# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pheochromocytoma_and_Paraganglioma"
cohort = "GSE33371"

# Input paths
in_trait_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma"
in_cohort_dir = "../DATA/GEO/Pheochromocytoma_and_Paraganglioma/GSE33371"

# Output paths
out_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/GSE33371.csv"
out_gene_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/gene_data/GSE33371.csv"
out_clinical_data_file = "./output/preprocess/3/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE33371.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
# From title and overall design, this dataset contains Affymetrix HG_U133_plus_2 arrays data
is_gene_available = True

# 2.1 Data availability
# trait_row: 3 - Clinical characteristics shows pheochromocytoma cases
trait_row = 3
# age_row: 0 - Age data is available
age_row = 0  
# gender_row: 1 - Sex data is available 
gender_row = 1

# 2.2 Data conversion functions
def convert_trait(x: str) -> Union[int, None]:
    """Convert clinical characteristics to binary labels"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    # Convert to binary: 1 for pheochromocytoma cases, 0 for others
    if 'pheochromocytoma' in value:
        return 1
    return 0

def convert_age(x: str) -> Union[float, None]:
    """Convert age to float"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1]
    try:
        if value == '<10':
            return 5.0  # Approximate middle value
        return float(value)
    except:
        return None

def convert_gender(x: str) -> Union[int, None]:
    """Convert gender to binary (0:Female, 1:Male)"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].upper()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    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
# Extract clinical features using the library function
clinical_df = geo_select_clinical_features(clinical_data, 
                                         trait='Pheochromocytoma',
                                         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_result = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview_result)

# 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])
# Observe the IDs from the gene expression data
# The identifiers follow the pattern like '1007_s_at', '1053_at' which are 
# Affymetrix probe IDs from microarray platforms
# These 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))
# Get gene mapping from annotation data
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Print first few rows and shape of the mapped gene data
print("Gene expression data after mapping:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 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, "Pheochromocytoma")

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

# 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)