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44,175 | astropy.modeling.core | _param_sets |
Implementation of the Model.param_sets property.
This internal implementation has a ``raw`` argument which controls
whether or not to return the raw parameter values (i.e. the values that
are actually stored in the ._parameters array, as opposed to the values
displayed to users. In most cases these are one in the same but there
are currently a few exceptions.
Note: This is notably an overcomplicated device and may be removed
entirely in the near future.
| def _param_sets(self, raw=False, units=False):
"""
Implementation of the Model.param_sets property.
This internal implementation has a ``raw`` argument which controls
whether or not to return the raw parameter values (i.e. the values that
are actually stored in the ._parameters array, as opposed to the values
displayed to users. In most cases these are one in the same but there
are currently a few exceptions.
Note: This is notably an overcomplicated device and may be removed
entirely in the near future.
"""
values = []
shapes = []
for name in self.param_names:
param = getattr(self, name)
if raw and param._setter:
value = param._internal_value
else:
value = param.value
broadcast_shape = self._param_metrics[name].get("broadcast_shape")
if broadcast_shape is not None:
value = value.reshape(broadcast_shape)
shapes.append(np.shape(value))
if len(self) == 1:
# Add a single param set axis to the parameter's value (thus
# converting scalars to shape (1,) array values) for
# consistency
value = np.array([value])
if units:
if raw and param.internal_unit is not None:
unit = param.internal_unit
else:
unit = param.unit
if unit is not None:
value = Quantity(value, unit, subok=True)
values.append(value)
if len(set(shapes)) != 1 or units:
# If the parameters are not all the same shape, converting to an
# array is going to produce an object array
# However the way Numpy creates object arrays is tricky in that it
# will recurse into array objects in the list and break them up
# into separate objects. Doing things this way ensures a 1-D
# object array the elements of which are the individual parameter
# arrays. There's not much reason to do this over returning a list
# except for consistency
psets = np.empty(len(values), dtype=object)
psets[:] = values
return psets
return np.array(values)
| (self, raw=False, units=False) | [
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|
44,176 | astropy.modeling.core | _parameters_to_array | null | def _parameters_to_array(self):
# Now set the parameter values (this will also fill
# self._parameters)
param_metrics = self._param_metrics
for name in self.param_names:
param = getattr(self, name)
value = param.value
if not isinstance(value, np.ndarray):
value = np.array([value])
self._parameters[param_metrics[name]["slice"]] = value.ravel()
# Finally validate all the parameters; we do this last so that
# validators that depend on one of the other parameters' values will
# work
| (self) | [
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|
44,177 | astropy.modeling.core | _post_evaluate |
Model specific post evaluation processing of outputs.
| def _post_evaluate(self, inputs, outputs, broadcasted_shapes, with_bbox, **kwargs):
"""
Model specific post evaluation processing of outputs.
"""
if self.get_bounding_box(with_bbox) is None and self.n_outputs == 1:
outputs = (outputs,)
outputs = self.prepare_outputs(broadcasted_shapes, *outputs, **kwargs)
outputs = self._process_output_units(inputs, outputs)
if self.n_outputs == 1:
return outputs[0]
return outputs
| (self, inputs, outputs, broadcasted_shapes, with_bbox, **kwargs) | [
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|
44,178 | astropy.modeling.core | _pre_evaluate |
Model specific input setup that needs to occur prior to model evaluation.
| def _pre_evaluate(self, *args, **kwargs):
"""
Model specific input setup that needs to occur prior to model evaluation.
"""
# Broadcast inputs into common size
inputs, broadcasted_shapes = self.prepare_inputs(*args, **kwargs)
# Setup actual model evaluation method
parameters = self._param_sets(raw=True, units=True)
def evaluate(_inputs):
return self.evaluate(*_inputs, *parameters)
return evaluate, inputs, broadcasted_shapes, kwargs
| (self, *args, **kwargs) | [
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|
44,179 | astropy.modeling.core | _prepare_inputs_model_set | null | def _prepare_inputs_model_set(self, params, inputs, model_set_axis_input, **kwargs):
reshaped = []
pivots = []
model_set_axis_param = self.model_set_axis # needed to reshape param
for idx, _input in enumerate(inputs):
max_param_shape = ()
if self._n_models > 1 and model_set_axis_input is not False:
# Use the shape of the input *excluding* the model axis
input_shape = (
_input.shape[:model_set_axis_input]
+ _input.shape[model_set_axis_input + 1 :]
)
else:
input_shape = _input.shape
for param in params:
try:
check_broadcast(
input_shape,
self._remove_axes_from_shape(param.shape, model_set_axis_param),
)
except IncompatibleShapeError:
raise ValueError(
f"Model input argument {self.inputs[idx]!r} of shape"
f" {input_shape!r} "
f"cannot be broadcast with parameter {param.name!r} of shape "
f"{self._remove_axes_from_shape(param.shape, model_set_axis_param)!r}."
)
if len(param.shape) - 1 > len(max_param_shape):
max_param_shape = self._remove_axes_from_shape(
param.shape, model_set_axis_param
)
# We've now determined that, excluding the model_set_axis, the
# input can broadcast with all the parameters
input_ndim = len(input_shape)
if model_set_axis_input is False:
if len(max_param_shape) > input_ndim:
# Just needs to prepend new axes to the input
n_new_axes = 1 + len(max_param_shape) - input_ndim
new_axes = (1,) * n_new_axes
new_shape = new_axes + _input.shape
pivot = model_set_axis_param
else:
pivot = input_ndim - len(max_param_shape)
new_shape = _input.shape[:pivot] + (1,) + _input.shape[pivot:]
new_input = _input.reshape(new_shape)
else:
if len(max_param_shape) >= input_ndim:
n_new_axes = len(max_param_shape) - input_ndim
pivot = self.model_set_axis
new_axes = (1,) * n_new_axes
new_shape = (
_input.shape[: pivot + 1] + new_axes + _input.shape[pivot + 1 :]
)
new_input = _input.reshape(new_shape)
else:
pivot = _input.ndim - len(max_param_shape) - 1
new_input = np.rollaxis(_input, model_set_axis_input, pivot + 1)
pivots.append(pivot)
reshaped.append(new_input)
if self.n_inputs < self.n_outputs:
pivots.extend([model_set_axis_input] * (self.n_outputs - self.n_inputs))
return reshaped, (pivots,)
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|
44,180 | astropy.modeling.core | _prepare_inputs_single_model | null | def _prepare_inputs_single_model(self, params, inputs, **kwargs):
broadcasts = []
for idx, _input in enumerate(inputs):
input_shape = _input.shape
# Ensure that array scalars are always upgrade to 1-D arrays for the
# sake of consistency with how parameters work. They will be cast back
# to scalars at the end
if not input_shape:
inputs[idx] = _input.reshape((1,))
if not params:
max_broadcast = input_shape
else:
max_broadcast = ()
for param in params:
try:
if self.standard_broadcasting:
broadcast = check_broadcast(input_shape, param.shape)
else:
broadcast = input_shape
except IncompatibleShapeError:
raise ValueError(
f"self input argument {self.inputs[idx]!r} of shape"
f" {input_shape!r} cannot be broadcast with parameter"
f" {param.name!r} of shape {param.shape!r}."
)
if len(broadcast) > len(max_broadcast):
max_broadcast = broadcast
elif len(broadcast) == len(max_broadcast):
max_broadcast = max(max_broadcast, broadcast)
broadcasts.append(max_broadcast)
if self.n_outputs > self.n_inputs:
extra_outputs = self.n_outputs - self.n_inputs
if not broadcasts:
# If there were no inputs then the broadcasts list is empty
# just add a None since there is no broadcasting of outputs and
# inputs necessary (see _prepare_outputs_single_self)
broadcasts.append(None)
broadcasts.extend([broadcasts[0]] * extra_outputs)
return inputs, (broadcasts,)
| (self, params, inputs, **kwargs) | [
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|
44,181 | astropy.modeling.core | _prepare_output_single_model | null | @staticmethod
def _prepare_output_single_model(output, broadcast_shape):
if broadcast_shape is not None:
if not broadcast_shape:
return output.item()
else:
try:
return output.reshape(broadcast_shape)
except ValueError:
try:
return output.item()
except ValueError:
return output
return output
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|
44,182 | astropy.modeling.core | _prepare_outputs_model_set | null | def _prepare_outputs_model_set(self, outputs, broadcasted_shapes, model_set_axis):
pivots = broadcasted_shapes[0]
# If model_set_axis = False was passed then use
# self._model_set_axis to format the output.
if model_set_axis is None or model_set_axis is False:
model_set_axis = self.model_set_axis
outputs = list(outputs)
for idx, output in enumerate(outputs):
pivot = pivots[idx]
if pivot < output.ndim and pivot != model_set_axis:
outputs[idx] = np.rollaxis(output, pivot, model_set_axis)
return tuple(outputs)
| (self, outputs, broadcasted_shapes, model_set_axis) | [
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|
44,183 | astropy.modeling.core | _prepare_outputs_single_model | null | def _prepare_outputs_single_model(self, outputs, broadcasted_shapes):
outputs = list(outputs)
for idx, output in enumerate(outputs):
try:
broadcast_shape = check_broadcast(*broadcasted_shapes[0])
except (IndexError, TypeError):
broadcast_shape = broadcasted_shapes[0][idx]
outputs[idx] = self._prepare_output_single_model(output, broadcast_shape)
return tuple(outputs)
| (self, outputs, broadcasted_shapes) | [
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|
44,184 | astropy.modeling.core | _process_output_units | null | def _process_output_units(self, inputs, outputs):
inputs_are_quantity = any(isinstance(i, Quantity) for i in inputs)
if self.return_units and inputs_are_quantity:
# We allow a non-iterable unit only if there is one output
if self.n_outputs == 1 and not isiterable(self.return_units):
return_units = {self.outputs[0]: self.return_units}
else:
return_units = self.return_units
outputs = tuple(
Quantity(out, return_units.get(out_name, None), subok=True)
for out, out_name in zip(outputs, self.outputs)
)
return outputs
| (self, inputs, outputs) | [
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|
44,185 | astropy.modeling.core | _remove_axes_from_shape |
Given a shape tuple as the first input, construct a new one by removing
that particular axis from the shape and all preceding axes. Negative axis
numbers are permittted, where the axis is relative to the last axis.
| @staticmethod
def _remove_axes_from_shape(shape, axis):
"""
Given a shape tuple as the first input, construct a new one by removing
that particular axis from the shape and all preceding axes. Negative axis
numbers are permittted, where the axis is relative to the last axis.
"""
if len(shape) == 0:
return shape
if axis < 0:
axis = len(shape) + axis
return shape[:axis] + shape[axis + 1 :]
if axis >= len(shape):
axis = len(shape) - 1
shape = shape[axis + 1 :]
return shape
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|
44,186 | astropy.modeling.core | _strip_ones | null | @staticmethod
def _strip_ones(intup):
return tuple(item for item in intup if item != 1)
| (intup) | [
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|
44,187 | astropy.modeling.core | _validate_input_shape | Perform basic validation of a single model input's shape.
The shape has the minimum dimensions for the given model_set_axis.
Returns the shape of the input if validation succeeds.
| def _validate_input_shape(
self, _input, idx, argnames, model_set_axis, check_model_set_axis
):
"""Perform basic validation of a single model input's shape.
The shape has the minimum dimensions for the given model_set_axis.
Returns the shape of the input if validation succeeds.
"""
input_shape = np.shape(_input)
# Ensure that the input's model_set_axis matches the model's
# n_models
if input_shape and check_model_set_axis:
# Note: Scalar inputs *only* get a pass on this
if len(input_shape) < model_set_axis + 1:
raise ValueError(
f"For model_set_axis={model_set_axis}, all inputs must be at "
f"least {model_set_axis + 1}-dimensional."
)
if input_shape[model_set_axis] != self._n_models:
try:
argname = argnames[idx]
except IndexError:
# the case of model.inputs = ()
argname = str(idx)
raise ValueError(
f"Input argument '{argname}' does not have the correct dimensions"
f" in model_set_axis={model_set_axis} for a model set with"
f" n_models={self._n_models}."
)
return input_shape
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|
44,188 | astropy.modeling.core | _validate_input_shapes |
Perform basic validation of model inputs
--that they are mutually broadcastable and that they have
the minimum dimensions for the given model_set_axis.
If validation succeeds, returns the total shape that will result from
broadcasting the input arrays with each other.
| def _validate_input_shapes(self, inputs, argnames, model_set_axis):
"""
Perform basic validation of model inputs
--that they are mutually broadcastable and that they have
the minimum dimensions for the given model_set_axis.
If validation succeeds, returns the total shape that will result from
broadcasting the input arrays with each other.
"""
check_model_set_axis = self._n_models > 1 and model_set_axis is not False
all_shapes = []
for idx, _input in enumerate(inputs):
all_shapes.append(
self._validate_input_shape(
_input, idx, argnames, model_set_axis, check_model_set_axis
)
)
try:
input_shape = check_broadcast(*all_shapes)
except IncompatibleShapeError as e:
raise ValueError(
"All inputs must have identical shapes or must be scalars."
) from e
return input_shape
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|
44,189 | astropy.modeling.core | _validate_input_units | null | def _validate_input_units(self, inputs, equivalencies=None, inputs_map=None):
inputs = list(inputs)
name = self.name or self.__class__.__name__
# Check that the units are correct, if applicable
if self.input_units is not None:
# If a leaflist is provided that means this is in the context of
# a compound model and it is necessary to create the appropriate
# alias for the input coordinate name for the equivalencies dict
if inputs_map:
edict = {}
for mod, mapping in inputs_map:
if self is mod:
edict[mapping[0]] = equivalencies[mapping[1]]
else:
edict = equivalencies
# We combine any instance-level input equivalencies with user
# specified ones at call-time.
input_units_equivalencies = _combine_equivalency_dict(
self.inputs, edict, self.input_units_equivalencies
)
# We now iterate over the different inputs and make sure that their
# units are consistent with those specified in input_units.
for i in range(len(inputs)):
input_name = self.inputs[i]
input_unit = self.input_units.get(input_name, None)
if input_unit is None:
continue
if isinstance(inputs[i], Quantity):
# We check for consistency of the units with input_units,
# taking into account any equivalencies
if inputs[i].unit.is_equivalent(
input_unit, equivalencies=input_units_equivalencies[input_name]
):
# If equivalencies have been specified, we need to
# convert the input to the input units - this is
# because some equivalencies are non-linear, and
# we need to be sure that we evaluate the model in
# its own frame of reference. If input_units_strict
# is set, we also need to convert to the input units.
if (
len(input_units_equivalencies) > 0
or self.input_units_strict[input_name]
):
inputs[i] = inputs[i].to(
input_unit,
equivalencies=input_units_equivalencies[input_name],
)
else:
# We consider the following two cases separately so as
# to be able to raise more appropriate/nicer exceptions
if input_unit is dimensionless_unscaled:
raise UnitsError(
f"{name}: Units of input '{self.inputs[i]}', "
f"{inputs[i].unit} ({inputs[i].unit.physical_type}),"
"could not be converted to "
"required dimensionless "
"input"
)
else:
raise UnitsError(
f"{name}: Units of input '{self.inputs[i]}', "
f"{inputs[i].unit} ({inputs[i].unit.physical_type}),"
" could not be "
"converted to required input"
f" units of {input_unit} ({input_unit.physical_type})"
)
else:
# If we allow dimensionless input, we add the units to the
# input values without conversion, otherwise we raise an
# exception.
if (
not self.input_units_allow_dimensionless[input_name]
and input_unit is not dimensionless_unscaled
and input_unit is not None
):
if np.any(inputs[i] != 0):
raise UnitsError(
f"{name}: Units of input '{self.inputs[i]}',"
" (dimensionless), could not be converted to required "
f"input units of {input_unit} "
f"({input_unit.physical_type})"
)
return inputs
| (self, inputs, equivalencies=None, inputs_map=None) | [
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|
44,190 | astropy.modeling.core | coerce_units |
Attach units to this (unitless) model.
Parameters
----------
input_units : dict or tuple, optional
Input units to attach. If dict, each key is the name of a model input,
and the value is the unit to attach. If tuple, the elements are units
to attach in order corresponding to `Model.inputs`.
return_units : dict or tuple, optional
Output units to attach. If dict, each key is the name of a model output,
and the value is the unit to attach. If tuple, the elements are units
to attach in order corresponding to `Model.outputs`.
input_units_equivalencies : dict, optional
Default equivalencies to apply to input values. If set, this should be a
dictionary where each key is a string that corresponds to one of the
model inputs.
input_units_allow_dimensionless : bool or dict, optional
Allow dimensionless input. If this is True, input values to evaluate will
gain the units specified in input_units. If this is a dictionary then it
should map input name to a bool to allow dimensionless numbers for that
input.
Returns
-------
`CompoundModel`
A `CompoundModel` composed of the current model plus
`~astropy.modeling.mappings.UnitsMapping` model(s) that attach the units.
Raises
------
ValueError
If the current model already has units.
Examples
--------
Wrapping a unitless model to require and convert units:
>>> from astropy.modeling.models import Polynomial1D
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = poly.coerce_units((u.m,), (u.s,))
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(1000, u.cm)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(10, u.cm)) # doctest: +FLOAT_CMP
<Quantity 1.2 s>
Wrapping a unitless model but still permitting unitless input:
>>> from astropy.modeling.models import Polynomial1D
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = poly.coerce_units((u.m,), (u.s,), input_units_allow_dimensionless=True)
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(10) # doctest: +FLOAT_CMP
<Quantity 21. s>
| def coerce_units(
self,
input_units=None,
return_units=None,
input_units_equivalencies=None,
input_units_allow_dimensionless=False,
):
"""
Attach units to this (unitless) model.
Parameters
----------
input_units : dict or tuple, optional
Input units to attach. If dict, each key is the name of a model input,
and the value is the unit to attach. If tuple, the elements are units
to attach in order corresponding to `Model.inputs`.
return_units : dict or tuple, optional
Output units to attach. If dict, each key is the name of a model output,
and the value is the unit to attach. If tuple, the elements are units
to attach in order corresponding to `Model.outputs`.
input_units_equivalencies : dict, optional
Default equivalencies to apply to input values. If set, this should be a
dictionary where each key is a string that corresponds to one of the
model inputs.
input_units_allow_dimensionless : bool or dict, optional
Allow dimensionless input. If this is True, input values to evaluate will
gain the units specified in input_units. If this is a dictionary then it
should map input name to a bool to allow dimensionless numbers for that
input.
Returns
-------
`CompoundModel`
A `CompoundModel` composed of the current model plus
`~astropy.modeling.mappings.UnitsMapping` model(s) that attach the units.
Raises
------
ValueError
If the current model already has units.
Examples
--------
Wrapping a unitless model to require and convert units:
>>> from astropy.modeling.models import Polynomial1D
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = poly.coerce_units((u.m,), (u.s,))
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(1000, u.cm)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(u.Quantity(10, u.cm)) # doctest: +FLOAT_CMP
<Quantity 1.2 s>
Wrapping a unitless model but still permitting unitless input:
>>> from astropy.modeling.models import Polynomial1D
>>> from astropy import units as u
>>> poly = Polynomial1D(1, c0=1, c1=2)
>>> model = poly.coerce_units((u.m,), (u.s,), input_units_allow_dimensionless=True)
>>> model(u.Quantity(10, u.m)) # doctest: +FLOAT_CMP
<Quantity 21. s>
>>> model(10) # doctest: +FLOAT_CMP
<Quantity 21. s>
"""
from .mappings import UnitsMapping
result = self
if input_units is not None:
if self.input_units is not None:
model_units = self.input_units
else:
model_units = {}
for unit in [model_units.get(i) for i in self.inputs]:
if unit is not None and unit != dimensionless_unscaled:
raise ValueError(
"Cannot specify input_units for model with existing input units"
)
if isinstance(input_units, dict):
if input_units.keys() != set(self.inputs):
message = (
f"""input_units keys ({", ".join(input_units.keys())}) """
f"""do not match model inputs ({", ".join(self.inputs)})"""
)
raise ValueError(message)
input_units = [input_units[i] for i in self.inputs]
if len(input_units) != self.n_inputs:
message = (
"input_units length does not match n_inputs: "
f"expected {self.n_inputs}, received {len(input_units)}"
)
raise ValueError(message)
mapping = tuple(
(unit, model_units.get(i)) for i, unit in zip(self.inputs, input_units)
)
input_mapping = UnitsMapping(
mapping,
input_units_equivalencies=input_units_equivalencies,
input_units_allow_dimensionless=input_units_allow_dimensionless,
)
input_mapping.inputs = self.inputs
input_mapping.outputs = self.inputs
result = input_mapping | result
if return_units is not None:
if self.return_units is not None:
model_units = self.return_units
else:
model_units = {}
for unit in [model_units.get(i) for i in self.outputs]:
if unit is not None and unit != dimensionless_unscaled:
raise ValueError(
"Cannot specify return_units for model "
"with existing output units"
)
if isinstance(return_units, dict):
if return_units.keys() != set(self.outputs):
message = (
f"""return_units keys ({", ".join(return_units.keys())}) """
f"""do not match model outputs ({", ".join(self.outputs)})"""
)
raise ValueError(message)
return_units = [return_units[i] for i in self.outputs]
if len(return_units) != self.n_outputs:
message = (
"return_units length does not match n_outputs: "
f"expected {self.n_outputs}, received {len(return_units)}"
)
raise ValueError(message)
mapping = tuple(
(model_units.get(i), unit)
for i, unit in zip(self.outputs, return_units)
)
return_mapping = UnitsMapping(mapping)
return_mapping.inputs = self.outputs
return_mapping.outputs = self.outputs
result = result | return_mapping
return result
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|
44,191 | astropy.modeling.core | copy |
Return a copy of this model.
Uses a deep copy so that all model attributes, including parameter
values, are copied as well.
| def copy(self):
"""
Return a copy of this model.
Uses a deep copy so that all model attributes, including parameter
values, are copied as well.
"""
return copy.deepcopy(self)
| (self) | [
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|
44,192 | astropy.modeling.core | deepcopy |
Return a deep copy of this model.
| def deepcopy(self):
"""
Return a deep copy of this model.
"""
return self.copy()
| (self) | [
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|
44,193 | gwcs.selector | evaluate | null | def evaluate(self, *args):
if self.inputs_mapping is not None:
args = self.inputs_mapping(*args)
if self.n_outputs == 1:
args = [args]
res = self.mapper(*args)
if np.isscalar(res):
res = np.array([res])
return np.array(res)
| (self, *args) | [
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|
44,194 | astropy.modeling.core | get_bounding_box |
Return the ``bounding_box`` of a model if it exists or ``None``
otherwise.
Parameters
----------
with_bbox :
The value of the ``with_bounding_box`` keyword argument
when calling the model. Default is `True` for usage when
looking up the model's ``bounding_box`` without risk of error.
| def get_bounding_box(self, with_bbox=True):
"""
Return the ``bounding_box`` of a model if it exists or ``None``
otherwise.
Parameters
----------
with_bbox :
The value of the ``with_bounding_box`` keyword argument
when calling the model. Default is `True` for usage when
looking up the model's ``bounding_box`` without risk of error.
"""
bbox = None
if not isinstance(with_bbox, bool) or with_bbox:
try:
bbox = self.bounding_box
except NotImplementedError:
pass
if isinstance(bbox, CompoundBoundingBox) and not isinstance(
with_bbox, bool
):
bbox = bbox[with_bbox]
return bbox
| (self, with_bbox=True) | [
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|
44,195 | astropy.modeling.core | has_inverse |
Returns True if the model has an analytic or user
inverse defined.
| def has_inverse(self):
"""
Returns True if the model has an analytic or user
inverse defined.
"""
try:
self.inverse # noqa: B018
except NotImplementedError:
return False
return True
| (self) | [
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|
44,196 | astropy.modeling.core | input_shape | Get input shape for bounding_box evaluation. | def input_shape(self, inputs):
"""Get input shape for bounding_box evaluation."""
return self._validate_input_shapes(inputs, self._argnames, self.model_set_axis)
| (self, inputs) | [
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|
44,197 | astropy.modeling.core | output_units |
Return a dictionary of output units for this model given a dictionary
of fitting inputs and outputs.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
This method will force extra model evaluations, which maybe computationally
expensive. To avoid this, one can add a return_units property to the model,
see :ref:`astropy:models_return_units`.
| def output_units(self, **kwargs):
"""
Return a dictionary of output units for this model given a dictionary
of fitting inputs and outputs.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
This method will force extra model evaluations, which maybe computationally
expensive. To avoid this, one can add a return_units property to the model,
see :ref:`astropy:models_return_units`.
"""
units = self.return_units
if units is None or units == {}:
inputs = {inp: kwargs[inp] for inp in self.inputs}
values = self(**inputs)
if self.n_outputs == 1:
values = (values,)
units = {
out: getattr(values[index], "unit", dimensionless_unscaled)
for index, out in enumerate(self.outputs)
}
return units
| (self, **kwargs) | [
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|
44,198 | astropy.modeling.core | prepare_inputs |
This method is used in `~astropy.modeling.Model.__call__` to ensure
that all the inputs to the model can be broadcast into compatible
shapes (if one or both of them are input as arrays), particularly if
there are more than one parameter sets. This also makes sure that (if
applicable) the units of the input will be compatible with the evaluate
method.
| def prepare_inputs(
self, *inputs, model_set_axis=None, equivalencies=None, **kwargs
):
"""
This method is used in `~astropy.modeling.Model.__call__` to ensure
that all the inputs to the model can be broadcast into compatible
shapes (if one or both of them are input as arrays), particularly if
there are more than one parameter sets. This also makes sure that (if
applicable) the units of the input will be compatible with the evaluate
method.
"""
# When we instantiate the model class, we make sure that __call__ can
# take the following two keyword arguments: model_set_axis and
# equivalencies.
if model_set_axis is None:
# By default the model_set_axis for the input is assumed to be the
# same as that for the parameters the model was defined with
# TODO: Ensure that negative model_set_axis arguments are respected
model_set_axis = self.model_set_axis
params = [getattr(self, name) for name in self.param_names]
inputs = [np.asanyarray(_input, dtype=float) for _input in inputs]
self._validate_input_shapes(inputs, self.inputs, model_set_axis)
inputs_map = kwargs.get("inputs_map", None)
inputs = self._validate_input_units(inputs, equivalencies, inputs_map)
# The input formatting required for single models versus a multiple
# model set are different enough that they've been split into separate
# subroutines
if self._n_models == 1:
return self._prepare_inputs_single_model(params, inputs, **kwargs)
else:
return self._prepare_inputs_model_set(
params, inputs, model_set_axis, **kwargs
)
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|
44,199 | astropy.modeling.core | prepare_outputs | null | def prepare_outputs(self, broadcasted_shapes, *outputs, **kwargs):
model_set_axis = kwargs.get("model_set_axis", None)
if len(self) == 1:
return self._prepare_outputs_single_model(outputs, broadcasted_shapes)
else:
return self._prepare_outputs_model_set(
outputs, broadcasted_shapes, model_set_axis
)
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|
44,200 | astropy.modeling.core | render |
Evaluate a model at fixed positions, respecting the ``bounding_box``.
The key difference relative to evaluating the model directly is that
this method is limited to a bounding box if the `Model.bounding_box`
attribute is set.
Parameters
----------
out : `numpy.ndarray`, optional
An array that the evaluated model will be added to. If this is not
given (or given as ``None``), a new array will be created.
coords : array-like, optional
An array to be used to translate from the model's input coordinates
to the ``out`` array. It should have the property that
``self(coords)`` yields the same shape as ``out``. If ``out`` is
not specified, ``coords`` will be used to determine the shape of
the returned array. If this is not provided (or None), the model
will be evaluated on a grid determined by `Model.bounding_box`.
Returns
-------
out : `numpy.ndarray`
The model added to ``out`` if ``out`` is not ``None``, or else a
new array from evaluating the model over ``coords``.
If ``out`` and ``coords`` are both `None`, the returned array is
limited to the `Model.bounding_box` limits. If
`Model.bounding_box` is `None`, ``arr`` or ``coords`` must be
passed.
Raises
------
ValueError
If ``coords`` are not given and the `Model.bounding_box` of
this model is not set.
Examples
--------
:ref:`astropy:bounding-boxes`
| def render(self, out=None, coords=None):
"""
Evaluate a model at fixed positions, respecting the ``bounding_box``.
The key difference relative to evaluating the model directly is that
this method is limited to a bounding box if the `Model.bounding_box`
attribute is set.
Parameters
----------
out : `numpy.ndarray`, optional
An array that the evaluated model will be added to. If this is not
given (or given as ``None``), a new array will be created.
coords : array-like, optional
An array to be used to translate from the model's input coordinates
to the ``out`` array. It should have the property that
``self(coords)`` yields the same shape as ``out``. If ``out`` is
not specified, ``coords`` will be used to determine the shape of
the returned array. If this is not provided (or None), the model
will be evaluated on a grid determined by `Model.bounding_box`.
Returns
-------
out : `numpy.ndarray`
The model added to ``out`` if ``out`` is not ``None``, or else a
new array from evaluating the model over ``coords``.
If ``out`` and ``coords`` are both `None`, the returned array is
limited to the `Model.bounding_box` limits. If
`Model.bounding_box` is `None`, ``arr`` or ``coords`` must be
passed.
Raises
------
ValueError
If ``coords`` are not given and the `Model.bounding_box` of
this model is not set.
Examples
--------
:ref:`astropy:bounding-boxes`
"""
try:
bbox = self.bounding_box
except NotImplementedError:
bbox = None
if isinstance(bbox, ModelBoundingBox):
bbox = bbox.bounding_box()
ndim = self.n_inputs
if (coords is None) and (out is None) and (bbox is None):
raise ValueError("If no bounding_box is set, coords or out must be input.")
# for consistent indexing
if ndim == 1:
if coords is not None:
coords = [coords]
if bbox is not None:
bbox = [bbox]
if coords is not None:
coords = np.asanyarray(coords, dtype=float)
# Check dimensions match out and model
assert len(coords) == ndim
if out is not None:
if coords[0].shape != out.shape:
raise ValueError("inconsistent shape of the output.")
else:
out = np.zeros(coords[0].shape)
if out is not None:
out = np.asanyarray(out)
if out.ndim != ndim:
raise ValueError(
"the array and model must have the same number of dimensions."
)
if bbox is not None:
# Assures position is at center pixel,
# important when using add_array.
pd = (
np.array([(np.mean(bb), np.ceil((bb[1] - bb[0]) / 2)) for bb in bbox])
.astype(int)
.T
)
pos, delta = pd
if coords is not None:
sub_shape = tuple(delta * 2 + 1)
sub_coords = np.array(
[extract_array(c, sub_shape, pos) for c in coords]
)
else:
limits = [slice(p - d, p + d + 1, 1) for p, d in pd.T]
sub_coords = np.mgrid[limits]
sub_coords = sub_coords[::-1]
if out is None:
out = self(*sub_coords)
else:
try:
out = add_array(out, self(*sub_coords), pos)
except ValueError:
raise ValueError(
"The `bounding_box` is larger than the input out in "
"one or more dimensions. Set "
"`model.bounding_box = None`."
)
else:
if coords is None:
im_shape = out.shape
limits = [slice(i) for i in im_shape]
coords = np.mgrid[limits]
coords = coords[::-1]
out += self(*coords)
return out
| (self, out=None, coords=None) | [
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|
44,201 | astropy.modeling.core | set_slice_args | null | def set_slice_args(self, *args):
if isinstance(self._user_bounding_box, CompoundBoundingBox):
self._user_bounding_box.slice_args = args
else:
raise RuntimeError("The bounding_box for this model is not compound")
| (self, *args) | [
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|
44,202 | astropy.modeling.core | strip_units_from_tree | null | def strip_units_from_tree(self):
for item in self._leaflist:
for parname in item.param_names:
par = getattr(item, parname)
par._set_unit(None, force=True)
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|
44,203 | astropy.modeling.core | sum_of_implicit_terms |
Evaluate the sum of any implicit model terms on some input variables.
This includes any fixed terms used in evaluating a linear model that
do not have corresponding parameters exposed to the user. The
prototypical case is `astropy.modeling.functional_models.Shift`, which
corresponds to a function y = a + bx, where b=1 is intrinsically fixed
by the type of model, such that sum_of_implicit_terms(x) == x. This
method is needed by linear fitters to correct the dependent variable
for the implicit term(s) when solving for the remaining terms
(ie. a = y - bx).
| def sum_of_implicit_terms(self, *args, **kwargs):
"""
Evaluate the sum of any implicit model terms on some input variables.
This includes any fixed terms used in evaluating a linear model that
do not have corresponding parameters exposed to the user. The
prototypical case is `astropy.modeling.functional_models.Shift`, which
corresponds to a function y = a + bx, where b=1 is intrinsically fixed
by the type of model, such that sum_of_implicit_terms(x) == x. This
method is needed by linear fitters to correct the dependent variable
for the implicit term(s) when solving for the remaining terms
(ie. a = y - bx).
"""
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|
44,204 | astropy.modeling.core | with_units_from_data |
Return an instance of the model which has units for which the parameter
values are compatible with the data units specified.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
The units that the parameters will gain are not necessarily the units
of the input data, but are derived from them. Model subclasses that
want fitting to work in the presence of quantities need to define a
``_parameter_units_for_data_units`` method that takes the input and output
units (as two dictionaries) and returns a dictionary giving the target
units for each parameter.
| def with_units_from_data(self, **kwargs):
"""
Return an instance of the model which has units for which the parameter
values are compatible with the data units specified.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
The units that the parameters will gain are not necessarily the units
of the input data, but are derived from them. Model subclasses that
want fitting to work in the presence of quantities need to define a
``_parameter_units_for_data_units`` method that takes the input and output
units (as two dictionaries) and returns a dictionary giving the target
units for each parameter.
"""
model = self.copy()
inputs_unit = {
inp: getattr(kwargs[inp], "unit", dimensionless_unscaled)
for inp in self.inputs
if kwargs[inp] is not None
}
outputs_unit = {
out: getattr(kwargs[out], "unit", dimensionless_unscaled)
for out in self.outputs
if kwargs[out] is not None
}
parameter_units = self._parameter_units_for_data_units(
inputs_unit, outputs_unit
)
# We are adding units to parameters that already have a value, but we
# don't want to convert the parameter, just add the unit directly,
# hence the call to ``_set_unit``.
for name, unit in parameter_units.items():
parameter = getattr(model, name)
parameter._set_unit(unit, force=True)
return model
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|
44,205 | astropy.modeling.core | without_units_for_data |
Return an instance of the model for which the parameter values have
been converted to the right units for the data, then the units have
been stripped away.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
The units that the parameters should be converted to are not
necessarily the units of the input data, but are derived from them.
Model subclasses that want fitting to work in the presence of
quantities need to define a ``_parameter_units_for_data_units`` method
that takes the input and output units (as two dictionaries) and
returns a dictionary giving the target units for each parameter.
| def without_units_for_data(self, **kwargs):
"""
Return an instance of the model for which the parameter values have
been converted to the right units for the data, then the units have
been stripped away.
The input and output Quantity objects should be given as keyword
arguments.
Notes
-----
This method is needed in order to be able to fit models with units in
the parameters, since we need to temporarily strip away the units from
the model during the fitting (which might be done by e.g. scipy
functions).
The units that the parameters should be converted to are not
necessarily the units of the input data, but are derived from them.
Model subclasses that want fitting to work in the presence of
quantities need to define a ``_parameter_units_for_data_units`` method
that takes the input and output units (as two dictionaries) and
returns a dictionary giving the target units for each parameter.
"""
model = self.copy()
inputs_unit = {
inp: getattr(kwargs[inp], "unit", dimensionless_unscaled)
for inp in self.inputs
if kwargs[inp] is not None
}
outputs_unit = {
out: getattr(kwargs[out], "unit", dimensionless_unscaled)
for out in self.outputs
if kwargs[out] is not None
}
parameter_units = self._parameter_units_for_data_units(
inputs_unit, outputs_unit
)
for name, unit in parameter_units.items():
parameter = getattr(model, name)
if parameter.unit is not None:
parameter.value = parameter.quantity.to(unit).value
parameter._set_unit(None, force=True)
if isinstance(model, CompoundModel):
model.strip_units_from_tree()
return model
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|
44,206 | gwcs.selector | LabelMapperArray |
Maps array locations to labels.
Parameters
----------
mapper : ndarray
An array of integers or strings where the values
correspond to a label in `~gwcs.selector.RegionsSelector` model.
For pixels for which the transform is not defined the value should
be set to 0 or " ".
inputs_mapping : `~astropy.modeling.mappings.Mapping`
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order
so that the output of it is (x, y) values to index the array.
name : str
The name of this transform.
Use case:
For an IFU observation, the array represents the detector and its
values correspond to the IFU slice label.
| class LabelMapperArray(_LabelMapper):
"""
Maps array locations to labels.
Parameters
----------
mapper : ndarray
An array of integers or strings where the values
correspond to a label in `~gwcs.selector.RegionsSelector` model.
For pixels for which the transform is not defined the value should
be set to 0 or " ".
inputs_mapping : `~astropy.modeling.mappings.Mapping`
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order
so that the output of it is (x, y) values to index the array.
name : str
The name of this transform.
Use case:
For an IFU observation, the array represents the detector and its
values correspond to the IFU slice label.
"""
n_inputs = 2
n_outputs = 1
linear = False
fittable = False
def __init__(self, mapper, inputs_mapping=None, name=None, **kwargs):
if mapper.dtype.type is not np.str_:
mapper = np.asanyarray(mapper, dtype=int)
_no_label = 0
else:
_no_label = ""
super(LabelMapperArray, self).__init__(mapper, _no_label, name=name, **kwargs)
self.inputs = ('x', 'y')
self.outputs = ('label',)
def evaluate(self, *args):
args = tuple([_toindex(a) for a in args])
try:
result = self._mapper[args[::-1]]
except IndexError as e:
raise LabelMapperArrayIndexingError(e)
return result
@classmethod
def from_vertices(cls, shape, regions):
"""
Create a `~gwcs.selector.LabelMapperArray` from
polygon vertices stores in a dict.
Parameters
----------
shape : tuple
shape of mapper array
regions: dict
{region_label : list_of_polygon_vertices}
The keys in this dictionary should match the region labels
in `~gwcs.selector.RegionsSelector`.
The list of vertices is ordered in such a way that when traversed in a
counterclockwise direction, the enclosed area is the polygon.
The last vertex must coincide with the first vertex, minimum
4 vertices are needed to define a triangle.
Returns
-------
mapper : `~gwcs.selector.LabelMapperArray`
This models is used with `~gwcs.selector.RegionsSelector`.
A model which takes the same inputs as `~gwcs.selector.RegionsSelector`
and returns a label.
Examples
--------
>>> regions = {1: [[795, 970], [2047, 970], [2047, 999], [795, 999], [795, 970]],
... 2: [[844, 1067], [2047, 1067], [2047, 1113], [844, 1113], [844, 1067]],
... 3: [[654, 1029], [2047, 1029], [2047, 1078], [654, 1078], [654, 1029]],
... 4: [[772, 990], [2047, 990], [2047, 1042], [772, 1042], [772, 990]]
... }
>>> mapper = LabelMapperArray.from_vertices((2400, 2400), regions)
"""
labels = np.array(list(regions.keys()))
mask = np.zeros(shape, dtype=labels.dtype)
for rid, vert in regions.items():
pol = region.Polygon(rid, vert)
mask = pol.scan(mask)
return cls(mask)
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|
44,209 | gwcs.selector | __call__ |
Evaluate this model using the given input(s) and the parameter values
that were specified when the model was instantiated.
| @staticmethod
def _has_overlapping(ranges):
"""
Test a list of tuple representing ranges of values has no overlapping ranges.
"""
d = dict(ranges)
start = ranges[:, 0]
end = ranges[:, 1]
start.sort()
l = []
for v in start:
l.append([v, d[v]])
l = np.array(l)
start = np.roll(l[:, 0], -1)
end = l[:, 1]
if any((end - start)[:-1] > 0) or any(start[-1] > end):
return True
else:
return False
| (self, *inputs, model_set_axis=None, with_bounding_box=False, fill_value=nan, equivalencies=None, inputs_map=None, **new_inputs) | [
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|
44,210 | gwcs.selector | __init__ | null | def __init__(self, mapper, inputs_mapping=None, name=None, **kwargs):
if mapper.dtype.type is not np.str_:
mapper = np.asanyarray(mapper, dtype=int)
_no_label = 0
else:
_no_label = ""
super(LabelMapperArray, self).__init__(mapper, _no_label, name=name, **kwargs)
self.inputs = ('x', 'y')
self.outputs = ('label',)
| (self, mapper, inputs_mapping=None, name=None, **kwargs) | [
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|
44,253 | gwcs.selector | evaluate | null | def evaluate(self, *args):
args = tuple([_toindex(a) for a in args])
try:
result = self._mapper[args[::-1]]
except IndexError as e:
raise LabelMapperArrayIndexingError(e)
return result
| (self, *args) | [
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|
44,266 | gwcs.selector | LabelMapperDict |
Maps a number to a transform, which when evaluated returns a label.
Use case: inverse transforms of an IFU.
For an IFU observation, the keys are constant angles (corresponding to a slice)
and values are transforms which return a slice number.
Parameters
----------
inputs : tuple of str
Names for the inputs, e.g. ('alpha', 'beta', lam')
mapper : dict
Maps key values to transforms.
inputs_mapping : `~astropy.modeling.mappings.Mapping`
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order.
It returns a number which is one of the keys of ``mapper``.
atol : float
Absolute tolerance when comparing inputs to ``mapper.keys``.
It is passed to np.isclose.
name : str
The name of this transform.
| class LabelMapperDict(_LabelMapper):
"""
Maps a number to a transform, which when evaluated returns a label.
Use case: inverse transforms of an IFU.
For an IFU observation, the keys are constant angles (corresponding to a slice)
and values are transforms which return a slice number.
Parameters
----------
inputs : tuple of str
Names for the inputs, e.g. ('alpha', 'beta', lam')
mapper : dict
Maps key values to transforms.
inputs_mapping : `~astropy.modeling.mappings.Mapping`
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order.
It returns a number which is one of the keys of ``mapper``.
atol : float
Absolute tolerance when comparing inputs to ``mapper.keys``.
It is passed to np.isclose.
name : str
The name of this transform.
"""
standard_broadcasting = False
linear = False
fittable = False
n_outputs = 1
def __init__(self, inputs, mapper, inputs_mapping=None, atol=10**-8, name=None, **kwargs):
self._atol = atol
_no_label = 0
self._inputs = inputs
self._n_inputs = len(inputs)
if not all([m.n_outputs == 1 for m in mapper.values()]):
raise TypeError("All transforms in mapper must have one output.")
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(LabelMapperDict, self).__init__(mapper, _no_label, inputs_mapping,
name=name, **kwargs)
self.outputs = ('labels',)
@property
def n_inputs(self):
return self._n_inputs
@property
def inputs(self):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
return self._inputs
@inputs.setter
def inputs(self, val):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
self._inputs = val
@property
def atol(self):
return self._atol
@atol.setter
def atol(self, val):
self._atol = val
def evaluate(self, *args):
shape = args[0].shape
args = [a.flatten() for a in args]
# if n_inputs > 1, determine which one is to be used as keys
if self.inputs_mapping is not None:
keys = self._inputs_mapping.evaluate(*args)
else:
keys = args
keys = keys.flatten()
# create an empty array for the results
res = np.zeros(keys.shape) + self._no_label
# If this is part of a combined transform, some of the inputs
# may be NaNs.
# Set NaNs to the ``_no_label`` value
mapper_keys = list(self.mapper.keys())
# Loop over the keys in mapper and compare to inputs.
# Find the indices where they are within ``atol``
# and evaluate the transform to get the corresponding label.
for key in mapper_keys:
ind = np.isclose(key, keys, atol=self._atol)
inputs = [a[ind] for a in args]
res[ind] = self.mapper[key](*inputs)
res.shape = shape
return res
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|
44,270 | gwcs.selector | __init__ | null | def __init__(self, inputs, mapper, inputs_mapping=None, atol=10**-8, name=None, **kwargs):
self._atol = atol
_no_label = 0
self._inputs = inputs
self._n_inputs = len(inputs)
if not all([m.n_outputs == 1 for m in mapper.values()]):
raise TypeError("All transforms in mapper must have one output.")
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(LabelMapperDict, self).__init__(mapper, _no_label, inputs_mapping,
name=name, **kwargs)
self.outputs = ('labels',)
| (self, inputs, mapper, inputs_mapping=None, atol=1e-08, name=None, **kwargs) | [
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|
44,313 | gwcs.selector | evaluate | null | def evaluate(self, *args):
shape = args[0].shape
args = [a.flatten() for a in args]
# if n_inputs > 1, determine which one is to be used as keys
if self.inputs_mapping is not None:
keys = self._inputs_mapping.evaluate(*args)
else:
keys = args
keys = keys.flatten()
# create an empty array for the results
res = np.zeros(keys.shape) + self._no_label
# If this is part of a combined transform, some of the inputs
# may be NaNs.
# Set NaNs to the ``_no_label`` value
mapper_keys = list(self.mapper.keys())
# Loop over the keys in mapper and compare to inputs.
# Find the indices where they are within ``atol``
# and evaluate the transform to get the corresponding label.
for key in mapper_keys:
ind = np.isclose(key, keys, atol=self._atol)
inputs = [a[ind] for a in args]
res[ind] = self.mapper[key](*inputs)
res.shape = shape
return res
| (self, *args) | [
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|
44,326 | gwcs.selector | LabelMapperRange |
The structure this class uses maps a range of values to a transform.
Given an input value it finds the range the value falls in and returns
the corresponding transform. When evaluated the transform returns a label.
Example: Pick a transform based on wavelength range.
For an IFU observation, the keys are (lambda_min, lambda_max) tuples
and values are transforms which return a label corresponding to a slice.
Parameters
----------
inputs : tuple of str
Names for the inputs, e.g. ('alpha', 'beta', 'lambda')
mapper : dict
Maps tuples of length 2 to transforms.
inputs_mapping : `~astropy.modeling.mappings.Mapping`
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order.
atol : float
Absolute tolerance when comparing inputs to ``mapper.keys``.
It is passed to np.isclose.
name : str
The name of this transform.
| class LabelMapperRange(_LabelMapper):
"""
The structure this class uses maps a range of values to a transform.
Given an input value it finds the range the value falls in and returns
the corresponding transform. When evaluated the transform returns a label.
Example: Pick a transform based on wavelength range.
For an IFU observation, the keys are (lambda_min, lambda_max) tuples
and values are transforms which return a label corresponding to a slice.
Parameters
----------
inputs : tuple of str
Names for the inputs, e.g. ('alpha', 'beta', 'lambda')
mapper : dict
Maps tuples of length 2 to transforms.
inputs_mapping : `~astropy.modeling.mappings.Mapping`
An optional Mapping model to be prepended to the LabelMapper
with the purpose to filter the inputs or change their order.
atol : float
Absolute tolerance when comparing inputs to ``mapper.keys``.
It is passed to np.isclose.
name : str
The name of this transform.
"""
standard_broadcasting = False
n_outputs = 1
linear = False
fittable = False
def __init__(self, inputs, mapper, inputs_mapping=None, name=None, **kwargs):
if self._has_overlapping(np.array(list(mapper.keys()))):
raise ValueError("Overlapping ranges of values are not supported.")
self._inputs = inputs
self._n_inputs = len(inputs)
_no_label = 0
if not all([m.n_outputs == 1 for m in mapper.values()]):
raise TypeError("All transforms in mapper must have one output.")
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(LabelMapperRange, self).__init__(mapper, _no_label, inputs_mapping,
name=name, **kwargs)
self.outputs = ('labels',)
@property
def n_inputs(self):
return self._n_inputs
@property
def inputs(self):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
return self._inputs
@inputs.setter
def inputs(self, val):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
self._inputs = val
@staticmethod
def _has_overlapping(ranges):
"""
Test a list of tuple representing ranges of values has no overlapping ranges.
"""
d = dict(ranges)
start = ranges[:, 0]
end = ranges[:, 1]
start.sort()
l = []
for v in start:
l.append([v, d[v]])
l = np.array(l)
start = np.roll(l[:, 0], -1)
end = l[:, 1]
if any((end - start)[:-1] > 0) or any(start[-1] > end):
return True
else:
return False
# move this to utils?
def _find_range(self, value_range, value):
"""
Returns the index of the tuple which holds value.
Parameters
----------
value_range : np.ndarray
an (2, 2) array of non-overlapping (min, max) values
value : float
The value
Returns
-------
ind : int
Index of the tuple which defines a range holding the input value.
None, if the input value is not within any available range.
"""
a, b = value_range[:, 0], value_range[:, 1]
ind = np.logical_and(value >= a, value <= b).nonzero()[0]
if ind.size > 1:
raise ValueError("There are overlapping ranges.")
elif ind.size == 0:
return None
else:
return ind.item()
def evaluate(self, *args):
shape = args[0].shape
args = [a.flatten() for a in args]
if self.inputs_mapping is not None:
keys = self._inputs_mapping.evaluate(*args)
else:
keys = args
keys = keys.flatten()
# Define an array for the results.
res = np.zeros(keys.shape) + self._no_label
nan_ind = np.isnan(keys)
res[nan_ind] = self._no_label
value_ranges = list(self.mapper.keys())
# For each tuple in mapper, find the indices of the inputs
# which fall within the range it defines.
for val_range in value_ranges:
temp = keys.copy()
temp[nan_ind] = np.nan
temp = np.where(np.logical_or(temp <= val_range[0],
temp >= val_range[1]),
np.nan, temp)
ind = ~np.isnan(temp)
if ind.any():
inputs = [a[ind] for a in args]
res[ind] = self.mapper[tuple(val_range)](*inputs)
else:
continue
res.shape = shape
if len(np.nonzero(res)[0]) == 0:
warnings.warn("All data is outside the valid range - {0}.".format(self.name))
return res
| (inputs, mapper, inputs_mapping=None, name=None, **kwargs) | [
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|
44,330 | gwcs.selector | __init__ | null | def __init__(self, inputs, mapper, inputs_mapping=None, name=None, **kwargs):
if self._has_overlapping(np.array(list(mapper.keys()))):
raise ValueError("Overlapping ranges of values are not supported.")
self._inputs = inputs
self._n_inputs = len(inputs)
_no_label = 0
if not all([m.n_outputs == 1 for m in mapper.values()]):
raise TypeError("All transforms in mapper must have one output.")
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(LabelMapperRange, self).__init__(mapper, _no_label, inputs_mapping,
name=name, **kwargs)
self.outputs = ('labels',)
| (self, inputs, mapper, inputs_mapping=None, name=None, **kwargs) | [
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|
44,344 | gwcs.selector | _find_range |
Returns the index of the tuple which holds value.
Parameters
----------
value_range : np.ndarray
an (2, 2) array of non-overlapping (min, max) values
value : float
The value
Returns
-------
ind : int
Index of the tuple which defines a range holding the input value.
None, if the input value is not within any available range.
| def _find_range(self, value_range, value):
"""
Returns the index of the tuple which holds value.
Parameters
----------
value_range : np.ndarray
an (2, 2) array of non-overlapping (min, max) values
value : float
The value
Returns
-------
ind : int
Index of the tuple which defines a range holding the input value.
None, if the input value is not within any available range.
"""
a, b = value_range[:, 0], value_range[:, 1]
ind = np.logical_and(value >= a, value <= b).nonzero()[0]
if ind.size > 1:
raise ValueError("There are overlapping ranges.")
elif ind.size == 0:
return None
else:
return ind.item()
| (self, value_range, value) | [
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]
|
44,350 | gwcs.selector | _has_overlapping |
Test a list of tuple representing ranges of values has no overlapping ranges.
| @staticmethod
def _has_overlapping(ranges):
"""
Test a list of tuple representing ranges of values has no overlapping ranges.
"""
d = dict(ranges)
start = ranges[:, 0]
end = ranges[:, 1]
start.sort()
l = []
for v in start:
l.append([v, d[v]])
l = np.array(l)
start = np.roll(l[:, 0], -1)
end = l[:, 1]
if any((end - start)[:-1] > 0) or any(start[-1] > end):
return True
else:
return False
| (ranges) | [
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|
44,375 | gwcs.selector | evaluate | null | def evaluate(self, *args):
shape = args[0].shape
args = [a.flatten() for a in args]
if self.inputs_mapping is not None:
keys = self._inputs_mapping.evaluate(*args)
else:
keys = args
keys = keys.flatten()
# Define an array for the results.
res = np.zeros(keys.shape) + self._no_label
nan_ind = np.isnan(keys)
res[nan_ind] = self._no_label
value_ranges = list(self.mapper.keys())
# For each tuple in mapper, find the indices of the inputs
# which fall within the range it defines.
for val_range in value_ranges:
temp = keys.copy()
temp[nan_ind] = np.nan
temp = np.where(np.logical_or(temp <= val_range[0],
temp >= val_range[1]),
np.nan, temp)
ind = ~np.isnan(temp)
if ind.any():
inputs = [a[ind] for a in args]
res[ind] = self.mapper[tuple(val_range)](*inputs)
else:
continue
res.shape = shape
if len(np.nonzero(res)[0]) == 0:
warnings.warn("All data is outside the valid range - {0}.".format(self.name))
return res
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|
44,388 | gwcs.wcs | NoConvergence |
An error class used to report non-convergence and/or divergence
of numerical methods. It is used to report errors in the
iterative solution used by
the :py:meth:`~astropy.wcs.WCS.all_world2pix`.
Attributes
----------
best_solution : `numpy.ndarray`
Best solution achieved by the numerical method.
accuracy : `numpy.ndarray`
Estimate of the accuracy of the ``best_solution``.
niter : `int`
Number of iterations performed by the numerical method
to compute ``best_solution``.
divergent : None, `numpy.ndarray`
Indices of the points in ``best_solution`` array
for which the solution appears to be divergent. If the
solution does not diverge, ``divergent`` will be set to `None`.
slow_conv : None, `numpy.ndarray`
Indices of the solutions in ``best_solution`` array
for which the solution failed to converge within the
specified maximum number of iterations. If there are no
non-converging solutions (i.e., if the required accuracy
has been achieved for all input data points)
then ``slow_conv`` will be set to `None`.
| class NoConvergence(Exception):
"""
An error class used to report non-convergence and/or divergence
of numerical methods. It is used to report errors in the
iterative solution used by
the :py:meth:`~astropy.wcs.WCS.all_world2pix`.
Attributes
----------
best_solution : `numpy.ndarray`
Best solution achieved by the numerical method.
accuracy : `numpy.ndarray`
Estimate of the accuracy of the ``best_solution``.
niter : `int`
Number of iterations performed by the numerical method
to compute ``best_solution``.
divergent : None, `numpy.ndarray`
Indices of the points in ``best_solution`` array
for which the solution appears to be divergent. If the
solution does not diverge, ``divergent`` will be set to `None`.
slow_conv : None, `numpy.ndarray`
Indices of the solutions in ``best_solution`` array
for which the solution failed to converge within the
specified maximum number of iterations. If there are no
non-converging solutions (i.e., if the required accuracy
has been achieved for all input data points)
then ``slow_conv`` will be set to `None`.
"""
def __init__(self, *args, best_solution=None, accuracy=None, niter=None,
divergent=None, slow_conv=None):
super().__init__(*args)
self.best_solution = best_solution
self.accuracy = accuracy
self.niter = niter
self.divergent = divergent
self.slow_conv = slow_conv
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|
44,389 | gwcs.wcs | __init__ | null | def __init__(self, *args, best_solution=None, accuracy=None, niter=None,
divergent=None, slow_conv=None):
super().__init__(*args)
self.best_solution = best_solution
self.accuracy = accuracy
self.niter = niter
self.divergent = divergent
self.slow_conv = slow_conv
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|
44,390 | gwcs.selector | RegionsSelector |
This model defines discontinuous transforms.
It maps inputs to their corresponding transforms.
It uses an instance of `_LabelMapper` as a proxy to map inputs to
the correct region.
Parameters
----------
inputs : list of str
Names of the inputs.
outputs : list of str
Names of the outputs.
selector : dict
Mapping of region labels to transforms.
Labels can be of type int or str, transforms are of type
`~astropy.modeling.Model`.
label_mapper : a subclass of `~gwcs.selector._LabelMapper`
A model which maps locations to region labels.
undefined_transform_value : float, np.nan (default)
Value to be returned if there's no transform defined for the inputs.
name : str
The name of this transform.
| class RegionsSelector(Model):
"""
This model defines discontinuous transforms.
It maps inputs to their corresponding transforms.
It uses an instance of `_LabelMapper` as a proxy to map inputs to
the correct region.
Parameters
----------
inputs : list of str
Names of the inputs.
outputs : list of str
Names of the outputs.
selector : dict
Mapping of region labels to transforms.
Labels can be of type int or str, transforms are of type
`~astropy.modeling.Model`.
label_mapper : a subclass of `~gwcs.selector._LabelMapper`
A model which maps locations to region labels.
undefined_transform_value : float, np.nan (default)
Value to be returned if there's no transform defined for the inputs.
name : str
The name of this transform.
"""
standard_broadcasting = False
linear = False
fittable = False
def __init__(self, inputs, outputs, selector, label_mapper, undefined_transform_value=np.nan,
name=None, **kwargs):
self._inputs = inputs
self._outputs = outputs
self._n_inputs = len(inputs)
self._n_outputs = len(outputs)
self.label_mapper = label_mapper
self._undefined_transform_value = undefined_transform_value
self._selector = selector # copy.deepcopy(selector)
if " " in selector.keys() or 0 in selector.keys():
raise ValueError('"0" and " " are not allowed as keys.')
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(RegionsSelector, self).__init__(n_models=1, name=name, **kwargs)
def set_input(self, rid):
"""
Sets one of the inputs and returns a transform associated with it.
"""
if rid in self._selector:
return self._selector[rid]
else:
raise RegionError("Region {0} not found".format(rid))
def inverse(self):
if self.label_mapper.inverse is not None:
try:
transforms_inv = {}
for rid in self._selector:
transforms_inv[rid] = self._selector[rid].inverse
except AttributeError:
raise NotImplementedError("The inverse of all regions must be defined"
"for RegionsSelector to have an inverse.")
return self.__class__(self.outputs, self.inputs, transforms_inv,
self.label_mapper.inverse)
else:
raise NotImplementedError("The label mapper must have an inverse "
"for RegionsSelector to have an inverse.")
def evaluate(self, *args):
"""
Parameters
----------
args : float or ndarray
Input pixel coordinate, one input for each dimension.
"""
# Get the region labels corresponding to these inputs
rids = self.label_mapper(*args).flatten()
# Raise an error if all pixels are outside regions
if (rids == self.label_mapper.no_label).all():
warnings.warn("The input positions are not inside any region.")
# Create output arrays and set any pixels not within regions to
# "undefined_transform_value"
no_trans_ind = (rids == self.label_mapper.no_label).nonzero()
outputs = [np.empty(rids.shape) for n in range(self.n_outputs)]
for out in outputs:
out[no_trans_ind] = self.undefined_transform_value
# Compute the transformations
args = [a.flatten() for a in args]
uniq = get_unique_regions(rids)
for rid in uniq:
ind = (rids == rid)
inputs = [a[ind] for a in args]
if rid in self._selector:
result = self._selector[rid](*inputs)
else:
# If there's no transform for a label, return np.nan
result = [np.empty(inputs[0].shape) +
self._undefined_transform_value for i in range(self.n_outputs)]
for j in range(self.n_outputs):
outputs[j][ind] = result[j]
return outputs
@property
def undefined_transform_value(self):
return self._undefined_transform_value
@undefined_transform_value.setter
def undefined_transform_value(self, value):
self._undefined_transform_value = value
@property
def outputs(self):
"""The name(s) of the output(s) of the model."""
return self._outputs
@property
def selector(self):
return self._selector
@property
def inputs(self):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
return self._inputs
@inputs.setter
def inputs(self, val):
"""
The name(s) of the input variable(s) on which a model is evaluated.
"""
self._inputs = val
@outputs.setter
def outputs(self, val):
"""
The name(s) of the output variable(s).
"""
self._outputs = val
@property
def n_inputs(self):
return self._n_inputs
@property
def n_outputs(self):
return self._n_outputs
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|
44,394 | gwcs.selector | __init__ | null | def __init__(self, inputs, outputs, selector, label_mapper, undefined_transform_value=np.nan,
name=None, **kwargs):
self._inputs = inputs
self._outputs = outputs
self._n_inputs = len(inputs)
self._n_outputs = len(outputs)
self.label_mapper = label_mapper
self._undefined_transform_value = undefined_transform_value
self._selector = selector # copy.deepcopy(selector)
if " " in selector.keys() or 0 in selector.keys():
raise ValueError('"0" and " " are not allowed as keys.')
self._input_units_strict = {key: False for key in self._inputs}
self._input_units_allow_dimensionless = {key: False for key in self._inputs}
super(RegionsSelector, self).__init__(n_models=1, name=name, **kwargs)
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|
44,419 | gwcs.selector | inverse | null | def inverse(self):
if self.label_mapper.inverse is not None:
try:
transforms_inv = {}
for rid in self._selector:
transforms_inv[rid] = self._selector[rid].inverse
except AttributeError:
raise NotImplementedError("The inverse of all regions must be defined"
"for RegionsSelector to have an inverse.")
return self.__class__(self.outputs, self.inputs, transforms_inv,
self.label_mapper.inverse)
else:
raise NotImplementedError("The label mapper must have an inverse "
"for RegionsSelector to have an inverse.")
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]
|
44,438 | gwcs.selector | evaluate |
Parameters
----------
args : float or ndarray
Input pixel coordinate, one input for each dimension.
| def evaluate(self, *args):
"""
Parameters
----------
args : float or ndarray
Input pixel coordinate, one input for each dimension.
"""
# Get the region labels corresponding to these inputs
rids = self.label_mapper(*args).flatten()
# Raise an error if all pixels are outside regions
if (rids == self.label_mapper.no_label).all():
warnings.warn("The input positions are not inside any region.")
# Create output arrays and set any pixels not within regions to
# "undefined_transform_value"
no_trans_ind = (rids == self.label_mapper.no_label).nonzero()
outputs = [np.empty(rids.shape) for n in range(self.n_outputs)]
for out in outputs:
out[no_trans_ind] = self.undefined_transform_value
# Compute the transformations
args = [a.flatten() for a in args]
uniq = get_unique_regions(rids)
for rid in uniq:
ind = (rids == rid)
inputs = [a[ind] for a in args]
if rid in self._selector:
result = self._selector[rid](*inputs)
else:
# If there's no transform for a label, return np.nan
result = [np.empty(inputs[0].shape) +
self._undefined_transform_value for i in range(self.n_outputs)]
for j in range(self.n_outputs):
outputs[j][ind] = result[j]
return outputs
| (self, *args) | [
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|
44,446 | gwcs.selector | set_input |
Sets one of the inputs and returns a transform associated with it.
| def set_input(self, rid):
"""
Sets one of the inputs and returns a transform associated with it.
"""
if rid in self._selector:
return self._selector[rid]
else:
raise RegionError("Region {0} not found".format(rid))
| (self, rid) | [
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|
44,452 | gwcs.coordinate_frames | SpectralFrame |
Represents Spectral Frame
Parameters
----------
axes_order : tuple or int
A dimension in the input data that corresponds to this axis.
reference_frame : astropy.coordinates.builtin_frames
Reference frame (usually used with output_frame to convert to world coordinate objects).
unit : str or units.Unit instance
Spectral unit.
axes_names : str
Spectral axis name.
name : str
Name for this frame.
reference_position : str
Reference position - one of ``STANDARD_REFERENCE_POSITION``
| class SpectralFrame(CoordinateFrame):
"""
Represents Spectral Frame
Parameters
----------
axes_order : tuple or int
A dimension in the input data that corresponds to this axis.
reference_frame : astropy.coordinates.builtin_frames
Reference frame (usually used with output_frame to convert to world coordinate objects).
unit : str or units.Unit instance
Spectral unit.
axes_names : str
Spectral axis name.
name : str
Name for this frame.
reference_position : str
Reference position - one of ``STANDARD_REFERENCE_POSITION``
"""
def __init__(self, axes_order=(0,), reference_frame=None, unit=None,
axes_names=None, name=None, axis_physical_types=None,
reference_position=None):
super(SpectralFrame, self).__init__(naxes=1, axes_type="SPECTRAL", axes_order=axes_order,
axes_names=axes_names, reference_frame=reference_frame,
unit=unit, name=name,
reference_position=reference_position,
axis_physical_types=axis_physical_types)
@property
def _default_axis_physical_types(self):
if self.unit[0].physical_type == "frequency":
return ("em.freq",)
elif self.unit[0].physical_type == "length":
return ("em.wl",)
elif self.unit[0].physical_type == "energy":
return ("em.energy",)
elif self.unit[0].physical_type == "speed":
return ("spect.dopplerVeloc",)
logging.warning("Physical type may be ambiguous. Consider "
"setting the physical type explicitly as "
"either 'spect.dopplerVeloc.optical' or "
"'spect.dopplerVeloc.radio'.")
else:
return ("custom:{}".format(self.unit[0].physical_type),)
@property
def _world_axis_object_classes(self):
return {'spectral': (
coord.SpectralCoord,
(),
{'unit': self.unit[0]})}
@property
def _world_axis_object_components(self):
return [('spectral', 0, 'value')]
def coordinates(self, *args):
# using SpectralCoord
if isinstance(args[0], coord.SpectralCoord):
return args[0].to(self.unit[0])
else:
if hasattr(args[0], 'unit'):
return coord.SpectralCoord(*args).to(self.unit[0])
else:
return coord.SpectralCoord(*args, self.unit[0])
def coordinate_to_quantity(self, *coords):
if hasattr(coords[0], 'unit'):
return coords[0]
return coords[0] * self.unit[0]
| (axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, axis_physical_types=None, reference_position=None) | [
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|
44,453 | gwcs.coordinate_frames | __init__ | null | def __init__(self, axes_order=(0,), reference_frame=None, unit=None,
axes_names=None, name=None, axis_physical_types=None,
reference_position=None):
super(SpectralFrame, self).__init__(naxes=1, axes_type="SPECTRAL", axes_order=axes_order,
axes_names=axes_names, reference_frame=reference_frame,
unit=unit, name=name,
reference_position=reference_position,
axis_physical_types=axis_physical_types)
| (self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, axis_physical_types=None, reference_position=None) | [
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|
44,457 | gwcs.coordinate_frames | coordinate_to_quantity | null | def coordinate_to_quantity(self, *coords):
if hasattr(coords[0], 'unit'):
return coords[0]
return coords[0] * self.unit[0]
| (self, *coords) | [
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|
44,458 | gwcs.coordinate_frames | coordinates | null | def coordinates(self, *args):
# using SpectralCoord
if isinstance(args[0], coord.SpectralCoord):
return args[0].to(self.unit[0])
else:
if hasattr(args[0], 'unit'):
return coord.SpectralCoord(*args).to(self.unit[0])
else:
return coord.SpectralCoord(*args, self.unit[0])
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|
44,459 | gwcs.wcs | Step |
Represents a ``step`` in the WCS pipeline.
Parameters
----------
frame : `~gwcs.coordinate_frames.CoordinateFrame`
A gwcs coordinate frame object.
transform : `~astropy.modeling.Model` or None
A transform from this step's frame to next step's frame.
The transform of the last step should be `None`.
| class Step:
"""
Represents a ``step`` in the WCS pipeline.
Parameters
----------
frame : `~gwcs.coordinate_frames.CoordinateFrame`
A gwcs coordinate frame object.
transform : `~astropy.modeling.Model` or None
A transform from this step's frame to next step's frame.
The transform of the last step should be `None`.
"""
def __init__(self, frame, transform=None):
self.frame = frame
self.transform = transform
@property
def frame(self):
return self._frame
@frame.setter
def frame(self, val):
if not isinstance(val, (cf.CoordinateFrame, str)):
raise TypeError('"frame" should be an instance of CoordinateFrame or a string.')
self._frame = val
@property
def transform(self):
return self._transform
@transform.setter
def transform(self, val):
if val is not None and not isinstance(val, (Model)):
raise TypeError('"transform" should be an instance of astropy.modeling.Model.')
self._transform = val
@property
def frame_name(self):
if isinstance(self.frame, str):
return self.frame
return self.frame.name
def __getitem__(self, ind):
warnings.warn("Indexing a WCS.pipeline step is deprecated. "
"Use the `frame` and `transform` attributes instead.", DeprecationWarning)
if ind not in (0, 1):
raise IndexError("Allowed inices are 0 (frame) and 1 (transform).")
if ind == 0:
return self.frame
return self.transform
def __str__(self):
return f"{self.frame_name}\t {getattr(self.transform, 'name', 'None') or self.transform.__class__.__name__}"
def __repr__(self):
return f"Step(frame={self.frame_name}, \
transform={getattr(self.transform, 'name', 'None') or self.transform.__class__.__name__})"
| (frame, transform=None) | [
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|
44,460 | gwcs.wcs | __getitem__ | null | def __getitem__(self, ind):
warnings.warn("Indexing a WCS.pipeline step is deprecated. "
"Use the `frame` and `transform` attributes instead.", DeprecationWarning)
if ind not in (0, 1):
raise IndexError("Allowed inices are 0 (frame) and 1 (transform).")
if ind == 0:
return self.frame
return self.transform
| (self, ind) | [
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|
44,461 | gwcs.wcs | __init__ | null | def __init__(self, frame, transform=None):
self.frame = frame
self.transform = transform
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|
44,462 | gwcs.wcs | __repr__ | null | def __repr__(self):
return f"Step(frame={self.frame_name}, \
transform={getattr(self.transform, 'name', 'None') or self.transform.__class__.__name__})"
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|
44,463 | gwcs.wcs | __str__ | null | def __str__(self):
return f"{self.frame_name}\t {getattr(self.transform, 'name', 'None') or self.transform.__class__.__name__}"
| (self) | [
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|
44,464 | gwcs.coordinate_frames | StokesFrame |
A coordinate frame for representing Stokes polarisation states.
Parameters
----------
name : str
Name of this frame.
axes_order : tuple
A dimension in the data that corresponds to this axis.
| class StokesFrame(CoordinateFrame):
"""
A coordinate frame for representing Stokes polarisation states.
Parameters
----------
name : str
Name of this frame.
axes_order : tuple
A dimension in the data that corresponds to this axis.
"""
def __init__(self, axes_order=(0,), axes_names=("stokes",), name=None, axis_physical_types=None):
super(StokesFrame, self).__init__(1, ["STOKES"], axes_order, name=name,
axes_names=axes_names, unit=u.one,
axis_physical_types=axis_physical_types)
@property
def _default_axis_physical_types(self):
return ("phys.polarization.stokes",)
@property
def _world_axis_object_classes(self):
return {'stokes': (
StokesCoord,
(),
{},
)}
@property
def _world_axis_object_components(self):
return [('stokes', 0, 'value')]
def coordinates(self, *args):
if isinstance(args[0], u.Quantity):
arg = args[0].value
else:
arg = args[0]
return StokesCoord(arg)
def coordinate_to_quantity(self, *coords):
if isinstance(coords[0], StokesCoord):
return coords[0].value << u.one
return coords[0]
| (axes_order=(0,), axes_names=('stokes',), name=None, axis_physical_types=None) | [
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|
44,465 | gwcs.coordinate_frames | __init__ | null | def __init__(self, axes_order=(0,), axes_names=("stokes",), name=None, axis_physical_types=None):
super(StokesFrame, self).__init__(1, ["STOKES"], axes_order, name=name,
axes_names=axes_names, unit=u.one,
axis_physical_types=axis_physical_types)
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|
44,469 | gwcs.coordinate_frames | coordinate_to_quantity | null | def coordinate_to_quantity(self, *coords):
if isinstance(coords[0], StokesCoord):
return coords[0].value << u.one
return coords[0]
| (self, *coords) | [
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|
44,470 | gwcs.coordinate_frames | coordinates | null | def coordinates(self, *args):
if isinstance(args[0], u.Quantity):
arg = args[0].value
else:
arg = args[0]
return StokesCoord(arg)
| (self, *args) | [
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|
44,471 | gwcs.coordinate_frames | TemporalFrame |
A coordinate frame for time axes.
Parameters
----------
reference_frame : `~astropy.time.Time`
A Time object which holds the time scale and format.
If data is provided, it is the time zero point.
To not set a zero point for the frame initialize ``reference_frame``
with an empty list.
unit : str or `~astropy.units.Unit`
Time unit.
axes_names : str
Time axis name.
axes_order : tuple or int
A dimension in the data that corresponds to this axis.
name : str
Name for this frame.
| class TemporalFrame(CoordinateFrame):
"""
A coordinate frame for time axes.
Parameters
----------
reference_frame : `~astropy.time.Time`
A Time object which holds the time scale and format.
If data is provided, it is the time zero point.
To not set a zero point for the frame initialize ``reference_frame``
with an empty list.
unit : str or `~astropy.units.Unit`
Time unit.
axes_names : str
Time axis name.
axes_order : tuple or int
A dimension in the data that corresponds to this axis.
name : str
Name for this frame.
"""
def __init__(self, reference_frame, unit=None, axes_order=(0,),
axes_names=None, name=None, axis_physical_types=None):
axes_names = axes_names or "{}({}; {}".format(reference_frame.format,
reference_frame.scale,
reference_frame.location)
super().__init__(naxes=1, axes_type="TIME", axes_order=axes_order,
axes_names=axes_names, reference_frame=reference_frame,
unit=unit, name=name, axis_physical_types=axis_physical_types)
self._attrs = {}
for a in self.reference_frame.info._represent_as_dict_extra_attrs:
try:
self._attrs[a] = getattr(self.reference_frame, a)
except AttributeError:
pass
@property
def _default_axis_physical_types(self):
return ("time",)
@property
def _world_axis_object_classes(self):
comp = (
time.Time,
(),
{'unit': self.unit[0], **self._attrs},
self._convert_to_time)
return {'temporal': comp}
@property
def _world_axis_object_components(self):
if isinstance(self.reference_frame.value, np.ndarray):
return [('temporal', 0, 'value')]
def offset_from_time_and_reference(time):
return (time - self.reference_frame).sec
return [('temporal', 0, offset_from_time_and_reference)]
def coordinates(self, *args):
if np.isscalar(args):
dt = args
else:
dt = args[0]
return self._convert_to_time(dt, unit=self.unit[0], **self._attrs)
def _convert_to_time(self, dt, *, unit, **kwargs):
if (not isinstance(dt, time.TimeDelta) and
isinstance(dt, time.Time) or
isinstance(self.reference_frame.value, np.ndarray)):
return time.Time(dt, **kwargs)
if not hasattr(dt, 'unit'):
dt = dt * unit
return self.reference_frame + dt
def coordinate_to_quantity(self, *coords):
if isinstance(coords[0], time.Time):
ref_value = self.reference_frame.value
if not isinstance(ref_value, np.ndarray):
return (coords[0] - self.reference_frame).to(self.unit[0])
else:
# If we can't convert to a quantity just drop the object out
# and hope the transform can cope.
return coords[0]
# Is already a quantity
elif hasattr(coords[0], 'unit'):
return coords[0]
if isinstance(coords[0], np.ndarray):
return coords[0] * self.unit[0]
else:
raise ValueError("Can not convert {} to Quantity".format(coords[0]))
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|
44,472 | gwcs.coordinate_frames | __init__ | null | def __init__(self, reference_frame, unit=None, axes_order=(0,),
axes_names=None, name=None, axis_physical_types=None):
axes_names = axes_names or "{}({}; {}".format(reference_frame.format,
reference_frame.scale,
reference_frame.location)
super().__init__(naxes=1, axes_type="TIME", axes_order=axes_order,
axes_names=axes_names, reference_frame=reference_frame,
unit=unit, name=name, axis_physical_types=axis_physical_types)
self._attrs = {}
for a in self.reference_frame.info._represent_as_dict_extra_attrs:
try:
self._attrs[a] = getattr(self.reference_frame, a)
except AttributeError:
pass
| (self, reference_frame, unit=None, axes_order=(0,), axes_names=None, name=None, axis_physical_types=None) | [
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|
44,475 | gwcs.coordinate_frames | _convert_to_time | null | def _convert_to_time(self, dt, *, unit, **kwargs):
if (not isinstance(dt, time.TimeDelta) and
isinstance(dt, time.Time) or
isinstance(self.reference_frame.value, np.ndarray)):
return time.Time(dt, **kwargs)
if not hasattr(dt, 'unit'):
dt = dt * unit
return self.reference_frame + dt
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|
44,477 | gwcs.coordinate_frames | coordinate_to_quantity | null | def coordinate_to_quantity(self, *coords):
if isinstance(coords[0], time.Time):
ref_value = self.reference_frame.value
if not isinstance(ref_value, np.ndarray):
return (coords[0] - self.reference_frame).to(self.unit[0])
else:
# If we can't convert to a quantity just drop the object out
# and hope the transform can cope.
return coords[0]
# Is already a quantity
elif hasattr(coords[0], 'unit'):
return coords[0]
if isinstance(coords[0], np.ndarray):
return coords[0] * self.unit[0]
else:
raise ValueError("Can not convert {} to Quantity".format(coords[0]))
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|
44,478 | gwcs.coordinate_frames | coordinates | null | def coordinates(self, *args):
if np.isscalar(args):
dt = args
else:
dt = args[0]
return self._convert_to_time(dt, unit=self.unit[0], **self._attrs)
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|
44,479 | gwcs.wcs | WCS |
Basic WCS class.
Parameters
----------
forward_transform : `~astropy.modeling.Model` or a list
The transform between ``input_frame`` and ``output_frame``.
A list of (frame, transform) tuples where ``frame`` is the starting frame and
``transform`` is the transform from this frame to the next one or ``output_frame``.
The last tuple is (transform, None), where None indicates the end of the pipeline.
input_frame : str, `~gwcs.coordinate_frames.CoordinateFrame`
A coordinates object or a string name.
output_frame : str, `~gwcs.coordinate_frames.CoordinateFrame`
A coordinates object or a string name.
name : str
a name for this WCS
| class WCS(GWCSAPIMixin):
"""
Basic WCS class.
Parameters
----------
forward_transform : `~astropy.modeling.Model` or a list
The transform between ``input_frame`` and ``output_frame``.
A list of (frame, transform) tuples where ``frame`` is the starting frame and
``transform`` is the transform from this frame to the next one or ``output_frame``.
The last tuple is (transform, None), where None indicates the end of the pipeline.
input_frame : str, `~gwcs.coordinate_frames.CoordinateFrame`
A coordinates object or a string name.
output_frame : str, `~gwcs.coordinate_frames.CoordinateFrame`
A coordinates object or a string name.
name : str
a name for this WCS
"""
def __init__(self, forward_transform=None, input_frame='detector', output_frame=None,
name=""):
#self.low_level_wcs = self
self._approx_inverse = None
self._available_frames = []
self._pipeline = []
self._name = name
self._initialize_wcs(forward_transform, input_frame, output_frame)
self._pixel_shape = None
pipe = []
for step in self._pipeline:
if isinstance(step, Step):
pipe.append(Step(step.frame, step.transform))
else:
pipe.append(Step(*step))
self._pipeline = pipe
def _initialize_wcs(self, forward_transform, input_frame, output_frame):
if forward_transform is not None:
if isinstance(forward_transform, Model):
if output_frame is None:
raise CoordinateFrameError("An output_frame must be specified "
"if forward_transform is a model.")
_input_frame, inp_frame_obj = self._get_frame_name(input_frame)
_output_frame, outp_frame_obj = self._get_frame_name(output_frame)
super(WCS, self).__setattr__(_input_frame, inp_frame_obj)
super(WCS, self).__setattr__(_output_frame, outp_frame_obj)
self._pipeline = [(input_frame, forward_transform.copy()),
(output_frame, None)]
elif isinstance(forward_transform, list):
for item in forward_transform:
if isinstance(item, Step):
name, frame_obj = self._get_frame_name(item.frame)
else:
name, frame_obj = self._get_frame_name(item[0])
super(WCS, self).__setattr__(name, frame_obj)
#self._pipeline.append((name, item[1]))
self._pipeline = forward_transform
else:
raise TypeError("Expected forward_transform to be a model or a "
"(frame, transform) list, got {0}".format(
type(forward_transform)))
else:
# Initialize a WCS without a forward_transform - allows building a WCS programmatically.
if output_frame is None:
raise CoordinateFrameError("An output_frame must be specified "
"if forward_transform is None.")
_input_frame, inp_frame_obj = self._get_frame_name(input_frame)
_output_frame, outp_frame_obj = self._get_frame_name(output_frame)
super(WCS, self).__setattr__(_input_frame, inp_frame_obj)
super(WCS, self).__setattr__(_output_frame, outp_frame_obj)
self._pipeline = [(_input_frame, None),
(_output_frame, None)]
def get_transform(self, from_frame, to_frame):
"""
Return a transform between two coordinate frames.
Parameters
----------
from_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Initial coordinate frame name of object.
to_frame : str, or instance of `~gwcs.coordinate_frames.CoordinateFrame`
End coordinate frame name or object.
Returns
-------
transform : `~astropy.modeling.Model`
Transform between two frames.
"""
if not self._pipeline:
return None
from_ind = self._get_frame_index(from_frame)
to_ind = self._get_frame_index(to_frame)
if to_ind < from_ind:
transforms = [step.transform for step in self._pipeline[to_ind: from_ind]]
transforms = [tr.inverse for tr in transforms[::-1]]
elif to_ind == from_ind:
return None
else:
transforms = [step.transform for step in self._pipeline[from_ind: to_ind]]
return functools.reduce(lambda x, y: x | y, transforms)
def set_transform(self, from_frame, to_frame, transform):
"""
Set/replace the transform between two coordinate frames.
Parameters
----------
from_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Initial coordinate frame.
to_frame : str, or instance of `~gwcs.coordinate_frames.CoordinateFrame`
End coordinate frame.
transform : `~astropy.modeling.Model`
Transform between ``from_frame`` and ``to_frame``.
"""
from_name, from_obj = self._get_frame_name(from_frame)
to_name, to_obj = self._get_frame_name(to_frame)
if not self._pipeline:
if from_name != self._input_frame:
raise CoordinateFrameError(
"Expected 'from_frame' to be {0}".format(self._input_frame))
if to_frame != self._output_frame:
raise CoordinateFrameError(
"Expected 'to_frame' to be {0}".format(self._output_frame))
try:
from_ind = self._get_frame_index(from_name)
except ValueError:
raise CoordinateFrameError("Frame {0} is not in the available frames".format(from_name))
try:
to_ind = self._get_frame_index(to_name)
except ValueError:
raise CoordinateFrameError("Frame {0} is not in the available frames".format(to_name))
if from_ind + 1 != to_ind:
raise ValueError("Frames {0} and {1} are not in sequence".format(from_name, to_name))
self._pipeline[from_ind].transform = transform
@property
def forward_transform(self):
"""
Return the total forward transform - from input to output coordinate frame.
"""
if self._pipeline:
#return functools.reduce(lambda x, y: x | y, [step[1] for step in self._pipeline[: -1]])
return functools.reduce(lambda x, y: x | y, [step.transform for step in self._pipeline[:-1]])
else:
return None
@property
def backward_transform(self):
"""
Return the total backward transform if available - from output to input coordinate system.
Raises
------
NotImplementedError :
An analytical inverse does not exist.
"""
try:
backward = self.forward_transform.inverse
except NotImplementedError as err:
raise NotImplementedError("Could not construct backward transform. \n{0}".format(err))
try:
backward.inverse
except NotImplementedError: # means "hasattr" won't work
backward.inverse = self.forward_transform
return backward
def _get_frame_index(self, frame):
"""
Return the index in the pipeline where this frame is locate.
"""
if isinstance(frame, cf.CoordinateFrame):
frame = frame.name
frame_names = [step.frame if isinstance(step.frame, str) else step.frame.name for step in self._pipeline]
try:
return frame_names.index(frame)
except ValueError as e:
raise CoordinateFrameError(f"Frame {frame} is not in the available frames") from e
def _get_frame_name(self, frame):
"""
Return the name of the frame and a ``CoordinateFrame`` object.
Parameters
----------
frame : str, `~gwcs.coordinate_f | (forward_transform=None, input_frame='detector', output_frame=None, name='') | [
-0.0048211682587862015,
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0.022200798615813255,
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0.011320303194224834,
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0.0177070964127779,
0.047843579202890396,
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0.03820602595806122,
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|
44,480 | gwcs.wcs | __call__ |
Executes the forward transform.
args : float or array-like
Inputs in the input coordinate system, separate inputs
for each dimension.
with_units : bool
If ``True`` returns a `~astropy.coordinates.SkyCoord` or
`~astropy.coordinates.SpectralCoord` object, by using the units of
the output cooridnate frame.
Optional, default=False.
with_bounding_box : bool, optional
If True(default) values in the result which correspond to
any of the inputs being outside the bounding_box are set
to ``fill_value``.
fill_value : float, optional
Output value for inputs outside the bounding_box
(default is np.nan).
| def __call__(self, *args, **kwargs):
"""
Executes the forward transform.
args : float or array-like
Inputs in the input coordinate system, separate inputs
for each dimension.
with_units : bool
If ``True`` returns a `~astropy.coordinates.SkyCoord` or
`~astropy.coordinates.SpectralCoord` object, by using the units of
the output cooridnate frame.
Optional, default=False.
with_bounding_box : bool, optional
If True(default) values in the result which correspond to
any of the inputs being outside the bounding_box are set
to ``fill_value``.
fill_value : float, optional
Output value for inputs outside the bounding_box
(default is np.nan).
"""
transform = self.forward_transform
if transform is None:
raise NotImplementedError("WCS.forward_transform is not implemented.")
with_units = kwargs.pop("with_units", False)
if 'with_bounding_box' not in kwargs:
kwargs['with_bounding_box'] = True
if 'fill_value' not in kwargs:
kwargs['fill_value'] = np.nan
if self.bounding_box is not None:
# Currently compound models do not attempt to combine individual model
# bounding boxes. Get the forward transform and assign the bounding_box to it
# before evaluating it. The order Model.bounding_box is reversed.
transform.bounding_box = self.bounding_box
result = transform(*args, **kwargs)
if with_units:
if self.output_frame.naxes == 1:
result = self.output_frame.coordinates(result)
else:
result = self.output_frame.coordinates(*result)
return result
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|
44,481 | gwcs.wcs | __init__ | null | def __init__(self, forward_transform=None, input_frame='detector', output_frame=None,
name=""):
#self.low_level_wcs = self
self._approx_inverse = None
self._available_frames = []
self._pipeline = []
self._name = name
self._initialize_wcs(forward_transform, input_frame, output_frame)
self._pixel_shape = None
pipe = []
for step in self._pipeline:
if isinstance(step, Step):
pipe.append(Step(step.frame, step.transform))
else:
pipe.append(Step(*step))
self._pipeline = pipe
| (self, forward_transform=None, input_frame='detector', output_frame=None, name='') | [
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|
44,482 | gwcs.wcs | __repr__ | null | def __repr__(self):
fmt = "<WCS(output_frame={0}, input_frame={1}, forward_transform={2})>".format(
self.output_frame, self.input_frame, self.forward_transform)
return fmt
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|
44,483 | gwcs.wcs | __str__ | null | def __str__(self):
from astropy.table import Table
col1 = [step.frame for step in self._pipeline]
col2 = []
for item in self._pipeline[: -1]:
model = item.transform
if model is None:
col2.append(None)
elif model.name is not None:
col2.append(model.name)
else:
col2.append(model.__class__.__name__)
col2.append(None)
t = Table([col1, col2], names=['From', 'Transform'])
return str(t)
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|
44,484 | gwcs.api | _add_units_input | null | def _add_units_input(self, arrays, transform, frame):
if transform.uses_quantity:
return tuple(u.Quantity(array, unit) for array, unit in zip(arrays, frame.unit))
return arrays
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|
44,485 | astropy.wcs.wcsapi.low_level_api | _as_mpl_axes | Compatibility hook for Matplotlib and WCSAxes.
With this method, one can do::
from astropy.wcs import WCS
import matplotlib.pyplot as plt
wcs = WCS('filename.fits')
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs)
...
and this will generate a plot with the correct WCS coordinates on the
axes.
| def _as_mpl_axes(self):
"""Compatibility hook for Matplotlib and WCSAxes.
With this method, one can do::
from astropy.wcs import WCS
import matplotlib.pyplot as plt
wcs = WCS('filename.fits')
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs)
...
and this will generate a plot with the correct WCS coordinates on the
axes.
"""
from astropy.visualization.wcsaxes import WCSAxes
return WCSAxes, {"wcs": self}
| (self) | [
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|
44,486 | gwcs.wcs | _calc_approx_inv |
Compute polynomial fit for the inverse transformation to be used as
initial aproximation/guess for the iterative solution.
| def _calc_approx_inv(self, max_inv_pix_error=5, inv_degree=None, npoints=16):
"""
Compute polynomial fit for the inverse transformation to be used as
initial aproximation/guess for the iterative solution.
"""
self._approx_inverse = None
try:
# try to use analytic inverse if available:
self._approx_inverse = functools.partial(self.backward_transform,
with_bounding_box=False)
return
except (NotImplementedError, KeyError):
pass
if not isinstance(self.output_frame, cf.CelestialFrame):
# The _calc_approx_inv method only works with celestial frame transforms
return
# Determine reference points.
if self.bounding_box is None:
# A bounding_box is needed to proceed.
return
crpix = np.mean(self.bounding_box, axis=1)
crval1, crval2 = self.forward_transform(*crpix)
# Rotate to native system and deproject. Set center of the projection
# transformation to the middle of the bounding box ("image") in order
# to minimize projection effects across the entire image,
# thus the initial shift.
ntransform = ((Shift(crpix[0]) & Shift(crpix[1])) | self.forward_transform
| RotateCelestial2Native(crval1, crval2, 180)
| Sky2Pix_TAN())
# standard sampling:
u, v = _make_sampling_grid(npoints, self.bounding_box, crpix=crpix)
undist_x, undist_y = ntransform(u, v)
# Double sampling to check if sampling is sufficient.
ud, vd = _make_sampling_grid(2 * npoints, self.bounding_box, crpix=crpix)
undist_xd, undist_yd = ntransform(ud, vd)
fit_inv_poly_u, fit_inv_poly_v, max_inv_resid = _fit_2D_poly(
None,
max_inv_pix_error, 1,
undist_x, undist_y, u, v,
undist_xd, undist_yd, ud, vd,
verbose=True
)
self._approx_inverse = (RotateCelestial2Native(crval1, crval2, 180) |
Sky2Pix_TAN() | Mapping((0, 1, 0, 1)) |
(fit_inv_poly_u & fit_inv_poly_v) |
(Shift(crpix[0]) & Shift(crpix[1])))
| (self, max_inv_pix_error=5, inv_degree=None, npoints=16) | [
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|
44,487 | gwcs.wcs | _get_axes_indices | null | def _get_axes_indices(self):
try:
axes_ind = np.argsort(self.input_frame.axes_order)
except AttributeError:
# the case of a frame being a string
axes_ind = np.arange(self.forward_transform.n_inputs)
return axes_ind
| (self) | [
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|
44,488 | gwcs.wcs | _get_frame_index |
Return the index in the pipeline where this frame is locate.
| def _get_frame_index(self, frame):
"""
Return the index in the pipeline where this frame is locate.
"""
if isinstance(frame, cf.CoordinateFrame):
frame = frame.name
frame_names = [step.frame if isinstance(step.frame, str) else step.frame.name for step in self._pipeline]
try:
return frame_names.index(frame)
except ValueError as e:
raise CoordinateFrameError(f"Frame {frame} is not in the available frames") from e
| (self, frame) | [
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|
44,489 | gwcs.wcs | _get_frame_name |
Return the name of the frame and a ``CoordinateFrame`` object.
Parameters
----------
frame : str, `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame.
Returns
-------
name : str
The name of the frame
frame_obj : `~gwcs.coordinate_frames.CoordinateFrame`
Frame instance or None (if `frame` is str)
| def _get_frame_name(self, frame):
"""
Return the name of the frame and a ``CoordinateFrame`` object.
Parameters
----------
frame : str, `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame.
Returns
-------
name : str
The name of the frame
frame_obj : `~gwcs.coordinate_frames.CoordinateFrame`
Frame instance or None (if `frame` is str)
"""
if isinstance(frame, str):
name = frame
frame_obj = None
else:
name = frame.name
frame_obj = frame
return name, frame_obj
| (self, frame) | [
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|
44,490 | gwcs.wcs | _initialize_wcs | null | def _initialize_wcs(self, forward_transform, input_frame, output_frame):
if forward_transform is not None:
if isinstance(forward_transform, Model):
if output_frame is None:
raise CoordinateFrameError("An output_frame must be specified "
"if forward_transform is a model.")
_input_frame, inp_frame_obj = self._get_frame_name(input_frame)
_output_frame, outp_frame_obj = self._get_frame_name(output_frame)
super(WCS, self).__setattr__(_input_frame, inp_frame_obj)
super(WCS, self).__setattr__(_output_frame, outp_frame_obj)
self._pipeline = [(input_frame, forward_transform.copy()),
(output_frame, None)]
elif isinstance(forward_transform, list):
for item in forward_transform:
if isinstance(item, Step):
name, frame_obj = self._get_frame_name(item.frame)
else:
name, frame_obj = self._get_frame_name(item[0])
super(WCS, self).__setattr__(name, frame_obj)
#self._pipeline.append((name, item[1]))
self._pipeline = forward_transform
else:
raise TypeError("Expected forward_transform to be a model or a "
"(frame, transform) list, got {0}".format(
type(forward_transform)))
else:
# Initialize a WCS without a forward_transform - allows building a WCS programmatically.
if output_frame is None:
raise CoordinateFrameError("An output_frame must be specified "
"if forward_transform is None.")
_input_frame, inp_frame_obj = self._get_frame_name(input_frame)
_output_frame, outp_frame_obj = self._get_frame_name(output_frame)
super(WCS, self).__setattr__(_input_frame, inp_frame_obj)
super(WCS, self).__setattr__(_output_frame, outp_frame_obj)
self._pipeline = [(_input_frame, None),
(_output_frame, None)]
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|
44,491 | gwcs.api | _remove_quantity_output | null | def _remove_quantity_output(self, result, frame):
if self.forward_transform.uses_quantity:
if self.output_frame.naxes == 1:
result = [result]
result = tuple(r.to_value(unit) for r, unit in zip(result, frame.unit))
# If we only have one output axes, we shouldn't return a tuple.
if self.output_frame.naxes == 1 and isinstance(result, tuple):
return result[0]
return result
| (self, result, frame) | [
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|
44,492 | gwcs.api | _sanitize_pixel_inputs | null | def _sanitize_pixel_inputs(self, *pixel_arrays):
pixels = []
if self.forward_transform.uses_quantity:
for i, pixel in enumerate(pixel_arrays):
if not isinstance(pixel, u.Quantity):
pixel = u.Quantity(value=pixel, unit=self.input_frame.unit[i])
pixels.append(pixel)
else:
for i, pixel in enumerate(pixel_arrays):
if isinstance(pixel, u.Quantity):
if pixel.unit != self.input_frame.unit[i]:
raise ValueError('Quantity input does not match the '
'input_frame unit.')
pixel = pixel.value
pixels.append(pixel)
return pixels
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|
44,493 | gwcs.wcs | _separable_groups |
This method finds sets (groups) of separable axes - axes that are
dependent on other axes within a set/group but do not depend on
axes from other groups. In other words, axes from different
groups are separable.
Parameters
----------
detect_celestial : bool
If `True`, will return, as the third return value, the group of
celestial axes separately from all other (groups of) axes. If
no celestial frame is detected, then return value for the
celestial axes group will be set to `None`.
Returns
-------
axes_groups : list of lists of ``_WorldAxisInfo``
Each inner list represents a group of non-separable (among
themselves) axes and each axis in a group is independent of axes
in *other* groups. Each axis in a group is represented through
the `_WorldAxisInfo` class used to store relevant information about
an axis. When ``detect_celestial`` is set to `True`, celestial axes
group is not included in this list.
world_axes : list of ``_WorldAxisInfo``
A flattened version of ``axes_groups``. Even though it is not
difficult to flatten ``axes_groups``, this list is a by-product
of other checks and returned here for efficiency. When
``detect_celestial`` is set to `True`, celestial axes
group is not included in this list.
celestial_group : list of ``_WorldAxisInfo``
A group of two celestial axes. This group is returned *only when*
``detect_celestial`` is set to `True`.
| def _separable_groups(self, detect_celestial):
"""
This method finds sets (groups) of separable axes - axes that are
dependent on other axes within a set/group but do not depend on
axes from other groups. In other words, axes from different
groups are separable.
Parameters
----------
detect_celestial : bool
If `True`, will return, as the third return value, the group of
celestial axes separately from all other (groups of) axes. If
no celestial frame is detected, then return value for the
celestial axes group will be set to `None`.
Returns
-------
axes_groups : list of lists of ``_WorldAxisInfo``
Each inner list represents a group of non-separable (among
themselves) axes and each axis in a group is independent of axes
in *other* groups. Each axis in a group is represented through
the `_WorldAxisInfo` class used to store relevant information about
an axis. When ``detect_celestial`` is set to `True`, celestial axes
group is not included in this list.
world_axes : list of ``_WorldAxisInfo``
A flattened version of ``axes_groups``. Even though it is not
difficult to flatten ``axes_groups``, this list is a by-product
of other checks and returned here for efficiency. When
``detect_celestial`` is set to `True`, celestial axes
group is not included in this list.
celestial_group : list of ``_WorldAxisInfo``
A group of two celestial axes. This group is returned *only when*
``detect_celestial`` is set to `True`.
"""
def find_frame(axis_number):
for frame in frames:
if axis_number in frame.axes_order:
return frame
else:
raise RuntimeError("Encountered an output axes that does not "
"belong to any output coordinate frames.")
# use correlation matrix to find separable axes:
corr_mat = self.axis_correlation_matrix
axes_sets = [set(np.flatnonzero(r)) for r in corr_mat.T]
k = 0
while len(axes_sets) - 1 > k:
for m in range(len(axes_sets) - 1, k, -1):
if axes_sets[k].isdisjoint(axes_sets[m]):
continue
axes_sets[k] = axes_sets[k].union(axes_sets[m])
del axes_sets[m]
k += 1
# create a mapping of output axes to input/image axes groups:
mapping = {k: tuple(np.flatnonzero(r)) for k, r in enumerate(corr_mat)}
axes_groups = []
world_axes = [] # flattened version of axes_groups
input_axes = [] # all input axes
if isinstance(self.output_frame, cf.CompositeFrame):
frames = self.output_frame.frames
else:
frames = [self.output_frame]
celestial_group = None
# identify which separable group of axes belong
for s in axes_sets:
axis_info_group = [] # group of separable output axes info
# Find the frame to which the first axis in the group belongs.
# Most likely this frame will be the frame of all other axes in
# this group; if not, we will update it later.
s = sorted(s)
frame = find_frame(s[0])
celestial = (detect_celestial and len(s) == 2 and
len(frame.axes_order) == 2 and
isinstance(frame, cf.CelestialFrame))
for axno in s:
if axno not in frame.axes_order:
frame = find_frame(axno)
celestial = False # Celestial axes must belong to the same frame
# index of the axis in this frame's
fidx = frame.axes_order.index(axno)
if hasattr(frame.unit[fidx], 'get_format_name'):
cunit = frame.unit[fidx].get_format_name(u.format.Fits).upper()
else:
cunit = ''
axis_info = _WorldAxisInfo(
axis=axno,
frame=frame,
world_axis_order=self.output_frame.axes_order.index(axno),
cunit=cunit,
ctype=cf.get_ctype_from_ucd(self.world_axis_physical_types[axno]),
input_axes=mapping[axno]
)
axis_info_group.append(axis_info)
input_axes.extend(mapping[axno])
world_axes.extend(axis_info_group)
if celestial:
celestial_group = axis_info_group
else:
axes_groups.append(axis_info_group)
# sanity check:
input_axes = set(sum((ax.input_axes for ax in world_axes),
world_axes[0].input_axes.__class__()))
n_inputs = len(input_axes)
if (n_inputs != self.pixel_n_dim or
max(input_axes) + 1 != n_inputs or
min(input_axes) < 0):
raise ValueError("Input axes indices are inconsistent with the "
"forward transformation.")
if detect_celestial:
return axes_groups, world_axes, celestial_group
else:
return axes_groups, world_axes
| (self, detect_celestial) | [
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|
44,494 | gwcs.wcs | _to_fits_sip |
Construct a SIP-based approximation to the WCS for the axes
corresponding to the `~gwcs.coordinate_frames.CelestialFrame`
in the form of a FITS header.
The default mode in using this attempts to achieve roughly 0.25 pixel
accuracy over the whole image.
Below we describe only parameters additional to the ones explained for
`to_fits_sip`.
Other Parameters
----------------
frame : gwcs.coordinate_frames.CelestialFrame
A celestial frame.
celestial_group : list of ``_WorldAxisInfo``
A group of two celestial axes to be represented using standard
image FITS WCS and maybe ``-SIP`` polynomials.
keep_axis_position : bool
This parameter controls whether to keep/preserve output axes
indices in this WCS object when creating FITS WCS and create a FITS
header with ``CTYPE`` axes indices preserved from the ``frame``
object or whether to reset the indices of output celestial axes
to 1 and 2 with ``CTYPE1``, ``CTYPE2``. Default is `False`.
.. warning::
Returned header will have both ``NAXIS`` and ``WCSAXES`` set
to 2. If ``max(axes_mapping) > 2`` this will lead to an invalid
WCS. It is caller's responsibility to adjust NAXIS to a valid
value.
.. note::
The ``lon``/``lat`` order is still preserved regardless of this
setting.
matrix_type : {'CD', 'PC-CDELT1', 'PC-SUM1', 'PC-DET1', 'PC-SCALE'}
Specifies formalism (``PC`` or ``CD``) to be used for the linear
transformation matrix and normalization for the ``PC`` matrix
*when non-linear polynomial terms are not required to achieve
requested accuracy*.
.. note:: ``CD`` matrix is always used when requested SIP
approximation accuracy requires non-linear terms (when
``CTYPE`` ends in ``-SIP``). This parameter is ignored when
non-linear polynomial terms are used.
- ``'CD'``: use ``CD`` matrix;
- ``'PC-CDELT1'``: set ``PC=CD`` and ``CDELTi=1``. This is the
behavior of `~astropy.wcs.WCS.to_header` method;
- ``'PC-SUM1'``: normalize ``PC`` matrix such that sum
of its squared elements is 1: :math:`\Sigma PC_{ij}^2=1`;
- ``'PC-DET1'``: normalize ``PC`` matrix such that :math:`|\det(PC)|=1`;
- ``'PC-SCALE'``: normalize ``PC`` matrix such that ``CDELTi``
are estimates of the linear pixel scales.
Returns
-------
FITS header with all SIP WCS keywords
Raises
------
ValueError
If the WCS is not at least 2D, an exception will be raised. If the
specified accuracy (both forward and inverse, both rms and maximum)
is not achieved an exception will be raised.
| def _to_fits_sip(self, celestial_group, keep_axis_position,
bounding_box, max_pix_error, degree,
max_inv_pix_error, inv_degree,
npoints, crpix, projection, matrix_type,
verbose):
r"""
Construct a SIP-based approximation to the WCS for the axes
corresponding to the `~gwcs.coordinate_frames.CelestialFrame`
in the form of a FITS header.
The default mode in using this attempts to achieve roughly 0.25 pixel
accuracy over the whole image.
Below we describe only parameters additional to the ones explained for
`to_fits_sip`.
Other Parameters
----------------
frame : gwcs.coordinate_frames.CelestialFrame
A celestial frame.
celestial_group : list of ``_WorldAxisInfo``
A group of two celestial axes to be represented using standard
image FITS WCS and maybe ``-SIP`` polynomials.
keep_axis_position : bool
This parameter controls whether to keep/preserve output axes
indices in this WCS object when creating FITS WCS and create a FITS
header with ``CTYPE`` axes indices preserved from the ``frame``
object or whether to reset the indices of output celestial axes
to 1 and 2 with ``CTYPE1``, ``CTYPE2``. Default is `False`.
.. warning::
Returned header will have both ``NAXIS`` and ``WCSAXES`` set
to 2. If ``max(axes_mapping) > 2`` this will lead to an invalid
WCS. It is caller's responsibility to adjust NAXIS to a valid
value.
.. note::
The ``lon``/``lat`` order is still preserved regardless of this
setting.
matrix_type : {'CD', 'PC-CDELT1', 'PC-SUM1', 'PC-DET1', 'PC-SCALE'}
Specifies formalism (``PC`` or ``CD``) to be used for the linear
transformation matrix and normalization for the ``PC`` matrix
*when non-linear polynomial terms are not required to achieve
requested accuracy*.
.. note:: ``CD`` matrix is always used when requested SIP
approximation accuracy requires non-linear terms (when
``CTYPE`` ends in ``-SIP``). This parameter is ignored when
non-linear polynomial terms are used.
- ``'CD'``: use ``CD`` matrix;
- ``'PC-CDELT1'``: set ``PC=CD`` and ``CDELTi=1``. This is the
behavior of `~astropy.wcs.WCS.to_header` method;
- ``'PC-SUM1'``: normalize ``PC`` matrix such that sum
of its squared elements is 1: :math:`\Sigma PC_{ij}^2=1`;
- ``'PC-DET1'``: normalize ``PC`` matrix such that :math:`|\det(PC)|=1`;
- ``'PC-SCALE'``: normalize ``PC`` matrix such that ``CDELTi``
are estimates of the linear pixel scales.
Returns
-------
FITS header with all SIP WCS keywords
Raises
------
ValueError
If the WCS is not at least 2D, an exception will be raised. If the
specified accuracy (both forward and inverse, both rms and maximum)
is not achieved an exception will be raised.
"""
if isinstance(matrix_type, str):
matrix_type = matrix_type.upper()
if matrix_type not in ['CD', 'PC-CDELT1', 'PC-SUM1', 'PC-DET1', 'PC-SCALE']:
raise ValueError(f"Unsupported 'matrix_type' value: {repr(matrix_type)}.")
if npoints < 8:
raise ValueError("Number of sampling points is too small. 'npoints' must be >= 8.")
if isinstance(projection, str):
projection = projection.upper()
try:
sky2pix_proj = getattr(projections, f'Sky2Pix_{projection}')(name=projection)
except AttributeError:
raise ValueError("Unsupported FITS WCS sky projection: {projection}")
elif isinstance(projection, projections.Sky2PixProjection):
sky2pix_proj = projection
projection = projection.name
if not projection or not isinstance(projection, str) or len(projection) != 3:
raise ValueError("Unsupported FITS WCS sky projection: {sky2pix_proj}")
try:
getattr(projections, f'Sky2Pix_{projection}')()
except AttributeError:
raise ValueError("Unsupported FITS WCS sky projection: {projection}")
else:
raise TypeError(
"'projection' must be either a FITS WCS string projection code "
"or an instance of astropy.modeling.projections.Pix2SkyProjection.")
frame = celestial_group[0].frame
lon_axis = frame.axes_order[0]
lat_axis = frame.axes_order[1]
# identify input axes:
input_axes = []
for wax in celestial_group:
input_axes.extend(wax.input_axes)
input_axes = sorted(set(input_axes))
if len(input_axes) != 2:
raise ValueError("Only CelestialFrame that correspond to two "
"input axes are supported.")
# Axis number for FITS axes.
# iax? - image axes; nlon, nlat - celestial axes:
if keep_axis_position:
nlon = lon_axis + 1
nlat = lat_axis + 1
iax1, iax2 = (i + 1 for i in input_axes)
else:
nlon, nlat = (1, 2) if lon_axis < lat_axis else (2, 1)
iax1 = 1
iax2 = 2
# Determine reference points.
if bounding_box is None and self.bounding_box is None:
raise ValueError("A bounding_box is needed to proceed.")
if bounding_box is None:
bounding_box = self.bounding_box
bb_center = np.mean(bounding_box, axis=1)
fixi_dict = {
k: bb_center[k] for k in set(range(self.pixel_n_dim)).difference(input_axes)
}
# transform = fix_inputs(self.forward_transform, fixi_dict)
# This is a workaround to the bug in https://github.com/astropy/astropy/issues/11360
# Once that bug is fixed, the code below can be replaced with fix_inputs
# statement commented out immediately above.
transform = _fix_transform_inputs(self.forward_transform, fixi_dict)
transform = transform | Mapping((lon_axis, lat_axis),
n_inputs=self.forward_transform.n_outputs)
(xmin, xmax) = bounding_box[input_axes[0]]
(ymin, ymax) = bounding_box[input_axes[1]]
# 0-based crpix:
if crpix is None:
crpix1 = round(bb_center[input_axes[0]], 1)
crpix2 = round(bb_center[input_axes[1]], 1)
else:
crpix1 = crpix[0] - 1
crpix2 = crpix[1] - 1
# check that the bounding box has some reasonable size:
if (xmax - xmin) < 1 or (ymax - ymin) < 1:
raise ValueError("Bounding box is too small for fitting a SIP polynomial")
lon, lat = transform(crpix1, crpix2)
# Now rotate to native system and deproject. Recall that transform
# expects pixels in the original coordinate system, but the SIP
# transform is relative to crpix coordinates, thus the initial shift.
ntransform = ((Shift(crpix1) & Shift(crpix2)) | transform
| RotateCelestial2Native(lon, lat, 180)
| sky2pix_proj)
# standard sampling:
u, v = _make_sampling_grid(
npoints,
tuple(bounding_box[k] for k in input_axes),
crpix=[crpix1, crpix2]
)
undist_x, undist_y = ntransform(u, v)
# Double sampling to check if sampling is sufficient.
ud, vd = _make_sampling_grid(
2 * npoints,
tuple(bounding_box[k] for k in input_axes),
crpix=[crpix1, crpix2]
)
undist_xd, undist_yd = ntransform(ud, vd)
# Determine approximate pixel scale in order to compute error threshold
# from the specified pixel error. Computed at the center of the array.
x0, y0 = ntransform(0, 0)
xx, xy = ntransform(1, 0)
yx, yy = ntransform(0, 1)
pixarea = np.abs((xx - x0) * (yy - y0) - (xy - y0) * (yx - x0))
plate_scale = np.sqrt(pixarea)
# The fitting section.
if verbose:
print("\nFitting forward SIP ...")
fit_poly_x, fit_poly_y, max_resid = _fit_2D_poly(
degree, max_pix_error, plate_scale,
u, v, undist_x, undist_y,
ud, vd, undist_xd, undist_yd,
verbose=verbose
)
# The following is necessary to put the fit into the SIP formalism.
cdmat, sip_poly_x, sip_poly_y = _reform_poly_coefficients(fit_poly_x, fit_poly_y)
# cdmat = np.arr | (self, celestial_group, keep_axis_position, bounding_box, max_pix_error, degree, max_inv_pix_error, inv_degree, npoints, crpix, projection, matrix_type, verbose) | [
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|
44,495 | gwcs.wcs | _to_fits_tab |
Construct a FITS WCS ``-TAB``-based approximation to the WCS
in the form of a FITS header and a binary table extension. For the
description of the FITS WCS ``-TAB`` convention, see
"Representations of spectral coordinates in FITS" in
`Greisen, E. W. et al. A&A 446 (2) 747-771 (2006)
<https://doi.org/10.1051/0004-6361:20053818>`_ .
Below we describe only parameters additional to the ones explained for
`to_fits_tab`.
.. warn::
For this helper function, parameters ``bounding_box`` and
``sampling`` (when provided as a tuple) are expected to have
the same length as the number of input axes in the *full* WCS
object. That is, the number of elements in ``bounding_box`` and
``sampling`` is not be affected by ``ignore_axes``.
Other Parameters
----------------
hdr : astropy.io.fits.Header, None
The first time this function is called, ``hdr`` should be set to
`None` or be an empty :py:class:`~astropy.io.fits.Header` object.
On subsequent calls, updated header from the previous iteration
should be provided.
world_axes_group : tuple of dict
A list of world axes to represent through FITS' -TAB convention.
This is a list of dictionaries with each dicti
axes_mapping : dict
A dictionary that maps output axis index to a tuple of input
axis indices. In a typical scenario of two input image axes
and two output celestial axes for a FITS-like WCS,
this dictionary would look like ``{0: (0, 1), 1: (0, 1)}``
with the two non-separable input axes.
fix_axes : dict
A dictionary containing as keys image axes' indices to be
fixed and as values - the values to which inputs should be kept
fixed. For example, this dictionary may be used to indicate the
celestial axes that should not be included into -TAB approximation
because they will be approximated using -SIP.
use_cd : bool
When `True` - CD-matrix formalism will be used instead of the
PC-matrix formalism.
bin_ext : str, tuple of str and int
Extension name and optionally version for the
`~astropy.io.fits.BinTableHDU` HDU. When only a string extension
name is provided, extension version will be set to 1.
When ``bin_ext`` is a tuple, first element should be extension
name and the second element is a positive integer extension version
number.
Returns
-------
hdr : `~astropy.io.fits.Header`
Header with WCS-TAB information associated (to be used) with image
data.
bin_table_hdu : `~astropy.io.fits.BinTableHDU`
Binary table extension containing the coordinate array.
Raises
------
ValueError
When ``bounding_box`` is not defined either through the input
``bounding_box`` parameter or this object's ``bounding_box``
property.
ValueError
When ``sampling`` is a `tuple` of length larger than 1 that
does not match the number of image axes.
ValueError
When extension version is smaller than 1.
TypeError
RuntimeError
If the number of image axes (``~gwcs.WCS.pixel_n_dim``) is larger
than the number of world axes (``~gwcs.WCS.world_n_dim``).
| def _to_fits_tab(self, hdr, world_axes_group, use_cd, bounding_box,
bin_ext, coord_col_name, sampling):
"""
Construct a FITS WCS ``-TAB``-based approximation to the WCS
in the form of a FITS header and a binary table extension. For the
description of the FITS WCS ``-TAB`` convention, see
"Representations of spectral coordinates in FITS" in
`Greisen, E. W. et al. A&A 446 (2) 747-771 (2006)
<https://doi.org/10.1051/0004-6361:20053818>`_ .
Below we describe only parameters additional to the ones explained for
`to_fits_tab`.
.. warn::
For this helper function, parameters ``bounding_box`` and
``sampling`` (when provided as a tuple) are expected to have
the same length as the number of input axes in the *full* WCS
object. That is, the number of elements in ``bounding_box`` and
``sampling`` is not be affected by ``ignore_axes``.
Other Parameters
----------------
hdr : astropy.io.fits.Header, None
The first time this function is called, ``hdr`` should be set to
`None` or be an empty :py:class:`~astropy.io.fits.Header` object.
On subsequent calls, updated header from the previous iteration
should be provided.
world_axes_group : tuple of dict
A list of world axes to represent through FITS' -TAB convention.
This is a list of dictionaries with each dicti
axes_mapping : dict
A dictionary that maps output axis index to a tuple of input
axis indices. In a typical scenario of two input image axes
and two output celestial axes for a FITS-like WCS,
this dictionary would look like ``{0: (0, 1), 1: (0, 1)}``
with the two non-separable input axes.
fix_axes : dict
A dictionary containing as keys image axes' indices to be
fixed and as values - the values to which inputs should be kept
fixed. For example, this dictionary may be used to indicate the
celestial axes that should not be included into -TAB approximation
because they will be approximated using -SIP.
use_cd : bool
When `True` - CD-matrix formalism will be used instead of the
PC-matrix formalism.
bin_ext : str, tuple of str and int
Extension name and optionally version for the
`~astropy.io.fits.BinTableHDU` HDU. When only a string extension
name is provided, extension version will be set to 1.
When ``bin_ext`` is a tuple, first element should be extension
name and the second element is a positive integer extension version
number.
Returns
-------
hdr : `~astropy.io.fits.Header`
Header with WCS-TAB information associated (to be used) with image
data.
bin_table_hdu : `~astropy.io.fits.BinTableHDU`
Binary table extension containing the coordinate array.
Raises
------
ValueError
When ``bounding_box`` is not defined either through the input
``bounding_box`` parameter or this object's ``bounding_box``
property.
ValueError
When ``sampling`` is a `tuple` of length larger than 1 that
does not match the number of image axes.
ValueError
When extension version is smaller than 1.
TypeError
RuntimeError
If the number of image axes (``~gwcs.WCS.pixel_n_dim``) is larger
than the number of world axes (``~gwcs.WCS.world_n_dim``).
"""
if isinstance(bin_ext, str):
bin_ext = (bin_ext, 1)
if isinstance(bounding_box, Bbox):
bounding_box = bounding_box.bounding_box(order='F')
if isinstance(bounding_box, list):
for index, bbox in enumerate(bounding_box):
if isinstance(bbox, Bbox):
bounding_box[index] = bbox.bounding_box(order='F')
# identify input axes:
input_axes = []
world_axes_idx = []
for ax in world_axes_group:
world_axes_idx.append(ax.axis)
input_axes.extend(ax.input_axes)
input_axes = sorted(set(input_axes))
n_inputs = len(input_axes)
n_outputs = len(world_axes_group)
world_axes_idx.sort()
# Create initial header and deal with non-degenerate axes
if hdr is None:
hdr = fits.Header()
hdr['NAXIS'] = n_inputs, 'number of array dimensions'
hdr['WCSAXES'] = n_outputs
hdr.insert('WCSAXES', ('WCSNAME', f'{self.output_frame.name}'), after=True)
else:
hdr['NAXIS'] += n_inputs
hdr['WCSAXES'] += n_outputs
# see what axes have been already populated in the header:
used_hdr_axes = []
for v in hdr['naxis*'].keys():
try:
used_hdr_axes.append(int(v.split('NAXIS')[1]) - 1)
except ValueError:
continue
degenerate_axis_start = max(
self.pixel_n_dim + 1,
max(used_hdr_axes) + 1 if used_hdr_axes else 1
)
# Deal with non-degenerate axes and add NAXISi to the header:
offset = hdr.index('NAXIS')
for iax in input_axes:
iiax = int(np.searchsorted(used_hdr_axes, iax))
hdr.insert(iiax + offset + 1, (f'NAXIS{iax + 1:d}', int(max(bounding_box[iiax])) + 1))
# 1D grid coordinates:
gcrds = []
cdelt = []
bb = [bounding_box[k] for k in input_axes]
for (xmin, xmax), s in zip(bb, sampling):
npix = max(2, 1 + int(np.ceil(abs((xmax - xmin) / s))))
gcrds.append(np.linspace(xmin, xmax, npix))
cdelt.append((npix - 1) / (xmax - xmin) if xmin != xmax else 1)
# In the forward transformation, select only inputs and outputs
# that we need given world_axes_group parameter:
bb_center = np.mean(bounding_box, axis=1)
fixi_dict = {
k: bb_center[k] for k in set(range(self.pixel_n_dim)).difference(input_axes)
}
transform = _fix_transform_inputs(self.forward_transform, fixi_dict)
transform = transform | Mapping(world_axes_idx,
n_inputs=self.forward_transform.n_outputs)
xyz = np.meshgrid(*gcrds[::-1], indexing='ij')[::-1]
shape = xyz[0].shape
xyz = [v.ravel() for v in xyz]
coord = np.stack(
transform(*xyz),
axis=-1
)
coord = coord.reshape(shape + (len(world_axes_group), ))
# create header with WCS info:
if hdr is None:
hdr = fits.Header()
for m, axis_info in enumerate(world_axes_group):
k = axis_info.axis
widx = world_axes_idx.index(k)
k1 = k + 1
ct = cf.get_ctype_from_ucd(self.world_axis_physical_types[k])
if len(ct) > 4:
raise ValueError("Axis type name too long.")
hdr[f'CTYPE{k1:d}'] = ct + (4 - len(ct)) * '-' + '-TAB'
hdr[f'CUNIT{k1:d}'] = self.world_axis_units[k]
hdr[f'PS{k1:d}_0'] = bin_ext[0]
hdr[f'PV{k1:d}_1'] = bin_ext[1]
hdr[f'PS{k1:d}_1'] = coord_col_name
hdr[f'PV{k1:d}_3'] = widx + 1
hdr[f'CRVAL{k1:d}'] = 1
if widx < n_inputs:
m1 = input_axes[widx] + 1
hdr[f'CRPIX{m1:d}'] = gcrds[widx][0] + 1
if use_cd:
hdr[f'CD{k1:d}_{m1:d}'] = cdelt[widx]
else:
if k1 != m1:
hdr[f'PC{k1:d}_{k1:d}'] = 0.0
hdr[f'PC{k1:d}_{m1:d}'] = 1.0
hdr[f'CDELT{k1:d}'] = cdelt[widx]
else:
m1 = degenerate_axis_start
degenerate_axis_start += 1
hdr[f'CRPIX{m1:d}'] = 1
if use_cd:
hdr[f'CD{k1:d}_{m1:d}'] = 1.0
else:
if k1 != m1:
hdr[f'PC{k1:d}_{k1:d}'] = 0.0
hdr[f'PC{k1:d}_{m1:d}'] = 1.0
hdr[f'CDELT{k1:d}'] = 1
# Uncomment 3 lines below to enable use of degenerate axes:
# hdr['NAXIS'] = hdr['NAXIS'] + 1
# naxisi_max = max(int(k[5:]) for k in hdr['naxis*'] if k[5:].strip())
# hdr.insert(f'NAXIS{naxisi_max:d}', (f'NAXIS{m1:d}', 1), after=True)
# NOTE: in this case make sure NAXIS=WCSAXES
coord = coord[None, :]
# structured array (data) for binary table HDU:
arr = np.array(
[(coord, )],
dtype=[
(coord_col_name, np. | (self, hdr, world_axes_group, use_cd, bounding_box, bin_ext, coord_col_name, sampling) | [
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|
44,496 | gwcs.wcs | _vectorized_fixed_point | null | def _vectorized_fixed_point(self, pix0, world, tolerance, maxiter,
adaptive, detect_divergence, quiet,
with_bounding_box, fill_value):
# ############################################################
# # INITIALIZE ITERATIVE PROCESS: ##
# ############################################################
# make a copy of the initial approximation
pix0 = np.atleast_2d(np.array(pix0)) # 0-order solution
pix = np.array(pix0)
world0 = np.atleast_2d(np.array(world))
world = np.array(world0)
# estimate pixel scale using approximate algorithm
# from https://trs.jpl.nasa.gov/handle/2014/40409
if self.bounding_box is None:
crpix = np.ones(self.pixel_n_dim)
else:
crpix = np.mean(self.bounding_box, axis=-1)
l1, phi1 = np.deg2rad(self.__call__(*(crpix - 0.5)))
l2, phi2 = np.deg2rad(self.__call__(*(crpix + [-0.5, 0.5])))
l3, phi3 = np.deg2rad(self.__call__(*(crpix + 0.5)))
l4, phi4 = np.deg2rad(self.__call__(*(crpix + [0.5, -0.5])))
area = np.abs(0.5 * ((l4 - l2) * (np.sin(phi1) - np.sin(phi3)) +
(l1 - l3) * (np.sin(phi2) - np.sin(phi4))))
inv_pscale = 1 / np.rad2deg(np.sqrt(area))
# form equation:
def f(x):
w = np.array(self.__call__(*(x.T), with_bounding_box=False)).T
dw = np.mod(np.subtract(w, world) - 180.0, 360.0) - 180.0
return np.add(inv_pscale * dw, x)
def froot(x):
return np.mod(np.subtract(self.__call__(*x, with_bounding_box=False), worldi) - 180.0, 360.0) - 180.0
# compute correction:
def correction(pix):
p1 = f(pix)
p2 = f(p1)
d = p2 - 2.0 * p1 + pix
idx = np.where(d != 0)
corr = pix - p2
corr[idx] = np.square(p1[idx] - pix[idx]) / d[idx]
return corr
# initial iteration:
dpix = correction(pix)
# Update initial solution:
pix -= dpix
# Norm (L2) squared of the correction:
dn = np.sum(dpix * dpix, axis=1)
dnprev = dn.copy() # if adaptive else dn
tol2 = tolerance**2
# Prepare for iterative process
k = 1
ind = None
inddiv = None
# Turn off numpy runtime warnings for 'invalid' and 'over':
old_invalid = np.geterr()['invalid']
old_over = np.geterr()['over']
np.seterr(invalid='ignore', over='ignore')
# ############################################################
# # NON-ADAPTIVE ITERATIONS: ##
# ############################################################
if not adaptive:
# Fixed-point iterations:
while (np.nanmax(dn) >= tol2 and k < maxiter):
# Find correction to the previous solution:
dpix = correction(pix)
# Compute norm (L2) squared of the correction:
dn = np.sum(dpix * dpix, axis=1)
# Check for divergence (we do this in two stages
# to optimize performance for the most common
# scenario when successive approximations converge):
if detect_divergence:
divergent = (dn >= dnprev)
if np.any(divergent):
# Find solutions that have not yet converged:
slowconv = (dn >= tol2)
inddiv, = np.where(divergent & slowconv)
if inddiv.shape[0] > 0:
# Update indices of elements that
# still need correction:
conv = (dn < dnprev)
iconv = np.where(conv)
# Apply correction:
dpixgood = dpix[iconv]
pix[iconv] -= dpixgood
dpix[iconv] = dpixgood
# For the next iteration choose
# non-divergent points that have not yet
# converged to the requested accuracy:
ind, = np.where(slowconv & conv)
world = world[ind]
dnprev[ind] = dn[ind]
k += 1
# Switch to adaptive iterations:
adaptive = True
break
# Save current correction magnitudes for later:
dnprev = dn
# Apply correction:
pix -= dpix
k += 1
# ############################################################
# # ADAPTIVE ITERATIONS: ##
# ############################################################
if adaptive:
if ind is None:
ind, = np.where(np.isfinite(pix).all(axis=1))
world = world[ind]
# "Adaptive" fixed-point iterations:
while (ind.shape[0] > 0 and k < maxiter):
# Find correction to the previous solution:
dpixnew = correction(pix[ind])
# Compute norm (L2) of the correction:
dnnew = np.sum(np.square(dpixnew), axis=1)
# Bookkeeping of corrections:
dnprev[ind] = dn[ind].copy()
dn[ind] = dnnew
if detect_divergence:
# Find indices of pixels that are converging:
conv = np.logical_or(dnnew < dnprev[ind], dnnew < tol2)
if not np.all(conv):
conv = np.ones_like(dnnew, dtype=bool)
iconv = np.where(conv)
iiconv = ind[iconv]
# Apply correction:
dpixgood = dpixnew[iconv]
pix[iiconv] -= dpixgood
dpix[iiconv] = dpixgood
# Find indices of solutions that have not yet
# converged to the requested accuracy
# AND that do not diverge:
subind, = np.where((dnnew >= tol2) & conv)
else:
# Apply correction:
pix[ind] -= dpixnew
dpix[ind] = dpixnew
# Find indices of solutions that have not yet
# converged to the requested accuracy:
subind, = np.where(dnnew >= tol2)
# Choose solutions that need more iterations:
ind = ind[subind]
world = world[subind]
k += 1
# ############################################################
# # FINAL DETECTION OF INVALID, DIVERGING, ##
# # AND FAILED-TO-CONVERGE POINTS ##
# ############################################################
# Identify diverging and/or invalid points:
invalid = ((~np.all(np.isfinite(pix), axis=1)) &
(np.all(np.isfinite(world0), axis=1)))
# When detect_divergence is False, dnprev is outdated
# (it is the norm of the very first correction).
# Still better than nothing...
inddiv, = np.where(((dn >= tol2) & (dn >= dnprev)) | invalid)
if inddiv.shape[0] == 0:
inddiv = None
# If there are divergent points, attempt to find a solution using
# scipy's 'hybr' method:
if detect_divergence and inddiv is not None and inddiv.size:
bad = []
for idx in inddiv:
worldi = world0[idx]
result = optimize.root(
froot,
pix0[idx],
method='hybr',
tol=tolerance / (np.linalg.norm(pix0[idx]) + 1),
options={'maxfev': 2 * maxiter}
)
if result['success']:
pix[idx, :] = result['x']
invalid[idx] = False
else:
bad.append(idx)
if bad:
inddiv = np.array(bad, dtype=int)
else:
inddiv = None
# Identify points that did not converge within 'maxiter'
# iterations:
if k >= maxiter:
ind, = np.where((dn >= tol2) & (dn < dnprev) & (~invalid))
if ind.shape[0] == 0:
ind = None
else:
ind = None
# Restore previous numpy error settings:
np.seterr(invalid=old_invalid, over=old_over)
# ############################################################
# # | (self, pix0, world, tolerance, maxiter, adaptive, detect_divergence, quiet, with_bounding_box, fill_value) | [
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|
44,497 | gwcs.api | array_index_to_world |
Convert array indices to world coordinates (represented by Astropy
objects).
| def array_index_to_world(self, *index_arrays):
"""
Convert array indices to world coordinates (represented by Astropy
objects).
"""
pixel_arrays = index_arrays[::-1]
pixels = self._sanitize_pixel_inputs(*pixel_arrays)
return self(*pixels, with_units=True)
| (self, *index_arrays) | [
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|
44,498 | gwcs.api | array_index_to_world_values |
Convert array indices to world coordinates.
This is the same as `~BaseLowLevelWCS.pixel_to_world_values` except that
the indices should be given in ``(i, j)`` order, where for an image
``i`` is the row and ``j`` is the column (i.e. the opposite order to
`~BaseLowLevelWCS.pixel_to_world_values`).
| def array_index_to_world_values(self, *index_arrays):
"""
Convert array indices to world coordinates.
This is the same as `~BaseLowLevelWCS.pixel_to_world_values` except that
the indices should be given in ``(i, j)`` order, where for an image
``i`` is the row and ``j`` is the column (i.e. the opposite order to
`~BaseLowLevelWCS.pixel_to_world_values`).
"""
pixel_arrays = index_arrays[::-1]
return self.pixel_to_world_values(*pixel_arrays)
| (self, *index_arrays) | [
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|
44,499 | gwcs.wcs | attach_compound_bounding_box | null | def attach_compound_bounding_box(self, cbbox, selector_args):
frames = self.available_frames
transform_0 = self.get_transform(frames[0], frames[1])
self.bounding_box = CompoundBoundingBox.validate(transform_0, cbbox, selector_args=selector_args,
order='F')
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|
44,500 | gwcs.wcs | fix_inputs |
Return a new unique WCS by fixing inputs to constant values.
Parameters
----------
fixed : dict
Keyword arguments with fixed values corresponding to ``self.selector``.
Returns
-------
new_wcs : `WCS`
A new unique WCS corresponding to the values in ``fixed``.
Examples
--------
>>> w = WCS(pipeline, selector={"spectral_order": [1, 2]}) # doctest: +SKIP
>>> new_wcs = w.set_inputs(spectral_order=2) # doctest: +SKIP
>>> new_wcs.inputs # doctest: +SKIP
("x", "y")
| def fix_inputs(self, fixed):
"""
Return a new unique WCS by fixing inputs to constant values.
Parameters
----------
fixed : dict
Keyword arguments with fixed values corresponding to ``self.selector``.
Returns
-------
new_wcs : `WCS`
A new unique WCS corresponding to the values in ``fixed``.
Examples
--------
>>> w = WCS(pipeline, selector={"spectral_order": [1, 2]}) # doctest: +SKIP
>>> new_wcs = w.set_inputs(spectral_order=2) # doctest: +SKIP
>>> new_wcs.inputs # doctest: +SKIP
("x", "y")
"""
new_pipeline = []
step0 = self.pipeline[0]
new_transform = fix_inputs(step0[1], fixed)
new_pipeline.append((step0[0], new_transform))
new_pipeline.extend(self.pipeline[1:])
return self.__class__(new_pipeline)
| (self, fixed) | [
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|
44,501 | gwcs.wcs | footprint |
Return the footprint in world coordinates.
Parameters
----------
bounding_box : tuple of floats: (start, stop)
``prop: bounding_box``
center : bool
If `True` use the center of the pixel, otherwise use the corner.
axis_type : str
A supported ``output_frame.axes_type`` or ``"all"`` (default).
One of [``'spatial'``, ``'spectral'``, ``'temporal'``] or a custom type.
Returns
-------
coord : ndarray
Array of coordinates in the output_frame mapping
corners to the output frame. For spatial coordinates the order
is clockwise, starting from the bottom left corner.
| def footprint(self, bounding_box=None, center=False, axis_type="all"):
"""
Return the footprint in world coordinates.
Parameters
----------
bounding_box : tuple of floats: (start, stop)
``prop: bounding_box``
center : bool
If `True` use the center of the pixel, otherwise use the corner.
axis_type : str
A supported ``output_frame.axes_type`` or ``"all"`` (default).
One of [``'spatial'``, ``'spectral'``, ``'temporal'``] or a custom type.
Returns
-------
coord : ndarray
Array of coordinates in the output_frame mapping
corners to the output frame. For spatial coordinates the order
is clockwise, starting from the bottom left corner.
"""
def _order_clockwise(v):
return np.asarray([[v[0][0], v[1][0]], [v[0][0], v[1][1]],
[v[0][1], v[1][1]], [v[0][1], v[1][0]]]).T
if bounding_box is None:
if self.bounding_box is None:
raise TypeError("Need a valid bounding_box to compute the footprint.")
bb = self.bounding_box
else:
bb = bounding_box
all_spatial = all([t.lower() == "spatial" for t in self.output_frame.axes_type])
if all_spatial:
vertices = _order_clockwise(bb)
else:
vertices = np.array(list(itertools.product(*bb))).T
if center:
vertices = utils._toindex(vertices)
result = np.asarray(self.__call__(*vertices, **{'with_bounding_box': False}))
axis_type = axis_type.lower()
if axis_type == 'spatial' and all_spatial:
return result.T
if axis_type != "all":
axtyp_ind = np.array([t.lower() for t in self.output_frame.axes_type]) == axis_type
if not axtyp_ind.any():
raise ValueError('This WCS does not have axis of type "{}".'.format(axis_type))
result = np.asarray([(r.min(), r.max()) for r in result[axtyp_ind]])
if axis_type == "spatial":
result = _order_clockwise(result)
else:
result.sort()
result = np.squeeze(result)
return result.T
| (self, bounding_box=None, center=False, axis_type='all') | [
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|
44,502 | gwcs.wcs | get_transform |
Return a transform between two coordinate frames.
Parameters
----------
from_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Initial coordinate frame name of object.
to_frame : str, or instance of `~gwcs.coordinate_frames.CoordinateFrame`
End coordinate frame name or object.
Returns
-------
transform : `~astropy.modeling.Model`
Transform between two frames.
| def get_transform(self, from_frame, to_frame):
"""
Return a transform between two coordinate frames.
Parameters
----------
from_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Initial coordinate frame name of object.
to_frame : str, or instance of `~gwcs.coordinate_frames.CoordinateFrame`
End coordinate frame name or object.
Returns
-------
transform : `~astropy.modeling.Model`
Transform between two frames.
"""
if not self._pipeline:
return None
from_ind = self._get_frame_index(from_frame)
to_ind = self._get_frame_index(to_frame)
if to_ind < from_ind:
transforms = [step.transform for step in self._pipeline[to_ind: from_ind]]
transforms = [tr.inverse for tr in transforms[::-1]]
elif to_ind == from_ind:
return None
else:
transforms = [step.transform for step in self._pipeline[from_ind: to_ind]]
return functools.reduce(lambda x, y: x | y, transforms)
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|
44,503 | gwcs.wcs | in_image |
This method tests if one or more of the input world coordinates are
contained within forward transformation's image and that it maps to
the domain of definition of the forward transformation.
In practical terms, this function tests
that input world coordinate(s) can be converted to input frame and that
it is within the forward transformation's ``bounding_box`` when
defined.
Parameters
----------
args : float, array like, `~astropy.coordinates.SkyCoord` or
`~astropy.units.Unit` coordinates to be inverted
kwargs : dict
keyword arguments to be passed either to ``backward_transform``
(when defined) or to the iterative invert method.
Returns
-------
result : bool, numpy.ndarray
A single boolean value or an array of boolean values with `True`
indicating that the WCS footprint contains the coordinate
and `False` if input is outside the footprint.
| def in_image(self, *args, **kwargs):
"""
This method tests if one or more of the input world coordinates are
contained within forward transformation's image and that it maps to
the domain of definition of the forward transformation.
In practical terms, this function tests
that input world coordinate(s) can be converted to input frame and that
it is within the forward transformation's ``bounding_box`` when
defined.
Parameters
----------
args : float, array like, `~astropy.coordinates.SkyCoord` or
`~astropy.units.Unit` coordinates to be inverted
kwargs : dict
keyword arguments to be passed either to ``backward_transform``
(when defined) or to the iterative invert method.
Returns
-------
result : bool, numpy.ndarray
A single boolean value or an array of boolean values with `True`
indicating that the WCS footprint contains the coordinate
and `False` if input is outside the footprint.
"""
kwargs['with_bounding_box'] = True
kwargs['fill_value'] = np.nan
coords = self.invert(*args, **kwargs)
result = np.isfinite(coords)
if self.input_frame.naxes > 1:
result = np.all(result, axis=0)
if self.bounding_box is None or not np.any(result):
return result
if self.input_frame.naxes == 1:
x1, x2 = self.bounding_box.bounding_box()
if len(np.shape(args[0])) > 0:
result[result] = (coords[result] >= x1) & (coords[result] <= x2)
elif result:
result = (coords >= x1) and (coords <= x2)
else:
if len(np.shape(args[0])) > 0:
for c, (x1, x2) in zip(coords, self.bounding_box):
result[result] = (c[result] >= x1) & (c[result] <= x2)
elif result:
result = all([(c >= x1) and (c <= x2) for c, (x1, x2) in zip(coords, self.bounding_box)])
return result
| (self, *args, **kwargs) | [
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|
44,504 | gwcs.wcs | insert_frame |
Insert a new frame into an existing pipeline. This frame must be
anchored to a frame already in the pipeline by a transform. This
existing frame is identified solely by its name, although an entire
`~gwcs.coordinate_frames.CoordinateFrame` can be passed (e.g., the
`input_frame` or `output_frame` attribute). This frame is never
modified.
Parameters
----------
input_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame at start of new transform
transform : `~astropy.modeling.Model`
New transform to be inserted in the pipeline
output_frame: str or `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame at end of new transform
| def insert_frame(self, input_frame, transform, output_frame):
"""
Insert a new frame into an existing pipeline. This frame must be
anchored to a frame already in the pipeline by a transform. This
existing frame is identified solely by its name, although an entire
`~gwcs.coordinate_frames.CoordinateFrame` can be passed (e.g., the
`input_frame` or `output_frame` attribute). This frame is never
modified.
Parameters
----------
input_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame at start of new transform
transform : `~astropy.modeling.Model`
New transform to be inserted in the pipeline
output_frame: str or `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame at end of new transform
"""
input_name, input_frame_obj = self._get_frame_name(input_frame)
output_name, output_frame_obj = self._get_frame_name(output_frame)
try:
input_index = self._get_frame_index(input_frame)
except CoordinateFrameError:
input_index = None
if input_frame_obj is None:
raise ValueError(f"New coordinate frame {input_name} must "
"be defined")
try:
output_index = self._get_frame_index(output_frame)
except CoordinateFrameError:
output_index = None
if output_frame_obj is None:
raise ValueError(f"New coordinate frame {output_name} must "
"be defined")
new_frames = [input_index, output_index].count(None)
if new_frames == 0:
raise ValueError("Could not insert frame as both frames "
f"{input_name} and {output_name} already exist")
elif new_frames == 2:
raise ValueError("Could not insert frame as neither frame "
f"{input_name} nor {output_name} exists")
if input_index is None:
self._pipeline = (self._pipeline[:output_index] +
[Step(input_frame_obj, transform)] +
self._pipeline[output_index:])
super(WCS, self).__setattr__(input_name, input_frame_obj)
else:
split_step = self._pipeline[input_index]
self._pipeline = (self._pipeline[:input_index] +
[Step(split_step.frame, transform),
Step(output_frame_obj, split_step.transform)] +
self._pipeline[input_index + 1:])
super(WCS, self).__setattr__(output_name, output_frame_obj)
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|
44,505 | gwcs.wcs | insert_transform |
Insert a transform before (default) or after a coordinate frame.
Append (or prepend) a transform to the transform connected to frame.
Parameters
----------
frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame which sets the point of insertion.
transform : `~astropy.modeling.Model`
New transform to be inserted in the pipeline
after : bool
If True, the new transform is inserted in the pipeline
immediately after ``frame``.
| def insert_transform(self, frame, transform, after=False):
"""
Insert a transform before (default) or after a coordinate frame.
Append (or prepend) a transform to the transform connected to frame.
Parameters
----------
frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Coordinate frame which sets the point of insertion.
transform : `~astropy.modeling.Model`
New transform to be inserted in the pipeline
after : bool
If True, the new transform is inserted in the pipeline
immediately after ``frame``.
"""
name, _ = self._get_frame_name(frame)
frame_ind = self._get_frame_index(name)
if not after:
current_transform = self._pipeline[frame_ind - 1].transform
self._pipeline[frame_ind - 1].transform = current_transform | transform
else:
current_transform = self._pipeline[frame_ind].transform
self._pipeline[frame_ind].transform = transform | current_transform
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|
44,506 | gwcs.wcs | invert |
Invert coordinates from output frame to input frame using analytical or
user-supplied inverse. When neither analytical nor user-supplied
inverses are defined, a numerical solution will be attempted using
:py:meth:`numerical_inverse`.
.. note::
Currently numerical inverse is implemented only for 2D imaging WCS.
Parameters
----------
args : float, array like, `~astropy.coordinates.SkyCoord` or `~astropy.units.Unit`
Coordinates to be inverted. The number of arguments must be equal
to the number of world coordinates given by ``world_n_dim``.
with_bounding_box : bool, optional
If `True` (default) values in the result which correspond to any
of the inputs being outside the bounding_box are set to
``fill_value``.
fill_value : float, optional
Output value for inputs outside the bounding_box (default is ``np.nan``).
with_units : bool, optional
If ``True`` returns a `~astropy.coordinates.SkyCoord` or
`~astropy.coordinates.SpectralCoord` object, by using the units of
the output cooridnate frame. Default is `False`.
Other Parameters
----------------
kwargs : dict
Keyword arguments to be passed to :py:meth:`numerical_inverse`
(when defined) or to the iterative invert method.
Returns
-------
result : tuple or value
Returns a tuple of scalar or array values for each axis. Unless
``input_frame.naxes == 1`` when it shall return the value.
| def invert(self, *args, **kwargs):
"""
Invert coordinates from output frame to input frame using analytical or
user-supplied inverse. When neither analytical nor user-supplied
inverses are defined, a numerical solution will be attempted using
:py:meth:`numerical_inverse`.
.. note::
Currently numerical inverse is implemented only for 2D imaging WCS.
Parameters
----------
args : float, array like, `~astropy.coordinates.SkyCoord` or `~astropy.units.Unit`
Coordinates to be inverted. The number of arguments must be equal
to the number of world coordinates given by ``world_n_dim``.
with_bounding_box : bool, optional
If `True` (default) values in the result which correspond to any
of the inputs being outside the bounding_box are set to
``fill_value``.
fill_value : float, optional
Output value for inputs outside the bounding_box (default is ``np.nan``).
with_units : bool, optional
If ``True`` returns a `~astropy.coordinates.SkyCoord` or
`~astropy.coordinates.SpectralCoord` object, by using the units of
the output cooridnate frame. Default is `False`.
Other Parameters
----------------
kwargs : dict
Keyword arguments to be passed to :py:meth:`numerical_inverse`
(when defined) or to the iterative invert method.
Returns
-------
result : tuple or value
Returns a tuple of scalar or array values for each axis. Unless
``input_frame.naxes == 1`` when it shall return the value.
"""
with_units = kwargs.pop('with_units', False)
if not utils.isnumerical(args[0]):
args = self.output_frame.coordinate_to_quantity(*args)
if self.output_frame.naxes == 1:
args = [args]
try:
if not self.backward_transform.uses_quantity:
args = utils.get_values(self.output_frame.unit, *args)
except (NotImplementedError, KeyError):
args = utils.get_values(self.output_frame.unit, *args)
if 'with_bounding_box' not in kwargs:
kwargs['with_bounding_box'] = True
if 'fill_value' not in kwargs:
kwargs['fill_value'] = np.nan
try:
# remove iterative inverse-specific keyword arguments:
akwargs = {k: v for k, v in kwargs.items() if k not in _ITER_INV_KWARGS}
result = self.backward_transform(*args, **akwargs)
except (NotImplementedError, KeyError):
result = self.numerical_inverse(*args, **kwargs, with_units=with_units)
if with_units and self.input_frame:
if self.input_frame.naxes == 1:
return self.input_frame.coordinates(result)
else:
return self.input_frame.coordinates(*result)
else:
return result
| (self, *args, **kwargs) | [
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|
44,507 | gwcs.wcs | numerical_inverse |
Invert coordinates from output frame to input frame using numerical
inverse.
.. note::
Currently numerical inverse is implemented only for 2D imaging WCS.
.. note::
This method uses a combination of vectorized fixed-point
iterations algorithm and `scipy.optimize.root`. The later is used
for input coordinates for which vectorized algorithm diverges.
Parameters
----------
args : float, array like, `~astropy.coordinates.SkyCoord` or `~astropy.units.Unit`
Coordinates to be inverted. The number of arguments must be equal
to the number of world coordinates given by ``world_n_dim``.
with_bounding_box : bool, optional
If `True` (default) values in the result which correspond to any
of the inputs being outside the bounding_box are set to
``fill_value``.
fill_value : float, optional
Output value for inputs outside the bounding_box (default is ``np.nan``).
with_units : bool, optional
If ``True`` returns a `~astropy.coordinates.SkyCoord` or
`~astropy.coordinates.SpectralCoord` object, by using the units of
the output cooridnate frame. Default is `False`.
tolerance : float, optional
*Absolute tolerance* of solution. Iteration terminates when the
iterative solver estimates that the "true solution" is
within this many pixels current estimate, more
specifically, when the correction to the solution found
during the previous iteration is smaller
(in the sense of the L2 norm) than ``tolerance``.
Default ``tolerance`` is 1.0e-5.
maxiter : int, optional
Maximum number of iterations allowed to reach a solution.
Default is 50.
quiet : bool, optional
Do not throw :py:class:`NoConvergence` exceptions when
the method does not converge to a solution with the
required accuracy within a specified number of maximum
iterations set by ``maxiter`` parameter. Instead,
simply return the found solution. Default is `True`.
adaptive : bool, optional
Specifies whether to adaptively select only points that
did not converge to a solution within the required
accuracy for the next iteration. Default (`True`) is recommended.
.. note::
The :py:meth:`numerical_inverse` uses a vectorized
implementation of the method of consecutive
approximations (see ``Notes`` section below) in which it
iterates over *all* input points *regardless* until
the required accuracy has been reached for *all* input
points. In some cases it may be possible that
*almost all* points have reached the required accuracy
but there are only a few of input data points for
which additional iterations may be needed (this
depends mostly on the characteristics of the geometric
distortions for a given instrument). In this situation
it may be advantageous to set ``adaptive`` = `True` in
which case :py:meth:`numerical_inverse` will continue
iterating *only* over the points that have not yet
converged to the required accuracy.
.. note::
When ``detect_divergence`` is `True`,
:py:meth:`numerical_inverse` will automatically switch
to the adaptive algorithm once divergence has been
detected.
detect_divergence : bool, optional
Specifies whether to perform a more detailed analysis
of the convergence to a solution. Normally
:py:meth:`numerical_inverse` may not achieve the required
accuracy if either the ``tolerance`` or ``maxiter`` arguments
are too low. However, it may happen that for some
geometric distortions the conditions of convergence for
the the method of consecutive approximations used by
:py:meth:`numerical_inverse` may not be satisfied, in which
case consecutive approximations to the solution will
diverge regardless of the ``tolerance`` or ``maxiter``
settings.
When ``detect_divergence`` is `False`, these divergent
points will be detected as not having achieved the
required accuracy (without further details). In addition,
if ``adaptive`` is `False` then the algorithm will not
know that the solution (for specific points) is diverging
and will continue iterating and trying to "improve"
diverging solutions. This may result in ``NaN`` or
``Inf`` values in the return results (in addition to a
performance penalties). Even when ``detect_divergence``
is `False`, :py:meth:`numerical_inverse`, at the end of the
iterative process, will identify invalid results
(``NaN`` or ``Inf``) as "diverging" solutions and will
raise :py:class:`NoConvergence` unless the ``quiet``
parameter is set to `True`.
When ``detect_divergence`` is `True` (default),
:py:meth:`numerical_inverse` will detect points for which
current correction to the coordinates is larger than
the correction applied during the previous iteration
**if** the requested accuracy **has not yet been
achieved**. In this case, if ``adaptive`` is `True`,
these points will be excluded from further iterations and
if ``adaptive`` is `False`, :py:meth:`numerical_inverse` will
automatically switch to the adaptive algorithm. Thus, the
reported divergent solution will be the latest converging
solution computed immediately *before* divergence
has been detected.
.. note::
When accuracy has been achieved, small increases in
current corrections may be possible due to rounding
errors (when ``adaptive`` is `False`) and such
increases will be ignored.
.. note::
Based on our testing using JWST NIRCAM images, setting
``detect_divergence`` to `True` will incur about 5-10%
performance penalty with the larger penalty
corresponding to ``adaptive`` set to `True`.
Because the benefits of enabling this
feature outweigh the small performance penalty,
especially when ``adaptive`` = `False`, it is
recommended to set ``detect_divergence`` to `True`,
unless extensive testing of the distortion models for
images from specific instruments show a good stability
of the numerical method for a wide range of
coordinates (even outside the image itself).
.. note::
Indices of the diverging inverse solutions will be
reported in the ``divergent`` attribute of the
raised :py:class:`NoConvergence` exception object.
Returns
-------
result : tuple
Returns a tuple of scalar or array values for each axis.
Raises
------
NoConvergence
The iterative method did not converge to a
solution to the required accuracy within a specified
number of maximum iterations set by the ``maxiter``
parameter. To turn off this exception, set ``quiet`` to
`True`. Indices of the points for which the requested
accuracy was not achieved (if any) will be listed in the
``slow_conv`` attribute of the
raised :py:class:`NoConvergence` exception object.
See :py:class:`NoConvergence` documentation for
more details.
NotImplementedError
Numerical inverse has not been implemented for this WCS.
ValueError
Invalid argument values.
Examples
--------
>>> from astropy.utils.data import get_pkg_data_filename
>>> from gwcs import NoConvergence
>>> import asdf
>>> import numpy as np
>>> filename = get_pkg_data_filename('data/nircamwcs.asdf', package='gwcs.tests')
>>> with asdf.open(filename, copy_arrays=True, lazy_load=False, ignore_missing_extensions=True) as af:
... w = af.tree['wcs']
>>> ra, dec = w([1,2,3], [1,1,1])
>>> assert np.allclose(ra, [5.927628, 5.92757069, 5.92751337]);
>>> assert np.allclose(dec, [-72.01341247, -72.01341273, -72.013413])
>>> x, y = w.numerical_inverse(ra, dec)
>>> assert np.allclose(x, [1.00000005, 2.00000005, 3.00000006]);
>>> assert np.allclose(y, [1.00000004, 0.99999979, 1.00000015]);
>>> x, y = w.numerical_inverse(ra, dec, maxiter=3, tolerance=1.0e-10, quiet=False)
Traceback (most recent call last):
...
gwcs.wcs.NoConvergence: 'WCS.numerical_inverse' failed to converge to the
requested accuracy after 3 iterations.
>>> w.numerical_inverse(
... *w([1, 300000, 3], [2, 1000000, 5], with_bounding_box=False),
... adaptive=False,
... detect_divergence=True,
... quiet=False,
... with_bounding_box=False
... )
Traceback (most recent call last):
...
gwcs.wcs.NoConvergence: 'WCS.numerical_inverse' failed to converge to the
requested accuracy. After 4 iterations, the solution is diverging at
least for one input point.
>>> # Now try to use some diverging data:
>>> divra, divdec = w([1, 300000, 3], [2, 1000000, 5], with_bounding_box=False)
>>> assert np.allclose(divra, [5.92762673, 148.21600848, 5.92750827])
>>> assert np.allclose(divdec, [-72.01339464, -7.80968079, -72.01334172])
>>> try: # doctest: +SKIP
... x, y = w.numerical_inverse(divra, divdec, maxiter=20,
... tolerance=1.0e-4, adaptive=True,
... detect_divergence=True,
... quiet=False)
... except NoConvergence as e:
... print(f"Indices of diverging points: {e.divergent}")
... print(f"Indices of poorly converging points: {e.slow_conv}")
... print(f"Best solution:\n{e.best_solution}")
... print(f"Achieved accuracy:\n{e.accuracy}")
Indices of diverging points: None
Indices of poorly converging points: [1]
Best solution:
[[1.00000040e+00 1.99999841e+00]
[6.33507833e+17 3.40118820e+17]
[3.00000038e+00 4.99999841e+00]]
Achieved accuracy:
[[2.75925982e-05 1.18471543e-05]
[3.65405005e+04 1.31364188e+04]
[2.76552923e-05 1.14789013e-05]]
| def numerical_inverse(self, *args, tolerance=1e-5, maxiter=50, adaptive=True,
detect_divergence=True, quiet=True, with_bounding_box=True,
fill_value=np.nan, with_units=False, **kwargs):
"""
Invert coordinates from output frame to input frame using numerical
inverse.
.. note::
Currently numerical inverse is implemented only for 2D imaging WCS.
.. note::
This method uses a combination of vectorized fixed-point
iterations algorithm and `scipy.optimize.root`. The later is used
for input coordinates for which vectorized algorithm diverges.
Parameters
----------
args : float, array like, `~astropy.coordinates.SkyCoord` or `~astropy.units.Unit`
Coordinates to be inverted. The number of arguments must be equal
to the number of world coordinates given by ``world_n_dim``.
with_bounding_box : bool, optional
If `True` (default) values in the result which correspond to any
of the inputs being outside the bounding_box are set to
``fill_value``.
fill_value : float, optional
Output value for inputs outside the bounding_box (default is ``np.nan``).
with_units : bool, optional
If ``True`` returns a `~astropy.coordinates.SkyCoord` or
`~astropy.coordinates.SpectralCoord` object, by using the units of
the output cooridnate frame. Default is `False`.
tolerance : float, optional
*Absolute tolerance* of solution. Iteration terminates when the
iterative solver estimates that the "true solution" is
within this many pixels current estimate, more
specifically, when the correction to the solution found
during the previous iteration is smaller
(in the sense of the L2 norm) than ``tolerance``.
Default ``tolerance`` is 1.0e-5.
maxiter : int, optional
Maximum number of iterations allowed to reach a solution.
Default is 50.
quiet : bool, optional
Do not throw :py:class:`NoConvergence` exceptions when
the method does not converge to a solution with the
required accuracy within a specified number of maximum
iterations set by ``maxiter`` parameter. Instead,
simply return the found solution. Default is `True`.
adaptive : bool, optional
Specifies whether to adaptively select only points that
did not converge to a solution within the required
accuracy for the next iteration. Default (`True`) is recommended.
.. note::
The :py:meth:`numerical_inverse` uses a vectorized
implementation of the method of consecutive
approximations (see ``Notes`` section below) in which it
iterates over *all* input points *regardless* until
the required accuracy has been reached for *all* input
points. In some cases it may be possible that
*almost all* points have reached the required accuracy
but there are only a few of input data points for
which additional iterations may be needed (this
depends mostly on the characteristics of the geometric
distortions for a given instrument). In this situation
it may be advantageous to set ``adaptive`` = `True` in
which case :py:meth:`numerical_inverse` will continue
iterating *only* over the points that have not yet
converged to the required accuracy.
.. note::
When ``detect_divergence`` is `True`,
:py:meth:`numerical_inverse` will automatically switch
to the adaptive algorithm once divergence has been
detected.
detect_divergence : bool, optional
Specifies whether to perform a more detailed analysis
of the convergence to a solution. Normally
:py:meth:`numerical_inverse` may not achieve the required
accuracy if either the ``tolerance`` or ``maxiter`` arguments
are too low. However, it may happen that for some
geometric distortions the conditions of convergence for
the the method of consecutive approximations used by
:py:meth:`numerical_inverse` may not be satisfied, in which
case consecutive approximations to the solution will
diverge regardless of the ``tolerance`` or ``maxiter``
settings.
When ``detect_divergence`` is `False`, these divergent
points will be detected as not having achieved the
required accuracy (without further details). In addition,
if ``adaptive`` is `False` then the algorithm will not
know that the solution (for specific points) is diverging
and will continue iterating and trying to "improve"
diverging solutions. This may result in ``NaN`` or
``Inf`` values in the return results (in addition to a
performance penalties). Even when ``detect_divergence``
is `False`, :py:meth:`numerical_inverse`, at the end of the
iterative process, will identify invalid results
(``NaN`` or ``Inf``) as "diverging" solutions and will
raise :py:class:`NoConvergence` unless the ``quiet``
parameter is set to `True`.
When ``detect_divergence`` is `True` (default),
:py:meth:`numerical_inverse` will detect points for which
current correction to the coordinates is larger than
the correction applied during the previous iteration
**if** the requested accuracy **has not yet been
achieved**. In this case, if ``adaptive`` is `True`,
these points will be excluded from further iterations and
if ``adaptive`` is `False`, :py:meth:`numerical_inverse` will
automatically switch to the adaptive algorithm. Thus, the
reported divergent solution will be the latest converging
solution computed immediately *before* divergence
has been detected.
.. note::
When accuracy has been achieved, small increases in
current corrections may be possible due to rounding
errors (when ``adaptive`` is `False`) and such
increases will be ignored.
.. note::
Based on our testing using JWST NIRCAM images, setting
``detect_divergence`` to `True` will incur about 5-10%
performance penalty with the larger penalty
corresponding to ``adaptive`` set to `True`.
Because the benefits of enabling this
feature outweigh the small performance penalty,
especially when ``adaptive`` = `False`, it is
recommended to set ``detect_divergence`` to `True`,
unless extensive testing of the distortion models for
images from specific instruments show a good stability
of the numerical method for a wide range of
coordinates (even outside the image itself).
.. note::
Indices of the diverging inverse solutions will be
reported in the ``divergent`` attribute of the
raised :py:class:`NoConvergence` exception object.
Returns
-------
result : tuple
Returns a tuple of scalar or array values for each axis.
Raises
------
NoConvergence
The iterative method did not converge to a
solution to the required accuracy within a specified
number of maximum iterations set by the ``maxiter``
parameter. To turn off this exception, set ``quiet`` to
`True`. Indices of the points for which the requested
accuracy was not achieved (if any) will be listed in the
``slow_conv`` attribute of the
raised :py:class:`NoConvergence` exception object.
See :py:class:`NoConvergence` documentation for
more details.
NotImplementedError
Numerical inverse has not been implemented for this WCS.
ValueError
Invalid argument values.
Examples
--------
>>> from astropy.utils.data import get_pkg_data_filename
>>> from gwcs import NoConvergence
>>> import asdf
>>> import numpy as np
>>> filename = get_pkg_data_filename('data/nircamwcs.asdf', | (self, *args, tolerance=1e-05, maxiter=50, adaptive=True, detect_divergence=True, quiet=True, with_bounding_box=True, fill_value=nan, with_units=False, **kwargs) | [
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|
44,508 | gwcs.api | pixel_to_world |
Convert pixel values to world coordinates.
| def pixel_to_world(self, *pixel_arrays):
"""
Convert pixel values to world coordinates.
"""
pixels = self._sanitize_pixel_inputs(*pixel_arrays)
return self(*pixels, with_units=True)
| (self, *pixel_arrays) | [
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|
44,509 | gwcs.api | pixel_to_world_values |
Convert pixel coordinates to world coordinates.
This method takes ``pixel_n_dim`` scalars or arrays as input, and pixel
coordinates should be zero-based. Returns ``world_n_dim`` scalars or
arrays in units given by ``world_axis_units``. Note that pixel
coordinates are assumed to be 0 at the center of the first pixel in each
dimension. If a pixel is in a region where the WCS is not defined, NaN
can be returned. The coordinates should be specified in the ``(x, y)``
order, where for an image, ``x`` is the horizontal coordinate and ``y``
is the vertical coordinate.
| def pixel_to_world_values(self, *pixel_arrays):
"""
Convert pixel coordinates to world coordinates.
This method takes ``pixel_n_dim`` scalars or arrays as input, and pixel
coordinates should be zero-based. Returns ``world_n_dim`` scalars or
arrays in units given by ``world_axis_units``. Note that pixel
coordinates are assumed to be 0 at the center of the first pixel in each
dimension. If a pixel is in a region where the WCS is not defined, NaN
can be returned. The coordinates should be specified in the ``(x, y)``
order, where for an image, ``x`` is the horizontal coordinate and ``y``
is the vertical coordinate.
"""
pixel_arrays = self._add_units_input(pixel_arrays, self.forward_transform, self.input_frame)
result = self(*pixel_arrays, with_units=False)
return self._remove_quantity_output(result, self.output_frame)
| (self, *pixel_arrays) | [
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|
44,510 | gwcs.wcs | set_transform |
Set/replace the transform between two coordinate frames.
Parameters
----------
from_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Initial coordinate frame.
to_frame : str, or instance of `~gwcs.coordinate_frames.CoordinateFrame`
End coordinate frame.
transform : `~astropy.modeling.Model`
Transform between ``from_frame`` and ``to_frame``.
| def set_transform(self, from_frame, to_frame, transform):
"""
Set/replace the transform between two coordinate frames.
Parameters
----------
from_frame : str or `~gwcs.coordinate_frames.CoordinateFrame`
Initial coordinate frame.
to_frame : str, or instance of `~gwcs.coordinate_frames.CoordinateFrame`
End coordinate frame.
transform : `~astropy.modeling.Model`
Transform between ``from_frame`` and ``to_frame``.
"""
from_name, from_obj = self._get_frame_name(from_frame)
to_name, to_obj = self._get_frame_name(to_frame)
if not self._pipeline:
if from_name != self._input_frame:
raise CoordinateFrameError(
"Expected 'from_frame' to be {0}".format(self._input_frame))
if to_frame != self._output_frame:
raise CoordinateFrameError(
"Expected 'to_frame' to be {0}".format(self._output_frame))
try:
from_ind = self._get_frame_index(from_name)
except ValueError:
raise CoordinateFrameError("Frame {0} is not in the available frames".format(from_name))
try:
to_ind = self._get_frame_index(to_name)
except ValueError:
raise CoordinateFrameError("Frame {0} is not in the available frames".format(to_name))
if from_ind + 1 != to_ind:
raise ValueError("Frames {0} and {1} are not in sequence".format(from_name, to_name))
self._pipeline[from_ind].transform = transform
| (self, from_frame, to_frame, transform) | [
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|
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