# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE212131"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212131"

# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE212131.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212131.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212131.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 contains mRNA gene expression data from microarray

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability
trait_row = None  # Disease duration not explicitly available in characteristics
age_row = None  # Age not available in characteristics
gender_row = 0  # Gender information available at row 0

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

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

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

# 3. Save Metadata
# Initial filtering based on gene and trait availability
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. Clinical Feature Extraction skipped 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)
# Examining the gene identifiers shows they are numeric probe IDs (starting with '23')
# These are not human gene symbols and need to be mapped to gene symbols
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("'gene_assignment' column: Contains gene symbol information in format:")
# Print example of gene_assignment format
print("\nExample gene_assignment value:")
print(gene_annotation['gene_assignment'].iloc[0])
# Extract and process gene mapping from annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Normalize gene symbols to standardized forms
gene_data = normalize_gene_symbols_in_index(gene_data)

print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
# 1. Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# 2-5. Since we know trait data is unavailable, document this as a bias
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,  # Consider lack of trait data as bias
    df=gene_data,    # Provide the gene data as required
    note="Dataset contains gene expression data but lacks trait information for analysis"
)

# 6. Skip saving linked data as it's not usable