import numpy as np
import plotly.graph_objects as go
import sys
[docs]def cumu_plot(clust_info, cores_queued, cores_running, resample_str='',
fig_out='', y_label='Usage', fig_title='', query_bounds=True,
running=[], queued=[], submit_run=[], submit_req=[], user_run=[],
plot_queued=False):
"""Cumulative usage plot.
Parameters
-------
clust_info: DataFrame
Frame which represents the cluster state at given time intervals.
See job_use from viewclust.
cores_queued: array_like of DataFrame
Series displaying queued resources at a particular time.
See job_use from viewclust.
cores_running: array_like of DataFrame
Series displaying running resources at a particular time.
See job_use from viewclust.
resample_str: pandas freq str, optional
Defaults to empty, meaning no resampling. Passing this parameter
does not do sanity checking and will only run the below code example.
cores_queued = cores_queued.resample('1D').sum()
fig_out: str, optional
Writes the generated figure to file as the given name.
If empty, skips writing. Defaults to empty.
y_label: str, optional
Makes the passed string the y-axis label.
fig_title: str, optional
Appends the given string to the title.
query_bounds: bool, optional
Draws red lines on the figure to represent where query is valid.
Defaults to true.
submit_run: DataFrame, optional
Draws a red line representing what would usage have looked like
if jobs had started instantly and ran for their elapsed duration.
Allows for easier interpretation of
the queued series. Defaults to not plotting.
submit_req: DataFrame, optional
Draws an orange line representing what usage would have looked like
if jobs had started instantly and ran for their requested duration.
Allows for easier interpretation of
the queued series. Defaults to not plotting.
See Also
-------
jobUse: Generates the input frames for this function.
"""
# Avoid recalculations via these:
clust_sum = np.cumsum(clust_info).divide(len(clust_info))
run_sum = np.cumsum(cores_running).divide(len(cores_running))
queue_sum = np.cumsum(cores_queued).divide(len(cores_queued))
if resample_str != '':
clust_sum = clust_sum.resample(resample_str).sum()
run_sum = run_sum.resample(resample_str).sum()
queue_sum = queue_sum.resample(resample_str).sum()
fig = go.Figure()
fig.add_trace(go.Scatter(x=clust_info.index,
y=clust_sum,
fill='tozeroy',
mode='none',
name='Allocation',
fillcolor='rgba(180, 180, 180, .3)'))
if len(user_run) > 0:
for user in user_run:
fig.add_trace(go.Scatter(
x=user_run.index,
y=np.cumsum(user_run[user]).divide(len(user_run[user])),
line=dict(width=0),
hoverinfo='x+y',
opacity=.1,
mode='none',
name=user,
stackgroup='use' # define stack group
))
if plot_queued:
fig.add_trace(go.Scatter(x=cores_queued.index,
y=queue_sum,
mode='lines',
name='Resources queued',
marker_color='rgba(160,160,220, .8)'))
if len(submit_run) > 0:
submit_run_tmp = submit_run.copy()
if resample_str != '':
submit_run_tmp = submit_run_tmp.resample(resample_str).sum()
fig.add_trace(go.Scatter(x=submit_run.index,
y=np.cumsum(submit_run_tmp).divide(
len(submit_run_tmp)),
mode='lines',
name='Resources run at submit (elapsed)',
marker_color='rgba(220,80,80, .8)'))
if len(submit_req) > 0:
submit_req_tmp = submit_req.copy()
if resample_str != '':
submit_req_tmp = submit_req_tmp.resample(resample_str).sum()
fig.add_trace(go.Scatter(x=submit_req_tmp.index,
y=np.cumsum(submit_req_tmp).divide(
len(submit_req_tmp)),
mode='lines',
name='Resources run at submit (timelimit)',
marker_color='rgba(220,160,00, .8)'))
fig.add_trace(go.Scatter(x=cores_running.index,
y=run_sum,
mode='lines',
name='Resources consumed',
marker_color='rgba(80,80,220, .8)'))
if query_bounds:
max_y = max(clust_sum.max(), run_sum.max(), queue_sum.max())
min_x = clust_info.index.min()
max_x = clust_info.index.max()
fig.add_shape(dict(type="line", x0=min_x, y0=0, x1=min_x, y1=max_y,
line=dict(color="Red", width=2)))
fig.add_shape(dict(type="line", x0=max_x, y0=0, x1=max_x, y1=max_y,
line=dict(color="Red", width=2)))
fig.update_layout(
title=go.layout.Title(
text="Cumulative resource usage: ",
xref="paper",
x=0
),
xaxis=go.layout.XAxis(
title=go.layout.xaxis.Title(
text="Date Time",
font=dict(
family="Courier New, monospace",
size=18,
color="#7f7f7f"
)
)
),
yaxis=go.layout.YAxis(
title=go.layout.yaxis.Title(
text="Core equivalent in time period",
font=dict(
family="Courier New, monospace",
size=18,
color="#7f7f7f"
)
)
)
)
if fig_out != '':
fig.write_html(fig_out)
return fig