DOI: 10.5281/zenodo.5758080
title: PYTHIA8/JEWEL Dataset -- The information content of jet quenching and machine learning assisted observable design

description: Dataset corresponding to: https://inspirehep.net/literature/1978762 The data contains four-vectors of jet constituents, Nsubjettiness values per jet, and jet labels (PYTHIA8 -- 0, JEWEL -- 1). The files are provided in HDF5 format before merging. They can be aggregated into a single "virtual dataset" (containing both the pp and AA files) that can be accessed as a single HDF5 file by using code such as the following python script: <pre><code class="language-python">''' Script to create a virtual dataset from a list of hdf5 files. The virtual dataset can then be accessed as you would a normal single hdf5 file. Author: James Mulligan &lt;james.mulligan@berkeley.edu&gt; ''' import os import h5py import numpy as np def aggregate(output_dir): # Create files with list of paths of the individual hdf5 files (including both pp and AA files) # You can use e.g.: find &lt;file_dir&gt; -name "*.h5" &gt;"files.txt" filelist = './files.txt' # Location of aggragated file output_dir = '.' # List of arrays to aggregate observables = ['X_four_vectors', 'X_Nsub', 'y'] jetR_list = [0.4] jet_pt_bins = [[100, 125]] max_distance_list = [0, 0.25] event_types = ['hard', 'combined_matched'] K_max = 30 # Create a list of keys to loop over # We have separate training data for: # - the hard-event and the combined-event # - different jet R # - different constituent subtraction R_max output_keys = [] for event_type in event_types: for jetR in jetR_list: for jet_pt_bin in jet_pt_bins: for R_max in max_distance_list: for observable in observables: if accepted_setting(event_type, R_max): output_key = f'{observable}_{event_type}_R{jetR}_pt{jet_pt_bin}_Rmax{R_max}' output_keys.append(output_key) # We will create a virtual dataset for each combination # See: https://docs.h5py.org/en/stable/vds.html # First, we need to find the total shape for each observable set shapes, total_shapes, N_list, beta_list = determine_shapes(output_keys, filelist, K_max) print('Determined shapes.') # Now, create the virtual dataset # We use keys equal to the keys in the input file output_filename_unshuffled = 'nsubjettiness_with_four_vectors_unshuffled.h5' with h5py.File(os.path.join(output_dir, output_filename_unshuffled), 'w') as hf: for output_key in output_keys: print(f'Creating virtual dataset for {output_key}') print(f' Total shape: {total_shapes[output_key]}') layout = h5py.VirtualLayout(shape=total_shapes[output_key], dtype=np.float64) # Loop through file list layout_index = 0 with open(filelist) as f: files = [line.rstrip() for line in f] for i,filename in enumerate(files): # Create virtual source source = h5py.VirtualSource(filename, output_key, shapes[output_key][i], dtype=np.float64) # Insert the source into the layout new_layout_index = layout_index + shapes[output_key][i][0] if len(total_shapes[output_key]) == 1: layout[layout_index:new_layout_index] = source elif len(total_shapes[output_key]) == 2: layout[layout_index:new_layout_index,:] = source else: layout[layout_index:new_layout_index,:,:] = source layout_index = new_layout_index # Add virtual dataset to output file hf.create_virtual_dataset(output_key, layout) # Write N_list, beta_list hf.create_dataset('N_list', data=np.array(N_list)) hf.create_dataset('beta_list', data=np.array(beta_list)) print('Virtual dataset created.') print() # Determine shapes of lists in all files def determine_shapes(output_keys, filelist, K_max): shapes = {} for output_key in output_keys: shapes[output_key] = [] with open(filelist) as f: files = [line.rstrip() for line in f] n_files = len(files) for i,filename in enumerate(files): with h5py.File(filename,'r') as hdf: if i%10 == 0: print(f'{i}/{n_files}') for output_key in output_keys: shapes[output_key].append(hdf[output_key][:].shape) if i==0: N_list = list(hdf['N_list'][:]) beta_list = list(hdf['beta_list'][:]) total_shapes = {} for key,val in shapes.items(): total_shape = np.sum([shape[0] for shape in val]) if len(val[0]) == 1: total_shapes[key] = (total_shape,) elif len(val[0]) == 2: total_shapes[key] = (total_shape, 3*K_max-4) else: total_shapes[key] = (total_shape, 800, 4) return shapes, total_shapes, N_list, beta_list def accepted_setting(event_type, R_max): if 'hard' in event_type and np.isclose(R_max, 0.): return True if 'combined' in event_type: if not np.isclose(R_max, 0.): return True return False ################################################################## if __name__ == '__main__': aggregate()</code></pre> Please contact james.mulligan@berkeley.edu with any questions.