# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Metabolic_Rate"
cohort = "GSE101492"

# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE101492"

# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE101492.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE101492.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE101492.csv"
json_path = "./output/preprocess/3/Metabolic_Rate/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("Background Information:")
print(background_info)
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
# Based on background info, this study examines lncRNAs and gene expression in adipose tissue
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait (insulin sensitivity) is in row 3
trait_row = 3
def convert_trait(x):
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().lower()
    if 'resistant' in value:
        return 1
    elif 'sensitive' in value:
        return 0
    return None

# Age is in row 2
age_row = 2
def convert_age(x):
    if not x or ':' not in x:
        return None
    try:
        return float(x.split(':')[1].strip())
    except:
        return None

# Gender is in row 1, but it's constant (all female)
gender_row = None
def convert_gender(x):
    return 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_features = 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 the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:", preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Looking at the ID format (18670xxx), these appear to be probe IDs or Illumina IDs,
# not standard HGNC gene symbols. Gene mapping will be required.
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Extract mapping data with proper parsing of gene_assignment field
relevant_rows = gene_annotation[~gene_annotation['gene_assignment'].str.contains('Housekeeping Controls', na=False)]

def parse_gene_assignment(text):
    if pd.isna(text) or '---' in str(text):
        return None
    parts = str(text).split('//')
    if len(parts) >= 3:
        gene_info = parts[2].strip()
        if gene_info.startswith('gene:'):
            return gene_info.split(':')[1].strip()
        return gene_info
    return None

relevant_rows['Gene'] = relevant_rows['gene_assignment'].apply(parse_gene_assignment)
mapping_data = relevant_rows[['ID', 'Gene']].dropna()

# Apply mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the results
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data only if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)