# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE208668"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE208668"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE208668.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE208668.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE208668.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 data availability based on background info
# Background explicitly states raw data was lost and not included -> no usable gene expression data.
is_gene_available = False

# Step 2: Identify rows for trait, age, and gender from the Sample Characteristics Dictionary
trait_row = 9        # 'history of depression: yes/no' -> aligns with trait "Depression"
age_row = 1          # 'age: <number>'
gender_row = 2       # 'gender: female/male'

# Data availability flags
is_trait_available = trait_row is not None

# Step 2.2: Define conversion functions
def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip().strip('"').strip("'")

def convert_trait(x):
    """
    Map history of depression to binary: no->0, yes->1
    """
    v = _after_colon(x)
    if v is None or v == '':
        return None
    v_lower = v.strip().lower()
    mapping_yes = {'yes', 'y', '1', 'true', 'present', 'positive', 'pos'}
    mapping_no = {'no', 'n', '0', 'false', 'absent', 'negative', 'neg'}
    if v_lower in mapping_yes:
        return 1
    if v_lower in mapping_no:
        return 0
    # Heuristic: if contains 'yes' or 'no' substrings
    if 'yes' in v_lower:
        return 1
    if 'no' in v_lower:
        return 0
    return None

def convert_age(x):
    """
    Convert age to continuous (float).
    """
    v = _after_colon(x)
    if v is None or v == '':
        return None
    try:
        return float(str(v).strip())
    except Exception:
        return None

def convert_gender(x):
    """
    Map gender to binary: female->0, male->1
    """
    v = _after_colon(x)
    if v is None or v == '':
        return None
    v_lower = v.strip().lower()
    if v_lower in {'female', 'f', 'woman', 'women', 'girl'}:
        return 0
    if v_lower in {'male', 'm', 'man', 'men', 'boy'}:
        return 1
    # Sometimes encoded as 0/1 or F/M
    if v_lower in {'0'}:
        return 0
    if v_lower in {'1'}:
        return 1
    return None

# Step 3: Initial filtering and save metadata
_ = 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 (only if trait_row 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
    )
    # Preview and save
    preview = preview_df(selected_clinical_df)
    print(preview)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)