wf_psf.psf_models.tf_psf_field module

class wf_psf.psf_models.tf_psf_field.TF_GT_physical_field(*args, **kwargs)[source]

Bases: Model

Ground truth PSF field forward model with a physical layer

Ground truth PSF field used for evaluation purposes.

Parameters:

zernike_maps (Tensor(n_batch, opd_dim, opd_dim)) – Zernike polynomial maps.
obscurations (Tensor(opd_dim, opd_dim)) – Predefined obscurations of the phase.
batch_size (int) – Batch size
obs_pos (Tensor(n_stars, 2)) – The positions of all the stars
zks_prior (Tensor(n_stars, n_zks)) – The Zernike coeffients of the prior for all the stars
output_Q (float) – Oversampling used. This should match the oversampling Q used to generate the diffraction zero padding that is found in the input packed_SEDs. We call this other Q the input_Q. In that case, we replicate the original sampling of the model used to calculate the input packed_SEDs. The final oversampling of the generated PSFs with respect to the original instrument sampling depend on the division input_Q/output_Q. It is not recommended to use output_Q < 1. Although it works with float values it is better to use integer values.
output_dim (int) – Output dimension of the PSF stamps.

Attributes:

activity_regularizer: Optional regularizer function for the output of this layer.
compute_dtype: The dtype of the layer’s computations.
distribute_strategy: The tf.distribute.Strategy this model was created under.
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.
layers
losses: List of losses added using the add_loss() API.
metrics: Returns the model’s metrics added using compile(), add_metric() APIs.
metrics_names: Returns the model’s display labels for all outputs.
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.
run_eagerly: Settable attribute indicating whether the model should run eagerly.
state_updates: Deprecated, do NOT use!
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

`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)	Builds the model based on input shapes received.
`call`(inputs[, training])	Define the PSF field forward model.
`compile`([optimizer, loss, metrics, ...])	Configures the model for training.
`compute_loss`([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
`compute_mask`(inputs[, mask])	Computes an output mask tensor.
`compute_metrics`(x, y, y_pred, sample_weight)	Update metric states and collect all metrics to be returned.
`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.
`compute_zernikes`(input_positions)	Compute Zernike coefficients at a batch of positions
`count_params`()	Count the total number of scalars composing the weights.
`evaluate`([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
`evaluate_generator`(generator[, steps, ...])	Evaluates the model on a data generator.
`finalize_state`()	Finalizes the layers state after updating layer weights.
`fit`([x, y, batch_size, epochs, verbose, ...])	Trains the model for a fixed number of epochs (iterations on a dataset).
`fit_generator`(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
`from_config`(config[, custom_objects])	Creates a layer from its config.
`get_config`()	Returns the config of the Model.
`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_layer`([name, index])	Retrieves a layer based on either its name (unique) or index.
`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`()	Retrieves the weights of the model.
`load_weights`(filepath[, by_name, ...])	Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
`make_predict_function`([force])	Creates a function that executes one step of inference.
`make_test_function`([force])	Creates a function that executes one step of evaluation.
`make_train_function`([force])	Creates a function that executes one step of training.
`predict`(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
`predict_generator`(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
`predict_mono_psfs`(input_positions, ...)	Predict a set of monochromatic PSF at desired positions.
`predict_on_batch`(x)	Returns predictions for a single batch of samples.
`predict_opd`(input_positions)	Predict the OPD at some positions.
`predict_step`(data[, evaluate_step])	Custom predict (inference) step.
`predict_zernikes`(input_positions)	Predict Zernike coefficients at a batch of positions
`reset_metrics`()	Resets the state of all the metrics in the model.
`save`(filepath[, overwrite, ...])	Saves the model to Tensorflow SavedModel or a single HDF5 file.
`save_spec`([dynamic_batch])	Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).
`save_weights`(filepath[, overwrite, ...])	Saves all layer weights.
`set_output_Q`(output_Q[, output_dim])	Set the value of the output_Q parameter.
`set_weights`(weights)	Sets the weights of the layer, from NumPy arrays.
`summary`([line_length, positions, print_fn, ...])	Prints a string summary of the network.
`test_on_batch`(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
`test_step`(data)	The logic for one evaluation step.
`to_json`(**kwargs)	Returns a JSON string containing the network configuration.
`to_yaml`(**kwargs)	Returns a yaml string containing the network configuration.
`train_on_batch`(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
`train_step`(data)	The logic for one training step.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

__call__
reset_states

call(inputs, training=True)[source]

Define the PSF field forward model.

[1] From positions to Zernike coefficients [2] From Zernike coefficients to OPD maps [3] From OPD maps and SED info to polychromatic PSFs

OPD: Optical Path Differences

compute_zernikes(input_positions)[source]

Compute Zernike coefficients at a batch of positions

This only includes the physical layer

Parameters:: input_positions (Tensor [batch_dim, 2]) – Positions to compute the Zernikes.
Returns:: zks_coeffs – Zernikes at requested positions
Return type:: Tensor [batch, n_zks_total, 1, 1]

predict_mono_psfs(input_positions, lambda_obs, phase_N)[source]

Predict a set of monochromatic PSF at desired positions.

Parameters:

input_positions (Tensor [batch_dim, 2]) – Positions at which to compute the PSF
lambda_obs (float) – Observed wavelength in um.
phase_N (int) – Required wavefront dimension. Should be calculated with as: simPSF_np = wf.SimPSFToolkit(...) phase_N = simPSF_np.feasible_N(lambda_obs)

predict_opd(input_positions)[source]

Predict the OPD at some positions.

Parameters:: input_positions (Tensor [batch_dim, 2]) – Positions to predict the OPD.
Returns:: opd_maps – OPD at requested positions.
Return type:: Tensor [batch, opd_dim, opd_dim]

predict_step(data, evaluate_step=False)[source]

Custom predict (inference) step.

It is needed as the physical layer requires a special interpolation (different from training).

predict_zernikes(input_positions)[source]

Predict Zernike coefficients at a batch of positions

This only includes the physical layer. For the moment, it is the same as the compute_zernikes. No interpolation done to avoid interpolation error in the metrics.

Parameters:: input_positions (Tensor [batch_dim, 2]) – Positions to compute the Zernikes.
Returns:: zks_coeffs – Zernikes at requested positions
Return type:: Tensor [batch, n_zks_total, 1, 1]

set_output_Q(output_Q, output_dim=None)[source]: Set the value of the output_Q parameter. Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.

class wf_psf.psf_models.tf_psf_field.TF_PSF_field_model(*args, **kwargs)[source]

Bases: Model

Parametric PSF field model!

Fully parametric model based on the Zernike polynomial basis.

Parameters:

zernike_maps (Tensor(n_batch, opd_dim, opd_dim)) – Zernike polynomial maps.
obscurations (Tensor(opd_dim, opd_dim)) – Predefined obscurations of the phase.
batch_size (int) – Batch size.
output_Q (float) – Oversampling used. This should match the oversampling Q used to generate the diffraction zero padding that is found in the input packed_SEDs. We call this other Q the input_Q. In that case, we replicate the original sampling of the model used to calculate the input packed_SEDs. The final oversampling of the generated PSFs with respect to the original instrument sampling depend on the division input_Q/output_Q. It is not recommended to use output_Q < 1. Although it works with float values it is better to use integer values.
l2_param (float) – Parameter going with the l2 loss on the opd. If it is 0. the loss is not added. Default is 0..
output_dim (int) – Output dimension of the PSF stamps.
n_zernikes (int) – Order of the Zernike polynomial for the parametric model.
d_max (int) – Maximum degree of the polynomial for the Zernike coefficient variations.
x_lims ([float, float]) – Limits for the x coordinate of the PSF field.
y_lims ([float, float]) – Limits for the x coordinate of the PSF field.
coeff_mat (Tensor or None) – Initialization of the coefficient matrix defining the parametric psf field model.

Attributes:

activity_regularizer: Optional regularizer function for the output of this layer.
compute_dtype: The dtype of the layer’s computations.
distribute_strategy: The tf.distribute.Strategy this model was created under.
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.
layers
losses: List of losses added using the add_loss() API.
metrics: Returns the model’s metrics added using compile(), add_metric() APIs.
metrics_names: Returns the model’s display labels for all outputs.
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.
run_eagerly: Settable attribute indicating whether the model should run eagerly.
state_updates: Deprecated, do NOT use!
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

`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)	Builds the model based on input shapes received.
`call`(inputs)	Define the PSF field forward model.
`compile`([optimizer, loss, metrics, ...])	Configures the model for training.
`compute_loss`([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
`compute_mask`(inputs[, mask])	Computes an output mask tensor.
`compute_metrics`(x, y, y_pred, sample_weight)	Update metric states and collect all metrics to be returned.
`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.
`evaluate`([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
`evaluate_generator`(generator[, steps, ...])	Evaluates the model on a data generator.
`finalize_state`()	Finalizes the layers state after updating layer weights.
`fit`([x, y, batch_size, epochs, verbose, ...])	Trains the model for a fixed number of epochs (iterations on a dataset).
`fit_generator`(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
`from_config`(config[, custom_objects])	Creates a layer from its config.
`get_coeff_matrix`()	Get coefficient matrix.
`get_config`()	Returns the config of the Model.
`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_layer`([name, index])	Retrieves a layer based on either its name (unique) or index.
`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`()	Retrieves the weights of the model.
`load_weights`(filepath[, by_name, ...])	Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
`make_predict_function`([force])	Creates a function that executes one step of inference.
`make_test_function`([force])	Creates a function that executes one step of evaluation.
`make_train_function`([force])	Creates a function that executes one step of training.
`predict`(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
`predict_generator`(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
`predict_mono_psfs`(input_positions, ...)	Predict a set of monochromatic PSF at desired positions.
`predict_on_batch`(x)	Returns predictions for a single batch of samples.
`predict_opd`(input_positions)	Predict the OPD at some positions.
`predict_step`(data)	The logic for one inference step.
`reset_metrics`()	Resets the state of all the metrics in the model.
`save`(filepath[, overwrite, ...])	Saves the model to Tensorflow SavedModel or a single HDF5 file.
`save_spec`([dynamic_batch])	Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).
`save_weights`(filepath[, overwrite, ...])	Saves all layer weights.
`set_output_Q`(output_Q[, output_dim])	Set the value of the output_Q parameter.
`set_weights`(weights)	Sets the weights of the layer, from NumPy arrays.
`summary`([line_length, positions, print_fn, ...])	Prints a string summary of the network.
`test_on_batch`(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
`test_step`(data)	The logic for one evaluation step.
`to_json`(**kwargs)	Returns a JSON string containing the network configuration.
`to_yaml`(**kwargs)	Returns a yaml string containing the network configuration.
`train_on_batch`(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
`train_step`(data)	The logic for one training step.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

__call__
reset_states

assign_coeff_matrix(coeff_mat)[source]: Assign coefficient matrix.

call(inputs)[source]

Define the PSF field forward model.

[1] From positions to Zernike coefficients [2] From Zernike coefficients to OPD maps [3] From OPD maps and SED info to polychromatic PSFs

OPD: Optical Path Differences

get_coeff_matrix()[source]: Get coefficient matrix.

predict_mono_psfs(input_positions, lambda_obs, phase_N)[source]

Predict a set of monochromatic PSF at desired positions.

input_positions: Tensor(batch_dim x 2)

lambda_obs: float: Observed wavelength in um.
phase_N: int: Required wavefront dimension. Should be calculated with as: simPSF_np = wf.SimPSFToolkit(...) phase_N = simPSF_np.feasible_N(lambda_obs)

predict_opd(input_positions)[source]

Predict the OPD at some positions.

Parameters:: input_positions (Tensor(batch_dim x 2)) – Positions to predict the OPD.
Returns:: opd_maps – OPD at requested positions.
Return type:: Tensor [batch x opd_dim x opd_dim]

set_output_Q(output_Q, output_dim=None)[source]: Set the value of the output_Q parameter. Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.

class wf_psf.psf_models.tf_psf_field.TF_SemiParam_field(*args, **kwargs)[source]

Bases: Model

PSF field forward model!

Semi parametric model based on the Zernike polynomial basis. The

Parameters:

zernike_maps (Tensor(n_batch, opd_dim, opd_dim)) – Zernike polynomial maps.
obscurations (Tensor(opd_dim, opd_dim)) – Predefined obscurations of the phase.
batch_size (int) – Batch sizet
output_Q (float) – Oversampling used. This should match the oversampling Q used to generate the diffraction zero padding that is found in the input packed_SEDs. We call this other Q the input_Q. In that case, we replicate the original sampling of the model used to calculate the input packed_SEDs. The final oversampling of the generated PSFs with respect to the original instrument sampling depend on the division input_Q/output_Q. It is not recommended to use output_Q < 1. Although it works with float values it is better to use integer values.
d_max_nonparam (int) – Maximum degree of the polynomial for the non-parametric variations.
l2_param (float) – Parameter going with the l2 loss on the opd. If it is 0. the loss is not added. Default is 0..
output_dim (int) – Output dimension of the PSF stamps.
n_zernikes (int) – Order of the Zernike polynomial for the parametric model.
d_max (int) – Maximum degree of the polynomial for the Zernike coefficient variations.
x_lims ([float, float]) – Limits for the x coordinate of the PSF field.
y_lims ([float, float]) – Limits for the x coordinate of the PSF field.
coeff_mat (Tensor or None) – Initialization of the coefficient matrix defining the parametric psf field model.

Attributes:

activity_regularizer: Optional regularizer function for the output of this layer.
compute_dtype: The dtype of the layer’s computations.
distribute_strategy: The tf.distribute.Strategy this model was created under.
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.
layers
losses: List of losses added using the add_loss() API.
metrics: Returns the model’s metrics added using compile(), add_metric() APIs.
metrics_names: Returns the model’s display labels for all outputs.
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.
run_eagerly: Settable attribute indicating whether the model should run eagerly.
state_updates: Deprecated, do NOT use!
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

`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.
`assign_coeff_matrix`(coeff_mat)	Assign coefficient matrix.
`build`(input_shape)	Builds the model based on input shapes received.
`call`(inputs)	Define the PSF field forward model.
`compile`([optimizer, loss, metrics, ...])	Configures the model for training.
`compute_loss`([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
`compute_mask`(inputs[, mask])	Computes an output mask tensor.
`compute_metrics`(x, y, y_pred, sample_weight)	Update metric states and collect all metrics to be returned.
`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.
`evaluate`([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
`evaluate_generator`(generator[, steps, ...])	Evaluates the model on a data generator.
`finalize_state`()	Finalizes the layers state after updating layer weights.
`fit`([x, y, batch_size, epochs, verbose, ...])	Trains the model for a fixed number of epochs (iterations on a dataset).
`fit_generator`(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
`from_config`(config[, custom_objects])	Creates a layer from its config.
`get_coeff_matrix`()	Get coefficient matrix.
`get_config`()	Returns the config of the Model.
`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_layer`([name, index])	Retrieves a layer based on either its name (unique) or index.
`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`()	Retrieves the weights of the model.
`load_weights`(filepath[, by_name, ...])	Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
`make_predict_function`([force])	Creates a function that executes one step of inference.
`make_test_function`([force])	Creates a function that executes one step of evaluation.
`make_train_function`([force])	Creates a function that executes one step of training.
`predict`(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
`predict_generator`(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
`predict_mono_psfs`(input_positions, ...)	Predict a set of monochromatic PSF at desired positions.
`predict_on_batch`(x)	Returns predictions for a single batch of samples.
`predict_opd`(input_positions)	Predict the OPD at some positions.
`predict_step`(data)	The logic for one inference step.
`project_DD_features`(tf_zernike_cube)	Project non-parametric wavefront onto first n_z Zernikes and transfer their parameters to the parametric model.
`reset_metrics`()	Resets the state of all the metrics in the model.
`save`(filepath[, overwrite, ...])	Saves the model to Tensorflow SavedModel or a single HDF5 file.
`save_spec`([dynamic_batch])	Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).
`save_weights`(filepath[, overwrite, ...])	Saves all layer weights.
`set_nonzero_nonparam`()	Set to non-zero the non-parametric part.
`set_output_Q`(output_Q[, output_dim])	Set the value of the output_Q parameter.
`set_trainable_layers`([param_bool, nonparam_bool])	Set the layers to be trainable or not.
`set_weights`(weights)	Sets the weights of the layer, from NumPy arrays.
`set_zero_nonparam`()	Set to zero the non-parametric part.
`summary`([line_length, positions, print_fn, ...])	Prints a string summary of the network.
`test_on_batch`(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
`test_step`(data)	The logic for one evaluation step.
`to_json`(**kwargs)	Returns a JSON string containing the network configuration.
`to_yaml`(**kwargs)	Returns a yaml string containing the network configuration.
`train_on_batch`(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
`train_step`(data)	The logic for one training step.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

__call__
reset_states

assign_S_mat(S_mat)[source]: Assign DD features matrix.

assign_coeff_matrix(coeff_mat)[source]: Assign coefficient matrix.

call(inputs)[source]

Define the PSF field forward model.

[1] From positions to Zernike coefficients [2] From Zernike coefficients to OPD maps [3] From OPD maps and SED info to polychromatic PSFs

OPD: Optical Path Differences

get_coeff_matrix()[source]: Get coefficient matrix.

predict_mono_psfs(input_positions, lambda_obs, phase_N)[source]

Predict a set of monochromatic PSF at desired positions.

input_positions: Tensor(batch_dim x 2)

lambda_obs: float: Observed wavelength in um.
phase_N: int: Required wavefront dimension. Should be calculated with as: simPSF_np = wf.SimPSFToolkit(...) phase_N = simPSF_np.feasible_N(lambda_obs)

predict_opd(input_positions)[source]

Predict the OPD at some positions.

Parameters:: input_positions (Tensor(batch_dim x 2)) – Positions to predict the OPD.
Returns:: opd_maps – OPD at requested positions.
Return type:: Tensor [batch x opd_dim x opd_dim]

project_DD_features(tf_zernike_cube)[source]: Project non-parametric wavefront onto first n_z Zernikes and transfer their parameters to the parametric model.

set_nonzero_nonparam()[source]: Set to non-zero the non-parametric part.

set_output_Q(output_Q, output_dim=None)[source]: Set the value of the output_Q parameter. Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.

set_trainable_layers(param_bool=True, nonparam_bool=True)[source]: Set the layers to be trainable or not.

set_zero_nonparam()[source]: Set to zero the non-parametric part.

class wf_psf.psf_models.tf_psf_field.TF_physical_poly_field(*args, **kwargs)[source]

Bases: Model

PSF field forward model with a physical layer

WaveDiff-original with a physical layer

Parameters:

zernike_maps (Tensor(n_batch, opd_dim, opd_dim)) – Zernike polynomial maps.
obscurations (Tensor(opd_dim, opd_dim)) – Predefined obscurations of the phase.
batch_size (int) – Batch size
obs_pos (Tensor(n_stars, 2)) – The positions of all the stars
zks_prior (Tensor(n_stars, n_zks)) – The Zernike coeffients of the prior for all the stars
output_Q (float) – Oversampling used. This should match the oversampling Q used to generate the diffraction zero padding that is found in the input packed_SEDs. We call this other Q the input_Q. In that case, we replicate the original sampling of the model used to calculate the input packed_SEDs. The final oversampling of the generated PSFs with respect to the original instrument sampling depend on the division input_Q/output_Q. It is not recommended to use output_Q < 1. Although it works with float values it is better to use integer values.
d_max_nonparam (int) – Maximum degree of the polynomial for the non-parametric variations.
l2_param (float) – Parameter going with the l2 loss on the opd. If it is 0. the loss is not added. Default is 0..
output_dim (int) – Output dimension of the PSF stamps.
n_zks_param (int) – Order of the Zernike polynomial for the parametric model.
d_max (int) – Maximum degree of the polynomial for the Zernike coefficient variations.
x_lims ([float, float]) – Limits for the x coordinate of the PSF field.
y_lims ([float, float]) – Limits for the x coordinate of the PSF field.
coeff_mat (Tensor or None) – Initialization of the coefficient matrix defining the parametric psf field model.
interpolation_type (str) – Option for the interpolation type of the physical layer. Default is no interpolation.
interpolation_args (dict) – Additional arguments for the interpolation.

Attributes:

activity_regularizer: Optional regularizer function for the output of this layer.
compute_dtype: The dtype of the layer’s computations.
distribute_strategy: The tf.distribute.Strategy this model was created under.
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.
layers
losses: List of losses added using the add_loss() API.
metrics: Returns the model’s metrics added using compile(), add_metric() APIs.
metrics_names: Returns the model’s display labels for all outputs.
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.
run_eagerly: Settable attribute indicating whether the model should run eagerly.
state_updates: Deprecated, do NOT use!
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

`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)	Builds the model based on input shapes received.
`call`(inputs[, training])	Define the PSF field forward model.
`compile`([optimizer, loss, metrics, ...])	Configures the model for training.
`compute_loss`([x, y, y_pred, sample_weight])	Compute the total loss, validate it, and return it.
`compute_mask`(inputs[, mask])	Computes an output mask tensor.
`compute_metrics`(x, y, y_pred, sample_weight)	Update metric states and collect all metrics to be returned.
`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.
`compute_zernikes`(input_positions)	Compute Zernike coefficients at a batch of positions
`count_params`()	Count the total number of scalars composing the weights.
`evaluate`([x, y, batch_size, verbose, ...])	Returns the loss value & metrics values for the model in test mode.
`evaluate_generator`(generator[, steps, ...])	Evaluates the model on a data generator.
`finalize_state`()	Finalizes the layers state after updating layer weights.
`fit`([x, y, batch_size, epochs, verbose, ...])	Trains the model for a fixed number of epochs (iterations on a dataset).
`fit_generator`(generator[, steps_per_epoch, ...])	Fits the model on data yielded batch-by-batch by a Python generator.
`from_config`(config[, custom_objects])	Creates a layer from its config.
`get_coeff_matrix`()	Get coefficient matrix.
`get_config`()	Returns the config of the Model.
`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_layer`([name, index])	Retrieves a layer based on either its name (unique) or index.
`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`()	Retrieves the weights of the model.
`load_weights`(filepath[, by_name, ...])	Loads all layer weights, either from a TensorFlow or an HDF5 weight file.
`make_predict_function`([force])	Creates a function that executes one step of inference.
`make_test_function`([force])	Creates a function that executes one step of evaluation.
`make_train_function`([force])	Creates a function that executes one step of training.
`predict`(x[, batch_size, verbose, steps, ...])	Generates output predictions for the input samples.
`predict_generator`(generator[, steps, ...])	Generates predictions for the input samples from a data generator.
`predict_mono_psfs`(input_positions, ...)	Predict a set of monochromatic PSF at desired positions.
`predict_on_batch`(x)	Returns predictions for a single batch of samples.
`predict_opd`(input_positions)	Predict the OPD at some positions.
`predict_step`(data[, evaluate_step])	Custom predict (inference) step.
`predict_zernikes`(input_positions)	Predict Zernike coefficients at a batch of positions
`reset_metrics`()	Resets the state of all the metrics in the model.
`save`(filepath[, overwrite, ...])	Saves the model to Tensorflow SavedModel or a single HDF5 file.
`save_spec`([dynamic_batch])	Returns the tf.TensorSpec of call inputs as a tuple (args, kwargs).
`save_weights`(filepath[, overwrite, ...])	Saves all layer weights.
`set_nonzero_nonparam`()	Set to non-zero the non-parametric part.
`set_output_Q`(output_Q[, output_dim])	Set the value of the output_Q parameter.
`set_trainable_layers`([param_bool, nonparam_bool])	Set the layers to be trainable or not.
`set_weights`(weights)	Sets the weights of the layer, from NumPy arrays.
`set_zero_nonparam`()	Set to zero the non-parametric part.
`summary`([line_length, positions, print_fn, ...])	Prints a string summary of the network.
`test_on_batch`(x[, y, sample_weight, ...])	Test the model on a single batch of samples.
`test_step`(data)	The logic for one evaluation step.
`to_json`(**kwargs)	Returns a JSON string containing the network configuration.
`to_yaml`(**kwargs)	Returns a yaml string containing the network configuration.
`train_on_batch`(x[, y, sample_weight, ...])	Runs a single gradient update on a single batch of data.
`train_step`(data)	The logic for one training step.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.
`zks_pad`(zk_param, zk_prior)	Pad the zernike coefficients with zeros to have the same length.

__call__
reset_states

assign_coeff_matrix(coeff_mat)[source]: Assign coefficient matrix.

call(inputs, training=True)[source]

Define the PSF field forward model.

[1] From positions to Zernike coefficients [2] From Zernike coefficients to OPD maps [3] From OPD maps and SED info to polychromatic PSFs

OPD: Optical Path Differences

compute_zernikes(input_positions)[source]

Compute Zernike coefficients at a batch of positions

This includes the parametric model and the physical layer

Parameters:: input_positions (Tensor [batch_dim, 2]) – Positions to compute the Zernikes.
Returns:: zks_coeffs – Zernikes at requested positions
Return type:: Tensor [batch, n_zks_total, 1, 1]

get_coeff_matrix()[source]: Get coefficient matrix.

predict_mono_psfs(input_positions, lambda_obs, phase_N)[source]

Predict a set of monochromatic PSF at desired positions.

Parameters:

input_positions (Tensor [batch_dim, 2]) – Positions at which to compute the PSF
lambda_obs (float) – Observed wavelength in um.
phase_N (int) – Required wavefront dimension. Should be calculated with as: simPSF_np = wf.SimPSFToolkit(...) phase_N = simPSF_np.feasible_N(lambda_obs)

predict_opd(input_positions)[source]

Predict the OPD at some positions.

Parameters:: input_positions (Tensor [batch_dim, 2]) – Positions to predict the OPD.
Returns:: opd_maps – OPD at requested positions.
Return type:: Tensor [batch, opd_dim, opd_dim]

predict_step(data, evaluate_step=False)[source]

Custom predict (inference) step.

It is needed as the physical layer requires a special interpolation (different from training).

predict_zernikes(input_positions)[source]

Predict Zernike coefficients at a batch of positions

This includes the parametric model and the physical layer. The prediction of the physical layer to positions is not used at training time.

Parameters:: input_positions (Tensor [batch_dim, 2]) – Positions to compute the Zernikes.
Returns:: zks_coeffs – Zernikes at requested positions
Return type:: Tensor [batch, n_zks_total, 1, 1]

set_nonzero_nonparam()[source]: Set to non-zero the non-parametric part.

set_output_Q(output_Q, output_dim=None)[source]: Set the value of the output_Q parameter. Useful for generating/predicting PSFs at a different sampling wrt the observation sampling.

set_trainable_layers(param_bool=True, nonparam_bool=True)[source]: Set the layers to be trainable or not.

set_zero_nonparam()[source]: Set to zero the non-parametric part.

zks_pad(zk_param, zk_prior)[source]

Pad the zernike coefficients with zeros to have the same length.

Pad them to have n_zks_total length.

Parameters:

zk_param (Tensor [batch, n_zks_param, 1, 1]) – Zernike coefficients for the parametric part
zk_prior (Tensor [batch, n_zks_prior, 1, 1]) – Zernike coefficients for the prior part

Returns:

zk_param (Tensor [batch, n_zks_total, 1, 1]) – Zernike coefficients for the parametric part
zk_prior (Tensor [batch, n_zks_total, 1, 1]) – Zernike coefficients for the prior part

wf_psf.psf_models.tf_psf_field.build_PSF_model(model_inst, optimizer=None, loss=None, metrics=None)[source]

Define the model-compilation parameters.

Specially the loss function, the optimizer and the metrics.