lognse
- postprocessinglib.evaluation.metrics.lognse(observed: DataFrame, simulated: Union[DataFrame, List[DataFrame]], stations: list[int] = []) float
Calculates the Logarithmic Nash-Sutcliffe Efficiency of the data
- Parameters:
observed (pd.DataFrame) – Observed values[1: Datetime ; 2+: Streamflow Values]
simulated (pd.DataFrame or list[pd.DataFrame]) – Simulated values[1: Datetime ; 2+: Streamflow Values]
stations (list[int]) – numbers pointing to the location of the stations in the list of stations. Values can be any number from 1 to number of stations in the data
- Returns:
the Logarithmic Nash-Sutcliffe Efficiency of the data
- Return type:
pd.DataFrame
Example
Calculate the Logarithmic Value of Nash-Sutcliffe Efficiency
>>> import numpy as np >>> import pandas as pd >>> from postprocessinglib.evaluation import metrics >>> # Create your index as an array >>> index = np.array([1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990]) >>> . >>> # Create a test dataframe >>> test_df = pd.DataFrame(data = np.random.rand(10, 4), columns = ("obs1", "sim1", "obs2", "sim2"), index = index) >>> print(test_df) obs1 sim1 obs2 sim2 1981 0.966878 0.348580 0.053977 0.043133 1982 0.188252 0.739990 0.941848 0.580866 1983 0.430902 0.292824 0.963190 0.798885 1984 0.718644 0.098746 0.031072 0.446317 1985 0.586581 0.479616 0.541689 0.639898 1986 0.380978 0.193639 0.737498 0.025509 1987 0.072452 0.095210 0.188173 0.357554 1988 0.833037 0.542694 0.913704 0.963027 1989 0.434239 0.817284 0.425448 0.865841 1990 0.698412 0.484796 0.693588 0.981778
>>> # Generate the observed and simulated Dataframes >>> obs = test_df.iloc[:, [0, 2]] >>> sim = test_df.iloc[:, [1, 3]] >>> . >>> Calculate the Log of Nash-Sutcliffe Efficiency >>> lognse = metrics.lognse(observed = obs, simulated = sim) >>> print(lognse) model1 Station 1 -0.4923 Station 2 -0.4228