def __init__(self,
label='data',
folder='.',
filename=False,
Col1=1,
Col2=3,
sens1=1000,
sens2=5,
tmin=False,
tmax=False,
tshift=False,
fmin=False,
fmax=False,
t=False,
ref=False,
samp=False):
if filename:
self.sens1 = sens1
self.sens2 = sens2
self.label = label
self.Col1 = Col1
self.Col2 = Col2
# load data, extract reference & sample field, calc FFT
self.dat = loadtxt(folder + '/' + filename)
self.t0 = -self.dat[:, 0]
L10 = self.dat[:, Col1] * sqrt(2) * sens1
L20 = self.dat[:, Col2] * sens2
# create waveform objects (note, extra assignments to save mem.)
self.ref0 = waveform(t=self.t0, wf=L10 + L20)
self.samp0 = waveform(t=self.t0, wf=L10 - L20)
self.ref0.t = self.t0
self.samp0.t = self.t0
self.f0 = self.ref0.f
self.samp0.f = self.ref0.f
if type(t) != bool:
assert type(ref) != bool, 'must provide reference data'
assert type(samp) != bool, 'must provide sample data'
self.t0 = t
self.ref0 = waveform(t=self.t0, wf=ref)
self.samp0 = waveform(t=self.t0, wf=samp)
self.ref0.t = self.t0
self.samp0.t = self.t0
self.f0 = self.ref0.f
self.samp0.f = self.ref0.f
# trim times & frequencies
self.ref = deepcopy(self.ref0)
self.samp = deepcopy(self.samp0)
self.trimTime(tmin, tmax)
self.samp.f = self.ref.f
self.f = self.ref.f
self.trimFreq(fmin, fmax)
self.calcTrans()
return
def send_wfms(self, ch_id, **kwargs):
id_list = kwargs.get('id_list', [ch_id,])
if ch_id in self.ch1_ids() and ch_id == self.ch1_ids(id_list)[0]:
bundle = waveform([self.ch1, self.mk11, self.mk12])
suffix = '_ch1'
elif ch_id in self.ch2_ids() and ch_id == self.ch2_ids(id_list)[0]:
bundle = waveform([self.ch2, self.mk21, self.mk22])
suffix = '_ch2'
else:
return
file_list, len_list, name_list = bundle.format_MAGIC1000()
for index, wfm_length in enumerate(len_list):
self._check_wfm_length(wfm_length, index)
for name, wfm_file in zip(name_list, file_list):
self._OPC()
self.instr.write_raw(':MMEM:DATA "%s%s.wfm",%s' % (name, suffix, IEEE_block_format(wfm_file)))
def waveform_creator(empty_elements,nonempty_elements):
WaveForm = []
Empty = []
for x in range(len(nonempty_elements)):
ch1 = [nonempty_elements[x][0],nonempty_elements[x][1],'ch1']
ch2 = [nonempty_elements[x][0],nonempty_elements[x][1],'ch2']
ch3 = [nonempty_elements[x][0],nonempty_elements[x][1],'ch3']
ch4 = [nonempty_elements[x][0],nonempty_elements[x][1],'ch4']
for y in range(2,len(nonempty_elements[x])):
if (nonempty_elements[x][y].channel == 'ch1'):
ch1.append(nonempty_elements[x][y])
elif (nonempty_elements[x][y].channel == 'ch2'):
ch2.append(nonempty_elements[x][y])
elif (nonempty_elements[x][y].channel == 'ch3'):
ch3.append(nonempty_elements[x][y])
elif (nonempty_elements[x][y].channel == 'ch4'):
ch4.append(nonempty_elements[x][y])
ch1 = waveform(ch1)
ch2 = waveform(ch2)
ch3 = waveform(ch3)
ch4 = waveform(ch4)
ch1.waveGenerator()
ch2.waveGenerator()
ch3.waveGenerator()
ch4.waveGenerator()
WaveForm.append(ch1)
WaveForm.append(ch2)
WaveForm.append(ch3)
WaveForm.append(ch4)
for x in range(len(empty_elements)):
Empty.append(waveform(empty_elements[x]))
return (WaveForm,Empty)
def proc_RX(WF_file,
shots,
rxData=None,
sigmas=np.arange(0, 5, 0.25),
deltas=np.arange(-1, 1.5, 0.5),
TX=None,
countPeaks=True,
WF_library=None,
TX_file=None,
scat_file=None,
catalogBuffer=None):
"""
Routine to process the wavefoms in an ATM waveform file. Can be run inside
the loop of fit_ATM_scat, or run on a few waveforms at a time for plot generation
"""
if TX is None and TX_file is not None:
with h5py.file(TX_file) as h5f:
TX = waveform(np.array(h5f['/TX/t']), np.array(h5f(['/TX/p'])))
TX.t -= TX.nSigmaMean()[0]
TX.tc = 0
# make the library of templates
if WF_library is None:
WF_library = dict()
WF_library.update({0.: TX})
if scat_file is not None:
WF_library.update(make_rx_scat_catalog(TX, h5_file=scat_file))
if rxData is None:
# make the return waveform structure
D = read_ATM_file(WF_file, shot0=shots[0], nShots=shots[-1] - shots[0])
rxData = D['RX']
rxData.t -= np.nanmean(rxData.t)
else:
D = dict()
if countPeaks:
rxData.nPeaks = rxData.count_peaks(W=4)
else:
rxData.nPeaks = np.ones(rxData.size)
threshold_mask = rxData.p >= 255
rxData.subBG(t50_minus=3.)
rxData.tc = [ii.nSigmaMean()[0] for ii in rxData]
#rxData.tc=rxData.t50()
rxData.p[threshold_mask] = np.NaN
# fit the data. Catalogbuffer contains waveform templates that have already been tried
D_out, catalogBuffer=fit_catalog(rxData, WF_library, sigmas, deltas, return_data_est=True, \
return_catalog=True, catalog=catalogBuffer)
return D_out, rxData, D, catalogBuffer
def _preprocessing(self, filename, start=0, end=float('inf')):
"""
Return the sub-fingerprint matrix from featureExtract.py
overlap: For the sake of avoiding unpleasant segmentation in the retrieving procedure.
"""
if start != 0:
start = int(float(start)*1000)
if end != float('inf'):
end = int(float(end)*1000)
pcm, samplerate = waveform(filename, start, end)
pcm = downsample(pcm, samplerate, self.sr)
if type(pcm) == int:
landmarks = 0
else:
landmarks = find_landmark(pcm, self.sr, self.density)
return landmarks
def main():
parser = argparse.ArgumentParser(
description=
'Fit the waveforms from an ATM file with a set of scattering parameters'
)
parser.add_argument('input_file', type=str)
parser.add_argument('output_file', type=str)
parser.add_argument('--startShot', '-s', type=int, default=0)
parser.add_argument('--scat_file', '-f', type=str, default=None)
parser.add_argument('--nShots', '-n', type=int, default=np.Inf)
parser.add_argument('--DOPLOT', '-P', action='store_true')
parser.add_argument('--IR', '-I', action='store_true')
parser.add_argument('--TXfromRX', action='store_true')
parser.add_argument('--skipRX', action='store_true', default=False)
parser.add_argument('--fitTX', action='store_true')
parser.add_argument('--everyNTX', type=int, default=100)
parser.add_argument('--TXfile', '-T', type=str, default=None)
parser.add_argument('--waveforms',
'-w',
action='store_true',
default=False)
args = parser.parse_args()
# get the waveform count from the output file
nWFs = np.minimum(
args.nShots,
read_ATM_file(args.input_file, getCountAndReturn=True) -
args.startShot)
lastShot = args.startShot + nWFs
# make the output file
if os.path.isfile(args.output_file):
os.remove(args.output_file)
outDS = ['shot', 'R', 'A', 'B', 'delta_t', 'sigma', 't0', 'K0', 'Kmin', 'Kmax',\
'latitude', 'longitude', 'elevation']
out_h5 = h5py.File(args.output_file, 'w')
for DS in outDS:
out_h5.create_dataset(DS, (nWFs, ), dtype='f8')
if args.waveforms:
out_h5.create_dataset('RX/p', (192, nWFs))
out_h5.create_dataset('RX/p_fit', (192, nWFs))
TxP = None
# get the transmit pulse
if args.TXfile is not None:
with h5py.File(args.TXfile, 'r') as fh:
TX = waveform(np.array(fh['/TX/t']), np.array(fh['/TX/p']))
TX.t -= TX.nSigmaMean()[0]
TX.tc = np.array(TX.nSigmaMean()[0])
TX.normalize()
else:
if args.TXfromRX is False:
TX, TxP = get_tx_est(args.input_file, nShots=5000)
else:
TX, TxP = get_tx_est(args.input_file, source='RX', pMin=150)
if args.fitTX:
print('fitting the transmit pulse')
dummy, TxP = get_tx_est(args.input_file,
source='TX',
TX0=TX,
skip_make_TX=True,
skip_n_tx=args.everyNTX)
out_h5.create_dataset("TX/t", data=TX.t.ravel())
out_h5.create_dataset("TX/p", data=TX.p.ravel())
if TxP is not None:
for field in ['t0', 'A', 'R', 'shot', 'sigma']:
out_h5.create_dataset('/TX/' + field, data=TxP[field])
if args.skipRX is True:
out_h5.close()
return
# check whether the scattering file exists
if args.scat_file is not None:
scat_file = args.scat_file
else:
# look for the default scattering file in the directory where the source file is found
scat_file = os.path.dirname(
os.path.abspath(__file__)) + '/subsurface_srf_no_BC.h5'
if not os.path.isfile(scat_file):
print("%s does not exist" % scat_file)
exit()
# make the library of templates
WF_library = dict()
WF_library.update({0.: TX})
if args.IR is False:
WF_library.update(make_rx_scat_catalog(TX, h5_file=scat_file))
print("Returns:")
# loop over start vals (one block at a time...)
# choose how to divide the output
blocksize = 1000
start_vals = args.startShot + np.arange(0, nWFs, blocksize, dtype=int)
catalogBuffer = None
time_old = time()
if args.IR is True:
countPeaks = False
sigmas = np.arange(0, 5, 0.125)
else:
countPeaks = True
sigmas = np.arange(0, 5, 0.25)
# choose a set of delta t values
deltas = np.arange(-1, 1.5, 0.5)
for shot0 in start_vals:
shots = np.arange(shot0, np.minimum(shot0 + blocksize, lastShot))
tic = time()
D_out, rxData, D, catalogBuffer=proc_RX(args.input_file, shots, sigmas=sigmas, deltas=deltas,\
TX=TX, WF_library=WF_library, catalogBuffer=catalogBuffer, countPeaks=countPeaks)
delta_time = time() - tic
N_out = D_out['shot'].size
outShot0 = shot0 - args.startShot
for key in outDS:
try:
if key in D_out:
out_h5[key][outShot0:outShot0 + N_out] = D_out[key].ravel()
else:
if key in D:
out_h5[key][outShot0:outShot0 + N_out] = D[key].ravel()
except OSError:
print("OSError for key=%s, outshot0=%d, outshotN=%d, nDS=%d" %
(key, outShot0, outShot0 + N_out, out_h5[key].size))
if args.waveforms:
out_h5['RX/p_fit'][:, outShot0:outShot0 + N_out] = D_out['wf_est']
out_h5['RX/p'][:, outShot0:outShot0 + N_out] = rxData.p
print(" shot=%d out of %d, N_keys=%d, dt=%5.1f" %
(shot0 + blocksize, start_vals[-1] + blocksize,
len(catalogBuffer.keys()), delta_time))
print(" time to fit RX=%3.2f" % (time() - time_old))
if args.waveforms:
out_h5.create_dataset('RX/t', data=rxData.t.ravel())
out_h5.close()
if asp.ch_play != 1:
raise ValueError("El código no está preparado para reproducir \
en 2 canales")
print("Canales para grabar: ch_rec=%i" % asp.ch_rec)
#%%
p = pyaudio.PyAudio()
s_f = []
n = int(bsp.dT / bsp.dt)
if form == 'freq':
s_0 = np.sin(2 * np.pi * np.arange(asp.sr * bsp.dT) * bsp.freq / asp.sr)
else:
s_0 = waveform(form, n)
s_0 = bsp.vol * s_0.astype(np.float32)
def callback(in_data, frame_count, time_info, status):
return (s_0, pyaudio.paContinue)
streamplay = p.open(format=asp.ft_play,
channels=asp.ch_play,
rate=asp.sr,
output=True,
stream_callback=callback)
streamrecord = p.open(format=asp.ft_rec,
channels=asp.ch_rec,
def play_record(
T, # duración en seg de la grabación
form='freq', # forma de onda {'freq', 'sin', 'squ', 'tri', 'saw'}
mode='txt', # modo de funcionamiento del programa {'txt', 'wav'}
savetxt=False,
showplot=True,
buffer={
'dT': 1,
'dt': 0.005,
'freq': 440,
'vol': 1
},
audio={
'sr': 44000,
'ch_play': 1,
'ch_rec': 1,
'ft_play': pyaudio.paInt16,
'ft_rec': pyaudio.paInt16
},
filendir={
'fname': 'output',
'fdir': os.getcwd()
},
):
"""Reproduce una señal de sonido y graba por jack al mismo tiempo.
Crea una señal cuya forma de onda está dada por 'form'. Ésta crea un \
buffer cuyos frames duran 'dt'. Reproduce y graba durante un tiempo \
'T'. Devuelve el array reproducido y la lista grabada.
Tiene dos modos de funcionamiento dados por 'mode'. Si 'mode=="wav", \
guarda un archivo de audio. Y si 'mode=="txt"',
Además, si 'savetxt==True', guarda un archivo de texto. Y si \
'showplot=="True", muestra un gráfico.
"""
import pyaudio
import numpy as np
import os
import wave
import matplotlib.pyplot as plt
from new_name import new_name
from waveform import waveform
from argparse import Namespace
home = os.getcwd()
ref = {
'dT': 1,
'dt': 0.005,
'freq': 440,
'vol': 1,
'sr': 44000,
'ch_play': 1,
'ch_rec': 1,
'ft_play': pyaudio.paInt16,
'ft_rec': pyaudio.paInt16,
'fname': 'output',
'fdir': os.getcwd()
}
bkey = ['dT', 'dt', 'freq', 'vol']
akey = ['sr', 'ch_play', 'ch_rec', 'ft_play', 'ft_rec']
fkey = ['fname', 'fdir']
for key in bkey:
if key not in buffer:
buffer.update({key: ref[key]})
for key in akey:
if key not in audio:
audio.update({key: ref[key]})
for key in fkey:
if key not in filendir:
filendir.update({key: ref[key]})
bsp = Namespace(**buffer) # buffer space
asp = Namespace(**audio) # audio space
fsp = Namespace(**filendir) # filendir space
if mode != 'txt':
print("Modo: 'wav'")
else:
print("Modo: 'txt'")
print("Guardar txt: %s" % savetxt)
print("Mostrar gráfico: %s" % showplot)
print("Longitud del buffer: %i" % int(bsp.dT / bsp.dt))
if int(bsp.dT / bsp.dt) != bsp.dT / bsp.dt:
print("¡Ojo! El intervalo dt no entra un número entero \
de veces en dT")
bsp.dt = bsp.dT / int(bsp.dT / bsp.dt)
print("Intervalo redefinido a %f s" % bsp.dt)
if int(T / bsp.dT) != T / bsp.dT:
print("¡Ojo! El tiempo de grabación \
no es un número entero de veces dT")
if int(T / bsp.dT) != 0:
T = int(T / bsp.dT) * bsp.dT
else:
print("¡Ojo! El tiempo de grabación era menor a dT")
T = bsp.dT
print("Tiempo de grabación redefinido a %f s" % T)
p = pyaudio.PyAudio()
s_f = []
n = int(bsp.dT / bsp.dt)
if form == 'freq':
s_0 = np.sin(2 * np.pi * np.arange(asp.sr * bsp.dT) * bsp.freq /
asp.sr)
else:
s_0 = waveform(form, n)
s_0 = bsp.vol * s_0.astype(np.float32)
def callback(in_data, frame_count, time_info, status):
return (s_0, pyaudio.paContinue)
streamplay = p.open(format=asp.ft_play,
channels=asp.ch_play,
rate=asp.sr,
output=True,
stream_callback=callback)
streamrecord = p.open(format=asp.ft_rec,
channels=asp.ch_rec,
rate=asp.sr,
input=True,
frames_per_buffer=n)
streamplay.start_stream()
print("* recording")
streamrecord.start_stream()
data = streamrecord.read(int(asp.sr * T))
print("* done recording")
streamrecord.stop_stream()
streamplay.stop_stream()
streamrecord.close()
streamplay.close()
p.terminate()
if mode == 'txt':
s_f.extend(np.fromstring(data, 'Float32'))
if savetxt:
os.chdir(fsp.fdir)
savetxt((new_name(fsp.fname, 'txt', fsp.fdir) + '.txt'), s_f)
os.chdir(home)
if showplot:
plt.figure()
plt.plot(s_f[2000:3000], 'ro-')
plt.ylabel('señal grabada')
plt.grid()
plt.show()
return s_0, s_f
else:
s_f.append(data)
fsp.fname = new_name(fsp.fname, 'wav', fsp.fdir)
os.chdir(fsp.fdir)
wf = wave.open((fsp.fname + '.wav'), 'wb')
wf.setnchannels(asp.ch_rec)
wf.setsampwidth(p.get_sample_size(asp.ft_rec))
wf.setframerate(asp.sr)
wf.writeframes(b''.join(s_f))
wf.close()
os.chdir(home)
def send_wfms(self, ch_id, **kwargs):
if ('id_list' in kwargs
and ch_id == kwargs['id_list'][0]) or kwargs.get(
'clear_buffer', False):
self.safe_write('SLISt:SEQuence:DELete ALL')
self.safe_write('WLISt:WAVeform:DELete ALL')
id_list = kwargs.get('id_list', [
ch_id,
])
if ch_id in self.ch1_ids() and ch_id == self.ch1_ids(id_list)[0]:
bundle = waveform([self.ch1, self.mk11, self.mk12])
suffix = '_ch1'
elif ch_id in self.ch2_ids() and ch_id == self.ch2_ids(id_list)[0]:
bundle = waveform([self.ch2, self.mk21, self.mk22])
suffix = '_ch2'
else:
return
chunk_size = 2**20
wave_data, mk_data, len_list, name_list = bundle.format_TekWFM_AWG70000(
chunk_size=chunk_size)
for index, wfm_length in enumerate(len_list):
self._check_wfm_length(wfm_length, index)
self._OPC() #complete all pending operations
for wave_block_chunked, mk_block_chunked, wfm_length, name in zip(
wave_data, mk_data, len_list, name_list):
info = ({'name': name + suffix, 'size': wfm_length})
self.safe_write(
'WLISt:WAVeform:NEW "{name}",{size}'.format(**info))
#self.safe_write('WLISt:WAVeform:NEW "{name}",{size},REAL'.format(**info))
for chunk_index in range(len(wave_block_chunked)):
numpnts = chunk_size if chunk_index < len(
wave_block_chunked
) - 1 else wfm_length - chunk_index * chunk_size
wave_block, mk_block = wave_block_chunked[
chunk_index], mk_block_chunked[chunk_index]
info.update({
'start_index': chunk_index * chunk_size,
'size': numpnts
})
info.update({
'wave_block_data': wave_block,
'marker_block_data': mk_block
})
self.instr.write_raw(
'WLISt:WAVeform:DATA "{name}",{start_index},{size},{wave_block_data}'
.format(**info))
self.instr.write_raw(
'WLISt:WAVeform:MARKer:DATA "{name}",{start_index},{size},{marker_block_data}'
.format(**info))
# cmd = 'WLISt:WAVeform:DATA "{name}",{start_index},{size},{wave_block_data}'.format(**info)
# header = 'WLISt:WAVeform:DATA "{name}",{start_index},{size},'.format(**info)
# print cmd[:len(header)+10]
# cmd = 'WLISt:WAVeform:MARKer:DATA "{name}",{start_index},{size},{marker_block_data}'.format(**info)
# header = 'WLISt:WAVeform:MARKer:DATA "{name}",{start_index},{size},'.format(**info)
# print cmd[:len(header)+10]
self._OPC()
