# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Intellectual_Disability"
cohort = "GSE200864"

# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE200864"

# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE200864.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE200864.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE200864.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
# Based on Series_title and Series_overall_design mentioning Affymetrix platform and gene expression
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability 
# Trait: Down Syndrome mentioned in title and summary, everyone has Down Syndrome based on background info
trait_row = None  # Everyone has intellectual disability (Down Syndrome), so constant feature

# Age and gender: Not available in characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return 1  # Would return 1 for all samples since all have Down Syndrome

def convert_age(x):
    return None  # Not used since age data not available

def convert_gender(x):
    return None  # Not used since gender data not available

# 3. Save Metadata
is_trait_available = True  # Although trait_row is None, we know everyone has intellectual disability
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
# Skip since trait_row is None (constant feature)
# 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 indices appear to be probe set IDs from Affymetrix microarray platform
# They are not standard human gene symbols and will need to be mapped
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)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Create gene mapping from probe IDs to gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Since trait is constant (all Down Syndrome), create a clinical features dataframe of all 1's
clinical_features = pd.DataFrame(1, index=gene_data.columns, columns=['trait'])

# 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 patients with Down Syndrome. Since all samples have Down Syndrome, the trait is constant (all 1's)."
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)