def ReIter(self):
[a, b] = ipVar.poptDir(self.RePath)
z = np.where(np.diff(np.sign(a)))[0]
i = z[0]
r1 = b[i]
r2 = b[i + 1]
print(r1)
print(r2)
print("the value of the ")
while (abs(r1 - r2) > 20):
self.ReList = []
ReCr = funcs.calcReC(a, b)
self.ReList.append(ReCr + 0.3 * abs(r1 - r2))
self.ReList.append(ReCr - 0.3 * abs(r1 - r2))
self.CritRe()
print(r1)
print(r2)
[a, b] = ipVar.poptDir(self.RePath)
z = np.where(np.diff(np.sign(a)))[0]
r1 = b[z[0]]
r2 = b[z[0] + 1]
[a, b] = ipVar.poptDir(self.RePath)
if (len(a) > 3): func = InterpolatedUnivariateSpline(b, a, k=3)
ReC = func.roots()
return ReC
def attenuation_dead_zone(f, s1, s2):
DZ = []
pat1 = re.compile(s1 + '(.*?)' + s2, re.S) ### used for otdr 1
for element in f:
filename = element.split('/')[-1].split('.')[0]
cond = filename.split('_')[2]
if float(cond) != 8:
data = open(element)
lines = data.read()
new_data = pat1.findall(lines)
x = []
y = []
for line in new_data:
line = line.split()
n = len(line)
for i in range(1, int(n * 0.5), 1):
newline = line[i].split(',')
x.append(float(newline[0]))
y.append(float(newline[1]))
spl = InterpolatedUnivariateSpline(x, y)
x_new_1 = np.linspace(0.15, 0.25, 100)
y_new_1 = spl(x_new_1)
left = np.average(abs(y_new_1))
n = len(y)
y_new = []
for i in range(n):
y_new.append(float(y[i]) - left)
spl1 = InterpolatedUnivariateSpline(x, y_new)
root = InterpolatedUnivariateSpline.roots(spl1)
error = (abs(root[-1] - root[-2])) * 500
DZ.append(error)
else:
DZ.append(0)
return DZ
def find_cell_interceps(u_par_grid, u_perp_grid, cur_BDOP, irhop):
u_par_interceps = u_par_grid[np.logical_and(
u_par_grid > np.min(cur_BDOP.u_par[irhop]),
u_par_grid < np.max(cur_BDOP.u_par[irhop]))]
u_perp_spl = InterpolatedUnivariateSpline(cur_BDOP.u_par[irhop],
cur_BDOP.u_perp[irhop])
u_perp_at_u_par_interceps = u_perp_spl(u_par_interceps)
u_perp_interceps = []
u_par_at_u_perp_inteceps = []
for u_perp_intercep in u_perp_grid:
u_perp_interceps_spl = InterpolatedUnivariateSpline(
cur_BDOP.u_par[irhop], cur_BDOP.u_perp[irhop] - u_perp_intercep)
roots = u_perp_interceps_spl.roots()
if (len(roots) > 0):
for root in roots:
if (root >= np.min(u_par_grid) and root <= np.max(u_par_grid)):
u_perp_interceps.append(u_perp_intercep)
u_par_at_u_perp_inteceps.append(root)
# Now create list of all intercep points (u_par, u_perp) sorted by ascending u_par
intercep_list = np.array([
np.concatenate([u_par_interceps, u_par_at_u_perp_inteceps]),
np.concatenate([u_perp_at_u_par_interceps, u_perp_interceps])
])
sortarray = np.argsort(intercep_list[0])
return intercep_list.T[sortarray]
def ReIter2(self, FT):
[a, b] = ipVar.poptDir(self.RePath)
z = np.where(np.diff(np.sign(a)))[0]
r1 = b[z[0]]
r2 = b[z[0] + 1]
while (abs(r1 - r2) > 11):
[a, b] = ipVar.poptDir(self.RePath)
ReCr = funcs.calcReC(a, b)
self.ReList.append(ReCr + 5)
self.ReList.append(ReCr - 5)
self.CritRe(FT)
[a, b] = ipVar.poptDir(self.RePath)
ReCr = funcs.calcReC(a, b)
z = np.where(np.diff(np.sign(a)))[0]
r1 = b[z[0]]
r2 = b[z[0] + 1]
if (abs(r1 - r2) < 11):
[a, b] = ipVar.poptDir(self.RePath)
ReCr = funcs.calcReC(a, b)
self.ReList.append(ReCr)
self.CritRe(FT)
[a, b] = ipVar.poptDir(self.RePath)
if (len(a) > 3): func = InterpolatedUnivariateSpline(b, a, k=3)
ReC = func.roots()
return ReC
def ReIter2(self,FT):
[a,b]=ipVar.poptDir(self.RePath);
z=np.where(np.diff(np.sign(a)))[0];
r1=b[z[0]];r2=b[z[0]+1];
while(abs(r1-r2)>11):
[a,b]=ipVar.poptDir(self.RePath);
ReCr=funcs.calcReC(a,b);
self.ReList.append(ReCr+5);
self.ReList.append(ReCr-5);
self.CritRe(FT);
[a,b]=ipVar.poptDir(self.RePath);
ReCr=funcs.calcReC(a,b);
z=np.where(np.diff(np.sign(a)))[0];
r1=b[z[0]];r2=b[z[0]+1];
if(abs(r1-r2)< 11):
[a,b]=ipVar.poptDir(self.RePath);
ReCr=funcs.calcReC(a,b);
self.ReList.append(ReCr);
self.CritRe(FT);
[a,b]=ipVar.poptDir(self.RePath);
if(len(a)>3):func=InterpolatedUnivariateSpline(b,a,k=3);
ReC=func.roots();
return ReC;
def get_key_points(t, x):
"""
Basic function used in ZeroKeypointsGenerator
finding t: x(t)=0 and filtering the outliers
"""
spl = InterpolatedUnivariateSpline(t, x)
roots = spl.roots()
key_points = roots[1::2]
if len(key_points) < 3:
return key_points, np.zeros_like(key_points)
outlier_scores = np.abs(
(key_points[2:] + key_points[:-2] - 2 * key_points[1:-1]) /
key_points[1:-1])
np.pad(np.abs((key_points[2:] + key_points[:-2] - 2 * key_points[1:-1]) /
key_points[1:-1]), (2, 2),
mode='constant',
constant_values=1)
threshold = 3 * np.percentile(outlier_scores, 75) - 2 * np.percentile(
outlier_scores, 25)
outliers = (np.convolve(
np.pad(outlier_scores > threshold, (2, 2),
mode='constant',
constant_values=1), [1, 1, 1]) == 3)[2:-2]
return key_points, outliers
def parsec_locus_jhjk(x0, y0, cer, feh, age, isochrones):
'''
Find the where a star would be located on the stellar locus in (J - K)-(J - H) color space.
(J - K) is the abscissa and (J - H) is the ordinate.
Inputs:
------
x0: J - K color of star
y0: J - H color of star
cer: color excess ratio [E(J - H)/E(J - K)]
feh: metallicity of star
age: age in Gyr
isochrones: set of parsec isochrones
Outputs:
-------
jk_cross: J - K of intersection point
jh_cross: J - H of intersection point
'''
single = isochrones[np.where((isochrones['logAge'] == closest(
np.log10(age * 10**9), isochrones['logAge'])) & (
isochrones['MH'] == closest(feh, isochrones['MH'])))]
xs = (single['Jmag'] - single['Ksmag'])
ys = (single['Jmag'] - single['Hmag'])
bins = np.arange(np.min(xs), np.max(xs), 0.005)
binned_color = binned_statistic(xs, ys, statistic='median',
bins=bins).statistic
fin = np.where((np.isfinite(bins[:-1]) == True)
& (np.isfinite(binned_color) == True))
bins = bins[:-1][fin]
binned_color = binned_color[fin]
# Interpolate Isochrone
spline = InterpolatedUnivariateSpline(bins, binned_color, ext=0)
y_spl = spline(bins)
# Find the crossing point
if y0 - spline(x0) > 0:
# what to do if star is above isochrone in color-color space
return np.squeeze([-9999.0, -9999.0])
else:
# Find roots in difference between the ccline and isochrone
func = y_spl - ccline(bins, cer, x0, y0)
func_spl = InterpolatedUnivariateSpline(bins, func, ext=0)
jk_cross = func_spl.roots()
jh_cross = spline(jk_cross)
if len(jk_cross) == 0:
# If cross12 is empty
return np.squeeze([-9999.0, -9999.0])
else:
# Return Cartesian coordinates of crossing point in color-color space
return np.squeeze(list(zip(jk_cross, jh_cross)))
def crossings(series, value):
"""Find the labels where the series passes through value.
The labels in series must be increasing numerical values.
series: Series
value: number
returns: sequence of labels
"""
interp = InterpolatedUnivariateSpline(series.index, series-value)
return interp.roots()
def find_FWHM(x, y):
"""
Find FWHM (full width at half maximum) of the waveform.
"""
xmax = x[np.argmax(y)]
yn = y/np.max(y)
spl = InterpolatedUnivariateSpline(x=x, y=yn-0.5)
roots = spl.roots()
x1 = roots[roots<xmax][-1]
x2 = roots[roots>xmax][0]
return x2-x1
def crossings(series, value):
"""Find the labels where the series passes through value.
The labels in series must be increasing numerical values.
series: Series
value: number
returns: sequence of labels
"""
interp = InterpolatedUnivariateSpline(series.index, series-value)
return interp.roots()
def fwhm(x, y):
"""Calculate the FWHM and peak height of a vector y with corresponding coordinates x."""
i_max_y = np.argmax(y)
y_norm = y - y[i_max_y] / 2
max_x = x[i_max_y]
spline = InterpolatedUnivariateSpline(x, y_norm, k=3, ext='raise')
# FWHM
r = spline.roots()
if len(r) < 2:
return np.nan
center = r[np.argsort(np.abs(r - max_x))[0:2]]
fwhm = np.abs(center[1] - center[0])
return fwhm
def CalculateHWHM(GF_A): ''' calculates the half-width at half-maximum of the Kondo resonance and the maximum of the spectral function ''' N = len(En_A) IntMin = int((N+1)/2-int(0.5/dE)) IntMax = int((N+1)/2+int(0.5/dE)) DOSmaxPos = sp.argmax(-sp.imag(GF_A[IntMin:IntMax])/sp.pi) DOSmax = -sp.imag(GF_A[IntMin+DOSmaxPos])/sp.pi # maximum of DoS wmax = En_A[IntMin+DOSmaxPos] # position of the maximum at energy axis DOS = InterpolatedUnivariateSpline(En_A-1e-12,-sp.imag(GF_A)/sp.pi-DOSmax/2.0) ## 1e-12 breaks symmetry for half-filling, otherway DOS.roots() loses one solution. DOSroots_A = sp.sort(sp.fabs(DOS.roots())) try: HWHM = (DOSroots_A[0] + DOSroots_A[1])/2.0 except IndexError: HWHM = 0.0 return [HWHM,DOSmax,wmax]
def CalculateHWHM(GF_A): ''' calculates the half-width at half-maximum of the Kondo resonance and the maximum of the spectral function ''' N = len(En_A) IntMin = int((N+1)/2-int(0.5/dE)) IntMax = int((N+1)/2+int(0.5/dE)) DOSmaxPos = sp.argmax(-sp.imag(GF_A[IntMin:IntMax])/sp.pi) DOSmax = -sp.imag(GF_A[IntMin+DOSmaxPos])/sp.pi # maximum of DoS wmax = En_A[IntMin+DOSmaxPos] # position of the maximum at energy axis DOS = InterpolatedUnivariateSpline(En_A-1e-12,-sp.imag(GF_A)/sp.pi-DOSmax/2.0) ## 1e-12 breaks symmetry for half-filling, otherway DOS.roots() loses one solution. DOSroots_A = sp.sort(sp.fabs(DOS.roots())) try: HWHM = (DOSroots_A[0] + DOSroots_A[1])/2.0 except IndexError: HWHM = 0.0 return [HWHM,DOSmax,wmax]
def retreat_terminus(profile, verbose=False):
"""
Retreats terminus position with interpolation to satisfy height above flotation criterion.
Parameters
----------
profile: Profile
Profile to advance.
verobse: bool, optional
Print out diagnostic messages. Default: False.
Returns
-------
new_profile: Profile
New Profile instance.
"""
# Create spline representation of HAF
spline = InterpolatedUnivariateSpline(profile.x, profile.HAF)
# Find roots
roots = spline.roots()
if len(roots) > 1:
raise ValueError('Found multiple zero crossings of HAF')
x_zero = roots[0]
# Compute rough number of grid points to remove
n_remove = int(np.round((profile.x[-1] - x_zero) / profile.dx))
if verbose:
print('Retreating %d cells' % n_remove)
# Create new grid of x points
x_new = np.linspace(profile.x[0], x_zero, profile.N - n_remove)
# Check if we need to remove an extra point
dx_thresh = profile.dx - 0.05 * profile.dx
if (x_new[1] - x_new[0]) < dx_thresh:
x_new = np.linspace(profile.x[0], x_zero, profile.N - n_remove - 1)
# Create new profile
new_profile = profile.update_coordinates(x_new, extrapolate=False)
new_profile.t = profile.t
if verbose:
print('New spacing:', new_profile.dx)
return new_profile
def ReIter(self, FT):
[a, b] = ipVar.poptDir(self.RePath)
while (all(i > 0 for i in a)):
self.ReList = []
ReNew = float(b[0]) - 0.2 * float(b[0])
self.ReList.append(ReNew)
self.CritRe(FT)
[a, b] = ipVar.poptDir(self.RePath)
while (all(i < 0 for i in a)):
self.ReList = []
ReNew = float(b[-1]) + 0.3 * float(b[-1])
self.ReList.append(ReNew)
self.CritRe(FT)
[a, b] = ipVar.poptDir(self.RePath)
[a, b] = ipVar.poptDir(self.RePath)
z = np.where(np.diff(np.sign(a)))[0]
i = z[0]
r1 = b[i]
r2 = b[i + 1]
print(r1)
print(r2)
print("the value of the ")
temp = [0]
ReCr = funcs.calcReC(a, b)
temp.append(ReCr)
while (abs(temp[-1] - temp[-2]) > 1):
self.ReList = []
ReCr = funcs.calcReC(a, b)
self.ReList.append(ReCr + 0.1 * abs(r1 - r2))
self.ReList.append(ReCr - 0.1 * abs(r1 - r2))
self.CritRe(FT)
print(r1)
print(r2)
ReCr = funcs.calcReC(a, b)
temp.append(ReCr)
[a, b] = ipVar.poptDir(self.RePath)
z = np.where(np.diff(np.sign(a)))[0]
r1 = b[z[0]]
r2 = b[z[0] + 1]
[a, b] = ipVar.poptDir(self.RePath)
if (len(a) > 3): func = InterpolatedUnivariateSpline(b, a, k=3)
ReC = func.roots()
return ReC
def score_from_efficiency_array(y_truth,
y_score,
efficiency_selected,
keep_lower=False):
"""
Return the score array corresponding to an external fixed efficiency
array.
Parameters
-----------------------------------------------
y_truth: array
Training or test set labels. The candidates for each
class should be labeled with 0, ..., N.
In case of binary classification, 0 should
correspond to the background while 1 to the signal
y_score: array
Estimated probabilities or decision function.
keep_lower: bool
If True compute the efficiency using the candidates with
score lower than the score threshold; otherwise using the
candidates with score higher than the score threshold.
efficiency_selected: list or array
Efficiency array along which calculate
the corresponding score array
Returns
-----------------------------------------------
out: numpy array
Score array corresponding to efficiency_selected
"""
score_list = []
eff, score = bdt_efficiency_array(y_truth,
y_score,
n_points=1000,
keep_lower=keep_lower)
for eff_val in efficiency_selected:
interp = InterpolatedUnivariateSpline(score, eff - eff_val)
score_list.append(interp.roots()[0])
score_array = np.array(score_list)
return score_array
def FindABS(Det_A):
""" determines ABS energies as zeroes of GF determinant """
DetG = InterpolatedUnivariateSpline(En_A[EdgePos1+1:EdgePos2],sp.real(Det_A[:]))
RootsG_A = DetG.roots()
NABS = len(RootsG_A)
ABSpos_A = sp.zeros(2)
Diff_A = sp.zeros(2)
if NABS == 0:
## assumes ABS states too close to gap edges
## this also happens when using brentq to calculate densities and
## it starts from wrong initial guess
print("# - Warning: FindABS: no ABS found: Probably too close to band edges.")
ABS_A = sp.array([-Delta+2.0*dE,Delta-2.0*dE])
ABSpos_A = sp.array([EdgePos1+1,EdgePos2-1])
Diff_A = sp.array([DetG.derivatives(ABS_A[0])[1],DetG.derivatives(ABS_A[1])[1]])
elif NABS == 1:
## ABS too close to each other?
print("# - Warning: FindABS: only one ABS found: {0: .6e}".format(RootsG_A[0]))
print("# - Assuming they are too close to Fermi energy.")
print("# - Using mirroring to get the other ABS, please check the result.")
ABS_A = [-sp.fabs(RootsG_A[0]),sp.fabs(RootsG_A[0])]
for i in range(2):
ABSpos_A[i] = FindInEnergies(ABS_A[i],En_A)
Diff_A[i] = DetG.derivatives(ABS_A[i])[1]
elif NABS == 2:
## two ABS states, ideal case
ABS_A = sp.copy(RootsG_A)
for i in range(2):
ABSpos_A[i] = FindInEnergies(RootsG_A[i],En_A)
Diff_A[i] = DetG.derivatives(RootsG_A[i])[1]
else:
print("# - Error: FindABS: Too many zeroes of the determinant.")
exit()
if sp.fabs(ABS_A[0]+ABS_A[1]) > 1e-6:
print("# - Warning: FindABS: positive and negative ABS energies don't match, diff = {0: .6e}"\
.format(sp.fabs(ABS_A[0]-ABS_A[1])))
if sp.fabs(ABS_A[0])<dE:
## ABS energy smaller than the energy resolution
print("# - Warning: FindABS: ABS energies smaller than energy resolution")
print("# - We put the poles to lowest possible energies.")
ABSpos_A = [Nhalf-1,Nhalf+1]
return [ABS_A,Diff_A,ABSpos_A]
def filter_fwhm(self):
"""
Returns
-------
float
Filter full width at half maximum (micron).
"""
data = self.get_filter()
spline = InterpolatedUnivariateSpline(
data[0, :], data[1, :] - np.max(data[1, :]) / 2.)
root = spline.roots()
diff = root - self.mean_wavelength()
root1 = np.amax(diff[diff < 0.])
root2 = np.amin(diff[diff > 0.])
return root2 - root1
def ReIter(self,FT):
[a,b]=ipVar.poptDir(self.RePath);
while(all(i > 0 for i in a)):
self.ReList=[];
ReNew=float(b[0])- 0.2*float(b[0]);
self.ReList.append(ReNew);
self.CritRe(FT);
[a,b]=ipVar.poptDir(self.RePath);
while(all(i < 0 for i in a)):
self.ReList=[];
ReNew=float(b[-1])+0.3*float(b[-1]);
self.ReList.append(ReNew);
self.CritRe(FT);
[a,b]=ipVar.poptDir(self.RePath);
[a,b]=ipVar.poptDir(self.RePath);
z=np.where(np.diff(np.sign(a)))[0];
i=z[0];
r1=b[i];r2=b[i+1];
print(r1);print(r2);
print("the value of the ");
temp=[0];
ReCr=funcs.calcReC(a,b);
temp.append(ReCr);
while(abs(temp[-1]-temp[-2])>1):
self.ReList=[];
ReCr=funcs.calcReC(a,b);
self.ReList.append(ReCr+0.1*abs(r1-r2));
self.ReList.append(ReCr-0.1*abs(r1-r2));
self.CritRe(FT);
print(r1);print(r2);
ReCr=funcs.calcReC(a,b);
temp.append(ReCr);
[a,b]=ipVar.poptDir(self.RePath);
z=np.where(np.diff(np.sign(a)))[0];
r1=b[z[0]];r2=b[z[0]+1];
[a,b]=ipVar.poptDir(self.RePath);
if(len(a)>3):func=InterpolatedUnivariateSpline(b,a,k=3);
ReC=func.roots();
return ReC;
def ReIter(self):
[a,b]=ipVar.poptDir(self.RePath);
z=np.where(np.diff(np.sign(a)))[0];
i=z[0];
r1=b[i];r2=b[i+1];
print(r1);print(r2);
print("the value of the ");
while(abs(r1-r2)>20):
self.ReList=[];
ReCr=funcs.calcReC(a,b);
self.ReList.append(ReCr+0.3*abs(r1-r2));
self.ReList.append(ReCr-0.3*abs(r1-r2));
self.CritRe();
print(r1);print(r2);
[a,b]=ipVar.poptDir(self.RePath);
z=np.where(np.diff(np.sign(a)))[0];
r1=b[z[0]];r2=b[z[0]+1];
[a,b]=ipVar.poptDir(self.RePath);
if(len(a)>3):func=InterpolatedUnivariateSpline(b,a,k=3);
ReC=func.roots();
return ReC;
def filter_fwhm(self) -> float:
"""
Calculate the full width at half maximum (FWHM) of the filter profile.
Returns
-------
float
Filter full width at half maximum (um).
"""
data = self.get_filter()
spline = InterpolatedUnivariateSpline(
data[:, 0], data[:, 1] - np.max(data[:, 1]) / 2.)
root = spline.roots()
diff = root - self.mean_wavelength()
root1 = np.amax(diff[diff < 0.])
root2 = np.amin(diff[diff > 0.])
return root2 - root1
def refl_dead_zone(f, s1, s2):
DZ = []
pat1 = re.compile(s1 + '(.*?)' + s2, re.S) ### used for otdr 1
for element in f:
filename = element.split('/')[-1].split('.')[0]
cond = filename.split('_')[2]
if float(cond) != 8:
data = open(element)
lines = data.read()
new_data = pat1.findall(lines)
x = []
y = []
for line in new_data:
line = line.split()
n = len(line)
for i in range(1, n, 1):
newline = line[i].split(',')
x.append(float(newline[0]))
y.append(float(newline[1]))
refl_peak = max(y)
refl_under_peak_1 = refl_peak - 1.5
index = y.index(refl_peak)
index_low = index - 50
index_high = index + 20
x_new = []
y_new = []
for i in range(index_low, index_high, 1):
x_new.append(float(x[i]))
y_new.append(float(y[i]) - refl_under_peak_1)
spl = InterpolatedUnivariateSpline(x_new, y_new)
root = InterpolatedUnivariateSpline.roots(spl)
pos_gap = (abs(root[0] - root[1])) * 1000
DZ.append(pos_gap)
else:
DZ.append(0)
return DZ
def find_contours(self, h):
# Contour for single level
# Sign changes of Z-h are recoreded in pen_points
# The recorded points are always above or left the penetration point
# Negative indices indicate a horizontal sign change
self.pen_points = []
#horizontal
# The zeroth x entry can never be a horizontal penetration point
for i in range(1, self.nx-1):
for j in range(0, self.ny):
if((self.Z[i,j] -h) * (self.Z[i + 1,j] - h) < 0.0):
self.pen_points.append(-np.array([i,j]))
# Edge
for i in range(self.nx-1, self.nx):
for j in range(0, self.ny):
if((self.Z[i,j] -h) * (self.Z[i - 1,j] - h) < 0.0):
if(np.all(np.sum(np.abs(self.pen_points - (-np.array([i - 1,j]))),axis=1) > 0)):
self.pen_points.append(-np.array([i - 1,j]))
#Vertical
for i in range(0, self.nx):
# The zeroth xy entry can never be a vertical penetration point
for j in range(1, self.ny-1):
if((self.Z[i,j] - h) * (self.Z[i,j + 1] - h) < 0.0):
self.pen_points.append(np.array([i,j]))
#Edge
for i in range(0, self.nx):
for j in range(self.ny-1, self.ny):
if((self.Z[i,j] - h) * (self.Z[i,j - 1] - h) < 0.0):
if(np.all(np.sum(np.abs(self.pen_points - (-np.array([i,j-1]))),axis=1) > 0)):
self.pen_points.append(np.array([i,j-1]))
if(len(self.pen_points) == 0):
self.contours_found = False
return
# Convert the pen_points to complex values which makes the index search much faster
# Also remove any duplicates we might have picked up
self.pen_points = list(np.unique(np.array(self.pen_points).T[0] + 1.j * np.array(self.pen_points).T[1]))
self.contours_found = True
self.finished_points = []
self.contour_lines = [[]]
self.contour_indices = [[]]
self.contour_closed = []
next_point = self.pen_points.pop(0)
Z_spl = RectBivariateSpline(self.x, self.y, self.Z - h)
N_int = 10
x_int = np.zeros(N_int)
y_int = np.zeros(N_int)
# Open contourlines are reversed to avoid bad starting points
contour_reversed = False
# Assemble contours
while True:
i_next = int(np.real(next_point))
j_next = int(np.imag(next_point))
if(i_next < 0 or j_next < 0):
# Horiontal penetration point
x_int[:] = np.linspace(self.x[-i_next], self.x[-i_next + 1], N_int)
y_int[:] = self.y[-j_next]
Z_int = Z_spl(x_int, y_int, grid=False)
root_spl = InterpolatedUnivariateSpline(x_int, Z_int)
roots = root_spl.roots()
if(len(roots) == 0):
print("Found no roots for this penetration point")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.contour(self.x, self.y, self.Z.T - h, levels=[0.0])
ax.vlines(self.x, np.min(self.y), np.max(self.y), linewidths=0.2)
ax.hlines(self.y, np.min(self.x), np.max(self.x), linewidths=0.2)
ax.plot(x_int, y_int, "r--", linewidth=4)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot(x_int, Z_int)
plt.show()
raise ValueError("Found no roots for this penetration point")
for root in roots:
self.contour_lines[-1].append([root, y_int[0]])
else:
# Horiontal penetration point
x_int[:] = self.x[i_next]
y_int[:] = np.linspace(self.y[j_next], self.y[j_next + 1], N_int)
Z_int = Z_spl(x_int, y_int, grid=False)
root_spl = InterpolatedUnivariateSpline(y_int, Z_int)
roots = root_spl.roots()
if(len(roots) == 0):
print("Found no roots for this penetration point")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.contour(self.x, self.y, self.Z.T - h, levels=[0.0])
ax.vlines(self.x, np.min(self.y), np.max(self.y), linewidths=0.2)
ax.hlines(self.y, np.min(self.x), np.max(self.x), linewidths=0.2)
ax.plot(x_int, y_int, "r--", linewidth=4)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.plot(y_int, Z_int)
plt.show()
raise ValueError("Found no roots for this penetration point")
for root in roots:
self.contour_lines[-1].append([x_int[0], root])
self.contour_indices[-1].append(next_point)
self.finished_points.append(next_point)
# Find the next point
found_next, isclosed, next_point = self._find_next(next_point)
if(not found_next):
if(isclosed and len(self.contour_lines[-1]) > 2):
# Close the contour
self.contour_lines[-1].append(self.contour_lines[-1][0])
self.contour_indices[-1].append(self.contour_indices[-1][0])
elif(not contour_reversed):
self.contour_lines[-1] = self.contour_lines[-1][::-1]
self.contour_indices[-1] = self.contour_indices[-1][::-1]
contour_reversed = True
found_next, isclosed, next_point = self._find_next(self.contour_indices[-1][-1])
if(not found_next):
contour_reversed = False
self.contour_closed.append(isclosed)
self.contour_lines[-1] = np.array(self.contour_lines[-1])
self.contour_indices[-1] = np.array(self.contour_indices[-1])
if(len(self.pen_points) == 0):
break
next_point = self.pen_points.pop(0)
self.contour_lines.append([])
self.contour_indices.append([])
def integrate_flux(
self,
wavel_int: Tuple[float, float],
interp_kind: str = "linear",
plot_filename: Optional[str] = "int_line.pdf",
) -> Union[np.float64, np.float64]:
"""
Method for calculating the integrated line flux and error. The
spectrum is first interpolated to :math:`R = 100000` and then
integrated across the specified wavelength range with the
composite trapezoidal rule of ``np.trapz``. The error is
estimated with a Monte Carlo approach from 1000 samples.
Parameters
----------
wavel_int : tuple(float, float)
Wavelength range (um) across which the flux
will be integrated.
interp_kind : str
Kind of interpolation kind for
``scipy.interpolate.interp1d`` (default: 'linear').
plot_filename : str, None
Filename for the plot with the interpolated line profile.
The plot is shown in an interface window if the argument
is set to ``None``.
Returns
-------
float
Integrated line flux (W m-2).
float
Flux error (W m-2).
"""
if plot_filename is None:
print("Plotting integrated line...", end="", flush=True)
else:
print(f"Plotting integrated line: {plot_filename}...", end="", flush=True)
n_samples = 1000
wavel_high_res = read_util.create_wavelengths(wavel_int, 1e5)
# Creating plot
mpl.rcParams["font.serif"] = ["Bitstream Vera Serif"]
mpl.rcParams["font.family"] = "serif"
plt.rc("axes", edgecolor="black", linewidth=2)
plt.rcParams["axes.axisbelow"] = False
plt.figure(1, figsize=(6, 3))
gs = mpl.gridspec.GridSpec(1, 1)
gs.update(wspace=0, hspace=0, left=0, right=1, bottom=0, top=1)
ax1 = plt.subplot(gs[0, 0])
ax2 = ax1.twiny()
ax1.tick_params(
axis="both",
which="major",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=5,
labelsize=12,
top=False,
bottom=True,
left=True,
right=True,
)
ax1.tick_params(
axis="both",
which="minor",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=3,
labelsize=12,
top=False,
bottom=True,
left=True,
right=True,
)
ax2.tick_params(
axis="both",
which="major",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=5,
labelsize=12,
top=True,
bottom=False,
left=False,
right=True,
)
ax2.tick_params(
axis="both",
which="minor",
colors="black",
labelcolor="black",
direction="in",
width=1,
length=3,
labelsize=12,
top=True,
bottom=False,
left=False,
right=True,
)
ax1.set_xlabel("Wavelength (µm)", fontsize=16)
ax1.set_ylabel("Flux (W m$^{-2}$ µm$^{-1}$)", fontsize=16)
ax2.set_xlabel("Velocity (km s$^{-1}$)", fontsize=16)
ax1.get_xaxis().set_label_coords(0.5, -0.12)
ax1.get_yaxis().set_label_coords(-0.1, 0.5)
ax2.get_xaxis().set_label_coords(0.5, 1.12)
ax1.plot(
self.spectrum[:, 0],
self.spectrum[:, 1],
color="black",
label=self.spec_name,
)
ax2.plot(self.spec_vrad, self.spectrum[:, 1], ls="-", lw=0.0)
flux_sample = np.zeros(n_samples)
fwhm_sample = np.zeros(n_samples)
mean_sample = np.zeros(n_samples)
vrad_sample = np.zeros(n_samples)
lum_sample = np.zeros(n_samples)
for i in range(n_samples):
# Sample fluxes from random errors
spec_rand = np.random.normal(self.spectrum[:, 1], self.spectrum[:, 2])
# Interpolate sampled spectrum
spec_interp = interp1d(
self.spectrum[:, 0], spec_rand, kind=interp_kind, bounds_error=False
)
# Resample to high-resolution wavelengths
flux_rand = spec_interp(wavel_high_res)
# Integrate line flux (W m-2)
flux_sample[i] = np.trapz(flux_rand, wavel_high_res)
# Line luminosity (Lsun)
lum_sample[i] = (
4.0 * np.pi * (1e3 * constants.PARSEC / self.parallax) ** 2 * flux_sample[i]
)
lum_sample[i] /= constants.L_SUN # (Lsun)
# Weighted (with flux) mean wavelength (um)
mean_sample[i] = np.trapz(
wavel_high_res * flux_rand, wavel_high_res
) / np.trapz(flux_rand, wavel_high_res)
# Radial velocity (km s-1)
vrad_sample[i] = (
1e-3
* constants.LIGHT
* (mean_sample[i] - self.lambda_rest)
/ self.lambda_rest
)
# Find full width at half maximum
spline = InterpolatedUnivariateSpline(
wavel_high_res, flux_rand - np.max(flux_rand) / 2.0
)
root = spline.roots()
diff = root - mean_sample[i]
root1 = np.amax(diff[diff < 0.0])
root2 = np.amin(diff[diff > 0.0])
fwhm_sample[i] = 1e-3 * constants.LIGHT * (root2 - root1) / mean_sample[i]
# Add 30 samples to the plot
if i == 0:
ax1.plot(
wavel_high_res,
flux_rand,
ls="-",
lw=0.5,
color="gray",
alpha=0.4,
label="Random samples",
)
elif i < 30:
ax1.plot(
wavel_high_res, flux_rand, ls="-", lw=0.5, color="gray", alpha=0.4
)
# Line flux from original, interpolated spectrum
spec_interp = interp1d(
self.spectrum[:, 0],
self.spectrum[:, 1],
kind=interp_kind,
bounds_error=False,
)
flux_high_res = spec_interp(wavel_high_res)
line_flux = np.trapz(flux_high_res, wavel_high_res)
ax1.plot(
wavel_high_res, flux_high_res, color="tab:blue", label="High resolution"
)
ax1.legend(loc="upper right", frameon=False, fontsize=12.0)
print(" [DONE]")
if plot_filename is None:
plt.show()
else:
plt.savefig(plot_filename, bbox_inches="tight")
plt.clf()
plt.close()
wavel_mean, wavel_std = np.mean(mean_sample), np.std(mean_sample)
print(f"Mean wavelength (nm): {1e3*wavel_mean:.2f} +/- {1e3*wavel_std:.2f}")
fwhm_mean, fwhm_std = np.mean(fwhm_sample), np.std(fwhm_sample)
print(f"FWHM (km s-1): {fwhm_mean:.2f} +/- {fwhm_std:.2f}")
vrad_mean, vrad_std = np.mean(vrad_sample), np.std(vrad_sample)
print(f"Radial velocity (km s-1): {vrad_mean:.1f} +/- {vrad_std:.1f}")
line_error = np.std(flux_sample)
print(f"Line flux (W m-2): {line_flux:.2e} +/- {line_error:.2e}")
lum_mean, lum_std = np.mean(lum_sample), np.std(lum_sample)
print(f"Line luminosity (Lsun): {lum_mean:.2e} +/- {lum_std:.2e}")
return line_flux, line_error
def SpecHWHM(GFint_A): ''' Half-width at half-maximum of the spectral function ''' N = len(En_A) DOSF = -sp.imag(GFint_A[N/2])/sp.pi # value at Fermi energy DOS = InterpolatedUnivariateSpline(En_A,-sp.imag(GFint_A)/sp.pi-DOSF/2.0) return sp.amin(sp.fabs(DOS.roots()))
def SpecHWHM(GFint_A): ''' Half-width at half-maximum of the spectral function ''' N = len(En_A) DOSF = -sp.imag(GFint_A[N/2])/sp.pi # value at Fermi energy DOS = InterpolatedUnivariateSpline(En_A,-sp.imag(GFint_A)/sp.pi-DOSF/2.0) return sp.amin(sp.fabs(DOS.roots()))
def calc_fwhm(x, y):
"""Return FWHM calculated by fitting a spline to y vs. x"""
y = N.array(y)
sp = InterpolatedUnivariateSpline(x, y - y.max() / 2)
lo, hi = sp.roots()
return hi - lo
x0 = [seed[0], seed[1], seed[2], seed[4]]
res = optimize.minimize(chi2_rel, x0, method='Nelder-Mead')
chi2vals.append(res.fun)
fig = plt.figure(figsize=(7, 6), dpi=200)
ax = plt.axes()
ax.plot(means, chi2vals, "k-")
ax.set_ylabel('$\\Delta\\chi^2$', position=(0., 1.), va='top', ha='right')
ax.set_xlabel('mean [keV]', position=(1., 0.), va='bottom', ha='right')
ax.yaxis.set_label_coords(-0.12, 1.)
ax.xaxis.set_label_coords(1.0, -0.1)
plt.savefig("chi2.png")
# find roots numerically via interpolation
from scipy.interpolate import InterpolatedUnivariateSpline
spline = InterpolatedUnivariateSpline(means, numpy.asarray(chi2vals)-1.)
roots = spline.roots()
print "best fit:", result.x[3], "+", roots[1]-result.x[3], "-", result.x[3]-roots[0]
def parsec_locus(x0, y0, mag1, mag2, mag3, mag4, cer, feh, age, parsec):
'''
Find the where a star would be located on the stellar locus in (1 - 2)-(3 - 4) color space.
(1 - 2) is the abscissa and (3 - 4) is the ordinate.
Inputs:
------
x0: (1 - 2) color of star off locus
y0: (3 - 4) color of star off locus
mag1: [str] first magnitude in (1 - 2) color
mag2: [str] second magnitude in (1 - 2) color
mag3: [str] first magnitude in (3 - 4) color
mag4: [str] second magnitude in (3 - 4) color
cer: color excess ratio [E(3 - 4)/E(1 - 2)]
feh: metallicity of star
age: age in Gyr
parsec: set of parsec isochrones
Outputs:
-------
cross12: (1 - 2) of intersection point
cross34: (3 - 4) of intersection point
'''
single = parsec[np.where(
(parsec['logAge'] == closest(np.log10(age * 10**9), parsec['logAge']))
& (parsec['MH'] == closest(feh, parsec['MH'])))]
xs = (single[mag1] - single[mag2])
ys = (single[mag3] - single[mag4])
bins = np.arange(np.min(xs), np.max(xs), 0.005)
binned_color = binned_statistic(xs, ys, statistic='median',
bins=bins).statistic
fin = np.where((np.isfinite(bins[:-1]) == True)
& (np.isfinite(binned_color) == True))
bins = bins[:-1][fin]
binned_color = binned_color[fin]
# Interpolate Isochrone
spline = InterpolatedUnivariateSpline(bins, binned_color, ext=0)
y_spl = spline(bins)
# Find the crossing point
if y0 - spline(x0) > 0:
# what to do if star is above isochrone in color-color space
return np.squeeze([-9999.0, -9999.0])
else:
# Find roots in difference between the ccline and isochrone
func = y_spl - ccline(bins, cer, x0, y0)
func_spl = InterpolatedUnivariateSpline(bins, func, ext=0)
cross12 = func_spl.roots()
cross34 = spline(cross12)
if len(cross12) == 0:
# If cross12 is empty
return np.squeeze([-9999.0, -9999.0])
else:
# Return Cartesian coordinates of crossing point in color-color space
return np.squeeze(list(zip(cross12, cross34)))
def upperlimit(self, printlevel=1):
"""
Returns the upper limit of the parameter of interest.
"""
pvalues = self.pvalues()
poinull = self.poinull
poivalues = poinull.value
poiname = poinull.name
poiparam = poinull.parameter
bestfitpoi = self.calculator.config.bestfit.params[poiparam]["value"]
sel = poivalues > bestfitpoi
if self.CLs:
k = "cls"
else:
k = "clsb"
values = {}
if isinstance(self.calculator, AsymptoticCalculator):
keys = [k]
else:
keys = [k, "exp", "exp_p1", "exp_m1", "exp_p2", "exp_m2"]
for k_ in keys:
p_ = pvalues[k_]
pvals = poivalues
if k_ not in ["exp_m1", "exp_m2"]:
p_ = p_[sel]
pvals = pvals[sel]
p_ = p_ - self.alpha
s = InterpolatedUnivariateSpline(pvals, p_)
val = s.roots()
if len(val) > 0:
poiul = val[0]
else:
poiul = None
if k_ == k:
k_ = "observed"
values[k_] = poiul
if isinstance(self.calculator, AsymptoticCalculator):
poiul = POI(poiparam, poiul)
exp_poi = self.calculator.expected_poi
sigmas = [0.0, 1.0, 2.0, -1.0, -2.0]
kwargs = dict(poinull=poiul,
poialt=self.poialt,
nsigma=sigmas,
alpha=self.alpha,
CLs=self.CLs)
results = exp_poi(**kwargs)
keys = ["exp", "exp_p1", "exp_p2", "exp_m1", "exp_m2"]
for r, k_ in zip(results, keys):
values[k_] = r
if printlevel > 0:
msg = "\nObserved upper limit: {0} = {1}"
print(msg.format(poiname, values["observed"]))
msg = "Expected upper limit: {0} = {1}"
print(msg.format(poiname, values["exp"]))
msg = "Expected upper limit +1 sigma: {0} = {1}"
print(msg.format(poiname, values["exp_p1"]))
msg = "Expected upper limit -1 sigma: {0} = {1}"
print(msg.format(poiname, values["exp_m1"]))
msg = "Expected upper limit +2 sigma: {0} = {1}"
print(msg.format(poiname, values["exp_p2"]))
msg = "Expected upper limit -2 sigma: {0} = {1}"
print(msg.format(poiname, values["exp_m2"]))
return values
def get_half_energy(p_spectrum, f, idx, prev_idx, next_idx):
a_spectrum = np.sqrt(p_spectrum)
p_value = a_spectrum[idx]
q_value = p_value / 2
ius = InterpolatedUnivariateSpline(f, a_spectrum - q_value)
p_ius = InterpolatedUnivariateSpline(f, p_spectrum)
roots = ius.roots()
if len(roots) == 0:
l_limit = find_minima(p_spectrum, f, prev_idx, idx, idx)
r_limit = find_minima(p_spectrum, f, idx, next_idx, idx)
elif len(roots) == 1:
#check the root if it lies on the left side or the right side
if roots[0] < f[prev_idx]:
l_limit = find_minima(p_spectrum, f, prev_idx, idx, idx)
r_limit = find_minima(p_spectrum, f, idx, next_idx, idx)
elif roots[0] >= f[prev_idx] and roots[0] < f[idx]:
l_limit = roots[0]
#find minina on the right side
r_limit = find_minima(p_spectrum, f, idx, next_idx, idx)
elif roots[0] >= f[idx] and roots[0] <= f[next_idx]:
r_limit = roots[0]
l_limit = find_minima(p_spectrum, f, prev_idx, idx, idx)
else:
l_limit = find_minima(p_spectrum, f, prev_idx, idx, idx)
r_limit = find_minima(p_spectrum, f, idx, next_idx, idx)
elif len(roots) >= 2:
#find left-closest root
t = [(f[idx] - root, root) for root in roots if root < f[idx]]
if len(t) == 0:
l_limit = find_minima(p_spectrum, f, prev_idx, idx, idx)
else:
(d, l_limit) = min(t)
if l_limit <= f[prev_idx]:
l_limit = find_minima(p_spectrum, f, prev_idx, idx, idx)
#find right-closest root
t = [(root - f[idx], root) for root in roots if root > f[idx]]
if len(t) == 0:
r_limit = find_minima(p_spectrum, f, idx, next_idx, idx)
else:
d, r_limit = min(t)
if r_limit >= f[next_idx]:
r_limit = find_minima(p_spectrum, f, idx, next_idx, idx)
'''diff = np.array([abs(root - f[idx]) for root in roots])
sorted_indices = np.argsort(diff)
i1,i2 = sorted_indices[0],sorted_indices[1]
if f[i1] >= f[i2]:
r_limit = roots[i1]
l_limit = roots[i2]
else:
r_limit = roots[i2]
l_limit = roots[i1]'''
assert r_limit > l_limit
if r_limit is None:
print "r_limit is None"
sys.exit()
if l_limit is None:
print "l_limit is None"
sys.exit()
num = int((r_limit - l_limit) *
StepStrideFrequencyModuleConstants.sample_points /
(Constants.sampling_frequency / 2))
x = np.linspace(l_limit, r_limit, num=num)
return np.trapz(p_ius(x), x)
U = get_vec(res_AF, 'U')
m_AF = get_vec(res_AF, 'm_AF')
chi_AF = get_vec(res_AF, 'chi')
assert( np.array_equal(np.abs(m_AF) < 1e-6, chi_AF.real > 0.) ), \
"Error: The AF susceptibility and order parameter do not agree."
m_FM = get_vec(res_FM, 'm_FM')
chi_FM = get_vec(res_FM, 'chi')
assert( np.array_equal(np.abs(m_FM) < 1e-6, chi_FM.real > 0.) ), \
"Error: The AF susceptibility and order parameter do not agree."
spl_chi_AF = InterpolatedUnivariateSpline(U[::-1], 1./chi_AF[::-1])
U_AF = spl_chi_AF.roots()[0]
print 'U_AF =', U_AF
spl_chi_FM = InterpolatedUnivariateSpline(U[::-1], 1./chi_FM[::-1])
U_FM = spl_chi_FM.roots()[0]
print 'U_FM =', U_FM
# -- Compare divergencies of chi with the
chi_m = get_vec(res_FM, 'chi_m')
e_AF = chi_m[:, -1] # AF is triply degenerate [spin-SU(2)]
e_FM = chi_m[:, -4] # FM is the 4th eigen mode
spl_e_AF = InterpolatedUnivariateSpline(U[::-1], 1./e_AF[::-1])
U_AF_ref = spl_e_AF.roots()[0]
def generate_interpolators(self, tablefile):
"""Generates the interpolators from the full hdf5 file.
"""
f = h5py.File(tablefile, 'r')
self.ye_grid = f['ye'][:]
self.rho_grid = f['logrho'][:] + np.log10(self.scaling['rho'])
self.energy_shift = f['energy_shift'][0] * self.scaling['eps']
rho_grid, ye_grid = np.meshgrid(self.rho_grid, self.ye_grid)
pres_values = f['logpress'][:, 0, :] + np.log10(self.scaling['pres'])
munu_values = f['munu'][:, 0, :] # * self.scaling['energy']
energy_values = f['logenergy'][:, 0, :] + np.log10(self.scaling['eps'])
f.close()
self.ye_bounds = (np.min(self.ye_grid), np.max(self.ye_grid))
self.rho_bounds = (np.min(self.rho_grid), np.max(self.rho_grid))
self.pres_bounds = (np.min(pres_values), np.max(pres_values))
self.energy_arr = np.zeros_like(self.rho_grid)
self.pres_arr = np.zeros_like(self.rho_grid)
self.ye_arr = np.zeros_like(self.rho_grid)
prev_root = 0
# for each rho, extract the values given ye such that munu = 0
for i, rho in enumerate(self.rho_grid):
munu_func = InterpolatedUnivariateSpline(self.ye_grid,
munu_values[:, i])
pres_func = interp1d(self.ye_grid, pres_values[:, i])
energy_func = interp1d(self.ye_grid, energy_values[:, i])
ye_roots = munu_func.roots()
if len(ye_roots) > 1:
# in case several roots, we pick the closest one
import warnings
warnings.warn(
'Error, more than one root in munu! Using closest to previous'
)
ye_root = ye_roots[np.argmin(
np.abs(np.array(ye_roots) - prev_root))]
elif len(ye_roots) == 0:
# in case of no roots, we chose the previous value
ye_root = prev_root
else:
ye_root = ye_roots[0]
prev_root = ye_root
self.ye_arr[i] = ye_root
self.pres_arr[i] = pres_func(ye_root)
self.energy_arr[i] = energy_func(ye_root)
# generate interpolators
self.rho_interp = interp1d(self.pres_arr,
self.rho_grid,
fill_value='extrapolate')
self.eps_interp = interp1d(self.rho_grid,
self.energy_arr,
fill_value='extrapolate')
self.pres_interp = interp1d(self.rho_grid,
self.pres_arr,
fill_value='extrapolate')
self.ye_interp = interp1d(self.rho_grid,
self.ye_arr,
fill_value='extrapolate')
