def long_interval(w, n, t):
"""
Return the interval of integration for longitudinal impedance, which
is of the form [0, root, inf], where root satisfies
d_l(root, w, n, t) = 0.
"""
if w < mp.sqrt(1+n):
return [0, mp.inf]
if n == 0:
guesses = [4, 6, 8, 10]
else:
guesses = [4, 6, 8, 10, 12, 14]
int_range = [0, mp.inf]
for guess in guesses:
try:
root = mp.findroot(lambda z: BiMax.d_l(z, w, n, t), guess)
except ValueError:
continue
unique = True
for z in int_range:
if mp.fabs(z - mp.fabs(root)) < 1e-4:
unique = False
if unique:
int_range += [mp.fabs(root)]
int_range = np.sort(int_range)
return int_range
def int_interval(wrel, n, t, k):
"""
find out the almost-singular point and
divide the integration integrals.
"""
if wrel < 1 or wrel > 1.3:
return [0, mp.inf]
else:
guesses = [4, 6, 8, 10, 12, 14]
wc = wrel * mp.sqrt(1+n)
int_range = [0, mp.inf]
for guess in guesses:
try:
root = mp.findroot(lambda zc: MaxKappa.e_l(zc, wc, n, t, k), guess)
except ValueError:
continue
unique = True
for z in int_range:
if mp.fabs(z - mp.fabs(root)) < 1e-4:
unique = False
if unique:
int_range += [mp.fabs(root)]
int_range = np.sort(int_range)
return int_range
def gamma_shot(self, wrel, l, n, t, tc):
"""
Calculate
1, transfer gain
2, electron shot noise
"""
if wrel > 1.0 and wrel < 1.2:
mp.mp.dps = 80
else:
mp.mp.dps = 40
wc = wrel * mp.sqrt(1+n)
za = self.za_l(wc, l, n, t, tc)
mp.mp.dps = 15
zr = self.zr_mp(wc, l, tc)
# below calculating shot noise:
ldc = self.ant_len/l
nc =permittivity * boltzmann * tc/ ldc**2 / echarge**2
vtc = np.sqrt(2 * boltzmann * tc/ emass)
ne = nc * vtc * (1 + n * mp.sqrt(t)) * 2 * np.pi * self.ant_rad * self.ant_len / np.sqrt(4 * np.pi)
###################
## a: coefficient in shot noise. see Issautier et al. 1999
###################
a = 1 + echarge * 3.6 / boltzmann/tc
shot_noise = 2 * a * echarge**2 * mp.fabs(za)**2 * ne
return [mp.fabs((zr+za)/zr)**2, shot_noise]
def gamma_shot(self, wrel, l, n, t, k, tc):
"""
Calculate
1, transfer gain
2, electron shot noise
"""
if wrel > 1.0 and wrel < 1.1:
mp.mp.dps = 40
else:
mp.mp.dps = 20
wc = wrel * mp.sqrt(1+n)
za_val = self.za(wrel, l, n, t, k, tc)
mp.mp.dps = 15
zr_val = self.zr(wc, l, tc)
# below calculating shot noise:
ldc = self.ant_len/l
nc =permittivity * boltzmann * tc/ ldc**2 / echarge**2
vtc = np.sqrt(2 * boltzmann * tc/ emass)
ne = nc * vtc * (1 + n * mp.sqrt(t)) * 2 * np.pi * self.ant_rad * self.ant_len / np.sqrt(4 * np.pi)
###################################
## a: coefficient in shot noise. ##
###################################
scpot= 4
a = 1 + echarge * scpot / boltzmann/tc
shot_noise = 2 * a * echarge**2 * mp.fabs(za_val)**2 * ne
return [mp.fabs((zr_val+za_val)/zr_val)**2, shot_noise]
def mpf_assert_allclose(res, std, atol=0, rtol=1e-17):
try:
len(res)
except TypeError:
res = list(res)
n = len(std)
if len(res) != n:
raise AssertionError("Lengths of inputs not equal.")
failures = []
for k in range(n):
try:
assert_(mp.fabs(res[k] - std[k]) <= atol + rtol*mp.fabs(std[k]))
except AssertionError:
failures.append(k)
ndigits = int(abs(np.log10(rtol)))
msg = [""]
msg.append("Bad results ({} out of {}) for the following points:"
.format(len(failures), n))
for k in failures:
resrep = mp.nstr(res[k], ndigits, min_fixed=0, max_fixed=0)
stdrep = mp.nstr(std[k], ndigits, min_fixed=0, max_fixed=0)
if std[k] == 0:
rdiff = "inf"
else:
rdiff = mp.fabs((res[k] - std[k])/std[k])
rdiff = mp.nstr(rdiff, 3)
msg.append("{}: {} != {} (rdiff {})".format(k, resrep, stdrep, rdiff))
if failures:
assert_(False, "\n".join(msg))
def __dip__(self, p: int, kmax: int, f: LambdaType, x: list,
eps: float) -> tuple:
"""
Calculates the df(s) = (s-1)-th derivative of f at O , for s = 1 .. p+1. Uses k-th degree polynomial for some k
satisfying p <= k <= kmax. If the relative change in df(s) from k - 1 to k is less than eps then this determines
k and success is true. Otherwise k = kmax and success is false.
:param p: maximum derivative to calculate; p <= length(x) <= kmax
:param kmax: maximum degree of the interpolating polynomial
:param f: function to derive
:param x: array of values around 0
:param eps: desired tolerance
:return: array of derivatives at 0
"""
self.C = mp.zeros(1, int(kmax * (kmax + 3) / 2) + 1)
df = mp.zeros(1, p + 1)
for k in range(0, kmax + 1):
self.__update__(int(k), int(p), x, f(x[int(k)]))
if k < p:
continue
self.r = mp.mpf(1)
for s in range(0, p + 1):
if self.r:
self.r = mp.mpf(
mp.fabs(self.C[k - s] - df[s]) <= eps *
mp.fabs(self.C[k - s]))
df[s] = self.C[k - s]
if self.r:
break
for s in range(1, p + 1):
df[s] = mp.factorial(s) * df[s]
return df
def find_cutoff(self, thresh):
# Find a cutoff to do finite integration
# Evaluate the modulus of the characteristic function
domain = np.linspace(0, 5, 500)
char_fn = list(map(lambda t: mp.fabs(self.char_fn(t)), domain))
# thresh 1e-40
thresh = 1e-30
thresh_check = [
domain[i] for i in range(500) if char_fn[i] < mp.mpf(thresh)
]
# Need to extend the domain
if len(list(thresh_check)) == 0:
j = 1
while len(list(thresh_check)) == 0:
domain = np.linspace(5 * j, 5 * (j + 1), 500)
char_fn = list(map(lambda t: mp.fabs(self.char_fn(t)), domain))
# thresh 1e-40
thresh_check = [
domain[i] for i in range(500)
if char_fn[i] < mp.mpf(thresh)
]
j += 1
cutoff = thresh_check[0]
return cutoff
def newton(f, f_x, x0, erro, max_iter, valor=mpf(0.0)):
u'''
Implementa:
Parâmetros:
- f: function(x) =>
- f_x: function(x) =>
- x0: num => chute inicial
- erro: num => valor de erro no qual o método vai parar
- max_iter: num => máximo de iterações
- valor: num => valor que o método espera a função alcançar
Retorna (valor, numero_iter), onde:
- valor: valor obtido pelo método
- numero_iter: número de iterações necessárias
'''
x0, erro, valor = mpf(x0), fabs(erro), mpf(valor)
for i in range(max_iter):
try:
x1 = x0 - (f(x0)-valor) / f_x(x0)
except TypeError:
x1 = x0 + 300.0
#raise ValueError('O metodo teve uma aberracao')
erro_iter = fabs(x1 - x0)
x0 = x1
if erro_iter < erro:
break
else:
raise ValueError('O metodo nao convergiu, o ultimo foi: %f' % x0)
return (x0, i)
def update_vardict(vardict):
# expand vardict into component mapping
vardict = expand_vardict(vardict)
# extract every free symbol present in vardict
symdict = {
symbol: None
for var in vardict if isinstance(vardict[var], sp.Basic)
for symbol in vardict[var].free_symbols
}
for free_symbol in symdict:
# seed random number generator with free_symbol hash value
random.seed(hash(free_symbol))
# update symdict with mapping: free_symbol -> unique random number
symdict[free_symbol] = mpf(random.random())
for var in vardict:
# apply CSE to every expression in vardict
replaced, reduced = sp.cse(vardict[var], order='none')
# calculate value after substituting the unique random number
# from each free symbol in symdict into every expression in vardict
value = compute_value(symdict, replaced, reduced, factor=1)
# double the precision (factor = 2) whenever value within range of zero
if fabs(value) != mpf('0.0') and fabs(value) < 10**(
(-2.0 / 3) * precision):
_value = compute_value(symdict, replaced, reduced, factor=2)
if fabs(_value) < 10**(-(4.0 / 3) * precision):
value = mpf('0.0')
# update vardict with mapping: variable -> (pseudo) unique number
vardict[var] = value
return vardict
def bissecao(func, a, b, erro, valor=mpf(0.0)):
u'''
Implementa o método da bisseção para encontrar o ponto onde a
função encontra o valor desejado.
Retorna (valor, numero_iter)
'''
f = lambda x: func(x) - valor
a, b = mpf(a), mpf(b)
erro = fabs(erro)
i = mpf(0)
while True:
i += 1
e = fabs(a-b) / 2
x = (a+b) / 2
if e <= erro:
return (x, i)
if f(a) * f(x) > 0:
a, b = x, b
elif f(x) * f(b) > 0:
a, b = a, x
else:
raise ValueError('Nao ha variacao de sinal entre os dois.')
def mp_assert_allclose(res, std, atol=0, rtol=1e-17):
"""
Compare lists of mpmath.mpf's or mpmath.mpc's directly so that it
can be done to higher precision than double.
"""
failures = []
for k, (resval, stdval) in enumerate(zip_longest(res, std)):
if resval is None or stdval is None:
raise ValueError('Lengths of inputs res and std are not equal.')
if mpmath.fabs(resval - stdval) > atol + rtol * mpmath.fabs(stdval):
failures.append((k, resval, stdval))
nfail = len(failures)
if nfail > 0:
ndigits = int(abs(np.log10(rtol)))
msg = [""]
msg.append(
"Bad results ({} out of {}) for the following points:".format(
nfail, k + 1))
for k, resval, stdval in failures:
resrep = mpmath.nstr(resval, ndigits, min_fixed=0, max_fixed=0)
stdrep = mpmath.nstr(stdval, ndigits, min_fixed=0, max_fixed=0)
if stdval == 0:
rdiff = "inf"
else:
rdiff = mpmath.fabs((resval - stdval) / stdval)
rdiff = mpmath.nstr(rdiff, 3)
msg.append("{}: {} != {} (rdiff {})".format(
k, resrep, stdrep, rdiff))
assert_(False, "\n".join(msg))
def compose_result_string(self, mpf_x, mpf_m, n, mpf_value, output_dps):
return ('{' + mp.nstr(mpf_x, output_dps * 2) + ',{' +
mp.nstr(mpf_m.real, output_dps * 2) + ',' +
mp.nstr(mpf_m.imag, output_dps * 2) + '},' + str(n) + ',{' +
mp.nstr(mpf_value.real, output_dps) + ',' +
mp.nstr(mpf_value.imag, output_dps) + '},' +
mp.nstr(mp.fabs(mpf_value.real * 10**-output_dps), 2) + ',' +
mp.nstr(mp.fabs(mpf_value.imag * 10**-output_dps), 2) + '},')
def _mp_round(n):
ceil = mp.ceil(n)
floor = mp.floor(n)
ceil_diff = mp.fabs(n - ceil)
floor_diff = mp.fabs(n - floor)
if ceil_diff <= floor_diff:
return ceil
else:
return floor
def cdf_segment(alpha_1, beta_1, alpha_2, beta_2, n=200):
'''The segment of cdf of Gamma_1 - Gamma_2
Compute the z variables of n(=200) segments of cdf.
For example, cdf = 1/n, 2/n, 3/n
Args:
alpha_1: An int. Alpha of Gamma1.
beta_1: An int. Beta of Gamma1.
alpha_2: An int. Alpha of Gamma2.
beta_2: An int. Beta of Gamma2.
n: An int. The number of segments.
Returns:
A list of z. The z corresponding to the segmented cumulative probability of Gamma1- Gamma2.
'''
center = float(alpha_1 ) / float(beta_1) - float(alpha_2) / float(beta_2)
standard_err = (float(alpha_1 ) / beta_1 ** 2 + float(alpha_2 ) / beta_2 ** 2 ) ** 0.5
x = center - 300 * standard_err
cdf_n = []
cdf = 0
pdf_1 = pdf_of_gamma_difference(x, alpha_1, beta_1, alpha_2, beta_2)
pdf_2 = pdf_1
delta = standard_err / 30.
epsilon = standard_err / 10000.
while pdf_1 * delta < 1e-10:
x += delta
pdf_2 = pdf_of_gamma_difference(x, alpha_1, beta_1, alpha_2, beta_2)
pdf_1 = pdf_2
if math.fabs(pdf_2 + pdf_1) * delta < 1e-8:
pass
elif math.fabs(pdf_2 - pdf_1) / math.fabs(pdf_2 + pdf_1) < 0.2:
delta = delta * 2
elif math.fabs(pdf_2 - pdf_1) / math.fabs(pdf_2 + pdf_1) > 0.5:
delta = delta / 2.
if delta < epsilon:
print 'Error'
return None
for i in range(1,n):
while cdf < float(i) / n:
pdf_1 = pdf_2
pdf_2 = pdf_of_gamma_difference(x + delta, alpha_1, beta_1, alpha_2, beta_2)
cdf += (pdf_1 + pdf_2) / 2 * delta
x += delta
if delta < epsilon:
print 'Error'
return None
if mpmath.fabs(pdf_2 + pdf_1) * delta < 1e-8:
pass
elif mpmath.fabs(pdf_2 - pdf_1) / mpmath.fabs(pdf_2 + pdf_1) < 0.001:
delta = delta * 2
elif mpmath.fabs(pdf_2 - pdf_1) / mpmath.fabs(pdf_2 + pdf_1) > 0.003:
delta = delta / 2.
cdf_n.append(float(x - delta * (cdf - float(i) / n) / ((pdf_1 + pdf_2) / 2 * delta)))
return cdf_n
def inverse_cdf(y, alpha_1, beta_1, alpha_2, beta_2):
'''The inverse function of cdf of Gamma_1 - Gamma_2
Given a cumulative probability y, compute z.
Args:
z: A float. x1 - x2.
alpha_1: An int. Alpha of Gamma1.
beta_1: An int. Beta of Gamma1.
alpha_2: An int. Alpha of Gamma2.
beta_2: An int. Beta of Gamma2.
Returns:
The z corresponding to the cumulative probability y of Gamma1- Gamma2.
'''
center = float(alpha_1 ) / float(beta_1) - float(alpha_2) / float(beta_2)
standard_err = (float(alpha_1 ) / beta_1 ** 2 + float(alpha_2 ) / beta_2 ** 2 ) ** 0.5
x = center - 300 * standard_err
cdf = 0
pdf_1 = pdf_of_gamma_difference(x, alpha_1, beta_1, alpha_2, beta_2)
pdf_2 = pdf_1
delta = standard_err / 30.
epsilon = standard_err / 10000.
if y < 1e-8 or y >= 1 - 1e-8:
return numpy.inf
while pdf_1 * delta < 1e-10:
x += delta
pdf_2 = pdf_of_gamma_difference(x, alpha_1, beta_1, alpha_2, beta_2)
pdf_1 = pdf_2
if math.fabs(pdf_2 + pdf_1) < 1e-8:
pass
elif math.fabs(pdf_2 - pdf_1) / math.fabs(pdf_2 + pdf_1) < 0.2:
delta = delta * 2
elif math.fabs(pdf_2 - pdf_1) / math.fabs(pdf_2 + pdf_1) > 0.5:
delta = delta / 2.
if delta < epsilon:
print 'Error'
return None
while cdf < y:
pdf_1 = pdf_2
pdf_2 = pdf_of_gamma_difference(x + delta, alpha_1, beta_1, alpha_2, beta_2)
cdf += (pdf_1 + pdf_2) / 2 * delta
x += delta
if delta < epsilon:
print 'Error'
return None
if mpmath.fabs(pdf_2 + pdf_1) * delta < 1e-8:
pass
elif mpmath.fabs(pdf_2 - pdf_1) / mpmath.fabs(pdf_2 + pdf_1) < 0.001:
delta = delta * 2.
elif mpmath.fabs(pdf_2 - pdf_1) / mpmath.fabs(pdf_2 + pdf_1) > 0.003:
delta = delta / 2.
x = x - delta * (cdf - y) / ((pdf_1 + pdf_2) / 2 * delta)
return x
def calc_hyper_volume(paretos, ref_point):
sum = 0
for idx, item in enumerate(paretos):
if idx == len(paretos) - 1:
sum += mpmath.fabs(paretos[idx][0] -
ref_point[0]) * mpmath.fabs(paretos[idx][1] -
ref_point[1])
else:
sum += mpmath.fabs(paretos[idx][0] - paretos[idx + 1][0]
) * mpmath.fabs(paretos[idx][0] - ref_point[1])
return sum
def getPrimeFactors( n, verbose = False ):
cutoff = primes[ -1 ] * primes[ -1 ]
smallFactors = [ ]
largeFactors = [ ]
qPrimes = [ ]
if verbose:
print( "\nFactoring", n, '...' )
remaining, smallFactors = getSmallFactors( n, verbose )
if remaining > 1 and g.factorCache is not None:
if remaining in g.factorCache:
if verbose:
print( 'cache hit:', remaining )
largeFactors.extend( g.factorCache[ remaining ] )
remaining = 1
while remaining > 1:
exponent = 0
P = getLargeFactors( remaining, verbose )
while True:
Z = remaining % P
if Z != 0:
break
exponent += 1
remaining //= P
if verbose:
if P < cutoff:
print( P, 'is a prime factor of', n )
if fabs( P ) > fabs( cutoff ):
if verbose:
print( P, 'is a q-prime factor of', n )
qPrimes.append( ( P, 1 ) )
largeFactors.append( ( P, exponent ) )
if verbose:
print( ' exponent:', exponent )
print( '--' )
if verbose:
print( 'factorization into primes and q-primes completed for', n )
return smallFactors, largeFactors, qPrimes
def eigen(M,dic,order):
M1 = M.xreplace(dic)
with mp.workdps(int(mp.mp.dps*2)):
M1 = M1.evalf(mp.mp.dps)
det =(M1).det(method = 'berkowitz')
detp = Poly(det,kz)
co = detp.all_coeffs()
co = [mp.mpc(str(re(k)),str(im(k))) for k in co]
maxsteps = 3000
extraprec = 500
ok =0
while ok == 0:
try:
sol,err = mp.polyroots(co,maxsteps =maxsteps,extraprec = extraprec,error =True)
sol = np.array(sol)
print("Error on polyroots =", err)
ok=1
except:
maxsteps = int(maxsteps*2)
extraprec = int(extraprec*1.5)
print("Poly roots fail precision increased: ",maxsteps,extraprec)
te = np.array([mp.fabs(m) < mp.mpf(10**mp.mp.dps) for m in sol])
solr = sol[te]
if Bound_nb == 1:
solr = solr[[mp.im(m) < 0 for m in solr]]
eigen1 = np.empty((len(solr),np.shape(M1)[0]),dtype = object)
with mp.workdps(int(mp.mp.dps*2)):
for i in range(len(solr)):
M2 = mpmathM(M1.xreplace({kz:solr[i]}))
eigen1[i] = null_space(M2)
solr1 = solr
div = [mp.fabs(x) for x in (order*kxl.xreplace(dic)*eigen1[:,4]+order*kyl.xreplace(dic)*eigen1[:,5]+solr1*eigen1[:,6])]
testdivB = [mp.almosteq(x,0,10**(-(mp.mp.dps/2))) for x in div]
eigen1 =eigen1[testdivB]
solr1 = solr1[testdivB]
if len(solr1) == 3:
print("Inviscid semi infinite domain")
elif len(solr1) == 6:
print("Inviscid 2 boundaries")
elif len(solr1) == 5:
print("Viscous semi infinite domain")
elif len(solr1) == 10:
print("Viscous 2 boundaries")
else:
print("number of solution inconsistent,",len(solr1))
return(solr1,eigen1,M1)
def ReflectedLightIntensity(self):
for i in range(self.numberOfLayers):
try:
self.reflectedLight[i] = 0
except IndexError:
print("IndexError is i =" + str(i))
self.StructuralReflectionCoefficient(self.numberOfLayers)
R = mpmath.fabs(self.reflectedLight[1])
#for i in range(len(self.reflectedLight)):
#self.reflectedLight[i] = mpmath.fabs(self.reflectedLight[i])**2
#print("self.reflectedLight[%s] = %s"%(i,self.reflectedLight[i]))
dep = mpmath.fabs(self.DetectionDepth(self.numberOfLayers))
return R**2, dep
def alpha(z, x, beta):
"""
Eq. (A4) from Ref[1]
Note that 'x' here corresponds to 'chi = x/rho',
and 'z' here corresponds to 'xi = z/2/rho' in the paper.
"""
arg1 = sqrt(2 * fabs(m(z, x, beta)))
arg2 = -2 * (m(z, x, beta) + nu(x, beta))
arg3 = 2 * eta(z, x, beta) / arg1
if z < 0:
return re(1 / 2 * (-arg1 + sqrt(fabs(arg2 + arg3))))
else:
return re(1 / 2 * (arg1 + sqrt(fabs(arg2 - arg3))))
def brot(c, depth=200, eps=0.001):
z = c
dz = 1
epsSq = eps * eps
for i in range(depth):
dz = dz * z * 2
z = mpmath.power(z, 2) + c
if mpmath.power(mpmath.fabs(z), 2) > 4:
return i
if mpmath.power(mpmath.fabs(dz), 2) < epsSq:
return -1
return -1
def logSpaceAdd(update, before):
'''
Assume a >= b
We want to calculate ln(exp(ln(a))+exp(ln(b))), thus
ln(
exp(ln(b))*(1+exp(ln(a)-ln(b)))
)
->
ln(b) + ln(1+exp(ln(a)-ln(b)))
->
ln(b) + ln1p(exp(ln(a)-ln(b)))
'''
#As there is no neutral element of addition in log space we only start adding when two values are given
if before == None:
return update
else:
a = None
b = None
#Check which value is larger
if update > before:
b = update
a = before
else:
b = before
a = update
x = mp.mpf(a) - mp.mpf(b)
#print(update,before)
xexp = memoexp(x)
#print('Exp:',xexp)
val = memolog1p(xexp)
#print('Log:',val)
val = val + b
if val == 0:
print('a:{} b:{} x:{} xexp:{} log1p:{}'.format(a, b, x, xexp, val))
raise ValueError(
'LogSpace Addition has resulted in a value of 0: a = {} b = {} (possible underflow)'
.format(a, b))
#elif val > 0:
#print('a:{} b:{} x:{} xexp:{} log1p:{}'.format(a,b,x,xexp,val))
#raise ValueError('Prior Update has resulted in a value > 0: a = {} b = {} val = {}'.format(a,b,val))
if before == val:
#print('a:{} b:{} x:{} xexp:{} log1p:{}'.format(a,b,x,xexp,val))
logging.warning(
'LogAddition had no effect: a = {} b = {} val = {}'.format(
a, b, val))
#raise ValueError('LogAddition had no effect: a = {} b = {} val = {}'.format(a,b,val))
#raise ValueError('LogAddition had no effect!')
if mp.isnan(val):
raise ValueError(
'LogSpace Addition has resulted in a value of nan: a = {} b = {}'
.format(a, b))
if mp.fabs(val) > 1000: #At this point who cares let's round a bit
val = mp.floor(val)
return val
def sig_figs_jy(jn, jn1, yn, yn1, z):
"""Check relation http://dlmf.nist.gov/10.50 .
Parameters
----------
jn : mpf
The value of j_n(x).
jn1 : mpf
The value of j_{n + 1}(x).
yn : mpf
The value of y_n(x).
yn1 : mpf
The value of y_{n + 1}(x).
Returns
-------
The estimated number of significant digits to which the computation of
the passed Bessel functions is correct.
"""
w = mpmath.fabs(z**2*(jn1*yn - jn*yn1) - 1)
if not mpmath.isfinite(w):
return w
if w > 0:
return 1 - mpmath.log10(w)
else:
return mpmath.mp.dps
def sig_figs_jy(jn, jn1, yn, yn1, z):
"""Check relation http://dlmf.nist.gov/10.50 .
Parameters
----------
jn : mpf
The value of j_n(x).
jn1 : mpf
The value of j_{n + 1}(x).
yn : mpf
The value of y_n(x).
yn1 : mpf
The value of y_{n + 1}(x).
Returns
-------
The estimated number of significant digits to which the computation of
the passed Bessel functions is correct.
"""
w = mpmath.fabs(z**2 * (jn1 * yn - jn * yn1) - 1)
if not mpmath.isfinite(w):
return w
if w > 0:
return 1 - mpmath.log10(w)
else:
return mpmath.mp.dps
def daubechies(N):
# p vanishing moments.
p = int(N / 2)
# make polynomial; see Mallat, 7.96
Py = [sm.binomial(p - 1 + k, k) for k in reversed(range(p))]
# get polynomial roots y[k]
Py_roots = sm.mp.polyroots(Py, maxsteps=200, extraprec=64)
z = []
for yk in Py_roots:
# substitute y = -1/4z + 1/2 - 1/4/z to factor f(y) = y - y[k]
# We've found the roots of P(y). We need the roots of Q(z) = P((1-z-1/z)/4)
f = [sm.mpf('-1/4'), sm.mpf('1/2') - yk, sm.mpf('-1/4')]
# get polynomial roots z[k]
z += sm.mp.polyroots(f)
# make polynomial using the roots within unit circle
h0z = sm.sqrt('2')
for zk in z:
if sm.fabs(zk) < 1:
# This calculation is superior to Mallat, (equation between 7.96 and 7.97)
h0z *= sympy.sympify('(z-zk)/(1-zk)').subs('zk', zk)
# adapt vanishing moments
hz = (sympy.sympify('(1+z)/2')**p * h0z).expand()
# get scaling coefficients
return [sympy.re(hz.coeff('z', k)) for k in reversed(range(p * 2))]
def run_stoch_eig(P, verbose=0):
"""
stoch_eig returns a stochastic vector x such that x P = x
for an irreducible stochstic matrix P.
"""
if verbose > 1:
print("original matrix (stoch_eig):\n", P)
x = mp.stoch_eig(P)
if verbose > 1:
print("x\n", x)
eps = mp.exp(0.8 * mp.log(mp.eps)) # From test_eigen.py
# x is a left eigenvector of P with eigenvalue unity
err0 = mp.norm(x*P-x, p=1)
if verbose > 0:
print("|xP - x| (stoch_eig):", err0)
assert err0 < eps
# x is a nonnegative vector
if verbose > 0:
print("min(x) (stoch_eig):", min(x))
assert min(x) >= 0 - eps
# 1-norm of x is one
err1 = mp.fabs(mp.norm(x, p=1) - 1)
if verbose > 0:
print("||x| - 1| (stoch_eig):", err1)
assert err1 < eps
def run_gth_solve(A, verbose=0):
"""
gth_solve returns a stochastic vector x such that x A = 0
for an irreducible transition rate matrix A.
"""
if verbose > 1:
print("original matrix (gth_solve):\n", A)
x = mp.gth_solve(A)
if verbose > 1:
print("x\n", x)
eps = mp.exp(0.8 * mp.log(mp.eps)) # test_eigen.py
# x is a solution to x A = 0
err0 = mp.norm(x*A, p=1)
if verbose > 0:
print("|xA| (gth_solve):", err0)
assert err0 < eps
# x is a nonnegative vector
if verbose > 0:
print("min(x) (gth_solve):", min(x))
assert min(x) >= 0 - eps
# 1-norm of x is one
err1 = mp.fabs(mp.norm(x, p=1) - 1)
if verbose > 0:
print("||x| - 1| (gth_solve):", err1)
assert err1 < eps
def my_assert_allclose_TFmp(H, b, a, tol):
if max(H.num.shape) != b.rows or max(H.den.shape) != a.rows:
raise ValueError('MP Transfer function is not the same size as the scipy transfer function!')
for i in range(0, b.rows):
if mpmath.fabs(b[i, 0] - H.num[0, i]) < tol:
assert True
else:
raise ValueError("MP transfer function is not close to the dTF")
for i in range(0, a.rows):
if mpmath.fabs(a[i, 0] - H.den[0, i]) < tol:
assert True
else:
raise ValueError("MP transfer function is not close to the dTF")
def daubechies(N):
# make polynomial
q_y = [mpmath.binomial(N - 1 + k, k) for k in reversed(range(N))]
# get polynomial roots y[k]
y = mpmath.mp.polyroots(q_y, maxsteps=200, extraprec=64)
z = []
for yk in y:
# subustitute y = -1/4z + 1/2 - 1/4/z to factor f(y) = y - y[k]
f = [mpmath.mpf('-1/4'), mpmath.mpf('1/2') - yk, mpmath.mpf('-1/4')]
# get polynomial roots z[k] within unit circle
z += [zk for zk in mpmath.mp.polyroots(f) if mpmath.fabs(zk) < 1]
# make polynomial using the roots
h0z = mpmath.sqrt('2')
for zk in z:
h0z *= sympy.sympify('(z-zk)/(1-zk)').subs('zk', zk)
# adapt vanishing moments
hz = (sympy.sympify('(1+z)/2')**N * h0z).expand()
# get scaling coefficients
return [sympy.re(hz.coeff('z', k)) for k in reversed(range(N * 2))]
def lognormal_minmax_log(kmin, kmax, mu, sigma):
'''returns the sum( 1/k*exp(-0.5*((lnk - mu)/sigma)^2),{k,kmin,kmax} ) with kmax=None for infty
'''
# print kmin,kmax,mu,sigma
mpm.mp.dps = 15
if kmax == None:
x = mpm.log(
mpm.sumem(
lambda k: 1.0 / (k - kmin + 1.0) * mpm.exp(-0.5 * (mpm.log(
(k - kmin + 1.0)) - mu) * (mpm.log(
(k - kmin + 1.0)) - mu) / sigma / sigma),
[kmin, mpm.inf]))
if mpm.im(x) != 0:
logS = float(mpm.fabs(x))
else:
logS = float(x)
else:
logS = (float(
mpm.log(
mpm.sumem(
lambda k: 1.0 / (k - kmin + 1.0) * mpm.exp(-0.5 * (mpm.log(
(k - kmin + 1.0)) - mu) * (mpm.log(
(k - kmin + 1.0)) - mu) / sigma / sigma),
[kmin, kmax]))))
return logS
def __init__(self, theta, phi, r=None, is_polar=True):
from mpmath import mpf, mp, norm, fabs, chop
if not r:
r = Point._scale
else:
r *= Point._scale
Point._can_change_the_scale = False
mp.dps = Point._precision
if not is_polar:
self.x = mpf(theta)
self.y = mpf(phi)
self.z = mpf(r)
self.norm = norm(self.get_coordinate(), 2)
self.set_polar_coordinate()
else:
self.norm = fabs(r)
self.theta = theta
self.phi = phi
self.set_coordinate()
# Setting float parameters
self.floatx = float(self.x)
self.floaty = float(self.y)
self.floatz = float(self.z)
def bimax_integrand(self, z, wc, l, n, t):
"""
Integrand of electron-noise integral.
"""
return f1(wc*l/z/mp.sqrt(2)) * z * \
(mp.exp(-z**2) + n/mp.sqrt(t)*mp.exp(-z**2 / t)) / \
(mp.fabs(BiMax.d_l(z, wc, n, t))**2 * wc**2)
def makeEulerBrick( _a, _b, _c ):
a, b, c = sorted( [ real( _a ), real( _b ), real( _c ) ] )
if fadd( power( a, 2 ), power( b, 2 ) ) != power( c, 2 ):
raise ValueError( "'euler_brick' requires a pythogorean triple" )
result = [ ]
a2 = fmul( a, a )
b2 = fmul( b, b )
c2 = fmul( c, c )
result.append( fabs( fmul( a, fsub( fmul( 4, b2 ), c2 ) ) ) )
result.append( fabs( fmul( b, fsub( fmul( 4, a2 ), c2 ) ) ) )
result.append( fprod( [ 4, a, b, c ] ) )
return sorted( result )
def assert_equal(vardict_1, vardict_2, suppress_message=False):
""" Assert SymPy Expression Equality
>>> from sympy import sin, cos
>>> from sympy.abc import x
>>> assert_equal(sin(2*x), 2*sin(x)*cos(x))
Assertion Passed!
>>> assert_equal(cos(2*x), cos(x)**2 - sin(x)**2)
Assertion Passed!
>>> assert_equal(cos(2*x), 1 - 2*sin(x)**2)
Assertion Passed!
>>> assert_equal(cos(2*x), 1 + 2*sin(x)**2)
Traceback (most recent call last):
...
AssertionError
>>> vardict_1 = {'A': sin(2*x), 'B': cos(2*x)}
>>> vardict_2 = {'A': 2*sin(x)*cos(x), 'B': cos(x)**2 - sin(x)**2}
>>> assert_equal(vardict_1, vardict_2)
Assertion Passed!
>>> assert_equal('(a^2 - b^2) - (a + b)*(a - b)', 0)
Assertion Passed!
"""
if not isinstance(vardict_1, dict):
vardict_1 = {'': vardict_1}
if not isinstance(vardict_2, dict):
vardict_2 = {'': vardict_2}
for var_1, var_2 in zip(vardict_1, vardict_2):
if not isinstance(vardict_1[var_1], sp.Basic):
vardict_1[var_1] = sp.sympify(vardict_1[var_1])
if not isinstance(vardict_2[var_2], sp.Basic):
vardict_2[var_2] = sp.sympify(vardict_2[var_2])
# update each vardict with mapping: variable -> (pseudo) unique number
vardict_1, vardict_2 = update_vardict(vardict_1), update_vardict(vardict_2)
# assert whether SDA >= (2/3) * precision, implying expression equality
for var_1, var_2 in zip(vardict_1, vardict_2):
n_1, n_2 = vardict_1[var_1], vardict_2[var_2]
if n_1 == n_2: continue
E_rel = 2 * fabs(n_1 - n_2)/(fabs(n_1) + fabs(n_2))
assert -log10(E_rel) + 1 >= (2.0/3) * precision
if not suppress_message:
print('Assertion Passed!')
def ponto_fixo(func, x0):
x0 = float(x0)
x = func(x0)
while fabs(x0 - x) > 0.000000001:
x0 = x
x = func(x0)
return x
def questao7():
chute = chute_temp()
t_star = 1319.7542885 # Obtido no item anterior
delta = fabs(t_star - chute) + 3
print 'Intervalo inicial: [1100, 1300]'
valor, n_iter = bissecao(temp, chute-delta, chute+delta, 0.000001, valor=50.0)
print 'Valor encontrado: %f' % valor
print 'Numero de iteracoes: %d' % n_iter
def maxkappa_integrand(zc, wc, lc, n, t, k):
"""
integrand of electron noise integral.
lc:= l/ldc
"""
num = f1(wc*lc/mp.sqrt(2)/zc) * MaxKappa.b(zc, n, t, k)
denom = mp.fabs(MaxKappa.e_l(zc, wc, n, t, k))**2
return num/denom
def error(coefs, progress=True):
(a, b) = coefs
xs = (x * mp.pi / mp.mpf(4096) for x in range(-4096, 4097))
err = max(fabs(sin(x) - f(x, a, b)) for x in xs)
if progress:
print('(a, b, c): ({}, {}, {})'.format(a, b, c(a, b)))
print('evaluated error: ', err)
print()
return float(err)
def test_fft():
n = 2**10
a = mp_rand_dist(n)
b = mp_rand_dist(n)
c = mp_conv(a, b)
d = mp_conv2(a, b)
eps = 0
for i in range(n):
eps = max(eps, mp.fabs(c[i] - d[i]))
return eps
def achar_ponto_fixo(l, n_iter=50):
l = float(l)
x0 = 0.1
vec = []
fixos = []
for n in range(n_iter):
for val in vec:
if fabs(val - x0) < 0.0000001:
for fixo in fixos:
if fabs(fixo - x0) < 0.0000001:
return fixos
else:
fixos.append(x0)
vec.append(x0)
x0 = iter_proc(x0, l)
return None
def getDecimalDigitList(n, k):
result = []
setAccuracy(k)
digits = floor(log10(n))
if digits < 0:
for _ in arange(fsub(fabs(digits), 1)):
result.append(0)
k = fsub(k, fsub(fabs(digits), 1))
value = fmul(n, power(10, fsub(k, fadd(digits, 1))))
for c in getMPFIntegerAsString(floor(value)):
result.append(int(c))
return result
def psi(t, eigenvalues, coefficients, f0=mp.mpf("1.0")):
'''The nearly periodic function'''
f = None
if len(eigenvalues) == len(coefficients):
f = [mp.expj(E*t) for E in eigenvalues]
f = mp.fdot(coefficients, f)
f = mp.fabs(f - f0)
return(f)
def _transformlng(lng, lat):
ret = 300.0 + lng + 2.0 * lat + 0.1 * lng * lng + \
0.1 * lng * lat + 0.1 * math.sqrt(math.fabs(lng))
ret += (20.0 * math.sin(6.0 * lng * pi) + 20.0 *
math.sin(2.0 * lng * pi)) * 2.0 / 3.0
ret += (20.0 * math.sin(lng * pi) + 40.0 *
math.sin(lng / 3.0 * pi)) * 2.0 / 3.0
ret += (150.0 * math.sin(lng / 12.0 * pi) + 300.0 *
math.sin(lng / 30.0 * pi)) * 2.0 / 3.0
return ret
def _transformlat(lng, lat):
ret = -100.0 + 2.0 * lng + 3.0 * lat + 0.2 * lat * lat + \
0.1 * lng * lat + 0.2 * math.sqrt(math.fabs(lng))
ret += (20.0 * math.sin(6.0 * lng * pi) + 20.0 *
math.sin(2.0 * lng * pi)) * 2.0 / 3.0
ret += (20.0 * math.sin(lat * pi) + 40.0 *
math.sin(lat / 3.0 * pi)) * 2.0 / 3.0
ret += (160.0 * math.sin(lat / 12.0 * pi) + 320 *
math.sin(lat * pi / 30.0)) * 2.0 / 3.0
return ret
def grandCountGN_UltraX1_Limited_wrapperMpmath (funcf, jacf, measdata:list, binit:list, bstart:list, bend:list, c, NSIG=10, NSIGGENERAL=10, implicit=False, verbose=False, verbose_wrapper=False, isBinitGood=True):
"""
Обёртка для grandCountGN_UltraX1_Limited для реализации общего алгоритма
:param funcf callable функция, параметры по формату x,b,c
:param jacf callable функция, параметры по формату x,b,c,y
:param measdata:list список словарей экспериментальных данных [{'x': [] 'y':[])},{'x': [] 'y':[])}]
:param binit:list начальное приближение b
:param bstart:list нижняя граница b
:param bend:list верхняя граница b
:param c словарь дополнительных постоянных
:param A матрица коэффициентов a
:param NSIG=3 точность (кол-во знаков после запятой)
:param implicit True если функция - неявная, иначе false
:param verbose Если True, то подробно принтить результаты итераций
:returns b, numiter, log - вектор оценки коэффициентов, число итераций, сообщения
"""
maxiter=10
b,bpriv=binit,binit
gknux=None
gknuxlist=list()
Skinit = makeSkInitMpmath(funcf,measdata,binit, c)
A=makeAinitMpmath(bstart, bend,Skinit,binit, isBinitGood)
log=''
if verbose_wrapper:
print ('==grandCountGN_UltraX1_Limited_wrapperMpmath is launched==\n\n')
for numiter in range (maxiter):
bpriv=copy.copy(b)
gknux=grandCountGN_UltraX1_mpmath_Limited (funcf, jacf, measdata, b, bstart, bend, c, A, NSIG, implicit, verbose) #посчитали b
if gknux is None:
print ("grandCountGN_UltraX1_Limited_wrapper crashed on some iteration")
continue
gknuxlist.append(gknux)
if verbose_wrapper:
print ('Iteration \n',numiter,'\n' ,gknux)
b=gknux[0]
if not gknux[2]=='':
#log+="On gknux iteration "+numiter+": "+ gknux[2]+"\n"
log+="On gknux iteration {0}: {1}\n".format (numiter, gknux[2])
for j in range (len(binit)): #уменьшили в два раза
A[j,0]*=mpm.mpf('0.5')
A[j,1]*=mpm.mpf('0.5')
condition=False
for i in range (len(b)):
if mpm.fabs ((b[i]-bpriv[i])/bpriv[i]) > math.pow(10,-1*NSIGGENERAL): #сравнение идёт по обычному флоат
condition=True
if not condition:
break
#мол если хоть один компонент вектора b значимо изменился, тогда продолжать. Иначе программа дойдёт до break и цикл прекратится
# print ('grandCountGN_UltraX1_Limited_wrapper iterations number:', numiter)
# print (gknux[0], gknux[1], log, gknux[3], gknux[4])
return gknux[0], gknux[1], log, gknux[3], gknux[4]
def norm(self):
"""
>>> import mpmapy
>>> import mpmath
>>> x = mpmapy.mpArray([1, -1])
>>> print x.norm()
1.4142135623731
>>> print x.norm() == mpmath.sqrt("2")
True
"""
return mpmath.sqrt(mpmath.fabs(self.inner()))
def __test_getEigen(self):
dim = random.randint(1,20)
qmin = -mpmath.rand()
qmax = mpmath.rand()
pmin = -mpmath.rand()
pmax = mpmath.rand()
domain = [[qmin,qmax],[pmin,pmax]]
qmapsys = QmapSystem(map=self.map, type='U', dim=dim, domain=domain)
evals, evecs = qmapsys.getEigen()
for evec in evecs:
self.assertTrue(mpmath.fabs(evec.norm() -mpmath.mpf(1) ) < 1e-32)
for i in range(len(evecs)):
vals = [ evecs[i].inner(evecs[j]) for j in range(len(evecs)) ]
self.assertTrue(mpmath.fabs(mpmath.fabs(vals[i]) - 1.0) < 1e-32)
vals.pop(i)
index = [x < 1e-32 for x in vals]
self.assertFalse(numpy.all(index))
for evec in evecs:
evec.hsmrep(10,10)
def str_var(v):
l = len(v)
var = mp.mpf(0)
eps = 1.0
for i in range(-l / 2, l / 2):
var += v[i] * (i * i)
eps -= v[i]
if mp.fabs(eps) > prq_eps:
return "!!! eps=" + str_log2(eps)
exit()
return mp.nstr(var).ljust(12)
def psi_x(z, x, beta):
"""
Eq.(24) from Ref[1] with argument zeta=0 and no constant factor e*beta**2/2/rho**2.
Note that 'x' here corresponds to 'chi = x/rho',
and 'z' here corresponds to 'xi = z/2/rho' in the paper.
"""
kap = kappa(z, x, beta)
alp = alpha(z, x, beta)
arg2 = -4 * (1 + x) / x**2
T1 = (1 / fabs(x) / (1 + x) *
((2 + 2 * x + x**2) * ellipf(alp, arg2) - x**2 * ellipe(alp, arg2)))
D = kap**2 - beta**2 * (1 + x)**2 * sin(2 * alp)**2
T2 = ((kap**2 - 2 * beta**2 * (1 + x)**2 + beta**2 * (1 + x) *
(2 + 2 * x + x**2) * cos(2 * alp)) / beta / (1 + x) / D)
T3 = -kap * sin(2 * alp) / D
T4 = kap * beta**2 * (1 + x) * sin(2 * alp) * cos(2 * alp) / D
T5 = 1 / fabs(x) * ellipf(alp, arg2) # psi_phi without e/rho**2 factor
out = re((T1 + T2 + T3 + T4) - 2 / beta**2 * T5)
return out
def asymptotic_expansion(self, omega):
# Evaluate the modulus of the characteristic function
domain = np.linspace(0, 5, 500)
char_fn = list(map(lambda t: mp.fabs(self.char_fn(t)), domain))
# thresh 1e-40
thresh_check = [
domain[i] for i in range(500) if char_fn[i] < mp.mpf(1e-50)
]
# Need to extend the domain
if len(list(thresh_check)) == 0:
j = 1
while len(list(thresh_check)) == 0:
domain = np.linspace(5 * j, 5 * (j + 1), 500)
char_fn = list(map(lambda t: mp.fabs(self.char_fn(t)), domain))
# thresh 1e-40
thresh_check = [
domain[i] for i in range(500) if char_fn[i] < mp.mpf(1e-50)
]
j += 1
cutoff = thresh_check[0]
# Generate the derivatives for the asymptotic expansion
order = 6
if not hasattr(self, 'diffs'):
self.gen_diffs(order)
# Evaluate the expansion
asym_series = mp.matrix(order, 1)
for i in range(1, order + 1):
asym_series[i - 1] = self.series_term(i, omega, cutoff)
# Sum up and take the real part
# We do not multiply by (-1) because the fact that we have g(x) = -x cancels this
return mp.re(mp.fsum(asym_series))
def compute_l2_norm(machine, initial_spin, steps):
_old_dps = mpmath.mp.dps
mpmath.mp.dps = 50
wave_function = {}
chain = MetropolisMC(machine, initial_spin)
for state in islice(chain, *steps):
spin = CompactSpin(state.spin)
wave_function[spin] = mpmath.exp(mpmath.mpc(state.log_wf()))
l2_norm = mpmath.sqrt(
sum(map(lambda x: mpmath.fabs(x)**2, wave_function.values()),
mpmath.mpf(0)) / len(wave_function))
mpmath.mp.dps = _old_dps
return float(l2_norm)
def getGCD( a, b = 0 ):
if real( b ) == 0:
a = list( a )
else:
a, b = fabs( a ), fabs( b )
while a:
b, a = a, fmod( b, a )
return b
if isinstance( a[ 0 ], ( list, RPNGenerator ) ):
return [ getGCD( real( arg ) ) for arg in a ]
else:
result = max( a )
for pair in itertools.combinations( a, 2 ):
gcd = getGCD( *pair )
if gcd < result:
result = gcd
return result
def makePythagoreanTriple( n, k ):
if real( n ) < 0 or real( k ) < 0:
raise ValueError( "'make_pyth_3' requires positive arguments" )
if n == k:
raise ValueError( "'make_pyth_3' requires unequal arguments" )
result = [ ]
result.append( fprod( [ 2, n, k ] ) )
result.append( fabs( fsub( fmul( n, n ), fmul( k, k ) ) ) )
result.append( fadd( fmul( n, n ), fmul( k, k ) ) )
return sorted( result )
def smallest_largest_elements(_matrix):
l = len(_matrix) - 1
smallest_element = mp.mpf(1e100) # initialize to very large number
largest_element = mp.mpf(0)
for i in range(l):
for j in range(l):
num = _matrix[i, j]
if num != 0:
abs_num = fabs(num)
if abs_num < smallest_element:
smallest_element = abs_num
if abs_num > largest_element:
largest_element = abs_num
return largest_element, smallest_element
def test_Matrix01(self):
a = mpmath.mpc(mpmath.pi,"0")
b = mpmath.mpc("0","1")
m = mpArray.mpArray([[a,b],[b,a]])
mat = mpArray.mpMatrix(m)
evals, evecs = mat.eigen(False, 'qeispack', verbose=True)
self.assertTrue(mpmath.fabs(mpmath.mpc(mpmath.pi,"1") - evals[0]) < 1e-32)
self.assertTrue(mpmath.fabs(mpmath.mpc(mpmath.pi,"-1") - evals[1]) < 1e-32)
self.assertTrue(numpy.all([mpmath.fabs(mpmath.mpf(1)/mpmath.sqrt("2") - mpmath.fabs(x)) < 1e-32 for x in evecs[0]]))
self.assertTrue(numpy.all([mpmath.fabs(mpmath.mpf(1)/mpmath.sqrt("2") - mpmath.fabs(x)) < 1e-32 for x in evecs[1]]))
self.assertTrue(mpmath.fabs(mpmath.fsum(evecs[0]*evecs[0].conj())) - mpmath.mpf(1) < 1e-32)
self.assertTrue(numpy.all([vec.abs2() -mpmath.mpf(1) < 1e-32 for vec in evecs]))
self.assertTrue(mpmath.fabs(evecs[0].inner(evecs[1])) < 1e-32)
# coefficients are computed for an infinite domain). Notice as well
# that these syntax only captures the cosine coefficients, which is
# correct for the standard initial condition.
def agnesi(x):
return dtheta/((x/aa)**2+r1)*mpm.sin(l*z)
THk0 = Fourier( Nk , agnesi )
T1 = time.clock(); print('Fourier time: %f'%(T1-T0))
# Preliminary check: compare the initial datum with its Fourier
# series.
TH0 = invFourier(x,THk0)
print('Inverse Fourier time: %f'%(time.clock()-T1))
err0 = [ mpm.fabs(agnesi(x[i])-TH0[i]) for i in xrange(len(x)) ]
T1 = time.clock(); print('Total setup time: %f'%(T1-T0))
print('Maximum initial error: %e' % max( float(ei) for ei in err0 ))
import pylab as py
py.figure(1)
py.plot([float(xi) for xi in x],[float(ei) for ei in err0],'go-')
py.title("Initial error")
py.figure(2)
py.title("Initial datum and Fourier series")
py.plot( \
[float(xi) for xi in x] , \
[float(ti) for ti in TH0], \
'mo-', label='Fourier series')
py.plot( \
def _distance(num): return mp.fabs(mp.fsub(num, mp.nint(num)))
def getNumberName( n, ordinal = False ):
units = ''
if isinstance( n, RPNMeasurement ):
value = n.value
if value == 1 or value == -1:
units = n.getUnitName( )
else:
units = n.getPluralUnitName( )
n = real_int( value )
if n == 0:
if ordinal:
name = 'zeroth'
else:
name = 'zero'
if units:
name += ' '
name += units
return name
current = fabs( n )
if current >= power( 10, 3003 ):
raise ValueError( 'value out of range for converting to an English name' )
group = 0
name = ''
firstTime = True
while current > 0:
section = getSmallNumberName( int( fmod( current, 1000 ) ), ordinal if firstTime else False )
firstTime = False
if section != '':
groupName = getNumberGroupName( group )
if groupName != '':
section += ' ' + groupName
if ordinal and name == '':
section += 'th'
if name == '':
name = section
else:
name = section + ' ' + name
current = floor( fdiv( current, 1000 ) )
group += 1
if n < 0:
name = 'negative ' + name
if units:
name += ' '
name += units
return name
