# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pheochromocytoma_and_Paraganglioma"
cohort = "GSE19987"

# Input paths
in_trait_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma"
in_cohort_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma/GSE19987"

# Output paths
out_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/GSE19987.csv"
out_gene_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/gene_data/GSE19987.csv"
out_clinical_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE19987.csv"
json_path = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/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, this dataset contains gene expression data for studying Pheochromocytoma
is_gene_available = True

# 2.1 Data Availability

# Trait can be determined from tumor location (row 2)
trait_row = 2 

# Age data not available
age_row = None  

# Gender data not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    # Extract value after colon
    if ':' in value:
        value = value.split(':')[1].strip()
    if value.lower() == 'adrenal':
        return 0  # Adrenal pheochromocytoma
    elif value.lower() == 'extraadrenal':
        return 1  # Extra-adrenal paraganglioma
    return None

def convert_age(value: str) -> float:
    # Not used since age data unavailable
    return None

def convert_gender(value: str) -> int:
    # Not used since 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=trait_row is not None
)

# 4. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical = 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
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical))
    
    # Save to CSV
    selected_clinical.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])
# Based on the row IDs ending with "_at" (e.g. "1007_s_at", "1053_at"), 
# these appear to be Affymetrix probe IDs rather than gene symbols,
# so they will need to be mapped to official 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. From inspection, 'ID' column contains probe IDs (e.g., "1007_s_at") matching gene expression data
# 'Gene Symbol' column contains the corresponding 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 expressions to gene expressions
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview results
print("First few rows and columns of mapped gene expression data:")
print(gene_data.head().iloc[:, :5])

print("\nShape:", gene_data.shape)
print("\nFirst 10 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 = "Dataset contains gene expression data from adrenal tissue samples, with cases identified by presence of pheochromocytoma"
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)