# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Parkinsons_Disease"
cohort = "GSE202665"

# Input paths
in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE202665"

# Output paths
out_data_file = "./output/preprocess/3/Parkinsons_Disease/GSE202665.csv"
out_gene_data_file = "./output/preprocess/3/Parkinsons_Disease/gene_data/GSE202665.csv"
out_clinical_data_file = "./output/preprocess/3/Parkinsons_Disease/clinical_data/GSE202665.csv"
json_path = "./output/preprocess/3/Parkinsons_Disease/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 - yes, mRNA array data is available
is_gene_available = True

# 2.1 Data Availability
trait_row = 0  # Disease state in row 0
age_row = 3    # Age in row 3
gender_row = 2 # Gender in row 2

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if value is None:
        return None
    value = value.lower().split(': ')[-1]
    if "parkinson" in value:
        return 1
    elif "healthy control" in value:
        return 0
    return None

def convert_age(value):
    if value is None:
        return None
    try:
        return float(value.split(': ')[-1])
    except:
        return None

def convert_gender(value):
    if value is None:
        return None
    value = value.lower().split(': ')[-1]
    if value == 'male':
        return 1
    elif value == 'female':
        return 0
    return None

# 3. Save Metadata (Initial Filtering)
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 processed clinical data
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical features
    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])
# The gene expression data appears to use probe IDs (simple numeric identifiers) rather than gene symbols
# For example, IDs like '1', '2', '3', etc. are numeric probe IDs that need to be mapped to human gene symbols
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))
# 1. Identify columns for mapping - 'ID' matches the probe IDs in expression data, 'GENE_SYMBOL' contains gene symbols
prob_col = 'ID'  
gene_col = 'GENE_SYMBOL'

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

# 3. Apply mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the converted gene data
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())

# Preview some gene symbols 
print("\nFirst 20 gene symbols:")
print(list(gene_data.index)[:20])
# 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)