def __init__(self, g_parameters, image_renderer, snr):
self.g_parameters = g_parameters
self.snr = snr
self.model = galfun.getGalaxiesModels(g_parameters=self.g_parameters)
self.image_renderer = image_renderer
#we do not want to mask or crop the images used to obtain the partials.
self.image_renderer_partials = galfun.ImageRenderer(stamp=self.image_renderer.stamp)
self.image = self.image_renderer.getImage(self.model)
_, self.var_noise = galfun.addNoise(self.image, self.snr, 0)
self.steps = defaults.getSteps(self.g_parameters, self.image_renderer)
self.param_names = g_parameters.ordered_fit_names
self.num_params = len(self.param_names)
self.num_galaxies = self.g_parameters.num_galaxies
self.derivatives_images = self.derivativesImages()
self.second_derivatives_images = self.secondDerivativesImages()
self.fisher_matrix_images = self.fisherMatrixImages()
self.fisher_matrix = self.fisherMatrix()
self.covariance_matrix = self.covarianceMatrix()
self.correlation_matrix = self.correlationMatrix()
self.bias_matrix_images = self.biasMatrixImages()
self.bias_matrix = self.biasMatrix()
self.bias_images = self.biasImages()
self.biases = self.getBiases()
self.fisher_condition_number = self.fisherConditionNumber()
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)
