def gather(s, nums, source, vanilla=0):
"""Wrapper for easy MPI Gather receive.
Receive data from source with tag.
Create appropriate buffer and receive data.
"""
control_info = get_control_info(s)
protocol = control_info[0]
typecode = control_info[1]
s_size = nums
if protocol == 'array':
import Numeric
x = Numeric.zeros(s_size * size(), typecode)
gather_array(s, s_size, x, source)
elif protocol == 'string':
x = ' ' * s_size * size()
gather_string(s, s_size, x, source)
elif protocol == 'vanilla':
raise "Protocol: %s unsupported for gather" % protocol
else:
raise "Unknown values for protocol: %s" % protocol
return x
def raw_gather(s, nums, d, source, vanilla=0):
"""Wrapper for MPI gather.
Gather s in nums element to d (of the same nums size) from source.
Automatically determine appropriate protocol
and call corresponding send function.
The variable s can be any (picklable) type, but
Numeric variables and text strings will most efficient.
Setting vanilla = 1 forces vanilla mode for any type.
"""
protocol = get_control_info(s, vanilla)[0]
if protocol == 'array':
gather_array(s, nums, d, source)
elif protocol == 'string':
gather_string(s, nums, d, source)
elif protocol == 'vanilla':
raise "Protocol: %s unsupported for gather" % protocol
else:
raise "Unknown values for protocol: %s" % protocol
return d
# 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]
# Save our most recent orbit and width information
cPickle.dump(widths[i], file, 1)
file.close()
# 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]
# Save our most recent orbit and width information
cPickle.dump(widths[i], file, 1)
file.close()