# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Intellectual_Disability"
cohort = "GSE285666"

# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE285666"

# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE285666.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE285666.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE285666.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 availability - Yes, it uses Affymetrix exon arrays
is_gene_available = True

# 2.1 Get row numbers for clinical features
trait_row = 0  # disease state row
age_row = None  # age not available 
gender_row = None  # gender not available

# 2.2 Define conversion functions
def convert_trait(value: str) -> int:
    """Convert disease state to binary: 1 for Williams syndrome, 0 for control"""
    if pd.isna(value) or value is None:
        return None
    if ':' in value:
        value = value.split(':')[1].strip().lower()
    if 'williams syndrome' in value:
        return 1
    elif 'unaffected' in value or 'control' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Placeholder function since age is not available"""
    return None
    
def convert_gender(value: str) -> int:
    """Placeholder function since gender is not available"""
    return None

# 3. Save metadata about dataset usability
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. Extract clinical features since trait data is available
clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait)

# Preview results
preview_result = preview_df(clinical_features)
print("Preview of extracted clinical features:")
print(preview_result)

# Save clinical data
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())
# These appear to be probe IDs from a microarray platform, not standard human gene symbols
# Examining the numeric format and length pattern confirms they need mapping
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', 'gene_assignment']:
    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['gene_assignment'].notna()].head()
print(json.dumps(preview_df(gene_rows), indent=2))
# Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
mapping_data = mapping_data[mapping_data['Gene'] != '---']

# Extract gene symbols from gene_assignment strings
def extract_gene_symbol(text):
    if pd.isna(text):
        return None
    parts = text.split('//')
    if len(parts) >= 2:
        return parts[1].strip()
    return None

mapping_data['Gene'] = mapping_data['Gene'].apply(extract_gene_symbol)
mapping_data = mapping_data.dropna()

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

# Print info about the converted data
print("Shape of probe-level data:", genetic_data.shape)
print("Shape of gene-level data:", gene_data.shape)
print("\nFirst few genes:")
print(gene_data.index[:10].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)