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SDSS_fratio.py
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SDSS_fratio.py
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import numpy as np
import matplotlib.pyplot as plt
import pyfits
import ADEUtils as ADE
from glob import glob
from matplotlib.backends.backend_pdf import PdfPages as PDF
from datetime import datetime
def kegstand(searchstr,EEfigs,Nfig,EE=0.50):
"""Given an input list of fits files this function will try to compute the
f-ratio of the SDSS test stand input beam.
"""
R = np.array([])
dprime = np.array([])
pp = PDF(EEfigs)
pp2 = PDF(Nfig)
in_files = glob(searchstr)
for image in in_files:
print image
HDU = pyfits.open(image)[0]
dp = float(image.split('_')[1].split('.fits')[0])
dprime = np.append(dprime, dp)
radius = get_radius(HDU.data,pp,EE)
R = np.append(R, radius)
print "d: {:4.3f}, R: {:4.3f}".format(dp,radius)
dprime *= -1
# Ns = np.array([])
# for k in range(numtrys):
# sampleidx = np.random.randint(dprime.size, size = dprime.size)
# tempdp = dprime[sampleidx]
# tempR = R[sampleidx]
# Ns = np.append(Ns,
# (2.*ADE.polyclip(tempdp,tempR,1,niter=10).c[0])**-1)
fit_coef = ADE.polyclip(dprime,R,1,niter=10).c
slope = fit_coef[0]
### compute the uncertainty
N = (2.*slope)**-1
# N = np.mean(Ns)
# N_err = np.std(Ns)
Nfit = np.poly1d(fit_coef)
fitd = np.linspace(dprime.min(),dprime.max(),50)
### compute the uncertainty
see = (np.sum((R - np.polyval(fit_coef,dprime))**2)/(R.size - 2))**0.5
slope_err = see * (1/(np.sum((dprime - np.mean(dprime))**2)))**0.5
N_err = slope_err/(2.*slope**2)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dprime,R,marker='s',linestyle='')
ax.plot(fitd,Nfit(fitd),'k:',label='linear fit')
ax.set_xlabel("$-d^'$ [mm]")
ax.set_ylabel('$R$ [mm]')
ax.legend(loc=0)
ax.set_title('{}\nN: {:3.2f}$\pm$ {:3.2f}'.\
format(datetime.now().isoformat(' '),N,N_err))
pp2.savefig(fig)
pp.close()
pp2.close()
return N, N_err
def get_radius(data,pp,EEcut):
"""Takes in a 2D numpy array and computes the radius of the beam profile.
This function uses a parabola fit to find the true beam radius.
"""
r, sb, err = ADE.fast_annulize(data,300)
r *= 0.0044
flux = np.cumsum(sb)
EE = flux/flux.max()
cutr = np.where(EE >= EEcut)[0][0]
EEfit = np.poly1d(np.polyfit(r[:cutr],EE[:cutr],2))
fitr = np.linspace(r.min(),r.max(),500)
fitEE = EEfit(fitr)
r1 = np.interp(1.0,fitEE,fitr)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(r,EE,marker='.',linestyle='',markersize=0.7)
ax.plot(fitr,fitEE,'-',alpha=0.4)
ax.set_xlabel('r [mm]')
ax.set_ylabel('EE')
ax.axvline(r1,linestyle='-',alpha=0.4)
ax.axhline(1.0,linestyle=':',color='k',alpha=0.2)
ax.set_ylim(0,1.1)
# ax.set_xlim(0,1.5*r1)
ax.set_title('r: {:3.2f} mm'.format(r1))
if pp:
pp.savefig(fig)
return r1
def clean_VI(searchstr):
"""Takes FITS files generated by LabView and:
1) Poor man's bkgrnd subtraction with ADE.mediclean
2) Sets the Primary Hdu to 0 instead of LabView's 1
"""
in_files = glob(searchstr)
for image in in_files:
HDU = pyfits.open(image)[1]
cleaned_data = ADE.mediclean(HDU.data[220:870,290:960])
clean_name = image.split('.fits')[0].split('/')[-1] + '.cleaned.fits'
pyfits.PrimaryHDU(cleaned_data,HDU.header).writeto(clean_name)
return
def radius_test(search_str):
file_list = glob(search_str)
zs = np.array([])
rs = np.array([])
for image in file_list:
print image
z = float(image.split('_')[1][0:4])
data = pyfits.open(image)[0].data
r = get_radius(data,None,0.2)/0.0044
zs = np.append(zs,z)
rs = np.append(rs,r)
ax = plt.figure().add_subplot(111)
ax.set_xlabel('z')
ax.set_ylabel('r')
ax.plot(zs,rs)
ax.get_figure().show()
return ax