def init_one_atom_normal(num_walkers, prop_range, num_molecules, filename):
# Read the xyz file generated by Webmo (used to calculate Ground State energy)
# Return the file in the shape of a ndarray
walk = out.read_xyz(filename, num_molecules)['w']
# Broadcasts xyz file output to inputted size given by num_walkers
# Propagate walkers by a prop_range and then return
walkers = np.broadcast_to(walk[0], (num_walkers, num_molecules, 3, 3)) + \
np.random.normal(-prop_range, prop_range, (num_walkers, num_molecules, 3, 3))
return walkers
# Filename (string)
# Used to initialize system. Should be a .xyz filename with the xyz positions of
# one walker in the system.
filename = 'm_tetramer.xyz'
# If using WebMO intitialization, uncomment this line below
# Reads in a .xyz file of a 1 walker system and broadcasts to n_walker array with
# some amount of propagation simulate some randomness but not so much that the system
# cannot equilibrate
# Propagation amount
prop_amount = .5
#walkers = np.random.uniform(-prop_amount, prop_amount, (n_walkers, num_molecules, 3, 3))
#walkers = np.load('r_tetramer.npy', allow_pickle = True)
walkers, num_molecules = out.gen_walker_array(filename, n_walkers, prop_amount,
num_molecules)
# Uncomment the code below if doing an initialization within a random range
# Initial 4D walker array
# Returns a uniform distribution cenetered at the given bond length
# Array axes are walkers, molecules, atoms, coordinates
#walkers = (np.random.rand(n_walkers, num_molecules, lib.atomic_masses.shape[0],lib.coord_const) - .5)
# Uncomment this line if loading in an already equliibrated walker array
#walkers = np.load('5000_walker.npy')
#######################################################################################
# Simulation
start = time.time()
# Equilibriate Walkers
[lib.sim_loop(snapshots[i],prop_steps,dt,do_dw=True)['a'] \
for j in range(prop_reps)],axis=-1),axis=1)
# print(ancestor_weights.shape)
# Calculate the distance between one of the OH vectors
# Used in the histogram and wave function plot
CO_lengths = np.linalg.norm(walkers[:, 0, 0] - walkers[:, 0, 1], axis=1)
plt.figure(i)
# Get the range to graph the wave function in
# Step is .001, which is usually a good smooth value
x = np.arange(CO_lengths.min(), CO_lengths.max(), step=.001)
plt.hist(CO_lengths, weights=ancestor_weights, bins=n_bins, density=True)
#TODO maybe square this function?
plt.plot(x,
lib.norm_constant *
np.exp(-((x - lib.eq_bond_length)**2) *
np.sqrt(lib.kCO * lib.reduced_mass) / 2),
label='Wave Function')
plt.xlabel('CO Bond Length')
plt.ylabel('Density')
plt.title(f'Density of CO Bond Length at Snapshot {i*prop_interval}')
np.save('CO_DW_snap_' + str(i) + '.npy', walkers)
out.write_array('CO_DW_snap_' + str(i),
ancestors=ancestor_weights,
atoms=['C', 'O'])
plt.show()