Esempio n. 1
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def load_pfds(dir=SAMPLE_FILES_DIR):
    SAMPLE_FILES = glob.glob(dir + '*.pfd')
    pfds = []
    for f in SAMPLE_FILES:
        pf = pfd(f)
        pfds.append(pf)
    return pfds
Esempio n. 2
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def load_pfds(dir=SAMPLE_FILES_DIR):
    SAMPLE_FILES = glob.glob(dir+'*.pfd')
    pfds = []
    for f in SAMPLE_FILES:
        pf = pfd(f)
        pfds.append(pf)
    return pfds
Esempio n. 3
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pfdfile = glob.glob('ubc_AI/pfd_files/*.pfd') + glob.glob('ubc_AI/pfd_files/*.ar') + glob.glob('ubc_AI/pfd_files/*.ar2') + glob.glob('ubc_AI/pfd_files/*.spd')
timeout = time.time() + 60*15 # 15 minutes from now
data = ''
i=0
while True:
    if time.time() > timeout:
        break
    line = sys.stdin.readline()
    if line.strip() != 'eof':
        data += line
        continue
    else:
        with open('/dev/shm/test.pfd', 'wb') as f:
            f.write(data)
        data = ''
    Metadata = pfd('/dev/shm/test.pfd')	
    print 'Name:',os.path.basename(os.path.normpath(pfdfile[i]))
    print 'Telescope:',Metadata.telescope
    print 'Barycentric Epoch:',Metadata.bepoch
    print 'Topocentric Epoch:',Metadata.tepoch
    print 'DM:',Metadata.bestdm
    print 'Period (topo):',Metadata.topo_p1
    print 'Pdot (topo):',Metadata.topo_p2
    print 'P\'\'(topo):',Metadata.topo_p3
    print 'Period (bary):',Metadata.bary_p1
    print 'Pdot (bary):',Metadata.bary_p2
    print 'Eccentricity:',Metadata.orb_e
    print 'Orbital period:',Metadata.orb_p
    print  ' '
    sys.stdout.flush()
    time.sleep(1)
Esempio n. 4
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import matplotlib.pyplot as plt
from scipy.interpolate import  RectBivariateSpline as interp
from scipy import ndimage, array, ogrid, mgrid
import sys,os




if __name__ == '__main__':
    f1 = sys.argv[1]
    f2 = sys.argv[2]
    if not (f1.endswith('pfd')):
        print 'file name %s not end with pfd ' % (f1)
        sys.exit(1)

    pfdfile = pfd(f1)
    pfdfile.dedisperse()
    profs = pfdfile.profs
    pshape = profs.shape
    x, y, z = profs.shape
    data = profs.reshape((-1,1))
    del pfdfile

    mean = np.mean(data)
    var = np.std(data)
    data = (data-mean)/var
    profs = data.reshape(pshape)
    X, Y, Z = ogrid[0:1:x,0:1:y,0:1:z]
    coords = array([X, Y, Z])
    coeffs = ndimage.spline_filter(profs )
    X, Y, Z = mgrid[0:1:8j,0:1:8j,0:1:8j]