Custom Plotting Tutorial

Once you’ve been sampling wastewater for long enough, you might find that the standard freyja plot function isn’t sufficiently flexible to build more complicated and/or curated plots of variant/lineage dynamics. In this tutorial, we’ll talk a little bit about how to customize plots as you see fit, using the Freyja Python library.

  1. Load in necessary packages and prepare an aggregated output file using the python freyja aggregate command. Note: this will be in tsv (tab separated value) format.

import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.dates as mdates

# make text illustrator
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42

agg_df = pd.read_csv('your-aggregated-data.tsv', skipinitialspace=True, sep='\t',index_col=0)
## filter samples by coverage (generally 60-70 is decent cutoff, but can vary across samples)
agg_df = agg_df[agg_df['coverage']>70.0]

  1. Read the aggregated data in, and convert to a more friendly format using the python prepLineageDict() and python prepSummarizedDict() functions:

from freyja.utils import prepLineageDict, prepSummaryDict

agg_df = prepSummaryDict(agg_df)
agg_df = prepLineageDict(agg_df)


# specify which sort of data you're looking to get out
lineages=True # False = variant summarized, True = lineage specific
if lineages:
    ## lineage focused analyses
    queryType = 'linDict'
else:
    ## analyses summarized by variant
    queryType = 'summarized'

  1. Add in dates. Then convert to a single dataframe ordered by date.

# load time metadata
times_df = pd.read_csv(times, skipinitialspace=True,
                               index_col=0)
        times_df['sample_collection_datetime'] = \
            pd.to_datetime(times_df['sample_collection_datetime'])

# build a dataframe
df_abundances = pd.DataFrame()
for i, sampLabel in enumerate(agg_df.index):
    dat = agg_df.loc[sampLabel, queryType]
    if isinstance(dat, list):
        df_abundances = df_abundances.append(
            pd.Series(agg_df.loc[sampLabel, queryType][0],
                      name=times_df.loc[sampLabel,
                                        'sample_collection_datetime']))
    else:
        df_abundances = df_abundances.append(
            pd.Series(agg_df.loc[sampLabel, queryType],
                      name=times_df.loc[sampLabel,
                                        'sample_collection_datetime']))

# fill nans, group data by the appropriate interval

interval = 'MS' # 'MS' is monthly, 'D' is daily
df_abundances = df_abundances.fillna(0)
df_abundances = df_abundances.groupby(pd.Grouper(freq=interval)).mean()

Optional: Add in custom groupings as desired. For example, if we want to group all AY lineages, we can adjust

df_abundances['AY.X'] = df_abundances.loc[:,df_abundances.columns.str.startswith('AY.')].sum(axis=1)
df_abundances = df_abundances.drop(columns= df_abundances.columns[df_abundances.columns.str.startswith('AY.') & ~(df_abundances.columns=='AY.X')])

For more complex groupings, relationships between lineages are made available via cov-lineages here and can be grouped accordingly.

  1. Build your custom plot, using either an existing colormap (like tab20 in matplotlib) or a custom colormap (see colors0 in the example below).

windowSize = 14 # set window size for rolling average if using daily interval

# replace "example" with whatever you'd like to call the output file
outputFn = 'example_' + queryType + '.pdf'

colors0 = []# if empty, use cmap
#example for user specified colors
# colors0 = [#DAF7A6,#FFC300,#FF5733,#1B4F72,#808B96,#45B39D]
cmap = plt.cm.tab20
fig, ax = plt.subplots()
if interval == 'D':
    df_abundances = df_abundances.rolling(windowSize, center=True,
                                          min_periods=0).mean()
    if len(colors0) == 0:
        ax.stackplot(df_abundances.index, df_abundances.to_numpy().T,
                     labels=df_abundances.columns, cmap=cmap)
    else:
        ax.stackplot(df_abundances.index, df_abundances.to_numpy().T,
                     labels=df_abundances.columns, colors=colors0)
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 4})
    ax.set_ylabel('Variant Prevalence')
    ax.set_ylim([0, 1])
    plt.setp(ax.get_xticklabels(), rotation=90)
    fig.tight_layout()
    plt.savefig(outputFn)
    plt.close()
elif interval == 'MS':
    for i in range(0, df_abundances.shape[1]):
        label = df_abundances.columns[i]
        # color = colorDict[label]
        if len(colors0) == 0:
            #this should work for up to 20 colors
            ax.bar(df_abundances.index, df_abundances.iloc[:, i],
                   width=14, bottom=df_abundances.iloc[:, 0:i].sum(axis=1),
                   label=label, color=cmap(i / 20.))
        else:
            ax.bar(df_abundances.index, df_abundances.iloc[:, i],
                   width=14, bottom=df_abundances.iloc[:, 0:i].sum(axis=1),
                   label=label, color=colors0[i])
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 4})
    ax.set_ylabel('Variant Prevalence')
    locator = mdates.MonthLocator(bymonthday=1)
    ax.xaxis.set_major_locator(locator)
    ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator))
    ax.set_ylim([0, 1])
    ax.set_aspect(150)
    fig.tight_layout()
    plt.savefig(outputFn)
    plt.close()