def genfits():
from LeastSquares import leastSquaresFit
global psrp, orbp, orbx, orbe, orbw, orbt
global widthp, widthx, widtht, widthe, widthw
global mf, z
yvals = {'Orb p': widthp, 'Orb x': widthx, 'Orb t': widtht}
xvals = {'Orb p': orbp / z, 'Orb x': 1.0 / z, 'Orb t': 1.0 / z}
xtits = {'Orb p': 'Orb p/z', 'Orb x': '1.0/z', 'Orb t': '1.0/z'}
for fitvar in ['Orb p', 'Orb x', 'Orb t']:
vals = []
for i in range(len(xvals[fitvar])):
vals.append((xvals[fitvar][i], yvals[fitvar][i]))
fit = leastSquaresFit(linear, (1.0, 0.0), vals)
print('%s width = %10.7f * %s + %10.7f (Acc: %f)' %
(fitvar, fit[0][0], xtits[fitvar], fit[0][1], fit[1]))
plotxy(yvals[fitvar],
xvals[fitvar],
laby=fitvar + ' Width (Fractional)',
labx=xtits[fitvar],
line=None,
font=2,
symbol=2,
color='red',
device='fits.ps/CPS')
plotxy([
fit[0][0] * min(xvals[fitvar]) + fit[0][1],
fit[0][0] * max(xvals[fitvar]) + fit[0][1]
], [min(xvals[fitvar]), max(xvals[fitvar])],
line=1,
symbol=None,
color='blue')
nextplotpage(1)
closeplot()
def genlogfits():
from LeastSquares import leastSquaresFit
global psrp, orbp, orbx, orbe, orbw, orbt
global widthp, widthx, widtht, widthe, widthw
global mf, z
yvals = {'Orb p':log(widthp), 'Orb x':log(widthx), 'Orb t':log(widtht)}
xvals = {'Orb p':log(orbp/z), 'Orb x':log(1.0/z), 'Orb t':log(1.0/z)}
xtits = {'Orb p':'log(Orb p/z)', 'Orb x':'log(1.0/z)',
'Orb t':'log(1.0/z)'}
for fitvar in ['Orb p', 'Orb x', 'Orb t']:
vals = []
for i in range(len(xvals[fitvar])):
vals.append((xvals[fitvar][i], yvals[fitvar][i]))
fit = leastSquaresFit(linear, (1.0, 0.0), vals)
print 'log(%s) width = %10.7f * %s + %10.7f (Acc: %f)' % (fitvar,
fit[0][0],
xtits[fitvar],
fit[0][1],
fit[1])
plotxy(yvals[fitvar], xvals[fitvar],
laby='log('+fitvar+') Width (Fractional)',
labx=xtits[fitvar], line=None, font=2,
symbol=2, color='red', device='logfits.ps/CPS')
plotxy([fit[0][0]*min(xvals[fitvar])+fit[0][1],
fit[0][0]*max(xvals[fitvar])+fit[0][1]],
[min(xvals[fitvar]), max(xvals[fitvar])],
line=1, symbol=None, color='blue')
nextplotpage(1)
closeplot()
if debugout:
# Print the most recent results
print ' %s: Delta = %10.6f Response = %8.5f' % \
(myjob, vals[-1][0], vals[-1][1])
if showplots and not parallel:
# Plot the results of the correlation
Pgplot.plotxy(respow, labx='Frequency',
laby='Relative Power')
a = raw_input("Press enter to continue...")
Pgplot.nextplotpage(1)
# A very rough adaptive stepsize
if abs(vals[-3][1] - vals[-1][1]) < 0.04:
ddelta = ddelta * 2.0
delta = delta + ddelta
# Fit a quadratic to the width values
fit = leastSquaresFit(quadratic, (-1.0, 0.0, 1.0), vals)
if debugout:
print '\n %sfit = %fx^2 + %fx + %f\n' % (myjob, fit[0][0],
fit[0][1],
fit[0][2])
width = 2.0*math.sqrt(-0.5/fit[0][0])
if parallel:
newwidths = mpi.gather_string(str(width), 0)
if myid==0:
for proc in range(numprocs):
psrlist.append(string.atof(newwidths[proc]))
else:
psrlist.append(width)
widths.append(psrlist)
if debugout:
print 'Widths are', widths[i]
if debugout:
# Print the most recent results
print ' %s: Delta = %10.6f Response = %8.5f' % \
(myjob, vals[-1][0], vals[-1][1])
if showplots and not parallel:
# Plot the results of the correlation
Pgplot.plotxy(respow, labx='Frequency',
laby='Relative Power')
a = raw_input("Press enter to continue...")
Pgplot.nextplotpage(1)
# A very rough adaptive stepsize
if abs(vals[-3][1] - vals[-1][1]) < 0.04:
ddelta = ddelta * 2.0
delta = delta + ddelta
# Fit a quadratic to the width values
fit = leastSquaresFit(quadratic, (-1.0, 0.0, 1.0), vals)
if debugout:
print '\n %sfit = %fx^2 + %fx + %f\n' % (myjob, fit[0][0],
fit[0][1],
fit[0][2])
width = 2.0*math.sqrt(-0.5/fit[0][0])
if parallel:
newwidths = mpi.gather_string(str(width), 0)
if myid==0:
for proc in range(numprocs):
psrlist.append(string.atof(newwidths[proc]))
else:
psrlist.append(width)
widths.append(psrlist)
if debugout:
print 'Widths are', widths[i]
