# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bile_Duct_Cancer"
cohort = "GSE107754"

# Input paths
in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE107754"

# Output paths
out_data_file = "./output/preprocess/3/Bile_Duct_Cancer/GSE107754.csv"
out_gene_data_file = "./output/preprocess/3/Bile_Duct_Cancer/gene_data/GSE107754.csv"
out_clinical_data_file = "./output/preprocess/3/Bile_Duct_Cancer/clinical_data/GSE107754.csv"
json_path = "./output/preprocess/3/Bile_Duct_Cancer/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info mentioning "whole human genome gene expression microarrays"
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 2  # Tissue type info in row 2
gender_row = 0  # Gender info in row 0
age_row = None  # Age data not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    if ":" not in x:
        return None
    value = x.split(": ")[1].lower()
    # Converting to binary based on bile duct cancer presence
    return 1 if "bile duct cancer" in value else 0

def convert_gender(x):
    if not isinstance(x, str):
        return None
    if ":" not in x:
        return None
    value = x.split(": ")[1].lower()
    return 0 if "female" in value else 1 if "male" in value else None

# Age conversion function not needed since age data is not available
convert_age = None

# 3. Save Metadata
# Initial filtering - trait data is available since trait_row is not None
is_usable = 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 features since 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,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# Preview the data
preview = preview_df(selected_clinical)
print("Selected clinical features preview:", preview)

# Save to CSV
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and some data preview to verify structure
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

print("\nData preview:")
preview_subset = genetic_data.iloc[:5, :5]
print(preview_subset)
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. Identify the mapping columns
# From the preview, 'ID' column contains identifiers matching gene expression data
# 'GENE_SYMBOL' column contains the target gene symbols
probe_col = 'ID'
gene_col = 'GENE_SYMBOL'

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_metadata, probe_col, gene_col)

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

# Preview the mapped data
print("\nGene expression data preview (first 5 genes, first 5 samples):")
print(gene_data.iloc[:5, :5])
# 1. Normalize gene symbols and save gene 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)

# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values 
linked_data = handle_missing_values(linked_data, trait)

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)