def _load_pointing_data(self):
file = DATA_DIR + 'f13_aid.fits'
f = fits.open(file)
altitude = f[1].data['targetalt'].byteswap().newbyteorder()
time = f[1].data['stime'].byteswap().newbyteorder()
ra = f[1].data['TargetRA'].byteswap().newbyteorder()
dec = f[1].data['TargetDEC'].byteswap().newbyteorder()
az = f[1].data['TargetAz'].byteswap().newbyteorder()
times = [
timedelta(seconds=t) -
timedelta(seconds=60 * 60 * 24 * 2730 + 8 * 60 * 60) +
datetime.datetime(2013, 9, 21) for t in time
]
target = {
'time': times,
'ra': ra,
'dec': dec,
'az': az,
'alt': altitude
}
target.units = (u('deg'), u('deg'), u('deg'), u('deg'))
target = pandas.DataFrame(dict)
target.index = target['time']
return target
def _load_location_data(self):
file = DATA_DIR + 'f13_gps.fits'
f = fits.open(file)
time = f[1].data['time']
height = f[1].data['height']
longitude = f[1].data['longitude']
latitude = f[1].data['latitude']
times = [convert_time(t) for t in time]
dict = {'time': times, 'height': height, 'latitude': latitude, 'longitude': longitude}
d = pandas.DataFrame(dict)
d.index = d['time']
d.units = (u('m'), u('deg'), u('deg'))
return d
def _load_pointing_data(self):
file = DATA_DIR + 'f13_aid.fits'
f = fits.open(file)
altitude = f[1].data['targetalt'].byteswap().newbyteorder()
time = f[1].data['stime'].byteswap().newbyteorder()
ra = f[1].data['TargetRA'].byteswap().newbyteorder()
dec = f[1].data['TargetDEC'].byteswap().newbyteorder()
az = f[1].data['TargetAz'].byteswap().newbyteorder()
times = [timedelta(seconds=t) - timedelta(seconds=60*60*24*2730+8*60*60) + datetime.datetime(2013, 9, 21) for t in time]
target = {'time': times, 'ra':ra, 'dec': dec, 'az':az, 'alt':altitude}
target.units = (u('deg'), u('deg'), u('deg'), u('deg'))
target = pandas.DataFrame(dict)
target.index = target['time']
return target
def test_scipy(data, z, plot=False):
f_tau = get_tau_funtcion(data)
tau = f_tau(z, nu).T
print(tau)
if plot is True:
plt.ion()
E = nu * u('Hz').to('TeV', equivalencies=spectral())
plt.loglog(E, np.exp(-tau), '-')
plt.grid()
plt.show()
def __repr__(self):
if self._c is not None:
s = '%s' % self._c
elif self._DL_cm is not None:
x = self._DL_cm * u('cm')
s = 'cosmology is not defined, the luminosity distance has been set to %s' % x
else:
s = 'you have to define either an astropy comoslogy model or set a luminosity distance in cm'
return s
def pixel_to_world(self, pixelxy):
"""Convert a pixel position to physical coordinates
Parameters
----------
pixelxy : `astropy.units.Quantity` (pixels)
Returns
-------
result : `astropy.units.Quantity` (physical)
"""
return (pixelxy - self.raw_center + 0.5 * u('pix')) * self.plate_scale
def _load_location_data(self):
file = DATA_DIR + 'f13_gps.fits'
f = fits.open(file)
time = f[1].data['time']
height = f[1].data['height']
longitude = f[1].data['longitude']
latitude = f[1].data['latitude']
times = [convert_time(t) for t in time]
dict = {
'time': times,
'height': height,
'latitude': latitude,
'longitude': longitude
}
d = pandas.DataFrame(dict)
d.index = d['time']
d.units = (u('m'), u('deg'), u('deg'))
return d
def pixel_to_world(self, pixelxy):
"""Convert a pixel position to physical coordinates
Parameters
----------
pixelxy : `astropy.units.Quantity` (pixels)
Returns
-------
result : `astropy.units.Quantity` (physical)
"""
return (pixelxy - self.raw_center + 0.5 * u('pix')) * self.plate_scale
def get_DL_cm(self, z=None):
if self._c is not None:
#THIS IS FIXING THE ERROR WITH PICKLED COSMO
#TODO open issue on astropy!
try:
_d = self._c.luminosity_distance(z).to('cm').value
except:
_d = self._c.luminosity_distance(z)
_d = _d.value * u(str(_d.unit))
_d = _d.to('cm').value
else:
_d = self._DL_cm
return _d
def __init__(self, isFront):
if isFront:
self.clock_angle = -32.425
self.center_offset_mils = np.array([124.68, -74.64])
self.twist = 180.0
else:
self.clock_angle = -52.175
self.center_offset_mils = np.array([-105.59, -48.64])
self.twist = 0.0
self.pixel_number = np.array([966, 1296])
self.arcsec_per_mil = 1.72
self.micron_per_mil = 25.4
self.pixel_size_um = 6.45 * u('micron')
self.focal_length_m = 2
self.screen_radius_mil = 3000
def __init__(self, isFront):
if isFront:
self.clock_angle = -32.425
self.center_offset_mils = np.array([124.68, -74.64])
self.twist = 180.0
else:
self.clock_angle = -52.175
self.center_offset_mils = np.array([-105.59, -48.64])
self.twist = 0.0
self.pixel_number = np.array([966, 1296])
self.arcsec_per_mil = 1.72
self.micron_per_mil = 25.4
self.pixel_size_um = 6.45 * u('micron')
self.focal_length_m = 2
self.screen_radius_mil = 3000
def get_pyas_aspect(front=True):
if front:
file = DATA_DIR + 'SAS1_pointing_data.csv'
else:
file = DATA_DIR + 'SAS2_pointing_data.csv'
names = ('ctl_az', 'ctl_el', 'offset_r', 'offset_x', 'offset_y', 'pointing_x', 'pointing_y')
data = pandas.read_csv(file, names=names, parse_dates=True, index_col=0, skiprows=1)
data.units = (u('arcsec'), u('arcsec'), u('arcsec'), u('arcsec'), u('arcsec'), u('arcsec'), u('arcsec'))
return data
def get_pyas_aspect(front=True):
if front:
file = DATA_DIR + 'SAS1_pointing_data.csv'
else:
file = DATA_DIR + 'SAS2_pointing_data.csv'
names = ('ctl_az', 'ctl_el', 'offset_r', 'offset_x', 'offset_y',
'pointing_x', 'pointing_y')
data = pandas.read_csv(file,
names=names,
parse_dates=True,
index_col=0,
skiprows=1)
data.units = (u('arcsec'), u('arcsec'), u('arcsec'), u('arcsec'),
u('arcsec'), u('arcsec'), u('arcsec'))
return data
def __init__(self,
x_values,
y_values,
z_values,
log_input_grid=False,
log_log_interp=False,
x_in_units=None,
y_in_units=None,
x_out_units='Hz',
y_out_units='erg cm-2 s-1',
z_in_units=None,
z_out_units=None,
nu_size=100,
name='TableModel2D',
zero=1E-100):
"""
"""
super(TemplateTable2D, self).__init__(name=name)
self._zero=zero
self._log_log_interp = log_log_interp
if log_input_grid is False:
self.x_values = x_values
self.y_values = y_values
self.z_values = z_values
else:
self.z_values = np.power(10., z_values)
self.x_values = np.power(10., x_values)
self.y_values = np.power(10., y_values)
if x_in_units is not None:
self.x_values *= u(x_in_units).to(x_out_units, equivalencies=spectral())
if hasattr(self.x_values,'value'):
_x = self.x_values.value
else:
_x = self.x_values
if y_in_units is not None:
self.y_values *= u(y_in_units).to(y_out_units, equivalencies=spectral())
if hasattr(self.y_values,'value'):
_y = self.y_values.value
else:
_y = self.y_values
if z_in_units is not None:
self.z_values *= u(z_in_units).to(z_out_units, equivalencies=spectral())
if hasattr(self.z_values, 'value'):
_z = self.z_values.value
else:
_z = self.z_values
if log_log_interp is True:
self._scale='log-log'
_x[_x<self._zero]=self._zero
self._x_grid = np.log10(_x)
self._y_grid = np.log10(_y)
self._z_grid = np.log10(_z)
else:
self._scale='lin-lin'
self._x_grid = _x
self._y_grid = _y
self._z_grid = _z
self.interp_func = interpolate.RectBivariateSpline(self._x_grid, self._y_grid, self._z_grid)
self.nu_size = nu_size
self.name = name
self.model_type = 'table2D'
import heroespy.science.spectrum as spec
from astropy.units import Unit as u
print(spec.atmospheric_column_density(40 * u('km')))
from astropy.table import Table
from astropy.units import Unit as u
from astropy.units import spectral
import glob
import numpy as np
from astropy.io import fits
f_list = glob.glob('tau_modelC_total*.dat')
_c_tau = []
_n_tau = []
_tau = []
z_array = []
for ID, f_name in enumerate(f_list):
z = f_name.split('_z')[1]
z = z.replace('z', '')
z = z.replace('.dat', '')
d = np.loadtxt(f_name)
if ID == 0:
E = d[:, 0] * u('TeV')
_c_tau.append(E)
_n_tau.append('energies')
_n_tau.append('z_%s' % z)
z_array.append(z)
_c_tau.append(d[:, 1])
t_tau = Table(data=_c_tau, names=_n_tau, meta={'redshift': z_array})
t_tau.write('tau_finke_2010.fits', format='fits', overwrite=True)
with pytest.raises(ValueError):
hist.get_axis('y')
assert (x_axis == hist.get_axis('x')).all
assert hist.dim == 1
data = np.arange(15)
with pytest.raises(ValueError):
hist.data = data
data = [random.expovariate(hist.axes[0][_].value + 1) for _ in range(10)]
hist.data = data
# Find nodes on x-axis
with pytest.raises(ValueError):
hist.find_node(x=[14 * u('s')])
idx = hist.get_axis('x').find_node(12 * u('m'))
assert idx[0] == 1
idx = hist.get_axis('x').find_node(1200 * u('cm'))
assert idx[0] == 1
vals = [13 * u('m'), 2500 * u('cm'), 600 * u('dm')]
idx = hist.get_axis('x').find_node(vals)
assert idx[0] == np.array([1, 2, 6]).all()
# Find nodes using array
with pytest.raises(ValueError):
hist.find_node(energy=5)
idx = hist.find_node(x=[12 * u('m'), 67 * u('m')])
assert idx[0][0] == 1
from astropy.table import Table
from astropy.units import Unit as u
from astropy.units import spectral
import glob
import numpy as np
from astropy.io import fits
_c_tau = []
_n_tau = []
_tau = []
z_array = []
data = np.loadtxt('tau_fran08.dat', usecols=(0, 2))
E = data[0:50, 0] * u('TeV')
_n_tau.append('energies')
_c_tau.append(E)
for i in range(int(len(data[:, 1]) / len(E))):
tau = data[i * 50:i * 50 + 50, 1]
_c_tau.append(tau.flatten())
z = 1e-3 * (i + 1.)
z_array.append(z)
_n_tau.append('z_%2.8f' % z)
#print(z,E,tau)
#MUST use ascii, fits forma can not handle table with more than 1000 cols
t_tau = Table(data=_c_tau, names=_n_tau, meta={'redshift': z_array})
t_tau.write(
'tau_franceschini_2008.dat',
format='ascii.ecsv',
overwrite=True,
)
with pytest.raises(ValueError):
hist.get_axis('y')
assert (x_axis == hist.get_axis('x')).all
assert hist.dim == 1
data = np.arange(15)
with pytest.raises(ValueError):
hist.data = data
data = [random.expovariate(hist.axes[0][_].value+1) for _ in range(10)]
hist.data = data
# Find nodes on x-axis
with pytest.raises(ValueError):
hist.find_node(x = [14 * u('s')])
idx = hist.get_axis('x').find_node(12 * u('m'))
assert idx[0] == 1
idx = hist.get_axis('x').find_node(1200 * u('cm'))
assert idx[0] == 1
vals = [13 * u('m'), 2500*u('cm'), 600 * u('dm')]
idx = hist.get_axis('x').find_node(vals)
assert idx[0] == np.array([1, 2, 6]).all()
# Find nodes using array
with pytest.raises(ValueError):
hist.find_node(energy = 5)
idx = hist.find_node(x = [12 * u('m'), 67 * u('m')])
assert idx[0][0] == 1
with pytest.raises(ValueError):
hist.get_axis('y')
assert (x_axis == hist.get_axis('x')).all
assert hist.dim == 1
data = np.arange(15)
with pytest.raises(ValueError):
hist.data = data
data = [random.expovariate(hist.axes[0][_].value + 1) for _ in range(10)]
hist.data = data
# Find nodes on x-axis
with pytest.raises(ValueError):
hist.find_node(x=[14 * u('s')])
idx = hist.get_axis('x').find_node(12 * u('m'))
assert idx[0] == 1
idx = hist.get_axis('x').find_node(1200 * u('cm'))
assert idx[0] == 1
vals = [13 * u('m'), 2500 * u('cm'), 600 * u('dm')]
idx = hist.get_axis('x').find_node(vals)
assert idx[0] == np.array([1, 2, 6]).all()
# Find nodes using array
with pytest.raises(ValueError):
hist.find_node(energy=5)
idx = hist.find_node(x=[12 * u('m'), 67 * u('m')])
assert idx[0][0] == 1
