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# Plot x-bar control chart

# Line chart
line_plot = df_grouped.hvplot.line(
x='sample_group',
y=['x_bar','UCL','+2s','+1s','x_bar_bar','-1s','-2s','LCL'],
xlabel="Sample Group",
title="x-bar chart",
height=500,
width=1000)

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# Get control limits
df_grouped['x_bar_bar'] = statistics.mean(df_grouped['x_bar'])
df_grouped['UCL'] = statistics.mean(df_grouped['x_bar'])+(0.577*statistics.mean(df_grouped['R']))
df_grouped['+2s'] = (df_grouped['UCL']-df_grouped['x_bar_bar'])/3*2+df_grouped['x_bar_bar']
df_grouped['+1s'] = (df_grouped['UCL']-df_grouped['x_bar_bar'])/3*1+df_grouped['x_bar_bar']
df_grouped['-1s'] = df_grouped['x_bar_bar']-(df_grouped['UCL']-df_grouped['x_bar_bar'])/3*1
df_grouped['-2s'] = df_grouped['x_bar_bar']- (df_grouped['UCL']-df_grouped['x_bar_bar'])/3*2
df_grouped['LCL'] = statistics.mean(df_grouped['x_bar'])-(0.577*statistics.mean(df_grouped['R']))
df_grouped.head()

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# Add R (range) column
df_max = df.groupby('sample_group').max()
df_min = df.groupby('sample_group').min()
df_grouped['R'] = df_max['data'] - df_min['data']
df_grouped.head()

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# Group masures by sample groups (x_bar)
df_grouped = df.groupby('sample_group').mean()

# Rename x-bar column
df_grouped.columns = ['x_bar']
df_grouped.head()

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# Import required libraries
import numpy as np
import pandas as pd
import statistics
import hvplot
import hvplot.pandas

# Set a random seed
np.random.seed(42)

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# Initialize lists where we will store the information for future analysis
customer_number_list = []
time_arrived_to_system_list = []
time_arrived_to_queue_list = []
time_start_order_placing_list =[]
time_end_order_placing_list = []
time_order_delivered_list = []
time_exit_system_list = []
total_time_placing_order_list = []
total_time_waiting_for_order = []

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# Import required libraries
import time
import datetime
import numpy as np
import pandas as pd

# Define the customer class (i.e., the agent)
class customer:
    def __init__(self):
        self.customer_number = 0

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# Import required libraries
from wordcloud import WordCloud
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import matplotlib as mpl

# Set wordcloud size
mpl.rcParams['figure.figsize'] = [20.0, 10.0]

# Define a wordcloud function

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# Import required libraries
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from string import punctuation

# Download list of stopwords
stop = stopwords.words('english')

# Create a list of additional stopwords
additional_stopwords = ["strong", "markup", "h3", "em", "class="]

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# Declare a function to remove '\xa0' from stores titles
def string_cleaning(text):

    correct_title = text.replace(u'\xa0', u' ')
    return correct_title
    
# Apply function to stories titles
df["title"] = df.title.apply(string_cleaning)