Modulus Metrics
- modulus.metrics.climate.acc.acc(pred: Tensor, target: Tensor, climatology: Tensor, lat: Tensor) → Tensor[source]
Calculates the Anomaly Correlation Coefficient
- Parameters
pred (Tensor) – […, H, W] Predicted tensor on a lat/long grid
target (Tensor) – […, H, W] Target tensor on a lat/long grid
climatology (Tensor) – […, H, W] climatology tensor
lat (Tensor) – [H] latitude tensor
- Returns
- Return type
ACC values for each field
Tensor
Note
- modulus.metrics.climate.efi.efi(pred_cdf: Tensor, bin_edges: Tensor, climatology_mean: Tensor, climatology_std: Tensor) → Tensor[source]
Calculates the Extreme Forecast Index (EFI) for an ensemble forecast against a climatological distribution.
- Parameters
pred_cdf (Tensor) – Cumulative distribution function of predictions of shape [N, …] where N is the number of bins. This cdf must be defined over the passed bin_edges.
bin_edges (Tensor) – Tensor of bin edges with shape [N+1, …] where N is the number of bins.
climatology_mean (Tensor) – Tensor of climatological mean with shape […]
climatology_std (Tensor) – Tensor of climatological std with shape […]
- Returns
- Return type
EFI values of each of the batched dimensions.
Tensor
Note
- modulus.metrics.climate.efi.normalized_entropy(pred_pdf: Tensor, bin_edges: Tensor, climatology_mean: Tensor, climatology_std: Tensor) → Tensor[source]
Calculates the relative entropy, or surprise, of using the prediction distribution as opposed to the climatology distribution.
- Parameters
pred_cdf (Tensor) – Cumulative distribution function of predictions of shape [N, …] where N is the number of bins. This cdf must be defined over the passed bin_edges.
bin_edges (Tensor) – Tensor of bin edges with shape [N+1, …] where N is the number of bins.
climatology_mean (Tensor) – Tensor of climatological mean with shape […]
climatology_std (Tensor) – Tensor of climatological std with shape […]
- Returns
- Return type
Relative Entropy values of each of the batched dimensions.
Tensor
Note
- modulus.metrics.climate.reduction.global_mean(x: Tensor, lat: Tensor, keepdims: bool = False) → Tensor[source]
Computes global mean
This function computs the global mean of a lat/lon grid by weighting over the latitude direction and then averaging over longitude
- Parameters
x (Tensor) – The lat/lon tensor […, H, W] over which the mean will be computed
lat (Tensor) – A one-dimension tensor [H] representing the latitudes at which the function will return weights for
keepdims (bool, optional) – Keep aggregated dimension, by default False
- Returns
- Return type
Global mean tensor
Tensor
- modulus.metrics.climate.reduction.global_var(x: Tensor, lat: Tensor, std: bool = False, keepdims: bool = False) → Tensor[source]
Computes global variance
This function computs the global variance of a lat/lon grid by weighting over the latitude direction and then averaging over longitude
- Parameters
x (Tensor) – The lat/lon tensor […, H, W] over which the variance will be computed
lat (Tensor) – A one-dimension tensor [H] representing the latitudes at which the function will return weights for
std (bool, optional) – Return global standard deviation, by default False
keepdims (bool, optional) – Keep aggregated dimension, by default False
- Returns
- Return type
Global variance tensor
Tensor
- modulus.metrics.climate.reduction.zonal_mean(x: Tensor, lat: Tensor, dim: int = -2, keepdims: bool = False) → Tensor[source]
Computes zonal mean, weighting over the latitude direction that is specified by dim
- Parameters
x (Tensor) – The tensor […, H, W] over which the mean will be computed
lat (Tensor) – A one-dimension tensor representing the latitudes at which the function will return weights for
dim (int, optional) – The int specifying which dimension of x the reduction will occur, by default -2
keepdims (bool, optional) – Keep aggregated dimension, by default False
- Returns
- Return type
Zonal mean tensor of x over the latitude dimension
Tensor
- modulus.metrics.climate.reduction.zonal_var(x: Tensor, lat: Tensor, std: bool = False, dim: int = -2, keepdims: bool = False) → Tensor[source]
Computes zonal variance, weighting over the latitude direction
- Parameters
x (Tensor) – The tensor […, H, W] over which the variance will be computed
lat (Tensor) – A one-dimension tensor [H] representing the latitudes at which the function will return weights for
std (bool, optional) – Return zonal standard deviation, by default False
dim (int, optional) – The int specifying which dimension of x the reduction will occur, by default -2
keepdims (bool, optional) – Keep aggregated dimension, by default False
- Returns
- Return type
The variance (or standard deviation) of x over the latitude dimension
Tensor