# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE142610"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE142610"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE142610.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE142610.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE142610.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/cohort_info.json"

# STEP1
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Determine if gene expression data is likely available
is_gene_available = True  # Based on the series summary describing transcriptomic (gene expression) analysis

# 2. Determine data availability (trait, age, gender)
#    We see that all samples are from a CF cell line without variation. Hence, trait is constant.
#    No age or gender information is provided. Therefore:
trait_row = None
age_row = None
gender_row = None

# 2.2 Define conversion functions.
#     Since trait_row, age_row, and gender_row are None, these functions will not be used here,
#     but we provide them for completeness.

def convert_trait(raw_value: str):
    return None

def convert_age(raw_value: str):
    return None

def convert_gender(raw_value: str):
    return None

# 3. Save metadata with initial filtering
is_trait_available = (trait_row is not None)
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Since trait_row is None (trait not available), we skip clinical feature extraction.
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# Based on inspection, some identifiers (e.g., "7A5", "A2BP1") appear to be synonyms or outdated symbols
# rather than standard HGNC gene symbols. Therefore, they may require mapping to unify them into
# current official human gene symbols.

print("Some gene identifiers are synonyms or aliases rather than current official gene symbols.")
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1. Decide which key in the gene annotation DataFrame matches the gene expression data IDs
#    and which key contains the gene symbols. 
#    From the preview, both "ID" and "ORF" columns appear to match the probe IDs in the expression data,
#    but "ORF" likely corresponds to the gene symbol we want.

# 2. Get a gene mapping DataFrame using the library function get_gene_mapping
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')

# 3. Convert (probe-level) gene expression data to (gene-level) data
gene_data = apply_gene_mapping(gene_data, mapping_df)