def compare_cumulative_yields(df, path, palette=None, title=None):
if palette is None:
palette = plotly.colors.DEFAULT_PLOTLY_COLORS * 5
dfs = check_valid_time_and_sort(df, "start_time").set_index("start_time")
logging.info(
"NanoComp: Creating cumulative yield plots using {} reads.".format(
len(dfs)))
cum_yield_gb = Plot(path=path +
"NanoComp_CumulativeYieldPlot_Gigabases.html",
title="Cumulative yield")
data = []
annotations = []
for sample, color in zip(df["dataset"].unique(), palette):
cumsum = dfs.loc[dfs["dataset"] == sample,
"lengths"].cumsum().resample('10T').max() / 1e9
data.append(
go.Scatter(x=cumsum.index.total_seconds() / 3600,
y=cumsum,
opacity=0.75,
name=sample,
marker=dict(color=color)))
annotations.append(
dict(xref='paper',
x=0.99,
y=cumsum[-1],
xanchor='left',
yanchor='middle',
text='{}Gb'.format(round(cumsum[-1])),
showarrow=False))
cum_yield_gb.html = plotly.offline.plot(
{
"data":
data,
"layout":
go.Layout(barmode='overlay',
title=title or cum_yield_gb.title,
xaxis=dict(title="Time (hours)"),
yaxis=dict(title="Yield (gigabase)"),
annotations=annotations)
},
output_type="div",
show_link=False)
cum_yield_gb.fig = go.Figure({
"data":
data,
"layout":
go.Layout(barmode='overlay',
title=title or cum_yield_gb.title,
xaxis=dict(title="Time (hours)"),
yaxis=dict(title="Yield (gigabase)"),
annotations=annotations)
})
cum_yield_gb.save()
return [cum_yield_gb]
def compare_cumulative_yields(df, path, palette=None, title=None):
if palette is None:
palette = plotly.colors.DEFAULT_PLOTLY_COLORS * 5
dfs = check_valid_time_and_sort(df, "start_time").set_index("start_time")
logging.info(
"Nanoplotter: Creating cumulative yield plots using {} reads.".format(
len(dfs)))
cum_yield_gb = Plot(path=path +
"NanoComp_CumulativeYieldPlot_Gigabases.html",
title="Cumulative yield")
data = []
for d, c in zip(df["dataset"].unique(), palette):
s = dfs.loc[dfs["dataset"] == d,
"lengths"].cumsum().resample('10T').max() / 1e9
data.append(
go.Scatter(x=s.index.total_seconds() / 3600,
y=s,
opacity=0.75,
name=d,
marker=dict(color=c)))
cum_yield_gb.html = plotly.offline.plot(
{
"data":
data,
"layout":
go.Layout(
barmode='overlay',
title=title or cum_yield_gb.title,
xaxis=dict(title="Time (hours)"),
yaxis=dict(title="Yield (gigabase)"),
)
},
output_type="div",
show_link=False)
cum_yield_gb.fig = go.Figure({
"data":
data,
"layout":
go.Layout(
barmode='overlay',
title=title or cum_yield_gb.title,
xaxis=dict(title="Time (hours)"),
yaxis=dict(title="Yield (gigabase)"),
)
})
cum_yield_gb.save()
return [cum_yield_gb]
def active_pores_over_time(df, path, palette=None, title=None):
if palette is None:
palette = plotly.colors.DEFAULT_PLOTLY_COLORS * 5
dfs = check_valid_time_and_sort(df, "start_time").set_index("start_time")
logging.info("NanoComp: Creating active pores plot using {} reads.".format(
len(dfs)))
active_pores = Plot(path=path + "NanoComp_ActivePoresOverTime.html",
title="Active pores over time")
data = []
for sample, color in zip(df["dataset"].unique(), palette):
pores = dfs.loc[dfs["dataset"] == sample,
"channelIDs"].resample('10T').nunique()
data.append(
go.Scatter(x=pores.index.total_seconds() / 3600,
y=pores,
opacity=0.75,
name=sample,
marker=dict(color=color)))
active_pores.html = plotly.offline.plot(
{
"data":
data,
"layout":
go.Layout(
barmode='overlay',
title=title or active_pores.title,
xaxis=dict(title="Time (hours)"),
yaxis=dict(title="Active pores (per 10 minutes)"),
)
},
output_type="div",
show_link=False)
active_pores.fig = go.Figure({
"data":
data,
"layout":
go.Layout(
barmode='overlay',
title=title or active_pores.title,
xaxis=dict(title="Time (hours)"),
yaxis=dict(title="Active pores (per 10 minutes)"),
)
})
active_pores.save()
return active_pores
def compare_sequencing_speed(df, figformat, path, title=None, palette=None):
logging.info(
"Nanoplotter: creating comparison of sequencing speed over time.")
seq_speed = Plot(path=path + "NanoComp_sequencing_speed_over_time." +
figformat,
title="Sequencing speed over time")
dfs = check_valid_time_and_sort(df, "start_time")
dfs['timebin'] = add_time_bins(dfs)
ax = sns.violinplot(x=dfs["timebin"],
y=dfs["lengths"] / dfs["duration"],
hue=dfs["dataset"],
inner=None,
cut=0,
linewidth=0)
ax.set(xlabel='Interval (hours)',
ylabel="Sequencing speed (nucleotides/second)")
plt.xticks(rotation=45, ha='center', fontsize=8)
seq_speed.fig = ax.get_figure()
seq_speed.save(format=figformat)
plt.close("all")
return [seq_speed]
def compare_sequencing_speed(df, path, settings, title=None):
logging.info(
"NanoComp: creating comparison of sequencing speed over time.")
seq_speed = Plot(path=path + "NanoComp_sequencing_speed_over_time.html",
title="Sequencing speed over time")
dfs = check_valid_time_and_sort(df, "start_time").set_index("start_time")
dfs = dfs.loc[dfs["duration"] > 0]
palette = plotly.colors.DEFAULT_PLOTLY_COLORS * 5
data = []
for sample, color in zip(df["dataset"].unique(), palette):
seqspeed = (dfs.loc[dfs["dataset"] == sample, "lengths"] /
dfs.loc[dfs["dataset"] == sample,
"duration"]).resample('30T').median()
data.append(
go.Scatter(x=seqspeed.index.total_seconds() / 3600,
y=seqspeed,
opacity=0.75,
name=sample,
mode='lines',
marker=dict(color=color)))
seq_speed.fig = go.Figure({"data": data})
seq_speed.fig.update_layout(
title=title or seq_speed.title,
title_x=0.5,
xaxis_title='Interval (hours)',
yaxis_title="Sequencing speed (nucleotides/second)")
seq_speed.html = seq_speed.fig.to_html(full_html=False,
include_plotlyjs='cdn')
seq_speed.save(settings)
return [seq_speed]