List of Metrics of Hydrologic Skill¶
HydroErr.HydroErr Module¶
HydroErr contains a library of goodness of fit metrics that measure hydrologic skill. Each metric is contained in function, and every function has the parameters to treat missing values as well as remove zero and negative values from the timeseries data.
Each function contains two properties, name and abbr. These can be used in the Hydrostats package when creating tables and adding metrics to the plots. Link to the hydrostats package: https://github.com/BYU-Hydroinformatics/Hydrostats. An example of this functionality is shown below.
>>> import HydroErr as he
>>>
>>> he.acc.name
'Anomaly Correlation Coefficient'
>>> he.acc.abbr
'ACC'
Functions¶
me(simulated_array, observed_array[, …]) |
Compute the mean error of the simulated and observed data. |
mae(simulated_array, observed_array[, …]) |
Compute the mean absolute error of the simulated and observed data. |
mse(simulated_array, observed_array[, …]) |
Compute the mean squared error of the simulated and observed data. |
mle(simulated_array, observed_array[, …]) |
Compute the mean log error of the simulated and observed data. |
male(simulated_array, observed_array[, …]) |
Compute the mean absolute log error of the simulated and observed data. |
msle(simulated_array, observed_array[, …]) |
Compute the mean squared log error of the simulated and observed data. |
mde(simulated_array, observed_array[, …]) |
Compute the median error (MdE) between the simulated and observed data. |
mdae(simulated_array, observed_array[, …]) |
Compute the median absolute error (MdAE) between the simulated and observed data. |
mdse(simulated_array, observed_array[, …]) |
Compute the median squared error (MdSE) between the simulated and observed data. |
ed(simulated_array, observed_array[, …]) |
Compute the Euclidean distance between predicted and observed values in vector space. |
ned(simulated_array, observed_array[, …]) |
Compute the normalized Euclidian distance between the simulated and observed data in vector space. |
rmse(simulated_array, observed_array[, …]) |
Compute the root mean square error between the simulated and observed data. |
rmsle(simulated_array, observed_array[, …]) |
Compute the root mean square log error between the simulated and observed data. |
nrmse_range(simulated_array, observed_array) |
Compute the range normalized root mean square error between the simulated and observed data. |
nrmse_mean(simulated_array, observed_array) |
Compute the mean normalized root mean square error between the simulated and observed data. |
nrmse_iqr(simulated_array, observed_array[, …]) |
Compute the IQR normalized root mean square error between the simulated and observed data. |
irmse(simulated_array, observed_array[, …]) |
Compute the inertial root mean square error (IRMSE) between the simulated and observed data. |
mase(simulated_array, observed_array[, m, …]) |
Compute the mean absolute scaled error between the simulated and observed data. |
r_squared(simulated_array, observed_array[, …]) |
Compute the the Coefficient of Determination (r2). |
pearson_r(simulated_array, observed_array[, …]) |
Compute the pearson correlation coefficient. |
spearman_r(simulated_array, observed_array) |
Compute the spearman rank correlation coefficient. |
acc(simulated_array, observed_array[, …]) |
Compute the the anomaly correlation coefficient (ACC). |
mape(simulated_array, observed_array[, …]) |
Compute the the mean absolute percentage error (MAPE). |
mapd(simulated_array, observed_array[, …]) |
Compute the the mean absolute percentage deviation (MAPD). |
maape(simulated_array, observed_array[, …]) |
Compute the the Mean Arctangent Absolute Percentage Error (MAAPE). |
smape1(simulated_array, observed_array[, …]) |
Compute the the Symmetric Mean Absolute Percentage Error (1) (SMAPE1). |
smape2(simulated_array, observed_array[, …]) |
Compute the the Symmetric Mean Absolute Percentage Error (2) (SMAPE2). |
d(simulated_array, observed_array[, …]) |
Compute the the index of agreement (d). |
d1(simulated_array, observed_array[, …]) |
Compute the the index of agreement (d1). |
dmod(simulated_array, observed_array[, j, …]) |
Compute the the modified index of agreement (dmod). |
drel(simulated_array, observed_array[, …]) |
Compute the the relative index of agreement (drel). |
dr(simulated_array, observed_array[, …]) |
Compute the the refined index of agreement (dr). |
watt_m(simulated_array, observed_array[, …]) |
Compute Watterson’s M (M). |
mb_r(simulated_array, observed_array[, …]) |
Compute Mielke-Berry R value (MB R). |
nse(simulated_array, observed_array[, …]) |
Compute the Nash-Sutcliffe Efficiency. |
nse_mod(simulated_array, observed_array[, …]) |
Compute the modified Nash-Sutcliffe efficiency (NSE mod). |
nse_rel(simulated_array, observed_array[, …]) |
Compute the relative Nash-Sutcliffe efficiency (NSE rel). |
kge_2009(simulated_array, observed_array[, …]) |
Compute the Kling-Gupta efficiency (2009). |
kge_2012(simulated_array, observed_array[, …]) |
Compute the Kling-Gupta efficiency (2012). |
lm_index(simulated_array, observed_array[, …]) |
Compute the Legate-McCabe Efficiency Index. |
d1_p(simulated_array, observed_array[, …]) |
Compute the Legate-McCabe Index of Agreement. |
ve(simulated_array, observed_array[, …]) |
Compute the Volumetric Efficiency (VE). |
sa(simulated_array, observed_array[, …]) |
Compute the Spectral Angle (SA). |
sc(simulated_array, observed_array[, …]) |
Compute the Spectral Correlation (SC). |
sid(simulated_array, observed_array[, …]) |
Compute the Spectral Information Divergence (SID). |
sga(simulated_array, observed_array[, …]) |
Compute the Spectral Gradient Angle (SGA). |
h1_mhe(simulated_array, observed_array[, …]) |
Compute the H1 mean error. |
h1_mahe(simulated_array, observed_array[, …]) |
Compute the H1 absolute error. |
h1_rmshe(simulated_array, observed_array[, …]) |
Compute the H1 root mean square error. |
h2_mhe(simulated_array, observed_array[, …]) |
Compute the H2 mean error. |
h2_mahe(simulated_array, observed_array[, …]) |
Compute the H2 mean absolute error. |
h2_rmshe(simulated_array, observed_array[, …]) |
Compute the H2 root mean square error. |
h3_mhe(simulated_array, observed_array[, …]) |
Compute the H3 mean error. |
h3_mahe(simulated_array, observed_array[, …]) |
Compute the H3 mean absolute error. |
h3_rmshe(simulated_array, observed_array[, …]) |
Compute the H3 root mean square error. |
h4_mhe(simulated_array, observed_array[, …]) |
Compute the H4 mean error. |
h4_mahe(simulated_array, observed_array[, …]) |
Compute the H4 mean absolute error. |
h4_rmshe(simulated_array, observed_array[, …]) |
Compute the H4 mean error. |
h5_mhe(simulated_array, observed_array[, …]) |
Compute the H5 mean error. |
h5_mahe(simulated_array, observed_array[, …]) |
Compute the H5 mean absolute error. |
h5_rmshe(simulated_array, observed_array[, …]) |
Compute the H5 root mean square error. |
h6_mhe(simulated_array, observed_array[, k, …]) |
Compute the H6 mean error. |
h6_mahe(simulated_array, observed_array[, …]) |
Compute the H6 mean absolute error. |
h6_rmshe(simulated_array, observed_array[, …]) |
Compute the H6 root mean square error. |
h7_mhe(simulated_array, observed_array[, …]) |
Compute the H7 mean error. |
h7_mahe(simulated_array, observed_array[, …]) |
Compute the H7 mean absolute error. |
h7_rmshe(simulated_array, observed_array[, …]) |
Compute the H7 root mean square error. |
h8_mhe(simulated_array, observed_array[, …]) |
Compute the H8 mean error. |
h8_mahe(simulated_array, observed_array[, …]) |
Compute the H8 mean absolute error. |
h8_rmshe(simulated_array, observed_array[, …]) |
Compute the H8 root mean square error. |
h10_mhe(simulated_array, observed_array[, …]) |
Compute the H10 mean error. |
h10_mahe(simulated_array, observed_array[, …]) |
Compute the H10 mean absolute error. |
h10_rmshe(simulated_array, observed_array[, …]) |
Compute the H10 root mean square error. |
g_mean_diff(simulated_array, observed_array) |
Compute the geometric mean difference. |
mean_var(simulated_array, observed_array[, …]) |
Compute the mean variance. |