ((start_s, start_t), (end_s, end_t))
else:
return None | [
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B_1(s_m) = B_2(1)
B_1(0) = B_2(t_0)
B_1(1) = B_2(t_n)
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nodes1 (numpy.ndarray): Set of control points for a
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nodes2 (numpy.ndarray): Set of control points for a
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Returns:
Optional[Tuple[Tuple[float, float], ...]]: A ``2 x 2`` array of
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dhermes/bezier | src/bezier/_geometric_intersection.py | check_lines | def check_lines(first, second):
"""Checks if two curves are lines and tries to intersect them.
.. note::
This is a helper for :func:`._all_intersections`.
If they are not lines / not linearized, immediately returns :data:`False`
with no "return value".
If they are lines, attempts to intersect them (even if they are parallel
and share a coincident segment).
Args:
first (Union[SubdividedCurve, Linearization]): First curve being
intersected.
second (Union[SubdividedCurve, Linearization]): Second curve being
intersected.
Returns:
Tuple[bool, Optional[Tuple[numpy.ndarray, bool]]]: A pair of
* Flag indicating if both candidates in the pair are lines.
* Optional "result" populated only if both candidates are lines.
When this result is populated, it will be a pair of
* array of parameters of intersection
* flag indicating if the two candidates share a coincident segment
"""
# NOTE: In the below we replace ``isinstance(a, B)`` with
# ``a.__class__ is B``, which is a 3-3.5x speedup.
if not (
first.__class__ is Linearization
and second.__class__ is Linearization
and first.error == 0.0
and second.error == 0.0
):
return False, None
s, t, success = segment_intersection(
first.start_node, first.end_node, second.start_node, second.end_node
)
if success:
if _helpers.in_interval(s, 0.0, 1.0) and _helpers.in_interval(
t, 0.0, 1.0
):
intersections = np.asfortranarray([[s], [t]])
result = intersections, False
else:
result = np.empty((2, 0), order="F"), False
else:
disjoint, params = parallel_lines_parameters(
first.start_node,
first.end_node,
second.start_node,
second.end_node,
)
if disjoint:
result = np.empty((2, 0), order="F"), False
else:
result = params, True
return True, result | python | def check_lines(first, second):
"""Checks if two curves are lines and tries to intersect them.
.. note::
This is a helper for :func:`._all_intersections`.
If they are not lines / not linearized, immediately returns :data:`False`
with no "return value".
If they are lines, attempts to intersect them (even if they are parallel
and share a coincident segment).
Args:
first (Union[SubdividedCurve, Linearization]): First curve being
intersected.
second (Union[SubdividedCurve, Linearization]): Second curve being
intersected.
Returns:
Tuple[bool, Optional[Tuple[numpy.ndarray, bool]]]: A pair of
* Flag indicating if both candidates in the pair are lines.
* Optional "result" populated only if both candidates are lines.
When this result is populated, it will be a pair of
* array of parameters of intersection
* flag indicating if the two candidates share a coincident segment
"""
# NOTE: In the below we replace ``isinstance(a, B)`` with
# ``a.__class__ is B``, which is a 3-3.5x speedup.
if not (
first.__class__ is Linearization
and second.__class__ is Linearization
and first.error == 0.0
and second.error == 0.0
):
return False, None
s, t, success = segment_intersection(
first.start_node, first.end_node, second.start_node, second.end_node
)
if success:
if _helpers.in_interval(s, 0.0, 1.0) and _helpers.in_interval(
t, 0.0, 1.0
):
intersections = np.asfortranarray([[s], [t]])
result = intersections, False
else:
result = np.empty((2, 0), order="F"), False
else:
disjoint, params = parallel_lines_parameters(
first.start_node,
first.end_node,
second.start_node,
second.end_node,
)
if disjoint:
result = np.empty((2, 0), order="F"), False
else:
result = params, True
return True, result | [
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If they are lines, attempts to intersect them (even if they are parallel
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dhermes/bezier | src/bezier/_geometric_intersection.py | SubdividedCurve.subdivide | def subdivide(self):
"""Split the curve into a left and right half.
See :meth:`.Curve.subdivide` for more information.
Returns:
Tuple[SubdividedCurve, SubdividedCurve]: The left and right
sub-curves.
"""
left_nodes, right_nodes = _curve_helpers.subdivide_nodes(self.nodes)
midpoint = 0.5 * (self.start + self.end)
left = SubdividedCurve(
left_nodes, self.original_nodes, start=self.start, end=midpoint
)
right = SubdividedCurve(
right_nodes, self.original_nodes, start=midpoint, end=self.end
)
return left, right | python | def subdivide(self):
"""Split the curve into a left and right half.
See :meth:`.Curve.subdivide` for more information.
Returns:
Tuple[SubdividedCurve, SubdividedCurve]: The left and right
sub-curves.
"""
left_nodes, right_nodes = _curve_helpers.subdivide_nodes(self.nodes)
midpoint = 0.5 * (self.start + self.end)
left = SubdividedCurve(
left_nodes, self.original_nodes, start=self.start, end=midpoint
)
right = SubdividedCurve(
right_nodes, self.original_nodes, start=midpoint, end=self.end
)
return left, right | [
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"=... | Split the curve into a left and right half.
See :meth:`.Curve.subdivide` for more information.
Returns:
Tuple[SubdividedCurve, SubdividedCurve]: The left and right
sub-curves. | [
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] | 4f941f82637a8e70a5b159a9203132192e23406b | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/_geometric_intersection.py#L1543-L1560 | train | 54,204 |
dhermes/bezier | src/bezier/curve.py | Curve.plot | def plot(self, num_pts, color=None, alpha=None, ax=None):
"""Plot the current curve.
Args:
num_pts (int): Number of points to plot.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
alpha (Optional[float]): The alpha channel for the color.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis.
Raises:
NotImplementedError: If the curve's dimension is not ``2``.
"""
if self._dimension != 2:
raise NotImplementedError(
"2D is the only supported dimension",
"Current dimension",
self._dimension,
)
s_vals = np.linspace(0.0, 1.0, num_pts)
points = self.evaluate_multi(s_vals)
if ax is None:
ax = _plot_helpers.new_axis()
ax.plot(points[0, :], points[1, :], color=color, alpha=alpha)
return ax | python | def plot(self, num_pts, color=None, alpha=None, ax=None):
"""Plot the current curve.
Args:
num_pts (int): Number of points to plot.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
alpha (Optional[float]): The alpha channel for the color.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis.
Raises:
NotImplementedError: If the curve's dimension is not ``2``.
"""
if self._dimension != 2:
raise NotImplementedError(
"2D is the only supported dimension",
"Current dimension",
self._dimension,
)
s_vals = np.linspace(0.0, 1.0, num_pts)
points = self.evaluate_multi(s_vals)
if ax is None:
ax = _plot_helpers.new_axis()
ax.plot(points[0, :], points[1, :], color=color, alpha=alpha)
return ax | [
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Args:
num_pts (int): Number of points to plot.
color (Optional[Tuple[float, float, float]]): Color as RGB profile.
alpha (Optional[float]): The alpha channel for the color.
ax (Optional[matplotlib.artist.Artist]): matplotlib axis object
to add plot to.
Returns:
matplotlib.artist.Artist: The axis containing the plot. This
may be a newly created axis.
Raises:
NotImplementedError: If the curve's dimension is not ``2``. | [
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] | 4f941f82637a8e70a5b159a9203132192e23406b | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/curve.py#L245-L274 | train | 54,205 |
dhermes/bezier | src/bezier/curve.py | Curve.intersect | def intersect(
self, other, strategy=IntersectionStrategy.GEOMETRIC, _verify=True
):
"""Find the points of intersection with another curve.
See :doc:`../../algorithms/curve-curve-intersection` for more details.
.. image:: ../../images/curve_intersect.png
:align: center
.. doctest:: curve-intersect
:options: +NORMALIZE_WHITESPACE
>>> nodes1 = np.asfortranarray([
... [0.0, 0.375, 0.75 ],
... [0.0, 0.75 , 0.375],
... ])
>>> curve1 = bezier.Curve(nodes1, degree=2)
>>> nodes2 = np.asfortranarray([
... [0.5, 0.5 ],
... [0.0, 0.75],
... ])
>>> curve2 = bezier.Curve(nodes2, degree=1)
>>> intersections = curve1.intersect(curve2)
>>> 3.0 * intersections
array([[2.],
[2.]])
>>> s_vals = intersections[0, :]
>>> curve1.evaluate_multi(s_vals)
array([[0.5],
[0.5]])
.. testcleanup:: curve-intersect
import make_images
make_images.curve_intersect(curve1, curve2, s_vals)
Args:
other (Curve): Other curve to intersect with.
strategy (Optional[~bezier.curve.IntersectionStrategy]): The
intersection algorithm to use. Defaults to geometric.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the input and current
curve. Can be disabled to speed up execution time.
Defaults to :data:`True`.
Returns:
numpy.ndarray: ``2 x N`` array of ``s``- and ``t``-parameters where
intersections occur (possibly empty).
Raises:
TypeError: If ``other`` is not a curve (and ``_verify=True``).
NotImplementedError: If at least one of the curves
isn't two-dimensional (and ``_verify=True``).
ValueError: If ``strategy`` is not a valid
:class:`.IntersectionStrategy`.
"""
if _verify:
if not isinstance(other, Curve):
raise TypeError(
"Can only intersect with another curve", "Received", other
)
if self._dimension != 2 or other._dimension != 2:
raise NotImplementedError(
"Intersection only implemented in 2D"
)
if strategy == IntersectionStrategy.GEOMETRIC:
all_intersections = _geometric_intersection.all_intersections
elif strategy == IntersectionStrategy.ALGEBRAIC:
all_intersections = _algebraic_intersection.all_intersections
else:
raise ValueError("Unexpected strategy.", strategy)
st_vals, _ = all_intersections(self._nodes, other._nodes)
return st_vals | python | def intersect(
self, other, strategy=IntersectionStrategy.GEOMETRIC, _verify=True
):
"""Find the points of intersection with another curve.
See :doc:`../../algorithms/curve-curve-intersection` for more details.
.. image:: ../../images/curve_intersect.png
:align: center
.. doctest:: curve-intersect
:options: +NORMALIZE_WHITESPACE
>>> nodes1 = np.asfortranarray([
... [0.0, 0.375, 0.75 ],
... [0.0, 0.75 , 0.375],
... ])
>>> curve1 = bezier.Curve(nodes1, degree=2)
>>> nodes2 = np.asfortranarray([
... [0.5, 0.5 ],
... [0.0, 0.75],
... ])
>>> curve2 = bezier.Curve(nodes2, degree=1)
>>> intersections = curve1.intersect(curve2)
>>> 3.0 * intersections
array([[2.],
[2.]])
>>> s_vals = intersections[0, :]
>>> curve1.evaluate_multi(s_vals)
array([[0.5],
[0.5]])
.. testcleanup:: curve-intersect
import make_images
make_images.curve_intersect(curve1, curve2, s_vals)
Args:
other (Curve): Other curve to intersect with.
strategy (Optional[~bezier.curve.IntersectionStrategy]): The
intersection algorithm to use. Defaults to geometric.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the input and current
curve. Can be disabled to speed up execution time.
Defaults to :data:`True`.
Returns:
numpy.ndarray: ``2 x N`` array of ``s``- and ``t``-parameters where
intersections occur (possibly empty).
Raises:
TypeError: If ``other`` is not a curve (and ``_verify=True``).
NotImplementedError: If at least one of the curves
isn't two-dimensional (and ``_verify=True``).
ValueError: If ``strategy`` is not a valid
:class:`.IntersectionStrategy`.
"""
if _verify:
if not isinstance(other, Curve):
raise TypeError(
"Can only intersect with another curve", "Received", other
)
if self._dimension != 2 or other._dimension != 2:
raise NotImplementedError(
"Intersection only implemented in 2D"
)
if strategy == IntersectionStrategy.GEOMETRIC:
all_intersections = _geometric_intersection.all_intersections
elif strategy == IntersectionStrategy.ALGEBRAIC:
all_intersections = _algebraic_intersection.all_intersections
else:
raise ValueError("Unexpected strategy.", strategy)
st_vals, _ = all_intersections(self._nodes, other._nodes)
return st_vals | [
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"TypeErr... | Find the points of intersection with another curve.
See :doc:`../../algorithms/curve-curve-intersection` for more details.
.. image:: ../../images/curve_intersect.png
:align: center
.. doctest:: curve-intersect
:options: +NORMALIZE_WHITESPACE
>>> nodes1 = np.asfortranarray([
... [0.0, 0.375, 0.75 ],
... [0.0, 0.75 , 0.375],
... ])
>>> curve1 = bezier.Curve(nodes1, degree=2)
>>> nodes2 = np.asfortranarray([
... [0.5, 0.5 ],
... [0.0, 0.75],
... ])
>>> curve2 = bezier.Curve(nodes2, degree=1)
>>> intersections = curve1.intersect(curve2)
>>> 3.0 * intersections
array([[2.],
[2.]])
>>> s_vals = intersections[0, :]
>>> curve1.evaluate_multi(s_vals)
array([[0.5],
[0.5]])
.. testcleanup:: curve-intersect
import make_images
make_images.curve_intersect(curve1, curve2, s_vals)
Args:
other (Curve): Other curve to intersect with.
strategy (Optional[~bezier.curve.IntersectionStrategy]): The
intersection algorithm to use. Defaults to geometric.
_verify (Optional[bool]): Indicates if extra caution should be
used to verify assumptions about the input and current
curve. Can be disabled to speed up execution time.
Defaults to :data:`True`.
Returns:
numpy.ndarray: ``2 x N`` array of ``s``- and ``t``-parameters where
intersections occur (possibly empty).
Raises:
TypeError: If ``other`` is not a curve (and ``_verify=True``).
NotImplementedError: If at least one of the curves
isn't two-dimensional (and ``_verify=True``).
ValueError: If ``strategy`` is not a valid
:class:`.IntersectionStrategy`. | [
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] | 4f941f82637a8e70a5b159a9203132192e23406b | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/curve.py#L317-L393 | train | 54,206 |
dhermes/bezier | src/bezier/curve.py | Curve.elevate | def elevate(self):
r"""Return a degree-elevated version of the current curve.
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n + 1}` where
.. math::
\begin{align*}
w_0 &= v_0 \\
w_j &= \frac{j}{n + 1} v_{j - 1} + \frac{n + 1 - j}{n + 1} v_j \\
w_{n + 1} &= v_n
\end{align*}
.. image:: ../../images/curve_elevate.png
:align: center
.. testsetup:: curve-elevate
import numpy as np
import bezier
.. doctest:: curve-elevate
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.5, 3.0],
... [0.0, 1.5, 0.0],
... ])
>>> curve = bezier.Curve(nodes, degree=2)
>>> elevated = curve.elevate()
>>> elevated
<Curve (degree=3, dimension=2)>
>>> elevated.nodes
array([[0., 1., 2., 3.],
[0., 1., 1., 0.]])
.. testcleanup:: curve-elevate
import make_images
make_images.curve_elevate(curve, elevated)
Returns:
Curve: The degree-elevated curve.
"""
new_nodes = _curve_helpers.elevate_nodes(self._nodes)
return Curve(new_nodes, self._degree + 1, _copy=False) | python | def elevate(self):
r"""Return a degree-elevated version of the current curve.
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n + 1}` where
.. math::
\begin{align*}
w_0 &= v_0 \\
w_j &= \frac{j}{n + 1} v_{j - 1} + \frac{n + 1 - j}{n + 1} v_j \\
w_{n + 1} &= v_n
\end{align*}
.. image:: ../../images/curve_elevate.png
:align: center
.. testsetup:: curve-elevate
import numpy as np
import bezier
.. doctest:: curve-elevate
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.5, 3.0],
... [0.0, 1.5, 0.0],
... ])
>>> curve = bezier.Curve(nodes, degree=2)
>>> elevated = curve.elevate()
>>> elevated
<Curve (degree=3, dimension=2)>
>>> elevated.nodes
array([[0., 1., 2., 3.],
[0., 1., 1., 0.]])
.. testcleanup:: curve-elevate
import make_images
make_images.curve_elevate(curve, elevated)
Returns:
Curve: The degree-elevated curve.
"""
new_nodes = _curve_helpers.elevate_nodes(self._nodes)
return Curve(new_nodes, self._degree + 1, _copy=False) | [
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"+",
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Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n + 1}` where
.. math::
\begin{align*}
w_0 &= v_0 \\
w_j &= \frac{j}{n + 1} v_{j - 1} + \frac{n + 1 - j}{n + 1} v_j \\
w_{n + 1} &= v_n
\end{align*}
.. image:: ../../images/curve_elevate.png
:align: center
.. testsetup:: curve-elevate
import numpy as np
import bezier
.. doctest:: curve-elevate
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.5, 3.0],
... [0.0, 1.5, 0.0],
... ])
>>> curve = bezier.Curve(nodes, degree=2)
>>> elevated = curve.elevate()
>>> elevated
<Curve (degree=3, dimension=2)>
>>> elevated.nodes
array([[0., 1., 2., 3.],
[0., 1., 1., 0.]])
.. testcleanup:: curve-elevate
import make_images
make_images.curve_elevate(curve, elevated)
Returns:
Curve: The degree-elevated curve. | [
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] | 4f941f82637a8e70a5b159a9203132192e23406b | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/curve.py#L395-L441 | train | 54,207 |
dhermes/bezier | src/bezier/curve.py | Curve.reduce_ | def reduce_(self):
r"""Return a degree-reduced version of the current curve.
.. _pseudo-inverse:
https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n - 1}` that correspond to
reversing the :meth:`elevate` process.
This uses the `pseudo-inverse`_ of the elevation matrix. For example
when elevating from degree 2 to 3, the matrix :math:`E_2` is given by
.. math::
\mathbf{v} = \left[\begin{array}{c c c} v_0 & v_1 & v_2
\end{array}\right] \longmapsto \left[\begin{array}{c c c c}
v_0 & \frac{v_0 + 2 v_1}{3} & \frac{2 v_1 + v_2}{3} & v_2
\end{array}\right] = \frac{1}{3} \mathbf{v}
\left[\begin{array}{c c c c} 3 & 1 & 0 & 0 \\
0 & 2 & 2 & 0 \\ 0 & 0 & 1 & 3 \end{array}\right]
and the (right) pseudo-inverse is given by
.. math::
R_2 = E_2^T \left(E_2 E_2^T\right)^{-1} = \frac{1}{20}
\left[\begin{array}{c c c} 19 & -5 & 1 \\
3 & 15 & -3 \\ -3 & 15 & 3 \\ 1 & -5 & 19
\end{array}\right].
.. warning::
Though degree-elevation preserves the start and end nodes, degree
reduction has no such guarantee. Rather, the nodes produced are
"best" in the least squares sense (when solving the normal
equations).
.. image:: ../../images/curve_reduce.png
:align: center
.. testsetup:: curve-reduce, curve-reduce-approx
import numpy as np
import bezier
.. doctest:: curve-reduce
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [-3.0, 0.0, 1.0, 0.0],
... [ 3.0, 2.0, 3.0, 6.0],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> reduced = curve.reduce_()
>>> reduced
<Curve (degree=2, dimension=2)>
>>> reduced.nodes
array([[-3. , 1.5, 0. ],
[ 3. , 1.5, 6. ]])
.. testcleanup:: curve-reduce
import make_images
make_images.curve_reduce(curve, reduced)
In the case that the current curve **is not** degree-elevated.
.. image:: ../../images/curve_reduce_approx.png
:align: center
.. doctest:: curve-reduce-approx
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.25, 3.75, 5.0],
... [2.5, 5.0 , 7.5 , 2.5],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> reduced = curve.reduce_()
>>> reduced
<Curve (degree=2, dimension=2)>
>>> reduced.nodes
array([[-0.125, 2.5 , 5.125],
[ 2.125, 8.125, 2.875]])
.. testcleanup:: curve-reduce-approx
import make_images
make_images.curve_reduce_approx(curve, reduced)
Returns:
Curve: The degree-reduced curve.
"""
new_nodes = _curve_helpers.reduce_pseudo_inverse(self._nodes)
return Curve(new_nodes, self._degree - 1, _copy=False) | python | def reduce_(self):
r"""Return a degree-reduced version of the current curve.
.. _pseudo-inverse:
https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n - 1}` that correspond to
reversing the :meth:`elevate` process.
This uses the `pseudo-inverse`_ of the elevation matrix. For example
when elevating from degree 2 to 3, the matrix :math:`E_2` is given by
.. math::
\mathbf{v} = \left[\begin{array}{c c c} v_0 & v_1 & v_2
\end{array}\right] \longmapsto \left[\begin{array}{c c c c}
v_0 & \frac{v_0 + 2 v_1}{3} & \frac{2 v_1 + v_2}{3} & v_2
\end{array}\right] = \frac{1}{3} \mathbf{v}
\left[\begin{array}{c c c c} 3 & 1 & 0 & 0 \\
0 & 2 & 2 & 0 \\ 0 & 0 & 1 & 3 \end{array}\right]
and the (right) pseudo-inverse is given by
.. math::
R_2 = E_2^T \left(E_2 E_2^T\right)^{-1} = \frac{1}{20}
\left[\begin{array}{c c c} 19 & -5 & 1 \\
3 & 15 & -3 \\ -3 & 15 & 3 \\ 1 & -5 & 19
\end{array}\right].
.. warning::
Though degree-elevation preserves the start and end nodes, degree
reduction has no such guarantee. Rather, the nodes produced are
"best" in the least squares sense (when solving the normal
equations).
.. image:: ../../images/curve_reduce.png
:align: center
.. testsetup:: curve-reduce, curve-reduce-approx
import numpy as np
import bezier
.. doctest:: curve-reduce
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [-3.0, 0.0, 1.0, 0.0],
... [ 3.0, 2.0, 3.0, 6.0],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> reduced = curve.reduce_()
>>> reduced
<Curve (degree=2, dimension=2)>
>>> reduced.nodes
array([[-3. , 1.5, 0. ],
[ 3. , 1.5, 6. ]])
.. testcleanup:: curve-reduce
import make_images
make_images.curve_reduce(curve, reduced)
In the case that the current curve **is not** degree-elevated.
.. image:: ../../images/curve_reduce_approx.png
:align: center
.. doctest:: curve-reduce-approx
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.25, 3.75, 5.0],
... [2.5, 5.0 , 7.5 , 2.5],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> reduced = curve.reduce_()
>>> reduced
<Curve (degree=2, dimension=2)>
>>> reduced.nodes
array([[-0.125, 2.5 , 5.125],
[ 2.125, 8.125, 2.875]])
.. testcleanup:: curve-reduce-approx
import make_images
make_images.curve_reduce_approx(curve, reduced)
Returns:
Curve: The degree-reduced curve.
"""
new_nodes = _curve_helpers.reduce_pseudo_inverse(self._nodes)
return Curve(new_nodes, self._degree - 1, _copy=False) | [
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",",
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"_degree",
"-",
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",",
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")"
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.. _pseudo-inverse:
https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n - 1}` that correspond to
reversing the :meth:`elevate` process.
This uses the `pseudo-inverse`_ of the elevation matrix. For example
when elevating from degree 2 to 3, the matrix :math:`E_2` is given by
.. math::
\mathbf{v} = \left[\begin{array}{c c c} v_0 & v_1 & v_2
\end{array}\right] \longmapsto \left[\begin{array}{c c c c}
v_0 & \frac{v_0 + 2 v_1}{3} & \frac{2 v_1 + v_2}{3} & v_2
\end{array}\right] = \frac{1}{3} \mathbf{v}
\left[\begin{array}{c c c c} 3 & 1 & 0 & 0 \\
0 & 2 & 2 & 0 \\ 0 & 0 & 1 & 3 \end{array}\right]
and the (right) pseudo-inverse is given by
.. math::
R_2 = E_2^T \left(E_2 E_2^T\right)^{-1} = \frac{1}{20}
\left[\begin{array}{c c c} 19 & -5 & 1 \\
3 & 15 & -3 \\ -3 & 15 & 3 \\ 1 & -5 & 19
\end{array}\right].
.. warning::
Though degree-elevation preserves the start and end nodes, degree
reduction has no such guarantee. Rather, the nodes produced are
"best" in the least squares sense (when solving the normal
equations).
.. image:: ../../images/curve_reduce.png
:align: center
.. testsetup:: curve-reduce, curve-reduce-approx
import numpy as np
import bezier
.. doctest:: curve-reduce
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [-3.0, 0.0, 1.0, 0.0],
... [ 3.0, 2.0, 3.0, 6.0],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> reduced = curve.reduce_()
>>> reduced
<Curve (degree=2, dimension=2)>
>>> reduced.nodes
array([[-3. , 1.5, 0. ],
[ 3. , 1.5, 6. ]])
.. testcleanup:: curve-reduce
import make_images
make_images.curve_reduce(curve, reduced)
In the case that the current curve **is not** degree-elevated.
.. image:: ../../images/curve_reduce_approx.png
:align: center
.. doctest:: curve-reduce-approx
:options: +NORMALIZE_WHITESPACE
>>> nodes = np.asfortranarray([
... [0.0, 1.25, 3.75, 5.0],
... [2.5, 5.0 , 7.5 , 2.5],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> reduced = curve.reduce_()
>>> reduced
<Curve (degree=2, dimension=2)>
>>> reduced.nodes
array([[-0.125, 2.5 , 5.125],
[ 2.125, 8.125, 2.875]])
.. testcleanup:: curve-reduce-approx
import make_images
make_images.curve_reduce_approx(curve, reduced)
Returns:
Curve: The degree-reduced curve. | [
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] | 4f941f82637a8e70a5b159a9203132192e23406b | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/curve.py#L443-L538 | train | 54,208 |
dhermes/bezier | src/bezier/curve.py | Curve.specialize | def specialize(self, start, end):
"""Specialize the curve to a given sub-interval.
.. image:: ../../images/curve_specialize.png
:align: center
.. doctest:: curve-specialize
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0],
... [0.0, 1.0, 0.0],
... ])
>>> curve = bezier.Curve(nodes, degree=2)
>>> new_curve = curve.specialize(-0.25, 0.75)
>>> new_curve.nodes
array([[-0.25 , 0.25 , 0.75 ],
[-0.625, 0.875, 0.375]])
.. testcleanup:: curve-specialize
import make_images
make_images.curve_specialize(curve, new_curve)
This is generalized version of :meth:`subdivide`, and can even
match the output of that method:
.. testsetup:: curve-specialize2
import numpy as np
import bezier
nodes = np.asfortranarray([
[0.0, 0.5, 1.0],
[0.0, 1.0, 0.0],
])
curve = bezier.Curve(nodes, degree=2)
.. doctest:: curve-specialize2
>>> left, right = curve.subdivide()
>>> also_left = curve.specialize(0.0, 0.5)
>>> np.all(also_left.nodes == left.nodes)
True
>>> also_right = curve.specialize(0.5, 1.0)
>>> np.all(also_right.nodes == right.nodes)
True
Args:
start (float): The start point of the interval we
are specializing to.
end (float): The end point of the interval we
are specializing to.
Returns:
Curve: The newly-specialized curve.
"""
new_nodes = _curve_helpers.specialize_curve(self._nodes, start, end)
return Curve(new_nodes, self._degree, _copy=False) | python | def specialize(self, start, end):
"""Specialize the curve to a given sub-interval.
.. image:: ../../images/curve_specialize.png
:align: center
.. doctest:: curve-specialize
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0],
... [0.0, 1.0, 0.0],
... ])
>>> curve = bezier.Curve(nodes, degree=2)
>>> new_curve = curve.specialize(-0.25, 0.75)
>>> new_curve.nodes
array([[-0.25 , 0.25 , 0.75 ],
[-0.625, 0.875, 0.375]])
.. testcleanup:: curve-specialize
import make_images
make_images.curve_specialize(curve, new_curve)
This is generalized version of :meth:`subdivide`, and can even
match the output of that method:
.. testsetup:: curve-specialize2
import numpy as np
import bezier
nodes = np.asfortranarray([
[0.0, 0.5, 1.0],
[0.0, 1.0, 0.0],
])
curve = bezier.Curve(nodes, degree=2)
.. doctest:: curve-specialize2
>>> left, right = curve.subdivide()
>>> also_left = curve.specialize(0.0, 0.5)
>>> np.all(also_left.nodes == left.nodes)
True
>>> also_right = curve.specialize(0.5, 1.0)
>>> np.all(also_right.nodes == right.nodes)
True
Args:
start (float): The start point of the interval we
are specializing to.
end (float): The end point of the interval we
are specializing to.
Returns:
Curve: The newly-specialized curve.
"""
new_nodes = _curve_helpers.specialize_curve(self._nodes, start, end)
return Curve(new_nodes, self._degree, _copy=False) | [
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.. image:: ../../images/curve_specialize.png
:align: center
.. doctest:: curve-specialize
>>> nodes = np.asfortranarray([
... [0.0, 0.5, 1.0],
... [0.0, 1.0, 0.0],
... ])
>>> curve = bezier.Curve(nodes, degree=2)
>>> new_curve = curve.specialize(-0.25, 0.75)
>>> new_curve.nodes
array([[-0.25 , 0.25 , 0.75 ],
[-0.625, 0.875, 0.375]])
.. testcleanup:: curve-specialize
import make_images
make_images.curve_specialize(curve, new_curve)
This is generalized version of :meth:`subdivide`, and can even
match the output of that method:
.. testsetup:: curve-specialize2
import numpy as np
import bezier
nodes = np.asfortranarray([
[0.0, 0.5, 1.0],
[0.0, 1.0, 0.0],
])
curve = bezier.Curve(nodes, degree=2)
.. doctest:: curve-specialize2
>>> left, right = curve.subdivide()
>>> also_left = curve.specialize(0.0, 0.5)
>>> np.all(also_left.nodes == left.nodes)
True
>>> also_right = curve.specialize(0.5, 1.0)
>>> np.all(also_right.nodes == right.nodes)
True
Args:
start (float): The start point of the interval we
are specializing to.
end (float): The end point of the interval we
are specializing to.
Returns:
Curve: The newly-specialized curve. | [
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dhermes/bezier | src/bezier/curve.py | Curve.locate | def locate(self, point):
r"""Find a point on the current curve.
Solves for :math:`s` in :math:`B(s) = p`.
This method acts as a (partial) inverse to :meth:`evaluate`.
.. note::
A unique solution is only guaranteed if the current curve has no
self-intersections. This code assumes, but doesn't check, that
this is true.
.. image:: ../../images/curve_locate.png
:align: center
.. doctest:: curve-locate
>>> nodes = np.asfortranarray([
... [0.0, -1.0, 1.0, -0.75 ],
... [2.0, 0.0, 1.0, 1.625],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> point1 = np.asfortranarray([
... [-0.09375 ],
... [ 0.828125],
... ])
>>> curve.locate(point1)
0.5
>>> point2 = np.asfortranarray([
... [0.0],
... [1.5],
... ])
>>> curve.locate(point2) is None
True
>>> point3 = np.asfortranarray([
... [-0.25 ],
... [ 1.375],
... ])
>>> curve.locate(point3) is None
Traceback (most recent call last):
...
ValueError: Parameters not close enough to one another
.. testcleanup:: curve-locate
import make_images
make_images.curve_locate(curve, point1, point2, point3)
Args:
point (numpy.ndarray): A (``D x 1``) point on the curve,
where :math:`D` is the dimension of the curve.
Returns:
Optional[float]: The parameter value (:math:`s`) corresponding
to ``point`` or :data:`None` if the point is not on
the ``curve``.
Raises:
ValueError: If the dimension of the ``point`` doesn't match the
dimension of the current curve.
"""
if point.shape != (self._dimension, 1):
point_dimensions = " x ".join(
str(dimension) for dimension in point.shape
)
msg = _LOCATE_ERROR_TEMPLATE.format(
self._dimension, self._dimension, point, point_dimensions
)
raise ValueError(msg)
return _curve_helpers.locate_point(self._nodes, point) | python | def locate(self, point):
r"""Find a point on the current curve.
Solves for :math:`s` in :math:`B(s) = p`.
This method acts as a (partial) inverse to :meth:`evaluate`.
.. note::
A unique solution is only guaranteed if the current curve has no
self-intersections. This code assumes, but doesn't check, that
this is true.
.. image:: ../../images/curve_locate.png
:align: center
.. doctest:: curve-locate
>>> nodes = np.asfortranarray([
... [0.0, -1.0, 1.0, -0.75 ],
... [2.0, 0.0, 1.0, 1.625],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> point1 = np.asfortranarray([
... [-0.09375 ],
... [ 0.828125],
... ])
>>> curve.locate(point1)
0.5
>>> point2 = np.asfortranarray([
... [0.0],
... [1.5],
... ])
>>> curve.locate(point2) is None
True
>>> point3 = np.asfortranarray([
... [-0.25 ],
... [ 1.375],
... ])
>>> curve.locate(point3) is None
Traceback (most recent call last):
...
ValueError: Parameters not close enough to one another
.. testcleanup:: curve-locate
import make_images
make_images.curve_locate(curve, point1, point2, point3)
Args:
point (numpy.ndarray): A (``D x 1``) point on the curve,
where :math:`D` is the dimension of the curve.
Returns:
Optional[float]: The parameter value (:math:`s`) corresponding
to ``point`` or :data:`None` if the point is not on
the ``curve``.
Raises:
ValueError: If the dimension of the ``point`` doesn't match the
dimension of the current curve.
"""
if point.shape != (self._dimension, 1):
point_dimensions = " x ".join(
str(dimension) for dimension in point.shape
)
msg = _LOCATE_ERROR_TEMPLATE.format(
self._dimension, self._dimension, point, point_dimensions
)
raise ValueError(msg)
return _curve_helpers.locate_point(self._nodes, point) | [
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This method acts as a (partial) inverse to :meth:`evaluate`.
.. note::
A unique solution is only guaranteed if the current curve has no
self-intersections. This code assumes, but doesn't check, that
this is true.
.. image:: ../../images/curve_locate.png
:align: center
.. doctest:: curve-locate
>>> nodes = np.asfortranarray([
... [0.0, -1.0, 1.0, -0.75 ],
... [2.0, 0.0, 1.0, 1.625],
... ])
>>> curve = bezier.Curve(nodes, degree=3)
>>> point1 = np.asfortranarray([
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... [ 0.828125],
... ])
>>> curve.locate(point1)
0.5
>>> point2 = np.asfortranarray([
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... ])
>>> curve.locate(point2) is None
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>>> point3 = np.asfortranarray([
... [-0.25 ],
... [ 1.375],
... ])
>>> curve.locate(point3) is None
Traceback (most recent call last):
...
ValueError: Parameters not close enough to one another
.. testcleanup:: curve-locate
import make_images
make_images.curve_locate(curve, point1, point2, point3)
Args:
point (numpy.ndarray): A (``D x 1``) point on the curve,
where :math:`D` is the dimension of the curve.
Returns:
Optional[float]: The parameter value (:math:`s`) corresponding
to ``point`` or :data:`None` if the point is not on
the ``curve``.
Raises:
ValueError: If the dimension of the ``point`` doesn't match the
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dhermes/bezier | scripts/clean_cython.py | clean_file | def clean_file(c_source, virtualenv_dirname):
"""Strip trailing whitespace and clean up "local" names in C source.
These source files are autogenerated from the ``cython`` CLI.
Args:
c_source (str): Path to a ``.c`` source file.
virtualenv_dirname (str): The name of the ``virtualenv``
directory where Cython is installed (this is part of a
relative path ``.nox/{NAME}/lib/...``).
"""
with open(c_source, "r") as file_obj:
contents = file_obj.read().rstrip()
# Replace the path to the Cython include files.
py_version = "python{}.{}".format(*sys.version_info[:2])
lib_path = os.path.join(
".nox", virtualenv_dirname, "lib", py_version, "site-packages", ""
)
contents = contents.replace(lib_path, "")
# Write the files back, but strip all trailing whitespace.
lines = contents.split("\n")
with open(c_source, "w") as file_obj:
for line in lines:
file_obj.write(line.rstrip() + "\n") | python | def clean_file(c_source, virtualenv_dirname):
"""Strip trailing whitespace and clean up "local" names in C source.
These source files are autogenerated from the ``cython`` CLI.
Args:
c_source (str): Path to a ``.c`` source file.
virtualenv_dirname (str): The name of the ``virtualenv``
directory where Cython is installed (this is part of a
relative path ``.nox/{NAME}/lib/...``).
"""
with open(c_source, "r") as file_obj:
contents = file_obj.read().rstrip()
# Replace the path to the Cython include files.
py_version = "python{}.{}".format(*sys.version_info[:2])
lib_path = os.path.join(
".nox", virtualenv_dirname, "lib", py_version, "site-packages", ""
)
contents = contents.replace(lib_path, "")
# Write the files back, but strip all trailing whitespace.
lines = contents.split("\n")
with open(c_source, "w") as file_obj:
for line in lines:
file_obj.write(line.rstrip() + "\n") | [
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dhermes/bezier | scripts/doc_template_release.py | get_version | def get_version():
"""Get the current version from ``setup.py``.
Assumes that importing ``setup.py`` will have no side-effects (i.e.
assumes the behavior is guarded by ``if __name__ == "__main__"``).
Returns:
str: The current version in ``setup.py``.
"""
# "Spoof" the ``setup.py`` helper modules.
sys.modules["setup_helpers"] = object()
sys.modules["setup_helpers_macos"] = object()
sys.modules["setup_helpers_windows"] = object()
filename = os.path.join(_ROOT_DIR, "setup.py")
loader = importlib.machinery.SourceFileLoader("setup", filename)
setup_mod = loader.load_module()
return setup_mod.VERSION | python | def get_version():
"""Get the current version from ``setup.py``.
Assumes that importing ``setup.py`` will have no side-effects (i.e.
assumes the behavior is guarded by ``if __name__ == "__main__"``).
Returns:
str: The current version in ``setup.py``.
"""
# "Spoof" the ``setup.py`` helper modules.
sys.modules["setup_helpers"] = object()
sys.modules["setup_helpers_macos"] = object()
sys.modules["setup_helpers_windows"] = object()
filename = os.path.join(_ROOT_DIR, "setup.py")
loader = importlib.machinery.SourceFileLoader("setup", filename)
setup_mod = loader.load_module()
return setup_mod.VERSION | [
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dhermes/bezier | scripts/doc_template_release.py | populate_readme | def populate_readme(
version, circleci_build, appveyor_build, coveralls_build, travis_build
):
"""Populates ``README.rst`` with release-specific data.
This is because ``README.rst`` is used on PyPI.
Args:
version (str): The current version.
circleci_build (Union[str, int]): The CircleCI build ID corresponding
to the release.
appveyor_build (str): The AppVeyor build ID corresponding to the
release.
coveralls_build (Union[str, int]): The Coveralls.io build ID
corresponding to the release.
travis_build (int): The Travis CI build ID corresponding to
the release.
"""
with open(RELEASE_README_FILE, "r") as file_obj:
template = file_obj.read()
contents = template.format(
version=version,
circleci_build=circleci_build,
appveyor_build=appveyor_build,
coveralls_build=coveralls_build,
travis_build=travis_build,
)
with open(README_FILE, "w") as file_obj:
file_obj.write(contents) | python | def populate_readme(
version, circleci_build, appveyor_build, coveralls_build, travis_build
):
"""Populates ``README.rst`` with release-specific data.
This is because ``README.rst`` is used on PyPI.
Args:
version (str): The current version.
circleci_build (Union[str, int]): The CircleCI build ID corresponding
to the release.
appveyor_build (str): The AppVeyor build ID corresponding to the
release.
coveralls_build (Union[str, int]): The Coveralls.io build ID
corresponding to the release.
travis_build (int): The Travis CI build ID corresponding to
the release.
"""
with open(RELEASE_README_FILE, "r") as file_obj:
template = file_obj.read()
contents = template.format(
version=version,
circleci_build=circleci_build,
appveyor_build=appveyor_build,
coveralls_build=coveralls_build,
travis_build=travis_build,
)
with open(README_FILE, "w") as file_obj:
file_obj.write(contents) | [
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dhermes/bezier | scripts/doc_template_release.py | populate_native_libraries | def populate_native_libraries(version):
"""Populates ``binary-extension.rst`` with release-specific data.
Args:
version (str): The current version.
"""
with open(BINARY_EXT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
contents = template.format(revision=version)
with open(BINARY_EXT_FILE, "w") as file_obj:
file_obj.write(contents) | python | def populate_native_libraries(version):
"""Populates ``binary-extension.rst`` with release-specific data.
Args:
version (str): The current version.
"""
with open(BINARY_EXT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
contents = template.format(revision=version)
with open(BINARY_EXT_FILE, "w") as file_obj:
file_obj.write(contents) | [
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dhermes/bezier | scripts/doc_template_release.py | populate_development | def populate_development(version):
"""Populates ``DEVELOPMENT.rst`` with release-specific data.
This is because ``DEVELOPMENT.rst`` is used in the Sphinx documentation.
Args:
version (str): The current version.
"""
with open(DEVELOPMENT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
contents = template.format(revision=version, rtd_version=version)
with open(DEVELOPMENT_FILE, "w") as file_obj:
file_obj.write(contents) | python | def populate_development(version):
"""Populates ``DEVELOPMENT.rst`` with release-specific data.
This is because ``DEVELOPMENT.rst`` is used in the Sphinx documentation.
Args:
version (str): The current version.
"""
with open(DEVELOPMENT_TEMPLATE, "r") as file_obj:
template = file_obj.read()
contents = template.format(revision=version, rtd_version=version)
with open(DEVELOPMENT_FILE, "w") as file_obj:
file_obj.write(contents) | [
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dhermes/bezier | scripts/doc_template_release.py | main | def main():
"""Populate the templates with release-specific fields.
Requires user input for the CircleCI, AppVeyor, Coveralls.io and Travis
build IDs.
"""
version = get_version()
circleci_build = six.moves.input("CircleCI Build ID: ")
appveyor_build = six.moves.input("AppVeyor Build ID: ")
coveralls_build = six.moves.input("Coveralls Build ID: ")
travis_build = six.moves.input("Travis Build ID: ")
populate_readme(
version, circleci_build, appveyor_build, coveralls_build, travis_build
)
populate_index(
version, circleci_build, appveyor_build, coveralls_build, travis_build
)
populate_native_libraries(version)
populate_development(version) | python | def main():
"""Populate the templates with release-specific fields.
Requires user input for the CircleCI, AppVeyor, Coveralls.io and Travis
build IDs.
"""
version = get_version()
circleci_build = six.moves.input("CircleCI Build ID: ")
appveyor_build = six.moves.input("AppVeyor Build ID: ")
coveralls_build = six.moves.input("Coveralls Build ID: ")
travis_build = six.moves.input("Travis Build ID: ")
populate_readme(
version, circleci_build, appveyor_build, coveralls_build, travis_build
)
populate_index(
version, circleci_build, appveyor_build, coveralls_build, travis_build
)
populate_native_libraries(version)
populate_development(version) | [
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dhermes/bezier | src/bezier/_curve_helpers.py | make_subdivision_matrices | def make_subdivision_matrices(degree):
"""Make the matrix used to subdivide a curve.
.. note::
This is a helper for :func:`_subdivide_nodes`. It does not have a
Fortran speedup because it is **only** used by a function which has
a Fortran speedup.
Args:
degree (int): The degree of the curve.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]: The matrices used to convert
the nodes into left and right nodes, respectively.
"""
left = np.zeros((degree + 1, degree + 1), order="F")
right = np.zeros((degree + 1, degree + 1), order="F")
left[0, 0] = 1.0
right[-1, -1] = 1.0
for col in six.moves.xrange(1, degree + 1):
half_prev = 0.5 * left[:col, col - 1]
left[:col, col] = half_prev
left[1 : col + 1, col] += half_prev # noqa: E203
# Populate the complement col (in right) as well.
complement = degree - col
# NOTE: We "should" reverse the results when using
# the complement, but they are symmetric so
# that would be a waste.
right[-(col + 1) :, complement] = left[: col + 1, col] # noqa: E203
return left, right | python | def make_subdivision_matrices(degree):
"""Make the matrix used to subdivide a curve.
.. note::
This is a helper for :func:`_subdivide_nodes`. It does not have a
Fortran speedup because it is **only** used by a function which has
a Fortran speedup.
Args:
degree (int): The degree of the curve.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]: The matrices used to convert
the nodes into left and right nodes, respectively.
"""
left = np.zeros((degree + 1, degree + 1), order="F")
right = np.zeros((degree + 1, degree + 1), order="F")
left[0, 0] = 1.0
right[-1, -1] = 1.0
for col in six.moves.xrange(1, degree + 1):
half_prev = 0.5 * left[:col, col - 1]
left[:col, col] = half_prev
left[1 : col + 1, col] += half_prev # noqa: E203
# Populate the complement col (in right) as well.
complement = degree - col
# NOTE: We "should" reverse the results when using
# the complement, but they are symmetric so
# that would be a waste.
right[-(col + 1) :, complement] = left[: col + 1, col] # noqa: E203
return left, right | [
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.. note::
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a Fortran speedup.
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degree (int): The degree of the curve.
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dhermes/bezier | src/bezier/_curve_helpers.py | _subdivide_nodes | def _subdivide_nodes(nodes):
"""Subdivide a curve into two sub-curves.
Does so by taking the unit interval (i.e. the domain of the surface) and
splitting it into two sub-intervals by splitting down the middle.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a B |eacute| zier curve.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]: The nodes for the two sub-curves.
"""
_, num_nodes = np.shape(nodes)
if num_nodes == 2:
left_nodes = _helpers.matrix_product(nodes, _LINEAR_SUBDIVIDE_LEFT)
right_nodes = _helpers.matrix_product(nodes, _LINEAR_SUBDIVIDE_RIGHT)
elif num_nodes == 3:
left_nodes = _helpers.matrix_product(nodes, _QUADRATIC_SUBDIVIDE_LEFT)
right_nodes = _helpers.matrix_product(
nodes, _QUADRATIC_SUBDIVIDE_RIGHT
)
elif num_nodes == 4:
left_nodes = _helpers.matrix_product(nodes, _CUBIC_SUBDIVIDE_LEFT)
right_nodes = _helpers.matrix_product(nodes, _CUBIC_SUBDIVIDE_RIGHT)
else:
left_mat, right_mat = make_subdivision_matrices(num_nodes - 1)
left_nodes = _helpers.matrix_product(nodes, left_mat)
right_nodes = _helpers.matrix_product(nodes, right_mat)
return left_nodes, right_nodes | python | def _subdivide_nodes(nodes):
"""Subdivide a curve into two sub-curves.
Does so by taking the unit interval (i.e. the domain of the surface) and
splitting it into two sub-intervals by splitting down the middle.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a B |eacute| zier curve.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]: The nodes for the two sub-curves.
"""
_, num_nodes = np.shape(nodes)
if num_nodes == 2:
left_nodes = _helpers.matrix_product(nodes, _LINEAR_SUBDIVIDE_LEFT)
right_nodes = _helpers.matrix_product(nodes, _LINEAR_SUBDIVIDE_RIGHT)
elif num_nodes == 3:
left_nodes = _helpers.matrix_product(nodes, _QUADRATIC_SUBDIVIDE_LEFT)
right_nodes = _helpers.matrix_product(
nodes, _QUADRATIC_SUBDIVIDE_RIGHT
)
elif num_nodes == 4:
left_nodes = _helpers.matrix_product(nodes, _CUBIC_SUBDIVIDE_LEFT)
right_nodes = _helpers.matrix_product(nodes, _CUBIC_SUBDIVIDE_RIGHT)
else:
left_mat, right_mat = make_subdivision_matrices(num_nodes - 1)
left_nodes = _helpers.matrix_product(nodes, left_mat)
right_nodes = _helpers.matrix_product(nodes, right_mat)
return left_nodes, right_nodes | [
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.. note::
There is also a Fortran implementation of this function, which
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Args:
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dhermes/bezier | src/bezier/_curve_helpers.py | _evaluate_multi_barycentric | def _evaluate_multi_barycentric(nodes, lambda1, lambda2):
r"""Evaluates a B |eacute| zier type-function.
Of the form
.. math::
B(\lambda_1, \lambda_2) = \sum_j \binom{n}{j}
\lambda_1^{n - j} \lambda_2^j \cdot v_j
for some set of vectors :math:`v_j` given by ``nodes``.
Does so via a modified Horner's method for each pair of values
in ``lambda1`` and ``lambda2``, rather than using the
de Casteljau algorithm.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
lambda1 (numpy.ndarray): Parameters along the curve (as a
1D array).
lambda2 (numpy.ndarray): Parameters along the curve (as a
1D array). Typically we have ``lambda1 + lambda2 == 1``.
Returns:
numpy.ndarray: The evaluated points as a two dimensional
NumPy array, with the columns corresponding to each pair of parameter
values and the rows to the dimension.
"""
# NOTE: We assume but don't check that lambda2 has the same shape.
num_vals, = lambda1.shape
dimension, num_nodes = nodes.shape
degree = num_nodes - 1
# Resize as row vectors for broadcast multiplying with
# columns of ``nodes``.
lambda1 = lambda1[np.newaxis, :]
lambda2 = lambda2[np.newaxis, :]
result = np.zeros((dimension, num_vals), order="F")
result += lambda1 * nodes[:, [0]]
binom_val = 1.0
lambda2_pow = np.ones((1, num_vals), order="F")
for index in six.moves.xrange(1, degree):
lambda2_pow *= lambda2
binom_val = (binom_val * (degree - index + 1)) / index
result += binom_val * lambda2_pow * nodes[:, [index]]
result *= lambda1
result += lambda2 * lambda2_pow * nodes[:, [degree]]
return result | python | def _evaluate_multi_barycentric(nodes, lambda1, lambda2):
r"""Evaluates a B |eacute| zier type-function.
Of the form
.. math::
B(\lambda_1, \lambda_2) = \sum_j \binom{n}{j}
\lambda_1^{n - j} \lambda_2^j \cdot v_j
for some set of vectors :math:`v_j` given by ``nodes``.
Does so via a modified Horner's method for each pair of values
in ``lambda1`` and ``lambda2``, rather than using the
de Casteljau algorithm.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
lambda1 (numpy.ndarray): Parameters along the curve (as a
1D array).
lambda2 (numpy.ndarray): Parameters along the curve (as a
1D array). Typically we have ``lambda1 + lambda2 == 1``.
Returns:
numpy.ndarray: The evaluated points as a two dimensional
NumPy array, with the columns corresponding to each pair of parameter
values and the rows to the dimension.
"""
# NOTE: We assume but don't check that lambda2 has the same shape.
num_vals, = lambda1.shape
dimension, num_nodes = nodes.shape
degree = num_nodes - 1
# Resize as row vectors for broadcast multiplying with
# columns of ``nodes``.
lambda1 = lambda1[np.newaxis, :]
lambda2 = lambda2[np.newaxis, :]
result = np.zeros((dimension, num_vals), order="F")
result += lambda1 * nodes[:, [0]]
binom_val = 1.0
lambda2_pow = np.ones((1, num_vals), order="F")
for index in six.moves.xrange(1, degree):
lambda2_pow *= lambda2
binom_val = (binom_val * (degree - index + 1)) / index
result += binom_val * lambda2_pow * nodes[:, [index]]
result *= lambda1
result += lambda2 * lambda2_pow * nodes[:, [degree]]
return result | [
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.. note::
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nodes (numpy.ndarray): The nodes defining a curve.
lambda1 (numpy.ndarray): Parameters along the curve (as a
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lambda2 (numpy.ndarray): Parameters along the curve (as a
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numpy.ndarray: The evaluated points as a two dimensional
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dhermes/bezier | src/bezier/_curve_helpers.py | _compute_length | def _compute_length(nodes):
r"""Approximately compute the length of a curve.
.. _QUADPACK: https://en.wikipedia.org/wiki/QUADPACK
If ``degree`` is :math:`n`, then the Hodograph curve
:math:`B'(s)` is degree :math:`d = n - 1`. Using this curve, we
approximate the integral:
.. math::
\int_{B\left(\left[0, 1\right]\right)} 1 \, d\mathbf{x} =
\int_0^1 \left\lVert B'(s) \right\rVert_2 \, ds
using `QUADPACK`_ (via SciPy).
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
Returns:
float: The length of the curve.
Raises:
OSError: If SciPy is not installed.
"""
_, num_nodes = np.shape(nodes)
# NOTE: We somewhat replicate code in ``evaluate_hodograph()``
# here. This is so we don't re-compute the nodes for the first
# derivative every time it is evaluated.
first_deriv = (num_nodes - 1) * (nodes[:, 1:] - nodes[:, :-1])
if num_nodes == 2:
# NOTE: We convert to 1D to make sure NumPy uses vector norm.
return np.linalg.norm(first_deriv[:, 0], ord=2)
if _scipy_int is None:
raise OSError("This function requires SciPy for quadrature.")
size_func = functools.partial(vec_size, first_deriv)
length, _ = _scipy_int.quad(size_func, 0.0, 1.0)
return length | python | def _compute_length(nodes):
r"""Approximately compute the length of a curve.
.. _QUADPACK: https://en.wikipedia.org/wiki/QUADPACK
If ``degree`` is :math:`n`, then the Hodograph curve
:math:`B'(s)` is degree :math:`d = n - 1`. Using this curve, we
approximate the integral:
.. math::
\int_{B\left(\left[0, 1\right]\right)} 1 \, d\mathbf{x} =
\int_0^1 \left\lVert B'(s) \right\rVert_2 \, ds
using `QUADPACK`_ (via SciPy).
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
Returns:
float: The length of the curve.
Raises:
OSError: If SciPy is not installed.
"""
_, num_nodes = np.shape(nodes)
# NOTE: We somewhat replicate code in ``evaluate_hodograph()``
# here. This is so we don't re-compute the nodes for the first
# derivative every time it is evaluated.
first_deriv = (num_nodes - 1) * (nodes[:, 1:] - nodes[:, :-1])
if num_nodes == 2:
# NOTE: We convert to 1D to make sure NumPy uses vector norm.
return np.linalg.norm(first_deriv[:, 0], ord=2)
if _scipy_int is None:
raise OSError("This function requires SciPy for quadrature.")
size_func = functools.partial(vec_size, first_deriv)
length, _ = _scipy_int.quad(size_func, 0.0, 1.0)
return length | [
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dhermes/bezier | src/bezier/_curve_helpers.py | _elevate_nodes | def _elevate_nodes(nodes):
r"""Degree-elevate a B |eacute| zier curves.
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n + 1}` where
.. math::
\begin{align*}
w_0 &= v_0 \\
w_j &= \frac{j}{n + 1} v_{j - 1} + \frac{n + 1 - j}{n + 1} v_j \\
w_{n + 1} &= v_n
\end{align*}
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
Returns:
numpy.ndarray: The nodes of the degree-elevated curve.
"""
dimension, num_nodes = np.shape(nodes)
new_nodes = np.empty((dimension, num_nodes + 1), order="F")
multipliers = np.arange(1, num_nodes, dtype=_FLOAT64)[np.newaxis, :]
denominator = float(num_nodes)
new_nodes[:, 1:-1] = (
multipliers * nodes[:, :-1]
+ (denominator - multipliers) * nodes[:, 1:]
)
# Hold off on division until the end, to (attempt to) avoid round-off.
new_nodes /= denominator
# After setting the internal nodes (which require division), set the
# boundary nodes.
new_nodes[:, 0] = nodes[:, 0]
new_nodes[:, -1] = nodes[:, -1]
return new_nodes | python | def _elevate_nodes(nodes):
r"""Degree-elevate a B |eacute| zier curves.
Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n + 1}` where
.. math::
\begin{align*}
w_0 &= v_0 \\
w_j &= \frac{j}{n + 1} v_{j - 1} + \frac{n + 1 - j}{n + 1} v_j \\
w_{n + 1} &= v_n
\end{align*}
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
Returns:
numpy.ndarray: The nodes of the degree-elevated curve.
"""
dimension, num_nodes = np.shape(nodes)
new_nodes = np.empty((dimension, num_nodes + 1), order="F")
multipliers = np.arange(1, num_nodes, dtype=_FLOAT64)[np.newaxis, :]
denominator = float(num_nodes)
new_nodes[:, 1:-1] = (
multipliers * nodes[:, :-1]
+ (denominator - multipliers) * nodes[:, 1:]
)
# Hold off on division until the end, to (attempt to) avoid round-off.
new_nodes /= denominator
# After setting the internal nodes (which require division), set the
# boundary nodes.
new_nodes[:, 0] = nodes[:, 0]
new_nodes[:, -1] = nodes[:, -1]
return new_nodes | [
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Does this by converting the current nodes :math:`v_0, \ldots, v_n`
to new nodes :math:`w_0, \ldots, w_{n + 1}` where
.. math::
\begin{align*}
w_0 &= v_0 \\
w_j &= \frac{j}{n + 1} v_{j - 1} + \frac{n + 1 - j}{n + 1} v_j \\
w_{n + 1} &= v_n
\end{align*}
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a curve.
Returns:
numpy.ndarray: The nodes of the degree-elevated curve. | [
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] | 4f941f82637a8e70a5b159a9203132192e23406b | https://github.com/dhermes/bezier/blob/4f941f82637a8e70a5b159a9203132192e23406b/src/bezier/_curve_helpers.py#L346-L385 | train | 54,221 |
dhermes/bezier | src/bezier/_curve_helpers.py | de_casteljau_one_round | def de_casteljau_one_round(nodes, lambda1, lambda2):
"""Perform one round of de Casteljau's algorithm.
.. note::
This is a helper for :func:`_specialize_curve`. It does not have a
Fortran speedup because it is **only** used by a function which has
a Fortran speedup.
The weights are assumed to sum to one.
Args:
nodes (numpy.ndarray): Control points for a curve.
lambda1 (float): First barycentric weight on interval.
lambda2 (float): Second barycentric weight on interval.
Returns:
numpy.ndarray: The nodes for a "blended" curve one degree
lower.
"""
return np.asfortranarray(lambda1 * nodes[:, :-1] + lambda2 * nodes[:, 1:]) | python | def de_casteljau_one_round(nodes, lambda1, lambda2):
"""Perform one round of de Casteljau's algorithm.
.. note::
This is a helper for :func:`_specialize_curve`. It does not have a
Fortran speedup because it is **only** used by a function which has
a Fortran speedup.
The weights are assumed to sum to one.
Args:
nodes (numpy.ndarray): Control points for a curve.
lambda1 (float): First barycentric weight on interval.
lambda2 (float): Second barycentric weight on interval.
Returns:
numpy.ndarray: The nodes for a "blended" curve one degree
lower.
"""
return np.asfortranarray(lambda1 * nodes[:, :-1] + lambda2 * nodes[:, 1:]) | [
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dhermes/bezier | src/bezier/_curve_helpers.py | _specialize_curve | def _specialize_curve(nodes, start, end):
"""Specialize a curve to a re-parameterization
.. note::
This assumes the curve is degree 1 or greater but doesn't check.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): Control points for a curve.
start (float): The start point of the interval we are specializing to.
end (float): The end point of the interval we are specializing to.
Returns:
numpy.ndarray: The control points for the specialized curve.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
_, num_nodes = np.shape(nodes)
# Uses start-->0, end-->1 to represent the specialization used.
weights = ((1.0 - start, start), (1.0 - end, end))
partial_vals = {
(0,): de_casteljau_one_round(nodes, *weights[0]),
(1,): de_casteljau_one_round(nodes, *weights[1]),
}
for _ in six.moves.xrange(num_nodes - 2, 0, -1):
new_partial = {}
for key, sub_nodes in six.iteritems(partial_vals):
# Our keys are ascending so we increment from the last value.
for next_id in six.moves.xrange(key[-1], 1 + 1):
new_key = key + (next_id,)
new_partial[new_key] = de_casteljau_one_round(
sub_nodes, *weights[next_id]
)
partial_vals = new_partial
result = np.empty(nodes.shape, order="F")
for index in six.moves.xrange(num_nodes):
key = (0,) * (num_nodes - index - 1) + (1,) * index
result[:, [index]] = partial_vals[key]
return result | python | def _specialize_curve(nodes, start, end):
"""Specialize a curve to a re-parameterization
.. note::
This assumes the curve is degree 1 or greater but doesn't check.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): Control points for a curve.
start (float): The start point of the interval we are specializing to.
end (float): The end point of the interval we are specializing to.
Returns:
numpy.ndarray: The control points for the specialized curve.
"""
# NOTE: There is no corresponding "enable", but the disable only applies
# in this lexical scope.
# pylint: disable=too-many-locals
_, num_nodes = np.shape(nodes)
# Uses start-->0, end-->1 to represent the specialization used.
weights = ((1.0 - start, start), (1.0 - end, end))
partial_vals = {
(0,): de_casteljau_one_round(nodes, *weights[0]),
(1,): de_casteljau_one_round(nodes, *weights[1]),
}
for _ in six.moves.xrange(num_nodes - 2, 0, -1):
new_partial = {}
for key, sub_nodes in six.iteritems(partial_vals):
# Our keys are ascending so we increment from the last value.
for next_id in six.moves.xrange(key[-1], 1 + 1):
new_key = key + (next_id,)
new_partial[new_key] = de_casteljau_one_round(
sub_nodes, *weights[next_id]
)
partial_vals = new_partial
result = np.empty(nodes.shape, order="F")
for index in six.moves.xrange(num_nodes):
key = (0,) * (num_nodes - index - 1) + (1,) * index
result[:, [index]] = partial_vals[key]
return result | [
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dhermes/bezier | src/bezier/_curve_helpers.py | _locate_point | def _locate_point(nodes, point):
r"""Locate a point on a curve.
Does so by recursively subdividing the curve and rejecting
sub-curves with bounding boxes that don't contain the point.
After the sub-curves are sufficiently small, uses Newton's
method to zoom in on the parameter value.
.. note::
This assumes, but does not check, that ``point`` is ``D x 1``,
where ``D`` is the dimension that ``curve`` is in.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a B |eacute| zier curve.
point (numpy.ndarray): The point to locate.
Returns:
Optional[float]: The parameter value (:math:`s`) corresponding
to ``point`` or :data:`None` if the point is not on the ``curve``.
Raises:
ValueError: If the standard deviation of the remaining start / end
parameters among the subdivided intervals exceeds a given
threshold (e.g. :math:`2^{-20}`).
"""
candidates = [(0.0, 1.0, nodes)]
for _ in six.moves.xrange(_MAX_LOCATE_SUBDIVISIONS + 1):
next_candidates = []
for start, end, candidate in candidates:
if _helpers.contains_nd(candidate, point.ravel(order="F")):
midpoint = 0.5 * (start + end)
left, right = subdivide_nodes(candidate)
next_candidates.extend(
((start, midpoint, left), (midpoint, end, right))
)
candidates = next_candidates
if not candidates:
return None
params = [(start, end) for start, end, _ in candidates]
if np.std(params) > _LOCATE_STD_CAP:
raise ValueError("Parameters not close enough to one another", params)
s_approx = np.mean(params)
s_approx = newton_refine(nodes, point, s_approx)
# NOTE: Since ``np.mean(params)`` must be in ``[0, 1]`` it's
# "safe" to push the Newton-refined value back into the unit
# interval.
if s_approx < 0.0:
return 0.0
elif s_approx > 1.0:
return 1.0
else:
return s_approx | python | def _locate_point(nodes, point):
r"""Locate a point on a curve.
Does so by recursively subdividing the curve and rejecting
sub-curves with bounding boxes that don't contain the point.
After the sub-curves are sufficiently small, uses Newton's
method to zoom in on the parameter value.
.. note::
This assumes, but does not check, that ``point`` is ``D x 1``,
where ``D`` is the dimension that ``curve`` is in.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes defining a B |eacute| zier curve.
point (numpy.ndarray): The point to locate.
Returns:
Optional[float]: The parameter value (:math:`s`) corresponding
to ``point`` or :data:`None` if the point is not on the ``curve``.
Raises:
ValueError: If the standard deviation of the remaining start / end
parameters among the subdivided intervals exceeds a given
threshold (e.g. :math:`2^{-20}`).
"""
candidates = [(0.0, 1.0, nodes)]
for _ in six.moves.xrange(_MAX_LOCATE_SUBDIVISIONS + 1):
next_candidates = []
for start, end, candidate in candidates:
if _helpers.contains_nd(candidate, point.ravel(order="F")):
midpoint = 0.5 * (start + end)
left, right = subdivide_nodes(candidate)
next_candidates.extend(
((start, midpoint, left), (midpoint, end, right))
)
candidates = next_candidates
if not candidates:
return None
params = [(start, end) for start, end, _ in candidates]
if np.std(params) > _LOCATE_STD_CAP:
raise ValueError("Parameters not close enough to one another", params)
s_approx = np.mean(params)
s_approx = newton_refine(nodes, point, s_approx)
# NOTE: Since ``np.mean(params)`` must be in ``[0, 1]`` it's
# "safe" to push the Newton-refined value back into the unit
# interval.
if s_approx < 0.0:
return 0.0
elif s_approx > 1.0:
return 1.0
else:
return s_approx | [
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dhermes/bezier | src/bezier/_curve_helpers.py | _reduce_pseudo_inverse | def _reduce_pseudo_inverse(nodes):
"""Performs degree-reduction for a B |eacute| zier curve.
Does so by using the pseudo-inverse of the degree elevation
operator (which is overdetermined).
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes in the curve.
Returns:
numpy.ndarray: The reduced nodes.
Raises:
.UnsupportedDegree: If the degree is not 1, 2, 3 or 4.
"""
_, num_nodes = np.shape(nodes)
if num_nodes == 2:
reduction = _REDUCTION0
denom = _REDUCTION_DENOM0
elif num_nodes == 3:
reduction = _REDUCTION1
denom = _REDUCTION_DENOM1
elif num_nodes == 4:
reduction = _REDUCTION2
denom = _REDUCTION_DENOM2
elif num_nodes == 5:
reduction = _REDUCTION3
denom = _REDUCTION_DENOM3
else:
raise _helpers.UnsupportedDegree(num_nodes - 1, supported=(1, 2, 3, 4))
result = _helpers.matrix_product(nodes, reduction)
result /= denom
return result | python | def _reduce_pseudo_inverse(nodes):
"""Performs degree-reduction for a B |eacute| zier curve.
Does so by using the pseudo-inverse of the degree elevation
operator (which is overdetermined).
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes in the curve.
Returns:
numpy.ndarray: The reduced nodes.
Raises:
.UnsupportedDegree: If the degree is not 1, 2, 3 or 4.
"""
_, num_nodes = np.shape(nodes)
if num_nodes == 2:
reduction = _REDUCTION0
denom = _REDUCTION_DENOM0
elif num_nodes == 3:
reduction = _REDUCTION1
denom = _REDUCTION_DENOM1
elif num_nodes == 4:
reduction = _REDUCTION2
denom = _REDUCTION_DENOM2
elif num_nodes == 5:
reduction = _REDUCTION3
denom = _REDUCTION_DENOM3
else:
raise _helpers.UnsupportedDegree(num_nodes - 1, supported=(1, 2, 3, 4))
result = _helpers.matrix_product(nodes, reduction)
result /= denom
return result | [
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dhermes/bezier | src/bezier/_curve_helpers.py | projection_error | def projection_error(nodes, projected):
"""Compute the error between ``nodes`` and the projected nodes.
.. note::
This is a helper for :func:`maybe_reduce`, which is in turn a helper
for :func:`_full_reduce`. Hence there is no corresponding Fortran
speedup.
For now, just compute the relative error in the Frobenius norm. But,
we may wish to consider the error per row / point instead.
Args:
nodes (numpy.ndarray): Nodes in a curve.
projected (numpy.ndarray): The ``nodes`` projected into the
space of degree-elevated nodes.
Returns:
float: The relative error.
"""
relative_err = np.linalg.norm(nodes - projected, ord="fro")
if relative_err != 0.0:
relative_err /= np.linalg.norm(nodes, ord="fro")
return relative_err | python | def projection_error(nodes, projected):
"""Compute the error between ``nodes`` and the projected nodes.
.. note::
This is a helper for :func:`maybe_reduce`, which is in turn a helper
for :func:`_full_reduce`. Hence there is no corresponding Fortran
speedup.
For now, just compute the relative error in the Frobenius norm. But,
we may wish to consider the error per row / point instead.
Args:
nodes (numpy.ndarray): Nodes in a curve.
projected (numpy.ndarray): The ``nodes`` projected into the
space of degree-elevated nodes.
Returns:
float: The relative error.
"""
relative_err = np.linalg.norm(nodes - projected, ord="fro")
if relative_err != 0.0:
relative_err /= np.linalg.norm(nodes, ord="fro")
return relative_err | [
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dhermes/bezier | src/bezier/_curve_helpers.py | maybe_reduce | def maybe_reduce(nodes):
r"""Reduce nodes in a curve if they are degree-elevated.
.. note::
This is a helper for :func:`_full_reduce`. Hence there is no
corresponding Fortran speedup.
We check if the nodes are degree-elevated by projecting onto the
space of degree-elevated curves of the same degree, then comparing
to the projection. We form the projection by taking the corresponding
(right) elevation matrix :math:`E` (from one degree lower) and forming
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Args:
nodes (numpy.ndarray): The nodes in the curve.
Returns:
Tuple[bool, numpy.ndarray]: Pair of values. The first indicates
if the ``nodes`` were reduced. The second is the resulting nodes,
either the reduced ones or the original passed in.
Raises:
.UnsupportedDegree: If the curve is degree 5 or higher.
"""
_, num_nodes = nodes.shape
if num_nodes < 2:
return False, nodes
elif num_nodes == 2:
projection = _PROJECTION0
denom = _PROJ_DENOM0
elif num_nodes == 3:
projection = _PROJECTION1
denom = _PROJ_DENOM1
elif num_nodes == 4:
projection = _PROJECTION2
denom = _PROJ_DENOM2
elif num_nodes == 5:
projection = _PROJECTION3
denom = _PROJ_DENOM3
else:
raise _helpers.UnsupportedDegree(
num_nodes - 1, supported=(0, 1, 2, 3, 4)
)
projected = _helpers.matrix_product(nodes, projection) / denom
relative_err = projection_error(nodes, projected)
if relative_err < _REDUCE_THRESHOLD:
return True, reduce_pseudo_inverse(nodes)
else:
return False, nodes | python | def maybe_reduce(nodes):
r"""Reduce nodes in a curve if they are degree-elevated.
.. note::
This is a helper for :func:`_full_reduce`. Hence there is no
corresponding Fortran speedup.
We check if the nodes are degree-elevated by projecting onto the
space of degree-elevated curves of the same degree, then comparing
to the projection. We form the projection by taking the corresponding
(right) elevation matrix :math:`E` (from one degree lower) and forming
:math:`E^T \left(E E^T\right)^{-1} E`.
Args:
nodes (numpy.ndarray): The nodes in the curve.
Returns:
Tuple[bool, numpy.ndarray]: Pair of values. The first indicates
if the ``nodes`` were reduced. The second is the resulting nodes,
either the reduced ones or the original passed in.
Raises:
.UnsupportedDegree: If the curve is degree 5 or higher.
"""
_, num_nodes = nodes.shape
if num_nodes < 2:
return False, nodes
elif num_nodes == 2:
projection = _PROJECTION0
denom = _PROJ_DENOM0
elif num_nodes == 3:
projection = _PROJECTION1
denom = _PROJ_DENOM1
elif num_nodes == 4:
projection = _PROJECTION2
denom = _PROJ_DENOM2
elif num_nodes == 5:
projection = _PROJECTION3
denom = _PROJ_DENOM3
else:
raise _helpers.UnsupportedDegree(
num_nodes - 1, supported=(0, 1, 2, 3, 4)
)
projected = _helpers.matrix_product(nodes, projection) / denom
relative_err = projection_error(nodes, projected)
if relative_err < _REDUCE_THRESHOLD:
return True, reduce_pseudo_inverse(nodes)
else:
return False, nodes | [
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We check if the nodes are degree-elevated by projecting onto the
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nodes (numpy.ndarray): The nodes in the curve.
Returns:
Tuple[bool, numpy.ndarray]: Pair of values. The first indicates
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Raises:
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dhermes/bezier | src/bezier/_curve_helpers.py | _full_reduce | def _full_reduce(nodes):
"""Apply degree reduction to ``nodes`` until it can no longer be reduced.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes in the curve.
Returns:
numpy.ndarray: The fully degree-reduced nodes.
"""
was_reduced, nodes = maybe_reduce(nodes)
while was_reduced:
was_reduced, nodes = maybe_reduce(nodes)
return nodes | python | def _full_reduce(nodes):
"""Apply degree reduction to ``nodes`` until it can no longer be reduced.
.. note::
There is also a Fortran implementation of this function, which
will be used if it can be built.
Args:
nodes (numpy.ndarray): The nodes in the curve.
Returns:
numpy.ndarray: The fully degree-reduced nodes.
"""
was_reduced, nodes = maybe_reduce(nodes)
while was_reduced:
was_reduced, nodes = maybe_reduce(nodes)
return nodes | [
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dhermes/bezier | scripts/rewrite_package_rst.py | get_desired | def get_desired():
"""Populate ``DESIRED_TEMPLATE`` with public members.
If there are no members, does nothing.
Returns:
str: The "desired" contents of ``bezier.rst``.
"""
public_members = get_public_members()
if public_members:
members = "\n :members: {}".format(", ".join(public_members))
else:
members = ""
return DESIRED_TEMPLATE.format(members=members) | python | def get_desired():
"""Populate ``DESIRED_TEMPLATE`` with public members.
If there are no members, does nothing.
Returns:
str: The "desired" contents of ``bezier.rst``.
"""
public_members = get_public_members()
if public_members:
members = "\n :members: {}".format(", ".join(public_members))
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members = ""
return DESIRED_TEMPLATE.format(members=members) | [
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dhermes/bezier | scripts/rewrite_package_rst.py | main | def main():
"""Main entry point to replace autogenerated contents.
Raises:
ValueError: If the file doesn't contain the expected or
desired contents.
"""
with open(FILENAME, "r") as file_obj:
contents = file_obj.read()
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if contents == EXPECTED:
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elif contents != desired:
raise ValueError("Unexpected contents", contents, "Expected", EXPECTED) | python | def main():
"""Main entry point to replace autogenerated contents.
Raises:
ValueError: If the file doesn't contain the expected or
desired contents.
"""
with open(FILENAME, "r") as file_obj:
contents = file_obj.read()
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if contents == EXPECTED:
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file_obj.write(desired)
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dhermes/bezier | setup_helpers_windows.py | run_cleanup | def run_cleanup(build_ext_cmd):
"""Cleanup after ``BuildFortranThenExt.run``.
For in-place builds, moves the built shared library into the source
directory.
"""
if not build_ext_cmd.inplace:
return
bezier_dir = os.path.join("src", "bezier")
shutil.move(os.path.join(build_ext_cmd.build_lib, LIB_DIR), bezier_dir)
shutil.move(os.path.join(build_ext_cmd.build_lib, DLL_DIR), bezier_dir) | python | def run_cleanup(build_ext_cmd):
"""Cleanup after ``BuildFortranThenExt.run``.
For in-place builds, moves the built shared library into the source
directory.
"""
if not build_ext_cmd.inplace:
return
bezier_dir = os.path.join("src", "bezier")
shutil.move(os.path.join(build_ext_cmd.build_lib, LIB_DIR), bezier_dir)
shutil.move(os.path.join(build_ext_cmd.build_lib, DLL_DIR), bezier_dir) | [
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dhermes/bezier | noxfile.py | clean | def clean(session):
"""Clean up build files.
Cleans up all artifacts that might get created during
other ``nox`` sessions.
There is no need for the session to create a ``virtualenv``
here (we are just pretending to be ``make``).
"""
clean_dirs = (
get_path(".cache"),
get_path(".coverage"),
get_path(".pytest_cache"),
get_path("__pycache__"),
get_path("build"),
get_path("dist"),
get_path("docs", "__pycache__"),
get_path("docs", "build"),
get_path("scripts", "macos", "__pycache__"),
get_path("scripts", "macos", "dist_wheels"),
get_path("scripts", "macos", "fixed_wheels"),
get_path("src", "bezier.egg-info"),
get_path("src", "bezier", "__pycache__"),
get_path("src", "bezier", "extra-dll"),
get_path("src", "bezier", "lib"),
get_path("tests", "__pycache__"),
get_path("tests", "functional", "__pycache__"),
get_path("tests", "unit", "__pycache__"),
get_path("wheelhouse"),
)
clean_globs = (
get_path(".coverage"),
get_path("*.mod"),
get_path("*.pyc"),
get_path("src", "bezier", "*.pyc"),
get_path("src", "bezier", "*.pyd"),
get_path("src", "bezier", "*.so"),
get_path("src", "bezier", "quadpack", "*.o"),
get_path("src", "bezier", "*.o"),
get_path("tests", "*.pyc"),
get_path("tests", "functional", "*.pyc"),
get_path("tests", "unit", "*.pyc"),
)
for dir_path in clean_dirs:
session.run(shutil.rmtree, dir_path, ignore_errors=True)
for glob_path in clean_globs:
for filename in glob.glob(glob_path):
session.run(os.remove, filename) | python | def clean(session):
"""Clean up build files.
Cleans up all artifacts that might get created during
other ``nox`` sessions.
There is no need for the session to create a ``virtualenv``
here (we are just pretending to be ``make``).
"""
clean_dirs = (
get_path(".cache"),
get_path(".coverage"),
get_path(".pytest_cache"),
get_path("__pycache__"),
get_path("build"),
get_path("dist"),
get_path("docs", "__pycache__"),
get_path("docs", "build"),
get_path("scripts", "macos", "__pycache__"),
get_path("scripts", "macos", "dist_wheels"),
get_path("scripts", "macos", "fixed_wheels"),
get_path("src", "bezier.egg-info"),
get_path("src", "bezier", "__pycache__"),
get_path("src", "bezier", "extra-dll"),
get_path("src", "bezier", "lib"),
get_path("tests", "__pycache__"),
get_path("tests", "functional", "__pycache__"),
get_path("tests", "unit", "__pycache__"),
get_path("wheelhouse"),
)
clean_globs = (
get_path(".coverage"),
get_path("*.mod"),
get_path("*.pyc"),
get_path("src", "bezier", "*.pyc"),
get_path("src", "bezier", "*.pyd"),
get_path("src", "bezier", "*.so"),
get_path("src", "bezier", "quadpack", "*.o"),
get_path("src", "bezier", "*.o"),
get_path("tests", "*.pyc"),
get_path("tests", "functional", "*.pyc"),
get_path("tests", "unit", "*.pyc"),
)
for dir_path in clean_dirs:
session.run(shutil.rmtree, dir_path, ignore_errors=True)
for glob_path in clean_globs:
for filename in glob.glob(glob_path):
session.run(os.remove, filename) | [
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allianceauth/allianceauth | allianceauth/hooks.py | register | def register(name, fn=None):
"""
Decorator to register a function as a hook
Register hook for ``hook_name``. Can be used as a decorator::
@register('hook_name')
def my_hook(...):
pass
or as a function call::
def my_hook(...):
pass
register('hook_name', my_hook)
:param name: str Name of the hook/callback to register it as
:param fn: function to register in the hook/callback
:return: function Decorator if applied as a decorator
"""
def _hook_add(func):
if name not in _hooks:
logger.debug("Creating new hook %s" % name)
_hooks[name] = []
logger.debug('Registering hook %s for function %s' % (name, fn))
_hooks[name].append(func)
if fn is None:
# Behave like a decorator
def decorator(func):
_hook_add(func)
return func
return decorator
else:
# Behave like a function, just register hook
_hook_add(fn) | python | def register(name, fn=None):
"""
Decorator to register a function as a hook
Register hook for ``hook_name``. Can be used as a decorator::
@register('hook_name')
def my_hook(...):
pass
or as a function call::
def my_hook(...):
pass
register('hook_name', my_hook)
:param name: str Name of the hook/callback to register it as
:param fn: function to register in the hook/callback
:return: function Decorator if applied as a decorator
"""
def _hook_add(func):
if name not in _hooks:
logger.debug("Creating new hook %s" % name)
_hooks[name] = []
logger.debug('Registering hook %s for function %s' % (name, fn))
_hooks[name].append(func)
if fn is None:
# Behave like a decorator
def decorator(func):
_hook_add(func)
return func
return decorator
else:
# Behave like a function, just register hook
_hook_add(fn) | [
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allianceauth/allianceauth | allianceauth/services/modules/seat/manager.py | SeatManager.exec_request | def exec_request(endpoint, func, raise_for_status=False, **kwargs):
""" Send an https api request """
try:
endpoint = '{0}/api/v1/{1}'.format(settings.SEAT_URL, endpoint)
headers = {'X-Token': settings.SEAT_XTOKEN, 'Accept': 'application/json'}
logger.debug(headers)
logger.debug(endpoint)
ret = getattr(requests, func)(endpoint, headers=headers, data=kwargs)
ret.raise_for_status()
return ret.json()
except requests.HTTPError as e:
if raise_for_status:
raise e
logger.exception("Error encountered while performing API request to SeAT with url {}".format(endpoint))
return {} | python | def exec_request(endpoint, func, raise_for_status=False, **kwargs):
""" Send an https api request """
try:
endpoint = '{0}/api/v1/{1}'.format(settings.SEAT_URL, endpoint)
headers = {'X-Token': settings.SEAT_XTOKEN, 'Accept': 'application/json'}
logger.debug(headers)
logger.debug(endpoint)
ret = getattr(requests, func)(endpoint, headers=headers, data=kwargs)
ret.raise_for_status()
return ret.json()
except requests.HTTPError as e:
if raise_for_status:
raise e
logger.exception("Error encountered while performing API request to SeAT with url {}".format(endpoint))
return {} | [
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allianceauth/allianceauth | allianceauth/services/modules/seat/manager.py | SeatManager.add_user | def add_user(cls, username, email):
""" Add user to service """
sanitized = str(cls.__sanitize_username(username))
logger.debug("Adding user to SeAT with username %s" % sanitized)
password = cls.__generate_random_pass()
ret = cls.exec_request('user', 'post', username=sanitized, email=str(email), password=password)
logger.debug(ret)
if cls._response_ok(ret):
logger.info("Added SeAT user with username %s" % sanitized)
return sanitized, password
logger.info("Failed to add SeAT user with username %s" % sanitized)
return None, None | python | def add_user(cls, username, email):
""" Add user to service """
sanitized = str(cls.__sanitize_username(username))
logger.debug("Adding user to SeAT with username %s" % sanitized)
password = cls.__generate_random_pass()
ret = cls.exec_request('user', 'post', username=sanitized, email=str(email), password=password)
logger.debug(ret)
if cls._response_ok(ret):
logger.info("Added SeAT user with username %s" % sanitized)
return sanitized, password
logger.info("Failed to add SeAT user with username %s" % sanitized)
return None, None | [
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allianceauth/allianceauth | allianceauth/services/modules/seat/manager.py | SeatManager._check_email_changed | def _check_email_changed(cls, username, email):
"""Compares email to one set on SeAT"""
ret = cls.exec_request('user/{}'.format(username), 'get', raise_for_status=True)
return ret['email'] != email | python | def _check_email_changed(cls, username, email):
"""Compares email to one set on SeAT"""
ret = cls.exec_request('user/{}'.format(username), 'get', raise_for_status=True)
return ret['email'] != email | [
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allianceauth/allianceauth | allianceauth/services/modules/seat/manager.py | SeatManager.update_user | def update_user(cls, username, email, password):
""" Edit user info """
if cls._check_email_changed(username, email):
# if we try to set the email to whatever it is already on SeAT, we get a HTTP422 error
logger.debug("Updating SeAT username %s with email %s and password" % (username, email))
ret = cls.exec_request('user/{}'.format(username), 'put', email=email)
logger.debug(ret)
if not cls._response_ok(ret):
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return username | python | def update_user(cls, username, email, password):
""" Edit user info """
if cls._check_email_changed(username, email):
# if we try to set the email to whatever it is already on SeAT, we get a HTTP422 error
logger.debug("Updating SeAT username %s with email %s and password" % (username, email))
ret = cls.exec_request('user/{}'.format(username), 'put', email=email)
logger.debug(ret)
if not cls._response_ok(ret):
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allianceauth/allianceauth | allianceauth/timerboard/views.py | AddUpdateMixin.get_form_kwargs | def get_form_kwargs(self):
"""
Inject the request user into the kwargs passed to the form
"""
kwargs = super(AddUpdateMixin, self).get_form_kwargs()
kwargs.update({'user': self.request.user})
return kwargs | python | def get_form_kwargs(self):
"""
Inject the request user into the kwargs passed to the form
"""
kwargs = super(AddUpdateMixin, self).get_form_kwargs()
kwargs.update({'user': self.request.user})
return kwargs | [
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allianceauth/allianceauth | allianceauth/groupmanagement/models.py | create_auth_group | def create_auth_group(sender, instance, created, **kwargs):
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"""
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Creates the AuthGroup model when a group is created
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allianceauth/allianceauth | allianceauth/services/modules/teamspeak3/util/ts3.py | TS3Proto.construct_command | def construct_command(self, command, keys=None, opts=None):
"""
Constructs a TS3 formatted command string
Keys can have a single nested list to construct a nested parameter
@param command: Command list
@type command: string
@param keys: Key/Value pairs
@type keys: dict
@param opts: Options
@type opts: list
"""
cstr = [command]
# Add the keys and values, escape as needed
if keys:
for key in keys:
if isinstance(keys[key], list):
ncstr = []
for nest in keys[key]:
ncstr.append("%s=%s" % (key, self._escape_str(nest)))
cstr.append("|".join(ncstr))
else:
cstr.append("%s=%s" % (key, self._escape_str(keys[key])))
# Add in options
if opts:
for opt in opts:
cstr.append("-%s" % opt)
return " ".join(cstr) | python | def construct_command(self, command, keys=None, opts=None):
"""
Constructs a TS3 formatted command string
Keys can have a single nested list to construct a nested parameter
@param command: Command list
@type command: string
@param keys: Key/Value pairs
@type keys: dict
@param opts: Options
@type opts: list
"""
cstr = [command]
# Add the keys and values, escape as needed
if keys:
for key in keys:
if isinstance(keys[key], list):
ncstr = []
for nest in keys[key]:
ncstr.append("%s=%s" % (key, self._escape_str(nest)))
cstr.append("|".join(ncstr))
else:
cstr.append("%s=%s" % (key, self._escape_str(keys[key])))
# Add in options
if opts:
for opt in opts:
cstr.append("-%s" % opt)
return " ".join(cstr) | [
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allianceauth/allianceauth | allianceauth/services/modules/teamspeak3/util/ts3.py | TS3Proto._escape_str | def _escape_str(value):
"""
Escape a value into a TS3 compatible string
@param value: Value
@type value: string/int
"""
if isinstance(value, int):
return "%d" % value
value = value.replace("\\", r'\\')
for i, j in ts3_escape.items():
value = value.replace(i, j)
return value | python | def _escape_str(value):
"""
Escape a value into a TS3 compatible string
@param value: Value
@type value: string/int
"""
if isinstance(value, int):
return "%d" % value
value = value.replace("\\", r'\\')
for i, j in ts3_escape.items():
value = value.replace(i, j)
return value | [
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allianceauth/allianceauth | allianceauth/services/modules/teamspeak3/util/ts3.py | TS3Proto._unescape_str | def _unescape_str(value):
"""
Unescape a TS3 compatible string into a normal string
@param value: Value
@type value: string/int
"""
if isinstance(value, int):
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value = value.replace(r"\\", "\\")
for i, j in ts3_escape.items():
value = value.replace(j, i)
return value | python | def _unescape_str(value):
"""
Unescape a TS3 compatible string into a normal string
@param value: Value
@type value: string/int
"""
if isinstance(value, int):
return "%d" % value
value = value.replace(r"\\", "\\")
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allianceauth/allianceauth | allianceauth/services/modules/teamspeak3/util/ts3.py | TS3Server.login | def login(self, username, password):
"""
Login to the TS3 Server
@param username: Username
@type username: str
@param password: Password
@type password: str
"""
d = self.send_command('login', keys={'client_login_name': username, 'client_login_password': password})
if d == 0:
self._log.info('Login Successful')
return True
return False | python | def login(self, username, password):
"""
Login to the TS3 Server
@param username: Username
@type username: str
@param password: Password
@type password: str
"""
d = self.send_command('login', keys={'client_login_name': username, 'client_login_password': password})
if d == 0:
self._log.info('Login Successful')
return True
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allianceauth/allianceauth | allianceauth/services/modules/teamspeak3/util/ts3.py | TS3Server.use | def use(self, id):
"""
Use a particular Virtual Server instance
@param id: Virtual Server ID
@type id: int
"""
if self._connected and id > 0:
self.send_command('use', keys={'sid': id}) | python | def use(self, id):
"""
Use a particular Virtual Server instance
@param id: Virtual Server ID
@type id: int
"""
if self._connected and id > 0:
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allianceauth/allianceauth | allianceauth/eveonline/autogroups/signals.py | pre_save_config | def pre_save_config(sender, instance, *args, **kwargs):
"""
Checks if enable was toggled on group config and
deletes groups if necessary.
"""
logger.debug("Received pre_save from {}".format(instance))
if not instance.pk:
# new model being created
return
try:
old_instance = AutogroupsConfig.objects.get(pk=instance.pk)
# Check if enable was toggled, delete groups?
if old_instance.alliance_groups is True and instance.alliance_groups is False:
instance.delete_alliance_managed_groups()
if old_instance.corp_groups is True and instance.corp_groups is False:
instance.delete_corp_managed_groups()
except AutogroupsConfig.DoesNotExist:
pass | python | def pre_save_config(sender, instance, *args, **kwargs):
"""
Checks if enable was toggled on group config and
deletes groups if necessary.
"""
logger.debug("Received pre_save from {}".format(instance))
if not instance.pk:
# new model being created
return
try:
old_instance = AutogroupsConfig.objects.get(pk=instance.pk)
# Check if enable was toggled, delete groups?
if old_instance.alliance_groups is True and instance.alliance_groups is False:
instance.delete_alliance_managed_groups()
if old_instance.corp_groups is True and instance.corp_groups is False:
instance.delete_corp_managed_groups()
except AutogroupsConfig.DoesNotExist:
pass | [
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allianceauth/allianceauth | allianceauth/eveonline/autogroups/signals.py | check_groups_on_profile_update | def check_groups_on_profile_update(sender, instance, created, *args, **kwargs):
"""
Trigger check when main character or state changes.
"""
AutogroupsConfig.objects.update_groups_for_user(instance.user) | python | def check_groups_on_profile_update(sender, instance, created, *args, **kwargs):
"""
Trigger check when main character or state changes.
"""
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allianceauth/allianceauth | allianceauth/eveonline/autogroups/signals.py | autogroups_states_changed | def autogroups_states_changed(sender, instance, action, reverse, model, pk_set, *args, **kwargs):
"""
Trigger group membership update when a state is added or removed from
an autogroup config.
"""
if action.startswith('post_'):
for pk in pk_set:
try:
state = State.objects.get(pk=pk)
instance.update_group_membership_for_state(state)
except State.DoesNotExist:
# Deleted States handled by the profile state change
pass | python | def autogroups_states_changed(sender, instance, action, reverse, model, pk_set, *args, **kwargs):
"""
Trigger group membership update when a state is added or removed from
an autogroup config.
"""
if action.startswith('post_'):
for pk in pk_set:
try:
state = State.objects.get(pk=pk)
instance.update_group_membership_for_state(state)
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# Deleted States handled by the profile state change
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allianceauth/allianceauth | allianceauth/srp/views.py | random_string | def random_string(string_length=10):
"""Returns a random string of length string_length."""
random = str(uuid.uuid4()) # Convert UUID format to a Python string.
random = random.upper() # Make all characters uppercase.
random = random.replace("-", "") # Remove the UUID '-'.
return random[0:string_length] | python | def random_string(string_length=10):
"""Returns a random string of length string_length."""
random = str(uuid.uuid4()) # Convert UUID format to a Python string.
random = random.upper() # Make all characters uppercase.
random = random.replace("-", "") # Remove the UUID '-'.
return random[0:string_length] | [
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tariqdaouda/pyGeno | pyGeno/Genome.py | getGenomeList | def getGenomeList() :
"""Return the names of all imported genomes"""
import rabaDB.filters as rfilt
f = rfilt.RabaQuery(Genome_Raba)
names = []
for g in f.iterRun() :
names.append(g.name)
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"""Return the names of all imported genomes"""
import rabaDB.filters as rfilt
f = rfilt.RabaQuery(Genome_Raba)
names = []
for g in f.iterRun() :
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tariqdaouda/pyGeno | pyGeno/Transcript.py | Transcript.iterCodons | def iterCodons(self) :
"""iterates through the codons"""
for i in range(len(self.cDNA)/3) :
yield self.getCodon(i) | python | def iterCodons(self) :
"""iterates through the codons"""
for i in range(len(self.cDNA)/3) :
yield self.getCodon(i) | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | removeDuplicates | def removeDuplicates(inFileName, outFileName) :
"""removes duplicated lines from a 'inFileName' CSV file, the results are witten in 'outFileName'"""
f = open(inFileName)
legend = f.readline()
data = ''
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h[legend] = 0
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for l in lines :
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h[l] = 0
data += l
f.flush()
f.close()
f = open(outFileName, 'w')
f.write(legend+data)
f.flush()
f.close() | python | def removeDuplicates(inFileName, outFileName) :
"""removes duplicated lines from a 'inFileName' CSV file, the results are witten in 'outFileName'"""
f = open(inFileName)
legend = f.readline()
data = ''
h = {}
h[legend] = 0
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f.flush()
f.close()
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f.write(legend+data)
f.flush()
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | catCSVs | def catCSVs(folder, ouputFileName, removeDups = False) :
"""Concatenates all csv in 'folder' and wites the results in 'ouputFileName'. My not work on non Unix systems"""
strCmd = r"""cat %s/*.csv > %s""" %(folder, ouputFileName)
os.system(strCmd)
if removeDups :
removeDuplicates(ouputFileName, ouputFileName) | python | def catCSVs(folder, ouputFileName, removeDups = False) :
"""Concatenates all csv in 'folder' and wites the results in 'ouputFileName'. My not work on non Unix systems"""
strCmd = r"""cat %s/*.csv > %s""" %(folder, ouputFileName)
os.system(strCmd)
if removeDups :
removeDuplicates(ouputFileName, ouputFileName) | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | joinCSVs | def joinCSVs(csvFilePaths, column, ouputFileName, separator = ',') :
"""csvFilePaths should be an iterable. Joins all CSVs according to the values in the column 'column'. Write the results in a new file 'ouputFileName' """
res = ''
legend = []
csvs = []
for f in csvFilePaths :
c = CSVFile()
c.parse(f)
csvs.append(c)
legend.append(separator.join(c.legend.keys()))
legend = separator.join(legend)
lines = []
for i in range(len(csvs[0])) :
val = csvs[0].get(i, column)
line = separator.join(csvs[0][i])
for c in csvs[1:] :
for j in range(len(c)) :
if val == c.get(j, column) :
line += separator + separator.join(c[j])
lines.append( line )
res = legend + '\n' + '\n'.join(lines)
f = open(ouputFileName, 'w')
f.write(res)
f.flush()
f.close()
return res | python | def joinCSVs(csvFilePaths, column, ouputFileName, separator = ',') :
"""csvFilePaths should be an iterable. Joins all CSVs according to the values in the column 'column'. Write the results in a new file 'ouputFileName' """
res = ''
legend = []
csvs = []
for f in csvFilePaths :
c = CSVFile()
c.parse(f)
csvs.append(c)
legend.append(separator.join(c.legend.keys()))
legend = separator.join(legend)
lines = []
for i in range(len(csvs[0])) :
val = csvs[0].get(i, column)
line = separator.join(csvs[0][i])
for c in csvs[1:] :
for j in range(len(c)) :
if val == c.get(j, column) :
line += separator + separator.join(c[j])
lines.append( line )
res = legend + '\n' + '\n'.join(lines)
f = open(ouputFileName, 'w')
f.write(res)
f.flush()
f.close()
return res | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.addField | def addField(self, field) :
"""add a filed to the legend"""
if field.lower() in self.legend :
raise ValueError("%s is already in the legend" % field.lower())
self.legend[field.lower()] = len(self.legend)
if len(self.strLegend) > 0 :
self.strLegend += self.separator + field
else :
self.strLegend += field | python | def addField(self, field) :
"""add a filed to the legend"""
if field.lower() in self.legend :
raise ValueError("%s is already in the legend" % field.lower())
self.legend[field.lower()] = len(self.legend)
if len(self.strLegend) > 0 :
self.strLegend += self.separator + field
else :
self.strLegend += field | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.parse | def parse(self, filePath, skipLines=0, separator = ',', stringSeparator = '"', lineSeparator = '\n') :
"""Loads a CSV file"""
self.filename = filePath
f = open(filePath)
if lineSeparator == '\n' :
lines = f.readlines()
else :
lines = f.read().split(lineSeparator)
f.flush()
f.close()
lines = lines[skipLines:]
self.lines = []
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for l in lines :
# print l
if len(l) != 0 and l[0] != "#" :
self.lines.append(l)
elif l[0] == "#" :
self.comments.append(l)
self.separator = separator
self.lineSeparator = lineSeparator
self.stringSeparator = stringSeparator
self.legend = collections.OrderedDict()
i = 0
for c in self.lines[0].lower().replace(stringSeparator, '').split(separator) :
legendElement = c.strip()
if legendElement not in self.legend :
self.legend[legendElement] = i
i+=1
self.strLegend = self.lines[0].replace('\r', '\n').replace('\n', '')
self.lines = self.lines[1:] | python | def parse(self, filePath, skipLines=0, separator = ',', stringSeparator = '"', lineSeparator = '\n') :
"""Loads a CSV file"""
self.filename = filePath
f = open(filePath)
if lineSeparator == '\n' :
lines = f.readlines()
else :
lines = f.read().split(lineSeparator)
f.flush()
f.close()
lines = lines[skipLines:]
self.lines = []
self.comments = []
for l in lines :
# print l
if len(l) != 0 and l[0] != "#" :
self.lines.append(l)
elif l[0] == "#" :
self.comments.append(l)
self.separator = separator
self.lineSeparator = lineSeparator
self.stringSeparator = stringSeparator
self.legend = collections.OrderedDict()
i = 0
for c in self.lines[0].lower().replace(stringSeparator, '').split(separator) :
legendElement = c.strip()
if legendElement not in self.legend :
self.legend[legendElement] = i
i+=1
self.strLegend = self.lines[0].replace('\r', '\n').replace('\n', '')
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.commitLine | def commitLine(self, line) :
"""Commits a line making it ready to be streamed to a file and saves the current buffer if needed. If no stream is active, raises a ValueError"""
if self.streamBuffer is None :
raise ValueError("Commit lines is only for when you are streaming to a file")
self.streamBuffer.append(line)
if len(self.streamBuffer) % self.writeRate == 0 :
for i in xrange(len(self.streamBuffer)) :
self.streamBuffer[i] = str(self.streamBuffer[i])
self.streamFile.write("%s\n" % ('\n'.join(self.streamBuffer)))
self.streamFile.flush()
self.streamBuffer = [] | python | def commitLine(self, line) :
"""Commits a line making it ready to be streamed to a file and saves the current buffer if needed. If no stream is active, raises a ValueError"""
if self.streamBuffer is None :
raise ValueError("Commit lines is only for when you are streaming to a file")
self.streamBuffer.append(line)
if len(self.streamBuffer) % self.writeRate == 0 :
for i in xrange(len(self.streamBuffer)) :
self.streamBuffer[i] = str(self.streamBuffer[i])
self.streamFile.write("%s\n" % ('\n'.join(self.streamBuffer)))
self.streamFile.flush()
self.streamBuffer = [] | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.closeStreamToFile | def closeStreamToFile(self) :
"""Appends the remaining commited lines and closes the stream. If no stream is active, raises a ValueError"""
if self.streamBuffer is None :
raise ValueError("Commit lines is only for when you are streaming to a file")
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self.streamFile.write('\n'.join(self.streamBuffer))
self.streamFile.close()
self.streamFile = None
self.writeRate = None
self.streamBuffer = None
self.keepInMemory = True | python | def closeStreamToFile(self) :
"""Appends the remaining commited lines and closes the stream. If no stream is active, raises a ValueError"""
if self.streamBuffer is None :
raise ValueError("Commit lines is only for when you are streaming to a file")
for i in xrange(len(self.streamBuffer)) :
self.streamBuffer[i] = str(self.streamBuffer[i])
self.streamFile.write('\n'.join(self.streamBuffer))
self.streamFile.close()
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self.writeRate = None
self.streamBuffer = None
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.newLine | def newLine(self) :
"""Appends an empty line at the end of the CSV and returns it"""
l = CSVEntry(self)
if self.keepInMemory :
self.lines.append(l)
return l | python | def newLine(self) :
"""Appends an empty line at the end of the CSV and returns it"""
l = CSVEntry(self)
if self.keepInMemory :
self.lines.append(l)
return l | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.insertLine | def insertLine(self, i) :
"""Inserts an empty line at position i and returns it"""
self.data.insert(i, CSVEntry(self))
return self.lines[i] | python | def insertLine(self, i) :
"""Inserts an empty line at position i and returns it"""
self.data.insert(i, CSVEntry(self))
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.save | def save(self, filePath) :
"""save the CSV to a file"""
self.filename = filePath
f = open(filePath, 'w')
f.write(self.toStr())
f.flush()
f.close() | python | def save(self, filePath) :
"""save the CSV to a file"""
self.filename = filePath
f = open(filePath, 'w')
f.write(self.toStr())
f.flush()
f.close() | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/CSVTools.py | CSVFile.toStr | def toStr(self) :
"""returns a string version of the CSV"""
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for l in self.lines :
s.append(l.toStr())
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tariqdaouda/pyGeno | pyGeno/pyGenoObjectBases.py | pyGenoRabaObjectWrapper.count | def count(self, objectType, *args, **coolArgs) :
"""Returns the number of elements satisfying the query"""
return self._makeLoadQuery(objectType, *args, **coolArgs).count() | python | def count(self, objectType, *args, **coolArgs) :
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tariqdaouda/pyGeno | pyGeno/pyGenoObjectBases.py | pyGenoRabaObjectWrapper.iterGet | def iterGet(self, objectType, *args, **coolArgs) :
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yield objectType(wrapped_object_and_bag = (e, self.bagKey))
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"""Same as get. But retuns the elements one by one, much more efficient for large outputs"""
for e in self._makeLoadQuery(objectType, *args, **coolArgs).iterRun() :
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tariqdaouda/pyGeno | pyGeno/importation/SNPs.py | deleteSNPs | def deleteSNPs(setName) :
"""deletes a set of polymorphisms"""
con = conf.db
try :
SMaster = SNPMaster(setName = setName)
con.beginTransaction()
SNPType = SMaster.SNPType
con.delete(SNPType, 'setName = ?', (setName,))
SMaster.delete()
con.endTransaction()
except KeyError :
raise KeyError("Can't delete the setName %s because i can't find it in SNPMaster, maybe there's not set by that name" % setName)
#~ printf("can't delete the setName %s because i can't find it in SNPMaster, maybe there's no set by that name" % setName)
return False
return True | python | def deleteSNPs(setName) :
"""deletes a set of polymorphisms"""
con = conf.db
try :
SMaster = SNPMaster(setName = setName)
con.beginTransaction()
SNPType = SMaster.SNPType
con.delete(SNPType, 'setName = ?', (setName,))
SMaster.delete()
con.endTransaction()
except KeyError :
raise KeyError("Can't delete the setName %s because i can't find it in SNPMaster, maybe there's not set by that name" % setName)
#~ printf("can't delete the setName %s because i can't find it in SNPMaster, maybe there's no set by that name" % setName)
return False
return True | [
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tariqdaouda/pyGeno | pyGeno/SNP.py | getSNPSetsList | def getSNPSetsList() :
"""Return the names of all imported snp sets"""
import rabaDB.filters as rfilt
f = rfilt.RabaQuery(SNPMaster)
names = []
for g in f.iterRun() :
names.append(g.setName)
return names | python | def getSNPSetsList() :
"""Return the names of all imported snp sets"""
import rabaDB.filters as rfilt
f = rfilt.RabaQuery(SNPMaster)
names = []
for g in f.iterRun() :
names.append(g.setName)
return names | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/FastaTools.py | FastaFile.parseFile | def parseFile(self, fil) :
"""Opens a file and parses it"""
f = open(fil)
self.parseStr(f.read())
f.close() | python | def parseFile(self, fil) :
"""Opens a file and parses it"""
f = open(fil)
self.parseStr(f.read())
f.close() | [
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tariqdaouda/pyGeno | pyGeno/tools/parsers/FastaTools.py | FastaFile.add | def add(self, header, data) :
"""appends a new entry to the file"""
if header[0] != '>' :
self.data.append(('>'+header, data))
else :
self.data.append((header, data)) | python | def add(self, header, data) :
"""appends a new entry to the file"""
if header[0] != '>' :
self.data.append(('>'+header, data))
else :
self.data.append((header, data)) | [
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tariqdaouda/pyGeno | pyGeno/tools/UsefulFunctions.py | findAll | def findAll(haystack, needle) :
"""returns a list of all occurances of needle in haystack"""
h = haystack
res = []
f = haystack.find(needle)
offset = 0
while (f >= 0) :
#print h, needle, f, offset
res.append(f+offset)
offset += f+len(needle)
h = h[f+len(needle):]
f = h.find(needle)
return res | python | def findAll(haystack, needle) :
"""returns a list of all occurances of needle in haystack"""
h = haystack
res = []
f = haystack.find(needle)
offset = 0
while (f >= 0) :
#print h, needle, f, offset
res.append(f+offset)
offset += f+len(needle)
h = h[f+len(needle):]
f = h.find(needle)
return res | [
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tariqdaouda/pyGeno | pyGeno/tools/UsefulFunctions.py | complementTab | def complementTab(seq=[]):
"""returns a list of complementary sequence without inversing it"""
complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'R': 'Y', 'Y': 'R', 'M': 'K', 'K': 'M',
'W': 'W', 'S': 'S', 'B': 'V', 'D': 'H', 'H': 'D', 'V': 'B', 'N': 'N', 'a': 't',
'c': 'g', 'g': 'c', 't': 'a', 'r': 'y', 'y': 'r', 'm': 'k', 'k': 'm', 'w': 'w',
's': 's', 'b': 'v', 'd': 'h', 'h': 'd', 'v': 'b', 'n': 'n'}
seq_tmp = []
for bps in seq:
if len(bps) == 0:
#Need manage '' for deletion
seq_tmp.append('')
elif len(bps) == 1:
seq_tmp.append(complement[bps])
else:
#Need manage 'ACT' for insertion
#The insertion need to be reverse complement (like seq)
seq_tmp.append(reverseComplement(bps))
#Doesn't work in the second for when bps==''
#seq = [complement[bp] if bp != '' else '' for bps in seq for bp in bps]
return seq_tmp | python | def complementTab(seq=[]):
"""returns a list of complementary sequence without inversing it"""
complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'R': 'Y', 'Y': 'R', 'M': 'K', 'K': 'M',
'W': 'W', 'S': 'S', 'B': 'V', 'D': 'H', 'H': 'D', 'V': 'B', 'N': 'N', 'a': 't',
'c': 'g', 'g': 'c', 't': 'a', 'r': 'y', 'y': 'r', 'm': 'k', 'k': 'm', 'w': 'w',
's': 's', 'b': 'v', 'd': 'h', 'h': 'd', 'v': 'b', 'n': 'n'}
seq_tmp = []
for bps in seq:
if len(bps) == 0:
#Need manage '' for deletion
seq_tmp.append('')
elif len(bps) == 1:
seq_tmp.append(complement[bps])
else:
#Need manage 'ACT' for insertion
#The insertion need to be reverse complement (like seq)
seq_tmp.append(reverseComplement(bps))
#Doesn't work in the second for when bps==''
#seq = [complement[bp] if bp != '' else '' for bps in seq for bp in bps]
return seq_tmp | [
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tariqdaouda/pyGeno | pyGeno/tools/UsefulFunctions.py | translateDNA_6Frames | def translateDNA_6Frames(sequence) :
"""returns 6 translation of sequence. One for each reading frame"""
trans = (
translateDNA(sequence, 'f1'),
translateDNA(sequence, 'f2'),
translateDNA(sequence, 'f3'),
translateDNA(sequence, 'r1'),
translateDNA(sequence, 'r2'),
translateDNA(sequence, 'r3'),
)
return trans | python | def translateDNA_6Frames(sequence) :
"""returns 6 translation of sequence. One for each reading frame"""
trans = (
translateDNA(sequence, 'f1'),
translateDNA(sequence, 'f2'),
translateDNA(sequence, 'f3'),
translateDNA(sequence, 'r1'),
translateDNA(sequence, 'r2'),
translateDNA(sequence, 'r3'),
)
return trans | [
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tariqdaouda/pyGeno | pyGeno/tools/UsefulFunctions.py | getSequenceCombinaisons | def getSequenceCombinaisons(polymorphipolymorphicDnaSeqSeq, pos = 0) :
"""Takes a dna sequence with polymorphismes and returns all the possible sequences that it can yield"""
if type(polymorphipolymorphicDnaSeqSeq) is not types.ListType :
seq = list(polymorphipolymorphicDnaSeqSeq)
else :
seq = polymorphipolymorphicDnaSeqSeq
if pos >= len(seq) :
return [''.join(seq)]
variants = []
if seq[pos] in polymorphicNucleotides :
chars = decodePolymorphicNucleotide(seq[pos])
else :
chars = seq[pos]#.split('/')
for c in chars :
rseq = copy.copy(seq)
rseq[pos] = c
variants.extend(getSequenceCombinaisons(rseq, pos + 1))
return variants | python | def getSequenceCombinaisons(polymorphipolymorphicDnaSeqSeq, pos = 0) :
"""Takes a dna sequence with polymorphismes and returns all the possible sequences that it can yield"""
if type(polymorphipolymorphicDnaSeqSeq) is not types.ListType :
seq = list(polymorphipolymorphicDnaSeqSeq)
else :
seq = polymorphipolymorphicDnaSeqSeq
if pos >= len(seq) :
return [''.join(seq)]
variants = []
if seq[pos] in polymorphicNucleotides :
chars = decodePolymorphicNucleotide(seq[pos])
else :
chars = seq[pos]#.split('/')
for c in chars :
rseq = copy.copy(seq)
rseq[pos] = c
variants.extend(getSequenceCombinaisons(rseq, pos + 1))
return variants | [
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tariqdaouda/pyGeno | pyGeno/tools/UsefulFunctions.py | getNucleotideCodon | def getNucleotideCodon(sequence, x1) :
"""Returns the entire codon of the nucleotide at pos x1 in sequence,
and the position of that nocleotide in the codon in a tuple"""
if x1 < 0 or x1 >= len(sequence) :
return None
p = x1%3
if p == 0 :
return (sequence[x1: x1+3], 0)
elif p ==1 :
return (sequence[x1-1: x1+2], 1)
elif p == 2 :
return (sequence[x1-2: x1+1], 2) | python | def getNucleotideCodon(sequence, x1) :
"""Returns the entire codon of the nucleotide at pos x1 in sequence,
and the position of that nocleotide in the codon in a tuple"""
if x1 < 0 or x1 >= len(sequence) :
return None
p = x1%3
if p == 0 :
return (sequence[x1: x1+3], 0)
elif p ==1 :
return (sequence[x1-1: x1+2], 1)
elif p == 2 :
return (sequence[x1-2: x1+1], 2) | [
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tariqdaouda/pyGeno | pyGeno/tools/UsefulFunctions.py | highlightSubsequence | def highlightSubsequence(sequence, x1, x2, start=' [', stop = '] ') :
"""returns a sequence where the subsequence in [x1, x2[ is placed
in bewteen 'start' and 'stop'"""
seq = list(sequence)
print x1, x2-1, len(seq)
seq[x1] = start + seq[x1]
seq[x2-1] = seq[x2-1] + stop
return ''.join(seq) | python | def highlightSubsequence(sequence, x1, x2, start=' [', stop = '] ') :
"""returns a sequence where the subsequence in [x1, x2[ is placed
in bewteen 'start' and 'stop'"""
seq = list(sequence)
print x1, x2-1, len(seq)
seq[x1] = start + seq[x1]
seq[x2-1] = seq[x2-1] + stop
return ''.join(seq) | [
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tariqdaouda/pyGeno | pyGeno/importation/Genomes.py | deleteGenome | def deleteGenome(species, name) :
"""Removes a genome from the database"""
printf('deleting genome (%s, %s)...' % (species, name))
conf.db.beginTransaction()
objs = []
allGood = True
try :
genome = Genome_Raba(name = name, species = species.lower())
objs.append(genome)
pBar = ProgressBar(label = 'preparing')
for typ in (Chromosome_Raba, Gene_Raba, Transcript_Raba, Exon_Raba, Protein_Raba) :
pBar.update()
f = RabaQuery(typ, namespace = genome._raba_namespace)
f.addFilter({'genome' : genome})
for e in f.iterRun() :
objs.append(e)
pBar.close()
pBar = ProgressBar(nbEpochs = len(objs), label = 'deleting objects')
for e in objs :
pBar.update()
e.delete()
pBar.close()
except KeyError as e :
#~ printf("\tWARNING, couldn't remove genome form db, maybe it's not there: ", e)
raise KeyError("\tWARNING, couldn't remove genome form db, maybe it's not there: ", e)
allGood = False
printf('\tdeleting folder')
try :
shutil.rmtree(conf.getGenomeSequencePath(species, name))
except OSError as e:
#~ printf('\tWARNING, Unable to delete folder: ', e)
OSError('\tWARNING, Unable to delete folder: ', e)
allGood = False
conf.db.endTransaction()
return allGood | python | def deleteGenome(species, name) :
"""Removes a genome from the database"""
printf('deleting genome (%s, %s)...' % (species, name))
conf.db.beginTransaction()
objs = []
allGood = True
try :
genome = Genome_Raba(name = name, species = species.lower())
objs.append(genome)
pBar = ProgressBar(label = 'preparing')
for typ in (Chromosome_Raba, Gene_Raba, Transcript_Raba, Exon_Raba, Protein_Raba) :
pBar.update()
f = RabaQuery(typ, namespace = genome._raba_namespace)
f.addFilter({'genome' : genome})
for e in f.iterRun() :
objs.append(e)
pBar.close()
pBar = ProgressBar(nbEpochs = len(objs), label = 'deleting objects')
for e in objs :
pBar.update()
e.delete()
pBar.close()
except KeyError as e :
#~ printf("\tWARNING, couldn't remove genome form db, maybe it's not there: ", e)
raise KeyError("\tWARNING, couldn't remove genome form db, maybe it's not there: ", e)
allGood = False
printf('\tdeleting folder')
try :
shutil.rmtree(conf.getGenomeSequencePath(species, name))
except OSError as e:
#~ printf('\tWARNING, Unable to delete folder: ', e)
OSError('\tWARNING, Unable to delete folder: ', e)
allGood = False
conf.db.endTransaction()
return allGood | [
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tariqdaouda/pyGeno | pyGeno/importation/Genomes.py | _importSequence | def _importSequence(chromosome, fastaFile, targetDir) :
"Serializes fastas into .dat files"
f = gzip.open(fastaFile)
header = f.readline()
strRes = f.read().upper().replace('\n', '').replace('\r', '')
f.close()
fn = '%s/chromosome%s.dat' % (targetDir, chromosome.number)
f = open(fn, 'w')
f.write(strRes)
f.close()
chromosome.dataFile = fn
chromosome.header = header
return len(strRes) | python | def _importSequence(chromosome, fastaFile, targetDir) :
"Serializes fastas into .dat files"
f = gzip.open(fastaFile)
header = f.readline()
strRes = f.read().upper().replace('\n', '').replace('\r', '')
f.close()
fn = '%s/chromosome%s.dat' % (targetDir, chromosome.number)
f = open(fn, 'w')
f.write(strRes)
f.close()
chromosome.dataFile = fn
chromosome.header = header
return len(strRes) | [
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tariqdaouda/pyGeno | pyGeno/configuration.py | createDefaultConfigFile | def createDefaultConfigFile() :
"""Creates a default configuration file"""
s = "[pyGeno_config]\nsettings_dir=%s\nremote_location=%s" % (pyGeno_SETTINGS_DIR, pyGeno_REMOTE_LOCATION)
f = open('%s/config.ini' % pyGeno_SETTINGS_DIR, 'w')
f.write(s)
f.close() | python | def createDefaultConfigFile() :
"""Creates a default configuration file"""
s = "[pyGeno_config]\nsettings_dir=%s\nremote_location=%s" % (pyGeno_SETTINGS_DIR, pyGeno_REMOTE_LOCATION)
f = open('%s/config.ini' % pyGeno_SETTINGS_DIR, 'w')
f.write(s)
f.close() | [
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tariqdaouda/pyGeno | pyGeno/configuration.py | getSettingsPath | def getSettingsPath() :
"""Returns the path where the settings are stored"""
parser = SafeConfigParser()
try :
parser.read(os.path.normpath(pyGeno_SETTINGS_DIR+'/config.ini'))
return parser.get('pyGeno_config', 'settings_dir')
except :
createDefaultConfigFile()
return getSettingsPath() | python | def getSettingsPath() :
"""Returns the path where the settings are stored"""
parser = SafeConfigParser()
try :
parser.read(os.path.normpath(pyGeno_SETTINGS_DIR+'/config.ini'))
return parser.get('pyGeno_config', 'settings_dir')
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tariqdaouda/pyGeno | pyGeno/configuration.py | pyGeno_init | def pyGeno_init() :
"""This function is automatically called at launch"""
global db, dbConf
global pyGeno_SETTINGS_PATH
global pyGeno_RABA_DBFILE
global pyGeno_DATA_PATH
if not checkPythonVersion() :
raise PythonVersionError("==> FATAL: pyGeno only works with python 2.7 and above, please upgrade your python version")
if not os.path.exists(pyGeno_SETTINGS_DIR) :
os.makedirs(pyGeno_SETTINGS_DIR)
pyGeno_SETTINGS_PATH = getSettingsPath()
pyGeno_RABA_DBFILE = os.path.normpath( os.path.join(pyGeno_SETTINGS_PATH, "pyGenoRaba.db") )
pyGeno_DATA_PATH = os.path.normpath( os.path.join(pyGeno_SETTINGS_PATH, "data") )
if not os.path.exists(pyGeno_SETTINGS_PATH) :
os.makedirs(pyGeno_SETTINGS_PATH)
if not os.path.exists(pyGeno_DATA_PATH) :
os.makedirs(pyGeno_DATA_PATH)
#launching the db
rabaDB.rabaSetup.RabaConfiguration(pyGeno_RABA_NAMESPACE, pyGeno_RABA_DBFILE)
db = rabaDB.rabaSetup.RabaConnection(pyGeno_RABA_NAMESPACE)
dbConf = rabaDB.rabaSetup.RabaConfiguration(pyGeno_RABA_NAMESPACE) | python | def pyGeno_init() :
"""This function is automatically called at launch"""
global db, dbConf
global pyGeno_SETTINGS_PATH
global pyGeno_RABA_DBFILE
global pyGeno_DATA_PATH
if not checkPythonVersion() :
raise PythonVersionError("==> FATAL: pyGeno only works with python 2.7 and above, please upgrade your python version")
if not os.path.exists(pyGeno_SETTINGS_DIR) :
os.makedirs(pyGeno_SETTINGS_DIR)
pyGeno_SETTINGS_PATH = getSettingsPath()
pyGeno_RABA_DBFILE = os.path.normpath( os.path.join(pyGeno_SETTINGS_PATH, "pyGenoRaba.db") )
pyGeno_DATA_PATH = os.path.normpath( os.path.join(pyGeno_SETTINGS_PATH, "data") )
if not os.path.exists(pyGeno_SETTINGS_PATH) :
os.makedirs(pyGeno_SETTINGS_PATH)
if not os.path.exists(pyGeno_DATA_PATH) :
os.makedirs(pyGeno_DATA_PATH)
#launching the db
rabaDB.rabaSetup.RabaConfiguration(pyGeno_RABA_NAMESPACE, pyGeno_RABA_DBFILE)
db = rabaDB.rabaSetup.RabaConnection(pyGeno_RABA_NAMESPACE)
dbConf = rabaDB.rabaSetup.RabaConfiguration(pyGeno_RABA_NAMESPACE) | [
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tariqdaouda/pyGeno | pyGeno/Exon.py | Exon.previousExon | def previousExon(self) :
"""Returns the previous exon of the transcript, or None if there is none"""
if self.number == 0 :
return None
try :
return self.transcript.exons[self.number-1]
except IndexError :
return None | python | def previousExon(self) :
"""Returns the previous exon of the transcript, or None if there is none"""
if self.number == 0 :
return None
try :
return self.transcript.exons[self.number-1]
except IndexError :
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tariqdaouda/pyGeno | pyGeno/tools/parsers/FastqTools.py | FastqFile.parseStr | def parseStr(self, st) :
"""Parses a string"""
self.data = st.replace('\r', '\n')
self.data = self.data.replace('\n\n', '\n')
self.data = self.data.split('\n') | python | def parseStr(self, st) :
"""Parses a string"""
self.data = st.replace('\r', '\n')
self.data = self.data.replace('\n\n', '\n')
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tariqdaouda/pyGeno | pyGeno/tools/parsers/FastqTools.py | FastqFile.get | def get(self, li) :
"""returns the ith entry"""
i = li*4
self.__splitEntry(i)
return self.data[i] | python | def get(self, li) :
"""returns the ith entry"""
i = li*4
self.__splitEntry(i)
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tariqdaouda/pyGeno | pyGeno/tools/parsers/FastqTools.py | FastqFile.newEntry | def newEntry(self, ident = "", seq = "", plus = "", qual = "") :
"""Appends an empty entry at the end of the CSV and returns it"""
e = FastqEntry()
self.data.append(e)
return e | python | def newEntry(self, ident = "", seq = "", plus = "", qual = "") :
"""Appends an empty entry at the end of the CSV and returns it"""
e = FastqEntry()
self.data.append(e)
return e | [
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tariqdaouda/pyGeno | pyGeno/bootstrap.py | listRemoteDatawraps | def listRemoteDatawraps(location = conf.pyGeno_REMOTE_LOCATION) :
"""Lists all the datawraps availabe from a remote a remote location."""
loc = location + "/datawraps.json"
response = urllib2.urlopen(loc)
js = json.loads(response.read())
return js | python | def listRemoteDatawraps(location = conf.pyGeno_REMOTE_LOCATION) :
"""Lists all the datawraps availabe from a remote a remote location."""
loc = location + "/datawraps.json"
response = urllib2.urlopen(loc)
js = json.loads(response.read())
return js | [
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tariqdaouda/pyGeno | pyGeno/bootstrap.py | listDatawraps | def listDatawraps() :
"""Lists all the datawraps pyGeno comes with"""
l = {"Genomes" : [], "SNPs" : []}
for f in os.listdir(os.path.join(this_dir, "bootstrap_data/genomes")) :
if f.find(".tar.gz") > -1 :
l["Genomes"].append(f)
for f in os.listdir(os.path.join(this_dir, "bootstrap_data/SNPs")) :
if f.find(".tar.gz") > -1 :
l["SNPs"].append(f)
return l | python | def listDatawraps() :
"""Lists all the datawraps pyGeno comes with"""
l = {"Genomes" : [], "SNPs" : []}
for f in os.listdir(os.path.join(this_dir, "bootstrap_data/genomes")) :
if f.find(".tar.gz") > -1 :
l["Genomes"].append(f)
for f in os.listdir(os.path.join(this_dir, "bootstrap_data/SNPs")) :
if f.find(".tar.gz") > -1 :
l["SNPs"].append(f)
return l | [
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tariqdaouda/pyGeno | pyGeno/bootstrap.py | printDatawraps | def printDatawraps() :
"""print all available datawraps for bootstraping"""
l = listDatawraps()
printf("Available datawraps for boostraping\n")
for k, v in l.iteritems() :
printf(k)
printf("~"*len(k) + "|")
for vv in v :
printf(" "*len(k) + "|" + "~~~:> " + vv)
printf('\n') | python | def printDatawraps() :
"""print all available datawraps for bootstraping"""
l = listDatawraps()
printf("Available datawraps for boostraping\n")
for k, v in l.iteritems() :
printf(k)
printf("~"*len(k) + "|")
for vv in v :
printf(" "*len(k) + "|" + "~~~:> " + vv)
printf('\n') | [
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tariqdaouda/pyGeno | pyGeno/bootstrap.py | importGenome | def importGenome(name, batchSize = 100) :
"""Import a genome shipped with pyGeno. Most of the datawraps only contain URLs towards data provided by third parties."""
path = os.path.join(this_dir, "bootstrap_data", "genomes/" + name)
PG.importGenome(path, batchSize) | python | def importGenome(name, batchSize = 100) :
"""Import a genome shipped with pyGeno. Most of the datawraps only contain URLs towards data provided by third parties."""
path = os.path.join(this_dir, "bootstrap_data", "genomes/" + name)
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"""Import a SNP set shipped with pyGeno. Most of the datawraps only contain URLs towards data provided by third parties."""
path = os.path.join(this_dir, "bootstrap_data", "SNPs/" + name)
PS.importSNPs(path) | python | def importSNPs(name) :
"""Import a SNP set shipped with pyGeno. Most of the datawraps only contain URLs towards data provided by third parties."""
path = os.path.join(this_dir, "bootstrap_data", "SNPs/" + name)
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tariqdaouda/pyGeno | pyGeno/tools/ProgressBar.py | ProgressBar.log | def log(self) :
"""logs stats about the progression, without printing anything on screen"""
self.logs['epochDuration'].append(self.lastEpochDuration)
self.logs['avg'].append(self.avg)
self.logs['runtime'].append(self.runtime)
self.logs['remtime'].append(self.remtime) | python | def log(self) :
"""logs stats about the progression, without printing anything on screen"""
self.logs['epochDuration'].append(self.lastEpochDuration)
self.logs['avg'].append(self.avg)
self.logs['runtime'].append(self.runtime)
self.logs['remtime'].append(self.remtime) | [
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tariqdaouda/pyGeno | pyGeno/tools/ProgressBar.py | ProgressBar.saveLogs | def saveLogs(self, filename) :
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f.close() | python | def saveLogs(self, filename) :
"""dumps logs into a nice pickle"""
f = open(filename, 'wb')
cPickle.dump(self.logs, f)
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tariqdaouda/pyGeno | pyGeno/SNPFiltering.py | DefaultSNPFilter.filter | def filter(self, chromosome, **kwargs) :
"""The default filter mixes applied all SNPs and ignores Insertions and Deletions."""
def appendAllele(alleles, sources, snp) :
pos = snp.start
if snp.alt[0] == '-' :
pass
# print warn % ('DELETION', snpSet, snp.start, snp.chromosomeNumber)
elif snp.ref[0] == '-' :
pass
# print warn % ('INSERTION', snpSet, snp.start, snp.chromosomeNumber)
else :
sources[snpSet] = snp
alleles.append(snp.alt) #if not an indel append the polymorphism
refAllele = chromosome.refSequence[pos]
alleles.append(refAllele)
sources['ref'] = refAllele
return alleles, sources
warn = 'Warning: the default snp filter ignores indels. IGNORED %s of SNP set: %s at pos: %s of chromosome: %s'
sources = {}
alleles = []
for snpSet, data in kwargs.iteritems() :
if type(data) is list :
for snp in data :
alleles, sources = appendAllele(alleles, sources, snp)
else :
allels, sources = appendAllele(alleles, sources, data)
#appends the refence allele to the lot
#optional we keep a record of the polymorphisms that were used during the process
return SequenceSNP(alleles, sources = sources) | python | def filter(self, chromosome, **kwargs) :
"""The default filter mixes applied all SNPs and ignores Insertions and Deletions."""
def appendAllele(alleles, sources, snp) :
pos = snp.start
if snp.alt[0] == '-' :
pass
# print warn % ('DELETION', snpSet, snp.start, snp.chromosomeNumber)
elif snp.ref[0] == '-' :
pass
# print warn % ('INSERTION', snpSet, snp.start, snp.chromosomeNumber)
else :
sources[snpSet] = snp
alleles.append(snp.alt) #if not an indel append the polymorphism
refAllele = chromosome.refSequence[pos]
alleles.append(refAllele)
sources['ref'] = refAllele
return alleles, sources
warn = 'Warning: the default snp filter ignores indels. IGNORED %s of SNP set: %s at pos: %s of chromosome: %s'
sources = {}
alleles = []
for snpSet, data in kwargs.iteritems() :
if type(data) is list :
for snp in data :
alleles, sources = appendAllele(alleles, sources, snp)
else :
allels, sources = appendAllele(alleles, sources, data)
#appends the refence allele to the lot
#optional we keep a record of the polymorphisms that were used during the process
return SequenceSNP(alleles, sources = sources) | [
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tariqdaouda/pyGeno | pyGeno/tools/SegmentTree.py | SegmentTree.insert | def insert(self, x1, x2, name = '', referedObject = []) :
"""Insert the segment in it's right place and returns it.
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elif xx1 <= self.children[i].x1 and self.children[i].x2 <= xx2 :
if rt == None :
if type(referedObject) is types.ListType :
rt = SegmentTree(xx1, xx2, name, referedObject, self, self.level+1)
else :
rt = SegmentTree(xx1, xx2, name, [referedObject], self, self.level+1)
insertId = i
rt.__addChild(self.children[i])
self.children[i].father = rt
childrenToRemove.append(self.children[i])
elif xx1 <= self.children[i].x1 and xx2 <= self.children[i].x2 :
insertId = i
break
if rt != None :
self.__addChild(rt, insertId)
for c in childrenToRemove :
self.children.remove(c)
else :
if type(referedObject) is types.ListType :
rt = SegmentTree(xx1, xx2, name, referedObject, self, self.level+1)
else :
rt = SegmentTree(xx1, xx2, name, [referedObject], self, self.level+1)
if insertId != None :
self.__addChild(rt, insertId)
else :
self.__addChild(rt)
return rt | python | def insert(self, x1, x2, name = '', referedObject = []) :
"""Insert the segment in it's right place and returns it.
If there's already a segment S as S.x1 == x1 and S.x2 == x2. S.name will be changed to 'S.name U name' and the
referedObject will be appended to the already existing list"""
if x1 > x2 :
xx1, xx2 = x2, x1
else :
xx1, xx2 = x1, x2
rt = None
insertId = None
childrenToRemove = []
for i in range(len(self.children)) :
if self.children[i].x1 == xx1 and xx2 == self.children[i].x2 :
self.children[i].name = self.children[i].name + ' U ' + name
self.children[i].referedObject.append(referedObject)
return self.children[i]
if self.children[i].x1 <= xx1 and xx2 <= self.children[i].x2 :
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elif xx1 <= self.children[i].x1 and self.children[i].x2 <= xx2 :
if rt == None :
if type(referedObject) is types.ListType :
rt = SegmentTree(xx1, xx2, name, referedObject, self, self.level+1)
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rt = SegmentTree(xx1, xx2, name, [referedObject], self, self.level+1)
insertId = i
rt.__addChild(self.children[i])
self.children[i].father = rt
childrenToRemove.append(self.children[i])
elif xx1 <= self.children[i].x1 and xx2 <= self.children[i].x2 :
insertId = i
break
if rt != None :
self.__addChild(rt, insertId)
for c in childrenToRemove :
self.children.remove(c)
else :
if type(referedObject) is types.ListType :
rt = SegmentTree(xx1, xx2, name, referedObject, self, self.level+1)
else :
rt = SegmentTree(xx1, xx2, name, [referedObject], self, self.level+1)
if insertId != None :
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self.__addChild(rt)
return rt | [
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tariqdaouda/pyGeno | pyGeno/tools/SegmentTree.py | SegmentTree.removeGaps | def removeGaps(self) :
"""Remove all gaps between regions"""
for i in range(1, len(self.children)) :
if self.children[i].x1 > self.children[i-1].x2:
aux_moveTree(self.children[i-1].x2-self.children[i].x1, self.children[i]) | python | def removeGaps(self) :
"""Remove all gaps between regions"""
for i in range(1, len(self.children)) :
if self.children[i].x1 > self.children[i-1].x2:
aux_moveTree(self.children[i-1].x2-self.children[i].x1, self.children[i]) | [
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tariqdaouda/pyGeno | pyGeno/tools/SegmentTree.py | SegmentTree.getIndexedLength | def getIndexedLength(self) :
"""Returns the total length of indexed regions"""
if self.x1 != None and self.x2 != None:
return self.x2 - self.x1
else :
if len(self.children) == 0 :
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l += self.children[i].x2 - self.children[i].x1 - max(0, self.children[i-1].x2 - self.children[i].x1)
return l | python | def getIndexedLength(self) :
"""Returns the total length of indexed regions"""
if self.x1 != None and self.x2 != None:
return self.x2 - self.x1
else :
if len(self.children) == 0 :
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l = self.children[0].x2 - self.children[0].x1
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tariqdaouda/pyGeno | pyGeno/tools/SegmentTree.py | SegmentTree.flatten | def flatten(self) :
"""Flattens the tree. The tree become a tree of depth 1 where overlapping regions have been merged together"""
if len(self.children) > 1 :
children = self.children
self.emptyChildren()
children[0].emptyChildren()
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name = children[i].name
if len(refObjs) == 1 :
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self.insert(x1, x2, name, refObjs) | python | def flatten(self) :
"""Flattens the tree. The tree become a tree of depth 1 where overlapping regions have been merged together"""
if len(self.children) > 1 :
children = self.children
self.emptyChildren()
children[0].emptyChildren()
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x2 = children[0].x2
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children[i].emptyChildren()
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x2 = children[i].x2
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name = children[i].name
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self.insert(x1, x2, name, refObjs) | [
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tariqdaouda/pyGeno | pyGeno/tools/SegmentTree.py | SegmentTree.move | def move(self, newX1) :
"""Moves tree to a new starting position, updates x1s of children"""
if self.x1 != None and self.x2 != None :
offset = newX1-self.x1
aux_moveTree(offset, self)
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offset = newX1-self.children[0].x1
aux_moveTree(offset, self) | python | def move(self, newX1) :
"""Moves tree to a new starting position, updates x1s of children"""
if self.x1 != None and self.x2 != None :
offset = newX1-self.x1
aux_moveTree(offset, self)
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offset = newX1-self.children[0].x1
aux_moveTree(offset, self) | [
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tariqdaouda/pyGeno | pyGeno/tools/BinarySequence.py | BinarySequence.__getSequenceVariants | def __getSequenceVariants(self, x1, polyStart, polyStop, listSequence) :
"""polyStop, is the polymorphisme at wixh number where the calcul of combinaisons stops"""
if polyStart < len(self.polymorphisms) and polyStart < polyStop:
sequence = copy.copy(listSequence)
ret = []
pk = self.polymorphisms[polyStart]
posInSequence = pk[0]-x1
if posInSequence < len(listSequence) :
for allele in pk[1] :
sequence[posInSequence] = allele
ret.extend(self.__getSequenceVariants(x1, polyStart+1, polyStop, sequence))
return ret
else :
return [''.join(listSequence)] | python | def __getSequenceVariants(self, x1, polyStart, polyStop, listSequence) :
"""polyStop, is the polymorphisme at wixh number where the calcul of combinaisons stops"""
if polyStart < len(self.polymorphisms) and polyStart < polyStop:
sequence = copy.copy(listSequence)
ret = []
pk = self.polymorphisms[polyStart]
posInSequence = pk[0]-x1
if posInSequence < len(listSequence) :
for allele in pk[1] :
sequence[posInSequence] = allele
ret.extend(self.__getSequenceVariants(x1, polyStart+1, polyStop, sequence))
return ret
else :
return [''.join(listSequence)] | [
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tariqdaouda/pyGeno | pyGeno/tools/BinarySequence.py | BinarySequence.getNbVariants | def getNbVariants(self, x1, x2 = -1) :
"""returns the nb of variants of sequences between x1 and x2"""
if x2 == -1 :
xx2 = len(self.defaultSequence)
else :
xx2 = x2
nbP = 1
for p in self.polymorphisms:
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nbP *= len(p[1])
return nbP | python | def getNbVariants(self, x1, x2 = -1) :
"""returns the nb of variants of sequences between x1 and x2"""
if x2 == -1 :
xx2 = len(self.defaultSequence)
else :
xx2 = x2
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tariqdaouda/pyGeno | pyGeno/tools/BinarySequence.py | BinarySequence._kmp_construct_next | def _kmp_construct_next(self, pattern):
"""the helper function for KMP-string-searching is to construct the DFA. pattern should be an integer array. return a 2D array representing the DFA for moving the pattern."""
next = [[0 for state in pattern] for input_token in self.ALPHABETA_KMP]
next[pattern[0]][0] = 1
restart_state = 0
for state in range(1, len(pattern)):
for input_token in self.ALPHABETA_KMP:
next[input_token][state] = next[input_token][restart_state]
next[pattern[state]][state] = state + 1
restart_state = next[pattern[state]][restart_state]
return next | python | def _kmp_construct_next(self, pattern):
"""the helper function for KMP-string-searching is to construct the DFA. pattern should be an integer array. return a 2D array representing the DFA for moving the pattern."""
next = [[0 for state in pattern] for input_token in self.ALPHABETA_KMP]
next[pattern[0]][0] = 1
restart_state = 0
for state in range(1, len(pattern)):
for input_token in self.ALPHABETA_KMP:
next[input_token][state] = next[input_token][restart_state]
next[pattern[state]][state] = state + 1
restart_state = next[pattern[state]][restart_state]
return next | [
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... | 474b1250bf78ce5c7e7c3bbbfdbad9635d5a7d14 | https://github.com/tariqdaouda/pyGeno/blob/474b1250bf78ce5c7e7c3bbbfdbad9635d5a7d14/pyGeno/tools/BinarySequence.py#L197-L207 | train | 54,298 |
tariqdaouda/pyGeno | pyGeno/tools/BinarySequence.py | BinarySequence._kmp_search_first | def _kmp_search_first(self, pInput_sequence, pPattern):
"""use KMP algorithm to search the first occurrence in the input_sequence of the pattern. both arguments are integer arrays. return the position of the occurence if found; otherwise, -1."""
input_sequence, pattern = pInput_sequence, [len(bin(e)) for e in pPattern]
n, m = len(input_sequence), len(pattern)
d = p = 0
next = self._kmp_construct_next(pattern)
while d < n and p < m:
p = next[len(bin(input_sequence[d]))][p]
d += 1
if p == m: return d - p
else: return -1 | python | def _kmp_search_first(self, pInput_sequence, pPattern):
"""use KMP algorithm to search the first occurrence in the input_sequence of the pattern. both arguments are integer arrays. return the position of the occurence if found; otherwise, -1."""
input_sequence, pattern = pInput_sequence, [len(bin(e)) for e in pPattern]
n, m = len(input_sequence), len(pattern)
d = p = 0
next = self._kmp_construct_next(pattern)
while d < n and p < m:
p = next[len(bin(input_sequence[d]))][p]
d += 1
if p == m: return d - p
else: return -1 | [
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"... | 474b1250bf78ce5c7e7c3bbbfdbad9635d5a7d14 | https://github.com/tariqdaouda/pyGeno/blob/474b1250bf78ce5c7e7c3bbbfdbad9635d5a7d14/pyGeno/tools/BinarySequence.py#L209-L219 | train | 54,299 |
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