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/open_darts-1.0.2-cp310-cp310-win_amd64.whl/darts/mesh/geometry/shapes.py | import numpy as np
import math
from dataclasses import dataclass, is_dataclass, field
# region geometry dataclasses
@dataclass
class Point:
idx: int
xyz: list
lc: int = 0
active: bool = True
embed: list = field(default_factory=list)
@dataclass
class Curve:
idx: int
curve_type: str
points: list
active: bool = True
embed: list = field(default_factory=list)
@dataclass
class Surface:
idx: int
points: list
curves: list = field(default_factory=list)
in_surfaces: list = field(default_factory=list)
holes: list = field(default_factory=list)
plane: bool = True
active: bool = True
embed: list = field(default_factory=list)
@dataclass
class Volume:
idx: int
surfaces: list
active: bool = True
@dataclass
class Physical:
tag: str
idxs: list
# endregion
class Shape:
def __init__(self):
self.points = []
self.curves = []
self.surfaces = []
self.volumes = []
self.physical_points = []
self.physical_curves = []
self.physical_surfaces = []
self.physical_volumes = []
# self.physical_points = {}
# self.physical_curves = {}
# self.physical_surfaces = {}
# self.physical_volumes = {}
self.boundary_tag = 90000
self.define_shapes()
def define_shapes(self):
pass
def connect_points(self):
"""
Function to connect points in surfaces specified. Points in surfaces must be defined in order
"""
curves = []
for curve in self.curves:
# If self.curves is not empty
curves.append(curve.points)
for j, surface in enumerate(self.surfaces):
points = surface.points
surface_curves = []
for i in range(len(points)-1):
curve_points = [points[i], points[i+1]]
reverse = [points[i+1], points[i]]
if curve_points not in curves and reverse not in curves:
curve_idx = len(curves) + 1
curves.append(curve_points)
curve_type = 'line'
self.curves.append(Curve(curve_idx, curve_type, curve_points))
surface_curves.append(curve_idx)
else:
if curve_points in curves:
curve_idx = curves.index(curve_points) + 1
surface_curves.append(curve_idx)
else:
curve_idx = curves.index(reverse) + 1
surface_curves.append(-curve_idx)
self.surfaces[j].curves = surface_curves
return
def add_boundary(self, boundary):
pass
def plot_shape_2D(self):
"""
Function to plot point and curves
"""
import matplotlib.pyplot as plt
plt.figure(dpi=400, figsize=(10, 5))
for point in self.points:
plt.scatter(point.xyz[0], point.xyz[2], c='k', s=0.5)
for curve in self.curves:
idx1 = curve.points[0]-1
idx2 = curve.points[1]-1
p1 = self.points[idx1].xyz
p2 = self.points[idx2].xyz
x = [p1[0], p2[0]]
z = [p1[2], p2[2]]
plt.plot(x, z, c='k', linewidth=1)
# Find limits
xmin, xmax = self.points[0].xyz[0], self.points[0].xyz[0]
zmin, zmax = self.points[0].xyz[2], self.points[0].xyz[2]
for point in self.points:
xi = point.xyz[0]
if xi < xmin:
xmin = xi
elif xi > xmax:
xmax = xi
zi = point.xyz[2]
if zi < zmin:
zmin = zi
elif zi > zmax:
zmax = zi
plt.axis('scaled')
plt.xlim([xmin, xmax])
plt.ylim([zmin, zmax])
plt.xlabel("x [m]")
plt.ylabel("z [m]")
return
class Box(Shape):
def __init__(self, xdim: list, ydim: list, zdim: list):
self.xdim, self.ydim, self.zdim = xdim, ydim, zdim
super().__init__()
self.connect_points()
surface_idxs = [surface.idx for surface in self.surfaces]
self.volumes = [Volume(1, surface_idxs)]
def define_shapes(self):
self.points = [Point(1, [self.xdim[0], self.ydim[0], self.zdim[0]]),
Point(2, [self.xdim[1], self.ydim[0], self.zdim[0]]),
Point(3, [self.xdim[1], self.ydim[1], self.zdim[0]]),
Point(4, [self.xdim[0], self.ydim[1], self.zdim[0]]),
Point(5, [self.xdim[0], self.ydim[0], self.zdim[1]]),
Point(6, [self.xdim[1], self.ydim[0], self.zdim[1]]),
Point(7, [self.xdim[1], self.ydim[1], self.zdim[1]]),
Point(8, [self.xdim[0], self.ydim[1], self.zdim[1]])]
self.surfaces = [Surface(1, points=[1, 4, 8, 5, 1]), # yz_min
Surface(2, points=[2, 3, 7, 6, 2]), # yz_plus
Surface(3, points=[1, 2, 6, 5, 1]), # xz_min
Surface(4, points=[4, 3, 7, 8, 4]), # xz_plus
Surface(5, points=[1, 2, 3, 4, 1]), # xy_min
Surface(6, points=[5, 6, 7, 8, 5])] # xy_plus
return
def add_boundary(self, boundary):
# xy_min, xy_plus, xz_min, xz_plus, yz_min, yz_plus
if boundary == "xy_min":
idxs = [5]
elif boundary == "xy_plus":
idxs = [6]
elif boundary == "xz_min":
idxs = [3]
elif boundary == "xz_plus":
idxs = [4]
elif boundary == "yz_min":
idxs = [1]
elif boundary == "yz_plus":
idxs = [2]
else:
raise Exception("Not a valid boundary")
surface = Physical(boundary, idxs)
self.physical_surfaces.append(surface)
self.boundary_tag += 1
return self.boundary_tag
class Cylinder(Shape):
def __init__(self, c0: list, radius: float, length: float, orientation: int, angle=360.):
self.c0, self.radius, self.length, self.orientation, self.angle = c0, radius, length, orientation, angle
super().__init__()
def define_shapes(self):
if self.angle == 360.: # need 3 curved surfaces
self.points, self.curves, self.surfaces, self.volumes = self.calc_faces_360(self.c0, self.radius, self.length, self.orientation)
elif self.angle >= 180.: # need 2 curved surfaces
self.points, self.curves, self.surfaces, self.volumes = self.calc_faces_180(self.c0, self.radius, self.length, self.orientation)
else: # need only 1 curved surface
self.points, self.curves, self.surfaces, self.volumes = self.calc_faces_180_(self.c0, self.radius, self.length, self.orientation)
return
def calc_faces_360(self, c0, r, l, orientation):
"""Function to calculate points, curves and surfaces for a full cylinder"""
"""Calculate points"""
segments = 3
curve_angle = 120
points = [Point(1, c0)]
for i in range(segments):
radians = math.radians(i*curve_angle)
point0 = self.calc_radial_points(c0, r, orientation, radians)
points.append(Point(i+2, point0))
for i, point in enumerate(points[:]):
point1 = point.xyz[:]
point1[orientation] += l
points.append(Point(i+segments+2, point1))
"""Define curves and surfaces"""
curves = [Curve(1, 'circle', [2, 1, 3]),
Curve(2, 'circle', [3, 1, 4]),
Curve(3, 'circle', [4, 1, 2]),
Curve(4, 'circle', [6, 5, 7]),
Curve(5, 'circle', [7, 5, 8]),
Curve(6, 'circle', [8, 5, 6]),
Curve(7, 'line', [1, 5]),
Curve(8, 'line', [2, 6]),
Curve(9, 'line', [3, 7]),
Curve(10, 'line', [4, 8])]
surfaces = [Surface(1, points=[2, 3, 4, 2], curves=[1, 2, 3]), # face0
Surface(2, [6, 7, 8, 6], [4, 5, 6]), # face1
Surface(3, [2, 3, 7, 6, 2], [1, 9, -4, -8], plane=False), # side12
Surface(4, [3, 4, 8, 7, 3], [2, 10, -5, -9], plane=False), # side23
Surface(5, [4, 2, 6, 8, 4], [3, 8, -6, -10], plane=False)] # side31
surface_idxs = [surface.idx for surface in surfaces]
volumes = [Volume(1, surface_idxs)]
return points, curves, surfaces, volumes
def calc_faces_180(self, c0, r, l, orientation):
"""Function to calculate points, curves and surfaces for a half cylinder"""
"""Calculate points"""
segments = 2
curve_angle = self.angle/2
points = [Point(1, c0)]
for i in range(segments + 1):
radians = math.radians(i*curve_angle)
point0 = self.calc_radial_points(c0, r, orientation, radians)
points.append(Point(i+2, point0))
for i, point in enumerate(points[:]):
point1 = point.xyz[:]
point1[orientation] += l
points.append(Point(i+segments+3, point1))
"""Define curves and surfaces"""
curves = [Curve(1, 'line', [1, 2]),
Curve(2, 'circle', [2, 1, 3]),
Curve(3, 'circle', [3, 1, 4]),
Curve(4, 'line', [4, 1]),
Curve(5, 'line', [5, 6]),
Curve(6, 'circle', [6, 5, 7]),
Curve(7, 'circle', [7, 5, 8]),
Curve(8, 'line', [8, 5]),
Curve(9, 'line', [1, 5]),
Curve(10, 'line', [2, 6]),
Curve(11, 'line', [3, 7]),
Curve(12, 'line', [4, 8])]
surfaces = [Surface(1, [1, 2, 3, 4, 1], [1, 2, 3, 4]), # face0
Surface(2, [5, 6, 7, 8, 5], [5, 6, 7, 8]), # face1
Surface(3, [1, 2, 6, 5, 1], [1, 10, -5, -9]), # side centre1
Surface(4, [2, 3, 7, 6, 2], [2, 11, -6, -10], plane=False), # side12
Surface(5, [3, 4, 8, 7, 3], [3, 12, -7, -11], plane=False), # side23
Surface(6, [1, 5, 8, 4, 1], [9, -8, -12, 4])] # side centre2
surface_idxs = [surface.idx for surface in surfaces]
volumes = [Volume(1, surface_idxs)]
return points, curves, surfaces, volumes
def calc_faces_180_(self, c0, r, l, orientation):
"""Function to calculate points, curves and surfaces for a less than half cylinder"""
"""Calculate points"""
segments = 1
curve_angle = self.angle
points = [Point(1, c0)]
for i in range(segments+1):
radians = math.radians(i*curve_angle)
point0 = self.calc_radial_points(c0, r, orientation, radians)
points.append(Point(i+2, point0))
for i, point in enumerate(points[:]):
point1 = point.xyz[:]
point1[orientation] += l
points.append(Point(i+segments+3, point1))
"""Define curves and surfaces"""
curves = [Curve(1, 'line', [1, 2]),
Curve(2, 'circle', [2, 1, 3]),
Curve(3, 'line', [3, 1]),
Curve(4, 'line', [4, 5]),
Curve(5, 'circle', [5, 4, 6]),
Curve(6, 'line', [6, 4]),
Curve(7, 'line', [1, 4]),
Curve(8, 'line', [2, 5]),
Curve(9, 'line', [3, 6])]
surfaces = [Surface(1, [1, 2, 3, 1], [1, 2, 3]), # face0
Surface(2, [4, 5, 6, 4], [4, 5, 6]), # face1
Surface(3, [1, 2, 5, 4, 1], [1, 8, -4, -7]), # side centre1
Surface(4, [2, 3, 6, 5, 2], [2, 9, -5, -8], plane=False), # side12
Surface(5, [1, 4, 6, 3, 1], [7, -6, -9, 3])] # side centre2
surface_idxs = [surface.idx for surface in surfaces]
volumes = [Volume(1, surface_idxs)]
return points, curves, surfaces, volumes
def calc_radial_points(self, c, r, orientation, angle):
point = c[:]
if orientation == 0: # x-dir
point[1] += np.round(r * np.sin(angle), 5)
point[2] += np.round(r * np.cos(angle), 5)
elif orientation == 1: # y-dir
point[0] += np.round(r * np.sin(angle), 5)
point[2] += np.round(r * np.cos(angle), 5)
else: # z-dir
point[0] += np.round(r * np.sin(angle), 5)
point[1] += np.round(r * np.cos(angle), 5)
return point
def add_boundary(self, boundary):
# bottom, top, outer
if boundary == "top":
idxs = [1]
elif boundary == "bottom":
idxs = [2]
elif boundary == "outer":
if self.angle < 180.:
idxs = [4]
elif self.angle < 360.:
idxs = [4, 5]
else:
idxs = [3, 4, 5]
else:
raise Exception("Not a valid boundary")
surface = Physical(boundary, idxs)
self.physical_surfaces.append(surface)
self.boundary_tag += 1
return self.boundary_tag
class CylinderWithHole(Cylinder):
def __init__(self, c0: list, c1: list, radius: float, rw: float, length: float, orientation: int, angle=360.):
self.rw = rw
super().__init__(c0, radius, length, orientation, angle)
def calc_faces_360(self, c0, r, l, orientation):
"""Function to calculate points, curves and surfaces for a full cylinder"""
"""Calculate points"""
segments = 3
curve_angle = 120
points = [Point(1, c0)]
for i in range(segments):
radians = math.radians(i * curve_angle)
point0 = self.calc_radial_points(c0, r, orientation, radians)
points.append(Point(i + 2, point0))
for i, point in enumerate(points[:]):
point1 = point.xyz[:]
point1[orientation] += l
points.append(Point(i + segments + 2, point1))
"""Define curves and surfaces"""
curves = [Curve(1, 'circle', [2, 1, 3]),
Curve(2, 'circle', [3, 1, 4]),
Curve(3, 'circle', [4, 1, 2]),
Curve(4, 'circle', [5, 1, 6]),
Curve(5, 'circle', [6, 1, 7]),
Curve(6, 'circle', [7, 1, 8]),
Curve(7, 'line', [1, 5]),
Curve(8, 'line', [2, 6]),
Curve(9, 'line', [3, 7]),
Curve(10, 'line', [4, 8])]
surfaces = [Surface(1, points=[2, 3, 4, 2], curves=[1, 2, 3]), # face0
Surface(2, [6, 7, 8, 6], [4, 5, 6]), # face1
Surface(3, [2, 3, 7, 6, 2], [1, 9, -4, -8], plane=False), # side12
Surface(4, [3, 4, 8, 7, 3], [2, 10, -5, -9], plane=False), # side23
Surface(5, [4, 2, 6, 8, 4], [3, 8, -6, -10], plane=False)] # side31
surface_idxs = [surface.idx for surface in surfaces]
volumes = [Volume(1, surface_idxs)]
return points, curves, surfaces, volumes
def calc_faces_180(self, c0, r, l, orientation):
"""Function to calculate points, curves and surfaces for a half cylinder"""
"""Calculate points"""
segments = 2
curve_angle = self.angle / 2
points = [Point(1, c0)]
for i in range(segments + 1):
radians = math.radians(i * curve_angle)
point0 = self.calc_radial_points(c0, r, orientation, radians)
points.append(Point(i + 2, point0))
for i, point in enumerate(points[:]):
point1 = point.xyz[:]
point1[orientation] += l
points.append(Point(i + segments + 3, point1))
"""Define curves and surfaces"""
curves = [Curve(1, 'line', [1, 2]),
Curve(2, 'circle', [2, 1, 3]),
Curve(3, 'circle', [3, 1, 4]),
Curve(4, 'line', [4, 1]),
Curve(5, 'line', [5, 6]),
Curve(6, 'circle', [6, 5, 7]),
Curve(7, 'circle', [7, 5, 8]),
Curve(8, 'line', [8, 5]),
Curve(9, 'line', [1, 5]),
Curve(10, 'line', [2, 6]),
Curve(11, 'line', [3, 7]),
Curve(12, 'line', [4, 8])]
surfaces = [Surface(1, [1, 2, 3, 4, 1], [1, 2, 3, 4]), # face0
Surface(2, [5, 6, 7, 8, 5], [5, 6, 7, 8]), # face1
Surface(3, [1, 2, 6, 5, 1], [1, 10, -5, -9]), # side centre1
Surface(4, [2, 3, 7, 6, 2], [2, 11, -6, -10], plane=False), # side12
Surface(5, [3, 4, 8, 7, 3], [3, 12, -7, -11], plane=False), # side23
Surface(6, [1, 5, 8, 4, 1], [9, -8, -12, 4])] # side centre2
surface_idxs = [surface.idx for surface in surfaces]
volumes = [Volume(1, surface_idxs)]
return points, curves, surfaces, volumes
def calc_faces_180_(self, c0, r, l, orientation):
"""Function to calculate points, curves and surfaces for a less than half cylinder"""
"""Calculate points"""
segments = 1
curve_angle = self.angle
points = [Point(1, c0)]
for i in range(segments + 1):
radians = math.radians(i * curve_angle)
point0 = self.calc_radial_points(c0, r, orientation, radians)
points.append(Point(i + 2, point0))
for i, point in enumerate(points[:]):
point1 = point.xyz[:]
point1[orientation] += l
points.append(Point(i + segments + 3, point1))
"""Define curves and surfaces"""
curves = [Curve(1, 'line', [1, 2]),
Curve(2, 'circle', [2, 1, 3]),
Curve(3, 'line', [3, 1]),
Curve(4, 'line', [4, 5]),
Curve(5, 'circle', [5, 4, 6]),
Curve(6, 'line', [6, 4]),
Curve(7, 'line', [1, 4]),
Curve(8, 'line', [2, 5]),
Curve(9, 'line', [3, 6])]
surfaces = [Surface(1, [1, 2, 3, 1], [1, 2, 3]), # face0
Surface(2, [4, 5, 6, 4], [4, 5, 6]), # face1
Surface(3, [1, 2, 5, 4, 1], [1, 8, -4, -7]), # side centre1
Surface(4, [2, 3, 6, 5, 2], [2, 9, -5, -8], plane=False), # side12
Surface(5, [1, 4, 6, 3, 1], [7, -6, -9, 3])] # side centre2
surface_idxs = [surface.idx for surface in surfaces]
volumes = [Volume(1, surface_idxs)]
return points, curves, surfaces, volumes
def add_boundary(self, boundary):
# bottom, top, outer
if boundary == "top":
idxs = [1]
elif boundary == "bottom":
idxs = [2]
elif boundary == "outer":
if self.angle < 180.:
idxs = [4]
elif self.angle < 360.:
idxs = [4, 5]
else:
idxs = [3, 4, 5]
else:
raise Exception("Not a valid boundary")
surface = Physical(boundary, idxs)
self.physical_surfaces.append(surface)
self.boundary_tag += 1
return self.boundary_tag
class Circle(Shape):
def __init__(self, c0: list, radius: float, orientation: int = 1, angle=360.):
self.c0, self.radius, self.orientation, self.angle = c0, radius, orientation, angle
super().__init__()
def define_shapes(self):
if self.angle == 360.: # need 3 curved lines
self.points = [Point(1, self.c0),
Point(2, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(0))),
Point(3, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(120))),
Point(4, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(240)))]
self.curves = [Curve(1, curve_type='circle', points=[2, 1, 3]),
Curve(2, curve_type='circle', points=[3, 1, 4]),
Curve(3, curve_type='circle', points=[4, 1, 2])]
self.surfaces = [Surface(1, points=[2, 3, 4, 2], curves=[1, 2, 3])]
elif self.angle >= 180.: # need 2 curved lines
self.points = [Point(1, self.c0),
Point(2, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(0))),
Point(3, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(self.angle/2))),
Point(4, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(self.angle)))]
self.curves = [Curve(1, curve_type='line', points=[1, 2]),
Curve(2, curve_type='circle', points=[2, 1, 3]),
Curve(3, curve_type='circle', points=[3, 1, 4]),
Curve(4, curve_type='line', points=[4, 1])]
self.surfaces = [Surface(1, points=[1, 2, 3, 4, 1], curves=[1, 2, 3, 4])]
else: # need only 1 curved line
self.points = [Point(1, self.c0),
Point(2, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(0))),
Point(3, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(self.angle)))]
self.curves = [Curve(1, curve_type='line', points=[1, 2]),
Curve(2, curve_type='circle', points=[2, 1, 3]),
Curve(3, curve_type='line', points=[3, 1])]
self.surfaces = [Surface(1, points=[1, 2, 3, 1], curves=[1, 2, 3])]
return
def calc_radial_points(self, c, r, orientation, angle):
point = c[:]
if orientation == 0: # x-dir
point[1] += np.round(r * np.sin(angle), 5)
point[2] += np.round(r * np.cos(angle), 5)
elif orientation == 1: # y-dir
point[0] += np.round(r * np.sin(angle), 5)
point[2] += np.round(r * np.cos(angle), 5)
else: # z-dir
point[0] += np.round(r * np.sin(angle), 5)
point[1] += np.round(r * np.cos(angle), 5)
return point
class CircleWithHole(Circle):
def __init__(self, c0: list, radius: float, rw: float, orientation: int = 1, angle=360.):
self.rw = rw
super().__init__(c0, radius, orientation, angle)
def define_shapes(self):
if self.angle == 360.: # need 3 curved lines
print("DOESN'T WORK YET")
# self.points = [Point(1, self.c0),
# Point(2, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(0))),
# Point(3, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(120))),
# Point(4, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(240))),
# Point(5, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(0)), lc=1),
# Point(6, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(120)), lc=1),
# Point(7, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(240)), lc=1)]
#
# self.curves = [Curve(1, curve_type='circle', points=[2, 1, 3]),
# Curve(2, curve_type='circle', points=[3, 1, 4]),
# Curve(3, curve_type='circle', points=[4, 1, 2]),
# Curve(4, curve_type='circle', points=[5, 1, 6]),
# Curve(5, curve_type='circle', points=[6, 1, 7]),
# Curve(6, curve_type='circle', points=[7, 1, 5])
# ]
#
# self.surfaces = [Surface(1, points=[2, 3, 4, 2, 5, 6, 7, 5], curves=[1, 2, 3, 4, 5, 6])]
#
# self.physical_curves = [Physical('inner', idxs=[4, 5, 6]),
# Physical('outer', idxs=[1, 2, 3])
# ]
elif self.angle >= 180.: # need 2 curved lines
self.points = [Point(1, self.c0),
Point(2, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(0)), lc=1),
Point(3, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(0))),
Point(4, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(self.angle/2))),
Point(5, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(self.angle))),
Point(6, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(self.angle)), lc=1),
Point(7, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(self.angle/2)), lc=1),
]
self.curves = [Curve(1, curve_type='line', points=[2, 3]),
Curve(2, curve_type='circle', points=[3, 1, 4]),
Curve(3, curve_type='circle', points=[4, 1, 5]),
Curve(4, curve_type='line', points=[5, 6]),
Curve(5, curve_type='circle', points=[6, 1, 7]),
Curve(6, curve_type='circle', points=[7, 1, 2]),]
self.surfaces = [Surface(1, points=[2, 3, 4, 5, 6, 7, 2], curves=[1, 2, 3, 4, 5, 6])]
self.physical_curves = [Physical('inner', idxs=[5, 6]),
Physical('outer', idxs=[2, 3])
]
# self.physical_curves['inner'] = Physical('inner', idxs=[5, 6])
# self.physical_curves['outer'] = Physical('outer', idxs=[2, 3])
else: # need only 1 curved line
self.points = [Point(1, self.c0),
Point(2, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(0)), lc=1),
Point(3, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(0))),
Point(4, self.calc_radial_points(self.c0, self.radius, self.orientation, math.radians(self.angle))),
Point(5, self.calc_radial_points(self.c0, self.rw, self.orientation, math.radians(self.angle)), lc=1)]
self.curves = [Curve(1, curve_type='line', points=[2, 3]),
Curve(2, curve_type='circle', points=[3, 1, 4]),
Curve(3, curve_type='line', points=[4, 5]),
Curve(4, curve_type='circle', points=[5, 1, 2])]
self.surfaces = [Surface(1, points=[2, 3, 4, 5, 2], curves=[1, 2, 3, 4])]
self.physical_curves = [Physical('inner', idxs=[4]),
Physical('outer', idxs=[2])
]
return | PypiClean |
/nwpoetry-1.0.0b100.tar.gz/nwpoetry-1.0.0b100/poetry/packages/constraints/multi_constraint.py | from .base_constraint import BaseConstraint
from .constraint import Constraint
class MultiConstraint(BaseConstraint):
def __init__(self, *constraints):
if any(c.operator == "==" for c in constraints):
raise ValueError(
"A multi-constraint can only be comprised of negative constraints"
)
self._constraints = constraints
@property
def constraints(self):
return self._constraints
def allows(self, other):
for constraint in self._constraints:
if not constraint.allows(other):
return False
return True
def allows_all(self, other):
if other.is_any():
return False
if other.is_empty():
return True
if isinstance(other, Constraint):
return self.allows(other)
our_constraints = iter(self._constraints)
their_constraints = iter(other.constraints)
our_constraint = next(our_constraints, None)
their_constraint = next(their_constraints, None)
while our_constraint and their_constraint:
if our_constraint.allows_all(their_constraint):
their_constraint = next(their_constraints, None)
else:
our_constraint = next(our_constraints, None)
return their_constraint is None
def allows_any(self, other):
if other.is_any():
return True
if other.is_empty():
return True
if isinstance(other, Constraint):
return self.allows(other)
if isinstance(other, MultiConstraint):
for c1 in self.constraints:
for c2 in other.constraints:
if c1.allows(c2):
return True
return False
def intersect(self, other):
if isinstance(other, Constraint):
constraints = self._constraints
if other not in constraints:
constraints += (other,)
else:
constraints = (other,)
if len(constraints) == 1:
return constraints[0]
return MultiConstraint(*constraints)
def __eq__(self, other):
if not isinstance(other, MultiConstraint):
return False
return sorted(
self._constraints, key=lambda c: (c.operator, c.version)
) == sorted(other.constraints, key=lambda c: (c.operator, c.version))
def __str__(self):
constraints = []
for constraint in self._constraints:
constraints.append(str(constraint))
return "{}".format(", ").join(constraints) | PypiClean |
/followee_notifier-0.0.1a0.tar.gz/followee_notifier-0.0.1a0/followee_notifier/platforms/twitter.py | import json
import time
import requests
from typing import Any
from http.cookies import SimpleCookie
from urllib.parse import quote, unquote, parse_qs
from followee_notifier.types import Follower
NAME = 'twitter'
FEATURES = {
'blue_business_profile_image_shape_enabled': True,
'responsive_web_graphql_exclude_directive_enabled': True,
'verified_phone_label_enabled': False,
'responsive_web_graphql_timeline_navigation_enabled': True,
'responsive_web_graphql_skip_user_profile_image_extensions_enabled': False,
'tweetypie_unmention_optimization_enabled': True,
'vibe_api_enabled': True,
'responsive_web_edit_tweet_api_enabled': True,
'graphql_is_translatable_rweb_tweet_is_translatable_enabled': True,
'view_counts_everywhere_api_enabled': True,
'longform_notetweets_consumption_enabled': True,
'tweet_awards_web_tipping_enabled': False,
'freedom_of_speech_not_reach_fetch_enabled': True,
'standardized_nudges_misinfo': True,
'tweet_with_visibility_results_prefer_gql_limited_actions_policy_enabled': False,
'interactive_text_enabled': True,
'responsive_web_text_conversations_enabled': False,
'longform_notetweets_rich_text_read_enabled': True,
'responsive_web_enhance_cards_enabled': False,
}
GRAPHQL_JSON_SEPARATOR = (',', ':')
REQUEST_INTERVAL = 3
def parse_cookies(cookies_str: str) -> dict[str, str]:
cookies = SimpleCookie()
cookies.load(cookies_str)
cookies = {key: morsel.value for key, morsel in cookies.items()}
for key in ['ct0', 'dnt', 'twid']:
if key in cookies.keys():
del cookies[key]
return cookies
def graphql_url(url: str, params: dict[str, Any]) -> str:
url += '?'
for key, value in params.items():
url += f'{key}={quote(json.dumps(value, separators=GRAPHQL_JSON_SEPARATOR))}&'
if url[-1] == '&':
url = url[:-1]
return url
def fetch(config: dict[str, Any]) -> list[Follower]:
result = []
cookies = parse_cookies(config['cookies'])
session = requests.Session()
session.cookies.update(cookies)
# Requset #1 - Get CSRF Token
url = f'https://twitter.com/{config["username"]}/followers'
print('[HTTP] >', 'GET', url)
res = session.get(url)
print('[HTTP] <', res.status_code)
referer = res.url
csrf_token = res.cookies['ct0']
twitter_id = parse_qs(unquote(res.cookies['twid']))['u'][0]
print(f'[DATA] Twitter ID: {twitter_id}, CSRF Token: {csrf_token}')
# Request #2 - Get Followers
next_cursor = None
while True:
graphql_variables = {"userId": str(twitter_id), "count":20, "includePromotedContent": False}
if next_cursor:
graphql_variables['cursor'] = next_cursor
url = graphql_url('https://twitter.com/i/api/graphql/djdTXDIk2qhd4OStqlUFeQ/Followers', {
'variables': graphql_variables,
'features': FEATURES,
})
print('[HTTP] >', 'GET', url)
res = session.get(url, headers={
'referer': referer,
'x-csrf-token': csrf_token,
'authorization': f'Bearer {config["auth"]}',
})
print('[HTTP] <', res.status_code)
if res.status_code == 429:
rate_limit_reset = res.headers['x-rate-limit-reset']
next_cursor = next_cursor
sleep_time = int(rate_limit_reset) - int(time.time())
print('[RATE LIMIT]', f'Wait for {sleep_time} seconds.')
time.sleep(sleep_time)
continue
assert res.status_code == 200
instructions = res.json()['data']['user']['result']['timeline']['timeline']['instructions']
followers = list(filter(lambda x: x['type'] == 'TimelineAddEntries', instructions))[0]['entries']
next_cursor = list(filter(lambda x: (x['content']['entryType'] == 'TimelineTimelineCursor') and (x['content'].get('cursorType') == 'Bottom'), followers))[0]
followers = list(filter(lambda x: (x['content']['entryType'] == 'TimelineTimelineItem'), followers))
followers = list(filter(lambda x: x['content'].get('itemContent', {}).get("itemType") == "TimelineUser", followers))
followers = [i['content']['itemContent']['user_results']['result'] for i in followers]
followers = [{
'id': i['rest_id'],
'url': f'https://twitter.com/i/user/{i["rest_id"]}',
'name': i['legacy']['screen_name'],
'display_name': i['legacy']['name'],
'avatar': i['legacy'].get('profile_image_url_https', 'data:,'),
'description': i['legacy'].get('description', ''),
} for i in followers]
result += followers
print(f'[DATA] Fetched {len(followers)} followers, total fatched {len(result)} followers.')
next_cursor = next_cursor['content']['value']
print(f'[DATA] Next cursor: {next_cursor}')
if next_cursor.startswith('0|'):
print('[DATA] No more followers. Stop fetching.')
break
time.sleep(REQUEST_INTERVAL)
return result | PypiClean |
/websauna.newsletter-1.0a3.tar.gz/websauna.newsletter-1.0a3/websauna/newsletter/importer.py | # Standard Library
import logging
from typing import Optional
# Pyramid
from transaction import TransactionManager
# Websauna
from websauna.system.model.retry import ensure_transactionless
from websauna.system.model.retry import retryable
from websauna.system.user.models import User
from .mailgun import Mailgun
logger = logging.getLogger(__name__)
def import_subscriber(mailgun: Mailgun, address: str, user: User, upsert=True) -> bool:
"""Add one subscriber to the mailing list.
:return: True if user was fresh and imported
"""
# Track import status in user_data JSON, so we don't do double requests if the user has already been subscribed once
mailing_list_subscribes = user.user_data.get("mailing_list_subscribes", [])
if address not in mailing_list_subscribes:
# Some sanity logic to filter out emails that are legit in some services, unlegit in Mailgun
first_part, second_part = address.split("@")
if first_part.startswith(".") or first_part.endswith("."):
logger.info("Bad email address: %s", address)
return False
logger.info("Subscribing %s to %s", user.email, address)
# Don't set subscribed field, so that we don't accidentally update unsubscribed users
data = {
"address": user.email,
"name": user.friendly_name,
"upsert": upsert and "yes" or "no",
}
try:
mailgun.update_subscription(address, data)
except Exception as e:
logger.error("Failed to subscribe email %s: %s", user.email, e)
return False
mailing_list_subscribes.append(address)
user.user_data["mailing_list_subscribes"] = mailing_list_subscribes
return True
return False
def import_all_users(mailgun: Mailgun, dbsession, address: str, tm: Optional[TransactionManager]=None) -> int:
"""Update Mail subscribers database from Websauna internal database.
:return: Imported count
"""
if tm is None:
tm = dbsession.transaction_manager
count = 0
for obj in dbsession:
print(obj)
# Make sure we don't have a transaction in progress as we do batching ourselves
ensure_transactionless(transaction_manager=tm)
@retryable(tm=tm)
def tx1():
"""Get user ids on the first transaction."""
return [u.id for u in dbsession.query(User.id).all()]
@retryable(tm=tm)
def tx_n(id):
"""For each user, import it in a subsequent transaction."""
u = dbsession.query(User).get(id)
if import_subscriber(mailgun, address, u):
return 1
else:
return 0
user_ids = tx1()
for id in user_ids:
count += tx_n(id)
logger.info("Imported %d users", count)
return count | PypiClean |
/django-dojo-0.0.1.tar.gz/django-dojo-0.0.1/dojo/static/dojo/dojox/grid/enhanced/plugins/GridSource.js | define([
"dojo/_base/declare",
"dojo/_base/array",
"dojo/_base/lang",
"dojo/dnd/Source",
"./DnD"
], function(declare, array, lang, Source, DnD){
var _joinToArray = function(arrays){
var a = arrays[0];
for(var i = 1; i < arrays.length; ++i){
a = a.concat(arrays[i]);
}
return a;
};
var GridDnDSource = lang.getObject("dojox.grid.enhanced.plugins.GridDnDSource");
return declare("dojox.grid.enhanced.plugins.GridSource", Source, {
// summary:
// A special source that can accept grid contents.
// Only for non-grid widgets or domNodes.
accept: ["grid/cells", "grid/rows", "grid/cols", "text"],
// insertNodesForGrid:
// If you'd like to insert some sort of nodes into your dnd source, turn this on,
// and override getCellContent/getRowContent/getColumnContent
// to populate the dnd data in your desired format.
insertNodesForGrid: false,
markupFactory: function(params, node){
cls = lang.getObject("dojox.grid.enhanced.plugins.GridSource");
return new cls(node, params);
},
checkAcceptance: function(source, nodes){
if(source instanceof GridDnDSource){
if(nodes[0]){
var item = source.getItem(nodes[0].id);
if(item && (array.indexOf(item.type, "grid/rows") >= 0 || array.indexOf(item.type, "grid/cells") >= 0) &&
!source.dndPlugin._allDnDItemsLoaded()){
return false;
}
}
this.sourcePlugin = source.dndPlugin;
}
return this.inherited(arguments);
},
onDraggingOver: function(){
if(this.sourcePlugin){
this.sourcePlugin._isSource = true;
}
},
onDraggingOut: function(){
if(this.sourcePlugin){
this.sourcePlugin._isSource = false;
}
},
onDropExternal: function(source, nodes, copy){
if(source instanceof GridDnDSource){
var ranges = array.map(nodes, function(node){
return source.getItem(node.id).data;
});
var item = source.getItem(nodes[0].id);
var grid = item.dndPlugin.grid;
var type = item.type[0];
var range;
try{
switch(type){
case "grid/cells":
nodes[0].innerHTML = this.getCellContent(grid, ranges[0].min, ranges[0].max) || "";
this.onDropGridCells(grid, ranges[0].min, ranges[0].max);
break;
case "grid/rows":
range = _joinToArray(ranges);
nodes[0].innerHTML = this.getRowContent(grid, range) || "";
this.onDropGridRows(grid, range);
break;
case "grid/cols":
range = _joinToArray(ranges);
nodes[0].innerHTML = this.getColumnContent(grid, range) || "";
this.onDropGridColumns(grid, range);
break;
}
if(this.insertNodesForGrid){
this.selectNone();
this.insertNodes(true, [nodes[0]], this.before, this.current);
}
item.dndPlugin.onDragOut(!copy);
}catch(e){
console.warn("GridSource.onDropExternal() error:",e);
}
}else{
this.inherited(arguments);
}
},
getCellContent: function(grid, leftTopCell, rightBottomCell){
// summary:
// Fill node innerHTML for dnd grid cells.
// example:
// | var cells = grid.layout.cells;
// | var store = grid.store;
// | var cache = grid._by_idx;
// | var res = "Grid Cells from " + grid.id + ":<br/>";
// | for(var r = leftTopCell.row; r <= rightBottomCell.row; ++r){
// | for(var c = leftTopCell.col; c <= rightBottomCell.col; ++c){
// | res += store.getValue(cache[r].item, cells[c].field) + ", ";
// | }
// | res = res.substring(0, res.length - 2) + ";<br/>";
// | }
// | return res;
},
getRowContent: function(grid, rowIndexes){
// summary:
// Fill node innerHTML for dnd grid rows.
// example:
// | var cells = grid.layout.cells;
// | var store = grid.store;
// | var cache = grid._by_idx;
// | var res = "Grid Rows from " + grid.id + ":<br/>";
// | for(var i = 0; i < rowIndexes.length; ++i){
// | var r = rowIndexes[i];
// | res += "Row " + r + ": ";
// | for(var j = 0; j < cells.length; ++j){
// | if(!cells[j].hidden){
// | res += store.getValue(cache[r].item, cells[j].field) + ", ";
// | }
// | }
// | res = res.substring(0, res.length - 2) + ";<br/>";
// | }
// | return res;
},
getColumnContent: function(grid, colIndexes){
// summary:
// Fill node innerHTML for dnd grid columns.
// example:
// | var cells = grid.layout.cells;
// | var res = "Grid Columns from " + grid.id + ":";
// | for(var i = 0; i < colIndexes.length; ++i){
// | var c = colIndexes[i];
// | res += (cells[c].name || cells[c].field) + ", ";
// | }
// | return res.substring(0, res.length - 2);
},
onDropGridCells: function(grid, leftTopCell, rightBottomCell){
},
onDropGridRows: function(grid, rowIndexes){
},
onDropGridColumns: function(grid, colIndexes){
}
});
}); | PypiClean |
/cdktf_cdktf_provider_aws-17.0.2-py3-none-any.whl/cdktf_cdktf_provider_aws/servicecatalog_portfolio_share/__init__.py | import abc
import builtins
import datetime
import enum
import typing
import jsii
import publication
import typing_extensions
from typeguard import check_type
from .._jsii import *
import cdktf as _cdktf_9a9027ec
import constructs as _constructs_77d1e7e8
class ServicecatalogPortfolioShare(
_cdktf_9a9027ec.TerraformResource,
metaclass=jsii.JSIIMeta,
jsii_type="@cdktf/provider-aws.servicecatalogPortfolioShare.ServicecatalogPortfolioShare",
):
'''Represents a {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share aws_servicecatalog_portfolio_share}.'''
def __init__(
self,
scope: _constructs_77d1e7e8.Construct,
id_: builtins.str,
*,
portfolio_id: builtins.str,
principal_id: builtins.str,
type: builtins.str,
accept_language: typing.Optional[builtins.str] = None,
id: typing.Optional[builtins.str] = None,
share_principals: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
share_tag_options: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
timeouts: typing.Optional[typing.Union["ServicecatalogPortfolioShareTimeouts", typing.Dict[builtins.str, typing.Any]]] = None,
wait_for_acceptance: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
connection: typing.Optional[typing.Union[typing.Union[_cdktf_9a9027ec.SSHProvisionerConnection, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.WinrmProvisionerConnection, typing.Dict[builtins.str, typing.Any]]]] = None,
count: typing.Optional[typing.Union[jsii.Number, _cdktf_9a9027ec.TerraformCount]] = None,
depends_on: typing.Optional[typing.Sequence[_cdktf_9a9027ec.ITerraformDependable]] = None,
for_each: typing.Optional[_cdktf_9a9027ec.ITerraformIterator] = None,
lifecycle: typing.Optional[typing.Union[_cdktf_9a9027ec.TerraformResourceLifecycle, typing.Dict[builtins.str, typing.Any]]] = None,
provider: typing.Optional[_cdktf_9a9027ec.TerraformProvider] = None,
provisioners: typing.Optional[typing.Sequence[typing.Union[typing.Union[_cdktf_9a9027ec.FileProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.LocalExecProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.RemoteExecProvisioner, typing.Dict[builtins.str, typing.Any]]]]] = None,
) -> None:
'''Create a new {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share aws_servicecatalog_portfolio_share} Resource.
:param scope: The scope in which to define this construct.
:param id_: The scoped construct ID. Must be unique amongst siblings in the same scope
:param portfolio_id: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#portfolio_id ServicecatalogPortfolioShare#portfolio_id}.
:param principal_id: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#principal_id ServicecatalogPortfolioShare#principal_id}.
:param type: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#type ServicecatalogPortfolioShare#type}.
:param accept_language: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#accept_language ServicecatalogPortfolioShare#accept_language}.
:param id: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#id ServicecatalogPortfolioShare#id}. Please be aware that the id field is automatically added to all resources in Terraform providers using a Terraform provider SDK version below 2. If you experience problems setting this value it might not be settable. Please take a look at the provider documentation to ensure it should be settable.
:param share_principals: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#share_principals ServicecatalogPortfolioShare#share_principals}.
:param share_tag_options: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#share_tag_options ServicecatalogPortfolioShare#share_tag_options}.
:param timeouts: timeouts block. Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#timeouts ServicecatalogPortfolioShare#timeouts}
:param wait_for_acceptance: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#wait_for_acceptance ServicecatalogPortfolioShare#wait_for_acceptance}.
:param connection:
:param count:
:param depends_on:
:param for_each:
:param lifecycle:
:param provider:
:param provisioners:
'''
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__e2a0362f1c93184e4efea7c8d3b10f0d51151d397a8f6aa504a7119279f6d31a)
check_type(argname="argument scope", value=scope, expected_type=type_hints["scope"])
check_type(argname="argument id_", value=id_, expected_type=type_hints["id_"])
config = ServicecatalogPortfolioShareConfig(
portfolio_id=portfolio_id,
principal_id=principal_id,
type=type,
accept_language=accept_language,
id=id,
share_principals=share_principals,
share_tag_options=share_tag_options,
timeouts=timeouts,
wait_for_acceptance=wait_for_acceptance,
connection=connection,
count=count,
depends_on=depends_on,
for_each=for_each,
lifecycle=lifecycle,
provider=provider,
provisioners=provisioners,
)
jsii.create(self.__class__, self, [scope, id_, config])
@jsii.member(jsii_name="putTimeouts")
def put_timeouts(
self,
*,
create: typing.Optional[builtins.str] = None,
delete: typing.Optional[builtins.str] = None,
read: typing.Optional[builtins.str] = None,
update: typing.Optional[builtins.str] = None,
) -> None:
'''
:param create: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#create ServicecatalogPortfolioShare#create}.
:param delete: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#delete ServicecatalogPortfolioShare#delete}.
:param read: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#read ServicecatalogPortfolioShare#read}.
:param update: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#update ServicecatalogPortfolioShare#update}.
'''
value = ServicecatalogPortfolioShareTimeouts(
create=create, delete=delete, read=read, update=update
)
return typing.cast(None, jsii.invoke(self, "putTimeouts", [value]))
@jsii.member(jsii_name="resetAcceptLanguage")
def reset_accept_language(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetAcceptLanguage", []))
@jsii.member(jsii_name="resetId")
def reset_id(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetId", []))
@jsii.member(jsii_name="resetSharePrincipals")
def reset_share_principals(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetSharePrincipals", []))
@jsii.member(jsii_name="resetShareTagOptions")
def reset_share_tag_options(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetShareTagOptions", []))
@jsii.member(jsii_name="resetTimeouts")
def reset_timeouts(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetTimeouts", []))
@jsii.member(jsii_name="resetWaitForAcceptance")
def reset_wait_for_acceptance(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetWaitForAcceptance", []))
@jsii.member(jsii_name="synthesizeAttributes")
def _synthesize_attributes(self) -> typing.Mapping[builtins.str, typing.Any]:
return typing.cast(typing.Mapping[builtins.str, typing.Any], jsii.invoke(self, "synthesizeAttributes", []))
@jsii.python.classproperty
@jsii.member(jsii_name="tfResourceType")
def TF_RESOURCE_TYPE(cls) -> builtins.str:
return typing.cast(builtins.str, jsii.sget(cls, "tfResourceType"))
@builtins.property
@jsii.member(jsii_name="accepted")
def accepted(self) -> _cdktf_9a9027ec.IResolvable:
return typing.cast(_cdktf_9a9027ec.IResolvable, jsii.get(self, "accepted"))
@builtins.property
@jsii.member(jsii_name="timeouts")
def timeouts(self) -> "ServicecatalogPortfolioShareTimeoutsOutputReference":
return typing.cast("ServicecatalogPortfolioShareTimeoutsOutputReference", jsii.get(self, "timeouts"))
@builtins.property
@jsii.member(jsii_name="acceptLanguageInput")
def accept_language_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "acceptLanguageInput"))
@builtins.property
@jsii.member(jsii_name="idInput")
def id_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "idInput"))
@builtins.property
@jsii.member(jsii_name="portfolioIdInput")
def portfolio_id_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "portfolioIdInput"))
@builtins.property
@jsii.member(jsii_name="principalIdInput")
def principal_id_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "principalIdInput"))
@builtins.property
@jsii.member(jsii_name="sharePrincipalsInput")
def share_principals_input(
self,
) -> typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]]:
return typing.cast(typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]], jsii.get(self, "sharePrincipalsInput"))
@builtins.property
@jsii.member(jsii_name="shareTagOptionsInput")
def share_tag_options_input(
self,
) -> typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]]:
return typing.cast(typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]], jsii.get(self, "shareTagOptionsInput"))
@builtins.property
@jsii.member(jsii_name="timeoutsInput")
def timeouts_input(
self,
) -> typing.Optional[typing.Union[_cdktf_9a9027ec.IResolvable, "ServicecatalogPortfolioShareTimeouts"]]:
return typing.cast(typing.Optional[typing.Union[_cdktf_9a9027ec.IResolvable, "ServicecatalogPortfolioShareTimeouts"]], jsii.get(self, "timeoutsInput"))
@builtins.property
@jsii.member(jsii_name="typeInput")
def type_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "typeInput"))
@builtins.property
@jsii.member(jsii_name="waitForAcceptanceInput")
def wait_for_acceptance_input(
self,
) -> typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]]:
return typing.cast(typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]], jsii.get(self, "waitForAcceptanceInput"))
@builtins.property
@jsii.member(jsii_name="acceptLanguage")
def accept_language(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "acceptLanguage"))
@accept_language.setter
def accept_language(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__4b570a3fed85a4cb18e8efa39595b5263d0172365a8712d03fc62b27986316b9)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "acceptLanguage", value)
@builtins.property
@jsii.member(jsii_name="id")
def id(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "id"))
@id.setter
def id(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__98b1fd2b7f07281d51541d66c7f10281cf184092191df31cff95d7bcab2034c4)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "id", value)
@builtins.property
@jsii.member(jsii_name="portfolioId")
def portfolio_id(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "portfolioId"))
@portfolio_id.setter
def portfolio_id(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__acc2cae5f1d65dff8ffa71551cd4d4033f37c922c9fe0ce2308c954eed2e9058)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "portfolioId", value)
@builtins.property
@jsii.member(jsii_name="principalId")
def principal_id(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "principalId"))
@principal_id.setter
def principal_id(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__963007e5c07679a02e2f51426af01eb6a53c5763453e56c32c6cb715a50ff946)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "principalId", value)
@builtins.property
@jsii.member(jsii_name="sharePrincipals")
def share_principals(
self,
) -> typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]:
return typing.cast(typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable], jsii.get(self, "sharePrincipals"))
@share_principals.setter
def share_principals(
self,
value: typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable],
) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__6bdc1e032fb1837768d0050f9f20ba20e35a5a39ad7aca6ae4de797d1666fb6b)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "sharePrincipals", value)
@builtins.property
@jsii.member(jsii_name="shareTagOptions")
def share_tag_options(
self,
) -> typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]:
return typing.cast(typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable], jsii.get(self, "shareTagOptions"))
@share_tag_options.setter
def share_tag_options(
self,
value: typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable],
) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__dbd73049b29d4fa30e6fa9f509a1fb5799586cad9e360643ac209ab82a10f59f)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "shareTagOptions", value)
@builtins.property
@jsii.member(jsii_name="type")
def type(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "type"))
@type.setter
def type(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__a5cc9949f13f969cae9dde441712da3a0a507df8ce55d19824dfaf0efafbf01f)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "type", value)
@builtins.property
@jsii.member(jsii_name="waitForAcceptance")
def wait_for_acceptance(
self,
) -> typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]:
return typing.cast(typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable], jsii.get(self, "waitForAcceptance"))
@wait_for_acceptance.setter
def wait_for_acceptance(
self,
value: typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable],
) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__dfeb81cfbefb7b31819fc305681d7a25095565a1efedf2de5689475a0b8d19c2)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "waitForAcceptance", value)
@jsii.data_type(
jsii_type="@cdktf/provider-aws.servicecatalogPortfolioShare.ServicecatalogPortfolioShareConfig",
jsii_struct_bases=[_cdktf_9a9027ec.TerraformMetaArguments],
name_mapping={
"connection": "connection",
"count": "count",
"depends_on": "dependsOn",
"for_each": "forEach",
"lifecycle": "lifecycle",
"provider": "provider",
"provisioners": "provisioners",
"portfolio_id": "portfolioId",
"principal_id": "principalId",
"type": "type",
"accept_language": "acceptLanguage",
"id": "id",
"share_principals": "sharePrincipals",
"share_tag_options": "shareTagOptions",
"timeouts": "timeouts",
"wait_for_acceptance": "waitForAcceptance",
},
)
class ServicecatalogPortfolioShareConfig(_cdktf_9a9027ec.TerraformMetaArguments):
def __init__(
self,
*,
connection: typing.Optional[typing.Union[typing.Union[_cdktf_9a9027ec.SSHProvisionerConnection, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.WinrmProvisionerConnection, typing.Dict[builtins.str, typing.Any]]]] = None,
count: typing.Optional[typing.Union[jsii.Number, _cdktf_9a9027ec.TerraformCount]] = None,
depends_on: typing.Optional[typing.Sequence[_cdktf_9a9027ec.ITerraformDependable]] = None,
for_each: typing.Optional[_cdktf_9a9027ec.ITerraformIterator] = None,
lifecycle: typing.Optional[typing.Union[_cdktf_9a9027ec.TerraformResourceLifecycle, typing.Dict[builtins.str, typing.Any]]] = None,
provider: typing.Optional[_cdktf_9a9027ec.TerraformProvider] = None,
provisioners: typing.Optional[typing.Sequence[typing.Union[typing.Union[_cdktf_9a9027ec.FileProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.LocalExecProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.RemoteExecProvisioner, typing.Dict[builtins.str, typing.Any]]]]] = None,
portfolio_id: builtins.str,
principal_id: builtins.str,
type: builtins.str,
accept_language: typing.Optional[builtins.str] = None,
id: typing.Optional[builtins.str] = None,
share_principals: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
share_tag_options: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
timeouts: typing.Optional[typing.Union["ServicecatalogPortfolioShareTimeouts", typing.Dict[builtins.str, typing.Any]]] = None,
wait_for_acceptance: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
) -> None:
'''
:param connection:
:param count:
:param depends_on:
:param for_each:
:param lifecycle:
:param provider:
:param provisioners:
:param portfolio_id: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#portfolio_id ServicecatalogPortfolioShare#portfolio_id}.
:param principal_id: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#principal_id ServicecatalogPortfolioShare#principal_id}.
:param type: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#type ServicecatalogPortfolioShare#type}.
:param accept_language: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#accept_language ServicecatalogPortfolioShare#accept_language}.
:param id: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#id ServicecatalogPortfolioShare#id}. Please be aware that the id field is automatically added to all resources in Terraform providers using a Terraform provider SDK version below 2. If you experience problems setting this value it might not be settable. Please take a look at the provider documentation to ensure it should be settable.
:param share_principals: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#share_principals ServicecatalogPortfolioShare#share_principals}.
:param share_tag_options: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#share_tag_options ServicecatalogPortfolioShare#share_tag_options}.
:param timeouts: timeouts block. Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#timeouts ServicecatalogPortfolioShare#timeouts}
:param wait_for_acceptance: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#wait_for_acceptance ServicecatalogPortfolioShare#wait_for_acceptance}.
'''
if isinstance(lifecycle, dict):
lifecycle = _cdktf_9a9027ec.TerraformResourceLifecycle(**lifecycle)
if isinstance(timeouts, dict):
timeouts = ServicecatalogPortfolioShareTimeouts(**timeouts)
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__e2c118b9f27a068600c34d0e2c4508f46c093ba883d65003c36c6f61750cf324)
check_type(argname="argument connection", value=connection, expected_type=type_hints["connection"])
check_type(argname="argument count", value=count, expected_type=type_hints["count"])
check_type(argname="argument depends_on", value=depends_on, expected_type=type_hints["depends_on"])
check_type(argname="argument for_each", value=for_each, expected_type=type_hints["for_each"])
check_type(argname="argument lifecycle", value=lifecycle, expected_type=type_hints["lifecycle"])
check_type(argname="argument provider", value=provider, expected_type=type_hints["provider"])
check_type(argname="argument provisioners", value=provisioners, expected_type=type_hints["provisioners"])
check_type(argname="argument portfolio_id", value=portfolio_id, expected_type=type_hints["portfolio_id"])
check_type(argname="argument principal_id", value=principal_id, expected_type=type_hints["principal_id"])
check_type(argname="argument type", value=type, expected_type=type_hints["type"])
check_type(argname="argument accept_language", value=accept_language, expected_type=type_hints["accept_language"])
check_type(argname="argument id", value=id, expected_type=type_hints["id"])
check_type(argname="argument share_principals", value=share_principals, expected_type=type_hints["share_principals"])
check_type(argname="argument share_tag_options", value=share_tag_options, expected_type=type_hints["share_tag_options"])
check_type(argname="argument timeouts", value=timeouts, expected_type=type_hints["timeouts"])
check_type(argname="argument wait_for_acceptance", value=wait_for_acceptance, expected_type=type_hints["wait_for_acceptance"])
self._values: typing.Dict[builtins.str, typing.Any] = {
"portfolio_id": portfolio_id,
"principal_id": principal_id,
"type": type,
}
if connection is not None:
self._values["connection"] = connection
if count is not None:
self._values["count"] = count
if depends_on is not None:
self._values["depends_on"] = depends_on
if for_each is not None:
self._values["for_each"] = for_each
if lifecycle is not None:
self._values["lifecycle"] = lifecycle
if provider is not None:
self._values["provider"] = provider
if provisioners is not None:
self._values["provisioners"] = provisioners
if accept_language is not None:
self._values["accept_language"] = accept_language
if id is not None:
self._values["id"] = id
if share_principals is not None:
self._values["share_principals"] = share_principals
if share_tag_options is not None:
self._values["share_tag_options"] = share_tag_options
if timeouts is not None:
self._values["timeouts"] = timeouts
if wait_for_acceptance is not None:
self._values["wait_for_acceptance"] = wait_for_acceptance
@builtins.property
def connection(
self,
) -> typing.Optional[typing.Union[_cdktf_9a9027ec.SSHProvisionerConnection, _cdktf_9a9027ec.WinrmProvisionerConnection]]:
'''
:stability: experimental
'''
result = self._values.get("connection")
return typing.cast(typing.Optional[typing.Union[_cdktf_9a9027ec.SSHProvisionerConnection, _cdktf_9a9027ec.WinrmProvisionerConnection]], result)
@builtins.property
def count(
self,
) -> typing.Optional[typing.Union[jsii.Number, _cdktf_9a9027ec.TerraformCount]]:
'''
:stability: experimental
'''
result = self._values.get("count")
return typing.cast(typing.Optional[typing.Union[jsii.Number, _cdktf_9a9027ec.TerraformCount]], result)
@builtins.property
def depends_on(
self,
) -> typing.Optional[typing.List[_cdktf_9a9027ec.ITerraformDependable]]:
'''
:stability: experimental
'''
result = self._values.get("depends_on")
return typing.cast(typing.Optional[typing.List[_cdktf_9a9027ec.ITerraformDependable]], result)
@builtins.property
def for_each(self) -> typing.Optional[_cdktf_9a9027ec.ITerraformIterator]:
'''
:stability: experimental
'''
result = self._values.get("for_each")
return typing.cast(typing.Optional[_cdktf_9a9027ec.ITerraformIterator], result)
@builtins.property
def lifecycle(self) -> typing.Optional[_cdktf_9a9027ec.TerraformResourceLifecycle]:
'''
:stability: experimental
'''
result = self._values.get("lifecycle")
return typing.cast(typing.Optional[_cdktf_9a9027ec.TerraformResourceLifecycle], result)
@builtins.property
def provider(self) -> typing.Optional[_cdktf_9a9027ec.TerraformProvider]:
'''
:stability: experimental
'''
result = self._values.get("provider")
return typing.cast(typing.Optional[_cdktf_9a9027ec.TerraformProvider], result)
@builtins.property
def provisioners(
self,
) -> typing.Optional[typing.List[typing.Union[_cdktf_9a9027ec.FileProvisioner, _cdktf_9a9027ec.LocalExecProvisioner, _cdktf_9a9027ec.RemoteExecProvisioner]]]:
'''
:stability: experimental
'''
result = self._values.get("provisioners")
return typing.cast(typing.Optional[typing.List[typing.Union[_cdktf_9a9027ec.FileProvisioner, _cdktf_9a9027ec.LocalExecProvisioner, _cdktf_9a9027ec.RemoteExecProvisioner]]], result)
@builtins.property
def portfolio_id(self) -> builtins.str:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#portfolio_id ServicecatalogPortfolioShare#portfolio_id}.'''
result = self._values.get("portfolio_id")
assert result is not None, "Required property 'portfolio_id' is missing"
return typing.cast(builtins.str, result)
@builtins.property
def principal_id(self) -> builtins.str:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#principal_id ServicecatalogPortfolioShare#principal_id}.'''
result = self._values.get("principal_id")
assert result is not None, "Required property 'principal_id' is missing"
return typing.cast(builtins.str, result)
@builtins.property
def type(self) -> builtins.str:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#type ServicecatalogPortfolioShare#type}.'''
result = self._values.get("type")
assert result is not None, "Required property 'type' is missing"
return typing.cast(builtins.str, result)
@builtins.property
def accept_language(self) -> typing.Optional[builtins.str]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#accept_language ServicecatalogPortfolioShare#accept_language}.'''
result = self._values.get("accept_language")
return typing.cast(typing.Optional[builtins.str], result)
@builtins.property
def id(self) -> typing.Optional[builtins.str]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#id ServicecatalogPortfolioShare#id}.
Please be aware that the id field is automatically added to all resources in Terraform providers using a Terraform provider SDK version below 2.
If you experience problems setting this value it might not be settable. Please take a look at the provider documentation to ensure it should be settable.
'''
result = self._values.get("id")
return typing.cast(typing.Optional[builtins.str], result)
@builtins.property
def share_principals(
self,
) -> typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#share_principals ServicecatalogPortfolioShare#share_principals}.'''
result = self._values.get("share_principals")
return typing.cast(typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]], result)
@builtins.property
def share_tag_options(
self,
) -> typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#share_tag_options ServicecatalogPortfolioShare#share_tag_options}.'''
result = self._values.get("share_tag_options")
return typing.cast(typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]], result)
@builtins.property
def timeouts(self) -> typing.Optional["ServicecatalogPortfolioShareTimeouts"]:
'''timeouts block.
Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#timeouts ServicecatalogPortfolioShare#timeouts}
'''
result = self._values.get("timeouts")
return typing.cast(typing.Optional["ServicecatalogPortfolioShareTimeouts"], result)
@builtins.property
def wait_for_acceptance(
self,
) -> typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#wait_for_acceptance ServicecatalogPortfolioShare#wait_for_acceptance}.'''
result = self._values.get("wait_for_acceptance")
return typing.cast(typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]], result)
def __eq__(self, rhs: typing.Any) -> builtins.bool:
return isinstance(rhs, self.__class__) and rhs._values == self._values
def __ne__(self, rhs: typing.Any) -> builtins.bool:
return not (rhs == self)
def __repr__(self) -> str:
return "ServicecatalogPortfolioShareConfig(%s)" % ", ".join(
k + "=" + repr(v) for k, v in self._values.items()
)
@jsii.data_type(
jsii_type="@cdktf/provider-aws.servicecatalogPortfolioShare.ServicecatalogPortfolioShareTimeouts",
jsii_struct_bases=[],
name_mapping={
"create": "create",
"delete": "delete",
"read": "read",
"update": "update",
},
)
class ServicecatalogPortfolioShareTimeouts:
def __init__(
self,
*,
create: typing.Optional[builtins.str] = None,
delete: typing.Optional[builtins.str] = None,
read: typing.Optional[builtins.str] = None,
update: typing.Optional[builtins.str] = None,
) -> None:
'''
:param create: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#create ServicecatalogPortfolioShare#create}.
:param delete: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#delete ServicecatalogPortfolioShare#delete}.
:param read: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#read ServicecatalogPortfolioShare#read}.
:param update: Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#update ServicecatalogPortfolioShare#update}.
'''
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__fbb419b22cd2801ebde2d069ab4cab35313fa7a2db9ca57b92be01477a2cfea8)
check_type(argname="argument create", value=create, expected_type=type_hints["create"])
check_type(argname="argument delete", value=delete, expected_type=type_hints["delete"])
check_type(argname="argument read", value=read, expected_type=type_hints["read"])
check_type(argname="argument update", value=update, expected_type=type_hints["update"])
self._values: typing.Dict[builtins.str, typing.Any] = {}
if create is not None:
self._values["create"] = create
if delete is not None:
self._values["delete"] = delete
if read is not None:
self._values["read"] = read
if update is not None:
self._values["update"] = update
@builtins.property
def create(self) -> typing.Optional[builtins.str]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#create ServicecatalogPortfolioShare#create}.'''
result = self._values.get("create")
return typing.cast(typing.Optional[builtins.str], result)
@builtins.property
def delete(self) -> typing.Optional[builtins.str]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#delete ServicecatalogPortfolioShare#delete}.'''
result = self._values.get("delete")
return typing.cast(typing.Optional[builtins.str], result)
@builtins.property
def read(self) -> typing.Optional[builtins.str]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#read ServicecatalogPortfolioShare#read}.'''
result = self._values.get("read")
return typing.cast(typing.Optional[builtins.str], result)
@builtins.property
def update(self) -> typing.Optional[builtins.str]:
'''Docs at Terraform Registry: {@link https://registry.terraform.io/providers/hashicorp/aws/5.15.0/docs/resources/servicecatalog_portfolio_share#update ServicecatalogPortfolioShare#update}.'''
result = self._values.get("update")
return typing.cast(typing.Optional[builtins.str], result)
def __eq__(self, rhs: typing.Any) -> builtins.bool:
return isinstance(rhs, self.__class__) and rhs._values == self._values
def __ne__(self, rhs: typing.Any) -> builtins.bool:
return not (rhs == self)
def __repr__(self) -> str:
return "ServicecatalogPortfolioShareTimeouts(%s)" % ", ".join(
k + "=" + repr(v) for k, v in self._values.items()
)
class ServicecatalogPortfolioShareTimeoutsOutputReference(
_cdktf_9a9027ec.ComplexObject,
metaclass=jsii.JSIIMeta,
jsii_type="@cdktf/provider-aws.servicecatalogPortfolioShare.ServicecatalogPortfolioShareTimeoutsOutputReference",
):
def __init__(
self,
terraform_resource: _cdktf_9a9027ec.IInterpolatingParent,
terraform_attribute: builtins.str,
) -> None:
'''
:param terraform_resource: The parent resource.
:param terraform_attribute: The attribute on the parent resource this class is referencing.
'''
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__0a14096fa0dbe10bc9a507243e1dd4f9160ba825bcaa4dc54a98cd85f190874f)
check_type(argname="argument terraform_resource", value=terraform_resource, expected_type=type_hints["terraform_resource"])
check_type(argname="argument terraform_attribute", value=terraform_attribute, expected_type=type_hints["terraform_attribute"])
jsii.create(self.__class__, self, [terraform_resource, terraform_attribute])
@jsii.member(jsii_name="resetCreate")
def reset_create(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetCreate", []))
@jsii.member(jsii_name="resetDelete")
def reset_delete(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetDelete", []))
@jsii.member(jsii_name="resetRead")
def reset_read(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetRead", []))
@jsii.member(jsii_name="resetUpdate")
def reset_update(self) -> None:
return typing.cast(None, jsii.invoke(self, "resetUpdate", []))
@builtins.property
@jsii.member(jsii_name="createInput")
def create_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "createInput"))
@builtins.property
@jsii.member(jsii_name="deleteInput")
def delete_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "deleteInput"))
@builtins.property
@jsii.member(jsii_name="readInput")
def read_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "readInput"))
@builtins.property
@jsii.member(jsii_name="updateInput")
def update_input(self) -> typing.Optional[builtins.str]:
return typing.cast(typing.Optional[builtins.str], jsii.get(self, "updateInput"))
@builtins.property
@jsii.member(jsii_name="create")
def create(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "create"))
@create.setter
def create(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__c7a1bd1ca2b536b69f80baa35871f702ec649ec74486d35627062e963de4eeda)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "create", value)
@builtins.property
@jsii.member(jsii_name="delete")
def delete(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "delete"))
@delete.setter
def delete(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__decab16dd2ff3d9972f87a0ad1a088cafef1859fd83cd0a9ad7a023e6c60b428)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "delete", value)
@builtins.property
@jsii.member(jsii_name="read")
def read(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "read"))
@read.setter
def read(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__641f4a59df6defc7be8ecc9a92a293fa2d1506ddc1a4ed5831e3e9710de413dd)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "read", value)
@builtins.property
@jsii.member(jsii_name="update")
def update(self) -> builtins.str:
return typing.cast(builtins.str, jsii.get(self, "update"))
@update.setter
def update(self, value: builtins.str) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__fc0dd06161f8effd09aef1eb13d332d1b485713ad2cb8c7d2d3a99884b976d69)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "update", value)
@builtins.property
@jsii.member(jsii_name="internalValue")
def internal_value(
self,
) -> typing.Optional[typing.Union[_cdktf_9a9027ec.IResolvable, ServicecatalogPortfolioShareTimeouts]]:
return typing.cast(typing.Optional[typing.Union[_cdktf_9a9027ec.IResolvable, ServicecatalogPortfolioShareTimeouts]], jsii.get(self, "internalValue"))
@internal_value.setter
def internal_value(
self,
value: typing.Optional[typing.Union[_cdktf_9a9027ec.IResolvable, ServicecatalogPortfolioShareTimeouts]],
) -> None:
if __debug__:
type_hints = typing.get_type_hints(_typecheckingstub__185f881c871d4f500c13daf506b92072ef866035514f49ca7c7145c3fb0018fe)
check_type(argname="argument value", value=value, expected_type=type_hints["value"])
jsii.set(self, "internalValue", value)
__all__ = [
"ServicecatalogPortfolioShare",
"ServicecatalogPortfolioShareConfig",
"ServicecatalogPortfolioShareTimeouts",
"ServicecatalogPortfolioShareTimeoutsOutputReference",
]
publication.publish()
def _typecheckingstub__e2a0362f1c93184e4efea7c8d3b10f0d51151d397a8f6aa504a7119279f6d31a(
scope: _constructs_77d1e7e8.Construct,
id_: builtins.str,
*,
portfolio_id: builtins.str,
principal_id: builtins.str,
type: builtins.str,
accept_language: typing.Optional[builtins.str] = None,
id: typing.Optional[builtins.str] = None,
share_principals: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
share_tag_options: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
timeouts: typing.Optional[typing.Union[ServicecatalogPortfolioShareTimeouts, typing.Dict[builtins.str, typing.Any]]] = None,
wait_for_acceptance: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
connection: typing.Optional[typing.Union[typing.Union[_cdktf_9a9027ec.SSHProvisionerConnection, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.WinrmProvisionerConnection, typing.Dict[builtins.str, typing.Any]]]] = None,
count: typing.Optional[typing.Union[jsii.Number, _cdktf_9a9027ec.TerraformCount]] = None,
depends_on: typing.Optional[typing.Sequence[_cdktf_9a9027ec.ITerraformDependable]] = None,
for_each: typing.Optional[_cdktf_9a9027ec.ITerraformIterator] = None,
lifecycle: typing.Optional[typing.Union[_cdktf_9a9027ec.TerraformResourceLifecycle, typing.Dict[builtins.str, typing.Any]]] = None,
provider: typing.Optional[_cdktf_9a9027ec.TerraformProvider] = None,
provisioners: typing.Optional[typing.Sequence[typing.Union[typing.Union[_cdktf_9a9027ec.FileProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.LocalExecProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.RemoteExecProvisioner, typing.Dict[builtins.str, typing.Any]]]]] = None,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__4b570a3fed85a4cb18e8efa39595b5263d0172365a8712d03fc62b27986316b9(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__98b1fd2b7f07281d51541d66c7f10281cf184092191df31cff95d7bcab2034c4(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__acc2cae5f1d65dff8ffa71551cd4d4033f37c922c9fe0ce2308c954eed2e9058(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__963007e5c07679a02e2f51426af01eb6a53c5763453e56c32c6cb715a50ff946(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__6bdc1e032fb1837768d0050f9f20ba20e35a5a39ad7aca6ae4de797d1666fb6b(
value: typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable],
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__dbd73049b29d4fa30e6fa9f509a1fb5799586cad9e360643ac209ab82a10f59f(
value: typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable],
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__a5cc9949f13f969cae9dde441712da3a0a507df8ce55d19824dfaf0efafbf01f(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__dfeb81cfbefb7b31819fc305681d7a25095565a1efedf2de5689475a0b8d19c2(
value: typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable],
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__e2c118b9f27a068600c34d0e2c4508f46c093ba883d65003c36c6f61750cf324(
*,
connection: typing.Optional[typing.Union[typing.Union[_cdktf_9a9027ec.SSHProvisionerConnection, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.WinrmProvisionerConnection, typing.Dict[builtins.str, typing.Any]]]] = None,
count: typing.Optional[typing.Union[jsii.Number, _cdktf_9a9027ec.TerraformCount]] = None,
depends_on: typing.Optional[typing.Sequence[_cdktf_9a9027ec.ITerraformDependable]] = None,
for_each: typing.Optional[_cdktf_9a9027ec.ITerraformIterator] = None,
lifecycle: typing.Optional[typing.Union[_cdktf_9a9027ec.TerraformResourceLifecycle, typing.Dict[builtins.str, typing.Any]]] = None,
provider: typing.Optional[_cdktf_9a9027ec.TerraformProvider] = None,
provisioners: typing.Optional[typing.Sequence[typing.Union[typing.Union[_cdktf_9a9027ec.FileProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.LocalExecProvisioner, typing.Dict[builtins.str, typing.Any]], typing.Union[_cdktf_9a9027ec.RemoteExecProvisioner, typing.Dict[builtins.str, typing.Any]]]]] = None,
portfolio_id: builtins.str,
principal_id: builtins.str,
type: builtins.str,
accept_language: typing.Optional[builtins.str] = None,
id: typing.Optional[builtins.str] = None,
share_principals: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
share_tag_options: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
timeouts: typing.Optional[typing.Union[ServicecatalogPortfolioShareTimeouts, typing.Dict[builtins.str, typing.Any]]] = None,
wait_for_acceptance: typing.Optional[typing.Union[builtins.bool, _cdktf_9a9027ec.IResolvable]] = None,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__fbb419b22cd2801ebde2d069ab4cab35313fa7a2db9ca57b92be01477a2cfea8(
*,
create: typing.Optional[builtins.str] = None,
delete: typing.Optional[builtins.str] = None,
read: typing.Optional[builtins.str] = None,
update: typing.Optional[builtins.str] = None,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__0a14096fa0dbe10bc9a507243e1dd4f9160ba825bcaa4dc54a98cd85f190874f(
terraform_resource: _cdktf_9a9027ec.IInterpolatingParent,
terraform_attribute: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__c7a1bd1ca2b536b69f80baa35871f702ec649ec74486d35627062e963de4eeda(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__decab16dd2ff3d9972f87a0ad1a088cafef1859fd83cd0a9ad7a023e6c60b428(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__641f4a59df6defc7be8ecc9a92a293fa2d1506ddc1a4ed5831e3e9710de413dd(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__fc0dd06161f8effd09aef1eb13d332d1b485713ad2cb8c7d2d3a99884b976d69(
value: builtins.str,
) -> None:
"""Type checking stubs"""
pass
def _typecheckingstub__185f881c871d4f500c13daf506b92072ef866035514f49ca7c7145c3fb0018fe(
value: typing.Optional[typing.Union[_cdktf_9a9027ec.IResolvable, ServicecatalogPortfolioShareTimeouts]],
) -> None:
"""Type checking stubs"""
pass | PypiClean |
/service_runner-1.0.0b3.tar.gz/service_runner-1.0.0b3/service_runner/service_runner/asset/consumers.py | from asgiref.sync import async_to_sync
from channels.generic.websocket import WebsocketConsumer, AsyncWebsocketConsumer
import json
import base64
from django.http.request import QueryDict
import paramiko
import socket
from threading import Thread
import time
import os
from django.utils.six import StringIO
from django.conf import settings
from .models import Host
def get_key_obj(pkeyobj, pkey_file=None, pkey_obj=None, password=None):
if pkey_file:
with open(pkey_file) as fo:
try:
pkey = pkeyobj.from_private_key(fo, password=password)
return pkey
except:
pass
else:
try:
pkey = pkeyobj.from_private_key(pkey_obj, password=password)
return pkey
except:
pkey_obj.seek(0)
class SSHBridge(object):
"""
桥接WebSocket和ssh
参考：https://blog.51cto.com/hongchen99/2336087
"""
def __init__(self, websocket, simpleuser):
self.websocket = websocket
self.simpleuser = simpleuser
def connect(self, host, user, pwd=None, key=None, port=22, timeout=6, term='xterm', pty_width=80, pty_height=24):
"""
建立SSH连接，放在 self.ssh_channel 通道中，之后直接在通道中交互数据
:param host:
:param user:
:param pwd:
:param key:
:param port:
:param timeout:
:param term:
:param pty_width:
:param pty_height:
:return:
"""
ssh_client = paramiko.SSHClient()
ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
try:
if key:
# 密钥方式认证
pkey = get_key_obj(paramiko.RSAKey, pkey_obj=key, password=pwd) or \
get_key_obj(paramiko.DSSKey, pkey_obj=key, password=pwd) or \
get_key_obj(paramiko.ECDSAKey, pkey_obj=key, password=pwd) or \
get_key_obj(paramiko.Ed25519Key,
pkey_obj=key, password=pwd)
ssh_client.connect(username=user, hostname=host,
port=port, pkey=pkey, timeout=timeout)
else:
ssh_client.connect(hostname=host, port=port,
username=user, password=pwd, timeout=timeout)
except Exception as e:
# print(e)
message = json.dumps({'flag': 'fail', 'message': str(e)})
self.websocket.send_message_or_team(message)
return
transport = ssh_client.get_transport()
"""
另一种方式建立通道
transport = paramiko.Transport((host, port,))
transport.start_client()
transport.auth_password(username=user, password=pwd)
"""
# 打开一个通道
self.ssh_channel = transport.open_session()
# 获取一个终端
self.ssh_channel.get_pty(term=term, width=pty_width, height=pty_height)
# 激活终端，这样就可以登录到终端了，就和我们用类似于xshell登录系统一样
self.ssh_channel.invoke_shell()
# 获取ssh连接主机后的返回内容，例如Linux，会显示上次登录等信息，把这些信息通过WebSocket显示到Web终端。
# 连接建立一次，之后交互数据不会再进入该方法
for i in range(2):
recv = self.ssh_channel.recv(1024).decode('utf-8')
message = json.dumps({'flag': 'msg', 'message': recv})
# print('【WS --websocket--> Web】建立SSH通道后，返回欢迎信息：', message)
self.websocket.send_message_or_team(message)
def close(self):
message = {'flag': 0, 'message': '关闭WebSocket和SSH连接'}
# 向WebSocket发送一个关闭消息
self.websocket.send_message_or_team(json.dumps(message))
try:
# 关闭ssh通道
self.ssh_channel.close()
# 关闭WebSocket连接
self.websocket.close()
except BaseException as e:
# print('关闭WebSocket和SSH连接产生异常：', e)
pass
def _ws_to_ssh(self, data):
"""
尝试发送数据到ssh通道，产生异常则关闭所有连接
"""
try:
# print('【Func --paramiko--> SSH】WebSocket中的数据发送数据到ssh通道：', data)
self.ssh_channel.send(data)
except OSError as e:
# print(e)
self.close()
def _ssh_to_ws(self):
try:
# while True:
while not self.ssh_channel.exit_status_ready():
data = self.ssh_channel.recv(1024).decode('utf-8')
# print('【SSH --paramiko--> Func】获取ssh通道返回的数据：', data)
if len(data) != 0:
message = {'flag': 'msg', 'message': data}
# print('【WS --websocket--> Web】通过WebSocket把信息发回前端，显示到Web终端：', message)
self.websocket.send_message_or_team(json.dumps(message))
else:
break
except:
self.close()
def shell(self, data):
Thread(target=self._ws_to_ssh, args=(data,)).start()
Thread(target=self._ssh_to_ws).start()
"""
t1 = Thread(target=self._ws_to_ssh, args=(data,))
t1.setDaemon(True)
t1.start()
t2 = Thread(target=self._ssh_to_ws)
t2.setDaemon(True)
t2.start()
"""
def resize_pty(self, cols, rows):
self.ssh_channel.resize_pty(width=cols, height=rows)
class WebsshConsumer(WebsocketConsumer):
"""
1、xterm.js 在浏览器端模拟 shell 终端, 监听用户输入通过 websocket 将用户输入的内容上传到 django
2、django 接受到用户上传的内容, 将用户在前端页面输入的内容通过 paramiko 建立的 ssh 通道上传到远程服务器执行
3、paramiko 将远程服务器的处理结果返回给 django
4、django 将 paramiko 返回的结果通过 websocket 返回给用户
5、xterm.js 接收 django 返回的数据并将其写入前端页面
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.host_id = ''
self.simple_user = ''
self.is_team = False
self.team_name = ''
def connect(self):
"""
建立WebSocket连接，并实例化SSHBridge类，在这个对象中建立SSH连接，放在 self.ssh_channel 通道中
:return:
"""
self.host_id = self.scope['url_route']['kwargs'].get('host_id')
# 获取session中的值
self.simple_user = self.scope["user"].username
# print('【Web --websocket--> WS】建立WebSocket通道，当前连接用户：', self.simple_user)
host_obj = Host.objects.get(id=self.host_id)
self.accept()
# WebSocket连接成功后，连接ssh
query_string = self.scope.get('query_string')
ws_args = QueryDict(query_string=query_string, encoding='utf-8')
# # print(ws_args)
# <QueryDict: {'user': ['admin'], 'host': ['192.168.96.20'], 'port': ['22'], 'auth': ['pwd'], 'pwd': ['ZGphbmdvYWRtaW4='], 'key': [''], 'width': ['113'], 'height': ['43']}>
# 根据参数判断是否是协作
team = ws_args.get('team')
if team:
self.is_team = True
self.team_name = "team_{}".format(self.host_id) # 加到这个通道组
async_to_sync(self.channel_layer.group_add)(
self.team_name,
self.channel_name
)
# 用户连接时，同一群组发送消息
self.send_message_or_team(json.dumps(
{'flag': 'user', 'message': '用户 {} 已连接本终端'.format(self.simple_user)}))
width = ws_args.get('width')
height = ws_args.get('height')
width = int(width)
height = int(height) # ssh连接要求int类型：required argument is an integer
ssh_connect_dict = {}
user = self.simple_user
host = host_obj.ip
port = host_obj.ssh_port
port = int(port)
auth = host_obj.ssh_user
pwd = host_obj.ssh_passwd
# if pwd:
# pwd = base64.b64decode(pwd).decode('utf-8')
sshkey_filename_path = host_obj.ssh_key.ssh_key.path if host_obj.ssh_key else None
ssh_connect_dict = {
'host': host,
'user': auth,
'port': port,
'timeout': 30,
'pty_width': width,
'pty_height': height,
'pwd': pwd
}
if sshkey_filename_path:
if not os.path.exists(sshkey_filename_path):
self.send(json.dumps(
{'flag': 'error', 'message': '密钥文件不存在'}))
else:
try:
f = open(sshkey_filename_path, 'r', encoding='utf-8')
key = f.read()
string_io = StringIO()
string_io.write(key)
string_io.flush()
string_io.seek(0)
ssh_connect_dict['key'] = string_io
# os.remove(sshkey_filename_path) # 用完之后删除key文件
except BaseException as e:
# print('打开密钥文件出错', e)
pass
# 建立SSH连接
self.ssh = SSHBridge(websocket=self, simpleuser=self.simple_user)
# print('【WS --SSHBridge--> SSH】连接SSH参数：', ssh_connect_dict)
self.ssh.connect(**ssh_connect_dict)
def disconnect(self, close_code):
# 断开连接
# print('用户 {} 断开WebSocket连接，断开SSH连接'.format(self.simple_user))
try:
if self.is_team:
# 用户连接时，同一群组发送消息
self.send_message_or_team(json.dumps(
{'flag': 'user', 'message': '用户 {} 已断开本终端'.format(self.simple_user)}))
# 退出群组
async_to_sync(self.channel_layer.group_discard)(
self.team_name,
self.channel_name
)
self.ssh.close()
except BaseException as e:
pass
def receive(self, text_data=None, bytes_data=None):
# 从WebSocket中接收消息
text_data = json.loads(text_data) # json字符串转字典
# print('\n\n【Web --websocket--> WS】Web终端按键内容通过WebSocket传到后端：', text_data)
if type(text_data) == dict:
if text_data.get('flag') == 'entered_key':
# 获取前端传过来输入的按键值，并传递给shell
data = text_data.get('entered_key', '')
# print('【WS --SSHBridge--> Func】WebSocket转发SSHBridge：', text_data)
self.ssh.shell(data=data)
else:
cols = text_data['cols']
rows = text_data['rows']
# 改变通道中终端大小
self.ssh.resize_pty(cols=cols, rows=rows)
else:
# print('【！！！】收到的数据不是dict类型')
pass
def send_message_or_team(self, message):
if self.is_team:
async_to_sync(self.channel_layer.group_send)(
self.team_name,
{
'type': 'team_message',
'message': message
}
)
else:
self.send(message)
def team_message(self, event):
message = event['message']
# 发送消息到WebSocket
self.send(message) | PypiClean |
/jupyterhub_url_sharing-0.1.0.tar.gz/jupyterhub_url_sharing-0.1.0/node_modules/jsonpointer/LICENSE.md | The MIT License (MIT)
Copyright (c) 2011-2015 Jan Lehnardt <[email protected]> & Marc Bachmann <https://github.com/marcbachmann>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
| PypiClean |
/botogram-beta-0.7b0.tar.gz/botogram-beta-0.7b0/botogram/api.py |
import os
import requests
# These API methods sends something to a chat
# This list is used to filter which method to check for unavailable chats
SEND_TO_CHAT_METHODS = (
"sendMessage",
"forwardMessage",
"sendPhoto",
"sendAudio",
"sendDocument",
"sendSticker",
"sendVideo",
"sendVoice",
"sendLocation",
"sendChatAction",
"getChat",
)
class APIError(Exception):
"""Something went wrong with the API"""
def __init__(self, response):
self.error_code = response["error_code"]
self.description = response["description"]
msg = "Request failed with code %s. Response from Telegram: \"%s\"" % (
self.error_code, self.description
)
super(APIError, self).__init__(msg)
class ChatUnavailableError(APIError):
"""A chat is unavailable, which means you can't send messages to it"""
def __init__(self, reason, chat_id):
self.reason = reason
self.chat_id = chat_id
if reason == "blocked":
msg = "The user with ID %s blocked your bot" % chat_id
elif reason == "account_deleted":
msg = "The user with ID %s deleted his account" % chat_id
elif reason == "not_contacted":
msg = "The user with ID %s didn't contact you before" % chat_id
elif reason == "not_found":
msg = "The chat with ID %s doesn't exist" % chat_id
elif reason == "kicked":
msg = "The bot was kicked from the group with ID %s" % chat_id
elif reason == "chat_moved":
msg = "The chat with ID %s moved, and the old ID is no longer " \
"valid" % chat_id
else:
raise ValueError("Unknown reason: %s" % reason)
Exception.__init__(self, msg)
class TelegramAPI:
"""Main interface to the Telegram API"""
def __init__(self, api_key, endpoint=None):
# Fill the default API endpoint
if endpoint is None:
endpoint = "https://api.telegram.org/"
self._api_key = api_key
self._endpoint = endpoint
self._session_cache = None
self._session_pid = -1
def _session(self):
"""Get the current requests session"""
# Ensure a new session is created if the PID changes. This is because
# sessions behaves badly if you use them after fork()
if self._session_pid != os.getpid() or self._session_cache is None:
self._session_cache = requests.Session()
self._session_pid = os.getpid()
return self._session_cache
def call(self, method, params=None, files=None, expect=None):
"""Call a method of the API"""
url = self._endpoint + "bot%s/%s" % (self._api_key, method)
response = self._session().get(url, params=params, files=files,
timeout=10)
content = response.json()
if not content["ok"]:
status = content["error_code"]
message = content["description"]
# Special handling for unavailable chats
if method in SEND_TO_CHAT_METHODS:
reason = None
# This happens when the bot tries to send messages to an user
# who blocked the bot
if status == 403 and "blocked" in message:
# Error code # 403
# Bot was blocked by the user
reason = "blocked"
# This happens when the user deleted its account
elif status == 403 and "deactivated" in message:
# Error code # 403
# Forbidden: user is deactivated
reason = "account_deleted"
# This happens, as @BotSupport says, when the Telegram API
# isn't able to determine why your bot can't contact an user
elif status == 400 and "PEER_ID_INVALID" in message:
# Error code # 400
# Bad request: PEER_ID_INVALID
reason = "not_found"
# This happens when the bot can't contact the user or the user
# doesn't exist
elif status == 400 and "not found" in message:
# Error code # 400
# Bad Request: chat not found
reason = "not_found"
# This happens when the bot is kicked from the group chat it's
# trying to send messages to
elif status == 403 and "kicked" in message:
# Error code # 403
# Forbidden: bot was kicked from the group chat
# Forbidden: bot was kicked from the supergroup chat
reason = "kicked"
# This happens when the ID points to a group chat, which was
# migrated to a supergroup chat, thus changing its ID
elif status == 400 and "migrated" in message:
# Error code # 400
# Bad Request: group chat is migrated to a supergroup chat
reason = "chat_moved"
if reason is not None:
raise ChatUnavailableError(reason, params["chat_id"])
raise APIError(content)
# If no special object is expected, return the decoded json.
# Else, return the "pythonized" result
if expect is None:
return content
else:
wrapped = expect(content["result"])
if hasattr(wrapped, "set_api"):
wrapped.set_api(self)
return wrapped
def file_content(self, path):
"""Get the content of an user-submitted file"""
url = self._endpoint + "file/bot%s/%s" % (self._api_key, path)
response = requests.get(url)
return response.content
@property
def token(self):
return self._api_key | PypiClean |
/ansible-kkvesper-2.3.2.0.tar.gz/ansible-kkvesper-2.3.2.0/lib/ansible/modules/storage/netapp/sf_volume_manager.py |
# (c) 2017, NetApp, Inc
#
# This file is part of Ansible
#
# Ansible is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Ansible is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <http://www.gnu.org/licenses/>.
#
ANSIBLE_METADATA = {'metadata_version': '1.0',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
module: sf_volume_manager
short_description: Manage SolidFire volumes
extends_documentation_fragment:
- netapp.solidfire
version_added: '2.3'
author: Sumit Kumar ([email protected])
description:
- Create, destroy, or update volumes on SolidFire
options:
state:
description:
- Whether the specified volume should exist or not.
required: true
choices: ['present', 'absent']
name:
description:
- The name of the volume to manage.
required: true
account_id:
description:
- Account ID for the owner of this volume.
required: true
512emulation:
description:
- Should the volume provide 512-byte sector emulation?
- Required when C(state=present)
required: false
qos:
description: Initial quality of service settings for this volume.
required: false
default: None
attributes:
description: A YAML dictionary of attributes that you would like to apply on this volume.
required: false
default: None
volume_id:
description:
- The ID of the volume to manage or update.
- In order to create multiple volumes with the same name, but different volume_ids, please declare the I(volume_id)
parameter with an arbitary value. However, the specified volume_id will not be assigned to the newly created
volume (since it's an auto-generated property).
required: false
default: None
size:
description:
- The size of the volume in (size_unit).
- Required when C(state = present).
required: false
size_unit:
description:
- The unit used to interpret the size parameter.
required: false
choices: ['bytes', 'b', 'kb', 'mb', 'gb', 'tb', 'pb', 'eb', 'zb', 'yb']
default: 'gb'
access:
required: false
choices: ['readOnly', 'readWrite', 'locked', 'replicationTarget']
description:
- "Access allowed for the volume."
- "readOnly: Only read operations are allowed."
- "readWrite: Reads and writes are allowed."
- "locked: No reads or writes are allowed."
- "replicationTarget: Identify a volume as the target volume for a paired set of volumes. If the volume is not paired, the access status is locked."
- "If unspecified, the access settings of the clone will be the same as the source."
default: None
'''
EXAMPLES = """
- name: Create Volume
sf_volume_manager:
hostname: "{{ solidfire_hostname }}"
username: "{{ solidfire_username }}"
password: "{{ solidfire_password }}"
state: present
name: AnsibleVol
account_id: 3
enable512e: False
size: 1
size_unit: gb
- name: Update Volume
sf_volume_manager:
hostname: "{{ solidfire_hostname }}"
username: "{{ solidfire_username }}"
password: "{{ solidfire_password }}"
state: present
name: AnsibleVol
account_id: 3
access: readWrite
- name: Delete Volume
sf_volume_manager:
hostname: "{{ solidfire_hostname }}"
username: "{{ solidfire_username }}"
password: "{{ solidfire_password }}"
state: absent
name: AnsibleVol
account_id: 2
"""
RETURN = """
msg:
description: Success message
returned: success
type: string
"""
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.pycompat24 import get_exception
import ansible.module_utils.netapp as netapp_utils
HAS_SF_SDK = netapp_utils.has_sf_sdk()
class SolidFireVolume(object):
def __init__(self):
self._size_unit_map = netapp_utils.SF_BYTE_MAP
self.argument_spec = netapp_utils.ontap_sf_host_argument_spec()
self.argument_spec.update(dict(
state=dict(required=True, choices=['present', 'absent']),
name=dict(required=True, type='str'),
account_id=dict(required=True, type='int'),
enable512e=dict(type='bool', aliases=['512emulation']),
qos=dict(required=False, type='str', default=None),
attributes=dict(required=False, type='dict', default=None),
volume_id=dict(type='int', default=None),
size=dict(type='int'),
size_unit=dict(default='gb',
choices=['bytes', 'b', 'kb', 'mb', 'gb', 'tb',
'pb', 'eb', 'zb', 'yb'], type='str'),
access=dict(required=False, type='str', default=None, choices=['readOnly', 'readWrite',
'locked', 'replicationTarget']),
))
self.module = AnsibleModule(
argument_spec=self.argument_spec,
required_if=[
('state', 'present', ['size', 'enable512e'])
],
supports_check_mode=True
)
p = self.module.params
# set up state variables
self.state = p['state']
self.name = p['name']
self.account_id = p['account_id']
self.enable512e = p['enable512e']
self.qos = p['qos']
self.attributes = p['attributes']
self.volume_id = p['volume_id']
self.size_unit = p['size_unit']
if p['size'] is not None:
self.size = p['size'] * self._size_unit_map[self.size_unit]
else:
self.size = None
self.access = p['access']
if HAS_SF_SDK is False:
self.module.fail_json(msg="Unable to import the SolidFire Python SDK")
else:
self.sfe = netapp_utils.create_sf_connection(module=self.module)
def get_volume(self):
"""
Return volume object if found
:return: Details about the volume. None if not found.
:rtype: dict
"""
volume_list = self.sfe.list_volumes_for_account(account_id=self.account_id)
for volume in volume_list.volumes:
if volume.name == self.name:
# Update self.volume_id
if self.volume_id is not None:
if volume.volume_id == self.volume_id and str(volume.delete_time) == "":
return volume
else:
if str(volume.delete_time) == "":
self.volume_id = volume.volume_id
return volume
return None
def create_volume(self):
try:
self.sfe.create_volume(name=self.name,
account_id=self.account_id,
total_size=self.size,
enable512e=self.enable512e,
qos=self.qos,
attributes=self.attributes)
except:
err = get_exception()
self.module.fail_json(msg="Error provisioning volume %s of size %s" % (self.name, self.size),
exception=str(err))
def delete_volume(self):
try:
self.sfe.delete_volume(volume_id=self.volume_id)
except:
err = get_exception()
self.module.fail_json(msg="Error deleting volume %s" % self.volume_id,
exception=str(err))
def update_volume(self):
try:
self.sfe.modify_volume(self.volume_id,
account_id=self.account_id,
access=self.access,
qos=self.qos,
total_size=self.size,
attributes=self.attributes)
except:
err = get_exception()
self.module.fail_json(msg="Error updating volume %s" % self.name,
exception=str(err))
def apply(self):
changed = False
volume_exists = False
update_volume = False
volume_detail = self.get_volume()
if volume_detail:
volume_exists = True
if self.state == 'absent':
# Checking for state change(s) here, and applying it later in the code allows us to support
# check_mode
changed = True
elif self.state == 'present':
if volume_detail.access is not None and self.access is not None and volume_detail.access != self.access:
update_volume = True
changed = True
elif volume_detail.account_id is not None and self.account_id is not None \
and volume_detail.account_id != self.account_id:
update_volume = True
changed = True
elif volume_detail.qos is not None and self.qos is not None and volume_detail.qos != self.qos:
update_volume = True
changed = True
elif volume_detail.total_size is not None and volume_detail.total_size != self.size:
size_difference = abs(float(volume_detail.total_size - self.size))
# Change size only if difference is bigger than 0.001
if size_difference/self.size > 0.001:
update_volume = True
changed = True
elif volume_detail.attributes is not None and self.attributes is not None and \
volume_detail.attributes != self.attributes:
update_volume = True
changed = True
else:
if self.state == 'present':
changed = True
result_message = ""
if changed:
if self.module.check_mode:
result_message = "Check mode, skipping changes"
pass
else:
if self.state == 'present':
if not volume_exists:
self.create_volume()
result_message = "Volume created"
elif update_volume:
self.update_volume()
result_message = "Volume updated"
elif self.state == 'absent':
self.delete_volume()
result_message = "Volume deleted"
self.module.exit_json(changed=changed, msg=result_message)
def main():
v = SolidFireVolume()
v.apply()
if __name__ == '__main__':
main() | PypiClean |
/Pyql700-0.4.9.tar.gz/Pyql700-0.4.9/README.md | # Project: pyql700
## Module: stripes
Uses **imagemagick** now for short texts,
` ./stripes.py textline 62 "Toto je text" `
when print is needed directly:
` ./stripes.py textline 62 "Toto je text" -p `
Prints an image file:
` ./stripes.py print_it 62 imagefile `
Games with an image file - prototype for dither/pattern:
` ./stripes.py image 62 imagefile `
## Module multextimg
Based on PIL and uses
- WIDTH
- maxchars_per_line,
it creates multiline text image with exact HEIGHT dimension. Prototype now.
`./image_ut.py mk 640 "Mnohokrát děkuji." 60
`
| PypiClean |
/tensorflow_cpu-2.14.0rc1-cp311-cp311-macosx_10_15_x86_64.whl/tensorflow/core/function/capture/restore_captures.py | import warnings
from tensorflow.core.function.polymorphism import function_type as function_type_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import handle_data_util
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.trackable import asset
from tensorflow.python.trackable import resource
def get_tensor_from_node(node):
"""Resolves a saved model graph node into a tensor to be captured.
Args:
node: a tensor, variable, or resource to be resolved into a capturable
tensor
Returns:
A list of tensors.
Raises:
ValueError: if the node cannot be converted into a tensor.
"""
with ops.init_scope():
# TODO(b/210144904): Use __tf_tensor__ instead of `is_[...]` checks
if getattr(node, "is_distributed_variable", False):
return node
elif getattr(node, "is_distributed_table", False):
return node
elif getattr(node, "is_sharded_variable", False):
return node
elif resource_variable_ops.is_resource_variable(node):
return node.handle
elif isinstance(node, asset.Asset):
return node.asset_path
elif tensor_util.is_tf_type(node):
return node
elif isinstance(node, resource.CapturableResource):
# Note: this executes restored functions in the CapturableResource.
return node.resource_handle
raise ValueError(f"Cannot convert node {node} to tensor.")
def restore_captures(concrete_function, inputs):
"""Restore captures for the concrete function.
Used at deserialization time. For functions that are being deserialized,
saved model restores objects that tensors were captured from, but functions
only know about their tensors -- object information is destroyed by tracing.
This additional logic extracts the tensors which the function originally
captured.
Args:
concrete_function: the concrete function for which to restore captures
inputs: a list tensors or other Python objects (such as variables) which
contain tensors that were originally captured by the function
"""
bound_inputs = [get_tensor_from_node(obj) for obj in inputs]
# pylint: disable=g-complex-comprehension
bound_variables = [
obj
for obj in inputs
if isinstance(
obj,
(variables_lib.Variable, resource_variable_ops.BaseResourceVariable),
)
]
# TODO(b/205010575): This is only injecting the captured inputs into the
# concrete function, note that we did not modify the FuncGraph
# itself.
captured_inputs_list = []
concrete_function.set_variables(bound_variables)
if bound_inputs:
for bound_input, internal_capture in zip(
bound_inputs, concrete_function.inputs[-len(bound_inputs) :]
):
# Distributed inputs have special logic for capturing, so we call their
# custom restoration methods
if hasattr(bound_input, "__tf_experimental_restore_capture__"):
captured_inputs_list.append(
bound_input.__tf_experimental_restore_capture__(
concrete_function, internal_capture
)
)
else:
captured_inputs_list.append(bound_input)
concrete_function.graph.replace_capture(bound_input, internal_capture)
if internal_capture.dtype == dtypes.resource:
if resource_variable_ops.is_resource_variable(bound_input):
try:
handle = bound_input.handle
except ValueError:
# For mirrored variables we'll copy handle data for components
# as they get captured.
pass
else:
handle_data_util.copy_handle_data(handle, internal_capture)
else:
# TODO(b/213451747): Remove need to call copy_handle_data
handle_data_util.copy_handle_data(bound_input, internal_capture)
# Setting "captures" first means "capture" won't create a new
# placeholder for this input.
concrete_function.graph.capture(bound_input)
if any([inp is None for inp in captured_inputs_list]):
warnings.warn(
"Trying to load ShardedVariables using tf.saved_model.load. "
"This won't work if using a tf.distribute.Strategy, and may "
"use excess memory if not using a Strategy. Ignore this "
"warning if using tf.keras.models.load_model."
)
concrete_function.set_external_captures(captured_inputs_list)
# Update FunctionType with new captures.
if concrete_function.function_type:
concrete_function._function_type = function_type_lib.FunctionType( # pylint: disable=protected-access
concrete_function.function_type.parameters.values(),
concrete_function.graph.function_captures.capture_types,
return_annotation=concrete_function.function_type.output,
) | PypiClean |
/byterec-0.0.1.tar.gz/byterec-0.0.1/torch_ctr/models/din.py | import torch
from torch_ctr.layers import FeaturesEmbedding, SequenceFeaturesEmbedding, MultiLayerPerceptron
from torch.nn import ModuleList, Sequential, Linear, PReLU
from ..activation import Dice
class DIN(torch.nn.Module):
def __init__(self, sequence_field_dims, sparse_field_dims, embed_dim, mlp_dims=None, attention_mlp_dims=[36], activation='dice', dropout=None):
super(DIN, self).__init__()
self.num_seq_fields = len(sequence_field_dims)
self.embedding_sequence = SequenceFeaturesEmbedding(sequence_field_dims, embed_dim, mode="concat")
self.embedding_sparse = FeaturesEmbedding(sparse_field_dims, embed_dim)
self.embed_output_dim = (len(sparse_field_dims) + 2*self.num_seq_fields)*embed_dim
self.mlp = MultiLayerPerceptron(self.embed_output_dim, mlp_dims, dropout, activation="dice")
self.attention_layers = ModuleList([
ActivationUnit(embed_dim, attention_mlp_dims, activation, use_softmax=False) for i in range(self.num_seq_fields)])
def forward(self, x_sequence, x_candidate, x_sparse):
"""
The original paper without dense feature.
:param x_sequence: Long tensor of size ``(batch_size, num_seq_fields, seq_length)``
# each fields is a tensor list
:param x_candidate: Long tensor of size ``(batch_size, num_seq_fields)``
# each fields is a candidate field id for x_sequence
:param x_sparse: Long tensor of size ``(batch_size, num_sparse_fields)``
# sparse: user_profile_feature, candidate_feature, context_feature
"""
#embed_x_sequence: (batch_size, num_seq_fields, seq_length, emb_dim)
#embed_x_candidate: (batch_size, num_seq_fields, emb_dim)
embed_x_sequence, embed_x_candidate = self.embedding_sequence(x_sequence, x_candidate)
embed_x_sparse = self.embedding_sparse(x_sparse) #(batch_size, num_sparse_fields, embed_dim)
pooling_concat = []
for i in range(self.num_seq_fields):
#为每一个序列特征都训练一个独立的激活单元 eg.历史序列item id，历史序列item id品牌
pooling_seq = self.attention_layers[i](embed_x_sequence[:,i,:,:], embed_x_candidate[:,i,:])
pooling_concat.append(pooling_seq.unsqueeze(1)) #(batch_size, 1, emb_dim)
pooling_concat = torch.cat(pooling_concat, dim=1) #(batch_size, num_seq_fields, emb_dim)
mlp_in = torch.cat([pooling_concat.flatten(start_dim=1), embed_x_candidate.flatten(start_dim=1), embed_x_sparse.flatten(start_dim=1)], dim=1) #(batch_size, N)
y = self.mlp(mlp_in)
return torch.sigmoid(y.squeeze(1))
class ActivationUnit(torch.nn.Module):
"""
DIN Attention Layer
"""
def __init__(self, emb_dim, attention_mlp_dims=[36], activation="dice", use_softmax=False):
super(ActivationUnit, self).__init__()
self.emb_dim = emb_dim
self.use_softmax = use_softmax
self.attention = MultiLayerPerceptron(4 * self.emb_dim, attention_mlp_dims, activation=activation)
def forward(self, history, candidate):
"""
:param history: Long tensor of size ``(batch_size, seq_length, emb_dim) ``
:param candidate: Long tensor of size ``(batch_size, emb_dim)``
"""
seq_length = history.size(1)
candidate = candidate.unsqueeze(1).expand(-1, seq_length, -1) #batch_size,seq_length,emb_dim
att_input = torch.cat([candidate, history, candidate - history, candidate * history], dim=-1) # batch_size,seq_length,4*emb_dim
att_weight = self.attention(att_input.view(-1, 4*self.emb_dim)) # #(batch_size*seq_length,4*emb_dim)
att_weight = att_weight.view(-1, seq_length) #(batch_size*seq_length, 1) -> (batch_size,seq_length)
if self.use_softmax:
att_weight = att_weight.softmax(dim=-1)
# (batch_size, seq_length, 1) * (batch_size, seq_length, emb_dim)
output = (att_weight.unsqueeze(-1) * history).sum(dim=1) #(batch_size,emb_dim)
return output | PypiClean |
/plone.app.event-ploneintegration-1.1.zip/plone.app.event-ploneintegration-1.1/README.rst | plone.app.event-ploneintegration
================================
This package integrates plone.app.event into Plone 4 releases, where
plone.app.event is not in the core. This is the case for all Plone 4 releases
at the time of this release.
Installation for Plone 4.1
--------------------------
To install plone.app.event for Plone 4.1, please use the
plone.app.event-ploneintegration package. Include it in your buildout config or
in your integration package's setup.py and apply the
"plone.app.event Plone4 integration" profile.
The plone.app.event-ploneintegration package pulls all dependencies, which are
needed for plone.app.event.
Warning
-------
!!!
Backup! Don't do this on a Plone setups in production, only install
plone.app.event for new setups or report any upgrade issues. Upgrading is yet
not tested and no upgrade steps are provided - this is still a task to do.
Expect weired behavior regarding date/time/timezones and any other bugs.
!!!
Bug reporting
-------------
Please report bugs here: https://github.com/collective/plone.app.event
This url may change to https://github.com/plone/plone.app.event some time soon!
More information
----------------
See: https://python.org/pypi/plone.app.event
| PypiClean |
/inspectnn-0.4.2-py3-none-any.whl/tflite/BuiltinOptions.py |
# namespace: tflite
class BuiltinOptions(object):
NONE = 0
Conv2DOptions = 1
DepthwiseConv2DOptions = 2
ConcatEmbeddingsOptions = 3
LSHProjectionOptions = 4
Pool2DOptions = 5
SVDFOptions = 6
RNNOptions = 7
FullyConnectedOptions = 8
SoftmaxOptions = 9
ConcatenationOptions = 10
AddOptions = 11
L2NormOptions = 12
LocalResponseNormalizationOptions = 13
LSTMOptions = 14
ResizeBilinearOptions = 15
CallOptions = 16
ReshapeOptions = 17
SkipGramOptions = 18
SpaceToDepthOptions = 19
EmbeddingLookupSparseOptions = 20
MulOptions = 21
PadOptions = 22
GatherOptions = 23
BatchToSpaceNDOptions = 24
SpaceToBatchNDOptions = 25
TransposeOptions = 26
ReducerOptions = 27
SubOptions = 28
DivOptions = 29
SqueezeOptions = 30
SequenceRNNOptions = 31
StridedSliceOptions = 32
ExpOptions = 33
TopKV2Options = 34
SplitOptions = 35
LogSoftmaxOptions = 36
CastOptions = 37
DequantizeOptions = 38
MaximumMinimumOptions = 39
ArgMaxOptions = 40
LessOptions = 41
NegOptions = 42
PadV2Options = 43
GreaterOptions = 44
GreaterEqualOptions = 45
LessEqualOptions = 46
SelectOptions = 47
SliceOptions = 48
TransposeConvOptions = 49
SparseToDenseOptions = 50
TileOptions = 51
ExpandDimsOptions = 52
EqualOptions = 53
NotEqualOptions = 54
ShapeOptions = 55
PowOptions = 56
ArgMinOptions = 57
FakeQuantOptions = 58
PackOptions = 59
LogicalOrOptions = 60
OneHotOptions = 61
LogicalAndOptions = 62
LogicalNotOptions = 63
UnpackOptions = 64
FloorDivOptions = 65
SquareOptions = 66
ZerosLikeOptions = 67
FillOptions = 68
BidirectionalSequenceLSTMOptions = 69
BidirectionalSequenceRNNOptions = 70
UnidirectionalSequenceLSTMOptions = 71
FloorModOptions = 72
RangeOptions = 73
ResizeNearestNeighborOptions = 74
LeakyReluOptions = 75
SquaredDifferenceOptions = 76
MirrorPadOptions = 77
AbsOptions = 78
SplitVOptions = 79
UniqueOptions = 80
ReverseV2Options = 81
AddNOptions = 82
GatherNdOptions = 83
CosOptions = 84
WhereOptions = 85
RankOptions = 86
ReverseSequenceOptions = 87
MatrixDiagOptions = 88
QuantizeOptions = 89
MatrixSetDiagOptions = 90
HardSwishOptions = 91
IfOptions = 92
WhileOptions = 93
DepthToSpaceOptions = 94
NonMaxSuppressionV4Options = 95
NonMaxSuppressionV5Options = 96
ScatterNdOptions = 97
SelectV2Options = 98
DensifyOptions = 99
SegmentSumOptions = 100
BatchMatMulOptions = 101 | PypiClean |
/cfw-fido-1.6.0rc3.tar.gz/cfw-fido-1.6.0rc3/fido/update_signatures.py | from __future__ import print_function
from argparse import ArgumentParser
import os
from shutil import rmtree
import sys
import time
from xml.etree import ElementTree as CET
import zipfile
from . import __version__, CONFIG_DIR, query_yes_no
from .prepare import run as prepare_pronom_to_fido
from .versions import get_local_versions
from .pronom.soap import get_pronom_sig_version, get_pronom_signature, NS
from .pronom.http import get_sig_xml_for_puid
DEFAULTS = {
'signatureFileName': 'DROID_SignatureFile-v{0}.xml',
'pronomZipFileName': 'pronom-xml-v{0}.zip',
'fidoSignatureVersion': 'format_extensions.xml',
'containerVersion': 'container-signature-UPDATE-ME.xml', # container version is frozen and needs human attention before updating,
}
OPTIONS = {
'http_throttle': 0.5, # in secs, to prevent DoS of PRONOM server
'tmp_dir': os.path.join(CONFIG_DIR, 'tmp'),
'deleteTempDirectory': True,
}
def run(defaults=None):
"""
Update PRONOM signatures.
Interactive script, requires keyboard input.
"""
print("FIDO signature updater v{}".format(__version__))
defaults = defaults or DEFAULTS
try:
print("Contacting PRONOM...")
currentVersion, signatureFile = sig_version_check(defaults)
download_sig_file(defaults, currentVersion, signatureFile)
print("Extracting PRONOM PUID's from signature file...")
tree = CET.parse(signatureFile)
format_eles = tree.findall('.//sig:FileFormat', NS)
print("Found {} PRONOM FileFormat elements".format(len(format_eles)))
tmpdir, resume = init_sig_download(defaults)
download_signatures(defaults, format_eles, resume, tmpdir)
create_zip_file(defaults, format_eles, currentVersion, tmpdir)
if defaults['deleteTempDirectory']:
print("Deleting temporary folder and files...")
rmtree(tmpdir, ignore_errors=True)
update_versions_xml(defaults, currentVersion)
# TODO: there should be a check here to handle prepare.main exit() signal (-1/0/1/...)
print("Preparing to convert PRONOM formats to FIDO signatures...")
prepare_pronom_to_fido()
print("FIDO signatures successfully updated")
except KeyboardInterrupt:
sys.exit('Aborting update...')
def sig_version_check(defaults):
"""Return a tuple consisting of current sig file version and the derived file name."""
print("Contacting PRONOM...")
currentVersion = get_pronom_sig_version()
if not currentVersion:
sys.exit('Failed to obtain PRONOM signature file version number, please try again.')
print("Querying latest signaturefile version...")
signatureFile = os.path.join(CONFIG_DIR, defaults['signatureFileName'].format(currentVersion))
if os.path.isfile(signatureFile):
print("You already have the latest PRONOM signature file, version", currentVersion)
if not query_yes_no("Update anyway?"):
sys.exit('Aborting update...')
return currentVersion, signatureFile
def download_sig_file(defaults, version, signatureFile):
"""Download the latest version of the PRONOM sigs to signatureFile."""
print("Downloading signature file version {}...".format(version))
currentFile, _ = get_pronom_signature()
if not currentFile:
sys.exit('Failed to obtain PRONOM signature file, please try again.')
print("Writing {0}...".format(defaults['signatureFileName'].format(version)))
with open(signatureFile, 'w') as file_:
file_.write(currentFile)
def init_sig_download(defaults):
"""
Initialise the download of individual PRONOM signatures.
Handles user input and resumption of interupted downloads.
Return a tuple of the temp directory for writing and a boolean resume flag.
"""
print("Downloading signatures can take a while")
if not query_yes_no("Continue and download signatures?"):
sys.exit('Aborting update...')
tmpdir = defaults['tmp_dir']
resume = False
if os.path.isdir(tmpdir):
print("Found previously created temporary folder for download:", tmpdir)
resume = query_yes_no('Do you want to resume download (yes) or start over (no)?')
if resume:
print("Resuming download...")
else:
print("Creating temporary folder for download:", tmpdir)
try:
os.mkdir(tmpdir)
except OSError:
pass
if not os.path.isdir(tmpdir):
sys.stderr.write("Failed to create temporary folder for PUID's, using: " + tmpdir)
return tmpdir, resume
def download_signatures(defaults, format_eles, resume, tmpdir):
"""Download PRONOM signatures and write to individual files."""
print("Downloading signatures, one moment please...")
numberPuids = len(format_eles)
one_percent = (float(numberPuids) / 100)
numfiles = 0
for format_ele in format_eles:
download_sig(format_ele, tmpdir, resume)
numfiles += 1
percent = int(float(numfiles) / one_percent)
print(r"{}/{} files [{}%]".format(numfiles, numberPuids, percent))
time.sleep(defaults['http_throttle'])
print("100%")
def download_sig(format_ele, tmpdir, resume):
"""
Download an individual PRONOM signature.
The signature to be downloaded is identified by the FileFormat element
parameter format_ele. The downloaded signature is written to tmpdir.
"""
puid, puidFileName = get_puid_file_name(format_ele)
filename = os.path.join(tmpdir, puidFileName)
if os.path.isfile(filename) and resume:
return
try:
xml = get_sig_xml_for_puid(puid)
except Exception as e:
sys.stderr.write("Failed to download signature file:" + puid)
sys.stderr.write("Error:" + str(e))
sys.exit('Please restart and resume download.')
with open(filename, 'wb') as file_:
file_.write(xml)
def create_zip_file(defaults, format_eles, currentVersion, tmpdir):
"""Create zip file of signatures."""
print("Creating PRONOM zip...")
compression = zipfile.ZIP_DEFLATED if 'zlib' in sys.modules else zipfile.ZIP_STORED
modes = {zipfile.ZIP_DEFLATED: 'deflated', zipfile.ZIP_STORED: 'stored'}
zf = zipfile.ZipFile(os.path.join(CONFIG_DIR, defaults['pronomZipFileName'].format(currentVersion)), mode='w')
print("Adding files with compression mode", modes[compression])
for format_ele in format_eles:
_, puidFileName = get_puid_file_name(format_ele)
filename = os.path.join(tmpdir, puidFileName)
if os.path.isfile(filename):
zf.write(filename, arcname=puidFileName, compress_type=compression)
if defaults['deleteTempDirectory']:
os.unlink(filename)
zf.close()
def get_puid_file_name(format_ele):
"""Return a tupe of PUID and PUID file name derived from format_ele."""
puid = format_ele.get('PUID')
puidType, puidNum = puid.split("/")
return puid, 'puid.{}.{}.xml'.format(puidType, puidNum)
def update_versions_xml(defaults, currentVersion):
"""Create new versions identified sig XML file."""
print('Updating versions.xml...')
versions = get_local_versions()
versions.pronom_version = str(currentVersion)
versions.pronom_signature = "formats-v" + str(currentVersion) + ".xml"
versions.pronom_container_signature = defaults['containerVersion']
versions.fido_extension_signature = defaults['fidoSignatureVersion']
versions.update_script = __version__
versions.write()
def main():
"""Main CLI entrypoint."""
parser = ArgumentParser(description='Download and convert the latest PRONOM signatures')
parser.add_argument('-tmpdir', default=OPTIONS['tmp_dir'], help='Location to store temporary files', dest='tmp_dir')
parser.add_argument('-keep_tmp', default=OPTIONS['deleteTempDirectory'], help='Do not delete temporary files after completion', dest='deleteTempDirectory', action='store_false')
parser.add_argument('-http_throttle', default=OPTIONS['http_throttle'], help='Time (in seconds) to wait between downloads', type=float, dest='http_throttle')
args = parser.parse_args()
opts = DEFAULTS.copy()
opts.update(vars(args))
run(opts)
if __name__ == '__main__':
main() | PypiClean |
/catcli-0.9.6.tar.gz/catcli-0.9.6/README.md | # CATCLI
[](https://github.com/deadc0de6/catcli/actions)
[](http://www.gnu.org/licenses/gpl-3.0)
[](https://coveralls.io/github/deadc0de6/catcli?branch=master)
[](https://badge.fury.io/py/catcli)
[](https://aur.archlinux.org/packages/catcli-git)
[](https://pypi.python.org/pypi/catcli)
[](https://ko-fi.com/deadc0de6)
*The command line catalog tool for your offline data*
Did you ever wanted to find back that specific file that should be on one of your
backup DVDs or one of your external hard drives? You usually go through all
of them hoping to find the right one on the first try?
[Catcli](https://github.com/deadc0de6/catcli) indexes external media
in a catalog file and allows to quickly find specific files or even navigate in the
catalog of indexed files while these are not connected to your host.
Features:
* Index any directories in a catalog
* Ability to search for files by name in the catalog
* Ability to navigate through indexed data à la `ls`
* Support for fuse to mount the indexed data as a virtual filesystem
* Handle archive files (zip, tar, ...) and index their content
* Save catalog to json for easy versioning with git
* Command line interface FTW
* Store files and directories sizes
* Store md5 hash of files
* Ability to update the catalog
* Support for `fzf` for finding files
* Tag your different storages with additional information
* Export catalog to CSV
<a href="https://asciinema.org/a/hRE22qbVtBGxOM1yxw2y4fBy8"><img src="https://asciinema.org/a/hRE22qbVtBGxOM1yxw2y4fBy8.png" width="50%" height="50%"></a>
Quick start:
```bash
# install catcli with pip
pip3 install catcli --user
# index a directory in the catalog
catcli index --meta='some description' log /var/log
# display the content
catcli ls -r
# navigate
catcli ls log
# find files/directories named '*log*'
catcli find log
```
see [usage](#usage) for specific info
## Why catcli?
[Catcli](https://github.com/deadc0de6/catcli) gives the ability to navigate,
explore and find your files that are stored on external media
(DVDs, hard drives, USB sticks, etc) when those are not connected.
Catcli can just as easily index any arbitrary directories.
See the [examples](#examples) for an overview of the available features.
---
**Table of Contents**
* [Installation](#installation)
* [Usage](#usage)
* [Index data](#index-data)
* [Index archive files](#index-archive-files)
* [Walk indexed files with ls](#walk-indexed-files-with-ls)
* [Find files](#find-files)
* [Mount catalog](#mount-catalog)
* [Display entire hierarchy](#display-entire-hierarchy)
* [Catalog graph](#catalog-graph)
* [Edit storage](#edit-storage)
* [Update catalog](#update-catalog)
* [CSV format](#csv-format)
* [Examples](#examples)
* [Contribution](#contribution)
* [Thank you](#thank-you)
# Installation
Install from Pypi
```bash
$ pip3 install catcli --user
```
Or from github directly
```bash
$ cd /tmp; git clone https://github.com/deadc0de6/catcli && cd catcli
$ sudo python3 setup.py install
$ catcli --help
```
To work with catcli without installing it, you can do the following
```bash
$ cd /tmp; git clone https://github.com/deadc0de6/catcli && cd catcli
$ pip3 install -r requirements.txt --user
$ python3 -m catcli.catcli --help
```
or install it in a virtualenv
```bash
$ cd /tmp; git clone https://github.com/deadc0de6/catcli && cd catcli
$ virtualenv -p python3 env
$ source env/bin/activate
$ python setup.py install
$ catcli --help
```
Catcli is also available on aur: https://aur.archlinux.org/packages/catcli-git/
# Usage
Each indexed directory is stored in the catalog. Multiple directories can be indexed
and they are all available through the command line interface of catcli.
Five different types of entry are present in a catalog:
* **top node**: this is the root of the hierarchy
* **storage node**: this represents an indexed storage (a DVD, an external
hard drive, an USB drive, some arbitrary directory, etc).
* **dir node**: this is a directory
* **file node**: this is a file
* **archive node**: this is a file contained in an archive (tar, zip, etc)
## Index data
Let's say the DVD or external hard drive that needs to be indexed
is mounted on `/media/mnt`. The following command
will index the entire directory `/media/mnt`
and store that in your catalog under the name `<short-name>`.
```bash
$ catcli index --meta=<some-description> <short-name> /media/mnt
```
If not specified otherwise (with the switch `--catalog`), the catalog is saved in the current
directory under `catcli.catalog`.
The `--meta` switch allows to add any additional information to store along in
the catalog like for example `the blue disk in my office`.
Catcli will calculate and store the total size of each node (directories, storages, etc)
unless the `-n --no-subsize` switch is used.
Using the `-a --archive` switch allows to also index archive files as explained
[below](#index-archive-files).
## Index archive files
Catcli is able to index and explore the content of archive files.
Following archive formats are supported: *tar*, *tar.gz*, *tar.xz*, *lzma*, *tar.bz2*, *zip*.
Catcli is also able to find files within indexed archive files.
See the [archive example](#archive-example) for more.
## Walk indexed files with ls
A catalog can be walked using the command `ls` as if the media
is mounted (File/directories separator is `/`).
```bash
$ catcli ls tmp/a/b/c
```
Resulting files can be sorted by size using `-S --sortsize`.
See the [examples](#examples) for more.
## Find files
Files and directories can be found based on their names
using the `find` command.
`Find` support two formats that allow to use `fzf` for
searching:
* `--format=fzf-native`: display the result in native format
* `--format=fzf-csv`: display the result in csv
See the [examples](#examples) for more.
## Mount catalog
The catalog can be mounted with [fuse](https://www.kernel.org/doc/html/next/filesystems/fuse.html)
and navigate like any filesystem.
```bash
$ mkdir /tmp/mnt
$ catcli index -c github .github
$ catcli mount /tmp/mnt
$ ls -laR /tmp/mnt
drwxrwxrwx - user 8 Mar 22:08 github
mnt/github:
.rwxrwxrwx 17 user 19 Oct 2022 FUNDING.yml
drwxrwxrwx - user 2 Mar 10:15 workflows
mnt/github/workflows:
.rwxrwxrwx 691 user 19 Oct 2022 pypi-release.yml
.rwxrwxrwx 635 user 8 Mar 21:08 testing.yml
```
## Display entire hierarchy
The entire catalog can be shown using the `ls -r` command.
Resulting files can be sorted by size using the `-S --sortsize` switch.
See the [examples](#examples) for more.
## Catalog graph
The catalog can be exported in a dot file that can be used to
generate a graph of the indexed files.
```bash
$ catcli graph
dot file created under "/tmp/catcli.dot"
create graph with "dot /tmp/catcli.dot -T png -o /tmp/tree.png" (you need graphviz)
$ dot /tmp/catcli.dot -T png -o /tmp/tree.png
```
## Edit storage
Storage entry can be edited with following catcli commands:
* `rename` - rename the storage
* `edit` - edit storage metadata
## Update catalog
The catalog can be updated with the `update` command.
Updates are based on the access time of each of the files and on the
hash checksum if present (catalog was indexed with `-c --hash` and
`update` is called with the switch `-c --hash`).
## CSV format
Results can be printed to CSV using `--format=csv`.
Fields are separated by a comma (`,`) and are quoted with double quotes (`"`).
Each line contains the following fields:
* **name**: the entry name
* **type**: the entry type (file, directory, storage, etc)
* **path**: the entry path
* **size**: the entry size
* **indexed_at**: when this entry was indexed
* **maccess**: the entry modification date/time
* **md5**: the entry checksum (if any)
* **nbfiles**: the number of children (empty for nodes that are not storage or directory)
* **free_space**: free space (empty for not storage nodes)
* **total_space**: total space (empty for not storage nodes)
* **meta**: meta information (empty for not storage nodes)
# Examples
## Simple example
Let's first create some files and directories:
```bash
$ mkdir -p /tmp/test/{a,b,c}
$ echo 'something in files in a' > /tmp/test/a/{1,2,3}
$ echo 'something else in files in b' > /tmp/test/b/{4,5,6}
$ echo 'some bytes' > /tmp/test/c/{7,8,9}
$ tree /tmp/test
/tmp/test
├── a
│ ├── 1
│ ├── 2
│ └── 3
├── b
│ ├── 4
│ ├── 5
│ └── 6
└── c
├── 7
├── 8
└── 9
3 directories, 9 files
```
First this directory is indexed with `catcli` as if it was some kind of
external storage:
```bash
$ catcli index --meta='my test directory' tmptest /tmp/test
```
Catcli creates its catalog file in the current directory as `catcli.catalog`.
Printing the entire catalog as a tree is done with the command `ls -r`
```
$ catcli ls -r
top
└── storage: tmptest (my test directory) (nbfiles:3, free:3.7G/3.7G, date:2019-01-26 19:59:47)
├── a [nbfiles:3, totsize:72]
│ ├── 1 [size:24]
│ ├── 2 [size:24]
│ └── 3 [size:24]
├── b [nbfiles:3, totsize:87]
│ ├── 4 [size:29]
│ ├── 5 [size:29]
│ └── 6 [size:29]
└── c [nbfiles:3, totsize:33]
├── 7 [size:11]
├── 8 [size:11]
└── 9 [size:11]
```
The catalog can be walked with `ls` as if it was a normal directory
```
$ catcli ls
top
- storage: tmptest (my test directory) (nbfiles:3, free:3.7G/3.7G, date:2019-01-26 19:59:47)
$ catcli ls tmptest
storage: tmptest (my test directory) (nbfiles:3, free:3.7G/3.7G, date:2019-01-26 19:59:47)
- a [nbfiles:3, totsize:72]
- b [nbfiles:3, totsize:87]
- c [nbfiles:3, totsize:33]
$ catcli ls tmptest/b
b [nbfiles:3, totsize:87]
- 4 [size:29]
- 5 [size:29]
- 6 [size:29]
```
And files can be found using the command `find`
```bash
$ catcli find 9
c/9 [size:11, storage:tmptest]
```
When using the `-b --script` switch, a one-liner is generated
that allows to handle the found file(s)
```
$ catcli find 9 --script
c/9 [size:11, storage:tmptest]
op=file; source=/media/mnt; $op ${source}/c/9
```
## Archive example
Let's consider a directory containing archive files:
```bash
$ ls -1 /tmp/catcli
catcli-0.3.1
v0.3.1.tar.gz
v0.3.1.zip
```
To enable the indexing of archive contents use
the `-a --archive` switch
```bash
$ catcli index -au some-name /tmp/catcli
```
Then any command can be used to explore the catalog as for normal
files but, by providing the `-a --archive` switch, archive content are displayed.
```bash
$ catcli ls some-name
storage: some-name (free:800G, total:1T)
- catcli-0.3.1 [nbfiles:11, totsize:80.5K]
- v0.3.1.tar.gz [size:24.2K]
- v0.3.1.zip [size:31.2K]
$ catcli ls -r some-name/v0.3.1.zip
v0.3.1.zip [size:31.2K]
$ catcli ls -ar some-name/v0.3.1.zip
v0.3.1.zip [size:31.2K]
├── catcli-0.3.1 [archive:v0.3.1.zip]
│ ├── catcli [archive:v0.3.1.zip]
│ │ ├── __init__.py [archive:v0.3.1.zip]
│ │ ├── catalog.py [archive:v0.3.1.zip]
│ │ ├── catcli.py [archive:v0.3.1.zip]
│ │ ├── logger.py [archive:v0.3.1.zip]
│ │ ├── noder.py [archive:v0.3.1.zip]
│ │ ├── utils.py [archive:v0.3.1.zip]
│ │ └── walker.py [archive:v0.3.1.zip]
│ ├── .gitignore [archive:v0.3.1.zip]
│ ├── LICENSE [archive:v0.3.1.zip]
│ ├── MANIFEST.in [archive:v0.3.1.zip]
│ ├── README.md [archive:v0.3.1.zip]
│ ├── requirements.txt [archive:v0.3.1.zip]
│ ├── setup.cfg [archive:v0.3.1.zip]
│ ├── setup.py [archive:v0.3.1.zip]
│ ├── tests [archive:v0.3.1.zip]
│ │ ├── __init__.py [archive:v0.3.1.zip]
│ │ ├── helpers.py [archive:v0.3.1.zip]
│ │ ├── test_find.py [archive:v0.3.1.zip]
│ │ ├── test_graph.py [archive:v0.3.1.zip]
│ │ ├── test_index.py [archive:v0.3.1.zip]
│ │ ├── test_ls.py [archive:v0.3.1.zip]
│ │ ├── test_rm.py [archive:v0.3.1.zip]
│ │ └── test_tree.py [archive:v0.3.1.zip]
│ ├── tests.sh [archive:v0.3.1.zip]
│ └── .travis.yml [archive:v0.3.1.zip]
└── catcli-0.3.1/ [archive:v0.3.1.zip]
```
# Contribution
If you are having trouble installing or using catcli, open an issue.
If you want to contribute, feel free to do a PR (please follow PEP8).
The `tests.sh` script can be run to check the code.
# Thank you
If you like catcli, [buy me a coffee](https://ko-fi.com/deadc0de6).
# License
This project is licensed under the terms of the GPLv3 license.
| PypiClean |
/allennlp-light-1.0.0.tar.gz/allennlp-light-1.0.0/allennlp_light/modules/attention/bilinear_attention.py | import torch
from torch.nn.parameter import Parameter
from allennlp_light.modules.attention.attention import Attention
from allennlp_light.nn import Activation
@Attention.register("bilinear")
class BilinearAttention(Attention):
"""
Computes attention between a vector and a matrix using a bilinear attention function. This
function has a matrix of weights `W` and a bias `b`, and the similarity between the vector
`x` and the matrix `y` is computed as `x^T W y + b`.
Registered as an `Attention` with name "bilinear".
# Parameters
vector_dim : `int`, required
The dimension of the vector, `x`, described above. This is `x.size()[-1]` - the length
of the vector that will go into the similarity computation. We need this so we can build
the weight matrix correctly.
matrix_dim : `int`, required
The dimension of the matrix, `y`, described above. This is `y.size()[-1]` - the length
of the vector that will go into the similarity computation. We need this so we can build
the weight matrix correctly.
activation : `Activation`, optional (default=`linear`)
An activation function applied after the `x^T W y + b` calculation. Default is
linear, i.e. no activation.
normalize : `bool`, optional (default=`True`)
If true, we normalize the computed similarities with a softmax, to return a probability
distribution for your attention. If false, this is just computing a similarity score.
"""
def __init__(
self,
vector_dim: int,
matrix_dim: int,
activation: Activation = None,
normalize: bool = True,
) -> None:
super().__init__(normalize)
self._weight_matrix = Parameter(torch.Tensor(vector_dim, matrix_dim))
self._bias = Parameter(torch.Tensor(1))
self._activation = activation or Activation.by_name("linear")()
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.xavier_uniform_(self._weight_matrix)
self._bias.data.fill_(0)
def _forward_internal(self, vector: torch.Tensor, matrix: torch.Tensor) -> torch.Tensor:
intermediate = vector.mm(self._weight_matrix).unsqueeze(1)
return self._activation(intermediate.bmm(matrix.transpose(1, 2)).squeeze(1) + self._bias) | PypiClean |
/memecomplete_desktop-0.2-py3-none-any.whl/memecomplete_desktop-0.2.dist-info/DESCRIPTION.rst | Unix: |Unix Build Status| Windows: |Windows Build Status|\ Metrics:
|Coverage Status| |Scrutinizer Code Quality|\ Usage: |PyPI Version|
|PyPI Downloads|
Overview
========
Desktop client for https://memecomplete.com.
Setup
=====
Requirements
------------
- Python 3.6+
- SpeechRecognition requirements:
https://github.com/Uberi/speech_recognition#requirements
- macOS: ``$ brew install flac portaudio swig``
Installation
------------
Install the client with pip:
.. code:: sh
$ pip install memecomplete-desktop
or directly from the source code:
.. code:: sh
$ git clone https://github.com/jacebrowning/memecomplete-desktop.git
$ cd memecomplete-desktop
$ python setup.py install
Usage
=====
Launch the GUI from the command-line:
.. code:: sh
$ memecomplete
.. |Unix Build Status| image:: http://img.shields.io/travis/jacebrowning/memecomplete-desktop/master.svg
:target: https://travis-ci.org/jacebrowning/memecomplete-desktop
.. |Windows Build Status| image:: https://img.shields.io/appveyor/ci/jacebrowning/memecomplete-desktop/master.svg
:target: https://ci.appveyor.com/project/jacebrowning/memecomplete-desktop
.. |Coverage Status| image:: http://img.shields.io/coveralls/jacebrowning/memecomplete-desktop/master.svg
:target: https://coveralls.io/r/jacebrowning/memecomplete-desktop
.. |Scrutinizer Code Quality| image:: http://img.shields.io/scrutinizer/g/jacebrowning/memecomplete-desktop.svg
:target: https://scrutinizer-ci.com/g/jacebrowning/memecomplete-desktop/?branch=master
.. |PyPI Version| image:: http://img.shields.io/pypi/v/memecomplete-desktop.svg
:target: https://pypi.python.org/pypi/memecomplete-desktop
.. |PyPI Downloads| image:: http://img.shields.io/pypi/dm/memecomplete-desktop.svg
:target: https://pypi.python.org/pypi/memecomplete-desktop
Revision History
================
0.2 (2017/06/12)
----------------
- Updated API to use https://memecomplete.com.
0.1.1 (2016/09/14)
------------------
- Fixed image loading.
0.1 (2016/09/05)
----------------
- Initial release.
| PypiClean |
/VIP-yt-2022.7.26.tar.gz/VIP-yt-2022.7.26/VIP_yt/extractor/epicon.py | import re
from .common import InfoExtractor
from ..utils import ExtractorError
class EpiconIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?epicon\.in/(?:documentaries|movies|tv-shows/[^/?#]+/[^/?#]+)/(?P<id>[^/?#]+)'
_TESTS = [{
'url': 'https://www.epicon.in/documentaries/air-battle-of-srinagar',
'info_dict': {
'id': 'air-battle-of-srinagar',
'ext': 'mp4',
'title': 'Air Battle of Srinagar',
'description': 'md5:c4de2013af9bc05ae4392e4115d518d7',
'thumbnail': r're:^https?://.*\.jpg$',
}
}, {
'url': 'https://www.epicon.in/movies/krit',
'info_dict': {
'id': 'krit',
'ext': 'mp4',
'title': 'Krit',
'description': 'md5:c12b35dad915d48ccff7f013c79bab4a',
'thumbnail': r're:^https?://.*\.jpg$',
}
}, {
'url': 'https://www.epicon.in/tv-shows/paapnaashini-ganga/season-1/vardaan',
'info_dict': {
'id': 'vardaan',
'ext': 'mp4',
'title': 'Paapnaashini Ganga - Season 1 - Ep 1 - VARDAAN',
'description': 'md5:f517058c3d0402398eefa6242f4dd6ae',
'thumbnail': r're:^https?://.*\.jpg$',
}
}, {
'url': 'https://www.epicon.in/movies/jayadev',
'info_dict': {
'id': 'jayadev',
'ext': 'mp4',
'title': 'Jayadev',
'description': 'md5:09e349eecd8e585a3b6466904f19df6c',
'thumbnail': r're:^https?://.*\.jpg$',
}
}]
def _real_extract(self, url):
id = self._match_id(url)
webpage = self._download_webpage(url, id)
cid = self._search_regex(r'class=\"mylist-icon\ iconclick\"\ id=\"(\d+)', webpage, 'cid')
headers = {'content-type': 'application/x-www-form-urlencoded; charset=UTF-8'}
data = f'cid={cid}&action=st&type=video'.encode()
data_json = self._parse_json(self._download_json('https://www.epicon.in/ajaxplayer/', id, headers=headers, data=data), id)
if not data_json['success']:
raise ExtractorError(data_json['message'], expected=True)
title = self._search_regex(r'setplaytitle=\"([^\"]+)', webpage, 'title')
description = self._og_search_description(webpage) or None
thumbnail = self._og_search_thumbnail(webpage) or None
formats = self._extract_m3u8_formats(data_json['url']['video_url'], id)
self._sort_formats(formats)
subtitles = {}
for subtitle in data_json.get('subtitles', []):
sub_url = subtitle.get('file')
if not sub_url:
continue
subtitles.setdefault(subtitle.get('lang', 'English'), []).append({
'url': self._proto_relative_url(sub_url),
})
return {
'id': id,
'formats': formats,
'title': title,
'description': description,
'thumbnail': thumbnail,
'subtitles': subtitles,
}
class EpiconSeriesIE(InfoExtractor):
_VALID_URL = r'(?!.*season)https?://(?:www\.)?epicon\.in/tv-shows/(?P<id>[^/?#]+)'
_TESTS = [{
'url': 'https://www.epicon.in/tv-shows/1-of-something',
'playlist_mincount': 5,
'info_dict': {
'id': '1-of-something',
},
}, {
'url': 'https://www.epicon.in/tv-shows/eco-india-english',
'playlist_mincount': 76,
'info_dict': {
'id': 'eco-india-english',
},
}, {
'url': 'https://www.epicon.in/tv-shows/s/',
'playlist_mincount': 25,
'info_dict': {
'id': 's',
},
}, {
'url': 'https://www.epicon.in/tv-shows/ekaant',
'playlist_mincount': 38,
'info_dict': {
'id': 'ekaant',
},
}]
def _real_extract(self, url):
id = self._match_id(url)
webpage = self._download_webpage(url, id)
episodes = re.findall(r'ct-tray-url=\"(tv-shows/%s/[^\"]+)' % id, webpage)
entries = [self.url_result('https://www.epicon.in/%s' % episode, ie=EpiconIE.ie_key()) for episode in episodes]
return self.playlist_result(entries, playlist_id=id) | PypiClean |
/formification-1.2.0-py3-none-any.whl/formulaic/static/admin/formulaic/ember-formulaic/node_modules/yargs/lib/usage.js | var cliui = require('cliui')
var decamelize = require('decamelize')
var wsize = require('window-size')
module.exports = function (yargs, y18n) {
var __ = y18n.__
var self = {}
// methods for ouputting/building failure message.
var fails = []
self.failFn = function (f) {
fails.push(f)
}
var failMessage = null
var showHelpOnFail = true
self.showHelpOnFail = function (enabled, message) {
if (typeof enabled === 'string') {
message = enabled
enabled = true
} else if (typeof enabled === 'undefined') {
enabled = true
}
failMessage = message
showHelpOnFail = enabled
return self
}
var failureOutput = false
self.fail = function (msg) {
if (fails.length) {
fails.forEach(function (f) {
f(msg)
})
} else {
// don't output failure message more than once
if (!failureOutput) {
failureOutput = true
if (showHelpOnFail) yargs.showHelp('error')
if (msg) console.error(msg)
if (failMessage) {
if (msg) console.error('')
console.error(failMessage)
}
}
if (yargs.getExitProcess()) {
process.exit(1)
} else {
throw new Error(msg)
}
}
}
// methods for ouputting/building help (usage) message.
var usage
self.usage = function (msg) {
usage = msg
}
var examples = []
self.example = function (cmd, description) {
examples.push([cmd, description || ''])
}
var commands = []
self.command = function (cmd, description) {
commands.push([cmd, description || ''])
}
self.getCommands = function () {
return commands
}
var descriptions = {}
self.describe = function (key, desc) {
if (typeof key === 'object') {
Object.keys(key).forEach(function (k) {
self.describe(k, key[k])
})
} else {
descriptions[key] = desc
}
}
self.getDescriptions = function () {
return descriptions
}
var epilog
self.epilog = function (msg) {
epilog = msg
}
var wrap = windowWidth()
self.wrap = function (cols) {
wrap = cols
}
var deferY18nLookupPrefix = '__yargsString__:'
self.deferY18nLookup = function (str) {
return deferY18nLookupPrefix + str
}
self.help = function () {
normalizeAliases()
var demanded = yargs.getDemanded()
var options = yargs.getOptions()
var keys = Object.keys(
Object.keys(descriptions)
.concat(Object.keys(demanded))
.concat(Object.keys(options.default))
.reduce(function (acc, key) {
if (key !== '_') acc[key] = true
return acc
}, {})
)
var ui = cliui({
width: wrap,
wrap: !!wrap
})
// the usage string.
if (usage) {
var u = usage.replace(/\$0/g, yargs.$0)
ui.div(u + '\n')
}
// your application's commands, i.e., non-option
// arguments populated in '_'.
if (commands.length) {
ui.div(__('Commands:'))
commands.forEach(function (command) {
ui.div(
{text: command[0], padding: [0, 2, 0, 2], width: maxWidth(commands) + 4},
{text: command[1]}
)
})
ui.div()
}
// the options table.
var aliasKeys = (Object.keys(options.alias) || [])
.concat(Object.keys(yargs.parsed.newAliases) || [])
keys = keys.filter(function (key) {
return !yargs.parsed.newAliases[key] && aliasKeys.every(function (alias) {
return (options.alias[alias] || []).indexOf(key) === -1
})
})
var switches = keys.reduce(function (acc, key) {
acc[key] = [ key ].concat(options.alias[key] || [])
.map(function (sw) {
return (sw.length > 1 ? '--' : '-') + sw
})
.join(', ')
return acc
}, {})
if (keys.length) {
ui.div(__('Options:'))
keys.forEach(function (key) {
var kswitch = switches[key]
var desc = descriptions[key] || ''
var type = null
if (~desc.lastIndexOf(deferY18nLookupPrefix)) desc = __(desc.substring(deferY18nLookupPrefix.length))
if (~options.boolean.indexOf(key)) type = '[' + __('boolean') + ']'
if (~options.count.indexOf(key)) type = '[' + __('count') + ']'
if (~options.string.indexOf(key)) type = '[' + __('string') + ']'
if (~options.normalize.indexOf(key)) type = '[' + __('string') + ']'
if (~options.array.indexOf(key)) type = '[' + __('array') + ']'
var extra = [
type,
demanded[key] ? '[' + __('required') + ']' : null,
options.choices && options.choices[key] ? '[' + __('choices:') + ' ' +
self.stringifiedValues(options.choices[key]) + ']' : null,
defaultString(options.default[key], options.defaultDescription[key])
].filter(Boolean).join(' ')
ui.span(
{text: kswitch, padding: [0, 2, 0, 2], width: maxWidth(switches) + 4},
desc
)
if (extra) ui.div({text: extra, padding: [0, 0, 0, 2], align: 'right'})
else ui.div()
})
ui.div()
}
// describe some common use-cases for your application.
if (examples.length) {
ui.div(__('Examples:'))
examples.forEach(function (example) {
example[0] = example[0].replace(/\$0/g, yargs.$0)
})
examples.forEach(function (example) {
ui.div(
{text: example[0], padding: [0, 2, 0, 2], width: maxWidth(examples) + 4},
example[1]
)
})
ui.div()
}
// the usage string.
if (epilog) {
var e = epilog.replace(/\$0/g, yargs.$0)
ui.div(e + '\n')
}
return ui.toString()
}
// return the maximum width of a string
// in the left-hand column of a table.
function maxWidth (table) {
var width = 0
// table might be of the form [leftColumn],
// or {key: leftColumn}}
if (!Array.isArray(table)) {
table = Object.keys(table).map(function (key) {
return [table[key]]
})
}
table.forEach(function (v) {
width = Math.max(v[0].length, width)
})
// if we've enabled 'wrap' we should limit
// the max-width of the left-column.
if (wrap) width = Math.min(width, parseInt(wrap * 0.5, 10))
return width
}
// make sure any options set for aliases,
// are copied to the keys being aliased.
function normalizeAliases () {
var demanded = yargs.getDemanded()
var options = yargs.getOptions()
;(Object.keys(options.alias) || []).forEach(function (key) {
options.alias[key].forEach(function (alias) {
// copy descriptions.
if (descriptions[alias]) self.describe(key, descriptions[alias])
// copy demanded.
if (demanded[alias]) yargs.demand(key, demanded[alias].msg)
// type messages.
if (~options.boolean.indexOf(alias)) yargs.boolean(key)
if (~options.count.indexOf(alias)) yargs.count(key)
if (~options.string.indexOf(alias)) yargs.string(key)
if (~options.normalize.indexOf(alias)) yargs.normalize(key)
if (~options.array.indexOf(alias)) yargs.array(key)
})
})
}
self.showHelp = function (level) {
level = level || 'error'
console[level](self.help())
}
self.functionDescription = function (fn) {
var description = fn.name ? decamelize(fn.name, '-') : __('generated-value')
return ['(', description, ')'].join('')
}
self.stringifiedValues = function (values, separator) {
var string = ''
var sep = separator || ', '
var array = [].concat(values)
if (!values || !array.length) return string
array.forEach(function (value) {
if (string.length) string += sep
string += JSON.stringify(value)
})
return string
}
// format the default-value-string displayed in
// the right-hand column.
function defaultString (value, defaultDescription) {
var string = '[' + __('default:') + ' '
if (value === undefined && !defaultDescription) return null
if (defaultDescription) {
string += defaultDescription
} else {
switch (typeof value) {
case 'string':
string += JSON.stringify(value)
break
case 'object':
string += JSON.stringify(value)
break
default:
string += value
}
}
return string + ']'
}
// guess the width of the console window, max-width 80.
function windowWidth () {
return wsize.width ? Math.min(80, wsize.width) : null
}
// logic for displaying application version.
var version = null
self.version = function (ver, opt, msg) {
version = ver
}
self.showVersion = function () {
if (typeof version === 'function') console.log(version())
else console.log(version)
}
return self
} | PypiClean |
/variant_tools-3.1.3-cp38-cp38-macosx_10_9_x86_64.whl/variant_tools/pipeline.py | import bz2
import collections
import copy
import csv
import glob
import gzip
import hashlib
import logging
import os
import pipes
import pprint
import random
import re
import shlex
import shutil
import subprocess
import sys
import tarfile
import tempfile
import threading
import time
import traceback
import zipfile
from collections import MutableMapping
from itertools import combinations, tee
from multiprocessing import Process
from .project import PipelineDescription, Project
from .ucsctools import showTrack
from .utils import (TEMP, FileInfo, ProgressBar, RuntimeFiles, calculateMD5,
decompressGzFile, delayedAction, downloadFile, downloadURL,
env, existAndNewerThan, substituteVars, typeOfValues,
validFieldName, which)
class NamedList:
'''This class implements a named list to assist users in inputting a large
number of items to a pipeline. Because these lists are often stored in a text
or excel file and are associated with a name or some other meta information,
users can input all them in the format of three ':' seprated parts
1. name (optional): name of the list.
2. values: values can be either a space or comma separated list such as A1 A2 ...
or A1,A2,A3,... or a field in a file in the format of fieldname@filename?query.
Fieldname should be one or more fields from the file joined with a non-alphabetical
character (e.g. '+'). Filename should be a file in .csv, text (tab delimited) or
EXCEL file. Query is a query that can limit the items from the retrieved list.
A query can consist other fields in the input data file with the entire file treated
as a retional database.
3. meta (optional): meta information for the list, which can be extra comment, weight (relative
to other list), and others, must be in the format of comma separated name=value
Examples:
[email protected]
A column called phenotype from "phenotype.xls". Age will be treated as
name of the list.
deceased:[email protected]
A list with name "deceased" retrieved from column "dead" of "phenotype.xls".
id1,id2,id3,id4
A list of IDs.
[email protected]?recurrance=="1"
get a list of IDs (column IDs) from "phenotype.xls" where the recurrance column has
value "1"
[email protected]:weight==1000
A named list with meta information 1000
The named list has attribute
name
Name of the list, default to "" unless default_name is given.
items
A list of items.
meta
Optional meta information as a dictionary. Default to {}.
'''
def __init__(self, value_string, default_name=""):
self.name = default_name
self.items = []
self.meta = {}
# if it is None etc
if value_string is None:
return
if not isinstance(value_string, str):
if len(value_string) == 1:
value_string = value_string[0]
else:
# if multiple items are passed, treat directly
# as list of strings.
self.items = value_string
return
#
self._parse(value_string, default_name)
def _parse(self, value_string, default_name):
if not value_string:
return
# single space separated string
if re.match('([\w\d-]+\s+)+[\w\d-]+', value_string):
self.items = value_string.split()
return
#
# comma separated named list
matched = re.match(
'^([\w\d-]+:)*((([\w\d-]+,)+[\w\d-]+)|([^:]*)@([^:?]*)(\?([^:]*))*)(:([^:]+))*$',
value_string)
if matched is None:
raise ValueError((
'"{}" is not a valid named list / query string, which should be name (optional) + comma separated list or '
'colname@filename with optional query string (?), with optional meta. Three parts should be separated by :.'
).format(value_string))
name = matched.group(1)
comma_list = matched.group(3)
colname = matched.group(5)
filename = matched.group(6)
query = matched.group(8)
meta = matched.group(10)
if name is not None:
self.name = name.rstrip(':')
if meta is not None:
matched = re.match(
'[\d\w_]+\s*=\s*[^=,]*(,[\d\w_]+\s*=\s*[^=,]*)*$', meta)
if not matched:
raise ValueError(
'Meta information is not in the format of key=value: {}'
.format(meta))
self.meta = {
x.split('=')[0].strip(): x.split('=')[1].strip()
for x in meta.split(',')
}
#
if comma_list is not None:
self.items = comma_list.split(',')
else:
import pandas as pd
filename = os.path.expanduser(filename)
if not os.path.isfile(filename):
raise ValueError('File does not exist: {}'.format(filename))
# pandas can be slow to import
if filename.endswith('.csv'):
data = pd.read_csv(filename)
else:
data = pd.read_excel(filename)
# convert everything to str, however, there are cases where 1 is converted to 1.0 when missing value
# is present (na is considered float, thus the behavior)
data = data.applymap(str)
env.logger.trace("{} records are loaded from {}".format(
data.shape[0], filename))
# env.logger.trace(data)
#
# if query?
if query is not None:
if re.match('.*[\d\w_]+\s*=\s*[\d\w_]+.*', query):
raise ValueError(
'Syntax "a=b" is not allowed. Please use "a==b" instead: {}'
.format(query))
try:
pre_filter = data.shape[0]
data = data.query(query)
if pre_filter != data.shape[0]:
env.logger.info(
'{} out of {} records are removed by filter {}'
.format(pre_filter - data.shape[0], pre_filter,
query))
except Exception as e:
raise ValueError(
'Failed to apply query "{}" to data file {}: {}'.format(
query, filename, e))
#
values = None
for col in re.split('([^\w\d_])', colname):
if re.match('[^\w\d_]', col):
if values is None:
raise ValueError(
'Leading non-ascii word is not allowed. {}'.format(
colname))
else:
values = [x + col for x in values]
continue
if col not in data.columns:
raise ValueError(
'File {} does not have column {}. Available columns are {}'
.format(filename, col, ', '.join(list(data.columns))))
if values is None:
values = list(data[col].fillna(''))
else:
values = [
x + y for x, y in zip(values, data[col].fillna(''))
]
#
self.items = values
if self.name == default_name:
self.name = colname
def rvec(vec):
return 'c(' + ','.join([repr(x) for x in vec]) + ')'
class SkipIf:
'''An input emitter that skips the step (does not pass any input to the
action) if certain condition is met. The input will be passed directly
as output by default. This emitter is equivalent to
``EmitInput(select=not cond, pass_unselected)`` '''
def __init__(self, cond=None, pass_unselected=True):
'''Does not emit input and skip the step if cond is ``True``.
Parameters:
cond (boolean):
A boolean value True or False. In practice ``cond`` is usually
a lambda function that checks the existence of a file or value
of a pipeline variable.
pass_unselected (boolean):
Pass input files to output if ``cond`` is not met.
'''
self.cond = cond
self.pass_unselected = pass_unselected
def __call__(self, ifiles, pipeline=None):
if self.cond:
return [], [], ifiles
else:
return [ifiles], [{}], []
class EmitInput:
'''An input emitter that emits input files individually, in pairs, or
altogether.'''
def __init__(self,
group_by='all',
select=True,
skip=False,
pass_unselected=True,
labels=None,
for_each=None,
loop_labels=None):
'''Select input files of certain types, group them, and send input files
to action. Selection criteria can be True (all input file types, default),
'False' (select no input file, but an empty list will still be passed to
pipeline action), 'fastq' (check content of files), or one or
more file extensions (e.g. ['.sam', '.bam']). Eligible files are by default
sent altogether (group_by='all') to action (${INPUT} equals ${INPUT#} where
# is the index of step, but can also be sent individually (group_by='single',
${INPUT} equals to a list of a single file) or in pairs
(group_by='paired', e.g. filename_1.txt and filename_2.txt), pairwise for
(a0, a1), (a1, a2), (a2, a3) ..., or combinations for all combination of
different input files. Unselected files are by default passed directly as
output of a step. If skip is set to True, the whole step will be skipped.
If labels is set to the name of one or more pipeline variable of type list,
values of these variables will be matched to each input file name, and
variables of name _${labels} will be set for each emitted group of files
(so that they are always list with the same length as ${input}.
If for_each is set to the name of one or more pipeline variables, the output will be
repeated for each item of this variable, with _${for_each} set to each item.
for_each='A,B' is equivalent to for _A,_B in zip(VARS[A], VARS[B]), and
for_each=['A', 'B'] is equivalent to nested loop of _A in VARS[A] and _B in VARS[B].
'''
self.group_by = group_by
#
if labels is None or not labels:
self.labels = []
elif isinstance(labels, str):
self.labels = [labels]
elif isinstance(labels, list):
self.labels = labels
else:
raise ValueError(
'Unacceptable value for parameter labels: {}'.format(labels))
#
if for_each is None or not for_each:
self.for_each = []
elif isinstance(for_each, str):
self.for_each = [for_each]
elif isinstance(for_each, list):
self.for_each = for_each
else:
raise ValueError(
'Unacceptable value for parameter for_each: {}'.format(
for_each))
#
if loop_labels is None or not loop_labels:
self.loop_labels = []
elif isinstance(loop_labels, str):
self.loop_labels = [loop_labels]
elif isinstance(loop_labels, list):
self.loop_labels = loop_labels
else:
raise ValueError(
'Unacceptable value for parameter loop_labels: {}'.format(
loop_labels))
#
if type(select) == str:
if select not in ['fastq', 'bam', 'sam'
] and not str(select).startswith('.'):
raise ValueError(
"Value to option select can only be True/False, "
"'fastq', or a file extension with leading '.': '{}' provided."
.format(select))
self.select = [select]
elif select in [True, False]:
self.select = select
else:
for s in select:
if s not in ['fastq', 'bam', 'sam'
] and not str(s).startswith('.'):
raise ValueError(
"Value to option select can only be True/False, "
"'fastq', or a file extension with leading '.': '{}' provided."
.format(s))
self.select = select
self.skip = skip
self.pass_unselected = pass_unselected
def _isFastq(self, filename):
try:
if not os.path.isfile(filename) and not os.path.isfile(
filename + '.file_info'):
raise RuntimeError('File not found')
fl = FileInfo(filename).firstline()
if fl is None:
env.logger.info(
'Cannot detect the type of file because the {} has been removed.'
.format(filename))
return filename.lower().split('.')[-1] not in [
'bam', 'sam', 'gz', 'zip'
]
if not fl.startswith('@'):
return False
if filename.endswith('.gz'):
env.logger.warning(
'{}: compressed fastq file might not be '
'acceptable to downstream analysis.'.format(filename))
except Exception as e:
env.logger.debug('Input file {} is not in fastq format: {}'.format(
filename, e))
return False
return True
def _is_paired(self, f1, f2, at=None):
if len(f1) != len(f2):
return False
if f1 >= f2:
return False
diffs = [x != y for x, y in zip(f1, f2)]
if sum(diffs) != 1:
return False
diff_at = diffs.index(True)
if sorted([f1[diff_at], f2[diff_at]]) != ['1', '2']:
return False
if at is not None and diff_at not in at:
return False
return True
def _pairByReadNames(self, selected, unselected):
# we should pair files by actual read names
read_map = {}
for filename in selected:
read = FileInfo(filename).firstline().strip()
if read[:-1] in read_map:
if read.endswith('1'):
if read_map[read[:-1]][0] is not None:
raise RuntimeError(
'Fastq file {} has the same first read as {}'
.format(filename, read_map[read[:-1]][0]))
else:
read_map[read[:-1]][0] = filename
elif read.endswith('2'):
if read_map[read[:-1]][1] is not None:
raise RuntimeError(
'Fastq file {} has the same first read as {}'
.format(filename, read_map[read[:-1]][1]))
else:
read_map[read[:-1]][1] = filename
else:
raise RuntimeError(
'Fastq file {} is not paired because its read name does '
'not end with 1 or 2'.format(filename))
else:
if read.endswith('1'):
read_map[read[:-1]] = [filename, None]
elif read.endswith('2'):
read_map[read[:-1]] = [None, filename]
else:
raise RuntimeError(
'Fastq file {} is not paired because its read name does '
'not end with 1 or 2'.format(filename))
# now, let us go through files
pairs = []
for read, filenames in read_map.items():
if filenames[0] is None:
raise RuntimeError(
'Fastq file {} is not paired (no matching read is found)'
.format(filenames[0]))
elif filenames[1] is None:
raise RuntimeError(
'Fastq file {} is not paired (no matching read is found)'
.format(filenames[1]))
else:
if not self._is_paired(filenames[0], filenames[1]):
env.logger.warning(
'{} and {} contain paired reads but the filenames '
'do not follow illumina filename convention'.format(
filenames[0], filenames[1]))
pairs.append(filenames)
return sorted(pairs), unselected
def _pairByFileName(self, selected, unselected):
#
# there is a possibility that one name differ at multiple parts
# with another name. e.g
#
# A1_TAGCTT_L007_R1_001.fastq.gz
#
# differ with the following two names by a number
#
# A1_TAGCTT_L007_R1_002.fastq.gz
# A1_TAGCTT_L007_R2_001.fastq.gz
#
# the code below tries to find good pairs first, then use matched
# locations to pair others
if not selected:
env.logger.warning(
'No file matching type "{}" is selected for pairing.'.format(
self.select))
return [], unselected
all_pairs = [
[x, y] for x in selected for y in selected if self._is_paired(x, y)
]
unpaired = [x for x in selected if not any([x in y for y in all_pairs])]
if unpaired:
raise ValueError('Failed to pair input filenames: {} is not paired'
'with any other names.'.format(
', '.join(unpaired)))
uniquely_paired = [
x for x in selected if sum([x in y for y in all_pairs]) == 1
]
# if some filenames are uniquely paired, we can use them to identify
# index locations.
if uniquely_paired:
pairs = [
x for x in all_pairs
if x[0] in uniquely_paired or x[1] in uniquely_paired
]
if len(pairs) != all_pairs:
# find the differentiating index of existing pairs
diff_at = set([[i != j
for i, j in zip(x[0], x[1])].index(True)
for x in pairs])
# use the diff_at locations to screen the rest of the pairs
pairs.extend([
x for x in all_pairs
if x not in pairs and self._is_paired(x[0], x[1], diff_at)
])
#
if len(pairs) * 2 != len(selected):
unpaired = [
x for x in selected if not any([x in y for y in pairs])
]
raise ValueError('Failed to pair input files because they '
'match multiple filenames: {}'.format(
', '.join(unpaired)))
return sorted(pairs), unselected
else:
# all filenames match to multiple names, so we try to get all
# differentiating indexes and see if one of them can pair filenames
# perfectly. We start from the end because we assume that _1 _2
# are close to the end of filenames.
#
diff_at = set([[i != j
for i, j in zip(x[0], x[1])].index(True)
for x in all_pairs])
acceptable_diff_at = []
for d in diff_at:
# try to pair all names at this location.
pairs = [
x for x in all_pairs if self._is_paired(x[0], x[1], [d])
]
if len(pairs) * 2 != len(selected):
continue
# all filename should appear once and only once
if not all(
[sum([x in y for y in pairs]) == 1 for x in selected]):
continue
acceptable_diff_at.append(d)
# fortunately, only one perfect pairing is found
if len(acceptable_diff_at) == 1:
pairs = [
x for x in all_pairs
if self._is_paired(x[0], x[1], acceptable_diff_at)
]
return sorted(pairs), unselected
elif len(acceptable_diff_at) > 1:
env.logger.warning(
'There are {} ways to match all filenames '
'perfectly. The one using a latter differentiating index '
'is used.'.format(len(acceptable_diff_at)))
diff_at = sorted(list(acceptable_diff_at))[-1]
pairs = [
x for x in all_pairs
if self._is_paired(x[0], x[1], [diff_at])
]
return sorted(pairs), unselected
else:
raise ValueError(
'All filenames match multiple names but no differentiating '
'index can pair filenames perfectly.')
def get_groups(self, ifiles, pipeline=None):
if self.skip:
return [], ifiles
selected = []
unselected = []
for filename in ifiles:
match = False
if self.select is True:
match = True
elif self.select is False:
pass
else: # list of types
for t in self.select:
if t == 'fastq':
if self._isFastq(filename):
match = True
break
if filename.lower().endswith('.' + t.lstrip('.').lower()):
match = True
break
#
if match:
selected.append(filename)
elif self.pass_unselected:
unselected.append(filename)
#
# for this special case, the step is skipped
if self.group_by == 'single':
return [[x] for x in selected], unselected
elif self.group_by == 'all':
return [selected], unselected
elif self.group_by == 'paired':
if 'fastq' in self.select:
try:
return self._pairByReadNames(selected, unselected)
except Exception as e:
# if failed to pair by read name, pair by filenames
env.logger.warning(
'Failed to pair fastq files by read names. '
'Trying to pair files by filenames: {}'.format(e))
else:
# this should not happen becase we do not need to pair non-fastq files
# at this point, but I will leave the code here anyway.
env.logger.warning(
'It is unsafe to pair input files by names instead of '
'their content. Please add option select="fastq" if you need to '
'pair input fastq files')
return self._pairByFileName(selected, unselected)
elif self.group_by == 'pairwise':
f1, f2 = tee(selected)
next(f2, None)
return [list(x) for x in zip(f1, f2)], unselected
elif self.group_by == 'combinations':
return [list(x) for x in combinations(selected, 2)], unselected
def __call__(self, ifiles, pipeline=None):
selected_groups, unselected = self.get_groups(ifiles, pipeline)
set_vars = [{} for x in selected_groups]
for wv in self.labels:
values = pipeline.VARS[wv]
if not isinstance(values, list):
raise ValueError(
'with_var variable {} is not a list ("{}")'.format(
wv, values))
if len(values) != len(ifiles):
raise ValueError(
'Length of variable {} (length {}) should match the number of input files (length {}).'
.format(wv, len(values), len(ifiles)))
file_map = {x: y for x, y in zip(ifiles, values)}
#env.logger.error('Paring {}'.format(file_map))
for idx, val in enumerate(values):
set_vars[idx]['_' +
wv] = [file_map[x] for x in selected_groups[idx]]
for fe_all in self.for_each:
loop_size = None
for fe in fe_all.split(','):
values = pipeline.VARS[fe]
if not isinstance(values, list):
raise ValueError(
'for_each variable {} is not a list ("{}")'.format(
fe, values))
if loop_size is None:
loop_size = len(values)
elif loop_size != len(values):
raise ValueError(
'Length of variable {} (length {}) should match the length of variable {} (length {}).'
.format(fe, len(values),
fe_all.split(',')[0], loop_size))
# expand
selected_groups = selected_groups * loop_size
tmp = []
for vidx in range(loop_size):
for idx in range(len(set_vars)):
for fe in fe_all.split(','):
set_vars[idx]['_' + fe] = pipeline.VARS[fe][vidx]
tmp.extend(copy.deepcopy(set_vars))
set_vars = tmp
# env.logger.trace('SELECTED GROUPS {}\nVARS {}\nUNSELECTED {}'
# .format( selected_groups, set_vars, unselected))
return selected_groups, set_vars, unselected
class PipelineAction:
'''Base class for all pipeline actions. If one or more output files
are specified, the pipeline will record the runtime signature of
this action in a file ``${cache_dir}/path/to/$OUTPUT[0].exe_info``, which
consists of the MD5 signature of input and output files, command used,
and additional information such as start and end time of execution,
standard and error outputs. An action will be skipped if the action
is re-run with the same input, output and command.
NOTE: The ``__call__`` function or a pipeline action implements the
runtime signature feature and calls function ``execute`` for actual work.
User-defined actions should either override the ``__call__`` function
(without the runtime signature feature) or function
``_execute(self, ifiles, pipeline)`` (with runtime signature feature).
User can also define function ``_bypass(self, ifiles, pipeline)`` if the
step is bypassed due to identical execution signatures.
'''
def __init__(self, cmd='', output=[]):
'''
Parameters:
cmd (string or list of strings):
one or more commands to be executed. It should capture
the name and all options used by the command.
output (string or list of strings):
Output files. If at least one output file is specified,
the runtime signature of this action will be saved to
$output[0].exe_info. The output directory will be created
if it does not exist.
'''
# multiple command is not allowed.
if not cmd:
self.cmd = []
elif isinstance(cmd, str):
self.cmd = [' '.join(cmd.split('\n'))]
else:
self.cmd = [' '.join(x.split('\n')) for x in cmd]
#
if not output:
self.output = []
elif isinstance(output, str):
self.output = [output]
else:
self.output = output
#
self.runtime = RuntimeFiles(self.output)
def _bypass(self, ifiles, pipeline=None):
'''Function called by ``__call__`` if the step is bypassed due to identical
execution signature. This function can be used, for example, to set pipeline
variable even when the step is not executed.'''
return True
def _execute(self, ifiles, pipeline=None):
'''Function called by ``__call__`` for actual action performed on ifiles. A user-defined
action should re-define __call__ or this function. This funciton should return ``True`` if
the action is completed successfully, ``False`` for pending (signature will be written later,
and raise an exception for errors. '''
raise RuntimeError(
'Please define your own execute function in an derived class of PipelineAction.'
)
def _write_info(self, pipeline=None):
if not self.output:
return
with open(self.runtime.proc_info, 'a') as exe_info:
exe_info.write('#End: {}\n'.format(time.asctime(time.localtime())))
for f in self.output:
if not os.path.isfile(f):
raise RuntimeError(
'Output file {} does not exist after completion of the job.'
.format(f))
# for performance considerations, use partial MD5
exe_info.write('{}\t{}\t{}\n'.format(
f, os.path.getsize(f), calculateMD5(f, partial=True)))
# write standard output to exe_info
exe_info.write('\n\nSTDOUT\n\n')
if os.path.isfile(self.runtime.proc_out):
with open(self.runtime.proc_out) as stdout:
for line in stdout:
exe_info.write(line)
# write standard error to exe_info
exe_info.write('\n\nSTDERR\n\n')
if os.path.isfile(self.runtime.proc_err):
with open(self.runtime.proc_err) as stderr:
for line in stderr:
exe_info.write(line)
# if command succeed, remove all out_ and err_ files,
self.runtime.clear()
def _useVars(self, text, pipeline_vars={}):
# reduce extra newline, space etc
text = ' '.join(text.split())
#
# if there are pipeline vars, try to use pipeline vars to replace the
# text
if pipeline_vars:
files_and_dirs = []
for key, item in pipeline_vars.items():
if isinstance(item, str) and (os.path.isfile(item) or
os.path.isdir(item)):
files_and_dirs.append([key, item])
# sort by length
files_and_dirs = sorted(files_and_dirs, key=lambda x: -len(x[1]))
#
# try to subsitute
pieces = text.split()
for key, item in files_and_dirs:
for idx, p in enumerate(pieces):
if p.startswith(item) and (len(p) == len(item) or
not (p[len(item)].isalpha() or
p[len(item)].isdigit())):
pieces[idx] = '${{{}}}{}'.format(
key, pieces[idx][len(item):])
text = ' '.join(pieces)
return text
def __call__(self, ifiles, pipeline=None):
'''Execute action with input files ``ifiles`` with runtime information
stored in ``pipeline``. This function is called by the pipeline and calls
user-defined ``execute`` function.
Parameters:
ifiles (string or list of strings):
input file names
pipeline (an pipeline object):
An Pipeline object for which the action is executed. The action
can set or retrieve runtime information from a dictionary
``pipeline.VARS``.
Result:
An action returns output files (parameter ``output`` of the action)
if any output is given. Otherwise input files (``ifiles``) are passed
through and returned.
'''
if self.output:
if os.path.isfile(self.runtime.proc_info):
with open(self.runtime.proc_info) as exe_info:
cmd = exe_info.readline().strip()
if self._useVars(cmd, pipeline.VARS) == self._useVars('; '.join(self.cmd), pipeline.VARS) \
and existAndNewerThan(self.output, ifiles + pipeline.step_dependent_files,
md5file=self.runtime.proc_info, pipeline=pipeline):
env.logger.info('Reuse existing {}'.format(', '.join(
self.output)))
self._bypass(ifiles, pipeline)
if self.output:
return self.output
else:
return ifiles
# create directory if output directory does not exist
for d in [
os.path.split(os.path.abspath(x))[0] for x in self.output
]:
if not os.path.isdir(d):
try:
os.makedirs(d)
except Exception as e:
raise RuntimeError(
'Failed to create directory {} for output file: {}'
.format(d, e))
# We cannot ignore this step, but do we have all the input files?
# If not, we will have to rewind the execution
for ifile in ifiles:
if not os.path.isfile(ifile):
env.logger.warning(
'Rewind execution because input file {} does not exist.'
.format(ifile))
raise RewindExecution(ifile)
#
if self.output:
with open(self.runtime.proc_info, 'w') as exe_info:
exe_info.write('{}\n'.format('; '.join(self.cmd)))
exe_info.write('#Start: {}\n'.format(
time.asctime(time.localtime())))
for f in ifiles + pipeline.step_dependent_files:
# for performance considerations, use partial MD5
exe_info.write('{}\t{}\t{}\n'.format(
f, os.path.getsize(f), calculateMD5(f, partial=True)))
# now, run the job, write info if it is successfully finished.
# Otherwise the job might be forked and it will record the signature by itself.
ret = self._execute(ifiles, pipeline)
if ret not in [True, False]:
env.logger.warning(
'User defined execute function of a PipelineAction should return True or False'
)
if ret:
self._write_info(pipeline)
#
if self.output:
return self.output
else:
return ifiles
# for backward compatibility
SkiptableAction = PipelineAction
try:
from .simulation import *
hasSimuPOP = True
except ImportError:
hasSimuPOP = False
class SequentialActions(PipelineAction):
'''Define an action that calls a list of actions, specified by Action1,
Action2 etc. This allows the specification of multiple small tasks in
a single pipeline step.
NOTE: this action is automatically applied if a list or tuple of actions
are specified in the SPEC file (e.g. action=Action1(), Action2()).
Examples:
action=CheckCommands('bowtie'), CheckOutput('bowtie --version', '1.1.1')
'''
def __init__(self, actions):
'''
Parameters:
actions (a tuple or list of actions):
A list of actions that will be applied to
'''
self.actions = []
for a in actions:
if hasattr(a, '__call__'):
self.actions.append(a.__call__)
else:
self.actions.append(a)
def __call__(self, ifiles, pipeline=None):
'''
Pass ifiles to the first action, take its output and pass it to
the second action, and so on. Return the output from the last
action as the result of this ``SequentialAction``.
'''
for a in self.actions:
# the input of the next action is the output of the
# previous action. However, ${INPUT} is the same for all
# options because it is substitute before ...
ifiles = a(ifiles, pipeline)
# return the output of the last action
return ifiles
class IfElse(PipelineAction):
'''Define an action that runs an action with a given condition.
'''
def __init__(self, cond, if_action=None, else_action=None):
'''
Parameters:
cond:
a condition, with True, 'True' as true, empty string,
False or 'False' as false.
if_action:
action if condition is met
else_action:
action if condition is not met
'''
self.if_action = if_action
self.else_action = else_action
self.cond = cond
def __call__(self, ifiles, pipeline=None):
'''
Pass ifiles to the first action, take its output and pass it to
the second action, and so on. Return the output from the last
action as the result of this ``SequentialAction``.
'''
if self.cond in [False, 'False', '']:
if self.else_action is not None:
return self.else_condition(ifiles, pipeline)
else:
if self.if_action is not None:
return self.if_condition(ifiles, pipeline)
# return the output of the last action
return ifiles
class CheckVariantToolsVersion(PipelineAction):
'''Check the version of variant tools and determine if it is
recent enough to execute the pipeline.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
Fail if the version of variant tools used to execute the
pipeline is older than the specified version.
Examples:
action=CheckVariantToolsVersion('2.5.0')
'''
def __init__(self, version=''):
'''
Parameters:
version (string):
Oldest version of variant tools that can be used
to execute this pipeline
'''
self.min_version = version
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
vtools_version = [
int(x)
for x in re.sub('\D', ' ', pipeline.VARS['vtools_version']).split()
]
# e.g. minimal 2.2.0, vtools 2.1.1
if [int(x) for x in re.sub('\D', ' ', self.min_version).split()
] > vtools_version:
raise RuntimeError(
'Version {} is required to execute this pipeline. '
'Please upgrade your installation of variant tools (version {})'
.format(self.min_version, pipeline.VARS['vtools_version']))
return ifiles
class ImportModules(PipelineAction):
'''Import functions and action from a Python module. This action passed input
files to output and does not change the pipeline.
File Flow: Input passthrough, but import symbols to pipeline.
Pipeline
^
INPUT =============> INPUT
Raises:
Raise a RuntimeError if one or more modules can not
be imported.
Examples:
action=ImportModules('DNASeq_tools.py')
action=ImportModules(['DNASeq_tools.py', 'simuPOP.demography'])
'''
def __init__(self, modules=[], script=''):
'''Import one or more modules to be used by the existing pipeline.
Parameters:
module (string or list of strings):
One or more module, which can be either the name of a system module
or a .py file. In the latter case, Variant Tools will try to locate
the file directly (a full path can be given), look for the module in
the path of the pipeline (if a local pipeline is used), or download
from the Variant Tools Repository under directory pipeline.
script (string or list of strings):
One or more in-line script that defines Python functions or customized
actions that will be used in this pipeline. This allows users to define
actions and utility functions that do not need to be shared with other
pipelines but might be used repeatedly in this pipeline. Otherwise a
ExecutePipelineFunction action can be used.
'''
if isinstance(modules, str):
self.modules = [modules]
else:
self.modules = modules
#
if isinstance(script, str):
self.script = script
else:
self.script = '\n'.join(script)
def __call__(self, ifiles, pipeline=None):
for module in self.modules:
# this is a path to a .py file
if module.endswith('.py'):
if os.path.isfile(module):
pyfile = module
# if the .py file locates in the same directory as the pipeline file
elif pipeline is not None \
and os.path.isfile(os.path.join(os.path.split(pipeline.spec_file)[0], module)):
pyfile = os.path.join(
os.path.split(pipeline.spec_file)[0], module)
else:
# try to download it from online
try:
pyfile = downloadFile('simulation/{}'.format(module))
except Exception:
try:
pyfile = downloadFile('pipeline/{}'.format(module))
except Exception as e:
raise ValueError(
'Failed to download required python module {}: {}'
.format(module, e))
try:
p, f = os.path.split(
os.path.abspath(os.path.expanduser(pyfile)))
sys.path.append(p)
local_dict = __import__(f[:-3] if f.endswith('.py') else f,
globals(), locals(),
module.split('.', 1)[-1:])
env.logger.info(
'{} symbols are imported form module {}'.format(
len(local_dict.__dict__), module))
pipeline.GLOBALS.update(local_dict.__dict__)
except Exception as e:
raise RuntimeError('Failed to import module {}: {}'.format(
module, e))
# now a system module
else:
try:
# allow loading from current directory
sys.path.append(os.getcwd())
local_dict = __import__(module, globals(), locals(),
module.split('.', 1)[-1:])
env.logger.info(
'{} symbols are imported form module {}'.format(
len(local_dict.__dict__), module))
pipeline.GLOBALS.update(local_dict.__dict__)
except ImportError as e:
raise RuntimeError('Failed to import module {}: {}'.format(
module, e))
# script
if self.script:
try:
local_dict = {}
exec(self.script, globals(), local_dict)
env.logger.info(
'{} symbols are imported form inline script'.format(
len(local_dict)))
pipeline.GLOBALS.update(local_dict)
except Exception as e:
raise RuntimeError('Failed to execute script "{}": {}'.format(
self.script, e))
return ifiles
class CheckCommands(PipelineAction):
'''Check the existence of specified commands and raise an error if one of
the commands does not exist.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
A RuntimeError will be raised if a command is not found.
Examples:
action=CheckCommands('java')
action=CheckCommands(['java', 'tophat2'])
'''
def __init__(self, commands):
'''
Parameters:
commands (string or list of strings):
Name of one of more commands to be checked. No option is allowed.
'''
PipelineAction.__init__(self)
if type(commands) == type(''):
self.commands = [commands]
else:
self.commands = commands
def __call__(self, ifiles, pipeline=None):
for cmd in self.commands:
if which(cmd) is None:
raise RuntimeError(
'Command {} does not exist. Please install it and try again.'
.format(cmd))
else:
env.logger.info('Command {} is located.'.format(cmd))
return ifiles
class CheckOutput(PipelineAction):
'''Run a command and check if its output matches at least one of specified
patterns. The pipeline will be terminated if failIfMismatch is set to True
(default). Otherwise a warning message will be printed.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
Raise a RuntimeError if the output of command does not match
any of the patterns, if ``failIfMismatch`` is set to ``True``.
Examples:
action=CheckOutput('tophat2 --version', ['v2.0.13', 'v2.0.14'])
# if strict_version is a command line parameter
action=CheckOutput('samtools', '0.1.19', %(strict_version)s)
'''
def __init__(self, command, patterns, failIfMismatch=True):
'''
Parameters:
command (string):
A command (with or without options)
patterns (string or list of strings):
One or more patterns (usually a piece of version string)
that will be compared to the output of ``command``
failIfMismatch (boolean):
If set to ``True`` (default), the action will terminate the
pipeline if the output of command does not match any of the
patterns. Otherwise a warning message will be printed when
the output of command does not match any of the patterns.
'''
self.command = command
if isinstance(patterns, str):
self.patterns = [patterns]
else:
self.patterns = patterns
self.fail = failIfMismatch
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
try:
# do not use subprocess.check_output because I need to get
# output even when the command returns non-zero return code
p = subprocess.Popen(
self.command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True)
odata, edata = p.communicate()
output = odata.decode() + edata.decode()
env.logger.trace('Output of command "{}" is "{}"'.format(
self.command, output))
except Exception as e:
raise RuntimeError('Failed to execute command "{}": {}'.format(
self.cmd, e))
#
if all(
[re.search(x, output, re.MULTILINE) is None for x in self.patterns
]):
msg = ('Output of command "{}" ("{}") does not ' +
'match specified regular expression {}.').format(
self.command, ' '.join(output[:40].split()),
' or '.join(self.patterns))
if self.fail:
raise RuntimeError(msg)
else:
env.logger.warning(msg)
return ifiles
class CheckFiles(PipelineAction):
'''Check the existence of specified files and raise an
error if one of the files does not exist.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
Raise a RuntimeError if any of the files is not found.
Example:
# assume gatk_path is a command line argument
action=CheckFile('%(gatk_path)s/GenomeAnalysisTK.jar',
'Please point --gatk_path to a directory with GenomeAnalysisTK.jar')
'''
def __init__(self, files, message=''):
'''
Parameters:
files (string or list of strings):
One or more files to check.
message (string):
A message when one of the files cannot be found.
'''
if type(files) == str:
self.files = [files]
else:
self.files = files
self.message = message
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
for f in self.files:
if os.path.isfile(os.path.expanduser(f)):
env.logger.info('{} is located.'.format(f))
else:
raise RuntimeError('Cannot locate {}: {}'.format(
f, self.message))
return ifiles
class CheckDirs(PipelineAction):
'''Check the existence of specified directories and raise an
error if one of the directories does not exist.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
Raise a RuntimeError if any of the directories is not found.
Example:
action=CheckDirs('${cmd_output}',
'Value of parameter --output need to be an existing directory')
'''
def __init__(self, dirs, message=''):
'''
Parameters:
files (string or list of strings):
One or more directories to check.
message (string):
A message when one of the directories cannot be found.
'''
if type(dirs) == str:
self.dirs = [dirs]
else:
self.dirs = dirs
self.message = message
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
for d in self.dirs:
if os.path.isdir(d):
env.logger.info('Directory {} is located.'.format(d))
else:
raise RuntimeError('Cannot locate directory {}. {}'.format(
d, self.message))
return ifiles
class TerminateIf(PipelineAction):
'''Terminate a pipeline if a condition is not met.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
A RuntimeError will be raised to terminate the pipeline if
the condition is met.
Examples:
action=TerminateIf(not '${cmd_output}', 'No --output is specified.')
'''
def __init__(self, cond, message=''):
'''
Parameters:
cond (boolean):
True or False. In practice, ``cond`` is usually
a lambda function that checks the existence of a file or value
of a pipeline variable.
message (string):
A message to be outputted when the condition is met.
'''
self.cond = cond
self.message = message
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
if self.cond:
raise RuntimeError(self.message)
return ifiles
class WarnIf(PipelineAction):
'''Send a warning message if a condition is not met.
File Flow: Input passthrough.
INPUT ====> INPUT
Examples:
action=WarnIf('%(LGD)' == 'NA', 'Default value of parameter --LGD is used.')
'''
def __init__(self, cond, message):
'''
Parameters:
cond (boolean):
True or False. In practice, ``cond`` is usually
a lambda function that checks the existence of a file or value
of a pipeline variable.
message (string):
A message to be outputted when the condition is met.
'''
self.cond = cond
self.message = message
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
if self.cond:
env.logger.warning(self.message)
return ifiles
class OutputText(PipelineAction):
'''Write specified text to standard output, or a file if a filename is
specified. The text can be a list of strings. A new line is added
automatically to each line of the text.
File Flow: Input passthrough.
INPUT ====> INPUT
Examples:
action=OutputText('Hey, the biggest part is done.')
'''
def __init__(self, text='', output=None, mode='a'):
'''
Parameters:
text (string or list of strings):
Text to be written to output.
output (a file name or None):
Output files. The text will be written to standard output
if no output is specified.
mode (string):
Mode to open file. 'a' for append and 'w' for overwrite.
'''
if not isinstance(text, str):
self.text = ''.join([str(x) + '\n' for x in text])
else:
self.text = text + '\n'
self.filename = output
self.mode = mode
PipelineAction.__init__(self, 'OutputText',
self.filename if self.filename is not None else '')
def __call__(self, ifiles, pipeline=None):
if self.filename is not None:
with open(self.filename, self.mode) as output:
output.write(self.text)
else:
sys.stdout.write(self.text)
return ifiles
class FieldsFromTextFile(PipelineAction):
'''Read a text file, guess its delimeter, field name (from header)
and create field descriptions. If a vcf file is encountered, all
fields will be exported.
File Flow: extract format of input and output format.
INPUT ==> Get Format ==> OUTPUT
Raises:
Raise a RuntimeError if this action failed to guess format (fields)
from the input file.
Examples:
action=FieldsFromTextFile('format.txt')
'''
def __init__(self, output):
'''
Parameters:
output:
Output file that records the format of the input files.
'''
PipelineAction.__init__(self, 'FieldsFromTextFile', output)
def _execute(self, ifiles, pipeline=None):
if len(ifiles) > 1:
env.logger.warning(
'Only the format of the first input file would be outputted.')
try:
if ifiles[0].endswith('.vcf') or ifiles[0].endswith('.vcf.gz'):
showTrack(ifiles[0], self.output[0])
else:
with open(self.output[0], 'w') as fo:
csv_dialect = csv.Sniffer().sniff(
open(ifiles[0], 'rU').read(8192))
fo.write('delimiter="{}"\n\n'.format(
csv_dialect.delimiter.replace('\t', r'\t')))
values = []
with open(ifiles[0], 'rU') as fi:
reader = csv.reader(fi, dialect=csv_dialect)
headers = reader.next()
values = [[] for x in headers]
for line in reader:
for idx in range(len(headers)):
values[idx].append(line[idx])
if len(values[0]) > 100:
break
#
for idx, header in enumerate(headers):
fo.write('[{}]\n'.format(validFieldName(header)))
fo.write('index={}\n'.format(idx + 1))
fo.write('type={}\n\n'.format(
typeOfValues(values[idx])))
except Exception as e:
raise RuntimeError('Failed to guess fields from {}: {}'.format(
ifiles[0], e))
#
return True
class RewindExecution(Exception):
pass
class NullAction(PipelineAction):
'''A pipeline action that does nothing. This is usually used when the goal
of the step is to change input, output, or assign variables to pipelines.
The action will be assumed if an empty action line is given.
File Flow: Input passthrough.
INPUT ====> INPUT
Example:
action=
action=NullAction()
'''
def __init__(self, output=[], *args, **kwargs):
'''A null action that does nothing.'''
PipelineAction.__init__(self, cmd='NullAction', output=output)
def _execute(self, ifiles, pipeline=None):
return True
class MonitorThread(threading.Thread):
def __init__(self,
group=None,
target=None,
name=None,
args=(),
kwargs={},
Verbose=None):
threading.Thread.__init__(self, group, target, name, args, kwargs,
Verbose)
self._return = None
def run(self):
if self._Thread__target is not None:
self._return = self._Thread__target(*self._Thread__args,
**self._Thread__kwargs)
def join(self):
threading.Thread.join(self)
return self._return
class SharedProcess:
def __init__(self, runtime):
self.runtime = runtime
def update_prog(self):
while True:
with open(self.runtime.proc_prog, 'w') as prog:
prog.write(' ')
time.sleep(30)
def __enter__(self):
# wait for the availability of lock
prog_time = None
while True:
# there is a progress file
if os.path.isfile(self.runtime.proc_prog):
if prog_time is None:
env.logger.trace('Job started with progress file {}'.format(
self.runtime.proc_prog))
os.remove(self.runtime.proc_prog)
prog_time = time.time()
#
if prog_time is None or time.time() - prog_time > 60:
# no progress by another thread
break
#
if os.path.isfile(self.runtime.proc_done):
break
#
# start monitoring process
self.update_process = Process(target=self.update_prog)
self.update_process.start()
def __exit__(self, type, value, traceback):
with open(self.runtime.proc_done, 'w') as prog:
prog.write('1')
self.runtime.clear(['prog'])
self.update_process.terminate()
class RunCommand(PipelineAction):
"""This action execute specified commands. If the pipeline is running
in parallel mode and a submitter is specified, it will use the submitter
command to execute the commands in a separate job.
File Flow:
Input passthrough if no output file is specified.
INPUT ====> INPUT
Generate output if one or more output files are specified.
INPUT ==> CMD ==> OUTPUT
Raises:
Raises an error if an command fails to execute.
Examples:
# simple commands without checking output
action=RunCommand(cmd='vtools init myproj -f')
action=RunCommand(cmd=[
'[ -d ${DIR1} ] || mkdir -p ${DIR1}',
'[ -d ${DIR2} ] || mkdir -p ${DIR2}'
])
# multiple commands, change working directory
# check output
action=RunCommand([
'update_blastdb.pl human_genomic --decompress || true',
'update_blastdb.pl nt --decompress || true',
],
working_dir='${NCBI_RESOURCE_DIR}/blast/db',
output=['${NCBI_RESOURCE_DIR}/blast/db/human_genomic.nal',
'${NCBI_RESOURCE_DIR}/blast/db/nt.nal']
)
# run command in background, with pipes
action=RunCommand('''samtools view -h ${INPUT}
| awk '$6 ~/N/' | awk '{ if ($9 ~ /^-/) {print $1"\t-"} else print $1"\t+" }'
| sort -T ${TEMP_DIR} -u | wc -l > ${ALIGNMENT_OUT}/junction.count''',
output='${ALIGNMENT_OUT}/junction.count',
submitter='sh {} &')
"""
def __init__(self,
cmd='',
output=[],
working_dir=None,
submitter=None,
wait=True,
max_jobs=None):
'''This action accepts one (a string) or more command (a list of strings)
and executes them in a shell environment, possibly as a separate job.
Parameters:
cmd (string or list of strings):
One or more commands to execute. The commands will be executed in
shell mode so pipes are allowed.
output (string or list of strings):
Expected output files of the action. If specified, the execution
signature will be created to record the input, output and command
of the action, and ignore the action if the signature matches of
a previous run.
working_dir (None or string):
Working directory of the command. Variant Tools will change to
this directory before executing the commands if a valid directory
is passed.
submitter (None or string):
If a submitter is specified and the pipeline is executed in multi-job
mode (e.g. --jobs 2), a shell script will be written with the commands
to be executed. The submitter command will be executed with ``{}`` in
parameter ``submitter`` replaced by the name of shell script. For
example, submitter='sh {} &' will run the job as a background job,
and submitter='qsub -q long < {}' will submit the shell script to the
long queue of a cluster system. Because the pipeline will be terminated
if the submitter command fails, `qsub new_job ... && false` can be used
to replace the running process by start a new job and terminate the
existing process intentionally.
wait (True, False, or number of seconds):
If a job is submitted, whether or not wait it to be completed. The default
is True, meaning that the master thread will continue to execute until
is will be waiting for the outcome of this command. If you set this parameter
to False, the pipeline execute will be stopped and you can re-run the
pipeline till the subcommand is completed. You can also set a number
to let the master thread wait for a pre-determined period of time. This
option is useful if the subprocess might die.
max_jobs: (deprecated)
'''
self.submitter = submitter
self.working_dir = working_dir
self.wait = wait
if type(output) == str:
self.output = [os.path.expanduser(output)]
else:
self.output = [os.path.expanduser(x) for x in output]
#
self.runtime = RuntimeFiles(self.output)
self.start_time = time.time()
if not cmd:
cmd = ['echo "None command executed."']
PipelineAction.__init__(self, cmd=cmd, output=output)
def _elapsed_time(self):
'''Return the elapsed time in human readable format since start time'''
second_elapsed = int(time.time() - self.start_time)
days_elapsed = second_elapsed // 86400
return ('{} days '.format(days_elapsed) if days_elapsed else '') + \
time.strftime('%H:%M:%S', time.gmtime(second_elapsed % 86400))
def _run_command(self):
'''Call a list of external command cmd, raise an error if any of them
fails. '''
if self.runtime.proc_lck:
env.lock(self.runtime.proc_lck, str(os.getpid()))
for cur_cmd in self.cmd:
if self.working_dir is not None and not os.path.isdir(
self.working_dir):
os.makedirs(self.working_dir)
env.logger.info('Running ``{}``{}'.format(
cur_cmd, ' under {}'.format(self.working_dir)
if self.working_dir else ''))
ret = subprocess.call(
cur_cmd,
shell=True,
stdout=None if self.runtime.proc_out is None else open(
self.runtime.proc_out, 'w'),
stderr=None if self.runtime.proc_err is None else open(
self.runtime.proc_err, 'w'),
cwd=self.working_dir)
if ret < 0:
if self.output:
try:
env.unlock(self.runtime.proc_lck, str(os.getpid()))
except:
pass
raise RuntimeError(
"Command '{}' was terminated by signal {} after executing {}"
.format(cur_cmd, -ret, self._elapsed_time()))
elif ret > 0:
if self.output:
with open(self.runtime.proc_err) as err:
for line in err.read().split('\n')[-50:]:
env.logger.error(line)
try:
env.unlock(self.runtime.proc_lck, str(os.getpid()))
except:
pass
raise RuntimeError(
"Execution of command '{}' failed after {} (return code {})."
.format(cur_cmd, self._elapsed_time(), ret))
def _monitor(self):
start_time = time.time()
prog_time = None
while True:
if os.path.isfile(self.runtime.proc_prog):
if prog_time is None:
env.logger.trace('Job started with progress file {}'.format(
self.runtime.proc_prog))
os.remove(self.runtime.proc_prog)
prog_time = time.time()
#
if prog_time is None:
# if the job has not been started for 10 minutes, quite
if time.time() - start_time > 600:
return (
'Background job has not been started after 10 minutes.')
else:
if time.time() - prog_time > 60:
return (
'Background job has not updated it progress for 1 minutes.'
)
#
if os.path.isfile(self.runtime.proc_done):
break
else:
if self.wait is False:
return (
'Do not wait for the completion of submitted job (wait=False).'
)
if self.wait is not True and isinstance(
self.wait, int) and prog_time is not None and time.time(
) - prog_time > self.wait:
return ('Quitted after waiting {} seconds.'.format(
self.wait))
time.sleep(10)
try:
env.unlock(self.runtime.proc_lck, str(os.getpid()))
except:
env.logger.warning('Failed to remove lock for file {}'.format(
self.output[0]))
pass
try:
with open(self.runtime.proc_done) as done:
ret = int(done.read().strip())
except Exception as e:
return (
'Failed to retrive return information for forked process from {}. {}'
.format(self.runtime.proc_done, e))
#
if ret < 0:
return (
"Command '{}' was terminated by signal {} after executing {}"
.format('; '.join(self.cmd), -ret, self._elapsed_time()))
elif ret > 0:
if self.output:
with open(self.runtime.proc_err) as err:
for line in err.read().split('\n')[-50:]:
env.logger.error(line)
return (
"Execution of command '{}' failed after {} (return code {})."
.format('; '.join(self.cmd), self._elapsed_time(), ret))
# remove the .done file
if not self.output[0] in self.pipeline.THREADS:
return ('Output is not waited by any threads')
# DO NOT POP FROM ANOTHER THREAD, this will cause race condition
# (unless we use thread safe dictionry). In this case, we only need
# to monitor the status of threads from the master threads.
# self.pipeline.THREADS.pop(self.output[0])
#
# the thread will end here
env.logger.trace('Thread for output {} ends.'.format(self.output[0]))
self.runtime.clear(['done'])
return ('')
def _submit_command(self):
'''Submit a job and wait for its completion.'''
# use full path because the command might be submitted to a remote machine
if os.path.isfile(self.runtime.proc_done):
os.remove(self.runtime.proc_done)
if self.runtime.proc_lck:
env.lock(self.runtime.proc_lck, str(os.getpid()))
#
if os.path.isfile(self.runtime.proc_cmd):
with open(self.runtime.proc_cmd) as old_cmd:
old_script = old_cmd.read()
else:
old_script = None
# create a batch file for execution
with open(self.runtime.proc_cmd, 'w') as sh_file:
sh_file.write('#PBS -o {}\n'.format(
os.path.abspath(self.runtime.proc_out)))
sh_file.write('#PBS -e {}\n'.format(
os.path.abspath(self.runtime.proc_err)))
sh_file.write('#PBS -N {}\n'.format(
os.path.basename(self.output[0])))
# sh_file.write('#PBS -N {}.{}_{}\n'.format(self.runtime.proc_err))
sh_file.write('#PBS -V\n')
# we try to reproduce the environment as much as possible becaus ehte
# script might be executed in a different environment
for k, v in os.environ.items():
if any([k.startswith(x) for x in ('SSH', 'PBS', '_')
]) or not k.replace('_', '').isalpha():
continue
sh_file.write('export {}="{}"\n'.format(k,
v.replace('\n', '\\n')))
#
sh_file.write('\ncd {}\n'.format(os.path.abspath(os.getcwd())))
if self.working_dir is not None:
sh_file.write('[ -d {0} ] || mkdir -p {0}\ncd {0}\n'.format(
os.path.abspath(self.working_dir)))
#
sh_file.write('''
progress() {{
while true
do
touch {}
sleep 30
done
}}
progress &
MYSELF=$!
'''.format(self.runtime.proc_prog))
sh_file.write('\n'.join(self.cmd))
#
sh_file.write(
'\n\nCMD_RET=$?\nif [ $CMD_RET == 0 ]; then vtools admin --record_exe_info {} {}; fi\n'
.format(os.getpid(), ' '.join(self.output)))
# a signal to show the successful completion of the job
sh_file.write(
'\nrm -f {}\nkill $MYSELF >/dev/null 2>&1\necho $CMD_RET > {}\n'
.format(self.runtime.proc_prog, self.runtime.proc_done))
#
if old_script is not None:
# with open(self.runtime.proc_cmd) as new_cmd:
# if old_script == new_cmd.read():
# env.logger.debug('Identical script {}'.format(self.runtime.proc_cmd))
# if there is no change in command
other_prog = glob.glob(
os.path.abspath(self.output[0]) + '.working_*')
if other_prog:
for op in other_prog:
# if the working file is less than 2 minutes old, ...
if time.time() - os.path.getmtime(op) < 120:
env.logger.info(
'Another process appears to be working on {}, checking ...'
.format(self.output[0]))
last_time = os.path.getmtime(op)
time.sleep(60)
# if the working file does not change after 60 seconds
if os.path.getmtime(op) != last_time:
raise RuntimeError(
'Failed to submit job because a job is currently running or has been failed within 2 minutes. Status file is {} (pid is {})'
.format(op, os.getpid()))
# try to submit command
if '{}' in self.submitter:
submit_cmd = self.submitter.replace('{}', self.runtime.proc_cmd)
else:
submit_cmd = self.submitter
#
env.logger.info(
'Running job {} with command "{}" from directory {}'.format(
self.runtime.proc_cmd, submit_cmd, os.getcwd()))
ret = subprocess.call(
submit_cmd,
shell=True,
stdout=open(self.runtime.proc_out, 'w'),
stderr=open(self.runtime.proc_err, 'w'),
cwd=self.working_dir)
if ret != 0:
try:
env.unlock(self.runtime.proc_out, str(os.getpid()))
except:
pass
#
if ret < 0:
raise RuntimeError(
"Failed to submit job {} due to signal {} (submitter='{}')"
.format(self.runtime.proc_cmd, -ret, self.submitter))
elif ret > 0:
if os.path.isfile(self.runtime.proc_err):
with open(self.runtime.proc_err) as err:
msg = err.read()
else:
msg = ''
raise RuntimeError(
"Failed to submit job {} using submiter '{}': {}".format(
self.runtime.proc_cmd, self.submitter, msg))
else:
t = MonitorThread(target=self._monitor)
t.daemon = True
t.start()
if self.output[0] in self.pipeline.THREADS:
raise RuntimeError(
'Two spawned jobs have the same self.output[0] file {}'
.format(self.output[0]))
self.pipeline.THREADS[self.output[0]] = t
def _execute(self, ifiles, pipeline=None):
# substitute cmd by input_files and output_files
if pipeline.jobs > 1 and self.submitter is not None and not self.output:
env.logger.warning(
'Fail to execute in parallel because no output is specified.')
#
self.pipeline = pipeline
# Submit the job on a cluster system
# 1. if there is output, otherwise we cannot track the status of the job
# 2. if a submit command is specified
# 3. if --jobs with a value greater than 1 is used.
if self.output and pipeline.jobs > 1 and self.submitter is not None:
self._submit_command()
return False
else:
self._run_command()
return True
class ExecutePythonCode(PipelineAction):
'''This action execute a piece of Python code under the pipeline namespace, which
means all pipeline variables will be available to the code. This action provides a
way to implement pipeline actions on the fly. Arbitary parameters can be passed
and be made available to the script. Pipeline variables are also available to the
script as a variabe "pvars".
File Flow:
Input passthrough if no output file is specified.
INPUT ====> INPUT
Generate output if one or more output files are specified.
INPUT ==> CMD ==> OUTPUT
Raises:
Raises an error if the python code fails to execute.
'''
def __init__(self, script='', output=[], modules=[], export=None, **kwargs):
'''This action accepts one or a list of strings and execute it as a piece of Python
code. Pipeline variables are made available as a dictionary "pvars".
Parameters:
script (string or list of strings):
One or more strings to execute. List of strings will be concatenated by new
lines.
modules (string or list of strings):
Modules to import for this step. It is similar to action ImportModules but
the imported symbols are available to this action only.
export (None or filename):
A filename to which the execute code will be exported. This option is useful
for debugging because pipeline variables will be prepended to the script so
that the exported script could be executed with no or minimal modification.
kwargs (additional parameters):
Any additional kwargs will be passed to the function executed.
'''
if not script:
env.logger.warning('No valid script is specified.')
script = ''
if isinstance(script, str):
self.script = script
else:
self.script = '\n'.join(script)
#
m = hashlib.md5()
m.update(self.script.encode('utf-8'))
#
self.kwargs = kwargs
self.modules = modules
self.export = export
#
PipelineAction.__init__(
self,
cmd='python -e {} {}'.format(m.hexdigest(), kwargs),
output=output)
def _execute(self, ifiles, pipeline=None):
if self.export is not None:
with open(self.export, 'w') as exported_script:
exported_script.write('#!/usr/env python\n')
exported_script.write(
'#\n#Script exported by action ExecutePythonCode\n')
# modules?
exported_script.write(''.join([
'import {}\n'.format(x) for x in ('sys', 'os', 're', 'glob')
]))
exported_script.write(
'\nfrom variant_tools.pipeline import *\n')
for module in self.modules:
exported_script.write('import {}\n'.format(
os.path.basename(module)[:-3] if module
.endswith('.py') else module))
#
# pipeline variables
exported_script.write('pvars=')
pprint.pprint(pipeline.VARS.dict(), stream=exported_script)
exported_script.write('\n')
#
exported_script.write(
'# Pipeline variables are case insensitive\n')
exported_script.write(
'pvars.update({x.upper():y for x,y in pvars.items()})\n')
exported_script.write(
'pvars.update({x.lower():y for x,y in pvars.items()})\n')
#
# script
exported_script.write(self.script)
env.logger.info('Python code exported to ``{}``'.format(
self.export))
for module in self.modules:
# this is a path to a .py file
if module.endswith('.py'):
if os.path.isfile(module):
pyfile = module
# if the .py file locates in the same directory as the pipeline file
elif pipeline is not None \
and os.path.isfile(os.path.join(os.path.split(pipeline.spec_file)[0], module)):
pyfile = os.path.join(
os.path.split(pipeline.spec_file)[0], module)
else:
# try to download it from online
try:
pyfile = downloadFile('simulation/{}'.format(module))
except Exception:
try:
pyfile = downloadFile('pipeline/{}'.format(module))
except Exception as e:
raise ValueError(
'Failed to download required python module {}: {}'
.format(module, e))
try:
p, f = os.path.split(
os.path.abspath(os.path.expanduser(pyfile)))
sys.path.append(p)
local_dict = __import__(f[:-3] if f.endswith('.py') else f,
globals(), locals(),
module.split('.', 1)[-1:])
env.logger.info(
'{} symbols are imported form module {}'.format(
len(local_dict.__dict__), module))
pipeline.GLOBALS.update(local_dict.__dict__)
except Exception as e:
raise RuntimeError('Failed to import module {}: {}'.format(
module, e))
# now a system module
else:
try:
# allow loading from current directory
sys.path.append(os.getcwd())
local_dict = __import__(module, globals(), locals(),
module.split('.', 1)[-1:])
env.logger.info(
'{} symbols are imported form module {}'.format(
len(local_dict.__dict__), module))
pipeline.GLOBALS.update(local_dict.__dict__)
except ImportError as e:
raise RuntimeError('Failed to import module {}: {}'.format(
module, e))
env.logger.info('Executing Python script:\n{}'.format(self.script))
try:
globals().update(pipeline.GLOBALS)
local_dict = self.kwargs
local_dict['pvars'] = pipeline.VARS
exec(self.script, globals(), local_dict)
except Exception as e:
ex_type, ex, tb = sys.exc_info()
traceback.print_tb(tb)
raise RuntimeError('Failed to execute script: {}'.format(e))
return True
class ExecuteScript(PipelineAction):
"""This action execute specified in-line script with specified command (bash, python, perl
etc). If the pipeline is running in parallel mode and a submitter is specified, it will use
the submitter command to execute the commands in a separate job. This action is the base
action for ExecuteRScript, ExecutePerlScript and ExecutePythonScript and is usually not
used directly.
File Flow:
Input passthrough if no output file is specified.
INPUT ====> INPUT
Generate output if one or more output files are specified.
INPUT ==> CMD ==> OUTPUT
Raises:
Raises an error if an command fails to execute.
"""
def __init__(self,
script='',
interpreter='',
args='',
output=[],
working_dir=None,
export=None,
submitter=None,
suffix=None,
wait=True):
'''This action accepts one or a list of strings, write them to a temporary file
and executes them by a interpreter, possibly as a separate job.
Parameters:
script (string or list of strings):
One or more strings to execute. List of strings will be concatenated by new
lines. The complete script will be written to a temporary file to be executed
by an interpreter.
interpreter (string):
An interpreter that will be used to execute the script. It is usually just a command
but more complex command line is allowed with '{}' replaced by the path to the
temporary script.
args (string or list of strings):
Command line arguments which can be a single string or a list of strings.
Filenames will be properly quoted if needed.
output (string or list of strings):
Expected output files of the action. If specified, the execution
signature will be created to record the input, output and command
of the action, and ignore the action if the signature matches of
a previous run.
working_dir (None or string):
Working directory of the command. Variant Tools will change to
this directory before executing the commands if a valid directory
is passed.
export (None or string):
A filename to which the script will be exported before execution.
This option makes it easier to debug the script because the script in the
spec file might contain pipeline variables.
submitter (None or string):
If a submitter is specified and the pipeline is executed in multi-job
mode (e.g. --jobs 2), the script will be submitted by the submitter.
The submitter command will be executed with ``{}`` in
parameter ``submitter`` replaced by the name of shell script. For
example, submitter='sh {} &' will run the job as a background job,
and submitter='qsub -q long < {}' will submit the shell script to the
long queue of a cluster system. Because the pipeline will be terminated
if the submitter command fails, `qsub new_job ... && false` can be used
to replace the running process by start a new job and terminate the
existing process intentionally.
suffix (None or string):
An optional suffix (file extension) to the temporary script.
wait (True, False, or number of seconds):
If a job is submitted, whether or not wait it to be completed. The default
is True, meaning that the master thread will continue to execute until
is will be waiting for the outcome of this command. If you set this parameter
to False, the pipeline execute will be stopped and you can re-run the
pipeline till the subcommand is completed. You can also set a number
to let the master thread wait for a pre-determined period of time. This
option is useful if the subprocess might die.
'''
self.interpreter = interpreter
self.submitter = submitter
self.working_dir = working_dir
self.wait = wait
if type(output) == str:
self.output = [os.path.expanduser(output)]
else:
self.output = [os.path.expanduser(x) for x in output]
#
self.runtime = RuntimeFiles(self.output)
self.start_time = time.time()
if not script:
raise ValueError('No valid script is specified.')
if isinstance(script, str):
self.script = script
else:
self.script = '\n'.join(script)
#
self.args = args
env.logger.info('Executing\n{}'.format(self.script))
#
m = hashlib.md5()
m.update(self.script.encode('utf-8'))
#
self.script_file = tempfile.NamedTemporaryFile(
mode='w+t', suffix=suffix, delete=False).name
with open(self.script_file, 'w') as script_file:
script_file.write(self.script)
#
if export is not None:
with open(export, 'w') as exported_script:
exported_script.write(self.script)
env.logger.info('Script exported to ``{}``'.format(export))
PipelineAction.__init__(
self, cmd='{} {}'.format(interpreter, m.hexdigest()), output=output)
def __del__(self):
try:
os.remove(self.script_file)
except Exception as e:
env.logger.debug(
'Failed to remove temporary script file {}: {}'.format(
self.script_file, e))
def _elapsed_time(self):
'''Return the elapsed time in human readable format since start time'''
second_elapsed = int(time.time() - self.start_time)
days_elapsed = second_elapsed // 86400
return ('{} days '.format(days_elapsed) if days_elapsed else '') + \
time.strftime('%H:%M:%S', time.gmtime(second_elapsed % 86400))
def _run_command(self):
'''Call a list of external command cmd, raise an error if any of them
fails. '''
if self.runtime.proc_lck:
env.lock(self.runtime.proc_lck, str(os.getpid()))
if '{}' in self.interpreter:
cmd = self.interpreter.replace('{}', pipes.quote(self.script_file))
else:
cmd = self.interpreter + ' ' + pipes.quote(self.script_file) + \
(self.args if isinstance(self.args, str)
else ' '.join(pipes.quote(x) for x in self.args))
env.logger.info('Running ``{}``'.format(cmd))
ret = subprocess.call(
cmd,
shell=True,
stdout=None if self.runtime.proc_out is None else open(
self.runtime.proc_out, 'w'),
stderr=None if self.runtime.proc_err is None else open(
self.runtime.proc_err, 'w'),
cwd=self.working_dir)
if ret < 0:
if self.output:
try:
env.unlock(self.runtime.proc_lck, str(os.getpid()))
except:
pass
raise RuntimeError(
"Command '{}' was terminated by signal {} after executing {}"
.format(cmd, -ret, self._elapsed_time()))
elif ret > 0:
if self.output:
with open(self.runtime.proc_err) as err:
for line in err.read().split('\n')[-50:]:
env.logger.error(line)
try:
env.unlock(self.runtime.proc_lck, str(os.getpid()))
except:
pass
raise RuntimeError(
"Execution of command '{}' failed after {} (return code {})."
.format(cmd, self._elapsed_time(), ret))
else:
# write standard out to terminal
if self.runtime.proc_out:
with open(self.runtime.proc_out) as proc_out:
for line in proc_out:
env.logger.info(line.rstrip())
if self.runtime.proc_err:
with open(self.runtime.proc_err) as proc_err:
for line in proc_err:
env.logger.warning(line.rstrip())
def _monitor(self):
start_time = time.time()
prog_time = None
while True:
if os.path.isfile(self.runtime.proc_prog):
if prog_time is None:
env.logger.trace('Job started with progress file {}'.format(
self.runtime.proc_prog))
os.remove(self.runtime.proc_prog)
prog_time = time.time()
#
if prog_time is None:
# if the job has not been started for 10 minutes, quite
if time.time() - start_time > 600:
return (
'Background job has not been started after 10 minutes.')
else:
if time.time() - prog_time > 60:
return (
'Background job has not updated it progress for 1 minutes.'
)
if os.path.isfile(self.runtime.proc_done):
break
else:
if self.wait is False:
return
if self.wait is not True and isinstance(
self.wait, int) and prog_time is not None and time.time(
) - prog_time > self.wait:
return ('Quitted after waiting {} seconds.'.format(
self.wait))
time.sleep(10)
try:
env.unlock(self.runtime.proc_lck, str(os.getpid()))
except:
env.logger.warning('Failed to remove lock for file {}'.format(
self.output[0]))
pass
with open(self.runtime.proc_done) as done:
ret = int(done.read().strip())
#
if ret < 0:
return (
"Command '{}' was terminated by signal {} after executing {}"
.format('; '.join(self.cmd), -ret, self._elapsed_time()))
elif ret > 0:
if self.output:
with open(self.runtime.proc_err) as err:
for line in err.read().split('\n')[-50:]:
env.logger.error(line)
return (
"Execution of command '{}' failed after {} (return code {})."
.format('; '.join(self.cmd), self._elapsed_time(), ret))
# remove the .done file
if not self.output[0] in self.pipeline.THREADS:
return ('Output is not waited by any threads')
# DO NOT POP FROM ANOTHER THREAD, this will cause race condition
# (unless we use thread safe dictionry). In this case, we only need
# to monitor the status of threads from the master threads.
# self.pipeline.THREADS.pop(self.output[0])
#
# the thread will end here
env.logger.info('{} has been successfully generated.'.format(
self.output[0]))
self.runtime.clear(['done'])
return ('')
def _submit_command(self):
'''Submit a job and wait for its completion.'''
# use full path because the command might be submitted to a remote machine
#
if os.path.isfile(self.runtime.proc_done):
os.remove(self.runtime.proc_done)
if self.runtime.proc_lck:
env.lock(self.runtime.proc_lck, str(os.getpid()))
#
if os.path.isfile(self.runtime.proc_cmd):
with open(self.runtime.proc_cmd) as old_cmd:
old_script = old_cmd.read()
else:
old_script = None
# create a batch file for execution
with open(self.runtime.proc_cmd, 'w') as sh_file:
sh_file.write('#PBS -o {}\n'.format(
os.path.abspath(self.runtime.proc_out)))
sh_file.write('#PBS -e {}\n'.format(
os.path.abspath(self.runtime.proc_err)))
sh_file.write('#PBS -N {}\n'.format(
os.path.basename(self.output[0])))
# sh_file.write('#PBS -N {}.{}_{}\n'.format(self.runtime.proc_err))
sh_file.write('#PBS -V\n')
# we try to reproduce the environment as much as possible becaus ehte
# script might be executed in a different environment
for k, v in os.environ.items():
if any([k.startswith(x) for x in ('SSH', 'PBS', '_')
]) or not k.replace('_', '').isalpha():
continue
sh_file.write('export {}="{}"\n'.format(k,
v.replace('\n', '\\n')))
#
sh_file.write('\ncd {}\n'.format(os.path.abspath(os.getcwd())))
if self.working_dir is not None:
sh_file.write('[ -d {0} ] || mkdir -p {0}\ncd {0}\n'.format(
os.path.abspath(self.working_dir)))
#
sh_file.write('''
progress() {{
while true
do
touch {}
sleep 30
done
}}
progress &
MYSELF=$!
'''.format(self.runtime.proc_prog))
# interpreter
if '{}' in self.interpreter:
sh_file.write(
self.interpreter.replace('{}', pipes.quote(
self.script_file)) +
(self.args if isinstance(self.args, str) else ' '.join(
pipes.quote(x) for x in self.args)) + '\n')
else:
sh_file.write(
self.interpreter + ' ' + pipes.quote(self.script_file) +
(self.args if isinstance(self.args, str) else ' '.join(
pipes.quote(x) for x in self.args)) + '\n')
#
sh_file.write(
'\n\nCMD_RET=$?\nif [ $CMD_RET == 0 ]; then vtools admin --record_exe_info {} {}; fi\n'
.format(os.getpid(), ' '.join(self.output)))
# a signal to show the successful completion of the job
sh_file.write(
'\nrm -f {}\nkill $MYSELF >/dev/null 2>&1\necho $CMD_RET > {}\n'
.format(self.runtime.proc_prog, self.runtime.proc_done))
#
# try to submit command
if '{}' in self.submitter:
submit_cmd = self.submitter.replace('{}', self.runtime.proc_cmd)
else:
submit_cmd = self.submitter
#
if old_script is not None:
# with open(self.runtime.proc_cmd) as new_cmd:
# if old_script == new_cmd.read():
# if there is no change in command
other_prog = glob.glob(
os.path.abspath(self.output[0]) + '.working_*')
if other_prog:
for op in other_prog:
if time.time() - os.path.getmtime(op) < 120:
env.logger.info(
'Another process appears to be working on {}, checking ...'
.format(self.output[0]))
last_time = os.path.getmtime(op)
time.sleep(60)
# if the working file does not change after 60 seconds
if os.path.getmtime(op) != last_time:
raise RuntimeError(
'Failed to submit job because a job is currently running or has been failed within 2 minutes. Status file is {} (pid is {})'
.format(op, os.getpid()))
env.logger.info(
'Running job {} with command "{}" from directory {}'.format(
self.runtime.proc_cmd, submit_cmd, os.getcwd()))
ret = subprocess.call(
submit_cmd,
shell=True,
stdout=open(self.runtime.proc_out, 'w'),
stderr=open(self.runtime.proc_err, 'w'),
cwd=self.working_dir)
if ret != 0:
try:
env.unlock(self.runtime.proc_out, str(os.getpid()))
except:
pass
#
if ret < 0:
raise RuntimeError(
"Failed to submit job {} due to signal {} (submitter='{}')"
.format(self.runtime.proc_cmd, -ret, self.submitter))
elif ret > 0:
if os.path.isfile(self.runtime.proc_err):
with open(self.runtime.proc_err) as err:
msg = err.read()
else:
msg = ''
raise RuntimeError(
"Failed to submit job {} using submiter '{}': {}".format(
self.runtime.proc_cmd, self.submitter, msg))
else:
t = MonitorThread(target=self._monitor)
t.daemon = True
t.start()
if self.output[0] in self.pipeline.THREADS:
raise RuntimeError(
'Two spawned jobs have the same self.output[0] file {}'
.format(self.output[0]))
self.pipeline.THREADS[self.output[0]] = t
def _execute(self, ifiles, pipeline=None):
# substitute cmd by input_files and output_files
if pipeline.jobs > 1 and self.submitter is not None and not self.output:
env.logger.warning(
'Fail to execute in parallel because no output is specified.')
#
self.pipeline = pipeline
# Submit the job on a cluster system
# 1. if there is output, otherwise we cannot track the status of the job
# 2. if a submit command is specified
# 3. if --jobs with a value greater than 1 is used.
if self.output and pipeline.jobs > 1 and self.submitter is not None:
self._submit_command()
return False
else:
self._run_command()
return True
class ExecuteRScript(ExecuteScript):
'''Execute in-line R script using Rscript as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='Rscript',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.R',
wait=wait)
class ExecuteShellScript(ExecuteScript):
'''Execute in-line shell script using bash as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='bash',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.sh',
wait=wait)
class ExecuteCShellScript(ExecuteScript):
'''Execute in-line shell script using bash as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='tcsh',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.csh',
wait=wait)
class ExecutePythonScript(ExecuteScript):
'''Execute in-line python script using python as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='python',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.py',
wait=wait)
class ExecutePython3Script(ExecuteScript):
'''Execute in-line python script using python3 as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='python3',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.py',
wait=wait)
class ExecutePerlScript(ExecuteScript):
'''Execute in-line perl script using perl as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='perl',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.perl',
wait=wait)
class ExecuteRubyScript(ExecuteScript):
'''Execute in-line perl script using perl as interpreter. Please
check action ExecuteScript for more details.
'''
def __init__(self,
script='',
args='',
output=[],
export=None,
working_dir=None,
submitter=None,
wait=True):
ExecuteScript.__init__(
self,
script=script,
interpreter='ruby',
args=args,
output=output,
export=export,
working_dir=working_dir,
submitter=submitter,
suffix='.rb',
wait=wait)
class CheckRLibraries(ExecuteRScript):
'''Check the existence of specified R libraries. If an package is not available,
it will try to install it using "install.package" and from bioconductor. The pipeline
will raise an error if one of the library is not available and cannot be installed.
File Flow: Input passthrough.
INPUT ====> INPUT
Raises:
A RuntimeError will be raised if a R library is not installed.
Examples:
action=CheckRLibraries('edgeR')
action=CheckRLibraries(['edgeR', 'AIMS'])
'''
def __init__(self, libraries):
'''
Parameters:
libraries (string or list of strings):
Name of one of more R libraries to be checked.
'''
PipelineAction.__init__(self)
if type(libraries) == type(''):
self.libraries = [libraries]
else:
self.libraries = libraries
# script
# get temp filename
self.output_file = tempfile.NamedTemporaryFile(
mode='w+t', suffix='.txt', delete=False).name
script = r'''
for (package in c({0})) {{
if (require(package, character.only=TRUE, quietly=TRUE)) {{
write(paste(package, "AVAILABLE"), file="{1}", append=TRUE)
next
}} else {{
install.packages(package, repos="http://cran.us.r-project.org",
quiet=TRUE)
}}
# if the package still does not exist
if (!require(package, character.only=TRUE, quietly=TRUE)) {{
source("http://bioconductor.org/biocLite.R")
biocLite(package, ask=FALSE)
}}
# if it still does not exist, write the package name to output
if (require(package, character.only=TRUE, quietly=TRUE)) {{
write(paste(package, "INSTALLED"), file="{1}", append=TRUE)
}} else {{
write(paste(package, "MISSING"), file="{1}", append=TRUE)
}}
}}
'''.format(', '.join(['"{}"'.format(lib) for lib in self.libraries]),
self.output_file)
ExecuteRScript.__init__(self, script=script, output=self.output_file)
def __call__(self, ifiles, pipeline=None):
ExecuteRScript.__call__(self, ifiles, pipeline)
with open(self.output_file) as tmp:
count = 0
for line in tmp:
lib, status = line.split()
if status.strip() == "MISSING":
env.logger.error(
'R Library {} is not available and cannot be installed.'
.format(lib))
count += 1
elif status.strip() == 'AVAILABLE':
env.logger.info('R library {} is available'.format(lib))
elif status.strip() == 'INSTALLED':
env.logger.info(
'R library {} has been installed'.format(lib))
else:
raise RuntimeError(
'This should not happen: {}'.format(line))
#
try:
os.remove(self.output_file)
except:
pass
if count > 0:
raise RuntimeError("One or more R libraries are not available.")
return ifiles
class DecompressFiles(PipelineAction):
'''This action gets a list of input files from input file, decompressing
input files (.tar.gz, .zip, etc) if necessary. The decompressed files
are returned as output. One particular feature of this action is that
it records content of large tar or tar.gz files to a manifest file and
ignores the step if the manifest file exists.
File Flow: Decompress input files
INPUT ==> Decompress ==> OUTPUT
Examples:
action=DecompressFiles()
'''
def __init__(self, dest_dir=None):
'''
Parameters:
dest_dir (None or string):
Destination directory, default to current directory.
'''
self.dest_dir = dest_dir if dest_dir else '.'
PipelineAction.__init__(self)
def _decompress(self, filename):
'''If the file ends in .tar.gz, .tar.bz2, .bz2, .gz, .tgz, .tbz2, decompress
it to dest_dir (current directory if unspecified), and return a list of files.
Uncompressed files will be returned untouched. If the destination files exist
and newer, this function will return immediately.'''
mode = None
if filename.lower().endswith('.tar.gz') or filename.lower().endswith(
'.tar.bz2'):
mode = 'r:gz'
elif filename.lower().endswith('.tbz2') or filename.lower().endswith(
'.tgz'):
mode = 'r:bz2'
elif filename.lower().endswith('.tar'):
mode = 'r'
elif filename.lower().endswith('.gz'):
dest_file = os.path.join(self.dest_dir,
os.path.basename(filename)[:-3])
if existAndNewerThan(ofiles=dest_file, ifiles=filename):
env.logger.info(
'Using existing decompressed file {}'.format(dest_file))
else:
env.logger.info('Decompressing {} to {}'.format(
filename, dest_file))
with gzip.open(filename,
'rb') as gzinput, open(TEMP(dest_file),
'wb') as output:
content = gzinput.read(10000000)
while content:
output.write(content)
content = gzinput.read(10000000)
# only rename the temporary file to the right one after finishing everything
# this avoids corrupted files
os.rename(TEMP(dest_file), dest_file)
return [dest_file]
elif filename.lower().endswith('.bz2'):
dest_file = os.path.join(self.dest_dir,
os.path.basename(filename)[:-4])
if existAndNewerThan(ofiles=dest_file, ifiles=filename):
env.logger.warning(
'Using existing decompressed file {}'.format(dest_file))
else:
env.logger.info('Decompressing {} to {}'.format(
filename, dest_file))
with bz2.BZ2File(filename,
'rb') as bzinput, open(TEMP(dest_file),
'wb') as output:
content = bzinput.read(10000000)
while content:
output.write(content)
content = bzinput.read(10000000)
# only rename the temporary file to the right one after finishing everything
# this avoids corrupted files
os.rename(TEMP(dest_file), dest_file)
return [dest_file]
elif filename.lower().endswith('.zip'):
bundle = zipfile.ZipFile(filename)
bundle.extractall(self.dest_dir)
env.logger.info('Decompressing {} to {}'.format(
filename, self.dest_dir))
return [
os.path.join(self.dest_dir, name) for name in bundle.namelist()
]
#
# if it is a tar file
if mode is not None:
env.logger.info(
'Extracting fastq sequences from tar file {}'.format(filename))
#
# MOTE: open a compressed tar file can take a long time because it needs to scan
# the whole file to determine its content. I am therefore creating a manifest
# file for the tar file in the dest_dir, and avoid re-opening when the tar file
# is processed again.
manifest = RuntimeFiles(filename).manifest
all_extracted = False
dest_files = []
if existAndNewerThan(ofiles=manifest, ifiles=filename):
all_extracted = True
for f in [x.strip() for x in open(manifest).readlines()]:
dest_file = os.path.join(self.dest_dir, os.path.basename(f))
if existAndNewerThan(ofiles=dest_file, ifiles=filename):
dest_files.append(dest_file)
env.logger.info(
'Using existing extracted file {}'.format(
dest_file))
else:
all_extracted = False
#
if all_extracted:
return dest_files
#
# create a temporary directory to avoid corrupted file due to interrupted decompress
try:
os.mkdir(os.path.join(self.dest_dir, 'tmp'))
except:
# directory might already exist
pass
#
dest_files = []
with tarfile.open(filename, mode) as tar:
# only extract files
files = [x.name for x in tar.getmembers() if x.isfile()]
# save content to a manifest
with open(manifest, 'w') as manifest:
for f in files:
manifest.write(f + '\n')
for f in files:
# if there is directory structure within tar file, decompress all to the current directory
dest_file = os.path.join(self.dest_dir, os.path.basename(f))
dest_files.append(dest_file)
if existAndNewerThan(ofiles=dest_file, ifiles=filename):
env.logger.info(
'Using existing extracted file {}'.format(
dest_file))
else:
env.logger.info('Extracting {} to {}'.format(
f, dest_file))
tar.extract(f, os.path.join(self.dest_dir, 'tmp'))
# move to the top directory with the right name only after the file has been properly extracted
shutil.move(
os.path.join(self.dest_dir, 'tmp', f), dest_file)
# set dest_files to the same modification time. This is used to
# mark the right time when the files are created and avoid the use
# of archieved but should-not-be-used files that might be generated later
[os.utime(x, None) for x in dest_files]
return dest_files
# return source file if nothing needs to be decompressed
return [filename]
def __call__(self, ifiles, pipeline=None):
# decompress input files and return a list of output files
filenames = []
for filename in ifiles:
filenames.extend(self._decompress(filename))
filenames.sort()
return filenames
class RemoveIntermediateFiles(PipelineAction):
'''This action removes specified files (not the step input files) and replaces
them with their signature (file size, md5 signature etc). A pipeline can bypass
completed steps with these files as input or output by checking the signatures.
In contrast, the steps would have to be re-run if the files are removed from the
file system.
File Flow: Input passthrough. Specified files are replaced by their signature.
INPUT ====> INPUT
Examples:
action=RemoveIntermediateFiles('${OUTPUT200}')
action=RemoveIntermediateFiles('${OUTPUT200} ${OUTPUT330}')
action=RemoveIntermediateFiles(['${OUTPUT200}', '${OUTPUT330}'])
'''
def __init__(self, files):
'''Replace ``files`` with their signatures. This pipeline passes its
input to output and does not change the flow of pipeline.
Parameters:
files (string or list of strings)
One or more files to be removed. Multiple files can be specified
in the same string if they are separated by spaces.
'''
if isinstance(files, str):
self.files_to_remove = [files]
else:
self.files_to_remove = files
PipelineAction.__init__(self)
def _getFiles(self):
for name in self.files_to_remove:
files = shlex.split(name)
for f in files:
yield f
def __call__(self, ifiles, pipeline=None):
env.logger.trace('Remove intermediate files {}'.format(' '.join(
self.files_to_remove)))
for f in self._getFiles():
if not os.path.isfile(f):
if os.path.isfile(f + '.file_info'):
env.logger.info('Keeping existing {}.file_info.'.format(f))
else:
raise RuntimeError(
'Failed to create {}.file_info: Missing input file.'
.format(f))
else:
FileInfo(f).save()
env.logger.info('Replace {0} with {0}.file_info'.format(f))
try:
os.remove(f)
except Exception:
env.logger.warning(
'Failed to remove intermediate file {}'.format(f))
return ifiles
class LinkToDir(PipelineAction):
'''Create hard links of input files to a specified directory. This is
usually used to link input files to a common cache directory so that
all operations can be performed on that directory.
File Flow: Link input files to specified destination directory.
INPUT == LINK ==> DEST_DIR/INPUT
Examples:
action=LinkToDir('cache')
'''
def __init__(self, dest_dir):
'''
Parameters:
dest_dir (string):
A directory to which input files will be linked to.
The directory will be created if it does not exist.
'''
self.dest = dest_dir
if not os.path.isdir(self.dest):
env.logger.info('Creating directory {}'.format(self.dest))
try:
os.makedirs(self.dest)
except Exception as e:
raise RuntimeError('Failed to create directory {}: {}'.format(
self.dest, e))
if not os.path.isdir(self.dest):
raise RuntimeError('Failed to create directory {}'.format(
self.dest))
PipelineAction.__init__(self)
def __call__(self, ifiles, pipeline=None):
ofiles = []
for filename in ifiles:
path, basename = os.path.split(filename)
if not os.path.isfile(filename):
if os.path.isfile(filename + '.file_info'):
dest_file = os.path.join(self.dest, basename) + '.file_info'
if os.path.isfile(dest_file):
if not os.path.samefile(filename + '.file_info',
dest_file):
os.remove(dest_file)
env.logger.info('Linking {} to {}'.format(
filename, self.dest))
os.link(
filename + '.file_info',
os.path.join(self.dest, basename) +
'.file_info')
else:
env.logger.trace(
'Reusing existing linked file_info file: {}'
.format(
os.path.join(self.dest, basename) +
'.file_info'))
else:
env.logger.info('Linking {} to {}'.format(
filename, self.dest))
os.link(
filename + '.file_info',
os.path.join(self.dest, basename) + '.file_info')
else:
raise RuntimeError(
'Failed to link {} to directory {}: file does not exist'
.format(filename, self.dest))
else:
dest_file = os.path.join(self.dest, basename)
if os.path.isfile(dest_file):
if not os.path.samefile(filename, dest_file):
os.remove(dest_file)
env.logger.info('Linking {} to {}'.format(
filename, self.dest))
os.link(filename, dest_file)
else:
env.logger.trace(
'Reusing existing linked file: {}'.format(
dest_file))
else:
env.logger.info('Linking {} to {}'.format(
filename, self.dest))
os.link(filename, dest_file)
ofiles.append(os.path.join(self.dest, basename))
return ofiles
class DownloadResource(PipelineAction):
'''Download resources to specified destination directory. dest_dir can
be a full path name or a directory relative to
$local_resource/pipeline_resource where $local_resource is the local
resource directory of the project (default to ~/.variant_tools,
see runtime option local_resource for details). The default pipeline
resource directory is $local_resource/pipeline_resource/NAME where NAME
is the name of the pipeline.
File Flow: Input passthrough.
INPUT ====> INPUT
Examples:
action=DownloadResource(resource='ftp://igenome:[email protected]/Homo_sapiens/UCSC/hg19/Homo_sapiens_UCSC_hg19.tar.gz',
dest_dir="${LOCAL_RESOURCE}/iGenomes")
action=DownloadResource(resource='ftp://[email protected]/bundle/2.8/hg19/1000G_omni2.5.hg19.sites.vcf.gz
ftp://[email protected]/bundle/2.8/hg19/1000G_omni2.5.hg19.sites.vcf.gz.md5',
dest_dir='${LOCAL_RESOURCE/GATK')
NOTE:
1. If FILE.md5 file is downloaded, it will be used to validate FILE.
2. The resources will be automatically decompressed if decompress=True (default). You would get both
FILE and FILE.gz if you downloaded FILE.gz.
'''
def __init__(self, resource, dest_dir, output=[], decompress=True):
'''Download resources from specified URLs in ``resource``.
Parameters:
dest_dir:
Directory where the downloaded resources will be placed.
'''
self.resource = [x for x in resource.split() if x]
if not dest_dir or type(dest_dir) != str:
raise ValueError('Invalid resource directory {}'.format(dest_dir))
else:
self.pipeline_resource = os.path.expanduser(dest_dir)
try:
if not os.path.isdir(self.pipeline_resource):
os.makedirs(self.pipeline_resource)
except:
raise RuntimeError(
'Failed to create pipeline resource directory '.format(
self.pipeline_resource))
self.decompress = decompress
PipelineAction.__init__(
self,
cmd='Download Resource {} to {}'.format(resource, dest_dir),
output=output)
def __call__(self, ifiles, pipeline=None):
saved_dir = os.getcwd()
os.chdir(self.pipeline_resource)
ofiles, md5files = self._downloadFiles(ifiles)
self._validate(md5files)
os.chdir(saved_dir)
return ofiles
def _validate(self, md5_files):
if md5_files:
prog = ProgressBar('Validating md5 signature',
sum([x[1] for x in md5_files]))
mismatched_files = []
for filename, s in md5_files:
try:
downloaded_md5 = open(filename +
'.md5').readline().split()[0]
calculated_md5 = calculateMD5(filename, partial=False)
if downloaded_md5 != calculated_md5:
mismatched_files.append(filename)
except Exception as e:
env.logger.warning(
'Failed to verify md5 signature of {}: {}'.format(
filename[:-4], e))
prog.update(prog.count + s)
prog.done()
if mismatched_files:
env.logger.warning('md5 signature of {} mismatch. '
'Please remove {} and try again.'.format(
', '.join(mismatched_files),
'this file' if len(mismatched_files) == 1
else 'these files'))
def _downloadFiles(self, ifiles):
'''Download resource'''
# decompress all .gz files
skipped = []
md5_files = []
for cnt, URL in enumerate(sorted(self.resource)):
filename = URL.rsplit('/', 1)[-1]
dest_file = os.path.join(self.pipeline_resource, filename)
try:
if os.path.isfile(dest_file):
skipped.append(filename)
else:
downloadURL(
URL,
dest_file,
False,
message='{}/{} {}'.format(cnt + 1, len(self.resource),
filename))
except KeyboardInterrupt as e:
raise e
except Exception as e:
raise RuntimeError('Failed to download {}: {} {}'.format(
filename,
type(e).__name__, e))
#
if filename.endswith('.tar.gz'):
manifest_file = RuntimeFiles(filename).manifest
env.logger.trace('Checking manifest {}'.format(manifest_file))
decompress = not os.path.isfile(manifest_file)
if not decompress:
with open(manifest_file) as mf:
for item in mf:
if not os.path.isfile(item.strip()):
decompress = True
break
if decompress:
with tarfile.open(filename, 'r:gz') as tar:
s = delayedAction(env.logger.info,
'Extracting {}'.format(filename))
tar.extractall(self.pipeline_resource)
del s
# only extract files
files = [x.name for x in tar.getmembers() if x.isfile()]
# save content to a manifest
with open(manifest_file, 'w') as manifest:
for f in files:
manifest.write(f + '\n')
elif filename.endswith('.gz'):
if not existAndNewerThan(ofiles=filename[:-3], ifiles=filename):
s = delayedAction(env.logger.info,
'Decompressing {}'.format(filename))
decompressGzFile(filename, inplace=False, force=True)
del s
elif filename.endswith('.zip'):
manifest_file = RuntimeFiles(filename).manifest
env.logger.trace('Checking manifest {}'.format(manifest_file))
decompress = not os.path.isfile(manifest_file)
if not decompress:
with open(manifest_file) as mf:
for item in mf:
if not os.path.isfile(item.strip()):
decompress = True
break
if decompress:
env.logger.trace('Decompressing {}'.format(filename))
s = delayedAction(env.logger.info,
'Decompressing {}'.format(filename))
bundle = zipfile.ZipFile(filename)
bundle.extractall(os.path.dirname(filename))
with open(manifest_file, 'w') as manifest:
for f in bundle.namelist():
manifest.write(f + '\n')
del s
#
if filename.endswith('.md5') and os.path.isfile(filename[:-4]):
md5_files.append(
[filename[:-4],
os.path.getsize(filename[:-4])])
if skipped:
env.logger.info('Using {} existing resource files under {}.'.format(
', '.join(skipped), self.pipeline_resource))
return ifiles, md5_files
class _CaseInsensitiveDict(MutableMapping):
"""A case-insensitive ``dict``-like object that
1. limits the type of items to string or list of strings.
2. returns '' is the key does not exist (yield a warning)
3. allows attribute-like access
"""
def __init__(self, data=None, **kwargs):
self._store = dict()
if data is None:
data = {}
self.update(data, **kwargs)
def __setitem__(self, key, value):
# Use the uppercased key for lookups, but store the actual
# key alongside the value.
# and value != self._store[key.upper()][1] else 'set'
reset = 'reset' if key.upper() in self._store else 'set'
if not isinstance(value, (str, list, tuple)):
value = str(value)
env.logger.warning(
'Pipeline variable {} is converted to "{}"'.format(key, value))
if isinstance(
value,
(list, tuple)) and not all([isinstance(x, str) for x in value]):
raise ValueError(
'Only string or list of strings are allowed for pipeline variables: {} for key {}'
.format(value, key))
self._store[key.upper()] = (key, value)
if isinstance(value, str) or len(value) <= 2 or len(str(value)) < 50:
# if not inputXXX, outputXXX ... (these variables are not recommended to use)
if re.match('^(input|INPUT|output|OUTPUT)\d+$', key) is None:
env.logger.debug(
'Pipeline variable ``{}`` is {} to ``{}``'.format(
key, reset, str(value)))
else: # should be a list or tuple
val = str(value).split(' ')[0] + \
' ...] ({} items)'.format(len(value))
# if not inputXXX, outputXXX ... (these variables are not recommended to use)
if re.match('^(input|INPUT|output|OUTPUT)\d+$', key) is None:
env.logger.debug(
'Pipeline variable ``{}`` is {} to ``{}``'.format(
key, reset, val))
def __contains__(self, key):
return key.upper() in self._store
def dict(self):
return {x: y for x, y in self._store.values()}
def __setattr__(self, key, value):
if key == '_store':
self.__dict__[key] = value
else:
self.__setitem__(key, value)
def __getattr__(self, key):
return self.__getitem__(key)
def __getitem__(self, key):
return self._store[key.upper()][1]
def __delitem__(self, key):
del self._store[key.upper()]
def __iter__(self):
return (casedkey for casedkey, mappedvalue in self._store.values())
def __len__(self):
return len(self._store)
def upper_items(self):
"""Like iteritems(), but with all uppercase keys."""
return (
(upperkey, keyval[1]) for (upperkey, keyval) in self._store.items())
def __eq__(self, other):
if isinstance(other, collections.Mapping):
other = _CaseInsensitiveDict(other)
else:
return NotImplemented
# Compare insensitively
return dict(self.upper_items()) == dict(other.upper_items())
# Copy is required
def copy(self):
return _CaseInsensitiveDict(self._store.values())
def __repr__(self):
return '%s(%r)' % (self.__class__.__name__, dict(self.items()))
class Pipeline:
'''The Variant Tools pipeline class. Its instance will be passed to each action
to provide runtime information. An action should not change any attribute of
the pipeline, except for setting additional variables through its ``VARS``
dictionary. Note that VARS is a case-insensitive dictionary but it is generally
recommended to use CAPTICAL names for pipeline variables. '''
def __init__(self,
name,
extra_args=[],
pipeline_type='pipeline',
verbosity=None,
jobs=1):
self.pipeline = PipelineDescription(name, extra_args, pipeline_type)
self.spec_file = self.pipeline.spec_file
self.verbosity = verbosity
self.jobs = jobs
def limit_steps(self, psteps, allowed_steps):
'''Restrict steps of a pipeline using allowed_steps'''
all_steps = {int(x.index): False for x in psteps}
#
for item in allowed_steps.split(','):
# remove space
item = ''.join([x for x in item if x != ' '])
if item.isdigit():
# pipeline:100
all_steps[int(item)] = True
elif '-' in item and item.count('-') == 1:
l, u = item.split('-')
if (l and not l.isdigit()) or (u and not u.isdigit()) or \
(l and u and int(l) > int(u)):
raise ValueError(
'Invalid pipeline step item {}'.format(item))
# pipeline:-100, pipeline:100+ or pipeline:10-100
if not l:
l = min(all_steps.keys())
if not u:
u = max(all_steps.keys())
#
for key in all_steps.keys():
if key >= int(l) and key <= int(u):
all_steps[key] = True
else:
raise ValueError('Invalid pipeline step item {}'.format(item))
#
# disable limited steps
for idx in range(len(psteps)):
if not all_steps[int(psteps[idx].index)]:
psteps[idx].options.append('skip')
env.logger.warning('Steps {} are skipped due to restriction {}'.format(
','.join([str(x) for x in all_steps.keys() if not all_steps[x]]),
allowed_steps))
def execute(self, pname, **kwargs):
allowed_steps = None
if not pname:
pname = ''
else:
if ':' in pname:
pname, allowed_steps = pname.split(':', 1)
if not pname:
if len(self.pipeline.pipelines) == 1:
pname = list(self.pipeline.pipelines.keys())[0]
elif 'default' in self.pipeline.pipelines:
pname = 'default'
else:
raise ValueError(
'Name of pipeline should be specified because '
'{}.pipeline defines more than one pipelines without a default one. '
'Available pipelines are: {}.'.format(
self.pipeline.name,
', '.join(self.pipeline.pipelines.keys())))
elif pname not in self.pipeline.pipelines.keys():
raise ValueError('Pipeline {} is undefined in configuraiton file '
'{}. Available pipelines are: {}'.format(
pname, self.pipeline.name,
', '.join(self.pipeline.pipelines.keys())))
#
psteps = self.pipeline.pipelines[pname]
if allowed_steps is not None:
self.limit_steps(psteps, allowed_steps)
#
# the project will be opened when needed
with Project(
mode=['ALLOW_NO_PROJ', 'READ_ONLY'],
verbosity=self.verbosity) as proj:
self.VARS = _CaseInsensitiveDict(
home=os.path.expanduser('~'),
temp_dir=env.temp_dir,
cache_dir=env.cache_dir,
local_resource=env.local_resource,
ref_genome_build=proj.build if proj.build is not None else '',
pipeline_name=pname,
spec_file=self.spec_file,
model_name=pname,
vtools_version=proj.version,
working_dir=os.getcwd(),
pipeline_format=self.pipeline.pipeline_format)
if not os.path.isdir(env.cache_dir):
os.makedirs(env.cache_dir)
# these are command line options
if float(self.pipeline.pipeline_format) <= 1.0:
if 'cmd_input' in self.pipeline.commandline_opts:
if not self.pipeline.commandline_opts['cmd_input']:
self.pipeline.commandline_opts['cmd_input'] = []
else:
self.pipeline.commandline_opts[
'cmd_input'] = self.pipeline.commandline_opts[
'cmd_input'].split(',')
if 'cmd_output' in self.pipeline.commandline_opts:
if not self.pipeline.commandline_opts['cmd_output']:
self.pipeline.commandline_opts['cmd_output'] = []
else:
self.pipeline.commandline_opts[
'cmd_output'] = self.pipeline.commandline_opts[
'cmd_output'].split(',')
self.VARS.update(self.pipeline.commandline_opts)
self.VARS.update({k: str(v) for k, v in kwargs.items()})
if 'cmd_input' not in self.VARS:
self.VARS['cmd_input'] = []
if 'cmd_output' not in self.VARS:
self.VARS['cmd_output'] = []
# if there is a output file, write log to .log
if self.VARS['cmd_output'] and 'logfile' not in self.VARS:
self.VARS['logfile'] = self.VARS['cmd_output'][0] + '.log'
#
self.GLOBALS = {}
self.GLOBALS.update(globals())
self.THREADS = {}
# we need to put self.pipeline.pipeline_vars in self.VARS because
# they might refer to each other
self.VARS.update(self.pipeline.pipeline_vars)
for key, val in self.pipeline.pipeline_vars.items():
# if key in ('vtools_version', 'spec_file', 'home', 'pipeline_name', 'model_name'):
# raise ValueError('Cannot reset read-only pipeline variable {}'.format(key))
self.VARS[key] = substituteVars(
val, self.VARS, self.GLOBALS, asString=False)
for key, val in self.VARS.items():
if key == 'working_dir' and val != os.getcwd():
env.logger.warning(
'Changing working directory to {}'.format(val))
os.chdir(val)
if key == 'cache_dir' and val != env.cache_dir:
env.logger.warning('Changing cache directory to {}'.format(val))
env.cache_dir = val
#
if 'logfile' in self.VARS:
env.logger.info('Logging information is saved to {}'.format(
self.VARS['logfile']))
if '/' in self.VARS['logfile']:
d = os.path.split(self.VARS['logfile'])[0]
if not os.path.isdir(d):
env.logger.info(
'Making directory {} for output file'.format(d))
os.makedirs(d)
ch = logging.FileHandler(self.VARS['logfile'].lstrip('>'), mode='a')
ch.setLevel(logging.DEBUG)
ch.setFormatter(
logging.Formatter('%(asctime)s: %(levelname)s: %(message)s'))
env.logger.addHandler(ch)
#
ifiles = self.VARS['cmd_input']
step_index = 0
rewind_count = 0
while True:
# step_index can jump back and forth depending on the
# execution status of each step
command = psteps[step_index]
if 'skip' in command.options:
step_index += 1
env.logger.info('Step {}.{}_{} is skipped'.format(
self.pipeline.name, pname, command.index))
if step_index == len(psteps):
break
step_output = []
continue
self.VARS['pipeline_step'] = command.index
env.logger.info('Executing ``{}.{}_{}``: {}'.format(
self.pipeline.name, pname, command.index,
' '.join(command.comment.split())))
# init
for key, val in command.init_action_vars:
self.VARS[key] = substituteVars(
val, self.VARS, self.GLOBALS, asString=False)
# substitute ${} variables
emitter = None
if 'no_input' in command.options or 'independent' in command.options:
step_input = []
step_named_input = []
elif command.input is None or not command.input.strip():
step_input = ifiles
step_named_input = [['', ifiles]]
else:
command_input_line = substituteVars(command.input, self.VARS,
self.GLOBALS)
if ':' in command_input_line:
input_line, emitter_part = command_input_line.split(':', 1)
else:
input_line = command_input_line
emitter_part = ''
#
if not input_line.strip():
step_input = ifiles
step_named_input = [['', ifiles]]
else:
# look for pattern of name=filenames
pieces = re.split('([\w\d_]+\s*=)', input_line)
step_named_input = []
for piece in pieces:
if not piece.strip():
continue
if piece.endswith('='):
step_named_input.append([piece[:-1].strip(), []])
else:
expanded_files = sum([
glob.glob(os.path.expanduser(x))
for x in shlex.split(piece)
], [])
if not expanded_files:
raise ValueError(
'{} does not expand to any valid file.'
.format(piece))
if not step_named_input:
step_named_input.append(['', expanded_files])
else:
step_named_input[-1][1].extend(expanded_files)
#
step_input = sum(
[x[1] for x in step_named_input if x[0] == ''], [])
if not step_input:
step_input = ifiles
if emitter_part:
try:
# remove ${INPUT} because it is determined by the emitter
if 'input' in self.VARS:
self.VARS.pop('input')
emitter = eval(
'EmitInput({})'.format(
substituteVars(emitter_part,
self.VARS, self.GLOBALS)),
globals(), self.GLOBALS)
except Exception as e:
raise ValueError(
'Failed to interpret input emit options "{}"'
.format(e))
#
#
self.VARS['input{}'.format(command.index)] = step_input
self.VARS['input'] = step_input
self.step_dependent_files = []
for n, f in step_named_input:
if n:
self.VARS['input{}_{}'.format(command.index, n)] = f
self.VARS['input_{}'.format(n)] = f
self.step_dependent_files.extend(f)
if self.step_dependent_files:
env.logger.debug('Step dependent files are {}'.format(', '.join(
self.step_dependent_files)))
#
saved_dir = os.getcwd()
for opt in command.options:
matched = re.match('^input_alias\s*=\s*([\w\d_]+)$', opt)
if matched:
self.VARS[matched.group(1)] = step_input
for n, f in step_named_input:
if n:
self.VARS['{}_{}'.format(matched.group(1), n)] = f
matched = re.match('^working_dir\s*=\s*(\S+)$', opt)
if matched:
working_dir = os.path.expanduser(matched.group(1))
if not os.path.isdir(working_dir):
raise ValueError(
'Invalid working directory: {}'.format(working_dir))
env.logger.info(
'Use working directory ``{}`` for {}_{}'.format(
working_dir, pname, command.index))
os.chdir(working_dir)
#
env.logger.trace('INPUT of step {}_{}: {}'.format(
pname, command.index, step_input))
#
# now, group input files
if not command.input_emitter:
if emitter is None: # if not defined in input:
emitter = EmitInput()
else:
if emitter is not None:
raise ValueError(
'Cannot define input emitter in both input and input_emitter'
)
try:
# ${CMD_INPUT} etc can be used.
emitter = eval(
substituteVars(command.input_emitter, self.VARS,
self.GLOBALS), globals(), self.GLOBALS)
except Exception as e:
raise RuntimeError(
'Failed to group input files: {}'.format(e))
# pass Pipeline itself to emitter
igroups, ivars, step_output = emitter(step_input, self)
try:
for ig, iv in zip(igroups, ivars):
if ig != self.VARS['input']:
self.VARS['input'] = ig
if not ig and float(self.pipeline.pipeline_format) <= 1.0:
env.logger.trace(
'Step skipped due to no input file (for pipeline format < 1.0 only)'
)
continue
for key, val in iv.items():
self.VARS[key] = substituteVars(
val, self.VARS, self.GLOBALS, asString=False)
# pre action variables are evaluated for each ig because they might involve
# changing ${input}
for key, val in command.pre_action_vars:
self.VARS[key] = substituteVars(
val, self.VARS, self.GLOBALS, asString=False)
action = substituteVars(command.action, self.VARS,
self.GLOBALS)
env.logger.trace('Emitted input of step {}_{}: {}'.format(
pname, command.index, ig))
env.logger.trace('Action of step {}_{}: {}'.format(
pname, command.index, action))
# check if the input file is ready. This is used for
# parallel execution of the pipeline while the input file
# might be worked on by another job
for ifile in ig:
# is ifile in any of the output files?
if ifile in self.THREADS:
# wait for the thread to complete
env.logger.info(
'Waiting for the input file {} to be available.'
.format(ifile))
# while self.THREADS[ifile].isAlive():
ret = self.THREADS[ifile].join()
# thread closed, remove from self.THREADS
self.THREADS.pop(ifile)
if ret:
raise RuntimeError(
'Failed to generate {}: {}'.format(
ifile, ret))
if not (os.path.isfile(ifile) or
os.path.isfile(ifile + '.file_info')):
#raise RewindExecution(ifile)
raise RuntimeError(
'Non-existent input file {} due to ongoing or failed background job'
.format(ifile))
#
if not action.strip():
action = 'NullAction()'
action = eval('(' + action + ')', globals(), self.GLOBALS)
if isinstance(action, (tuple, list)):
action = SequentialActions(action)
if not issubclass(action.__class__, PipelineAction):
env.logger.warning(
'Pipeline action {} is not a subclass of PipelineAction'
.format(action.__class__))
# pass the Pipeline object itself to action
# this allows the action to have access to pipeline variables
# and other options
if 'blocking' in command.options:
self.runtime = RuntimeFiles('{}_{}'.format(
pname, command.index))
with SharedProcess(self.runtime):
ofiles = action(ig, self)
else:
ofiles = action(ig, self)
if type(ofiles) == str:
step_output.append(ofiles)
else:
step_output.extend(ofiles)
# wait for all pending jobs to finish
self.VARS['output{}'.format(command.index)] = step_output
self.VARS['output'.format(command.index)] = step_output
for opt in command.options:
matched = re.match('^output_alias\s*=\s*([\w\d_]+)$', opt)
if matched:
env.logger.debug('Setting variable {} to {}'.format(
matched.group(1), step_output))
self.VARS[matched.group(1)] = step_output
env.logger.trace('OUTPUT of step {}_{}: {}'.format(
pname, command.index, step_output))
for f in step_output:
if not (os.path.isfile(f) or
os.path.isfile(f + '.file_info') or
f in self.THREADS):
raise RuntimeError(
'Output file {} does not exist after '
'completion of step {}_{} (working directory: {})'
.format(f, pname, command.index, os.getcwd()))
for key, val in command.post_action_vars:
self.VARS[key] = substituteVars(
val, self.VARS, self.GLOBALS, asString=False)
#
# In case of passthrough, the original input files will be passed to
# the next step regardless what has been produced during the step.
if 'independent' not in command.options:
ifiles = step_output
# this step is successful, go to next
os.chdir(saved_dir)
step_index += 1
env.logger.debug(
'Step {}.{}_{} is executed successfully.'.format(
self.pipeline.name, pname, command.index))
if step_index == len(psteps):
break
except RewindExecution:
rewind_count += 1
if rewind_count >= 3:
raise RuntimeError(
'Failed to execute pipeline {}.{}: excessive '
'rewind during execution.'.format(
self.pipeline.name, pname))
# unfortunately, an input file has been removed (replaced by .file_info) but
# a later steps need it. We will have to figure out how to create this
# file by looking backward ...
to_be_regenerated = [
x for x in step_input if not os.path.isfile(x)
]
# we need to check if this file is actually generated at all before
# otherwise a misspecified input file would cause the whole pipline
# to start from step 1 again and again
all_input_and_output_files = []
for k, v in self.VARS.items():
if ((k.startswith('INPUT') and k[5:].isdigit() and int(k[5:]) < int(command.index)) or
(k.startswith('OUTPUT') and k[6:].isdigit() and int(k[6:]) < int(command.index))) and \
isinstance(v, (tuple, list)):
all_input_and_output_files.extend(v)
#
for x in to_be_regenerated:
if x not in all_input_and_output_files:
raise RuntimeError(
'Specified input file "{}" does not exist and is not '
'generated by any previous step.'.format(x))
# remove all fony files so that they will be re-generated
if os.path.isfile(x + '.file_info'):
os.remove(x + '.file_info')
remaining = [x for x in to_be_regenerated]
env.logger.debug('Missing input file {}'.format(
', '.join(remaining)))
while step_index > 0:
step_index -= 1
command = psteps[step_index]
# remove all fony files so that they will be re-generated
for x in self.VARS['input{}'.format(command.index)]:
if os.path.isfile(x + '.file_info'):
env.logger.debug(
'Remove file info {}'.format(x + '.file_info'))
os.remove(x + '.file_info')
# if any of the input file does not exist, go back further
if not all([
os.path.isfile(x)
for x in self.VARS['input{}'.format(command.index)]
]):
env.logger.debug(
'Not all input files are available: {}'.format(
', '.join(self.VARS['input{}'.format(
command.index)])))
continue
# check if a real file can be generated at this step
remaining = [
x for x in remaining
if x not in self.VARS['output{}'.format(command.index)]
]
if not remaining:
break
if step_index > 1:
ifiles = self.VARS['output{}'.format(psteps[step_index -
1].index)]
else:
ifiles = self.VARS['cmd_input']
env.logger.warning(
'Rewinding to ``{}.{}_{}``: input files {} need to be re-generated.'
.format(self.pipeline.name, pname, command.index,
', '.join(to_be_regenerated)))
os.chdir(saved_dir)
except Exception as e:
env.logger.debug('Failed to execute step {}.{}_{}.'.format(
self.pipeline.name, pname, command.index))
raise RuntimeError('Failed to execute step {}_{}: {}'.format(
pname, command.index, e))
#
# clear variables that are local to step
for n, f in step_named_input:
if n:
self.VARS.pop('input_{}'.format(n))
#
# at the end of pipeline wait for all threads to complete
if self.THREADS:
for k, v in self.THREADS.items():
env.logger.trace('Waiting for {} to be completed.'.format(k))
while v.isAlive():
v.join(5)
# thread closed, remove from self.THREADS
self.THREADS.pop(ifile)
env.logger.info(
'Execution of pipeline {}.{} is successful with output {}'.format(
self.pipeline.name, pname, ', '.join(step_output)))
def executeArguments(parser):
parser.add_argument(
'specfile',
metavar='SPECFILE',
help='''Name of a pipeline configuration file, which can be a
path to a .pipeline file (with or without extension) or one
of the online pipelines listed by command "vtools show pipelines".
For backward compatibility, if no input and output files are
specified (options --input and --output), values of this option
is treated as a SQL query that will be executed against the project
database, with project genotype database attached as "genotype" and
annotation databases attached by their names.''')
parser.add_argument(
'pipelines',
nargs='*',
metavar='PIPELINES',
help='''Name of one or more pipelines defined in SPECFILE, which can be
ignored if the SPECFILE only defines one pipeline. One or more steps
can be specified in the form of 'pipeline:5' (step_5 only),
'pipeline:-5' (up to step 5), 'pipeline:5-' (from step 5),
'pipeline:2,5' (step 2 and 5), 'pipeline:2-5' (step 2 to 5). This
essentially adds an option "skip" to the unselected pipeline steps and
it is up to the user to ensure that the pipeline is executable
with only a subset of steps. Please use command "vtools show pipeline
SPECFILE" for details of available pipelines in SPECFILE, including
pipeline-specific parameters that could be used to change the default
behavior of the pipelines.''')
parser.add_argument(
'-j',
'--jobs',
default=1,
type=int,
help='''Execute the pipeline in parallel model if a number other than
1 is specified. In this mode, the RunCommand action will create
a shell script and submit the job using a command specified by
option ``submitter``, if this parameter is defined.''')
parser.add_argument(
'-d',
'--delimiter',
default='\t',
help='''Delimiter used to output results of a SQL query.''')
def execute(args):
# to keep backward compatibility, the vtools execute command
# can execute a SQL query and a pipeline
def executeQuery():
with Project(verbosity=args.verbosity) as proj:
# if there is no output,
if proj.store == "sqlite":
proj.db.attach('{}_genotype'.format(proj.name), 'genotype')
# for backward compatibility
proj.db.attach('{}_genotype'.format(proj.name))
cur = proj.db.cursor()
#
query = args.specfile + ' ' + ' '.join(args.pipelines)
if query.upper().startswith('SELECT'):
env.logger.trace('Analyze statement: "{}"'.format(query))
cur.execute('EXPLAIN QUERY PLAN ' + query)
for rec in cur:
env.logger.trace('\t'.join([str(x) for x in rec]))
# really execute the query
try:
cur.execute(query)
except Exception as e:
raise RuntimeError(
'Failed to execute SQL query "{}": {}'.format(query, e))
proj.db.commit()
sep = args.delimiter
for rec in cur:
print(sep.join(['{}'.format(x) for x in rec]))
#
def executePipeline():
pipeline = Pipeline(
args.specfile,
extra_args=args.unknown_args,
verbosity=args.verbosity,
jobs=args.jobs)
# unspecified
if not args.pipelines:
pipeline.execute(None)
else:
for name in args.pipelines:
pipeline.execute(name)
#
try:
env.verbosity = args.verbosity
env.logger = None
# definitely a pipeline
if args.specfile.endswith('.pipeline') or args.unknown_args:
executePipeline()
# definitely a sql query
elif args.delimiter != '\t':
executeQuery()
else:
try:
# try to execute as a SQL query
executeQuery()
except (RuntimeError, ValueError) as e:
env.logger.debug('Failed to execute {} as SQL query: {}'.format(
' '.join(args.pipelines), e))
executePipeline()
except Exception as e:
env.unlock_all()
env.logger.error(e)
sys.exit(1)
#
# vtools simulate is implemented using the pipeline execution mechanism. It
# essentially execute a pipeline that calls various simulation functions
# to simulate data.
#
def simulateArguments(parser):
parser.add_argument(
'specfile',
metavar='SPECFILE',
help='''Name of a model specification file, which can be the name of an
online specification file, or path to a local .pipeline file. Please
use command "vtools show simulations" to get a list all available
simulation models.''')
parser.add_argument(
'models',
nargs='*',
metavar='MODELS',
help='''Name of one or more simulation models defined in SPECFILE, which
can be ignored if the SPECFILE only defines one simulation model.
Please use command "vtools show simulation SPECFILE" for details
of available models in SPECFILE, including model-specific parameters
that could be used to change the default behavior of these models.'''
)
parser.add_argument(
'--seed',
type=int,
help='''Random seed for the simulation. A random seed will be used by
default but a specific seed could be used to reproduce a previously
executed simulation.''')
parser.add_argument(
'--replicates',
default=1,
type=int,
help='''Number of consecutive replications to simulate''')
parser.add_argument(
'-j',
'--jobs',
default=1,
type=int,
help='''Maximum number of concurrent jobs to execute, for steps
of a pipeline that allows multi-processing.''')
def simulate_replicate(args, rep):
try:
env.verbosity = args.verbosity
env.logger = None
# step 1, create a simulation configuration file.
model_name = os.path.basename(args.specfile).split('.', 1)[0]
if args.seed is None:
args.seed = random.randint(1, 2**32 - 1)
if not os.path.isdir(env.cache_dir):
os.mkdir(env.cache_dir)
# set random seed of simulators
random.seed(args.seed + rep)
if not args.models:
cfg_file = '{}/{}_{}.cfg'.format(env.cache_dir, model_name,
args.seed + rep)
else:
cfg_file = '{}/{}_{}_{}.cfg'.format(env.cache_dir, model_name,
'_'.join(args.models),
args.seed + rep)
#
with open(cfg_file, 'w') as cfg:
cfg.write('model={} {}\n'.format(args.specfile,
' '.join(args.models)))
cfg.write('seed={}\n'.format(args.seed + rep))
if '--seed' in sys.argv:
# skip the seed option so to stop pipeline from distinguishing the two commands
cmd_args = sys.argv[:sys.argv.index(
'--seed')] + sys.argv[sys.argv.index('--seed') + 2:]
cfg.write("command=vtools {}\n".format(
subprocess.list2cmdline(cmd_args[1:])))
else:
cfg.write("command={}\n".format(env.command_line))
#
env.logger.info('Starting simulation ``{}``'.format(cfg_file))
opt = args.unknown_args
opt.extend(['--input'] + [cfg_file])
pipeline = Pipeline(
args.specfile,
extra_args=opt,
pipeline_type='simulation',
verbosity=args.verbosity,
jobs=args.jobs)
# using a pool of simulators
if not args.models:
pipeline.execute(None, seed=args.seed + rep)
else:
for name in args.models:
pipeline.execute(name, seed=args.seed + rep)
except Exception as e:
env.logger.error(
'Failed to simulate replicate {} of model {}: {}'.format(
rep, model_name, e))
sys.exit(1)
def simulate(args):
#
try:
ret = 0
if args.replicates <= 0:
raise ValueError('No replication studies is requested.')
#
for rep in range(args.replicates):
p = Process(target=simulate_replicate, args=(args, rep))
p.start()
p.join()
if ret == 0:
ret = p.exitcode
# return fail if any of the replicates fails
sys.exit(ret)
except Exception as e:
env.logger.error(e)
sys.exit(1)
if __name__ == '__main__':
pass | PypiClean |
/django_graphql_jwt_using_rest_framework_jwt-1.2.0-py3-none-any.whl/graphql_jwt/refresh_token/models.py | import binascii
import os
from calendar import timegm
from django.conf import settings
from django.db import models
from django.utils import timezone
from django.utils.translation import gettext_lazy as _
from ..settings import jwt_settings
from . import managers, signals
class AbstractRefreshToken(models.Model):
id = models.BigAutoField(primary_key=True)
user = models.ForeignKey(
settings.AUTH_USER_MODEL,
on_delete=models.CASCADE,
related_name="refresh_tokens",
verbose_name=_("user"),
)
token = models.CharField(_("token"), max_length=255, editable=False)
created = models.DateTimeField(_("created"), auto_now_add=True)
revoked = models.DateTimeField(_("revoked"), null=True, blank=True)
objects = managers.RefreshTokenQuerySet.as_manager()
class Meta:
abstract = True
verbose_name = _("refresh token")
verbose_name_plural = _("refresh tokens")
unique_together = ("token", "revoked")
def __str__(self):
return self.token
def save(self, *args, **kwargs):
if not self.token:
self.token = self._cached_token = self.generate_token()
super().save(*args, **kwargs)
def generate_token(self):
return binascii.hexlify(
os.urandom(jwt_settings.JWT_REFRESH_TOKEN_N_BYTES),
).decode()
def get_token(self):
if hasattr(self, "_cached_token"):
return self._cached_token
return self.token
def is_expired(self, request=None):
orig_iat = timegm(self.created.timetuple())
return jwt_settings.JWT_REFRESH_EXPIRED_HANDLER(orig_iat, request)
def revoke(self, request=None):
self.revoked = timezone.now()
self.save(update_fields=["revoked"])
signals.refresh_token_revoked.send(
sender=AbstractRefreshToken,
request=request,
refresh_token=self,
)
def reuse(self, request=None):
self.token = ""
self.created = timezone.now()
self.save(update_fields=["token", "created"])
class RefreshToken(AbstractRefreshToken):
"""RefreshToken default model""" | PypiClean |
/zope.html-2.4.2.zip/zope.html-2.4.2/src/zope/html/fckeditor/2.6.4.1/fckeditor/editor/_source/classes/fckw3crange.js | /*
* FCKeditor - The text editor for Internet - http://www.fckeditor.net
* Copyright (C) 2003-2009 Frederico Caldeira Knabben
*
* == BEGIN LICENSE ==
*
* Licensed under the terms of any of the following licenses at your
* choice:
*
* - GNU General Public License Version 2 or later (the "GPL")
* http://www.gnu.org/licenses/gpl.html
*
* - GNU Lesser General Public License Version 2.1 or later (the "LGPL")
* http://www.gnu.org/licenses/lgpl.html
*
* - Mozilla Public License Version 1.1 or later (the "MPL")
* http://www.mozilla.org/MPL/MPL-1.1.html
*
* == END LICENSE ==
*
* This class partially implements the W3C DOM Range for browser that don't
* support the standards (like IE):
* http://www.w3.org/TR/DOM-Level-2-Traversal-Range/ranges.html
*/
var FCKW3CRange = function( parentDocument )
{
this._Document = parentDocument ;
this.startContainer = null ;
this.startOffset = null ;
this.endContainer = null ;
this.endOffset = null ;
this.collapsed = true ;
}
FCKW3CRange.CreateRange = function( parentDocument )
{
// We could opt to use the Range implementation of the browsers. The problem
// is that every browser have different bugs on their implementations,
// mostly related to different interpretations of the W3C specifications.
// So, for now, let's use our implementation and pray for browsers fixings
// soon. Otherwise will go crazy on trying to find out workarounds.
/*
// Get the browser implementation of the range, if available.
if ( parentDocument.createRange )
{
var range = parentDocument.createRange() ;
if ( typeof( range.startContainer ) != 'undefined' )
return range ;
}
*/
return new FCKW3CRange( parentDocument ) ;
}
FCKW3CRange.CreateFromRange = function( parentDocument, sourceRange )
{
var range = FCKW3CRange.CreateRange( parentDocument ) ;
range.setStart( sourceRange.startContainer, sourceRange.startOffset ) ;
range.setEnd( sourceRange.endContainer, sourceRange.endOffset ) ;
return range ;
}
FCKW3CRange.prototype =
{
_UpdateCollapsed : function()
{
this.collapsed = ( this.startContainer == this.endContainer && this.startOffset == this.endOffset ) ;
},
// W3C requires a check for the new position. If it is after the end
// boundary, the range should be collapsed to the new start. It seams we
// will not need this check for our use of this class so we can ignore it for now.
setStart : function( refNode, offset )
{
this.startContainer = refNode ;
this.startOffset = offset ;
if ( !this.endContainer )
{
this.endContainer = refNode ;
this.endOffset = offset ;
}
this._UpdateCollapsed() ;
},
// W3C requires a check for the new position. If it is before the start
// boundary, the range should be collapsed to the new end. It seams we
// will not need this check for our use of this class so we can ignore it for now.
setEnd : function( refNode, offset )
{
this.endContainer = refNode ;
this.endOffset = offset ;
if ( !this.startContainer )
{
this.startContainer = refNode ;
this.startOffset = offset ;
}
this._UpdateCollapsed() ;
},
setStartAfter : function( refNode )
{
this.setStart( refNode.parentNode, FCKDomTools.GetIndexOf( refNode ) + 1 ) ;
},
setStartBefore : function( refNode )
{
this.setStart( refNode.parentNode, FCKDomTools.GetIndexOf( refNode ) ) ;
},
setEndAfter : function( refNode )
{
this.setEnd( refNode.parentNode, FCKDomTools.GetIndexOf( refNode ) + 1 ) ;
},
setEndBefore : function( refNode )
{
this.setEnd( refNode.parentNode, FCKDomTools.GetIndexOf( refNode ) ) ;
},
collapse : function( toStart )
{
if ( toStart )
{
this.endContainer = this.startContainer ;
this.endOffset = this.startOffset ;
}
else
{
this.startContainer = this.endContainer ;
this.startOffset = this.endOffset ;
}
this.collapsed = true ;
},
selectNodeContents : function( refNode )
{
this.setStart( refNode, 0 ) ;
this.setEnd( refNode, refNode.nodeType == 3 ? refNode.data.length : refNode.childNodes.length ) ;
},
insertNode : function( newNode )
{
var startContainer = this.startContainer ;
var startOffset = this.startOffset ;
// If we are in a text node.
if ( startContainer.nodeType == 3 )
{
startContainer.splitText( startOffset ) ;
// Check if it is necessary to update the end boundary.
if ( startContainer == this.endContainer )
this.setEnd( startContainer.nextSibling, this.endOffset - this.startOffset ) ;
// Insert the new node it after the text node.
FCKDomTools.InsertAfterNode( startContainer, newNode ) ;
return ;
}
else
{
// Simply insert the new node before the current start node.
startContainer.insertBefore( newNode, startContainer.childNodes[ startOffset ] || null ) ;
// Check if it is necessary to update the end boundary.
if ( startContainer == this.endContainer )
{
this.endOffset++ ;
this.collapsed = false ;
}
}
},
deleteContents : function()
{
if ( this.collapsed )
return ;
this._ExecContentsAction( 0 ) ;
},
extractContents : function()
{
var docFrag = new FCKDocumentFragment( this._Document ) ;
if ( !this.collapsed )
this._ExecContentsAction( 1, docFrag ) ;
return docFrag ;
},
// The selection may be lost when cloning (due to the splitText() call).
cloneContents : function()
{
var docFrag = new FCKDocumentFragment( this._Document ) ;
if ( !this.collapsed )
this._ExecContentsAction( 2, docFrag ) ;
return docFrag ;
},
_ExecContentsAction : function( action, docFrag )
{
var startNode = this.startContainer ;
var endNode = this.endContainer ;
var startOffset = this.startOffset ;
var endOffset = this.endOffset ;
var removeStartNode = false ;
var removeEndNode = false ;
// Check the start and end nodes and make the necessary removals or changes.
// Start from the end, otherwise DOM mutations (splitText) made in the
// start boundary may interfere on the results here.
// For text containers, we must simply split the node and point to the
// second part. The removal will be handled by the rest of the code .
if ( endNode.nodeType == 3 )
endNode = endNode.splitText( endOffset ) ;
else
{
// If the end container has children and the offset is pointing
// to a child, then we should start from it.
if ( endNode.childNodes.length > 0 )
{
// If the offset points after the last node.
if ( endOffset > endNode.childNodes.length - 1 )
{
// Let's create a temporary node and mark it for removal.
endNode = FCKDomTools.InsertAfterNode( endNode.lastChild, this._Document.createTextNode('') ) ;
removeEndNode = true ;
}
else
endNode = endNode.childNodes[ endOffset ] ;
}
}
// For text containers, we must simply split the node. The removal will
// be handled by the rest of the code .
if ( startNode.nodeType == 3 )
{
startNode.splitText( startOffset ) ;
// In cases the end node is the same as the start node, the above
// splitting will also split the end, so me must move the end to
// the second part of the split.
if ( startNode == endNode )
endNode = startNode.nextSibling ;
}
else
{
// If the start container has children and the offset is pointing
// to a child, then we should start from its previous sibling.
// If the offset points to the first node, we don't have a
// sibling, so let's use the first one, but mark it for removal.
if ( startOffset == 0 )
{
// Let's create a temporary node and mark it for removal.
startNode = startNode.insertBefore( this._Document.createTextNode(''), startNode.firstChild ) ;
removeStartNode = true ;
}
else if ( startOffset > startNode.childNodes.length - 1 )
{
// Let's create a temporary node and mark it for removal.
startNode = startNode.appendChild( this._Document.createTextNode('') ) ;
removeStartNode = true ;
}
else
startNode = startNode.childNodes[ startOffset ].previousSibling ;
}
// Get the parent nodes tree for the start and end boundaries.
var startParents = FCKDomTools.GetParents( startNode ) ;
var endParents = FCKDomTools.GetParents( endNode ) ;
// Compare them, to find the top most siblings.
var i, topStart, topEnd ;
for ( i = 0 ; i < startParents.length ; i++ )
{
topStart = startParents[i] ;
topEnd = endParents[i] ;
// The compared nodes will match until we find the top most
// siblings (different nodes that have the same parent).
// "i" will hold the index in the parents array for the top
// most element.
if ( topStart != topEnd )
break ;
}
var clone, levelStartNode, levelClone, currentNode, currentSibling ;
if ( docFrag )
clone = docFrag.RootNode ;
// Remove all successive sibling nodes for every node in the
// startParents tree.
for ( var j = i ; j < startParents.length ; j++ )
{
levelStartNode = startParents[j] ;
// For Extract and Clone, we must clone this level.
if ( clone && levelStartNode != startNode ) // action = 0 = Delete
levelClone = clone.appendChild( levelStartNode.cloneNode( levelStartNode == startNode ) ) ;
currentNode = levelStartNode.nextSibling ;
while( currentNode )
{
// Stop processing when the current node matches a node in the
// endParents tree or if it is the endNode.
if ( currentNode == endParents[j] || currentNode == endNode )
break ;
// Cache the next sibling.
currentSibling = currentNode.nextSibling ;
// If cloning, just clone it.
if ( action == 2 ) // 2 = Clone
clone.appendChild( currentNode.cloneNode( true ) ) ;
else
{
// Both Delete and Extract will remove the node.
currentNode.parentNode.removeChild( currentNode ) ;
// When Extracting, move the removed node to the docFrag.
if ( action == 1 ) // 1 = Extract
clone.appendChild( currentNode ) ;
}
currentNode = currentSibling ;
}
if ( clone )
clone = levelClone ;
}
if ( docFrag )
clone = docFrag.RootNode ;
// Remove all previous sibling nodes for every node in the
// endParents tree.
for ( var k = i ; k < endParents.length ; k++ )
{
levelStartNode = endParents[k] ;
// For Extract and Clone, we must clone this level.
if ( action > 0 && levelStartNode != endNode ) // action = 0 = Delete
levelClone = clone.appendChild( levelStartNode.cloneNode( levelStartNode == endNode ) ) ;
// The processing of siblings may have already been done by the parent.
if ( !startParents[k] || levelStartNode.parentNode != startParents[k].parentNode )
{
currentNode = levelStartNode.previousSibling ;
while( currentNode )
{
// Stop processing when the current node matches a node in the
// startParents tree or if it is the startNode.
if ( currentNode == startParents[k] || currentNode == startNode )
break ;
// Cache the next sibling.
currentSibling = currentNode.previousSibling ;
// If cloning, just clone it.
if ( action == 2 ) // 2 = Clone
clone.insertBefore( currentNode.cloneNode( true ), clone.firstChild ) ;
else
{
// Both Delete and Extract will remove the node.
currentNode.parentNode.removeChild( currentNode ) ;
// When Extracting, mode the removed node to the docFrag.
if ( action == 1 ) // 1 = Extract
clone.insertBefore( currentNode, clone.firstChild ) ;
}
currentNode = currentSibling ;
}
}
if ( clone )
clone = levelClone ;
}
if ( action == 2 ) // 2 = Clone.
{
// No changes in the DOM should be done, so fix the split text (if any).
var startTextNode = this.startContainer ;
if ( startTextNode.nodeType == 3 )
{
startTextNode.data += startTextNode.nextSibling.data ;
startTextNode.parentNode.removeChild( startTextNode.nextSibling ) ;
}
var endTextNode = this.endContainer ;
if ( endTextNode.nodeType == 3 && endTextNode.nextSibling )
{
endTextNode.data += endTextNode.nextSibling.data ;
endTextNode.parentNode.removeChild( endTextNode.nextSibling ) ;
}
}
else
{
// Collapse the range.
// If a node has been partially selected, collapse the range between
// topStart and topEnd. Otherwise, simply collapse it to the start. (W3C specs).
if ( topStart && topEnd && ( startNode.parentNode != topStart.parentNode || endNode.parentNode != topEnd.parentNode ) )
{
var endIndex = FCKDomTools.GetIndexOf( topEnd ) ;
// If the start node is to be removed, we must correct the
// index to reflect the removal.
if ( removeStartNode && topEnd.parentNode == startNode.parentNode )
endIndex-- ;
this.setStart( topEnd.parentNode, endIndex ) ;
}
// Collapse it to the start.
this.collapse( true ) ;
}
// Cleanup any marked node.
if( removeStartNode )
startNode.parentNode.removeChild( startNode ) ;
if( removeEndNode && endNode.parentNode )
endNode.parentNode.removeChild( endNode ) ;
},
cloneRange : function()
{
return FCKW3CRange.CreateFromRange( this._Document, this ) ;
}
} ; | PypiClean |
/jk_svg-0.2021.1.2.tar.gz/jk_svg-0.2021.1.2/jk_svg/_GroupElementsMixin.py | from .AbstractSVGElement import AbstractSVGElement
from .SVGGenericElement import SVGGenericElement
from .SVGLine import SVGLine
from .SVGEllipse import SVGEllipse
from .SVGCircle import SVGCircle
from .SVGRect import SVGRect
from .SVGPolygon import SVGPolygon
from .SVGPolyline import SVGPolyline
from .SVGPath import SVGPath
from .SVGText import SVGText
class _GroupElementsMixin:
################################################################################################################################
## Constructor
################################################################################################################################
################################################################################################################################
## Public Properties
################################################################################################################################
################################################################################################################################
## Helper Methods
################################################################################################################################
################################################################################################################################
## Public Methods
################################################################################################################################
def createElement(self, tagName:str) -> SVGGenericElement:
assert isinstance(tagName, str)
assert tagName
if tagName == "path":
return self.createPath()
elif tagName == "line":
return self.createLine()
elif tagName == "ellipse":
return self.createEllipse()
elif tagName == "circle":
return self.createCircle()
elif tagName == "rect":
return self.createRect()
elif tagName == "polygon":
return self.createPolygon()
elif tagName == "polyline":
return self.createPolyline()
elif tagName == "text":
return self.createText()
ret = SVGGenericElement(tagName)
self._children.append(ret)
return ret
#
def createPath(self) -> SVGPath:
ret = SVGPath()
self._children.append(ret)
return ret
#
def createLine(self) -> SVGLine:
ret = SVGLine()
self._children.append(ret)
return ret
#
def createEllipse(self) -> SVGEllipse:
ret = SVGEllipse()
self._children.append(ret)
return ret
#
def createCircle(self) -> SVGCircle:
ret = SVGCircle()
self._children.append(ret)
return ret
#
def createRect(self) -> SVGRect:
ret = SVGRect()
self._children.append(ret)
return ret
#
def createPolygon(self) -> SVGPolygon:
ret = SVGPolygon()
self._children.append(ret)
return ret
#
def createPolyline(self) -> SVGPolyline:
ret = SVGPolyline()
self._children.append(ret)
return ret
#
def createText(self) -> SVGText:
ret = SVGText()
self._children.append(ret)
return ret
#
# | PypiClean |
/hh_applicant_tool-0.2.1.tar.gz/hh_applicant_tool-0.2.1/hh_applicant_tool/main.py | from __future__ import annotations
import argparse
import logging
import sys
from abc import ABCMeta, abstractmethod
from importlib import import_module
from os import getenv
from pathlib import Path
from pkgutil import iter_modules
from typing import Sequence
from .color_log import ColorHandler
from .utils import Config
DEFAULT_CONFIG_PATH = (
Path(getenv("XDG_CONFIG_PATH", Path.home() / ".config"))
/ __package__.replace("_", "-")
/ "config.json"
)
logger = logging.getLogger(__package__)
class BaseOperation(metaclass=ABCMeta):
def setup_parser(self, parser: argparse.ArgumentParser) -> None:
...
@abstractmethod
def run(self, args: argparse.Namespace) -> None | int:
...
OPERATIONS = "operations"
class Namespace(argparse.Namespace):
config: Config
verbosity: int
class HHApplicantTool:
"""Утилита для автоматизации действий соискателя на сайте hh.ru.
Описание, исходники и предложения: <https://github.com/s3rgeym/hh-applicant-tool>.
"""
def create_parser(self) -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description=self.__doc__,
)
parser.add_argument(
"-c",
"--config",
help="config path",
type=Config,
default=Config(DEFAULT_CONFIG_PATH),
)
parser.add_argument(
"-v",
"--verbosity",
help="increase verbosity",
action="count",
default=0,
)
subparsers = parser.add_subparsers(help="commands")
package_dir = Path(__file__).resolve().parent / OPERATIONS
for _, module_name, _ in iter_modules([str(package_dir)]):
mod = import_module(f"{__package__}.{OPERATIONS}.{module_name}")
op: BaseOperation = mod.Operation()
op_parser = subparsers.add_parser(
module_name.replace("_", "-"), description=op.__doc__
)
op_parser.set_defaults(run=op.run)
op.setup_parser(op_parser)
parser.set_defaults(run=None)
return parser
def run(self, argv: Sequence[str] | None) -> None | int:
parser = self.create_parser()
args = parser.parse_args(argv, namespace=Namespace())
log_level = max(logging.DEBUG, logging.WARNING - args.verbosity * 10)
logger.setLevel(log_level)
handler = ColorHandler()
# [C] Critical Error Occurred
handler.setFormatter(
logging.Formatter("[%(levelname).1s] %(message)s")
)
logger.addHandler(handler)
if args.run:
try:
return args.run(args)
except Exception as e:
logger.exception(e)
return 1
parser.print_help(file=sys.stderr)
return 2
def main(argv: Sequence[str] | None = None) -> None | int:
return HHApplicantTool().run(argv) | PypiClean |
/p-template-generator-0.1.79.tar.gz/p-template-generator-0.1.79/template_generator/binary.py | import sys
import os
import subprocess
import json
import random
from pathlib import Path
import shutil
import zipfile
import stat
import requests
import hashlib
import logging
def getOssResource(rootDir, url, md5, name):
localFile = os.path.join(rootDir, name)
localFileIsRemote = False
if os.path.exists(localFile):
with open(localFile, 'rb') as fp:
file_data = fp.read()
fp.close()
file_md5 = hashlib.md5(file_data).hexdigest()
if file_md5 == md5:
localFileIsRemote = True
if localFileIsRemote == False: #download
if os.path.exists(localFile):
os.remove(localFile)
s = requests.session()
s.keep_alive = False
print(f"download {url} ")
file = s.get(url, verify=False)
with open(localFile, "wb") as c:
c.write(file.content)
c.close()
s.close()
fname = name[0:name.index(".")]
fext = name[name.index("."):]
unzipDir = os.path.join(rootDir, fname)
if os.path.exists(unzipDir):
shutil.rmtree(unzipDir)
print(f"unzip {url} -> {unzipDir}")
def readDirChecksum(dir):
f = os.path.join(dir, "checksum.txt")
txt = ""
if os.path.exists(f):
with open(f, "r", encoding="UTF-8") as f1:
txt = f1.read()
f1.close()
return txt
def writeDirChecksum(dir, zipFile):
if os.path.exists(zipFile) == False:
return
with open(zipFile, 'rb') as fp:
fdata = fp.read()
fp.close()
fmd5 = hashlib.md5(fdata).hexdigest()
with open(os.path.join(dir, "checksum.txt"), "w") as f:
f.write(fmd5)
f.close()
def checkFileMd5(rootDir):
data = {
"fonts.zip.py" : "b1f190ba1cea49177eccde2eb2a6cb13",
"subEffect.zip.py" : "08651251e4351fd8cd5829b2ef65a8b9"
}
for key in data:
fpath = os.path.join(rootDir, key)
if os.path.exists(fpath):
with open(fpath, 'rb') as fp:
fdata = fp.read()
fp.close()
fmd5 = hashlib.md5(fdata).hexdigest()
fname = key[0:key.index(".")]
fext = key[key.index("."):]
fdirpath = os.path.join(rootDir, fname)
if os.path.exists(fdirpath) and fmd5 != readDirChecksum(fdirpath):
logging.info(f"remove old {fdirpath}")
shutil.rmtree(fdirpath)
def updateBin(rootDir):
getOssResource(rootDir, "https://m.mecordai.com/res/ffmpeg.zip", "a9e6b05ac70f6416d5629c07793b4fcf", "ffmpeg.zip.py")
getOssResource(rootDir, "https://m.mecordai.com/res/skymedia_20230825.zip", "bd305b4c3c5caa6f754276e1986f2146", "skymedia.zip.py")
getOssResource(rootDir, "https://m.mecordai.com/res/randomTemplates_20230625.zip", "e0cf7eaed4a90d59fe82f41b02f3d17e", "randomTemplates.zip.py")
checkFileMd5(rootDir)
for root,dirs,files in os.walk(rootDir):
for file in files:
if file.find(".") <= 0:
continue
name = file[0:file.index(".")]
ext = file[file.index("."):]
if ext == ".zip.py" and os.path.exists(os.path.join(root, name)) == False:
print(f"unzip {os.path.join(root, name)}")
with zipfile.ZipFile(os.path.join(root, file), "r") as zipf:
zipf.extractall(os.path.join(root, name))
writeDirChecksum(os.path.join(root, name), os.path.join(root, file))
if root != files:
break
def realBinPath(searchPath):
binDir = ""
if len(searchPath) <= 0 or os.path.exists(searchPath) == False:
binDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "bin")
updateBin(binDir)
else:
binDir = searchPath
return binDir
def ffmpegPath(searchPath):
return os.path.join(realBinPath(searchPath), "ffmpeg")
def skymediaPath(searchPath):
return os.path.join(realBinPath(searchPath), "skymedia")
def subEffectPath(searchPath):
return os.path.join(realBinPath(searchPath), "subEffect")
def randomEffectPath(searchPath):
return os.path.join(realBinPath(searchPath), "randomTemplates")
def fontPath(searchPath):
return os.path.join(realBinPath(searchPath), "fonts") | PypiClean |
/pbt_service-5.0.0-py3-none-any.whl/service/log/formatter.py | import os
import logging
import json
from collections.abc import Mapping
from pathlib import Path
from . import logger
from . import term
def formatter_from_event_type(event_type: int):
"""Map formatter to event."""
log_styles = {
logging.NOTSET: term.StyleDefault,
logging.DEBUG: term.StyleDebug,
logging.INFO: term.StyleInfo,
logging.WARNING: term.StyleWarning,
logging.CRITICAL: term.StyleError,
logging.ERROR: term.StyleError,
logging.FATAL: term.StyleError,
}
try:
return log_styles[event_type]
except (AttributeError, KeyError, IndexError):
return term.StyleDefault
class Formatter(logger.JSONFormatter):
"""Default log formatter."""
def format(self, record: logger.LogRecord):
"""Format."""
super().format(record)
message = {
field_name: logger.formatter_value(record, field_value)
for field_name, field_value in self.fields.items()
}
if isinstance(record.msg, Mapping):
message.update(record.msg)
else:
message[self.message_field_name] = super().formatMessage(record)
message.update(logger.log_extra_attrs(record))
message['msg'] = record.msg or record.message
if record.name == 'sqlalchemy.engine.Engine':
_no_sql = ('\n', '\t')
for _spec in _no_sql:
message['msg'] = str(message['msg']).replace(_spec, ' ')
caller = Path(record.pathname)
message['path'] = '../{parent}/{name}/{script}'.format(
parent=caller.parent.parent.name,
name=caller.parent.name,
script=caller.name,
)
_debug = 'true' in str(os.environ.get('DEBUG', '')).lower()
if record.exc_info:
message['exception'] = {
'type': record.exc_info[0].__name__,
'msg': str(record.exc_info[1]),
}
if _debug:
message['exception'].update({
'stack': logger.Traceback(record.exc_info[2]).as_dict(),
})
if len(message) == 1 and self.message_field_name in message:
return super().formatMessage(record)
_fmt_style = formatter_from_event_type(record.levelno)
return term.highlight(
json.dumps(
message,
default=logger.default_converter,
indent=4,
ensure_ascii=False,
),
lexer=term.JsonLexer(),
formatter=term.Terminal256Formatter(style=_fmt_style),
)
class RPCFormatter(Formatter):
"""RPC Log formatter."""
pass
__all__ = ('Formatter', 'RPCFormatter') | PypiClean |
/skytime-0.16.1-py3-none-any.whl/build/lib/build/lib/sktime/_contrib/distance_based/_proximity_forest.py | # linkedin.com/goastler; github.com/goastler
# github.com/moradisten
__author__ = ["gastler", "Morad A. Azaz"]
__all__ = ["ProximityForest", "_CachedTransformer", "ProximityStump", "ProximityTree"]
from logging import exception
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from scipy import stats
from sklearn.preprocessing import LabelEncoder, normalize
from sklearn.utils import check_random_state
from sktime.classification.base import BaseClassifier
from sktime.datatypes._panel._convert import (
from_nested_to_2d_array,
from_nested_to_3d_numpy,
)
from sktime.distances import ( # twe_distance,
dtw_distance,
erp_distance,
lcss_distance,
msm_distance,
wdtw_distance,
)
from sktime.transformations.base import _PanelToPanelTransformer
from sktime.transformations.panel.summarize import DerivativeSlopeTransformer
from sktime.utils.validation.panel import check_X, check_X_y
# todo unit tests / sort out current unit tests
# todo logging package rather than print to screen
# todo get params avoid func pointer - use name
# todo set params use func name or func pointer
# todo constructor accept str name func / pointer
# todo duck-type functions
def stdp(X):
"""Proximity forest util function (deprecated)."""
warn(
"This function has moved to classification/distance_based/_proximity_forest as "
"a private function. This version will be removed in V0.10",
FutureWarning,
)
sum = 0
sum_sq = 0
num_instances = X.shape[0]
num_dimensions = X.shape[1]
num_values = 0
for instance_index in range(0, num_instances):
for dimension_index in range(0, num_dimensions):
instance = X.iloc[instance_index, dimension_index]
for value in instance:
num_values += 1
sum += value
sum_sq += value**2 # todo missing values NaN messes
# this up!
mean = sum / num_values
stdp = np.math.sqrt(sum_sq / num_values - mean**2)
return stdp
# find index of min value in array, randomly breaking ties
def _arg_min(array, rand, getter=None):
"""Proximity forest util function."""
return rand.choice(arg_mins(array, getter))
def max_instance_length(X):
"""Proximity forest util function."""
max_length = len(X.iloc[0, 0])
# max = -1
# for dimension in range(0, instances.shape[1]):
# length = max_instance_dimension_length(instances, dimension)
# if length > max:
# max = length
return max_length
class _CachedTransformer(_PanelToPanelTransformer):
"""Transformer that transforms data and adds the transformed version to a cache.
If the transformation is called again on already seen data the data is
fetched from the cache rather than performing the expensive transformation.
Parameters
----------
transformer : the transformer to transform uncached data
Attributes
----------
cache : location to store transforms seen before for fast look up
"""
_required_parameters = ["transformer"]
def __init__(self, transformer):
self.cache = {}
self.transformer = transformer
super(_CachedTransformer, self).__init__()
def clear(self):
"""Clear the cache."""
self.cache = {}
def transform(self, X, y=None):
"""
Fit transformer, creating a cache for transformation.
Parameters
----------
X : pandas DataFrame of shape [n_instances, n_features]
Input data
y : pandas Series, shape (n_instances), optional
Targets for supervised learning.
Returns
-------
cached_instances.
"""
# for each instance, get transformed instance from cache or
# transform and add to cache
cached_instances = {}
uncached_indices = []
for index in X.index.values:
try:
cached_instances[index] = self.cache[index]
except Exception:
uncached_indices.append(index)
if len(uncached_indices) > 0:
uncached_instances = X.loc[uncached_indices, :]
transformed_uncached_instances = self.transformer.fit_transform(
uncached_instances
)
transformed_uncached_instances.index = uncached_instances.index
transformed_uncached_instances = transformed_uncached_instances.to_dict(
"index"
)
self.cache.update(transformed_uncached_instances)
cached_instances.update(transformed_uncached_instances)
cached_instances = pd.DataFrame.from_dict(cached_instances, orient="index")
return cached_instances
def __str__(self):
"""Transform string."""
return self.transformer.__str__()
def _derivative_distance(distance_measure, transformer):
"""Take derivative before conducting distance measure.
:param distance_measure: the distance measure to use
:param transformer: the transformer to use
:return: a distance measure function with built in transformation
"""
def distance(instance_a, instance_b, **params):
df = pd.DataFrame([instance_a, instance_b])
df = transformer.transform(X=df)
instance_a = df.iloc[0, :]
instance_b = df.iloc[1, :]
return distance_measure(instance_a, instance_b, **params)
return distance
def distance_predefined_params(distance_measure, **params):
"""Conduct distance measurement with a predefined set of parameters.
:param distance_measure: the distance measure to use
:param params: the parameters to use in the distance measure
:return: a distance measure with no parameters
"""
def distance(instance_a, instance_b):
return distance_measure(instance_a, instance_b, **params)
return distance
def numba_wrapper(distance_measure):
"""Wrap a distance measure in cython conversion.
(to 1 column per dimension format)
:param distance_measure: distance measure to wrap
:return: a distance measure which automatically formats data for cython
distance measures
"""
def distance(instance_a, instance_b, **params):
# find distance
instance_a = from_nested_to_2d_array(
instance_a, return_numpy=True
) # todo use specific
# dimension rather than whole
# thing?
instance_b = from_nested_to_2d_array(
instance_b, return_numpy=True
) # todo use specific
# dimension rather than whole thing?
instance_a = np.transpose(instance_a)
instance_b = np.transpose(instance_b)
return distance_measure(instance_a, instance_b, **params)
return distance
def pure(y):
"""Test whether a set of class labels are pure (i.e. all the same).
Parameters
----------
y : 1d array like
array of class labels
Returns
-------
result : boolean
whether the set of class labels is pure
"""
# get unique class labels
unique_class_labels = np.unique(np.array(y))
# if more than 1 unique then not pure
return len(unique_class_labels) <= 1
def gini_gain(y, y_subs):
"""Get gini score of a split, i.e. the gain from parent to children.
Parameters
----------
y : 1d array like
array of class labels at parent
y_subs : list of 1d array like
list of array of class labels, one array per child
Returns
-------
score : float
gini score of the split from parent class labels to children. Note a
higher score means better gain,
i.e. a better split
"""
y = np.array(y)
# find number of instances overall
parent_n_instances = y.shape[0]
# if parent has no instances then is pure
if parent_n_instances == 0:
for child in y_subs:
if len(child) > 0:
raise ValueError("children populated but parent empty")
return 0.5
# find gini for parent node
score = gini(y)
# sum the children's gini scores
for index in range(len(y_subs)):
child_class_labels = y_subs[index]
# ignore empty children
if len(child_class_labels) > 0:
# find gini score for this child
child_score = gini(child_class_labels)
# weight score by proportion of instances at child compared to
# parent
child_size = len(child_class_labels)
child_score *= child_size / parent_n_instances
# add to cumulative sum
score -= child_score
return score
def gini(y):
"""Get gini score at a specific node.
Parameters
----------
y : 1d numpy array
array of class labels
Returns
-------
score : float
gini score for the set of class labels (i.e. how pure they are). A
larger score means more impurity. Zero means
pure.
"""
y = np.array(y)
# get number instances at node
n_instances = y.shape[0]
if n_instances > 0:
# count each class
unique_class_labels, class_counts = np.unique(y, return_counts=True)
# subtract class entropy from current score for each class
class_counts = np.divide(class_counts, n_instances)
class_counts = np.power(class_counts, 2)
class_counts_sum = np.sum(class_counts)
return 1 - class_counts_sum
else:
# y is empty, therefore considered pure
raise ValueError(" y empty")
def get_one_exemplar_per_class_proximity(proximity):
"""Unpack proximity object into X, y and random_state for picking exemplars.
Parameters
----------
proximity : Proximity object
Proximity like object containing the X, y and random_state variables
required for picking exemplars.
Returns
-------
result : function
function choosing one exemplar per class
"""
return get_one_exemplar_per_class(proximity.X, proximity.y, proximity.random_state)
def get_one_exemplar_per_class(X, y, random_state):
"""Pick one exemplar instance per class in the dataset.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples. If a Pandas data frame is passed,
the column _dim_to_use is extracted
y : array-like, shape = [n_samples] or [n_samples, n_outputs]
The class labels.
random_state : numpy RandomState
a random state for sampling random numbers
Returns
-------
chosen_instances : list
list of the chosen exemplar instances.
chosen_class_labels : array
list of corresponding class labels for each of the chosen exemplar
instances.
"""
# find unique class labels
unique_class_labels = np.unique(y)
n_unique_class_labels = len(unique_class_labels)
chosen_instances = [None] * n_unique_class_labels
# for each class randomly choose and instance
for class_label_index in range(n_unique_class_labels):
class_label = unique_class_labels[class_label_index]
# filter class labels for desired class and get indices
indices = np.argwhere(y == class_label)
# flatten numpy output
indices = np.ravel(indices)
# random choice
index = random_state.choice(indices)
# record exemplar instance and class label
instance = X.iloc[index, :]
chosen_instances[class_label_index] = instance
# convert lists to numpy arrays
return chosen_instances, unique_class_labels
def dtw_distance_measure_getter(X):
"""Generate the dtw distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
return {
"distance_measure": [numba_wrapper(dtw_distance)],
"w": stats.uniform(0, 0.25),
}
def msm_distance_measure_getter(X):
"""Generate the msm distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
n_dimensions = 1 # todo use other dimensions
return {
"distance_measure": [numba_wrapper(dtw_distance)],
"dim_to_use": stats.randint(low=0, high=n_dimensions),
"c": [
0.01,
0.01375,
0.0175,
0.02125,
0.025,
0.02875,
0.0325,
0.03625,
0.04,
0.04375,
0.0475,
0.05125,
0.055,
0.05875,
0.0625,
0.06625,
0.07,
0.07375,
0.0775,
0.08125,
0.085,
0.08875,
0.0925,
0.09625,
0.1,
0.136,
0.172,
0.208,
0.244,
0.28,
0.316,
0.352,
0.388,
0.424,
0.46,
0.496,
0.532,
0.568,
0.604,
0.64,
0.676,
0.712,
0.748,
0.784,
0.82,
0.856,
0.892,
0.928,
0.964,
1,
1.36,
1.72,
2.08,
2.44,
2.8,
3.16,
3.52,
3.88,
4.24,
4.6,
4.96,
5.32,
5.68,
6.04,
6.4,
6.76,
7.12,
7.48,
7.84,
8.2,
8.56,
8.92,
9.28,
9.64,
10,
13.6,
17.2,
20.8,
24.4,
28,
31.6,
35.2,
38.8,
42.4,
46,
49.6,
53.2,
56.8,
60.4,
64,
67.6,
71.2,
74.8,
78.4,
82,
85.6,
89.2,
92.8,
96.4,
100,
],
}
def erp_distance_measure_getter(X):
"""Generate the erp distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
stdp = _stdp(X)
instance_length = max_instance_length(X) # todo should this use the max instance
# length for unequal length dataset instances?
max_raw_warping_window = np.floor((instance_length + 1) / 4)
n_dimensions = 1 # todo use other dimensions
return {
"distance_measure": [numba_wrapper(erp_distance)],
"dim_to_use": stats.randint(low=0, high=n_dimensions),
"g": stats.uniform(0.2 * stdp, 0.8 * stdp - 0.2 * stdp),
"band_size": stats.randint(low=0, high=max_raw_warping_window + 1)
# scipy stats randint is exclusive on the max value, hence + 1
}
def lcss_distance_measure_getter(X):
"""Generate the lcss distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
stdp = _stdp(X)
instance_length = max_instance_length(X) # todo should this use the max instance
# length for unequal length dataset instances?
max_raw_warping_window = np.floor((instance_length + 1) / 4)
n_dimensions = 1 # todo use other dimensions
return {
"distance_measure": [numba_wrapper(lcss_distance)],
"dim_to_use": stats.randint(low=0, high=n_dimensions),
"epsilon": stats.uniform(0.2 * stdp, stdp - 0.2 * stdp),
# scipy stats randint is exclusive on the max value, hence + 1
"delta": stats.randint(low=0, high=max_raw_warping_window + 1),
}
def twe_distance_measure_getter(X):
"""Generate the twe distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
return {
"distance_measure": [numba_wrapper(twe_distance)],
"penalty": [
0,
0.011111111,
0.022222222,
0.033333333,
0.044444444,
0.055555556,
0.066666667,
0.077777778,
0.088888889,
0.1,
],
"stiffness": [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1],
}
def wdtw_distance_measure_getter(X):
"""Generate the wdtw distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
return {
"distance_measure": [numba_wrapper(weighted_dtw_distance)],
"g": stats.uniform(0, 1),
}
def euclidean_distance_measure_getter(X):
"""Generate the ed distance measure.
:param X: dataset to derive parameter ranges from
:return: distance measure and parameter range dictionary
"""
return {"distance_measure": [numba_wrapper(dtw_distance)], "w": [0]}
def setup_wddtw_distance_measure_getter(transformer):
"""Generate the wddtw distance measure by baking the derivative transformer.
into the wdtw distance measure
:param transformer: the transformer to use
:return: a getter to produce the distance measure
"""
def getter(X):
return {
"distance_measure": [
_derivative_distance(numba_wrapper(weighted_dtw_distance), transformer)
],
"g": stats.uniform(0, 1),
}
return getter
def setup_ddtw_distance_measure_getter(transformer):
"""Generate the ddtw distance measure by baking the derivative transformer.
into the dtw distance measure
:param transformer: the transformer to use
:return: a getter to produce the distance measure
"""
def getter(X):
return {
"distance_measure": [
_derivative_distance(numba_wrapper(dtw_distance), transformer)
],
"w": stats.uniform(0, 0.25),
}
return getter
def setup_all_distance_measure_getter(proximity):
"""Set all distance measure getter functions from a proximity object.
:param proximity: a PT / PF / PS
:return: a list of distance measure getters
"""
transformer = _CachedTransformer(DerivativeSlopeTransformer())
distance_measure_getters = [
euclidean_distance_measure_getter,
dtw_distance_measure_getter,
setup_ddtw_distance_measure_getter(transformer),
wdtw_distance_measure_getter,
setup_wddtw_distance_measure_getter(transformer),
msm_distance_measure_getter,
lcss_distance_measure_getter,
erp_distance_measure_getter,
# twe_distance_measure_getter,
]
def pick_rand_distance_measure(proximity):
"""Generate a distance measure from a range of parameters.
:param proximity: proximity object containing distance measures,
ranges and dataset
:return: a distance measure with no parameters
"""
random_state = proximity.random_state
X = proximity.X
distance_measure_getter = random_state.choice(distance_measure_getters)
distance_measure_perm = distance_measure_getter(X)
param_perm = pick_rand_param_perm_from_dict(distance_measure_perm, random_state)
distance_measure = param_perm["distance_measure"]
del param_perm["distance_measure"]
return distance_predefined_params(distance_measure, **param_perm)
return pick_rand_distance_measure
def pick_rand_param_perm_from_dict(param_pool, random_state):
"""Pick a parameter permutation.
Given a list of dictionaries contain potential values OR a list of values OR a
distribution of values (a distribution must have the .rvs() function to
sample values)
param_pool : list of dicts OR list OR distribution
parameters in the same format as GridSearchCV from scikit-learn.
example:
param_grid = [
{'C': [1, 10, 100, 1000], 'kernel': ['linear']},
{'C': [1, 10, 100, 1000], 'gamma': [{'C': [1, 10, 100, 1000],
'kernel': ['linear']}],
'kernel': ['rbf']},
]
Returns
-------
param_perm : dict
distance measure and corresponding parameters in dictionary format
"""
# construct empty permutation
param_perm = {}
# for each parameter
for param_name, param_values in param_pool.items():
# if it is a list
if isinstance(param_values, list):
# randomly pick a value
param_value = param_values[random_state.randint(len(param_values))]
# if the value is another dict then get a random parameter
# permutation from that dict (recursive over
# 2 funcs)
# if isinstance(param_value, dict): # no longer require
# recursive param perms
# param_value = _pick_param_permutation(param_value,
# random_state)
# else if parameter is a distribution
elif hasattr(param_values, "rvs"):
# sample from the distribution
param_value = param_values.rvs(random_state=random_state)
else:
# otherwise we don't know how to obtain a value from the parameter
raise Exception("unknown type of parameter pool")
# add parameter name and value to permutation
param_perm[param_name] = param_value
return param_perm
def pick_rand_param_perm_from_list(params, random_state):
"""Get a random parameter permutation.
Providing a distance measure and corresponding parameters
params : list of dicts
parameters in the same format as GridSearchCV from scikit-learn.
example:
param_grid = [
{'C': [1, 10, 100, 1000], 'kernel': ['linear']},
{'C': [1, 10, 100, 1000], 'gamma': [{'C': [1, 10, 100, 1000],
'kernel': ['linear']}], 'kernel': ['rbf']},
]
Returns
-------
permutation : dict
distance measure and corresponding parameters in dictionary format
"""
#
param_pool = random_state.choice(params)
permutation = pick_rand_param_perm_from_dict(param_pool, random_state)
return permutation
def best_of_n_stumps(n):
"""Generate the function to pick the best of n stump evaluations.
Parameters
----------
n : int
the number of stumps to evaluate before picking the best. Must be 1
or more.
Returns
-------
find_best_stump : func
function to find the best of n stumps.
"""
if n < 1:
raise ValueError("n cannot be less than 1")
def find_best_stump(proximity):
"""Pick the best of n stump evaluations.
Parameters
----------
proximity : Proximity like object
the proximity object to split data from.
Returns
-------
stump : ProximityStump
the best stump / split of data of the n attempts.
"""
stumps = []
# for n stumps
for _ in range(n):
# duplicate tree configuration
stump = ProximityStump(
random_state=proximity.random_state,
get_exemplars=proximity.get_exemplars,
distance_measure=proximity.distance_measure,
setup_distance_measure=proximity.setup_distance_measure,
get_distance_measure=proximity.get_distance_measure,
get_gain=proximity.get_gain,
verbosity=proximity.verbosity,
n_jobs=proximity.n_jobs,
)
# grow the stump
stump.fit(proximity.X, proximity.y)
stump.grow()
stumps.append(stump)
# pick the best stump based upon gain
stump = _max(stumps, proximity.random_state, lambda stump: stump.entropy)
return stump
return find_best_stump
class ProximityStump(BaseClassifier):
"""Proximity Stump."""
np.random.seed(1234)
def __init__(
self,
random_state=0,
setup_distance_measure=setup_all_distance_measure_getter,
get_distance_measure=None,
distance_measure=dtw_distance,
X=None,
y=None,
label=None,
verbosity=0,
n_stumps=5,
n_jobs=1,
):
self.setup_distance_measure = setup_distance_measure
self.random_state = random_state
self.n_stumps = n_stumps
self.get_distance_measure = get_distance_measure
self.distance_measure = distance_measure
self.verbosity = verbosity
self.n_jobs = n_jobs
# set in fit
self.num_children = None
self.label_encoder = None
# exemplars
self.y_exemplar = None
self.X_exemplar = None
# temp_exemplars
self.temp_exemplar = dict()
# best_splits
self.X_best_splits = None
self.y_best_splits = None
# Datasets
self.X = X
self.y = y
# splits
self.children = list()
self.is_leaf = False
self.classes_ = dict()
self.label = label
self.entropy = None
super(ProximityStump, self).__init__()
def set_X(self, X):
"""Set X."""
self.X = X
def set_y(self, y):
"""Set y."""
self.y = y
@staticmethod
def gini_gain(y, y_subs):
"""Get gini score of a split, i.e. the gain from parent to children.
Parameters
----------
y : 1d array like
array of class labels at parent
y_subs : list of 1d array like
list of array of class labels, one array per child
Returns
-------
score : float
gini score of the split from parent class labels to children. Note a
higher score means better gain,
i.e. a better split
"""
y = np.array(y)
# find number of instances overall
parent_n_instances = y.shape[0]
# if parent has no instances then is pure
if parent_n_instances == 0:
for child in y_subs:
if len(child) > 0:
raise ValueError("children populated but parent empty")
return 0.5
# find gini for parent node
score = ProximityStump.gini(y)
# sum the children's gini scores
for index in range(len(y_subs)):
child_class_labels = y_subs[index]
# ignore empty children
if len(child_class_labels) > 0:
# find gini score for this child
child_score = ProximityStump.gini(child_class_labels)
# weight score by proportion of instances at child compared to
# parent
child_size = len(child_class_labels)
child_score *= child_size / parent_n_instances
# add to cumulative sum
score -= child_score
return score
@staticmethod
def gini(y):
"""Get gini score at a specific node.
Parameters
----------
y : 1d numpy array
array of class labels
Returns
-------
score : float
gini score for the set of class labels (i.e. how pure they are). A
larger score means more impurity. Zero means
pure.
"""
y = np.array(y)
# get number instances at node
try:
n_instances = y.shape[0]
except exception:
n_instances = 0
if n_instances > 0:
# count each class
unique_class_labels, class_counts = np.unique(y, return_counts=True)
# subtract class entropy from current score for each class
class_counts = np.divide(class_counts, n_instances)
class_counts = np.power(class_counts, 2)
sum = np.sum(class_counts)
return 1 - sum
else:
# y is empty, therefore considered pure
return 0
# raise ValueError(' y empty')
@staticmethod
def split_X_per_class(X, y):
"""Split by class.
:param X: Array-like containing instances
:param y: Array-like containing class labels
:return: Returns a dictionary {Label: [sub_X]} in which sub_X contains all
instances that match that label
"""
split_class_x = dict()
y_size = len(y)
for index in range(y_size):
label = y[index]
if not split_class_x.keys().__contains__(label):
split_class_x[label] = list()
if X.shape == 3:
split_class_x[label].append(X[index][0])
else:
split_class_x[label].append(X[index])
return split_class_x
@staticmethod
def calculate_dist_to_exemplars_inst(exemplars, instance, distance_measure):
"""Calculate distance to exemplars."""
distances = list()
indices = list()
if len(exemplars) == 0:
return None
for index in range(len(exemplars)):
exemplar = exemplars[index]
try:
distance = distance_measure(instance, exemplar)
except exception:
distance = np.inf
distances.append(distance)
indices.append(index)
return distances, indices
@staticmethod
def find_closest_distances_inst(exemplars, instance, distance_measure):
"""Find closest distance instance."""
distances = list()
indices = list()
min_distance = np.math.inf
if (exemplars is None) or len(exemplars) == 0:
return None, None
for index in range(len(exemplars)):
exemplar = exemplars[index][0]
try:
distance = distance_measure(instance, exemplar)
except Exception:
distance = np.inf
if len(indices) == 0:
min_distance = distance
distances.append(distance)
indices.append(index)
else:
if distance < min_distance:
min_distance = distance
distances.clear()
distances.append(distance)
indices.clear()
indices.append(index)
elif distance == min_distance:
distances.append(distance)
indices.append(index)
return distances, indices
@staticmethod
def find_closest_distance(exemplars, instance, distance_measure):
"""Find closest distance."""
distance, indices = ProximityStump.find_closest_distances_inst(
exemplars, instance, distance_measure
)
if distance is None:
return -1, -1
elif len(distance) == 1:
return distance[0], indices[0]
else:
r = np.random.randint(0, len(distance))
return distance[r], indices[r]
def find_closest_distance_(self, instance, distance_measure):
"""Find closest distance."""
return ProximityStump.find_closest_distance(
self.X_exemplar, instance, distance_measure
)
def find_closest_exemplar_indices(self, X):
"""Find closes exemplar indices."""
check_X(X) # todo make checks optional and propogate from forest downwards
n_instances = X.shape[0]
distances = self.distance_to_exemplars(X)
indices = np.empty(X.shape[0], dtype=int)
for index in range(n_instances):
exemplar_distances = distances[index]
closest_exemplar_index = arg_mins(exemplar_distances, self.random_state)
indices[index] = closest_exemplar_index[0]
return indices
def split_stump(self, X, y, dataset_per_class):
"""Split stump."""
splits_x = dict() # {index: x_list}
splits_y = dict() # {index: y_list}
label_branch = 0
for label in dataset_per_class.keys():
if len(dataset_per_class[label]) == 0:
continue
else:
sub_X = dataset_per_class[
label
] # sub_X is a list of series/arrays who belong to a label
r = np.random.randint(0, len(sub_X)) # select a random element in sub_X
splits_x[label_branch] = list()
splits_y[label_branch] = list()
self.temp_exemplar[label_branch] = sub_X[r] # sub_X[r] is a serie
self.classes_[label_branch] = label
label_branch = label_branch + 1
for j in range(X.shape[0]):
instance = self.X[j][0]
closest_distance, index = ProximityStump.find_closest_distance(
self.temp_exemplar, instance, self.distance_measure
)
if closest_distance == -1:
return splits_x, splits_y
splits_x[index].append(X[j][0])
splits_y[index].append(y[j])
return splits_x, splits_y # <index, list_x>, <index, list_y>
def find_best_stumps(self, X, y):
"""Find best stumps."""
x_per_label = self.split_X_per_class(X, y)
best_weighted_gini = np.inf
x_size = len(X)
for _ in range(self.n_stumps):
splits_x, splits_y = self.split_stump(X, y, x_per_label)
if len(splits_x) == 0:
return self.X_best_splits, self.y_best_splits
weighted_gini = self.weighted_gini(x_size, splits_x, splits_y)
if weighted_gini < best_weighted_gini:
best_weighted_gini = weighted_gini
self.X_best_splits = splits_x
self.y_best_splits = splits_y
self.X_exemplar = self.temp_exemplar
self.y_exemplar = self.temp_exemplar.keys()
self.num_children = len(self.X_best_splits)
return self.X_best_splits, self.y_best_splits
@staticmethod
def weighted_gini(x_size, splits_x, splits_y):
"""Find weighted Gini."""
wgini = 0.0
for index in range(len(splits_x)):
spt_x = splits_x[index]
spt_y = splits_y[index]
wgini = wgini + (len(spt_x) / x_size) * ProximityStump.gini(spt_y)
return wgini
def calculate_distance_to_exemplars(self, X):
"""Find distance to exemplars.
:param X: the dataset containing a list of instances
:return: 2d numpy array of distances from each instance to each
exemplar (instance by exemplar)
"""
check_X(X)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
distances = parallel(
delayed(self.calculate_dist_to_exemplars_inst)(
self.X_exemplar, X[0][index, :], self.distance_measure
)
for index in range(X.shape[0])
)
else:
distances = [
self.calculate_dist_to_exemplars_inst(
self.X_exemplar, X[index, :][0], self.distance_measure
)
for index in range(X.shape[0])
]
return distances
def distance_to_exemplars(self, X):
"""Distance to exemplars."""
distances = self.calculate_distance_to_exemplars(X)
distances = [x[0][0] for x in distances]
distances = np.vstack(np.array(distances))
return distances
def distance_to_exemplars_indices(self, X):
"""Distance to exemplars indices."""
distances_indices = self.calculate_distance_to_exemplars(X)
indices = [x[1][0] for x in distances_indices]
return indices
def fit(self, X=None, y=None):
"""Fit."""
if X is None:
X = self.X
if y is None:
y = self.y
if len(y) == 0:
return
gini = ProximityStump.gini(y)
if gini == 0:
self.label = int(y[0])
self.is_leaf = True
return
if len(X.shape) == 2:
X = X.reshape((X.shape[0], 1, X.shape[1]))
self.find_best_stumps(X, y)
if len(self.X_best_splits) > 0:
for i in range(0, len(self.X_best_splits.values())):
self.children.append(
ProximityStump(
X=X, y=y, label=y[i], distance_measure=self.distance_measure
)
)
counter = 0
for index in self.X_best_splits.keys():
x_branches = self.X_best_splits[index]
y_branches = self.y_best_splits[index]
try:
splits_x = np.array(x_branches)
splits_y = np.array(y_branches)
if splits_x.shape == 2:
splits_x = splits_x.reshape(
(splits_x.shape[0], 1, splits_x.shape[1])
)
self.children[counter].fit(splits_x, splits_y)
except RecursionError:
return
counter = counter + 1
def predict_proba(self, X):
"""Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = check_X(X, enforce_univariate=True)
distances = self.distance_to_exemplars(X)
ones = np.ones(distances.shape)
distances = np.add(distances, ones)
distributions = np.divide(ones, distances)
normalize(distributions, copy=False, norm="l1")
return distributions
class ProximityTree(BaseClassifier):
"""Proximity Tree class to model a distance based decision tree.
Attributes
----------
label_encoder: label encoder to change string labels to numeric indices
classes_: unique list of classes
random_state: the random state
get_exemplars: function to extract exemplars from a dataframe and
class value list
setup_distance_measure: function to setup the distance measure
getters from dataframe and class value list
get_distance_measure: distance measure getters
distance_measure: distance measures
get_gain: function to score the quality of a split
verbosity: logging verbosity
n_jobs: number of jobs to run in parallel *across threads"
find_stump: function to find the best split of data
max_depth: max tree depth
depth: current depth of tree, as each node is a tree itself,
therefore can have a depth of >=0
X: train data
y: train data labels
stump: the stump used to split data at this node
branches: the partitions of data driven by the stump
"""
def __init__(
self,
# note: any changes of these params must be reflected in
# the fit method for building trees / clones
random_state=0,
get_exemplars=get_one_exemplar_per_class_proximity,
distance_measure=dtw_distance,
get_distance_measure=None,
setup_distance_measure=setup_all_distance_measure_getter,
get_gain=gini_gain,
max_depth=np.math.inf,
is_leaf=pure,
verbosity=0,
n_jobs=1,
n_stump_evaluations=5,
find_stump=None,
):
"""Build a Proximity Tree object.
:param random_state: the random state
:param get_exemplars: get the exemplars from a given dataframe and
list of class labels
:param distance_measure: distance measure to use
:param get_distance_measure: method to get the distance measure if
no already set
:param setup_distance_measure: method to setup the distance measures
based upon the dataset given
:param get_gain: method to find the gain of a data split
:param max_depth: maximum depth of the tree
:param is_leaf: function to decide when to mark a node as a leaf node
:param verbosity: number reflecting the verbosity of logging
:param n_jobs: number of parallel threads to use while building
:param find_stump: method to find the best split of data / stump at
a node
:param n_stump_evaluations: number of stump evaluations to do if
find_stump method is None
"""
super().__init__()
self.verbosity = verbosity
self.n_stump_evaluations = n_stump_evaluations
self.find_stump = find_stump
self.max_depth = max_depth
self.get_distance_measure = distance_measure
self.random_state = check_random_state(random_state)
self.get_distance_measure = get_distance_measure
self.setup_distance_measure = setup_all_distance_measure_getter
self.get_gain = get_gain
self.n_jobs = n_jobs
self.depth = 0
# below set in fit method
self.label_encoder = None
self.distance_measure = distance_measure
self.root_stump = None
self.branches = None
self.X = None
self.y = None
self._is_fitted = False
self.classes_ = None
def fit(self, X, y, random_state=0):
"""Fit."""
self.classes_ = np.unique(y)
self.root_stump = ProximityStump(
X=X,
y=y,
n_stumps=self.n_stump_evaluations,
distance_measure=self.distance_measure,
random_state=self.random_state,
)
self.root_stump.fit()
self._is_fitted = True
def predict_class_label(self, query):
"""Predict the label of a query.
:param query:
:return:
"""
stump = self.root_stump
while not stump.is_leaf:
child_index = stump.find_closest_distance_(query, self.distance_measure)[1]
if child_index == -1:
stump.is_leaf = True
continue
stump = stump.children[child_index]
return stump.label
def predict_proba(self, X):
"""Predict proba."""
n_instances = X.shape[0]
predictions = np.zeros(n_instances, dtype=int)
for index in range(n_instances):
query = X[index][0]
class_label = self.predict_class_label(query)
predictions[index] = class_label
return predictions
class ProximityForest(BaseClassifier):
"""Proximity Forest class to model a decision tree forest.
Which uses distance measures to partition data.
@article{lucas19proximity,
title={Proximity Forest: an effective and scalable distance-based
classifier for time series},
author={B. Lucas and A. Shifaz and C. Pelletier and L. O’Neill and N.
Zaidi and B. Goethals and F. Petitjean and G. Webb},
journal={Data Mining and Knowledge Discovery},
volume={33},
number={3},
pages={607--635},
year={2019}
}
Attributes
----------
label_encoder: label encoder to change string labels to numeric indices
classes_: unique list of classes
random_state: the random state
get_exemplars: function to extract exemplars from a dataframe and
class value list
setup_distance_measure_getter: function to setup the distance
measure getters from dataframe and class value list
get_distance_measure: distance measure getters
distance_measure: distance measures
get_gain: function to score the quality of a split
verbosity: logging verbosity
n_jobs: number of jobs to run in parallel *across threads"
find_stump: function to find the best split of data
max_depth: max tree depth
X: train data
y: train data labels
trees: list of trees in the forest
"""
def __init__(
self,
random_state=0,
n_estimators=100,
distance_measure=dtw_distance,
get_distance_measure=None,
get_exemplars=get_one_exemplar_per_class_proximity,
get_gain=gini_gain,
verbosity=0,
max_depth=np.math.inf,
is_leaf=pure,
n_jobs=1,
n_stump_evaluations=5,
find_stump=None,
setup_distance_measure_getter=setup_all_distance_measure_getter,
):
"""Build a Proximity Forest object.
:param random_state: the random state
:param get_exemplars: get the exemplars from a given dataframe and
list of class labels
:param distance_measure: distance measure to use
:param get_distance_measure: method to get the distance measure if
no already set
:param setup_distance_measure_getter: method to setup the distance
measures based upon the dataset given
:param get_gain: method to find the gain of a data split
:param max_depth: maximum depth of the tree
:param is_leaf: function to decide when to mark a node as a leaf node
:param verbosity: number reflecting the verbosity of logging
:param n_jobs: number of parallel threads to use while building
:param find_stump: method to find the best split of data / stump at
a node
:param n_stump_evaluations: number of stump evaluations to do if
find_stump method is None
:param n_estimators: number of trees to construct
"""
self.verbosity = verbosity
self.max_depth = max_depth
self.get_exemplars = get_exemplars
self.get_gain = get_gain
self.random_state = random_state
self.n_estimators = n_estimators
self.n_jobs = n_jobs
self.n_stump_evaluations = n_stump_evaluations
self.get_distance_measure = get_distance_measure
self.setup_distance_measure_getter = setup_distance_measure_getter
self.distance_measure = distance_measure
self.find_stump = find_stump
# set in fit method
self.label_encoder = None
self.X = None
self.y = None
self.classes_ = None
self.trees = list()
self.num_classes_predicted = dict()
for _ in range(self.n_estimators):
self.trees.append(
ProximityTree(
n_stump_evaluations=self.n_stump_evaluations,
distance_measure=self.distance_measure,
)
)
super(ProximityForest, self).__init__()
def _fit_tree(self, X, y, index, random_state=0):
self.trees[index].fit(X, y, random_state)
return self.trees[index]
@staticmethod
def _predict_proba_tree(X, tree):
return tree.predict_proba(X)
def fit_tree(self, X, y, index, random_state):
"""Build the classifier on the training set (X, y).
X : array-like or sparse matrix of shape = [n_instances,n_columns]
The training input samples. If a Pandas data frame is passed,
column 0 is extracted.
y : array-like, shape = [n_instances]
The class labels.
index : index of the tree to be constructed
random_state: random_state to send to the tree to be constructed
Returns
-------
self : object
"""
if self.verbosity > 0:
print("tree " + str(index) + " building") # noqa
tree = ProximityTree(
random_state=random_state,
verbosity=self.verbosity,
distance_measure=self.distance_measure,
get_distance_measure=self.get_distance_measure,
max_depth=self.max_depth,
n_jobs=1,
find_stump=self.find_stump,
n_stump_evaluations=self.n_stump_evaluations,
)
tree.fit(X, y)
return tree
def fit(self, X, y):
"""Fit."""
X, y = check_X_y(X, y, enforce_univariate=True)
X = from_nested_to_3d_numpy(X)
self.random_state = check_random_state(self.random_state)
# setup label encoding
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y = self.label_encoder.fit_transform(y)
self.y = y
self.classes_ = self.label_encoder.classes_
if self.distance_measure is None:
if self.get_distance_measure is None:
self.get_distance_measure = self.setup_distance_measure_getter(self)
self.distance_measure = self.get_distance_measure(self)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
self.trees = parallel(
delayed(self._fit_tree)(
X, y, index, self.random_state.randint(0, self.n_estimators)
)
for index in range(self.n_estimators)
)
else:
self.trees = [
self._fit_tree(
X, y, index, self.random_state.randint(0, self.n_estimators)
)
for index in range(self.n_estimators)
]
self._is_fitted = True
return self
def predict_proba(self, X):
"""Find probability estimates for each class for all cases in X.
Parameters
----------
X : array-like or sparse matrix of shape = [n_instances, n_columns]
The training input samples.
If a Pandas data frame is passed (sktime format)
If a Pandas data frame is passed, a check is performed that it
only has one column.
If not, an exception is thrown, since this classifier does not
yet have
multivariate capability.
Returns
-------
output : array of shape = [n_instances, n_classes] of probabilities
"""
X = from_nested_to_3d_numpy(X)
X = check_X(X, enforce_univariate=True)
if self.n_jobs > 1 or self.n_jobs < 0:
parallel = Parallel(self.n_jobs)
predictions_per_tree = parallel(
delayed(self._predict_proba_tree)(X, tree) for tree in self.trees
)
else:
predictions_per_tree = [
self._predict_proba_tree(X, tree) for tree in self.trees
]
distributions = self.calculate_distributions(predictions_per_tree, X.shape[0])
distributions = np.array(distributions)
normalize(distributions, copy=False, norm="l1")
return distributions
def calculate_prediction_counts(self, predictions):
"""Pick an array of labels predicted by the trees.
Reorganize it into a dictionary {label: times predicted}
:param predictions: Array-like which contains the label predicted by each tree
:return:
"""
arr = np.array(predictions)
count_arr = np.bincount(arr)
prediction_counts = dict()
for label in np.sort(np.unique(self.y)):
try:
prediction_counts[label] = count_arr[label]
except IndexError:
prediction_counts[label] = 0
return prediction_counts
def calculate_distributions(self, predictions_per_tree, size):
"""Find probability estimates for each class for all cases in X.
:param predictions_per_tree: Array-like of shape
[n_instances,[n_tree_estimators,labels]] which contains an array of labels
predicted by each tree for each instance.
:param size: Size of X dataset containing the instances to predict
:return:
"""
distributions = np.zeros((size, len(np.unique(self.classes_))))
for index in range(size):
predicted_classes = list()
for predict_tree in predictions_per_tree:
predicted_classes.append(predict_tree[index])
prediction_counts = self.calculate_prediction_counts(predicted_classes)
prediction_counts = list(prediction_counts.items())
prediction_counts_to_array = np.array(prediction_counts)
sub_distribution = prediction_counts_to_array[
np.argsort(prediction_counts_to_array[:, 0])
][:, 1]
np.add.at(distributions, index, sub_distribution)
return distributions | PypiClean |
/alipay-sdk-python-pycryptodome-3.3.202.tar.gz/alipay-sdk-python-pycryptodome-3.3.202/alipay/aop/api/request/ZhimaCreditEpSceneRatingInitializeRequest.py | import json
from alipay.aop.api.FileItem import FileItem
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.ZhimaCreditEpSceneRatingInitializeModel import ZhimaCreditEpSceneRatingInitializeModel
class ZhimaCreditEpSceneRatingInitializeRequest(object):
def __init__(self, biz_model=None):
self._biz_model = biz_model
self._biz_content = None
self._version = "1.0"
self._terminal_type = None
self._terminal_info = None
self._prod_code = None
self._notify_url = None
self._return_url = None
self._udf_params = None
self._need_encrypt = False
@property
def biz_model(self):
return self._biz_model
@biz_model.setter
def biz_model(self, value):
self._biz_model = value
@property
def biz_content(self):
return self._biz_content
@biz_content.setter
def biz_content(self, value):
if isinstance(value, ZhimaCreditEpSceneRatingInitializeModel):
self._biz_content = value
else:
self._biz_content = ZhimaCreditEpSceneRatingInitializeModel.from_alipay_dict(value)
@property
def version(self):
return self._version
@version.setter
def version(self, value):
self._version = value
@property
def terminal_type(self):
return self._terminal_type
@terminal_type.setter
def terminal_type(self, value):
self._terminal_type = value
@property
def terminal_info(self):
return self._terminal_info
@terminal_info.setter
def terminal_info(self, value):
self._terminal_info = value
@property
def prod_code(self):
return self._prod_code
@prod_code.setter
def prod_code(self, value):
self._prod_code = value
@property
def notify_url(self):
return self._notify_url
@notify_url.setter
def notify_url(self, value):
self._notify_url = value
@property
def return_url(self):
return self._return_url
@return_url.setter
def return_url(self, value):
self._return_url = value
@property
def udf_params(self):
return self._udf_params
@udf_params.setter
def udf_params(self, value):
if not isinstance(value, dict):
return
self._udf_params = value
@property
def need_encrypt(self):
return self._need_encrypt
@need_encrypt.setter
def need_encrypt(self, value):
self._need_encrypt = value
def add_other_text_param(self, key, value):
if not self.udf_params:
self.udf_params = dict()
self.udf_params[key] = value
def get_params(self):
params = dict()
params[P_METHOD] = 'zhima.credit.ep.scene.rating.initialize'
params[P_VERSION] = self.version
if self.biz_model:
params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':'))
if self.biz_content:
if hasattr(self.biz_content, 'to_alipay_dict'):
params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':'))
else:
params['biz_content'] = self.biz_content
if self.terminal_type:
params['terminal_type'] = self.terminal_type
if self.terminal_info:
params['terminal_info'] = self.terminal_info
if self.prod_code:
params['prod_code'] = self.prod_code
if self.notify_url:
params['notify_url'] = self.notify_url
if self.return_url:
params['return_url'] = self.return_url
if self.udf_params:
params.update(self.udf_params)
return params
def get_multipart_params(self):
multipart_params = dict()
return multipart_params | PypiClean |
/pydda-1.4.0.tar.gz/pydda-1.4.0/doc/source/user_guide/dealiasing_velocities.rst | .. _dealiasing-velocities:
Radar Data Quality Control - Dealiasing
=======================================
In this notebook, we will showcase how to preform quality control of your
radar files, specifically dealiasing velocities. By doing this we can provide
PyDDA with quality controlled doppler velocities for dual doppler analysis.
-------------
Read the Data
-------------
For this example, we use test data found in Py-ART for two X-Band Scanning ARM
Precipitation Radars (X-SAPR) found at the Atmospheric Radiation Measurment
(ARM) Southern Great Plains (SGP) site in Oklahoma. For more information on
reading the radar data, consult :ref:`reading-radar-data`.
Get test data::
https://arm-doe.github.io/pyart/API/generated/pyart.testing.get_test_data.html
Reading CF-Radial::
https://arm-doe.github.io/pyart/API/generated/pyart.io.read_cfradial.html
.. code-block:: python
# read in the data from both XSAPR radars
xsapr_sw_file = get_test_data("swx_20120520_0641.nc")
xsapr_se_file = get_test_data("sex_20120520_0641.nc")
radar_sw = pyart.io.read_cfradial(xsapr_sw_file)
radar_se = pyart.io.read_cfradial(xsapr_se_file)
++++++++++++++++++++++++++++
Plot Velocity of Both Radars
++++++++++++++++++++++++++++
.. code-block:: python
fig = plt.figure(figsize=(16, 6))
ax = plt.subplot(121, projection=ccrs.PlateCarree())
# Plot the southwestern radar
disp1 = pyart.graph.RadarMapDisplay(radar_sw)
disp1.plot_ppi_map(
"mean_doppler_velocity",
sweep=1,
ax=ax,
vmin=-32,
vmax=32,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap='twilight_shifted'
)
# Plot the southeastern radar
ax2 = plt.subplot(122, projection=ccrs.PlateCarree())
disp2 = pyart.graph.RadarMapDisplay(radar_se)
disp2.plot_ppi_map(
"mean_doppler_velocity",
sweep=1,
ax=ax2,
vmin=-32,
vmax=32,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap='twilight_shifted'
)
.. plot::
import warnings
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import pyart
from pyart.testing import get_test_data
warnings.filterwarnings("ignore")
# read in the data from both XSAPR radars
xsapr_sw_file = get_test_data("swx_20120520_0641.nc")
xsapr_se_file = get_test_data("sex_20120520_0641.nc")
radar_sw = pyart.io.read_cfradial(xsapr_sw_file)
radar_se = pyart.io.read_cfradial(xsapr_se_file)
fig = plt.figure(figsize=(16, 6))
ax = plt.subplot(121, projection=ccrs.PlateCarree())
# Plot the southwestern radar
disp1 = pyart.graph.RadarMapDisplay(radar_sw)
disp1.plot_ppi_map(
"mean_doppler_velocity",
sweep=1,
ax=ax,
vmin=-32,
vmax=32,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap='twilight_shifted'
)
# Plot the southeastern radar
ax2 = plt.subplot(122, projection=ccrs.PlateCarree())
disp2 = pyart.graph.RadarMapDisplay(radar_se)
disp2.plot_ppi_map(
"mean_doppler_velocity",
sweep=1,
ax=ax2,
vmin=-32,
vmax=32,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap='twilight_shifted'
)
-----------------------------------------------
Determining Artifacts within Doppler Velocities
-----------------------------------------------
Before dealiasing the radar velocities, we need to remove noise and clutter
from the radar objects. Utilizing Py-ART, we will accomplish this by
calculating the velocity texture, or the standard deviation of velocity
surrounding a gate.
Py-ART's calculate_velocity_texture function::
https://arm-doe.github.io/pyart/API/generated/pyart.retrieve.calculate_velocity_texture.html
.. code-block:: python
# Calculate the Velocity Texture and apply the PyART GateFilter Utilityx
vel_tex_sw = pyart.retrieve.calculate_velocity_texture(radar_sw,
vel_field='mean_doppler_velocity',
nyq=19
)
vel_tex_se = pyart.retrieve.calculate_velocity_texture(radar_se,
vel_field='mean_doppler_velocity',
nyq=19
)
## Add velocity texture to the radar objects
radar_sw.add_field('velocity_texture', vel_tex_sw, replace_existing=True)
radar_se.add_field('velocity_texture', vel_tex_se, replace_existing=True)
+++++++++++++++++++++++++
Velocity Texture Displays
+++++++++++++++++++++++++
Let's see what this velocity texture looks like.
Additionally, a histogram of velocity texture values will allow for
the determination of a threshold to distingiush the hydrometeor signal
from artifacts.
.. code-block:: python
# Display the calculated velocity texture
fig = plt.figure(figsize=[8, 6])
display = pyart.graph.RadarDisplay(radar_sw)
display.plot_ppi('velocity_texture',
sweep=0,
vmin=0,
vmax=10,
cmap=plt.get_cmap('twilight_shifted')
)
# Plot a histogram of the velocity textures
fig = plt.figure(figsize=[8, 8])
hist, bins = np.histogram(radar_sw.fields['velocity_texture']['data'],
bins=150)
bins = (bins[1:]+bins[:-1])/2.0
plt.plot(bins,
hist,
label='Velocity Texture Frequency'
)
plt.axvline(3,
color='r',
label='Proposed Velocity Texture Threshold'
)
plt.xlabel('Velocity texture')
plt.ylabel('Count')
plt.legend()
.. plot::
import warnings
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import pyart
from pyart.testing import get_test_data
warnings.filterwarnings("ignore")
# read in the data from both XSAPR radars
xsapr_sw_file = get_test_data("swx_20120520_0641.nc")
xsapr_se_file = get_test_data("sex_20120520_0641.nc")
radar_sw = pyart.io.read_cfradial(xsapr_sw_file)
radar_se = pyart.io.read_cfradial(xsapr_se_file)
# Calculate the Velocity Texture and apply the PyART GateFilter Utilityx
vel_tex_sw = pyart.retrieve.calculate_velocity_texture(radar_sw,
vel_field='mean_doppler_velocity',
nyq=19
)
vel_tex_se = pyart.retrieve.calculate_velocity_texture(radar_se,
vel_field='mean_doppler_velocity',
nyq=19
)
## Add velocity texture to the radar objects
radar_sw.add_field('velocity_texture', vel_tex_sw, replace_existing=True)
radar_se.add_field('velocity_texture', vel_tex_se, replace_existing=True)
fig = plt.figure(figsize=(8, 6))
display = pyart.graph.RadarDisplay(radar_sw)
display.plot_ppi('velocity_texture',
sweep=0,
vmin=0,
vmax=10,
cmap=plt.get_cmap('twilight_shifted')
)
# Plot a histogram of the velocity textures
fig = plt.figure(figsize=[8, 8])
hist, bins = np.histogram(radar_sw.fields['velocity_texture']['data'],
bins=150)
bins = (bins[1:]+bins[:-1])/2.0
plt.plot(bins,
hist,
label='Velocity Texture Frequency'
)
plt.axvline(3,
color='r',
label='Proposed Velocity Texture Threshold'
)
plt.xlabel('Velocity texture')
plt.ylabel('Count')
plt.legend()
+++++++++++++++++++++++++++++++++
Filter Doppler Velocity Artifacts
+++++++++++++++++++++++++++++++++
Now that we have determined which velocity texture values correspond to
artifacts within the doppler velocity data, we utilize Py-ART's GateFilter
to filter out these artifacts
Py-ART's GateFilter function::
https://arm-doe.github.io/pyart/API/generated/pyart.filters.GateFilter.html
.. code-block:: python
# Apply a GateFilter
gatefilter_sw = pyart.filters.GateFilter(radar_sw)
gatefilter_sw.exclude_above('velocity_texture', 3)
gatefilter_se = pyart.filters.GateFilter(radar_se)
gatefilter_se.exclude_above('velocity_texture', 3)
----------------
Apply Dealiasing
----------------
Now that we have removed artifacts, we can proceed with dealiasing
the doppler velocity data with Py-ART's Region-Based Dealiasing
Algorithm.
The Region-Based Dealiasing finds regions of similar velocities and
unfolds and merges these pairs of regions until all data are unfolded.
Py-ART's Region Based Dealiasing Correction::
https://arm-doe.github.io/pyart/API/generated/pyart.correct.dealias_region_based.html
.. code-block:: python
# Apply Region Based DeAlising Utiltiy
vel_dealias_sw = pyart.correct.dealias_region_based(radar_sw,
vel_field='mean_doppler_velocity',
nyquist_vel=19,
centered=True,
gatefilter=gatefilter_sw
)
# Apply Region Based DeAlising Utiltiy
vel_dealias_se = pyart.correct.dealias_region_based(radar_se,
vel_field='mean_doppler_velocity',
nyquist_vel=19,
centered=True,
gatefilter=gatefilter_se
)
# Add our data dictionary to the radar object
radar_se.add_field('corrected_velocity', vel_dealias_se, replace_existing=True)
radar_sw.add_field('corrected_velocity', vel_dealias_sw, replace_existing=True)
+++++++++++++++++++++++++++++++++
Display Corrected Velocity Fields
+++++++++++++++++++++++++++++++++
Let's check on our corrected velocity fields!
.. code-block:: python
fig = plt.figure(figsize=(16, 6))
# Plot the southwestern radar
ax = plt.subplot(121, projection=ccrs.PlateCarree())
disp1 = pyart.graph.RadarMapDisplay(radar_sw)
disp1.plot_ppi_map("corrected_velocity",
sweep=1,
ax=ax,
vmin=-35,
vmax=35,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap=plt.get_cmap('twilight_shifted')
)
# Plot the southeastern radar
ax2 = plt.subplot(122, projection=ccrs.PlateCarree())
disp2 = pyart.graph.RadarMapDisplay(radar_se)
disp2.plot_ppi_map("corrected_velocity",
sweep=1,
ax=ax2,
vmin=-35,
vmax=35,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap=plt.get_cmap('twilight_shifted')
)
.. plot::
import warnings
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import numpy as np
import pyart
from pyart.testing import get_test_data
warnings.filterwarnings("ignore")
# read in the data from both XSAPR radars
xsapr_sw_file = get_test_data("swx_20120520_0641.nc")
xsapr_se_file = get_test_data("sex_20120520_0641.nc")
radar_sw = pyart.io.read_cfradial(xsapr_sw_file)
radar_se = pyart.io.read_cfradial(xsapr_se_file)
# Calculate the Velocity Texture and apply the PyART GateFilter Utilityx
vel_tex_sw = pyart.retrieve.calculate_velocity_texture(radar_sw,
vel_field='mean_doppler_velocity',
nyq=19
)
vel_tex_se = pyart.retrieve.calculate_velocity_texture(radar_se,
vel_field='mean_doppler_velocity',
nyq=19
)
## Add velocity texture to the radar objects
radar_sw.add_field('velocity_texture', vel_tex_sw, replace_existing=True)
radar_se.add_field('velocity_texture', vel_tex_se, replace_existing=True)
# Apply a GateFilter
gatefilter_sw = pyart.filters.GateFilter(radar_sw)
gatefilter_sw.exclude_above('velocity_texture', 3)
gatefilter_se = pyart.filters.GateFilter(radar_se)
gatefilter_se.exclude_above('velocity_texture', 3)
# Apply Region Based DeAlising Utiltiy
vel_dealias_sw = pyart.correct.dealias_region_based(radar_sw,
vel_field='mean_doppler_velocity',
nyquist_vel=19,
centered=True,
gatefilter=gatefilter_sw
)
# Apply Region Based DeAlising Utiltiy
vel_dealias_se = pyart.correct.dealias_region_based(radar_se,
vel_field='mean_doppler_velocity',
nyquist_vel=19,
centered=True,
gatefilter=gatefilter_se
)
# Add our data dictionary to the radar object
radar_se.add_field('corrected_velocity', vel_dealias_se, replace_existing=True)
radar_sw.add_field('corrected_velocity', vel_dealias_sw, replace_existing=True)
fig = plt.figure(figsize=(16, 6))
# Plot the southwestern radar
ax = plt.subplot(121, projection=ccrs.PlateCarree())
disp1 = pyart.graph.RadarMapDisplay(radar_sw)
disp1.plot_ppi_map("corrected_velocity",
sweep=1,
ax=ax,
vmin=-35,
vmax=35,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap=plt.get_cmap('twilight_shifted')
)
# Plot the southeastern radar
ax2 = plt.subplot(122, projection=ccrs.PlateCarree())
disp2 = pyart.graph.RadarMapDisplay(radar_se)
disp2.plot_ppi_map("corrected_velocity",
sweep=1,
ax=ax2,
vmin=-35,
vmax=35,
min_lat=36,
max_lat=37,
min_lon=-98,
max_lon=-97,
lat_lines=np.arange(36, 37.25, 0.25),
lon_lines=np.arange(-98, -96.75, 0.25),
cmap=plt.get_cmap('twilight_shifted')
)
-------
Summary
-------
Utilizing Py-ART, we read in two radar files within close proximity to each other.
We then corrected the radar doppler velocities to remove artifacts and clutter.
Finally, utilizing Py-ART, we applied a region-based dealiasing alogrithm to
unfold the doppler velocities
Now that we have corrected velocities, incorporating these radars into PyDDA
will be shown in the next notebook.
| PypiClean |
/FiPy-3.4.4.tar.gz/FiPy-3.4.4/fipy/variables/faceGradVariable.py | from __future__ import division
from __future__ import unicode_literals
__docformat__ = 'restructuredtext'
__all__ = []
from fipy.variables.faceVariable import FaceVariable
from fipy.tools import numerix
from fipy.tools import inline
class _FaceGradVariable(FaceVariable):
"""
Test case for a vector cell variable
>>> from fipy import *
>>> m = Grid2D(nx=3, ny=3)
>>> x, y = m.cellCenters
>>> v = CellVariable(mesh=m, elementshape=(3,))
>>> v[0] = x
>>> v[1] = y
>>> v[2] = x**2
>>> v0 = CellVariable(mesh=m, value=x)
>>> v1 = CellVariable(mesh=m, value=y)
>>> v2 = CellVariable(mesh=m, value=x**2)
>>> numerix.allequal(v.faceGrad.globalValue.shape, (2, 3, 24))
True
>>> print(v0.faceGrad.allclose([[ 0.5, 1., 0.5, 0.5, 1., 0.5, 0.5, 1., 0.5, 0.5, 1., 0.5, 0., 1., 1.,
... 0., 0., 1., 1., 0., 0., 1., 1., 0. ],
... [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
... 0., 0., 0., 0., 0., 0., 0., 0., 0. ]]))
True
>>> print((v0.faceGrad.globalValue == v.faceGrad.globalValue[:, 0]).all())
True
>>> print((v1.faceGrad.globalValue == v.faceGrad.globalValue[:, 1]).all())
True
>>> print((v2.faceGrad.globalValue == v.faceGrad.globalValue[:, 2]).all())
True
"""
def __init__(self, var):
FaceVariable.__init__(self, mesh=var.mesh, elementshape=(var.mesh.dim,) + var.shape[:-1])
self.var = self._requires(var)
def _calcValue(self):
if inline.doInline and self.var.rank == 0:
return self._calcValueInline()
else:
return self._calcValueNoInline()
def _calcValueInline(self):
id1, id2 = self.mesh._adjacentCellIDs
tangents1 = self.mesh._faceTangents1
tangents2 = self.mesh._faceTangents2
val = self._array.copy()
faceNormals = self.mesh._orientedFaceNormals
if numerix.MA.isMaskedArray(faceNormals):
faceNormals = faceNormals.filled()
inline._runIterateElementInline("""
int j;
double t1grad1, t1grad2, t2grad1, t2grad2, N, N2;
int ID1 = ITEM(id1, i, NULL);
int ID2 = ITEM(id2, i, NULL);
if ITEM(exteriorFaces, i, NULL) {
N2 = ITEM(facevar, i, NULL);
} else {
N2 = ITEM(var, ID2, NULL);
}
N = (N2 - ITEM(var, ID1, NULL)) / ITEM(dAP, i, NULL);
t1grad1 = t1grad2 = t2grad1 = t2grad2 = 0.;
t1grad1 += ITEM(tangents1, i, vec) * ITEM(cellGrad, ID1, vec);
t1grad2 += ITEM(tangents1, i, vec) * ITEM(cellGrad, ID2, vec);
t2grad1 += ITEM(tangents2, i, vec) * ITEM(cellGrad, ID1, vec);
t2grad2 += ITEM(tangents2, i, vec) * ITEM(cellGrad, ID2, vec);
ITEM(val, i, vec) = ITEM(normals, i, vec) * N;
ITEM(val, i, vec) += ITEM(tangents1, i, vec) * (t1grad1 + t1grad2) / 2.;
ITEM(val, i, vec) += ITEM(tangents2, i, vec) * (t2grad1 + t2grad2) / 2.;
""", tangents1 = tangents1,
tangents2 = tangents2,
cellGrad = self.var.grad.numericValue,
normals = faceNormals,
id1 = id1,
id2 = id2,
dAP = numerix.array(self.mesh._cellDistances),
var = self.var.numericValue,
facevar = self.var.faceValue.numericValue,
exteriorFaces = self.mesh.exteriorFaces.numericValue,
val = val,
ni = tangents1.shape[1],
shape=numerix.array(numerix.shape(tangents1)))
return self._makeValue(value = val)
def _calcValueNoInline(self):
dAP = self.mesh._cellDistances
id1, id2 = self.mesh._adjacentCellIDs
N2 = numerix.take(self.var.value, id2, axis=-1)
faceMask = numerix.array(self.mesh.exteriorFaces)
## The following conditional is required because empty
## indexing is not altogether functional. This
## numpy.empty((0,))[[]] and this numpy.empty((0,))[...,[]]
## both work, but this numpy.empty((3, 0))[...,[]] is
## broken.
if self.var.faceValue.shape[-1] != 0:
s = (Ellipsis, faceMask)
else:
s = (faceMask,)
N2[s] = self.var.faceValue[s]
N = (N2 - numerix.take(self.var, id1, axis=-1)) / dAP
normals = self.mesh._orientedFaceNormals
tangents1 = self.mesh._faceTangents1
tangents2 = self.mesh._faceTangents2
cellGrad = self.var.grad.numericValue
grad1 = numerix.take(cellGrad, id1, axis=-1)
grad2 = numerix.take(cellGrad, id2, axis=-1)
s = (slice(0, None, None),) + (numerix.newaxis,) * (len(grad1.shape) - 2) + (slice(0, None, None),)
t1grad1 = numerix.sum(tangents1[s] * grad1, 0)
t1grad2 = numerix.sum(tangents1[s] * grad2, 0)
t2grad1 = numerix.sum(tangents2[s] * grad1, 0)
t2grad2 = numerix.sum(tangents2[s] * grad2, 0)
T1 = (t1grad1 + t1grad2) / 2.
T2 = (t2grad1 + t2grad2) / 2.
return normals[s] * N[numerix.newaxis] + tangents1[s] * T1[numerix.newaxis] + tangents2[s] * T2[numerix.newaxis]
def _test():
import fipy.tests.doctestPlus
return fipy.tests.doctestPlus.testmod()
if __name__ == "__main__":
_test() | PypiClean |
/js.ace-1.4.11.tar.gz/js.ace-1.4.11/js/ace/resources/mode-puppet.js | ace.define("ace/mode/puppet_highlight_rules",["require","exports","module","ace/lib/oop","ace/mode/text_highlight_rules"],function(e,t,n){"use strict";var r=e("../lib/oop"),i=e("./text_highlight_rules").TextHighlightRules,s=function(){this.$rules={start:[{token:["keyword.type.puppet","constant.class.puppet","keyword.inherits.puppet","constant.class.puppet"],regex:'^\\s*(class)(\\s+(?:[-_A-Za-z0-9".]+::)*[-_A-Za-z0-9".]+\\s*)(?:(inherits\\s*)(\\s+(?:[-_A-Za-z0-9".]+::)*[-_A-Za-z0-9".]+\\s*))?'},{token:["storage.function.puppet","name.function.puppet","punctuation.lpar"],regex:"(^\\s*define)(\\s+[a-zA-Z0-9_:]+\\s*)(\\()",push:[{token:"punctuation.rpar.puppet",regex:"\\)",next:"pop"},{include:"constants"},{include:"variable"},{include:"strings"},{include:"operators"},{defaultToken:"string"}]},{token:["language.support.class","keyword.operator"],regex:"\\b([a-zA-Z_]+)(\\s+=>)"},{token:["exported.resource.puppet","keyword.name.resource.puppet","paren.lparen"],regex:"(\\@\\@)?(\\s*[a-zA-Z_]*)(\\s*\\{)"},{token:"qualified.variable.puppet",regex:"(\\$([a-z][a-z0-9_]*)?(::[a-z][a-z0-9_]*)*::[a-z0-9_][a-zA-Z0-9_]*)"},{token:"singleline.comment.puppet",regex:"#(.)*$"},{token:"multiline.comment.begin.puppet",regex:"^\\s*\\/\\*\\s*$",push:"blockComment"},{token:"keyword.control.puppet",regex:"\\b(case|if|unless|else|elsif|in|default:|and|or)\\s+(?!::)"},{token:"keyword.control.puppet",regex:"\\b(import|default|inherits|include|require|contain|node|application|consumes|environment|site|function|produces)\\b"},{token:"support.function.puppet",regex:"\\b(lest|str2bool|escape|gsub|Timestamp|Timespan|with|alert|crit|debug|notice|sprintf|split|step|strftime|slice|shellquote|type|sha1|defined|scanf|reverse_each|regsubst|return|emerg|reduce|err|failed|fail|versioncmp|file|generate|then|info|realize|search|tag|tagged|template|epp|warning|hiera_include|each|assert_type|binary_file|create_resources|dig|digest|filter|lookup|find_file|fqdn_rand|hiera_array|hiera_hash|inline_epp|inline_template|map|match|md5|new|next)\\b"},{token:"constant.types.puppet",regex:"\\b(String|File|Package|Service|Class|Integer|Array|Catalogentry|Variant|Boolean|Undef|Number|Hash|Float|Numeric|NotUndef|Callable|Optional|Any|Regexp|Sensitive|Sensitive.new|Type|Resource|Default|Enum|Scalar|Collection|Data|Pattern|Tuple|Struct)\\b"},{token:"paren.lparen",regex:"[[({]"},{token:"paren.rparen",regex:"[\\])}]"},{include:"variable"},{include:"constants"},{include:"strings"},{include:"operators"},{token:"regexp.begin.string.puppet",regex:"\\s*(\\/(\\S)+)\\/"}],blockComment:[{regex:"^\\s*\\/\\*\\s*$",token:"multiline.comment.begin.puppet",push:"blockComment"},{regex:"^\\s*\\*\\/\\s*$",token:"multiline.comment.end.puppet",next:"pop"},{defaultToken:"comment"}],constants:[{token:"constant.language.puppet",regex:"\\b(false|true|running|stopped|installed|purged|latest|file|directory|held|undef|present|absent|link|mounted|unmounted)\\b"}],variable:[{token:"variable.puppet",regex:"(\\$[a-z0-9_{][a-zA-Z0-9_]*)"}],strings:[{token:"punctuation.quote.puppet",regex:"'",push:[{token:"punctuation.quote.puppet",regex:"'",next:"pop"},{include:"escaped_chars"},{defaultToken:"string"}]},{token:"punctuation.quote.puppet",regex:'"',push:[{token:"punctuation.quote.puppet",regex:'"',next:"pop"},{include:"escaped_chars"},{include:"variable"},{defaultToken:"string"}]}],escaped_chars:[{token:"constant.escaped_char.puppet",regex:"\\\\."}],operators:[{token:"keyword.operator",regex:"\\+\\.|\\-\\.|\\*\\.|\\/\\.|#|;;|\\+|\\-|\\*|\\*\\*\\/|\\/\\/|%|<<|>>|&|\\||\\^|~|<|>|<=|=>|==|!=|<>|<-|=|::|,"}]},this.normalizeRules()};r.inherits(s,i),t.PuppetHighlightRules=s}),ace.define("ace/mode/folding/cstyle",["require","exports","module","ace/lib/oop","ace/range","ace/mode/folding/fold_mode"],function(e,t,n){"use strict";var r=e("../../lib/oop"),i=e("../../range").Range,s=e("./fold_mode").FoldMode,o=t.FoldMode=function(e){e&&(this.foldingStartMarker=new RegExp(this.foldingStartMarker.source.replace(/\|[^|]*?$/,"|"+e.start)),this.foldingStopMarker=new RegExp(this.foldingStopMarker.source.replace(/\|[^|]*?$/,"|"+e.end)))};r.inherits(o,s),function(){this.foldingStartMarker=/([\{\[\(])[^\}\]\)]*$|^\s*(\/\*)/,this.foldingStopMarker=/^[^\[\{\(]*([\}\]\)])|^[\s\*]*(\*\/)/,this.singleLineBlockCommentRe=/^\s*(\/\*).*\*\/\s*$/,this.tripleStarBlockCommentRe=/^\s*(\/\*\*\*).*\*\/\s*$/,this.startRegionRe=/^\s*(\/\*|\/\/)#?region\b/,this._getFoldWidgetBase=this.getFoldWidget,this.getFoldWidget=function(e,t,n){var r=e.getLine(n);if(this.singleLineBlockCommentRe.test(r)&&!this.startRegionRe.test(r)&&!this.tripleStarBlockCommentRe.test(r))return"";var i=this._getFoldWidgetBase(e,t,n);return!i&&this.startRegionRe.test(r)?"start":i},this.getFoldWidgetRange=function(e,t,n,r){var i=e.getLine(n);if(this.startRegionRe.test(i))return this.getCommentRegionBlock(e,i,n);var s=i.match(this.foldingStartMarker);if(s){var o=s.index;if(s[1])return this.openingBracketBlock(e,s[1],n,o);var u=e.getCommentFoldRange(n,o+s[0].length,1);return u&&!u.isMultiLine()&&(r?u=this.getSectionRange(e,n):t!="all"&&(u=null)),u}if(t==="markbegin")return;var s=i.match(this.foldingStopMarker);if(s){var o=s.index+s[0].length;return s[1]?this.closingBracketBlock(e,s[1],n,o):e.getCommentFoldRange(n,o,-1)}},this.getSectionRange=function(e,t){var n=e.getLine(t),r=n.search(/\S/),s=t,o=n.length;t+=1;var u=t,a=e.getLength();while(++t<a){n=e.getLine(t);var f=n.search(/\S/);if(f===-1)continue;if(r>f)break;var l=this.getFoldWidgetRange(e,"all",t);if(l){if(l.start.row<=s)break;if(l.isMultiLine())t=l.end.row;else if(r==f)break}u=t}return new i(s,o,u,e.getLine(u).length)},this.getCommentRegionBlock=function(e,t,n){var r=t.search(/\s*$/),s=e.getLength(),o=n,u=/^\s*(?:\/\*|\/\/|--)#?(end)?region\b/,a=1;while(++n<s){t=e.getLine(n);var f=u.exec(t);if(!f)continue;f[1]?a--:a++;if(!a)break}var l=n;if(l>o)return new i(o,r,l,t.length)}}.call(o.prototype)}),ace.define("ace/mode/matching_brace_outdent",["require","exports","module","ace/range"],function(e,t,n){"use strict";var r=e("../range").Range,i=function(){};(function(){this.checkOutdent=function(e,t){return/^\s+$/.test(e)?/^\s*\}/.test(t):!1},this.autoOutdent=function(e,t){var n=e.getLine(t),i=n.match(/^(\s*\})/);if(!i)return 0;var s=i[1].length,o=e.findMatchingBracket({row:t,column:s});if(!o||o.row==t)return 0;var u=this.$getIndent(e.getLine(o.row));e.replace(new r(t,0,t,s-1),u)},this.$getIndent=function(e){return e.match(/^\s*/)[0]}}).call(i.prototype),t.MatchingBraceOutdent=i}),ace.define("ace/mode/puppet",["require","exports","module","ace/lib/oop","ace/mode/text","ace/mode/puppet_highlight_rules","ace/mode/behaviour/cstyle","ace/mode/folding/cstyle","ace/mode/matching_brace_outdent"],function(e,t,n){"use strict";var r=e("../lib/oop"),i=e("./text").Mode,s=e("./puppet_highlight_rules").PuppetHighlightRules,o=e("./behaviour/cstyle").CstyleBehaviour,u=e("./folding/cstyle").FoldMode,a=e("./matching_brace_outdent").MatchingBraceOutdent,f=function(){i.call(this),this.HighlightRules=s,this.$outdent=new a,this.$behaviour=new o,this.foldingRules=new u};r.inherits(f,i),function(){this.$id="ace/mode/puppet"}.call(f.prototype),t.Mode=f}); (function() {
ace.require(["ace/mode/puppet"], function(m) {
if (typeof module == "object" && typeof exports == "object" && module) {
module.exports = m;
}
});
})(); | PypiClean |
/google-dataproc-templates-0.4.0b0.tar.gz/google-dataproc-templates-0.4.0b0/dataproc_templates/gcs/text_to_bigquery.py |
from typing import Dict, Sequence, Optional, Any
from logging import Logger
import argparse
import pprint
from pyspark.sql import SparkSession
from dataproc_templates import BaseTemplate
from dataproc_templates.util.argument_parsing import add_spark_options
from dataproc_templates.util.dataframe_reader_wrappers import ingest_dataframe_from_cloud_storage
import dataproc_templates.util.template_constants as constants
__all__ = ['TextToBigQueryTemplate']
class TextToBigQueryTemplate(BaseTemplate):
"""
Dataproc template implementing Text loads from GCS into BigQuery
"""
@staticmethod
def parse_args(args: Optional[Sequence[str]] = None) -> Dict[str, Any]:
parser: argparse.ArgumentParser = argparse.ArgumentParser()
parser.add_argument(
f'--{constants.TEXT_BQ_INPUT_LOCATION}',
dest=constants.TEXT_BQ_INPUT_LOCATION,
required=True,
help='Cloud Storage location of the input text files'
)
add_spark_options(parser, constants.get_csv_input_spark_options("text.bigquery.input."))
parser.add_argument(
f'--{constants.TEXT_BQ_OUTPUT_DATASET}',
dest=constants.TEXT_BQ_OUTPUT_DATASET,
required=True,
help='BigQuery dataset for the output table'
)
parser.add_argument(
f'--{constants.TEXT_BQ_OUTPUT_TABLE}',
dest=constants.TEXT_BQ_OUTPUT_TABLE,
required=True,
help='BigQuery output table name'
)
parser.add_argument(
f'--{constants.TEXT_BQ_LD_TEMP_BUCKET_NAME}',
dest=constants.TEXT_BQ_LD_TEMP_BUCKET_NAME,
required=True,
help='Spark BigQuery connector temporary bucket'
)
parser.add_argument(
f'--{constants.TEXT_BQ_OUTPUT_MODE}',
dest=constants.TEXT_BQ_OUTPUT_MODE,
required=False,
default=constants.OUTPUT_MODE_APPEND,
help=(
'Output write mode '
'(one of: append,overwrite,ignore,errorifexists) '
'(Defaults to append)'
),
choices=[
constants.OUTPUT_MODE_OVERWRITE,
constants.OUTPUT_MODE_APPEND,
constants.OUTPUT_MODE_IGNORE,
constants.OUTPUT_MODE_ERRORIFEXISTS
]
)
parser.add_argument(
f'--{constants.TEXT_INPUT_COMPRESSION}',
dest=constants.TEXT_INPUT_COMPRESSION,
required=True,
help='Input file compression format (one of: bzip2,deflate,lz4,gzip,None)',
default=None,
choices=[
constants.COMPRESSION_BZIP2,
constants.COMPRESSION_GZIP,
constants.COMPRESSION_DEFLATE,
constants.COMPRESSION_LZ4,
constants.COMPRESSION_NONE
]
)
parser.add_argument(
f'--{constants.TEXT_INPUT_DELIMITER}',
dest=constants.TEXT_INPUT_DELIMITER,
required=False,
help=(
'Input column delimiter '
'(example: ",", ";", "|", "/","\" ) '
)
)
known_args: argparse.Namespace
known_args, _ = parser.parse_known_args(args)
return vars(known_args)
def run(self, spark: SparkSession, args: Dict[str, Any]) -> None:
logger: Logger = self.get_logger(spark=spark)
# Arguments
input_location: str = args[constants.TEXT_BQ_INPUT_LOCATION]
big_query_dataset: str = args[constants.TEXT_BQ_OUTPUT_DATASET]
big_query_table: str = args[constants.TEXT_BQ_OUTPUT_TABLE]
bq_temp_bucket: str = args[constants.TEXT_BQ_LD_TEMP_BUCKET_NAME]
output_mode: str = args[constants.TEXT_BQ_OUTPUT_MODE]
# These options are redundant but left in place until this template is removed
# via issue https://github.com/GoogleCloudPlatform/dataproc-templates/issues/721
# input_delimiter: str = args[constants.TEXT_INPUT_DELIMITER]
# input_file_codec_format: str = args[constants.TEXT_INPUT_COMPRESSION]
logger.info(
"Starting GCS to Bigquery Spark job with parameters:\n"
f"{pprint.pformat(args)}"
)
# Read
input_data = ingest_dataframe_from_cloud_storage(
spark,
args,
input_location,
constants.FORMAT_CSV,
"text.bigquery.input.",
)
# Write
input_data.write \
.format(constants.FORMAT_BIGQUERY) \
.option(constants.TABLE, big_query_dataset + "." + big_query_table) \
.option(constants.TEXT_BQ_TEMP_BUCKET, bq_temp_bucket) \
.mode(output_mode) \
.save() | PypiClean |
/shanhe-sdk-1.0.0.tar.gz/shanhe-sdk-1.0.0/shanhe/iaas/actions/volume.py |
from shanhe.iaas import constants as const
from shanhe.misc.utils import filter_out_none
class VolumeAction(object):
def __init__(self, conn):
self.conn = conn
def describe_volumes(self, volumes=None,
volume_type=None,
instance_id=None,
status=None,
owner=None,
search_word=None,
verbose=0,
offset=None,
limit=None,
tags=None,
**ignore):
""" Describe volumes filtered by conditions
@param volumes : the array of IDs of volumes.
@param volume_type : the type of volume, 0 is high performance, 1 is high capacity
@param instance_id: ID of the instance that volume is currently attached to, if has.
@param status: pending, available, in-use, deleted.
@param search_word: the combined search column.
@param verbose: the number to specify the verbose level, larger the number, the more detailed information will be returned.
@param offset: the starting offset of the returning results.
@param limit: specify the number of the returning results.
@param tags : the array of IDs of tags.
"""
valid_keys = ['volumes', 'instance_id', 'status', 'search_word',
'volume_type', 'verbose', 'offset', 'limit', 'tags', 'owner']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=[],
integer_params=[
'offset', 'limit', 'verbose'],
list_params=[
'volumes', 'status', 'tags']
):
return None
return self.conn.send_request(const.ACTION_DESCRIBE_VOLUMES, body)
def create_volumes(self, size,
volume_name="",
volume_type=0,
count=1,
target_user=None,
**ignore):
""" Create one or more volumes.
@param size : the size of each volume. Unit is GB.
@param volume_name : the short name of volume
@param volume_type : the type of volume, 0 is high performance, 1 is high capacity
@param count : the number of volumes to create.
@param target_user: ID of user who will own this resource, should be one of your sub-accounts
"""
action = const.ACTION_CREATE_VOLUMES
valid_keys = ['size', 'volume_name', 'volume_type', 'count', 'target_user']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=['size'],
integer_params=['size', 'count'],
list_params=[]
):
return None
return self.conn.send_request(action, body)
def delete_volumes(self, volumes,
**ignore):
""" Delete one or more volumes.
@param volumes : An array including IDs of the volumes you want to delete.
"""
action = const.ACTION_DELETE_VOLUMES
body = {'volumes': volumes}
if not self.conn.req_checker.check_params(body,
required_params=['volumes'],
integer_params=[],
list_params=['volumes']
):
return None
return self.conn.send_request(action, body)
def attach_volumes(self, volumes,
instance,
**ignore):
""" Attach one or more volumes to same instance
@param volumes : an array including IDs of the volumes you want to attach.
@param instance : the ID of instance the volumes will be attached to.
"""
action = const.ACTION_ATTACH_VOLUMES
valid_keys = ['volumes', 'instance']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=[
'volumes', 'instance'],
integer_params=[],
list_params=['volumes']
):
return None
return self.conn.send_request(action, body)
def detach_volumes(self, volumes,
instance,
**ignore):
""" Detach one or more volumes from same instance.
@param volumes : An array including IDs of the volumes you want to attach.
@param instance : the ID of instance the volumes will be detached from.
"""
action = const.ACTION_DETACH_VOLUMES
valid_keys = ['volumes', 'instance']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=[
'volumes', 'instance'],
integer_params=[],
list_params=['volumes']
):
return None
return self.conn.send_request(action, body)
def resize_volumes(self, volumes,
size,
**ignore):
""" Extend one or more volumes' size.
@param volumes: The IDs of the volumes you want to resize.
@param size : The new larger size of the volumes, unit is GB
"""
action = const.ACTION_RESIZE_VOLUMES
valid_keys = ['volumes', 'size']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=[
'volumes', 'size'],
integer_params=['size'],
list_params=['volumes']
):
return None
return self.conn.send_request(action, body)
def modify_volume_attributes(self, volume,
volume_name=None,
description=None,
**ignore):
""" Modify volume attributes.
@param volume: the ID of volume whose attributes you want to modify.
@param volume_name: Name of the volume. It's a short name for
the volume that more meaningful than volume id.
@param description: The detailed description of the resource.
"""
action = const.ACTION_MODIFY_VOLUME_ATTRIBUTES
valid_keys = ['volume', 'volume_name', 'description']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=['volume'],
integer_params=[],
list_params=[]
):
return None
return self.conn.send_request(action, body)
def clone_volumes(self,
zone,
volume,
volume_name="",
volume_type=None,
count=1,
**ignore):
""" Clone an existed volume to one or more new volumes.
@param zone: the ID of zone for new volume.
@param volume: the ID of volume you want to clone.
@param volume_name: name of the volume. It's a short name for
the volume that more meaningful than volume id.
@param volume_type: type of the volume.
@param count: how many volumes will be created.
"""
action = const.ACTION_CLONE_VOLUMES
valid_keys = ['zone', 'volume', 'volume_name', 'volume_type', 'count']
body = filter_out_none(locals(), valid_keys)
if not self.conn.req_checker.check_params(body,
required_params=['zone', 'volume'],
integer_params=['count'],
list_params=[]
):
return None
return self.conn.send_request(action, body) | PypiClean |
/heppyfwk-3.0.1.tar.gz/heppyfwk-3.0.1/heppy/particles/isolation.py | from heppy.utils.deltar import deltaR2
class Area(object):
'''Base Area interface.'''
def is_inside(self, *args):
'''returns True if *args describes a particle inside the EtaPhiCircle.
*args may be the particle itself, assuming it has eta() and phi() methods,
or eta, phi.
'''
pass
class EtaPhiCircle(Area):
'''Circle in (eta, phi) space.
When running on a lepton collider, eta is replaced by theta.
'''
def __init__(self, R):
'''Create a circle of radius R'''
self.R = R
self._R2 = R**2
def is_inside(self, *args):
dR2 = deltaR2(*args)
return dR2 < self._R2
class IsolationInfo(object):
'''Holds the results of an isolation calculation.'''
def __init__(self, label, lepton):
'''Create an IsolationInfo.
Attributes:
lepton = the lepton
particles = list of particles around the lepton used in the calculation.
the following quantities are computed for these particles
sumpt = total pT for the particles
sume = total energy for the particles
num = total number of particles
'''
self.particles = []
self.label = label
self.lepton = lepton
self.sumpt = 0
self.sume = 0
self.num = 0
def add_particle(self, ptc):
'''Add a new particle and update counters.'''
self.particles.append(ptc)
self.sumpt += ptc.pt()
self.sume += ptc.e()
self.num += 1
def rm_particle(self, ptc):
'''Remove a particle and update counters (e.g. for FSR recovery)'''
self.particles.remove(ptc)
self.sumpt -= ptc.pt()
self.sume -= ptc.e()
self.num -= 1
def __iadd__(self, other):
self.particles.extend(other.particles)
self.sumpt += other.sumpt
self.sume += other.sume
self.num += other.num
return self
def __str__(self):
return 'iso {label:>3}: sumpt = {sumpt:5.2f}, sume = {sume:5.2f}, num = {num}'.format(
label = self.label,
sumpt = self.sumpt,
sume = self.sume,
num = self.num
)
class IsolationComputer(object):
'''Computes isolation for a given lepton.'''
def __init__(self, on_areas, off_areas=None,
pt_thresh=0, e_thresh=0, label=''):
'''Creates the isolation computer.
Particles around the lepton are considered in the isolation if:
- they pass both thresholds:
pt_thresh : pt threshold
e_thresh : energy threshold
- they are in an active area around the lepton
areas should
on_areas and off_areas are lists of areas in which particles
around the should be considered
or ignored, respectively.
for a given particle
'''
self.on_areas = on_areas
if off_areas is None:
off_areas = []
self.off_areas = off_areas
self.pt_thresh = pt_thresh
self.e_thresh = e_thresh
self.label = label
def compute(self, lepton, particles):
'''Compute the isolation for lepton, using particles.
returns an IsolationInfo.
'''
isolation = IsolationInfo(self.label, lepton)
for ptc in particles:
if ptc is lepton:
continue
if ptc.e()<self.e_thresh or \
ptc.pt()<self.pt_thresh:
continue
is_on = False
for area in self.on_areas:
if area.is_inside(lepton, ptc):
is_on = True
break
if not is_on:
continue
for area in self.off_areas:
if area.is_inside(lepton, ptc):
is_on = False
break
if is_on:
isolation.add_particle(ptc)
return isolation | PypiClean |
/practice_problems_builder_ktraff-0.0.14-py3-none-any.whl/ppb/workers.py |
# Copyright (c) [year] [fullname]
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import logging
import re
import os
import subprocess
from pkg_resources import resource_filename
from typing import List
import yaml
from jinja2 import Environment, PackageLoader
from ppb.exceptions import NotImplementedException, DependenciesNotMetException
from ppb.logging import setup_logger, set_log_level
_log = setup_logger(__name__)
_j2 = Environment(
loader=PackageLoader('ppb', 'templates'),
)
def _check_if_executables_exist(*executables):
for executable in executables:
_log.info(f'Checking whether {executable} is installed.')
output = subprocess.run(['which', executable], stdout=subprocess.PIPE).stdout.decode('utf-8').strip()
_log.debug(output)
if executable not in output:
raise DependenciesNotMetException(f'No suitable "{executable}" found. Please install and try again.')
def _check_python_dependencies(python_version):
_check_if_executables_exist(python_version, 'virtualenv', 'make')
def _check_rust_dependencies():
_check_if_executables_exist('rustup', 'rustc', 'cargo')
version_info = subprocess.run(['rustc', '--version'], stdout=subprocess.PIPE).stdout.decode('utf-8').strip()
_log.debug(version_info)
if not version_info:
raise DependenciesNotMetException('rustc appears to be installed incorrectly.')
def _check_golang_dependencies():
_check_if_executables_exist('go')
def _setup_python(problem_path: str):
_log.info('Creating virtual environment for python')
_log.debug(subprocess.run(
['make', 'venv_create'],
stdout=subprocess.PIPE,
cwd=os.path.join(problem_path, 'python')
).stdout.decode('utf-8'))
_log.info('Building practice problems')
_log.debug(subprocess.run(
['make', 'build'],
stdout=subprocess.PIPE,
cwd=os.path.join(problem_path, 'python')
).stdout.decode('utf-8'))
def _setup_rust():
pass
def _setup_golang():
pass
class Worker:
def run(self):
raise NotImplementedException()
def _get_config(self, path: str):
with open(os.path.join(path, 'config.yml'), 'r') as infile:
return yaml.safe_load(infile.read())
class TemplateGenerator:
"""Helps workers to generate jinja templates.
This class is intended to be used as a mixin for worker classes.
"""
def _include_templates(self, path: str):
"""By default, every template is included. Override this in your class to modify it's behavior."""
return True
def _generate_templates(
self,
template_args: dict,
template_path: str,
destination_path: str,
) -> None:
"""Walks a directory containing templates and renders them to a destination path.
Args:
- template_args: a list of variables to be passed into jinja templates during rendering.
- template_path: the absolute path to the root folder where templates exist. This path is
relative to the root path of the `_j2` template loader.
- destination_path: where to save the templates
"""
full_template_path = resource_filename('ppb', f'templates/{template_path}')
for subdir, dirs, files in os.walk(full_template_path):
relative_path = re.sub(re.compile(f'{full_template_path}/?'), '', subdir)
# Substitute any variable names in paths
interpolated_path = relative_path.format(**template_args)
if self._include_templates(relative_path):
for a_dir in dirs:
if self._include_templates(os.path.join(relative_path, a_dir)):
dir_path = os.path.join(destination_path, interpolated_path, a_dir.format(**template_args))
_log.debug(f'Creating directory {dir_path}')
os.makedirs(dir_path)
for a_file in files:
template = _j2.get_template(os.path.join(template_path, relative_path, a_file))
# Remove the .j2 extension when saving the template to the destination
dest_file = re.sub(r'.j2$', '', a_file)
dest_path = os.path.join(destination_path, interpolated_path, dest_file)
with open(dest_path, 'w') as outfile:
_log.debug(f'Writing template {dest_path}')
outfile.write(template.render(**template_args))
class LogWorker(Worker):
def __init__(self, debug=False):
super().__init__()
self.debug = debug
def run(self):
if self.debug:
set_log_level(logging.DEBUG)
else:
set_log_level(logging.INFO)
_log.debug(f'Logging initialized for level {logging.getLevelName(_log.getEffectiveLevel())}')
class LanguageSetupMixin:
"""Used to setup dependencies for workers that use multiple languages."""
def _check_dependencies(self):
if 'python' in self.languages:
_check_python_dependencies(self.python_version)
if 'rust' in self.languages:
_check_rust_dependencies()
if 'golang' in self.languages:
_check_golang_dependencies()
def _setup_languages(self):
"""Configures each programming languages with necessary tools and environment"""
if 'python' in self.languages:
_setup_python(self.problem_path)
if 'rust' in self.languages:
_setup_rust()
if 'golang' in self.languages:
_setup_golang()
class NewProblemWorker(Worker, TemplateGenerator, LanguageSetupMixin):
"""Creates a workspace for a new practice problem"""
def __init__(
self,
problem_name: str,
languages: List[str],
target: str,
title: str,
description: str,
difficulty: int,
tags: List[str],
resources: List[str],
python_version: str,
author_name: str,
author_email: str,
rust_compiler_edition: str,
github_username: str
):
super().__init__()
self.problem_name = problem_name
self.languages = languages
self.target = target
self.title = title
self.description = description
self.difficulty = difficulty
self.tags = tags
self.resources = resources
self.python_version = python_version
self.author_name = author_name
self.author_email = author_email
self.rust_compiler_edition = rust_compiler_edition
self.github_username = github_username
@property
def problem_path(self):
return f'{os.path.join(self.target, self.problem_name)}'
def _include_templates(self, relative_path: str):
"""Returns True if the path to the provided template should be included when generating a new practice problem.
Templates for languages that have not been included will be skipped.
Arguments:
relative_path (str): A path to a template directory, relative to the root of the template folder.
This path shouldn't have any variable interpolation done on it, i.e. variable
names in the path like "{problem_name}" should be passed without any substitutions
made on them.
"""
match = re.match(r'\{problem_name\}\/(python|golang|rust)', relative_path)
if match: # We are parsing a template folder for a specific language
if match.group(1) in self.languages:
return True
return False
return True
def _create_workspace(self):
"""The main function for creating a new practice problem in the target destination"""
template_args = {
'problem_name': self.problem_name,
'title': self.title,
'description': self.description,
'difficulty': self.difficulty,
'tags': self.tags,
'resources': self.resources,
'python_version': self.python_version,
'author_name': self.author_name,
'author_email': self.author_email,
'github_username': self.github_username,
'rust_compiler_edition': self.rust_compiler_edition,
'target': self.target,
}
self._generate_templates(template_args, 'create', self.target)
def run(self):
_log.info(
f'Creating a new problem "{self.problem_name}" with {", ".join(self.languages)} '
f'into {self.problem_path}'
)
self._check_dependencies()
self._create_workspace()
self._setup_languages()
class AddProblemWorker(Worker, TemplateGenerator, LanguageSetupMixin):
"""Generates language template(s) for an existing practice problem."""
def __init__(
self,
languages: List[str],
target: str,
python_version: str,
rust_compiler_edition: str,
github_username: str
):
super().__init__()
self.languages = languages
self.target = target
self.python_version = python_version
self.rust_compiler_edition = rust_compiler_edition
self.github_username = github_username
def _create_workspace(self):
template_args = self._get_config(self.target)
template_args['python_version'] = self.python_version
template_args['rust_compiler_edition'] = self.rust_compiler_edition
template_args['github_username'] = self.github_username
template_args['target'] = self.target
for language in self.languages:
dest_path = os.path.join(self.target, language)
os.makedirs(dest_path)
self._generate_templates(template_args, f'create/{{problem_name}}/{language}', dest_path)
def run(self):
_log.info(f'Adding {", ".join(self.languages)} to {self.target}')
self._check_dependencies()
self._create_workspace()
self._setup_languages() | PypiClean |
/Mopidy-Spotmop-2.10.1.tar.gz/Mopidy-Spotmop-2.10.1/mopidy_spotmop/static/vendor/jquery.event.drop.js |
;(function($){ // secure $ jQuery alias
// Events: drop, dropstart, dropend
// add the jquery instance method
$.fn.drop = function( str, arg, opts ){
// figure out the event type
var type = typeof str == "string" ? str : "",
// figure out the event handler...
fn = $.isFunction( str ) ? str : $.isFunction( arg ) ? arg : null;
// fix the event type
if ( type.indexOf("drop") !== 0 )
type = "drop"+ type;
// were options passed
opts = ( str == fn ? arg : opts ) || {};
// trigger or bind event handler
return fn ? this.bind( type, opts, fn ) : this.trigger( type );
};
// DROP MANAGEMENT UTILITY
// returns filtered drop target elements, caches their positions
$.drop = function( opts ){
opts = opts || {};
// safely set new options...
drop.multi = opts.multi === true ? Infinity :
opts.multi === false ? 1 : !isNaN( opts.multi ) ? opts.multi : drop.multi;
drop.delay = opts.delay || drop.delay;
drop.tolerance = $.isFunction( opts.tolerance ) ? opts.tolerance :
opts.tolerance === null ? null : drop.tolerance;
drop.mode = opts.mode || drop.mode || 'intersect';
};
// local refs (increase compression)
var $event = $.event,
$special = $event.special,
// configure the drop special event
drop = $.event.special.drop = {
// these are the default settings
multi: 1, // allow multiple drop winners per dragged element
delay: 20, // async timeout delay
mode: 'overlap', // drop tolerance mode
// internal cache
targets: [],
// the key name for stored drop data
datakey: "dropdata",
// prevent bubbling for better performance
noBubble: true,
// count bound related events
add: function( obj ){
// read the interaction data
var data = $.data( this, drop.datakey );
// count another realted event
data.related += 1;
},
// forget unbound related events
remove: function(){
$.data( this, drop.datakey ).related -= 1;
},
// configure the interactions
setup: function(){
// check for related events
if ( $.data( this, drop.datakey ) )
return;
// initialize the drop element data
var data = {
related: 0,
active: [],
anyactive: 0,
winner: 0,
location: {}
};
// store the drop data on the element
$.data( this, drop.datakey, data );
// store the drop target in internal cache
drop.targets.push( this );
},
// destroy the configure interaction
teardown: function(){
var data = $.data( this, drop.datakey ) || {};
// check for related events
if ( data.related )
return;
// remove the stored data
$.removeData( this, drop.datakey );
// reference the targeted element
var element = this;
// remove from the internal cache
drop.targets = $.grep( drop.targets, function( target ){
return ( target !== element );
});
},
// shared event handler
handler: function( event, dd ){
// local vars
var results, $targets;
// make sure the right data is available
if ( !dd )
return;
// handle various events
switch ( event.type ){
// draginit, from $.event.special.drag
case 'mousedown': // DROPINIT >>
case 'touchstart': // DROPINIT >>
// collect and assign the drop targets
$targets = $( drop.targets );
if ( typeof dd.drop == "string" )
$targets = $targets.filter( dd.drop );
// reset drop data winner properties
$targets.each(function(){
var data = $.data( this, drop.datakey );
data.active = [];
data.anyactive = 0;
data.winner = 0;
});
// set available target elements
dd.droppable = $targets;
// activate drop targets for the initial element being dragged
$special.drag.hijack( event, "dropinit", dd );
break;
// drag, from $.event.special.drag
case 'mousemove': // TOLERATE >>
case 'touchmove': // TOLERATE >>
drop.event = event; // store the mousemove event
if ( !drop.timer )
// monitor drop targets
drop.tolerate( dd );
break;
// dragend, from $.event.special.drag
case 'mouseup': // DROP >> DROPEND >>
case 'touchend': // DROP >> DROPEND >>
drop.timer = clearTimeout( drop.timer ); // delete timer
if ( dd.propagates ){
$special.drag.hijack( event, "drop", dd );
$special.drag.hijack( event, "dropend", dd );
}
break;
}
},
// returns the location positions of an element
locate: function( elem, index ){
var data = $.data( elem, drop.datakey ),
$elem = $( elem ),
posi = $elem.offset() || {},
height = $elem.outerHeight(),
width = $elem.outerWidth(),
location = {
elem: elem,
width: width,
height: height,
top: posi.top,
left: posi.left,
right: posi.left + width,
bottom: posi.top + height
};
// drag elements might not have dropdata
if ( data ){
data.location = location;
data.index = index;
data.elem = elem;
}
return location;
},
// test the location positions of an element against another OR an X,Y coord
contains: function( target, test ){ // target { location } contains test [x,y] or { location }
return ( ( test[0] || test.left ) >= target.left && ( test[0] || test.right ) <= target.right
&& ( test[1] || test.top ) >= target.top && ( test[1] || test.bottom ) <= target.bottom );
},
// stored tolerance modes
modes: { // fn scope: "$.event.special.drop" object
// target with mouse wins, else target with most overlap wins
'intersect': function( event, proxy, target ){
return this.contains( target, [ event.pageX, event.pageY ] ) ? // check cursor
1e9 : this.modes.overlap.apply( this, arguments ); // check overlap
},
// target with most overlap wins
'overlap': function( event, proxy, target ){
// calculate the area of overlap...
return Math.max( 0, Math.min( target.bottom, proxy.bottom ) - Math.max( target.top, proxy.top ) )
* Math.max( 0, Math.min( target.right, proxy.right ) - Math.max( target.left, proxy.left ) );
},
// proxy is completely contained within target bounds
'fit': function( event, proxy, target ){
return this.contains( target, proxy ) ? 1 : 0;
},
// center of the proxy is contained within target bounds
'middle': function( event, proxy, target ){
return this.contains( target, [ proxy.left + proxy.width * .5, proxy.top + proxy.height * .5 ] ) ? 1 : 0;
}
},
// sort drop target cache by by winner (dsc), then index (asc)
sort: function( a, b ){
return ( b.winner - a.winner ) || ( a.index - b.index );
},
// async, recursive tolerance execution
tolerate: function( dd ){
// declare local refs
var i, drp, drg, data, arr, len, elem,
// interaction iteration variables
x = 0, ia, end = dd.interactions.length,
// determine the mouse coords
xy = [ drop.event.pageX, drop.event.pageY ],
// custom or stored tolerance fn
tolerance = drop.tolerance || drop.modes[ drop.mode ];
// go through each passed interaction...
do if ( ia = dd.interactions[x] ){
// check valid interaction
if ( !ia )
return;
// initialize or clear the drop data
ia.drop = [];
// holds the drop elements
arr = [];
len = ia.droppable.length;
// determine the proxy location, if needed
if ( tolerance )
drg = drop.locate( ia.proxy );
// reset the loop
i = 0;
// loop each stored drop target
do if ( elem = ia.droppable[i] ){
data = $.data( elem, drop.datakey );
drp = data.location;
if ( !drp ) continue;
// find a winner: tolerance function is defined, call it
data.winner = tolerance ? tolerance.call( drop, drop.event, drg, drp )
// mouse position is always the fallback
: drop.contains( drp, xy ) ? 1 : 0;
arr.push( data );
} while ( ++i < len ); // loop
// sort the drop targets
arr.sort( drop.sort );
// reset the loop
i = 0;
// loop through all of the targets again
do if ( data = arr[ i ] ){
// winners...
if ( data.winner && ia.drop.length < drop.multi ){
// new winner... dropstart
if ( !data.active[x] && !data.anyactive ){
// check to make sure that this is not prevented
if ( $special.drag.hijack( drop.event, "dropstart", dd, x, data.elem )[0] !== false ){
data.active[x] = 1;
data.anyactive += 1;
}
// if false, it is not a winner
else
data.winner = 0;
}
// if it is still a winner
if ( data.winner )
ia.drop.push( data.elem );
}
// losers...
else if ( data.active[x] && data.anyactive == 1 ){
// former winner... dropend
$special.drag.hijack( drop.event, "dropend", dd, x, data.elem );
data.active[x] = 0;
data.anyactive -= 1;
}
} while ( ++i < len ); // loop
} while ( ++x < end ) // loop
// check if the mouse is still moving or is idle
if ( drop.last && xy[0] == drop.last.pageX && xy[1] == drop.last.pageY )
delete drop.timer; // idle, don't recurse
else // recurse
drop.timer = setTimeout(function(){
drop.tolerate( dd );
}, drop.delay );
// remember event, to compare idleness
drop.last = drop.event;
}
};
// share the same special event configuration with related events...
$special.dropinit = $special.dropstart = $special.dropend = drop;
})(jQuery); // confine scope | PypiClean |
/zephyrus-sc2-parser-0.3.8.tar.gz/zephyrus-sc2-parser-0.3.8/zephyrus_sc2_parser/s2protocol_fixed/versions/protocol16755.py |
from zephyrus_sc2_parser.s2protocol_fixed.decoders import *
# Decoding instructions for each protocol type.
typeinfos = [
('_int',[(0,7)]), #0
('_int',[(0,4)]), #1
('_int',[(0,6)]), #2
('_int',[(0,14)]), #3
('_int',[(0,22)]), #4
('_int',[(0,32)]), #5
('_choice',[(0,2),{0:('m_uint6',2),1:('m_uint14',3),2:('m_uint22',4),3:('m_uint32',5)}]), #6
('_blob',[(0,8)]), #7
('_int',[(0,8)]), #8
('_struct',[[('m_flags',8,0),('m_major',8,1),('m_minor',8,2),('m_revision',8,3),('m_build',5,4),('m_baseBuild',5,5)]]), #9
('_int',[(0,3)]), #10
('_struct',[[('m_signature',7,0),('m_version',9,1),('m_type',10,2),('m_elapsedGameLoops',5,3)]]), #11
('_fourcc',[]), #12
('_blob',[(0,7)]), #13
('_int',[(0,64)]), #14
('_struct',[[('m_region',8,0),('m_programId',12,1),('m_realm',5,2),('m_name',13,3),('m_id',14,4)]]), #15
('_struct',[[('m_a',8,0),('m_r',8,1),('m_g',8,2),('m_b',8,3)]]), #16
('_int',[(0,2)]), #17
('_struct',[[('m_name',7,0),('m_toon',15,1),('m_race',7,2),('m_color',16,3),('m_control',8,4),('m_teamId',1,5),('m_handicap',0,6),('m_observe',17,7),('m_result',17,8)]]), #18
('_array',[(0,5),18]), #19
('_optional',[19]), #20
('_blob',[(0,10)]), #21
('_blob',[(0,11)]), #22
('_struct',[[('m_file',22,0)]]), #23
('_bool',[]), #24
('_int',[(-9223372036854775808,64)]), #25
('_blob',[(0,12)]), #26
('_blob',[(40,0)]), #27
('_array',[(0,4),27]), #28
('_optional',[28]), #29
('_struct',[[('m_playerList',20,0),('m_title',21,1),('m_difficulty',7,2),('m_thumbnail',23,3),('m_isBlizzardMap',24,4),('m_timeUTC',25,5),('m_timeLocalOffset',25,6),('m_description',26,7),('m_imageFilePath',22,8),('m_mapFileName',22,9),('m_cacheHandles',29,10),('m_miniSave',24,11),('m_gameSpeed',10,12),('m_defaultDifficulty',2,13)]]), #30
('_optional',[8]), #31
('_struct',[[('m_race',31,-1)]]), #32
('_struct',[[('m_team',31,-1)]]), #33
('_struct',[[('m_name',7,-7),('m_randomSeed',5,-6),('m_racePreference',32,-5),('m_teamPreference',33,-4),('m_testMap',24,-3),('m_testAuto',24,-2),('m_observe',17,-1)]]), #34
('_array',[(0,5),34]), #35
('_struct',[[('m_lockTeams',24,-11),('m_teamsTogether',24,-10),('m_advancedSharedControl',24,-9),('m_randomRaces',24,-8),('m_battleNet',24,-7),('m_amm',24,-6),('m_ranked',24,-5),('m_noVictoryOrDefeat',24,-4),('m_fog',17,-3),('m_observers',17,-2),('m_userDifficulty',17,-1)]]), #36
('_int',[(0,5)]), #37
('_int',[(1,4)]), #38
('_int',[(1,5)]), #39
('_int',[(1,8)]), #40
('_bitarray',[(0,6)]), #41
('_bitarray',[(0,8)]), #42
('_bitarray',[(0,2)]), #43
('_struct',[[('m_allowedColors',41,-5),('m_allowedRaces',42,-4),('m_allowedDifficulty',41,-3),('m_allowedControls',42,-2),('m_allowedObserveTypes',43,-1)]]), #44
('_array',[(0,5),44]), #45
('_struct',[[('m_randomValue',5,-23),('m_gameCacheName',21,-22),('m_gameOptions',36,-21),('m_gameSpeed',10,-20),('m_gameType',10,-19),('m_maxUsers',37,-18),('m_maxObservers',37,-17),('m_maxPlayers',37,-16),('m_maxTeams',38,-15),('m_maxColors',39,-14),('m_maxRaces',40,-13),('m_maxControls',40,-12),('m_mapSizeX',8,-11),('m_mapSizeY',8,-10),('m_mapFileSyncChecksum',5,-9),('m_mapFileName',22,-8),('m_mapAuthorName',7,-7),('m_modFileSyncChecksum',5,-6),('m_slotDescriptions',45,-5),('m_defaultDifficulty',2,-4),('m_cacheHandles',28,-3),('m_isBlizzardMap',24,-2),('m_isPremadeFFA',24,-1)]]), #46
('_optional',[1]), #47
('_optional',[37]), #48
('_struct',[[('m_color',48,-1)]]), #49
('_array',[(0,5),5]), #50
('_struct',[[('m_control',8,-9),('m_userId',47,-8),('m_teamId',1,-7),('m_colorPref',49,-6),('m_racePref',32,-5),('m_difficulty',2,-4),('m_handicap',0,-3),('m_observe',17,-2),('m_rewards',50,-1)]]), #51
('_array',[(0,5),51]), #52
('_struct',[[('m_phase',10,-9),('m_maxUsers',37,-8),('m_maxObservers',37,-7),('m_slots',52,-6),('m_randomSeed',5,-5),('m_hostUserId',47,-4),('m_isSinglePlayer',24,-3),('m_gameDuration',5,-2),('m_defaultDifficulty',2,-1)]]), #53
('_struct',[[('m_userInitialData',35,-3),('m_gameDescription',46,-2),('m_lobbyState',53,-1)]]), #54
('_struct',[[('m_syncLobbyState',54,-1)]]), #55
('_struct',[[('m_name',13,-5)]]), #56
('_blob',[(0,6)]), #57
('_struct',[[('m_name',57,-5)]]), #58
('_struct',[[('m_name',57,-7),('m_type',5,-6),('m_data',13,-5)]]), #59
('_struct',[[('m_type',5,-7),('m_name',57,-6),('m_data',26,-5)]]), #60
('_struct',[[('m_developmentCheatsEnabled',24,-8),('m_multiplayerCheatsEnabled',24,-7),('m_syncChecksummingEnabled',24,-6),('m_isMapToMapTransition',24,-5)]]), #61
('_struct',[[]]), #62
('_struct',[[('m_fileName',22,-9),('m_automatic',24,-8),('m_overwrite',24,-7),('m_name',7,-6),('m_description',21,-5)]]), #63
('_int',[(-2147483648,32)]), #64
('_struct',[[('x',64,-2),('y',64,-1)]]), #65
('_struct',[[('m_point',65,-4),('m_time',64,-3),('m_verb',21,-2),('m_arguments',21,-1)]]), #66
('_struct',[[('m_data',66,-5)]]), #67
('_int',[(0,17)]), #68
('_int',[(0,16)]), #69
('_struct',[[('m_abilLink',69,-3),('m_abilCmdIndex',37,-2),('m_abilCmdData',31,-1)]]), #70
('_optional',[70]), #71
('_null',[]), #72
('_int',[(0,20)]), #73
('_struct',[[('x',73,-3),('y',73,-2),('z',64,-1)]]), #74
('_struct',[[('m_targetUnitFlags',8,-6),('m_timer',8,-5),('m_tag',5,-4),('m_snapshotUnitLink',69,-3),('m_snapshotPlayerId',47,-2),('m_snapshotPoint',74,-1)]]), #75
('_choice',[(0,2),{0:('None',72),1:('TargetPoint',74),2:('TargetUnit',75),3:('Data',5)}]), #76
('_optional',[5]), #77
('_struct',[[('m_cmdFlags',68,-8),('m_abil',71,-7),('m_data',76,-6),('m_otherUnit',77,-5)]]), #78
('_array',[(0,8),8]), #79
('_choice',[(0,2),{0:('None',72),1:('Mask',42),2:('OneIndices',79),3:('ZeroIndices',79)}]), #80
('_struct',[[('m_unitLink',69,-3),('m_intraSubgroupPriority',8,-2),('m_count',8,-1)]]), #81
('_array',[(0,8),81]), #82
('_array',[(0,8),5]), #83
('_struct',[[('m_subgroupIndex',8,-4),('m_removeMask',80,-3),('m_addSubgroups',82,-2),('m_addUnitTags',83,-1)]]), #84
('_struct',[[('m_controlGroupId',1,-6),('m_delta',84,-5)]]), #85
('_struct',[[('m_controlGroupIndex',1,-7),('m_controlGroupUpdate',17,-6),('m_mask',80,-5)]]), #86
('_struct',[[('m_count',8,-6),('m_subgroupCount',8,-5),('m_activeSubgroupIndex',8,-4),('m_unitTagsChecksum',5,-3),('m_subgroupIndicesChecksum',5,-2),('m_subgroupsChecksum',5,-1)]]), #87
('_struct',[[('m_controlGroupId',1,-6),('m_selectionSyncData',87,-5)]]), #88
('_array',[(0,3),64]), #89
('_struct',[[('m_recipientId',1,-6),('m_resources',89,-5)]]), #90
('_struct',[[('m_chatMessage',21,-5)]]), #91
('_int',[(-128,8)]), #92
('_struct',[[('x',64,-3),('y',64,-2),('z',64,-1)]]), #93
('_struct',[[('m_beacon',92,-11),('m_ally',92,-10),('m_autocast',92,-9),('m_targetUnitTag',5,-8),('m_targetUnitSnapshotUnitLink',69,-7),('m_targetUnitSnapshotPlayerId',47,-6),('m_targetPoint',93,-5)]]), #94
('_struct',[[('m_speed',10,-5)]]), #95
('_struct',[[('m_delta',92,-5)]]), #96
('_struct',[[('m_verb',21,-6),('m_arguments',21,-5)]]), #97
('_struct',[[('m_alliance',5,-6),('m_control',5,-5)]]), #98
('_struct',[[('m_unitTag',5,-5)]]), #99
('_struct',[[('m_unitTag',5,-6),('m_flags',8,-5)]]), #100
('_struct',[[('m_conversationId',64,-6),('m_replyId',64,-5)]]), #101
('_struct',[[('m_purchaseItemId',64,-5)]]), #102
('_struct',[[('m_difficultyLevel',64,-5)]]), #103
('_choice',[(0,3),{0:('None',72),1:('Checked',24),2:('ValueChanged',5),3:('SelectionChanged',64),4:('TextChanged',22)}]), #104
('_struct',[[('m_controlId',64,-7),('m_eventType',64,-6),('m_eventData',104,-5)]]), #105
('_struct',[[('m_soundHash',5,-6),('m_length',5,-5)]]), #106
('_struct',[[('m_soundHash',83,-2),('m_length',83,-1)]]), #107
('_struct',[[('m_syncInfo',107,-5)]]), #108
('_struct',[[('m_sound',5,-5)]]), #109
('_struct',[[('m_transmissionId',64,-5)]]), #110
('_struct',[[('x',69,-2),('y',69,-1)]]), #111
('_optional',[69]), #112
('_struct',[[('m_target',111,-8),('m_distance',112,-7),('m_pitch',112,-6),('m_yaw',112,-5)]]), #113
('_int',[(0,1)]), #114
('_struct',[[('m_skipType',114,-5)]]), #115
('_struct',[[('m_button',5,-11),('m_down',24,-10),('m_posXUI',5,-9),('m_posYUI',5,-8),('m_posXWorld',64,-7),('m_posYWorld',64,-6),('m_posZWorld',64,-5)]]), #116
('_struct',[[('m_soundtrack',5,-5)]]), #117
('_struct',[[('m_planetId',64,-5)]]), #118
('_struct',[[('m_key',92,-6),('m_flags',92,-5)]]), #119
('_struct',[[('m_resources',89,-5)]]), #120
('_struct',[[('m_fulfillRequestId',64,-5)]]), #121
('_struct',[[('m_cancelRequestId',64,-5)]]), #122
('_struct',[[('m_researchItemId',64,-5)]]), #123
('_struct',[[('m_laggingPlayerId',1,-5)]]), #124
('_struct',[[('m_mercenaryId',64,-5)]]), #125
('_struct',[[('m_battleReportId',64,-6),('m_difficultyLevel',64,-5)]]), #126
('_struct',[[('m_battleReportId',64,-5)]]), #127
('_int',[(0,19)]), #128
('_struct',[[('m_decrementMs',128,-5)]]), #129
('_struct',[[('m_portraitId',64,-5)]]), #130
('_struct',[[('m_functionName',13,-5)]]), #131
('_struct',[[('m_result',64,-5)]]), #132
('_struct',[[('m_gameMenuItemIndex',64,-5)]]), #133
('_struct',[[('m_reason',92,-5)]]), #134
('_struct',[[('m_purchaseCategoryId',64,-5)]]), #135
('_struct',[[('m_button',69,-5)]]), #136
('_struct',[[('m_recipient',17,-3),('m_string',22,-2)]]), #137
('_struct',[[('m_recipient',17,-3),('m_point',65,-2)]]), #138
('_struct',[[('m_progress',64,-2)]]), #139
]
# Map from protocol NNet.Game.*Event eventid to (typeid, name)
game_event_types = {
5: (62, 'NNet.Game.SUserFinishedLoadingSyncEvent'),
7: (56, 'NNet.Game.SBankFileEvent'),
8: (58, 'NNet.Game.SBankSectionEvent'),
9: (59, 'NNet.Game.SBankKeyEvent'),
10: (60, 'NNet.Game.SBankValueEvent'),
11: (61, 'NNet.Game.SUserOptionsEvent'),
22: (63, 'NNet.Game.SSaveGameEvent'),
23: (62, 'NNet.Game.SSaveGameDoneEvent'),
25: (62, 'NNet.Game.SPlayerLeaveEvent'),
26: (67, 'NNet.Game.SGameCheatEvent'),
27: (78, 'NNet.Game.SCmdEvent'),
28: (85, 'NNet.Game.SSelectionDeltaEvent'),
29: (86, 'NNet.Game.SControlGroupUpdateEvent'),
30: (88, 'NNet.Game.SSelectionSyncCheckEvent'),
31: (90, 'NNet.Game.SResourceTradeEvent'),
32: (91, 'NNet.Game.STriggerChatMessageEvent'),
33: (94, 'NNet.Game.SAICommunicateEvent'),
34: (95, 'NNet.Game.SSetAbsoluteGameSpeedEvent'),
35: (96, 'NNet.Game.SAddAbsoluteGameSpeedEvent'),
37: (97, 'NNet.Game.SBroadcastCheatEvent'),
38: (98, 'NNet.Game.SAllianceEvent'),
39: (99, 'NNet.Game.SUnitClickEvent'),
40: (100, 'NNet.Game.SUnitHighlightEvent'),
41: (101, 'NNet.Game.STriggerReplySelectedEvent'),
44: (62, 'NNet.Game.STriggerSkippedEvent'),
45: (106, 'NNet.Game.STriggerSoundLengthQueryEvent'),
46: (109, 'NNet.Game.STriggerSoundOffsetEvent'),
47: (110, 'NNet.Game.STriggerTransmissionOffsetEvent'),
48: (110, 'NNet.Game.STriggerTransmissionCompleteEvent'),
49: (113, 'NNet.Game.SCameraUpdateEvent'),
50: (62, 'NNet.Game.STriggerAbortMissionEvent'),
51: (102, 'NNet.Game.STriggerPurchaseMadeEvent'),
52: (62, 'NNet.Game.STriggerPurchaseExitEvent'),
53: (103, 'NNet.Game.STriggerPlanetMissionLaunchedEvent'),
54: (62, 'NNet.Game.STriggerPlanetPanelCanceledEvent'),
55: (105, 'NNet.Game.STriggerDialogControlEvent'),
56: (108, 'NNet.Game.STriggerSoundLengthSyncEvent'),
57: (115, 'NNet.Game.STriggerConversationSkippedEvent'),
58: (116, 'NNet.Game.STriggerMouseClickedEvent'),
63: (62, 'NNet.Game.STriggerPlanetPanelReplayEvent'),
64: (117, 'NNet.Game.STriggerSoundtrackDoneEvent'),
65: (118, 'NNet.Game.STriggerPlanetMissionSelectedEvent'),
66: (119, 'NNet.Game.STriggerKeyPressedEvent'),
67: (131, 'NNet.Game.STriggerMovieFunctionEvent'),
68: (62, 'NNet.Game.STriggerPlanetPanelBirthCompleteEvent'),
69: (62, 'NNet.Game.STriggerPlanetPanelDeathCompleteEvent'),
70: (120, 'NNet.Game.SResourceRequestEvent'),
71: (121, 'NNet.Game.SResourceRequestFulfillEvent'),
72: (122, 'NNet.Game.SResourceRequestCancelEvent'),
73: (62, 'NNet.Game.STriggerResearchPanelExitEvent'),
74: (62, 'NNet.Game.STriggerResearchPanelPurchaseEvent'),
75: (123, 'NNet.Game.STriggerResearchPanelSelectionChangedEvent'),
76: (124, 'NNet.Game.SLagMessageEvent'),
77: (62, 'NNet.Game.STriggerMercenaryPanelExitEvent'),
78: (62, 'NNet.Game.STriggerMercenaryPanelPurchaseEvent'),
79: (125, 'NNet.Game.STriggerMercenaryPanelSelectionChangedEvent'),
80: (62, 'NNet.Game.STriggerVictoryPanelExitEvent'),
81: (62, 'NNet.Game.STriggerBattleReportPanelExitEvent'),
82: (126, 'NNet.Game.STriggerBattleReportPanelPlayMissionEvent'),
83: (127, 'NNet.Game.STriggerBattleReportPanelPlaySceneEvent'),
84: (127, 'NNet.Game.STriggerBattleReportPanelSelectionChangedEvent'),
85: (103, 'NNet.Game.STriggerVictoryPanelPlayMissionAgainEvent'),
86: (62, 'NNet.Game.STriggerMovieStartedEvent'),
87: (62, 'NNet.Game.STriggerMovieFinishedEvent'),
88: (129, 'NNet.Game.SDecrementGameTimeRemainingEvent'),
89: (130, 'NNet.Game.STriggerPortraitLoadedEvent'),
90: (132, 'NNet.Game.STriggerCustomDialogDismissedEvent'),
91: (133, 'NNet.Game.STriggerGameMenuItemSelectedEvent'),
92: (134, 'NNet.Game.STriggerCameraMoveEvent'),
93: (102, 'NNet.Game.STriggerPurchasePanelSelectedPurchaseItemChangedEvent'),
94: (135, 'NNet.Game.STriggerPurchasePanelSelectedPurchaseCategoryChangedEvent'),
95: (136, 'NNet.Game.STriggerButtonPressedEvent'),
96: (62, 'NNet.Game.STriggerGameCreditsFinishedEvent'),
}
# The typeid of the NNet.Game.EEventId enum.
game_eventid_typeid = 0
# Map from protocol NNet.Game.*Message eventid to (typeid, name)
message_event_types = {
0: (137, 'NNet.Game.SChatMessage'),
1: (138, 'NNet.Game.SPingMessage'),
2: (139, 'NNet.Game.SLoadingProgressMessage'),
3: (62, 'NNet.Game.SServerPingMessage'),
}
# The typeid of the NNet.Game.EMessageId enum.
message_eventid_typeid = 1
# Map from protocol NNet.Replay.Tracker.*Event eventid to (typeid, name)
tracker_event_types = {
}
# NOTE: older builds may not support some types and the generated methods
# may fail to function properly, if specific backwards compatibility is
# needed these values should be tested against for None
# The typeid of the NNet.Replay.Tracker.EEventId enum.
tracker_eventid_typeid = None
# The typeid of NNet.SVarUint32 (the type used to encode gameloop deltas).
svaruint32_typeid = 6
# The typeid of NNet.Replay.SGameUserId (the type used to encode player ids).
replay_userid_typeid = None
# The typeid of NNet.Replay.SHeader (the type used to store replay game version and length).
replay_header_typeid = 11
# The typeid of NNet.Game.SDetails (the type used to store overall replay details).
game_details_typeid = 30
# The typeid of NNet.Replay.SInitData (the type used to store the inital lobby).
replay_initdata_typeid = 55
def _varuint32_value(value):
# Returns the numeric value from a SVarUint32 instance.
for v in value.values():
return v
return 0
def _decode_event_stream(decoder, eventid_typeid, event_types, decode_user_id):
# Decodes events prefixed with a gameloop and possibly userid
gameloop = 0
while not decoder.done():
start_bits = decoder.used_bits()
# decode the gameloop delta before each event
delta = _varuint32_value(decoder.instance(svaruint32_typeid))
gameloop += delta
# decode the userid before each event
if decode_user_id:
userid = decoder.instance(replay_userid_typeid)
# decode the event id
eventid = decoder.instance(eventid_typeid)
typeid, typename = event_types.get(eventid, (None, None))
if typeid is None:
raise CorruptedError('eventid({}) at {}'.format(eventid, decoder))
# decode the event struct instance
event = decoder.instance(typeid)
event['_event'] = typename
event['_eventid'] = eventid
# insert gameloop and userid
event['_gameloop'] = gameloop
if decode_user_id:
event['_userid'] = userid
# the next event is byte aligned
decoder.byte_align()
# insert bits used in stream
event['_bits'] = decoder.used_bits() - start_bits
yield event
def decode_replay_game_events(contents):
"""Decodes and yields each game event from the contents byte string."""
decoder = BitPackedDecoder(contents, typeinfos)
for event in _decode_event_stream(decoder,
game_eventid_typeid,
game_event_types,
decode_user_id=True):
yield event
def decode_replay_message_events(contents):
"""Decodes and yields each message event from the contents byte string."""
decoder = BitPackedDecoder(contents, typeinfos)
for event in _decode_event_stream(decoder,
message_eventid_typeid,
message_event_types,
decode_user_id=True):
yield event
def decode_replay_tracker_events(contents):
"""Decodes and yields each tracker event from the contents byte string."""
decoder = VersionedDecoder(contents, typeinfos)
for event in _decode_event_stream(decoder,
tracker_eventid_typeid,
tracker_event_types,
decode_user_id=False):
yield event
def decode_replay_header(contents):
"""Decodes and return the replay header from the contents byte string."""
decoder = VersionedDecoder(contents, typeinfos)
return decoder.instance(replay_header_typeid)
def decode_replay_details(contents):
"""Decodes and returns the game details from the contents byte string."""
decoder = VersionedDecoder(contents, typeinfos)
return decoder.instance(game_details_typeid)
def decode_replay_initdata(contents):
"""Decodes and return the replay init data from the contents byte string."""
decoder = BitPackedDecoder(contents, typeinfos)
return decoder.instance(replay_initdata_typeid)
def decode_replay_attributes_events(contents):
"""Decodes and yields each attribute from the contents byte string."""
buffer = BitPackedBuffer(contents, 'little')
attributes = {}
if not buffer.done():
attributes['source'] = buffer.read_bits(8)
attributes['mapNamespace'] = buffer.read_bits(32)
count = buffer.read_bits(32)
attributes['scopes'] = {}
while not buffer.done():
value = {}
value['namespace'] = buffer.read_bits(32)
value['attrid'] = attrid = buffer.read_bits(32)
scope = buffer.read_bits(8)
value['value'] = buffer.read_aligned_bytes(4)[::-1].strip(b'\x00')
if not scope in attributes['scopes']:
attributes['scopes'][scope] = {}
if not attrid in attributes['scopes'][scope]:
attributes['scopes'][scope][attrid] = []
attributes['scopes'][scope][attrid].append(value)
return attributes
def unit_tag(unitTagIndex, unitTagRecycle):
return (unitTagIndex << 18) + unitTagRecycle
def unit_tag_index(unitTag):
return (unitTag >> 18) & 0x00003fff
def unit_tag_recycle(unitTag):
return (unitTag) & 0x0003ffff | PypiClean |
/visual-automata-demo-0.0.4.tar.gz/visual-automata-demo-0.0.4/visual_automata/fa/nfa.py | import copy
import random
import sys
from typing import Generator, Union
import numpy as np
import pandas as pd
from automata.fa.nfa import NFA
from colormath.color_objects import sRGBColor
from forbiddenfruit import curse
from graphviz import Digraph
from IPython.display import display
from pandas import DataFrame
from visual_automata.colors import (
create_palette,
hex_to_rgb_color,
list_cycler,
)
sys.setrecursionlimit(10 ** 6)
def deepcopy(self) -> dict:
return copy.deepcopy(self)
curse(dict, "deepcopy", deepcopy)
class VisualNFA:
"""A wrapper for an automata-lib non-deterministic finite automaton."""
def __init__(
self,
nfa: NFA = None,
*,
states: set = None,
input_symbols: set = None,
transitions: dict = None,
initial_state: str = None,
final_states: set = None,
):
if nfa:
self.nfa = nfa.copy()
else:
if not states:
states = {*transitions.keys()}
if not input_symbols:
input_symbols = set()
for v in transitions.values():
symbols = [*v.keys()]
for symbol in symbols:
if symbol != "":
input_symbols.add(symbol)
self.nfa = NFA(
states=states.copy(),
input_symbols=input_symbols.copy(),
transitions=transitions.deepcopy(),
initial_state=initial_state,
final_states=final_states.copy(),
)
self.nfa.validate()
# -------------------------------------------------------------------------
# Mimic behavior of automata-lib NFA.
@property
def states(self) -> set:
"""Pass on .states from the NFA"""
return self.nfa.states
@states.setter
def states(self, states: set):
"""Set .states on the NFA"""
self.nfa.states = states
@property
def input_symbols(self) -> set:
"""Pass on .input_symbols from the NFA"""
return self.nfa.input_symbols
@input_symbols.setter
def input_symbols(self, input_symbols: set):
"""Set .input_symbols on the NFA"""
self.nfa.input_symbols = input_symbols
@property
def transitions(self) -> dict:
"""Pass on .transitions from the NFA"""
return self.nfa.transitions
@transitions.setter
def transitions(self, transitions: dict):
"""Set .transitions on the NFA"""
self.nfa.transitions = transitions
@property
def initial_state(self) -> str:
"""Pass on .initial_state from the NFA"""
return self.nfa.initial_state
@initial_state.setter
def initial_state(self, initial_state: str):
"""Set .initial_state on the NFA"""
self.nfa.initial_state = initial_state
@property
def final_states(self) -> set:
"""Pass on .final_states from the NFA"""
return self.nfa.final_states
@final_states.setter
def final_states(self, final_states: set):
"""Set .final_states on the NFA"""
self.nfa.final_states = final_states
def copy(self):
"""Create a deep copy of the automaton."""
return self.__class__(**vars(self))
def validate(self) -> bool:
"""Return True if this NFA is internally consistent."""
return self.nfa.validate()
def accepts_input(self, input_str: str) -> bool:
"""Return True if this automaton accepts the given input."""
return self.nfa.accepts_input(input_str=input_str)
def read_input(self, input_str: str) -> set:
"""
Check if the given string is accepted by this automaton.
Return the automaton's final configuration if this string is valid.
"""
return self.nfa.read_input(input_str=input_str)
def read_input_stepwise(self, input_str: str) -> Generator:
"""
Check if the given string is accepted by this automaton.
Return the automaton's final configuration if this string is valid.
"""
return self.nfa.read_input_stepwise(input_str=input_str)
def _get_lambda_closure(self, start_state: str) -> set:
"""
Return the lambda closure for the given state.
The lambda closure of a state q is the set containing q, along with
every state that can be reached from q by following only lambda
transitions.
"""
return self.nfa._get_lambda_closure(start_state=start_state)
def _get_next_current_states(
self, current_states: set, input_symbol: str
) -> set:
"""Return the next set of current states given the current set."""
return self.nfa._get_next_current_states(current_states, input_symbol)
# -------------------------------------------------------------------------
# Define new attributes and their helper methods.
@property
def table(self) -> DataFrame:
"""
Generates a transition table of the given VisualNFA.
Returns:
DataFrame: A transition table of the VisualNFA.
"""
final_states = "".join(self.nfa.final_states)
transitions = self._add_lambda(
all_transitions=self.nfa.transitions,
input_symbols=self.nfa.input_symbols,
)
table: dict = {}
for state, transition in sorted(transitions.items()):
if state == self.nfa.initial_state and state in final_states:
state = "→*" + state
elif state == self.nfa.initial_state:
state = "→" + state
elif state in final_states:
state = "*" + state
row: dict = {}
for input_symbol, next_states in transition.items():
cell: list = []
for next_state in sorted(next_states):
if next_state in final_states:
cell.append("*" + next_state)
else:
cell.append(next_state)
if not cell and len(cell) == 1:
cell = np.nan
elif len(cell) == 1:
cell = cell.pop()
else:
cell = "{" + ",".join(cell) + "}"
row[input_symbol] = cell
table[state] = row
table = pd.DataFrame.from_dict(table).fillna("∅").T
table = table.reindex(sorted(table.columns), axis=1)
return table
@staticmethod
def _add_lambda(all_transitions: dict, input_symbols: str) -> dict:
"""
Replacing '' key name for empty string (lambda/epsilon) transitions.
Args:
all_transitions (dict): The NFA's transitions with '' for lambda transitions.
input_symbols (str): The NFA's input symbols/alphabet.
Returns:
dict: Transitions with λ for lambda transitions
"""
all_transitions = all_transitions.deepcopy()
input_symbols = input_symbols.copy()
# Replacing '' key name for empty string (lambda/epsilon) transitions.
for transitions in all_transitions.values():
for state, transition in list(transitions.items()):
if state == "":
transitions["λ"] = transition
del transitions[""]
input_symbols.add("λ")
return all_transitions
# -------------------------------------------------------------------------
# Define new class methods and their helper methods.
@property
def _lambda_transition_exists(self) -> bool:
"""
Checks if the nfa has lambda transitions.
Returns:
bool: If the nfa has lambda transitions, returns True; else False.
"""
status = False
for transitions in self.nfa.transitions.values():
if "" in transitions:
return True
return status
@classmethod
def eliminate_lambda(cls, nfa):
"""
Eliminates lambda transitions, and returns a new nfa.
Args:
nfa (VisualNFA): A VisualNFA object.
Returns:
VisualNFA: A VisualNFA object without lambda transitions.
"""
if nfa._lambda_transition_exists:
nfa_lambda_eliminated = nfa.copy()
for state in sorted(nfa_lambda_eliminated.transitions):
# Find lambda closure for the state.
closures = nfa_lambda_eliminated._get_lambda_closure(state)
if nfa_lambda_eliminated.initial_state == state:
if closures.difference(state).issubset(
nfa_lambda_eliminated.final_states
):
[
nfa_lambda_eliminated.final_states.add(state)
for state in closures.intersection(state)
]
for input_symbol in nfa_lambda_eliminated.input_symbols:
next_states = nfa.nfa._get_next_current_states(
closures, input_symbol
)
# Check if a dead state was returned.
if next_states != set():
# Update the transition after lambda move has been eliminated.
nfa_lambda_eliminated.transitions[state][
input_symbol
] = next_states
# Delete the lambda transition.
if "" in nfa_lambda_eliminated.transitions[state]:
del nfa_lambda_eliminated.transitions[state][""]
return nfa_lambda_eliminated
else:
return nfa
# -------------------------------------------------------------------------
# Define new methods and their helper methods.
def _pathfinder(
self,
input_str: str,
status: bool = False,
counter: int = 0,
main_counter: int = 0,
) -> Union[bool, list]: # pragma: no cover. Too many possibilities.
"""
Searches for a appropriate path to return to input_check.
Args:
input_str (str): Input symbols
status (bool, optional): If a path is found. Defaults to False.
counter (int, optional): To keep track of recursion limit in __pathsearcher. Defaults to 0.
main_counter (int, optional): To keep track of recursion limit in _pathfinder. Defaults to 0.
Returns:
Union[bool, list]: If a path is found, and a list of transition tuples.
"""
counter += 1
nfa = self.copy()
recursion_limit = 50
result = self.__pathsearcher(nfa, input_str, status)
if result:
return status, result
else:
main_counter += 1
if main_counter <= recursion_limit:
return self._pathfinder(
input_str, status, counter, main_counter=main_counter
)
else:
status = (
"[NO VALID PATH FOUND]\n"
"Try to eliminate lambda transitions and try again.\n"
"Example: nfa_lambda_removed = nfa.eliminate_lambda()"
)
return status, []
@staticmethod
def __pathsearcher(
nfa, input_str: str, status: bool = False, counter: int = 0
) -> list: # pragma: no cover. Too many possibilities.
"""
Searches for a appropriate path to return to _pathfinder.
Args:
nfa (VisualNFA): A VisualNFA object.
input_str (str): Input symbols.
status (bool, optional): If a path is found. Defaults to False.
counter (int, optional): To keep track of recursion limit. Defaults to 0.
Returns:
list: a list of transition tuples.
"""
recursion_limit = 20000
counter += 1
current_state = {(nfa.initial_state)}
path = []
for symbol in input_str:
next_curr = nfa._get_next_current_states(current_state, symbol)
if next_curr == set():
if not status:
state = {}
path.append(("".join(current_state), state, symbol))
return path
else:
break
else:
state = random.choice(list(next_curr))
path.append(("".join(current_state), state, symbol))
current_state = {(state)}
# Accepted path opptained.
if (
status
and len(input_str) == (len(path))
and path[-1][1] in nfa.final_states
):
return path
# Rejected path opptained.
elif not status and len(input_str) == (len(path)):
return path
# No path opptained. Try again.
else:
if counter <= recursion_limit:
return nfa.__pathsearcher(nfa, input_str, status, counter)
else:
return False
@staticmethod
def _transition_steps(
initial_state,
final_states,
input_str: str,
transitions_taken: list,
status: bool,
) -> DataFrame: # pragma: no cover. Too many possibilities.
"""
Generates a table of taken transitions based on the input string and it's result.
Args:
initial_state (str): The NFA's initial state.
final_states (set): The NFA's final states.
input_str (str): The input string to run on the NFA.
transitions_taken (list): Transitions taken from the input string.
status (bool): The result of the input string.
Returns:
DataFrame: Table of taken transitions based on the input string and it's result.
"""
current_states = transitions_taken.copy()
for i, state in enumerate(current_states):
if state == "" or state == {}:
current_states[i] = "∅"
elif state == initial_state and state in final_states:
current_states[i] = "→*" + state
elif state == initial_state:
current_states[i] = "→" + state
elif state in final_states:
current_states[i] = "*" + state
new_states = current_states.copy()
del current_states[-1]
del new_states[0]
inputs = [str(x) for x in input_str]
inputs = inputs[: len(current_states)]
transition_steps: dict = {
"Current state:": current_states,
"Input symbol:": inputs,
"New state:": new_states,
}
transition_steps = pd.DataFrame.from_dict(transition_steps)
transition_steps.index += 1
transition_steps = pd.DataFrame.from_dict(
transition_steps
).rename_axis("Step:", axis=1)
if status:
transition_steps.columns = pd.MultiIndex.from_product(
[["[Accepted]"], transition_steps.columns]
)
return transition_steps, inputs
else:
transition_steps.columns = pd.MultiIndex.from_product(
[["[Rejected]"], transition_steps.columns]
)
return transition_steps, inputs
@staticmethod
def _transitions_pairs(
all_transitions: dict,
) -> list: # pragma: no cover. Too many possibilities.
"""
Generates a list of all possible transitions pairs for all input symbols.
Args:
transition_dict (dict): NFA transitions.
Returns:
list: All possible transitions for all the given input symbols.
"""
all_transitions = all_transitions.deepcopy()
transition_possibilities: list = []
for state, state_transitions in all_transitions.items():
for symbol, transitions in state_transitions.items():
if len(transitions) < 2:
if transitions != "" and transitions != {}:
transitions = transitions.pop()
transition_possibilities.append(
(state, transitions, symbol)
)
else:
for transition in transitions:
transition_possibilities.append(
(state, transition, symbol)
)
return transition_possibilities
def input_check(
self, input_str: str, return_result=False
) -> Union[
bool, list, DataFrame
]: # pragma: no cover. Too many possibilities.
"""
Checks if string of input symbols results in final state.
Args:
input_str (str): The input string to run on the NFA.
return_result (bool, optional): Returns results to the show_diagram method. Defaults to False.
Raises:
TypeError: To let the user know a string has to be entered.
Returns:
Union[bool, list, list]: If the last state is the final state, transition pairs, and steps taken.
"""
if not isinstance(input_str, str):
raise TypeError(
f"input_str should be a string. "
f"{input_str} is {type(input_str)}, not a string."
)
# Check if input string is accepted.
status: bool = self.nfa.accepts_input(input_str=input_str)
status, taken_transitions_pairs = self._pathfinder(
input_str=input_str, status=status
)
if not isinstance(status, bool):
if return_result:
return status, [], DataFrame, input_str
else:
return status
current_states = self.initial_state
transitions_taken = [current_states]
for transition in range(len(taken_transitions_pairs)):
transitions_taken.append(taken_transitions_pairs[transition][1])
taken_steps, inputs = self._transition_steps(
initial_state=self.nfa.initial_state,
final_states=self.final_states,
input_str=input_str,
transitions_taken=transitions_taken,
status=status,
)
if return_result:
return status, taken_transitions_pairs, taken_steps, inputs
else:
return taken_steps
def show_diagram(
self,
input_str: str = None,
filename: str = None,
format_type: str = "png",
path: str = None,
*,
view=False,
cleanup: bool = True,
horizontal: bool = True,
reverse_orientation: bool = False,
fig_size: tuple = (8, 8),
font_size: float = 14.0,
arrow_size: float = 0.85,
state_seperation: float = 0.5,
) -> Digraph: # pragma: no cover. Too many possibilities.
"""
Generates the graph associated with the given NFA.
Args:
nfa (NFA): Deterministic Finite Automata to graph.
input_str (str, optional): String list of input symbols. Defaults to None.
filename (str, optional): Name of output file. Defaults to None.
format_type (str, optional): File format [svg/png/...]. Defaults to "png".
path (str, optional): Folder path for output file. Defaults to None.
view (bool, optional): Storing and displaying the graph as a pdf. Defaults to False.
cleanup (bool, optional): Garbage collection. Defaults to True.
horizontal (bool, optional): Direction of node layout. Defaults to True.
reverse_orientation (bool, optional): Reverse direction of node layout. Defaults to False.
fig_size (tuple, optional): Figure size. Defaults to (8, 8).
font_size (float, optional): Font size. Defaults to 14.0.
arrow_size (float, optional): Arrow head size. Defaults to 0.85.
state_seperation (float, optional): Node distance. Defaults to 0.5.
Returns:
Digraph: The graph in dot format.
"""
# Converting to graphviz preferred input type,
# keeping the conventional input styles; i.e fig_size(8,8)
fig_size = ", ".join(map(str, fig_size))
font_size = str(font_size)
arrow_size = str(arrow_size)
state_seperation = str(state_seperation)
# Defining the graph.
graph = Digraph(strict=False)
graph.attr(
size=fig_size,
ranksep=state_seperation,
)
if horizontal:
graph.attr(rankdir="LR")
if reverse_orientation:
if horizontal:
graph.attr(rankdir="RL")
else:
graph.attr(rankdir="BT")
# Defining arrow to indicate the initial state.
graph.node("Initial", label="", shape="point", fontsize=font_size)
# Defining all states.
for state in sorted(self.nfa.states):
if (
state in self.nfa.initial_state
and state in self.nfa.final_states
):
graph.node(state, shape="doublecircle", fontsize=font_size)
elif state in self.nfa.initial_state:
graph.node(state, shape="circle", fontsize=font_size)
elif state in self.nfa.final_states:
graph.node(state, shape="doublecircle", fontsize=font_size)
else:
graph.node(state, shape="circle", fontsize=font_size)
# Point initial arrow to the initial state.
graph.edge("Initial", self.nfa.initial_state, arrowsize=arrow_size)
# Define all tansitions in the finite state machine.
all_transitions_pairs = self._transitions_pairs(self.nfa.transitions)
# Replacing '' key name for empty string (lambda/epsilon) transitions.
for i, pair in enumerate(all_transitions_pairs):
if pair[2] == "":
all_transitions_pairs[i] = (pair[0], pair[1], "λ")
if input_str is None:
for pair in all_transitions_pairs:
graph.edge(
pair[0],
pair[1],
label=" {} ".format(pair[2]),
arrowsize=arrow_size,
fontsize=font_size,
)
status = None
else:
(
status,
taken_transitions_pairs,
taken_steps,
inputs,
) = self.input_check(input_str=input_str, return_result=True)
if not isinstance(status, bool):
print(status)
return
remaining_transitions_pairs = [
x
for x in all_transitions_pairs
if x not in taken_transitions_pairs
]
# Define color palette for transitions
if status:
start_color = hex_to_rgb_color("#FFFF00")
end_color = hex_to_rgb_color("#00FF00")
else:
start_color = hex_to_rgb_color("#FFFF00")
end_color = hex_to_rgb_color("#FF0000")
number_of_colors = len(inputs)
palette = create_palette(
start_color, end_color, number_of_colors, sRGBColor
)
color_gen = list_cycler(palette)
# Define all tansitions in the finite state machine with traversal.
counter = 0
for i, pair in enumerate(taken_transitions_pairs):
dead_state = "\u00D8"
edge_color = next(color_gen)
counter += 1
if pair[1] != {}:
graph.edge(
pair[0],
pair[1],
label=" [{}]\n{} ".format(counter, pair[2]),
arrowsize=arrow_size,
fontsize=font_size,
color=edge_color,
penwidth="2.5",
)
else:
graph.node(dead_state, shape="circle", fontsize=font_size)
graph.edge(
pair[0],
dead_state,
label=" [{}]\n{} ".format(counter, inputs[-1]),
arrowsize=arrow_size,
fontsize=font_size,
color=edge_color,
penwidth="2.5",
)
for pair in remaining_transitions_pairs:
graph.edge(
pair[0],
pair[1],
label=" {} ".format(pair[2]),
arrowsize=arrow_size,
fontsize=font_size,
)
# Write diagram to file. PNG, SVG, etc.
if filename:
graph.render(
filename=filename,
format=format_type,
directory=path,
cleanup=cleanup,
)
if view:
graph.render(view=True)
if input_str:
display(taken_steps)
return graph
else:
return graph | PypiClean |
/purplship_python-2021.7-py3-none-any.whl/purplship/api/webhooks_api.py | import re # noqa: F401
import sys # noqa: F401
from purplship.api_client import ApiClient, Endpoint as _Endpoint
from purplship.model_utils import ( # noqa: F401
check_allowed_values,
check_validations,
date,
datetime,
file_type,
none_type,
validate_and_convert_types
)
from purplship.model.error_response import ErrorResponse
from purplship.model.operation import Operation
from purplship.model.webhook import Webhook
from purplship.model.webhook_data import WebhookData
from purplship.model.webhook_list import WebhookList
from purplship.model.webhook_test_request import WebhookTestRequest
class WebhooksApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def __create(
self,
data,
**kwargs
):
"""Create a webhook # noqa: E501
Create a new webhook. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create(data, async_req=True)
>>> result = thread.get()
Args:
data (WebhookData):
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
Webhook
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['data'] = \
data
return self.call_with_http_info(**kwargs)
self.create = _Endpoint(
settings={
'response_type': (Webhook,),
'auth': [
'Token'
],
'endpoint_path': '/v1/webhooks',
'operation_id': 'create',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'data',
],
'required': [
'data',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'data':
(WebhookData,),
},
'attribute_map': {
},
'location_map': {
'data': 'body',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create
)
def __list(
self,
**kwargs
):
"""List all webhooks # noqa: E501
Retrieve all webhooks. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.list(async_req=True)
>>> result = thread.get()
Keyword Args:
limit (int): Number of results to return per page.. [optional]
offset (int): The initial index from which to return the results.. [optional]
test_mode (bool, none_type): This flag filter out webhooks created in test or live mode. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
WebhookList
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.call_with_http_info(**kwargs)
self.list = _Endpoint(
settings={
'response_type': (WebhookList,),
'auth': [
'Token'
],
'endpoint_path': '/v1/webhooks',
'operation_id': 'list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'limit',
'offset',
'test_mode',
],
'required': [],
'nullable': [
'test_mode',
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'limit':
(int,),
'offset':
(int,),
'test_mode':
(bool, none_type,),
},
'attribute_map': {
'limit': 'limit',
'offset': 'offset',
'test_mode': 'test_mode',
},
'location_map': {
'limit': 'query',
'offset': 'query',
'test_mode': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__list
)
def __remove(
self,
id,
**kwargs
):
"""Remove a webhook # noqa: E501
Remove a webhook. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.remove(id, async_req=True)
>>> result = thread.get()
Args:
id (str):
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
Operation
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
return self.call_with_http_info(**kwargs)
self.remove = _Endpoint(
settings={
'response_type': (Operation,),
'auth': [
'Token'
],
'endpoint_path': '/v1/webhooks/{id}',
'operation_id': 'remove',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'id',
],
'required': [
'id',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__remove
)
def __retrieve(
self,
id,
**kwargs
):
"""Retrieve a webhook # noqa: E501
Retrieve a webhook. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.retrieve(id, async_req=True)
>>> result = thread.get()
Args:
id (str):
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
Webhook
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
return self.call_with_http_info(**kwargs)
self.retrieve = _Endpoint(
settings={
'response_type': (Webhook,),
'auth': [
'Token'
],
'endpoint_path': '/v1/webhooks/{id}',
'operation_id': 'retrieve',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'id',
],
'required': [
'id',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__retrieve
)
def __test(
self,
id,
data,
**kwargs
):
"""Test a webhook # noqa: E501
test a webhook. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.test(id, data, async_req=True)
>>> result = thread.get()
Args:
id (str):
data (WebhookTestRequest):
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
Operation
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
kwargs['data'] = \
data
return self.call_with_http_info(**kwargs)
self.test = _Endpoint(
settings={
'response_type': (Operation,),
'auth': [
'Token'
],
'endpoint_path': '/v1/webhooks/{id}/test',
'operation_id': 'test',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'id',
'data',
],
'required': [
'id',
'data',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
'data':
(WebhookTestRequest,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
'data': 'body',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__test
)
def __update(
self,
id,
data,
**kwargs
):
"""Update a webhook # noqa: E501
update a webhook. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update(id, data, async_req=True)
>>> result = thread.get()
Args:
id (str):
data (WebhookData):
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
Webhook
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
kwargs['data'] = \
data
return self.call_with_http_info(**kwargs)
self.update = _Endpoint(
settings={
'response_type': (Webhook,),
'auth': [
'Token'
],
'endpoint_path': '/v1/webhooks/{id}',
'operation_id': 'update',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'id',
'data',
],
'required': [
'id',
'data',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
'data':
(WebhookData,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
'data': 'body',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__update
) | PypiClean |
/h2o_engine_manager-0.5.3-py3-none-any.whl/h2o_engine_manager/gen/dai_engine_constraint_set_service/api/dai_engine_constraint_set_service_api.py | import re # noqa: F401
import sys # noqa: F401
from h2o_engine_manager.gen.dai_engine_constraint_set_service.api_client import ApiClient, Endpoint as _Endpoint
from h2o_engine_manager.gen.dai_engine_constraint_set_service.model_utils import ( # noqa: F401
check_allowed_values,
check_validations,
date,
datetime,
file_type,
none_type,
validate_and_convert_types
)
from h2o_engine_manager.gen.dai_engine_constraint_set_service.model.rpc_status import RpcStatus
from h2o_engine_manager.gen.dai_engine_constraint_set_service.model.v1_get_dai_engine_constraint_set_response import V1GetDAIEngineConstraintSetResponse
class DAIEngineConstraintSetServiceApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
self.d_ai_engine_constraint_set_service_get_dai_engine_constraint_set_endpoint = _Endpoint(
settings={
'response_type': (V1GetDAIEngineConstraintSetResponse,),
'auth': [],
'endpoint_path': '/v1/{name}',
'operation_id': 'd_ai_engine_constraint_set_service_get_dai_engine_constraint_set',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'name',
],
'required': [
'name',
],
'nullable': [
],
'enum': [
],
'validation': [
'name',
]
},
root_map={
'validations': {
('name',): {
'regex': {
'pattern': r'workspaces\/[^\/]+\/daiEngineConstraintSet', # noqa: E501
},
},
},
'allowed_values': {
},
'openapi_types': {
'name':
(str,),
},
'attribute_map': {
'name': 'name',
},
'location_map': {
'name': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client
)
def d_ai_engine_constraint_set_service_get_dai_engine_constraint_set(
self,
name,
**kwargs
):
"""Returns the current DAIEngineConstraintSet. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.d_ai_engine_constraint_set_service_get_dai_engine_constraint_set(name, async_req=True)
>>> result = thread.get()
Args:
name (str): DAIEngineConstraintSet resource name. Format: workspaces/{workspace}/daiEngineConstraintSet
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_content_type (str/None): force body content-type.
Default is None and content-type will be predicted by allowed
content-types and body.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
_request_auths (list): set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
Default is None
async_req (bool): execute request asynchronously
Returns:
V1GetDAIEngineConstraintSetResponse
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_spec_property_naming'] = kwargs.get(
'_spec_property_naming', False
)
kwargs['_content_type'] = kwargs.get(
'_content_type')
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['_request_auths'] = kwargs.get('_request_auths', None)
kwargs['name'] = \
name
return self.d_ai_engine_constraint_set_service_get_dai_engine_constraint_set_endpoint.call_with_http_info(**kwargs) | PypiClean |
/deepNets-0.1.8.tar.gz/deepNets-0.1.8/.eggs/pluggy-1.0.0.dev0-py3.8.egg/pluggy/hooks.py | import inspect
import sys
import warnings
class HookspecMarker(object):
""" Decorator helper class for marking functions as hook specifications.
You can instantiate it with a project_name to get a decorator.
Calling :py:meth:`.PluginManager.add_hookspecs` later will discover all marked functions
if the :py:class:`.PluginManager` uses the same project_name.
"""
def __init__(self, project_name):
self.project_name = project_name
def __call__(
self, function=None, firstresult=False, historic=False, warn_on_impl=None
):
""" if passed a function, directly sets attributes on the function
which will make it discoverable to :py:meth:`.PluginManager.add_hookspecs`.
If passed no function, returns a decorator which can be applied to a function
later using the attributes supplied.
If ``firstresult`` is ``True`` the 1:N hook call (N being the number of registered
hook implementation functions) will stop at I<=N when the I'th function
returns a non-``None`` result.
If ``historic`` is ``True`` calls to a hook will be memorized and replayed
on later registered plugins.
"""
def setattr_hookspec_opts(func):
if historic and firstresult:
raise ValueError("cannot have a historic firstresult hook")
setattr(
func,
self.project_name + "_spec",
dict(
firstresult=firstresult,
historic=historic,
warn_on_impl=warn_on_impl,
),
)
return func
if function is not None:
return setattr_hookspec_opts(function)
else:
return setattr_hookspec_opts
class HookimplMarker(object):
""" Decorator helper class for marking functions as hook implementations.
You can instantiate with a ``project_name`` to get a decorator.
Calling :py:meth:`.PluginManager.register` later will discover all marked functions
if the :py:class:`.PluginManager` uses the same project_name.
"""
def __init__(self, project_name):
self.project_name = project_name
def __call__(
self,
function=None,
hookwrapper=False,
optionalhook=False,
tryfirst=False,
trylast=False,
specname=None,
):
""" if passed a function, directly sets attributes on the function
which will make it discoverable to :py:meth:`.PluginManager.register`.
If passed no function, returns a decorator which can be applied to a
function later using the attributes supplied.
If ``optionalhook`` is ``True`` a missing matching hook specification will not result
in an error (by default it is an error if no matching spec is found).
If ``tryfirst`` is ``True`` this hook implementation will run as early as possible
in the chain of N hook implementations for a specification.
If ``trylast`` is ``True`` this hook implementation will run as late as possible
in the chain of N hook implementations.
If ``hookwrapper`` is ``True`` the hook implementations needs to execute exactly
one ``yield``. The code before the ``yield`` is run early before any non-hookwrapper
function is run. The code after the ``yield`` is run after all non-hookwrapper
function have run. The ``yield`` receives a :py:class:`.callers._Result` object
representing the exception or result outcome of the inner calls (including other
hookwrapper calls).
If ``specname`` is provided, it will be used instead of the function name when
matching this hook implementation to a hook specification during registration.
"""
def setattr_hookimpl_opts(func):
setattr(
func,
self.project_name + "_impl",
dict(
hookwrapper=hookwrapper,
optionalhook=optionalhook,
tryfirst=tryfirst,
trylast=trylast,
specname=specname,
),
)
return func
if function is None:
return setattr_hookimpl_opts
else:
return setattr_hookimpl_opts(function)
def normalize_hookimpl_opts(opts):
opts.setdefault("tryfirst", False)
opts.setdefault("trylast", False)
opts.setdefault("hookwrapper", False)
opts.setdefault("optionalhook", False)
opts.setdefault("specname", None)
if hasattr(inspect, "getfullargspec"):
def _getargspec(func):
return inspect.getfullargspec(func)
else:
def _getargspec(func):
return inspect.getargspec(func)
_PYPY3 = hasattr(sys, "pypy_version_info") and sys.version_info.major == 3
def varnames(func):
"""Return tuple of positional and keywrord argument names for a function,
method, class or callable.
In case of a class, its ``__init__`` method is considered.
For methods the ``self`` parameter is not included.
"""
cache = getattr(func, "__dict__", {})
try:
return cache["_varnames"]
except KeyError:
pass
if inspect.isclass(func):
try:
func = func.__init__
except AttributeError:
return (), ()
elif not inspect.isroutine(func): # callable object?
try:
func = getattr(func, "__call__", func)
except Exception:
return (), ()
try: # func MUST be a function or method here or we won't parse any args
spec = _getargspec(func)
except TypeError:
return (), ()
args, defaults = tuple(spec.args), spec.defaults
if defaults:
index = -len(defaults)
args, kwargs = args[:index], tuple(args[index:])
else:
kwargs = ()
# strip any implicit instance arg
# pypy3 uses "obj" instead of "self" for default dunder methods
implicit_names = ("self",) if not _PYPY3 else ("self", "obj")
if args:
if inspect.ismethod(func) or (
"." in getattr(func, "__qualname__", ()) and args[0] in implicit_names
):
args = args[1:]
try:
cache["_varnames"] = args, kwargs
except TypeError:
pass
return args, kwargs
class _HookRelay(object):
""" hook holder object for performing 1:N hook calls where N is the number
of registered plugins.
"""
class _HookCaller(object):
def __init__(self, name, hook_execute, specmodule_or_class=None, spec_opts=None):
self.name = name
self._wrappers = []
self._nonwrappers = []
self._hookexec = hook_execute
self.argnames = None
self.kwargnames = None
self.spec = None
if specmodule_or_class is not None:
assert spec_opts is not None
self.set_specification(specmodule_or_class, spec_opts)
def has_spec(self):
return self.spec is not None
def set_specification(self, specmodule_or_class, spec_opts):
assert not self.has_spec()
self.spec = HookSpec(specmodule_or_class, self.name, spec_opts)
if spec_opts.get("historic"):
self._call_history = []
def is_historic(self):
return hasattr(self, "_call_history")
def _remove_plugin(self, plugin):
def remove(wrappers):
for i, method in enumerate(wrappers):
if method.plugin == plugin:
del wrappers[i]
return True
if remove(self._wrappers) is None:
if remove(self._nonwrappers) is None:
raise ValueError("plugin %r not found" % (plugin,))
def get_hookimpls(self):
# Order is important for _hookexec
return self._nonwrappers + self._wrappers
def _add_hookimpl(self, hookimpl):
"""Add an implementation to the callback chain.
"""
if hookimpl.hookwrapper:
methods = self._wrappers
else:
methods = self._nonwrappers
if hookimpl.trylast:
methods.insert(0, hookimpl)
elif hookimpl.tryfirst:
methods.append(hookimpl)
else:
# find last non-tryfirst method
i = len(methods) - 1
while i >= 0 and methods[i].tryfirst:
i -= 1
methods.insert(i + 1, hookimpl)
def __repr__(self):
return "<_HookCaller %r>" % (self.name,)
def __call__(self, *args, **kwargs):
if args:
raise TypeError("hook calling supports only keyword arguments")
assert not self.is_historic()
if self.spec and self.spec.argnames:
notincall = set(self.spec.argnames) - set(kwargs.keys())
if notincall:
warnings.warn(
"Argument(s) {} which are declared in the hookspec "
"can not be found in this hook call".format(tuple(notincall)),
stacklevel=2,
)
return self._hookexec(self, self.get_hookimpls(), kwargs)
def call_historic(self, result_callback=None, kwargs=None, proc=None):
"""Call the hook with given ``kwargs`` for all registered plugins and
for all plugins which will be registered afterwards.
If ``result_callback`` is not ``None`` it will be called for for each
non-``None`` result obtained from a hook implementation.
.. note::
The ``proc`` argument is now deprecated.
"""
if proc is not None:
warnings.warn(
"Support for `proc` argument is now deprecated and will be"
"removed in an upcoming release.",
DeprecationWarning,
)
result_callback = proc
self._call_history.append((kwargs or {}, result_callback))
# historizing hooks don't return results
res = self._hookexec(self, self.get_hookimpls(), kwargs)
if result_callback is None:
return
# XXX: remember firstresult isn't compat with historic
for x in res or []:
result_callback(x)
def call_extra(self, methods, kwargs):
""" Call the hook with some additional temporarily participating
methods using the specified ``kwargs`` as call parameters. """
old = list(self._nonwrappers), list(self._wrappers)
for method in methods:
opts = dict(hookwrapper=False, trylast=False, tryfirst=False)
hookimpl = HookImpl(None, "<temp>", method, opts)
self._add_hookimpl(hookimpl)
try:
return self(**kwargs)
finally:
self._nonwrappers, self._wrappers = old
def _maybe_apply_history(self, method):
"""Apply call history to a new hookimpl if it is marked as historic.
"""
if self.is_historic():
for kwargs, result_callback in self._call_history:
res = self._hookexec(self, [method], kwargs)
if res and result_callback is not None:
result_callback(res[0])
class HookImpl(object):
def __init__(self, plugin, plugin_name, function, hook_impl_opts):
self.function = function
self.argnames, self.kwargnames = varnames(self.function)
self.plugin = plugin
self.opts = hook_impl_opts
self.plugin_name = plugin_name
self.__dict__.update(hook_impl_opts)
def __repr__(self):
return "<HookImpl plugin_name=%r, plugin=%r>" % (self.plugin_name, self.plugin)
class HookSpec(object):
def __init__(self, namespace, name, opts):
self.namespace = namespace
self.function = function = getattr(namespace, name)
self.name = name
self.argnames, self.kwargnames = varnames(function)
self.opts = opts
self.warn_on_impl = opts.get("warn_on_impl") | PypiClean |
/pyfloat-0.0.3.tar.gz/pyfloat-0.0.3/README.md | ## PyFloat Lib
Python Library for doing math operations with floats without lossing precission
## How to use
```
>> from pyfloat import PyFloat
>> PyFloat(2.1234e-12)
'0.0000000000021234'
>> PyFloat(-2.1234e+12)
'-2123400000000'
>> PyFloat(0.1452)
'0.1452'
>> PyFloat(PyFloat(152.455))
'152.455'
>> a = PyFloat("123451.1234551230000000004444445551122000000011")
>> b = PyFloat(-8123994.000002234100000001323400000001232112221)
>> a + b
'-8000542.8765471111000000008789554448890321122199'
>> a - b
'8247445.1234573571000000017678445551134321122221'
>> a * b
'-1002916186242.9543234169148643344158458369442274183843912848765430855693663896461975553234431'
>> a == b
'False'
>> a != b
'True'
>> a > b
'True'
>>> PyFloat(0.00239419391).round(10)
'0.0023941939'
>>> PyFloat(0.00239419391).round(7)
'0.0023942'
>>> PyFloat(-0.00239419391).round(7)
'-0.0023942'
>>> PyFloat(0.00239419391).round(4)
'0.0024'
>>> PyFloat(-1234.5678).abs()
'1234.5678'
>>> PyFloat(0.0000089778).truncate(8)
'0.00000897'
>>> PyFloat(-0.0000089778).truncate(8)
-0.00000897
```
## Test
```
python pytfloat_test.py
``` | PypiClean |
/intel_tensorflow-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl/tensorflow/python/keras/layers/convolutional_recurrent.py | """Convolutional-recurrent layers."""
import numpy as np
from tensorflow.python.keras import activations
from tensorflow.python.keras import backend
from tensorflow.python.keras import constraints
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.keras.engine.input_spec import InputSpec
from tensorflow.python.keras.layers.recurrent import DropoutRNNCellMixin
from tensorflow.python.keras.layers.recurrent import RNN
from tensorflow.python.keras.utils import conv_utils
from tensorflow.python.keras.utils import generic_utils
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.ops import array_ops
from tensorflow.python.util.tf_export import keras_export
class ConvRNN2D(RNN):
"""Base class for convolutional-recurrent layers.
Args:
cell: A RNN cell instance. A RNN cell is a class that has:
- a `call(input_at_t, states_at_t)` method, returning
`(output_at_t, states_at_t_plus_1)`. The call method of the
cell can also take the optional argument `constants`, see
section "Note on passing external constants" below.
- a `state_size` attribute. This can be a single integer
(single state) in which case it is
the number of channels of the recurrent state
(which should be the same as the number of channels of the cell
output). This can also be a list/tuple of integers
(one size per state). In this case, the first entry
(`state_size[0]`) should be the same as
the size of the cell output.
return_sequences: Boolean. Whether to return the last output.
in the output sequence, or the full sequence.
return_state: Boolean. Whether to return the last state
in addition to the output.
go_backwards: Boolean (default False).
If True, process the input sequence backwards and return the
reversed sequence.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
input_shape: Use this argument to specify the shape of the
input when this layer is the first one in a model.
Call arguments:
inputs: A 5D tensor.
mask: Binary tensor of shape `(samples, timesteps)` indicating whether
a given timestep should be masked.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is for use with cells that use dropout.
initial_state: List of initial state tensors to be passed to the first
call of the cell.
constants: List of constant tensors to be passed to the cell at each
timestep.
Input shape:
5D tensor with shape:
`(samples, timesteps, channels, rows, cols)`
if data_format='channels_first' or 5D tensor with shape:
`(samples, timesteps, rows, cols, channels)`
if data_format='channels_last'.
Output shape:
- If `return_state`: a list of tensors. The first tensor is
the output. The remaining tensors are the last states,
each 4D tensor with shape:
`(samples, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)`
if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
- If `return_sequences`: 5D tensor with shape:
`(samples, timesteps, filters, new_rows, new_cols)`
if data_format='channels_first'
or 5D tensor with shape:
`(samples, timesteps, new_rows, new_cols, filters)`
if data_format='channels_last'.
- Else, 4D tensor with shape:
`(samples, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)`
if data_format='channels_last'.
Masking:
This layer supports masking for input data with a variable number
of timesteps.
Note on using statefulness in RNNs:
You can set RNN layers to be 'stateful', which means that the states
computed for the samples in one batch will be reused as initial states
for the samples in the next batch. This assumes a one-to-one mapping
between samples in different successive batches.
To enable statefulness:
- Specify `stateful=True` in the layer constructor.
- Specify a fixed batch size for your model, by passing
- If sequential model:
`batch_input_shape=(...)` to the first layer in your model.
- If functional model with 1 or more Input layers:
`batch_shape=(...)` to all the first layers in your model.
This is the expected shape of your inputs
*including the batch size*.
It should be a tuple of integers,
e.g. `(32, 10, 100, 100, 32)`.
Note that the number of rows and columns should be specified
too.
- Specify `shuffle=False` when calling fit().
To reset the states of your model, call `.reset_states()` on either
a specific layer, or on your entire model.
Note on specifying the initial state of RNNs:
You can specify the initial state of RNN layers symbolically by
calling them with the keyword argument `initial_state`. The value of
`initial_state` should be a tensor or list of tensors representing
the initial state of the RNN layer.
You can specify the initial state of RNN layers numerically by
calling `reset_states` with the keyword argument `states`. The value of
`states` should be a numpy array or list of numpy arrays representing
the initial state of the RNN layer.
Note on passing external constants to RNNs:
You can pass "external" constants to the cell using the `constants`
keyword argument of `RNN.__call__` (as well as `RNN.call`) method. This
requires that the `cell.call` method accepts the same keyword argument
`constants`. Such constants can be used to condition the cell
transformation on additional static inputs (not changing over time),
a.k.a. an attention mechanism.
"""
def __init__(self,
cell,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs):
if unroll:
raise TypeError('Unrolling isn\'t possible with '
'convolutional RNNs.')
if isinstance(cell, (list, tuple)):
# The StackedConvRNN2DCells isn't implemented yet.
raise TypeError('It is not possible at the moment to'
'stack convolutional cells.')
super(ConvRNN2D, self).__init__(cell,
return_sequences,
return_state,
go_backwards,
stateful,
unroll,
**kwargs)
self.input_spec = [InputSpec(ndim=5)]
self.states = None
self._num_constants = None
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
cell = self.cell
if cell.data_format == 'channels_first':
rows = input_shape[3]
cols = input_shape[4]
elif cell.data_format == 'channels_last':
rows = input_shape[2]
cols = input_shape[3]
rows = conv_utils.conv_output_length(rows,
cell.kernel_size[0],
padding=cell.padding,
stride=cell.strides[0],
dilation=cell.dilation_rate[0])
cols = conv_utils.conv_output_length(cols,
cell.kernel_size[1],
padding=cell.padding,
stride=cell.strides[1],
dilation=cell.dilation_rate[1])
if cell.data_format == 'channels_first':
output_shape = input_shape[:2] + (cell.filters, rows, cols)
elif cell.data_format == 'channels_last':
output_shape = input_shape[:2] + (rows, cols, cell.filters)
if not self.return_sequences:
output_shape = output_shape[:1] + output_shape[2:]
if self.return_state:
output_shape = [output_shape]
if cell.data_format == 'channels_first':
output_shape += [(input_shape[0], cell.filters, rows, cols)
for _ in range(2)]
elif cell.data_format == 'channels_last':
output_shape += [(input_shape[0], rows, cols, cell.filters)
for _ in range(2)]
return output_shape
@tf_utils.shape_type_conversion
def build(self, input_shape):
# Note input_shape will be list of shapes of initial states and
# constants if these are passed in __call__.
if self._num_constants is not None:
constants_shape = input_shape[-self._num_constants:] # pylint: disable=E1130
else:
constants_shape = None
if isinstance(input_shape, list):
input_shape = input_shape[0]
batch_size = input_shape[0] if self.stateful else None
self.input_spec[0] = InputSpec(shape=(batch_size, None) + input_shape[2:5])
# allow cell (if layer) to build before we set or validate state_spec
if isinstance(self.cell, Layer):
step_input_shape = (input_shape[0],) + input_shape[2:]
if constants_shape is not None:
self.cell.build([step_input_shape] + constants_shape)
else:
self.cell.build(step_input_shape)
# set or validate state_spec
if hasattr(self.cell.state_size, '__len__'):
state_size = list(self.cell.state_size)
else:
state_size = [self.cell.state_size]
if self.state_spec is not None:
# initial_state was passed in call, check compatibility
if self.cell.data_format == 'channels_first':
ch_dim = 1
elif self.cell.data_format == 'channels_last':
ch_dim = 3
if [spec.shape[ch_dim] for spec in self.state_spec] != state_size:
raise ValueError(
'An initial_state was passed that is not compatible with '
'`cell.state_size`. Received `state_spec`={}; '
'However `cell.state_size` is '
'{}'.format([spec.shape for spec in self.state_spec],
self.cell.state_size))
else:
if self.cell.data_format == 'channels_first':
self.state_spec = [InputSpec(shape=(None, dim, None, None))
for dim in state_size]
elif self.cell.data_format == 'channels_last':
self.state_spec = [InputSpec(shape=(None, None, None, dim))
for dim in state_size]
if self.stateful:
self.reset_states()
self.built = True
def get_initial_state(self, inputs):
# (samples, timesteps, rows, cols, filters)
initial_state = backend.zeros_like(inputs)
# (samples, rows, cols, filters)
initial_state = backend.sum(initial_state, axis=1)
shape = list(self.cell.kernel_shape)
shape[-1] = self.cell.filters
initial_state = self.cell.input_conv(initial_state,
array_ops.zeros(tuple(shape),
initial_state.dtype),
padding=self.cell.padding)
if hasattr(self.cell.state_size, '__len__'):
return [initial_state for _ in self.cell.state_size]
else:
return [initial_state]
def call(self,
inputs,
mask=None,
training=None,
initial_state=None,
constants=None):
# note that the .build() method of subclasses MUST define
# self.input_spec and self.state_spec with complete input shapes.
inputs, initial_state, constants = self._process_inputs(
inputs, initial_state, constants)
if isinstance(mask, list):
mask = mask[0]
timesteps = backend.int_shape(inputs)[1]
kwargs = {}
if generic_utils.has_arg(self.cell.call, 'training'):
kwargs['training'] = training
if constants:
if not generic_utils.has_arg(self.cell.call, 'constants'):
raise ValueError('RNN cell does not support constants')
def step(inputs, states):
constants = states[-self._num_constants:] # pylint: disable=invalid-unary-operand-type
states = states[:-self._num_constants] # pylint: disable=invalid-unary-operand-type
return self.cell.call(inputs, states, constants=constants, **kwargs)
else:
def step(inputs, states):
return self.cell.call(inputs, states, **kwargs)
last_output, outputs, states = backend.rnn(step,
inputs,
initial_state,
constants=constants,
go_backwards=self.go_backwards,
mask=mask,
input_length=timesteps)
if self.stateful:
updates = [
backend.update(self_state, state)
for self_state, state in zip(self.states, states)
]
self.add_update(updates)
if self.return_sequences:
output = outputs
else:
output = last_output
if self.return_state:
if not isinstance(states, (list, tuple)):
states = [states]
else:
states = list(states)
return [output] + states
else:
return output
def reset_states(self, states=None):
if not self.stateful:
raise AttributeError('Layer must be stateful.')
input_shape = self.input_spec[0].shape
state_shape = self.compute_output_shape(input_shape)
if self.return_state:
state_shape = state_shape[0]
if self.return_sequences:
state_shape = state_shape[:1].concatenate(state_shape[2:])
if None in state_shape:
raise ValueError('If a RNN is stateful, it needs to know '
'its batch size. Specify the batch size '
'of your input tensors: \n'
'- If using a Sequential model, '
'specify the batch size by passing '
'a `batch_input_shape` '
'argument to your first layer.\n'
'- If using the functional API, specify '
'the time dimension by passing a '
'`batch_shape` argument to your Input layer.\n'
'The same thing goes for the number of rows and '
'columns.')
# helper function
def get_tuple_shape(nb_channels):
result = list(state_shape)
if self.cell.data_format == 'channels_first':
result[1] = nb_channels
elif self.cell.data_format == 'channels_last':
result[3] = nb_channels
else:
raise KeyError
return tuple(result)
# initialize state if None
if self.states[0] is None:
if hasattr(self.cell.state_size, '__len__'):
self.states = [backend.zeros(get_tuple_shape(dim))
for dim in self.cell.state_size]
else:
self.states = [backend.zeros(get_tuple_shape(self.cell.state_size))]
elif states is None:
if hasattr(self.cell.state_size, '__len__'):
for state, dim in zip(self.states, self.cell.state_size):
backend.set_value(state, np.zeros(get_tuple_shape(dim)))
else:
backend.set_value(self.states[0],
np.zeros(get_tuple_shape(self.cell.state_size)))
else:
if not isinstance(states, (list, tuple)):
states = [states]
if len(states) != len(self.states):
raise ValueError('Layer ' + self.name + ' expects ' +
str(len(self.states)) + ' states, ' +
'but it received ' + str(len(states)) +
' state values. Input received: ' + str(states))
for index, (value, state) in enumerate(zip(states, self.states)):
if hasattr(self.cell.state_size, '__len__'):
dim = self.cell.state_size[index]
else:
dim = self.cell.state_size
if value.shape != get_tuple_shape(dim):
raise ValueError('State ' + str(index) +
' is incompatible with layer ' +
self.name + ': expected shape=' +
str(get_tuple_shape(dim)) +
', found shape=' + str(value.shape))
# TODO(anjalisridhar): consider batch calls to `set_value`.
backend.set_value(state, value)
class ConvLSTM2DCell(DropoutRNNCellMixin, Layer):
"""Cell class for the ConvLSTM2D layer.
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of n integers, specifying the
dimensions of the convolution window.
strides: An integer or tuple/list of n integers,
specifying the strides of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
dilation_rate: An integer or tuple/list of n integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
activation: Activation function to use.
If you don't specify anything, no activation is applied
(ie. "linear" activation: `a(x) = x`).
recurrent_activation: Activation function to use
for the recurrent step.
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Use in combination with `bias_initializer="zeros"`.
This is recommended in [Jozefowicz et al., 2015](
http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
kernel_constraint: Constraint function applied to
the `kernel` weights matrix.
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
Call arguments:
inputs: A 4D tensor.
states: List of state tensors corresponding to the previous timestep.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. Only relevant when `dropout` or
`recurrent_dropout` is used.
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(ConvLSTM2DCell, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2,
'dilation_rate')
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.state_size = (self.filters, self.filters)
def build(self, input_shape):
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters * 4)
self.kernel_shape = kernel_shape
recurrent_kernel_shape = self.kernel_size + (self.filters, self.filters * 4)
self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=recurrent_kernel_shape,
initializer=self.recurrent_initializer,
name='recurrent_kernel',
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
if self.use_bias:
if self.unit_forget_bias:
def bias_initializer(_, *args, **kwargs):
return backend.concatenate([
self.bias_initializer((self.filters,), *args, **kwargs),
initializers.get('ones')((self.filters,), *args, **kwargs),
self.bias_initializer((self.filters * 2,), *args, **kwargs),
])
else:
bias_initializer = self.bias_initializer
self.bias = self.add_weight(
shape=(self.filters * 4,),
name='bias',
initializer=bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.built = True
def call(self, inputs, states, training=None):
h_tm1 = states[0] # previous memory state
c_tm1 = states[1] # previous carry state
# dropout matrices for input units
dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=4)
# dropout matrices for recurrent units
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
h_tm1, training, count=4)
if 0 < self.dropout < 1.:
inputs_i = inputs * dp_mask[0]
inputs_f = inputs * dp_mask[1]
inputs_c = inputs * dp_mask[2]
inputs_o = inputs * dp_mask[3]
else:
inputs_i = inputs
inputs_f = inputs
inputs_c = inputs
inputs_o = inputs
if 0 < self.recurrent_dropout < 1.:
h_tm1_i = h_tm1 * rec_dp_mask[0]
h_tm1_f = h_tm1 * rec_dp_mask[1]
h_tm1_c = h_tm1 * rec_dp_mask[2]
h_tm1_o = h_tm1 * rec_dp_mask[3]
else:
h_tm1_i = h_tm1
h_tm1_f = h_tm1
h_tm1_c = h_tm1
h_tm1_o = h_tm1
(kernel_i, kernel_f,
kernel_c, kernel_o) = array_ops.split(self.kernel, 4, axis=3)
(recurrent_kernel_i,
recurrent_kernel_f,
recurrent_kernel_c,
recurrent_kernel_o) = array_ops.split(self.recurrent_kernel, 4, axis=3)
if self.use_bias:
bias_i, bias_f, bias_c, bias_o = array_ops.split(self.bias, 4)
else:
bias_i, bias_f, bias_c, bias_o = None, None, None, None
x_i = self.input_conv(inputs_i, kernel_i, bias_i, padding=self.padding)
x_f = self.input_conv(inputs_f, kernel_f, bias_f, padding=self.padding)
x_c = self.input_conv(inputs_c, kernel_c, bias_c, padding=self.padding)
x_o = self.input_conv(inputs_o, kernel_o, bias_o, padding=self.padding)
h_i = self.recurrent_conv(h_tm1_i, recurrent_kernel_i)
h_f = self.recurrent_conv(h_tm1_f, recurrent_kernel_f)
h_c = self.recurrent_conv(h_tm1_c, recurrent_kernel_c)
h_o = self.recurrent_conv(h_tm1_o, recurrent_kernel_o)
i = self.recurrent_activation(x_i + h_i)
f = self.recurrent_activation(x_f + h_f)
c = f * c_tm1 + i * self.activation(x_c + h_c)
o = self.recurrent_activation(x_o + h_o)
h = o * self.activation(c)
return h, [h, c]
def input_conv(self, x, w, b=None, padding='valid'):
conv_out = backend.conv2d(x, w, strides=self.strides,
padding=padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if b is not None:
conv_out = backend.bias_add(conv_out, b,
data_format=self.data_format)
return conv_out
def recurrent_conv(self, x, w):
conv_out = backend.conv2d(x, w, strides=(1, 1),
padding='same',
data_format=self.data_format)
return conv_out
def get_config(self):
config = {'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'recurrent_activation': activations.serialize(
self.recurrent_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(
self.kernel_initializer),
'recurrent_initializer': initializers.serialize(
self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'kernel_regularizer': regularizers.serialize(
self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(
self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'kernel_constraint': constraints.serialize(
self.kernel_constraint),
'recurrent_constraint': constraints.serialize(
self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout}
base_config = super(ConvLSTM2DCell, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@keras_export('keras.layers.ConvLSTM2D')
class ConvLSTM2D(ConvRNN2D):
"""2D Convolutional LSTM layer.
A convolutional LSTM is similar to an LSTM, but the input transformations
and recurrent transformations are both convolutional. This layer is typically
used to process timeseries of images (i.e. video-like data).
It is known to perform well for weather data forecasting,
using inputs that are timeseries of 2D grids of sensor values.
It isn't usually applied to regular video data, due to its high computational
cost.
Args:
filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
kernel_size: An integer or tuple/list of n integers, specifying the
dimensions of the convolution window.
strides: An integer or tuple/list of n integers,
specifying the strides of the convolution.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
padding: One of `"valid"` or `"same"` (case-insensitive).
`"valid"` means no padding. `"same"` results in padding evenly to
the left/right or up/down of the input such that output has the same
height/width dimension as the input.
data_format: A string,
one of `channels_last` (default) or `channels_first`.
The ordering of the dimensions in the inputs.
`channels_last` corresponds to inputs with shape
`(batch, time, ..., channels)`
while `channels_first` corresponds to
inputs with shape `(batch, time, channels, ...)`.
It defaults to the `image_data_format` value found in your
Keras config file at `~/.keras/keras.json`.
If you never set it, then it will be "channels_last".
dilation_rate: An integer or tuple/list of n integers, specifying
the dilation rate to use for dilated convolution.
Currently, specifying any `dilation_rate` value != 1 is
incompatible with specifying any `strides` value != 1.
activation: Activation function to use.
By default hyperbolic tangent activation function is applied
(`tanh(x)`).
recurrent_activation: Activation function to use
for the recurrent step.
use_bias: Boolean, whether the layer uses a bias vector.
kernel_initializer: Initializer for the `kernel` weights matrix,
used for the linear transformation of the inputs.
recurrent_initializer: Initializer for the `recurrent_kernel`
weights matrix,
used for the linear transformation of the recurrent state.
bias_initializer: Initializer for the bias vector.
unit_forget_bias: Boolean.
If True, add 1 to the bias of the forget gate at initialization.
Use in combination with `bias_initializer="zeros"`.
This is recommended in [Jozefowicz et al., 2015](
http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
kernel_regularizer: Regularizer function applied to
the `kernel` weights matrix.
recurrent_regularizer: Regularizer function applied to
the `recurrent_kernel` weights matrix.
bias_regularizer: Regularizer function applied to the bias vector.
activity_regularizer: Regularizer function applied to.
kernel_constraint: Constraint function applied to
the `kernel` weights matrix.
recurrent_constraint: Constraint function applied to
the `recurrent_kernel` weights matrix.
bias_constraint: Constraint function applied to the bias vector.
return_sequences: Boolean. Whether to return the last output
in the output sequence, or the full sequence. (default False)
return_state: Boolean Whether to return the last state
in addition to the output. (default False)
go_backwards: Boolean (default False).
If True, process the input sequence backwards.
stateful: Boolean (default False). If True, the last state
for each sample at index i in a batch will be used as initial
state for the sample of index i in the following batch.
dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the inputs.
recurrent_dropout: Float between 0 and 1.
Fraction of the units to drop for
the linear transformation of the recurrent state.
Call arguments:
inputs: A 5D float tensor (see input shape description below).
mask: Binary tensor of shape `(samples, timesteps)` indicating whether
a given timestep should be masked.
training: Python boolean indicating whether the layer should behave in
training mode or in inference mode. This argument is passed to the cell
when calling it. This is only relevant if `dropout` or `recurrent_dropout`
are set.
initial_state: List of initial state tensors to be passed to the first
call of the cell.
Input shape:
- If data_format='channels_first'
5D tensor with shape:
`(samples, time, channels, rows, cols)`
- If data_format='channels_last'
5D tensor with shape:
`(samples, time, rows, cols, channels)`
Output shape:
- If `return_state`: a list of tensors. The first tensor is
the output. The remaining tensors are the last states,
each 4D tensor with shape:
`(samples, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)`
if data_format='channels_last'.
`rows` and `cols` values might have changed due to padding.
- If `return_sequences`: 5D tensor with shape:
`(samples, timesteps, filters, new_rows, new_cols)`
if data_format='channels_first'
or 5D tensor with shape:
`(samples, timesteps, new_rows, new_cols, filters)`
if data_format='channels_last'.
- Else, 4D tensor with shape:
`(samples, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)`
if data_format='channels_last'.
Raises:
ValueError: in case of invalid constructor arguments.
References:
- [Shi et al., 2015](http://arxiv.org/abs/1506.04214v1)
(the current implementation does not include the feedback loop on the
cells output).
Example:
```python
steps = 10
height = 32
width = 32
input_channels = 3
output_channels = 6
inputs = tf.keras.Input(shape=(steps, height, width, input_channels))
layer = tf.keras.layers.ConvLSTM2D(filters=output_channels, kernel_size=3)
outputs = layer(inputs)
```
"""
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
cell = ConvLSTM2DCell(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
bias_initializer=bias_initializer,
unit_forget_bias=unit_forget_bias,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
dtype=kwargs.get('dtype'))
super(ConvLSTM2D, self).__init__(cell,
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
def call(self, inputs, mask=None, training=None, initial_state=None):
return super(ConvLSTM2D, self).call(inputs,
mask=mask,
training=training,
initial_state=initial_state)
@property
def filters(self):
return self.cell.filters
@property
def kernel_size(self):
return self.cell.kernel_size
@property
def strides(self):
return self.cell.strides
@property
def padding(self):
return self.cell.padding
@property
def data_format(self):
return self.cell.data_format
@property
def dilation_rate(self):
return self.cell.dilation_rate
@property
def activation(self):
return self.cell.activation
@property
def recurrent_activation(self):
return self.cell.recurrent_activation
@property
def use_bias(self):
return self.cell.use_bias
@property
def kernel_initializer(self):
return self.cell.kernel_initializer
@property
def recurrent_initializer(self):
return self.cell.recurrent_initializer
@property
def bias_initializer(self):
return self.cell.bias_initializer
@property
def unit_forget_bias(self):
return self.cell.unit_forget_bias
@property
def kernel_regularizer(self):
return self.cell.kernel_regularizer
@property
def recurrent_regularizer(self):
return self.cell.recurrent_regularizer
@property
def bias_regularizer(self):
return self.cell.bias_regularizer
@property
def kernel_constraint(self):
return self.cell.kernel_constraint
@property
def recurrent_constraint(self):
return self.cell.recurrent_constraint
@property
def bias_constraint(self):
return self.cell.bias_constraint
@property
def dropout(self):
return self.cell.dropout
@property
def recurrent_dropout(self):
return self.cell.recurrent_dropout
def get_config(self):
config = {'filters': self.filters,
'kernel_size': self.kernel_size,
'strides': self.strides,
'padding': self.padding,
'data_format': self.data_format,
'dilation_rate': self.dilation_rate,
'activation': activations.serialize(self.activation),
'recurrent_activation': activations.serialize(
self.recurrent_activation),
'use_bias': self.use_bias,
'kernel_initializer': initializers.serialize(
self.kernel_initializer),
'recurrent_initializer': initializers.serialize(
self.recurrent_initializer),
'bias_initializer': initializers.serialize(self.bias_initializer),
'unit_forget_bias': self.unit_forget_bias,
'kernel_regularizer': regularizers.serialize(
self.kernel_regularizer),
'recurrent_regularizer': regularizers.serialize(
self.recurrent_regularizer),
'bias_regularizer': regularizers.serialize(self.bias_regularizer),
'activity_regularizer': regularizers.serialize(
self.activity_regularizer),
'kernel_constraint': constraints.serialize(
self.kernel_constraint),
'recurrent_constraint': constraints.serialize(
self.recurrent_constraint),
'bias_constraint': constraints.serialize(self.bias_constraint),
'dropout': self.dropout,
'recurrent_dropout': self.recurrent_dropout}
base_config = super(ConvLSTM2D, self).get_config()
del base_config['cell']
return dict(list(base_config.items()) + list(config.items()))
@classmethod
def from_config(cls, config):
return cls(**config) | PypiClean |
/google_ads_megalista-18.0.2-py3-none-any.whl/google/ads/googleads/v13/services/services/keyword_plan_campaign_service/transports/grpc.py | import warnings
from typing import Callable, Dict, Optional, Sequence, Tuple
from google.api_core import grpc_helpers
from google.api_core import gapic_v1
import google.auth # type: ignore
from google.auth import credentials as ga_credentials # type: ignore
from google.auth.transport.grpc import SslCredentials # type: ignore
import grpc # type: ignore
from google.ads.googleads.v13.services.types import (
keyword_plan_campaign_service,
)
from .base import KeywordPlanCampaignServiceTransport, DEFAULT_CLIENT_INFO
class KeywordPlanCampaignServiceGrpcTransport(
KeywordPlanCampaignServiceTransport
):
"""gRPC backend transport for KeywordPlanCampaignService.
Service to manage Keyword Plan campaigns.
This class defines the same methods as the primary client, so the
primary client can load the underlying transport implementation
and call it.
It sends protocol buffers over the wire using gRPC (which is built on
top of HTTP/2); the ``grpcio`` package must be installed.
"""
_stubs: Dict[str, Callable]
def __init__(
self,
*,
host: str = "googleads.googleapis.com",
credentials: ga_credentials.Credentials = None,
credentials_file: str = None,
scopes: Sequence[str] = None,
channel: grpc.Channel = None,
api_mtls_endpoint: str = None,
client_cert_source: Callable[[], Tuple[bytes, bytes]] = None,
ssl_channel_credentials: grpc.ChannelCredentials = None,
client_cert_source_for_mtls: Callable[[], Tuple[bytes, bytes]] = None,
quota_project_id: Optional[str] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
always_use_jwt_access: Optional[bool] = False,
) -> None:
"""Instantiate the transport.
Args:
host (Optional[str]):
The hostname to connect to.
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
This argument is ignored if ``channel`` is provided.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is ignored if ``channel`` is provided.
scopes (Optional(Sequence[str])): A list of scopes. This argument is
ignored if ``channel`` is provided.
channel (Optional[grpc.Channel]): A ``Channel`` instance through
which to make calls.
api_mtls_endpoint (Optional[str]): Deprecated. The mutual TLS endpoint.
If provided, it overrides the ``host`` argument and tries to create
a mutual TLS channel with client SSL credentials from
``client_cert_source`` or application default SSL credentials.
client_cert_source (Optional[Callable[[], Tuple[bytes, bytes]]]):
Deprecated. A callback to provide client SSL certificate bytes and
private key bytes, both in PEM format. It is ignored if
``api_mtls_endpoint`` is None.
ssl_channel_credentials (grpc.ChannelCredentials): SSL credentials
for the grpc channel. It is ignored if ``channel`` is provided.
client_cert_source_for_mtls (Optional[Callable[[], Tuple[bytes, bytes]]]):
A callback to provide client certificate bytes and private key bytes,
both in PEM format. It is used to configure a mutual TLS channel. It is
ignored if ``channel`` or ``ssl_channel_credentials`` is provided.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
always_use_jwt_access (Optional[bool]): Whether self signed JWT should
be used for service account credentials.
Raises:
google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport
creation failed for any reason.
google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``
and ``credentials_file`` are passed.
"""
self._grpc_channel = None
self._ssl_channel_credentials = ssl_channel_credentials
self._stubs: Dict[str, Callable] = {}
if api_mtls_endpoint:
warnings.warn("api_mtls_endpoint is deprecated", DeprecationWarning)
if client_cert_source:
warnings.warn(
"client_cert_source is deprecated", DeprecationWarning
)
if channel:
# Ignore credentials if a channel was passed.
credentials = False
# If a channel was explicitly provided, set it.
self._grpc_channel = channel
self._ssl_channel_credentials = None
else:
if api_mtls_endpoint:
host = api_mtls_endpoint
# Create SSL credentials with client_cert_source or application
# default SSL credentials.
if client_cert_source:
cert, key = client_cert_source()
self._ssl_channel_credentials = grpc.ssl_channel_credentials(
certificate_chain=cert, private_key=key
)
else:
self._ssl_channel_credentials = (
SslCredentials().ssl_credentials
)
else:
if client_cert_source_for_mtls and not ssl_channel_credentials:
cert, key = client_cert_source_for_mtls()
self._ssl_channel_credentials = grpc.ssl_channel_credentials(
certificate_chain=cert, private_key=key
)
# The base transport sets the host, credentials and scopes
super().__init__(
host=host,
credentials=credentials,
credentials_file=credentials_file,
scopes=scopes,
quota_project_id=quota_project_id,
client_info=client_info,
always_use_jwt_access=always_use_jwt_access,
)
if not self._grpc_channel:
self._grpc_channel = type(self).create_channel(
self._host,
# use the credentials which are saved
credentials=self._credentials,
# Set ``credentials_file`` to ``None`` here as
# the credentials that we saved earlier should be used.
credentials_file=None,
scopes=self._scopes,
ssl_credentials=self._ssl_channel_credentials,
quota_project_id=quota_project_id,
options=[
("grpc.max_send_message_length", -1),
("grpc.max_receive_message_length", -1),
],
)
# Wrap messages. This must be done after self._grpc_channel exists
self._prep_wrapped_messages(client_info)
@classmethod
def create_channel(
cls,
host: str = "googleads.googleapis.com",
credentials: ga_credentials.Credentials = None,
credentials_file: str = None,
scopes: Optional[Sequence[str]] = None,
quota_project_id: Optional[str] = None,
**kwargs,
) -> grpc.Channel:
"""Create and return a gRPC channel object.
Args:
host (Optional[str]): The host for the channel to use.
credentials (Optional[~.Credentials]): The
authorization credentials to attach to requests. These
credentials identify this application to the service. If
none are specified, the client will attempt to ascertain
the credentials from the environment.
credentials_file (Optional[str]): A file with credentials that can
be loaded with :func:`google.auth.load_credentials_from_file`.
This argument is mutually exclusive with credentials.
scopes (Optional[Sequence[str]]): A optional list of scopes needed for this
service. These are only used when credentials are not specified and
are passed to :func:`google.auth.default`.
quota_project_id (Optional[str]): An optional project to use for billing
and quota.
kwargs (Optional[dict]): Keyword arguments, which are passed to the
channel creation.
Returns:
grpc.Channel: A gRPC channel object.
Raises:
google.api_core.exceptions.DuplicateCredentialArgs: If both ``credentials``
and ``credentials_file`` are passed.
"""
return grpc_helpers.create_channel(
host,
credentials=credentials,
credentials_file=credentials_file,
quota_project_id=quota_project_id,
default_scopes=cls.AUTH_SCOPES,
scopes=scopes,
default_host=cls.DEFAULT_HOST,
**kwargs,
)
@property
def grpc_channel(self) -> grpc.Channel:
"""Return the channel designed to connect to this service.
"""
return self._grpc_channel
@property
def mutate_keyword_plan_campaigns(
self,
) -> Callable[
[keyword_plan_campaign_service.MutateKeywordPlanCampaignsRequest],
keyword_plan_campaign_service.MutateKeywordPlanCampaignsResponse,
]:
r"""Return a callable for the mutate keyword plan campaigns method over gRPC.
Creates, updates, or removes Keyword Plan campaigns. Operation
statuses are returned.
List of thrown errors: `AuthenticationError <>`__
`AuthorizationError <>`__ `DatabaseError <>`__ `FieldError <>`__
`FieldMaskError <>`__ `HeaderError <>`__ `InternalError <>`__
`KeywordPlanCampaignError <>`__ `KeywordPlanError <>`__
`ListOperationError <>`__ `MutateError <>`__ `QuotaError <>`__
`RangeError <>`__ `RequestError <>`__
`ResourceCountLimitExceededError <>`__
Returns:
Callable[[~.MutateKeywordPlanCampaignsRequest],
~.MutateKeywordPlanCampaignsResponse]:
A function that, when called, will call the underlying RPC
on the server.
"""
# Generate a "stub function" on-the-fly which will actually make
# the request.
# gRPC handles serialization and deserialization, so we just need
# to pass in the functions for each.
if "mutate_keyword_plan_campaigns" not in self._stubs:
self._stubs[
"mutate_keyword_plan_campaigns"
] = self.grpc_channel.unary_unary(
"/google.ads.googleads.v13.services.KeywordPlanCampaignService/MutateKeywordPlanCampaigns",
request_serializer=keyword_plan_campaign_service.MutateKeywordPlanCampaignsRequest.serialize,
response_deserializer=keyword_plan_campaign_service.MutateKeywordPlanCampaignsResponse.deserialize,
)
return self._stubs["mutate_keyword_plan_campaigns"]
def close(self):
self.grpc_channel.close()
__all__ = ("KeywordPlanCampaignServiceGrpcTransport",) | PypiClean |
/pyr2-2.0.0-py3-none-macosx_10_14_x86_64.whl/r2/r_magic.py | import ctypes
from .r_libs import r_anal as _libr_anal
from .r_libs import r_asm as _libr_asm
from .r_libs import r_bin as _libr_bin
from .r_libs import r_bp as _libr_bp
from .r_libs import r_config as _libr_config
from .r_libs import r_cons as _libr_cons
from .r_libs import r_core as _libr_core
from .r_libs import r_crypto as _libr_crypto
from .r_libs import r_debug as _libr_debug
from .r_libs import r_egg as _libr_egg
from .r_libs import r_flag as _libr_flag
from .r_libs import r_fs as _libr_fs
from .r_libs import r_hash as _libr_hash
from .r_libs import r_io as _libr_io
from .r_libs import r_lang as _libr_lang
from .r_libs import r_magic as _libr_magic
from .r_libs import r_main as _libr_main
from .r_libs import r_parse as _libr_parse
from .r_libs import r_reg as _libr_reg
from .r_libs import r_search as _libr_search
from .r_libs import r_socket as _libr_socket
from .r_libs import r_syscall as _libr_syscall
from .r_libs import r_util as _libr_util
_libraries = {}
def string_cast(char_pointer, encoding='utf-8', errors='strict'):
value = ctypes.cast(char_pointer, ctypes.c_char_p).value
if value is not None and encoding is not None:
value = value.decode(encoding, errors=errors)
return value
def char_pointer_cast(string, encoding='utf-8'):
if encoding is not None:
try:
string = string.encode(encoding)
except AttributeError:
# In Python3, bytes has no encode attribute
pass
string = ctypes.c_char_p(string)
return ctypes.cast(string, ctypes.POINTER(ctypes.c_char))
class AsDictMixin:
@classmethod
def as_dict(cls, self):
result = {}
if not isinstance(self, AsDictMixin):
# not a structure, assume it's already a python object
return self
if not hasattr(cls, "_fields_"):
return result
# sys.version_info >= (3, 5)
# for (field, *_) in cls._fields_: # noqa
for field_tuple in cls._fields_: # noqa
field = field_tuple[0]
if field.startswith('PADDING_'):
continue
value = getattr(self, field)
type_ = type(value)
if hasattr(value, "_length_") and hasattr(value, "_type_"):
# array
if not hasattr(type_, "as_dict"):
value = [v for v in value]
else:
type_ = type_._type_
value = [type_.as_dict(v) for v in value]
elif hasattr(value, "contents") and hasattr(value, "_type_"):
# pointer
try:
if not hasattr(type_, "as_dict"):
value = value.contents
else:
type_ = type_._type_
value = type_.as_dict(value.contents)
except ValueError:
# nullptr
value = None
elif isinstance(value, AsDictMixin):
# other structure
value = type_.as_dict(value)
result[field] = value
return result
class Structure(ctypes.Structure, AsDictMixin):
def __init__(self, *args, **kwds):
# We don't want to use positional arguments fill PADDING_* fields
args = dict(zip(self.__class__._field_names_(), args))
args.update(kwds)
super(Structure, self).__init__(**args)
@classmethod
def _field_names_(cls):
if hasattr(cls, '_fields_'):
return (f[0] for f in cls._fields_ if not f[0].startswith('PADDING'))
else:
return ()
@classmethod
def get_type(cls, field):
for f in cls._fields_:
if f[0] == field:
return f[1]
return None
@classmethod
def bind(cls, bound_fields):
fields = {}
for name, type_ in cls._fields_:
if hasattr(type_, "restype"):
if name in bound_fields:
# use a closure to capture the callback from the loop scope
fields[name] = (
type_((lambda callback: lambda *args: callback(*args))(
bound_fields[name]))
)
del bound_fields[name]
else:
# default callback implementation (does nothing)
try:
default_ = type_(0).restype().value
except TypeError:
default_ = None
fields[name] = type_((
lambda default_: lambda *args: default_)(default_))
else:
# not a callback function, use default initialization
if name in bound_fields:
fields[name] = bound_fields[name]
del bound_fields[name]
else:
fields[name] = type_()
if len(bound_fields) != 0:
raise ValueError(
"Cannot bind the following unknown callback(s) {}.{}".format(
cls.__name__, bound_fields.keys()
))
return cls(**fields)
class Union(ctypes.Union, AsDictMixin):
pass
c_int128 = ctypes.c_ubyte*16
c_uint128 = c_int128
void = None
if ctypes.sizeof(ctypes.c_longdouble) == 16:
c_long_double_t = ctypes.c_longdouble
else:
c_long_double_t = ctypes.c_ubyte*16
r_magic_version = _libr_magic.r_magic_version
r_magic_version.restype = ctypes.POINTER(ctypes.c_char)
r_magic_version.argtypes = []
class union_VALUETYPE(Union):
pass
union_VALUETYPE._pack_ = 1 # source:False
union_VALUETYPE._fields_ = [
('b', ctypes.c_ubyte),
('h', ctypes.c_uint16),
('l', ctypes.c_uint32),
('q', ctypes.c_uint64),
('hs', ctypes.c_ubyte * 2),
('hl', ctypes.c_ubyte * 4),
('hq', ctypes.c_ubyte * 8),
('s', ctypes.c_char * 32),
('f', ctypes.c_float),
('d', ctypes.c_double),
('PADDING_0', ctypes.c_ubyte * 24),
]
class struct_r_magic(Structure):
pass
class union_r_magic_0(Union):
pass
class struct_r_magic_0_0(Structure):
pass
struct_r_magic_0_0._pack_ = 1 # source:False
struct_r_magic_0_0._fields_ = [
('_count', ctypes.c_uint32),
('_flags', ctypes.c_uint32),
]
union_r_magic_0._pack_ = 1 # source:False
union_r_magic_0._fields_ = [
('_mask', ctypes.c_uint64),
('_s', struct_r_magic_0_0),
]
struct_r_magic._pack_ = 1 # source:False
struct_r_magic._fields_ = [
('cont_level', ctypes.c_uint16),
('flag', ctypes.c_ubyte),
('dummy1', ctypes.c_ubyte),
('reln', ctypes.c_ubyte),
('vallen', ctypes.c_ubyte),
('type', ctypes.c_ubyte),
('in_type', ctypes.c_ubyte),
('in_op', ctypes.c_ubyte),
('mask_op', ctypes.c_ubyte),
('cond', ctypes.c_ubyte),
('dummy2', ctypes.c_ubyte),
('offset', ctypes.c_uint32),
('in_offset', ctypes.c_uint32),
('lineno', ctypes.c_uint32),
('_u', union_r_magic_0),
('value', union_VALUETYPE),
('desc', ctypes.c_char * 64),
('mimetype', ctypes.c_char * 64),
]
class struct_mlist(Structure):
pass
struct_mlist._pack_ = 1 # source:False
struct_mlist._fields_ = [
('magic', ctypes.POINTER(struct_r_magic)),
('nmagic', ctypes.c_uint32),
('mapped', ctypes.c_int32),
('next', ctypes.POINTER(struct_mlist)),
('prev', ctypes.POINTER(struct_mlist)),
]
class struct_r_magic_set(Structure):
pass
class struct_out(Structure):
pass
struct_out._pack_ = 1 # source:False
struct_out._fields_ = [
('buf', ctypes.POINTER(ctypes.c_char)),
('pbuf', ctypes.POINTER(ctypes.c_char)),
]
class struct_cont(Structure):
pass
class struct_level_info(Structure):
pass
struct_cont._pack_ = 1 # source:False
struct_cont._fields_ = [
('len', ctypes.c_uint64),
('li', ctypes.POINTER(struct_level_info)),
]
class struct_r_magic_set_2(Structure):
pass
struct_r_magic_set_2._pack_ = 1 # source:False
struct_r_magic_set_2._fields_ = [
('s', ctypes.POINTER(ctypes.c_char)),
('s_len', ctypes.c_uint64),
('offset', ctypes.c_uint64),
('rm_len', ctypes.c_uint64),
]
struct_r_magic_set._pack_ = 1 # source:False
struct_r_magic_set._fields_ = [
('mlist', ctypes.POINTER(struct_mlist)),
('c', struct_cont),
('o', struct_out),
('offset', ctypes.c_uint32),
('error', ctypes.c_int32),
('flags', ctypes.c_int32),
('haderr', ctypes.c_int32),
('file', ctypes.POINTER(ctypes.c_char)),
('line', ctypes.c_uint64),
('search', struct_r_magic_set_2),
('ms_value', union_VALUETYPE),
]
struct_level_info._pack_ = 1 # source:False
struct_level_info._fields_ = [
('off', ctypes.c_int32),
('got_match', ctypes.c_int32),
('last_match', ctypes.c_int32),
('last_cond', ctypes.c_int32),
]
RMagic = struct_r_magic_set
r_magic_new = _libr_magic.r_magic_new
r_magic_new.restype = ctypes.POINTER(struct_r_magic_set)
r_magic_new.argtypes = [ctypes.c_int32]
r_magic_free = _libr_magic.r_magic_free
r_magic_free.restype = None
r_magic_free.argtypes = [ctypes.POINTER(struct_r_magic_set)]
r_magic_file = _libr_magic.r_magic_file
r_magic_file.restype = ctypes.POINTER(ctypes.c_char)
r_magic_file.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.POINTER(ctypes.c_char)]
r_magic_descriptor = _libr_magic.r_magic_descriptor
r_magic_descriptor.restype = ctypes.POINTER(ctypes.c_char)
r_magic_descriptor.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.c_int32]
size_t = ctypes.c_uint64
r_magic_buffer = _libr_magic.r_magic_buffer
r_magic_buffer.restype = ctypes.POINTER(ctypes.c_char)
r_magic_buffer.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.POINTER(None), size_t]
r_magic_error = _libr_magic.r_magic_error
r_magic_error.restype = ctypes.POINTER(ctypes.c_char)
r_magic_error.argtypes = [ctypes.POINTER(struct_r_magic_set)]
r_magic_setflags = _libr_magic.r_magic_setflags
r_magic_setflags.restype = None
r_magic_setflags.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.c_int32]
r_magic_load = _libr_magic.r_magic_load
r_magic_load.restype = ctypes.c_bool
r_magic_load.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.POINTER(ctypes.c_char)]
r_magic_load_buffer = _libr_magic.r_magic_load_buffer
r_magic_load_buffer.restype = ctypes.c_bool
r_magic_load_buffer.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.POINTER(ctypes.c_char)]
r_magic_compile = _libr_magic.r_magic_compile
r_magic_compile.restype = ctypes.c_bool
r_magic_compile.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.POINTER(ctypes.c_char)]
r_magic_check = _libr_magic.r_magic_check
r_magic_check.restype = ctypes.c_bool
r_magic_check.argtypes = [ctypes.POINTER(struct_r_magic_set), ctypes.POINTER(ctypes.c_char)]
r_magic_errno = _libr_magic.r_magic_errno
r_magic_errno.restype = ctypes.c_int32
r_magic_errno.argtypes = [ctypes.POINTER(struct_r_magic_set)]
__all__ = \
['RMagic', 'r_magic_buffer', 'r_magic_check', 'r_magic_compile',
'r_magic_descriptor', 'r_magic_errno', 'r_magic_error',
'r_magic_file', 'r_magic_free', 'r_magic_load',
'r_magic_load_buffer', 'r_magic_new', 'r_magic_setflags',
'r_magic_version', 'size_t', 'struct_cont', 'struct_level_info',
'struct_mlist', 'struct_out', 'struct_r_magic',
'struct_r_magic_0_0', 'struct_r_magic_set',
'struct_r_magic_set_2', 'union_VALUETYPE', 'union_r_magic_0'] | PypiClean |
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//# sourceMappingURL=76045-Swd8I3BgC9s.js.map | PypiClean |
/PyTikTokAPI-0.0.5.tar.gz/PyTikTokAPI-0.0.5/TikTokAPI/tiktokapi.py | import os
import string
import random
import urllib.parse
from .utils import random_key, build_get_url, get_req_json, get_req_content, get_req_text
from .tiktok_browser import TikTokBrowser
class VideoException(Exception):
pass
class TikTokAPI(object):
def __init__(self, cookie=None, language='en', browser_lang="en-US", timezone="Asia/Kolkata", region='IN'):
self.base_url = "https://t.tiktok.com/api"
self.user_agent = "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:79.0) Gecko/20100101 Firefox/79.0"
if cookie is None:
cookie = {}
self.verifyFp = cookie.get("s_v_web_id", "verify_kjf974fd_y7bupmR0_3uRm_43kF_Awde_8K95qt0GcpBk")
self.tt_webid = cookie.get("tt_webid", "6913027209393473025")
self.headers = {
'Host': 't.tiktok.com',
'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:79.0) Gecko/20100101 Firefox/79.0',
'Referer': 'https://www.tiktok.com/',
'Cookie': 'tt_webid_v2={}; tt_webid={}'.format(self.tt_webid, self.tt_webid)
}
self.language = language
self.browser_lang = browser_lang
self.timezone = timezone
self.region = region
self.default_params = {
"aid": "1988",
"app_name": "tiktok_web",
"device_platform": "web",
"referer": "",
"user_agent": urllib.parse.quote_plus(self.user_agent),
"cookie_enabled": "true",
"screen_width": "1920",
"screen_height": "1080",
"browser_language": self.browser_lang,
"browser_platform": "Linux+x86_64",
"browser_name": "Mozilla",
"browser_version": "5.0+(X11)",
"browser_online": "true",
"timezone_name": self.timezone,
# "page_referer": "https://www.tiktok.com/foryou?lang=en",
"priority_region": self.region,
"appId": "1180",
"region": self.region,
"appType": "t",
"isAndroid": "false",
"isMobile": "false",
"isIOS": "false",
"OS": "linux",
"tt-web-region": self.region,
"language": self.language,
"verifyFp": self.verifyFp
}
self.signature_key = "_signature"
self.did_key = "did"
self.tiktok_browser = TikTokBrowser(self.user_agent)
def send_get_request(self, url, params, extra_headers=None):
url = build_get_url(url, params)
did = ''.join(random.choice(string.digits) for num in range(19))
url = build_get_url(url, {self.did_key: did}, append=True)
signature = self.tiktok_browser.fetch_auth_params(url, language=self.language)
url = build_get_url(url, {self.signature_key: signature}, append=True)
if extra_headers is None:
headers = self.headers
else:
headers = {}
for key, val in extra_headers.items():
headers[key] = val
for key, val in self.headers.items():
headers[key] = val
data = get_req_json(url, params=None, headers=self.headers)
return data
def getTrending(self, count=30):
url = self.base_url + "/item_list/"
req_default_params = {
"id": "1",
"type": "5",
"secUid": "",
"maxCursor": "0",
"minCursor": "0",
"sourceType": "12",
}
params = {
"count": str(count)
}
for key, val in req_default_params.items():
params[key] = val
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def getUserByName(self, user_name):
url = "https://t.tiktok.com/node/share/user/@" + user_name
params = {
"uniqueId": user_name,
"validUniqueId": user_name,
}
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def getVideosByUserName(self, user_name, count=30):
user_data = self.getUserByName(user_name)
user_obj = user_data["userInfo"]["user"]
user_id = user_obj["id"]
secUid = user_obj["secUid"]
url = self.base_url + "/item_list/"
req_default_params = {
"type": "1",
"maxCursor": "0",
"minCursor": "0",
"sourceType": "8",
}
params = {
"id": user_id,
"secUid": secUid,
"count": str(count)
}
for key, val in req_default_params.items():
params[key] = val
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def getLikesByUserName(self, user_name, count=30):
user_data = self.getUserByName(user_name)
user_obj = user_data["userInfo"]["user"]
user_id = user_obj["id"]
secUid = user_obj["secUid"]
url = self.base_url + "/item_list/"
req_default_params = {
"type": "2",
"maxCursor": "0",
"minCursor": "0",
"sourceType": "9",
}
params = {
"id": user_id,
"secUid": secUid,
"count": str(count)
}
for key, val in req_default_params.items():
params[key] = val
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def getHashTag(self, hashTag):
url = self.base_url + "/challenge/detail/"
params = {
"challengeName": hashTag.replace("#", "")
}
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def getVideosByHashTag(self, hashTag, count=30):
hashTag = hashTag.replace("#", "")
hashTag_obj = self.getHashTag(hashTag)
hashTag_id = hashTag_obj["challengeInfo"]["challenge"]["id"]
url = self.base_url + "/challenge/item_list/"
req_default_params = {
"secUid": "",
"type": "3",
"minCursor": "0",
"maxCursor": "0",
"shareUid": "",
"recType": ""
}
params = {
"challengeID": str(hashTag_id),
"count": str(count),
"cursor": "0",
}
for key, val in req_default_params.items():
params[key] = val
for key, val in self.default_params.items():
params[key] = val
extra_headers = {"Referer": "https://www.tiktok.com/tag/" + str(hashTag)}
return self.send_get_request(url, params, extra_headers=extra_headers)
def getMusic(self, music_id):
url = self.base_url + "/music/detail/"
params = {
"musicId": music_id
}
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def getVideosByMusic(self, music_id, count=30):
url = self.base_url + "/music/item_list/"
req_default_params = {
"secUid": "",
"type": "4",
"minCursor": "0",
"maxCursor": "0",
"shareUid": "",
"recType": ""
}
params = {
"musicID": str(music_id),
"count": str(count),
"cursor": "0",
}
for key, val in req_default_params.items():
params[key] = val
for key, val in self.default_params.items():
params[key] = val
extra_headers = {"Referer": "https://www.tiktok.com/music/original-sound-" + str(music_id)}
return self.send_get_request(url, params, extra_headers=extra_headers)
def getVideoById(self, video_id):
url = self.base_url + "/item/detail/"
params = {
"itemId": str(video_id)
}
for key, val in self.default_params.items():
params[key] = val
return self.send_get_request(url, params)
def downloadVideoById(self, video_id, save_path):
video_info = self.getVideoById(video_id)
video_url = video_info["itemInfo"]["itemStruct"]["video"]["playAddr"]
video_data = get_req_content(video_url, params=None, headers=self.headers)
with open(save_path, 'wb') as f:
f.write(video_data)
def downloadVideoByIdNoWatermark(self, video_id, save_path):
video_info = self.getVideoById(video_id)
video_url = video_info["itemInfo"]["itemStruct"]["video"]["downloadAddr"]
video_data = get_req_text(video_url, params=None, headers=self.headers)
pos = video_data.find("vid:")
if pos == -1:
raise VideoException("Video without watermark not available in new videos")
video_url_no_wm = "https://api2-16-h2.musical.ly/aweme/v1/play/?video_id={" \
"}&vr_type=0&is_play_url=1&source=PackSourceEnum_PUBLISH&media_type=4" \
.format(video_data[pos + 4:pos + 36])
video_data_no_wm = get_req_content(video_url_no_wm, params=None, headers=self.headers)
with open(save_path, 'wb') as f:
f.write(video_data_no_wm) | PypiClean |
/accelbyte_py_sdk-0.48.0.tar.gz/accelbyte_py_sdk-0.48.0/accelbyte_py_sdk/api/ugc/operations/admin_group/single_admin_get_all_groups.py |
# template file: ags_py_codegen
# pylint: disable=duplicate-code
# pylint: disable=line-too-long
# pylint: disable=missing-function-docstring
# pylint: disable=missing-module-docstring
# pylint: disable=too-many-arguments
# pylint: disable=too-many-branches
# pylint: disable=too-many-instance-attributes
# pylint: disable=too-many-lines
# pylint: disable=too-many-locals
# pylint: disable=too-many-public-methods
# pylint: disable=too-many-return-statements
# pylint: disable=too-many-statements
# pylint: disable=unused-import
# AccelByte Gaming Services Ugc Service (2.12.0)
from __future__ import annotations
from typing import Any, Dict, List, Optional, Tuple, Union
from .....core import Operation
from .....core import HeaderStr
from .....core import HttpResponse
from ...models import ModelsPaginatedGroupResponse
from ...models import ResponseError
class SingleAdminGetAllGroups(Operation):
"""Get all user groups (SingleAdminGetAllGroups)
Required permission ADMIN:NAMESPACE:{namespace}:USER:{userId}:CONTENTGROUP [READ].
Required Permission(s):
- ADMIN:NAMESPACE:{namespace}:USER:{userId}:CONTENTGROUP [READ]
Properties:
url: /ugc/v1/admin/namespaces/{namespace}/groups
method: GET
tags: ["Admin Group"]
consumes: ["application/json"]
produces: ["application/json"]
securities: [BEARER_AUTH]
namespace: (namespace) REQUIRED str in path
limit: (limit) OPTIONAL int in query
offset: (offset) OPTIONAL int in query
Responses:
200: OK - ModelsPaginatedGroupResponse (OK)
401: Unauthorized - ResponseError (Unauthorized)
404: Not Found - ResponseError (Not Found)
500: Internal Server Error - ResponseError (Internal Server Error)
"""
# region fields
_url: str = "/ugc/v1/admin/namespaces/{namespace}/groups"
_method: str = "GET"
_consumes: List[str] = ["application/json"]
_produces: List[str] = ["application/json"]
_securities: List[List[str]] = [["BEARER_AUTH"]]
_location_query: str = None
namespace: str # REQUIRED in [path]
limit: int # OPTIONAL in [query]
offset: int # OPTIONAL in [query]
# endregion fields
# region properties
@property
def url(self) -> str:
return self._url
@property
def method(self) -> str:
return self._method
@property
def consumes(self) -> List[str]:
return self._consumes
@property
def produces(self) -> List[str]:
return self._produces
@property
def securities(self) -> List[List[str]]:
return self._securities
@property
def location_query(self) -> str:
return self._location_query
# endregion properties
# region get methods
# endregion get methods
# region get_x_params methods
def get_all_params(self) -> dict:
return {
"path": self.get_path_params(),
"query": self.get_query_params(),
}
def get_path_params(self) -> dict:
result = {}
if hasattr(self, "namespace"):
result["namespace"] = self.namespace
return result
def get_query_params(self) -> dict:
result = {}
if hasattr(self, "limit"):
result["limit"] = self.limit
if hasattr(self, "offset"):
result["offset"] = self.offset
return result
# endregion get_x_params methods
# region is/has methods
# endregion is/has methods
# region with_x methods
def with_namespace(self, value: str) -> SingleAdminGetAllGroups:
self.namespace = value
return self
def with_limit(self, value: int) -> SingleAdminGetAllGroups:
self.limit = value
return self
def with_offset(self, value: int) -> SingleAdminGetAllGroups:
self.offset = value
return self
# endregion with_x methods
# region to methods
def to_dict(self, include_empty: bool = False) -> dict:
result: dict = {}
if hasattr(self, "namespace") and self.namespace:
result["namespace"] = str(self.namespace)
elif include_empty:
result["namespace"] = ""
if hasattr(self, "limit") and self.limit:
result["limit"] = int(self.limit)
elif include_empty:
result["limit"] = 0
if hasattr(self, "offset") and self.offset:
result["offset"] = int(self.offset)
elif include_empty:
result["offset"] = 0
return result
# endregion to methods
# region response methods
# noinspection PyMethodMayBeStatic
def parse_response(
self, code: int, content_type: str, content: Any
) -> Tuple[
Union[None, ModelsPaginatedGroupResponse],
Union[None, HttpResponse, ResponseError],
]:
"""Parse the given response.
200: OK - ModelsPaginatedGroupResponse (OK)
401: Unauthorized - ResponseError (Unauthorized)
404: Not Found - ResponseError (Not Found)
500: Internal Server Error - ResponseError (Internal Server Error)
---: HttpResponse (Undocumented Response)
---: HttpResponse (Unexpected Content-Type Error)
---: HttpResponse (Unhandled Error)
"""
pre_processed_response, error = self.pre_process_response(
code=code, content_type=content_type, content=content
)
if error is not None:
return None, None if error.is_no_content() else error
code, content_type, content = pre_processed_response
if code == 200:
return ModelsPaginatedGroupResponse.create_from_dict(content), None
if code == 401:
return None, ResponseError.create_from_dict(content)
if code == 404:
return None, ResponseError.create_from_dict(content)
if code == 500:
return None, ResponseError.create_from_dict(content)
return self.handle_undocumented_response(
code=code, content_type=content_type, content=content
)
# endregion response methods
# region static methods
@classmethod
def create(
cls,
namespace: str,
limit: Optional[int] = None,
offset: Optional[int] = None,
**kwargs,
) -> SingleAdminGetAllGroups:
instance = cls()
instance.namespace = namespace
if limit is not None:
instance.limit = limit
if offset is not None:
instance.offset = offset
return instance
@classmethod
def create_from_dict(
cls, dict_: dict, include_empty: bool = False
) -> SingleAdminGetAllGroups:
instance = cls()
if "namespace" in dict_ and dict_["namespace"] is not None:
instance.namespace = str(dict_["namespace"])
elif include_empty:
instance.namespace = ""
if "limit" in dict_ and dict_["limit"] is not None:
instance.limit = int(dict_["limit"])
elif include_empty:
instance.limit = 0
if "offset" in dict_ and dict_["offset"] is not None:
instance.offset = int(dict_["offset"])
elif include_empty:
instance.offset = 0
return instance
@staticmethod
def get_field_info() -> Dict[str, str]:
return {
"namespace": "namespace",
"limit": "limit",
"offset": "offset",
}
@staticmethod
def get_required_map() -> Dict[str, bool]:
return {
"namespace": True,
"limit": False,
"offset": False,
}
# endregion static methods | PypiClean |
/django_htmx_ui_adminlte-0.1.13-py3-none-any.whl/django_htmx_ui_adminlte/forms.py | from django import forms
from django.contrib.auth.models import User
class SignupForm(forms.Form):
email = forms.EmailField(label='',
widget=forms.EmailInput(attrs={'id': 'email',
'class': "form-control",
'placeholder': 'Email'}),
required=True)
password = forms.CharField(label='',
widget=forms.PasswordInput(attrs={'id':'password',
'class': "form-control",
'placeholder': 'Password'}),
min_length=4,
required=True)
confirm_password = forms.CharField(label='',
widget=forms.PasswordInput(attrs={'id': 'confirm_password',
'class': "form-control",
'placeholder': 'Confirm password'}),
min_length=4,
required=True)
def clean(self):
email = self.cleaned_data.get('email')
password = self.cleaned_data.get('password')
confirm_password = self.cleaned_data.get('confirm_password')
if password != confirm_password:
self.add_error('password', 'Passwords do not match.')
return
if len(User.objects.filter(username=email)) > 0:
self.add_error('email', 'There is already an account with this email.')
return
return self.cleaned_data
class LoginForm(forms.Form):
email = forms.EmailField(label='',
widget=forms.EmailInput(attrs={'id': 'email',
'class': "form-control",
'placeholder': 'Email'}),
required=True)
password = forms.CharField(label='',
widget=forms.PasswordInput(attrs={'id': 'password',
'class': "form-control",
'placeholder': 'Password'}),
min_length=4,
required=True) | PypiClean |
/package-0.1.1.tar.gz/package-0.1.1/doc/Design.rst | This document describes the design goals and details of `package`, the package
package package.
Glossary
--------
author
A person who writes and distributes Python packages.
package
`package` has a double meaning in Python. It means either `namespace` or
`distribution`. In this text, it means the latter.
user
A person who installs and uses Python packages.
Design Goals
------------
* Give authors a simple way to start new packages.
* Put all the author/package info in a yaml file. (instead of `setup.py`)
* Lead users through the installation matrix with lots of helpful suggestions.
* Provide authors with a set of simple standards for various author tasks.
* Provide an easy upgrade path for new releases of package-py.
| PypiClean |
/aisdb-1.6.5-cp39-none-win_amd64.whl/aisdb_web/dist_map_bingmaps/assets/render-0afdbaa3.js | import{w as on,u as Qn,l as gn}from"./map-e7b21464.js";import"./main-b50358e6.js";import"./proj-04f64360.js";import"./constants-f1a52cc9.js";/*!
* html2canvas 1.4.1 <https://html2canvas.hertzen.com>
* Copyright (c) 2022 Niklas von Hertzen <https://hertzen.com>
* Released under MIT License
*//*! *****************************************************************************
Copyright (c) Microsoft Corporation.
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
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 P P P P P    + +  + P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P P + + + + + + + + + + +                                              P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P    P                   P        P P P P P P P P P P     K K K K K K K K K K  P P P P P P P P P P P P P P P P    + P P P P P P P P + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P + + + P P P P + +  P        + +   + +    P + + + + + + + +  + + + + P P + P P P   + + K K K K K K K K K K P P   P P P P P L   P   + +    + P P P P P P + + + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P P + P P + P P + +  +      + + + +   + +    + + +  + + + + + + + P P P P + P + + + + + + + K K K K K K K K K K   P P P   + + + + + + + + + +    + P P P P P P P P P + P P P + P P P P P P P P P P P P P P + P P P P P P P + P P + P P P P P + +  P         +    +    + + P + + + + + + + + + + + + + + + P P   + + K K K K K K K K K K   + + + + + + + P       +    + P P P P P P P P + + P P + + P P P P P P P P P P P P P      + +   + +    + + + + + + +    + + + + P P + P P P   + + K K K K K K K K K K  P P P P P P P + + + + + + + + + +  P + P P P P P P + + + P P P + P P P P + + + P P + P + P P + + + P P + + + P P P + + + P P P P P P P P P P P P + + + +      + + +    +     + + P + + + + + +  + + + + + + + + + + K K K K K K K K K K P P P         + + + + +      P P P P P P P P + P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P + + + P        +    +     + + + + + + +   + P P P + + + + + P P   + + K K K K K K K K K K + + + + + + +  P P P P P P P  P     P P P P P P P P + P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P + P P P P P + +  P        +    +     + + + + + + +   + + + + + + + P + P P   + + K K K K K K K K K K + P P + + + + + + + + + + + + +     P P P P P P P P P + P P P + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P        +    +     P  + + + + P P P  P P P P P P P P P P   + + K K K K K K K K K K P P P P P P P P P  P P P P P P +    + P P P P P P P P P P P P P P P P P P + + + P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P + P + + P P P P P P P + + +  + + + +       +  +         + + + + + + K K K K K K K K K K + +    + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * \ \ * * * * * * * + + + +  \ \ \ \ \ \ \ * * * * * * * *  K K K K K K K K K K   + + + + + \ \ + \ + \ \ \ \ \ + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + \ + \ \ \ \ \ \ \ \ \ \ * \ \ * * * * * * * * * \ + + \ \ \ \ \ + \ + * * * * * * + + K K K K K K K K K K + + \ \ \ \ P        	    	      	              K K K K K K K K K K P P P P P P P P P P             P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P + + + +                        P P P P P            +                                 +                +            	 	 + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * * * * * * * * * \ K K K K K K K K K K       \ \ \ \ \ \ * * * * \ \ \ \ * * * \ * * * \ \ * * * * * * * \ \ \ * * * * \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * \ * K K K K K K K K K K * * * * * * P P P P P P + P + + + + + P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P  P P P P X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z P P P P P P P P P + P P P P + + P P P P P P P + P + P P P P + + P P P P P P P P P + P P P P + + P P P P P P P P P P P P P P P P P + P P P P + + P P P P P P P + P + P P P P + + P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P + P P P P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + +             P P P P P P P P P P P P P P P P P P P P + + + P P P P P P P P P P P P P P P P           + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + P P P P P P + +  P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P P P P P P P P P  P P P P P P P P P P P P P P P P P P P P P P P P P P   + + + P P P P P P P P P P P    P P P P P P P P P P P + + + + + + + P P P P P P P P P P P P P + P P P P    + + + + + + + + + + + P P P P P P P P P P P P P P P P P P      + + + + + + + + + P P P P P P P P P P P P P P P P P P   + + + + + + + + + + + + P P P P P P P P P P P P P + P P P +   + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * * * * * * * * * * *    \     \ * + + K K K K K K K K K K + + + + + + P P P P P P P P P P + + + + + +               	 + K K K K K K K K K K + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + P P P P P   P P P P P P P P P P P P P P P P P P P P P P P P P P  P + + + + + P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P +             + + + +             + + + +  + + +   K K K K K K K K K K \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + \ \ \ \ \ + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + + + + + K K K K K K K K K K \ + + + * * P P P P P P P P P P P P P P P P P P P P P P P      + +   \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * * * * * * * * + * * * * * * * * * * * * * * * * * * * * * * * * * * * * * + +  K K K K K K K K K K + + + + + + K K K K K K K K K K + + + + + + * * * * * * * \ * * * * * * + +                  + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +      P P P P P P P P P P P P P P P P P P P P P P P P P P P      P P P P P P P + + + + K K K K K K K K K K                                    + + +    P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P              P P K K K K K K K K K K P P P P P P P P               + + + + + + + +     P P P P                     + + +      K K K K K K K K K K + + + P P P K K K K K K K K K K P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + + P P P         + + + + + + + +                  P P P P  P P P P P P  P P    P + + + + +                           +                            + + P P P P P P + + P P P P P P P P + P + P + P +                                + +                      +         P    P P P +           P P P P + +       +                    + + P P P +          +        	          	                              	                                                      +           P P + + O P P P P P      O P O O O O P P P P P      + P P P P P P P P P P P P P + + +                                                                   P     P            O  P    P P P P P       P  P  P  P O P P        P P P P P                 P  P P P P O O P P P P P O P P O P O O O O O O O O O O O O P P P P O O O O O O O O O O P P P P P P P P P   P P P P O   + + + +                                                                                                                                                                                                                                                                                           % %                                 % % % %                + + + + + + + + + + + + + + + + + + + + + + + + + O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O                                   O O O O O O O O O O O O O O O O O O O O O P                                                                                                                                                                             % % % %                 % %   %  % % %   % %                          % % %                                             %                       %                                                              % % % % % % % % % % % %     %  % % %  % %    % %   %   % % %         %       % % % % %  % %   %   % % % % % % % %    % %                                               %                  O O O O O O O O O O O O O O O O O O O O O O                                                                                                                                                                                                             + +                         +                              P P P P                     + + + + +     P   P P P P P P P P + + + + + + + P  + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + P P P P P P P + P P P P P P P + P P P P P P P + P P P P P P P +                                                P                                    + + + + + + + + + + + + + % % % % % % % % % % % % % % % % % % % % % % % % % % + % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % + + + + + + + + + + + + % % % % % % % % % % % % % % % % % % % % % % + + + + + + + + + + + + + + + + + + % % % % % % % % % % % % + + + +    % % G W W           % %             % W W W W W W W W W       % W W W W 6 % % W W W G G % % % + Q W Q W Q W Q W Q W W W W W W W W W W W W W W W W W W W W W W W W W Q W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W Q W Q W Q W W W W W W Q W W W W W W Q Q + +     G G W  Q W Q W Q W Q W Q W W W W W W W W W W W W W W W W W W W W W W W W W Q W Q W Q W W W W W W Q W W W W W W Q Q W W W W  Q G G W + + + + + W W W W W W W W W W W W W W W W W W W W W W W W W W W + W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W + % % W W W W % % % % % % % % % % + + + + + + + + + + + + Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % + W W W W W W W W W W % % % % % % % % % % % % % % % % % % % % % % O O O O O O O O % W W W W W W W W W W W W W W W % % % % % % % % % % % % % % % % % W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W % % % W W W W W W W W W W W W W W W W W W W W W G W W W W W W W W W W W W W W W + + + % % % % % % % % % % % % % % % % % % % + + + + + + + + + P P P P P P P P P P P P P P P P P    P P P P P P P P P P P P P P P P K K K K K K K K K K P P + + + + + + + + + + + + + + + + + + + +               P                 P                             P P   P P P P P P P P P P P P P P P P         + + + + + + + +                        P P P P P P P P P                 P                P       P                 + +          + + + + + + + + + + + + + + + + + + + + + +   P P P P P P P P P P P  P P P  P P P P  P P P P P P P P P P P P P P P P P P P P P P P           + + + P P P P P P     + + + + + + P P P P P P P P P P P P P P P P P P P P     + + + + + + + +   P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P       + + + + + + + +   K K K K K K K K K K + + + + + +                   P P P P P P    P  P P  P P P P P P           P P P P P P P P P P P P P P P P P P P              + + + + + + + + + + +  X X X X X X X X X X X X X X X X X X X X X X X X X X X X X + + +               + P K K K K K K K K K K + + + +   \ \ \ \ \ * \ \ \ \ \ \ \ \ \ \ K K K K K K K K K K \ \ \ \ \ + P P P P P P P P P               + + + + + + + + + P P P  P P P P P P P P   + + K K K K K K K K K K + +     \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * * * \ * * * \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ * \ * * * \ \ * * \ \ \ \ \ * * \ * \ + + + + + + + + + + + + + + + + + + + + + + + + \ \ \ * * P P P P P P P P P P P        P P P   + + + + + + + + + + P P P P P P + + P P P P P P + + P P P P P P + + + + + + + + + P P P P P P P + P P P P P P P + P P P P P P P P P P P P P P P P P P   + + + + P P P P P P P P P P P P P P P P P P P            + + K K K K K K K K K K + + + + + + T U U U U U U U U U U U U U U U U U U U U U U U U U U U T U U U U U U U U U U U U U U U U U U U U U U U U U U U + + + + + + + + + + + + Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y + + + + Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z Z + + + +                                 W W W W W W W W W W W W W W % % W W W W W W W W W W W W W W W W W W W W W W W W W W % % % % % % P P P P P P P + + + + + + + + + + + + P P P P P + + + + + V  V V V V V V V V V V  V V V V V V V V V V V V V + V V V V V + V + V V + V V + V V V V V V V V V V P P P P P P P P P P P P P P P P P P                 + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P   + +                           + + + + + +                 % % % % %             % %   % % % % % % %  %  +     %       % % % % % % % % + %   % + + + + P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + +  +  % %   % %   % %  %  % W W W W W W W W W W   % % %  % W W W W W W W W W W W W W W W W W W W W W W W W W W  %  % % % W W W W W W W W W W W W W W W W W W W W W W W W W W  %  %        W Q Q Q Q Q Q Q Q Q Q W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W W G G + + W W W W W W + + W W W W W W + + W W W W W W + + W W W + + +   % % %   +        + + + + + + + + + +      + + P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P + P P P P P P P P P P P P P P P P P P P + P P + P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + +    + + + + P P P P P P P P P P P P P P P P P P P P P P P P P + + +          P P P P P P P P P P P P P P P P P P P P P P P P P              P P    +              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +                               + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + + + + P P P P P P P P P P P P P P P P P P P P P P      + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P +  P P P P + + + + P P P P P P P P  P P P P P + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + + + P P P P P P P P P P P P P P P P + + + + + + + + + + +  + + + + + + + + + + + + + + + + P P P P P P + + P + P P P P P P P P P P P P P P P P P P P P P P P P + P P + + + P + + P P P P P P P P P P P P P P P P P P P P P P P +  P P P P P P P P P P P P P P P P P P P P P P P   P P P P P P P + + + + + + + P P P P P P P P P + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + P P + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P + + + + P P P P P    +   + + + + +     P P P P + P P P + P P P P P P P P P P P P P P P P P P P P P P P P P + +    + + + +  P P P P P P P P P + + + + + + +          + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P P  P P P P P P P P  P P P P P P P P P P P P P P P P P P P P P P P P   + + + + P P P P P        + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P + + +        P P P P P P P P P P P P P P P P P P + + + + + + +     + + + + + + + + + + + + P P P P P P P + + + + + + + + + + + + + + + + P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P + + + + + + + P P P P P P     + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P P P +    + + P P + + + + + + + + + + + + + + P P P P P P            P P P P      + + + + + +               + + + + P P P P P P P P P P P P P P P P K K K K K K K K K K + + + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P                   + + + + + + + + + + +  + + P P P P P P P P P P P P P P P P P + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P               + K K K K K K K K K K     P   P + + + + + + + + P P P P P P P P P P P P P P P P P P P    P + + + + + + + + +  P P P P            K K K K K K K K K K P  P    + P P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + P P P P P P P P P P P P P P P P P P + P P P P P P P P P P P P P                    + P P P P P P P + P + P P P P + P P P P P P P P P P P P P P P + P P P P P P P P P P  + + + + + + P P P P P P P P P P P P P P P P            + + + + + K K K K K K K K K K + + + + + +     + P P P P P P P P + + P P + + P P P P P P P P P P P P P P + P P P P P P P + P P + P P P P P +   P        + +   + +    + + P + + + + + +  + + + + + P P P P P   + +        + + +      + + + + + + + + + + +        P P P P      K K K K K K K K K K   +   P P P + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +     P P  P + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P P P P P P P P        + +                                 P P P P   + +     P + + + + + + + + + + + K K K K K K K K K K + + + + + +              + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P              P + + + + + + + K K K K K K K K K K + + + + + + + + + + + + + + + + + + + + + + \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ + + * * * * * * * * * * * * * * * + + + + K K K K K K K K K K \ \    * P P P P P P P P P P P P                 + + + + K K K K K K K K K K P P P P P P P P P + + + + + + + + + + + + P P P P P P P P + + P + + P P P P P P P P + P P + P P P P P P P P P P P P P P P P       +   + +     P  P      + + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P P P + + P P P P P P P P P P P P P P P P P P P P P P P        + +        P  P  + + + + + + + + + + + + + + + + + + + + + + + + + + + P           P P P P P P P P P P P P P P P P P P P P P P P P        P              + + + + + + + + P            P P P P P P P P P P                    P      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P + P P P P P P P P P P P P P P P P P P P P P P P P P         +         P      + + + + + + + + + + K K K K K K K K K K P P P P P P P P P P P P P P P + + +   P P P P P P P P P P P P P P P P P P + +               +               + + + + + + + + + P P P P P P P + P P + P P P P P P P P P P P P P P P P P P P P P P       + + +  +   +        P  + + + + + + + + K K K K K K K K K K + + + + + + P P P P P P + P P + P P P P P P P P P P P P P P P P P P P P P P P P      +   +      P + + + + + + + P P P P P P P P P P P P P P P P P P P       + + + + + + + + + + + + + + + + + + + P + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P                              + + + + + + + + + + + + +  P P P P P P P P P P P P P P P P P P P P P P P P P P + + + + + + P P P P P P P P P P P P P P P +      + + + + + + + + + + + P P P P + + + + + + + + + + + + + + + + + + + + + + + + + + + + P P P P P P P P P P P P P P P P P P P P P P P P H H H C C C P P P P C P P P H C H C P P P P P P P P P P P P P P P P P P P P P P P P P P P H C C P P P P P P P P P P P P P P P + 	 	 	 	 	 	 	   + + + + + + + P P P P P P P P P P P P P P H C P P P P P P P P P P P P P P P P P P P + + + + + + + + + + + + + + + + + + + + + + + + + K K K K K K K K K K + + + +   + + + + + + + + + + + + + + + + P P P P P P P P P P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e===Xe||e===qn||e===jn},_e=function(e){return Ls(e)||Ds(e)||e===es},dt=function(e){return _e(e)||P(e)||e===Z},bs=function(e){return e>=Cs&&e<=fs||e===Us||e>=Fs&&e<=hs||e===ds},UA=function(e,A){return e!==ne?!1:A!==Xe},Ke=function(e,A,t){return e===Z?_e(A)||UA(A,t):_e(e)?!0:!!(e===ne&&UA(e,A))},ur=function(e,A,t){return e===DA||e===Z?P(A)?!0:A===ge&&P(t):P(e===ge?A:e)},xs=function(e){var A=0,t=1;(e[A]===DA||e[A]===Z)&&(e[A]===Z&&(t=-1),A++);for(var r=[];P(e[A]);)r.push(e[A++]);var B=r.length?parseInt(S.apply(void 0,r),10):0;e[A]===ge&&A++;for(var n=[];P(e[A]);)n.push(e[A++]);var s=n.length,i=s?parseInt(S.apply(void 0,n),10):0;(e[A]===lB||e[A]===cB)&&A++;var a=1;(e[A]===DA||e[A]===Z)&&(e[A]===Z&&(a=-1),A++);for(var o=[];P(e[A]);)o.push(e[A++]);var Q=o.length?parseInt(S.apply(void 0,o),10):0;return t*(B+i*Math.pow(10,-s))*Math.pow(10,a*Q)},Ts={type:2},Ss={type:3},Os={type:4},Ms={type:13},Gs={type:8},Rs={type:21},Vs={type:9},Ns={type:10},Xs={type:11},_s={type:12},Ps={type:14},me={type:23},Js={type:1},ks={type:25},Ys={type:24},Ws={type:26},Zs={type:27},qs={type:28},js={type:29},zs={type:31},Nr={type:32},fB=function(){function e(){this._value=[]}return e.prototype.write=function(A){this._value=this._value.concat($e(A))},e.prototype.read=function(){for(var A=[],t=this.consumeToken();t!==Nr;)A.push(t),t=this.consumeToken();return A},e.prototype.consumeToken=function(){var A=this.consumeCodePoint();switch(A){case He:return this.consumeStringToken(He);case zn:var t=this.peekCodePoint(0),r=this.peekCodePoint(1),B=this.peekCodePoint(2);if(dt(t)||UA(r,B)){var n=Ke(t,r,B)?Zn:Wn,s=this.consumeName();return{type:5,value:s,flags:n}}break;case $n:if(this.peekCodePoint(0)===qA)return this.consumeCodePoint(),Ms;break;case pe:return this.consumeStringToken(pe);case Ie:return Ts;case jA:return Ss;case cr:if(this.peekCodePoint(0)===qA)return this.consumeCodePoint(),Ps;break;case DA:if(ur(A,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(A),this.consumeNumericToken();break;case cs:return Os;case Z:var i=A,a=this.peekCodePoint(0),o=this.peekCodePoint(1);if(ur(i,a,o))return this.reconsumeCodePoint(A),this.consumeNumericToken();if(Ke(i,a,o))return this.reconsumeCodePoint(A),this.consumeIdentLikeToken();if(a===Z&&o===Bs)return this.consumeCodePoint(),this.consumeCodePoint(),Ys;break;case ge:if(ur(A,this.peekCodePoint(0),this.peekCodePoint(1)))return this.reconsumeCodePoint(A),this.consumeNumericToken();break;case Ut:if(this.peekCodePoint(0)===cr)for(this.consumeCodePoint();;){var Q=this.consumeCodePoint();if(Q===cr&&(Q=this.consumeCodePoint(),Q===Ut))return this.consumeToken();if(Q===nA)return this.consumeToken()}break;case us:return Ws;case ls:return Zs;case ts:if(this.peekCodePoint(0)===rs&&this.peekCodePoint(1)===Z&&this.peekCodePoint(2)===Z)return this.consumeCodePoint(),this.consumeCodePoint(),ks;break;case ns:var g=this.peekCodePoint(0),w=this.peekCodePoint(1),f=this.peekCodePoint(2);if(Ke(g,w,f)){var s=this.consumeName();return{type:7,value:s}}break;case ss:return qs;case ne:if(UA(A,this.peekCodePoint(0)))return this.reconsumeCodePoint(A),this.consumeIdentLikeToken();break;case as:return js;case is:if(this.peekCodePoint(0)===qA)return this.consumeCodePoint(),Gs;break;case os:return Xs;case Qs:return _s;case Hs:case Is:var c=this.peekCodePoint(0),u=this.peekCodePoint(1);return c===DA&&(MA(u)||u===ve)&&(this.consumeCodePoint(),this.consumeUnicodeRangeToken()),this.reconsumeCodePoint(A),this.consumeIdentLikeToken();case Ft:if(this.peekCodePoint(0)===qA)return this.consumeCodePoint(),Vs;if(this.peekCodePoint(0)===Ft)return this.consumeCodePoint(),Rs;break;case gs:if(this.peekCodePoint(0)===qA)return this.consumeCodePoint(),Ns;break;case nA:return Nr}return ye(A)?(this.consumeWhiteSpace(),zs):P(A)?(this.reconsumeCodePoint(A),this.consumeNumericToken()):_e(A)?(this.reconsumeCodePoint(A),this.consumeIdentLikeToken()):{type:6,value:S(A)}},e.prototype.consumeCodePoint=function(){var A=this._value.shift();return typeof A>"u"?-1:A},e.prototype.reconsumeCodePoint=function(A){this._value.unshift(A)},e.prototype.peekCodePoint=function(A){return A>=this._value.length?-1:this._value[A]},e.prototype.consumeUnicodeRangeToken=function(){for(var A=[],t=this.consumeCodePoint();MA(t)&&A.length<6;)A.push(t),t=this.consumeCodePoint();for(var r=!1;t===ve&&A.length<6;)A.push(t),t=this.consumeCodePoint(),r=!0;if(r){var B=parseInt(S.apply(void 0,A.map(function(a){return a===ve?gB:a})),16),n=parseInt(S.apply(void 0,A.map(function(a){return a===ve?CB:a})),16);return{type:30,start:B,end:n}}var s=parseInt(S.apply(void 0,A),16);if(this.peekCodePoint(0)===Z&&MA(this.peekCodePoint(1))){this.consumeCodePoint(),t=this.consumeCodePoint();for(var i=[];MA(t)&&i.length<6;)i.push(t),t=this.consumeCodePoint();var n=parseInt(S.apply(void 0,i),16);return{type:30,start:s,end:n}}else return{type:30,start:s,end:s}},e.prototype.consumeIdentLikeToken=function(){var A=this.consumeName();return A.toLowerCase()==="url"&&this.peekCodePoint(0)===Ie?(this.consumeCodePoint(),this.consumeUrlToken()):this.peekCodePoint(0)===Ie?(this.consumeCodePoint(),{type:19,value:A}):{type:20,value:A}},e.prototype.consumeUrlToken=function(){var A=[];if(this.consumeWhiteSpace(),this.peekCodePoint(0)===nA)return{type:22,value:""};var t=this.peekCodePoint(0);if(t===pe||t===He){var r=this.consumeStringToken(this.consumeCodePoint());return r.type===0&&(this.consumeWhiteSpace(),this.peekCodePoint(0)===nA||this.peekCodePoint(0)===jA)?(this.consumeCodePoint(),{type:22,value:r.value}):(this.consumeBadUrlRemnants(),me)}for(;;){var B=this.consumeCodePoint();if(B===nA||B===jA)return{type:22,value:S.apply(void 0,A)};if(ye(B))return this.consumeWhiteSpace(),this.peekCodePoint(0)===nA||this.peekCodePoint(0)===jA?(this.consumeCodePoint(),{type:22,value:S.apply(void 0,A)}):(this.consumeBadUrlRemnants(),me);if(B===He||B===pe||B===Ie||bs(B))return this.consumeBadUrlRemnants(),me;if(B===ne)if(UA(B,this.peekCodePoint(0)))A.push(this.consumeEscapedCodePoint());else return this.consumeBadUrlRemnants(),me;else A.push(B)}},e.prototype.consumeWhiteSpace=function(){for(;ye(this.peekCodePoint(0));)this.consumeCodePoint()},e.prototype.consumeBadUrlRemnants=function(){for(;;){var A=this.consumeCodePoint();if(A===jA||A===nA)return;UA(A,this.peekCodePoint(0))&&this.consumeEscapedCodePoint()}},e.prototype.consumeStringSlice=function(A){for(var t=5e4,r="";A>0;){var B=Math.min(t,A);r+=S.apply(void 0,this._value.splice(0,B)),A-=B}return this._value.shift(),r},e.prototype.consumeStringToken=function(A){var t="",r=0;do{var B=this._value[r];if(B===nA||B===void 0||B===A)return t+=this.consumeStringSlice(r),{type:0,value:t};if(B===Xe)return this._value.splice(0,r),Js;if(B===ne){var n=this._value[r+1];n!==nA&&n!==void 0&&(n===Xe?(t+=this.consumeStringSlice(r),r=-1,this._value.shift()):UA(B,n)&&(t+=this.consumeStringSlice(r),t+=S(this.consumeEscapedCodePoint()),r=-1))}r++}while(!0)},e.prototype.consumeNumber=function(){var A=[],t=ce,r=this.peekCodePoint(0);for((r===DA||r===Z)&&A.push(this.consumeCodePoint());P(this.peekCodePoint(0));)A.push(this.consumeCodePoint());r=this.peekCodePoint(0);var B=this.peekCodePoint(1);if(r===ge&&P(B))for(A.push(this.consumeCodePoint(),this.consumeCodePoint()),t=ft;P(this.peekCodePoint(0));)A.push(this.consumeCodePoint());r=this.peekCodePoint(0),B=this.peekCodePoint(1);var n=this.peekCodePoint(2);if((r===lB||r===cB)&&((B===DA||B===Z)&&P(n)||P(B)))for(A.push(this.consumeCodePoint(),this.consumeCodePoint()),t=ft;P(this.peekCodePoint(0));)A.push(this.consumeCodePoint());return[xs(A),t]},e.prototype.consumeNumericToken=function(){var A=this.consumeNumber(),t=A[0],r=A[1],B=this.peekCodePoint(0),n=this.peekCodePoint(1),s=this.peekCodePoint(2);if(Ke(B,n,s)){var i=this.consumeName();return{type:15,number:t,flags:r,unit:i}}return B===As?(this.consumeCodePoint(),{type:16,number:t,flags:r}):{type:17,number:t,flags:r}},e.prototype.consumeEscapedCodePoint=function(){var A=this.consumeCodePoint();if(MA(A)){for(var t=S(A);MA(this.peekCodePoint(0))&&t.length<6;)t+=S(this.consumeCodePoint());ye(this.peekCodePoint(0))&&this.consumeCodePoint();var r=parseInt(t,16);return r===0||ys(r)||r>1114111?ht:r}return A===nA?ht:A},e.prototype.consumeName=function(){for(var A="";;){var t=this.consumeCodePoint();if(dt(t))A+=S(t);else if(UA(t,this.peekCodePoint(0)))A+=S(this.consumeEscapedCodePoint());else return this.reconsumeCodePoint(t),A}},e}(),UB=function(){function e(A){this._tokens=A}return e.create=function(A){var t=new fB;return t.write(A),new e(t.read())},e.parseValue=function(A){return e.create(A).parseComponentValue()},e.parseValues=function(A){return e.create(A).parseComponentValues()},e.prototype.parseComponentValue=function(){for(var A=this.consumeToken();A.type===31;)A=this.consumeToken();if(A.type===32)throw new SyntaxError("Error parsing CSS component value, unexpected EOF");this.reconsumeToken(A);var t=this.consumeComponentValue();do A=this.consumeToken();while(A.type===31);if(A.type===32)return t;throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one")},e.prototype.parseComponentValues=function(){for(var A=[];;){var t=this.consumeComponentValue();if(t.type===32)return A;A.push(t),A.push()}},e.prototype.consumeComponentValue=function(){var A=this.consumeToken();switch(A.type){case 11:case 28:case 2:return this.consumeSimpleBlock(A.type);case 19:return this.consumeFunction(A)}return A},e.prototype.consumeSimpleBlock=function(A){for(var t={type:A,values:[]},r=this.consumeToken();;){if(r.type===32||Aa(r,A))return t;this.reconsumeToken(r),t.values.push(this.consumeComponentValue()),r=this.consumeToken()}},e.prototype.consumeFunction=function(A){for(var t={name:A.value,values:[],type:18};;){var r=this.consumeToken();if(r.type===32||r.type===3)return t;this.reconsumeToken(r),t.values.push(this.consumeComponentValue())}},e.prototype.consumeToken=function(){var A=this._tokens.shift();return typeof A>"u"?Nr:A},e.prototype.reconsumeToken=function(A){this._tokens.unshift(A)},e}(),ue=function(e){return e.type===15},kA=function(e){return e.type===17},D=function(e){return e.type===20},$s=function(e){return e.type===0},Xr=function(e,A){return D(e)&&e.value===A},FB=function(e){return e.type!==31},JA=function(e){return e.type!==31&&e.type!==4},sA=function(e){var A=[],t=[];return e.forEach(function(r){if(r.type===4){if(t.length===0)throw new Error("Error parsing function args, zero tokens for arg");A.push(t),t=[];return}r.type!==31&&t.push(r)}),t.length&&A.push(t),A},Aa=function(e,A){return 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e}(),pQ=1e4,IQ=function(e){tA(A,e);function A(t,r){var B=e.call(this,t,r)||this;return B._activeEffects=[],B.canvas=r.canvas?r.canvas:document.createElement("canvas"),B.ctx=B.canvas.getContext("2d"),r.canvas||(B.canvas.width=Math.floor(r.width*r.scale),B.canvas.height=Math.floor(r.height*r.scale),B.canvas.style.width=r.width+"px",B.canvas.style.height=r.height+"px"),B.fontMetrics=new HQ(document),B.ctx.scale(B.options.scale,B.options.scale),B.ctx.translate(-r.x,-r.y),B.ctx.textBaseline="bottom",B._activeEffects=[],B.context.logger.debug("Canvas renderer initialized ("+r.width+"x"+r.height+") with scale "+r.scale),B}return A.prototype.applyEffects=function(t){for(var r=this;this._activeEffects.length;)this.popEffect();t.forEach(function(B){return r.applyEffect(B)})},A.prototype.applyEffect=function(t){this.ctx.save(),wQ(t)&&(this.ctx.globalAlpha=t.opacity),gQ(t)&&(this.ctx.translate(t.offsetX,t.offsetY),this.ctx.transform(t.matrix[0],t.matrix[1],t.matrix[2],t.matrix[3],t.matrix[4],t.matrix[5]),this.ctx.translate(-t.offsetX,-t.offsetY)),en(t)&&(this.path(t.path),this.ctx.clip()),this._activeEffects.push(t)},A.prototype.popEffect=function(){this._activeEffects.pop(),this.ctx.restore()},A.prototype.renderStack=function(t){return J(this,void 0,void 0,function(){var r;return _(this,function(B){switch(B.label){case 0:return r=t.element.container.styles,r.isVisible()?[4,this.renderStackContent(t)]:[3,2];case 1:B.sent(),B.label=2;case 2:return[2]}})})},A.prototype.renderNode=function(t){return J(this,void 0,void 0,function(){return _(this,function(r){switch(r.label){case 0:if(G(t.container.flags,16))debugger;return t.container.styles.isVisible()?[4,this.renderNodeBackgroundAndBorders(t)]:[3,3];case 1:return r.sent(),[4,this.renderNodeContent(t)];case 2:r.sent(),r.label=3;case 3:return[2]}})})},A.prototype.renderTextWithLetterSpacing=function(t,r,B){var n=this;if(r===0)this.ctx.fillText(t.text,t.bounds.left,t.bounds.top+B);else{var s=rt(t.text);s.reduce(function(i,a){return n.ctx.fillText(a,i,t.bounds.top+B),i+n.ctx.measureText(a).width},t.bounds.left)}},A.prototype.createFontStyle=function(t){var r=t.fontVariant.filter(function(s){return s==="normal"||s==="small-caps"}).join(""),B=LQ(t.fontFamily).join(", "),n=ue(t.fontSize)?""+t.fontSize.number+t.fontSize.unit:t.fontSize.number+"px";return[[t.fontStyle,r,t.fontWeight,n,B].join(" "),B,n]},A.prototype.renderTextNode=function(t,r){return J(this,void 0,void 0,function(){var B,n,s,i,a,o,Q,g,w=this;return _(this,function(f){return B=this.createFontStyle(r),n=B[0],s=B[1],i=B[2],this.ctx.font=n,this.ctx.direction=r.direction===1?"rtl":"ltr",this.ctx.textAlign="left",this.ctx.textBaseline="alphabetic",a=this.fontMetrics.getMetrics(s,i),o=a.baseline,Q=a.middle,g=r.paintOrder,t.textBounds.forEach(function(c){g.forEach(function(u){switch(u){case 0:w.ctx.fillStyle=R(r.color),w.renderTextWithLetterSpacing(c,r.letterSpacing,o);var H=r.textShadow;H.length&&c.text.trim().length&&(H.slice(0).reverse().forEach(function(h){w.ctx.shadowColor=R(h.color),w.ctx.shadowOffsetX=h.offsetX.number*w.options.scale,w.ctx.shadowOffsetY=h.offsetY.number*w.options.scale,w.ctx.shadowBlur=h.blur.number,w.renderTextWithLetterSpacing(c,r.letterSpacing,o)}),w.ctx.shadowColor="",w.ctx.shadowOffsetX=0,w.ctx.shadowOffsetY=0,w.ctx.shadowBlur=0),r.textDecorationLine.length&&(w.ctx.fillStyle=R(r.textDecorationColor||r.color),r.textDecorationLine.forEach(function(h){switch(h){case 1:w.ctx.fillRect(c.bounds.left,Math.round(c.bounds.top+o),c.bounds.width,1);break;case 2:w.ctx.fillRect(c.bounds.left,Math.round(c.bounds.top),c.bounds.width,1);break;case 3:w.ctx.fillRect(c.bounds.left,Math.ceil(c.bounds.top+Q),c.bounds.width,1);break}}));break;case 1:r.webkitTextStrokeWidth&&c.text.trim().length&&(w.ctx.strokeStyle=R(r.webkitTextStrokeColor),w.ctx.lineWidth=r.webkitTextStrokeWidth,w.ctx.lineJoin=window.chrome?"miter":"round",w.ctx.strokeText(c.text,c.bounds.left,c.bounds.top+o)),w.ctx.strokeStyle="",w.ctx.lineWidth=0,w.ctx.lineJoin="miter";break}})}),[2]})})},A.prototype.renderReplacedElement=function(t,r,B){if(B&&t.intrinsicWidth>0&&t.intrinsicHeight>0){var n=je(t),s=qe(r);this.path(s),this.ctx.save(),this.ctx.clip(),this.ctx.drawImage(B,0,0,t.intrinsicWidth,t.intrinsicHeight,n.left,n.top,n.width,n.height),this.ctx.restore()}},A.prototype.renderNodeContent=function(t){return J(this,void 0,void 0,function(){var r,B,n,s,i,a,F,F,o,Q,g,w,p,f,c,d,u,H,h,F,K,p,d;return _(this,function(C){switch(C.label){case 0:this.applyEffects(t.getEffects(4)),r=t.container,B=t.curves,n=r.styles,s=0,i=r.textNodes,C.label=1;case 1:return s<i.length?(a=i[s],[4,this.renderTextNode(a,n)]):[3,4];case 2:C.sent(),C.label=3;case 3:return s++,[3,1];case 4:if(!(r instanceof GB))return[3,8];C.label=5;case 5:return C.trys.push([5,7,,8]),[4,this.context.cache.match(r.src)];case 6:return F=C.sent(),this.renderReplacedElement(r,B,F),[3,8];case 7:return C.sent(),this.context.logger.error("Error loading image "+r.src),[3,8];case 8:if(r instanceof RB&&this.renderReplacedElement(r,B,r.canvas),!(r instanceof VB))return[3,12];C.label=9;case 9:return C.trys.push([9,11,,12]),[4,this.context.cache.match(r.svg)];case 10:return F=C.sent(),this.renderReplacedElement(r,B,F),[3,12];case 11:return C.sent(),this.context.logger.error("Error loading svg "+r.svg.substring(0,255)),[3,12];case 12:return r instanceof PB&&r.tree?(o=new A(this.context,{scale:this.options.scale,backgroundColor:r.backgroundColor,x:0,y:0,width:r.width,height:r.height}),[4,o.render(r.tree)]):[3,14];case 13:Q=C.sent(),r.width&&r.height&&this.ctx.drawImage(Q,0,0,r.width,r.height,r.bounds.left,r.bounds.top,r.bounds.width,r.bounds.height),C.label=14;case 14:if(r instanceof tt&&(g=Math.min(r.bounds.width,r.bounds.height),r.type===Je?r.checked&&(this.ctx.save(),this.path([new l(r.bounds.left+g*.39363,r.bounds.top+g*.79),new l(r.bounds.left+g*.16,r.bounds.top+g*.5549),new l(r.bounds.left+g*.27347,r.bounds.top+g*.44071),new l(r.bounds.left+g*.39694,r.bounds.top+g*.5649),new l(r.bounds.left+g*.72983,r.bounds.top+g*.23),new l(r.bounds.left+g*.84,r.bounds.top+g*.34085),new l(r.bounds.left+g*.39363,r.bounds.top+g*.79)]),this.ctx.fillStyle=R(Pt),this.ctx.fill(),this.ctx.restore()):r.type===ke&&r.checked&&(this.ctx.save(),this.ctx.beginPath(),this.ctx.arc(r.bounds.left+g/2,r.bounds.top+g/2,g/4,0,Math.PI*2,!0),this.ctx.fillStyle=R(Pt),this.ctx.fill(),this.ctx.restore())),vQ(r)&&r.value.length){switch(w=this.createFontStyle(n),p=w[0],f=w[1],c=this.fontMetrics.getMetrics(p,f).baseline,this.ctx.font=p,this.ctx.fillStyle=R(n.color),this.ctx.textBaseline="alphabetic",this.ctx.textAlign=KQ(r.styles.textAlign),d=je(r),u=0,r.styles.textAlign){case 1:u+=d.width/2;break;case 2:u+=d.width;break}H=d.add(u,0,0,-d.height/2+1),this.ctx.save(),this.path([new l(d.left,d.top),new l(d.left+d.width,d.top),new l(d.left+d.width,d.top+d.height),new l(d.left,d.top+d.height)]),this.ctx.clip(),this.renderTextWithLetterSpacing(new ie(r.value,H),n.letterSpacing,c),this.ctx.restore(),this.ctx.textBaseline="alphabetic",this.ctx.textAlign="left"}if(!G(r.styles.display,2048))return[3,20];if(r.styles.listStyleImage===null)return[3,19];if(h=r.styles.listStyleImage,h.type!==0)return[3,18];F=void 0,K=h.url,C.label=15;case 15:return C.trys.push([15,17,,18]),[4,this.context.cache.match(K)];case 16:return F=C.sent(),this.ctx.drawImage(F,r.bounds.left-(F.width+10),r.bounds.top),[3,18];case 17:return C.sent(),this.context.logger.error("Error loading list-style-image "+K),[3,18];case 18:return[3,20];case 19:t.listValue&&r.styles.listStyleType!==-1&&(p=this.createFontStyle(n)[0],this.ctx.font=p,this.ctx.fillStyle=R(n.color),this.ctx.textBaseline="middle",this.ctx.textAlign="right",d=new cA(r.bounds.left,r.bounds.top+b(r.styles.paddingTop,r.bounds.width),r.bounds.width,yt(n.lineHeight,n.fontSize.number)/2+1),this.renderTextWithLetterSpacing(new ie(t.listValue,d),n.letterSpacing,yt(n.lineHeight,n.fontSize.number)/2+2),this.ctx.textBaseline="bottom",this.ctx.textAlign="left"),C.label=20;case 20:return[2]}})})},A.prototype.renderStackContent=function(t){return J(this,void 0,void 0,function(){var r,B,h,n,s,h,i,a,h,o,Q,h,g,w,h,f,c,h,u,H,h;return _(this,function(F){switch(F.label){case 0:if(G(t.element.container.flags,16))debugger;return[4,this.renderNodeBackgroundAndBorders(t.element)];case 1:F.sent(),r=0,B=t.negativeZIndex,F.label=2;case 2:return r<B.length?(h=B[r],[4,this.renderStack(h)]):[3,5];case 3:F.sent(),F.label=4;case 4:return r++,[3,2];case 5:return[4,this.renderNodeContent(t.element)];case 6:F.sent(),n=0,s=t.nonInlineLevel,F.label=7;case 7:return n<s.length?(h=s[n],[4,this.renderNode(h)]):[3,10];case 8:F.sent(),F.label=9;case 9:return n++,[3,7];case 10:i=0,a=t.nonPositionedFloats,F.label=11;case 11:return i<a.length?(h=a[i],[4,this.renderStack(h)]):[3,14];case 12:F.sent(),F.label=13;case 13:return i++,[3,11];case 14:o=0,Q=t.nonPositionedInlineLevel,F.label=15;case 15:return o<Q.length?(h=Q[o],[4,this.renderStack(h)]):[3,18];case 16:F.sent(),F.label=17;case 17:return o++,[3,15];case 18:g=0,w=t.inlineLevel,F.label=19;case 19:return g<w.length?(h=w[g],[4,this.renderNode(h)]):[3,22];case 20:F.sent(),F.label=21;case 21:return g++,[3,19];case 22:f=0,c=t.zeroOrAutoZIndexOrTransformedOrOpacity,F.label=23;case 23:return f<c.length?(h=c[f],[4,this.renderStack(h)]):[3,26];case 24:F.sent(),F.label=25;case 25:return f++,[3,23];case 26:u=0,H=t.positiveZIndex,F.label=27;case 27:return u<H.length?(h=H[u],[4,this.renderStack(h)]):[3,30];case 28:F.sent(),F.label=29;case 29:return u++,[3,27];case 30:return[2]}})})},A.prototype.mask=function(t){this.ctx.beginPath(),this.ctx.moveTo(0,0),this.ctx.lineTo(this.canvas.width,0),this.ctx.lineTo(this.canvas.width,this.canvas.height),this.ctx.lineTo(0,this.canvas.height),this.ctx.lineTo(0,0),this.formatPath(t.slice(0).reverse()),this.ctx.closePath()},A.prototype.path=function(t){this.ctx.beginPath(),this.formatPath(t),this.ctx.closePath()},A.prototype.formatPath=function(t){var r=this;t.forEach(function(B,n){var s=$(B)?B.start:B;n===0?r.ctx.moveTo(s.x,s.y):r.ctx.lineTo(s.x,s.y),$(B)&&r.ctx.bezierCurveTo(B.startControl.x,B.startControl.y,B.endControl.x,B.endControl.y,B.end.x,B.end.y)})},A.prototype.renderRepeat=function(t,r,B,n){this.path(t),this.ctx.fillStyle=r,this.ctx.translate(B,n),this.ctx.fill(),this.ctx.translate(-B,-n)},A.prototype.resizeImage=function(t,r,B){var n;if(t.width===r&&t.height===B)return t;var s=(n=this.canvas.ownerDocument)!==null&&n!==void 0?n:document,i=s.createElement("canvas");i.width=Math.max(1,r),i.height=Math.max(1,B);var a=i.getContext("2d");return a.drawImage(t,0,0,t.width,t.height,0,0,r,B),i},A.prototype.renderBackgroundImage=function(t){return J(this,void 0,void 0,function(){var r,B,n,s,i,a;return _(this,function(o){switch(o.label){case 0:r=t.styles.backgroundImage.length-1,B=function(Q){var g,w,f,O,Y,W,L,V,y,c,O,Y,W,L,V,u,H,h,F,K,p,d,C,v,y,E,O,k,q,L,V,uA,Y,W,IA,BA,lA,vA,yA,iA,KA,oA;return _(this,function(TA){switch(TA.label){case 0:if(Q.type!==0)return[3,5];g=void 0,w=Q.url,TA.label=1;case 1:return TA.trys.push([1,3,,4]),[4,n.context.cache.match(w)];case 2:return g=TA.sent(),[3,4];case 3:return TA.sent(),n.context.logger.error("Error loading background-image "+w),[3,4];case 4:return g&&(f=Kr(t,r,[g.width,g.height,g.width/g.height]),O=f[0],Y=f[1],W=f[2],L=f[3],V=f[4],y=n.ctx.createPattern(n.resizeImage(g,L,V),"repeat"),n.renderRepeat(O,y,Y,W)),[3,6];case 5:Qa(Q)?(c=Kr(t,r,[null,null,null]),O=c[0],Y=c[1],W=c[2],L=c[3],V=c[4],u=na(Q.angle,L,V),H=u[0],h=u[1],F=u[2],K=u[3],p=u[4],d=document.createElement("canvas"),d.width=L,d.height=V,C=d.getContext("2d"),v=C.createLinearGradient(h,K,F,p),It(Q.stops,H).forEach(function(YA){return v.addColorStop(YA.stop,R(YA.color))}),C.fillStyle=v,C.fillRect(0,0,L,V),L>0&&V>0&&(y=n.ctx.createPattern(d,"repeat"),n.renderRepeat(O,y,Y,W))):ga(Q)&&(E=Kr(t,r,[null,null,null]),O=E[0],k=E[1],q=E[2],L=E[3],V=E[4],uA=Q.position.length===0?[$r]:Q.position,Y=b(uA[0],L),W=b(uA[uA.length-1],V),IA=sa(Q,Y,W,L,V),BA=IA[0],lA=IA[1],BA>0&&lA>0&&(vA=n.ctx.createRadialGradient(k+Y,q+W,0,k+Y,q+W,BA),It(Q.stops,BA*2).forEach(function(YA){return vA.addColorStop(YA.stop,R(YA.color))}),n.path(O),n.ctx.fillStyle=vA,BA!==lA?(yA=t.bounds.left+.5*t.bounds.width,iA=t.bounds.top+.5*t.bounds.height,KA=lA/BA,oA=1/KA,n.ctx.save(),n.ctx.translate(yA,iA),n.ctx.transform(1,0,0,KA,0,0),n.ctx.translate(-yA,-iA),n.ctx.fillRect(k,oA*(q-iA)+iA,L,V*oA),n.ctx.restore()):n.ctx.fill())),TA.label=6;case 6:return r--,[2]}})},n=this,s=0,i=t.styles.backgroundImage.slice(0).reverse(),o.label=1;case 1:return s<i.length?(a=i[s],[5,B(a)]):[3,4];case 2:o.sent(),o.label=3;case 3:return s++,[3,1];case 4:return[2]}})})},A.prototype.renderSolidBorder=function(t,r,B){return J(this,void 0,void 0,function(){return _(this,function(n){return this.path(rB(B,r)),this.ctx.fillStyle=R(t),this.ctx.fill(),[2]})})},A.prototype.renderDoubleBorder=function(t,r,B,n){return J(this,void 0,void 0,function(){var s,i;return _(this,function(a){switch(a.label){case 0:return r<3?[4,this.renderSolidBorder(t,B,n)]:[3,2];case 1:return a.sent(),[2];case 2:return s=lQ(n,B),this.path(s),this.ctx.fillStyle=R(t),this.ctx.fill(),i=CQ(n,B),this.path(i),this.ctx.fill(),[2]}})})},A.prototype.renderNodeBackgroundAndBorders=function(t){return J(this,void 0,void 0,function(){var r,B,n,s,i,a,o,Q,g=this;return _(this,function(w){switch(w.label){case 0:return this.applyEffects(t.getEffects(2)),r=t.container.styles,B=!HA(r.backgroundColor)||r.backgroundImage.length,n=[{style:r.borderTopStyle,color:r.borderTopColor,width:r.borderTopWidth},{style:r.borderRightStyle,color:r.borderRightColor,width:r.borderRightWidth},{style:r.borderBottomStyle,color:r.borderBottomColor,width:r.borderBottomWidth},{style:r.borderLeftStyle,color:r.borderLeftColor,width:r.borderLeftWidth}],s=yQ(XA(r.backgroundClip,0),t.curves),B||r.boxShadow.length?(this.ctx.save(),this.path(s),this.ctx.clip(),HA(r.backgroundColor)||(this.ctx.fillStyle=R(r.backgroundColor),this.ctx.fill()),[4,this.renderBackgroundImage(t.container)]):[3,2];case 1:w.sent(),this.ctx.restore(),r.boxShadow.slice(0).reverse().forEach(function(f){g.ctx.save();var c=Ze(t.curves),u=f.inset?0:pQ,H=cQ(c,-u+(f.inset?1:-1)*f.spread.number,(f.inset?1:-1)*f.spread.number,f.spread.number*(f.inset?-2:2),f.spread.number*(f.inset?-2:2));f.inset?(g.path(c),g.ctx.clip(),g.mask(H)):(g.mask(c),g.ctx.clip(),g.path(H)),g.ctx.shadowOffsetX=f.offsetX.number+u,g.ctx.shadowOffsetY=f.offsetY.number,g.ctx.shadowColor=R(f.color),g.ctx.shadowBlur=f.blur.number,g.ctx.fillStyle=f.inset?R(f.color):"rgba(0,0,0,1)",g.ctx.fill(),g.ctx.restore()}),w.label=2;case 2:i=0,a=0,o=n,w.label=3;case 3:return a<o.length?(Q=o[a],Q.style!==0&&!HA(Q.color)&&Q.width>0?Q.style!==2?[3,5]:[4,this.renderDashedDottedBorder(Q.color,Q.width,i,t.curves,2)]:[3,11]):[3,13];case 4:return w.sent(),[3,11];case 5:return Q.style!==3?[3,7]:[4,this.renderDashedDottedBorder(Q.color,Q.width,i,t.curves,3)];case 6:return w.sent(),[3,11];case 7:return Q.style!==4?[3,9]:[4,this.renderDoubleBorder(Q.color,Q.width,i,t.curves)];case 8:return w.sent(),[3,11];case 9:return[4,this.renderSolidBorder(Q.color,i,t.curves)];case 10:w.sent(),w.label=11;case 11:i++,w.label=12;case 12:return a++,[3,3];case 13:return[2]}})})},A.prototype.renderDashedDottedBorder=function(t,r,B,n,s){return J(this,void 0,void 0,function(){var i,a,o,Q,g,w,f,c,u,H,h,F,K,p,d,C,d,C;return _(this,function(v){return this.ctx.save(),i=fQ(n,B),a=rB(n,B),s===2&&(this.path(a),this.ctx.clip()),$(a[0])?(o=a[0].start.x,Q=a[0].start.y):(o=a[0].x,Q=a[0].y),$(a[1])?(g=a[1].end.x,w=a[1].end.y):(g=a[1].x,w=a[1].y),B===0||B===2?f=Math.abs(o-g):f=Math.abs(Q-w),this.ctx.beginPath(),s===3?this.formatPath(i):this.formatPath(a.slice(0,2)),c=r<3?r*3:r*2,u=r<3?r*2:r,s===3&&(c=r,u=r),H=!0,f<=c*2?H=!1:f<=c*2+u?(h=f/(2*c+u),c*=h,u*=h):(F=Math.floor((f+u)/(c+u)),K=(f-F*c)/(F-1),p=(f-(F+1)*c)/F,u=p<=0||Math.abs(u-K)<Math.abs(u-p)?K:p),H&&(s===3?this.ctx.setLineDash([0,c+u]):this.ctx.setLineDash([c,u])),s===3?(this.ctx.lineCap="round",this.ctx.lineWidth=r):this.ctx.lineWidth=r*2+1.1,this.ctx.strokeStyle=R(t),this.ctx.stroke(),this.ctx.setLineDash([]),s===2&&($(a[0])&&(d=a[3],C=a[0],this.ctx.beginPath(),this.formatPath([new l(d.end.x,d.end.y),new l(C.start.x,C.start.y)]),this.ctx.stroke()),$(a[1])&&(d=a[1],C=a[2],this.ctx.beginPath(),this.formatPath([new l(d.end.x,d.end.y),new 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/love_course_2016_2019-2023.3.1.0-py3-none-any.whl/LoveCourse20162019/docs/an-xiao-yao/1-260节聊天课：202.把女生聊嗨，就用这个方法.md | # 1-260节聊天课：202.把女生聊嗨，就用这个方法
哈喽大家好我是你们的小鱼女朋友，今天呢我来教大家怎么在微信上和女孩子聊天，如果你没有办法吸引女人按照字点上的定义，你等于没有反制能力，大自然会毫不流情地尖面你的基因，听起来是不是觉得很可怕。
不过没有关系啊，当你听到这个节目的时候呢，你就得救了，众所周知女生都喜欢高价值的男人那么包括哪些方面呢，我们来分析一下啊，高价值分为应性指标和软性指标，那应性指标包括什么，外形生活习惯经济实力。
那软性指标包括生活方式，社交认证，语言，行相气质，自身的性格，如果你觉得刚才小语老师说的各个方面，你都达到了标准啊，那就没有标再听我的节目了啊，省点时间去做有用的事吧，如果你意识到自己的价值并不高。
而且想立刻改变的话，那么请往下听寻找答案，当然呢，如果你想知道更多的追女孩的技巧，微信公众号一来搜搜小路情感男士，关注以后回复数字二一二，填写并提教你的手机号和微信号，小语老师的私人助理会联系到你。
送你免费的恋爱聊天密集，刚认识一个女孩，你对她有好感并且想追求她，那要如何让她了解你并喜欢上你呢，当然是从聊天开始了，别以为聊天很容易啊，说话这门艺术是需要不断学习的，特别是当我们带着某种需求。
比如为了吸引对方而聊天，往往是很难的，当你面对喜欢的女生的时候，说话有时候会口吃街吧，甚至变成亚版找不到话题，看着对方脸上逐渐失去笑容，平凡打喊显，或者是网上聊着聊着，她突然打断你说，好的，天起来不错。
我去洗澡了，是不是都中了，不要闹，不会靠聊天吸引对方，这个问题是可以解决的，你只需要一些幽默的聊天技巧，那今天的节目呢，小雨老师就教大家几招，第一招假装头响，这种方法呢。
是用于当女生提出某方面的要求的时候，你可以先让她一步，然后再打击她，比如，当女生说，不要再教我小公主了，再教我就不理你了，你就可以说，好的，那我该教你什么呢，小公主，虽然天起来很前走。
但是可以根据女生的反应来说话，一般情况下，女生会觉得你很完毕，处处在斗塔，她也会不但地跟你回应，甚至也会降米的尼撑，互相伴嘴，有说有笑，第二招故意开车，这种幽默技巧在生活中很常见，就是制造一个误解。
让女生响入非非，最后再跟她解释你的意思，并打去她的思想不健康，比如说，男生说，你知道事上什么东西最硬，女人最喜欢了，特别是结婚后的女人，这个时候你可以根据她的表情，和她的反应，如果发现她迟疑或者坏笑。
说明她想歪了，你可以接小答案，你想哪去了，我说的是钻石，当然这种幽默的方式，不能逢人就用，甚至不能多用，因为有些女生，拒绝跟男人谈之种耻度的话题，而说说效果还是可以的，第三点，假装否认再承认。
女生经常会问男生一些问题，比如你是不是教过很多女朋友，你是不是很讨厌你的前任等等，反正遇到问题你也躲不了，那么到女生制问你的时候，该怎么有趣的回答她呢，比如女生问你，你经常搭扇女生吗。
你是不是立马向否认，没有啊，我只搭扇你一个而已，然后就结束了，很多男生觉得撒谎也不对，说实话也不对，总结了女生不会饶过自己，那你完全可以比她更扯，你可以先假装否认，然后再承认制造幽默效果，比如你可以回。
不经常啊，一天只是认识十几个吧，再比如，当女孩问你经常带女生回家吗，那你就可以回答，怎么可能啊，都是女生带我回家，这样天起来，是不是有趣多了呢，第四招假装复合，跟女生聊天的时候。
但她说了一些你不认同的话，你会怎么回呢，记住千万不要反驳她，否则没有好下场的，比如女生说，我最讨厌男人吸烟了，臭死了，你要是突然忍不住站起来，替吸烟难通包身员，对她让让到，你们有人屁事比我们还多。
我先怎么了，那女生保证提包走人啊，那要怎么做呢，教你照先来，教你照先假装复合她，然后再去踩她，女孩子说，我最讨厌男人吸烟了，那你就可以回答，就是就是，臭烟应该是女人的事，男人臭什么热闹，对吧，这样呢。
既不得罪女生，又让她觉得你说话，非常有趣，第五招，夸张赞美，这个很容易理解啊，就是用夸张的与其赞美，公为对方，虽然表面上有点，炒粉的意思，但实际上呢，也是在夸女生，这种方式的赞美，更能增加趣味性，比如。
给女生拍照的时候，女生说，怎么样，拍的好看吗，男生可以回复，美的刘宾鞋，先与下反了吧，女生说，再拍一张，你就可以回答，美的猫胖，当女生说，这个字是好不好看呢，你可以说，每到枯了。
刘宾鞋都摆不出你这个动作，女生说，讨厌那样呢，男生可以回复，非常到位，你都可以出道了，反正，小悦老师告诉你，使劲的夸，这样重复夸张的赞美女生，很快让女生知道你再斗她，而且那些还会偷偷暗洗，最后呢。
我们来总结一下，如何用幽默的方式，给女生聊天，有五点方法，第一，假装头型，女生提出某方面要求的时候，先让她一步再去打击她，第二，故意开车，让她想入飞飞，再打去她思想不健康，第三，假装否认，再承认。
是走幽默的效果，第四，假装复合，不容易得罪女生，又能斗她笑，第五张，夸张在美，让聊天更加有趣味，好了，今天呢，小悦老师就跟大家赞起，分享这些技巧，所以大家都知道，聊天是需要学习的对不对，不会聊天。
但是已经下定决心小，学习聊天的朋友呢，可以直接关注我们的微信公众号，小路情感男士，关注以后，回复数字，2，1，2，填写病题，交表单，小悦老师，会送您免费的，恋爱。
聊天密集 | PypiClean |
/tensorflow_tflex-1.13.1rc1-cp27-cp27mu-manylinux1_x86_64.whl/tensorflow_tflex-1.13.1rc1.data/purelib/tensorflow/contrib/recurrent/python/ops/functional_rnn.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
from tensorflow.contrib.recurrent.python.ops import recurrent
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.util import nest
def _GetDTypesFromStructure(struct):
dtypes_list = []
for x in nest.flatten(struct):
x = ops.convert_to_tensor(x)
dtypes_list.append(x.dtype)
return dtypes_list
def _SetShapeFromTemplate(struct, struct_template):
as_list = nest.flatten(struct)
template_as_list = nest.flatten(struct_template)
for element, template in zip(as_list, template_as_list):
element.set_shape(template.shape)
class _FunctionalRnnCell(object):
"""Wrapper around RNNCell which separates state from computation.
This class accomplishes the following:
* Turn the cell's `__call__` function into a pure function. The global
side effects are separated as `theta`. They are the variables created
for the weights of the computation.
* Unless the output is aliased as part of the state, extend the state to
contain the output so that we store the history in `Recurrent`.
* Set static shapes as required.
"""
def __init__(self, rnn_cell, seq_inputs, initial_state):
assert initial_state is not None
# TODO(drpng): Dtype needs to be configurable.
input_dtypes = [seq_inputs.dtype] + _GetDTypesFromStructure(initial_state)
# See _index.
like_inputs_t = nest.map_structure(
lambda x: array_ops.stop_gradient(array_ops.gather(x, 0)), seq_inputs)
input_structure = (like_inputs_t, initial_state)
@function.Defun(*input_dtypes)
def FlatCellStep(*flat_inputs):
"""The flattened version of `rnn_cell`."""
inputs_t, state0 = nest.pack_sequence_as(input_structure, flat_inputs)
_SetShapeFromTemplate(state0, initial_state)
_SetShapeFromTemplate(inputs_t, like_inputs_t)
outputs_t, state1 = rnn_cell(inputs_t, state0)
state_list = nest.flatten(state1)
self._output_shape = outputs_t.shape
if outputs_t in state_list:
output_index_in_state = state_list.index(outputs_t)
else:
output_index_in_state = None
if output_index_in_state is None:
self._prepend_output = True
self._output_state_idx = 0
return [outputs_t] + state_list
else:
self._output_state_idx = output_index_in_state
self._prepend_output = False
# To save memory, we don't store return the output separately
# from the state list, since we know it's the same.
return state_list
def _ToPureFunction(func):
# NOTE: This forces the creating of the function.
if func.captured_inputs:
pure_func = copy.copy(func)
# pylint: disable=protected-access
pure_func._extra_inputs = []
return pure_func
return func
pure_flat_cell_step = _ToPureFunction(FlatCellStep)
def CellStep(theta, extended_state0, inputs_t):
"""Performs one time steps on structured inputs.
The purpose of this function is to turn the parameters into flattened
versions, and to resolve the parameter order difference between
`Recurrent` and `RNNCell`.
In the event the cell returns a transformed output that is not aliased
within its state, the `extended_state0` also contains the output as its
first element.
Args:
theta: Weights required for the computation. A structure of tensors.
extended_state0: the state0, and possibly the output at the previous
time step. A structure of tensors.
inputs_t: the inputs at time t.
Returns:
A pair of the next state (inclusive of the output), and an empty list
(unused `extras`).
The next state is congruent to state0.
"""
extended_state0_flat = nest.flatten(extended_state0)
state0_flat = self.MaybeRemoveOutputFromState(extended_state0_flat)
full_inputs = [inputs_t] + state0_flat + theta
# Note that the thetas are additional inputs appeneded as extra
# parameters.
cell_out = pure_flat_cell_step(*full_inputs)
return cell_out, []
self._cell_step = CellStep
self._theta = FlatCellStep.captured_inputs
self._zero_state = rnn_cell.zero_state
self._state_template = initial_state
self._output_size = rnn_cell.output_size
@property
def extended_initial_state(self):
if self._prepend_output:
return [array_ops.zeros(
self._output_shape,
dtype=_GetDTypesFromStructure(self._state_template)[0]),
self._state_template]
else:
# The base case, where the output is just the hidden state.
return self._state_template
@property
def cell_step(self):
return self._cell_step
@property
def theta(self):
return self._theta
@property
def state_template(self):
return self._state_template
@property
def output_shape(self):
return self._output_shape
def GetOutputFromState(self, state):
return nest.flatten(state)[self._output_state_idx]
def MaybeRemoveOutputFromState(self, flat_state):
if self._prepend_output:
return flat_state[1:]
return flat_state
def _ApplyLengthsToBatch(sequence_lengths, tf_output):
# TODO(drpng): just use Update so that we don't carry over the gradients?
"""Sets the output to be zero at the end of the sequence."""
# output is batch major.
shape = array_ops.shape(tf_output)
batch_size, max_time, vector_size = shape[0], shape[1], shape[2]
output_time = array_ops.tile(math_ops.range(0, max_time), [batch_size])
output_time = array_ops.reshape(output_time, [batch_size, max_time])
lengths = array_ops.tile(
array_ops.reshape(sequence_lengths, [-1, 1]), [1, max_time])
is_less = math_ops.cast(
math_ops.less(output_time, lengths), dtype=tf_output.dtype)
keep_mask = array_ops.tile(
array_ops.expand_dims(is_less, -1),
[1, 1, vector_size])
final_output = keep_mask * tf_output
return final_output
def _PickFinalStateFromHistory(acc_state, sequence_length):
"""Implements acc_state[sequence_length - 1]."""
# This will work on all platforms, unlike the regular slice.
last_value = []
for state_var in nest.flatten(acc_state):
# We compute the following with matrix operations:
# last_var = state_var[sequence_length - 1]
shape = array_ops.shape(state_var)
max_time, batch_size = shape[0], shape[1]
output_time = array_ops.tile(math_ops.range(0, max_time), [batch_size])
output_time = array_ops.reshape(output_time, [batch_size, max_time])
lengths = array_ops.tile(array_ops.reshape(sequence_length,
[-1, 1]), [1, max_time])
last_idx = math_ops.cast(math_ops.equal(output_time, lengths - 1),
dtype=state_var.dtype)
last_idx = array_ops.transpose(last_idx)
last_idx_for_bcast = array_ops.expand_dims(last_idx, -1)
sliced = math_ops.multiply(last_idx_for_bcast, state_var)
last_var = math_ops.reduce_sum(sliced, 0)
last_value += [last_var]
return nest.pack_sequence_as(acc_state, last_value)
def _PostProcessOutput(extended_acc_state, extended_final_state, func_cell,
total_time, inputs_lengths, is_reversed):
"""Post-process output of recurrent.
This function takes the accumulated extended state and extracts the requested
state and output.
When `inputs_lengths` has been set, it extracts the output from the
accumulated state. It also sets outputs past.
When `is_reversed` is true, the output will be reversed in this function.
It also sets the static shape information.
Args:
extended_acc_state: A structure containing the accumulated state at each
time. It may contain the output at each time as well.
extended_final_state: A structure containing the final state. It may
contain the output at the final time.
func_cell: The functional wrapper around the cell.
total_time: A scalar integer tensor.
inputs_lengths: An integer tensor with one entry per input.
is_reversed: A boolean to indicate if the sequence is reversed.
Returns:
A tuple with the outputs at each time, and the final state.
"""
if inputs_lengths is None or is_reversed:
flat_final_state = func_cell.MaybeRemoveOutputFromState(
nest.flatten(extended_final_state))
tf_state = nest.pack_sequence_as(func_cell.state_template, flat_final_state)
else:
# The accumulated state is over the entire sequence, so we pick it
# out from the acc_state sequence.
flat_acc_state = func_cell.MaybeRemoveOutputFromState(
nest.flatten(extended_acc_state))
acc_state = nest.pack_sequence_as(
func_cell.state_template, flat_acc_state)
tf_state = _PickFinalStateFromHistory(acc_state, inputs_lengths)
output_from_state = func_cell.GetOutputFromState(extended_acc_state)
if is_reversed:
output_from_state = array_ops.reverse(output_from_state, [0])
tf_output = array_ops.transpose(output_from_state, [1, 0, 2])
tf_output.set_shape(
[func_cell.output_shape[0], total_time, func_cell.output_shape[1]])
if inputs_lengths is not None:
# Need set the outputs to zero.
tf_output = _ApplyLengthsToBatch(inputs_lengths, tf_output)
_SetShapeFromTemplate(tf_state, func_cell.state_template)
return tf_output, tf_state
# pylint: disable=invalid-name
def functional_rnn(cell,
inputs,
sequence_length=None,
initial_state=None,
dtype=None,
time_major=False,
scope=None,
use_tpu=False,
reverse=False):
"""Same interface as `tf.nn.dynamic_rnn`."""
with variable_scope.variable_scope(scope or 'rnn'):
if not time_major:
inputs = nest.map_structure(
lambda t: array_ops.transpose(t, [1, 0, 2]), inputs)
inputs_flat = nest.flatten(inputs)
batch_size = array_ops.shape(inputs_flat[0])[1]
if initial_state is None:
initial_state = cell.zero_state(batch_size, dtype)
func_cell = _FunctionalRnnCell(cell, inputs, initial_state)
if sequence_length is not None:
max_length = math_ops.reduce_max(sequence_length)
else:
max_length = None
if reverse:
inputs = array_ops.reverse(inputs, [0])
extended_acc_state, extended_final_state = recurrent.Recurrent(
theta=func_cell.theta,
state0=func_cell.extended_initial_state,
inputs=inputs,
cell_fn=func_cell.cell_step,
max_input_length=max_length,
use_tpu=use_tpu,
aligned_end=reverse)
tf_output, tf_state = _PostProcessOutput(
extended_acc_state,
extended_final_state,
func_cell,
inputs_flat[0].shape[0],
sequence_length,
is_reversed=reverse)
if time_major:
tf_output = array_ops.transpose(tf_output, [1, 0, 2])
return tf_output, tf_state
def bidirectional_functional_rnn(cell_fw,
cell_bw,
inputs,
initial_state_fw=None,
initial_state_bw=None,
dtype=None,
sequence_length=None,
time_major=False,
use_tpu=False,
fast_reverse=False,
scope=None):
"""Creates a bidirectional recurrent neural network.
Performs fully dynamic unrolling of inputs in both directions. Built to be API
compatible with `tf.nn.bidirectional_dynamic_rnn`, but implemented with
functional control flow for TPU compatibility.
Args:
cell_fw: An instance of `tf.contrib.rnn.RNNCell`.
cell_bw: An instance of `tf.contrib.rnn.RNNCell`.
inputs: The RNN inputs. If time_major == False (default), this must be a
Tensor (or hierarchical structure of Tensors) of shape
[batch_size, max_time, ...]. If time_major == True, this must be a Tensor
(or hierarchical structure of Tensors) of shape:
[max_time, batch_size, ...]. The first two dimensions must match across
all the inputs, but otherwise the ranks and other shape components may
differ.
initial_state_fw: An optional initial state for `cell_fw`. Should match
`cell_fw.zero_state` in structure and type.
initial_state_bw: An optional initial state for `cell_bw`. Should match
`cell_bw.zero_state` in structure and type.
dtype: (optional) The data type for the initial state and expected output.
Required if initial_states are not provided or RNN state has a
heterogeneous dtype.
sequence_length: An optional int32/int64 vector sized [batch_size]. Used to
copy-through state and zero-out outputs when past a batch element's
sequence length. So it's more for correctness than performance.
time_major: Whether the `inputs` tensor is in "time major" format.
use_tpu: Whether to enable TPU-compatible operation. If True, does not truly
reverse `inputs` in the backwards RNN. Once b/69305369 is fixed, we can
remove this flag.
fast_reverse: Whether to use fast tf.reverse to replace tf.reverse_sequence.
This is only possible when either all sequence lengths are the same inside
the batch, or when the cell function does not change the state on padded
input.
scope: An optional scope name for the dynamic RNN.
Returns:
outputs: A tuple of `(output_fw, output_bw)`. The output of the forward and
backward RNN. If time_major == False (default), these will
be Tensors shaped: [batch_size, max_time, cell.output_size]. If
time_major == True, these will be Tensors shaped:
[max_time, batch_size, cell.output_size]. Note, if cell.output_size is a
(possibly nested) tuple of integers or TensorShape objects, then the
output for that direction will be a tuple having the same structure as
cell.output_size, containing Tensors having shapes corresponding to the
shape data in cell.output_size.
final_states: A tuple of `(final_state_fw, final_state_bw)`. A Tensor or
hierarchical structure of Tensors indicating the final cell state in each
direction. Must have the same structure and shape as cell.zero_state.
Raises:
ValueError: If `initial_state_fw` is None or `initial_state_bw` is None and
`dtype` is not provided.
"""
# Keep this code in sync with tf.nn.dynamic_rnn for compatibility.
with variable_scope.variable_scope(scope or 'bidirectional_rnn'):
# Forward direction
with variable_scope.variable_scope('fw') as fw_scope:
output_fw, output_state_fw = functional_rnn(
cell=cell_fw, inputs=inputs, sequence_length=sequence_length,
initial_state=initial_state_fw, dtype=dtype,
time_major=time_major, scope=fw_scope, use_tpu=use_tpu)
# Backward direction
if not time_major:
time_dim = 1
batch_dim = 0
else:
time_dim = 0
batch_dim = 1
def _reverse(input_, seq_lengths, seq_dim, batch_dim):
if seq_lengths is not None:
return array_ops.reverse_sequence(
input=input_, seq_lengths=seq_lengths,
seq_dim=seq_dim, batch_dim=batch_dim)
else:
# See b/69305369.
assert not use_tpu, (
'Bidirectional with variable sequence lengths unsupported on TPU')
return array_ops.reverse(input_, axis=[seq_dim])
with variable_scope.variable_scope('bw') as bw_scope:
if not fast_reverse:
inputs = _reverse(
inputs,
seq_lengths=sequence_length,
seq_dim=time_dim,
batch_dim=batch_dim)
output_bw, output_state_bw = functional_rnn(
cell=cell_bw,
inputs=inputs,
sequence_length=sequence_length,
initial_state=initial_state_bw,
dtype=dtype,
time_major=time_major,
scope=bw_scope,
use_tpu=use_tpu,
reverse=fast_reverse)
if not fast_reverse:
output_bw = _reverse(
output_bw,
seq_lengths=sequence_length,
seq_dim=time_dim,
batch_dim=batch_dim)
outputs = (output_fw, output_bw)
output_states = (output_state_fw, output_state_bw)
return (outputs, output_states)
# pylint: enable=invalid-name | PypiClean |
/mmcif-0.69.tar.gz/mmcif-0.69/modules/pybind11/.github/ISSUE_TEMPLATE/feature-request.md | ---
name: Feature Request
about: File an issue about adding a feature
title: "[FEAT] "
---
Make sure you've completed the following steps before submitting your issue -- thank you!
1. Check if your feature has already been mentioned / rejected / planned in other issues.
2. If those resources didn't help, consider asking in the [Gitter chat room][] to see if this is interesting / useful to a larger audience and possible to implement reasonably,
4. If you have a useful feature that passes the previous items (or not suitable for chat), please fill in the details below.
[Gitter chat room]: https://gitter.im/pybind/Lobby
*After reading, remove this checklist.*
| PypiClean |
/GladTeX-3.1.0.tar.gz/GladTeX-3.1.0/gleetex/unicode.py |
import enum
class LaTeXMode(enum.Enum):
"""Represent either math or text mode. Math mode in LaTeX is e.g.
everything between $ and $."""
mathmode = 1
textmode = 0
unicode_table = {
161: {LaTeXMode.textmode: '\\textexclamdown ', LaTeXMode.mathmode: '\\textexclamdown'},
162: {LaTeXMode.textmode: '\\textcent ', LaTeXMode.mathmode: '\\mbox{\\textcent}'},
163: {LaTeXMode.textmode: '\\textsterling ', LaTeXMode.mathmode: '\\sterling'},
164: {LaTeXMode.textmode: '\\textcurrency ', LaTeXMode.mathmode: '\\mbox{\\textcurrency}'},
165: {LaTeXMode.textmode: '\\textyen ', LaTeXMode.mathmode: '\\yen'},
166: {LaTeXMode.textmode: '\\textbrokenbar '},
167: {LaTeXMode.textmode: '\\textsection ', LaTeXMode.mathmode: '\\S'},
168: {LaTeXMode.textmode: '\\textasciidieresis '},
169: {LaTeXMode.textmode: '\\textcopyright ', LaTeXMode.mathmode: '\\copyright'},
170: {LaTeXMode.textmode: '\\textordfeminine '},
171: {LaTeXMode.textmode: '\\guillemotleft ', LaTeXMode.mathmode: '\\mbox{\\guillemotleft}'},
172: {LaTeXMode.textmode: '\\lnot ', LaTeXMode.mathmode: '\\neg'},
173: {LaTeXMode.textmode: '\\-'},
174: {LaTeXMode.textmode: '\\textregistered ', LaTeXMode.mathmode: '\\circledR'},
175: {LaTeXMode.textmode: '\\textasciimacron '},
176: {LaTeXMode.textmode: '\\textdegree ', LaTeXMode.mathmode: '\\degree'},
177: {LaTeXMode.textmode: '\\pm ', LaTeXMode.mathmode: '\\pm'},
178: {LaTeXMode.textmode: '{^2}', LaTeXMode.mathmode: '^2'},
179: {LaTeXMode.textmode: '{^3}', LaTeXMode.mathmode: '^3'},
180: {LaTeXMode.textmode: '\\textasciiacute ', LaTeXMode.mathmode: '\\textasciiacute'},
181: {LaTeXMode.textmode: '\\mathrm{\\mu}', LaTeXMode.mathmode: '\\mu'},
182: {LaTeXMode.textmode: '\\textparagraph ', LaTeXMode.mathmode: '\\P'},
183: {LaTeXMode.textmode: '\\cdot ', LaTeXMode.mathmode: '\\cdotp'},
184: {LaTeXMode.textmode: '\\c{}', LaTeXMode.mathmode: '\\mbox{\\c{}}'},
185: {LaTeXMode.textmode: '{^1}', LaTeXMode.mathmode: '^1'},
186: {LaTeXMode.textmode: '\\textordmasculine '},
187: {LaTeXMode.textmode: '\\guillemotright ', LaTeXMode.mathmode: '\\mbox{\\guillemotright}'},
188: {LaTeXMode.textmode: '\\textonequarter '},
189: {LaTeXMode.textmode: '\\textonehalf '},
190: {LaTeXMode.textmode: '\\textthreequarters '},
191: {LaTeXMode.textmode: '\\textquestiondown ', LaTeXMode.mathmode: '\\textquestiondown'},
192: {LaTeXMode.textmode: '\\`{A}', LaTeXMode.mathmode: '\\grave{A}'},
193: {LaTeXMode.textmode: "\\'{A}", LaTeXMode.mathmode: '\\acute{A}'},
194: {LaTeXMode.textmode: '\\^{A}', LaTeXMode.mathmode: '\\hat{A}'},
195: {LaTeXMode.textmode: '\\~{A}', LaTeXMode.mathmode: '\\tilde{A}'},
196: {LaTeXMode.textmode: '\\"{A}', LaTeXMode.mathmode: '\\ddot{A}'},
197: {LaTeXMode.textmode: '\\AA '},
198: {LaTeXMode.textmode: '\\AE ', LaTeXMode.mathmode: '\\AE'},
199: {LaTeXMode.textmode: '\\c{C}', LaTeXMode.mathmode: '\\mbox{\\c{C}}'},
200: {LaTeXMode.textmode: '\\`{E}', LaTeXMode.mathmode: '\\grave{E}'},
201: {LaTeXMode.textmode: "\\'{E}", LaTeXMode.mathmode: '\\acute{E}'},
202: {LaTeXMode.textmode: '\\^{E}', LaTeXMode.mathmode: '\\hat{E}'},
203: {LaTeXMode.textmode: '\\"{E}', LaTeXMode.mathmode: '\\ddot{E}'},
204: {LaTeXMode.textmode: '\\`{I}', LaTeXMode.mathmode: '\\grave{I}'},
205: {LaTeXMode.textmode: "\\'{I}", LaTeXMode.mathmode: '\\acute{I}'},
206: {LaTeXMode.textmode: '\\^{I}', LaTeXMode.mathmode: '\\hat{I}'},
207: {LaTeXMode.textmode: '\\"{I}', LaTeXMode.mathmode: '\\ddot{I}'},
208: {LaTeXMode.textmode: '\\DH '},
209: {LaTeXMode.textmode: '\\~{N}', LaTeXMode.mathmode: '\\tilde{N}'},
210: {LaTeXMode.textmode: '\\`{O}', LaTeXMode.mathmode: '\\grave{O}'},
211: {LaTeXMode.textmode: "\\'{O}", LaTeXMode.mathmode: '\\acute{O}'},
212: {LaTeXMode.textmode: '\\^{O}', LaTeXMode.mathmode: '\\hat{O}'},
213: {LaTeXMode.textmode: '\\~{O}', LaTeXMode.mathmode: '\\tilde{O}'},
214: {LaTeXMode.textmode: '\\"{O}', LaTeXMode.mathmode: '\\ddot{O}'},
215: {LaTeXMode.textmode: '\\texttimes ', LaTeXMode.mathmode: '\\times'},
216: {LaTeXMode.textmode: '\\O ', LaTeXMode.mathmode: '\\O'},
217: {LaTeXMode.textmode: '\\`{U}', LaTeXMode.mathmode: '\\grave{U}'},
218: {LaTeXMode.textmode: "\\'{U}", LaTeXMode.mathmode: '\\acute{U}'},
219: {LaTeXMode.textmode: '\\^{U}', LaTeXMode.mathmode: '\\hat{U}'},
220: {LaTeXMode.textmode: '\\"{U}', LaTeXMode.mathmode: '\\ddot{U}'},
221: {LaTeXMode.textmode: "\\'{Y}", LaTeXMode.mathmode: '\\acute{Y}'},
222: {LaTeXMode.textmode: '\\TH '},
223: {LaTeXMode.textmode: '\\ss ', LaTeXMode.mathmode: '\\ss'},
224: {LaTeXMode.textmode: '\\`{a}', LaTeXMode.mathmode: '\\grave{a}'},
225: {LaTeXMode.textmode: "\\'{a}", LaTeXMode.mathmode: '\\acute{a}'},
226: {LaTeXMode.textmode: '\\^{a}', LaTeXMode.mathmode: '\\hat{a}'},
227: {LaTeXMode.textmode: '\\~{a}', LaTeXMode.mathmode: '\\tilde{a}'},
228: {LaTeXMode.textmode: '\\"{a}', LaTeXMode.mathmode: '\\ddot{a}'},
229: {LaTeXMode.textmode: '\\aa '},
230: {LaTeXMode.textmode: '\\ae ', LaTeXMode.mathmode: '\\ae'},
231: {LaTeXMode.textmode: '\\c{c}', LaTeXMode.mathmode: '\\mbox{\\c{c}}'},
232: {LaTeXMode.textmode: '\\`{e}', LaTeXMode.mathmode: '\\grave{e}'},
233: {LaTeXMode.textmode: "\\'{e}", LaTeXMode.mathmode: '\\acute{e}'},
234: {LaTeXMode.textmode: '\\^{e}', LaTeXMode.mathmode: '\\hat{e}'},
235: {LaTeXMode.textmode: '\\"{e}', LaTeXMode.mathmode: '\\ddot{e}'},
236: {LaTeXMode.textmode: '\\`{\\i}', LaTeXMode.mathmode: '\\grave{\\imath}'},
237: {LaTeXMode.textmode: "\\'{\\i}", LaTeXMode.mathmode: '\\acute{\\imath}'},
238: {LaTeXMode.textmode: '\\^{\\i}', LaTeXMode.mathmode: '\\hat{\\imath}'},
239: {LaTeXMode.textmode: '\\"{\\i}', LaTeXMode.mathmode: '\\ddot{\\imath}'},
240: {LaTeXMode.textmode: '\\dh ', LaTeXMode.mathmode: '\\eth'},
241: {LaTeXMode.textmode: '\\~{n}', LaTeXMode.mathmode: '\\tilde{n}'},
242: {LaTeXMode.textmode: '\\`{o}', LaTeXMode.mathmode: '\\grave{o}'},
243: {LaTeXMode.textmode: "\\'{o}", LaTeXMode.mathmode: '\\acute{o}'},
244: {LaTeXMode.textmode: '\\^{o}', LaTeXMode.mathmode: '\\hat{o}'},
245: {LaTeXMode.textmode: '\\~{o}', LaTeXMode.mathmode: '\\tilde{o}'},
246: {LaTeXMode.textmode: '\\"{o}', LaTeXMode.mathmode: '\\ddot{o}'},
247: {LaTeXMode.textmode: '\\div ', LaTeXMode.mathmode: '\\div'},
248: {LaTeXMode.textmode: '\\o ', LaTeXMode.mathmode: '\\o'},
249: {LaTeXMode.textmode: '\\`{u}', LaTeXMode.mathmode: '\\grave{u}'},
250: {LaTeXMode.textmode: "\\'{u}", LaTeXMode.mathmode: '\\acute{u}'},
251: {LaTeXMode.textmode: '\\^{u}', LaTeXMode.mathmode: '\\hat{u}'},
252: {LaTeXMode.textmode: '\\"{u}', LaTeXMode.mathmode: '\\ddot{u}'},
253: {LaTeXMode.textmode: "\\'{y}", LaTeXMode.mathmode: '\\acute{y}'},
254: {LaTeXMode.textmode: '\\th '},
255: {LaTeXMode.textmode: '\\"{y}', LaTeXMode.mathmode: '\\ddot{y}'},
256: {LaTeXMode.textmode: '\\={A}', LaTeXMode.mathmode: '\\bar{A}'},
257: {LaTeXMode.textmode: '\\={a}', LaTeXMode.mathmode: '\\bar{a}'},
258: {LaTeXMode.textmode: '\\u{A}', LaTeXMode.mathmode: '\\breve{A}'},
259: {LaTeXMode.textmode: '\\u{a}', LaTeXMode.mathmode: '\\u{a}'},
260: {LaTeXMode.textmode: '\\k{A}'},
261: {LaTeXMode.textmode: '\\k{a}'},
262: {LaTeXMode.textmode: "\\'{C}", LaTeXMode.mathmode: '\\acute{C}'},
263: {LaTeXMode.textmode: "\\'{c}", LaTeXMode.mathmode: '\\acute{c}'},
264: {LaTeXMode.textmode: '\\^{C}', LaTeXMode.mathmode: '\\hat{C}'},
265: {LaTeXMode.textmode: '\\^{c}', LaTeXMode.mathmode: '\\hat{c}'},
266: {LaTeXMode.textmode: '\\.{C}', LaTeXMode.mathmode: '\\dot{C}'},
267: {LaTeXMode.textmode: '\\.{c}', LaTeXMode.mathmode: '\\dot{c}'},
268: {LaTeXMode.textmode: '\\v{C}', LaTeXMode.mathmode: '\\check{C}'},
269: {LaTeXMode.textmode: '\\v{c}', LaTeXMode.mathmode: '\\check{c}'},
270: {LaTeXMode.textmode: '\\v{D}', LaTeXMode.mathmode: '\\check{D}'},
271: {LaTeXMode.textmode: '\\v{d}', LaTeXMode.mathmode: '\\check{d}'},
272: {LaTeXMode.textmode: '\\DJ '},
273: {LaTeXMode.textmode: '\\dj '},
274: {LaTeXMode.textmode: '\\={E}', LaTeXMode.mathmode: '\\bar{E}'},
275: {LaTeXMode.textmode: '\\={e}', LaTeXMode.mathmode: '\\bar{e}'},
276: {LaTeXMode.textmode: '\\u{E}', LaTeXMode.mathmode: '\\breve{E}'},
277: {LaTeXMode.textmode: '\\u{e}', LaTeXMode.mathmode: '\\breve{e}'},
278: {LaTeXMode.textmode: '\\.{E}', LaTeXMode.mathmode: '\\dot{E}'},
279: {LaTeXMode.textmode: '\\.{e}', LaTeXMode.mathmode: '\\dot{e}'},
280: {LaTeXMode.textmode: '\\k{E}', LaTeXMode.mathmode: '\\k{E}'},
281: {LaTeXMode.textmode: '\\k{e}'},
282: {LaTeXMode.textmode: '\\v{E}', LaTeXMode.mathmode: '\\check{E}'},
283: {LaTeXMode.textmode: '\\v{e}', LaTeXMode.mathmode: '\\check{e}'},
284: {LaTeXMode.textmode: '\\^{G}', LaTeXMode.mathmode: '\\hat{G}'},
285: {LaTeXMode.textmode: '\\^{g}', LaTeXMode.mathmode: '\\hat{g}'},
286: {LaTeXMode.textmode: '\\u{G}', LaTeXMode.mathmode: '\\breve{G}'},
287: {LaTeXMode.textmode: '\\u{g}', LaTeXMode.mathmode: '\\breve{g}'},
288: {LaTeXMode.textmode: '\\.{G}', LaTeXMode.mathmode: '\\dot{G}'},
289: {LaTeXMode.textmode: '\\.{g}', LaTeXMode.mathmode: '\\dot{g}'},
290: {LaTeXMode.textmode: '\\c{G}', LaTeXMode.mathmode: '\\mbox{\\c{G}}'},
291: {LaTeXMode.textmode: '\\c{g}', LaTeXMode.mathmode: '\\mbox{\\c{g}}'},
292: {LaTeXMode.textmode: '\\^{H}', LaTeXMode.mathmode: '\\hat{H}'},
293: {LaTeXMode.textmode: '\\^{h}', LaTeXMode.mathmode: '\\hat{h}'},
294: {LaTeXMode.textmode: '{\\fontencoding{LELA}\\selectfont\\char40}'},
295: {LaTeXMode.textmode: '\\Elzxh '},
296: {LaTeXMode.textmode: '\\~{I}', LaTeXMode.mathmode: '\\tilde{I}'},
297: {LaTeXMode.textmode: '\\~{\\i}', LaTeXMode.mathmode: '\\tilde{\\imath}'},
298: {LaTeXMode.textmode: '\\={I}', LaTeXMode.mathmode: '\\bar{I}'},
299: {LaTeXMode.textmode: '\\={\\i}', LaTeXMode.mathmode: '\\bar{\\imath}'},
300: {LaTeXMode.textmode: '\\u{I}', LaTeXMode.mathmode: '\\breve{I}'},
301: {LaTeXMode.textmode: '\\u{\\i}', LaTeXMode.mathmode: '\\breve{\\imath}'},
302: {LaTeXMode.textmode: '\\k{I}'},
303: {LaTeXMode.textmode: '\\k{i}'},
304: {LaTeXMode.textmode: '\\.{I}', LaTeXMode.mathmode: '\\dot{I}'},
305: {LaTeXMode.textmode: '\\i ', LaTeXMode.mathmode: '\\imath'},
306: {LaTeXMode.textmode: 'IJ'},
307: {LaTeXMode.textmode: 'ij'},
308: {LaTeXMode.textmode: '\\^{J}', LaTeXMode.mathmode: '\\hat{J}'},
309: {LaTeXMode.textmode: '\\^{\\j}', LaTeXMode.mathmode: '\\hat{\\jmath}'},
310: {LaTeXMode.textmode: '\\c{K}', LaTeXMode.mathmode: '\\mbox{\\c{K}}'},
311: {LaTeXMode.textmode: '\\c{k}', LaTeXMode.mathmode: '\\mbox{\\c{k}}'},
312: {LaTeXMode.textmode: '{\\fontencoding{LELA}\\selectfont\\char91}'},
313: {LaTeXMode.textmode: "\\'{L}", LaTeXMode.mathmode: '\\acute{L}'},
314: {LaTeXMode.textmode: "\\'{l}", LaTeXMode.mathmode: '\\acute{l}'},
315: {LaTeXMode.textmode: '\\c{L}', LaTeXMode.mathmode: '\\mbox{\\c{L}}'},
316: {LaTeXMode.textmode: '\\c{l}', LaTeXMode.mathmode: '\\mbox{\\c{l}}'},
317: {LaTeXMode.textmode: '\\v{L}', LaTeXMode.mathmode: '\\check{L}'},
318: {LaTeXMode.textmode: '\\v{l}', LaTeXMode.mathmode: '\\check{l}'},
319: {LaTeXMode.textmode: '{\\fontencoding{LELA}\\selectfont\\char201}'},
320: {LaTeXMode.textmode: '{\\fontencoding{LELA}\\selectfont\\char202}'},
321: {LaTeXMode.textmode: '\\L ', LaTeXMode.mathmode: '\\L'},
322: {LaTeXMode.textmode: '\\l ', LaTeXMode.mathmode: '\\l'},
323: {LaTeXMode.textmode: "\\'{N}", LaTeXMode.mathmode: '\\acute{N}'},
324: {LaTeXMode.textmode: "\\'{n}", LaTeXMode.mathmode: '\\acute{n}'},
325: {LaTeXMode.textmode: '\\c{N}', LaTeXMode.mathmode: '\\mbox{\\c{N}}'},
326: {LaTeXMode.textmode: '\\c{n}', LaTeXMode.mathmode: '\\mbox{\\c{n}}'},
327: {LaTeXMode.textmode: '\\v{N}', LaTeXMode.mathmode: '\\check{N}'},
328: {LaTeXMode.textmode: '\\v{n}', LaTeXMode.mathmode: '\\check{n}'},
329: {LaTeXMode.textmode: "'n"},
330: {LaTeXMode.textmode: '\\NG '},
331: {LaTeXMode.textmode: '\\ng '},
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120697: {LaTeXMode.textmode: '\\mathsfbf{\\kappa}'},
120698: {LaTeXMode.textmode: '\\mathsfbf{\\lambda}'},
120699: {LaTeXMode.textmode: '\\mathsfbf{\\mu}'},
120700: {LaTeXMode.textmode: '\\mathsfbf{\\nu}'},
120701: {LaTeXMode.textmode: '\\mathsfbf{\\xi}'},
120702: {LaTeXMode.textmode: '\\mathsfbf{o}'},
120703: {LaTeXMode.textmode: '\\mathsfbf{\\pi}'},
120704: {LaTeXMode.textmode: '\\mathsfbf{\\rho}'},
120705: {LaTeXMode.textmode: '\\mathsfbf{\\varsigma}'},
120706: {LaTeXMode.textmode: '\\mathsfbf{\\sigma}'},
120707: {LaTeXMode.textmode: '\\mathsfbf{\\tau}'},
120708: {LaTeXMode.textmode: '\\mathsfbf{\\upsilon}'},
120709: {LaTeXMode.textmode: '\\mathsfbf{\\phi}'},
120710: {LaTeXMode.textmode: '\\mathsfbf{\\chi}'},
120711: {LaTeXMode.textmode: '\\mathsfbf{\\psi}'},
120712: {LaTeXMode.textmode: '\\mathsfbf{\\omega}'},
120713: {LaTeXMode.textmode: '\\partial '},
120714: {LaTeXMode.textmode: '\\mathsfbf{\\varepsilon}'},
120715: {LaTeXMode.textmode: '\\mathsfbf{\\vartheta}'},
120716: {LaTeXMode.textmode: '\\mathsfbf{\\varkappa}'},
120717: {LaTeXMode.textmode: '\\mathsfbf{\\phi}'},
120718: {LaTeXMode.textmode: '\\mathsfbf{\\varrho}'},
120719: {LaTeXMode.textmode: '\\mathsfbf{\\varpi}'},
120720: {LaTeXMode.textmode: '\\mathsfbfsl{\\Alpha}'},
120721: {LaTeXMode.textmode: '\\mathsfbfsl{\\Beta}'},
120722: {LaTeXMode.textmode: '\\mathsfbfsl{\\Gamma}'},
120723: {LaTeXMode.textmode: '\\mathsfbfsl{\\Delta}'},
120724: {LaTeXMode.textmode: '\\mathsfbfsl{\\Epsilon}'},
120725: {LaTeXMode.textmode: '\\mathsfbfsl{\\Zeta}'},
120726: {LaTeXMode.textmode: '\\mathsfbfsl{\\Eta}'},
120727: {LaTeXMode.textmode: '\\mathsfbfsl{\\vartheta}'},
120728: {LaTeXMode.textmode: '\\mathsfbfsl{\\Iota}'},
120729: {LaTeXMode.textmode: '\\mathsfbfsl{\\Kappa}'},
120730: {LaTeXMode.textmode: '\\mathsfbfsl{\\Lambda}'},
120731: {LaTeXMode.textmode: '\\mathsfbfsl{M}'},
120732: {LaTeXMode.textmode: '\\mathsfbfsl{N}'},
120733: {LaTeXMode.textmode: '\\mathsfbfsl{\\Xi}'},
120734: {LaTeXMode.textmode: 'O'},
120735: {LaTeXMode.textmode: '\\mathsfbfsl{\\Pi}'},
120736: {LaTeXMode.textmode: '\\mathsfbfsl{\\Rho}'},
120737: {LaTeXMode.textmode: '\\mathsfbfsl{\\vartheta}'},
120738: {LaTeXMode.textmode: '\\mathsfbfsl{\\Sigma}'},
120739: {LaTeXMode.textmode: '\\mathsfbfsl{\\Tau}'},
120740: {LaTeXMode.textmode: '\\mathsfbfsl{\\Upsilon}'},
120741: {LaTeXMode.textmode: '\\mathsfbfsl{\\Phi}'},
120742: {LaTeXMode.textmode: '\\mathsfbfsl{\\Chi}'},
120743: {LaTeXMode.textmode: '\\mathsfbfsl{\\Psi}'},
120744: {LaTeXMode.textmode: '\\mathsfbfsl{\\Omega}'},
120745: {LaTeXMode.textmode: '\\mathsfbfsl{\\nabla}'},
120746: {LaTeXMode.textmode: '\\mathsfbfsl{\\alpha}'},
120747: {LaTeXMode.textmode: '\\mathsfbfsl{\\beta}'},
120748: {LaTeXMode.textmode: '\\mathsfbfsl{\\gamma}'},
120749: {LaTeXMode.textmode: '\\mathsfbfsl{\\delta}'},
120750: {LaTeXMode.textmode: '\\mathsfbfsl{\\epsilon}'},
120751: {LaTeXMode.textmode: '\\mathsfbfsl{\\zeta}'},
120752: {LaTeXMode.textmode: '\\mathsfbfsl{\\eta}'},
120753: {LaTeXMode.textmode: '\\mathsfbfsl{\\vartheta}'},
120754: {LaTeXMode.textmode: '\\mathsfbfsl{\\iota}'},
120755: {LaTeXMode.textmode: '\\mathsfbfsl{\\kappa}'},
120756: {LaTeXMode.textmode: '\\mathsfbfsl{\\lambda}'},
120757: {LaTeXMode.textmode: '\\mathsfbfsl{\\mu}'},
120758: {LaTeXMode.textmode: '\\mathsfbfsl{\\nu}'},
120759: {LaTeXMode.textmode: '\\mathsfbfsl{\\xi}'},
120760: {LaTeXMode.textmode: '\\mathsfbfsl{o}'},
120761: {LaTeXMode.textmode: '\\mathsfbfsl{\\pi}'},
120762: {LaTeXMode.textmode: '\\mathsfbfsl{\\rho}'},
120763: {LaTeXMode.textmode: '\\mathsfbfsl{\\varsigma}'},
120764: {LaTeXMode.textmode: '\\mathsfbfsl{\\sigma}'},
120765: {LaTeXMode.textmode: '\\mathsfbfsl{\\tau}'},
120766: {LaTeXMode.textmode: '\\mathsfbfsl{\\upsilon}'},
120767: {LaTeXMode.textmode: '\\mathsfbfsl{\\phi}'},
120768: {LaTeXMode.textmode: '\\mathsfbfsl{\\chi}'},
120769: {LaTeXMode.textmode: '\\mathsfbfsl{\\psi}'},
120770: {LaTeXMode.textmode: '\\mathsfbfsl{\\omega}'},
120771: {LaTeXMode.textmode: '\\partial '},
120772: {LaTeXMode.textmode: '\\in'},
120773: {LaTeXMode.textmode: '\\mathsfbfsl{\\vartheta}'},
120774: {LaTeXMode.textmode: '\\mathsfbfsl{\\varkappa}'},
120775: {LaTeXMode.textmode: '\\mathsfbfsl{\\phi}'},
120776: {LaTeXMode.textmode: '\\mathsfbfsl{\\varrho}'},
120777: {LaTeXMode.textmode: '\\mathsfbfsl{\\varpi}'},
120782: {LaTeXMode.textmode: '\\mathbf{0}'},
120783: {LaTeXMode.textmode: '\\mathbf{1}'},
120784: {LaTeXMode.textmode: '\\mathbf{2}'},
120785: {LaTeXMode.textmode: '\\mathbf{3}'},
120786: {LaTeXMode.textmode: '\\mathbf{4}'},
120787: {LaTeXMode.textmode: '\\mathbf{5}'},
120788: {LaTeXMode.textmode: '\\mathbf{6}'},
120789: {LaTeXMode.textmode: '\\mathbf{7}'},
120790: {LaTeXMode.textmode: '\\mathbf{8}'},
120791: {LaTeXMode.textmode: '\\mathbf{9}'},
120792: {LaTeXMode.textmode: '\\mathbb{0}'},
120793: {LaTeXMode.textmode: '\\mathbb{1}'},
120794: {LaTeXMode.textmode: '\\mathbb{2}'},
120795: {LaTeXMode.textmode: '\\mathbb{3}'},
120796: {LaTeXMode.textmode: '\\mathbb{4}'},
120797: {LaTeXMode.textmode: '\\mathbb{5}'},
120798: {LaTeXMode.textmode: '\\mathbb{6}'},
120799: {LaTeXMode.textmode: '\\mathbb{7}'},
120800: {LaTeXMode.textmode: '\\mathbb{8}'},
120801: {LaTeXMode.textmode: '\\mathbb{9}'},
120802: {LaTeXMode.textmode: '\\mathsf{0}'},
120803: {LaTeXMode.textmode: '\\mathsf{1}'},
120804: {LaTeXMode.textmode: '\\mathsf{2}'},
120805: {LaTeXMode.textmode: '\\mathsf{3}'},
120806: {LaTeXMode.textmode: '\\mathsf{4}'},
120807: {LaTeXMode.textmode: '\\mathsf{5}'},
120808: {LaTeXMode.textmode: '\\mathsf{6}'},
120809: {LaTeXMode.textmode: '\\mathsf{7}'},
120810: {LaTeXMode.textmode: '\\mathsf{8}'},
120811: {LaTeXMode.textmode: '\\mathsf{9}'},
120812: {LaTeXMode.textmode: '\\mathsfbf{0}'},
120813: {LaTeXMode.textmode: '\\mathsfbf{1}'},
120814: {LaTeXMode.textmode: '\\mathsfbf{2}'},
120815: {LaTeXMode.textmode: '\\mathsfbf{3}'},
120816: {LaTeXMode.textmode: '\\mathsfbf{4}'},
120817: {LaTeXMode.textmode: '\\mathsfbf{5}'},
120818: {LaTeXMode.textmode: '\\mathsfbf{6}'},
120819: {LaTeXMode.textmode: '\\mathsfbf{7}'},
120820: {LaTeXMode.textmode: '\\mathsfbf{8}'},
120821: {LaTeXMode.textmode: '\\mathsfbf{9}'},
120822: {LaTeXMode.textmode: '\\mathtt{0}'},
120823: {LaTeXMode.textmode: '\\mathtt{1}'},
120824: {LaTeXMode.textmode: '\\mathtt{2}'},
120825: {LaTeXMode.textmode: '\\mathtt{3}'},
120826: {LaTeXMode.textmode: '\\mathtt{4}'},
120827: {LaTeXMode.textmode: '\\mathtt{5}'},
120828: {LaTeXMode.textmode: '\\mathtt{6}'},
120829: {LaTeXMode.textmode: '\\mathtt{7}'},
120830: {LaTeXMode.textmode: '\\mathtt{8}'},
120831: {LaTeXMode.textmode: '\\mathtt{9}'},
} | PypiClean |
/lyberry_dmenu-0.0.1-py3-none-any.whl/lyberry_dmenu/lyberrydmenu.py | import lyberry_api
# This will first try to use a dmenu wrapper library, which you can get off of PyPI as simply dmenu.
# If that is not installed it will make a class that emulates part of the library.
try:
import dmenu
except:
import subprocess
class dmenu:
def show(self, items: list, lines: int = 0, prompt: str = ""):
items_str = ""
for item in items:
items_str += f"{item}\n"
return (
subprocess.check_output(
["dmenu", "-l", str(lines), "-p", prompt], input=items_str.encode()
)
.decode()
.rstrip()
)
dmenu = dmenu()
class LyBerry_Dmenu:
def __init__(self):
self.lbry = lyberry_api.LBRY_Api()
def repl(self):
commands = ["search", "following"]
selected = dmenu.show(commands, lines=len(commands), prompt="Command to run:")
if selected == "search":
self.search()
if selected == "following":
self.following()
def search(self):
query = dmenu.show([], prompt="Search for:")
if query:
results = self.lbry.lbrynet_search_feed(text=query)
self.play_from_results(results)
def following(self):
results = self.lbry.sub_feed
self.play_from_results(results)
def play_from_results(self, results):
items = []
for i in range(20):
try:
items.append(next(results))
except StopIteration:
break
options = []
for item in items:
options.append(
item.title + " - " + item.channel.title
if hasattr(item, "channel")
else "Anonymous"
)
selection = dmenu.show(options, lines=20, prompt="Claim to open:")
if selection:
selected_item = items[options.index(selection)]
self.open_external(selected_item)
def open_external(self, pub):
file_type = pub.media_type.split("/")[0]
if file_type == "video" or file_type == "audio":
lyberry_api.settings.media_player(pub.streaming_url)
elif file_type == "text":
lyberry_api.settings.text_viewer(pub.streaming_url)
def main():
LyBerry_Dmenu().repl()
if __name__ == "__main__":
main() | PypiClean |
/sage-utils-0.5.6.tar.gz/sage-utils-0.5.6/sage_utils/amqp/clients.py | import asyncio
import json
from copy import deepcopy
class RpcAmqpClient(object):
CONTENT_TYPE = 'application/json'
DEFAULT_PROPERTIES = {
'content_type': CONTENT_TYPE,
'delivery_mode': 2,
'correlation_id': 'event-name'
}
def __init__(self, app, routing_key, request_exchange='',
response_queue=None, response_exchange='', loop=None):
self.app = app
self.loop = loop or getattr(self.app, 'loop', None) or asyncio.get_event_loop()
self.routing_key = routing_key
self.request_exchange = request_exchange
self.response_queue = response_queue
self.response_exchange = response_exchange
self.transport = None
self.protocol = None
self.channel = None
self.waiter = asyncio.Event()
self._response_queue_name = None
self._response = None
@property
def response_queue_name(self):
return self._response_queue_name
async def connect(self, consume_timeout=None):
self.transport, self.protocol = await self.app.amqp.connect()
self.channel = await self.protocol.channel()
if self.response_queue is not None:
result = await self.channel.queue_declare(
queue_name=self.response_queue,
exclusive=True,
durable=True,
passive=False,
auto_delete=True,
)
self._response_queue_name = result['queue']
await self.channel.queue_bind(
queue_name=self.response_queue_name,
exchange_name=self.response_exchange,
routing_key=self.response_queue_name
)
await self.channel.basic_qos(
prefetch_count=1,
prefetch_size=0,
connection_global=False
)
await asyncio.wait_for(
self.channel.basic_consume(
self.on_response,
queue_name=self._response_queue_name,
),
timeout=consume_timeout,
loop=self.loop
)
async def on_response(self, _channel, body, _envelope, _properties):
self._response = json.loads(body)
self.waiter.set()
async def send(self, payload={}, properties={}, raw_data=False, consume_timeout=None):
if not self.protocol:
await self.connect(consume_timeout=consume_timeout)
request_properties = deepcopy(self.DEFAULT_PROPERTIES)
request_properties.update({'reply_to': self.response_queue_name})
request_properties.update(properties)
await self.channel.publish(
payload if raw_data else json.dumps(payload),
exchange_name=self.request_exchange,
routing_key=self.routing_key,
properties=request_properties
)
response = None
if self.response_queue_name is not None:
await self.waiter.wait()
response = self._response
await self.protocol.close()
self.protocol = None
self.transport = None
return response | PypiClean |
/gwu_nn-0.2.0.tar.gz/gwu_nn-0.2.0/gwu_nn/layers.py | import numpy as np
from abc import ABC, abstractmethod
from gwu_nn.activation_layers import Sigmoid, RELU, Softmax
activation_functions = {'relu': RELU, 'sigmoid': Sigmoid, 'softmax': Softmax}
def apply_activation_forward(forward_pass):
"""Decorator that ensures that a layer's activation function is applied after the layer during forward
propagation.
"""
def wrapper(*args):
output = forward_pass(args[0], args[1])
if args[0].activation:
return args[0].activation.forward_propagation(output)
else:
return output
return wrapper
def apply_activation_backward(backward_pass):
"""Decorator that ensures that a layer's activation function's derivative is applied before the layer during
backwards propagation.
"""
def wrapper(*args):
output_error = args[1]
learning_rate = args[2]
if args[0].activation:
output_error = args[0].activation.backward_propagation(output_error, learning_rate)
return backward_pass(args[0], output_error, learning_rate)
return wrapper
class Layer():
"""The Layer layer is an abstract object used to define the template
for other layer types to inherit"""
def __init__(self, activation=None):
"""Because Layer is an abstract object, we don't provide any detailing
on the initializtion"""
self.type = "Layer"
if activation:
self.activation = activation_functions[activation]()
else:
self.activation = None
@apply_activation_forward
def forward_propagation(cls, input):
""":noindex:"""
pass
@apply_activation_backward
def backward_propogation(cls, output_error, learning_rate):
""":noindex:"""
pass
class Dense(Layer):
"""The Dense layer class creates a layer that is fully connected with the previous
layer. This means that the number of weights will be MxN where M is number of
nodes in the previous layer and N = number of nodes in the current layer.
"""
def __init__(self, output_size, add_bias=False, activation=None, input_size=None):
super().__init__(activation)
self.type = None
self.name = "Dense"
self.input_size = input_size
self.output_size = output_size
self.add_bias = add_bias
def init_weights(self, input_size):
"""Initialize the weights for the layer based on input and output size
Args:
input_size (numpy array): dimensions for the input array
"""
if self.input_size is None:
self.input_size = input_size
self.weights = np.random.randn(input_size, self.output_size) / np.sqrt(input_size + self.output_size)
# TODO: Batching of inputs has broken how bias works. Need to address in next iteration
if self.add_bias:
self.bias = np.random.randn(1, self.output_size) / np.sqrt(input_size + self.output_size)
@apply_activation_forward
def forward_propagation(self, input):
"""Applies the forward propagation for a densely connected layer. This will compute the dot product between the
input value (calculated during forward propagation) and the layer's weight tensor.
Args:
input (np.array): Input tensor calculated during forward propagation up to this layer.
Returns:
np.array(float): The dot product of the input and the layer's weight tensor."""
self.input = input
output = np.dot(input, self.weights)
if self.add_bias:
return output + self.bias
else:
return output
@apply_activation_backward
def backward_propagation(self, output_error, learning_rate):
"""Applies the backward propagation for a densely connected layer. This will calculate the output error
(dot product of the output_error and the layer's weights) and will calculate the update gradient for the
weights (dot product of the layer's input values and the output_error).
Args:
output_error (np.array): The gradient of the error up to this point in the network.
Returns:
np.array(float): The gradient of the error up to and including this layer."""
input_error = np.dot(output_error, self.weights.T)
weights_error = np.dot(self.input.T, output_error)
self.weights -= learning_rate * weights_error
if self.add_bias:
self.bias -= learning_rate * output_error
return input_error | PypiClean |
/mis_modulos-0.1.tar.gz/mis_modulos-0.1/tensorflow/python/platform/tf_logging.py |
"""Logging utilities."""
# pylint: disable=unused-import
# pylint: disable=g-bad-import-order
# pylint: disable=invalid-name
import logging as _logging
import os as _os
import sys as _sys
import _thread
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging import INFO
from logging import WARN
import threading
from tensorflow.python.util.tf_export import tf_export
# Don't use this directly. Use get_logger() instead.
_logger = None
_logger_lock = threading.Lock()
def _get_caller(offset=3):
"""Returns a code and frame object for the lowest non-logging stack frame."""
# Use sys._getframe(). This avoids creating a traceback object.
# pylint: disable=protected-access
f = _sys._getframe(offset)
# pylint: enable=protected-access
our_file = f.f_code.co_filename
f = f.f_back
while f:
code = f.f_code
if code.co_filename != our_file:
return code, f
f = f.f_back
return None, None
# The definition of `findCaller` changed in Python 3.2,
# and further changed in Python 3.8
if _sys.version_info.major >= 3 and _sys.version_info.minor >= 8:
def _logger_find_caller(stack_info=False, stacklevel=1): # pylint: disable=g-wrong-blank-lines
code, frame = _get_caller(4)
sinfo = None
if stack_info:
sinfo = '\n'.join(_traceback.format_stack())
if code:
return (code.co_filename, frame.f_lineno, code.co_name, sinfo)
else:
return '(unknown file)', 0, '(unknown function)', sinfo
elif _sys.version_info.major >= 3 and _sys.version_info.minor >= 2:
def _logger_find_caller(stack_info=False): # pylint: disable=g-wrong-blank-lines
code, frame = _get_caller(4)
sinfo = None
if stack_info:
sinfo = '\n'.join(_traceback.format_stack())
if code:
return (code.co_filename, frame.f_lineno, code.co_name, sinfo)
else:
return '(unknown file)', 0, '(unknown function)', sinfo
else:
def _logger_find_caller(): # pylint: disable=g-wrong-blank-lines
code, frame = _get_caller(4)
if code:
return (code.co_filename, frame.f_lineno, code.co_name)
else:
return '(unknown file)', 0, '(unknown function)'
@tf_export('get_logger')
def get_logger():
"""Return TF logger instance."""
global _logger
# Use double-checked locking to avoid taking lock unnecessarily.
if _logger:
return _logger
_logger_lock.acquire()
try:
if _logger:
return _logger
# Scope the TensorFlow logger to not conflict with users' loggers.
logger = _logging.getLogger('tensorflow')
# Override findCaller on the logger to skip internal helper functions
logger.findCaller = _logger_find_caller
# Don't further configure the TensorFlow logger if the root logger is
# already configured. This prevents double logging in those cases.
if not _logging.getLogger().handlers:
# Determine whether we are in an interactive environment
_interactive = False
try:
# This is only defined in interactive shells.
if _sys.ps1: _interactive = True
except AttributeError:
# Even now, we may be in an interactive shell with `python -i`.
_interactive = _sys.flags.interactive
# If we are in an interactive environment (like Jupyter), set loglevel
# to INFO and pipe the output to stdout.
if _interactive:
logger.setLevel(INFO)
_logging_target = _sys.stdout
else:
_logging_target = _sys.stderr
# Add the output handler.
_handler = _logging.StreamHandler(_logging_target)
_handler.setFormatter(_logging.Formatter(_logging.BASIC_FORMAT, None))
logger.addHandler(_handler)
_logger = logger
return _logger
finally:
_logger_lock.release()
@tf_export(v1=['logging.log'])
def log(level, msg, *args, **kwargs):
get_logger().log(level, msg, *args, **kwargs)
@tf_export(v1=['logging.debug'])
def debug(msg, *args, **kwargs):
get_logger().debug(msg, *args, **kwargs)
@tf_export(v1=['logging.error'])
def error(msg, *args, **kwargs):
get_logger().error(msg, *args, **kwargs)
@tf_export(v1=['logging.fatal'])
def fatal(msg, *args, **kwargs):
get_logger().fatal(msg, *args, **kwargs)
@tf_export(v1=['logging.info'])
def info(msg, *args, **kwargs):
get_logger().info(msg, *args, **kwargs)
@tf_export(v1=['logging.warn'])
def warn(msg, *args, **kwargs):
get_logger().warning(msg, *args, **kwargs)
@tf_export(v1=['logging.warning'])
def warning(msg, *args, **kwargs):
get_logger().warning(msg, *args, **kwargs)
_level_names = {
FATAL: 'FATAL',
ERROR: 'ERROR',
WARN: 'WARN',
INFO: 'INFO',
DEBUG: 'DEBUG',
}
# Mask to convert integer thread ids to unsigned quantities for logging
# purposes
_THREAD_ID_MASK = 2 * _sys.maxsize + 1
_log_prefix = None # later set to google2_log_prefix
# Counter to keep track of number of log entries per token.
_log_counter_per_token = {}
@tf_export(v1=['logging.TaskLevelStatusMessage'])
def TaskLevelStatusMessage(msg):
error(msg)
@tf_export(v1=['logging.flush'])
def flush():
raise NotImplementedError()
# Code below is taken from pyglib/logging
@tf_export(v1=['logging.vlog'])
def vlog(level, msg, *args, **kwargs):
get_logger().log(level, msg, *args, **kwargs)
def _GetNextLogCountPerToken(token):
"""Wrapper for _log_counter_per_token.
Args:
token: The token for which to look up the count.
Returns:
The number of times this function has been called with
*token* as an argument (starting at 0)
"""
global _log_counter_per_token # pylint: disable=global-variable-not-assigned
_log_counter_per_token[token] = 1 + _log_counter_per_token.get(token, -1)
return _log_counter_per_token[token]
@tf_export(v1=['logging.log_every_n'])
def log_every_n(level, msg, n, *args):
"""Log 'msg % args' at level 'level' once per 'n' times.
Logs the 1st call, (N+1)st call, (2N+1)st call, etc.
Not threadsafe.
Args:
level: The level at which to log.
msg: The message to be logged.
n: The number of times this should be called before it is logged.
*args: The args to be substituted into the msg.
"""
count = _GetNextLogCountPerToken(_GetFileAndLine())
log_if(level, msg, not (count % n), *args)
@tf_export(v1=['logging.log_first_n'])
def log_first_n(level, msg, n, *args): # pylint: disable=g-bad-name
"""Log 'msg % args' at level 'level' only first 'n' times.
Not threadsafe.
Args:
level: The level at which to log.
msg: The message to be logged.
n: The number of times this should be called before it is logged.
*args: The args to be substituted into the msg.
"""
count = _GetNextLogCountPerToken(_GetFileAndLine())
log_if(level, msg, count < n, *args)
@tf_export(v1=['logging.log_if'])
def log_if(level, msg, condition, *args):
"""Log 'msg % args' at level 'level' only if condition is fulfilled."""
if condition:
vlog(level, msg, *args)
def _GetFileAndLine():
"""Returns (filename, linenumber) for the stack frame."""
code, f = _get_caller()
if not code:
return ('<unknown>', 0)
return (code.co_filename, f.f_lineno)
def google2_log_prefix(level, timestamp=None, file_and_line=None):
"""Assemble a logline prefix using the google2 format."""
# pylint: disable=global-variable-not-assigned
global _level_names
# pylint: enable=global-variable-not-assigned
# Record current time
now = timestamp or _time.time()
now_tuple = _time.localtime(now)
now_microsecond = int(1e6 * (now % 1.0))
(filename, line) = file_and_line or _GetFileAndLine()
basename = _os.path.basename(filename)
# Severity string
severity = 'I'
if level in _level_names:
severity = _level_names[level][0]
s = '%c%02d%02d %02d:%02d:%02d.%06d %5d %s:%d] ' % (
severity,
now_tuple[1], # month
now_tuple[2], # day
now_tuple[3], # hour
now_tuple[4], # min
now_tuple[5], # sec
now_microsecond,
_get_thread_id(),
basename,
line)
return s
@tf_export(v1=['logging.get_verbosity'])
def get_verbosity():
"""Return how much logging output will be produced."""
return get_logger().getEffectiveLevel()
@tf_export(v1=['logging.set_verbosity'])
def set_verbosity(v):
"""Sets the threshold for what messages will be logged."""
get_logger().setLevel(v)
def _get_thread_id():
"""Get id of current thread, suitable for logging as an unsigned quantity."""
thread_id = _thread.get_ident()
return thread_id & _THREAD_ID_MASK
_log_prefix = google2_log_prefix
tf_export(v1=['logging.DEBUG']).export_constant(__name__, 'DEBUG')
tf_export(v1=['logging.ERROR']).export_constant(__name__, 'ERROR')
tf_export(v1=['logging.FATAL']).export_constant(__name__, 'FATAL')
tf_export(v1=['logging.INFO']).export_constant(__name__, 'INFO')
tf_export(v1=['logging.WARN']).export_constant(__name__, 'WARN') | PypiClean |
/kivy_garden.ebs.clocks-1.0.1.tar.gz/kivy_garden.ebs.clocks-1.0.1/README.md | Clock Widgets for Kivy
======================
[](https://github.com/ebs-universe/kivy_garden.ebs.clocks/actions)
This package provides relatively simple Clock widgets for Kivy.
Presently it has only a single and trivial clock and is certainly not deserving
of a whole package. In time, this package is intended to hold a curated
collection of clocks in both Python and .kv.
The presently included widgets are easily implemented from scratch in both
Python and Kv, and for most non-EBS applications, you'd probably just want
to roll your own or use some .kv based clock widget available in the wild.
The typical EBS application can get unwieldy pretty quickly though, so this
package affords a separation of concerns which aids in maintainability.
If you are looking for something you can just pip install and which just works,
and don't care that it might be written in python and might pull in additional
dependencies, then this might be an option.
Included clock widgets :
- SimpleDigitalClock
No clock which have its own kivy_garden package will be added to this collection,
and the collection itself will grow (very) slowly.
This package is part of the EBS widget collection for Kivy. It is written in
mostly Python and depends on the EBS core widgets and widget infrastructure package.
For more information, see [kivy_garden.ebs.core](https://github.com/ebs-universe/kivy_garden.ebs.core)
See https://kivy-garden.github.io/ebs.flower/ for the rendered flower docs.
Please see the garden [instructions](https://kivy-garden.github.io) for
how to use kivy garden flowers.
CI
--
Every push or pull request run the [GitHub Action](https://github.com/kivy-garden/flower/actions) CI.
It tests the code on various OS and also generates wheels that can be released on PyPI upon a
tag. Docs are also generated and uploaded to the repo as well as artifacts of the CI.
TODO
-------
* add your code
Contributing
--------------
Check out our [contribution guide](CONTRIBUTING.md) and feel free to improve the flower.
License
---------
This software is released under the terms of the MIT License.
Please see the [LICENSE.txt](LICENSE.txt) file.
How to release
===============
See the garden [instructions](https://kivy-garden.github.io/#makingareleaseforyourflower) for how to make a new release.
| PypiClean |
/onlinepayments-sdk-python2-4.14.0.zip/onlinepayments-sdk-python2-4.14.0/README.rst | Online Payments Python SDK
==========================
Introduction
------------
The Python SDK helps you to communicate with the payment platform server
API. Its primary features are:
- convenient Python library for the API calls and responses
- marshalls Python request objects to HTTP requests
- unmarshalls HTTP responses to Python response objects or Python
exceptions
- handling of all the details concerning authentication
- handling of required meta data
Its use is demonstrated by an example for each possible call. The
examples execute a call using the provided API key.
Structure of this repository
----------------------------
This repository consists out of three main components:
#. The source code of the SDK itself: ``/onlinepayments/sdk/``
#. The source code of the SDK unit tests: ``/tests/unit/``
#. The source code of the example integration tests:
``/tests/integration/``
Note that the source code of the unit tests and integration tests can
only be found on GitHub.
Requirements
------------
Python 2.7 is required. In addition, the following package is required:
- `requests <https://requests.readthedocs.io/>`__ 2.20.0 or higher
This package will be installed automatically if the SDK is installed
manually or using pip following the below instructions.
Installation
------------
To install the SDK using pip, execute the following command:
::
pip install onlinepayments-sdk-python2
Alternatively, you can install the SDK from a source distribution file:
#. Download the latest version of the Python SDK from GitHub. Choose the
``onlinepayments-sdk-python2-x.y.z.zip`` file from the
`releases <https://github.com/wl-online-payments-direct/sdk-python2/releases>`__
page, where ``x.y.z`` is the version number.
#. Execute the following command in the folder where the SDK was
downloaded to:
::
pip install onlinepayments-sdk-python2-x.y.z.zip
Uninstalling
------------
After the Python SDK has been installed, it can be uninstalled using the
following command:
::
pip uninstall onlinepayments-sdk-python2
The required package can be uninstalled in the same way.
Running tests
-------------
| There are two types of tests: unit tests and integration tests. The
unit tests will work out-of-the-box; for the integration tests some
configuration is required.
| First, some environment variables need to be set:
- ``onlinePayments.api.apiKeyId`` for the API key id to use.
- ``onlinePayments.api.secretApiKey`` for the secret API key to use.
- ``onlinePayments.api.merchantId`` for your merchant ID.
In order to run the unit and integration tests, the
`mock <https://pypi.python.org/pypi/mock>`__ backport and
`mockito <https://pypi.python.org/pypi/mockito>`__ packages are
required. These can be installed using the following command:
::
pip install mock mockito
The following commands can then be executed from the ``tests`` directory
to execute the tests:
- Unit tests:
::
python run_unit_tests.py
- Integration tests:
::
python run_integration_tests.py
- Both unit and integration tests:
::
python run_all_tests.py
Note: in the current version of the unit tests, two errors will pop up
([Errno 10053] for Windows and [Errno 32] for Linux), indicating that
there was a client disconnect. These errors occur during cleanup of the
tests and do not hinder the tests in any way, and should therefore be
ignored.
| PypiClean |
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//# sourceMappingURL=d3398bcb.js.map | PypiClean |
/monk_keras_cuda100_test-0.0.1-py3-none-any.whl/monk/gluon/finetune/level_8_layers_main.py | import math
from monk.gluon.finetune.imports import *
from monk.system.imports import *
from monk.gluon.finetune.level_7_aux_main import prototype_aux
class prototype_layers(prototype_aux):
'''
Main class for all layers and activations
- Layers and activations while appending to base network
- Layers and activations while creating custom network
Args:
verbose (int): Set verbosity levels
0 - Print Nothing
1 - Print desired details
'''
@accepts("self", verbose=int, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def __init__(self, verbose=1):
super().__init__(verbose=verbose);
#####################################################################################################################################
@warning_checks(None, num_neurons=["lt", 10000], final_layer=None, post_trace=False)
@error_checks(None, num_neurons=["gt", 0], final_layer=None, post_trace=False)
@accepts("self", num_neurons=[int, bool], final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_linear(self, num_neurons=False, final_layer=False):
'''
Append dense (fully connected) layer to base network in transfer learning
Args:
num_neurons (int): Number of neurons in the dense layer
final_layer (bool): If True, then number of neurons are directly set as number of classes in dataset for single label type classification
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
if(not num_neurons):
num_neurons = self.system_dict["dataset"]["params"]["num_classes"];
self.system_dict = layer_linear(self.system_dict, num_neurons=num_neurons, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, probability=["lt", 0.7], final_layer=None, post_trace=False)
@error_checks(None, probability=["gt", 0, "lt", 1], final_layer=None, post_trace=False)
@accepts("self", probability=float, final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_dropout(self, probability=0.5, final_layer=False):
'''
Append dropout layer to base network in transfer learning
Args:
probability (float): Droping probability of neurons in next layer
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = layer_dropout(self.system_dict, probability=probability, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_relu(self, final_layer=False):
'''
Append rectified linear unit activation to base network in transfer learning
Args:
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_relu(self.system_dict, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_sigmoid(self, final_layer=False):
'''
Append sigmoid activation to base network in transfer learning
Args:
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_sigmoid(self.system_dict, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_tanh(self, final_layer=False):
'''
Append tanh activation to base network in transfer learning
Args:
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_tanh(self.system_dict, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", beta=[float, int], threshold=[float, int], final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_softplus(self, beta=1, threshold=20, final_layer=False):
'''
Append softplus activation to base network in transfer learning
Args:
beta (int): Multiplicative factor
threshold (int): softplus (thresholded relu) limit
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_softplus(self.system_dict, beta=1, threshold=20, final_layer=False);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_softsign(self, final_layer=False):
'''
Append softsign activation to base network in transfer learning
Args:
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_softsign(self.system_dict, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, negative_slope=["lt", 0.2], final_layer=None, post_trace=False)
@error_checks(None, negative_slope=["gt", 0], final_layer=None, post_trace=False)
@accepts("self", negative_slope=[float, int], final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_leakyrelu(self, negative_slope=0.01, final_layer=False):
'''
Append Leaky - ReLU activation to base network in transfer learning
Args:
negative_slope (float): Multiplicatve factor towards negative spectrum of real numbers.
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_leakyrelu(self.system_dict, negative_slope=negative_slope, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, num_parameters=["lt", 1], final_layer=None, post_trace=False)
@error_checks(None, num_parameters=["gt", 0], init=["gt", 0], final_layer=None, post_trace=False)
@accepts("self", num_parameters=int, init=[int, float], final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_prelu(self, num_parameters=1, init=0.25, final_layer=False):
'''
Append Learnable parameerized rectified linear unit activation to base network in transfer learning
Args:
init (float): Initialization value for multiplicatve factor towards negative spectrum of real numbers.
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_prelu(self.system_dict, num_parameters=num_parameters, init=init, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", alpha=[float, int], final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_elu(self, alpha=1.0, final_layer=False):
'''
Append exponential linear unit activation to base network in transfer learning
Args:
alpha (float): Multiplicatve factor.
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_elu(self.system_dict, alpha=alpha, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@accepts("self", final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_selu(self, final_layer=False):
'''
Append scaled exponential linear unit activation to base network in transfer learning
Args:
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_selu(self.system_dict, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, beta=["lt", 2], final_layer=None, post_trace=False)
@error_checks(None, beta=["gt", 0], final_layer=None, post_trace=False)
@accepts("self", beta=[float, int], final_layer=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def append_swish(self, beta=1.0, final_layer=False):
'''
Append swish activation to base network in transfer learning
Args:
beta (bool): Multiplicative factor
final_layer (bool): Indicator that this layer marks the end of network.
Returns:
None
'''
if(self.system_dict["model"]["final_layer"]):
msg = "Cannot append more layers.\n";
msg += "Tip: Previously appended layer termed as final layer";
raise ConstraintError(msg);
else:
self.system_dict = activation_swish(self.system_dict, beta=beta, final_layer=final_layer);
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=int, stride=int, padding=[str, int],
groups=int, dilation=int, use_bias=bool, layout=str, uid=[type(None), str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def convolution1d(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
groups=1, dilation=1, use_bias=True, layout='NCW', uid=None):
'''
Append 1d-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCW' - order
2) 'NWC' - order
- N: Number of elements in batches
- C: Number of channels
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "convolution1d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=[int, tuple], stride=[int, tuple], padding=[str, int, tuple],
groups=int, dilation=int, use_bias=bool, layout=str, uid=[type(None), str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def convolution2d(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
groups=1, dilation=1, use_bias=True, layout='NCHW', uid=None):
'''
Append 2d-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "convolution2d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=[int, tuple], stride=[int, tuple], padding=[str, int, tuple],
groups=int, dilation=int, use_bias=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def convolution(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
groups=1, dilation=1, use_bias=True, layout='NCHW', uid=None):
'''
Append 2d-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "convolution2d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCDHW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=int, stride=int, padding=[str, int],
groups=int, dilation=int, use_bias=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def convolution3d(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
groups=1, dilation=1, use_bias=True, layout='NCDHW', uid=None):
'''
Append 3d-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCDHW' - Order
2) 'NDHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- D: Depth of features in layers
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "convolution3d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
output_padding=None, groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
output_padding=["gte, 0"], groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=int, stride=int, padding=[str, int],
output_padding=int, groups=int, dilation=int, use_bias=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def transposed_convolution1d(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
output_padding=0, groups=1, dilation=1, use_bias=True, layout='NCW', uid=None):
'''
Append 1d-transposed-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
output_padding (int): Additional padding applied to output
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCW' - order
2) 'NWC' - order
- N: Number of elements in batches
- C: Number of channels
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "transposed_convolution1d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["output_padding"] = output_padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
output_padding=None, groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
output_padding=["gte, 0"], groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=int, stride=int, padding=[str, int],
output_padding=int, groups=int, dilation=int, use_bias=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def transposed_convolution(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
output_padding=0, groups=1, dilation=1, use_bias=True, layout='NCHW', uid=None):
'''
Append 2d-transposed-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
output_padding (int): Additional padding applied to output
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "transposed_convolution2d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["output_padding"] = output_padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
output_padding=None, groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
output_padding=["gte, 0"], groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=int, stride=int, padding=[str, int],
output_padding=int, groups=int, dilation=int, use_bias=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def transposed_convolution2d(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
output_padding=0, groups=1, dilation=1, use_bias=True, layout='NCHW', uid=None):
'''
Append 2d-transposed-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
output_padding (int): Additional padding applied to output
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "transposed_convolution2d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["output_padding"] = output_padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=["lt", 2048], kernel_size=["lt", 16], stride=["lt", 16], padding=None,
output_padding=None, groups=None, dilation=None, use_bias=None, layout=None, uid=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], kernel_size=["gt", 0], stride=["gte", 1], padding=None,
output_padding=["gte, 0"], groups=["gte", 1], dilation=["gte", 1], use_bias=None, layout=["eq", "NCDHW"], uid=None, post_trace=False)
@accepts("self", output_channels=int, kernel_size=int, stride=int, padding=[str, int],
output_padding=int, groups=int, dilation=int, use_bias=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def transposed_convolution3d(self, output_channels=3, kernel_size=3, stride=1, padding="in_eq_out",
output_padding=0, groups=1, dilation=1, use_bias=True, layout='NCDHW', uid=None):
'''
Append 3d-transposed-convolution to custom network
Args:
output_channels (int): Number of output features for this layer
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
output_padding (int): Additional padding applied to output
groups (int): Number of groups for grouped convolution
dilation (int): Factor for dilated convolution
use_bias (bool): If True, learnable bias is added
layout (str): Either of these values (order)
1) 'NCDHW' - Order
2) 'NDHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- D: Depth of features in layers
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp={};
tmp["uid"] = uid;
tmp["name"] = "transposed_convolution3d";
tmp["params"] = {};
tmp["params"]["output_channels"] = output_channels;
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["output_padding"] = output_padding;
tmp["params"]["groups"] = groups;
tmp["params"]["dilation"] = dilation;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, layout=["eq", "NCW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def max_pooling1d(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, layout='NCW', uid=None):
'''
Append 1d-max-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
layout (str): Either of these values (order)
1) 'NCW' - order
2) 'NWC' - order
- N: Number of elements in batches
- C: Number of channels
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "max_pooling1d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def max_pooling2d(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, layout='NCHW', uid=None):
'''
Append 2d-max-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "max_pooling2d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def max_pooling(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, layout='NCHW', uid=None):
'''
Append 2d-max-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "max_pooling2d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, layout=["eq", "NCDHW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def max_pooling3d(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, layout='NCDHW', uid=None):
'''
Append 3d-max-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
layout (str): Either of these values (order)
1) 'NCDHW' - Order
2) 'NDHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- D: Depth of features in layers
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "max_pooling3d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=["eq", "NCW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, include_padding_in_calculation=bool,
layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def average_pooling1d(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, include_padding_in_calculation=True,
layout='NCW', uid=None):
'''
Append 1d-average-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
include_padding_in_calculation (bool): If True, padding will be considered.
ceil_mode (bool): If True, apply ceil math operation post pooling
layout (str): Either of these values (order)
1) 'NCW' - order
2) 'NWC' - order
- N: Number of elements in batches
- C: Number of channels
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "average_pooling1d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["include_padding_in_calculation"] = include_padding_in_calculation;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, include_padding_in_calculation=bool,
layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def average_pooling2d(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, include_padding_in_calculation=True,
layout='NCHW', uid=None):
'''
Append 2d-average-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
include_padding_in_calculation (bool): If True, padding will be considered.
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "average_pooling2d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["include_padding_in_calculation"] = include_padding_in_calculation;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, include_padding_in_calculation=bool,
layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def average_pooling(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, include_padding_in_calculation=True,
layout='NCHW', uid=None):
'''
Append 2d-average-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
include_padding_in_calculation (bool): If True, padding will be considered.
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "average_pooling2d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["include_padding_in_calculation"] = include_padding_in_calculation;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, kernel_size=["lt", 16], stride=["lt", 16], padding=None, dilation=None,
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=None, uid=None, post_trace=False)
@error_checks(None, kernel_size=["gt", 0], stride=["gte", 1], padding=None, dilation=["gte", 1],
return_indices=None, ceil_mode=None, include_padding_in_calculation=None,
layout=["eq", "NCDHW"], uid=None, post_trace=False)
@accepts("self", kernel_size=int, stride=[int, None], padding=[str, int], dilation=int,
return_indices=bool, ceil_mode=bool, include_padding_in_calculation=bool,
layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def average_pooling3d(self, kernel_size=2, stride=None, padding=0, dilation=1,
return_indices=False, ceil_mode=False, include_padding_in_calculation=True,
layout='NCDHW', uid=None):
'''
Append 3d-average-pooling to custom network
Args:
kernel_size (int, tuple): kernel matrix size
stride (int, tuple): kernel movement stride
padding (int, tuple, str): Zero padding applied to input
1) "in_eq_out": Automated padding applied to keep output shape same as input
2) integer or tuple value: Manually add padding
dilation (int): Factor for dilated pooling
return_indices (bool): Fixed value set as False
ceil_mode (bool): If True, apply ceil math operation post pooling
include_padding_in_calculation (bool): If True, padding will be considered.
layout (str): Either of these values (order)
1) 'NCDHW' - Order
2) 'NDHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- D: Depth of features in layers
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "average_pooling3d";
tmp["params"] = {};
tmp["params"]["kernel_size"] = kernel_size;
tmp["params"]["stride"] = stride;
tmp["params"]["padding"] = padding;
tmp["params"]["dilation"] = dilation;
tmp["params"]["return_indices"] = return_indices;
tmp["params"]["ceil_mode"] = ceil_mode;
tmp["params"]["include_padding_in_calculation"] = include_padding_in_calculation;
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_max_pooling1d(self, layout='NCW', uid=None):
'''
Append 1d-global-max-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCW' - order
2) 'NWC' - order
- N: Number of elements in batches
- C: Number of channels
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_max_pooling1d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_max_pooling2d(self, layout='NCHW', uid=None):
'''
Append 2d-global-max-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_max_pooling2d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_max_pooling(self, layout='NCHW', uid=None):
'''
Append 2d-global-max-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_max_pooling2d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCDHW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_max_pooling3d(self, layout='NCDHW', uid=None):
'''
Append 3d-global-max-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCDHW' - Order
2) 'NDHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- D: Depth of features in layers
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_max_pooling3d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_average_pooling1d(self, layout='NCW', uid=None):
'''
Append 1d-global-average-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCW' - order
2) 'NWC' - order
- N: Number of elements in batches
- C: Number of channels
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_average_pooling1d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_average_pooling2d(self, layout='NCHW', uid=None):
'''
Append 2d-global-average-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_average_pooling2d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCHW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_average_pooling(self, layout='NCHW', uid=None):
'''
Append 2d-global-average-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCHW' - Order
2) 'NHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_average_pooling2d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, layout=None, uid=None, post_trace=False)
@error_checks(None, layout=["eq", "NCDHW"], uid=None, post_trace=False)
@accepts("self", layout=str, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def global_average_pooling3d(self, layout='NCDHW', uid=None):
'''
Append 3d-global-average-pooling to custom network
Args:
layout (str): Either of these values (order)
1) 'NCDHW' - Order
2) 'NDHWC' - Order
- N: Number of elements in batches
- C: Number of channels
- D: Depth of features in layers
- H: Height of features in layers
- W: Number of features in layers
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "global_average_pooling3d";
tmp["params"] = {};
tmp["params"]["layout"] = layout;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, units=None, use_bias=None, flatten=None, uid=None, post_trace=False)
@error_checks(None, units=["gt", 0], use_bias=None, flatten=None, uid=None, post_trace=False)
@accepts("self", units=int, use_bias=bool, flatten=bool, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def fully_connected(self, units=512, use_bias=True, flatten=True, uid=None):
'''
Append fully-connected (dense) layer to custom network
Args:
units (int): Number of neurons in the layer
use_bias (bool): If True, learnable bias is added
flatten (bool): Fixed to True
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "fully_connected";
tmp["params"] = {};
tmp["params"]["units"] = units;
tmp["params"]["use_bias"] = use_bias;
tmp["params"]["flatten"] = flatten;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, drop_probability=["lt", 0.5], use_bias=None, flatten=None, uid=None, post_trace=False)
@error_checks(None, drop_probability=["gte", 0.0, "lt", 1.0], use_bias=None, flatten=None, uid=None, post_trace=False)
@accepts("self", drop_probability=[int, float], axes=tuple, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def dropout(self, drop_probability=0.2, axes=(), uid=None):
'''
Append dropout layer to custom network
Args:
drop_probability (float): Probability for not considering neurons in the output
axes (tuple): Channel axis to implement dropout over
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "dropout";
tmp["params"] = {};
tmp["params"]["drop_probability"] = drop_probability;
tmp["params"]["axes"] = axes;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def flatten(self, uid=None):
'''
Append flatten layer to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "flatten";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def identity(self, uid=None):
'''
Append identity layer to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "identity";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def add(self, uid=None):
'''
Append elementwise addition layer to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "add";
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def concatenate(self, uid=None):
'''
Append concatenation layer to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "concatenate";
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, moving_average_momentum=["lt", 1.0], epsilon=["lt", 0.0001], use_trainable_parameters=None,
activate_scale_shift_operation=None, uid=None, post_trace=False)
@error_checks(None, moving_average_momentum=["gte", 0], epsilon=["gte", 0], use_trainable_parameters=None,
activate_scale_shift_operation=None, uid=None, post_trace=False)
@accepts("self", moving_average_momentum=[float, int], epsilon=float, use_trainable_parameters=bool,
activate_scale_shift_operation=bool, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def batch_normalization(self, moving_average_momentum=0.9, epsilon=0.00001, use_trainable_parameters=True,
activate_scale_shift_operation=False, uid=None):
'''
Append batch normalization layer to custom network
Args:
moving_average_momentum (float): Normalization momentum value
epsilon (float): Value to avoid division by zero
use_trainable_paramemetrs (bool): If True, batch norm turns into a trainable layer
activate_scale_shift_operation (bool): Fixed status - False
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "batch_normalization";
tmp["params"] = {};
tmp["params"]["moving_average_momentum"] = moving_average_momentum;
tmp["params"]["epsilon"] = epsilon;
tmp["params"]["use_trainable_parameters"] = use_trainable_parameters;
tmp["params"]["activate_scale_shift_operation"] = activate_scale_shift_operation;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, moving_average_momentum=["lt", 1.0], epsilon=["lt", 0.0001], use_trainable_parameters=None,
uid=None, post_trace=False)
@error_checks(None, moving_average_momentum=["gte", 0], epsilon=["gte", 0], use_trainable_parameters=None,
uid=None, post_trace=False)
@accepts("self", moving_average_momentum=[float, int], epsilon=float, use_trainable_parameters=bool,
uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def instance_normalization(self, moving_average_momentum=0.9, epsilon=0.00001, use_trainable_parameters=True,
uid=None):
'''
Append instace normalization layer to custom network
Args:
moving_average_momentum (float): Normalization momentum value
epsilon (float): Value to avoid division by zero
use_trainable_paramemetrs (bool): If True, batch norm turns into a trainable layer
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "instance_normalization";
tmp["params"] = {};
tmp["params"]["epsilon"] = epsilon;
tmp["params"]["use_trainable_parameters"] = use_trainable_parameters;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, moving_average_momentum=["lt", 1.0], epsilon=["lt", 0.0001], use_trainable_parameters=None,
uid=None, post_trace=False)
@error_checks(None, moving_average_momentum=["gte", 0], epsilon=["gte", 0], use_trainable_parameters=None,
uid=None, post_trace=False)
@accepts("self", moving_average_momentum=[float, int], epsilon=float, use_trainable_parameters=bool,
uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def layer_normalization(self, moving_average_momentum=0.9, epsilon=0.00001, use_trainable_parameters=True,
uid=None):
'''
Append layer normalization layer to custom network
Args:
moving_average_momentum (float): Normalization momentum value
epsilon (float): Value to avoid division by zero
use_trainable_paramemetrs (bool): If True, batch norm turns into a trainable layer
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "layer_normalization";
tmp["params"] = {};
tmp["params"]["epsilon"] = epsilon;
tmp["params"]["use_trainable_parameters"] = use_trainable_parameters;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def relu(self, uid=None):
'''
Append rectified linear unit activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "relu";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def sigmoid(self, uid=None):
'''
Append sigmoid activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "sigmoid";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def tanh(self, uid=None):
'''
Append tanh activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "tanh";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, beta=None, threshold=None, uid=None, post_trace=False)
@error_checks(None, beta=["gt", 0], threshold=None, uid=None, post_trace=False)
@accepts("self", beta=[int, float], threshold=[int, float], uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def softplus(self, beta=1, threshold=20, uid=None):
'''
Append softplus activation to custom network
Args:
beta (int): Multiplicative factor
threshold (int): softplus (thresholded relu) limit
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "softplus";
tmp["params"] = {};
tmp["params"]["beta"] = beta;
tmp["params"]["threshold"] = threshold;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def softsign(self, uid=None):
'''
Append softsign activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "softsign";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, alpha=None, uid=None, post_trace=False)
@error_checks(None, alpha=["gt", 0], uid=None, post_trace=False)
@accepts("self", alpha=float, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def elu(self, alpha=1.0, uid=None):
'''
Append exponential linear unit activation to custom network
Args:
alpha (float): Multiplicative factor
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "elu";
tmp["params"] = {};
tmp["params"]["alpha"] = alpha;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def gelu(self, uid=None):
'''
Append gated exponential linear unit activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "gelu";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, alpha=None, uid=None, post_trace=False)
@error_checks(None, alpha=["gt", 0], uid=None, post_trace=False)
@accepts("self", alpha=float, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def leaky_relu(self, alpha=0.01, uid=None):
'''
Append leaky relu activation to custom network
Args:
alpha (float): Multiplicatve factor towards negative spectrum of real numbers.
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "leaky_relu";
tmp["params"] = {};
tmp["params"]["alpha"] = alpha;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def prelu(self, uid=None):
'''
Append paramemeterized rectified linear unit activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "prelu";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, uid=None, post_trace=False)
@error_checks(None, uid=None, post_trace=False)
@accepts("self", uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def selu(self, uid=None):
'''
Append scaled exponential linear unit activation to custom network
Args:
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "selu";
tmp["params"] = {};
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, beta=None, uid=None, post_trace=False)
@error_checks(None, beta=["gt", 0], uid=None, post_trace=False)
@accepts("self", beta=float, uid=[None, str], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def swish(self, beta=1.0, uid=None):
'''
Append swish activation to custom network
Args:
beta (float): Multiplicative factor
uid (str): Unique name for layer, if not mentioned then dynamically assigned
Returns:
dict: Containing all the parameters set as per function arguments
'''
tmp = {};
tmp["uid"] = uid;
tmp["name"] = "swish";
tmp["params"] = {};
tmp["params"]["beta"] = beta;
return tmp;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, stride=None, downsample=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], stride=None, downsample=None, post_trace=False)
@accepts("self", output_channels=int, stride=[None, int, tuple], downsample=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def resnet_v1_block(self, output_channels=16, stride=1, downsample=True):
'''
Append Resnet V1 Block to custom network
Args:
output_channels (int): Number of output features for this block
stride (int, tuple): kernel movement stride
downsample (bool): If False, residual branch is a shortcut,
Else, residual branch has non-identity layers
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=3, stride=stride));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=3, stride=1));
branch_1.append(self.batch_normalization());
branch_2 = [];
if(downsample):
branch_2.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=stride));
branch_2.append(self.batch_normalization());
else:
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork)
network.append(self.relu());
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, stride=None, downsample=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], stride=None, downsample=None, post_trace=False)
@accepts("self", output_channels=int, stride=[None, int, tuple], downsample=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def resnet_v2_block(self, output_channels=16, stride=1, downsample=True):
'''
Append Resnet V2 Block to custom network
Args:
output_channels (int): Number of output features for this block
stride (int, tuple): kernel movement stride
downsample (bool): If False, residual branch is a shortcut,
Else, residual branch has non-identity layers
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
network.append(self.batch_normalization());
network.append(self.relu());
subnetwork = [];
branch_1 = [];
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=3, stride=stride));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=3, stride=1));
branch_2 = [];
if(downsample):
branch_2.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=stride));
else:
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, stride=None, downsample=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], stride=None, downsample=None, post_trace=False)
@accepts("self", output_channels=int, stride=[None, int, tuple], downsample=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def resnet_v1_bottleneck_block(self, output_channels=16, stride=1, downsample=True):
'''
Append Resnet V1 Bottleneck Block to custom network
Args:
output_channels (int): Number of output features for this block
stride (int, tuple): kernel movement stride
downsample (bool): If False, residual branch is a shortcut,
Else, residual branch has non-identity layers
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_1.append(self.convolution(output_channels=output_channels//4, kernel_size=1, stride=stride));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels//4, kernel_size=3, stride=1));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_2 = [];
if(downsample):
branch_2.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=stride));
branch_2.append(self.batch_normalization());
else:
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork)
network.append(self.relu())
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, stride=None, downsample=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], stride=None, downsample=None, post_trace=False)
@accepts("self", output_channels=int, stride=[None, int, tuple], downsample=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def resnet_v2_bottleneck_block(self, output_channels=16, stride=1, downsample=True):
'''
Append Resnet V2 Bottleneck Block to custom network
Args:
output_channels (int): Number of output features for this block
stride (int): kernel movement stride
downsample (bool): If False, residual branch is a shortcut,
Else, residual branch has non-identity layers
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
network.append(self.batch_normalization());
network.append(self.relu());
subnetwork = [];
branch_1 = [];
branch_1.append(self.convolution(output_channels=output_channels//4, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels//4, kernel_size=3, stride=stride));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=1));
branch_2 = [];
if(downsample):
branch_2.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=stride));
else:
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork)
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, cardinality=None, bottleneck_width=None, stride=None,
downsample=None, post_trace=False)
@error_checks(None, output_channels=["gt", 0], cardinality=None, bottleneck_width=None, stride=None,
downsample=None, post_trace=False)
@accepts("self", output_channels=int, cardinality=int, bottleneck_width=int, stride=[int, tuple],
downsample=bool, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def resnext_block(self, output_channels=256, cardinality=8, bottleneck_width=4, stride=1, downsample=True):
'''
Append Resnext Block to custom network
Args:
output_channels (int): Number of output features for this block
cardinality (int): cardinality dimensions for complex transformations
bottleneck_width (int): Bottleneck dimensions for reducing number of features
stride (int): kernel movement stride
downsample (bool): If False, residual branch is a shortcut,
Else, residual branch has non-identity layers
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
channels = output_channels//4;
D = int(math.floor(channels * (bottleneck_width / 64)))
group_width = cardinality * D
subnetwork = [];
branch_1 = [];
branch_1.append(self.convolution(output_channels=group_width, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=group_width, kernel_size=3, stride=stride));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_2 = [];
if(downsample):
branch_2.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=stride));
branch_2.append(self.batch_normalization());
else:
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork)
network.append(self.relu());
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, bottleneck_width=None, stride=None,
post_trace=False)
@error_checks(None, output_channels=["gt", 0], bottleneck_width=None, stride=None,
post_trace=False)
@accepts("self", output_channels=int, bottleneck_width=int, stride=[int, tuple],
post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def mobilenet_v2_linear_bottleneck_block(self, output_channels=32, bottleneck_width=4, stride=1):
'''
Append Mobilenet V2 Linear Bottleneck Block to custom network
Args:
output_channels (int): Number of output features for this block
stride (int): kernel movement stride
bottleneck_width (int): Bottleneck dimensions for reducing number of features
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
if(bottleneck_width != 1):
branch_1.append(self.convolution(output_channels=output_channels*bottleneck_width,
kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels*bottleneck_width,
kernel_size=3, stride=stride));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_2 = [];
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, input_channels=None, output_channels=None, kernel_size=None, stride=None,
padding=None, post_trace=False)
@error_checks(None, input_channels=["gt", 0], output_channels=["gt", 0], kernel_size=None, stride=None,
padding=None, post_trace=False)
@accepts("self", input_channels=int, output_channels=int, kernel_size=int, stride=[None, int, tuple],
padding=[None, int, tuple], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def separable_convolution_block(self, input_channels=16, output_channels=32, kernel_size=3, stride=1, padding=1):
'''
Append Separable convolution Block to custom network
Args:
input_channels (int): Number of input features for this block
output_channels (int): Number of output features for this block
kernel_size (int): Kernel matrix shape for all layers in this block
stride (int): kernel movement stride
padding (int, tuple): external zero padding on input
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
network.append(self.convolution(output_channels=input_channels, kernel_size=kernel_size,
stride=stride, padding=padding, groups=input_channels));
network.append(self.convolution(output_channels=output_channels, kernel_size=1,
stride=1));
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, output_channels=None, bottleneck_width=None, stride=None,
post_trace=False)
@error_checks(None, output_channels=["gt", 0], bottleneck_width=None, stride=None,
post_trace=False)
@accepts("self", output_channels=int, bottleneck_width=int, stride=[int, tuple],
post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def mobilenet_v2_inverted_linear_bottleneck_block(self, output_channels=32, bottleneck_width=4, stride=1):
'''
Append Mobilenet V2 Inverted Linear Bottleneck Block to custom network
Args:
output_channels (int): Number of output features for this block
stride (int): kernel movement stride
bottleneck_width (int): Bottleneck dimensions for reducing number of features
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
if(bottleneck_width != 1):
branch_1.append(self.convolution(output_channels=output_channels//bottleneck_width,
kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
sep_conv = self.separable_convolution_block(input_channels=output_channels//bottleneck_width,
output_channels=output_channels//bottleneck_width,
kernel_size=3, stride=stride);
branch_1.append(sep_conv);
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=output_channels, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_2 = [];
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.add());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, squeeze_channels=None, expand_channels_1x1=None, expand_channels_3x3=None,
post_trace=False)
@error_checks(None, squeeze_channels=["gt", 0], expand_channels_1x1=["gt", 0], expand_channels_3x3=["gt", 0],
post_trace=False)
@accepts("self", squeeze_channels=int, expand_channels_1x1=int, expand_channels_3x3=int,
post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def squeezenet_fire_block(self, squeeze_channels=16, expand_channels_1x1=32, expand_channels_3x3=64):
'''
Append Squeezenet Fire Block to custom network
Args:
squeeze_channels (int): Number of output features for this block
expand_channels_1x1 (int): Number of convolution_1x1 features for this block
expand_channels_3x3 (int): Number of convolution_3x3 features for this block
bottleneck_width (int): Bottleneck dimensions for reducing number of features
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
network.append(self.convolution(output_channels=squeeze_channels, kernel_size=1, stride=1));
network.append(self.relu());
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_1.append(self.convolution(output_channels=expand_channels_1x1, kernel_size=1, stride=1));
branch_1.append(self.relu());
branch_2.append(self.convolution(output_channels=expand_channels_3x3, kernel_size=3, stride=1));
branch_2.append(self.relu());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, bottleneck_size=None, growth_rate=None, dropout=None,
post_trace=False)
@error_checks(None, bottleneck_size=["gt", 0], growth_rate=None, dropout=["gte", 0, "lte", 1],
post_trace=False)
@accepts("self", bottleneck_size=int, growth_rate=int, dropout=[int, float],
post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def densenet_block(self, bottleneck_size=4, growth_rate=16, dropout=0.2):
'''
Append Densenet Block to custom network
Args:
bottleneck_size (int): Bottleneck dimensions for reducing number of features
growth_rate (int): Expansion rate for convolution layers for this block
dropout (float): Prbability for dropout layer post convolution
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=bottleneck_size*growth_rate, kernel_size=1, stride=1));
branch_1.append(self.batch_normalization());
branch_1.append(self.relu());
branch_1.append(self.convolution(output_channels=growth_rate, kernel_size=3, stride=1));
branch_1.append(self.dropout(drop_probability=dropout));
branch_2.append(self.identity());
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, input_channels=None, output_channels=None, kernel_size=None, stride=None,
padding=None, post_trace=False)
@error_checks(None, input_channels=["gt", 0], output_channels=["gt", 0], kernel_size=None, stride=None,
padding=None, post_trace=False)
@accepts("self", input_channels=int, output_channels=int, kernel_size=[int, tuple], stride=[None, int, tuple],
padding=[None, int, tuple], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def conv_bn_relu_block(self, output_channels=64, kernel_size=1, stride=1, padding=None):
'''
Append Conv->batch_norm->relu Block to custom network
Args:
output_channels (int): Number of output features for this block
kernel_size (int): Kernel matrix shape for all layers in this block
stride (int): kernel movement stride
padding (int, tuple): external zero padding on input
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
if(padding):
network.append(self.convolution(output_channels=output_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding));
else:
network.append(self.convolution(output_channels=output_channels,
kernel_size=kernel_size,
stride=stride));
network.append(self.batch_normalization());
network.append(self.relu());
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, pooling_branch_channels=None, pool_type=None, post_trace=False)
@error_checks(None, pooling_branch_channels=["gt", 0], pool_type=None, post_trace=False)
@accepts("self", pooling_branch_channels=int, pool_type=str, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def inception_a_block(self, pooling_branch_channels=32, pool_type="avg"):
'''
Append Inception-A Block to custom network
Args:
pooling_branch_channels (int): Number of features for conv layers in pooling branch
pool_type (str): Either of these types
- "avg" - Average pooling
- "max" - Max pooling
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_3 = [];
branch_4 = [];
branch_1.append(self.conv_bn_relu_block(output_channels=64, kernel_size=1))
branch_2.append(self.conv_bn_relu_block(output_channels=48, kernel_size=1));
branch_2.append(self.conv_bn_relu_block(output_channels=64, kernel_size=5));
branch_3.append(self.conv_bn_relu_block(output_channels=64, kernel_size=1));
branch_3.append(self.conv_bn_relu_block(output_channels=96, kernel_size=3));
branch_3.append(self.conv_bn_relu_block(output_channels=96, kernel_size=3));
if(pool_type=="avg"):
branch_4.append(self.average_pooling(kernel_size=3, stride=1, padding=1));
else:
branch_4.append(self.max_pooling(kernel_size=3, stride=1, padding=1));
branch_4.append(self.conv_bn_relu_block(output_channels=pooling_branch_channels, kernel_size=1));
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(branch_3);
subnetwork.append(branch_4);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, pool_type=None, post_trace=False)
@error_checks(None, pool_type=None, post_trace=False)
@accepts("self", pool_type=str, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def inception_b_block(self, pool_type="avg"):
'''
Append Inception-B Block to custom network
Args:
pool_type (str): Either of these types
- "avg" - Average pooling
- "max" - Max pooling
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_3 = [];
branch_4 = [];
branch_1.append(self.conv_bn_relu_block(output_channels=384, kernel_size=3))
branch_2.append(self.conv_bn_relu_block(output_channels=64, kernel_size=1));
branch_2.append(self.conv_bn_relu_block(output_channels=96, kernel_size=3));
branch_2.append(self.conv_bn_relu_block(output_channels=96, kernel_size=3));
if(pool_type=="avg"):
branch_3.append(self.average_pooling(kernel_size=3, stride=1, padding=1));
else:
branch_3.append(self.max_pooling(kernel_size=3, stride=1, padding=1));
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(branch_3);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, channels_7x7=None, pool_type=None, post_trace=False)
@error_checks(None, channels_7x7=["gt", 0], pool_type=None, post_trace=False)
@accepts("self", channels_7x7=int, pool_type=str, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def inception_c_block(self, channels_7x7=3, pool_type="avg"):
'''
Append Inception-C Block to custom network
Args:
channels_7x7 (int): Number of features for conv layers in channels_7x7 branch
pool_type (str): Either of these types
- "avg" - Average pooling
- "max" - Max pooling
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_3 = [];
branch_4 = [];
branch_1.append(self.conv_bn_relu_block(output_channels=192, kernel_size=1))
branch_2.append(self.conv_bn_relu_block(output_channels=channels_7x7, kernel_size=1));
branch_2.append(self.conv_bn_relu_block(output_channels=channels_7x7, kernel_size=(1, 7), padding=(0, 3)));
branch_2.append(self.conv_bn_relu_block(output_channels=192, kernel_size=(7, 1), padding=(3, 0)));
branch_3.append(self.conv_bn_relu_block(output_channels=channels_7x7, kernel_size=1));
branch_3.append(self.conv_bn_relu_block(output_channels=channels_7x7, kernel_size=(1, 7), padding=(0, 3)));
branch_3.append(self.conv_bn_relu_block(output_channels=channels_7x7, kernel_size=(7, 1), padding=(3, 0)));
branch_3.append(self.conv_bn_relu_block(output_channels=channels_7x7, kernel_size=(1, 7), padding=(0, 3)));
branch_3.append(self.conv_bn_relu_block(output_channels=192, kernel_size=(7, 1), padding=(3, 0)));
if(pool_type=="avg"):
branch_4.append(self.average_pooling(kernel_size=3, stride=1, padding=1));
else:
branch_4.append(self.max_pooling(kernel_size=3, stride=1, padding=1));
branch_4.append(self.conv_bn_relu_block(output_channels=192, kernel_size=1));
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(branch_3);
subnetwork.append(branch_4);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, pool_type=None, post_trace=False)
@error_checks(None, pool_type=None, post_trace=False)
@accepts("self", pool_type=str, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def inception_d_block(self, pool_type="avg"):
'''
Append Inception-D Block to custom network
Args:
pool_type (str): Either of these types
- "avg" - Average pooling
- "max" - Max pooling
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_3 = [];
branch_4 = [];
branch_1.append(self.conv_bn_relu_block(output_channels=192, kernel_size=1))
branch_1.append(self.conv_bn_relu_block(output_channels=320, kernel_size=3, stride=2))
branch_2.append(self.conv_bn_relu_block(output_channels=192, kernel_size=1));
branch_2.append(self.conv_bn_relu_block(output_channels=192, kernel_size=(1, 7), padding=(0, 3)));
branch_2.append(self.conv_bn_relu_block(output_channels=192, kernel_size=(7, 1), padding=(3, 0)));
branch_2.append(self.conv_bn_relu_block(output_channels=192, kernel_size=3, stride=2));
if(pool_type=="avg"):
branch_3.append(self.average_pooling(kernel_size=3, stride=2));
else:
branch_3.append(self.max_pooling(kernel_size=3, stride=2));
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(branch_3);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, post_trace=False)
@error_checks(None, post_trace=False)
@accepts("self", post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def subbranch_block(self):
'''
Append sub-branch Block to custom network
Args:
None
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_1.append(self.conv_bn_relu_block(output_channels=384, kernel_size=(1, 3), padding=(0, 1)));
branch_2.append(self.conv_bn_relu_block(output_channels=384, kernel_size=(3, 1), padding=(1, 0)));
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(self.concatenate());
return subnetwork;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, pool_type=None, post_trace=False)
@error_checks(None, pool_type=None, post_trace=False)
@accepts("self", pool_type=str, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def inception_e_block(self, pool_type="avg"):
'''
Append Inception-C Block to custom network
Args:
pool_type (str): Either of these types
- "avg" - Average pooling
- "max" - Max pooling
Returns:
list: Containing all the layer dictionaries arranged as per function arguments
'''
network = [];
subnetwork = [];
branch_1 = [];
branch_2 = [];
branch_3 = [];
branch_4 = [];
branch_1.append(self.conv_bn_relu_block(output_channels=320, kernel_size=1))
branch_2.append(self.conv_bn_relu_block(output_channels=384, kernel_size=1));
branch_2.append(self.subbranch_block());
branch_3.append(self.conv_bn_relu_block(output_channels=448, kernel_size=1));
branch_3.append(self.conv_bn_relu_block(output_channels=384, kernel_size=3, padding=1));
branch_3.append(self.subbranch_block());
if(pool_type=="avg"):
branch_4.append(self.average_pooling(kernel_size=3, stride=1, padding=1));
else:
branch_4.append(self.max_pooling(kernel_size=3, stride=1, padding=1));
branch_4.append(self.conv_bn_relu_block(output_channels=192, kernel_size=1));
subnetwork.append(branch_1);
subnetwork.append(branch_2);
subnetwork.append(branch_3);
subnetwork.append(branch_4);
subnetwork.append(self.concatenate());
network.append(subnetwork);
return network;
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, None, num_filters=None, post_trace=False)
@error_checks(None, None, num_filters=None, post_trace=False)
@accepts("self", str, num_filters=int, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def study_convolution_filters(self, img_name, num_filters=4):
"""
Visualize the effect of a parameter in convolution - number of filters
Args:
img_name (str): Path to test images
num_filters(int): Number of filters to use
Returns:
None
"""
network = [];
network.append(self.convolution(output_channels=num_filters));
self.Compile_Network(network,
data_shape=(3, 224, 224),
use_gpu=False,
network_initializer="xavier_normal",
debug=False);
self.system_dict["local"]["transforms_test"] = [];
self.system_dict["local"]["transforms_test"].append(transforms.Resize(size=(224, 224)));
self.system_dict["local"]["transforms_test"].append(transforms.ToTensor());
self.system_dict["local"]["data_transforms"]["test"] = transforms.Compose(self.system_dict["local"]["transforms_test"]);
img = image.imread(img_name)
img = self.system_dict["local"]["data_transforms"]["test"](img).expand_dims(axis=0);
img = img.copyto(self.system_dict["local"]["ctx"][0]);
print("Input shape - {}".format(img.shape))
outputs = self.system_dict["local"]["model"].forward(img);
print("Output shape - {}".format(outputs.shape))
if(os.path.isdir("tmp")):
os.system("rm -r tmp");
os.system("mkdir tmp");
else:
os.system("mkdir tmp");
out = outputs[0];
for i in range(out.shape[0]):
tmp = out[i].asnumpy()
cv2.imwrite("tmp/test" + str(i) + ".jpg", tmp*255)
weights = self.system_dict["local"]["model"][0].weight.data().asnumpy()
for i in range(weights.shape[0]):
tmp = weights[i]
cv2.imwrite("tmp/test_filter" + str(i) + ".jpg", tmp*255)
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, None, kernel_size=None, post_trace=False)
@error_checks(None, None, kernel_size=["gt", 0], post_trace=False)
@accepts("self", str, kernel_size=[int, tuple], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def study_convolution_kernels(self, img_name, kernel_size=4):
"""
Visualize the effect of a parameter in convolution - KErnel size
Args:
img_name (str): Path to test images
kernel_size(int): Convolution kernel shape
Returns:
None
"""
network = [];
network.append(self.convolution(output_channels=4,
kernel_size=kernel_size));
self.Compile_Network(network,
data_shape=(3, 224, 224),
use_gpu=False,
network_initializer="xavier_normal",
debug=False);
self.system_dict["local"]["transforms_test"] = [];
self.system_dict["local"]["transforms_test"].append(transforms.Resize(size=(224, 224)));
self.system_dict["local"]["transforms_test"].append(transforms.ToTensor());
self.system_dict["local"]["data_transforms"]["test"] = transforms.Compose(self.system_dict["local"]["transforms_test"]);
img = image.imread(img_name)
img = self.system_dict["local"]["data_transforms"]["test"](img).expand_dims(axis=0);
img = img.copyto(self.system_dict["local"]["ctx"][0]);
print("Input shape - {}".format(img.shape))
outputs = self.system_dict["local"]["model"].forward(img);
print("Output shape - {}".format(outputs.shape))
if(os.path.isdir("tmp")):
os.system("rm -r tmp");
os.system("mkdir tmp");
else:
os.system("mkdir tmp");
out = outputs[0];
for i in range(out.shape[0]):
tmp = out[i].asnumpy()
cv2.imwrite("tmp/test" + str(i) + ".jpg", tmp*255)
weights = self.system_dict["local"]["model"][0].weight.data().asnumpy()
for i in range(weights.shape[0]):
tmp = weights[i]
tmp = np.swapaxes(tmp, 0, 1)
tmp = np.swapaxes(tmp, 1, 2)
cv2.imwrite("tmp/test_filter" + str(i) + ".jpg", tmp*255)
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, None, stride=None, post_trace=False)
@error_checks(None, None, stride=["gt", 0], post_trace=False)
@accepts("self", str, stride=[int, tuple], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def study_convolution_stride(self, img_name, stride=4):
"""
Visualize the effect of a parameter in convolution - KErnel size
Args:
img_name (str): Path to test images
kernel_size(int): Convolution kernel shape
Returns:
None
"""
network = [];
network.append(self.convolution(output_channels=4,
kernel_size=5,
stride=stride));
self.Compile_Network(network,
data_shape=(3, 224, 224),
use_gpu=False,
network_initializer="xavier_normal",
debug=False);
self.system_dict["local"]["transforms_test"] = [];
self.system_dict["local"]["transforms_test"].append(transforms.Resize(size=(224, 224)));
self.system_dict["local"]["transforms_test"].append(transforms.ToTensor());
self.system_dict["local"]["data_transforms"]["test"] = transforms.Compose(self.system_dict["local"]["transforms_test"]);
img = image.imread(img_name)
img = self.system_dict["local"]["data_transforms"]["test"](img).expand_dims(axis=0);
img = img.copyto(self.system_dict["local"]["ctx"][0]);
print("Input shape - {}".format(img.shape))
outputs = self.system_dict["local"]["model"].forward(img);
print("Output shape - {}".format(outputs.shape))
if(os.path.isdir("tmp")):
os.system("rm -r tmp");
os.system("mkdir tmp");
else:
os.system("mkdir tmp");
out = outputs[0];
for i in range(out.shape[0]):
tmp = out[i].asnumpy()
cv2.imwrite("tmp/test" + str(i) + ".jpg", tmp*255)
weights = self.system_dict["local"]["model"][0].weight.data().asnumpy()
for i in range(weights.shape[0]):
tmp = weights[i]
tmp = np.swapaxes(tmp, 0, 1)
tmp = np.swapaxes(tmp, 1, 2)
cv2.imwrite("tmp/test_filter" + str(i) + ".jpg", tmp*255)
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, None, padding=None, post_trace=False)
@error_checks(None, None, padding=["gte", 0], post_trace=False)
@accepts("self", str, padding=[int, tuple], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def study_convolution_padding(self, img_name, padding=0):
"""
Visualize the effect of a parameter in convolution - KErnel size
Args:
img_name (str): Path to test images
kernel_size(int): Convolution kernel shape
Returns:
None
"""
network = [];
network.append(self.convolution(output_channels=4,
kernel_size=5,
stride=1,
padding=padding));
self.Compile_Network(network,
data_shape=(3, 224, 224),
use_gpu=False,
network_initializer="xavier_normal",
debug=False);
self.system_dict["local"]["transforms_test"] = [];
self.system_dict["local"]["transforms_test"].append(transforms.Resize(size=(224, 224)));
self.system_dict["local"]["transforms_test"].append(transforms.ToTensor());
self.system_dict["local"]["data_transforms"]["test"] = transforms.Compose(self.system_dict["local"]["transforms_test"]);
img = image.imread(img_name)
img = self.system_dict["local"]["data_transforms"]["test"](img).expand_dims(axis=0);
img = img.copyto(self.system_dict["local"]["ctx"][0]);
print("Input shape - {}".format(img.shape))
outputs = self.system_dict["local"]["model"].forward(img);
print("Output shape - {}".format(outputs.shape))
if(os.path.isdir("tmp")):
os.system("rm -r tmp");
os.system("mkdir tmp");
else:
os.system("mkdir tmp");
out = outputs[0];
for i in range(out.shape[0]):
tmp = out[i].asnumpy()
cv2.imwrite("tmp/test" + str(i) + ".jpg", tmp*255)
weights = self.system_dict["local"]["model"][0].weight.data().asnumpy()
for i in range(weights.shape[0]):
tmp = weights[i]
tmp = np.swapaxes(tmp, 0, 1)
tmp = np.swapaxes(tmp, 1, 2)
cv2.imwrite("tmp/test_filter" + str(i) + ".jpg", tmp*255)
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, None, groups=None, post_trace=False)
@error_checks(None, None, groups=["gt", 0], post_trace=False)
@accepts("self", str, groups=int, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def study_convolution_grouping(self, img_name, groups=1):
"""
Visualize the effect of a parameter in convolution - KErnel size
Args:
img_name (str): Path to test images
kernel_size(int): Convolution kernel shape
Returns:
None
"""
network = [];
network.append(self.convolution(output_channels=4*groups,
kernel_size=5,
stride=1,
groups=1));
network.append(self.convolution(output_channels=groups*4,
kernel_size=5,
stride=1,
groups=groups));
self.Compile_Network(network,
data_shape=(3, 224, 224),
use_gpu=False,
network_initializer="xavier_normal",
debug=False);
self.system_dict["local"]["transforms_test"] = [];
self.system_dict["local"]["transforms_test"].append(transforms.Resize(size=(224, 224)));
self.system_dict["local"]["transforms_test"].append(transforms.ToTensor());
self.system_dict["local"]["data_transforms"]["test"] = transforms.Compose(self.system_dict["local"]["transforms_test"]);
img = image.imread(img_name)
img = self.system_dict["local"]["data_transforms"]["test"](img).expand_dims(axis=0);
img = img.copyto(self.system_dict["local"]["ctx"][0]);
print("Input shape - {}".format(img.shape))
outputs = self.system_dict["local"]["model"].forward(img);
print("Output shape - {}".format(outputs.shape))
if(os.path.isdir("tmp")):
os.system("rm -r tmp");
os.system("mkdir tmp");
else:
os.system("mkdir tmp");
out = outputs[0];
for i in range(out.shape[0]):
tmp = out[i].asnumpy()
cv2.imwrite("tmp/test" + str(i) + ".jpg", tmp*255)
weights = self.system_dict["local"]["model"][0].weight.data().asnumpy()
for i in range(weights.shape[0]):
tmp = weights[i]
tmp = np.swapaxes(tmp, 0, 1)
tmp = np.swapaxes(tmp, 1, 2)
cv2.imwrite("tmp/test_filter" + str(i) + ".jpg", tmp*255)
#####################################################################################################################################
#####################################################################################################################################
@warning_checks(None, None, factor=None, post_trace=False)
@error_checks(None, None, factor=["gt", 0], post_trace=False)
@accepts("self", str, factor=int, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def study_convolution_dilation(self, img_name, factor=0):
"""
Visualize the effect of a parameter in convolution - KErnel size
Args:
img_name (str): Path to test images
kernel_size(int): Convolution kernel shape
Returns:
None
"""
network = [];
network.append(self.convolution(output_channels=4,
kernel_size=5,
stride=1,
dilation=factor));
self.Compile_Network(network,
data_shape=(3, 224, 224),
use_gpu=False,
network_initializer="xavier_normal",
debug=False);
self.system_dict["local"]["transforms_test"] = [];
self.system_dict["local"]["transforms_test"].append(transforms.Resize(size=(224, 224)));
self.system_dict["local"]["transforms_test"].append(transforms.ToTensor());
self.system_dict["local"]["data_transforms"]["test"] = transforms.Compose(self.system_dict["local"]["transforms_test"]);
img = image.imread(img_name)
img = self.system_dict["local"]["data_transforms"]["test"](img).expand_dims(axis=0);
img = img.copyto(self.system_dict["local"]["ctx"][0]);
print("Input shape - {}".format(img.shape))
outputs = self.system_dict["local"]["model"].forward(img);
print("Output shape - {}".format(outputs.shape))
if(os.path.isdir("tmp")):
os.system("rm -r tmp");
os.system("mkdir tmp");
else:
os.system("mkdir tmp");
out = outputs[0];
for i in range(out.shape[0]):
tmp = out[i].asnumpy()
cv2.imwrite("tmp/test" + str(i) + ".jpg", tmp*255)
weights = self.system_dict["local"]["model"][0].weight.data().asnumpy()
for i in range(weights.shape[0]):
tmp = weights[i]
tmp = np.swapaxes(tmp, 0, 1)
tmp = np.swapaxes(tmp, 1, 2)
cv2.imwrite("tmp/test_filter" + str(i) + ".jpg", tmp*255)
##################################################################################################################################### | PypiClean |
/msgraph_beta_sdk-1.0.0a9-py3-none-any.whl/msgraph/generated/users/item/calendar/events/item/extensions/count/count_request_builder.py | from __future__ import annotations
from dataclasses import dataclass
from kiota_abstractions.get_path_parameters import get_path_parameters
from kiota_abstractions.method import Method
from kiota_abstractions.request_adapter import RequestAdapter
from kiota_abstractions.request_information import RequestInformation
from kiota_abstractions.request_option import RequestOption
from kiota_abstractions.response_handler import ResponseHandler
from kiota_abstractions.serialization import Parsable, ParsableFactory
from typing import Any, Callable, Dict, List, Optional, TYPE_CHECKING, Union
if TYPE_CHECKING:
from ........models.o_data_errors import o_data_error
class CountRequestBuilder():
"""
Provides operations to count the resources in the collection.
"""
def __init__(self,request_adapter: RequestAdapter, path_parameters: Optional[Union[Dict[str, Any], str]] = None) -> None:
"""
Instantiates a new CountRequestBuilder and sets the default values.
Args:
pathParameters: The raw url or the Url template parameters for the request.
requestAdapter: The request adapter to use to execute the requests.
"""
if path_parameters is None:
raise Exception("path_parameters cannot be undefined")
if request_adapter is None:
raise Exception("request_adapter cannot be undefined")
# Url template to use to build the URL for the current request builder
self.url_template: str = "{+baseurl}/users/{user%2Did}/calendar/events/{event%2Did}/extensions/$count{?%24filter}"
url_tpl_params = get_path_parameters(path_parameters)
self.path_parameters = url_tpl_params
self.request_adapter = request_adapter
async def get(self,request_configuration: Optional[CountRequestBuilderGetRequestConfiguration] = None) -> Optional[int]:
"""
Get the number of the resource
Args:
requestConfiguration: Configuration for the request such as headers, query parameters, and middleware options.
Returns: Optional[int]
"""
request_info = self.to_get_request_information(
request_configuration
)
from ........models.o_data_errors import o_data_error
error_mapping: Dict[str, ParsableFactory] = {
"4XX": o_data_error.ODataError,
"5XX": o_data_error.ODataError,
}
if not self.request_adapter:
raise Exception("Http core is null")
return await self.request_adapter.send_primitive_async(request_info, "int", error_mapping)
def to_get_request_information(self,request_configuration: Optional[CountRequestBuilderGetRequestConfiguration] = None) -> RequestInformation:
"""
Get the number of the resource
Args:
requestConfiguration: Configuration for the request such as headers, query parameters, and middleware options.
Returns: RequestInformation
"""
request_info = RequestInformation()
request_info.url_template = self.url_template
request_info.path_parameters = self.path_parameters
request_info.http_method = Method.GET
request_info.headers["Accept"] = ["text/plain"]
if request_configuration:
request_info.add_request_headers(request_configuration.headers)
request_info.set_query_string_parameters_from_raw_object(request_configuration.query_parameters)
request_info.add_request_options(request_configuration.options)
return request_info
@dataclass
class CountRequestBuilderGetQueryParameters():
"""
Get the number of the resource
"""
def get_query_parameter(self,original_name: Optional[str] = None) -> str:
"""
Maps the query parameters names to their encoded names for the URI template parsing.
Args:
originalName: The original query parameter name in the class.
Returns: str
"""
if original_name is None:
raise Exception("original_name cannot be undefined")
if original_name == "filter":
return "%24filter"
return original_name
# Filter items by property values
filter: Optional[str] = None
@dataclass
class CountRequestBuilderGetRequestConfiguration():
"""
Configuration for the request such as headers, query parameters, and middleware options.
"""
# Request headers
headers: Optional[Dict[str, Union[str, List[str]]]] = None
# Request options
options: Optional[List[RequestOption]] = None
# Request query parameters
query_parameters: Optional[CountRequestBuilder.CountRequestBuilderGetQueryParameters] = None | PypiClean |
/BioTEMPy-2.1.0a0-py3-none-any.whl/TEMPy/cli/arg_parser.py | import argparse
import os
import urllib
import tempfile
from TEMPy.protein.structure_parser import PDBParser, mmCIFParser
from TEMPy.maps.map_parser import MapParser
class Model:
NUM_MODELS = 0
DEFAULT_COLOURS = ["#3498db", "#28b463", "#f1c40f"]
def __init__(self, data):
self._data = data
self._name = None
self._color = None
self._id = Model.NUM_MODELS
Model.NUM_MODELS += 1
def get_data(self):
return self._data
def get_name(self):
if self._name is not None:
return self._name
return os.path.basename(self._data.filename).split(".")[0]
def get_color(self):
if self._color is not None:
return self._color
return Model.DEFAULT_COLOURS[self._id]
def set_name(self, name):
self._name = name
def set_color(self, color):
self._color = color
def parse_color(color_str):
import string
if len(color_str) > 6:
if color_str[0] != "#":
raise argparse.ArgumentTypeError(
"Expected color to be of in hexdecimal format. E.g. #12ab34 or 12ab34"
)
color_str = color_str[1:]
if len(color_str) != 6:
raise argparse.ArgumentTypeError(
"Expected color to have 6 hexadecimal digits. E.g. #12ab34 or 12ab34"
)
if not all(c in string.hexdigits for c in color_str):
raise argparse.ArgumentTypeError(
"Expected color to have 6 hexadecimal digits. E.g. #12ab34 or 12ab34"
)
return color_str
def parse_plot_unmodelled(action):
action = action.lower()
if action in ("band", "dash", "blank"):
return action
else:
raise argparse.ArgumentTypeError("Expected one of: 'band', 'dash', 'blank'")
def parse_format(format):
format = format.lower()
if format in ("tsv", "csv", "json", "pdf", "png"):
return format
raise argparse.ArgumentTypeError(
"Expected format to be one or more of: tsv, csv, json, pdf or png"
)
def parse_plot_normalize(format):
format = format.lower()
if format in ("zscore"):
return format
raise argparse.ArgumentTypeError("Expected one of 'zscore'")
def parse_plot_type(format):
format = format.lower()
if format in ("residue", "violin"):
return format
raise argparse.ArgumentTypeError(
"Expected format to be one or more of: residue, violin"
)
def parse_plot_residue_range(residue_range):
try:
chain, residue_range = residue_range.split(":")
except Exception:
chain = None
try:
start, end = residue_range.split("-")
start_int = int(start)
end_int = int(end)
return [chain, start_int, end_int]
except Exception:
raise argparse.ArgumentTypeError(
"Expected residue range to be in the format START-END. E.g. 42-69"
)
def parse_plot_span(span):
try:
chain, residue_range, color = span.split(":")
except Exception:
chain = None
try:
start, end = residue_range.split("-")
start_int = int(start)
end_int = int(end)
color = parse_color(color)
return [chain, start_int, end_int, color]
except argparse.ArgumentTypeError as e:
raise e
except Exception as e:
print(e)
raise argparse.ArgumentTypeError(
"Expected residue range to be in the format START-END:COLOR. E.g. 42-69:ff00ff"
)
def _is_rcsb_link(path):
return path.startswith("rcsb:")
def _is_emdb_link(path):
return path.startswith("emdb:")
def _get_tempfile(filename):
temp_dir = tempfile.gettempdir()
return f"/{temp_dir}/{filename}"
def _read_from_rcsb(path):
parts = path.split(":")
if len(parts) == 2:
_, pdb_id = parts
local_file = _get_tempfile(f"rcsb_{pdb_id}.cif")
if not os.path.isfile(local_file):
url = f"http://www.rcsb.org/pdb/files/{pdb_id}.cif"
print(f"Downloading {pdb_id} from RCSB")
urllib.request.urlretrieve(url, filename=local_file)
elif len(parts) == 3:
_, pdb_id, version = parts
local_file = _get_tempfile(f"rcsb_{pdb_id}_v{version}.cif.gz")
if not os.path.isfile(local_file):
print(f"Downloading {pdb_id} v{version} from RCSB")
# RCSB publishes latest minor revision only. We just increment
# until we find the file. We could be clever and FTP query later.
max_minor = 10
found_minor = True
for minor in range(max_minor):
url = f"https://ftp-versioned.rcsb.org/pdb_versioned/data/entries/{pdb_id[1:3]}/pdb_0000{pdb_id}/pdb_0000{pdb_id}_xyz_v{version}-{minor}.cif.gz"
try:
urllib.request.urlretrieve(url, filename=local_file)
found_minor = True
break
except Exception:
continue
if not found_minor:
raise Exception(
f"Couldn't find major version {version} of {pdb_id} on RCSB server"
)
return mmCIFParser.read_mmCIF_file(local_file)
def _read_from_emdb(path):
emdb_id = path[5:]
local_file = _get_tempfile(f"emdb_{emdb_id}.mrc.gz")
if not os.path.isfile(local_file):
url = f"https://ftp.ebi.ac.uk/pub/databases/emdb/structures/EMD-{emdb_id}/map/emd_{emdb_id}.map.gz"
print(f"Downloading {emdb_id} from EMDB")
urllib.request.urlretrieve(url, filename=local_file)
return MapParser.readMRC(local_file)
def _read_half_map_from_emdb(path, half):
emdb_id = path[5:]
local_file = _get_tempfile(f"emdb_{emdb_id}_{half}.mrc.gz")
if not os.path.isfile(local_file):
url = f"https://ftp.ebi.ac.uk/pub/databases/emdb/structures/EMD-{emdb_id}/other/emd_{emdb_id}_half_map_{half}.map.gz"
print(f"Downloading half_map {emdb_id}_{half} from EMDB")
urllib.request.urlretrieve(url, filename=local_file)
return MapParser.readMRC(local_file)
def parse_model(path):
"""Given a path or PDB accession tries to return a map.
Args:
path (str): A filename or PDB accession in the form of rcsb:1ake
Returns:
Map:
Raise:
argparse.ArgumentTypeError
"""
if _is_rcsb_link(path):
try:
return _read_from_rcsb(path)
except Exception as e:
raise argparse.ArgumentTypeError(e)
try:
return PDBParser().read_PDB_file("test", path)
except Exception:
pass
try:
return mmCIFParser(path).read_mmCIF_file(path)
except Exception:
pass
raise argparse.ArgumentTypeError(f"Failed to read model file: {path}")
def parse_map(path):
"""Given a path or EMDB accession tries to return a map.
Args:
path (str): A filename or EMDB accession in the form of emdb:1234
Returns:
Map:
Raise:
argparse.ArgumentTypeError
"""
if _is_emdb_link(path):
try:
return _read_from_emdb(path)
except Exception:
raise argparse.ArgumentTypeError(f"Failed to get map with id: {path[5:]}")
try:
return MapParser.readMRC(path)
except Exception:
raise argparse.ArgumentTypeError(f"Failed to read map file: {path}")
def _parse_half_map(path, half):
if _is_emdb_link(path):
try:
return _read_half_map_from_emdb(path, half)
except Exception:
raise argparse.ArgumentTypeError(f"Failed to get map with id: {path[5:]}")
try:
return MapParser.readMRC(path)
except Exception:
raise argparse.ArgumentTypeError(f"Failed to read map file: {path}")
def parse_half_map_1(path):
return _parse_half_map(path, 1)
def parse_half_map_2(path):
return _parse_half_map(path, 2)
class TEMPyArgParser:
def __init__(self, script_name):
self.parser = argparse.ArgumentParser(script_name)
self.default_group = self.parser.add_argument_group("standard TEMPy arguments")
self.args = None
def parse_args(self):
self.args = self.parser.parse_args()
self.check_one_range_per_chain()
return self.args
def check_one_range_per_chain(self):
seen = set()
try:
if self.args.plot_residue_range:
for chain, start, end in self.args.plot_residue_range:
if chain in seen:
self.parser.error(
f"Only one range per chain can be specified. Chain {chain} has more than one range specified."
)
seen.add(chain)
except:
pass
def get_models(self):
if len(self.args.model_colors) > 0:
if len(self.args.model_colors) != len(self.args.model):
self.parser.error(
f"The number of models {len(self.args.model)} and colors {len(self.args.model_colors)} do not match"
)
if len(self.args.model_names) > 0:
if len(self.args.model_names) != len(self.args.model):
self.parser.error(
f"The number of models {len(self.args.model)} and names {len(self.args.model_names)} do not match"
)
models = [Model(m) for m in self.args.model]
for model, color in zip(models, self.args.model_colors):
model.set_color(color)
for model, name in zip(models, self.args.model_names):
model.set_name(name)
return models
def add_model_arg(self, multiple=False):
self.model_group = self.parser.add_argument_group(
"model", "Set additional model information"
)
if multiple:
self.model_group.add_argument(
"-p",
"--models",
help="A model file in PDB, CIF or mmCIF formats. Alternatively, the accession number eg. rcsb:1234",
dest="model",
required=True,
nargs="+",
type=parse_model,
)
else:
self.model_group.add_argument(
"-p",
"--models",
help="A model file in PDB, CIF or mmCIF formats. Alternatively, the accession number eg. rcsb:1234",
dest="model",
required=True,
type=parse_model,
)
self.model_group.add_argument(
"--model-colors",
help="Color of the models for plotting",
dest="model_colors",
default=[],
nargs="+",
type=parse_color,
)
self.model_group.add_argument(
"--model-names",
help="Names of the models for plotting",
dest="model_names",
default=[],
nargs="+",
)
def add_map_arg(self):
self.default_group.add_argument(
"-m",
"--map",
help="A EM file in MRC format. Alternatively, the accession number eg. emdb:1234",
dest="map",
required=True,
type=parse_map,
)
def add_half_map_args(self, required=True):
self.default_group.add_argument(
"-hm1",
"--half-map-1",
help="A EM half map file in MRC format. Alternatively, the accession number eg. emdb:1234",
dest="hmap1",
required=required,
type=parse_half_map_1,
)
self.default_group.add_argument(
"-hm2",
"--half-map-2",
help="A EM half map file in MRC format. Alternatively, the accession number eg. emdb:1234",
dest="hmap2",
required=required,
type=parse_half_map_2,
)
def add_resolution_arg(self):
self.default_group.add_argument(
"-r",
"--resolution",
dest="resolution",
help="Estimated resolution of EM map",
required=True,
type=float,
)
def add_residue_output_writer(self):
output_group = self.parser.add_argument_group("output")
output_group.add_argument(
"--output-format",
dest="output_formats",
help="Output format: CSV, TSV, JSON, PDF, PNG",
nargs="+",
required=True,
default=["tsv"],
type=parse_format,
)
output_group.add_argument(
"--output-prefix",
dest="output_prefix",
help="Use a custom prefix for output files",
required=False,
default=None,
type=str,
)
plot_group = self.parser.add_argument_group("plot")
plot_group.add_argument(
"--plot-type",
dest="plot_types",
help="Plot type: residue, violin",
nargs="+",
default=["residue"],
type=parse_plot_type,
)
plot_group.add_argument(
"--plot-residue-range",
dest="plot_residue_range",
help="Plot only the specified range",
default=None,
nargs="*",
type=parse_plot_residue_range,
)
plot_group.add_argument(
"--plot-normalize",
dest="plot_normalize",
help="Normalize the plot",
default=None,
type=parse_plot_normalize,
)
plot_group.add_argument(
"--plot-bands",
dest="plot_bands",
help="Draw colored bands around ranges of residues. E.g. A:10-20:00ff00 A:70-110:ff0000",
default=None,
nargs="*",
type=parse_plot_span,
)
plot_group.add_argument(
"--plot-unmodelled",
dest="plot_unmodelled",
help="Draw colored bands around ranges of residues. E.g. A:10-20:00ff00 A:70-110:ff0000",
default="blank",
type=parse_plot_unmodelled,
) | PypiClean |
/mpp-solar-0.7.76.tar.gz/mpp-solar-0.7.76/mppsolar/protocols/jk02.py | import logging
from .jkabstractprotocol import jkAbstractProtocol
log = logging.getLogger("jk02")
NEW_COMMANDS = {
"getCellData": {
"name": "getCellData",
"command_code": "96",
"record_type": "2",
"description": "BLE Cell Data inquiry",
"help": " -- queries the ble device for the cell data",
"type": "QUERY",
"response_type": "POSITIONAL",
"response": [
["Hex2Str", 4, "Header", ""],
["Hex2Str", 1, "Record_Type", ""],
["Hex2Int", 1, "Record_Counter", ""],
["LittleHex2Short:r/1000", 2, "Voltage_Cell01", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell02", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell03", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell04", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell05", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell06", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell07", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell08", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell09", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell10", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell11", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell12", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell13", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell14", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell15", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell16", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell17", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell18", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell19", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell20", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell21", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell22", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell23", "V"],
["LittleHex2Short:r/1000", 2, "Voltage_Cell24", "V"],
["discard", 4, "discard1", ""],
["LittleHex2Short:r/1000", 2, "Average_Cell_Voltage", "V"],
["LittleHex2Short:r/1000", 2, "Delta_Cell_Voltage", "V"],
["LittleHex2Short:r/1000", 2, "Current_Balancer", ""],
["LittleHex2Short:r/1000", 2, "Resistance_Cell01", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell02", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell03", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell04", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell05", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell06", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell07", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell08", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell09", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell10", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell11", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell12", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell13", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell14", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell15", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell16", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell17", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell18", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell19", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell20", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell21", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell22", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell23", "Ohm"],
["LittleHex2Short:r/1000", 2, "Resistance_Cell24", "Ohm"],
["discard", 6, "discard2", ""],
["LittleHex2UInt:r/1000", 4, "Battery_Voltage", "V"],
["LittleHex2Float", 4, "Battery_Power", "W"],
["LittleHex2Float", 4, "Charge_Current", "A"], # signed int32
# ["discard", 8, "discard3", ""],
["LittleHex2Short:r/10", 2, "Battery_T1", "°C"],
["LittleHex2Short:r/10", 2, "Battery_T2", "°C"],
["LittleHex2Short:r/10", 2, "MOS_Temp", "°C"],
["discard", 4, "discard4", ""], # discard4
["discard", 1, "discard4_1", ""], # added
["Hex2Int", 1, "Percent_Remain", ""],
["LittleHex2UInt:r/1000", 4, "Capacity_Remain", ""], # Unknown6+7
["LittleHex2UInt:r/1000", 4, "Nominal_Capacity", ""], # Unknown8+9
["Hex2Str", 4, "Cycle_Count", ""],
# ["discard", 2, "Unknown10", ""],
# ["discard", 2, "Unknown11", ""],
["LittleHex2UInt:r/1000", 4, "Capacity_Cycle", ""], # Unknown10+11
["discard", 2, "Unknown12", ""],
["discard", 2, "Unknown13", ""],
["uptime", 3, "Time", ""],
["discard", 2, "Unknown15", ""],
["discard", 2, "Unknown16", ""],
["discard", 2, "Unknown17", ""],
["discard", 12, "discard6", ""],
["discard", 2, "Unknown18", ""],
["discard", 2, "Unknown19", ""],
["discard", 2, "Unknown20", ""],
["LittleHex2Short:r/1000", 2, "Current_Charge", ""], # Unknown21
["LittleHex2Short:r/1000", 2, "Current_Discharge", ""], # Unknown22
["discard", 2, "Unknown23", ""],
["discard", 2, "Unknown24", ""],
["discard", 2, "Unknown25", ""],
["discard", 2, "Unknown26", ""],
["discard", 2, "Unknown27", ""],
["discard", 2, "Unknown28", ""],
["discard", 2, "Unknown29", ""],
["discard", 93, "Unknown30", ""],
],
"test_responses": [
bytes.fromhex(
"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"
),
bytes.fromhex(
"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"
),
bytes.fromhex(
"55 AA EB 90 02 10 AD 0E 52 0E 53 0E 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 07 00 00 00 70 0E 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 51 2B 00 00 00 00 00 00 00 00 00 00 30 F8 30 F8 53 01 00 00 0C 01 00 00 C2 14 00 00 70 17 00 00 00 00 00 00 8F 01 00 00 00 00 51 07 AF 69 00 00 00 00 CB 06 00 00 00 00 00 00 00 00 00 00 00 00 01 00 00 00 00 00 11 04 00 00 00 00 92 4A 3B 40 00 00 00 00 AD 08 00 00 00 00 00 01 00 01 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 0C"
),
b"U\xaa\xeb\x90\x02\xa7\xd8\x0e\xd8\x0e\xd7\x0e\xd8\x0e\xd8\x0e\xda\x0e\xd7\x0e\xda\x0e\xd7\x0e\xd8\x0e\xd8\x0e\xd8\x0e\xc3\x0e\xda\x0e\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xff?\x00\x00\xd7\x0e\x19\x00\x0c\x02m\x00n\x00o\x00n\x00o\x00m\x00p\x00l\x00l\x00l\x00l\x00l\x00o\x00l\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xc1\xcf\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf5\x00\xda\x00\x05\x01\x00\x00\x02\xf9\x02EN!\x00\x00\xc7\x00\x00\x03\x00\x00\x00\x87\x91\x00\x00W\x00)\x03\x16\x11\x04\x00\x01\x01\xa8\x06\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x07\x00\x01\x00\x00\x00\x11\x04\x00\x00\x00\x00\xee(@@\x7f\x00\x00\x00i\xfd\xff\xff\x00\x00\x00\x01\x00\x01\x00\x00t\xa3(\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00F",
],
},
}
class jk02(jkAbstractProtocol):
"""
JK02 - Handler for JKBMS 2 byte data communication
- e.g. ASAS = ??V
"""
def __init__(self, *args, **kwargs) -> None:
super().__init__()
self._protocol_id = b"JK02"
self.COMMANDS.update(NEW_COMMANDS)
self.STATUS_COMMANDS = [
"getCellData",
]
self.SETTINGS_COMMANDS = [
"getInfo",
]
self.DEFAULT_COMMAND = "getCellData" | PypiClean |
/remita_rits-1.0.6-py3-none-any.whl/GetAccountEnquiry/AccountEnquiry.py | from datetime import datetime
import requests
from requests import ConnectTimeout, ReadTimeout
from RemitaInterbankService.BaseResponse import BaseResponse
from RemitaInterbankService.EncryptionUtil import EncryptionConfig
from RemitaInterbankService.EnvironmentConfig import EnvironmentConfig
from RemitaInterbankService.SdkResponseCode import SdkResponseCode
from RemitaInterbankService.Timestamp import Timestamp
class AccountEnquiry(object):
def account_enquiry(self, account_enquiry_payload, credentials):
try:
get_response = EnvironmentConfig()
if not get_response.credential_available(credentials):
return get_response.throw_exception(status=get_response.empty_credential_code,
data=get_response.empty_credential_msg)
else:
rpg_environment = EnvironmentConfig.set_rpg_environment(credentials)
headers = self.set_header(account_enquiry_payload, credentials)
url = rpg_environment['ACCOUNT_ENQUIRY_URL']
if not credentials.connection_timeout:
credentials.connection_timeout = 30000
config = EncryptionConfig()
payload = {
'accountNo': config.AES128(credentials.enc_key, account_enquiry_payload.account_number,credentials.enc_vector),
'bankCode': config.AES128(credentials.enc_key, account_enquiry_payload.bank_code, credentials.enc_vector)
}
try:
response = requests.post(url, headers=headers, json=payload, timeout=credentials.connection_timeout)
account_enquiry_response = BaseResponse(response.content)
except ConnectTimeout:
return get_response.throw_exception(status=SdkResponseCode.CONNECTION_TIMEOUT_CODE,
data=SdkResponseCode.CONNECTION_TIMEOUT)
except ValueError:
return get_response.throw_exception(status=SdkResponseCode.ERROR_IN_VALUE_CODE,
data=SdkResponseCode.ERROR_IN_VALUE)
except ReadTimeout:
return get_response.throw_exception(status=SdkResponseCode.CONNECTION_TIMEOUT_CODE,
data=SdkResponseCode.CONNECTION_TIMEOUT)
except ConnectionError as e: # This is the correct syntax
return get_response.throw_exception(status=SdkResponseCode.ERROR_WHILE_CONNECTING_CODE,
data=SdkResponseCode.ERROR_WHILE_CONNECTING)
return account_enquiry_response
except Exception:
return get_response.throw_exception(status=SdkResponseCode.ERROR_PROCESSING_REQUEST_CODE,
data=SdkResponseCode.ERROR_PROCESSING_REQUEST)
def set_header(self, account_enquiry_payload, credentials):
hash_string = credentials.api_key + account_enquiry_payload.request_id + credentials.api_token
txn_hash = EncryptionConfig.sha512(hash_string)
time_stamp = Timestamp()
headers = {'Content-Type': 'application/json', 'MERCHANT_ID':credentials.merchant_id, 'API_KEY':credentials.api_key,
'REQUEST_ID':account_enquiry_payload.request_id, 'REQUEST_TS':time_stamp.dateTimeObj(dateTimeObj=datetime.now()),
'API_DETAILS_HASH': txn_hash}
return headers | PypiClean |
/mailman_pgp-0.3.0.tar.gz/mailman_pgp-0.3.0/src/mailman_pgp/mta/deliver.py |
""""""
import logging
import time
from mailman.config import config
from mailman.interfaces.mailinglist import Personalization
from mailman.interfaces.mta import SomeRecipientsFailed
from mailman.mta.bulk import BulkDelivery
from mailman.mta.deliver import Deliver
from mailman.utilities.string import expand
from public import public
from mailman_pgp.model.list import PGPMailingList
from mailman_pgp.mta.bulk import PGPBulkDelivery
from mailman_pgp.mta.personalized import PGPPersonalizedDelivery
COMMA = ','
log = logging.getLogger('mailman.smtp')
@public
def deliver(mlist, msg, msgdata):
"""Deliver a message to the outgoing mail server."""
# If there are no recipients, there's nothing to do.
recipients = msgdata.get('recipients')
if not recipients:
# Could be None, could be an empty sequence.
return
# Which delivery agent should we use? Several situations can cause us to
# use individual delivery. If not specified, use bulk delivery. See the
# to-outgoing handler for when the 'verp' key is set in the metadata.
personalized_agent = Deliver
bulk_agent = BulkDelivery
pgp_list = PGPMailingList.for_list(mlist)
if pgp_list and msgdata.get('pgp_is_posting', False):
personalized_agent = PGPPersonalizedDelivery
bulk_agent = PGPBulkDelivery
if msgdata.get('verp', False):
agent = personalized_agent()
elif mlist.personalize != Personalization.none:
agent = personalized_agent()
else:
agent = bulk_agent(int(config.mta.max_recipients))
log.debug('Using agent: %s', agent)
# Keep track of the original recipients and the original sender for
# logging purposes.
original_recipients = msgdata['recipients']
original_sender = msgdata.get('original-sender', msg.sender)
# Let the agent attempt to deliver to the recipients. Record all failures
# for re-delivery later.
t0 = time.time()
refused = agent.deliver(mlist, msg, msgdata)
t1 = time.time()
# Log this posting.
size = getattr(msg, 'original_size', msgdata.get('original_size'))
if size is None:
size = len(msg.as_string())
substitutions = dict(
msgid=msg.get('message-id', 'n/a'), # noqa: E221, E251
listname=mlist.fqdn_listname, # noqa: E221, E251
sender=original_sender, # noqa: E221, E251
recip=len(original_recipients), # noqa: E221, E251
size=size, # noqa: E221, E251
time=t1 - t0, # noqa: E221, E251
refused=len(refused), # noqa: E221, E251
smtpcode='n/a', # noqa: E221, E251
smtpmsg='n/a', # noqa: E221, E251
)
template = config.logging.smtp.every
if template.lower() != 'no':
log.info('%s', expand(template, mlist, substitutions))
if refused:
template = config.logging.smtp.refused
if template.lower() != 'no':
log.info('%s', expand(template, mlist, substitutions))
else:
# Log the successful post, but if it was not destined to the mailing
# list (e.g. to the owner or admin), print the actual recipients
# instead of just the number.
if not msgdata.get('tolist', False):
recips = msg.get_all('to', [])
recips.extend(msg.get_all('cc', []))
substitutions['recips'] = COMMA.join(recips)
template = config.logging.smtp.success
if template.lower() != 'no':
log.info('%s', expand(template, mlist, substitutions))
# Process any failed deliveries.
temporary_failures = []
permanent_failures = []
for recipient, (code, smtp_message) in refused.items():
# RFC 5321, $4.5.3.1.10 says:
#
# RFC 821 [1] incorrectly listed the error where an SMTP server
# exhausts its implementation limit on the number of RCPT commands
# ("too many recipients") as having reply code 552. The correct
# reply code for this condition is 452. Clients SHOULD treat a 552
# code in this case as a temporary, rather than permanent, failure
# so the logic below works.
#
if code >= 500 and code != 552:
# A permanent failure
permanent_failures.append(recipient)
else:
# Deal with persistent transient failures by queuing them up for
# future delivery. TBD: this could generate lots of log entries!
temporary_failures.append(recipient)
template = config.logging.smtp.failure
if template.lower() != 'no':
substitutions.update(
recip=recipient, # noqa: E221, E251
smtpcode=code, # noqa: E221, E251
smtpmsg=smtp_message # noqa: E221, E251
)
log.info('%s', expand(template, mlist, substitutions))
# Return the results
if temporary_failures or permanent_failures:
raise SomeRecipientsFailed(temporary_failures, permanent_failures) | PypiClean |
/gbptesthorizon-2014.2.tar.gz/gbptesthorizon-2014.2.dev67.g70d5b01/tools/install_venv_common.py | from __future__ import print_function
import optparse
import os
import subprocess
import sys
class InstallVenv(object):
def __init__(self, root, venv, requirements,
test_requirements, py_version,
project):
self.root = root
self.venv = venv
self.requirements = requirements
self.test_requirements = test_requirements
self.py_version = py_version
self.project = project
def die(self, message, *args):
print(message % args, file=sys.stderr)
sys.exit(1)
def check_python_version(self):
if sys.version_info < (2, 6):
self.die("Need Python Version >= 2.6")
def run_command_with_code(self, cmd, redirect_output=True,
check_exit_code=True):
"""Runs a command in an out-of-process shell.
Returns the output of that command. Working directory is self.root.
"""
if redirect_output:
stdout = subprocess.PIPE
else:
stdout = None
proc = subprocess.Popen(cmd, cwd=self.root, stdout=stdout)
output = proc.communicate()[0]
if check_exit_code and proc.returncode != 0:
self.die('Command "%s" failed.\n%s', ' '.join(cmd), output)
return (output, proc.returncode)
def run_command(self, cmd, redirect_output=True, check_exit_code=True):
return self.run_command_with_code(cmd, redirect_output,
check_exit_code)[0]
def get_distro(self):
if (os.path.exists('/etc/fedora-release') or
os.path.exists('/etc/redhat-release')):
return Fedora(
self.root, self.venv, self.requirements,
self.test_requirements, self.py_version, self.project)
else:
return Distro(
self.root, self.venv, self.requirements,
self.test_requirements, self.py_version, self.project)
def check_dependencies(self):
self.get_distro().install_virtualenv()
def create_virtualenv(self, no_site_packages=True):
"""Creates the virtual environment and installs PIP.
Creates the virtual environment and installs PIP only into the
virtual environment.
"""
if not os.path.isdir(self.venv):
print('Creating venv...', end=' ')
if no_site_packages:
self.run_command(['virtualenv', '-q', '--no-site-packages',
self.venv])
else:
self.run_command(['virtualenv', '-q', self.venv])
print('done.')
else:
print("venv already exists...")
pass
def pip_install(self, *args):
self.run_command(['tools/with_venv.sh',
'pip', 'install', '--upgrade'] + list(args),
redirect_output=False)
def install_dependencies(self):
print('Installing dependencies with pip (this can take a while)...')
# First things first, make sure our venv has the latest pip and
# setuptools and pbr
self.pip_install('pip>=1.4')
self.pip_install('setuptools')
self.pip_install('pbr')
self.pip_install('-r', self.requirements, '-r', self.test_requirements)
def parse_args(self, argv):
"""Parses command-line arguments."""
parser = optparse.OptionParser()
parser.add_option('-n', '--no-site-packages',
action='store_true',
help="Do not inherit packages from global Python "
"install")
return parser.parse_args(argv[1:])[0]
class Distro(InstallVenv):
def check_cmd(self, cmd):
return bool(self.run_command(['which', cmd],
check_exit_code=False).strip())
def install_virtualenv(self):
if self.check_cmd('virtualenv'):
return
if self.check_cmd('easy_install'):
print('Installing virtualenv via easy_install...', end=' ')
if self.run_command(['easy_install', 'virtualenv']):
print('Succeeded')
return
else:
print('Failed')
self.die('ERROR: virtualenv not found.\n\n%s development'
' requires virtualenv, please install it using your'
' favorite package management tool' % self.project)
class Fedora(Distro):
"""This covers all Fedora-based distributions.
Includes: Fedora, RHEL, CentOS, Scientific Linux
"""
def check_pkg(self, pkg):
return self.run_command_with_code(['rpm', '-q', pkg],
check_exit_code=False)[1] == 0
def install_virtualenv(self):
if self.check_cmd('virtualenv'):
return
if not self.check_pkg('python-virtualenv'):
self.die("Please install 'python-virtualenv'.")
super(Fedora, self).install_virtualenv() | PypiClean |
/netoprmgr-1.3.5.tar.gz/netoprmgr-1.3.5/netmiko/eltex/eltex_esr_ssh.py | import time
from netmiko.cisco_base_connection import CiscoSSHConnection
class EltexEsrSSH(CiscoSSHConnection):
"""Netmiko support for routers Eltex ESR."""
def session_preparation(self):
"""Prepare the session after the connection has been established."""
self.ansi_escape_codes = True
self._test_channel_read()
self.set_base_prompt()
self.disable_paging(command="terminal datadump")
# Clear the read buffer
time.sleep(0.3 * self.global_delay_factor)
self.clear_buffer()
def config_mode(self, config_command="configure", pattern=r")#"):
"""Enter configuration mode."""
return super().config_mode(config_command=config_command, pattern=pattern)
def check_config_mode(self, check_string="(config", pattern=""):
"""Checks whether in configuration mode. Returns a boolean."""
return super().check_config_mode(check_string=check_string, pattern=pattern)
def save_config(self, *args, **kwargs):
"""Not Implemented (use commit() method)"""
raise NotImplementedError
def commit(self, delay_factor=1):
"""
Commit the candidate configuration.
Commit the entered configuration.
Raise an error and return the failure
if the commit fails.
default:
command_string = commit
"""
delay_factor = self.select_delay_factor(delay_factor)
error_marker = "Can't commit configuration"
command_string = "commit"
if self.check_config_mode():
self.exit_config_mode()
output = self.send_command(
command_string=command_string, delay_factor=delay_factor
)
if error_marker in output:
raise ValueError(
"Commit failed with following errors:\n\n{}".format(output)
)
return output
def _confirm(self, delay_factor=1):
"""
Confirm the candidate configuration.
Raise an error and return the failure if the confirm fails.
"""
delay_factor = self.select_delay_factor(delay_factor)
error_marker = "Nothing to confirm in configuration"
command_string = "confirm"
if self.check_config_mode():
self.exit_config_mode()
output = self.send_command(
command_string=command_string, delay_factor=delay_factor
)
if error_marker in output:
raise ValueError(
"Confirm failed with following errors:\n\n{}".format(output)
)
return output
def _restore(self, delay_factor=1):
"""
Restore the candidate configuration.
Raise an error and return the failure if the restore fails.
"""
delay_factor = self.select_delay_factor(delay_factor)
error_marker = "Can't find backup of previous configuration!"
command_string = "restore"
if self.check_config_mode():
self.exit_config_mode()
output = self.send_command(
command_string=command_string, delay_factor=delay_factor
)
if error_marker in output:
raise ValueError(
"Restore failed with following errors:\n\n{}".format(output)
)
return output | PypiClean |
/py-opengauss-1.3.10.tar.gz/py-opengauss-1.3.10/py_opengauss/documentation/sphinx/copyman.rst | .. _pg_copyman:
***************
Copy Management
***************
The `postgresql.copyman` module provides a way to quickly move COPY data coming
from one connection to many connections. Alternatively, it can be sourced
by arbitrary iterators and target arbitrary callables.
Statement execution methods offer a way for running COPY operations
with iterators, but the cost of allocating objects for each row is too
significant for transferring gigabytes of COPY data from one connection to
another. The interfaces available on statement objects are primarily intended to
be used when transferring COPY data to and from arbitrary Python
objects.
Direct connection-to-connection COPY operations can be performed using the
high-level `postgresql.copyman.transfer` function::
>>> from postgresql import copyman
>>> send_stmt = source.prepare("COPY (SELECT i FROM generate_series(1, 1000000) AS g(i)) TO STDOUT")
>>> destination.execute("CREATE TEMP TABLE loading_table (i int8)")
>>> receive_stmt = destination.prepare("COPY loading_table FROM STDIN")
>>> total_rows, total_bytes = copyman.transfer(send_stmt, receive_stmt)
However, if more control is needed, the `postgresql.copyman.CopyManager` class
should be used directly.
Copy Managers
=============
The `postgresql.copyman.CopyManager` class manages the Producer and the
Receivers involved in a COPY operation. Normally,
`postgresql.copyman.StatementProducer` and
`postgresql.copyman.StatementReceiver` instances. Naturally, a Producer is the
object that produces the COPY data to be given to the Manager's Receivers.
Using a Manager directly means that there is a need for more control over
the operation. The Manager is both a context manager and an iterator. The
context manager interfaces handle initialization and finalization of the COPY
state, and the iterator provides an event loop emitting information about the
amount of COPY data transferred this cycle. Normal usage takes the form::
>>> from postgresql import copyman
>>> send_stmt = source.prepare("COPY (SELECT i FROM generate_series(1, 1000000) AS g(i)) TO STDOUT")
>>> destination.execute("CREATE TEMP TABLE loading_table (i int8)")
>>> receive_stmt = destination.prepare("COPY loading_table FROM STDIN")
>>> producer = copyman.StatementProducer(send_stmt)
>>> receiver = copyman.StatementReceiver(receive_stmt)
>>>
>>> with source.xact(), destination.xact():
... with copyman.CopyManager(producer, receiver) as copy:
... for num_messages, num_bytes in copy:
... update_rate(num_bytes)
As an alternative to a for-loop inside a with-statement block, the `run` method
can be called to perform the operation::
>>> with source.xact(), destination.xact():
... copyman.CopyManager(producer, receiver).run()
However, there is little benefit beyond using the high-level
`postgresql.copyman.transfer` function.
Manager Interface Points
------------------------
Primarily, the `postgresql.copyman.CopyManager` provides a context manager and
an iterator for controlling the COPY operation.
``CopyManager.run()``
Perform the entire COPY operation.
``CopyManager.__enter__()``
Start the COPY operation. Connections taking part in the COPY should **not**
be used until ``__exit__`` is ran.
``CopyManager.__exit__(typ, val, tb)``
Finish the COPY operation. Fails in the case of an incomplete
COPY, or an untrapped exception. Either returns `None` or raises the generalized
exception, `postgresql.copyman.CopyFail`.
``CopyManager.__iter__()``
Returns the CopyManager instance.
``CopyManager.__next__()``
Transfer the next chunk of COPY data to the receivers. Yields a tuple
consisting of the number of messages and bytes transferred,
``(num_messages, num_bytes)``. Raises `StopIteration` when complete.
Raises `postgresql.copyman.ReceiverFault` when a Receiver raises an
exception.
Raises `postgresql.copyman.ProducerFault` when the Producer raises an
exception. The original exception is available via the exception's
``__context__`` attribute.
``CopyManager.reconcile(faulted_receiver)``
Reconcile a faulted receiver. When a receiver faults, it will no longer
be in the set of Receivers. This method is used to signal to the manager that the
problem has been corrected, and the receiver is again ready to receive.
``CopyManager.receivers``
The `builtins.set` of Receivers involved in the COPY operation.
``CopyManager.producer``
The Producer emitting the data to be given to the Receivers.
Faults
======
The CopyManager generalizes any exceptions that occur during transfer. While
inside the context manager, `postgresql.copyman.Fault` may be raised if a
Receiver or a Producer raises an exception. A `postgresql.copyman.ProducerFault`
in the case of the Producer, and `postgresql.copyman.ReceiverFault` in the case
of the Receivers.
.. note::
Faults are only raised by `postgresql.copyman.CopyManager.__next__`. The
``run()`` method will only raise `postgresql.copyman.CopyFail`.
Receiver Faults
---------------
The Manager assumes the Fault is fatal to a Receiver, and immediately removes
it from the set of target receivers. Additionally, if the Fault exception goes
untrapped, the copy will ultimately fail.
The Fault exception references the Manager that raised the exception, and the
actual exceptions that occurred associated with the Receiver that caused them.
In order to identify the exception that caused a Fault, the ``faults`` attribute
on the `postgresql.copyman.ReceiverFault` must be referenced::
>>> from postgresql import copyman
>>> send_stmt = source.prepare("COPY (SELECT i FROM generate_series(1, 1000000) AS g(i)) TO STDOUT")
>>> destination.execute("CREATE TEMP TABLE loading_table (i int8)")
>>> receive_stmt = destination.prepare("COPY loading_table FROM STDIN")
>>> producer = copyman.StatementProducer(send_stmt)
>>> receiver = copyman.StatementReceiver(receive_stmt)
>>>
>>> with source.xact(), destination.xact():
... with copyman.CopyManager(producer, receiver) as copy:
... while copy.receivers:
... try:
... for num_messages, num_bytes in copy:
... update_rate(num_bytes)
... break
... except copyman.ReceiverFault as cf:
... # Access the original exception using the receiver as the key.
... original_exception = cf.faults[receiver]
... if unknown_failure(original_exception):
... ...
... raise
ReceiverFault Properties
~~~~~~~~~~~~~~~~~~~~~~~~
The following attributes exist on `postgresql.copyman.ReceiverFault` instances:
``ReceiverFault.manager``
The subject `postgresql.copyman.CopyManager` instance.
``ReceiverFault.faults``
A dictionary mapping the Receiver to the exception raised by that Receiver.
Reconciliation
~~~~~~~~~~~~~~
When a `postgresql.copyman.ReceiverFault` is raised, the Manager immediately
removes the Receiver so that the COPY operation can continue. Continuation of
the COPY can occur by trapping the exception and continuing the iteration of the
Manager. However, if the fault is recoverable, the
`postgresql.copyman.CopyManager.reconcile` method must be used to reintroduce the
Receiver into the Manager's set. Faults must be trapped from within the
Manager's context::
>>> import socket
>>> from postgresql import copyman
>>> send_stmt = source.prepare("COPY (SELECT i FROM generate_series(1, 1000000) AS g(i)) TO STDOUT")
>>> destination.execute("CREATE TEMP TABLE loading_table (i int8)")
>>> receive_stmt = destination.prepare("COPY loading_table FROM STDIN")
>>> producer = copyman.StatementProducer(send_stmt)
>>> receiver = copyman.StatementReceiver(receive_stmt)
>>>
>>> with source.xact(), destination.xact():
... with copyman.CopyManager(producer, receiver) as copy:
... while copy.receivers:
... try:
... for num_messages, num_bytes in copy:
... update_rate(num_bytes)
... except copyman.ReceiverFault as cf:
... if isinstance(cf.faults[receiver], socket.timeout):
... copy.reconcile(receiver)
... else:
... raise
Recovering from Faults does add significant complexity to a COPY operation,
so, often, it's best to avoid conditions in which reconciliable Faults may
occur.
Producer Faults
---------------
Producer faults are normally fatal to the COPY operation and should rarely be
trapped. However, the Manager makes no state changes when a Producer faults,
so, unlike Receiver Faults, no reconciliation process is necessary; rather,
if it's safe to continue, the Manager's iterator should continue to be
processed.
ProducerFault Properties
~~~~~~~~~~~~~~~~~~~~~~~~
The following attributes exist on `postgresql.copyman.ProducerFault` instances:
``ReceiverFault.manager``
The subject `postgresql.copyman.CopyManager`.
``ReceiverFault.__context__``
The original exception raised by the Producer.
Failures
========
When a COPY operation is aborted, either by an exception or by the iterator
being broken, a `postgresql.copyman.CopyFail` exception will be raised by the
Manager's ``__exit__()`` method. The `postgresql.copyman.CopyFail` exception
offers to record any exceptions that occur during the exit of the context
managers of the Producer and the Receivers.
CopyFail Properties
-------------------
The following properties exist on `postgresql.copyman.CopyFail` exceptions:
``CopyFail.manager``
The Manager whose COPY operation failed.
``CopyFail.receiver_faults``
A dictionary mapping a `postgresql.copyman.Receiver` to the exception raised
by that Receiver's ``__exit__``. `None` if no exceptions were raised by the
Receivers.
``CopyFail.producer_fault``
The exception Raised by the `postgresql.copyman.Producer`. `None` if none.
Producers
=========
The following Producers are available:
``postgresql.copyman.StatementProducer(postgresql.api.Statement)``
Given a Statement producing COPY data, construct a Producer.
``postgresql.copyman.IteratorProducer(collections.abc.Iterator)``
Given an Iterator producing *chunks* of COPY lines, construct a Producer to
manage the data coming from the iterator.
Receivers
=========
``postgresql.copyman.StatementReceiver(postgresql.api.Statement)``
Given a Statement producing COPY data, construct a Producer.
``postgresql.copyman.CallReceiver(callable)``
Given a callable, construct a Receiver that will transmit COPY data in chunks
of lines. That is, the callable will be given a list of COPY lines for each
transfer cycle.
Terminology
===========
The following terms are regularly used to describe the implementation and
processes of the `postgresql.copyman` module:
Manager
The object used to manage data coming from a Producer and being given to the
Receivers. It also manages the necessary initialization and finalization steps
required by those factors.
Producer
The object used to produce the COPY data to be given to the Receivers. The
source.
Receiver
An object that consumes COPY data. A target.
Fault
Specifically, `postgresql.copyman.Fault` exceptions. A Fault is raised
when a Receiver or a Producer raises an exception during the COPY operation.
Reconciliation
Generally, the steps performed by the "reconcile" method on
`postgresql.copyman.CopyManager` instances. More precisely, the
necessary steps for a Receiver's reintroduction into the COPY operation after
a Fault.
Failed Copy
A failed copy is an aborted COPY operation. This occurs in situations of
untrapped exceptions or an incomplete COPY. Specifically, the COPY will be
noted as failed in cases where the Manager's iterator is *not* ran until
exhaustion.
Realignment
The process of providing compensating data to the Receivers so that the
connection will be on a message boundary. Occurs when the COPY operation
is aborted.
| PypiClean |
/pyeasysql-3.3.3-py3-none-any.whl/EasySQL/EasyInstances.py | from typing import Optional
from .ABC import CHARSET
from .Classes import EasyDatabase, EasyColumn
from .Types import *
__all__ = ['EasyLocalHost', 'EasyInt64Column', 'EasyInt32Column', 'EasyInt24Column', 'EasyInt16Column', 'EasyInt08Column', 'EasyCharColumn',
'EasyBitColumn', 'EasyBoolColumn', 'EasyFloatColumn', 'EasyDoubleColumn', 'EasyDecimalColumn', 'EasyStringColumn']
class EasyLocalHost(EasyDatabase):
_host = "127.0.0.1"
_port = 3306
_user = "root"
_password = ""
def __init__(self, database, charset: Optional[CHARSET]):
self._database = database
self._charset = charset if charset is not None else None
super().__init__()
class EasyInt64Column(EasyColumn):
def __init__(self, name: str):
super().__init__(name, INT64)
class EasyInt32Column(EasyColumn):
def __init__(self, name: str):
super().__init__(name, INT32)
class EasyInt24Column(EasyColumn):
def __init__(self, name: str):
super().__init__(name, INT24)
class EasyInt16Column(EasyColumn):
def __init__(self, name: str):
super().__init__(name, INT16)
class EasyInt08Column(EasyColumn):
def __init__(self, name: str):
super().__init__(name, INT8)
class EasyBitColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, BIT)
class EasyBoolColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, BOOL)
class EasyFloatColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, FLOAT)
class EasyDoubleColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, DOUBLE)
class EasyDecimalColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, DECIMAL)
class EasyStringColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, BIGINT)
class EasyCharColumn(EasyColumn):
def __init__(self, name: str):
super().__init__(name, BIGINT) | PypiClean |
/qlogging-1.3.1.tar.gz/qlogging-1.3.1/README.md | # Python Quick Logging | QLogging
Beautifully colored, quick and simple Python logging. This logger is based on [Python logging package](https://docs.python.org/3/library/logging.html)
[](https://github.com/sinkingtitanic/qlogging)
[](https://pypi.org/project/qlogging/)
[](https://www.python.org/downloads/)
[](https://pypi.org/project/qlogging/)
## Screenshots:
### Terminal/CMD
### Notebooks:
### Windows:
## Features
* Color logging in Terminal and CMD
* Color logging in Jupyter Notebook and Jupyter Lab
* Color logging in Kaggle Notebook
* Color logging in Google Colab Notebook
* Know which function the logger was called from
* Know while line number the logger was called from
* Support logging to a file
* Simple and clean one-liner
* Customizable
## Installation
```
$ pip install qlogging
```
## Examples
### Logging only to console/notebook:
```
import qlogging
logger = qlogging.get_logger(level='debug')
logger.debug("This is debug")
logger.info("This is info")
logger.warning("This is warning")
logger.error("This is an error")
logger.critical("This is a critical")
```
Output (output format: `<time> <function_name>,<line#>| <log_message>`):
```
12:21:37 foo(),3| This is debug
12:21:37 foo(),4| This is info
12:21:37 foo(),5| This is warning
12:21:37 foo(),6| This is an error
12:21:37 foo(),7| This is a critical
```
____
### Logging to console/terminal and a log file (append if log file exists):
```
import qlogging
logger = qlogging.get_logger(level='debug', logfile='my_log.log')
```
____
### Logging to console/terminal and a log file (overwrite if log file exists):
```
import qlogging
logger = qlogging.get_logger(level='debug', logfile='my_log.log', logfilemode='w')
```
____
### Logging with `loggingmode='long'` (default is `loggingmode='short'`):
```
import qlogging
logger = qlogging.get_logger(level='debug', loggingmode='long')
logger.debug("This is debug")
```
Output (output format: `<date> <time> | <file_name> | <function_name>,<line#>| <log_message>`):
```
2021-05-18 12:38:22 | <main.py> | <foo()>,4 | This is debug
```
## Easy Customization
Customize your logger based on the following `get_logger()` function parameters
```
def get_logger(
level="info",
logfile=None,
logfilemode="a",
loggingmode="short",
format_str=None,
file_format_str=None,
format_date=None,
colors=None,
logger_config=None,
):
"""
returns Python logging based logger formatted with colors
:param level: (DEFAULT='info') str of logging level, each str option is mapped to Python logging levels, str options:
'info': logging.INFO,
'debug': logging.DEBUG,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL,
'notset': logging.NOTSET
:param logfile: (DEFAULT=None) str path where to save log file, example: '/tmp/my_log.log'
:param logfilemode: (DEFAULT='a') str of log file writing mode, same as the ones documented at Python logging package. options:
'a': appends to logfile
'w': overwrites logfile
:param loggingmode: (DEFAULT='short') str logging mode to be selected. options:
'short': will use short str format ('%(asctime)s %(funcName)s,%(lineno)s| %(message)s') and short date format ('%H:%M:%S')
'medium': will use long str format ('%(asctime)s | %(filename)s | %(funcName)s,%(lineno)s | %(message)s') and long date format ('%Y-%m-%d %H:%M:%S')
'manual': you need to set :param format_str: and :param format_date: yourself
:param format_str: (DEFAULT=None) str of format logging string for console, only set this if you selected :param loggingmode: as 'manual'. example (the style is always '%', see python logging module for more info):
'%(asctime)s | %(filename)s | %(funcName)s,%(lineno)s | %(message)s'
:param file_format_str: (DEFAULT=None) str of format logging string for logfile (if you keep it None, we will use what you passed in :param format_str:), only set this if you selected :param loggingmode: as 'manual'. example (the style is always '%', see python logging module for more info):
'%(asctime)s | %(filename)s | %(funcName)s,%(lineno)s | %(message)s'
:param date_str: (DEFAULT=None) str of date logging string, only set this if you selected :param loggingmode: as 'manual'. example:
'%Y-%m-%d %H:%M:%S'
:param colors: (DEFAULT=None) dict of color settings, only set this if you selected :param loggingmode: as 'manual'. example:
{
'DEBUG': Fore.CYAN + Style.BRIGHT,
'INFO': Fore.GREEN + Style.BRIGHT,
'WARNING': Fore.YELLOW + Style.BRIGHT,
'ERROR': Fore.RED + Style.BRIGHT,
'CRITICAL': Fore.RED + Back.WHITE + Style.BRIGHT,
}
:param logger_config: (DEFAULT=None) dict python logger config if you want to fully overwrite configs. example:
{
"version": 1,
"disable_existing_loggers": False,
"formatters": {
"qlog": {
"()": "qlogging.qlogging.ColoredFormatter",
"colors": {
'DEBUG': Fore.CYAN + Style.BRIGHT,
'INFO': Fore.GREEN + Style.BRIGHT,
'WARNING': Fore.YELLOW + Style.BRIGHT,
'ERROR': Fore.RED + Style.BRIGHT,
'CRITICAL': Fore.RED + Back.WHITE + Style.BRIGHT,
},
"format": "%(asctime)s [%(levelname)s] %(name)s: %(message)s",
"datefmt":'%H:%M:%S'
},
},
"handlers": {
"console": {
"level": "DEBUG",
"formatter": "qlog",
"class": "logging.StreamHandler",
"stream": "ext://sys.stdout",
},
},
"loggers": {
"": {
"handlers": ["console"],
"level": "DEBUG",
"propagate": True,
},
},
}
:return: formated Python logging instance
"""
```
## Alternatives
* [coloredlogs 15.0](https://pypi.org/project/coloredlogs/): does not support coloring in notebooks.
* [colorlog 5.0.1](https://pypi.org/project/colorlog/): does not support coloring in notebooks.
## Credit:
* [colorama](https://github.com/tartley/colorama)
* [joshbode](https://gist.github.com/joshbode/58fac7ababc700f51e2a9ecdebe563ad)
## License
MIT License
Copyright (c) 2021 Github Account SinkingTitanic Owner
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
| PypiClean |
/ais_dom-2023.7.2-py3-none-any.whl/homeassistant/components/octoprint/sensor.py | from __future__ import annotations
from datetime import datetime, timedelta
import logging
from pyoctoprintapi import OctoprintJobInfo, OctoprintPrinterInfo
from homeassistant.components.sensor import (
SensorDeviceClass,
SensorEntity,
SensorStateClass,
)
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import PERCENTAGE, UnitOfTemperature
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.entity_platform import AddEntitiesCallback
from homeassistant.helpers.update_coordinator import CoordinatorEntity
from . import OctoprintDataUpdateCoordinator
from .const import DOMAIN
_LOGGER = logging.getLogger(__name__)
JOB_PRINTING_STATES = ["Printing from SD", "Printing"]
def _is_printer_printing(printer: OctoprintPrinterInfo) -> bool:
return (
printer
and printer.state
and printer.state.flags
and printer.state.flags.printing
)
async def async_setup_entry(
hass: HomeAssistant,
config_entry: ConfigEntry,
async_add_entities: AddEntitiesCallback,
) -> None:
"""Set up the available OctoPrint binary sensors."""
coordinator: OctoprintDataUpdateCoordinator = hass.data[DOMAIN][
config_entry.entry_id
]["coordinator"]
device_id = config_entry.unique_id
assert device_id is not None
known_tools = set()
@callback
def async_add_tool_sensors() -> None:
if not coordinator.data["printer"]:
return
new_tools = []
for tool in [
tool
for tool in coordinator.data["printer"].temperatures
if tool.name not in known_tools
]:
assert device_id is not None
known_tools.add(tool.name)
for temp_type in ("actual", "target"):
new_tools.append(
OctoPrintTemperatureSensor(
coordinator,
tool.name,
temp_type,
device_id,
)
)
async_add_entities(new_tools)
config_entry.async_on_unload(coordinator.async_add_listener(async_add_tool_sensors))
if coordinator.data["printer"]:
async_add_tool_sensors()
entities: list[SensorEntity] = [
OctoPrintStatusSensor(coordinator, device_id),
OctoPrintJobPercentageSensor(coordinator, device_id),
OctoPrintEstimatedFinishTimeSensor(coordinator, device_id),
OctoPrintStartTimeSensor(coordinator, device_id),
]
async_add_entities(entities)
class OctoPrintSensorBase(
CoordinatorEntity[OctoprintDataUpdateCoordinator], SensorEntity
):
"""Representation of an OctoPrint sensor."""
def __init__(
self,
coordinator: OctoprintDataUpdateCoordinator,
sensor_type: str,
device_id: str,
) -> None:
"""Initialize a new OctoPrint sensor."""
super().__init__(coordinator)
self._device_id = device_id
self._attr_name = f"OctoPrint {sensor_type}"
self._attr_unique_id = f"{sensor_type}-{device_id}"
@property
def device_info(self):
"""Device info."""
return self.coordinator.device_info
class OctoPrintStatusSensor(OctoPrintSensorBase):
"""Representation of an OctoPrint sensor."""
_attr_icon = "mdi:printer-3d"
def __init__(
self, coordinator: OctoprintDataUpdateCoordinator, device_id: str
) -> None:
"""Initialize a new OctoPrint sensor."""
super().__init__(coordinator, "Current State", device_id)
@property
def native_value(self):
"""Return sensor state."""
printer: OctoprintPrinterInfo = self.coordinator.data["printer"]
if not printer:
return None
return printer.state.text
@property
def available(self) -> bool:
"""Return if entity is available."""
return self.coordinator.last_update_success and self.coordinator.data["printer"]
class OctoPrintJobPercentageSensor(OctoPrintSensorBase):
"""Representation of an OctoPrint sensor."""
_attr_native_unit_of_measurement = PERCENTAGE
_attr_icon = "mdi:file-percent"
def __init__(
self, coordinator: OctoprintDataUpdateCoordinator, device_id: str
) -> None:
"""Initialize a new OctoPrint sensor."""
super().__init__(coordinator, "Job Percentage", device_id)
@property
def native_value(self):
"""Return sensor state."""
job: OctoprintJobInfo = self.coordinator.data["job"]
if not job:
return None
if not (state := job.progress.completion):
return 0
return round(state, 2)
class OctoPrintEstimatedFinishTimeSensor(OctoPrintSensorBase):
"""Representation of an OctoPrint sensor."""
_attr_device_class = SensorDeviceClass.TIMESTAMP
def __init__(
self, coordinator: OctoprintDataUpdateCoordinator, device_id: str
) -> None:
"""Initialize a new OctoPrint sensor."""
super().__init__(coordinator, "Estimated Finish Time", device_id)
@property
def native_value(self) -> datetime | None:
"""Return sensor state."""
job: OctoprintJobInfo = self.coordinator.data["job"]
if (
not job
or not job.progress.print_time_left
or not _is_printer_printing(self.coordinator.data["printer"])
):
return None
read_time = self.coordinator.data["last_read_time"]
return (read_time + timedelta(seconds=job.progress.print_time_left)).replace(
second=0
)
class OctoPrintStartTimeSensor(OctoPrintSensorBase):
"""Representation of an OctoPrint sensor."""
_attr_device_class = SensorDeviceClass.TIMESTAMP
def __init__(
self, coordinator: OctoprintDataUpdateCoordinator, device_id: str
) -> None:
"""Initialize a new OctoPrint sensor."""
super().__init__(coordinator, "Start Time", device_id)
@property
def native_value(self) -> datetime | None:
"""Return sensor state."""
job: OctoprintJobInfo = self.coordinator.data["job"]
if (
not job
or not job.progress.print_time
or not _is_printer_printing(self.coordinator.data["printer"])
):
return None
read_time = self.coordinator.data["last_read_time"]
return (read_time - timedelta(seconds=job.progress.print_time)).replace(
second=0
)
class OctoPrintTemperatureSensor(OctoPrintSensorBase):
"""Representation of an OctoPrint sensor."""
_attr_native_unit_of_measurement = UnitOfTemperature.CELSIUS
_attr_device_class = SensorDeviceClass.TEMPERATURE
_attr_state_class = SensorStateClass.MEASUREMENT
def __init__(
self,
coordinator: OctoprintDataUpdateCoordinator,
tool: str,
temp_type: str,
device_id: str,
) -> None:
"""Initialize a new OctoPrint sensor."""
super().__init__(coordinator, f"{temp_type} {tool} temp", device_id)
self._temp_type = temp_type
self._api_tool = tool
@property
def native_value(self):
"""Return sensor state."""
printer: OctoprintPrinterInfo = self.coordinator.data["printer"]
if not printer:
return None
for temp in printer.temperatures:
if temp.name == self._api_tool:
val = (
temp.actual_temp
if self._temp_type == "actual"
else temp.target_temp
)
if val is None:
return None
return round(val, 2)
return None
@property
def available(self) -> bool:
"""Return if entity is available."""
return self.coordinator.last_update_success and self.coordinator.data["printer"] | PypiClean |
/sonatype_nexus_sdk-0.1.3-py3-none-any.whl/nexus_sdk/paths/v1_repositories_nuget_hosted/post.py | import decimal # noqa: F401
import functools # noqa: F401
import io # noqa: F401
import re # noqa: F401
import typing # noqa: F401
import uuid # noqa: F401
from dataclasses import dataclass
from datetime import date, datetime # noqa: F401
import frozendict # noqa: F401
import typing_extensions # noqa: F401
import urllib3
from urllib3._collections import HTTPHeaderDict
from nexus_sdk import schemas # noqa: F401
from nexus_sdk import api_client, exceptions
from nexus_sdk.model.nuget_hosted_repository_api_request import (
NugetHostedRepositoryApiRequest,
)
from . import path
# body param
SchemaForRequestBodyApplicationJson = NugetHostedRepositoryApiRequest
request_body_body = api_client.RequestBody(
content={
'application/json': api_client.MediaType(
schema=SchemaForRequestBodyApplicationJson),
},
)
@dataclass
class ApiResponseFor201(api_client.ApiResponse):
response: urllib3.HTTPResponse
body: typing.Union[
]
headers: schemas.Unset = schemas.unset
_response_for_201 = api_client.OpenApiResponse(
response_cls=ApiResponseFor201,
)
@dataclass
class ApiResponseFor401(api_client.ApiResponse):
response: urllib3.HTTPResponse
body: typing.Union[
]
headers: schemas.Unset = schemas.unset
_response_for_401 = api_client.OpenApiResponse(
response_cls=ApiResponseFor401,
)
@dataclass
class ApiResponseFor403(api_client.ApiResponse):
response: urllib3.HTTPResponse
body: typing.Union[
]
headers: schemas.Unset = schemas.unset
_response_for_403 = api_client.OpenApiResponse(
response_cls=ApiResponseFor403,
)
_status_code_to_response = {
'201': _response_for_201,
'401': _response_for_401,
'403': _response_for_403,
}
class BaseApi(api_client.Api):
@typing.overload
def _create_repository12_oapg(
self,
content_type: typing_extensions.Literal["application/json"] = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: typing_extensions.Literal[False] = ...,
) -> typing.Union[
ApiResponseFor201,
]: ...
@typing.overload
def _create_repository12_oapg(
self,
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: typing_extensions.Literal[False] = ...,
) -> typing.Union[
ApiResponseFor201,
]: ...
@typing.overload
def _create_repository12_oapg(
self,
skip_deserialization: typing_extensions.Literal[True],
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
) -> api_client.ApiResponseWithoutDeserialization: ...
@typing.overload
def _create_repository12_oapg(
self,
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: bool = ...,
) -> typing.Union[
ApiResponseFor201,
api_client.ApiResponseWithoutDeserialization,
]: ...
def _create_repository12_oapg(
self,
content_type: str = 'application/json',
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: bool = False,
):
"""
Create NuGet hosted repository
:param skip_deserialization: If true then api_response.response will be set but
api_response.body and api_response.headers will not be deserialized into schema
class instances
"""
used_path = path.value
_headers = HTTPHeaderDict()
# TODO add cookie handling
_fields = None
_body = None
if body is not schemas.unset:
serialized_data = request_body_body.serialize(body, content_type)
_headers.add('Content-Type', content_type)
if 'fields' in serialized_data:
_fields = serialized_data['fields']
elif 'body' in serialized_data:
_body = serialized_data['body']
response = self.api_client.call_api(
resource_path=used_path,
method='post'.upper(),
headers=_headers,
fields=_fields,
body=_body,
stream=stream,
timeout=timeout,
)
if skip_deserialization:
api_response = api_client.ApiResponseWithoutDeserialization(response=response)
else:
response_for_status = _status_code_to_response.get(str(response.status))
if response_for_status:
api_response = response_for_status.deserialize(response, self.api_client.configuration)
else:
api_response = api_client.ApiResponseWithoutDeserialization(response=response)
if not 200 <= response.status <= 299:
raise exceptions.ApiException(
status=response.status,
reason=response.reason,
api_response=api_response
)
return api_response
class CreateRepository12(BaseApi):
# this class is used by api classes that refer to endpoints with operationId fn names
@typing.overload
def create_repository12(
self,
content_type: typing_extensions.Literal["application/json"] = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: typing_extensions.Literal[False] = ...,
) -> typing.Union[
ApiResponseFor201,
]: ...
@typing.overload
def create_repository12(
self,
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: typing_extensions.Literal[False] = ...,
) -> typing.Union[
ApiResponseFor201,
]: ...
@typing.overload
def create_repository12(
self,
skip_deserialization: typing_extensions.Literal[True],
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
) -> api_client.ApiResponseWithoutDeserialization: ...
@typing.overload
def create_repository12(
self,
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: bool = ...,
) -> typing.Union[
ApiResponseFor201,
api_client.ApiResponseWithoutDeserialization,
]: ...
def create_repository12(
self,
content_type: str = 'application/json',
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: bool = False,
):
return self._create_repository12_oapg(
body=body,
content_type=content_type,
stream=stream,
timeout=timeout,
skip_deserialization=skip_deserialization
)
class ApiForpost(BaseApi):
# this class is used by api classes that refer to endpoints by path and http method names
@typing.overload
def post(
self,
content_type: typing_extensions.Literal["application/json"] = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: typing_extensions.Literal[False] = ...,
) -> typing.Union[
ApiResponseFor201,
]: ...
@typing.overload
def post(
self,
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: typing_extensions.Literal[False] = ...,
) -> typing.Union[
ApiResponseFor201,
]: ...
@typing.overload
def post(
self,
skip_deserialization: typing_extensions.Literal[True],
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
) -> api_client.ApiResponseWithoutDeserialization: ...
@typing.overload
def post(
self,
content_type: str = ...,
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: bool = ...,
) -> typing.Union[
ApiResponseFor201,
api_client.ApiResponseWithoutDeserialization,
]: ...
def post(
self,
content_type: str = 'application/json',
body: typing.Union[SchemaForRequestBodyApplicationJson, schemas.Unset] = schemas.unset,
stream: bool = False,
timeout: typing.Optional[typing.Union[int, typing.Tuple]] = None,
skip_deserialization: bool = False,
):
return self._create_repository12_oapg(
body=body,
content_type=content_type,
stream=stream,
timeout=timeout,
skip_deserialization=skip_deserialization
) | PypiClean |
/Brian2-2.5.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl/brian2/memory/dynamicarray.py | import numpy as np
__all__ = ["DynamicArray", "DynamicArray1D"]
def getslices(shape, from_start=True):
if from_start:
return tuple(slice(0, x) for x in shape)
else:
return tuple(slice(x, None) for x in shape)
class DynamicArray:
"""
An N-dimensional dynamic array class
The array can be resized in any dimension, and the class will handle
allocating a new block of data and copying when necessary.
.. warning::
The data will NOT be contiguous for >1D arrays. To ensure this, you will
either need to use 1D arrays, or to copy the data, or use the shrink
method with the current size (although note that in both cases you
negate the memory and efficiency benefits of the dynamic array).
Initialisation arguments:
``shape``, ``dtype``
The shape and dtype of the array to initialise, as in Numpy. For 1D
arrays, shape can be a single int, for ND arrays it should be a tuple.
``factor``
The resizing factor (see notes below). Larger values tend to lead to
more wasted memory, but more computationally efficient code.
``use_numpy_resize``, ``refcheck``
Normally, when you resize the array it creates a new array and copies
the data. Sometimes, it is possible to resize an array without a copy,
and if this option is set it will attempt to do this. However, this can
cause memory problems if you are not careful so the option is off by
default. You need to ensure that you do not create slices of the array
so that no references to the memory exist other than the main array
object. If you are sure you know what you're doing, you can switch this
reference check off. Note that resizing in this way is only done if you
resize in the first dimension.
The array is initialised with zeros. The data is stored in the attribute
``data`` which is a Numpy array.
Some numpy methods are implemented and can work directly on the array object,
including ``len(arr)``, ``arr[...]`` and ``arr[...]=...``. In other cases,
use the ``data`` attribute.
Examples
--------
>>> x = DynamicArray((2, 3), dtype=int)
>>> x[:] = 1
>>> x.resize((3, 3))
>>> x[:] += 1
>>> x.resize((3, 4))
>>> x[:] += 1
>>> x.resize((4, 4))
>>> x[:] += 1
>>> x.data[:] = x.data**2
>>> x.data
array([[16, 16, 16, 4],
[16, 16, 16, 4],
[ 9, 9, 9, 4],
[ 1, 1, 1, 1]])
Notes
-----
The dynamic array returns a ``data`` attribute which is a view on the larger
``_data`` attribute. When a resize operation is performed, and a specific
dimension is enlarged beyond the size in the ``_data`` attribute, the size
is increased to the larger of ``cursize*factor`` and ``newsize``. This
ensures that the amortized cost of increasing the size of the array is O(1).
"""
def __init__(
self, shape, dtype=float, factor=2, use_numpy_resize=False, refcheck=True
):
if isinstance(shape, int):
shape = (shape,)
self._data = np.zeros(shape, dtype=dtype)
self.data = self._data
self.dtype = dtype
self.shape = self._data.shape
self.factor = factor
self.use_numpy_resize = use_numpy_resize
self.refcheck = refcheck
def resize(self, newshape):
"""
Resizes the data to the new shape, which can be a different size to the
current data, but should have the same rank, i.e. same number of
dimensions.
"""
datashapearr = np.array(self._data.shape)
newshapearr = np.array(newshape)
resizedimensions = newshapearr > datashapearr
if resizedimensions.any():
# resize of the data is needed
minnewshapearr = datashapearr # .copy()
dimstoinc = minnewshapearr[resizedimensions]
incdims = np.array(dimstoinc * self.factor, dtype=int)
newdims = np.maximum(incdims, dimstoinc + 1)
minnewshapearr[resizedimensions] = newdims
newshapearr = np.maximum(newshapearr, minnewshapearr)
do_resize = False
if self.use_numpy_resize and self._data.flags["C_CONTIGUOUS"]:
if sum(resizedimensions) == resizedimensions[0]:
do_resize = True
if do_resize:
self.data = None
self._data.resize(tuple(newshapearr), refcheck=self.refcheck)
else:
newdata = np.zeros(tuple(newshapearr), dtype=self.dtype)
slices = getslices(self._data.shape)
newdata[slices] = self._data
self._data = newdata
elif (newshapearr < self.shape).any():
# If we reduced the size, set the no longer used memory to 0
self._data[getslices(newshape, from_start=False)] = 0
# Reduce our view to the requested size if necessary
self.data = self._data[getslices(newshape, from_start=True)]
self.shape = self.data.shape
def resize_along_first(self, newshape):
new_dimension = newshape[0]
if new_dimension > self._data.shape[0]:
new_size = np.maximum(self._data.shape[0] * self.factor, new_dimension + 1)
final_new_shape = np.array(self._data.shape)
final_new_shape[0] = new_size
if self.use_numpy_resize and self._data.flags["C_CONTIGUOUS"]:
self.data = None
self._data.resize(tuple(final_new_shape), refcheck=self.refcheck)
else:
newdata = np.zeros(tuple(final_new_shape), dtype=self.dtype)
slices = getslices(self._data.shape)
newdata[slices] = self._data
self._data = newdata
elif newshape < self.shape:
# If we reduced the size, set the no longer used memory to 0
self._data[new_dimension:] = 0
# Reduce our view to the requested size if necessary
self.data = self._data[:new_dimension]
self.shape = newshape
def shrink(self, newshape):
"""
Reduces the data to the given shape, which should be smaller than the
current shape. `resize` can also be used with smaller values, but
it will not shrink the allocated memory, whereas `shrink` will
reallocate the memory. This method should only be used infrequently, as
if it is used frequently it will negate the computational efficiency
benefits of the DynamicArray.
"""
if isinstance(newshape, int):
newshape = (newshape,)
shapearr = np.array(self.shape)
newshapearr = np.array(newshape)
if (newshapearr <= shapearr).all():
newdata = np.zeros(newshapearr, dtype=self.dtype)
newdata[:] = self._data[getslices(newshapearr)]
self._data = newdata
self.shape = tuple(newshapearr)
self.data = self._data
def __getitem__(self, item):
return self.data.__getitem__(item)
def __setitem__(self, item, val):
self.data.__setitem__(item, val)
def __len__(self):
return len(self.data)
def __str__(self):
return self.data.__str__()
def __repr__(self):
return self.data.__repr__()
class DynamicArray1D(DynamicArray):
"""
Version of `DynamicArray` with specialised ``resize`` method designed
to be more efficient.
"""
def resize(self, newshape):
(datashape,) = self._data.shape
if newshape > datashape:
(shape,) = self.shape # we work with int shapes only
newdatashape = max(newshape, int(shape * self.factor) + 1)
if self.use_numpy_resize and self._data.flags["C_CONTIGUOUS"]:
self.data = None
self._data.resize(newdatashape, refcheck=self.refcheck)
else:
newdata = np.zeros(newdatashape, dtype=self.dtype)
newdata[:shape] = self.data
self._data = newdata
elif newshape < self.shape[0]:
# If we reduced the size, set the no longer used memory to 0
self._data[newshape:] = 0
# Reduce our view to the requested size if necessary
self.data = self._data[:newshape]
self.shape = (newshape,) | PypiClean |
/Dabo-0.9.16.tar.gz/Dabo-0.9.16/dabo/lib/specParser.py | import xml.sax
from StringIO import StringIO
import os.path
class specHandler(xml.sax.ContentHandler):
_IsContainer = False
def __init__(self):
self.appDict = {}
self.relaDict = {}
self.currTableDict = {}
self.currTable = ""
# self.currFieldDict = {}
def startElement(self, name, attrs):
if name == "table":
# New table starting
self.currTable = attrs.getValue("name")
self.currTableDict = {}
#self.currFieldDict = {}
self.currRelaDict = {}
elif name == "field":
fieldDict = {}
for att in attrs.keys():
if att == "name":
fldName = attrs.getValue("name")
else:
fieldDict[att] = attrs.getValue(att)
self.currTableDict[fldName] = fieldDict.copy()
elif name == "join":
joinDict = {}
for att in attrs.keys():
if att == "order":
name = "_join%s" % attrs["order"]
joinDict[att] = attrs[att]
self.currTableDict[name] = joinDict.copy()
elif name == "relation":
relType = attrs.getValue("relationType")
if relType == "1M":
nm = attrs.getValue("name")
self.relaDict[nm] = {}
self.relaDict[nm]["relationType"] = "1M"
self.relaDict[nm]["source"] = nm.split(":")[0].strip()
self.relaDict[nm]["target"] = attrs.getValue("target")
self.relaDict[nm]["sourceField"] = attrs.getValue("sourceField")
self.relaDict[nm]["targetField"] = attrs.getValue("targetField")
def endElement(self, name):
if name == "table":
# Save it to the app dict
self.appDict[self.currTable] = self.currTableDict.copy()
def getFieldDict(self):
return self.appDict
def getRelationDict(self):
return self.relaDict
def importFieldSpecs(file=None, tbl=None):
if file is None:
return None
file = fileRef(file)
sh = specHandler()
xml.sax.parse(file, sh)
ret = sh.getFieldDict()
# Limit it to a specific table if requested
if tbl is not None:
ret = ret[tbl]
return ret
def importRelationSpecs(file=None):
if file is None:
return None
file = fileRef(file)
sh = specHandler()
xml.sax.parse(file, sh)
ret = sh.getRelationDict()
return ret
def fileRef(ref=""):
""" Handles the passing of file names, file objects, or raw
XML to the parser. Returns a file-like object, or None.
"""
ret = None
if isinstance(ref, basestring):
if os.path.exists(ref):
ret = file(ref)
else:
ret = StringIO(ref)
return ret | PypiClean |
/verse16-0.1.2.tar.gz/verse16-0.1.2/docs/installation.rst | .. highlight:: shell
============
Installation
============
Stable release
--------------
To install verse16, generate this command in your terminal:
.. code-block:: console
$ pip install verse16
This is the preferred method to install verse16, as it will always install the most recent stable release.
If you don't have `pip`_ installed, this `Python installation guide`_ can guide
you through the process.
.. _pip: https://pip.pypa.io
.. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
From sources
------------
The sources for verse16 can be downloaded from the `Github repo`_.
You can either clone the public repository:
.. code-block:: console
$ git clone git://github.com/pelgo14/verse16
Or download the `tarball`_:
.. code-block:: console
$ curl -OJL https://github.com/pelgo14/verse16/tarball/master
Once you have a copy of the source, you can install it with:
.. code-block:: console
$ python setup.py install
.. _Github repo: https://github.com/pelgo14/verse16
.. _tarball: https://github.com/pelgo14/verse16/tarball/master
| PypiClean |
/Charm-Crypto-0.43.tar.gz/Charm-Crypto-0.43/charm/toolbox/matrixops.py | def GaussEliminationinGroups(m):
#The code was original found at: http://ine.scripts.mit.edu/blog/2011/05/gaussian-elimination-in-python/
#Here is an example: suppose you have A= [[1,2],
# [3,4]]
#and you want AX = I.
#if X = [[x1,x2],[x3,x4]] and I = [[1,0],[0,1]]
#GaussEliminationinGroups([1,2,1],[3,4,0])-->[x1,x3]
#GaussEliminationinGroups([1,2,0],[3,4,1])-->[x2,x4]
#then X = MatrixTransGroups[[x1,x3],[x2,x4]]
#eliminate columns
for col in range(len(m[0])):
for row in range(col+1, len(m)):
r = [(rowValue * (-(m[row][col] / m[col][col]))) for rowValue in m[col]]
m[row] = [ (pair[0]+pair[1]) for pair in zip(m[row], r)]
#now backsolve by substitution
ans = []
m.reverse() #makes it easier to backsolve
for sol in range(len(m)):
if sol == 0:
ans.append(m[sol][-1] / m[sol][-2])
else:
inner = 0
#substitute in all known coefficients
for x in range(sol):
inner += (ans[x]*m[sol][-2-x])
#the equation is now reduced to ax + b = c form
#solve with (c - b) / a
ans.append((m[sol][-1]-inner)/m[sol][-sol-2])
ans.reverse()
return ans
def MatrixMulGroups(matrix1,matrix2):
# Matrix multiplication
if len(matrix1[0]) != len(matrix2):
# Check matrix dimensions
print('Matrices must be m*n and n*p to multiply!')
else:
# Multiply if correct dimensions
new_matrix = [[0 for row in range(len(matrix2[0]))] for col in range(len(matrix1))]
for i in range(len(matrix1)):
for j in range(len(matrix2[0])):
for k in range(len(matrix2)):
new_matrix[i][j] += matrix1[i][k]*matrix2[k][j]
return new_matrix
def MatrixAddGroups(matrix1,matrix2):
# Matrix Addition
if (len(matrix1[0]) != len(matrix2[0]) or len(matrix1) != len(matrix2)):
# Check matrix dimensions
print('Matrices must be m*m and m*m to Add!')
else:
# Add if correct dimensions
rows = len(matrix1)
columns =len(matrix1[0])
result = [[matrix1[row][col] + matrix2[row][col] for col in range(columns)] for row in range(rows)]
return result
def MatrixScalarMulGroups(lamda , matrix):
# Matrix Scalar Mul
rows = len(matrix)
columns =len(matrix[0])
result = [[matrix[row][col] * lamda for col in range(columns)] for row in range(rows)]
return result
def MatrixTransGroups(matrix):
# Matrix transpose,
result = [[r[col] for r in matrix] for col in range(len(matrix[0]))]
return result | PypiClean |
/fastly-3.0.0.tar.gz/fastly-3.0.0/docs/Results.md | # Results
The [results](#results-data-model) of the query, grouped by service (and optionally, region), and aggregated over the appropriate time span.
## Properties
Name | Type | Description | Notes
------------ | ------------- | ------------- | -------------
**requests** | **int** | Number of requests processed. | [optional]
**hits** | **int** | Number of cache hits. | [optional]
**hits_time** | **float** | Total amount of time spent processing cache hits (in seconds). | [optional]
**miss** | **int** | Number of cache misses. | [optional]
**miss_time** | **float** | Total amount of time spent processing cache misses (in seconds). | [optional]
**_pass** | **int** | Number of requests that passed through the CDN without being cached. | [optional]
**pass_time** | **float** | Total amount of time spent processing cache passes (in seconds). | [optional]
**errors** | **int** | Number of cache errors. | [optional]
**restarts** | **int** | Number of restarts performed. | [optional]
**hit_ratio** | **float, none_type** | Ratio of cache hits to cache misses (between 0 and 1). | [optional]
**bandwidth** | **int** | Total bytes delivered (`resp_header_bytes` + `resp_body_bytes` + `bereq_header_bytes` + `bereq_body_bytes` + `compute_resp_header_bytes` + `compute_resp_body_bytes` + `compute_bereq_header_bytes` + `compute_bereq_body_bytes` + `websocket_resp_header_bytes` + `websocket_resp_body_bytes` + `websocket_bereq_header_bytes` + `websocket_bereq_body_bytes` + `fanout_resp_header_bytes` + `fanout_resp_body_bytes` + `fanout_bereq_header_bytes` + `fanout_bereq_body_bytes`). | [optional]
**body_size** | **int** | Total body bytes delivered (alias for resp_body_bytes). | [optional]
**header_size** | **int** | Total header bytes delivered (alias for resp_header_bytes). | [optional]
**req_body_bytes** | **int** | Total body bytes received. | [optional]
**req_header_bytes** | **int** | Total header bytes received. | [optional]
**resp_body_bytes** | **int** | Total body bytes delivered (edge_resp_body_bytes + shield_resp_body_bytes). | [optional]
**resp_header_bytes** | **int** | Total header bytes delivered (edge_resp_header_bytes + shield_resp_header_bytes). | [optional]
**bereq_body_bytes** | **int** | Total body bytes sent to origin. | [optional]
**bereq_header_bytes** | **int** | Total header bytes sent to origin. | [optional]
**uncacheable** | **int** | Number of requests that were designated uncachable. | [optional]
**pipe** | **int** | Optional. Pipe operations performed (legacy feature). | [optional]
**synth** | **int** | Number of requests that returned a synthetic response (i.e., response objects created with the `synthetic` VCL statement). | [optional]
**tls** | **int** | Number of requests that were received over TLS. | [optional]
**tls_v10** | **int** | Number of requests received over TLS 1.0. | [optional]
**tls_v11** | **int** | Number of requests received over TLS 1.1. | [optional]
**tls_v12** | **int** | Number of requests received over TLS 1.2. | [optional]
**tls_v13** | **int** | Number of requests received over TLS 1.3. | [optional]
**edge_requests** | **int** | Number of requests sent by end users to Fastly. | [optional]
**edge_resp_header_bytes** | **int** | Total header bytes delivered from Fastly to the end user. | [optional]
**edge_resp_body_bytes** | **int** | Total body bytes delivered from Fastly to the end user. | [optional]
**edge_hit_requests** | **int** | Number of requests sent by end users to Fastly that resulted in a hit at the edge. | [optional]
**edge_miss_requests** | **int** | Number of requests sent by end users to Fastly that resulted in a miss at the edge. | [optional]
**origin_fetches** | **int** | Number of requests sent to origin. | [optional]
**origin_fetch_header_bytes** | **int** | Total request header bytes sent to origin. | [optional]
**origin_fetch_body_bytes** | **int** | Total request body bytes sent to origin. | [optional]
**origin_fetch_resp_header_bytes** | **int** | Total header bytes received from origin. | [optional]
**origin_fetch_resp_body_bytes** | **int** | Total body bytes received from origin. | [optional]
**origin_revalidations** | **int** | Number of responses received from origin with a `304` status code in response to an `If-Modified-Since` or `If-None-Match` request. Under regular scenarios, a revalidation will imply a cache hit. However, if using Fastly Image Optimizer or segmented caching this may result in a cache miss. | [optional]
**origin_cache_fetches** | **int** | The total number of completed requests made to backends (origins) that returned cacheable content. | [optional]
**shield** | **int** | Number of requests from edge to the shield POP. | [optional]
**shield_resp_body_bytes** | **int** | Total body bytes delivered via a shield. | [optional]
**shield_resp_header_bytes** | **int** | Total header bytes delivered via a shield. | [optional]
**shield_fetches** | **int** | Number of requests made from one Fastly POP to another, as part of shielding. | [optional]
**shield_fetch_header_bytes** | **int** | Total request header bytes sent to a shield. | [optional]
**shield_fetch_body_bytes** | **int** | Total request body bytes sent to a shield. | [optional]
**shield_fetch_resp_header_bytes** | **int** | Total response header bytes sent from a shield to the edge. | [optional]
**shield_fetch_resp_body_bytes** | **int** | Total response body bytes sent from a shield to the edge. | [optional]
**shield_revalidations** | **int** | Number of responses received from origin with a `304` status code, in response to an `If-Modified-Since` or `If-None-Match` request to a shield. Under regular scenarios, a revalidation will imply a cache hit. However, if using segmented caching this may result in a cache miss. | [optional]
**shield_cache_fetches** | **int** | The total number of completed requests made to shields that returned cacheable content. | [optional]
**ipv6** | **int** | Number of requests that were received over IPv6. | [optional]
**otfp** | **int** | Number of responses that came from the Fastly On-the-Fly Packaging service for video-on-demand. | [optional]
**otfp_resp_body_bytes** | **int** | Total body bytes delivered from the Fastly On-the-Fly Packaging service for video-on-demand. | [optional]
**otfp_resp_header_bytes** | **int** | Total header bytes delivered from the Fastly On-the-Fly Packaging service for video-on-demand. | [optional]
**otfp_shield_resp_body_bytes** | **int** | Total body bytes delivered via a shield for the Fastly On-the-Fly Packaging service for video-on-demand. | [optional]
**otfp_shield_resp_header_bytes** | **int** | Total header bytes delivered via a shield for the Fastly On-the-Fly Packaging service for video-on-demand. | [optional]
**otfp_manifests** | **int** | Number of responses that were manifest files from the Fastly On-the-Fly Packaging service for video-on-demand. | [optional]
**otfp_deliver_time** | **float** | Total amount of time spent delivering a response from the Fastly On-the-Fly Packaging service for video-on-demand (in seconds). | [optional]
**otfp_shield_time** | **float** | Total amount of time spent delivering a response via a shield from the Fastly On-the-Fly Packaging service for video-on-demand (in seconds). | [optional]
**video** | **int** | Number of responses with the video segment or video manifest MIME type (i.e., application/x-mpegurl, application/vnd.apple.mpegurl, application/f4m, application/dash+xml, application/vnd.ms-sstr+xml, ideo/mp2t, audio/aac, video/f4f, video/x-flv, video/mp4, audio/mp4). | [optional]
**pci** | **int** | Number of responses with the PCI flag turned on. | [optional]
**log** | **int** | Number of log lines sent. | [optional]
**log_bytes** | **int** | Total log bytes sent. | [optional]
**http2** | **int** | Number of requests received over HTTP/2. | [optional]
**http3** | **int** | Number of requests received over HTTP/3. | [optional]
**waf_logged** | **int** | Number of requests that triggered a WAF rule and were logged. | [optional]
**waf_blocked** | **int** | Number of requests that triggered a WAF rule and were blocked. | [optional]
**waf_passed** | **int** | Number of requests that triggered a WAF rule and were passed. | [optional]
**attack_req_body_bytes** | **int** | Total body bytes received from requests that triggered a WAF rule. | [optional]
**attack_req_header_bytes** | **int** | Total header bytes received from requests that triggered a WAF rule. | [optional]
**attack_logged_req_body_bytes** | **int** | Total body bytes received from requests that triggered a WAF rule that was logged. | [optional]
**attack_logged_req_header_bytes** | **int** | Total header bytes received from requests that triggered a WAF rule that was logged. | [optional]
**attack_blocked_req_body_bytes** | **int** | Total body bytes received from requests that triggered a WAF rule that was blocked. | [optional]
**attack_blocked_req_header_bytes** | **int** | Total header bytes received from requests that triggered a WAF rule that was blocked. | [optional]
**attack_passed_req_body_bytes** | **int** | Total body bytes received from requests that triggered a WAF rule that was passed. | [optional]
**attack_passed_req_header_bytes** | **int** | Total header bytes received from requests that triggered a WAF rule that was passed. | [optional]
**attack_resp_synth_bytes** | **int** | Total bytes delivered for requests that triggered a WAF rule and returned a synthetic response. | [optional]
**imgopto** | **int** | Number of responses that came from the Fastly Image Optimizer service. If the service receives 10 requests for an image, this stat will be 10 regardless of how many times the image was transformed. | [optional]
**imgopto_resp_body_bytes** | **int** | Total body bytes delivered from the Fastly Image Optimizer service, including shield traffic. | [optional]
**imgopto_resp_header_bytes** | **int** | Total header bytes delivered from the Fastly Image Optimizer service, including shield traffic. | [optional]
**imgopto_shield_resp_body_bytes** | **int** | Total body bytes delivered via a shield from the Fastly Image Optimizer service. | [optional]
**imgopto_shield_resp_header_bytes** | **int** | Total header bytes delivered via a shield from the Fastly Image Optimizer service. | [optional]
**imgvideo** | **int** | Number of video responses that came from the Fastly Image Optimizer service. | [optional]
**imgvideo_frames** | **int** | Number of video frames that came from the Fastly Image Optimizer service. A video frame is an individual image within a sequence of video. | [optional]
**imgvideo_resp_header_bytes** | **int** | Total header bytes of video delivered from the Fastly Image Optimizer service. | [optional]
**imgvideo_resp_body_bytes** | **int** | Total body bytes of video delivered from the Fastly Image Optimizer service. | [optional]
**imgvideo_shield_resp_header_bytes** | **int** | Total header bytes of video delivered via a shield from the Fastly Image Optimizer service. | [optional]
**imgvideo_shield_resp_body_bytes** | **int** | Total body bytes of video delivered via a shield from the Fastly Image Optimizer service. | [optional]
**imgvideo_shield** | **int** | Number of video responses delivered via a shield that came from the Fastly Image Optimizer service. | [optional]
**imgvideo_shield_frames** | **int** | Number of video frames delivered via a shield that came from the Fastly Image Optimizer service. A video frame is an individual image within a sequence of video. | [optional]
**status_200** | **int** | Number of responses sent with status code 200 (Success). | [optional]
**status_204** | **int** | Number of responses sent with status code 204 (No Content). | [optional]
**status_206** | **int** | Number of responses sent with status code 206 (Partial Content). | [optional]
**status_301** | **int** | Number of responses sent with status code 301 (Moved Permanently). | [optional]
**status_302** | **int** | Number of responses sent with status code 302 (Found). | [optional]
**status_304** | **int** | Number of responses sent with status code 304 (Not Modified). | [optional]
**status_400** | **int** | Number of responses sent with status code 400 (Bad Request). | [optional]
**status_401** | **int** | Number of responses sent with status code 401 (Unauthorized). | [optional]
**status_403** | **int** | Number of responses sent with status code 403 (Forbidden). | [optional]
**status_404** | **int** | Number of responses sent with status code 404 (Not Found). | [optional]
**status_406** | **int** | Number of responses sent with status code 406 (Not Acceptable). | [optional]
**status_416** | **int** | Number of responses sent with status code 416 (Range Not Satisfiable). | [optional]
**status_429** | **int** | Number of responses sent with status code 429 (Too Many Requests). | [optional]
**status_500** | **int** | Number of responses sent with status code 500 (Internal Server Error). | [optional]
**status_501** | **int** | Number of responses sent with status code 501 (Not Implemented). | [optional]
**status_502** | **int** | Number of responses sent with status code 502 (Bad Gateway). | [optional]
**status_503** | **int** | Number of responses sent with status code 503 (Service Unavailable). | [optional]
**status_504** | **int** | Number of responses sent with status code 504 (Gateway Timeout). | [optional]
**status_505** | **int** | Number of responses sent with status code 505 (HTTP Version Not Supported). | [optional]
**status_1xx** | **int** | Number of \"Informational\" category status codes delivered. | [optional]
**status_2xx** | **int** | Number of \"Success\" status codes delivered. | [optional]
**status_3xx** | **int** | Number of \"Redirection\" codes delivered. | [optional]
**status_4xx** | **int** | Number of \"Client Error\" codes delivered. | [optional]
**status_5xx** | **int** | Number of \"Server Error\" codes delivered. | [optional]
**object_size_1k** | **int** | Number of objects served that were under 1KB in size. | [optional]
**object_size_10k** | **int** | Number of objects served that were between 1KB and 10KB in size. | [optional]
**object_size_100k** | **int** | Number of objects served that were between 10KB and 100KB in size. | [optional]
**object_size_1m** | **int** | Number of objects served that were between 100KB and 1MB in size. | [optional]
**object_size_10m** | **int** | Number of objects served that were between 1MB and 10MB in size. | [optional]
**object_size_100m** | **int** | Number of objects served that were between 10MB and 100MB in size. | [optional]
**object_size_1g** | **int** | Number of objects served that were between 100MB and 1GB in size. | [optional]
**recv_sub_time** | **float** | Time spent inside the `vcl_recv` Varnish subroutine (in seconds). | [optional]
**recv_sub_count** | **int** | Number of executions of the `vcl_recv` Varnish subroutine. | [optional]
**hash_sub_time** | **float** | Time spent inside the `vcl_hash` Varnish subroutine (in seconds). | [optional]
**hash_sub_count** | **int** | Number of executions of the `vcl_hash` Varnish subroutine. | [optional]
**miss_sub_time** | **float** | Time spent inside the `vcl_miss` Varnish subroutine (in seconds). | [optional]
**miss_sub_count** | **int** | Number of executions of the `vcl_miss` Varnish subroutine. | [optional]
**fetch_sub_time** | **float** | Time spent inside the `vcl_fetch` Varnish subroutine (in seconds). | [optional]
**fetch_sub_count** | **int** | Number of executions of the `vcl_fetch` Varnish subroutine. | [optional]
**pass_sub_time** | **float** | Time spent inside the `vcl_pass` Varnish subroutine (in seconds). | [optional]
**pass_sub_count** | **int** | Number of executions of the `vcl_pass` Varnish subroutine. | [optional]
**pipe_sub_time** | **float** | Time spent inside the `vcl_pipe` Varnish subroutine (in seconds). | [optional]
**pipe_sub_count** | **int** | Number of executions of the `vcl_pipe` Varnish subroutine. | [optional]
**deliver_sub_time** | **float** | Time spent inside the `vcl_deliver` Varnish subroutine (in seconds). | [optional]
**deliver_sub_count** | **int** | Number of executions of the `vcl_deliver` Varnish subroutine. | [optional]
**error_sub_time** | **float** | Time spent inside the `vcl_error` Varnish subroutine (in seconds). | [optional]
**error_sub_count** | **int** | Number of executions of the `vcl_error` Varnish subroutine. | [optional]
**hit_sub_time** | **float** | Time spent inside the `vcl_hit` Varnish subroutine (in seconds). | [optional]
**hit_sub_count** | **int** | Number of executions of the `vcl_hit` Varnish subroutine. | [optional]
**prehash_sub_time** | **float** | Time spent inside the `vcl_prehash` Varnish subroutine (in seconds). | [optional]
**prehash_sub_count** | **int** | Number of executions of the `vcl_prehash` Varnish subroutine. | [optional]
**predeliver_sub_time** | **float** | Time spent inside the `vcl_predeliver` Varnish subroutine (in seconds). | [optional]
**predeliver_sub_count** | **int** | Number of executions of the `vcl_predeliver` Varnish subroutine. | [optional]
**tls_handshake_sent_bytes** | **int** | Number of bytes transferred during TLS handshake. | [optional]
**hit_resp_body_bytes** | **int** | Total body bytes delivered for cache hits. | [optional]
**miss_resp_body_bytes** | **int** | Total body bytes delivered for cache misses. | [optional]
**pass_resp_body_bytes** | **int** | Total body bytes delivered for cache passes. | [optional]
**segblock_origin_fetches** | **int** | Number of `Range` requests to origin for segments of resources when using segmented caching. | [optional]
**segblock_shield_fetches** | **int** | Number of `Range` requests to a shield for segments of resources when using segmented caching. | [optional]
**compute_requests** | **int** | The total number of requests that were received for your service by Fastly. | [optional]
**compute_request_time_ms** | **float** | The total, actual amount of time used to process your requests, including active CPU time (in milliseconds). | [optional]
**compute_request_time_billed_ms** | **float** | The total amount of request processing time you will be billed for, measured in 50 millisecond increments. | [optional]
**compute_ram_used** | **int** | The amount of RAM used for your service by Fastly (in bytes). | [optional]
**compute_execution_time_ms** | **float** | The amount of active CPU time used to process your requests (in milliseconds). | [optional]
**compute_req_header_bytes** | **int** | Total header bytes received by Compute@Edge. | [optional]
**compute_req_body_bytes** | **int** | Total body bytes received by Compute@Edge. | [optional]
**compute_resp_header_bytes** | **int** | Total header bytes sent from Compute@Edge to end user. | [optional]
**compute_resp_body_bytes** | **int** | Total body bytes sent from Compute@Edge to end user. | [optional]
**compute_resp_status_1xx** | **int** | Number of \"Informational\" category status codes delivered by Compute@Edge. | [optional]
**compute_resp_status_2xx** | **int** | Number of \"Success\" category status codes delivered by Compute@Edge. | [optional]
**compute_resp_status_3xx** | **int** | Number of \"Redirection\" category status codes delivered by Compute@Edge. | [optional]
**compute_resp_status_4xx** | **int** | Number of \"Client Error\" category status codes delivered by Compute@Edge. | [optional]
**compute_resp_status_5xx** | **int** | Number of \"Server Error\" category status codes delivered by Compute@Edge. | [optional]
**compute_bereq_header_bytes** | **int** | Total header bytes sent to backends (origins) by Compute@Edge. | [optional]
**compute_bereq_body_bytes** | **int** | Total body bytes sent to backends (origins) by Compute@Edge. | [optional]
**compute_beresp_header_bytes** | **int** | Total header bytes received from backends (origins) by Compute@Edge. | [optional]
**compute_beresp_body_bytes** | **int** | Total body bytes received from backends (origins) by Compute@Edge. | [optional]
**compute_bereqs** | **int** | Number of backend requests started. | [optional]
**compute_bereq_errors** | **int** | Number of backend request errors, including timeouts. | [optional]
**compute_resource_limit_exceeded** | **int** | Number of times a guest exceeded its resource limit, includes heap, stack, globals, and code execution timeout. | [optional]
**compute_heap_limit_exceeded** | **int** | Number of times a guest exceeded its heap limit. | [optional]
**compute_stack_limit_exceeded** | **int** | Number of times a guest exceeded its stack limit. | [optional]
**compute_globals_limit_exceeded** | **int** | Number of times a guest exceeded its globals limit. | [optional]
**compute_guest_errors** | **int** | Number of times a service experienced a guest code error. | [optional]
**compute_runtime_errors** | **int** | Number of times a service experienced a guest runtime error. | [optional]
**edge_hit_resp_body_bytes** | **int** | Body bytes delivered for edge hits. | [optional]
**edge_hit_resp_header_bytes** | **int** | Header bytes delivered for edge hits. | [optional]
**edge_miss_resp_body_bytes** | **int** | Body bytes delivered for edge misses. | [optional]
**edge_miss_resp_header_bytes** | **int** | Header bytes delivered for edge misses. | [optional]
**origin_cache_fetch_resp_body_bytes** | **int** | Body bytes received from origin for cacheable content. | [optional]
**origin_cache_fetch_resp_header_bytes** | **int** | Header bytes received from an origin for cacheable content. | [optional]
**shield_hit_requests** | **int** | Number of requests that resulted in a hit at a shield. | [optional]
**shield_miss_requests** | **int** | Number of requests that resulted in a miss at a shield. | [optional]
**shield_hit_resp_header_bytes** | **int** | Header bytes delivered for shield hits. | [optional]
**shield_hit_resp_body_bytes** | **int** | Body bytes delivered for shield hits. | [optional]
**shield_miss_resp_header_bytes** | **int** | Header bytes delivered for shield misses. | [optional]
**shield_miss_resp_body_bytes** | **int** | Body bytes delivered for shield misses. | [optional]
**websocket_req_header_bytes** | **int** | Total header bytes received from end users over passthrough WebSocket connections. | [optional]
**websocket_req_body_bytes** | **int** | Total message content bytes received from end users over passthrough WebSocket connections. | [optional]
**websocket_resp_header_bytes** | **int** | Total header bytes sent to end users over passthrough WebSocket connections. | [optional]
**websocket_resp_body_bytes** | **int** | Total message content bytes sent to end users over passthrough WebSocket connections. | [optional]
**websocket_bereq_header_bytes** | **int** | Total header bytes sent to backends over passthrough WebSocket connections. | [optional]
**websocket_bereq_body_bytes** | **int** | Total message content bytes sent to backends over passthrough WebSocket connections. | [optional]
**websocket_beresp_header_bytes** | **int** | Total header bytes received from backends over passthrough WebSocket connections. | [optional]
**websocket_beresp_body_bytes** | **int** | Total message content bytes received from backends over passthrough WebSocket connections. | [optional]
**websocket_conn_time_ms** | **int** | Total duration of passthrough WebSocket connections with end users. | [optional]
**fanout_recv_publishes** | **int** | Total published messages received from the publish API endpoint. | [optional]
**fanout_send_publishes** | **int** | Total published messages sent to end users. | [optional]
**kv_store_class_a_operations** | **int** | The total number of class a operations for the KV store. | [optional]
**kv_store_class_b_operations** | **int** | The total number of class b operations for the KV store. | [optional]
**object_store_class_a_operations** | **int** | Use kv_store_class_a_operations. | [optional]
**object_store_class_b_operations** | **int** | Use kv_store_class_b_operations. | [optional]
**fanout_req_header_bytes** | **int** | Total header bytes received from end users over Fanout connections. | [optional]
**fanout_req_body_bytes** | **int** | Total body or message content bytes received from end users over Fanout connections. | [optional]
**fanout_resp_header_bytes** | **int** | Total header bytes sent to end users over Fanout connections. | [optional]
**fanout_resp_body_bytes** | **int** | Total body or message content bytes sent to end users over Fanout connections, excluding published message content. | [optional]
**fanout_bereq_header_bytes** | **int** | Total header bytes sent to backends over Fanout connections. | [optional]
**fanout_bereq_body_bytes** | **int** | Total body or message content bytes sent to backends over Fanout connections. | [optional]
**fanout_beresp_header_bytes** | **int** | Total header bytes received from backends over Fanout connections. | [optional]
**fanout_beresp_body_bytes** | **int** | Total body or message content bytes received from backends over Fanout connections. | [optional]
**fanout_conn_time_ms** | **int** | Total duration of Fanout connections with end users. | [optional]
**ddos_action_limit_streams_connections** | **int** | For HTTP/2, the number of connections the limit-streams action was applied to. The limit-streams action caps the allowed number of concurrent streams in a connection. | [optional]
**ddos_action_limit_streams_requests** | **int** | For HTTP/2, the number of requests made on a connection for which the limit-streams action was taken. The limit-streams action caps the allowed number of concurrent streams in a connection. | [optional]
**ddos_action_tarpit_accept** | **int** | The number of times the tarpit-accept action was taken. The tarpit-accept action adds a delay when accepting future connections. | [optional]
**ddos_action_tarpit** | **int** | The number of times the tarpit action was taken. The tarpit action delays writing the response to the client. | [optional]
**ddos_action_close** | **int** | The number of times the close action was taken. The close action aborts the connection as soon as possible. The close action takes effect either right after accept, right after the client hello, or right after the response was sent. | [optional]
**ddos_action_blackhole** | **int** | The number of times the blackhole action was taken. The blackhole action quietly closes a TCP connection without sending a reset. The blackhole action quietly closes a TCP connection without notifying its peer (all TCP state is dropped). | [optional]
**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
| PypiClean |
/availsim4-2.0.2.tar.gz/availsim4-2.0.2/availsim4core/src/discrete_event_simulation/event/b_event/failure_event/detectable_failure_event.py |
from __future__ import annotations
from typing import Set, TYPE_CHECKING, Tuple, List
if TYPE_CHECKING:
from availsim4core.src.context.system.component_tree.basic import Basic
from availsim4core.src.discrete_event_simulation.event.b_event.b_event import BEventPriority
from availsim4core.src.discrete_event_simulation.event.c_event.c_event import CEvent, CEventPriority
from availsim4core.src.context.context import Context
from availsim4core.src.context.system.component_tree.status import Status
from availsim4core.src.context.system.failure_mode import FailureMode
from availsim4core.src.discrete_event_simulation.event.b_event.failure_event.failure_event import FailureEvent
from availsim4core.src.context.phase.phase_manager import PhaseManager
from availsim4core.src.discrete_event_simulation.event.c_event.held_event.order_end_holding_event import \
OrderEndHoldingEvent
from availsim4core.src.discrete_event_simulation.event.c_event.repair_event.order_repair_event import OrderRepairEvent
class DetectableFailureEvent(FailureEvent):
"""
Class dealing with detectable failure events. A detectable failure can trigger a repair because that failure is ,
in real life, detected by the monitoring system
"""
def __init__(self,
absolute_occurrence_time: float,
context: Context,
basic: Basic,
failure_mode: FailureMode):
super().__init__(absolute_occurrence_time, context, basic, failure_mode,
priority=BEventPriority.DETECTABLE_FAILURE_EVENT)
def postpone(self, duration):
return DetectableFailureEvent(self.absolute_occurrence_time + duration,
self.context,
self.basic,
self.failure_mode)
def execute(self):
return self.basic.update_status(Status.FAILED,
self.absolute_occurrence_time,
self.context.phase_manager.current_phase, # or phase of the failure mode?!
f"{self.failure_mode.name} failure mode of component {self.basic.name}_{self.basic.local_id}_{self.basic.global_id}",
self.context)
def update_b_event_collection(self,
event_set: Set,
types_of_event_to_clean: List[type]) -> Tuple[Set, Set]:
"""
The input event_set is cleaned from all the events of types provided by types_of_event_to_clean.
Returns a clean set of Events and a set containing the removed events.
"""
if self.failure_mode.phase_change_trigger in ["AFTER_REPAIR", "AFTER_FAILURE"]:
# we clean the flow of the phases as for failures, the tye_of_event_to_clean are only the phase event
b_event_to_remove_set = {event
for event in event_set
if type(event) in types_of_event_to_clean}
return event_set - b_event_to_remove_set, b_event_to_remove_set
elif self.failure_mode.phase_change_trigger in ["NEVER"]:
# we do not change the normal flow of the phases
return event_set, set()
def generate_c_event(self, **kwargs) -> Set[CEvent]:
c_event_set = set()
order_repair_event = OrderRepairEvent(
priority=CEventPriority.ORDER_REPAIR_EVENT,
context=self.context,
component=self.basic,
event=self,
failure_mode=self.failure_mode
)
if self.failure_mode.held_before_repair_phase_set != {PhaseManager.HELD_FOREVER}:
if self.context.phase_manager.current_phase not in self.failure_mode.held_before_repair_phase_set:
c_event_set.add(OrderEndHoldingEvent(
priority=CEventPriority.ORDER_END_HOLDING_EVENT,
context=self.context,
component=self.basic,
event=self,
failure_mode=self.failure_mode,
held_event=order_repair_event,
held_until_phase_set=self.failure_mode.held_before_repair_phase_set
))
else:
c_event_set.add(order_repair_event)
if self.failure_mode.phase_change_trigger in ["AFTER_FAILURE"]:
from availsim4core.src.discrete_event_simulation.event.c_event.phase_event.order_next_phase_if_failure_event import \
OrderNextPhaseIfFailureEvent
c_event_set.add(
OrderNextPhaseIfFailureEvent(
priority=CEventPriority.ORDER_NEXT_PHASE_EVENT,
context=self.context,
failure_mode=self.failure_mode
)
)
return c_event_set | PypiClean |
/collective.jqueryui-1.6rc5.tar.gz/collective.jqueryui-1.6rc5/collective/jqueryui/jquery.ui/ui/minified/ui.slider.min.js | (function(a){a.widget("ui.slider",a.extend({},a.ui.mouse,{_init:function(){var b=this,c=this.options;this._keySliding=false;this._handleIndex=null;this.orientation=c.orientation=="auto"?(this.element[0].offsetWidth/this.element[0].offsetHeight>1?"horizontal":"vertical"):c.orientation;this._mouseInit();this.element.addClass("ui-slider ui-slider-"+this.orientation+" ui-widget ui-widget-content ui-corner-all");this.range=a([]);if(c.range){if(c.range===true){this.range=a("<div></div>");if(!c.values){c.values=[this._valueMin(),this._valueMin()]}if(c.values.length&&c.values.length!=2){c.values=[c.values[0],c.values[0]]}}else{this.range=a("<div></div>")}this.range.appendTo(this.element).addClass("ui-slider-range ui-widget-header");(c.range=="min")&&(this.orientation=="horizontal")&&this.range.css({left:0});(c.range=="max")&&(this.orientation=="horizontal")&&this.range.css({right:0});(c.range=="min")&&(this.orientation=="vertical")&&this.range.css({bottom:0});(c.range=="max")&&(this.orientation=="vertical")&&this.range.css({top:0})}if(a(".ui-slider-handle",this.element).length==0){a('<a href="#"></a>').appendTo(this.element).addClass("ui-slider-handle")}if(c.values&&c.values.length){while(a(".ui-slider-handle",this.element).length<c.values.length){a('<a href="#"></a>').appendTo(this.element).addClass("ui-slider-handle")}}this.handles=a(".ui-slider-handle",this.element).addClass("ui-state-default ui-corner-all");this.handle=this.handles.eq(0);this.handles.add(this.range).filter("a").click(function(d){d.preventDefault()}).hover(function(){a(this).addClass("ui-state-hover")},function(){a(this).removeClass("ui-state-hover")}).focus(function(){b.handles.removeClass("ui-state-focus");a(this).addClass("ui-state-focus")}).blur(function(){a(this).removeClass("ui-state-focus")});this.handles.each(function(d){a(this).data("index.ui-slider-handle",d)});this.handles.keydown(function(h){var e=a(this).data("index.ui-slider-handle");if(b.options.disabled){return}switch(h.keyCode){case a.ui.keyCode.HOME:case a.ui.keyCode.END:case a.ui.keyCode.UP:case a.ui.keyCode.RIGHT:case a.ui.keyCode.DOWN:case a.ui.keyCode.LEFT:if(!b._keySliding){b._keySliding=true;a(this).addClass("ui-state-active");b._start(h)}break}var f,d,g=b._step();if(b.options.values&&b.options.values.length){f=d=b.values(e)}else{f=d=b.value()}switch(h.keyCode){case a.ui.keyCode.HOME:d=b._valueMin();break;case a.ui.keyCode.END:d=b._valueMax();break;case a.ui.keyCode.UP:case a.ui.keyCode.RIGHT:d=f+g;break;case a.ui.keyCode.DOWN:case a.ui.keyCode.LEFT:d=f-g;break}b._slide(h,e,d)}).keyup(function(d){if(b._keySliding){b._stop(d);b._change(d);b._keySliding=false;a(this).removeClass("ui-state-active")}});this._refreshValue()},destroy:function(){this.handles.remove();this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-slider-disabled ui-widget ui-widget-content ui-corner-all").removeData("slider").unbind(".slider");this._mouseDestroy()},_mouseCapture:function(g){var h=this.options;if(h.disabled){return false}this._start(g);this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()};this.elementOffset=this.element.offset();var b={x:g.pageX,y:g.pageY};var e=this._normValueFromMouse(b);var i=this._valueMax(),c;var d=this,f;this.handles.each(function(j){var k=Math.abs(e-d.values(j));if(i>k){i=k;c=a(this);f=j}});d._handleIndex=f;c.addClass("ui-state-active").focus();this._slide(g,f,e);return true},_mouseStart:function(b){return true},_mouseDrag:function(d){var b={x:d.pageX,y:d.pageY};var c=this._normValueFromMouse(b);this._slide(d,this._handleIndex,c);return false},_mouseStop:function(b){this.handles.removeClass("ui-state-active");this._stop(b);this._change(b);this._handleIndex=null;return false},_normValueFromMouse:function(d){var c,h;if("horizontal"==this.orientation){c=this.elementSize.width;h=d.x-this.elementOffset.left}else{c=this.elementSize.height;h=d.y-this.elementOffset.top}var f=(h/c);if(f>1){f=1}if(f<0){f=0}if("vertical"==this.orientation){f=1-f}var e=this._valueMax()-this._valueMin(),i=f*e,b=i%this.options.step,g=this._valueMin()+i-b;if(b>(this.options.step/2)){g+=this.options.step}return g},_start:function(b){this._trigger("start",b,{value:this.value()})},_slide:function(f,e,d){if(this.options.values&&this.options.values.length){var g=this.handles[e];var b=this.values(e?0:1);if((e==0&&d>=b)||(e==1&&d<=b)){d=b}if(d!=this.values(e)){var c=this.values();c[e]=d;var h=this._trigger("slide",f,{handle:g,value:d,values:c});var b=this.values(e?0:1);if(h!==false){this.values(e,d)}}}else{if(d!=this.value()){var h=this._trigger("slide",f,{value:d});if(h!==false){this._setData("value",d)}}}},_stop:function(b){this._trigger("stop",b,{value:this.value()})},_change:function(b){this._trigger("change",b,{value:this.value()})},value:function(b){if(arguments.length){this._setData("value",b);this._change()}return this._value()},values:function(b,c){if(arguments.length>1){this.options.values[b]=c;this._refreshValue();this._change()}if(arguments.length){if(this.options.values&&this.options.values.length){return this._values(b)}else{return this.value()}}else{return this._values()}},_setData:function(b,c){a.widget.prototype._setData.apply(this,arguments);switch(b){case"orientation":this.orientation=this.options.orientation=="auto"?(this.element[0].offsetWidth/this.element[0].offsetHeight>1?"horizontal":"vertical"):this.options.orientation;this.element.removeClass("ui-slider-horizontal ui-slider-vertical").addClass("ui-slider-"+this._orientation());this._refreshValue();break;case"value":this._refreshValue();break}},_step:function(){var b=this.options.step;return b},_value:function(){var b=this.options.value;if(b<this._valueMin()){b=this._valueMin()}if(b>this._valueMax()){b=this._valueMax()}return b},_values:function(b){if(arguments.length){var c=this.options.values[b];if(c<this._valueMin()){c=this._valueMin()}if(c>this._valueMax()){c=this._valueMax()}return c}else{return this.options.values}},_valueMin:function(){var b=this.options.min;return b},_valueMax:function(){var b=this.options.max;return b},_refreshValue:function(){var e=this.options.range;if(this.options.values&&this.options.values.length){var c=this,b,f;this.handles.each(function(k,g){var h=(c.values(k)-c._valueMin())/(c._valueMax()-c._valueMin())*100;a(this).css(c.orientation=="horizontal"?"left":"bottom",h+"%");if(c.options.range===true){if(c.orientation=="horizontal"){(k==0)&&c.range.css("left",h+"%");(k==1)&&c.range.css("width",(h-lastValPercent)+"%")}else{(k==0)&&c.range.css("bottom",(h)+"%");(k==1)&&c.range.css("height",(h-lastValPercent)+"%")}}lastValPercent=h})}else{var d=(this.value()-this._valueMin())/(this._valueMax()-this._valueMin())*100;this.handle.css(this.orientation=="horizontal"?"left":"bottom",d+"%");(e=="min")&&(this.orientation=="horizontal")&&this.range.css({left:0,width:d+"%"});(e=="max")&&(this.orientation=="horizontal")&&this.range.css({left:d+"%",width:(100-d)+"%"});(e=="min")&&(this.orientation=="vertical")&&this.range.css({top:(100-d)+"%",height:d+"%"});(e=="max")&&(this.orientation=="vertical")&&this.range.css({bottom:d+"%",height:(100-d)+"%"})}}}));a.extend(a.ui.slider,{getter:"value values",version:"1.6rc5",eventPrefix:"slide",defaults:{delay:0,distance:0,max:100,min:0,orientation:"auto",range:false,step:1,value:0,values:null}})})(jQuery); | PypiClean |
/email-filter-0.2.12.linux-x86_64.tar.gz/usr/local/lib/python2.7/dist-packages/email_filter/migrations/0002_auto__add_field_emaillog_raw_email__add_field_emaillog_message_id__add.py | import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'EmailLog.raw_email'
db.add_column(u'email_filter_emaillog', 'raw_email', self.gf('django.db.models.fields.files.FileField')(max_length=100, null=True), keep_default=False)
# Adding field 'EmailLog.message_id'
db.add_column(u'email_filter_emaillog', 'message_id', self.gf('django.db.models.fields.CharField')(default='', max_length=255, blank=True), keep_default=False)
# Adding field 'EmailLog.in_reply_to'
db.add_column(u'email_filter_emaillog', 'in_reply_to', self.gf('django.db.models.fields.CharField')(default='', max_length=255, blank=True), keep_default=False)
# Adding unique constraint on 'EmailRedirect', fields ['email_in', 'email_redirect']
db.create_unique(u'email_filter_emailredirect', ['email_in', 'email_redirect'])
def backwards(self, orm):
# Removing unique constraint on 'EmailRedirect', fields ['email_in', 'email_redirect']
db.delete_unique(u'email_filter_emailredirect', ['email_in', 'email_redirect'])
# Deleting field 'EmailLog.raw_email'
db.delete_column(u'email_filter_emaillog', 'raw_email')
# Deleting field 'EmailLog.message_id'
db.delete_column(u'email_filter_emaillog', 'message_id')
# Deleting field 'EmailLog.in_reply_to'
db.delete_column(u'email_filter_emaillog', 'in_reply_to')
models = {
u'email_filter.emaillog': {
'Meta': {'object_name': 'EmailLog'},
'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'in_reply_to': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '255', 'blank': 'True'}),
'message_id': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '255', 'blank': 'True'}),
'raw_body': ('django.db.models.fields.TextField', [], {}),
'raw_email': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True'}),
'recipient': ('django.db.models.fields.CharField', [], {'max_length': '750'}),
'sender': ('django.db.models.fields.EmailField', [], {'max_length': '250'}),
'sent': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'subject': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
u'email_filter.emailredirect': {
'Meta': {'unique_together': "(('email_in', 'email_redirect'),)", 'object_name': 'EmailRedirect'},
'email_in': ('django.db.models.fields.EmailField', [], {'unique': 'True', 'max_length': '250'}),
'email_redirect': ('django.db.models.fields.EmailField', [], {'max_length': '250'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'})
}
}
complete_apps = ['email_filter'] | PypiClean |
/hops-apache-beam-2.24.0.2.tar.gz/hops-apache-beam-2.24.0.2/apache_beam/typehints/trivial_inference.py | # pytype: skip-file
from __future__ import absolute_import
from __future__ import print_function
import collections
import dis
import inspect
import pprint
import sys
import traceback
import types
from builtins import object
from builtins import zip
from functools import reduce
from apache_beam.typehints import Any
from apache_beam.typehints import row_type
from apache_beam.typehints import typehints
# pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports
try: # Python 2
import __builtin__ as builtins
except ImportError: # Python 3
import builtins # type: ignore
# pylint: enable=wrong-import-order, wrong-import-position, ungrouped-imports
class TypeInferenceError(ValueError):
"""Error to raise when type inference failed."""
pass
def instance_to_type(o):
"""Given a Python object o, return the corresponding type hint.
"""
t = type(o)
if o is None:
return type(None)
elif t not in typehints.DISALLOWED_PRIMITIVE_TYPES:
# pylint: disable=deprecated-types-field
if sys.version_info[0] == 2 and t == types.InstanceType:
return o.__class__
if t == BoundMethod:
return types.MethodType
return t
elif t == tuple:
return typehints.Tuple[[instance_to_type(item) for item in o]]
elif t == list:
if len(o) > 0:
return typehints.List[typehints.Union[[
instance_to_type(item) for item in o
]]]
else:
return typehints.List[typehints.Any]
elif t == set:
if len(o) > 0:
return typehints.Set[typehints.Union[[
instance_to_type(item) for item in o
]]]
else:
return typehints.Set[typehints.Any]
elif t == frozenset:
if len(o) > 0:
return typehints.FrozenSet[typehints.Union[[
instance_to_type(item) for item in o
]]]
else:
return typehints.FrozenSet[typehints.Any]
elif t == dict:
if len(o) > 0:
return typehints.Dict[
typehints.Union[[instance_to_type(k) for k, v in o.items()]],
typehints.Union[[instance_to_type(v) for k, v in o.items()]],
]
else:
return typehints.Dict[typehints.Any, typehints.Any]
else:
raise TypeInferenceError('Unknown forbidden type: %s' % t)
def union_list(xs, ys):
assert len(xs) == len(ys)
return [union(x, y) for x, y in zip(xs, ys)]
class Const(object):
def __init__(self, value):
self.value = value
self.type = instance_to_type(value)
def __eq__(self, other):
return isinstance(other, Const) and self.value == other.value
def __ne__(self, other):
# TODO(BEAM-5949): Needed for Python 2 compatibility.
return not self == other
def __hash__(self):
return hash(self.value)
def __repr__(self):
return 'Const[%s]' % str(self.value)[:100]
@staticmethod
def unwrap(x):
if isinstance(x, Const):
return x.type
return x
@staticmethod
def unwrap_all(xs):
return [Const.unwrap(x) for x in xs]
class FrameState(object):
"""Stores the state of the frame at a particular point of execution.
"""
def __init__(self, f, local_vars=None, stack=()):
self.f = f
self.co = f.__code__
self.vars = list(local_vars)
self.stack = list(stack)
def __eq__(self, other):
return isinstance(other, FrameState) and self.__dict__ == other.__dict__
def __ne__(self, other):
# TODO(BEAM-5949): Needed for Python 2 compatibility.
return not self == other
def __hash__(self):
return hash(tuple(sorted(self.__dict__.items())))
def copy(self):
return FrameState(self.f, self.vars, self.stack)
def const_type(self, i):
return Const(self.co.co_consts[i])
def get_closure(self, i):
num_cellvars = len(self.co.co_cellvars)
if i < num_cellvars:
return self.vars[i]
else:
return self.f.__closure__[i - num_cellvars].cell_contents
def closure_type(self, i):
"""Returns a TypeConstraint or Const."""
val = self.get_closure(i)
if isinstance(val, typehints.TypeConstraint):
return val
else:
return Const(val)
def get_global(self, i):
name = self.get_name(i)
if name in self.f.__globals__:
return Const(self.f.__globals__[name])
if name in builtins.__dict__:
return Const(builtins.__dict__[name])
return Any
def get_name(self, i):
return self.co.co_names[i]
def __repr__(self):
return 'Stack: %s Vars: %s' % (self.stack, self.vars)
def __or__(self, other):
if self is None:
return other.copy()
elif other is None:
return self.copy()
return FrameState(
self.f,
union_list(self.vars, other.vars),
union_list(self.stack, other.stack))
def __ror__(self, left):
return self | left
def union(a, b):
"""Returns the union of two types or Const values.
"""
if a == b:
return a
elif not a:
return b
elif not b:
return a
a = Const.unwrap(a)
b = Const.unwrap(b)
# TODO(robertwb): Work this into the Union code in a more generic way.
if type(a) == type(b) and element_type(a) == typehints.Union[()]:
return b
elif type(a) == type(b) and element_type(b) == typehints.Union[()]:
return a
return typehints.Union[a, b]
def finalize_hints(type_hint):
"""Sets type hint for empty data structures to Any."""
def visitor(tc, unused_arg):
if isinstance(tc, typehints.DictConstraint):
empty_union = typehints.Union[()]
if tc.key_type == empty_union:
tc.key_type = Any
if tc.value_type == empty_union:
tc.value_type = Any
if isinstance(type_hint, typehints.TypeConstraint):
type_hint.visit(visitor, None)
def element_type(hint):
"""Returns the element type of a composite type.
"""
hint = Const.unwrap(hint)
if isinstance(hint, typehints.SequenceTypeConstraint):
return hint.inner_type
elif isinstance(hint, typehints.TupleHint.TupleConstraint):
return typehints.Union[hint.tuple_types]
return Any
def key_value_types(kv_type):
"""Returns the key and value type of a KV type.
"""
# TODO(robertwb): Unions of tuples, etc.
# TODO(robertwb): Assert?
if (isinstance(kv_type, typehints.TupleHint.TupleConstraint) and
len(kv_type.tuple_types) == 2):
return kv_type.tuple_types
return Any, Any
known_return_types = {
len: int,
hash: int,
}
class BoundMethod(object):
"""Used to create a bound method when we only know the type of the instance.
"""
def __init__(self, func, type):
"""Instantiates a bound method object.
Args:
func (types.FunctionType): The method's underlying function
type (type): The class of the method.
"""
self.func = func
self.type = type
def hashable(c):
try:
hash(c)
return True
except TypeError:
return False
def infer_return_type(c, input_types, debug=False, depth=5):
"""Analyses a callable to deduce its return type.
Args:
c: A Python callable to infer the return type of.
input_types: A sequence of inputs corresponding to the input types.
debug: Whether to print verbose debugging information.
depth: Maximum inspection depth during type inference.
Returns:
A TypeConstraint that that the return value of this function will (likely)
satisfy given the specified inputs.
"""
try:
if hashable(c) and c in known_return_types:
return known_return_types[c]
elif isinstance(c, types.FunctionType):
return infer_return_type_func(c, input_types, debug, depth)
elif isinstance(c, types.MethodType):
if c.__self__ is not None:
input_types = [Const(c.__self__)] + input_types
return infer_return_type_func(c.__func__, input_types, debug, depth)
elif isinstance(c, BoundMethod):
input_types = [c.type] + input_types
return infer_return_type_func(c.func, input_types, debug, depth)
elif inspect.isclass(c):
if c in typehints.DISALLOWED_PRIMITIVE_TYPES:
return {
list: typehints.List[Any],
set: typehints.Set[Any],
frozenset: typehints.FrozenSet[Any],
tuple: typehints.Tuple[Any, ...],
dict: typehints.Dict[Any, Any]
}[c]
return c
else:
return Any
except TypeInferenceError:
if debug:
traceback.print_exc()
return Any
except Exception:
if debug:
sys.stdout.flush()
raise
else:
return Any
def infer_return_type_func(f, input_types, debug=False, depth=0):
"""Analyses a function to deduce its return type.
Args:
f: A Python function object to infer the return type of.
input_types: A sequence of inputs corresponding to the input types.
debug: Whether to print verbose debugging information.
depth: Maximum inspection depth during type inference.
Returns:
A TypeConstraint that that the return value of this function will (likely)
satisfy given the specified inputs.
Raises:
TypeInferenceError: if no type can be inferred.
"""
if debug:
print()
print(f, id(f), input_types)
dis.dis(f)
from . import opcodes
simple_ops = dict((k.upper(), v) for k, v in opcodes.__dict__.items())
co = f.__code__
code = co.co_code
end = len(code)
pc = 0
extended_arg = 0 # Python 2 only.
free = None
yields = set()
returns = set()
# TODO(robertwb): Default args via inspect module.
local_vars = list(input_types) + [typehints.Union[()]] * (
len(co.co_varnames) - len(input_types))
state = FrameState(f, local_vars)
states = collections.defaultdict(lambda: None)
jumps = collections.defaultdict(int)
# In Python 3, use dis library functions to disassemble bytecode and handle
# EXTENDED_ARGs.
is_py3 = sys.version_info[0] == 3
if is_py3:
ofs_table = {} # offset -> instruction
for instruction in dis.get_instructions(f):
ofs_table[instruction.offset] = instruction
# Python 2 - 3.5: 1 byte opcode + optional 2 byte arg (1 or 3 bytes).
# Python 3.6+: 1 byte opcode + 1 byte arg (2 bytes, arg may be ignored).
if sys.version_info >= (3, 6):
inst_size = 2
opt_arg_size = 0
else:
inst_size = 1
opt_arg_size = 2
last_pc = -1
while pc < end: # pylint: disable=too-many-nested-blocks
start = pc
if is_py3:
instruction = ofs_table[pc]
op = instruction.opcode
else:
op = ord(code[pc])
if debug:
print('-->' if pc == last_pc else ' ', end=' ')
print(repr(pc).rjust(4), end=' ')
print(dis.opname[op].ljust(20), end=' ')
pc += inst_size
if op >= dis.HAVE_ARGUMENT:
if is_py3:
arg = instruction.arg
else:
arg = ord(code[pc]) + ord(code[pc + 1]) * 256 + extended_arg
extended_arg = 0
pc += opt_arg_size
if op == dis.EXTENDED_ARG:
extended_arg = arg * 65536
if debug:
print(str(arg).rjust(5), end=' ')
if op in dis.hasconst:
print('(' + repr(co.co_consts[arg]) + ')', end=' ')
elif op in dis.hasname:
print('(' + co.co_names[arg] + ')', end=' ')
elif op in dis.hasjrel:
print('(to ' + repr(pc + arg) + ')', end=' ')
elif op in dis.haslocal:
print('(' + co.co_varnames[arg] + ')', end=' ')
elif op in dis.hascompare:
print('(' + dis.cmp_op[arg] + ')', end=' ')
elif op in dis.hasfree:
if free is None:
free = co.co_cellvars + co.co_freevars
print('(' + free[arg] + ')', end=' ')
# Actually emulate the op.
if state is None and states[start] is None:
# No control reaches here (yet).
if debug:
print()
continue
state |= states[start]
opname = dis.opname[op]
jmp = jmp_state = None
if opname.startswith('CALL_FUNCTION'):
if sys.version_info < (3, 6):
# Each keyword takes up two arguments on the stack (name and value).
standard_args = (arg & 0xFF) + 2 * (arg >> 8)
var_args = 'VAR' in opname
kw_args = 'KW' in opname
pop_count = standard_args + var_args + kw_args + 1
if depth <= 0:
return_type = Any
elif arg >> 8:
if not var_args and not kw_args and not arg & 0xFF:
# Keywords only, maybe it's a call to Row.
if isinstance(state.stack[-pop_count], Const):
from apache_beam.pvalue import Row
if state.stack[-pop_count].value == Row:
fields = state.stack[-pop_count + 1::2]
types = state.stack[-pop_count + 2::2]
return_type = row_type.RowTypeConstraint(
zip([fld.value for fld in fields], Const.unwrap_all(types)))
else:
return_type = Any
else:
# TODO(robertwb): Handle this case.
return_type = Any
elif isinstance(state.stack[-pop_count], Const):
# TODO(robertwb): Handle this better.
if var_args or kw_args:
state.stack[-1] = Any
state.stack[-var_args - kw_args] = Any
return_type = infer_return_type(
state.stack[-pop_count].value,
state.stack[1 - pop_count:],
debug=debug,
depth=depth - 1)
else:
return_type = Any
state.stack[-pop_count:] = [return_type]
else: # Python 3.6+
if opname == 'CALL_FUNCTION':
pop_count = arg + 1
if depth <= 0:
return_type = Any
elif isinstance(state.stack[-pop_count], Const):
return_type = infer_return_type(
state.stack[-pop_count].value,
state.stack[1 - pop_count:],
debug=debug,
depth=depth - 1)
else:
return_type = Any
elif opname == 'CALL_FUNCTION_KW':
# TODO(udim): Handle keyword arguments. Requires passing them by name
# to infer_return_type.
pop_count = arg + 2
if isinstance(state.stack[-pop_count], Const):
from apache_beam.pvalue import Row
if state.stack[-pop_count].value == Row:
fields = state.stack[-1].value
return_type = row_type.RowTypeConstraint(
zip(fields, Const.unwrap_all(state.stack[-pop_count + 1:-1])))
else:
return_type = Any
else:
return_type = Any
elif opname == 'CALL_FUNCTION_EX':
# stack[-has_kwargs]: Map of keyword args.
# stack[-1 - has_kwargs]: Iterable of positional args.
# stack[-2 - has_kwargs]: Function to call.
has_kwargs = arg & 1 # type: int
pop_count = has_kwargs + 2
if has_kwargs:
# TODO(udim): Unimplemented. Requires same functionality as a
# CALL_FUNCTION_KW implementation.
return_type = Any
else:
args = state.stack[-1]
_callable = state.stack[-2]
if isinstance(args, typehints.ListConstraint):
# Case where there's a single var_arg argument.
args = [args]
elif isinstance(args, typehints.TupleConstraint):
args = list(args._inner_types())
return_type = infer_return_type(
_callable.value, args, debug=debug, depth=depth - 1)
else:
raise TypeInferenceError('unable to handle %s' % opname)
state.stack[-pop_count:] = [return_type]
elif opname == 'CALL_METHOD':
pop_count = 1 + arg
# LOAD_METHOD will return a non-Const (Any) if loading from an Any.
if isinstance(state.stack[-pop_count], Const) and depth > 0:
return_type = infer_return_type(
state.stack[-pop_count].value,
state.stack[1 - pop_count:],
debug=debug,
depth=depth - 1)
else:
return_type = typehints.Any
state.stack[-pop_count:] = [return_type]
elif opname in simple_ops:
if debug:
print("Executing simple op " + opname)
simple_ops[opname](state, arg)
elif opname == 'RETURN_VALUE':
returns.add(state.stack[-1])
state = None
elif opname == 'YIELD_VALUE':
yields.add(state.stack[-1])
elif opname == 'JUMP_FORWARD':
jmp = pc + arg
jmp_state = state
state = None
elif opname == 'JUMP_ABSOLUTE':
jmp = arg
jmp_state = state
state = None
elif opname in ('POP_JUMP_IF_TRUE', 'POP_JUMP_IF_FALSE'):
state.stack.pop()
jmp = arg
jmp_state = state.copy()
elif opname in ('JUMP_IF_TRUE_OR_POP', 'JUMP_IF_FALSE_OR_POP'):
jmp = arg
jmp_state = state.copy()
state.stack.pop()
elif opname == 'FOR_ITER':
jmp = pc + arg
jmp_state = state.copy()
jmp_state.stack.pop()
state.stack.append(element_type(state.stack[-1]))
else:
raise TypeInferenceError('unable to handle %s' % opname)
if jmp is not None:
# TODO(robertwb): Is this guaranteed to converge?
new_state = states[jmp] | jmp_state
if jmp < pc and new_state != states[jmp] and jumps[pc] < 5:
jumps[pc] += 1
pc = jmp
states[jmp] = new_state
if debug:
print()
print(state)
pprint.pprint(dict(item for item in states.items() if item[1]))
if yields:
result = typehints.Iterable[reduce(union, Const.unwrap_all(yields))]
else:
result = reduce(union, Const.unwrap_all(returns))
finalize_hints(result)
if debug:
print(f, id(f), input_types, '->', result)
return result | PypiClean |
/ansible-8.3.0-py3-none-any.whl/ansible_collections/fortinet/fortimanager/plugins/modules/fmgr_pkg_firewall_hyperscalepolicy.py | from __future__ import absolute_import, division, print_function
# Copyright 2019-2023 Fortinet, Inc.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
__metaclass__ = type
ANSIBLE_METADATA = {'status': ['preview'],
'supported_by': 'community',
'metadata_version': '1.1'}
DOCUMENTATION = '''
---
module: fmgr_pkg_firewall_hyperscalepolicy
short_description: Configure IPv4/IPv6 policies.
description:
- This module is able to configure a FortiManager device.
- Examples include all parameters and values which need to be adjusted to data sources before usage.
version_added: "2.2.0"
author:
- Xinwei Du (@dux-fortinet)
- Xing Li (@lix-fortinet)
- Jie Xue (@JieX19)
- Link Zheng (@chillancezen)
- Frank Shen (@fshen01)
- Hongbin Lu (@fgtdev-hblu)
notes:
- Running in workspace locking mode is supported in this FortiManager module, the top
level parameters workspace_locking_adom and workspace_locking_timeout help do the work.
- To create or update an object, use state present directive.
- To delete an object, use state absent directive.
- Normally, running one module can fail when a non-zero rc is returned. you can also override
the conditions to fail or succeed with parameters rc_failed and rc_succeeded
options:
access_token:
description: The token to access FortiManager without using username and password.
required: false
type: str
bypass_validation:
description: Only set to True when module schema diffs with FortiManager API structure, module continues to execute without validating parameters.
required: false
type: bool
default: false
enable_log:
description: Enable/Disable logging for task.
required: false
type: bool
default: false
forticloud_access_token:
description: Authenticate Ansible client with forticloud API access token.
required: false
type: str
proposed_method:
description: The overridden method for the underlying Json RPC request.
required: false
type: str
choices:
- update
- set
- add
rc_succeeded:
description: The rc codes list with which the conditions to succeed will be overriden.
type: list
required: false
elements: int
rc_failed:
description: The rc codes list with which the conditions to fail will be overriden.
type: list
required: false
elements: int
state:
description: The directive to create, update or delete an object.
type: str
required: true
choices:
- present
- absent
workspace_locking_adom:
description: The adom to lock for FortiManager running in workspace mode, the value can be global and others including root.
required: false
type: str
workspace_locking_timeout:
description: The maximum time in seconds to wait for other user to release the workspace lock.
required: false
type: int
default: 300
adom:
description: the parameter (adom) in requested url
type: str
required: true
pkg:
description: the parameter (pkg) in requested url
type: str
required: true
pkg_firewall_hyperscalepolicy:
description: the top level parameters set
required: false
type: dict
suboptions:
action:
type: str
description: Policy action
choices:
- 'deny'
- 'accept'
auto-asic-offload:
type: str
description: Enable/disable policy traffic ASIC offloading.
choices:
- 'disable'
- 'enable'
cgn-eif:
type: str
description: Enable/Disable CGN endpoint independent filtering.
choices:
- 'disable'
- 'enable'
cgn-eim:
type: str
description: Enable/Disable CGN endpoint independent mapping
choices:
- 'disable'
- 'enable'
cgn-log-server-grp:
type: str
description: NP log server group name
cgn-resource-quota:
type: int
description: resource quota
cgn-session-quota:
type: int
description: session quota
comments:
type: str
description: Comment.
delay-tcp-npu-session:
type: str
description: Enable TCP NPU session delay to guarantee packet order of 3-way handshake.
choices:
- 'disable'
- 'enable'
dstaddr:
description: description
type: str
dstaddr-negate:
type: str
description: When enabled dstaddr/dstaddr6 specifies what the destination address must NOT be.
choices:
- 'disable'
- 'enable'
dstaddr6:
description: description
type: str
dstintf:
description: description
type: str
firewall-session-dirty:
type: str
description: How to handle sessions if the configuration of this firewall policy changes.
choices:
- 'check-all'
- 'check-new'
global-label:
type: str
description: Label for the policy that appears when the GUI is in Global View mode.
ippool:
type: str
description: Enable to use IP Pools for source NAT.
choices:
- 'disable'
- 'enable'
label:
type: str
description: Label for the policy that appears when the GUI is in Section View mode.
name:
type: str
description: Policy name.
nat:
type: str
description: Enable/disable source NAT.
choices:
- 'disable'
- 'enable'
policy-offload:
type: str
description: Enable/Disable hardware session setup for CGNAT.
choices:
- 'disable'
- 'enable'
policyid:
type: int
description: Policy ID
poolname:
description: description
type: str
poolname6:
description: description
type: str
send-deny-packet:
type: str
description: Enable to send a reply when a session is denied or blocked by a firewall policy.
choices:
- 'disable'
- 'enable'
service:
description: description
type: str
service-negate:
type: str
description: When enabled service specifies what the service must NOT be.
choices:
- 'disable'
- 'enable'
srcaddr:
description: description
type: str
srcaddr-negate:
type: str
description: When enabled srcaddr/srcaddr6 specifies what the source address must NOT be.
choices:
- 'disable'
- 'enable'
srcaddr6:
description: description
type: str
srcintf:
description: description
type: str
status:
type: str
description: Enable or disable this policy.
choices:
- 'disable'
- 'enable'
tcp-timeout-pid:
type: str
description: TCP timeout profile ID
traffic-shaper:
type: str
description: Traffic shaper.
traffic-shaper-reverse:
type: str
description: Reverse traffic shaper.
udp-timeout-pid:
type: str
description: UDP timeout profile ID
uuid:
type: str
description: Universally Unique Identifier
'''
EXAMPLES = '''
- hosts: fortimanager-inventory
collections:
- fortinet.fortimanager
connection: httpapi
vars:
ansible_httpapi_use_ssl: True
ansible_httpapi_validate_certs: False
ansible_httpapi_port: 443
tasks:
- name: Configure IPv4/IPv6 policies.
fmgr_pkg_firewall_hyperscalepolicy:
bypass_validation: False
workspace_locking_adom: <value in [global, custom adom including root]>
workspace_locking_timeout: 300
rc_succeeded: [0, -2, -3, ...]
rc_failed: [-2, -3, ...]
adom: <your own value>
pkg: <your own value>
state: <value in [present, absent]>
pkg_firewall_hyperscalepolicy:
action: <value in [deny, accept]>
auto-asic-offload: <value in [disable, enable]>
cgn-eif: <value in [disable, enable]>
cgn-eim: <value in [disable, enable]>
cgn-log-server-grp: <value of string>
cgn-resource-quota: <value of integer>
cgn-session-quota: <value of integer>
comments: <value of string>
delay-tcp-npu-session: <value in [disable, enable]>
dstaddr: <value of string>
dstaddr-negate: <value in [disable, enable]>
dstaddr6: <value of string>
dstintf: <value of string>
firewall-session-dirty: <value in [check-all, check-new]>
global-label: <value of string>
ippool: <value in [disable, enable]>
label: <value of string>
name: <value of string>
nat: <value in [disable, enable]>
policy-offload: <value in [disable, enable]>
policyid: <value of integer>
poolname: <value of string>
poolname6: <value of string>
send-deny-packet: <value in [disable, enable]>
service: <value of string>
service-negate: <value in [disable, enable]>
srcaddr: <value of string>
srcaddr-negate: <value in [disable, enable]>
srcaddr6: <value of string>
srcintf: <value of string>
status: <value in [disable, enable]>
tcp-timeout-pid: <value of string>
traffic-shaper: <value of string>
traffic-shaper-reverse: <value of string>
udp-timeout-pid: <value of string>
uuid: <value of string>
'''
RETURN = '''
meta:
description: The result of the request.
type: dict
returned: always
contains:
request_url:
description: The full url requested.
returned: always
type: str
sample: /sys/login/user
response_code:
description: The status of api request.
returned: always
type: int
sample: 0
response_data:
description: The api response.
type: list
returned: always
response_message:
description: The descriptive message of the api response.
type: str
returned: always
sample: OK.
system_information:
description: The information of the target system.
type: dict
returned: always
rc:
description: The status the request.
type: int
returned: always
sample: 0
version_check_warning:
description: Warning if the parameters used in the playbook are not supported by the current FortiManager version.
type: list
returned: complex
'''
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.connection import Connection
from ansible_collections.fortinet.fortimanager.plugins.module_utils.napi import NAPIManager
from ansible_collections.fortinet.fortimanager.plugins.module_utils.napi import check_galaxy_version
from ansible_collections.fortinet.fortimanager.plugins.module_utils.napi import check_parameter_bypass
def main():
jrpc_urls = [
'/pm/config/adom/{adom}/pkg/{pkg}/firewall/hyperscale-policy'
]
perobject_jrpc_urls = [
'/pm/config/adom/{adom}/pkg/{pkg}/firewall/hyperscale-policy/{hyperscale-policy}'
]
url_params = ['adom', 'pkg']
module_primary_key = 'name'
module_arg_spec = {
'access_token': {
'type': 'str',
'required': False,
'no_log': True
},
'bypass_validation': {
'type': 'bool',
'required': False,
'default': False
},
'enable_log': {
'type': 'bool',
'required': False,
'default': False
},
'forticloud_access_token': {
'type': 'str',
'required': False,
'no_log': True
},
'proposed_method': {
'type': 'str',
'required': False,
'choices': [
'set',
'update',
'add'
]
},
'rc_succeeded': {
'required': False,
'type': 'list',
'elements': 'int'
},
'rc_failed': {
'required': False,
'type': 'list',
'elements': 'int'
},
'state': {
'type': 'str',
'required': True,
'choices': [
'present',
'absent'
]
},
'workspace_locking_adom': {
'type': 'str',
'required': False
},
'workspace_locking_timeout': {
'type': 'int',
'required': False,
'default': 300
},
'adom': {
'required': True,
'type': 'str'
},
'pkg': {
'required': True,
'type': 'str'
},
'pkg_firewall_hyperscalepolicy': {
'required': False,
'type': 'dict',
'revision': {
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.0': True
},
'options': {
'action': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'deny',
'accept'
],
'type': 'str'
},
'auto-asic-offload': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'disable',
'enable'
],
'type': 'str'
},
'cgn-eif': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'disable',
'enable'
],
'type': 'str'
},
'cgn-eim': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'disable',
'enable'
],
'type': 'str'
},
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'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'disable',
'enable'
],
'type': 'str'
},
'srcaddr': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'srcaddr-negate': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'disable',
'enable'
],
'type': 'str'
},
'srcaddr6': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': False,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'srcintf': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'status': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'choices': [
'disable',
'enable'
],
'type': 'str'
},
'tcp-timeout-pid': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'traffic-shaper': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'traffic-shaper-reverse': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'udp-timeout-pid': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
},
'uuid': {
'required': False,
'revision': {
'7.2.0': True,
'6.2.0': False,
'6.2.2': False,
'6.2.6': False,
'6.2.7': False,
'6.2.8': False,
'6.2.9': False,
'6.2.10': False,
'6.2.11': False,
'6.4.1': False,
'6.4.3': False,
'6.4.4': False,
'6.4.6': False,
'6.4.7': True,
'6.4.8': True,
'6.4.9': True,
'6.4.10': True,
'6.4.11': True,
'6.4.12': True,
'7.0.1': True,
'7.0.2': True,
'7.0.3': True,
'7.0.4': True,
'7.0.5': True,
'7.0.6': True,
'7.0.7': True,
'7.0.8': True,
'7.2.1': False,
'7.2.2': False,
'7.2.3': False,
'7.4.0': False
},
'type': 'str'
}
}
}
}
params_validation_blob = []
check_galaxy_version(module_arg_spec)
module = AnsibleModule(argument_spec=check_parameter_bypass(module_arg_spec, 'pkg_firewall_hyperscalepolicy'),
supports_check_mode=False)
fmgr = None
if module._socket_path:
connection = Connection(module._socket_path)
connection.set_option('access_token', module.params['access_token'] if 'access_token' in module.params else None)
connection.set_option('enable_log', module.params['enable_log'] if 'enable_log' in module.params else False)
connection.set_option('forticloud_access_token',
module.params['forticloud_access_token'] if 'forticloud_access_token' in module.params else None)
fmgr = NAPIManager(jrpc_urls, perobject_jrpc_urls, module_primary_key, url_params, module, connection, top_level_schema_name='data')
fmgr.validate_parameters(params_validation_blob)
fmgr.process_curd(argument_specs=module_arg_spec)
else:
module.fail_json(msg='MUST RUN IN HTTPAPI MODE')
module.exit_json(meta=module.params)
if __name__ == '__main__':
main() | PypiClean |
/fhir.resources-7.0.2.tar.gz/fhir.resources-7.0.2/fhir/resources/DSTU2/location.py | from typing import List as ListType
from pydantic import Field
from . import fhirtypes
from .backboneelement import BackboneElement
from .domainresource import DomainResource
class Location(DomainResource):
"""Details and position information for a physical place.
Details and position information for a physical place where services are
provided and resources and participants may be stored, found, contained or
accommodated.
"""
resource_type = Field("Location", const=True)
address: fhirtypes.AddressType = Field(
None,
alias="address",
title="Type `Address` (represented as `dict` in JSON)",
description="Physical location",
)
description: fhirtypes.String = Field(
None,
alias="description",
title="Type `String` (represented as `dict` in JSON)",
description=(
"Additional details about the location that could be displayed as "
"further information to identify the location beyond its name"
),
)
identifier: ListType[fhirtypes.IdentifierType] = Field(
None,
alias="identifier",
title="List of `Identifier` items (represented as `dict` in JSON)",
description="Unique code or number identifying the location to its users",
)
managingOrganization: fhirtypes.ReferenceType = Field(
None,
alias="managingOrganization",
title=(
"Type `Reference` referencing `Organization` (represented as `dict` in "
"JSON)"
),
description="Organization responsible for provisioning and upkeep",
)
mode: fhirtypes.Code = Field(
None,
alias="mode",
title="Type `Code` (represented as `dict` in JSON)",
description="instance | kind",
)
name: fhirtypes.String = Field(
None,
alias="name",
title="Type `String` (represented as `dict` in JSON)",
description="Name of the location as used by humans",
)
partOf: fhirtypes.ReferenceType = Field(
None,
alias="partOf",
title=(
"Type `Reference` referencing `Location` (represented as `dict` in " "JSON)"
),
description="Another Location this one is physically part of",
)
physicalType: fhirtypes.CodeableConceptType = Field(
None,
alias="physicalType",
title="Type `CodeableConcept` (represented as `dict` in JSON)",
description="Physical form of the location",
)
position: fhirtypes.LocationPositionType = Field(
None,
alias="position",
title="Type `LocationPosition` (represented as `dict` in JSON)",
description="The absolute geographic location",
)
status: fhirtypes.Code = Field(
None,
alias="status",
title="Type `Code` (represented as `dict` in JSON)",
description="active | suspended | inactive",
)
telecom: ListType[fhirtypes.ContactPointType] = Field(
None,
alias="telecom",
title="List of `ContactPoint` items (represented as `dict` in JSON)",
description="Contact details of the location",
)
type: fhirtypes.CodeableConceptType = Field(
None,
alias="type",
title="Type `CodeableConcept` (represented as `dict` in JSON)",
description="Type of function performed",
)
class LocationPosition(BackboneElement):
"""The absolute geographic location.
The absolute geographic location of the Location, expressed using the WGS84
datum (This is the same co-ordinate system used in KML).
"""
resource_type = Field("LocationPosition", const=True)
altitude: fhirtypes.Decimal = Field(
None,
alias="altitude",
title="Type `Decimal` (represented as `dict` in JSON)",
description="Altitude with WGS84 datum",
)
latitude: fhirtypes.Decimal = Field(
...,
alias="latitude",
title="Type `Decimal` (represented as `dict` in JSON)",
description="Latitude with WGS84 datum",
)
longitude: fhirtypes.Decimal = Field(
...,
alias="longitude",
title="Type `Decimal` (represented as `dict` in JSON)",
description="Longitude with WGS84 datum",
) | PypiClean |
/ipp_toolkit-0.1.2.tar.gz/ipp_toolkit-0.1.2/ipp_toolkit/utils/rl/agents/DaggerAgent.py | from ipp_toolkit.utils.rl.agents.BaseAgent import BaseAgent
from ipp_toolkit.utils.rl.agents.PerfectAgent import PerfectAgent
from ipp_toolkit.utils.rl.agents.UCBAgent import UCBAgent
from imitation.algorithms import bc
from imitation.data import rollout
from imitation.data.wrappers import RolloutInfoWrapper
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.evaluation import evaluate_policy
import numpy as np
from imitation.scripts.train_preference_comparisons import save_model
from pathlib import Path
import os
from imitation.algorithms.bc import reconstruct_policy
import torch
import tempfile
from imitation.algorithms.dagger import SimpleDAggerTrainer
from imitation.data.types import TransitionsMinimal
from tqdm import tqdm
import imitation
#from stable_baselines3.common.policies import ActorCriticPolicy
class DaggerAgent(BaseAgent):
def __init__(self, env):
self.name = "DA"
self.policy = None
self.model_name = "da_model"
def train(
self,
env,
cfg,
):
model_dir = cfg["model_dir"]
savefile = Path(model_dir, self.model_name + ".zip")
if not os.path.exists(model_dir):
os.mkdir(model_dir)
rng = np.random.default_rng(0)
bc_trainer = bc.BC(
observation_space=env.observation_space,
action_space=env.action_space,
rng=rng,
batch_size=256,
)
expert = PerfectAgent(env)
#TODO
#not really sure if wrapper is needed but it worked so keeping it
#venv = DummyVecEnv([lambda: env])
venv = DummyVecEnv([lambda: RolloutInfoWrapper(env)])
with tempfile.TemporaryDirectory(prefix="dagger_example_") as tmpdir:
print(tmpdir)
dagger_trainer = SimpleDAggerTrainer(
venv=venv,
scratch_dir=tmpdir,
expert_policy=expert,
rng=rng,
bc_trainer=bc_trainer,
)
dagger_trainer.train(10000,
rollout_round_min_episodes=100,
bc_train_kwargs={"n_epochs": 100},
#policy=ActorCriticPolicy,
)
dagger_trainer.save_policy(savefile)
def load_model(self, model_dir):
self.policy = reconstruct_policy(Path(model_dir, self.model_name + ".zip"))
def get_action(self, observation, env):
observation = torch.Tensor(observation).cuda()
observation = torch.unsqueeze(observation, dim=0)
action, _, _ = self.policy(observation)
action = action.squeeze().detach().cpu().numpy()
return action, None | PypiClean |
/torch_geometric-2.3.1.tar.gz/torch_geometric-2.3.1/torch_geometric/datasets/coma.py | import os.path as osp
from glob import glob
from typing import Callable, List, Optional
import torch
from torch_geometric.data import InMemoryDataset, extract_zip
from torch_geometric.io import read_ply
class CoMA(InMemoryDataset):
r"""The CoMA 3D faces dataset from the `"Generating 3D faces using
Convolutional Mesh Autoencoders" <https://arxiv.org/abs/1807.10267>`_
paper, containing 20,466 meshes of extreme expressions captured over 12
different subjects.
.. note::
Data objects hold mesh faces instead of edge indices.
To convert the mesh to a graph, use the
:obj:`torch_geometric.transforms.FaceToEdge` as :obj:`pre_transform`.
To convert the mesh to a point cloud, use the
:obj:`torch_geometric.transforms.SamplePoints` as :obj:`transform` to
sample a fixed number of points on the mesh faces according to their
face area.
Args:
root (str): Root directory where the dataset should be saved.
train (bool, optional): If :obj:`True`, loads the training dataset,
otherwise the test dataset. (default: :obj:`True`)
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - #graphs
- #nodes
- #edges
- #features
- #classes
* - 20,465
- 5,023
- 29,990
- 3
- 12
"""
url = 'https://coma.is.tue.mpg.de/'
categories = [
'bareteeth',
'cheeks_in',
'eyebrow',
'high_smile',
'lips_back',
'lips_up',
'mouth_down',
'mouth_extreme',
'mouth_middle',
'mouth_open',
'mouth_side',
'mouth_up',
]
def __init__(self, root: str, train: bool = True,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None):
super().__init__(root, transform, pre_transform, pre_filter)
path = self.processed_paths[0] if train else self.processed_paths[1]
self.data, self.slices = torch.load(path)
@property
def raw_file_names(self) -> str:
return 'COMA_data.zip'
@property
def processed_file_names(self) -> List[str]:
return ['training.pt', 'test.pt']
def download(self):
raise RuntimeError(
f"Dataset not found. Please download 'COMA_data.zip' from "
f"'{self.url}' and move it to '{self.raw_dir}'")
def process(self):
folders = sorted(glob(osp.join(self.raw_dir, 'FaceTalk_*')))
if len(folders) == 0:
extract_zip(self.raw_paths[0], self.raw_dir, log=False)
folders = sorted(glob(osp.join(self.raw_dir, 'FaceTalk_*')))
train_data_list, test_data_list = [], []
for folder in folders:
for i, category in enumerate(self.categories):
files = sorted(glob(osp.join(folder, category, '*.ply')))
for j, f in enumerate(files):
data = read_ply(f)
data.y = torch.tensor([i], dtype=torch.long)
if self.pre_filter is not None and\
not self.pre_filter(data):
continue
if self.pre_transform is not None:
data = self.pre_transform(data)
if (j % 100) < 90:
train_data_list.append(data)
else:
test_data_list.append(data)
torch.save(self.collate(train_data_list), self.processed_paths[0])
torch.save(self.collate(test_data_list), self.processed_paths[1]) | PypiClean |
/Yodine-0.4.0rc3.tar.gz/Yodine-0.4.0rc3/yodine/core/vector.py | import math
try:
raise ImportError("blah")
import numpy as np
class _Vector(np.ndarray):
def vsize(self) -> float:
return np.linalg.norm(self)
def sqsize(self) -> float:
return np.linalg.norm(self) ** 2
def fisrsize(self) -> float:
return 1 / (np.linalg.norm(self) ** 2)
def unit(self) -> "Vector":
sz = self.vsize()
if sz == 0:
return Vector(0, 0)
elif sz == 1:
return self
else:
return self / sz
def ints(self) -> "Vector":
return Vector(int(self[0]), int(self[1]))
def rotate(self, angle: float) -> "Vector":
c = math.cos(angle)
s = math.sin(angle)
return Vector(self.x * c - self.y * s, self.x * s + self.y * c)
@property
def x(self) -> float:
return self[0]
@property
def y(self) -> float:
return self[1]
@x.setter
def x(self, v: float):
self[0] = v
@y.setter
def y(self, v: float):
self[1] = v
def dot(self, b: "Vector") -> float:
return self[0] * b.vec[0] + self[1] * b.vec[1]
@classmethod
def new(cls, _x=None, _y=None):
# get initial values
if _y is not None:
x = _x
y = _y
elif _x is not None:
try:
x, y = _x
except TypeError:
raise ValueError("Non-vectorial value: {}".format(repr(_x)))
else:
x = 0.0
y = 0.0
return super(_Vector, cls).__new__(
cls, (2,), dtype=np.float128, buffer=np.array([x, y], dtype=np.float128)
)
def __iadd__(self, b: "Vector") -> "Vector":
self.x += b.x
self.y += b.y
return self
def __isub__(self, b: "Vector") -> "Vector":
self.x -= res.x
self.y -= res.y
return self
def __imul__(self, b: "Vector") -> "Vector":
try:
self.x *= b.x
self.y *= b.y
except AttributeError:
self.x *= b
self.y *= b
return self
def __itruediv__(self, b: "Vector") -> "Vector":
try:
self.x /= b.x
self.y /= b.y
except AttributeError:
self.x /= b
self.y /= b
return self
def Vector(*args, **kwargs):
return _Vector.new(*args, **kwargs)
except ImportError:
import struct
class _Vector(object):
def __init__(self, x=None, y=None):
if y is not None:
self.x = x
self.y = y
elif x is not None:
try:
self.x, self.y = x
except TypeError:
raise ValueError("Non-vectorial value: {}".format(repr(x)))
else:
self.x = 0
self.y = 0
def __iter__(self):
return iter((self.x, self.y))
def __getitem__(self, co):
if co in (0, "x"):
return self.x
if co in (1, "y"):
return self.y
raise KeyError("No such key in a vector: ", repr(co))
def ints(self):
return Vector(int(self[0]), int(self[1]))
def rotate(self, angle: float) -> "Vector":
c = math.cos(angle)
s = math.sin(angle)
return Vector(
self.x * c - self.y * s,
self.x * s + self.y * c,
)
def vsize(self) -> float:
return math.sqrt(self[0] ** 2 + self[1] ** 2)
def sqsize(self) -> float:
return float(self[0] ** 2 + self[1] ** 2)
def fisrsize(self, sqs=None) -> float:
y = sqs or self.sqsize()
threehalfs = 1.5
x2 = y * 0.5
packed_y = struct.pack("f", y)
i = struct.unpack("i", packed_y)[0] # treat float's bytes as int
i = 0x5F3759DF - (i >> 1) # arithmetic with magic number
packed_i = struct.pack("i", i)
y = struct.unpack("f", packed_i)[0] # treat int's bytes as float
y = y * (threehalfs - (x2 * y * y)) # Newton's method
return y
def unit(self) -> "Vector":
sz = self.sqsize()
fz = self.fisrsize(sz)
if sz == 0:
return Vector(0, 0)
elif sz == 1:
return self
else:
return self * fz
def __add__(self, b: "Vector") -> "Vector":
return Vector(self[0] + b.x, self[1] + b.y)
def __neg__(self) -> "Vector":
return Vector(-self[0], -self[1])
def __mul__(self, b) -> "Vector":
try:
return Vector(self[0] * b.x, self[1] * b.y)
except AttributeError:
return Vector(self[0] * b, self[1] * b)
def __truediv__(self, b) -> "Vector":
try:
return Vector(self[0] / b.x, self[1] / b.y)
except AttributeError:
return Vector(self[0] / b, self[1] / b)
def __iadd__(self, b: "Vector") -> "ComponentVector":
res = self + b
self.x = res.x
self.y = res.y
return self
def __isub__(self, b: "Vector") -> "ComponentVector":
res = self - b
self.x = res.x
self.y = res.y
return self
def __imul__(self, b: "Vector") -> "ComponentVector":
res = self * b
self.x = res.x
self.y = res.y
return self
def __itruediv__(self, b: "Vector") -> "ComponentVector":
res = self / b
self.x = res.x
self.y = res.y
return self
def dot(self, b: "Vector") -> float:
return self[0] * b.vec[0] + self[1] * b.vec[1]
def __sub__(self, b: "Vector") -> "Vector":
return self + (-b)
def __lshift__(self, b: "Vector") -> "ComponentVector":
self.x = b.x
self.y = b.y
return self
def __repr__(self) -> str:
return "{}(x={},y={})".format(type(self).__name__, self[0], self[1])
Vector = _Vector
def ComponentVector(component: "Component"):
res = component.get()
if not (hasattr(res, "x") and hasattr(res, "y")):
from .entity import VectorComponent
res = component.entity.create_component(
component.name, component.value, VectorComponent
)
return res.get()
return res | PypiClean |
/Products.ResourceRegistries-3.0.8.tar.gz/Products.ResourceRegistries-3.0.8/Products/ResourceRegistries/tools/KSSRegistry.py | from .packer import CSSPacker
from AccessControl import ClassSecurityInfo
from App.class_init import InitializeClass
from Products.PageTemplates.PageTemplateFile import PageTemplateFile
from Products.ResourceRegistries import config
from Products.ResourceRegistries import permissions
from Products.ResourceRegistries.interfaces import IKSSRegistry
from Products.ResourceRegistries.tools.BaseRegistry import BaseRegistryTool
from Products.ResourceRegistries.tools.BaseRegistry import Resource
from zope.interface import implementer
class KineticStylesheet(Resource):
security = ClassSecurityInfo()
def __init__(self, id, **kwargs):
Resource.__init__(self, id, **kwargs)
self._data['compression'] = kwargs.get('compression', 'safe')
if self.isExternal:
self._data['compression'] = 'none' #External resources are not compressable
security.declarePublic('getCompression')
def getCompression(self):
# as this is a new property, old instance might not have that value, so
# return 'safe' as default
compression = self._data.get('compression', 'safe')
if compression in config.KSS_COMPRESSION_METHODS:
return compression
return 'none'
security.declareProtected(permissions.ManagePortal, 'setCompression')
def setCompression(self, compression):
if self.isExternalResource() and compression not in config.KSS_EXTERNAL_COMPRESSION_METHODS:
raise ValueError("Compression method %s must be one of: %s for External Resources" % (
compression, ', '.join(config.KSS_EXTERNAL_COMPRESSION_METHODS)))
self._data['compression'] = compression
InitializeClass(KineticStylesheet)
@implementer(IKSSRegistry)
class KSSRegistryTool(BaseRegistryTool):
"""A Plone registry for managing the linking to kss files."""
id = config.KSSTOOLNAME
meta_type = config.KSSTOOLTYPE
title = 'KSS Registry'
security = ClassSecurityInfo()
#
# ZMI stuff
#
manage_kssForm = PageTemplateFile('www/kssconfig', config.GLOBALS)
manage_kssComposition = PageTemplateFile('www/ksscomposition', config.GLOBALS)
manage_options = (
{
'label': 'KSS Registry',
'action': 'manage_kssForm',
},
{
'label': 'Merged KSS Composition',
'action': 'manage_kssComposition',
},
) + BaseRegistryTool.manage_options
attributes_to_compare = ('getAuthenticated', 'getExpression',
'getCookable', 'getCacheable',
'getConditionalcomment')
filename_base = 'ploneStyles'
filename_appendix = '.kss'
merged_output_prefix = u''
cache_duration = config.KSS_CACHE_DURATION
resource_class = KineticStylesheet
@property
def manage_workspace_url(self):
return "%s/manage_workspace" % self.absolute_url_path()
#
# Private Methods
#
security.declarePrivate('clearKineticStylesheets')
def clearKineticStylesheets(self):
self.clearResources()
def _compressKSS(self, content, level='safe'):
if level == 'full':
return CSSPacker('full').pack(content)
elif level == 'safe':
return CSSPacker('safe').pack(content)
else:
return content
security.declarePrivate('finalizeContent')
def finalizeContent(self, resource, content):
"""Finalize the resource content."""
compression = resource.getCompression()
if compression != 'none' and not self.getDebugMode():
orig_url = "%s/%s?original=1" % (self.absolute_url(), resource.getId())
content = "/* %s */\n%s" % (orig_url,
self._compressKSS(content, compression))
return content
#
# ZMI Methods
#
security.declareProtected(permissions.ManagePortal, 'manage_addKineticStylesheet')
def manage_addKineticStylesheet(self, id, expression='', media='screen',
rel='stylesheet', title='', rendering='import',
enabled=False, cookable=True, compression='safe',
cacheable=True, conditionalcomment='', authenticated=False,
bundle='default', REQUEST=None):
"""Register a kineticstylesheet from a TTW request."""
self.registerKineticStylesheet(id, expression, enabled,
cookable, compression, cacheable,
conditionalcomment, authenticated, bundle=bundle)
if REQUEST:
REQUEST.RESPONSE.redirect("manage_workspace")
security.declareProtected(permissions.ManagePortal, 'manage_saveKineticStylesheets')
def manage_saveKineticStylesheets(self, REQUEST=None):
"""Save kineticstylesheets from the ZMI.
Updates the whole sequence. For editing and reordering.
"""
if REQUEST and not REQUEST.form:
REQUEST.RESPONSE.redirect("manage_workspace")
return
debugmode = REQUEST.get('debugmode', False)
self.setDebugMode(debugmode)
records = REQUEST.get('kineticstylesheets', [])
records.sort(lambda a, b: a.sort - b.sort)
self.resources = ()
kineticstylesheets = []
for r in records:
kss = self.resource_class(
r.get('id'),
expression=r.get('expression', ''),
enabled=r.get('enabled', True),
cookable=r.get('cookable', True),
cacheable=r.get('cacheable', True),
compression=r.get('compression', 'safe'),
conditionalcomment=r.get('conditionalcomment',''),
authenticated=r.get('authenticated', False),
bundle=r.get('bundle', 'default'))
kineticstylesheets.append(kss)
self.resources = tuple(kineticstylesheets)
self.cookResources()
if REQUEST:
REQUEST.RESPONSE.redirect("manage_workspace")
security.declareProtected(permissions.ManagePortal, 'manage_removeKineticStylesheet')
def manage_removeKineticStylesheet(self, id, REQUEST=None):
"""Remove kineticstylesheet from the ZMI."""
self.unregisterResource(id)
if REQUEST:
REQUEST.RESPONSE.redirect("manage_workspace")
#
# Protected Methods
#
security.declareProtected(permissions.ManagePortal, 'registerKineticStylesheet')
def registerKineticStylesheet(self, id, expression='', enabled=1,
cookable=True, compression='safe',
cacheable=True, conditionalcomment='',
authenticated=False,
skipCooking=False, bundle='default'):
"""Register a kineticstylesheet."""
kineticstylesheet = self.resource_class(id,
expression=expression,
enabled=enabled,
cookable=cookable,
compression=compression,
cacheable=cacheable,
conditionalcomment=conditionalcomment,
authenticated=authenticated,
bundle=bundle)
self.storeResource(kineticstylesheet, skipCooking=skipCooking)
security.declareProtected(permissions.ManagePortal, 'updateKineticStylesheet')
def updateKineticStylesheet(self, id, **data):
kineticstylesheet = self.getResourcesDict().get(id, None)
if kineticstylesheet is None:
raise ValueError('Invalid resource id %s' % (id))
if data.get('expression', None) is not None:
kineticstylesheet.setExpression(data['expression'])
if data.get('authenticated', None) is not None:
kineticstylesheet.setAuthenticated(data['authenticated'])
if data.get('enabled', None) is not None:
kineticstylesheet.setEnabled(data['enabled'])
if data.get('cookable', None) is not None:
kineticstylesheet.setCookable(data['cookable'])
if data.get('compression', None) is not None:
kineticstylesheet.setCompression(data['compression'])
if data.get('cacheable', None) is not None:
kineticstylesheet.setCacheable(data['cacheable'])
if data.get('conditionalcomment', None) is not None:
kineticstylesheet.setConditionalcomment(data['conditionalcomment'])
if data.get('bundle', None) is not None:
kineticstylesheet.setBundle(data['bundle'])
security.declareProtected(permissions.ManagePortal, 'getCompressionOptions')
def getCompressionOptions(self):
"""Compression methods for use in ZMI forms."""
return config.KSS_COMPRESSION_METHODS
security.declareProtected(permissions.ManagePortal, 'getExternalCompressionOptions')
def getExternalCompressionOptions(self):
"""Compression methods for use in ZMI forms."""
return config.KSS_EXTERNAL_COMPRESSION_METHODS
security.declareProtected(permissions.View, 'getContentType')
def getContentType(self):
"""Return the registry content type."""
return 'text/css;charset=utf-8'
InitializeClass(KSSRegistryTool) | PypiClean |
/Transcrypt-3.7.16.tar.gz/Transcrypt-3.7.16/transcrypt/demos/parcel_demo/node_modules/cosmiconfig/dist/createExplorer.js | 'use strict';
const path = require('path');
const loaders = require('./loaders');
const readFile = require('./readFile');
const cacheWrapper = require('./cacheWrapper');
const getDirectory = require('./getDirectory');
const MODE_SYNC = 'sync';
// An object value represents a config object.
// null represents that the loader did not find anything relevant.
// undefined represents that the loader found something relevant
// but it was empty.
class Explorer {
constructor(options ) {
this.loadCache = options.cache ? new Map() : null;
this.loadSyncCache = options.cache ? new Map() : null;
this.searchCache = options.cache ? new Map() : null;
this.searchSyncCache = options.cache ? new Map() : null;
this.config = options;
this.validateConfig();
}
clearLoadCache() {
if (this.loadCache) {
this.loadCache.clear();
}
if (this.loadSyncCache) {
this.loadSyncCache.clear();
}
}
clearSearchCache() {
if (this.searchCache) {
this.searchCache.clear();
}
if (this.searchSyncCache) {
this.searchSyncCache.clear();
}
}
clearCaches() {
this.clearLoadCache();
this.clearSearchCache();
}
validateConfig() {
const config = this.config;
config.searchPlaces.forEach(place => {
const loaderKey = path.extname(place) || 'noExt';
const loader = config.loaders[loaderKey];
if (!loader) {
throw new Error(
`No loader specified for ${getExtensionDescription(
place
)}, so searchPlaces item "${place}" is invalid`
);
}
});
}
search(searchFrom ) {
searchFrom = searchFrom || process.cwd();
return getDirectory(searchFrom).then(dir => {
return this.searchFromDirectory(dir);
});
}
searchFromDirectory(dir ) {
const absoluteDir = path.resolve(process.cwd(), dir);
const run = () => {
return this.searchDirectory(absoluteDir).then(result => {
const nextDir = this.nextDirectoryToSearch(absoluteDir, result);
if (nextDir) {
return this.searchFromDirectory(nextDir);
}
return this.config.transform(result);
});
};
if (this.searchCache) {
return cacheWrapper(this.searchCache, absoluteDir, run);
}
return run();
}
searchSync(searchFrom ) {
searchFrom = searchFrom || process.cwd();
const dir = getDirectory.sync(searchFrom);
return this.searchFromDirectorySync(dir);
}
searchFromDirectorySync(dir ) {
const absoluteDir = path.resolve(process.cwd(), dir);
const run = () => {
const result = this.searchDirectorySync(absoluteDir);
const nextDir = this.nextDirectoryToSearch(absoluteDir, result);
if (nextDir) {
return this.searchFromDirectorySync(nextDir);
}
return this.config.transform(result);
};
if (this.searchSyncCache) {
return cacheWrapper(this.searchSyncCache, absoluteDir, run);
}
return run();
}
searchDirectory(dir ) {
return this.config.searchPlaces.reduce((prevResultPromise, place) => {
return prevResultPromise.then(prevResult => {
if (this.shouldSearchStopWithResult(prevResult)) {
return prevResult;
}
return this.loadSearchPlace(dir, place);
});
}, Promise.resolve(null));
}
searchDirectorySync(dir ) {
let result = null;
for (const place of this.config.searchPlaces) {
result = this.loadSearchPlaceSync(dir, place);
if (this.shouldSearchStopWithResult(result)) break;
}
return result;
}
shouldSearchStopWithResult(result ) {
if (result === null) return false;
if (result.isEmpty && this.config.ignoreEmptySearchPlaces) return false;
return true;
}
loadSearchPlace(dir , place ) {
const filepath = path.join(dir, place);
return readFile(filepath).then(content => {
return this.createCosmiconfigResult(filepath, content);
});
}
loadSearchPlaceSync(dir , place ) {
const filepath = path.join(dir, place);
const content = readFile.sync(filepath);
return this.createCosmiconfigResultSync(filepath, content);
}
nextDirectoryToSearch(
currentDir ,
currentResult
) {
if (this.shouldSearchStopWithResult(currentResult)) {
return null;
}
const nextDir = nextDirUp(currentDir);
if (nextDir === currentDir || currentDir === this.config.stopDir) {
return null;
}
return nextDir;
}
loadPackageProp(filepath , content ) {
const parsedContent = loaders.loadJson(filepath, content);
const packagePropValue = parsedContent[this.config.packageProp];
return packagePropValue || null;
}
getLoaderEntryForFile(filepath ) {
if (path.basename(filepath) === 'package.json') {
const loader = this.loadPackageProp.bind(this);
return { sync: loader, async: loader };
}
const loaderKey = path.extname(filepath) || 'noExt';
return this.config.loaders[loaderKey] || {};
}
getSyncLoaderForFile(filepath ) {
const entry = this.getLoaderEntryForFile(filepath);
if (!entry.sync) {
throw new Error(
`No sync loader specified for ${getExtensionDescription(filepath)}`
);
}
return entry.sync;
}
getAsyncLoaderForFile(filepath ) {
const entry = this.getLoaderEntryForFile(filepath);
const loader = entry.async || entry.sync;
if (!loader) {
throw new Error(
`No async loader specified for ${getExtensionDescription(filepath)}`
);
}
return loader;
}
loadFileContent(
mode ,
filepath ,
content
) {
if (content === null) {
return null;
}
if (content.trim() === '') {
return undefined;
}
const loader =
mode === MODE_SYNC
? this.getSyncLoaderForFile(filepath)
: this.getAsyncLoaderForFile(filepath);
return loader(filepath, content);
}
loadedContentToCosmiconfigResult(
filepath ,
loadedContent
) {
if (loadedContent === null) {
return null;
}
if (loadedContent === undefined) {
return { filepath, config: undefined, isEmpty: true };
}
return { config: loadedContent, filepath };
}
createCosmiconfigResult(
filepath ,
content
) {
return Promise.resolve()
.then(() => {
return this.loadFileContent('async', filepath, content);
})
.then(loaderResult => {
return this.loadedContentToCosmiconfigResult(filepath, loaderResult);
});
}
createCosmiconfigResultSync(
filepath ,
content
) {
const loaderResult = this.loadFileContent('sync', filepath, content);
return this.loadedContentToCosmiconfigResult(filepath, loaderResult);
}
validateFilePath(filepath ) {
if (!filepath) {
throw new Error('load and loadSync must pass a non-empty string');
}
}
load(filepath ) {
return Promise.resolve().then(() => {
this.validateFilePath(filepath);
const absoluteFilePath = path.resolve(process.cwd(), filepath);
return cacheWrapper(this.loadCache, absoluteFilePath, () => {
return readFile(absoluteFilePath, { throwNotFound: true })
.then(content => {
return this.createCosmiconfigResult(absoluteFilePath, content);
})
.then(this.config.transform);
});
});
}
loadSync(filepath ) {
this.validateFilePath(filepath);
const absoluteFilePath = path.resolve(process.cwd(), filepath);
return cacheWrapper(this.loadSyncCache, absoluteFilePath, () => {
const content = readFile.sync(absoluteFilePath, { throwNotFound: true });
const result = this.createCosmiconfigResultSync(
absoluteFilePath,
content
);
return this.config.transform(result);
});
}
}
module.exports = function createExplorer(options ) {
const explorer = new Explorer(options);
return {
search: explorer.search.bind(explorer),
searchSync: explorer.searchSync.bind(explorer),
load: explorer.load.bind(explorer),
loadSync: explorer.loadSync.bind(explorer),
clearLoadCache: explorer.clearLoadCache.bind(explorer),
clearSearchCache: explorer.clearSearchCache.bind(explorer),
clearCaches: explorer.clearCaches.bind(explorer),
};
};
function nextDirUp(dir ) {
return path.dirname(dir);
}
function getExtensionDescription(filepath ) {
const ext = path.extname(filepath);
return ext ? `extension "${ext}"` : 'files without extensions';
} | PypiClean |
/py-pure-client-1.38.0.tar.gz/py-pure-client-1.38.0/pypureclient/flasharray/FA_2_22/models/network_interface_port_details_static_tx_power_thresholds.py | import pprint
import re
import six
import typing
from ....properties import Property
if typing.TYPE_CHECKING:
from pypureclient.flasharray.FA_2_22 import models
class NetworkInterfacePortDetailsStaticTxPowerThresholds(object):
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'alarm_low': 'float',
'warn_low': 'float',
'warn_high': 'float',
'alarm_high': 'float'
}
attribute_map = {
'alarm_low': 'alarm_low',
'warn_low': 'warn_low',
'warn_high': 'warn_high',
'alarm_high': 'alarm_high'
}
required_args = {
}
def __init__(
self,
alarm_low=None, # type: float
warn_low=None, # type: float
warn_high=None, # type: float
alarm_high=None, # type: float
):
"""
Keyword args:
alarm_low (float): The alarm low threshold for Tx output power in mW.
warn_low (float): The warn low threshold for Tx output power in mW.
warn_high (float): The warn high threshold for Tx output power in mW.
alarm_high (float): The alarm high threshold for Tx output power in mW.
"""
if alarm_low is not None:
self.alarm_low = alarm_low
if warn_low is not None:
self.warn_low = warn_low
if warn_high is not None:
self.warn_high = warn_high
if alarm_high is not None:
self.alarm_high = alarm_high
def __setattr__(self, key, value):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `NetworkInterfacePortDetailsStaticTxPowerThresholds`".format(key))
self.__dict__[key] = value
def __getattribute__(self, item):
value = object.__getattribute__(self, item)
if isinstance(value, Property):
raise AttributeError
else:
return value
def __getitem__(self, key):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `NetworkInterfacePortDetailsStaticTxPowerThresholds`".format(key))
return object.__getattribute__(self, key)
def __setitem__(self, key, value):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `NetworkInterfacePortDetailsStaticTxPowerThresholds`".format(key))
object.__setattr__(self, key, value)
def __delitem__(self, key):
if key not in self.attribute_map:
raise KeyError("Invalid key `{}` for `NetworkInterfacePortDetailsStaticTxPowerThresholds`".format(key))
object.__delattr__(self, key)
def keys(self):
return self.attribute_map.keys()
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
if hasattr(self, attr):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(NetworkInterfacePortDetailsStaticTxPowerThresholds, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, NetworkInterfacePortDetailsStaticTxPowerThresholds):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other | PypiClean |
/Pyccuracy-1.2.47.tar.gz/Pyccuracy-1.2.47/pyccuracy/help.py | import os
import re
CURR_DIR = os.path.dirname(__file__) or '.'
class LanguageViewer(object):
ACTIONS = ['page', 'button', 'checkbox', 'div', 'image',
'link', 'radio', 'select', 'textbox', 'element', ]
def __init__(self, language='en-us'):
self.languages_dir = CURR_DIR + '/languages/data'
self.language = language
self.actions = {}
self._set_all_actions()
def _set_all_actions(self):
language_filename = os.path.join(self.languages_dir, '%s.txt' % self.language)
if not os.path.exists(language_filename):
raise Exception, 'Language file not found: %s' % language_filename
language_file = open(language_filename)
possible_action_lines = []
for line in language_file:
line = line.strip()
if not line.startswith('#') and '=' in line:
values = line.split('=')
left = values[0].strip()
right = "=".join(values[1:]).strip()
splitted_left_operand = left.split('_')
if splitted_left_operand[-1] == 'regex' and splitted_left_operand[0] in self.ACTIONS:
action_name = '_'.join(splitted_left_operand[:-1])
new_right_value = self.make_it_readable(right)
self.actions[action_name] = new_right_value
language_file.close()
def make_it_readable(self, value):
url_regex = "(?P<url>[\\\"](([\w:/._-]|\=|\?|\&|\\\"|\;|\%)+)[\\\"]|([\w\s_.-]+))$"
value = value.replace(url_regex, '[page|"url"]') #replace urls
value = value.replace('(?P<url>([\w\s_.-]+))', 'page')
value = value.replace('(?P<parameters>.+)', 'parameters')
value = re.sub(r'\(\?\P\<([\w\s]*)\>\<([\w\s]*)\>\)', r'[\1|\2]', value)
value = re.sub(r'\(\?\P\<([\w\s]*)\>\[\^\"\]\+\)', r'\1', value)
value = re.sub(r'\(\?\P\<([\w\s]*)\>\.\+\)', r'\1', value)
value = re.sub(r'\(\?\P\<([\w\s]*)\>\\d\+\)', r'X', value)
value = re.sub(r'\(\?\P\<\w\>(.*)\)', r'\1', value)
value = re.sub(r'\(\?\P\<\w*\>\\d\+\(\[\.\]\\d\+\)\?\)', '[X|X.X]', value)
value = re.sub(r'\P\<\w*\>', '', value)
value = value.replace('[\\"]', '"') #replace quotes
value = value.replace('(.+)', 'blah') #replace random text
value = value.replace('\d+', 'X').replace('(X)', 'X') #replace digits
value = value.replace('[\\\"\\\']', '"').replace('[\\\'\\\"]', '"') #replace quotes
value = value.replace('X([.]X)?', '[X|X.X]')
value = value.replace('?', '').replace('$', '').replace('^', '')
value = value.replace('{1}', '')
return value
def get_actions(self, term):
matches = {}
for key in self.actions.keys():
if term in key:
matches[key] = self.actions.get(key)
return matches | PypiClean |
/pulumi_gcp-6.65.0a1693462587.tar.gz/pulumi_gcp-6.65.0a1693462587/pulumi_gcp/iap/get_web_backend_service_iam_policy.py |
import copy
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = [
'GetWebBackendServiceIamPolicyResult',
'AwaitableGetWebBackendServiceIamPolicyResult',
'get_web_backend_service_iam_policy',
'get_web_backend_service_iam_policy_output',
]
@pulumi.output_type
class GetWebBackendServiceIamPolicyResult:
"""
A collection of values returned by getWebBackendServiceIamPolicy.
"""
def __init__(__self__, etag=None, id=None, policy_data=None, project=None, web_backend_service=None):
if etag and not isinstance(etag, str):
raise TypeError("Expected argument 'etag' to be a str")
pulumi.set(__self__, "etag", etag)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if policy_data and not isinstance(policy_data, str):
raise TypeError("Expected argument 'policy_data' to be a str")
pulumi.set(__self__, "policy_data", policy_data)
if project and not isinstance(project, str):
raise TypeError("Expected argument 'project' to be a str")
pulumi.set(__self__, "project", project)
if web_backend_service and not isinstance(web_backend_service, str):
raise TypeError("Expected argument 'web_backend_service' to be a str")
pulumi.set(__self__, "web_backend_service", web_backend_service)
@property
@pulumi.getter
def etag(self) -> str:
"""
(Computed) The etag of the IAM policy.
"""
return pulumi.get(self, "etag")
@property
@pulumi.getter
def id(self) -> str:
"""
The provider-assigned unique ID for this managed resource.
"""
return pulumi.get(self, "id")
@property
@pulumi.getter(name="policyData")
def policy_data(self) -> str:
"""
(Required only by `iap.WebBackendServiceIamPolicy`) The policy data generated by
a `organizations_get_iam_policy` data source.
"""
return pulumi.get(self, "policy_data")
@property
@pulumi.getter
def project(self) -> str:
return pulumi.get(self, "project")
@property
@pulumi.getter(name="webBackendService")
def web_backend_service(self) -> str:
return pulumi.get(self, "web_backend_service")
class AwaitableGetWebBackendServiceIamPolicyResult(GetWebBackendServiceIamPolicyResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetWebBackendServiceIamPolicyResult(
etag=self.etag,
id=self.id,
policy_data=self.policy_data,
project=self.project,
web_backend_service=self.web_backend_service)
def get_web_backend_service_iam_policy(project: Optional[str] = None,
web_backend_service: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetWebBackendServiceIamPolicyResult:
"""
Retrieves the current IAM policy data for webbackendservice
## example
```python
import pulumi
import pulumi_gcp as gcp
policy = gcp.iap.get_web_backend_service_iam_policy(project=google_compute_backend_service["default"]["project"],
web_backend_service=google_compute_backend_service["default"]["name"])
```
:param str project: The ID of the project in which the resource belongs.
If it is not provided, the project will be parsed from the identifier of the parent resource. If no project is provided in the parent identifier and no project is specified, the provider project is used.
:param str web_backend_service: Used to find the parent resource to bind the IAM policy to
"""
__args__ = dict()
__args__['project'] = project
__args__['webBackendService'] = web_backend_service
opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)
__ret__ = pulumi.runtime.invoke('gcp:iap/getWebBackendServiceIamPolicy:getWebBackendServiceIamPolicy', __args__, opts=opts, typ=GetWebBackendServiceIamPolicyResult).value
return AwaitableGetWebBackendServiceIamPolicyResult(
etag=pulumi.get(__ret__, 'etag'),
id=pulumi.get(__ret__, 'id'),
policy_data=pulumi.get(__ret__, 'policy_data'),
project=pulumi.get(__ret__, 'project'),
web_backend_service=pulumi.get(__ret__, 'web_backend_service'))
@_utilities.lift_output_func(get_web_backend_service_iam_policy)
def get_web_backend_service_iam_policy_output(project: Optional[pulumi.Input[Optional[str]]] = None,
web_backend_service: Optional[pulumi.Input[str]] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetWebBackendServiceIamPolicyResult]:
"""
Retrieves the current IAM policy data for webbackendservice
## example
```python
import pulumi
import pulumi_gcp as gcp
policy = gcp.iap.get_web_backend_service_iam_policy(project=google_compute_backend_service["default"]["project"],
web_backend_service=google_compute_backend_service["default"]["name"])
```
:param str project: The ID of the project in which the resource belongs.
If it is not provided, the project will be parsed from the identifier of the parent resource. If no project is provided in the parent identifier and no project is specified, the provider project is used.
:param str web_backend_service: Used to find the parent resource to bind the IAM policy to
"""
... | PypiClean |
/recipe_scrapers-14.11.0.tar.gz/recipe_scrapers-14.11.0/recipe_scrapers/vegolosi.py | from ._abstract import AbstractScraper
from ._utils import get_minutes, get_yields, normalize_string
class Vegolosi(AbstractScraper):
@classmethod
def host(cls):
return "vegolosi.it"
def title(self):
return self.soup.find("h1").get_text().strip()
def preparation_time(self):
possible_time_info_elements = self.soup.findAll(
"span", {"class": "tasty-recipes-prep-time"}
)
return sum([get_minutes(element) for element in possible_time_info_elements])
def cooking_time(self):
possible_time_info_elements = self.soup.findAll(
"span", {"class": "tasty-recipes-cook-time"}
)
return sum([get_minutes(element) for element in possible_time_info_elements])
def total_time(self):
possible_time_info_elements = self.soup.findAll(
"span", {"class": "tasty-recipes-total-time"}
)
return sum([get_minutes(element) for element in possible_time_info_elements])
def yields(self):
possible_yields_info_elements = self.soup.findAll(
"span", {"class": "tasty-recipes-yield"}
)
for element in possible_yields_info_elements:
if "persone" in element.get_text():
return get_yields(element)
def ingredients(self):
ingredients = self.soup.select(".tasty-recipe-ingredients > ul > li")
if not ingredients:
ingredients = self.soup.findAll("li", {"class": "ingredient"})
return [normalize_string(ingredient.get_text()) for ingredient in ingredients]
def instructions(self):
instructions = self.soup.findAll("div", {"class": "tasty-recipe-instructions"})
return "\n".join(
[normalize_string(instruction.get_text()) for instruction in instructions]
)
def ratings(self):
return round(
float(
self.soup.find("div", {"class": "tasty-recipe-rating rating_panel"})
.get("data-content-rate")
.replace(",", ".")
),
2,
) | PypiClean |
/volatility3-2.4.1.tar.gz/volatility3-2.4.1/doc/source/simple-plugin.rst | How to Write a Simple Plugin
============================
This guide will step through how to construct a simple plugin using Volatility 3.
The example plugin we'll use is :py:class:`~volatility3.plugins.windows.dlllist.DllList`, which features the main traits
of a normal plugin, and reuses other plugins appropriately.
.. note::
This document will not include the complete code necessary for a
working plugin (such as imports, etc) since it's designed to focus on the necessary components for writing a plugin.
For complete and functioning plugins, the ``framework/plugins`` directory should be consulted.
Inherit from PluginInterface
----------------------------
The first step is to define a class that inherits from :py:class:`~volatility3.framework.interfaces.plugins.PluginInterface`.
Volatility automatically finds all plugins defined under the various plugin directories by importing them and then
making use of any classes that inherit from :py:class:`~volatility3.framework.interfaces.plugins.PluginInterface`.
::
from volatility3.framework import interfaces
class DllList(interfaces.plugins.PluginInterface):
The next step is to define the requirements of the plugin, these will be converted into options the user can provide
based on the User Interface.
Define the plugin requirements
------------------------------
These requirements are the names of variables that will need to be populated in the configuration tree for the plugin
to be able to run properly. Any that are defined as optional need not necessarily be provided.
::
_version = (1, 0, 0)
_required_framework_version = (2, 0, 0)
@classmethod
def get_requirements(cls):
return [requirements.ModuleRequirement(name = 'kernel', description = 'Windows kernel',
architectures = ["Intel32", "Intel64"]),
requirements.ListRequirement(name = 'pid',
element_type = int,
description = "Process IDs to include (all other processes are excluded)",
optional = True),
requirements.PluginRequirement(name = 'pslist',
plugin = pslist.PsList,
version = (2, 0, 0))]
This is a classmethod, because it is called before the specific plugin object has been instantiated (in order to know how
to instantiate the plugin). At the moment these requirements are fairly straightforward:
::
requirements.ModuleRequirement(name = 'kernel', description = 'Windows kernel',
architectures = ["Intel32", "Intel64"]),
This requirement specifies the need for a particular submodule. Each module requires a
:py:class:`TranslationLayer <volatility3.framework.interfaces.layers.TranslationLayerInterface>` and a
:py:class:`SymbolTable <volatility3.framework.interfaces.symbols.SymbolTableInterface>`, which are fulfilled by two
subrequirements: a
:py:class:`~volatility3.framework.configuration.requirements.TranslationLayerRequirement` and a
:py:class:`~volatility3.framework.configuration.requirements.SymbolTableRequirement`. At the moment, the automagic
only fills `ModuleRequirements` with kernels, and so has relatively few parameters. It requires the architecture for
the underlying TranslationLayer, and the offset of the module within that layer.
The name of the module will be stored in the ``kernel`` configuration option, and the module object itself
can be accessed from the ``context.modules`` collection. This requirement is a Complex Requirement and therefore will
not be requested directly from the user.
.. note::
In previous versions of volatility 3, there was no `ModuleRequirement`, and instead two requirements were defined
a :py:class:`TranslationLayer <volatility3.framework.interfaces.layers.TranslationLayerInterface>` and a `SymbolTableRequirement`. These still exist, and can be used, most plugins just
define a single `ModuleRequirement` for the kernel, which the automagic will populate. The `ModuleRequirement` has
two automatic sub-requirements, a `TranslationLayerRequirement` and a `SymbolTableRequirement`, but the module also
includes the offset of the module, and will allow future expansion to specify specific modules when application
level plugins become more common. Below are how the requirements would be specified:
::
requirements.TranslationLayerRequirement(name = 'primary',
description = 'Memory layer for the kernel',
architectures = ["Intel32", "Intel64"]),
This requirement indicates that the plugin will operate on a single
:py:class:`TranslationLayer <volatility3.framework.interfaces.layers.TranslationLayerInterface>`. The name of the
loaded layer will appear in the plugin's configuration under the name ``primary``. Requirement values can be
accessed within the plugin through the plugin's `config` attribute (for example ``self.config['pid']``).
.. note:: The name itself is dynamic depending on the other layers already present in the Context. Always use the value
from the configuration rather than attempting to guess what the layer will be called.
Finally, this defines that the translation layer must be on the Intel Architecture. At the moment, this acts as a filter,
failing to be satisfied by memory images that do not match the architecture required.
Most plugins will only operate on a single layer, but it is entirely possible for a plugin to request two different
layers, for example a plugin that carries out some form of difference or statistics against multiple memory images.
This requirement (and the next two) are known as Complex Requirements, and user interfaces will likely not directly
request a value for this from a user. The value stored in the configuration tree for a
:py:class:`~volatility3.framework.configuration.requirements.TranslationLayerRequirement` is
the string name of a layer present in the context's memory that satisfies the requirement.
::
requirements.SymbolTableRequirement(name = "nt_symbols",
description = "Windows kernel symbols"),
This requirement specifies the need for a particular
:py:class:`SymbolTable <volatility3.framework.interfaces.symbols.SymbolTableInterface>`
to be loaded. This gets populated by various
:py:class:`Automagic <volatility3.framework.interfaces.automagic.AutoMagicInterface>` as the nearest sibling to a particular
:py:class:`~volatility3.framework.configuration.requirements.TranslationLayerRequirement`.
This means that if the :py:class:`~volatility3.framework.configuration.requirements.TranslationLayerRequirement`
is satisfied and the :py:class:`Automagic <volatility3.framework.interfaces.automagic.AutoMagicInterface>` can determine
the appropriate :py:class:`SymbolTable <volatility3.framework.interfaces.symbols.SymbolTableInterface>`, the
name of the :py:class:`SymbolTable <volatility3.framework.interfaces.symbols.SymbolTableInterface>` will be stored in the configuration.
This requirement is also a Complex Requirement and therefore will not be requested directly from the user.
::
requirements.ListRequirement(name = 'pid',
description = 'Filter on specific process IDs',
element_type = int,
optional = True),
The next requirement is a List Requirement, populated by integers. The description will be presented to the user to
describe what the value represents. The optional flag indicates that the plugin can function without the ``pid`` value
being defined within the configuration tree at all.
::
requirements.PluginRequirement(name = 'pslist',
plugin = pslist.PsList,
version = (2, 0, 0))]
This requirement indicates that the plugin will make use of another plugin's code, and specifies the version requirements
on that plugin. The version is specified in terms of Semantic Versioning meaning that, to be compatible, the major
versions must be identical and the minor version must be equal to or higher than the one provided. This requirement
does not make use of any data from the configuration, even if it were provided, it is merely a functional check before
running the plugin. To define the version of a plugin, populate the `_version` class variable as a tuple of version
numbers `(major, minor, patch)`. So for example:
::
_version = (1, 0, 0)
The plugin may also require a specific version of the framework, and this also uses Semantic Versioning, and can be
set by defining the `_required_framework_version`. The major version should match the version of volatility the plugin
is to be used with, which at the time of writing would be 2.2.0, and so would be specified as below. If only features, for example,
from 2.0.0 are used, then the lowest applicable version number should be used to support the greatest number of
installations:
::
_required_framework_version = (2, 0, 0)
Define the `run` method
-----------------------
The run method is the primary method called on a plugin. It takes no parameters (these have been passed through the
context's configuration tree, and the context is provided at plugin initialization time) and returns an unpopulated
:py:class:`~volatility3.framework.interfaces.renderers.TreeGrid` object. These are typically constructed based on a
generator that carries out the bulk of the plugin's processing. The
:py:class:`~volatility3.framework.interfaces.renderers.TreeGrid` also specifies the column names and types
that will be output as part of the :py:class:`~volatility3.framework.interfaces.renderers.TreeGrid`.
::
def run(self):
filter_func = pslist.PsList.create_pid_filter(self.config.get('pid', None))
kernel = self.context.modules[self.config['kernel']]
return renderers.TreeGrid([("PID", int),
("Process", str),
("Base", format_hints.Hex),
("Size", format_hints.Hex),
("Name", str),
("Path", str)],
self._generator(pslist.PsList.list_processes(self.context,
kernel.layer_name,
kernel.symbol_table_name,
filter_func = filter_func)))
In this instance, the plugin constructs a filter (using the PsList plugin's *classmethod* for creating filters).
It checks the plugin's configuration for the ``pid`` value, and passes it in as a list if it finds it, or None if
it does not. The :py:func:`~volatility3.plugins.windows.pslist.PsList.create_pid_filter` method accepts a list of process
identifiers that are included in the list. If the list is empty, all processes are returned.
The next line specifies the columns by their name and type. The types are simple types (int, str, bytes, float, and bool)
but can also provide hints as to how the output should be displayed (such as a hexadecimal number, using
:py:class:`volatility3.framework.renderers.format_hints.Hex`).
This indicates to user interfaces that the value should be displayed in a particular way, but does not guarantee that the value
will be displayed that way (for example, if it doesn't make sense to do so in a particular interface).
Finally, the generator is provided. The generator accepts a list of processes, which is gathered using a different plugin,
the :py:class:`~volatility3.plugins.windows.pslist.PsList` plugin. That plugin features a *classmethod*,
so that other plugins can call it. As such, it takes all the necessary parameters rather than accessing them
from a configuration. Since it must be portable code, it takes a context, as well as the layer name,
symbol table and optionally a filter. In this instance we unconditionally
pass it the values from the configuration for the layer and symbol table from the kernel module object, constructed from
the ``kernel`` configuration requirement. This will generate a list
of :py:class:`~volatility3.framework.symbols.windows.extensions.EPROCESS` objects, as provided by the :py:class:`~volatility.plugins.windows.pslist.PsList` plugin,
and is not covered here but is used as an example for how to share code across plugins
(both as the provider and the consumer of the shared code).
Define the generator
--------------------
The :py:class:`~volatility3.framework.interfaces.renderers.TreeGrid` can be populated without a generator,
but it is quite a common model to use. This is where the main processing for this plugin lives.
::
def _generator(self, procs):
for proc in procs:
for entry in proc.load_order_modules():
BaseDllName = FullDllName = renderers.UnreadableValue()
try:
BaseDllName = entry.BaseDllName.get_string()
# We assume that if the BaseDllName points to an invalid buffer, so will FullDllName
FullDllName = entry.FullDllName.get_string()
except exceptions.InvalidAddressException:
pass
yield (0, (proc.UniqueProcessId,
proc.ImageFileName.cast("string", max_length = proc.ImageFileName.vol.count,
errors = 'replace'),
format_hints.Hex(entry.DllBase), format_hints.Hex(entry.SizeOfImage),
BaseDllName, FullDllName))
This iterates through the list of processes and for each one calls the :py:meth:`~volatility3.framework.symbols.windows.extensions.EPROCESS.load_order_modules` method on it. This provides
a list of the loaded modules within the process.
The plugin then defaults the ``BaseDllName`` and ``FullDllName`` variables to an :py:class:`~volatility3.framework.renderers.UnreadableValue`,
which is a way of indicating to the user interface that the value couldn't be read for some reason (but that it isn't fatal).
There are currently four different reasons a value may be unreadable:
* **Unreadable**: values which are empty because the data cannot be read
* **Unparsable**: values which are empty because the data cannot be interpreted correctly
* **NotApplicable**: values which are empty because they don't make sense for this particular entry
* **NotAvailable**: values which cannot be provided now (but might in a future run, via new symbols or an updated plugin)
This is a safety provision to ensure that the data returned by the Volatility library is accurate and describes why
information may not be provided.
The plugin then takes the process's ``BaseDllName`` value, and calls :py:meth:`~volatility3.framework.symbols.windows.extensions.UNICODE_STRING.get_string` on it. All structure attributes,
as defined by the symbols, are directly accessible and use the case-style of the symbol library it came from (in Windows,
attributes are CamelCase), such as ``entry.BaseDllName`` in this instance. Any attributes not defined by the symbol but added
by Volatility extensions cannot be properties (in case they overlap with the attributes defined in the symbol libraries)
and are therefore always methods and prepended with ``get_``, in this example ``BaseDllName.get_string()``.
Finally, ``FullDllName`` is populated. These operations read from memory, and as such, the memory image may be unable to
read the data at a particular offset. This will cause an exception to be thrown. In Volatility 3, exceptions are thrown
as a means of communicating when something exceptional happens. It is the responsibility of the plugin developer to
appropriately catch and handle any non-fatal exceptions and otherwise allow the exception to be thrown by the user interface.
In this instance, the :py:class:`~volatility3.framework.exceptions.InvalidAddressException` class is caught, which is thrown
by any layer which cannot access an offset requested of it. Since we have already populated both values with ``UnreadableValue``
we do not need to write code for the exception handler.
Finally, we yield the record in the format required by the :py:class:`~volatility3.framework.interfaces.renderers.TreeGrid`,
a tuple, listing the indentation level (for trees) and then the list of values for each column.
This plugin demonstrates casting a value ``ImageFileName`` to ensure it's returned
as a string with a specific maximum length, rather than its original type (potentially an array of characters, etc).
This is carried out using the :py:meth:`~volatility3.framework.interfaces.objects.ObjectInterface.cast` method which takes a type (either a native type, such as string or pointer, or a
structure type defined in a :py:class:`SymbolTable <volatility3.framework.interfaces.symbols.SymbolTableInterface>`
such as ``<table>!_UNICODE``) and the parameters to that type.
Since the cast value must populate a string typed column, it had to be a Python string (such as being cast to the native
type string) and could not have been a special Structure such as ``_UNICODE``. For the format hint columns, the format
hint type must be used to ensure the error checking does not fail.
| PypiClean |
/baiduads-sdk-auto-snapshot-2022.2.1.5.tar.gz/baiduads-sdk-auto-snapshot-2022.2.1.5/baiduads/extaudience/model/get_audience_list_response_wrapper.py | import re # noqa: F401
import sys # noqa: F401
from baiduads.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
OpenApiModel
)
from baiduads.exceptions import ApiAttributeError
def lazy_import():
from baiduads.common.model.api_response_header import ApiResponseHeader
from baiduads.extaudience.model.get_audience_list_response_wrapper_body import GetAudienceListResponseWrapperBody
globals()['ApiResponseHeader'] = ApiResponseHeader
globals()['GetAudienceListResponseWrapperBody'] = GetAudienceListResponseWrapperBody
class GetAudienceListResponseWrapper(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
}
validations = {
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'header': (ApiResponseHeader,), # noqa: E501
'body': (GetAudienceListResponseWrapperBody,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'header': 'header', # noqa: E501
'body': 'body', # noqa: E501
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs): # noqa: E501
"""GetAudienceListResponseWrapper - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
header (ApiResponseHeader): [optional] # noqa: E501
body (GetAudienceListResponseWrapperBody): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""GetAudienceListResponseWrapper - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
header (ApiResponseHeader): [optional] # noqa: E501
body (GetAudienceListResponseWrapperBody): [optional] # noqa: E501
"""
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes.") | PypiClean |
/dag_workflows-1.0.5-py3-none-any.whl/workflows/dags.py | import importlib.util
import logging
import os
import re
import yaml
from workflows import bases
logger = logging.getLogger(__name__)
def register_directory(folder_path: str):
"""
Given a folder, register all DAG folders inside it.
"""
registry = bases.DagRegistry()
for entry in os.scandir(folder_path): # type: os.DirEntry
if entry.is_dir():
if entry.name in ("__pycache__",):
continue # skip
try:
registry[entry.name] = register_dag(entry.path)
logger.debug("Registered DAG %s", entry.path)
except Exception as exc: # pylint: disable=too-broad-exception
registry[entry.name] = exc
logger.warning("Could not register DAG in %s: %s", entry.path, exc)
registry.validate()
return registry
def register_dag(folder_path: str) -> bases.DAG:
"""
Given a folder, register it if it has a workflow.yaml file.
"""
spec_file = os.path.join(folder_path, "workflow.yaml")
if not os.path.isfile(spec_file):
raise bases.RegistrationError(f"No workflow.yaml file found in {folder_path}")
spec = yaml.safe_load(open(spec_file))
dag = bases.DAG(folder=folder_path, **spec)
for entry in os.scandir(folder_path): # type: os.DirEntry
if entry.is_file():
if entry.name in ("__init__.py", ".DS_Store", "workflow.yaml"):
continue # skip non-transforms
register_task(dag, entry.path)
dag.validate()
return dag
SQL_COMMENT = re.compile(r"/\*(.*)\*/", re.MULTILINE + re.DOTALL)
def register_task(dag: bases.DAG, file_path: str) -> None:
"""
Load a transform file and add it to the DAG
"""
if file_path.endswith(".py"):
module_spec = importlib.util.spec_from_file_location("task", file_path)
module = importlib.util.module_from_spec(module_spec)
module_spec.loader.exec_module(module)
spec = yaml.safe_load(module.__doc__)
spec["run"] = module.run
elif file_path.endswith(".sql"):
sql = open(file_path).read()
spec_text = SQL_COMMENT.match(sql)
if not spec_text:
raise bases.RegistrationError("Cannot find a YAML spec in file %s", file_path)
spec = yaml.safe_load(spec_text.group(1))
spec["sql"] = sql
elif file_path.endswith(".yaml"):
spec = yaml.safe_load(open(file_path))
else:
raise bases.RegistrationError("Unsupported file type: %s", file_path)
task_type = spec.pop("type", None)
if not task_type:
raise bases.RegistrationError("`type` is a required field in task %s", file_path)
task_class = bases.load_class(task_type, bases.TASK_TYPES)
name = os.path.basename(file_path)
task_class(dag=dag, name=name, **spec) | PypiClean |