sup3r.models.linear.LinearInterp
- class LinearInterp(lr_features, s_enhance, t_enhance, t_centered=False)[source]
Bases:
AbstractInterface
Simple model to do linear interpolation on the spatial and temporal axes
- Parameters:
lr_features (list) – List of feature names that this model will operate on for both input and output. This must match the feature axis ordering in the array input to generate().
s_enhance (int) – Integer factor by which the spatial axes is to be enhanced.
t_enhance (int) – Integer factor by which the temporal axes is to be enhanced.
t_centered (bool) – Flag to switch time axis from time-beginning (Default, e.g. interpolate 00:00 01:00 to 00:00 00:30 01:00 01:30) to time-centered (e.g. interp 01:00 02:00 to 00:45 01:15 01:45 02:15)
Methods
generate
(low_res[, norm_in, un_norm_out, ...])Use the generator model to generate high res data from low res input.
Compute factor by which model will enhance spatial resolution from layer attributes.
Compute factor by which model will enhance temporal resolution from layer attributes.
load
(model_dir[, verbose])Load the LinearInterp model with its params saved to the model_dir created with LinearInterp.save(model_dir)
save
(out_dir)save_params
(out_dir)seed
([s])Set the random seed for reproducible results.
set_model_params
(**kwargs)Set parameters used for training the model
Attributes
Returns an empty list for LinearInterp model
Get the list of output feature names that the generative model outputs
Get dimension of model generator input.
'...km', 'temporal': '...min'}.
Check if model expects spatial only input
Check if model expects spatiotemporal input
Get the list of input feature names that the generative model was trained on.
Get meta data dictionary that defines the model params
Model parameters, used to save model to disc
Resolution of output data.
Factor by which model will enhance spatial resolution.
Get the list of smoothed input feature names that the generative model was trained on.
Value of smoothing parameter used in gaussian filtering of coarsened high res data.
Factor by which model will enhance temporal resolution.
A record of important versions that this model was built with.
- classmethod load(model_dir, verbose=False)[source]
Load the LinearInterp model with its params saved to the model_dir created with LinearInterp.save(model_dir)
- Parameters:
model_dir (str) – Directory to load LinearInterp model files from. Must have a model_params.json file containing “meta” key with all of the class init args.
verbose (bool) – Flag to log information about the loaded model.
- Returns:
out (LinearInterp) – Returns an initialized LinearInterp model
- property meta
Get meta data dictionary that defines the model params
- property lr_features
Get the list of input feature names that the generative model was trained on.
- property hr_out_features
Get the list of output feature names that the generative model outputs
- property hr_exo_features
Returns an empty list for LinearInterp model
- save(out_dir)[source]
- Parameters:
out_dir (str) – Directory to save linear model params. This directory will be created if it does not already exist.
- generate(low_res, norm_in=False, un_norm_out=False, exogenous_data=None)[source]
Use the generator model to generate high res data from low res input. This is the public generate function.
- Parameters:
low_res (np.ndarray) – Low-resolution spatiotemporal input data, a 5D array of shape: (n_obs, spatial_1, spatial_2, temporal, n_features)
norm_in (bool) – This doesnt do anything for this LinearInterp, but is kept to keep the same interface as Sup3rGan
un_norm_out (bool) – This doesnt do anything for this LinearInterp, but is kept to keep the same interface as Sup3rGan
exogenous_data (list) – This doesnt do anything for this LinearInterp, but is kept to keep the same interface as Sup3rGan
- Returns:
hi_res (ndarray) – high-resolution spatial output data, a 5D array of shape: (n_obs, spatial_1, spatial_2, temporal, n_features)
- get_s_enhance_from_layers()
Compute factor by which model will enhance spatial resolution from layer attributes. Used in model training during high res coarsening
- get_t_enhance_from_layers()
Compute factor by which model will enhance temporal resolution from layer attributes. Used in model training during high res coarsening
- property input_dims
Get dimension of model generator input. This is usually 4D for spatial models and 5D for spatiotemporal models. This gives the input to the first step if the model is multi-step. Returns 5 for linear models.
- Returns:
int
- property input_resolution
‘…km’, ‘temporal’: ‘…min’}. The numbers are required to be integers in the units specified. The units are not strict as long as the resolution of the exogenous data, when extracting exogenous data, is specified in the same units.
- Type:
Resolution of input data. Given as a dictionary {‘spatial’
- property is_4d
Check if model expects spatial only input
- property is_5d
Check if model expects spatiotemporal input
- property model_params
Model parameters, used to save model to disc
- Returns:
dict
- property output_resolution
Resolution of output data. Given as a dictionary {‘spatial’: ‘…km’, ‘temporal’: ‘…min’}. This is computed from the input resolution and the enhancement factors.
- property s_enhance
Factor by which model will enhance spatial resolution. Used in model training during high res coarsening
- save_params(out_dir)
- Parameters:
out_dir (str) – Directory to save linear model params. This directory will be created if it does not already exist.
- static seed(s=0)
Set the random seed for reproducible results.
- Parameters:
s (int) – Random seed
- set_model_params(**kwargs)
Set parameters used for training the model
- Parameters:
kwargs (dict) – Keyword arguments including ‘input_resolution’, ‘lr_features’, ‘hr_exo_features’, ‘hr_out_features’, ‘smoothed_features’, ‘s_enhance’, ‘t_enhance’, ‘smoothing’
- property smoothed_features
Get the list of smoothed input feature names that the generative model was trained on.
- property smoothing
Value of smoothing parameter used in gaussian filtering of coarsened high res data.
- property t_enhance
Factor by which model will enhance temporal resolution. Used in model training during high res coarsening
- property version_record
A record of important versions that this model was built with.
- Returns:
dict