# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE42842"

# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE42842"

# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE42842.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE42842.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE42842.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/cohort_info.json"

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

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# Since Series_overall_design mentions two color experiments, this is a gene expression microarray dataset
is_gene_available = True

# 2.1 Data Availability 
# Feature 2 shows disease state, which indicates RA vs non-RA
trait_row = 2
# Age is not available in sample characteristics
age_row = None  
# Gender is available in Feature 0
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert disease state to binary"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if 'rheumatoid arthritis' in value:
        return 1
    return None

def convert_gender(x):
    """Convert gender to binary (0=female, 1=male)"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
    if value == 'f':
        return 0
    elif value == 'm':
        return 1
    return None

convert_age = None

# 3. Save initial 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. 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 data
    print("Preview of selected clinical features:")
    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 gene identifiers are just numerical indices (1,2,3...)
# They are not human gene symbols and need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation data
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Check if gene annotation data is usable by looking at gene-related columns
gene_cols = ['GENE', 'GENE_SYMBOL', 'GENE_NAME', 'REFSEQ', 'GB_ACC', 'UNIGENE_ID', 'ENSEMBL_ID']
has_gene_info = any(gene_annotation[col].notna().any() for col in gene_cols)

if not has_gene_info:
    # Save metadata indicating this dataset is not usable
    validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,  # Set to False since gene annotations are missing
        is_trait_available=True,
        note="Dataset lacks proper gene annotations - all gene identifier fields are empty"
    )
    
    print("\nWARNING: This dataset lacks proper gene annotations.")
    print("All gene identifier fields (GENE, GENE_SYMBOL, REFSEQ, etc.) are empty.")
    print("Stopping processing as gene mapping cannot be performed without annotations.")
    
    # Exit further processing as dataset is not suitable
    raise ValueError("Dataset lacks proper gene annotations")