flowcal_html_output.qmd

---
title: title_name
author: "Prashant Kalvapalle's script"
date: today
format: 
  html:
    theme: default # there's not point doing flatly. Only 1 section
    embed-resources: true
    output-file: 'FACS_analysis/html_outputs/title_name.html' # call-override
echo: false
editor: visual
# knitr: # not knitting directly ; using quarto_render() now 
#   opts_knit: 
#     root.dir: 'C:/Users/new/Box Sync/Stadler lab/Data/Flow cytometry (FACS)'
---

<!--# TODO : make title a variable from R, set flatly theme with table of content ; figure out how to call the render with an output name set -->

```{r setup, include = FALSE}
# knitr::opts_knit$set(root.dir = normalizePath('..')) # set path to the project head (if this qmd lies within a subfolder)

# reticulate options

# Using the python from the conda env so flowcal functions are loaded!
Sys.setenv(RETICULATE_PYTHON = 'C:/Users/new/.conda/envs/flowcal/python.exe')
library(reticulate) # needed to connect python to Rstudio 
```

```{r get-directory-name}

config <- import('scripts_general_fns.g10_user_config')

# reload user config
importlib <- import("importlib")
importlib$reload(config)

title_name <- config$fcs_experiment_folder
```

# `r title_name` : data folder

## flowcytometry cleanup steps

Clean \`.fcs\` data by excluding saturated events, retaining singlets and events among densest region to exclude debris

```{python check-working-dir}
# import os
# # os.chdir("..") ; os.getcwd() # changing working dir;try global defaults
# os.getcwd()

```

```{python}
#| echo: false

from analyze_fcs_flowcal import process_fcs_dir # function for complete processing
from scripts_general_fns.g10_user_config import make_processing_plots # Import plotting key

process_fcs_dir(make_processing_plots) # Run the pipeline
# reading .fcs data, beads processing, cleanup and calibration, saving summary statistics and cleaned files

# TODO : test output file destination, css formatting. title_name as var
```