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spectrogram.py
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"""
Calculate temporal fourier transform.
mpiexec_mpt -np 1 python3 spectrogram.py Ra1e10/slices/slices_c_s* Ra1e10/slices_rst/slices_rst_c_s* Ra1e10/slices_rst2/slices_rst2_c_s* --start=3
Usage:
spectrum_freq.py <files>... [options]
Options:
--start=<start> Start index of file list, default 0.
--output=<output> Output filename, default based off file name.
"""
import numpy as np
import h5py
from dedalus.tools.general import natural_sort
import pathlib
import os
from scipy import signal
from docopt import docopt
from dedalus.tools import logging as logging_setup
import logging
logger = logging.getLogger(__name__)
def rearrange(time, data, start):
num_transitions = 2
dt = np.abs(time[1:] - time[:-1])
transitions = np.argpartition(dt, -num_transitions)[-num_transitions:]
data_list = []
time_list = []
for i in range(num_transitions+1):
if i == 0:
s = slice(0, transitions[i]+1)
elif i == num_transitions:
s = slice(transitions[i-1]+2, -1)
else:
s = slice(transitions[i-1]+2, transitions[i])
time_list.append(time[s])
data_list.append(data[s])
sort = np.argsort([t[0] for t in time_list])
time_list = [time_list[i] for i in sort]
data_list = [data_list[i] for i in sort]
for j in range(num_transitions):
i = np.argmin(np.abs(time_list[j+1] - time_list[j][-1]))
time_list[j+1] = time_list[j+1][i+1:]
data_list[j+1] = data_list[j+1][i+1:]
time = np.concatenate(time_list)
data = np.concatenate(data_list)
i_start = start*200
time = time[i_start:]
data = data[i_start:,:]
dt = time[1:] - time[:-1]
logger.info(np.max(dt))
logger.info(np.min(dt))
return time, data
def frequency_spectrum(task, files, start):
data_list = []
time_list = []
for file in files:
f = h5py.File(file)
time_list.append(np.array(f['scales/sim_time']))
data_list.append(np.array(f['tasks/%s' %task]))
f.close()
time = np.hstack(time_list)
data = np.vstack(data_list)
time, data = rearrange(time, data, start)
logger.info(time[0])
logger.info(time[-1])
win_len = 2000
spectrum_list = []
time_list = []
dt = 200
for i in range(0,len(time)-win_len, dt):
if i % 100 == 0:
logger.info(i)
window = signal.hann(win_len)
window = window.reshape((win_len, 1))
data_norm = np.sum(np.abs(data[i:i+win_len])**2,axis=0)
data_win = data[i:i+win_len]*window
data_new_norm = np.sum(np.abs(data_win)**2,axis=0)
# renormalize data because of window function
data_win *= np.sqrt( data_norm/data_new_norm )
#logger.info( np.sqrt( data_norm/data_new_norm )[1:4] )
spectrum_freq = np.fft.fft(data_win,axis=0)/win_len
freq = np.fft.fftfreq(win_len,d = time[-1]-time[-2])
spectrum_list.append(spectrum_freq)
time_list.append(time[i+win_len//2])
#energy_g = np.sum(np.abs(data_win)**2,axis=0)/win_len # average energy
#energy_c = np.sum(np.abs(spectrum_freq)**2,axis=0) # should also be average energy
#logger.info(energy_g[1])
#logger.info(energy_c[1])
time = np.array(time_list)
spectrum = np.array(spectrum_list)
logger.info('done')
return freq, time, spectrum
def calculate_spectrum(files, start, output):
file = files[0]
f = h5py.File(file)
tasks = tuple(f['tasks'].keys())
kx = np.array(f['scales/kx'])
f.close()
new_file = output
spectra_file = pathlib.Path(new_file).absolute()
if os.path.exists(str(spectra_file)):
spectra_file.unlink()
spectra_f = h5py.File('{:s}'.format(str(spectra_file)), 'a')
scale_group = spectra_f.create_group('scales')
scale_group.create_dataset(name='kx', data = kx)
task_group = spectra_f.create_group('tasks')
for task in ['u z=1.0']:
logger.info(task)
freq, time, spectrum_freq = frequency_spectrum(task, files, start)
task_group.create_dataset(name=task, data = spectrum_freq)
scale_group.create_dataset(name='f', data = freq)
scale_group.create_dataset(name='t', data = time)
spectra_f.close()
if __name__ == "__main__":
args = docopt(__doc__)
files = natural_sort(args['<files>'])
logger.info(files)
if args['--start'] == None: start = 0
else: start = int(args['--start'])
if args['--output'] is None:
file = files[0]
index_under = file.rfind('_')
new_file = file[:index_under-2] + '_spectrogram.h5'
else:
new_file = args['--output']
calculate_spectrum(files, start, new_file)