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#!/usr/bin/env python
import cProfile
import pstats
from subprocess import PIPE, Popen
import numpy as np
from lmfit import Parameters, minimize
def get_git_version():
proc = Popen(['git', 'rev-parse', '--short', 'HEAD'], stdout=PIPE)
return proc.communicate()[0].strip()
# define objective function: returns the array to be minimized
def fcn2min(params, x, data):
"""model decaying sine wave, subtract data"""
amp = params['amp']
shift = params['shift']
omega = params['omega']
decay = params['decay']
model = amp * np.sin(x * omega + shift) * np.exp(-x*x*decay)
return model - data
def run_fit(nruns=100):
# create data to be fitted
x = np.linspace(0, 15, 601)
np.random.seed(201)
for i in range(nruns):
data = (5. * np.sin(2 * x - 0.1) * np.exp(-x*x*0.025) +
np.random.normal(size=len(x), scale=0.1))
params = Parameters()
params.add('amp', value=1.0, min=0.0, max=100.0)
params.add('decay', value=0.0, min=-1.0, max=10.0)
params.add('shift', value=0.0, min=-np.pi/2., max=np.pi/2)
params.add('omega', value=1.0, min=-10.0, max=10.0)
out = minimize(fcn2min, params, args=(x, data))
# print(out.params['amp'])
assert out.params['amp'].value < 5.5
assert out.params['amp'].value > 4.5
assert out.params['omega'].value < 2.25
assert out.params['omega'].value > 1.75
# print(out.params['amp'])
def show_profile(filename):
stats = pstats.Stats(filename)
stats.strip_dirs().sort_stats('tottime').print_stats(20)
def profile_command(command, filename=None):
gitversion = get_git_version()
if filename is None:
filename = '%s.prof' % gitversion
prof = cProfile.run(command, filename=filename)
show_profile(filename)
profile_command('run_fit()')
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