# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE244117"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE244117"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE244117.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE244117.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE244117.csv"
json_path = "./output/preprocess/3/Lung_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on background info, this contains spatial transcriptomics data of human ONB samples
is_gene_available = True

# 2.1 Data Type Availability 
# Trait (lung cancer status) can be inferred from grade (1)
trait_row = 1
# Age is available in row 5
age_row = 5  
# Gender is available in row 4
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    val = x.split(':')[1].strip().lower()
    # Normal samples are controls (0), any grade is case (1)
    if val == 'normal':
        return 0
    elif val in ['ii', 'iii', 'iv']:
        return 1
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    try:
        # Extract numeric age value after colon
        return float(x.split(':')[1].strip())
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    val = x.split(':')[1].strip().upper()
    # Convert F->0, M->1
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    return None

# 3. 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=trait_row is not None
)

# 4. Clinical Feature Extraction
if trait_row is not None:
    # Extract features using library function
    clinical_features = 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 the extracted features
    preview = preview_df(clinical_features)
    print("Preview of extracted clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Looking at the identifiers like A1BG, A1CF, A2M - these appear to be standard HGNC gene symbols
# The gene names follow standard human gene nomenclature conventions and match known human genes
# Therefore no mapping is needed

requires_gene_mapping = False
# Since gene symbols are already standardized, skip normalization
gene_data.index = gene_data.index.astype(str)  # Ensure string index
gene_data.to_csv(out_gene_data_file)

# Load clinical data from previous steps
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Record cohort information
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Contains standardized gene expression data and clinical features."
)

# Save data if usable
if is_usable:
    linked_data.to_csv(out_data_file)