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import numpy as np
from matplotlib import pyplot as plt
from matplotlib import patches
from collections import Counter
import pandas as pd
import seaborn as sns
ngl = pd.read_table('../data/precomputed/ngless_benchmark.tsv')
mocat = pd.read_csv('../data/precomputed/mocat_benchmark.csv', index_col=0)
hts = pd.read_table('../data/precomputed/htseq-count_benchmark.tsv')
hts.rename(columns={
hts.columns[0]: 'dataset',
hts.columns[1]: 'action',
hts.columns[2]: 'sample-id',
hts.columns[3]: 'rep-nr',
}, inplace=True)
hts = hts.groupby(['dataset', 'action', 'rep-nr']).sum().reset_index()
hts['action'] = hts.action.map({'map':'Screen', 'htseq-count':'Profile'})
hts['target'] = hts.dataset.map({'gut': 'IGC', 'tara' : 'OM-RGC'})
htsfull = hts.groupby(['dataset', 'rep-nr']).sum().reset_index()
htsfull['action'] = 'Full'
htsfull['target'] = '-'
hts = pd.concat((hts, htsfull))
hts['tool'] = 'bwa/htseq-count'
mocat.rename(columns={
'Elapsed time': 'wallclock',
'Maximum resident set size (kbytes)': 'max_rss',
}, inplace=True)
mocat = mocat[~ (mocat.target == 'RefMG.v1.padded')]
mocat = mocat[~ (mocat.target == 'mOTU.v1.padded')]
mocat = mocat[~ (mocat.target == 'hg19')]
command = {
'gut.ngl' : ('gut', 'Full', ''),
'gut0-rtf.ngl': ('gut', 'ReadTrimFilter', ''),
'gut1-s.hg19.ngl': ('gut', 'Screen', 'hg19'),
'gut2-f.hg19.ngl': ('gut', 'Filter', 'hg19'),
'gut3-motus.ngl': ('gut', 'motus', ''),
'gut4-s.igc.ngl': ('gut', 'Screen', 'IGC'),
'gut5-f.igc.ngl': ('gut', 'Filter', 'IGC'),
'gut6-p.igc.ngl': ('gut', 'Profile', 'IGC'),
'ocean.ngl' : ('tara', 'Full', ''),
'ocean0-rtf.ngl' : ('tara', 'ReadTrimFilter', ''),
'ocean1-s-om-rgc.ngl' : ('tara', 'Screen', 'OM-RGC.1'),
'ocean2-f-om-rgc.ngl' : ('tara', 'Filter', 'OM-RGC.1'),
'ocean3-p-functional-om-rgc.ngl' : ('tara', 'Profile', 'OM-RGC.1'),
'ocean4-p-seqname-om-rgc.ngl' : ('tara', 'Profile', 'OM-RGC.1'),
}
ngl['dataset'] = ngl.command.map(lambda c: command[c.split()[2]][0])
ngl['action'] = ngl.command.map(lambda c: command[c.split()[2]][1])
ngl['target'] = ngl.command.map(lambda c: command[c.split()[2]][2])
ngl = ngl.groupby([ngl.columns[0], ngl.columns[1]]).sum()
ngl['tool'] = 'ngless'
mocat['tool'] = 'mocat'
ngl['dataset'] = ngl.index.get_level_values(0).map(lambda c: command[c][0])
ngl['action'] = ngl.index.get_level_values(0).map(lambda c: command[c][1])
ngl['target'] = ngl.index.get_level_values(0).map(lambda c: command[c][2])
ngl = ngl.drop([
'ocean4-p-seqname-om-rgc.ngl',
'gut3-motus.ngl',
'gut1-s.hg19.ngl',
'gut2-f.hg19.ngl',
])
COLS = ['dataset', 'action', 'target', 'tool', 'wallclock']
mocat = mocat[COLS]
tara_full = mocat[mocat.dataset == 'tara'].reset_index()
tara_full = pd.DataFrame([('tara', 'Full', '-', 'mocat', wc) for wc in tara_full.groupby(lambda i : i // 4).sum()['wallclock'].values], columns=COLS)
gut_full = mocat[mocat.dataset == 'gut'].reset_index()
gut_full = pd.DataFrame([('gut', 'Full', '-', 'mocat', wc) for wc in gut_full.groupby(lambda i : i // 4).sum()['wallclock'].values], columns=COLS)
data = pd.concat((ngl[COLS], mocat[COLS], hts[COLS], gut_full, tara_full))
data.reset_index(inplace=True)
data.fillna('-', inplace=True)
ACTIONS = ['Full', 'ReadTrimFilter', 'Screen', 'Filter', 'Profile']
DATASETS = ['gut', 'tara']
COLORS = ['#1b9e77', '#d95f02', '#7570b3']
MARKERS = "oDs"
TOOLS = ['bwa/htseq-count', 'mocat', 'ngless']
fig,ax = plt.subplots()
rep = Counter()
for k, row in data.iterrows():
base = ACTIONS.index(row['action'])*3 + DATASETS.index(row['dataset'])
key = tuple(row[['action', 'dataset', 'tool']].values)
base += rep[key]/4
rep[key] += 1
x = base
y = row['wallclock']
c = COLORS[TOOLS.index(row['tool'])]
m = MARKERS[TOOLS.index(row['tool'])]
ax.scatter([x], [y], c=c, marker=m, s=12)
for ac in range(len(ACTIONS)):
x = 3*ac+.75
y = 0
ax.add_patch(patches.Rectangle([x,y], 1, max(data['wallclock']) * 1.1, zorder=-1, color='#0091ff26'))
ax.set_yscale('log')
ax.set_xticks(np.arange(len(ACTIONS))*3+0.5)
ax.set_xticklabels(ACTIONS)
fig.tight_layout()
fig.savefig('ngless-mocat-htseq-count-compare.png', dpi=150)
fig.savefig('ngless-mocat-htseq-count-compare.svg')
fig.savefig('ngless-mocat-htseq-count-compare.pdf')