def correlation_MRRtest(rx1y, rx2y, rx1x2, n):
"""Meng-Rosenthal-Rubin method comparing two correlated
correlation coefficients.
:param float rx1y: the correlation coefficient between variables x1 and y
:param float rx2y: the correlation coefficient between variables x2 and y
:param float rx1x2: the correlation coefficient between variables x1 and x2
:param int n: sample size
:return: two-tailed p-value
Reference: Meng et al. (1992), Psychol. Bull. 111, 172,
"""
assert (-1<=rx1y<=1) and (-1<=rx2y<=1) and (-1<=rx1x2<=1), \
"Correlation coefficients should be in ]-1,1["
from math import sqrt, atanh
z1 = atanh(rx1y) # Fisher's Z-transformation
z2 = atanh(rx2y)
rs = (rx1y**2 + rx2y**2)/2.
f = min((1 - rx1x2)/(2*(1 - rs)), 1)
h = (1 - f*rs) / (1 - rs)
dz = (z1 - z2) * sqrt((n - 3)/(2 * (1 - rx1x2) * h))
p = 2 * S.distributions.norm.sf(abs(dz)) # Two-tailed probability
return p
def test_atanh(self):
import math
self.ftest(math.atanh(0), 0)
self.ftest(math.atanh(0.5), 0.54930614433405489)
self.ftest(math.atanh(-0.5), -0.54930614433405489)
raises(ValueError, math.atanh, 1.)
assert math.isnan(math.atanh(float("nan")))
def fromwgs84(lat, lng, pkm=False):
"""
Convert coordintes from WGS84 to TWD97
pkm true for Penghu, Kinmen and Matsu area
The latitude and longitude can be in the following formats:
[+/-]DDD°MMM'SSS.SSSS" (unicode)
[+/-]DDD°MMM.MMMM' (unicode)
[+/-]DDD.DDDDD (string, unicode or float)
The returned coordinates are in meters
"""
_lng0 = lng0pkm if pkm else lng0
lat = radians(todegdec(lat))
lng = radians(todegdec(lng))
t = sinh((atanh(sin(lat)) - 2*pow(n,0.5)/(1+n)*atanh(2*pow(n,0.5)/(1+n)*sin(lat))))
epsilonp = atan(t/cos(lng-_lng0))
etap = atan(sin(lng-_lng0) / pow(1+t*t, 0.5))
E = E0 + k0*A*(etap + alpha1*cos(2*1*epsilonp)*sinh(2*1*etap) +
alpha2*cos(2*2*epsilonp)*sinh(2*2*etap) +
alpha3*cos(2*3*epsilonp)*sinh(2*3*etap))
N = N0 + k0*A*(epsilonp + alpha1*sin(2*1*epsilonp)*cosh(2*1*etap) +
alpha2*sin(2*2*epsilonp)*cosh(2*2*etap) +
alpha3*sin(2*3*epsilonp)*cosh(2*3*etap))
return E*1000, N*1000
def pwmTOu(pwm):
u = np.array([0.0,0.0])
u[0] = math.atanh((pwm[0]-1800)/200)+1
u[1] = math.atanh((pwm[1]-1500)/500)
return u
# # # FIN PARTIE SIMULATION # # #
def testAtanh(self):
self.assertRaises(TypeError, math.atan)
self.ftest('atanh(0)', math.atanh(0), 0)
self.ftest('atanh(0.5)', math.atanh(0.5), 0.54930614433405489)
self.ftest('atanh(-0.5)', math.atanh(-0.5), -0.54930614433405489)
self.assertRaises(ValueError, math.atanh, 1)
self.assertRaises(ValueError, math.atanh, -1)
self.assertRaises(ValueError, math.atanh, INF)
self.assertRaises(ValueError, math.atanh, NINF)
self.assert_(math.isnan(math.atanh(NAN)))
def min_f(self, a, b):
"""Custom minimum function using inverse hyperbolic function."""
if a >= 2:
a = 1.99
if a <= 0:
a = 0.01
if b >= 2:
b = 1.99
if b <= 0:
b = 0.01
return 1 - math.tanh(math.atanh(1 - a) + math.atanh(1 - b))
def correlation(x, y):
"""Calculate correlation with p-value and confidence interval."""
assert len(x) == len(y)
#r, p = sp.stats.pearsonr(x, y)
#r, p = sp.stats.kendalltau(x, y)
r, p = sp.stats.spearmanr(x, y)
n = len(x)
stderr = 1.0 / math.sqrt(n-3)
delta = 1.96 * stderr
lower = math.tanh(math.atanh(r) - delta)
upper = math.tanh(math.atanh(r) + delta)
return dict(r=r, p=p, lower=lower, upper=upper)
def getEta(pEvent, index):
pTot = math.sqrt(
math.pow(pEvent.px[index], 2) + math.pow(pEvent.py[index], 2) +
math.pow(pEvent.pz[index], 2))
eta = math.atanh(pEvent.pz[index] / pTot)
return eta
def test_moments_basic():
"""Test that we can properly recover adaptive moments for Gaussians."""
import time
t1 = time.time()
for sig in gaussian_sig_values:
for g1 in shear_values:
for g2 in shear_values:
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0*math.atanh(total_shear))/total_shear
distortion_1 = g1*conversion_factor
distortion_2 = g2*conversion_factor
gal = galsim.Gaussian(flux = 1.0, sigma = sig)
gal.applyShear(g1=g1, g2=g2)
gal_image = gal.draw(scale = pixel_scale)
result = gal_image.FindAdaptiveMom()
# make sure we find the right Gaussian sigma
np.testing.assert_almost_equal(np.fabs(result.moments_sigma-sig/pixel_scale), 0.0,
err_msg = "- incorrect dsigma", decimal = decimal)
# make sure we find the right e
np.testing.assert_almost_equal(result.observed_shape.e1,
distortion_1, err_msg = "- incorrect e1",
decimal = decimal_shape)
np.testing.assert_almost_equal(result.observed_shape.e2,
distortion_2, err_msg = "- incorrect e2",
decimal = decimal_shape)
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def __init__(self, file_name):
proc = subprocess.Popen("../bin/meas_moments %s" % file_name, stdout=subprocess.PIPE, shell=True)
buf = os.read(proc.stdout.fileno(), 1000)
while proc.poll() == None:
pass
if proc.returncode != 0:
raise RuntimeError("meas_moments exited with an error code")
results = buf.split()
if results[0] is not "0":
raise RuntimeError("meas_moments returned an error status")
self.mxx = float(results[1])
self.myy = float(results[2])
self.mxy = float(results[3])
self.e1 = float(results[4])
self.e2 = float(results[5])
# These are distortions e1,e2
# Find the corresponding shear:
esq = self.e1 * self.e1 + self.e2 * self.e2
if esq > 0.0:
e = math.sqrt(esq)
g = math.tanh(0.5 * math.atanh(e))
self.g1 = self.e1 * (g / e)
self.g2 = self.e2 * (g / e)
else:
self.g1 = 0.0
self.g2 = 0.0
def km2_area_wgs84cell(center_lat, pixel_size): #DS re-ordered, center_lat):
"""Calculate km^2 area of a wgs84 square pixel. As with area_of_pixel above
but with atanh idea from same website
Adapted from: https://gis.stackexchange.com/a/127327/2397
Parameters:
pixel_size (float): length of side of pixel in degrees.
center_lat (float): latitude of the center of the pixel. Note this
value +/- half the `pixel-size` must not exceed 90/-90 degrees
latitude or an invalid area will be calculated.
Returns:
Area of square pixel of side length `pixel_size` centered at
`center_lat` in km^2.
"""
a = 6378137 # meters
b = 6356752.3142 # meters
e = math.sqrt(1 - (b / a)**2)
area_list = []
for f in [center_lat + pixel_size / 2, center_lat - pixel_size / 2]:
zm = 1 - e * math.sin(math.radians(f))
zp = 1 + e * math.sin(math.radians(f))
area_list.append(math.pi * b**2 * (
2 * atanh(e * sin(math.radians(f))) / (2 * e) +
#math.log(zp/zm) / (2*e) +
math.sin(math.radians(f)) / (zp * zm)))
return 1.0e-6 * pixel_size / 360. * (area_list[0] - area_list[1]
) #DS now km2
def bachelierFormulaImpliedVol(optionType,
strike,
forward,
tte,
bachelierPrice,
discount=1.0):
strike = float(strike)
forward = float(forward)
tte = float(tte)
bachelierPrice = float(bachelierPrice)
discount = float(discount)
pyFinAssert(tte > 0, ValueError,
"tte ({0:f}) must be positive".format(tte))
SQRT_QL_EPSILON = math.sqrt(MathConstants.QL_EPSILON)
forwardPremium = bachelierPrice / discount
if optionType == OptionType.Call:
straddlePremium = 2.0 * forwardPremium - (forward - strike)
else:
straddlePremium = 2.0 * forwardPremium + (forward - strike)
nu = (forward - strike) / straddlePremium
nu = max(-1.0 + MathConstants.QL_EPSILON,
min(nu, 1.0 - MathConstants.QL_EPSILON))
eta = 1.0 if (abs(nu) < SQRT_QL_EPSILON) else (nu / math.atanh(nu))
heta = HCalculator.calculate(eta)
impliedBpvol = math.sqrt(MathConstants.M_PI /
(2 * tte)) * straddlePremium * heta
return impliedBpvol
def __init__(self, file_name, file_name_epsf, array_shape):
proc = subprocess.Popen(
"../bin/meas_shape %s %s %f %f 0.0 REGAUSS 0.0"
% (file_name, file_name_epsf, 0.5 * array_shape[0], 0.5 * array_shape[1]),
stdout=subprocess.PIPE,
shell=True,
)
buf = os.read(proc.stdout.fileno(), 1000)
while proc.poll() == None:
pass
if proc.returncode != 0:
raise RuntimeError("meas_shape exited with an error code, %d" % proc.returncode)
results = buf.split()
if results[0] is not "0":
raise RuntimeError("meas_shape returned an error status")
self.e1 = float(results[1])
self.e2 = float(results[2])
self.r2 = float(results[5])
# These are distortions e1,e2
# Find the corresponding shear:
esq = self.e1 * self.e1 + self.e2 * self.e2
e = math.sqrt(esq)
g = math.tanh(0.5 * math.atanh(e))
self.g1 = self.e1 * (g / e)
self.g2 = self.e2 * (g / e)
def fromGeographic(self, lat, lon):
lat_rad = radians(lat)
lon_rad = radians(lon)
B = cos(lat_rad) * sin(lon_rad - self.lon_rad)
x = self.radius * atanh(B)
y = self.radius * (atan(tan(lat_rad) / cos(lon_rad - self.lon_rad)) - self.lat_rad)
return x, y
def compute_nfw(R):
if R < Rcore:
R2 = Rcore
else:
R2 = R
if (R2 / Rs) < 0.999:
func = (1 - 2 * math.atanh(math.sqrt(
(1 - R2 / Rs) /
(R2 / Rs + 1))) / math.sqrt(1 -
(R2 / Rs)**2)) / ((R2 / Rs)**2 - 1)
# There are some computational issues as R2/Rs -> 1, using taylor expansion of the function
# around this point
elif (R2 / Rs) < 1.001:
func = -(20 / 63) * ((R2 / Rs)**3 - 1) + (13 / 35) * (
(R2 / Rs)**2 - 1) - (2 / 5) * (R2 / Rs) + (11 / 15)
else:
func = (1 - 2 * math.atan(math.sqrt(
(R2 / Rs - 1) /
(R2 / Rs + 1))) / math.sqrt((R2 / Rs)**2 - 1)) / (
(R2 / Rs)**2 - 1)
return norm_constant * 2 * math.pi * R * func
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()
inputValueXHandle = data.inputValue(self.aInputValueX)
inputValueX = inputValueXHandle.asFloat()
inputValueYHandle = data.inputValue(self.aInputValueY)
inputValueY = inputValueYHandle.asFloat()
# Math tanus
outputValue = 0
if operationType == 0:
outputValue = math.atan(inputValueX)
if operationType == 1:
outputValue = math.tan(inputValueX)
if operationType == 2:
outputValue = math.atanh(inputValueX)
if operationType == 3:
outputValue = math.tanh(inputValueX)
if operationType == 4:
outputValue = math.tanh(inputValueY, inputValueX)
# Output Value
output_data = data.outputValue(self.aOutputVaue)
output_data.setFloat(outputValue)
# Clean plug
data.setClean(plug)
def latlon2(self, datum=None):
'''Convert this WM coordinate to a lat- and longitude.
@kwarg datum: Optional ellipsoidal datum (C{Datum}).
@return: A L{LatLon2Tuple}C{(lat, lon)}.
@raise TypeError: Non-ellipsoidal B{C{datum}}.
@see: Method C{toLatLon}.
'''
r = self.radius
x = self._x / r
y = 2 * atan(exp(self._y / r)) - PI_2
if datum is not None:
_xinstanceof(Datum, datum=datum)
E = datum.ellipsoid
if not E.isEllipsoidal:
raise _IsnotError(_ellipsoidal_, datum=datum)
# <https://Earth-Info.NGA.mil/GandG/wgs84/web_mercator/
# %28U%29%20NGA_SIG_0011_1.0.0_WEBMERC.pdf>
y = y / r
if E.e:
y -= E.e * atanh(E.e * tanh(y)) # == E.es_atanh(tanh(y))
y *= E.a
x *= E.a / r
r = LatLon2Tuple(Lat(degrees90(y)), Lon(degrees180(x)))
return self._xnamed(r)
def setup_method(self):
self.functions = [
utils.cos_close_0,
utils.sinc_close_0,
utils.inv_sinc_close_0,
utils.inv_tanc_close_0,
{
"coefficients": utils.arctanh_card_close_0["coefficients"],
"function": lambda x: math.atanh(x) / x,
},
{
"coefficients": utils.cosc_close_0["coefficients"],
"function": lambda x: (1 - math.cos(x)) / x**2,
},
utils.sinch_close_0,
utils.cosh_close_0,
{
"coefficients": utils.inv_sinch_close_0["coefficients"],
"function": lambda x: x / math.sinh(x),
},
{
"coefficients": utils.inv_tanh_close_0["coefficients"],
"function": lambda x: x / math.tanh(x),
},
]
def normal_cdf(x):
if abs(x) > 7:
return 1 if x > 0 else 0
temp = 27 * x / 294
temp = 111 * math.atanh(temp) - 358 * x / 23
temp = 1 + math.exp(temp)
return 1. / temp
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 ci(n, corr, clevel):
"""return clevel-% lower and upper confidence levels for each pair
n and corr are vectors of counts and correlations
Correlations very close to +-1 do not get a CI
"""
critz = abs(idfNormal((1 - clevel) / 2))
res = []
for nn, cc in zip(n, corr):
if nn <= 3 or cc is None or abs(
abs(cc) -
1) < 1e-12: # fuzz factor because of floating point properties
res.append((None, None))
else:
try:
Z = math.atanh(cc)
zse = critz / math.sqrt(nn - 3)
lowci = (math.tanh(Z) -
math.tanh(zse)) / (1 - math.tanh(Z) * math.tanh(zse))
upperci = (math.tanh(Z) + math.tanh(zse)) / (
1 + math.tanh(Z) * math.tanh(zse))
res.append((lowci, upperci))
except:
res.append((None, None))
return res
def update_cn_to_vr_msgs(H, vr_to_cn_msgs, cn_to_vr_msgs, LLRs):
for j in range(len(H)):
#print('ck11')
for i in neighbors_cn(H, j):
#print('ck12')
product = 1.0
for k in neighbors_cn(H, j):
#print('ck13')
if(k!=i):
product*=math.tanh(0.5*vr_to_cn_msgs[k][j])
#print('ck14')
#print('ck15')
if(abs(product)<0.9999999999999999):
cn_to_vr_msgs[j][i] = 2*math.atanh(product)
else:
cn_to_vr_msgs[j][i] = 2*math.atanh(0.9999999999999999)
def test_shearest_basic():
"""Test that we can recover shears for Gaussian galaxies and PSFs."""
for sig in gaussian_sig_values:
for g1 in shear_values:
for g2 in shear_values:
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0*math.atanh(total_shear))/total_shear
distortion_1 = g1*conversion_factor
distortion_2 = g2*conversion_factor
gal = galsim.Gaussian(flux = 1.0, sigma = sig)
psf = galsim.Gaussian(flux = 1.0, sigma = sig)
gal = gal.shear(g1=g1, g2=g2)
psf = psf.shear(g1=0.1*g1, g2=0.05*g2)
final = galsim.Convolve([gal, psf])
final_image = final.drawImage(scale=pixel_scale, method='no_pixel')
epsf_image = psf.drawImage(scale=pixel_scale, method='no_pixel')
result = galsim.hsm.EstimateShear(final_image, epsf_image)
# make sure we find the right e after PSF correction
# with regauss, which returns a distortion
np.testing.assert_almost_equal(result.corrected_e1,
distortion_1, err_msg = "- incorrect e1",
decimal = decimal_shape)
np.testing.assert_almost_equal(result.corrected_e2,
distortion_2, err_msg = "- incorrect e2",
decimal = decimal_shape)
def test_shearest_basic():
"""Test that we can recover shears for Gaussian galaxies and PSFs."""
import time
t1 = time.time()
for sig in gaussian_sig_values:
for g1 in shear_values:
for g2 in shear_values:
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0*math.atanh(total_shear))/total_shear
distortion_1 = g1*conversion_factor
distortion_2 = g2*conversion_factor
gal = galsim.Gaussian(flux = 1.0, sigma = sig)
psf = galsim.Gaussian(flux = 1.0, sigma = sig)
gal.applyShear(g1=g1, g2=g2)
final = galsim.Convolve([gal, psf])
final_image = final.draw(dx = pixel_scale)
epsf_image = psf.draw(dx = pixel_scale)
result = galsim.EstimateShearHSM(final_image, epsf_image)
# make sure we find the right e after PSF correction
# with regauss, which returns a distortion
np.testing.assert_almost_equal(result.corrected_e1,
distortion_1, err_msg = "- incorrect e1",
decimal = decimal_shape)
np.testing.assert_almost_equal(result.corrected_e2,
distortion_2, err_msg = "- incorrect e2",
decimal = decimal_shape)
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def dG12dsMN(s, par, B, ff, ff0, lep, wc):
flavio.citations.register("Melikhov:2004mk")
mlh = par['m_' + lep] / par['m_' + B]
pref = 4 * prefactor(s, par, B, ff, lep,
wc) * par['f_' + B] / par['m_' + B] * (1 - s)
return pref * atanh(sqrt(1 - 4 * mlh**2 / s)) * B120(
s, par, B, ff, ff0, lep, wc)
def _get_distorted_indices(self, nb_images):
inverse = random.randint(0, 1)
if inverse:
scale = random.random()
scale *= 0.21
scale += 0.6
else:
scale = random.random()
scale *= 0.6
scale += 0.8
frames_per_clip = nb_images
indices = np.linspace(-scale, scale, frames_per_clip).tolist()
if inverse:
values = [math.atanh(x) for x in indices]
else:
values = [math.tanh(x) for x in indices]
values = [x / values[-1] for x in values]
values = [
int(round(((x + 1) / 2) * (frames_per_clip - 1), 0))
for x in values
]
return values
def to2ll(self, datum=None):
'''Convert this WM coordinate to a geodetic lat- and longitude.
@keyword datum: Optional datum (C{Datum}).
@return: 2-Tuple (lat, lon) in (C{degrees90}, C{degrees180}).
@raise TypeError: Non-ellipsoidal I{datum}.
@raise ValueError: Invalid I{radius}.
'''
r = self.radius
x = self._x / r
y = 2 * atan(exp(self._y / r)) - PI_2
if datum:
E = datum.ellipsoid
if not E.isEllipsoidal:
raise TypeError('%s not %s: %r' %
('datum', 'ellipsoidal', datum))
# <http://Earth-Info.NGA.mil/GandG/wgs84/web_mercator/
# %28U%29%20NGA_SIG_0011_1.0.0_WEBMERC.pdf>
y = y / r
if E.e:
y -= E.e * atanh(E.e * tanh(y))
y *= E.a
x *= E.a / r
return degrees90(y), degrees180(x)
def set_e1e2(self, e1, e2):
etot = sqrt(e1**2 + e2**2)
if etot >= 1:
mess="e values must be < 1, found %.16g (%.16g,%.16g)"
mess = mess % (etot,e1,e2)
raise ShapeRangeError(mess)
if etot==0:
self.eta1,self.eta2,self.g1,self.g2=(0.,0.,0.,0.)
self.e1,self.e2=(0., 0.)
return
eta = atanh(etot)
gtot = tanh(eta/2)
cos2theta = e1/etot
sin2theta = e2/etot
self.e1=e1
self.e2=e2
self.g1=gtot*cos2theta
self.g2=gtot*sin2theta
self.eta1=eta*cos2theta
self.eta2=eta*sin2theta
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 test_moments_basic():
"""Test that we can properly recover adaptive moments for Gaussians."""
import time
t1 = time.time()
for sig in gaussian_sig_values:
for g1 in shear_values:
for g2 in shear_values:
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0*math.atanh(total_shear))/total_shear
distortion_1 = g1*conversion_factor
distortion_2 = g2*conversion_factor
gal = galsim.Gaussian(flux = 1.0, sigma = sig)
gal.applyShear(g1=g1, g2=g2)
gal_image = gal.draw(dx = pixel_scale)
result = gal_image.FindAdaptiveMom()
# make sure we find the right Gaussian sigma
np.testing.assert_almost_equal(np.fabs(result.moments_sigma-sig/pixel_scale), 0.0,
err_msg = "- incorrect dsigma", decimal = decimal)
# make sure we find the right e
np.testing.assert_almost_equal(result.observed_shape.e1,
distortion_1, err_msg = "- incorrect e1",
decimal = decimal_shape)
np.testing.assert_almost_equal(result.observed_shape.e2,
distortion_2, err_msg = "- incorrect e2",
decimal = decimal_shape)
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def _constants(self, stages, bits): if self.func_mode == "circular": s = range(stages) a = [atan(2**-i) for i in s] g = [sqrt(1 + 2**(-2*i)) for i in s] #zmax = sum(a) # use pi anyway as the input z can cause overflow # and we need the range for quadrant mapping zmax = pi elif self.func_mode == "linear": s = range(stages) a = [2**-i for i in s] g = [1 for i in s] #zmax = sum(a) # use 2 anyway as this simplifies a and scaling zmax = 2. else: # hyperbolic s = [] # need to repeat some stages: j = 4 for i in range(stages): if i == j: s.append(j) j = 3*j + 1 s.append(i + 1) a = [atanh(2**-i) for i in s] g = [sqrt(1 - 2**(-2*i)) for i in s] zmax = sum(a)*2 a = [int(ai*2**(bits - 1)/zmax) for ai in a] # round here helps the width=2**i - 1 case but hurts the # important width=2**i case gain = 1. for gi in g: gain *= gi return a, s, zmax, gain
def bachelierFormulaImpliedVol(optionType,
strike,
forward,
tte,
bachelierPrice,
discount=1.0):
strike = float(strike)
forward = float(forward)
tte = float(tte)
bachelierPrice = float(bachelierPrice)
discount = float(discount)
pyFinAssert(tte > 0, ValueError, "tte ({0:f}) must be positive".format(tte))
SQRT_QL_EPSILON = math.sqrt(MathConstants.QL_EPSILON)
forwardPremium = bachelierPrice / discount
if optionType == OptionType.Call:
straddlePremium = 2.0 * forwardPremium - (forward - strike)
else:
straddlePremium = 2.0 * forwardPremium + (forward - strike)
nu = (forward - strike) / straddlePremium
nu = max(-1.0 + MathConstants.QL_EPSILON, min(nu, 1.0 - MathConstants.QL_EPSILON))
eta = 1.0 if (abs(nu) < SQRT_QL_EPSILON) else (nu / math.atanh(nu))
heta = HCalculator.calculate(eta)
impliedBpvol = math.sqrt(MathConstants.M_PI / (2 * tte)) * straddlePremium * heta
return impliedBpvol
def g1g2_to_eta1eta2(g1,g2):
gtot=sqrt(g1**2 + g2**2)
if isinstance(gtot,numpy.ndarray):
eta1=numpy.zeros(gtot.size)
eta2=numpy.zeros(gtot.size)
w,=numpy.where(gtot != 0)
if w.size > 0:
cos2theta=g1[w]/gtot[w]
sin2theta=g2[w]/gtot[w]
eta = 2*arctanh(gtot[w])
eta1[w]=eta*cos2theta
eta2[w]=eta*sin2theta
else:
from math import atanh
if gtot == 0:
eta1,eta2=0.0,0.0
else:
cos2theta=g1/gtot
sin2theta=g2/gtot
eta = 2*atanh(gtot)
eta1=eta*cos2theta
eta2=eta*sin2theta
return eta1,eta2
def test_shearest_basic():
"""Test that we can recover shears for Gaussian galaxies and PSFs."""
for sig in gaussian_sig_values:
for g1 in shear_values:
for g2 in shear_values:
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(
2.0 * math.atanh(total_shear)) / total_shear
distortion_1 = g1 * conversion_factor
distortion_2 = g2 * conversion_factor
gal = galsim.Gaussian(flux=1.0, sigma=sig)
psf = galsim.Gaussian(flux=1.0, sigma=sig)
gal = gal.shear(g1=g1, g2=g2)
psf = psf.shear(g1=0.1 * g1, g2=0.05 * g2)
final = galsim.Convolve([gal, psf])
final_image = final.drawImage(scale=pixel_scale,
method='no_pixel')
epsf_image = psf.drawImage(scale=pixel_scale,
method='no_pixel')
result = galsim.hsm.EstimateShear(final_image, epsf_image)
# make sure we find the right e after PSF correction
# with regauss, which returns a distortion
np.testing.assert_almost_equal(result.corrected_e1,
distortion_1,
err_msg="- incorrect e1",
decimal=decimal_shape)
np.testing.assert_almost_equal(result.corrected_e2,
distortion_2,
err_msg="- incorrect e2",
decimal=decimal_shape)
def checkio(height, width):
'''height是拉长，width是半径，返回表面积和体积'''
from math import pi
volume = 4 * pi / 3 * (width / 2)**3 * height / width
# 椭球的近似表面积
# surface_area = pi/3 * (width**2+width*height*2)
# 球体的表面积，拉长
# surface_area = pi * width**2 * (height/width)**(2/3)
if height == width:
# 球体的表面积
surface_area = pi * width**2
# 新的公式..
# 不学数学就是不行
# http://mathworld.wolfram.com/ProlateSpheroid.html
elif height < width:
import math
e = math.sqrt(1 - height**2 / width**2)
surface_area = 0.5 * pi * (width**2) * (1 +
(1 - e**2) / e * math.atanh(e))
else:
import math
e = math.sqrt(1 - width**2 / height**2)
surface_area = 0.5 * pi * width**2 + 0.5 * pi * width * height / e * math.asin(
e)
return [round(volume, 2), round(surface_area, 2)]
def test_shearest_shape():
"""Test that shear estimation is insensitive to shape of input images."""
# this test can help reveal bugs having to do with x / y indexing issues
# just do test for one particular gaussian
g1 = shear_values[1]
g2 = shear_values[2]
e1_psf = 0.05
e2_psf = -0.04
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0 * math.atanh(total_shear)) / total_shear
distortion_1 = g1 * conversion_factor
distortion_2 = g2 * conversion_factor
gal = galsim.Exponential(flux=1.0, half_light_radius=1.)
gal = gal.shear(g1=g1, g2=g2)
psf = galsim.Kolmogorov(flux=1.0, fwhm=0.7)
psf = psf.shear(e1=e1_psf, e2=e2_psf)
final = galsim.Convolve([gal, psf])
imsize = [128, 256]
for method_index in range(len(correction_methods)):
print(correction_methods[method_index])
save_e1 = -100.
save_e2 = -100.
for gal_x_imsize in imsize:
for gal_y_imsize in imsize:
for psf_x_imsize in imsize:
for psf_y_imsize in imsize:
final_image = galsim.ImageF(gal_x_imsize, gal_y_imsize)
epsf_image = galsim.ImageF(psf_x_imsize, psf_y_imsize)
final.drawImage(image=final_image,
scale=pixel_scale,
method='no_pixel')
psf.drawImage(image=epsf_image,
scale=pixel_scale,
method='no_pixel')
result = galsim.hsm.EstimateShear(
final_image,
epsf_image,
shear_est=correction_methods[method_index])
e1 = result.corrected_e1
e2 = result.corrected_e2
# make sure answers don't change as we vary image size
tot_e = np.sqrt(save_e1**2 + save_e2**2)
if tot_e < 99.:
np.testing.assert_almost_equal(
e1,
save_e1,
err_msg="- incorrect e1",
decimal=decimal_shape)
np.testing.assert_almost_equal(
e2,
save_e2,
err_msg="- incorrect e2",
decimal=decimal_shape)
save_e1 = e1
save_e2 = e2
def p_expression_trig(t):
'''expression : COS expression
| SEN expression
| TAN expression
| SEC expression
| CSC expression
| COT expression
| COSH expression
| SENH expression
| TANH expression
| SECH expression
| CSCH expression
| COTH expression
| ACOS expression
| ASEN expression
| ATAN expression
| ACOSH expression
| ASENH expression
| ATANH expression
'''
try:
if t[1] == 'cos' or t[1] == 'COS' : t[0] = m.cos(t[2])
elif t[1] == 'sen' or t[1] == 'SEN' : t[0] = m.sin(t[2])
elif t[1] == 'tan' or t[1] == 'TAN' : t[0] = m.tan(t[2])
elif t[1] == 'sec' or t[1] == 'SEC' : t[0] = 1/m.cos(t[2])
elif t[1] == 'csc' or t[1] == 'CSC' : t[0] = 1/m.sin(t[2])
elif t[1] == 'cot' or t[1] == 'COT' : t[0] = 1/m.tan(t[2])
##Hiperbólicas
elif t[1] == 'cosh' or t[1] == 'COSH' : t[0] = m.cosh(t[2])
elif t[1] == 'senh' or t[1] == 'SENH' : t[0] = m.sinh(t[2])
elif t[1] == 'tanh' or t[1] == 'TANH' : t[0] = m.tanh(t[2])
elif t[1] == 'sech' or t[1] == 'SECH' : t[0] = 1/m.cosh(t[2])
elif t[1] == 'csch' or t[1] == 'CSCH' : t[0] = 1/m.sinh(t[2])
elif t[1] == 'coth' or t[1] == 'COTH' : t[0] = 1/m.tanh(t[2])
##Inversas
elif t[1] == 'acos' or t[1] == 'ACOS' : t[0] = m.acos(t[2])
elif t[1] == 'asen' or t[1] == 'ASEN' : t[0] = m.asin(t[2])
elif t[1] == 'atan' or t[1] == 'ATAN' : t[0] = m.atan(t[2])
##Inversas hiperbolicas
elif t[1] == 'acosh' or t[1] == 'ACOSH':
try:
t[0] = m.acosh(t[2])
except:
print("Math error: acosh(%s)" %t[2])
t[0] = 0
elif t[1] == 'asenh' or t[1] == 'ASENH':
try:
t[0] = m.asinh(t[2])
except:
print("Math error: asenh(%s)" %t[2])
t[0] = 0
elif t[1] == 'atanh' or t[1] == 'ATANH':
try:
t[0] = m.atanh(t[2])
except:
print("Math error: atanh(%s)" %t[2])
t[0] = 0
except:
print("Syntax error: Trigonometric function")
def arctangenth(self):
x = self.xinput.get('1.0', 'end-1c')
x = float(x)
z = float(math.atanh(x))
print(z)
z = str(z)
z = z + "\n"
self.display.insert(END, z)
def atanh(x=('FloatPin', 0.0), Result=("Reference", ('BoolPin', False))):
'''Return the inverse hyperbolic tangent of `x`.'''
try:
Result(True)
return math.atanh(x)
except:
Result(False)
return -1
def fromGeographic(self, lat, lon):
lat_rad = radians(lat)
lon_rad = radians(lon)
B = cos(lat_rad) * sin(lon_rad - self.lon_rad)
x = self.radius * atanh(B)
y = self.radius * (atan(tan(lat_rad) / cos(lon_rad - self.lon_rad)) -
self.lat_rad)
return x, y
def G_e(Z, W, endpoint):
beta = beta_rel(W)
if W <= 1:
result = 0
else:
W0 = endpoint / ELECTRON_MASS + 1
result = 3 * math.log(NUCLEON_MASS / ELECTRON_MASS) - 0.75 + 4 * (
math.atanh(beta) / beta - 1) * (
(W0 - W) / (3 * W) - 1.5 + math.log(2 * (W0 + 0.01 - W))
) + 4 / beta * dilog(2 * beta /
(1 + beta)) + math.atanh(beta) / beta * (
2 * (1 + beta**2) +
(W0 - W)**2 /
(6 * W**2) - 4 * math.atanh(beta))
result = ALPHA * result / (2 * PI) + 1
return result
def F(self, e, theta):
"""
:param e:
:param theta:
:return: Hyperbolic anomaly (F)
"""
F = 2 * atanh(sqrt((e - 1) / (e + 1)) * tan(theta / 2))
return F + 2 * pi if F <= 0 else F
def pearson_confidence(r, num, interval=0.95):
"""
Calculate upper and lower 95% CI for a Pearson r (not R**2)
Inspired by https://stats.stackexchange.com/questions/18887
:param r: Pearson's R
:param num: number of data points
:param interval: confidence interval (0-1.0)
:return: lower bound, upper bound
"""
stderr = 1.0 / math.sqrt(num - 3)
z_score = norm.ppf(
interval +
(1.0 - interval) / 2.0) # There was a bug in the original code
delta = z_score * stderr
lower = math.tanh(math.atanh(r) - delta)
upper = math.tanh(math.atanh(r) + delta)
return lower, upper
def model1(t, a, beta):
row = (a * N) / beta
alpha = m.sqrt(((S0_1 / row) - 1)**2 + (2 * S0_1 * (N - S0_1)) / (row**2))
phai = m.atanh((1 / alpha) * ((S0_1 / row) - 1))
rt = ((row**2) / S0_1) * (((S0_1 / row) - 1) + alpha *
(np.tanh(((a * alpha * t) / 2) - phai)))
return rt
def atanh(space, d):
""" atanh - Inverse hyperbolic tangent """
try:
return space.wrap(math.atanh(d))
except OverflowError:
return space.wrap(rfloat.INFINITY)
except ValueError:
return space.wrap(rfloat.NAN)
def ChkOp(self): for i in self.ChkNodes: msgvec=[] for j in i.edges: msgvec.append(math.tanh(0.5*j.msg1)) extr=extrProd(msgvec) for j in range(0,len(i.edges)): i.edges[j].msg2=2.0*math.atanh(extr[j])
def adjustValue(value):
pMark = 0.65 #> percentage mark where values gets pushed up or down
result = value + (1.5*math.atanh(value - pMark)) * value
upperLimit = 0.9
lowerLimit = 0.0
result = min(result,upperLimit)
result = max(result,lowerLimit)
return result
def atanh(space, d):
""" atanh - Inverse hyperbolic tangent """
try:
return space.wrap(math.atanh(d))
except OverflowError:
return space.wrap(rfloat.INFINITY)
except ValueError:
return space.wrap(rfloat.NAN)
def op_atanh(x):
"""Returns the inverse hyperbolic tangent of this mathematical object."""
if isinstance(x, list):
return [op_atanh(a) for a in x]
elif isinstance(x, complex):
return cmath.atanh(x)
else:
return math.atanh(x)
def convertToShear(e1,e2):
# Convert a distortion (e1,e2) to a shear (g1,g2)
import math
e = math.sqrt(e1*e1 + e2*e2)
g = math.tanh( 0.5 * math.atanh(e) )
g1 = e1 * (g/e)
g2 = e2 * (g/e)
return (g1,g2)
def convert_pixcel_tolonglati(pixel_x, pixel_y, z):
Longitude, Latitude = 0, 0
Longitude = 180 * (pixel_x / math.pow(2, z + 7) - 1)
Latitude = 180 / PAI \
* (math.asin(math.tanh(-1 * (PAI / math.pow(2, z + 7) * pixel_y) \
+ math.atanh(math.sin((PAI / 180) * L)))))
return Latitude, Longitude
def convertToShear(e1, e2):
# Convert a distortion (e1,e2) to a shear (g1,g2)
import math
e = math.sqrt(e1 * e1 + e2 * e2)
g = math.tanh(0.5 * math.atanh(e))
g1 = e1 * (g / e)
g2 = e2 * (g / e)
return (g1, g2)
def coords2pixel(self, zoom, coords):
TILESIZE = self.TILESIZE
(lat, lon) = coords
lon = lon / 360 + 0.5
lat = math.atanh(math.sin(lat / 180 * math.pi))
lat = - lat / (2 * math.pi) + 0.5
scale = 2 ** zoom * TILESIZE
return (lon * scale, lat * scale)
def test_moments_basic():
"""Test that we can properly recover adaptive moments for Gaussians."""
import time
t1 = time.time()
first_test=True
for sig in gaussian_sig_values:
for g1 in shear_values:
for g2 in shear_values:
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0*math.atanh(total_shear))/total_shear
distortion_1 = g1*conversion_factor
distortion_2 = g2*conversion_factor
gal = galsim.Gaussian(flux = 1.0, sigma = sig)
gal = gal.shear(g1=g1, g2=g2)
gal_image = gal.drawImage(scale = pixel_scale, method='no_pixel')
result = gal_image.FindAdaptiveMom()
# make sure we find the right Gaussian sigma
np.testing.assert_almost_equal(np.fabs(result.moments_sigma-sig/pixel_scale), 0.0,
err_msg = "- incorrect dsigma", decimal = decimal)
# make sure we find the right e
np.testing.assert_almost_equal(result.observed_shape.e1,
distortion_1, err_msg = "- incorrect e1",
decimal = decimal_shape)
np.testing.assert_almost_equal(result.observed_shape.e2,
distortion_2, err_msg = "- incorrect e2",
decimal = decimal_shape)
# if this is the first time through this loop, just make sure it runs and gives the
# same result for ImageView and ConstImageViews:
if first_test:
result = gal_image.view().FindAdaptiveMom()
first_test=False
np.testing.assert_almost_equal(
np.fabs(result.moments_sigma-sig/pixel_scale), 0.0,
err_msg = "- incorrect dsigma (ImageView)", decimal = decimal)
np.testing.assert_almost_equal(
result.observed_shape.e1,
distortion_1, err_msg = "- incorrect e1 (ImageView)",
decimal = decimal_shape)
np.testing.assert_almost_equal(
result.observed_shape.e2,
distortion_2, err_msg = "- incorrect e2 (ImageView)",
decimal = decimal_shape)
result = gal_image.view(make_const=True).FindAdaptiveMom()
np.testing.assert_almost_equal(
np.fabs(result.moments_sigma-sig/pixel_scale), 0.0,
err_msg = "- incorrect dsigma (ConstImageView)", decimal = decimal)
np.testing.assert_almost_equal(
result.observed_shape.e1,
distortion_1, err_msg = "- incorrect e1 (ConstImageView)",
decimal = decimal_shape)
np.testing.assert_almost_equal(
result.observed_shape.e2,
distortion_2, err_msg = "- incorrect e2 (ConstImageView)",
decimal = decimal_shape)
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
def utm2LL(Easting, Northing, zone, zoneQuadrant=None, North=None, datum='wgs84'):
a, b = Datums[datum]
zone_cm = getZoneCM(zone) # zone central meridian in degrees longitude
if zoneQuadrant: # if both North and zoneQuadrant are specified, use zoneQuadrant to determine North
North = isNorth(zoneQuadrant)
elif North is None:
raise Exception("Either 'zoneQuadrant' or 'North' arguments must be specified")
e, n, AA = getDatumProperties(a, b)
b1, b2, b3, b4, b5, b6, b7 = getBetaSeries(n) # (Krüger 1912, pg. 21, eqn. 41)
if North:
xi = Northing / (k0 * AA) # (Karney 2010, pg.3, eqn. 15)
else:
xi = Northing / (1e7 - Northing) / (k0 * AA)
eta = (Easting - falseEasting) / (k0 * AA) # (Karney 2010, pg.3, eqn. 13)
# (Karney 2010, pg.3, eqn. 11) which is an inversion of (Karney 2010, pg. 2, eqn. 7-9)
xi_prime = (xi - (
b1 * sin(2 * xi) * cosh(2 * eta) + b2 * sin(4 * xi) * cosh(4 * eta) + b3 * sin(6 * xi) * cosh(
6 * eta) + b4 * sin(8 * xi) * cosh(8 * eta) + b5 * sin(10 * xi) * cosh(10 * eta) + b6 * sin(
12 * xi) * cosh(12 * eta) + b7 * sin(14 * xi) * cosh(14 * eta)))
eta_prime = (eta - (
b1 * cos(2 * xi) * sinh(2 * eta) + b2 * cos(4 * xi) * sinh(4 * eta) + b3 * cos(6 * xi) * sinh(
6 * eta) + b4 * cos(8 * xi) * sinh(8 * eta) + b5 * cos(10 * xi) * sinh(10 * eta) + b6 * cos(
12 * xi) * sinh(12 * eta) + b7 * cos(14 * xi) * sinh(14 * eta)))
tau_prime = sin(xi_prime) / sqrt(sinh(eta_prime) ** 2 + cos(xi_prime) ** 2)
relative_long_rad = atan(sinh(eta_prime) / cos(xi_prime))
relative_long = degrees(relative_long_rad) # longitude relative to central meridian
# We are in a pickle here. Since tau is unknown and sigma is a function of tau, we need to use an approximation
# for newton's method with a first guess for tau, say tau = tau_prime. With each iteration, f converges closer and
# closer to zero and tau stabilizes. f should converge after 2-3 iterations, but other programs have used 7.
# Likewise, we will be using 7 iterations.
# source: [https: // stevedutch.net / usefuldata / utmformulas.htm] analogous to (Karney 2010, pg.3, eqn. 19-21)
tau = tau_prime
for iteration in range(7):
sigma = sinh(e * atanh(e * tau / sqrt(1 + tau ** 2)))
f_tau = tau * sqrt(1 + sigma ** 2) - sigma * sqrt(1 + tau ** 2) - tau_prime
d_tau = (sqrt((1 + sigma ** 2) * (1 + tau ** 2)) - sigma * tau) * (1 - e ** 2) * sqrt(1 + tau ** 2) / (
1 + (1 - e ** 2) * tau ** 2)
tau = tau - f_tau / d_tau
lat = degrees(atan(tau)) # Return latitude back to degrees.
if North: # Convert latitude to negative if South.
lat = abs(lat)
else:
lat = abs(lat) * -1
long = relative_long + zone_cm
return lat, long
def _ellipsoidal_area(a, b, lambda12, phi1, phi2, alpha1, alpha2):
""" Area of a single quatrilateral defined by two meridians, the equator,
and another geodesic.
"""
f = (a-b)/a
e2 = f*(2-f)
ep2 = e2/(1-e2)
e = sqrt(e2)
# Authalic radius
c = sqrt(a**2/2 + b**2/2*atanh(e)/e)
beta1 = atan((1-f)*tan(phi1))
beta2 = atan((1-f)*tan(phi2))
_i = sqrt(cos(alpha1)**2 + (sin(alpha1)*sin(beta1))**2)
alpha0 = atan2(sin(alpha1)*cos(beta1), _i)
sigma1, omega1 = _solve_NEA(alpha0, alpha1, beta1)
_, sigma2, omega2 = _solve_NEB(alpha0, alpha1, beta1, beta2)
omega12 = omega2 - omega1
# Bessel identity for alpha2 - alpha1
alpha12 = 2*atan(sin(0.5*beta1+0.5*beta2)/cos(0.5*beta2-0.5*beta1) \
* tan(0.5*omega12))
sph_term = c**2 * alpha12
# compute integrals for ellipsoidal correction
k2 = ep2*cos(alpha0)**2
C40 = (2.0/3 - ep2/15 + 4*ep2**2/105 - 8*ep2**3/315 + 64*ep2**4/3465 - 128*ep2**5/9009) \
- (1.0/20 - ep2/35 + 2*ep2**2/105 - 16*ep2**3/1155 + 32*ep2**4/3003) * k2 \
+ (1.0/42 - ep2/63 + 8*ep2**2/693 - 90*ep2**3/9009) * k2**2 \
- (1.0/72 - ep2/99 + 10*ep2**2/1287) * k2**3 \
+ (1.0/110 - ep2/143) * k2**4 - k2**5/156
C41 = (1.0/180 - ep2/315 + 2*ep2**2/945 - 16*ep2**3/10395 + 32*ep2**4/27027) * k2 \
- (1.0/252 - ep2/378 + 4*ep2**2/2079 - 40*ep2**3/27027) * k2**2 \
+ (1.0/360 - ep2/495 + 2*ep2**2/1287) * k2**3 \
- (1.0/495 - 2*ep2/1287) * k2**4 + 5*k2**5/3276
C42 = (1.0/2100 - ep2/3150 + 4*ep2**2/17325 - 8*ep2**3/45045) * k2**2 \
- (1.0/1800 - ep2/2475 + 2*ep2**2/6435) * k2**3 \
+ (1.0/1925 - 2*ep2/5005) * k2**4 - k2**5/2184
C43 = (1.0/17640 - ep2/24255 + 2*ep2**2/63063) * k2**3 \
- (1.0/10780 - ep2/14014) * k2**4 + 5*k2**5/45864
C44 = (1.0/124740 - ep2/162162) * k2**4 - 1*k2**5/58968
C45 = k2**5/792792
Cs = [C40, C41, C42, C43, C44, C45]
I4s1 = sum(c*cos((2*i+1)*sigma1) for i,c in enumerate(Cs))
I4s2 = sum(c*cos((2*i+1)*sigma2) for i,c in enumerate(Cs))
S12 = sph_term + e**2*a**2 * cos(alpha0)*sin(alpha0) * (I4s2-I4s1)
return S12
def lambert93_to_WGPS(lambertE, lambertN):
"""
Converts coordinates given in
`Lambert 93 <https://fr.wikipedia.org/wiki/Projection_conique_conforme_de_Lambert>`_
system, this system is used by `IGN <http://http://professionnels.ign.fr/>`_
and their :epkg:`GEOFLA` file format.
@param lambertE east
@param lambertN north
@return longitude, latitude
The function is inspired from
`lam93toLatLon.py <https://gist.github.com/flire/0a305eeec77bc84a73af8ddc8f9ec043>`_.
.. faqref::
:tag: geo
:title: Les fichiers GEOFLA ne contiennent pas de longitude, latitude ?
Les coordonnées contenues dans les fichiers :epkg:`GEOFLA`
ne sont pas toujours des longitudes, latitudes mais des coordonnées exprimées dans un système
de projection conique `Lambert 93 <https://fr.wikipedia.org/wiki/Projection_conique_conforme_de_Lambert>`_.
Il faut convertir les coordonnées avant de pouvoir tracer la carte ou changer la projection
utilisée par :epkg:`cartopy` :
`Lambert Conformal Projection <https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html#lambertconformal>`_.
"""
class constantes:
GRS80E = 0.081819191042816
LONG_0 = 3
XS = 700000
YS = 12655612.0499
n = 0.7256077650532670
C = 11754255.4261
delX = lambertE - constantes.XS
delY = lambertN - constantes.YS
gamma = math.atan(-delX / delY)
R = math.sqrt(delX * delX + delY * delY)
latiso = math.log(constantes.C / R) / constantes.n
sinPhiit0 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * math.sin(1)))
sinPhiit1 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * sinPhiit0))
sinPhiit2 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * sinPhiit1))
sinPhiit3 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * sinPhiit2))
sinPhiit4 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * sinPhiit3))
sinPhiit5 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * sinPhiit4))
sinPhiit6 = math.tanh(latiso + constantes.GRS80E *
math.atanh(constantes.GRS80E * sinPhiit5))
longRad = math.asin(sinPhiit6)
latRad = gamma / constantes.n + constantes.LONG_0 / 180 * math.pi
longitude = latRad / math.pi * 180
latitude = longRad / math.pi * 180
return longitude, latitude
def transMercator (cls, lat, lon, centerlon=0.0):
"""
Transversal Mercator projection - returns (x,y) tuple (unit: m)
"""
_lat = math.radians(lat)
_lon = math.radians(lon-centerlon)
x = cls.r_earth * math.atanh(math.sin(_lon) * math.cos(_lat))
y = cls.r_earth * math.atan(math.tan(_lat) / math.cos(_lon))
return x,y
def test_shearest_shape():
"""Test that shear estimation is insensitive to shape of input images."""
# this test can help reveal bugs having to do with x / y indexing issues
import time
t1 = time.time()
# just do test for one particular gaussian
g1 = shear_values[1]
g2 = shear_values[2]
e1_psf = 0.05
e2_psf = -0.04
total_shear = np.sqrt(g1**2 + g2**2)
conversion_factor = np.tanh(2.0*math.atanh(total_shear))/total_shear
distortion_1 = g1*conversion_factor
distortion_2 = g2*conversion_factor
gal = galsim.Exponential(flux = 1.0, half_light_radius = 1.)
gal.applyShear(g1=g1, g2=g2)
psf = galsim.Kolmogorov(flux = 1.0, fwhm = 0.7)
psf.applyShear(e1=e1_psf, e2=e2_psf)
final = galsim.Convolve([gal, psf])
imsize = [128, 256]
for method_index in range(len(correction_methods)):
print correction_methods[method_index]
save_e1 = -100.
save_e2 = -100.
for gal_x_imsize in imsize:
for gal_y_imsize in imsize:
for psf_x_imsize in imsize:
for psf_y_imsize in imsize:
print gal_x_imsize, gal_y_imsize, psf_x_imsize, psf_y_imsize
final_image = galsim.ImageF(gal_x_imsize, gal_y_imsize)
epsf_image = galsim.ImageF(psf_x_imsize, psf_y_imsize)
final_image = final.draw(image = final_image, dx = pixel_scale)
epsf_image = psf.draw(image = epsf_image, dx = pixel_scale)
result = galsim.EstimateShearHSM(final_image, epsf_image,
shear_est = correction_methods[method_index])
e1 = result.corrected_e1
e2 = result.corrected_e2
# make sure answers don't change as we vary image size
tot_e = np.sqrt(save_e1**2 + save_e2**2)
if tot_e < 99.:
print "Testing!"
np.testing.assert_almost_equal(e1, save_e1,
err_msg = "- incorrect e1",
decimal = decimal_shape)
np.testing.assert_almost_equal(e2, save_e2,
err_msg = "- incorrect e2",
decimal = decimal_shape)
print save_e1, save_e2, e1, e2
save_e1 = e1
save_e2 = e2
t2 = time.time()
print 'time for %s = %.2f'%(funcname(),t2-t1)
