import src.model_Production as modPro import src.surface_Brightness_Profiles as SBPro import numpy as np #Single Run - Derivative print 'Running' imageParams = modPro.default_ModelParameter_Dictionary() imageParams['SNR'] = 35. imageParams['e1'] = 0.3 imageParams['e2'] = 0. imageParams['size'] = 0.84853 imageParams['flux'] = 4.524 imageParams['stamp_size'] = [10, 10] imageParams['centroid'] = (np.array(imageParams['stamp_size']) + 1) / 2. ###Get image using GALSIM default models #image, imageParams = modPro.get_Pixelised_Model(imageParams, noiseType = None, outputImage = True) #image, imageParams = modPro.get_Pixelised_Model(imageParams, noiseType = None, outputImage = True, sbProfileFunc = modPro.gaussian_SBProfile) ##A Halls version image = np.genfromtxt('/home/cajd/Downloads/fid_image.dat') dimage = np.genfromtxt('/home/cajd/Downloads/dfid_image.dat') ddimage = np.genfromtxt('/home/cajd/Downloads/ddfid_image.dat') image = image.T dimage = dimage.T ddimage = ddimage.T print 'Got Original' print 'Halls:', np.power(image, 2.).sum()
import src.model_Production as modPro import src.measure_Bias as mBias import src.image_measurement_ML as ML import numpy as np ## Bias Measurement S0 = 1.2; derLabel = 'T' imageParams = modPro.default_ModelParameter_Dictionary() imageParams['SNR'] = 50. imageParams['size'] = 1.2 imageParams[derLabel] = S0 imageParams['stamp_size'] = np.array([50,50]) imageParams['centroid'] = (imageParams['stamp_size']+1)/2. ###Get image image, imageParams = modPro.get_Pixelised_Model(imageParams, noiseType = 'G') modelParams = imageParams.copy() print 'modelParams test:', modelParams['stamp_size'], modelParams['noise'] print 'Estimated variance:', ML.estimate_Noise(image, maskCentroid = modelParams['centroid']) raw_input('Check') ##Produce analytic bias print 'Getting analytic bias:' anbias = mBias.analytic_GaussianLikelihood_Bias(S0, derLabel, modelParams, diffType = 'ana') print '\n ****** Analytic Bias is:', anbias ##Produce analytic bias print 'Getting analytic bias:' numanbias = mBias.analytic_GaussianLikelihood_Bias(S0, derLabel, modelParams, diffType = 'num')
fitParamsLabels = fittedParameters.keys(); fitParamsValues = fittedParameters.values() ## preSearchMethod defines whether a grid-based method is used to define an initial guess. Will give a lot of slow-down for large parameter spaces, but likely to reduce the effect of local mimina or dependancies on initial guesses preSearchMethod = 'grid' ## bruteRange must be a tuple of 2-element lists (or three element slice), even in the 1D case #bruteRange = [(-0.9, 0.9)] bruteRange = [(0.21, 0.39), (0.21, 0.39)] ## If >=1, the ML Estiamtor routine will correct to that order (only coded to first order as of 31 Aug 2015) biasCorrect = 1 ##Initial Galaxy Set up imageShape = (15., 15.) #size = 0.84853 imageParams = modPro.default_ModelParameter_Dictionary(SB = dict(size = 1.2, e1 = 0.0, e2 = 0.0, magnification = 1., shear = [0., 0.], flux = 4.524, modelType = 'gaussian'),\ centroid = (np.array(imageShape)+1)/2, noise = 10., SNR = 50., stamp_size = imageShape, pixel_scale = 1.,\ PSF = dict(PSF_Type = 0, PSF_size = 1., PSF_Gauss_e1 = 0.1, PSF_Gauss_e2 = 0.0) ) ## Model Lookup Defintions - Overridden in the number of fitted parameters is greater than one useLookup = False ## 1D Ellipticity lookup ''' lookupRange = [-0.99, 0.99] lookupWidth = [0.001] ''' ##2D Ellipticity Lookup ''' lookupRange = [[-0.99, 0.99],[-0.99, 0.99]] lookupWidth = [0.01,0.01] '''