# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE212134"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212134"

# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE212134.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212134.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212134.csv"
json_path = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/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
is_gene_available = True  # Dataset mentions mRNA, indicating gene expression data

# 2.1 Variable Keys
trait_row = None # Cannot find any disease status indication in sample characteristics
age_row = None  # Age data not available in sample characteristics
gender_row = 0   # Gender data is in first row (index 0)

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    return None  # No trait data available

def convert_age(value): 
    return None  # No age data available

def convert_gender(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save Metadata
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. Skip clinical feature extraction since trait_row is None
# 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)
# These IDs are numeric probe IDs from a microarray platform, not gene symbols
# They need to be mapped to human gene symbols for meaningful analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'Symbol' column: Gene name mapping")
# Step 1: 'ID' column in annotation matches numeric probe IDs in expression data
# For gene symbols, need to extract from 'gene_assignment' which has format:
# "NR_024005 // DDX11L2 // description // location // ID"

# Extract gene symbols from gene_assignment column
def extract_gene_symbol(assignment):
    if pd.isna(assignment) or assignment == '---':
        return None
    # Split by // and take the second item which is the gene symbol
    parts = assignment.split('//')
    if len(parts) >= 2:
        return parts[1].strip()
    return None

# Add Symbol column with extracted gene symbols
gene_annotation['Symbol'] = gene_annotation['gene_assignment'].apply(extract_gene_symbol)

# Step 2: Create mapping dataframe with probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# Step 3: Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview the mapped gene data
print("Gene expression data after mapping:")
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Since no trait data is available (trait_row was None), validate and mark dataset as unusable
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Dataset without trait data is biased/unusable
    df=gene_data,
    note="Gene expression data successfully processed but no trait information available for analysis"
)