def avg_feature(timestep):
"""
calculates the average feature of all available files for a particular timestep
@param :timestep: timestep of interest
"""
# initialization
if timestep >= 0 and timestep <= 50000:
iterations = 50000
dump_interval = 50
directory = ".\\dump\\young\\"
extension = ".YOUNG"
elif timestep >= 4000000 and timestep <= 5000000:
iterations = 1000000
dump_interval = 1000
timestep -= 4000000
directory = ".\\dump\\old\\"
extension = ".OLD"
else:
raise ValueError("No data available for this timestep")
if timestep % dump_interval != 0:
raise ValueError("Timestep has to be dividible by the dump_interval")
timestep = int(timestep / dump_interval)
amount = 0
# iterate through dump files
for file in os.listdir(os.fsencode(directory)):
filename = os.fsdecode(file)
if filename.endswith(extension):
print("Calculating file " + str(amount) + ' for timestep ' +
str(timestep),
sep=' ',
end='\r',
file=sys.stdout,
flush=False)
_, types, _, _, _, _, Data = read_data(directory + filename,
iterations, dump_interval)
if amount == 0:
rdf = np.asarray(calc_rdf(Data[0, timestep], types[timestep]))
SF = np.asarray(calc_SF(rdf))
else:
rdf_new = np.asarray(
calc_rdf(Data[0, timestep], types[timestep]))
rdf += rdf_new
SF += np.asarray(calc_SF(rdf))
amount += 1
rdf /= amount
SF /= amount
return rdf, SF
def avg_voronoi(timestep):
"""
Returns the voronoi area and the amount of voronoi edges for each particle
averaged over all dump files for given timestep
"""
# initialization
if timestep >= 0 and timestep <= 50000:
iterations = 50000
dump_interval = 50
directory = ".\\dump\\young\\"
extension = ".YOUNG"
elif timestep >= 4000000 and timestep <= 5000000:
timestep -= 4000000
iterations = 1000000
dump_interval = 1000
directory = ".\\dump\\old\\"
extension = ".OLD"
else:
raise ValueError("No data available for this timestep")
if timestep % dump_interval != 0:
raise ValueError("Timestep has to be dividible by the dump_interval")
timestep = int(timestep / dump_interval)
amount = 0
# iterate through dump files
for file in os.listdir(os.fsencode(directory)):
filename = os.fsdecode(file)
if filename.endswith(extension):
print("Calculating file " + str(amount) + ' for timestep ' + str(timestep * dump_interval), sep=' ', end='\r', file=sys.stdout, flush=False)
_, types, _, _, vor_area, vor_amn, Data = read_data(directory + filename, iterations, dump_interval)
if amount == 0:
voronoi_area = vor_area[timestep]
voronoi_amount = vor_amn[timestep]
else:
voronoi_area += vor_area[timestep]
voronoi_amount += vor_amn[timestep]
amount += 1
voronoi_area /= amount
voronoi_amount /= amount
return voronoi_area, voronoi_amount
def calc_rdf_cutoff(age):
dr = params['dr']
if age == 'young':
directory = ".\\dump\\young\\"
file_ending = ".YOUNG"
iterations = 50000
dump_interval = 50
if age == 'old':
directory = ".\\dump\\old\\"
file_ending = ".OLD"
iterations = 1000000
dump_interval = 1000
grAA, grBB, grAB = [], [], []
for file in os.listdir(os.fsencode(directory)):
filename = os.fsdecode(file)
if filename.endswith(file_ending):
print('calculating', filename)
# load data
timesteps, types, q6_re, q6_im, Data = read_data(
directory + filename, iterations, dump_interval)
posData = Data[0]
# get minimum for each timestep
grAA_amin, grBB_amin, grAB_amin = calc_rdf_minimum(posData, types)
print(grAA_amin.mean())
# get mean of cutoff for different times
grAA.append(grAA_amin.mean())
grBB.append(grBB_amin.mean())
grAB.append(grAB_amin.mean())
grAA = np.array(grAA)
grBB = np.array(grBB)
grAB = np.array(grAB)
# get mean of cutoff for different files
AA_cutoff = np.mean(grAA) * dr
BB_cutoff = np.mean(grBB) * dr
AB_cutoff = np.mean(grAB) * dr
return AA_cutoff, BB_cutoff, AB_cutoff
rdf = True
q6 = False
directory = ".\\dump\\young\\"
extension = ".YOUNG"
amount = 0
# iterate through all dump files
for file in os.listdir(os.fsencode(directory)):
filename = os.fsdecode(file)
if filename.endswith(extension):
savename = './/saves//' + filename.split(".")[0] + '-rdf.npy'
# retrieve and process data
timesteps, types, q6_re, q6_im, Data = read_data(directory + filename, iterations, dump_interval)
Data = {'position': Data[0, :1], 'force': Data[1], 'q6_re': q6_re, 'q6_im': q6_im}
types = types[:1]
# declare variable for the first run
if amount == 0:
if rdf:
gr = save_load(lambda: calc_avg_rdf(Data['position'], types), savename)
if q6:
mean_q6 = np.asarray((calc_mean(Data['q6_re']), calc_mean(Data['q6_im'])))
var_q6 = np.asarray((calc_variance(Data['q6_re'], mean_q6[0]), calc_variance(Data['q6_im'], mean_q6[1])))
else:
if rdf:
def extract_features(directory, filename, iterations, dump_interval):
timesteps, types, q6_re, q6_im, vor_area, vor_amn, Data = read_data(
directory + filename, iterations, dump_interval)
Data = {
'position': Data[0],
'force': Data[1],
'q6_re': q6_re,
'q6_im': q6_im,
'vor_area': vor_area,
'vor_amn': vor_amn
}
savename = './/saves//' + filename.split(".")[0]
# get the mean square displacement and the variance square displacement of the position data (deprecated)
# msd = msd(Data['position'])
# vsd = vsd(Data['position'], msd)
# calculate the mean and variance nearest neighbour distance per timestep
mnn_distance, mnn_amount = save_load(lambda: mean_nn(Data['position'], 1),
savename + '-mean_nn.npy')
vnn_distance, vnn_amount = save_load(
lambda: variance_nn(Data['position'], mnn_distance, mnn_amount, 1),
savename + '-var_nn.npy')
# calculate the mean and variance of the norm of the force
mean_force = calc_mean(Data['force'])
variance_force = calc_variance(Data['force'], mean_force)
# calculate the mean and variance of the real q6 parameters
mean_q6_re = calc_mean(Data['q6_re'])
variance_q6_re = calc_variance(Data['q6_re'], mean_q6_re)
# calculate the mean and variance of the imaginary q6 parameters
mean_q6_im = calc_mean(Data['q6_im'])
variance_q6_im = calc_variance(Data['q6_im'], mean_q6_im)
# calculate the mean and the variance of the voronoi area
mean_vor_area = calc_mean(Data['vor_area'])
variance_vor_area = calc_variance(Data['vor_area'], mean_vor_area)
# calculate the mean and the variance of the voronoi area side amount
mean_vor_amn = calc_mean(Data['vor_amn'])
variance_vor_amn = calc_variance(Data['vor_amn'], mean_vor_amn)
# calculate the magnitude and count of the max two peaks of the voronoi area and amount
area_peak1_count, area_peak2_count, area_peak1_mag, area_peak2_mag, amount_peak1_count, amount_peak2_count, amount_peak1_mag, amount_peak2_mag = calc_voronoi_peaks(
timesteps, vor_area, vor_amn)
# prepare features in single array
features = np.column_stack([
mnn_distance, vnn_distance, mean_force, variance_force, mnn_amount,
vnn_amount, mean_q6_re, variance_q6_re, mean_q6_im, variance_q6_im,
mean_vor_area, variance_vor_area, mean_vor_amn, variance_vor_amn,
area_peak1_count, area_peak2_count, area_peak1_mag, area_peak2_mag,
amount_peak1_count, amount_peak2_count, amount_peak1_mag,
amount_peak2_mag
])
return features