def read_data(path, dtype=np.float64, bin_factor=None, **kargs):
"""
Read SOHO / STEREO data files and output a Data instance
Input :
path : path of the data set
dtype : cast of the data array
kargs : arguments of the data filtering
"""
if not os.path.isdir(path):
raise ValueError('Directory does not exist')
# read files
fnames = os.listdir(path)
files = [pyfits.fitsopen(os.path.join(path, fname))[0] for fname in fnames]
files = filter_files(files, **kargs)
fits_arrays = list()
for f in files:
fits_array = fa.hdu2fitsarray(f)
if bin_factor is not None:
fits_array = fits_array.bin(bin_factor)
fits_array.header['RSUN'] /= bin_factor
update_header(fits_array)
fits_arrays.append(fits_array)
data = fa.infoarrays2infoarray(fits_arrays)
data = data.astype(dtype)
return data
def circular_trajectory_data(**kargs):
"""
Generate a circular trajectory of n images at a given radius
Inputs
------
radius : float
radius of the trajectory
dtype : data-type, optional (default np.float64)
data type of the output array
n_images : int (default 1)
number of images
min_lon : float (default 0.)
first longitude value in radians
max_lon : float (default 2 * np.pi)
last longitude value in radians
kargs :
other keyword arguments are treated as keywords of
the image header.
Outputs
-------
data : InfoArray
An empty InfoArray filled with appropriate metadata. The last axis
is image index. The header elements are 1d arrays of length
n_images.
Exemple
-------
>>> data = circular_trajectory_data(**default_image_dict)
>>> data.shape
(1, 1, 1)
"""
radius = kargs.pop('radius', 1.)
dtype = kargs.pop('dtype', np.float64)
n_images = kargs.pop('n_images', 1)
min_lon = kargs.pop('min_lon', 0.)
max_lon = kargs.pop('max_lon', 2 * np.pi)
longitudes = np.linspace(min_lon, max_lon, n_images)
images = []
for i, lon in enumerate(longitudes):
header = kargs.copy()
shape = header['NAXIS1'], header['NAXIS2']
images.append(Image(shape, header=dict(header), dtype=dtype))
images[-1].update('LON', lon)
images[-1].update('D', radius)
data = fa.infoarrays2infoarray(images)
# set values to zeros
data[:] = 0.
# compute rotation matrices
siddon.full_rotation_matrix(data)
return data
def circular_trajectory_data(**kargs):
"""
Generate a circular trajectory of n images at a given radius
Inputs
------
radius : float
radius of the trajectory
dtype : data-type, optional (default np.float64)
data type of the output array
n_images : int (default 1)
number of images
min_lon : float (default 0.)
first longitude value in radians
max_lon : float (default 2 * np.pi)
last longitude value in radians
kargs :
other keyword arguments are treated as keywords of
the image header.
Outputs
-------
data : InfoArray
An empty InfoArray filled with appropriate metadata. The last axis
is image index. The header elements are 1d arrays of length
n_images.
Exemple
-------
>>> data = circular_trajectory_data(**default_image_dict)
>>> data.shape
(1, 1, 1)
"""
radius = kargs.pop('radius', 1.)
dtype = kargs.pop('dtype', np.float64)
n_images = kargs.pop('n_images', 1)
min_lon = kargs.pop('min_lon', 0.)
max_lon = kargs.pop('max_lon', 2 * np.pi)
longitudes = np.linspace(min_lon, max_lon, n_images)
images = []
for i, lon in enumerate(longitudes):
header = kargs.copy()
shape = header['NAXIS1'], header['NAXIS2']
images.append(Image(shape, header=dict(header), dtype=dtype))
images[-1].update('LON', lon)
images[-1].update('D', radius)
data = fa.infoarrays2infoarray(images)
# set values to zeros
data[:] = 0.
# compute rotation matrices
siddon.full_rotation_matrix(data)
return data