Info: diag-eff needs Python >= 3.6!
In case you use diag-eff in other software or scientific publications,
please reference this package. It is published and has a DOI. It can be cited
as:
Schwemmle, R., Demand, D., and Weiler, M.: Technical note: Diagnostic
efficiency – specific evaluation of model performance, Hydrol. Earth Syst.
Sci., 25, 2187–2198, https://doi.org/10.5194/hess-25-2187-2021, 2021.
The full documentation can be found at: https://diag-eff.readthedocs.io
This software can be distributed freely under the GPL v3 license. Please read the LICENSE for further information.
© 2021, Robin Schwemmle (robin.schwemmle@hydrology.uni-freiburg.de)
diag-eff is an open-source toolbox written in Python for specific evaluation of
model performance. The toolbox provides functions to calculate the Diagnostic
Efficiency metric and and functions to visualize contribution of metric terms
by diagnostic polar plots. Additionally, functions to calculate KGE and NSE
are available.
PyPI:
pip install diag-effGIT:
git clone https://github.com/Hydrology-IFH/diag-eff.git
cd diag-eff
pip install -r requirements.txt
pip install -e .from pathlib import Path # OS-independent path handling
from de import de
from de import util
# set path to example data
path_cam = Path('./examples/13331500_94_model_output.txt')
# import example data as dataframe
df_cam = util.import_camels_obs_sim(path_cam)
# make arrays
obs_arr = df_cam['Qobs'].values
sim_arr = df_cam['Qsim'].values
# calculate diagnostic efficiency
eff_de = de.calc_de(obs_arr, sim_arr)
# diagnostic polar plot
de.diag_polar_plot(obs_arr, sim_arr)In order to run diag-eff in R, reticulate
can be used as an interface to Python.
Non-interactive mode:
install.packages("reticulate")
library(reticulate)
# pip installation
py_install("numpy")
py_install("pandas")
py_install("scipy")
py_install("matplotlib")
py_install("seaborn")
py_install("diag-eff")
# import Python modules
np <- import("numpy")
pd <- import("pandas")
sp <- import("scipy")
mpl <- import("matplotlib")
plt <- import("matplotlib.pyplot")
sns <- import("seaborn")
de <- import("de")
# set path to example data
path_cam <- file.path('./examples/13331500_94_model_output.txt')
# import example data as dataframe
df_cam <- import_camels_obs_sim(path_cam)
# calculate diagnostic efficiency
eff_de <- calc_de(df_cam$Qobs, df_cam$Qsim)
# diagnostic polar plot
fig <- diag_polar_plot(df_cam$Qobs, df_cam$Qsim)
# currently figures cannot be displayed interactively in a R environment
fig$savefig('diagnostic_polar_plot.png')Interactive mode using a Python interpreter in R:
install.packages("reticulate")
library(reticulate)
# pip installation
py_install("numpy")
py_install("pandas")
py_install("scipy")
py_install("matplotlib")
py_install("seaborn")
py_install("tk")
py_install("diag-eff")
# start Python interpreter in R
repl_python()# copy+paste the lines below to the interpreter
from pathlib import Path # OS-independent path handling
from de import de
from de import util
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
# set path to example data
path = Path('./examples/13331500_94_model_output.txt')
# import example data as dataframe
df_cam = util.import_camels_obs_sim(path)
# make arrays
obs_arr = df_cam['Qobs'].values
sim_arr = df_cam['Qsim'].values
plt.plot(obs_arr)
plt.show()
# calculate diagnostic efficiency
eff_de = de.calc_de(obs_arr, sim_arr)
# diagnostic polar plot
de.diag_polar_plot(obs_arr, sim_arr)
plt.show()
# quit the interpreter
exit