def main(argv):
current_fit_number, snr, project, existing_fits = (
int(argv[1]), float(argv[2]), argv[3], int(argv[4]))
noise_seed = current_fit_number + existing_fits
if not os.path.isdir(os.path.join(project, defaults.RESULTS_DIR)):
os.mkdir(os.path.join(project, defaults.RESULTS_DIR))
g_parameters = galfun.GParameters(project)
image_renderer = galfun.ImageRenderer(pixel_scale=defaults.PIXEL_SCALE,nx=defaults.NX,ny=defaults.NY)
fish = fisher.Fisher(g_parameters=g_parameters,image_renderer=image_renderer, snr=snr)
orig_image = deepcopy(fish.image)
mins = defaults.getMinimums(g_parameters, orig_image)
maxs = defaults.getMaximums(g_parameters, orig_image)
init_values = defaults.getInitialValuesFit(g_parameters)
nfit_params = g_parameters.nfit_params
noisy_image, variance_noise = galfun.addNoise(orig_image, snr, noise_seed)
fit_params = lmfit.Parameters()
for param in g_parameters.fit_params:
fit_params.add(param,
value=init_values[param],
min=mins[param],
max=maxs[param])
results = lmfit.minimize(objFunc, fit_params, kws=dict(image_renderer=image_renderer,
data=noisy_image,
variance_noise=(variance_noise),
**nfit_params))
filename = ''.join([defaults.RESULTS_DIR, str(noise_seed), '.csv'])
result_filename = os.path.join(project, defaults.RESULTS_DIR, filename)
# write results of fits into a file,
with open(result_filename, 'w') as csvfile:
row_to_write = fit_params.valuesdict()
row_to_write['chi2'] = results.chisqr
row_to_write['success'] = results.success
row_to_write['errorbars'] = results.errorbars
row_to_write['nfev'] = results.nfev
row_to_write['nvarys'] = results.nvarys
row_to_write['ndata'] = results.ndata
row_to_write['nfree'] = results.nfree
row_to_write['redchi'] = results.redchi
writer = csv.DictWriter(csvfile, fieldnames=row_to_write.keys())
writer.writeheader()
writer.writerow(row_to_write)
def read_results(project,g_parameters, fish):
orig_image = fish.image
mins = defaults.getMinimums(g_parameters, orig_image)
maxs = defaults.getMaximums(g_parameters, orig_image)
residuals = {}
pulls = {}
redchis = [] #list containing values of reduced chi2 for each fit.
rltsdir = os.path.join(project, defaults.RESULTS_DIR)
# read results from rltsdir's files.
for filename in os.listdir(rltsdir):
with open(os.path.join(rltsdir, filename)) as csvfile:
reader = csv.DictReader(csvfile)
for i,row in enumerate(reader):
print i,row
redchis.append(float(row['redchi']))
for param in g_parameters.fit_params:
if param not in residuals:
residuals[param] = []
if param not in pulls:
pulls[param] = []
residual = (float(row[param]) -
float(g_parameters.params[param]))
pull = (residual /
math.sqrt(fish.covariance_matrix[param, param]))
residuals[param].append(residual)
pulls[param].append(pull)
biases = {param: np.mean(residuals[param]) for param in residuals}
pull_means = {param: np.mean(pulls[param]) for param in residuals}
res_stds = {param: np.std(residuals[param]) for param in residuals}
pull_mins = {param: ((mins[param] - float(g_parameters.params[param])) /
math.sqrt(fish.covariance_matrix[param, param])) for
param in residuals}
pull_maxs = {param: ((maxs[param] - float(g_parameters.params[param])) /
math.sqrt(fish.covariance_matrix[param, param])) for
param in residuals}
return pulls,residuals,biases,pull_means,res_stds,pull_mins,pull_maxs,redchis
