def measure_all_histograms(models,options,pool):
#Look at a sample map
sample_map = ConvergenceMap.fromfilename(models[0].getNames(z=1.0,realizations=[1])[0],loader=load_fits_default_convergence)
#Initialize Gaussian shape noise generator for the sample map shape and angle
generator = GaussianNoiseGenerator.forMap(sample_map)
#Parsed from options
num_realizations = options.getint("analysis","num_realizations")
smoothing_scales = [float(scale) for scale in options.get("analysis","smoothing_scales").split(",")]
bin_edges = np.ogrid[options.getfloat("analysis","bin_edge_low"):options.getfloat("analysis","bin_edge_high"):(options.getint("analysis","num_bins") - 2)*1j]
bin_edges = np.hstack((-10.0,bin_edges,10.0))
z = options.getfloat("analysis","redshift")
bin_midpoints = 0.5*(bin_edges[1:] + bin_edges[:-1])
#Create smoothing scale index for the histograms
idx = Indexer.stack([PDF(bin_edges) for scale in smoothing_scales])
#Build the data type of the structure array in output
data_type = [(model.name,Ensemble) for model in models]
#Append info about the smoothing scale
data_type = [("Smooth",np.float),] + data_type
#Create output struct array
ensemble_array = np.zeros(len(smoothing_scales),dtype=data_type)
#Write smoothing scale information
ensemble_array["Smooth"] = np.array(smoothing_scales)
#The for loop runs the distributed computations
for model in models:
#Build Ensemble instance with the maps to analyze
map_ensemble = Ensemble.fromfilelist(range(1,num_realizations+1))
#Measure the histograms and load the data in the ensemble
map_ensemble.load(callback_loader=compute_map_histograms,pool=pool,simulation_set=model,smoothing_scales=smoothing_scales,index=idx,generator=generator,bin_edges=bin_edges,redshift=z)
#Split the ensemble between different smoothing scales
map_ensemble_list = map_ensemble.split(idx)
#Add to output struct array
ensemble_array[model.name] = np.array(map_ensemble_list)
return ensemble_array
def compute_map_histograms(args):
assert "map_id" in args.keys()
assert "simulation_set" in args.keys()
assert "smoothing_scales" in args.keys()
assert "redshift" in args.keys()
assert "index" in args.keys()
assert "generator" in args.keys()
assert "bin_edges" in args.keys()
assert len(args["index"].descriptor_list) == len(args["smoothing_scales"])
z = args["redshift"]
#Get map name to analyze
map_name = args["simulation_set"].getNames(z=z,realizations=[args["map_id"]])[0]
#Load the convergence map
convergence_map = ConvergenceMap.fromfilename(map_name,loader=load_fits_default_convergence)
#Generate the shape noise map
noise_map = args["generator"].getShapeNoise(z=z,ngal=15.0,seed=args["map_id"])
#Add the noise
convergence_map += noise_map
#Measure the features
hist_output = np.zeros(args["index"].size)
for n,descriptor in enumerate(args["index"].descriptor_list):
logging.debug("Processing {0} x {1} arcmin".format(map_name,args["smoothing_scales"][n]))
smoothed_map = convergence_map.smooth(args["smoothing_scales"][n])
v,hist_output[descriptor.first:descriptor.last] = smoothed_map.pdf(args["bin_edges"])
#Return the histograms in array format
return hist_output
