# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE249638"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE249638"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE249638.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE249638.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE249638.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/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
# Yes - this is a transcriptomic profiling study of CD4+ T cells
is_gene_available = True

# 2.1 Data Availability & 2.2 Data Type Conversion
# Trait (AML status) is available in Feature 1, using binary type
trait_row = 1
def convert_trait(x):
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().lower()
    if 'acute myeloid leukemia' in value:
        return 1
    elif 'healthy control' in value:
        return 0
    return None

# Age not available
age_row = None
convert_age = None

# Gender not available  
gender_row = None
convert_gender = 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. Clinical Feature Extraction
if trait_row is not None:
    clinical_features = 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 features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(gene_data.index[:20])
# The identifiers like '2824546_st' are probe IDs from Affymetrix microarray platform, not human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 2. Get mapping between probe IDs and gene symbols
gene_annotation = gene_annotation.drop('ID', axis=1)  # Drop the original ID column 
gene_annotation = gene_annotation.rename(columns={'probeset_id': 'ID'})
mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# 3. Apply the mapping to convert probe-level measurements to gene expression data 
gene_data = apply_gene_mapping(gene_data, mapping)

# Preview first few genes and their expression values
print("\nPreview of mapped gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values systematically 
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save metadata
# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
# based on cell subtypes (AMKL vs non-AMKL).
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)