def render_bytes_sent_received(
figure: Figure, df: pd.DataFrame, label: str, read_col: str, write_col: str
):
time = df[DATETIME_KEY]
def accumulate(column):
values = df[column]
values = values - values.min()
return resample(values, time)
read = accumulate(read_col)
write = accumulate(write_col)
data = ColumnDataSource(
dict(rx=read, rx_kb=read / 1024, tx=write, tx_kb=write / 1024, x=read.index)
)
tooltips = [("RX", "@rx_kb KiB"), ("TX", "@tx_kb KiB")]
figure.add_tools(HoverTool(tooltips=tooltips))
figure.yaxis[0].formatter = NumeralTickFormatter(format="0.0b")
y_max = max(max(data.data["rx"]), max(data.data["tx"]))
figure.y_range = Range1d(0, y_max + 0.01)
figure.line(
x="x", y="rx", color="blue", legend_label="{} RX".format(label), source=data
)
figure.line(
x="x", y="tx", color="red", legend_label="{} TX".format(label), source=data
)
def render_node_network_connections(figure: Figure, df: pd.DataFrame):
connections_series = df[NETWORK_CONNECTIONS_KEY]
connections = resample(connections_series, df[DATETIME_KEY])
figure.y_range = Range1d(0, connections_series.max() + 10)
figure.add_tools(HoverTool(tooltips=[("Connection count", "@count")]))
data = ColumnDataSource(dict(count=connections, x=connections.index))
figure.line(x="x", y="count", legend_label="Network connections", source=data)
def plot(self, figure, color_attr=None, history_step=None, solid_color='#0485d1', circle_attr=None,
circle_attr_transform=lambda x: x, circle_line_color='#cb7723', circle_fill_color='#fcb001',
circle_hover_attrs=[], color_attr_bounds=None):
""" To simply plot the network in a solid color, use color_attr=None and history_step=None.
To plot a fixed attribute of each network reach such as redd capacity, set color_attr to the
name of that attribute and history_step=None.
To plot an attribute of the network's history that varies over time, use history_step along
with the name of that attribute.
color_attr_bounds is None to use the min and max values of that variable in the current plot, or
specifiable to use a standard color range across multiple plots"""
lines = [reach.points for reach in self.reaches]
source = ColumnDataSource({'xs': [list(np.array(line).T[0]) for line in lines],
'ys': [list(np.array(line).T[1]) for line in lines],
'line_widths': [0.5 * reach.strahler_order for reach in self.reaches]})
figure.add_layout(Label(x=self.migration_reach.midpoint[0], y=self.migration_reach.midpoint[1]+750,
text='Migration', text_align='center'))
figure.add_layout(Label(x=self.ocean_reach.midpoint[0], y=self.ocean_reach.midpoint[1]+750,
text='Ocean', text_align='center'))
if history_step is not None:
year = 1 + math.floor(history_step / time_settings['WEEKS_PER_YEAR'])
step_within_year = history_step % time_settings['WEEKS_PER_YEAR']
days_into_year = 1 + step_within_year * time_settings['DAYS_PER_WEEK']
date1 = datetime.date.fromordinal(days_into_year).strftime("%b %e")
date2 = datetime.date.fromordinal(days_into_year + 7).strftime("%b %e")
timestring = "Timestep {0} (step {1} of year {2}, {3} - {4})".format(history_step,
1 + step_within_year,
year,
date1,
date2)
figure.add_layout(Label(x=30, y=700, x_units='screen', y_units='screen', text=timestring))
season, season_color = self.season_label(history_step)
figure.add_layout(Label(x=650, y=700, x_units='screen', y_units='screen', text=season, text_color=season_color))
if color_attr is None:
figure.multi_line('xs', 'ys', source=source, line_color=solid_color, line_width='line_widths')
else:
source.add([reach.reach_statistic(color_attr, history_step) for reach in self.reaches], name='color_values')
color_low_value = min(source.data['color_values']) if color_attr_bounds is None else color_attr_bounds[0]
color_high_value = max(source.data['color_values']) if color_attr_bounds is None else color_attr_bounds[1]
mapper = LinearColorMapper(palette='Viridis256', low=color_low_value, high=color_high_value)
figure.multi_line('xs', 'ys', source=source, line_color={'field': 'color_values', 'transform': mapper}, line_width='line_widths')
fmt = NumeralTickFormatter(format='00')
color_bar = ColorBar(color_mapper=mapper, location=(0, 0), title=color_attr, formatter=fmt, label_standoff=7)
figure.add_layout(color_bar, 'right')
if circle_attr is not None:
circle_source = ColumnDataSource({'xs': [reach.midpoint[0] for reach in self.reaches],
'ys': [reach.midpoint[1] for reach in self.reaches]})
circle_source.add([circle_attr_transform(reach.reach_statistic(circle_attr, history_step))
for reach in self.reaches], name='circle_sizes')
for attr in circle_hover_attrs:
circle_source.add([reach.reach_statistic(attr, history_step) for reach in self.reaches], name=attr)
figure.scatter('xs', 'ys', source=circle_source, name='scatterplot', marker='circle', size='circle_sizes',
line_color=circle_line_color, fill_color=circle_fill_color, alpha=0.5)
hover_tooltips = []
for attr in circle_hover_attrs:
hover_tooltips.append((attr, "@"+attr))
figure.add_tools(HoverTool(tooltips=hover_tooltips, names=['scatterplot']))
figure.toolbar.logo = None
def render_node_mem_pct_utilization(figure: Figure, df: pd.DataFrame):
mem = resample(df[MEM_KEY], df[DATETIME_KEY])
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.y_range = Range1d(0, 1)
figure.add_tools(HoverTool(tooltips=[("Memory", "@mem")]))
data = ColumnDataSource(dict(mem=mem / 100.0, x=mem.index))
figure.line(x="x", y="mem", color="red", legend_label="Memory", source=data)
def render_node_per_cpu_pct_utilization(figure: Figure, df: pd.DataFrame):
time = df[DATETIME_KEY]
cpu_series = df[CPU_KEY]
cpu_count = len(cpu_series.iloc[0])
cpus = [
resample(cpu_series.apply(lambda res: res[i]), time) for i in range(cpu_count)
]
cpu_mean = resample(cpu_series.apply(lambda res: average(res)), time)
figure.yaxis[0].formatter = NumeralTickFormatter(format="0 %")
figure.y_range = Range1d(0, 1)
data = {}
tooltips = []
for (i, cpu) in enumerate(cpus):
key = f"cpu_{i}"
data[key] = cpu / 100.0
data[f"{key}_x"] = cpu.index
tooltips.append((f"CPU #{i}", f"@{key}"))
data["cpu_mean"] = cpu_mean / 100.0
data["cpu_mean_x"] = cpu_mean.index
tooltips.append(("CPU avg.", "@cpu_mean"))
figure.add_tools(HoverTool(tooltips=tooltips))
data = ColumnDataSource(data)
colors = select_colors(cpus)
for (i, color) in enumerate(colors):
figure.line(
x=f"cpu_{i}_x",
y=f"cpu_{i}",
color=color,
legend_label=f"CPU #{i}",
source=data,
)
figure.line(
x="cpu_mean_x",
y="cpu_mean",
color="red",
legend_label="Average CPU",
line_dash="dashed",
line_width=5,
source=data,
)
def add_hover(self, figure, tooltips=[('Country', '@iso_code'), ('Cases', '@total_cases')]):
hover = HoverTool(tooltips=tooltips)
figure.add_tools(hover)
