background_fill_alpha=0.6,
x_offset=8,
#y_offset=5,
source=mohr_sigma_label_data)
mohr_plot.add_layout(mohr_sigma_labels)
mohr_plot.legend.location = "top_left"
##############################################################################
### TABLE ###
##############################################################################
columns = [
TableColumn(field='zw',
title='Zw (m)',
formatter=models.NumberFormatter(format='0.000')),
TableColumn(field='zl',
title='Zl (m)',
formatter=models.NumberFormatter(format='0.000')),
TableColumn(field='z',
title='Z (m)',
formatter=models.NumberFormatter(format='0.000')),
TableColumn(field='Ja',
title='J\u03B1',
formatter=models.NumberFormatter(format='0.000')),
TableColumn(field='a_a',
title='\u03B1\u2090 (deg)',
formatter=models.NumberFormatter(format='0.000')),
TableColumn(field='Ka',
title='K\u03B1',
formatter=models.NumberFormatter(format='0.000')),
def create_panel(k):
# Create plot
p[k] = bpl.figure(tools=[TOOLS,hover],toolbar_location="right")
p[k].circle(x=inp_xaxis.value, y=inp_yaxis.value,
source = pdf_ts[k],
size=12)
p[k].xaxis.axis_label = "climate change signal {}".format(inp_xaxis.value)
p[k].yaxis.axis_label = "climate change signal {}".format(inp_yaxis.value)
# Horizontal line
hline = bmo.Span(location=0, dimension='width', line_color='black', line_width=3)
vline = bmo.Span(location=0, dimension='height', line_color='black', line_width=3)
p[k].renderers.extend([vline, hline])
# Create table
columns = [
bmo.widgets.TableColumn(field="index", title="model"),
bmo.widgets.TableColumn(field="{}".format(inp_xaxis.value), title="{}".format(inp_xaxis.value.title()), width=65,formatter=bmo.NumberFormatter(format="0.000")),
bmo.widgets.TableColumn(field="{}_percentiles".format(inp_xaxis.value), title="{} Perc.".format(inp_xaxis.value.title()), width=70,formatter=bmo.NumberFormatter(format="0.000")),
bmo.widgets.TableColumn(field="{}".format(inp_yaxis.value), title="{}".format(inp_yaxis.value.title()), width=65,formatter=bmo.NumberFormatter(format="0.000")),
bmo.widgets.TableColumn(field="{}_percentiles".format(inp_yaxis.value), title="{} Perc.".format(inp_yaxis.value.title()), width=70,formatter=bmo.NumberFormatter(format="0.000")),
]
data_table = bmo.widgets.DataTable(source=pdf_ts[k], columns=columns, fit_columns=False,
selectable='checkbox', height=p[k].plot_height-100, index_position=None)
down_button = bmo.widgets.Button(label="Download CSV", button_type="primary")
down_button.callback = bmo.CustomJS(args=dict(source=pdf_ts[k], filename="{}_{}_{}.csv".format(k, inp_time_mean.value, inp_exp.value)),
code=open(join(dirname(__file__), "download.js")).read())
dct_buttons[k] = down_button
l_panel = bo.layouts.row([
bo.layouts.column([p[k]]),
bo.layouts.column([down_button, data_table]),
])
panel = bmo.widgets.Panel(child=l_panel, title=time_description[k])
return panel
def perform_slicing(s: pd.Series):
"""
Perform slicing of the packet IAT series `s` into following bins:
'0 - 10',
'10 - 100',
'100 - 500',
'500 - 1,000',
'1,000 - 5,000',
'5,000 - 10,000',
'10,000 - 50,000',
'50,000 - 100,000',
'100,000 - 500,000',
where each diapason is specified in microseconds (us).
Attributes:
s: `
pd.Series` of packet inter-arrival time in microseconds (us).
Returns:
`bokeh` bar chart figure and table with data.
"""
bins = [0, 10, 100, 500, 1000, 5000, 10000, 50000, 100000, 500000]
hist, edges = np.histogram(s, bins=bins)
bins_str = [
'0 - 10',
'10 - 100',
'100 - 500',
'500 - 1,000',
'1,000 - 5,000',
'5,000 - 10,000',
'10,000 - 50,000',
'50,000 - 100,000',
'100,000 - 500,000'
]
d = {}
d['edges.us'] = bins_str
d['packets'] = hist
df = pd.DataFrame(d)
n = df['packets'].sum()
df['packets_cumsum'] = df['packets'].cumsum()
df['percentage'] = df['packets'] * 100 / n
df['percentage_cumsum'] = round(df['percentage'].cumsum(), 4)
df['percentage'] = round(df['percentage'], 4)
# Figure
fig = plotting.figure(
plot_height=300,
plot_width=1000,
x_range=bins_str,
tools=TOOLS
)
fig.title.text = 'Inter-arrival packet time diapason vs Packets'
fig.xaxis.axis_label = 'IAT, us'
fig.yaxis.axis_label = 'Packets'
fig.yaxis.formatter = models.NumeralTickFormatter(format='0,0')
# fig.xaxis.major_label_orientation = math.pi/4
fig.vbar(x=bins_str, top=hist, width=0.9)
# Table
source = models.ColumnDataSource(df)
columns = [
models.widgets.TableColumn(field='edges.us', title='IAT, us'),
models.widgets.TableColumn(field='packets', title='Packets', formatter=models.NumberFormatter(format='0,0')),
models.widgets.TableColumn(field='packets_cumsum', title='Packets cumsum', formatter=models.NumberFormatter(format='0,0')),
models.widgets.TableColumn(field='percentage', title='Packets, %'),
models.widgets.TableColumn(field='percentage_cumsum', title='Packets cumsum, %'),
]
table = models.widgets.DataTable(columns=columns, source=source)
return fig, table