# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE85550"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE85550"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE85550.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE85550.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE85550.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
# This dataset has liver biopsy samples for expression profiling
# No indicators of miRNA/methylation data, likely contains gene expression data
is_gene_available = True

# 2. Variable availability and data type conversion
# Looking at sample characteristics:
# trait_row = 2 (time_point indicates disease progression in hepatitis fibrosis)
# age_row = None as age data is not recorded 
# gender_row = None as gender data is not recorded

trait_row = 2
age_row = None  
gender_row = None

def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    if value == 'Baseline':
        return 0  # Early/mild fibrosis
    elif value == 'Follow-up':
        return 1  # Progressed fibrosis
    return None

def convert_age(x):
    # Not needed since age data not available
    return None

def convert_gender(x):
    # Not needed since gender data not available
    return None

# 3. Save metadata about dataset usability
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 since trait_row is not None
clinical_features = geo_select_clinical_features(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 clinical features:")
print(preview)

# Save clinical features
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)
# Based on examination of the gene identifiers shown in the data, they appear to be human gene symbols (e.g. AARS, ABLIM1, ACOT2, etc.)
# These are official HGNC gene symbols, so no mapping is needed
requires_gene_mapping = False
# Skip normalization since data already uses standard symbols
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 standard gene symbol expression data and clinical data."
)

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