wf_psf.psf_models.tf_layers module

class wf_psf.psf_models.tf_layers.OLD_TF_batch_poly_PSF(*args, **kwargs)

Bases: Layer

Calculate a polychromatic PSF from an OPD and stored SED values.

The calculation of the packed values with the respective SED is done with the SimPSFToolkit class but outside the TF class.

obscurations: Tensor(pupil_len, pupil_len)

Obscurations to apply to the wavefront.

packed_SED_data: Tensor(batch_size, 3, n_bins_lda)

Comes from: tf.convert_to_tensor(list(list(Tensor,Tensor,Tensor)))

Where each inner list consist of a packed_elem:

packed_elems: Tuple of tensors Contains three 1D tensors with the parameters needed for the calculation of one monochromatic PSF.

packed_elems[0]: phase_N packed_elems[1]: lambda_obs packed_elems[2]: SED_norm_val

The SED data is constant in a FoV.

psf_batch: Tensor(batch_size, output_dim, output_dim)

Tensor containing the psfs that will be updated each time a calculation is required.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
calculate_mono_PSF(packed_elems)	Calculate monochromatic PSF from packed elements.
calculate_poly_PSF(packed_elems)	Calculate a polychromatic PSF.
call(inputs)	Calculate the batch poly PSFs.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
set_psf_batch(psf_batch)	Set poly PSF batch.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

calculate_mono_PSF(packed_elems)

Calculate monochromatic PSF from packed elements.

packed_elems[0]: phase_N packed_elems[1]: lambda_obs packed_elems[2]: SED_norm_val

calculate_poly_PSF(packed_elems)

Calculate a polychromatic PSF.

call(inputs)

Calculate the batch poly PSFs.

set_psf_batch(psf_batch)

Set poly PSF batch.

class wf_psf.psf_models.tf_layers.TF_NP_GRAPH_OPD(*args, **kwargs)

Bases: Layer

Non-parametric OPD generation with only graph-cosntraint variations.

Parameters:

• obs_pos (tensor(n_stars, 2)) – Observed positions of the n_stars in the dataset. The indexing of the positions has to correspond to the indexing in the spatial_dic.

• spatial_dic (tensor(n_stars, n_dic_elems)) – Dictionary containing the spatial-constraint dictionary. n_stars corresponds to the total number of stars in the dataset. n_dic_elems corresponds to the number of elements of the dictionary, not to be confounded with n_comp, the total number of non-parametric features of the wavefront-PSF.

• x_lims ([int, int]) – Limits of the x axis.

• y_lims ([int, int]) – Limits of the y axis.

• graph_comps (int) – Number of wavefront-PSF features correspondign to the graph constraint.

• d_max (int) – Max degree of polynomial determining the FoV variations. The number of wavefront-PSF features of the polynomial part is computed (d_max+1)*(d_max+2)/2.

• opd_dim (int) – Dimension of the OPD maps. Same as pupil diameter.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(positions)	Calculate the OPD maps for the given positions.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
init_vars()	Initialize trainable variables.
predict(positions)	Prediction step.
set_alpha_identity()	Set alpha matrix to the identity.
set_alpha_zero()	Set alpha matrix to zero.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

call(positions)

Calculate the OPD maps for the given positions.

Calculating: batch(spatial_dict) x alpha x S

Parameters:

positions (Tensor(batch, 2)) – First element is x-axis, second is y-axis.

Returns:

opd_maps

Return type:

Tensor(batch, opd_dim, opd_dim)

init_vars()

Initialize trainable variables.

Basic initialization. Random uniform for S and identity for alpha.

predict(positions)

Prediction step.

set_alpha_identity()

Set alpha matrix to the identity.

set_alpha_zero()

Set alpha matrix to zero.

class wf_psf.psf_models.tf_layers.TF_NP_MCCD_OPD_v2(*args, **kwargs)

Bases: Layer

Non-parametric OPD generation with hybrid-MCCD variations.

Parameters:

• obs_pos (tensor(n_stars, 2)) – Observed positions of the n_stars in the dataset. The indexing of the positions has to correspond to the indexing in the spatial_dic.

• spatial_dic (tensor(n_stars, n_dic_elems)) – Dictionary containing the spatial-constraint dictionary. n_stars corresponds to the total number of stars in the dataset. n_dic_elems corresponds to the number of elements of the dictionary, not to be confounded with n_comp, the total number of non-parametric features of the wavefront-PSF.

• x_lims ([int, int]) – Limits of the x axis.

• y_lims ([int, int]) – Limits of the y axis.

• graph_comps (int) – Number of wavefront-PSF features correspondign to the graph constraint.

• d_max (int) – Max degree of polynomial determining the FoV variations. The number of wavefront-PSF features of the polynomial part is computed (d_max+1)*(d_max+2)/2.

• opd_dim (int) – Dimension of the OPD maps. Same as pupil diameter.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(positions)	Calculate the OPD maps for the given positions.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
init_vars()	Initialize trainable variables.
predict(positions)	Prediction step.
set_alpha_identity()	Set alpha matrix to the identity.
set_alpha_zero()	Set alpha matrix to zero.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

call(positions)

Calculate the OPD maps for the given positions.

Calculating: batch(spatial_dict) x alpha x S

Parameters:

positions (Tensor(batch, 2)) – First element is x-axis, second is y-axis.

Returns:

opd_maps

Return type:

Tensor(batch, opd_dim, opd_dim)

init_vars()

Initialize trainable variables.

Basic initialization. Random uniform for S and identity for alpha.

predict(positions)

Prediction step.

set_alpha_identity()

Set alpha matrix to the identity.

set_alpha_zero()

Set alpha matrix to zero.

class wf_psf.psf_models.tf_layers.TF_NP_poly_OPD(*args, **kwargs)

Bases: Layer

Non-parametric OPD generation with polynomial variations.

Parameters:

• x_lims ([int, int]) – Limits of the x axis.

• y_lims ([int, int]) – Limits of the y axis.

• random_seed (int) – Random seed initialization for Tensor Flow

• d_max (int) – Max degree of polynomial determining the FoV variations.

• opd_dim (int) – Dimension of the OPD maps. Same as pupil diameter.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
assign_S_mat(S_mat)	Assign DD features matrix.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(positions)	Calculate the OPD maps for the given positions.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
init_vars()	Initialize trainable variables.
set_alpha_identity()	Set alpha matrix to the identity.
set_alpha_zero()	Set alpha matrix to zero.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

assign_S_mat(S_mat)

Assign DD features matrix.

call(positions)

Calculate the OPD maps for the given positions.

Calculating: Pi(pos) x alpha x S

Parameters:

positions (Tensor(batch, 2)) – First element is x-axis, second is y-axis.

Returns:

opd_maps

Return type:

Tensor(batch, opd_dim, opd_dim)

init_vars()

Initialize trainable variables.

Basic initialization. Random uniform for S and identity for alpha.

set_alpha_identity()

Set alpha matrix to the identity.

set_alpha_zero()

Set alpha matrix to zero.

class wf_psf.psf_models.tf_layers.TF_batch_mono_PSF(*args, **kwargs)

Bases: Layer

Calculate a monochromatic PSF from a batch of OPDs.

The calculation of the phase_N variable is done with the SimPSFToolkit class but outside the TF class.

Parameters:

• obscurations (Tensor [opd_dim, opd_dim]) – Obscurations to apply to the wavefront.

• psf_batch (Tensor [batch_size, output_dim, output_dim]) – Tensor containing the psfs that will be updated each time a calculation is required. Can be started with zeros.

• output_Q (int) – Output oversampling value.

• output_dim (int) – Output PSF stamp dimension.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
calculate_mono_PSF(current_opd)	Calculate monochromatic PSF from OPD info.
call(opd_batch)	Calculate the batch poly PSFs.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
init_mono_PSF()	Initialise or restart the PSF generator.
set_lambda_phaseN([phase_N, lambda_obs])	Set the lambda value for monochromatic PSFs and the phaseN.
set_output_params(output_Q, output_dim)	Set output patams, Q and dimension.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

calculate_mono_PSF(current_opd)

Calculate monochromatic PSF from OPD info.

call(opd_batch)

Calculate the batch poly PSFs.

init_mono_PSF()

Initialise or restart the PSF generator.

set_lambda_phaseN(phase_N=914, lambda_obs=0.7)

Set the lambda value for monochromatic PSFs and the phaseN.

set_output_params(output_Q, output_dim)

Set output patams, Q and dimension.

class wf_psf.psf_models.tf_layers.TF_batch_poly_PSF(*args, **kwargs)

Bases: Layer

Calculate a polychromatic PSF from an OPD and stored SED values.

The calculation of the packed values with the respective SED is done with the SimPSFToolkit class but outside the TF class.

Parameters:

• obscurations (Tensor [opd_dim, opd_dim]) – Obscurations to apply to the wavefront.

• packed_SED_data (Tensor [batch_size, 3, n_bins_lda]) –

  Comes from: tf.convert_to_tensor(list(list(Tensor,Tensor,Tensor))) Where each inner list consist of a packed_elem:

  packed_elems: Tuple of tensors Contains three 1D tensors with the parameters needed for the calculation of one monochromatic PSF.

  packed_elems[0]: phase_N packed_elems[1]: lambda_obs packed_elems[2]: SED_norm_val

  The SED data is constant in a FoV.

• psf_batch (Tensor [batch_size, output_dim, output_dim]) – Tensor containing the psfs that will be updated each time a calculation is required. REMOVED!

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
calculate_mono_PSF(packed_elems)	Calculate monochromatic PSF from packed elements.
calculate_poly_PSF(packed_elems)	Calculate a polychromatic PSF.
call(inputs)	Calculate the batch poly PSFs.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

calculate_mono_PSF(packed_elems)

Calculate monochromatic PSF from packed elements.

packed_elems[0]: phase_N packed_elems[1]: lambda_obs packed_elems[2]: SED_norm_val

calculate_poly_PSF(packed_elems)

Calculate a polychromatic PSF.

call(inputs)

Calculate the batch poly PSFs.

class wf_psf.psf_models.tf_layers.TF_physical_layer(*args, **kwargs)

Bases: Layer

Store and calculate the zernike coefficients for a given position.

This layer gives the Zernike contribution of the physical layer. It is fixed and not trainable.

Parameters:

• obs_pos (Tensor (n_stars, 2)) – Observed positions of the n_stars in the dataset. The indexing of the positions has to correspond to the indexing in the zks_prior.

• n_zernikes (int) – Number of Zernike polynomials

• zks_prior (Tensor (n_stars, n_zernikes)) – Zernike coefficients for each position

• interpolation_type (str) – Type of interpolation to be used. Options are: ‘none’, ‘all’, ‘top_K’, ‘independent_Zk’. Default is ‘none’.

• interpolation_args (dict) – Interpolation hyper-parameters. The order of the RBF interpolation, and the K elements in the top_K interpolation.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(positions)	Calculate the prior zernike coefficients for a given position.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
interpolate_all(positions)	Zernike interpolation
interpolate_independent_Zk(positions)	Zernike interpolation
interpolate_top_K(positions)	Zernike interpolation
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

call(positions)

Calculate the prior zernike coefficients for a given position.

The position polynomial matrix and the coefficients should be set before calling this function.

Parameters:

positions (Tensor(batch, 2)) – First element is x-axis, second is y-axis.

Returns:

zernikes_coeffs

Return type:

Tensor(batch, n_zernikes, 1, 1)

interpolate_all(positions)

Zernike interpolation

Right now all the input elements are used to build the RBF interpolant that is going to be used for the interpolation.

interpolate_independent_Zk(positions)

Zernike interpolation

The class wf.utils.IndependentZernikeInterpolation allows to interpolate each order of the Zernike polynomials independently using all the points avaialble to build the interpolant.

interpolate_top_K(positions)

Zernike interpolation

The class wf.utils.ZernikeInterpolation allows to use only the K closest elements for the interpolation. Even though, the interpolation error is smaller the computing time is bigger.

class wf_psf.psf_models.tf_layers.TF_poly_Z_field(*args, **kwargs)

Bases: Layer

Calculate the zernike coefficients for a given position.

This module implements a polynomial model of Zernike coefficient variation.

Parameters:

• n_zernikes (int) – Number of Zernike polynomials to consider

• d_max (int) – Max degree of polynomial determining the FoV variations.

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
assign_coeff_matrix(coeff_mat)	Assign coefficient matrix.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(positions)	Calculate the zernike coefficients for a given position.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_coeff_matrix()	Get coefficient matrix.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_poly_coefficients_shape()	Return the shape of the coefficient matrix.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
init_coeff_matrix()	Initialize coefficient matrix.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

assign_coeff_matrix(coeff_mat)

Assign coefficient matrix.

call(positions)

Calculate the zernike coefficients for a given position.

The position polynomial matrix and the coefficients should be set before calling this function.

Parameters:

positions (Tensor(batch, 2)) – First element is x-axis, second is y-axis.

Returns:

zernikes_coeffs

Return type:

Tensor(batch, n_zernikes, 1, 1)

get_coeff_matrix()

Get coefficient matrix.

get_poly_coefficients_shape()

Return the shape of the coefficient matrix.

init_coeff_matrix()

Initialize coefficient matrix.

class wf_psf.psf_models.tf_layers.TF_zernike_OPD(*args, **kwargs)

Bases: Layer

Turn zernike coefficients into an OPD.

Will use all of the Zernike maps provided. Both the Zernike maps and the Zernike coefficients must be provided.

Parameters:

• zernike_maps (Tensor (Num_coeffs, x_dim, y_dim)) –

• z_coeffs (Tensor (batch_size, n_zernikes, 1, 1)) –

Returns:

opd

Return type:

Tensor (batch_size, x_dim, y_dim)

Attributes:

activity_regularizer

Optional regularizer function for the output of this layer.

compute_dtype

The dtype of the layer’s computations.

dtype

The dtype of the layer weights.

dtype_policy

The dtype policy associated with this layer.

dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

inbound_nodes

Return Functional API nodes upstream of this layer.

input

Retrieves the input tensor(s) of a layer.

input_mask

Retrieves the input mask tensor(s) of a layer.

input_shape

Retrieves the input shape(s) of a layer.

input_spec

InputSpec instance(s) describing the input format for this layer.

losses

List of losses added using the add_loss() API.

metrics

List of metrics added using the add_metric() API.

name

Name of the layer (string), set in the constructor.

name_scope

Returns a tf.name_scope instance for this class.

non_trainable_variables

Sequence of non-trainable variables owned by this module and its submodules.

non_trainable_weights

List of all non-trainable weights tracked by this layer.

outbound_nodes

Return Functional API nodes downstream of this layer.

output

Retrieves the output tensor(s) of a layer.

output_mask

Retrieves the output mask tensor(s) of a layer.

output_shape

Retrieves the output shape(s) of a layer.

stateful

submodules

Sequence of all sub-modules.

supports_masking

Whether this layer supports computing a mask using compute_mask.

trainable

trainable_variables

Sequence of trainable variables owned by this module and its submodules.

trainable_weights

List of all trainable weights tracked by this layer.

updates

variable_dtype

Alias of Layer.dtype, the dtype of the weights.

variables

Returns the list of all layer variables/weights.

weights

Returns the list of all layer variables/weights.

Methods

__call__(*args, **kwargs)	Wraps call, applying pre- and post-processing steps.
add_loss(losses, **kwargs)	Add loss tensor(s), potentially dependent on layer inputs.
add_metric(value[, name])	Adds metric tensor to the layer.
add_update(updates)	Add update op(s), potentially dependent on layer inputs.
add_variable(*args, **kwargs)	Deprecated, do NOT use! Alias for add_weight.
add_weight([name, shape, dtype, ...])	Adds a new variable to the layer.
build(input_shape)	Creates the variables of the layer (optional, for subclass implementers).
call(z_coeffs)	Perform the weighted sum of Zernikes coeffs and maps.
compute_mask(inputs[, mask])	Computes an output mask tensor.
compute_output_shape(input_shape)	Computes the output shape of the layer.
compute_output_signature(input_signature)	Compute the output tensor signature of the layer based on the inputs.
count_params()	Count the total number of scalars composing the weights.
finalize_state()	Finalizes the layers state after updating layer weights.
from_config(config)	Creates a layer from its config.
get_config()	Returns the config of the layer.
get_input_at(node_index)	Retrieves the input tensor(s) of a layer at a given node.
get_input_mask_at(node_index)	Retrieves the input mask tensor(s) of a layer at a given node.
get_input_shape_at(node_index)	Retrieves the input shape(s) of a layer at a given node.
get_output_at(node_index)	Retrieves the output tensor(s) of a layer at a given node.
get_output_mask_at(node_index)	Retrieves the output mask tensor(s) of a layer at a given node.
get_output_shape_at(node_index)	Retrieves the output shape(s) of a layer at a given node.
get_weights()	Returns the current weights of the layer, as NumPy arrays.
set_weights(weights)	Sets the weights of the layer, from NumPy arrays.
with_name_scope(method)	Decorator to automatically enter the module name scope.

call(z_coeffs)

Perform the weighted sum of Zernikes coeffs and maps.

Returns:

opd

Return type:

Tensor (batch_size, x_dim, y_dim)