#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import modelskill as ms


# In[2]:


fn = '../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu'
mr = ms.model_result(fn, name='HKZN_local', item=0)
mr.data


# Configuration of comparison, see [SW_DutchCoast.ipynb](SW_DutchCoast.ipynb) for more details.

# In[3]:


o1 = ms.PointObservation('../tests/testdata/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name="HKNA")
o2 = ms.PointObservation("../tests/testdata/SW/eur_Hm0.dfs0", item=0, x=3.2760, y=51.9990, name="EPL")
o3 = ms.TrackObservation("../tests/testdata/SW/Alti_c2_Dutch.dfs0", item=3, name="c2")
cc = ms.match([o1, o2, o3], mr)
cc


# Standard set of metrics

# In[4]:


cc.skill()


# Select a specific metric

# In[5]:


cc.skill(metrics="mean_absolute_error")


# Some metrics has parameters, which require a bit special treatment.

# In[6]:


import modelskill.metrics as mtr
from modelskill.metrics import hit_ratio

def hit_ratio_05_pct(obs, model):
    return hit_ratio(obs, model, 0.5) * 100

def hit_ratio_01_pct(obs, model):
    return hit_ratio(obs, model, 0.1) * 100


cc.skill(metrics=[hit_ratio_05_pct, hit_ratio_01_pct])


# And you are of course always free to specify your own special metric or import metrics from other libraries, e.g. scikit-learn.

# In[7]:


def my_special_metric_with_long_descriptive_name(obs, model):

    res = obs - model

    res_clipped = np.clip(res,0,np.inf)

    return np.mean(np.abs(res_clipped))

# short alias to avoid long column names in output
def mcae(obs, model): return my_special_metric_with_long_descriptive_name(obs, model)

cc.skill(metrics=mcae)