# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ovarian_Cancer"
cohort = "GSE132342"

# Input paths
in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE132342"

# Output paths
out_data_file = "./output/preprocess/3/Ovarian_Cancer/GSE132342.csv"
out_gene_data_file = "./output/preprocess/3/Ovarian_Cancer/gene_data/GSE132342.csv"
out_clinical_data_file = "./output/preprocess/3/Ovarian_Cancer/clinical_data/GSE132342.csv"
json_path = "./output/preprocess/3/Ovarian_Cancer/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on series summary mentioning "Expression of 276 genes" and discussion of gene expression signature,
# this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 0  # "diagnosis: High-grade serous ovarian cancer (HGSOC)"
age_row = 8  # "age: q1", "age: q2" etc.
gender_row = 1  # "Sex: Female" - but all female so not useful
gender_row = None  # Set to None since constant

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Binary: 1 for HGSOC, 0 for others
    if not x:
        return None
    val = x.split(": ")[1].strip().lower()
    if "high-grade serous ovarian cancer" in val or "hgsoc" in val:
        return 1
    return 0

def convert_age(x):
    # Convert quartile groups to estimated continuous values
    if not x:
        return None
    val = x.split(": ")[1].strip().lower()
    # Map quartiles to approximate ages based on typical ovarian cancer age distribution
    age_map = {
        'q1': 45,  # Representing ~40-50 years
        'q2': 55,  # Representing ~50-60 years
        'q3': 65,  # Representing ~60-70 years
        'q4': 75   # Representing ~70-80 years
    }
    return age_map.get(val)

def convert_gender(x):
    # Not needed since gender is constant (all female)
    pass

# 3. Save Metadata 
# Run initial validation (trait data is available since trait_row is not None)
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=True
)

# 4. Clinical Feature Extraction
if is_usable and 'clinical_data' in locals():
    clinical_df = geo_select_clinical_features(
        clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age
    )

    # Preview the processed clinical data
    print("Preview of processed clinical data:")
    print(preview_df(clinical_df))

    # Save clinical data
    clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated 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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# The identifiers appear to be NCBI RefSeq Transcript IDs (NM_* format) and some Ensembl Transcript IDs (ENST*)
# These need to be mapped to standard human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. Identify columns: 'ID' matches gene expression indices, 'ORF' contains gene symbols
prob_col = 'ID'
gene_col = 'ORF'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply the mapping to convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the mapped gene expression data
print("\nMapped gene expression data preview:")
print("\nShape:", gene_data.shape)
print("\nFirst few genes and samples:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data  
# Use correct clinical features from Step 2 rather than reading from file
clinical_df = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=4,  # Using status row for survival outcome
    convert_trait=lambda x: int(x.split(": ")[1]) if x else None,  # Convert status 0/1 directly 
    age_row=age_row,
    convert_age=convert_age
)
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available, 
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Gene expression study of HGSOC patients using vital status (0/1) as outcome measure."
)

# 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)