def __init__(self):
data = [
["data_dir", None, "Base data directory", validator_is(str), str, str, 0],
["fits_extension", 0, "Extension index", validator_is(int), int, str, 0],
["stack_image_filename", "full_stack_image.fits", "Stack Image filename", nputils.validator_is(str), str, str, 2],
["ref_image_filename", "reference_image", "Reference image filename", validator_is(str), str, str, 0],
["mask_filename", "mask.fits", "Mask filename", validator_is(str), str, str, 0],
["bg_fct", None, "Background extraction fct", is_callable, None, None, 2],
["bg_coords", None, "Background region in coordinates [Xa,Ya,Xb,Yb]", validator_list(4, (int, float)),
str2floatlist, jp.encode, 0],
["bg_use_ksigma_method", False, "Use ksigma method to estimate the background level", validator_is(bool), str2bool, str, 0],
["roi_coords", None, "Region of interest in coordinates [Xa,Ya,Xb,Yb]", validator_list(4, (int, float)),
str2floatlist, jp.encode, 0],
["core_offset_filename", "core.dat", "Core offset filename", validator_is(str), str, str, 0],
["core_offset_fct", None, "Core offset generation fct", is_callable, None, None, 2],
["pre_bg_process_fct", None, "Initial processing before bg extraction", is_callable, None, None, 2],
["pre_process_fct", None, "Pre detection processing", is_callable, None, None, 2],
["post_process_fct", None, "Post detection processing", is_callable, None, None, 2],
["crval", None, "CRVAL", validator_is(list), jp.decode, jp.encode, 1],
["crpix", None, "CRPIX", validator_is(list), jp.decode, jp.encode, 1],
["projection_unit", u.mas, "Unit used for the projection", validator_is(u.Unit), u.Unit, str, 0],
["projection_relative", True, "Use relative projection", validator_is(bool), str2bool, str, 0],
["projection_center", "pix_ref", "Method used to get the center", validator_is(str), str, str, 0],
["object_distance", None, "Object distance", validator_is(u.Quantity), quantity_decode, str, 0],
["object_z", 0, "Object z", validator_in_range(0, 5), float, str, 0],
]
# nputils.BaseConfiguration.__init__(self, data, title="Finder configuration")
super(DataConfiguration, self).__init__(data, title="Data configuration")
def __init__(self):
data = [
["unit", u.mas / u.year, "Velocity unit", nputils.validator_is(u.Unit)],
["bounds", [1, 1, 1, 1], "Velocity bounds", nputils.validator_list(4, (int, float))],
["filter1", None, "Filter for the first image", nputils.validator_is((NoneType, nputils.AbstractFilter))],
["filter2", None, "Filter for the second image", nputils.validator_is((NoneType, nputils.AbstractFilter))],
["tol_pix_range", [4, 25], "Allowed range of pixel velocity resolution", nputils.validator_list(2, int)],
["ncc_threshold", 0.6, "Threshold for the NCC", nputils.validator_is(bool)],
["factor", 10, "Zoom factor of the resulting map", nputils.validator_in_range(1, 20)],
["method", 'ncc_peaks_direct', "Method used to compute the SCC", lambda v: v in ['ncc', 'ncc_peaks', 'ncc_peaks_direct']],
["vector_direction", None, "Project the result on this direction", lambda v: v == 'position_angle' or nputils.is_callable(v) or isinstance(v, (list, np.ndarray, NoneType))],
["velocity_trans", None, "Do any transform on the velocity vector, pre projection", lambda v: nputils.is_callable(v)],
["rnd_pos_shift", False, "Randomly shift the segments position", nputils.validator_is((bool, NoneType))],
["rnd_pos_factor", 1.5, "Factor of the standart deviation of the shift", nputils.validator_in_range(0.1, 5)],
["img_rnd_shift", 0, "Randomly shift the images (pixel std)", nputils.validator_in_range(0, 10)],
["shuffle", False, "Suffle the list of images", nputils.validator_is(bool)],
]
nputils.BaseConfiguration.__init__(self, data, title="Stack cross correlation configuration")
def __init__(self):
data = [
["alpha_threashold", 3, "Significance threshold", validator_in_range(0.1, 20), float, str, 0],
["alpha_detection", 4, "Detection threshold", validator_in_range(0.1, 20), float, str, 0],
["min_scale", 1, "Minimum Wavelet scale", validator_in_range(0, 10, instance=int), int, str, 0],
["max_scale", 4, "Maximum Wavelet scale", validator_in_range(1, 10, instance=int), int, str, 0],
["scales_snr_filter", None, "Per scales detection threshold", validator_is(dict), jp.decode, jp.encode, 1],
["ms_dec_klass", WaveletMultiscaleDecomposition, "Multiscale decompostion class",
validator_is_class(AbstractMultiScaleDecomposition), lambda s: jp.decode(str2jsonclass(s)), jp.encode, 1],
["use_iwd", False, "Use Intermediate Wavelet Decomposition", validator_is(bool), str2bool, str, 0],
["dec", wtutils.uiwt, "Multiscale decompostion class", is_callable, lambda s: jp.decode(str2jsonfunction(s)), jp.encode, 1],
["wd_wavelet", 'b1', "Wavelet to use for the Wavelet Decomposition", validator_is(str), str, str, 1],
["iwd_wavelet", 'b3', "Wavelet to use for the Intermediate Wavelet Decomposition", validator_is(str), str, str, 1],
["dog_step", True, "DOG", validator_is(int), None, None, 2],
["dog_angle", True, "DOG", validator_is((int, float)), None, None, 2],
["dog_ellipticity", True, "DOG", validator_is((int, float)), None, None, 2],
["exclude_border_dist", 1, "Number of pixel from border to exclude", validator_is(int), int, str, 0],
["exclude_noise", True, "Include coefficients below threshold in resulting image", validator_is(bool), str2bool, str, 1],
]
super(FinderConfiguration, self).__init__(data, title="Finder configuration")
def __init__(self):
data = [
[
"unit", u.mas / u.year, "Velocity unit",
nputils.validator_is(u.Unit)
],
[
"bounds", [1, 1, 1, 1], "Velocity bounds",
nputils.validator_list(4, (int, float))
],
[
"filter1", None, "Filter for the first image",
nputils.validator_is((NoneType, nputils.AbstractFilter))
],
[
"filter2", None, "Filter for the second image",
nputils.validator_is((NoneType, nputils.AbstractFilter))
],
[
"tol_pix_range", [4, 25],
"Allowed range of pixel velocity resolution",
nputils.validator_list(2, int)
],
[
"ncc_threshold", 0.6, "Threshold for the NCC",
nputils.validator_is(bool)
],
[
"factor", 10, "Zoom factor of the resulting map",
nputils.validator_in_range(1, 20)
],
[
"method", 'ncc_peaks_direct', "Method used to compute the SCC",
lambda v: v in ['ncc', 'ncc_peaks', 'ncc_peaks_direct']
],
[
"vector_direction", None,
"Project the result on this direction",
lambda v: v == 'position_angle' or nputils.is_callable(
v) or isinstance(v, (list, np.ndarray, NoneType))
],
[
"velocity_trans", None,
"Do any transform on the velocity vector, pre projection",
lambda v: nputils.is_callable(v)
],
[
"rnd_pos_shift", False, "Randomly shift the segments position",
nputils.validator_is((bool, NoneType))
],
[
"rnd_pos_factor", 1.5,
"Factor of the standart deviation of the shift",
nputils.validator_in_range(0.1, 5)
],
[
"img_rnd_shift", 0, "Randomly shift the images (pixel std)",
nputils.validator_in_range(0, 10)
],
[
"shuffle", False, "Suffle the list of images",
nputils.validator_is(bool)
],
]
nputils.BaseConfiguration.__init__(
self, data, title="Stack cross correlation configuration")
def __init__(self):
data = [
[
"alpha_threashold", 3, "Significance threshold",
validator_in_range(0.1, 20), float, str, 0
],
[
"alpha_detection", 4, "Detection threshold",
validator_in_range(0.1, 20), float, str, 0
],
[
"min_scale", 1, "Minimum Wavelet scale",
validator_in_range(0, 10, instance=int), int, str, 0
],
[
"max_scale", 4, "Maximum Wavelet scale",
validator_in_range(1, 10, instance=int), int, str, 0
],
[
"scales_snr_filter", None, "Per scales detection threshold",
validator_is(dict), jp.decode, jp.encode, 1
],
[
"ms_dec_klass", WaveletMultiscaleDecomposition,
"Multiscale decompostion class",
validator_is_class(AbstractMultiScaleDecomposition),
lambda s: jp.decode(str2jsonclass(s)), jp.encode, 1
],
[
"use_iwd", False, "Use Intermediate Wavelet Decomposition",
validator_is(bool), str2bool, str, 0
],
[
"dec", wtutils.uiwt, "Multiscale decompostion class",
is_callable, lambda s: jp.decode(str2jsonfunction(s)),
jp.encode, 1
],
[
"wd_wavelet", 'b1',
"Wavelet to use for the Wavelet Decomposition",
validator_is(str), str, str, 1
],
[
"iwd_wavelet", 'b3',
"Wavelet to use for the Intermediate Wavelet Decomposition",
validator_is(str), str, str, 1
],
["dog_step", True, "DOG",
validator_is(int), None, None, 2],
[
"dog_angle", True, "DOG",
validator_is((int, float)), None, None, 2
],
[
"dog_ellipticity", True, "DOG",
validator_is((int, float)), None, None, 2
],
[
"exclude_border_dist", 1,
"Number of pixel from border to exclude",
validator_is(int), int, str, 0
],
[
"exclude_noise", True,
"Include coefficients below threshold in resulting image",
validator_is(bool), str2bool, str, 1
],
]
super(FinderConfiguration, self).__init__(data,
title="Finder configuration")
def __init__(self):
data = [
[
"data_dir", None, "Base data directory",
validator_is(str), str, str, 0
],
[
"fits_extension", 0, "Extension index",
validator_is(int), int, str, 0
],
[
"stack_image_filename", "full_stack_image.fits",
"Stack Image filename",
nputils.validator_is(str), str, str, 2
],
[
"ref_image_filename", "reference_image",
"Reference image filename",
validator_is(str), str, str, 0
],
[
"mask_filename", "mask.fits", "Mask filename",
validator_is(str), str, str, 0
],
[
"bg_fct", None, "Background extraction fct", is_callable, None,
None, 2
],
[
"bg_coords", None,
"Background region in coordinates [Xa,Ya,Xb,Yb]",
validator_list(4, (int, float)), str2floatlist, jp.encode, 0
],
[
"bg_use_ksigma_method", False,
"Use ksigma method to estimate the background level",
validator_is(bool), str2bool, str, 0
],
[
"roi_coords", None,
"Region of interest in coordinates [Xa,Ya,Xb,Yb]",
validator_list(4, (int, float)), str2floatlist, jp.encode, 0
],
[
"core_offset_filename", "core.dat", "Core offset filename",
validator_is(str), str, str, 0
],
[
"core_offset_fct", None, "Core offset generation fct",
is_callable, None, None, 2
],
[
"pre_bg_process_fct", None,
"Initial processing before bg extraction", is_callable, None,
None, 2
],
[
"pre_process_fct", None, "Pre detection processing",
is_callable, None, None, 2
],
[
"post_process_fct", None, "Post detection processing",
is_callable, None, None, 2
],
[
"crval", None, "CRVAL",
validator_is(list), jp.decode, jp.encode, 1
],
[
"crpix", None, "CRPIX",
validator_is(list), jp.decode, jp.encode, 1
],
[
"projection_unit", u.mas, "Unit used for the projection",
validator_is(u.Unit), u.Unit, str, 0
],
[
"projection_relative", True, "Use relative projection",
validator_is(bool), str2bool, str, 0
],
[
"projection_center", "pix_ref",
"Method used to get the center",
validator_is(str), str, str, 0
],
[
"object_distance", None, "Object distance",
validator_is(u.Quantity), quantity_decode, str, 0
],
[
"object_z", 0, "Object z",
validator_in_range(0, 5), float, str, 0
],
]
# nputils.BaseConfiguration.__init__(self, data, title="Finder configuration")
super(DataConfiguration, self).__init__(data,
title="Data configuration")
