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Commit 45503263 authored by Yoann Dufresne's avatar Yoann Dufresne
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figures update

parent df809076
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deconvolution/d2graph/d2_path.py
View file @ 45503263
......@@ -16,6 +16,7 @@ class Path(list):
self.barcode_order = []
self.lcp_per_multiset = []
self.barcode_score = 0
self.size_score = 0
def append(self, obj) -> None:
lcp = self.d2g.get_lcp(obj)
......
deconvolution/main/fig_gen.py
View file @ 45503263
......@@ -20,16 +20,21 @@ def parse_args():
def main():
args = parse_args()
data = pd.read_csv(args.filename, sep='\t')
print(data)
plot = (p9.ggplot(data=data,
mapping=p9.aes(x='path length', y='Accuracy', color='experiment'))
+ p9.geom_line())
plot.save(args.output + "_acc.png", width=10, height=3, dpi=300)
mapping=p9.aes(x='path length', y='Accuracy', color='experiment', ))
+ p9.geom_line()
+ p9.themes.theme_classic()
+ p9.scales.scale_x_log10()
# + p9.scale_colour_manual(values=["red", "blue", "red", "blue", "red", "blue", "red", "blue"])
)
plot.save(args.output + "_acc.png", width=6, height=3, dpi=300)
plot = (p9.ggplot(data=data,
mapping=p9.aes(x='path length', y='Sensitivity', color='experiment'))
+ p9.geom_line())
plot.save(args.output + "_sens.png", width=10, height=3, dpi=300)
+ p9.geom_line()
+ p9.themes.theme_classic()
+ p9.scales.scale_x_log10())
plot.save(args.output + "_sens.png", width=6, height=3, dpi=300)
......
figs/fig1_data.tsv
View file @ 45503263
experiment path length Accuracy Sensitivity
5000-2-0 Gb 2 0.47240795939740593 0.9995999199839968
5000-2-0 Gb 4 0.09528 0.9987992795677406
5000-2-0 Gb 10 0.00024 0.996393508314967
5000-2-0 Gb 25 0.0 0.9903536977491961
5000-2-0 Gb 50 0.0 0.9802060189860634
5000-2-0 Gb 75 0.0 0.9699553390174583
5000-2-0 Gb 100 0.0 0.9596000816159967
5000-2-0 Gb 150 0.0 0.9385693671407958
5000-2-0 lcp 2 1.0 0.9985997199439888
5000-2-0 lcp 4 0.9999078001106398 0.9941965179107465
5000-2-0 lcp 10 0.9992509363295881 0.9873772791023843
5000-2-0 lcp 25 0.9987136354640808 0.9722668810289389
5000-2-0 lcp 50 0.9989362833741091 0.9468794182993334
5000-2-0 lcp 75 0.9990801425779005 0.9212342671538774
5000-2-0 lcp 100 0.9988800398208064 0.8953274841869007
5000-2-0 lcp 150 0.9986753017368266 0.8427128427128427
5000-2-1 Gb 2 0.46545819062734944 0.9995999199839968
5000-2-1 Gb 4 0.0897627343620375 0.9987992795677406
5000-2-1 Gb 10 0.0011613438407299876 0.996393508314967
5000-2-1 Gb 25 0.0 0.9903536977491961
5000-2-1 Gb 50 0.0 0.9802060189860634
5000-2-1 Gb 75 0.0 0.9699553390174583
5000-2-1 Gb 100 0.0 0.9596000816159967
5000-2-1 Gb 150 0.0 0.9385693671407958
5000-2-1 lcp 2 0.9997357759379955 0.9977995599119824
5000-2-1 lcp 4 0.9994972769166317 0.9927956774064439
5000-2-1 lcp 10 0.9987872487872488 0.9817671809256662
5000-2-1 lcp 25 0.9980111753006914 0.9545819935691319
5000-2-1 lcp 50 0.9974154399370716 0.9089072914562715
5000-2-1 lcp 75 0.9969027171617626 0.8627689809175801
5000-2-1 lcp 100 0.9976555455365194 0.8161599673536013
5000-2-1 lcp 150 0.9956602603843769 0.7241805813234384
5000-3-0 Gb 2 0.32474102625873286 0.9995999199839968
5000-3-0 Gb 4 0.03035392921415717 0.9987992795677406
5000-3-0 Gb 10 0.0 0.996393508314967
5000-3-0 Gb 25 0.0 0.9903536977491961
5000-3-0 Gb 50 0.0 0.9802060189860634
5000-3-0 Gb 75 0.0 0.9699553390174583
5000-3-0 Gb 100 0.0 0.9596000816159967
5000-3-0 Gb 150 0.0 0.9385693671407958
5000-3-0 lcp 2 0.9995081564062628 0.9907981596319264
5000-3-0 lcp 4 0.9992268439771146 0.9763858314988993
5000-3-0 lcp 10 0.9978075517661389 0.9455019034261671
5000-3-0 lcp 25 0.9980715456561566 0.8729903536977492
5000-3-0 lcp 50 0.9973603778827452 0.7618662896384569
5000-3-0 lcp 75 0.9964375523889355 0.6591555014210313
5000-3-0 lcp 100 0.994793474488025 0.5596816976127321
5000-3-0 lcp 150 0.9910813823857302 0.39806225520511235
5000-3-1 Gb 2 0.32885662431941926 0.9993998799759952
5000-3-1 Gb 4 0.028898254063816978 0.998198919351611
5000-3-1 Gb 10 0.0 0.9945902624724504
5000-3-1 Gb 25 0.0 0.9855305466237942
5000-3-1 Gb 50 0.0 0.9703090284790952
5000-3-1 Gb 75 0.0 0.9549330085261876
5000-3-1 Gb 100 0.0 0.9394001224239951
5000-3-1 Gb 150 0.0 0.9078540507111935
5000-3-1 lcp 2 0.9987888880478389 0.9857971594318864
5000-3-1 lcp 4 0.9968935329003107 0.9655793476085651
5000-3-1 lcp 10 0.9928609402293613 0.9196553796834301
5000-3-1 lcp 25 0.9922175155157127 0.8203376205787781
5000-3-1 lcp 50 0.9913057478666881 0.6806705716016966
5000-3-1 lcp 75 0.9872557349192863 0.5629313844904588
5000-3-1 lcp 100 0.9843342036553525 0.4572536217098551
5000-3-1 lcp 150 0.9775510204081632 0.28633271490414347
5000-2-0 Gb 2 0.47240795939740593 0.9995999199839968
5000-2-0 Gb 4 0.09528 0.9987992795677406
5000-2-0 Gb 10 0.00024 0.996393508314967
5000-2-0 Gb 25 0.0 0.9903536977491961
5000-2-0 Gb 50 0.0 0.9802060189860634
5000-2-0 Gb 75 0.0 0.9699553390174583
5000-2-0 Gb 100 0.0 0.9596000816159967
5000-2-0 Gb 150 0.0 0.9385693671407958
5000-2-0 lcp 2 1.0 0.9985997199439888
5000-2-0 lcp 4 0.9999078001106398 0.9941965179107465
5000-2-0 lcp 10 0.9992509363295881 0.9873772791023843
5000-2-0 lcp 25 0.9987136354640808 0.9722668810289389
5000-2-0 lcp 50 0.9989362833741091 0.9468794182993334
5000-2-0 lcp 75 0.9990801425779005 0.9212342671538774
5000-2-0 lcp 100 0.9988800398208064 0.8953274841869007
5000-2-0 lcp 150 0.9986753017368266 0.8427128427128427
5000-2-1 Gb 2 0.46545819062734944 0.9995999199839968
5000-2-1 Gb 4 0.0897627343620375 0.9987992795677406
5000-2-1 Gb 10 0.0011613438407299876 0.996393508314967
5000-2-1 Gb 25 0.0 0.9903536977491961
5000-2-1 Gb 50 0.0 0.9802060189860634
5000-2-1 Gb 75 0.0 0.9699553390174583
5000-2-1 Gb 100 0.0 0.9596000816159967
5000-2-1 Gb 150 0.0 0.9385693671407958
5000-2-1 lcp 2 0.9997357759379955 0.9977995599119824
5000-2-1 lcp 4 0.9994972769166317 0.9927956774064439
5000-2-1 lcp 10 0.9987872487872488 0.9817671809256662
5000-2-1 lcp 25 0.9980111753006914 0.9545819935691319
5000-2-1 lcp 50 0.9974154399370716 0.9089072914562715
5000-2-1 lcp 75 0.9969027171617626 0.8627689809175801
5000-2-1 lcp 100 0.9976555455365194 0.8161599673536013
5000-2-1 lcp 150 0.9956602603843769 0.7241805813234384
5000-3-0 Gb 2 0.32474102625873286 0.9995999199839968
5000-3-0 Gb 4 0.03035392921415717 0.9987992795677406
5000-3-0 Gb 10 0.0 0.996393508314967
5000-3-0 Gb 25 0.0 0.9903536977491961
5000-3-0 Gb 50 0.0 0.9802060189860634
5000-3-0 Gb 75 0.0 0.9699553390174583
5000-3-0 Gb 100 0.0 0.9596000816159967
5000-3-0 Gb 150 0.0 0.9385693671407958
5000-3-0 lcp 2 0.9995081564062628 0.9907981596319264
5000-3-0 lcp 4 0.9992268439771146 0.9763858314988993
5000-3-0 lcp 10 0.9978075517661389 0.9455019034261671
5000-3-0 lcp 25 0.9980715456561566 0.8729903536977492
5000-3-0 lcp 50 0.9973603778827452 0.7618662896384569
5000-3-0 lcp 75 0.9964375523889355 0.6591555014210313
5000-3-0 lcp 100 0.994793474488025 0.5596816976127321
5000-3-0 lcp 150 0.9910813823857302 0.39806225520511235
5000-3-1 Gb 2 0.32885662431941926 0.9993998799759952
5000-3-1 Gb 4 0.028898254063816978 0.998198919351611
5000-3-1 Gb 10 0.0 0.9945902624724504
5000-3-1 Gb 25 0.0 0.9855305466237942
5000-3-1 Gb 50 0.0 0.9703090284790952
5000-3-1 Gb 75 0.0 0.9549330085261876
5000-3-1 Gb 100 0.0 0.9394001224239951
5000-3-1 Gb 150 0.0 0.9078540507111935
5000-3-1 lcp 2 0.9987888880478389 0.9857971594318864
5000-3-1 lcp 4 0.9968935329003107 0.9655793476085651
5000-3-1 lcp 10 0.9928609402293613 0.9196553796834301
5000-3-1 lcp 25 0.9922175155157127 0.8203376205787781
5000-3-1 lcp 50 0.9913057478666881 0.6806705716016966
5000-3-1 lcp 75 0.9872557349192863 0.5629313844904588
5000-3-1 lcp 100 0.9843342036553525 0.4572536217098551
5000-3-1 lcp 150 0.9775510204081632 0.28633271490414347
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figs/fig1_data_acc.png
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figs/fig1_data_sens.png
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