parameter.py
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""
Define UVParameters: data and metadata objects for interferometric data sets.
UVParameters are objects to hold specific data and metadata associated with
interferometric data sets. They are used as attributes for classes based on
UVBase. This module also includes specialized subclasses for particular types
of metadata.
"""
import builtins
import warnings
import astropy.units as units
import numpy as np
from astropy.coordinates import SkyCoord
from . import utils
__all__ = ["UVParameter", "AngleParameter", "LocationParameter"]
def _get_generic_type(expected_type, strict_type_check=False):
"""Return tuple of more generic types.
Allows for more flexible type checking in the case when a Parameter's value
changes precision or to/from a numpy dtype but still is the desired generic type.
If a generic type cannot be found, the expected_type is returned
Parameters
----------
expected_type : Type or string
The expected type of a Parameter object or a string of the name of a type.
strict_type_check : bool
If True the input expected_type is return exactly
if strict_type_check:
return expected_type exactly
Returns
-------
Tuple of types based on input expected_type
"""
if isinstance(expected_type, str):
try:
expected_type = getattr(builtins, expected_type)
except AttributeError as err:
raise ValueError(
f"Input expected_type is a string with value: '{expected_type}'. "
"When the expected_type is a string, it must be a Python builtin type."
) from err
if strict_type_check:
return expected_type
for types in [
(bool, np.bool_),
(float, np.floating),
(np.unsignedinteger), # unexpected but just in case
(int, np.integer),
(complex, np.complexfloating),
]:
if issubclass(expected_type, types):
return types
return expected_type
def _param_dict_equal(this_dict, other_dict):
"""
Test if dicts are equal for parameter equality.
Helper function pulled out to allow recursion for nested dicts
"""
try:
# Try a naive comparison first
# this will fail if keys are the same
# but cases differ.
# so only look for exact equality
# then default to the long test below.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "elementwise comparison failed")
if this_dict == other_dict:
return True, ""
except (ValueError, TypeError):
pass
# this dict may contain arrays or Nones
# we will need to check each item individually
# check to see if they are equal other than
# upper/lower case keys
this_lower = {
(k.lower() if isinstance(k, str) else k): v for k, v in this_dict.items()
}
other_lower = {
(k.lower() if isinstance(k, str) else k): v for k, v in other_dict.items()
}
if set(this_lower.keys()) != set(other_lower.keys()):
message_str = ", keys are not the same."
return False, message_str
else:
# need to check if values are close,
# not just equal
for key in this_lower.keys():
if isinstance(this_lower[key], dict):
# nested dict, use recursion
subdict_equal, subdict_message = _param_dict_equal(
this_lower[key], other_lower[key]
)
if subdict_equal:
continue
else:
message_str = f", key {key} is a dict" + subdict_message
return False, message_str
# this is not a dict, use other methods
if this_lower[key] is None or other_lower[key] is None:
if this_lower[key] is None and other_lower[key] is None:
continue
else:
message_str = f", key {key} is not equal"
return False, message_str
if isinstance(this_lower[key], (list, np.ndarray, tuple)) and isinstance(
other_lower[key], (list, np.ndarray, tuple)
):
this_array = np.asarray(this_lower[key])
other_array = np.asarray(other_lower[key])
if this_array.shape != other_array.shape:
message_str = f", key {key} is not equal"
return False, message_str
if np.allclose(this_array, other_array):
continue
else:
message_str = f", key {key} is not equal"
return False, message_str
else:
# this isn't a list, array or tuple
try:
if np.isclose(this_lower[key], other_lower[key]):
continue
else:
message_str = f", key {key} is not equal"
return False, message_str
except TypeError:
# this isn't a type that can be
# handled by np.isclose,
# test for equality
if this_lower[key] == other_lower[key]:
continue
else:
message_str = f", key {key} is not equal"
return False, message_str
return True, ""
class UVParameter(object):
"""
Data and metadata objects for interferometric data sets.
Parameters
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : 'str', int or tuple
Either 'str' or an int (if a single value) or tuple giving information about the
expected shape of the value. Elements of the tuple may be the name of other
UVParameters that indicate data shapes.
Form examples:
- 'str': a string value
- ('Nblts', 3): the value should be an array of shape:
Nblts (another UVParameter name), 3
- (): a single numeric value
- 3: the value should be an array of shape (3, )
description : str
Description of the data or metadata in the object.
expected_type
The type that the data or metadata should be. Default is int or str if
form is 'str'.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : float or 2-tuple of float
Tolerances for testing the equality of UVParameters. Either a single
absolute value or a tuple of relative and absolute values to be used by
np.isclose()
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precisions or to/from numpy
dtypes to not break checks.
Attributes
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : 'str', int or tuple
Either 'str' or an int (if a single value) or tuple giving information about the
expected shape of the value. Elements of the tuple may be the name of other
UVParameters that indicate data shapes.
Form examples:
- 'str': a string value
- ('Nblts', 3): the value should be an array of shape:
Nblts (another UVParameter name), 3
- (): a single numeric value
- 3: the value should be an array of shape (3, )
description : str
Description of the data or metadata in the object.
expected_type
The type that the data or metadata should be. Default is int or str if
form is 'str'.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : 2-tuple of float
Relative and absolute tolerances for testing the equality of UVParameters, to be
used by np.isclose()
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precisions or to/from numpy
dtypes to not break checks.
"""
def __init__(
self,
name,
required=True,
value=None,
spoof_val=None,
form=(),
description="",
expected_type=int,
acceptable_vals=None,
acceptable_range=None,
tols=(1e-05, 1e-08),
strict_type_check=False,
):
"""Init UVParameter object."""
self.name = name
self.required = required
# cannot set a spoof_val for required parameters
if not self.required:
self.spoof_val = spoof_val
self.value = value
self.description = description
self.form = form
if self.form == "str":
self.expected_type = str
self.strict_type = True
else:
self.expected_type = _get_generic_type(
expected_type, strict_type_check=strict_type_check
)
self.strict_type = strict_type_check
self.acceptable_vals = acceptable_vals
self.acceptable_range = acceptable_range
if np.size(tols) == 1:
# Only one tolerance given, assume absolute, set relative to zero
self.tols = (0, tols)
else:
# relative and absolute tolerances to be used in np.isclose
self.tols = tols
def __eq__(self, other, silent=False):
"""
Test if classes match and values are within tolerances.
Parameters
----------
other : UVParameter or subclass
The other UVParameter to compare with this one.
silent : bool
When set to False (default), descriptive text is printed out when parameters
do not match. If set to True, this text is not printed.
"""
if isinstance(other, self.__class__):
if self.value is None:
if other.value is not None:
if not silent:
print(f"{self.name} is None on left, but not right")
return False
else:
return True
if other.value is None:
if self.value is not None:
if not silent:
print(f"{self.name} is None on right, but not left")
return False
if isinstance(self.value, np.ndarray) and not isinstance(
self.value.item(0), (str, np.str_)
):
if not isinstance(other.value, np.ndarray):
if not silent:
print(f"{self.name} parameter value is array, but other is not")
return False
if self.value.shape != other.value.shape:
if not silent:
print(
f"{self.name} parameter value is array, shapes are "
"different"
)
return False
if isinstance(self.value, units.Quantity):
if not self.value.unit.is_equivalent(other.value.unit):
if not silent:
print(
f"{self.name} parameter value is an astropy Quantity, "
"units are not equivalent"
)
return False
if not isinstance(self.tols[1], units.Quantity):
atol_use = self.tols[1] * self.value.unit
else:
atol_use = self.tols[1]
if not units.quantity.allclose(
self.value,
other.value,
rtol=self.tols[0],
atol=atol_use,
equal_nan=True,
):
if not silent:
print(
f"{self.name} parameter value is an astropy Quantity, "
"values are not close"
)
return False
else:
# check to see if strict types are used
if self.strict_type:
# types must match
if other.strict_type:
# both strict, expected_type must match
if self.expected_type != other.expected_type:
if not silent:
print(
f"{self.name} parameter has incompatible "
f"types. Left is {self.expected_type}, right "
f"is {other.expected_type}"
)
return False
elif not isinstance(self.value.item(0), other.expected_type):
if not silent:
print(
f"{self.name} parameter has incompatible dtypes. "
f"Left requires {self.expected_type}, right is "
f"{other.value.dtype}"
)
return False
elif other.strict_type:
# types must match in the other direction
if not isinstance(other.value.item(0), self.expected_type):
if not silent:
print(
f"{self.name} parameter has incompatible dtypes. "
f"Left is {self.value.dtype}, right requires "
f"{other.expected_type}"
)
return False
if not np.allclose(
self.value,
other.value,
rtol=self.tols[0],
atol=self.tols[1],
equal_nan=True,
):
if not silent:
print(
f"{self.name} parameter value is array, values are not "
"close"
)
return False
else:
# check to see if strict types are used
if self.strict_type:
# types must match
if not isinstance(self.value, other.expected_type):
if not silent:
print(
f"{self.name} parameter has incompatible types. Left "
f"requires {type(self.value)}, right is "
f"{other.expected_type}"
)
return False
if other.strict_type:
# types must match in the other direction
if not isinstance(other.value, self.expected_type):
if not silent:
print(
f"{self.name} parameter has incompatible types. Left "
f"is {self.expected_type}, right requires "
f"{type(other.value)}"
)
return False
str_type = False
if isinstance(self.value, str):
str_type = True
if isinstance(self.value, (list, np.ndarray, tuple)):
if isinstance(self.value[0], str):
str_type = True
if not str_type:
if isinstance(other.value, np.ndarray):
if not silent:
print(
f"{self.name} parameter value is not an array, "
"but other is not"
)
return False
try:
if not np.allclose(
np.array(self.value),
np.array(other.value),
rtol=self.tols[0],
atol=self.tols[1],
equal_nan=True,
):
if not silent:
print(
f"{self.name} parameter value can be cast to an "
"array and tested with np.allclose. The values are "
"not close"
)
return False
except (TypeError):
if isinstance(self.value, dict):
message_str = f"{self.name} parameter is a dict"
dict_equal, dict_message_str = _param_dict_equal(
self.value, other.value
)
if dict_equal:
return True
else:
message_str += dict_message_str
if not silent:
print(message_str)
return False
else:
if self.value != other.value:
if not silent:
print(
f"{self.name} parameter value is not a string "
"or a dict and cannot be cast as a numpy "
"array. The values are not equal."
)
return False
else:
if isinstance(self.value, (list, np.ndarray, tuple)):
if [s.strip() for s in self.value] != [
s.strip() for s in other.value
]:
if not silent:
print(
f"{self.name} parameter value is a list of "
"strings, values are different"
)
return False
else:
if self.value.strip() != other.value.strip():
if self.value.replace("\n", "").replace(
" ", ""
) != other.value.replace("\n", "").replace(" ", ""):
if not silent:
print(
f"{self.name} parameter value is a string, "
"values are different"
)
return False
return True
else:
if not silent:
print(f"{self.name} parameter classes are different")
return False
def __ne__(self, other, silent=True):
"""
Test if classes do not match or values are not within tolerances.
Parameters
----------
other : UVParameter or subclass
The other UVParameter to compare with this one.
silent : bool
When set to False (default), descriptive text is printed out when parameters
do not match. If set to True, this text is not printed.
"""
return not self.__eq__(other)
def apply_spoof(self):
"""Set value to spoof_val for non-required UVParameters."""
self.value = self.spoof_val
def expected_shape(self, uvbase):
"""
Get the expected shape of the value based on the form.
Parameters
----------
uvbase : object
Object with this UVParameter as an attribute. Needed
because the form can refer to other UVParameters on this object.
Returns
-------
tuple
The expected shape of the value.
"""
if self.form == "str":
return self.form
elif isinstance(self.form, (int, np.integer)):
# Fixed shape, just return the form
return (self.form,)
else:
# Given by other attributes, look up values
eshape = ()
for p in self.form:
if isinstance(p, (int, np.integer)):
eshape = eshape + (p,)
else:
val = getattr(uvbase, p)
if val is None:
raise ValueError(
f"Missing UVBase parameter {p} needed to "
f"calculate expected shape of parameter {self.name}"
)
eshape = eshape + (val,)
return eshape
def check_acceptability(self):
"""Check that values are acceptable."""
if self.acceptable_vals is None and self.acceptable_range is None:
return True, "No acceptability check"
else:
# either acceptable_vals or acceptable_range is set. Prefer acceptable_vals
if self.acceptable_vals is not None:
# acceptable_vals are a list of allowed values
if self.expected_type is str:
# strings need to be converted to lower case
if isinstance(self.value, str):
value_set = {self.value.lower()}
else:
# this is a list or array of strings, make them all lower case
value_set = {x.lower() for x in self.value}
acceptable_vals = [x.lower() for x in self.acceptable_vals]
else:
if isinstance(self.value, (list, np.ndarray)):
value_set = set(self.value)
else:
value_set = {self.value}
acceptable_vals = self.acceptable_vals
for elem in value_set:
if elem not in acceptable_vals:
message = (
f"Value {elem}, is not in allowed values: {acceptable_vals}"
)
return False, message
return True, "Value is acceptable"
else:
# acceptable_range is a tuple giving a range of allowed magnitudes
testval = np.mean(np.abs(self.value))
if (testval >= self.acceptable_range[0]) and (
testval <= self.acceptable_range[1]
):
return True, "Value is acceptable"
else:
message = (
f"Mean of abs values, {testval}, is not in allowed range: "
f"{self.acceptable_range}"
)
return False, message
class AngleParameter(UVParameter):
"""
Subclass of UVParameter for Angle type parameters.
Adds extra methods for conversion to & from degrees (used by UVBase objects
for _degrees properties associated with these parameters).
Parameters
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : 'str', int or tuple
Either 'str' or an int (if a single value) or tuple giving information about the
expected shape of the value. Elements of the tuple may be the name of other
UVParameters that indicate data shapes.
Form examples:
- 'str': a string value
- ('Nblts', 3): the value should be an array of shape:
Nblts (another UVParameter name), 3
- (): a single numeric value
- 3: the value should be an array of shape (3, )
description : str
Description of the data or metadata in the object.
expected_type
The type that the data or metadata should be. Default is int or str if
form is 'str'.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : float or 2-tuple of float
Tolerances for testing the equality of UVParameters. Either a single
absolute value or a tuple of relative and absolute values to be used by
np.isclose()
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precicions or to/from numpy
dtypes to not break checks.
Attributes
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : 'str', int or tuple
Either 'str' or an int (if a single value) or tuple giving information about the
expected shape of the value. Elements of the tuple may be the name of other
UVParameters that indicate data shapes.
Form examples:
- 'str': a string value
- ('Nblts', 3): the value should be an array of shape:
Nblts (another UVParameter name), 3
- (): a single numeric value
- 3: the value should be an array of shape (3, )
description : str
Description of the data or metadata in the object.
expected_type
The type that the data or metadata should be. Default is int or str if
form is 'str'.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : 2-tuple of float
Relative and absolute tolerances for testing the equality of UVParameters, to be
used by np.isclose()
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precicions or to/from numpy
dtypes to not break checks.
"""
def degrees(self):
"""Get value in degrees."""
if self.value is None:
return None
else:
return self.value * 180.0 / np.pi
def set_degrees(self, degree_val):
"""
Set value in degrees.
Parameters
----------
degree_val : float
Value in degrees to use to set the value attribute.
"""
if degree_val is None:
self.value = None
else:
self.value = degree_val * np.pi / 180.0
class LocationParameter(UVParameter):
"""
Subclass of UVParameter for location type parameters.
Adds extra methods for conversion to & from lat/lon/alt in radians or
degrees (used by UVBase objects for _lat_lon_alt and _lat_lon_alt_degrees
properties associated with these parameters).
Parameters
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
description : str
Description of the data or metadata in the object.
frame : str, optional
Coordinate frame. Valid options are "itrs" (default) or "mcmf".
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : float or 2-tuple of float
Tolerances for testing the equality of UVParameters. Either a single
absolute value or a tuple of relative and absolute values to be used by
np.isclose()
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precicions or to/from numpy
dtypes to not break checks.
Attributes
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : int
Always set to 3.
description : str
Description of the data or metadata in the object.
frame : str, optional
Coordinate frame. Valid options are "itrs" (default) or "mcmf".
expected_type
Always set to float.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : 2-tuple of float
Relative and absolute tolerances for testing the equality of UVParameters, to be
used by np.isclose()
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precicions or to/from numpy
dtypes to not break checks.
"""
def __init__(
self,
name,
required=True,
value=None,
spoof_val=None,
description="",
frame="itrs",
acceptable_range=-1,
tols=1e-3,
):
if acceptable_range == -1:
if frame == "itrs":
acceptable_range = (6.35e6, 6.39e6)
elif frame == "mcmf":
acceptable_range = (1717100.0, 1757100.0)
else:
acceptable_range = None
super(LocationParameter, self).__init__(
name,
required=required,
value=value,
spoof_val=spoof_val,
form=3,
description=description,
expected_type=float,
acceptable_range=acceptable_range,
tols=tols,
)
self.frame = frame
def lat_lon_alt(self):
"""Get value in (latitude, longitude, altitude) tuple in radians."""
if self.value is None:
return None
else:
# check defaults to False b/c exposed check kwarg exists in UVData
return utils.LatLonAlt_from_XYZ(
self.value, check_acceptability=False, frame=self.frame
)
def set_lat_lon_alt(self, lat_lon_alt):
"""
Set value from (latitude, longitude, altitude) tuple in radians.
Parameters
----------
lat_lon_alt : 3-tuple of float
Tuple with the latitude (radians), longitude (radians)
and altitude (meters) to use to set the value attribute.
"""
if lat_lon_alt is None:
self.value = None
else:
self.value = utils.XYZ_from_LatLonAlt(
lat_lon_alt[0], lat_lon_alt[1], lat_lon_alt[2], frame=self.frame
)
def lat_lon_alt_degrees(self):
"""Get value in (latitude, longitude, altitude) tuple in degrees."""
if self.value is None:
return None
else:
latitude, longitude, altitude = self.lat_lon_alt()
return latitude * 180.0 / np.pi, longitude * 180.0 / np.pi, altitude
def set_lat_lon_alt_degrees(self, lat_lon_alt_degree):
"""
Set value from (latitude, longitude, altitude) tuple in degrees.
Parameters
----------
lat_lon_alt : 3-tuple of float
Tuple with the latitude (degrees), longitude (degrees)
and altitude (meters) to use to set the value attribute.
"""
if lat_lon_alt_degree is None:
self.value = None
else:
latitude, longitude, altitude = lat_lon_alt_degree
self.value = utils.XYZ_from_LatLonAlt(
latitude * np.pi / 180.0,
longitude * np.pi / 180.0,
altitude,
frame=self.frame,
)
def check_acceptability(self):
"""Check that vector magnitudes are in range."""
if self.acceptable_range is None:
return True, "No acceptability check"
else:
# acceptable_range is a tuple giving a range of allowed vector magnitudes
testval = np.sqrt(np.sum(np.abs(self.value) ** 2))
if (testval >= self.acceptable_range[0]) and (
testval <= self.acceptable_range[1]
):
return True, "Value is acceptable"
else:
message = (
f"Value {testval}, is not in allowed range: {self.acceptable_range}"
)
return False, message
class SkyCoordParameter(UVParameter):
"""
Subclass of UVParameter for SkyCoord parameters.
Needed for handling tolerances properly. The `tols` attribute is interpreted as the
tolerance of the sky separation in radians.
Parameters
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : 'str', int or tuple
Either 'str' or an int (if a single value) or tuple giving information about the
expected shape of the value. Elements of the tuple may be the name of other
UVParameters that indicate data shapes.
Form examples:
- 'str': a string value
- ('Nblts', 3): the value should be an array of shape:
Nblts (another UVParameter name), 3
- (): a single numeric value
- 3: the value should be an array of shape (3, )
description : str
Description of the data or metadata in the object.
expected_type
The type that the data or metadata should be. Default is int or str if
form is 'str'.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
radian_tol : float
Tolerance of the sky separation in radians.
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precicions or to/from numpy
dtypes to not break checks.
Attributes
----------
name : str
The name of the attribute. Used as the associated property name in
classes based on UVBase.
required : bool
Flag indicating whether this is required metadata for
the class with this UVParameter as an attribute. Default is True.
value
The value of the data or metadata.
spoof_val
A fake value that can be assigned to a non-required UVParameter if the
metadata is required for a particular file-type.
This is not an attribute of required UVParameters.
form : 'str', int or tuple
Either 'str' or an int (if a single value) or tuple giving information about the
expected shape of the value. Elements of the tuple may be the name of other
UVParameters that indicate data shapes.
Form examples:
- 'str': a string value
- ('Nblts', 3): the value should be an array of shape:
Nblts (another UVParameter name), 3
- (): a single numeric value
- 3: the value should be an array of shape (3, )
description : str
Description of the data or metadata in the object.
expected_type
Always set to SkyCoord.
acceptable_vals : list, optional
List giving allowed values for elements of value.
acceptable_range: 2-tuple, optional
Tuple giving a range of allowed magnitudes for elements of value.
tols : 2-tuple of float
Set to (0, `radian_tol`).
strict_type_check : bool
When True, the input expected_type is used exactly, otherwise a more
generic type is found to allow changes in precicions or to/from numpy
dtypes to not break checks.
"""
def __init__(
self,
name,
required=True,
value=None,
spoof_val=None,
form=(),
description="",
acceptable_range=None,
# standard angle tolerance: 1 mas in radians.
radian_tol=1 * 2 * np.pi * 1e-3 / (60.0 * 60.0 * 360.0),
):
super(SkyCoordParameter, self).__init__(
name,
required=required,
value=value,
spoof_val=spoof_val,
form=form,
description=description,
expected_type=SkyCoord,
acceptable_range=acceptable_range,
tols=(0, radian_tol),
)
def __eq__(self, other):
if not issubclass(self.value.__class__, SkyCoord) or not issubclass(
other.value.__class__, SkyCoord
):
return super(SkyCoordParameter, self).__eq__(other)
if self.value.shape != other.value.shape:
print(f"{self.name} parameter shapes are different")
return False
this_frame = self.value.frame.name
other_frame = other.value.frame.name
if this_frame != other_frame:
print(
f"{self.name} parameter has different frames, {this_frame} vs "
f"{other_frame}."
)
return False
this_rep_type = self.value.representation_type
other_rep_type = other.value.representation_type
if this_rep_type != other_rep_type:
print(
f"{self.name} parameter has different representation_types, "
f"{this_rep_type} vs {other_rep_type}."
)
return False
# finally calculate on sky separations
sky_separation = self.value.separation(other.value).rad
if np.any(sky_separation > self.tols[1]):
print(f"{self.name} parameter is not close. ")
return False
return True
