sup3r.preprocessing.conditional_moment_batch_handling.BatchMom2SepSF

class BatchMom2SepSF(low_res, high_res, output, mask)[source]

Bases: BatchMom1SF

Batch of low_res, high_res and output data when learning second moment of subfilter vel separate from first moment

Stores low, high res, output and mask data

Parameters:

low_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
high_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
output (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
mask (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

Methods

`get_coarse_batch`(high_res, s_enhance[, ...])	Coarsen high res data and return Batch with high res and low res data
`make_mask`(high_res[, s_padding, t_padding, ...])	Make mask for output.
`make_output`(low_res, high_res[, s_enhance, ...])	Make custom batch output

Attributes

`high_res`	Get the high-resolution data for the batch.
`low_res`	Get the low-resolution data for the batch.
`mask`	Get the mask for the batch.
`output`	Get the output for the batch.
`shape`	Get the (low_res_shape, high_res_shape) shapes.

static make_output(low_res, high_res, s_enhance=None, t_enhance=None, model_mom1=None, hr_features_ind=None, t_enhance_mode='constant')[source]

Make custom batch output

Parameters:

low_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
high_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
s_enhance (int | None) – Spatial enhancement factor
t_enhance (int | None) – Temporal enhancement factor
model_mom1 (Sup3rCondMom | None) – Model used to modify the make the batch output
hr_features_ind (list | np.ndarray | None) – List/array of feature channel indices that are used for generative output, without any feature indices used only for training.
t_enhance_mode (str) – Enhancing mode for temporal subfilter. Can be either constant or linear

Returns:

SF**2 (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features) SF is subfilter, HR is high-res and LR is low-res SF = HR - LR

classmethod get_coarse_batch(high_res, s_enhance, t_enhance=1, temporal_coarsening_method='subsample', temporal_enhancing_method='constant', hr_features_ind=None, features=None, smoothing=None, smoothing_ignore=None, model_mom1=None, s_padding=None, t_padding=None, end_t_padding=False)

Coarsen high res data and return Batch with high res and low res data

Parameters:

high_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
s_enhance (int) – Factor by which to coarsen spatial dimensions of the high resolution data
t_enhance (int) – Factor by which to coarsen temporal dimension of the high resolution data
temporal_coarsening_method (str) – Method to use for temporal coarsening. Can be subsample, average, or total
temporal_enhancing_method (str) – [constant, linear] Method to enhance temporally when constructing subfilter. At every temporal location, a low-res temporal data is substracted from the high-res temporal data predicted. constant will assume that the low-res temporal data is constant between landmarks. linear will linearly interpolate between landmarks to generate the low-res data to remove from the high-res.
hr_features_ind (list | np.ndarray | None) – List/array of feature channel indices that are used for generative output, without any feature indices used only for training.
features (list | None) – Ordered list of low-resolution training features input to the generative model
smoothing (float | None) – Standard deviation to use for gaussian filtering of the coarse data. This can be tuned by matching the kinetic energy of a low resolution simulation with the kinetic energy of a coarsened and smoothed high resolution simulation. If None no smoothing is performed.
smoothing_ignore (list | None) – List of features to ignore for the smoothing filter. None will smooth all features if smoothing kwarg is not None
model_mom1 (Sup3rCondMom | None) – Model used to modify the make the batch output
s_padding (int | None) – Width of spatial padding to predict only middle part. If None, no padding is used
t_padding (int | None) – Width of temporal padding to predict only middle part. If None, no padding is used
end_t_padding (bool | False) – Zero pad the end of temporal space. Ensures that loss is calculated only if snapshot is surrounded by temporal landmarks. False by default

Returns:

Batch – Batch instance with low and high res data

property high_res: Get the high-resolution data for the batch.

property low_res: Get the low-resolution data for the batch.

static make_mask(high_res, s_padding=None, t_padding=None, end_t_padding=False, t_enhance=None)

Make mask for output. The mask is used to ensure consistency when training conditional moments. Consider the case of learning E(HR|LR) where HR is the high_res and LR is the low_res. In theory, the conditional moment estimation works if the full LR is passed as input and predicts the full HR. In practice, only the LR data that overlaps and surrounds the HR data is useful, ie E(HR|LR) = E(HR|LR_nei) where LR_nei is the LR data that surrounds the HR data. Physically, this is equivalent to saying that data far away from a region of interest does not matter. This allows learning the conditional moments on spatial and temporal chunks only if one restricts the high_res output as being overlapped and surrounded by the input low_res. The role of the mask is to ensure that the input low_res always surrounds the output high_res.

Parameters:

high_res (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)
s_padding (int | None) – Spatial padding size. If None or 0, no padding is applied. None by default
t_padding (int | None) – Temporal padding size. If None or 0, no padding is applied. None by default
end_t_padding (bool | False) – Zero pad the end of temporal space. Ensures that loss is calculated only if snapshot is surrounded by temporal landmarks. False by default
t_enhance (int | None) – Temporal enhancement factor to define end padding. None by default
model_mom1 (Sup3rCondMom | None) – Model used to modify the make the batch output
hr_features_ind (list | np.ndarray | None) – List/array of feature channel indices that are used for generative output, without any feature indices used only for training.

Returns:

mask (np.ndarray) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

property mask: Get the mask for the batch.

property output: Get the output for the batch. Output predicted by the neural net can be different than the high_res when doing moment estimation. For ex: output may be (high_res)**2 We distinguish output from high_res since it may not be possible to recover high_res from output.

property shape: Get the (low_res_shape, high_res_shape) shapes.