"Node_fig = sns.distplot(Node_cutoff['ReqMemNode'], kde=False, label='User Requested RAM per Node for Array and Non Array Jobs') #color = 'darkblue')\n",
"Node_fig.set_yscale('log')\n",
"\n",
"plt.legend(prop={'size': 12})\n",
"plt.title('User Requested RAM per CPU and per Node together for all Jobs')\n",
plt.title('User Requested RAM per Node for Array Jobs vs Not Array Jobs %i gigs or less'%UpperlimitGB)
plt.xlabel('Requested Gigs of RAM')
plt.ylabel('Number of Users Requesting')
plt.ylabel('Number of Jobs Requesting')
```
%% Cell type:markdown id: tags:
# These are Plotly Express Graphs of the some of the above Seaborn graphs. Run them only if you need more details about the data in the graph. They will make your notebook run slower.
%% Cell type:markdown id: tags:
Graphs: <br>
User Requested RAM per CPU for all Jobs
<br>
User Requested RAM per CPU for Non Array Jobs
<br>
User Requested RAM per CPU for Array Jobs
<br>
User Requested RAM per Node for all Jobs
<br>
User Requested RAM per Node for Non Array Jobs
<br>
User Requested RAM per Node for Array Jobs
<br>
These graphs create histograms using the data for the month of March 2020.
The x axis measures the amount of requested RAM in gigs per CPU/Node, from 0 to the max declared in the upperRAMlimit variable above - 5 gigs.
The y axis measures how many users requested that amount RAM per CPU or Node.
Can also show box or violin graph above to show where min, max, median, and 3rd quartile is.
%% Cell type:code id: tags:
```
CPU_fig = px.histogram(CPU_cutoff, x="ReqMemCPU",
title='User Requested RAM per CPU for all Jobs',
title='User Requested RAM per CPU for all Jobs %i gigs or less'%UpperlimitGB,
labels={'ReqMemCPU':'ReqMemCPU'}, # can specify one label per df column
opacity=0.8,
log_y=True, # represent bars with log scale
marginal="box", # can be `box`, `violin`
hover_data=CPU_cutoff.columns,
nbins=30,
color_discrete_sequence=['goldenrod'] # color of histogram bars