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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f60a93633ec0cdca46c588929e221bac483a4f51 | 18,604 | py | Python | miniframe/kernels.py | jdavidrcamacho/mini-frame | a07b9ef83f57ae1a7178e73092297ca8b68a845e | [
"MIT"
] | 3 | 2018-12-11T20:53:42.000Z | 2021-11-04T16:23:34.000Z | miniframe/kernels.py | jdavidrcamacho/mini-frame | a07b9ef83f57ae1a7178e73092297ca8b68a845e | [
"MIT"
] | 6 | 2018-03-06T20:17:56.000Z | 2018-06-22T13:02:03.000Z | miniframe/kernels.py | jdavidrcamacho/mini-frame | a07b9ef83f57ae1a7178e73092297ca8b68a845e | [
"MIT"
] | 1 | 2018-03-06T20:13:55.000Z | 2018-03-06T20:13:55.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
pi = np.pi
class kernel(object):
""" Definition the base kernel class """
is_kernel = True
def __init__(self, *args):
""" Puts all kernel arguments in an array pars """
self.pars = np.array(args)
def __call__(self, r):
""" r = t - t' """
raise NotImplementedError
def __add__(self, b):
if not hasattr(b, "is_kernel"):
return Sum(Constant(c=float(b)), self)
return Sum(self, b)
def __radd__(self, b):
return self.__add__(b)
def __mul__(self, b):
if not hasattr(b, "is_kernel"):
return Product(Constant(c=float(b)), self)
return Product(self, b)
def __rmul__(self, b):
return self.__mul__(b)
def __repr__(self):
""" Representation of each kernel instance """
return "{0}({1})".format(self.__class__.__name__,
", ".join(map(str, self.pars)))
class _operator(kernel):
""" To allow operations between two kernels """
def __init__(self, k1, k2):
self.k1 = k1
self.k2 = k2
@property
def pars(self):
return np.append(self.k1.pars, self.k2.pars)
class Sum(_operator):
""" Sum of two kernels """
def __repr__(self):
return "{0} + {1}".format(self.k1, self.k2)
def __call__(self, r):
return self.k1(r) + self.k2(r)
class Product(_operator):
""" Product of two kernels """
def __repr__(self):
return "{0} * {1}".format(self.k1, self.k2)
def __call__(self, r):
return self.k1(r) * self.k2(r)
class Constant(kernel):
""" This kernel returns its constant argument c """
def __init__(self, c):
super(Constant, self).__init__(c)
self.c = c
def __call__(self, r):
return self.c * np.ones_like(r)
# Squared exponential kernel
class SquaredExponential(kernel):
"""
Squared Exponential kernel, also known as radial basis function
(RBF kernel) in other works.
Parameters:
ell: float
Length-scale, lambda in the paper
wn: float
White noise amplitude
"""
def __init__(self, ell, wn):
super(SquaredExponential, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r**2
f2 = self.ell**2
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return np.exp(-0.5 *f1/f2) + fwn
except ValueError:
f1 = r**2
f2 = self.ell**2
return np.exp(-0.5 *f1/f2)
class dSE_dt1(SquaredExponential):
"""
Derivative of the SquaredExponential kernel in order to t1.
"""
def __init__(self, ell, wn):
super(dSE_dt1, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r
f2 = self.ell**2
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return -f1/f2 *np.exp(-0.5*f1*f1/f2) + fwn
except ValueError:
f1 = r
f2 = self.ell**2
return -f1/f2 *np.exp(-0.5*f1*f1/f2)
class dSE_dt2(SquaredExponential):
"""
Derivative of the SquaredExponential kernel in order to t2.
"""
def __init__(self, ell, wn):
super(dSE_dt2, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r
f2 = self.ell**2
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return f1/f2 *np.exp(-0.5*f1*f1/f2) + fwn
except ValueError:
f1 = r
f2 = self.ell**2
return f1/f2 *np.exp(-0.5*f1*f1/f2)
class ddSE_dt2dt1(SquaredExponential):
"""
Derivative of the SquaredExponential kernel, one time in order to t1 and
another in order to t2.
"""
def __init__(self, ell, wn):
super(ddSE_dt2dt1, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r**2
f2 = self.ell**2
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (1.0/f2 -f1/f2**2) *np.exp(-0.5*f1/f2) + fwn
except ValueError:
f1 = r**2
f2 = self.ell**2
return (1.0/f2 -f1/f2**2) *np.exp(-0.5*f1/f2)
class dddSE_dt2ddt1(SquaredExponential):
"""
Derivative of the SquaredExponential kernel, two times in order to t1 and
one in order to t2.
"""
def __init__(self, ell, wn):
super(dddSE_dt2ddt1, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r
f11 = r**2
f111 = r**3
f2 = self.ell**2
f22 = self.ell**4
f222 = self.ell**6
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (f111/f222 -3.0*f1/f22) *np.exp(-0.5*f11/f2) + fwn
except ValueError:
f1 = r
f11 = r**2
f111 = r**3
f2 = self.ell**2
f22 = self.ell**4
f222 = self.ell**6
return (f111/f222 -3.0*f1/f22) *np.exp(-0.5*f11/f2)
class dddSE_ddt2dt1(SquaredExponential):
"""
Derivative of the SquaredExponential kernel, one time in order to t1 and
two times in order to t2. Equation A6 in the paper, for N=1.
"""
def __init__(self, ell, wn):
super(dddSE_ddt2dt1, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r
f11 = r**2
f111 = r**3
f2 = self.ell**2
f22 = self.ell**4
f222 = self.ell**6
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (-f111/f222 +3.0*f1/f22) *np.exp(-0.5*f11/f2) + fwn
except ValueError:
f1 = r
f11 = r**2
f111 = r**3
f2 = self.ell**2
f22 = self.ell**4
f222 = self.ell**6
return (-f111/f222 +3.0*f1/f22) *np.exp(-0.5*f11/f2)
class ddddSE_ddt2ddt1(SquaredExponential):
"""
Derivative of the SquaredExponential kernel, two times in order to t1 and
two times in order to t2. Equation A6 in the paper, for N=1.
"""
def __init__(self, ell, wn):
super(ddddSE_ddt2ddt1, self).__init__(ell, wn)
self.ell = ell
self.wn = wn
def __call__(self, r):
try:
f1 = r**2
f11 = r**4
f2 = self.ell**2
f22 = self.ell**4
f222 = self.ell**6
f2222 = self.ell**8
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (f11/f2222 -6.0*f1/f222 +3.0/f22) *np.exp(-0.5*f1/f2) + fwn
except ValueError:
f1 = r**2
f11 = r**4
f2 = self.ell**2
f22 = self.ell**4
f222 = self.ell**6
f2222 = self.ell**8
return (f11/f2222 -6.0*f1/f222 +3.0/f22) *np.exp(-0.5*f1/f2)
# Quasi-periodic kernel
class QuasiPeriodic(kernel):
"""
This kernel is the product between the exponential sine squared kernel and
the squared exponential kernel. It is known as the quasi-periodic kernel.
Equation 27 in the paper.
Parameters
----------
theta: float
Kernel amplitude
ell_e: float
Evolutionary time scale
ell_p: float
Length scale of the periodic component
Period: float
Kernel periodicity
wn: float
White noise amplitude
"""
def __init__(self, ell_e, ell_p, period, wn):
super(QuasiPeriodic, self).__init__(ell_e, ell_p, period, wn)
self.ell_e = ell_e
self.ell_p = ell_p
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return np.exp( -(2.0*f5*f5/f2) -0.5*f1*f1/f3 ) + fwn
except ValueError:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
return np.exp( -(2.0*f5*f5/f2) -0.5*f1*f1/f3)
class dQP_dt1(QuasiPeriodic):
"""
Derivative of the QuasiPeriodic kernel, in order to t1. Equation A8 in the
paper.
"""
def __init__(self, ell_e, ell_p, period, wn):
super(dQP_dt1, self).__init__(ell_e, ell_p, period, wn)
self.ell_e = ell_e
self.ell_p = ell_p
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
f6 = np.cos(pi*f1/f4)
f7 = np.exp( - 2.0*f5*f5/f2 - 0.5*f1*f1/f3 )
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (-(4*pi*f5*f6)/(f2*f4) -f1/f3) *f7 +fwn
except ValueError:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
f6 = np.cos(pi*f1/f4)
f7 = np.exp( - 2.0*f5*f5/f2 - 0.5*f1*f1/f3 )
return (-(4*pi*f5*f6)/(f2*f4) -f1/f3) *f7
class dQP_dt2(QuasiPeriodic):
"""
Derivative of the QuasiPeriodic kernel, in order to t2. Equation A9 in the
paper.
"""
def __init__(self, ell_e, ell_p, period, wn):
super(dQP_dt2, self).__init__(ell_e, ell_p, period, wn)
self.ell_e = ell_e
self.ell_p = ell_p
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
f6 = np.cos(pi*f1/f4)
f7 = np.exp( -(2.0*f5*f5/f2) - 0.5*f1*f1/f3 )
fwn = self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return ((4*pi*f5*f6)/(f2*f4) +f1/f3) *f7 +fwn
except ValueError:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
f6 = np.cos(pi*f1/f4)
f7 = np.exp( -(2.0*f5*f5/f2) - 0.5*f1*f1/f3 )
return ((4*pi*f5*f6)/(f2*f4) +f1/f3) *f7
class ddQP_dt2dt1(QuasiPeriodic):
"""
Derivative of the QuasiPeriodic kernel, one time in order to t1 and another
in order to t2. Equation A10 in the paper.
"""
def __init__(self, ell_e, ell_p, period, wn):
super(ddQP_dt2dt1, self).__init__(ell_e, ell_p, period, wn)
self.ell_e = ell_e
self.ell_p = ell_p
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
f6 = np.cos(pi*f1/f4)
f7 = np.exp( -(2.0*f5*f5/f2) - 0.5*f1*f1/f3 )
f8 = (-(4*pi*f5*f6)/(f2*f4) - f1/f3)
f9 = ((4*pi*f5*f6)/(f2*f4) + f1/f3)
fwn=self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (f8*f9 +1.0/f3 +4*pi*pi*f6*f6/(f2*f4*f4) \
-4*pi*pi*f5*f5/(f2*f4*f4)) *f7 +fwn
except ValueError:
f1 = r
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f5 = np.sin(pi*f1/f4)
f6 = np.cos(pi*f1/f4)
f7 = np.exp( -(2.0*f5*f5/f2) - 0.5*f1*f1/f3 )
f8 = (-(4*pi*f5*f6)/(f2*f4) - f1/f3)
f9 = ((4*pi*f5*f6)/(f2*f4) + f1/f3)
return (f8*f9 +1.0/f3 +4*pi*pi*f6*f6/(f2*f4*f4) \
-4*pi*pi*f5*f5/(f2*f4*f4)) *f7
class dddQP_dt2ddt1(QuasiPeriodic):
"""
Derivative of the QuasiPeriodic kernel, two times in order to t1t1 and one
time in order t2. Equation A10 in the paper.
"""
def __init__(self, ell_e, ell_p, period, wn):
super(dddQP_dt2ddt1, self).__init__(ell_e, ell_p, period, wn)
self.ell_e = ell_e
self.ell_p = ell_p
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f11 = r**2
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f44 = self.period**2
f444 = self.period**3
f5 = np.sin(pi*f1/f4)
f55 = np.sin(pi*f1/f4)**2
f6 = np.cos(pi*f1/f4)
f66 = np.cos(pi*f1/f4)**2
f7 = np.exp( -(2.0*f55/f2) - 0.5*f11/f3 )
j1 = -1/f3 -4*pi*pi*f66/(f2*f44) +4*pi*pi*f55/(f2*f44)
j2 = f1/f3 + 4*pi*f5*f5/(f2*f4)
j3 = (-j2)**2
j4 = j2
j5 = -j1
j6 = -j2
j8 = 16*pi*pi*pi*f6*f5/(f2*f444)
fwn=self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (j1*j2 + j3*j4 + 2*j5*j6 - j8) *f7 +fwn
except ValueError:
f1 = r
f11 = r**2
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f44 = self.period**2
f444 = self.period**3
f5 = np.sin(pi*f1/f4)
f55 = np.sin(pi*f1/f4)**2
f6 = np.cos(pi*f1/f4)
f66 = np.cos(pi*f1/f4)**2
f7 = np.exp( -(2.0*f55/f2) - 0.5*f11/f3 )
j1 = -1/f3 -4*pi*pi*f66/(f2*f44) +4*pi*pi*f55/(f2*f44)
j2 = f1/f3 + 4*pi*f5*f5/(f2*f4)
j3 = (-j2)**2
j4 = j2
j5 = -j1
j6 = -j2
j8 = 16*pi*pi*pi*f6*f5/(f2*f444)
return (j1*j2 + j3*j4 + 2*j5*j6 - j8) *f7
class dddQP_ddt2dt1(QuasiPeriodic):
"""
Second derivative of the QuasiPeriodic kernel, one time in order to t1t1
and two times in order t2. Equation A10 in the paper.
"""
def __init__(self, ell_e, ell_p, period, wn):
super(dddQP_ddt2dt1, self).__init__(ell_e, ell_p, period, wn)
self.ell_p = ell_p
self.ell_e = ell_e
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f11 = r**2
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f44 = self.period**2
f444 = self.period**3
f5 = np.sin(pi*f1/f4)
f55 = np.sin(pi*f1/f4)**2
f6 = np.cos(pi*f1/f4)
f66 = np.cos(pi*f1/f4)**2
f7 = np.exp( -(2.0*f55/f2) - 0.5*f11/f3 )
j1 = -1/f3 -4*pi*pi*f66/(f2*f44) +4*pi*pi*f55/(f2*f44)
j2 = f1/f3 + 4*pi*f5*f5/(f2*f4)
j3 = (-j2)**2
j4 = j2
j5 = -j1
j6 = -j2
j8 = 16*pi*pi*pi*f6*f5/(f2*f444)
fwn=self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return -(j1*j2 + j3*j4 + 2*j5*j6 - j8) *f7 +fwn
except ValueError:
f1 = r
f11 = r**2
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f44 = self.period**2
f444 = self.period**3
f5 = np.sin(pi*f1/f4)
f55 = np.sin(pi*f1/f4)**2
f6 = np.cos(pi*f1/f4)
f66 = np.cos(pi*f1/f4)**2
f7 = np.exp( -(2.0*f55/f2) - 0.5*f11/f3 )
j1 = -1/f3 -4*pi*pi*f66/(f2*f44) +4*pi*pi*f55/(f2*f44)
j2 = f1/f3 + 4*pi*f5*f5/(f2*f4)
j3 = (-j2)**2
j4 = j2
j5 = -j1
j6 = -j2
j8 = 16*pi*pi*pi*f6*f5/(f2*f444)
return -(j1*j2 + j3*j4 + 2*j5*j6 - j8) *f7
class ddddQP_ddt2ddt1(QuasiPeriodic):
"""
Second derivative of the QuasiPeriodic kernel, two times in order to t1
and two times in order to t2. Equation A6 in the paper, for N=1.
"""
def __init__(self, ell_e, ell_p, period, wn):
super(ddddQP_ddt2ddt1, self).__init__(ell_e, ell_p, period, wn)
self.ell_p = ell_p
self.ell_e = ell_e
self.period = period
self.wn = wn
def __call__(self, r):
try:
f1 = r
f11 = r**2
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f44 = self.period**2
f444 = self.period**3
f4444 = self.period**4
f5 = np.sin(pi*f1/f4)
f55 = np.sin(pi*f1/f4)**2
f6 = np.cos(pi*f1/f4)
f66 = np.cos(pi*f1/f4)**2
f7 = np.exp( -0.5*f11/f3 - 2*f55/f2)
j1 = 1./f3 + 4*pi*pi*f66/(f2*f44) - 4*pi*pi*f55/(f2*f44)
j2 = -f1/f3 - 4*pi*f6*f5/(f2*f4)
j3 = f1/f3 + 4*pi*f6*f5/(f2*f4)
j4 = 32*pi*pi*pi*f6*f5*j3/(f2*f444)
j5 = 32*pi*pi*pi*f6*f5*j2/(f2*f444)
j6 = 16*pi*pi*pi*pi*f55/(f2*f4444)
j7 = 16*pi*pi*pi*pi*f66/(f2*f4444)
j8 = -j1
j9 = j3**2
j10 = j2**2
j11 = (-j1)**2
fwn=self.wn**2 *np.diag(np.diag(np.ones_like(r)))
return (4*j1*j2*j3 -j4 +j5 -j6 +j7 +j8*j9 \
+j10*j9 +j8*j10 +j11 +2*j1**2) *f7 +fwn
except ValueError:
f1 = r
f11 = r**2
f2 = self.ell_p**2
f3 = self.ell_e**2
f4 = self.period
f44 = self.period**2
f444 = self.period**3
f4444 = self.period**4
f5 = np.sin(pi*f1/f4)
f55 = np.sin(pi*f1/f4)**2
f6 = np.cos(pi*f1/f4)
f66 = np.cos(pi*f1/f4)**2
f7 = np.exp( -0.5*f11/f3 - 2*f55/f2)
j1 = 1./f3 + 4*pi*pi*f66/(f2*f44) - 4*pi*pi*f55/(f2*f44)
j2 = -f1/f3 - 4*pi*f6*f5/(f2*f4)
j3 = f1/f3 + 4*pi*f6*f5/(f2*f4)
j4 = 32*pi*pi*pi*f6*f5*j3/(f2*f444)
j5 = 32*pi*pi*pi*f6*f5*j2/(f2*f444)
j6 = 16*pi*pi*pi*pi*f55/(f2*f4444)
j7 = 16*pi*pi*pi*pi*f66/(f2*f4444)
j8 = -j1
j9 = j3**2
j10 = j2**2
j11 = (-j1)**2
return (4*j1*j2*j3 -j4 +j5 -j6 +j7 +j8*j9 \
+j10*j9 +j8*j10 +j11 +2*j1**2) *f7
| 30.054927 | 83 | 0.480596 | 2,847 | 18,604 | 3.008781 | 0.067088 | 0.074364 | 0.026617 | 0.021013 | 0.833761 | 0.827457 | 0.8152 | 0.803993 | 0.803993 | 0.761382 | 0 | 0.116901 | 0.372823 | 18,604 | 618 | 84 | 30.10356 | 0.617244 | 0.118147 | 0 | 0.793333 | 0 | 0 | 0.002872 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.095556 | false | 0 | 0.002222 | 0.017778 | 0.233333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f6596a4dd489e707722a249e6e4933745bd6c60e | 165,429 | py | Python | qr_code/tests/tests.py | HackRoboy/CoinBoy | 5e10e763fe2e1e492f733fdf2531c77f13cef3a4 | [
"BSD-3-Clause"
] | null | null | null | qr_code/tests/tests.py | HackRoboy/CoinBoy | 5e10e763fe2e1e492f733fdf2531c77f13cef3a4 | [
"BSD-3-Clause"
] | null | null | null | qr_code/tests/tests.py | HackRoboy/CoinBoy | 5e10e763fe2e1e492f733fdf2531c77f13cef3a4 | [
"BSD-3-Clause"
] | null | null | null | """Tests for qr_code application."""
import base64
import re
import os
from datetime import date
from django.template import Template, Context
from django.test import SimpleTestCase, override_settings
from django.utils.safestring import mark_safe
from django.utils.html import escape
from qr_code.qrcode.image import SVG_FORMAT_NAME, PNG_FORMAT_NAME
from qr_code.qrcode.maker import make_embedded_qr_code
from qr_code.qrcode.constants import ERROR_CORRECTION_DICT, DEFAULT_IMAGE_FORMAT, DEFAULT_MODULE_SIZE, \
DEFAULT_ERROR_CORRECTION, DEFAULT_VERSION
from qr_code.qrcode.serve import make_qr_code_url
from qr_code.qrcode.utils import ContactDetail, WifiConfig, QRCodeOptions, Coordinates
from qr_code.templatetags.qr_code import qr_from_text, qr_url_from_text
BASE64_PNG_IMAGE_TEMPLATE = '<img src="data:image/png;base64, %salt="Hello World!">'
TEST_TEXT = 'Hello World!'
COMPLEX_TEST_TEXT = '/%+¼@#=<>àé'
TEST_CONTACT_DETAIL = dict(
first_name='John',
last_name='Doe',
first_name_reading='jAAn',
last_name_reading='dOH',
tel='+41769998877',
email='j.doe@company.com',
url='http://www.company.com',
birthday=date(year=1985, month=10, day=2),
address='Cras des Fourches 987, 2800 Delémont, Jura, Switzerland',
memo='Development Manager',
org='Company Ltd',
)
TEST_WIFI_CONFIG = dict(
ssid='my-wifi',
authentication=WifiConfig.AUTHENTICATION.WPA,
password='wifi-password'
)
OVERRIDE_CACHES_SETTING = {'default': {'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', },
'qr-code': {'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
'LOCATION': 'qr-code-cache', 'TIMEOUT': 3600}}
SVG_REF_SUFFIX = '.ref.svg'
PNG_REF_SUFFIX = '.ref.png'
def get_resources_path():
tests_dir = os.path.dirname(os.path.abspath(__file__))
resources_dir = os.path.join(tests_dir, 'resources')
return resources_dir
def _make_closing_path_tag(svg):
return svg.replace(' /></svg>', '></path></svg>')
class TestApps(SimpleTestCase):
def test_apps_attributes(self):
from qr_code.apps import QrCodeConfig
self.assertEqual(QrCodeConfig.name, 'qr_code')
self.assertEqual(QrCodeConfig.verbose_name, 'Django QR code')
class TestQRCodeOptions(SimpleTestCase):
def test_qr_code_options(self):
with self.assertRaises(ValueError):
QRCodeOptions(foo='bar')
options = QRCodeOptions()
self.assertEqual(options.border, 4)
self.assertEqual(options.size, DEFAULT_MODULE_SIZE)
self.assertEqual(options.image_format, DEFAULT_IMAGE_FORMAT)
self.assertEqual(options.version, DEFAULT_VERSION)
self.assertEqual(options.error_correction, DEFAULT_ERROR_CORRECTION)
options = QRCodeOptions(image_format='invalid-image-format')
self.assertEqual(options.image_format, DEFAULT_IMAGE_FORMAT)
class TestContactDetail(SimpleTestCase):
def test_make_qr_code_text(self):
data = dict(**TEST_CONTACT_DETAIL)
c1 = ContactDetail(**data)
data['nickname'] = 'buddy'
c2 = ContactDetail(**data)
data['last_name'] = "O'Hara;,:"
data['tel_av'] = 'n/a'
c3 = ContactDetail(**data)
del data['last_name']
c4 = ContactDetail(**data)
self.assertEqual(c1.make_qr_code_text(), r'MECARD:N:Doe,John;SOUND:dOH,jAAn;TEL:+41769998877;EMAIL:j.doe@company.com;NOTE:Development Manager;BDAY:19851002;ADR:Cras des Fourches 987, 2800 Delémont, Jura, Switzerland;URL:http\://www.company.com;ORG:Company Ltd;;')
self.assertEqual(c2.make_qr_code_text(), r'MECARD:N:Doe,John;SOUND:dOH,jAAn;TEL:+41769998877;EMAIL:j.doe@company.com;NOTE:Development Manager;BDAY:19851002;ADR:Cras des Fourches 987, 2800 Delémont, Jura, Switzerland;URL:http\://www.company.com;NICKNAME:buddy;ORG:Company Ltd;;')
self.assertEqual(c3.make_qr_code_text(),
r"MECARD:N:O'Hara\;\,\:,John;SOUND:dOH,jAAn;TEL:+41769998877;TEL-AV:n/a;EMAIL:j.doe@company.com;NOTE:Development Manager;BDAY:19851002;ADR:Cras des Fourches 987, 2800 Delémont, Jura, Switzerland;URL:http\://www.company.com;NICKNAME:buddy;ORG:Company Ltd;;")
self.assertEqual(c4.make_qr_code_text(),
r"MECARD:N:John;SOUND:dOH,jAAn;TEL:+41769998877;TEL-AV:n/a;EMAIL:j.doe@company.com;NOTE:Development Manager;BDAY:19851002;ADR:Cras des Fourches 987, 2800 Delémont, Jura, Switzerland;URL:http\://www.company.com;NICKNAME:buddy;ORG:Company Ltd;;")
class TestWifiConfig(SimpleTestCase):
def test_make_qr_code_text(self):
wifi1 = WifiConfig(**TEST_WIFI_CONFIG)
wifi2 = WifiConfig(hidden=True, **TEST_WIFI_CONFIG)
self.assertEqual(wifi1.make_qr_code_text(), 'WIFI:S:my-wifi;T:WPA;P:wifi-password;;')
self.assertEqual(wifi2.make_qr_code_text(), 'WIFI:S:my-wifi;T:WPA;P:wifi-password;H:true;;')
class TestCoordinates(SimpleTestCase):
def test_coordinates(self):
c1 = Coordinates(latitude=586000.32, longitude=250954.19)
c2 = Coordinates(latitude=586000.32, longitude=250954.19, altitude=500)
self.assertEqual(c1.__str__(), 'latitude: 586000.32, longitude: 250954.19')
self.assertEqual(c2.__str__(), 'latitude: 586000.32, longitude: 250954.19, altitude: 500')
@override_settings()
class TestQRUrlFromTextResult(SimpleTestCase):
"""
Ensures that serving images representing QR codes works as expected (with or without caching, and with or without
protection against external requests).
"""
svg_result = b'<?xml version=\'1.0\' encoding=\'UTF-8\'?>\n<svg height="2.9mm" version="1.1" viewBox="0 0 2.9 2.9" width="2.9mm" xmlns="http://www.w3.org/2000/svg"><path d="M 2 1 L 2 1.1 L 2.1 1.1 L 2.1 1 z M 1.8 2.1 L 1.8 2.2 L 1.9 2.2 L 1.9 2.1 z M 1.6 0.5 L 1.6 0.6 L 1.7 0.6 L 1.7 0.5 z M 1.4 2.1 L 1.4 2.2 L 1.5 2.2 L 1.5 2.1 z M 0.4 2.4 L 0.4 2.5 L 0.5 2.5 L 0.5 2.4 z M 2 1.3 L 2 1.4 L 2.1 1.4 L 2.1 1.3 z M 1.8 0.8 L 1.8 0.9 L 1.9 0.9 L 1.9 0.8 z M 2.2 0.8 L 2.2 0.9 L 2.3 0.9 L 2.3 0.8 z M 1.7 2.1 L 1.7 2.2 L 1.8 2.2 L 1.8 2.1 z M 0.5 1 L 0.5 1.1 L 0.6 1.1 L 0.6 1 z M 0.4 1.4 L 0.4 1.5 L 0.5 1.5 L 0.5 1.4 z M 2.2 2.3 L 2.2 2.4 L 2.3 2.4 L 2.3 2.3 z M 1.2 0.9 L 1.2 1.0 L 1.3 1.0 L 1.3 0.9 z M 0.9 1.2 L 0.9 1.3 L 1.0 1.3 L 1.0 1.2 z M 0.8 0.4 L 0.8 0.5 L 0.9 0.5 L 0.9 0.4 z M 1.4 1.2 L 1.4 1.3 L 1.5 1.3 L 1.5 1.2 z M 1.3 0.4 L 1.3 0.5 L 1.4 0.5 L 1.4 0.4 z M 1.4 1.1 L 1.4 1.2 L 1.5 1.2 L 1.5 1.1 z M 2 0.7 L 2 0.8 L 2.1 0.8 L 2.1 0.7 z M 1 1.4 L 1 1.5 L 1.1 1.5 L 1.1 1.4 z M 1.6 1 L 1.6 1.1 L 1.7 1.1 L 1.7 1 z M 2.3 1.8 L 2.3 1.9 L 2.4 1.9 L 2.4 1.8 z M 2.1 0.6 L 2.1 0.7 L 2.2 0.7 L 2.2 0.6 z M 0.4 2.1 L 0.4 2.2 L 0.5 2.2 L 0.5 2.1 z M 1.8 0.5 L 1.8 0.6 L 1.9 0.6 L 1.9 0.5 z M 2.4 1.3 L 2.4 1.4 L 2.5 1.4 L 2.5 1.3 z M 2.2 1.3 L 2.2 1.4 L 2.3 1.4 L 2.3 1.3 z M 0.4 0.8 L 0.4 0.9 L 0.5 0.9 L 0.5 0.8 z M 2.4 0.8 L 2.4 0.9 L 2.5 0.9 L 2.5 0.8 z M 1.2 1.9 L 1.2 2.0 L 1.3 2.0 L 1.3 1.9 z M 0.8 1.4 L 0.8 1.5 L 0.9 1.5 L 0.9 1.4 z M 0.7 0.6 L 0.7 0.7 L 0.8 0.7 L 0.8 0.6 z M 0.6 1 L 0.6 1.1 L 0.7 1.1 L 0.7 1 z M 1.3 1.8 L 1.3 1.9 L 1.4 1.9 L 1.4 1.8 z M 1.2 0.6 L 1.2 0.7 L 1.3 0.7 L 1.3 0.6 z M 1 0.4 L 1 0.5 L 1.1 0.5 L 1.1 0.4 z M 0.8 2 L 0.8 2.1 L 0.9 2.1 L 0.9 2 z M 1.5 0.4 L 1.5 0.5 L 1.6 0.5 L 1.6 0.4 z M 1.3 2 L 1.3 2.1 L 1.4 2.1 L 1.4 2 z M 2 0.4 L 2 0.5 L 2.1 0.5 L 2.1 0.4 z M 2.1 1.7 L 2.1 1.8 L 2.2 1.8 L 2.2 1.7 z M 2 2.3 L 2 2.4 L 2.1 2.4 L 2.1 2.3 z M 1.8 0.6 L 1.8 0.7 L 1.9 0.7 L 1.9 0.6 z M 1.7 1.4 L 1.7 1.5 L 1.8 1.5 L 1.8 1.4 z M 0.6 2.1 L 0.6 2.2 L 0.7 2.2 L 0.7 2.1 z M 2.2 1.4 L 2.2 1.5 L 2.3 1.5 L 2.3 1.4 z M 0.4 0.5 L 0.4 0.6 L 0.5 0.6 L 0.5 0.5 z M 0.5 1.6 L 0.5 1.7 L 0.6 1.7 L 0.6 1.6 z M 1.2 1.6 L 1.2 1.7 L 1.3 1.7 L 1.3 1.6 z M 0.9 1.8 L 0.9 1.9 L 1.0 1.9 L 1.0 1.8 z M 0.7 2.2 L 0.7 2.3 L 0.8 2.3 L 0.8 2.2 z M 1.2 2.2 L 1.2 2.3 L 1.3 2.3 L 1.3 2.2 z M 1.5 1.9 L 1.5 2.0 L 1.6 2.0 L 1.6 1.9 z M 1.3 2.3 L 1.3 2.4 L 1.4 2.4 L 1.4 2.3 z M 1.9 2 L 1.9 2.1 L 2.0 2.1 L 2.0 2 z M 1.8 1.2 L 1.8 1.3 L 1.9 1.3 L 1.9 1.2 z M 1 2 L 1 2.1 L 1.1 2.1 L 1.1 2 z M 1.6 1.2 L 1.6 1.3 L 1.7 1.3 L 1.7 1.2 z M 2.4 2 L 2.4 2.1 L 2.5 2.1 L 2.5 2 z M 2.2 0.4 L 2.2 0.5 L 2.3 0.5 L 2.3 0.4 z M 2.1 1.2 L 2.1 1.3 L 2.2 1.3 L 2.2 1.2 z M 2.4 0.7 L 2.4 0.8 L 2.5 0.8 L 2.5 0.7 z M 0.6 2.2 L 0.6 2.3 L 0.7 2.3 L 0.7 2.2 z M 2.2 1.9 L 2.2 2.0 L 2.3 2.0 L 2.3 1.9 z M 0.4 1.8 L 0.4 1.9 L 0.5 1.9 L 0.5 1.8 z M 0.6 1.6 L 0.6 1.7 L 0.7 1.7 L 0.7 1.6 z M 0.7 2.1 L 0.7 2.2 L 0.8 2.2 L 0.8 2.1 z M 1 1.8 L 1 1.9 L 1.1 1.9 L 1.1 1.8 z M 1.5 1.4 L 1.5 1.5 L 1.6 1.5 L 1.6 1.4 z M 2.1 1 L 2.1 1.1 L 2.2 1.1 L 2.2 1 z M 1.8 0.9 L 1.8 1.0 L 1.9 1.0 L 1.9 0.9 z M 1.6 1.7 L 1.6 1.8 L 1.7 1.8 L 1.7 1.7 z M 2.1 1.5 L 2.1 1.6 L 2.2 1.6 L 2.2 1.5 z M 1.9 0.4 L 1.9 0.5 L 2.0 0.5 L 2.0 0.4 z M 2.4 0.4 L 2.4 0.5 L 2.5 0.5 L 2.5 0.4 z M 0.4 1.5 L 0.4 1.6 L 0.5 1.6 L 0.5 1.5 z M 0.7 1 L 0.7 1.1 L 0.8 1.1 L 0.8 1 z M 0.6 0.6 L 0.6 0.7 L 0.7 0.7 L 0.7 0.6 z M 0.5 1.4 L 0.5 1.5 L 0.6 1.5 L 0.6 1.4 z M 1.2 1 L 1.2 1.1 L 1.3 1.1 L 1.3 1 z M 1.3 1.1 L 1.3 1.2 L 1.4 1.2 L 1.4 1.1 z M 1 0.8 L 1 0.9 L 1.1 0.9 L 1.1 0.8 z M 1.5 0.8 L 1.5 0.9 L 1.6 0.9 L 1.6 0.8 z M 1.4 0.8 L 1.4 0.9 L 1.5 0.9 L 1.5 0.8 z M 1.3 2.4 L 1.3 2.5 L 1.4 2.5 L 1.4 2.4 z M 2 0.8 L 2 0.9 L 2.1 0.9 L 2.1 0.8 z M 1.6 1.1 L 1.6 1.2 L 1.7 1.2 L 1.7 1.1 z M 0.4 2.2 L 0.4 2.3 L 0.5 2.3 L 0.5 2.2 z M 1.8 1 L 1.8 1.1 L 1.9 1.1 L 1.9 1 z M 1.6 2.2 L 1.6 2.3 L 1.7 2.3 L 1.7 2.2 z M 2.2 1 L 2.2 1.1 L 2.3 1.1 L 2.3 1 z M 0.4 0.9 L 0.4 1.0 L 0.5 1.0 L 0.5 0.9 z M 1.7 2.3 L 1.7 2.4 L 1.8 2.4 L 1.8 2.3 z M 2.4 0.9 L 2.4 1.0 L 2.5 1.0 L 2.5 0.9 z M 0.5 0.4 L 0.5 0.5 L 0.6 0.5 L 0.6 0.4 z M 0.4 1.2 L 0.4 1.3 L 0.5 1.3 L 0.5 1.2 z M 1.3 1.7 L 1.3 1.8 L 1.4 1.8 L 1.4 1.7 z M 1.2 0.7 L 1.2 0.8 L 1.3 0.8 L 1.3 0.7 z M 0.8 1 L 0.8 1.1 L 0.9 1.1 L 0.9 1 z M 1 0.5 L 1 0.6 L 1.1 0.6 L 1.1 0.5 z M 0.8 2.1 L 0.8 2.2 L 0.9 2.2 L 0.9 2.1 z M 1.8 1.6 L 1.8 1.7 L 1.9 1.7 L 1.9 1.6 z M 0.9 2.4 L 0.9 2.5 L 1.0 2.5 L 1.0 2.4 z M 1.6 0.8 L 1.6 0.9 L 1.7 0.9 L 1.7 0.8 z M 1 2.4 L 1 2.5 L 1.1 2.5 L 1.1 2.4 z M 1.4 2.4 L 1.4 2.5 L 1.5 2.5 L 1.5 2.4 z M 1.5 2.4 L 1.5 2.5 L 1.6 2.5 L 1.6 2.4 z M 1.9 1.3 L 1.9 1.4 L 2.0 1.4 L 2.0 1.3 z M 1.8 0.7 L 1.8 0.8 L 1.9 0.8 L 1.9 0.7 z M 1.7 1.3 L 1.7 1.4 L 1.8 1.4 L 1.8 1.3 z M 2 2.4 L 2 2.5 L 2.1 2.5 L 2.1 2.4 z M 2.2 1.5 L 2.2 1.6 L 2.3 1.6 L 2.3 1.5 z M 0.4 0.6 L 0.4 0.7 L 0.5 0.7 L 0.5 0.6 z M 2.1 2.1 L 2.1 2.2 L 2.2 2.2 L 2.2 2.1 z M 1.2 1.7 L 1.2 1.8 L 1.3 1.8 L 1.3 1.7 z M 0.8 1.2 L 0.8 1.3 L 0.9 1.3 L 0.9 1.2 z M 0.7 0.4 L 0.7 0.5 L 0.8 0.5 L 0.8 0.4 z M 0.6 1.2 L 0.6 1.3 L 0.7 1.3 L 0.7 1.2 z M 1.3 1.2 L 1.3 1.3 L 1.4 1.3 L 1.4 1.2 z M 1.4 1.9 L 1.4 2.0 L 1.5 2.0 L 1.5 1.9 z M 1 0.6 L 1 0.7 L 1.1 0.7 L 1.1 0.6 z M 1.3 2.2 L 1.3 2.3 L 1.4 2.3 L 1.4 2.2 z M 1.8 1.3 L 1.8 1.4 L 1.9 1.4 L 1.9 1.3 z M 1 2.1 L 1 2.2 L 1.1 2.2 L 1.1 2.1 z M 1.6 1.3 L 1.6 1.4 L 1.7 1.4 L 1.7 1.3 z M 0.5 2.4 L 0.5 2.5 L 0.6 2.5 L 0.6 2.4 z M 2.1 1.9 L 2.1 2.0 L 2.2 2.0 L 2.2 1.9 z M 2 2.1 L 2 2.2 L 2.1 2.2 L 2.1 2.1 z M 1.7 1.6 L 1.7 1.7 L 1.8 1.7 L 1.8 1.6 z M 2.4 1.6 L 2.4 1.7 L 2.5 1.7 L 2.5 1.6 z M 1.6 2.4 L 1.6 2.5 L 1.7 2.5 L 1.7 2.4 z M 0.4 1.9 L 0.4 2.0 L 0.5 2.0 L 0.5 1.9 z M 0.7 1.4 L 0.7 1.5 L 0.8 1.5 L 0.8 1.4 z M 0.5 1.8 L 0.5 1.9 L 0.6 1.9 L 0.6 1.8 z M 1.3 1.5 L 1.3 1.6 L 1.4 1.6 L 1.4 1.5 z M 0.9 0.4 L 0.9 0.5 L 1.0 0.5 L 1.0 0.4 z M 0.7 2 L 0.7 2.1 L 0.8 2.1 L 0.8 2 z M 1.4 0.4 L 1.4 0.5 L 1.5 0.5 L 1.5 0.4 z M 1.2 2 L 1.2 2.1 L 1.3 2.1 L 1.3 2 z M 1 1.9 L 1 2.0 L 1.1 2.0 L 1.1 1.9 z M 2 1.5 L 2 1.6 L 2.1 1.6 L 2.1 1.5 z M 1.8 1.4 L 1.8 1.5 L 1.9 1.5 L 1.9 1.4 z M 1 2.2 L 1 2.3 L 1.1 2.3 L 1.1 2.2 z M 1.6 1.8 L 1.6 1.9 L 1.7 1.9 L 1.7 1.8 z M 2.3 1 L 2.3 1.1 L 2.4 1.1 L 2.4 1 z M 2.2 0.6 L 2.2 0.7 L 2.3 0.7 L 2.3 0.6 z M 2.1 1.4 L 2.1 1.5 L 2.2 1.5 L 2.2 1.4 z M 1.7 1.9 L 1.7 2.0 L 1.8 2.0 L 1.8 1.9 z M 2.4 0.5 L 2.4 0.6 L 2.5 0.6 L 2.5 0.5 z M 0.6 2.4 L 0.6 2.5 L 0.7 2.5 L 0.7 2.4 z M 0.7 1.3 L 0.7 1.4 L 0.8 1.4 L 0.8 1.3 z M 0.6 0.7 L 0.6 0.8 L 0.7 0.8 L 0.7 0.7 z M 1.2 1.1 L 1.2 1.2 L 1.3 1.2 L 1.3 1.1 z M 0.8 0.6 L 0.8 0.7 L 0.9 0.7 L 0.9 0.6 z M 0.6 1.8 L 0.6 1.9 L 0.7 1.9 L 0.7 1.8 z M 1 0.9 L 1 1.0 L 1.1 1.0 L 1.1 0.9 z M 1.5 1.1 L 1.5 1.2 L 1.6 1.2 L 1.6 1.1 z M 1.4 0.9 L 1.4 1.0 L 1.5 1.0 L 1.5 0.9 z M 1 1.2 L 1 1.3 L 1.1 1.3 L 1.1 1.2 z M 1.8 2 L 1.8 2.1 L 1.9 2.1 L 1.9 2 z M 2.3 2 L 2.3 2.1 L 2.4 2.1 L 2.4 2 z M 2.1 0.4 L 2.1 0.5 L 2.2 0.5 L 2.2 0.4 z M 2 1.2 L 2 1.3 L 2.1 1.3 L 2.1 1.2 z M 0.4 2.3 L 0.4 2.4 L 0.5 2.4 L 0.5 2.3 z M 1.6 2.3 L 1.6 2.4 L 1.7 2.4 L 1.7 2.3 z M 0.4 1 L 0.4 1.1 L 0.5 1.1 L 0.5 1 z M 1.9 1 L 1.9 1.1 L 2.0 1.1 L 2.0 1 z M 2.4 1 L 2.4 1.1 L 2.5 1.1 L 2.5 1 z M 2.2 2.2 L 2.2 2.3 L 2.3 2.3 L 2.3 2.2 z M 0.7 0.8 L 0.7 0.9 L 0.8 0.9 L 0.8 0.8 z M 0.6 0.8 L 0.6 0.9 L 0.7 0.9 L 0.7 0.8 z M 1.3 1.6 L 1.3 1.7 L 1.4 1.7 L 1.4 1.6 z M 1.2 0.8 L 1.2 0.9 L 1.3 0.9 L 1.3 0.8 z M 1.4 1.5 L 1.4 1.6 L 1.5 1.6 L 1.5 1.5 z M 1 1 L 1 1.1 L 1.1 1.1 L 1.1 1 z M 0.9 1 L 0.9 1.1 L 1.0 1.1 L 1.0 1 z M 0.8 2.2 L 0.8 2.3 L 0.9 2.3 L 0.9 2.2 z M 1.5 0.6 L 1.5 0.7 L 1.6 0.7 L 1.6 0.6 z M 1.4 1 L 1.4 1.1 L 1.5 1.1 L 1.5 1 z M 2 0.6 L 2 0.7 L 2.1 0.7 L 2.1 0.6 z M 1.6 0.9 L 1.6 1.0 L 1.7 1.0 L 1.7 0.9 z M 2.1 0.7 L 2.1 0.8 L 2.2 0.8 L 2.2 0.7 z M 1.9 1.2 L 1.9 1.3 L 2.0 1.3 L 2.0 1.2 z M 1.8 0.4 L 1.8 0.5 L 1.9 0.5 L 1.9 0.4 z M 0.4 2 L 0.4 2.1 L 0.5 2.1 L 0.5 2 z M 1.6 2 L 1.6 2.1 L 1.7 2.1 L 1.7 2 z M 2.3 0.4 L 2.3 0.5 L 2.4 0.5 L 2.4 0.4 z M 2.2 1.2 L 2.2 1.3 L 2.3 1.3 L 2.3 1.2 z M 0.4 0.7 L 0.4 0.8 L 0.5 0.8 L 0.5 0.7 z M 0.7 1.8 L 0.7 1.9 L 0.8 1.9 L 0.8 1.8 z M 0.8 1.3 L 0.8 1.4 L 0.9 1.4 L 0.9 1.3 z M 0.7 0.7 L 0.7 0.8 L 0.8 0.8 L 0.8 0.7 z M 0.6 1.3 L 0.6 1.4 L 0.7 1.4 L 0.7 1.3 z M 1.3 1.9 L 1.3 2.0 L 1.4 2.0 L 1.4 1.9 z M 0.9 1.6 L 0.9 1.7 L 1.0 1.7 L 1.0 1.6 z M 0.8 0.8 L 0.8 0.9 L 0.9 0.9 L 0.9 0.8 z M 0.7 2.4 L 0.7 2.5 L 0.8 2.5 L 0.8 2.4 z M 1.4 1.6 L 1.4 1.7 L 1.5 1.7 L 1.5 1.6 z M 1.2 2.4 L 1.2 2.5 L 1.3 2.5 L 1.3 2.4 z M 1 0.7 L 1 0.8 L 1.1 0.8 L 1.1 0.7 z M 1.3 2.1 L 1.3 2.2 L 1.4 2.2 L 1.4 2.1 z M 1.9 2.2 L 1.9 2.3 L 2.0 2.3 L 2.0 2.2 z M 1.8 1.8 L 1.8 1.9 L 1.9 1.9 L 1.9 1.8 z M 2.3 1.4 L 2.3 1.5 L 2.4 1.5 L 2.4 1.4 z M 1.9 1.9 L 1.9 2.0 L 2.0 2.0 L 2.0 1.9 z M 1.7 1.5 L 1.7 1.6 L 1.8 1.6 L 1.8 1.5 z M 0.6 2 L 0.6 2.1 L 0.7 2.1 L 0.7 2 z M 0.4 0.4 L 0.4 0.5 L 0.5 0.5 L 0.5 0.4 z M 2.1 2.3 L 2.1 2.4 L 2.2 2.4 L 2.2 2.3 z M 0.8 1.8 L 0.8 1.9 L 0.9 1.9 L 0.9 1.8 z M 1.4 0.5 L 1.4 0.6 L 1.5 0.6 L 1.5 0.5 z M 1.2 2.1 L 1.2 2.2 L 1.3 2.2 L 1.3 2.1 z M 1 1.6 L 1 1.7 L 1.1 1.7 L 1.1 1.6 z M 1.8 2.4 L 1.8 2.5 L 1.9 2.5 L 1.9 2.4 z M 0.8 2.4 L 0.8 2.5 L 0.9 2.5 L 0.9 2.4 z M 1.5 1.6 L 1.5 1.7 L 1.6 1.7 L 1.6 1.6 z M 2.3 2.4 L 2.3 2.5 L 2.4 2.5 L 2.4 2.4 z M 2.1 0.8 L 2.1 0.9 L 2.2 0.9 L 2.2 0.8 z M 1.1 1.3 L 1.1 1.4 L 1.2 1.4 L 1.2 1.3 z M 1.9 2.1 L 1.9 2.2 L 2.0 2.2 L 2.0 2.1 z M 1 2.3 L 1 2.4 L 1.1 2.4 L 1.1 2.3 z M 1.6 1.9 L 1.6 2.0 L 1.7 2.0 L 1.7 1.9 z M 2.2 0.7 L 2.2 0.8 L 2.3 0.8 L 2.3 0.7 z M 2.1 1.3 L 2.1 1.4 L 2.2 1.4 L 2.2 1.3 z M 2.4 0.6 L 2.4 0.7 L 2.5 0.7 L 2.5 0.6 z M 2.2 1.8 L 2.2 1.9 L 2.3 1.9 L 2.3 1.8 z M 0.7 1.2 L 0.7 1.3 L 0.8 1.3 L 0.8 1.2 z M 0.6 0.4 L 0.6 0.5 L 0.7 0.5 L 0.7 0.4 z M 0.8 0.7 L 0.8 0.8 L 0.9 0.8 L 0.9 0.7 z M 1.3 0.9 L 1.3 1.0 L 1.4 1.0 L 1.4 0.9 z" id="qr-path" style="fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none" /></svg>'
png_result = b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x1d\x00\x00\x00\x1d\x01\x00\x00\x00\x00~\xe8Z\xa2\x00\x00\x00\x83IDATx\x9cm\xcd1\x0e\x01Q\x10\x80\xe1\x7f\xc6&:j\x07\x90,\x9db\x0f q\x0e\x8e \x91H\x88\x08\xb7\xa0\xd3ju.\xa0\xd7\x8aj[\x95e\x937\x13\xd6Sj4_\xfbI\x04W\x80\x1fI\x91\xee\x8fZ?\xed\xd0j>A\x11P\xaa5Z[6P\x1f\xe4\x10\xef+\xa3l\xf5\x8d\xf7Xf<\x86[#\xf8\xc1\xc47\xcd\x0b\xf1\xb90-\xd2\x1c\xc2\xb9cR\x8e^\xdd\x84O6U\xf4\x06\xe1\xda3\xf5\xac\x8d\xfc\xc9\xbfO\x8703\xef(\x96\xc2\x00\x00\x00\x00IEND\xaeB`\x82'
def test_svg_url(self):
for cache_enabled in [True, False]:
url1 = make_qr_code_url(TEST_TEXT, QRCodeOptions(size=1), cache_enabled=cache_enabled)
url2 = qr_url_from_text(TEST_TEXT, size=1, cache_enabled=cache_enabled)
url3 = qr_url_from_text(TEST_TEXT, image_format='svg', size=1, cache_enabled=cache_enabled)
url4 = qr_url_from_text(TEST_TEXT, image_format='SVG', size=1, cache_enabled=cache_enabled)
url5 = qr_url_from_text(TEST_TEXT, options=QRCodeOptions(image_format='SVG', size=1), cache_enabled=cache_enabled)
# Using an invalid image format should fallback to SVG.
url6 = qr_url_from_text(TEST_TEXT, image_format='invalid-format-name', size=1, cache_enabled=cache_enabled)
url = url1
token_regex = re.compile(r"token=.+&?")
urls = list(map(lambda x: token_regex.sub('', x), (url1, url2, url3, url4, url5, url6)))
self.assertEqual(urls[0], urls[1])
self.assertEqual(urls[0], urls[2])
self.assertEqual(urls[0], urls[3])
self.assertEqual(urls[0], urls[4])
self.assertEqual(urls[0], urls[5])
response = self.client.get(url)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content, TestQRUrlFromTextResult.svg_result)
def test_png_url(self):
for cache_enabled in [True, False]:
url1 = make_qr_code_url(TEST_TEXT, QRCodeOptions(image_format='png', size=1), cache_enabled=cache_enabled)
url2 = qr_url_from_text(TEST_TEXT, image_format='png', size=1, cache_enabled=cache_enabled)
url3 = qr_url_from_text(TEST_TEXT, image_format='PNG', size=1, cache_enabled=cache_enabled)
url4 = qr_url_from_text(TEST_TEXT, options=QRCodeOptions(image_format='PNG', size=1), cache_enabled=cache_enabled)
url = url1
token_regex = re.compile(r"token=.+&?")
urls = list(map(lambda x: token_regex.sub('', x), (url1, url2, url3, url4)))
self.assertEqual(urls[0], urls[1])
self.assertEqual(urls[0], urls[2])
self.assertEqual(urls[0], urls[3])
response = self.client.get(url)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content, TestQRUrlFromTextResult.png_result)
@override_settings(CACHES=OVERRIDE_CACHES_SETTING, QR_CODE_CACHE_ALIAS=None)
def test_svg_with_cache_but_no_alias(self):
self.test_svg_url()
@override_settings(CACHES=OVERRIDE_CACHES_SETTING)
def test_png_with_cache(self):
self.test_png_url()
@override_settings(CACHES=OVERRIDE_CACHES_SETTING, QR_CODE_CACHE_ALIAS=None)
def test_png_with_cache_but_no_alias(self):
self.test_png_url()
@override_settings(QR_CODE_URL_PROTECTION=dict(TOKEN_LENGTH=30, SIGNING_KEY='my-secret-signing-key',
SIGNING_SALT='my-signing-salt',
ALLOWS_EXTERNAL_REQUESTS_FOR_REGISTERED_USER=True))
def test_with_url_protection_settings_1(self):
self.test_svg_url()
self.test_png_url()
response = self.client.get(make_qr_code_url(TEST_TEXT, include_url_protection_token=False, cache_enabled=False))
# Registered users can access the URL externally, but since we are not logged in, we must expect an HTTP 403.
self.assertEqual(response.status_code, 403)
@override_settings(QR_CODE_URL_PROTECTION=dict(ALLOWS_EXTERNAL_REQUESTS_FOR_REGISTERED_USER=False))
def test_with_url_protection_settings_2(self):
self.test_svg_url()
self.test_png_url()
response = self.client.get(make_qr_code_url(TEST_TEXT, include_url_protection_token=False, cache_enabled=False))
self.assertEqual(response.status_code, 403)
@override_settings(QR_CODE_URL_PROTECTION=dict(ALLOWS_EXTERNAL_REQUESTS_FOR_REGISTERED_USER=lambda user: False))
def test_with_url_protection_settings_3(self):
self.test_svg_url()
self.test_png_url()
response = self.client.get(make_qr_code_url(TEST_TEXT, include_url_protection_token=False, cache_enabled=False))
self.assertEqual(response.status_code, 403)
@override_settings(QR_CODE_URL_PROTECTION=dict(ALLOWS_EXTERNAL_REQUESTS_FOR_REGISTERED_USER=lambda user: True))
def test_with_url_protection_settings_4(self):
self.test_svg_url()
self.test_png_url()
response = self.client.get(make_qr_code_url(TEST_TEXT, include_url_protection_token=False, cache_enabled=False))
# The callable for ALLOWS_EXTERNAL_REQUESTS_FOR_REGISTERED_USER always return True, event for anonymous user.
# Therefore, we must expect an HTTP 403.
self.assertEqual(response.status_code, 200)
def test_svg_error_correction(self):
for correction_level in ERROR_CORRECTION_DICT:
print('Testing SVG URL with error correction: %s' % correction_level)
url1 = make_qr_code_url(COMPLEX_TEST_TEXT, QRCodeOptions(error_correction=correction_level), cache_enabled=False)
url2 = qr_url_from_text(COMPLEX_TEST_TEXT, error_correction=correction_level, cache_enabled=False)
url3 = qr_url_from_text(COMPLEX_TEST_TEXT, error_correction=correction_level, image_format='svg', cache_enabled=False)
url4 = qr_url_from_text(COMPLEX_TEST_TEXT, error_correction=correction_level, image_format='SVG', cache_enabled=False)
url5 = qr_url_from_text(COMPLEX_TEST_TEXT, options=QRCodeOptions(error_correction=correction_level, image_format='SVG'), cache_enabled=False)
# Using an invalid image format should fallback to SVG.
url6 = qr_url_from_text(COMPLEX_TEST_TEXT, error_correction=correction_level, image_format='invalid-format-name', cache_enabled=False)
url = url1
token_regex = re.compile(r"token=.+&?")
urls = list(map(lambda x: token_regex.sub('', x), (url1, url2, url3, url4, url5, url6)))
self.assertEqual(urls[0], urls[1])
self.assertEqual(urls[0], urls[2])
self.assertEqual(urls[0], urls[3])
self.assertEqual(urls[0], urls[4])
self.assertEqual(urls[0], urls[5])
response = self.client.get(url)
self.assertEqual(response.status_code, 200)
source_image_data = response.content.decode('utf-8')
# Skip header and adjust tag format.
source_image_data = source_image_data[source_image_data.index('\n') + 1:]
source_image_data = _make_closing_path_tag(source_image_data)
ref_image_data = get_svg_content_from_file_name('qrfromtextsvgresult_error_correction_%s%s' % (correction_level.lower(), SVG_REF_SUFFIX), skip_header=False)
self.assertEqual(source_image_data, ref_image_data)
def test_png_error_correction(self):
for correction_level in ERROR_CORRECTION_DICT:
print('Testing PNG URL with error correction: %s' % correction_level)
url1 = make_qr_code_url(COMPLEX_TEST_TEXT, QRCodeOptions(error_correction=correction_level, image_format='png'), cache_enabled=False)
url2 = make_qr_code_url(COMPLEX_TEST_TEXT, QRCodeOptions(error_correction=correction_level, image_format='PNG'), cache_enabled=False)
url3 = qr_url_from_text(COMPLEX_TEST_TEXT, error_correction=correction_level, image_format='png', cache_enabled=False)
url4 = qr_url_from_text(COMPLEX_TEST_TEXT, error_correction=correction_level, image_format='PNG', cache_enabled=False)
url5 = qr_url_from_text(COMPLEX_TEST_TEXT, options=QRCodeOptions(error_correction=correction_level, image_format='PNG'), cache_enabled=False)
url = url1
token_regex = re.compile(r"token=.+&?")
urls = list(map(lambda x: token_regex.sub('', x), (url1, url2, url3, url4, url5)))
self.assertEqual(urls[0], urls[1])
self.assertEqual(urls[0], urls[2])
self.assertEqual(urls[0], urls[3])
self.assertEqual(urls[0], urls[4])
response = self.client.get(url)
self.assertEqual(response.status_code, 200)
source_image_data = response.content
ref_image_data = get_png_content_from_file_name('qrfromtextpngresult_error_correction_%s%s' % (correction_level.lower(), PNG_REF_SUFFIX))
self.assertEqual(source_image_data, ref_image_data)
class TestQRFromTextSvgResult(SimpleTestCase):
"""
Ensures that produced QR codes in SVG format coincide with verified references.
The tests cover direct call to tag function, rendering of tag, and direct call to qr_code API.
"""
def test_size(self):
sizes = ['t', 'T', 's', 'S', None, -1, 0, 'm', 'M', 'l', 'L', 'h', 'H', '6', 6, '8', 8]
rt = """<svg height="17.4mm" version="1.1" viewBox="0 0 17.4 17.4" width="17.4mm" xmlns="http://www.w3.org/2000/svg"><path d="M 12 6 L 12 6.6 L 12.6 6.6 L 12.6 6 z M 10.8 12.6 L 10.8 13.2 L 11.4 13.2 L 11.4 12.6 z M 9.6 3 L 9.6 3.6 L 10.2 3.6 L 10.2 3 z M 8.4 12.6 L 8.4 13.2 L 9.0 13.2 L 9.0 12.6 z M 2.4 14.4 L 2.4 15.0 L 3.0 15.0 L 3.0 14.4 z M 12 7.8 L 12 8.4 L 12.6 8.4 L 12.6 7.8 z M 10.8 4.8 L 10.8 5.4 L 11.4 5.4 L 11.4 4.8 z M 13.2 4.8 L 13.2 5.4 L 13.8 5.4 L 13.8 4.8 z M 10.2 12.6 L 10.2 13.2 L 10.8 13.2 L 10.8 12.6 z M 3 6 L 3 6.6 L 3.6 6.6 L 3.6 6 z M 2.4 8.4 L 2.4 9.0 L 3.0 9.0 L 3.0 8.4 z M 13.2 13.8 L 13.2 14.4 L 13.8 14.4 L 13.8 13.8 z M 7.2 5.4 L 7.2 6.0 L 7.8 6.0 L 7.8 5.4 z M 5.4 7.2 L 5.4 7.8 L 6.0 7.8 L 6.0 7.2 z M 4.8 2.4 L 4.8 3.0 L 5.4 3.0 L 5.4 2.4 z M 8.4 7.2 L 8.4 7.8 L 9.0 7.8 L 9.0 7.2 z M 7.8 2.4 L 7.8 3.0 L 8.4 3.0 L 8.4 2.4 z M 8.4 6.6 L 8.4 7.2 L 9.0 7.2 L 9.0 6.6 z M 12 4.2 L 12 4.8 L 12.6 4.8 L 12.6 4.2 z M 6 8.4 L 6 9.0 L 6.6 9.0 L 6.6 8.4 z M 9.6 6 L 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120.0 L 115.2 115.2 z M 100.8 38.4 L 100.8 43.2 L 105.6 43.2 L 105.6 38.4 z M 52.8 62.4 L 52.8 67.2 L 57.6 67.2 L 57.6 62.4 z M 91.2 100.8 L 91.2 105.6 L 96.0 105.6 L 96.0 100.8 z M 48 110.4 L 48 115.2 L 52.8 115.2 L 52.8 110.4 z M 76.8 91.2 L 76.8 96.0 L 81.6 96.0 L 81.6 91.2 z M 105.6 33.6 L 105.6 38.4 L 110.4 38.4 L 110.4 33.6 z M 100.8 62.4 L 100.8 67.2 L 105.6 67.2 L 105.6 62.4 z M 115.2 28.8 L 115.2 33.6 L 120.0 33.6 L 120.0 28.8 z M 105.6 86.4 L 105.6 91.2 L 110.4 91.2 L 110.4 86.4 z M 33.6 57.6 L 33.6 62.4 L 38.4 62.4 L 38.4 57.6 z M 28.8 19.2 L 28.8 24.0 L 33.6 24.0 L 33.6 19.2 z M 38.4 33.6 L 38.4 38.4 L 43.2 38.4 L 43.2 33.6 z M 62.4 43.2 L 62.4 48.0 L 67.2 48.0 L 67.2 43.2 z" id="qr-path" style="fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"></path></svg>"""
r6 = rt
r8 = """<svg height="23.2mm" version="1.1" viewBox="0 0 23.2 23.2" width="23.2mm" xmlns="http://www.w3.org/2000/svg"><path d="M 16 8 L 16 8.8 L 16.8 8.8 L 16.8 8 z M 14.4 16.8 L 14.4 17.6 L 15.2 17.6 L 15.2 16.8 z M 12.8 4 L 12.8 4.8 L 13.6 4.8 L 13.6 4 z M 11.2 16.8 L 11.2 17.6 L 12.0 17.6 L 12.0 16.8 z M 3.2 19.2 L 3.2 20.0 L 4.0 20.0 L 4.0 19.2 z M 16 10.4 L 16 11.2 L 16.8 11.2 L 16.8 10.4 z M 14.4 6.4 L 14.4 7.2 L 15.2 7.2 L 15.2 6.4 z M 17.6 6.4 L 17.6 7.2 L 18.4 7.2 L 18.4 6.4 z M 13.6 16.8 L 13.6 17.6 L 14.4 17.6 L 14.4 16.8 z M 4 8 L 4 8.8 L 4.8 8.8 L 4.8 8 z M 3.2 11.2 L 3.2 12.0 L 4.0 12.0 L 4.0 11.2 z M 17.6 18.4 L 17.6 19.2 L 18.4 19.2 L 18.4 18.4 z M 9.6 7.2 L 9.6 8.0 L 10.4 8.0 L 10.4 7.2 z M 7.2 9.6 L 7.2 10.4 L 8.0 10.4 L 8.0 9.6 z M 6.4 3.2 L 6.4 4.0 L 7.2 4.0 L 7.2 3.2 z M 11.2 9.6 L 11.2 10.4 L 12.0 10.4 L 12.0 9.6 z M 10.4 3.2 L 10.4 4.0 L 11.2 4.0 L 11.2 3.2 z M 11.2 8.8 L 11.2 9.6 L 12.0 9.6 L 12.0 8.8 z M 16 5.6 L 16 6.4 L 16.8 6.4 L 16.8 5.6 z M 8 11.2 L 8 12.0 L 8.8 12.0 L 8.8 11.2 z M 12.8 8 L 12.8 8.8 L 13.6 8.8 L 13.6 8 z M 18.4 14.4 L 18.4 15.2 L 19.2 15.2 L 19.2 14.4 z M 16.8 4.8 L 16.8 5.6 L 17.6 5.6 L 17.6 4.8 z M 3.2 16.8 L 3.2 17.6 L 4.0 17.6 L 4.0 16.8 z M 14.4 4 L 14.4 4.8 L 15.2 4.8 L 15.2 4 z M 19.2 10.4 L 19.2 11.2 L 20.0 11.2 L 20.0 10.4 z M 17.6 10.4 L 17.6 11.2 L 18.4 11.2 L 18.4 10.4 z M 3.2 6.4 L 3.2 7.2 L 4.0 7.2 L 4.0 6.4 z M 19.2 6.4 L 19.2 7.2 L 20.0 7.2 L 20.0 6.4 z M 9.6 15.2 L 9.6 16.0 L 10.4 16.0 L 10.4 15.2 z M 6.4 11.2 L 6.4 12.0 L 7.2 12.0 L 7.2 11.2 z M 5.6 4.8 L 5.6 5.6 L 6.4 5.6 L 6.4 4.8 z M 4.8 8 L 4.8 8.8 L 5.6 8.8 L 5.6 8 z M 10.4 14.4 L 10.4 15.2 L 11.2 15.2 L 11.2 14.4 z M 9.6 4.8 L 9.6 5.6 L 10.4 5.6 L 10.4 4.8 z M 8 3.2 L 8 4.0 L 8.8 4.0 L 8.8 3.2 z M 6.4 16 L 6.4 16.8 L 7.2 16.8 L 7.2 16 z M 12 3.2 L 12 4.0 L 12.8 4.0 L 12.8 3.2 z M 10.4 16 L 10.4 16.8 L 11.2 16.8 L 11.2 16 z M 16 3.2 L 16 4.0 L 16.8 4.0 L 16.8 3.2 z M 16.8 13.6 L 16.8 14.4 L 17.6 14.4 L 17.6 13.6 z M 16 18.4 L 16 19.2 L 16.8 19.2 L 16.8 18.4 z M 14.4 4.8 L 14.4 5.6 L 15.2 5.6 L 15.2 4.8 z M 13.6 11.2 L 13.6 12.0 L 14.4 12.0 L 14.4 11.2 z M 4.8 16.8 L 4.8 17.6 L 5.6 17.6 L 5.6 16.8 z M 17.6 11.2 L 17.6 12.0 L 18.4 12.0 L 18.4 11.2 z M 3.2 4 L 3.2 4.8 L 4.0 4.8 L 4.0 4 z M 4 12.8 L 4 13.6 L 4.8 13.6 L 4.8 12.8 z M 9.6 12.8 L 9.6 13.6 L 10.4 13.6 L 10.4 12.8 z M 7.2 14.4 L 7.2 15.2 L 8.0 15.2 L 8.0 14.4 z M 5.6 17.6 L 5.6 18.4 L 6.4 18.4 L 6.4 17.6 z M 9.6 17.6 L 9.6 18.4 L 10.4 18.4 L 10.4 17.6 z M 12 15.2 L 12 16.0 L 12.8 16.0 L 12.8 15.2 z M 10.4 18.4 L 10.4 19.2 L 11.2 19.2 L 11.2 18.4 z M 15.2 16 L 15.2 16.8 L 16.0 16.8 L 16.0 16 z M 14.4 9.6 L 14.4 10.4 L 15.2 10.4 L 15.2 9.6 z M 8 16 L 8 16.8 L 8.8 16.8 L 8.8 16 z M 12.8 9.6 L 12.8 10.4 L 13.6 10.4 L 13.6 9.6 z M 19.2 16 L 19.2 16.8 L 20.0 16.8 L 20.0 16 z M 17.6 3.2 L 17.6 4.0 L 18.4 4.0 L 18.4 3.2 z M 16.8 9.6 L 16.8 10.4 L 17.6 10.4 L 17.6 9.6 z M 19.2 5.6 L 19.2 6.4 L 20.0 6.4 L 20.0 5.6 z M 4.8 17.6 L 4.8 18.4 L 5.6 18.4 L 5.6 17.6 z M 17.6 15.2 L 17.6 16.0 L 18.4 16.0 L 18.4 15.2 z M 3.2 14.4 L 3.2 15.2 L 4.0 15.2 L 4.0 14.4 z M 4.8 12.8 L 4.8 13.6 L 5.6 13.6 L 5.6 12.8 z M 5.6 16.8 L 5.6 17.6 L 6.4 17.6 L 6.4 16.8 z M 8 14.4 L 8 15.2 L 8.8 15.2 L 8.8 14.4 z M 12 11.2 L 12 12.0 L 12.8 12.0 L 12.8 11.2 z M 16.8 8 L 16.8 8.8 L 17.6 8.8 L 17.6 8 z M 14.4 7.2 L 14.4 8.0 L 15.2 8.0 L 15.2 7.2 z M 12.8 13.6 L 12.8 14.4 L 13.6 14.4 L 13.6 13.6 z M 16.8 12 L 16.8 12.8 L 17.6 12.8 L 17.6 12 z M 15.2 3.2 L 15.2 4.0 L 16.0 4.0 L 16.0 3.2 z M 19.2 3.2 L 19.2 4.0 L 20.0 4.0 L 20.0 3.2 z M 3.2 12 L 3.2 12.8 L 4.0 12.8 L 4.0 12 z M 5.6 8 L 5.6 8.8 L 6.4 8.8 L 6.4 8 z M 4.8 4.8 L 4.8 5.6 L 5.6 5.6 L 5.6 4.8 z M 4 11.2 L 4 12.0 L 4.8 12.0 L 4.8 11.2 z M 9.6 8 L 9.6 8.8 L 10.4 8.8 L 10.4 8 z M 10.4 8.8 L 10.4 9.6 L 11.2 9.6 L 11.2 8.8 z M 8 6.4 L 8 7.2 L 8.8 7.2 L 8.8 6.4 z M 12 6.4 L 12 7.2 L 12.8 7.2 L 12.8 6.4 z M 11.2 6.4 L 11.2 7.2 L 12.0 7.2 L 12.0 6.4 z M 10.4 19.2 L 10.4 20.0 L 11.2 20.0 L 11.2 19.2 z M 16 6.4 L 16 7.2 L 16.8 7.2 L 16.8 6.4 z M 12.8 8.8 L 12.8 9.6 L 13.6 9.6 L 13.6 8.8 z M 3.2 17.6 L 3.2 18.4 L 4.0 18.4 L 4.0 17.6 z M 14.4 8 L 14.4 8.8 L 15.2 8.8 L 15.2 8 z M 12.8 17.6 L 12.8 18.4 L 13.6 18.4 L 13.6 17.6 z M 17.6 8 L 17.6 8.8 L 18.4 8.8 L 18.4 8 z M 3.2 7.2 L 3.2 8.0 L 4.0 8.0 L 4.0 7.2 z M 13.6 18.4 L 13.6 19.2 L 14.4 19.2 L 14.4 18.4 z M 19.2 7.2 L 19.2 8.0 L 20.0 8.0 L 20.0 7.2 z M 4 3.2 L 4 4.0 L 4.8 4.0 L 4.8 3.2 z M 3.2 9.6 L 3.2 10.4 L 4.0 10.4 L 4.0 9.6 z M 10.4 13.6 L 10.4 14.4 L 11.2 14.4 L 11.2 13.6 z M 9.6 5.6 L 9.6 6.4 L 10.4 6.4 L 10.4 5.6 z M 6.4 8 L 6.4 8.8 L 7.2 8.8 L 7.2 8 z M 8 4 L 8 4.8 L 8.8 4.8 L 8.8 4 z M 6.4 16.8 L 6.4 17.6 L 7.2 17.6 L 7.2 16.8 z M 14.4 12.8 L 14.4 13.6 L 15.2 13.6 L 15.2 12.8 z M 7.2 19.2 L 7.2 20.0 L 8.0 20.0 L 8.0 19.2 z M 12.8 6.4 L 12.8 7.2 L 13.6 7.2 L 13.6 6.4 z M 8 19.2 L 8 20.0 L 8.8 20.0 L 8.8 19.2 z M 11.2 19.2 L 11.2 20.0 L 12.0 20.0 L 12.0 19.2 z M 12 19.2 L 12 20.0 L 12.8 20.0 L 12.8 19.2 z M 15.2 10.4 L 15.2 11.2 L 16.0 11.2 L 16.0 10.4 z M 14.4 5.6 L 14.4 6.4 L 15.2 6.4 L 15.2 5.6 z M 13.6 10.4 L 13.6 11.2 L 14.4 11.2 L 14.4 10.4 z M 16 19.2 L 16 20.0 L 16.8 20.0 L 16.8 19.2 z M 17.6 12 L 17.6 12.8 L 18.4 12.8 L 18.4 12 z M 3.2 4.8 L 3.2 5.6 L 4.0 5.6 L 4.0 4.8 z M 16.8 16.8 L 16.8 17.6 L 17.6 17.6 L 17.6 16.8 z M 9.6 13.6 L 9.6 14.4 L 10.4 14.4 L 10.4 13.6 z M 6.4 9.6 L 6.4 10.4 L 7.2 10.4 L 7.2 9.6 z M 5.6 3.2 L 5.6 4.0 L 6.4 4.0 L 6.4 3.2 z M 4.8 9.6 L 4.8 10.4 L 5.6 10.4 L 5.6 9.6 z M 10.4 9.6 L 10.4 10.4 L 11.2 10.4 L 11.2 9.6 z M 11.2 15.2 L 11.2 16.0 L 12.0 16.0 L 12.0 15.2 z M 8 4.8 L 8 5.6 L 8.8 5.6 L 8.8 4.8 z M 10.4 17.6 L 10.4 18.4 L 11.2 18.4 L 11.2 17.6 z M 14.4 10.4 L 14.4 11.2 L 15.2 11.2 L 15.2 10.4 z M 8 16.8 L 8 17.6 L 8.8 17.6 L 8.8 16.8 z M 12.8 10.4 L 12.8 11.2 L 13.6 11.2 L 13.6 10.4 z M 4 19.2 L 4 20.0 L 4.8 20.0 L 4.8 19.2 z M 16.8 15.2 L 16.8 16.0 L 17.6 16.0 L 17.6 15.2 z M 16 16.8 L 16 17.6 L 16.8 17.6 L 16.8 16.8 z M 13.6 12.8 L 13.6 13.6 L 14.4 13.6 L 14.4 12.8 z M 19.2 12.8 L 19.2 13.6 L 20.0 13.6 L 20.0 12.8 z M 12.8 19.2 L 12.8 20.0 L 13.6 20.0 L 13.6 19.2 z M 3.2 15.2 L 3.2 16.0 L 4.0 16.0 L 4.0 15.2 z M 5.6 11.2 L 5.6 12.0 L 6.4 12.0 L 6.4 11.2 z M 4 14.4 L 4 15.2 L 4.8 15.2 L 4.8 14.4 z M 10.4 12 L 10.4 12.8 L 11.2 12.8 L 11.2 12 z M 7.2 3.2 L 7.2 4.0 L 8.0 4.0 L 8.0 3.2 z M 5.6 16 L 5.6 16.8 L 6.4 16.8 L 6.4 16 z M 11.2 3.2 L 11.2 4.0 L 12.0 4.0 L 12.0 3.2 z M 9.6 16 L 9.6 16.8 L 10.4 16.8 L 10.4 16 z M 8 15.2 L 8 16.0 L 8.8 16.0 L 8.8 15.2 z M 16 12 L 16 12.8 L 16.8 12.8 L 16.8 12 z M 14.4 11.2 L 14.4 12.0 L 15.2 12.0 L 15.2 11.2 z M 8 17.6 L 8 18.4 L 8.8 18.4 L 8.8 17.6 z M 12.8 14.4 L 12.8 15.2 L 13.6 15.2 L 13.6 14.4 z M 18.4 8 L 18.4 8.8 L 19.2 8.8 L 19.2 8 z M 17.6 4.8 L 17.6 5.6 L 18.4 5.6 L 18.4 4.8 z M 16.8 11.2 L 16.8 12.0 L 17.6 12.0 L 17.6 11.2 z M 13.6 15.2 L 13.6 16.0 L 14.4 16.0 L 14.4 15.2 z M 19.2 4 L 19.2 4.8 L 20.0 4.8 L 20.0 4 z M 4.8 19.2 L 4.8 20.0 L 5.6 20.0 L 5.6 19.2 z M 5.6 10.4 L 5.6 11.2 L 6.4 11.2 L 6.4 10.4 z M 4.8 5.6 L 4.8 6.4 L 5.6 6.4 L 5.6 5.6 z M 9.6 8.8 L 9.6 9.6 L 10.4 9.6 L 10.4 8.8 z M 6.4 4.8 L 6.4 5.6 L 7.2 5.6 L 7.2 4.8 z M 4.8 14.4 L 4.8 15.2 L 5.6 15.2 L 5.6 14.4 z M 8 7.2 L 8 8.0 L 8.8 8.0 L 8.8 7.2 z M 12 8.8 L 12 9.6 L 12.8 9.6 L 12.8 8.8 z M 11.2 7.2 L 11.2 8.0 L 12.0 8.0 L 12.0 7.2 z M 8 9.6 L 8 10.4 L 8.8 10.4 L 8.8 9.6 z M 14.4 16 L 14.4 16.8 L 15.2 16.8 L 15.2 16 z M 18.4 16 L 18.4 16.8 L 19.2 16.8 L 19.2 16 z M 16.8 3.2 L 16.8 4.0 L 17.6 4.0 L 17.6 3.2 z M 16 9.6 L 16 10.4 L 16.8 10.4 L 16.8 9.6 z M 3.2 18.4 L 3.2 19.2 L 4.0 19.2 L 4.0 18.4 z M 12.8 18.4 L 12.8 19.2 L 13.6 19.2 L 13.6 18.4 z M 3.2 8 L 3.2 8.8 L 4.0 8.8 L 4.0 8 z M 15.2 8 L 15.2 8.8 L 16.0 8.8 L 16.0 8 z M 19.2 8 L 19.2 8.8 L 20.0 8.8 L 20.0 8 z M 17.6 17.6 L 17.6 18.4 L 18.4 18.4 L 18.4 17.6 z M 5.6 6.4 L 5.6 7.2 L 6.4 7.2 L 6.4 6.4 z M 4.8 6.4 L 4.8 7.2 L 5.6 7.2 L 5.6 6.4 z M 10.4 12.8 L 10.4 13.6 L 11.2 13.6 L 11.2 12.8 z M 9.6 6.4 L 9.6 7.2 L 10.4 7.2 L 10.4 6.4 z M 11.2 12 L 11.2 12.8 L 12.0 12.8 L 12.0 12 z M 8 8 L 8 8.8 L 8.8 8.8 L 8.8 8 z M 7.2 8 L 7.2 8.8 L 8.0 8.8 L 8.0 8 z M 6.4 17.6 L 6.4 18.4 L 7.2 18.4 L 7.2 17.6 z M 12 4.8 L 12 5.6 L 12.8 5.6 L 12.8 4.8 z M 11.2 8 L 11.2 8.8 L 12.0 8.8 L 12.0 8 z M 16 4.8 L 16 5.6 L 16.8 5.6 L 16.8 4.8 z M 12.8 7.2 L 12.8 8.0 L 13.6 8.0 L 13.6 7.2 z M 16.8 5.6 L 16.8 6.4 L 17.6 6.4 L 17.6 5.6 z M 15.2 9.6 L 15.2 10.4 L 16.0 10.4 L 16.0 9.6 z M 14.4 3.2 L 14.4 4.0 L 15.2 4.0 L 15.2 3.2 z M 3.2 16 L 3.2 16.8 L 4.0 16.8 L 4.0 16 z M 12.8 16 L 12.8 16.8 L 13.6 16.8 L 13.6 16 z M 18.4 3.2 L 18.4 4.0 L 19.2 4.0 L 19.2 3.2 z M 17.6 9.6 L 17.6 10.4 L 18.4 10.4 L 18.4 9.6 z M 3.2 5.6 L 3.2 6.4 L 4.0 6.4 L 4.0 5.6 z M 5.6 14.4 L 5.6 15.2 L 6.4 15.2 L 6.4 14.4 z M 6.4 10.4 L 6.4 11.2 L 7.2 11.2 L 7.2 10.4 z M 5.6 5.6 L 5.6 6.4 L 6.4 6.4 L 6.4 5.6 z M 4.8 10.4 L 4.8 11.2 L 5.6 11.2 L 5.6 10.4 z M 10.4 15.2 L 10.4 16.0 L 11.2 16.0 L 11.2 15.2 z M 7.2 12.8 L 7.2 13.6 L 8.0 13.6 L 8.0 12.8 z M 6.4 6.4 L 6.4 7.2 L 7.2 7.2 L 7.2 6.4 z M 5.6 19.2 L 5.6 20.0 L 6.4 20.0 L 6.4 19.2 z M 11.2 12.8 L 11.2 13.6 L 12.0 13.6 L 12.0 12.8 z M 9.6 19.2 L 9.6 20.0 L 10.4 20.0 L 10.4 19.2 z M 8 5.6 L 8 6.4 L 8.8 6.4 L 8.8 5.6 z M 10.4 16.8 L 10.4 17.6 L 11.2 17.6 L 11.2 16.8 z M 15.2 17.6 L 15.2 18.4 L 16.0 18.4 L 16.0 17.6 z M 14.4 14.4 L 14.4 15.2 L 15.2 15.2 L 15.2 14.4 z M 18.4 11.2 L 18.4 12.0 L 19.2 12.0 L 19.2 11.2 z M 15.2 15.2 L 15.2 16.0 L 16.0 16.0 L 16.0 15.2 z M 13.6 12 L 13.6 12.8 L 14.4 12.8 L 14.4 12 z M 4.8 16 L 4.8 16.8 L 5.6 16.8 L 5.6 16 z M 3.2 3.2 L 3.2 4.0 L 4.0 4.0 L 4.0 3.2 z M 16.8 18.4 L 16.8 19.2 L 17.6 19.2 L 17.6 18.4 z M 6.4 14.4 L 6.4 15.2 L 7.2 15.2 L 7.2 14.4 z M 11.2 4 L 11.2 4.8 L 12.0 4.8 L 12.0 4 z M 9.6 16.8 L 9.6 17.6 L 10.4 17.6 L 10.4 16.8 z M 8 12.8 L 8 13.6 L 8.8 13.6 L 8.8 12.8 z M 14.4 19.2 L 14.4 20.0 L 15.2 20.0 L 15.2 19.2 z M 6.4 19.2 L 6.4 20.0 L 7.2 20.0 L 7.2 19.2 z M 12 12.8 L 12 13.6 L 12.8 13.6 L 12.8 12.8 z M 18.4 19.2 L 18.4 20.0 L 19.2 20.0 L 19.2 19.2 z M 16.8 6.4 L 16.8 7.2 L 17.6 7.2 L 17.6 6.4 z M 8.8 10.4 L 8.8 11.2 L 9.6 11.2 L 9.6 10.4 z M 15.2 16.8 L 15.2 17.6 L 16.0 17.6 L 16.0 16.8 z M 8 18.4 L 8 19.2 L 8.8 19.2 L 8.8 18.4 z M 12.8 15.2 L 12.8 16.0 L 13.6 16.0 L 13.6 15.2 z M 17.6 5.6 L 17.6 6.4 L 18.4 6.4 L 18.4 5.6 z M 16.8 10.4 L 16.8 11.2 L 17.6 11.2 L 17.6 10.4 z M 19.2 4.8 L 19.2 5.6 L 20.0 5.6 L 20.0 4.8 z M 17.6 14.4 L 17.6 15.2 L 18.4 15.2 L 18.4 14.4 z M 5.6 9.6 L 5.6 10.4 L 6.4 10.4 L 6.4 9.6 z M 4.8 3.2 L 4.8 4.0 L 5.6 4.0 L 5.6 3.2 z M 6.4 5.6 L 6.4 6.4 L 7.2 6.4 L 7.2 5.6 z M 10.4 7.2 L 10.4 8.0 L 11.2 8.0 L 11.2 7.2 z" id="qr-path" style="fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"></path></svg>"""
results = [rt] * 2 + [rs] * 2 + [rm] * 5 + [rl] * 2 + [rh] * 2 + [r6] * 2 + [r8] * 2
for i in range(len(sizes)):
size = sizes[i]
print('Testing SVG with size %s' % size)
result = results[i]
qr1 = make_embedded_qr_code(TEST_TEXT, QRCodeOptions(size=size))
qr2 = qr_from_text(TEST_TEXT, size=size)
qr3 = qr_from_text(TEST_TEXT, size=size, image_format='svg')
qr4 = qr_from_text(TEST_TEXT, options=QRCodeOptions(size=size, image_format='svg'))
qr5 = qr_from_text(TEST_TEXT, size=size, image_format='invalid-format-name')
self.assertEqual(qr1, qr2)
self.assertEqual(qr1, qr3)
self.assertEqual(qr1, qr4)
self.assertEqual(qr1, qr5)
self.assertEqual(qr1, result)
# print("\"\"\"%s\"\"\"," % qr1)
# print("\"\"\"{%% qr_from_text '%s' %%}\"\"\"," % qr1)
def test_version(self):
versions = [None, -1, 0, 41, '-1', '0', '41', 'blabla', 1, '1', 2, '2', 4, '4']
default_result = """<svg height="52.2mm" version="1.1" viewBox="0 0 52.2 52.2" width="52.2mm" xmlns="http://www.w3.org/2000/svg"><path d="M 36 18 L 36 19.8 L 37.8 19.8 L 37.8 18 z M 32.4 37.8 L 32.4 39.6 L 34.2 39.6 L 34.2 37.8 z M 28.8 9 L 28.8 10.8 L 30.6 10.8 L 30.6 9 z M 25.2 37.8 L 25.2 39.6 L 27.0 39.6 L 27.0 37.8 z M 7.2 43.2 L 7.2 45.0 L 9.0 45.0 L 9.0 43.2 z M 36 23.4 L 36 25.2 L 37.8 25.2 L 37.8 23.4 z M 32.4 14.4 L 32.4 16.2 L 34.2 16.2 L 34.2 14.4 z M 39.6 14.4 L 39.6 16.2 L 41.4 16.2 L 41.4 14.4 z M 30.6 37.8 L 30.6 39.6 L 32.4 39.6 L 32.4 37.8 z M 9 18 L 9 19.8 L 10.8 19.8 L 10.8 18 z M 7.2 25.2 L 7.2 27.0 L 9.0 27.0 L 9.0 25.2 z M 39.6 41.4 L 39.6 43.2 L 41.4 43.2 L 41.4 41.4 z M 21.6 16.2 L 21.6 18.0 L 23.4 18.0 L 23.4 16.2 z M 16.2 21.6 L 16.2 23.4 L 18.0 23.4 L 18.0 21.6 z M 14.4 7.2 L 14.4 9.0 L 16.2 9.0 L 16.2 7.2 z M 25.2 21.6 L 25.2 23.4 L 27.0 23.4 L 27.0 21.6 z M 23.4 7.2 L 23.4 9.0 L 25.2 9.0 L 25.2 7.2 z M 25.2 19.8 L 25.2 21.6 L 27.0 21.6 L 27.0 19.8 z M 36 12.6 L 36 14.4 L 37.8 14.4 L 37.8 12.6 z M 18 25.2 L 18 27.0 L 19.8 27.0 L 19.8 25.2 z M 28.8 18 L 28.8 19.8 L 30.6 19.8 L 30.6 18 z M 41.4 32.4 L 41.4 34.2 L 43.2 34.2 L 43.2 32.4 z M 37.8 10.8 L 37.8 12.6 L 39.6 12.6 L 39.6 10.8 z M 7.2 37.8 L 7.2 39.6 L 9.0 39.6 L 9.0 37.8 z M 32.4 9 L 32.4 10.8 L 34.2 10.8 L 34.2 9 z M 43.2 23.4 L 43.2 25.2 L 45.0 25.2 L 45.0 23.4 z M 39.6 23.4 L 39.6 25.2 L 41.4 25.2 L 41.4 23.4 z M 7.2 14.4 L 7.2 16.2 L 9.0 16.2 L 9.0 14.4 z M 43.2 14.4 L 43.2 16.2 L 45.0 16.2 L 45.0 14.4 z M 21.6 34.2 L 21.6 36.0 L 23.4 36.0 L 23.4 34.2 z M 14.4 25.2 L 14.4 27.0 L 16.2 27.0 L 16.2 25.2 z M 12.6 10.8 L 12.6 12.6 L 14.4 12.6 L 14.4 10.8 z M 10.8 18 L 10.8 19.8 L 12.6 19.8 L 12.6 18 z M 23.4 32.4 L 23.4 34.2 L 25.2 34.2 L 25.2 32.4 z M 21.6 10.8 L 21.6 12.6 L 23.4 12.6 L 23.4 10.8 z M 18 7.2 L 18 9.0 L 19.8 9.0 L 19.8 7.2 z M 14.4 36 L 14.4 37.8 L 16.2 37.8 L 16.2 36 z M 27 7.2 L 27 9.0 L 28.8 9.0 L 28.8 7.2 z M 23.4 36 L 23.4 37.8 L 25.2 37.8 L 25.2 36 z M 36 7.2 L 36 9.0 L 37.8 9.0 L 37.8 7.2 z M 37.8 30.6 L 37.8 32.4 L 39.6 32.4 L 39.6 30.6 z M 36 41.4 L 36 43.2 L 37.8 43.2 L 37.8 41.4 z M 32.4 10.8 L 32.4 12.6 L 34.2 12.6 L 34.2 10.8 z M 30.6 25.2 L 30.6 27.0 L 32.4 27.0 L 32.4 25.2 z M 10.8 37.8 L 10.8 39.6 L 12.6 39.6 L 12.6 37.8 z M 39.6 25.2 L 39.6 27.0 L 41.4 27.0 L 41.4 25.2 z M 7.2 9 L 7.2 10.8 L 9.0 10.8 L 9.0 9 z M 9 28.8 L 9 30.6 L 10.8 30.6 L 10.8 28.8 z M 21.6 28.8 L 21.6 30.6 L 23.4 30.6 L 23.4 28.8 z M 16.2 32.4 L 16.2 34.2 L 18.0 34.2 L 18.0 32.4 z M 12.6 39.6 L 12.6 41.4 L 14.4 41.4 L 14.4 39.6 z M 21.6 39.6 L 21.6 41.4 L 23.4 41.4 L 23.4 39.6 z M 27 34.2 L 27 36.0 L 28.8 36.0 L 28.8 34.2 z M 23.4 41.4 L 23.4 43.2 L 25.2 43.2 L 25.2 41.4 z M 34.2 36 L 34.2 37.8 L 36.0 37.8 L 36.0 36 z M 32.4 21.6 L 32.4 23.4 L 34.2 23.4 L 34.2 21.6 z M 18 36 L 18 37.8 L 19.8 37.8 L 19.8 36 z M 28.8 21.6 L 28.8 23.4 L 30.6 23.4 L 30.6 21.6 z M 43.2 36 L 43.2 37.8 L 45.0 37.8 L 45.0 36 z M 39.6 7.2 L 39.6 9.0 L 41.4 9.0 L 41.4 7.2 z M 37.8 21.6 L 37.8 23.4 L 39.6 23.4 L 39.6 21.6 z M 43.2 12.6 L 43.2 14.4 L 45.0 14.4 L 45.0 12.6 z M 10.8 39.6 L 10.8 41.4 L 12.6 41.4 L 12.6 39.6 z M 39.6 34.2 L 39.6 36.0 L 41.4 36.0 L 41.4 34.2 z M 7.2 32.4 L 7.2 34.2 L 9.0 34.2 L 9.0 32.4 z M 10.8 28.8 L 10.8 30.6 L 12.6 30.6 L 12.6 28.8 z M 12.6 37.8 L 12.6 39.6 L 14.4 39.6 L 14.4 37.8 z M 18 32.4 L 18 34.2 L 19.8 34.2 L 19.8 32.4 z M 27 25.2 L 27 27.0 L 28.8 27.0 L 28.8 25.2 z M 37.8 18 L 37.8 19.8 L 39.6 19.8 L 39.6 18 z M 32.4 16.2 L 32.4 18.0 L 34.2 18.0 L 34.2 16.2 z M 28.8 30.6 L 28.8 32.4 L 30.6 32.4 L 30.6 30.6 z M 37.8 27 L 37.8 28.8 L 39.6 28.8 L 39.6 27 z M 34.2 7.2 L 34.2 9.0 L 36.0 9.0 L 36.0 7.2 z M 43.2 7.2 L 43.2 9.0 L 45.0 9.0 L 45.0 7.2 z M 7.2 27 L 7.2 28.8 L 9.0 28.8 L 9.0 27 z M 12.6 18 L 12.6 19.8 L 14.4 19.8 L 14.4 18 z M 10.8 10.8 L 10.8 12.6 L 12.6 12.6 L 12.6 10.8 z M 9 25.2 L 9 27.0 L 10.8 27.0 L 10.8 25.2 z M 21.6 18 L 21.6 19.8 L 23.4 19.8 L 23.4 18 z M 23.4 19.8 L 23.4 21.6 L 25.2 21.6 L 25.2 19.8 z M 18 14.4 L 18 16.2 L 19.8 16.2 L 19.8 14.4 z M 27 14.4 L 27 16.2 L 28.8 16.2 L 28.8 14.4 z M 25.2 14.4 L 25.2 16.2 L 27.0 16.2 L 27.0 14.4 z M 23.4 43.2 L 23.4 45.0 L 25.2 45.0 L 25.2 43.2 z M 36 14.4 L 36 16.2 L 37.8 16.2 L 37.8 14.4 z M 28.8 19.8 L 28.8 21.6 L 30.6 21.6 L 30.6 19.8 z M 7.2 39.6 L 7.2 41.4 L 9.0 41.4 L 9.0 39.6 z M 32.4 18 L 32.4 19.8 L 34.2 19.8 L 34.2 18 z M 28.8 39.6 L 28.8 41.4 L 30.6 41.4 L 30.6 39.6 z M 39.6 18 L 39.6 19.8 L 41.4 19.8 L 41.4 18 z M 7.2 16.2 L 7.2 18.0 L 9.0 18.0 L 9.0 16.2 z M 30.6 41.4 L 30.6 43.2 L 32.4 43.2 L 32.4 41.4 z M 43.2 16.2 L 43.2 18.0 L 45.0 18.0 L 45.0 16.2 z M 9 7.2 L 9 9.0 L 10.8 9.0 L 10.8 7.2 z M 7.2 21.6 L 7.2 23.4 L 9.0 23.4 L 9.0 21.6 z M 23.4 30.6 L 23.4 32.4 L 25.2 32.4 L 25.2 30.6 z M 21.6 12.6 L 21.6 14.4 L 23.4 14.4 L 23.4 12.6 z M 14.4 18 L 14.4 19.8 L 16.2 19.8 L 16.2 18 z M 18 9 L 18 10.8 L 19.8 10.8 L 19.8 9 z M 14.4 37.8 L 14.4 39.6 L 16.2 39.6 L 16.2 37.8 z M 32.4 28.8 L 32.4 30.6 L 34.2 30.6 L 34.2 28.8 z M 16.2 43.2 L 16.2 45.0 L 18.0 45.0 L 18.0 43.2 z M 28.8 14.4 L 28.8 16.2 L 30.6 16.2 L 30.6 14.4 z M 18 43.2 L 18 45.0 L 19.8 45.0 L 19.8 43.2 z M 25.2 43.2 L 25.2 45.0 L 27.0 45.0 L 27.0 43.2 z M 27 43.2 L 27 45.0 L 28.8 45.0 L 28.8 43.2 z M 34.2 23.4 L 34.2 25.2 L 36.0 25.2 L 36.0 23.4 z M 32.4 12.6 L 32.4 14.4 L 34.2 14.4 L 34.2 12.6 z M 30.6 23.4 L 30.6 25.2 L 32.4 25.2 L 32.4 23.4 z M 36 43.2 L 36 45.0 L 37.8 45.0 L 37.8 43.2 z M 39.6 27 L 39.6 28.8 L 41.4 28.8 L 41.4 27 z M 7.2 10.8 L 7.2 12.6 L 9.0 12.6 L 9.0 10.8 z M 37.8 37.8 L 37.8 39.6 L 39.6 39.6 L 39.6 37.8 z M 21.6 30.6 L 21.6 32.4 L 23.4 32.4 L 23.4 30.6 z M 14.4 21.6 L 14.4 23.4 L 16.2 23.4 L 16.2 21.6 z M 12.6 7.2 L 12.6 9.0 L 14.4 9.0 L 14.4 7.2 z M 10.8 21.6 L 10.8 23.4 L 12.6 23.4 L 12.6 21.6 z M 23.4 21.6 L 23.4 23.4 L 25.2 23.4 L 25.2 21.6 z M 25.2 34.2 L 25.2 36.0 L 27.0 36.0 L 27.0 34.2 z M 18 10.8 L 18 12.6 L 19.8 12.6 L 19.8 10.8 z M 23.4 39.6 L 23.4 41.4 L 25.2 41.4 L 25.2 39.6 z M 32.4 23.4 L 32.4 25.2 L 34.2 25.2 L 34.2 23.4 z M 18 37.8 L 18 39.6 L 19.8 39.6 L 19.8 37.8 z M 28.8 23.4 L 28.8 25.2 L 30.6 25.2 L 30.6 23.4 z M 9 43.2 L 9 45.0 L 10.8 45.0 L 10.8 43.2 z M 37.8 34.2 L 37.8 36.0 L 39.6 36.0 L 39.6 34.2 z M 36 37.8 L 36 39.6 L 37.8 39.6 L 37.8 37.8 z M 30.6 28.8 L 30.6 30.6 L 32.4 30.6 L 32.4 28.8 z M 43.2 28.8 L 43.2 30.6 L 45.0 30.6 L 45.0 28.8 z M 28.8 43.2 L 28.8 45.0 L 30.6 45.0 L 30.6 43.2 z M 7.2 34.2 L 7.2 36.0 L 9.0 36.0 L 9.0 34.2 z M 12.6 25.2 L 12.6 27.0 L 14.4 27.0 L 14.4 25.2 z M 9 32.4 L 9 34.2 L 10.8 34.2 L 10.8 32.4 z M 23.4 27 L 23.4 28.8 L 25.2 28.8 L 25.2 27 z M 16.2 7.2 L 16.2 9.0 L 18.0 9.0 L 18.0 7.2 z M 12.6 36 L 12.6 37.8 L 14.4 37.8 L 14.4 36 z M 25.2 7.2 L 25.2 9.0 L 27.0 9.0 L 27.0 7.2 z M 21.6 36 L 21.6 37.8 L 23.4 37.8 L 23.4 36 z M 18 34.2 L 18 36.0 L 19.8 36.0 L 19.8 34.2 z M 36 27 L 36 28.8 L 37.8 28.8 L 37.8 27 z M 32.4 25.2 L 32.4 27.0 L 34.2 27.0 L 34.2 25.2 z M 18 39.6 L 18 41.4 L 19.8 41.4 L 19.8 39.6 z M 28.8 32.4 L 28.8 34.2 L 30.6 34.2 L 30.6 32.4 z M 41.4 18 L 41.4 19.8 L 43.2 19.8 L 43.2 18 z M 39.6 10.8 L 39.6 12.6 L 41.4 12.6 L 41.4 10.8 z M 37.8 25.2 L 37.8 27.0 L 39.6 27.0 L 39.6 25.2 z M 30.6 34.2 L 30.6 36.0 L 32.4 36.0 L 32.4 34.2 z M 43.2 9 L 43.2 10.8 L 45.0 10.8 L 45.0 9 z M 10.8 43.2 L 10.8 45.0 L 12.6 45.0 L 12.6 43.2 z M 12.6 23.4 L 12.6 25.2 L 14.4 25.2 L 14.4 23.4 z M 10.8 12.6 L 10.8 14.4 L 12.6 14.4 L 12.6 12.6 z M 21.6 19.8 L 21.6 21.6 L 23.4 21.6 L 23.4 19.8 z M 14.4 10.8 L 14.4 12.6 L 16.2 12.6 L 16.2 10.8 z M 10.8 32.4 L 10.8 34.2 L 12.6 34.2 L 12.6 32.4 z M 18 16.2 L 18 18.0 L 19.8 18.0 L 19.8 16.2 z M 27 19.8 L 27 21.6 L 28.8 21.6 L 28.8 19.8 z M 25.2 16.2 L 25.2 18.0 L 27.0 18.0 L 27.0 16.2 z M 18 21.6 L 18 23.4 L 19.8 23.4 L 19.8 21.6 z M 32.4 36 L 32.4 37.8 L 34.2 37.8 L 34.2 36 z M 41.4 36 L 41.4 37.8 L 43.2 37.8 L 43.2 36 z M 37.8 7.2 L 37.8 9.0 L 39.6 9.0 L 39.6 7.2 z M 36 21.6 L 36 23.4 L 37.8 23.4 L 37.8 21.6 z M 7.2 41.4 L 7.2 43.2 L 9.0 43.2 L 9.0 41.4 z M 28.8 41.4 L 28.8 43.2 L 30.6 43.2 L 30.6 41.4 z M 7.2 18 L 7.2 19.8 L 9.0 19.8 L 9.0 18 z M 34.2 18 L 34.2 19.8 L 36.0 19.8 L 36.0 18 z M 43.2 18 L 43.2 19.8 L 45.0 19.8 L 45.0 18 z M 39.6 39.6 L 39.6 41.4 L 41.4 41.4 L 41.4 39.6 z M 12.6 14.4 L 12.6 16.2 L 14.4 16.2 L 14.4 14.4 z M 10.8 14.4 L 10.8 16.2 L 12.6 16.2 L 12.6 14.4 z M 23.4 28.8 L 23.4 30.6 L 25.2 30.6 L 25.2 28.8 z M 21.6 14.4 L 21.6 16.2 L 23.4 16.2 L 23.4 14.4 z M 25.2 27 L 25.2 28.8 L 27.0 28.8 L 27.0 27 z M 18 18 L 18 19.8 L 19.8 19.8 L 19.8 18 z M 16.2 18 L 16.2 19.8 L 18.0 19.8 L 18.0 18 z M 14.4 39.6 L 14.4 41.4 L 16.2 41.4 L 16.2 39.6 z M 27 10.8 L 27 12.6 L 28.8 12.6 L 28.8 10.8 z M 25.2 18 L 25.2 19.8 L 27.0 19.8 L 27.0 18 z M 36 10.8 L 36 12.6 L 37.8 12.6 L 37.8 10.8 z M 28.8 16.2 L 28.8 18.0 L 30.6 18.0 L 30.6 16.2 z M 37.8 12.6 L 37.8 14.4 L 39.6 14.4 L 39.6 12.6 z M 34.2 21.6 L 34.2 23.4 L 36.0 23.4 L 36.0 21.6 z M 32.4 7.2 L 32.4 9.0 L 34.2 9.0 L 34.2 7.2 z M 7.2 36 L 7.2 37.8 L 9.0 37.8 L 9.0 36 z M 28.8 36 L 28.8 37.8 L 30.6 37.8 L 30.6 36 z M 41.4 7.2 L 41.4 9.0 L 43.2 9.0 L 43.2 7.2 z M 39.6 21.6 L 39.6 23.4 L 41.4 23.4 L 41.4 21.6 z M 7.2 12.6 L 7.2 14.4 L 9.0 14.4 L 9.0 12.6 z M 12.6 32.4 L 12.6 34.2 L 14.4 34.2 L 14.4 32.4 z M 14.4 23.4 L 14.4 25.2 L 16.2 25.2 L 16.2 23.4 z M 12.6 12.6 L 12.6 14.4 L 14.4 14.4 L 14.4 12.6 z M 10.8 23.4 L 10.8 25.2 L 12.6 25.2 L 12.6 23.4 z M 23.4 34.2 L 23.4 36.0 L 25.2 36.0 L 25.2 34.2 z M 16.2 28.8 L 16.2 30.6 L 18.0 30.6 L 18.0 28.8 z M 14.4 14.4 L 14.4 16.2 L 16.2 16.2 L 16.2 14.4 z M 12.6 43.2 L 12.6 45.0 L 14.4 45.0 L 14.4 43.2 z M 25.2 28.8 L 25.2 30.6 L 27.0 30.6 L 27.0 28.8 z M 21.6 43.2 L 21.6 45.0 L 23.4 45.0 L 23.4 43.2 z M 18 12.6 L 18 14.4 L 19.8 14.4 L 19.8 12.6 z M 23.4 37.8 L 23.4 39.6 L 25.2 39.6 L 25.2 37.8 z M 34.2 39.6 L 34.2 41.4 L 36.0 41.4 L 36.0 39.6 z M 32.4 32.4 L 32.4 34.2 L 34.2 34.2 L 34.2 32.4 z M 41.4 25.2 L 41.4 27.0 L 43.2 27.0 L 43.2 25.2 z M 34.2 34.2 L 34.2 36.0 L 36.0 36.0 L 36.0 34.2 z M 30.6 27 L 30.6 28.8 L 32.4 28.8 L 32.4 27 z M 10.8 36 L 10.8 37.8 L 12.6 37.8 L 12.6 36 z M 7.2 7.2 L 7.2 9.0 L 9.0 9.0 L 9.0 7.2 z M 37.8 41.4 L 37.8 43.2 L 39.6 43.2 L 39.6 41.4 z M 14.4 32.4 L 14.4 34.2 L 16.2 34.2 L 16.2 32.4 z M 25.2 9 L 25.2 10.8 L 27.0 10.8 L 27.0 9 z M 21.6 37.8 L 21.6 39.6 L 23.4 39.6 L 23.4 37.8 z M 18 28.8 L 18 30.6 L 19.8 30.6 L 19.8 28.8 z M 32.4 43.2 L 32.4 45.0 L 34.2 45.0 L 34.2 43.2 z M 14.4 43.2 L 14.4 45.0 L 16.2 45.0 L 16.2 43.2 z M 27 28.8 L 27 30.6 L 28.8 30.6 L 28.8 28.8 z M 41.4 43.2 L 41.4 45.0 L 43.2 45.0 L 43.2 43.2 z M 37.8 14.4 L 37.8 16.2 L 39.6 16.2 L 39.6 14.4 z M 19.8 23.4 L 19.8 25.2 L 21.6 25.2 L 21.6 23.4 z M 34.2 37.8 L 34.2 39.6 L 36.0 39.6 L 36.0 37.8 z M 18 41.4 L 18 43.2 L 19.8 43.2 L 19.8 41.4 z M 28.8 34.2 L 28.8 36.0 L 30.6 36.0 L 30.6 34.2 z M 39.6 12.6 L 39.6 14.4 L 41.4 14.4 L 41.4 12.6 z M 37.8 23.4 L 37.8 25.2 L 39.6 25.2 L 39.6 23.4 z M 43.2 10.8 L 43.2 12.6 L 45.0 12.6 L 45.0 10.8 z M 39.6 32.4 L 39.6 34.2 L 41.4 34.2 L 41.4 32.4 z M 12.6 21.6 L 12.6 23.4 L 14.4 23.4 L 14.4 21.6 z M 10.8 7.2 L 10.8 9.0 L 12.6 9.0 L 12.6 7.2 z M 14.4 12.6 L 14.4 14.4 L 16.2 14.4 L 16.2 12.6 z M 23.4 16.2 L 23.4 18.0 L 25.2 18.0 L 25.2 16.2 z" id="qr-path" style="fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"></path></svg>"""
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28.8 z M 57.6 57.6 L 57.6 59.4 L 59.4 59.4 L 59.4 57.6 z M 10.8 27 L 10.8 28.8 L 12.6 28.8 L 12.6 27 z M 12.6 39.6 L 12.6 41.4 L 14.4 41.4 L 14.4 39.6 z M 14.4 45 L 14.4 46.8 L 16.2 46.8 L 16.2 45 z M 19.8 21.6 L 19.8 23.4 L 21.6 23.4 L 21.6 21.6 z M 46.8 64.8 L 46.8 66.6 L 48.6 66.6 L 48.6 64.8 z M 43.2 36 L 43.2 37.8 L 45.0 37.8 L 45.0 36 z M 39.6 7.2 L 39.6 9.0 L 41.4 9.0 L 41.4 7.2 z M 61.2 64.8 L 61.2 66.6 L 63.0 66.6 L 63.0 64.8 z M 37.8 59.4 L 37.8 61.2 L 39.6 61.2 L 39.6 59.4 z M 46.8 54 L 46.8 55.8 L 48.6 55.8 L 48.6 54 z M 21.6 57.6 L 21.6 59.4 L 23.4 59.4 L 23.4 57.6 z M 64.8 23.4 L 64.8 25.2 L 66.6 25.2 L 66.6 23.4 z M 28.8 10.8 L 28.8 12.6 L 30.6 12.6 L 30.6 10.8 z M 25.2 61.2 L 25.2 63.0 L 27.0 63.0 L 27.0 61.2 z M 34.2 48.6 L 34.2 50.4 L 36.0 50.4 L 36.0 48.6 z M 54 59.4 L 54 61.2 L 55.8 61.2 L 55.8 59.4 z M 7.2 50.4 L 7.2 52.2 L 9.0 52.2 L 9.0 50.4 z M 28.8 50.4 L 28.8 52.2 L 30.6 52.2 L 30.6 50.4 z M 55.8 36 L 55.8 37.8 L 57.6 37.8 L 57.6 36 z M 57.6 55.8 L 57.6 57.6 L 59.4 57.6 L 59.4 55.8 z M 10.8 10.8 L 10.8 12.6 L 12.6 12.6 L 12.6 10.8 z M 59.4 54 L 59.4 55.8 L 61.2 55.8 L 61.2 54 z M 18 14.4 L 18 16.2 L 19.8 16.2 L 19.8 14.4 z M 14.4 57.6 L 14.4 59.4 L 16.2 59.4 L 16.2 57.6 z M 41.4 57.6 L 41.4 59.4 L 43.2 59.4 L 43.2 57.6 z M 16.2 37.8 L 16.2 39.6 L 18.0 39.6 L 18.0 37.8 z M 43.2 63 L 43.2 64.8 L 45.0 64.8 L 45.0 63 z M 36 48.6 L 36 50.4 L 37.8 50.4 L 37.8 48.6 z M 18 61.2 L 18 63.0 L 19.8 63.0 L 19.8 61.2 z M 41.4 10.8 L 41.4 12.6 L 43.2 12.6 L 43.2 10.8 z M 37.8 46.8 L 37.8 48.6 L 39.6 48.6 L 39.6 46.8 z M 46.8 59.4 L 46.8 61.2 L 48.6 61.2 L 48.6 59.4 z M 39.6 45 L 39.6 46.8 L 41.4 46.8 L 41.4 45 z M 21.6 12.6 L 21.6 14.4 L 23.4 14.4 L 23.4 12.6 z M 64.8 7.2 L 64.8 9.0 L 66.6 9.0 L 66.6 7.2 z M 23.4 10.8 L 23.4 12.6 L 25.2 12.6 L 25.2 10.8 z M 32.4 52.2 L 32.4 54.0 L 34.2 54.0 L 34.2 52.2 z M 25.2 52.2 L 25.2 54.0 L 27.0 54.0 L 27.0 52.2 z M 7.2 63 L 7.2 64.8 L 9.0 64.8 L 9.0 63 z M 50.4 28.8 L 50.4 30.6 L 52.2 30.6 L 52.2 28.8 z M 32.4 12.6 L 32.4 14.4 L 34.2 14.4 L 34.2 12.6 z M 52.2 23.4 L 52.2 25.2 L 54.0 25.2 L 54.0 23.4 z M 57.6 39.6 L 57.6 41.4 L 59.4 41.4 L 59.4 39.6 z M 10.8 59.4 L 10.8 61.2 L 12.6 61.2 L 12.6 59.4 z M 54 32.4 L 54 34.2 L 55.8 34.2 L 55.8 32.4 z M 12.6 7.2 L 12.6 9.0 L 14.4 9.0 L 14.4 7.2 z M 55.8 34.2 L 55.8 36.0 L 57.6 36.0 L 57.6 34.2 z M 34.2 64.8 L 34.2 66.6 L 36.0 66.6 L 36.0 64.8 z M 57.6 21.6 L 57.6 23.4 L 59.4 23.4 L 59.4 21.6 z M 63 30.6 L 63 32.4 L 64.8 32.4 L 64.8 30.6 z M 16.2 54 L 16.2 55.8 L 18.0 55.8 L 18.0 54 z M 36 32.4 L 36 34.2 L 37.8 34.2 L 37.8 32.4 z M 46.8 43.2 L 46.8 45.0 L 48.6 45.0 L 48.6 43.2 z M 43.2 28.8 L 43.2 30.6 L 45.0 30.6 L 45.0 28.8 z M 39.6 28.8 L 39.6 30.6 L 41.4 30.6 L 41.4 28.8 z M 48.6 37.8 L 48.6 39.6 L 50.4 39.6 L 50.4 37.8 z M 45 9 L 45 10.8 L 46.8 10.8 L 46.8 9 z M 21.6 25.2 L 21.6 27.0 L 23.4 27.0 L 23.4 25.2 z M 25.2 7.2 L 25.2 9.0 L 27.0 9.0 L 27.0 7.2 z M 21.6 36 L 21.6 37.8 L 23.4 37.8 L 23.4 36 z M 27 30.6 L 27 32.4 L 28.8 32.4 L 28.8 30.6 z M 50.4 41.4 L 50.4 43.2 L 52.2 43.2 L 52.2 41.4 z M 30.6 34.2 L 30.6 36.0 L 32.4 36.0 L 32.4 34.2 z M 7.2 28.8 L 7.2 30.6 L 9.0 30.6 L 9.0 28.8 z M 12.6 23.4 L 12.6 25.2 L 14.4 25.2 L 14.4 23.4 z M 14.4 10.8 L 14.4 12.6 L 16.2 12.6 L 16.2 10.8 z M 10.8 32.4 L 10.8 34.2 L 12.6 34.2 L 12.6 32.4 z M 63 25.2 L 63 27.0 L 64.8 27.0 L 64.8 25.2 z M 59.4 18 L 59.4 19.8 L 61.2 19.8 L 61.2 18 z M 36 16.2 L 36 18.0 L 37.8 18.0 L 37.8 16.2 z M 18 21.6 L 18 23.4 L 19.8 23.4 L 19.8 21.6 z M 37.8 7.2 L 37.8 9.0 L 39.6 9.0 L 39.6 7.2 z M 63 36 L 63 37.8 L 64.8 37.8 L 64.8 36 z M 59.4 64.8 L 59.4 66.6 L 61.2 66.6 L 61.2 64.8 z M 39.6 19.8 L 39.6 21.6 L 41.4 21.6 L 41.4 19.8 z M 36 55.8 L 36 57.6 L 37.8 57.6 L 37.8 55.8 z M 45 25.2 L 45 27.0 L 46.8 27.0 L 46.8 25.2 z M 37.8 54 L 37.8 55.8 L 39.6 55.8 L 39.6 54 z M 46.8 9 L 46.8 10.8 L 48.6 10.8 L 48.6 9 z M 27 10.8 L 27 12.6 L 28.8 12.6 L 28.8 10.8 z M 50.4 54 L 50.4 55.8 L 52.2 55.8 L 52.2 54 z M 30.6 21.6 L 30.6 23.4 L 32.4 23.4 L 32.4 21.6 z M 27 50.4 L 27 52.2 L 28.8 52.2 L 28.8 50.4 z M 54 36 L 54 37.8 L 55.8 37.8 L 55.8 36 z M 7.2 12.6 L 7.2 14.4 L 9.0 14.4 L 9.0 12.6 z M 12.6 32.4 L 12.6 34.2 L 14.4 34.2 L 14.4 32.4 z M 57.6 61.2 L 57.6 63.0 L 59.4 63.0 L 59.4 61.2 z M 10.8 23.4 L 10.8 25.2 L 12.6 25.2 L 12.6 23.4 z M 16.2 28.8 L 16.2 30.6 L 18.0 30.6 L 18.0 28.8 z M 59.4 63 L 59.4 64.8 L 61.2 64.8 L 61.2 63 z M 12.6 43.2 L 12.6 45.0 L 14.4 45.0 L 14.4 43.2 z M 18 12.6 L 18 14.4 L 19.8 14.4 L 19.8 12.6 z M 14.4 48.6 L 14.4 50.4 L 16.2 50.4 L 16.2 48.6 z M 41.4 52.2 L 41.4 54.0 L 43.2 54.0 L 43.2 52.2 z M 63 52.2 L 63 54.0 L 64.8 54.0 L 64.8 52.2 z M 16.2 46.8 L 16.2 48.6 L 18.0 48.6 L 18.0 46.8 z M 36 39.6 L 36 41.4 L 37.8 41.4 L 37.8 39.6 z M 64.8 45 L 64.8 46.8 L 66.6 46.8 L 66.6 45 z M 39.6 50.4 L 39.6 52.2 L 41.4 52.2 L 41.4 50.4 z M 64.8 27 L 64.8 28.8 L 66.6 28.8 L 66.6 27 z M 23.4 63 L 23.4 64.8 L 25.2 64.8 L 25.2 63 z M 32.4 43.2 L 32.4 45.0 L 34.2 45.0 L 34.2 43.2 z M 52.2 57.6 L 52.2 59.4 L 54.0 59.4 L 54.0 57.6 z M 27 37.8 L 27 39.6 L 28.8 39.6 L 28.8 37.8 z M 7.2 54 L 7.2 55.8 L 9.0 55.8 L 9.0 54 z M 55.8 39.6 L 55.8 41.4 L 57.6 41.4 L 57.6 39.6 z M 30.6 55.8 L 30.6 57.6 L 32.4 57.6 L 32.4 55.8 z M 57.6 45 L 57.6 46.8 L 59.4 46.8 L 59.4 45 z M 10.8 7.2 L 10.8 9.0 L 12.6 9.0 L 12.6 7.2 z M 54 16.2 L 54 18.0 L 55.8 18.0 L 55.8 16.2 z M 32.4 64.8 L 32.4 66.6 L 34.2 66.6 L 34.2 64.8 z M 12.6 59.4 L 12.6 61.2 L 14.4 61.2 L 14.4 59.4 z M 55.8 21.6 L 55.8 23.4 L 57.6 23.4 L 57.6 21.6 z M 14.4 61.2 L 14.4 63.0 L 16.2 63.0 L 16.2 61.2 z M 41.4 61.2 L 41.4 63.0 L 43.2 63.0 L 43.2 61.2 z" id="qr-path" style="fill:#000000;fill-opacity:1;fill-rule:nonzero;stroke:none"></path></svg>"""
results = [default_result] * 10 + [
result_version_2,
result_version_2,
result_version_4,
result_version_4
]
for i in range(len(versions)):
version = versions[i]
print('Testing SVG with version %s' % version)
result = results[i]
qr1 = make_embedded_qr_code(TEST_TEXT, QRCodeOptions(version=version))
qr2 = qr_from_text(TEST_TEXT, version=version)
qr3 = qr_from_text(TEST_TEXT, version=version, image_format='svg')
qr4 = qr_from_text(TEST_TEXT, version=version, image_format='SVG')
qr5 = qr_from_text(TEST_TEXT, options=QRCodeOptions(version=version, image_format='SVG'))
qr6 = qr_from_text(TEST_TEXT, version=version, image_format='invalid-format-name')
self.assertEqual(qr1, qr2)
self.assertEqual(qr1, qr3)
self.assertEqual(qr1, qr4)
self.assertEqual(qr1, qr5)
self.assertEqual(qr1, qr6)
self.assertEqual(qr1, result)
# print("\"\"\"%s\"\"\"," % qr1)
# print("\"\"\"{%% qr_from_text '%s' %%}\"\"\"," % qr1)
def test_error_correction(self):
file_base_name = 'qrfromtextsvgresult_error_correction'
tests_data = []
for correction_level in ERROR_CORRECTION_DICT.keys():
ref_file_name = '%s_%s%s' % (file_base_name, correction_level, SVG_REF_SUFFIX)
tests_data.append(dict(source='{% qr_from_text "' + COMPLEX_TEST_TEXT + '" image_format="svg" error_correction="' + correction_level + '" %}', ref_file_name=ref_file_name.lower()))
for test_data in tests_data:
print('Testing template: %s' % test_data['source'])
html_source = mark_safe('{% load qr_code %}' + test_data['source'])
template = Template(html_source)
context = Context()
source_image_data = template.render(context).strip()
# Debug code for updating reference file.
# write_svg_content_to_file(test_data['ref_file_name'], source_image_data)
ref_image_data = get_svg_content_from_file_name(test_data['ref_file_name'], skip_header=False)
self.assertEqual(source_image_data, ref_image_data)
class TestQRFromTextPngResult(SimpleTestCase):
"""
Ensures that produced QR codes in PNG format coincide with verified references.
The tests cover direct call to tag function, rendering of tag, and direct call to qr_code API.
"""
def test_size(self):
sizes = ['t', 'T', 's', 'S', None, -1, 0, 'm', 'M', 'l', 'L', 'h', 'H', '6', 6, '8', 8, 10, '10']
rt = """iVBORw0KGgoAAAANSUhEUgAAAK4AAACuAQAAAACHdwtDAAABBklEQVR4nO2XMU5EMQxEn9eRKPNv5H81bvb3KHsApKRE8mooskDBStCAC9ZVNI2fovE4MXGnrqd7Kjzk/yojLadEggaAS1kE6JIGPqIRN7Cqq5rWoPXnBLO9kuRW8fct78vTWjlJV8J1C0A6CkmuZrbhAzibWRGJfaS9geftXJX2bZrZngTT6manATnxPLuwDaBlXcb2w0dIR66krclYJLkkKYnsgigkSSIJIIkFVkWyii6XsqvoTk6A2ZodmLtfqvLk3ScjAPqRZT5pAHPzF+ijXTbpXDTFn0uPub82rI/85Zbfy/140qHL/octv8hdCWsdr5itfccCPsJ1FDnWHj+vh/wD+Q2xAZu1VAND1QAAAABJRU5ErkJggg==" """
rs = """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" """
rm = """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" """
rl = """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" """
rh = """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" """
r6 = rt
r8 = """iVBORw0KGgoAAAANSUhEUgAAAOgAAADoAQAAAADN0pXVAAABBElEQVR4nO2YwRKDIAxE33b8/1/eHgJUe/BQEDMWD2jmHdwdIkmUObleZ3DRRRe9mW4AimejskSUVXO3XwwgsCy3KKvmfr+xsciWW3SjqnnUp/S6986i2zHU0XFOzQP8FpPCYmc5p+Z+v+VIDrctyqq5h2qfvoK/yGfFvtrC2rvOqbmbGopbt2x+aP19ASiqbl3smueJNf9Ia3/laDji/uB+Q6V5dKtFKv3kM/vnr/PZn6y+U9Wk+aiUX7dNzql52HwkBLKq3aSah81H4KjFjpMrqeaBNA4tRSVOo+o6amTXDzmNqoH0MB9ZxNB/t6pJ85HKP51alpJqHlZ/J7530UUX7advq/JW1h0TisgAAAAASUVORK5CYII=" """
r10 = """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" """
results = [rt] * 2 + [rs] * 2 + [rm] * 5 + [rl] * 2 + [rh] * 2 + [r6] * 2 + [r8] * 2 + [r10] * 2
for i in range(len(sizes)):
size = sizes[i]
print('Testing PNG with size %s' % size)
result = results[i]
qr1 = make_embedded_qr_code(TEST_TEXT, QRCodeOptions(size=size, image_format='png'))
qr2 = qr_from_text(TEST_TEXT, size=size, image_format='png')
qr3 = qr_from_text(TEST_TEXT, options=QRCodeOptions(size=size, image_format='png'))
self.assertEqual(qr1, qr2)
self.assertEqual(qr1, qr3)
self.assertEqual(qr1, BASE64_PNG_IMAGE_TEMPLATE % result)
# print("\"\"\"%s\"\"\"," % qr1)
# print("\"\"\"{%% qr_from_text '%s' %%}\"\"\"," % qr1)
def test_version(self):
versions = [None, -1, 0, 41, '-1', '0', '41', 'blabla', 1, '1', 2, '2', 4, '4']
default_result = """iVBORw0KGgoAAAANSUhEUgAAAgoAAAIKAQAAAABqulr4AAACO0lEQVR4nO3cQW6kMBCF4VfjlmbJ3GCOAkeHo+QGsIwEqiyMwZA0MBM6tNR/rWiTfCo2likXNtc3o/n1XUGCgICAgICAgICAgICAgICAgID4X8KWcZOkJl5LUre6Xz0mCwgICIjjROlTtGmkVXCPc1SYb/s94oQsICAgII4T3WIdZVap8Hcz9zqNTMuwB2YBAQEBcT9uu38R9nojnuNBICAgICStV19XZQEBAQGRYrnWKlbrqsak0MvS7y+XXc/xIBAQEC9INPMO4Z84UqayvCQN2Q7iA7OAgICA2Ip5rfXVOqoxaYhT1HoZdmoWEBAQEP9OmFV5U9ZNiluHIfY5LG89LAsICAiInfA82jhWunuf3Vr9S3DPmrvq53gQCAiIlyPMzP56H3909jtNVkPq1wru8WLIOrjOzgICAgJiO/K3vbKeurPGKlZTzSOtNG4mhv7sLCAgICCORl6NH/scxskqNTwMJpfUWejnir2prE/MAgICAuJw+P2QpCJezFWsOJIFdS0ICIhriM9nPuTFK3Vx67D0N/q1ICAgLiTyjcLFmQ9htXVYxF3FWsvDH1hrQUBAXEl8/upwekN8S2st93fO14KAgLiM2DvzYSzCN/Gd0CQV7eoUiOd4EAgIiBch9matseGhdGm4zZuJJ2cBAQEBcTS2znxwNdV42VjRBteiCn9iFhAQEBCHY/OznSnKVJ9ff+xDNR4CAuJnia0zHxYjhU8j1LUgICAgICAgICAgICAgICAgIK4mPgAhWy686mP48AAAAABJRU5ErkJggg==" """
result_version_2 = """iVBORw0KGgoAAAANSUhEUgAAAlIAAAJSAQAAAAAgpBbeAAADC0lEQVR4nO3dQZLbIBCF4deRqmYp3yBHkY9ujjI3sPZWdRaABPbYk6RsWSQ/K4+k+Qo2FDQNmOtZJfx4GiVhYWFhYWFhYWFhYWFhYWFhYWFhYbVjWV16yeyYX4b4UJKmqw+PX1jPrBcWFhZWW1Zf/B5Py8/pIMnzD2l0ac7fdpf1f+xF9cLCwsL6N6ypGoKaHTW4u0vBDup8zR1YxrTb1AsLCwurHat/8M4VbDjPvWJ/OveqxrQvrRcWFhZWq9ajflUafbKHH1Rlr23EwsLC2tKq+9Wh2iVg+UnsWztfo6lf7ibYaxuxsLCw3mWFdZH/EJ+M/hmDqKOf1+/mIhtgi3phYWFhtWSt49XbIWgKq5qkYIOnJ8Pdna97bSMWFhbWeyyzo6a8xD/FsWiwn74kVc05VWDOiax1jmsDbcTCwsLa1gpx+m9mZXcqyd39nPOsUt8bcqzg9fXCwsLCatNyP2nwS+xFP+3DPYZVu5i/umSrDlWX+/p6YWFhYbVl3cmzKtIAJLlGnw5z/ruLEdbhPFsVld1rG7GwsLDeZy1zfLM+Dlw9jk7Tfiv30xqDlTp34qtYWFhY962UTBW3r17yStaHXycAlOtWW9QLCwsLqyVr7RrjLoA0tZ+su6Ssqtnk8dyVPOvv7gRY99pGLCwsrC2tNNlfyuVqAeucp/9D9Y2k8om7u5/22kYsLCysLa16vLpsVk3HAoTj4Mty1XyzxrUcyfL8emFhYWG1al2PV8siKZ8TOOYn4/pKcSjLeBULCwvra+v2voDifIDqvoD0yvPa1mvrhYWFhdWW9fi+gGDpfIDxlC4OuD4fgDgAFhYW1lUpZv3LoVVjXq5a5/hSdV+AxLoVFhYW1l9Yt4cAjDed8O9af1awsLCwWrUe3RcQEwHmPuWvKt/MIkkKOU1gjR7stY1YWFhYW1qP7gtIGwRcUjim7rS4mDU9eU29sLCwsFq1yvhqVW6jqRepOOGq+EF8FQsLC2st398XsJR8jFV1cQD5AFhYWFhYWFhYWFhYWFhYWFhYWFj/q/ULXEmDCUnFmPAAAAAASUVORK5CYII=" """
result_version_4 = """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" """
results = [default_result] * 10 + [
result_version_2,
result_version_2,
result_version_4,
result_version_4
]
for i in range(len(versions)):
version = versions[i]
print('Testing PNG with version %s' % version)
result = results[i]
qr1 = make_embedded_qr_code(TEST_TEXT, QRCodeOptions(version=version, image_format='png'))
qr2 = qr_from_text(TEST_TEXT, version=version, image_format='png')
qr3 = qr_from_text(TEST_TEXT, version=version, image_format='PNG')
qr4 = qr_from_text(TEST_TEXT, options=QRCodeOptions(version=version, image_format='PNG'))
self.assertEqual(qr1, qr2)
self.assertEqual(qr1, qr3)
self.assertEqual(qr1, qr4)
# print(BASE64_PNG_IMAGE_TEMPLATE % result)
self.assertEqual(qr1, BASE64_PNG_IMAGE_TEMPLATE % result)
# print("\"\"\"%s\"\"\"," % qr1)
# print("\"\"\"{%% qr_from_text '%s' %%}\"\"\"," % qr1)
def test_error_correction(self):
file_base_name = 'qrfromtextpngresult_error_correction'
tests_data = []
for correction_level in ERROR_CORRECTION_DICT.keys():
ref_file_name = '%s_%s%s' % (file_base_name, correction_level, PNG_REF_SUFFIX)
tests_data.append(dict(source='{% qr_from_text "' + COMPLEX_TEST_TEXT + '" image_format="png" error_correction="' + correction_level + '" %}', ref_file_name=ref_file_name.lower()))
for test_data in tests_data:
print('Testing template: %s' % test_data['source'])
html_source = mark_safe('{% load qr_code %}' + test_data['source'])
template = Template(html_source)
context = Context()
source_image = template.render(context).strip()
source_image_data = source_image[33:-len('" alt="%s"' % escape(COMPLEX_TEST_TEXT))]
source_image_data = base64.b64decode(source_image_data)
# Debug code for updating reference file.
# write_png_content_to_file(test_data['ref_file_name'], source_image_data)
ref_image_data = get_png_content_from_file_name(test_data['ref_file_name'])
self.assertEqual(source_image_data, ref_image_data)
class TestQRForApplications(SimpleTestCase):
@staticmethod
def _make_test_data(tag_pattern, ref_file_name, tag_args, template_context=dict()):
tag_content = tag_pattern
for key, value in tag_args.items():
if isinstance(value, str):
tag_content += ' %s="%s"' % (key, value)
else:
tag_content += ' %s=%s' % (key, value)
return dict(source='{% ' + tag_content + ' %}', ref_file_name=ref_file_name, template_context=template_context)
@staticmethod
def _make_tests_data(embedded=True, image_format=SVG_FORMAT_NAME):
contact_detail1 = dict(**TEST_CONTACT_DETAIL)
contact_detail2 = ContactDetail(
**contact_detail1
)
wifi_config1 = dict(**TEST_WIFI_CONFIG)
wifi_config2 = WifiConfig(
**wifi_config1
)
google_maps_coordinates = Coordinates(latitude=586000.32, longitude=250954.19)
geolocation_coordinates = Coordinates(latitude=586000.32, longitude=250954.19, altitude=500)
if image_format == SVG_FORMAT_NAME:
ref_suffix = SVG_REF_SUFFIX
else:
ref_suffix = PNG_REF_SUFFIX
tag_prefix = 'qr_for_' if embedded else 'qr_url_for_'
tag_args = dict(image_format=image_format)
if image_format == PNG_FORMAT_NAME:
tag_args['size'] = 't'
if not embedded:
# Deactivate cache for URL.
tag_args['cache_enabled'] = False
raw_data = (
('email', '"john.doe@domain.com"', None),
('tel', ' "+41769998877"', None),
('sms', ' "+41769998877"', None),
('geolocation', 'latitude=586000.32 longitude=250954.19 altitude=500', None),
('geolocation', 'coordinates=coordinates', {'coordinates': geolocation_coordinates}),
('google_maps', 'latitude=586000.32 longitude=250954.19', None),
('google_maps', 'coordinates=coordinates', {'coordinates': google_maps_coordinates}),
('wifi', 'wifi_config', {'wifi_config': wifi_config1}),
('wifi', 'wifi_config', {'wifi_config': wifi_config2}),
('wifi', 'wifi_config=wifi_config', {'wifi_config': wifi_config2}),
('contact', 'contact_detail', {'contact_detail': contact_detail1}),
('contact', 'contact_detail', {'contact_detail': contact_detail2}),
('contact', 'contact_detail=contact_detail', {'contact_detail': contact_detail2}),
('youtube', '"J9go2nj6b3M"', None),
('youtube', 'video_id', {'video_id': "J9go2nj6b3M"}),
('google_play', '"ch.admin.meteoswiss"', None),
)
tests_data = []
for tag_base_name, tag_data, template_context in raw_data:
test_data = TestQRForApplications._make_test_data(tag_pattern='%s%s %s' % (tag_prefix, tag_base_name, tag_data),
ref_file_name='qr_for_%s%s' % (tag_base_name, ref_suffix),
tag_args=tag_args,
template_context=template_context)
tests_data.append(test_data)
return tests_data
@staticmethod
def _get_rendered_template(template_source, template_context):
html_source = mark_safe('{% load qr_code %}' + template_source)
template = Template(html_source)
context = Context()
if template_context:
context.update(template_context)
return template.render(context).strip()
def test_demo_samples_embedded_in_svg_format(self):
tests_data = self._make_tests_data(embedded=True)
for test_data in tests_data:
print('Testing template: %s' % test_data['source'])
source_image_data = TestQRForApplications._get_rendered_template(test_data['source'], test_data.get('template_context'))
ref_image_data = get_svg_content_from_file_name(test_data['ref_file_name'])
self.assertEqual(source_image_data, ref_image_data)
def test_demo_samples_embedded_in_png_format(self):
tests_data = self._make_tests_data(embedded=True, image_format=PNG_FORMAT_NAME)
image_data_re = re.compile(r'data:image/png;base64, (?P<data>[\w/+=]+)')
for test_data in tests_data:
print('Testing template: %s' % test_data['source'])
source_image_data = TestQRForApplications._get_rendered_template(test_data['source'], test_data.get('template_context'))
match = image_data_re.search(source_image_data)
source_image_data = match.group('data')
source_image_data = base64.b64decode(source_image_data)
ref_image_data = get_png_content_from_file_name(test_data['ref_file_name'])
# write_png_content_to_file(test_data['ref_file_name'], source_image_data)
self.assertEqual(source_image_data, ref_image_data)
def test_demo_sample_urls_in_svg_format(self):
tests_data = self._make_tests_data(embedded=False)
for test_data in tests_data:
source_image_data = self._check_url_for_test_data(test_data).content.decode('utf-8')
source_image_data = _make_closing_path_tag(source_image_data)
ref_image_data = get_svg_content_from_file_name(test_data['ref_file_name'], skip_header=False)
self.assertEqual(source_image_data, ref_image_data)
def test_demo_sample_urls_in_png_format(self):
tests_data = self._make_tests_data(embedded=False, image_format=PNG_FORMAT_NAME)
for test_data in tests_data:
source_image_data = self._check_url_for_test_data(test_data).content
ref_image_data = get_png_content_from_file_name(test_data['ref_file_name'])
# write_png_content_to_file(test_data['ref_file_name'], source_image_data)
self.assertEqual(source_image_data, ref_image_data)
def _check_url_for_test_data(self, test_data):
print('Testing template: %s' % test_data['source'])
source_image_url = TestQRForApplications._get_rendered_template(test_data['source'],
test_data.get('template_context'))
response = self.client.get(source_image_url)
self.assertEqual(response.status_code, 200)
return response
class TestIssues(SimpleTestCase):
def test_reverse_lazy_url(self):
from django.urls import reverse, reverse_lazy
options = QRCodeOptions(image_format='svg', size=1)
url1 = make_qr_code_url(reverse('qr_code:serve_qr_code_image'), options)
url2 = make_qr_code_url(reverse_lazy('qr_code:serve_qr_code_image'), options)
self.assertEqual(url1, url2)
svg1 = make_embedded_qr_code(reverse('qr_code:serve_qr_code_image'), options)
svg2 = make_embedded_qr_code(reverse_lazy('qr_code:serve_qr_code_image'), options)
self.assertEqual(svg1, svg2)
def get_svg_content_from_file_name(file_name, skip_header=True):
with open(os.path.join(get_resources_path(), file_name), 'r', encoding='utf-8') as file:
if skip_header:
# Skip SVG header.
file.readline()
image_data = file.read().strip()
return image_data
def get_png_content_from_file_name(file_name):
with open(os.path.join(get_resources_path(), file_name), 'rb') as file:
image_data = file.read()
return image_data
# Uncomment in order to renew some of the reference files.
# def write_png_content_to_file(file_name, image_content):
# with open(os.path.join(get_resources_path(), file_name), 'wb') as file:
# file.write(image_content)
#
#
# def write_svg_content_to_file(file_name, image_content):
# with open(os.path.join(get_resources_path(), file_name), 'wt', encoding='utf-8') as file:
# file.write(image_content)
| 288.706806 | 26,664 | 0.611259 | 57,445 | 165,429 | 1.739194 | 0.01323 | 0.053729 | 0.016065 | 0.004484 | 0.791618 | 0.657265 | 0.541388 | 0.434209 | 0.412369 | 0.40218 | 0 | 0.478013 | 0.253414 | 165,429 | 572 | 26,665 | 289.211538 | 0.330888 | 0.012936 | 0 | 0.355748 | 0 | 0.05423 | 0.851625 | 0.077515 | 0 | 1 | 0 | 0 | 0.158351 | 1 | 0.075922 | false | 0.006508 | 0.034707 | 0.002169 | 0.154013 | 0.023861 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
9ca94d8c5d1c4d184a812b08cde4e2b27f6425ae | 7,756 | py | Python | tests/Maps/test_visual.py | aperezlebel/meta_analysis | 10f983a4f3a94d385b9cd69a13c36ac610b1be93 | [
"MIT"
] | null | null | null | tests/Maps/test_visual.py | aperezlebel/meta_analysis | 10f983a4f3a94d385b9cd69a13c36ac610b1be93 | [
"MIT"
] | null | null | null | tests/Maps/test_visual.py | aperezlebel/meta_analysis | 10f983a4f3a94d385b9cd69a13c36ac610b1be93 | [
"MIT"
] | null | null | null | """Visual tests on a single example."""
import pytest
import matplotlib.pyplot as plt
import nilearn
from meta_analysis import Maps, plotting
from globals_test import template, atlas, df
# Parameters
sigma = 2.
# Maps
maps = Maps(df, template=template, groupby_col='pmid')
maps_dense = Maps(df, template=template, groupby_col='pmid', save_memory=False)
maps_atlas = Maps(df, template=template, groupby_col='pmid', atlas=atlas)
avg, var = maps.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=False)
avg_dense, var_dense = maps_dense.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=False)
avg_atlas, var_atlas = maps_atlas.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=False)
avg_biased, var_biased = maps.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=True)
avg_dense_biased, var_dense_biased = maps_dense.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=True)
avg_atlas_biased, var_atlas_biased = maps_atlas.iterative_smooth_avg_var(compute_var=True, sigma=sigma, bias=True)
@pytest.mark.mpl_image_compare
def test_sum():
"""Test sum of maps."""
sum = maps.summed_map()
return plotting.plot_activity_map(sum.to_img())
@pytest.mark.mpl_image_compare
def test_avg():
"""Test avg of maps."""
avg = maps.avg()
return plotting.plot_activity_map(avg.to_img())
@pytest.mark.mpl_image_compare
def test_var():
"""Test var of maps."""
var = maps.var()
return plotting.plot_activity_map(var.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg():
"""Test iterative avg of maps."""
avg, _ = maps.iterative_smooth_avg_var(compute_var=False, sigma=sigma, bias=False)
return plotting.plot_activity_map(avg.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_1():
"""Test iterative avg of maps."""
return plotting.plot_activity_map(avg.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_2():
"""Test iterative var of maps."""
return plotting.plot_activity_map(var.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_1():
"""Test iterative avg thresholded of maps."""
return plotting.plot_activity_map(avg.to_img(), threshold=0.0007)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_2():
"""Test iterative var thresholded of maps."""
return plotting.plot_activity_map(var.to_img(), threshold=0.00002)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_1_dense():
"""Test iterative avg of dense maps."""
return plotting.plot_activity_map(avg_dense.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_2_dense():
"""Test iterative var of dense maps."""
return plotting.plot_activity_map(var_dense.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_1_dense():
"""Test iterative avg of dense maps thresholded."""
return plotting.plot_activity_map(avg_dense.to_img(), threshold=0.0007)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_2_dense():
"""Test iterative var of maps thresholded."""
return plotting.plot_activity_map(var_dense.to_img(), threshold=0.00002)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_1_biased():
"""Test iterative biased avg of maps."""
return plotting.plot_activity_map(avg_biased.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_2_biased():
"""Test iterative biased var of maps."""
return plotting.plot_activity_map(var_biased.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_1_biased():
"""Test iterative biased avg of maps thresholded."""
return plotting.plot_activity_map(avg_biased.to_img(), threshold=0.0007)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_2_biased():
"""Test iterative biased var of maps thresholded."""
return plotting.plot_activity_map(var_biased.to_img(), threshold=0.00002)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_1_dense_biased():
"""Test iterative biased avg of dense maps."""
return plotting.plot_activity_map(avg_dense_biased.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_2_dense_biased():
"""Test iterative biased var of dense maps."""
return plotting.plot_activity_map(var_dense_biased.to_img())
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_1_dense_biased():
"""Test iterative biased avg of dense maps thresholded."""
return plotting.plot_activity_map(avg_dense_biased.to_img(), threshold=0.0007)
@pytest.mark.mpl_image_compare
def test_iterative_avg_var_thresholded_2_dense_biased():
"""Test iterative biased var of dense maps thresholded."""
return plotting.plot_activity_map(var_dense_biased.to_img(), threshold=0.00002)
@pytest.mark.mpl_image_compare
def test_atlas_sum():
"""Test sum of maps on atlas."""
sum = maps_atlas.summed_map()
return plotting.plot_activity_map(sum.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_avg():
"""Test avg of maps on atlas."""
avg = maps_atlas.avg()
return plotting.plot_activity_map(avg.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_var():
"""Test var of maps on atlas."""
var = maps_atlas.var()
return plotting.plot_activity_map(var.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg():
"""Test iterative avg of maps on atlas."""
avg, _ = maps_atlas.iterative_smooth_avg_var(compute_var=False, sigma=sigma, bias=False)
return plotting.plot_activity_map(avg.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_1():
"""Test iterative avg of maps on atlas."""
return plotting.plot_activity_map(avg_atlas.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_2():
"""Test iterative var of maps on atlas."""
return plotting.plot_activity_map(var_atlas.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_thresholded_1():
"""Test iterative avg of maps on atlas."""
return plotting.plot_activity_map(avg_atlas.to_img_atlas(ignore_bg=True), threshold=0.0007)
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_thresholded_2():
"""Test iterative var of maps on atlas."""
return plotting.plot_activity_map(var_atlas.to_img_atlas(ignore_bg=True), threshold=0.00002)
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_1_biased():
"""Test iterative biased avg of maps on atlas."""
return plotting.plot_activity_map(avg_atlas_biased.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_2_biased():
"""Test iterative biased var of maps on atlas."""
return plotting.plot_activity_map(var_atlas_biased.to_img_atlas(ignore_bg=True))
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_thresholded_1_biased():
"""Test thresholded iterative avg of maps on atlas."""
return plotting.plot_activity_map(avg_atlas_biased.to_img_atlas(ignore_bg=True), threshold=0.0007)
@pytest.mark.mpl_image_compare
def test_atlas_iterative_avg_var_thresholded_2_biased():
"""Test thresholded iterative var of maps on atlas."""
return plotting.plot_activity_map(var_atlas_biased.to_img_atlas(ignore_bg=True), threshold=0.00002)
@pytest.mark.mpl_image_compare
def test_atlas_cov():
"""Test cov computation on atlas."""
cov, labels = maps_atlas.cov()
fig = plt.figure(figsize=(20, 20))
nilearn.plotting.plot_matrix(cov, labels=labels, figure=fig)
return fig
| 33.431034 | 114 | 0.776818 | 1,198 | 7,756 | 4.666945 | 0.059265 | 0.095332 | 0.07673 | 0.106242 | 0.908782 | 0.890181 | 0.88553 | 0.855661 | 0.813092 | 0.773744 | 0 | 0.013838 | 0.114879 | 7,756 | 231 | 115 | 33.575758 | 0.800583 | 0.162068 | 0 | 0.32 | 0 | 0 | 0.001897 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.264 | false | 0 | 0.04 | 0 | 0.568 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 |
9cb1cb234cb45329b115e44a99dd2489728caf45 | 81 | py | Python | src/data_functions/__init__.py | cuevas1208/Tradebot | f6499bc75d625414c9a474c774912cb502a153d8 | [
"MIT"
] | null | null | null | src/data_functions/__init__.py | cuevas1208/Tradebot | f6499bc75d625414c9a474c774912cb502a153d8 | [
"MIT"
] | null | null | null | src/data_functions/__init__.py | cuevas1208/Tradebot | f6499bc75d625414c9a474c774912cb502a153d8 | [
"MIT"
] | null | null | null | from . import data_load
from . import data_prepare
from . import prepare_CNNData
| 20.25 | 29 | 0.814815 | 12 | 81 | 5.25 | 0.5 | 0.47619 | 0.444444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.148148 | 81 | 3 | 30 | 27 | 0.913043 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
9ce2bef3b48df04b706f04ddc3a248e544327266 | 108,760 | py | Python | RFEM/Loads/membersetload.py | r0m30d4c/DlubalRFEM6 | 4bd0d744007bdc27d86d6ce535a507cdc81552ca | [
"MIT"
] | null | null | null | RFEM/Loads/membersetload.py | r0m30d4c/DlubalRFEM6 | 4bd0d744007bdc27d86d6ce535a507cdc81552ca | [
"MIT"
] | null | null | null | RFEM/Loads/membersetload.py | r0m30d4c/DlubalRFEM6 | 4bd0d744007bdc27d86d6ce535a507cdc81552ca | [
"MIT"
] | null | null | null | from RFEM.initModel import *
from RFEM.enums import *
class MemberSetLoad():
def __init__(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_direction = LoadDirectionType.LOAD_DIRECTION_LOCAL_Z,
magnitude: float = 0,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_direction (enum): Load Case Enumeration
magnitude (float): Load Magnitude
comment (str, optional): Comments
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Member Sets No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_FORCE
clientObject.load_type = load_type.name
# Member Load Distribution
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
clientObject.magnitude = magnitude
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Force(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution= MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction= MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
force_eccentricity: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameter
force_eccentricity (bool): Force Eccentricity Option
comment (str, optional): Comments
params (dict, optional): Parameters
for LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [magnitude]
for LOAD_DISTRIBUTION_UNIFORM_TOTAL:
load_parameter = [magnitude]
for LOAD_DISTRIBUTION_CONCENTRATED_1:
load_parameter = [relative_distance = False, magnitude, distance_a]
for LOAD_DISTRIBUTION_CONCENTRATED_N:
load_parameter = [relative_distance_a = False, relative_distance_b = False, magnitude, count_n, distance_a, distance_b]
for LOAD_DISTRIBUTION_CONCENTRATED_2x2:
load_parameter = [relative_distance_a = False, relative_distance_b = False, relative_distance_c = False, magnitude, distance_a, distance_b, distance_c]
for LOAD_DISTRIBUTION_CONCENTRATED_2:
load_parameter = [relative_distance_a = False, relative_distance_b = False, magnitude_1, magnitude_2, distance_a, distance_b]
for LOAD_DISTRIBUTION_CONCENTRATED_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
for LOAD_DISTRIBUTION_TRAPEZOIDAL:
load_parameter = [relative_distance_a = False, relative_distance_b = False,magnitude_1, magnitude_2, distance_a, distance_b]
for LOAD_DISTRIBUTION_TAPERED:
load_parameter = [relative_distance_a = False, relative_distance_b = False,magnitude_1, magnitude_2, distance_a, distance_b]
for LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [magnitude_1, magnitude_2, magnitude_3]
for LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
for LOAD_DISTRIBUTION_VARYING_IN_Z:
load_parameter = [[distance, delta_distance, magnitude], ...]
params:
{'eccentricity_horizontal_alignment': MemberSetLoadEccentricityHorizontalAlignment.ALIGN_NONE,
'eccentricity_vertical_alignment': MemberSetLoadEccentricityVerticalAlignment.ALIGN_NONE,
'eccentricity_section_middle': MemberSetLoadEccentricitySectionMiddle.LOAD_ECCENTRICITY_SECTION_MIDDLE_CENTER_OF_GRAVITY,
'is_eccentricity_at_end_different_from_start': False,
'eccentricity_y_at_end': 0.0,
'eccentricity_y_at_start': 0.0,
'eccentricity_z_at_end': 0.0,
'eccentricity_z_at_start': 0.0}
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_FORCE
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution= load_distribution.name
#Load Magnitude and Parameters
if load_parameter == []:
raise Exception("WARNING: Load parameter cannot be empty. Kindly check list inputs completeness and correctness.")
else:
if load_distribution.name == "LOAD_DISTRIBUTION_UNIFORM" or load_distribution.name == "LOAD_DISTRIBUTION_UNIFORM_TOTAL":
if len(load_parameter) == 1:
clientObject.magnitude = load_parameter[0]
else:
raise Exception("WARNING: Load parameter array length should be 1 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_1":
if len(load_parameter) == 3:
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
if load_parameter[0] == False:
clientObject.magnitude = load_parameter[1]
clientObject.distance_a_absolute = load_parameter[2]
else:
clientObject.magnitude = load_parameter[1]
clientObject.distance_a_relative = load_parameter[2]
else:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_CONCENTRATED_1. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_N":
if len(load_parameter) == 6:
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude = load_parameter[2]
clientObject.count_n = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
else:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_CONCENTRATED_N. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_2x2":
if len(load_parameter) == 7:
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.distance_c_is_defined_as_relative = load_parameter[2]
clientObject.magnitude = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
if load_parameter[2] == False:
clientObject.distance_c_absolute = load_parameter[6]
else:
clientObject.distance_c_relative = load_parameter[6]
else:
raise Exception("WARNING: Load parameter array length should be 7 for LOAD_DISTRIBUTION_CONCENTRATED_N. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_2":
if len(load_parameter) == 6:
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude_1 = load_parameter[2]
clientObject.magnitude_2 = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
else:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_CONCENTRATED_2. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_VARYING":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
elif load_distribution.name == "LOAD_DISTRIBUTION_TRAPEZOIDAL":
if len(load_parameter) == 6:
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude_1 = load_parameter[2]
clientObject.magnitude_2 = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
else:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_TAPERED":
if len(load_parameter)==6:
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude_1 = load_parameter[2]
clientObject.magnitude_2 = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
else:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_PARABOLIC":
if len(load_parameter)==3:
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.magnitude_3 = load_parameter[2]
else:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING_IN_Z":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Member Load Direction
clientObject.load_direction = load_direction.name
#Force Eccentiricity
clientObject.has_force_eccentricity = force_eccentricity
if force_eccentricity == True:
if 'eccentricity_horizontal_alignment' and 'eccentricity_vertical_alignment' and 'eccentricity_section_middle' \
'is_eccentricity_at_end_different_from_start' and 'eccentricity_y_at_end' and 'eccentricity_y_at_start' \
'eccentricity_z_at_end' and 'eccentricity_z_at_start' in params:
pass
else:
raise Exception("WARNING: Params does not contain all the necessary parameters. Kindly check dictionary")
params_ecc = {'eccentricity_horizontal_alignment': MemberSetLoadEccentricityHorizontalAlignment.ALIGN_NONE,
'eccentricity_vertical_alignment': MemberSetLoadEccentricityVerticalAlignment.ALIGN_NONE,
'eccentricity_section_middle': MemberSetLoadEccentricitySectionMiddle.LOAD_ECCENTRICITY_SECTION_MIDDLE_CENTER_OF_GRAVITY,
'is_eccentricity_at_end_different_from_start': False,
'eccentricity_y_at_end': 0.0,
'eccentricity_y_at_start': 0.0,
'eccentricity_z_at_end': 0.0,
'eccentricity_z_at_start': 0.0}
params_ecc.update(params)
if params_ecc['is_eccentricity_at_end_different_from_start'] == False:
clientObject.eccentricity_horizontal_alignment= params_ecc['eccentricity_horizontal_alignment'].name
clientObject.eccentricity_vertical_alignment= params_ecc['eccentricity_vertical_alignment'].name
clientObject.eccentricity_section_middle = params_ecc['eccentricity_section_middle'].name
clientObject.eccentricity_y_at_end= params_ecc['eccentricity_y_at_start']
clientObject.eccentricity_y_at_start= params_ecc['eccentricity_y_at_start']
clientObject.eccentricity_z_at_end= params_ecc['eccentricity_z_at_start']
clientObject.eccentricity_z_at_start= params_ecc['eccentricity_z_at_start']
elif params_ecc['is_eccentricity_at_end_different_from_start'] == True:
clientObject.eccentricity_horizontal_alignment= params_ecc['eccentricity_horizontal_alignment']
clientObject.eccentricity_vertical_alignment= params_ecc['eccentricity_vertical_alignment']
clientObject.eccentricity_section_middle = params_ecc['eccentricity_section_middle']
clientObject.eccentricity_y_at_end= params_ecc['eccentricity_y_at_end']
clientObject.eccentricity_y_at_start= params_ecc['eccentricity_y_at_start']
clientObject.eccentricity_z_at_end= params_ecc['eccentricity_z_at_end']
clientObject.eccentricity_z_at_start= params_ecc['eccentricity_z_at_start']
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
if 'eccentricity_horizontal_alignment' or 'eccentricity_vertical_alignment' or 'eccentricity_section_middle' or 'is_eccentricity_at_end_different_from_start' or 'eccentricity_y_at_end' or 'eccentricity_y_at_start' or 'eccentricity_z_at_end' or 'eccentricity_z_at_start':
pass
else:
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Moment(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution= MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction= MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameters
comment (str, optional): Comments
params (dict, optional): Parameters
for LOAD_DISTRIBUTION_UNIFORM:
load_parameter = magnitude
for LOAD_DISTRIBUTION_CONCENTRATED_1:
load_parameter = [relative_distance = False, magnitude, distance_a]
for LOAD_DISTRIBUTION_CONCENTRATED_N:
load_parameter = [relative_distance_a = False, relative_distance_b = False, magnitude, count_n, distance_a, distance_b]
for LOAD_DISTRIBUTION_CONCENTRATED_2x2:
load_parameter = [relative_distance_a = False, relative_distance_b = False, relative_distance_c = False, magnitude, distance_a, distance_b, distance_c]
for LOAD_DISTRIBUTION_CONCENTRATED_2:
load_parameter = [relative_distance_a = False, relative_distance_b = False, magnitude_1, magnitude_2, distance_a, distance_b]
for LOAD_DISTRIBUTION_CONCENTRATED_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
for LOAD_DISTRIBUTION_TRAPEZOIDAL:
load_parameter = [relative_distance_a = False, relative_distance_b = False,magnitude_1, magnitude_2, distance_a, distance_b]
for LOAD_DISTRIBUTION_TAPERED:
load_parameter = [relative_distance_a = False, relative_distance_b = False,magnitude_1, magnitude_2, distance_a, distance_b]
for LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [magnitude_1, magnitude_2, magnitude_3]
for LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_MOMENT
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution= load_distribution.name
#Load Magnitude and Parameters
if load_distribution.name == "LOAD_DISTRIBUTION_UNIFORM":
try:
len(load_parameter)==1
except:
raise Exception("WARNING: Load parameter array length should be 1 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_1":
try:
len(load_parameter)==3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_CONCENTRATED_1. Kindly check list inputs completeness and correctness.")
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
if load_parameter[0] == False:
clientObject.magnitude = load_parameter[1]
clientObject.distance_a_absolute = load_parameter[2]
else:
clientObject.magnitude = load_parameter[1]
clientObject.distance_a_relative = load_parameter[2]
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_N":
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_CONCENTRATED_N. Kindly check list inputs completeness and correctness.")
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude = load_parameter[2]
clientObject.count_n = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_2x2":
try:
len(load_parameter)==7
except:
raise Exception("WARNING: Load parameter array length should be 7 for LOAD_DISTRIBUTION_CONCENTRATED_2x2. Kindly check list inputs completeness and correctness.")
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.distance_c_is_defined_as_relative = load_parameter[2]
clientObject.magnitude = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
if load_parameter[2] == False:
clientObject.distance_c_absolute = load_parameter[6]
else:
clientObject.distance_c_relative = load_parameter[6]
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_2":
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_CONCENTRATED_2. Kindly check list inputs completeness and correctness.")
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude_1 = load_parameter[2]
clientObject.magnitude_2 = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
elif load_distribution.name == "LOAD_DISTRIBUTION_CONCENTRATED_VARYING":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
elif load_distribution.name == "LOAD_DISTRIBUTION_TRAPEZOIDAL":
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude_1 = load_parameter[2]
clientObject.magnitude_2 = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
elif load_distribution.name == "LOAD_DISTRIBUTION_TAPERED":
try:
len(load_parameter)==4
except:
raise Exception("WARNING: Load parameter array length should be 4 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.distance_a_is_defined_as_relative = load_parameter[0]
clientObject.distance_b_is_defined_as_relative = load_parameter[1]
clientObject.magnitude_1 = load_parameter[2]
clientObject.magnitude_2 = load_parameter[3]
if load_parameter[0] == False:
clientObject.distance_a_absolute = load_parameter[4]
else:
clientObject.distance_a_relative = load_parameter[4]
if load_parameter[1] == False:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.distance_b_relative = load_parameter[5]
elif load_distribution.name == "LOAD_DISTRIBUTION_PARABOLIC":
try:
len(load_parameter)==3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.magnitude_3 = load_parameter[2]
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Member Load Direction
clientObject.load_direction = load_direction.name
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Mass(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
individual_mass_components: bool=False,
mass_components = [],
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
individual_mass_components (bool): Individiual Mass Components Option
mass_components (list): Mass Components
comment (str, optional): Comment
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
clientObject.load_type = MemberSetLoadType.E_TYPE_MASS.name
# Member Load Distribution
clientObject.load_distribution= MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM.name
# Individual Mass Components
if type(individual_mass_components) == bool:
pass
else:
raise Exception("WARNING: Type of individual mass components should be bool. Kindly check inputs correctness.")
clientObject.individual_mass_components = individual_mass_components
# Mass magnitude
if individual_mass_components == False:
clientObject.mass_global = mass_components[0]
else:
clientObject.mass_x = mass_components[0]
clientObject.mass_y = mass_components[1]
clientObject.mass_z = mass_components[2]
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Temperature(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
load_over_total_length: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameters
load_over_total_length (bool): Load Over Total Length Option
comment (str, optional): Comment
params (dict, optional): Parameters
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [tt, tb]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZIODAL:
for load_over_total_length: bool= False:
load_parameter = [tt1, tt2, tb1, tb2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_over_total_length: bool= True:
load_parameter = [tt1, tt2, tb1, tb2]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [tt1, tt2, tb1, tb2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [tb1, tb2, tb3, tt1, tt2, tt3]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_TEMPERATURE
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
if load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
try:
len(load_parameter)==2
except:
raise Exception("WARNING: Load parameter array length should be 2 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_t_b = load_parameter[0]
clientObject.magnitude_t_t = load_parameter[1]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZOIDAL:
try:
len(load_parameter)==8
except:
raise Exception("WARNING: Load parameter array length should be 8 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_t_b_1 = load_parameter[0]
clientObject.magnitude_t_b_2 = load_parameter[1]
clientObject.magnitude_t_t_1 = load_parameter[2]
clientObject.magnitude_t_t_2 = load_parameter[3]
if type(load_over_total_length) == bool:
pass
else:
raise Exception("WARNING: Type of load over total length should be bool. Kindly check inputs correctness.")
if load_over_total_length == False:
if load_parameter[4] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[6]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[6]
if load_parameter[5] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[7]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[7]
else:
clientObject.load_is_over_total_length = True
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
try:
len(load_parameter)==8
except:
raise Exception("WARNING: Load parameter array length should be 8 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_t_b_1 = load_parameter[0]
clientObject.magnitude_t_b_2 = load_parameter[1]
clientObject.magnitude_t_t_1 = load_parameter[2]
clientObject.magnitude_t_t_2 = load_parameter[3]
if type(load_parameter[4]) == bool:
pass
else:
raise Exception("WARNING: Type of the fourth load parameter should be bool. Kindly check inputs correctness.")
if load_parameter[4] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[6]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[6]
if type(load_parameter[5]) == bool:
pass
else:
raise Exception("WARNING: Type of the fifth load parameter should be bool. Kindly check inputs correctness.")
if load_parameter[5] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[7]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[7]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_t_b_1 = load_parameter[0]
clientObject.magnitude_t_b_2 = load_parameter[1]
clientObject.magnitude_t_b_3 = load_parameter[2]
clientObject.magnitude_t_t_1 = load_parameter[3]
clientObject.magnitude_t_t_2 = load_parameter[4]
clientObject.magnitude_t_t_3 = load_parameter[5]
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING":
try:
len(load_parameter[0])==4
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = load_parameter[i][2]
mlvlp.magnitude_delta_t = load_parameter[i][3]
mlvlp.magnitude_t_t = load_parameter[i][2]
mlvlp.magnitude_t_b = load_parameter[i][3]
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def TemperatureChange(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
load_over_total_length: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameters
load_over_total_length (bool): Load Over Total Length Option
comment (str, optional): Comment
params (dict, optional): Parameters
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [tc, delta_t]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZIODAL:
for load_over_total_length: bool= False:
load_parameter = [delta_t_1, delta_t_2, t_c_1, t_c_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_over_total_length: bool= True:
load_parameter = [delta_t_1, delta_t_2, t_c_1, t_c_2]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [delta_t_1, delta_t_2, t_c_1, t_c_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [delta_t_1, delta_t_2, delta_t_3, t_c_1, t_c_2, t_c_3]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_TEMPERATURE_CHANGE
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
if load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
try:
len(load_parameter)==2
except:
raise Exception("WARNING: Load parameter array length should be 2 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_delta_t = load_parameter[0]
clientObject.magnitude_t_c = load_parameter[1]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZOIDAL:
try:
len(load_parameter)==8
except:
raise Exception("WARNING: Load parameter array length should be 8 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_delta_t_1 = load_parameter[0]
clientObject.magnitude_delta_t_2 = load_parameter[1]
clientObject.magnitude_t_c_1 = load_parameter[2]
clientObject.magnitude_t_c_2 = load_parameter[3]
if type(load_over_total_length) == bool:
pass
else:
raise Exception("WARNING: Type of the load over total length should be bool. Kindly check inputs correctness.")
if load_over_total_length == False:
if load_parameter[4] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[6]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[6]
if load_parameter[5] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[7]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[7]
else:
clientObject.load_is_over_total_length = True
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
try:
len(load_parameter)==8
except:
raise Exception("WARNING: Load parameter array length should be 8 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_delta_t_1 = load_parameter[0]
clientObject.magnitude_delta_t_2 = load_parameter[1]
clientObject.magnitude_t_c_1 = load_parameter[2]
clientObject.magnitude_t_c_2 = load_parameter[3]
if load_parameter[4] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[6]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[6]
if load_parameter[5] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[7]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[7]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_delta_t_1 = load_parameter[0]
clientObject.magnitude_delta_t_2 = load_parameter[1]
clientObject.magnitude_delta_t_3 = load_parameter[2]
clientObject.magnitude_t_c_1 = load_parameter[3]
clientObject.magnitude_t_c_2 = load_parameter[4]
clientObject.magnitude_t_c_3 = load_parameter[5]
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING":
try:
len(load_parameter[0])==4
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = load_parameter[i][2]
mlvlp.magnitude_delta_t = load_parameter[i][3]
mlvlp.magnitude_t_t = load_parameter[i][2]
mlvlp.magnitude_t_b = load_parameter[i][3]
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def AxialStrain(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_X,
load_parameter = [],
load_over_total_length: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameters
load_over_total_length (bool): Load Over Total Length Option
comment (str, optional): Comment
params (dict, optional): Parameters
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [epsilon]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZIODAL:
load_parameter = [epsilon1, epsilon2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [epsilon1, epsilon2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [epsilon1, epsilon2, epsilon3]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_AXIAL_STRAIN
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
if load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
try:
len(load_parameter)==1
except:
raise Exception("WARNING: Load parameter array length should be 1 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZOIDAL:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
if type(load_over_total_length) == bool:
pass
else:
raise Exception("WARNING: Type of the load over total length should be bool. Kindly check inputs correctness.")
if load_over_total_length == False:
if load_parameter[2] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.load_is_over_total_length = True
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
if load_parameter[2] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[5]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
try:
len(load_parameter)==3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.magnitude_3 = load_parameter[2]
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def AxialDisplacement(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_X,
magnitude : float = 0.0,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Set
load_direction (enum): Load Direction Enumeration
magnitude (float): Load Magnitude
comment (str, optional): Comments
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_AXIAL_DISPLACEMENT
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
clientObject.magnitude = magnitude
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Precamber(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
load_over_total_length: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (enum): Load Parameters
load_over_total_length (bool): Load Over Total Lenth Option
comment (str, optional): Comment
params (dict, optional): Parameters
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [magnitude]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZIODAL:
load_parameter = [magnitude_1, magnitude_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [magnitude_1, magnitude_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [magnitude_1, magnitude_2, magnitude_3]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_PRECAMBER
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
if load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
try:
len(load_parameter)==1
except:
raise Exception("WARNING: Load parameter array length should be 1 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZOIDAL:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
if type(load_over_total_length) == bool:
pass
else:
raise Exception("WARNING: Type of the load over total length should be bool. Kindly check inputs correctness.")
if load_over_total_length == False:
if load_parameter[2] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.load_is_over_total_length = True
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
if load_parameter[2] == True:
clientObject.distance_a_is_defined_as_relative = True
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_is_defined_as_relative = False
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3] == True:
clientObject.distance_b_is_defined_as_relative = True
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_is_defined_as_relative = False
clientObject.distance_b_absolute = load_parameter[5]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
try:
len(load_parameter)==3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.magnitude_3 = load_parameter[2]
elif load_distribution.name == "LOAD_DISTRIBUTION_VARYING":
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def InitialPrestress(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_X,
magnitude : float = 0.0,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_direction (enum): Load Direction Enumeration
magnitude (float): Load Magnitude
comment (str, optional): Comment
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_INITIAL_PRESTRESS
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
clientObject.magnitude = magnitude
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Displacement(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
load_over_total_length: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameters
load_over_total_length (bool): Load Over Total Length Option
comment (str, optional): Comment
params (dict, optional): Parameters
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [magnitude]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_1:
load_parameter = [magnitude, distance_a_is_defined_as_relative = False, distance_a]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_N:
load_parameter = [magnitude, distance_a_is_defined_as_relative = False, distance_b_is_defined_as_relative = False, distance_a, distance_b]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2x2:
load_parameter = [magnitude, distance_a_is_defined_as_relative = False, distance_b_is_defined_as_relative = False, distance_c_is_defined_as_relative = False, distance_a, distance_b, distance_c]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2:
load_parameter = [magnitude_1, magnitude_2, distance_a_is_defined_as_relative = False, distance_b_is_defined_as_relative = False, distance_a, distance_b]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZIODAL:
load_parameter = [magnitude_1, magnitude_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [magnitude_1, magnitude_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [magnitude_1, magnitude_2, magnitude_3]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_DISPLACEMENT
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
if load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
try:
len(load_parameter)==1
except:
raise Exception("WARNING: Load parameter array length should be 1 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_1:
try:
len(load_parameter)==3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_CONCENTRATED_1. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
clientObject.distance_a_is_defined_as_relative = load_parameter[1]
if load_parameter[1]:
clientObject.distance_a_relative = load_parameter[2]
else:
clientObject.distance_a_absolute = load_parameter[2]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_N:
try:
len(load_parameter)==5
except:
raise Exception("WARNING: Load parameter array length should be 5 for LOAD_DISTRIBUTION_CONCENTRATED_N. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
clientObject.distance_a_is_defined_as_relative = load_parameter[1]
clientObject.distance_b_is_defined_as_relative = load_parameter[2]
if load_parameter[1]:
clientObject.distance_a_relative = load_parameter[3]
else:
clientObject.distance_a_absolute = load_parameter[3]
if load_parameter[2]:
clientObject.distance_b_relative = load_parameter[4]
else:
clientObject.distance_b_absolute = load_parameter[4]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2x2:
try:
len(load_parameter)==7
except:
raise Exception("WARNING: Load parameter array length should be 7 for LOAD_DISTRIBUTION_CONCENTRATED_2x2. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
clientObject.distance_a_is_defined_as_relative = load_parameter[1]
clientObject.distance_b_is_defined_as_relative = load_parameter[2]
clientObject.distance_c_is_defined_as_relative = load_parameter[3]
if load_parameter[1]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[2]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
if load_parameter[3]:
clientObject.distance_c_relative = load_parameter[6]
else:
clientObject.distance_c_absolute = load_parameter[6]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_CONCENTRATED_2. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.distance_a_is_defined_as_relative = load_parameter[2]
clientObject.distance_b_is_defined_as_relative = load_parameter[3]
if load_parameter[2]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_VARYING:
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZOIDAL:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
if type(load_over_total_length) == bool:
pass
else:
raise Exception("WARNING: Type of the load over total length should be bool. Kindly check inputs correctness.")
if load_over_total_length == False:
clientObject.distance_a_is_defined_as_relative = load_parameter[2]
clientObject.distance_b_is_defined_as_relative = load_parameter[3]
if load_parameter[2]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.load_is_over_total_length = True
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
try:
len(load_parameter)==6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.distance_a_is_defined_as_relative = load_parameter[2]
clientObject.distance_b_is_defined_as_relative = load_parameter[3]
if load_parameter[2]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
try:
len(load_parameter)==3
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.magnitude_3 = load_parameter[2]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def Rotation(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM,
load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_Z,
load_parameter = [],
load_over_total_length: bool= False,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_distribution (enum): Load Distribution Enumeration
load_direction (enum): Load Direction Enumeration
load_parameter (list): Load Parameters
load_over_total_length (bool): Load Over Total Length
comment (str, optional): Comment
params (dict, optional): Parameters
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
load_parameter = [magnitude]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_1:
load_parameter = [magnitude, distance_a_is_defined_as_relative = False, distance_a]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_N:
load_parameter = [magnitude, distance_a_is_defined_as_relative = False, distance_b_is_defined_as_relative = False, distance_a, distance_b]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2x2:
load_parameter = [magnitude, distance_a_is_defined_as_relative = False, distance_b_is_defined_as_relative = False, distance_c_is_defined_as_relative = False, distance_a, distance_b, distance_c]
for load_distrubition = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2:
load_parameter = [magnitude_1, magnitude_2, distance_a_is_defined_as_relative = False, distance_b_is_defined_as_relative = False, distance_a, distance_b]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZIODAL:
load_parameter = [magnitude_1, magnitude_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
load_parameter = [magnitude_1, magnitude_2, distance_a_relative = False, distance_a_relative = False, a_distance, b_distance]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
load_parameter = [magnitude_1, magnitude_2, magnitude_3]
for load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
load_parameter = [[distance, delta_distance, magnitude], ...]
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_ROTATION
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = load_distribution.name
# Member Load Direction
clientObject.load_direction = load_direction.name
#Load Magnitude
if load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM:
try:
len(load_parameter)==1
except:
raise Exception("WARNING: Load parameter array length should be 1 for LOAD_DISTRIBUTION_UNIFORM. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_1:
try:
len(load_parameter) == 3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_CONCENTRATED_1. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
clientObject.distance_a_is_defined_as_relative = load_parameter[1]
if load_parameter[1]:
clientObject.distance_a_relative = load_parameter[2]
else:
clientObject.distance_a_absolute = load_parameter[2]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_N:
try:
len(load_parameter) == 5
except:
raise Exception("WARNING: Load parameter array length should be 5 for LOAD_DISTRIBUTION_CONCENTRATED_N. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
clientObject.distance_a_is_defined_as_relative = load_parameter[1]
clientObject.distance_b_is_defined_as_relative = load_parameter[2]
if load_parameter[1]:
clientObject.distance_a_relative = load_parameter[3]
else:
clientObject.distance_a_absolute = load_parameter[3]
if load_parameter[2]:
clientObject.distance_b_relative = load_parameter[4]
else:
clientObject.distance_b_absolute = load_parameter[4]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2x2:
try:
len(load_parameter) == 7
except:
raise Exception("WARNING: Load parameter array length should be 7 for LOAD_DISTRIBUTION_CONCENTRATED_2x2. Kindly check list inputs completeness and correctness.")
clientObject.magnitude = load_parameter[0]
clientObject.distance_a_is_defined_as_relative = load_parameter[1]
clientObject.distance_b_is_defined_as_relative = load_parameter[2]
clientObject.distance_c_is_defined_as_relative = load_parameter[3]
if load_parameter[1]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[2]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
if load_parameter[3]:
clientObject.distance_c_relative = load_parameter[6]
else:
clientObject.distance_c_absolute = load_parameter[6]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_2:
try:
len(load_parameter) == 6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_CONCENTRATED_2. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.distance_a_is_defined_as_relative = load_parameter[2]
clientObject.distance_b_is_defined_as_relative = load_parameter[3]
if load_parameter[2]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_CONCENTRATED_VARYING:
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TRAPEZOIDAL:
try:
len(load_parameter) == 6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TRAPEZOIDAL. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
if type(load_over_total_length) == bool:
pass
else:
raise Exception("WARNING: Type of the load over total length should be bool. Kindly check inputs correctness.")
if load_over_total_length == False:
clientObject.distance_a_is_defined_as_relative = load_parameter[2]
clientObject.distance_b_is_defined_as_relative = load_parameter[3]
if load_parameter[2]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
else:
clientObject.load_is_over_total_length = True
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_TAPERED:
try:
len(load_parameter) == 6
except:
raise Exception("WARNING: Load parameter array length should be 6 for LOAD_DISTRIBUTION_TAPERED. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.distance_a_is_defined_as_relative = load_parameter[2]
clientObject.distance_b_is_defined_as_relative = load_parameter[3]
if load_parameter[2]:
clientObject.distance_a_relative = load_parameter[4]
else:
clientObject.distance_a_absolute = load_parameter[4]
if load_parameter[3]:
clientObject.distance_b_relative = load_parameter[5]
else:
clientObject.distance_b_absolute = load_parameter[5]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_PARABOLIC:
try:
len(load_parameter) == 3
except:
raise Exception("WARNING: Load parameter array length should be 3 for LOAD_DISTRIBUTION_PARABOLIC. Kindly check list inputs completeness and correctness.")
clientObject.magnitude_1 = load_parameter[0]
clientObject.magnitude_2 = load_parameter[1]
clientObject.magnitude_3 = load_parameter[2]
elif load_distribution == MemberSetLoadDistribution.LOAD_DISTRIBUTION_VARYING:
try:
len(load_parameter[0])==3
except:
print("WARNING: MemberSetLoad no: %x, load case: %x - Wrong data input." % (no, load_case_no))
clientObject.varying_load_parameters = clientModel.factory.create('ns0:member_set_load.varying_load_parameters')
for i in range(len(load_parameter)):
mlvlp = clientModel.factory.create('ns0:member_set_load_varying_load_parameters')
mlvlp.no = i+1
mlvlp.distance = load_parameter[i][0]
mlvlp.delta_distance = load_parameter[i][1]
mlvlp.magnitude = load_parameter[i][2]
mlvlp.note = None
mlvlp.magnitude_t_c = 0.0
mlvlp.magnitude_delta_t = 0.0
mlvlp.magnitude_t_t = 0.0
mlvlp.magnitude_t_b = 0.0
clientObject.varying_load_parameters.member_set_load_varying_load_parameters.append(mlvlp)
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def PipeContentFull(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_direction_orientation = MemberSetLoadDirectionOrientation.LOAD_DIRECTION_FORWARD,
specific_weight : float = 0.0,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_direction_orientation (enum): Load Direction Orientation Enumeration
specific_weight (float): Specific Weight
comment (str, optional): Comment
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_PIPE_CONTENT_FULL
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM.name
# Member Load Direction
clientObject.load_direction = MemberSetLoadDirection.LOAD_DIRECTION_GLOBAL_Z_OR_USER_DEFINED_W_TRUE.name
#Member Load Orientation
clientObject.load_direction_orientation = load_direction_orientation.name
#Load Magnitude
clientObject.magnitude = specific_weight
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def PipeContentPartial(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
load_direction_orientation = MemberSetLoadDirectionOrientation.LOAD_DIRECTION_FORWARD,
specific_weight : float = 0.0,
filling_height : float = 0.0,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
load_direction_orientation (enum): Load Direction Orientation Enumeration
specific_weight (float): Specific Weight
filling_height (float): Filling Height
comment (str, optional): Comment
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_PIPE_CONTENT_PARTIAL
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM.name
# Member Load Direction
clientObject.load_direction = MemberSetLoadDirection.LOAD_DIRECTION_GLOBAL_Z_OR_USER_DEFINED_W_TRUE.name
#Member Load Orientation
clientObject.load_direction_orientation = load_direction_orientation.name
#Load Magnitude
clientObject.magnitude = specific_weight
#Filling Height
clientObject.filling_height = filling_height
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def PipeInternalPressure(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
pressure : float = 0.0,
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
pressure (float): Pressure
comment (str, optional): Comment
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_PIPE_INTERNAL_PRESSURE
clientObject.load_type = load_type.name
# Member Load Distribution
clientObject.load_distribution = MemberSetLoadDistribution.LOAD_DISTRIBUTION_UNIFORM.name
# Member Load Direction
clientObject.load_direction = MemberSetLoadDirection.LOAD_DIRECTION_LOCAL_X.name
#Load Magnitude
clientObject.magnitude = pressure
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject)
def RotaryMotion(self,
no: int = 1,
load_case_no: int = 1,
member_sets: str = '1',
angular_acceleration : float = 0.0,
angular_velocity : float = 0.0,
axis_definition_type = MemberSetLoadAxisDefinitionType.AXIS_DEFINITION_TWO_POINTS,
axis_orientation = MemberSetLoadAxisDefinitionAxisOrientation.AXIS_POSITIVE,
axis_definition = MemberSetLoadAxisDefinition.AXIS_X,
axis_definition_p1 = [],
axis_definition_p2 = [],
comment: str = '',
params: dict = {}):
"""
Args:
no (int): Load Tag
load_case_no (int): Assigned Load Case
member_sets (str): Assigned Member Sets
angular_acceleration (float): Angular Acceleration
angular_velocity (float): Angular Velocity
axis_definition_type (enum): Axis Definition Type Enumeration
axis_orientation (enum): Axis Orientation Enumeration
axis_definition (enum): Axis Definition Enumeration
axis_definition_p1 (list):Axis Definition First Point
axis_definition_p2 (list): Axis Definition Second Point
comment (str, optional): Comment
params (dict, optional): Parameters
"""
# Client model | Member Load
clientObject = clientModel.factory.create('ns0:member_set_load')
# Clears object atributes | Sets all atributes to None
clearAtributes(clientObject)
# Member Load No.
clientObject.no = no
# Load Case No.
clientObject.load_case = load_case_no
# Members No. (e.g. '5 6 7 12')
clientObject.member_sets = ConvertToDlString(member_sets)
# Member Load Type
load_type = MemberSetLoadType.LOAD_TYPE_ROTARY_MOTION
clientObject.load_type = load_type.name
#Angular Acceleration
clientObject.angular_acceleration = angular_acceleration
#Angular Velocity
clientObject.angular_velocity = angular_velocity
#Axis Definition Type
clientObject.axis_definition_type = axis_definition_type.name
#Axis definition
if axis_definition_type == MemberSetLoadAxisDefinitionType.AXIS_DEFINITION_TWO_POINTS.name:
clientObject.axis_definition_p1_x = axis_definition_p1[0]
clientObject.axis_definition_p1_y = axis_definition_p1[1]
clientObject.axis_definition_p1_z = axis_definition_p1[2]
clientObject.axis_definition_p2_x = axis_definition_p2[0]
clientObject.axis_definition_p2_y = axis_definition_p2[1]
clientObject.axis_definition_p2_z = axis_definition_p2[2]
elif axis_definition_type == MemberSetLoadAxisDefinitionType.AXIS_DEFINITION_POINT_AND_AXIS.name:
clientObject.axis_definition_p1_x = axis_definition_p1[0]
clientObject.axis_definition_p1_y = axis_definition_p1[1]
clientObject.axis_definition_p1_z = axis_definition_p1[2]
clientObject.axis_definition_axis = axis_definition.name
clientObject.axis_definition_axis_orientation = axis_orientation.name
# Comment
clientObject.comment = comment
# Adding optional parameters via dictionary
for key in params:
clientObject[key] = params[key]
# Add Load Member Load to client model
clientModel.service.set_member_set_load(load_case_no, clientObject) | 47.55575 | 278 | 0.636401 | 11,387 | 108,760 | 5.762888 | 0.021077 | 0.118863 | 0.035842 | 0.027796 | 0.95916 | 0.955518 | 0.949758 | 0.941346 | 0.937475 | 0.922892 | 0 | 0.013969 | 0.299641 | 108,760 | 2,287 | 279 | 47.55575 | 0.84754 | 0.200717 | 0 | 0.910598 | 0 | 0 | 0.139522 | 0.052337 | 0 | 0 | 0 | 0 | 0 | 1 | 0.011536 | false | 0.007931 | 0.001442 | 0 | 0.013699 | 0.009373 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
9ceb23e87249c8e7891a32d92bff3f3fc9d97d3c | 6,721 | py | Python | src/aspire/aspire/em_classavg/image_denoising/image_denoising/ConverterModel/test.py | janden/ASPIRE-Python | 5bcf831881fd0e42630c3b99671c5ed08de260ea | [
"MIT"
] | null | null | null | src/aspire/aspire/em_classavg/image_denoising/image_denoising/ConverterModel/test.py | janden/ASPIRE-Python | 5bcf831881fd0e42630c3b99671c5ed08de260ea | [
"MIT"
] | null | null | null | src/aspire/aspire/em_classavg/image_denoising/image_denoising/ConverterModel/test.py | janden/ASPIRE-Python | 5bcf831881fd0e42630c3b99671c5ed08de260ea | [
"MIT"
] | null | null | null | import numpy as np
from ConverterModel.Converter import Converter
from scipy.misc import imresize
import time
import os
def test():
data_path = os.path.join('test_data', 'example_data_np_array.npy')
images = np.load(data_path)
num_images = images.shape[2]
bandlimit_ratio = 1.0
truncation_parameter = 1
resolutions = [64]
images_multiplier = 100
n = images_multiplier * num_images
for resolution in resolutions:
# testing with odd grid
scaled_images = np.zeros((2 * resolution + 1, 2 * resolution + 1, num_images))
for j in range(num_images):
scaled_images[:, :, j] = imresize(images[:, :, j], (2 * resolution + 1, 2 * resolution + 1))
scaled_images = np.repeat(scaled_images, images_multiplier, axis=2)
print("testing images of size {}\n".format(scaled_images.shape[0]))
# initializing models
tic1 = time.clock()
converter = Converter(scaled_images.shape[0], truncation_parameter, beta=bandlimit_ratio)
tic2 = time.clock()
converter.init_fast()
tic3 = time.clock()
converter.init_direct()
tic4 = time.clock()
print("finished initializing PSWF2D in {}".format(tic2 - tic1))
print("finished initializing FastModel in {}".format(tic3 - tic2))
print("finished initializing DirectModel in {}\n".format(tic4 - tic3))
# forwarding images
tic = time.clock()
coefficients_fast = converter.fast_forward(scaled_images)
toc = time.clock()
t = toc - tic
tpi = t/n
print("finished fast forwarding {} images in {} seconds, average of {} seconds per image".format(n, t, tpi))
tic = time.clock()
coefficients_direct = converter.direct_forward(scaled_images)
toc = time.clock()
t = toc - tic
tpi = t/n
print("finished direct forwarding {} images in {} seconds, average of {} seconds per image\n".format(n, t, tpi))
# test if coefficients are the same
print("Maximum absolute difference between coefficients is {}\n".format(np.max(np.absolute(coefficients_fast - coefficients_direct))))
# test reconstruction error
tic = time.clock()
reconstructed_images_direct = converter.direct_backward(coefficients_direct)
reconstructed_images_fast = converter.direct_backward(coefficients_fast)
toc = time.clock()
t = toc - tic
tpi = t / (2 * n)
print("finished backward of {} images in {} seconds, average of {} seconds per image\n".format(2 * n, t, tpi))
x_1d_grid = range(-resolution, resolution + 1)
x_2d_grid, y_2d_grid = np.meshgrid(x_1d_grid, x_1d_grid)
r_2d_grid = np.sqrt(np.square(x_2d_grid) + np.square(y_2d_grid))
points_inside_the_circle = r_2d_grid <= resolution
err_slow = reconstructed_images_direct - scaled_images
e_slow = np.mean(np.square(np.absolute(err_slow)), axis=2)
e_slow = np.sum(e_slow[points_inside_the_circle])
err_fast = reconstructed_images_fast - scaled_images
e_fast = np.mean(np.square(np.absolute(err_fast)), axis=2)
e_fast = np.sum(e_fast[points_inside_the_circle])
p = np.mean(np.square(np.absolute(scaled_images)), axis=2)
p = np.sum(p[points_inside_the_circle])
print("odd images with resolution {} fast coefficients reconstructed error: {}".format(resolution, e_fast / p))
print("odd images with resolution {} direct coefficients reconstructed error: {}\n".format(resolution, e_slow / p))
# testing with even grid
scaled_images = np.zeros((2 * resolution, 2 * resolution, num_images))
for j in range(num_images):
scaled_images[:, :, j] = imresize(images[:, :, j], (2 * resolution, 2 * resolution))
scaled_images = np.repeat(scaled_images, images_multiplier, axis=2)
print("testing images of size {}\n".format(scaled_images.shape[0]))
# initializing models
tic1 = time.clock()
converter = Converter(scaled_images.shape[0], truncation_parameter, beta=bandlimit_ratio)
tic2 = time.clock()
converter.init_fast()
tic3 = time.clock()
converter.init_direct()
tic4 = time.clock()
print("finished initializing PSWF2D in {}".format(tic2 - tic1))
print("finished initializing FastModel in {}".format(tic3 - tic2))
print("finished initializing DirectModel in {}\n".format(tic4 - tic3))
# forwarding images
tic = time.clock()
coefficients_fast = converter.fast_forward(scaled_images)
toc = time.clock()
t = toc - tic
tpi = t / n
print("finished fast forwarding {} images in {} seconds, average of {} seconds per image".format(n, t, tpi))
tic = time.clock()
coefficients_direct = converter.direct_forward(scaled_images)
toc = time.clock()
t = toc - tic
tpi = t / n
print("finished direct forwarding {} images in {} seconds, average of {} seconds per image\n".format(n, t, tpi))
# test if coefficients are the same
print("Maximum absolute difference between coefficients is {}\n".format(np.max(np.absolute(coefficients_fast - coefficients_direct))))
# test reconstruction error
tic = time.clock()
reconstructed_images_direct = converter.direct_backward(coefficients_direct)
reconstructed_images_fast = converter.direct_backward(coefficients_fast)
toc = time.clock()
t = toc - tic
tpi = t / (2 * n)
print("finished backward of {} images in {} seconds, average of {} seconds per image\n".format(2 * n, t, tpi))
x_1d_grid = range(-resolution, resolution)
x_2d_grid, y_2d_grid = np.meshgrid(x_1d_grid, x_1d_grid)
r_2d_grid = np.sqrt(np.square(x_2d_grid) + np.square(y_2d_grid))
points_inside_the_circle = r_2d_grid <= resolution
err_slow = reconstructed_images_direct - scaled_images
e_slow = np.mean(np.square(np.absolute(err_slow)), axis=2)
e_slow = np.sum(e_slow[points_inside_the_circle])
err_fast = reconstructed_images_fast - scaled_images
e_fast = np.mean(np.square(np.absolute(err_fast)), axis=2)
e_fast = np.sum(e_fast[points_inside_the_circle])
p = np.mean(np.square(np.absolute(scaled_images)), axis=2)
p = np.sum(p[points_inside_the_circle])
print("even images with resolution {} fast coefficients reconstructed error: {}".format(resolution, e_fast / p))
print("even images with resolution {} direct coefficients reconstructed error: {}\n".format(resolution, e_slow / p))
test()
| 43.642857 | 142 | 0.652433 | 873 | 6,721 | 4.83047 | 0.127148 | 0.062604 | 0.028456 | 0.039839 | 0.911548 | 0.908229 | 0.902063 | 0.885938 | 0.885938 | 0.885938 | 0 | 0.015181 | 0.23553 | 6,721 | 153 | 143 | 43.928105 | 0.805566 | 0.035709 | 0 | 0.77193 | 0 | 0 | 0.186708 | 0.003864 | 0 | 0 | 0 | 0 | 0 | 1 | 0.008772 | false | 0 | 0.04386 | 0 | 0.052632 | 0.175439 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1413c46fc707943afd71728d3671c25052a801be | 16 | py | Python | main.py | lukeyeager/github-testing | 03cadc83d4587bf0c4787e4a308056019aa8d6f6 | [
"MIT"
] | null | null | null | main.py | lukeyeager/github-testing | 03cadc83d4587bf0c4787e4a308056019aa8d6f6 | [
"MIT"
] | 10 | 2015-07-07T23:39:54.000Z | 2016-08-30T23:40:38.000Z | main.py | lukeyeager/github-testing | 03cadc83d4587bf0c4787e4a308056019aa8d6f6 | [
"MIT"
] | null | null | null | print('10.1.0')
| 8 | 15 | 0.5625 | 4 | 16 | 2.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.266667 | 0.0625 | 16 | 1 | 16 | 16 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0.375 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
14946f08002b0427fded5f5eed961e9123e8a523 | 32,765 | py | Python | bert/models/bert/bert.py | fanshiqing/DAPPLE | b2d2ceda90f6033b316f672ec05f45123234f130 | [
"BSD-3-Clause"
] | 50 | 2020-02-02T09:24:44.000Z | 2022-03-01T03:22:19.000Z | bert/models/bert/bert.py | fanshiqing/DAPPLE | b2d2ceda90f6033b316f672ec05f45123234f130 | [
"BSD-3-Clause"
] | 1 | 2020-02-04T03:50:02.000Z | 2020-02-04T04:41:37.000Z | bert/models/bert/bert.py | AlibabaPAI/DAPPLE | fd75dcfbc6c73a7624b9fd9d8c3334e5d04bcd20 | [
"BSD-3-Clause"
] | 9 | 2020-02-02T09:23:31.000Z | 2021-09-22T07:24:34.000Z | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from models.bert import modeling
from models.bert import modeling_slice
import tensorflow as tf
from tensorflow import logging
import sys
slim = tf.contrib.slim
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor
def bert_arg_scope(
weight_decay=0.00004,
batch_norm_decay=0.9997,
batch_norm_epsilon=0.001,
activation_fn=tf.nn.relu,
batch_norm_updates_collections=tf.GraphKeys.UPDATE_OPS):
"""Returns the scope with the default parameters.
Args:
weight_decay: the weight decay for weights variables.
batch_norm_decay: decay for the moving average of batch_norm momentums.
batch_norm_epsilon: small float added to variance to avoid dividing by zero.
activation_fn: Activation function for conv2d.
batch_norm_updates_collections: Collection for the update ops for
batch norm.
Returns:
a arg_scope with the parameters.
"""
# Set weight_decay for weights in conv2d and fully_connected layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_regularizer=slim.l2_regularizer(weight_decay),
biases_regularizer=slim.l2_regularizer(weight_decay)):
batch_norm_params = {
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'updates_collections': batch_norm_updates_collections,
'fused': None, # Use fused batch norm if possible.
}
# Set activation_fn and parameters for batch_norm.
with slim.arg_scope([slim.conv2d], activation_fn=activation_fn,
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as scope:
return scope
class BertFinetune(object):
"""
Fintune Method based on Bert.
"""
def __init__(self, bert_config_file, max_seq_length, is_training,
input_ids, input_mask, segment_ids, labels, use_one_hot_embeddings,
model_type='classification', kwargs=None):
bert_config = modeling.BertConfig.from_json_file(bert_config_file)
if max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(max_seq_length, bert_config.max_position_embeddings))
self.model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
self.bert_config = bert_config
self.kwargs = kwargs
self.labels = labels
self.input_ids = input_ids
if model_type == 'classification':
self.build_output_layer_classification()
elif model_type == 'regression':
self.build_output_layer_regression()
elif model_type == 'mrc':
self.build_output_layer_squad()
elif model_type == 'pretrain':
self.build_pretrain()
else:
raise ValueError("model_type should be one of ['classification', "
"'regression', pretrain', 'mrc'].")
self.saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=2)
def restore(self, saver_directory, sess):
checkpoint = tf.train.latest_checkpoint(saver_directory)
if not checkpoint:
logging.info("Couldn't find trained model at %s." % saver_directory)
else:
logging.info('restore from {}'.format(checkpoint))
self.saver.restore(sess, checkpoint)
def save(self, saver_directory, sess, step=None):
logging.info("Save to %s." % saver_directory)
if step is not None:
self.saver.save(sess, saver_directory, global_step=step)
else:
self.saver.save(sess, saver_directory)
def build_pretrain(self):
(masked_lm_loss,
masked_lm_example_loss, masked_lm_log_probs) = self.get_masked_lm_output(
self.bert_config,
self.model.get_sequence_output(),
self.model.get_embedding_table(),
self.kwargs['masked_lm_positions'],
self.kwargs['masked_lm_ids'],
self.kwargs['masked_lm_weights'])
(next_sentence_loss, next_sentence_example_loss,
next_sentence_log_probs) = self.get_next_sentence_output(
self.bert_config,
self.model.get_pooled_output(),
self.kwargs['next_sentence_labels'])
self.loss = masked_lm_loss + next_sentence_loss
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
[-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = tf.argmax(
masked_lm_log_probs, axis=-1, output_type=tf.int32)
masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
masked_lm_ids = tf.reshape(self.kwargs['masked_lm_ids'], [-1])
masked_lm_weights = tf.reshape(self.kwargs['masked_lm_weights'], [-1])
masked_lm_accuracy = tf.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
next_sentence_log_probs = tf.reshape(
next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
next_sentence_predictions = tf.argmax(
next_sentence_log_probs, axis=-1, output_type=tf.int32)
next_sentence_labels = tf.reshape(self.kwargs['next_sentence_labels'], [-1])
next_sentence_accuracy = tf.metrics.accuracy(
labels=next_sentence_labels, predictions=next_sentence_predictions)
next_sentence_mean_loss = tf.metrics.mean(
values=next_sentence_example_loss)
self.eval_metric = {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
def get_masked_lm_output(self, bert_config, input_tensor, output_weights, positions,
label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
# log_probs = tf.nn.log_softmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(self, bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.variable_scope("cls/seq_relationship"):
output_weights = tf.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
# log_probs = tf.nn.log_softmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def build_output_layer_regression(self):
with tf.variable_scope("src-output-layer"):
self.src_estimation = tf.contrib.layers.fully_connected(
inputs=self.model.get_pooled_output(),
num_outputs=1,
activation_fn=None, #tf.nn.sigmoid
weights_initializer=tf.contrib.layers.xavier_initializer(),
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=1e-3),
biases_initializer=tf.constant_initializer(1e-04),
scope="FC"
)
self.src_prediction = self.src_estimation
self.src_pred_cost = tf.add(
tf.reduce_mean(tf.pow(self.src_prediction - self.labels, 2)),
tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)),
name="src_cost")
self.loss = self.src_pred_cost
self.logits = self.src_estimation
self.predictions = self.src_prediction
self.accuracy = tf.metrics.accuracy(self.labels, self.predictions)
print('loss', self.loss)
print('logits', self.logits)
print('predictions', self.predictions)
print('accuracy', self.accuracy)
def build_output_layer_classification(self):
with tf.variable_scope("src-output-layer"):
self.src_estimation = tf.contrib.layers.fully_connected(
inputs=self.model.get_pooled_output(),
num_outputs=2,
activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer(),
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=1e-3),
biases_initializer=tf.constant_initializer(1e-04),
scope="FC"
)
self.src_prediction = tf.contrib.layers.softmax(self.src_estimation)[:, 1]
self.src_pred_cost = tf.add(
tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.src_estimation, labels=self.labels)),
tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)),
name="src_cost")
self.loss = self.src_pred_cost
self.logits = self.src_estimation
self.predictions = self.src_prediction
self.accuracy = tf.metrics.accuracy(self.labels, self.predictions)
print('logits', self.logits)
print('predictions', self.predictions)
print('accuracy', self.accuracy)
def build_output_layer_squad(self, is_training=False):
final_hidden = self.model.get_sequence_output()
final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
batch_size = final_hidden_shape[0]
seq_length = final_hidden_shape[1]
hidden_size = final_hidden_shape[2]
output_weights = tf.get_variable(
"cls/squad/output_weights", [2, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
final_hidden_matrix = tf.reshape(final_hidden,
[batch_size * seq_length, hidden_size])
logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [batch_size, seq_length, 2])
logits = tf.transpose(logits, [2, 0, 1])
unstacked_logits = tf.unstack(logits, axis=0)
(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
# compute loss
seq_length = modeling.get_shape_list(self.input_ids)[1]
def compute_loss(logits, positions):
one_hot_positions = tf.one_hot(
positions, depth=seq_length, dtype=tf.float32)
log_probs = tf.nn.log_softmax(logits)
loss = -tf.reduce_mean(
tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
return loss
def def_loss():
start_positions = self.kwargs["start_positions"]
end_positions = self.kwargs["end_positions"]
start_loss = compute_loss(start_logits, start_positions)
end_loss = compute_loss(end_logits, end_positions)
loss = (start_loss + end_loss) / 2.0
return loss
self.loss = def_loss()
def build_output_layer(self, is_training):
output_layer = self.model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [2, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [2], initializer=tf.zeros_initializer())
print('output_layer', output_layer.shape)
print('output_weights', output_weights.shape)
print('output_bias', output_bias.shape)
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
self.logits = logits
log_probs = tf.nn.log_softmax(self.logits)
print('logits', logits.shape)
one_hot_labels = tf.one_hot(self.labels, depth=2,
dtype=tf.float32)
self.per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
self.loss = tf.reduce_mean(self.per_example_loss)
self.predictions = tf.argmax(self.logits, axis=-1, output_type=tf.int32)
self.accuracy = tf.metrics.accuracy(self.labels, self.predictions)
class BertFinetuneSlice(object):
"""
Fintune Method based on Bert.
"""
def __init__(self, bert_config_file, max_seq_length, is_training,
input_ids, input_mask, segment_ids, labels, use_one_hot_embeddings,
model_type='classification', slice_devices="/device:GPU:0",
dep_outputs=None, kwargs=None):
bert_config = modeling_slice.BertConfig.from_json_file(bert_config_file)
if max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(max_seq_length, bert_config.max_position_embeddings))
self.model = modeling_slice.BertModelSlice(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
if not isinstance(slice_devices, list):
logging.info("SLICE DEVICES: ", slice_devices)
self.devices = slice_devices.split(",")
else:
self.devices = slice_devices
self.stages = self.model.stages
ndev = len(self.devices)
nstage = len(self.stages)
def calc_device(i):
# Bert-24
if nstage == 27:
# 11:13
return 0 if i < 13 else 1
# Bert-48
elif nstage == 51:
# 23:25
return 0 if i < 25 else 1
else:
print("Unrecognized nstage, only bert-24 and bert-48 are supported.")
sys.exit(0)
# idx = int((i+2) / ((nstage+1) / ndev + 1))
# return idx
self.stage_outputs = []
prev_output = input_ids
prev_device_idx = 0
for i in xrange(nstage):
device_idx = calc_device(i)
if (i == 0 or device_idx != prev_device_idx) and \
(dep_outputs is not None and dep_outputs[device_idx] is not None):
#print ("***DEPS***", dep_outputs[device_idx])
dep = dep_outputs[device_idx] if isinstance(dep_outputs[device_idx], list) else [dep_outputs[device_idx]]
with tf.control_dependencies(dep), tf.device(self.devices[device_idx]):
output = self.stages[i](prev_output)
if device_idx != prev_device_idx:
self.stage_outputs.append(prev_output)
prev_device_idx = device_idx
prev_output = output
continue
if device_idx != prev_device_idx:
self.stage_outputs.append(prev_output)
prev_device_idx = device_idx
#with tf.control_dependencies([prev_output]), tf.device(self.devices[device_idx]):
with tf.device(self.devices[device_idx]):
output = self.stages[i](prev_output)
prev_output = output
self.bert_config = bert_config
self.kwargs = kwargs
self.labels = labels
self.input_ids = input_ids
if model_type == 'classification':
self.build_output_layer_classification()
elif model_type == 'regression':
self.build_output_layer_regression()
elif model_type == 'mrc':
with tf.device(self.devices[device_idx]):
self.build_output_layer_squad()
self.stage_outputs.append(self.loss)
elif model_type == 'pretrain':
self.build_pretrain()
else:
raise ValueError("model_type should be one of ['classification', "
"'regression', pretrain', 'mrc'].")
self.saver = tf.train.Saver(
var_list=tf.global_variables(),
max_to_keep=2)
def restore(self, saver_directory, sess):
checkpoint = tf.train.latest_checkpoint(saver_directory)
if not checkpoint:
logging.info("Couldn't find trained model at %s." % saver_directory)
else:
logging.info('restore from {}'.format(checkpoint))
self.saver.restore(sess, checkpoint)
def save(self, saver_directory, sess, step=None):
logging.info("Save to %s." % saver_directory)
if step is not None:
self.saver.save(sess, saver_directory, global_step=step)
else:
self.saver.save(sess, saver_directory)
def build_pretrain(self):
(masked_lm_loss,
masked_lm_example_loss, masked_lm_log_probs) = self.get_masked_lm_output(
self.bert_config,
self.model.get_sequence_output(),
self.model.get_embedding_table(),
self.kwargs['masked_lm_positions'],
self.kwargs['masked_lm_ids'],
self.kwargs['masked_lm_weights'])
(next_sentence_loss, next_sentence_example_loss,
next_sentence_log_probs) = self.get_next_sentence_output(
self.bert_config,
self.model.get_pooled_output(),
self.kwargs['next_sentence_labels'])
self.loss = masked_lm_loss + next_sentence_loss
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
[-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = tf.argmax(
masked_lm_log_probs, axis=-1, output_type=tf.int32)
masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
masked_lm_ids = tf.reshape(self.kwargs['masked_lm_ids'], [-1])
masked_lm_weights = tf.reshape(self.kwargs['masked_lm_weights'], [-1])
masked_lm_accuracy = tf.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
next_sentence_log_probs = tf.reshape(
next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
next_sentence_predictions = tf.argmax(
next_sentence_log_probs, axis=-1, output_type=tf.int32)
next_sentence_labels = tf.reshape(self.kwargs['next_sentence_labels'], [-1])
next_sentence_accuracy = tf.metrics.accuracy(
labels=next_sentence_labels, predictions=next_sentence_predictions)
next_sentence_mean_loss = tf.metrics.mean(
values=next_sentence_example_loss)
self.eval_metric = {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
}
def get_masked_lm_output(self, bert_config, input_tensor, output_weights, positions,
label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling_slice.get_activation(bert_config.hidden_act),
kernel_initializer=modeling_slice.create_initializer(
bert_config.initializer_range))
input_tensor = modeling_slice.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
# log_probs = tf.nn.log_softmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs)
def get_next_sentence_output(self, bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.variable_scope("cls/seq_relationship"):
output_weights = tf.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling_slice.create_initializer(bert_config.initializer_range))
output_bias = tf.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
# log_probs = tf.nn.log_softmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def build_output_layer_regression(self):
with tf.variable_scope("src-output-layer"):
self.src_estimation = tf.contrib.layers.fully_connected(
inputs=self.model.get_pooled_output(),
num_outputs=1,
activation_fn=None, #tf.nn.sigmoid
weights_initializer=tf.contrib.layers.xavier_initializer(),
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=1e-3),
biases_initializer=tf.constant_initializer(1e-04),
scope="FC"
)
self.src_prediction = self.src_estimation
self.src_pred_cost = tf.add(
tf.reduce_mean(tf.pow(self.src_prediction - self.labels, 2)),
tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)),
name="src_cost")
self.loss = self.src_pred_cost
self.logits = self.src_estimation
self.predictions = self.src_prediction
self.accuracy = tf.metrics.accuracy(self.labels, self.predictions)
print('loss', self.loss)
print('logits', self.logits)
print('predictions', self.predictions)
print('accuracy', self.accuracy)
def build_output_layer_classification(self):
with tf.variable_scope("src-output-layer"):
self.src_estimation = tf.contrib.layers.fully_connected(
inputs=self.model.get_pooled_output(),
num_outputs=2,
activation_fn=None,
weights_initializer=tf.contrib.layers.xavier_initializer(),
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=1e-3),
biases_initializer=tf.constant_initializer(1e-04),
scope="FC"
)
self.src_prediction = tf.contrib.layers.softmax(self.src_estimation)[:, 1]
self.src_pred_cost = tf.add(
tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.src_estimation, labels=self.labels)),
tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)),
name="src_cost")
self.loss = self.src_pred_cost
self.logits = self.src_estimation
self.predictions = self.src_prediction
self.accuracy = tf.metrics.accuracy(self.labels, self.predictions)
print('logits', self.logits)
print('predictions', self.predictions)
print('accuracy', self.accuracy)
def build_output_layer_squad(self, is_training=False):
final_hidden = self.model.get_sequence_output()
final_hidden_shape = modeling_slice.get_shape_list(final_hidden, expected_rank=3)
batch_size = final_hidden_shape[0]
seq_length = final_hidden_shape[1]
hidden_size = final_hidden_shape[2]
with tf.variable_scope("cls/squad", reuse=tf.AUTO_REUSE):
output_weights = tf.get_variable(
"output_weights", [2, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [2], initializer=tf.zeros_initializer())
final_hidden_matrix = tf.reshape(final_hidden,
[batch_size * seq_length, hidden_size])
logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [batch_size, seq_length, 2])
logits = tf.transpose(logits, [2, 0, 1])
unstacked_logits = tf.unstack(logits, axis=0)
(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
# compute loss
seq_length = modeling_slice.get_shape_list(self.input_ids)[1]
def compute_loss(logits, positions):
one_hot_positions = tf.one_hot(
positions, depth=seq_length, dtype=tf.float32)
log_probs = tf.nn.log_softmax(logits)
loss = -tf.reduce_mean(
tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
return loss
def def_loss():
start_positions = self.kwargs["start_positions"]
end_positions = self.kwargs["end_positions"]
start_loss = compute_loss(start_logits, start_positions)
end_loss = compute_loss(end_logits, end_positions)
loss = (start_loss + end_loss) / 2.0
return loss
self.loss = def_loss()
def build_output_layer(self, is_training):
output_layer = self.model.get_pooled_output()
hidden_size = output_layer.shape[-1].value
output_weights = tf.get_variable(
"output_weights", [2, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [2], initializer=tf.zeros_initializer())
print('output_layer', output_layer.shape)
print('output_weights', output_weights.shape)
print('output_bias', output_bias.shape)
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
self.logits = logits
log_probs = tf.nn.log_softmax(self.logits)
print('logits', logits.shape)
one_hot_labels = tf.one_hot(self.labels, depth=2,
dtype=tf.float32)
self.per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
self.loss = tf.reduce_mean(self.per_example_loss)
self.predictions = tf.argmax(self.logits, axis=-1, output_type=tf.int32)
self.accuracy = tf.metrics.accuracy(self.labels, self.predictions)
| 43.339947 | 117 | 0.635648 | 4,005 | 32,765 | 4.897878 | 0.085144 | 0.026101 | 0.008157 | 0.012592 | 0.885808 | 0.871533 | 0.858993 | 0.857157 | 0.857157 | 0.844515 | 0 | 0.010475 | 0.27157 | 32,765 | 755 | 118 | 43.397351 | 0.811413 | 0.078956 | 0 | 0.832452 | 0 | 0 | 0.057486 | 0.002971 | 0 | 0 | 0 | 0 | 0 | 1 | 0.047619 | false | 0 | 0.014109 | 0 | 0.08642 | 0.042328 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1adf9ed69c7aab99d4a8e06e355d660e1f43e607 | 298 | py | Python | 03/03/zfill.py | pylangstudy/201708 | 126b1af96a1d1f57522d5a1d435b58597bea2e57 | [
"CC0-1.0"
] | null | null | null | 03/03/zfill.py | pylangstudy/201708 | 126b1af96a1d1f57522d5a1d435b58597bea2e57 | [
"CC0-1.0"
] | 39 | 2017-07-31T22:54:01.000Z | 2017-08-31T00:19:03.000Z | 03/03/zfill.py | pylangstudy/201708 | 126b1af96a1d1f57522d5a1d435b58597bea2e57 | [
"CC0-1.0"
] | null | null | null | print(b'12'.zfill(8))
print(b'-12'.zfill(8))
print(b'-12.3'.zfill(8))
print(b'+12.3'.zfill(8))
print(b'z12.3x'.zfill(8))
print(bytearray(b'12').zfill(8))
print(bytearray(b'-12').zfill(8))
print(bytearray(b'-12.3').zfill(8))
print(bytearray(b'+12.3').zfill(8))
print(bytearray(b'z12.3x').zfill(8))
| 24.833333 | 36 | 0.654362 | 61 | 298 | 3.196721 | 0.147541 | 0.307692 | 0.507692 | 0.512821 | 1 | 0.917949 | 0.917949 | 0.917949 | 0.805128 | 0.805128 | 0 | 0.125436 | 0.036913 | 298 | 11 | 37 | 27.090909 | 0.554007 | 0 | 0 | 0 | 0 | 0 | 0.14094 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 11 |
1aeed00c1f3e3390988151d2ad63b93e9805fa6e | 34,028 | py | Python | crowdemotion_api_client_python/apis/respondent_api.py | CrowdEmotion/crowdemotion-api-client-python | b5ec57030e36d2b2c32cc5a43b804d7a34401c16 | [
"Apache-2.0"
] | 1 | 2018-06-14T05:12:54.000Z | 2018-06-14T05:12:54.000Z | python/crowdemotion_api_client_python/apis/respondent_api.py | CrowdEmotion/crowdemotion-api-clients-examples | 9e4bd38279399e5694cf3cec6cc7fb0b3149bc39 | [
"MIT"
] | null | null | null | python/crowdemotion_api_client_python/apis/respondent_api.py | CrowdEmotion/crowdemotion-api-clients-examples | 9e4bd38279399e5694cf3cec6cc7fb0b3149bc39 | [
"MIT"
] | null | null | null | # coding: utf-8
"""
CloudEmotion API v1
CrowdEmotion API
OpenAPI spec version: 1.1.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import absolute_import
import sys
import os
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class RespondentApi(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
config = Configuration()
if api_client:
self.api_client = api_client
else:
if not config.api_client:
config.api_client = ApiClient()
self.api_client = config.api_client
def respondent_get(self, research_id, **kwargs):
"""
Find all Respondents of a Research
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_get(research_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int research_id: Search by research id. (required)
:param int skip: The number of results to skip.
:param int limit: The maximum number of results to return.
:param str where: JSON formatted string.
:param str sort: Attribute used to sort results.
:return: list[Respondent]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_get_with_http_info(research_id, **kwargs)
else:
(data) = self.respondent_get_with_http_info(research_id, **kwargs)
return data
def respondent_get_with_http_info(self, research_id, **kwargs):
"""
Find all Respondents of a Research
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_get_with_http_info(research_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int research_id: Search by research id. (required)
:param int skip: The number of results to skip.
:param int limit: The maximum number of results to return.
:param str where: JSON formatted string.
:param str sort: Attribute used to sort results.
:return: list[Respondent]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['research_id', 'skip', 'limit', 'where', 'sort']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'research_id' is set
if ('research_id' not in params) or (params['research_id'] is None):
raise ValueError("Missing the required parameter `research_id` when calling `respondent_get`")
resource_path = '/respondent'.replace('{format}', 'json')
path_params = {}
query_params = {}
if 'research_id' in params:
query_params['research_id'] = params['research_id']
if 'skip' in params:
query_params['skip'] = params['skip']
if 'limit' in params:
query_params['limit'] = params['limit']
if 'where' in params:
query_params['where'] = params['where']
if 'sort' in params:
query_params['sort'] = params['sort']
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[Respondent]',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def respondent_post(self, body, **kwargs):
"""
Create a Respondent
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_post(body, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Respondent body: Request body (required)
:return: Respondent
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_post_with_http_info(body, **kwargs)
else:
(data) = self.respondent_post_with_http_info(body, **kwargs)
return data
def respondent_post_with_http_info(self, body, **kwargs):
"""
Create a Respondent
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_post_with_http_info(body, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param Respondent body: Request body (required)
:return: Respondent
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_post" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'body' is set
if ('body' not in params) or (params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `respondent_post`")
resource_path = '/respondent'.replace('{format}', 'json')
path_params = {}
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Respondent',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def respondent_respondent_id_delete(self, respondent_id, **kwargs):
"""
Delete a Respondent
<p><strong>Permissions:</strong> ✗ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_delete(respondent_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: (required)
:return: str
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_respondent_id_delete_with_http_info(respondent_id, **kwargs)
else:
(data) = self.respondent_respondent_id_delete_with_http_info(respondent_id, **kwargs)
return data
def respondent_respondent_id_delete_with_http_info(self, respondent_id, **kwargs):
"""
Delete a Respondent
<p><strong>Permissions:</strong> ✗ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_delete_with_http_info(respondent_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: (required)
:return: str
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['respondent_id']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_respondent_id_delete" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'respondent_id' is set
if ('respondent_id' not in params) or (params['respondent_id'] is None):
raise ValueError("Missing the required parameter `respondent_id` when calling `respondent_respondent_id_delete`")
resource_path = '/respondent/{respondent_id}'.replace('{format}', 'json')
path_params = {}
if 'respondent_id' in params:
path_params['respondent_id'] = params['respondent_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='str',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def respondent_respondent_id_get(self, respondent_id, **kwargs):
"""
Find a Respondent
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_get(respondent_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: Search by research id. (required)
:return: Respondent
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_respondent_id_get_with_http_info(respondent_id, **kwargs)
else:
(data) = self.respondent_respondent_id_get_with_http_info(respondent_id, **kwargs)
return data
def respondent_respondent_id_get_with_http_info(self, respondent_id, **kwargs):
"""
Find a Respondent
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_get_with_http_info(respondent_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: Search by research id. (required)
:return: Respondent
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['respondent_id']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_respondent_id_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'respondent_id' is set
if ('respondent_id' not in params) or (params['respondent_id'] is None):
raise ValueError("Missing the required parameter `respondent_id` when calling `respondent_respondent_id_get`")
resource_path = '/respondent/{respondent_id}'.replace('{format}', 'json')
path_params = {}
if 'respondent_id' in params:
path_params['respondent_id'] = params['respondent_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Respondent',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def respondent_respondent_id_metadata_get(self, respondent_id, **kwargs):
"""
Find Respondent Metadata
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_metadata_get(respondent_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: ID of the Respondent (required)
:return: RespondentMetadataResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_respondent_id_metadata_get_with_http_info(respondent_id, **kwargs)
else:
(data) = self.respondent_respondent_id_metadata_get_with_http_info(respondent_id, **kwargs)
return data
def respondent_respondent_id_metadata_get_with_http_info(self, respondent_id, **kwargs):
"""
Find Respondent Metadata
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_metadata_get_with_http_info(respondent_id, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: ID of the Respondent (required)
:return: RespondentMetadataResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['respondent_id']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_respondent_id_metadata_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'respondent_id' is set
if ('respondent_id' not in params) or (params['respondent_id'] is None):
raise ValueError("Missing the required parameter `respondent_id` when calling `respondent_respondent_id_metadata_get`")
resource_path = '/respondent/{respondent_id}/metadata'.replace('{format}', 'json')
path_params = {}
if 'respondent_id' in params:
path_params['respondent_id'] = params['respondent_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RespondentMetadataResponse',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def respondent_respondent_id_metadata_post(self, respondent_id, body, **kwargs):
"""
Add Respondent Metadata
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_metadata_post(respondent_id, body, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: (required)
:param RespondentMetadata body: Request body (required)
:return: RespondentMetadataResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_respondent_id_metadata_post_with_http_info(respondent_id, body, **kwargs)
else:
(data) = self.respondent_respondent_id_metadata_post_with_http_info(respondent_id, body, **kwargs)
return data
def respondent_respondent_id_metadata_post_with_http_info(self, respondent_id, body, **kwargs):
"""
Add Respondent Metadata
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_metadata_post_with_http_info(respondent_id, body, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: (required)
:param RespondentMetadata body: Request body (required)
:return: RespondentMetadataResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['respondent_id', 'body']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_respondent_id_metadata_post" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'respondent_id' is set
if ('respondent_id' not in params) or (params['respondent_id'] is None):
raise ValueError("Missing the required parameter `respondent_id` when calling `respondent_respondent_id_metadata_post`")
# verify the required parameter 'body' is set
if ('body' not in params) or (params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `respondent_respondent_id_metadata_post`")
resource_path = '/respondent/{respondent_id}/metadata'.replace('{format}', 'json')
path_params = {}
if 'respondent_id' in params:
path_params['respondent_id'] = params['respondent_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RespondentMetadataResponse',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
def respondent_respondent_id_put(self, respondent_id, body, **kwargs):
"""
Update a Respondent
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_put(respondent_id, body, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: (required)
:param Respondent body: Request body (required)
:return: Respondent
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('callback'):
return self.respondent_respondent_id_put_with_http_info(respondent_id, body, **kwargs)
else:
(data) = self.respondent_respondent_id_put_with_http_info(respondent_id, body, **kwargs)
return data
def respondent_respondent_id_put_with_http_info(self, respondent_id, body, **kwargs):
"""
Update a Respondent
<p><strong>Permissions:</strong> ✓ Respondent ✗ Customer ✓ Manager</p>
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please define a `callback` function
to be invoked when receiving the response.
>>> def callback_function(response):
>>> pprint(response)
>>>
>>> thread = api.respondent_respondent_id_put_with_http_info(respondent_id, body, callback=callback_function)
:param callback function: The callback function
for asynchronous request. (optional)
:param int respondent_id: (required)
:param Respondent body: Request body (required)
:return: Respondent
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['respondent_id', 'body']
all_params.append('callback')
all_params.append('_return_http_data_only')
params = locals()
for key, val in iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method respondent_respondent_id_put" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'respondent_id' is set
if ('respondent_id' not in params) or (params['respondent_id'] is None):
raise ValueError("Missing the required parameter `respondent_id` when calling `respondent_respondent_id_put`")
# verify the required parameter 'body' is set
if ('body' not in params) or (params['body'] is None):
raise ValueError("Missing the required parameter `body` when calling `respondent_respondent_id_put`")
resource_path = '/respondent/{respondent_id}'.replace('{format}', 'json')
path_params = {}
if 'respondent_id' in params:
path_params['respondent_id'] = params['respondent_id']
query_params = {}
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.\
select_header_accept(['application/json'])
if not header_params['Accept']:
del header_params['Accept']
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.\
select_header_content_type(['application/json'])
# Authentication setting
auth_settings = ['api_key']
return self.api_client.call_api(resource_path, 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Respondent',
auth_settings=auth_settings,
callback=params.get('callback'),
_return_http_data_only=params.get('_return_http_data_only'))
| 41.958076 | 132 | 0.582491 | 3,526 | 34,028 | 5.409813 | 0.061826 | 0.079895 | 0.054207 | 0.026422 | 0.935203 | 0.929541 | 0.923408 | 0.916855 | 0.911088 | 0.90291 | 0 | 0.000486 | 0.334813 | 34,028 | 810 | 133 | 42.009877 | 0.84038 | 0.350829 | 0 | 0.778667 | 1 | 0 | 0.175023 | 0.053588 | 0 | 0 | 0 | 0 | 0 | 1 | 0.04 | false | 0 | 0.018667 | 0 | 0.117333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
1af943e305d8a425da297353336883836376ed57 | 13,856 | py | Python | model.py | BenMaxGCU/Honours | 5a9314d843c090891ab20151663f07cbc766f28e | [
"MIT"
] | null | null | null | model.py | BenMaxGCU/Honours | 5a9314d843c090891ab20151663f07cbc766f28e | [
"MIT"
] | null | null | null | model.py | BenMaxGCU/Honours | 5a9314d843c090891ab20151663f07cbc766f28e | [
"MIT"
] | null | null | null | import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras import backend as keras
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from custom_activations import swish
def unet(pretrained_weights = None,input_size = (320,480,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
# Convolutional neural network created based upon the U-Net architecture
def unet_cracks(pretrained_weights = None,input_size = (320,480,1)):
inputs = Input(input_size)
conv1 = Conv2D(64,3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128,3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256,3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512,3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.3)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(pool4))
merge7 = Concatenate(axis=3)([conv3,up7])
conv7 = Conv2D(256,3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = Concatenate(axis=3)([conv2,up8])
conv8 = Conv2D(128,3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = Concatenate(axis=3)([conv1,up9])
conv9 = Conv2D(64,3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2,3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1,1, activation='sigmoid')(conv9) # Returns the prediction as either 1 or 0
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
# Smaller network still based off of U-Net architecture
def simple_unet(pretrained_weights = None,input_size = (320,480,1)):
inputs = Input(input_size)
conv1 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128,3, activation='swish', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256,3, activation='swish', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512,3, activation='swish', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
up5 = Conv2D(256, 2, activation='swish', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(drop4))
trans5 = Conv2DTranspose(256, (2,2), strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(up5)
merge5 = Concatenate(axis=3)([conv3,trans5])
conv5 = Conv2D(256,3, activation='swish', padding='same', kernel_initializer='he_normal')(merge5)
conv5 = Conv2D(256,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv5)
up6 = Conv2D(128, 2, activation='swish', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(conv5))
trans6 = Conv2DTranspose(128, (2,2), strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(up6)
merge6 = Concatenate(axis=3)([conv2,trans6])
conv6 = Conv2D(128,3, activation='swish', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(128,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(64, 2, activation='swish', padding='same', kernel_initializer='he_normal')(UpSampling2D(size = (2,2))(conv6))
trans7 = Conv2DTranspose(64, (2,2), strides=1, padding='same', activation='relu', kernel_initializer='he_normal')(up7)
merge7 = Concatenate(axis=3)([conv1,trans7])
conv7 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv7)
conv7 = Conv2D(2,3, activation='swish', padding='same', kernel_initializer='he_normal')(conv7)
conv8 = Conv2D(1,1, activation='sigmoid')(conv7) # Returns the prediction as either 1 or 0
model = Model(input = inputs, output = conv8)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
def crf_unet(pretrained_weights = None,input_size = (320,480,1)):
inputs = Input(input_size)
conv1 = Conv2D(32,3, activation='swish', padding='same', kernel_initializer='he_normal')(inputs)
drop1 = Dropout(0.2)(conv1)
conv1 = Conv2D(32,3, activation='swish', padding='same', kernel_initializer='he_normal')(drop1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(pool1)
drop2 = Dropout(0.2)(conv2)
conv2 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(drop2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128,3, activation='swish', padding='same', kernel_initializer='he_normal')(pool2)
drop3 = Dropout(0.2)(conv3)
conv3 = Conv2D(128,3, activation='swish', padding='same', kernel_initializer='he_normal')(drop3)
up3 = UpSampling2D(size=(2, 2))(conv3)
up3 = Concatenate(axis=3)([conv2,up3])
conv4 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(up3)
drop4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64,3, activation='swish', padding='same', kernel_initializer='he_normal')(drop4)
up5 = UpSampling2D(size = (2,2))(conv4)
up5 = Concatenate(axis=3)([conv1,up5])
conv5 = Conv2D(32,3, activation='swish', padding='same', kernel_initializer='he_normal')(up5)
drop5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32,3, activation='swish', padding='same', kernel_initializer='he_normal')(drop5)
conv6 = Conv2D(2, 1, activation='swish', padding='same', kernel_initializer='he_normal')(conv5)
conv7 = Conv2D(1,1, activation='sigmoid')(conv6)
model = Model(input = inputs, output = conv7)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
# Smaller network still based off of U-Net architecture using LeakyReLu
def lrcrf_unet(pretrained_weights = None,input_size = (250,250,1)):
inputs = Input(input_size)
leaky_relu = LeakyReLU(alpha=0.2)
conv1 = Conv2D(32,3, padding='same', kernel_initializer='he_normal')(inputs)
conv1 = leaky_relu(conv1)
drop1 = Dropout(0.2)(conv1)
conv1 = Conv2D(32,3, padding='same', kernel_initializer='he_normal')(drop1)
conv1 = leaky_relu(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64,3, padding='same', kernel_initializer='he_normal')(pool1)
conv2 = leaky_relu(conv2)
drop2 = Dropout(0.2)(conv2)
conv2 = Conv2D(64,3, padding='same', kernel_initializer='he_normal')(drop2)
conv2 = leaky_relu(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(128,3, padding='same', kernel_initializer='he_normal')(pool2)
conv3 = leaky_relu(conv3)
drop3 = Dropout(0.2)(conv3)
conv3 = Conv2D(128,3, padding='same', kernel_initializer='he_normal')(drop3)
conv3 = leaky_relu(conv3)
up3 = UpSampling2D(size=(2, 2))(conv3)
up3 = Concatenate(axis=3)([conv2,up3])
conv4 = Conv2D(64,3, padding='same', kernel_initializer='he_normal')(up3)
conv4 = leaky_relu(conv4)
drop4 = Dropout(0.2)(conv4)
conv4 = Conv2D(64,3, padding='same', kernel_initializer='he_normal')(drop4)
conv4 = leaky_relu(conv4)
up5 = UpSampling2D(size = (2,2))(conv4)
up5 = Concatenate(axis=3)([conv1,up5])
conv5 = Conv2D(32,3, padding='same', kernel_initializer='he_normal')(up5)
conv5 = leaky_relu(conv5)
drop5 = Dropout(0.2)(conv5)
conv5 = Conv2D(32,3, padding='same', kernel_initializer='he_normal')(drop5)
conv5 = leaky_relu(conv5)
conv6 = Conv2D(2, 1, padding='same', kernel_initializer='he_normal')(conv5)
conv6 = leaky_relu(conv6)
conv7 = Conv2D(1,1, activation='sigmoid')(conv6)
model = Model(input = inputs, output = conv7)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model | 50.569343 | 132 | 0.690026 | 1,806 | 13,856 | 5.163898 | 0.075858 | 0.099078 | 0.171134 | 0.225177 | 0.886018 | 0.880763 | 0.877118 | 0.865537 | 0.854815 | 0.823933 | 0 | 0.07541 | 0.145352 | 13,856 | 274 | 133 | 50.569343 | 0.712126 | 0.02526 | 0 | 0.587629 | 0 | 0 | 0.117285 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025773 | false | 0 | 0.056701 | 0 | 0.108247 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2105c3135b74b036ddfff8fbe6b797c9dfa145a0 | 398 | py | Python | FreePIEScript_HapticFeedbackSample.py | ahinore/NoloFreePIEPlugin | e587fdc20067e9c6d04ad7851827b5e87cf82dde | [
"MIT"
] | 1,946 | 2018-05-25T11:29:44.000Z | 2022-03-24T09:15:54.000Z | FreePIEScript_HapticFeedbackSample.py | ahinore/NoloFreePIEPlugin | e587fdc20067e9c6d04ad7851827b5e87cf82dde | [
"MIT"
] | 708 | 2018-05-27T09:56:07.000Z | 2021-11-08T11:26:30.000Z | FreePIEScript_HapticFeedbackSample.py | ahinore/NoloFreePIEPlugin | e587fdc20067e9c6d04ad7851827b5e87cf82dde | [
"MIT"
] | 382 | 2018-05-25T20:13:24.000Z | 2022-03-29T18:33:12.000Z |
diagnostics.watch(alvr.input_haptic_feedback[0][0]) #fAmplitude
diagnostics.watch(alvr.input_haptic_feedback[0][1]) #fDurationSeconds
diagnostics.watch(alvr.input_haptic_feedback[0][2]) #fFrequency
diagnostics.watch(alvr.input_haptic_feedback[1][0]) #fAmplitude
diagnostics.watch(alvr.input_haptic_feedback[1][1]) #fDurationSeconds
diagnostics.watch(alvr.input_haptic_feedback[1][2]) #fFrequency
| 44.222222 | 69 | 0.829146 | 54 | 398 | 5.888889 | 0.222222 | 0.301887 | 0.377358 | 0.471698 | 0.930818 | 0.930818 | 0.930818 | 0.666667 | 0 | 0 | 0 | 0.03125 | 0.035176 | 398 | 8 | 70 | 49.75 | 0.796875 | 0.180905 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
2113bf3861e3a27ad3188c3513d1b29be47a27b5 | 5,015 | py | Python | cent21.py | shadowp2810/python_WebScraper_cent21RealEstate | 186e663fbf8a333433b1d64731431c91aa59589c | [
"MIT"
] | null | null | null | cent21.py | shadowp2810/python_WebScraper_cent21RealEstate | 186e663fbf8a333433b1d64731431c91aa59589c | [
"MIT"
] | null | null | null | cent21.py | shadowp2810/python_WebScraper_cent21RealEstate | 186e663fbf8a333433b1d64731431c91aa59589c | [
"MIT"
] | null | null | null | #!/usr/bin/env python
# coding: utf-8
# In[121]:
import requests
from bs4 import BeautifulSoup
r = requests.get(
"http://www.pyclass.com/real-estate/rock-springs-wy/LCWYROCKSPRINGS/" ,
headers = {
'User-agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0' } )
c = r.content
soup = BeautifulSoup( c , "html.parser" )
# print( soup.prettify() )
all = soup.find_all(
"div" ,
{ "class" : "propertyRow" } )
# all # len( all ) # all[ 0 ]
all[ 0 ].find(
"h4" ,
{ "class" : "propPrice" } ).text.replace(
"\n" , "" ).replace( " " , "" )
# In[122]:
l = [] #list to store dictionaries of items
for item in all:
d = {} #dictionary to store all items
d[ "Price" ] = item.find( "h4" ,
{ "class" , "propPrice" } ).text.replace(
"\n" , "" ).replace( " " , "" )
d[ "Address" ] = item.find_all( "span" ,
{ "class" , "propAddressCollapse" } )[0].text
d[ "Locality" ] = item.find_all( "span" ,
{ "class" , "propAddressCollapse" } )[1].text
try:
d[ "Beds" ] = item.find( "span" ,
{ "class" , "infoBed"} ).find( "b" ).text
except:
d[ "Beds" ] = None
try:
d[ "Area" ] = item.find( "span" ,
{ "class" : "infoSqFt"} ).find( "b" ).text
except:
d[ "Area" ] = None
try:
d[ "Full Bath" ] = item.find( "span" ,
{ "class" : "infoValueFullBath"} ).find( "b" ).text
except:
d[ "Full Bath" ] = None
try:
d[ "Half Bath" ] = item.find( "span" ,
{ "class" : "infoValueHalfBath"} ).find( "b" ).text
except:
d[ "Half Bath" ] = None
for column_group in item.find_all(
"div" , { "class" , "columnGroup" } ) :
# print( column_group )
for feature_group , feature_name in zip(
column_group.find_all( "span" ,
{ "class" , "featureGroup" } ) ,
column_group.find_all( "span" ,
{ "class" , "featureName" } ) ) :
if "Lot Size" in feature_group.text :
d[ "Lot Size" ] = feature_name.text
l.append( d )
l
# In[123]:
import pandas
df = pandas.DataFrame( l )
df
# In[124]:
df.to_csv( "Output.csv" )
# In[130]:
l_allPages = [] #list to store dictionaries of items
final_page_nbr = soup.find_all(
"a" ,
{ "class" : "Page" } )[-1].text
base_url = "http://www.pyclass.com/real-estate/rock-springs-wy/LCWYROCKSPRINGS/t=0&s="
for page in range( 0 , int( final_page_nbr ) * 10 , 10 ) :
print( base_url + str( page ) + ".html" )
r = requests.get(
( base_url + str( page ) + ".html" ) ,
headers = {
'User-agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0' } )
c = r.content
soup = BeautifulSoup( c , "html.parser" )
# print( soup.prettify() )
all = soup.find_all(
"div" ,
{ "class" : "propertyRow" } )
for item in all:
d = {} #dictionary to store all items
d[ "Price" ] = item.find( "h4" ,
{ "class" , "propPrice" } ).text.replace(
"\n" , "" ).replace( " " , "" )
d[ "Address" ] = item.find_all( "span" ,
{ "class" , "propAddressCollapse" } )[0].text
try:
d[ "Locality" ] = item.find_all( "span" ,
{ "class" , "propAddressCollapse" } )[1].text
except:
d[ "Locality" ] = None
try:
d[ "Beds" ] = item.find( "span" ,
{ "class" , "infoBed"} ).find( "b" ).text
except:
d[ "Beds" ] = None
try:
d[ "Area" ] = item.find( "span" ,
{ "class" : "infoSqFt"} ).find( "b" ).text
except:
d[ "Area" ] = None
try:
d[ "Full Bath" ] = item.find( "span" ,
{ "class" : "infoValueFullBath"} ).find( "b" ).text
except:
d[ "Full Bath" ] = None
try:
d[ "Half Bath" ] = item.find( "span" ,
{ "class" : "infoValueHalfBath"} ).find( "b" ).text
except:
d[ "Half Bath" ] = None
for column_group in item.find_all(
"div" , { "class" , "columnGroup" } ) :
# print( column_group )
for feature_group , feature_name in zip(
column_group.find_all( "span" ,
{ "class" , "featureGroup" } ) ,
column_group.find_all( "span" ,
{ "class" , "featureName" } ) ) :
if "Lot Size" in feature_group.text :
d[ "Lot Size" ] = feature_name.text
l_allPages.append( d )
l_allPages
# In[131]:
import pandas
df = pandas.DataFrame( l_allPages )
df
# In[132]:
df.to_csv( "Output_allPages.csv" )
| 24.950249 | 109 | 0.470588 | 540 | 5,015 | 4.287037 | 0.233333 | 0.055292 | 0.042765 | 0.055292 | 0.851404 | 0.835853 | 0.784017 | 0.784017 | 0.767171 | 0.767171 | 0 | 0.025994 | 0.36331 | 5,015 | 200 | 110 | 25.075 | 0.699029 | 0.069591 | 0 | 0.804878 | 0 | 0.02439 | 0.230786 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.03252 | 0 | 0.03252 | 0.00813 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2126f74663032c5a5941bab4aca0447914b4be98 | 6,964 | py | Python | dash/data/penguin.py | klabacher/dash | e8eeee80e7deef07bcc139c212947c916543898f | [
"MIT"
] | null | null | null | dash/data/penguin.py | klabacher/dash | e8eeee80e7deef07bcc139c212947c916543898f | [
"MIT"
] | null | null | null | dash/data/penguin.py | klabacher/dash | e8eeee80e7deef07bcc139c212947c916543898f | [
"MIT"
] | null | null | null | from dash.data import db
class Penguin(db.Model):
__tablename__ = 'penguin'
id = db.Column(db.Integer, primary_key=True, server_default=db.text("nextval('\"penguin_id_seq\"'::regclass)"))
username = db.Column(db.String(12), nullable=False, unique=True)
nickname = db.Column(db.String(30), nullable=False)
password = db.Column(db.CHAR(60), nullable=False)
email = db.Column(db.String(255), nullable=False, index=True)
registration_date = db.Column(db.DateTime, nullable=False, server_default=db.text("now()"))
active = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
safe_chat = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
last_paycheck = db.Column(db.DateTime, nullable=False, server_default=db.text("now()"))
minutes_played = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
moderator = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
stealth_moderator = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
character = db.Column(db.ForeignKey('character.id', ondelete='CASCADE', onupdate='CASCADE'))
igloo = db.Column(db.ForeignKey('penguin_igloo_room.id', ondelete='CASCADE', onupdate='CASCADE'))
coins = db.Column(db.Integer, nullable=False, server_default=db.text("500"))
color = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
head = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
face = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
neck = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
body = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
hand = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
feet = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
photo = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
flag = db.Column(db.ForeignKey('item.id', ondelete='CASCADE', onupdate='CASCADE'))
permaban = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
book_modified = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
book_color = db.Column(db.SmallInteger, nullable=False, server_default=db.text("1"))
book_highlight = db.Column(db.SmallInteger, nullable=False, server_default=db.text("1"))
book_pattern = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
book_icon = db.Column(db.SmallInteger, nullable=False, server_default=db.text("1"))
agent_status = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
field_op_status = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
career_medals = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
agent_medals = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
last_field_op = db.Column(db.DateTime, nullable=False, server_default=db.text("now()"))
com_message_read_date = db.Column(db.DateTime, nullable=False, server_default=db.text("now()"))
ninja_rank = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
ninja_progress = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
fire_ninja_rank = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
fire_ninja_progress = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
water_ninja_rank = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
water_ninja_progress = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
ninja_matches_won = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
fire_matches_won = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
water_matches_won = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
rainbow_adoptability = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
has_dug = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
puffle_handler = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
nuggets = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
walking = db.Column(db.ForeignKey('penguin_puffle.id', ondelete='CASCADE', onupdate='CASCADE'))
opened_playercard = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
special_wave = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
special_dance = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
special_snowball = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
map_category = db.Column(db.SmallInteger, nullable=False, server_default=db.text("0"))
status_field = db.Column(db.Integer, nullable=False, server_default=db.text("0"))
timer_active = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
timer_start = db.Column(db.Time, nullable=False, server_default=db.text("'00:00:00'::time without time zone"))
timer_end = db.Column(db.Time, nullable=False, server_default=db.text("'23:59:59'::time without time zone"))
timer_total = db.Column(db.Interval, nullable=False, server_default=db.text("'01:00:00'::interval"))
grounded = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
approval_en = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
approval_pt = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
approval_fr = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
approval_es = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
approval_de = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
approval_ru = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
rejection_en = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
rejection_pt = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
rejection_fr = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
rejection_es = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
rejection_de = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
rejection_ru = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
class ActivationKey(db.Model):
__tablename__ = 'activation_key'
penguin_id = db.Column(db.ForeignKey('penguin.id', ondelete='CASCADE', onupdate='CASCADE'), primary_key=True,
nullable=False)
activation_key = db.Column(db.CHAR(255), primary_key=True, nullable=False)
| 79.136364 | 115 | 0.730902 | 983 | 6,964 | 5.038657 | 0.134283 | 0.121139 | 0.151423 | 0.218655 | 0.83727 | 0.768827 | 0.762366 | 0.762366 | 0.760549 | 0.760549 | 0 | 0.008671 | 0.105686 | 6,964 | 87 | 116 | 80.045977 | 0.786609 | 0 | 0 | 0 | 0 | 0 | 0.088311 | 0.003016 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.012346 | 0.012346 | 0 | 0.987654 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 |
212ca0e136dbb1fa89e55f7c25909878647d9226 | 126,185 | py | Python | issues/issue2.py | lalitaalaalitah/siddhantakaumudi | cfd071f085caed300946c757781f4751a5c8dfe2 | [
"MIT"
] | 13 | 2017-01-02T00:55:15.000Z | 2021-08-13T16:29:34.000Z | issues/issue2.py | kmadathil/siddhantakaumudi | 105b3ca1595527c3d5e67d52213de7c5e9dffca7 | [
"MIT"
] | 70 | 2017-01-15T11:14:31.000Z | 2021-01-15T21:45:35.000Z | issues/issue2.py | kmadathil/siddhantakaumudi | 105b3ca1595527c3d5e67d52213de7c5e9dffca7 | [
"MIT"
] | 4 | 2017-01-31T06:20:35.000Z | 2020-03-25T07:41:38.000Z | # This Python file uses the following encoding: utf-8
"""
Usage:
python issue2.py
"""
import re,codecs,sys
import lxml
sys.path.insert(0,'..')
import transcoder
# Data taken on loan from function.php of SanskritVerb repository.
ASdata=["1.1.1:संज्ञा:वृद्धिरादैच्","1.1.2:संज्ञा:अदेङ् गुणः","1.1.3:परिभाषा:इको गुणवृद्धी","1.1.4::न धातुलोप आर्धधातुके","1.1.5::ग्क्ङिति च","1.1.6::दीधीवेवीटाम्","1.1.7:संज्ञा:हलोऽनन्तराः संयोगः","1.1.8:संज्ञा:मुखनासिकावचनोऽनुनासिकः","1.1.9:संज्ञा:तुल्यास्यप्रयत्नं सवर्णम्","1.1.10:संज्ञा:नाज्झलौ","1.1.11:संज्ञा:ईदूदेद्द्विवचनं प्रगृह्यम्","1.1.12:संज्ञा:अदसो मात्","1.1.13:संज्ञा:शे","1.1.14:संज्ञा:निपात एकाजनाङ्","1.1.15:संज्ञा:ओत्","1.1.16:संज्ञा:सम्बुद्धौ शाकल्यस्येतावनार्षे","1.1.17:संज्ञा:उञः","1.1.18:संज्ञा:ऊँ","1.1.19:संज्ञा:ईदूतौ च सप्तम्यर्थे","1.1.20:संज्ञा:दाधा घ्वदाप्","1.1.21:परिभाषा:आद्यन्तवदेकस्मिन्","1.1.22:संज्ञा:तरप्तमपौ घः","1.1.23:संज्ञा:बहुगणवतुडति संख्या","1.1.24:संज्ञा:ष्णान्ता षट्","1.1.25:संज्ञा:डति च","1.1.26:संज्ञा:क्तक्तवतू निष्ठा","1.1.27:संज्ञा:सर्वादीनि सर्वनामानि","1.1.28:संज्ञा:विभाषा दिक्समासे बहुव्रीहौ","1.1.29:संज्ञा:न बहुव्रीहौ","1.1.30:संज्ञा:तृतीयासमासे","1.1.31:संज्ञा:द्वन्द्वे च","1.1.32:संज्ञा:विभाषा 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इजुपधात्","8.4.32:अतिदेशः:इजादेः सनुमः","8.4.33:अतिदेशः:वा निंसनिक्षनिन्दाम्","8.4.34:अतिदेशः:न भाभूपूकमिगमिप्यायीवेपाम्","8.4.35:अतिदेशः:षात् पदान्तात्","8.4.36:अतिदेशः:नशेः षान्तस्य","8.4.37:अतिदेशः:पदान्तस्य","8.4.38:अतिदेशः:पदव्यवायेऽपि","8.4.39:अतिदेशः:क्षुभ्नाऽऽदिषु च","8.4.40:अतिदेशः:स्तोः श्चुना श्चुः","8.4.41:अतिदेशः:ष्टुना ष्टुः","8.4.42:अतिदेशः:न पदान्ताट्टोरनाम्","8.4.43:अतिदेशः:तोः षि","8.4.44:अतिदेशः:शात्","8.4.45:अतिदेशः:यरोऽनुनासिकेऽनुनासिको वा","8.4.46:अतिदेशः:अचो रहाभ्यां द्वे","8.4.47:अतिदेशः:अनचि च","8.4.48:अतिदेशः:नादिन्याक्रोशे पुत्रस्य","8.4.49:अतिदेशः:शरोऽचि","8.4.50:अतिदेशः:त्रिप्रभृतिषु शाकटायनस्य","8.4.51:अतिदेशः:सर्वत्र शाकल्यस्य","8.4.52:अतिदेशः:दीर्घादाचार्याणाम्","8.4.53:अतिदेशः:झलां जश् झशि","8.4.54:अतिदेशः:अभ्यासे चर्च्च","8.4.55:अतिदेशः:खरि च","8.4.56:अतिदेशः:वाऽवसाने","8.4.57:अतिदेशः:अणोऽप्रगृह्यस्यानुनासिकः","8.4.58:अतिदेशः:अनुस्वारस्य ययि परसवर्णः","8.4.59:अतिदेशः:वा पदान्तस्य","8.4.60:अतिदेशः:तोर्लि","8.4.61:अतिदेशः:उदः स्थास्तम्भोः पूर्वस्य","8.4.62:अतिदेशः:झयो होऽन्यतरस्याम्","8.4.63:अतिदेशः:शश्छोऽटि","8.4.64:अतिदेशः:हलो यमां यमि लोपः","8.4.65:अतिदेशः:झरो झरि सवर्णे","8.4.66:अतिदेशः:उदात्तादनुदात्तस्य स्वरितः","8.4.67:अतिदेशः:नोदात्तस्वरितोदयमगार्ग्यकाश्यपगालवानाम्","8.4.68:अतिदेशः:अ अ इति"]
def basedata():
global ASdata
sutrawise = {}
sutratextonly = []
sutrawisespaceless = {}
sutratextonlyspaceless = []
for member in ASdata:
(a,b,c) = member.split(':')
orig = c.decode('utf-8')
c = c.decode('utf-8')
c = transcoder.transcoder_processString(c,'deva','slp1')
c = c.replace(u'\u200c',u'')
c = c.replace(u'\u200d',u'')
c = c.replace(u"'",u"")
c = re.sub('[NYRnmM]','M',c)
c = re.sub('cC','C',c)
c = re.sub('-','',c)
c = c.replace('.','')
a = a.replace('.','-')
sutrawise[a] = orig
sutratextonly.append(orig)
sutrawisespaceless[a] = c
sutratextonlyspaceless.append(c.replace(' ',''))
return sutrawise, sutratextonly, sutrawisespaceless, sutratextonlyspaceless
sutrawise, sutratextonly, sutrawisespaceless, sutratextonlyspaceless = basedata()
class sutra():
def __init__(self,line):
m = re.search(u'{#([0-9]+)#}[ ]*(.*)[ ]*{@([0-9-]+)@}',line)
self.base = m.group(2).strip()
c = unicode(transcoder.transcoder_processString(self.base,'deva','slp1'))
c = c.replace(u'\u200c',u'')
c = c.replace(u'\u200d',u'')
c = re.sub('[NYRnmM]','M',c)
c = re.sub('cC','C',c)
c = re.sub('-','',c)
c = c.replace('.','')
self.text = c
self.num = m.group(3)
self.sk = m.group(1)
self.textspaceless = self.text.replace(' ','')
def maketest(line):
global sutratextonlyspaceless, sutrawise
if re.search(u'{#([0-9]+)#}(.*){@([0-9-]+)@}',line):
sutradata = sutra(line)
#print sutradata.num, sutradata.text.encode('utf-8')
"""
linestripped = re.sub(u'{#([0-9]+)#}(.*){@([0-9-]+)@}',u'\g<2>',line)
#linestripped = linestripped.replace(' ','')
linestripped = linestripped.encode('utf-8')
linestripped = linestripped.strip()
print linestripped
"""
if sutradata.textspaceless.replace(u"'","") in sutratextonlyspaceless:
pass
elif sutradata.textspaceless not in sutratextonlyspaceless:
#print '; '+sutradata.sk+' '+sutradata.text.encode('utf-8')+' '+sutradata.num.encode('utf-8')
print '; '+line.strip().encode('utf-8')
if sutradata.num in sutrawise:
print transcoder.transcoder_processString(sutrawise[sutradata.num],'slp1','deva').encode('utf-8')
else:
print ''
data = codecs.open('../sk1.txt','r','utf-8')
for line in data:
maketest(line)
| 1,249.356436 | 123,697 | 0.49666 | 73,935 | 126,185 | 1.272767 | 0.032664 | 0.030796 | 0.017247 | 0.022061 | 0.518395 | 0.394986 | 0.309483 | 0.218454 | 0.135087 | 0.088978 | 0 | 0.136615 | 0.028585 | 126,185 | 100 | 123,698 | 1,261.85 | 0.374602 | 0.002053 | 0 | 0.2 | 0 | 29.65 | 0.89075 | 0.418815 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.016667 | 0.05 | null | null | 0.05 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
0d4bc802d10f50ee49a3c56f9594678f5e15ada3 | 38,035 | py | Python | tests/test_cyhynotificationmessage.py | dhs-ncats/cyhy-mailer | 7050d6774e64dffb61c11a2cba358b7236f73054 | [
"CC0-1.0"
] | 3 | 2017-12-06T07:45:35.000Z | 2018-06-25T20:08:09.000Z | tests/test_cyhynotificationmessage.py | dhs-ncats/cyhy-mailer | 7050d6774e64dffb61c11a2cba358b7236f73054 | [
"CC0-1.0"
] | 33 | 2017-12-11T16:07:04.000Z | 2019-02-25T14:09:45.000Z | tests/test_cyhynotificationmessage.py | dhs-ncats/cyhy-mailer | 7050d6774e64dffb61c11a2cba358b7236f73054 | [
"CC0-1.0"
] | 2 | 2018-03-30T21:46:14.000Z | 2018-07-02T18:01:23.000Z | """This module contains the tests for the CyhyNotificationMessage class."""
# Standard Python Libraries
import unittest
# cisagov Libraries
from cyhy.mailer.CyhyNotificationMessage import CyhyNotificationMessage
class Test(unittest.TestCase):
"""The tests for the CyhyNotificationMessage class."""
def test_four_params_single_recipient_fed(self):
"""Test the 4-parameter Federal version of the constructor."""
to = ["recipient@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
agency_acronym = "FEDTEST"
is_federal = True
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to, pdf, agency_acronym, is_federal, report_date
)
self.assertEqual(message["From"], "reports@cyber.dhs.gov")
self.assertEqual(
message["Subject"],
"FEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message.get("CC"), None)
self.assertEqual(message["BCC"], "cyhy_reports@hq.dhs.gov")
self.assertEqual(message["To"], "recipient@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
text_body = """Greetings FEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
As part of BOD 19-02, critical findings need to be remediated within 15 days and high findings remediated within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), text_body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings FEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>As part of <a href="https://cyber.dhs.gov/bod/19-02/">BOD 19-02</a>, critical findings need to be remediated within 15 days and high findings remediated within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_four_params_multiple_recipients_fed(self):
"""Test the 4-parameter Federal version of the constructor."""
to = ["recipient@example.com", "recipient2@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
agency_acronym = "FEDTEST"
is_federal = True
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to, pdf, agency_acronym, is_federal, report_date
)
self.assertEqual(message["From"], "reports@cyber.dhs.gov")
self.assertEqual(
message["Subject"],
"FEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message.get("CC"), None)
self.assertEqual(message["BCC"], "cyhy_reports@hq.dhs.gov")
self.assertEqual(message["To"], "recipient@example.com,recipient2@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
body = """Greetings FEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
As part of BOD 19-02, critical findings need to be remediated within 15 days and high findings remediated within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings FEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>As part of <a href="https://cyber.dhs.gov/bod/19-02/">BOD 19-02</a>, critical findings need to be remediated within 15 days and high findings remediated within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_six_params_single_cc_fed(self):
"""Test the 6-parameter Federal version of the constructor."""
to = ["recipient@example.com", "recipient2@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
fm = "sender@example.com"
cc = ["cc@example.com"]
bcc = ["bcc@example.com"]
agency_acronym = "FEDTEST"
is_federal = True
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to,
pdf,
agency_acronym,
is_federal,
report_date,
from_addr=fm,
cc_addrs=cc,
bcc_addrs=bcc,
)
self.assertEqual(message["From"], fm)
self.assertEqual(
message["Subject"],
"FEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message["CC"], "cc@example.com")
self.assertEqual(message["BCC"], "bcc@example.com")
self.assertEqual(message["To"], "recipient@example.com,recipient2@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
body = """Greetings FEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
As part of BOD 19-02, critical findings need to be remediated within 15 days and high findings remediated within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings FEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>As part of <a href="https://cyber.dhs.gov/bod/19-02/">BOD 19-02</a>, critical findings need to be remediated within 15 days and high findings remediated within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_six_params_multiple_cc_fed(self):
"""Test the 6-parameter Federal version of the constructor."""
to = ["recipient@example.com", "recipient2@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
fm = "sender@example.com"
cc = ["cc@example.com", "cc2@example.com"]
bcc = ["bcc@example.com", "bcc2@example.com"]
agency_acronym = "FEDTEST"
is_federal = True
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to,
pdf,
agency_acronym,
is_federal,
report_date,
from_addr=fm,
cc_addrs=cc,
bcc_addrs=bcc,
)
self.assertEqual(message["From"], fm)
self.assertEqual(
message["Subject"],
"FEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message["CC"], "cc@example.com,cc2@example.com")
self.assertEqual(message["BCC"], "bcc@example.com,bcc2@example.com")
self.assertEqual(message["To"], "recipient@example.com,recipient2@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
body = """Greetings FEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
As part of BOD 19-02, critical findings need to be remediated within 15 days and high findings remediated within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings FEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>As part of <a href="https://cyber.dhs.gov/bod/19-02/">BOD 19-02</a>, critical findings need to be remediated within 15 days and high findings remediated within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_four_params_single_recipient_nonfed(self):
"""Test the 4-parameter non-Federal version of the constructor."""
to = ["recipient@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
agency_acronym = "NONFEDTEST"
is_federal = False
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to, pdf, agency_acronym, is_federal, report_date
)
self.assertEqual(message["From"], "reports@cyber.dhs.gov")
self.assertEqual(
message["Subject"],
"NONFEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message.get("CC"), None)
self.assertEqual(message["BCC"], "cyhy_reports@hq.dhs.gov")
self.assertEqual(message["To"], "recipient@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
text_body = """Greetings NONFEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
CISA recommends remediating critical findings within 15 days and high findings within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), text_body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings NONFEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>CISA recommends remediating critical findings within 15 days and high findings within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_four_params_multiple_recipients_nonfed(self):
"""Test the 4-parameter non-Federal version of the constructor."""
to = ["recipient@example.com", "recipient2@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
agency_acronym = "NONFEDTEST"
is_federal = False
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to, pdf, agency_acronym, is_federal, report_date
)
self.assertEqual(message["From"], "reports@cyber.dhs.gov")
self.assertEqual(
message["Subject"],
"NONFEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message.get("CC"), None)
self.assertEqual(message["BCC"], "cyhy_reports@hq.dhs.gov")
self.assertEqual(message["To"], "recipient@example.com,recipient2@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
body = """Greetings NONFEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
CISA recommends remediating critical findings within 15 days and high findings within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings NONFEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>CISA recommends remediating critical findings within 15 days and high findings within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_six_params_single_cc_nonfed(self):
"""Test the 6-parameter non-Federal version of the constructor."""
to = ["recipient@example.com", "recipient2@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
fm = "sender@example.com"
cc = ["cc@example.com"]
bcc = ["bcc@example.com"]
agency_acronym = "NONFEDTEST"
is_federal = False
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to,
pdf,
agency_acronym,
is_federal,
report_date,
from_addr=fm,
cc_addrs=cc,
bcc_addrs=bcc,
)
self.assertEqual(message["From"], fm)
self.assertEqual(
message["Subject"],
"NONFEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message["CC"], "cc@example.com")
self.assertEqual(message["BCC"], "bcc@example.com")
self.assertEqual(message["To"], "recipient@example.com,recipient2@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
body = """Greetings NONFEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
CISA recommends remediating critical findings within 15 days and high findings within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings NONFEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>CISA recommends remediating critical findings within 15 days and high findings within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
def test_six_params_multiple_cc_nonfed(self):
"""Test the 6-parameter non-Federal version of the constructor."""
to = ["recipient@example.com", "recipient2@example.com"]
pdf = "./tests/data/pdf-sample.pdf"
fm = "sender@example.com"
cc = ["cc@example.com", "cc2@example.com"]
bcc = ["bcc@example.com", "bcc2@example.com"]
agency_acronym = "NONFEDTEST"
is_federal = False
report_date = "December 15, 2001"
message = CyhyNotificationMessage(
to,
pdf,
agency_acronym,
is_federal,
report_date,
from_addr=fm,
cc_addrs=cc,
bcc_addrs=bcc,
)
self.assertEqual(message["From"], fm)
self.assertEqual(
message["Subject"],
"NONFEDTEST - Cyber Hygiene Alert - December 15, 2001",
)
self.assertEqual(message["CC"], "cc@example.com,cc2@example.com")
self.assertEqual(message["BCC"], "bcc@example.com,bcc2@example.com")
self.assertEqual(message["To"], "recipient@example.com,recipient2@example.com")
# Grab the bytes that comprise the attachment
bytes = open(pdf, "rb").read()
# Make sure the correct body and PDF attachments were added
for part in message.walk():
# multipart/* are just containers
if part.get_content_type() == "application/pdf":
self.assertEqual(part.get_payload(decode=True), bytes)
self.assertEqual(part.get_filename(), "pdf-sample.pdf")
elif part.get_content_type() == "text/plain":
body = """Greetings NONFEDTEST,
Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
* New critical and/or high vulnerabilities
* New potentially risky services
CISA recommends remediating critical findings within 15 days and high findings within 30 days.
CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.
The details are in the attached PDF, which has the same password as your Cyber Hygiene report.
If you have any questions, please contact our office.
Cheers,
CISA Cyber Assessments - Cyber Hygiene
Cybersecurity and Infrastructure Security Agency
vulnerability@cisa.dhs.gov
WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid "need-to-know" without prior approval of an authorized DHS official.
"""
self.assertEqual(part.get_payload(), body)
elif part.get_content_type() == "text/html":
html_body = """<html>
<head></head>
<body>
<p>Greetings NONFEDTEST,</p>
<p>Cyber Hygiene scans of your host(s) conducted in the past day have detected one or both of the following:
<ul>
<li>New critical and/or high vulnerabilities</li>
<li>New potentially risky services</li>
</ul>
</p>
<p>CISA recommends remediating critical findings within 15 days and high findings within 30 days.</p>
<p>CISA also recommends reviewing hosts with potentially risky open services (e.g. RDP, Telnet, etc.) to ensure that each service is intended to be available to the public and, where applicable, the service is up-to-date on the latest version, correctly configured, and uses strong authentication.</p>
<p>The details are in the attached PDF, which has the same password as your Cyber Hygiene report.</p>
<p>If you have any questions, please contact our office.</p>
<p>Cheers,<br>
CISA Cyber Assessments - Cyber Hygiene<br>
Cybersecurity and Infrastructure Security Agency<br>
<a href="mailto:vulnerability@cisa.dhs.gov">vulnerability@cisa.dhs.gov</a></p>
<p>WARNING: This message and any attached document(s) is FOR OFFICIAL USE ONLY (FOUO). It contains information that may be exempt from public release under the Freedom of Information Act (5 U.S.G. 552). It is to be controlled, stored, handled, transmitted, distributed, and disposed of in accordance with DHS policy relating to FOUO information and is not to be released to the public or other personnel who do not have a valid “need-to-know” without prior approval of an authorized DHS official.</p>
</body>
</html>
"""
self.assertEqual(part.get_payload(), html_body)
if __name__ == "__main__":
unittest.main()
| 51.677989 | 512 | 0.707611 | 5,493 | 38,035 | 4.861096 | 0.044056 | 0.040446 | 0.032956 | 0.026365 | 0.993109 | 0.993109 | 0.988203 | 0.988203 | 0.988203 | 0.988203 | 0 | 0.009896 | 0.202945 | 38,035 | 735 | 513 | 51.748299 | 0.870893 | 0.04488 | 0 | 0.942699 | 0 | 0.133087 | 0.729836 | 0.058976 | 0 | 0 | 0 | 0 | 0.133087 | 1 | 0.014787 | false | 0.029575 | 0.003697 | 0 | 0.020333 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
0d501eefe3ce134fe25493327643330889e3892b | 189 | py | Python | dvc/utils/flatten.py | lucasalavapena/dvc | 230eb7087df7f063ded7422af7ae45bd04eb794a | [
"Apache-2.0"
] | 9,136 | 2018-05-30T05:10:44.000Z | 2022-03-31T16:58:52.000Z | dvc/utils/flatten.py | 4nalog/dvc | 13c1314099df131f526177b2a75bda02dfc0cdbf | [
"Apache-2.0"
] | 4,804 | 2018-05-30T00:36:42.000Z | 2022-03-31T18:34:54.000Z | dvc/utils/flatten.py | 4nalog/dvc | 13c1314099df131f526177b2a75bda02dfc0cdbf | [
"Apache-2.0"
] | 1,072 | 2018-05-30T07:59:35.000Z | 2022-03-28T20:43:49.000Z | def flatten(d):
import flatten_dict
return flatten_dict.flatten(d, reducer="dot")
def unflatten(d):
import flatten_dict
return flatten_dict.unflatten(d, splitter="dot")
| 17.181818 | 52 | 0.714286 | 26 | 189 | 5.038462 | 0.384615 | 0.335878 | 0.21374 | 0.274809 | 0.534351 | 0.534351 | 0.534351 | 0 | 0 | 0 | 0 | 0 | 0.179894 | 189 | 10 | 53 | 18.9 | 0.845161 | 0 | 0 | 0.333333 | 0 | 0 | 0.031746 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
b4e8bec0f85ca78a79d80de607eee0f6602a21f9 | 79 | py | Python | region/api.py | Dexterzhao/region | 596476ad291bfbbeb7d88bb70503aff89c1df59c | [
"BSD-3-Clause"
] | 15 | 2018-05-17T07:17:43.000Z | 2022-02-20T19:00:58.000Z | region/api.py | Dexterzhao/region | 596476ad291bfbbeb7d88bb70503aff89c1df59c | [
"BSD-3-Clause"
] | 29 | 2017-09-23T20:46:26.000Z | 2019-12-18T20:16:56.000Z | region/api.py | Dexterzhao/region | 596476ad291bfbbeb7d88bb70503aff89c1df59c | [
"BSD-3-Clause"
] | 17 | 2017-06-23T17:37:44.000Z | 2020-04-15T16:45:35.000Z | from .max_p_regions import api as maxp
from .p_regions import api as p_regions
| 26.333333 | 39 | 0.822785 | 16 | 79 | 3.8125 | 0.5 | 0.393443 | 0.459016 | 0.557377 | 0.622951 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.151899 | 79 | 2 | 40 | 39.5 | 0.910448 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
b4e9604f2cacda1cd40451bb5e8757ad8146f43d | 4,845 | py | Python | lit_nlp/examples/models/glue_models_test.py | eichinflo/lit | b46c0cac34baa571242637b53b78cfd69de536d0 | [
"Apache-2.0"
] | 2,854 | 2020-08-12T15:51:12.000Z | 2022-03-31T08:24:13.000Z | lit_nlp/examples/models/glue_models_test.py | soma2000-lang/lit | b46c0cac34baa571242637b53b78cfd69de536d0 | [
"Apache-2.0"
] | 156 | 2020-08-16T21:09:05.000Z | 2022-03-30T18:04:53.000Z | lit_nlp/examples/models/glue_models_test.py | soma2000-lang/lit | b46c0cac34baa571242637b53b78cfd69de536d0 | [
"Apache-2.0"
] | 301 | 2020-08-14T05:52:56.000Z | 2022-03-25T22:48:01.000Z | """Tests for lit_nlp.examples.models.glue_models."""
from absl.testing import absltest
from lit_nlp.examples.models import glue_models
import numpy as np
class GlueModelForTesting(glue_models.GlueModel):
"""Glue model for testing, which skips Huggingface initializations."""
def _load_model(self, model_name_or_path):
pass
class GlueModelsTest(absltest.TestCase):
def test_scatter_all_embeddings_single_input(self):
glue_model = GlueModelForTesting(
model_name_or_path="bert-base-uncased",
text_a_name="sentence1")
emb_size = 10
# We'll inject zeros for the embeddings of 'hi',
# while special tokens get vectors of 1s.
embs_a = np.zeros((1, emb_size))
input_embs = np.ones((1, 3, emb_size))
# Scatter embs_a into input_embs
result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
"input_embs_sentence1": embs_a,
}], input_embs)
target = [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]
np.testing.assert_almost_equal(result, target)
def test_scatter_all_embeddings_both_inputs(self):
glue_model = GlueModelForTesting(
model_name_or_path="bert-base-uncased",
text_a_name="sentence1",
text_b_name="sentence2")
emb_size = 10
# Inject zeros at positions corresponding to real tokens
# in each segment. Special tokens get vectors of 1s.
embs_a = np.zeros((1, emb_size))
embs_b = np.zeros((3, emb_size))
input_embs = np.ones((1, 7, emb_size))
# Scatter embs_a and embs_b into input_embs
result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
"input_embs_sentence1": embs_a,
"sentence2": "how are you",
"input_embs_sentence2": embs_b
}], input_embs)
target = [[[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]]
np.testing.assert_almost_equal(result, target)
def test_scatter_all_embeddings_multi_batch(self):
glue_model = GlueModelForTesting(
model_name_or_path="bert-base-uncased",
text_a_name="sentence1")
emb_size = 4
embs_a = np.zeros((1, emb_size))
embs_b = np.zeros((2, emb_size))
input_embs = np.ones((2, 4, emb_size))
# Scatter embs_a and embs_b into input_embs
result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
"input_embs_sentence1": embs_a,
},
{"sentence1": "hi there",
"input_embs_sentence1": embs_b,
}], input_embs)
target = [[[1, 1, 1, 1],
[0, 0, 0, 0],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 1, 1]]]
np.testing.assert_almost_equal(result, target)
# Scatter only embs_a into input_embs
result = glue_model.scatter_all_embeddings([{"sentence1": "hi",
"input_embs_sentence1": embs_a,
},
{"sentence1": "hi there"
}], input_embs)
target = [[[1, 1, 1, 1],
[0, 0, 0, 0],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]]]
np.testing.assert_almost_equal(result, target)
# Scatter only embs_b into input_embs
result = glue_model.scatter_all_embeddings([{"sentence1": "hi"},
{"sentence1": "hi there",
"input_embs_sentence1": embs_b,
}], input_embs)
target = [[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 1, 1]]]
np.testing.assert_almost_equal(result, target)
if __name__ == "__main__":
absltest.main()
| 40.041322 | 80 | 0.461507 | 599 | 4,845 | 3.507513 | 0.160267 | 0.104712 | 0.139933 | 0.163732 | 0.754879 | 0.735364 | 0.724893 | 0.70633 | 0.70633 | 0.70633 | 0 | 0.082048 | 0.411352 | 4,845 | 120 | 81 | 40.375 | 0.654628 | 0.101548 | 0 | 0.715789 | 0 | 0 | 0.083295 | 0 | 0 | 0 | 0 | 0 | 0.052632 | 1 | 0.042105 | false | 0.010526 | 0.031579 | 0 | 0.094737 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
3706d9a147c7efb0f1f4adf8064c640a42be33e0 | 1,539 | py | Python | diffractsim/diffractive_elements/fresnel_zone_plate.py | rafael-fuente/diffractsim | 7287635d2bfa76f8b1eb24c6208796f761dd6144 | [
"BSD-3-Clause"
] | 29 | 2022-01-01T01:16:29.000Z | 2022-03-31T00:42:52.000Z | diffractsim/diffractive_elements/fresnel_zone_plate.py | rafael-fuente/diffractsim | 7287635d2bfa76f8b1eb24c6208796f761dd6144 | [
"BSD-3-Clause"
] | 2 | 2022-01-02T17:33:00.000Z | 2022-01-03T17:51:39.000Z | diffractsim/diffractive_elements/fresnel_zone_plate.py | rafael-fuente/diffractsim | 7287635d2bfa76f8b1eb24c6208796f761dd6144 | [
"BSD-3-Clause"
] | 6 | 2022-02-07T22:44:42.000Z | 2022-03-23T12:34:54.000Z | import numpy as np
from ..util.backend_functions import backend as bd
from .diffractive_element import DOE
class BinaryFZP(DOE):
def __init__(self, f, λ, radius = None, aberration = None):
"""
Creates a Phase Binary Fresnel Zone Plate with a focal length equal to f for a wavelength λ
"""
global bd
from ..util.backend_functions import backend as bd
self.f = f
self.FZP_λ = λ
self.radius = radius
def get_transmittance(self, xx, yy, λ):
t = 1
if self.radius != None:
t = bd.where((xx**2 + yy**2) < self.radius**2, t, bd.zeros_like(xx))
r_2 = xx**2 + yy**2
phase_shift = bd.pi* (bd.sign(((2*bd.pi/self.FZP_λ * (bd.sqrt(f**2 + r_2) - f))) % (2*bd.pi) - bd.pi ))/2.
t = t*bd.exp(1j*phase_shift)
return t
class FZP(DOE):
def __init__(self, f, λ, radius = None, aberration = None):
"""
Creates a Phase Blazed (Ideal) Fresnel Zone Plate with a focal length equal to f for a wavelength λ
"""
global bd
from ..util.backend_functions import backend as bd
self.f = f
self.FZP_λ = λ
self.radius = radius
def get_transmittance(self, xx, yy, λ):
t = 1
if self.radius != None:
t = bd.where((xx**2 + yy**2) < self.radius**2, t, bd.zeros_like(xx))
r_2 = xx**2 + yy**2
phase_shift = -(2*bd.pi/λ * (bd.sqrt(self.f**2 + r_2) - self.f))
t = t*bd.exp(1j*phase_shift)
return t
| 27.482143 | 118 | 0.556205 | 244 | 1,539 | 3.397541 | 0.241803 | 0.036188 | 0.024125 | 0.028951 | 0.814234 | 0.814234 | 0.814234 | 0.814234 | 0.764777 | 0.702051 | 0 | 0.022727 | 0.31384 | 1,539 | 55 | 119 | 27.981818 | 0.762311 | 0.124107 | 0 | 0.818182 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.121212 | false | 0 | 0.151515 | 0 | 0.393939 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2eba4bdc69f90d559c48e1af4ef9affe05f3e336 | 45 | py | Python | src/models/__init__.py | andriihomiak/literate-enigma | 179a52432a0c9b67e916c5c9157e8f3051a20619 | [
"MIT"
] | null | null | null | src/models/__init__.py | andriihomiak/literate-enigma | 179a52432a0c9b67e916c5c9157e8f3051a20619 | [
"MIT"
] | null | null | null | src/models/__init__.py | andriihomiak/literate-enigma | 179a52432a0c9b67e916c5c9157e8f3051a20619 | [
"MIT"
] | null | null | null | from .classification import ClassificationNet | 45 | 45 | 0.911111 | 4 | 45 | 10.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.066667 | 45 | 1 | 45 | 45 | 0.97619 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
2edd530ef6e56e4b94e5d9e4b2c7ca1933139845 | 1,329 | py | Python | lowres/model/lowres/structural_blocks.py | mdpi2020lowres/mdpi2020lowres | 1a73ab011063998e57a31db13b170d604f71a794 | [
"Apache-2.0"
] | null | null | null | lowres/model/lowres/structural_blocks.py | mdpi2020lowres/mdpi2020lowres | 1a73ab011063998e57a31db13b170d604f71a794 | [
"Apache-2.0"
] | null | null | null | lowres/model/lowres/structural_blocks.py | mdpi2020lowres/mdpi2020lowres | 1a73ab011063998e57a31db13b170d604f71a794 | [
"Apache-2.0"
] | null | null | null | import torch.nn as nn
class PreActivation3dNoBN(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, bias=True):
super().__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=bias)
self.activation = nn.ReLU(inplace=True)
def forward(self, x):
return self.conv(self.activation(x))
class PostActivation3dNoBN(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, bias=True):
super().__init__()
self.conv = nn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, padding=padding, bias=bias)
self.activation = nn.ReLU(inplace=True)
def forward(self, x):
return self.activation(self.conv(x))
class ResBlock3dNoBN(nn.Module):
def __init__(self, n_channels, kernel_size=3, padding=1, bias=True):
super().__init__()
self.path = nn.Sequential(
nn.Conv3d(n_channels, n_channels, kernel_size=kernel_size, padding=padding, bias=bias),
nn.ReLU(inplace=True),
nn.Conv3d(n_channels, n_channels, kernel_size=kernel_size, padding=padding, bias=bias),
nn.ReLU(inplace=True),
)
def forward(self, x):
x_path = self.path(x)
return x + x_path
| 33.225 | 109 | 0.669676 | 180 | 1,329 | 4.666667 | 0.188889 | 0.130952 | 0.15 | 0.1 | 0.802381 | 0.779762 | 0.779762 | 0.779762 | 0.761905 | 0.761905 | 0 | 0.012357 | 0.208427 | 1,329 | 39 | 110 | 34.076923 | 0.786122 | 0 | 0 | 0.592593 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.037037 | 0.074074 | 0.481481 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
25a18a2801476bffae7d8c653c93a397575b7605 | 226,652 | py | Python | eeauditor/auditors/aws/Amazon_EC2_Security_Group_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 442 | 2020-03-15T20:56:36.000Z | 2022-03-31T22:13:07.000Z | eeauditor/auditors/aws/Amazon_EC2_Security_Group_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 57 | 2020-03-15T22:09:56.000Z | 2022-03-31T13:17:06.000Z | eeauditor/auditors/aws/Amazon_EC2_Security_Group_Auditor.py | kbhagi/ElectricEye | 31960e1e1cfb75c5d354844ea9e07d5295442823 | [
"Apache-2.0"
] | 59 | 2020-03-15T21:19:10.000Z | 2022-03-31T15:01:31.000Z | #This file is part of ElectricEye.
#SPDX-License-Identifier: Apache-2.0
#Licensed to the Apache Software Foundation (ASF) under one
#or more contributor license agreements. See the NOTICE file
#distributed with this work for additional information
#regarding copyright ownership. The ASF licenses this file
#to you under the Apache License, Version 2.0 (the
#"License"); you may not use this file except in compliance
#with the License. You may obtain a copy of the License at
#http://www.apache.org/licenses/LICENSE-2.0
#Unless required by applicable law or agreed to in writing,
#software distributed under the License is distributed on an
#"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
#KIND, either express or implied. See the License for the
#specific language governing permissions and limitations
#under the License.
import boto3
import datetime
from check_register import CheckRegister
registry = CheckRegister()
ec2 = boto3.client("ec2")
# loop through security groups
def describe_security_groups(cache):
response = cache.get("describe_security_groups")
if response:
return response
cache["describe_security_groups"] = ec2.describe_security_groups()
return cache["describe_security_groups"]
@registry.register_check("ec2")
def security_group_all_open_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.1] Security groups should not allow unrestricted access to all ports and protocols"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if ipProtocol == "-1" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-all-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "CRITICAL"},
"Confidence": 99,
"Title": "[SecurityGroup.1] Security groups should not allow unrestricted access to all ports and protocols",
"Description": "Security group "
+ sgName
+ " allows unrestricted access to all ports and protocols. Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif ipProtocol == "-1" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-all-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.1] Security groups should not allow unrestricted access to all ports and protocols",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted access to all ports and protocols. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_ftp_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.2] Security groups should not allow unrestricted File Transfer Protocol (FTP) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort == "20" and fromPort == "21" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-ftp-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.2] Security groups should not allow unrestricted File Transfer Protocol (FTP) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted File Transfer Protocol (FTP) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort == "20" and fromPort == "21" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-ftp-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.2] Security groups should not allow unrestricted File Transfer Protocol (FTP) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted File Transfer Protocol (FTP) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_telnet_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.3] Security groups should not allow unrestricted TelNet access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "23" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-telnet-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.3] Security groups should not allow unrestricted TelNet access",
"Description": "Security group "
+ sgName
+ " allows unrestricted TelNet access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "23" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-telnet-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.3] Security groups should not allow unrestricted TelNet access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted TelNet access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_dcom_rpc_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.4] Security groups should not allow unrestricted Windows RPC DCOM access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "135" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-rpc-dcom-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.4] Security groups should not allow unrestricted Windows RPC DCOM access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Windows RPC DCOM access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Attack signature information, refer to Threatl Intel Source URL",
"Source": "Symantec Security Center",
"SourceUrl": "https://www.symantec.com/security_response/attacksignatures/detail.jsp?asid=20387",
}
],
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
if toPort and fromPort == "135" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-rpc-dcom-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.4] Security groups should not allow unrestricted Windows RPC DCOM access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Windows RPC DCOM access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Attack signature information, refer to Threatl Intel Source URL",
"Source": "Symantec Security Center",
"SourceUrl": "https://www.symantec.com/security_response/attacksignatures/detail.jsp?asid=20387",
}
],
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_smb_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.5] Security groups should not allow unrestricted Server Message Blocks (SMB) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "445" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-smb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.5] Security groups should not allow unrestricted Server Message Blocks (SMB) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Server Message Blocks (SMB) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "MS17-010 EternalBlue SMB Remote Windows Kernel Pool Corruption",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/modules/exploit/windows/smb/ms17_010_eternalblue",
},
{
"Category": "BACKDOOR",
"Value": "How to use EternalBlue to Exploit SMB Port using Public Wi-Fi",
"Source": "Medium",
"SourceUrl": "https://medium.com/@melvinshb/how-to-use-eternalblue-to-exploit-smb-port-using-public-wi-fi-79a996821767",
},
],
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "445" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-smb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.5] Security groups should not allow unrestricted Server Message Blocks (SMB) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Server Message Blocks (SMB) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "MS17-010 EternalBlue SMB Remote Windows Kernel Pool Corruption",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/modules/exploit/windows/smb/ms17_010_eternalblue",
},
{
"Category": "BACKDOOR",
"Value": "How to use EternalBlue to Exploit SMB Port using Public Wi-Fi",
"Source": "Medium",
"SourceUrl": "https://medium.com/@melvinshb/how-to-use-eternalblue-to-exploit-smb-port-using-public-wi-fi-79a996821767",
},
],
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_mssql_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.6] Security groups should not allow unrestricted Microsoft SQL Server (MSSQL) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "1433" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-mssql-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.6] Security groups should not allow unrestricted Microsoft SQL Server (MSSQL) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Microsoft SQL Server (MSSQL) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2020-0618: Microsoft SQL Server Reporting Services Remote Code Execution Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0618",
},
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2019-0819: Microsoft SQL Server Analysis Services Information Disclosure Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2019-0819",
},
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2018-8273: Microsoft SQL Server Remote Code Execution Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2018-8273",
},
],
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "1433" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-mssql-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.6] Security groups should not allow unrestricted Microsoft SQL Server (MSSQL) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Microsoft SQL Server (MSSQL) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2020-0618: Microsoft SQL Server Reporting Services Remote Code Execution Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0618",
},
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2019-0819: Microsoft SQL Server Analysis Services Information Disclosure Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2019-0819",
},
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2018-8273: Microsoft SQL Server Remote Code Execution Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2018-8273",
},
],
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_oracle_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.7] Security groups should not allow unrestricted Oracle database (TCP 1521) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "1521" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-oracledb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.7] Security groups should not allow unrestricted Oracle database (TCP 1521) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Oracle database (TCP 1521) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "1521" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-oracledb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.7] Security groups should not allow unrestricted Oracle database (TCP 1521) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Oracle database (TCP 1521) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_mysql_mariadb_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.8] Security groups should not allow unrestricted MySQL or MariaDB database (TCP 3306) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "3306" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn
+ "/"
+ ipProtocol
+ "/security-group-mysql-mariadb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.8] Security groups should not allow unrestricted MySQL or MariaDB database (TCP 3306) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted MySQL or MariaDB database (TCP 3306) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
if toPort and fromPort == "3306" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn
+ "/"
+ ipProtocol
+ "/security-group-mysql-mariadb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.8] Security groups should not allow unrestricted MySQL or MariaDB database (TCP 3306) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted MySQL or MariaDB database (TCP 3306) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_rdp_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.9] Security groups should not allow unrestricted Remote Desktop Protocol (RDP) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "3389" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-rdp-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "CRITICAL"},
"Confidence": 99,
"Title": "[SecurityGroup.9] Security groups should not allow unrestricted Remote Desktop Protocol (RDP) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Remote Desktop Protocol (RDP) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2020-0660: Windows Remote Desktop Protocol (RDP) Denial of Service Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0660",
},
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2020-0610: Windows Remote Desktop Gateway (RD Gateway) Remote Code Execution Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0610",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "3389" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-rdp-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.9] Security groups should not allow unrestricted Remote Desktop Protocol (RDP) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Remote Desktop Protocol (RDP) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2020-0660: Windows Remote Desktop Protocol (RDP) Denial of Service Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0660",
},
{
"Category": "BACKDOOR",
"Value": "Microsoft CVE-2020-0610: Windows Remote Desktop Gateway (RD Gateway) Remote Code Execution Vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0610",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_postgresql_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.10] Security groups should not allow unrestricted PostgreSQL datbase (TCP 5432) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "5432" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-postgresql-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.10] Security groups should not allow unrestricted PostgreSQL datbase (TCP 5432) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted PostgreSQL datbase (TCP 5432) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "5432" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-postgresql-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.10] Security groups should not allow unrestricted PostgreSQL datbase (TCP 5432) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted PostgreSQL datbase (TCP 5432) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_kibana_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.11] Security groups should not allow unrestricted access to Kibana (TCP 5601)"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "5601" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-kibana-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.11] Security groups should not allow unrestricted access to Kibana (TCP 5601)",
"Description": "Security group "
+ sgName
+ " allows unrestricted access to Kibana (TCP 5601) on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "CVE-2019-7609: Exploit Script Available for Kibana Remote Code Execution Vulnerability",
"Source": "Tenable Blog",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0660",
},
{
"Category": "BACKDOOR",
"Value": "Red Hat OpenShift: CVE-2019-7608: kibana: Cross-site scripting vulnerability permits perform destructive actions on behalf of other Kibana users",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/redhat-openshift-cve-2019-7608",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "5601" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-kibana-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.11] Security groups should not allow unrestricted access to Kibana (TCP 5601)",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted access to Kibana (TCP 5601) on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "CVE-2019-7609: Exploit Script Available for Kibana Remote Code Execution Vulnerability",
"Source": "Tenable Blog",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/msft-cve-2020-0660",
},
{
"Category": "BACKDOOR",
"Value": "Red Hat OpenShift: CVE-2019-7608: kibana: Cross-site scripting vulnerability permits perform destructive actions on behalf of other Kibana users",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/redhat-openshift-cve-2019-7608",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_redis_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.12] Security groups should not allow unrestricted Redis (TCP 6379) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "6379" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-redis-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.12] Security groups should not allow unrestricted Redis (TCP 6379) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Redis (TCP 6379) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Redis 4.x / 5.x - Unauthenticated Code Execution (Metasploit)",
"Source": "ExploitDB",
"SourceUrl": "https://www.exploit-db.com/exploits/47195",
},
{
"Category": "BACKDOOR",
"Value": "Redis: Improper Input Validation (CVE-2013-0178)",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/redislabs-redis-cve-2013-0178",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "6379" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-redis-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.12] Security groups should not allow unrestricted Redis (TCP 6379) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Redis (TCP 6379) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Redis 4.x / 5.x - Unauthenticated Code Execution (Metasploit)",
"Source": "ExploitDB",
"SourceUrl": "https://www.exploit-db.com/exploits/47195",
},
{
"Category": "BACKDOOR",
"Value": "Redis: Improper Input Validation (CVE-2013-0178)",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/redislabs-redis-cve-2013-0178",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_splunkd_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.13] Security groups should not allow unrestricted Splunkd (TCP 8089) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "8089" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-splunkd-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.13] Security groups should not allow unrestricted Splunkd (TCP 8089) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Splunkd (TCP 8089) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Splunk - Remote Command Execution",
"Source": "ExploitDB",
"SourceUrl": "https://www.exploit-db.com/exploits/18245",
},
{
"Category": "BACKDOOR",
"Value": "Splunk Web Interface Login Utility",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/modules/auxiliary/scanner/http/splunk_web_login",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "8089" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-splunkd-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.13] Security groups should not allow unrestricted Splunkd (TCP 8089) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Splunkd (TCP 8089) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "Splunk - Remote Command Execution",
"Source": "ExploitDB",
"SourceUrl": "https://www.exploit-db.com/exploits/18245",
},
{
"Category": "BACKDOOR",
"Value": "Splunk Web Interface Login Utility",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/modules/auxiliary/scanner/http/splunk_web_login",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_elasticsearch1_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.14] Security groups should not allow unrestricted Elasticsearch (TCP 9200) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "9200" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn
+ "/"
+ ipProtocol
+ "/security-group-elasticsearch-9200-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.14] Security groups should not allow unrestricted Elasticsearch (TCP 9200) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Elasticsearch (TCP 9200) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "9200" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn
+ "/"
+ ipProtocol
+ "/security-group-elasticsearch-9200-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.14] Security groups should not allow unrestricted Elasticsearch (TCP 9200) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Elasticsearch (TCP 9200) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_elasticsearch2_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.15] Security groups should not allow unrestricted Elasticsearch (TCP 9300) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "9300" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn
+ "/"
+ ipProtocol
+ "/security-group-elasticsearch-9300-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.15] Security groups should not allow unrestricted Elasticsearch (TCP 9300) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Elasticsearch (TCP 9300) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "9300" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn
+ "/"
+ ipProtocol
+ "/security-group-elasticsearch-9300-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.15] Security groups should not allow unrestricted Elasticsearch (TCP 9300) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Elasticsearch (TCP 9300) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_memcached_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.16] Security groups should not allow unrestricted Memcached (UDP 11211) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if (
toPort
and fromPort == "11211"
and ipProtocol == "udp"
and cidrIpRange == "0.0.0.0/0"
):
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-memcached-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.16] Security groups should not allow unrestricted Memcached (UDP 11211) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Memcached (UDP 11211) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "memcached 1.4.2 - Memory Consumption Remote Denial of Service",
"Source": "ExploitDB",
"SourceUrl": "https://www.exploit-db.com/exploits/33850",
},
{
"Category": "BACKDOOR",
"Value": "Ubuntu: USN-4125-1 (CVE-2019-15026): Memcached vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/ubuntu-cve-2019-15026",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif (
toPort
and fromPort == "11211"
and ipProtocol == "udp"
and cidrIpRange != "0.0.0.0/0"
):
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-memcached-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.16] Security groups should not allow unrestricted Memcached (UDP 11211) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Memcached (UDP 11211) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"ThreatIntelIndicators": [
{
"Category": "BACKDOOR",
"Value": "memcached 1.4.2 - Memory Consumption Remote Denial of Service",
"Source": "ExploitDB",
"SourceUrl": "https://www.exploit-db.com/exploits/33850",
},
{
"Category": "BACKDOOR",
"Value": "Ubuntu: USN-4125-1 (CVE-2019-15026): Memcached vulnerability",
"Source": "Rapid7 Vulnerability & Exploit Database",
"SourceUrl": "https://www.rapid7.com/db/vulnerabilities/ubuntu-cve-2019-15026",
},
],
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_redshift_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.17] Security groups should not allow unrestricted Redshift (TCP 5439) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "5439" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-redshift-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.17] Security groups should not allow unrestricted Redshift (TCP 5439) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Redshift (TCP 5439) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "5439" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-redshift-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.17] Security groups should not allow unrestricted Redshift (TCP 5439) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Redshift (TCP 5439) access on "
+ ipProtocol
+ ". Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_documentdb_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.18] Security groups should not allow unrestricted DocumentDB (TCP 27017) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "27017" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-documentdb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.18] Security groups should not allow unrestricted DocumentDB (TCP 27017) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted DocumentDB (TCP 27017) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "27017" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-documentdb-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.18] Security groups should not allow unrestricted DocumentDB (TCP 27017) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted DocumentDB (TCP 27017) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_cassandra_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.19] Security groups should not allow unrestricted Cassandra (TCP 9142) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "9142" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-cassandra-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.19] Security groups should not allow unrestricted Cassandra (TCP 9142) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Cassandra (TCP 9142) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "9142" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-cassandra-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.19] Security groups should not allow unrestricted Cassandra (TCP 9142) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Cassandra (TCP 9142) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_kafka_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.20] Security groups should not allow unrestricted Kafka streams (TCP 9092) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "9092" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-kafka-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.20] Security groups should not allow unrestricted Kafka streams (TCP 9092) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Kafka streams (TCP 9092) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "9092" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-kafka-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.20] Security groups should not allow unrestricted Kafka streams (TCP 9092) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Kafka streams (TCP 9092) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_nfs_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.21] Security groups should not allow unrestricted NFS (TCP 2049) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "2049" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-nfs-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.21] Security groups should not allow unrestricted NFS (TCP 2049) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted NFS (TCP 2049) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "2049" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-nfs-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.21] Security groups should not allow unrestricted NFS (TCP 2049) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted NFS (TCP 2049) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_rsync_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.22] Security groups should not allow unrestricted Rsync (TCP 873) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "873" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-rsync-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.22] Security groups should not allow unrestricted Rsync (TCP 873) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Rsync (TCP 873) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "873" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-rsync-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.22] Security groups should not allow unrestricted Rsync (TCP 873) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Rsync (TCP 873) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_tftp_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.23] Security groups should not allow unrestricted TFTP (UDP 69) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "69" and cidrIpRange == "0.0.0.0/0" and ipProtocol == "udp":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-tftp-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "MEDIUM"},
"Confidence": 99,
"Title": "[SecurityGroup.23] Security groups should not allow unrestricted TFTP (UDP 69) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted TFTP (UDP 69) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "69" and cidrIpRange != "0.0.0.0/0" and ipProtocol == "udp":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-tftp-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.23] Security groups should not allow unrestricted TFTP (UDP 69) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted TFTP (UDP 69) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue
@registry.register_check("ec2")
def security_group_open_docker_check(cache: dict, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:
"""[SecurityGroup.24] Security groups should not allow unrestricted Docker (TCP 2375) access"""
response = describe_security_groups(cache)
mySgs = response["SecurityGroups"]
for secgroup in mySgs:
sgName = str(secgroup["GroupName"])
sgId = str(secgroup["GroupId"])
sgArn = f"arn:{awsPartition}:ec2:{awsRegion}:{awsAccountId}:security-group/{sgId}"
for permissions in secgroup["IpPermissions"]:
try:
fromPort = str(permissions["FromPort"])
except Exception as e:
if str(e) == "'FromPort'":
continue
else:
print(e)
try:
toPort = str(permissions["ToPort"])
except Exception as e:
if str(e) == "'ToPort'":
continue
else:
print(e)
try:
ipProtocol = str(permissions["IpProtocol"])
except Exception as e:
print(e)
ipRanges = permissions["IpRanges"]
for cidrs in ipRanges:
cidrIpRange = str(cidrs["CidrIp"])
iso8601Time = (
datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
)
if toPort and fromPort == "2375" and cidrIpRange == "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-docker-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "HIGH"},
"Confidence": 99,
"Title": "[SecurityGroup.24] Security groups should not allow unrestricted Docker (TCP 2375) access",
"Description": "Security group "
+ sgName
+ " allows unrestricted Docker (TCP 2375) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "FAILED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "NEW"},
"RecordState": "ACTIVE",
}
yield finding
elif toPort and fromPort == "2375" and cidrIpRange != "0.0.0.0/0":
finding = {
"SchemaVersion": "2018-10-08",
"Id": sgArn + "/" + ipProtocol + "/security-group-docker-open-check",
"ProductArn": f"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default",
"GeneratorId": sgArn,
"AwsAccountId": awsAccountId,
"Types": [
"Software and Configuration Checks/AWS Security Best Practices",
"Effects/Data Exposure",
],
"FirstObservedAt": iso8601Time,
"CreatedAt": iso8601Time,
"UpdatedAt": iso8601Time,
"Severity": {"Label": "INFORMATIONAL"},
"Confidence": 99,
"Title": "[SecurityGroup.24] Security groups should not allow unrestricted Docker (TCP 2375) access",
"Description": "Security group "
+ sgName
+ " does not allow unrestricted Docker (TCP 2375) access on "
+ ipProtocol
+ ". Refer to the remediation instructions to remediate this behavior. Your security group should still be audited to ensure any other rules are compliant with organizational or regulatory requirements.",
"Remediation": {
"Recommendation": {
"Text": "For more information on modifying security group rules refer to the Adding, Removing, and Updating Rules section of the Amazon Virtual Private Cloud User Guide",
"Url": "https://docs.aws.amazon.com/vpc/latest/userguide/VPC_SecurityGroups.html#AddRemoveRules",
}
},
"ProductFields": {"Product Name": "ElectricEye"},
"Resources": [
{
"Type": "AwsEc2SecurityGroup",
"Id": sgArn,
"Partition": awsPartition,
"Region": awsRegion,
"Details": {
"AwsEc2SecurityGroup": {"GroupName": sgName, "GroupId": sgId,}
},
}
],
"Compliance": {
"Status": "PASSED",
"RelatedRequirements": [
"NIST CSF PR.AC-3",
"NIST SP 800-53 AC-1",
"NIST SP 800-53 AC-17",
"NIST SP 800-53 AC-19",
"NIST SP 800-53 AC-20",
"NIST SP 800-53 SC-15",
"AICPA TSC CC6.6",
"ISO 27001:2013 A.6.2.1",
"ISO 27001:2013 A.6.2.2",
"ISO 27001:2013 A.11.2.6",
"ISO 27001:2013 A.13.1.1",
"ISO 27001:2013 A.13.2.1",
],
},
"Workflow": {"Status": "RESOLVED"},
"RecordState": "ARCHIVED",
}
yield finding
else:
continue | 55.483966 | 282 | 0.413533 | 16,749 | 226,652 | 5.581766 | 0.026151 | 0.033373 | 0.023104 | 0.028239 | 0.987934 | 0.987731 | 0.987731 | 0.983388 | 0.979474 | 0.974767 | 0 | 0.064484 | 0.491701 | 226,652 | 4,085 | 283 | 55.483966 | 0.747003 | 0.013655 | 0 | 0.86145 | 0 | 0.037877 | 0.356659 | 0.035726 | 0 | 0 | 0 | 0 | 0 | 1 | 0.00623 | false | 0.005981 | 0.000748 | 0 | 0.007476 | 0.017443 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
25dd5514913ac597638a5910a2757191aa413eaf | 1,146 | py | Python | tests/test_sum_of_all_pixels.py | elsandal/pyclesperanto_prototype | 7bda828813b86b44b63d73d5e8f466d9769cded1 | [
"BSD-3-Clause"
] | 64 | 2020-03-18T12:11:22.000Z | 2022-03-31T08:19:18.000Z | tests/test_sum_of_all_pixels.py | elsandal/pyclesperanto_prototype | 7bda828813b86b44b63d73d5e8f466d9769cded1 | [
"BSD-3-Clause"
] | 148 | 2020-05-14T06:14:11.000Z | 2022-03-26T15:02:31.000Z | tests/test_sum_of_all_pixels.py | elsandal/pyclesperanto_prototype | 7bda828813b86b44b63d73d5e8f466d9769cded1 | [
"BSD-3-Clause"
] | 16 | 2020-05-31T00:53:44.000Z | 2022-03-23T13:20:57.000Z | import pyclesperanto_prototype as cle
import numpy as np
def test_sum_of_all_pixels_3d():
test1 = cle.push(np.asarray([
[
[0, 4, 0, 0, 2],
[0, 0, 0, 8, 0],
[3, 0, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 2, 0, 0]
]
]))
s = cle.sum_of_all_pixels(test1)
assert s == 20
def test_sum_of_all_pixels_2d():
test1 = cle.push(np.asarray([
[0, 4, 0, 0, 2],
[0, 0, 0, 8, 0],
[3, 0, 0, 0, 0],
[0, 0, 0, 0, 1],
[0, 0, 2, 0, 0]
]))
s = cle.sum_of_all_pixels(test1)
assert s == 20
def test_sum_of_all_pixels_1d():
test1 = cle.push(np.asarray(
[0, 4, 0, 0, 2]
))
s = cle.sum_of_all_pixels(test1)
assert s == 6
def test_sum_of_all_pixels_1d_y():
test1 = cle.push(np.asarray(
[[0], [4], [0], [0], [2]]
))
s = cle.sum_of_all_pixels(test1)
assert s == 6
def test_sum_of_all_pixels_1d_z():
test1 = cle.push(np.asarray(
[[[0]], [[4]], [[0]], [[0]], [[2]]]
))
s = cle.sum_of_all_pixels(test1)
assert s == 6
| 19.1 | 47 | 0.466841 | 186 | 1,146 | 2.645161 | 0.166667 | 0.109756 | 0.085366 | 0.284553 | 0.890244 | 0.890244 | 0.847561 | 0.843496 | 0.843496 | 0.843496 | 0 | 0.118046 | 0.356894 | 1,146 | 59 | 48 | 19.423729 | 0.549525 | 0 | 0 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119048 | 1 | 0.119048 | false | 0 | 0.047619 | 0 | 0.166667 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d350bb9b62ad027f30840ef90fb50efb1e318f02 | 11,411 | py | Python | test_jostar.py | amirhszd/jostar | 26a6769ca40081383537f5b38bdb1777c104f00b | [
"MIT"
] | 5 | 2021-07-09T17:59:25.000Z | 2021-09-18T21:29:40.000Z | test_jostar.py | amirhszd/jostar | 26a6769ca40081383537f5b38bdb1777c104f00b | [
"MIT"
] | 1 | 2021-09-19T18:38:55.000Z | 2021-09-28T18:15:48.000Z | test_jostar.py | amirhszd/jostar | 26a6769ca40081383537f5b38bdb1777c104f00b | [
"MIT"
] | 2 | 2021-07-12T14:03:16.000Z | 2021-07-23T11:11:38.000Z | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 31 14:57:23 2021
@author: Amirh
"""
from sklearn.datasets import make_classification, make_regression
from sklearn.metrics import r2_score
from jostar.algorithms import ACO, GA, SA, PSO, PlusLMinusR, DE, NSGA2, SBS, SFS
from sklearn.svm import SVR, SVC
from sklearn.base import is_classifier, is_regressor
from sklearn.model_selection import KFold
import warnings
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
import matplotlib
def eval_opt_model_output_regression(opt_model, n_f):
rank_models = ["GA", "SA", "PSO", "ACO", "DE"]
seq_models = ["PlusLMinusR", "SFS", "SBS"]
if opt_model._name_ in rank_models:
assert len(opt_model.best_fits) == 1
assert len(opt_model.best_sol) == n_f
assert is_regressor(opt_model.model_best)
assert len(opt_model.rankings) == 2
assert opt_model.display_results() is not None
elif opt_model._name_ in seq_models:
if opt_model._name_ != "SBS":
assert len(opt_model.best_fits) == n_f
assert len(opt_model.best_sol) == n_f
assert is_regressor(opt_model.model_best)
assert opt_model.display_results() is not None
else:
assert len(opt_model.best_sol) == n_f
assert is_regressor(opt_model.model_best)
assert opt_model.display_results() is not None
else:
result_df = opt_model.res_df
assert isinstance(result_df, pd.core.frame.DataFrame)
assert opt_model.display_results(0) is not None
plt.close('all')
pbar.update(1)
def eval_opt_model_output_classification(opt_model, n_f):
rank_models = ["GA", "SA", "PSO", "ACO", "DE"]
seq_models = ["PlusLMinusR", "SFS", "SBS"]
if opt_model._name_ in rank_models:
assert len(opt_model.best_fits) == 1
assert len(opt_model.best_sol) == n_f
assert is_classifier(opt_model.model_best)
rank_models = ["GA", "SA", "PSO", "ACO", "DE"]
if opt_model._name_ in rank_models:
assert len(opt_model.rankings) == 2
assert opt_model.display_results() is not None
elif opt_model._name_ in seq_models:
if opt_model._name_ != "SBS":
assert len(opt_model.best_fits) == n_f
assert len(opt_model.best_sol) == n_f
assert is_classifier(opt_model.model_best)
assert opt_model.display_results() is not None
else:
assert len(opt_model.best_sol) == n_f
assert is_classifier(opt_model.model_best)
assert opt_model.display_results() is not None
else:
result_df = opt_model.res_df
assert isinstance(result_df, pd.core.frame.DataFrame)
assert opt_model.display_results(0) is not None
plt.close('all')
pbar.update(1)
def test_all_regression():
global pbar
cv = KFold(5)
n_f = 5
x, y = make_regression(100, 10)
model = SVR()
# regression
# with CV
pbar = tqdm(total=18)
ga_opt_model = GA(model, n_f, +1, r2_score, n_gen=1,
n_pop=20, cv=cv, verbose=False)
sa_opt_model = SA(model, n_f, +1, r2_score, n_iter=1,
n_sub_iter=20, cv=cv, verbose=False)
de_opt_model = DE(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=cv, verbose=False)
aco_opt_model = ACO(model, n_f, +1, r2_score, n_iter=1,
n_ant=20, cv=cv, verbose=False)
pso_opt_model = PSO(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=cv, verbose=False)
lrs_opt_model = PlusLMinusR(model, n_f, +1, r2_score, cv=cv, verbose=False)
nsga_opt_model = NSGA2(model, n_f, (+1, -1), r2_score,
n_gen=1, n_pop=20, cv=cv, verbose=False)
sbs_opt_model = SBS(model, n_f, +1, r2_score, cv=cv, verbose=False)
sfs_opt_model = SFS(model, n_f, +1, r2_score, cv=cv, verbose=False)
ga_opt_model.fit(x, y, decor=0.95, scale=True)
sa_opt_model.fit(x, y, decor=0.95, scale=True)
de_opt_model.fit(x, y, decor=0.95, scale=True)
aco_opt_model.fit(x, y, decor=0.95, scale=True)
pso_opt_model.fit(x, y, decor=0.95, scale=True)
lrs_opt_model.fit(x, y, decor=0.95, scale=True)
nsga_opt_model.fit(x, y, decor=0.95, scale=True)
sbs_opt_model.fit(x, y, decor=0.95, scale=True)
sfs_opt_model.fit(x, y, decor=0.95, scale=True)
eval_opt_model_output_regression(ga_opt_model, n_f)
eval_opt_model_output_regression(sa_opt_model, n_f)
eval_opt_model_output_regression(de_opt_model, n_f)
eval_opt_model_output_regression(aco_opt_model, n_f)
eval_opt_model_output_regression(pso_opt_model, n_f)
eval_opt_model_output_regression(lrs_opt_model, n_f)
eval_opt_model_output_regression(nsga_opt_model, n_f)
eval_opt_model_output_regression(sbs_opt_model, n_f)
eval_opt_model_output_regression(sfs_opt_model, n_f)
# with test size
ga_opt_model = GA(model, n_f, +1, r2_score, n_gen=1,
n_pop=20, cv=None, verbose=False)
sa_opt_model = SA(model, n_f, +1, r2_score, n_iter=1,
n_sub_iter=20, cv=None, verbose=False)
de_opt_model = DE(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=None, verbose=False)
aco_opt_model = ACO(model, n_f, +1, r2_score, n_iter=1,
n_ant=20, cv=None, verbose=False)
pso_opt_model = PSO(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=None, verbose=False)
lrs_opt_model = PlusLMinusR(
model, n_f, +1, r2_score, cv=None, verbose=False)
nsga_opt_model = NSGA2(model, n_f, (+1, -1), r2_score,
n_gen=1, n_pop=20, cv=None, verbose=False)
sbs_opt_model = SBS(model, n_f, +1, r2_score, cv=None, verbose=False)
sfs_opt_model = SFS(model, n_f, +1, r2_score, cv=None, verbose=False)
ga_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
sa_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
de_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
aco_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
pso_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
lrs_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
nsga_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
sbs_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
sfs_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
eval_opt_model_output_regression(ga_opt_model, n_f)
eval_opt_model_output_regression(sa_opt_model, n_f)
eval_opt_model_output_regression(de_opt_model, n_f)
eval_opt_model_output_regression(aco_opt_model, n_f)
eval_opt_model_output_regression(pso_opt_model, n_f)
eval_opt_model_output_regression(lrs_opt_model, n_f)
eval_opt_model_output_regression(nsga_opt_model, n_f)
eval_opt_model_output_regression(sbs_opt_model, n_f)
eval_opt_model_output_regression(sfs_opt_model, n_f)
def test_all_classification():
global pbar
cv = KFold(5)
n_f = 5
x, y = make_classification(100, 10)
model = SVC(probability=True)
# regression
# with CV
pbar = tqdm(total=18)
ga_opt_model = GA(model, n_f, +1, r2_score, n_gen=1,
n_pop=20, cv=cv, verbose=False)
sa_opt_model = SA(model, n_f, +1, r2_score, n_iter=1,
n_sub_iter=20, cv=cv, verbose=False)
de_opt_model = DE(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=cv, verbose=False)
aco_opt_model = ACO(model, n_f, +1, r2_score, n_iter=1,
n_ant=20, cv=cv, verbose=False)
pso_opt_model = PSO(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=cv, verbose=False)
lrs_opt_model = PlusLMinusR(model, n_f, +1, r2_score, cv=cv, verbose=False)
nsga_opt_model = NSGA2(model, n_f, (+1, -1), r2_score,
n_gen=1, n_pop=20, cv=cv, verbose=False)
sbs_opt_model = SBS(model, n_f, +1, r2_score, cv=cv, verbose=False)
sfs_opt_model = SFS(model, n_f, +1, r2_score, cv=cv, verbose=False)
ga_opt_model.fit(x, y, decor=0.95, scale=True)
sa_opt_model.fit(x, y, decor=0.95, scale=True)
de_opt_model.fit(x, y, decor=0.95, scale=True)
aco_opt_model.fit(x, y, decor=0.95, scale=True)
pso_opt_model.fit(x, y, decor=0.95, scale=True)
lrs_opt_model.fit(x, y, decor=0.95, scale=True)
nsga_opt_model.fit(x, y, decor=0.95, scale=True)
sbs_opt_model.fit(x, y, decor=0.95, scale=True)
sfs_opt_model.fit(x, y, decor=0.95, scale=True)
eval_opt_model_output_classification(ga_opt_model, n_f)
eval_opt_model_output_classification(sa_opt_model, n_f)
eval_opt_model_output_classification(de_opt_model, n_f)
eval_opt_model_output_classification(aco_opt_model, n_f)
eval_opt_model_output_classification(pso_opt_model, n_f)
eval_opt_model_output_classification(lrs_opt_model, n_f)
eval_opt_model_output_classification(nsga_opt_model, n_f)
eval_opt_model_output_classification(sbs_opt_model, n_f)
eval_opt_model_output_classification(sfs_opt_model, n_f)
# with test size
ga_opt_model = GA(model, n_f, +1, r2_score, n_gen=1,
n_pop=20, cv=None, verbose=False)
sa_opt_model = SA(model, n_f, +1, r2_score, n_iter=1,
n_sub_iter=20, cv=None, verbose=False)
de_opt_model = DE(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=None, verbose=False)
aco_opt_model = ACO(model, n_f, +1, r2_score, n_iter=1,
n_ant=20, cv=None, verbose=False)
pso_opt_model = PSO(model, n_f, +1, r2_score, n_iter=1,
n_pop=20, cv=None, verbose=False)
lrs_opt_model = PlusLMinusR(
model, n_f, +1, r2_score, cv=None, verbose=False)
nsga_opt_model = NSGA2(model, n_f, (+1, -1), r2_score,
n_gen=1, n_pop=20, cv=None, verbose=False)
sbs_opt_model = SBS(model, n_f, +1, r2_score, cv=None, verbose=False)
sfs_opt_model = SFS(model, n_f, +1, r2_score, cv=None, verbose=False)
ga_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
sa_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
de_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
aco_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
pso_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
lrs_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
nsga_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
sbs_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
sfs_opt_model.fit(x, y, decor=0.95, scale=True, test_size=0.3)
eval_opt_model_output_classification(ga_opt_model, n_f)
eval_opt_model_output_classification(sa_opt_model, n_f)
eval_opt_model_output_classification(de_opt_model, n_f)
eval_opt_model_output_classification(aco_opt_model, n_f)
eval_opt_model_output_classification(pso_opt_model, n_f)
eval_opt_model_output_classification(lrs_opt_model, n_f)
eval_opt_model_output_classification(nsga_opt_model, n_f)
eval_opt_model_output_classification(sbs_opt_model, n_f)
eval_opt_model_output_classification(sfs_opt_model, n_f)
if __name__ == '__main__':
test_all_regression()
test_all_classification()
print(" ")
print("All tests passed!")
| 45.102767 | 80 | 0.67023 | 1,976 | 11,411 | 3.526822 | 0.062753 | 0.210073 | 0.074329 | 0.098149 | 0.918066 | 0.909026 | 0.909026 | 0.905869 | 0.905869 | 0.905869 | 0 | 0.037515 | 0.212777 | 11,411 | 252 | 81 | 45.281746 | 0.738283 | 0.012444 | 0 | 0.888889 | 0 | 0 | 0.009596 | 0 | 0 | 0 | 0 | 0 | 0.12963 | 1 | 0.018519 | false | 0.00463 | 0.050926 | 0 | 0.069444 | 0.009259 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d3635ad98eac34644d41591f180b459df7dae6ed | 27 | py | Python | week_0_to_2/inclass/test/sub2/cake_recipes.py | ScriptingBeyondCS/CS-35 | 1ee6135bbb2b8cfa5961007ccafbe77a2356020d | [
"MIT"
] | null | null | null | week_0_to_2/inclass/test/sub2/cake_recipes.py | ScriptingBeyondCS/CS-35 | 1ee6135bbb2b8cfa5961007ccafbe77a2356020d | [
"MIT"
] | null | null | null | week_0_to_2/inclass/test/sub2/cake_recipes.py | ScriptingBeyondCS/CS-35 | 1ee6135bbb2b8cfa5961007ccafbe77a2356020d | [
"MIT"
] | null | null | null | def getFive():
return 5 | 13.5 | 14 | 0.62963 | 4 | 27 | 4.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0.259259 | 27 | 2 | 15 | 13.5 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
d392cb95337f1359637a26c016fd8e9968c765a2 | 18,489 | py | Python | Lil JadenBot/word-rnn.py | oduwa/pyRNN | d6c60724da68b76e2e9cf941431e6aa67ee0f329 | [
"MIT"
] | null | null | null | Lil JadenBot/word-rnn.py | oduwa/pyRNN | d6c60724da68b76e2e9cf941431e6aa67ee0f329 | [
"MIT"
] | null | null | null | Lil JadenBot/word-rnn.py | oduwa/pyRNN | d6c60724da68b76e2e9cf941431e6aa67ee0f329 | [
"MIT"
] | null | null | null | import numpy as np
import nltk
import pickle
import random
import argparse
# parse cli arguments
ap = argparse.ArgumentParser(description="Minimal RNN for text generation")
ap.add_argument('-t', '--train', help = 'Set this flag to train the RNN', action='store_true', default=False)
ap.add_argument('-t2', '--train2', help = 'Set this flag to train the RNN from the last point', action='store_true', default=False)
ap.add_argument('-f', '--file', help = 'The seed file for training or text generation', required=False)
args = vars(ap.parse_args())
filename = args['file']
isTrainingPhase = args['train']
isContinuingTraining = args['train2']
if(args['file']):
filename = args['file']
else:
filename = 'fanfic2.txt'
if(isTrainingPhase):
print("-------- TRAINING --------")
# data I/O
data_file = filename
data = open(data_file, 'r').read()
#print data
data_words = nltk.word_tokenize(data.decode('utf8'))
# Make input into a set to remove duplicates and then make it into a list
words = list(set(data_words))
data_size, vocab_size = len(data_words), len(words)
print 'Data has %d characters, %d unique.' % (data_size,vocab_size)
# Create dictionaries mapping a word to an index and vice versa
word_to_ix = { ch:i for i,ch in enumerate(words) }
ix_to_word = { i:ch for i,ch in enumerate(words) }
# hyperparameters
hidden_size = 100 # size of hidden layer of neurons
seq_length = 25 # number of steps to unroll the RNN for
learning_rate = 1e-1
# initialise model parameters
Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden
Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden
Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output
bh = np.zeros((hidden_size, 1)) # hidden bias
by = np.zeros((vocab_size, 1)) # output bias
# Initialise rnn for serialization
rnn = {}
rnn["hidden_size"] = hidden_size
rnn["seq_length"] = seq_length
rnn["learning_rate"] = learning_rate
def lossFun(inputs, targets, hprev):
"""
inputs,targets are both list of integers.
hprev is Hx1 array of initial hidden state
returns the loss, gradients on model parameters, and last hidden state
"""
# dictionaries for values at each timestep indexed by timestep
# xs[t]-> input_t, hs[t]->hiddenState_t, ys[t]->output_t, ps[t]->probabilities_t,
xs, hs, ys, ps = {}, {}, {}, {}
hs[-1] = np.copy(hprev)
loss = 0
# FORWARD PASS
# Go through each timestep t
for t in xrange(len(inputs)):
# encode input in one-hot encoding (aka 1-of-k encoding)
xs[t] = np.zeros((vocab_size,1))
xs[t][inputs[t]] = 1
# Update our hidden state according to the recurrent function f_W(x_t, h_t-1)
# given as h_t = tanh(W_xh.x_t + W_hh.h_t-1 + bias)
hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state
# Compute our output
ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars
ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars
# Accumulate the loss for this time step as the negative log of the predicted probability.
# Ideally, we would have a probability of 1 for the actual next character. If it is 1, the loss is 0, log(1) = 0.
loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)
# BACKWARD PASS: compute gradients going backwards
dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why) # Initialise gradients of weight matrices
dbh, dby = np.zeros_like(bh), np.zeros_like(by) # Initialise gradients of biases
dhnext = np.zeros_like(hs[0]) # Initialise gradient for next timestep
for t in reversed(xrange(len(inputs))):
dy = np.copy(ps[t])
dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here
dWhy += np.dot(dy, hs[t].T)
dby += dy
dh = np.dot(Why.T, dy) + dhnext # backprop into h
dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity
dbh += dhraw
dWxh += np.dot(dhraw, xs[t].T)
dWhh += np.dot(dhraw, hs[t-1].T)
dhnext = np.dot(Whh.T, dhraw)
for dparam in [dWxh, dWhh, dWhy, dbh, dby]:
np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients
rnn["loss"] = loss
rnn["dWxh"] = dWxh
rnn["dWhh"] = dWhh
rnn["dWhy"] = dWhy
rnn["dbh"] = dbh
rnn["dby"] = dby
rnn["Wxh"] = Wxh
rnn["Whh"] = Whh
rnn["Why"] = Why
rnn["bh"] = bh
rnn["by"] = by
return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]
def sample(h, seed_ix, n):
"""
sample a sequence of integers from the model
h is memory state, seed_ix is seed letter for first time step
"""
# Set up our one-hot encoded input vector based on the seed character.
x = np.zeros((vocab_size, 1))
x[seed_ix] = 1
# Set up an array to keep track of our sequence.
ixes = []
# For each timestep
for t in xrange(n):
# Update hidden state and generate output and apply softmax to get probabilities
h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)
y = np.dot(Why, h) + by
p = np.exp(y) / np.sum(np.exp(y))
# Select the index of the character with the highest probability
ix = np.random.choice(range(vocab_size), p=p.ravel())
# Create new one-hot encoding input for selected character
x = np.zeros((vocab_size, 1))
x[ix] = 1
ixes.append(ix)
return ixes
# TRAINING
# n is the number of training iterations we've done. p is the index into our training data for where we are now.
n, p = 0, 0
# Set up memory variables for the Adagrad algorithm
mWxh, mWhh, mWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)
mbh, mby = np.zeros_like(bh), np.zeros_like(by) # memory variables for Adagrad
smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0
# Trraining loop
try:
while True:
# prepare inputs (we're sweeping from left to right in steps seq_length long)
if p+seq_length+1 >= len(data_words) or n == 0:
hprev = np.zeros((hidden_size,1)) # reset RNN memory
p = 0 # go from start of data
# Fetch inputs and targets of length seq_length at a time
inputs = [word_to_ix[w] for w in data_words[p:p+seq_length]]
# we're predicting the next character so the target for data[i] is data[i+1]
targets = [word_to_ix[w] for w in data_words[p+1:p+seq_length+1]]
# print to the terminal a sample every 100 training steps so we can see how its doing
if n % 100 == 0:
sample_ix = sample(hprev, inputs[0], 200)
txt = ' '.join(ix_to_word[ix] for ix in sample_ix)
print '----\n %s \n----' % (txt, )
# forward seq_length characters through the net and fetch gradient
loss, dWxh, dWhh, dWhy, dbh, dby, hprev = lossFun(inputs, targets, hprev)
smooth_loss = smooth_loss * 0.999 + loss * 0.001 # Adagrad stuff
if n % 100 == 0: print 'iter %d, loss: %f' % (n, smooth_loss) # print progress
# perform parameter update with Adagrad
for param, dparam, mem in zip([Wxh, Whh, Why, bh, by],
[dWxh, dWhh, dWhy, dbh, dby],
[mWxh, mWhh, mWhy, mbh, mby]):
mem += dparam * dparam
param += -learning_rate * dparam / np.sqrt(mem + 1e-8) # adagrad update
p += seq_length # move data pointer tonext chunk of size seq_length
n += 1 # iteration counter
rnn["h"] = hprev
rnn["nuber_of_iterations"] = n
rnn["position_in_data"] = p
except KeyboardInterrupt:
#Serialize hidden state to use for prediction later
f = open('rnn.ser', 'wb')
pickle.dump(rnn, f)
f.close()
exit()
elif(isContinuingTraining):
print("-------- RESUMING TRAINING FROM SERIALIZED POINT --------")
# Load serialized rnn parameters
f = open("rnn.ser", 'rb')
rnn = pickle.load(f)
f.close()
# data I/O
data_file = filename
data = open(data_file, 'r').read()
#print data
data_words = nltk.word_tokenize(data.decode('utf8'))
# Make input into a set to remove duplicates and then make it into a list
words = list(set(data_words))
data_size, vocab_size = len(data_words), len(words)
print 'Data has %d characters, %d unique.' % (data_size,vocab_size)
# Create dictionaries mapping a word to an index and vice versa
word_to_ix = { ch:i for i,ch in enumerate(words) }
ix_to_word = { i:ch for i,ch in enumerate(words) }
# hyperparameters
hidden_size = 100 # size of hidden layer of neurons
seq_length = 25 # number of steps to unroll the RNN for
learning_rate = 1e-1
# Model weights and biases
Wxh = rnn["Wxh"]
Whh = rnn["Whh"]
Why = rnn["Why"]
bh = rnn["bh"]
by = rnn["by"]
hprev = rnn["h"]
# Load rnn parameters
hidden_size = rnn["hidden_size"]
seq_length = rnn["seq_length"]
learning_rate = rnn["learning_rate"]
def lossFun(inputs, targets, hprev):
"""
inputs,targets are both list of integers.
hprev is Hx1 array of initial hidden state
returns the loss, gradients on model parameters, and last hidden state
"""
# dictionaries for values at each timestep indexed by timestep
# xs[t]-> input_t, hs[t]->hiddenState_t, ys[t]->output_t, ps[t]->probabilities_t,
xs, hs, ys, ps = {}, {}, {}, {}
hs[-1] = np.copy(hprev)
loss = 0
# FORWARD PASS
# Go through each timestep t
for t in xrange(len(inputs)):
# encode input in one-hot encoding (aka 1-of-k encoding)
xs[t] = np.zeros((vocab_size,1))
xs[t][inputs[t]] = 1
# Update our hidden state according to the recurrent function f_W(x_t, h_t-1)
# given as h_t = tanh(W_xh.x_t + W_hh.h_t-1 + bias)
hs[t] = np.tanh(np.dot(Wxh, xs[t]) + np.dot(Whh, hs[t-1]) + bh) # hidden state
# Compute our output
ys[t] = np.dot(Why, hs[t]) + by # unnormalized log probabilities for next chars
ps[t] = np.exp(ys[t]) / np.sum(np.exp(ys[t])) # probabilities for next chars
# Accumulate the loss for this time step as the negative log of the predicted probability.
# Ideally, we would have a probability of 1 for the actual next character. If it is 1, the loss is 0, log(1) = 0.
loss += -np.log(ps[t][targets[t],0]) # softmax (cross-entropy loss)
# BACKWARD PASS: compute gradients going backwards
dWxh, dWhh, dWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why) # Initialise gradients of weight matrices
dbh, dby = np.zeros_like(bh), np.zeros_like(by) # Initialise gradients of biases
dhnext = np.zeros_like(hs[0]) # Initialise gradient for next timestep
for t in reversed(xrange(len(inputs))):
dy = np.copy(ps[t])
dy[targets[t]] -= 1 # backprop into y. see http://cs231n.github.io/neural-networks-case-study/#grad if confused here
dWhy += np.dot(dy, hs[t].T)
dby += dy
dh = np.dot(Why.T, dy) + dhnext # backprop into h
dhraw = (1 - hs[t] * hs[t]) * dh # backprop through tanh nonlinearity
dbh += dhraw
dWxh += np.dot(dhraw, xs[t].T)
dWhh += np.dot(dhraw, hs[t-1].T)
dhnext = np.dot(Whh.T, dhraw)
for dparam in [dWxh, dWhh, dWhy, dbh, dby]:
np.clip(dparam, -5, 5, out=dparam) # clip to mitigate exploding gradients
rnn["loss"] = loss
rnn["dWxh"] = dWxh
rnn["dWhh"] = dWhh
rnn["dWhy"] = dWhy
rnn["dbh"] = dbh
rnn["dby"] = dby
rnn["Wxh"] = Wxh
rnn["Whh"] = Whh
rnn["Why"] = Why
rnn["bh"] = bh
rnn["by"] = by
return loss, dWxh, dWhh, dWhy, dbh, dby, hs[len(inputs)-1]
def sample(h, seed_ix, n):
"""
sample a sequence of integers from the model
h is memory state, seed_ix is seed letter for first time step
"""
# Set up our one-hot encoded input vector based on the seed character.
x = np.zeros((vocab_size, 1))
x[seed_ix] = 1
# Set up an array to keep track of our sequence.
ixes = []
# For each timestep
for t in xrange(n):
# Update hidden state and generate output and apply softmax to get probabilities
h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)
y = np.dot(Why, h) + by
p = np.exp(y) / np.sum(np.exp(y))
# Select the index of the character with the highest probability
ix = np.random.choice(range(vocab_size), p=p.ravel())
# Create new one-hot encoding input for selected character
x = np.zeros((vocab_size, 1))
x[ix] = 1
ixes.append(ix)
return ixes
# TRAINING
# n is the number of training iterations we've done. p is the index into our training data for where we are now.
n = rnn["nuber_of_iterations"]
p = rnn["position_in_data"]
# Set up memory variables for the Adagrad algorithm
mWxh, mWhh, mWhy = np.zeros_like(Wxh), np.zeros_like(Whh), np.zeros_like(Why)
mbh, mby = np.zeros_like(bh), np.zeros_like(by) # memory variables for Adagrad
smooth_loss = -np.log(1.0/vocab_size)*seq_length # loss at iteration 0
# Training loop
print "ITERTIONS: %d , DATA POS: %d" % (n,p)
try:
while True:
# prepare inputs (we're sweeping from left to right in steps seq_length long)
if p+seq_length+1 >= len(data_words) or n == 0:
hprev = np.zeros((hidden_size,1)) # reset RNN memory
p = 0 # go from start of data
# Fetch inputs and targets of length seq_length at a time
inputs = [word_to_ix[w] for w in data_words[p:p+seq_length]]
# we're predicting the next character so the target for data[i] is data[i+1]
targets = [word_to_ix[w] for w in data_words[p+1:p+seq_length+1]]
# print to the terminal a sample every 100 training steps so we can see how its doing
if n % 100 == 0:
sample_ix = sample(hprev, inputs[0], 200)
txt = ' '.join(ix_to_word[ix] for ix in sample_ix)
print '----\n %s \n----' % (txt, )
# forward seq_length characters through the net and fetch gradient
loss, dWxh, dWhh, dWhy, dbh, dby, hprev = lossFun(inputs, targets, hprev)
smooth_loss = smooth_loss * 0.999 + loss * 0.001 # Adagrad stuff
if n % 100 == 0: print 'iter %d, loss: %f' % (n, smooth_loss) # print progress
# perform parameter update with Adagrad
for param, dparam, mem in zip([Wxh, Whh, Why, bh, by],
[dWxh, dWhh, dWhy, dbh, dby],
[mWxh, mWhh, mWhy, mbh, mby]):
mem += dparam * dparam
param += -learning_rate * dparam / np.sqrt(mem + 1e-8) # adagrad update
p += seq_length # move data pointer tonext chunk of size seq_length
n += 1 # iteration counter
rnn["h"] = hprev
rnn["nuber_of_iterations"] = n
rnn["position_in_data"] = p
except KeyboardInterrupt:
#Serialize hidden state to use for prediction later
f = open('rnn.ser', 'wb')
pickle.dump(rnn, f)
f.close()
exit()
else:
# data I/O
data_file = filename
data = open(data_file, 'r').read()
#print data
data_words = nltk.word_tokenize(data.decode('utf8'))
# Make input into a set to remove duplicates and then make it into a list
words = list(set(data_words))
data_size, vocab_size = len(data_words), len(words)
print 'Data has %d characters, %d unique.' % (data_size,vocab_size)
# Load serialized rnn parameters
f = open("rnn_jaden.ser", 'rb')
rnn = pickle.load(f)
f.close()
# model hyperparameters
hidden_size = rnn["hidden_size"]
seq_length = rnn["seq_length"]
seq_length = rnn["learning_rate"]
# Create dictionaries mapping a word to an index and vice versa
word_to_ix = { ch:i for i,ch in enumerate(words) }
ix_to_word = { i:ch for i,ch in enumerate(words) }
# Model weights and biases
Wxh = rnn["Wxh"]
Whh = rnn["Whh"]
Why = rnn["Why"]
bh = rnn["bh"]
by = rnn["by"]
h = rnn["h"]
def sample(h, seed_ix, n):
"""
sample a sequence of integers from the model
h is memory state, seed_ix is seed letter for first time step
"""
# Set up our one-hot encoded input vector based on the seed character.
x = np.zeros((vocab_size, 1))
x[seed_ix] = 1
# Set up an array to keep track of our sequence.
ixes = []
# For each timestep
for t in xrange(n):
# Update hidden state and generate output and apply softmax to get probabilities
h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)
y = np.dot(Why, h) + by
p = np.exp(y) / np.sum(np.exp(y))
# Select the index of the character with the highest probability
ix = np.random.choice(range(vocab_size), p=p.ravel())
# Create new one-hot encoding input for selected character
x = np.zeros((vocab_size, 1))
x[ix] = 1
ixes.append(ix)
return ixes
# Sample and generate words
sample_ix = sample(h, word_to_ix[random.choice(data_words)], 200)
txt = ' '.join(ix_to_word[ix] for ix in sample_ix)
print '----\n %s \n----' % (txt, )
| 41.362416 | 131 | 0.584456 | 2,737 | 18,489 | 3.867738 | 0.126416 | 0.022483 | 0.02286 | 0.013603 | 0.917249 | 0.909975 | 0.909975 | 0.89864 | 0.875401 | 0.875401 | 0 | 0.013204 | 0.299529 | 18,489 | 446 | 132 | 41.455157 | 0.804185 | 0.308237 | 0 | 0.860806 | 0 | 0 | 0.075962 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.018315 | null | null | 0.040293 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d395e1bc5e809649e705f3337f40fd55712924b5 | 8,433 | py | Python | prepare.py | Prettyfinger/Twostream_reID | 8e340e0c03bd248b04ff1b48398ca99b6aeaa508 | [
"MIT"
] | 6 | 2019-05-17T03:40:59.000Z | 2021-04-09T11:01:54.000Z | prepare.py | Prettyfinger/Twostream_reID | 8e340e0c03bd248b04ff1b48398ca99b6aeaa508 | [
"MIT"
] | null | null | null | prepare.py | Prettyfinger/Twostream_reID | 8e340e0c03bd248b04ff1b48398ca99b6aeaa508 | [
"MIT"
] | 2 | 2019-09-12T06:19:05.000Z | 2020-06-12T11:34:12.000Z | #*************RGB***********************
# import os
# from shutil import copyfile
#
# # You only need to change this line to your dataset download path
# download_path = '../Market'
#
# if not os.path.isdir(download_path):
# print('please change the download_path')
#
# save_path = download_path + '/pytorch'
# if not os.path.isdir(save_path):
# os.mkdir(save_path)
# #-----------------------------------------
# #query
# query_path = download_path + '/query'
# query_save_path = download_path + '/pytorch/query'
# if not os.path.isdir(query_save_path):
# os.mkdir(query_save_path)
#
# for root, dirs, files in os.walk(query_path, topdown=True):
# for name in files:
# if not name[-3:]=='jpg':
# continue
# ID = name.split('_')
# src_path = query_path + '/' + name
# dst_path = query_save_path + '/' + ID[0]
# if not os.path.isdir(dst_path):
# os.mkdir(dst_path)
# copyfile(src_path, dst_path + '/' + name)
#
# #-----------------------------------------
# #multi-query
# query_path = download_path + '/gt_bbox'
# # for dukemtmc-reid, we do not need multi-query
# if os.path.isdir(query_path):
# query_save_path = download_path + '/pytorch/multi-query'
# if not os.path.isdir(query_save_path):
# os.mkdir(query_save_path)
#
# for root, dirs, files in os.walk(query_path, topdown=True):
# for name in files:
# if not name[-3:]=='jpg':
# continue
# ID = name.split('_')
# src_path = query_path + '/' + name
# dst_path = query_save_path + '/' + ID[0]
# if not os.path.isdir(dst_path):
# os.mkdir(dst_path)
# copyfile(src_path, dst_path + '/' + name)
#
# #-----------------------------------------
# #gallery
# gallery_path = download_path + '/bounding_box_test'
# gallery_save_path = download_path + '/pytorch/gallery'
# if not os.path.isdir(gallery_save_path):
# os.mkdir(gallery_save_path)
#
# for root, dirs, files in os.walk(gallery_path, topdown=True):
# for name in files:
# if not name[-3:]=='jpg':
# continue
# ID = name.split('_')
# src_path = gallery_path + '/' + name
# dst_path = gallery_save_path + '/' + ID[0]
# if not os.path.isdir(dst_path):
# os.mkdir(dst_path)
# copyfile(src_path, dst_path + '/' + name)
#
# #---------------------------------------
# #train_all
# train_path = download_path + '/bounding_box_train'
# train_save_path = download_path + '/pytorch/train_all'
# if not os.path.isdir(train_save_path):
# os.mkdir(train_save_path)
#
# for root, dirs, files in os.walk(train_path, topdown=True):
# for name in files:
# if not name[-3:]=='jpg':
# continue
# ID = name.split('_')
# src_path = train_path + '/' + name
# dst_path = train_save_path + '/' + ID[0]
# if not os.path.isdir(dst_path):
# os.mkdir(dst_path)
# copyfile(src_path, dst_path + '/' + name)
#
#
# #---------------------------------------
# #train_val
# train_path = download_path + '/bounding_box_train'
# train_save_path = download_path + '/pytorch/train'
# val_save_path = download_path + '/pytorch/val'
# if not os.path.isdir(train_save_path):
# os.mkdir(train_save_path)
# os.mkdir(val_save_path)
#
# for root, dirs, files in os.walk(train_path, topdown=True):
# for name in files:
# if not name[-3:]=='jpg':
# continue
# ID = name.split('_')
# src_path = train_path + '/' + name
# dst_path = train_save_path + '/' + ID[0]
# if not os.path.isdir(dst_path):
# os.mkdir(dst_path)
# dst_path = val_save_path + '/' + ID[0] #first image is used as val image
# os.mkdir(dst_path)
# copyfile(src_path, dst_path + '/' + name)
#****************RGB TO HSV****************************
import os
from shutil import copyfile
import cv2
# You only need to change this line to your dataset download path
download_path = '/home/mcii216/fmx/dataset_ReID/Market-1501'
if not os.path.isdir(download_path):
print('please change the download_path')
save_path = download_path + '/pytorchsv'
if not os.path.isdir(save_path):
os.mkdir(save_path)
#-----------------------------------------
# query
query_path = download_path + '/query'
query_save_path = download_path + '/pytorchsv/query'
if not os.path.isdir(query_save_path):
os.mkdir(query_save_path)
for root, dirs, files in os.walk(query_path, topdown=True):
for name in files:
if not name[-3:]=='jpg':
continue
ID = name.split('_')
src_path = query_path + '/' + name
img = cv2.imread(src_path)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
sv_path = query_save_path + '/' + ID[0] + '/' + name
dst_path = query_save_path + '/' + ID[0]
if not os.path.isdir(dst_path):
os.mkdir(dst_path)
cv2.imwrite(sv_path, img_hsv)
#-----------------------------------------
#multi-query
query_path = download_path + '/gt_bbox'
# for dukemtmc-reid, we do not need multi-query
if os.path.isdir(query_path):
query_save_path = download_path + '/pytorchsv/multi-query'
if not os.path.isdir(query_save_path):
os.mkdir(query_save_path)
for root, dirs, files in os.walk(query_path, topdown=True):
for name in files:
if not name[-3:]=='jpg':
continue
ID = name.split('_')
src_path = query_path + '/' + name
img = cv2.imread(src_path)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
sv_path = query_save_path + '/' + ID[0] + '/' + name
dst_path = query_save_path + '/' + ID[0]
if not os.path.isdir(dst_path):
os.mkdir(dst_path)
cv2.imwrite(sv_path, img_hsv)
#-----------------------------------------
#gallery
gallery_path = download_path + '/bounding_box_test'
gallery_save_path = download_path + '/pytorchsv/gallery'
if not os.path.isdir(gallery_save_path):
os.mkdir(gallery_save_path)
for root, dirs, files in os.walk(gallery_path, topdown=True):
for name in files:
if not name[-3:]=='jpg':
continue
ID = name.split('_')
src_path = gallery_path + '/' + name
img = cv2.imread(src_path)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
sv_path = gallery_save_path + '/' + ID[0] + '/' + name
dst_path = gallery_save_path + '/' + ID[0]
if not os.path.isdir(dst_path):
os.mkdir(dst_path)
cv2.imwrite(sv_path, img_hsv)
#---------------------------------------
#train_all
train_path = download_path + '/bounding_box_train'
train_save_path = download_path + '/pytorchsv/train_all'
if not os.path.isdir(train_save_path):
os.mkdir(train_save_path)
for root, dirs, files in os.walk(train_path, topdown=True):
for name in files:
if not name[-3:]=='jpg':
continue
ID = name.split('_')
src_path = train_path + '/' + name
img = cv2.imread(src_path)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
sv_path = train_save_path + '/' + ID[0] + '/' + name
dst_path = train_save_path + '/' + ID[0]
if not os.path.isdir(dst_path):
os.mkdir(dst_path)
cv2.imwrite(sv_path, img_hsv)
#---------------------------------------
#train_val
train_path = download_path + '/bounding_box_train'
train_save_path = download_path + '/pytorchsv/train'
val_save_path = download_path + '/pytorchsv/val'
if not os.path.isdir(train_save_path):
os.mkdir(train_save_path)
os.mkdir(val_save_path)
for root, dirs, files in os.walk(train_path, topdown=True):
for name in files:
if not name[-3:]=='jpg':
continue
ID = name.split('_')
src_path = train_path + '/' + name
img = cv2.imread(src_path)
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# sv_path = train_save_path + '/' + ID[0] + '/' + name
dst_path = train_save_path + '/' + ID[0]
if not os.path.isdir(dst_path):
os.mkdir(dst_path)
dst_path = val_save_path + '/' + ID[0]
os.mkdir(dst_path)
cv2.imwrite(dst_path + '/' + name, img_hsv)
cv2.imwrite(dst_path + '/' + name, img_hsv)
| 34.847107 | 87 | 0.569667 | 1,122 | 8,433 | 4.025847 | 0.069519 | 0.100952 | 0.092097 | 0.058446 | 0.980739 | 0.970113 | 0.934027 | 0.919637 | 0.919637 | 0.919637 | 0 | 0.009561 | 0.243448 | 8,433 | 241 | 88 | 34.991701 | 0.698433 | 0.490454 | 0 | 0.717172 | 0 | 0 | 0.07215 | 0.015392 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.030303 | 0 | 0.030303 | 0.010101 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
6cb995d209b91ed26bb29c4df78d7a3613962375 | 65,027 | py | Python | subPrograms/Hesap_Makinesi/Ui_calculator.py | birhann/Student-Tracking-System_Ogrenci-Takip-Sistemi | 0e01add14fa207861fbb573df6977c6701632cf5 | [
"Unlicense"
] | 2 | 2021-01-09T12:53:54.000Z | 2021-08-12T18:37:17.000Z | subPrograms/Hesap_Makinesi/Ui_calculator.py | birhann/Student-Tracking-System_Ogrenci-Takip-Sistemi | 0e01add14fa207861fbb573df6977c6701632cf5 | [
"Unlicense"
] | null | null | null | subPrograms/Hesap_Makinesi/Ui_calculator.py | birhann/Student-Tracking-System_Ogrenci-Takip-Sistemi | 0e01add14fa207861fbb573df6977c6701632cf5 | [
"Unlicense"
] | 3 | 2021-01-26T06:02:02.000Z | 2021-06-20T15:52:05.000Z | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'c:\Users\Lenovo\Desktop\Python\Programlar\Hesap_Makinesi\calculator.ui'
#
# Created by: PyQt5 UI code generator 5.11.3
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Form(object):
def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(429, 362)
Form.setMinimumSize(QtCore.QSize(429, 362))
Form.setMaximumSize(QtCore.QSize(429, 500))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 62, 211))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 255, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(189, 247, 244))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(88, 160, 155))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(121, 128, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 244, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(215, 202, 98))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(91, 79, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.HighlightedText, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 247, 244))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(176, 255, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 62, 211))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 255, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(189, 247, 244))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(88, 160, 155))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(121, 128, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 244, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(215, 202, 98))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(91, 79, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.HighlightedText, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 247, 244))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(176, 255, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 62, 211))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 255, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(189, 247, 244))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(88, 160, 155))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(121, 128, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 120, 215))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(91, 79, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.HighlightedText, brush)
brush = QtGui.QBrush(QtGui.QColor(132, 240, 233))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(176, 255, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush)
Form.setPalette(palette)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap("Calculator_icon.svg.png"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
Form.setWindowIcon(icon)
self.verticalLayout = QtWidgets.QVBoxLayout(Form)
self.verticalLayout.setObjectName("verticalLayout")
self.operation_history = QtWidgets.QLabel(Form)
self.operation_history.setMinimumSize(QtCore.QSize(0, 25))
self.operation_history.setMaximumSize(QtCore.QSize(16777215, 10))
font = QtGui.QFont()
font.setFamily("MingLiU-ExtB")
font.setPointSize(12)
self.operation_history.setFont(font)
self.operation_history.setText("")
self.operation_history.setObjectName("operation_history")
self.verticalLayout.addWidget(self.operation_history)
self.writing_area = QtWidgets.QTextEdit(Form)
self.writing_area.setEnabled(False)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(50)
sizePolicy.setHeightForWidth(self.writing_area.sizePolicy().hasHeightForWidth())
self.writing_area.setSizePolicy(sizePolicy)
self.writing_area.setMinimumSize(QtCore.QSize(0, 0))
self.writing_area.setMaximumSize(QtCore.QSize(16777215, 85))
self.writing_area.setSizeIncrement(QtCore.QSize(0, 0))
self.writing_area.setBaseSize(QtCore.QSize(0, 0))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(55, 57, 55))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(55, 57, 55))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 42, 42))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(226, 249, 249))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 120, 215))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
self.writing_area.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(37)
font.setKerning(True)
self.writing_area.setFont(font)
self.writing_area.viewport().setProperty("cursor", QtGui.QCursor(QtCore.Qt.OpenHandCursor))
self.writing_area.setMouseTracking(True)
self.writing_area.setFocusPolicy(QtCore.Qt.NoFocus)
self.writing_area.setAccessibleName("")
self.writing_area.setStyleSheet("")
self.writing_area.setObjectName("writing_area")
self.verticalLayout.addWidget(self.writing_area)
self.verticalLayout_5 = QtWidgets.QVBoxLayout()
self.verticalLayout_5.setSpacing(7)
self.verticalLayout_5.setObjectName("verticalLayout_5")
self.horizontalLayout = QtWidgets.QHBoxLayout()
self.horizontalLayout.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)
self.horizontalLayout.setContentsMargins(-1, -1, -1, 0)
self.horizontalLayout.setSpacing(5)
self.horizontalLayout.setObjectName("horizontalLayout")
self.verticalLayout_2 = QtWidgets.QVBoxLayout()
self.verticalLayout_2.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)
self.verticalLayout_2.setSpacing(0)
self.verticalLayout_2.setObjectName("verticalLayout_2")
self.clear_line = QtWidgets.QPushButton(Form)
self.clear_line.setMinimumSize(QtCore.QSize(0, 0))
self.clear_line.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.clear_line.setFont(font)
self.clear_line.setIconSize(QtCore.QSize(20, 20))
self.clear_line.setShortcut("")
self.clear_line.setCheckable(False)
self.clear_line.setObjectName("clear_line")
self.verticalLayout_2.addWidget(self.clear_line)
self.seven = QtWidgets.QPushButton(Form)
self.seven.setMinimumSize(QtCore.QSize(0, 0))
self.seven.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
self.seven.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.seven.setFont(font)
self.seven.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.seven.setIconSize(QtCore.QSize(20, 20))
self.seven.setShortcut("")
self.seven.setCheckable(False)
self.seven.setObjectName("seven")
self.verticalLayout_2.addWidget(self.seven)
self.four = QtWidgets.QPushButton(Form)
self.four.setMinimumSize(QtCore.QSize(0, 0))
self.four.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.four.setFont(font)
self.four.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.four.setIconSize(QtCore.QSize(20, 20))
self.four.setShortcut("")
self.four.setCheckable(False)
self.four.setObjectName("four")
self.verticalLayout_2.addWidget(self.four)
self.one = QtWidgets.QPushButton(Form)
self.one.setMinimumSize(QtCore.QSize(0, 0))
self.one.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.one.setFont(font)
self.one.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.one.setIconSize(QtCore.QSize(20, 20))
self.one.setShortcut("")
self.one.setCheckable(False)
self.one.setObjectName("one")
self.verticalLayout_2.addWidget(self.one)
self.arti_eksi = QtWidgets.QPushButton(Form)
self.arti_eksi.setMinimumSize(QtCore.QSize(0, 0))
self.arti_eksi.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.arti_eksi.setFont(font)
self.arti_eksi.setIconSize(QtCore.QSize(20, 20))
self.arti_eksi.setShortcut("")
self.arti_eksi.setCheckable(False)
self.arti_eksi.setObjectName("arti_eksi")
self.verticalLayout_2.addWidget(self.arti_eksi)
self.horizontalLayout.addLayout(self.verticalLayout_2)
self.verticalLayout_3 = QtWidgets.QVBoxLayout()
self.verticalLayout_3.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)
self.verticalLayout_3.setSpacing(0)
self.verticalLayout_3.setObjectName("verticalLayout_3")
self.clear_all = QtWidgets.QPushButton(Form)
self.clear_all.setMinimumSize(QtCore.QSize(0, 0))
self.clear_all.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(101, 196, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(148, 245, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(101, 196, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(148, 245, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(101, 196, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(221, 243, 244))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
self.clear_all.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.clear_all.setFont(font)
self.clear_all.setIconSize(QtCore.QSize(20, 20))
self.clear_all.setShortcut("")
self.clear_all.setCheckable(False)
self.clear_all.setDefault(False)
self.clear_all.setObjectName("clear_all")
self.verticalLayout_3.addWidget(self.clear_all)
self.eight = QtWidgets.QPushButton(Form)
self.eight.setMinimumSize(QtCore.QSize(0, 0))
self.eight.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.eight.setFont(font)
self.eight.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.eight.setIconSize(QtCore.QSize(20, 20))
self.eight.setShortcut("")
self.eight.setCheckable(False)
self.eight.setObjectName("eight")
self.verticalLayout_3.addWidget(self.eight)
self.five = QtWidgets.QPushButton(Form)
self.five.setMinimumSize(QtCore.QSize(0, 0))
self.five.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 52, 52))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(167, 250, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 52, 52))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(167, 250, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(167, 250, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
self.five.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.five.setFont(font)
self.five.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.five.setIconSize(QtCore.QSize(20, 20))
self.five.setShortcut("")
self.five.setCheckable(False)
self.five.setObjectName("five")
self.verticalLayout_3.addWidget(self.five)
self.two = QtWidgets.QPushButton(Form)
self.two.setMinimumSize(QtCore.QSize(0, 0))
self.two.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
self.two.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.two.setFont(font)
self.two.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.two.setIconSize(QtCore.QSize(20, 20))
self.two.setShortcut("")
self.two.setCheckable(False)
self.two.setObjectName("two")
self.verticalLayout_3.addWidget(self.two)
self.zero = QtWidgets.QPushButton(Form)
self.zero.setMinimumSize(QtCore.QSize(0, 0))
self.zero.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.zero.setFont(font)
self.zero.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.zero.setIconSize(QtCore.QSize(20, 20))
self.zero.setShortcut("")
self.zero.setCheckable(False)
self.zero.setObjectName("zero")
self.verticalLayout_3.addWidget(self.zero)
self.horizontalLayout.addLayout(self.verticalLayout_3)
self.verticalLayout_4 = QtWidgets.QVBoxLayout()
self.verticalLayout_4.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)
self.verticalLayout_4.setSpacing(0)
self.verticalLayout_4.setObjectName("verticalLayout_4")
self.back = QtWidgets.QPushButton(Form)
self.back.setMinimumSize(QtCore.QSize(0, 0))
self.back.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(True)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(75)
font.setStrikeOut(False)
font.setKerning(True)
self.back.setFont(font)
self.back.setIconSize(QtCore.QSize(20, 20))
self.back.setShortcut("")
self.back.setCheckable(False)
self.back.setObjectName("back")
self.verticalLayout_4.addWidget(self.back)
self.nine = QtWidgets.QPushButton(Form)
self.nine.setMinimumSize(QtCore.QSize(0, 0))
self.nine.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(66, 120, 116))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
self.nine.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.nine.setFont(font)
self.nine.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.nine.setIconSize(QtCore.QSize(20, 20))
self.nine.setShortcut("")
self.nine.setCheckable(False)
self.nine.setObjectName("nine")
self.verticalLayout_4.addWidget(self.nine)
self.six = QtWidgets.QPushButton(Form)
self.six.setMinimumSize(QtCore.QSize(0, 0))
self.six.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(213, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(149, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(56, 170, 170))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(213, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(149, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(56, 170, 170))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(170, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(213, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(149, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(56, 170, 170))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(42, 127, 127))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(85, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 220))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush)
self.six.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.six.setFont(font)
self.six.setLayoutDirection(QtCore.Qt.LeftToRight)
self.six.setAutoFillBackground(False)
self.six.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.six.setIconSize(QtCore.QSize(20, 20))
self.six.setShortcut("")
self.six.setCheckable(False)
self.six.setObjectName("six")
self.verticalLayout_4.addWidget(self.six)
self.three = QtWidgets.QPushButton(Form)
self.three.setMinimumSize(QtCore.QSize(0, 0))
self.three.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 70, 70))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 31, 31))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(81, 111, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(160, 52, 52))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 25, 25))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 255))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.HighlightedText, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 27, 27))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(209, 222, 92))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.NoRole, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 47, 47))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 70, 70))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 31, 31))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(81, 111, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(160, 52, 52))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 25, 25))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.HighlightedText, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 27, 27))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(209, 222, 92))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.NoRole, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 47, 47))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipText, brush)
brush = QtGui.QBrush(QtGui.QColor(120, 115, 43))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.WindowText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Button, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 70, 70))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Light, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 31, 31))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Midlight, brush)
brush = QtGui.QBrush(QtGui.QColor(112, 120, 36))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Dark, brush)
brush = QtGui.QBrush(QtGui.QColor(160, 52, 52))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Mid, brush)
brush = QtGui.QBrush(QtGui.QColor(81, 111, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Text, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 25, 25))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.BrightText, brush)
brush = QtGui.QBrush(QtGui.QColor(81, 111, 120))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ButtonText, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Base, brush)
brush = QtGui.QBrush(QtGui.QColor(240, 240, 240))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Window, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Shadow, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 255, 161))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.Highlight, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 24, 8))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.HighlightedText, brush)
brush = QtGui.QBrush(QtGui.QColor(247, 27, 27))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.AlternateBase, brush)
brush = QtGui.QBrush(QtGui.QColor(209, 222, 92))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.NoRole, brush)
brush = QtGui.QBrush(QtGui.QColor(193, 47, 47))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(0, 0, 0))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipText, brush)
self.three.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.three.setFont(font)
self.three.setStyleSheet("background-color: rgb(240, 240, 240);\n"
"selection-background-color: rgb(255, 255, 161);")
self.three.setIconSize(QtCore.QSize(20, 20))
self.three.setShortcut("")
self.three.setCheckable(False)
self.three.setObjectName("three")
self.verticalLayout_4.addWidget(self.three)
self.virgul = QtWidgets.QPushButton(Form)
self.virgul.setMinimumSize(QtCore.QSize(0, 0))
self.virgul.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.virgul.setFont(font)
self.virgul.setIconSize(QtCore.QSize(20, 20))
self.virgul.setShortcut("")
self.virgul.setCheckable(False)
self.virgul.setObjectName("virgul")
self.verticalLayout_4.addWidget(self.virgul)
self.horizontalLayout.addLayout(self.verticalLayout_4)
self.verticalLayout_7 = QtWidgets.QVBoxLayout()
self.verticalLayout_7.setSizeConstraint(QtWidgets.QLayout.SetDefaultConstraint)
self.verticalLayout_7.setSpacing(0)
self.verticalLayout_7.setObjectName("verticalLayout_7")
self.bolme = QtWidgets.QPushButton(Form)
self.bolme.setEnabled(True)
self.bolme.setMinimumSize(QtCore.QSize(0, 0))
self.bolme.setMaximumSize(QtCore.QSize(16777215, 68))
palette = QtGui.QPalette()
brush = QtGui.QBrush(QtGui.QColor(255, 21, 21))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Active, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 21, 21))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.ToolTipBase, brush)
brush = QtGui.QBrush(QtGui.QColor(255, 21, 21))
brush.setStyle(QtCore.Qt.SolidPattern)
palette.setBrush(QtGui.QPalette.Disabled, QtGui.QPalette.ToolTipBase, brush)
self.bolme.setPalette(palette)
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.bolme.setFont(font)
self.bolme.setIconSize(QtCore.QSize(20, 20))
self.bolme.setShortcut("")
self.bolme.setCheckable(False)
self.bolme.setObjectName("bolme")
self.verticalLayout_7.addWidget(self.bolme)
self.carpma = QtWidgets.QPushButton(Form)
self.carpma.setEnabled(True)
self.carpma.setMinimumSize(QtCore.QSize(0, 0))
self.carpma.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.carpma.setFont(font)
self.carpma.setStyleSheet("")
self.carpma.setIconSize(QtCore.QSize(20, 20))
self.carpma.setShortcut("")
self.carpma.setCheckable(False)
self.carpma.setObjectName("carpma")
self.verticalLayout_7.addWidget(self.carpma)
self.cikarma = QtWidgets.QPushButton(Form)
self.cikarma.setMinimumSize(QtCore.QSize(0, 0))
self.cikarma.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.cikarma.setFont(font)
self.cikarma.setIconSize(QtCore.QSize(20, 20))
self.cikarma.setShortcut("")
self.cikarma.setCheckable(False)
self.cikarma.setObjectName("cikarma")
self.verticalLayout_7.addWidget(self.cikarma)
self.toplama = QtWidgets.QPushButton(Form)
self.toplama.setMinimumSize(QtCore.QSize(0, 0))
self.toplama.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.toplama.setFont(font)
self.toplama.setIconSize(QtCore.QSize(20, 20))
self.toplama.setShortcut("")
self.toplama.setCheckable(False)
self.toplama.setObjectName("toplama")
self.verticalLayout_7.addWidget(self.toplama)
self.esittir = QtWidgets.QPushButton(Form)
self.esittir.setMinimumSize(QtCore.QSize(0, 0))
self.esittir.setMaximumSize(QtCore.QSize(16777215, 68))
font = QtGui.QFont()
font.setPointSize(16)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.esittir.setFont(font)
self.esittir.setIconSize(QtCore.QSize(20, 20))
self.esittir.setShortcut("")
self.esittir.setCheckable(False)
self.esittir.setObjectName("esittir")
self.verticalLayout_7.addWidget(self.esittir)
self.horizontalLayout.addLayout(self.verticalLayout_7)
self.verticalLayout_5.addLayout(self.horizontalLayout)
self.verticalLayout.addLayout(self.verticalLayout_5)
self.retranslateUi(Form)
QtCore.QMetaObject.connectSlotsByName(Form)
def retranslateUi(self, Form):
_translate = QtCore.QCoreApplication.translate
Form.setWindowTitle(_translate("Form", "CalculatorOne (3.7)"))
self.writing_area.setToolTip(_translate("Form", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:\'MS Shell Dlg 2\'; font-size:37pt; font-weight:400; font-style:normal;\">\n"
"<p align=\"right\" dir=\'rtl\' style=\"-qt-paragraph-type:empty; margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><br /></p></body></html>"))
self.clear_line.setText(_translate("Form", "CE"))
self.seven.setText(_translate("Form", "7"))
self.four.setText(_translate("Form", "4"))
self.one.setText(_translate("Form", "1"))
self.arti_eksi.setText(_translate("Form", "+-"))
self.clear_all.setText(_translate("Form", "C"))
self.eight.setText(_translate("Form", "8"))
self.five.setText(_translate("Form", "5"))
self.two.setText(_translate("Form", "2"))
self.zero.setText(_translate("Form", "0"))
self.back.setText(_translate("Form", "↵"))
self.nine.setText(_translate("Form", "9"))
self.six.setText(_translate("Form", "6"))
self.three.setToolTip(_translate("Form", "<html><head/><body><p>ddd</p></body></html>"))
self.three.setText(_translate("Form", "3"))
self.virgul.setText(_translate("Form", "."))
self.bolme.setText(_translate("Form", "÷"))
self.carpma.setText(_translate("Form", "x"))
self.cikarma.setText(_translate("Form", "-"))
self.toplama.setText(_translate("Form", "+"))
self.esittir.setText(_translate("Form", "="))
| 53.919569 | 204 | 0.683516 | 7,565 | 65,027 | 5.857237 | 0.042829 | 0.140239 | 0.084496 | 0.1109 | 0.842316 | 0.820853 | 0.789348 | 0.782848 | 0.781223 | 0.781223 | 0 | 0.046857 | 0.190336 | 65,027 | 1,205 | 205 | 53.964315 | 0.794701 | 0.003706 | 0 | 0.732607 | 1 | 0 | 0.023866 | 0.005187 | 0 | 0 | 0 | 0 | 0 | 1 | 0.001676 | false | 0 | 0.000838 | 0 | 0.003353 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
6cf8eb813884f44ad603adab7611de76c34460ee | 21,605 | py | Python | poker/models/networks.py | MorGriffiths/PokerAI | a68400f4918f10dde82574ad19654243c9a65024 | [
"MIT"
] | 2 | 2020-05-24T12:21:36.000Z | 2022-02-08T03:02:17.000Z | poker/models/networks.py | MorGriffiths/PokerAI | a68400f4918f10dde82574ad19654243c9a65024 | [
"MIT"
] | 3 | 2017-04-28T00:25:18.000Z | 2018-03-18T20:51:20.000Z | poker/models/networks.py | C5ipo7i/PokerAI | a68400f4918f10dde82574ad19654243c9a65024 | [
"MIT"
] | 2 | 2020-11-05T11:57:04.000Z | 2021-03-17T17:57:24.000Z | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.distributions import Categorical
from poker_env.datatypes import Action
from models.model_utils import padding_index,count_parameters
from models.buffers import PriorityReplayBuffer,PriorityTree
from models.model_layers import EncoderAttention,VectorAttention,Embedder,GaussianNoise,PreProcessLayer,CTransformer,NetworkFunctions,IdentityBlock
from models.model_utils import mask_,hard_update,combined_masks,norm_frequencies,strip_padding,copy_weights
class BetAgent(object):
def __init__(self):
pass
def name(self):
return 'baseline_evaluation'
def __call__(self,state,action_mask,betsize_mask,target=False):
if betsize_mask.sum() > 0:
action = np.argmax(betsize_mask,axis=-1) + 3
else:
action = np.argmax(action_mask,axis=-1)
actor_outputs = {
'action':action,
'action_category':int(np.where(action_mask > 0)[-1][-1]),
'action_probs':torch.zeros(5).fill_(2.),
'action_prob':torch.tensor([1.]),
'betsize' : int(np.argmax(betsize_mask,axis=-1))
}
return actor_outputs
################################################
# Holdem Networks #
################################################
class Network(nn.Module):
def __init__(self):
super().__init__()
@property
def summary(self):
count_parameters(self)
class HoldemBaseline(Network):
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.maxlen = params['maxlen']
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.mapping = params['mapping']
self.hand_emb = Embedder(5,64)
self.action_emb = Embedder(6,64)
self.betsize_emb = Embedder(self.nB,64)
self.noise = GaussianNoise()
self.emb = 1248
n_heads = 8
depth = 2
self.lstm = nn.LSTM(self.emb, 128)
# self.transformer = CTransformer(emb,n_heads,depth,self.max_length,self.nA)
self.fc1 = nn.Linear(528,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(1280,self.combined_output)
self.dropout = nn.Dropout(0.5)
def forward(self,state,action_mask,betsize_mask):
mask = combined_masks(action_mask,betsize_mask)
x = state
if x.dim() == 2:
x = x.unsqueeze(0)
out = self.process_input(x).unsqueeze(0)
B,M,c = out.size()
n_padding = max(self.maxlen - M,0)
padding = torch.zeros(B,n_padding,out.size(-1))
h = torch.cat((out,padding),dim=1)
lstm_out,_ = self.lstm(h)
t_logits = self.fc3(lstm_out.view(-1))
category_logits = self.noise(t_logits)
action_soft = F.softmax(category_logits,dim=-1)
action_probs = norm_frequencies(action_soft,mask)
m = Categorical(action_probs)
action = m.sample()
action_category,betsize_category = self.helper_functions.unwrap_action(action,state[:,-1,self.mapping['state']['previous_action']])
outputs = {
'action':action,
'action_category':action_category,
'action_prob':m.log_prob(action),
'action_probs':action_probs,
'betsize':betsize_category
}
return outputs
class HoldemBaselineCritic(Network):
def __init__(self,seed,nO,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
# self.seed = torch.manual_seed(seed)
self.mapping = params['mapping']
self.process_input = PreProcessLayer(params,critic=True)
self.fc1 = nn.Linear(304,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(hidden_dims[1],nA)
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(64,1)
self.advantage_output = nn.Linear(64,self.nA)
def forward(self,x,action):
M,c = x.size()
ranks = x[:,self.mapping['observation']['rank']].long()
suits = x[:,self.mapping['observation']['suit']].long()
vil_rank = x[:,self.mapping['observation']['vil_ranks']].long()
vil_suit = x[:,self.mapping['observation']['vil_suits']].long()
board_ranks = x[:,self.mapping['observation']['board_ranks']].long()
board_suits = x[:,self.mapping['observation']['board_suits']].long()
rank_input = torch.cat((ranks,board_ranks),dim=-1)
suit_input = torch.cat((suits,board_suits),dim=-1)
hot_ranks = self.one_hot_ranks[rank_input]
hot_suits = self.one_hot_suits[suit_input]
s = self.suit_conv(hot_suits.float())
r = self.rank_conv(hot_ranks.float())
hero = torch.cat((r,s),dim=-1)
rank_input2 = torch.cat((vil_rank,board_ranks),dim=-1)
suit_input2 = torch.cat((vil_suit,board_suits),dim=-1)
hot_ranks2 = self.one_hot_ranks[rank_input2]
hot_suits2 = self.one_hot_suits[suit_input2]
s2 = self.suit_conv(hot_suits2.float())
r2 = self.rank_conv(hot_ranks2.float())
villain = torch.cat((r2,s2),dim=-1)
# should be (b,64,88)
winner = hero - villain
last_action = x[:,self.mapping['observation']['previous_action']].long()
last_action = self.action_emb(last_action)
x = torch.cat([winner.view(M,-1),last_action],dim=-1)
x = self.activation(self.fc1(x))
x = self.activation(self.fc2(x))
outputs = {
'value':torch.tanh(self.fc3(x))
}
return outputs
class HoldemQCritic(Network):
def __init__(self,seed,nO,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.process_input = PreProcessLayer(params)
self.maxlen = params['maxlen']
self.mapping = params['mapping']
emb = 1248
n_heads = 8
depth = 2
self.transformer = CTransformer(emb,n_heads,depth,self.maxlen,self.nA)
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(5,1)
self.advantage_output = nn.Linear(5,self.nA)
def forward(self,state):
x = state
if x.ndim == 2:
x = x.unsqueeze(0)
out = self.process_input(x).unsqueeze(0)
B,M,c = out.size()
q_input = self.transformer(out)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(-1,keepdim=True).expand_as(a)
outputs = {
'value':q.squeeze(0)
}
return outputs
################################################
# Omaha Networks #
################################################
class OmahaBatchActor(Network):
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.maxlen = params['maxlen']
self.device = params['device']
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.state_mapping = params['state_mapping']
self.hand_emb = Embedder(5,64)
self.action_emb = Embedder(Action.UNOPENED,64)
self.betsize_emb = Embedder(self.nB,64)
self.noise = GaussianNoise(self.device)
self.emb = 1248
n_heads = 8
depth = 2
self.lstm = nn.LSTM(1280, 128,bidirectional=True)
self.batchnorm = nn.BatchNorm1d(self.maxlen)
# self.blocks = nn.Sequential(
# IdentityBlock(hidden_dims=(2560,2560,512),activation=F.leaky_relu),
# IdentityBlock(hidden_dims=(512,512,256),activation=F.leaky_relu),
# )
self.fc_final = nn.Linear(2560,self.combined_output)
self.dropout = nn.Dropout(0.5)
def forward(self,state,action_mask,betsize_mask):
x = torch.tensor(state,dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,dtype=torch.float).to(self.device)
betsize_mask = torch.tensor(betsize_mask,dtype=torch.float).to(self.device)
mask = combined_masks(action_mask,betsize_mask)
out = self.process_input(x)
B,M,c = out.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:,-self.maxlen:,:]
else:
padding = torch.zeros(B,n_padding,out.size(-1)).to(self.device)
h = torch.cat((out,padding),dim=1)
lstm_out,_ = self.lstm(h)
norm = self.batchnorm(lstm_out)
# blocks_out = self.blocks(lstm_out.view(-1))
t_logits = self.fc_final(norm.view(-1))
category_logits = self.noise(t_logits)
action_soft = F.softmax(category_logits,dim=-1)
action_probs = norm_frequencies(action_soft,mask)
m = Categorical(action_probs)
action = m.sample()
action_category,betsize_category = self.helper_functions.unwrap_action(action,state[:,-1,self.state_mapping['last_action']])
outputs = {
'action':action.item(),
'action_category':action_category.item(),
'action_prob':m.log_prob(action),
'action_probs':action_probs,
'betsize':betsize_category.item()
}
return outputs
class OmahaBatchObsQCritic(Network):
def __init__(self,seed,nO,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.combined_output = nA - 2 + nB
self.process_input = PreProcessLayer(params,critic=True)
self.maxlen = params['maxlen']
self.mapping = params['state_mapping']
self.device = params['device']
# self.emb = params['embedding_size']
# self.lstm = nn.LSTM(1280, 128)
emb = params['transformer_in']
n_heads = 8
depth = 2
self.transformer = CTransformer(emb,n_heads,depth,self.maxlen,params['transformer_out'])
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(params['transformer_out'],1)
self.advantage_output = nn.Linear(params['transformer_out'],self.combined_output)
def forward(self,obs):
x = torch.tensor(obs,dtype=torch.float32).to(self.device)
out = self.process_input(x)
q_input = self.transformer(out)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(-1,keepdim=True).expand_as(a)
outputs = {
'value':q.squeeze(0)
}
return outputs
class OmahaActor(Network):
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.maxlen = params['maxlen']
self.device = params['device']
self.epsilon = params['epsilon']
self.epsilon_weights = params['epsilon_weights'].to(self.device)
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.state_mapping = params['state_mapping']
self.action_emb = Embedder(Action.UNOPENED,64)
self.betsize_emb = Embedder(self.nB,64)
self.noise = GaussianNoise(self.device)
self.emb = 1248
n_heads = 8
depth = 2
# self.attention = EncoderAttention(params['lstm_in'],params['lstm_out'])
self.lstm = nn.LSTM(params['lstm_in'],params['lstm_out'],bidirectional=True)
self.batchnorm = nn.BatchNorm1d(self.maxlen)
# self.blocks = nn.Sequential(
# IdentityBlock(hidden_dims=(2560,2560,512),activation=F.leaky_relu),
# IdentityBlock(hidden_dims=(512,512,256),activation=F.leaky_relu),
# )
self.fc_final = nn.Linear(5120,self.combined_output)
def set_device(self,device):
self.device = device
self.process_input.set_device(device)
def forward(self,state,action_mask,betsize_mask,target=False):
"""
state: B,M,39
"""
if not isinstance(state,torch.Tensor):
state = torch.tensor(state,dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,dtype=torch.float32).to(self.device)
betsize_mask = torch.tensor(betsize_mask,dtype=torch.float32).to(self.device)
mask = combined_masks(action_mask,betsize_mask)
if target and np.random.random() < self.epsilon:
B = state.size(0)
# pick random legal move
action_masked = self.epsilon_weights * mask
action_probs = action_masked / action_masked.sum(-1).unsqueeze(-1)
action = action_probs.multinomial(num_samples=1, replacement=False)
action_prob = torch.zeros(B,1)
else:
out = self.process_input(state)
B,M,c = state.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:,-self.maxlen:,:]
else:
padding = torch.zeros(B,n_padding,out.size(-1)).to(self.device)
h = torch.cat((padding,out),dim=1)
lstm_out,hidden_states = self.lstm(h)
norm = self.batchnorm(lstm_out)
# self.attention(out)
# blocks_out = self.blocks(lstm_out.view(-1))
t_logits = self.fc_final(norm.view(B,-1))
category_logits = self.noise(t_logits)
# skip connection
# category_logits += h
action_soft = F.softmax(category_logits,dim=-1)
action_probs = norm_frequencies(action_soft,mask)
m = Categorical(action_probs)
action = m.sample()
action_prob = m.log_prob(action)
previous_action = torch.as_tensor(state[:,-1,self.state_mapping['last_action']]).to(self.device)
action_category,betsize_category = self.helper_functions.batch_unwrap_action(action,previous_action)
if B > 1:
# batch training
outputs = {
'action':action,
'action_category':action_category,
'action_prob':action_prob,
'action_probs':action_probs,
'betsize':betsize_category
}
else:
# playing hand
outputs = {
'action':action.item(),
'action_category':action_category.item(),
'action_prob':action_prob,
'action_probs':action_probs,
'betsize':betsize_category.item()
}
return outputs
class OmahaQCritic(Network):
def __init__(self,seed,nO,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.combined_output = nA - 2 + nB
self.process_input = PreProcessLayer(params)
self.maxlen = params['maxlen']
self.mapping = params['state_mapping']
self.device = params['device']
# self.emb = params['embedding_size']
# self.lstm = nn.LSTM(1280, 128)
emb = params['transformer_in']
n_heads = 8
depth = 2
self.transformer = CTransformer(emb,n_heads,depth,self.maxlen,params['transformer_out'])
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(params['transformer_out'],1)
self.advantage_output = nn.Linear(params['transformer_out'],self.combined_output)
def set_device(self,device):
self.device = device
self.process_input.set_device(device)
def forward(self,state):
x = torch.tensor(state,dtype=torch.float32).to(self.device)
out = self.process_input(x)
# B,M,c = out.size()
# n_padding = max(self.maxlen - M,0)
# padding = torch.zeros(B,n_padding,out.size(-1))
# h = torch.cat((out,padding),dim=1)
q_input = self.transformer(out)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(-1,keepdim=True).expand_as(a)
outputs = {
'value':q.squeeze(0)
}
return outputs
class OmahaObsQCritic(Network):
def __init__(self,seed,nO,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.combined_output = nA - 2 + nB
# self.attention = VectorAttention(params['transformer_in'])
self.process_input = PreProcessLayer(params,critic=True)
self.maxlen = params['maxlen']
self.mapping = params['state_mapping']
self.device = params['device']
# self.emb = params['embedding_size']
emb = params['transformer_in']
n_heads = 8
depth = 2
self.transformer = CTransformer(emb,n_heads,depth,self.maxlen,params['transformer_out'])
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(params['transformer_out'],1)
self.advantage_output = nn.Linear(params['transformer_out'],self.combined_output)
def set_device(self,device):
self.device = device
self.process_input.set_device(device)
def forward(self,obs):
if not isinstance(obs,torch.Tensor):
obs = torch.tensor(obs,dtype=torch.float32).to(self.device)
out = self.process_input(obs)
# context = self.attention(out)
q_input = self.transformer(out)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(-1,keepdim=True).expand_as(a)
outputs = {
'value':q.squeeze(0)
}
return outputs
class CombinedNet(Network):
def __init__(self,seed,nO,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.maxlen = params['maxlen']
self.mapping = params['state_mapping']
self.device = params['device']
# self.emb = params['embedding_size']
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.process_input = PreProcessLayer(params)
self.lstm = nn.LSTM(1280, 128)
self.policy_out = nn.Linear(1280,self.combined_output)
self.noise = GaussianNoise(self.device)
emb = params['transformer_in']
n_heads = 8
depth = 2
self.transformer = CTransformer(emb,n_heads,depth,self.maxlen,params['transformer_out'])
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(params['transformer_out'],1)
self.advantage_output = nn.Linear(params['transformer_out'],self.combined_output)
def forward(self,state,action_mask,betsize_mask):
x = torch.tensor(state,dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,dtype=torch.float).to(self.device)
betsize_mask = torch.tensor(betsize_mask,dtype=torch.float).to(self.device)
mask = combined_masks(action_mask,betsize_mask)
out = self.process_input(x)
# Actor
B,M,c = out.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:,-self.maxlen:,:]
else:
padding = torch.zeros(B,n_padding,out.size(-1)).to(self.device)
h = torch.cat((out,padding),dim=1)
lstm_out,_ = self.lstm(h)
t_logits = self.policy_out(lstm_out.view(-1))
category_logits = self.noise(t_logits)
action_soft = F.softmax(category_logits,dim=-1)
action_probs = norm_frequencies(action_soft,mask)
m = Categorical(action_probs)
action = m.sample()
action_category,betsize_category = self.helper_functions.unwrap_action(action,state[:,-1,self.mapping['last_action']])
outputs = {
'action':action.item(),
'action_category':action_category.item(),
'action_prob':m.log_prob(action),
'action_probs':action_probs,
'betsize':betsize_category.item()
}
# Critic
q_input = self.transformer(out)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(-1,keepdim=True).expand_as(a)
outputs['value'] = q.squeeze(0)
return outputs | 39.935305 | 147 | 0.604166 | 2,696 | 21,605 | 4.646884 | 0.089763 | 0.025543 | 0.025543 | 0.020754 | 0.806992 | 0.768678 | 0.753033 | 0.733637 | 0.715597 | 0.704342 | 0 | 0.021182 | 0.261421 | 21,605 | 541 | 148 | 39.935305 | 0.763928 | 0.064985 | 0 | 0.701794 | 0 | 0 | 0.050336 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.058296 | false | 0.002242 | 0.022422 | 0.002242 | 0.130045 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
9f544a61164f2e6c40e09f35ddb0ae3a30a89eeb | 7,890 | py | Python | dialogue-engine/test/programytest/config/brain/test_security.py | cotobadesign/cotoba-agent-oss | 3833d56e79dcd7529c3e8b3a3a8a782d513d9b12 | [
"MIT"
] | 104 | 2020-03-30T09:40:00.000Z | 2022-03-06T22:34:25.000Z | dialogue-engine/test/programytest/config/brain/test_security.py | cotobadesign/cotoba-agent-oss | 3833d56e79dcd7529c3e8b3a3a8a782d513d9b12 | [
"MIT"
] | 25 | 2020-06-12T01:36:35.000Z | 2022-02-19T07:30:44.000Z | dialogue-engine/test/programytest/config/brain/test_security.py | cotobadesign/cotoba-agent-oss | 3833d56e79dcd7529c3e8b3a3a8a782d513d9b12 | [
"MIT"
] | 10 | 2020-04-02T23:43:56.000Z | 2021-05-14T13:47:01.000Z | """
Copyright (c) 2020 COTOBA DESIGN, Inc.
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 unittest
from programy.config.file.yaml_file import YamlConfigurationFile
from programy.config.brain.security import BrainSecurityAuthorisationConfiguration
from programy.config.brain.security import BrainSecurityAuthenticationConfiguration
from programy.clients.events.console.config import ConsoleConfiguration
class BrainSecurityConfigurationTests(unittest.TestCase):
def test_authorisation_with_data_denied_srai(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
security:
authorisation:
classname: programy.security.authorise.passthrough.PassThroughAuthorisationService
denied_srai: AUTHORISATION_FAILED
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
self.assertIsNotNone(brain_config)
services_config = yaml.get_section("security", brain_config)
self.assertIsNotNone(services_config)
service_config = BrainSecurityAuthorisationConfiguration()
service_config.load_config_section(yaml, services_config, ".")
self.assertEqual("programy.security.authorise.passthrough.PassThroughAuthorisationService", service_config.classname)
self.assertEqual("AUTHORISATION_FAILED", service_config.denied_srai)
self.assertEqual(BrainSecurityAuthorisationConfiguration.DEFAULT_ACCESS_DENIED, service_config.denied_text)
def test_authorisation_with_data_denied_text(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
security:
authorisation:
classname: programy.security.authorise.passthrough.PassThroughAuthorisationService
denied_text: Authorisation Failed
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
self.assertIsNotNone(brain_config)
services_config = yaml.get_section("security", brain_config)
self.assertIsNotNone(services_config)
service_config = BrainSecurityAuthorisationConfiguration()
service_config.load_config_section(yaml, services_config, ".")
self.assertEqual("programy.security.authorise.passthrough.PassThroughAuthorisationService", service_config.classname)
self.assertEqual("Authorisation Failed", service_config.denied_text)
self.assertIsNone(service_config.denied_srai)
def test_authorisation_with_data_neither_denied_srai_or_text(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
security:
authorisation:
classname: programy.security.authorise.passthrough.PassThroughAuthorisationService
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
self.assertIsNotNone(brain_config)
services_config = yaml.get_section("security", brain_config)
self.assertIsNotNone(services_config)
service_config = BrainSecurityAuthorisationConfiguration()
service_config.load_config_section(yaml, services_config, ".")
self.assertEqual("programy.security.authorise.passthrough.PassThroughAuthorisationService", service_config.classname)
self.assertEqual(BrainSecurityAuthorisationConfiguration.DEFAULT_ACCESS_DENIED, service_config.denied_text)
self.assertIsNone(service_config.denied_srai)
def test_authentication_with_data_denied_srai(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
security:
authentication:
classname: programy.security.authenticate.passthrough.PassThroughAuthenticationService
denied_srai: AUTHENTICATION_FAILED
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
self.assertIsNotNone(brain_config)
services_config = yaml.get_section("security", brain_config)
self.assertIsNotNone(services_config)
service_config = BrainSecurityAuthenticationConfiguration()
service_config.load_config_section(yaml, services_config, ".")
self.assertEqual("programy.security.authenticate.passthrough.PassThroughAuthenticationService", service_config.classname)
self.assertEqual("AUTHENTICATION_FAILED", service_config.denied_srai)
self.assertEqual(BrainSecurityAuthenticationConfiguration.DEFAULT_ACCESS_DENIED, service_config.denied_text)
def test_authentication_with_data_denied_text(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
security:
authentication:
classname: programy.security.authenticate.passthrough.PassThroughAuthenticationService
denied_text: Authentication failed
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
self.assertIsNotNone(brain_config)
services_config = yaml.get_section("security", brain_config)
self.assertIsNotNone(services_config)
service_config = BrainSecurityAuthenticationConfiguration()
service_config.load_config_section(yaml, services_config, ".")
self.assertEqual("programy.security.authenticate.passthrough.PassThroughAuthenticationService", service_config.classname)
self.assertEqual("Authentication failed", service_config.denied_text)
self.assertIsNone(service_config.denied_srai)
def test_authentication_with_data_neither_denied_srai_or_text(self):
yaml = YamlConfigurationFile()
self.assertIsNotNone(yaml)
yaml.load_from_text("""
brain:
security:
authentication:
classname: programy.security.authenticate.passthrough.PassThroughAuthenticationService
""", ConsoleConfiguration(), ".")
brain_config = yaml.get_section("brain")
self.assertIsNotNone(brain_config)
services_config = yaml.get_section("security", brain_config)
self.assertIsNotNone(services_config)
service_config = BrainSecurityAuthenticationConfiguration()
service_config.load_config_section(yaml, services_config, ".")
self.assertEqual("programy.security.authenticate.passthrough.PassThroughAuthenticationService", service_config.classname)
self.assertEqual(BrainSecurityAuthenticationConfiguration.DEFAULT_ACCESS_DENIED, service_config.denied_text)
self.assertEqual(BrainSecurityAuthenticationConfiguration.DEFAULT_ACCESS_DENIED, service_config.denied_text)
| 49.006211 | 129 | 0.735488 | 747 | 7,890 | 7.542169 | 0.188755 | 0.069223 | 0.027689 | 0.042599 | 0.79819 | 0.795882 | 0.774405 | 0.772985 | 0.772985 | 0.770501 | 0 | 0.000627 | 0.191255 | 7,890 | 160 | 130 | 49.3125 | 0.882307 | 0.134601 | 0 | 0.830508 | 0 | 0 | 0.281965 | 0.134604 | 0 | 0 | 0 | 0 | 0.305085 | 1 | 0.050847 | false | 0.101695 | 0.042373 | 0 | 0.101695 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
9fb36c76c0f027c09d1a14f3ed61c4c5fba3b1ea | 164 | py | Python | sic_financeiro/core/models/__init__.py | diegoMasin/project-sic-financeiro | a06d4c873014e0835afa37437ad8f57c24f78163 | [
"MIT"
] | null | null | null | sic_financeiro/core/models/__init__.py | diegoMasin/project-sic-financeiro | a06d4c873014e0835afa37437ad8f57c24f78163 | [
"MIT"
] | null | null | null | sic_financeiro/core/models/__init__.py | diegoMasin/project-sic-financeiro | a06d4c873014e0835afa37437ad8f57c24f78163 | [
"MIT"
] | null | null | null | from sic_financeiro.core.models.contas import Conta
from sic_financeiro.core.models.tags import Tag
from sic_financeiro.core.models.tipo_despesa import TipoDespesa
| 41 | 63 | 0.871951 | 25 | 164 | 5.56 | 0.52 | 0.151079 | 0.366906 | 0.453237 | 0.582734 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.073171 | 164 | 3 | 64 | 54.666667 | 0.914474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
4cd16d12961de1ebd54ce8ecce9a9f41a432ec9c | 213 | py | Python | neuro_logging/testing_utils.py | neuro-inc/neuro-logging | e3173a40d0e2559f113f1420ed8a3fd4a0e76dde | [
"Apache-2.0"
] | null | null | null | neuro_logging/testing_utils.py | neuro-inc/neuro-logging | e3173a40d0e2559f113f1420ed8a3fd4a0e76dde | [
"Apache-2.0"
] | 50 | 2021-08-20T00:10:05.000Z | 2022-02-21T16:44:46.000Z | neuro_logging/testing_utils.py | neuro-inc/neuro-logging | e3173a40d0e2559f113f1420ed8a3fd4a0e76dde | [
"Apache-2.0"
] | null | null | null | # shim file for testing purpose,
# _find_caller_version() should be called from a package
from neuro_logging.trace import _find_caller_version
def _get_test_version() -> str:
return _find_caller_version(1)
| 23.666667 | 56 | 0.793427 | 32 | 213 | 4.875 | 0.75 | 0.192308 | 0.326923 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005495 | 0.14554 | 213 | 8 | 57 | 26.625 | 0.851648 | 0.399061 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 7 |
4cf415afee101a6a4d308d53426f8649f55baad0 | 11,500 | py | Python | bc/inlineindex/migrations/0001_initial.py | Buckinghamshire-Digital-Service/buckinghamshire-council | bbbdb52b515bcdfc79a2bd9198dfa4828405370e | [
"BSD-3-Clause"
] | 1 | 2021-02-27T07:27:17.000Z | 2021-02-27T07:27:17.000Z | bc/inlineindex/migrations/0001_initial.py | Buckinghamshire-Digital-Service/buckinghamshire-council | bbbdb52b515bcdfc79a2bd9198dfa4828405370e | [
"BSD-3-Clause"
] | null | null | null | bc/inlineindex/migrations/0001_initial.py | Buckinghamshire-Digital-Service/buckinghamshire-council | bbbdb52b515bcdfc79a2bd9198dfa4828405370e | [
"BSD-3-Clause"
] | 1 | 2021-06-09T15:56:54.000Z | 2021-06-09T15:56:54.000Z | # Generated by Django 2.2.9 on 2019-12-19 22:10
import django.db.models.deletion
from django.db import migrations, models
import wagtail.core.blocks
import wagtail.core.fields
class Migration(migrations.Migration):
initial = True
dependencies = [
("wagtailcore", "0041_group_collection_permissions_verbose_name_plural"),
]
operations = [
migrations.CreateModel(
name="InlineIndex",
fields=[
(
"page_ptr",
models.OneToOneField(
auto_created=True,
on_delete=django.db.models.deletion.CASCADE,
parent_link=True,
primary_key=True,
serialize=False,
to="wagtailcore.Page",
),
),
(
"subtitle",
models.CharField(
default="Introduction",
help_text="Title that appears on the index. (e.g. Introduction)",
max_length=255,
),
),
(
"body",
wagtail.core.fields.StreamField(
[
(
"heading",
wagtail.core.blocks.CharBlock(
classname="full title",
icon="title",
template="patterns/molecules/streamfield/blocks/heading_block.html",
),
),
(
"paragraph",
wagtail.core.blocks.RichTextBlock(
features=[
"bold",
"italic",
"ol",
"ul",
"link",
"document-link",
]
),
),
(
"local_area_links",
wagtail.core.blocks.StructBlock(
[
(
"introduction",
wagtail.core.blocks.RichTextBlock(
default="<p>Select your local area for information:</p>",
features=[
"bold",
"italic",
"ol",
"ul",
"link",
"document-link",
],
),
),
(
"aylesbury_vale_url",
wagtail.core.blocks.URLBlock(
label="Aylesbury Vale URL",
required=False,
),
),
(
"chiltern_url",
wagtail.core.blocks.URLBlock(
label="Chiltern URL", required=False
),
),
(
"south_bucks_url",
wagtail.core.blocks.URLBlock(
label="South Bucks URL", required=False
),
),
(
"wycombe_url",
wagtail.core.blocks.URLBlock(
label="Wycombe URL", required=False
),
),
(
"postscript",
wagtail.core.blocks.RichTextBlock(
default='<p>Or <a href="https://www.gov.uk/find-local-council">click here</a> to find your area based on your postcode.</p>',
features=[
"bold",
"italic",
"ol",
"ul",
"link",
"document-link",
],
required=False,
),
),
]
),
),
]
),
),
],
options={"abstract": False,},
bases=("wagtailcore.page",),
),
migrations.CreateModel(
name="InlineIndexChild",
fields=[
(
"page_ptr",
models.OneToOneField(
auto_created=True,
on_delete=django.db.models.deletion.CASCADE,
parent_link=True,
primary_key=True,
serialize=False,
to="wagtailcore.Page",
),
),
(
"body",
wagtail.core.fields.StreamField(
[
(
"heading",
wagtail.core.blocks.CharBlock(
classname="full title",
icon="title",
template="patterns/molecules/streamfield/blocks/heading_block.html",
),
),
(
"paragraph",
wagtail.core.blocks.RichTextBlock(
features=[
"bold",
"italic",
"ol",
"ul",
"link",
"document-link",
]
),
),
(
"local_area_links",
wagtail.core.blocks.StructBlock(
[
(
"introduction",
wagtail.core.blocks.RichTextBlock(
default="<p>Select your local area for information:</p>",
features=[
"bold",
"italic",
"ol",
"ul",
"link",
"document-link",
],
),
),
(
"aylesbury_vale_url",
wagtail.core.blocks.URLBlock(
label="Aylesbury Vale URL",
required=False,
),
),
(
"chiltern_url",
wagtail.core.blocks.URLBlock(
label="Chiltern URL", required=False
),
),
(
"south_bucks_url",
wagtail.core.blocks.URLBlock(
label="South Bucks URL", required=False
),
),
(
"wycombe_url",
wagtail.core.blocks.URLBlock(
label="Wycombe URL", required=False
),
),
(
"postscript",
wagtail.core.blocks.RichTextBlock(
default='<p>Or <a href="https://www.gov.uk/find-local-council">click here</a> to find your area based on your postcode.</p>',
features=[
"bold",
"italic",
"ol",
"ul",
"link",
"document-link",
],
required=False,
),
),
]
),
),
]
),
),
],
options={"abstract": False,},
bases=("wagtailcore.page",),
),
]
| 47.520661 | 173 | 0.220087 | 442 | 11,500 | 5.644796 | 0.280543 | 0.096994 | 0.129459 | 0.064128 | 0.821643 | 0.821643 | 0.821643 | 0.821643 | 0.821643 | 0.821643 | 0 | 0.00679 | 0.718261 | 11,500 | 241 | 174 | 47.717842 | 0.763272 | 0.003913 | 0 | 0.781116 | 1 | 0.008584 | 0.10888 | 0.014407 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.017167 | 0 | 0.034335 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
4cfcd64060161d716d88575c8a0ec4caae4d9d39 | 112 | py | Python | tests/test_utils.py | elstevi/libbhyve | 9d746073b80fc21d58d7931a21a891836d0748a6 | [
"BSD-2-Clause"
] | 1 | 2020-06-18T17:58:01.000Z | 2020-06-18T17:58:01.000Z | tests/test_utils.py | elstevi/libbhyve | 9d746073b80fc21d58d7931a21a891836d0748a6 | [
"BSD-2-Clause"
] | null | null | null | tests/test_utils.py | elstevi/libbhyve | 9d746073b80fc21d58d7931a21a891836d0748a6 | [
"BSD-2-Clause"
] | null | null | null | import pytest
from libbhyve.utils import log
def test_log():
assert log('crit', 'world') == "[crit] world"
| 18.666667 | 49 | 0.678571 | 16 | 112 | 4.6875 | 0.6875 | 0.24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.169643 | 112 | 5 | 50 | 22.4 | 0.806452 | 0 | 0 | 0 | 0 | 0 | 0.1875 | 0 | 0 | 0 | 0 | 0 | 0.25 | 1 | 0.25 | true | 0 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
e24c8a9efc3697032166cdea2148f93cda8a5b57 | 77 | py | Python | pclpy/view/__init__.py | toinsson/pclpy | e44d261c4996bc5fd4080bf813542ccdffbca601 | [
"MIT"
] | 293 | 2018-05-21T21:50:11.000Z | 2022-03-30T02:43:08.000Z | pclpy/view/__init__.py | toinsson/pclpy | e44d261c4996bc5fd4080bf813542ccdffbca601 | [
"MIT"
] | 97 | 2018-04-23T20:45:20.000Z | 2022-03-28T09:00:25.000Z | pclpy/view/__init__.py | toinsson/pclpy | e44d261c4996bc5fd4080bf813542ccdffbca601 | [
"MIT"
] | 56 | 2018-05-16T08:59:09.000Z | 2022-02-24T02:21:11.000Z |
from pclpy.view.cloudcompare import cloudcompare
from pclpy.view import vtk | 19.25 | 48 | 0.844156 | 11 | 77 | 5.909091 | 0.545455 | 0.276923 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.116883 | 77 | 4 | 49 | 19.25 | 0.955882 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
e27801e662da382246d346122a3a292d5cf33927 | 69 | py | Python | VTree/vtree/__init__.py | MarcoMuellner/VTree | c4bd509daeb80652075df1937b5861fa3e281dff | [
"MIT"
] | null | null | null | VTree/vtree/__init__.py | MarcoMuellner/VTree | c4bd509daeb80652075df1937b5861fa3e281dff | [
"MIT"
] | null | null | null | VTree/vtree/__init__.py | MarcoMuellner/VTree | c4bd509daeb80652075df1937b5861fa3e281dff | [
"MIT"
] | null | null | null | from VTree.vtree.VTree import VTree
from VTree.vtree.Node import Node | 34.5 | 35 | 0.84058 | 12 | 69 | 4.833333 | 0.333333 | 0.517241 | 0.482759 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.101449 | 69 | 2 | 36 | 34.5 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
e298bb8bb616ba4db97bb5f236bb14ddb4a5171e | 1,276 | py | Python | sqlhandler/custom/field.py | matthewgdv/sqlhandler | b82fd159195f6bb63175bb8a8d81fc421e7d5835 | [
"MIT"
] | null | null | null | sqlhandler/custom/field.py | matthewgdv/sqlhandler | b82fd159195f6bb63175bb8a8d81fc421e7d5835 | [
"MIT"
] | null | null | null | sqlhandler/custom/field.py | matthewgdv/sqlhandler | b82fd159195f6bb63175bb8a8d81fc421e7d5835 | [
"MIT"
] | null | null | null | from __future__ import annotations
from sqlalchemy import types
from subtypes import DateTime, Date
class BitLiteral(types.TypeDecorator):
impl = types.DateTime
def process_literal_param(self, value, dialect):
return str(int(value))
class SubtypesDateTime(types.TypeDecorator):
impl = types.DateTime
string = types.String()
def process_bind_param(self, value, dialect):
return None if value is None else DateTime.infer(value).to_isoformat()
def process_literal_param(self, value, dialect):
return None if value is None else self.string.literal_processor(dialect)(DateTime.infer(value).to_isoformat())
def process_result_value(self, value, dialect):
return None if value is None else DateTime.infer(value)
class SubtypesDate(types.TypeDecorator):
impl = types.Date
string = types.String()
def process_bind_param(self, value, dialect):
return None if value is None else Date.infer(value).to_isoformat()
def process_literal_param(self, value, dialect):
return None if value is None else self.string.literal_processor(dialect)(Date.infer(value).to_isoformat())
def process_result_value(self, value, dialect):
return None if value is None else Date.infer(value)
| 31.121951 | 118 | 0.734326 | 171 | 1,276 | 5.339181 | 0.210526 | 0.07667 | 0.122673 | 0.168675 | 0.806134 | 0.729463 | 0.729463 | 0.71632 | 0.668127 | 0.668127 | 0 | 0 | 0.181034 | 1,276 | 40 | 119 | 31.9 | 0.873684 | 0 | 0 | 0.44 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.28 | false | 0 | 0.12 | 0.28 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 8 |
2c61d4849e47a71765b84f72bc8e34a5430fe083 | 10,004 | py | Python | graphical_analysis/query_scenario_additional_operators_analysis.py | sjuenger/WikiMETA | 13ed293b4bda8ff0fc10b532907ca35c24a12616 | [
"MIT"
] | null | null | null | graphical_analysis/query_scenario_additional_operators_analysis.py | sjuenger/WikiMETA | 13ed293b4bda8ff0fc10b532907ca35c24a12616 | [
"MIT"
] | null | null | null | graphical_analysis/query_scenario_additional_operators_analysis.py | sjuenger/WikiMETA | 13ed293b4bda8ff0fc10b532907ca35c24a12616 | [
"MIT"
] | null | null | null | import os
import json
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# plot only the NON-redundant data
def plot_additional_second_level_operator_information_about_scenarios_per_timeframe_for_OPTIONAL(timeframes,
metadata,
scenario):
if metadata not in ["reference_metadata", "rank_metadata", "qualifier_metadata"]:
raise Exception
if scenario not in ["optional"]:
raise Exception
csv_ready_scenario_dict = {}
csv_ready_scenario_dict["timeframe"] = []
csv_ready_scenario_dict["datatype"] = []
csv_ready_scenario_dict["base scenario"] = []
csv_ready_scenario_dict["operator name"] = []
csv_ready_scenario_dict["operator count"] = []
csv_ready_scenario_dict["operator percentage"] = []
csv_ready_scenario_dict["total operators"] = []
for timeframe in timeframes:
# get the path to the location information of the timeframe scenario data per
# datatype
information_path = "data/" + timeframe[:21] + "/" + timeframe[22:] + \
"/" + metadata + \
"/scenarios/non_redundant/" + scenario + "_statistical_information.json"
if os.path.exists(information_path):
with open(information_path, "r") as stat_info_scenarios_data:
stat_info_scenarios_dict = json.load(stat_info_scenarios_data)
for operator in stat_info_scenarios_dict["in_found_prop_path_found_operators_overall"]:
csv_ready_scenario_dict["operator name"].append(operator)
csv_ready_scenario_dict["operator count"]. \
append(stat_info_scenarios_dict["in_found_prop_path_found_operators_overall"][operator])
total_occurrences = stat_info_scenarios_dict["in_found_prop_path_total_found_operators"]
if total_occurrences > 0:
csv_ready_scenario_dict["operator percentage"]. \
append(
int(stat_info_scenarios_dict["in_found_prop_path_found_operators_overall"][operator]) /
total_occurrences)
else:
csv_ready_scenario_dict["operator percentage"]. \
append(0)
csv_ready_scenario_dict["total operators"].append(total_occurrences)
csv_ready_scenario_dict["timeframe"]. \
append(timeframe[:21].replace("_", "-\n"))
csv_ready_scenario_dict["datatype"].append(metadata)
csv_ready_scenario_dict["base scenario"].append(scenario)
# insert the total data
overall_information_path = "data/statistical_information/query_research/" + "non_redundant" + \
"/" + metadata + "/scenarios/additional_layer/" \
+ scenario +"_statistical_information.json"
with open(overall_information_path, "r") as overall_data:
overall_dict = json.load(overall_data)
for operator in overall_dict["in_found_prop_path_found_operators_overall"]:
csv_ready_scenario_dict["operator name"].append(operator)
csv_ready_scenario_dict["operator count"]. \
append(overall_dict["in_found_prop_path_found_operators_overall"][operator])
total_occurrences = overall_dict["in_found_prop_path_total_found_operators"]
if total_occurrences > 0:
csv_ready_scenario_dict["operator percentage"]. \
append(
int(overall_dict["in_found_prop_path_found_operators_overall"][operator]) /
total_occurrences)
else:
csv_ready_scenario_dict["operator percentage"]. \
append(0)
csv_ready_scenario_dict["total operators"].append(total_occurrences)
csv_ready_scenario_dict["timeframe"]. \
append("total")
csv_ready_scenario_dict["datatype"].append(metadata)
csv_ready_scenario_dict["base scenario"].append(scenario)
# plot the data in a heatmap
tmp_dict = {}
tmp_dict["operator name"] = []
tmp_dict["timeframe"] = []
tmp_dict["operator percentage"] = []
for i in range(len(csv_ready_scenario_dict["timeframe"])):
tmp_dict["operator name"].append(csv_ready_scenario_dict["operator name"][i])
tmp_dict["timeframe"].append(csv_ready_scenario_dict["timeframe"][i])
tmp_dict["operator percentage"].append(\
csv_ready_scenario_dict["operator percentage"][i])
df = pd.DataFrame(tmp_dict)
df = pd.pivot_table(data=df,
index='operator name',
values='operator percentage',
columns='timeframe', sort=True)
mask = (df == 0)
fig, ax = plt.subplots(figsize=(9, 5)) # 16 10
tmp = sns.heatmap(df, ax=ax, annot=True, vmin = 0, vmax = 1, mask=mask, cmap="Reds",
linewidths=.5)
tmp.figure.tight_layout()
tmp.figure.subplots_adjust(left=0.15, bottom=0.3)
# set the yticks "upright" with 0, as opposed to sideways with 90
plt.yticks(rotation=0)
plt.gcf().autofmt_xdate()
save_path = "data/statistical_information/query_research/non_redundant/" \
+ metadata + "/scenarios/additional_layer/" + \
scenario + "_prop_path_operators.pdf"
tmp.get_figure().savefig(save_path)
plt.close()
# plot only the NON-redundant data
def plot_additional_second_level_operator_information_about_scenarios_per_datatype_for_OPTIONAL(timeframes,
metadata,
scenario):
if metadata not in ["reference_metadata", "rank_metadata", "qualifier_metadata"]:
raise Exception
if scenario not in ["optional"]:
raise Exception
csv_ready_scenario_dict = {}
csv_ready_scenario_dict["datatype"] = []
csv_ready_scenario_dict["base scenario"] = []
csv_ready_scenario_dict["operator name"] = []
csv_ready_scenario_dict["operator count"] = []
csv_ready_scenario_dict["operator percentage"] = []
csv_ready_scenario_dict["total operators"] = []
# insert the total data
overall_information_path = "data/statistical_information/query_research/" + "non_redundant" + \
"/" + metadata + "/scenarios/additional_layer/" \
+ scenario +"_statistical_information.json"
with open(overall_information_path, "r") as overall_data:
overall_dict = json.load(overall_data)
for datatype in overall_dict["in_found_prop_path_found_operators_per_datatype"]:
for operator in overall_dict["in_found_prop_path_found_operators_per_datatype"][datatype]:
csv_ready_scenario_dict["operator name"].append(operator)
csv_ready_scenario_dict["operator count"]. \
append(overall_dict["in_found_prop_path_found_operators_per_datatype"][datatype][operator])
total_occurrences = overall_dict["in_found_prop_path_total_found_operators"]
if total_occurrences > 0:
csv_ready_scenario_dict["operator percentage"]. \
append(
int(overall_dict["in_found_prop_path_found_operators_per_datatype"][datatype][operator]) /
total_occurrences)
else:
csv_ready_scenario_dict["operator percentage"]. \
append(0)
csv_ready_scenario_dict["total operators"].append(total_occurrences)
# e.g. reference_metadata/only_derived -> only_derived
csv_ready_scenario_dict["datatype"].append(datatype.split("/")[1].
replace("_+_", " +\n").
replace("e_", "e\n"))
csv_ready_scenario_dict["base scenario"].append(scenario)
# plot the data in a heatmap
tmp_dict = {}
tmp_dict["operator name"] = []
tmp_dict["datatype"] = []
tmp_dict["operator percentage"] = []
for i in range(len(csv_ready_scenario_dict["datatype"])):
tmp_dict["operator name"].append(csv_ready_scenario_dict["operator name"][i])
tmp_dict["datatype"].append(csv_ready_scenario_dict["datatype"][i])
tmp_dict["operator percentage"].append(\
csv_ready_scenario_dict["operator percentage"][i])
df = pd.DataFrame(tmp_dict)
df = pd.pivot_table(data=df,
index='operator name',
values='operator percentage',
columns='datatype', sort=True)
mask = (df == 0)
fig, ax = plt.subplots(figsize=(6, 6))
tmp = sns.heatmap(df, ax=ax, annot=True, vmin = 0, vmax = 1, mask=mask, cmap="Greys",
linewidths=.5)
tmp.figure.subplots_adjust()
tmp.figure.tight_layout()
tmp.figure.subplots_adjust(left=0.15, bottom=0)
# set the yticks "upright" with 0, as opposed to sideways with 90
plt.yticks(rotation=0)
plt.gcf().autofmt_xdate()
save_path = "data/statistical_information/query_research/non_redundant/" \
+ metadata + "/scenarios/additional_layer/" + \
scenario + "_prop_path_operators_per_datatype.pdf"
tmp.get_figure().savefig(save_path)
plt.close()
| 41.510373 | 115 | 0.592463 | 1,041 | 10,004 | 5.332373 | 0.132565 | 0.066294 | 0.132589 | 0.165736 | 0.883805 | 0.863268 | 0.857143 | 0.857143 | 0.85408 | 0.840389 | 0 | 0.00667 | 0.310576 | 10,004 | 240 | 116 | 41.683333 | 0.798173 | 0.043683 | 0 | 0.701863 | 0 | 0 | 0.216372 | 0.109808 | 0.012422 | 0 | 0 | 0 | 0 | 1 | 0.012422 | false | 0 | 0.031056 | 0 | 0.043478 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
2c668aa48dc5db25540507a87523989f031dc268 | 2,434 | py | Python | Bugscan_exploits-master/exp_list/exp－1723.py | csadsl/poc_exp | e3146262e7403f19f49ee2db56338fa3f8e119c9 | [
"MIT"
] | 11 | 2020-05-30T13:53:49.000Z | 2021-03-17T03:20:59.000Z | Bugscan_exploits-master/exp_list/exp－1723.py | csadsl/poc_exp | e3146262e7403f19f49ee2db56338fa3f8e119c9 | [
"MIT"
] | 6 | 2020-05-13T03:25:18.000Z | 2020-07-21T06:24:16.000Z | Bugscan_exploits-master/exp_list/exp－1723.py | csadsl/poc_exp | e3146262e7403f19f49ee2db56338fa3f8e119c9 | [
"MIT"
] | 6 | 2020-05-30T13:53:51.000Z | 2020-12-01T21:44:26.000Z | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#author:小光
#refer:http://www.wooyun.org/bugs/wooyun-2015-0138680
import time
def assign(service, arg):
if service == "yongyou_nc":
return True, arg
def audit(arg):
url = arg + 'nc/servlet/nc.ui.iufo.login.LoginUI'
postdatas ={
'LoginButton=%e7%99%bb%e5%bd%95(Login)¤tDate=2015-09-02&dschoice=aorwpw5ufcw6&hidBack=&languagechoice=simpchn&operType=null&refrence=%e5%8f%82%e7%85%a7(Ref)&timeRef=%e5%8f%82%e7%85%a7(Ref)&UserCodeText=wxbsisqq&UserPassText=wxbsisqq&UserSeleLang=simpchn&UserUnitText=asd%27%29%20AND%208148%3DDBMS_PIPE.RECEIVE_MESSAGE%28CHR%2872%29%7C%7CCHR%2867%29%7C%7CCHR%2885%29%7C%7CCHR%2876%29%2C5%29%20AND%20%28%271%27%3D%271':'LoginButton=%e7%99%bb%e5%bd%95(Login)¤tDate=2015-09-02&dschoice=aorwpw5ufcw6&hidBack=&languagechoice=simpchn&operType=null&refrence=%e5%8f%82%e7%85%a7(Ref)&timeRef=%e5%8f%82%e7%85%a7(Ref)&UserCodeText=wxbsisqq&UserPassText=wxbsisqq&UserSeleLang=simpchn&UserUnitText=asd%27%29%20AND%208148%3DDBMS_PIPE.RECEIVE_MESSAGE%28CHR%2872%29%7C%7CCHR%2867%29%7C%7CCHR%2885%29%7C%7CCHR%2876%29%2C1%29%20AND%20%28%271%27%3D%271',
'LoginButton=%e7%99%bb%e5%bd%95(Login)¤tDate=2015-09-02&dschoice=aorwpw5ufcw6&hidBack=&languagechoice=simpchn&operType=null&refrence=%e5%8f%82%e7%85%a7(Ref)&timeRef=%e5%8f%82%e7%85%a7(Ref)&UserCodeText=wxbsisqq&UserPassText=wxbsisqq&UserSeleLang=simpchn&UserUnitText=asd%27%29%3BWAITFOR%20DELAY%20%270%3A0%3A5%27--':'LoginButton=%e7%99%bb%e5%bd%95(Login)¤tDate=2015-09-02&dschoice=aorwpw5ufcw6&hidBack=&languagechoice=simpchn&operType=null&refrence=%e5%8f%82%e7%85%a7(Ref)&timeRef=%e5%8f%82%e7%85%a7(Ref)&UserCodeText=wxbsisqq&UserPassText=wxbsisqq&UserSeleLang=simpchn&UserUnitText=asd%27%29%3BWAITFOR%20DELAY%20%270%3A0%3A1%27--'
}
for postdata in postdatas:
t1 = time.time()
code1, head, res1, errcode, _ = curl.curl2(url,postdata)
t2 = time.time()
code2, head, res2, errcode, _ = curl.curl2(url,postdatas[postdata])
t3 = time.time()
if code1==200 and code2 == 200 and (2*t2 - t1 - t3 > 3):
security_hole(url + " :post Injection")
if __name__ == '__main__':
from dummy import *
audit(assign('yongyou_nc', 'http://61.135.227.114/')[1])
audit(assign('yongyou_nc', 'http://101.95.113.130/')[1]) | 76.0625 | 851 | 0.69926 | 371 | 2,434 | 4.539084 | 0.342318 | 0.019002 | 0.028504 | 0.038005 | 0.729216 | 0.700713 | 0.700713 | 0.700713 | 0.700713 | 0.700713 | 0 | 0.163858 | 0.122432 | 2,434 | 32 | 852 | 76.0625 | 0.624532 | 0.042317 | 0 | 0 | 0 | 0.181818 | 0.70222 | 0.658685 | 0 | 0 | 0 | 0 | 0 | 1 | 0.090909 | false | 0.090909 | 0.090909 | 0 | 0.227273 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
2cacd3ac099a9a692b63bde28cfa56e0872ca82b | 25 | py | Python | python/testData/MockSdkWithBinaryModules/python_stubs/oldnumpy/core/umath.py | adehtiarov/intellij-community | 82826022ae57a7ae5e7f8fe3430f2ea4fc1a8f86 | [
"Apache-2.0"
] | 2 | 2018-12-29T09:53:39.000Z | 2018-12-29T09:53:42.000Z | python/testData/MockSdkWithBinaryModules/python_stubs/oldnumpy/core/umath.py | tnorbye/intellij-community | f01cf262fc196bf4dbb99e20cd937dee3705a7b6 | [
"Apache-2.0"
] | null | null | null | python/testData/MockSdkWithBinaryModules/python_stubs/oldnumpy/core/umath.py | tnorbye/intellij-community | f01cf262fc196bf4dbb99e20cd937dee3705a7b6 | [
"Apache-2.0"
] | null | null | null | def log():
return 1.0 | 12.5 | 14 | 0.56 | 5 | 25 | 2.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 0.28 | 25 | 2 | 14 | 12.5 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0.5 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
e2f110ca62c190bddaaabb3c870620163646540d | 1,017 | py | Python | examples/simple_book.py | kapinga/piecash | ec30cf469198cccf35f7ba968f889d360cfe1824 | [
"MIT"
] | 223 | 2015-01-12T22:02:53.000Z | 2022-03-03T22:05:42.000Z | examples/simple_book.py | kapinga/piecash | ec30cf469198cccf35f7ba968f889d360cfe1824 | [
"MIT"
] | 158 | 2015-03-16T19:57:29.000Z | 2022-01-31T23:22:57.000Z | examples/simple_book.py | kapinga/piecash | ec30cf469198cccf35f7ba968f889d360cfe1824 | [
"MIT"
] | 84 | 2015-02-06T14:17:17.000Z | 2022-03-14T02:13:50.000Z | from __future__ import print_function
from piecash import create_book
# create by default an in memory sqlite version
with create_book(echo=False) as book:
print("Book is saved:", book.is_saved, end=" ")
print(" ==> book description:", book.root_account.description)
print("changing description...")
book.root_account.description = "hello, book"
print("Book is saved:", book.is_saved, end=" ")
print(" ==> book description:", book.root_account.description)
print("saving...")
book.save()
print("Book is saved:", book.is_saved, end=" ")
print(" ==> book description:", book.root_account.description)
print("changing description...")
book.root_account.description = "nevermind, book"
print("Book is saved:", book.is_saved, end=" ")
print(" ==> book description:", book.root_account.description)
print("cancel...")
book.cancel()
print("Book is saved:", book.is_saved, end=" ")
print(" ==> book description:", book.root_account.description)
| 32.806452 | 66 | 0.672566 | 126 | 1,017 | 5.277778 | 0.246032 | 0.135338 | 0.165414 | 0.273684 | 0.754887 | 0.754887 | 0.754887 | 0.754887 | 0.754887 | 0.754887 | 0 | 0 | 0.169125 | 1,017 | 30 | 67 | 33.9 | 0.786982 | 0.044248 | 0 | 0.571429 | 0 | 0 | 0.283505 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.095238 | 0 | 0.095238 | 0.714286 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 8 |
3939092a735662ab4f640101981b68eeb5655262 | 29,111 | py | Python | locan/tests/data/test_aggregate.py | super-resolution/Locan | 94ed7759f7d7ceddee7c7feaabff80010cfedf30 | [
"BSD-3-Clause"
] | 8 | 2021-11-25T20:05:49.000Z | 2022-03-27T17:45:00.000Z | locan/tests/data/test_aggregate.py | super-resolution/Locan | 94ed7759f7d7ceddee7c7feaabff80010cfedf30 | [
"BSD-3-Clause"
] | 4 | 2021-12-15T22:39:20.000Z | 2022-03-11T17:35:34.000Z | locan/tests/data/test_aggregate.py | super-resolution/Locan | 94ed7759f7d7ceddee7c7feaabff80010cfedf30 | [
"BSD-3-Clause"
] | 1 | 2022-03-22T19:53:13.000Z | 2022-03-22T19:53:13.000Z | import boost_histogram as bh
import numpy as np
import pytest
from locan import Bins, histogram
from locan.data.aggregate import (
_bin_edges_to_bin_centers,
_bin_edges_to_bin_size,
_bin_edges_to_bin_size_one_dimension,
_bin_edges_to_n_bins,
_bin_edges_to_n_bins_one_dimension,
_bin_size_to_bin_edges_one_dimension,
_BinsFromBoostHistogramAxis,
_BinsFromEdges,
_BinsFromNumber,
_BinsFromSize,
_indices_to_bin_centers,
_is_1d_array_of_scalar,
_is_1d_array_of_two_scalar,
_is_2d_homogeneous_array,
_is_2d_inhomogeneous_array,
_is_2d_inhomogeneous_array_of_1d_array_of_scalar,
_is_scalar,
_is_single_element,
_n_bins_to_bin_edges_one_dimension,
)
data_scalars = {"1": 1, "()": ()}
data_tuples = {
"((),)": ((),),
"(1,)": (1,),
"(1, 2)": (1, 2),
"(1, 2, 3)": (1, 2, 3),
"((1, 2),)": ((1, 2),),
"((1, 2), (1, 2))": ((1, 2), (1, 2)),
"((1, 2), (1, 2, 3))": ((1, 2), (1, 2, 3)),
"((1, 2), ((1, 2), (1, 2)))": ((1, 2), ((1, 2), (1, 2))),
"(1, (1, 2))": (1, (1, 2)),
}
data_lists = {
"[[]]": [[]],
"[1]": [1],
"[1, 2]": [1, 2],
"[1, 2, 3]": [1, 2, 3],
"[[1, 2]]": [[1, 2]],
"[[1, 2], [1, 2]]": [[1, 2], [1, 2]],
"[[1, 2], [1, 2, 3]]": [[1, 2], [1, 2, 3]],
"[[1, 2], [[1, 2], [1, 2]]]": [[1, 2], [[1, 2], [1, 2]]],
"[1, [1, 2]]": [1, [1, 2]],
}
data_ndarrays = {
"np.array((1))": np.array((1)),
"np.array((1,))": np.array((1,)),
"np.array((1, 2))": np.array((1, 2)),
"np.array([(1, 2)])": np.array([(1, 2)]),
"np.array([(1, 2)], dtype=object)": np.array([(1, 2)], dtype=object),
"np.array([(1, 2), (1, 2, 3)], dtype=object)": np.array(
[(1, 2), (1, 2, 3)], dtype=object
),
}
data_all = {**data_scalars, **data_tuples, **data_lists, **data_ndarrays}
expect_is_scalar = ["1", "np.array((1))"]
expect_is_single_element = ["1", "(1,)", "[1]", "np.array((1))", "np.array((1,))"]
expect_is_1d_array_of_scalar = [
"(1, 2)",
"(1, 2, 3)",
"[1, 2]",
"[1, 2, 3]",
"np.array((1, 2))",
"(1,)",
"[1]",
"np.array((1,))",
]
expect_is_1d_array_of_two_scalar = ["(1, 2)", "[1, 2]", "np.array((1, 2))"]
expect_is_2d_homogeneous_array = [
"((1, 2),)",
"((1, 2), (1, 2))",
"[[1, 2]]",
"[[1, 2], [1, 2]]",
"np.array([(1, 2)])",
]
expect_is_2d_inhomogeneous_array = [
"((1, 2), (1, 2, 3))",
"(1, (1, 2))",
"[[1, 2], [1, 2, 3]]",
"[1, [1, 2]]",
"np.array([(1, 2), (1, 2, 3)], dtype=object)",
]
expect_is_2d_inhomogeneous_array_of_1d_array_of_scalar = [
"((1, 2), (1, 2, 3))",
"[[1, 2], [1, 2, 3]]",
"np.array([(1, 2), (1, 2, 3)], dtype=object)",
]
def test__is_scalar():
for key, value in data_all.items():
if key in expect_is_scalar:
assert _is_scalar(value)
else:
assert not _is_scalar(value)
def test__is_single_element():
for key, value in data_all.items():
if key in expect_is_single_element:
assert _is_single_element(value)
else:
assert not _is_single_element(value)
def test__is_1d_array_of_scalar():
for key, value in data_all.items():
if key in expect_is_1d_array_of_scalar:
assert _is_1d_array_of_scalar(value)
else:
assert not _is_1d_array_of_scalar(value)
def test__is_1d_array_of_two_scalar():
for key, value in data_all.items():
if key in expect_is_1d_array_of_two_scalar:
assert _is_1d_array_of_two_scalar(value)
else:
assert not _is_1d_array_of_two_scalar(value)
def test__is_2d_homogeneous_array():
for key, value in data_all.items():
if key in expect_is_2d_homogeneous_array:
assert _is_2d_homogeneous_array(value)
else:
assert not _is_2d_homogeneous_array(value)
def test__is_2d_inhomogeneous_array():
for key, value in data_all.items():
if key in expect_is_2d_inhomogeneous_array:
assert _is_2d_inhomogeneous_array(value)
else:
assert not _is_2d_inhomogeneous_array(value)
def test__is_2d_inhomogeneous_array_of_1d_array_of_scalar():
for key, value in data_all.items():
if key in expect_is_2d_inhomogeneous_array_of_1d_array_of_scalar:
assert _is_2d_inhomogeneous_array_of_1d_array_of_scalar(value)
else:
assert not _is_2d_inhomogeneous_array_of_1d_array_of_scalar(value)
def test__n_bins_to_bin_edges_one_dimension():
bin_edges = _n_bins_to_bin_edges_one_dimension(10, (10, 20))
assert bin_edges.shape == (11,)
def test__bin_size_to_bin_edges_one_dimension():
bin_edges = _bin_size_to_bin_edges_one_dimension(4, (1, 10), extend_range=None)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 5, 9))
bin_edges = _bin_size_to_bin_edges_one_dimension(4, (1, 10), extend_range=True)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 5, 9, 13))
bin_edges = _bin_size_to_bin_edges_one_dimension(4, (1, 10), extend_range=False)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 5, 9, 10))
bin_edges = _bin_size_to_bin_edges_one_dimension(20, (1, 10), extend_range=None)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 10))
bin_edges = _bin_size_to_bin_edges_one_dimension(20, (1, 10), extend_range=True)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 21))
bin_edges = _bin_size_to_bin_edges_one_dimension(20, (1, 10), extend_range=False)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 10))
bin_edges = _bin_size_to_bin_edges_one_dimension(
(1, 2, 3, 3, 2), (1, 11), extend_range=None
)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 2, 4, 7, 10))
bin_edges = _bin_size_to_bin_edges_one_dimension(
(1, 2, 3, 3, 2), (1, 11), extend_range=True
)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 2, 4, 7, 10, 12))
bin_edges = _bin_size_to_bin_edges_one_dimension(
(1, 2, 3, 3, 2), (1, 11), extend_range=False
)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 2, 4, 7, 10, 11))
bin_edges = _bin_size_to_bin_edges_one_dimension(
(10, 20, 30), (1, 2), extend_range=None
)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 2))
bin_edges = _bin_size_to_bin_edges_one_dimension(
(10, 20, 30), (1, 2), extend_range=True
)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 11))
bin_edges = _bin_size_to_bin_edges_one_dimension(
(10, 20, 30), (1, 2), extend_range=False
)
isinstance(bin_edges, np.ndarray)
assert np.array_equal(bin_edges, (1, 2))
with pytest.raises(TypeError):
_bin_size_to_bin_edges_one_dimension(((4,), 2), (0, 10), extend_range=False)
def test__bin_edges_to_n_bins_one_dimension():
n_bins = _bin_edges_to_n_bins_one_dimension((1, 3, 5))
assert n_bins == 2
n_bins = _bin_edges_to_n_bins_one_dimension([1, 2, 4])
assert n_bins == 2
def test__bin_edges_to_n_bins():
n_bins = _bin_edges_to_n_bins([1, 3, 5])
assert n_bins == (2,)
n_bins = _bin_edges_to_n_bins(([1, 3, 5],))
assert n_bins == (2,)
n_bins = _bin_edges_to_n_bins([1, 2, 4])
assert n_bins == (2,)
n_bins = _bin_edges_to_n_bins(((1, 3, 5), (1, 2, 4, 5)))
assert np.array_equal(n_bins, (2, 3))
n_bins = _bin_edges_to_n_bins([[1, 3, 5], [1, 2, 3, 4]])
assert np.array_equal(n_bins, (2, 3))
n_bins = _bin_edges_to_n_bins(np.array([[1, 3, 5], [1, 2, 3]]))
assert np.array_equal(n_bins, (2, 2))
def test__bin_edges_to_bin_size_one_dimension():
bin_size = _bin_edges_to_bin_size_one_dimension((1, 3, 5))
assert bin_size == 2
bin_size = _bin_edges_to_bin_size_one_dimension([1, 2, 4])
assert np.array_equal(bin_size, (1, 2))
bin_size = _bin_edges_to_bin_size_one_dimension([1, 2])
assert bin_size == 1
def test__bin_edges_to_bin_size():
bin_size = _bin_edges_to_bin_size([1, 3, 5])
assert bin_size == (2,)
bin_size = _bin_edges_to_bin_size(([1, 3, 5],))
assert bin_size == (2,)
bin_size = _bin_edges_to_bin_size([1, 2, 4])
assert np.array_equal(bin_size[0], (1, 2))
bin_size = _bin_edges_to_bin_size(((1, 3, 5), (1, 2, 4, 5)))
assert bin_size[0] == 2
assert np.array_equal(bin_size[1], (1, 2, 1))
bin_size = _bin_edges_to_bin_size([[1, 3, 5], [1, 2, 3, 4]])
assert bin_size == (2, 1)
bin_size = _bin_edges_to_bin_size(np.array([[1, 3, 5], [1, 2, 3]]))
assert bin_size == (2, 1)
def test__bin_edges_to_bin_centers():
bin_centers = _bin_edges_to_bin_centers([1, 3, 5])
assert np.array_equal(bin_centers, ((2, 4),))
bin_centers = _bin_edges_to_bin_centers(([1, 3, 5],))
assert np.array_equal(bin_centers, ((2, 4),))
bin_centers = _bin_edges_to_bin_centers(((1, 3, 5), (1, 2, 4, 6)))
expected = ((2, 4), (1.5, 3, 5))
for bc, ex in zip(bin_centers, expected):
assert np.array_equal(bc, ex)
bin_edges = np.array([[0, 1, 2, 4, 8, 9], [0, 1, 4, 8]], dtype=object)
bin_centers = _bin_edges_to_bin_centers(bin_edges)
expected = (np.array([0.5, 1.5, 3.0, 6, 8.5]), np.array([0.5, 2.5, 6]))
for bc, ex in zip(bin_centers, expected):
assert np.array_equal(bc, ex)
def test__indices_to_bin_centers():
indices = 2
bin_edges = np.array([0, 1, 2, 4, 8, 9])
bin_centers = _indices_to_bin_centers(bin_edges, indices)
expected = 3
assert np.array_equal(bin_centers, expected)
indices = np.array([0, 2, 1])
bin_edges = np.array([0, 1, 2, 4, 8, 9])
bin_centers = _indices_to_bin_centers(bin_edges, indices)
expected = np.array([0.5, 3, 1.5])
assert np.array_equal(bin_centers, expected)
indices = np.array([[0, 1], [2, 2], [4, 3]])
bin_edges = np.array([0, 1, 2, 4, 8, 9])
bin_centers = _indices_to_bin_centers(bin_edges, indices)
expected = np.array([[0.5, 1.5], [3, 3], [8.5, 6]])
assert np.array_equal(bin_centers, expected)
indices = np.array([[0, 1], [2, 2], [4, 3]])
bin_edges = np.array([[0, 1, 2, 4, 8, 9], [1, 2, 4, 8, 9]], dtype=object)
bin_centers = _indices_to_bin_centers(bin_edges, indices)
expected = np.array([[0.5, 3], [3, 6], [8.5, 8.5]])
assert np.array_equal(bin_centers, expected)
def test__BinsFromBoostHistogramAxis():
bhaxis = bh.axis.Regular(5, 0, 10)
bins = _BinsFromBoostHistogramAxis(bins=bhaxis)
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == ((2, 2, 2, 2, 2),)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_centers[0], np.array([1, 3, 5, 7, 9]))
bhaxes = bh.axis.AxesTuple((bh.axis.Regular(5, 0, 10), bh.axis.Regular(2, 0, 10)))
bins = _BinsFromBoostHistogramAxis(bins=bhaxes)
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 10.0), (0.0, 10.0))
assert bins.n_bins == (5, 2)
assert bins.bin_size == ((2.0, 2.0, 2.0, 2.0, 2.0), (5.0, 5.0))
expected_edges = [np.array([0, 2, 4, 6, 8, 10]), np.array([0, 5, 10])]
for bin_edges, edges in zip(bins.bin_edges, expected_edges):
assert np.array_equal(bin_edges, edges)
expected_centers = [np.array([1, 3, 5, 7, 9]), np.array([2.5, 7.5])]
for bin_centers, expected_cents in zip(bins.bin_centers, expected_centers):
assert np.array_equal(bin_centers, expected_cents)
def test__BinsFromEdges():
bins = _BinsFromEdges(bin_edges=(0, 2, 4))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 4.0),)
assert bins.n_bins == (2,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4]))
bins = _BinsFromEdges(bin_edges=((0, 2, 4),))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 4.0),)
assert bins.n_bins == (2,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4]))
bins = _BinsFromEdges(bin_edges=(1, 2, 5))
assert bins.dimension == 1
assert bins.bin_range == ((1, 5),)
assert bins.n_bins == (2,)
assert np.array_equal(bins.bin_size[0], np.array([1, 3]))
assert np.array_equal(bins.bin_edges[0], np.array([1, 2, 5]))
bins = _BinsFromEdges(bin_edges=((0, 2, 4), (1, 2, 5)))
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 4.0), (1.0, 5.0))
assert bins.n_bins == (2, 2)
assert bins.bin_size[0] == 2
assert np.array_equal(bins.bin_size[1], np.array([1, 3]))
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4]))
assert np.array_equal(bins.bin_edges[-1], np.array([1, 2, 5]))
with pytest.raises(TypeError):
_BinsFromEdges(bin_edges=())
with pytest.raises(TypeError):
_BinsFromEdges(bin_edges=[(0, 2, 4), ((0, 2, 4), (0, 2, 4))])
with pytest.raises(TypeError):
_BinsFromEdges(bin_edges=[(0, 2, 4), 2])
def test__BinsFromNumber_():
bins = _BinsFromNumber(n_bins=5, bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
bins = _BinsFromNumber(n_bins=(5,), bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
bins = _BinsFromNumber(n_bins=(2, 5), bin_range=((0, 10), (0, 5)))
assert bins.dimension == 2
assert bins.bin_range == ((0, 10), (0, 5))
assert bins.n_bins == (2, 5)
assert bins.bin_size == (5, 1)
assert np.array_equal(bins.bin_edges[0], np.array([0, 5, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 1, 2, 3, 4, 5]))
bins = _BinsFromNumber(n_bins=2, bin_range=((0, 10), (0, 5)))
assert bins.dimension == 2
assert bins.bin_range == ((0, 10), (0, 5))
assert bins.n_bins == (2, 2)
assert bins.bin_size == (5, 2.5)
assert np.array_equal(bins.bin_edges[0], np.array([0, 5, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 2.5, 5]))
bins = _BinsFromNumber(n_bins=(2, 5, 2), bin_range=(0, 10))
assert bins.dimension == 3
assert bins.bin_range == ((0, 10), (0, 10), (0, 10))
assert bins.n_bins == (2, 5, 2)
assert bins.bin_size == (5, 2, 5)
assert np.array_equal(bins.bin_edges[0], np.array([0, 5, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_edges[2], np.array([0, 5, 10]))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=5, bin_range=1)
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=5, bin_range=(0,))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=5, bin_range=(1, 2, 3))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(5, (1, 2)), bin_range=(1, 2, 3))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(5,), bin_range=1)
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(5,), bin_range=(0,))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(5,), bin_range=(1, 2, 3))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(2,), bin_range=((0, 10), (0, 5)))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(2, 5, 2), bin_range=1)
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(2, 5, 2), bin_range=(0,))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(2, 5, 2), bin_range=(1, 2, 3))
with pytest.raises(TypeError):
_BinsFromNumber(n_bins=(5, (1, 2)), bin_range=(1, 2))
def test__BinsFromSize():
bins = _BinsFromSize(bin_size=2, bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
bins = _BinsFromSize(bin_size=(2,), bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
bins = _BinsFromSize(bin_size=((1, 2, 3, 4),), bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (4,)
assert np.array_equal(bins.bin_size[0], (1, 2, 3, 4))
assert np.array_equal(bins.bin_edges[0], np.array([0, 1, 3, 6, 10]))
bins = _BinsFromSize(bin_size=3, bin_range=(0, 10), extend_range=False)
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (4,)
assert np.array_equal(bins.bin_size[0], (3.0, 3.0, 3.0, 1.0))
assert np.array_equal(bins.bin_edges[0], np.array([0, 3, 6, 9, 10]))
bins = _BinsFromSize(bin_size=(5, 1), bin_range=((0, 10), (0, 5)))
assert bins.dimension == 2
assert bins.bin_range == ((0, 10), (0, 5))
assert bins.n_bins == (2, 5)
assert bins.bin_size == (5, 1)
assert np.array_equal(bins.bin_edges[0], np.array([0, 5, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 1, 2, 3, 4, 5]))
bins = _BinsFromSize(bin_size=2, bin_range=((0, 10), (0, 5)))
assert bins.dimension == 2
assert bins.bin_range == ((0, 10), (0, 4))
assert bins.n_bins == (5, 2)
assert bins.bin_size == (2, 2)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 2, 4]))
bins = _BinsFromSize(bin_size=(2, 5), bin_range=(0, 10))
assert bins.dimension == 2
assert bins.bin_range == ((0, 10), (0, 10))
assert bins.n_bins == (5, 2)
assert bins.bin_size == (2, 5)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 5, 10]))
bins = _BinsFromSize(bin_size=((1, 2, 3, 4), (1, 2, 3, 1)), bin_range=(0, 10))
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 10.0), (0.0, 7.0))
assert bins.n_bins == (4, 4)
assert np.array_equal(bins.bin_size[0], (1, 2, 3, 4))
assert np.array_equal(bins.bin_size[1], (1, 2, 3, 1))
assert np.array_equal(bins.bin_edges[0], np.array([0, 1, 3, 6, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 1, 3, 6, 7]))
bins = _BinsFromSize(
bin_size=((1, 2, 3, 4), (1, 2, 3, 4)), bin_range=((0, 10), (0, 10))
)
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 10.0), (0.0, 10.0))
assert bins.n_bins == (4, 4)
assert np.array_equal(bins.bin_size[0], (1, 2, 3, 4))
assert np.array_equal(bins.bin_size[1], (1, 2, 3, 4))
assert np.array_equal(bins.bin_edges[0], np.array([0, 1, 3, 6, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 1, 3, 6, 10]))
bins = _BinsFromSize(bin_size=(2, (1, 2, 3, 4)), bin_range=(0, 10))
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 10.0), (0.0, 10.0))
assert bins.n_bins == (5, 4)
assert bins.bin_size[0] == 2
assert np.array_equal(bins.bin_size[1], (1, 2, 3, 4))
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 1, 3, 6, 10]))
bins = _BinsFromSize(bin_size=(2, (1, 2, 3, 4)), bin_range=((0, 10), (0, 20)))
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 10.0), (0.0, 10.0))
assert bins.n_bins == (5, 4)
assert bins.bin_size[0] == 2
assert np.array_equal(bins.bin_size[1], (1, 2, 3, 4))
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_edges[1], np.array([0, 1, 3, 6, 10]))
with pytest.raises(TypeError):
_BinsFromSize(bin_size=5, bin_range=(0,))
with pytest.raises(TypeError):
_BinsFromSize(bin_size=5, bin_range=(1, 2, 3))
with pytest.raises(TypeError):
_BinsFromSize(bin_size=(2, 2, (1, 2, 3, 4)), bin_range=((0, 10), (0, 20)))
with pytest.raises(TypeError):
_BinsFromSize(
bin_size=((1, 2, 3, 4), (1, 2, 3, 4), (1, 2, 3, 4)),
bin_range=((0, 10), (0, 20)),
)
def test_Bins():
bins = Bins(bin_edges=(0, 2, 4))
assert bins.dimension == 1
assert bins.bin_range == ((0, 4),)
assert bins.n_bins == (2,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4]))
assert np.array_equal(bins.bin_centers[0], np.array([1, 3]))
assert bins.is_equally_sized == (True,)
bins = Bins(n_bins=5, bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_centers[0], np.array([1, 3, 5, 7, 9]))
assert bins.is_equally_sized == (True,)
bins = Bins(bin_size=2, bin_range=(0, 10))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_centers[0], np.array([1, 3, 5, 7, 9]))
assert bins.is_equally_sized == (True,)
bins = Bins(bin_size=(2, (1, 2, 3)), bin_range=(0, 10))
assert bins.dimension == 2
assert bins.bin_range == ((0.0, 10.0), (0.0, 6.0))
assert bins.n_bins == (5, 3)
assert np.array_equal(bins.bin_size[0], 2)
assert np.array_equal(bins.bin_size[1], (1, 2, 3))
assert bins.is_equally_sized == (True, False)
bins = Bins(bins=Bins(n_bins=5, bin_range=(0, 10)))
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == (2,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_centers[0], np.array([1, 3, 5, 7, 9]))
assert bins.is_equally_sized == (True,)
bins = Bins(n_bins=(2, 5), bin_range=(0, 10), labels=["position_x", "position_y"])
assert bins.labels == ["position_x", "position_y"]
assert bins.dimension == 2
assert bins.is_equally_sized == (True, True)
bins = Bins(bins=Bins(n_bins=5, bin_range=(0, 10)), labels=["position_x"])
assert bins.labels == ["position_x"]
assert bins.dimension == 1
bins = Bins(bins=Bins(n_bins=5, bin_range=(0, 10), labels=["position_x"]))
assert bins.labels == ["position_x"]
assert bins.dimension == 1
with pytest.raises(ValueError):
Bins(n_bins=5, bin_range=(0, 10), labels=["position_x", "position_y"])
def test_Bins_with_boost_histogram():
bhaxis = bh.axis.Regular(5, 0, 10)
bins = Bins(bins=bhaxis)
assert bins.dimension == 1
assert bins.bin_range == ((0.0, 10.0),)
assert bins.n_bins == (5,)
assert bins.bin_size == ((2, 2, 2, 2, 2),)
assert np.array_equal(bins.bin_edges[0], np.array([0, 2, 4, 6, 8, 10]))
assert np.array_equal(bins.bin_centers[0], np.array([1, 3, 5, 7, 9]))
assert bins.is_equally_sized == (True,)
def test_Bins_methods():
bins = Bins(bin_edges=(0, 1, 2, 4))
assert bins.dimension == 1
assert bins.bin_range == ((0, 4),)
assert bins.n_bins == (3,)
assert np.array_equal(bins.bin_size[0], (1, 1, 2))
assert np.array_equal(bins.bin_edges[0], np.array([0, 1, 2, 4]))
assert bins.is_equally_sized == (False,)
bins = Bins(bin_edges=(0, 1, 2, 4)).equalize_bin_size()
assert bins.dimension == 1
assert bins.bin_range == ((0, 4),)
assert bins.n_bins == (4,)
assert bins.bin_size == (1,)
assert np.array_equal(bins.bin_edges[0], np.array([0, 1, 2, 3, 4]))
assert bins.is_equally_sized == (True,)
def test_histogram(locdata_blobs_2d):
hist = histogram(locdata_blobs_2d, n_bins=10)
assert hist.labels == ["position_x", "position_y", "counts"]
assert hist.data.dtype == "float64"
assert hist.data.ndim == 2
assert np.max(hist.data) == 7
hist = histogram(locdata_blobs_2d, n_bins=10, bin_range=((500, 1000), (500, 1000)))
assert hist.data.ndim == 2
assert np.max(hist.data) == 5
assert hist.data.shape == (10, 10)
hist = histogram(locdata_blobs_2d, bin_size=10, loc_properties="position_x")
assert hist.labels == ["position_x", "counts"]
assert hist.data.shape == (89,)
with pytest.raises(ValueError):
histogram(
locdata_blobs_2d,
bin_size=10,
loc_properties="position_x",
bin_range=((500, 1000), (500, 1000)),
)
hist = histogram(locdata_blobs_2d, bin_size=10, loc_properties=["position_x"])
assert hist.labels == ["position_x", "counts"]
assert hist.data.shape == (89,)
hist = histogram(
locdata_blobs_2d, bin_size=10, loc_properties=["position_x", "position_y"]
)
assert hist.labels == ["position_x", "position_y", "counts"]
assert hist.data.shape == (89, 55)
hist = histogram(
locdata_blobs_2d, bin_size=10, loc_properties=["position_x", "cluster_label"]
)
assert hist.labels == ["position_x", "cluster_label", "counts"]
assert hist.data.shape == (89, 1)
with pytest.raises(ValueError):
histogram(locdata_blobs_2d, bin_size=10, loc_properties="position_z")
with pytest.raises(ValueError):
histogram(locdata_blobs_2d, bin_size=10, loc_properties=["position_z"])
with pytest.raises(ValueError):
histogram(
locdata_blobs_2d, bin_size=10, loc_properties=["position_x", "position_z"]
)
hist = histogram(
locdata_blobs_2d,
bin_edges=((500, 600, 700, 800, 900, 1000), (500, 600, 700, 800, 900, 1000)),
)
assert hist.data.ndim == 2
assert np.max(hist.data) == 7
hist = histogram(locdata_blobs_2d, bin_size=10, other_property="position_y")
assert hist.labels == ["position_x", "position_y", "position_y"]
assert hist.data.shape == (89, 55)
def test_histogram_1d(locdata_1d):
hist = histogram(locdata_1d, n_bins=10)
assert hist.labels == ["position_x", "counts"]
assert hist.data.dtype == "float64"
assert hist.data.ndim == 1
assert np.max(hist.data) == 2
assert hist.data.shape == (10,)
hist = histogram(locdata_1d, n_bins=5, bin_range=(5, 10))
assert np.max(hist.data) == 1
assert hist.data.shape == (5,)
hist = histogram(locdata_1d, bin_edges=(5, 6, 7, 8, 9, 10))
assert hist.data.shape == (5,)
hist = histogram(locdata_1d, n_bins=10, other_property="intensity")
assert hist.labels == ["position_x", "intensity"]
assert hist.data.shape == (10,)
assert np.nanmax(hist.data) == 125
def test_histogram_3d(locdata_blobs_3d):
hist = histogram(locdata_blobs_3d, n_bins=10)
assert hist.labels == ["position_x", "position_y", "position_z", "counts"]
assert hist.data.dtype == "float64"
assert hist.data.ndim == 3
assert np.max(hist.data) == 6
assert hist.data.shape == (10, 10, 10)
hist = histogram(
locdata_blobs_3d, n_bins=10, bin_range=((500, 1000), (500, 1000), (500, 1000))
)
assert np.max(hist.data) == 4
assert hist.data.shape == (10, 10, 10)
hist = histogram(
locdata_blobs_3d,
bin_edges=(
(500, 600, 700, 800, 900, 1000),
(500, 600, 700, 800, 900, 1000),
(500, 600, 700, 800, 900, 1000),
),
)
assert hist.data.shape == (5, 5, 5)
hist = histogram(locdata_blobs_3d, n_bins=10, other_property="position_y")
assert hist.labels == ["position_x", "position_y", "position_z", "position_y"]
assert hist.data.shape == (10, 10, 10)
assert np.nanmax(hist.data) == 787
def test_histogram_empty(locdata_empty):
with pytest.raises(TypeError):
hist = histogram(locdata_empty, n_bins=10)
def test_histogram_single_value(locdata_single_localization_3d):
hist = histogram(locdata_single_localization_3d, n_bins=3)
assert hist.data.shape == (3, 3, 3)
assert np.array_equal(hist.bins.bin_range, [[1, 2], [1, 2], [1, 2]])
hist = histogram(locdata_single_localization_3d, bin_size=0.2)
assert hist.data.shape == (5, 5, 5)
assert np.array_equal(
hist.bins.bin_range,
[[1, pytest.approx(2)], [1, pytest.approx(2)], [1, pytest.approx(2)]],
)
hist = histogram(locdata_single_localization_3d, bin_size=2)
assert hist.data.shape == (1, 1, 1)
assert np.array_equal(hist.bins.bin_range, [[1, 2], [1, 2], [1, 2]])
def test_histogram_2d_negative_values(locdata_2d_negative):
hist = histogram(locdata_2d_negative, n_bins=10)
assert hist.labels == ["position_x", "position_y", "counts"]
assert hist.data.shape == (10, 10)
| 37.226343 | 87 | 0.619766 | 4,814 | 29,111 | 3.489821 | 0.028251 | 0.074167 | 0.069643 | 0.096429 | 0.915298 | 0.874881 | 0.842798 | 0.79375 | 0.752024 | 0.702083 | 0 | 0.081037 | 0.200955 | 29,111 | 781 | 88 | 37.274008 | 0.641202 | 0 | 0 | 0.447167 | 0 | 0 | 0.044107 | 0 | 0 | 0 | 0 | 0 | 0.445636 | 1 | 0.042879 | false | 0 | 0.007657 | 0 | 0.050536 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
395096e5187ec5fb3359421a8017c93ceccd8139 | 17,556 | py | Python | RLBotPack/HP OMEN/omen.py | Dan-SmashRepair/RLBotPack | 638dd2dfae660e715dfa15c7cf7af71633090f5a | [
"MIT"
] | null | null | null | RLBotPack/HP OMEN/omen.py | Dan-SmashRepair/RLBotPack | 638dd2dfae660e715dfa15c7cf7af71633090f5a | [
"MIT"
] | null | null | null | RLBotPack/HP OMEN/omen.py | Dan-SmashRepair/RLBotPack | 638dd2dfae660e715dfa15c7cf7af71633090f5a | [
"MIT"
] | null | null | null | from gettext import find
from tools import *
from objects import *
from routines import *
from threading import local
from utils import *
import time
from tools import *
from rlbot.agents.base_agent import BaseAgent, SimpleControllerState
from rlbot.messages.flat.QuickChatSelection import QuickChatSelection
from rlbot.utils.structures.game_data_struct import GameTickPacket
from util.ball_prediction_analysis import find_slice_at_time
from util.boost_pad_tracker import BoostPadTracker
from util.drive import steer_toward_target
from util.sequence import Sequence, ControlStep
from util.vec import Vec3
#This file is for strategy
class omen(GoslingAgent):
def run(agent):
if len(agent.friends) == 0:
if agent.team > 0.1:
team_multiplier = 1
if agent.team < 0.1:
team_multiplier = -1
if agent.team == 0:
team_lol = 0
else:
team_lol = 1
left_field = Vector3(4200 * -side(agent.team), agent.ball.location.y + (1000 * -side(agent.team)), 0)
right_field = Vector3(4200 * side(agent.team), agent.ball.location.y + (1000 * side(agent.team)), 0)
future_ball = Vec3(0, 0, 0)
future_ball_2 = 0
ball_in_future = 0
ball_in_future_2 = 0
packet = GameTickPacket()
ball_prediction = agent.get_ball_prediction_struct()
ball_in_future = find_slice_at_time(ball_prediction, agent.time + 1)
ball_in_future_2 = find_slice_at_time(ball_prediction, agent.time + 2)
if ball_in_future is not None:
future_ball = Vec3(ball_in_future.physics.location)
elif ball_in_future_2 is not None:
future_ball = Vec3(ball_in_future_2.physics.location)
my_goal_to_ball,my_ball_distance = (agent.ball.location - agent.friend_goal.location).normalize(True)
goal_to_me = agent.me.location - agent.friend_goal.location
my_distance = my_goal_to_ball.dot(goal_to_me)
large_boosts = [boost for boost in agent.boosts if boost.large and boost.active]
foe_goal_to_ball,foe_ball_distance = (agent.ball.location - agent.foe_goal.location).normalize(True)
foe_goal_to_foe = agent.foes[0].location - agent.foe_goal.location
foe_distance = foe_goal_to_ball.dot(foe_goal_to_foe)
closest_foe_to_ball = agent.foes[0]
for foe in agent.foes:
if (closest_foe_to_ball.location - agent.ball.location).magnitude() > (foe.location - agent.ball.location).magnitude():
closest_foe = foe
left_field = Vector3(4200*-side(agent.team),agent.ball.location.y + (1000*-side(agent.team)),0)
right_field = Vector3(4200*side(agent.team),agent.ball.location.y + (1000*side(agent.team)),0)
targets = {"goal": (agent.foe_goal.left_post, agent.foe_goal.right_post),
"upfield": (left_field, right_field),
"not_my_net": (agent.friend_goal.right_post, agent.friend_goal.left_post)}
shots = find_hits(agent, targets)
x = 1
me_onside = my_distance - 200 < my_ball_distance
foe_onside = foe_distance - 200 < foe_ball_distance
close = (agent.me.location - agent.ball.location).magnitude() < 3000
foe_close = (closest_foe_to_ball.location - agent.ball.location).magnitude() < 3000
have_boost = agent.me.boost > 20
defense_location = Vector3(agent.ball.location.x, agent.ball.location.y + (4000 * team_multiplier), 0)
closest_foe = agent.foes[0]
for foe in agent.foes:
if (closest_foe.location - agent.me.location).magnitude() > (foe.location - agent.me.location).magnitude():
closest_foe = foe
x = 1
if agent.team == 0:
agent.debug_stack()
agent.line(agent.friend_goal.location, agent.ball.location, [255,255,255])
my_point = agent.friend_goal.location + (my_goal_to_ball * my_distance)
agent.line(my_point - Vector3(0, 0, 100), my_point + Vector3(0, 0, 100), [0,255,0])
def get_closest_boost(agent):
large_boosts = [boost for boost in agent.boosts if boost.large and boost.active]
closest_boost = large_boosts[0]
for item in large_boosts:
if (closest_boost.location - agent.me.location).magnitude() > (
item.location - agent.me.location).magnitude():
closest_boost = item
agent.stack = []
agent.push(goto_boost(closest_boost))
def demo(agent):
relative_target = closest_foe.location - agent.me.location
local_target = agent.me.local(relative_target)
defaultPD(agent, local_target)
defaultThrottle(agent, 2300)
if (agent.me.location - closest_foe.location).magnitude() < 200:
agent.push(flip(agent.me.local(closest_foe.location - agent.me.location)))
if agent.team == 0:
agent.debug_stack()
agent.line(agent.friend_goal.location, agent.ball.location, [255,255,255])
my_point = agent.friend_goal.location + (my_goal_to_ball * my_distance)
agent.line(my_point - Vector3(0, 0, 100), my_point + Vector3(0, 0, 100), [0,255,0])
if agent.team == 0:
agent.debug_stack()
if len(agent.stack) < 1:
if agent.kickoff_flag:
if agent.me.location.x > 300 or agent.me.location.x < -300:
agent.push(kickoff())
else:
agent.controller.throttle = 0
elif (agent.me.location - agent.friend_goal.location).magnitude() < 2000 and -1000 < agent.ball.location.x < 1000 and -1000 < closest_foe_to_ball.location.x < 1000:
if len(shots["not_my_net"]) > 0:
agent.push(shots["not_my_net"][0])
elif (close and me_onside) and (foe_onside and foe_close) and (agent.me.location - agent.ball.location).magnitude() > 50 and agent.ball.location.z < 200:
while (agent.me.location - agent.ball.location).magnitude() > 50:
relative_target = agent.ball.location - agent.me.location
local_target = agent.me.local(relative_target)
defaultPD(agent, local_target)
defaultThrottle(agent, 2300)
break
elif (close and me_onside) or me_onside and (closest_foe_to_ball.location - agent.ball.location).magnitude() > (agent.me.location - agent.ball.location).magnitude():
if len(shots["goal"]) > 0:
agent.push(shots["goal"][0])
elif len(shots["upfield"]) > 0:
agent.push(shots["upfield"][0])
elif (agent.ball.location - agent.friend_goal.location).magnitude() > 6000:
agent.push(goto(defense_location))
elif (agent.ball.location - agent.friend_goal.location).magnitude() > 4000 and (closest_foe.location - agent.ball.location).magnitude() > 3000 and agent.me.boost < 30 or (agent.ball.location - agent.friend_goal.location).magnitude() > 8000 and agent.me.boost < 30:
closest_boost = large_boosts[0]
for item in large_boosts:
if (closest_boost.location - agent.me.location).magnitude() > (
item.location - agent.me.location).magnitude():
closest_boost = item
agent.stack = []
agent.push(goto_boost(closest_boost))
elif (agent.ball.location - agent.friend_goal.location).magnitude() < 5000 and len(shots["not_my_net"]) > 0:
agent.push(shots["not_my_net"][0])
else:
demo(agent)
elif len(agent.friends) > 0:
if agent.team > 0.1:
team_multiplier = 1
if agent.team < 0.1:
team_multiplier = -1
if agent.team == 0:
team_lol = 0
else:
team_lol = 1
if len(agent.friends) > 0:
for friend in agent.friends:
if (agent.me.location - agent.ball.location).magnitude() > (friend.location - agent.ball.location).magnitude():
is_closest_friend_to_ball = False
else:
is_closest_friend_to_ball = True
closest_foe = agent.foes[0]
for foe in agent.foes:
if (closest_foe.location - agent.me.location).magnitude() > (foe.location - agent.me.location).magnitude():
closest_foe = foe
left_field = Vector3(4200 * -side(agent.team), agent.ball.location.y + (1000 * -side(agent.team)), 0)
right_field = Vector3(4200 * side(agent.team), agent.ball.location.y + (1000 * side(agent.team)), 0)
future_ball = Vec3(0, 0, 0)
future_ball_2 = 0
ball_in_future = 0
ball_in_future_2 = 0
packet = GameTickPacket()
ball_prediction = agent.get_ball_prediction_struct()
ball_in_future = find_slice_at_time(ball_prediction, agent.time + 1)
ball_in_future_2 = find_slice_at_time(ball_prediction, agent.time + 2)
if ball_in_future is not None:
future_ball = Vec3(ball_in_future.physics.location)
elif ball_in_future_2 is not None:
future_ball = Vec3(ball_in_future_2.physics.location)
my_goal_to_ball, my_ball_distance = (agent.ball.location - agent.friend_goal.location).normalize(True)
goal_to_me = agent.me.location - agent.friend_goal.location
my_distance = my_goal_to_ball.dot(goal_to_me)
large_boosts = [boost for boost in agent.boosts if boost.large and boost.active]
foe_goal_to_ball, foe_ball_distance = (agent.ball.location - agent.foe_goal.location).normalize(True)
foe_goal_to_foe = agent.foes[0].location - agent.foe_goal.location
foe_distance = foe_goal_to_ball.dot(foe_goal_to_foe)
closest_foe_to_ball = agent.foes[0]
for foe in agent.foes:
if (closest_foe_to_ball.location - agent.ball.location).magnitude() > (
foe.location - agent.ball.location).magnitude():
closest_foe_to_ball = foe
if len(agent.friends) > 0:
closest_friend_to_ball = agent.friends[0]
for friend in agent.friends:
if (closest_friend_to_ball.location - agent.ball.location).magnitude() > (
friend.location - agent.ball.location).magnitude():
closest_friend_to_ball = friend
closest_friend_to_goal = agent.friends[0]
for friend in agent.friends:
if (closest_friend_to_goal.location - agent.friend_goal.location).magnitude() > (
friend.location - agent.friend_goal.location).magnitude():
closest_friend_to_goal = friend
closest_friend = agent.friends[0]
for friend in agent.friends:
if (closest_friend.location - agent.me.location).magnitude() > (
friend.location - agent.me.location).magnitude():
closest_friend = friend
closest_foe = agent.foes[0]
for foe in agent.foes:
if (closest_foe_to_ball.location - agent.me.location).magnitude() > (
foe.location - agent.me.location).magnitude():
closest_foe = foe
left_field = Vector3(4200 * -side(agent.team), agent.ball.location.y + (1000 * -side(agent.team)), 0)
right_field = Vector3(4200 * side(agent.team), agent.ball.location.y + (1000 * side(agent.team)), 0)
targets = {"goal": (agent.foe_goal.left_post, agent.foe_goal.right_post),
"upfield": (left_field, right_field),
"not_my_net": (agent.friend_goal.right_post, agent.friend_goal.left_post)}
shots = find_hits(agent, targets)
x = 1
me_onside = my_distance - 200 < my_ball_distance
foe_onside = foe_distance - 200 < foe_ball_distance
close = (agent.me.location - agent.ball.location).magnitude() < 3000
foe_close = (closest_foe_to_ball.location - agent.ball.location).magnitude() < 3000
have_boost = agent.me.boost > 20
defense_location = Vector3(agent.ball.location.x, agent.ball.location.y + (4000 * team_multiplier), 0)
x = 1
if agent.team == 0:
agent.debug_stack()
agent.line(agent.friend_goal.location, agent.ball.location, [255, 255, 255])
my_point = agent.friend_goal.location + (my_goal_to_ball * my_distance)
agent.line(my_point - Vector3(0, 0, 100), my_point + Vector3(0, 0, 100), [0, 255, 0])
def get_closest_boost(agent):
large_boosts = [boost for boost in agent.boosts if boost.large and boost.active]
closest_boost = large_boosts[0]
for item in large_boosts:
if (closest_boost.location - agent.me.location).magnitude() > (
item.location - agent.me.location).magnitude():
closest_boost = item
agent.stack = []
agent.push(goto_boost(closest_boost))
def demo(agent):
relative_target = closest_foe.location - agent.me.location
local_target = agent.me.local(relative_target)
defaultPD(agent, local_target)
defaultThrottle(agent, 2300)
if (agent.me.location - closest_foe.location).magnitude() < 200:
agent.push(flip(agent.me.local(closest_foe.location - agent.me.location)))
if agent.team == 0:
agent.debug_stack()
agent.line(agent.friend_goal.location, agent.ball.location, [255, 255, 255])
my_point = agent.friend_goal.location + (my_goal_to_ball * my_distance)
agent.line(my_point - Vector3(0, 0, 100), my_point + Vector3(0, 0, 100), [0, 255, 0])
if agent.team == 0:
agent.debug_stack()
if len(agent.stack) < 1:
if agent.kickoff_flag:
if (closest_friend_to_ball.location - agent.ball.location).magnitude() > (agent.me.location - agent.ball.location).magnitude() or (closest_friend_to_ball.location - agent.ball.location).magnitude() == (agent.me.location - agent.ball.location).magnitude() and closest_friend_to_ball.location.x < agent.me.location.x:
agent.push(kickoff())
else:
get_closest_boost(agent)
elif (close and me_onside) and (foe_onside and foe_close) and (
agent.me.location - agent.ball.location).magnitude() > 50 and agent.ball.location.z < 200 and (closest_friend_to_ball.location - agent.ball.location).magnitude() > 1000:
while (agent.me.location - agent.ball.location).magnitude() > 50:
relative_target = agent.ball.location - agent.me.location
local_target = agent.me.local(relative_target)
defaultPD(agent, local_target)
defaultThrottle(agent, 2300)
break
elif (close and me_onside) or (not foe_onside and me_onside) or (agent.me.location - agent.ball.location).magnitude() < (closest_friend_to_ball.location - agent.ball.location).magnitude() and me_onside:
if len(shots["goal"]) > 0:
agent.push(shots["goal"][0])
elif len(shots["upfield"]) > 0:
agent.push(shots["upfield"][0])
elif (agent.ball.location - agent.friend_goal.location).magnitude() > 4000 and (
closest_foe.location - agent.ball.location).magnitude() > 3000 and agent.me.boost < 30 or (
agent.ball.location - agent.friend_goal.location).magnitude() > 8000 and agent.me.boost < 30:
get_closest_boost(agent)
elif (agent.ball.location - agent.friend_goal.location).magnitude() > 5000 and (closest_friend_to_ball.location - agent.ball.location).magnitude() < (agent.me.location - agent.ball.location).magnitude():
demo(agent)
elif (agent.ball.location.y - agent.friend_goal.location.y) > 4000:
agent.push(goto(defense_location))
elif (agent.ball.location - agent.friend_goal.location).magnitude() < 4000 and len(shots["not_my_net"]) > 0:
agent.push(shots["not_my_net"][0])
else:
demo(agent)
| 53.039275 | 335 | 0.582308 | 2,105 | 17,556 | 4.647031 | 0.067933 | 0.094459 | 0.109487 | 0.084339 | 0.908812 | 0.886731 | 0.866387 | 0.861071 | 0.861071 | 0.845328 | 0 | 0.036668 | 0.314935 | 17,556 | 330 | 336 | 53.2 | 0.776669 | 0.001424 | 0 | 0.786765 | 0 | 0 | 0.008329 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.018382 | false | 0 | 0.058824 | 0 | 0.080882 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1ab689a434c0a2be1718478c74519e57e2a34182 | 186 | py | Python | fs0GetHResultDefineName.py | SkyLined/mWindowsSDK | 931cc9d30316893662a3dc4e200dabe97122d216 | [
"CC-BY-4.0"
] | 2 | 2019-08-01T15:08:25.000Z | 2021-01-30T07:29:34.000Z | fs0GetHResultDefineName.py | SkyLined/mWindowsSDK | 931cc9d30316893662a3dc4e200dabe97122d216 | [
"CC-BY-4.0"
] | null | null | null | fs0GetHResultDefineName.py | SkyLined/mWindowsSDK | 931cc9d30316893662a3dc4e200dabe97122d216 | [
"CC-BY-4.0"
] | null | null | null | from .mWindowsConstants.dsHResultDefineName_by_uValue import dsHResultDefineName_by_uValue;
def fs0GetHResultDefineName(uHResult):
return dsHResultDefineName_by_uValue.get(uHResult);
| 37.2 | 91 | 0.887097 | 18 | 186 | 8.833333 | 0.611111 | 0.396226 | 0.509434 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005714 | 0.05914 | 186 | 4 | 92 | 46.5 | 0.902857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 7 |
46f3be5bc8c1602bbcf543437ad01f991de45a03 | 197 | py | Python | tests/__init__.py | Rippling/mongoengine | c3b6fa6ffdfe05fcf6f49857c1a89fee0175a05f | [
"MIT"
] | null | null | null | tests/__init__.py | Rippling/mongoengine | c3b6fa6ffdfe05fcf6f49857c1a89fee0175a05f | [
"MIT"
] | 28 | 2016-11-30T03:15:18.000Z | 2022-02-25T15:57:02.000Z | tests/__init__.py | Rippling/mongoengine | c3b6fa6ffdfe05fcf6f49857c1a89fee0175a05f | [
"MIT"
] | 1 | 2021-11-10T05:33:18.000Z | 2021-11-10T05:33:18.000Z | from __future__ import absolute_import
from tests.all_warnings import AllWarnings
from tests.document import *
from tests.queryset import *
from tests.fields import *
from tests.migration import *
| 28.142857 | 42 | 0.832487 | 27 | 197 | 5.851852 | 0.444444 | 0.28481 | 0.379747 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121827 | 197 | 6 | 43 | 32.833333 | 0.913295 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
649e1ad64ebcb40ba329809013e4004161f0652b | 2,211 | py | Python | convert_data.py | cuizelin99/strategyqa | 1a7abf3dc98d5a0335126d9f4080ce376631be07 | [
"MIT"
] | null | null | null | convert_data.py | cuizelin99/strategyqa | 1a7abf3dc98d5a0335126d9f4080ce376631be07 | [
"MIT"
] | null | null | null | convert_data.py | cuizelin99/strategyqa | 1a7abf3dc98d5a0335126d9f4080ce376631be07 | [
"MIT"
] | null | null | null | import json
import sys
json_file = open(sys.argv[1])
creak_lines = json_file.readlines()
out_file = sys.argv[2]
#data = json.load(json_file)
with open(out_file, "w") as outfile:
outfile.write('[\n')
for i in range(len(creak_lines) - 2):
example = creak_lines[i]
outfile.write('\t{\n')
data = json.loads(example)
uid = data['ex_id']
entity = data['entity']
sentence = data['sentence']
label = data['label']
desc = ""
if '(' in entity and ')' in entity:
desc = entity.split('(')[1].split(')')[0]
entity = entity.split(' (')[0]
id_line = "\t\t\"qid\": \"{}\",\n".format(uid)
term_line = "\t\t\"term\": \"{}\",\n".format(entity)
description_line = "\t\t\"description\": \"{}\",\n".format(desc)
question_line = "\t\t\"question\": \"{}\",\n".format(sentence)
answer_line = "\t\t\"answer\": {},\n".format(label)
fact_line = "\t\t\"facts\": [],\n"
decomp_line = "\t\t\"decomposition\": [],\n"
evidence_line = "\t\t\"evidence\": []\n"
outfile.write(id_line)
outfile.write(term_line)
outfile.write(description_line)
outfile.write(question_line)
outfile.write(answer_line)
outfile.write(fact_line)
outfile.write(decomp_line)
outfile.write(evidence_line)
outfile.write('\t},\n')
example = creak_lines[-2]
outfile.write('\t{\n')
data = json.loads(example)
uid = data['ex_id']
entity = data['entity']
sentence = data['sentence']
label = data['label']
desc = ""
if '(' in entity and ')' in entity:
desc = entity.split('(')[1].split(')')[0]
entity = entity.split(' (')[0]
id_line = "\t\t\"qid\": \"{}\",\n".format(uid)
term_line = "\t\t\"term\": \"{}\",\n".format(entity)
description_line = "\t\t\"description\": \"{}\",\n".format(desc)
question_line = "\t\t\"question\": \"{}\",\n".format(sentence)
answer_line = "\t\t\"answer\": {},\n".format(label)
fact_line = "\t\t\"facts\": [],\n"
decomp_line = "\t\t\"decomposition\": [],\n"
evidence_line = "\t\t\"evidence\": []\n"
outfile.write(id_line)
outfile.write(term_line)
outfile.write(description_line)
outfile.write(question_line)
outfile.write(answer_line)
outfile.write(fact_line)
outfile.write(decomp_line)
outfile.write(evidence_line)
outfile.write('\t}\n')
outfile.write(']\n') | 32.043478 | 66 | 0.631389 | 318 | 2,211 | 4.254717 | 0.160377 | 0.195122 | 0.070953 | 0.04139 | 0.842572 | 0.842572 | 0.842572 | 0.842572 | 0.842572 | 0.842572 | 0 | 0.005173 | 0.125735 | 2,211 | 69 | 67 | 32.043478 | 0.694775 | 0.012212 | 0 | 0.8 | 0 | 0 | 0.123626 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.030769 | 0 | 0.030769 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
649fc46b3782e29accaa8dc7d7945204f27eeded | 9,989 | py | Python | test/compliance_tool/test_compliance_check_xml.py | eclipse-basyx/basyx-sdk-python | 1249f49803a6ef5e594bb61410ad1c7939c2bdb7 | [
"MIT"
] | 4 | 2022-01-07T01:30:49.000Z | 2022-02-21T07:58:14.000Z | test/compliance_tool/test_compliance_check_xml.py | eclipse-basyx/basyx-sdk-python | 1249f49803a6ef5e594bb61410ad1c7939c2bdb7 | [
"MIT"
] | 5 | 2022-02-22T15:24:22.000Z | 2022-03-28T11:42:28.000Z | test/compliance_tool/test_compliance_check_xml.py | eclipse-basyx/basyx-sdk-python | 1249f49803a6ef5e594bb61410ad1c7939c2bdb7 | [
"MIT"
] | 2 | 2021-11-15T10:24:02.000Z | 2022-03-17T14:44:39.000Z | # Copyright (c) 2020 the Eclipse BaSyx Authors
#
# This program and the accompanying materials are made available under the terms of the MIT License, available in
# the LICENSE file of this project.
#
# SPDX-License-Identifier: MIT
import os
import unittest
import basyx.aas.compliance_tool.compliance_check_xml as compliance_tool
from basyx.aas.compliance_tool.state_manager import ComplianceToolStateManager, Status
class ComplianceToolXmlTest(unittest.TestCase):
def test_check_schema(self) -> None:
manager = ComplianceToolStateManager()
script_dir = os.path.dirname(__file__)
file_path_1 = os.path.join(script_dir, 'files/test_not_found.xml')
compliance_tool.check_schema(file_path_1, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.FAILED, manager.steps[0].status)
self.assertEqual(Status.NOT_EXECUTED, manager.steps[1].status)
self.assertEqual(Status.NOT_EXECUTED, manager.steps[2].status)
self.assertIn("No such file or directory", manager.format_step(0, verbose_level=1))
manager.steps = []
file_path_3 = os.path.join(script_dir, 'files/test_missing_submodels.xml')
compliance_tool.check_schema(file_path_3, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
manager.steps = []
file_path_4 = os.path.join(script_dir, 'files/test_empty.xml')
compliance_tool.check_schema(file_path_4, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
manager.steps = []
file_path_5 = os.path.join(script_dir, 'files/test_demo_full_example.xml')
compliance_tool.check_schema(file_path_5, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
def test_check_deserialization(self) -> None:
manager = ComplianceToolStateManager()
script_dir = os.path.dirname(__file__)
file_path_1 = os.path.join(script_dir, 'files/test_not_found.xml')
compliance_tool.check_deserialization(file_path_1, manager)
self.assertEqual(2, len(manager.steps))
self.assertEqual(Status.FAILED, manager.steps[0].status)
self.assertEqual(Status.NOT_EXECUTED, manager.steps[1].status)
self.assertIn("No such file or directory", manager.format_step(0, verbose_level=1))
manager.steps = []
file_path_2 = os.path.join(script_dir, 'files/test_not_deserializable_aas.xml')
compliance_tool.check_deserialization(file_path_2, manager)
self.assertEqual(2, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.FAILED, manager.steps[1].status)
self.assertIn("child of aas:assetAdministrationShells", manager.format_step(1, verbose_level=1))
self.assertIn("doesn't match the expected tag aas:assetAdministrationShell",
manager.format_step(1, verbose_level=1))
manager.steps = []
file_path_3 = os.path.join(script_dir, 'files/test_deserializable_aas_warning.xml')
compliance_tool.check_deserialization(file_path_3, manager)
self.assertEqual(2, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.FAILED, manager.steps[1].status)
self.assertIn("ValueError: A revision requires a version", manager.format_step(1, verbose_level=1))
manager.steps = []
file_path_4 = os.path.join(script_dir, 'files/test_empty.xml')
compliance_tool.check_deserialization(file_path_4, manager)
self.assertEqual(2, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
manager.steps = []
file_path_4 = os.path.join(script_dir, 'files/test_empty.xml')
compliance_tool.check_deserialization(file_path_4, manager)
self.assertEqual(2, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
def test_check_aas_example(self) -> None:
manager = ComplianceToolStateManager()
script_dir = os.path.dirname(__file__)
file_path_2 = os.path.join(script_dir, 'files/test_demo_full_example.xml')
compliance_tool.check_aas_example(file_path_2, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
manager.steps = []
file_path_1 = os.path.join(script_dir, 'files/test_not_deserializable_aas.xml')
compliance_tool.check_aas_example(file_path_1, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.FAILED, manager.steps[1].status)
self.assertEqual(Status.NOT_EXECUTED, manager.steps[2].status)
self.assertIn("child of aas:assetAdministrationShells", manager.format_step(1, verbose_level=1))
self.assertIn("doesn't match the expected tag aas:assetAdministrationShell",
manager.format_step(1, verbose_level=1))
manager.steps = []
file_path_3 = os.path.join(script_dir, 'files/test_demo_full_example_wrong_attribute.xml')
compliance_tool.check_aas_example(file_path_3, manager)
self.assertEqual(3, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.FAILED, manager.steps[2].status)
self.assertIn('Asset administration shell AssetAdministrationShell[Identifier(IRI=https://acplt.org/'
'Test_AssetAdministrationShell)] must exist in given asset administrationshell list',
manager.format_step(2, verbose_level=1))
def test_check_xml_files_equivalence(self) -> None:
manager = ComplianceToolStateManager()
script_dir = os.path.dirname(__file__)
file_path_1 = os.path.join(script_dir, 'files/test_not_deserializable_aas.xml')
file_path_2 = os.path.join(script_dir, 'files/test_empty.xml')
compliance_tool.check_xml_files_equivalence(file_path_1, file_path_2, manager)
self.assertEqual(5, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.FAILED, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
self.assertEqual(Status.SUCCESS, manager.steps[3].status)
self.assertEqual(Status.NOT_EXECUTED, manager.steps[4].status)
manager.steps = []
compliance_tool.check_xml_files_equivalence(file_path_2, file_path_1, manager)
self.assertEqual(5, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
self.assertEqual(Status.FAILED, manager.steps[3].status)
self.assertEqual(Status.NOT_EXECUTED, manager.steps[4].status)
manager.steps = []
file_path_3 = os.path.join(script_dir, 'files/test_demo_full_example.xml')
file_path_4 = os.path.join(script_dir, 'files/test_demo_full_example.xml')
compliance_tool.check_xml_files_equivalence(file_path_3, file_path_4, manager)
self.assertEqual(5, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
self.assertEqual(Status.SUCCESS, manager.steps[3].status)
self.assertEqual(Status.SUCCESS, manager.steps[4].status)
manager.steps = []
file_path_3 = os.path.join(script_dir, 'files/test_demo_full_example.xml')
file_path_4 = os.path.join(script_dir, 'files/test_demo_full_example_wrong_attribute.xml')
compliance_tool.check_xml_files_equivalence(file_path_3, file_path_4, manager)
self.assertEqual(5, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
self.assertEqual(Status.SUCCESS, manager.steps[3].status)
self.assertEqual(Status.FAILED, manager.steps[4].status)
manager.steps = []
compliance_tool.check_xml_files_equivalence(file_path_4, file_path_3, manager)
self.assertEqual(5, len(manager.steps))
self.assertEqual(Status.SUCCESS, manager.steps[0].status)
self.assertEqual(Status.SUCCESS, manager.steps[1].status)
self.assertEqual(Status.SUCCESS, manager.steps[2].status)
self.assertEqual(Status.SUCCESS, manager.steps[3].status)
self.assertEqual(Status.FAILED, manager.steps[4].status)
self.assertIn('Asset administration shell AssetAdministrationShell[Identifier(IRI=https://acplt.org/'
'Test_AssetAdministrationShell)] must exist in given asset administrationshell list',
manager.format_step(4, verbose_level=1))
| 54.884615 | 113 | 0.712284 | 1,271 | 9,989 | 5.397325 | 0.088906 | 0.150437 | 0.171429 | 0.163265 | 0.914286 | 0.914286 | 0.909475 | 0.882653 | 0.877114 | 0.873178 | 0 | 0.016366 | 0.174192 | 9,989 | 181 | 114 | 55.187845 | 0.81525 | 0.021924 | 0 | 0.75817 | 0 | 0 | 0.121581 | 0.068012 | 0 | 0 | 0 | 0 | 0.535948 | 1 | 0.026144 | false | 0 | 0.026144 | 0 | 0.058824 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
64a98181d44f28dd2971020bcbaf25452ead0b83 | 41 | py | Python | develop/src/rawdata_builder/__init__.py | pjw960408/binance-trader-c1 | dae91cc721591257334ab1ddcf3a4f6d86644435 | [
"MIT"
] | null | null | null | develop/src/rawdata_builder/__init__.py | pjw960408/binance-trader-c1 | dae91cc721591257334ab1ddcf3a4f6d86644435 | [
"MIT"
] | null | null | null | develop/src/rawdata_builder/__init__.py | pjw960408/binance-trader-c1 | dae91cc721591257334ab1ddcf3a4f6d86644435 | [
"MIT"
] | 1 | 2021-05-06T14:14:56.000Z | 2021-05-06T14:14:56.000Z | from .build_rawdata import build_rawdata
| 20.5 | 40 | 0.878049 | 6 | 41 | 5.666667 | 0.666667 | 0.705882 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097561 | 41 | 1 | 41 | 41 | 0.918919 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
b3c9f85c92e0d95e7e5a588eda50f835acc4bb85 | 250 | py | Python | src/lqc/generate/css/util/integer.py | tysmith/layout-quickcheck | c5ba9431a40f650a594140541e32af7c8ff21695 | [
"MIT"
] | 2 | 2021-03-05T19:00:21.000Z | 2021-03-15T18:23:04.000Z | src/lqc/generate/css/util/integer.py | tysmith/layout-quickcheck | c5ba9431a40f650a594140541e32af7c8ff21695 | [
"MIT"
] | 7 | 2021-03-05T19:10:28.000Z | 2021-10-20T19:26:18.000Z | src/lqc/generate/css/util/integer.py | tysmith/layout-quickcheck | c5ba9431a40f650a594140541e32af7c8ff21695 | [
"MIT"
] | 1 | 2021-09-27T18:56:34.000Z | 2021-09-27T18:56:34.000Z | from random import choice, randint
MAX_NUMBER = 2000
prefixes = ["", "+", "-"]
def generate_prefix():
return choice(prefixes)
def generate():
prefix = generate_prefix()
number = randint(0, MAX_NUMBER)
return f"{prefix}{number}"
| 15.625 | 35 | 0.656 | 29 | 250 | 5.517241 | 0.517241 | 0.2625 | 0.2375 | 0.3125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.025 | 0.2 | 250 | 15 | 36 | 16.666667 | 0.775 | 0 | 0 | 0 | 1 | 0 | 0.072 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.222222 | false | 0 | 0.111111 | 0.111111 | 0.555556 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
b3dc459c21a7e820048a88c7737092ffc6888b22 | 407 | py | Python | name building software.py | Ayush2007A/Code-master | fafe4a020adc3f8e78c78f6b8b2b08b5c3005613 | [
"Unlicense"
] | 1 | 2021-02-05T10:29:30.000Z | 2021-02-05T10:29:30.000Z | name building software.py | Ayush2007A/Code-master | fafe4a020adc3f8e78c78f6b8b2b08b5c3005613 | [
"Unlicense"
] | null | null | null | name building software.py | Ayush2007A/Code-master | fafe4a020adc3f8e78c78f6b8b2b08b5c3005613 | [
"Unlicense"
] | null | null | null | import random
s_letters=['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
c_letters=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
print(random.choice(c_letters)+random.choice(s_letters)+random.choice(s_letters)+random.choice(s_letters)+random.choice(s_letters)+random.choice(s_letters))
| 81.4 | 157 | 0.523342 | 83 | 407 | 2.46988 | 0.373494 | 0.234146 | 0.463415 | 0.487805 | 0.843902 | 0.843902 | 0.843902 | 0.843902 | 0.843902 | 0.843902 | 0 | 0 | 0.022113 | 407 | 4 | 158 | 101.75 | 0.515075 | 0 | 0 | 0 | 0 | 0 | 0.129032 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.25 | 0 | 0.25 | 0.25 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
377f8e0bfe1df5b5e5aa7b62ab4b9ca25f265b69 | 41,130 | py | Python | c19_synthesis/cellular_automata.py | octaviomtz/nbdev_c19_synthesis | 45079757af6c05c3763d5c7147f566862171de9b | [
"Apache-2.0"
] | null | null | null | c19_synthesis/cellular_automata.py | octaviomtz/nbdev_c19_synthesis | 45079757af6c05c3763d5c7147f566862171de9b | [
"Apache-2.0"
] | null | null | null | c19_synthesis/cellular_automata.py | octaviomtz/nbdev_c19_synthesis | 45079757af6c05c3763d5c7147f566862171de9b | [
"Apache-2.0"
] | null | null | null | # AUTOGENERATED! DO NOT EDIT! File to edit: 01_cellular_automata.ipynb (unless otherwise specified).
__all__ = ['to_rgb', 'correct_label_in_plot', 'create_sobel_and_identity', 'prepare_seed', 'epochs_in_inner_loop',
'ca_model_baseline', 'ca_model_perception', 'plot_loss_and_lesion_synthesis', 'ca_model_perception_clamp',
'ca_model_step_size', 'CeA_00', 'ca_model_laplacian_regularizer', 'ca_model_l2reg']
# Cell
import cv2
import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
from IPython.display import Image, HTML, clear_output
import matplotlib
import io
import sys
# Cell
def to_rgb(img, channel=1):
'''return visible channel'''
# rgb, a = img[:,:,:1], img[:,:,1:2]
rgb, a = img[:,:,:channel], img[:,:,channel:channel+1]
return 1.0-a+rgb
# Cell
def correct_label_in_plot(model):
'''get a string with the network architecture to print in the figure'''
# https://www.kite.com/python/answers/how-to-redirect-print-output-to-a-variable-in-python
old_stdout = sys.stdout
new_stdout = io.StringIO()
sys.stdout = new_stdout
print(model);
output = new_stdout.getvalue()
sys.stdout = old_stdout
model_str = [i.split(', k')[0] for i in output.split('\n')]
model_str_layers = [i.split(':')[-1] for i in model_str[2:-3]]
model_str = [model_str[0]]+model_str_layers
model_str = str(model_str).replace("', '",'\n')
return model_str
# Cell
def create_sobel_and_identity(device='cuda'):
ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
return ident, sobel_x, lap
# Cell
def prepare_seed(target, this_seed, device, num_channels = 16, pool_size = 1024):
# prepare seed
height, width, _ = np.shape(target)
seed = np.zeros([1, height, width, num_channels], np.float32)
for i in range(num_channels-1):
seed[:,..., i+1] = this_seed
# Preparing the seed pool
seed_tensor = torch.tensor(seed).permute(0,-1,1,2).to(device)
seed_pool = torch.repeat_interleave(seed_tensor, repeats = pool_size, dim = 0)
return seed, seed_tensor, seed_pool
# Cell
def epochs_in_inner_loop(i, inner_iter_aux=0, inner_iter=0, thresh_do_nothing=100, thresh_do_something=200, increase=10, inner_iter_max=100):
if i < thresh_do_nothing:
inner_iter = 100
elif i % thresh_do_something == 0:
inner_iter_aux = inner_iter_aux + increase
inner_iter = np.min([inner_iter_aux, inner_iter_max])
else:
inner_iter=inner_iter
return inner_iter, inner_iter_aux
# Cell
class ca_model_baseline(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda'):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(ca_model_baseline, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, epochs_inside, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for i in range(epochs_inside):
x, alive_mask = self.forward(x)
target_loss = target_loss_func(x[:,:2, :,:], target) # used to synthesize almost all nodules
loss = target_loss
return loss, x, alive_mask.cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
alive_mask = self.get_alive_mask(x)
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1)
y = self.perception(x)
out = x + self.model(y)*mask
out *= alive_mask
return out, alive_mask
# Cell
class ca_model_perception(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda'):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(ca_model_perception, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, iters, current_epoch = 1000, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for i in range(iters):
x, alive_mask = self.forward(x,i, current_epoch)
# print(x[:,:4, :,:].shape, target.shape)
# batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,:1, :,:] - target)**2),dim=0)
batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,0, :,:] - target[:,0,:,:])**2),dim=0)
if masked_loss == True:
alive_mask_dilated = (F.max_pool2d(alive_mask[0], 3,1, padding =1 ) > 0.1).float()
# alive_mask_dilated = torch.from_numpy(binary_closing(alive_mask[0].cpu().numpy() > 0.1)).float().to('cuda')
target_loss = target_loss_func(x[:,:1, :,:] * alive_mask_dilated, target * alive_mask_dilated)
else:
target_loss = target_loss_func(x[:,:2, :,:] * target[:,1:,...], target * target[:,1:,...]) # used to synthesize almost all nodules
loss = target_loss
return loss, x, alive_mask.cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x, i, current_epoch):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
if current_epoch < 100:
alive_mask = self.get_alive_mask(x)
else:
if i % 3 == 0:
alive_mask = self.get_alive_mask(x)
else:
# alive_mask = self.get_alive_mask(x)
alive_mask = (x[:,1:2,:,:] > 0.1).float()
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1)
y = self.perception(x)
out = x + self.model(y)*mask
out *= alive_mask
return out, alive_mask
# Cell
def plot_loss_and_lesion_synthesis(losses, optimizer, model_str, i, loss, sample_size, out):
clear_output(True)
f, (ax0, ax1) = plt.subplots(2, 1, figsize=(12,10), gridspec_kw={'height_ratios': [4, 1]})
lr_info = f'\nlr_init={optimizer.param_groups[0]["initial_lr"]:.1E}\nlr_last={optimizer.param_groups[0]["lr"]:.1E}'
model_str_final = model_str+lr_info
ax0.plot(losses, label=model_str_final)
ax0.set_yscale('log')
ax0.legend(loc='upper right', fontsize=16)
stack = []
for z in range(sample_size):
stack.append(to_rgb(out[z].permute(-2, -1,0).cpu().detach().numpy()))
ax1.imshow(np.clip(np.hstack(np.squeeze(stack)), 0,1))
ax1.axis('off')
plt.show()
print(i, loss.item(), flush = True)
return model_str_final
# Cell
class ca_model_perception_clamp(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda'):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(ca_model_perception_clamp, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, iters, current_epoch = 1000, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for i in range(iters):
x, alive_mask, mask_diff = self.forward(x,i, current_epoch)
# print(x[:,:4, :,:].shape, target.shape)
# batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,:1, :,:] - target)**2),dim=0)
batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,0, :,:] - target[:,0,:,:])**2),dim=0)
if masked_loss == True:
alive_mask_dilated = (F.max_pool2d(alive_mask[0], 3,1, padding =1 ) > 0.1).float()
# alive_mask_dilated = torch.from_numpy(binary_closing(alive_mask[0].cpu().numpy() > 0.1)).float().to('cuda')
target_loss = target_loss_func(x[:,:1, :,:] * alive_mask_dilated, target * alive_mask_dilated)
else:
target_loss = target_loss_func(x[:,:2, :,:] * target[:,1:,...], target * target[:,1:,...]) # used to synthesize almost all nodules
loss = target_loss
return loss, x, alive_mask.cpu().numpy(), mask_diff.cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x, i, current_epoch):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
mask_previous = alive_mask = (x[:,1:2,:,:] > 0.1).float()
if current_epoch < 100:
alive_mask = self.get_alive_mask(x)
else:
if i % 3 == 0:
alive_mask = self.get_alive_mask(x)
else:
# alive_mask = self.get_alive_mask(x)
alive_mask = (x[:,1:2,:,:] > 0.1).float()
mask_diff = alive_mask - mask_previous
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1)
y = self.perception(x)
mask_new_cells_clamped = torch.clip((1-mask_diff)+.19,0,1) #make sure this is only applied to the first channel
mask_new_cells_clamped_ones = torch.ones_like(torch.squeeze(mask_new_cells_clamped))
mask_new_cells_clamped2 = torch.repeat_interleave(mask_new_cells_clamped,16,1)
for i in np.arange(1,16,1):
mask_new_cells_clamped2[:,i,:,:] = mask_new_cells_clamped_ones
out = x + self.model(y)*mask*mask_new_cells_clamped2
out *= alive_mask
return out, alive_mask, mask_diff
# Cell
class ca_model_step_size(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda', grow_on_k_iter=3, background_intensity=.19, step_size=1, scale_mask=1):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(ca_model_step_size, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
self.grow_on_k_iter = grow_on_k_iter
self.background_intensity = background_intensity
self.step_size = step_size
self.scale_mask = scale_mask
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, iters, current_epoch = 1000, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for i in range(iters):
x, alive_mask, other_mask = self.forward(x,i, current_epoch)
# print(x[:,:4, :,:].shape, target.shape)
# batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,:1, :,:] - target)**2),dim=0)
batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,0, :,:] - target[:,0,:,:])**2),dim=0)
if masked_loss == True:
alive_mask_dilated = (F.max_pool2d(alive_mask[0], 3,1, padding =1 ) > 0.1).float()
# alive_mask_dilated = torch.from_numpy(binary_closing(alive_mask[0].cpu().numpy() > 0.1)).float().to('cuda')
target_loss = target_loss_func(x[:,:1, :,:] * alive_mask_dilated, target * alive_mask_dilated)
else:
target_loss = target_loss_func(x[:,:2, :,:] * target[:,1:,...], target * target[:,1:,...]) # used to synthesize almost all nodules
loss = target_loss
return loss, x, alive_mask.cpu().numpy(), other_mask.cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x, i, current_epoch):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
mask_previous = alive_mask = (x[:,1:2,:,:] > 0.1).float()
if current_epoch < 100:
alive_mask = self.get_alive_mask(x)
else:
if i % self.grow_on_k_iter == 0:
alive_mask = self.get_alive_mask(x)
else:
alive_mask = (x[:,1:2,:,:] > 0.1).float()
mask_diff = alive_mask - mask_previous
mask_new_cells_clamped = torch.clip((1-mask_diff) + self.background_intensity,0,self.step_size) #make sure this is only applied to the first channel
mask_new_cells_clamped_ones = torch.ones_like(torch.squeeze(mask_new_cells_clamped))*self.scale_mask
mask_new_cells_clamped2 = torch.repeat_interleave(mask_new_cells_clamped,16,1)
for idx_channel in np.arange(1,16,1):
mask_new_cells_clamped2[:,idx_channel,:,:] = mask_new_cells_clamped_ones
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1) # original mask used
y = self.perception(x)
out = x + self.model(y)*mask*mask_new_cells_clamped2
out *= alive_mask
return out, alive_mask, mask_new_cells_clamped2
# Cell
class CeA_00(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda', grow_on_k_iter=3, background_intensity=.19, step_size=1, scale_mask=1, pretrain_thres=100):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(CeA_00, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
self.grow_on_k_iter = grow_on_k_iter
self.background_intensity = background_intensity
self.step_size = step_size
self.scale_mask = scale_mask
self.pretrain_thres = pretrain_thres
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, epochs_inside, epoch_outside = 1000, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for epoch_in in range(epochs_inside):
x, alive_mask, other = self.forward(x, epoch_in, epoch_outside)
if masked_loss == True:
alive_mask_dilated = (F.max_pool2d(alive_mask[0], 3,1, padding =1 ) > 0.1).float()
target_loss = target_loss_func(x[:,:1, :,:] * alive_mask_dilated, target * alive_mask_dilated)
else:
target_loss = target_loss_func(x[:,:2, :,:] * target[:,1:,...], target * target[:,1:,...]) # used to synthesize almost all nodules
# target_loss = target_loss_func(x[:,:2, :,:], target) # ORIGINAL
loss = target_loss
return loss, x, alive_mask.cpu().numpy(), other.detach().cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x, epoch_in, epoch_outside):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
mask_previous = alive_mask = (x[:,1:2,:,:] > 0.1).float()
# self_pretraining
if epoch_outside < self.pretrain_thres:
alive_mask = self.get_alive_mask(x)
else:
if epoch_in % self.grow_on_k_iter == 0:
alive_mask = self.get_alive_mask(x)
else:
alive_mask = (x[:,1:2,:,:] > 0.1).float()
mask_previous = torch.zeros_like(alive_mask)#OMM added in CeA
# MASK CLAMP
# | = self.background_intensity
# X = self.step_size
# S = self.scale_mask
# ch0 ch1 ch2 ...
# |||||||||||||| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
# |||||XXXX||||| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
# |||XX||||XX||| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
# ||XX||||||XX|| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
# |||XX||||XX||| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
# |||||XXXX||||| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
# |||||||||||||| SSSSSSSSSSSSS SSSSSSSSSSSSS SSSSSSSSSSSSS
mask_diff = alive_mask - mask_previous
mask_clamp_ch0 = torch.clip((1-mask_diff) + self.background_intensity,0,self.step_size) #make sure this is only applied to the first channel
mask_clamp = torch.repeat_interleave(mask_clamp_ch0,16,1)
mask_clamp_ones = torch.ones_like(torch.squeeze(mask_clamp_ch0))*self.scale_mask
for idx_channel in np.arange(1,16,1):
mask_clamp[:,idx_channel,:,:] = mask_clamp_ones
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1)
P = self.perception(x)
Y = self.model(P)
out = x + (Y * mask * mask_clamp)
out *= alive_mask
return out, alive_mask, mask_clamp
# Cell
class ca_model_laplacian_regularizer(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda', grow_on_k_iter=3, background_intensity=.19, step_size=1, scale_mask=1, reg1=1):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(ca_model_laplacian_regularizer, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
self.grow_on_k_iter = grow_on_k_iter
self.background_intensity = background_intensity
self.step_size = step_size
self.scale_mask = scale_mask
self.reg1 = reg1
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, epochs_inside, epoch_outside = 1000, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for epoch_in in range(epochs_inside):
x, alive_mask, other_mask = self.forward(x, epoch_in, epoch_outside)
# print(x[:,:4, :,:].shape, target.shape)
# batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,:1, :,:] - target)**2),dim=0)
batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,0, :,:] - target[:,0,:,:])**2),dim=0)
if masked_loss == True:
alive_mask_dilated = (F.max_pool2d(alive_mask[0], 3,1, padding =1 ) > 0.1).float()
# alive_mask_dilated = torch.from_numpy(binary_closing(alive_mask[0].cpu().numpy() > 0.1)).float().to('cuda')
target_loss = target_loss_func(x[:,:1, :,:] * alive_mask_dilated, target * alive_mask_dilated)
else:
target_loss = target_loss_func(x[:,:2, :,:] * target[:,1:,...], target * target[:,1:,...]) # used to synthesize almost all nodules
sobel_regularizer = sobel_reg(x)
# print(f' out(x)={x.shape}, sobel_regularizer={sobel_regularizer}')
loss = target_loss + (sobel_regularizer*self.reg1)
return loss, x, alive_mask.cpu().numpy(), other_mask.cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x, epoch_in, epoch_outside):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
mask_previous = alive_mask = (x[:,1:2,:,:] > 0.1).float()
mode = 0
if epoch_outside < 100:
alive_mask = self.get_alive_mask(x)
mode = 1
else:
if epoch_in % self.grow_on_k_iter == 0:
alive_mask = self.get_alive_mask(x)
mode = 2
else:
alive_mask = (x[:,1:2,:,:] > 0.1).float()
mode = 3
mask_diff = alive_mask - mask_previous
mask_new_cells_clamped = torch.clip((1-mask_diff) + self.background_intensity,0,self.step_size) #make sure this is only applied to the first channel
mask_new_cells_clamped_ones = torch.ones_like(torch.squeeze(mask_new_cells_clamped))*self.scale_mask
mask_new_cells_clamped2 = torch.repeat_interleave(mask_new_cells_clamped,16,1)
for idx_channel in np.arange(1,16,1):
mask_new_cells_clamped2[:,idx_channel,:,:] = mask_new_cells_clamped_ones
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1) # original mask used
y = self.perception(x)
out = x + self.model(y)*mask*mask_new_cells_clamped2
out *= alive_mask
# print(f'({epoch_in}) ({mode}) y={y.shape} alive_mask={alive_mask.shape} out={out.shape}')
return out, alive_mask, mask_new_cells_clamped2
# Cell
class ca_model_l2reg(nn.Module):
def __init__(self, checkpoint = None, seq_layers = None, device = 'cuda', grow_on_k_iter=3, background_intensity=.19, step_size=1, scale_mask=1, l2reg=0):
'''
Kind of a modular class for a CA model
args:
checkpoint = 'path/to/model.pt'
seq_layers = nn.Sequential(your, pytorch, layers)
device = 'cuda' or 'cpu'
'''
super(ca_model_l2reg, self).__init__()
self.ident = torch.tensor([[0.0,0.0,0.0],[0.0,1.0,0.0],[0.0,0.0,0.0]]).to(device)
self.sobel_x = (torch.tensor([[-1.0,0.0,1.0],[-2.0,0.0,2.0],[-1.0,0.0,1.0]])/8.0).to(device)
self.lap = (torch.tensor([[1.0,2.0,1.0],[2.0,-12,2.0],[1.0,2.0,1.0]])/16.0).to(device)
self.grow_on_k_iter = grow_on_k_iter
self.background_intensity = background_intensity
self.step_size = step_size
self.scale_mask = scale_mask
self.l2reg = l2reg
if seq_layers is not None:
self.model = seq_layers
else:
self.model = nn.Sequential(
nn.Conv2d(64, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size = 3,padding =1, bias = True),
nn.ReLU(),
nn.Conv2d(256, 16, kernel_size = 1, bias = True),
)
'''
initial condition for "do nothing" behaviour:
* all biases should be zero
* the weights of the last layer should be zero
'''
for l in range(len(self.model)):
if isinstance(self.model[l], nn.Conv2d):
self.model[l].bias.data.fill_(0)
if l == len(self.model) -1:
self.model[l].weight.data.fill_(0)
if checkpoint is not None:
self.load_state_dict(torch.load(checkpoint))
self.to(device= device)
def perchannel_conv(self, x, filters):
'''filters: [filter_n, h, w]'''
b, ch, h, w = x.shape
y = x.reshape(b*ch, 1, h, w)
y = torch.nn.functional.pad(y, [1, 1, 1, 1], 'circular')
y = torch.nn.functional.conv2d(y, filters[:,None])
return y.reshape(b, -1, h, w)
def perception(self, x):
filters = torch.stack([self.ident, self.sobel_x, self.sobel_x.T, self.lap])
return self.perchannel_conv(x, filters)
def normalize_grads(self):
'''
gradient normalization for constant step size and to avoid spikes
'''
for p in self.parameters():
p.grad.data = p.grad.data/(p.grad.data.norm()+1e-8)
def get_alive_mask(self, x):
'''
looks for cells that have values over 0.1,
and allows only their adjacent cells to participate in growth
'''
alpha = x[:,1:2,:,:]
pooled = (F.max_pool2d(alpha, 3,1, padding =1 ) > 0.1).float()
return pooled
def train_step(self, seed, target, target_loss_func, epochs_inside, epoch_outside = 1000, masked_loss=False):
'''
a single training step for the model,
feel free to play around with different loss functions like L1 loss
the loss is calculated for only the first 4 channels of the output
'''
x = seed
for epoch_in in range(epochs_inside):
x, alive_mask, Y = self.forward(x, epoch_in, epoch_outside)
# print(x[:,:4, :,:].shape, target.shape)
# batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,:1, :,:] - target)**2),dim=0)
batch_mean_rmse_per_pixel = torch.mean(torch.sqrt((x[:,0, :,:] - target[:,0,:,:])**2),dim=0)
if masked_loss == True:
alive_mask_dilated = (F.max_pool2d(alive_mask[0], 3,1, padding =1 ) > 0.1).float()
# alive_mask_dilated = torch.from_numpy(binary_closing(alive_mask[0].cpu().numpy() > 0.1)).float().to('cuda')
target_loss = target_loss_func(x[:,:1, :,:] * alive_mask_dilated, target * alive_mask_dilated)
else:
target_loss = target_loss_func(x[:,:2, :,:] * target[:,1:,...], target * target[:,1:,...]) # used to synthesize almost all nodules
loss_reg = torch.sum(torch.abs(Y[:,:1,...]))
loss = target_loss + (loss_reg*self.l2reg)
return loss, x, alive_mask.cpu().numpy(), Y.detach().cpu().numpy() #batch_mean_rmse_per_pixel.detach().cpu().numpy()
def forward(self, x, epoch_in, epoch_outside):
'''
nice little forward function for the model
1. fetches an alive mask
2. generates another random mask of 0's and 1's
3. updates the input
4. applies alive mask
'''
mask_previous = alive_mask = (x[:,1:2,:,:] > 0.1).float()
if epoch_outside < 100:
alive_mask = self.get_alive_mask(x)
else:
if epoch_in % self.grow_on_k_iter == 0:
alive_mask = self.get_alive_mask(x)
else:
alive_mask = (x[:,1:2,:,:] > 0.1).float()
mask_diff = alive_mask - mask_previous
mask_new_cells_clamped = torch.clip((1-mask_diff) + self.background_intensity,0,self.step_size) #make sure this is only applied to the first channel
mask_new_cells_clamped_ones = torch.ones_like(torch.squeeze(mask_new_cells_clamped))*self.scale_mask
mask_new_cells_clamped2 = torch.repeat_interleave(mask_new_cells_clamped,16,1)
for idx_channel in np.arange(1,16,1):
mask_new_cells_clamped2[:,idx_channel,:,:] = mask_new_cells_clamped_ones
mask = torch.clamp(torch.round(torch.rand_like(x[:,:1,:,:])) , 0,1) # original mask used
P = self.perception(x)
Y = self.model(P)
out = x + Y *mask*mask_new_cells_clamped2
out *= alive_mask
return out, alive_mask, Y #mask_new_cells_clamped2 | 40.522167 | 169 | 0.586774 | 6,054 | 41,130 | 3.817476 | 0.059465 | 0.014539 | 0.016615 | 0.015231 | 0.876552 | 0.867725 | 0.867206 | 0.855999 | 0.851023 | 0.848081 | 0 | 0.042658 | 0.269317 | 41,130 | 1,015 | 170 | 40.522167 | 0.72635 | 0.200219 | 0 | 0.769634 | 1 | 0.001745 | 0.01574 | 0.007721 | 0 | 0 | 0 | 0 | 0 | 1 | 0.095986 | false | 0 | 0.019197 | 0 | 0.198953 | 0.00349 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
3780f41176c4335cc90b285843e882a8598adb65 | 34,202 | py | Python | menpofit/benchmark/predefined.py | trigeorgis/menpofit | 742f4d1aeeb822a615d88ac499df40009b05795f | [
"BSD-3-Clause"
] | 1 | 2015-07-26T18:33:56.000Z | 2015-07-26T18:33:56.000Z | menpofit/benchmark/predefined.py | ersisimou/menpofit | 55ec53205ba31fd42ca054b2ce07590490decb8c | [
"BSD-3-Clause"
] | null | null | null | menpofit/benchmark/predefined.py | ersisimou/menpofit | 55ec53205ba31fd42ca054b2ce07590490decb8c | [
"BSD-3-Clause"
] | null | null | null | from menpo.landmark import ibug_face_68_trimesh
from menpo.feature import sparse_hog, igo
from menpofit.lucaskanade import AIC
from menpofit.transform import OrthoMDTransform, DifferentiablePiecewiseAffine
from menpofit.modelinstance import OrthoPDM
from menpofit.gradientdescent import RLMS
from menpofit.clm.classifier import linear_svm_lr
from .io import import_bounding_boxes
from .base import (aam_build_benchmark, aam_fit_benchmark,
clm_build_benchmark, clm_fit_benchmark,
sdm_build_benchmark, sdm_fit_benchmark,
load_database, convert_fitting_results_to_ced,
plot_fitting_curves)
def aam_fastest_alternating_noise(training_db_path, fitting_db_path,
features=igo, noise_std=0.04,
verbose=False, plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.2,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'features': igo,
'transform': DifferentiablePiecewiseAffine,
'trilist': ibug_face_68_trimesh,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 2,
'scaled_shape_models': True,
'max_shape_components': 25,
'max_appearance_components': 250,
'boundary': 3
}
fitting_options = {'algorithm': AIC,
'md_transform': OrthoMDTransform,
'n_shape': [3, 6, 12],
'n_appearance': 50,
'max_iters': 50,
'error_type': 'me_norm'
}
perturb_options = {'noise_std': 0.04,
'rotation': False}
# set passed parameters
training_options['features'] = features
perturb_options['noise_std'] = noise_std
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
aam = aam_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
fitting_results = aam_fit_benchmark(fitting_images, aam,
perturb_options=perturb_options,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "AAMs using {} and Alternating IC".format(
training_options['features'].__name__)
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def aam_fastest_alternating_bbox(training_db_path, fitting_db_path,
fitting_bboxes_path, features=igo,
verbose=False, plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.1,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'features': [igo] * 3,
'transform': DifferentiablePiecewiseAffine,
'trilist': ibug_face_68_trimesh,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 2,
'scaled_shape_models': True,
'max_shape_components': 25,
'max_appearance_components': 250,
'boundary': 3
}
fitting_options = {'algorithm': AIC,
'md_transform': OrthoMDTransform,
'n_shape': [3, 6, 12],
'n_appearance': 50,
'max_iters': 50,
'error_type': 'me_norm'
}
# set passed parameters
training_options['features'] = features
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
aam = aam_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
# import bounding boxes
bboxes_list = import_bounding_boxes(fitting_bboxes_path)
# for all fittings, we crop to 0.5
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
bounding_boxes=bboxes_list,
verbose=verbose)
fitting_results = aam_fit_benchmark(fitting_images, aam,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "AAMs using {} and Alternating IC".format(
training_options['features'].__name__)
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def aam_best_performance_alternating_noise(training_db_path, fitting_db_path,
features=igo, noise_std=0.04,
verbose=False, plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.2,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'features': igo,
'transform': DifferentiablePiecewiseAffine,
'trilist': ibug_face_68_trimesh,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 1.2,
'scaled_shape_models': False,
'max_shape_components': 25,
'max_appearance_components': 250,
'boundary': 3
}
fitting_options = {'algorithm': AIC,
'md_transform': OrthoMDTransform,
'n_shape': [3, 6, 12],
'n_appearance': 50,
'max_iters': 50,
'error_type': error_type
}
perturb_options = {'noise_std': 0.04,
'rotation': False}
# set passed parameters
training_options['features'] = features
perturb_options['noise_std'] = noise_std
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
aam = aam_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
fitting_results = aam_fit_benchmark(fitting_images, aam,
perturb_options=perturb_options,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "AAMs using {} and Alternating IC".format(
training_options['features'].__name__)
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def aam_best_performance_alternating_bbox(training_db_path, fitting_db_path,
fitting_bboxes_path,
features=igo, verbose=False,
plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.5,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'features': igo,
'transform': DifferentiablePiecewiseAffine,
'trilist': ibug_face_68_trimesh,
'normalization_diagonal': 200,
'n_levels': 3,
'downscale': 2,
'scaled_shape_models': True,
'max_shape_components': 25,
'max_appearance_components': 100,
'boundary': 3
}
fitting_options = {'algorithm': AIC,
'md_transform': OrthoMDTransform,
'n_shape': [3, 6, 12],
'n_appearance': 50,
'max_iters': 50,
'error_type': error_type
}
# set passed parameters
training_options['features'] = features
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
aam = aam_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
# import bounding boxes
bboxes_list = import_bounding_boxes(fitting_bboxes_path)
# for all fittings, we crop to 0.5
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
bounding_boxes=bboxes_list,
verbose=verbose)
fitting_results = aam_fit_benchmark(fitting_images, aam,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "AAMs using {} and Alternating IC".format(
training_options['features'].__name__)
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def clm_basic_noise(training_db_path, fitting_db_path,
features=sparse_hog, classifier_trainers=linear_svm_lr,
noise_std=0.04, verbose=False, plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.4,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'classifier_trainers': linear_svm_lr,
'patch_shape': (5, 5),
'features': [sparse_hog] * 3,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 1.1,
'scaled_shape_models': True,
'max_shape_components': None,
'boundary': 3
}
fitting_options = {'algorithm': RLMS,
'pdm_transform': OrthoPDM,
'n_shape': [3, 6, 12],
'max_iters': 50,
'error_type': error_type
}
perturb_options = {'noise_std': 0.01,
'rotation': False}
# set passed parameters
training_options['features'] = features
training_options['classifier_trainers'] = classifier_trainers
perturb_options['noise_std'] = noise_std
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
clm = clm_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
fitting_results = clm_fit_benchmark(fitting_images, clm,
perturb_options=perturb_options,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "CLMs with {} and {} classifier using RLMS".format(
training_options['features'].__name__,
training_options['classifier_trainers'])
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def clm_basic_bbox(training_db_path, fitting_db_path, fitting_bboxes_path,
features=sparse_hog, classifier_trainers=linear_svm_lr,
verbose=False, plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.5,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'classifier_trainers': linear_svm_lr,
'patch_shape': (5, 5),
'features': [sparse_hog] * 3,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 1.1,
'scaled_shape_models': True,
'max_shape_components': None,
'boundary': 3
}
fitting_options = {'algorithm': RLMS,
'pdm_transform': OrthoPDM,
'n_shape': [3, 6, 12],
'max_iters': 50,
'error_type': error_type
}
# set passed parameters
training_options['features'] = features
training_options['classifier_trainers'] = classifier_trainers
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
clm = clm_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
# import bounding boxes
bboxes_list = import_bounding_boxes(fitting_bboxes_path)
# for all fittings, we crop to 0.5
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
bounding_boxes=bboxes_list,
verbose=verbose)
fitting_results = clm_fit_benchmark(fitting_images, clm,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "CLMs with {} and {} classifier using RLMS".format(
training_options['features'].__name__,
training_options['classifier_trainers'])
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def sdm_fastest_bbox(training_db_path, fitting_db_path,
fitting_bboxes_path, features=None,
verbose=False, plot=False):
# predefined options
error_type = 'me_norm'
db_loading_options = {'crop_proportion': 0.8,
'convert_to_grey': True
}
training_options = {'group': 'PTS',
'normalization_diagonal': 200,
'n_levels': 4,
'downscale': 1.01,
'noise_std': 0.08,
'patch_shape': (16, 16),
'n_perturbations': 15,
}
fitting_options = {
'error_type': error_type
}
# run experiment
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
sdm = sdm_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
# import bounding boxes
bboxes_list = import_bounding_boxes(fitting_bboxes_path)
# for all fittings, we crop to 0.5
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
bounding_boxes=bboxes_list,
verbose=verbose)
fitting_results = sdm_fit_benchmark(fitting_images, sdm,
fitting_options=fitting_options,
verbose=verbose)
# convert results
max_error_bin = 0.05
bins_error_step = 0.005
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(fitting_results,
max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
# plot results
if plot:
title = "SDMs using default (sparse hogs)".format(
training_options['features'].__name__)
y_axis = [final_error_curve, initial_error_curve]
legend = ['Fitting', 'Initialization']
plot_fitting_curves(error_bins, y_axis, title, new_figure=True,
x_limit=max_error_bin, legend_entries=legend,
line_colour=['r', 'b'],
marker_face_colour=['r', 'b'],
marker_style=['o', 'x'])
return fitting_results, final_error_curve, initial_error_curve, error_bins
def aam_params_combinations_noise(training_db_path, fitting_db_path,
n_experiments=1, features=None,
scaled_shape_models=None,
n_shape=None,
n_appearance=None, noise_std=None,
rotation=None, verbose=False, plot=False):
# parse input
if features is None:
features = [igo] * n_experiments
elif len(features) is not n_experiments:
raise ValueError("features has wrong length")
if scaled_shape_models is None:
scaled_shape_models = [True] * n_experiments
elif len(scaled_shape_models) is not n_experiments:
raise ValueError("scaled_shape_models has wrong length")
if n_shape is None:
n_shape = [[3, 6, 12]] * n_experiments
elif len(n_shape) is not n_experiments:
raise ValueError("n_shape has wrong length")
if n_appearance is None:
n_appearance = [50] * n_experiments
elif len(n_appearance) is not n_experiments:
raise ValueError("n_appearance has wrong length")
if noise_std is None:
noise_std = [0.04] * n_experiments
elif len(noise_std) is not n_experiments:
raise ValueError("noise_std has wrong length")
if rotation is None:
rotation = [False] * n_experiments
elif len(rotation) is not n_experiments:
raise ValueError("rotation has wrong length")
# load images
db_loading_options = {'crop_proportion': 0.1,
'convert_to_grey': True
}
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
# run experiments
max_error_bin = 0.05
bins_error_step = 0.005
curves_to_plot = []
all_fitting_results = []
for i in range(n_experiments):
if verbose:
print("\nEXPERIMENT {}/{}:".format(i + 1, n_experiments))
print("- features: {}\n- scaled_shape_models: {}\n"
"- n_shape: {}\n"
"- n_appearance: {}\n- noise_std: {}\n"
"- rotation: {}".format(
features[i], scaled_shape_models[i],
n_shape[i], n_appearance[i], noise_std[i], rotation[i]))
# predefined option dictionaries
error_type = 'me_norm'
training_options = {'group': 'PTS',
'features': igo,
'transform': DifferentiablePiecewiseAffine,
'trilist': ibug_face_68_trimesh,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 1.1,
'scaled_shape_models': True,
'max_shape_components': 25,
'max_appearance_components': 250,
'boundary': 3
}
fitting_options = {'algorithm': AIC,
'md_transform': OrthoMDTransform,
'n_shape': [3, 6, 12],
'n_appearance': 50,
'max_iters': 50,
'error_type': error_type
}
pertrub_options = {'noise_std': 0.04,
'rotation': False}
# training
training_options['features'] = features[i]
training_options['scaled_shape_models'] = scaled_shape_models[i]
aam = aam_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
# fitting
fitting_options['n_shape'] = n_shape[i]
fitting_options['n_appearance'] = n_appearance[i]
pertrub_options['noise_std'] = noise_std[i]
pertrub_options['rotation'] = rotation[i]
fitting_results = aam_fit_benchmark(fitting_images, aam,
perturb_options=pertrub_options,
fitting_options=fitting_options,
verbose=verbose)
all_fitting_results.append(fitting_results)
# convert results
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(
fitting_results, max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
curves_to_plot.append(final_error_curve)
if i == n_experiments - 1:
curves_to_plot.append(initial_error_curve)
# plot results
if plot:
title = "AAMs using Alternating IC"
colour_list = ['r', 'b', 'g', 'y', 'c'] * n_experiments
marker_list = ['o', 'x', 'v', 'd'] * n_experiments
plot_fitting_curves(error_bins, curves_to_plot, title, new_figure=True,
x_limit=max_error_bin, line_colour=colour_list,
marker_face_colour=colour_list,
marker_style=marker_list)
return all_fitting_results
def clm_params_combinations_noise(training_db_path, fitting_db_path,
n_experiments=1, classifier_trainers=None,
patch_shape=None, features=None,
scaled_shape_models=None, n_shape=None,
noise_std=None, rotation=None, verbose=False,
plot=False):
# parse input
if classifier_trainers is None:
classifier_trainers = [linear_svm_lr] * n_experiments
elif len(classifier_trainers) is not n_experiments:
raise ValueError("classifier_trainers has wrong length")
if patch_shape is None:
patch_shape = [(5, 5)] * n_experiments
elif len(patch_shape) is not n_experiments:
raise ValueError("patch_shape has wrong length")
if features is None:
features = [igo] * n_experiments
elif len(features) is not n_experiments:
raise ValueError("features has wrong length")
if scaled_shape_models is None:
scaled_shape_models = [True] * n_experiments
elif len(scaled_shape_models) is not n_experiments:
raise ValueError("scaled_shape_models has wrong length")
if n_shape is None:
n_shape = [[3, 6, 12]] * n_experiments
elif len(n_shape) is not n_experiments:
raise ValueError("n_shape has wrong length")
if noise_std is None:
noise_std = [0.04] * n_experiments
elif len(noise_std) is not n_experiments:
raise ValueError("noise_std has wrong length")
if rotation is None:
rotation = [False] * n_experiments
elif len(rotation) is not n_experiments:
raise ValueError("rotation has wrong length")
# load images
db_loading_options = {'crop_proportion': 0.4,
'convert_to_grey': True
}
training_images = load_database(training_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
fitting_images = load_database(fitting_db_path,
db_loading_options=db_loading_options,
verbose=verbose)
# run experiments
max_error_bin = 0.05
bins_error_step = 0.005
curves_to_plot = []
all_fitting_results = []
for i in range(n_experiments):
if verbose:
print("\nEXPERIMENT {}/{}:".format(i + 1, n_experiments))
print("- classifiers: {}\n- patch_shape: {}\n"
"- features: {}\n- scaled_shape_models: {}\n"
"- n_shape: {}\n"
"- noise_std: {}\n- rotation: {}".format(
classifier_trainers[i], patch_shape[i], features[i],
scaled_shape_models[i], n_shape[i],
noise_std[i], rotation[i]))
# predefined option dictionaries
error_type = 'me_norm'
training_options = {'group': 'PTS',
'classifier_trainers': linear_svm_lr,
'patch_shape': (5, 5),
'features': sparse_hog,
'normalization_diagonal': None,
'n_levels': 3,
'downscale': 1.1,
'scaled_shape_models': False,
'max_shape_components': None,
'boundary': 3
}
fitting_options = {'algorithm': RLMS,
'pdm_transform': OrthoPDM,
'n_shape': [3, 6, 12],
'max_iters': 50,
'error_type': error_type
}
perturb_options = {'noise_std': 0.01,
'rotation': False}
# training
training_options['classifier_trainers'] = classifier_trainers[i]
training_options['patch_shape'] = patch_shape[i]
training_options['features'] = features[i]
training_options['scaled_shape_models'] = scaled_shape_models[i]
clm = clm_build_benchmark(training_images,
training_options=training_options,
verbose=verbose)
# fitting
fitting_options['n_shape'] = n_shape[i]
perturb_options['noise_std'] = noise_std[i]
perturb_options['rotation'] = rotation[i]
fitting_results = clm_fit_benchmark(fitting_images, clm,
perturb_options=perturb_options,
fitting_options=fitting_options,
verbose=verbose)
all_fitting_results.append(fitting_results)
# convert results
final_error_curve, initial_error_curve, error_bins = \
convert_fitting_results_to_ced(
fitting_results, max_error_bin=max_error_bin,
bins_error_step=bins_error_step,
error_type=error_type)
curves_to_plot.append(final_error_curve)
if i == n_experiments - 1:
curves_to_plot.append(initial_error_curve)
# plot results
if plot:
title = "CLMs using RLMS"
colour_list = ['r', 'b', 'g', 'y', 'c'] * n_experiments
marker_list = ['o', 'x', 'v', 'd'] * n_experiments
plot_fitting_curves(error_bins, curves_to_plot, title, new_figure=True,
x_limit=max_error_bin, line_colour=colour_list,
marker_face_colour=colour_list,
marker_style=marker_list)
return all_fitting_results
| 43.848718 | 79 | 0.527455 | 3,282 | 34,202 | 5.105119 | 0.058806 | 0.044763 | 0.042972 | 0.0302 | 0.933691 | 0.924799 | 0.910654 | 0.902477 | 0.898418 | 0.880215 | 0 | 0.013999 | 0.39638 | 34,202 | 779 | 80 | 43.905006 | 0.797578 | 0.029998 | 0 | 0.826019 | 0 | 0 | 0.107286 | 0.009753 | 0 | 0 | 0 | 0 | 0 | 1 | 0.014107 | false | 0 | 0.020376 | 0 | 0.048589 | 0.00627 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
37959c4763801fd3b2d0574049dda7b7f037ae25 | 36 | py | Python | networks/__init__.py | chenpan0615/FCCDN_pytorch | 0589988a62b031d678358c7462b2f236c0f17555 | [
"MIT"
] | 18 | 2021-08-11T07:04:19.000Z | 2022-03-29T02:08:32.000Z | networks/__init__.py | chenpan0615/FCCDN_pytorch | 0589988a62b031d678358c7462b2f236c0f17555 | [
"MIT"
] | 5 | 2021-10-02T03:18:26.000Z | 2022-03-29T02:59:48.000Z | networks/__init__.py | chenpan0615/FCCDN_pytorch | 0589988a62b031d678358c7462b2f236c0f17555 | [
"MIT"
] | 5 | 2021-11-18T14:54:10.000Z | 2022-03-29T02:08:34.000Z | from .GenerateNet import GenerateNet | 36 | 36 | 0.888889 | 4 | 36 | 8 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 36 | 1 | 36 | 36 | 0.969697 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
37bc78283f980d2bcb555def50a5cafef2f72354 | 13,662 | py | Python | sdk/python/pulumi_azure/synapse/workspace_key.py | ScriptBox99/pulumi-azure | 1b8c6d5479ccabc39094741eac25a8ca44c8833a | [
"ECL-2.0",
"Apache-2.0"
] | 109 | 2018-06-18T00:19:44.000Z | 2022-02-20T05:32:57.000Z | sdk/python/pulumi_azure/synapse/workspace_key.py | ScriptBox99/pulumi-azure | 1b8c6d5479ccabc39094741eac25a8ca44c8833a | [
"ECL-2.0",
"Apache-2.0"
] | 663 | 2018-06-18T21:08:46.000Z | 2022-03-31T20:10:11.000Z | sdk/python/pulumi_azure/synapse/workspace_key.py | ScriptBox99/pulumi-azure | 1b8c6d5479ccabc39094741eac25a8ca44c8833a | [
"ECL-2.0",
"Apache-2.0"
] | 41 | 2018-07-19T22:37:38.000Z | 2022-03-14T10:56:26.000Z | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['WorkspaceKeyArgs', 'WorkspaceKey']
@pulumi.input_type
class WorkspaceKeyArgs:
def __init__(__self__, *,
active: pulumi.Input[bool],
synapse_workspace_id: pulumi.Input[str],
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a WorkspaceKey resource.
"""
pulumi.set(__self__, "active", active)
pulumi.set(__self__, "synapse_workspace_id", synapse_workspace_id)
if cusomter_managed_key_name is not None:
warnings.warn("""As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""", DeprecationWarning)
pulumi.log.warn("""cusomter_managed_key_name is deprecated: As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""")
if cusomter_managed_key_name is not None:
pulumi.set(__self__, "cusomter_managed_key_name", cusomter_managed_key_name)
if customer_managed_key_name is not None:
pulumi.set(__self__, "customer_managed_key_name", customer_managed_key_name)
if customer_managed_key_versionless_id is not None:
pulumi.set(__self__, "customer_managed_key_versionless_id", customer_managed_key_versionless_id)
@property
@pulumi.getter
def active(self) -> pulumi.Input[bool]:
return pulumi.get(self, "active")
@active.setter
def active(self, value: pulumi.Input[bool]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="synapseWorkspaceId")
def synapse_workspace_id(self) -> pulumi.Input[str]:
return pulumi.get(self, "synapse_workspace_id")
@synapse_workspace_id.setter
def synapse_workspace_id(self, value: pulumi.Input[str]):
pulumi.set(self, "synapse_workspace_id", value)
@property
@pulumi.getter(name="cusomterManagedKeyName")
def cusomter_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "cusomter_managed_key_name")
@cusomter_managed_key_name.setter
def cusomter_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cusomter_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyName")
def customer_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_name")
@customer_managed_key_name.setter
def customer_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyVersionlessId")
def customer_managed_key_versionless_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_versionless_id")
@customer_managed_key_versionless_id.setter
def customer_managed_key_versionless_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_versionless_id", value)
@pulumi.input_type
class _WorkspaceKeyState:
def __init__(__self__, *,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering WorkspaceKey resources.
"""
if active is not None:
pulumi.set(__self__, "active", active)
if cusomter_managed_key_name is not None:
warnings.warn("""As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""", DeprecationWarning)
pulumi.log.warn("""cusomter_managed_key_name is deprecated: As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""")
if cusomter_managed_key_name is not None:
pulumi.set(__self__, "cusomter_managed_key_name", cusomter_managed_key_name)
if customer_managed_key_name is not None:
pulumi.set(__self__, "customer_managed_key_name", customer_managed_key_name)
if customer_managed_key_versionless_id is not None:
pulumi.set(__self__, "customer_managed_key_versionless_id", customer_managed_key_versionless_id)
if synapse_workspace_id is not None:
pulumi.set(__self__, "synapse_workspace_id", synapse_workspace_id)
@property
@pulumi.getter
def active(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "active")
@active.setter
def active(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="cusomterManagedKeyName")
def cusomter_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "cusomter_managed_key_name")
@cusomter_managed_key_name.setter
def cusomter_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cusomter_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyName")
def customer_managed_key_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_name")
@customer_managed_key_name.setter
def customer_managed_key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_name", value)
@property
@pulumi.getter(name="customerManagedKeyVersionlessId")
def customer_managed_key_versionless_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "customer_managed_key_versionless_id")
@customer_managed_key_versionless_id.setter
def customer_managed_key_versionless_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "customer_managed_key_versionless_id", value)
@property
@pulumi.getter(name="synapseWorkspaceId")
def synapse_workspace_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "synapse_workspace_id")
@synapse_workspace_id.setter
def synapse_workspace_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "synapse_workspace_id", value)
class WorkspaceKey(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Create a WorkspaceKey resource with the given unique name, props, and options.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: WorkspaceKeyArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Create a WorkspaceKey resource with the given unique name, props, and options.
:param str resource_name: The name of the resource.
:param WorkspaceKeyArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(WorkspaceKeyArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = WorkspaceKeyArgs.__new__(WorkspaceKeyArgs)
if active is None and not opts.urn:
raise TypeError("Missing required property 'active'")
__props__.__dict__["active"] = active
if cusomter_managed_key_name is not None and not opts.urn:
warnings.warn("""As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""", DeprecationWarning)
pulumi.log.warn("""cusomter_managed_key_name is deprecated: As this property name contained a typo originally, please switch to using 'customer_managed_key_name' instead.""")
__props__.__dict__["cusomter_managed_key_name"] = cusomter_managed_key_name
__props__.__dict__["customer_managed_key_name"] = customer_managed_key_name
__props__.__dict__["customer_managed_key_versionless_id"] = customer_managed_key_versionless_id
if synapse_workspace_id is None and not opts.urn:
raise TypeError("Missing required property 'synapse_workspace_id'")
__props__.__dict__["synapse_workspace_id"] = synapse_workspace_id
super(WorkspaceKey, __self__).__init__(
'azure:synapse/workspaceKey:WorkspaceKey',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
cusomter_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_name: Optional[pulumi.Input[str]] = None,
customer_managed_key_versionless_id: Optional[pulumi.Input[str]] = None,
synapse_workspace_id: Optional[pulumi.Input[str]] = None) -> 'WorkspaceKey':
"""
Get an existing WorkspaceKey resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _WorkspaceKeyState.__new__(_WorkspaceKeyState)
__props__.__dict__["active"] = active
__props__.__dict__["cusomter_managed_key_name"] = cusomter_managed_key_name
__props__.__dict__["customer_managed_key_name"] = customer_managed_key_name
__props__.__dict__["customer_managed_key_versionless_id"] = customer_managed_key_versionless_id
__props__.__dict__["synapse_workspace_id"] = synapse_workspace_id
return WorkspaceKey(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def active(self) -> pulumi.Output[bool]:
return pulumi.get(self, "active")
@property
@pulumi.getter(name="cusomterManagedKeyName")
def cusomter_managed_key_name(self) -> pulumi.Output[str]:
return pulumi.get(self, "cusomter_managed_key_name")
@property
@pulumi.getter(name="customerManagedKeyName")
def customer_managed_key_name(self) -> pulumi.Output[str]:
return pulumi.get(self, "customer_managed_key_name")
@property
@pulumi.getter(name="customerManagedKeyVersionlessId")
def customer_managed_key_versionless_id(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "customer_managed_key_versionless_id")
@property
@pulumi.getter(name="synapseWorkspaceId")
def synapse_workspace_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "synapse_workspace_id")
| 48.792857 | 190 | 0.694701 | 1,605 | 13,662 | 5.522741 | 0.092835 | 0.104919 | 0.104242 | 0.081904 | 0.80009 | 0.782265 | 0.776512 | 0.75722 | 0.733078 | 0.706679 | 0 | 0.000093 | 0.21278 | 13,662 | 279 | 191 | 48.967742 | 0.824082 | 0.082931 | 0 | 0.699531 | 1 | 0.014085 | 0.192819 | 0.104393 | 0 | 0 | 0 | 0 | 0 | 1 | 0.150235 | false | 0.004695 | 0.023474 | 0.070423 | 0.262911 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
807c1d5498f15e6de8d068bf147332465a66369d | 137 | py | Python | python/testData/override/indent_after.py | teddywest32/intellij-community | e0268d7a1da1d318b441001448cdd3e8929b2f29 | [
"Apache-2.0"
] | null | null | null | python/testData/override/indent_after.py | teddywest32/intellij-community | e0268d7a1da1d318b441001448cdd3e8929b2f29 | [
"Apache-2.0"
] | 11 | 2017-02-27T22:35:32.000Z | 2021-12-24T08:07:40.000Z | python/testData/override/indent_after.py | teddywest32/intellij-community | e0268d7a1da1d318b441001448cdd3e8929b2f29 | [
"Apache-2.0"
] | 1 | 2019-02-06T14:50:03.000Z | 2019-02-06T14:50:03.000Z | class Dialog:
def validate(self): pass
class B(Dialog):
def validate(self):
<selection>Dialog.validate(self)</selection> | 22.833333 | 52 | 0.678832 | 17 | 137 | 5.470588 | 0.470588 | 0.387097 | 0.365591 | 0.451613 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.189781 | 137 | 6 | 52 | 22.833333 | 0.837838 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0.2 | 0 | null | null | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
80fc0749713257291dc93a80b4688cdadbbb9c01 | 6,959 | py | Python | loldib/getratings/models/NA/na_tahmkench/na_tahmkench_mid.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_tahmkench/na_tahmkench_mid.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_tahmkench/na_tahmkench_mid.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | from getratings.models.ratings import Ratings
class NA_TahmKench_Mid_Aatrox(Ratings):
pass
class NA_TahmKench_Mid_Ahri(Ratings):
pass
class NA_TahmKench_Mid_Akali(Ratings):
pass
class NA_TahmKench_Mid_Alistar(Ratings):
pass
class NA_TahmKench_Mid_Amumu(Ratings):
pass
class NA_TahmKench_Mid_Anivia(Ratings):
pass
class NA_TahmKench_Mid_Annie(Ratings):
pass
class NA_TahmKench_Mid_Ashe(Ratings):
pass
class NA_TahmKench_Mid_AurelionSol(Ratings):
pass
class NA_TahmKench_Mid_Azir(Ratings):
pass
class NA_TahmKench_Mid_Bard(Ratings):
pass
class NA_TahmKench_Mid_Blitzcrank(Ratings):
pass
class NA_TahmKench_Mid_Brand(Ratings):
pass
class NA_TahmKench_Mid_Braum(Ratings):
pass
class NA_TahmKench_Mid_Caitlyn(Ratings):
pass
class NA_TahmKench_Mid_Camille(Ratings):
pass
class NA_TahmKench_Mid_Cassiopeia(Ratings):
pass
class NA_TahmKench_Mid_Chogath(Ratings):
pass
class NA_TahmKench_Mid_Corki(Ratings):
pass
class NA_TahmKench_Mid_Darius(Ratings):
pass
class NA_TahmKench_Mid_Diana(Ratings):
pass
class NA_TahmKench_Mid_Draven(Ratings):
pass
class NA_TahmKench_Mid_DrMundo(Ratings):
pass
class NA_TahmKench_Mid_Ekko(Ratings):
pass
class NA_TahmKench_Mid_Elise(Ratings):
pass
class NA_TahmKench_Mid_Evelynn(Ratings):
pass
class NA_TahmKench_Mid_Ezreal(Ratings):
pass
class NA_TahmKench_Mid_Fiddlesticks(Ratings):
pass
class NA_TahmKench_Mid_Fiora(Ratings):
pass
class NA_TahmKench_Mid_Fizz(Ratings):
pass
class NA_TahmKench_Mid_Galio(Ratings):
pass
class NA_TahmKench_Mid_Gangplank(Ratings):
pass
class NA_TahmKench_Mid_Garen(Ratings):
pass
class NA_TahmKench_Mid_Gnar(Ratings):
pass
class NA_TahmKench_Mid_Gragas(Ratings):
pass
class NA_TahmKench_Mid_Graves(Ratings):
pass
class NA_TahmKench_Mid_Hecarim(Ratings):
pass
class NA_TahmKench_Mid_Heimerdinger(Ratings):
pass
class NA_TahmKench_Mid_Illaoi(Ratings):
pass
class NA_TahmKench_Mid_Irelia(Ratings):
pass
class NA_TahmKench_Mid_Ivern(Ratings):
pass
class NA_TahmKench_Mid_Janna(Ratings):
pass
class NA_TahmKench_Mid_JarvanIV(Ratings):
pass
class NA_TahmKench_Mid_Jax(Ratings):
pass
class NA_TahmKench_Mid_Jayce(Ratings):
pass
class NA_TahmKench_Mid_Jhin(Ratings):
pass
class NA_TahmKench_Mid_Jinx(Ratings):
pass
class NA_TahmKench_Mid_Kalista(Ratings):
pass
class NA_TahmKench_Mid_Karma(Ratings):
pass
class NA_TahmKench_Mid_Karthus(Ratings):
pass
class NA_TahmKench_Mid_Kassadin(Ratings):
pass
class NA_TahmKench_Mid_Katarina(Ratings):
pass
class NA_TahmKench_Mid_Kayle(Ratings):
pass
class NA_TahmKench_Mid_Kayn(Ratings):
pass
class NA_TahmKench_Mid_Kennen(Ratings):
pass
class NA_TahmKench_Mid_Khazix(Ratings):
pass
class NA_TahmKench_Mid_Kindred(Ratings):
pass
class NA_TahmKench_Mid_Kled(Ratings):
pass
class NA_TahmKench_Mid_KogMaw(Ratings):
pass
class NA_TahmKench_Mid_Leblanc(Ratings):
pass
class NA_TahmKench_Mid_LeeSin(Ratings):
pass
class NA_TahmKench_Mid_Leona(Ratings):
pass
class NA_TahmKench_Mid_Lissandra(Ratings):
pass
class NA_TahmKench_Mid_Lucian(Ratings):
pass
class NA_TahmKench_Mid_Lulu(Ratings):
pass
class NA_TahmKench_Mid_Lux(Ratings):
pass
class NA_TahmKench_Mid_Malphite(Ratings):
pass
class NA_TahmKench_Mid_Malzahar(Ratings):
pass
class NA_TahmKench_Mid_Maokai(Ratings):
pass
class NA_TahmKench_Mid_MasterYi(Ratings):
pass
class NA_TahmKench_Mid_MissFortune(Ratings):
pass
class NA_TahmKench_Mid_MonkeyKing(Ratings):
pass
class NA_TahmKench_Mid_Mordekaiser(Ratings):
pass
class NA_TahmKench_Mid_Morgana(Ratings):
pass
class NA_TahmKench_Mid_Nami(Ratings):
pass
class NA_TahmKench_Mid_Nasus(Ratings):
pass
class NA_TahmKench_Mid_Nautilus(Ratings):
pass
class NA_TahmKench_Mid_Nidalee(Ratings):
pass
class NA_TahmKench_Mid_Nocturne(Ratings):
pass
class NA_TahmKench_Mid_Nunu(Ratings):
pass
class NA_TahmKench_Mid_Olaf(Ratings):
pass
class NA_TahmKench_Mid_Orianna(Ratings):
pass
class NA_TahmKench_Mid_Ornn(Ratings):
pass
class NA_TahmKench_Mid_Pantheon(Ratings):
pass
class NA_TahmKench_Mid_Poppy(Ratings):
pass
class NA_TahmKench_Mid_Quinn(Ratings):
pass
class NA_TahmKench_Mid_Rakan(Ratings):
pass
class NA_TahmKench_Mid_Rammus(Ratings):
pass
class NA_TahmKench_Mid_RekSai(Ratings):
pass
class NA_TahmKench_Mid_Renekton(Ratings):
pass
class NA_TahmKench_Mid_Rengar(Ratings):
pass
class NA_TahmKench_Mid_Riven(Ratings):
pass
class NA_TahmKench_Mid_Rumble(Ratings):
pass
class NA_TahmKench_Mid_Ryze(Ratings):
pass
class NA_TahmKench_Mid_Sejuani(Ratings):
pass
class NA_TahmKench_Mid_Shaco(Ratings):
pass
class NA_TahmKench_Mid_Shen(Ratings):
pass
class NA_TahmKench_Mid_Shyvana(Ratings):
pass
class NA_TahmKench_Mid_Singed(Ratings):
pass
class NA_TahmKench_Mid_Sion(Ratings):
pass
class NA_TahmKench_Mid_Sivir(Ratings):
pass
class NA_TahmKench_Mid_Skarner(Ratings):
pass
class NA_TahmKench_Mid_Sona(Ratings):
pass
class NA_TahmKench_Mid_Soraka(Ratings):
pass
class NA_TahmKench_Mid_Swain(Ratings):
pass
class NA_TahmKench_Mid_Syndra(Ratings):
pass
class NA_TahmKench_Mid_TahmKench(Ratings):
pass
class NA_TahmKench_Mid_Taliyah(Ratings):
pass
class NA_TahmKench_Mid_Talon(Ratings):
pass
class NA_TahmKench_Mid_Taric(Ratings):
pass
class NA_TahmKench_Mid_Teemo(Ratings):
pass
class NA_TahmKench_Mid_Thresh(Ratings):
pass
class NA_TahmKench_Mid_Tristana(Ratings):
pass
class NA_TahmKench_Mid_Trundle(Ratings):
pass
class NA_TahmKench_Mid_Tryndamere(Ratings):
pass
class NA_TahmKench_Mid_TwistedFate(Ratings):
pass
class NA_TahmKench_Mid_Twitch(Ratings):
pass
class NA_TahmKench_Mid_Udyr(Ratings):
pass
class NA_TahmKench_Mid_Urgot(Ratings):
pass
class NA_TahmKench_Mid_Varus(Ratings):
pass
class NA_TahmKench_Mid_Vayne(Ratings):
pass
class NA_TahmKench_Mid_Veigar(Ratings):
pass
class NA_TahmKench_Mid_Velkoz(Ratings):
pass
class NA_TahmKench_Mid_Vi(Ratings):
pass
class NA_TahmKench_Mid_Viktor(Ratings):
pass
class NA_TahmKench_Mid_Vladimir(Ratings):
pass
class NA_TahmKench_Mid_Volibear(Ratings):
pass
class NA_TahmKench_Mid_Warwick(Ratings):
pass
class NA_TahmKench_Mid_Xayah(Ratings):
pass
class NA_TahmKench_Mid_Xerath(Ratings):
pass
class NA_TahmKench_Mid_XinZhao(Ratings):
pass
class NA_TahmKench_Mid_Yasuo(Ratings):
pass
class NA_TahmKench_Mid_Yorick(Ratings):
pass
class NA_TahmKench_Mid_Zac(Ratings):
pass
class NA_TahmKench_Mid_Zed(Ratings):
pass
class NA_TahmKench_Mid_Ziggs(Ratings):
pass
class NA_TahmKench_Mid_Zilean(Ratings):
pass
class NA_TahmKench_Mid_Zyra(Ratings):
pass
| 16.688249 | 46 | 0.780572 | 972 | 6,959 | 5.162551 | 0.151235 | 0.192507 | 0.440016 | 0.522519 | 0.819051 | 0.819051 | 0 | 0 | 0 | 0 | 0 | 0 | 0.159649 | 6,959 | 416 | 47 | 16.728365 | 0.858071 | 0 | 0 | 0.498195 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.498195 | 0.00361 | 0 | 0.501805 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 7 |
80fe31c3aefdaccaae877a965dfa7f1bec006821 | 43 | py | Python | src/lib/sre_compile.py | DTenore/skulpt | 098d20acfb088d6db85535132c324b7ac2f2d212 | [
"MIT"
] | 2,671 | 2015-01-03T08:23:25.000Z | 2022-03-31T06:15:48.000Z | src/lib/sre_compile.py | wakeupmuyunhe/skulpt | a8fb11a80fb6d7c016bab5dfe3712517a350b347 | [
"MIT"
] | 972 | 2015-01-05T08:11:00.000Z | 2022-03-29T13:47:15.000Z | src/lib/sre_compile.py | wakeupmuyunhe/skulpt | a8fb11a80fb6d7c016bab5dfe3712517a350b347 | [
"MIT"
] | 845 | 2015-01-03T19:53:36.000Z | 2022-03-29T18:34:22.000Z | import _sk_fail; _sk_fail._("sre_compile")
| 21.5 | 42 | 0.790698 | 7 | 43 | 4 | 0.714286 | 0.428571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.069767 | 43 | 1 | 43 | 43 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0.255814 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
0380a906483de0d0bd7facd4ba3ee4af9934c511 | 95 | py | Python | games/pyxel-games-master/WhatAmI/utils.py | rkrishnasanka/Pyxel-Paradise-Launcher | 16727098bbc7acfbb26d34331505a18da60a2649 | [
"BSD-3-Clause"
] | 1 | 2020-02-04T03:06:32.000Z | 2020-02-04T03:06:32.000Z | games/pyxel-games-master/WhatAmI/utils.py | rkrishnasanka/Pyxel-Paradise-Launcher | 16727098bbc7acfbb26d34331505a18da60a2649 | [
"BSD-3-Clause"
] | null | null | null | games/pyxel-games-master/WhatAmI/utils.py | rkrishnasanka/Pyxel-Paradise-Launcher | 16727098bbc7acfbb26d34331505a18da60a2649 | [
"BSD-3-Clause"
] | null | null | null | import math
def get_tile_from_pos(x, y):
return (math.floor(y / 8), math.floor(x / 8)) | 23.75 | 53 | 0.631579 | 18 | 95 | 3.166667 | 0.666667 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.026667 | 0.210526 | 95 | 4 | 53 | 23.75 | 0.733333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 7 |
0392c01c80c751ff0aa087ae1f36628c1d84b242 | 202 | py | Python | kirby/builtins/available_plugins/routes.py | kirby6/kirby | d58086c53b0b1957a701328c4539712512a68464 | [
"MIT"
] | 5 | 2019-01-31T19:47:52.000Z | 2019-03-06T09:44:47.000Z | kirby/builtins/available_plugins/routes.py | kirby6/kirby | d58086c53b0b1957a701328c4539712512a68464 | [
"MIT"
] | null | null | null | kirby/builtins/available_plugins/routes.py | kirby6/kirby | d58086c53b0b1957a701328c4539712512a68464 | [
"MIT"
] | null | null | null | import json
from kirby.core import web_api
from .controller import get_all_available_plugins
@web_api.route('/')
def get_available_plugins_route():
return json.dumps(get_all_available_plugins())
| 20.2 | 50 | 0.806931 | 30 | 202 | 5.066667 | 0.533333 | 0.315789 | 0.197368 | 0.289474 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.108911 | 202 | 9 | 51 | 22.444444 | 0.844444 | 0 | 0 | 0 | 0 | 0 | 0.004951 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.166667 | true | 0 | 0.5 | 0.166667 | 0.833333 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 7 |
03ee7db85a1eb441860e09d31b2166b08f149537 | 2,230 | py | Python | pytorch_toolbox/probe/runtime.py | MathGaron/pytorch_toolbox | 2afd13e50ba71dfce66467a4b070d9b922668502 | [
"MIT"
] | 10 | 2018-02-26T04:51:11.000Z | 2021-10-01T02:30:37.000Z | pytorch_toolbox/probe/runtime.py | MathGaron/pytorch_toolbox | 2afd13e50ba71dfce66467a4b070d9b922668502 | [
"MIT"
] | 9 | 2017-11-16T16:11:16.000Z | 2020-02-13T13:10:55.000Z | pytorch_toolbox/probe/runtime.py | MathGaron/pytorch_toolbox | 2afd13e50ba71dfce66467a4b070d9b922668502 | [
"MIT"
] | 7 | 2018-02-12T19:06:14.000Z | 2021-03-25T19:13:51.000Z | import torch
from torch.autograd import Variable
import time
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
def compute_test_time(network_class, input_size, max_batch_size, step_size=1, is_cuda=False):
backend = "cpu"
if is_cuda:
backend = "cuda"
model = network_class()
if is_cuda:
model = model.cuda()
model.eval()
time_log = []
# make sure that everything is in memorybefore the actual tests
batch = Variable(torch.FloatTensor(1, *input_size))
if is_cuda:
batch = batch.cuda()
model(batch)
print("Compute {} test time".format(backend))
for i in tqdm(range(0, max_batch_size, step_size)):
batch = Variable(torch.FloatTensor(i+1, *input_size))
if is_cuda:
batch = batch.cuda()
time_start = time.time()
model(batch)
time_log.append(time.time() - time_start)
plt.plot(np.arange(1, max_batch_size + 1, step_size), time_log)
plt.title("{} test time w.r.t minibatch size".format(backend))
plt.ylabel("Time (s)")
plt.xlabel("Batch size")
def compute_train_time(network_class, input_size, max_batch_size, step_size=1, is_cuda=False, backward_only=False):
backend = "cpu"
if is_cuda:
backend = "cuda"
model = network_class()
if is_cuda:
model = model.cuda()
model.train()
time_log = []
# make sure that everything is in memorybefore the actual tests
batch = Variable(torch.FloatTensor(1, *input_size))
if is_cuda:
batch = batch.cuda()
model(batch)
print("Compute {} test time".format(backend))
for i in tqdm(range(0, max_batch_size, step_size)):
batch = Variable(torch.FloatTensor(i+1, *input_size))
if is_cuda:
batch = batch.cuda()
time_start = time.time()
prediction = model(batch)
out = torch.sum(prediction)
if backward_only:
time_start = time.time()
out.backward()
time_log.append(time.time() - time_start)
plt.plot(np.arange(1, max_batch_size + 1, step_size), time_log)
plt.title("{} train time w.r.t minibatch size".format(backend))
plt.ylabel("Time (s)")
plt.xlabel("Batch size") | 31.857143 | 115 | 0.643049 | 315 | 2,230 | 4.377778 | 0.212698 | 0.04351 | 0.04641 | 0.04641 | 0.808557 | 0.808557 | 0.808557 | 0.808557 | 0.808557 | 0.808557 | 0 | 0.007071 | 0.239013 | 2,230 | 70 | 116 | 31.857143 | 0.805539 | 0.055157 | 0 | 0.733333 | 0 | 0 | 0.074584 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.033333 | false | 0 | 0.1 | 0 | 0.133333 | 0.033333 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
03fe732f4d809199493b38ad904ff9d1e867457e | 14,498 | py | Python | sequencers/tests/test_views.py | bihealth/digestiflow-server | 298c53f95dbf56e7be0d0b8bcceacabc21257d5f | [
"MIT"
] | 13 | 2019-11-27T19:12:15.000Z | 2021-12-01T21:32:18.000Z | sequencers/tests/test_views.py | bihealth/digestiflow-server | 298c53f95dbf56e7be0d0b8bcceacabc21257d5f | [
"MIT"
] | 60 | 2019-03-27T14:43:19.000Z | 2022-03-22T09:12:53.000Z | sequencers/tests/test_views.py | bihealth/digestiflow-server | 298c53f95dbf56e7be0d0b8bcceacabc21257d5f | [
"MIT"
] | 3 | 2020-11-09T07:08:42.000Z | 2022-02-09T11:37:54.000Z | # TODO: check timeline events
from django.shortcuts import reverse
from test_plus.test import TestCase
from digestiflow.test_utils import SetupUserMixin, SetupProjectMixin, AuthenticatedRequestMixin
from ..models import INDEX_WORKFLOW_A, MACHINE_MODEL_HISEQ2000, SequencingMachine
from ..tests import SetupSequencingMachineMixin
class SequencingMachineListViewTest(
AuthenticatedRequestMixin,
SetupSequencingMachineMixin,
SetupProjectMixin,
SetupUserMixin,
TestCase,
):
"""Test the ``SequencingMachineListView``"""
def runGet(self, user, project=None):
return super().runGet(
user, "sequencers:sequencer-list", project=(project or self.project).sodar_uuid
)
def testGet(self):
"""Test that rendering the machine list works (with super user)"""
response = self.runGet(self.root)
self.response_200(response)
def testGetAccessDenied(self):
"""Test that access is denied if role assignment is missing"""
for user in (self.norole, None, self.unrelated_owner):
response = self.runGet(user)
self.assertUnauthorizedRedirect(user, response)
def testAccessAllowed(self):
"""Test that access is allowed if role assignment is correct"""
for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):
response = self.runGet(user)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("object_list")
class SequencingMachineDetailViewTest(
AuthenticatedRequestMixin,
SetupSequencingMachineMixin,
SetupProjectMixin,
SetupUserMixin,
TestCase,
):
"""Test the ``SequencingMachineDetailView``"""
def runGet(self, user, project=None):
return super().runGet(
user,
"sequencers:sequencer-detail",
project=(project or self.project).sodar_uuid,
sequencer=self.hiseq2000.sodar_uuid,
)
def testGet(self):
"""Test that rendering the machine detail works (with super user)"""
response = self.runGet(self.root)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("object")
def testAccessAllowed(self):
"""Test that access is denied if role assignment is correct"""
for user in (self.guest, self.contributor, self.delegate, self.owner, self.root):
response = self.runGet(user)
self.response_200(response)
def testGetAccessDenied(self):
"""Test that access is allowed if role assignment is missing"""
for user in (self.norole, None):
response = self.runGet(user)
self.assertUnauthorizedRedirect(user, response)
# Members of unrelated projects should not be able to see the object in their project...
self.runGet(self.unrelated_owner, self.unrelated_project)
self.response_404()
class SequencingMachineCreateViewTest(
AuthenticatedRequestMixin,
SetupSequencingMachineMixin,
SetupProjectMixin,
SetupUserMixin,
TestCase,
):
"""Test the ``SequencingMachineCreateView``"""
url_name = "sequencers:sequencer-create"
def setUp(self):
super().setUp()
self.form_data = {
"vendor_id": "Hzzzzzzzz",
"label": "Another test machine",
"machine_model": MACHINE_MODEL_HISEQ2000,
"slot_count": 2,
"dual_index_workflow": INDEX_WORKFLOW_A,
}
def runGet(self, user, project=None):
return super().runGet(user, self.url_name, project=(project or self.project).sodar_uuid)
def runPost(self, user, data, project=None):
return super().runPost(
user, self.url_name, project=(project or self.project).sodar_uuid, data=data
)
def testGet(self):
"""Test that rendering the machine create form works (with super user)"""
response = self.runGet(self.root)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("form")
def testGetAccessDenied(self):
"""Test that access is denied if role assignment is missing"""
for user in (self.guest, self.norole, None):
response = self.runGet(user)
self.assertUnauthorizedRedirect(user, response)
def testAccessAllowed(self):
"""Test that access is denied if role assignment is correct"""
for user in (self.contributor, self.delegate, self.owner, self.root):
response = self.runGet(user)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("form")
def testPost(self):
"""Test that the create view works (with super user)"""
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(self.root, self.form_data)
self.assertRedirects(
response,
SequencingMachine.objects.order_by("-date_created").first().get_absolute_url(),
fetch_redirect_response=False,
)
self.assertEqual(SequencingMachine.objects.count(), 2)
def testPostAccessDenied(self):
"""Test that access is denied if necessary role assignment is missing"""
for user in (self.norole, self.guest, None, self.unrelated_owner):
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(user, data=self.form_data)
self.assertUnauthorizedRedirect(user, response)
def testPostAccessAllowed(self):
"""Test that access is allowed if role assignment is correct"""
for user in (self.contributor, self.delegate, self.owner, self.root):
SequencingMachine.objects.all().delete()
response = self.runPost(user, data=self.form_data)
self.assertEqual(SequencingMachine.objects.count(), 1)
self.response_200(response)
self.assertRedirects(
response,
SequencingMachine.objects.order_by("-date_created").first().get_absolute_url(),
fetch_redirect_response=False,
)
class SequencingMachineUpdateViewTest(
AuthenticatedRequestMixin,
SetupSequencingMachineMixin,
SetupProjectMixin,
SetupUserMixin,
TestCase,
):
"""Test the ``SequencingMachineUpdateView``"""
url_name = "sequencers:sequencer-update"
def setUp(self):
super().setUp()
self.form_data = {
"vendor_id": "Haaaaaaaa",
"label": "UPDATED",
"machine_model": MACHINE_MODEL_HISEQ2000,
"slot_count": 2,
"dual_index_workflow": INDEX_WORKFLOW_A,
}
def runGet(self, user, project=None):
return super().runGet(
user,
self.url_name,
project=(project or self.project).sodar_uuid,
sequencer=self.hiseq2000.sodar_uuid,
)
def runPost(self, user, data, project=None):
return super().runPost(
user,
self.url_name,
project=(project or self.project).sodar_uuid,
sequencer=self.hiseq2000.sodar_uuid,
data=data,
)
def testGet(self):
"""Test that rendering the machine update form works (with super user)"""
response = self.runGet(self.root)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("object")
self.assertInContext("form")
def testGetAccessDenied(self):
"""Test that access is denied if role assignment is missing"""
for user in (self.guest, self.norole, None):
response = self.runGet(user)
self.assertUnauthorizedRedirect(user, response)
# Members of unrelated projects should not be able to see the object in their project...
self.runGet(self.unrelated_owner, self.unrelated_project)
self.response_404()
def testAccessAllowed(self):
"""Test that access is denied if role assignment is correct"""
for user in (self.contributor, self.delegate, self.owner, self.root):
response = self.runGet(user)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("object")
self.assertInContext("form")
def testPost(self):
"""Test that the update view works (with super user)"""
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(self.root, self.form_data)
self.assertRedirects(
response,
SequencingMachine.objects.order_by("-date_created").first().get_absolute_url(),
fetch_redirect_response=False,
)
self.assertEqual(SequencingMachine.objects.count(), 1)
instrument = SequencingMachine.objects.first()
self.assertEqual(instrument.vendor_id, self.form_data["vendor_id"])
self.assertEqual(instrument.label, self.form_data["label"])
def testPostAccessDenied(self):
"""Test that access is denied if necessary role assignment is missing"""
for user in (self.norole, self.guest, None):
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(user, data=self.form_data)
self.assertUnauthorizedRedirect(user, response)
# Members of unrelated projects should not be able to see the object in their project...
self.runPost(self.unrelated_owner, data=self.form_data, project=self.unrelated_project)
self.response_404()
def testPostAccessAllowed(self):
"""Test that access is allowed if role assignment is correct"""
for user in (self.contributor, self.delegate, self.owner, self.root):
response = self.runPost(user, data=self.form_data)
self.assertEqual(SequencingMachine.objects.count(), 1)
self.response_200(response)
self.assertRedirects(
response,
SequencingMachine.objects.order_by("-date_created").first().get_absolute_url(),
fetch_redirect_response=False,
)
class SequencingMachineDeleteViewTest(
AuthenticatedRequestMixin,
SetupSequencingMachineMixin,
SetupProjectMixin,
SetupUserMixin,
TestCase,
):
"""Test the ``SequencingMachineDeleteView``"""
url_name = "sequencers:sequencer-delete"
def setUp(self):
super().setUp()
def runGet(self, user, sequencer, project=None):
return super().runGet(
user,
self.url_name,
project=(project or self.project).sodar_uuid,
sequencer=sequencer.sodar_uuid,
)
def runPost(self, user, sequencer, project=None):
return super().runPost(
user,
self.url_name,
project=(project or self.project).sodar_uuid,
sequencer=sequencer.sodar_uuid,
)
def testGet(self):
"""Test that rendering the machine update form works (with super user)"""
response = self.runGet(self.root, self.hiseq2000)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("object")
def testGetAccessDenied(self):
"""Test that access is denied if role assignment is missing"""
for user in (self.guest, self.norole, None):
response = self.runGet(user, self.hiseq2000)
self.assertUnauthorizedRedirect(user, response)
# Members of unrelated projects should not be able to see the object in their project...
self.runGet(self.unrelated_owner, sequencer=self.hiseq2000, project=self.unrelated_project)
self.response_404()
def testAccessAllowed(self):
"""Test that access is denied if role assignment is correct"""
for user in (self.contributor, self.delegate, self.owner, self.root):
response = self.runGet(user, self.hiseq2000)
self.response_200(response)
self.assertInContext("project")
self.assertInContext("object")
def testPost(self):
"""Test that the delete view works (with super user)"""
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(self.root, SequencingMachine.objects.first())
self.assertEqual(SequencingMachine.objects.count(), 0)
self.response_200(response)
self.assertRedirects(
response,
reverse("sequencers:sequencer-list", kwargs={"project": self.project.sodar_uuid}),
fetch_redirect_response=False,
)
def testPostAccessDenied(self):
"""Test that access is denied if necessary role assignment is missing"""
for user in (self.norole, self.guest, None):
SequencingMachine.objects.all().delete()
self.make_machine()
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(user, SequencingMachine.objects.first())
self.assertEqual(SequencingMachine.objects.count(), 1)
self.assertUnauthorizedRedirect(user, response)
# Members of unrelated projects should not be able to see the object in their project...
SequencingMachine.objects.all().delete()
self.make_machine()
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(
self.unrelated_owner,
sequencer=SequencingMachine.objects.first(),
project=self.unrelated_project,
)
self.assertEqual(SequencingMachine.objects.count(), 1)
self.response_404(response)
def testPostAccessAllowed(self):
"""Test that access is allowed if role assignment is correct"""
for user in (self.contributor, self.delegate, self.owner, self.root):
SequencingMachine.objects.all().delete()
self.make_machine()
self.assertEqual(SequencingMachine.objects.count(), 1)
response = self.runPost(user, SequencingMachine.objects.first())
self.assertEqual(SequencingMachine.objects.count(), 0)
self.response_200(response)
self.assertRedirects(
response,
reverse("sequencers:sequencer-list", kwargs={"project": self.project.sodar_uuid}),
fetch_redirect_response=False,
)
| 39.289973 | 99 | 0.648641 | 1,502 | 14,498 | 6.178429 | 0.092543 | 0.047845 | 0.031034 | 0.031034 | 0.894935 | 0.878556 | 0.875216 | 0.817672 | 0.803341 | 0.798384 | 0 | 0.010585 | 0.250655 | 14,498 | 368 | 100 | 39.396739 | 0.843612 | 0.144227 | 0 | 0.798587 | 0 | 0 | 0.043489 | 0.014931 | 0 | 0 | 0 | 0.002717 | 0.176678 | 1 | 0.123675 | false | 0 | 0.017668 | 0.028269 | 0.19788 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ff1711e6c129402e09892db6af6aed09d07bf428 | 39,893 | py | Python | skyline_apiserver/policy/manager/heat.py | openstack/skyline-apiserver | 60144767cd5513bd581fbb8eac7791887d5b276f | [
"Apache-2.0"
] | null | null | null | skyline_apiserver/policy/manager/heat.py | openstack/skyline-apiserver | 60144767cd5513bd581fbb8eac7791887d5b276f | [
"Apache-2.0"
] | null | null | null | skyline_apiserver/policy/manager/heat.py | openstack/skyline-apiserver | 60144767cd5513bd581fbb8eac7791887d5b276f | [
"Apache-2.0"
] | null | null | null | # flake8: noqa
from . import base
list_rules = (
base.Rule(
name="context_is_admin",
check_str=("(role:admin and is_admin_project:True) OR (role:admin and system_scope:all)"),
description="Decides what is required for the 'is_admin:True' check to succeed.",
),
base.Rule(
name="project_admin",
check_str=("role:admin"),
description="Default rule for project admin.",
),
base.Rule(
name="deny_stack_user",
check_str=("not role:heat_stack_user"),
description="Default rule for deny stack user.",
),
base.Rule(
name="deny_everybody",
check_str=("!"),
description="Default rule for deny everybody.",
),
base.Rule(
name="allow_everybody",
check_str=(""),
description="Default rule for allow everybody.",
),
base.Rule(
name="cloudformation:ListStacks",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:CreateStack",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:DescribeStacks",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:DeleteStack",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:UpdateStack",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:CancelUpdateStack",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:DescribeStackEvents",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:ValidateTemplate",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:GetTemplate",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:EstimateTemplateCost",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:DescribeStackResource",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s) or (role:heat_stack_user and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:DescribeStackResources",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="cloudformation:ListStackResources",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
description="No description",
),
base.Rule(
name="resource_types:OS::Nova::Flavor",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Cinder::EncryptedVolumeType",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Cinder::VolumeType",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Cinder::Quota",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::Quota",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Nova::Quota",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Octavia::Quota",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Manila::ShareType",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::ProviderNet",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::QoSPolicy",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::QoSBandwidthLimitRule",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::QoSDscpMarkingRule",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::QoSMinimumBandwidthRule",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Neutron::Segment",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Nova::HostAggregate",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Cinder::QoSSpecs",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Cinder::QoSAssociation",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Keystone::*",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Blazar::Host",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Octavia::Flavor",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="resource_types:OS::Octavia::FlavorProfile",
check_str=("rule:project_admin"),
description="No description",
),
base.Rule(
name="service:index",
check_str=("role:reader and system_scope:all"),
description="No description",
),
base.APIRule(
name="actions:action",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Performs non-lifecycle operations on the stack (Snapshot, Resume, Cancel update, or check stack resources). This is the default for all actions but can be overridden by more specific policies for individual actions.",
scope_types=["project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="actions:snapshot",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Create stack snapshot",
scope_types=["system", "project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="actions:suspend",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Suspend a stack.",
scope_types=["system", "project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="actions:resume",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Resume a suspended stack.",
scope_types=["system", "project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="actions:check",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Check stack resources.",
scope_types=["system", "project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="actions:cancel_update",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Cancel stack operation and roll back.",
scope_types=["system", "project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="actions:cancel_without_rollback",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Cancel stack operation without rolling back.",
scope_types=["system", "project"],
operations=[
{"method": "POST", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/actions"},
],
),
base.APIRule(
name="build_info:build_info",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=("@"),
description="Show build information.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/build_info"}],
),
base.APIRule(
name="events:index",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List events.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/events"},
],
),
base.APIRule(
name="events:show",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show event.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/resources/{resource_name}/events/{event_id}",
},
],
),
base.APIRule(
name="resource:index",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List resources.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/resources"},
],
),
base.APIRule(
name="resource:metadata",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s) or (role:heat_stack_user and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s or role:heat_stack_user"
),
description="Show resource metadata.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/resources/{resource_name}/metadata",
},
],
),
base.APIRule(
name="resource:signal",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s) or (role:heat_stack_user and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:heat_stack_user"
),
description="Signal resource.",
scope_types=["system", "project"],
operations=[
{
"method": "POST",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/resources/{resource_name}/signal",
},
],
),
base.APIRule(
name="resource:mark_unhealthy",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Mark resource as unhealthy.",
scope_types=["system", "project"],
operations=[
{
"method": "PATCH",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/resources/{resource_name_or_physical_id}",
},
],
),
base.APIRule(
name="resource:show",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show resource.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/resources/{resource_name}",
},
],
),
base.APIRule(
name="software_configs:global_index",
check_str=("role:reader and system_scope:all"),
basic_check_str=("role:admin or role:reader"),
description="List configs globally.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/software_configs"}],
),
base.APIRule(
name="software_configs:index",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List configs.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/software_configs"}],
),
base.APIRule(
name="software_configs:create",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Create config.",
scope_types=["system", "project"],
operations=[{"method": "POST", "path": "/v1/{tenant_id}/software_configs"}],
),
base.APIRule(
name="software_configs:show",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show config details.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/software_configs/{config_id}"}],
),
base.APIRule(
name="software_configs:delete",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Delete config.",
scope_types=["system", "project"],
operations=[{"method": "DELETE", "path": "/v1/{tenant_id}/software_configs/{config_id}"}],
),
base.APIRule(
name="software_deployments:index",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List deployments.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/software_deployments"}],
),
base.APIRule(
name="software_deployments:create",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Create deployment.",
scope_types=["system", "project"],
operations=[{"method": "POST", "path": "/v1/{tenant_id}/software_deployments"}],
),
base.APIRule(
name="software_deployments:show",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show deployment details.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/software_deployments/{deployment_id}"},
],
),
base.APIRule(
name="software_deployments:update",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Update deployment.",
scope_types=["system", "project"],
operations=[
{"method": "PUT", "path": "/v1/{tenant_id}/software_deployments/{deployment_id}"},
],
),
base.APIRule(
name="software_deployments:delete",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Delete deployment.",
scope_types=["system", "project"],
operations=[
{"method": "DELETE", "path": "/v1/{tenant_id}/software_deployments/{deployment_id}"},
],
),
base.APIRule(
name="software_deployments:metadata",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s) or (role:heat_stack_user and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s or role:heat_stack_user"
),
description="Show server configuration metadata.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/software_deployments/metadata/{server_id}",
},
],
),
base.APIRule(
name="stacks:abandon",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Abandon stack.",
scope_types=["system", "project"],
operations=[
{
"method": "DELETE",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/abandon",
},
],
),
base.APIRule(
name="stacks:create",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Create stack.",
scope_types=["system", "project"],
operations=[{"method": "POST", "path": "/v1/{tenant_id}/stacks"}],
),
base.APIRule(
name="stacks:delete",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Delete stack.",
scope_types=["system", "project"],
operations=[
{"method": "DELETE", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}"},
],
),
base.APIRule(
name="stacks:detail",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List stacks in detail.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/stacks"}],
),
base.APIRule(
name="stacks:export",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Export stack.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/export"},
],
),
base.APIRule(
name="stacks:generate_template",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Generate stack template.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/template"},
],
),
base.APIRule(
name="stacks:global_index",
check_str=("role:reader and system_scope:all"),
basic_check_str=("role:admin or role:reader"),
description="List stacks globally.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/stacks"}],
),
base.APIRule(
name="stacks:index",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List stacks.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/stacks"}],
),
base.APIRule(
name="stacks:list_resource_types",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=("@"),
description="List resource types.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/resource_types"}],
),
base.APIRule(
name="stacks:list_template_versions",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=("@"),
description="List template versions.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/template_versions"}],
),
base.APIRule(
name="stacks:list_template_functions",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=("@"),
description="List template functions.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/template_versions/{template_version}/functions",
},
],
),
base.APIRule(
name="stacks:lookup",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s) or (role:heat_stack_user and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s or role:heat_stack_user"
),
description="Find stack.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_identity}"}],
),
base.APIRule(
name="stacks:preview",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Preview stack.",
scope_types=["system", "project"],
operations=[{"method": "POST", "path": "/v1/{tenant_id}/stacks/preview"}],
),
base.APIRule(
name="stacks:resource_schema",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=("@"),
description="Show resource type schema.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/resource_types/{type_name}"}],
),
base.APIRule(
name="stacks:show",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show stack.",
scope_types=["system", "project"],
operations=[{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_identity}"}],
),
base.APIRule(
name="stacks:template",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Get stack template.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/template"},
],
),
base.APIRule(
name="stacks:environment",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Get stack environment.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/environment",
},
],
),
base.APIRule(
name="stacks:files",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Get stack files.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/files"},
],
),
base.APIRule(
name="stacks:update",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Update stack.",
scope_types=["system", "project"],
operations=[{"method": "PUT", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}"}],
),
base.APIRule(
name="stacks:update_patch",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Update stack (PATCH).",
scope_types=["system", "project"],
operations=[
{"method": "PATCH", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}"},
],
),
base.APIRule(
name="stacks:update_no_change",
check_str=("rule:stacks:update_patch"),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Update stack (PATCH) with no changes.",
scope_types=["system", "project"],
operations=[
{"method": "PATCH", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}"},
],
),
base.APIRule(
name="stacks:preview_update",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Preview update stack.",
scope_types=["system", "project"],
operations=[
{"method": "PUT", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/preview"},
],
),
base.APIRule(
name="stacks:preview_update_patch",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Preview update stack (PATCH).",
scope_types=["system", "project"],
operations=[
{"method": "PATCH", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/preview"},
],
),
base.APIRule(
name="stacks:validate_template",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Validate template.",
scope_types=["system", "project"],
operations=[{"method": "POST", "path": "/v1/{tenant_id}/validate"}],
),
base.APIRule(
name="stacks:snapshot",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Snapshot Stack.",
scope_types=["system", "project"],
operations=[
{
"method": "POST",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/snapshots",
},
],
),
base.APIRule(
name="stacks:show_snapshot",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show snapshot.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/snapshots/{snapshot_id}",
},
],
),
base.APIRule(
name="stacks:delete_snapshot",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Delete snapshot.",
scope_types=["system", "project"],
operations=[
{
"method": "DELETE",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/snapshots/{snapshot_id}",
},
],
),
base.APIRule(
name="stacks:list_snapshots",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List snapshots.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/snapshots"},
],
),
base.APIRule(
name="stacks:restore_snapshot",
check_str=(
"(role:admin and system_scope:all) or (role:member and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s"
),
description="Restore snapshot.",
scope_types=["system", "project"],
operations=[
{
"method": "POST",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/snapshots/{snapshot_id}/restore",
},
],
),
base.APIRule(
name="stacks:list_outputs",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="List outputs.",
scope_types=["system", "project"],
operations=[
{"method": "GET", "path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/outputs"},
],
),
base.APIRule(
name="stacks:show_output",
check_str=(
"(role:reader and system_scope:all) or (role:reader and project_id:%(project_id)s)"
),
basic_check_str=(
"role:admin or role:reader or role:admin and project_id:%(project_id)s or role:member and project_id:%(project_id)s or role:reader and project_id:%(project_id)s"
),
description="Show outputs.",
scope_types=["system", "project"],
operations=[
{
"method": "GET",
"path": "/v1/{tenant_id}/stacks/{stack_name}/{stack_id}/outputs/{output_key}",
},
],
),
)
__all__ = ("list_rules",)
| 39.303448 | 238 | 0.585015 | 4,733 | 39,893 | 4.717515 | 0.037397 | 0.157202 | 0.104801 | 0.165935 | 0.914636 | 0.897886 | 0.892646 | 0.884405 | 0.884405 | 0.881673 | 0 | 0.001979 | 0.265184 | 39,893 | 1,014 | 239 | 39.342209 | 0.759705 | 0.000301 | 0 | 0.765347 | 0 | 0.116832 | 0.538905 | 0.245593 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.00099 | 0 | 0.00099 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
20e42b0760bd695c7563d341bb64cc368a52c816 | 120 | py | Python | betfund_event_broker/__init__.py | betfund/betfund-event-broker | 524aec73d9cf66cbeeb0fab67e6816b836c1d98e | [
"MIT"
] | 1 | 2020-09-23T02:36:35.000Z | 2020-09-23T02:36:35.000Z | betfund_event_broker/__init__.py | betfund/betfund-event-broker | 524aec73d9cf66cbeeb0fab67e6816b836c1d98e | [
"MIT"
] | 5 | 2020-04-13T23:55:07.000Z | 2020-06-04T15:09:12.000Z | betfund_event_broker/__init__.py | betfund/betfund-event-broker | 524aec73d9cf66cbeeb0fab67e6816b836c1d98e | [
"MIT"
] | null | null | null | """Betfund Event Broker namespace."""
from .flows import * # noqa: F403, F401
from .tasks import * # noqa: F403, F401
| 30 | 40 | 0.675 | 16 | 120 | 5.0625 | 0.6875 | 0.246914 | 0.345679 | 0.444444 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.122449 | 0.183333 | 120 | 3 | 41 | 40 | 0.704082 | 0.55 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
45b6187791563009ae33d98d57ecf55f4ca74025 | 113,677 | py | Python | boto3_type_annotations_with_docs/boto3_type_annotations/sms/client.py | cowboygneox/boto3_type_annotations | 450dce1de4e066b939de7eac2ec560ed1a7ddaa2 | [
"MIT"
] | 119 | 2018-12-01T18:20:57.000Z | 2022-02-02T10:31:29.000Z | boto3_type_annotations_with_docs/boto3_type_annotations/sms/client.py | cowboygneox/boto3_type_annotations | 450dce1de4e066b939de7eac2ec560ed1a7ddaa2 | [
"MIT"
] | 15 | 2018-11-16T00:16:44.000Z | 2021-11-13T03:44:18.000Z | boto3_type_annotations_with_docs/boto3_type_annotations/sms/client.py | cowboygneox/boto3_type_annotations | 450dce1de4e066b939de7eac2ec560ed1a7ddaa2 | [
"MIT"
] | 11 | 2019-05-06T05:26:51.000Z | 2021-09-28T15:27:59.000Z | from typing import Optional
from botocore.client import BaseClient
from typing import Dict
from botocore.paginate import Paginator
from datetime import datetime
from botocore.waiter import Waiter
from typing import Union
from typing import List
class Client(BaseClient):
def can_paginate(self, operation_name: str = None):
"""
Check if an operation can be paginated.
:type operation_name: string
:param operation_name: The operation name. This is the same name
as the method name on the client. For example, if the
method name is ``create_foo``, and you\'d normally invoke the
operation as ``client.create_foo(**kwargs)``, if the
``create_foo`` operation can be paginated, you can use the
call ``client.get_paginator(\"create_foo\")``.
:return: ``True`` if the operation can be paginated,
``False`` otherwise.
"""
pass
def create_app(self, name: str = None, description: str = None, roleName: str = None, clientToken: str = None, serverGroups: List = None, tags: List = None) -> Dict:
"""
Creates an application. An application consists of one or more server groups. Each server group contain one or more servers.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/CreateApp>`_
**Request Syntax**
::
response = client.create_app(
name='string',
description='string',
roleName='string',
clientToken='string',
serverGroups=[
{
'serverGroupId': 'string',
'name': 'string',
'serverList': [
{
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
]
},
],
tags=[
{
'key': 'string',
'value': 'string'
},
]
)
**Response Syntax**
::
{
'appSummary': {
'appId': 'string',
'name': 'string',
'description': 'string',
'status': 'CREATING'|'ACTIVE'|'UPDATING'|'DELETING'|'DELETED'|'DELETE_FAILED',
'statusMessage': 'string',
'replicationStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_REPLICATION'|'VALIDATION_IN_PROGRESS'|'REPLICATION_PENDING'|'REPLICATION_IN_PROGRESS'|'REPLICATED'|'DELTA_REPLICATION_IN_PROGRESS'|'DELTA_REPLICATED'|'DELTA_REPLICATION_FAILED'|'REPLICATION_FAILED'|'REPLICATION_STOPPING'|'REPLICATION_STOP_FAILED'|'REPLICATION_STOPPED',
'replicationStatusMessage': 'string',
'latestReplicationTime': datetime(2015, 1, 1),
'launchStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_LAUNCH'|'VALIDATION_IN_PROGRESS'|'LAUNCH_PENDING'|'LAUNCH_IN_PROGRESS'|'LAUNCHED'|'DELTA_LAUNCH_IN_PROGRESS'|'DELTA_LAUNCH_FAILED'|'LAUNCH_FAILED'|'TERMINATE_IN_PROGRESS'|'TERMINATE_FAILED'|'TERMINATED',
'launchStatusMessage': 'string',
'launchDetails': {
'latestLaunchTime': datetime(2015, 1, 1),
'stackName': 'string',
'stackId': 'string'
},
'creationTime': datetime(2015, 1, 1),
'lastModified': datetime(2015, 1, 1),
'roleName': 'string',
'totalServerGroups': 123,
'totalServers': 123
},
'serverGroups': [
{
'serverGroupId': 'string',
'name': 'string',
'serverList': [
{
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
]
},
],
'tags': [
{
'key': 'string',
'value': 'string'
},
]
}
**Response Structure**
- *(dict) --*
- **appSummary** *(dict) --*
Summary description of the application.
- **appId** *(string) --*
Unique ID of the application.
- **name** *(string) --*
Name of the application.
- **description** *(string) --*
Description of the application.
- **status** *(string) --*
Status of the application.
- **statusMessage** *(string) --*
A message related to the status of the application
- **replicationStatus** *(string) --*
Replication status of the application.
- **replicationStatusMessage** *(string) --*
A message related to the replication status of the application.
- **latestReplicationTime** *(datetime) --*
Timestamp of the application's most recent successful replication.
- **launchStatus** *(string) --*
Launch status of the application.
- **launchStatusMessage** *(string) --*
A message related to the launch status of the application.
- **launchDetails** *(dict) --*
Details about the latest launch of the application.
- **latestLaunchTime** *(datetime) --*
Latest time this application was launched successfully.
- **stackName** *(string) --*
Name of the latest stack launched for this application.
- **stackId** *(string) --*
Identifier of the latest stack launched for this application.
- **creationTime** *(datetime) --*
Time of creation of this application.
- **lastModified** *(datetime) --*
Timestamp of the application's creation.
- **roleName** *(string) --*
Name of the service role in the customer's account used by AWS SMS.
- **totalServerGroups** *(integer) --*
Number of server groups present in the application.
- **totalServers** *(integer) --*
Number of servers present in the application.
- **serverGroups** *(list) --*
List of server groups included in the application.
- *(dict) --*
A logical grouping of servers.
- **serverGroupId** *(string) --*
Identifier of a server group.
- **name** *(string) --*
Name of a server group.
- **serverList** *(list) --*
List of servers belonging to a server group.
- *(dict) --*
Represents a server.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **tags** *(list) --*
List of taags associated with the application.
- *(dict) --*
A label that can be assigned to an application.
- **key** *(string) --*
Tag key.
- **value** *(string) --*
Tag value.
:type name: string
:param name:
Name of the new application.
:type description: string
:param description:
Description of the new application
:type roleName: string
:param roleName:
Name of service role in customer\'s account to be used by AWS SMS.
:type clientToken: string
:param clientToken:
A unique, case-sensitive identifier you provide to ensure idempotency of application creation.
:type serverGroups: list
:param serverGroups:
List of server groups to include in the application.
- *(dict) --*
A logical grouping of servers.
- **serverGroupId** *(string) --*
Identifier of a server group.
- **name** *(string) --*
Name of a server group.
- **serverList** *(list) --*
List of servers belonging to a server group.
- *(dict) --*
Represents a server.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
:type tags: list
:param tags:
List of tags to be associated with the application.
- *(dict) --*
A label that can be assigned to an application.
- **key** *(string) --*
Tag key.
- **value** *(string) --*
Tag value.
:rtype: dict
:returns:
"""
pass
def create_replication_job(self, serverId: str, seedReplicationTime: datetime, frequency: int = None, runOnce: bool = None, licenseType: str = None, roleName: str = None, description: str = None, numberOfRecentAmisToKeep: int = None, encrypted: bool = None, kmsKeyId: str = None) -> Dict:
"""
Creates a replication job. The replication job schedules periodic replication runs to replicate your server to AWS. Each replication run creates an Amazon Machine Image (AMI).
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/CreateReplicationJob>`_
**Request Syntax**
::
response = client.create_replication_job(
serverId='string',
seedReplicationTime=datetime(2015, 1, 1),
frequency=123,
runOnce=True|False,
licenseType='AWS'|'BYOL',
roleName='string',
description='string',
numberOfRecentAmisToKeep=123,
encrypted=True|False,
kmsKeyId='string'
)
**Response Syntax**
::
{
'replicationJobId': 'string'
}
**Response Structure**
- *(dict) --*
- **replicationJobId** *(string) --*
The unique identifier of the replication job.
:type serverId: string
:param serverId: **[REQUIRED]**
The identifier of the server.
:type seedReplicationTime: datetime
:param seedReplicationTime: **[REQUIRED]**
The seed replication time.
:type frequency: integer
:param frequency:
The time between consecutive replication runs, in hours.
:type runOnce: boolean
:param runOnce:
:type licenseType: string
:param licenseType:
The license type to be used for the AMI created by a successful replication run.
:type roleName: string
:param roleName:
The name of the IAM role to be used by the AWS SMS.
:type description: string
:param description:
The description of the replication job.
:type numberOfRecentAmisToKeep: integer
:param numberOfRecentAmisToKeep:
The maximum number of SMS-created AMIs to retain. The oldest will be deleted once the maximum number is reached and a new AMI is created.
:type encrypted: boolean
:param encrypted:
When *true* , the replication job produces encrypted AMIs. See also ``KmsKeyId`` below.
:type kmsKeyId: string
:param kmsKeyId:
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer\'s default KMS key for EBS is used.
:rtype: dict
:returns:
"""
pass
def delete_app(self, appId: str = None, forceStopAppReplication: bool = None, forceTerminateApp: bool = None) -> Dict:
"""
Deletes an existing application. Optionally deletes the launched stack associated with the application and all AWS SMS replication jobs for servers in the application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/DeleteApp>`_
**Request Syntax**
::
response = client.delete_app(
appId='string',
forceStopAppReplication=True|False,
forceTerminateApp=True|False
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application to delete.
:type forceStopAppReplication: boolean
:param forceStopAppReplication:
While deleting the application, stop all replication jobs corresponding to the servers in the application.
:type forceTerminateApp: boolean
:param forceTerminateApp:
While deleting the application, terminate the stack corresponding to the application.
:rtype: dict
:returns:
"""
pass
def delete_app_launch_configuration(self, appId: str = None) -> Dict:
"""
Deletes existing launch configuration for an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/DeleteAppLaunchConfiguration>`_
**Request Syntax**
::
response = client.delete_app_launch_configuration(
appId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application associated with the launch configuration.
:rtype: dict
:returns:
"""
pass
def delete_app_replication_configuration(self, appId: str = None) -> Dict:
"""
Deletes existing replication configuration for an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/DeleteAppReplicationConfiguration>`_
**Request Syntax**
::
response = client.delete_app_replication_configuration(
appId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application associated with the replication configuration.
:rtype: dict
:returns:
"""
pass
def delete_replication_job(self, replicationJobId: str) -> Dict:
"""
Deletes the specified replication job.
After you delete a replication job, there are no further replication runs. AWS deletes the contents of the Amazon S3 bucket used to store AWS SMS artifacts. The AMIs created by the replication runs are not deleted.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/DeleteReplicationJob>`_
**Request Syntax**
::
response = client.delete_replication_job(
replicationJobId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type replicationJobId: string
:param replicationJobId: **[REQUIRED]**
The identifier of the replication job.
:rtype: dict
:returns:
"""
pass
def delete_server_catalog(self) -> Dict:
"""
Deletes all servers from your server catalog.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/DeleteServerCatalog>`_
**Request Syntax**
::
response = client.delete_server_catalog()
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:rtype: dict
:returns:
"""
pass
def disassociate_connector(self, connectorId: str) -> Dict:
"""
Disassociates the specified connector from AWS SMS.
After you disassociate a connector, it is no longer available to support replication jobs.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/DisassociateConnector>`_
**Request Syntax**
::
response = client.disassociate_connector(
connectorId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type connectorId: string
:param connectorId: **[REQUIRED]**
The identifier of the connector.
:rtype: dict
:returns:
"""
pass
def generate_change_set(self, appId: str = None, changesetFormat: str = None) -> Dict:
"""
Generates a target change set for a currently launched stack and writes it to an Amazon S3 object in the customer’s Amazon S3 bucket.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GenerateChangeSet>`_
**Request Syntax**
::
response = client.generate_change_set(
appId='string',
changesetFormat='JSON'|'YAML'
)
**Response Syntax**
::
{
's3Location': {
'bucket': 'string',
'key': 'string'
}
}
**Response Structure**
- *(dict) --*
- **s3Location** *(dict) --*
Location of the Amazon S3 object.
- **bucket** *(string) --*
Amazon S3 bucket name.
- **key** *(string) --*
Amazon S3 bucket key.
:type appId: string
:param appId:
ID of the application associated with the change set.
:type changesetFormat: string
:param changesetFormat:
Format for the change set.
:rtype: dict
:returns:
"""
pass
def generate_presigned_url(self, ClientMethod: str = None, Params: Dict = None, ExpiresIn: int = None, HttpMethod: str = None):
"""
Generate a presigned url given a client, its method, and arguments
:type ClientMethod: string
:param ClientMethod: The client method to presign for
:type Params: dict
:param Params: The parameters normally passed to
``ClientMethod``.
:type ExpiresIn: int
:param ExpiresIn: The number of seconds the presigned url is valid
for. By default it expires in an hour (3600 seconds)
:type HttpMethod: string
:param HttpMethod: The http method to use on the generated url. By
default, the http method is whatever is used in the method\'s model.
:returns: The presigned url
"""
pass
def generate_template(self, appId: str = None, templateFormat: str = None) -> Dict:
"""
Generates an Amazon CloudFormation template based on the current launch configuration and writes it to an Amazon S3 object in the customer’s Amazon S3 bucket.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GenerateTemplate>`_
**Request Syntax**
::
response = client.generate_template(
appId='string',
templateFormat='JSON'|'YAML'
)
**Response Syntax**
::
{
's3Location': {
'bucket': 'string',
'key': 'string'
}
}
**Response Structure**
- *(dict) --*
- **s3Location** *(dict) --*
Location of the Amazon S3 object.
- **bucket** *(string) --*
Amazon S3 bucket name.
- **key** *(string) --*
Amazon S3 bucket key.
:type appId: string
:param appId:
ID of the application associated with the Amazon CloudFormation template.
:type templateFormat: string
:param templateFormat:
Format for generating the Amazon CloudFormation template.
:rtype: dict
:returns:
"""
pass
def get_app(self, appId: str = None) -> Dict:
"""
Retrieve information about an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetApp>`_
**Request Syntax**
::
response = client.get_app(
appId='string'
)
**Response Syntax**
::
{
'appSummary': {
'appId': 'string',
'name': 'string',
'description': 'string',
'status': 'CREATING'|'ACTIVE'|'UPDATING'|'DELETING'|'DELETED'|'DELETE_FAILED',
'statusMessage': 'string',
'replicationStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_REPLICATION'|'VALIDATION_IN_PROGRESS'|'REPLICATION_PENDING'|'REPLICATION_IN_PROGRESS'|'REPLICATED'|'DELTA_REPLICATION_IN_PROGRESS'|'DELTA_REPLICATED'|'DELTA_REPLICATION_FAILED'|'REPLICATION_FAILED'|'REPLICATION_STOPPING'|'REPLICATION_STOP_FAILED'|'REPLICATION_STOPPED',
'replicationStatusMessage': 'string',
'latestReplicationTime': datetime(2015, 1, 1),
'launchStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_LAUNCH'|'VALIDATION_IN_PROGRESS'|'LAUNCH_PENDING'|'LAUNCH_IN_PROGRESS'|'LAUNCHED'|'DELTA_LAUNCH_IN_PROGRESS'|'DELTA_LAUNCH_FAILED'|'LAUNCH_FAILED'|'TERMINATE_IN_PROGRESS'|'TERMINATE_FAILED'|'TERMINATED',
'launchStatusMessage': 'string',
'launchDetails': {
'latestLaunchTime': datetime(2015, 1, 1),
'stackName': 'string',
'stackId': 'string'
},
'creationTime': datetime(2015, 1, 1),
'lastModified': datetime(2015, 1, 1),
'roleName': 'string',
'totalServerGroups': 123,
'totalServers': 123
},
'serverGroups': [
{
'serverGroupId': 'string',
'name': 'string',
'serverList': [
{
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
]
},
],
'tags': [
{
'key': 'string',
'value': 'string'
},
]
}
**Response Structure**
- *(dict) --*
- **appSummary** *(dict) --*
Information about the application.
- **appId** *(string) --*
Unique ID of the application.
- **name** *(string) --*
Name of the application.
- **description** *(string) --*
Description of the application.
- **status** *(string) --*
Status of the application.
- **statusMessage** *(string) --*
A message related to the status of the application
- **replicationStatus** *(string) --*
Replication status of the application.
- **replicationStatusMessage** *(string) --*
A message related to the replication status of the application.
- **latestReplicationTime** *(datetime) --*
Timestamp of the application's most recent successful replication.
- **launchStatus** *(string) --*
Launch status of the application.
- **launchStatusMessage** *(string) --*
A message related to the launch status of the application.
- **launchDetails** *(dict) --*
Details about the latest launch of the application.
- **latestLaunchTime** *(datetime) --*
Latest time this application was launched successfully.
- **stackName** *(string) --*
Name of the latest stack launched for this application.
- **stackId** *(string) --*
Identifier of the latest stack launched for this application.
- **creationTime** *(datetime) --*
Time of creation of this application.
- **lastModified** *(datetime) --*
Timestamp of the application's creation.
- **roleName** *(string) --*
Name of the service role in the customer's account used by AWS SMS.
- **totalServerGroups** *(integer) --*
Number of server groups present in the application.
- **totalServers** *(integer) --*
Number of servers present in the application.
- **serverGroups** *(list) --*
List of server groups belonging to the application.
- *(dict) --*
A logical grouping of servers.
- **serverGroupId** *(string) --*
Identifier of a server group.
- **name** *(string) --*
Name of a server group.
- **serverList** *(list) --*
List of servers belonging to a server group.
- *(dict) --*
Represents a server.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **tags** *(list) --*
List of tags associated with the application.
- *(dict) --*
A label that can be assigned to an application.
- **key** *(string) --*
Tag key.
- **value** *(string) --*
Tag value.
:type appId: string
:param appId:
ID of the application whose information is being retrieved.
:rtype: dict
:returns:
"""
pass
def get_app_launch_configuration(self, appId: str = None) -> Dict:
"""
Retrieves the application launch configuration associated with an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetAppLaunchConfiguration>`_
**Request Syntax**
::
response = client.get_app_launch_configuration(
appId='string'
)
**Response Syntax**
::
{
'appId': 'string',
'roleName': 'string',
'serverGroupLaunchConfigurations': [
{
'serverGroupId': 'string',
'launchOrder': 123,
'serverLaunchConfigurations': [
{
'server': {
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
'logicalId': 'string',
'vpc': 'string',
'subnet': 'string',
'securityGroup': 'string',
'ec2KeyName': 'string',
'userData': {
's3Location': {
'bucket': 'string',
'key': 'string'
}
},
'instanceType': 'string',
'associatePublicIpAddress': True|False
},
]
},
]
}
**Response Structure**
- *(dict) --*
- **appId** *(string) --*
ID of the application associated with the launch configuration.
- **roleName** *(string) --*
Name of the service role in the customer's account that Amazon CloudFormation uses to launch the application.
- **serverGroupLaunchConfigurations** *(list) --*
List of launch configurations for server groups in this application.
- *(dict) --*
Launch configuration for a server group.
- **serverGroupId** *(string) --*
Identifier of the server group the launch configuration is associated with.
- **launchOrder** *(integer) --*
Launch order of servers in the server group.
- **serverLaunchConfigurations** *(list) --*
Launch configuration for servers in the server group.
- *(dict) --*
Launch configuration for a server.
- **server** *(dict) --*
Identifier of the server the launch configuration is associated with.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **logicalId** *(string) --*
Logical ID of the server in the Amazon CloudFormation template.
- **vpc** *(string) --*
Identifier of the VPC the server should be launched into.
- **subnet** *(string) --*
Identifier of the subnet the server should be launched into.
- **securityGroup** *(string) --*
Identifier of the security group that applies to the launched server.
- **ec2KeyName** *(string) --*
Name of the EC2 SSH Key to be used for connecting to the launched server.
- **userData** *(dict) --*
Location of the user-data script to be executed when launching the server.
- **s3Location** *(dict) --*
Amazon S3 location of the user-data script.
- **bucket** *(string) --*
Amazon S3 bucket name.
- **key** *(string) --*
Amazon S3 bucket key.
- **instanceType** *(string) --*
Instance type to be used for launching the server.
- **associatePublicIpAddress** *(boolean) --*
If true, a publicly accessible IP address is created when launching the server.
:type appId: string
:param appId:
ID of the application launch configuration.
:rtype: dict
:returns:
"""
pass
def get_app_replication_configuration(self, appId: str = None) -> Dict:
"""
Retrieves an application replication configuration associatd with an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetAppReplicationConfiguration>`_
**Request Syntax**
::
response = client.get_app_replication_configuration(
appId='string'
)
**Response Syntax**
::
{
'serverGroupReplicationConfigurations': [
{
'serverGroupId': 'string',
'serverReplicationConfigurations': [
{
'server': {
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
'serverReplicationParameters': {
'seedTime': datetime(2015, 1, 1),
'frequency': 123,
'runOnce': True|False,
'licenseType': 'AWS'|'BYOL',
'numberOfRecentAmisToKeep': 123,
'encrypted': True|False,
'kmsKeyId': 'string'
}
},
]
},
]
}
**Response Structure**
- *(dict) --*
- **serverGroupReplicationConfigurations** *(list) --*
Replication configurations associated with server groups in this application.
- *(dict) --*
Replication configuration for a server group.
- **serverGroupId** *(string) --*
Identifier of the server group this replication configuration is associated with.
- **serverReplicationConfigurations** *(list) --*
Replication configuration for servers in the server group.
- *(dict) --*
Replication configuration of a server.
- **server** *(dict) --*
Identifier of the server this replication configuration is associated with.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **serverReplicationParameters** *(dict) --*
Parameters for replicating the server.
- **seedTime** *(datetime) --*
Seed time for creating a replication job for the server.
- **frequency** *(integer) --*
Frequency of creating replication jobs for the server.
- **runOnce** *(boolean) --*
- **licenseType** *(string) --*
License type for creating a replication job for the server.
- **numberOfRecentAmisToKeep** *(integer) --*
Number of recent AMIs to keep when creating a replication job for this server.
- **encrypted** *(boolean) --*
When true, the replication job produces encrypted AMIs. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer's default KMS key for EBS is used.
:type appId: string
:param appId:
ID of the application associated with the replication configuration.
:rtype: dict
:returns:
"""
pass
def get_connectors(self, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Describes the connectors registered with the AWS SMS.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetConnectors>`_
**Request Syntax**
::
response = client.get_connectors(
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'connectorList': [
{
'connectorId': 'string',
'version': 'string',
'status': 'HEALTHY'|'UNHEALTHY',
'capabilityList': [
'VSPHERE'|'SCVMM'|'HYPERV-MANAGER'|'SNAPSHOT_BATCHING',
],
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmManagerId': 'string',
'ipAddress': 'string',
'macAddress': 'string',
'associatedOn': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **connectorList** *(list) --*
Information about the registered connectors.
- *(dict) --*
Represents a connector.
- **connectorId** *(string) --*
The identifier of the connector.
- **version** *(string) --*
The connector version.
- **status** *(string) --*
The status of the connector.
- **capabilityList** *(list) --*
The capabilities of the connector.
- *(string) --*
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The VM management product.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **ipAddress** *(string) --*
The IP address of the connector.
- **macAddress** *(string) --*
The MAC address of the connector.
- **associatedOn** *(datetime) --*
The time the connector was associated.
- **nextToken** *(string) --*
The token required to retrieve the next set of results. This value is null when there are no more results to return.
:type nextToken: string
:param nextToken:
The token for the next set of results.
:type maxResults: integer
:param maxResults:
The maximum number of results to return in a single call. The default value is 50. To retrieve the remaining results, make another call with the returned ``NextToken`` value.
:rtype: dict
:returns:
"""
pass
def get_paginator(self, operation_name: str = None) -> Paginator:
"""
Create a paginator for an operation.
:type operation_name: string
:param operation_name: The operation name. This is the same name
as the method name on the client. For example, if the
method name is ``create_foo``, and you\'d normally invoke the
operation as ``client.create_foo(**kwargs)``, if the
``create_foo`` operation can be paginated, you can use the
call ``client.get_paginator(\"create_foo\")``.
:raise OperationNotPageableError: Raised if the operation is not
pageable. You can use the ``client.can_paginate`` method to
check if an operation is pageable.
:rtype: L{botocore.paginate.Paginator}
:return: A paginator object.
"""
pass
def get_replication_jobs(self, replicationJobId: str = None, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Describes the specified replication job or all of your replication jobs.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetReplicationJobs>`_
**Request Syntax**
::
response = client.get_replication_jobs(
replicationJobId='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'replicationJobList': [
{
'replicationJobId': 'string',
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'seedReplicationTime': datetime(2015, 1, 1),
'frequency': 123,
'runOnce': True|False,
'nextReplicationRunStartTime': datetime(2015, 1, 1),
'licenseType': 'AWS'|'BYOL',
'roleName': 'string',
'latestAmiId': 'string',
'state': 'PENDING'|'ACTIVE'|'FAILED'|'DELETING'|'DELETED'|'COMPLETED'|'PAUSED_ON_FAILURE'|'FAILING',
'statusMessage': 'string',
'description': 'string',
'numberOfRecentAmisToKeep': 123,
'encrypted': True|False,
'kmsKeyId': 'string',
'replicationRunList': [
{
'replicationRunId': 'string',
'state': 'PENDING'|'MISSED'|'ACTIVE'|'FAILED'|'COMPLETED'|'DELETING'|'DELETED',
'type': 'ON_DEMAND'|'AUTOMATIC',
'stageDetails': {
'stage': 'string',
'stageProgress': 'string'
},
'statusMessage': 'string',
'amiId': 'string',
'scheduledStartTime': datetime(2015, 1, 1),
'completedTime': datetime(2015, 1, 1),
'description': 'string',
'encrypted': True|False,
'kmsKeyId': 'string'
},
]
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **replicationJobList** *(list) --*
Information about the replication jobs.
- *(dict) --*
Represents a replication job.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **seedReplicationTime** *(datetime) --*
The seed replication time.
- **frequency** *(integer) --*
The time between consecutive replication runs, in hours.
- **runOnce** *(boolean) --*
- **nextReplicationRunStartTime** *(datetime) --*
The start time of the next replication run.
- **licenseType** *(string) --*
The license type to be used for the AMI created by a successful replication run.
- **roleName** *(string) --*
The name of the IAM role to be used by the Server Migration Service.
- **latestAmiId** *(string) --*
The ID of the latest Amazon Machine Image (AMI).
- **state** *(string) --*
The state of the replication job.
- **statusMessage** *(string) --*
The description of the current status of the replication job.
- **description** *(string) --*
The description of the replication job.
- **numberOfRecentAmisToKeep** *(integer) --*
Number of recent AMIs to keep in the customer's account for a replication job. By default the value is set to zero, meaning that all AMIs are kept.
- **encrypted** *(boolean) --*
Whether the replication job should produce encrypted AMIs or not. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer's default KMS key for EBS is used.
- **replicationRunList** *(list) --*
Information about the replication runs.
- *(dict) --*
Represents a replication run.
- **replicationRunId** *(string) --*
The identifier of the replication run.
- **state** *(string) --*
The state of the replication run.
- **type** *(string) --*
The type of replication run.
- **stageDetails** *(dict) --*
Details of the current stage of the replication run.
- **stage** *(string) --*
String describing the current stage of a replication run.
- **stageProgress** *(string) --*
String describing the progress of the current stage of a replication run.
- **statusMessage** *(string) --*
The description of the current status of the replication job.
- **amiId** *(string) --*
The identifier of the Amazon Machine Image (AMI) from the replication run.
- **scheduledStartTime** *(datetime) --*
The start time of the next replication run.
- **completedTime** *(datetime) --*
The completion time of the last replication run.
- **description** *(string) --*
The description of the replication run.
- **encrypted** *(boolean) --*
Whether the replication run should produce encrypted AMI or not. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer's default KMS key for EBS is used.
- **nextToken** *(string) --*
The token required to retrieve the next set of results. This value is null when there are no more results to return.
:type replicationJobId: string
:param replicationJobId:
The identifier of the replication job.
:type nextToken: string
:param nextToken:
The token for the next set of results.
:type maxResults: integer
:param maxResults:
The maximum number of results to return in a single call. The default value is 50. To retrieve the remaining results, make another call with the returned ``NextToken`` value.
:rtype: dict
:returns:
"""
pass
def get_replication_runs(self, replicationJobId: str, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Describes the replication runs for the specified replication job.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetReplicationRuns>`_
**Request Syntax**
::
response = client.get_replication_runs(
replicationJobId='string',
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'replicationJob': {
'replicationJobId': 'string',
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'seedReplicationTime': datetime(2015, 1, 1),
'frequency': 123,
'runOnce': True|False,
'nextReplicationRunStartTime': datetime(2015, 1, 1),
'licenseType': 'AWS'|'BYOL',
'roleName': 'string',
'latestAmiId': 'string',
'state': 'PENDING'|'ACTIVE'|'FAILED'|'DELETING'|'DELETED'|'COMPLETED'|'PAUSED_ON_FAILURE'|'FAILING',
'statusMessage': 'string',
'description': 'string',
'numberOfRecentAmisToKeep': 123,
'encrypted': True|False,
'kmsKeyId': 'string',
'replicationRunList': [
{
'replicationRunId': 'string',
'state': 'PENDING'|'MISSED'|'ACTIVE'|'FAILED'|'COMPLETED'|'DELETING'|'DELETED',
'type': 'ON_DEMAND'|'AUTOMATIC',
'stageDetails': {
'stage': 'string',
'stageProgress': 'string'
},
'statusMessage': 'string',
'amiId': 'string',
'scheduledStartTime': datetime(2015, 1, 1),
'completedTime': datetime(2015, 1, 1),
'description': 'string',
'encrypted': True|False,
'kmsKeyId': 'string'
},
]
},
'replicationRunList': [
{
'replicationRunId': 'string',
'state': 'PENDING'|'MISSED'|'ACTIVE'|'FAILED'|'COMPLETED'|'DELETING'|'DELETED',
'type': 'ON_DEMAND'|'AUTOMATIC',
'stageDetails': {
'stage': 'string',
'stageProgress': 'string'
},
'statusMessage': 'string',
'amiId': 'string',
'scheduledStartTime': datetime(2015, 1, 1),
'completedTime': datetime(2015, 1, 1),
'description': 'string',
'encrypted': True|False,
'kmsKeyId': 'string'
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **replicationJob** *(dict) --*
Information about the replication job.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **seedReplicationTime** *(datetime) --*
The seed replication time.
- **frequency** *(integer) --*
The time between consecutive replication runs, in hours.
- **runOnce** *(boolean) --*
- **nextReplicationRunStartTime** *(datetime) --*
The start time of the next replication run.
- **licenseType** *(string) --*
The license type to be used for the AMI created by a successful replication run.
- **roleName** *(string) --*
The name of the IAM role to be used by the Server Migration Service.
- **latestAmiId** *(string) --*
The ID of the latest Amazon Machine Image (AMI).
- **state** *(string) --*
The state of the replication job.
- **statusMessage** *(string) --*
The description of the current status of the replication job.
- **description** *(string) --*
The description of the replication job.
- **numberOfRecentAmisToKeep** *(integer) --*
Number of recent AMIs to keep in the customer's account for a replication job. By default the value is set to zero, meaning that all AMIs are kept.
- **encrypted** *(boolean) --*
Whether the replication job should produce encrypted AMIs or not. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer's default KMS key for EBS is used.
- **replicationRunList** *(list) --*
Information about the replication runs.
- *(dict) --*
Represents a replication run.
- **replicationRunId** *(string) --*
The identifier of the replication run.
- **state** *(string) --*
The state of the replication run.
- **type** *(string) --*
The type of replication run.
- **stageDetails** *(dict) --*
Details of the current stage of the replication run.
- **stage** *(string) --*
String describing the current stage of a replication run.
- **stageProgress** *(string) --*
String describing the progress of the current stage of a replication run.
- **statusMessage** *(string) --*
The description of the current status of the replication job.
- **amiId** *(string) --*
The identifier of the Amazon Machine Image (AMI) from the replication run.
- **scheduledStartTime** *(datetime) --*
The start time of the next replication run.
- **completedTime** *(datetime) --*
The completion time of the last replication run.
- **description** *(string) --*
The description of the replication run.
- **encrypted** *(boolean) --*
Whether the replication run should produce encrypted AMI or not. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer's default KMS key for EBS is used.
- **replicationRunList** *(list) --*
Information about the replication runs.
- *(dict) --*
Represents a replication run.
- **replicationRunId** *(string) --*
The identifier of the replication run.
- **state** *(string) --*
The state of the replication run.
- **type** *(string) --*
The type of replication run.
- **stageDetails** *(dict) --*
Details of the current stage of the replication run.
- **stage** *(string) --*
String describing the current stage of a replication run.
- **stageProgress** *(string) --*
String describing the progress of the current stage of a replication run.
- **statusMessage** *(string) --*
The description of the current status of the replication job.
- **amiId** *(string) --*
The identifier of the Amazon Machine Image (AMI) from the replication run.
- **scheduledStartTime** *(datetime) --*
The start time of the next replication run.
- **completedTime** *(datetime) --*
The completion time of the last replication run.
- **description** *(string) --*
The description of the replication run.
- **encrypted** *(boolean) --*
Whether the replication run should produce encrypted AMI or not. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer's default KMS key for EBS is used.
- **nextToken** *(string) --*
The token required to retrieve the next set of results. This value is null when there are no more results to return.
:type replicationJobId: string
:param replicationJobId: **[REQUIRED]**
The identifier of the replication job.
:type nextToken: string
:param nextToken:
The token for the next set of results.
:type maxResults: integer
:param maxResults:
The maximum number of results to return in a single call. The default value is 50. To retrieve the remaining results, make another call with the returned ``NextToken`` value.
:rtype: dict
:returns:
"""
pass
def get_servers(self, nextToken: str = None, maxResults: int = None, vmServerAddressList: List = None) -> Dict:
"""
Describes the servers in your server catalog.
Before you can describe your servers, you must import them using ImportServerCatalog .
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/GetServers>`_
**Request Syntax**
::
response = client.get_servers(
nextToken='string',
maxResults=123,
vmServerAddressList=[
{
'vmManagerId': 'string',
'vmId': 'string'
},
]
)
**Response Syntax**
::
{
'lastModifiedOn': datetime(2015, 1, 1),
'serverCatalogStatus': 'NOT_IMPORTED'|'IMPORTING'|'AVAILABLE'|'DELETED'|'EXPIRED',
'serverList': [
{
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **lastModifiedOn** *(datetime) --*
The time when the server was last modified.
- **serverCatalogStatus** *(string) --*
The status of the server catalog.
- **serverList** *(list) --*
Information about the servers.
- *(dict) --*
Represents a server.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **nextToken** *(string) --*
The token required to retrieve the next set of results. This value is null when there are no more results to return.
:type nextToken: string
:param nextToken:
The token for the next set of results.
:type maxResults: integer
:param maxResults:
The maximum number of results to return in a single call. The default value is 50. To retrieve the remaining results, make another call with the returned ``NextToken`` value.
:type vmServerAddressList: list
:param vmServerAddressList:
List of ``VmServerAddress`` objects
- *(dict) --*
Represents a VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
:rtype: dict
:returns:
"""
pass
def get_waiter(self, waiter_name: str = None) -> Waiter:
"""
Returns an object that can wait for some condition.
:type waiter_name: str
:param waiter_name: The name of the waiter to get. See the waiters
section of the service docs for a list of available waiters.
:returns: The specified waiter object.
:rtype: botocore.waiter.Waiter
"""
pass
def import_server_catalog(self) -> Dict:
"""
Gathers a complete list of on-premises servers. Connectors must be installed and monitoring all servers that you want to import.
This call returns immediately, but might take additional time to retrieve all the servers.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/ImportServerCatalog>`_
**Request Syntax**
::
response = client.import_server_catalog()
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:rtype: dict
:returns:
"""
pass
def launch_app(self, appId: str = None) -> Dict:
"""
Launches an application stack.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/LaunchApp>`_
**Request Syntax**
::
response = client.launch_app(
appId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application to launch.
:rtype: dict
:returns:
"""
pass
def list_apps(self, appIds: List = None, nextToken: str = None, maxResults: int = None) -> Dict:
"""
Returns a list of summaries for all applications.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/ListApps>`_
**Request Syntax**
::
response = client.list_apps(
appIds=[
'string',
],
nextToken='string',
maxResults=123
)
**Response Syntax**
::
{
'apps': [
{
'appId': 'string',
'name': 'string',
'description': 'string',
'status': 'CREATING'|'ACTIVE'|'UPDATING'|'DELETING'|'DELETED'|'DELETE_FAILED',
'statusMessage': 'string',
'replicationStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_REPLICATION'|'VALIDATION_IN_PROGRESS'|'REPLICATION_PENDING'|'REPLICATION_IN_PROGRESS'|'REPLICATED'|'DELTA_REPLICATION_IN_PROGRESS'|'DELTA_REPLICATED'|'DELTA_REPLICATION_FAILED'|'REPLICATION_FAILED'|'REPLICATION_STOPPING'|'REPLICATION_STOP_FAILED'|'REPLICATION_STOPPED',
'replicationStatusMessage': 'string',
'latestReplicationTime': datetime(2015, 1, 1),
'launchStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_LAUNCH'|'VALIDATION_IN_PROGRESS'|'LAUNCH_PENDING'|'LAUNCH_IN_PROGRESS'|'LAUNCHED'|'DELTA_LAUNCH_IN_PROGRESS'|'DELTA_LAUNCH_FAILED'|'LAUNCH_FAILED'|'TERMINATE_IN_PROGRESS'|'TERMINATE_FAILED'|'TERMINATED',
'launchStatusMessage': 'string',
'launchDetails': {
'latestLaunchTime': datetime(2015, 1, 1),
'stackName': 'string',
'stackId': 'string'
},
'creationTime': datetime(2015, 1, 1),
'lastModified': datetime(2015, 1, 1),
'roleName': 'string',
'totalServerGroups': 123,
'totalServers': 123
},
],
'nextToken': 'string'
}
**Response Structure**
- *(dict) --*
- **apps** *(list) --*
A list of application summaries.
- *(dict) --*
Information about the application.
- **appId** *(string) --*
Unique ID of the application.
- **name** *(string) --*
Name of the application.
- **description** *(string) --*
Description of the application.
- **status** *(string) --*
Status of the application.
- **statusMessage** *(string) --*
A message related to the status of the application
- **replicationStatus** *(string) --*
Replication status of the application.
- **replicationStatusMessage** *(string) --*
A message related to the replication status of the application.
- **latestReplicationTime** *(datetime) --*
Timestamp of the application's most recent successful replication.
- **launchStatus** *(string) --*
Launch status of the application.
- **launchStatusMessage** *(string) --*
A message related to the launch status of the application.
- **launchDetails** *(dict) --*
Details about the latest launch of the application.
- **latestLaunchTime** *(datetime) --*
Latest time this application was launched successfully.
- **stackName** *(string) --*
Name of the latest stack launched for this application.
- **stackId** *(string) --*
Identifier of the latest stack launched for this application.
- **creationTime** *(datetime) --*
Time of creation of this application.
- **lastModified** *(datetime) --*
Timestamp of the application's creation.
- **roleName** *(string) --*
Name of the service role in the customer's account used by AWS SMS.
- **totalServerGroups** *(integer) --*
Number of server groups present in the application.
- **totalServers** *(integer) --*
Number of servers present in the application.
- **nextToken** *(string) --*
The token required to retrieve the next set of results. This value is null when there are no more results to return.
:type appIds: list
:param appIds:
- *(string) --*
:type nextToken: string
:param nextToken:
The token for the next set of results.
:type maxResults: integer
:param maxResults:
The maximum number of results to return in a single call. The default value is 50. To retrieve the remaining results, make another call with the returned ``NextToken`` value.
:rtype: dict
:returns:
"""
pass
def put_app_launch_configuration(self, appId: str = None, roleName: str = None, serverGroupLaunchConfigurations: List = None) -> Dict:
"""
Creates a launch configuration for an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/PutAppLaunchConfiguration>`_
**Request Syntax**
::
response = client.put_app_launch_configuration(
appId='string',
roleName='string',
serverGroupLaunchConfigurations=[
{
'serverGroupId': 'string',
'launchOrder': 123,
'serverLaunchConfigurations': [
{
'server': {
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
'logicalId': 'string',
'vpc': 'string',
'subnet': 'string',
'securityGroup': 'string',
'ec2KeyName': 'string',
'userData': {
's3Location': {
'bucket': 'string',
'key': 'string'
}
},
'instanceType': 'string',
'associatePublicIpAddress': True|False
},
]
},
]
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application associated with the launch configuration.
:type roleName: string
:param roleName:
Name of service role in the customer\'s account that Amazon CloudFormation uses to launch the application.
:type serverGroupLaunchConfigurations: list
:param serverGroupLaunchConfigurations:
Launch configurations for server groups in the application.
- *(dict) --*
Launch configuration for a server group.
- **serverGroupId** *(string) --*
Identifier of the server group the launch configuration is associated with.
- **launchOrder** *(integer) --*
Launch order of servers in the server group.
- **serverLaunchConfigurations** *(list) --*
Launch configuration for servers in the server group.
- *(dict) --*
Launch configuration for a server.
- **server** *(dict) --*
Identifier of the server the launch configuration is associated with.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **logicalId** *(string) --*
Logical ID of the server in the Amazon CloudFormation template.
- **vpc** *(string) --*
Identifier of the VPC the server should be launched into.
- **subnet** *(string) --*
Identifier of the subnet the server should be launched into.
- **securityGroup** *(string) --*
Identifier of the security group that applies to the launched server.
- **ec2KeyName** *(string) --*
Name of the EC2 SSH Key to be used for connecting to the launched server.
- **userData** *(dict) --*
Location of the user-data script to be executed when launching the server.
- **s3Location** *(dict) --*
Amazon S3 location of the user-data script.
- **bucket** *(string) --*
Amazon S3 bucket name.
- **key** *(string) --*
Amazon S3 bucket key.
- **instanceType** *(string) --*
Instance type to be used for launching the server.
- **associatePublicIpAddress** *(boolean) --*
If true, a publicly accessible IP address is created when launching the server.
:rtype: dict
:returns:
"""
pass
def put_app_replication_configuration(self, appId: str = None, serverGroupReplicationConfigurations: List = None) -> Dict:
"""
Creates or updates a replication configuration for an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/PutAppReplicationConfiguration>`_
**Request Syntax**
::
response = client.put_app_replication_configuration(
appId='string',
serverGroupReplicationConfigurations=[
{
'serverGroupId': 'string',
'serverReplicationConfigurations': [
{
'server': {
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
'serverReplicationParameters': {
'seedTime': datetime(2015, 1, 1),
'frequency': 123,
'runOnce': True|False,
'licenseType': 'AWS'|'BYOL',
'numberOfRecentAmisToKeep': 123,
'encrypted': True|False,
'kmsKeyId': 'string'
}
},
]
},
]
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application tassociated with the replication configuration.
:type serverGroupReplicationConfigurations: list
:param serverGroupReplicationConfigurations:
Replication configurations for server groups in the application.
- *(dict) --*
Replication configuration for a server group.
- **serverGroupId** *(string) --*
Identifier of the server group this replication configuration is associated with.
- **serverReplicationConfigurations** *(list) --*
Replication configuration for servers in the server group.
- *(dict) --*
Replication configuration of a server.
- **server** *(dict) --*
Identifier of the server this replication configuration is associated with.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **serverReplicationParameters** *(dict) --*
Parameters for replicating the server.
- **seedTime** *(datetime) --*
Seed time for creating a replication job for the server.
- **frequency** *(integer) --*
Frequency of creating replication jobs for the server.
- **runOnce** *(boolean) --*
- **licenseType** *(string) --*
License type for creating a replication job for the server.
- **numberOfRecentAmisToKeep** *(integer) --*
Number of recent AMIs to keep when creating a replication job for this server.
- **encrypted** *(boolean) --*
When true, the replication job produces encrypted AMIs. See also ``KmsKeyId`` below.
- **kmsKeyId** *(string) --*
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer\'s default KMS key for EBS is used.
:rtype: dict
:returns:
"""
pass
def start_app_replication(self, appId: str = None) -> Dict:
"""
Starts replicating an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/StartAppReplication>`_
**Request Syntax**
::
response = client.start_app_replication(
appId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application to replicate.
:rtype: dict
:returns:
"""
pass
def start_on_demand_replication_run(self, replicationJobId: str, description: str = None) -> Dict:
"""
Starts an on-demand replication run for the specified replication job. This replication run starts immediately. This replication run is in addition to the ones already scheduled.
There is a limit on the number of on-demand replications runs you can request in a 24-hour period.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/StartOnDemandReplicationRun>`_
**Request Syntax**
::
response = client.start_on_demand_replication_run(
replicationJobId='string',
description='string'
)
**Response Syntax**
::
{
'replicationRunId': 'string'
}
**Response Structure**
- *(dict) --*
- **replicationRunId** *(string) --*
The identifier of the replication run.
:type replicationJobId: string
:param replicationJobId: **[REQUIRED]**
The identifier of the replication job.
:type description: string
:param description:
The description of the replication run.
:rtype: dict
:returns:
"""
pass
def stop_app_replication(self, appId: str = None) -> Dict:
"""
Stops replicating an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/StopAppReplication>`_
**Request Syntax**
::
response = client.stop_app_replication(
appId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application to stop replicating.
:rtype: dict
:returns:
"""
pass
def terminate_app(self, appId: str = None) -> Dict:
"""
Terminates the stack for an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/TerminateApp>`_
**Request Syntax**
::
response = client.terminate_app(
appId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type appId: string
:param appId:
ID of the application to terminate.
:rtype: dict
:returns:
"""
pass
def update_app(self, appId: str = None, name: str = None, description: str = None, roleName: str = None, serverGroups: List = None, tags: List = None) -> Dict:
"""
Updates an application.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/UpdateApp>`_
**Request Syntax**
::
response = client.update_app(
appId='string',
name='string',
description='string',
roleName='string',
serverGroups=[
{
'serverGroupId': 'string',
'name': 'string',
'serverList': [
{
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
]
},
],
tags=[
{
'key': 'string',
'value': 'string'
},
]
)
**Response Syntax**
::
{
'appSummary': {
'appId': 'string',
'name': 'string',
'description': 'string',
'status': 'CREATING'|'ACTIVE'|'UPDATING'|'DELETING'|'DELETED'|'DELETE_FAILED',
'statusMessage': 'string',
'replicationStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_REPLICATION'|'VALIDATION_IN_PROGRESS'|'REPLICATION_PENDING'|'REPLICATION_IN_PROGRESS'|'REPLICATED'|'DELTA_REPLICATION_IN_PROGRESS'|'DELTA_REPLICATED'|'DELTA_REPLICATION_FAILED'|'REPLICATION_FAILED'|'REPLICATION_STOPPING'|'REPLICATION_STOP_FAILED'|'REPLICATION_STOPPED',
'replicationStatusMessage': 'string',
'latestReplicationTime': datetime(2015, 1, 1),
'launchStatus': 'READY_FOR_CONFIGURATION'|'CONFIGURATION_IN_PROGRESS'|'CONFIGURATION_INVALID'|'READY_FOR_LAUNCH'|'VALIDATION_IN_PROGRESS'|'LAUNCH_PENDING'|'LAUNCH_IN_PROGRESS'|'LAUNCHED'|'DELTA_LAUNCH_IN_PROGRESS'|'DELTA_LAUNCH_FAILED'|'LAUNCH_FAILED'|'TERMINATE_IN_PROGRESS'|'TERMINATE_FAILED'|'TERMINATED',
'launchStatusMessage': 'string',
'launchDetails': {
'latestLaunchTime': datetime(2015, 1, 1),
'stackName': 'string',
'stackId': 'string'
},
'creationTime': datetime(2015, 1, 1),
'lastModified': datetime(2015, 1, 1),
'roleName': 'string',
'totalServerGroups': 123,
'totalServers': 123
},
'serverGroups': [
{
'serverGroupId': 'string',
'name': 'string',
'serverList': [
{
'serverId': 'string',
'serverType': 'VIRTUAL_MACHINE',
'vmServer': {
'vmServerAddress': {
'vmManagerId': 'string',
'vmId': 'string'
},
'vmName': 'string',
'vmManagerName': 'string',
'vmManagerType': 'VSPHERE'|'SCVMM'|'HYPERV-MANAGER',
'vmPath': 'string'
},
'replicationJobId': 'string',
'replicationJobTerminated': True|False
},
]
},
],
'tags': [
{
'key': 'string',
'value': 'string'
},
]
}
**Response Structure**
- *(dict) --*
- **appSummary** *(dict) --*
Summary description of the application.
- **appId** *(string) --*
Unique ID of the application.
- **name** *(string) --*
Name of the application.
- **description** *(string) --*
Description of the application.
- **status** *(string) --*
Status of the application.
- **statusMessage** *(string) --*
A message related to the status of the application
- **replicationStatus** *(string) --*
Replication status of the application.
- **replicationStatusMessage** *(string) --*
A message related to the replication status of the application.
- **latestReplicationTime** *(datetime) --*
Timestamp of the application's most recent successful replication.
- **launchStatus** *(string) --*
Launch status of the application.
- **launchStatusMessage** *(string) --*
A message related to the launch status of the application.
- **launchDetails** *(dict) --*
Details about the latest launch of the application.
- **latestLaunchTime** *(datetime) --*
Latest time this application was launched successfully.
- **stackName** *(string) --*
Name of the latest stack launched for this application.
- **stackId** *(string) --*
Identifier of the latest stack launched for this application.
- **creationTime** *(datetime) --*
Time of creation of this application.
- **lastModified** *(datetime) --*
Timestamp of the application's creation.
- **roleName** *(string) --*
Name of the service role in the customer's account used by AWS SMS.
- **totalServerGroups** *(integer) --*
Number of server groups present in the application.
- **totalServers** *(integer) --*
Number of servers present in the application.
- **serverGroups** *(list) --*
List of updated server groups in the application.
- *(dict) --*
A logical grouping of servers.
- **serverGroupId** *(string) --*
Identifier of a server group.
- **name** *(string) --*
Name of a server group.
- **serverList** *(list) --*
List of servers belonging to a server group.
- *(dict) --*
Represents a server.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
- **tags** *(list) --*
List of tags associated with the application.
- *(dict) --*
A label that can be assigned to an application.
- **key** *(string) --*
Tag key.
- **value** *(string) --*
Tag value.
:type appId: string
:param appId:
ID of the application to update.
:type name: string
:param name:
New name of the application.
:type description: string
:param description:
New description of the application.
:type roleName: string
:param roleName:
Name of the service role in the customer\'s account used by AWS SMS.
:type serverGroups: list
:param serverGroups:
List of server groups in the application to update.
- *(dict) --*
A logical grouping of servers.
- **serverGroupId** *(string) --*
Identifier of a server group.
- **name** *(string) --*
Name of a server group.
- **serverList** *(list) --*
List of servers belonging to a server group.
- *(dict) --*
Represents a server.
- **serverId** *(string) --*
The identifier of the server.
- **serverType** *(string) --*
The type of server.
- **vmServer** *(dict) --*
Information about the VM server.
- **vmServerAddress** *(dict) --*
Information about the VM server location.
- **vmManagerId** *(string) --*
The identifier of the VM manager.
- **vmId** *(string) --*
The identifier of the VM.
- **vmName** *(string) --*
The name of the VM.
- **vmManagerName** *(string) --*
The name of the VM manager.
- **vmManagerType** *(string) --*
The type of VM management product.
- **vmPath** *(string) --*
The VM folder path in the vCenter Server virtual machine inventory tree.
- **replicationJobId** *(string) --*
The identifier of the replication job.
- **replicationJobTerminated** *(boolean) --*
Indicates whether the replication job is deleted or failed.
:type tags: list
:param tags:
List of tags to associate with the application.
- *(dict) --*
A label that can be assigned to an application.
- **key** *(string) --*
Tag key.
- **value** *(string) --*
Tag value.
:rtype: dict
:returns:
"""
pass
def update_replication_job(self, replicationJobId: str, frequency: int = None, nextReplicationRunStartTime: datetime = None, licenseType: str = None, roleName: str = None, description: str = None, numberOfRecentAmisToKeep: int = None, encrypted: bool = None, kmsKeyId: str = None) -> Dict:
"""
Updates the specified settings for the specified replication job.
See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/sms-2016-10-24/UpdateReplicationJob>`_
**Request Syntax**
::
response = client.update_replication_job(
replicationJobId='string',
frequency=123,
nextReplicationRunStartTime=datetime(2015, 1, 1),
licenseType='AWS'|'BYOL',
roleName='string',
description='string',
numberOfRecentAmisToKeep=123,
encrypted=True|False,
kmsKeyId='string'
)
**Response Syntax**
::
{}
**Response Structure**
- *(dict) --*
:type replicationJobId: string
:param replicationJobId: **[REQUIRED]**
The identifier of the replication job.
:type frequency: integer
:param frequency:
The time between consecutive replication runs, in hours.
:type nextReplicationRunStartTime: datetime
:param nextReplicationRunStartTime:
The start time of the next replication run.
:type licenseType: string
:param licenseType:
The license type to be used for the AMI created by a successful replication run.
:type roleName: string
:param roleName:
The name of the IAM role to be used by AWS SMS.
:type description: string
:param description:
The description of the replication job.
:type numberOfRecentAmisToKeep: integer
:param numberOfRecentAmisToKeep:
The maximum number of SMS-created AMIs to retain. The oldest will be deleted once the maximum number is reached and a new AMI is created.
:type encrypted: boolean
:param encrypted:
When true, the replication job produces encrypted AMIs . See also ``KmsKeyId`` below.
:type kmsKeyId: string
:param kmsKeyId:
KMS key ID for replication jobs that produce encrypted AMIs. Can be any of the following:
* KMS key ID
* KMS key alias
* ARN referring to KMS key ID
* ARN referring to KMS key alias
If encrypted is *true* but a KMS key id is not specified, the customer\'s default KMS key for EBS is used.
:rtype: dict
:returns:
"""
pass
| 46.799918 | 403 | 0.468899 | 9,024 | 113,677 | 5.86691 | 0.057735 | 0.024932 | 0.024083 | 0.022439 | 0.859132 | 0.837958 | 0.820354 | 0.807038 | 0.795044 | 0.785203 | 0 | 0.008418 | 0.431512 | 113,677 | 2,428 | 404 | 46.819193 | 0.810829 | 0.799229 | 0 | 0.438356 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.438356 | false | 0.438356 | 0.123288 | 0 | 0.575342 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
afe83ae51facb95287c36d5db5fa532c2c0f177d | 11,828 | py | Python | poetrytools/tests/test_ru_poetics.py | standfromunder/Poetry-Tools | fa81b677be41b542e2288f39a1d3cc5d2b761922 | [
"MIT"
] | 1 | 2020-12-01T22:51:03.000Z | 2020-12-01T22:51:03.000Z | poetrytools/tests/test_ru_poetics.py | standfromunder/Poetry-Tools | fa81b677be41b542e2288f39a1d3cc5d2b761922 | [
"MIT"
] | null | null | null | poetrytools/tests/test_ru_poetics.py | standfromunder/Poetry-Tools | fa81b677be41b542e2288f39a1d3cc5d2b761922 | [
"MIT"
] | 1 | 2021-03-04T17:36:32.000Z | 2021-03-04T17:36:32.000Z | import os
import unittest
from poetrytools.PoetryRU import PoetryRU
class TestENPoems(unittest.TestCase):
def setUp(self):
self.poetryRUS = PoetryRU('cmudict/ru_cmudict.json')
self.number = 0
def open_poem(self, poem):
with open(os.path.join('..','poems', 'ru', poem), encoding='utf-8') as f:
return self.poetryRUS.tokenize(f.read())
def open_test(self, test):
with open(os.path.join('..', 'poems', 'test_files', test), encoding='utf-8') as f:
return self.poetryRUS.tokenize(f.read())
def test_easy_rhyme(self):
easy_test = self.open_test('easy_tests.txt')
for pair in easy_test:
print(pair[0], pair[1])
self.assertTrue(self.poetryRUS.rhymes(pair[0], pair[1]))
def test_rhyme(self):
self.assertTrue(self.poetryRUS.rhymes('раскис', 'вниз'))
def test_rhyme_scheme(self):
poem = """В прозрачных пространствах Эфира,
Над сумраком дольнего мира,
Над шумом забытой метели,
Два светлые духа летели."""
tokenized_poem = self.poetryRUS.tokenize(poem)
rhyme_scheme = self.poetryRUS.guess_rhyme_type(tokenized_poem)
print('Rhyme scheme: {}'.format(rhyme_scheme))
def test_hard_rhyme(self):
hard_test = self.open_test('hard_tests.txt')
for pair in hard_test:
print(pair[0], pair[1])
self.assertTrue(self.poetryRUS.rhymes(pair[0], pair[1]))
def test_poem_1(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aa')
def test_poem_2(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aaaa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aaaa')
def test_poem_3(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aaabaaabbbabbcdcdb.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aaabaaabbbabbcdcdb')
def test_poem_4(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aabb.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aabb')
def test_poem_5(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aabbcc.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aabbcc')
def test_poem_6(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aabbccaa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aabbccaa')
def test_poem_7(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aabbccdd.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aabbccdd')
def test_poem_8(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aabcbc.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aabcbc')
def test_poem_9(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aax.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aaX')
def test_poem_10(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aaxa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aaXa')
def test_poem_11(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_aaxb.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aaXX')
def test_poem_12(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_ab.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'XX')
def test_poem_14(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_abab.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'abab')
def test_poem_15(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_ababcdcd.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'ababcdcd')
'''def test_poem_16(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_ababcdcdefef.txt'))
for stanza in stanzas:
rhyme_scheme_string, rhyme = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'ababcdcdefef')'''
def test_poem_17(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_abaxb.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'abaXb')
def test_poem_18(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_abba.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'abba')
def test_poem_19(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_abbaa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'abbaa')
def test_poem_20(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_abbab.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'abbab')
'''def test_poem_21(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_abbabcbcddefef.txt'))
for stanza in stanzas:
rhyme_scheme_string, rhyme = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'abbabcbcddefef')'''
def test_poem_22(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_axa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aXa')
def test_poem_23(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_xaa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'Xaa')
def test_poem_24(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_xabab.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'Xabab')
def test_poem_25(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_xaxbab.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'XaXbab')
def test_poem_26(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('rhyme_axaa.txt'))
for stanza in stanzas:
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanza)
for line in stanza:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aXaa')
def test_poem_27(self):
stanzas = self.poetryRUS.split_into_stanzas(self.open_poem('problems.txt'))
rhyme_scheme_string = self.poetryRUS.guess_rhyme_type(stanzas[0][:4])
for line in stanzas[0]:
print(' '.join(line))
print(rhyme_scheme_string, '\n')
self.assertTrue(rhyme_scheme_string == 'aaaa')
if __name__ == '__main__':
unittest.main()
| 42.242857 | 99 | 0.626564 | 1,446 | 11,828 | 4.851314 | 0.089903 | 0.128582 | 0.189024 | 0.088525 | 0.828083 | 0.814683 | 0.808125 | 0.808125 | 0.801853 | 0.801853 | 0 | 0.006552 | 0.264457 | 11,828 | 279 | 100 | 42.394265 | 0.79977 | 0 | 0 | 0.553097 | 0 | 0 | 0.07434 | 0.004641 | 0 | 0 | 0 | 0 | 0.119469 | 1 | 0.137168 | false | 0 | 0.013274 | 0 | 0.163717 | 0.225664 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
b35ed8a98dcaad04bb8a91be98408900fc46e34a | 10,413 | py | Python | models/develset/src/utils/unit_tests/test_case1.py | phdyang007/pytorch-CycleGAN-and-pix2pix | 8057392a37d47a17ac0e8f7cc4642bec86bf2e43 | [
"BSD-3-Clause"
] | 1 | 2022-01-26T00:45:06.000Z | 2022-01-26T00:45:06.000Z | models/develset/src/utils/unit_tests/test_case1.py | phdyang007/pytorch-CycleGAN-and-pix2pix | 8057392a37d47a17ac0e8f7cc4642bec86bf2e43 | [
"BSD-3-Clause"
] | null | null | null | models/develset/src/utils/unit_tests/test_case1.py | phdyang007/pytorch-CycleGAN-and-pix2pix | 8057392a37d47a17ac0e8f7cc4642bec86bf2e43 | [
"BSD-3-Clause"
] | null | null | null | '''
Author: Guojin Chen @ CUHK-CSE
Homepage: https://dekura.github.io/
Date: 2020-12-26 17:07:15
LastEditTime: 2021-04-09 17:00:56
Contact: cgjhaha@qq.com
Description: the unit tests for the case4 target image
'''
from src.models.const import *
'''
8 9
0 1 2
3 x 4
5 6 7
10 11
'''
def test_ul_corner(x, y, type_corner, image, ls):
x1 = x + 512
y1 = y + 512
px = (0, 0, y1, x1)
px0 = (0, 0, y1 - 1, x1 - 1)
px1 = (0, 0, y1 - 1, x1)
px2 = (0, 0, y1 - 1, x1 + 1)
px3 = (0, 0, y1, x1 - 1)
px4 = (0, 0, y1, x1 + 1)
px5 = (0, 0, y1 + 1, x1 - 1)
px6 = (0, 0, y1 + 1, x1)
px7 = (0, 0, y1 + 1, x1 + 1)
px8 = (0, 0, y1 - 2, x1 - 2)
px9 = (0, 0, y1 - 2, x1 + 2)
px10 = (0, 0, y1 + 2, x1 - 2)
px11 = (0, 0, y1 + 2, x1 + 2)
assert image[px] == 1, 'the {} corner of ({},{}) should be {}'.format(type_corner, x, y, 1)
assert image[px0] == 0, 'the {} corner of ({},{}) position 0 should be {}'.format(type_corner, x, y, 0)
assert image[px7] == 1, 'the {} corner of ({},{}) position 0 should be {}'.format(type_corner, x, y, 1)
assert ls[px] == 0, 'the {} corner levelset of ({},{}) should be {}'.format(type_corner, x, y, 0)
assert ls[px7] == -1, 'the {} corner levelset of ({},{}) position7 should be {}'.format(type_corner, x, y, -1)
print('test {} corner of ({},{}) pass all'.format(type_corner, x, y))
'''
8 9
0 1 2
3 x 4
5 6 7
10 11
'''
def test_ll_corner(x, y, type_corner, image, ls):
x1 = x + 512
y1 = y + 512
px = (0, 0, y1, x1)
px0 = (0, 0, y1 - 1, x1 - 1)
px1 = (0, 0, y1 - 1, x1)
px2 = (0, 0, y1 - 1, x1 + 1)
px3 = (0, 0, y1, x1 - 1)
px4 = (0, 0, y1, x1 + 1)
px5 = (0, 0, y1 + 1, x1 - 1)
px6 = (0, 0, y1 + 1, x1)
px7 = (0, 0, y1 + 1, x1 + 1)
px8 = (0, 0, y1 - 2, x1 - 2)
px9 = (0, 0, y1 - 2, x1 + 2)
px10 = (0, 0, y1 + 2, x1 - 2)
px11 = (0, 0, y1 + 2, x1 + 2)
assert image[px] == 1, 'the {} corner of ({},{}) should be {}'.format(type_corner, x, y, 1)
assert image[px0] == 0, 'the {} corner of ({},{}) px0 should be {}'.format(type_corner, x, y, 0)
assert image[px7] == 1, 'the {} corner of ({},{}) px7 should be {}'.format(type_corner, x, y, 1)
assert ls[px] == 0, 'the {} corner levelset of ({},{}) should be {}'.format(type_corner, x, y, 0)
assert ls[px7] == -1, 'the {} corner levelset of ({},{}) position7 should be {}'.format(type_corner, x, y, -1)
print('the {} corner ls[px0] is {}, which should be sqrt(2)'.format(type_corner, ls[px0]))
print('the {} corner ls[px7] is {}, which should be -1'.format(type_corner, ls[px7]))
print('check the output to see whether pass all test {} corner of ({},{}) pass all'.format(type_corner, x, y))
print('\n========================\n')
'''
8 9
0 1 2
3 x 4
5 6 7
10 11
'''
def test_ur_corner(x, y, type_corner, image, ls):
x1 = x + 512
y1 = y + 512
px = (0, 0, y1, x1)
px0 = (0, 0, y1 - 1, x1 - 1)
px1 = (0, 0, y1 - 1, x1)
px2 = (0, 0, y1 - 1, x1 + 1)
px3 = (0, 0, y1, x1 - 1)
px4 = (0, 0, y1, x1 + 1)
px5 = (0, 0, y1 + 1, x1 - 1)
px6 = (0, 0, y1 + 1, x1)
px7 = (0, 0, y1 + 1, x1 + 1)
px8 = (0, 0, y1 - 2, x1 - 2)
px9 = (0, 0, y1 - 2, x1 + 2)
px10 = (0, 0, y1 + 2, x1 - 2)
px11 = (0, 0, y1 + 2, x1 + 2)
assert image[px] == 0, 'the {} corner of ({},{}) should be {}'.format(type_corner, x, y, 0)
assert image[px0] == 1, 'the {} corner of ({},{}) position 0 should be {}'.format(type_corner, x, y, 1)
assert image[px7] == 0, 'the {} corner of ({},{}) position 7 should be {}'.format(type_corner, x, y, 0)
assert ls[px0] == 0, 'the {} corner levelset of ({},{}) position 0 should be {}'.format(type_corner, x, y, 0)
print('the {} corner ls[px] is {}, which should be sqrt(2)'.format(type_corner, ls[px]))
print('the {} corner ls[px7] is {}, which should be sqrt(8)'.format(type_corner, ls[px7]))
print('check the output to see whether pass all test {} corner of ({},{}) pass all.'.format(type_corner, x, y))
print('\n========================\n')
'''
8 9
0 1 2
3 x 4
5 6 7
10 11
'''
def test_lr_convex(x, y, type_convex, image, ls):
x1 = x + 512
y1 = y + 512
px = (0, 0, y1, x1)
px0 = (0, 0, y1 - 1, x1 - 1)
px1 = (0, 0, y1 - 1, x1)
px2 = (0, 0, y1 - 1, x1 + 1)
px3 = (0, 0, y1, x1 - 1)
px4 = (0, 0, y1, x1 + 1)
px5 = (0, 0, y1 + 1, x1 - 1)
px6 = (0, 0, y1 + 1, x1)
px7 = (0, 0, y1 + 1, x1 + 1)
px8 = (0, 0, y1 - 2, x1 - 2)
px9 = (0, 0, y1 - 2, x1 + 2)
px10 = (0, 0, y1 + 2, x1 - 2)
px11 = (0, 0, y1 + 2, x1 + 2)
assert image[px] == 1, 'the {} convex of ({},{}) should be {}'.format(type_convex, x, y, 1)
assert image[px0] == 1, 'the {} convex of ({},{}) px0 should be {}'.format(type_convex, x, y, 1)
assert image[px7] == 1, 'the {} convex of ({},{}) px7 should be {}'.format(type_convex, x, y, 1)
assert image[px1] == 0, 'the {} convex of ({},{}) px1 should be {}'.format(type_convex, x, y, 0)
assert ls[px] == 0, 'the {} convex levelset of ({},{}) should be {}'.format(type_convex, x, y, 0)
assert ls[px1] == 1, 'the {} convex levelset of ({},{}) px1 should be {}'.format(type_convex, x, y, 1)
assert ls[px7] == -1, 'the {} convex levelset of ({},{}) px7 should be {}'.format(type_convex, x, y, -1)
assert ls[px9] == 2, 'the {} convex levelset of ({},{}) px7 should be {}'.format(type_convex, x, y, 2)
print('check the output to see whether pass all test {} convex of ({},{}) pass all'.format(type_convex, x, y))
print('\n========================\n')
'''
8 9
0 1 2
3 x 4
5 6 7
10 11
'''
def test_ul_convex(x, y, type_convex, image, ls):
x1 = x + 512
y1 = y + 512
px = (0, 0, y1, x1)
px0 = (0, 0, y1 - 1, x1 - 1)
px1 = (0, 0, y1 - 1, x1)
px2 = (0, 0, y1 - 1, x1 + 1)
px3 = (0, 0, y1, x1 - 1)
px4 = (0, 0, y1, x1 + 1)
px5 = (0, 0, y1 + 1, x1 - 1)
px6 = (0, 0, y1 + 1, x1)
px7 = (0, 0, y1 + 1, x1 + 1)
px8 = (0, 0, y1 - 2, x1 - 2)
px9 = (0, 0, y1 - 2, x1 + 2)
px10 = (0, 0, y1 + 2, x1 - 2)
px11 = (0, 0, y1 + 2, x1 + 2)
assert image[px] == 1, 'the {} convex of ({},{}) should be {}'.format(type_convex, x, y, 1)
assert image[px0] == 1, 'the {} convex of ({},{}) px0 should be {}'.format(type_convex, x, y, 1)
assert image[px1] == 1, 'the {} convex of ({},{}) px1 should be {}'.format(type_convex, x, y, 1)
assert image[px3] == 0, 'the {} convex of ({},{}) px3 should be {}'.format(type_convex, x, y, 0)
assert image[px5] == 0, 'the {} convex of ({},{}) px5 should be {}'.format(type_convex, x, y, 0)
assert image[px6] == 1, 'the {} convex of ({},{}) px6 should be {}'.format(type_convex, x, y, 1)
assert image[px7] == 1, 'the {} convex of ({},{}) px7 should be {}'.format(type_convex, x, y, 1)
assert ls[px] == 0, 'the {} convex levelset of ({},{}) should be {}'.format(type_convex, x, y, 0)
# assert ls[px1] == 1, 'the {} convex levelset of ({},{}) px1 should be {}'.format(type_convex, x, y, 1)
assert ls[px7] == -1, 'the {} convex levelset of ({},{}) px7 should be {}'.format(type_convex, x, y, -1)
assert ls[px3] == 1, 'the {} convex levelset of ({},{}) px3 should be {}'.format(type_convex, x, y, 1)
print('the {} convex ls[px1] is {}, which should be 0'.format(type_convex, ls[px1]))
print('the {} convex ls[px8] is {}, which should be -1'.format(type_convex, ls[px8]))
print('the {} convex ls[px9] is {}, which should be -sqrt(5)'.format(type_convex, ls[px9]))
print('check the output to see whether pass all test {} convex of ({},{}) pass all'.format(type_convex, x, y))
print('\n========================\n')
'''
x, y is the coord in glp
'''
def test_corner(x, y, type_corner, image, ls):
if type_corner == 'ur':
test_ur_corner(x, y, type_corner, image, ls)
elif type_corner == 'll':
test_ll_corner(x, y, type_corner, image, ls)
else:
raise 'not implmentation'
def test_convex(x, y, type_convex, image, ls):
if type_convex == 'lr':
test_lr_convex(x, y, type_convex, image, ls)
elif type_convex == 'ul':
test_ul_convex(x, y, type_convex, image, ls)
else:
raise 'not implmentation'
def test_outer(x, y, image, ls):
type_convex = 'outer'
x1 = x + 512
y1 = y + 512
px = (0, 0, y1, x1)
px0 = (0, 0, y1 - 1, x1 - 1)
px1 = (0, 0, y1 - 1, x1)
px2 = (0, 0, y1 - 1, x1 + 1)
px3 = (0, 0, y1, x1 - 1)
px4 = (0, 0, y1, x1 + 1)
px5 = (0, 0, y1 + 1, x1 - 1)
px6 = (0, 0, y1 + 1, x1)
px7 = (0, 0, y1 + 1, x1 + 1)
px8 = (0, 0, y1 - 2, x1 - 2)
px9 = (0, 0, y1 - 2, x1 + 2)
px10 = (0, 0, y1 + 2, x1 - 2)
px11 = (0, 0, y1 + 2, x1 + 2)
assert image[px] == 0, 'the {} convex of ({},{}) should be {}'.format(type_convex, x, y, 0)
assert image[px0] == 0, 'the {} convex of ({},{}) px0 should be {}'.format(type_convex, x, y, 0)
assert image[px1] == 0, 'the {} convex of ({},{}) px1 should be {}'.format(type_convex, x, y, 0)
assert image[px3] == 0, 'the {} convex of ({},{}) px3 should be {}'.format(type_convex, x, y, 0)
assert image[px5] == 0, 'the {} convex of ({},{}) px5 should be {}'.format(type_convex, x, y, 0)
assert image[px6] == 0, 'the {} convex of ({},{}) px6 should be {}'.format(type_convex, x, y, 0)
assert image[px7] == 0, 'the {} convex of ({},{}) px7 should be {}'.format(type_convex, x, y, 0)
assert ls[px] == UP_TRUNCATED_D, 'the {} convex levelset of ({},{}) should be {}'.format(type_convex, x, y, UP_TRUNCATED_D)
# assert ls[px1] == 1, 'the {} convex levelset of ({},{}) px1 should be {}'.format(type_convex, x, y, 1)
assert ls[px7] == UP_TRUNCATED_D, 'the {} convex levelset of ({},{}) px7 should be {}'.format(type_convex, x, y, UP_TRUNCATED_D)
assert ls[px3] == UP_TRUNCATED_D, 'the {} convex levelset of ({},{}) px3 should be {}'.format(type_convex, x, y, UP_TRUNCATED_D)
# print('the {} convex ls[px1] is {}, which should be 0'.format(type_convex, ls[px1]))
# print('the {} convex ls[px8] is {}, which should be -1'.format(type_convex, ls[px8]))
# print('the {} convex ls[px9] is {}, which should be -sqrt(5)'.format(type_convex, ls[px9]))
print('check the output to see whether pass all test {} convex of ({},{}) pass all'.format(type_convex, x, y))
print('\n========================\n')
| 42.851852 | 133 | 0.521464 | 1,801 | 10,413 | 2.95447 | 0.062743 | 0.029318 | 0.058636 | 0.148844 | 0.934599 | 0.925578 | 0.922195 | 0.905845 | 0.893629 | 0.856418 | 0 | 0.108496 | 0.257371 | 10,413 | 242 | 134 | 43.028926 | 0.579594 | 0.064823 | 0 | 0.72093 | 0 | 0 | 0.296324 | 0.01479 | 0 | 0 | 0 | 0 | 0.244186 | 1 | 0.046512 | false | 0.034884 | 0.005814 | 0 | 0.052326 | 0.104651 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
6428146be1f47649727e610bf6ebecb3343763fb | 20,730 | py | Python | test/Coveringarray_server_test.py | DrewCross/Coveringarray | db8f833f7cbbf16d8800ab3e085e8eeff4c7ed7b | [
"MIT"
] | null | null | null | test/Coveringarray_server_test.py | DrewCross/Coveringarray | db8f833f7cbbf16d8800ab3e085e8eeff4c7ed7b | [
"MIT"
] | null | null | null | test/Coveringarray_server_test.py | DrewCross/Coveringarray | db8f833f7cbbf16d8800ab3e085e8eeff4c7ed7b | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import os
import subprocess
import time
import unittest
from configparser import ConfigParser
from Coveringarray.CoveringarrayImpl import Coveringarray
from Coveringarray.CoveringarrayServer import MethodContext
from Coveringarray.authclient import KBaseAuth as _KBaseAuth
from installed_clients.WorkspaceClient import Workspace
class CoveringarrayTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
token = os.environ.get('KB_AUTH_TOKEN', None)
config_file = os.environ.get('KB_DEPLOYMENT_CONFIG', None)
cls.cfg = {}
config = ConfigParser()
config.read(config_file)
for nameval in config.items('Coveringarray'):
cls.cfg[nameval[0]] = nameval[1]
# Getting username from Auth profile for token
authServiceUrl = cls.cfg['auth-service-url']
auth_client = _KBaseAuth(authServiceUrl)
user_id = auth_client.get_user(token)
# WARNING: don't call any logging methods on the context object,
# it'll result in a NoneType error
cls.ctx = MethodContext(None)
cls.ctx.update({'token': token,
'user_id': user_id,
'provenance': [
{'service': 'Coveringarray',
'method': 'please_never_use_it_in_production',
'method_params': []
}],
'authenticated': 1})
cls.wsURL = cls.cfg['workspace-url']
cls.wsClient = Workspace(cls.wsURL)
cls.serviceImpl = Coveringarray(cls.cfg)
cls.scratch = cls.cfg['scratch']
cls.callback_url = os.environ['SDK_CALLBACK_URL']
suffix = int(time.time() * 1000)
cls.wsName = "_test_Cover_" + str(suffix)
ret = cls.wsClient.create_workspace({'workspace': cls.wsName}) # noqa
@classmethod
def tearDownClass(cls):
if hasattr(cls, 'wsName'):
cls.wsClient.delete_workspace({'workspace': cls.wsName})
print('Test workspace was deleted')
def getWsClient(self):
return self.__class__.wsClient
def getWsName(self):
if hasattr(self.__class__, 'wsName'):
return self.__class__.wsName
suffix = int(time.time() * 1000)
wsName = "test_Cover_"+str(suffix)
ret = self.getWsClient().create_workspace({'workspace':wsName})
self.__class__.wsName = wsName
return wsName
def getImpl(self):
return self.__class__.serviceImpl
def getContext(self):
return self.__class__.ctx
# NOTE: According to Python unittest naming rules test method names should start from 'test'. # noqa
def testManualInput(self):
testMedia = {"__VERSION__":1,"id":"kb|media.664","isDefined":0,"isMinimal":0,"mediacompounds":[{"compound_ref":"kbase/default/compounds/id/cpd00205",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00242","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00048","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00009",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00007","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00013","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00971",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00067","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00001","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00036",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00100","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00023","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00027",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd10516","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00058","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00099",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00137","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00063","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00254",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00030","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00034","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00149",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00244","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd10515","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd11574",
"concentration":0.001,"maxFlux":100,"minFlux":-100}],"name":"7H9","source_id":"7H9","type":"unspecified"}
#weka has the object saved in the setupclass method
mediaObject = self.getWsClient().save_objects({'workspace': self.getWsName(),'objects': [{'name':'Mediain',
'type':'KBaseBiochem.Media',
'data':testMedia }]
})[0]
# Prepare test objects in workspace if needed using
#
# Run your method by
# ret = self.getImpl().your_method(self.getContext(), parameters...)
#
# Check returned data with
# self.assertEqual(ret[...], ...) or other unittest methods
ret = self.serviceImpl.run_Coveringarray(self.ctx, {'workspace_name': self.wsName,'option_0':"2",
'container_object': [{"option_1":"Firefox", "option_2":["on","off"]},
{"option_1":"Network", "option_2":["on","off"]},
{"option_1":"Feature", "option_2":["ready","unready","unsure"]},{"option_1":"os", "option_2":["low","medium","high","very high"]}
],
'input_media':'','evaluation_options':'',
'output_media':'matrixout',
'output_json_check':1,
'output_media_check':0})
arrayValid = int(subprocess.check_output(['/kb/module/./checkpairs','/kb/module/anneal.out']))
self.assertEqual(arrayValid,0,"Produced incorrect coverage array")
def testMediaInput(self):
testMedia = {"__VERSION__":1,"id":"kb|media.664","isDefined":0,"isMinimal":0,"mediacompounds":[{"compound_ref":"kbase/default/compounds/id/cpd00205",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00242","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00048","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00009",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00007","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00013","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00971",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00067","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00001","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00036",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00100","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00023","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00027",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd10516","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00058","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00099",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00137","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00063","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00254",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00030","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00034","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00149",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00244","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd10515","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd11574",
"concentration":0.001,"maxFlux":100,"minFlux":-100}],"name":"7H9","source_id":"7H9","type":"unspecified"}
#weka has the object saved in the setupclass method
mediaObject = self.getWsClient().save_objects({'workspace': self.getWsName(),'objects': [{'name':'Mediain',
'type':'KBaseBiochem.Media',
'data':testMedia }]
})[0]
ret = self.serviceImpl.run_Coveringarray(self.ctx, {'workspace_name': self.wsName, 'container_object':[{'option_1':'appendedmedia','option_2':['apoption1','apoption2']}],
'option_0':"2",'input_media':mediaObject[0],'evaluation_options':'append_media','output_media':'matrixout',
'output_json_check':1,
'output_media_check':0})
arrayValid = int(subprocess.check_output(['/kb/module/./checkpairs','/kb/module/anneal.out']))
self.assertEqual(arrayValid,0,"Produced incorrect coverage array")
#identify consistent member of tool output that indicates success
#self.assertEqual(ret, "OK")
def testManualandMediaInputExclusive(self):
testMedia = {"__VERSION__":1,"id":"kb|media.664","isDefined":0,"isMinimal":0,"mediacompounds":[{"compound_ref":"kbase/default/compounds/id/cpd00205",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00242","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00048","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00009",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00007","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00013","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00971",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00067","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00001","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00036",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00100","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00023","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00027",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd10516","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00058","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00099",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00137","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00063","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00254",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00030","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00034","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00149",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00244","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd10515","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd11574",
"concentration":0.001,"maxFlux":100,"minFlux":-100}],"name":"7H9","source_id":"7H9","type":"unspecified"}
#weka has the object saved in the setupclass method
mediaObject = self.getWsClient().save_objects({'workspace': self.getWsName(),'objects': [{'name':'Mediain',
'type':'KBaseBiochem.Media',
'data':testMedia }]
})[0]
ret = self.serviceImpl.run_Coveringarray(self.ctx, {'workspace_name': self.wsName,'option_0':"2",
'container_object': [{"option_1":"cpd00007", "option_2":["100","0"]},
{"option_1":"cpd00009", "option_2":["100","0"],'output_media':'matrixout'}
],
'input_media':mediaObject[0],'evaluation_options':'isolate_media','output_media':'matrixout',
'output_json_check':1,
'output_media_check':1})
arrayValid = int(subprocess.check_output(['/kb/module/./checkpairs','/kb/module/anneal.out']))
self.assertEqual(arrayValid,0,"Produced incorrect coverage array")
def testManualandMediaInputInclusive(self):
testMedia = {"__VERSION__":1,"id":"kb|media.664","isDefined":0,"isMinimal":0,"mediacompounds":[{"compound_ref":"kbase/default/compounds/id/cpd00205",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00242","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00048","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00009",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00007","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00013","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00971",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00067","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00001","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00036",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00100","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00023","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00027",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd10516","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00058","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00099",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00137","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00063","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00254",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00030","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd00034","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00149",
"concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd00244","concentration":0.001,"maxFlux":100,"minFlux":-100},
{"compound_ref":"kbase/default/compounds/id/cpd10515","concentration":0.001,"maxFlux":100,"minFlux":-100},{"compound_ref":"kbase/default/compounds/id/cpd11574",
"concentration":0.001,"maxFlux":100,"minFlux":-100}],"name":"7H9","source_id":"7H9","type":"unspecified"}
#weka has the object saved in the setupclass method
mediaObject = self.getWsClient().save_objects({'workspace': self.getWsName(),'objects': [{'name':'Mediain',
'type':'KBaseBiochem.Media',
'data':testMedia }]
})[0]
ret = self.serviceImpl.run_Coveringarray(self.ctx, {'workspace_name': self.wsName,'option_0':"2",
'container_object': [{"option_1":"cpd00009", "option_2":["90","80"]},
{"option_1":"cpd00007", "option_2":["70","60"],'output_media':'matrixout'}
],
'input_media':mediaObject[0], 'evaluation_options':'overwrite_media','output_media':'mediaout',
'output_media_check':1,
'output_json_check':1})
arrayValid = int(subprocess.check_output(['/kb/module/./checkpairs','/kb/module/anneal.out']))
self.assertEqual(arrayValid,0,"Produced incorrect coverage array")
#sef.assertEqual(ret, "OK")
| 88.589744 | 193 | 0.614568 | 2,221 | 20,730 | 5.622692 | 0.10896 | 0.088085 | 0.128123 | 0.184177 | 0.825432 | 0.816144 | 0.805653 | 0.805653 | 0.805653 | 0.796204 | 0 | 0.098923 | 0.202701 | 20,730 | 233 | 194 | 88.969957 | 0.656643 | 0.03864 | 0 | 0.6 | 0 | 0 | 0.474707 | 0.186316 | 0 | 0 | 0 | 0 | 0.021622 | 1 | 0.054054 | false | 0 | 0.048649 | 0.016216 | 0.135135 | 0.005405 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ff3afed53bb7d3063c02272328b7de9d9b95b4f6 | 16,344 | py | Python | nova/tests/unit/api/openstack/compute/test_server_start_stop.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | null | null | null | nova/tests/unit/api/openstack/compute/test_server_start_stop.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | null | null | null | nova/tests/unit/api/openstack/compute/test_server_start_stop.py | bopopescu/nova-token | ec98f69dea7b3e2b9013b27fd55a2c1a1ac6bfb2 | [
"Apache-2.0"
] | 2 | 2017-07-20T17:31:34.000Z | 2020-07-24T02:42:19.000Z | begin_unit
comment|'# Copyright (c) 2012 Midokura Japan K.K.'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Licensed under the Apache License, Version 2.0 (the "License"); you may'
nl|'\n'
comment|'# not use this file except in compliance with the License. You may obtain'
nl|'\n'
comment|'# a copy of the License at'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# http://www.apache.org/licenses/LICENSE-2.0'
nl|'\n'
comment|'#'
nl|'\n'
comment|'# Unless required by applicable law or agreed to in writing, software'
nl|'\n'
comment|'# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT'
nl|'\n'
comment|'# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the'
nl|'\n'
comment|'# License for the specific language governing permissions and limitations'
nl|'\n'
comment|'# under the License.'
nl|'\n'
nl|'\n'
name|'from'
name|'mox3'
name|'import'
name|'mox'
newline|'\n'
name|'import'
name|'six'
newline|'\n'
name|'import'
name|'webob'
newline|'\n'
nl|'\n'
name|'from'
name|'oslo_policy'
name|'import'
name|'policy'
name|'as'
name|'oslo_policy'
newline|'\n'
nl|'\n'
name|'from'
name|'nova'
op|'.'
name|'api'
op|'.'
name|'openstack'
op|'.'
name|'compute'
name|'import'
name|'extension_info'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'api'
op|'.'
name|'openstack'
op|'.'
name|'compute'
name|'import'
name|'servers'
name|'as'
name|'server_v21'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'compute'
name|'import'
name|'api'
name|'as'
name|'compute_api'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'exception'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'policy'
newline|'\n'
name|'from'
name|'nova'
name|'import'
name|'test'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'tests'
op|'.'
name|'unit'
op|'.'
name|'api'
op|'.'
name|'openstack'
name|'import'
name|'fakes'
newline|'\n'
name|'from'
name|'nova'
op|'.'
name|'tests'
name|'import'
name|'uuidsentinel'
name|'as'
name|'uuids'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_instance_get
name|'def'
name|'fake_instance_get'
op|'('
name|'context'
op|','
name|'instance_id'
op|','
nl|'\n'
name|'columns_to_join'
op|'='
name|'None'
op|','
name|'use_slave'
op|'='
name|'False'
op|')'
op|':'
newline|'\n'
indent|' '
name|'result'
op|'='
name|'fakes'
op|'.'
name|'stub_instance'
op|'('
name|'id'
op|'='
number|'1'
op|','
name|'uuid'
op|'='
name|'instance_id'
op|')'
newline|'\n'
name|'result'
op|'['
string|"'created_at'"
op|']'
op|'='
name|'None'
newline|'\n'
name|'result'
op|'['
string|"'deleted_at'"
op|']'
op|'='
name|'None'
newline|'\n'
name|'result'
op|'['
string|"'updated_at'"
op|']'
op|'='
name|'None'
newline|'\n'
name|'result'
op|'['
string|"'deleted'"
op|']'
op|'='
number|'0'
newline|'\n'
name|'result'
op|'['
string|"'info_cache'"
op|']'
op|'='
op|'{'
string|"'network_info'"
op|':'
string|"'[]'"
op|','
nl|'\n'
string|"'instance_uuid'"
op|':'
name|'result'
op|'['
string|"'uuid'"
op|']'
op|'}'
newline|'\n'
name|'return'
name|'result'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_start_stop_not_ready
dedent|''
name|'def'
name|'fake_start_stop_not_ready'
op|'('
name|'self'
op|','
name|'context'
op|','
name|'instance'
op|')'
op|':'
newline|'\n'
indent|' '
name|'raise'
name|'exception'
op|'.'
name|'InstanceNotReady'
op|'('
name|'instance_id'
op|'='
name|'instance'
op|'['
string|'"uuid"'
op|']'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_start_stop_locked_server
dedent|''
name|'def'
name|'fake_start_stop_locked_server'
op|'('
name|'self'
op|','
name|'context'
op|','
name|'instance'
op|')'
op|':'
newline|'\n'
indent|' '
name|'raise'
name|'exception'
op|'.'
name|'InstanceIsLocked'
op|'('
name|'instance_uuid'
op|'='
name|'instance'
op|'['
string|"'uuid'"
op|']'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|function|fake_start_stop_invalid_state
dedent|''
name|'def'
name|'fake_start_stop_invalid_state'
op|'('
name|'self'
op|','
name|'context'
op|','
name|'instance'
op|')'
op|':'
newline|'\n'
indent|' '
name|'raise'
name|'exception'
op|'.'
name|'InstanceIsLocked'
op|'('
name|'instance_uuid'
op|'='
name|'instance'
op|'['
string|"'uuid'"
op|']'
op|')'
newline|'\n'
nl|'\n'
nl|'\n'
DECL|class|ServerStartStopTestV21
dedent|''
name|'class'
name|'ServerStartStopTestV21'
op|'('
name|'test'
op|'.'
name|'TestCase'
op|')'
op|':'
newline|'\n'
DECL|variable|start_policy
indent|' '
name|'start_policy'
op|'='
string|'"os_compute_api:servers:start"'
newline|'\n'
DECL|variable|stop_policy
name|'stop_policy'
op|'='
string|'"os_compute_api:servers:stop"'
newline|'\n'
nl|'\n'
DECL|member|setUp
name|'def'
name|'setUp'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'super'
op|'('
name|'ServerStartStopTestV21'
op|','
name|'self'
op|')'
op|'.'
name|'setUp'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'_setup_controller'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'req'
op|'='
name|'fakes'
op|'.'
name|'HTTPRequest'
op|'.'
name|'blank'
op|'('
string|"''"
op|')'
newline|'\n'
nl|'\n'
DECL|member|_setup_controller
dedent|''
name|'def'
name|'_setup_controller'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'ext_info'
op|'='
name|'extension_info'
op|'.'
name|'LoadedExtensionInfo'
op|'('
op|')'
newline|'\n'
name|'self'
op|'.'
name|'controller'
op|'='
name|'server_v21'
op|'.'
name|'ServersController'
op|'('
nl|'\n'
name|'extension_info'
op|'='
name|'ext_info'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_start
dedent|''
name|'def'
name|'test_start'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'mox'
op|'.'
name|'StubOutWithMock'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'start'"
op|')'
newline|'\n'
name|'compute_api'
op|'.'
name|'API'
op|'.'
name|'start'
op|'('
name|'mox'
op|'.'
name|'IgnoreArg'
op|'('
op|')'
op|','
name|'mox'
op|'.'
name|'IgnoreArg'
op|'('
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'mox'
op|'.'
name|'ReplayAll'
op|'('
op|')'
newline|'\n'
nl|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_start_server'
op|'('
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_start_policy_failed
dedent|''
name|'def'
name|'test_start_policy_failed'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'rules'
op|'='
op|'{'
nl|'\n'
name|'self'
op|'.'
name|'start_policy'
op|':'
string|'"project_id:non_fake"'
nl|'\n'
op|'}'
newline|'\n'
name|'policy'
op|'.'
name|'set_rules'
op|'('
name|'oslo_policy'
op|'.'
name|'Rules'
op|'.'
name|'from_dict'
op|'('
name|'rules'
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'exc'
op|'='
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'exception'
op|'.'
name|'PolicyNotAuthorized'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_start_server'
op|','
nl|'\n'
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIn'
op|'('
name|'self'
op|'.'
name|'start_policy'
op|','
name|'exc'
op|'.'
name|'format_message'
op|'('
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_start_not_ready
dedent|''
name|'def'
name|'test_start_not_ready'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'start'"
op|','
name|'fake_start_stop_not_ready'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPConflict'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_start_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_start_locked_server
dedent|''
name|'def'
name|'test_start_locked_server'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'start'"
op|','
name|'fake_start_stop_locked_server'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPConflict'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_start_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_start_invalid_state
dedent|''
name|'def'
name|'test_start_invalid_state'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'start'"
op|','
name|'fake_start_stop_invalid_state'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'ex'
op|'='
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPConflict'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_start_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIn'
op|'('
string|"'is locked'"
op|','
name|'six'
op|'.'
name|'text_type'
op|'('
name|'ex'
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_stop
dedent|''
name|'def'
name|'test_stop'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'mox'
op|'.'
name|'StubOutWithMock'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'stop'"
op|')'
newline|'\n'
name|'compute_api'
op|'.'
name|'API'
op|'.'
name|'stop'
op|'('
name|'mox'
op|'.'
name|'IgnoreArg'
op|'('
op|')'
op|','
name|'mox'
op|'.'
name|'IgnoreArg'
op|'('
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'mox'
op|'.'
name|'ReplayAll'
op|'('
op|')'
newline|'\n'
nl|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'stop'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_stop_server'
op|'('
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_stop_policy_failed
dedent|''
name|'def'
name|'test_stop_policy_failed'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'rules'
op|'='
op|'{'
nl|'\n'
name|'self'
op|'.'
name|'stop_policy'
op|':'
string|'"project_id:non_fake"'
nl|'\n'
op|'}'
newline|'\n'
name|'policy'
op|'.'
name|'set_rules'
op|'('
name|'oslo_policy'
op|'.'
name|'Rules'
op|'.'
name|'from_dict'
op|'('
name|'rules'
op|')'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'stop'
op|'='
string|'""'
op|')'
newline|'\n'
name|'exc'
op|'='
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'exception'
op|'.'
name|'PolicyNotAuthorized'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_stop_server'
op|','
nl|'\n'
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIn'
op|'('
name|'self'
op|'.'
name|'stop_policy'
op|','
name|'exc'
op|'.'
name|'format_message'
op|'('
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_stop_not_ready
dedent|''
name|'def'
name|'test_stop_not_ready'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'stop'"
op|','
name|'fake_start_stop_not_ready'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'stop'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPConflict'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_stop_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_stop_locked_server
dedent|''
name|'def'
name|'test_stop_locked_server'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'stop'"
op|','
name|'fake_start_stop_locked_server'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'stop'
op|'='
string|'""'
op|')'
newline|'\n'
name|'ex'
op|'='
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPConflict'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_stop_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertIn'
op|'('
string|"'is locked'"
op|','
name|'six'
op|'.'
name|'text_type'
op|'('
name|'ex'
op|')'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_stop_invalid_state
dedent|''
name|'def'
name|'test_stop_invalid_state'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'self'
op|'.'
name|'stub_out'
op|'('
string|"'nova.db.instance_get_by_uuid'"
op|','
name|'fake_instance_get'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'stubs'
op|'.'
name|'Set'
op|'('
name|'compute_api'
op|'.'
name|'API'
op|','
string|"'stop'"
op|','
name|'fake_start_stop_invalid_state'
op|')'
newline|'\n'
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPConflict'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_stop_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_start_with_bogus_id
dedent|''
name|'def'
name|'test_start_with_bogus_id'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'body'
op|'='
name|'dict'
op|'('
name|'start'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPNotFound'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_start_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
nl|'\n'
DECL|member|test_stop_with_bogus_id
dedent|''
name|'def'
name|'test_stop_with_bogus_id'
op|'('
name|'self'
op|')'
op|':'
newline|'\n'
indent|' '
name|'body'
op|'='
name|'dict'
op|'('
name|'stop'
op|'='
string|'""'
op|')'
newline|'\n'
name|'self'
op|'.'
name|'assertRaises'
op|'('
name|'webob'
op|'.'
name|'exc'
op|'.'
name|'HTTPNotFound'
op|','
nl|'\n'
name|'self'
op|'.'
name|'controller'
op|'.'
name|'_stop_server'
op|','
name|'self'
op|'.'
name|'req'
op|','
name|'uuids'
op|'.'
name|'instance'
op|','
name|'body'
op|')'
newline|'\n'
dedent|''
dedent|''
endmarker|''
end_unit
| 12.325792 | 88 | 0.60952 | 2,453 | 16,344 | 3.949857 | 0.071341 | 0.201878 | 0.085664 | 0.098256 | 0.853339 | 0.830839 | 0.799257 | 0.735576 | 0.71865 | 0.686139 | 0 | 0.001422 | 0.096366 | 16,344 | 1,325 | 89 | 12.335094 | 0.654614 | 0 | 0 | 0.940377 | 0 | 0 | 0.358725 | 0.053965 | 0 | 0 | 0 | 0 | 0.010566 | 0 | null | null | 0 | 0.009057 | null | null | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
ff3b1827b738fe5764e1717591fd89a6f63efa9f | 86,263 | py | Python | pippi_script.py | patscott/pippi | 4818fa11abe6c9b0b62f6e31036ff975eb120bca | [
"Unlicense"
] | 10 | 2015-09-08T15:38:20.000Z | 2019-04-29T12:46:37.000Z | pippi_script.py | patscott/pippi | 4818fa11abe6c9b0b62f6e31036ff975eb120bca | [
"Unlicense"
] | 7 | 2016-11-18T13:28:24.000Z | 2021-07-01T01:43:23.000Z | pippi_script.py | patscott/pippi | 4818fa11abe6c9b0b62f6e31036ff975eb120bca | [
"Unlicense"
] | 3 | 2019-07-18T04:06:47.000Z | 2021-08-01T10:43:05.000Z |
#############################################################
# pippi: parse it, plot it
# ------------------------
# Program for creating plotting scripts for pippi.
#
# Author: Pat Scott (patscott@physics.mcgill.ca)
# Originally developed: March 2012
#############################################################
left_margin = 0.16
right_margin = 0.03
top_margin = 0.05
bottom_margin = 0.16
plot_scale = 1.1
import subprocess
import os
from pippi_utils import *
from pippi_read import *
#Define pip file entries required from parsing
parsedir = dataObject('parse_dir',safe_string)
# Define script-specific pip file entries
scriptdir = dataObject('script_dir',safe_string)
doComparison = dataObject('plot_comparison',boolean)
postMeanOnPost = dataObject('plot_posterior_mean_on_posterior_pdf',boolean)
postMeanOnProf = dataObject('plot_posterior_mean_on_profile_like',boolean)
bestFitOnPost = dataObject('plot_best_fit_on_posterior_pdf',boolean)
bestFitOnProf = dataObject('plot_best_fit_on_profile_like',boolean)
doLegend1D = dataObject('legend_on_1D',int_list)
doLegend2D = dataObject('legend_on_2D',intuple_list)
legendLoc1D = dataObject('legend_locations_1D',string_dictionary)
legendLoc2D = dataObject('legend_locations_2D',int_pair_string_dictionary)
doKey1D = dataObject('key_on_1D',int_list)
doKey2D = dataObject('key_on_2D',intuple_list)
keyLoc1D = dataObject('key_locations_1D',string_dictionary)
keyLoc2D = dataObject('key_locations_2D',int_pair_string_dictionary)
doColourbar = dataObject('plot_colourbar_2D',intuple_list)
doHistograms = dataObject('plot_as_histograms_1D',boolean)
legendLines = dataObject('extra_legend_lines',string_list)
plotSize = dataObject('plot_size',string)
blame = dataObject('blame',string)
logoFile = dataObject('logo_file',string)
logoLoc = dataObject('logo_loc',floatuple_list)
logoWidth = dataObject('logo_width',floater)
colours = dataObject('colour_scheme',internal)
axisRanges = dataObject('axis_ranges',floatuple_dictionary)
yAxisAngle = dataObject('yaxis_number_angle',floater)
refPoint = dataObject('reference_point',float_dictionary)
refKey = dataObject('reference_text',string)
keys = keys+[scriptdir,doComparison,postMeanOnPost,postMeanOnProf,bestFitOnPost,
bestFitOnProf,doColourbar,doLegend1D,doLegend2D,legendLoc1D,legendLoc2D,
doHistograms,legendLines,blame,colours,axisRanges,yAxisAngle,refPoint,
refKey,doKey1D,doKey2D,keyLoc1D,keyLoc2D,parsedir,logoFile,logoLoc,logoWidth]
# Define pip file entries to be read from savedkeys file
labels = dataObject('quantity_labels',string_dictionary)
dataRanges = dataObject('data_ranges',floatuple_dictionary)
lookupKeys = dataObject('lookup_keys',int_dictionary)
# Constants
blameFractionalVerticalOffset = 1.2e-2
PosteriorIsMainInComboPlot = True
likeColourbarString = 'Profile likelihood ratio $\Lambda=\mathcal{L}/\mathcal{L}_\mathrm{max}$'
postColourbarString = 'Relative probability $P/P_\mathrm{max}$'
defaultLegendLocation = 'bl'
defaultKeyLocation = 'tr'
defaultRefKey = 'Ref.\ point'
keyYSep = 0.055
keyXSep = 0.04
keyYVals = {'t':[0.94 - x*keyYSep for x in range(3)], 'c':[0.44 + x*keyYSep for x in range(3)], 'b':[0.065 + x*keyYSep for x in range(3)]}
keyXVals = {'r':[0.74 + x*keyXSep for x in range(2)], 'c':[0.45 + x*keyXSep for x in range(2)], 'l':[0.06 + x*keyXSep for x in range(2)]}
def script(filename):
# input: filename = the name of the pip file
print
# Parse pip file
getIniData(filename,keys)
# Make sure that comparison is turned off if comparison filename is missing
if doComparison.value and secChain.value is None:
print ' Warning: comparison curves requested but no comparison file specified.\n Skipping comparison...\n'
doComparison.value = False
# Work out where the parse output is located
if parsedir.value is None:
# No parse_dir; default to searching the directory containing chain(s)
parseFiledir = re.sub(r'/.*?$', '/', mainChain.value)
else:
# Search in parse_dir
parseFiledir = parsedir.value+'/'
# Work out where the script output is to be located
if scriptdir.value is None:
# No script_dir; default to parse directory
baseFiledir = parseFiledir
else:
# Save in script_dir
baseFiledir = scriptdir.value+'/'
# Make sure script_dir exists, make it if not
safe_dir(scriptdir.value)
# Work out how to reference the parse dir from the script dir
if parseFiledir[0] == '/' or parseFiledir[0] == '~':
# The parse output path is absolute; easy-peasy
parseFiledirFromScriptFiledir = parseFiledir
else:
# The parse output path is a relative one
if baseFiledir[0] == '/' or baseFiledir[0] == '~':
# The script output is to be placed in an absolute path; need to convert the parse path to absolute too
parseFiledirFromScriptFiledir = os.getcwd() + '/' + parseFiledir
else:
# The script output is also to be placed in a relative path
parseFiledirFromScriptFiledir = re.sub(r'.+?/', '../', baseFiledir+'/') + parseFiledir
# Locate and scale logo (if any)
if logoFile.value is not None:
if logoFile.value == 'pippi': logoFile.value = sys.path[0]+'/pippi'
# Work out how to reference the logo file from the script dir
if logoFile.value[0] != '/':
if baseFiledir[0] == '/':
# The script output is to be placed in an absolute path; need to convert the logo path to absolute too
logoFile.value = os.getcwd() + '/' + logoFile.value
else:
# The script output is also to be placed in a relative path
logoFile.value = re.sub(r'.+?/', '../', baseFiledir+'/') + logoFile.value
# Strip extensions off chain filenames
baseFilename = baseFiledir + re.sub(r'.*/|\..?.?.?$', '', mainChain.value)
parseFilename = parseFiledir + re.sub(r'.*/|\..?.?.?$', '', mainChain.value)
parseFilenameFromScriptFiledir = parseFiledirFromScriptFiledir + re.sub(r'.*/|\..?.?.?$', '', mainChain.value)
if doComparison.value:
secParseFilename = parseFiledir + re.sub(r'.*/|\..?.?.?$', '', secChain.value) + '_comparison'
secParseFilenameFromScriptFiledir = parseFiledirFromScriptFiledir + re.sub(r'.*/|\..?.?.?$', '', secChain.value) + '_comparison'
# Retrieve labels and data ranges saved in earlier parsing run
getIniData([parseFilename+'_savedkeys.pip'],[labels,dataRanges,lookupKeys])
#Work out whether to do posteriors and check that flags match up
if doPosterior.value and not any(x in labels.value for x in permittedMults):
print ' Warning: do_posterior_pdf = T but no multiplicity in chain labels.\n Skipping posterior PDF...'
doPosterior.value = False
# set colour scheme if it is undefined
if colours.value is None: colours.value = basic
# Create 1D plotting scripts
if oneDplots.value is not None:
# Determine whether histograms are required or not
histString = '' if doHistograms.value is None or not doHistograms.value else 'hist'
# Loop over requested plots
for plot in oneDplots.value:
print ' Writing scripts for 1D plots of quantity ',plot
# Set up filenames
currentBase = baseFilename+'_'+str(plot)
currentParse = parseFilenameFromScriptFiledir+'_'+str(plot)
currentBaseMinimal = re.sub(r'.*/', '', currentBase)
if doComparison.value: currentSecParse = secParseFilenameFromScriptFiledir+'_'+str(plot)
# Get plot limits
xtrema = dictFallback(axisRanges,dataRanges,plot)
xRange = xtrema[1] - xtrema[0]
ytrema = [0.0,1.0]
yRange = 1.0
# Locate and scale logo (if any)
if logoFile.value is not None:
logoCoords = [xtrema[0]+logoLoc.value[0][0]*xRange,logoLoc.value[0][1]]
logoString = '\'\\includegraphics[width = '+str(logoWidth.value*8.8)+'cm]{'+logoFile.value+'}\''
# Determine reference point
if refPoint.value is not None and plot in refPoint.value:
plotRef = True
refString = ' --draw-marker '+str(refPoint.value[plot])+','+str(yRange*colours.value.referenceMarkerInnerScale/40.0)+' '+\
colours.value.referenceMarkerInner+' /color \''+colours.value.referenceMarkerInnerColour+\
'\' /scale '+str(colours.value.referenceMarkerInnerScale)+' \\\n'+\
' --draw-marker '+str(refPoint.value[plot])+','+str(yRange*colours.value.referenceMarkerOuterScale/40.0)+' '+\
colours.value.referenceMarkerOuter+' /color \''+colours.value.referenceMarkerOuterColour+\
'\' /scale '+str(colours.value.referenceMarkerOuterScale)+' \\\n'
else:
plotRef = False
# Determine plot size
if plotSize.value is None or plotSize.value is '':
plotSizeInternal = '11cm x 4in'
else:
plotSizeInternal = plotSize.value
# Make profile likelihood plotting scripts
if doProfile.value:
# Get contours
if contours1D.value is not None:
contourLevels = getContours(parseFilename,plot,'like')
# Determine keys
keyString = ''
if doKey1D.value is not None and plot in doKey1D.value:
# Get gross key location
try:
keyLoc = keyLoc1D.value[plot]
except (KeyError, TypeError):
keyLoc = defaultKeyLocation
# Get text to be used for reference point
refText = defaultRefKey if refKey.value is None else refKey.value
# Get x and y coordinates for 3 possible keys (for markers and text)
yVals = ytrema[0] + np.array(keyYVals[keyLoc[0]])*yRange
xVals = xtrema[0] + np.array(keyXVals[keyLoc[1]])*xRange
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter, colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale, refText, colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour, colours.value.referenceMarkerInnerScale/
colours.value.referenceMarkerOuterScale])
# Get details of key for posterior mean
if postMeanOnProf.value: markers.append([colours.value.mainPostMeanMarker, colours.value.mainPostMeanColour1D,
colours.value.mainPostMeanMarkerScale, 'Mean'])
# Get details of key for best fit
if bestFitOnProf.value: markers.append([colours.value.mainBestFitMarker, colours.value.mainBestFitColour1D,
colours.value.mainBestFitMarkerScale, 'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[2] /= 1.5
if key[2] > 1.0: key[2] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 7: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[4]+' /color \''+\
key[5]+'\' /scale '+str(key[6]*key[2])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /color \''+key[1]+'\' /scale '+str(key[2])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[3]+'\' /color \''+colours.value.keyTextColour1D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(currentBase+'_like1D.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_like1D')
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
outfile.write(' --frame-margins '+str(left_margin)+','
+str(right_margin)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange 0:1\\\n')
outfile.write(' --ylabel \'Profile likelihood ratio $\Lambda=\mathcal{L}/\mathcal{L}_\mathrm{max}$\' /shift 2.1\\\n')
outfile.write(' --xlabel \''+labels.value[plot]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.15')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write('\\\n')
if contours1D is not None:
for i, contour in enumerate(contourLevels):
outfile.write(' --draw-line '+str(xtrema[0])+','+contour+' '+str(xtrema[1])+','+contour+' /color \'Black\' '+
'/style Dashes /width '+str(float(colours.value.lineWidth1D)*0.5)+'\\\n')
outfile.write(' --draw-text '+str(xtrema[0]+0.045*(xtrema[1]-xtrema[0]))+','+str(float(contour)+0.005)+' \''+str(contours1D.value[i])+
'\%CL\' /color \'Black\' /scale 0.5 /justification left /alignment bottom\\\n')
if doComparison.value:
# Do everything for comparison chain
outfile.write(' --plot '+currentSecParse+'_like1D'+histString+'.ct2@1:2 /fill xaxis /fill-transparency '+colours.value.fillTransparency1D+
' /fill-color '+colours.value.comparisonProfColour1D+' /color '+colours.value.comparisonProfColour1D+
' /line-style '+colours.value.comparison1DLineStyle+' /line-width '+colours.value.lineWidth1D+'\\\n')
if bestFitOnProf.value and colours.value.comparisonBestFitMarker is not None:
# Get best-fit point and plot it
bestFit = getCentralVal(secParseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit)+','+str(yRange*colours.value.comparisonBestFitMarkerScale/40.0)+' '+
colours.value.comparisonBestFitMarker+' /color \''+colours.value.comparisonBestFitColour+
'\' /scale '+str(colours.value.comparisonBestFitMarkerScale)+' \\\n')
if postMeanOnProf.value and colours.value.comparisonPostMeanMarker is not None:
# Get posterior mean and plot it
postMean = getCentralVal(secParseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_profile_like = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean)+','+str(yRange*colours.value.comparisonPostMeanMarkerScale/40.0)+' '+
colours.value.comparisonPostMeanMarker+' /color \''+colours.value.comparisonPostMeanColour+
'\' /scale '+str(colours.value.comparisonPostMeanMarkerScale)+' \\\n')
outfile.write(' --plot '+currentParse+'_like1D'+histString+'.ct2@1:2 /fill xaxis /fill-transparency '+colours.value.fillTransparency1D+
' /fill-color '+colours.value.mainProfColour1D+' /color '+colours.value.mainProfColour1D+
' /line-style '+colours.value.main1DLineStyle+' /line-width '+colours.value.lineWidth1D+'\\\n')
if doLegend1D.value is not None and plot in doLegend1D.value:
# Write legend
try:
legendLocation = legendLoc1D.value[plot]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour1D+'\'\\\n')
outfile.write(' --legend-line \'Prof.~likelihood\' /color \''+colours.value.legendTextColour1D+'\'\\\n')
if bestFitOnProf.value:
# Get best-fit point and plot it
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit)+','+str(yRange*colours.value.mainBestFitMarkerScale/40.0)+' '+
colours.value.mainBestFitMarker+' /color \''+colours.value.mainBestFitColour1D+
'\' /scale '+str(colours.value.mainBestFitMarkerScale)+' \\\n')
if postMeanOnProf.value:
# Get posterior mean and plot it
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_profile_like = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean)+','+str(yRange*colours.value.mainPostMeanMarkerScale/40.0)+' '+
colours.value.mainPostMeanMarker+' /color \''+colours.value.mainPostMeanColour1D+
'\' /scale '+str(colours.value.mainPostMeanMarkerScale)+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour1D+'\'\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_like1D.bsh', shell=True)
# Make posterior pdf plotting scripts
if doPosterior.value:
# Get contours
if contours1D.value is not None:
mainContourLevels = getContours(parseFilename,plot,'post')
if doComparison.value: secContourLevels = getContours(secParseFilename,plot,'post')
# Determine keys
keyString = ''
if doKey1D.value is not None and plot in doKey1D.value:
# Get gross key location
try:
keyLoc = keyLoc1D.value[plot]
except (KeyError, TypeError):
keyLoc = defaultKeyLocation
# Get text to be used for reference point
refText = defaultRefKey if refKey.value is None else refKey.value
# Get x and y coordinates for 3 possible keys (for markers and text)
yVals = ytrema[0] + np.array(keyYVals[keyLoc[0]])*yRange
xVals = xtrema[0] + np.array(keyXVals[keyLoc[1]])*xRange
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter, colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale, refText, colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour, colours.value.referenceMarkerInnerScale/
colours.value.referenceMarkerOuterScale])
# Get details of key for posterior mean
if postMeanOnPost.value: markers.append([colours.value.mainPostMeanMarker, colours.value.mainPostMeanColour1D,
colours.value.mainPostMeanMarkerScale, 'Mean'])
# Get details of key for best fit
if bestFitOnPost.value: markers.append([colours.value.mainBestFitMarker, colours.value.mainBestFitColour1D,
colours.value.mainBestFitMarkerScale, 'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[2] /= 1.5
if key[2] > 1.0: key[2] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 7: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[4]+' /color \''+\
key[5]+'\' /scale '+str(key[6]*key[2])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /color \''+key[1]+'\' /scale '+str(key[2])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[3]+'\' /color \''+colours.value.keyTextColour1D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(currentBase+'_post1D.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_post1D')
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
outfile.write(' --frame-margins '+str(left_margin)+','
+str(right_margin)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange 0:1\\\n')
outfile.write(' --ylabel \'Relative probability $P/P_\mathrm{max}$\' /shift 2.1\\\n')
outfile.write(' --xlabel \''+labels.value[plot]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.15')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write('\\\n')
if contours1D is not None:
for i, contour in enumerate(mainContourLevels):
outfile.write(' --draw-line '+str(xtrema[0])+','+contour+' '+str(xtrema[1])+','+contour+' /color \''+colours.value.mainPostColour1D+
'\' /style Dashes /width '+str(float(colours.value.lineWidth1D)*0.5)+'\\\n')
outfile.write(' --draw-text '+str(xtrema[0]+0.045*(xtrema[1]-xtrema[0]))+','+str(float(contour)+0.005)+' \''+str(contours1D.value[i])+
'\%CR\' /color \''+colours.value.mainPostColour1D+'\' /scale 0.5 /justification left /alignment bottom\\\n')
if doComparison.value:
# Do everything for comparison chain
if contours1D is not None:
for i, contour in enumerate(secContourLevels):
outfile.write(' --draw-line '+str(xtrema[0])+','+contour+' '+str(xtrema[1])+','+contour+' /color \''+colours.value.comparisonPostColour1D+
'\' /style Dashes /width '+str(float(colours.value.lineWidth1D)*0.5)+'\\\n')
outfile.write(' --draw-text '+str(xtrema[0]+0.045*(xtrema[1]-xtrema[0]))+','+str(float(contour)+0.005)+' \''+str(contours1D.value[i])+
'\%CR\' /color \''+colours.value.comparisonPostColour1D+'\' /scale 0.5 /justification left /alignment bottom\\\n')
outfile.write(' --plot '+currentSecParse+'_post1D'+histString+'.ct2@1:2 /fill xaxis /fill-transparency '+colours.value.fillTransparency1D+
' /fill-color '+colours.value.comparisonPostColour1D+' /color '+colours.value.comparisonPostColour1D+
' /line-style '+colours.value.comparison1DLineStyle+' /line-width '+colours.value.lineWidth1D+'\\\n')
if bestFitOnPost.value and colours.value.comparisonBestFitMarker is not None:
# Get best-fit point and plot it
bestFit = getCentralVal(secParseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit)+','+str(yRange*colours.value.comparisonBestFitMarkerScale/40.0)+' '+
colours.value.comparisonBestFitMarker+' /color \''+colours.value.comparisonBestFitColour+
'\' /scale '+str(colours.value.comparisonBestFitMarkerScale)+' \\\n')
if postMeanOnPost.value and colours.value.comparisonPostMeanMarker is not None:
# Get posterior mean and plot it
postMean = getCentralVal(secParseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_posterior_pdf = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean)+','+str(yRange*colours.value.comparisonPostMeanMarkerScale/40.0)+' '+
colours.value.comparisonPostMeanMarker+' /color \''+colours.value.comparisonPostMeanColour+
'\' /scale '+str(colours.value.comparisonPostMeanMarkerScale)+' \\\n')
outfile.write(' --plot '+currentParse+'_post1D'+histString+'.ct2@1:2 /fill xaxis /fill-transparency '+colours.value.fillTransparency1D+
' /fill-color '+colours.value.mainPostColour1D+' /color '+colours.value.mainPostColour1D+
' /line-style '+colours.value.main1DLineStyle+' /line-width '+colours.value.lineWidth1D+'\\\n')
if doLegend1D.value is not None and plot in doLegend1D.value:
# Write legend
try:
legendLocation = legendLoc1D.value[plot]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour1D+'\'\\\n')
outfile.write(' --legend-line \'Marg.~posterior\' /color \''+colours.value.legendTextColour1D+'\'\\\n')
if bestFitOnPost.value:
# Get best-fit point and plot it
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit)+','+str(yRange*colours.value.mainBestFitMarkerScale/40.0)+' '+
colours.value.mainBestFitMarker+' /color \''+colours.value.mainBestFitColour1D+
'\' /scale '+str(colours.value.mainBestFitMarkerScale)+' \\\n')
if postMeanOnPost.value:
# Get posterior mean and plot it
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_posterior_pdf = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean)+','+str(yRange*colours.value.mainPostMeanMarkerScale/40.0)+' '+
colours.value.mainPostMeanMarker+' /color \''+colours.value.mainPostMeanColour1D+
'\' /scale '+str(colours.value.mainPostMeanMarkerScale)+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour1D+'\'\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_post1D.bsh', shell=True)
# Make profile-posterior comparison plotting scripts
if doProfile.value and doPosterior.value:
bestFitData = [colours.value.mainBestFitMarker, colours.value.mainBestFitColour1D, colours.value.mainBestFitMarkerScale, colours.value.mainProfColour1D]
postMeanData = [colours.value.mainPostMeanMarker, colours.value.mainPostMeanColour1D, colours.value.mainPostMeanMarkerScale, colours.value.mainPostColour1D]
# Work out which is the main and which is the comparison
if PosteriorIsMainInComboPlot:
[main, sec] = ['post', 'like']
[mainData, secData] = [postMeanData, bestFitData]
else:
[main, sec] = ['like', 'post']
[mainData, secData] = [bestFitData, postMeanData]
# Get contours
if contours1D.value is not None:
mainContourLevels = getContours(parseFilename,plot,main)
secContourLevels = getContours(parseFilename,plot,sec)
# Determine keys
keyString = ''
if doKey1D.value is not None and plot in doKey1D.value:
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter, colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale, refText, colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour, colours.value.referenceMarkerInnerScale/
colours.value.referenceMarkerOuterScale])
# Get details of key for posterior mean
markers.append([postMeanData[0], postMeanData[1], postMeanData[2], 'Mean'])
# Get details of key for best fit
markers.append([bestFitData[0], bestFitData[1], bestFitData[2], 'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[2] /= 1.5
if key[2] > 1.0: key[2] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 7: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[4]+' /color \''+\
key[5]+'\' /scale '+str(key[6]*key[2])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /color \''+key[1]+'\' /scale '+str(key[2])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[3]+'\' /color \''+colours.value.keyTextColour1D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(currentBase+'_combo1D.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_combo1D')
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
outfile.write(' --frame-margins '+str(left_margin)+','
+str(right_margin)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange 0:1\\\n')
outfile.write(' --ylabel \'Relative probability $P/P_\mathrm{max}$\' /shift 2.1\\\n')
outfile.write(' --xlabel \''+labels.value[plot]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.15')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write('\\\n')
if contours1D is not None:
if main == 'like':
main_colour = colours.value.mainProfColour1D
main_text = 'CL'
sec_colour = colours.value.mainPostColour1D
sec_text = 'CR'
else:
main_colour = colours.value.mainPostColour1D
main_text = 'CR'
sec_colour = colours.value.mainProfColour1D
sec_text = 'CL'
for i, contour in enumerate(mainContourLevels):
outfile.write(' --draw-line '+str(xtrema[0])+','+contour+' '+str(xtrema[1])+','+contour+' /color \''+main_colour+
'\' /style Dashes /width '+str(float(colours.value.lineWidth1D)*0.5)+'\\\n')
outfile.write(' --draw-text '+str(xtrema[0]+0.045*(xtrema[1]-xtrema[0]))+','+str(float(contour)+0.005)+' \''+str(contours1D.value[i])+
'\%'+main_text+'\' /color \''+main_colour+'\' /scale 0.5 /justification left /alignment bottom\\\n')
for i, contour in enumerate(secContourLevels):
outfile.write(' --draw-line '+str(xtrema[0])+','+contour+' '+str(xtrema[1])+','+contour+' /color \''+sec_colour+
'\' /style Dashes /width '+str(float(colours.value.lineWidth1D)*0.5)+'\\\n')
outfile.write(' --draw-text '+str(xtrema[0]+0.045*(xtrema[1]-xtrema[0]))+','+str(float(contour)+0.005)+' \''+str(contours1D.value[i])+
'\%'+sec_text+'\' /color \''+sec_colour+'\' /scale 0.5 /justification left /alignment bottom\\\n')
# Plot comparison distribution
outfile.write(' --plot '+currentParse+'_'+sec+'1D'+histString+'.ct2@1:2 /fill xaxis /fill-transparency '+colours.value.fillTransparency1D+
' /fill-color '+secData[3]+' /color '+secData[3]+
' /line-style '+colours.value.comparison1DLineStyle+' /line-width '+colours.value.lineWidth1D+'\\\n')
# Plot main distribution
outfile.write(' --plot '+currentParse+'_'+main+'1D'+histString+'.ct2@1:2 /fill xaxis /fill-transparency '+colours.value.fillTransparency1D+
' /fill-color '+mainData[3]+' /color '+mainData[3]+
' /line-style '+colours.value.main1DLineStyle+' /line-width '+colours.value.lineWidth1D+'\\\n')
if doLegend1D.value is not None and plot in doLegend1D.value:
# Write legend
try:
legendLocation = legendLoc1D.value[plot]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour1D+'\'\\\n')
outfile.write(' --legend-line \'Like vs. Posterior\' /color \''+colours.value.legendTextColour1D+'\'\\\n')
# Get best-fit point
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
# Get posterior mean
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
# Always plot both best fit and posterior mean on comparison plot
outfile.write(' --draw-marker '+str(bestFit)+','+str(yRange*bestFitData[2]/40.0)+' '+bestFitData[0]+' /color \''+bestFitData[1]+
'\' /scale '+str(bestFitData[2])+' \\\n')
if postMean: outfile.write(' --draw-marker '+str(postMean)+','+str(yRange*postMeanData[2]/40.0)+' '+postMeanData[0]+' /color \''+postMeanData[1]+
'\' /scale '+str(postMeanData[2])+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour1D+'\'\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_combo1D.bsh', shell=True)
# Create 2D plotting scripts
if twoDplots.value is not None:
# Loop over requested plots
for plot in twoDplots.value:
print ' Writing scripts for 2D plots of quantities ',plot
# Set up filenames
currentBase = baseFilename+'_'+'_'.join([str(x) for x in plot])
currentParse = parseFilenameFromScriptFiledir+'_'+'_'.join([str(x) for x in plot])
currentBaseMinimal = re.sub(r'.*/', '', currentBase)
if doComparison.value: currentSecParse = secParseFilenameFromScriptFiledir+'_'+'_'.join([str(x) for x in plot])
# Get plot limits
xtrema = dictFallback(axisRanges,dataRanges,plot[0])
ytrema = dictFallback(axisRanges,dataRanges,plot[1])
xRange = xtrema[1] - xtrema[0]
yRange = ytrema[1] - ytrema[0]
# Locate and scale logo (if any)
if logoFile.value is not None:
logoCoords = [xtrema[0]+logoLoc.value[0][0]*xRange,ytrema[0]+logoLoc.value[0][1]*yRange]
logoString = '\'\\includegraphics[width = '+str(logoWidth.value*8.8)+'cm]{'+logoFile.value+'}\''
# Determine reference point
if refPoint.value is not None and all([x in refPoint.value for x in plot]):
plotRef = True
refString = ' --draw-marker '+str(refPoint.value[plot[0]])+','+str(refPoint.value[plot[1]])+' '+\
colours.value.referenceMarkerInner+' /color \''+colours.value.referenceMarkerInnerColour+\
'\' /scale '+str(colours.value.referenceMarkerInnerScale)+' \\\n'+\
' --draw-marker '+str(refPoint.value[plot[0]])+','+str(refPoint.value[plot[1]])+' '+\
colours.value.referenceMarkerOuter+' /color \''+colours.value.referenceMarkerOuterColour+\
'\' /scale '+str(colours.value.referenceMarkerOuterScale)+' \\\n'
else:
plotRef = False
# Determine plot size
if plotSize.value is None or plotSize.value is '':
if doColourbar.value is not None and plot in doColourbar.value:
plotSizeInternal = '12.5cm x 4in'
else:
plotSizeInternal = '11cm x 4in'
else:
plotSizeInternal = plotSize.value
# Make profile likelihood plotting scripts
if doProfile.value:
# Get contours
if contours2D.value is not None:
contourLevels = getContours(parseFilename,plot,'like')
# Determine keys
keyString = ''
if doKey2D.value is not None and plot in doKey2D.value:
# Get gross key location
try:
keyLoc = keyLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
keyLoc = defaultKeyLocation
# Get text to be used for reference point
refText = defaultRefKey if refKey.value is None else refKey.value
# Get x and y coordinates for 3 possible keys (for markers and text)
yVals = ytrema[0] + np.array(keyYVals[keyLoc[0]])*yRange
xVals = xtrema[0] + np.array(keyXVals[keyLoc[1]])*xRange
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale,
refText,
colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour,
colours.value.referenceMarkerInnerScale/colours.value.referenceMarkerOuterScale])
# Get details of key for posterior mean
if postMeanOnProf.value: markers.append([colours.value.mainPostMeanMarker,
colours.value.mainPostMeanColour2D,
colours.value.mainPostMeanColourOutline2D,
colours.value.mainPostMeanMarkerScale,
'Mean'])
# Get details of key for best fit
if bestFitOnProf.value: markers.append([colours.value.mainBestFitMarker,
colours.value.mainBestFitColour2D,
colours.value.mainBestFitColourOutline2D,
colours.value.mainBestFitMarkerScale,
'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[3] /= 1.5
if key[3] > 1.0: key[3] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 8: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[5]+' /color \''+\
key[6]+'\' /scale '+str(key[7]*key[3])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /fill-color \''+str(key[1])+'\' /stroke-color \''+str(key[2])+'\' /scale '+str(key[3])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[4]+'\' /color \''+colours.value.keyTextColour2D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(currentBase+'_like2D.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_like2D')
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --frame-margins '+str(left_margin+0.03)+','
+str(right_margin+0.15)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
else:
outfile.write(' --frame-margins '+str(left_margin+0.05)+','
+str(right_margin+0.02)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+labels.value[plot[1]]+'\' /shift 2.9\\\n')
outfile.write(' --xlabel \''+labels.value[plot[0]]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.75')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write(" /valign 'midheight'")
outfile.write('\\\n --xyz-map\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --new-zaxis zvalues /location right /bar_size \'0.5cm\'\\\n')
outfile.write(" --label-style zvalues /angle 270 /shift 0.4 /valign 'midheight'\\\n")
outfile.write(' --plot '+currentParse+'_like2D.ct2@1:2:3 ')
if doColourbar.value is not None and plot in doColourbar.value: outfile.write('/zaxis zvalues ')
outfile.write('/color-map \''+colours.value.colourMap(contourLevels,'like')+'\'\\\n')
if doComparison.value:
# Do everything for comparison chain
if contours2D.value is not None:
# Plot contours
outfile.write(' --plot '+currentSecParse+'_like2D.ct2@1:2:3 /fill-transparency 1\\\n')
for contour in contourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.comparisonProfContourColour2D+
' /style '+colours.value.comparisonContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if bestFitOnProf.value and colours.value.comparisonBestFitMarker is not None:
# Get best-fit point and plot it
bestFit = getCentralVal(secParseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+
colours.value.comparisonBestFitMarker+' /color \''+colours.value.comparisonBestFitColour+
'\' /scale '+str(colours.value.comparisonBestFitMarkerScale)+' \\\n')
if postMeanOnProf.value and colours.value.comparisonPostMeanMarker is not None:
# Get posterior mean and plot it
postMean = getCentralVal(secParseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_profile_like = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+
colours.value.comparisonPostMeanMarker+' /color \''+colours.value.comparisonPostMeanColour+
'\' /scale '+str(colours.value.comparisonPostMeanMarkerScale)+' \\\n')
outfile.write(' --plot '+currentParse+'_like2D.ct2@1:2:3 /fill-transparency 1\\\n')
if contours2D.value is not None:
# Plot contours
for contour in contourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.mainProfContourColour2D+
' /style '+colours.value.mainContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if doLegend2D.value is not None and plot in doLegend2D.value:
# Write legend
try:
legendLocation = legendLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
outfile.write(' --legend-line \'Prof.~likelihood\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
if bestFitOnProf.value:
# Get best-fit point and plot it
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+
colours.value.mainBestFitMarker+' /fill-color \''+str(colours.value.mainBestFitColour2D)+'\' /stroke-color \''+str(colours.value.mainBestFitColourOutline2D)+
'\' /scale '+str(colours.value.mainBestFitMarkerScale)+' \\\n')
if postMeanOnProf.value:
# Get posterior mean and plot it
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_profile_like = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+
colours.value.mainPostMeanMarker+' /fill-color \''+str(colours.value.mainPostMeanColour2D)+'\' /stroke-color \''+str(colours.value.mainPostMeanColourOutline2D)+
'\' /scale '+str(colours.value.mainPostMeanMarkerScale)+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour2D+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
# Do labelling for colourbar
outfile.write(' --y2 --plot '+currentParse+'_like2D.ct2@1:2:3 /fill-transparency 1\\\n')
outfile.write(' --axis-style y /decoration ticks --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+likeColourbarString+'\' /shift 3.5 /angle 180 /scale 0.8\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_like2D.bsh', shell=True)
# Make posterior pdf plotting scripts
if doPosterior.value:
# Get contours
if contours2D.value is not None:
mainContourLevels = getContours(parseFilename,plot,'post')
if doComparison.value: secContourLevels = getContours(secParseFilename,plot,'post')
# Determine keys
keyString = ''
if doKey2D.value is not None and plot in doKey2D.value:
# Get gross key location
try:
keyLoc = keyLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
keyLoc = defaultKeyLocation
# Get text to be used for reference point
refText = defaultRefKey if refKey.value is None else refKey.value
# Get x and y coordinates for 3 possible keys (for markers and text)
yVals = ytrema[0] + np.array(keyYVals[keyLoc[0]])*yRange
xVals = xtrema[0] + np.array(keyXVals[keyLoc[1]])*xRange
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale,
refText,
colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour,
colours.value.referenceMarkerInnerScale/colours.value.referenceMarkerOuterScale])
# Get details of key for posterior mean
if postMeanOnPost.value: markers.append([colours.value.mainPostMeanMarker,
colours.value.mainPostMeanColour2D,
colours.value.mainPostMeanColourOutline2D,
colours.value.mainPostMeanMarkerScale,
'Mean'])
# Get details of key for best fit
if bestFitOnPost.value: markers.append([colours.value.mainBestFitMarker,
colours.value.mainBestFitColour2D,
colours.value.mainBestFitColourOutline2D,
colours.value.mainBestFitMarkerScale,
'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[3] /= 1.5
if key[3] > 1.0: key[3] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 8: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[5]+' /color \''+\
key[6]+'\' /scale '+str(key[7]*key[3])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /fill-color \''+str(key[1])+'\' /stroke-color \''+str(key[2])+'\' /scale '+str(key[3])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[4]+'\' /color \''+colours.value.keyTextColour2D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(currentBase+'_post2D.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_post2D')
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --frame-margins '+str(left_margin+0.03)+','
+str(right_margin+0.15)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
else:
outfile.write(' --frame-margins '+str(left_margin+0.05)+','
+str(right_margin+0.02)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+labels.value[plot[1]]+'\' /shift 2.9\\\n')
outfile.write(' --xlabel \''+labels.value[plot[0]]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.75')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write(" /valign 'midheight'")
outfile.write('\\\n --xyz-map\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --new-zaxis zvalues /location right /bar_size \'0.5cm\'\\\n')
outfile.write(" --label-style zvalues /angle 270 /shift 0.4 /valign 'midheight'\\\n")
outfile.write(' --plot '+currentParse+'_post2D.ct2@1:2:3 ')
if doColourbar.value is not None and plot in doColourbar.value: outfile.write('/zaxis zvalues ')
outfile.write('/color-map \''+colours.value.colourMap(mainContourLevels,'post')+'\'\\\n')
if doComparison.value:
# Do everything for comparison chain
if contours2D.value is not None:
# Plot contours
outfile.write(' --plot '+currentSecParse+'_post2D.ct2@1:2:3 /fill-transparency 1\\\n')
for contour in secContourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.comparisonPostContourColour2D+
' /style '+colours.value.comparisonContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if bestFitOnPost.value and colours.value.comparisonBestFitMarker is not None:
# Get best-fit point and plot it
bestFit = getCentralVal(secParseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+
colours.value.comparisonBestFitMarker+' /color \''+colours.value.comparisonBestFitColour+
'\' /scale '+str(colours.value.comparisonBestFitMarkerScale)+' \\\n')
if postMeanOnPost.value and colours.value.comparisonPostMeanMarker is not None:
# Get posterior mean and plot it
postMean = getCentralVal(secParseFilename,plot,'post',lookupKeys)
outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+
colours.value.comparisonPostMeanMarker+' /color \''+colours.value.comparisonPostMeanColour+
'\' /scale '+str(colours.value.comparisonPostMeanMarkerScale)+' \\\n')
outfile.write(' --plot '+currentParse+'_post2D.ct2@1:2:3 /fill-transparency 1\\\n')
if contours2D.value is not None:
# Plot contours
for contour in mainContourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.mainPostContourColour2D+
' /style '+colours.value.mainContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if doLegend2D.value is not None and plot in doLegend2D.value:
# Write legend
try:
legendLocation = legendLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
outfile.write(' --legend-line \'Marg.~posterior\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
if bestFitOnPost.value:
# Get best-fit point and plot it
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+
colours.value.mainBestFitMarker+' /fill-color \''+str(colours.value.mainBestFitColour2D)+'\' /stroke-color \''+str(colours.value.mainBestFitColourOutline2D)+
'\' /scale '+str(colours.value.mainBestFitMarkerScale)+' \\\n')
if postMeanOnPost.value:
# Get posterior mean and plot it
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+
colours.value.mainPostMeanMarker+' /fill-color \''+str(colours.value.mainPostMeanColour2D)+'\' /stroke-color \''+str(colours.value.mainPostMeanColourOutline2D)+
'\' /scale '+str(colours.value.mainPostMeanMarkerScale)+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour2D+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
# Do labelling for colourbar
outfile.write(' --y2 --plot '+currentParse+'_post2D.ct2@1:2:3 /fill-transparency 1\\\n')
outfile.write(' --axis-style y /decoration ticks --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+postColourbarString+'\' /shift 3.5 /angle 180 /scale 0.8\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_post2D.bsh', shell=True)
# Make observable plotting scripts
#if doObservable.value:
if obsPlots.value is not None:
for column in obsPlots.value:
# Get contours
if contours2D.value is not None:
contourLevelsLike = getContours(parseFilename,plot,'like')
contourLevelsObs = getContours_obs(parseFilename,plot,column)
# Determine keys
keyString = ''
if doKey2D.value is not None and plot in doKey2D.value:
# Get gross key location
try:
keyLoc = keyLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
keyLoc = defaultKeyLocation
# Get text to be used for reference point
refText = defaultRefKey if refKey.value is None else refKey.value
# Get x and y coordinates for 3 possible keys (for markers and text)
yVals = ytrema[0] + np.array(keyYVals[keyLoc[0]])*yRange
xVals = xtrema[0] + np.array(keyXVals[keyLoc[1]])*xRange
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale,
refText,
colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour,
colours.value.referenceMarkerInnerScale/colours.value.referenceMarkerOuterScale])
# Get details of key for posterior mean
if postMeanOnProf.value: markers.append([colours.value.mainPostMeanMarker,
colours.value.mainPostMeanColour2D,
colours.value.mainPostMeanColourOutline2D,
colours.value.mainPostMeanMarkerScale,
'Mean'])
# Get details of key for best fit
if bestFitOnProf.value: markers.append([colours.value.mainBestFitMarker,
colours.value.mainBestFitColour2D,
colours.value.mainBestFitColourOutline2D,
colours.value.mainBestFitMarkerScale,
'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[3] /= 1.5
if key[3] > 1.0: key[3] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 8: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[5]+' /color \''+\
key[6]+'\' /scale '+str(key[7]*key[3])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /fill-color \''+str(key[1])+'\' /stroke-color \''+str(key[2])+'\' /scale '+str(key[3])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[4]+'\' /color \''+colours.value.keyTextColour2D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(currentBase+'_obs2D_'+str(column)+'.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_obs2D_'+str(column))
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --frame-margins '+str(left_margin+0.03)+','
+str(right_margin+0.15)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
else:
outfile.write(' --frame-margins '+str(left_margin+0.05)+','
+str(right_margin+0.02)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+labels.value[plot[1]]+'\' /shift 2.9\\\n')
outfile.write(' --xlabel \''+labels.value[plot[0]]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.75')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write(" /valign 'midheight'")
outfile.write('\\\n --xyz-map\\\n')
outfile.write(' --plot '+currentParse+'_obs2D_'+str(column)+'.ct2@1:2:3 ')
#if doColourbar.value is not None and plot in doColourbar.value: outfile.write('/zaxis zvalues ')
outfile.write('/color-map \''+colours.value.colourMap(contourLevelsObs,'obs')+'\'\\\n')
if doComparison.value:
# Do everything for comparison chain
if contours2D.value is not None:
# Plot contours
outfile.write(' --plot '+currentSecParse+'_like2D.ct2@1:2:3 /fill-transparency 1\\\n')
for contour in contourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.comparisonProfContourColour2D+
' /style '+colours.value.comparisonContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if bestFitOnProf.value and colours.value.comparisonBestFitMarker is not None:
# Get best-fit point and plot it
bestFit = getCentralVal(secParseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+
colours.value.comparisonBestFitMarker+' /color \''+colours.value.comparisonBestFitColour+
'\' /scale '+str(colours.value.comparisonBestFitMarkerScale)+' \\\n')
if postMeanOnProf.value and colours.value.comparisonPostMeanMarker is not None:
# Get posterior mean and plot it
postMean = getCentralVal(secParseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_profile_like = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+
colours.value.comparisonPostMeanMarker+' /color \''+colours.value.comparisonPostMeanColour+
'\' /scale '+str(colours.value.comparisonPostMeanMarkerScale)+' \\\n')
outfile.write(' --plot '+currentParse+'_like2D.ct2@1:2:3 /fill-transparency 1\\\n')
if contours2D.value is not None:
# Plot contours
for contour in contourLevelsLike:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.mainProfContourColour2D+
' /style '+colours.value.mainContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if doLegend2D.value is not None and plot in doLegend2D.value:
# Write legend
try:
legendLocation = legendLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
outfile.write(' --legend-line \'Prof.~likelihood\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
if bestFitOnProf.value:
# Get best-fit point and plot it
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+
colours.value.mainBestFitMarker+' /fill-color \''+str(colours.value.mainBestFitColour2D)+'\' /stroke-color \''+str(colours.value.mainBestFitColourOutline2D)+
'\' /scale '+str(colours.value.mainBestFitMarkerScale)+' \\\n')
if postMeanOnProf.value:
# Get posterior mean and plot it
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
if not postMean: sys.exit('Error: plot_posterior_mean_on_profile_like = T but no multiplicity given!')
outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+
colours.value.mainPostMeanMarker+' /fill-color \''+str(colours.value.mainPostMeanColour2D)+'\' /stroke-color \''+str(colours.value.mainPostMeanColourOutline2D)+
'\' /scale '+str(colours.value.mainPostMeanMarkerScale)+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour2D+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
# Do colourbar
outfile.write(' --xyz-map\\\n')
outfile.write(' --new-zaxis zvalues /location right /bar_size \'0.5cm\'\\\n')
outfile.write(" --label-style zvalues /angle 270 /shift 0.4 /valign 'midheight'\\\n")
outfile.write(' --y2 --plot '+currentParse+'_obs2D_'+str(column)+'_colorbar.ct2@1:2:3 /zaxis zvalues ')
outfile.write('/color-map \''+colours.value.colourMap(contourLevelsObs,'obs')+'\' /fill-transparency 1\\\n')
outfile.write(' --axis-style y /decoration ticks --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+labels.value[column]+'\' /shift 3.5 /angle 180 /scale 0.8\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_obs2D_'+str(column)+'.bsh', shell=True)
# Make profile-posterior comparison plotting scripts
if doProfile.value and doPosterior.value:
# Work out which is the main and which is the comparison
[main, sec] = ['post', 'like'] if PosteriorIsMainInComboPlot else ['like', 'post']
# Get contours
if contours2D.value is not None:
mainContourLevels = getContours(parseFilename,plot,main)
secContourLevels = getContours(parseFilename,plot,sec)
# Determine keys
keyString = ''
if doKey2D.value is not None and plot in doKey2D.value:
markers = []
# Get details of key for reference point
if plotRef: markers.append([colours.value.referenceMarkerOuter,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterColour,
colours.value.referenceMarkerOuterScale,
refText,
colours.value.referenceMarkerInner,
colours.value.referenceMarkerInnerColour,
colours.value.referenceMarkerInnerScale/colours.value.referenceMarkerOuterScale])
if PosteriorIsMainInComboPlot:
# Get details of key for posterior mean
markers.append([colours.value.mainPostMeanMarker,
colours.value.mainPostMeanColour2D,
colours.value.mainPostMeanColourOutline2D,
colours.value.mainPostMeanMarkerScale,
'Mean'])
# Get details of key for best fit
markers.append([colours.value.comparisonBestFitMarker,
colours.value.comparisonBestFitColour,
colours.value.comparisonBestFitColour,
colours.value.comparisonBestFitMarkerScale,
'Best fit'])
else:
# Get details of key for posterior mean
markers.append([colours.value.comparisonPostMeanMarker,
colours.value.comparisonPostMeanColour,
colours.value.comparisonPostMeanColour,
colours.value.comparisonPostMeanMarkerScale,
'Mean'])
# Get details of key for best fit
markers.append([colours.value.mainBestFitMarker,
colours.value.mainBestFitColour2D,
colours.value.mainBestFitColourOutline2D,
colours.value.mainBestFitMarkerScale,
'Best fit'])
# Reverse vertical ordering if keys are to be placed at the top of the page, so as to fill from the top down
if keyLoc[0] == 't': markers.reverse()
# Construct ctioga2 command for each key
for i,key in enumerate(markers):
if key[0] == 'Bullet' or key[0] == 'BulletOpen': key[3] /= 1.5
if key[3] > 1.0: key[3] = 1.0
# Write the extra marker overlay for the reference point
if len(key) == 8: keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[5]+' /color \''+\
key[6]+'\' /scale '+str(key[7]*key[3])+'\\\n'
# Write the main marker
keyString += ' --draw-marker '+str(xVals[0])+','+str(yVals[i])+' '+key[0]+' /fill-color \''+str(key[1])+'\' /stroke-color \''+str(key[2])+'\' /scale '+str(key[3])+'\\\n'
# Write the key text
keyString += ' --draw-text '+str(xVals[1])+','+str(yVals[i])+' \''+key[4]+'\' /color \''+colours.value.keyTextColour2D
keyString += '\' /justification left /scale 0.75 /alignment center \\\n'
# Open plotting shell script file for writing
outfile = smart_open(baseFilename+'_'+'_'.join([str(x) for x in plot])+'_combo2D.bsh','w')
outfile.write('#!/usr/bin/env bash\n')
outfile.write('# This plot script created by pippi '+pippiVersion+' on '+datetime.datetime.now().strftime('%c')+'\n')
outfile.write('ctioga2\\\n')
outfile.write(' --name '+currentBaseMinimal+'_combo2D')
outfile.write(' --plot-scale \''+str(plot_scale)+'\'\\\n')
outfile.write(' --page-size \''+plotSizeInternal+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --frame-margins '+str(left_margin+0.03)+','
+str(right_margin+0.15)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
else:
outfile.write(' --frame-margins '+str(left_margin+0.05)+','
+str(right_margin+0.02)+','
+str(top_margin)+','
+str(bottom_margin)+'\\\n')
outfile.write(' --xrange '+str(xtrema[0])+':'+str(xtrema[1])+'\\\n')
outfile.write(' --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+labels.value[plot[1]]+'\' /shift 2.9\\\n')
outfile.write(' --xlabel \''+labels.value[plot[0]]+'\'\\\n')
outfile.write(' --label-style x /scale 1.0 /shift 0.15 --label-style y /scale 1.0 /shift 0.75')
if yAxisAngle.value is not None: outfile.write(' /angle '+str(yAxisAngle.value))
outfile.write('\\\n --xyz-map\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
outfile.write(' --new-zaxis zvalues /location right /bar_size \'0.5cm\'\\\n')
outfile.write(" --label-style zvalues /angle 270 /shift 0.4 /valign 'midheight'\\\n")
outfile.write(' --plot '+currentParse+'_'+main+'2D.ct2@1:2:3 ')
if doColourbar.value is not None and plot in doColourbar.value: outfile.write('/zaxis zvalues ')
outfile.write('/color-map \''+colours.value.colourMap(mainContourLevels,main)+'\'\\\n')
if contours2D.value is not None:
# Plot comparison contours
outfile.write(' --plot '+currentParse+'_'+sec+'2D.ct2@1:2:3 /fill-transparency 1\\\n')
for contour in secContourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.comparisonPostContourColour2D+
' /style '+colours.value.comparisonContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
outfile.write(' --plot '+currentParse+'_'+main+'2D.ct2@1:2:3 /fill-transparency 1\\\n')
if contours2D.value is not None:
# Plot contours
for contour in mainContourLevels:
outfile.write(' --draw-contour '+contour+' /color '+colours.value.mainPostContourColour2D+
' /style '+colours.value.mainContourStyle+' /width '+colours.value.lineWidth2D+'\\\n')
if doLegend2D.value is not None and plot in doLegend2D.value:
# Write legend
try:
legendLocation = legendLoc2D.value[plot[0]][plot[1]]
except (KeyError, TypeError):
legendLocation = defaultLegendLocation
outfile.write(' --legend-inside \''+legendLocation+'\' /scale 1.0 /vpadding 0.1\\\n')
if legendLines.value is not None:
for x in legendLines.value: outfile.write(' --legend-line \''+x+'\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
outfile.write(' --legend-line \'Like vs. Posterior\' /color \''+colours.value.legendTextColour2D+'\'\\\n')
# Get best-fit point
bestFit = getCentralVal(parseFilename,plot,'like',lookupKeys)
# Get posterior mean
postMean = getCentralVal(parseFilename,plot,'post',lookupKeys)
# Always plot both best fit and posterior mean on comparison plot
if PosteriorIsMainInComboPlot:
bestFitData = [colours.value.comparisonBestFitMarker, colours.value.comparisonBestFitColour, colours.value.comparisonBestFitColour, colours.value.comparisonBestFitMarkerScale]
postMeanData = [colours.value.mainPostMeanMarker, colours.value.mainPostMeanColour2D, colours.value.mainPostMeanColourOutline2D, colours.value.mainPostMeanMarkerScale]
else:
bestFitData = [colours.value.mainBestFitMarker, colours.value.mainBestFitColour2D, colours.value.mainBestFitColourOutline2D, colours.value.mainBestFitMarkerScale]
postMeanData = [colours.value.comparisonPostMeanMarker, colours.value.comparisonPostMeanColour, colours.value.comparisonPostMeanColour, colours.value.comparisonPostMeanMarkerScale]
outfile.write(' --draw-marker '+str(bestFit[0])+','+str(bestFit[1])+' '+bestFitData[0]+' /fill-color \''+str(bestFitData[1])+'\' /stroke-color \''+str(bestFitData[2])+
'\' /scale '+str(bestFitData[3])+' \\\n')
if postMean: outfile.write(' --draw-marker '+str(postMean[0])+','+str(postMean[1])+' '+postMeanData[0]+' /fill-color \''+str(postMeanData[1])+'\' /stroke-color \''+str(postMeanData[2])+
'\' /scale '+str(postMeanData[3])+' \\\n')
# Plot reference point
if plotRef: outfile.write(refString)
# Draw key
outfile.write(keyString)
# Write credits
if blame.value is not None:
blameYCoordinate = str(blameFractionalVerticalOffset * yRange + ytrema[1])
outfile.write(' --draw-text '+str(xtrema[1])+','+blameYCoordinate+' \''+blame.value+'\' /scale 0.5 /justification right\\\n')
# Add logo
if logoFile.value is not None:
outfile.write(' --draw-text '+str(logoCoords[0])+','+str(logoCoords[1])+' '+logoString+'\\\n')
# Set axis colours
for x in ['top', 'bottom', 'left', 'right']:
outfile.write(' --axis-style '+x+' /stroke_color \''+colours.value.axisColour2D+'\'\\\n')
if doColourbar.value is not None and plot in doColourbar.value:
# Do labelling for colourbar
outfile.write(' --y2 --plot '+currentParse+'_'+main+'2D.ct2@1:2:3 /fill-transparency 1\\\n')
outfile.write(' --axis-style y /decoration ticks --yrange '+str(ytrema[0])+':'+str(ytrema[1])+'\\\n')
outfile.write(' --ylabel \''+postColourbarString+'\' /shift 3.5 /angle 180 /scale 0.8\\\n')
outfile.close
subprocess.call('chmod +x '+currentBase+'_combo2D.bsh', shell=True)
def getContours(parseFilename,plot,statistic):
# Construct dimensionality of plot and string indicating specific plot (if any)
if type(plot) == list:
[dim, plot] = [str(len(plot)), '' if statistic == 'like' else '_'+'_'.join([str(x) for x in plot])]
else:
[dim, plot] = ['1', '' if statistic == 'like' else '_'+str(plot)]
# Open contour file
contourfile = safe_open(parseFilename+plot+'_'+statistic+dim+'D.contours')
# Read contents
fileContents = contourfile.readline()
while fileContents[0] == '#': fileContents = contourfile.readline()
#Shut it
contourfile.close
levels = fileContents.split()
return levels
def getContours_obs(parseFilename,plot,observable):
# Construct dimensionality of plot and string indicating specific plot (if any)
if type(plot) == list:
[dim, plot] = [str(len(plot)), '_'+'_'.join([str(x) for x in plot])]
# Open contour file
contourfile = safe_open(parseFilename+plot+'_obs'+dim+'D_' + str(observable) + '.contours')
# Read contents
fileContents = contourfile.readline()
while fileContents[0] == '#': fileContents = contourfile.readline()
#Shut it
contourfile.close
levels = fileContents.split()
return levels
def getCentralVal(parseFilename,plot,statistic,lk):
# Find central value (either best fit or posterior mean) for requested plot
# Open .best file
bestfile = safe_open(parseFilename+'.best')
# Read contents
fileContents = bestfile.readline()
while fileContents[0] == '#': fileContents = bestfile.readline()
fileContents = bestfile.readlines()
# Shut it
bestfile.close
if statistic == 'like':
# Extract best fit
point = fileContents[1].split()
elif statistic == 'post':
try:
# Extract posterior pdf
point = fileContents[3].split()
except IndexError:
return None
else:
# Never get here
sys.exit('Error: unrecognised statistic in pippi_script.getCentralVal.\nQuitting...')
# Choose the coordinates corresponding to the axes of the current plot
if type(plot) == list:
coordinates = [point[lk.value[x]] for x in plot]
else:
coordinates = point[lk.value[plot]]
return coordinates
def dictFallback(risky,safe,key):
# Try to extract entry corresponding to key from risky dataObject, otherwise use safe dataObject
try:
return risky.value[key]
except (KeyError, TypeError):
return safe.value[key]
| 63.615782 | 194 | 0.589534 | 9,134 | 86,263 | 5.534706 | 0.059448 | 0.077145 | 0.026487 | 0.022431 | 0.859081 | 0.848004 | 0.836037 | 0.831131 | 0.827551 | 0.813922 | 0 | 0.018847 | 0.265583 | 86,263 | 1,355 | 195 | 63.662731 | 0.779126 | 0.098397 | 0 | 0.745597 | 0 | 0.006849 | 0.194426 | 0.043738 | 0.000978 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.003914 | null | null | 0.004892 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
80ffd8af584b8a15cca887740482b4f326725e0b | 113 | py | Python | alphapose/version.py | jinfagang/AlphaPose | af9b49f8d9b156d0468472942e58d525258cee91 | [
"Apache-2.0"
] | null | null | null | alphapose/version.py | jinfagang/AlphaPose | af9b49f8d9b156d0468472942e58d525258cee91 | [
"Apache-2.0"
] | null | null | null | alphapose/version.py | jinfagang/AlphaPose | af9b49f8d9b156d0468472942e58d525258cee91 | [
"Apache-2.0"
] | null | null | null | # GENERATED VERSION FILE
# TIME: Fri Apr 1 13:21:51 2022
__version__ = '0.5.0+8b99d03'
short_version = '0.5.0'
| 18.833333 | 32 | 0.690265 | 21 | 113 | 3.47619 | 0.714286 | 0.219178 | 0.246575 | 0.273973 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.234043 | 0.168142 | 113 | 5 | 33 | 22.6 | 0.542553 | 0.469027 | 0 | 0 | 1 | 0 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
440a47454c60e828bffee782f663b4def519662f | 71,415 | py | Python | TEST3D/GUI/0010505_page_skel/log.py | usnistgov/OOF3D | 4fd423a48aea9c5dc207520f02de53ae184be74c | [
"X11"
] | 31 | 2015-04-01T15:59:36.000Z | 2022-03-18T20:21:47.000Z | TEST3D/GUI/0010505_page_skel/log.py | usnistgov/OOF3D | 4fd423a48aea9c5dc207520f02de53ae184be74c | [
"X11"
] | 3 | 2015-02-06T19:30:24.000Z | 2017-05-25T14:14:31.000Z | TEST3D/GUI/0010505_page_skel/log.py | usnistgov/OOF3D | 4fd423a48aea9c5dc207520f02de53ae184be74c | [
"X11"
] | 7 | 2015-01-23T15:19:22.000Z | 2021-06-09T09:03:59.000Z | # -*- python -*-
# This software was produced by NIST, an agency of the U.S. government,
# and by statute is not subject to copyright in the United States.
# Recipients of this software assume all responsibilities associated
# with its operation, modification and maintenance. However, to
# facilitate maintenance we ask that before distributing modified
# versions of this software, you first contact the authors at
# oof_manager@nist.gov.
import tests
#This GUI test case is tight to the skeleton page global test.
#It aims to check if the skeleton Surface Smooth Method is reliabily working according
#to the sensitization of the OK button in case of an Heterogenity, Selection , Group situations.
#This case has no targets. Base on our comments on the 0010501 in this test we should just check that the OK Button is always sensitized in all cases.
findWidget('OOF3D').resize(550, 350)
#Loading the script log file of the entry general skeleton page test case 0010500.
findMenu(findWidget('OOF3D:MenuBar'), 'File:Load:Script').activate()
checkpoint toplevel widget mapped Dialog-Script
findWidget('Dialog-Script').resize(190, 67)
findWidget('Dialog-Script:filename').set_text('TEST_DATA/skelpagetestbase.log')
findWidget('Dialog-Script:gtk-ok').clicked()
checkpoint meshable button set
checkpoint meshable button set
checkpoint microstructure page sensitized
checkpoint pixel page updated
checkpoint active area status updated
checkpoint microstructure page sensitized
checkpoint meshable button set
checkpoint Field page sensitized
checkpoint Materials page updated
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint pinnodes page sensitized
checkpoint boundary page updated
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page groups sensitized
checkpoint Solver page sensitized
checkpoint OOF.Microstructure.New
checkpoint meshable button set
checkpoint meshable button set
checkpoint microstructure page sensitized
checkpoint microstructure page sensitized
checkpoint meshable button set
checkpoint Field page sensitized
checkpoint Materials page updated
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint pinnodes page sensitized
checkpoint boundary page updated
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page groups sensitized
checkpoint microstructure page sensitized
checkpoint OOF.Microstructure.Create_From_ImageFile
checkpoint Move Node toolbox info updated
checkpoint toplevel widget mapped OOF3D Graphics 1
checkpoint OOF.Windows.Graphics.New
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 705))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 705))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 705))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 705))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 705))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 705))
findWidget('OOF3D Graphics 1').resize(1000, 800)
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 705))
findWidget('OOF3D Messages 1').resize(593, 200)
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint Solver page sensitized
checkpoint OOF.Skeleton.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page groups sensitized
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
findWidget('OOF3D Activity Viewer').resize(400, 300)
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page groups sensitized
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Copy
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page groups sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Delete
checkpoint OOF.File.Save.Skeleton
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint skeleton selection page groups sensitized
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint pinnodes page sensitized
checkpoint boundary page updated
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint OOF.Skeleton.New
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint OOF.Graphics_1.Layer.Select
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Copy
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Delete
checkpoint OOF.File.Save.Skeleton
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.New
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.New
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.New
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint mesh page subproblems sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Rename
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page selection sensitized
checkpoint skeleton selection page groups sensitized
checkpoint skeleton selection page updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Field page sensitized
checkpoint mesh page sensitized
checkpoint mesh page sensitized
checkpoint OOF.Skeleton.Simple
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint OOF.Graphics_1.Layer.Select
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint OOF.Graphics_1.Layer.Select
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.New
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Hide
checkpoint OOF.File.Load.Script
widget_0=findWidget('OOF3D Activity Viewer')
handled_0=widget_0.event(event(gtk.gdk.DELETE,window=widget_0.window))
postpone if not handled_0: widget_0.destroy()
checkpoint OOF.ActivityViewer.File.Close
#Going to the Skeleton Page
setComboBox(findWidget('OOF3D:Navigation:PageMenu'), 'Skeleton')
checkpoint page installed Skeleton
findWidget('OOF3D').resize(601, 357)
findWidget('OOF3D:Skeleton Page:Pane').set_position(250)
checkpoint skeleton page sensitized
checkpoint skeleton page info updated
checkpoint skeleton page info updated
checkpoint skeleton page sensitized
#Selecting the Microstrure '0color'
setComboBox(findWidget('OOF3D:Skeleton Page:Microstructure'), '0color')
checkpoint skeleton page info updated
checkpoint skeleton page info updated
checkpoint skeleton page sensitized
findWidget('OOF3D Graphics 1').resize(1000, 802)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 707))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 707))
findWidget('OOF3D Graphics 1').resize(1000, 806)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 711))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 711))
findWidget('OOF3D Graphics 1').resize(1000, 832)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 737))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 737))
findWidget('OOF3D Graphics 1').resize(1000, 857)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 762))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 762))
findWidget('OOF3D Graphics 1').resize(1000, 873)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 778))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 778))
findWidget('OOF3D Graphics 1').resize(1000, 883)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 788))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 788))
findWidget('OOF3D Graphics 1').resize(1000, 894)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 799))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 799))
findWidget('OOF3D Graphics 1').resize(1000, 897)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 802))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 802))
findWidget('OOF3D Graphics 1').resize(1000, 901)
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 806))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 806))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 805))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 805))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 801))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 801))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 793))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 793))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 784))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 784))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 776))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 776))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 770))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 770))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 763))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 763))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 756))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 756))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 750))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 750))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 745))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 745))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 739))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 739))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 736))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 736))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 724))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 724))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 715))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 715))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 709))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 709))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 704))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 704))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 697))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 697))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 691))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 691))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 687))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 687))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 683))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 683))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 682))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 682))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 681))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 681))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 680))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 680))
findWidget('OOF3D Graphics 1:Pane0:Pane2:ToolboxFrame').size_allocate(gtk.gdk.Rectangle(0, 29, 380, 679))
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 679))
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 2.7979576943884e+01)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 5.5959153887769e+01)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 8.3938730831653e+01)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 1.1191830777554e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 1.3989788471942e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 1.6787746166331e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 1.9585703860719e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 2.2383661555107e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 2.5181619249496e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 2.7979576943884e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 3.0777534638273e+02)
findWidget('OOF3D Graphics 1:Pane0:LayerScroll').get_vadjustment().set_value( 3.3200000000000e+02)
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '29')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((29,))
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Hide
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '28')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((28,))
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Show
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '24')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((24,))
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Show
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 679))
findWidget('OOF3D Graphics 1:Pane0:Pane2:tumble').clicked()
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 679))
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.BUTTON_PRESS,x= 1.8600000000000e+02,y= 2.0100000000000e+02,button=1,state=16,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.8600000000000e+02,y= 2.0200000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.8700000000000e+02,y= 2.0300000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.8800000000000e+02,y= 2.0400000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.8900000000000e+02,y= 2.0600000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.9200000000000e+02,y= 2.0800000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.9700000000000e+02,y= 2.1300000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 1.9900000000000e+02,y= 2.1400000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 2.0000000000000e+02,y= 2.1500000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.MOTION_NOTIFY,x= 2.0300000000000e+02,y= 2.1500000000000e+02,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
window = findOOFWindow('Graphics_1')
oldsize = window.setCanvasSize(614, 645)
canvasobj = findCanvasDrawingArea(findWidget('OOF3D Graphics 1:Pane0:Pane2:Canvas'), windowname='Graphics_1')
canvasobj.emit('event', event(gtk.gdk.BUTTON_RELEASE,x= 2.0500000000000e+02,y= 2.1700000000000e+02,button=1,state=272,window=findCanvasGdkWindow('Graphics_1')))
window.setCanvasSize(oldsize[0], oldsize[1])
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 679))
checkpoint OOF.Graphics_1.Settings.Camera.View
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 679))
findWidget('OOF3D Graphics 1:Pane0:Pane2:fill').clicked()
findWidget('OOF3D Graphics 1:Pane0:Pane2').size_allocate(gtk.gdk.Rectangle(0, 29, 1000, 679))
findWidget('OOF3D').resize(601, 357)
#Selecting the Surface Smooth method
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Chooser'), 'Surface Smooth')
assert tests.skeletonPageModificationSensitivityCheck1()
assert tests.skeletonMethodListCheck('Refine','Snap Nodes','Anneal','Smooth','Surface Smooth','Rationalize','Fix Illegal Elements','Snap Refine',)
assert tests.currentSkeletonMethodCheck('Surface Smooth')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Average Energy')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D').resize(601, 401)
findWidget('OOF3D:Skeleton Page:Pane').set_position(274)
checkpoint skeleton page sensitized
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(285)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Unconditional')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Unconditional')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(285)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Average Energy')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Average Energy')
findWidget('OOF3D:Skeleton Page:Pane').set_position(230)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(230)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Unconditional')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Unconditional')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(230)
#Selecting the microstructure '5color'
setComboBox(findWidget('OOF3D:Skeleton Page:Microstructure'), '5color')
checkpoint skeleton page sensitized
checkpoint skeleton page info updated
checkpoint skeleton page info updated
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Average Energy')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Average Energy')
findWidget('OOF3D:Skeleton Page:Pane').set_position(285)
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '28')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((28,))
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Hide
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '24')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((24,))
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Hide
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '29')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((29,))
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Show
findCellRenderer(findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList'), col=0, rend=0).emit('toggled', '23')
findWidget('OOF3D Graphics 1:Pane0:LayerScroll:LayerList').get_selection().select_path((23,))
checkpoint Move Node toolbox writable changed
checkpoint Move Node toolbox info updated
checkpoint Graphics_1 Move Nodes sensitized
checkpoint Graphics_1 Voxel Info updated
checkpoint Graphics_1 Pin Nodes updated
checkpoint OOF.Graphics_1.Layer.Select
checkpoint OOF.Graphics_1.Layer.Show
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(285)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Unconditional')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Unconditional')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(285)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Average Energy')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Average Energy')
findWidget('OOF3D:Skeleton Page:Pane').set_position(230)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(230)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Unconditional')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Unconditional')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Conditional Iteration')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Conditional Iteration')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
findWidget('OOF3D').resize(612, 475)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Energy Reduction Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Energy Reduction Rate')
findWidget('OOF3D:Skeleton Page:Pane').set_position(123)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Both')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Both')
findWidget('OOF3D').resize(612, 497)
findWidget('OOF3D:Skeleton Page:Pane').set_position(110)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Either')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Either')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Conditional Iteration:condition:Chooser'), 'Acceptance Rate')
assert tests.skeletonMethodIterationConditionListCheck('Surface Smooth','Conditional Iteration','Acceptance Rate','Energy Reduction Rate','Both','Either',)
assert tests.currentSkeletonMethodIterationConditionCheck('Surface Smooth','Conditional Iteration','Acceptance Rate')
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:iteration:Chooser'), 'Fixed Iterations')
assert tests.skeletonMethodIterationListCheck('Surface Smooth','Fixed Iterations','Conditional Iteration')
assert tests.currentSkeletonMethodIterationCheck('Surface Smooth','Fixed Iterations')
findWidget('OOF3D:Skeleton Page:Pane').set_position(230)
setComboBox(findWidget('OOF3D:Skeleton Page:Pane:Modification:Method:Surface Smooth:criterion:Chooser'), 'Average Energy')
assert tests.skeletonMethodCriterionListCheck('Surface Smooth','Average Energy','Unconditional')
assert tests.currentSkeletonMethodCriterionCheck('Surface Smooth','Average Energy')
findWidget('OOF3D:Skeleton Page:Pane').set_position(285)
findMenu(findWidget('OOF3D:MenuBar'), 'File:Save:Python_Log').activate()
checkpoint toplevel widget mapped Dialog-Python_Log
findWidget('Dialog-Python_Log').resize(190, 95)
findWidget('Dialog-Python_Log:filename').set_text('skelpagesurfsmooth.log')
findWidget('Dialog-Python_Log:gtk-ok').clicked()
checkpoint OOF.File.Save.Python_Log
assert tests.filediff('skelpagesurfsmooth.log')
widget_0=findWidget('OOF3D')
handled_0=widget_0.event(event(gtk.gdk.DELETE,window=widget_0.window)) | 63.87746 | 161 | 0.8396 | 8,640 | 71,415 | 6.889699 | 0.041551 | 0.056546 | 0.051708 | 0.067264 | 0.958691 | 0.954542 | 0.944748 | 0.939389 | 0.938381 | 0.938381 | 0 | 0.039933 | 0.073584 | 71,415 | 1,118 | 162 | 63.87746 | 0.85981 | 0.014381 | 0 | 0.87135 | 0 | 0.029197 | 0.278765 | 0.084666 | 0 | 0 | 0 | 0 | 0.124088 | 0 | null | null | 0 | 0.000912 | null | null | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
440b1e5e1ea330bdc10364b287379d7e2d438888 | 961 | py | Python | packages/pyright-internal/src/tests/samples/constants1.py | sasano8/pyright | e804f324ee5dbd25fd37a258791b3fd944addecd | [
"MIT"
] | 4,391 | 2019-05-07T01:18:57.000Z | 2022-03-31T20:45:44.000Z | packages/pyright-internal/src/tests/samples/constants1.py | sasano8/pyright | e804f324ee5dbd25fd37a258791b3fd944addecd | [
"MIT"
] | 2,740 | 2019-05-07T03:29:30.000Z | 2022-03-31T12:57:46.000Z | packages/pyright-internal/src/tests/samples/constants1.py | sasano8/pyright | e804f324ee5dbd25fd37a258791b3fd944addecd | [
"MIT"
] | 455 | 2019-05-07T12:55:14.000Z | 2022-03-31T17:09:15.000Z | # This sample tests that the type checker flags certain values
# that cannot be deleted or assigned to.
# This should generate an error
True = 3
# This should generate an error
False = 4
# This should generate an error
None = True
# This should generate an error
__debug__ = 4
# This should generate an error
del True
# This should generate an error
del None
# This should generate an error
-3 = 2
# This should generate an error
[4] = [2]
# This should generate an error
[True] = [3]
# This should generate an error
(True) = 3
# This should generate an error
del -3
# This should generate an error
3 + 4 = 2
# This should generate an error
del 3 + 4
# This should generate an error
del -(4)
# This should generate an error
del __debug__
# This should generate an error
del {}
# This should generate an error
... = 3
# This should generate an error
del ...
# This should generate an error
(...) = 3
# This should generate an error
del ...
| 14.784615 | 62 | 0.705515 | 154 | 961 | 4.350649 | 0.181818 | 0.298507 | 0.537313 | 0.597015 | 0.843284 | 0.843284 | 0.597015 | 0.41194 | 0.41194 | 0.41194 | 0 | 0.024161 | 0.224766 | 961 | 64 | 63 | 15.015625 | 0.875168 | 0.727367 | 0 | 0.1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
4453fdbf000f5801f56dbc110fe60e0baba414c7 | 173 | py | Python | cauldron/cli/sync/__init__.py | DanMayhew/cauldron | ac41481830fc1a363c145f4b58ce785aac054d10 | [
"MIT"
] | null | null | null | cauldron/cli/sync/__init__.py | DanMayhew/cauldron | ac41481830fc1a363c145f4b58ce785aac054d10 | [
"MIT"
] | null | null | null | cauldron/cli/sync/__init__.py | DanMayhew/cauldron | ac41481830fc1a363c145f4b58ce785aac054d10 | [
"MIT"
] | null | null | null | from cauldron.cli.sync import sync_io as io
from cauldron.cli.sync import files
from cauldron.cli.sync import comm
from cauldron.cli.sync.threads import send_remote_command
| 34.6 | 57 | 0.843931 | 30 | 173 | 4.766667 | 0.433333 | 0.335664 | 0.41958 | 0.531469 | 0.524476 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.104046 | 173 | 4 | 58 | 43.25 | 0.922581 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
447bdfa1588e693079ed4c2a79721ffbd6f65096 | 8,334 | py | Python | lib/solver_interface/pyoptsolver/src/snopt-interface/pyscript/snoptWrapper.py | paperstiger/trajOptLib | 5e86a33537d89c0d1e35df7a436f9266fe817c49 | [
"MIT"
] | 6 | 2020-04-29T05:02:30.000Z | 2021-04-19T15:42:35.000Z | lib/solver_interface/pyoptsolver/src/snopt-interface/pyscript/snoptWrapper.py | paperstiger/trajOptLib | 5e86a33537d89c0d1e35df7a436f9266fe817c49 | [
"MIT"
] | null | null | null | lib/solver_interface/pyoptsolver/src/snopt-interface/pyscript/snoptWrapper.py | paperstiger/trajOptLib | 5e86a33537d89c0d1e35df7a436f9266fe817c49 | [
"MIT"
] | null | null | null | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2018 Gao Tang <gt70@duke.edu>
#
# Distributed under terms of the MIT license.
"""
snoptWrapper.py
Wrapper functions for calling snopt
"""
import numpy as np
import libpysnopt as libsnopt
def parseResult(rst):
"""Parse the results returned by snopt and convert to a dict."""
return {'flag': rst.flag, 'obj': rst.obj, 'x': rst.sol, 'f': rst.fval}
def directSolve(fun, x0, nf=None, xlb=None, xub=None, clb=None, cub=None, cfg=None):
"""Directly solve the optimization problem described using fun with guess x0
:param fun: A function like y = f(x) where x, y are np.ndarray
:param x0: np.ndarray (nx,) the initial guess to the solver
:param nf: int, length of y
:param xlb: np.ndarray (nx,) lower bound on decision variable x
:param xub: np.ndarray (nx,) upper bound on decision variable x
:param clb: np.ndarray (nc,) lower bound on return function c
:param cub: np.ndarray (nc,) upper bound on return function c
:param cfg: libsnopt.SnoptConfig, configuration of snopt solver
:returns: a dictionary containing the solution
"""
nx = len(x0)
if nf is None:
if clb is not None and cub is not None:
assert len(clb) == len(cub)
nf = len(clb)
else:
y = fun(x0)
nf = len(y)
if xlb is None or xub is None:
xlb = np.empty(0)
xub = np.empty(0)
if clb is None or cub is None:
clb = np.empty(0)
cub = np.empty(0)
if cfg is None:
cfg = libsnopt.SnoptConfig()
rst = libsnopt.directSolve(fun, x0, nx, nf, xlb, xub, clb, cub, cfg)
return parseResult(rst)
def inDirectSolve(fun, x0, nf=None, xlb=None, xub=None, clb=None, cub=None, cfg=None):
"""Directly solve the optimization problem described using fun with guess x0
:param fun: A function like f(x, y) where x, y are np.ndarray
:param x0: np.ndarray (nx,) the initial guess to the solver
:param nf: int, length of y
:param xlb: np.ndarray (nx,) lower bound on decision variable x
:param xub: np.ndarray (nx,) upper bound on decision variable x
:param clb: np.ndarray (nc,) lower bound on return function c
:param cub: np.ndarray (nc,) upper bound on return function c
:param cfg: libsnopt.SnoptConfig, configuration of snopt solver
:returns: a dictionary containing the solution
"""
nx = len(x0)
if nf is None:
if clb is not None and cub is not None:
assert len(clb) == len(cub)
nf = len(clb)
assert nf is not None
if xlb is None or xub is None:
xlb = np.empty(0)
xub = np.empty(0)
if clb is None or cub is None:
clb = np.empty(0)
cub = np.empty(0)
if cfg is None:
cfg = libsnopt.SnoptConfig()
rst = libsnopt.inDirectSolve(fun, x0, nx, nf, xlb, xub, clb, cub, cfg)
return parseResult(rst)
def gradSolve(fun, x0, nf=None, xlb=None, xub=None, clb=None, cub=None, cfg=None):
"""Directly solve the optimization problem described using fun with guess x0
:param fun: A function like y, J = f(x) where x, y, J are np.ndarray
:param x0: np.ndarray (nx,) the initial guess to the solver
:param nf: int, length of y
:param xlb: np.ndarray (nx,) lower bound on decision variable x
:param xub: np.ndarray (nx,) upper bound on decision variable x
:param clb: np.ndarray (nc,) lower bound on return function c
:param cub: np.ndarray (nc,) upper bound on return function c
:param cfg: libsnopt.SnoptConfig, configuration of snopt solver
:returns: a dictionary containing the solution
"""
nx = len(x0)
if nf is None:
if clb is not None and cub is not None:
assert len(clb) == len(cub)
nf = len(clb)
else:
y = fun(x0)
nf = len(y)
if xlb is None or xub is None:
xlb = np.empty(0)
xub = np.empty(0)
if clb is None or cub is None:
clb = np.empty(0)
cub = np.empty(0)
if cfg is None:
cfg = libsnopt.SnoptConfig()
rst = libsnopt.gradSolve(fun, x0, nx, nf, xlb, xub, clb, cub, cfg)
return parseResult(rst)
def inGradSolve(fun, x0, nf=None, xlb=None, xub=None, clb=None, cub=None, cfg=None):
"""Directly solve the optimization problem described using fun with guess x0
:param fun: A function like f(x, y, J) where x, y, J are np.ndarray
:param x0: np.ndarray (nx,) the initial guess to the solver
:param nf: int, length of y
:param xlb: np.ndarray (nx,) lower bound on decision variable x
:param xub: np.ndarray (nx,) upper bound on decision variable x
:param clb: np.ndarray (nc,) lower bound on return function c
:param cub: np.ndarray (nc,) upper bound on return function c
:param cfg: libsnopt.SnoptConfig, configuration of snopt solver
:returns: a dictionary containing the solution
"""
nx = len(x0)
if nf is None:
if clb is not None and cub is not None:
assert len(clb) == len(cub)
nf = len(clb)
assert nf is not None
if xlb is None or xub is None:
xlb = np.empty(0)
xub = np.empty(0)
if clb is None or cub is None:
clb = np.empty(0)
cub = np.empty(0)
if cfg is None:
cfg = libsnopt.SnoptConfig()
rst = libsnopt.inGradSolve(fun, x0, nx, nf, xlb, xub, clb, cub, cfg)
return parseResult(rst)
def spGradSolve(fun, x0, nf=None, nG=None, xlb=None, xub=None, clb=None, cub=None, cfg=None):
"""Directly solve the optimization problem described using fun with guess x0
:param fun: A function like y, spJ = f(x) where x, y are np.ndarray, J is scipy.sparse.csc_matrix
:param nf: int, length of y
:param nG: int, nnz of spJ
:param x0: np.ndarray (nx,) the initial guess to the solver
:param xlb: np.ndarray (nx,) lower bound on decision variable x
:param xub: np.ndarray (nx,) upper bound on decision variable x
:param clb: np.ndarray (nc,) lower bound on return function c
:param cub: np.ndarray (nc,) upper bound on return function c
:param cfg: libsnopt.SnoptConfig, configuration of snopt solver
:returns: a dictionary containing the solution
"""
nx = len(x0)
if nf is None:
if clb is not None and cub is not None:
assert len(clb) == len(cub)
nf = len(clb)
else:
y, spJ = fun(x0)
nf = len(y)
nG = spJ.nnz
if nG is None:
y, spJ = fun(x0)
nG = spJ.nnz
assert nf is not None
assert nG is not None
if xlb is None or xub is None:
xlb = np.empty(0)
xub = np.empty(0)
if clb is None or cub is None:
clb = np.empty(0)
cub = np.empty(0)
if cfg is None:
cfg = libsnopt.SnoptConfig()
rst = libsnopt.spGradSolve(fun, x0, nx, nf, nG, xlb, xub, clb, cub, cfg)
return parseResult(rst)
def inSpGradSolve(fun, x0, nf=None, nG=None, xlb=None, xub=None, clb=None, cub=None, cfg=None):
"""Directly solve the optimization problem described using fun with guess x0
:param fun: A function like f(x, y, G, row, col, rec) where x, y are np.ndarray, J is scipy.sparse.csc_matrix
:param x0: np.ndarray (nx,) the initial guess to the solver
:param nf: int, number of f
:param nG: int number nonzero in Jacobian
:param xlb: np.ndarray (nx,) lower bound on decision variable x
:param xub: np.ndarray (nx,) upper bound on decision variable x
:param clb: np.ndarray (nc,) lower bound on return function c
:param cub: np.ndarray (nc,) upper bound on return function c
:param cfg: libsnopt.SnoptConfig, configuration of snopt solver
:returns: a dictionary containing the solution
"""
nx = len(x0)
if nf is None:
if clb is not None and cub is not None:
assert len(clb) == len(cub)
nf = len(clb)
assert nf is not None
assert nG is not None
if xlb is None or xub is None:
xlb = np.empty(0)
xub = np.empty(0)
if clb is None or cub is None:
clb = np.empty(0)
cub = np.empty(0)
if cfg is None:
cfg = libsnopt.SnoptConfig()
rst = libsnopt.inSpGradSolve(fun, x0, nx, nf, nG, xlb, xub, clb, cub, cfg)
return parseResult(rst)
| 36.393013 | 113 | 0.62707 | 1,344 | 8,334 | 3.887649 | 0.096726 | 0.042488 | 0.036746 | 0.052823 | 0.896268 | 0.89378 | 0.89378 | 0.890144 | 0.889378 | 0.888804 | 0 | 0.010886 | 0.272498 | 8,334 | 228 | 114 | 36.552632 | 0.850734 | 0.474202 | 0 | 0.857143 | 0 | 0 | 0.00221 | 0 | 0 | 0 | 0 | 0 | 0.10084 | 1 | 0.058824 | false | 0 | 0.016807 | 0 | 0.134454 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
44969c415e8c6eaa548ddf4a55ec09ee734afa10 | 135,036 | py | Python | files/runs_small/cores_8/barnes/power.py | ST4NSB/sniper-simulator-predictions | 1f0fe2a10fda55fceea053464ea202bfe2effafc | [
"MIT"
] | 1 | 2021-03-08T03:39:23.000Z | 2021-03-08T03:39:23.000Z | files/runs_small/cores_8/barnes/power.py | ST4NSB/sniper-simulator-predictions | 1f0fe2a10fda55fceea053464ea202bfe2effafc | [
"MIT"
] | null | null | null | files/runs_small/cores_8/barnes/power.py | ST4NSB/sniper-simulator-predictions | 1f0fe2a10fda55fceea053464ea202bfe2effafc | [
"MIT"
] | null | null | null | power = {'BUSES': {'Area': 3.70399,
'Bus/Area': 3.70399,
'Bus/Gate Leakage': 0.00993673,
'Bus/Peak Dynamic': 0.216542,
'Bus/Runtime Dynamic': 0.0281445,
'Bus/Subthreshold Leakage': 0.103619,
'Bus/Subthreshold Leakage with power gating': 0.0388573,
'Gate Leakage': 0.00993673,
'Peak Dynamic': 0.216542,
'Runtime Dynamic': 0.0281445,
'Subthreshold Leakage': 0.103619,
'Subthreshold Leakage with power gating': 0.0388573},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0723132,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.259487,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.391893,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.476432,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.321039,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.555923,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.36686,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.24382,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.257251,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.359953,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.07797,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0740369,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0116379,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.111174,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0860694,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.18521,
'Execution Unit/Register Files/Runtime Dynamic': 0.0977073,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.288452,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.726224,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.16362,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00168503,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00168503,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00147496,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000574968,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00123639,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00608142,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0158954,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0827408,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.26302,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.202846,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.281025,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 7.74087,
'Instruction Fetch Unit/Runtime Dynamic': 0.588588,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0322611,
'L2/Runtime Dynamic': 0.00680373,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 4.68097,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.65482,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.111417,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.111417,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.20925,
'Load Store Unit/Runtime Dynamic': 2.3157,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.274735,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.54947,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0975043,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0979881,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.327235,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0332409,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.6543,
'Memory Management Unit/Runtime Dynamic': 0.131229,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 24.2763,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.258297,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.0195243,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.163068,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.44089,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 6.64684,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0791433,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.264851,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.427065,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.491905,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.352513,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.610425,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.402439,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.36538,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.282968,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.385727,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.19487,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0806818,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0127789,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.122051,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0945075,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.202732,
'Execution Unit/Register Files/Runtime Dynamic': 0.107286,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.316606,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.796848,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.41199,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00182918,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00182918,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00159979,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000622896,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00135761,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00661576,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0173034,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0908525,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.779,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.219694,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.308576,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.28227,
'Instruction Fetch Unit/Runtime Dynamic': 0.643042,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
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'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0541553,
'L2/Runtime Dynamic': 0.0114114,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Data Cache/Peak Dynamic': 5.01726,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.81622,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.122297,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.122297,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
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'Load Store Unit/Peak Dynamic': 5.59713,
'Load Store Unit/Runtime Dynamic': 2.54164,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.301563,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.603125,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Peak Dynamic': 0.107026,
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'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Peak Dynamic': 0.702842,
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'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
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'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
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'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.179184,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.482076,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 7.23401,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0758444,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.26226,
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'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.337593,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.58459,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.385514,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.3077,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.270852,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.373508,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.13883,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0774411,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.012238,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.116878,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0905077,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.194319,
'Execution Unit/Register Files/Runtime Dynamic': 0.102746,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.303204,
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'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.29371,
'Execution Unit/Subthreshold Leakage': 1.83518,
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'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00176219,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00176219,
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'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00154193,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000600772,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00130015,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00636647,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0166433,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0870074,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.53441,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.211193,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.295516,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.02563,
'Instruction Fetch Unit/Runtime Dynamic': 0.616727,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0455747,
'L2/Runtime Dynamic': 0.00960554,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 4.85698,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.73924,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.117111,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.117111,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.41226,
'Load Store Unit/Runtime Dynamic': 2.4339,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.288776,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.577552,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Peak Dynamic': 0.102488,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.103171,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Memory Management Unit/Peak Dynamic': 0.679789,
'Memory Management Unit/Runtime Dynamic': 0.137783,
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'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
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'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.270174,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
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'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
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'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
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'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
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'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.462241,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 6.95397,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
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'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
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'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
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'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
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'Execution Unit/Floating Point Units/Runtime Dynamic': 0.480808,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.330139,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.571682,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.377117,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.27894,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.264722,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.367404,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.11148,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0759158,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0119678,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.114311,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0885092,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.190227,
'Execution Unit/Register Files/Runtime Dynamic': 0.100477,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.296567,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.746537,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.23518,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00172664,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00172664,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.0015109,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.00058872,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00127144,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00623563,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.016305,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0850862,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.41221,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.207506,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.288991,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 7.89741,
'Instruction Fetch Unit/Runtime Dynamic': 0.604123,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0392334,
'L2/Runtime Dynamic': 0.00836828,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 4.77778,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.70139,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.114549,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.114549,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.32091,
'Load Store Unit/Runtime Dynamic': 2.38085,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.282458,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.564916,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.100245,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.100834,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.336512,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.034007,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.668315,
'Memory Management Unit/Runtime Dynamic': 0.134841,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 24.599,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.264854,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.0200685,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.167732,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.452654,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 6.81602,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0787304,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.264527,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.424947,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.490974,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.350462,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.606874,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.400147,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.35748,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.281267,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.384066,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.18751,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0802817,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0127045,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.121354,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0939578,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.201636,
'Execution Unit/Register Files/Runtime Dynamic': 0.106662,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.31481,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.792375,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.39609,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00181551,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00181551,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00158702,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000617489,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00134971,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00656775,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0172027,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0903241,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.74538,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.218765,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.306781,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.247,
'Instruction Fetch Unit/Runtime Dynamic': 0.639641,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0510915,
'L2/Runtime Dynamic': 0.0108015,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 4.99657,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.8063,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.121627,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.121627,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.57326,
'Load Store Unit/Runtime Dynamic': 2.52775,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.299912,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.599823,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.10644,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.107206,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.357227,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0358527,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.699739,
'Memory Management Unit/Runtime Dynamic': 0.143059,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
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'Peak Dynamic': 25.3203,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.280085,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.0212911,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.178133,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.479509,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 7.19685,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0789439,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.264694,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.425997,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.491435,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.35196,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.609468,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.401759,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.36319,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.282587,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.385229,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.19216,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0804801,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0127588,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.121831,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0943595,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.202311,
'Execution Unit/Register Files/Runtime Dynamic': 0.107118,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.316021,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.795281,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.40695,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00183089,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00183089,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00160163,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000623801,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00135548,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00661889,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0173072,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0907102,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 5.76995,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.21938,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.308092,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.27277,
'Instruction Fetch Unit/Runtime Dynamic': 0.642109,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.051916,
'L2/Runtime Dynamic': 0.0109555,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 5.0097,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.81258,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.122052,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.122052,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 5.5884,
'Load Store Unit/Runtime Dynamic': 2.53655,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.300959,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.601918,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.106811,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.10759,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.358754,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0359537,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.701909,
'Memory Management Unit/Runtime Dynamic': 0.143544,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 25.3689,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.280776,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.021376,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.178902,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.481054,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 7.22116,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0713146,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.258703,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.386822,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.474201,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.31606,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.547302,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.361286,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.22465,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.253108,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.355881,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.06018,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.073079,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0114574,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.109482,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0847348,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.182561,
'Execution Unit/Register Files/Runtime Dynamic': 0.0961922,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.28409,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.715316,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 3.12494,
'Execution Unit/Subthreshold Leakage': 1.83518,
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'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
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'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00599874,
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'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
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'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
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'Execution Unit/Area': 8.2042,
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'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00639793,
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'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0441599,
'L2/Runtime Dynamic': 0.00944066,
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'Load Store Unit/Data Cache/Area': 6.84535,
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'Peak Dynamic': 0.026291,
'Runtime Dynamic': 0.0159925,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364},
{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.0256158,
'Runtime Dynamic': 0.0152321,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 388.384,
'Gate Leakage': 3.09074,
'Peak Dynamic': 199.133,
'Peak Power': 265.679,
'Runtime Dynamic': 55.679,
'Subthreshold Leakage': 63.4555,
'Subthreshold Leakage with power gating': 28.0572,
'Total Cores/Area': 260.865,
'Total Cores/Gate Leakage': 2.98397,
'Total Cores/Peak Dynamic': 198.864,
'Total Cores/Runtime Dynamic': 55.6196,
'Total Cores/Subthreshold Leakage': 49.7502,
'Total Cores/Subthreshold Leakage with power gating': 20.6649,
'Total L3s/Area': 123.815,
'Total L3s/Gate Leakage': 0.0968273,
'Total L3s/Peak Dynamic': 0.0519068,
'Total L3s/Runtime Dynamic': 0.0312246,
'Total L3s/Subthreshold Leakage': 13.6017,
'Total L3s/Subthreshold Leakage with power gating': 6.64728,
'Total Leakage': 66.5462,
'Total NoCs/Area': 3.70399,
'Total NoCs/Gate Leakage': 0.00993673,
'Total NoCs/Peak Dynamic': 0.216542,
'Total NoCs/Runtime Dynamic': 0.0281445,
'Total NoCs/Subthreshold Leakage': 0.103619,
'Total NoCs/Subthreshold Leakage with power gating': 0.0388573}} | 75.692825 | 124 | 0.684136 | 15,928 | 135,036 | 5.794011 | 0.044576 | 0.122292 | 0.114426 | 0.094661 | 0.961143 | 0.959669 | 0.951174 | 0.925851 | 0.910161 | 0.904439 | 0 | 0.130598 | 0.222755 | 135,036 | 1,784 | 125 | 75.692825 | 0.748695 | 0 | 0 | 0.723094 | 0 | 0 | 0.660811 | 0.048875 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
92213088c2ff56c7d8ae6f9ffd14590d7ceda74e | 120 | py | Python | openks/models/pytorch/ke_modules/nero_modules/__init__.py | zhengkangjie/OpenKS | 4e010c3d3cc6acec3968fd92e21a473e02e76f70 | [
"Apache-2.0"
] | 1 | 2021-03-12T02:41:05.000Z | 2021-03-12T02:41:05.000Z | openks/models/pytorch/ke_modules/nero_modules/__init__.py | zhengkangjie/OpenKS | 4e010c3d3cc6acec3968fd92e21a473e02e76f70 | [
"Apache-2.0"
] | null | null | null | openks/models/pytorch/ke_modules/nero_modules/__init__.py | zhengkangjie/OpenKS | 4e010c3d3cc6acec3968fd92e21a473e02e76f70 | [
"Apache-2.0"
] | 1 | 2021-02-08T11:08:26.000Z | 2021-02-08T11:08:26.000Z | from .main import *
from .models.soft_match_bert import *
from .models.pat_match import *
from . import semeval_constant | 30 | 37 | 0.8 | 18 | 120 | 5.111111 | 0.555556 | 0.326087 | 0.347826 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 120 | 4 | 38 | 30 | 0.87619 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
9230e5b177de103567c3aea96cef7fe8ece15cbd | 34,977 | py | Python | tb_rest_client/api/api_pe/sig_fox_integration_controller_api.py | maksonlee/python_tb_rest_client | a6cd17ef4de31f68c3226b7a9835292fbac4b1fa | [
"Apache-2.0"
] | 1 | 2021-07-19T10:09:04.000Z | 2021-07-19T10:09:04.000Z | tb_rest_client/api/api_pe/sig_fox_integration_controller_api.py | moravcik94/python_tb_rest_client | 985361890cdf4ccce93d2b24905ad9003c8dfcaa | [
"Apache-2.0"
] | null | null | null | tb_rest_client/api/api_pe/sig_fox_integration_controller_api.py | moravcik94/python_tb_rest_client | 985361890cdf4ccce93d2b24905ad9003c8dfcaa | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
# Copyright 2020. ThingsBoard
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# http://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from tb_rest_client.api_client import ApiClient
class SigFoxIntegrationControllerApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def process_request_using_delete1(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_delete1(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_delete1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_delete1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_delete1_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_delete1_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_delete1`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_delete1`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_delete1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_get1(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_get1(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_get1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_get1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_get1_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_get1_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_get1`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_get1`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_get1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_head1(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_head1(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_head1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_head1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_head1_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_head1_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_head1`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_head1`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_head1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'HEAD',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_options1(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_options1(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_options1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_options1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_options1_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_options1_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_options1`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_options1`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_options1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'OPTIONS',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_patch1(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_patch1(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_patch1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_patch1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_patch1_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_patch1_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_patch1`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_patch1`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_patch1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'PATCH',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_post5(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_post5(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_post5_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_post5_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_post5_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_post5_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_post5`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_post5`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_post5`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_put1(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_put1(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_put1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_put1_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_put1_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_put1_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_put1`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_put1`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_put1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/sigfox/{routingKey}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 43.77597 | 140 | 0.638648 | 3,975 | 34,977 | 5.351195 | 0.049811 | 0.044756 | 0.056274 | 0.046072 | 0.960651 | 0.957783 | 0.957783 | 0.955949 | 0.954116 | 0.954116 | 0 | 0.0173 | 0.271207 | 34,977 | 798 | 141 | 43.830827 | 0.817151 | 0.326643 | 0 | 0.845972 | 0 | 0 | 0.230096 | 0.071048 | 0 | 0 | 0 | 0 | 0 | 1 | 0.035545 | false | 0 | 0.009479 | 0 | 0.097156 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
a63aa0bc9d4ef0d60aaacfdedbd99a2bcc1bcb78 | 258 | py | Python | src/cmarkgfm/__init__.py | waldyrious/cmarkgfm | 21112ab4bd8ff11b9e121472c2eab64ca7de1509 | [
"MIT"
] | null | null | null | src/cmarkgfm/__init__.py | waldyrious/cmarkgfm | 21112ab4bd8ff11b9e121472c2eab64ca7de1509 | [
"MIT"
] | 1 | 2020-04-08T14:28:08.000Z | 2020-04-08T14:28:08.000Z | src/cmarkgfm/__init__.py | waldyrious/cmarkgfm | 21112ab4bd8ff11b9e121472c2eab64ca7de1509 | [
"MIT"
] | 1 | 2020-03-30T15:48:20.000Z | 2020-03-30T15:48:20.000Z | from cmarkgfm.cmark import (
github_flavored_markdown_to_html,
markdown_to_html,
markdown_to_html_with_extensions)
__all__ = [
'github_flavored_markdown_to_html',
'markdown_to_html',
'markdown_to_html_with_extensions',
]
| 21.5 | 40 | 0.736434 | 31 | 258 | 5.354839 | 0.387097 | 0.361446 | 0.506024 | 0.53012 | 0.843373 | 0.843373 | 0.843373 | 0.843373 | 0.843373 | 0.843373 | 0 | 0 | 0.197674 | 258 | 11 | 41 | 23.454545 | 0.801932 | 0 | 0 | 0 | 0 | 0 | 0.323887 | 0.259109 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.111111 | 0 | 0.111111 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
a67c131d134953ca6b422520cb038388225e850c | 2,462 | py | Python | tests/table/test_batch.py | teners/piccolo | e5c32a4810badf39fc61e465747b7343309d7e12 | [
"MIT"
] | 1 | 2021-08-22T03:29:08.000Z | 2021-08-22T03:29:08.000Z | tests/table/test_batch.py | teners/piccolo | e5c32a4810badf39fc61e465747b7343309d7e12 | [
"MIT"
] | null | null | null | tests/table/test_batch.py | teners/piccolo | e5c32a4810badf39fc61e465747b7343309d7e12 | [
"MIT"
] | null | null | null | import asyncio
import math
from ..base import DBTestCase
from ..example_app.tables import Manager
class TestBatchSelect(DBTestCase):
def _check_results(self, batch):
"""
Make sure the data is returned in the correct format.
"""
self.assertTrue(type(batch) == list)
if len(batch) > 0:
row = batch[0]
self.assertTrue(type(row) == dict)
self.assertTrue("name" in row.keys())
self.assertTrue("id" in row.keys())
async def run_batch(self, batch_size):
row_count = 0
iterations = 0
async with await Manager.select().batch(
batch_size=batch_size
) as batch:
async for _batch in batch:
self._check_results(_batch)
_row_count = len(_batch)
row_count += _row_count
iterations += 1
return row_count, iterations
def test_batch(self):
row_count = 1000
self.insert_many_rows(row_count)
batch_size = 10
_row_count, iterations = asyncio.run(
self.run_batch(batch_size=batch_size), debug=True
)
_iterations = math.ceil(row_count / batch_size)
self.assertTrue(_row_count == row_count)
self.assertTrue(iterations == _iterations)
class TestBatchObjects(DBTestCase):
def _check_results(self, batch):
"""
Make sure the data is returned in the correct format.
"""
self.assertTrue(type(batch) == list)
if len(batch) > 0:
row = batch[0]
self.assertTrue(isinstance(row, Manager))
async def run_batch(self, batch_size):
row_count = 0
iterations = 0
async with await Manager.objects().batch(
batch_size=batch_size
) as batch:
async for _batch in batch:
self._check_results(_batch)
_row_count = len(_batch)
row_count += _row_count
iterations += 1
return row_count, iterations
def test_batch(self):
row_count = 1000
self.insert_many_rows(row_count)
batch_size = 10
_row_count, iterations = asyncio.run(
self.run_batch(batch_size=batch_size), debug=True
)
_iterations = math.ceil(row_count / batch_size)
self.assertTrue(_row_count == row_count)
self.assertTrue(iterations == _iterations)
| 27.355556 | 61 | 0.586515 | 284 | 2,462 | 4.827465 | 0.214789 | 0.128373 | 0.078775 | 0.055434 | 0.841721 | 0.841721 | 0.841721 | 0.841721 | 0.841721 | 0.841721 | 0 | 0.013301 | 0.328188 | 2,462 | 89 | 62 | 27.662921 | 0.815599 | 0.043461 | 0 | 0.774194 | 0 | 0 | 0.0026 | 0 | 0 | 0 | 0 | 0 | 0.16129 | 1 | 0.064516 | false | 0 | 0.064516 | 0 | 0.193548 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
a69ade6877e9ce4ef06cfdc9b9ff0769096f4696 | 231 | py | Python | torch/ao/sparsity/__init__.py | mdmn07C5/pytorch | b14bde466d5d9c5329eff15c07a92dbe96be7b35 | [
"Intel"
] | 1 | 2022-01-25T15:48:31.000Z | 2022-01-25T15:48:31.000Z | torch/ao/sparsity/__init__.py | mdmn07C5/pytorch | b14bde466d5d9c5329eff15c07a92dbe96be7b35 | [
"Intel"
] | null | null | null | torch/ao/sparsity/__init__.py | mdmn07C5/pytorch | b14bde466d5d9c5329eff15c07a92dbe96be7b35 | [
"Intel"
] | null | null | null | # Parametrizations
from .experimental.pruner.parametrization import PruningParametrization
from .experimental.pruner.parametrization import ActivationReconstruction
# Pruner
from .experimental.pruner.base_pruner import BasePruner
| 33 | 73 | 0.878788 | 21 | 231 | 9.619048 | 0.47619 | 0.237624 | 0.326733 | 0.366337 | 0.425743 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.073593 | 231 | 6 | 74 | 38.5 | 0.943925 | 0.099567 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
a6d1b33ffe3d99422e8e050f86c0ee900b8b342c | 133 | py | Python | hubconf.py | Ivan1248/FCHarDNet | 5d6926aff93e8a1e1a2c8904975b05c19063914a | [
"MIT"
] | null | null | null | hubconf.py | Ivan1248/FCHarDNet | 5d6926aff93e8a1e1a2c8904975b05c19063914a | [
"MIT"
] | null | null | null | hubconf.py | Ivan1248/FCHarDNet | 5d6926aff93e8a1e1a2c8904975b05c19063914a | [
"MIT"
] | null | null | null | from ptsemseg.models import get_model
def fc_hardnet_70(n_classes):
return get_model(dict(arch='hardnet'), n_classes=n_classes)
| 26.6 | 63 | 0.796992 | 22 | 133 | 4.5 | 0.681818 | 0.242424 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016807 | 0.105263 | 133 | 4 | 64 | 33.25 | 0.815126 | 0 | 0 | 0 | 0 | 0 | 0.052632 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 7 |
5b910ac371c2f76cf6e8712a1c6b711f6bf529c2 | 91,457 | py | Python | examples/psi4_interface/eom_ccsd.py | maxscheurer/pdaggerq | e9fef3466e0d0170afc3094ab79e603200e78dfb | [
"Apache-2.0"
] | 37 | 2020-09-17T19:29:18.000Z | 2022-03-03T16:29:16.000Z | examples/psi4_interface/eom_ccsd.py | maxscheurer/pdaggerq | e9fef3466e0d0170afc3094ab79e603200e78dfb | [
"Apache-2.0"
] | 7 | 2021-02-28T19:22:12.000Z | 2022-02-22T15:17:47.000Z | examples/psi4_interface/eom_ccsd.py | maxscheurer/pdaggerq | e9fef3466e0d0170afc3094ab79e603200e78dfb | [
"Apache-2.0"
] | 6 | 2021-02-16T22:34:29.000Z | 2021-12-04T19:37:23.000Z | # pdaggerq - A code for bringing strings of creation / annihilation operators to normal order.
# Copyright (C) 2020 A. Eugene DePrince III
#
# This file is part of the pdaggerq package.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Functions to build EOM-CCSD Hamiltonian
"""
import numpy as np
from numpy import einsum
def build_eom_ccsd_H_by_block(kd, f, g, o, v, t1, t2):
# H(0;0) = <0| e(-T) H e(T) |0>
# 1.0000 f(i,i)
H00 = 1.000000000000000 * einsum('ii', f[o, o])
# 1.0000 f(i,a)*t1(a,i)
H00 += 1.000000000000000 * einsum('ia,ai', f[o, v], t1)
# -0.5000 <j,i||j,i>
H00 += -0.500000000000000 * einsum('jiji', g[o, o, o, o])
# 0.2500 <j,i||a,b>*t2(a,b,j,i)
H00 += 0.250000000000000 * einsum('jiab,abji', g[o, o, v, v], t2)
# -0.5000 <j,i||a,b>*t1(a,i)*t1(b,j)
H00 += -0.500000000000000 * einsum('jiab,ai,bj', g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# H(m,e;0) = <0|e1(m,e) e(-T) H e(T) |0>
# 1.0000 f(e,m)
Hs0 = 1.000000000000000 * einsum('em->em', f[v, o])
# -1.0000 f(i,m)*t1(e,i)
Hs0 += -1.000000000000000 * einsum('im,ei->em', f[o, o], t1)
# 1.0000 f(e,a)*t1(a,m)
Hs0 += 1.000000000000000 * einsum('ea,am->em', f[v, v], t1)
# -1.0000 f(i,a)*t2(a,e,m,i)
Hs0 += -1.000000000000000 * einsum('ia,aemi->em', f[o, v], t2)
# -1.0000 f(i,a)*t1(a,m)*t1(e,i)
Hs0 += -1.000000000000000 * einsum('ia,am,ei->em', f[o, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# 1.0000 <i,e||a,m>*t1(a,i)
Hs0 += 1.000000000000000 * einsum('ieam,ai->em', g[o, v, v, o], t1)
# -0.5000 <j,i||a,m>*t2(a,e,j,i)
Hs0 += -0.500000000000000 * einsum('jiam,aeji->em', g[o, o, v, o], t2)
# -0.5000 <i,e||a,b>*t2(a,b,m,i)
Hs0 += -0.500000000000000 * einsum('ieab,abmi->em', g[o, v, v, v], t2)
# 1.0000 <j,i||a,b>*t1(a,i)*t2(b,e,m,j)
Hs0 += 1.000000000000000 * einsum('jiab,ai,bemj->em', g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
# 0.5000 <j,i||a,b>*t1(a,m)*t2(b,e,j,i)
Hs0 += 0.500000000000000 * einsum('jiab,am,beji->em', g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 2), (0, 1)])
# 0.5000 <j,i||a,b>*t1(e,i)*t2(a,b,m,j)
Hs0 += 0.500000000000000 * einsum('jiab,ei,abmj->em', g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 2), (0, 1)])
# 1.0000 <j,i||a,m>*t1(a,i)*t1(e,j)
Hs0 += 1.000000000000000 * einsum('jiam,ai,ej->em', g[o, o, v, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# 1.0000 <i,e||a,b>*t1(a,i)*t1(b,m)
Hs0 += 1.000000000000000 * einsum('ieab,ai,bm->em', g[o, v, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# 1.0000 <j,i||a,b>*t1(a,i)*t1(b,m)*t1(e,j)
Hs0 += 1.000000000000000 * einsum('jiab,ai,bm,ej->em', g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
# H(0;i,a) = <0| e(-T) H e(T) e1(a,i)|0>
# 1.0000 f(i,a)
H0s = 1.000000000000000 * einsum('ia->ai', f[o, v])
# -1.0000 <i,j||b,a>*t1(b,j)
H0s += -1.000000000000000 * einsum('ijba,bj->ai', g[o, o, v, v], t1)
# H(m,n,e,f;0) = <0|e2(m,n,f,e) e(-T) H e(T) |0>
# -1.0000 P(m,n)f(i,n)*t2(e,f,m,i)
contracted_intermediate = -1.000000000000000 * einsum('in,efmi->efmn', f[o, o], t2)
Hd0 = 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# 1.0000 P(e,f)f(e,a)*t2(a,f,m,n)
contracted_intermediate = 1.000000000000000 * einsum('ea,afmn->efmn', f[v, v], t2)
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# -1.0000 P(m,n)f(i,a)*t1(a,n)*t2(e,f,m,i)
contracted_intermediate = -1.000000000000000 * einsum('ia,an,efmi->efmn', f[o, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# -1.0000 P(e,f)f(i,a)*t1(e,i)*t2(a,f,m,n)
contracted_intermediate = -1.000000000000000 * einsum('ia,ei,afmn->efmn', f[o, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# 1.0000 <e,f||m,n>
Hd0 += 1.000000000000000 * einsum('efmn->efmn', g[v, v, o, o])
# 1.0000 P(e,f)<i,e||m,n>*t1(f,i)
contracted_intermediate = 1.000000000000000 * einsum('iemn,fi->efmn', g[o, v, o, o], t1)
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# 1.0000 P(m,n)<e,f||a,n>*t1(a,m)
contracted_intermediate = 1.000000000000000 * einsum('efan,am->efmn', g[v, v, v, o], t1)
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# 0.5000 <j,i||m,n>*t2(e,f,j,i)
Hd0 += 0.500000000000000 * einsum('jimn,efji->efmn', g[o, o, o, o], t2)
# 1.0000 P(m,n)*P(e,f)<i,e||a,n>*t2(a,f,m,i)
contracted_intermediate = 1.000000000000000 * einsum('iean,afmi->efmn', g[o, v, v, o], t2)
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate) + -1.00000 * einsum('efmn->femn', contracted_intermediate) + 1.00000 * einsum('efmn->fenm', contracted_intermediate)
# 0.5000 <e,f||a,b>*t2(a,b,m,n)
Hd0 += 0.500000000000000 * einsum('efab,abmn->efmn', g[v, v, v, v], t2)
# 1.0000 P(m,n)<j,i||a,n>*t1(a,i)*t2(e,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('jian,ai,efmj->efmn', g[o, o, v, o], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# 0.5000 P(m,n)<j,i||a,n>*t1(a,m)*t2(e,f,j,i)
contracted_intermediate = 0.500000000000000 * einsum('jian,am,efji->efmn', g[o, o, v, o], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# -1.0000 P(m,n)*P(e,f)<j,i||a,n>*t1(e,i)*t2(a,f,m,j)
contracted_intermediate = -1.000000000000000 * einsum('jian,ei,afmj->efmn', g[o, o, v, o], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate) + -1.00000 * einsum('efmn->femn', contracted_intermediate) + 1.00000 * einsum('efmn->fenm', contracted_intermediate)
# 1.0000 P(e,f)<i,e||a,b>*t1(a,i)*t2(b,f,m,n)
contracted_intermediate = 1.000000000000000 * einsum('ieab,ai,bfmn->efmn', g[o, v, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# -1.0000 P(m,n)*P(e,f)<i,e||a,b>*t1(a,n)*t2(b,f,m,i)
contracted_intermediate = -1.000000000000000 * einsum('ieab,an,bfmi->efmn', g[o, v, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate) + -1.00000 * einsum('efmn->femn', contracted_intermediate) + 1.00000 * einsum('efmn->fenm', contracted_intermediate)
# 0.5000 P(e,f)<i,e||a,b>*t1(f,i)*t2(a,b,m,n)
contracted_intermediate = 0.500000000000000 * einsum('ieab,fi,abmn->efmn', g[o, v, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# -1.0000 <j,i||m,n>*t1(e,i)*t1(f,j)
Hd0 += -1.000000000000000 * einsum('jimn,ei,fj->efmn', g[o, o, o, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# 1.0000 P(m,n)*P(e,f)<i,e||a,n>*t1(a,m)*t1(f,i)
contracted_intermediate = 1.000000000000000 * einsum('iean,am,fi->efmn', g[o, v, v, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate) + -1.00000 * einsum('efmn->femn', contracted_intermediate) + 1.00000 * einsum('efmn->fenm', contracted_intermediate)
# -1.0000 <e,f||a,b>*t1(a,n)*t1(b,m)
Hd0 += -1.000000000000000 * einsum('efab,an,bm->efmn', g[v, v, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# -0.5000 P(m,n)<j,i||a,b>*t2(a,b,n,i)*t2(e,f,m,j)
contracted_intermediate = -0.500000000000000 * einsum('jiab,abni,efmj->efmn', g[o, o, v, v], t2, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# 0.2500 <j,i||a,b>*t2(a,b,m,n)*t2(e,f,j,i)
Hd0 += 0.250000000000000 * einsum('jiab,abmn,efji->efmn', g[o, o, v, v], t2, t2, optimize=['einsum_path', (0, 1), (0, 1)])
# -0.5000 <j,i||a,b>*t2(a,e,j,i)*t2(b,f,m,n)
Hd0 += -0.500000000000000 * einsum('jiab,aeji,bfmn->efmn', g[o, o, v, v], t2, t2, optimize=['einsum_path', (0, 1), (0, 1)])
# 1.0000 P(m,n)<j,i||a,b>*t2(a,e,n,i)*t2(b,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('jiab,aeni,bfmj->efmn', g[o, o, v, v], t2, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# -0.5000 <j,i||a,b>*t2(a,e,m,n)*t2(b,f,j,i)
Hd0 += -0.500000000000000 * einsum('jiab,aemn,bfji->efmn', g[o, o, v, v], t2, t2, optimize=['einsum_path', (0, 2), (0, 1)])
# 1.0000 P(m,n)<j,i||a,b>*t1(a,i)*t1(b,n)*t2(e,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('jiab,ai,bn,efmj->efmn', g[o, o, v, v], t1, t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# 1.0000 P(e,f)<j,i||a,b>*t1(a,i)*t1(e,j)*t2(b,f,m,n)
contracted_intermediate = 1.000000000000000 * einsum('jiab,ai,ej,bfmn->efmn', g[o, o, v, v], t1, t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# -0.5000 <j,i||a,b>*t1(a,n)*t1(b,m)*t2(e,f,j,i)
Hd0 += -0.500000000000000 * einsum('jiab,an,bm,efji->efmn', g[o, o, v, v], t1, t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
# 1.0000 P(m,n)*P(e,f)<j,i||a,b>*t1(a,n)*t1(e,i)*t2(b,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('jiab,an,ei,bfmj->efmn', g[o, o, v, v], t1, t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate) + -1.00000 * einsum('efmn->femn', contracted_intermediate) + 1.00000 * einsum('efmn->fenm', contracted_intermediate)
# -0.5000 <j,i||a,b>*t1(e,i)*t1(f,j)*t2(a,b,m,n)
Hd0 += -0.500000000000000 * einsum('jiab,ei,fj,abmn->efmn', g[o, o, v, v], t1, t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
# -1.0000 P(m,n)<j,i||a,n>*t1(a,m)*t1(e,i)*t1(f,j)
contracted_intermediate = -1.000000000000000 * einsum('jian,am,ei,fj->efmn', g[o, o, v, o], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->efnm', contracted_intermediate)
# -1.0000 P(e,f)<i,e||a,b>*t1(a,n)*t1(b,m)*t1(f,i)
contracted_intermediate = -1.000000000000000 * einsum('ieab,an,bm,fi->efmn', g[o, v, v, v], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 2), (0, 1)])
Hd0 += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmn->femn', contracted_intermediate)
# 1.0000 <j,i||a,b>*t1(a,n)*t1(b,m)*t1(e,i)*t1(f,j)
Hd0 += 1.000000000000000 * einsum('jiab,an,bm,ei,fj->efmn', g[o, o, v, v], t1, t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 3), (0, 2), (0, 1)])
# H(0;i,j,a,b) = <0| e(-T) H e(T) e2(a,b,j,i)|0>
# 1.0000 <i,j||a,b>
H0d = 1.000000000000000 * einsum('ijab->abij', g[o, o, v, v])
# H(m,e;i,a) = <0|e1(m,e) e(-T) H e(T) e1(a,i)|0>
# 1.0000 d(e,a)*d(m,i)*f(j,j)
Hss = 1.000000000000000 * einsum('ea,mi,jj->emai', kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 2), (0, 1)])
# -1.0000 d(e,a)*f(i,m)
Hss += -1.000000000000000 * einsum('ea,im->emai', kd[v, v], f[o, o])
# 1.0000 d(m,i)*f(e,a)
Hss += 1.000000000000000 * einsum('mi,ea->emai', kd[o, o], f[v, v])
# 1.0000 d(e,a)*d(m,i)*f(j,b)*t1(b,j)
Hss += 1.000000000000000 * einsum('ea,mi,jb,bj->emai', kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (2, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*f(i,b)*t1(b,m)
Hss += -1.000000000000000 * einsum('ea,ib,bm->emai', kd[v, v], f[o, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(m,i)*f(j,a)*t1(e,j)
Hss += -1.000000000000000 * einsum('mi,ja,ej->emai', kd[o, o], f[o, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -0.5000 d(e,a)*d(m,i)*<k,j||k,j>
Hss += -0.500000000000000 * einsum('ea,mi,kjkj->emai', kd[v, v], kd[o, o], g[o, o, o, o], optimize=['einsum_path', (0, 2), (0, 1)])
# 1.0000 <i,e||a,m>
Hss += 1.000000000000000 * einsum('ieam->emai', g[o, v, v, o])
# 1.0000 d(e,a)*<i,j||b,m>*t1(b,j)
Hss += 1.000000000000000 * einsum('ea,ijbm,bj->emai', kd[v, v], g[o, o, v, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 <i,j||a,m>*t1(e,j)
Hss += -1.000000000000000 * einsum('ijam,ej->emai', g[o, o, v, o], t1)
# 1.0000 d(m,i)*<j,e||b,a>*t1(b,j)
Hss += 1.000000000000000 * einsum('mi,jeba,bj->emai', kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 <i,e||b,a>*t1(b,m)
Hss += -1.000000000000000 * einsum('ieba,bm->emai', g[o, v, v, v], t1)
# 0.2500 d(e,a)*d(m,i)*<k,j||b,c>*t2(b,c,k,j)
Hss += 0.250000000000000 * einsum('ea,mi,kjbc,bckj->emai', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (2, 3), (0, 2), (0, 1)])
# -0.5000 d(e,a)*<i,j||b,c>*t2(b,c,m,j)
Hss += -0.500000000000000 * einsum('ea,ijbc,bcmj->emai', kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -0.5000 d(m,i)*<k,j||b,a>*t2(b,e,k,j)
Hss += -0.500000000000000 * einsum('mi,kjba,bekj->emai', kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 <i,j||b,a>*t2(b,e,m,j)
Hss += 1.000000000000000 * einsum('ijba,bemj->emai', g[o, o, v, v], t2)
# -0.5000 d(e,a)*d(m,i)*<k,j||b,c>*t1(b,j)*t1(c,k)
Hss += -0.500000000000000 * einsum('ea,mi,kjbc,bj,ck->emai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (2, 3), (2, 3), (0, 2), (0, 1)])
# 1.0000 d(e,a)*<i,j||b,c>*t1(b,j)*t1(c,m)
Hss += 1.000000000000000 * einsum('ea,ijbc,bj,cm->emai', kd[v, v], g[o, o, v, v], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 d(m,i)*<k,j||b,a>*t1(b,j)*t1(e,k)
Hss += 1.000000000000000 * einsum('mi,kjba,bj,ek->emai', kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 <i,j||b,a>*t1(b,m)*t1(e,j)
Hss += 1.000000000000000 * einsum('ijba,bm,ej->emai', g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1)])
# H(m,e;i,j,a,b) = <0|e1(m,e) e(-T) H e(T) e2(a,b,j,i)|0>
# -1.0000 d(e,b)*d(m,i)*f(j,a)
Hsd = -1.000000000000000 * einsum('eb,mi,ja->emabij', kd[v, v], kd[o, o], f[o, v], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,b)*d(m,j)*f(i,a)
Hsd += 1.000000000000000 * einsum('eb,mj,ia->emabij', kd[v, v], kd[o, o], f[o, v], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,a)*d(m,i)*f(j,b)
Hsd += 1.000000000000000 * einsum('ea,mi,jb->emabij', kd[v, v], kd[o, o], f[o, v], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,a)*d(m,j)*f(i,b)
Hsd += -1.000000000000000 * einsum('ea,mj,ib->emabij', kd[v, v], kd[o, o], f[o, v], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,b)*<i,j||a,m>
Hsd += -1.000000000000000 * einsum('eb,ijam->emabij', kd[v, v], g[o, o, v, o])
# 1.0000 d(e,a)*<i,j||b,m>
Hsd += 1.000000000000000 * einsum('ea,ijbm->emabij', kd[v, v], g[o, o, v, o])
# -1.0000 d(m,i)*<j,e||a,b>
Hsd += -1.000000000000000 * einsum('mi,jeab->emabij', kd[o, o], g[o, v, v, v])
# 1.0000 d(m,j)*<i,e||a,b>
Hsd += 1.000000000000000 * einsum('mj,ieab->emabij', kd[o, o], g[o, v, v, v])
# 1.0000 d(e,b)*d(m,i)*<j,k||c,a>*t1(c,k)
Hsd += 1.000000000000000 * einsum('eb,mi,jkca,ck->emabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,b)*d(m,j)*<i,k||c,a>*t1(c,k)
Hsd += -1.000000000000000 * einsum('eb,mj,ikca,ck->emabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(m,i)*<j,k||c,b>*t1(c,k)
Hsd += -1.000000000000000 * einsum('ea,mi,jkcb,ck->emabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(m,j)*<i,k||c,b>*t1(c,k)
Hsd += 1.000000000000000 * einsum('ea,mj,ikcb,ck->emabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*<i,j||c,a>*t1(c,m)
Hsd += 1.000000000000000 * einsum('eb,ijca,cm->emabij', kd[v, v], g[o, o, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(e,a)*<i,j||c,b>*t1(c,m)
Hsd += -1.000000000000000 * einsum('ea,ijcb,cm->emabij', kd[v, v], g[o, o, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(m,i)*<j,k||a,b>*t1(e,k)
Hsd += 1.000000000000000 * einsum('mi,jkab,ek->emabij', kd[o, o], g[o, o, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(m,j)*<i,k||a,b>*t1(e,k)
Hsd += -1.000000000000000 * einsum('mj,ikab,ek->emabij', kd[o, o], g[o, o, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# H(m,n,e,f;i,a) = <0|e2(m,n,f,e) e(-T) H e(T) e1(a,i)|0>
# -1.0000 d(f,a)*d(m,i)*f(e,n)
Hds = -1.000000000000000 * einsum('fa,mi,en->efmnai', kd[v, v], kd[o, o], f[v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(f,a)*d(n,i)*f(e,m)
Hds += 1.000000000000000 * einsum('fa,ni,em->efmnai', kd[v, v], kd[o, o], f[v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,a)*d(m,i)*f(f,n)
Hds += 1.000000000000000 * einsum('ea,mi,fn->efmnai', kd[v, v], kd[o, o], f[v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,a)*d(n,i)*f(f,m)
Hds += -1.000000000000000 * einsum('ea,ni,fm->efmnai', kd[v, v], kd[o, o], f[v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(f,a)*d(m,i)*f(j,n)*t1(e,j)
Hds += 1.000000000000000 * einsum('fa,mi,jn,ej->efmnai', kd[v, v], kd[o, o], f[o, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(m,i)*f(j,n)*t1(f,j)
Hds += -1.000000000000000 * einsum('ea,mi,jn,fj->efmnai', kd[v, v], kd[o, o], f[o, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(n,i)*f(j,m)*t1(e,j)
Hds += -1.000000000000000 * einsum('fa,ni,jm,ej->efmnai', kd[v, v], kd[o, o], f[o, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(n,i)*f(j,m)*t1(f,j)
Hds += 1.000000000000000 * einsum('ea,ni,jm,fj->efmnai', kd[v, v], kd[o, o], f[o, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*f(e,b)*t1(b,n)
Hds += -1.000000000000000 * einsum('fa,mi,eb,bn->efmnai', kd[v, v], kd[o, o], f[v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*f(e,b)*t1(b,m)
Hds += 1.000000000000000 * einsum('fa,ni,eb,bm->efmnai', kd[v, v], kd[o, o], f[v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(m,i)*f(f,b)*t1(b,n)
Hds += 1.000000000000000 * einsum('ea,mi,fb,bn->efmnai', kd[v, v], kd[o, o], f[v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*f(f,b)*t1(b,m)
Hds += -1.000000000000000 * einsum('ea,ni,fb,bm->efmnai', kd[v, v], kd[o, o], f[v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(m,i)*f(j,b)*t2(b,e,n,j)
Hds += 1.000000000000000 * einsum('fa,mi,jb,benj->efmnai', kd[v, v], kd[o, o], f[o, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(n,i)*f(j,b)*t2(b,e,m,j)
Hds += -1.000000000000000 * einsum('fa,ni,jb,bemj->efmnai', kd[v, v], kd[o, o], f[o, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*f(i,b)*t2(b,e,m,n)
Hds += 1.000000000000000 * einsum('fa,ib,bemn->efmnai', kd[v, v], f[o, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(e,a)*d(m,i)*f(j,b)*t2(b,f,n,j)
Hds += -1.000000000000000 * einsum('ea,mi,jb,bfnj->efmnai', kd[v, v], kd[o, o], f[o, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(n,i)*f(j,b)*t2(b,f,m,j)
Hds += 1.000000000000000 * einsum('ea,ni,jb,bfmj->efmnai', kd[v, v], kd[o, o], f[o, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*f(i,b)*t2(b,f,m,n)
Hds += -1.000000000000000 * einsum('ea,ib,bfmn->efmnai', kd[v, v], f[o, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(m,i)*f(j,a)*t2(e,f,n,j)
Hds += 1.000000000000000 * einsum('mi,ja,efnj->efmnai', kd[o, o], f[o, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(n,i)*f(j,a)*t2(e,f,m,j)
Hds += -1.000000000000000 * einsum('ni,ja,efmj->efmnai', kd[o, o], f[o, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(f,a)*d(m,i)*f(j,b)*t1(b,n)*t1(e,j)
Hds += 1.000000000000000 * einsum('fa,mi,jb,bn,ej->efmnai', kd[v, v], kd[o, o], f[o, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(m,i)*f(j,b)*t1(b,n)*t1(f,j)
Hds += -1.000000000000000 * einsum('ea,mi,jb,bn,fj->efmnai', kd[v, v], kd[o, o], f[o, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(n,i)*f(j,b)*t1(b,m)*t1(e,j)
Hds += -1.000000000000000 * einsum('fa,ni,jb,bm,ej->efmnai', kd[v, v], kd[o, o], f[o, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(n,i)*f(j,b)*t1(b,m)*t1(f,j)
Hds += 1.000000000000000 * einsum('ea,ni,jb,bm,fj->efmnai', kd[v, v], kd[o, o], f[o, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,a)*<i,e||m,n>
Hds += 1.000000000000000 * einsum('fa,iemn->efmnai', kd[v, v], g[o, v, o, o])
# -1.0000 d(e,a)*<i,f||m,n>
Hds += -1.000000000000000 * einsum('ea,ifmn->efmnai', kd[v, v], g[o, v, o, o])
# 1.0000 d(m,i)*<e,f||a,n>
Hds += 1.000000000000000 * einsum('mi,efan->efmnai', kd[o, o], g[v, v, v, o])
# -1.0000 d(n,i)*<e,f||a,m>
Hds += -1.000000000000000 * einsum('ni,efam->efmnai', kd[o, o], g[v, v, v, o])
# -1.0000 d(f,a)*<i,j||m,n>*t1(e,j)
Hds += -1.000000000000000 * einsum('fa,ijmn,ej->efmnai', kd[v, v], g[o, o, o, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(e,a)*<i,j||m,n>*t1(f,j)
Hds += 1.000000000000000 * einsum('ea,ijmn,fj->efmnai', kd[v, v], g[o, o, o, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<j,e||b,n>*t1(b,j)
Hds += -1.000000000000000 * einsum('fa,mi,jebn,bj->efmnai', kd[v, v], kd[o, o], g[o, v, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 P(m,n)d(f,a)*<i,e||b,n>*t1(b,m)
contracted_intermediate = 1.000000000000000 * einsum('fa,iebn,bm->efmnai', kd[v, v], g[o, v, v, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# 1.0000 P(e,f)d(m,i)*<j,e||a,n>*t1(f,j)
contracted_intermediate = 1.000000000000000 * einsum('mi,jean,fj->efmnai', kd[o, o], g[o, v, v, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 1.0000 d(f,a)*d(n,i)*<j,e||b,m>*t1(b,j)
Hds += 1.000000000000000 * einsum('fa,ni,jebm,bj->efmnai', kd[v, v], kd[o, o], g[o, v, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 P(e,f)d(n,i)*<j,e||a,m>*t1(f,j)
contracted_intermediate = -1.000000000000000 * einsum('ni,jeam,fj->efmnai', kd[o, o], g[o, v, v, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 1.0000 d(e,a)*d(m,i)*<j,f||b,n>*t1(b,j)
Hds += 1.000000000000000 * einsum('ea,mi,jfbn,bj->efmnai', kd[v, v], kd[o, o], g[o, v, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 P(m,n)d(e,a)*<i,f||b,n>*t1(b,m)
contracted_intermediate = -1.000000000000000 * einsum('ea,ifbn,bm->efmnai', kd[v, v], g[o, v, v, o], t1, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# -1.0000 d(e,a)*d(n,i)*<j,f||b,m>*t1(b,j)
Hds += -1.000000000000000 * einsum('ea,ni,jfbm,bj->efmnai', kd[v, v], kd[o, o], g[o, v, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(m,i)*<e,f||b,a>*t1(b,n)
Hds += -1.000000000000000 * einsum('mi,efba,bn->efmnai', kd[o, o], g[v, v, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(n,i)*<e,f||b,a>*t1(b,m)
Hds += 1.000000000000000 * einsum('ni,efba,bm->efmnai', kd[o, o], g[v, v, v, v], t1, optimize=['einsum_path', (1, 2), (0, 1)])
# 0.5000 d(f,a)*d(m,i)*<k,j||b,n>*t2(b,e,k,j)
Hds += 0.500000000000000 * einsum('fa,mi,kjbn,bekj->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 P(m,n)d(f,a)*<i,j||b,n>*t2(b,e,m,j)
contracted_intermediate = -1.000000000000000 * einsum('fa,ijbn,bemj->efmnai', kd[v, v], g[o, o, v, o], t2, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# -0.5000 d(e,a)*d(m,i)*<k,j||b,n>*t2(b,f,k,j)
Hds += -0.500000000000000 * einsum('ea,mi,kjbn,bfkj->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 P(m,n)d(e,a)*<i,j||b,n>*t2(b,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('ea,ijbn,bfmj->efmnai', kd[v, v], g[o, o, v, o], t2, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# 0.5000 d(m,i)*<k,j||a,n>*t2(e,f,k,j)
Hds += 0.500000000000000 * einsum('mi,kjan,efkj->efmnai', kd[o, o], g[o, o, v, o], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 P(m,n)<i,j||a,n>*t2(e,f,m,j)
contracted_intermediate = -1.000000000000000 * einsum('ijan,efmj->efmnai', g[o, o, v, o], t2)
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# -0.5000 d(f,a)*d(n,i)*<k,j||b,m>*t2(b,e,k,j)
Hds += -0.500000000000000 * einsum('fa,ni,kjbm,bekj->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 0.5000 d(e,a)*d(n,i)*<k,j||b,m>*t2(b,f,k,j)
Hds += 0.500000000000000 * einsum('ea,ni,kjbm,bfkj->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -0.5000 d(n,i)*<k,j||a,m>*t2(e,f,k,j)
Hds += -0.500000000000000 * einsum('ni,kjam,efkj->efmnai', kd[o, o], g[o, o, v, o], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 0.5000 d(f,a)*d(m,i)*<j,e||b,c>*t2(b,c,n,j)
Hds += 0.500000000000000 * einsum('fa,mi,jebc,bcnj->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -0.5000 d(f,a)*d(n,i)*<j,e||b,c>*t2(b,c,m,j)
Hds += -0.500000000000000 * einsum('fa,ni,jebc,bcmj->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 0.5000 d(f,a)*<i,e||b,c>*t2(b,c,m,n)
Hds += 0.500000000000000 * einsum('fa,iebc,bcmn->efmnai', kd[v, v], g[o, v, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 P(e,f)d(m,i)*<j,e||b,a>*t2(b,f,n,j)
contracted_intermediate = -1.000000000000000 * einsum('mi,jeba,bfnj->efmnai', kd[o, o], g[o, v, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 1.0000 P(e,f)d(n,i)*<j,e||b,a>*t2(b,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('ni,jeba,bfmj->efmnai', kd[o, o], g[o, v, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# -1.0000 P(e,f)<i,e||b,a>*t2(b,f,m,n)
contracted_intermediate = -1.000000000000000 * einsum('ieba,bfmn->efmnai', g[o, v, v, v], t2)
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# -0.5000 d(e,a)*d(m,i)*<j,f||b,c>*t2(b,c,n,j)
Hds += -0.500000000000000 * einsum('ea,mi,jfbc,bcnj->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 0.5000 d(e,a)*d(n,i)*<j,f||b,c>*t2(b,c,m,j)
Hds += 0.500000000000000 * einsum('ea,ni,jfbc,bcmj->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -0.5000 d(e,a)*<i,f||b,c>*t2(b,c,m,n)
Hds += -0.500000000000000 * einsum('ea,ifbc,bcmn->efmnai', kd[v, v], g[o, v, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<k,j||b,c>*t1(b,j)*t2(c,e,n,k)
Hds += -1.000000000000000 * einsum('fa,mi,kjbc,bj,cenk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*<k,j||b,c>*t1(b,j)*t2(c,e,m,k)
Hds += 1.000000000000000 * einsum('fa,ni,kjbc,bj,cemk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,a)*<i,j||b,c>*t1(b,j)*t2(c,e,m,n)
Hds += -1.000000000000000 * einsum('fa,ijbc,bj,cemn->efmnai', kd[v, v], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 d(e,a)*d(m,i)*<k,j||b,c>*t1(b,j)*t2(c,f,n,k)
Hds += 1.000000000000000 * einsum('ea,mi,kjbc,bj,cfnk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*<k,j||b,c>*t1(b,j)*t2(c,f,m,k)
Hds += -1.000000000000000 * einsum('ea,ni,kjbc,bj,cfmk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,a)*<i,j||b,c>*t1(b,j)*t2(c,f,m,n)
Hds += 1.000000000000000 * einsum('ea,ijbc,bj,cfmn->efmnai', kd[v, v], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -1.0000 d(m,i)*<k,j||b,a>*t1(b,j)*t2(e,f,n,k)
Hds += -1.000000000000000 * einsum('mi,kjba,bj,efnk->efmnai', kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 d(n,i)*<k,j||b,a>*t1(b,j)*t2(e,f,m,k)
Hds += 1.000000000000000 * einsum('ni,kjba,bj,efmk->efmnai', kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -0.5000 d(f,a)*d(m,i)*<k,j||b,c>*t1(b,n)*t2(c,e,k,j)
Hds += -0.500000000000000 * einsum('fa,mi,kjbc,bn,cekj->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 1.0000 P(m,n)d(f,a)*<i,j||b,c>*t1(b,n)*t2(c,e,m,j)
contracted_intermediate = 1.000000000000000 * einsum('fa,ijbc,bn,cemj->efmnai', kd[v, v], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# 0.5000 d(e,a)*d(m,i)*<k,j||b,c>*t1(b,n)*t2(c,f,k,j)
Hds += 0.500000000000000 * einsum('ea,mi,kjbc,bn,cfkj->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -1.0000 P(m,n)d(e,a)*<i,j||b,c>*t1(b,n)*t2(c,f,m,j)
contracted_intermediate = -1.000000000000000 * einsum('ea,ijbc,bn,cfmj->efmnai', kd[v, v], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# -0.5000 d(m,i)*<k,j||b,a>*t1(b,n)*t2(e,f,k,j)
Hds += -0.500000000000000 * einsum('mi,kjba,bn,efkj->efmnai', kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 P(m,n)<i,j||b,a>*t1(b,n)*t2(e,f,m,j)
contracted_intermediate = 1.000000000000000 * einsum('ijba,bn,efmj->efmnai', g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# 0.5000 d(f,a)*d(n,i)*<k,j||b,c>*t1(b,m)*t2(c,e,k,j)
Hds += 0.500000000000000 * einsum('fa,ni,kjbc,bm,cekj->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -0.5000 d(e,a)*d(n,i)*<k,j||b,c>*t1(b,m)*t2(c,f,k,j)
Hds += -0.500000000000000 * einsum('ea,ni,kjbc,bm,cfkj->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 0.5000 d(n,i)*<k,j||b,a>*t1(b,m)*t2(e,f,k,j)
Hds += 0.500000000000000 * einsum('ni,kjba,bm,efkj->efmnai', kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -0.5000 d(f,a)*d(m,i)*<k,j||b,c>*t1(e,j)*t2(b,c,n,k)
Hds += -0.500000000000000 * einsum('fa,mi,kjbc,ej,bcnk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 0.5000 d(f,a)*d(n,i)*<k,j||b,c>*t1(e,j)*t2(b,c,m,k)
Hds += 0.500000000000000 * einsum('fa,ni,kjbc,ej,bcmk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -0.5000 d(f,a)*<i,j||b,c>*t1(e,j)*t2(b,c,m,n)
Hds += -0.500000000000000 * einsum('fa,ijbc,ej,bcmn->efmnai', kd[v, v], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 P(e,f)d(m,i)*<k,j||b,a>*t1(e,j)*t2(b,f,n,k)
contracted_intermediate = 1.000000000000000 * einsum('mi,kjba,ej,bfnk->efmnai', kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# -1.0000 P(e,f)d(n,i)*<k,j||b,a>*t1(e,j)*t2(b,f,m,k)
contracted_intermediate = -1.000000000000000 * einsum('ni,kjba,ej,bfmk->efmnai', kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 1.0000 P(e,f)<i,j||b,a>*t1(e,j)*t2(b,f,m,n)
contracted_intermediate = 1.000000000000000 * einsum('ijba,ej,bfmn->efmnai', g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 0.5000 d(e,a)*d(m,i)*<k,j||b,c>*t1(f,j)*t2(b,c,n,k)
Hds += 0.500000000000000 * einsum('ea,mi,kjbc,fj,bcnk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -0.5000 d(e,a)*d(n,i)*<k,j||b,c>*t1(f,j)*t2(b,c,m,k)
Hds += -0.500000000000000 * einsum('ea,ni,kjbc,fj,bcmk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t2, optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 0.5000 d(e,a)*<i,j||b,c>*t1(f,j)*t2(b,c,m,n)
Hds += 0.500000000000000 * einsum('ea,ijbc,fj,bcmn->efmnai', kd[v, v], g[o, o, v, v], t1, t2, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<k,j||b,n>*t1(b,j)*t1(e,k)
Hds += -1.000000000000000 * einsum('fa,mi,kjbn,bj,ek->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(m,i)*<k,j||b,n>*t1(b,j)*t1(f,k)
Hds += 1.000000000000000 * einsum('ea,mi,kjbn,bj,fk->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 P(m,n)d(f,a)*<i,j||b,n>*t1(b,m)*t1(e,j)
contracted_intermediate = -1.000000000000000 * einsum('fa,ijbn,bm,ej->efmnai', kd[v, v], g[o, o, v, o], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# 1.0000 P(m,n)d(e,a)*<i,j||b,n>*t1(b,m)*t1(f,j)
contracted_intermediate = 1.000000000000000 * einsum('ea,ijbn,bm,fj->efmnai', kd[v, v], g[o, o, v, o], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->efnmai', contracted_intermediate)
# -1.0000 d(m,i)*<k,j||a,n>*t1(e,j)*t1(f,k)
Hds += -1.000000000000000 * einsum('mi,kjan,ej,fk->efmnai', kd[o, o], g[o, o, v, o], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*<k,j||b,m>*t1(b,j)*t1(e,k)
Hds += 1.000000000000000 * einsum('fa,ni,kjbm,bj,ek->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*<k,j||b,m>*t1(b,j)*t1(f,k)
Hds += -1.000000000000000 * einsum('ea,ni,kjbm,bj,fk->efmnai', kd[v, v], kd[o, o], g[o, o, v, o], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(n,i)*<k,j||a,m>*t1(e,j)*t1(f,k)
Hds += 1.000000000000000 * einsum('ni,kjam,ej,fk->efmnai', kd[o, o], g[o, o, v, o], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<j,e||b,c>*t1(b,j)*t1(c,n)
Hds += -1.000000000000000 * einsum('fa,mi,jebc,bj,cn->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*<j,e||b,c>*t1(b,j)*t1(c,m)
Hds += 1.000000000000000 * einsum('fa,ni,jebc,bj,cm->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,a)*<i,e||b,c>*t1(b,n)*t1(c,m)
Hds += -1.000000000000000 * einsum('fa,iebc,bn,cm->efmnai', kd[v, v], g[o, v, v, v], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -1.0000 P(e,f)d(m,i)*<j,e||b,a>*t1(b,n)*t1(f,j)
contracted_intermediate = -1.000000000000000 * einsum('mi,jeba,bn,fj->efmnai', kd[o, o], g[o, v, v, v], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 1.0000 P(e,f)d(n,i)*<j,e||b,a>*t1(b,m)*t1(f,j)
contracted_intermediate = 1.000000000000000 * einsum('ni,jeba,bm,fj->efmnai', kd[o, o], g[o, v, v, v], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
Hds += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnai->femnai', contracted_intermediate)
# 1.0000 d(e,a)*d(m,i)*<j,f||b,c>*t1(b,j)*t1(c,n)
Hds += 1.000000000000000 * einsum('ea,mi,jfbc,bj,cn->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*<j,f||b,c>*t1(b,j)*t1(c,m)
Hds += -1.000000000000000 * einsum('ea,ni,jfbc,bj,cm->efmnai', kd[v, v], kd[o, o], g[o, v, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,a)*<i,f||b,c>*t1(b,n)*t1(c,m)
Hds += 1.000000000000000 * einsum('ea,ifbc,bn,cm->efmnai', kd[v, v], g[o, v, v, v], t1, t1, optimize=['einsum_path', (1, 2), (1, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<k,j||b,c>*t1(b,j)*t1(c,n)*t1(e,k)
Hds += -1.000000000000000 * einsum('fa,mi,kjbc,bj,cn,ek->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 3), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(m,i)*<k,j||b,c>*t1(b,j)*t1(c,n)*t1(f,k)
Hds += 1.000000000000000 * einsum('ea,mi,kjbc,bj,cn,fk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 3), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*<k,j||b,c>*t1(b,j)*t1(c,m)*t1(e,k)
Hds += 1.000000000000000 * einsum('fa,ni,kjbc,bj,cm,ek->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*<k,j||b,c>*t1(b,j)*t1(c,m)*t1(f,k)
Hds += -1.000000000000000 * einsum('ea,ni,kjbc,bj,cm,fk->efmnai', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 3), (0, 2), (0, 1)])
# 1.0000 d(f,a)*<i,j||b,c>*t1(b,n)*t1(c,m)*t1(e,j)
Hds += 1.000000000000000 * einsum('fa,ijbc,bn,cm,ej->efmnai', kd[v, v], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (1, 2), (1, 3), (1, 2), (0, 1)])
# -1.0000 d(e,a)*<i,j||b,c>*t1(b,n)*t1(c,m)*t1(f,j)
Hds += -1.000000000000000 * einsum('ea,ijbc,bn,cm,fj->efmnai', kd[v, v], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (1, 2), (1, 3), (1, 2), (0, 1)])
# 1.0000 d(m,i)*<k,j||b,a>*t1(b,n)*t1(e,j)*t1(f,k)
Hds += 1.000000000000000 * einsum('mi,kjba,bn,ej,fk->efmnai', kd[o, o], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (1, 2), (1, 3), (1, 2), (0, 1)])
# -1.0000 d(n,i)*<k,j||b,a>*t1(b,m)*t1(e,j)*t1(f,k)
Hds += -1.000000000000000 * einsum('ni,kjba,bm,ej,fk->efmnai', kd[o, o], g[o, o, v, v], t1, t1, t1, optimize=['einsum_path', (1, 2), (1, 3), (1, 2), (0, 1)])
# H(m,n,e,f;i,j,a,b) = <0|e2(m,n,f,e) e(-T) H e(T) e2(a,b,j,i)|0>
# 1.0000 d(e,a)*d(f,b)*d(n,j)*d(m,i)*f(k,k)
Hdd = 1.000000000000000 * einsum('ea,fb,nj,mi,kk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(m,j)*d(n,i)*f(k,k)
Hdd += -1.000000000000000 * einsum('ea,fb,mj,ni,kk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(n,j)*d(m,i)*f(k,k)
Hdd += -1.000000000000000 * einsum('fa,eb,nj,mi,kk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(m,j)*d(n,i)*f(k,k)
Hdd += 1.000000000000000 * einsum('fa,eb,mj,ni,kk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(m,i)*f(j,n)
Hdd += -1.000000000000000 * einsum('ea,fb,mi,jn->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(e,a)*d(f,b)*d(m,j)*f(i,n)
Hdd += 1.000000000000000 * einsum('ea,fb,mj,in->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(f,a)*d(e,b)*d(m,i)*f(j,n)
Hdd += 1.000000000000000 * einsum('fa,eb,mi,jn->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(f,a)*d(e,b)*d(m,j)*f(i,n)
Hdd += -1.000000000000000 * einsum('fa,eb,mj,in->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(e,a)*d(f,b)*d(n,i)*f(j,m)
Hdd += 1.000000000000000 * einsum('ea,fb,ni,jm->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(e,a)*d(f,b)*d(n,j)*f(i,m)
Hdd += -1.000000000000000 * einsum('ea,fb,nj,im->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(f,a)*d(e,b)*d(n,i)*f(j,m)
Hdd += -1.000000000000000 * einsum('fa,eb,ni,jm->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(f,a)*d(e,b)*d(n,j)*f(i,m)
Hdd += 1.000000000000000 * einsum('fa,eb,nj,im->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, o], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(f,b)*d(n,j)*d(m,i)*f(e,a)
Hdd += 1.000000000000000 * einsum('fb,nj,mi,ea->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(f,b)*d(m,j)*d(n,i)*f(e,a)
Hdd += -1.000000000000000 * einsum('fb,mj,ni,ea->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(f,a)*d(n,j)*d(m,i)*f(e,b)
Hdd += -1.000000000000000 * einsum('fa,nj,mi,eb->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(f,a)*d(m,j)*d(n,i)*f(e,b)
Hdd += 1.000000000000000 * einsum('fa,mj,ni,eb->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(e,b)*d(n,j)*d(m,i)*f(f,a)
Hdd += -1.000000000000000 * einsum('eb,nj,mi,fa->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(e,b)*d(m,j)*d(n,i)*f(f,a)
Hdd += 1.000000000000000 * einsum('eb,mj,ni,fa->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(e,a)*d(n,j)*d(m,i)*f(f,b)
Hdd += 1.000000000000000 * einsum('ea,nj,mi,fb->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# -1.0000 d(e,a)*d(m,j)*d(n,i)*f(f,b)
Hdd += -1.000000000000000 * einsum('ea,mj,ni,fb->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[v, v], optimize=['einsum_path', (0, 1, 2, 3)])
# 1.0000 d(e,a)*d(f,b)*d(n,j)*d(m,i)*f(k,c)*t1(c,k)
Hdd += 1.000000000000000 * einsum('ea,fb,nj,mi,kc,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(m,j)*d(n,i)*f(k,c)*t1(c,k)
Hdd += -1.000000000000000 * einsum('ea,fb,mj,ni,kc,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(n,j)*d(m,i)*f(k,c)*t1(c,k)
Hdd += -1.000000000000000 * einsum('fa,eb,nj,mi,kc,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(m,j)*d(n,i)*f(k,c)*t1(c,k)
Hdd += 1.000000000000000 * einsum('fa,eb,mj,ni,kc,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(m,i)*f(j,c)*t1(c,n)
Hdd += -1.000000000000000 * einsum('ea,fb,mi,jc,cn->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(f,b)*d(m,j)*f(i,c)*t1(c,n)
Hdd += 1.000000000000000 * einsum('ea,fb,mj,ic,cn->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(m,i)*f(j,c)*t1(c,n)
Hdd += 1.000000000000000 * einsum('fa,eb,mi,jc,cn->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(m,j)*f(i,c)*t1(c,n)
Hdd += -1.000000000000000 * einsum('fa,eb,mj,ic,cn->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(f,b)*d(n,i)*f(j,c)*t1(c,m)
Hdd += 1.000000000000000 * einsum('ea,fb,ni,jc,cm->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(n,j)*f(i,c)*t1(c,m)
Hdd += -1.000000000000000 * einsum('ea,fb,nj,ic,cm->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(n,i)*f(j,c)*t1(c,m)
Hdd += -1.000000000000000 * einsum('fa,eb,ni,jc,cm->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(n,j)*f(i,c)*t1(c,m)
Hdd += 1.000000000000000 * einsum('fa,eb,nj,ic,cm->efmnabij', kd[v, v], kd[v, v], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,b)*d(n,j)*d(m,i)*f(k,a)*t1(e,k)
Hdd += -1.000000000000000 * einsum('fb,nj,mi,ka,ek->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,b)*d(m,j)*d(n,i)*f(k,a)*t1(e,k)
Hdd += 1.000000000000000 * einsum('fb,mj,ni,ka,ek->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(n,j)*d(m,i)*f(k,b)*t1(e,k)
Hdd += 1.000000000000000 * einsum('fa,nj,mi,kb,ek->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,j)*d(n,i)*f(k,b)*t1(e,k)
Hdd += -1.000000000000000 * einsum('fa,mj,ni,kb,ek->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,b)*d(n,j)*d(m,i)*f(k,a)*t1(f,k)
Hdd += 1.000000000000000 * einsum('eb,nj,mi,ka,fk->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(e,b)*d(m,j)*d(n,i)*f(k,a)*t1(f,k)
Hdd += -1.000000000000000 * einsum('eb,mj,ni,ka,fk->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(n,j)*d(m,i)*f(k,b)*t1(f,k)
Hdd += -1.000000000000000 * einsum('ea,nj,mi,kb,fk->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(m,j)*d(n,i)*f(k,b)*t1(f,k)
Hdd += 1.000000000000000 * einsum('ea,mj,ni,kb,fk->efmnabij', kd[v, v], kd[o, o], kd[o, o], f[o, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(e,a)*d(f,b)*d(n,j)*d(m,i)*<l,k||l,k>
Hdd += -0.500000000000000 * einsum('ea,fb,nj,mi,lklk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 0.5000 d(e,a)*d(f,b)*d(m,j)*d(n,i)*<l,k||l,k>
Hdd += 0.500000000000000 * einsum('ea,fb,mj,ni,lklk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 0.5000 d(f,a)*d(e,b)*d(n,j)*d(m,i)*<l,k||l,k>
Hdd += 0.500000000000000 * einsum('fa,eb,nj,mi,lklk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# -0.5000 d(f,a)*d(e,b)*d(m,j)*d(n,i)*<l,k||l,k>
Hdd += -0.500000000000000 * einsum('fa,eb,mj,ni,lklk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, o, o], optimize=['einsum_path', (0, 1), (0, 2), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(f,b)*<i,j||m,n>
Hdd += 1.000000000000000 * einsum('ea,fb,ijmn->efmnabij', kd[v, v], kd[v, v], g[o, o, o, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(f,a)*d(e,b)*<i,j||m,n>
Hdd += -1.000000000000000 * einsum('fa,eb,ijmn->efmnabij', kd[v, v], kd[v, v], g[o, o, o, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(f,b)*d(m,i)*<j,e||a,n>
Hdd += 1.000000000000000 * einsum('fb,mi,jean->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(f,b)*d(m,j)*<i,e||a,n>
Hdd += -1.000000000000000 * einsum('fb,mj,iean->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(f,a)*d(m,i)*<j,e||b,n>
Hdd += -1.000000000000000 * einsum('fa,mi,jebn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(f,a)*d(m,j)*<i,e||b,n>
Hdd += 1.000000000000000 * einsum('fa,mj,iebn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(f,b)*d(n,i)*<j,e||a,m>
Hdd += -1.000000000000000 * einsum('fb,ni,jeam->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(f,b)*d(n,j)*<i,e||a,m>
Hdd += 1.000000000000000 * einsum('fb,nj,ieam->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(f,a)*d(n,i)*<j,e||b,m>
Hdd += 1.000000000000000 * einsum('fa,ni,jebm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(f,a)*d(n,j)*<i,e||b,m>
Hdd += -1.000000000000000 * einsum('fa,nj,iebm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,b)*d(m,i)*<j,f||a,n>
Hdd += -1.000000000000000 * einsum('eb,mi,jfan->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,b)*d(m,j)*<i,f||a,n>
Hdd += 1.000000000000000 * einsum('eb,mj,ifan->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,a)*d(m,i)*<j,f||b,n>
Hdd += 1.000000000000000 * einsum('ea,mi,jfbn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,a)*d(m,j)*<i,f||b,n>
Hdd += -1.000000000000000 * einsum('ea,mj,ifbn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,b)*d(n,i)*<j,f||a,m>
Hdd += 1.000000000000000 * einsum('eb,ni,jfam->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,b)*d(n,j)*<i,f||a,m>
Hdd += -1.000000000000000 * einsum('eb,nj,ifam->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(e,a)*d(n,i)*<j,f||b,m>
Hdd += -1.000000000000000 * einsum('ea,ni,jfbm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,a)*d(n,j)*<i,f||b,m>
Hdd += 1.000000000000000 * einsum('ea,nj,ifbm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, o], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(n,j)*d(m,i)*<e,f||a,b>
Hdd += 1.000000000000000 * einsum('nj,mi,efab->efmnabij', kd[o, o], kd[o, o], g[v, v, v, v], optimize=['einsum_path', (0, 1, 2)])
# -1.0000 d(m,j)*d(n,i)*<e,f||a,b>
Hdd += -1.000000000000000 * einsum('mj,ni,efab->efmnabij', kd[o, o], kd[o, o], g[v, v, v, v], optimize=['einsum_path', (0, 1, 2)])
# 1.0000 d(e,a)*d(f,b)*d(m,i)*<j,k||c,n>*t1(c,k)
Hdd += 1.000000000000000 * einsum('ea,fb,mi,jkcn,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(m,j)*<i,k||c,n>*t1(c,k)
Hdd += -1.000000000000000 * einsum('ea,fb,mj,ikcn,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(m,i)*<j,k||c,n>*t1(c,k)
Hdd += -1.000000000000000 * einsum('fa,eb,mi,jkcn,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(m,j)*<i,k||c,n>*t1(c,k)
Hdd += 1.000000000000000 * einsum('fa,eb,mj,ikcn,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 P(m,n)d(e,a)*d(f,b)*<i,j||c,n>*t1(c,m)
contracted_intermediate = 1.000000000000000 * einsum('ea,fb,ijcn,cm->efmnabij', kd[v, v], kd[v, v], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
Hdd += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnabij->efnmabij', contracted_intermediate)
# -1.0000 P(m,n)d(f,a)*d(e,b)*<i,j||c,n>*t1(c,m)
contracted_intermediate = -1.000000000000000 * einsum('fa,eb,ijcn,cm->efmnabij', kd[v, v], kd[v, v], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
Hdd += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnabij->efnmabij', contracted_intermediate)
# -1.0000 d(f,b)*d(m,i)*<j,k||a,n>*t1(e,k)
Hdd += -1.000000000000000 * einsum('fb,mi,jkan,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,b)*d(m,j)*<i,k||a,n>*t1(e,k)
Hdd += 1.000000000000000 * einsum('fb,mj,ikan,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(m,i)*<j,k||b,n>*t1(e,k)
Hdd += 1.000000000000000 * einsum('fa,mi,jkbn,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(m,j)*<i,k||b,n>*t1(e,k)
Hdd += -1.000000000000000 * einsum('fa,mj,ikbn,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*d(m,i)*<j,k||a,n>*t1(f,k)
Hdd += 1.000000000000000 * einsum('eb,mi,jkan,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,b)*d(m,j)*<i,k||a,n>*t1(f,k)
Hdd += -1.000000000000000 * einsum('eb,mj,ikan,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(m,i)*<j,k||b,n>*t1(f,k)
Hdd += -1.000000000000000 * einsum('ea,mi,jkbn,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(m,j)*<i,k||b,n>*t1(f,k)
Hdd += 1.000000000000000 * einsum('ea,mj,ikbn,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(n,i)*<j,k||c,m>*t1(c,k)
Hdd += -1.000000000000000 * einsum('ea,fb,ni,jkcm,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(f,b)*d(n,j)*<i,k||c,m>*t1(c,k)
Hdd += 1.000000000000000 * einsum('ea,fb,nj,ikcm,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(n,i)*<j,k||c,m>*t1(c,k)
Hdd += 1.000000000000000 * einsum('fa,eb,ni,jkcm,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(n,j)*<i,k||c,m>*t1(c,k)
Hdd += -1.000000000000000 * einsum('fa,eb,nj,ikcm,ck->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,b)*d(n,i)*<j,k||a,m>*t1(e,k)
Hdd += 1.000000000000000 * einsum('fb,ni,jkam,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,b)*d(n,j)*<i,k||a,m>*t1(e,k)
Hdd += -1.000000000000000 * einsum('fb,nj,ikam,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(n,i)*<j,k||b,m>*t1(e,k)
Hdd += -1.000000000000000 * einsum('fa,ni,jkbm,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(n,j)*<i,k||b,m>*t1(e,k)
Hdd += 1.000000000000000 * einsum('fa,nj,ikbm,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,b)*d(n,i)*<j,k||a,m>*t1(f,k)
Hdd += -1.000000000000000 * einsum('eb,ni,jkam,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*d(n,j)*<i,k||a,m>*t1(f,k)
Hdd += 1.000000000000000 * einsum('eb,nj,ikam,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(n,i)*<j,k||b,m>*t1(f,k)
Hdd += 1.000000000000000 * einsum('ea,ni,jkbm,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(n,j)*<i,k||b,m>*t1(f,k)
Hdd += -1.000000000000000 * einsum('ea,nj,ikbm,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, o], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,b)*d(n,j)*d(m,i)*<k,e||c,a>*t1(c,k)
Hdd += 1.000000000000000 * einsum('fb,nj,mi,keca,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,b)*d(m,j)*d(n,i)*<k,e||c,a>*t1(c,k)
Hdd += -1.000000000000000 * einsum('fb,mj,ni,keca,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(n,j)*d(m,i)*<k,e||c,b>*t1(c,k)
Hdd += -1.000000000000000 * einsum('fa,nj,mi,kecb,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(m,j)*d(n,i)*<k,e||c,b>*t1(c,k)
Hdd += 1.000000000000000 * einsum('fa,mj,ni,kecb,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(f,b)*d(m,i)*<j,e||c,a>*t1(c,n)
Hdd += -1.000000000000000 * einsum('fb,mi,jeca,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,b)*d(m,j)*<i,e||c,a>*t1(c,n)
Hdd += 1.000000000000000 * einsum('fb,mj,ieca,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(m,i)*<j,e||c,b>*t1(c,n)
Hdd += 1.000000000000000 * einsum('fa,mi,jecb,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(m,j)*<i,e||c,b>*t1(c,n)
Hdd += -1.000000000000000 * einsum('fa,mj,iecb,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,b)*d(n,i)*<j,e||c,a>*t1(c,m)
Hdd += 1.000000000000000 * einsum('fb,ni,jeca,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,b)*d(n,j)*<i,e||c,a>*t1(c,m)
Hdd += -1.000000000000000 * einsum('fb,nj,ieca,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(n,i)*<j,e||c,b>*t1(c,m)
Hdd += -1.000000000000000 * einsum('fa,ni,jecb,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(n,j)*<i,e||c,b>*t1(c,m)
Hdd += 1.000000000000000 * einsum('fa,nj,iecb,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 P(e,f)d(n,j)*d(m,i)*<k,e||a,b>*t1(f,k)
contracted_intermediate = 1.000000000000000 * einsum('nj,mi,keab,fk->efmnabij', kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
Hdd += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnabij->femnabij', contracted_intermediate)
# -1.0000 P(e,f)d(m,j)*d(n,i)*<k,e||a,b>*t1(f,k)
contracted_intermediate = -1.000000000000000 * einsum('mj,ni,keab,fk->efmnabij', kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
Hdd += 1.00000 * contracted_intermediate + -1.00000 * einsum('efmnabij->femnabij', contracted_intermediate)
# -1.0000 d(e,b)*d(n,j)*d(m,i)*<k,f||c,a>*t1(c,k)
Hdd += -1.000000000000000 * einsum('eb,nj,mi,kfca,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,b)*d(m,j)*d(n,i)*<k,f||c,a>*t1(c,k)
Hdd += 1.000000000000000 * einsum('eb,mj,ni,kfca,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(n,j)*d(m,i)*<k,f||c,b>*t1(c,k)
Hdd += 1.000000000000000 * einsum('ea,nj,mi,kfcb,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(m,j)*d(n,i)*<k,f||c,b>*t1(c,k)
Hdd += -1.000000000000000 * einsum('ea,mj,ni,kfcb,ck->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(e,b)*d(m,i)*<j,f||c,a>*t1(c,n)
Hdd += 1.000000000000000 * einsum('eb,mi,jfca,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,b)*d(m,j)*<i,f||c,a>*t1(c,n)
Hdd += -1.000000000000000 * einsum('eb,mj,ifca,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(m,i)*<j,f||c,b>*t1(c,n)
Hdd += -1.000000000000000 * einsum('ea,mi,jfcb,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(m,j)*<i,f||c,b>*t1(c,n)
Hdd += 1.000000000000000 * einsum('ea,mj,ifcb,cn->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,b)*d(n,i)*<j,f||c,a>*t1(c,m)
Hdd += -1.000000000000000 * einsum('eb,ni,jfca,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*d(n,j)*<i,f||c,a>*t1(c,m)
Hdd += 1.000000000000000 * einsum('eb,nj,ifca,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(n,i)*<j,f||c,b>*t1(c,m)
Hdd += 1.000000000000000 * einsum('ea,ni,jfcb,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(n,j)*<i,f||c,b>*t1(c,m)
Hdd += -1.000000000000000 * einsum('ea,nj,ifcb,cm->efmnabij', kd[v, v], kd[o, o], g[o, v, v, v], t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 0.2500 d(e,a)*d(f,b)*d(n,j)*d(m,i)*<l,k||c,d>*t2(c,d,l,k)
Hdd += 0.250000000000000 * einsum('ea,fb,nj,mi,lkcd,cdlk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# -0.2500 d(e,a)*d(f,b)*d(m,j)*d(n,i)*<l,k||c,d>*t2(c,d,l,k)
Hdd += -0.250000000000000 * einsum('ea,fb,mj,ni,lkcd,cdlk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# -0.2500 d(f,a)*d(e,b)*d(n,j)*d(m,i)*<l,k||c,d>*t2(c,d,l,k)
Hdd += -0.250000000000000 * einsum('fa,eb,nj,mi,lkcd,cdlk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# 0.2500 d(f,a)*d(e,b)*d(m,j)*d(n,i)*<l,k||c,d>*t2(c,d,l,k)
Hdd += 0.250000000000000 * einsum('fa,eb,mj,ni,lkcd,cdlk->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (2, 3), (0, 3), (0, 2), (0, 1)])
# -0.5000 d(e,a)*d(f,b)*d(m,i)*<j,k||c,d>*t2(c,d,n,k)
Hdd += -0.500000000000000 * einsum('ea,fb,mi,jkcd,cdnk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(e,a)*d(f,b)*d(m,j)*<i,k||c,d>*t2(c,d,n,k)
Hdd += 0.500000000000000 * einsum('ea,fb,mj,ikcd,cdnk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(f,a)*d(e,b)*d(m,i)*<j,k||c,d>*t2(c,d,n,k)
Hdd += 0.500000000000000 * einsum('fa,eb,mi,jkcd,cdnk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(f,a)*d(e,b)*d(m,j)*<i,k||c,d>*t2(c,d,n,k)
Hdd += -0.500000000000000 * einsum('fa,eb,mj,ikcd,cdnk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(e,a)*d(f,b)*d(n,i)*<j,k||c,d>*t2(c,d,m,k)
Hdd += 0.500000000000000 * einsum('ea,fb,ni,jkcd,cdmk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(e,a)*d(f,b)*d(n,j)*<i,k||c,d>*t2(c,d,m,k)
Hdd += -0.500000000000000 * einsum('ea,fb,nj,ikcd,cdmk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(f,a)*d(e,b)*d(n,i)*<j,k||c,d>*t2(c,d,m,k)
Hdd += -0.500000000000000 * einsum('fa,eb,ni,jkcd,cdmk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(f,a)*d(e,b)*d(n,j)*<i,k||c,d>*t2(c,d,m,k)
Hdd += 0.500000000000000 * einsum('fa,eb,nj,ikcd,cdmk->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(e,a)*d(f,b)*<i,j||c,d>*t2(c,d,m,n)
Hdd += 0.500000000000000 * einsum('ea,fb,ijcd,cdmn->efmnabij', kd[v, v], kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -0.5000 d(f,a)*d(e,b)*<i,j||c,d>*t2(c,d,m,n)
Hdd += -0.500000000000000 * einsum('fa,eb,ijcd,cdmn->efmnabij', kd[v, v], kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -0.5000 d(f,b)*d(n,j)*d(m,i)*<l,k||c,a>*t2(c,e,l,k)
Hdd += -0.500000000000000 * einsum('fb,nj,mi,lkca,celk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(f,b)*d(m,j)*d(n,i)*<l,k||c,a>*t2(c,e,l,k)
Hdd += 0.500000000000000 * einsum('fb,mj,ni,lkca,celk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(f,a)*d(n,j)*d(m,i)*<l,k||c,b>*t2(c,e,l,k)
Hdd += 0.500000000000000 * einsum('fa,nj,mi,lkcb,celk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(f,a)*d(m,j)*d(n,i)*<l,k||c,b>*t2(c,e,l,k)
Hdd += -0.500000000000000 * einsum('fa,mj,ni,lkcb,celk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 1.0000 d(f,b)*d(m,i)*<j,k||c,a>*t2(c,e,n,k)
Hdd += 1.000000000000000 * einsum('fb,mi,jkca,cenk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,b)*d(m,j)*<i,k||c,a>*t2(c,e,n,k)
Hdd += -1.000000000000000 * einsum('fb,mj,ikca,cenk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<j,k||c,b>*t2(c,e,n,k)
Hdd += -1.000000000000000 * einsum('fa,mi,jkcb,cenk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(m,j)*<i,k||c,b>*t2(c,e,n,k)
Hdd += 1.000000000000000 * einsum('fa,mj,ikcb,cenk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,b)*d(n,i)*<j,k||c,a>*t2(c,e,m,k)
Hdd += -1.000000000000000 * einsum('fb,ni,jkca,cemk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,b)*d(n,j)*<i,k||c,a>*t2(c,e,m,k)
Hdd += 1.000000000000000 * einsum('fb,nj,ikca,cemk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*<j,k||c,b>*t2(c,e,m,k)
Hdd += 1.000000000000000 * einsum('fa,ni,jkcb,cemk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,a)*d(n,j)*<i,k||c,b>*t2(c,e,m,k)
Hdd += -1.000000000000000 * einsum('fa,nj,ikcb,cemk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(f,b)*<i,j||c,a>*t2(c,e,m,n)
Hdd += -1.000000000000000 * einsum('fb,ijca,cemn->efmnabij', kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(f,a)*<i,j||c,b>*t2(c,e,m,n)
Hdd += 1.000000000000000 * einsum('fa,ijcb,cemn->efmnabij', kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 0.5000 d(e,b)*d(n,j)*d(m,i)*<l,k||c,a>*t2(c,f,l,k)
Hdd += 0.500000000000000 * einsum('eb,nj,mi,lkca,cflk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(e,b)*d(m,j)*d(n,i)*<l,k||c,a>*t2(c,f,l,k)
Hdd += -0.500000000000000 * einsum('eb,mj,ni,lkca,cflk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -0.5000 d(e,a)*d(n,j)*d(m,i)*<l,k||c,b>*t2(c,f,l,k)
Hdd += -0.500000000000000 * einsum('ea,nj,mi,lkcb,cflk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# 0.5000 d(e,a)*d(m,j)*d(n,i)*<l,k||c,b>*t2(c,f,l,k)
Hdd += 0.500000000000000 * einsum('ea,mj,ni,lkcb,cflk->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (1, 2), (0, 2), (0, 1)])
# -1.0000 d(e,b)*d(m,i)*<j,k||c,a>*t2(c,f,n,k)
Hdd += -1.000000000000000 * einsum('eb,mi,jkca,cfnk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*d(m,j)*<i,k||c,a>*t2(c,f,n,k)
Hdd += 1.000000000000000 * einsum('eb,mj,ikca,cfnk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(m,i)*<j,k||c,b>*t2(c,f,n,k)
Hdd += 1.000000000000000 * einsum('ea,mi,jkcb,cfnk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(m,j)*<i,k||c,b>*t2(c,f,n,k)
Hdd += -1.000000000000000 * einsum('ea,mj,ikcb,cfnk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*d(n,i)*<j,k||c,a>*t2(c,f,m,k)
Hdd += 1.000000000000000 * einsum('eb,ni,jkca,cfmk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,b)*d(n,j)*<i,k||c,a>*t2(c,f,m,k)
Hdd += -1.000000000000000 * einsum('eb,nj,ikca,cfmk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*<j,k||c,b>*t2(c,f,m,k)
Hdd += -1.000000000000000 * einsum('ea,ni,jkcb,cfmk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,a)*d(n,j)*<i,k||c,b>*t2(c,f,m,k)
Hdd += 1.000000000000000 * einsum('ea,nj,ikcb,cfmk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# 1.0000 d(e,b)*<i,j||c,a>*t2(c,f,m,n)
Hdd += 1.000000000000000 * einsum('eb,ijca,cfmn->efmnabij', kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(e,a)*<i,j||c,b>*t2(c,f,m,n)
Hdd += -1.000000000000000 * einsum('ea,ijcb,cfmn->efmnabij', kd[v, v], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 0.5000 d(n,j)*d(m,i)*<l,k||a,b>*t2(e,f,l,k)
Hdd += 0.500000000000000 * einsum('nj,mi,lkab,eflk->efmnabij', kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -0.5000 d(m,j)*d(n,i)*<l,k||a,b>*t2(e,f,l,k)
Hdd += -0.500000000000000 * einsum('mj,ni,lkab,eflk->efmnabij', kd[o, o], kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (0, 1), (0, 1), (0, 1)])
# -1.0000 d(m,i)*<j,k||a,b>*t2(e,f,n,k)
Hdd += -1.000000000000000 * einsum('mi,jkab,efnk->efmnabij', kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(m,j)*<i,k||a,b>*t2(e,f,n,k)
Hdd += 1.000000000000000 * einsum('mj,ikab,efnk->efmnabij', kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# 1.0000 d(n,i)*<j,k||a,b>*t2(e,f,m,k)
Hdd += 1.000000000000000 * einsum('ni,jkab,efmk->efmnabij', kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -1.0000 d(n,j)*<i,k||a,b>*t2(e,f,m,k)
Hdd += -1.000000000000000 * einsum('nj,ikab,efmk->efmnabij', kd[o, o], g[o, o, v, v], t2, optimize=['einsum_path', (1, 2), (0, 1)])
# -0.5000 d(e,a)*d(f,b)*d(n,j)*d(m,i)*<l,k||c,d>*t1(c,k)*t1(d,l)
Hdd += -0.500000000000000 * einsum('ea,fb,nj,mi,lkcd,ck,dl->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (2, 3), (2, 4), (0, 3), (0, 2), (0, 1)])
# 0.5000 d(e,a)*d(f,b)*d(m,j)*d(n,i)*<l,k||c,d>*t1(c,k)*t1(d,l)
Hdd += 0.500000000000000 * einsum('ea,fb,mj,ni,lkcd,ck,dl->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (2, 3), (2, 4), (0, 3), (0, 2), (0, 1)])
# 0.5000 d(f,a)*d(e,b)*d(n,j)*d(m,i)*<l,k||c,d>*t1(c,k)*t1(d,l)
Hdd += 0.500000000000000 * einsum('fa,eb,nj,mi,lkcd,ck,dl->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (2, 3), (2, 4), (0, 3), (0, 2), (0, 1)])
# -0.5000 d(f,a)*d(e,b)*d(m,j)*d(n,i)*<l,k||c,d>*t1(c,k)*t1(d,l)
Hdd += -0.500000000000000 * einsum('fa,eb,mj,ni,lkcd,ck,dl->efmnabij', kd[v, v], kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (2, 3), (2, 4), (0, 3), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(f,b)*d(m,i)*<j,k||c,d>*t1(c,k)*t1(d,n)
Hdd += 1.000000000000000 * einsum('ea,fb,mi,jkcd,ck,dn->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(m,j)*<i,k||c,d>*t1(c,k)*t1(d,n)
Hdd += -1.000000000000000 * einsum('ea,fb,mj,ikcd,ck,dn->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(m,i)*<j,k||c,d>*t1(c,k)*t1(d,n)
Hdd += -1.000000000000000 * einsum('fa,eb,mi,jkcd,ck,dn->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(m,j)*<i,k||c,d>*t1(c,k)*t1(d,n)
Hdd += 1.000000000000000 * einsum('fa,eb,mj,ikcd,ck,dn->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*d(n,i)*<j,k||c,d>*t1(c,k)*t1(d,m)
Hdd += -1.000000000000000 * einsum('ea,fb,ni,jkcd,ck,dm->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(f,b)*d(n,j)*<i,k||c,d>*t1(c,k)*t1(d,m)
Hdd += 1.000000000000000 * einsum('ea,fb,nj,ikcd,ck,dm->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*d(n,i)*<j,k||c,d>*t1(c,k)*t1(d,m)
Hdd += 1.000000000000000 * einsum('fa,eb,ni,jkcd,ck,dm->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(e,b)*d(n,j)*<i,k||c,d>*t1(c,k)*t1(d,m)
Hdd += -1.000000000000000 * einsum('fa,eb,nj,ikcd,ck,dm->efmnabij', kd[v, v], kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(f,b)*d(n,j)*d(m,i)*<l,k||c,a>*t1(c,k)*t1(e,l)
Hdd += 1.000000000000000 * einsum('fb,nj,mi,lkca,ck,el->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(f,b)*d(m,j)*d(n,i)*<l,k||c,a>*t1(c,k)*t1(e,l)
Hdd += -1.000000000000000 * einsum('fb,mj,ni,lkca,ck,el->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(n,j)*d(m,i)*<l,k||c,b>*t1(c,k)*t1(e,l)
Hdd += -1.000000000000000 * einsum('fa,nj,mi,lkcb,ck,el->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(m,j)*d(n,i)*<l,k||c,b>*t1(c,k)*t1(e,l)
Hdd += 1.000000000000000 * einsum('fa,mj,ni,lkcb,ck,el->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(e,b)*d(n,j)*d(m,i)*<l,k||c,a>*t1(c,k)*t1(f,l)
Hdd += -1.000000000000000 * einsum('eb,nj,mi,lkca,ck,fl->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(e,b)*d(m,j)*d(n,i)*<l,k||c,a>*t1(c,k)*t1(f,l)
Hdd += 1.000000000000000 * einsum('eb,mj,ni,lkca,ck,fl->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(n,j)*d(m,i)*<l,k||c,b>*t1(c,k)*t1(f,l)
Hdd += 1.000000000000000 * einsum('ea,nj,mi,lkcb,ck,fl->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(m,j)*d(n,i)*<l,k||c,b>*t1(c,k)*t1(f,l)
Hdd += -1.000000000000000 * einsum('ea,mj,ni,lkcb,ck,fl->efmnabij', kd[v, v], kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (1, 2), (1, 3), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(f,b)*<i,j||c,d>*t1(c,n)*t1(d,m)
Hdd += -1.000000000000000 * einsum('ea,fb,ijcd,cn,dm->efmnabij', kd[v, v], kd[v, v], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(e,b)*<i,j||c,d>*t1(c,n)*t1(d,m)
Hdd += 1.000000000000000 * einsum('fa,eb,ijcd,cn,dm->efmnabij', kd[v, v], kd[v, v], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,b)*d(m,i)*<j,k||c,a>*t1(c,n)*t1(e,k)
Hdd += 1.000000000000000 * einsum('fb,mi,jkca,cn,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,b)*d(m,j)*<i,k||c,a>*t1(c,n)*t1(e,k)
Hdd += -1.000000000000000 * einsum('fb,mj,ikca,cn,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(m,i)*<j,k||c,b>*t1(c,n)*t1(e,k)
Hdd += -1.000000000000000 * einsum('fa,mi,jkcb,cn,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(m,j)*<i,k||c,b>*t1(c,n)*t1(e,k)
Hdd += 1.000000000000000 * einsum('fa,mj,ikcb,cn,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,b)*d(m,i)*<j,k||c,a>*t1(c,n)*t1(f,k)
Hdd += -1.000000000000000 * einsum('eb,mi,jkca,cn,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,b)*d(m,j)*<i,k||c,a>*t1(c,n)*t1(f,k)
Hdd += 1.000000000000000 * einsum('eb,mj,ikca,cn,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(m,i)*<j,k||c,b>*t1(c,n)*t1(f,k)
Hdd += 1.000000000000000 * einsum('ea,mi,jkcb,cn,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(m,j)*<i,k||c,b>*t1(c,n)*t1(f,k)
Hdd += -1.000000000000000 * einsum('ea,mj,ikcb,cn,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,b)*d(n,i)*<j,k||c,a>*t1(c,m)*t1(e,k)
Hdd += -1.000000000000000 * einsum('fb,ni,jkca,cm,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,b)*d(n,j)*<i,k||c,a>*t1(c,m)*t1(e,k)
Hdd += 1.000000000000000 * einsum('fb,nj,ikca,cm,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(f,a)*d(n,i)*<j,k||c,b>*t1(c,m)*t1(e,k)
Hdd += 1.000000000000000 * einsum('fa,ni,jkcb,cm,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(f,a)*d(n,j)*<i,k||c,b>*t1(c,m)*t1(e,k)
Hdd += -1.000000000000000 * einsum('fa,nj,ikcb,cm,ek->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,b)*d(n,i)*<j,k||c,a>*t1(c,m)*t1(f,k)
Hdd += 1.000000000000000 * einsum('eb,ni,jkca,cm,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,b)*d(n,j)*<i,k||c,a>*t1(c,m)*t1(f,k)
Hdd += -1.000000000000000 * einsum('eb,nj,ikca,cm,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(e,a)*d(n,i)*<j,k||c,b>*t1(c,m)*t1(f,k)
Hdd += -1.000000000000000 * einsum('ea,ni,jkcb,cm,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(e,a)*d(n,j)*<i,k||c,b>*t1(c,m)*t1(f,k)
Hdd += 1.000000000000000 * einsum('ea,nj,ikcb,cm,fk->efmnabij', kd[v, v], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# -1.0000 d(n,j)*d(m,i)*<l,k||a,b>*t1(e,k)*t1(f,l)
Hdd += -1.000000000000000 * einsum('nj,mi,lkab,ek,fl->efmnabij', kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
# 1.0000 d(m,j)*d(n,i)*<l,k||a,b>*t1(e,k)*t1(f,l)
Hdd += 1.000000000000000 * einsum('mj,ni,lkab,ek,fl->efmnabij', kd[o, o], kd[o, o], g[o, o, v, v], t1, t1, optimize=['einsum_path', (0, 1), (0, 1), (0, 2), (0, 1)])
return H00, Hs0, H0s, Hd0, H0d, Hss, Hsd, Hds, Hdd
def pack_eom_ccsd_H(H00, Hs0, H0s, Hd0, H0d, Hss, Hsd, Hds, Hdd, nsocc, nsvirt):
dim = int(1 + nsvirt*(nsvirt-1)/2*nsocc*(nsocc-1)/2 + nsvirt*nsocc)
H = np.zeros((dim,dim))
# 00 block
H[0,0] = H00
# 0s, s0 blocks
for a in range (0,nsvirt):
for i in range (0,nsocc):
ai = 1 + a*nsocc + i
H[ai,0] = Hs0[a,i]
H[0,ai] = H0s[a,i]
# ss block
for a in range (0,nsvirt):
for i in range (0,nsocc):
ai = 1 + a*nsocc + i
for e in range (0,nsvirt):
for m in range (0,nsocc):
em = 1 + e*nsocc + m
H[ai,em] = Hss[a,i,e,m]
# sd, ds blocks
for a in range (0,nsvirt):
for i in range (0,nsocc):
ai = 1 + a*nsocc + i
efmn = 1 + nsocc*nsvirt
for e in range (0,nsvirt):
for f in range (e+1,nsvirt):
for m in range (0,nsocc):
for n in range (m+1,nsocc):
H[ai,efmn] = Hsd[a,i,e,f,m,n]
H[efmn,ai] = Hds[e,f,m,n,a,i]
efmn += 1
# 0d, d0 blocks
abij = 1 + nsocc*nsvirt
for a in range (0,nsvirt):
for b in range (a+1,nsvirt):
for i in range (0,nsocc):
for j in range (i+1,nsocc):
H[abij,0] = Hd0[a,b,i,j]
H[0,abij] = H0d[a,b,i,j]
abij += 1
# dd blocks
abij = 1 + nsocc*nsvirt
for a in range (0,nsvirt):
for b in range (a+1,nsvirt):
for i in range (0,nsocc):
for j in range (i+1,nsocc):
efmn = 1 + nsocc*nsvirt
for e in range (0,nsvirt):
for f in range (e+1,nsvirt):
for m in range (0,nsocc):
for n in range (m+1,nsocc):
H[abij,efmn] = Hdd[a,b,i,j,e,f,m,n]
efmn += 1
abij += 1
return H
def build_eom_ccsd_H(f, g, o, v, t1, t2, nsocc, nsvirt):
kd = np.zeros((nsocc+nsvirt,nsocc+nsvirt))
for i in range (0,nsocc+nsvirt):
kd[i,i] = 1.0
H00, Hs0, H0s, Hd0, H0d, Hss, Hsd, Hds, Hdd = build_eom_ccsd_H_by_block(kd,f, g, o, v, t1, t2)
H = pack_eom_ccsd_H(H00, Hs0, H0s, Hd0, H0d, Hss, Hsd, Hds, Hdd, nsocc, nsvirt)
return H
| 66.273188 | 225 | 0.508206 | 19,198 | 91,457 | 2.393531 | 0.018544 | 0.032426 | 0.142587 | 0.041392 | 0.953124 | 0.937695 | 0.913843 | 0.885574 | 0.844182 | 0.803922 | 0 | 0.168836 | 0.191839 | 91,457 | 1,379 | 226 | 66.321247 | 0.452863 | 0.204883 | 0 | 0.159763 | 0 | 0 | 0.185434 | 0.089625 | 0 | 0 | 0 | 0 | 0 | 1 | 0.005917 | false | 0 | 0.003945 | 0 | 0.015779 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
5bd059b5f4c7a944020810a86b2965b4db2a1be5 | 32,679 | py | Python | f5/bigip/tm/asm/test/functional/test_tasks.py | nghia-tran/f5-common-python | acb23a6e5830a119b460c19a578654113419f5c3 | [
"Apache-2.0"
] | 272 | 2016-02-23T06:05:44.000Z | 2022-02-20T02:09:32.000Z | f5/bigip/tm/asm/test/functional/test_tasks.py | nghia-tran/f5-common-python | acb23a6e5830a119b460c19a578654113419f5c3 | [
"Apache-2.0"
] | 1,103 | 2016-02-11T17:48:03.000Z | 2022-02-15T17:13:37.000Z | f5/bigip/tm/asm/test/functional/test_tasks.py | nghia-tran/f5-common-python | acb23a6e5830a119b460c19a578654113419f5c3 | [
"Apache-2.0"
] | 167 | 2016-02-11T17:48:21.000Z | 2022-01-17T20:13:05.000Z | # Copyright 2015 F5 Networks Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import pytest
import tempfile
import time
from distutils.version import LooseVersion
from f5.bigip.tm.asm.tasks import Apply_Policy
from f5.bigip.tm.asm.tasks import Check_Signature
from f5.bigip.tm.asm.tasks import Export_Policy
from f5.bigip.tm.asm.tasks import Export_Signature
from f5.bigip.tm.asm.tasks import Import_Policy
from f5.bigip.tm.asm.tasks import Import_Vulnerabilities
from f5.bigip.tm.asm.tasks import Update_Signature
from f5.sdk_exception import MissingRequiredCreationParameter
from f5.sdk_exception import UnsupportedOperation
from jinja2 import Environment
from jinja2 import FileSystemLoader
from requests.exceptions import HTTPError
if LooseVersion(pytest.config.getoption('--release')) >= LooseVersion('12.1.0'):
SCAN = 'trustwave'
else:
SCAN = 'cenzic-hailstorm'
F = ''
def file_read():
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
dirpath = os.path.dirname(__file__)
path = os.path.join(dirpath, 'test_files')
loader = FileSystemLoader(path)
env = Environment(
loader=loader
)
template = env.get_template('fake_policy.xml')
result = template.render(fake_policy=name)
return result
def remove_policies(mgmt_root, policy=None):
policies = mgmt_root.tm.asm.policies_s.get_collection()
if policy is None:
resources = policies
else:
resources = [p for p in policies if p.name == policy]
for resource in resources:
resource.delete()
@pytest.fixture(scope='function')
def partition(mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
partitions = mgmt_root.tm.auth.partitions.partition
local = partitions.create(name=name)
yield local
local.delete()
@pytest.fixture(scope='function')
def check_sig(mgmt_root):
task = mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.fetch()
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def update_sig(mgmt_root):
task = mgmt_root.tm.asm.tasks.update_signatures_s.update_signature.fetch()
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def export_basic(mgmt_root):
task = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.create(
filename='fake_export.xml'
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def set_policy(mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
pol1 = mgmt_root.tm.asm.policies_s.policy.create(
name=name
)
pol1.vulnerability_assessment.modify(scannerType=SCAN)
yield pol1.selfLink
@pytest.fixture(scope='function')
def set_policy2(mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
pol1 = mgmt_root.tm.asm.policies_s.policy.create(
name=name
)
yield pol1.selfLink
pol1.delete()
@pytest.fixture(scope='function')
def apply_policy(mgmt_root, set_policy2):
reference = {'link': set_policy2}
task = mgmt_root.tm.asm.tasks.apply_policy_s.apply_policy.create(
policyReference=reference
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def export_policy(mgmt_root, set_policy2):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
reference = {'link': set_policy2}
exp1 = mgmt_root.tm.asm.tasks.export_policy_s.export_policy.create(
filename=name + '.xml',
policyReference=reference
)
while True:
exp1.refresh()
if exp1.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield exp1
exp1.delete()
@pytest.fixture(scope='function')
def export_policy_inline(mgmt_root, set_policy2):
reference = {'link': set_policy2}
exp1 = mgmt_root.tm.asm.tasks.export_policy_s.export_policy.create(
inline=True,
policyReference=reference
)
while True:
exp1.refresh()
if exp1.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield exp1
exp1.delete()
@pytest.fixture(scope='function')
def import_policy_base64(mgmt_root):
content = file_read()
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
task = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
file=content,
name=name,
isBase64=True
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def import_policy_template(mgmt_root):
tmpl = mgmt_root.tm.asm.policy_templates_s.get_collection()
link = {'link': tmpl[0].selfLink}
f = tempfile.NamedTemporaryFile()
name = os.path.basename(f.name)
task = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
policyTemplateReference=link,
name=name,
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def import_policy(mgmt_root):
content = file_read()
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
task = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
file=content,
name=name
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def import_partitioned_policy(mgmt_root, partition):
content = file_read()
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
task = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
file=content,
fullPath='/{0}/{1}'.format(partition.name, name)
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def import_partitioned_policy2(mgmt_root, partition):
content = file_read()
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
task = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
file=content,
name=name,
partition=partition.name
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
@pytest.fixture(scope='function')
def import_vuln(mgmt_root, set_policy):
reference = {'link': set_policy}
imports = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
content = file_read()
file = tempfile.NamedTemporaryFile()
fh = open(file.name, 'w')
fh.write(content)
fh.close()
mgmt_root.tm.asm.file_transfer.uploads.upload_file(file.name)
task = imports.import_vulnerabilities.create(
filename=file.name,
policyReference=reference,
importAllDomainNames=True
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
yield task
task.delete()
class TestApplyPolicy(object):
def test_create_req_arg(self, apply_policy, set_policy2):
reference = {'link': set_policy2}
ap = apply_policy
assert ap.status == 'COMPLETED'
assert ap.kind == 'tm:asm:tasks:apply-policy:apply-policy-taskstate'
assert ap.policyReference == reference
def test_refresh(self, apply_policy, set_policy2):
reference = {'link': set_policy2}
ap = apply_policy
hashid = str(ap.id)
link = ap.selfLink
ap.refresh()
assert ap.kind == 'tm:asm:tasks:apply-policy:apply-policy-taskstate'
assert ap.policyReference == reference
assert ap.id == hashid
assert ap.selfLink == link
def test_load_no_object(self, mgmt_root):
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.apply_policy_s.apply_policy.load(
id='Lx3553-321')
assert err.value.response.status_code == 404
def test_load(self, apply_policy, mgmt_root):
ap = apply_policy
ap2 = mgmt_root.tm.asm.tasks.apply_policy_s.apply_policy.load(id=ap.id)
assert ap.id == ap2.id
assert ap.selfLink == ap2.selfLink
assert ap.policyReference == ap2.policyReference
def test_exists(self, apply_policy):
ap = apply_policy
hashid = str(ap.id)
assert ap.exists(id=hashid)
def test_delete(self, mgmt_root, set_policy2):
reference = {'link': set_policy2}
task = mgmt_root.tm.asm.tasks.apply_policy_s.apply_policy.create(
policyReference=reference
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
task.delete()
assert task.__dict__['deleted']
def test_apply_policy_collection(self, mgmt_root, apply_policy, set_policy2):
reference = {'link': set_policy2}
ap = apply_policy
assert ap.status == 'COMPLETED'
assert ap.kind == 'tm:asm:tasks:apply-policy:apply-policy-taskstate'
assert ap.policyReference == reference
col = mgmt_root.tm.asm.tasks.apply_policy_s.get_collection()
assert isinstance(col, list)
assert len(col)
assert isinstance(col[0], Apply_Policy)
class TestExportPolicy(object):
def test_create_req_arg(self, export_policy):
exp1 = export_policy
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-policy/'
final_uri = base_uri+endpoint
assert exp1.selfLink.startswith(final_uri)
assert exp1.status == 'COMPLETED'
assert exp1.kind == 'tm:asm:tasks:export-policy:export-policy-taskstate'
assert exp1.inline is False
def test_create_inline_export(self, export_policy_inline):
exp1 = export_policy_inline
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-policy/'
final_uri = base_uri+endpoint
assert exp1.selfLink.startswith(final_uri)
assert exp1.kind == 'tm:asm:tasks:export-policy:export-policy-taskstate'
assert exp1.inline is True
def test_create_optional_args(self, mgmt_root, set_policy2):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
reference = {'link': set_policy2}
exp1 = mgmt_root.tm.asm.tasks.export_policy_s.export_policy.create(
filename=name + '.xml',
policyReference=reference,
inline=True
)
while True:
exp1.refresh()
if exp1.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-policy/'
final_uri = base_uri + endpoint
assert exp1.selfLink.startswith(final_uri)
assert exp1.status == 'COMPLETED'
assert exp1.kind == 'tm:asm:tasks:export-policy:export-policy-taskstate'
assert exp1.inline is True
def test_refresh(self, export_policy, mgmt_root):
exp1 = export_policy
exp2 = mgmt_root.tm.asm.tasks.export_policy_s.export_policy.load(id=exp1.id)
assert exp1.selfLink == exp2.selfLink
exp1.refresh()
assert exp1.selfLink == exp2.selfLink
def test_load_no_object(self, mgmt_root):
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.export_policy_s.export_policy.load(id='Lx3553-321')
assert err.value.response.status_code == 404
def test_load(self, export_policy, mgmt_root):
exp1 = export_policy
exp2 = mgmt_root.tm.asm.tasks.export_policy_s.export_policy.load(id=exp1.id)
assert exp1.selfLink == exp2.selfLink
def test_delete(self, mgmt_root, set_policy2):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
reference = {'link': set_policy2}
exp1 = mgmt_root.tm.asm.tasks.export_policy_s.export_policy.create(
filename=name + '.xml',
policyReference=reference
)
while True:
exp1.refresh()
if exp1.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
hashid = str(exp1.id)
exp1.delete()
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.export_policy_s.export_policy.load(id=hashid)
assert err.value.response.status_code == 404
def test_policy_export_collection(self, export_policy, mgmt_root):
exp1 = export_policy
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-policy/'
final_uri = base_uri + endpoint
assert exp1.selfLink.startswith(final_uri)
assert exp1.status == 'COMPLETED'
assert exp1.kind == 'tm:asm:tasks:export-policy:export-policy-taskstate'
assert exp1.inline is False
sc = mgmt_root.tm.asm.tasks.export_policy_s.get_collection()
assert isinstance(sc, list)
assert len(sc)
assert isinstance(sc[0], Export_Policy)
class TestImportPolicy(object):
def test_create_req_arg(self, import_policy):
imp1 = import_policy
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.status == 'COMPLETED'
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.isBase64 is False
def test_create_import_template(self, import_policy_template):
imp1 = import_policy_template
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.isBase64 is False
def test_create_import_partitioned(self, mgmt_root, import_partitioned_policy):
imp1 = import_partitioned_policy
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.isBase64 is False
remove_policies(mgmt_root, os.path.basename(imp1.fullPath))
def test_create_import_partitioned2(self, mgmt_root, import_partitioned_policy2):
imp1 = import_partitioned_policy2
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.isBase64 is False
remove_policies(mgmt_root, os.path.basename(imp1.fullPath))
def test_create_import_fails(self, import_policy_base64):
imp1 = import_policy_base64
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.status == 'FAILURE'
def test_create_optional_args(self, mgmt_root):
content = file_read()
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
imp1 = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
file=content,
isBase64=True,
name=name
)
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri+endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.status == 'NEW'
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.isBase64 is True
def test_refresh(self, import_policy, mgmt_root):
imp1 = import_policy
imp2 = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.load(id=imp1.id)
assert imp1.selfLink == imp2.selfLink
imp1.refresh()
assert imp1.selfLink == imp2.selfLink
def test_load_no_object(self, mgmt_root):
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.import_policy_s.import_policy.load(id='Lx3553-321')
assert err.value.response.status_code == 404
def test_load(self, import_policy, mgmt_root):
imp1 = import_policy
imp2 = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.load(id=imp1.id)
assert imp1.selfLink == imp2.selfLink
def test_delete(self, mgmt_root):
content = file_read()
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
task = mgmt_root.tm.asm.tasks.import_policy_s.import_policy.create(
file=content,
name=name
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
hash_id = task.id
task.delete()
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.import_policy_s.import_policy.load(
id=hash_id
)
assert err.value.response.status_code == 404
def test_policy_import_collection(self, import_policy, mgmt_root):
imp1 = import_policy
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-policy/'
final_uri = base_uri+endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.status == 'COMPLETED'
assert imp1.kind == 'tm:asm:tasks:import-policy:import-policy-taskstate'
assert imp1.isBase64 is False
sc = mgmt_root.tm.asm.tasks.import_policy_s.get_collection()
assert isinstance(sc, list)
assert len(sc)
assert isinstance(sc[0], Import_Policy)
class TestCheckSignature(object):
def test_fetch(self, mgmt_root):
chk1 = mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.fetch()
endpoint = str(chk1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/check-signatures/'
final_uri = base_uri + endpoint
assert hasattr(chk1, 'id')
assert hasattr(chk1, 'status')
assert hasattr(chk1, 'selfLink')
assert not hasattr(chk1, 'generation')
assert chk1.status == 'NEW'
assert chk1.selfLink.startswith(final_uri)
assert chk1.kind == 'tm:asm:tasks:check-signatures:check-signatures-taskstate'
def test_load_no_object(self, mgmt_root):
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.load(
id='Lx3553-321'
)
assert err.value.response.status_code == 404
def test_load(self, check_sig, mgmt_root):
chk1 = check_sig
hashid = str(chk1.id)
t1 = mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.load(id=hashid)
assert t1.id == chk1.id
assert t1.selfLink == chk1.selfLink
def test_exists(self, check_sig):
chk1 = check_sig
hashid = str(chk1.id)
assert chk1.exists(id=hashid)
def test_refresh(self, check_sig):
chk1 = check_sig
hashid = str(chk1.id)
link = chk1.selfLink
chk1.refresh()
assert chk1.id == hashid
assert chk1.selfLink == link
def test_delete(self, mgmt_root):
task = mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.fetch()
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
task.delete()
assert task.__dict__['deleted']
def test_signature_update_collection(self, mgmt_root):
chk1 = mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.fetch()
endpoint = str(chk1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/check-signatures/'
final_uri = base_uri+endpoint
assert hasattr(chk1, 'id')
assert hasattr(chk1, 'status')
assert hasattr(chk1, 'selfLink')
assert not hasattr(chk1, 'generation')
assert chk1.status == 'NEW'
assert chk1.selfLink.startswith(final_uri)
assert chk1.kind == 'tm:asm:tasks:check-signatures:check-signatures-taskstate'
sc = mgmt_root.tm.asm.tasks.check_signatures_s.get_collection()
assert isinstance(sc, list)
assert len(sc)
assert isinstance(sc[0], Check_Signature)
class TestExportSignature(object):
def test_create_req_arg(self, mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
exp1 = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.create(
filename=name + '.xml'
)
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-signatures/'
final_uri = base_uri+endpoint
assert exp1.filename == name + '.xml'
assert exp1.selfLink.startswith(final_uri)
assert exp1.status == 'NEW'
assert exp1.kind == 'tm:asm:tasks:export-signatures:export-signatures-taskstate'
assert exp1.inline is False
def test_create_optional_args(self, mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
exp1 = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.create(
filename=name + '.xml',
inline=True
)
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-signatures/'
final_uri = base_uri + endpoint
assert exp1.filename == name + '.xml'
assert exp1.selfLink.startswith(final_uri)
assert exp1.status == 'NEW'
assert exp1.kind == 'tm:asm:tasks:export-signatures:export-signatures-taskstate'
assert exp1.inline is True
def test_refresh(self, mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
exp1 = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.create(
filename=name + '.xml'
)
exp2 = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.load(id=exp1.id)
assert exp1.selfLink == exp2.selfLink
exp1.refresh()
assert exp1.selfLink == exp2.selfLink
def test_load_no_object(self, mgmt_root):
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.load(id='Lx3553-321')
assert err.value.response.status_code == 404
def test_load(self, export_basic, mgmt_root):
exp1 = export_basic
exp2 = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.load(id=exp1.id)
assert exp1.selfLink == exp2.selfLink
def test_delete(self, mgmt_root):
file = tempfile.NamedTemporaryFile()
name = os.path.basename(file.name)
exp1 = mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.create(
filename=name + '.xml'
)
hashid = str(exp1.id)
exp1.delete()
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.export_signatures_s.export_signature.load(
id=hashid
)
assert err.value.response.status_code == 404
def test_signature_export_collection(self, export_basic, mgmt_root):
exp1 = export_basic
endpoint = str(exp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/export-signatures/'
final_uri = base_uri + endpoint
assert exp1.selfLink.startswith(final_uri)
assert exp1.status == 'COMPLETED'
assert exp1.kind == 'tm:asm:tasks:export-signatures:export-signatures-taskstate'
sc = mgmt_root.tm.asm.tasks.export_signatures_s.get_collection()
assert isinstance(sc, list)
assert len(sc)
assert isinstance(sc[0], Export_Signature)
@pytest.mark.skipif(
LooseVersion(pytest.config.getoption('--release')) < LooseVersion('12.0.0'),
reason='This collection is completely broken on 11.6.0.'
)
class TestUpdateSignature(object):
def test_fetch(self, mgmt_root):
chk1 = mgmt_root.tm.asm.tasks.update_signatures_s.update_signature.fetch()
endpoint = str(chk1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/update-signatures/'
final_uri = base_uri+endpoint
assert hasattr(chk1, 'id')
assert hasattr(chk1, 'status')
assert hasattr(chk1, 'selfLink')
assert not hasattr(chk1, 'generation')
assert chk1.status in ['COMPLETED', 'NEW']
assert chk1.selfLink.startswith(final_uri)
assert chk1.kind == 'tm:asm:tasks:update-signatures:update-signatures-taskstate'
def test_load_no_object(self, mgmt_root):
with pytest.raises(HTTPError) as err:
mgmt_root.tm.asm.tasks.update_signatures_s.update_signature.load(
id='Lx3553-321'
)
assert err.response.status_code == 404
def test_load(self, update_sig, mgmt_root):
chk1 = update_sig
hashid = str(chk1.id)
time.sleep(6)
t1 = mgmt_root.tm.asm.tasks.update_signatures_s.update_signature.load(id=hashid)
assert t1.id == chk1.id
assert t1.selfLink == chk1.selfLink
def test_exists(self, update_sig):
chk1 = update_sig
hashid = str(chk1.id)
assert chk1.exists(id=hashid)
def test_refresh(self, update_sig):
chk1 = update_sig
hashid = str(chk1.id)
link = chk1.selfLink
chk1.refresh()
assert chk1.id == hashid
assert chk1.selfLink == link
def test_delete(self, mgmt_root):
chk1 = mgmt_root.tm.asm.tasks.check_signatures_s.check_signature.fetch()
chk1.delete()
assert chk1.__dict__['deleted']
def test_signature_update_collection(self, mgmt_root):
chk1 = mgmt_root.tm.asm.tasks.update_signatures_s.update_signature.fetch()
endpoint = str(chk1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/update-signatures/'
final_uri = base_uri + endpoint
assert hasattr(chk1, 'id')
assert hasattr(chk1, 'status')
assert hasattr(chk1, 'selfLink')
assert not hasattr(chk1, 'generation')
assert chk1.status in ['COMPLETED', 'NEW']
assert chk1.selfLink.startswith(final_uri)
assert chk1.kind == 'tm:asm:tasks:update-signatures:update-signatures-taskstate'
sc = mgmt_root.tm.asm.tasks.update_signatures_s.get_collection()
assert isinstance(sc, list)
assert len(sc)
assert isinstance(sc[0], Update_Signature)
@pytest.mark.skipif(
LooseVersion(pytest.config.getoption('--release')) < LooseVersion('11.6.0'),
reason='This collection is fully implemented on 11.6.0 or greater.'
)
class TestImportVulnerabilities(object):
def test_modify_raises(self, mgmt_root):
rc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
with pytest.raises(UnsupportedOperation):
rc.import_vulnerabilities.modify()
def test_create_mandatory_arg_missing(self, mgmt_root, set_policy):
reference = {'link': set_policy}
rc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
content = file_read()
file = tempfile.NamedTemporaryFile()
fh = open(file.name, 'w')
fh.write(content)
fh.close()
with pytest.raises(MissingRequiredCreationParameter) as err:
rc.import_vulnerabilities.create(
filename=file.name,
policyReference=reference
)
assert 'This resource requires at least one of the' in str(err.value)
def test_create_req_arg(self, import_vuln):
imp1 = import_vuln
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-vulnerabilities/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.status == 'COMPLETED'
assert imp1.kind == 'tm:asm:tasks:import-vulnerabilities:import-vulnerabilities-taskstate'
assert imp1.importAllDomainNames is True
def test_refresh(self, import_vuln, mgmt_root):
rc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
imp1 = import_vuln
imp2 = rc.import_vulnerabilities.load(id=imp1.id)
assert imp1.selfLink == imp2.selfLink
assert imp1.importAllDomainNames == imp2.importAllDomainNames
imp1.refresh()
assert imp1.selfLink == imp2.selfLink
assert imp1.importAllDomainNames == imp2.importAllDomainNames
def test_load_no_object(self, mgmt_root):
rc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
with pytest.raises(HTTPError) as err:
rc.import_vulnerabilities.load(id='Lx3553-321')
assert err.value.response.status_code == 404
def test_load(self, mgmt_root, import_vuln):
rc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
imp1 = import_vuln
imp2 = rc.import_vulnerabilities.load(id=imp1.id)
assert imp1.selfLink == imp2.selfLink
assert imp1.importAllDomainNames == imp2.importAllDomainNames
def test_delete(self, mgmt_root, set_policy):
reference = {'link': set_policy}
imports = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
content = file_read()
file = tempfile.NamedTemporaryFile()
fh = open(file.name, 'w')
fh.write(content)
fh.close()
mgmt_root.tm.asm.file_transfer.uploads.upload_file(file.name)
task = imports.import_vulnerabilities.create(
filename=file.name,
policyReference=reference,
importAllDomainNames=True
)
while True:
task.refresh()
if task.status in ['COMPLETED', 'FAILURE']:
break
time.sleep(1)
hashid = str(task.id)
task.delete()
rc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s
with pytest.raises(HTTPError) as err:
rc.import_vulnerabilities.load(id=hashid)
assert err.value.response.status_code == 404
def test_import_vuln_collection(self, mgmt_root, import_vuln):
imp1 = import_vuln
endpoint = str(imp1.id)
base_uri = 'https://localhost/mgmt/tm/asm/tasks/import-vulnerabilities/'
final_uri = base_uri + endpoint
assert imp1.selfLink.startswith(final_uri)
assert imp1.status == 'COMPLETED'
assert imp1.kind == 'tm:asm:tasks:import-vulnerabilities:import-vulnerabilities-taskstate'
assert imp1.importAllDomainNames is True
sc = mgmt_root.tm.asm.tasks.import_vulnerabilities_s.get_collection()
assert isinstance(sc, list)
assert len(sc)
assert isinstance(sc[0], Import_Vulnerabilities)
| 36.189369 | 98 | 0.658557 | 4,083 | 32,679 | 5.103355 | 0.065148 | 0.048375 | 0.052311 | 0.040553 | 0.864184 | 0.854154 | 0.842684 | 0.82032 | 0.784134 | 0.779431 | 0 | 0.017976 | 0.235656 | 32,679 | 902 | 99 | 36.22949 | 0.816238 | 0.016892 | 0 | 0.731362 | 0 | 0 | 0.104976 | 0.038366 | 0 | 0 | 0 | 0 | 0.209512 | 1 | 0.092545 | false | 0 | 0.134961 | 0 | 0.237789 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
7551270451f7f94b2a01c04c1fbe6e3d0ec016ca | 5,633 | py | Python | test/testapi.py | sailingfree/Python-VPP | c4730494ae86dc78260ccd94fe05c85141760360 | [
"MIT"
] | 11 | 2020-06-03T15:06:03.000Z | 2022-02-05T19:01:33.000Z | test/testapi.py | sailingfree/Python-VPP | c4730494ae86dc78260ccd94fe05c85141760360 | [
"MIT"
] | 2 | 2020-06-03T15:24:51.000Z | 2020-10-24T21:21:28.000Z | test/testapi.py | sailingfree/Python-VPP | c4730494ae86dc78260ccd94fe05c85141760360 | [
"MIT"
] | 8 | 2020-06-07T18:28:34.000Z | 2021-08-20T16:55:29.000Z | import requests
import json
import os, sys
import numpy as np
sys.path.append(os.path.realpath("."))
from src.YachtMod import Yacht, Keel, Rudder
from src.SailMod import Main, Jib, Kite
from src.VPPMod import VPP
def test_interaction():
"""Test interaction between api and request by asking to sum arguments."""
url = "http://0.0.0.0:5000/api/sum/"
data = [
[14.34, 1.68, 2.7, 25.0, 98.0, 2.8, 1.31, 0.53, 2.7, 13.0, 0.57, 1.96, 660.0]
]
j_data = json.dumps(data)
headers = {"content-type": "application/json", "Accept-Charset": "UTF-8"}
r = requests.post(url, data=j_data, headers=headers)
print(r, r.text)
def test_local_vpp_solution():
"""
Return the dictionary produced by the VPP from an API call.
Pass the list of parameters as a dictionary.
Recieve the results as a dictionary.
"""
Keel1 = Keel(Cu=1.00, Cl=0.78, Span=1.90)
Rudder1 = Rudder(Cu=0.48, Cl=0.22, Span=1.15)
YD41 = Yacht(
Name="YD41",
Lwl=11.90,
Vol=6.05,
Bwl=3.18,
Tc=0.4,
WSA=28.20,
Tmax=2.30,
Amax=1.051,
Mass=6500,
Ff=1.5,
Fa=1.5,
Boa=4.2,
Loa=12.5,
App=[Keel1, Rudder1],
Sails=[
Main(P=16.60, E=5.60, Roach=0.1, BAD=1.0),
Jib(I=16.20, J=5.10, LPG=5.40, HBI=1.8),
Kite(area=150.0, vce=9.55),
],
)
yacht = dict(
{
"Name": "YD41",
"Lwl": 11.90,
"Vol": 6.05,
"Bwl": 3.18,
"Tc": 0.4,
"WSA": 28.20,
"Tmax": 2.30,
"Amax": 1.051,
"Mass": 6500,
"Ff": 1.5,
"Fa": 1.5,
"Boa": 4.2,
"Loa": 12.5,
}
)
keel = dict({"Cu": 1.00, "Cl": 0.78, "Span": 1.90})
rudder = dict({"Cu": 0.48, "Cl": 0.22, "Span": 1.15})
main = dict({"P": 16.60, "E": 5.60, "Roach": 0.1, "BAD": 1.0})
jib = dict({"I": 16.20, "J": 5.10, "LPG": 5.40, "HBI": 1.8})
kite = dict({"area": 150.0, "vce": 9.55})
tws_range = np.array([10.0]).tolist()
twa_range = [i for i in np.linspace(30.0, 180.0, 5)]
d = {
"name": yacht["Name"],
"yacht": yacht,
"keel": keel,
"rudder": rudder,
"main": main,
"jib": jib,
"kite": kite,
"tws_range": tws_range,
"twa_range": twa_range,
}
json_string = json.dumps(d)
url = "http://0.0.0.0:5000/api/vpp/"
headers = {"content-type": "application/json", "Accept-Charset": "UTF-8"}
response = requests.post(url, data=json_string, headers=headers).json()
vpp = VPP(Yacht=YD41)
vpp.set_analysis(
tws_range=np.array([10.0]), twa_range=np.linspace(30.0, 180.0, 5),
)
vpp.run(verbose=True)
results = vpp.result()
print(results["tws"] == response["tws"])
print(results["twa"] == response["twa"])
print(
np.isclose(results["perf"], response["perf"], rtol=0.1)
) # the results aren't always repeatable beyond 0.1 d.p.
def test_remote_vpp_solution():
"""
Return the dictionary produced by the VPP from an API call.
Pass the list of parameters as a dictionary.
Recieve the results as a dictionary.
"""
Keel1 = Keel(Cu=1.00, Cl=0.78, Span=1.90)
Rudder1 = Rudder(Cu=0.48, Cl=0.22, Span=1.15)
YD41 = Yacht(
Name="YD41",
Lwl=11.90,
Vol=6.05,
Bwl=3.18,
Tc=0.4,
WSA=28.20,
Tmax=2.30,
Amax=1.051,
Mass=6500,
Ff=1.5,
Fa=1.5,
Boa=4.2,
Loa=12.5,
App=[Keel1, Rudder1],
Sails=[
Main(P=16.60, E=5.60, Roach=0.1, BAD=1.0),
Jib(I=16.20, J=5.10, LPG=5.40, HBI=1.8),
Kite(area=150.0, vce=9.55),
],
)
yacht = dict(
{
"Name": "YD41",
"Lwl": 11.90,
"Vol": 6.05,
"Bwl": 3.18,
"Tc": 0.4,
"WSA": 28.20,
"Tmax": 2.30,
"Amax": 1.051,
"Mass": 6500,
"Ff": 1.5,
"Fa": 1.5,
"Boa": 4.2,
"Loa": 12.5,
}
)
keel = dict({"Cu": 1.00, "Cl": 0.78, "Span": 1.90})
rudder = dict({"Cu": 0.48, "Cl": 0.22, "Span": 1.15})
main = dict({"P": 16.60, "E": 5.60, "Roach": 0.1, "BAD": 1.0})
jib = dict({"I": 16.20, "J": 5.10, "LPG": 5.40, "HBI": 1.8})
kite = dict({"area": 150.0, "vce": 9.55})
tws_range = np.array([10.0]).tolist()
twa_range = [i for i in np.linspace(30.0, 180.0, 5)]
d = {
"name": yacht["Name"],
"yacht": yacht,
"keel": keel,
"rudder": rudder,
"main": main,
"jib": jib,
"kite": kite,
"tws_range": tws_range,
"twa_range": twa_range,
}
json_string = json.dumps(d)
url = "http://python-vpp-api.herokuapp.com/api/vpp/"
headers = {"content-type": "application/json", "Accept-Charset": "UTF-8"}
response = requests.post(url, data=json_string, headers=headers).json()
vpp = VPP(Yacht=YD41)
vpp.set_analysis(
tws_range=np.array([10.0]), twa_range=np.linspace(30.0, 180.0, 5),
)
vpp.run(verbose=True)
results = vpp.result()
print(results["tws"] == response["tws"])
print(results["twa"] == response["twa"])
print(
np.isclose(results["perf"], response["perf"], rtol=0.1)
) # the results aren't always repeatable beyond 0.1 d.p.
if __name__ == "__main__":
# test_interaction()
# test_local_vpp_solution()
test_remote_vpp_solution()
| 27.612745 | 85 | 0.499024 | 850 | 5,633 | 3.252941 | 0.201176 | 0.00868 | 0.00434 | 0.010127 | 0.83038 | 0.83038 | 0.83038 | 0.83038 | 0.820976 | 0.802893 | 0 | 0.12069 | 0.310137 | 5,633 | 203 | 86 | 27.748768 | 0.590839 | 0.090183 | 0 | 0.784431 | 0 | 0 | 0.113178 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.017964 | false | 0 | 0.041916 | 0 | 0.05988 | 0.041916 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f32e759f4045c5119734de5082dff62b6bb8d513 | 3,383 | py | Python | _blog_stuff/markdownplus/fabfile.py | draapho/Blog | 599ff2ec00a7fc17974df39db53d372e1697fe70 | [
"MIT"
] | 7 | 2016-11-13T19:08:00.000Z | 2020-03-27T04:38:25.000Z | _blog_stuff/markdownplus/fabfile.py | draapho/Blog | 599ff2ec00a7fc17974df39db53d372e1697fe70 | [
"MIT"
] | null | null | null | _blog_stuff/markdownplus/fabfile.py | draapho/Blog | 599ff2ec00a7fc17974df39db53d372e1697fe70 | [
"MIT"
] | 3 | 2018-05-17T05:47:17.000Z | 2021-02-18T08:19:05.000Z | from fabric.api import local
def css():
local('cp -r node_modules/markdown-core/dist/*.css dist/')
local('cp -r node_modules/markdown-core/dist/fonts dist/')
local('curl https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.12.0/jquery-ui.min.css > dist/markdown-plus.css')
local('curl https://cdn.jsdelivr.net/jquery.layout/1.4.3/layout-default.css >> dist/markdown-plus.css')
local('curl https://cdnjs.cloudflare.com/ajax/libs/remodal/1.1.0/remodal.min.css >> dist/markdown-plus.css')
local('curl https://cdnjs.cloudflare.com/ajax/libs/remodal/1.1.0/remodal-default-theme.min.css >> dist/markdown-plus.css')
local('cat dist/markdown-core.min.css >> dist/markdown-plus.css')
local('rm dist/markdown-core.min.css')
local('cat markdown-plus.css >> dist/markdown-plus.css')
local('cleancss -o dist/markdown-plus.min.css dist/markdown-plus.css')
local('rm dist/markdown-plus.css')
def js():
local('curl https://cdn.jsdelivr.net/underscorejs/1.8.3/underscore-min.js > dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('cat node_modules/markdown-core/dist/markdown-core.min.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.12.0/jquery-ui.min.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdn.jsdelivr.net/jquery.layout/1.4.3/jquery.layout.min.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/remodal/1.1.0/remodal.min.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/ace/1.2.5/ace.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/ace/1.2.5/keybinding-vim.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/ace/1.2.5/keybinding-emacs.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/ace/1.2.5/mode-markdown.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/ace/1.2.5/ext-searchbox.js >> dist/markdown-plus.js')
for theme in ['tomorrow_night_eighties', 'tomorrow_night_blue', 'tomorrow', 'kuroir']:
local('echo "\n" >> dist/markdown-plus.js')
local('curl https://cdnjs.cloudflare.com/ajax/libs/ace/1.2.5/theme-{0}.js >> dist/markdown-plus.js'.format(theme))
local('echo "\n" >> dist/markdown-plus.js')
local('cat sync_scroll.js >> dist/markdown-plus.js')
local('echo "\n" >> dist/markdown-plus.js')
local('cat markdown-plus.js >> dist/markdown-plus.js')
local('uglifyjs dist/markdown-plus.js -cmo dist/markdown-plus.min.js')
local('rm dist/markdown-plus.js')
def dist():
local('rm -rf node_modules')
local('npm install')
css()
js()
def mdp():
local('cp -rf dist ~/src/swift/markdown-plus/Markdown\ Plus/markdown-plus/')
local('cp -f index.html ~/src/swift/markdown-plus/Markdown\ Plus/markdown-plus/')
local('cp -f icon.png ~/src/swift/markdown-plus/Markdown\ Plus/markdown-plus/')
| 56.383333 | 126 | 0.683121 | 540 | 3,383 | 4.262963 | 0.15 | 0.250217 | 0.257168 | 0.211121 | 0.807124 | 0.754996 | 0.732407 | 0.706342 | 0.65682 | 0.631625 | 0 | 0.01502 | 0.114396 | 3,383 | 59 | 127 | 57.338983 | 0.753338 | 0 | 0 | 0.235294 | 0 | 0.352941 | 0.775643 | 0.325155 | 0 | 0 | 0 | 0 | 0 | 1 | 0.078431 | false | 0 | 0.019608 | 0 | 0.098039 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
f3c7e4cb5ec68e67de5cc67bcf6c26ca4ec02187 | 129 | py | Python | neurokit2/eeg/__init__.py | purpl3F0x/NeuroKit | bd41f2bf7692bc8ed4c85608daa535293a33a1d6 | [
"MIT"
] | 1 | 2020-05-26T09:46:57.000Z | 2020-05-26T09:46:57.000Z | neurokit2/eeg/__init__.py | purpl3F0x/NeuroKit | bd41f2bf7692bc8ed4c85608daa535293a33a1d6 | [
"MIT"
] | null | null | null | neurokit2/eeg/__init__.py | purpl3F0x/NeuroKit | bd41f2bf7692bc8ed4c85608daa535293a33a1d6 | [
"MIT"
] | 1 | 2020-10-27T06:47:51.000Z | 2020-10-27T06:47:51.000Z | """Submodule for NeuroKit."""
from .mne_channel_add import mne_channel_add
from .mne_channel_extract import mne_channel_extract
| 25.8 | 52 | 0.837209 | 19 | 129 | 5.263158 | 0.473684 | 0.4 | 0.28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.093023 | 129 | 4 | 53 | 32.25 | 0.854701 | 0.178295 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
f3c88a2395ad8886f4dec358684468d9b24bb235 | 71 | py | Python | other/dingding/dingtalk/api/__init__.py | hth945/pytest | 83e2aada82a2c6a0fdd1721320e5bf8b8fd59abc | [
"Apache-2.0"
] | null | null | null | other/dingding/dingtalk/api/__init__.py | hth945/pytest | 83e2aada82a2c6a0fdd1721320e5bf8b8fd59abc | [
"Apache-2.0"
] | null | null | null | other/dingding/dingtalk/api/__init__.py | hth945/pytest | 83e2aada82a2c6a0fdd1721320e5bf8b8fd59abc | [
"Apache-2.0"
] | null | null | null | from dingtalk.api.rest import *
from dingtalk.api.base import FileItem
| 23.666667 | 38 | 0.816901 | 11 | 71 | 5.272727 | 0.636364 | 0.413793 | 0.517241 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.112676 | 71 | 2 | 39 | 35.5 | 0.920635 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
45f4ff0ebf88bd6baa930b21957a2a4776515857 | 1,700 | py | Python | examples/tower_defense/map_data.py | yuehaowang/pylash_engine | 338c1552ff55e1088534bc127cfc5aafbda61227 | [
"MIT"
] | 38 | 2015-09-12T15:09:51.000Z | 2021-08-12T10:49:28.000Z | examples/tower_defense/map_data.py | yuehaowang/pylash_engine | 338c1552ff55e1088534bc127cfc5aafbda61227 | [
"MIT"
] | 2 | 2021-03-12T07:03:14.000Z | 2021-11-17T11:29:23.000Z | examples/tower_defense/map_data.py | yuehaowang/pylash_engine | 338c1552ff55e1088534bc127cfc5aafbda61227 | [
"MIT"
] | 21 | 2016-03-15T02:18:37.000Z | 2021-03-02T06:41:16.000Z | mapImageList = [
[18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18],
[18, 18, 18, 18, 18, 19, 18, 18, 18, 18, 18, 18, 18, 18, 18],
[18, 18, 18, 18, 19, 19, 17, 17, 22, 23, 24, 17, 18, 18, 18],
[18, 18, 18, 19, 19, 17, 17, 17, 25, 26, 27, 17, 18, 18, 18],
[18, 18, 17, 17, 17, 17, 17, 17, 17, 16, 17, 17, 18, 18, 18],
[18, 17, 17, 17, 17, 17, 17, 17, 17, 16, 17, 17, 17, 18, 18],
[18, 16, 16, 16, 16, 17, 17, 17, 17, 16, 17, 17, 17, 17, 18],
[18, 16, 17, 17, 16, 16, 16, 17, 17, 16, 17, 17, 17, 17, 18],
[18, 16, 17, 17, 17, 17, 16, 17, 17, 16, 17, 17, 17, 17, 17],
[18, 16, 16, 16, 17, 17, 16, 16, 16, 16, 17, 17, 17, 17, 17],
[18, 17, 17, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18],
[18, 17, 17, 16, 16, 16, 16, 17, 17, 17, 17, 19, 19, 18, 18],
[18, 17, 17, 17, 17, 17, 16, 17, 17, 19, 19, 19, 18, 18, 18],
[18, 16, 16, 16, 16, 16, 16, 17, 17, 19, 19, 18, 18, 18, 18],
[18, 16, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18]
]
terrainList = [
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 2],
[2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2],
[2, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 2],
[2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2],
[2, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2],
[2, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 2],
[2, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[2, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
[2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2],
[2, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 2],
[2, 0, 0, 0, 0, 0, 1, 0, 0, 2, 2, 2, 2, 2, 2],
[2, 1, 1, 1, 1, 1, 1, 0, 0, 2, 2, 2, 2, 2, 2],
[2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
] | 48.571429 | 62 | 0.410588 | 452 | 1,700 | 1.544248 | 0.033186 | 0.246418 | 0.309456 | 0.34957 | 0.946991 | 0.941261 | 0.881089 | 0.805158 | 0.679083 | 0.636103 | 0 | 0.556931 | 0.287059 | 1,700 | 35 | 63 | 48.571429 | 0.018977 | 0 | 0 | 0.058824 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
340184320b631c1172079e7f62c07f907c1fb2c6 | 6,094 | py | Python | tests/test_master.py | Craven-Biostat-Lab/synmod | 433e2f1726e68acbc45f226b1235f15508156de1 | [
"MIT"
] | 1 | 2020-05-21T14:56:14.000Z | 2020-05-21T14:56:14.000Z | tests/test_master.py | Craven-Biostat-Lab/synmod | 433e2f1726e68acbc45f226b1235f15508156de1 | [
"MIT"
] | 3 | 2020-10-20T08:17:07.000Z | 2021-09-08T02:34:51.000Z | tests/test_master.py | Craven-Biostat-Lab/synmod | 433e2f1726e68acbc45f226b1235f15508156de1 | [
"MIT"
] | 1 | 2021-12-14T21:16:53.000Z | 2021-12-14T21:16:53.000Z | """Tests for master script"""
import json
import subprocess
import sys
from unittest.mock import patch
import cloudpickle
import numpy as np
import synmod
from synmod import master, constants
from tests.utils import pre_test, post_test, round_fp
# pylint: disable = invalid-name, redefined-outer-name, protected-access
def test_regressor1(tmpdir, caplog):
"""Test synthetic data generation"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type regressor -num_instances 100 -num_features 10 -sequence_length 20 "
f"-fraction_relevant_features 0.5 -include_interaction_only_features 1 -output_dir {output_dir} -seed {constants.SEED}")
pass_args = cmd.split()[2:]
with patch.object(sys, 'argv', pass_args):
master.main()
post_test(caplog, output_dir)
def test_subprocess1(tmpdir, caplog):
"""Test synthetic data generation"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type regressor -num_instances 100 -num_features 10 -sequence_length 20 "
f"-fraction_relevant_features 0.5 -include_interaction_only_features 1 -output_dir {output_dir} -seed {constants.SEED}")
subprocess.check_call(cmd, shell=True)
post_test(caplog, output_dir)
def test_classifier1(tmpdir, caplog):
"""Test synthetic data generation"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type classifier -num_instances 100 -num_features 10 -sequence_length 20 "
f"-fraction_relevant_features 0.5 -include_interaction_only_features 1 -output_dir {output_dir} -seed {constants.SEED}")
pass_args = cmd.split()[2:]
with patch.object(sys, 'argv', pass_args):
master.main()
post_test(caplog, output_dir)
def test_reproducible_classifier(tmpdir, data_regression, caplog):
"""Reproducibility of results regression test"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type classifier -num_instances 100 -num_features 10 -sequence_length 20 "
f"-fraction_relevant_features 0.8 -include_interaction_only_features 1 -output_dir {output_dir} -seed {constants.SEED}")
pass_args = cmd.split()[2:]
with patch.object(sys, 'argv', pass_args):
_, data, model = master.main()
post_test(caplog, output_dir)
labels = model.predict(data, labels=True)
data_regression.check(round_fp(data).tobytes() + labels.tobytes())
def test_reproducible_regressor(tmpdir, data_regression, caplog):
"""Reproducibility of results regression test"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type regressor -num_instances 100 -num_features 10 -sequence_length 20 "
f"-fraction_relevant_features 0.8 -include_interaction_only_features 1 -output_dir {output_dir} -seed {constants.SEED}")
pass_args = cmd.split()[2:]
with patch.object(sys, 'argv', pass_args):
_, data, model = master.main()
post_test(caplog, output_dir)
labels = model.predict(data)
data_regression.check(round_fp(data).tobytes() + round_fp(labels).tobytes())
def test_reproducible_write_outputs(tmpdir, data_regression, file_regression, caplog):
"""Regression test to test reproducible human-readable summary of config/model/features and output files"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type classifier -num_instances 100 -num_features 10 -sequence_length 20 "
f"-fraction_relevant_features 0.8 -include_interaction_only_features 1 -write_outputs 1 -output_dir {output_dir} -seed {constants.SEED}")
pass_args = cmd.split()[2:]
with patch.object(sys, 'argv', pass_args):
master.main()
data = np.load(f"{output_dir}/{constants.INSTANCES_FILENAME}")
post_test(caplog, output_dir)
with open(f"{output_dir}/{constants.SUMMARY_FILENAME}", "rb") as summary_file:
summary = json.load(summary_file)
file_regression.check(json.dumps(round_fp(summary), indent=2), extension=".json")
with open(f"{output_dir}/{constants.MODEL_FILENAME}", "rb") as model_file:
model = cloudpickle.load(model_file)
labels = model.predict(data, labels=True)
data_regression.check(round_fp(data).tobytes() + labels.tobytes())
def test_reproducible_standardize_features(tmpdir, data_regression, file_regression, caplog):
"""Regression test to test reproducibility with standardized features"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
cmd = ("python -m synmod -synthesis_type temporal -model_type classifier -num_instances 100 -num_features 10 -sequence_length 20 "
"-fraction_relevant_features 0.8 -include_interaction_only_features 1 -write_outputs 1 "
f"-standardize_features 1 -output_dir {output_dir} -seed {constants.SEED}")
pass_args = cmd.split()[2:]
with patch.object(sys, 'argv', pass_args):
master.main()
data = np.load(f"{output_dir}/{constants.INSTANCES_FILENAME}")
post_test(caplog, output_dir)
with open(f"{output_dir}/{constants.SUMMARY_FILENAME}", "rb") as summary_file:
summary = json.load(summary_file)
file_regression.check(json.dumps(round_fp(summary), indent=2), extension=".json")
with open(f"{output_dir}/{constants.MODEL_FILENAME}", "rb") as model_file:
model = cloudpickle.load(model_file)
labels = model.predict(data, labels=True)
data_regression.check(round_fp(data).tobytes() + labels.tobytes())
def test_interface(tmpdir, caplog):
"""Test API"""
output_dir = pre_test(sys._getframe().f_code.co_name, tmpdir, caplog)
_ = synmod.synthesize(output_dir=output_dir, num_features=2, num_instances=10, synthesis_type=constants.TEMPORAL, sequence_length=5)
| 52.534483 | 148 | 0.735478 | 834 | 6,094 | 5.111511 | 0.139089 | 0.078114 | 0.022519 | 0.030026 | 0.848464 | 0.840957 | 0.840957 | 0.82524 | 0.82524 | 0.82524 | 0 | 0.016959 | 0.148507 | 6,094 | 115 | 149 | 52.991304 | 0.804587 | 0.074171 | 0 | 0.724138 | 0 | 0.011494 | 0.357756 | 0.124017 | 0 | 0 | 0 | 0 | 0 | 1 | 0.091954 | false | 0.137931 | 0.103448 | 0 | 0.195402 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
3416c3bea52a9895349d55551775c3351c880109 | 11,515 | py | Python | rdmo/questions/migrations/0007_refactoring.py | Raspeanut/rdmo | 9f785010a499c372a2f8368ccf76d2ea4150adcb | [
"Apache-2.0"
] | 77 | 2016-08-09T11:40:20.000Z | 2022-03-06T11:03:26.000Z | rdmo/questions/migrations/0007_refactoring.py | Raspeanut/rdmo | 9f785010a499c372a2f8368ccf76d2ea4150adcb | [
"Apache-2.0"
] | 377 | 2016-07-01T13:59:36.000Z | 2022-03-30T13:53:19.000Z | rdmo/questions/migrations/0007_refactoring.py | Raspeanut/rdmo | 9f785010a499c372a2f8368ccf76d2ea4150adcb | [
"Apache-2.0"
] | 47 | 2016-06-23T11:32:19.000Z | 2022-03-01T11:34:37.000Z | # -*- coding: utf-8 -*-
# Generated by Django 1.9 on 2017-01-26 16:01
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('questions', '0006_auto_20160803_1619'),
]
operations = [
migrations.AddField(
model_name='catalog',
name='comment',
field=models.TextField(blank=True, help_text='Additional information about this catalog.', null=True, verbose_name='Comment'),
),
migrations.AddField(
model_name='catalog',
name='key',
field=models.SlugField(blank=True, help_text='The internal identifier of this catalog. The URI will be generated from this key.', max_length=128, null=True, verbose_name='Key'),
),
migrations.AddField(
model_name='catalog',
name='uri',
field=models.URLField(blank=True, help_text='The Uniform Resource Identifier of this catalog (auto-generated).', max_length=640, null=True, verbose_name='URI'),
),
migrations.AddField(
model_name='catalog',
name='uri_prefix',
field=models.URLField(blank=True, help_text='The prefix for the URI of this catalog.', max_length=256, null=True, verbose_name='URI Prefix'),
),
migrations.AddField(
model_name='questionentity',
name='comment',
field=models.TextField(blank=True, help_text='Additional information about this question/questionset.', null=True, verbose_name='Comment'),
),
migrations.AddField(
model_name='questionentity',
name='key',
field=models.SlugField(blank=True, help_text='The internal identifier of this question/questionset. The URI will be generated from this key.', max_length=128, null=True, verbose_name='Key'),
),
migrations.AddField(
model_name='questionentity',
name='uri',
field=models.URLField(blank=True, help_text='The Uniform Resource Identifier of this question/questionset (auto-generated).', max_length=640, null=True, verbose_name='URI'),
),
migrations.AddField(
model_name='questionentity',
name='uri_prefix',
field=models.URLField(blank=True, help_text='The prefix for the URI of this question/questionset.', max_length=256, null=True, verbose_name='URI Prefix'),
),
migrations.AddField(
model_name='section',
name='comment',
field=models.TextField(blank=True, help_text='Additional information about this section.', null=True, verbose_name='Comment'),
),
migrations.AddField(
model_name='section',
name='key',
field=models.SlugField(blank=True, help_text='The internal identifier of this section. The URI will be generated from this key.', max_length=128, null=True, verbose_name='Key'),
),
migrations.AddField(
model_name='section',
name='uri',
field=models.URLField(blank=True, help_text='The Uniform Resource Identifier of this section (auto-generated).', max_length=640, null=True, verbose_name='URI'),
),
migrations.AddField(
model_name='section',
name='uri_prefix',
field=models.URLField(blank=True, help_text='The prefix for the URI of this section.', max_length=256, null=True, verbose_name='URI Prefix'),
),
migrations.AddField(
model_name='subsection',
name='comment',
field=models.TextField(blank=True, help_text='Additional information about this subsection.', null=True, verbose_name='Comment'),
),
migrations.AddField(
model_name='subsection',
name='key',
field=models.SlugField(blank=True, help_text='The internal identifier of this subsection. The URI will be generated from this key.', max_length=128, null=True, verbose_name='Key'),
),
migrations.AddField(
model_name='subsection',
name='uri',
field=models.URLField(blank=True, help_text='The Uniform Resource Identifier of this subsection (auto-generated).', max_length=640, null=True, verbose_name='URI'),
),
migrations.AddField(
model_name='subsection',
name='uri_prefix',
field=models.URLField(blank=True, help_text='The prefix for the URI of this subsection.', max_length=256, null=True, verbose_name='URI Prefix'),
),
migrations.AlterField(
model_name='catalog',
name='order',
field=models.IntegerField(default=0, help_text='The position of this catalog in lists.', verbose_name='Order'),
),
migrations.AlterField(
model_name='catalog',
name='title_de',
field=models.CharField(help_text='The German title for this catalog.', max_length=256, verbose_name='Title (de)'),
),
migrations.AlterField(
model_name='catalog',
name='title_en',
field=models.CharField(help_text='The English title for this catalog.', max_length=256, verbose_name='Title (en)'),
),
migrations.AlterField(
model_name='question',
name='parent',
field=models.ForeignKey(blank=True, help_text='The question set this question belongs to.', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='questions', to='questions.QuestionEntity', verbose_name='Parent'),
),
migrations.AlterField(
model_name='question',
name='text_de',
field=models.TextField(help_text='The German text for this question.', verbose_name='Text (de)'),
),
migrations.AlterField(
model_name='question',
name='text_en',
field=models.TextField(help_text='The English text for this question.', verbose_name='Text (en)'),
),
migrations.AlterField(
model_name='question',
name='widget_type',
field=models.CharField(choices=[('text', 'Text'), ('textarea', 'Textarea'), ('yesno', 'Yes/No'), ('checkbox', 'Checkboxes'), ('radio', 'Radio buttons'), ('select', 'Select drop-down'), ('range', 'Range slider'), ('date', 'Date picker')], help_text='Type of widget for this question.', max_length=12, verbose_name='Widget type'),
),
migrations.AlterField(
model_name='questionentity',
name='attribute_entity',
field=models.ForeignKey(blank=True, help_text='The attribute/entity this question belongs to.', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='domain.AttributeEntity', verbose_name='Attribute entity'),
),
migrations.AlterField(
model_name='questionentity',
name='help_de',
field=models.TextField(blank=True, help_text='The German help text for this question/questionset.', null=True, verbose_name='Help (de)'),
),
migrations.AlterField(
model_name='questionentity',
name='help_en',
field=models.TextField(blank=True, help_text='The English help text for this question/questionset.', null=True, verbose_name='Help (en)'),
),
migrations.AlterField(
model_name='questionentity',
name='label_de',
field=models.TextField(help_text='The German label for this question/questionset (auto-generated).', verbose_name='Label (de)'),
),
migrations.AlterField(
model_name='questionentity',
name='label_en',
field=models.TextField(help_text='The English label for this question/questionset (auto-generated).', verbose_name='Label (en)'),
),
migrations.AlterField(
model_name='questionentity',
name='order',
field=models.IntegerField(default=0, help_text='The position of this subsection in lists.', verbose_name='Order'),
),
migrations.AlterField(
model_name='questionentity',
name='subsection',
field=models.ForeignKey(help_text='The section this question belongs to.', on_delete=django.db.models.deletion.CASCADE, related_name='entities', to='questions.Subsection', verbose_name='Catalog'),
),
migrations.AlterField(
model_name='section',
name='catalog',
field=models.ForeignKey(help_text='The catalog this section belongs to.', on_delete=django.db.models.deletion.CASCADE, related_name='sections', to='questions.Catalog', verbose_name='Catalog'),
),
migrations.AlterField(
model_name='section',
name='label_de',
field=models.TextField(help_text='The German label for this section (auto-generated).', verbose_name='Label (de)'),
),
migrations.AlterField(
model_name='section',
name='label_en',
field=models.TextField(help_text='The English label for this section (auto-generated).', verbose_name='Label (en)'),
),
migrations.AlterField(
model_name='section',
name='order',
field=models.IntegerField(default=0, help_text='The position of this section in lists.', verbose_name='Order'),
),
migrations.AlterField(
model_name='section',
name='title_de',
field=models.CharField(help_text='The German title for this section.', max_length=256, verbose_name='Title (de)'),
),
migrations.AlterField(
model_name='section',
name='title_en',
field=models.CharField(help_text='The English title for this section.', max_length=256, verbose_name='Title (en)'),
),
migrations.AlterField(
model_name='subsection',
name='label_de',
field=models.TextField(help_text='The German label for this subsection (auto-generated).', verbose_name='Label (de)'),
),
migrations.AlterField(
model_name='subsection',
name='label_en',
field=models.TextField(help_text='The English label for this subsection (auto-generated).', verbose_name='Label (en)'),
),
migrations.AlterField(
model_name='subsection',
name='order',
field=models.IntegerField(default=0, help_text='The position of this subsection in lists.', verbose_name='Order'),
),
migrations.AlterField(
model_name='subsection',
name='section',
field=models.ForeignKey(help_text='The section this subsection belongs to.', on_delete=django.db.models.deletion.CASCADE, related_name='subsections', to='questions.Section', verbose_name='Catalog'),
),
migrations.AlterField(
model_name='subsection',
name='title_de',
field=models.CharField(help_text='The German title for this subsection.', max_length=256, verbose_name='Title (de)'),
),
migrations.AlterField(
model_name='subsection',
name='title_en',
field=models.CharField(help_text='The English title for this subsection.', max_length=256, verbose_name='Title (en)'),
),
]
| 50.726872 | 340 | 0.62475 | 1,276 | 11,515 | 5.492163 | 0.094828 | 0.050228 | 0.058076 | 0.107591 | 0.875285 | 0.866153 | 0.830908 | 0.74258 | 0.695919 | 0.627426 | 0 | 0.010548 | 0.250803 | 11,515 | 226 | 341 | 50.951327 | 0.801785 | 0.005645 | 0 | 0.730594 | 1 | 0 | 0.297283 | 0.0152 | 0.004566 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.013699 | 0 | 0.027397 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
1b37fb4d04904cef091e66d1e7ce3047f30e0754 | 7,357 | py | Python | tests/unit/models/field/color.py | RaenonX/Jelly-Bot-API | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 5 | 2020-08-26T20:12:00.000Z | 2020-12-11T16:39:22.000Z | tests/unit/models/field/color.py | RaenonX/Jelly-Bot | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 234 | 2019-12-14T03:45:19.000Z | 2020-08-26T18:55:19.000Z | tests/unit/models/field/color.py | RaenonX/Jelly-Bot-API | c7da1e91783dce3a2b71b955b3a22b68db9056cf | [
"MIT"
] | 2 | 2019-10-23T15:21:15.000Z | 2020-05-22T09:35:55.000Z | from typing import Type, Any, Tuple
from extutils.color import Color, ColorFactory
from models.field import ColorField, BaseField
from models.field.exceptions import (
FieldTypeMismatchError, FieldNoneNotAllowedError, FieldValueInvalidError, FieldError
)
from ._test_val import TestFieldValue
from ._test_prop import TestFieldProperty
__all__ = ["TestColorFieldProperty", "TestColorFieldValueAllowNone",
"TestColorFieldValueDefault", "TestColorFieldValueNoAutoCast"]
class TestColorFieldProperty(TestFieldProperty.TestClass):
def get_field_class(self) -> Type[BaseField]:
return ColorField
def valid_not_none_obj_value(self) -> Any:
return ColorFactory.WHITE
def expected_none_object(self) -> Any:
return ColorFactory.DEFAULT
def get_valid_default_values(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, Color(5723991)),
("#575757", Color(5723991)),
("575757", Color(5723991)),
(Color(5723991), Color(5723991))
)
def get_invalid_default_values(self) -> Tuple[Any, ...]:
return True, -8000, 20000000, "GGGGGG"
def get_expected_types(self) -> Tuple[Type[Any], ...]:
return Color, int, str
def get_desired_type(self) -> Type[Any]:
return Color
class TestColorFieldValueDefault(TestFieldValue.TestClass):
def get_field(self) -> BaseField:
return ColorField("k")
def get_value_type_match_test(self) -> Tuple[Tuple[Any, bool], ...]:
return (
(None, False),
(ColorFactory.DEFAULT, True),
(5723991, True),
("#575757", True),
("575757", True),
(Color(5723991), True),
(True, False),
(-8000, True),
(20000000, True),
("GGGGGG", True)
)
def get_value_validity_test(self) -> Tuple[Tuple[Any, bool], ...]:
return (
(None, False),
(ColorFactory.DEFAULT, True),
(5723991, True),
("#575757", True),
("575757", True),
(Color(5723991), True),
(True, False),
(-8000, False),
(20000000, False),
("GGGGGG", False)
)
def is_auto_cast(self) -> bool:
return True
def get_values_to_cast(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, Color(5723991)),
("#575757", Color(5723991)),
("575757", Color(5723991)),
(Color(5723991), Color(5723991))
)
def get_valid_value_to_set(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, Color(5723991)),
("#575757", Color(5723991)),
("575757", Color(5723991)),
(Color(5723991), Color(5723991))
)
def get_invalid_value_to_set(self) -> Tuple[Tuple[Any, Type[FieldError]], ...]:
return (
(None, FieldNoneNotAllowedError),
(True, FieldTypeMismatchError),
(-8000, FieldValueInvalidError),
(20000000, FieldValueInvalidError),
("GGGGGG", FieldValueInvalidError),
)
class TestColorFieldValueAllowNone(TestFieldValue.TestClass):
def get_field(self) -> BaseField:
return ColorField("k", allow_none=True)
def get_value_type_match_test(self) -> Tuple[Tuple[Any, bool], ...]:
return (
(None, True),
(ColorFactory.DEFAULT, True),
(5723991, True),
("#575757", True),
("575757", True),
(Color(5723991), True),
(True, False),
(-8000, True),
(20000000, True),
("GGGGGG", True)
)
def get_value_validity_test(self) -> Tuple[Tuple[Any, bool], ...]:
return (
(None, True),
(ColorFactory.DEFAULT, True),
(5723991, True),
("#575757", True),
("575757", True),
(Color(5723991), True),
(True, False),
(-8000, False),
(20000000, False),
("GGGGGG", False)
)
def is_auto_cast(self) -> bool:
return True
def get_values_to_cast(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(None, None),
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, Color(5723991)),
("#575757", Color(5723991)),
("575757", Color(5723991)),
(Color(5723991), Color(5723991))
)
def get_valid_value_to_set(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(None, None),
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, Color(5723991)),
("#575757", Color(5723991)),
("575757", Color(5723991)),
(Color(5723991), Color(5723991))
)
def get_invalid_value_to_set(self) -> Tuple[Tuple[Any, Type[FieldError]], ...]:
return (
(True, FieldTypeMismatchError),
(-8000, FieldValueInvalidError),
(20000000, FieldValueInvalidError),
("GGGGGG", FieldValueInvalidError),
)
class TestColorFieldValueNoAutoCast(TestFieldValue.TestClass):
def get_field(self) -> BaseField:
return ColorField("k", auto_cast=False)
def get_value_type_match_test(self) -> Tuple[Tuple[Any, bool], ...]:
return (
(None, False),
(ColorFactory.DEFAULT, True),
(5723991, True),
("#575757", True),
("575757", True),
(Color(5723991), True),
(True, False),
(-8000, True),
(20000000, True),
("GGGGGG", True)
)
def get_value_validity_test(self) -> Tuple[Tuple[Any, bool], ...]:
return (
(None, False),
(ColorFactory.DEFAULT, True),
(5723991, True),
("#575757", True),
("575757", True),
(Color(5723991), True),
(True, False),
(-8000, False),
(20000000, False),
("GGGGGG", False)
)
def is_auto_cast(self) -> bool:
return False
def get_values_to_cast(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, Color(5723991)),
("#575757", Color(5723991)),
("575757", Color(5723991)),
(Color(5723991), Color(5723991))
)
def get_valid_value_to_set(self) -> Tuple[Tuple[Any, Any], ...]:
return (
(ColorFactory.DEFAULT, ColorFactory.DEFAULT),
(5723991, 5723991),
("#575757", "#575757"),
("575757", "575757"),
(Color(5723991), Color(5723991))
)
def get_invalid_value_to_set(self) -> Tuple[Tuple[Any, Type[FieldError]], ...]:
return (
(None, FieldNoneNotAllowedError),
(True, FieldTypeMismatchError),
(-8000, FieldValueInvalidError),
(20000000, FieldValueInvalidError),
("GGGGGG", FieldValueInvalidError),
)
| 31.575107 | 88 | 0.541253 | 643 | 7,357 | 6.051322 | 0.102644 | 0.117194 | 0.092778 | 0.069905 | 0.76587 | 0.764328 | 0.764328 | 0.764328 | 0.764328 | 0.733488 | 0 | 0.129958 | 0.318064 | 7,357 | 232 | 89 | 31.711207 | 0.645605 | 0 | 0 | 0.746193 | 0 | 0 | 0.047574 | 0.014272 | 0 | 0 | 0 | 0 | 0 | 1 | 0.142132 | false | 0 | 0.030457 | 0.142132 | 0.335025 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
1b71c41b49693648dbe9613cc7d39e5c2be6f5e4 | 34,817 | py | Python | pypykatz/alsadecryptor/packages/msv/templates.py | wisdark/pypykatz | 7dccf2fa52532586da9b5ae3e849928b4ba5a3ba | [
"MIT"
] | 1,861 | 2018-05-26T11:16:39.000Z | 2022-03-24T19:48:55.000Z | pypykatz/alsadecryptor/packages/msv/templates.py | wisdark/pypykatz | 7dccf2fa52532586da9b5ae3e849928b4ba5a3ba | [
"MIT"
] | 77 | 2018-05-28T21:43:31.000Z | 2021-12-05T00:11:31.000Z | pypykatz/alsadecryptor/packages/msv/templates.py | wisdark/pypykatz | 7dccf2fa52532586da9b5ae3e849928b4ba5a3ba | [
"MIT"
] | 270 | 2018-05-26T16:42:14.000Z | 2022-03-24T03:05:08.000Z | #!/usr/bin/env python3
#
# Author:
# Tamas Jos (@skelsec)
#
import io
from pypykatz.commons.common import KatzSystemArchitecture, WindowsMinBuild, WindowsBuild
from pypykatz.alsadecryptor.win_datatypes import BOOLEAN, HANDLE, USHORT, ULONG, LSA_UNICODE_STRING, LSAISO_DATA_BLOB, \
BYTE, PVOID, WORD, DWORD, POINTER, LUID, PSID, ANSI_STRING
from pypykatz.alsadecryptor.package_commons import PackageTemplate
class MsvTemplate(PackageTemplate):
def __init__(self):
super().__init__('Msv')
self.signature = None
self.first_entry_offset = None
self.offset2 = None
self.list_entry = None
self.encrypted_credentials_list_struct = None
self.encrypted_credential_struct = None
self.decrypted_credential_struct = None
@staticmethod
def get_template(sysinfo):
template = MsvTemplate()
template.encrypted_credentials_list_struct = KIWI_MSV1_0_CREDENTIAL_LIST
template.log_template('encrypted_credentials_list_struct', template.encrypted_credentials_list_struct)
template.encrypted_credential_struct = KIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC
template.log_template('encrypted_credential_struct', template.encrypted_credential_struct)
#identify credential session list structure to be used
if sysinfo.buildnumber < WindowsMinBuild.WIN_2K3.value:
template.list_entry = PKIWI_MSV1_0_LIST_51
elif sysinfo.buildnumber < WindowsMinBuild.WIN_VISTA.value:
template.list_entry = PKIWI_MSV1_0_LIST_52
elif sysinfo.buildnumber < WindowsMinBuild.WIN_7.value:
template.list_entry = PKIWI_MSV1_0_LIST_60
elif sysinfo.buildnumber < WindowsMinBuild.WIN_8.value:
#do not do that :)
if sysinfo.msv_dll_timestamp > 0x53480000:
template.list_entry = PKIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ
else:
template.list_entry = PKIWI_MSV1_0_LIST_61
elif sysinfo.buildnumber < WindowsMinBuild.WIN_BLUE.value:
#template.list_entry = PKIWI_MSV1_0_LIST_62
if sysinfo.msv_dll_timestamp > 0x53480000:
template.list_entry = PKIWI_MSV1_0_LIST_63
else:
template.list_entry = PKIWI_MSV1_0_LIST_62
else:
template.list_entry = PKIWI_MSV1_0_LIST_63
template.log_template('list_entry', template.list_entry)
if sysinfo.buildnumber < WindowsBuild.WIN_10_1507.value:
template.decrypted_credential_struct = MSV1_0_PRIMARY_CREDENTIAL_DEC
elif sysinfo.buildnumber < WindowsBuild.WIN_10_1511.value:
template.decrypted_credential_struct = MSV1_0_PRIMARY_CREDENTIAL_10_OLD_DEC
elif sysinfo.buildnumber < WindowsBuild.WIN_10_1607.value:
template.decrypted_credential_struct = MSV1_0_PRIMARY_CREDENTIAL_10_DEC
else:
template.decrypted_credential_struct = MSV1_0_PRIMARY_CREDENTIAL_10_1607_DEC
template.log_template('decrypted_credential_struct', template.decrypted_credential_struct)
if sysinfo.architecture == KatzSystemArchitecture.X64:
if WindowsMinBuild.WIN_XP.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_2K3.value:
template.signature = b'\x4c\x8b\xdf\x49\xc1\xe3\x04\x48\x8b\xcb\x4c\x03\xd8'
template.first_entry_offset = -4
template.offset2 = 0
elif WindowsMinBuild.WIN_2K3.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_VISTA.value:
template.signature = b'\x4c\x8b\xdf\x49\xc1\xe3\x04\x48\x8b\xcb\x4c\x03\xd8'
template.first_entry_offset = -4
template.offset2 = -45
elif WindowsMinBuild.WIN_VISTA.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_7.value:
template.signature = b'\x33\xff\x45\x85\xc0\x41\x89\x75\x00\x4c\x8b\xe3\x0f\x84'
template.first_entry_offset = 21#-4
template.offset2 = -4
elif WindowsMinBuild.WIN_7.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_8.value:
template.signature = b'\x33\xf6\x45\x89\x2f\x4c\x8b\xf3\x85\xff\x0f\x84'
template.first_entry_offset = 19
template.offset2 = -4
elif WindowsMinBuild.WIN_8.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_BLUE.value:
template.signature = b'\x33\xff\x41\x89\x37\x4c\x8b\xf3\x45\x85\xc0\x74'
template.first_entry_offset = 16
template.offset2 = -4
elif WindowsMinBuild.WIN_BLUE.value <= sysinfo.buildnumber < WindowsBuild.WIN_10_1507.value:
template.signature = b'\x8b\xde\x48\x8d\x0c\x5b\x48\xc1\xe1\x05\x48\x8d\x05'
template.first_entry_offset = 36
template.offset2 = -6
elif WindowsBuild.WIN_10_1507.value <= sysinfo.buildnumber < WindowsBuild.WIN_10_1703.value:
#1503 and 1603
template.signature = b'\x33\xff\x41\x89\x37\x4c\x8b\xf3\x45\x85\xc0\x74'
template.first_entry_offset = 16
template.offset2 = -4
elif WindowsBuild.WIN_10_1703.value <= sysinfo.buildnumber < WindowsBuild.WIN_10_1803.value:
#1703
template.signature = b'\x33\xff\x45\x89\x37\x48\x8b\xf3\x45\x85\xc9\x74'
template.first_entry_offset = 23
template.offset2 = -4
elif WindowsBuild.WIN_10_1803.value <= sysinfo.buildnumber < WindowsBuild.WIN_10_1903.value:
#1803
template.signature = b'\x33\xff\x41\x89\x37\x4c\x8b\xf3\x45\x85\xc9\x74'
template.first_entry_offset = 23
template.offset2 = -4
else:
#1903
template.signature = b'\x33\xff\x41\x89\x37\x4c\x8b\xf3\x45\x85\xc0\x74'
template.first_entry_offset = 23
template.offset2 = -4
elif sysinfo.architecture == KatzSystemArchitecture.X86:
if WindowsMinBuild.WIN_XP.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_2K3.value:
template.signature = b'\xff\x50\x10\x85\xc0\x0f\x84'
template.first_entry_offset = 24
template.offset2 = 0
elif WindowsMinBuild.WIN_2K3.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_VISTA.value:
template.signature = b'\x89\x71\x04\x89\x30\x8d\x04\xbd'
template.first_entry_offset = -11
template.offset2 = -43
elif WindowsMinBuild.WIN_VISTA.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_8.value:
template.signature = b'\x89\x71\x04\x89\x30\x8d\x04\xbd'
template.first_entry_offset = -11
template.offset2 = -42
elif WindowsMinBuild.WIN_8.value <= sysinfo.buildnumber < WindowsMinBuild.WIN_BLUE.value:
template.signature = b'\x8b\x45\xf8\x8b\x55\x08\x8b\xde\x89\x02\x89\x5d\xf0\x85\xc9\x74'
template.first_entry_offset = 18
template.offset2 = -4
elif WindowsMinBuild.WIN_BLUE.value <= sysinfo.buildnumber < WindowsBuild.WIN_10_1507.value:
template.signature = b'\x8b\x4d\xe4\x8b\x45\xf4\x89\x75\xe8\x89\x01\x85\xff\x74'
template.first_entry_offset = 16
template.offset2 = -4
elif sysinfo.buildnumber >= WindowsBuild.WIN_10_1507.value:
template.signature = b'\x8b\x4d\xe8\x8b\x45\xf4\x89\x75\xec\x89\x01\x85\xff\x74'
template.first_entry_offset = 16
template.offset2 = -4
else:
raise Exception('Could not identify template! sysinfo.buildnumber: %d' % sysinfo.buildnumber)
else:
raise Exception('Unknown Architecture: %s , Build number %s' % (sysinfo.architecture, sysinfo.buildnumber))
return template
class MSV1_0_PRIMARY_CREDENTIAL_STRANGE_DEC:
#this structure doesnt have username nor domainname, but has credentials :S
#starts with
size = 0x60
def __init__(self):
self.unk1 = None
self.unk2 = None
self.unk_tag = None
self.unk_remaining_size = None
self.LengthOfNtOwfPassword = None
self.NtOwfPassword = None
self.LengthOfShaOwfPassword = None
self.ShaOwPassword = None
self.LogonDomainName = None
self.UserName = None
self.LmOwfPassword = None
self.isNtOwfPassword = None
self.isLmOwfPassword = None
self.isShaOwPassword = None
@staticmethod
async def load(reader):
res = MSV1_0_PRIMARY_CREDENTIAL_STRANGE_DEC()
res.unk1 = await USHORT.loadvalue(reader)
res.unk2 = await USHORT.loadvalue(reader)
res.unk_tag = await reader.read(4) #0xcccccc
res.unk_remaining_size = await ULONG.loadvalue(reader)
await reader.read(40)
res.LengthOfNtOwfPassword = await ULONG.loadvalue(reader)
res.NtOwfPassword = await reader.read(16)
res.LengthOfShaOwfPassword = await ULONG.loadvalue(reader)
res.ShaOwPassword = await reader.read(20)
res.LogonDomainName = None
res.UserName = None
res.LmOwfPassword = None
res.isNtOwfPassword = None
res.isLmOwfPassword = None
res.isShaOwPassword = None
return res
class MSV1_0_PRIMARY_CREDENTIAL_DEC:
def __init__(self):
self.LogonDomainName = None
self.UserName = None
self.NtOwfPassword = None
self.LmOwfPassword = None
self.ShaOwPassword = None
self.isNtOwfPassword = None
self.isLmOwfPassword = None
self.isShaOwPassword = None
@staticmethod
async def load(reader):
res = MSV1_0_PRIMARY_CREDENTIAL_DEC()
res.LogonDomainName = await LSA_UNICODE_STRING.load(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.NtOwfPassword = await reader.read(16)
res.LmOwfPassword = await reader.read(16)
res.ShaOwPassword = await reader.read(20)
res.isNtOwfPassword = await BOOLEAN.loadvalue(reader)
res.isLmOwfPassword = await BOOLEAN.loadvalue(reader)
res.isShaOwPassword = await BOOLEAN.loadvalue(reader)
return res
class MSV1_0_PRIMARY_CREDENTIAL_10_OLD_DEC:
def __init__(self):
self.LogonDomainName = None
self.UserName = None
self.isIso = None
self.isNtOwfPassword = None
self.isLmOwfPassword = None
self.isShaOwPassword = None
self.align0 = None
self.align1 = None
self.NtOwfPassword = None
self.LmOwfPassword = None
self.ShaOwPassword = None
@staticmethod
async def load(reader):
res = MSV1_0_PRIMARY_CREDENTIAL_10_OLD_DEC()
res.LogonDomainName = await LSA_UNICODE_STRING.load(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.isIso = await BOOLEAN.loadvalue(reader)
res.isNtOwfPassword = await BOOLEAN.loadvalue(reader)
res.isLmOwfPassword = await BOOLEAN.loadvalue(reader)
res.isShaOwPassword = await BOOLEAN.loadvalue(reader)
res.align0 = await BYTE.loadvalue(reader)
res.align1 = await BYTE.loadvalue(reader)
res.NtOwfPassword = await reader.read(16)
res.LmOwfPassword = await reader.read(16)
res.ShaOwPassword = await reader.read(20)
return res
class MSV1_0_PRIMARY_CREDENTIAL_10_DEC:
def __init__(self):
self.LogonDomainName = None
self.UserName = None
self.isIso = None
self.isNtOwfPassword = None
self.isLmOwfPassword = None
self.isShaOwPassword = None
self.align0 = None
self.align1 = None
self.align2 = None
self.align3 = None
self.NtOwfPassword = None
self.LmOwfPassword = None
self.ShaOwPassword = None
@staticmethod
async def load(reader):
res = MSV1_0_PRIMARY_CREDENTIAL_10_DEC()
res.LogonDomainName = await LSA_UNICODE_STRING.load(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.isIso = await BOOLEAN.loadvalue(reader)
res.isNtOwfPassword = await BOOLEAN.loadvalue(reader)
res.isLmOwfPassword = await BOOLEAN.loadvalue(reader)
res.isShaOwPassword = await BOOLEAN.loadvalue(reader)
res.align0 = await BYTE.loadvalue(reader)
res.align1 = await BYTE.loadvalue(reader)
res.align2 = await BYTE.loadvalue(reader)
res.align3 = await BYTE.loadvalue(reader)
res.NtOwfPassword = await reader.read(16)
res.LmOwfPassword = await reader.read(16)
res.ShaOwPassword = await reader.read(20)
return res
class MSV1_0_PRIMARY_CREDENTIAL_10_1607_DEC:
def __init__(self):
self.LogonDomainName = None
self.UserName = None
self.pNtlmCredIsoInProc = None
self.isIso = None
self.isNtOwfPassword = None
self.isLmOwfPassword = None
self.isShaOwPassword = None
self.isDPAPIProtected = None
self.align0 = None
self.align1 = None
self.align2 = None
self.unkD = None
# stuff to be done! #pragma pack(push, 2)
self.isoSize = None
self.DPAPIProtected = None
self.align3 = None
# stuff to be done! #pragma pack(pop)
self.NtOwfPassword = None
self.LmOwfPassword = None
self.ShaOwPassword = None
@staticmethod
async def load(reader):
res = MSV1_0_PRIMARY_CREDENTIAL_10_1607_DEC()
res.LogonDomainName = await LSA_UNICODE_STRING.load(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.pNtlmCredIsoInProc = await PVOID.loadvalue(reader)
res.isIso = await BOOLEAN.loadvalue(reader)
res.isNtOwfPassword = await BOOLEAN.loadvalue(reader)
res.isLmOwfPassword = await BOOLEAN.loadvalue(reader)
res.isShaOwPassword = await BOOLEAN.loadvalue(reader)
res.isDPAPIProtected = await BOOLEAN.loadvalue(reader)
res.align0 = await BYTE.loadvalue(reader)
res.align1 = await BYTE.loadvalue(reader)
res.align2 = await BYTE.loadvalue(reader)
res.unkD = await DWORD.loadvalue(reader) # // 1/2
# stuff to be done! #pragma pack(push, 2)
res.isoSize = await WORD.loadvalue(reader) #// 0000
res.DPAPIProtected = await reader.read(16)
res.align3 = await DWORD.loadvalue(reader) #// 00000000
# stuff to be done! #pragma pack(pop)
res.NtOwfPassword = await reader.read(16)
res.LmOwfPassword = await reader.read(16)
res.ShaOwPassword = await reader.read(20)
return res
class KIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC:
def __init__(self):
self.Flink = None
self.Primary = None
self.encrypted_credentials = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC()
res.Flink = await PKIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC.load(reader)
res.Primary = await ANSI_STRING.load(reader)
await reader.align()
res.encrypted_credentials = await LSA_UNICODE_STRING.load(reader)
return res
class PKIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC
return p
#class PKIWI_MSV1_0_CREDENTIAL_LIST(POINTER):
# def __init__(self, reader):
# super().__init__(reader, PKIWI_MSV1_0_CREDENTIAL_LIST)
class KIWI_MSV1_0_CREDENTIAL_LIST:
def __init__(self):
self.Flink = None
self.AuthenticationPackageId = None
self.PrimaryCredentials_ptr = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_CREDENTIAL_LIST()
res.Flink = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.AuthenticationPackageId = await DWORD.loadvalue(reader)
await reader.align()
res.PrimaryCredentials_ptr = await PKIWI_MSV1_0_PRIMARY_CREDENTIAL_ENC.load(reader)
return res
class PKIWI_MSV1_0_CREDENTIAL_LIST(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_CREDENTIAL_LIST()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_CREDENTIAL_LIST
return p
class PKIWI_MSV1_0_LIST_51(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_51()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_51
return p
class KIWI_MSV1_0_LIST_51:
def __init__(self):
self.Flink = None
self.Blink = None
self.LocallyUniqueIdentifier = None
self.UserName = None
self.Domaine = None
self.unk0 = None
self.unk1 = None
self.pSid = None
self.LogonType = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.unk23 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_51()
res.Flink = await PKIWI_MSV1_0_LIST_51.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_51.load(reader)
res.LocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await PVOID.loadvalue(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align(8)
t = t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
await reader.align()
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await ULONG.loadvalue(reader)
await reader.align()
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await PVOID.loadvalue(reader)
res.unk23 = await ULONG.loadvalue(reader)
await reader.align()
res.CredentialManager = await PVOID.load(reader)
return res
class PKIWI_MSV1_0_LIST_52(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_52()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_52
return p
class KIWI_MSV1_0_LIST_52:
def __init__(self):
self.Flink = None
self.Blink = None
self.LocallyUniqueIdentifier = None
self.UserName = None
self.Domaine = None
self.unk0 = None
self.unk1 = None
self.pSid = None
self.LogonType = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_52()
res.Flink = await PKIWI_MSV1_0_LIST_52.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_52.load(reader)
res.LocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await PVOID.loadvalue(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align(8)
t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await ULONG.loadvalue(reader)
await reader.align()
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await ULONG.loadvalue(reader)
await reader.align()
res.CredentialManager = await PVOID.load(reader)
return res
class PKIWI_MSV1_0_LIST_60(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_60()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_60
return p
class KIWI_MSV1_0_LIST_60:
def __init__(self):
self.Flink = None
self.Blink = None
self.unk0 = None
self.unk1 = None
self.unk2 = None
self.unk3 = None
self.unk4 = None
self.unk5 = None
self.hSemaphore6 = None
self.unk7 = None
self.hSemaphore8 = None
self.unk9 = None
self.unk10 = None
self.unk11 = None
self.unk12 = None
self.unk13 = None
self.LocallyUniqueIdentifier = None
self.SecondaryLocallyUniqueIdentifier = None
self.UserName = None
self.Domaine = None
self.unk14 = None
self.unk15 = None
self.pSid = None
self.LogonType = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.unk23 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_60()
res.Flink = await PKIWI_MSV1_0_LIST_60.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_60.load(reader)
await reader.align()
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await ULONG.loadvalue(reader)
await reader.align()
res.unk2 = await PVOID.loadvalue(reader)
res.unk3 = await ULONG.loadvalue(reader)
res.unk4 = await ULONG.loadvalue(reader)
res.unk5 = await ULONG.loadvalue(reader)
await reader.align()
res.hSemaphore6 = await HANDLE.loadvalue(reader)
await reader.align()
res.unk7 = await PVOID.loadvalue(reader)
await reader.align()
res.hSemaphore8 = await HANDLE.loadvalue(reader)
await reader.align()
res.unk9 = await PVOID.loadvalue(reader)
await reader.align()
res.unk10 = await PVOID.loadvalue(reader)
res.unk11 = await ULONG.loadvalue(reader)
res.unk12 = await ULONG.loadvalue(reader)
await reader.align()
res.unk13 = await PVOID.loadvalue(reader)
await reader.align()
t = await reader.read(8)
res.LocallyUniqueIdentifier = int.from_bytes(t, byteorder = 'little', signed = False)
t = await reader.read(8)
res.SecondaryLocallyUniqueIdentifier = int.from_bytes(t, byteorder = 'little', signed = False)
await reader.align()
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk14 = await PVOID.loadvalue(reader)
res.unk15 = await PVOID.loadvalue(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align(8)
t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await ULONG.loadvalue(reader)
await reader.align()
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await PVOID.loadvalue(reader)
res.unk23 = await ULONG.loadvalue(reader)
await reader.align()
res.CredentialManager = await PVOID.load(reader)
return res
class PKIWI_MSV1_0_LIST_61(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_61()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_61
return p
class KIWI_MSV1_0_LIST_61:
def __init__(self):
self.Flink = None
self.Blink = None
self.unk0 = None
self.unk1 = None
self.unk2 = None
self.unk3 = None
self.unk4 = None
self.unk5 = None
self.hSemaphore6 = None
self.unk7 = None
self.hSemaphore8 = None
self.unk9 = None
self.unk10 = None
self.unk11 = None
self.unk12 = None
self.unk13 = None
self.LocallyUniqueIdentifier = None
self.SecondaryLocallyUniqueIdentifier = None
self.UserName = None
self.Domaine = None
self.unk14 = None
self.unk15 = None
self.pSid = None
self.LogonType = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_61()
res.Flink = await PKIWI_MSV1_0_LIST_61.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_61.load(reader)
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await ULONG.loadvalue(reader)
await reader.align()
res.unk2 = await PVOID.loadvalue(reader)
res.unk3 = await ULONG.loadvalue(reader)
res.unk4 = await ULONG.loadvalue(reader)
res.unk5 = await ULONG.loadvalue(reader)
await reader.align()
res.hSemaphore6 = await HANDLE.loadvalue(reader)
res.unk7 = await PVOID.loadvalue(reader)
res.hSemaphore8 = await HANDLE.loadvalue(reader)
res.unk9 = await PVOID.loadvalue(reader)
res.unk10 = await PVOID.loadvalue(reader)
res.unk11 = await ULONG.loadvalue(reader)
res.unk12 = await ULONG.loadvalue(reader)
res.unk13 = await PVOID.loadvalue(reader)
res.LocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.SecondaryLocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk14 = await PVOID.loadvalue(reader)
res.unk15 = await PVOID.loadvalue(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align(8)
t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await PVOID.loadvalue(reader)
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await ULONG.loadvalue(reader)
await reader.align()
res.CredentialManager = await PVOID.load(reader)
return res
class PKIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ
return p
class KIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ:
def __init__(self):
self.Flink = None
self.Blink = None
self.unk0 = None
self.unk1 = None
self.unk2 = None
self.unk3 = None
self.unk4 = None
self.unk5 = None
self.hSemaphore6 = None
self.unk7 = None
self.hSemaphore8 = None
self.unk9 = None
self.unk10 = None
self.unk11 = None
self.unk12 = None
self.unk13 = None
self.LocallyUniqueIdentifier = None
self.SecondaryLocallyUniqueIdentifier = None
self.waza = None
self.UserName = None
self.Domaine = None
self.unk14 = None
self.unk15 = None
self.pSid = None
self.LogonType = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ()
res.Flink = await PKIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_61_ANTI_MIMIKATZ.load(reader)
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await ULONG.loadvalue(reader)
await reader.align()
res.unk2 = await PVOID.loadvalue(reader)
res.unk3 = await ULONG.loadvalue(reader)
res.unk4 = await ULONG.loadvalue(reader)
res.unk5 = await ULONG.loadvalue(reader)
await reader.align()
res.hSemaphore6 = await HANDLE.loadvalue(reader)
res.unk7 = await PVOID.loadvalue(reader)
res.hSemaphore8 = await HANDLE.loadvalue(reader)
res.unk9 = await PVOID.loadvalue(reader)
res.unk10 = await PVOID.loadvalue(reader)
res.unk11 = await ULONG.loadvalue(reader)
res.unk12 = await ULONG.loadvalue(reader)
res.unk13 = await PVOID.loadvalue(reader)
res.LocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.SecondaryLocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.waza = await reader.read(12)
await reader.align()
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk14 = await PVOID.loadvalue(reader)
res.unk15 = await PVOID.loadvalue(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align(8)
t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await PVOID.loadvalue(reader)
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await ULONG.loadvalue(reader)
await reader.align()
res.CredentialManager = await PVOID.load(reader)
return res
class PKIWI_MSV1_0_LIST_62(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_62()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_62
return p
class KIWI_MSV1_0_LIST_62:
def __init__(self):
self.Flink = None
self.Blink = None
self.unk0 = None
self.unk1 = None
self.unk2 = None
self.unk3 = None
self.unk4 = None
self.unk5 = None
self.hSemaphore6 = None
self.unk7 = None
self.hSemaphore8 = None
self.unk9 = None
self.unk10 = None
self.unk11 = None
self.unk12 = None
self.unk13 = None
self.LocallyUniqueIdentifier = None
self.SecondaryLocallyUniqueIdentifier = None
self.UserName = None
self.Domaine = None
self.unk14 = None
self.unk15 = None
self.Type = None
self.pSid = None
self.LogonType = None
self.unk18 = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.unk23 = None
self.unk24 = None
self.unk25 = None
self.unk26 = None
self.unk27 = None
self.unk28 = None
self.unk29 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_62()
res.Flink = await PKIWI_MSV1_0_LIST_62.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_62.load(reader)
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await ULONG.loadvalue(reader)
await reader.align()
res.unk2 = await PVOID.loadvalue(reader)
res.unk3 = await ULONG.loadvalue(reader)
res.unk4 = await ULONG.loadvalue(reader)
res.unk5 = await ULONG.loadvalue(reader)
await reader.align()
res.hSemaphore6 = await HANDLE.loadvalue(reader)
res.unk7 = await PVOID.loadvalue(reader)
res.hSemaphore8 = await HANDLE.loadvalue(reader)
res.unk9 = await PVOID.loadvalue(reader)
res.unk10 = await PVOID.loadvalue(reader)
res.unk11 = await ULONG.loadvalue(reader)
res.unk12 = await ULONG.loadvalue(reader)
res.unk13 = await PVOID.loadvalue(reader)
res.LocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.SecondaryLocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk14 = await PVOID.loadvalue(reader)
res.unk15 = await PVOID.loadvalue(reader)
res.Type = await LSA_UNICODE_STRING.load(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
await reader.align()
res.unk18 = await PVOID.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align()
t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await PVOID.loadvalue(reader)
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await ULONG.loadvalue(reader)
res.unk23 = await ULONG.loadvalue(reader)
res.unk24 = await ULONG.loadvalue(reader)
res.unk25 = await ULONG.loadvalue(reader)
res.unk26 = await ULONG.loadvalue(reader)
await reader.align()
res.unk27 = await PVOID.loadvalue(reader)
res.unk28 = await PVOID.loadvalue(reader)
res.unk29 = await PVOID.loadvalue(reader)
res.CredentialManager = await PVOID.load(reader)
return res
class PKIWI_MSV1_0_LIST_63(POINTER):
def __init__(self):
super().__init__()
@staticmethod
async def load(reader):
p = PKIWI_MSV1_0_LIST_63()
p.location = reader.tell()
p.value = await reader.read_uint()
p.finaltype = KIWI_MSV1_0_LIST_63
return p
class KIWI_MSV1_0_LIST_63:
def __init__(self):
self.Flink = None
self.Blink = None
self.unk0 = None
self.unk1 = None
self.unk2 = None
self.unk3 = None
self.unk4 = None
self.unk5 = None
self.hSemaphore6 = None
self.unk7 = None
self.hSemaphore8 = None
self.unk9 = None
self.unk10 = None
self.unk11 = None
self.unk12 = None
self.unk13 = None
self.LocallyUniqueIdentifier = None
self.SecondaryLocallyUniqueIdentifier = None
self.waza = None
self.UserName = None
self.Domaine = None
self.unk14 = None
self.unk15 = None
self.Type = None
self.pSid = None
self.LogonType = None
self.unk18 = None
self.Session = None
self.LogonTime = None
self.LogonServer = None
self.Credentials_list_ptr = None
self.unk19 = None
self.unk20 = None
self.unk21 = None
self.unk22 = None
self.unk23 = None
self.unk24 = None
self.unk25 = None
self.unk26 = None
self.unk27 = None
self.unk28 = None
self.unk29 = None
self.CredentialManager = None
@staticmethod
async def load(reader):
res = KIWI_MSV1_0_LIST_63()
res.Flink = await PKIWI_MSV1_0_LIST_63.load(reader)
res.Blink = await PKIWI_MSV1_0_LIST_63.load(reader)
res.unk0 = await PVOID.loadvalue(reader)
res.unk1 = await ULONG.loadvalue(reader)
await reader.align()
res.unk2 = await PVOID.loadvalue(reader)
res.unk3 = await ULONG.loadvalue(reader)
res.unk4 = await ULONG.loadvalue(reader)
res.unk5 = await ULONG.loadvalue(reader)
await reader.align()
res.hSemaphore6 = await HANDLE.loadvalue(reader)
res.unk7 = await PVOID.loadvalue(reader)
res.hSemaphore8 = await HANDLE.loadvalue(reader)
res.unk9 = await PVOID.loadvalue(reader)
res.unk10 = await PVOID.loadvalue(reader)
res.unk11 = await ULONG.loadvalue(reader)
res.unk12 = await ULONG.loadvalue(reader)
res.unk13 = await PVOID.loadvalue(reader)
await reader.align()
res.LocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.SecondaryLocallyUniqueIdentifier = await LUID.loadvalue(reader)
res.waza = await reader.read(12)
await reader.align()
res.UserName = await LSA_UNICODE_STRING.load(reader)
res.Domaine = await LSA_UNICODE_STRING.load(reader)
res.unk14 = await PVOID.loadvalue(reader)
res.unk15 = await PVOID.loadvalue(reader)
res.Type = await LSA_UNICODE_STRING.load(reader)
res.pSid = await PSID.load(reader)
res.LogonType = await ULONG.loadvalue(reader)
await reader.align()
res.unk18 = await PVOID.loadvalue(reader)
res.Session = await ULONG.loadvalue(reader)
await reader.align(8)
t = await reader.read(8)
res.LogonTime = int.from_bytes(t, byteorder = 'little', signed = False) #autoalign x86
res.LogonServer = await LSA_UNICODE_STRING.load(reader)
res.Credentials_list_ptr = await PKIWI_MSV1_0_CREDENTIAL_LIST.load(reader)
res.unk19 = await PVOID.loadvalue(reader)
res.unk20 = await PVOID.loadvalue(reader)
res.unk21 = await PVOID.loadvalue(reader)
res.unk22 = await ULONG.loadvalue(reader)
res.unk23 = await ULONG.loadvalue(reader)
res.unk24 = await ULONG.loadvalue(reader)
res.unk25 = await ULONG.loadvalue(reader)
res.unk26 = await ULONG.loadvalue(reader)
await reader.align()
#input('CredentialManager\n' + hexdump(reader.peek(0x100)))
res.unk27 = await PVOID.loadvalue(reader)
res.unk28 = await PVOID.loadvalue(reader)
res.unk29 = await PVOID.loadvalue(reader)
res.CredentialManager = await PVOID.load(reader)
return res | 33.445725 | 120 | 0.748887 | 4,790 | 34,817 | 5.269102 | 0.064092 | 0.089702 | 0.104838 | 0.072309 | 0.907286 | 0.883593 | 0.853679 | 0.816633 | 0.78561 | 0.756805 | 0 | 0.045288 | 0.152081 | 34,817 | 1,041 | 121 | 33.445725 | 0.809633 | 0.021139 | 0 | 0.805348 | 0 | 0.013904 | 0.029844 | 0.025115 | 0 | 0 | 0.000705 | 0 | 0 | 1 | 0.026738 | false | 0.068449 | 0.004278 | 0 | 0.083422 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
1bd1cb0d628d618b8c01369a12188e64043924f6 | 81,160 | py | Python | sdk/python/pulumi_spotinst/aws/ocean.py | pulumi/pulumi-spotinst | 75592d6293d63f6cec703722f2e02ff1fb1cca44 | [
"ECL-2.0",
"Apache-2.0"
] | 4 | 2019-12-21T20:50:43.000Z | 2021-12-01T20:57:38.000Z | sdk/python/pulumi_spotinst/aws/ocean.py | pulumi/pulumi-spotinst | 75592d6293d63f6cec703722f2e02ff1fb1cca44 | [
"ECL-2.0",
"Apache-2.0"
] | 103 | 2019-12-09T22:03:16.000Z | 2022-03-30T17:07:34.000Z | sdk/python/pulumi_spotinst/aws/ocean.py | pulumi/pulumi-spotinst | 75592d6293d63f6cec703722f2e02ff1fb1cca44 | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['OceanArgs', 'Ocean']
@pulumi.input_type
class OceanArgs:
def __init__(__self__, *,
security_groups: pulumi.Input[Sequence[pulumi.Input[str]]],
subnet_ids: pulumi.Input[Sequence[pulumi.Input[str]]],
associate_public_ip_address: Optional[pulumi.Input[bool]] = None,
autoscaler: Optional[pulumi.Input['OceanAutoscalerArgs']] = None,
blacklists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
controller_id: Optional[pulumi.Input[str]] = None,
desired_capacity: Optional[pulumi.Input[int]] = None,
draining_timeout: Optional[pulumi.Input[int]] = None,
ebs_optimized: Optional[pulumi.Input[bool]] = None,
fallback_to_ondemand: Optional[pulumi.Input[bool]] = None,
grace_period: Optional[pulumi.Input[int]] = None,
iam_instance_profile: Optional[pulumi.Input[str]] = None,
image_id: Optional[pulumi.Input[str]] = None,
instance_metadata_options: Optional[pulumi.Input['OceanInstanceMetadataOptionsArgs']] = None,
key_name: Optional[pulumi.Input[str]] = None,
load_balancers: Optional[pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]]] = None,
logging: Optional[pulumi.Input['OceanLoggingArgs']] = None,
max_size: Optional[pulumi.Input[int]] = None,
min_size: Optional[pulumi.Input[int]] = None,
monitoring: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
root_volume_size: Optional[pulumi.Input[int]] = None,
scheduled_tasks: Optional[pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]]] = None,
spot_percentage: Optional[pulumi.Input[int]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]]] = None,
update_policy: Optional[pulumi.Input['OceanUpdatePolicyArgs']] = None,
use_as_template_only: Optional[pulumi.Input[bool]] = None,
user_data: Optional[pulumi.Input[str]] = None,
utilize_commitments: Optional[pulumi.Input[bool]] = None,
utilize_reserved_instances: Optional[pulumi.Input[bool]] = None,
whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
"""
The set of arguments for constructing a Ocean resource.
:param pulumi.Input[Sequence[pulumi.Input[str]]] security_groups: One or more security group ids.
:param pulumi.Input[Sequence[pulumi.Input[str]]] subnet_ids: A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
:param pulumi.Input[bool] associate_public_ip_address: Configure public IP address allocation.
:param pulumi.Input['OceanAutoscalerArgs'] autoscaler: Describes the Ocean Kubernetes Auto Scaler.
:param pulumi.Input[Sequence[pulumi.Input[str]]] blacklists: Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
:param pulumi.Input[str] controller_id: A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
:param pulumi.Input[int] desired_capacity: The number of instances to launch and maintain in the cluster.
:param pulumi.Input[int] draining_timeout: The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
:param pulumi.Input[bool] ebs_optimized: Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
:param pulumi.Input[bool] fallback_to_ondemand: If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
:param pulumi.Input[int] grace_period: The amount of time, in seconds, after the instance has launched to start checking its health.
:param pulumi.Input[str] iam_instance_profile: The instance profile iam role.
:param pulumi.Input[str] image_id: ID of the image used to launch the instances.
:param pulumi.Input['OceanInstanceMetadataOptionsArgs'] instance_metadata_options: Ocean instance metadata options object for IMDSv2.
:param pulumi.Input[str] key_name: The key pair to attach the instances.
:param pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]] load_balancers: - Array of load balancer objects to add to ocean cluster
:param pulumi.Input['OceanLoggingArgs'] logging: Logging configuration.
:param pulumi.Input[int] max_size: The upper limit of instances the cluster can scale up to.
:param pulumi.Input[int] min_size: The lower limit of instances the cluster can scale down to.
:param pulumi.Input[bool] monitoring: Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
:param pulumi.Input[str] name: Required if type is set to `CLASSIC`
:param pulumi.Input[str] region: The region the cluster will run in.
:param pulumi.Input[int] root_volume_size: The size (in Gb) to allocate for the root volume. Minimum `20`.
:param pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]] scheduled_tasks: Set scheduling object.
:param pulumi.Input[int] spot_percentage: The percentage of Spot instances that would spin up from the `desired_capacity` number.
:param pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]] tags: Optionally adds tags to instances launched in an Ocean cluster.
:param pulumi.Input[bool] use_as_template_only: launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
:param pulumi.Input[str] user_data: Base64-encoded MIME user data to make available to the instances.
:param pulumi.Input[bool] utilize_reserved_instances: If Reserved instances exist, Ocean will utilize them before launching Spot instances.
:param pulumi.Input[Sequence[pulumi.Input[str]]] whitelists: Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
pulumi.set(__self__, "security_groups", security_groups)
pulumi.set(__self__, "subnet_ids", subnet_ids)
if associate_public_ip_address is not None:
pulumi.set(__self__, "associate_public_ip_address", associate_public_ip_address)
if autoscaler is not None:
pulumi.set(__self__, "autoscaler", autoscaler)
if blacklists is not None:
pulumi.set(__self__, "blacklists", blacklists)
if controller_id is not None:
pulumi.set(__self__, "controller_id", controller_id)
if desired_capacity is not None:
pulumi.set(__self__, "desired_capacity", desired_capacity)
if draining_timeout is not None:
pulumi.set(__self__, "draining_timeout", draining_timeout)
if ebs_optimized is not None:
pulumi.set(__self__, "ebs_optimized", ebs_optimized)
if fallback_to_ondemand is not None:
pulumi.set(__self__, "fallback_to_ondemand", fallback_to_ondemand)
if grace_period is not None:
pulumi.set(__self__, "grace_period", grace_period)
if iam_instance_profile is not None:
pulumi.set(__self__, "iam_instance_profile", iam_instance_profile)
if image_id is not None:
pulumi.set(__self__, "image_id", image_id)
if instance_metadata_options is not None:
pulumi.set(__self__, "instance_metadata_options", instance_metadata_options)
if key_name is not None:
pulumi.set(__self__, "key_name", key_name)
if load_balancers is not None:
pulumi.set(__self__, "load_balancers", load_balancers)
if logging is not None:
pulumi.set(__self__, "logging", logging)
if max_size is not None:
pulumi.set(__self__, "max_size", max_size)
if min_size is not None:
pulumi.set(__self__, "min_size", min_size)
if monitoring is not None:
pulumi.set(__self__, "monitoring", monitoring)
if name is not None:
pulumi.set(__self__, "name", name)
if region is not None:
pulumi.set(__self__, "region", region)
if root_volume_size is not None:
pulumi.set(__self__, "root_volume_size", root_volume_size)
if scheduled_tasks is not None:
pulumi.set(__self__, "scheduled_tasks", scheduled_tasks)
if spot_percentage is not None:
pulumi.set(__self__, "spot_percentage", spot_percentage)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if update_policy is not None:
pulumi.set(__self__, "update_policy", update_policy)
if use_as_template_only is not None:
pulumi.set(__self__, "use_as_template_only", use_as_template_only)
if user_data is not None:
pulumi.set(__self__, "user_data", user_data)
if utilize_commitments is not None:
pulumi.set(__self__, "utilize_commitments", utilize_commitments)
if utilize_reserved_instances is not None:
pulumi.set(__self__, "utilize_reserved_instances", utilize_reserved_instances)
if whitelists is not None:
pulumi.set(__self__, "whitelists", whitelists)
@property
@pulumi.getter(name="securityGroups")
def security_groups(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
"""
One or more security group ids.
"""
return pulumi.get(self, "security_groups")
@security_groups.setter
def security_groups(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]):
pulumi.set(self, "security_groups", value)
@property
@pulumi.getter(name="subnetIds")
def subnet_ids(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
"""
A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
"""
return pulumi.get(self, "subnet_ids")
@subnet_ids.setter
def subnet_ids(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]):
pulumi.set(self, "subnet_ids", value)
@property
@pulumi.getter(name="associatePublicIpAddress")
def associate_public_ip_address(self) -> Optional[pulumi.Input[bool]]:
"""
Configure public IP address allocation.
"""
return pulumi.get(self, "associate_public_ip_address")
@associate_public_ip_address.setter
def associate_public_ip_address(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "associate_public_ip_address", value)
@property
@pulumi.getter
def autoscaler(self) -> Optional[pulumi.Input['OceanAutoscalerArgs']]:
"""
Describes the Ocean Kubernetes Auto Scaler.
"""
return pulumi.get(self, "autoscaler")
@autoscaler.setter
def autoscaler(self, value: Optional[pulumi.Input['OceanAutoscalerArgs']]):
pulumi.set(self, "autoscaler", value)
@property
@pulumi.getter
def blacklists(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
"""
return pulumi.get(self, "blacklists")
@blacklists.setter
def blacklists(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "blacklists", value)
@property
@pulumi.getter(name="controllerId")
def controller_id(self) -> Optional[pulumi.Input[str]]:
"""
A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
"""
return pulumi.get(self, "controller_id")
@controller_id.setter
def controller_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "controller_id", value)
@property
@pulumi.getter(name="desiredCapacity")
def desired_capacity(self) -> Optional[pulumi.Input[int]]:
"""
The number of instances to launch and maintain in the cluster.
"""
return pulumi.get(self, "desired_capacity")
@desired_capacity.setter
def desired_capacity(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "desired_capacity", value)
@property
@pulumi.getter(name="drainingTimeout")
def draining_timeout(self) -> Optional[pulumi.Input[int]]:
"""
The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
"""
return pulumi.get(self, "draining_timeout")
@draining_timeout.setter
def draining_timeout(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "draining_timeout", value)
@property
@pulumi.getter(name="ebsOptimized")
def ebs_optimized(self) -> Optional[pulumi.Input[bool]]:
"""
Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
"""
return pulumi.get(self, "ebs_optimized")
@ebs_optimized.setter
def ebs_optimized(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "ebs_optimized", value)
@property
@pulumi.getter(name="fallbackToOndemand")
def fallback_to_ondemand(self) -> Optional[pulumi.Input[bool]]:
"""
If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
"""
return pulumi.get(self, "fallback_to_ondemand")
@fallback_to_ondemand.setter
def fallback_to_ondemand(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "fallback_to_ondemand", value)
@property
@pulumi.getter(name="gracePeriod")
def grace_period(self) -> Optional[pulumi.Input[int]]:
"""
The amount of time, in seconds, after the instance has launched to start checking its health.
"""
return pulumi.get(self, "grace_period")
@grace_period.setter
def grace_period(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "grace_period", value)
@property
@pulumi.getter(name="iamInstanceProfile")
def iam_instance_profile(self) -> Optional[pulumi.Input[str]]:
"""
The instance profile iam role.
"""
return pulumi.get(self, "iam_instance_profile")
@iam_instance_profile.setter
def iam_instance_profile(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "iam_instance_profile", value)
@property
@pulumi.getter(name="imageId")
def image_id(self) -> Optional[pulumi.Input[str]]:
"""
ID of the image used to launch the instances.
"""
return pulumi.get(self, "image_id")
@image_id.setter
def image_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "image_id", value)
@property
@pulumi.getter(name="instanceMetadataOptions")
def instance_metadata_options(self) -> Optional[pulumi.Input['OceanInstanceMetadataOptionsArgs']]:
"""
Ocean instance metadata options object for IMDSv2.
"""
return pulumi.get(self, "instance_metadata_options")
@instance_metadata_options.setter
def instance_metadata_options(self, value: Optional[pulumi.Input['OceanInstanceMetadataOptionsArgs']]):
pulumi.set(self, "instance_metadata_options", value)
@property
@pulumi.getter(name="keyName")
def key_name(self) -> Optional[pulumi.Input[str]]:
"""
The key pair to attach the instances.
"""
return pulumi.get(self, "key_name")
@key_name.setter
def key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "key_name", value)
@property
@pulumi.getter(name="loadBalancers")
def load_balancers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]]]:
"""
- Array of load balancer objects to add to ocean cluster
"""
return pulumi.get(self, "load_balancers")
@load_balancers.setter
def load_balancers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]]]):
pulumi.set(self, "load_balancers", value)
@property
@pulumi.getter
def logging(self) -> Optional[pulumi.Input['OceanLoggingArgs']]:
"""
Logging configuration.
"""
return pulumi.get(self, "logging")
@logging.setter
def logging(self, value: Optional[pulumi.Input['OceanLoggingArgs']]):
pulumi.set(self, "logging", value)
@property
@pulumi.getter(name="maxSize")
def max_size(self) -> Optional[pulumi.Input[int]]:
"""
The upper limit of instances the cluster can scale up to.
"""
return pulumi.get(self, "max_size")
@max_size.setter
def max_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_size", value)
@property
@pulumi.getter(name="minSize")
def min_size(self) -> Optional[pulumi.Input[int]]:
"""
The lower limit of instances the cluster can scale down to.
"""
return pulumi.get(self, "min_size")
@min_size.setter
def min_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_size", value)
@property
@pulumi.getter
def monitoring(self) -> Optional[pulumi.Input[bool]]:
"""
Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
"""
return pulumi.get(self, "monitoring")
@monitoring.setter
def monitoring(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "monitoring", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Required if type is set to `CLASSIC`
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
The region the cluster will run in.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter(name="rootVolumeSize")
def root_volume_size(self) -> Optional[pulumi.Input[int]]:
"""
The size (in Gb) to allocate for the root volume. Minimum `20`.
"""
return pulumi.get(self, "root_volume_size")
@root_volume_size.setter
def root_volume_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "root_volume_size", value)
@property
@pulumi.getter(name="scheduledTasks")
def scheduled_tasks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]]]:
"""
Set scheduling object.
"""
return pulumi.get(self, "scheduled_tasks")
@scheduled_tasks.setter
def scheduled_tasks(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]]]):
pulumi.set(self, "scheduled_tasks", value)
@property
@pulumi.getter(name="spotPercentage")
def spot_percentage(self) -> Optional[pulumi.Input[int]]:
"""
The percentage of Spot instances that would spin up from the `desired_capacity` number.
"""
return pulumi.get(self, "spot_percentage")
@spot_percentage.setter
def spot_percentage(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "spot_percentage", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]]]:
"""
Optionally adds tags to instances launched in an Ocean cluster.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="updatePolicy")
def update_policy(self) -> Optional[pulumi.Input['OceanUpdatePolicyArgs']]:
return pulumi.get(self, "update_policy")
@update_policy.setter
def update_policy(self, value: Optional[pulumi.Input['OceanUpdatePolicyArgs']]):
pulumi.set(self, "update_policy", value)
@property
@pulumi.getter(name="useAsTemplateOnly")
def use_as_template_only(self) -> Optional[pulumi.Input[bool]]:
"""
launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
"""
return pulumi.get(self, "use_as_template_only")
@use_as_template_only.setter
def use_as_template_only(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "use_as_template_only", value)
@property
@pulumi.getter(name="userData")
def user_data(self) -> Optional[pulumi.Input[str]]:
"""
Base64-encoded MIME user data to make available to the instances.
"""
return pulumi.get(self, "user_data")
@user_data.setter
def user_data(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "user_data", value)
@property
@pulumi.getter(name="utilizeCommitments")
def utilize_commitments(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "utilize_commitments")
@utilize_commitments.setter
def utilize_commitments(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "utilize_commitments", value)
@property
@pulumi.getter(name="utilizeReservedInstances")
def utilize_reserved_instances(self) -> Optional[pulumi.Input[bool]]:
"""
If Reserved instances exist, Ocean will utilize them before launching Spot instances.
"""
return pulumi.get(self, "utilize_reserved_instances")
@utilize_reserved_instances.setter
def utilize_reserved_instances(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "utilize_reserved_instances", value)
@property
@pulumi.getter
def whitelists(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
return pulumi.get(self, "whitelists")
@whitelists.setter
def whitelists(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "whitelists", value)
@pulumi.input_type
class _OceanState:
def __init__(__self__, *,
associate_public_ip_address: Optional[pulumi.Input[bool]] = None,
autoscaler: Optional[pulumi.Input['OceanAutoscalerArgs']] = None,
blacklists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
controller_id: Optional[pulumi.Input[str]] = None,
desired_capacity: Optional[pulumi.Input[int]] = None,
draining_timeout: Optional[pulumi.Input[int]] = None,
ebs_optimized: Optional[pulumi.Input[bool]] = None,
fallback_to_ondemand: Optional[pulumi.Input[bool]] = None,
grace_period: Optional[pulumi.Input[int]] = None,
iam_instance_profile: Optional[pulumi.Input[str]] = None,
image_id: Optional[pulumi.Input[str]] = None,
instance_metadata_options: Optional[pulumi.Input['OceanInstanceMetadataOptionsArgs']] = None,
key_name: Optional[pulumi.Input[str]] = None,
load_balancers: Optional[pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]]] = None,
logging: Optional[pulumi.Input['OceanLoggingArgs']] = None,
max_size: Optional[pulumi.Input[int]] = None,
min_size: Optional[pulumi.Input[int]] = None,
monitoring: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
root_volume_size: Optional[pulumi.Input[int]] = None,
scheduled_tasks: Optional[pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]]] = None,
security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
spot_percentage: Optional[pulumi.Input[int]] = None,
subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]]] = None,
update_policy: Optional[pulumi.Input['OceanUpdatePolicyArgs']] = None,
use_as_template_only: Optional[pulumi.Input[bool]] = None,
user_data: Optional[pulumi.Input[str]] = None,
utilize_commitments: Optional[pulumi.Input[bool]] = None,
utilize_reserved_instances: Optional[pulumi.Input[bool]] = None,
whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
"""
Input properties used for looking up and filtering Ocean resources.
:param pulumi.Input[bool] associate_public_ip_address: Configure public IP address allocation.
:param pulumi.Input['OceanAutoscalerArgs'] autoscaler: Describes the Ocean Kubernetes Auto Scaler.
:param pulumi.Input[Sequence[pulumi.Input[str]]] blacklists: Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
:param pulumi.Input[str] controller_id: A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
:param pulumi.Input[int] desired_capacity: The number of instances to launch and maintain in the cluster.
:param pulumi.Input[int] draining_timeout: The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
:param pulumi.Input[bool] ebs_optimized: Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
:param pulumi.Input[bool] fallback_to_ondemand: If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
:param pulumi.Input[int] grace_period: The amount of time, in seconds, after the instance has launched to start checking its health.
:param pulumi.Input[str] iam_instance_profile: The instance profile iam role.
:param pulumi.Input[str] image_id: ID of the image used to launch the instances.
:param pulumi.Input['OceanInstanceMetadataOptionsArgs'] instance_metadata_options: Ocean instance metadata options object for IMDSv2.
:param pulumi.Input[str] key_name: The key pair to attach the instances.
:param pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]] load_balancers: - Array of load balancer objects to add to ocean cluster
:param pulumi.Input['OceanLoggingArgs'] logging: Logging configuration.
:param pulumi.Input[int] max_size: The upper limit of instances the cluster can scale up to.
:param pulumi.Input[int] min_size: The lower limit of instances the cluster can scale down to.
:param pulumi.Input[bool] monitoring: Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
:param pulumi.Input[str] name: Required if type is set to `CLASSIC`
:param pulumi.Input[str] region: The region the cluster will run in.
:param pulumi.Input[int] root_volume_size: The size (in Gb) to allocate for the root volume. Minimum `20`.
:param pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]] scheduled_tasks: Set scheduling object.
:param pulumi.Input[Sequence[pulumi.Input[str]]] security_groups: One or more security group ids.
:param pulumi.Input[int] spot_percentage: The percentage of Spot instances that would spin up from the `desired_capacity` number.
:param pulumi.Input[Sequence[pulumi.Input[str]]] subnet_ids: A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
:param pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]] tags: Optionally adds tags to instances launched in an Ocean cluster.
:param pulumi.Input[bool] use_as_template_only: launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
:param pulumi.Input[str] user_data: Base64-encoded MIME user data to make available to the instances.
:param pulumi.Input[bool] utilize_reserved_instances: If Reserved instances exist, Ocean will utilize them before launching Spot instances.
:param pulumi.Input[Sequence[pulumi.Input[str]]] whitelists: Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
if associate_public_ip_address is not None:
pulumi.set(__self__, "associate_public_ip_address", associate_public_ip_address)
if autoscaler is not None:
pulumi.set(__self__, "autoscaler", autoscaler)
if blacklists is not None:
pulumi.set(__self__, "blacklists", blacklists)
if controller_id is not None:
pulumi.set(__self__, "controller_id", controller_id)
if desired_capacity is not None:
pulumi.set(__self__, "desired_capacity", desired_capacity)
if draining_timeout is not None:
pulumi.set(__self__, "draining_timeout", draining_timeout)
if ebs_optimized is not None:
pulumi.set(__self__, "ebs_optimized", ebs_optimized)
if fallback_to_ondemand is not None:
pulumi.set(__self__, "fallback_to_ondemand", fallback_to_ondemand)
if grace_period is not None:
pulumi.set(__self__, "grace_period", grace_period)
if iam_instance_profile is not None:
pulumi.set(__self__, "iam_instance_profile", iam_instance_profile)
if image_id is not None:
pulumi.set(__self__, "image_id", image_id)
if instance_metadata_options is not None:
pulumi.set(__self__, "instance_metadata_options", instance_metadata_options)
if key_name is not None:
pulumi.set(__self__, "key_name", key_name)
if load_balancers is not None:
pulumi.set(__self__, "load_balancers", load_balancers)
if logging is not None:
pulumi.set(__self__, "logging", logging)
if max_size is not None:
pulumi.set(__self__, "max_size", max_size)
if min_size is not None:
pulumi.set(__self__, "min_size", min_size)
if monitoring is not None:
pulumi.set(__self__, "monitoring", monitoring)
if name is not None:
pulumi.set(__self__, "name", name)
if region is not None:
pulumi.set(__self__, "region", region)
if root_volume_size is not None:
pulumi.set(__self__, "root_volume_size", root_volume_size)
if scheduled_tasks is not None:
pulumi.set(__self__, "scheduled_tasks", scheduled_tasks)
if security_groups is not None:
pulumi.set(__self__, "security_groups", security_groups)
if spot_percentage is not None:
pulumi.set(__self__, "spot_percentage", spot_percentage)
if subnet_ids is not None:
pulumi.set(__self__, "subnet_ids", subnet_ids)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if update_policy is not None:
pulumi.set(__self__, "update_policy", update_policy)
if use_as_template_only is not None:
pulumi.set(__self__, "use_as_template_only", use_as_template_only)
if user_data is not None:
pulumi.set(__self__, "user_data", user_data)
if utilize_commitments is not None:
pulumi.set(__self__, "utilize_commitments", utilize_commitments)
if utilize_reserved_instances is not None:
pulumi.set(__self__, "utilize_reserved_instances", utilize_reserved_instances)
if whitelists is not None:
pulumi.set(__self__, "whitelists", whitelists)
@property
@pulumi.getter(name="associatePublicIpAddress")
def associate_public_ip_address(self) -> Optional[pulumi.Input[bool]]:
"""
Configure public IP address allocation.
"""
return pulumi.get(self, "associate_public_ip_address")
@associate_public_ip_address.setter
def associate_public_ip_address(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "associate_public_ip_address", value)
@property
@pulumi.getter
def autoscaler(self) -> Optional[pulumi.Input['OceanAutoscalerArgs']]:
"""
Describes the Ocean Kubernetes Auto Scaler.
"""
return pulumi.get(self, "autoscaler")
@autoscaler.setter
def autoscaler(self, value: Optional[pulumi.Input['OceanAutoscalerArgs']]):
pulumi.set(self, "autoscaler", value)
@property
@pulumi.getter
def blacklists(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
"""
return pulumi.get(self, "blacklists")
@blacklists.setter
def blacklists(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "blacklists", value)
@property
@pulumi.getter(name="controllerId")
def controller_id(self) -> Optional[pulumi.Input[str]]:
"""
A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
"""
return pulumi.get(self, "controller_id")
@controller_id.setter
def controller_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "controller_id", value)
@property
@pulumi.getter(name="desiredCapacity")
def desired_capacity(self) -> Optional[pulumi.Input[int]]:
"""
The number of instances to launch and maintain in the cluster.
"""
return pulumi.get(self, "desired_capacity")
@desired_capacity.setter
def desired_capacity(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "desired_capacity", value)
@property
@pulumi.getter(name="drainingTimeout")
def draining_timeout(self) -> Optional[pulumi.Input[int]]:
"""
The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
"""
return pulumi.get(self, "draining_timeout")
@draining_timeout.setter
def draining_timeout(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "draining_timeout", value)
@property
@pulumi.getter(name="ebsOptimized")
def ebs_optimized(self) -> Optional[pulumi.Input[bool]]:
"""
Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
"""
return pulumi.get(self, "ebs_optimized")
@ebs_optimized.setter
def ebs_optimized(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "ebs_optimized", value)
@property
@pulumi.getter(name="fallbackToOndemand")
def fallback_to_ondemand(self) -> Optional[pulumi.Input[bool]]:
"""
If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
"""
return pulumi.get(self, "fallback_to_ondemand")
@fallback_to_ondemand.setter
def fallback_to_ondemand(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "fallback_to_ondemand", value)
@property
@pulumi.getter(name="gracePeriod")
def grace_period(self) -> Optional[pulumi.Input[int]]:
"""
The amount of time, in seconds, after the instance has launched to start checking its health.
"""
return pulumi.get(self, "grace_period")
@grace_period.setter
def grace_period(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "grace_period", value)
@property
@pulumi.getter(name="iamInstanceProfile")
def iam_instance_profile(self) -> Optional[pulumi.Input[str]]:
"""
The instance profile iam role.
"""
return pulumi.get(self, "iam_instance_profile")
@iam_instance_profile.setter
def iam_instance_profile(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "iam_instance_profile", value)
@property
@pulumi.getter(name="imageId")
def image_id(self) -> Optional[pulumi.Input[str]]:
"""
ID of the image used to launch the instances.
"""
return pulumi.get(self, "image_id")
@image_id.setter
def image_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "image_id", value)
@property
@pulumi.getter(name="instanceMetadataOptions")
def instance_metadata_options(self) -> Optional[pulumi.Input['OceanInstanceMetadataOptionsArgs']]:
"""
Ocean instance metadata options object for IMDSv2.
"""
return pulumi.get(self, "instance_metadata_options")
@instance_metadata_options.setter
def instance_metadata_options(self, value: Optional[pulumi.Input['OceanInstanceMetadataOptionsArgs']]):
pulumi.set(self, "instance_metadata_options", value)
@property
@pulumi.getter(name="keyName")
def key_name(self) -> Optional[pulumi.Input[str]]:
"""
The key pair to attach the instances.
"""
return pulumi.get(self, "key_name")
@key_name.setter
def key_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "key_name", value)
@property
@pulumi.getter(name="loadBalancers")
def load_balancers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]]]:
"""
- Array of load balancer objects to add to ocean cluster
"""
return pulumi.get(self, "load_balancers")
@load_balancers.setter
def load_balancers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanLoadBalancerArgs']]]]):
pulumi.set(self, "load_balancers", value)
@property
@pulumi.getter
def logging(self) -> Optional[pulumi.Input['OceanLoggingArgs']]:
"""
Logging configuration.
"""
return pulumi.get(self, "logging")
@logging.setter
def logging(self, value: Optional[pulumi.Input['OceanLoggingArgs']]):
pulumi.set(self, "logging", value)
@property
@pulumi.getter(name="maxSize")
def max_size(self) -> Optional[pulumi.Input[int]]:
"""
The upper limit of instances the cluster can scale up to.
"""
return pulumi.get(self, "max_size")
@max_size.setter
def max_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_size", value)
@property
@pulumi.getter(name="minSize")
def min_size(self) -> Optional[pulumi.Input[int]]:
"""
The lower limit of instances the cluster can scale down to.
"""
return pulumi.get(self, "min_size")
@min_size.setter
def min_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_size", value)
@property
@pulumi.getter
def monitoring(self) -> Optional[pulumi.Input[bool]]:
"""
Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
"""
return pulumi.get(self, "monitoring")
@monitoring.setter
def monitoring(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "monitoring", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Required if type is set to `CLASSIC`
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
The region the cluster will run in.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter(name="rootVolumeSize")
def root_volume_size(self) -> Optional[pulumi.Input[int]]:
"""
The size (in Gb) to allocate for the root volume. Minimum `20`.
"""
return pulumi.get(self, "root_volume_size")
@root_volume_size.setter
def root_volume_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "root_volume_size", value)
@property
@pulumi.getter(name="scheduledTasks")
def scheduled_tasks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]]]:
"""
Set scheduling object.
"""
return pulumi.get(self, "scheduled_tasks")
@scheduled_tasks.setter
def scheduled_tasks(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanScheduledTaskArgs']]]]):
pulumi.set(self, "scheduled_tasks", value)
@property
@pulumi.getter(name="securityGroups")
def security_groups(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
One or more security group ids.
"""
return pulumi.get(self, "security_groups")
@security_groups.setter
def security_groups(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "security_groups", value)
@property
@pulumi.getter(name="spotPercentage")
def spot_percentage(self) -> Optional[pulumi.Input[int]]:
"""
The percentage of Spot instances that would spin up from the `desired_capacity` number.
"""
return pulumi.get(self, "spot_percentage")
@spot_percentage.setter
def spot_percentage(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "spot_percentage", value)
@property
@pulumi.getter(name="subnetIds")
def subnet_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
"""
return pulumi.get(self, "subnet_ids")
@subnet_ids.setter
def subnet_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "subnet_ids", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]]]:
"""
Optionally adds tags to instances launched in an Ocean cluster.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['OceanTagArgs']]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="updatePolicy")
def update_policy(self) -> Optional[pulumi.Input['OceanUpdatePolicyArgs']]:
return pulumi.get(self, "update_policy")
@update_policy.setter
def update_policy(self, value: Optional[pulumi.Input['OceanUpdatePolicyArgs']]):
pulumi.set(self, "update_policy", value)
@property
@pulumi.getter(name="useAsTemplateOnly")
def use_as_template_only(self) -> Optional[pulumi.Input[bool]]:
"""
launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
"""
return pulumi.get(self, "use_as_template_only")
@use_as_template_only.setter
def use_as_template_only(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "use_as_template_only", value)
@property
@pulumi.getter(name="userData")
def user_data(self) -> Optional[pulumi.Input[str]]:
"""
Base64-encoded MIME user data to make available to the instances.
"""
return pulumi.get(self, "user_data")
@user_data.setter
def user_data(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "user_data", value)
@property
@pulumi.getter(name="utilizeCommitments")
def utilize_commitments(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "utilize_commitments")
@utilize_commitments.setter
def utilize_commitments(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "utilize_commitments", value)
@property
@pulumi.getter(name="utilizeReservedInstances")
def utilize_reserved_instances(self) -> Optional[pulumi.Input[bool]]:
"""
If Reserved instances exist, Ocean will utilize them before launching Spot instances.
"""
return pulumi.get(self, "utilize_reserved_instances")
@utilize_reserved_instances.setter
def utilize_reserved_instances(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "utilize_reserved_instances", value)
@property
@pulumi.getter
def whitelists(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
return pulumi.get(self, "whitelists")
@whitelists.setter
def whitelists(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "whitelists", value)
class Ocean(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
associate_public_ip_address: Optional[pulumi.Input[bool]] = None,
autoscaler: Optional[pulumi.Input[pulumi.InputType['OceanAutoscalerArgs']]] = None,
blacklists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
controller_id: Optional[pulumi.Input[str]] = None,
desired_capacity: Optional[pulumi.Input[int]] = None,
draining_timeout: Optional[pulumi.Input[int]] = None,
ebs_optimized: Optional[pulumi.Input[bool]] = None,
fallback_to_ondemand: Optional[pulumi.Input[bool]] = None,
grace_period: Optional[pulumi.Input[int]] = None,
iam_instance_profile: Optional[pulumi.Input[str]] = None,
image_id: Optional[pulumi.Input[str]] = None,
instance_metadata_options: Optional[pulumi.Input[pulumi.InputType['OceanInstanceMetadataOptionsArgs']]] = None,
key_name: Optional[pulumi.Input[str]] = None,
load_balancers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanLoadBalancerArgs']]]]] = None,
logging: Optional[pulumi.Input[pulumi.InputType['OceanLoggingArgs']]] = None,
max_size: Optional[pulumi.Input[int]] = None,
min_size: Optional[pulumi.Input[int]] = None,
monitoring: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
root_volume_size: Optional[pulumi.Input[int]] = None,
scheduled_tasks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanScheduledTaskArgs']]]]] = None,
security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
spot_percentage: Optional[pulumi.Input[int]] = None,
subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanTagArgs']]]]] = None,
update_policy: Optional[pulumi.Input[pulumi.InputType['OceanUpdatePolicyArgs']]] = None,
use_as_template_only: Optional[pulumi.Input[bool]] = None,
user_data: Optional[pulumi.Input[str]] = None,
utilize_commitments: Optional[pulumi.Input[bool]] = None,
utilize_reserved_instances: Optional[pulumi.Input[bool]] = None,
whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
"""
Create a Ocean resource with the given unique name, props, and options.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] associate_public_ip_address: Configure public IP address allocation.
:param pulumi.Input[pulumi.InputType['OceanAutoscalerArgs']] autoscaler: Describes the Ocean Kubernetes Auto Scaler.
:param pulumi.Input[Sequence[pulumi.Input[str]]] blacklists: Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
:param pulumi.Input[str] controller_id: A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
:param pulumi.Input[int] desired_capacity: The number of instances to launch and maintain in the cluster.
:param pulumi.Input[int] draining_timeout: The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
:param pulumi.Input[bool] ebs_optimized: Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
:param pulumi.Input[bool] fallback_to_ondemand: If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
:param pulumi.Input[int] grace_period: The amount of time, in seconds, after the instance has launched to start checking its health.
:param pulumi.Input[str] iam_instance_profile: The instance profile iam role.
:param pulumi.Input[str] image_id: ID of the image used to launch the instances.
:param pulumi.Input[pulumi.InputType['OceanInstanceMetadataOptionsArgs']] instance_metadata_options: Ocean instance metadata options object for IMDSv2.
:param pulumi.Input[str] key_name: The key pair to attach the instances.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanLoadBalancerArgs']]]] load_balancers: - Array of load balancer objects to add to ocean cluster
:param pulumi.Input[pulumi.InputType['OceanLoggingArgs']] logging: Logging configuration.
:param pulumi.Input[int] max_size: The upper limit of instances the cluster can scale up to.
:param pulumi.Input[int] min_size: The lower limit of instances the cluster can scale down to.
:param pulumi.Input[bool] monitoring: Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
:param pulumi.Input[str] name: Required if type is set to `CLASSIC`
:param pulumi.Input[str] region: The region the cluster will run in.
:param pulumi.Input[int] root_volume_size: The size (in Gb) to allocate for the root volume. Minimum `20`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanScheduledTaskArgs']]]] scheduled_tasks: Set scheduling object.
:param pulumi.Input[Sequence[pulumi.Input[str]]] security_groups: One or more security group ids.
:param pulumi.Input[int] spot_percentage: The percentage of Spot instances that would spin up from the `desired_capacity` number.
:param pulumi.Input[Sequence[pulumi.Input[str]]] subnet_ids: A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanTagArgs']]]] tags: Optionally adds tags to instances launched in an Ocean cluster.
:param pulumi.Input[bool] use_as_template_only: launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
:param pulumi.Input[str] user_data: Base64-encoded MIME user data to make available to the instances.
:param pulumi.Input[bool] utilize_reserved_instances: If Reserved instances exist, Ocean will utilize them before launching Spot instances.
:param pulumi.Input[Sequence[pulumi.Input[str]]] whitelists: Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: OceanArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Create a Ocean resource with the given unique name, props, and options.
:param str resource_name: The name of the resource.
:param OceanArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(OceanArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
associate_public_ip_address: Optional[pulumi.Input[bool]] = None,
autoscaler: Optional[pulumi.Input[pulumi.InputType['OceanAutoscalerArgs']]] = None,
blacklists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
controller_id: Optional[pulumi.Input[str]] = None,
desired_capacity: Optional[pulumi.Input[int]] = None,
draining_timeout: Optional[pulumi.Input[int]] = None,
ebs_optimized: Optional[pulumi.Input[bool]] = None,
fallback_to_ondemand: Optional[pulumi.Input[bool]] = None,
grace_period: Optional[pulumi.Input[int]] = None,
iam_instance_profile: Optional[pulumi.Input[str]] = None,
image_id: Optional[pulumi.Input[str]] = None,
instance_metadata_options: Optional[pulumi.Input[pulumi.InputType['OceanInstanceMetadataOptionsArgs']]] = None,
key_name: Optional[pulumi.Input[str]] = None,
load_balancers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanLoadBalancerArgs']]]]] = None,
logging: Optional[pulumi.Input[pulumi.InputType['OceanLoggingArgs']]] = None,
max_size: Optional[pulumi.Input[int]] = None,
min_size: Optional[pulumi.Input[int]] = None,
monitoring: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
root_volume_size: Optional[pulumi.Input[int]] = None,
scheduled_tasks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanScheduledTaskArgs']]]]] = None,
security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
spot_percentage: Optional[pulumi.Input[int]] = None,
subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanTagArgs']]]]] = None,
update_policy: Optional[pulumi.Input[pulumi.InputType['OceanUpdatePolicyArgs']]] = None,
use_as_template_only: Optional[pulumi.Input[bool]] = None,
user_data: Optional[pulumi.Input[str]] = None,
utilize_commitments: Optional[pulumi.Input[bool]] = None,
utilize_reserved_instances: Optional[pulumi.Input[bool]] = None,
whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = OceanArgs.__new__(OceanArgs)
__props__.__dict__["associate_public_ip_address"] = associate_public_ip_address
__props__.__dict__["autoscaler"] = autoscaler
__props__.__dict__["blacklists"] = blacklists
__props__.__dict__["controller_id"] = controller_id
__props__.__dict__["desired_capacity"] = desired_capacity
__props__.__dict__["draining_timeout"] = draining_timeout
__props__.__dict__["ebs_optimized"] = ebs_optimized
__props__.__dict__["fallback_to_ondemand"] = fallback_to_ondemand
__props__.__dict__["grace_period"] = grace_period
__props__.__dict__["iam_instance_profile"] = iam_instance_profile
__props__.__dict__["image_id"] = image_id
__props__.__dict__["instance_metadata_options"] = instance_metadata_options
__props__.__dict__["key_name"] = key_name
__props__.__dict__["load_balancers"] = load_balancers
__props__.__dict__["logging"] = logging
__props__.__dict__["max_size"] = max_size
__props__.__dict__["min_size"] = min_size
__props__.__dict__["monitoring"] = monitoring
__props__.__dict__["name"] = name
__props__.__dict__["region"] = region
__props__.__dict__["root_volume_size"] = root_volume_size
__props__.__dict__["scheduled_tasks"] = scheduled_tasks
if security_groups is None and not opts.urn:
raise TypeError("Missing required property 'security_groups'")
__props__.__dict__["security_groups"] = security_groups
__props__.__dict__["spot_percentage"] = spot_percentage
if subnet_ids is None and not opts.urn:
raise TypeError("Missing required property 'subnet_ids'")
__props__.__dict__["subnet_ids"] = subnet_ids
__props__.__dict__["tags"] = tags
__props__.__dict__["update_policy"] = update_policy
__props__.__dict__["use_as_template_only"] = use_as_template_only
__props__.__dict__["user_data"] = user_data
__props__.__dict__["utilize_commitments"] = utilize_commitments
__props__.__dict__["utilize_reserved_instances"] = utilize_reserved_instances
__props__.__dict__["whitelists"] = whitelists
super(Ocean, __self__).__init__(
'spotinst:aws/ocean:Ocean',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
associate_public_ip_address: Optional[pulumi.Input[bool]] = None,
autoscaler: Optional[pulumi.Input[pulumi.InputType['OceanAutoscalerArgs']]] = None,
blacklists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
controller_id: Optional[pulumi.Input[str]] = None,
desired_capacity: Optional[pulumi.Input[int]] = None,
draining_timeout: Optional[pulumi.Input[int]] = None,
ebs_optimized: Optional[pulumi.Input[bool]] = None,
fallback_to_ondemand: Optional[pulumi.Input[bool]] = None,
grace_period: Optional[pulumi.Input[int]] = None,
iam_instance_profile: Optional[pulumi.Input[str]] = None,
image_id: Optional[pulumi.Input[str]] = None,
instance_metadata_options: Optional[pulumi.Input[pulumi.InputType['OceanInstanceMetadataOptionsArgs']]] = None,
key_name: Optional[pulumi.Input[str]] = None,
load_balancers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanLoadBalancerArgs']]]]] = None,
logging: Optional[pulumi.Input[pulumi.InputType['OceanLoggingArgs']]] = None,
max_size: Optional[pulumi.Input[int]] = None,
min_size: Optional[pulumi.Input[int]] = None,
monitoring: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
root_volume_size: Optional[pulumi.Input[int]] = None,
scheduled_tasks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanScheduledTaskArgs']]]]] = None,
security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
spot_percentage: Optional[pulumi.Input[int]] = None,
subnet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanTagArgs']]]]] = None,
update_policy: Optional[pulumi.Input[pulumi.InputType['OceanUpdatePolicyArgs']]] = None,
use_as_template_only: Optional[pulumi.Input[bool]] = None,
user_data: Optional[pulumi.Input[str]] = None,
utilize_commitments: Optional[pulumi.Input[bool]] = None,
utilize_reserved_instances: Optional[pulumi.Input[bool]] = None,
whitelists: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None) -> 'Ocean':
"""
Get an existing Ocean resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] associate_public_ip_address: Configure public IP address allocation.
:param pulumi.Input[pulumi.InputType['OceanAutoscalerArgs']] autoscaler: Describes the Ocean Kubernetes Auto Scaler.
:param pulumi.Input[Sequence[pulumi.Input[str]]] blacklists: Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
:param pulumi.Input[str] controller_id: A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
:param pulumi.Input[int] desired_capacity: The number of instances to launch and maintain in the cluster.
:param pulumi.Input[int] draining_timeout: The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
:param pulumi.Input[bool] ebs_optimized: Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
:param pulumi.Input[bool] fallback_to_ondemand: If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
:param pulumi.Input[int] grace_period: The amount of time, in seconds, after the instance has launched to start checking its health.
:param pulumi.Input[str] iam_instance_profile: The instance profile iam role.
:param pulumi.Input[str] image_id: ID of the image used to launch the instances.
:param pulumi.Input[pulumi.InputType['OceanInstanceMetadataOptionsArgs']] instance_metadata_options: Ocean instance metadata options object for IMDSv2.
:param pulumi.Input[str] key_name: The key pair to attach the instances.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanLoadBalancerArgs']]]] load_balancers: - Array of load balancer objects to add to ocean cluster
:param pulumi.Input[pulumi.InputType['OceanLoggingArgs']] logging: Logging configuration.
:param pulumi.Input[int] max_size: The upper limit of instances the cluster can scale up to.
:param pulumi.Input[int] min_size: The lower limit of instances the cluster can scale down to.
:param pulumi.Input[bool] monitoring: Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
:param pulumi.Input[str] name: Required if type is set to `CLASSIC`
:param pulumi.Input[str] region: The region the cluster will run in.
:param pulumi.Input[int] root_volume_size: The size (in Gb) to allocate for the root volume. Minimum `20`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanScheduledTaskArgs']]]] scheduled_tasks: Set scheduling object.
:param pulumi.Input[Sequence[pulumi.Input[str]]] security_groups: One or more security group ids.
:param pulumi.Input[int] spot_percentage: The percentage of Spot instances that would spin up from the `desired_capacity` number.
:param pulumi.Input[Sequence[pulumi.Input[str]]] subnet_ids: A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['OceanTagArgs']]]] tags: Optionally adds tags to instances launched in an Ocean cluster.
:param pulumi.Input[bool] use_as_template_only: launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
:param pulumi.Input[str] user_data: Base64-encoded MIME user data to make available to the instances.
:param pulumi.Input[bool] utilize_reserved_instances: If Reserved instances exist, Ocean will utilize them before launching Spot instances.
:param pulumi.Input[Sequence[pulumi.Input[str]]] whitelists: Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _OceanState.__new__(_OceanState)
__props__.__dict__["associate_public_ip_address"] = associate_public_ip_address
__props__.__dict__["autoscaler"] = autoscaler
__props__.__dict__["blacklists"] = blacklists
__props__.__dict__["controller_id"] = controller_id
__props__.__dict__["desired_capacity"] = desired_capacity
__props__.__dict__["draining_timeout"] = draining_timeout
__props__.__dict__["ebs_optimized"] = ebs_optimized
__props__.__dict__["fallback_to_ondemand"] = fallback_to_ondemand
__props__.__dict__["grace_period"] = grace_period
__props__.__dict__["iam_instance_profile"] = iam_instance_profile
__props__.__dict__["image_id"] = image_id
__props__.__dict__["instance_metadata_options"] = instance_metadata_options
__props__.__dict__["key_name"] = key_name
__props__.__dict__["load_balancers"] = load_balancers
__props__.__dict__["logging"] = logging
__props__.__dict__["max_size"] = max_size
__props__.__dict__["min_size"] = min_size
__props__.__dict__["monitoring"] = monitoring
__props__.__dict__["name"] = name
__props__.__dict__["region"] = region
__props__.__dict__["root_volume_size"] = root_volume_size
__props__.__dict__["scheduled_tasks"] = scheduled_tasks
__props__.__dict__["security_groups"] = security_groups
__props__.__dict__["spot_percentage"] = spot_percentage
__props__.__dict__["subnet_ids"] = subnet_ids
__props__.__dict__["tags"] = tags
__props__.__dict__["update_policy"] = update_policy
__props__.__dict__["use_as_template_only"] = use_as_template_only
__props__.__dict__["user_data"] = user_data
__props__.__dict__["utilize_commitments"] = utilize_commitments
__props__.__dict__["utilize_reserved_instances"] = utilize_reserved_instances
__props__.__dict__["whitelists"] = whitelists
return Ocean(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="associatePublicIpAddress")
def associate_public_ip_address(self) -> pulumi.Output[Optional[bool]]:
"""
Configure public IP address allocation.
"""
return pulumi.get(self, "associate_public_ip_address")
@property
@pulumi.getter
def autoscaler(self) -> pulumi.Output[Optional['outputs.OceanAutoscaler']]:
"""
Describes the Ocean Kubernetes Auto Scaler.
"""
return pulumi.get(self, "autoscaler")
@property
@pulumi.getter
def blacklists(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
Instance types not allowed in the Ocean cluster. Cannot be configured if `whitelist` is configured.
"""
return pulumi.get(self, "blacklists")
@property
@pulumi.getter(name="controllerId")
def controller_id(self) -> pulumi.Output[Optional[str]]:
"""
A unique identifier used for connecting the Ocean SaaS platform and the Kubernetes cluster. Typically, the cluster name is used as its identifier.
"""
return pulumi.get(self, "controller_id")
@property
@pulumi.getter(name="desiredCapacity")
def desired_capacity(self) -> pulumi.Output[int]:
"""
The number of instances to launch and maintain in the cluster.
"""
return pulumi.get(self, "desired_capacity")
@property
@pulumi.getter(name="drainingTimeout")
def draining_timeout(self) -> pulumi.Output[Optional[int]]:
"""
The time in seconds, the instance is allowed to run while detached from the ELB. This is to allow the instance time to be drained from incoming TCP connections before terminating it, during a scale down operation.
"""
return pulumi.get(self, "draining_timeout")
@property
@pulumi.getter(name="ebsOptimized")
def ebs_optimized(self) -> pulumi.Output[Optional[bool]]:
"""
Enable EBS optimized for cluster. Flag will enable optimized capacity for high bandwidth connectivity to the EB service for non EBS optimized instance types. For instances that are EBS optimized this flag will be ignored.
"""
return pulumi.get(self, "ebs_optimized")
@property
@pulumi.getter(name="fallbackToOndemand")
def fallback_to_ondemand(self) -> pulumi.Output[Optional[bool]]:
"""
If not Spot instance markets are available, enable Ocean to launch On-Demand instances instead.
"""
return pulumi.get(self, "fallback_to_ondemand")
@property
@pulumi.getter(name="gracePeriod")
def grace_period(self) -> pulumi.Output[Optional[int]]:
"""
The amount of time, in seconds, after the instance has launched to start checking its health.
"""
return pulumi.get(self, "grace_period")
@property
@pulumi.getter(name="iamInstanceProfile")
def iam_instance_profile(self) -> pulumi.Output[Optional[str]]:
"""
The instance profile iam role.
"""
return pulumi.get(self, "iam_instance_profile")
@property
@pulumi.getter(name="imageId")
def image_id(self) -> pulumi.Output[Optional[str]]:
"""
ID of the image used to launch the instances.
"""
return pulumi.get(self, "image_id")
@property
@pulumi.getter(name="instanceMetadataOptions")
def instance_metadata_options(self) -> pulumi.Output[Optional['outputs.OceanInstanceMetadataOptions']]:
"""
Ocean instance metadata options object for IMDSv2.
"""
return pulumi.get(self, "instance_metadata_options")
@property
@pulumi.getter(name="keyName")
def key_name(self) -> pulumi.Output[Optional[str]]:
"""
The key pair to attach the instances.
"""
return pulumi.get(self, "key_name")
@property
@pulumi.getter(name="loadBalancers")
def load_balancers(self) -> pulumi.Output[Optional[Sequence['outputs.OceanLoadBalancer']]]:
"""
- Array of load balancer objects to add to ocean cluster
"""
return pulumi.get(self, "load_balancers")
@property
@pulumi.getter
def logging(self) -> pulumi.Output[Optional['outputs.OceanLogging']]:
"""
Logging configuration.
"""
return pulumi.get(self, "logging")
@property
@pulumi.getter(name="maxSize")
def max_size(self) -> pulumi.Output[Optional[int]]:
"""
The upper limit of instances the cluster can scale up to.
"""
return pulumi.get(self, "max_size")
@property
@pulumi.getter(name="minSize")
def min_size(self) -> pulumi.Output[int]:
"""
The lower limit of instances the cluster can scale down to.
"""
return pulumi.get(self, "min_size")
@property
@pulumi.getter
def monitoring(self) -> pulumi.Output[Optional[bool]]:
"""
Enable detailed monitoring for cluster. Flag will enable Cloud Watch detailed monitoring (one minute increments). Note: there are additional hourly costs for this service based on the region used.
"""
return pulumi.get(self, "monitoring")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Required if type is set to `CLASSIC`
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def region(self) -> pulumi.Output[Optional[str]]:
"""
The region the cluster will run in.
"""
return pulumi.get(self, "region")
@property
@pulumi.getter(name="rootVolumeSize")
def root_volume_size(self) -> pulumi.Output[Optional[int]]:
"""
The size (in Gb) to allocate for the root volume. Minimum `20`.
"""
return pulumi.get(self, "root_volume_size")
@property
@pulumi.getter(name="scheduledTasks")
def scheduled_tasks(self) -> pulumi.Output[Optional[Sequence['outputs.OceanScheduledTask']]]:
"""
Set scheduling object.
"""
return pulumi.get(self, "scheduled_tasks")
@property
@pulumi.getter(name="securityGroups")
def security_groups(self) -> pulumi.Output[Sequence[str]]:
"""
One or more security group ids.
"""
return pulumi.get(self, "security_groups")
@property
@pulumi.getter(name="spotPercentage")
def spot_percentage(self) -> pulumi.Output[Optional[int]]:
"""
The percentage of Spot instances that would spin up from the `desired_capacity` number.
"""
return pulumi.get(self, "spot_percentage")
@property
@pulumi.getter(name="subnetIds")
def subnet_ids(self) -> pulumi.Output[Sequence[str]]:
"""
A comma-separated list of subnet identifiers for the Ocean cluster. Subnet IDs should be configured with auto assign public IP.
"""
return pulumi.get(self, "subnet_ids")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Sequence['outputs.OceanTag']]]:
"""
Optionally adds tags to instances launched in an Ocean cluster.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="updatePolicy")
def update_policy(self) -> pulumi.Output[Optional['outputs.OceanUpdatePolicy']]:
return pulumi.get(self, "update_policy")
@property
@pulumi.getter(name="useAsTemplateOnly")
def use_as_template_only(self) -> pulumi.Output[Optional[bool]]:
"""
launch specification defined on the Ocean object will function only as a template for virtual node groups.
When set to true, on Ocean resource creation please make sure your custom VNG has an initial_nodes parameter to create nodes for your VNG.
"""
return pulumi.get(self, "use_as_template_only")
@property
@pulumi.getter(name="userData")
def user_data(self) -> pulumi.Output[Optional[str]]:
"""
Base64-encoded MIME user data to make available to the instances.
"""
return pulumi.get(self, "user_data")
@property
@pulumi.getter(name="utilizeCommitments")
def utilize_commitments(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "utilize_commitments")
@property
@pulumi.getter(name="utilizeReservedInstances")
def utilize_reserved_instances(self) -> pulumi.Output[Optional[bool]]:
"""
If Reserved instances exist, Ocean will utilize them before launching Spot instances.
"""
return pulumi.get(self, "utilize_reserved_instances")
@property
@pulumi.getter
def whitelists(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
Instance types allowed in the Ocean cluster. Cannot be configured if `blacklist` is configured.
"""
return pulumi.get(self, "whitelists")
| 51.108312 | 270 | 0.676762 | 9,690 | 81,160 | 5.467595 | 0.03612 | 0.104434 | 0.101131 | 0.034427 | 0.968498 | 0.961779 | 0.948717 | 0.943282 | 0.937147 | 0.920236 | 0 | 0.00057 | 0.22171 | 81,160 | 1,587 | 271 | 51.140517 | 0.838188 | 0.309808 | 0 | 0.908999 | 1 | 0 | 0.124784 | 0.036747 | 0 | 0 | 0 | 0 | 0 | 1 | 0.168857 | false | 0.001011 | 0.007078 | 0.006067 | 0.277048 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
941592e7be5e1d67879a16f149bcf752840dbbee | 32,550 | py | Python | pysnmp/VERITAS-CLUSTER-MIB.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 11 | 2021-02-02T16:27:16.000Z | 2021-08-31T06:22:49.000Z | pysnmp/VERITAS-CLUSTER-MIB.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 75 | 2021-02-24T17:30:31.000Z | 2021-12-08T00:01:18.000Z | pysnmp/VERITAS-CLUSTER-MIB.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 10 | 2019-04-30T05:51:36.000Z | 2022-02-16T03:33:41.000Z | #
# PySNMP MIB module VERITAS-CLUSTER-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/VERITAS-CLUSTER-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 21:26:59 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
SingleValueConstraint, ValueRangeConstraint, ConstraintsUnion, ValueSizeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsUnion", "ValueSizeConstraint", "ConstraintsIntersection")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
enterprises, Unsigned32, Bits, Counter32, ObjectIdentity, Gauge32, Counter64, NotificationType, IpAddress, ModuleIdentity, NotificationType, MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, Integer32, iso, TimeTicks = mibBuilder.importSymbols("SNMPv2-SMI", "enterprises", "Unsigned32", "Bits", "Counter32", "ObjectIdentity", "Gauge32", "Counter64", "NotificationType", "IpAddress", "ModuleIdentity", "NotificationType", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Integer32", "iso", "TimeTicks")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
veritassoftware = MibIdentifier((1, 3, 6, 1, 4, 1, 1302))
products = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3))
veritasCluster = ModuleIdentity((1, 3, 6, 1, 4, 1, 1302, 3, 8))
if mibBuilder.loadTexts: veritasCluster.setLastUpdated('03202001')
if mibBuilder.loadTexts: veritasCluster.setOrganization('VERITAS Software, Inc.')
clustertraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10))
clustertrapvars = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1))
clustertrapsGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2))
resourcesTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1))
groupsTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2))
systemsTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3))
vcsHeartbeatTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 4))
gcmHeartbeatTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 5))
vcsTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 6))
gcmSiteTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 7))
agentsTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 8))
externalTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 9))
rdcTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10))
trapOrigin = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 1), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: trapOrigin.setStatus('mandatory')
entityType = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 2), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entityType.setStatus('mandatory')
entitySubType = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 3), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entitySubType.setStatus('mandatory')
entityName = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 4), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entityName.setStatus('mandatory')
entityOwner = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 5), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entityOwner.setStatus('mandatory')
systemName = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 6), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 80))).setMaxAccess("readonly")
if mibBuilder.loadTexts: systemName.setStatus('mandatory')
systemLocation = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 7), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 80))).setMaxAccess("readonly")
if mibBuilder.loadTexts: systemLocation.setStatus('mandatory')
entityState = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 8), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entityState.setStatus('mandatory')
entityContainerType = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 9), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entityContainerType.setStatus('mandatory')
entityContainerName = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 10), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: entityContainerName.setStatus('mandatory')
peerSystemName = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 11), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 80))).setMaxAccess("readonly")
if mibBuilder.loadTexts: peerSystemName.setStatus('mandatory')
peerSystemLocation = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 12), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 80))).setMaxAccess("readonly")
if mibBuilder.loadTexts: peerSystemLocation.setStatus('mandatory')
message = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 13), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 80))).setMaxAccess("readonly")
if mibBuilder.loadTexts: message.setStatus('mandatory')
eventTime = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 14), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 50))).setMaxAccess("readonly")
if mibBuilder.loadTexts: eventTime.setStatus('mandatory')
severityId = MibScalar((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 1, 15), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2, 3))).clone(namedValues=NamedValues(("information", 0), ("warning", 1), ("error", 2), ("severeError", 3)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: severityId.setStatus('mandatory')
clusterResourceStateUnknownTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1) + (0,1)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterResourceMonitorTimeoutTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1) + (0,2)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterResourceNotGoingOfflineTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1) + (0,3)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterResourceRestartingByAgentTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1) + (0,4)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterResourceWentOnlineByItselfTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1) + (0,5)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterResourceFaultedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 1) + (0,6)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupOnlineTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,1)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupOfflineTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,2)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupAutoDisabledTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,3)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupFaultedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,4)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupFaultedAndNowhereToFailoverTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,5)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupRestartingTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,6)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupInitiatingForSwitchingTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,7)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupConcurencyViolationTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,8)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGroupRestInRspnToPerstResGoOnlineTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 2) + (0,9)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterFirstSystemUpTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,1)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemRestartingByHashadowTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,2)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemInJeopardyTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,3)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemFaultedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,4)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemJoinedClusterTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,5)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemExitedManuallyTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,6)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemUpButNotInClusterTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,7)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterSystemUsageExceededThresholdTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 3) + (0,8)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterGUIUserLoginTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 6) + (0,2)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterAgentRestartingTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 8) + (0,1)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterAgentFaultedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 8) + (0,2)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCRlinkInconsistentTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,1)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCRlinkNotUpToDateTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,2)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCTakeoverFailedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,3)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCMigrateFailedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,4)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCTakeoverSuccessTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,5)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCMigrateSuccessTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,6)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCActingSecondaryTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,7)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCResyncFailedTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,8)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCResyncSuccessTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,9)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
clusterRDCGroupOfflineTrap = NotificationType((1, 3, 6, 1, 4, 1, 1302, 3, 8, 10, 2, 10) + (0,10)).setObjects(("VERITAS-CLUSTER-MIB", "severityId"), ("VERITAS-CLUSTER-MIB", "eventTime"), ("VERITAS-CLUSTER-MIB", "entityName"), ("VERITAS-CLUSTER-MIB", "entityType"), ("VERITAS-CLUSTER-MIB", "entitySubType"), ("VERITAS-CLUSTER-MIB", "entityState"), ("VERITAS-CLUSTER-MIB", "trapOrigin"), ("VERITAS-CLUSTER-MIB", "systemName"), ("VERITAS-CLUSTER-MIB", "systemLocation"), ("VERITAS-CLUSTER-MIB", "entityContainerName"), ("VERITAS-CLUSTER-MIB", "entityContainerType"), ("VERITAS-CLUSTER-MIB", "entityOwner"), ("VERITAS-CLUSTER-MIB", "message"))
mibBuilder.exportSymbols("VERITAS-CLUSTER-MIB", clusterRDCGroupOfflineTrap=clusterRDCGroupOfflineTrap, clusterSystemJoinedClusterTrap=clusterSystemJoinedClusterTrap, clusterGroupOnlineTrap=clusterGroupOnlineTrap, clusterSystemUpButNotInClusterTrap=clusterSystemUpButNotInClusterTrap, clusterGroupInitiatingForSwitchingTrap=clusterGroupInitiatingForSwitchingTrap, message=message, clusterGroupAutoDisabledTrap=clusterGroupAutoDisabledTrap, clusterSystemRestartingByHashadowTrap=clusterSystemRestartingByHashadowTrap, clustertraps=clustertraps, clusterSystemUsageExceededThresholdTrap=clusterSystemUsageExceededThresholdTrap, clusterAgentFaultedTrap=clusterAgentFaultedTrap, veritasCluster=veritasCluster, gcmHeartbeatTraps=gcmHeartbeatTraps, clusterResourceRestartingByAgentTrap=clusterResourceRestartingByAgentTrap, clusterFirstSystemUpTrap=clusterFirstSystemUpTrap, entityState=entityState, vcsHeartbeatTraps=vcsHeartbeatTraps, clusterGroupRestartingTrap=clusterGroupRestartingTrap, systemLocation=systemLocation, clusterSystemInJeopardyTrap=clusterSystemInJeopardyTrap, products=products, groupsTraps=groupsTraps, clusterRDCMigrateSuccessTrap=clusterRDCMigrateSuccessTrap, gcmSiteTraps=gcmSiteTraps, resourcesTraps=resourcesTraps, clusterRDCActingSecondaryTrap=clusterRDCActingSecondaryTrap, clusterGUIUserLoginTrap=clusterGUIUserLoginTrap, entityType=entityType, clusterGroupFaultedAndNowhereToFailoverTrap=clusterGroupFaultedAndNowhereToFailoverTrap, PYSNMP_MODULE_ID=veritasCluster, clusterGroupRestInRspnToPerstResGoOnlineTrap=clusterGroupRestInRspnToPerstResGoOnlineTrap, systemName=systemName, clusterSystemExitedManuallyTrap=clusterSystemExitedManuallyTrap, clusterResourceWentOnlineByItselfTrap=clusterResourceWentOnlineByItselfTrap, systemsTraps=systemsTraps, entityOwner=entityOwner, clusterRDCTakeoverFailedTrap=clusterRDCTakeoverFailedTrap, clusterRDCResyncSuccessTrap=clusterRDCResyncSuccessTrap, clusterResourceNotGoingOfflineTrap=clusterResourceNotGoingOfflineTrap, agentsTraps=agentsTraps, entityName=entityName, peerSystemLocation=peerSystemLocation, clusterAgentRestartingTrap=clusterAgentRestartingTrap, clusterRDCRlinkInconsistentTrap=clusterRDCRlinkInconsistentTrap, clustertrapvars=clustertrapvars, externalTraps=externalTraps, eventTime=eventTime, clusterGroupConcurencyViolationTrap=clusterGroupConcurencyViolationTrap, severityId=severityId, clusterRDCResyncFailedTrap=clusterRDCResyncFailedTrap, trapOrigin=trapOrigin, entityContainerType=entityContainerType, rdcTraps=rdcTraps, entitySubType=entitySubType, clusterResourceMonitorTimeoutTrap=clusterResourceMonitorTimeoutTrap, clusterRDCMigrateFailedTrap=clusterRDCMigrateFailedTrap, entityContainerName=entityContainerName, clusterResourceStateUnknownTrap=clusterResourceStateUnknownTrap, veritassoftware=veritassoftware, clusterGroupFaultedTrap=clusterGroupFaultedTrap, clusterRDCTakeoverSuccessTrap=clusterRDCTakeoverSuccessTrap, peerSystemName=peerSystemName, clustertrapsGroups=clustertrapsGroups, clusterResourceFaultedTrap=clusterResourceFaultedTrap, clusterSystemFaultedTrap=clusterSystemFaultedTrap, clusterRDCRlinkNotUpToDateTrap=clusterRDCRlinkNotUpToDateTrap, clusterGroupOfflineTrap=clusterGroupOfflineTrap, vcsTraps=vcsTraps)
| 328.787879 | 3,217 | 0.727803 | 3,435 | 32,550 | 6.89607 | 0.055022 | 0.278369 | 0.338019 | 0.011314 | 0.751055 | 0.740966 | 0.740966 | 0.740966 | 0.739995 | 0.738475 | 0 | 0.041877 | 0.06682 | 32,550 | 98 | 3,218 | 332.142857 | 0.737975 | 0.0102 | 0 | 0 | 0 | 0 | 0.472073 | 0.001366 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.065934 | 0 | 0.065934 | 0 | 0 | 0 | 0 | null | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
94306cc9ff26de33fef5807edc851246fd4527d6 | 69 | py | Python | msgbuzz/__init__.py | sihendra/msgbus | ac67be1211732def95581e541239eb1ab1e6c00d | [
"BSD-3-Clause"
] | 2 | 2020-03-23T09:14:00.000Z | 2020-04-17T03:55:24.000Z | msgbuzz/__init__.py | sihendra/msgbuzz | ac67be1211732def95581e541239eb1ab1e6c00d | [
"BSD-3-Clause"
] | null | null | null | msgbuzz/__init__.py | sihendra/msgbuzz | ac67be1211732def95581e541239eb1ab1e6c00d | [
"BSD-3-Clause"
] | null | null | null | from .generic import MessageBus
from .generic import ConsumerConfirm
| 23 | 36 | 0.855072 | 8 | 69 | 7.375 | 0.625 | 0.372881 | 0.576271 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115942 | 69 | 2 | 37 | 34.5 | 0.967213 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
946fef83ed286e5659b4c0099f7966545eff1a50 | 2,890 | py | Python | contest/Contest_NAQP.py | dfannin/loggy | 9bb0eb7167966615666f1d15106dad72fcd8daa9 | [
"BSD-3-Clause"
] | 2 | 2017-01-28T17:54:33.000Z | 2017-05-13T11:46:18.000Z | contest/Contest_NAQP.py | dfannin/loggy | 9bb0eb7167966615666f1d15106dad72fcd8daa9 | [
"BSD-3-Clause"
] | null | null | null | contest/Contest_NAQP.py | dfannin/loggy | 9bb0eb7167966615666f1d15106dad72fcd8daa9 | [
"BSD-3-Clause"
] | null | null | null |
from . import Contest
class NAQP_SSB(Contest.Contest):
def __init__(self,config):
super(NAQP_SSB, self).__init__(config)
self.name = "NAQP-SSB"
self.description = "North American QSO Party - Single Side Band"
def _transform(self,row):
if row['mode'] == "SSB":
return row
else:
return []
def _checkmulti(self):
band = self.qso['band']
mode = self.qso['mode']
comment = self.qso['comment'].split(',')
qth = comment[2].upper()
bandmodeqth = band + " " + mode + " " + qth
if not bandmodeqth in self.multilist:
self.multilist[bandmodeqth] = 1
else:
self.multilist[bandmodeqth] += 1
def format_cabrillo_row(self):
if not self.qso:
return ''
freq = int( float(self.qso['freq']) * 1000.0 )
mode = self.qso['mode']
if ( mode == 'SSB' ):
mode = 'PH'
qso_date = self.qso['qso_date']
qso_date = qso_date[:4] + '-' + qso_date[4:6] + '-' + qso_date[6:]
comment = self.qso['comment'].split(',')
name = comment[1].upper()
qth = comment[2].upper()
return 'QSO: %5s %2s %s %s %-15s %-10s %-3s %-15s %-10s %-3s' % (freq, mode, qso_date, self.qso['time_on'] , self.config['default']['call'].upper(), self.config['contest']['name'].upper(), self.config['contest']['qth'].upper(), self.qso['call'], name, qth)
class NAQP_CW(Contest.Contest):
def __init__(self,config):
super(NAQP_CW, self).__init__(config)
self.name = "NAQP-CW"
self.description = "North American QSO Party - Continuous Wave"
def _transform(self,row):
if row['mode'] == "CW":
return row
else:
return []
def _checkmulti(self):
band = self.qso['band']
mode = self.qso['mode']
comment = self.qso['comment'].split(',')
qth = comment[2].upper()
bandmodeqth = band + " " + mode + " " + qth
if not bandmodeqth in self.multilist:
self.multilist[bandmodeqth] = 1
else:
self.multilist[bandmodeqth] += 1
def format_cabrillo_row(self):
if not self.qso:
return ''
freq = int( float(self.qso['freq']) * 1000.0 )
mode = self.qso['mode']
if ( mode == 'SSB' ):
mode = 'PH'
qso_date = self.qso['qso_date']
qso_date = qso_date[:4] + '-' + qso_date[4:6] + '-' + qso_date[6:]
comment = self.qso['comment'].split(',')
name = comment[1].upper()
qth = comment[2].upper()
return 'QSO: %5s %2s %s %s %-15s %-10s %-3s %-15s %-10s %-3s' % (freq, mode, qso_date, self.qso['time_on'] , self.config['default']['call'].upper(), self.config['contest']['name'].upper(), self.config['contest']['qth'].upper(), self.qso['call'], name, qth)
| 36.582278 | 264 | 0.535986 | 356 | 2,890 | 4.227528 | 0.171348 | 0.093023 | 0.029236 | 0.039867 | 0.944851 | 0.944851 | 0.862458 | 0.825249 | 0.772093 | 0.772093 | 0 | 0.025218 | 0.286505 | 2,890 | 78 | 265 | 37.051282 | 0.704656 | 0 | 0 | 0.835821 | 0 | 0.029851 | 0.138802 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.119403 | false | 0 | 0.014925 | 0 | 0.283582 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ca4ffbe85585c61f0ca246cd2516197b32f1b9f7 | 34,967 | py | Python | tb_rest_client/api/api_pe/ocean_connect_integration_controller_api.py | maksonlee/python_tb_rest_client | a6cd17ef4de31f68c3226b7a9835292fbac4b1fa | [
"Apache-2.0"
] | 1 | 2021-07-19T10:09:04.000Z | 2021-07-19T10:09:04.000Z | tb_rest_client/api/api_pe/ocean_connect_integration_controller_api.py | moravcik94/python_tb_rest_client | 985361890cdf4ccce93d2b24905ad9003c8dfcaa | [
"Apache-2.0"
] | null | null | null | tb_rest_client/api/api_pe/ocean_connect_integration_controller_api.py | moravcik94/python_tb_rest_client | 985361890cdf4ccce93d2b24905ad9003c8dfcaa | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
# Copyright 2020. ThingsBoard
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# http://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from tb_rest_client.api_client import ApiClient
class OceanConnectIntegrationControllerApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def process_request_using_delete(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_delete(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_delete_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_delete_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_delete_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_delete_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_delete`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_delete`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_get(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_get(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_get_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_get_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_get_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_get_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_get`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_get`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_head(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_head(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_head_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_head_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_head_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_head_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_head`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_head`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_head`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'HEAD',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_options(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_options(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_options_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_options_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_options_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_options_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_options`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_options`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_options`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'OPTIONS',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_patch(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_patch(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_patch_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_patch_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_patch_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_patch_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_patch`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_patch`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_patch`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'PATCH',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_post4(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_post4(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_post4_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_post4_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_post4_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_post4_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_post4`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_post4`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_post4`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def process_request_using_put(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_put(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.process_request_using_put_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
else:
(data) = self.process_request_using_put_with_http_info(routing_key, msg, request_headers, **kwargs) # noqa: E501
return data
def process_request_using_put_with_http_info(self, routing_key, msg, request_headers, **kwargs): # noqa: E501
"""processRequest # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api_pe.process_request_using_put_with_http_info(routing_key, msg, request_headers, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str routing_key: routingKey (required)
:param str msg: msg (required)
:param object request_headers: requestHeaders (required)
:return: DeferredResultResponseEntity
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['routing_key', 'msg', 'request_headers'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
params[key] = val
del params['kwargs']
# verify the required parameter 'routing_key' is set
if ('routing_key' not in params or
params['routing_key'] is None):
raise ValueError("Missing the required parameter `routing_key` when calling `process_request_using_put`") # noqa: E501
# verify the required parameter 'msg' is set
if ('msg' not in params or
params['msg'] is None):
raise ValueError("Missing the required parameter `msg` when calling `process_request_using_put`") # noqa: E501
# verify the required parameter 'request_headers' is set
if ('request_headers' not in params or
params['request_headers'] is None):
raise ValueError("Missing the required parameter `request_headers` when calling `process_request_using_put`") # noqa: E501
collection_formats = {}
path_params = {}
if 'routing_key' in params:
path_params['routingKey'] = params['routing_key'] # noqa: E501
query_params = []
header_params = {}
if 'request_headers' in params:
header_params['requestHeaders'] = params['request_headers'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'msg' in params:
body_params = params['msg']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['*/*']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/v1/integrations/oceanconnect/{routingKey}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DeferredResultResponseEntity', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 43.763454 | 139 | 0.638659 | 3,975 | 34,967 | 5.349686 | 0.048302 | 0.044768 | 0.05629 | 0.046085 | 0.960357 | 0.957771 | 0.957771 | 0.956219 | 0.954667 | 0.954667 | 0 | 0.015185 | 0.27117 | 34,967 | 798 | 140 | 43.818296 | 0.819227 | 0.326279 | 0 | 0.845972 | 0 | 0 | 0.231127 | 0.072122 | 0 | 0 | 0 | 0 | 0 | 1 | 0.035545 | false | 0 | 0.009479 | 0 | 0.097156 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
047d4eab926676a1b060945ea252eb410fe8fc92 | 5,236 | py | Python | tests/dhcpv6/classification/test_v6_ipxe.py | isc-projects/forge | dfec8b41003d6b5a229f69ee93616e0e5cc6d71b | [
"0BSD"
] | 22 | 2015-02-27T11:51:05.000Z | 2022-02-28T12:39:29.000Z | tests/dhcpv6/classification/test_v6_ipxe.py | isc-projects/forge | dfec8b41003d6b5a229f69ee93616e0e5cc6d71b | [
"0BSD"
] | 16 | 2018-10-30T15:00:12.000Z | 2019-01-11T17:55:13.000Z | tests/dhcpv6/classification/test_v6_ipxe.py | isc-projects/forge | dfec8b41003d6b5a229f69ee93616e0e5cc6d71b | [
"0BSD"
] | 11 | 2015-02-27T11:51:36.000Z | 2021-03-30T08:33:54.000Z | """DHCPv6 iPXE boot tests"""
# pylint: disable=invalid-name,line-too-long
import pytest
import misc
import srv_control
import srv_msg
@pytest.mark.v6
@pytest.mark.iPXE
def test_v6_IPXE_1():
misc.test_setup()
srv_control.config_srv_subnet('2001:db8::/64', '$(EMPTY)')
srv_control.create_new_class('a-ipxe')
srv_control.add_test_to_class(1, 'test', 'substring(option[15].hex,2,4) == \'iPXE\'')
srv_control.add_option_to_defined_class(1,
'bootfile-url',
'http://[2001:db8::1]/ubuntu.cfg')
# Server is configured with client-classification option in subnet 0 with name a-ipxe.
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_sets_value('Client', 'archtypes', 7)
srv_msg.client_does_include('Client', 'client-arch-type')
srv_msg.client_does_include('Client', 'client-id')
srv_msg.client_does_include('Client', 'IA-NA')
srv_msg.client_sets_value('Client', 'user_class_data', 'iPXE')
srv_msg.client_does_include('Client', 'user-class')
srv_msg.client_requests_option(59)
srv_msg.client_send_msg('SOLICIT')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ADVERTISE')
srv_msg.response_check_include_option(59)
srv_msg.response_check_option_content(59, 'optdata', 'http://[2001:db8::1]/ubuntu.cfg')
srv_msg.response_check_include_option(3)
srv_msg.response_check_option_content(3, 'sub-option', 13)
srv_msg.response_check_suboption_content(13, 3, 'statuscode', 2)
@pytest.mark.v6
@pytest.mark.iPXE
def test_v6_IPXE_2():
misc.test_setup()
srv_control.config_srv_subnet('2001:db8::/64', '$(EMPTY)')
srv_control.create_new_class('a-ipxe')
srv_control.add_test_to_class(1, 'test', 'option[61].hex == 0x0007')
srv_control.add_option_to_defined_class(1, 'bootfile-url', 'http://[2001:db8::1]/ipxe.efi')
# Server is configured with client-classification option in subnet 0 with name a-ipxe.
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_sets_value('Client', 'archtypes', 7)
srv_msg.client_does_include('Client', 'client-arch-type')
srv_msg.client_does_include('Client', 'client-id')
srv_msg.client_does_include('Client', 'IA-NA')
srv_msg.client_requests_option(59)
srv_msg.client_send_msg('SOLICIT')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ADVERTISE')
srv_msg.response_check_include_option(59)
srv_msg.response_check_option_content(59, 'optdata', 'http://[2001:db8::1]/ipxe.efi')
srv_msg.response_check_include_option(3)
srv_msg.response_check_option_content(3, 'sub-option', 13)
srv_msg.response_check_suboption_content(13, 3, 'statuscode', 2)
@pytest.mark.v6
@pytest.mark.iPXE
def test_v6_IPXE_combined():
misc.test_setup()
srv_control.config_srv_subnet('2001:db8::/64', '$(EMPTY)')
srv_control.create_new_class('a-ipxe')
srv_control.add_test_to_class(1, 'test', 'substring(option[15].hex,2,4) == \'iPXE\'')
srv_control.add_option_to_defined_class(1,
'bootfile-url',
'http://[2001:db8::1]/ubuntu.cfg')
srv_control.create_new_class('b-ipxe')
srv_control.add_test_to_class(2, 'test', 'option[61].hex == 0x0007')
srv_control.add_option_to_defined_class(2, 'bootfile-url', 'http://[2001:db8::1]/ipxe.efi')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_sets_value('Client', 'archtypes', 7)
srv_msg.client_does_include('Client', 'client-arch-type')
srv_msg.client_does_include('Client', 'client-id')
srv_msg.client_does_include('Client', 'IA-NA')
srv_msg.client_requests_option(59)
srv_msg.client_send_msg('SOLICIT')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ADVERTISE')
srv_msg.response_check_include_option(59)
srv_msg.response_check_option_content(59, 'optdata', 'http://[2001:db8::1]/ipxe.efi')
srv_msg.response_check_include_option(3)
srv_msg.response_check_option_content(3, 'sub-option', 13)
srv_msg.response_check_suboption_content(13, 3, 'statuscode', 2)
misc.test_procedure()
srv_msg.client_sets_value('Client', 'archtypes', 7)
srv_msg.client_does_include('Client', 'client-arch-type')
srv_msg.client_does_include('Client', 'client-id')
srv_msg.client_does_include('Client', 'IA-NA')
srv_msg.client_sets_value('Client', 'user_class_data', 'iPXE')
srv_msg.client_does_include('Client', 'user-class')
srv_msg.client_requests_option(59)
srv_msg.client_send_msg('SOLICIT')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ADVERTISE')
srv_msg.response_check_include_option(59)
srv_msg.response_check_option_content(59, 'optdata', 'http://[2001:db8::1]/ubuntu.cfg')
srv_msg.response_check_include_option(3)
srv_msg.response_check_option_content(3, 'sub-option', 13)
srv_msg.response_check_suboption_content(13, 3, 'statuscode', 2)
| 41.228346 | 95 | 0.710275 | 767 | 5,236 | 4.478488 | 0.130378 | 0.092576 | 0.097817 | 0.110626 | 0.965939 | 0.959825 | 0.959825 | 0.950801 | 0.940029 | 0.940029 | 0 | 0.038031 | 0.146295 | 5,236 | 126 | 96 | 41.555556 | 0.730425 | 0.045073 | 0 | 0.881188 | 0 | 0 | 0.21274 | 0.011619 | 0 | 0 | 0.002404 | 0 | 0 | 1 | 0.029703 | true | 0.039604 | 0.039604 | 0 | 0.069307 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
049bb07740ef931bfadbdeed93e59ba49575bc92 | 4,195 | py | Python | regexlib/python_re2_test_file/regexlib_7920.py | yetingli/ReDoS-Benchmarks | f5b5094d835649e957bf3fec6b8bd4f6efdb35fc | [
"MIT"
] | 1 | 2022-01-24T14:43:23.000Z | 2022-01-24T14:43:23.000Z | regexlib/python_re2_test_file/regexlib_7920.py | yetingli/ReDoS-Benchmarks | f5b5094d835649e957bf3fec6b8bd4f6efdb35fc | [
"MIT"
] | null | null | null | regexlib/python_re2_test_file/regexlib_7920.py | yetingli/ReDoS-Benchmarks | f5b5094d835649e957bf3fec6b8bd4f6efdb35fc | [
"MIT"
] | null | null | null | # 7920
# ^v=spf1[ \t]+[+?~-]?(?:(?:all)|(?:ip4(?:[:][0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})?(?:/[0-9]{1,2})?)|(?:ip6(?:[:]([0-9A-Fa-f]{1,4}:){7}[0-9A-Fa-f]{1,4})?(?:/[0-9]{1,2})?)|(?:a(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:mx(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:ptr(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exists(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:include(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:redirect(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exp(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|)(?:(?:[ \t]+[+?~-]?(?:(?:all)|(?:ip4(?:[:][0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})?(?:/[0-9]{1,2})?)|(?:ip6(?:[:]([0-9A-Fa-f]{1,4}:){7}[0-9A-Fa-f]{1,4})?(?:/[0-9]{1,2})?)|(?:a(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:mx(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:ptr(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exists(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:include(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:redirect(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exp(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|))*)?$
# EXPONENT
# nums:5
# EXPONENT AttackString:"v=spf1 "+" "*32+"! _1_NQ"
import re2 as re
from time import perf_counter
regex = """^v=spf1[ \t]+[+?~-]?(?:(?:all)|(?:ip4(?:[:][0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})?(?:/[0-9]{1,2})?)|(?:ip6(?:[:]([0-9A-Fa-f]{1,4}:){7}[0-9A-Fa-f]{1,4})?(?:/[0-9]{1,2})?)|(?:a(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:mx(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:ptr(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exists(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:include(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:redirect(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exp(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|)(?:(?:[ \t]+[+?~-]?(?:(?:all)|(?:ip4(?:[:][0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3})?(?:/[0-9]{1,2})?)|(?:ip6(?:[:]([0-9A-Fa-f]{1,4}:){7}[0-9A-Fa-f]{1,4})?(?:/[0-9]{1,2})?)|(?:a(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:mx(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+)?(?:/[0-9]{1,2})?)|(?:ptr(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exists(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:include(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:redirect(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|(?:exp(?:[:][A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?(?:\.[A-Za-z0-9](?:[A-Za-z0-9-]*[A-Za-z0-9])?)+))|))*)?$"""
REGEX = re.compile(regex)
for i in range(0, 150000):
ATTACK = "v=spf1 " + " " * i * 1 + "! _1_NQ"
LEN = len(ATTACK)
BEGIN = perf_counter()
m = REGEX.search(ATTACK)
# m = REGEX.match(ATTACK)
DURATION = perf_counter() - BEGIN
print(f"{i *1}: took {DURATION} seconds!") | 220.789474 | 1,892 | 0.390465 | 963 | 4,195 | 1.693666 | 0.05919 | 0.309013 | 0.515021 | 0.618026 | 0.842428 | 0.842428 | 0.842428 | 0.842428 | 0.842428 | 0.842428 | 0 | 0.129765 | 0.024553 | 4,195 | 19 | 1,893 | 220.789474 | 0.268817 | 0.470083 | 0 | 0 | 0 | 0.090909 | 0.86829 | 0.84258 | 0.090909 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.181818 | 0 | 0.181818 | 0.090909 | 0 | 0 | 1 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 |
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