# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lung_Cancer"
cohort = "GSE249262"

# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE249262"

# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE249262.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE249262.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE249262.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
is_gene_available = True  # RNA microarray data is mentioned in background

# 2. Clinical Data Analysis
# For trait: Use status field (Feature 3) to determine disease progression
trait_row = 3 
def convert_trait(x):
    if not isinstance(x, str): return None
    val = x.split(': ')[1] if ': ' in x else x
    if 'progression' in val.lower():
        return 1
    elif 'stable' in val.lower():
        return 0
    return None

# Age and gender not available in characteristics
age_row = None
gender_row = None
convert_age = None  
convert_gender = None

# 3. Save Initial Filtering Results
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. Extract Clinical Features 
if trait_row is not None:
    selected_clinical = 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 processed data
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical))
    
    # Save to CSV
    selected_clinical.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)
# The identifiers appear to be numeric probe IDs (e.g. 23064070) rather than standard gene symbols
# Based on the ID format and my knowledge of microarray data, these are likely probe IDs that need 
# mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
    sample_values = gene_metadata[col].dropna().head().tolist()
    print(f"\n{col}:")
    print(sample_values)

# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    header = []
    for i, line in enumerate(f):
        header.append(line.strip())
        if i >= 10:  # Preview first 10 lines
            break
print('\n'.join(header))
# First inspect the platform table structure
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if "!Platform_table_begin" in line:
            print("Header:", next(f).strip())
            print("First data row:", next(f).strip())
            break

# Extract platform data with proper column headers
platform_rows = []
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    platform_found = False
    for line in f:
        if "!Platform_table_begin" in line:
            platform_found = True
            header = next(f).strip().split('\t')
            continue
        if platform_found:
            if "!Platform_table_end" in line:
                break
            row = line.strip().split('\t')
            if len(row) == len(header):
                platform_rows.append(row)

platform_data = pd.DataFrame(platform_rows, columns=header)

print("\nAvailable columns:", platform_data.columns.tolist())

# Create mapping between probe IDs and gene symbols
mapping_df = pd.DataFrame()
id_col = [col for col in platform_data.columns if 'id' in col.lower()][0]
gene_col = [col for col in platform_data.columns if 'gene' in col.lower() or 'symbol' in col.lower()][0]

mapping_df['ID'] = platform_data[id_col] 
mapping_df['Gene'] = platform_data[gene_col]

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Print info about the mapped data
print("\nShape of mapped gene expression data:", gene_data.shape)
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
    sample_values = gene_metadata[col].dropna().head().tolist()
    print(f"\n{col}:")
    print(sample_values)

# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    header = []
    for i, line in enumerate(f):
        header.append(line.strip())
        if i >= 10:  # Preview first 10 lines
            break
print('\n'.join(header))
# Since gene mapping failed in a previous step, we'll fall back to using probe IDs
# Load clinical data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Save raw gene expression data with probe IDs
gene_data.to_csv(out_gene_data_file)

# 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 numerical probe-level expression data and clinical data. Gene symbol mapping was not completed."
)

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