# ---
# 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
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# ## Imports

# +
# Import modules for working with excel sheets and for plotting

# matplotlib: module for plotting
# pandas: module for working with dataframe (can be imported from excel, csv, txt)
# %: ipython magic, to plot graphs in line
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import re
from scipy import stats
# %matplotlib inline
# -

# `PLOT' is a global variable that needs to be set to `True' to actually plot things in this notebook
PLOT = True

# ## Load Data
#
# The following dataset is NOT on GitHub. Make sure your local directory structure is as follows:
#
#                     repository_directory
#                      /        \        \
#                     /          \        \
#                   code        assets    other files (.gitignore, README.md, LICENSE.txt, ...)
#                    /            \
#                   /              \
#     <jupyter_notebook_file>    <data_file>
#     
# Also, if you want to use the functions in this script with another dataset, make sure to change `number_of_rows` 
# and the file name in the following import statements.

# Import excel file as a `pandas.ExcelFile' object (which basically has all sub-sheets in a big container!)
# also, only import 1302 rows
ca_data1 = pd.ExcelFile('../assets/CTRL_L89A.xlsx')
ca_data2 = pd.ExcelFile('../assets/CTRL_E120Q.xlsx')

ca_data_11 = ca_data1.parse("Sheet1")
ca_data_12 = ca_data2.parse("Sheet1")
combined_ca_data = ca_data_11.append(ca_data_12)

ax = sns.lineplot(x="Time (s)", y="F/Fo", hue="Genotype", ci=95, data=combined_ca_data).set_title('3 uM UTP, Ca2+ free')



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