def get_fs(waterfall):
"""
Gets frequency values from filterbank file.
Parameters
----------
waterfall : str or Waterfall
Name of filterbank file or Waterfall object
Returns
-------
fs : ndarray
Frequency values
"""
if isinstance(waterfall, str):
fch1 = read_header(waterfall)[b'fch1']
df = read_header(waterfall)[b'foff']
fchans = read_header(waterfall)[b'nchans']
elif isinstance(waterfall, Waterfall):
fch1 = waterfall.header[b'fch1']
df = waterfall.header[b'foff']
fchans = waterfall.header[b'nchans']
else:
sys.exit('Invalid data file!')
return np.arange(fch1, fch1 + fchans * df, df)
def test_voyager_fix_header():
filename = '/workdata/bl/data/voyager_f1032192_t300_v2.fil'
new_filename = 'voyager_ext.fil'
fb = Filterbank(filename, f_start=8420.1, f_stop=8420.3)
fb.write_to_filterbank(new_filename)
fb = Filterbank(new_filename)
filename = new_filename
assert read_header(filename)['ibeam'] == 1
fix_header(filename, 'ibeam', 7)
assert read_header(filename)['ibeam'] == 7
fix_header(filename, 'ibeam', 1)
assert read_header(filename)['ibeam'] == 1
fix_header(filename, 'ibeam', 13)
assert read_header(filename)['ibeam'] == 13
pprint(read_header(filename))
fix_header(filename, 'rawdatafile',
'./blc3_9bit_guppi_57386_VOYAGER1_0004.0000.raw')
assert read_header(filename)[
'rawdatafile'] == './blc3_9bit_guppi_57386_VOYAGER1_0004.0000.raw'
fix_header(filename, 'rawdatafile',
'./blc3_2bit_guppi_57386_VOYAGER1_0004.0000.raw')
assert read_header(filename)[
'rawdatafile'] == './blc3_2bit_guppi_57386_VOYAGER1_0004.0000.raw'
os.remove(new_filename)
def get_ts(waterfall):
"""
Gets time values from filterbank file.
Parameters
----------
waterfall : str or Waterfall
Name of filterbank file or Waterfall object
Returns
-------
ts : ndarray
Time values
"""
if isinstance(waterfall, str):
tsamp = read_header(waterfall)[b'tsamp']
elif isinstance(waterfall, Waterfall):
tsamp = waterfall.header[b'tsamp']
else:
sys.exit('Invalid fil file!')
if isinstance(waterfall, str):
fch1 = read_header(waterfall)[b'fch1']
df = read_header(waterfall)[b'foff']
else:
fch1 = waterfall.header[b'fch1']
df = waterfall.header[b'foff']
waterfall0 = Waterfall(waterfall, f_start=fch1, f_stop=fch1 + df)
try:
tchans = get_data(waterfall0).shape[0]
except Exception as e:
sys.exit('No data in filterbank file!')
return np.arange(0, tchans * tsamp, tsamp)
def split_waterfall_generator(waterfall_fn, fchans, tchans=None, f_shift=None):
"""
Creates a generator that returns smaller Waterfall objects by 'splitting'
an input filterbank file according to the number of frequency samples.
Since this function only loads in data in chunks according to fchans,
it handles very large observations well. Specifically, it will not attempt
to load all the data into memory before splitting, which won't work when
the data is very large anyway.
Parameters
----------
waterfall_fn : str
Filterbank filename with .fil extension
fchans : int
Number of frequency samples per new filterbank file
tchans : int, optional
Number of time samples to select - will default from start of observation.
If None, just uses the entire integration time
f_shift : int, optional
Number of samples to shift when splitting filterbank. If
None, defaults to `f_shift=fchans` so that there is no
overlap between new filterbank files
Returns
-------
split : Waterfall
A blimpy Waterfall object containing a smaller section of the data
"""
fch1 = read_header(waterfall_fn)[b'fch1']
nchans = read_header(waterfall_fn)[b'nchans']
df = abs(read_header(waterfall_fn)[b'foff'])
tchans_tot = Waterfall(waterfall_fn,
load_data=False).container.selection_shape[0]
if f_shift is None:
f_shift = fchans
if tchans is None:
tchans = tchans_tot
elif tchans > tchans_tot:
raise ValueError('tchans value must be less than the total number of \
time samples in the observation')
# Note that df is negative!
f_start = fch1 - fchans * df
f_stop = fch1
# Iterates down frequencies, starting from highest
while f_start >= fch1 - nchans * df:
waterfall = Waterfall(waterfall_fn,
f_start=f_start,
f_stop=f_stop,
t_start=0,
t_stop=tchans)
yield waterfall
f_start -= f_shift * df
f_stop -= f_shift * df
fn = '/datax/scratch/bbrzycki/data/blc00_guppi_58331_12383_DIAG_SGR_B2_0014.gpuspec.0000.fil'
##### Search! #####
notable_hits = {
'choppy_rfi': [],
'constant': [],
'noise': [],
'scintillated': [],
'unclear': []
}
with open('notable_hits.json', 'w') as f:
json.dump(notable_hits, f)
fch1 = read_header(fn)[b'fch1']
nchans = read_header(fn)[b'nchans']
notable_indices = []
for index in range(0, int(nchans / fchans)):
f_stop = fch1 + index * fchans * df
f_start = fch1 + (index + 1) * fchans * df
frame = stg.get_data(Waterfall(fn, f_start=f_start, f_stop=f_stop))
normalized = stg.normalize(frame, cols = 128, exclude = 0.2, use_median=False)
# Predict each frame
plt.imsave('temp_normalized.png', normalized)
img = load_img('temp_normalized.png',False,target_size=(32, 1024))
x = img_to_array(img)