def __init__(self,
filename,
f_start=None,
f_stop=None,
t_start=None,
t_stop=None,
load_data=True,
max_load=1.):
""" Constructor.
Args:
filename (str): filename of blimpy file.
f_start (float): start frequency, in MHz
f_stop (float): stop frequency, in MHz
t_start (int): start time bin
t_stop (int): stop time bin
"""
super(FilReader, self).__init__()
self.header_keywords_types = sigproc.header_keyword_types
if filename and os.path.isfile(filename):
self.filename = filename
self.load_data = load_data
self.header = self.read_header()
self.file_size_bytes = os.path.getsize(self.filename)
self.idx_data = sigproc.len_header(self.filename)
self.n_channels_in_file = self.header[b'nchans']
self.n_beams_in_file = self.header[
b'nifs'] #Placeholder for future development.
self.n_pols_in_file = 1 #Placeholder for future development.
self._n_bytes = int(self.header[b'nbits'] /
8) #number of bytes per digit.
self._d_type = self._setup_dtype()
self._setup_n_ints_in_file()
self.file_shape = (self.n_ints_in_file, self.n_beams_in_file,
self.n_channels_in_file)
if self.header[b'foff'] < 0:
self.f_end = self.header[b'fch1']
self.f_begin = self.f_end + self.n_channels_in_file * self.header[
b'foff']
else:
self.f_begin = self.header[b'fch1']
self.f_end = self.f_begin + self.n_channels_in_file * self.header[
b'foff']
self.t_begin = 0
self.t_end = self.n_ints_in_file
#Taking care all the frequencies are assigned correctly.
self._setup_selection_range(f_start=f_start,
f_stop=f_stop,
t_start=t_start,
t_stop=t_stop,
init=True)
#Convert input frequencies into what their corresponding channel number would be.
self._setup_chans()
#Update frequencies ranges from channel number.
self._setup_freqs()
self.freq_axis = 2
self.time_axis = 0
self.beam_axis = 1 # Place holder
#EE ie.
# spec = np.squeeze(fil_file.data)
# set start of data, at real length of header (future development.)
# self.datastart=self.hdrraw.find('HEADER_END')+len('HEADER_END')+self.startsample*self.channels
#Applying data size limit to load.
if max_load is not None:
if max_load > 1.0:
logger.warning(
'Setting data limit != 1GB, please handle with care!')
self.MAX_DATA_ARRAY_SIZE = max_load * MAX_DATA_ARRAY_SIZE_UNIT
else:
self.MAX_DATA_ARRAY_SIZE = MAX_DATA_ARRAY_SIZE_UNIT
if self.file_size_bytes > self.MAX_DATA_ARRAY_SIZE:
self.large_file = True
else:
self.large_file = False
if self.load_data:
if self.large_file:
if self.f_start or self.f_stop or self.t_start or self.t_stop:
if self.isheavy():
logger.warning(
"Selection size of %.2f GB, exceeding our size limit %.2f GB. Instance created, header loaded, but data not loaded, please try another (t,v) selection."
% (self._calc_selection_size() /
(1024.**3), self.MAX_DATA_ARRAY_SIZE /
(1024.**3)))
self._init_empty_selection()
else:
self.read_data()
else:
logger.warning(
"The file is of size %.2f GB, exceeding our size limit %.2f GB. Instance created, header loaded, but data not loaded. You could try another (t,v) selection."
% (self.file_size_bytes /
(1024.**3), self.MAX_DATA_ARRAY_SIZE /
(1024.**3)))
self._init_empty_selection()
else:
self.read_data()
else:
logger.info("Skipping loading data ...")
self._init_empty_selection()
else:
raise IOError("Need a file to open, please give me one!")
def __init__(self, filename,f_start=None, f_stop=None,t_start=None, t_stop=None, load_data=True, max_load=1.):
""" Constructor.
Args:
filename (str): filename of blimpy file.
f_start (float): start frequency, in MHz
f_stop (float): stop frequency, in MHz
t_start (int): start time bin
t_stop (int): stop time bin
"""
super(FilReader, self).__init__()
self.header_keywords_types = sigproc.header_keyword_types
if filename and os.path.isfile(filename):
self.filename = filename
self.load_data = load_data
self.header = self.read_header()
self.file_size_bytes = os.path.getsize(self.filename)
self.idx_data = sigproc.len_header(self.filename)
self.n_channels_in_file = self.header[b'nchans']
self.n_beams_in_file = self.header[b'nifs'] #Placeholder for future development.
self.n_pols_in_file = 1 #Placeholder for future development.
self._n_bytes = int(self.header[b'nbits'] / 8) #number of bytes per digit.
self._d_type = self._setup_dtype()
self._setup_n_ints_in_file()
self.file_shape = (self.n_ints_in_file,self.n_beams_in_file,self.n_channels_in_file)
if self.header[b'foff'] < 0:
self.f_end = self.header[b'fch1']
self.f_begin = self.f_end + self.n_channels_in_file*self.header[b'foff']
else:
self.f_begin = self.header[b'fch1']
self.f_end = self.f_begin + self.n_channels_in_file*self.header[b'foff']
self.t_begin = 0
self.t_end = self.n_ints_in_file
#Taking care all the frequencies are assigned correctly.
self._setup_selection_range(f_start=f_start, f_stop=f_stop, t_start=t_start, t_stop=t_stop, init=True)
#Convert input frequencies into what their corresponding channel number would be.
self._setup_chans()
#Update frequencies ranges from channel number.
self._setup_freqs()
self.freq_axis = 2
self.time_axis = 0
self.beam_axis = 1 # Place holder
#EE ie.
# spec = np.squeeze(fil_file.data)
# set start of data, at real length of header (future development.)
# self.datastart=self.hdrraw.find('HEADER_END')+len('HEADER_END')+self.startsample*self.channels
#Applying data size limit to load.
if max_load is not None:
if max_load > 1.0:
logger.warning('Setting data limit != 1GB, please handle with care!')
self.MAX_DATA_ARRAY_SIZE = max_load * MAX_DATA_ARRAY_SIZE_UNIT
else:
self.MAX_DATA_ARRAY_SIZE = MAX_DATA_ARRAY_SIZE_UNIT
if self.file_size_bytes > self.MAX_DATA_ARRAY_SIZE:
self.large_file = True
else:
self.large_file = False
if self.load_data:
if self.large_file:
if self.f_start or self.f_stop or self.t_start or self.t_stop:
if self.isheavy():
logger.warning("Selection size of %.2f GB, exceeding our size limit %.2f GB. Instance created, header loaded, but data not loaded, please try another (t,v) selection." % (self._calc_selection_size() / (1024. ** 3), self.MAX_DATA_ARRAY_SIZE / (1024. ** 3)))
self._init_empty_selection()
else:
self.read_data()
else:
logger.warning("The file is of size %.2f GB, exceeding our size limit %.2f GB. Instance created, header loaded, but data not loaded. You could try another (t,v) selection."%(self.file_size_bytes/(1024.**3), self.MAX_DATA_ARRAY_SIZE/(1024.**3)))
self._init_empty_selection()
else:
self.read_data()
else:
logger.info("Skipping loading data ...")
self._init_empty_selection()
else:
raise IOError("Need a file to open, please give me one!")
#://github.com/UCBerkeleySETI/blimpy/blob/master/blimpy/io/fil_reader.py
# plots filterbank file and extracts candidates
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal
from scipy import stats
from pathlib import Path
import sigproc
import sys
#fname = '/datax2/devfil/beam_data/20201110104110_HAT-P-44C.fil';
fname = sys.argv[1];
fhead = sigproc.read_header(fname);
headlen = sigproc.len_header(fname);
nSpec = int((Path(fname).stat().st_size - headlen)/fhead['nchans']/4);
spec = np.zeros((fhead['nchans']));
for k in range(nSpec):
print(str(k+1) + ' / ' + str(nSpec));
spec += np.fromfile(fname, dtype=np.uint32, count=int(fhead['nchans']), offset=headlen+int(4*k*fhead['nchans']));
plt.figure();
plt.plot(np.linspace(fhead['fch1'],fhead['fch1']+fhead['foff']*fhead['nchans'],fhead['nchans']),10.*np.log10(spec));
plt.grid();
plt.xlabel('frequency [MHz]');
plt.ylabel('power [dB]');
plt.suptitle(fhead['source_name']);
plt.show();
'/mnt/data/dsa110/T3/corr07/03dec20/corr07_BF.fil',\
'/mnt/data/dsa110/T3/corr08/03dec20/corr08_BF.fil',\
'/mnt/data/dsa110/T3/corr09/03dec20/corr09_BF.fil',\
'/mnt/data/dsa110/T3/corr10/03dec20/corr10_BF.fil',\
'/mnt/data/dsa110/T3/corr11/03dec20/corr11_BF.fil',\
'/mnt/data/dsa110/T3/corr12/03dec20/corr12_BF.fil',\
'/mnt/data/dsa110/T3/corr13/03dec20/corr13_BF.fil',\
'/mnt/data/dsa110/T3/corr14/03dec20/corr14_BF.fil',\
'/mnt/data/dsa110/T3/corr15/03dec20/corr15_BF.fil',\
'/mnt/data/dsa110/T3/corr16/03dec20/corr16_BF.fil']
nChans = 384
fname = fnames[0]
print('processing file ' + fname)
header_len = sigproc.len_header(fname)
filsize = int(Path(fname).stat().st_size)
nSam = int((filsize - header_len) / nChans)
data = np.fromfile(fname, dtype=np.uint8, count=-1, offset=header_len)
alldata = np.reshape(data, (nChans, nSam), order='F')
for fname in fnames[1:]:
print('processing file ' + fname)
header_len = sigproc.len_header(fname)
filsize = int(Path(fname).stat().st_size)
nSam = int((filsize - header_len) / nChans)
data = np.fromfile(fname, dtype=np.uint8, count=-1, offset=header_len)
data = np.reshape(data, (nChans, nSam), order='F')
alldata = np.concatenate((alldata, data), axis=0)
header_keyword_types = {