# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE178631"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE178631"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE178631.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE178631.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE178631.csv"
json_path = "./output/preprocess/3/Prostate_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 check
# Based on background info mentioning "gene expression data" and the use case of RNeasy/miRNeasy kits
is_gene_available = True

# 2.1 Feature availability analysis
# For trait: Use ISUP grade group (Feature 3) as binary indicator of tumor aggressiveness
trait_row = 3

# Age and gender data not found in characteristics
age_row = None 
gender_row = None

# 2.2 Data type conversion functions
def convert_trait(value):
    if pd.isna(value):
        return None
    # Extract numeric grade after colon
    grade = value.split(': ')[1]
    if grade.isdigit():
        # Convert to binary: ISUP grade >=3 indicates more aggressive disease
        return 1 if int(grade) >= 3 else 0
    return None

def convert_age(value):
    return None  # Not used

def convert_gender(value):
    return None  # Not used

# 3. Save metadata about data availability
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_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 extracted data
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save clinical data
    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)
# Based on the identifier pattern "ILMN_", these are Illumina probes 
# rather than direct gene symbols, so mapping will be required
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))
# Identify mapping columns from annotation data
# 'Probe_Id' matches the IDs in gene expression data
# 'ILMN_Gene' contains the gene symbols to map to
mapping_df = get_gene_mapping(gene_metadata, prob_col='Probe_Id', gene_col='ILMN_Gene')

# Convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)

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