def read_table_rg(filename):
# astropy.io.ascii.convert_numpy
converters = { 'col1': [asciitable.convert_list(str)],
'col2': [asciitable.convert_list(float)],
'col3': [asciitable.convert_list(float)],
'col4': [asciitable.convert_list(float)],
'col5': [asciitable.convert_list(float)],
'col6': [asciitable.convert_list(str)],
'col7': [asciitable.convert_list(str)],
'col8': [asciitable.convert_list(str)],
'col9': [asciitable.convert_list(str)],
'col10': [asciitable.convert_list(float)]}
names = ('POINT_NAME', 'RA_PNT', 'DEC_PNT', 'BACK','NH', 'MOS1FILTER', 'MOS2FILTER', 'PNFILTER', 'EXPOTIME')
data = asciitable.read(filename, delimiter='|', converters=converters)
npoint = data.size
name = data['col1'].tolist()
back = data['col4']
nh = data['col5']
m1filt = data['col6'].tolist()
m2filt = data['col7'].tolist()
pnfilt = data['col8'].tolist()
timestamp = data['col9']
area = data['col10']
return (name, back, nh, m1filt, m2filt, pnfilt, timestamp, area, npoint)
def test_types_from_dat(numpy):
if numpy:
converters = {'a': [asciitable.convert_numpy(np.float)],
'e': [asciitable.convert_numpy(np.str)]}
else:
converters = {'a': [asciitable.convert_list(float)],
'e': [asciitable.convert_list(str)]}
dat = asciitable.read(['a b c d e', '1 1 cat 2.1 4.2'], Reader=asciitable.Basic,
converters=converters, numpy=numpy)
reader = asciitable.get_reader(Reader=asciitable.Memory, numpy=numpy)
reader.read(dat)
print('numpy=%s' % numpy)
print('dat=%s' % repr(dat))
print('reader.table=%s' % repr(reader.table))
print('types=%s' % repr([x.type for x in reader.cols]))
assert_true(issubclass(reader.cols[0].type, asciitable.FloatType))
assert_true(issubclass(reader.cols[1].type, asciitable.IntType))
assert_true(issubclass(reader.cols[2].type, asciitable.StrType))
assert_true(issubclass(reader.cols[3].type, asciitable.FloatType))
assert_true(issubclass(reader.cols[4].type, asciitable.StrType))
def read_NX2(self, filename, date, corr_bsp=1., origin=None, timeoffset=2, verbose=True):
'''read in csv data and initialize table
Parameters
----------
filename : string
filename as string or other input compatible with asciitable
date: tuple of integers ``(day, month, year)``
date of measurement
corr_bsp: float
multiplictive correction factor for BSP
origin: tuple
(lat, lon) in deg of x,y origin
default: lat, lon at first datapoint
timeoffset: float
hours to be added to convert UT to local
'''
include_names = ['TIME', 'LAT', 'LON', 'AWA', 'AWS',
'BSP', 'COG', 'DFT', 'HDC', 'SET', 'SOG', 'TWA', 'TWS']
converters = {'TIME': asciitable.convert_list(float)}
try:
atpy.Table.__init__(self, filename, type='ascii', delimiter=',', fill_values=(
'', 'nan'), data_start = 5, include_names = include_names, guess = False)
if verbose:
print 'Reading new format NX2 table - Export with 1.08 or later'
except InconsistentTableError:
if verbose:
print 'Reading NX2 table, which was exported with 1.05'
# 30 header values, but only 29 table entries, manually delete the last header value
names = [
'DATE', 'TIME', 'LAT', 'LON', 'AWA', 'AWS', 'BOD', 'BSP', 'BTW', 'CMG', 'COG', 'CTS', 'DEP', 'DFT',
'DMG', 'DST', 'DTW', 'HDC', 'LOG', 'RDR', 'SET', 'SOG', 'TBS', 'TEMP', 'TWA', 'TWS', 'VAR', 'VMG', 'WCV']
atpy.Table.__init__(self, filename, type='ascii', delimiter=',', names=names, fill_values=(
'', 'nan'), data_start = 5, include_names = include_names)
self.read_date = date
self.filename = filename
if origin:
self.origin = origin
else:
self.origin = (self.LAT[0], self.LON[0])
# remove all columns which contain only NaNs
# interpolate nans in those columns with only a few nans
for name in self.names:
valid = np.isfinite(self[name])
if valid.all():
pass
elif (~valid).all():
self.remove_columns(name)
elif (np.sum(valid, dtype=np.float) / len(valid)) >= 0.98:
self.fill_nans(name)
elif name == 'TIME':
warn('TIME contains > 2 % nans. Interpolating ...', NX2InterpolationWarning)
self.fill_nans(name)
else:
warn(NX2InterpolationWarning(name))
self.add_empty_column('year', np.int_)
self.add_empty_column('month', np.int_)
self.add_empty_column('day', np.int_)
self.add_empty_column('hour', np.int_)
self.add_empty_column('minute', np.int_)
self.add_empty_column('sec', np.int_)
self.year[:] = date[2]
self.month[:] = date[1]
self.day[:] = date[0]
# TBD: self.TIME has non-unique entries
# change [1,1,1] -> [1,1.33,1.66] ? Needs sub-s times then
TIME = timeoffset * 3600 + self.TIME
h, m, s = sec2hms(TIME)
self.hour[:] = h
self.minute[:] = m
self.sec[:] = s
r_earth = 6300e3 # in Si unit - meter
self.add_column('y', 2.*np.pi*r_earth/360.*(self.LAT-self.origin[0]))
self.add_column('x', 2.*np.pi*r_earth*np.cos(
self.LAT/180.*np.pi)/360.*(self.LON-self.origin[1]))
# 2012 Haltern data was taken / exported without all sensers available
if 'BSP' in self.columns:
self.BSP = self.BSP * corr_bsp
if 'TWS' in self.columns:
self.TWS = self.TWS / mps2knots
if 'AWS' in self.columns:
self.AWS = self.AWS / mps2knots
