# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE248830"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE248830"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE248830.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE248830.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE248830.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 the Series_summary mentioning "Targeted gene expression profiles" and NanoString panel
is_gene_available = True

# 2.1 Data Availability
# Trait can be inferred from histology which has breast cancer vs lung cancer info
trait_row = 2  

# Age data available in row 0
age_row = 0

# Gender data available in row 1
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon and strip whitespace
    value = x.split(':', 1)[1].strip().lower()
    # Return 1 for lung cancer, 0 for breast cancer related histology
    if 'adenocarcinoma' in value:
        return 1
    elif any(v in value for v in ['tnbc', 'er', 'pr', 'her2']):
        return 0
    return None

def convert_age(x):
    try:
        # Extract value after colon and convert to float
        value = x.split(':', 1)[1].strip()
        if value.lower() == 'n.a.':
            return None
        return float(value)
    except:
        return None

def convert_gender(x):
    # Extract value after colon
    value = x.split(':', 1)[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save initial 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)

# 4. Extract clinical features
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 the data
    preview = preview_df(selected_clinical_df)
    print("Preview of clinical data:")
    print(preview)
    
    # Save to CSV
    selected_clinical_df.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)
# Review gene identifiers
# Based on the preview of identifiers (e.g., A2M, ACVR1C, ADAM12), these appear to be 
# standard HGNC gene symbols and do not require mapping
requires_gene_mapping = False
# Since gene data already uses standardized symbols (as determined in Step 4),
# we can directly use it without normalization
gene_data = pd.DataFrame(gene_data, dtype=float)  # Ensure numeric expression values
gene_data.index = gene_data.index.astype(str)  # Convert index to strings to match sample IDs
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 information for lung cancer analysis."
)

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