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#!/bin/bash
"""
Using refnx in a highly parallelised environment using mpi.
You'll need to install:
- refnx
- numpy
- cython
- schwimmbad
- mpi4py
Usage
-----
mpiexec -n 4 python mpi_parallelisation.py
"""
# Start off by importing necessary packages
import sys
import os.path
import refnx
from schwimmbad import MPIPool
from refnx.reflect import SLD, Slab, ReflectModel
from refnx.dataset import ReflectDataset
from refnx.analysis import (Objective, CurveFitter, Transform, GlobalObjective)
def setup():
# load the data.
DATASET_NAME = os.path.join(refnx.__path__[0],
'analysis',
'test',
'c_PLP0011859_q.txt')
# load the data
data = ReflectDataset(DATASET_NAME)
# the materials we're using
si = SLD(2.07, name='Si')
sio2 = SLD(3.47, name='SiO2')
film = SLD(2, name='film')
d2o = SLD(6.36, name='d2o')
structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3)
structure[1].thick.setp(vary=True, bounds=(15., 50.))
structure[1].rough.setp(vary=True, bounds=(1., 6.))
structure[2].thick.setp(vary=True, bounds=(200, 300))
structure[2].sld.real.setp(vary=True, bounds=(0.1, 3))
structure[2].rough.setp(vary=True, bounds=(1, 6))
model = ReflectModel(structure, bkg=9e-6, scale=1.)
model.bkg.setp(vary=True, bounds=(1e-8, 1e-5))
model.scale.setp(vary=True, bounds=(0.9, 1.1))
model.threads = 1
# fit on a logR scale, but use weighting
objective = Objective(model, data, transform=Transform('logY'),
use_weights=True)
return objective
def structure_plot(obj, samples=0):
# plot sld profiles
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
if isinstance(obj, GlobalObjective):
if samples > 0:
savedparams = np.array(obj.parameters)
for pvec in obj.parameters.pgen(ngen=samples):
obj.setp(pvec)
for o in obj.objectives:
if hasattr(o.model, 'structure'):
ax.plot(*o.model.structure.sld_profile(),
color="k", alpha=0.01)
# put back saved_params
obj.setp(savedparams)
for o in obj.objectives:
if hasattr(o.model, 'structure'):
ax.plot(*o.model.structure.sld_profile(), zorder=20)
ax.set_ylabel('SLD / $10^{-6}\\AA^{-2}$')
ax.set_xlabel("z / $\\AA$")
elif isinstance(obj, Objective) and hasattr(obj.model, 'structure'):
fig, ax = obj.model.structure.plot(samples=samples)
fig.savefig('steps_sld.png', dpi=1000)
if __name__ == "__main__":
with MPIPool() as pool:
if not pool.is_master():
pool.wait()
sys.exit(0)
# buffering so the program doesn't try to write to the file
# constantly
with open('steps.chain', 'w', buffering=500000) as f:
objective = setup()
# Create the fitter and fit
fitter = CurveFitter(objective, nwalkers=300)
fitter.initialise('prior')
fitter.fit('differential_evolution')
# thin by 10 so we have a smaller filesize
fitter.sample(100, pool=pool.map, f=f, verbose=False, nthin=10);
f.flush()
try:
# create graphs of reflectivity and SLD profiles
import matplotlib
import matplotlib.pyplot as plt
matplotlib.use('agg')
fig, ax = objective.plot(samples=1000)
ax.set_ylabel('R')
ax.set_xlabel("Q / $\\AA$")
fig.savefig('steps.png', dpi=1000)
structure_plot(objective, samples=1000)
# corner plot
fig = objective.corner()
fig.savefig('steps_corner.png')
# plot the Autocorrelation function of the chain
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(fitter.acf())
ax.set_ylabel('autocorrelation')
ax.set_xlabel('step')
fig.savefig('steps-autocorrelation.png')
except ImportError:
pass
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