# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"
cohort = "GSE109178"

# Input paths
in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE109178"

# Output paths
out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE109178.csv"
out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE109178.csv"
out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE109178.csv"
json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# The background info mentions "mRNA profiles" and "HG-U133 Plus 2.0 microarrays"
# which indicates this is gene expression data
is_gene_available = True

# 2.1 Data Availability
# Trait can be inferred from the mutation data in key 4
trait_row = 4

# Age data is available in key 0
age_row = 0 

# Gender data is available in key 3
gender_row = 3

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert mutation info to binary trait status (DMD vs non-DMD)"""
    if pd.isna(value) or ":" not in value:
        return None
    value = value.split(":")[1].strip()
    # Deletions/duplications/mutations indicate DMD
    if any(x in value.lower() for x in ['deletion', 'duplication', 'mutation', 'exon']):
        return 1
    # Pathology notes indicate non-DMD
    return 0

def convert_age(value: str) -> float:
    """Convert age string to float value"""
    if pd.isna(value) or ":" not in value:
        return None
    value = value.split(":")[1].strip()
    if value == "NA":
        return None
    try:
        return float(value)
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender string to binary (0=female, 1=male)"""
    if pd.isna(value) or ":" not in value:
        return None
    value = value.split(":")[1].strip().upper()
    if value in ["M", "MALE"]:
        return 1
    elif value in ["F", "FEMALE"]:
        return 0
    return None

# 3. Save metadata 
# Trait data is available (trait_row is not None)
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path, is_gene_available, is_trait_available)

# 4. Extract clinical features
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
)

print("Preview of extracted clinical features:")
print(preview_df(selected_clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Based on the format like '1007_s_at', these appear to be Affymetrix probe IDs
# rather than standard human gene symbols. They need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# From the preview, we can see that 'ID' column matches the gene expression data identifiers (e.g. '1007_s_at')
# and 'Gene Symbol' column contains the human gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

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

# Print size of mapped data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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=trait_biased,
    df=linked_data,
    note="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)