# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE107754"

# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE107754"

# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE107754.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE107754.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE107754.csv"
json_path = "./output/preprocess/3/Thyroid_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
# The background shows this is human genome gene expression microarray data
is_gene_available = True

# 2.1 Data Availability
# Trait (thyroid cancer vs other cancers) from tissue field
trait_row = 2  
# Age not available
age_row = None
# Gender available
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not value:
        return None
    value = value.lower()
    if 'tissue:' in value:
        value = value.split('tissue:')[1].strip()
    elif ':' in value:
        value = value.split(':')[1].strip()
    return 1 if value == 'thyroid cancer' else 0

def convert_gender(value):
    if not value or ':' not in value:
        return None
    value = value.split(': ')[1].lower()
    return 1 if value == 'male' else 0

convert_age = None

# 3. Save Metadata
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. Clinical Feature Extraction 
if trait_row is not None:
    clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                             age_row=age_row, convert_age=convert_age,
                                             gender_row=gender_row, convert_gender=convert_gender)
    
    print("Preview of clinical data:")
    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 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)
# Observing the gene identifiers starting with "A_23_P" which are Agilent probe IDs
# These are not human gene symbols and need to be mapped
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)
# Based on previous output, 'ID' in gene annotation matches probe IDs in expression data
# and 'GENE_SYMBOL' contains the corresponding gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# Map probe IDs to gene symbols and aggregate the expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# 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  
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, 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="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
)

# 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)