#install libraries !pip install pandas !pip install numpy !pip install plotly

#import libraries import pandas as pd import numpy as np import plotly.express as px

#generate synthetic data #function to create linear changes over time (ups and downs) def linear_cycles(changes, begin): """ Linear Cycles: Function to generate arrays that have up and down changes over time changes: list, specify each change that occurs over time, will generate 25 data points for each linear change begin: int, data point that changes will start from """ appended_cycles = [] #initiate list for change in changes: #loop through changes list #create linear array starting with begin or from where changes ended, 25 total data points per array cycle = list(np.linspace(start = begin, stop = begin + change, num = 25)) #combine lists appended_cycles = appended_cycles + cycle #create new starting point for next iteration through the loop begin = begin + change return appended_cycles #return the combined linear cycles awake = linear_cycles(changes=[10, -10, 20, -35, 0, 0, 0], begin = 20) #awake cylce rem = linear_cycles(changes=[0, 0, 30, -10, 20, 10, 40], begin = 10) #rem cycle light = linear_cycles(changes=[55, -20, 20, -30, 65, -50, -40], begin = 10) #light cycle deep = linear_cycles(changes=[55, -20, 20, -30, 65, -50, -40], begin = 10) #deep cycle #create data frame with all sleep cycles sleepdata = pd.DataFrame({'Deep': deep, 'Light': light, 'REM': rem, 'Awake': awake}) #create column day based on the index (arbitrary for plotting purposes) sleepdata['Day'] = sleepdata.index #melt the data frame for plotting purposes data = pd.melt(sleepdata, id_vars = ['Day'], value_vars=["Awake", "REM", "Light", "Deep"], value_name= "Proportion of Time", var_name = "Sleep Cycle") display(data)

#begin with generic plot, color coded and correctly ordered to match image fig = px.area(data, x = 'Day', y = 'Proportion of Time', color = 'Sleep Cycle', line_group='Sleep Cycle', color_discrete_map={'REM': '#77c9f1', 'Light': '#438fc3', 'Deep': '#1b497d', 'Awake': '#fcfbfc'}, category_orders={"Sleep Cycle": ["Deep", "Light", "REM", "Awake"]}) fig.show()

#change background to match image fig.update_layout(paper_bgcolor="#e5ddd2", plot_bgcolor="#e5ddd2") fig.show()

#update axes, remove the grid and tick labels and expand the range of x and y, remove the zeroline fig.update_xaxes(showgrid=False, showticklabels=False, title = ' ', range = [-10, 185], zeroline=False) fig.update_yaxes(showgrid=False, showticklabels=False, title = ' ', range = [-25, 455], zeroline=False) fig.show()

#add the title fig.update_layout( title={ 'text' : 'REM sleep trends to decrease in summer' + '<br>' + 'and increase in winter.', 'x':0.5, 'xanchor': 'center', 'font': {'size': 30} })

#move the legend to the correct position fig.update_layout(legend=dict( orientation="h", yanchor="bottom", y=-0.15, xanchor="right", x=0.8 )) fig.show()

from PIL import Image #upload summer and winter images summer = Image.open("summer.png") winter = Image.open("winter.png") #add images to the plot fig.add_layout_image( dict( source=summer, xref="x", yref="y", x=0, y=430, sizex=150, sizey=150, opacity=1, layer="above") ) fig.add_layout_image( dict( source=winter, xref="x", yref="y", x=145, y=430, sizex=150, sizey=150, opacity=1, layer="below") ) fig.show()

#add annotations to highlight the changes fig.add_annotation(text = 'Decreased', x = 10, y = 70, xref='x', bgcolor='lightblue') fig.add_annotation(text = 'Increased', x = 170, y = 120, xref='x', bgcolor = 'lightblue') fig.show()