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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,)
(self, params, inputs, model_set_axis_input, **kwargs)
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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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0.005561610218137503, 0.025998396798968315, 0.03628785163164139 ]
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
(output, broadcast_shape)
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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
(shape, axis)
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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
(self, _input, idx, argnames, model_set_axis, check_model_set_axis)
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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
(self, inputs, argnames, model_set_axis)
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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
(self, input_units=None, return_units=None, input_units_equivalencies=None, input_units_allow_dimensionless=False)
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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 )
(self, *inputs, model_set_axis=None, equivalencies=None, **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 )
(self, broadcasted_shapes, *outputs, **kwargs)
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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)
(self)
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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). """
(self, *args, **kwargs)
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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
(self, **kwargs)
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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
(self, **kwargs)
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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)
(mapper, inputs_mapping=None, name=None, **kwargs)
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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
(inputs, mapper, inputs_mapping=None, atol=1e-08, name=None, **kwargs)
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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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0.03019842505455017, -0.06987506151199341, 0.020559871569275856 ]
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
(self, *args)
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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
(*args, best_solution=None, accuracy=None, niter=None, divergent=None, slow_conv=None)
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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
(self, *args, best_solution=None, accuracy=None, niter=None, divergent=None, slow_conv=None)
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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
(inputs, outputs, selector, label_mapper, undefined_transform_value=nan, name=None, **kwargs)
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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)
(self, inputs, outputs, selector, label_mapper, undefined_transform_value=nan, name=None, **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.")
(self)
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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])
(self, *args)
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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
(self, frame, transform=None)
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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__})"
(self)
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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)
(self, axes_order=(0,), axes_names=('stokes',), name=None, axis_physical_types=None)
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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]))
(reference_frame, unit=None, axes_order=(0,), axes_names=None, name=None, axis_physical_types=None)
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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
(self, dt, *, unit, **kwargs)
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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]))
(self, *coords)
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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)
(self, *args)
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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='')
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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
(self, *args, **kwargs)
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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
(self)
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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)
(self)
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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
(self, arrays, transform, frame)
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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)]
(self, forward_transform, input_frame, output_frame)
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0.06095333397388458, 0.038064587861299515, 0.013810018077492714 ]
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
(self, *pixel_arrays)
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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')
(self, cbbox, selector_args)
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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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0.014756234362721443, 0.009253831580281258 ]
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)
(self, from_frame, to_frame)
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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)
(self, input_frame, transform, output_frame)
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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
(self, frame, transform, after=False)
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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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