# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE52937"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE52937"

# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE52937.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE52937.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE52937.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
# This is likely a gene expression dataset based on series title and design
# investigating genetic factors in ALS, not miRNA or methylation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# No trait data comparing ALS vs controls - just cell lines with SETX knockdown
trait_row = None  # No ALS trait data
age_row = None  # Age not available 
gender_row = None  # Gender not available

def convert_trait(x):
    return None

def convert_age(x):
    return None
    
def convert_gender(x):
    return None

# 3. Save Metadata 
# Initial filtering - trait_row is None so trait data not available
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path, 
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
# Skip since trait_row is None and clinical data not available
# 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)
# Observe gene identifiers starting with 'ILMN_', which indicates Illumina probe IDs
# These are not human gene symbols and need to be mapped
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")
# 1. Extract mapping columns from gene annotation data
prob_col = 'ID'  # Column with Illumina probe IDs (ILMN_*)
gene_col = 'Symbol'  # Column with gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# 3. Normalize gene symbols to ensure consistency
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene data
gene_data.to_csv(out_gene_data_file)
# Since trait data is not available (trait_row=None from Step 2), skip data linking
# Only need to save normalized gene data and validate cohort info

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# Validate and save cohort info, indicating trait data not available
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No trait data available
    is_biased=True,  # Set to True since dataset lacks trait data
    df=gene_data,    # Pass gene expression data
    note="Dataset contains gene expression data but lacks trait information"
)