# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pheochromocytoma_and_Paraganglioma"
cohort = "GSE67066"

# Input paths
in_trait_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma"
in_cohort_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma/GSE67066"

# Output paths
out_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/GSE67066.csv"
out_gene_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/gene_data/GSE67066.csv"
out_clinical_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE67066.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
# The background indicates this is mRNA expression data 
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait data (benign vs malignant) is available in row 1
trait_row = 1

def convert_trait(value: str) -> int:
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'benign' in value:
        return 0
    elif 'malignant' in value:
        return 1
    return None

# Age and gender data not found in sample characteristics
age_row = None
gender_row = None
convert_age = None 
convert_gender = None

# 3. Save metadata about data availability
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. Extract clinical features
clinical_df = geo_select_clinical_features(clinical_data, 
                                         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 extracted features                                         
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)
# 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 format of the gene identifiers (e.g. "1007_s_at"), which appear to be Affymetrix probe IDs,
# we need to map these 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))
# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

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

# Preview resulting gene expression data
print("Preview of mapped gene expression data:")
print(gene_data.head())
print("\nShape:", gene_data.shape)

# Print some gene symbols to verify mapping
print("\nFirst 20 gene symbols:")
print(list(gene_data.index)[:20])

# Get a few column names to verify sample IDs are preserved
print("\nFirst 5 column names:")
print(list(gene_data.columns)[:5])
# 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)