Commit 0bab399e authored by Carine Rey's avatar Carine Rey
Browse files

fix some typos

parent 725d12e7
......@@ -192,7 +192,7 @@ let derive_from_det_meth ~det_meth ~(dataset : Dataset.t) ~preview =
match det_meth with
| `Pcoc -> `Pcoc (Pcoc.pcoc ~catx_est:10 ~plot_complete:true ~gamma:false ~faa ~tree:tree_sc)
| `Pcoc_gamma -> `Pcoc_gamma (Pcoc.pcoc ~catx_est:10 ~plot_complete: true ~gamma:true ~faa ~tree:tree_sc)
| `Pcoc_C60 -> `Pcoc_gamma (Pcoc.pcoc ~catx_est:60 ~plot_complete: true ~gamma:false ~faa ~tree:tree_sc)
| `Pcoc_C60 -> `Pcoc_C60 (Pcoc.pcoc ~catx_est:60 ~plot_complete: true ~gamma:false ~faa ~tree:tree_sc)
| `Tdg09 -> `Tdg09 (Tamuri.tdg09 ~faa ~tree:tree_sc)
| `Diffsel -> `Diffsel (Diffsel.diffsel ~phy_n ~tree:diffsel_tree ~w_every ~n_cycles ~id:1 ~tag:"v1.0" )
| `Identical_LG -> `Identical_LG (Identical.identical ~faa ~tree_id ~tree_sc ~prot_model:"LG08")
......
......@@ -100,7 +100,7 @@ if args.pcoc_gamma :
df_pcoc_gamma = df_pcoc_gamma[['Sites','PCOC','PC','OC']]
df_pcoc_gamma.rename(columns={'PCOC': 'PCOC_gamma',
'PC': 'PC_gamma',
'OC': 'OC_C60'}, inplace=True)
'OC': 'OC_gamma'}, inplace=True)
if args.pcoc_C60 :
df_pcoc_C60 = pd.read_csv(args.pcoc_C60, sep="\t")
df_pcoc_C60 = df_pcoc_C60[['Sites','PCOC','PC','OC']]
......@@ -132,14 +132,15 @@ if args.topological_LG :
if args.topological_WAG :
df_topological_WAG = pd.read_csv(args.topological_WAG, sep="\t")
df_topological_WAG.rename(columns={'Topological': 'Topological_WAG'}, inplace=True)
if args.tdg09 :
df_tdg09 = pd.read_csv(args.tdg09, sep="\t")
if args.multinomial :
df_multinomial = pd.read_csv(args.multinomial, sep="\t")
df_multinomial.rename(columns={'1MinusLRT': 'Mutinomial_1MinusLRT',
'LikelihoodRatio' :"Multinomial_LikelihoodRatio"}, inplace=True)
df_multinomial = df_multinomial[['Sites','1MinusLRT']]
df_multinomial.rename(columns={'1MinusLRT': 'Mutinomial_1MinusLRT'}, inplace=True)
df_list = [df for df in [df_pcoc, df_pcoc_gamma, df_pcoc_C60,
......
......@@ -50,15 +50,32 @@ alpha = 0.7
x_labs = "# of common AA between Conv and not Conv Leaves / # of AA"
y_labs = "# of sites"
plot = ggplot(df, aes(x=CommonRate, fill=hyp)) + theme_bw() + labs(x=x_labs, y=y_labs)
plot = plot + geom_histogram(binwidth = 0.05)
plot = plot + facet_grid(hyp ~ tree)
plotA = ggplot(df, aes(x=CommonRate, fill=hyp)) + theme_bw() + labs(x=x_labs, y=y_labs)
plotA = plotA + geom_histogram(binwidth = 0.05)
plotA = plotA + facet_grid(hyp ~ tree)
alpha = 0.7
x_labs = "# of AA only in ConvLeaves"
y_labs = "# of sites"
plotB = ggplot(df, aes(x=NbOnlyConvAA, fill=hyp)) + theme_bw() + labs(x=x_labs, y=y_labs)
plotB = plotB + geom_histogram(binwidth = 0.05)
plotB = plotB + facet_grid(hyp ~ tree)
alpha = 0.7
x_labs = " # of AA only in NonConvLeaves"
y_labs = "# of sites"
plotC = ggplot(df, aes(x=NbOnlyNonConvAA, fill=hyp)) + theme_bw() + labs(x=x_labs, y=y_labs)
plotC = plotC + geom_histogram(binwidth = 0.05)
plotC = plotC + facet_grid(hyp ~ tree)
plot_all <- plot_grid(plotA, plotB, plotC,
labels=c("AUTO"), nrow = 3)
output_pdf = paste0(opt$out,".pdf")
save_plot(output_pdf,
plot,
plot_all,
ncol = 0.8 * length(unique(df$tree)),
nrow = 0.35 * length(unique(df$hyp)),
nrow = 0.35 * length(unique(df$hyp)) * 3,
base_aspect_ratio = 2,
limitsize = FALSE
)
......
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