# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Telomere_Length"
cohort = "GSE52237"

# Input paths
in_trait_dir = "../DATA/GEO/Telomere_Length"
in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE52237"

# Output paths
out_data_file = "./output/preprocess/3/Telomere_Length/GSE52237.csv"
out_gene_data_file = "./output/preprocess/3/Telomere_Length/gene_data/GSE52237.csv"
out_clinical_data_file = "./output/preprocess/3/Telomere_Length/clinical_data/GSE52237.csv"
json_path = "./output/preprocess/3/Telomere_Length/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
# Based on background info, this is a study of gene expression and telomere length in airway epithelium
is_gene_available = True

# 2.1 Data Availability
# Smoking status can be used as trait since it's binary and complete
trait_row = 0  

# Age and gender not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip()
    # Convert smoking status to binary (0=nonsmoker, 1=smoker)
    if value == 'nonsmoker':
        return 0
    elif value in ['smoker', 'S']:
        return 1
    return None

def convert_age(value):
    # Not needed as age data unavailable
    return None

def convert_gender(value):
    # Not needed as gender data unavailable
    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=True)

# 4. Extract Clinical Features
# Use the geo_select_clinical_features function
clinical_df = 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
print("Preview of clinical features:")
print(preview_df(clinical_df))

# Save clinical data
clinical_df.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])
# Review the gene identifiers
# The IDs like "1007_s_at", "1053_at", etc. are Affymetrix probe IDs 
# from the HG-U133 array, not standard human gene symbols
# They need to be mapped to gene symbols for analysis
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)
# From the preview, 'ID' column matches the probe IDs in expression data
# 'Gene Symbol' column contains the gene symbols to map to
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol') 

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

# Preview the converted gene data 
print("Gene expression data preview:")
print(gene_data.head())
print("\nShape:", gene_data.shape) 

# Save the gene data
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) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)