def process_gains(self, proc_dir, send_dir):
# find the most recent gains file
files = [
file for file in os.listdir(proc_dir)
if file.lower().endswith(".gains")
]
self.trace("files=" + str(files))
if len(files) > 0:
gains_time_file = proc_dir + "/gains.time"
cmd = ""
# combine all the gains files together
if os.path.exists(gains_time_file):
cmd = "uwb_adaptive_filter_tappend " + gains_time_file
for file in files:
cmd = cmd + " " + proc_dir + "/" + file
cmd = cmd + " " + gains_time_file
else:
if len(files) == 1:
cmd = "cp " + proc_dir + "/" + files[0] + " " + \
gains_time_file
else:
cmd = "uwb_adaptive_filter_tappend"
for file in files:
cmd = cmd + " " + proc_dir + "/" + file
cmd = cmd + " " + gains_time_file
self.info(cmd)
rval, lines = self.system(cmd, 2)
if not rval == 0:
self.warn("failed to tappend gains files")
return
# read the data from file into a numpy array
file_size = os.path.getsize(gains_time_file)
header_size = 4096
data_size = file_size - header_size
gains_file = open(gains_time_file, "rb")
header_str = gains_file.read(header_size)
header = Config.readDictFromString(header_str)
npol = int(header["NPOL"])
ndim = int(header["NDIM"])
nchan = int(header["NCHAN"])
nant = int(header["NANT"])
nbit = int(header["NBIT"])
freq = float(header["FREQ"])
bw = float(header["BW"])
tsamp = float(header["TSAMP"])
self.info("npol=" + str(npol) + " ndim=" + str(ndim) + " nchan=" +
str(nchan) + " nant=" + str(nant) + " nbit=" +
str(nbit) + " freq=" + str(freq) + " bw=" + str(bw))
# check that the nbit is 32
bytes_per_sample = (npol * ndim * nchan * nant * nbit) / 8
ndat = data_size / bytes_per_sample
nval = ndat * nant * nchan * npol * ndim
self.info("ndat=" + str(ndat) + " bytes_per_sample=" +
str(bytes_per_sample) + " nval=" + str(nval))
raw = np.fromfile(gains_file, dtype=np.float32, count=nval)
gains_file.close()
self.info("np.shape(raw)=" + str(np.shape(raw)))
self.info("npol=" + str(npol) + " nchan=" + str(nchan))
# reshape the raw data into a numpy array with specified dimensions
data = raw.reshape((ndat, nant, npol, nchan, ndim))
# acquire the results lock
self.results["lock"].acquire()
# generate an empty numpy array with ndat values
xvals = np.zeros(ndat)
gains_time = np.zeros((npol, ndat))
gains_freq = np.zeros((npol, nchan))
for idat in range(ndat):
xvals[idat] = float(idat) * tsamp / 1e6
for ipol in range(npol):
for isig in range(nant):
for ichan in range(nchan):
power = 0
for idim in range(ndim):
g = data[idat][isig][ipol][ichan][idim]
power += g * g
if power > gains_time[ipol][idat]:
gains_time[ipol][idat] = power
if idat == ndat - 1:
gains_freq[ipol][ichan] = power
if npol == 1:
labels = ["AA+BB"]
colours = ["red"]
if npol == 2:
labels = ["AA", "BB"]
colours = ["red", "green"]
else:
labels = []
colours = []
self.gains_time_plot.plot(240, 180, True, xvals, gains_time,
labels, colours)
self.results["gainstime_lo"] = self.gains_time_plot.getRawImage()
self.gains_time_plot.plot(1024, 768, False, xvals, gains_time,
labels, colours)
self.results["gainstime_hi"] = self.gains_time_plot.getRawImage()
self.gains_freq_plot.plot_npol(240, 180, True, nchan, freq, bw,
gains_freq, labels)
self.results["gainsfreq_lo"] = self.gains_freq_plot.getRawImage()
self.gains_freq_plot.plot_npol(1024, 768, False, nchan, freq, bw,
gains_freq, labels)
self.results["gainsfreq_hi"] = self.gains_freq_plot.getRawImage()
self.gains_valid = True
self.results["lock"].release()
for file in files:
os.rename(proc_dir + "/" + file, send_dir + "/" + file)
return len(files)
def process_gains(self, proc_dir, send_dir):
# find the most recent gains file
files = [file for file in os.listdir(proc_dir)
if file.lower().endswith(".gains")]
self.trace("files=" + str(files))
if len(files) > 0:
gains_time_file = proc_dir + "/gains.time"
cmd = ""
# combine all the gains files together
if os.path.exists(gains_time_file):
cmd = "uwb_adaptive_filter_tappend " + gains_time_file
for file in files:
cmd = cmd + " " + proc_dir + "/" + file
cmd = cmd + " " + gains_time_file
else:
if len(files) == 1:
cmd = "cp " + proc_dir + "/" + files[0] + " " + \
gains_time_file
else:
cmd = "uwb_adaptive_filter_tappend"
for file in files:
cmd = cmd + " " + proc_dir + "/" + file
cmd = cmd + " " + gains_time_file
self.info(cmd)
rval, lines = self.system(cmd, 2)
if not rval == 0:
self.warn("failed to tappend gains files")
return
# read the data from file into a numpy array
file_size = os.path.getsize(gains_time_file)
header_size = 4096
data_size = file_size - header_size
gains_file = open(gains_time_file, "rb")
header_str = gains_file.read(header_size)
header = Config.readDictFromString(header_str)
npol = int(header["NPOL"])
ndim = int(header["NDIM"])
nchan = int(header["NCHAN"])
nant = int(header["NANT"])
nbit = int(header["NBIT"])
freq = float(header["FREQ"])
bw = float(header["BW"])
tsamp = float(header["TSAMP"])
self.info("npol=" + str(npol) + " ndim=" + str(ndim) + " nchan=" +
str(nchan) + " nant=" + str(nant) + " nbit=" +
str(nbit) + " freq=" + str(freq) + " bw=" + str(bw))
# check that the nbit is 32
bytes_per_sample = (npol * ndim * nchan * nant * nbit) / 8
ndat = data_size / bytes_per_sample
nval = ndat*nant*nchan*npol*ndim
self.info("ndat=" + str(ndat) + " bytes_per_sample=" +
str(bytes_per_sample) + " nval=" + str(nval))
raw = np.fromfile(gains_file, dtype=np.float32, count=nval)
gains_file.close()
self.info("np.shape(raw)=" + str(np.shape(raw)))
self.info("npol=" + str(npol) + " nchan=" + str(nchan))
# reshape the raw data into a numpy array with specified dimensions
data = raw.reshape((ndat, nant, npol, nchan, ndim))
# acquire the results lock
self.results["lock"].acquire()
# generate an empty numpy array with ndat values
xvals = np.zeros(ndat)
gains_time = np.zeros((npol, ndat))
gains_freq = np.zeros((npol, nchan))
for idat in range(ndat):
xvals[idat] = float(idat) * tsamp / 1e6
for ipol in range(npol):
for isig in range(nant):
for ichan in range(nchan):
power = 0
for idim in range(ndim):
g = data[idat][isig][ipol][ichan][idim]
power += g * g
if power > gains_time[ipol][idat]:
gains_time[ipol][idat] = power
if idat == ndat-1:
gains_freq[ipol][ichan] = power
if npol == 1:
labels = ["AA+BB"]
colours = ["red"]
if npol == 2:
labels = ["AA", "BB"]
colours = ["red", "green"]
else:
labels = []
colours = []
self.gains_time_plot.plot(240, 180, True, xvals, gains_time,
labels, colours)
self.results["gainstime_lo"] = self.gains_time_plot.getRawImage()
self.gains_time_plot.plot(1024, 768, False, xvals, gains_time,
labels, colours)
self.results["gainstime_hi"] = self.gains_time_plot.getRawImage()
self.gains_freq_plot.plot_npol(240, 180, True, nchan, freq, bw,
gains_freq, labels)
self.results["gainsfreq_lo"] = self.gains_freq_plot.getRawImage()
self.gains_freq_plot.plot_npol(1024, 768, False, nchan, freq, bw,
gains_freq, labels)
self.results["gainsfreq_hi"] = self.gains_freq_plot.getRawImage()
self.gains_valid = True
self.results["lock"].release()
for file in files:
os.rename(proc_dir + "/" + file, send_dir + "/" + file)
return len(files)
def process_cleaned(self, proc_dir, send_dir):
files = [
file for file in os.listdir(proc_dir)
if file.lower().endswith(".cleaned")
]
self.trace("files=" + str(files))
if len(files) > 0:
# process only the most recent file
last_file = proc_dir + "/" + files[-1]
self.info("file=" + last_file)
# read the data from file into a numpy array
file_size = os.path.getsize(last_file)
header_size = 4096
data_size = file_size - header_size
# read the data from file into a numpy array
fptr = open(last_file, "rb")
header_str = fptr.read(header_size)
header = Config.readDictFromString(header_str)
npol = int(header["NPOL"])
ndim = int(header["NDIM"])
nchan = int(header["NCHAN"])
nant = int(header["NANT"])
nbit = int(header["NBIT"])
freq = float(header["FREQ"])
bw = float(header["BW"])
self.info("npol=" + str(npol) + " ndim=" + str(ndim) + " nchan=" +
str(nchan) + " nant=" + str(nant) + " nbit=" +
str(nbit) + " freq=" + str(freq) + " bw=" + str(bw))
bytes_per_sample = (npol * ndim * nchan * nant * nbit) / 8
ndat = data_size / bytes_per_sample
# TODO check that nbit==32 ndat==1 nant=1
nval = ndat * nant * nchan * npol * ndim
self.info("bytes_per_sample=" + str(bytes_per_sample) + " nval=" +
str(nval))
raw = np.fromfile(fptr, dtype=np.float32, count=nval)
fptr.close()
# reshape the raw data into a numpy array with specified dimensions
self.info("np.shape(raw)=" + str(np.shape(raw)))
self.info("npol=" + str(npol) + " nchan=" + str(nchan))
data = raw.reshape((npol, nchan))
if npol == 1:
labels = ["AA"]
elif npol == 2:
labels = ["AA", "BB"]
else:
labels = []
# acquire the results lock
self.results["lock"].acquire()
self.info("len(data)=" + str(len(data)))
# generate plots multi polarisation plots
self.cleaned_plot.plot_npol(240, 180, True, nchan, freq, bw, data,
labels)
self.results["cleaned_lo"] = self.cleaned_plot.getRawImage()
self.cleaned_plot.plot_npol(1024, 768, False, nchan, freq, bw,
data, labels)
self.results["cleaned_hi"] = self.cleaned_plot.getRawImage()
self.cleaned_valid = True
self.results["lock"].release()
for file in files:
os.rename(proc_dir + "/" + file, send_dir + "/" + file)
return len(files)
def process_cleaned(self, proc_dir, send_dir):
files = [file for file in os.listdir(proc_dir)
if file.lower().endswith(".cleaned")]
self.trace("files=" + str(files))
if len(files) > 0:
# process only the most recent file
last_file = proc_dir + "/" + files[-1]
self.info("file=" + last_file)
# read the data from file into a numpy array
file_size = os.path.getsize(last_file)
header_size = 4096
data_size = file_size - header_size
# read the data from file into a numpy array
fptr = open(last_file, "rb")
header_str = fptr.read(header_size)
header = Config.readDictFromString(header_str)
npol = int(header["NPOL"])
ndim = int(header["NDIM"])
nchan = int(header["NCHAN"])
nant = int(header["NANT"])
nbit = int(header["NBIT"])
freq = float(header["FREQ"])
bw = float(header["BW"])
self.info("npol=" + str(npol) + " ndim=" + str(ndim) + " nchan=" +
str(nchan) + " nant=" + str(nant) + " nbit=" +
str(nbit) + " freq=" + str(freq) + " bw=" + str(bw))
bytes_per_sample = (npol * ndim * nchan * nant * nbit) / 8
ndat = data_size / bytes_per_sample
# TODO check that nbit==32 ndat==1 nant=1
nval = ndat*nant*nchan*npol*ndim
self.info("bytes_per_sample=" + str(bytes_per_sample) + " nval=" +
str(nval))
raw = np.fromfile(fptr, dtype=np.float32, count=nval)
fptr.close()
# reshape the raw data into a numpy array with specified dimensions
self.info("np.shape(raw)=" + str(np.shape(raw)))
self.info("npol=" + str(npol) + " nchan=" + str(nchan))
data = raw.reshape((npol, nchan))
if npol == 1:
labels = ["AA"]
elif npol == 2:
labels = ["AA", "BB"]
else:
labels = []
# acquire the results lock
self.results["lock"].acquire()
self.info("len(data)=" + str(len(data)))
# generate plots multi polarisation plots
self.cleaned_plot.plot_npol(240, 180, True, nchan, freq, bw,
data, labels)
self.results["cleaned_lo"] = self.cleaned_plot.getRawImage()
self.cleaned_plot.plot_npol(1024, 768, False, nchan, freq, bw,
data, labels)
self.results["cleaned_hi"] = self.cleaned_plot.getRawImage()
self.cleaned_valid = True
self.results["lock"].release()
for file in files:
os.rename(proc_dir + "/" + file, send_dir + "/" + file)
return len(files)