# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE138297"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE138297"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE138297.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE138297.csv"
json_path = "./output/z2/preprocess/Depression/cohort_info.json"


# Step 1: Initial Data Loading
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)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# Step 1: Determine gene expression availability
is_gene_available = True  # Microarray analysis on sigmoid biopsies indicates gene expression data

# Step 2: Identify rows and define converters
trait_row = None  # No Depression-related data available in this cohort
age_row = 3
gender_row = 1

def convert_trait(x):
    # Trait (Depression) not available in this dataset
    return None

def convert_age(x):
    try:
        # Extract value after colon
        val = str(x).split(":", 1)[1].strip()
    except Exception:
        val = str(x).strip()
    # Handle missing/unknown
    if val in {"", "NA", "N/A", "nan", "NaN", None}:
        return None
    # Convert to float
    try:
        return float(val)
    except Exception:
        return None

def convert_gender(x):
    s = str(x)
    # Extract value after colon, but keep header for potential mapping hints
    parts = s.split(":", 1)
    header = parts[0].lower() if parts else ""
    val = parts[1].strip() if len(parts) > 1 else s.strip()
    vlow = val.lower()

    # Direct string mapping
    if any(k in vlow for k in ["female", "f"]):
        return 0
    if any(k in vlow for k in ["male", "m"]):
        return 1

    # Numeric mapping with hint in header (female=1, male=0)
    if "female=1" in header and "male=0" in header:
        if val == "1":
            return 0  # female -> 0
        if val == "0":
            return 1  # male -> 1

    # Fallback: try common encodings
    if val in {"0", "1"}:
        # Without reliable header, assume 0=male, 1=female then convert to required scheme female=0, male=1
        # But given this dataset includes header, this path is unlikely.
        return 1 if val == "0" else 0

    return None

# Step 3: Initial filtering and save metadata
is_trait_available = trait_row is not None
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
)

# Step 4: Clinical feature extraction (skip because trait_row is None)
# If in future trait_row becomes available, the following pattern should be used:
# 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
# )
# preview = preview_df(selected_clinical_df)
# selected_clinical_df.to_csv(out_clinical_data_file)