# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_kidney_disease"
cohort = "GSE127136"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE127136"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE127136.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE127136.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE127136.csv"
json_path = "./output/preprocess/1/Chronic_kidney_disease/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. Gene Expression Data Availability
is_gene_available = True  # Single-cell RNA-seq suggests gene expression data is available.

# 2. Variable Availability and Data Type Conversion
# From the sample characteristics dictionary, row 1 contains multiple disease states (IgAN, kidney cancer, normal).
# We will treat "IgAN" as having the CKD trait = 1, and others (kidney cancer/normal) as 0.
trait_row = 1
age_row = None
gender_row = None

def convert_trait(value: str):
    """
    Convert disease state values to binary indicating CKD (IgAN) or not.
    """
    parts = value.split(':', 1)
    val = parts[1].strip() if len(parts) > 1 else parts[0].strip()
    if val.lower() == 'igan':
        return 1
    elif val.lower() in ['kidney cancer', 'normal']:
        return 0
    else:
        return 0

# Since age and gender are not available, set their conversion functions to None
convert_age = None
convert_gender = None

# 3. Save Metadata using 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. Clinical Feature Extraction (only if trait data is available)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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])