update

RC_styles** @ 7a2fd654

-Subproject commit 7a2fd65482b64345b1cd923f38846433f7c33399

slurm-2sql.ipynb

 %% Cell type:markdown id: tags:
 
 # Notebook Setup
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 import sqlite3
 import slurm2sql
 import pandas as pd
 import matplotlib.pyplot as plt
 %matplotlib inline
 import seaborn as sns
 import plotly.express as px
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
+from RC_styles import rc_styles as style
+```
+
+%% Cell type:code id: tags:
+
+``` 
 # must run
 
 # creates database of info from March 2020 using sqlite 3
 db = sqlite3.connect('/data/rc/rc-team/slurm-since-March.sqlite3')
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # creates database of allocation info from March 2020 using sqlite 3
 # not using this right now, but is here as an option
 #db_allocation = sqlite3.connect('/data/rc/rc-team/slurm-since-March-allocation.sqlite3')
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # variable for to be used in names of plots to describe the max gigs measured
 #UpperlimitGB = 5
 
 # variable for max gigs of RAM requested - Charts range from 0 to upperRAMlimit gigs
 #upperRAMlimit = UpperlimitGB * 10e+10 # 5 gigs
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df is starting database
 df = pd.read_sql('SELECT * FROM slurm', db)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # voluntary
 
 # for displaying all available column options
 pd.set_option('display.max_columns', None)
 df.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # converts units in ReqMemCPU column from bytes to gigs
 df['ReqMemCPU'] = df['ReqMemCPU'].div(1024**3)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_1 is dataframe of all completed jobs
 df_1 = df[df.State.str.contains('COMPLETED')]
 df_1.head(5)
 ```
 
 %% Cell type:markdown id: tags:
 
 # Average RAM per CPU Requested by User
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_2 is database of completed jobs with only User and ReqMemCpu
 # it is used for the user dataframes
 
 df_2 = df_1.loc[:,['User','ReqMemCPU']]
 df_2.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # fills empty strings in User column with NaN and then filters them out to give a dataset of users with no empty strings
 
 nan_value = float("NaN")
 
 df_2.replace("", nan_value, inplace=True)
 
 df_2.dropna(subset = ["User"], inplace=True)
 df_2.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # count = count of jobs per user
 # mean,std,min,25%,50%,75%, and max refers to the gigs of memory per cpu requested by that user for all their jobs
 df_user = df_2.groupby('User')['ReqMemCPU'].describe().reset_index()
 df_user.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # creates user number column of strings of numbers from 0 to the total number of users
 # used in graphs in place of usernames
 usernames = df_user['User']
 user_numbers = [str(i) for i in range(len(usernames))]
 
 df_user['User Number'] = user_numbers
 df_user
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_user_graph is df_user sorted in decending order by mean for easy readibility of graph
 df_user_graph = df_user.sort_values(by='mean', ascending=False)
 df_user_graph.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # bar graph for jobs run per user - shows average requested RAM per CPU for all jobs by user
 user_graph = px.bar(df_user_graph, x='User Number', y='mean', color = 'mean',
              hover_data=['max','count'],
              labels={'mean':'Average Requested RAM per CPU (Gigs)'},
              height=400)
 
 user_graph.update_xaxes(showticklabels=False)
 
 user_graph.update_layout(
     xaxis_type = 'category',
     title={
         'text': "Average Requested RAM per CPU by User for all Users",
         'y':0.9,
         'x':0.5,
         'xanchor': 'center',
         'yanchor': 'top'})
 
 user_graph.show()
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # dataset of all users whose number of jobs equal 1000 or less.
 df_without_outlier = df_user[df_user['count'] <= 1060]
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_without_outlier_graph is df_without_outlier sorted in decending order by mean for easy readibility of graph
 df_without_outlier_graph = df_without_outlier.sort_values(by='mean', ascending=False)
 df_without_outlier_graph.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # bar graph jobs run per user for all users whose number of jobs equal 1000 or less.
 # shows average requested RAM per CPU for jobs by user, with the color of the bars being the count of jobs run for that user
 # represents 21 out of 162 users
 without_outlier_graph = px.bar(df_without_outlier_graph, x='User Number', y='mean',
              hover_data=['count', 'max'], color='mean',
              labels={'mean':'Average Requested RAM per CPU (Gigs)'}, height=400)
 
 without_outlier_graph.update_xaxes(showticklabels=False)
 
 without_outlier_graph.update_layout(
     xaxis_type = 'category',
     title={
         'text': "Average Requested RAM per CPU by User for all Users Running 1060 Jobs or Less",
         'y':0.9,
         'x':0.5,
         'xanchor': 'center',
         'yanchor': 'top'})
 
 without_outlier_graph.show()
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 #voluntary
 
 # gives description of the counts (number of jobs ran) for all the users
 df_count = df_user['count'].describe()
 df_count
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # dataset of users whose number of jobs ran falls in the lower 25 percentile of jobs ran
 df_25percent = df_user[df_user['count'] <= 4.25]
 print(df_25percent.User.count(),'users in the lower 25% out of', df_user.User.count(),'users total')
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_25percent_graph is df_25percent sorted in decending order by mean for easy readibility of graph
 df_25percent_graph = df_25percent.sort_values(by='mean', ascending=False)
 df_25percent_graph.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # bar graph jobs run per user for all users whose number of jobs fall in the lower 25th percentile.
 # shows average requested RAM per CPU for jobs by user, with the color of the bars being the count of jobs run for that user
 # represents 41 out of 162 users
 lower25percent = px.bar(df_25percent_graph, x='User Number', y='mean',
              hover_data=['count', 'max'], color='mean',
              labels={'mean':'Average Requested RAM per CPU (Gigs)'}, height=400)
 
 lower25percent.update_xaxes(showticklabels=False)
 
 lower25percent.update_layout(
     xaxis_type = 'category',
     title={
         'text': "Average Requested RAM per CPU by User for all Users Running 4.25 Jobs or Less",
         'y':0.9,
         'x':0.5,
         'xanchor': 'center',
         'yanchor': 'top'})
 
 
 lower25percent.show()
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # dataset of users whose number of jobs ran falls between the median and 75th percentile of jobs ran
 df_mid = df_user[(df_user['count'] > 4.25) & (df_user['count'] < 145)]
 print(df_mid.User.count(),'users in the middle range out of', df_user.User.count(),'users total')
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_mid_graph is df_mid sorted in decending order by mean for easy readibility of graph
 df_mid_graph = df_mid.sort_values(by='mean', ascending=False)
 df_mid_graph.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # bar graph jobs run per user for all users whose number of jobs fall in the middle range between the lower and upper 25th percentile.
 # shows average requested RAM per CPU for jobs by user, with the color of the bars being the count of jobs run for that user
 # represents 80 out of 162 users
 mid_graph = px.bar(df_mid_graph, x='User Number', y='mean',
              hover_data=['count', 'max'], color='mean',
              labels={'mean':'Average Requested RAM per CPU (Gigs)'}, height=400)
 
 mid_graph.update_xaxes(showticklabels=False)
 
 mid_graph.update_layout(
     xaxis_type = 'category',
     title={
         'text': "Average Requested RAM per CPU by User for all Users Running between 4.25 and 145 Jobs",
         'y':0.9,
         'x':0.5,
         'xanchor': 'center',
         'yanchor': 'top'})
 
 mid_graph.show()
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # dataset of users whose number of jobs ran falls in the upper 25th percentile of jobs ran (or the 75th percentile)
 df_75percent = df_user[df_user['count'] >= 145]
 print(df_75percent.User.count(),'users in the upper 75% out of', df_user.User.count(),'users total')
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # df_75percent_graph is df_75percent sorted in decending order by mean for easy readibility of graph
 df_75percent_graph = df_75percent.sort_values(by='mean', ascending=False)
 df_75percent_graph.head(5)
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # bar graph jobs run per user for all users whose number of jobs fall in the upper 25th percentile.
 # shows average requested RAM per CPU for jobs by user, with the color of the bars being the count of jobs run for that user
 # represents 41 out of 162 users
 upper25percent = px.bar(df_75percent_graph, x='User Number', y='mean',
              hover_data=['count', 'max'], color='mean',
              labels={'mean':'Average Requested RAM per CPU (Gigs)'}, height=400)
 
 upper25percent.update_xaxes(showticklabels=False)
 
 upper25percent.update_layout(
     xaxis_type = 'category',
     title={
         'text': "Average Requested RAM per CPU by User for all Users Running 145 Jobs or Less",
         'y':0.9,
         'x':0.5,
         'xanchor': 'center',
         'yanchor': 'top'})
 
 upper25percent.show()
 ```
 
 %% Cell type:code id: tags:
 
 ``` 
 # must run
 
 # dataset of users whose number of jobs ran falls in the lower half of the upper 25th percentile of jobs ran (or the 75th percentile)
 df_lower75percent = df_user[(df_user['count'] >= 145) & (df_user['count'] <= 1060)]