def test_acosh(self):
import math
self.ftest(math.acosh(1), 0)
self.ftest(math.acosh(2), 1.3169578969248168)
assert math.isinf(math.asinh(float("inf")))
raises(ValueError, math.acosh, 0)
def test_funcs_multi(self):
pi = math.pi
# sin family
self.assertQuantity(data.sin(Quantity( (0,pi/2), (2,2))), (0,1), (2,0))
self.assertQuantity(data.sinh(Quantity((0,1), (2,2))), (0, math.sinh(1)), (2, math.cosh(1)*2))
self.assertQuantity(data.asin(Quantity((0,0.5), (2,2))), (0,math.asin(0.5)), (2,2/math.sqrt(1-0.5**2)))
self.assertQuantity(data.asinh(Quantity((0,1), (2,2))), (0,math.asinh(1)), (2,2/math.sqrt(1+1**2)))
# cos family
self.assertQuantity(data.cos(Quantity((0,pi/2), (2,2))), (1,0), (0,-2))
self.assertQuantity(data.cosh(Quantity((0,1), (2,2))), (1,math.cosh(1)), (0,math.sinh(1)*2))
self.assertQuantity(data.acos(Quantity((0,0.5), (2,2))), (math.acos(0),math.acos(0.5)), (-2,-2/math.sqrt(1-0.5**2)))
self.assertQuantity(data.acosh(Quantity((2,3), (2,2))), (math.acosh(2), math.acosh(3)), (2/math.sqrt(2**2-1),2/math.sqrt(3**2-1)))
# tan family
self.assertQuantity(data.tan(Quantity((0,1), (2,2))), (0,math.tan(1)), (2,2/math.cos(1)**2))
self.assertQuantity(data.tanh(Quantity((0,1), (2,2))), (0,math.tanh(1)), (2,2/math.cosh(1)**2))
self.assertQuantity(data.atan(Quantity((0,1), (2,2))), (0, math.atan(1)), (2,2/(1+1**2)))
self.assertQuantity(data.atan2(Quantity((0,1), (2,2)), Quantity((1,1), (0,0))), (0,math.atan(1)), (2,2/(1+1**2)))
self.assertQuantity(data.atanh(Quantity((0,0.5), (2,2))), (0,math.atanh(0.5)), (2,2/(1-0.5**2)))
#misc
self.assertQuantity(data.sqrt(Quantity((1,4), (2,2))), (1,2), (1,1/2))
self.assertQuantity(data.exp(Quantity((1,4), (2,2))), (math.e, math.e**4), (2 * math.e,2*math.e**4))
self.assertQuantity(data.log(Quantity((1,4), (2,2))), (0, math.log(4)), (2,1/2))
def cheb(f,n,e):
if abs(f)<1:
t=math.cos(float(n)*math.acos(float(f)))
elif f>=1:
t=math.cosh(float(n)*math.acosh(float(f)))
elif f<=-1:
t=-1**n*math.cosh(float(n)*math.acosh(float(-f)))
return 1.0/(float(float(1.0+float(e**2)*float(t**2)))**.5)
def testAcosh(self):
self.assertRaises(TypeError, math.acosh)
self.ftest('acosh(1)', math.acosh(1), 0)
self.ftest('acosh(2)', math.acosh(2), 1.3169578969248168)
self.assertRaises(ValueError, math.acosh, 0)
self.assertRaises(ValueError, math.acosh, -1)
self.assertEquals(math.acosh(INF), INF)
self.assertRaises(ValueError, math.acosh, NINF)
self.assert_(math.isnan(math.acosh(NAN)))
def hexagon_sides(a, b, c):
cosha, coshb, coshc = map(math.cosh, [a, b, c])
sinha, sinhb, sinhc = map(math.sinh, [a, b, c])
sides = [a,
math.acosh((cosha * coshb + coshc) / (sinha * sinhb)),
b,
math.acosh((coshb * coshc + cosha) / (sinhb * sinhc)),
c,
math.acosh((coshc * cosha + coshb) / (sinhc * sinha))]
return sides
def __init__(self, S, a, metal, dielec):
'''\n
S: center-to-center conductor separation, [unit: m]\n
a: conductor radius, [unit: m]\n
metal: conductor material, [unit: material]\n
dielec: dielectric material between conductors, [unit: material]'''
self.S = S
self.a = a
self.sigma = cond(metal)
self.eps = dielec_const(dielec)
self.mu = 1*spc.mu_0 # mu_r = 1
self.L = self.mu*math.acosh(S/(2*a))/math.pi
self.C = math.pi*self.eps.real/math.acosh(S/(2*a))
def hyperbolic_fun_cal(inp_val1, opn_type):
oprn_dic = {
'1': 'inverse hyperbolic cosine of x', '2':'inverse hyperbolic sine of x',
'3':' inverse hyperbolic tangent of x', '4':'hyperbolic cosine of x',
'5':'hyperbolic sine of x', '6':'hyperbolic tangent of x'}
if int(opn_type) == 1:
output = math.acosh(float(inp_val1))
return str(output)
if int(opn_type) == 2:
output = math.asinh(float(inp_val1))
return str(output)
if int(opn_type) == 3:
output = math.atanh(float(inp_val1))
return str(output)
if int(opn_type) == 4:
output = math.cosh(float(inp_val1))
return str(output)
if int(opn_type) == 5:
output = math.sinh(float(inp_val1))
return str(output)
if int(opn_type) == 6:
output = math.tanh(float(inp_val1))
return str(output)
else:
return "Invalid Operation"
def cosh_const_effective_mass_errors(self, dt):
jkasv = self.jackknife_average_sub_vev()
jkemass = {}
for cfg in self.configs:
asvc = jkasv[cfg]
emass = {}
asvct = {t: asvc[t+dt] - asvc[t] for t in self.times[:-dt]}
for t in self.times[dt:-(dt+1)]:
if t in self.emass_skip_times:
emass[t] = 0.0
continue
try:
emass[t] = (1.0 / float(dt))*math.acosh((asvc[t+dt] + asvc[t-dt])/(2.0*asvc[t]))
except ValueError:
#logging.debug("invalid argument to log, setting to zero")
emass[t] = 0.0
except ZeroDivisionError:
logging.debug("div by zero, setting to zero")
emass[t] = 0.0
except KeyError:
logging.error("index out of range")
jkemass[cfg] = emass
jkemassobj = configtimeobj.Cfgtimeobj.fromDataDict(jkemass)
effmass_dt = self.cosh_const_effective_mass(dt)
return {t: jackknife.errorbars(effmass_dt[t], jkemassobj.get(time=t))
for t in self.times[dt:-(dt+dt)]}
def cosh_effective_mass_errors(self, dt, fast=True, period=None):
if fast: logging.info("cosh emass computed fast method")
period = self.period
T = self.period_check(period)
jkasv = self.jackknife_average_sub_vev()
jkemass = {}
for cfg in self.configs:
asvc = jkasv[cfg]
emass = {}
for t in self.times[dt:-dt]:
if t in self.emass_skip_times:
emass[t] = 0.0
continue
try:
guess = (1.0 / float(dt))*math.acosh((asvc[t+dt] + asvc[t-dt])/(2.0*asvc[t]))
if fast:
emass[t] = guess
else:
emass[t] = newton.newton_cosh_for_m(t,t+dt,asvc, guess,T)
except ValueError:
#logging.debug("invalid argument to log, setting to zero")
emass[t] = 0.0
except ZeroDivisionError:
logging.debug("div by zero, setting to zero")
emass[t] = 0.0
except KeyError:
logging.error("index out of range")
jkemass[cfg] = emass
jkemassobj = configtimeobj.Cfgtimeobj.fromDataDict(jkemass)
effmass_dt = self.cosh_effective_mass(dt, fast=fast, period=period)
return {t: jackknife.errorbars(effmass_dt[t], jkemassobj.get(time=t))
for t in self.times[dt:-dt]}
def compute(self, plug, data):
# Check if output value is connected
if plug == self.aOutputVaue:
# Get input datas
operationTypeHandle = data.inputValue(self.aOperationType)
operationType = operationTypeHandle.asInt()
inputValueHandle = data.inputValue(self.aInputValue)
inputValue = inputValueHandle.asFloat()
# Math Cosinus
outputValue = 0
if operationType == 0:
outputValue = math.acos(inputValue)
if operationType == 1:
outputValue = math.cos(inputValue)
if operationType == 2:
outputValue = math.acosh(inputValue)
if operationType == 3:
outputValue = math.cosh(inputValue)
# Output Value
output_data = data.outputValue(self.aOutputVaue)
output_data.setFloat(outputValue)
# Clean plug
data.setClean(plug)
def cosh_effective_mass(self, dt, fast=True, period=None):
if fast: logging.info("cosh emass computed fast method")
T = self.period_check(period)
asv = self.average_sub_vev()
emass = {}
for t in self.times[dt:-dt]:
if t in self.emass_skip_times:
emass[t] = 0.0
continue
try:
guess = (1.0 / float(dt))*math.acosh((asv[t+dt] + asv[t-dt])/(2.0*asv[t]))
if fast:
emass[t] = guess
else:
emass[t] = newton.newton_cosh_for_m(t,t+dt,asv, guess, T)
except ValueError:
logging.debug("invalid argument to acosh, setting to zero")
emass[t] = float('NaN')
except KeyError:
logging.error("index out of range")
except ZeroDivisionError:
logging.error("Div by zero either dt:{} or average value sub vev {}".format(dt,asv[t]))
emass[t] = float('NaN')
return emass
def pmuboost3d(jets, met, lep):
mw = 80.385
wwwtlv = ROOT.TLorentzVector(1e-9,1e-9,1e-9,1e-9)
mettlv = ROOT.TLorentzVector(1e-9,1e-9,1e-9,1e-9)
leptlv = ROOT.TLorentzVector(1e-9,1e-9,1e-9,1e-9)
for jet in jets:
# if(e.jetPt[ij] > 30.):
tlvaux = ROOT.TLorentzVector(1e-9,1e-9,1e-9,1e-9)
tlvaux.SetPtEtaPhiM(jet['pt'],0.,jet['phi'],0.)
wwwtlv -= tlvaux
mettlv.SetPtEtaPhiM(met['pt'],0.,met['phi'],0.)
leptlv.SetPtEtaPhiM(lep['pt'],lep['eta'],lep['phi'], 0.)
# leptlv.SetPtEtaPhiM(e.muPt[0],e.muEta[0],e.muPhi[0],0.)
# calculate eta(W) estimate; take the + solution
ptl = (wwwtlv - mettlv).Pt()
ptn = mettlv.Pt()
ptw = wwwtlv.Pt()
etal = leptlv.Eta()
A = (mw*mw + ptw*ptw - ptl*ptl - ptn*ptn) / (2.*ptl*ptn)
if(A<1.): A=1.
etaw = asinh((ptl*sinh(etal)+ptn*sinh(etal+acosh(A)))/ptw)
if(abs(etaw)>10.): etaw=10*etaw/abs(etaw) # avoid too large values
wwwtlv.SetPtEtaPhiM(wwwtlv.Pt(),etaw,wwwtlv.Phi(),mw)
# calulate boosted lepton momentum
beta = wwwtlv.BoostVector()
leptlv.Boost(-beta)
return leptlv.P()
def test_arccosh(self):
import math
from _numpypy import arccosh
for v in [1.0, 1.1, 2]:
assert math.acosh(v) == arccosh(v)
for v in [-1.0, 0, .99]:
assert math.isnan(arccosh(v))
def chebyParam(n, r):
n = float(n)
r = float(r)
iterator = math.cosh(1 / n * math.acosh(r))
a = ((iterator - 1) / 2).real
b = ((iterator + 1) / 2).real
# returns the list of chebyshev's parameters
return [a, b]
def acosh(space, d):
""" acosh - Inverse hyperbolic cosine """
try:
return space.wrap(math.acosh(d))
except OverflowError:
return space.wrap(rfloat.INFINITY)
except ValueError:
return space.wrap(rfloat.NAN)
def startingTime(self):
angle = self.launchAngle
F = math.acosh((self.e+math.cos(angle))/(1.0+self.e*math.cos(angle)))
M = self.e*math.sinh(F)-F
ht = pow(pow(self.apoapsis,3.0)/self.gm, .5) * M
if angle>0:
return -ht
else:
return ht
def testArithmeticFunctionsDeepcopy(self):
data = {'x': 1}
test = Exp('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.exp(data['x']), copied.value)
test = Log('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.log(data['x']), copied.value)
test = Sqrt('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.sqrt(data['x']), copied.value)
data['x'] = -1.
test = Pow('x', 2)
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(data['x']**2, copied.value)
test = Abs('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(abs(data['x']), copied.value)
test = Sign('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(-1., copied.value)
data['x'] = 1.
test = Acos('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.acos(data['x']), copied.value)
test = Asin('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.asin(data['x']), copied.value)
test = Acosh('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.acosh(data['x']), copied.value)
test = Asinh('x')
test.push(data)
copied = copy.deepcopy(test)
self.assertAlmostEqual(math.asinh(data['x']), copied.value)
def operator(self, tok):
""" Return operator handler if tok is a valid operator. Otherwise return False. """
def stacksum(stack):
""" Return sum of all elements on stack (++ operator). """
acc = 0
while stack:
acc += stack.pop()
return acc
def stackproduct(stack):
""" Return product of all elements on stack (** operator). """
acc = 1
while stack:
acc *= stack.pop()
return acc
# Operator handlers.
operators = {
'++': stacksum,
'**': stackproduct,
'+': lambda stack: stack.pop(-2) + stack.pop(),
'-': lambda stack: stack.pop(-2) - stack.pop(),
'*': lambda stack: stack.pop(-2) * stack.pop(),
'/': lambda stack: stack.pop(-2) / stack.pop(),
'%': lambda stack: stack.pop(-2) % stack.pop(),
'^': lambda stack: stack.pop(-2) ** stack.pop(),
'!': lambda stack: math.factorial(stack.pop()),
'ln': lambda stack: math.log(stack.pop()),
'log': lambda stack: math.log(stack.pop(-2), stack.pop()),
'log2': lambda stack: math.log(stack.pop(), 2),
'log10': lambda stack: math.log(stack.pop(), 10),
'rand': lambda stack: random.random(),
'sqrt': lambda stack: math.sqrt(stack.pop()),
'exp': lambda stack: math.exp(stack.pop()),
'sin': lambda stack: math.sin(stack.pop()),
'cos': lambda stack: math.cos(stack.pop()),
'tan': lambda stack: math.tan(stack.pop()),
'sinh': lambda stack: math.sinh(stack.pop()),
'cosh': lambda stack: math.cosh(stack.pop()),
'tanh': lambda stack: math.tanh(stack.pop()),
'arcsin': lambda stack: math.asin(stack.pop()),
'arccos': lambda stack: math.acos(stack.pop()),
'arctan': lambda stack: math.atan(stack.pop()),
'arcsinh': lambda stack: math.asinh(stack.pop()),
'arccosh': lambda stack: math.acosh(stack.pop()),
'arctanh': lambda stack: math.atanh(stack.pop()),
'max': lambda stack: max(stack.pop(), stack.pop()),
'min': lambda stack: min(stack.pop(), stack.pop()),
'abs': lambda stack: abs(stack.pop()),
'ans': lambda stack: self.ans,
}
# Return operation handler if exists, otherwise return False.
if tok in operators:
return operators[tok]
return False
def acosh(a=(DataTypes.Float, 0.0), Result=(DataTypes.Reference, (DataTypes.Bool, False))):
'''
Return the inverse hyperbolic cosine of x
'''
try:
Result(True)
return math.acosh(a)
except:
Result(False)
return -1
def acosh(num):
value = math.acosh(num.value)
string = "\\text{arccosh} " + enclose(num.string)
string_nums = "\\text{arccosh} " + enclose(rstr(num.value))
error = num.error / math.sqrt(num.value**2 - 1)
error_vars = fraction("1", root(square(enclose(num.string)) + " - 1")) \
+ PRODUCT + diff(num.string)
error_nums = fraction("1", root(square(rstr(num.value)) + " - 1")) \
+ PRODUCT + rstr(num.value)
return Number(value, string, error, string_nums, error_vars, error_nums)
def native_disk_distance(r_1, theta_1, r_2, theta_2):
if r_1 == r_2 and theta_1 == theta_2:
return 0
d_phi = pi - abs(pi - abs(theta_1 - theta_2))
if d_phi == 0:
return r_1 + r_2
elif d_phi == pi:
return abs(r_2 - r_1)
else:
return acosh(cosh(r_1)*cosh(r_2) - sinh(r_1)*sinh(r_2)*cos(d_phi))
def ACOSH(value):
"""
Returns the inverse hyperbolic cosine of a number.
>>> ACOSH(1)
0.0
>>> round(ACOSH(10), 7)
2.9932228
"""
return _math.acosh(value)
def poincareDist(a, b):
# print(a,b)
try:
sigma = norm(np.subtract(a, b))**2.0 / ((1 - np.dot(a, a)) *
(1 - np.dot(b, b)))
return math.acosh(1 + 2 * sigma)
except ValueError as e:
print(a)
print(b)
raise (e)
def acosh(column):
i = 0
column = mt.convert_num_col(column)
result = list()
while i < len(column):
if (column[i] >= 1):
result.insert(i + 1, math.acosh(column[i]))
else:
result.insert(i + 1, "Error de dominio")
i += 1
return result
def check_rapidity_allowed(S123,PperpSquared,y): """A function to check if the rapidity is in the allowed region of phase space.""" ##Using equation 49 of Initial-state showering based on colour dipoles connected to incoming parton lines. assert (type(S123) == float) assert (type(PperpSquared) == float) assert (type(y) == float) __yMagMax = math.acosh(math.sqrt(S123/PperpSquared)/2.0) if (abs(y) > __yMagMax): return False else: return True
def st_acosh(x):
"""Inverse hyperbolic cosine function.
Parameters
----------
x : float, int, MissingValue instance, or None
Returns
-------
Inverse hyperbolic cosine when x is non-missing and x >= 1,
MISSING (".") otherwise
"""
if isinstance(x, StataVarVals):
return StataVarVals([
mv if _is_missing(v) or v < 1 else math.acosh(v) for v in x.values
])
if _is_missing(x) or x < 1:
return mv
return math.acosh(x)
def check_rapidity_allowed(S123, PperpSquared, y):
"""A function to check if the rapidity is in the allowed region of phase space."""
##Using equation 49 of Initial-state showering based on colour dipoles connected to incoming parton lines.
assert (type(S123) == float)
assert (type(PperpSquared) == float)
assert (type(y) == float)
__yMagMax = math.acosh(math.sqrt(S123 / PperpSquared) / 2.0)
if (abs(y) > __yMagMax):
return False
else:
return True
def func_arcosh(x):
"""Return arcosh(x) where the answer is in radians"""
# invalid cases
if x < 1:
raise CalcOperationError(
"Inverse hyperbolic cosine is undefined for values less than 1",
"arcosh", [x])
# use the function from the 'math' library
return acosh(x)
def execute(self, table, tree):
super().execute(table, tree)
exp = self.exp.execute(table, tree)
try:
return math.acosh(exp)
except ValueError:
raise (Error(self.line, self.column, ErrorType.SEMANTIC, 'ValueError: Math domain error.'))
except TypeError:
raise (Error(self.line, self.column, ErrorType.SEMANTIC,
'TypeError: must be real number, not ' + str(type(exp))))
except:
raise (Error(self.line, self.column, ErrorType.SEMANTIC, 'ACOSH() function argument error'))
def calcCubeExp(self, A, B, C):
'''Решение кубического уравнения тригонометрической формулой Виета
x^3+Ax^2+Bx+C=0
'''
def sgn(x):
if x > 0:
return 1
if x == 0:
return 0
if x < 0:
return -1
x1 = 'null'
x2 = 'null'
x3 = 'null'
Q = (A * A - 3 * B) / 9
R = (2 * A * A * A - 9 * A * B + 27 * C) / 54
S = Q * Q * Q - R * R
if S > 0:
fi = 1 / 3 * math.acos(R / math.pow(Q, 3 / 2))
x1 = -2 * math.sqrt(Q) * math.cos(fi) - A / 3
if x1 < 0:
x1 = 'null'
x2 = -2 * math.sqrt(Q) * math.cos(fi + 2 / 3 * 3.14) - A / 3
if x2 < 0:
x2 = 'null'
x3 = -2 * math.sqrt(Q) * math.cos(fi - 2 / 3 * 3.14) - A / 3
if x3 < 0:
x3 = 'null'
if S < 0:
if Q > 0:
fi = 1 / 3 * math.acosh(abs(R) / math.pow(Q, 3 / 2))
x1 = -2 * sgn(R) * math.sqrt(Q) * math.cosh(fi) - A / 3
if x1 < 0:
x1 = 'null'
if Q < 0:
sss = abs(R) / math.pow(abs(Q), 3 / 2)
fi = 1 / 3 * math.asinh(sss)
x1 = -2 * sgn(R) * math.sqrt(abs(Q)) * math.sinh(fi) - A / 3
if x1 < 0:
x1 = 'null'
if Q == 0:
x1 = -math.pow(C - A * A * A / 27, 1 / 3) - A / 3
if x1 < 0:
x1 = 'null'
if S == 0:
x1 = -2 * sgn(R) * math.sqrt(Q) - A / 3
if x1 < 0:
x1 = 'null'
x2 = sgn(R) * math.sqrt(Q) - A / 3
if x2 < 0:
x2 = 'null'
return x1, x2, x3
def acosh(input):
"""Coseno inverso hiperbólico
Evalúa el coseno inverso hiperbólico a un número real enviado en input y arroja un resultado en radianes.
Args:
input(Number): valor real
Returns:
Number
"""
return math.acosh(input)
def kubik_uravn(a, b, c, d):
div_a = a
a = b / div_a
b = c / div_a
c = d / div_a
# x^3 + a*x^2 + b*x + c
q = (a**2 - 3 * b) / 9
r = (2 * a**3 - 9 * a * b + 27 * c) / 54
s = q**3 - r**2
if s > 0:
fi = 1 / 3 * math.acos(r / (q**3)**(1 / 2))
x1 = -2 * q**(1 / 2) * math.cos(fi) - a / 3
x2 = -2 * q**(1 / 2) * math.cos(fi + 2 / 3 * math.pi) - a / 3
x3 = -2 * q**(1 / 2) * math.cos(fi - 2 / 3 * math.pi) - a / 3
print('S = %s, Q = %s, R = %s, sgn(R) = %s' % (s, q, r, sgn(r)))
return x1, x2, x3
elif s < 0:
if q > 0:
fi = 1 / 3 * math.acosh(abs(r) / ((q**3)**(1 / 2)))
x1 = -2 * sgn(r) * q**(1 / 2) * math.cosh(fi) - a / 3
x2 = complex(
sgn(r) * q**(1 / 2) * math.cosh(fi) - a / 3,
3**(1 / 2) * q**(1 / 2) * math.sinh(fi))
x3 = complex(
sgn(r) * q**(1 / 2) * math.cosh(fi) - a / 3,
-3**(1 / 2) * q**(1 / 2) * math.sinh(fi))
print('S = %s, Q = %s, R = %s, sgn(R) = %s' % (s, q, r, sgn(r)))
return x1, x2, x3
if q < 0:
fi = math.asinh(abs(r) / (abs(q**3)**(1 / 2)))
x1 = -2 * sgn(r) * abs(q)**(1 / 2) * math.sinh(fi) - a / 3
x2 = complex(
sgn(r) * abs(q)**(1 / 2) * math.sinh(fi) - a / 3,
3**(1 / 2) * abs(q)**(1 / 2) * math.cosh(fi))
x3 = complex(
sgn(r) * abs(q)**(1 / 2) * math.sinh(fi) - a / 3,
-3**(1 / 2) * abs(q)**(1 / 2) * math.cosh(fi))
print('S = %s, Q = %s, R = %s, sgn(R) = %s' % (s, q, r, sgn(r)))
return x1, x2, x3
if q == 0:
x1 = -rootodd(c - a**3 / 27, 3) - a / 3
x2 = complex(-(a + x1) / 2,
1 / 2 * abs((a - 3 * x1) * (a + x1) - 4 * b)**(1 / 2))
x3 = complex(
-(a + x1) / 2,
-1 / 2 * abs((a - 3 * x1) * (a + x1) - 4 * b)**(1 / 2))
print('S = %s, Q = %s, R = %s, sgn(R) = %s' % (s, q, r, sgn(r)))
return x1, x2, x3
elif s == 0:
x1 = -2 * sgn(r) * q**(1 / 2) - a / 3 # -2*rootodd(r, 3) - a/3
x2 = sgn(r) * q**(1 / 2) - a / 3 # rootodd(r, 3) - a/3
print('S = %s, Q = %s, R = %s, sgn(R) = %s' % (s, q, r, sgn(r)))
return x1, x2
def chebyParamOptimized(n, r, k0):
n = float(n)
r = float(r)
iterator = math.cosh(1 / n * math.acosh(r))
a = ((iterator - 1) / 2).real
b = ((iterator + 1) / 2).real
optimized_dist = (2 * math.pi - math.acos((1 - a) / b)) / k0
output = open("valori_a_b.txt", "a")
output.write(" a = " + str(a) + " b = " + str(b) + " N = " + str(n * 2 + 1) + "\n")
output.close()
print("N=" + str(n * 2 + 1) + " d=" + str(optimized_dist))
return [a, b, optimized_dist]
def st_acosh(x):
"""Inverse hyperbolic cosine function.
Parameters
----------
x : float, int, MissingValue instance, or None
Returns
-------
Inverse hyperbolic cosine when x is non-missing and x >= 1,
MISSING (".") otherwise
"""
if isinstance(x, StataVarVals):
return StataVarVals([
mv if _is_missing(v) or v < 1
else math.acosh(v) for v in x.values
])
if _is_missing(x) or x < 1:
return mv
return math.acosh(x)
def chebyParamOptimized(n, r, k0):
n = float(n)
r = float(r)
iterator = math.cosh(1 / n * math.acosh(r))
a = ((iterator - 1) / 2).real
b = ((iterator + 1) / 2).real
optimized_dist = (2 * math.pi - math.acos((1 - a) / b)) / k0
output = open("valori_a_b.txt", 'a')
output.write(" a = " + str(a) + " b = " + str(b) + " N = " +
str(n * 2 + 1) + "\n")
output.close()
print("N=" + str(n * 2 + 1) + " d=" + str(optimized_dist))
return [a, b, optimized_dist]
def acosh(self):
'''
Method to perform element-wise inverse hyperbolic cosine in radians
on the vector.
@return: result vector as a list.
'''
try:
values = [math.acosh(x) for x in self.values]
except:
raise VectorError('Failure in Vector.acosh()')
self.values = values
return self.values
def chebyschev_lowpass(f_pass, f_stop, tol_pass, tol_stop):
"""
Returns the Chebyschev analog lowpass filter function corresponding to
passband and stopband frequencies and tolerances.
Inputs:
- f_pass: The passband boundary frequency in Hz
- f_stop: The stopband boundary frequency in Hz
- tol_pass: The tolerance required in the passband
- tol_stop: The tolerance required in the stopband.
Returns a function that takes a frequency (in Hz) as input and returns
the filter magnitude at that frequency as the output.
"""
if f_pass == f_stop:
raise ValueError(
'The passband and stopband frequencies cannot be equal.')
if tol_pass == 0 or tol_stop == 0:
raise ValueError('Tolerance values cannot be zero.')
D1 = 1 / (1 - tol_pass)**2 - 1
D2 = 1 / tol_stop**2 - 1
# Optimal epsilon to get minimum N possible
# epsilon = math.sqrt(D1)
# Minimum order of the filter that is required
# (Note that D1 is the same as epsilon**2)
N = math.acosh(D2 / D1) / math.acosh(f_stop / f_pass)
N = math.ceil(N)
def filter_func(f):
# Note that D1 is the same as epsilon**2
mag2 = 1 / (1 + D1 * chebyshev_polynomial(f / f_pass, N)**2)
return math.sqrt(mag2)
return filter_func
def __init__(self, a: float, b: float, x_max: float, n: int):
t_max = acosh(x_max / a)
dt = t_max / (n - 1)
cf = cosh(dt)
sf = sinh(dt)
a_b = float(a) / b
sf_a_b = sf * a_b
sf_b_a = sf / a_b
super().__init__(1, 0, lambda x, y: x * cf + y * sf_a_b,
lambda x, y: x * sf_b_a + y * cf, n, False)
def to_prolate_spheroidal(coords):
focus = 0.3525E+02
x = coords[0]
y = coords[1]
z = coords[2]
theta = math.atan(z / y)
mu = math.acos(1 / (2 * focus) * ((y * y + z * z + (x + focus)**2)**0.5 -
(y * y + z * z + (x - focus)**2)**0.5))
lam = math.acosh(1 / (2 * focus) * ((y * y + z * z + (x + focus)**2)**0.5 +
(y * y + z * z + (x - focus)**2)**0.5))
return ([lam, mu, theta])
def hyperbola(a, b, begin, end, n):
th_min = m.acosh(1.0 * begin / a)
th_max = m.acosh(1.0 * end / a)
delta_theta = (th_max - th_min) / (n - 1)
start = [a * m.cosh(th_min), b * m.sinh(th_min)]
points = [start]
cosh = m.cosh(delta_theta)
sinh = m.sinh(delta_theta)
for i in range(1, n):
xi, yi = points[i - 1]
new_point = [
xi * cosh + yi * a / b * sinh, a / b * xi * sinh + yi * cosh
]
points.append(new_point)
points_rev = [[p[0], -p[1]] for p in reversed(points)]
points = points_rev + points
points_rev = [[-p[0], p[1]] for p in reversed(points)]
return np.array(points + points_rev)
def acosh_click(self, event):
try:
self.txt_box1.SetForegroundColour('Default')
self.txt_box1.SetValue(
str(math.acosh(eval(self.txt_box.GetValue()))))
except ZeroDivisionError:
self.txt_box1.SetForegroundColour('red')
self.txt_box1.SetValue(u'除数为0，请重新输入！')
except SyntaxError:
self.txt_box1.SetForegroundColour('red')
self.txt_box1.SetValue(u'输入算式错误，请重新输入！')
except ValueError:
self.txt_box1.SetForegroundColour('red')
self.txt_box1.SetValue(u'输入值错误，不能运算，请重新输入！')
def hyperbolicDistance(vector1, vector2):
if (len(vector1) != num_dimensions):
print("error vector1")
if (len(vector2) != num_dimensions):
print("error vector2")
L2Distance = findL2Distance(vector1, vector2)
vector1norm = findL2Norm(vector1)
vector2norm = findL2Norm(vector2)
inside = 1 + (2 * np.square(L2Distance)) / ((1 - np.square(vector1norm)) *
(1 - np.square(vector2norm)))
return math.acosh(inside)
def resolverFuncionTrigonometrica(self, nodo, resultado):
if nodo.valor.lower() == 'acos':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.acos(a)
resultado.append(c)
elif nodo.valor.lower() == 'asin':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.asin(a)
resultado.append(c)
elif nodo.valor.lower() == 'atan':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.atan(a)
resultado.append(c)
elif nodo.valor.lower() == 'cos':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.cos(a)
resultado.append(c)
elif nodo.valor.lower() == 'sin':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.sin(a)
resultado.append(c)
elif nodo.valor.lower() == 'tan':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.tan(a)
resultado.append(c)
elif nodo.valor.lower() == 'sinh':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.sinh(a)
resultado.append(c)
elif nodo.valor.lower() == 'cosh':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.cosh(a)
resultado.append(c)
elif nodo.valor.lower() == 'tanh':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.tanh(a)
resultado.append(c)
elif nodo.valor.lower() == 'asinh':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.asinh(a)
resultado.append(c)
elif nodo.valor.lower() == 'acosh':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
c = math.acosh(a)
resultado.append(c)
elif nodo.valor.lower() == 'atanh':
a = self.expresion_aritmetica(nodo.hijos[0], [], [], [])
print(a)
c = math.atanh(a)
resultado.append(c)
def calc_order(Apmax, Asmin, wp, ws):
"""
Parameters:
Apmax - maximum passand loss
Asmin - minimum stopband loss
wp - passband frequency
ws - stopband frequency
----
output: order of the required chebyshev filter
"""
n = 0
A = calc_epsilon(Asmin) / calc_epsilon(Apmax)
omega = ws/wp
if omega > 1:
n = math.acosh(A)/math.acosh(omega)
else:
n = math.acos(A)/math.acos(omega)
n = math.ceil(n)
return n
def acosh(column):
i = 0
column = mt.convert_num_col(column)
result = list()
while i < len(column):
if isinstance(column[i], int) or isinstance(column[i], float):
if column[i] >= 1:
result.append(math.acosh(column[i]))
else:
result.append("Error de dominio")
list_errors_tg.append("Error: 22003: la entrada esta fuera del dominio")
else:
result.append(column[i])
i += 1
return result
def compute_eff_energies(self, eff_en_type):
for k in range(self.corrs.shape[0]):
self.eff_energies.append( [] )
for j in range(self.corrs.shape[1]-1):
#for j in range(self.corrs.shape[1]/2):
if eff_en_type=="exp":
self.eff_energies[len(self.eff_energies)-1].append( log( self.corrs[k,j]/self.corrs[k,j+1] ) )
elif eff_en_type=="3pt":
self.eff_energies[len(self.eff_energies)-1].append( acosh( (self.corrs[k,j+1]+self.corrs[k,j-1]) / (2*self.corrs[k,j]) ) )
else:
raise Exception("Effective energy type not available.")
self.eff_energies = np.array(self.eff_energies)
def getHyperbolicDistance(sourceNode, destNode):
"""
Return hyperbolic or geohyperbolic distance between two nodes. The distance is computed
on the basis of following algorithm/mathematics
ref: https://en.wikipedia.org/wiki/Hyperbolic_geometry
"""
r1 = [key for key in sourceNode][0]
r2 = [key for key in destNode][0]
zeta = 1.0
dtheta = calculateAngularDistance(sourceNode[r1], destNode[r2])
hyperbolicDistance = (1. / zeta) * acosh(cosh(zeta * r1) * cosh(zeta * r2) - \
sinh(zeta * r1) * sinh(zeta * r2) * cos(dtheta))
debug("Distance from {} to {} is {}".format(sourceNode, destNode,
hyperbolicDistance))
return hyperbolicDistance
def S_isothermal_pipe_eccentric_to_isothermal_pipe(D1, D2, Z, L=1.):
r'''Returns the Shape factor `S` of a pipe of constant outer temperature
and of outer diameter `D1` which is `Z` distance from the center of another
pipe of outer diameter`D2`. Length `L` must be provided, but can be set to
1 to obtain a dimensionless shape factor used in some sources.
.. math::
S = \frac{2\pi L}{\cosh^{-1}
\left(\frac{D_2^2 + D_1^2 - 4Z^2}{2D_1D_2}\right)}
Parameters
----------
D1 : float
Diameter of inner pipe, [m]
D2 : float
Diameter of outer pipe, [m]
Z : float
Distance from the middle of inner pipe to the center of the other, [m]
L : float, optional
Length of the pipe, [m]
Returns
-------
S : float
Shape factor [m]
Examples
--------
>>> S_isothermal_pipe_eccentric_to_isothermal_pipe(.1, .4, .05, 10)
47.709841915608976
Notes
-----
L should be much larger than both diameters. D2 should be larger than D1.
.. math::
Q = Sk(T_1 - T_2) \\ R_{\text{shape}}=\frac{1}{Sk}
References
----------
.. [1] Kreith, Frank, Raj Manglik, and Mark Bohn. Principles of Heat
Transfer. Cengage, 2010.
.. [2] Bergman, Theodore L., Adrienne S. Lavine, Frank P. Incropera, and
David P. DeWitt. Introduction to Heat Transfer. 6E. Hoboken, NJ:
Wiley, 2011.
'''
S = 2*pi*L/acosh((D2**2 + D1**2 - 4*Z**2)/(2*D1*D2))
return S
def S_isothermal_pipe_eccentric_to_isothermal_pipe(D1, D2, Z, L=1.):
r'''Returns the Shape factor `S` of a pipe of constant outer temperature
and of outer diameter `D1` which is `Z` distance from the center of another
pipe of outer diameter`D2`. Length `L` must be provided, but can be set to
1 to obtain a dimensionless shape factor used in some sources.
.. math::
S = \frac{2\pi L}{\cosh^{-1}
\left(\frac{D_2^2 + D_1^2 - 4Z^2}{2D_1D_2}\right)}
Parameters
----------
D1 : float
Diameter of inner pipe, [m]
D2 : float
Diameter of outer pipe, [m]
Z : float
Distance from the middle of inner pipe to the center of the other, [m]
L : float, optional
Length of the pipe, [m]
Returns
-------
S : float
Shape factor [m]
Examples
--------
>>> S_isothermal_pipe_eccentric_to_isothermal_pipe(.1, .4, .05, 10)
47.709841915608976
Notes
-----
L should be much larger than both diameters. D2 should be larger than D1.
.. math::
Q = Sk(T_1 - T_2) \\ R_{\text{shape}}=\frac{1}{Sk}
References
----------
.. [1] Kreith, Frank, Raj Manglik, and Mark Bohn. Principles of Heat
Transfer. Cengage, 2010.
.. [2] Bergman, Theodore L., Adrienne S. Lavine, Frank P. Incropera, and
David P. DeWitt. Introduction to Heat Transfer. 6E. Hoboken, NJ:
Wiley, 2011.
'''
S = 2*pi*L/acosh((D2**2 + D1**2 - 4*Z**2)/(2*D1*D2))
return S
def computeTOF(self):
mu = self.myUniverse.mu
r = (self.periapseState[0]**2 + self.periapseState[1]**2 +
self.periapseState[2]**2)**0.5
rSOI = self.myUniverse.r_SOI
SMA = -mu / self.vMAG / self.vMAG
ECC = 1 - r / SMA
H = acosh(1.0 / ECC * (rSOI / -SMA + 1.0))
N = ECC * sinh(H) - H
self.TOF = N / sqrt(mu / -(SMA * SMA * SMA))
self.DeltaTA = 2.0 * atan(
((ECC + 1.0) / (ECC - 1.0))**0.5 * tanh(0.5 * H))
DeltaTA2 = acos(1.0 / ECC * (SMA * (1 - ECC**2) / rSOI - 1.0))
def testAcoshFunction(self):
ma5 = MovingAverage(5, 'close')
holder = Acosh(ma5)
sampleClose = np.cosh(self.sampleClose)
for i, close in enumerate(sampleClose):
data = {'close': close}
ma5.push(data)
holder.push(data)
expected = math.acosh(ma5.result())
calculated = holder.result()
self.assertAlmostEqual(calculated, expected, 12, "at index {0:d}\n"
"expected: {1:f}\n"
"calculated: {2:f}".format(i, expected, calculated))
def acosh(x):
"""
Return the inverse hyperbolic cosine of x.
"""
if isinstance(x,ADF):
ad_funcs = list(map(to_auto_diff,[x]))
x = ad_funcs[0].x
########################################
# Nominal value of the constructed ADF:
f = acosh(x)
########################################
variables = ad_funcs[0]._get_variables(ad_funcs)
if not variables or isinstance(f, bool):
return f
########################################
# Calculation of the derivatives with respect to the arguments
# of f (ad_funcs):
lc_wrt_args = [1/sqrt(x**2 - 1)]
qc_wrt_args = [-x/(x**2 - 1)**1.5]
cp_wrt_args = 0.0
########################################
# Calculation of the derivative of f with respect to all the
# variables (Variable) involved.
lc_wrt_vars,qc_wrt_vars,cp_wrt_vars = _apply_chain_rule(
ad_funcs,variables,lc_wrt_args,qc_wrt_args,
cp_wrt_args)
# The function now returns an ADF object:
return ADF(f, lc_wrt_vars, qc_wrt_vars, cp_wrt_vars)
else:
# try: # pythonic: fails gracefully when x is not an array-like object
# return [acosh(xi) for xi in x]
# except TypeError:
if x.imag:
return cmath.acosh(x)
else:
return math.acosh(x.real)
def acosh(*args):
if args[0].__class__.__name__ == "vmath":
self = args[0]
else:
self = vmath()
arg = vmath.self_remove(args)
if self.arg_type != None:
arg[0] = self.arg_type(arg[0])
arg_name = arg[0].__class__.__name__
pn_class_much = [x for x in vmath.pn_class if x == arg_name]
if len(pn_class_much) == 1:
answer = arg[0].math.acosh(arg[0])
else:
answer = math.acosh(arg[0])
if self.return_type != None:
answer = self.return_type(answer)
return answer
def S_isothermal_pipe_to_plane(D, Z, L=1):
r'''Returns the Shape factor `S` of a pipe of constant outer temperature
and of outer diameter `D` which is `Z` distance from an infinite plane.
Length `L` must be provided, but can be set to 1 to obtain a dimensionless
shape factor used in some sources.
.. math::
S = \frac{2\pi L}{\cosh^{-1}(2z/D)}
Parameters
----------
D : float
Diameter of the pipe, [m]
Z : float
Distance from the middle of the pipe to the infinite plane, [m]
L : float, optional
Length of the pipe, [m]
Returns
-------
S : float
Shape factor [m]
Examples
--------
>>> S_isothermal_pipe_to_plane(1, 100, 3)
3.146071454894645
Notes
-----
L should be much larger than D.
.. math::
Q = Sk(T_1 - T_2) \\ R_{\text{shape}}=\frac{1}{Sk}
References
----------
.. [1] Kreith, Frank, Raj Manglik, and Mark Bohn. Principles of Heat
Transfer. Cengage, 2010.
.. [2] Bergman, Theodore L., Adrienne S. Lavine, Frank P. Incropera, and
David P. DeWitt. Introduction to Heat Transfer. 6E. Hoboken, NJ:
Wiley, 2011.
'''
S = 2*pi*L/acosh(2*Z/D)
return S
def add_fit_info(filename, ax=None):
if not ax:
ax = plt
funmap = {"two_exp": two_exp, "single_exp": single_exp, "periodic_two_exp": periodic_two_exp,
"fwd-back-exp": periodic_exp, "periodic_two_exp_const": periodic_two_exp_const, "fwd-back-exp_const": periodic_exp_const}
try:
fittype, function, tmin, tmax, fitparams, fiterrors, Nt = get_fit(filename)
fun = funmap[function](Nt)
massindex = fun.parameter_names.index("mass")
mass = fitparams[massindex]
masserror = fiterrors[massindex]
if fittype == "#fit":
logging.info("correlator fit info")
xpoints = np.arange(tmin, tmax, 0.3)
fitpoints = fun.formula(fitparams, xpoints)
ax.plot(xpoints, fitpoints, ls="dashed", color="r", lw=2, zorder=5)
if args.fitfunction:
return fun.template.format(*fitparams)
if fittype == "#fit_emass":
xpoints = np.arange(tmin, tmax+1, 1.0)
fitpoints = fun.formula(fitparams, xpoints)
emassfit = []
dt = 3
for i in range(len(fitpoints))[:-dt]:
try:
#emass = (1.0 / float(dt)) * np.log(fitpoints[i] / fitpoints[i + dt])
emass = (1.0 / float(dt)) * math.acosh((fitpoints[i+dt] + fitpoints[i-dt])/(2.0*fitpoints[i]))
emassfit.append(emass)
except:
emassfit.append(np.nan)
if args.fit_errors:
ax.plot(xpoints[:-dt], np.full_like(xpoints[:-dt],mass+masserror), ls="--", color="k", lw=2, zorder=50)
ax.plot(xpoints[:-dt], np.full_like(xpoints[:-dt],mass-masserror), ls="--", color="k", lw=2, zorder=50)
else:
ax.plot(xpoints[:-dt], emassfit, ls="dashed", color="r", lw=2, zorder=5)
if masserror == 0:
return "{}".format(mass)
digits = -1.0*round(math.log10(masserror))
formated_error = int(round(masserror * (10**(digits + 1))))
formated_mass = "{m:.{d}f}".format(d=int(digits) + 1, m=mass)
#return "{m}({e})".format(m=formated_mass, e=formated_error)
return print_paren_error(mass, masserror)
except RuntimeError:
logging.error("File {} had no fit into".format(filename))
def cosh_const_effective_mass(self, dt):
asv = self.average_sub_vev()
asvt = {t: asv[t+dt]-asv[t] for t in self.times[:-dt] }
emass = {}
for t in self.times[dt:-(dt+dt)]:
if t in self.emass_skip_times:
emass[t] = 0.0
continue
try:
emass[t] = (1.0 / float(dt))*math.acosh((asvt[t+dt] + asvt[t-dt])/(2.0*asvt[t]))
except ValueError:
logging.debug("invalid argument to acosh, setting to nan")
emass[t] = float('NaN')
except KeyError:
logging.error("index out of range")
except ZeroDivisionError:
logging.error("Div by zero either dt:{} or average value sub vev {}".format(dt,asv[t]))
emass[t] = 0.0
return emass
def taylor_win(n, sll):
"""
http://www.dsprelated.com/showcode/6.php
from http://mathforum.org/kb/message.jspa?messageID=925929:
A Taylor window is very similar to Chebychev weights. While Chebychev
weights provide the tighest beamwidth for a given side-lobe level, taylor
weights provide the least taper loss for a given sidelobe level.
'Antenna Theory: Analysis and Design' by Constantine Balanis, 2nd
edition, 1997, pp. 358-368, or 'Modern Antenna Design' by Thomas
Milligan, 1985, pp.141-148.
"""
def calculate_fm(m, sp2, a, nbar):
n = np.arange(1, nbar)
p = np.hstack([np.arange(1, m), np.arange(m + 1, nbar)])
num = np.prod((1 - (m**2 / sp2) / (a**2 + (n - 0.5)**2)))
den = np.prod(1 - m**2 / p**2)
return ((-1)**(m + 1) * num) / (2 * den)
nbar = int(np.ceil(2.0 * (acosh(10**(-sll / 20.0)) / pi)**2 + 0.5))
n *= 2
a = np.arccosh(10**(-sll / 20)) / pi
sp2 = nbar**2 / (a**2 + (nbar - 0.5)**2)
w = np.ones(n)
fm = np.zeros(nbar)
summation = 0
k = np.arange(n)
xi = (k - 0.5 * n + 0.5) / n
for m in range(1, nbar):
fm[m] = calculate_fm(m, sp2, a, nbar)
summation += fm[m] * np.cos(2 * pi * m * xi)
w += w * summation
w /= w.max()
w = w[n//2:]
return w
def testACosh(self):
vectorA = m.Vector([1, 2, 3, 4])
result = [math.acosh(x) for x in vectorA.values]
self.assertEqual(vectorA.acosh(), result)
def nud(self):
return math.acosh(self.expression())
