Bases: `object`

Compute quality metrics from a set of observations (`obs`) and estimates (`est`).

First create an instance of the class using the set of observations and estimates. Then compute quality metrics using the class methods. A dictionary of all available quality metrics is returned using the `all` method, or printed to the screen using the `pprint` method.

The `ix` member variable indicates valid pairs of `obs` and `est`, based on NaNs and `minval`.

Parameters

Examples

```>>> obs = np.random.uniform(0, 10, 100)
>>> est = np.random.uniform(0, 10, 100)
>>> metrics = ErrorMetrics(obs, est)
>>> metrics.all()
>>> metrics.pprint()
>>> metrics.ix
```
corr()[source]#

Correlation coefficient

r2()[source]#

Coefficient of determination

spearman()[source]#

Spearman rank correlation coefficient

nash()[source]#

Nash-Sutcliffe Efficiency

sse()[source]#

Sum of Squared Errors

mse()[source]#

Mean Squared Error

rmse()[source]#

Root Mean Squared Error

mas()[source]#

Mean Absolute Error

meanerr()[source]#

Mean Error

ratio()[source]#

Mean ratio between observed and estimated

pbias()[source]#

Percent bias

all()[source]#

Returns a dictionary of all error metrics

pprint()[source]#

Pretty prints a summary of error metrics

 Returns a dictionary of all error metrics Correlation coefficient Mean Absolute Error Mean Error Mean Squared Error Nash-Sutcliffe Efficiency Percent bias Pretty prints a summary of error metrics Coefficient of determination Mean ratio between observed and estimated Root Mean Squared Error Spearman rank correlation coefficient Sum of Squared Errors