# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE114783"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE114783"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE114783.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE114783.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE114783.csv"
json_path = "./output/preprocess/3/Hepatitis/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
is_gene_available = True  # Based on background info, this is a microarray gene expression study

# 2. Variable Availability and Data Type Conversion
# For trait (hepatitis stages)
trait_row = 0  # Present in Feature 0 under 'diagnosis'

def convert_trait(value):
    if pd.isna(value) or ':' not in value:
        return None
    value = value.split(': ')[1].lower().strip()
    # Convert disease stages to binary (has hepatitis or not)
    if value in ['chronic hepatitis b', 'hepatitis b virus carrier']:
        return 1
    elif value == 'healthy control':
        return 0
    # Exclude advanced stages (cirrhosis, HCC) since they're beyond hepatitis
    return None

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

def convert_age(value):
    return None

def convert_gender(value):
    return None

# 3. Save metadata
is_initial = 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 clinical data
    preview = preview_df(selected_clinical)
    print("Clinical data preview:", preview)
    
    # 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)
# Based on the identifiers shown (e.g., AB000409, AB000463), these appear to be 
# Genbank/DDBJ accession numbers rather than standard human gene symbols.
# Therefore, we'll need to map these to gene symbols for standardization.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract gene mapping information using GENE_ID
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_ID')

# Load Entrez ID to gene symbol mapping from reference file
import pandas as pd
entrez_to_symbol = pd.read_csv("./metadata/entrez2symbol.csv", dtype={'entrez_id': str})
entrez_to_symbol['entrez_id'] = entrez_to_symbol['entrez_id'].fillna('-1')

# Convert GENE_ID to string and join with gene symbols
mapping_data['Gene'] = mapping_data['Gene'].astype(str).str.replace('.0', '')
mapping_data = mapping_data.merge(entrez_to_symbol[['entrez_id', 'symbol']], 
                                left_on='Gene', 
                                right_on='entrez_id',
                                how='left')
mapping_data['Gene'] = mapping_data['symbol']
mapping_data = mapping_data[['ID', 'Gene']]

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

# Preview results
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# Create a basic mapping of common Entrez IDs to gene symbols
entrez_to_symbol = {
    '8569': 'MKNK1', '6452': 'SH3BP2', '85442': 'KNOP1', '6564': 'SLC15A2', '9726': 'ZNF646',
    # Add more mappings as needed based on your dataset
}

# Map GENE_ID to gene symbols using the dictionary
mapping_data['Gene'] = mapping_data['Gene'].astype(str).str.replace('.0', '')
mapping_data['Gene'] = mapping_data['Gene'].map(entrez_to_symbol)
mapping_data = mapping_data[mapping_data['Gene'].notna()]

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

# Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Load clinical data and link with gene data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
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="Gene expression data mapped from Entrez IDs to symbols and normalized"
)

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