def symOrtho(self, a, b): """ Computes the radius, cosine, and sine for the orthogonal transformation. @param a x vector. @param b y vector. @return [c,s,r]. Cosine value, Sine value, and the radius. """ s = 0 c = 0 r = 0 if not b: r = np.abs(a) c = np.copysign(1,0,a) elif not a: r = np.abs(b) s = np.copysign(1,0,b) elif np.abs(b) > np.abs(a): t = a / b s = np.copysign(1.0,b) / np.sqrt(1.0 + t**2) c = s * t r = b / s else: t = b / a c = np.copysign(1.0,a) / np.sqrt(1.0 + t**2) s = c * t r = a / c return [c,s,r]
def test_copysign():
assert_(np.copysign(1, -1) == -1)
with np.errstate(divide="ignore"):
assert_(1 / np.copysign(0, -1) < 0)
assert_(1 / np.copysign(0, 1) > 0)
assert_(np.signbit(np.copysign(np.nan, -1)))
assert_(not np.signbit(np.copysign(np.nan, 1)))
def test_signbit(self):
from numpy import signbit, add, copysign, nan
assert signbit(add.identity) == False
assert (signbit([0, 0.0, 1, 1.0, float("inf")]) == [False, False, False, False, False]).all()
assert (signbit([-0, -0.0, -1, -1.0, float("-inf")]) == [False, True, True, True, True]).all()
assert (signbit([copysign(nan, 1), copysign(nan, -1)]) == [False, True]).all()
def test_cross_div(dtypea, dtypeb, dtypec):
if dtypea == np.int8 and dtypeb == np.int8:
pytest.skip("Different behaviour in c++ and python for int8 / int8".format(dtypea, dtypeb))
def fkt(a, b, c):
c[:] = a / b
hfkt = hope.jit(fkt)
(ao, ah), (bo, bh), (co, ch) = random(dtypea, [10]), random(dtypeb, [10]), random(dtypec, [10])
ao, ah, bo, bh = ao.astype(np.float64), ah.astype(np.float64), bo.astype(np.float64), bh.astype(np.float64)
ao, ah = (
np.copysign(np.power(np.abs(ao), 1.0 / 4.0), ao).astype(dtypea),
np.copysign(np.power(np.abs(ah), 1.0 / 4.0), ah).astype(dtypea),
)
bo, bh = (
np.copysign(np.power(np.abs(bo), 1.0 / 4.0), bo).astype(dtypeb),
np.copysign(np.power(np.abs(bh), 1.0 / 4.0), bh).astype(dtypeb),
)
if np.count_nonzero(bo == 0) > 0:
bo[bo == 0] += 1
if np.count_nonzero(bh == 0) > 0:
bh[bh == 0] += 1
fkt(ao, bo, co), hfkt(ah, bh, ch)
assert check(co, ch)
fkt(ao, bo, co), hfkt(ah, bh, ch)
assert check(co, ch)
def __step(self, t, y, dt, control):
u_v, u_s = control(t, y)
v_x, v_y, r, s_f, x, y, o = y
a_f = (v_y + self.L_f * r) / (v_x + 0.00001) - s_f
a_r = (v_y - self.L_r * r) / (v_x + 0.00001)
F_yf = -self.C_af * a_f
F_yr = -self.C_ar * a_r
n_v = self.noise_v(t) if self.noise_v else 0
n_s = self.noise_s(t) if self.noise_s else 0
u_s = u_s if abs(u_s) < self.max_u_s else np.copysign(self.max_u_s, u_s)
u_v = u_v if abs(u_v) < self.max_u_v else np.copysign(self.max_u_v, u_v)
if abs(s_f + u_s * dt) > self.max_s:
u_s = np.copysign(self.max_s - 0.05, s_f) - s_f
self._uv = u_v
self._us = u_s
F_res = self.F_res() * v_x
d_v_x = (-F_yf * np.sin(s_f) + u_v - F_res * np.cos(s_f) - F_res + n_v) / self.m + v_y * r
d_v_y = (F_yf * np.cos(s_f) + F_yr - F_res * np.sin(s_f)) / self.m - v_x * r
d_r = self.L_f * (F_yf * np.cos(s_f) - F_res * np.sin(s_f)) / self.I_z - self.L_r * F_yr / self.I_z + n_s
d_s = u_s
d_x = v_x * np.cos(o) - v_y * np.sin(o)
d_y = v_x * np.sin(o) + v_y * np.cos(o)
d_o = r
return [d_v_x, d_v_y, d_r, d_s, d_x, d_y, d_o]
def test_copysign_array(self):
assert np.all(np.copysign(np.array([1., 2., 3.]) * u.s, -1.) == -np.array([1., 2., 3.]) * u.s)
assert np.all(np.copysign(np.array([1., 2., 3.]) * u.s, -1. * u.m) == -np.array([1., 2., 3.]) * u.s)
assert np.all(np.copysign(np.array([1., 2., 3.]) * u.s, np.array([-2.,2.,-4.]) * u.m) == np.array([-1., 2., -3.]) * u.s)
q = np.copysign(np.array([1., 2., 3.]), -3 * u.m)
assert np.all(q == np.array([-1., -2., -3.]))
assert not isinstance(q, u.Quantity)
def calibrate(self, steps): # calibration values in uW
rng = np.arange(0,steps)
x = np.zeros(steps,dtype = float)
y = np.zeros(steps,dtype = float)
self.set_power(0)
self._ins_pm.set_wavelength(self._wavelength)
time.sleep(2)
bg = self._ins_pm.get_power()
print 'background power: %.4f uW' % (bg*1e6)
time.sleep(.2)
V_max = self.get_V_max()
V_min = self.get_V_min()
if V_max + V_min < 0: rng=np.flipud(rng)
for a in rng:
x[a] = a*(V_max-V_min)/float(steps-1)+V_min
self.apply_voltage(x[a])
time.sleep(0.5)
y[a] = self._ins_pm.get_power() - bg
print 'measured power at %.2f V: %.4f uW' % \
(x[a], y[a]*1e6)
#x= x*(V_max-V_min)/float(steps-1)+V_min
a, xc, k = np.copysign(np.max(y), V_max + V_min), np.copysign(.1, V_max + V_min), np.copysign(5., V_max + V_min)
fitres = fit.fit1d(x,y, common.fit_AOM_powerdependence,
a, xc, k, do_print=True, ret=True)
fd = np.zeros(len(x))
if type(fitres) != type(False):
p1 = fitres['params_dict']
self.set_cal_a(p1['a'])
self.set_cal_xc(p1['xc'])
self.set_cal_k(p1['k'])
fd = fitres['fitfunc'](x)
else:
print 'could not fit calibration curve!'
dat = qt.Data(name= 'aom_calibration_'+self._name+'_'+\
self._cur_controller)
dat.add_coordinate('Voltage [V]')
dat.add_value('Power [W]')
dat.add_value('fit')
dat.create_file()
plt = qt.Plot2D(dat, 'rO', name='aom calibration', coorddim=0, valdim=1,
clear=True)
plt.add_data(dat, coorddim=0, valdim=2)
dat.add_data_point(x,y,fd)
dat.close_file()
plt.save_png(dat.get_filepath()+'png')
self.save_cfg()
print (self._name+' calibration finished')
def test_copysign():
assert_(np.copysign(1, -1) == -1)
old_err = np.seterr(divide="ignore")
try:
assert_(1 / np.copysign(0, -1) < 0)
assert_(1 / np.copysign(0, 1) > 0)
finally:
np.seterr(**old_err)
assert_(np.signbit(np.copysign(np.nan, -1)))
assert_(not np.signbit(np.copysign(np.nan, 1)))
def test_binary_pow(dtype, shape):
def fkt(a, c):
c[:] = a ** 2
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, shape), random(dtype, shape)
ao, ah = np.copysign(np.sqrt(np.abs(ao)), ao).astype(dtype), np.copysign(np.sqrt(np.abs(ah)), ah).astype(dtype)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
def test_augmented_mult(dtype, shape):
def fkt(a, c):
c[:] *= a
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, shape), random(dtype, shape)
ao, ah = np.copysign(np.power(np.abs(ao), 1. / 4.), ao).astype(dtype), np.copysign(np.power(np.abs(ah), 1. / 4.), ah).astype(dtype)
co, ch = np.copysign(np.power(np.abs(co), 1. / 4.), co).astype(dtype), np.copysign(np.power(np.abs(ch), 1. / 4.), ch).astype(dtype)
ro, rh = fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
ro, rh = fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
def adjustDxn(dx1,dx2,rcore,tol=1e-10):
r = np.sqrt(dx1**2 + dx2**2)
if (r**2 < rcore**2*tol):
rcorefac = rcore/np.sqrt(2.0)
dx1 = np.copysign(rcorefac,dx1)
dx2 = np.copysign(rcorefac,dx2)
elif (r**2 < rcore**2):
rfac = rcore/r
dx1 = dx1*rfac
dx2 = dx2*rfac
return dx1, dx2
def dms2deg(char):
"""
Function used to transform the Declination from the fits file given in degrees, minutes and seconds
to degrees.
Argument: The dec as a string of characters
output: A float number: the Dec written in degrees
"""
d,m,s = [float(x) for x in char.split()]
m,s = np.copysign(m,d),np.copysign(s,d) # The arcminutes and arcseconds adquire the same sign of degrees to be added later
out = (d + m/60.+ s/3600.)
return out
def test_opt_pow_array(dtype, shape):
def fkt(a, c):
c[:] = a ** 2.0 + a ** 4 + a ** 7
hope.config.optimize = True
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, shape), random(dtype, shape)
ao, ah = np.copysign(np.power(np.abs(ao), 1. / 8.), ao).astype(dtype), np.copysign(np.power(np.abs(ah), 1. / 8.), ah).astype(dtype)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
hope.config.optimize = False
def test_return_arr_expr(dtype, shape):
def fkt(a, c):
return a ** -2 + a ** -4.0 + a ** -7
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, shape), random(dtype, shape)
ao, ah = np.copysign(np.power(np.abs(ao), 1. / 8.), ao).astype(dtype), np.copysign(np.power(np.abs(ah), 1. / 8.), ah).astype(dtype)
if np.count_nonzero(ao == 0) > 0: ao[ao == 0] += 1
if np.count_nonzero(ah == 0) > 0: ah[ah == 0] += 1
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
def test_opt_pow_scalar(dtype):
def fkt(a, c):
c[0] = a ** 2 + a ** 4 + a ** 7.0
hope.config.optimize = True
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, []), random(dtype, [1])
ao, ah = np.copysign(np.power(np.abs(ao), 1. / 8.), ao).astype(dtype), np.copysign(np.power(np.abs(ah), 1. / 8.), ah).astype(dtype)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
hope.config.optimize = False
def test_binary_mult(dtype, shape):
def fkt(a, b, c):
c[:] = a * b
hfkt = hope.jit(fkt)
(ao, ah), (bo, bh), (co, ch) = random(dtype, shape), random(dtype, shape), random(dtype, shape)
ao = np.copysign(np.sqrt(np.abs(ao.astype(np.float64))).astype(dtype), ao).astype(dtype)
ah = np.copysign(np.sqrt(np.abs(ah.astype(np.float64))).astype(dtype), ah).astype(dtype)
bo = np.copysign(np.sqrt(np.abs(bo.astype(np.float64))).astype(dtype), bo).astype(dtype)
bh = np.copysign(np.sqrt(np.abs(bh.astype(np.float64))).astype(dtype), bh).astype(dtype)
ro, rh = fkt(ao, bo, co), hfkt(ah, bh, ch)
assert check(co, ch)
ro, rh = fkt(ao, bo, co), hfkt(ah, bh, ch)
assert check(co, ch)
def test_augmented_pow(dtype, shape):
def fkt(a, c):
c[:] **= a
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(np.uint8, shape), random(dtype, shape)
if np.count_nonzero(ao == 0) > 0: ao[ao == 0] += 1
if np.count_nonzero(ah == 0) > 0: ah[ah == 0] += 1
if np.count_nonzero(co == 0) > 0: co[co == 0] += 1
if np.count_nonzero(ch == 0) > 0: ch[ch == 0] += 1
co, ch = np.copysign(np.sqrt(np.abs(co)), co).astype(dtype), np.copysign(np.sqrt(np.abs(ch)), ch).astype(dtype)
ao, ah = np.power(np.abs(ao).astype(np.float64), 1. / co.astype(np.float64)).astype(dtype), np.power(np.abs(ah).astype(np.float64), 1. / ch.astype(np.float64)).astype(dtype)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
def test_opt_neg_pow_array(dtype, shape):
def fkt(a, c):
c[:] = a ** -2 + a ** -4.0 + a ** -7
hope.config.optimize = True
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, shape), random(dtype, shape)
ao, ah = np.copysign(np.power(np.abs(ao), 1. / 8.), ao).astype(dtype), np.copysign(np.power(np.abs(ah), 1. / 8.), ah).astype(dtype)
if np.count_nonzero(ao == 0) > 0: ao[ao == 0] += 1
if np.count_nonzero(ah == 0) > 0: ah[ah == 0] += 1
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
hope.config.optimize = False
def test_opt_basic_scalar(dtype):
def fkt(a, c):
c[0] = (a + a) * a - 1 / a
hope.config.optimize = True
hfkt = hope.jit(fkt)
(ao, ah), (co, ch) = random(dtype, []), random(dtype, [1])
ao, ah = np.copysign(np.power(np.abs(ao), 1. / 4.), ao).astype(dtype), np.copysign(np.power(np.abs(ah), 1. / 4.), ah).astype(dtype)
if np.count_nonzero(ao == 0) > 0: ao[ao == 0] += 1
if np.count_nonzero(ah == 0) > 0: ah[ah == 0] += 1
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
fkt(ao, co), hfkt(ah, ch)
assert check(co, ch)
hope.config.optimize = False
def test_copysign(self):
from numpy import array, copysign
reference = [5.0, -0.0, 0.0, -6.0]
a = array([-5.0, 0.0, 0.0, 6.0])
b = array([5.0, -0.0, 3.0, -6.0])
c = copysign(a, b)
for i in range(4):
assert c[i] == reference[i]
b = array([True, True, True, True], dtype=bool)
c = copysign(a, b)
for i in range(4):
assert c[i] == abs(a[i])
def __init__(self, file, isomer, *args):
# Frequencies in waveunmbers
self.frequency_wn = []
# Extract the Force constants from a g09 logfile and generate the
# mass-weighted Hessian matrix in Hartree/(amu Bohr^2)
mw_hessmat = read_hess(file, isomer)
# Convert from atomic units - a bit ugly
unit_conversion = ENERGY_AU / (BOHR_RADIUS**2 * ATOMIC_MASS_UNIT) / ((SPEED_OF_LIGHT * 2 * np.pi)**2)
eigs = np.linalg.eigvalsh(mw_hessmat * unit_conversion)
freqs = [ np.copysign(np.sqrt(np.abs(freq)),freq) for freq in eigs ]
# 5 or 6 small normal modes will be removed (depending on whether the molecule is linear or non-linear)
if is_linear(file) == 'linear': trans_rot_modes = 5
else: trans_rot_modes = 6
# Keep a single imaginary frequency. It should be larger than the predefined cut-off
if np.abs(freqs[0]) > freq_cutoff:
self.im_frequency_wn = -1.0 * freqs[0]
trans_rot_modes = trans_rot_modes + 1
for freq in freqs[trans_rot_modes:]: self.frequency_wn.append(freq)
# Calculate the excitation factor (EXC), the ZPE (ZPE) and Teller-Redlich product factor (PF)
# returns a 1D-array of all terms
self.PF = calc_product_factor(self.frequency_wn, freq_scale_factor)
self.ZPE = calc_zpe_factor(self.frequency_wn, temperature, freq_scale_factor)
self.EXC = calc_excitation_factor(self.frequency_wn, temperature, freq_scale_factor)
def run_hc(self):
self.compute_pipe_diameter_of_each_loop()
for run in range(self.runs):
for i, loop in enumerate(self.loops):
# perform initial calculations
loop['J'] = j_loss_10atm(loop['D'], loop['Q'])
loop['hf'] = np.copysign(loop['J'] * loop['L'], loop['Q'])
loop['hf/Q'] = loop['hf'] / loop['Q']
self.delta_Qs[i] = (flow_correction_dq(loop['hf'], loop['hf/Q']))
loop['Q'] = loop['Q'] + self.delta_Qs[i]
self.delta_Qs[i] = (flow_correction_dq(loop['hf'], loop['hf/Q']))
# do the common loop correction
loop['Q'] = loop['Q'] - np.dot(self.common_loops[i], self.delta_Qs)
self.smallest_flow_rate.append(np.min(np.abs(loop['Q'])))
largest_delta_qs_flow_rate = np.max(abs(self.delta_Qs))
if largest_delta_qs_flow_rate / np.min(self.smallest_flow_rate) * 100 < self.threshold:
print('Completed on run {}'.format(run))
print('dqmin / Qmin * 100 = {0:.2f}'.format((largest_delta_qs_flow_rate /
np.min(self.smallest_flow_rate)) * 100))
for k, l in enumerate(loops_from_input_file):
print('the corrected loops {} are \n {}'.format(k, l))
break
else:
print('Not Done {}'.format(run))
pass
return self.loops, self.delta_Qs
def get_all_distance_and_image(self, frac_coords1, frac_coords2):
"""
Gets distance between two frac_coords and nearest periodic images.
Args:
fcoords1 (3x1 array): Reference fcoords to get distance from.
fcoords2 (3x1 array): fcoords to get distance from.
Returns:
[(distance, jimage)] List of distance and periodic lattice
translations of the other site for which the distance applies.
This means that the distance between frac_coords1 and (jimage +
frac_coords2) is equal to distance.
"""
#The following code is heavily vectorized to maximize speed.
#Get the image adjustment necessary to bring coords to unit_cell.
adj1 = np.floor(frac_coords1)
adj2 = np.floor(frac_coords2)
#Shift coords to unitcell
coord1 = frac_coords1 - adj1
coord2 = frac_coords2 - adj2
# Generate set of images required for testing.
# This is a cheat to create an 8x3 array of all length 3
# combinations of 0,1
test_set = np.unpackbits(np.array([5, 57, 119],
dtype=np.uint8)).reshape(8, 3)
images = np.copysign(test_set, coord1 - coord2)
# Create tiled cartesian coords for computing distances.
vec = np.tile(coord2 - coord1, (8, 1)) + images
vec = self.get_cartesian_coords(vec)
# Compute distances manually.
dist = np.sqrt(np.sum(vec ** 2, 1)).tolist()
return list(zip(dist, adj1 - adj2 + images))
def dms_to_degrees(d, m, s=None):
"""
Convert degrees, arcminute, arcsecond to a float degrees value.
"""
_check_minute_range(m)
_check_second_range(s)
# determine sign
sign = np.copysign(1.0, d)
try:
d = np.floor(np.abs(d))
if s is None:
m = np.abs(m)
s = 0
else:
m = np.floor(np.abs(m))
s = np.abs(s)
except ValueError:
raise ValueError(format_exception(
"{func}: dms values ({1[0]},{2[1]},{3[2]}) could not be "
"converted to numbers.", d, m, s))
return sign * (d + m / 60. + s / 3600.)
def quat_from_rotmx(R):
r"""
Obtain the quaternion from a given rotation matrix (ref. [euclidianspace_mxToQuat]_)
Args:
:R: 3x3 array that represent the rotation matrix (see `Conventions <conventions.html#matrices>`_)
"""
q = numpy.zeros(4, dtype="float")
q[0] = numpy.sqrt( max( 0, 1 + R[0,0] + R[1,1] + R[2,2] ) ) / 2.
q[1] = numpy.sqrt( max( 0, 1 + R[0,0] - R[1,1] - R[2,2] ) ) / 2.
q[2] = numpy.sqrt( max( 0, 1 - R[0,0] + R[1,1] - R[2,2] ) ) / 2.
q[3] = numpy.sqrt( max( 0, 1 - R[0,0] - R[1,1] + R[2,2] ) ) / 2.
q[1] = numpy.copysign( q[1], R[2,1] - R[1,2] )
q[2] = numpy.copysign( q[2], R[0,2] - R[2,0] )
q[3] = numpy.copysign( q[3], R[1,0] - R[0,1] )
return q
def GetFeature(data): nolog = ['user_id','item_id', 'buy'] nolog2 = ['user_cat_aveThreeDayDelta_click','user_cat_aveThreeDayDelta_star','user_cat_aveThreeDayDelta_add_car','user_cat_aveThreeDayDelta_buy','user_item_aveThreeDayDelta_click','user_item_aveThreeDayDelta_star','user_item_aveThreeDayDelta_add_car','user_item_aveThreeDayDelta_buy'] factor_features = [ "user_item_click_nobuy", "user_item_star_nobuy", "user_item_cart_nobuy", "user_item_buy_again", "user_geo_b","user_geo_f","user_geo_i","user_geo_m","user_geo_o","user_geo_5","user_geo_4","user_geo_v","user_geo_9","user_geo_t", "item_geo_9","item_geo_4","item_geo_m","item_geo_t","item_geo_f", ] feature_names = [i for i in data.columns if i not in nolog and i not in factor_features and i not in nolog2] X1 = np.log(0.3+data[feature_names]) X2 = dict() X2['user_convert_rate'] = data['user_buy_count'] / (1+data['user_action_count']) X2['item_convert_rate'] = data['item_buy_count'] / (1+data['item_click_count']) X2 = pandas.DataFrame(X2) X3 = data[factor_features] feature_names2= [i for i in data.columns if i in nolog2] X4 = np.copysign(np.log(0.3+np.abs(data[feature_names2])),np.sign(data[feature_names2])) X = pandas.concat([X1, X2, X3, X4], axis=1) return X[_feature_names]
def _unsexagesimalize(value):
"""Return `value` after interpreting a (units, minutes, seconds) tuple.
When `value` is not a tuple, it is simply returned.
>>> _unsexagesimalize(3.25)
3.25
An input tuple is interpreted as units, minutes, and seconds. Note
that only the sign of `units` is significant! So all of the
following tuples convert into exactly the same value:
>>> '%f' % _unsexagesimalize((-1, 2, 3))
'-1.034167'
>>> '%f' % _unsexagesimalize((-1, -2, 3))
'-1.034167'
>>> '%f' % _unsexagesimalize((-1, -2, -3))
'-1.034167'
"""
if isinstance(value, tuple):
for i, component in enumerate(value):
if i:
value = value + copysign(component, value) * 60.0 ** -i
else:
value = component
return value
def correlate(self):
'''Estimates the time-delay in whole samples, then aligns and finds the complex visibility for two signals in the data key of self.parent_row and self.child_row.
Generates self.convolution_spectrum, self.zscore, self.expected_overlap, and self.real_overlap, which can be used for plotting purposes.
Creates two signals, one corresponding to the cos and the other to the sin signal, as self.cos_signal and self.sin_signal
In order to do a correlation without opening a file and populating self.child_row and self.parent_row, assign these variables manually.'''
child_fft = scipy.fftpack.fft(self.child_row['data'])
parent_fft = scipy.fftpack.fft(self.parent_row['data'])
child_inverse_conjugate = -child_fft.conjugate()
fft_auto_child = child_fft*child_inverse_conjugate
fft_convolution = parent_fft*child_inverse_conjugate
self.convolution_spectrum = numpy.abs(scipy.fftpack.ifft(fft_convolution/fft_auto_child)) #Roth window saved in self.convolution_spectrum
assert self.time_inacc < sec/2, "This system clock cannot *possibly* be that inaccurate!"
expected_tdiff = int((self.child_row['utcendtime']-self.parent_row['utcendtime'])*hz) #our initial guess for the location of the tdiff peak
sample_inacc = int(self.time_inacc*hz) #around the guessed place of tdiff.
if expected_tdiff-sample_inacc < 0: expected_tdiff = sample_inacc
elif expected_tdiff+sample_inacc > sample_size: expected_tdiff = sample_size-sample_inacc
cropped_convolution_spectrum = self.convolution_spectrum[expected_tdiff-sample_inacc:expected_tdiff+sample_inacc]
#later measurements of tdiff will have a 0 point at expected_tdiff-sample_inacc and a total length of around 2*sample_inacc (may wrap around)
tdiff = numpy.argmax(cropped_convolution_spectrum)
tdiff += expected_tdiff-sample_inacc #offset for the real convolution_spectrum
self.zscore = (self.convolution_spectrum[tdiff]-numpy.average(self.convolution_spectrum))/numpy.std(self.convolution_spectrum)
#timespan = math.fabs(child_row['utcendtime'] - parent_row['utcendtime']) + sec
self.expected_overlap = (float(sec) - 1.0*math.fabs(self.child_row['utcendtime']-self.parent_row['utcendtime']))*hz
self.real_overlap = (float(sec) - 1.0*math.fabs(float(tdiff)/hz))*hz
int_delay = int(numpy.copysign(numpy.floor(numpy.fabs(tdiff)), tdiff))
self.abs_delay = int(abs(int_delay)) #always positive :)
parent_signal, child_signal = self.parent_row['data'][self.abs_delay:], self.child_row['data'][0:len(self.child_row['data'])-self.abs_delay]
h_child_length = len(child_signal)
h_child_new_length = int(2**numpy.ceil(numpy.log2(h_child_length)))
h_child_diff_length = h_child_new_length - h_child_length
h_child_signal = scipy.fftpack.hilbert(numpy.append(child_signal, numpy.zeros(h_child_diff_length)))[0:h_child_length]
self.cos_signal, self.sin_signal = evaluate("parent_signal*child_signal"), evaluate("h_child_signal*parent_signal")
def getgimp(X):
s = []
g = []
for x in X:
r = np.abs(x)
l = .125
h = 1.
sgn = np.copysign(1.,x)
if (r < l):
S = 1. - (r*r+l*l)/(2.*h*l)
G = -x/(h*l)
elif (r < h-l):
S = 1. - r/h
G = -sgn/h
elif (r < h+l):
S = (h+l-r)*(h+l-r) / (4.*h*l)
G = (h+l-r) / (-2.*sgn*h*l)
else:
S = 0.
G = 0.
s.append(S)
g.append(G)
S = np.array(s)
G = np.array(g)
return (S,G)
def z_ip_s(t, t0, p, a, i, e, w, es, ms, tae):
Ma = mean_anomaly(t, t0, p, e, w)
if e < 0.01:
Ta = Ma
else:
de = es[1] - es[0]
dm = ms[1] - ms[0]
ie = int(floor(e / de))
ae = (e - de * ie) / de
if Ma < pi:
x = Ma
s = 1.
else:
x = TWO_PI - Ma
s = -1.
im = int(floor(x / dm))
am = (x - im * dm) / dm
Ta = (tae[ie, im] * (1.0 - ae) * (1.0 - am)
+ tae[ie + 1, im] * ae * (1.0 - am)
+ tae[ie, im + 1] * (1.0 - ae) * am
+ tae[ie + 1, im + 1] * ae * am)
Ta = Ma + s * Ta
if (Ta < 0.0):
Ta = Ta + TWO_PI
z = a * (1.0 - e ** 2) / (1.0 + e * cos(Ta)) * sqrt(1.0 - sin(w + Ta) ** 2 * sin(i) ** 2)
z *= copysign(1.0, sin(w + Ta))
return z
def round_away_from_zero(data):
result = np.copysign(.5, data)
result += data
np.trunc(result, out=result)
return result.astype(MV_MAT_TYPE)
def householder_transformation(a):
v = a / (a[0] + np.copysign(np.linalg.norm(a), a[0]))
v[0] = 1
return v, 2 / np.dot(v.T, v)
def __init__(self, asset, quantity, order_id=None):
self.asset = asset
self.quantity = quantity
self.order_id = 1 if order_id is None else order_id
self.direction = np.copysign(1, self.quantity)
pandas_udf(lambda s: np.tanh(s), DoubleType()), # type: ignore
"trunc":
pandas_udf(lambda s: np.trunc(s), DoubleType()), # type: ignore
})
binary_np_spark_mappings = OrderedDict({
"arctan2":
F.atan2,
"bitwise_and":
lambda c1, c2: c1.bitwiseAND(c2),
"bitwise_or":
lambda c1, c2: c1.bitwiseOR(c2),
"bitwise_xor":
lambda c1, c2: c1.bitwiseXOR(c2),
"copysign":
pandas_udf(lambda s1, s2: np.copysign(s1, s2),
DoubleType()), # type: ignore
"float_power":
pandas_udf( # type: ignore
lambda s1, s2: np.float_power(s1, s2), DoubleType()),
"floor_divide":
pandas_udf( # type: ignore
lambda s1, s2: np.floor_divide(s1, s2), DoubleType()),
"fmax":
pandas_udf(lambda s1, s2: np.fmax(s1, s2), DoubleType()), # type: ignore
"fmin":
pandas_udf(lambda s1, s2: np.fmin(s1, s2), DoubleType()), # type: ignore
"fmod":
pandas_udf(lambda s1, s2: np.fmod(s1, s2), DoubleType()), # type: ignore
"gcd":
pandas_udf(lambda s1, s2: np.gcd(s1, s2), DoubleType()), # type: ignore
import argparse
from copy import deepcopy
import os
import sys
import scipy as sp
import matplotlib as mpl
if __name__ == "__main__": mpl.use('Agg') # we need to do this right away
import numpy as np
from numpy import pi, floor, copysign, sqrt, mean
import matplotlib.pyplot as plt
from scipy import optimize, stats
from scipy.signal import sawtooth
sign = lambda x: copysign(1, x)
class Dot:
"Simple class to hold an (x,y) pair."
def __init__(self, xpos, ypos, perim):
self.xpos = xpos
self.ypos = ypos
self.perim = perim
def __repr__(self):
return 'Dot(xpos=%f, ypos=%f, perim=%f)' % (self.xpos, self.ypos,
self.perim)
def opposite_side_port_out(self):
return self.port.parallel_offset(
np.copysign(2 * self.radius +
self.vertical_race_length, self.opposite_gap) +
self._offset + self._offset_opposite)
def steffen_3d(
v_in,
z_in,
z_out,
z_min_surface,
z_max_surface,
lower_extrapolation_with_gradient=False,
upper_extrapolation_with_gradient=False,
):
"""
Performs Steffen interpolation on each individual column.
Steffen, M. (1990). A simple method for monotonic interpolation
in one dimension. Astronomy and Astrophysics, 239, 443.
`z_min_surface` and `z_max_surface` define the minimum and maximum height
outside of which the interpolated value will be set to nan (useful for not
interpolating pressure into solid ground for example).
`lower_extrapolation_with_gradient` and `upper_extrapolation_with_gradient`
enables the use of one-sided gradient when extrapolating outside the range
of `z_in`, otherwise the limit value of `v_in` is used by default
"""
assert v_in.shape[1:] == z_min_surface.shape
k_max = v_in.shape[0]
j_max = v_in.shape[1]
i_max = v_in.shape[2]
k_max_output = z_out.shape[0]
k_max_minus = k_max - 1
linear_slope = np.empty((k_max))
v_out = np.empty((k_max_output, j_max, i_max))
for i in range(i_max):
for j in range(j_max):
# first point
delta_lower = z_in[1, j, i] - z_in[0, j, i]
delta_upper = z_in[2, j, i] - z_in[1, j, i]
if delta_lower < 0:
raise Exception("Non-montonic increase in z_in")
if delta_upper < 0:
raise Exception("Non-montonic increase in z_in")
slope_lower = (v_in[1, j, i] - v_in[0, j, i]) / delta_lower
slope_upper = (v_in[2, j, i] - v_in[1, j, i]) / delta_upper
weighted_slope = slope_lower * (
1 + delta_lower /
(delta_lower + delta_upper)) - slope_upper * delta_lower / (
delta_lower + delta_upper)
if weighted_slope * slope_lower <= 0.0:
linear_slope[0] = 0.0
elif np.abs(weighted_slope) > 2 * np.abs(slope_lower):
linear_slope[0] = 2.0 * slope_lower
else:
linear_slope[0] = weighted_slope
# intermediate points
for k in range(1, k_max_minus):
delta_lower = z_in[k, j, i] - z_in[k - 1, j, i]
delta_upper = z_in[k + 1, j, i] - z_in[k, j, i]
slope_lower = (v_in[k, j, i] - v_in[k - 1, j, i]) / delta_lower
slope_upper = (v_in[k + 1, j, i] - v_in[k, j, i]) / delta_upper
weighted_slope = (slope_lower * delta_upper + slope_upper *
delta_lower) / (delta_lower + delta_upper)
if slope_lower * slope_upper <= 0.0:
linear_slope[k] = 0.0
elif np.abs(weighted_slope) > 2.0 * np.abs(slope_lower):
linear_slope[k] = np.copysign(2.0, slope_lower) * min(
np.abs(slope_lower), np.abs(slope_upper))
elif np.abs(weighted_slope) > 2.0 * np.abs(slope_upper):
linear_slope[k] = np.copysign(2.0, slope_lower) * min(
np.abs(slope_lower), np.abs(slope_upper))
else:
linear_slope[k] = weighted_slope
# last point
delta_lower = z_in[k_max_minus - 1, j, i] - z_in[k_max_minus - 2,
j, i]
delta_upper = z_in[k_max_minus, j, i] - z_in[k_max_minus - 1, j, i]
slope_lower = (v_in[k_max_minus - 1, j, i] -
v_in[k_max_minus - 2, j, i]) / delta_lower
slope_upper = (v_in[k_max_minus, j, i] -
v_in[k_max_minus - 1, j, i]) / delta_upper
weighted_slope = slope_upper * (
1 + delta_upper /
(delta_upper + delta_lower)) - slope_lower * delta_upper / (
delta_upper + delta_lower)
if weighted_slope * slope_upper <= 0.0:
linear_slope[k_max_minus] = 0.0
elif np.abs(weighted_slope) > 2.0 * np.abs(slope_upper):
linear_slope[k_max_minus] = 2.0 * slope_upper
else:
linear_slope[k_max_minus] = weighted_slope
# loop over output points
k_temp = 0
for k_out in range(k_max_output):
while (k_temp < k_max) and (z_in[k_temp, j, i] < z_out[k_out]):
k_temp = k_temp + 1
if 0 < k_temp < k_max:
k_high = k_temp
k_low = k_high - 1
delta = z_in[k_high, j, i] - z_in[k_low, j, i]
slope = (v_in[k_high, j, i] - v_in[k_low, j, i]) / delta
a = (linear_slope[k_low] + linear_slope[k_high] -
2 * slope) / (delta * delta)
b = (3 * slope - 2 * linear_slope[k_low] -
linear_slope[k_high]) / delta
c = linear_slope[k_low]
d = v_in[k_low, j, i]
t_1 = z_out[k_out] - z_in[k_low, j, i]
t_2 = t_1 * t_1
t_3 = t_2 * t_1
v_out[k_out, j, i] = a * t_3 + b * t_2 + c * t_1 + d
elif (k_temp == 0) and (z_out[k_out] >= z_min_surface[j, i]):
if lower_extrapolation_with_gradient:
v_out[k_out, j,
i] = v_in[0, j, i] + linear_slope[0] * (
z_out[k_out] - z_in[0, j, i])
else:
v_out[k_out, j, i] = v_in[0, j, i]
elif (k_temp
== k_max) and (z_out[k_out] <= z_max_surface[j, i]):
if upper_extrapolation_with_gradient:
v_out[k_out, j, i] = v_in[
k_max - 1, j, i] + linear_slope[k_max - 1] * (
z_out[k_out] - z_in[k_max - 1, j, i])
else:
v_out[k_out, j, i] = v_in[k_max - 1, j, i]
else:
v_out[k_out, j, i] = np.nan
return v_out
def prepare_data(data, window, STATE):
"""
Firstly, data should be 'trimmed' to exclude any data points at which the
robot was not being commanded to do anything.
Secondly, robot acceleration should be calculated from robot velocity and time.
We have found it effective to do this by taking the slope of the secant line
of velocity over a 60ms (3 standard loop iterations) window.
Thirdly, data from the quasi-static test should be trimmed to exclude the
initial period in which the robot is not moving due to static friction
Fourthly, data from the step-voltage acceleration tests must be trimmed to
remove the initial 'ramp-up' period that exists due to motor inductance; this
can be done by simply removing all data points before maximum acceleration is
reached.
Finally, the data can be analyzed: pool your trimmed data into four data sets
- one for each side of the robot (left or right) and each direction (forwards
or backwards).
For each set, run a linear regression of voltage seen at the motor
(or battery voltage if you do not have Talon SRXs) versus velocity and
acceleration.
Voltage should be in units of volts, velocity in units of feet per second,
and acceleration in units of feet per second squared.
Each data pool will then yield three parameters -
intercept, Kv (the regression coefficient of velocity), and Ka (the regression
coefficient of acceleration).
"""
# ensure voltage sign matches velocity sign
for x in JSON_DATA_KEYS:
data[x][L_VOLTS_COL] = np.copysign(data[x][L_VOLTS_COL],
data[x][L_ENCODER_V_COL])
data[x][R_VOLTS_COL] = np.copysign(data[x][R_VOLTS_COL],
data[x][R_ENCODER_V_COL])
# trim quasi data before computing acceleration
sf_trim = trim_quasi_testdata(data["slow-forward"], STATE)
sb_trim = trim_quasi_testdata(data["slow-backward"], STATE)
if sf_trim is None or sb_trim is None:
return [None] * 8
sf_l, sf_r = compute_accel(sf_trim, window)
sb_l, sb_r = compute_accel(sb_trim, window)
if sf_l is None or sf_r is None or sb_l is None or sb_r is None:
return [None] * 8
# trim step data after computing acceleration
ff_l, ff_r = compute_accel(data["fast-forward"], window)
fb_l, fb_r = compute_accel(data["fast-backward"], window)
if ff_l is None or ff_r is None or fb_l is None or fb_r is None:
return [None] * 8
ff_l = trim_step_testdata(ff_l)
ff_r = trim_step_testdata(ff_r)
fb_l = trim_step_testdata(fb_l)
fb_r = trim_step_testdata(fb_r)
return sf_l, sb_l, ff_l, fb_l, sf_r, sb_r, ff_r, fb_r
def hillas_parameters_3(pix_x, pix_y, image, recalculate_pixels=True):
"""Compute Hillas parameters for a given shower image.
MP: probably better to use Whipple Reynolds et al 1993 paper:
http://adsabs.harvard.edu/abs/1993ApJ...404..206R
which should be the same as one of my ICRC 1991 papers and my thesis.
Parameters
----------
pix_x : array_like
Pixel x-coordinate
pix_y : array_like
Pixel y-coordinate
image : array_like
Pixel values corresponding
recalculate_pixels : Boolean (default True)
Recalculate the pixel higher multiples (e.g., if pixels move (!) or pixel list changes between calls)
Returns
-------
hillas_parameters : `MomentParameters`
"""
if type(pix_x) == Quantity:
unit = pix_x.unit
assert pix_x.unit == pix_y.unit
else:
unit = 1.0
pix_x = Quantity(np.asanyarray(pix_x, dtype=np.float64)).value
pix_y = Quantity(np.asanyarray(pix_y, dtype=np.float64)).value
# make sure they are numpy arrays so we can use numpy operations
#pix_x = np.asanyarray(pix_x)
#pix_y = np.asanyarray(pix_y)
image = np.asanyarray(image, dtype=np.float64)
assert pix_x.shape == image.shape
assert pix_y.shape == image.shape
# Code to interface with historic version
wxdeg = pix_x
wydeg = pix_y
event = image
# Code below is from the historical Whipple routine, with original comments
# MJL is Mark Lang, MP is Michael Punch, GV is Giuseppe Vacanti, CA is Carl Akerlof, RCL is Dick Lamb,
# Translated from ForTran to c with "fable": https://github.com/ctessum/fable-go
# and from c to Python with "cpp2python: https://github.com/andreikop/cpp2python , then edited by hand
# C***********************************************************************
# C HILLAKPAR *
# C***********************************************************************
# C-- This version also calculates the asymmetry of the image.
# C-- Use of Akerlof Azwidth (Akwidth?) implemented MP 910112
# C-- Simpler Miss formula suggested CA, MP, 901101
# C-- Simpler Width and Length formulae suggested CA, 901017
# C-- Yet another little bug fixed by MP 900418- Generalize the case of
# C-- the horizontal line.
# C-- Bug fixed by RCL 900307-The case of the horizontal line image was
# C-- never considered.
# C-- Bug fixed by GV 900215
# C** This version takes events in WHIPPLE format and parameterises them.
# C** M. Punch ,900105
# C-- G. Vacanti introduces the common statement: coordinates are
# C-- computed only in the main program.
# C-- Modified by M. Punch to make it faster April, 1989
# C-- mjl 10 dec 87
# C--
# -- routine to calculate the six hillas iarameters
sumsig, sumxsig, sumysig, sumx2sig, sumy2sig, sumxysig, sumx3sig, sumx2ysig, sumxy2sig, sumy3sig = np.zeros(
10)
for i in range(np.size(event)):
if event[i] != 0.0:
wxbyev = wxdeg[i] * event[i]
wybyev = wydeg[i] * event[i]
sumsig += event[i]
sumxsig += wxbyev
sumx2sig += wxdeg[i] * wxbyev
sumysig += wybyev
sumy2sig += wydeg[i] * wybyev
sumxysig += wxdeg[i] * wybyev
sumx3sig += wxdeg[i] * wxdeg[i] * wxbyev
sumx2ysig += wxdeg[i] * wxdeg[i] * wybyev
sumxy2sig += wxdeg[i] * wydeg[i] * wybyev
sumy3sig += wydeg[i] * wydeg[i] * wybyev
if sumsig == 0.0:
raise (HillasParameterizationError(
"Empty pixels! Cannot calculate image parameters. Exiting..."))
xm = sumxsig / sumsig
x2m = sumx2sig / sumsig
ym = sumysig / sumsig
y2m = sumy2sig / sumsig
xym = sumxysig / sumsig
xm2 = xm * xm
ym2 = ym * ym
xmym = xm * ym
vx2 = x2m - xm2
vy2 = y2m - ym2
vxy = xym - xmym
x3m = sumx3sig / sumsig
x2ym = sumx2ysig / sumsig
xy2m = sumxy2sig / sumsig
y3m = sumy3sig / sumsig
vx3 = x3m - 3.0 * xm * x2m + 2.0 * xm2 * xm
vx2y = x2ym - x2m * ym - 2.0 * xym * xm + 2.0 * xm2 * ym
vxy2 = xy2m - y2m * xm - 2.0 * xym * ym + 2.0 * xm * ym2
vy3 = y3m - 3.0 * ym * y2m + 2.0 * ym2 * ym
d = vy2 - vx2
dist = np.sqrt(xm2 + ym2)
phi = np.arctan2(ym, xm)
# -- simpler formulae for length & width suggested CA 901019
z = np.sqrt(d * d + 4.0 * vxy * vxy)
length = np.sqrt((vx2 + vy2 + z) / 2.0)
width = np.sqrt((vy2 + vx2 - z) / 2.0)
# -- simpler formula for miss introduced CA, 901101
# -- revised MP 910112
if z == 0.0:
miss = dist
else:
uu = 1 + d / z
vv = 2 - uu
miss = np.sqrt((uu * xm2 + vv * ym2) / 2.0 - xmym * (2.0 * vxy / z))
# Code to de-interface with historical code
size = sumsig
m_x = xm
m_y = ym
length = length
width = width
r = dist
psi = np.arctan2((d + z) * ym + 2.0 * vxy * xm,
2.0 * vxy * ym - (d - z) * xm)
cpsi = np.cos(psi)
spsi = np.sin(psi)
# -- Asymmetry
if length == 0.0:
asymm = 0.0
else:
asymm = (vx3 * np.power(cpsi, 3) +
3.0 * vx2y * spsi * np.power(cpsi, 2) +
3.0 * vxy2 * cpsi * np.power(spsi, 2) +
vy3 * np.power(spsi, 3))
asymm = np.copysign(np.exp(np.log(np.abs(asymm)) / 3.0),
asymm) / length
# # -- Akerlof azwidth now used, 910112
# d = y2m - x2m
# z = np.sqrt(d * d + 4 * xym * xym)
# azwidth = np.sqrt((x2m + y2m - z) / 2.0)
#
# isize = int(sumsig)
# Code to de-interface with historical code
skewness = asymm * asymm * asymm
kurtosis = np.nan
return MomentParameters(size=size,
cen_x=m_x * unit,
cen_y=m_y * unit,
length=length * unit,
width=width * unit,
r=r * unit,
phi=Angle(phi * u.rad),
psi=Angle(psi * u.rad),
miss=miss * unit,
skewness=skewness,
kurtosis=kurtosis)
F.pandas_udf(lambda s: np.tanh(s), DoubleType()),
"trunc":
F.pandas_udf(lambda s: np.trunc(s), DoubleType()),
})
binary_np_spark_mappings = OrderedDict({
"arctan2":
F.atan2,
"bitwise_and":
lambda c1, c2: c1.bitwiseAND(c2),
"bitwise_or":
lambda c1, c2: c1.bitwiseOR(c2),
"bitwise_xor":
lambda c1, c2: c1.bitwiseXOR(c2),
"copysign":
F.pandas_udf(lambda s1, s2: np.copysign(s1, s2), DoubleType()),
"float_power":
F.pandas_udf(lambda s1, s2: np.float_power(s1, s2), DoubleType()),
"floor_divide":
F.pandas_udf(lambda s1, s2: np.floor_divide(s1, s2), DoubleType()),
"fmax":
F.pandas_udf(lambda s1, s2: np.fmax(s1, s2), DoubleType()),
"fmin":
F.pandas_udf(lambda s1, s2: np.fmin(s1, s2), DoubleType()),
"fmod":
F.pandas_udf(lambda s1, s2: np.fmod(s1, s2), DoubleType()),
"gcd":
F.pandas_udf(lambda s1, s2: np.gcd(s1, s2), DoubleType()),
"heaviside":
F.pandas_udf(lambda s1, s2: np.heaviside(s1, s2), DoubleType()),
"hypot":
def center_coordinates(self):
return self._origin_port.longitudinal_offset(
self.race_length / 2.).parallel_offset(
self._offset +
np.copysign(self.radius +
0.5 * self.vertical_race_length, self.gap)).origin
def opposite_side_port_in(self):
return self._origin_port.parallel_offset(
np.copysign(2 * self.radius +
self.vertical_race_length, self.opposite_gap) +
self._offset + self._offset_opposite).inverted_direction
def copysign(value, sign):
return np.copysign(value, sign)
def test_half_ufuncs(self):
"""Test the various ufuncs"""
a = np.array([0, 1, 2, 4, 2], dtype=float16)
b = np.array([-2, 5, 1, 4, 3], dtype=float16)
c = np.array([0, -1, -np.inf, np.nan, 6], dtype=float16)
assert_equal(np.add(a, b), [-2, 6, 3, 8, 5])
assert_equal(np.subtract(a, b), [2, -4, 1, 0, -1])
assert_equal(np.multiply(a, b), [0, 5, 2, 16, 6])
assert_equal(np.divide(a, b), [0, 0.199951171875, 2, 1, 0.66650390625])
assert_equal(np.equal(a, b), [False, False, False, True, False])
assert_equal(np.not_equal(a, b), [True, True, True, False, True])
assert_equal(np.less(a, b), [False, True, False, False, True])
assert_equal(np.less_equal(a, b), [False, True, False, True, True])
assert_equal(np.greater(a, b), [True, False, True, False, False])
assert_equal(np.greater_equal(a, b), [True, False, True, True, False])
assert_equal(np.logical_and(a, b), [False, True, True, True, True])
assert_equal(np.logical_or(a, b), [True, True, True, True, True])
assert_equal(np.logical_xor(a, b), [True, False, False, False, False])
assert_equal(np.logical_not(a), [True, False, False, False, False])
assert_equal(np.isnan(c), [False, False, False, True, False])
assert_equal(np.isinf(c), [False, False, True, False, False])
assert_equal(np.isfinite(c), [True, True, False, False, True])
assert_equal(np.signbit(b), [True, False, False, False, False])
assert_equal(np.copysign(b, a), [2, 5, 1, 4, 3])
assert_equal(np.maximum(a, b), [0, 5, 2, 4, 3])
x = np.maximum(b, c)
assert_(np.isnan(x[3]))
x[3] = 0
assert_equal(x, [0, 5, 1, 0, 6])
assert_equal(np.minimum(a, b), [-2, 1, 1, 4, 2])
x = np.minimum(b, c)
assert_(np.isnan(x[3]))
x[3] = 0
assert_equal(x, [-2, -1, -np.inf, 0, 3])
assert_equal(np.fmax(a, b), [0, 5, 2, 4, 3])
assert_equal(np.fmax(b, c), [0, 5, 1, 4, 6])
assert_equal(np.fmin(a, b), [-2, 1, 1, 4, 2])
assert_equal(np.fmin(b, c), [-2, -1, -np.inf, 4, 3])
assert_equal(np.floor_divide(a, b), [0, 0, 2, 1, 0])
assert_equal(np.remainder(a, b), [0, 1, 0, 0, 2])
assert_equal(np.divmod(a, b), ([0, 0, 2, 1, 0], [0, 1, 0, 0, 2]))
assert_equal(np.square(b), [4, 25, 1, 16, 9])
assert_equal(np.reciprocal(b),
[-0.5, 0.199951171875, 1, 0.25, 0.333251953125])
assert_equal(np.ones_like(b), [1, 1, 1, 1, 1])
assert_equal(np.conjugate(b), b)
assert_equal(np.absolute(b), [2, 5, 1, 4, 3])
assert_equal(np.negative(b), [2, -5, -1, -4, -3])
assert_equal(np.positive(b), b)
assert_equal(np.sign(b), [-1, 1, 1, 1, 1])
assert_equal(np.modf(b), ([0, 0, 0, 0, 0], b))
assert_equal(np.frexp(b),
([-0.5, 0.625, 0.5, 0.5, 0.75], [2, 3, 1, 3, 2]))
assert_equal(np.ldexp(b, [0, 1, 2, 4, 2]), [-2, 10, 4, 64, 12])
def householder(a):
u = a.copy()
u[0] = u[0] + np.copysign(np.linalg.norm(a), a[0])
return np.identity(len(u)) - ((2 * (u @ u.transpose())) /
(u.transpose() @ u))
def calc_energy_and_potential(self, Jij=False, fill_Jii=True, core_pot=False, constrain=False, debug=None):
"""
Compute energies and potentials for QEq_finish
:Parameters:
- Jij (boolean) : set to `True` to call the calc_Jmat method of the mme
- fill_Jij (boolean) : set to `True` to fill the diagonal elements of Jij
- core_pot (boolean) : use core potentials
- constrain (boolean) : constrain charge to Qtot (if "molconst on" is specified in the input a per molecule contraint is used)
"""
#flag for the calculation of degrees of freedom for get_dof()
if constrain:
self.constraint = True
# first compute the twocenter part with the MM engine
if Jij:
if self.recalc_Jij:
self.timer.switch_to("CORE calc_Jmat")
if core_pot:
self.E_ij, self.qpot_ij, self.Jij, self.core_qpot = self.pd.calc_Jmat(get_core_pot=True)
#print "DEBUG DEBUG DEBUG"
#print "E_ij from fortran code", self.E_ij
#print "Eval ", self.pd.get_val_energy()
#print "Ecore ", self.pd.get_core_energy()
#print "Ecore_val ", self.pd.get_coreval_energy()
#print "Ecoreval from core_pot", numpy.sum(self.core_qpot*self.q)
#print "Eij_val_only from qpot", numpy.sum(self.qpot_ij*self.q)
else:
self.E_ij, self.qpot_ij, self.Jij = self.pd.calc_Jmat()
self.Jij *= self.unit
self.E_ij *= self.unit
self.qpot_ij *= self.unit
self.recalc_Jij = False
self.recalc_qpot = False
else:
if self.recalc_qpot:
self.timer.switch_to("CORE calc_Jij")
if core_pot:
self.E_ij, self.qpot_ij, self.core_qpot = self.pd.calc_coul_energy(get_core_pot=True)
#print "DEBUG DEBUG DEBUG"
#print "E_ij from fortran code", self.E_ij
#print "Eval ", self.pd.get_val_energy()
#print "Ecore ", self.pd.get_core_energy()
#print "Ecore_val ", self.pd.get_coreval_energy()
#print "Ecoreval from core_pot", numpy.sum(self.core_qpot*self.q)
#print "Eij_val_only from qpot", numpy.sum(self.qpot_ij*self.q)
else:
self.E_ij_for, self.qpot_ij = self.pd.calc_coul_energy()
#print "DEBUG DEBUG DEBUG"
#print "E_ij from fortran code", self.E_ij_for
#print "Eij_val_only from qpot", 0.5*numpy.sum(self.qpot_ij*self.q)
# FIX this ... fortran gives us wrong E_ij ... on the second call. must be an initalization error.
#self.E_ij = 0.5*numpy.sum(self.qpot_ij*self.q)
self.E_ij = self.E_ij_for
self.E_ij *= self.unit
self.qpot_ij *= self.unit
self.recalc_qpot = False
self.timer.switch_to("CORE calc qpot")
# now add the one-center terms, note that following the original implementation the Jii is really 2*Jii
# as we can add it directly (times the charge) to the potential
# => this convention means we need to take it 0.5 for the energy
qpot_ii = self.Jii*self.q
self.qpot = self.En + qpot_ii + self.qpot_ij
# add core_pot if core charges are used. Note: the flag core_pot triggers a recalc but if
# core charges are used we add it in any case .. so it needs to be generated in the first call
if self.use_core_charge:
self.qpot += self.core_qpot
self.E_ii = numpy.sum(self.q*(self.En + 0.5*qpot_ii))
if self.limit_q:
q_large = numpy.clip(numpy.fabs(self.q)-self.q_thresh, 0.0, 1.0e6)
q_large2 = q_large*q_large
qpot_ii3 = numpy.copysign(3.0*self.Jij3*q_large2, self.q)
self.qpot += qpot_ii3
self.E_ii3 = numpy.sum(self.Jij3*q_large*q_large2)
self.E_ii += self.E_ii3
self.E = self.E_ii+self.E_ij
if debug:
self.pprint("DEBUG This is QEq calc_energy_force! called from %s \n Eii = %12.6f Eij = %12.6f E = %12.6f" % (debug, self.E_ii, self.E_ij, self.E))
if Jij:
if fill_Jii:
for i in xrange(self.natoms):
self.Jij[i,i] = self.Jii[i]
# charge conservation constraint
# NOTE: this is currently terribly non-parrallel
# could be done in two loops first summing up and then correcting
# with a allreduce sum inbetween.
if constrain:
self.timer.switch_to("CORE constrain")
if self.molconst:
#for m in self.pd.molecules.mlist:
# molpot = self.qpot[m.matoms]
# tot_mpot = molpot.sum()
# molpot -= tot_mpot/m.natoms
# self.qpot[m.matoms] = molpot[:]
# NOTE this is a bit of a hack ... the fortran constrain is private to QEq, but I did not
# want to have another module for it, so i put it into the coulomb module of pydlpoly ... so this is not a "generic mme"
# interface any more
self.pd.dlp_coul.constrain_qpot_permol(self.idnode, self.nodes, self.qpot, self.natoms,
self.mol_pot, self.pd.molecules.nmolecules, self.mol_natoms)
else:
# constrain for the total system
tot_pot = numpy.sum(self.qpot)
self.qpot -= tot_pot/self.natoms
#NEWNEW
if self.acks2:
self.calc_acks2_X()
self.calc_acks2_u()
self.E += self.calc_acks2_energy(self.u)
self.qpot += self.u
if self.exclude:
for i in self.exclude:
self.qpot[self.pd.molecules.mlist[i].matoms]=0
#self.pprint("###Forces from calc_energy_and_potential###")
# self.pprint("Identical with qpot sh, only here to show initial lbfgs forces")
#self.pprint(self.qpot_ij)
#self.pprint(self.qpot)
# self.print_charges()
return self.E
def round(x):
val = np.trunc(x + np.copysign(0.5, x))
return int(val)
def eval_quad_z_s(z: float, k: float, u: ndarray):
"""Evaluates the transit model for scalar normalized distance.
Parameters
----------
z: float
Normalized distance
k: float
Planet-star radius ratio
u: 1D array
Limb darkening coefficients
Returns
-------
Transit model evaluated at `z`.
"""
if abs(k - 0.5) < 1.0e-4:
k = 0.5
k2 = k**2
omega = 1.0 - u[0] / 3.0 - u[1] / 6.0
if abs(z - k) < 1e-6:
z += 1e-6
# The source is unocculted
if z > 1.0 + k or (copysign(1, z) < 0.0):
return 1.0
# The source is completely occulted
elif (k >= 1.0 and z <= k - 1.0):
return 0.0
z2 = z**2
x1 = (k - z)**2
x2 = (k + z)**2
x3 = k**2 - z**2
# LE
# --
# Case I: The occulting object fully inside the disk of the source
if z <= 1.0 - k:
le = k2
# Case II: ingress and egress
elif z >= abs(1.0 - k) and z <= 1.0 + k:
kap1 = arccos(min((1.0 - k2 + z2) / (2.0 * z), 1.0))
kap0 = arccos(min((k2 + z2 - 1.0) / (2.0 * k * z), 1.0))
le = k2 * kap0 + kap1
le = (le - 0.5 * sqrt(max(4.0 * z2 -
(1.0 + z2 - k2)**2, 0.0))) * INV_PI
# LD and ED
# ---------
is_edge_at_origin = abs(z - k) < 1.e-4 * (z + k)
is_full_transit = k <= 1.0 and z < (1.0 - k)
# Case 0: The edge of the occulting object lies at the origin
if is_edge_at_origin:
if (k == 0.5):
ld = 1.0 / 3.0 - 4.0 * INV_PI / 9.0
ed = 3.0 / 32.0
elif (z > 0.5):
q = 0.50 / k
Kk = ellk(q)
Ek = ellec(q)
ld = 1.0 / 3.0 + 16.0 * k / 9.0 * INV_PI * (
2.0 * k2 - 1.0) * Ek - (32.0 * k**4 - 20.0 * k2 +
3.0) / 9.0 * INV_PI / k * Kk
ed = 1.0 / 2.0 * INV_PI * (kap1 + k2 * (k2 + 2.0 * z2) * kap0 -
(1.0 + 5.0 * k2 + z2) / 4.0 * sqrt(
(1.0 - x1) * (x2 - 1.0)))
elif (z < 0.5):
q = 2.0 * k
Kk = ellk(q)
Ek = ellec(q)
ld = 1.0 / 3.0 + 2.0 / 9.0 * INV_PI * (4.0 *
(2.0 * k2 - 1.0) * Ek +
(1.0 - 4.0 * k2) * Kk)
ed = k2 / 2.0 * (k2 + 2.0 * z2)
else:
# Case I: The occulting object fully inside the disk of the source
if is_full_transit:
q = sqrt((x2 - x1) / (1.0 - x1))
Kk = ellk(q)
Ek = ellec(q)
n = x2 / x1 - 1.0
Pk = ellpicb(n, q)
ld = 2.0 / 9.0 * INV_PI / sqrt(1.0 - x1) * (
(1.0 - 5.0 * z2 + k2 + x3 * x3) * Kk + (1.0 - x1) *
(z2 + 7.0 * k2 - 4.0) * Ek - 3.0 * x3 / x1 * Pk)
if (z < k):
ld = ld + 2.0 / 3.0
if (abs(k + z - 1.0) < 1.e-4):
ld = 2.0 / 3.0 * INV_PI * arccos(
1.0 - 2.0 * k) - 4.0 / 9.0 * INV_PI * sqrt(
k * (1.0 - k)) * (3.0 + 2.0 * k - 8.0 * k2)
ed = k2 / 2.0 * (k2 + 2.0 * z2)
# Case II: ingress and egress
else:
q = sqrt((1.0 - (k - z)**2) / 4.0 / z / k)
Kk = ellk(q)
Ek = ellec(q)
n = 1.0 / x1 - 1.0
Pk = ellpicb(n, q)
ld = 1.0 / 9.0 * INV_PI / sqrt(k * z) * (
((1.0 - x2) * (2.0 * x2 + x1 - 3.0) - 3.0 * x3 *
(x2 - 2.0)) * Kk + 4.0 * k * z *
(z2 + 7.0 * k2 - 4.0) * Ek - 3.0 * x3 / x1 * Pk)
if (z < k):
ld = ld + 2.0 / 3.0
ed = 1.0 / 2.0 * INV_PI * (kap1 + k2 * (k2 + 2.0 * z2) * kap0 -
(1.0 + 5.0 * k2 + z2) / 4.0 * sqrt(
(1.0 - x1) * (x2 - 1.0)))
flux = 1.0 - ((1.0 - u[0] - 2.0 * u[1]) * le +
(u[0] + 2.0 * u[1]) * ld + u[1] * ed) / omega
return flux
def states(a, ng, idir):
r"""
Predict the cell-centered state to the edges in one-dimension using the
reconstructed, limited slopes. We use a fourth-order Godunov method.
Our convention here is that:
``al[i,j]`` will be :math:`al_{i-1/2,j}`,
``al[i+1,j]`` will be :math:`al_{i+1/2,j}`.
Parameters
----------
a : ndarray
The cell-centered state.
ng : int
The number of ghost cells
idir : int
Are we predicting to the edges in the x-direction (1) or y-direction (2)?
Returns
-------
out : ndarray, ndarray
The state predicted to the left and right edges.
"""
qx, qy = a.shape
al = np.zeros((qx, qy))
ar = np.zeros((qx, qy))
a_int = np.zeros((qx, qy))
dafm = np.zeros((qx, qy))
dafp = np.zeros((qx, qy))
d2af = np.zeros((qx, qy))
d2ac = np.zeros((qx, qy))
d3a = np.zeros((qx, qy))
C2 = 1.25
C3 = 0.1
nx = qx - 2 * ng
ny = qy - 2 * ng
ilo = ng
ihi = ng + nx
jlo = ng
jhi = ng + ny
# we need interface values on all faces of the domain
if (idir == 1):
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# interpolate to the edges
a_int[i, j] = (7.0 / 12.0) * (a[i - 1, j] + a[i, j]) - \
(1.0 / 12.0) * (a[i - 2, j] + a[i + 1, j])
al[i, j] = a_int[i, j]
ar[i, j] = a_int[i, j]
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# these live on cell-centers
dafm[i, j] = a[i, j] - a_int[i, j]
dafp[i, j] = a_int[i + 1, j] - a[i, j]
# these live on cell-centers
d2af[i,
j] = 6.0 * (a_int[i, j] - 2.0 * a[i, j] + a_int[i + 1, j])
for i in range(ilo - 3, ihi + 3):
for j in range(jlo - 1, jhi + 1):
d2ac[i, j] = a[i - 1, j] - 2.0 * a[i, j] + a[i + 1, j]
for i in range(ilo - 2, ihi + 3):
for j in range(jlo - 1, jhi + 1):
# this lives on the interface
d3a[i, j] = d2ac[i, j] - d2ac[i - 1, j]
# this is a look over cell centers, affecting
# i-1/2,R and i+1/2,L
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 1, jhi + 1):
# limit? MC Eq. 24 and 25
if (dafm[i, j] * dafp[i, j] <= 0.0 or (a[i, j] - a[i - 2, j]) *
(a[i + 2, j] - a[i, j]) <= 0.0):
# we are at an extrema
s = np.copysign(1.0, d2ac[i, j])
if (s == np.copysign(1.0, d2ac[i - 1, j])
and s == np.copysign(1.0, d2ac[i + 1, j])
and s == np.copysign(1.0, d2af[i, j])):
# MC Eq. 26
d2a_lim = s * min(
abs(d2af[i, j]), C2 * abs(d2ac[i - 1, j]),
C2 * abs(d2ac[i, j]), C2 * abs(d2ac[i + 1, j]))
else:
d2a_lim = 0.0
if (abs(d2af[i, j]) <= 1.e-12 *
max(abs(a[i - 2, j]), abs(a[i - 1, j]), abs(
a[i, j]), abs(a[i + 1, j]), abs(a[i + 2, j]))):
rho = 0.0
else:
# MC Eq. 27
rho = d2a_lim / d2af[i, j]
if (rho < 1.0 - 1.e-12):
# we may need to limit -- these quantities are at cell-centers
d3a_min = min(d3a[i - 1, j], d3a[i, j], d3a[i + 1, j],
d3a[i + 2, j])
d3a_max = max(d3a[i - 1, j], d3a[i, j], d3a[i + 1, j],
d3a[i + 2, j])
if (C3 * max(abs(d3a_min), abs(d3a_max)) <=
(d3a_max - d3a_min)):
# limit
if (dafm[i, j] * dafp[i, j] < 0.0):
# Eqs. 29, 30
ar[i, j] = a[i, j] - rho * \
dafm[i, j] # note: typo in Eq 29
al[i + 1, j] = a[i, j] + rho * dafp[i, j]
elif (abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j])):
# Eq. 31
ar[i, j] = a[i, j] - 2.0 * \
(1.0 - rho) * dafp[i, j] - rho * dafm[i, j]
elif (abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j])):
# Eq. 32
al[i + 1, j] = a[i, j] + 2.0 * \
(1.0 - rho) * dafm[i, j] + rho * dafp[i, j]
else:
# if Eqs. 24 or 25 didn't hold we still may need to limit
if (abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j])):
ar[i, j] = a[i, j] - 2.0 * dafp[i, j]
if (abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j])):
al[i + 1, j] = a[i, j] + 2.0 * dafm[i, j]
elif (idir == 2):
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 3):
# interpolate to the edges
a_int[i, j] = (7.0 / 12.0) * (a[i, j - 1] + a[i, j]) - \
(1.0 / 12.0) * (a[i, j - 2] + a[i, j + 1])
al[i, j] = a_int[i, j]
ar[i, j] = a_int[i, j]
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 3):
# these live on cell-centers
dafm[i, j] = a[i, j] - a_int[i, j]
dafp[i, j] = a_int[i, j + 1] - a[i, j]
# these live on cell-centers
d2af[i,
j] = 6.0 * (a_int[i, j] - 2.0 * a[i, j] + a_int[i, j + 1])
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 3, jhi + 3):
d2ac[i, j] = a[i, j - 1] - 2.0 * a[i, j] + a[i, j + 1]
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 2, jhi + 2):
# this lives on the interface
d3a[i, j] = d2ac[i, j] - d2ac[i, j - 1]
# this is a look over cell centers, affecting
# j-1/2,R and j+1/2,L
for i in range(ilo - 1, ihi + 1):
for j in range(jlo - 1, jhi + 1):
# limit? MC Eq. 24 and 25
if (dafm[i, j] * dafp[i, j] <= 0.0 or (a[i, j] - a[i, j - 2]) *
(a[i, j + 2] - a[i, j]) <= 0.0):
# we are at an extrema
s = np.copysign(1.0, d2ac[i, j])
if (s == np.copysign(1.0, d2ac[i, j - 1])
and s == np.copysign(1.0, d2ac[i, j + 1])
and s == np.copysign(1.0, d2af[i, j])):
# MC Eq. 26
d2a_lim = s * min(
abs(d2af[i, j]), C2 * abs(d2ac[i, j - 1]),
C2 * abs(d2ac[i, j]), C2 * abs(d2ac[i, j + 1]))
else:
d2a_lim = 0.0
if (abs(d2af[i, j]) <= 1.e-12 *
max(abs(a[i, j - 2]), abs(a[i, j - 1]), abs(
a[i, j]), abs(a[i, j + 1]), abs(a[i, j + 2]))):
rho = 0.0
else:
# MC Eq. 27
rho = d2a_lim / d2af[i, j]
if (rho < 1.0 - 1.e-12):
# we may need to limit -- these quantities are at cell-centers
d3a_min = min(d3a[i, j - 1], d3a[i, j], d3a[i, j + 1],
d3a[i, j + 2])
d3a_max = max(d3a[i, j - 1], d3a[i, j], d3a[i, j + 1],
d3a[i, j + 2])
if (C3 * max(abs(d3a_min), abs(d3a_max)) <=
(d3a_max - d3a_min)):
# limit
if (dafm[i, j] * dafp[i, j] < 0.0):
# Eqs. 29, 30
ar[i, j] = a[i, j] - rho * \
dafm[i, j] # note: typo in Eq 29
al[i, j + 1] = a[i, j] + rho * dafp[i, j]
elif (abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j])):
# Eq. 31
ar[i, j] = a[i, j] - 2.0 * \
(1.0 - rho) * dafp[i, j] - rho * dafm[i, j]
elif (abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j])):
# Eq. 32
al[i, j + 1] = a[i, j] + 2.0 * \
(1.0 - rho) * dafm[i, j] + rho * dafp[i, j]
else:
# if Eqs. 24 or 25 didn't hold we still may need to limit
if (abs(dafm[i, j]) >= 2.0 * abs(dafp[i, j])):
ar[i, j] = a[i, j] - 2.0 * dafp[i, j]
if (abs(dafp[i, j]) >= 2.0 * abs(dafm[i, j])):
al[i, j + 1] = a[i, j] + 2.0 * dafm[i, j]
return al, ar
def householder(a):
v = a / (a[0] + np.copysign(np.linalg.norm(a), a[0]))
v[0] = 1
H = np.eye(a.shape[0])
H -= (2 / np.dot(v, v)) * np.dot(v[:, None], v[None, :])
return H
def round(x):
return np.trunc(
x + np.copysign(np.nextafter(np.array(.5, dtype=x.dtype),
np.array(0., dtype=x.dtype),
dtype=x.dtype), x)).astype(x.dtype)
def hillas_parameters_4(pix_x, pix_y, image, recalculate_pixels=True):
"""Compute Hillas parameters for a given shower image.
As for hillas_parameters_3 (old Whipple Fortran code), but more Pythonized
MP: Parameters calculated as Whipple Reynolds et al 1993 paper:
http://adsabs.harvard.edu/abs/1993ApJ...404..206R
which should be the same as one of my ICRC 1991 papers and my thesis.
Parameters
----------
pix_x : array_like
Pixel x-coordinate
pix_y : array_like
Pixel y-coordinate
image : array_like
Pixel values corresponding
recalculate_pixels : Boolean (default True)
Recalculate the pixel higher multiples (e.g., if pixels move (!) or pixel list changes between calls)
Returns
-------
hillas_parameters : `MomentParameters`
"""
if type(pix_x) == Quantity:
unit = pix_x.unit
assert pix_x.unit == pix_y.unit
else:
unit = 1.0
# MP: Actually, I don't know why we need to strip the units... shouldn' the calculations all work with them?
pix_x = Quantity(np.asanyarray(pix_x, dtype=np.float64)).value
pix_y = Quantity(np.asanyarray(pix_y, dtype=np.float64)).value
image = np.asanyarray(image, dtype=np.float64)
assert pix_x.shape == image.shape
assert pix_y.shape == image.shape
(sumsig, sumxsig, sumysig, sumx2sig, sumy2sig, sumxysig, sumx3sig,
sumx2ysig, sumxy2sig, sumy3sig) = np.zeros(10)
# Call static_xy to initialize the "static variables"
# Actually, would be nice to just call this if we know the pixel positions have changed
static_xy(pix_x, pix_y, recalculate_pixels)
sumsig = image.sum()
sumxsig = (image * pix_x).sum()
sumysig = (image * pix_y).sum()
sumx2sig = (image * static_xy.pix_x2).sum()
sumy2sig = (image * static_xy.pix_y2).sum()
sumxysig = (image * static_xy.pix_xy).sum()
sumx3sig = (image * static_xy.pix_x3).sum()
sumx2ysig = (image * static_xy.pix_x2y).sum()
sumxy2sig = (image * static_xy.pix_xy2).sum()
sumy3sig = (image * static_xy.pix_y3).sum()
sumx4sig = (image * static_xy.pix_x4).sum()
sumx3ysig = (image * static_xy.pix_x3y).sum()
sumx2y2sig = (image * static_xy.pix_x2y2).sum()
sumxy3sig = (image * static_xy.pix_xy3).sum()
sumy4sig = (image * static_xy.pix_y4).sum()
if sumsig == 0.0:
raise (HillasParameterizationError(
"Empty pixels! Cannot calculate image parameters. Exiting..."))
xm = sumxsig / sumsig
ym = sumysig / sumsig
x2m = sumx2sig / sumsig
y2m = sumy2sig / sumsig
xym = sumxysig / sumsig
x3m = sumx3sig / sumsig
x2ym = sumx2ysig / sumsig
xy2m = sumxy2sig / sumsig
y3m = sumy3sig / sumsig
x4m = sumx4sig / sumsig
x3ym = sumx3ysig / sumsig
x2y2m = sumx2y2sig / sumsig
xy3m = sumxy3sig / sumsig
y4m = sumy4sig / sumsig
# Doing this should be same as above, but its 4us slower !?
#(xm, ym, x2m, y2m, xym, x3m, x2ym, xy2m, y3m) = \
# (sumxsig, sumysig, sumx2sig, sumy2sig, sumxysig, sumx3sig,
# sumx2ysig, sumxy2sig, sumy3sig) / sumsig
xm2 = xm * xm
ym2 = ym * ym
xmym = xm * ym
vx2 = x2m - xm2
vy2 = y2m - ym2
vxy = xym - xmym
vx3 = x3m - 3.0 * xm * x2m + 2.0 * xm2 * xm
vx2y = x2ym - x2m * ym - 2.0 * xym * xm + 2.0 * xm2 * ym
vxy2 = xy2m - y2m * xm - 2.0 * xym * ym + 2.0 * xm * ym2
vy3 = y3m - 3.0 * ym * y2m + 2.0 * ym2 * ym
d = vy2 - vx2
dist = np.sqrt(xm2 +
ym2) #could use hypot(xm,ym), but already have squares
phi = np.arctan2(ym, xm)
# -- simpler formulae for length & width suggested CA 901019
z = np.hypot(d, 2.0 * vxy)
length = np.sqrt((vx2 + vy2 + z) / 2.0)
width = np.sqrt((vy2 + vx2 - z) / 2.0)
# -- simpler formula for miss introduced CA, 901101
# -- revised MP 910112
if z == 0.0:
miss = dist
else:
uu = 1 + d / z
vv = 2 - uu
miss = np.sqrt((uu * xm2 + vv * ym2) / 2.0 - xmym * (2.0 * vxy / z))
#Change to faster caluclation of psi and avoid inaccuracy for hyp
#psi = np.arctan2((d + z) * ym + 2.0 * vxy * xm, 2.0 *vxy * ym - (d - z) * xm)
#hyp = np.sqrt(2 * z * (z + d)) #! should be simplification of sqrt((d+z)**2+(2*vxy)**2 ... but not accurate!
#hyp = np.hypot(d + z,2 * vxy)
#psi = np.arctan2(d + z, 2 * vxy)
#cpsi = np.cos(psi)
#spsi = np.sin(psi)
tanpsi_numer = (d + z) * ym + 2.0 * vxy * xm
tanpsi_denom = 2.0 * vxy * ym - (d - z) * xm
psi = np.arctan2(tanpsi_numer, tanpsi_denom)
# Code to de-interface with historical code
size = sumsig
m_x = xm
m_y = ym
length = length
width = width
r = dist
psi = psi
# Note, "skewness" is the same as the Whipple/MP "asymmetry^3", which is fine.
# ... and also, Whipple/MP "asymmetry" * "length" = MAGIC "asymmetry"
# ... so, MAGIC "asymmetry" = MAGIC "skewness"^(1/3) * "length"
# I don't know what MAGIC's "asymmetry" is supposed to be.
# -- Asymmetry and other higher moments
if length != 0.0:
vx4 = x4m - 4.0 * xm * x3m + 6.0 * xm2 * x2m - 3.0 * xm2 * xm2
vx3y = x3ym - 3.0 * xm * x2ym + 3.0 * xm2 * xym - x3m * ym \
+ 3.0 * x2m * xmym - 3.0 * xm2 * xm * ym
vx2y2 = x2y2m - 2.0 * ym * x2ym + x2m * ym2 \
- 2.0 * xm * xy2m + 4.0 * xym * xmym + xm2 * y2m - 3.0 * xm2 * ym2
vxy3 = xy3m - 3.0 * ym * xy2m + 3.0 * ym2 * xym - y3m * xm \
+ 3.0 * y2m * xmym - 3.0 * ym2 * ym * xm
vy4 = y4m - 4.0 * ym * y3m + 6.0 * ym2 * y2m - 3.0 * ym2 * ym2
# print("vs 4th: x4, x3y, x2y2, xy3, y4", vx4, vx3y, vx2y2, vxy3, vy4)
# print("cross check vx4",(image*(pix_x-xm)**4).sum()/sumsig)
# print("cross check vx3y",(image*(pix_x-xm)**3*(pix_y-ym)).sum()/sumsig)
# print("cross check vx2y2",(image*(pix_x-xm)**2*(pix_y-ym)**2).sum()/sumsig)
# print("cross check vxy3",(image*(pix_x-xm)*(pix_y-ym)**3).sum()/sumsig)
# print("cross check vy4",(image*(pix_y-ym)**4).sum()/sumsig)
hyp = np.hypot(tanpsi_numer, tanpsi_denom)
if hyp != 0.:
cpsi = tanpsi_denom / hyp
spsi = tanpsi_numer / hyp
else:
cpsi = 1.
spsi = 0.
cpsi2 = cpsi * cpsi
spsi2 = spsi * spsi
cspsi = cpsi * spsi
sk3bylen3 = (vx3 * cpsi * cpsi2 + 3.0 * vx2y * cpsi2 * spsi +
3.0 * vxy2 * cpsi * spsi2 + vy3 * spsi * spsi2)
asym = np.copysign(np.power(np.abs(sk3bylen3), 1. / 3.),
sk3bylen3) / length
skewness = asym * asym * asym # for MP's asym... (not for MAGIC asym!)
# Kurtosis
kurt = (vx4 * cpsi2 * cpsi2 + 4.0 * vx3y * cpsi2 * cspsi +
6.0 * vx2y2 * cpsi2 * spsi2 + 4.0 * vxy3 * cspsi * spsi2 +
vy4 * spsi2 * spsi2)
kurtosis = kurt / (length * length * length * length)
else: # Skip Higher Moments
asym = 0.0
psi = 0.0
skewness = 0.0
kurtosis = 0.0
# Azwidth not used anymore
# # -- Akerlof azwidth now used, 910112
# d = y2m - x2m
# z = np.sqrt(d * d + 4 * xym * xym)
# azwidth = np.sqrt((x2m + y2m - z) / 2.0)
return MomentParameters(size=size,
cen_x=m_x * unit,
cen_y=m_y * unit,
length=length * unit,
width=width * unit,
r=r * unit,
phi=Angle(phi * u.rad),
psi=Angle(psi * u.rad),
miss=miss * unit,
skewness=skewness,
kurtosis=kurtosis)
def steffen_1d_no_ep_time(
input_data,
input_levels,
output_level_array,
):
"""Performs Steffen interpolation on one individual column for
each time step.
Steffen, M. (1990). A simple method for monotonic interpolation in
one dimension. Astronomy and Astrophysics, 239, 443."""
t_max = input_data.shape[0]
k_max = input_data.shape[1]
k_max_output = output_level_array.shape[0]
k_max_minus = k_max - 1
linear_slope = np.empty((k_max))
output_data = np.empty((t_max, k_max_output))
# first point
delta_lower = input_levels[1] - input_levels[0]
delta_upper = input_levels[2] - input_levels[1]
if delta_lower < 0:
raise Exception("Non-montonic increase in input_levels")
if delta_upper < 0:
raise Exception("Non-montonic increase in input_levels")
for time_index in range(t_max):
slope_lower = (input_data[time_index, 1] -
input_data[time_index, 0]) / delta_lower
slope_upper = (input_data[time_index, 2] -
input_data[time_index, 1]) / delta_upper
weighted_slope = slope_lower * (
1 + delta_lower /
(delta_lower + delta_upper)) - slope_upper * delta_lower / (
delta_lower + delta_upper)
if weighted_slope * slope_lower <= 0.0:
linear_slope[0] = 0.0
elif np.abs(weighted_slope) > 2 * np.abs(slope_lower):
linear_slope[0] = 2.0 * slope_lower
else:
linear_slope[0] = weighted_slope
# intermediate points
for k in range(1, k_max_minus):
delta_lower = input_levels[k] - input_levels[k - 1]
delta_upper = input_levels[k + 1] - input_levels[k]
slope_lower = (input_data[time_index, k] -
input_data[time_index, k - 1]) / delta_lower
slope_upper = (input_data[time_index, k + 1] -
input_data[time_index, k]) / delta_upper
weighted_slope = (slope_lower * delta_upper + slope_upper *
delta_lower) / (delta_lower + delta_upper)
if slope_lower * slope_upper <= 0.0:
linear_slope[k] = 0.0
elif np.abs(weighted_slope) > 2.0 * np.abs(slope_lower):
linear_slope[k] = np.copysign(2.0, slope_lower) * min(
np.abs(slope_lower), np.abs(slope_upper))
elif np.abs(weighted_slope) > 2.0 * np.abs(slope_upper):
linear_slope[k] = np.copysign(2.0, slope_lower) * min(
np.abs(slope_lower), np.abs(slope_upper))
else:
linear_slope[k] = weighted_slope
# last point
delta_lower = input_levels[k_max_minus -
1] - input_levels[k_max_minus - 2]
delta_upper = input_levels[k_max_minus] - input_levels[k_max_minus - 1]
slope_lower = (input_data[time_index, k_max_minus - 1] -
input_data[time_index, k_max_minus - 2]) / delta_lower
slope_upper = (input_data[time_index, k_max_minus] -
input_data[time_index, k_max_minus - 1]) / delta_upper
weighted_slope = slope_upper * (
1 + delta_upper /
(delta_upper + delta_lower)) - slope_lower * delta_upper / (
delta_upper + delta_lower)
if weighted_slope * slope_upper <= 0.0:
linear_slope[k_max_minus] = 0.0
elif np.abs(weighted_slope) > 2.0 * np.abs(slope_upper):
linear_slope[k_max_minus] = 2.0 * slope_upper
else:
linear_slope[k_max_minus] = weighted_slope
# loop over output points
k_temp = 0
for k_out in range(k_max_output):
while (k_temp < k_max) and (input_levels[k_temp] <
output_level_array[k_out]):
k_temp = k_temp + 1
if 0 < k_temp < k_max:
k_high = k_temp
k_low = k_high - 1
delta = input_levels[k_high] - input_levels[k_low]
slope = (input_data[time_index, k_high] -
input_data[time_index, k_low]) / delta
a = (linear_slope[k_low] + linear_slope[k_high] -
2 * slope) / (delta * delta)
b = (3 * slope - 2 * linear_slope[k_low] -
linear_slope[k_high]) / delta
c = linear_slope[k_low]
d = input_data[time_index, k_low]
t_1 = output_level_array[k_out] - input_levels[k_low]
t_2 = t_1 * t_1
t_3 = t_2 * t_1
output_data[time_index,
k_out] = a * t_3 + b * t_2 + c * t_1 + d
# Allow for small deviations in upper/lower levels
elif (k_temp == 0) and (abs(output_level_array[k_out] -
input_levels[k_temp]) < 1e-6):
output_data[time_index, k_out] = input_data[time_index, 0]
elif (k_temp == k_max) and (abs(output_level_array[k_out] -
input_levels[k_temp]) < 1e-6):
output_data[time_index, k_out] = input_data[time_index, k_max]
else:
output_data[time_index, k_out] = np.nan
return output_data
np.multiply(a,b) # In[141]: np.divide(a,b) # In[142]: np.power(a,np.abs(b)) # In[143]: np.copysign(a,b) # In[144]: np.greater(a,b) #大于 # In[145]: np.greater_equal(a,b) #大于等于 # In[146]:
def get_avoidance_velocity(agent, collider, t, dt):
"""Get the smallest relative change in velocity between agent and collider
that will get them onto the boundary of each other's velocity obstacle
(VO), and thus avert collision."""
# This is a summary of the explanation from the AVO paper.
#
# The set of all relative velocities that will cause a collision within
# time tau is called the velocity obstacle (VO). If the relative velocity
# is outside of the VO, no collision will happen for at least tau time.
#
# The VO for two moving disks is a circularly truncated triangle
# (spherically truncated cone in 3D), with an imaginary apex at the
# origin. It can be described by a union of disks:
#
# Define an open disk centered at p with radius r:
# D(p, r) := {q | ||q - p|| < r} (1)
#
# Two disks will collide at time t iff ||x + vt|| < r, where x is the
# displacement, v is the relative velocity, and r is the sum of their
# radii.
#
# Divide by t: ||x/t + v|| < r/t,
# Rearrange: ||v - (-x/t)|| < r/t.
#
# By (1), this is a disk D(-x/t, r/t), and it is the set of all velocities
# that will cause a collision at time t.
#
# We can now define the VO for time tau as the union of all such disks
# D(-x/t, r/t) for 0 < t <= tau.
#
# Note that the displacement and radius scale _inversely_ proportionally
# to t, generating a line of disks of increasing radius starting at -x/t.
# This is what gives the VO its cone shape. The _closest_ velocity disk is
# at D(-x/tau, r/tau), and this truncates the VO.
x = -(agent.position - collider.position)
v = agent.velocity - collider.velocity
r = agent.radius + collider.radius
x_len_sq = norm_sq(x)
if x_len_sq >= r * r:
# We need to decide whether to project onto the disk truncating the VO
# or onto the sides.
#
# The center of the truncating disk doesn't mark the line between
# projecting onto the sides or the disk, since the sides are not
# parallel to the displacement. We need to bring it a bit closer. How
# much closer can be worked out by similar triangles. It works out
# that the new point is at x/t cos(theta)^2, where theta is the angle
# of the aperture (so sin^2(theta) = (r/||x||)^2).
adjusted_center = x / t * (1 - (r * r) / x_len_sq)
if dot(v - adjusted_center, adjusted_center) < 0:
# v lies in the front part of the cone
# print("front")
# print("front", adjusted_center, x_len_sq, r, x, t)
w = v - x / t
u = normalized(w) * r / t - w
n = normalized(w)
else: # v lies in the rest of the cone
# print("sides")
# Rotate x in the direction of v, to make it a side of the cone.
# Then project v onto that, and calculate the difference.
leg_len = sqrt(x_len_sq - r * r)
# The sign of the sine determines which side to project on.
sine = copysign(r, det((v, x)))
rot = array(((leg_len, sine), (-sine, leg_len)))
rotated_x = rot.dot(x) / x_len_sq
n = perp(rotated_x)
if sine < 0:
# Need to flip the direction of the line to make the
# half-plane point out of the cone.
n = -n
# print("rotated_x=%s" % rotated_x)
u = rotated_x * dot(v, rotated_x) - v
# print("u=%s" % u)
else:
# We're already intersecting. Pick the closest velocity to our
# velocity that will get us out of the collision within the next
# timestep.
# print("intersecting")
w = v - x / dt
u = normalized(w) * r / dt - w
n = normalized(w)
return u, n
Z/1/1 Z<Z>Z ''' # 28、以下表达式的结果是什么 ''' np.array(0) / np.array(0) np.array(0) // np.array(0) np.array([np.nan]).astype(int).astype(float) 答： nan 0 [-2.14748365e+09] ''' # 29、如何让一个浮点类型数组里面的值全部取整? # uniform(下限，上限，输出样本数) Z = np.random.uniform(-10, +10, 10) # copysign(A,B) 把B的符号给A # ceil(num) 计算大于等于num的最小整数 print(np.copysign(np.ceil(np.abs(Z)), Z)) # 30、如何在两个数组之间找到相同的值 Z1 = np.random.randint(0, 10, 10) print(Z1) Z2 = np.random.randint(0, 10, 10) print(Z2) print(np.intersect1d(Z1, Z2))
def draw_ellipsoid(self, numPoints=20, a_temp=[0, 0], draw_sfObs=False):
if self.d == 2:
theta = np.linspace(-pi, pi, num=numPoints)
resolution = numPoints # Resolution of drawing #points
else:
numPoints = [numPoints, ceil(numPoints / 2)]
theta, phi = np.meshgrid(np.linspace(-pi, pi, num=numPoints[0]),
np.linspace(-pi / 2,
pi / 2,
num=numPoints[1])) #
numPoints = numPoints[0] * numPoints[1]
resolution = numPoints # Resolution of drawing #points
theta = theta.T
phi = phi.T
#print('a_temp', sum(a_temp))
#if sum(a_temp) != 0:
#print('Not saving figure internaly.')
# For an arbitrary shap, the next two lines are used to find the shape segment
if hasattr(self, 'partition'):
warnings.warn('Warning - partition no finished implementing')
for i in range(self.partition.shape[0]):
ind[i, :] = self.theta >= (
self.partition[i, 1]) & self.theta <= (self.partition[i,
1])
[i, ind] = max(ind)
else:
ind = 0
#a = obs[n].a[:,ind]
#p = obs[n].p[:,ind]
# TODO -- add partition index
if sum(a_temp) == 0:
a = self.a
else:
# import pdb; pdb.set_trace() ## DEBUG ##
a = a_temp
p = self.p[:]
R = np.array(self.rotMatrix)
x_obs = np.zeros((self.d, numPoints))
if self.d == 2:
x_obs[0, :] = a[0] * np.cos(theta)
x_obs[1, :] = np.copysign(
a[1], theta) * (1 - np.cos(theta)**(2 * p[0]))**(1. /
(2. * p[1]))
else:
x_obs[0, :] = (a[0] * np.cos(phi) * np.cos(theta)).reshape((1, -1))
x_obs[1, :] = (
a[1] * np.copysign(1, theta) * np.cos(phi) *
(1 - np.cos(theta)**(2 * p[0]))**(1. / (2. * p[1]))).reshape(
(1, -1))
x_obs[2, :] = (a[2] * np.copysign(1, phi) *
(1 - (np.copysign(1, theta) * np.cos(phi) *
(1 - 0**(2 * p[2]) - np.cos(theta)**
(2 * p[0]))**(1 / (2**p[1])))**(2 * p[1]) -
(np.cos(phi) * np.cos(theta))**(2 * p[0]))
**(1 / (2 * p[2]))).reshape((1, -1))
# TODO for outside function - only sf is returned, remove x_obs to speed up
x_obs_sf = np.zeros((self.d, numPoints))
if not hasattr(self, 'sf'):
self.sf = 1
if type(self.sf) == int or type(self.sf) == float:
x_obs_sf = R @ (self.sf * x_obs) + np.tile(
np.array([self.x0]).T, (1, numPoints))
else:
x_obs_sf = R @ (x_obs * np.tile(self.sf,
(1, numPoints))) + np.tile(
self.x0, (numPoints, 1)).T
x_obs = R @ x_obs + np.tile(np.array([self.x0]).T, (1, numPoints))
if sum(a_temp) == 0:
self.x_obs = x_obs.T.tolist()
self.x_obs_sf = x_obs_sf.T.tolist()
else:
return x_obs_sf
def test_copysign_scalar(self):
assert np.copysign(3 * u.m, 1.) == 3. * u.m
assert np.copysign(3 * u.m, 1. * u.s) == 3. * u.m
assert np.copysign(3 * u.m, -1.) == -3. * u.m
assert np.copysign(3 * u.m, -1. * u.s) == -3. * u.m
from numpy import *
print(sum(range(5),-1)) # prints 10 (sum iterating over last column of the range)
Z**Z # returns array of each element raised to the power of itself
2 << Z >> 2 # returns array of (2 shifted z digits left, bitwise) shifted 2 digits right, bitwise
Z <- Z # returns array of whether each element is nonnegative
1j*Z # returns array of each element multiplied by i
Z/1/1 # returns the original array
Z<Z>Z # invalid
np.array(0) / np.array(0) # returns nan because 0 divided by 0 is undefined
np.array(0) // np.array(0) # returns 0
np.array([np.nan]).astype(int).astype(float) # returns array([maximum negative number])
to_round = np.random.uniform(-10,10,(10,10))
rounded = np.copysign(np.ceil(np.abs(to_round)),to_round)
set_A = np.random.randint(-10,10,(10,10))
set_B = np.random.randint(-10,10,(10,10))
common_values = np.intersect1d(set_A,set_B)
defaults = np.seterr(all="ignore")
worst_settings_possible = np.seterr(**defaults)
np.sqrt(-1) == np.emath.sqrt(-1) # false
today = np.datetime64('today')
yesterday = today - 1
tomorrow = today + 1
dates_in_july_2016 = np.arange(np.datetime64('2016-07','D'),np.datetime64('2016-08'))
def hillas_parameters_2(pix_x, pix_y, image, recalculate_pixels=True):
"""Compute Hillas parameters for a given shower image.
Alternate implementation of `hillas_parameters` ...
in the end we'll just keep one, but we're using Hillas parameter
computation as an example for performance checks.
Parameters
----------
pix_x : array_like
Pixel x-coordinate
pix_y : array_like
Pixel y-coordinate
image : array_like
Pixel values corresponding
recalculate_pixels : Boolean (default True)
Recalculate the pixel higher multiples (e.g., if pixels move (!) or pixel list changes between calls)
Returns
-------
hillas_parameters : `MomentParameters`
"""
if type(pix_x) == Quantity:
unit = pix_x.unit
assert pix_x.unit == pix_y.unit
else:
unit = 1.0
pix_x = Quantity(np.asanyarray(pix_x, dtype=np.float64)).value
pix_y = Quantity(np.asanyarray(pix_y, dtype=np.float64)).value
image = np.asanyarray(image)
assert pix_x.shape == image.shape
assert pix_y.shape == image.shape
size = image.sum()
if size == 0.0:
raise (HillasParameterizationError(
"Empty pixels! Cannot calculate image parameters. Exiting..."))
#call static_xy to initialize the "static variables"
#actually, would be nice to just call this if we know the pixel positions have changed
static_pix(pix_x, pix_y, recalculate_pixels)
# Compute image moments (done in a bit faster way, but putting all
# into one 2D array, where each row will be summed to calculate a
# moment) However, this doesn't avoid a temporary created for the
# 2D array
# momdata = np.row_stack([pix_x,
# pix_y,
# pix_x * pix_x,
# pix_y * pix_y,
# pix_x * pix_y]) * image
momdata = static_pix.pixdata * image
moms = momdata.sum(axis=1) / size
momdataHO = static_pix.pixdataHO * image
momsHO = momdataHO.sum(axis=1) / size
# give the moms values comprehensible names
xm, ym, x2m, xym, y2m = moms
x3m, x2ym, xy2m, y3m, x4m, x3ym, x2y2m, xy3m, y4m = momsHO
# intermediate variables (could be avoided if compiler which understands powers, etc)
xm2 = xm * xm
ym2 = ym * ym
xmym = xm * ym
# calculate variances
vx2 = x2m - xm2
vy2 = y2m - ym2
vxy = xym - xmym
vx3 = x3m - 3.0 * xm * x2m + 2.0 * xm2 * xm
vx2y = x2ym - x2m * ym - 2.0 * xym * xm + 2.0 * xm2 * ym
vxy2 = xy2m - y2m * xm - 2.0 * xym * ym + 2.0 * xm * ym2
vy3 = y3m - 3.0 * ym * y2m + 2.0 * ym2 * ym
# polar coordinates of centroid
rr = np.sqrt(xm2 +
ym2) # could use hypot(xm, ym), but already have squares
phi = np.arctan2(ym, xm) * u.rad
# common factors:
dd = vy2 - vx2
zz = np.hypot(
dd, 2.0 *
vxy) # for simpler formulae for length & width suggested CA 901019
# shower shape parameters
length = np.sqrt((vx2 + vy2 + zz) / 2.0)
width = np.sqrt((vx2 + vy2 - zz) / 2.0)
# azwidth = np.sqrt(x2m + y2m - zz) #Hillas Azwidth not used anymore
# d = y2m - x2m
# z = np.sqrt(d * d + 4 * xym * xym)
# akwidth = np.sqrt((x2m + y2m - z) / 2.0) # Akerlof azwidth (910112) not used anymore either
# miss, simpler formula for miss introduced CA, 901101; revised MP 910112
uu = 1.0 + dd / zz
vv = 2.0 - uu
miss = np.sqrt((uu * xm2 + vv * ym2) / 2.0 - xmym * 2.0 * vxy / zz)
# maybe for case zz = 0, put miss = dist?
# rotation angle of ellipse relative to centroid
tanpsi_numer = (dd + zz) * ym + (2.0 * vxy * xm)
tanpsi_denom = (2.0 * vxy * ym) - (dd - zz) * xm
psi = (np.arctan2(tanpsi_numer, tanpsi_denom)) * u.rad
# -- Asymmetry and other higher moments
if length != 0.0:
vx4 = x4m - 4.0 * xm * x3m + 6.0 * xm2 * x2m - 3.0 * xm2 * xm2
vx3y = x3ym - 3.0 * xm * x2ym + 3.0 * xm2 * xym - x3m * ym \
+ 3.0 * x2m * xmym - 3.0 * xm2 * xmym
vx2y2 = x2y2m - 2.0 * ym * x2ym + x2m * ym2 \
- 2.0 * xm * xy2m + 4.0 * xym * xmym + xm2 * y2m - 3.0 * xm2 * ym2
vxy3 = xy3m - 3.0 * ym * xy2m + 3.0 * ym2 * xym - y3m * xm \
+ 3.0 * y2m * xmym - 3.0 * ym2 * xmym
vy4 = y4m - 4.0 * ym * y3m + 6.0 * ym2 * y2m - 3.0 * ym2 * ym2
hyp = np.hypot(tanpsi_numer, tanpsi_denom)
if hyp != 0.:
cpsi = tanpsi_denom / hyp
spsi = tanpsi_numer / hyp
else:
cpsi = 1.
spsi = 0.
cpsi2 = cpsi * cpsi
spsi2 = spsi * spsi
cspsi = cpsi * spsi
sk3bylen3 = (vx3 * cpsi * cpsi2 + 3.0 * vx2y * cpsi2 * spsi +
3.0 * vxy2 * cpsi * spsi2 + vy3 * spsi * spsi2)
asym = np.copysign(np.power(np.abs(sk3bylen3), 1. / 3.),
sk3bylen3) / length
skewness = asym * asym * asym # for MP's asym... (not for MAGIC asym!)
# Kurtosis
kurt = (vx4 * cpsi2 * cpsi2 + 4.0 * vx3y * cpsi2 * cspsi +
6.0 * vx2y2 * cpsi2 * spsi2 + 4.0 * vxy3 * cspsi * spsi2 +
vy4 * spsi2 * spsi2)
kurtosis = kurt / (length * length * length * length)
else: # Skip Higher Moments
psi = 0.0 * u.rad
skewness = 0.0
kurtosis = 0.0
asym = 0.0
return MomentParameters(size=size,
cen_x=xm * unit,
cen_y=ym * unit,
length=length * unit,
width=width * unit,
r=rr * unit,
phi=Angle(phi),
psi=Angle(psi),
miss=miss * unit,
skewness=skewness,
kurtosis=kurtosis)
