def int_pl(x1, x2, gam, nstp=None):
if nstp is None:
nstp = 1000
x = np.linspace(x1, x2, nstp)
y = x**(1 - gam)
#whats this? to normalize! when calculating ``intrinsic luminosity'', assuming an intrinsic power-law spectrum of gamma=1.8
y0 = x**(1 - 1.8)
interp_func0 = interpol(y0, x)
interp_func = interpol(y, x)
norm = interp_func0(10.) / interp_func(10.)
y *= norm
integral = tsum(y, x)
return integral
def int_pl(x1, x2, gam, nstp=None):
if nstp is None:
nstp = 1000
x = np.linspace(x1, x2, nstp)
y = x ** (1 - gam)
# whats this? to normalize! when calculating ``intrinsic luminosity'', assuming an intrinsic power-law spectrum of gamma=1.8
y0 = x ** (1 - 1.8)
interp_func0 = interpol(y0, x)
interp_func = interpol(y, x)
norm = interp_func0(10.0) / interp_func(10.0)
y *= norm
integral = tsum(y, x)
return integral
def __init__(self, points: list):
# ensure the curve starts at 0, 0
has_zero = False
for point in points:
if point[0] == 0:
has_zero = True
if not has_zero:
points.insert(0, [0, 0])
points.sort(key=lambda s: s[0])
temps = []
speeds = []
for set_ in points:
temps.append(set_[0])
speeds.append(set_[1])
self._array = []
# this involved alot of stack overflow and admittedly im not 100% sure how it works
# basically given a set of points it extrapolates that into a line consisting of one
# point per degree.
x = np.asarray(temps)
y = np.asarray(speeds)
pch = interpol(x, y)
xx = np.linspace(x[0], x[-1], x[-1])
line2d = plt.plot(xx, pch(xx), 'g-')
self.temps = line2d[0].get_xdata()
self.speeds = line2d[0].get_ydata()
def get_riskfree_libor(date, yte):
# compute only once per date
if date in functions_dict:
f = functions_dict[date]
else:
try:
df = df_yields.query('index==@date')
dr = df.iloc[0]
rates = ([0.0, dr['ON'] / 100, dr['w1'] / 100, dr['m1'] / 100, dr['m2'] / 100, dr['m3'] / 100, dr['m6'] / 100, dr['m12'] / 100])
df_inter = pandas.DataFrame(columns=['0', 'ON', 'w1', 'm1', 'm2', 'm3', 'm6', 'm12'])
df_inter.loc[0] = years
df_inter.loc[1] = rates
df_inter = df_inter.dropna(axis='columns')
f = interpol(df_inter.loc[0], df_inter.loc[1], k=1, bbox=[0.0, 4.0])
functions_dict[date] = f
except:
print (str(date))
functions_dict[date] = 0
f = functions_dict[date]
y = float(yte)
rf = f(y) / 100
rf = np.round(rf, decimals=4)
return rf
def ext_smc_mky(wav,ebv):
path='/Users/ctchen/analysis/SED/templates/'
wav_ext,kappa_ext=np.loadtxt(path+'ext_assef_kappa.dat',unpack=True)
interpfunc=interpol(wav_ext,kappa_ext)
kappa=interpfunc(wav)
kappa[np.where(kappa < 0)] = 0.
ext=10.**(-kappa*ebv)
ext[np.where(ext < 1e-10)]==0
return ext
def main():
obj=plt.imread('jerichoObject.bmp')
ref=plt.imread('jerichoRef.bmp')
img=obj-ref
K=np.empty(img.shape)+0j
temp=np.empty(img.shape)+0j
wavelength=405e-9
k=2*np.pi/(wavelength)
z=250e-6
#z=13e-3
#z=13e-3-250e-6
distX=6e-6
distY=6e-6
# n,m= img.shape
n=img.shape[0]
m=img.shape[1]
# a,b = np.mgrid[0:n,0:m]
a = np.mgrid[0:n,0:m][0]
b = np.mgrid[0:n,0:m][1]
pts=np.array((a.ravel(),b.ravel())).T
first=time.time()
# for (i,j),k in np.ndenumerate(K):
for i in xrange(K.shape[0]):
for j in xrange(K.shape[1]):
print(i,j)
r=(i*distX,j*distY,z)
# for (x,y),value in np.ndenumerate(img):
for x in xrange(img.shape[0]):
for y in xrange(img.shape[1]):
ksi=(x*distX,y*distY,z)
ksiNorm=np.linalg.norm(ksi)
ksiDotR=np.dot(ksi,r)
temp[x,y]=img[x,y]*np.exp(1j*k*ksiDotR/ksiNorm)
tempRavel=temp.ravel()
surf=interpol(pts,tempRavel)
func=lambda y,x: surf([[x,y]])
K[i,j]=dblquad(func,0,m,lambda x:0, lambda x:n )
timeTook=time.time()-first
print(timeTook)
from __future__ import division, print_function
import numpy as np
from scipy.interpolate import LinearNDInterpolator as interpol
import scipy.integrate as sciInt
a, b = np.mgrid[0:11, 0:11]
Z = np.ones((11, 11)) * 10
pts = np.array((a.ravel(), b.ravel())).T
zr = Z.ravel()
surf = interpol(pts, zr)
func = lambda y, x: surf([[x, y]])
res = np.empty(Z.shape)
for (i, j), k in np.ndenumerate(res):
res[i, j] = sciInt.dblquad(func, 0.0, 10.0, lambda x: 0.0, lambda x: 10.0)[0]
print(res[0])
val[out[0]] = 0.
norm = ann_spec_flux_norm(gp, gm, q)
val *= p[2] / norm[0]
return val
lecr = pd.read_csv('gam_lecr_pdcompatible.spc', skiprows=3, sep=',')
e_cr = lecr['Eg(MeV)']
f_cr = lecr['Fg(10^-5 ph/cm^2/s/sr/MeV)']
sr_norm = (np.sin(np.deg2rad(25)) - np.sin(np.deg2rad(-25))) * np.deg2rad(50)
from scipy.interpolate import interp1d as interpol
lecr_interpol = interpol(e_cr * 1000,
f_cr / 1000 * sr_norm * 1e-5,
bounds_error=False,
fill_value=0)
def lecr_spectrum(energy, p):
return p * lecr_interpol(energy)
"""
Created on Wed Sep 6 15:41:22 2017
@author: mpleinti
Collection of different secondary functions
adapted by tsiegert from mpleinti from tsiegert
