# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE41177"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE41177"

# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE41177.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE41177.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE41177.csv"
json_path = "./output/preprocess/3/Atrial_Fibrillation/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
is_gene_available = True  # Dataset contains microarray gene expression data per background info

# 2.1 Data Availability
trait_row = 3  # 'af duration' indicates AF status duration
age_row = 2    # Age data available
gender_row = 1 # Gender data available 

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    # Convert AF duration to binary - any duration indicates AF presence
    if value == '0M':
        return 0
    elif 'M' in value:  # Has months duration
        return 1
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    if value.endswith('Y'):
        try:
            return float(value[:-1])  # Remove 'Y' and convert to float
        except:
            return None
    return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip().lower()
    if value == 'female':
        return 0
    elif value == 'male':
        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. Extract Clinical Features
selected_clinical_df = 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_df(selected_clinical_df))

# Save clinical data
selected_clinical_df.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
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These are Affymetrix probe IDs (starting with numbers and containing "_at"), not human gene symbols
# They need to be mapped to standard gene symbols for analysis
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)
# Get gene mapping dataframe from annotation data
# 'ID' column contains probe IDs matching genetic_data, 'Gene Symbol' contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Apply gene mapping to convert from probes to genes
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the first few rows and columns of the mapped gene data
print("\nFirst few rows of mapped gene expression data:")
print(preview_df(gene_data))
# 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 
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, 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="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)

# 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)