Wrappers#

Applefy allows to compute contrast curves with different post-processing algorithms. However, it does not come with any implementation of these techniques. Instead, we use existing implementations in packages like PynPoint or VIP.

PynPoint Wrappers#

The following wrapper classes allow to use PynPoint with applefy. PynPoint is not on the requirement list of applefy. It has to be installed separately.

class applefy.wrappers.pynpoint.SimplePCAPynPoint(num_pca)#

Bases: DataReductionInterface

The SimplePCAPynPoint is a wrapper around the simple full frame PCA implemented in PynPoint. It only allows to compute the residuals for a fixed number of PCA components. The advantage of this wrapper over MultiComponentPCAPynPoint() is, that it does not require to create a PynPoint database file.

__init__(num_pca)#

Constructor of the class.

Parameters: num_pca (int) – The number of PCA components to be used.

get_method_keys()#

Get the method name “PCA (#num_pca components)”.

Return type: List[str]
Returns: A list with one string “PCA (#num_pca components)”.

__call__(stack_with_fake_planet, parang_rad, psf_template, exp_id)#

Compute the full-frame PCA.

Parameters

stack_with_fake_planet (ndarray) – A 3d numpy array of the observation sequence. Fake plants are inserted by applefy in advance.
parang_rad (ndarray) – A 1d numpy array containing the parallactic angles in radians.
psf_template (ndarray) – A 2d numpy array with the psf-template (usually the unsaturated star).
exp_id (str) – Experiment ID of the config used to add the fake planet. It is a unique string and can be used to store intermediate results. See generate_fake_planet_experiments() for more information about the config files.

Return type

Dict[str, ndarray]

Returns

A dictionary which contains the residual of the PCA reduction with the dict-key “PCA (#num_pca components)”.

class applefy.wrappers.pynpoint.MultiComponentPCAPynPoint(num_pcas, scratch_dir, num_cpus_pynpoint=1)#

Bases: DataReductionInterface

The MultiComponentPCAPynPoint is a wrapper around the full frame PCA implemented in PynPoint. While the wrapper SimplePCAPynPoint() only accepts a single fixed number of PCA components, MultiComponentPCAPynPoint computes several residuals with different number of components. This can be more efficient as the PCA basis needs to be computed only once. The disadvantage over SimplePCAPynPoint() is that MultiComponentPCAPynPoint needs to create a Pynpoint database and delete it after computing the residuals.

__init__(num_pcas, scratch_dir, num_cpus_pynpoint=1)#

Constructor of the class.

Parameters

num_pcas (List[int]) – List of the number of PCA components to be used.
scratch_dir (Path) – A directory to store the Pynpoint database. Any Pynpoint database created during the computation will be deleted afterwards.
num_cpus_pynpoint (int) – Number of CPU cores used by Pynpoint.

get_method_keys()#

Get the method name “PCA (#num_pca components)”.

Return type: List[str]
Returns: A list with strings “PCA (#num_pca components)” (one for each value in num_pcas.

__call__(stack_with_fake_planet, parang_rad, psf_template, exp_id)#

Compute the full-frame PCA for several numbers of PCA components.

Parameters

stack_with_fake_planet (ndarray) – A 3d numpy array of the observation sequence. Fake plants are inserted by applefy in advance.
parang_rad (ndarray) – A 1d numpy array containing the parallactic angles in radians.
psf_template (ndarray) – A 2d numpy array with the psf-template (usually the unsaturated star).
exp_id (str) – Experiment ID of the config used to add the fake planet. It is a unique string and can be used to store intermediate results. See generate_fake_planet_experiments() for more information about the config files.

Return type

Dict[str, ndarray]

Returns

A dictionary which contains the residuals of the PCA reduction with the dict-keys “PCA (#num_pca components)”.

VIP Wrappers#

The following wrapper classes allow to use VIP with applefy. VIP is not on the requirement list of applefy. It has to be installed separately.

class applefy.wrappers.vip.SimplePCAvip(num_pca, kwarg=None)#

Bases: DataReductionInterface

The SimplePCAvip is a wrapper around the simple full frame PCA implemented in VIP. It only allows to compute the residuals for a fixed number of PCA components.

__init__(num_pca, kwarg=None)#

Constructor of the class.

Parameters

num_pca (int) – The number of PCA components to be used.
kwarg (Optional[dict]) – Additional arguments (see documentation in VIP).

get_method_keys()#

Get the method name “PCA (#num_pca components)”.

Return type: List[str]
Returns: A list with one string “PCA (#num_pca components)”.

__call__(stack_with_fake_planet, parang_rad, psf_template, exp_id)#

Compute the full-frame PCA.

Parameters

stack_with_fake_planet (ndarray) – A 3d numpy array of the observation sequence. Fake plants are inserted by applefy in advance.
parang_rad (ndarray) – A 1d numpy array containing the parallactic angles in radians.
psf_template (ndarray) – A 2d numpy array with the psf-template (usually the unsaturated star).
exp_id (str) – Experiment ID of the config used to add the fake planet. It is a unique string and can be used to store intermediate results. See generate_fake_planet_experiments() for more information about the config files.

Return type

Dict[str, ndarray]

Returns

A dictionary which contains the residual of the PCA reduction with the dict-key “PCA (#num_pca components)”.

class applefy.wrappers.vip.MultiComponentPCAvip(num_pcas, kwarg=None)#

Bases: DataReductionInterface

The MultiComponentPCAvip is a wrapper around the full frame PCA implemented in VIP. While the wrapper SimplePCAvip() only accepts a single fixed number of PCA components, MultiComponentPCAvip computes several residuals with different number of components.

__init__(num_pcas, kwarg=None)#

Constructor of the class.

Parameters

num_pcas (Union[Tuple[int, int, int], List[int]]) – Either a list which defines the number of components to be computed, or a tuple which defines the range. If a tuple: (Min components, Max components, steps).
kwarg (Optional[dict]) – Additional arguments (see documentation in VIP).

get_method_keys()#

Get the method name “PCA (#num_pca components)”.

Return type: List[str]
Returns: A list with strings “PCA (#num_pca components)” (one for each value in num_pcas.

__call__(stack_with_fake_planet, parang_rad, psf_template, exp_id)#

Compute the full-frame PCA for several numbers of PCA components.

Parameters

stack_with_fake_planet (ndarray) – A 3d numpy array of the observation sequence. Fake plants are inserted by applefy in advance.
parang_rad (ndarray) – A 1d numpy array containing the parallactic angles in radians.
psf_template (ndarray) – A 2d numpy array with the psf-template (usually the unsaturated star).
exp_id (str) – Experiment ID of the config used to add the fake planet. It is a unique string and can be used to store intermediate results. See generate_fake_planet_experiments() for more information about the config files.

Return type

Dict[str, ndarray]

Returns

A dictionary which contains the residuals of the PCA reduction with the dict-keys “PCA (#num_pca components)”.

class applefy.wrappers.vip.PCAannvip(num_pca, kwarg=None)#

Bases: DataReductionInterface

The PCAannvip is a wrapper around the annular PCA implemented in VIP. It only allows to compute the residuals for a fixed number of PCA components.

__init__(num_pca, kwarg=None)#

Constructor of the class.

Parameters

num_pca (int) – The number of PCA components to be used.
kwarg (Optional[dict]) – Additional arguments (see documentation in VIP).

get_method_keys()#

Get the method name “PCAann (#num_pca components)”.

Return type: List[str]
Returns: A list with one string “PCAann (#num_pca components)”.

__call__(stack_with_fake_planet, parang_rad, psf_template, exp_id)#

Compute the annular PCA.

Parameters

stack_with_fake_planet (ndarray) – A 3d numpy array of the observation sequence. Fake plants are inserted by applefy in advance.
parang_rad (ndarray) – A 1d numpy array containing the parallactic angles in radians.
psf_template (ndarray) – A 2d numpy array with the psf-template (usually the unsaturated star).
exp_id (str) – Experiment ID of the config used to add the fake planet. It is a unique string and can be used to store intermediate results. See generate_fake_planet_experiments() for more information about the config files.

Return type

Dict[str, ndarray]

Returns

A dictionary which contains the residual of the PCA reduction with the dict-key “PCAann (#num_pca components)”.

class applefy.wrappers.vip.MultiComponentPCAannvip(num_pcas, kwarg=None)#

Bases: DataReductionInterface

The MultiComponentPCAannvip is a wrapper around the annular PCA implemented in VIP. While the wrapper PCAannvip() only accepts a single fixed number of PCA components, MultiComponentPCAannvip computes several residuals with different number of components.

__init__(num_pcas, kwarg=None)#

Constructor of the class.

Parameters

num_pcas (List[int]) – A list which defines the number of components to be computed.
kwarg (Optional[dict]) – Additional arguments (see documentation in VIP).

get_method_keys()#

Get the method name “PCAann (#num_pca components)”.

Return type: List[str]
Returns: A list with strings “PCAann (#num_pca components)” (one for each value in num_pcas.

__call__(stack_with_fake_planet, parang_rad, psf_template, exp_id)#

Compute the full-frame PCA for several numbers of PCA components.

Parameters

stack_with_fake_planet (ndarray) – A 3d numpy array of the observation sequence. Fake plants are inserted by applefy in advance.
parang_rad (ndarray) – A 1d numpy array containing the parallactic angles in radians.
psf_template (ndarray) – A 2d numpy array with the psf-template (usually the unsaturated star).
exp_id (str) – Experiment ID of the config used to add the fake planet. It is a unique string and can be used to store intermediate results. See generate_fake_planet_experiments() for more information about the config files.

Return type

Dict[str, ndarray]

Returns

A dictionary which contains the residuals of the PCA reduction with the dict-keys “PCA (#num_pca components)”.