# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypothyroidism"
cohort = "GSE75678"

# Input paths
in_trait_dir = "../DATA/GEO/Hypothyroidism"
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75678"

# Output paths
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE75678.csv"
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE75678.csv"
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE75678.csv"
json_path = "./output/preprocess/3/Hypothyroidism/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
is_gene_available = True   # Based on series title and summary indicating gene expression data

# 2. Variable Availability and Data Type Conversion
# Hypothyroidism data is in row 21 (personal pathological history)
trait_row = 21
age_row = 19  # Age at diagnosis
gender_row = 1  # Gender data is in row 1

def convert_trait(x):
    if pd.isna(x):
        return None
    val = x.split(': ')[1] if ': ' in x else x
    if 'Hypothyroidism' in val:
        return 1
    return 0

def convert_age(x):
    if pd.isna(x):
        return None
    val = x.split(': ')[1] if ': ' in x else x
    try:
        return float(val)
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    val = x.split(': ')[1] if ': ' in x else x
    if val.lower() == 'female':
        return 0
    elif val.lower() == 'male':
        return 1
    return None

# 3. Save Metadata
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 4. Clinical Feature Extraction
selected_clinical = 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 and save clinical data
print(preview_df(selected_clinical))
selected_clinical.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())
# Looking at the row IDs, they appear to be simple numeric indices rather than gene symbols
# This indicates we need to map these identifiers to actual 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 ['GENE', 'GENE_SYMBOL', 'GENE_NAME']:
    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_SYMBOL'].notna()].head()
print(json.dumps(preview_df(gene_rows), indent=2))
# Extract mapping between numeric IDs and gene symbols from annotation data
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the gene data shape 
print("Gene expression data shape:", gene_data.shape)

# Preview first few gene symbols and samples
print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
print("\nFirst few samples:", gene_data.columns[:5].tolist())
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_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, genetic_data)

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

# 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 breast cancer patients, with clinical annotations including hypothyroidism status."
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)