def test_open_stream(self, tmpdir):
# Check that we can use the baseband open command for HDF5 format.
filename = str(tmpdir.join('copy.hdf5'))
stream = self.fh
# Writing always requires a format argument.
with baseband.open(filename, 'w', format='hdf5',
template=stream) as f5w:
assert isinstance(f5w, hdf5.base.HDF5StreamWriter)
f5w.write(self.data)
# Reading with a format argument should just work.
with baseband.open(filename, 'r', format='hdf5') as f5r:
assert isinstance(f5r, hdf5.base.HDF5StreamReader)
self.check(stream, f5r)
data = f5r.read()
assert_array_equal(data, self.data)
# Check that basic info works, since next step relies on it.
assert f5r.info.format == 'hdf5'
# Without a format argument we are relying on our info.
with baseband.open(filename, 'r') as f5r:
assert isinstance(f5r, hdf5.base.HDF5StreamReader)
self.check(stream, f5r)
data = f5r.read()
assert_array_equal(data, self.data)
def setup(self):
self.fh = baseband.open(baseband.data.SAMPLE_VDIF)
self.data = self.fh.read()
self.fh.seek(0)
frequency = 311.25 * u.MHz + (np.arange(8.) // 2) * 16. * u.MHz
sideband = np.tile([-1, +1], 4)
self.wrapped = SetAttribute(self.fh,
frequency=frequency,
sideband=sideband)
def waterfall_by_sec(filename,
seconds,
time_step=0.002 * u.s,
Output_name="output",
start_time=None):
"""Produce a waterfall given a from the baseband data
=== Parameters ===
filename: Name of baseband data -> Code modelled after vdif format, hopefully should work fo
mark5b too
seconds: The number of seconds produced in the waterfall
time_step: The length of each time bin in the waterfall: Defaults to 2 microseconds
Output_name: The name of the Output waterfall: Defaults to output.npy
Start: The start time for the waterfall. Defaults to the start of the scan
"""
fh = baseband.open(filename)
# Determine number of seconds in each time step
dt = 0
fh.seek(0)
st = fh.time
fn = fh.time
while (time_step > fn - st):
fh.read(512)
fn = fh.time
dt += 512
print("Length of each time bin: ", fn - st)
print("Number of bins in each time bin: ", dt)
fh.seek(0)
# Set start time:
if start_time is None:
start_time = fh.time
fh.seek(start_time)
print("Start time of waterfall; ", start_time)
# Make one time bin at a time
I = []
while (seconds > fh.time - start_time):
data = fh.read(dt)
data = data.reshape(
-1, 512
) # Not entirely confident about this line: Rebins nearby values
ft = np.fft.rfft(data, axis=-1)
ft = np.fft.fftshift(ft)
spec = ft.sum(axis=0)
I.append(spec)
return np.savez(Output_name, I=np.array(I))