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"""
PWMSearch

Searches through a fasta sequence for the relevant Position Weight Matrices 
from the Jaspar Database.

"""
from __future__ import division

from optparse import OptionParser

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# Plot 18 arabidopsis ecotypes' locations on map.

## Author: Ji Huang
## Date: 2018-10-25

# The longitude and latitude info is provided by Chia-Yi on "EighteenAccession" 
# in the email. I changed two locations for the obvious wrong location.

library(ggmap)
library(ggrepel)

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# The function is the basically the same as https://github.com/mikelove/DESeq2/blob/master/R/plots.R.
# Inspired by https://www.biostars.org/p/243695/. 
# I added two variables, pp1 and pp2 to let user chose which principle to plot. pp1 and pp2 only take integer values.

# Last modified: 2021-12-03

plotPCA_jh = function(pp1=1, pp2=2, 
                      object, intgroup="condition", 
                      ntop=1000, returnData=FALSE) {
  

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# Date: 2019-09-24

list_of_files <- list.files(path = here("data", "dap_download_may2016_genes"), 
                            recursive = TRUE, pattern = "\\.txt$", full.names = TRUE)

# I discard the amp-DAP data.
list_of_files <- list_of_files[!str_detect(list_of_files, "amp")]

dap <- list_of_files %>%
    set_names(.) %>%

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# Calculate random network AUPR.

## Author: Ji Huang
## Date: 2020-04-15


library(here)
library(tidyverse)
library(precrec)

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I used the precision curve only to decide the cutoff value. I chose a precision cutoff 0.2, then the correspondent normalized ranking is *~0.01*. The meaning of the normalized ranking is explained [here](https://github.com/takayasaito/precrec/issues/12).

Therefore, to calculate the **weight** value for precision 0.2, we got the following calculation: (rank-1)/(n-1) = 0.01. In this case, `n=nrow(dfg_ortho_label)`, so the rank is *649*. We went back to the *649* row of the `dfg_ortho_label`, the `weight` is 1.57. Therefore, we kept the edge that has weight higher than 1.57.

```{r, fig.width=5, fig.height=5, fig.align="center"}
scurve_os_b <- evalmod(scores = dfg_ortho_label$weight, labels = dfg_ortho_label$label, 
                     mode = "basic")
sos_df_b <- fortify(scurve_os_b)
p2 <- ggplot(subset(sos_df_b, curvetype == "precision"), aes(x = x, y = y))+
   geom_point(color = "blue", size = 0.4)+ ylim(0:1)

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#
# If all files excluded and you will include only specific sub-directories
# the parent path must matched before.
#

/**
!/.gitignore

###############################
# Un-ignore the affected subdirectory

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# An R function to save pheatmap figure into pdf
# This was copied from Stackflow: https://stackoverflow.com/questions/43051525/how-to-draw-pheatmap-plot-to-screen-and-also-save-to-file


save_pheatmap_pdf <- function(x, filename, width=7, height=7) {
    stopifnot(!missing(x))
    stopifnot(!missing(filename))
    pdf(filename, width=width, height=height)
    grid::grid.newpage()
    grid::grid.draw(x$gtable)

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library(ggplot2)
theme_jh <- function (base_size = 11, base_family = "Arial", 
                      base_line_size = base_size/22, 
                      base_rect_size = base_size/22) {
    theme_grey(base_size = base_size, base_family = base_family, 
               base_line_size = base_line_size, 
               base_rect_size = base_rect_size) +
        theme(panel.background = element_rect(fill = "white", colour = NA),
              panel.border = element_rect(fill = NA, colour = "grey20"), 
              panel.grid = element_line(colour = "grey92"), 

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# Test on GENIE3 whether
# (Q1) if using more genes as regulator, for the same regulator-target edge 
# do I get the same order? No. The edge order will be different.

# (Q2) if using more genes as targets, for the same regulator-target edges,
# do I get the same order? Yes. The same edge will have the exact same weight.

# Author: Ji Huang
# Date: 2021-01-06