def __init__(self, fname=None, obsfile=None, size=2**25):
obs = {}
conf = configobj.ConfigObj(r"{0}".format(obsfile))
for key, val in conf.iteritems():
obs[key] = val
self.dtype = obs["dtype"]
self.sample_rate = float(obs["srate"]) * u.MHz
dm = float(obs["dm"]) * u.pc / u.cm**3
self.dm = DispersionMeasure(dm)
self.thread_ids = list(obs["threads"])
self.fedge = np.array(obs["fedge"]).astype('float') * u.MHz
if 'forder' in obs:
self.forder = np.array(obs["forder"]).astype('int')
else:
self.forder = np.ones(len(self.fedge))
if 'ntrack' in obs:
self.ntrack = int(obs["ntrack"])
else:
self.ntrack = 64
if 'nIF' in obs:
self.nIF = int(obs["nIF"])
self.dt1 = 1 / self.sample_rate
self.size = size
self.f = self.fedge + self.forder * np.fft.rfftfreq(
size, self.dt1)[:, np.newaxis]
self.fref = np.amax(self.f)
self.dmloss = self.dm.time_delay(np.amin(self.f), self.fref)
self.samploss = int(
np.ceil((self.dmloss * self.sample_rate).decompose()).value)
self.step = int(size - 2**(np.ceil(np.log2(self.samploss))))
self.npol = len(self.thread_ids)
print("{0} and {1} samples lost to de-dispersion".format(
self.dmloss, self.samploss))
print("Taking blocks of {0}, steps of {1} samples".format(
self.size, self.step))
if fname:
self.open(fname)
def __init__(self, fname=None, obsfile=None, size=2**25):
obs = {}
conf = configobj.ConfigObj(r"{0}".format(obsfile))
for key, val in conf.iteritems():
obs[key] = val
self.dtype = obs["dtype"]
self.sample_rate = float(obs["srate"]) * u.MHz
dm = float(obs["dm"]) * u.pc / u.cm**3
self.dm = DispersionMeasure(dm)
self.thread_ids = list(obs["threads"])
self.fedge = np.array(obs["fedge"]).astype('float') * u.MHz
if 'forder' in obs:
self.forder = np.array(obs["forder"]).astype('int')
else:
self.forder = np.ones(len(self.fedge))
if 'ntrack' in obs:
self.ntrack = int(obs["ntrack"])
else:
self.ntrack = 64
if 'nIF' in obs:
self.nIF = int(obs["nIF"])
self.dt1 = 1/self.sample_rate
self.size = size
self.f = self.fedge + self.forder*np.fft.rfftfreq(size, self.dt1)[:, np.newaxis]
self.fref = np.amax(self.f)
self.dmloss = self.dm.time_delay(np.amin(self.f), self.fref)
self.samploss = int(np.ceil( (self.dmloss * self.sample_rate).decompose() ).value)
self.step = int(size - 2**(np.ceil(np.log2(self.samploss))))
self.npol = len(self.thread_ids)
print("{0} and {1} samples lost to de-dispersion".format(self.dmloss, self.samploss))
print("Taking blocks of {0}, steps of {1} samples".format(self.size, self.step))
if fname:
self.open(fname)
class ReadDD:
"""Reader class to return de-dispersed data directy from raw binaries
Requires a configuration file with data type and frequency information
of your observation
Parameters
----------
fname: filename of binary file to be opened
obsfile: filename of config file for observation
requires
srate : int
sample_rate in MHz
dtype : string
one of 'vdif', 'mark4', 'mark5b', 'dada'
dm : float
dispersion measure, in pc/cm**3
threads:
IFs to be read
fedge: list, int
edge frequencies of IFs in MHz
optional
forder: list, int
list of 1 or -1 indicating upper or lower sideband
default assumption is upper
ntrack: int
ntrack parameter for mark4 data, default = 64
nIF: int
total number of IFs, required to open mark5b data
size: int
number of samples to read
"""
def __init__(self, fname=None, obsfile=None, size=2**25):
obs = {}
conf = configobj.ConfigObj(r"{0}".format(obsfile))
for key, val in conf.iteritems():
obs[key] = val
self.dtype = obs["dtype"]
self.sample_rate = float(obs["srate"]) * u.MHz
dm = float(obs["dm"]) * u.pc / u.cm**3
self.dm = DispersionMeasure(dm)
self.thread_ids = list(obs["threads"])
self.fedge = np.array(obs["fedge"]).astype('float') * u.MHz
if 'forder' in obs:
self.forder = np.array(obs["forder"]).astype('int')
else:
self.forder = np.ones(len(self.fedge))
if 'ntrack' in obs:
self.ntrack = int(obs["ntrack"])
else:
self.ntrack = 64
if 'nIF' in obs:
self.nIF = int(obs["nIF"])
self.dt1 = 1/self.sample_rate
self.size = size
self.f = self.fedge + self.forder*np.fft.rfftfreq(size, self.dt1)[:, np.newaxis]
self.fref = np.amax(self.f)
self.dmloss = self.dm.time_delay(np.amin(self.f), self.fref)
self.samploss = int(np.ceil( (self.dmloss * self.sample_rate).decompose() ).value)
self.step = int(size - 2**(np.ceil(np.log2(self.samploss))))
self.npol = len(self.thread_ids)
print("{0} and {1} samples lost to de-dispersion".format(self.dmloss, self.samploss))
print("Taking blocks of {0}, steps of {1} samples".format(self.size, self.step))
if fname:
self.open(fname)
def open(self, fname):
"""Open data with appropriate baseband reader"""
if self.dtype == 'vdif':
self.fh = vdif.open(fname, mode='rs', sample_rate=self.sample_rate)
if self.dtype == 'mark4':
self.fh = mark4.open(fname, mode='rs', decade=2010, ntrack=self.ntrack,
sample_rate=self.sample_rate, thread_ids=self.thread_ids)
if self.dtype == 'mark5b':
self.fh = mark5b.open(fname, mode='rs', nchan=self.nIF, ref_mjd=57000,
sample_rate=self.sample_rate, thread_ids=self.thread_ids)
def seek(self, nsamples):
"""Seek as in a baseband stream"""
self.fh.seek(nsamples)
def readCoherent(self, size=2**25):
"""Return coherently dedispersed timestream
Read size number of samples, coherently dedisperse, take first
step samples to chop off wraparound
"""
if size != self.size or not 'dd' in self.__dict__ :
# Only compute dedispersion phases and fft plans once for given size
print("Calculating de-dispersion phase factors for size {0}".format(size))
self.f = self.fedge + self.forder*np.fft.rfftfreq(size, self.dt1)[:, np.newaxis]
self.dd = self.dm.phase_factor(self.f, self.fref)
for j in range(len(self.forder)):
if self.forder[j] == 1:
self.dd[...,j] = np.conj(self.dd[...,j])
self.size = size
self.step = int(size - 2**(np.ceil(np.log2(self.samploss))))
a = pyfftw.empty_aligned((self.size, self.npol), dtype='float32', n=16)
b = pyfftw.empty_aligned((self.size//2+1, self.npol), dtype='complex64', n=16)
print("planning FFTs for coherent dedispersion...")
self.fft_ts = pyfftw.FFTW(a,b, axes=(0,), direction='FFTW_FORWARD',
planning_timelimit=1.0, threads=8 )
print("...")
self.ifft_ts = pyfftw.FFTW(b,a, axes=(0,), direction='FFTW_BACKWARD',
planning_timelimit=1.0, threads=8 )
d = pyfftw.empty_aligned((size,self.npol), dtype='float32')
#d = self.fh.read(size)
# need better solution...
if self.dtype == 'vdif':
d[:] = self.fh.read(size)[self.thread_ids]
else:
d[:] = self.fh.read(size)
#ft = np.fft.rfft(d, axis=0)
ft = self.fft_ts(d)
ft *= self.dd
dift = pyfftw.empty_aligned((size//2+1,self.npol), dtype='complex64')
dift[:] = ft
d = self.ifft_ts(dift)
#d = np.fft.irfft(ft, axis=0)[:self.step]
return d
def readIncoherent(self, size=2**25, nchan=512):
"""Return incoherently dedispersed, channelized data
Read size number of samples, channelize to nchan, incoherently
dedisperse, take first step samples to chop off wraparound
"""
self.f = self.fedge + self.forder*np.fft.rfftfreq(2*nchan, self.dt1)[:, np.newaxis]
npol = len(self.fh.thread_ids)
dm_delay = self.dm.time_delay(self.f, self.fref)
dm_sample = np.floor( (dm_delay / (2*nchan*self.dt1)).decompose()).value.astype('int')
d = self.fh.read(size)
dchan = np.fft.rfft(d.reshape(-1, 2*nchan, npol), axis=1)
for chan in range(nchan):
for pol in range(npol):
dchan[:,chan,pol] = np.roll(dchan[:,chan,pol], -dm_sample[chan,pol], axis=0)
return dchan[:(self.step//(2*nchan))]
class ReadDD:
"""Reader class to return de-dispersed data directy from raw binaries
Requires a configuration file with data type and frequency information
of your observation
Parameters
----------
fname: filename of binary file to be opened
obsfile: filename of config file for observation
requires
srate : int
sample_rate in MHz
dtype : string
one of 'vdif', 'mark4', 'mark5b', 'dada'
dm : float
dispersion measure, in pc/cm**3
threads:
IFs to be read
fedge: list, int
edge frequencies of IFs in MHz
optional
forder: list, int
list of 1 or -1 indicating upper or lower sideband
default assumption is upper
ntrack: int
ntrack parameter for mark4 data, default = 64
nIF: int
total number of IFs, required to open mark5b data
size: int
number of samples to read
"""
def __init__(self, fname=None, obsfile=None, size=2**25):
obs = {}
conf = configobj.ConfigObj(r"{0}".format(obsfile))
for key, val in conf.iteritems():
obs[key] = val
self.dtype = obs["dtype"]
self.sample_rate = float(obs["srate"]) * u.MHz
dm = float(obs["dm"]) * u.pc / u.cm**3
self.dm = DispersionMeasure(dm)
self.thread_ids = list(obs["threads"])
self.fedge = np.array(obs["fedge"]).astype('float') * u.MHz
if 'forder' in obs:
self.forder = np.array(obs["forder"]).astype('int')
else:
self.forder = np.ones(len(self.fedge))
if 'ntrack' in obs:
self.ntrack = int(obs["ntrack"])
else:
self.ntrack = 64
if 'nIF' in obs:
self.nIF = int(obs["nIF"])
self.dt1 = 1 / self.sample_rate
self.size = size
self.f = self.fedge + self.forder * np.fft.rfftfreq(
size, self.dt1)[:, np.newaxis]
self.fref = np.amax(self.f)
self.dmloss = self.dm.time_delay(np.amin(self.f), self.fref)
self.samploss = int(
np.ceil((self.dmloss * self.sample_rate).decompose()).value)
self.step = int(size - 2**(np.ceil(np.log2(self.samploss))))
self.npol = len(self.thread_ids)
print("{0} and {1} samples lost to de-dispersion".format(
self.dmloss, self.samploss))
print("Taking blocks of {0}, steps of {1} samples".format(
self.size, self.step))
if fname:
self.open(fname)
def open(self, fname):
"""Open data with appropriate baseband reader"""
if self.dtype == 'vdif':
self.fh = vdif.open(fname, mode='rs', sample_rate=self.sample_rate)
if self.dtype == 'mark4':
self.fh = mark4.open(fname,
mode='rs',
decade=2010,
ntrack=self.ntrack,
sample_rate=self.sample_rate,
thread_ids=self.thread_ids)
if self.dtype == 'mark5b':
self.fh = mark5b.open(fname,
mode='rs',
nchan=self.nIF,
ref_mjd=57000,
sample_rate=self.sample_rate,
thread_ids=self.thread_ids)
def seek(self, nsamples):
"""Seek as in a baseband stream"""
self.fh.seek(nsamples)
def readCoherent(self, size=2**25):
"""Return coherently dedispersed timestream
Read size number of samples, coherently dedisperse, take first
step samples to chop off wraparound
"""
if size != self.size or not 'dd' in self.__dict__:
# Only compute dedispersion phases and fft plans once for given size
print(
"Calculating de-dispersion phase factors for size {0}".format(
size))
self.f = self.fedge + self.forder * np.fft.rfftfreq(
size, self.dt1)[:, np.newaxis]
self.dd = self.dm.phase_factor(self.f, self.fref)
for j in range(len(self.forder)):
if self.forder[j] == 1:
self.dd[..., j] = np.conj(self.dd[..., j])
self.size = size
self.step = int(size - 2**(np.ceil(np.log2(self.samploss))))
a = pyfftw.empty_aligned((self.size, self.npol),
dtype='float32',
n=16)
b = pyfftw.empty_aligned((self.size // 2 + 1, self.npol),
dtype='complex64',
n=16)
print("planning FFTs for coherent dedispersion...")
self.fft_ts = pyfftw.FFTW(a,
b,
axes=(0, ),
direction='FFTW_FORWARD',
planning_timelimit=1.0,
threads=8)
print("...")
self.ifft_ts = pyfftw.FFTW(b,
a,
axes=(0, ),
direction='FFTW_BACKWARD',
planning_timelimit=1.0,
threads=8)
d = pyfftw.empty_aligned((size, self.npol), dtype='float32')
#d = self.fh.read(size)
# need better solution...
if self.dtype == 'vdif':
d[:] = self.fh.read(size)[self.thread_ids]
else:
d[:] = self.fh.read(size)
#ft = np.fft.rfft(d, axis=0)
ft = self.fft_ts(d)
ft *= self.dd
dift = pyfftw.empty_aligned((size // 2 + 1, self.npol),
dtype='complex64')
dift[:] = ft
d = self.ifft_ts(dift)
#d = np.fft.irfft(ft, axis=0)[:self.step]
return d
def readIncoherent(self, size=2**25, nchan=512):
"""Return incoherently dedispersed, channelized data
Read size number of samples, channelize to nchan, incoherently
dedisperse, take first step samples to chop off wraparound
"""
self.f = self.fedge + self.forder * np.fft.rfftfreq(
2 * nchan, self.dt1)[:, np.newaxis]
npol = len(self.fh.thread_ids)
dm_delay = self.dm.time_delay(self.f, self.fref)
dm_sample = np.floor(
(dm_delay /
(2 * nchan * self.dt1)).decompose()).value.astype('int')
d = self.fh.read(size)
dchan = np.fft.rfft(d.reshape(-1, 2 * nchan, npol), axis=1)
for chan in range(nchan):
for pol in range(npol):
dchan[:, chan, pol] = np.roll(dchan[:, chan, pol],
-dm_sample[chan, pol],
axis=0)
return dchan[:(self.step // (2 * nchan))]
