# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Intellectual_Disability"
cohort = "GSE98697"

# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE98697"

# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE98697.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE98697.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE98697.csv"
json_path = "./output/preprocess/3/Intellectual_Disability/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes - the dataset contains both coding and non-coding gene expression data according to title and design
is_gene_available = True

# 2.1 Data Row Numbers
# Trait: Not directly given but subtype shows Down syndrome cases, can infer from aml subtype
trait_row = 2
# Age not available
age_row = None  
# Gender not available
gender_row = None

# 2.2 Type Conversion Functions
def convert_trait(x):
    # Extract value after colon
    if ':' in x:
        x = x.split(':', 1)[1].strip()
    # Convert to binary - 1 for Down syndrome AMKL, 0 for other types
    if 'Down-syndrome' in x:
        return 1
    elif 'aml' in x.lower():  # Other AML types
        return 0
    return None

def convert_age(x):
    return None  # Not used as age is not available

def convert_gender(x):
    return None  # Not used as gender is not available

# 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
    preview_df(clinical_features)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# Observe that the identifiers are just '1', '2', '3' etc
# These are numeric indices and not standard gene symbols
# Therefore we need to map these IDs to proper gene symbols

requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Display non-NaN value counts for key gene identifier columns
print("\nNumber of non-NaN values in key columns:")
for col in ['ID', 'FINAL_SYMBOL']:
    print(f"{col}: {gene_metadata[col].notna().sum()}")

# Preview rows with actual gene information
print("\nPreview of rows with gene information:")
gene_rows = gene_metadata[gene_metadata['FINAL_SYMBOL'].notna()].head()
print(json.dumps(preview_df(gene_rows), indent=2))
# Extract the gene mapping data
# From observing the data, we need to map numeric 'ID' to 'FINAL_SYMBOL'
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='FINAL_SYMBOL')

# Apply the gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Display the shape of the gene expression data before and after mapping
print(f"Shape before mapping (probes × samples): {genetic_data.shape}")
print(f"Shape after mapping (genes × samples): {gene_data.shape}")

# Preview the first few gene symbols
print("\nFirst few gene symbols:")
print(gene_data.index[:5].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = geo_select_clinical_features(
    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
)

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

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

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
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=note
)

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