# ---
# jupyter:
#   jupytext:
#     text_representation:
#       extension: .py
#       format_name: light
#       format_version: '1.5'
#       jupytext_version: 1.14.4
#   kernelspec:
#     display_name: Python 3
#     language: python
#     name: python3
# ---

import numpy as np
import pandas as pd

# +

# %matplotlib inline  
# -

df = pd.DataFrame()
df['Time'] = range(15);
df['Sig1'] = df['Time'] / 14;
df['Sig2'] = np.sin(df['Time']);
df['Sig3'] = (np.sin(df['Sig1']) + np.cos(df['Sig2'])) / 2;


df.plot.line('Time' , ['Sig1', 'Sig2' , 'Sig3'])

df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['b' , 'r' , 'g']) 

df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['r' , 'g' , 'b']) 

df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['#FF0000' , '#00FF00' , '#0000FF']) 

# http://matplotlib.org/api/colors_api.html
#
# Commands which take color arguments can use several formats to specify the colors. For the basic built-in colors, you can use a single letter
#
#         b: blue
#         g: green
#         r: red
#         c: cyan
#         m: magenta
#         y: yellow
#         k: black
#         w: white
#
#

df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['c' , 'm' , 'y']) 

df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['#800000' , '#008000' , '#000080']) 

# ## pre-defined colors
#
# signal => blue
#
# trend => 
#
# trend-residue => red
#
# cycle => yellow
#
# cycle-residue => red
#
# ar => 
#
# ar-residue => red
#
# forecast => green
#
# upper-bound , lower-bound , shaded-area => green, green, light-blue
#
#
# ticks on left and right sides.
#
#

df.plot.line('Time' , ['Sig1', 'Sig2', 'Sig3'], color=['r' , 'g' , 'b']) 

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