def output_chart(issues_df, output_mode='static'):
import datetime
import bokeh
from bokeh.models import HoverTool
# Add timestamp to title
issues_chart = Bar(issues_df,
label='value_delivered',
values='status',
agg='count',
stack='status',
title=ISSUES_TITLE + " (Updated " +
datetime.datetime.now().strftime('%m/%d/%Y') + ")",
xlabel="Value Delivered",
ylabel="Number of Use Cases",
legend='top_right',
tools='hover',
color=brewer["GnBu"][3])
issues_chart.plot_width = DESTINATION_FRAME_WIDTH - (HTML_BODY_MARGIN * 2)
issues_chart.plot_height = DESTINATION_FRAME_HEIGHT - (HTML_BODY_MARGIN *
2)
issues_chart.logo = None
issues_chart.toolbar_location = None
hover = issues_chart.select(dict(type=HoverTool))
hover.tooltips = [("Value Delivered", "$x")]
#--- Configure output ---
reset_output()
if output_mode == 'static':
# Static file. CDN is most space efficient
output_file(ISSUES_FILE,
title=ISSUES_TITLE,
autosave=False,
mode='cdn',
root_dir=None) # Generate file
save(issues_chart, filename=ISSUES_FILE)
elif output_mode == 'notebook':
output_notebook() # Show inline
show(issues_chart)
else:
# Server (using internal server IP, rather than localhost or external)
session = bokeh.session.Session(root_url=BOKEH_SERVER_IP,
load_from_config=False)
output_server("ddod_chart", session=session)
show(issues_chart)
def plot_average_dts(df, hours):
lasti = 0
tmp = 0
tmplist = []
### Currently using a fixed method timeset, if there is a better way to do a log loop this could be changed. ###
lasti = 0
tmp = 0
tmplist = []
xlist = []
for i in range(1,10):
tmp = 0
for item in df['noao_time']:
if item > lasti and item < i:
tmp = tmp + 1
tmplist.append(tmp)
xlist.append(i)
lasti = i
for i in range(10,100,10):
tmp = 0
for item in df['noao_time']:
if item > lasti and item < i:
tmp = tmp + 1
tmplist.append(tmp)
xlist.append(i)
lasti = i
for i in range(100,1000,100):
tmp = 0
for item in df['noao_time']:
if item > lasti and item < i:
tmp = tmp + 1
tmplist.append(tmp)
xlist.append(i)
lasti = i
graph_info = {}
graph_info['values'] = tmplist
graph_info['Time in minutes'] = xlist
p = Bar(graph_info, values='values', label='Time in minutes', ylabel='Number of transfers', color='navy', title='DTS time plot')
p.plot_height = 500
p.plot_width = 1000
return p
def output_chart(issues_df,output_mode='static'):
import datetime
import bokeh
from bokeh.models import HoverTool
# Add timestamp to title
issues_chart = Bar(issues_df, label='value_delivered',
values='status', agg='count', stack='status',
title=ISSUES_TITLE+" (Updated "+datetime.datetime.now().strftime('%m/%d/%Y')+")",
xlabel="Value Delivered",ylabel="Number of Use Cases",
legend='top_right',
tools='hover',
color=brewer["GnBu"][3]
)
issues_chart.plot_width = DESTINATION_FRAME_WIDTH - (HTML_BODY_MARGIN * 2)
issues_chart.plot_height = DESTINATION_FRAME_HEIGHT - (HTML_BODY_MARGIN * 2)
issues_chart.logo = None
issues_chart.toolbar_location = None
hover = issues_chart.select(dict(type=HoverTool))
hover.tooltips = [ ("Value Delivered", "$x")]
#--- Configure output ---
reset_output()
if output_mode == 'static':
# Static file. CDN is most space efficient
output_file(ISSUES_FILE, title=ISSUES_TITLE,
autosave=False, mode='cdn',
root_dir=None
) # Generate file
save(issues_chart,filename=ISSUES_FILE)
elif output_mode == 'notebook':
output_notebook() # Show inline
show(issues_chart)
else:
# Server (using internal server IP, rather than localhost or external)
session = bokeh.session.Session(root_url = BOKEH_SERVER_IP, load_from_config=False)
output_server("ddod_chart", session=session)
show(issues_chart)
def generate_single_bar(df, time_str, color_dict, colors):
df = df[df.percent_total >= 3]
title = "Failure Rate " + time_str
fill = [color_dict[model_key] if model_key in color_dict else 'grey' for model_key in df.model]
source = ColumnDataSource(dict(color=[c for c in df['color']],
model=[m for m in df['model']],
failure_rate=[f for f in df['failure_rate']],
count=[co for co in df['count']]))
plot = Bar(df, 'model', values='failure_rate', title=title, source=source, tools=['hover'],color='color', legend=None)
# outline_line_color="color", border_fill_color='color',
hover = plot.select(dict(type=HoverTool))
hover.tooltips = [
("Model ", "@model"),
("Failure rate ", "@y"),
("Number of drives", "@count") #("Time ", "@timeline"),
]
hover.mode = 'mouse'
plot.xaxis.axis_label = 'Model Serial Number'
plot.yaxis.axis_label = 'Naive Failure Rate'
plot.title_text_font_size="18px"
plot.grid.grid_line_alpha = 0
plot.ygrid.grid_line_color = None
plot.toolbar.logo = None
plot.outline_line_width = 0
plot.outline_line_color = "white"
plot.plot_height = 600
plot.plot_width = 800
plot.xaxis.major_tick_line_color = None
plot.yaxis.major_tick_line_color = None
plot.xaxis.axis_line_width = 2
plot.yaxis.axis_line_width = 2
plot.title.text_font_size = '16pt'
plot.xaxis.axis_label_text_font_size = "14pt"
plot.xaxis.major_label_text_font_size = "14pt"
plot.yaxis.axis_label_text_font_size = "14pt"
plot.yaxis.major_label_text_font_size = "14pt"
return(plot)
