### ----------------------------------------------------------------- ###
##  ------------------------- Options -------------------------------  ##
### ----------------------------------------------------------------- ###

library(optparse)

option_list <- list(
    make_option(c("--count_file"), type = "character", default = NULL,
                help = "Path to count file (cells as columns, genes as rows)"),
    make_option(c("--meta_file"), type = "character", default = NULL,
                help = "Path to file with meta data infos")
);
 
opt_parser <- OptionParser(option_list = option_list);
opt <- parse_args(opt_parser);

if (is.null(opt$count_file) || is.null(opt$meta_file)) {
      print_help(opt_parser)
  stop("Please specify a path to the counts and meta data file.",
        call. = FALSE)
}

count_file <- opt$count_file
meta_file <- opt$meta_file

from omnibenchmark.utils.build_omni_object import get_omni_object_from_yaml
from omnibenchmark.renku_commands.general import renku_save

## Load config
omni_obj = get_omni_object_from_yaml('src/config.yaml')

Warning: Skipped parameter because of missing information. Please specify at least names.
WARNING: No input files in the current project detected.
Run OmniObject.update_object() to update and import input datasets.
Otherwise check the specified prefixes: {'count_file': ['_counts'], 'meta_file': ['_meta']}
WARNING: No outputs/output file mapping in the current project detected.
Run OmniObject.update_object() to update outputs, inputs and parameter.
Currently this object can not be used to generate a workflow.

from omnibenchmark.utils.build_omni_object import get_omni_object_from_yaml
from omnibenchmark.renku_commands.general import renku_save

## Load config
omni_obj = get_omni_object_from_yaml('src/config.yaml')

## Update object
omni_obj.update_object()
renku_save()

## Check object
omni_obj.__dict__

{'name': 'filter_hvg',
 'keyword': ['omni_clustering_filter'],
 'title': 'Filtering based on the most variable genes.',
 'description': '(Normalized) counts and reduced dimensions of filtered omni_clustering datasets, based on the top 10% most variable genes.',
 'command': <omnibenchmark.core.output_classes.OmniCommand at 0x7f3478402820>,
 'inputs': <omnibenchmark.core.input_classes.OmniInput at 0x7f3450edcb20>,
 'outputs': <omnibenchmark.core.output_classes.OmniOutput at 0x7f3450ef5910>,
 'parameter': None,
 'script': 'src/filter_hvg.R',
 'omni_plan': None,
 'orchestrator': 'https://renkulab.io/knowledge-graph/projects/omnibenchmark/omni_clustering/orchestrator-clustering',
 'benchmark_name': 'omni_clustering',
 'wflow_name': 'filter_hvg',
 'dataset_name': 'filter_hvg',
 'renku': True,
 'kg_url': 'https://renkulab.io/knowledge-graph'}

## Create output dataset

omni_obj.create_dataset()

Renku dataset with name filter_hvg and the following keywords ['omni_clustering_filter'] was generated.
<Dataset c98a1b7958c6463bbea9fc2e314c6190 filter_hvg>

## Run workflow
omni_obj.run_renku(all=False)

Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

...

## Update Output dataset
omni_obj.update_result_dataset()
renku_save()

from omnibenchmark.management.data_commands import update_dataset_files

### Update Output dataset
meta_files = [files["meta_file"] for files in omni_obj.inputs.input_files.values()]
update_dataset_files(urls=meta_files, dataset_name=omni_obj.name)

Added the following files to filter_hvg:
 ['data/koh/koh_meta.json', 'data/kumar/kumar_meta.json']

DirtyRepository: The repository is dirty. Please use the "git" command to clean it.

On branch master
Your branch is up to date with 'origin/master'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   src/filter_hvg.R

no changes added to commit (use "git add" and/or "git commit -a")

Once you have added the untracked files, commit them with "git commit".

renku save

from omnibenchmark.renku_commands.general import renku_save

renku_save()

parameter:
  names: ["order", "hvg", "k"]
  keywords: ["omni_batch_parameter"]
  default:
    order: "auto"
    hvg: "sig"
    k: 20
  filter:
    k:
      upper: 30
      lower: 5

parameter:
  names: ["param1", "param2"]
  values:
    param1: [10, 20]
    param2: ["test", "another_test"]
  combinations:
    comb1:
      param1: 10
      param2: "test"
    comb2:
      param1: 20
      param2: "another_test"

renku run --name word_count --  wc < src/config.yaml > wc_config.txt

renku run --name word_count --  wc < src/config.yaml > wc_config.txt
renku update --all
renku rerun wc_config.txt
renku workflow execute --set input-1="README.md" \
		      --set output-2="wc_readme.txt" \
                      word_count