"""
========
i. ridge
========
.. currentmodule:: easy_mpl

This file shows the usage of :func:`ridge` function.

"""

# sphinx_gallery_thumbnail_number = 2

import numpy as np
import pandas as pd
from easy_mpl import ridge
import matplotlib.pyplot as plt
from easy_mpl.utils import version_info

version_info()  # print version information of all the packages being used

#############################

data_ = np.random.random(size=100)
_ = ridge(data_)

# %%

data_ = np.random.random((100, 3))
_ = ridge(data_)

#############################
# specifying colormap

_ = ridge(data_, color="Blues")

#############################
# The data can also be in the form of pandas DataFrame

_ = ridge(pd.DataFrame(data_))

# %%
# if we don't want to fill the ridge, we can specify the color as white

_ = ridge(np.random.random(100), color=["white"])

# %%
# we can draw all the ridges on same axes as below

df = pd.DataFrame(np.random.random((100, 3)), dtype='object')
_ = ridge(df, share_axes=True, fill_kws={"alpha": 0.5})

# %%
# we can also provide an existing axes to plot on

_, ax = plt.subplots()
_ = ridge(df, ax=ax, fill_kws={"alpha": 0.5})

# %%
# The data can also be in the form of list of arrays

x1 = np.random.random(100)
x2 = np.random.random(100)
_ = ridge([x1, x2], color=np.random.random((3, 2)))

# %%
# The length of arrays need not to be constant/same. We can use arrays of different lengths

x1 = np.random.random(100)
x2 = np.random.random(90)
_ = ridge([x1, x2], color=np.random.random((3, 2)))

# %%
f = "https://raw.githubusercontent.com/AtrCheema/AI4Water/master/ai4water/datasets/arg_busan.csv"
df = pd.read_csv(f, index_col='index')
print(df.shape)
df.head()
# %%

cols = ['air_temp_c',
        'wat_temp_c',
        'sal_psu',
        'tide_cm',
        'rel_hum',
        'pcp12_mm',
        'wind_dir_deg',
        'wind_speed_mps'
        ]

_ = ridge(df[cols])

# %%
f = 'https://media.githubusercontent.com/media/HakaiInstitute/essd2021-hydromet-datapackage/main/2013-2019_Discharge1015_5min.csv'
df = pd.read_csv(f)
df.index = pd.to_datetime(df.pop('Datetime'))
print(df.shape)
df.head()
# %%
groupby_year = df.groupby(lambda x: x.year)

_ = ridge(
    [grp['Qrate'].resample('D').interpolate(method='linear') for _, grp in groupby_year],
    labels=[name for name, _ in groupby_year],
    )