# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE123993"

# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE123993"

# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE123993.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE123993.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/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)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# From background info, this is microarray data using Affymetrix HuGene arrays
is_gene_available = True

# 2.1 Data Availability
# Vitamin D levels can be inferred from the intervention + time information (rows 3 and 4)
trait_row = 3
# Age is not recorded in sample characteristics 
age_row = None
# Gender is in row 1 as "Sex"
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> float:
    """Convert intervention status to vitamin D level indicator.
    25(OH)D3 supplementation increases vitamin D compared to placebo"""
    if not value or ":" not in value:
        return None
    value = value.split(":")[1].strip()
    # Return 1 for supplementation group, 0 for placebo
    if "25-hydroxycholecalciferol" in value:
        return 1.0
    elif "Placebo" in value:
        return 0.0
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if not value or ":" not in value:
        return None
    value = value.split(":")[1].strip().lower()
    if value == "female":
        return 0
    elif value == "male":
        return 1
    return None

# 3. Save Metadata
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
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                         gender_row=gender_row, convert_gender=convert_gender)

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

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

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# Looking at the probe IDs like '16650001', they appear to be Illumina BeadArray probe IDs
# These are not human gene symbols and need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure 
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# The probe identifiers in gene_annotation['ID'] match the expression data's index 
# The gene symbols are in the 'gene_assignment' column and need extraction
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Normalize gene symbols using NCBI synonyms database
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print preview of gene data
print("Gene expression data preview:")
print(preview_df(gene_data))

# Save gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

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

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

# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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_trait_biased,
    df=linked_data,
    note=note
)

# 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)