# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE115574"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574"

# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE115574.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE115574.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
# The background info mentions "Affymetrix human gene expression microarrays"
is_gene_available = True

# 2.1 Variable Availability
trait_row = 0  # Disease state field contains AF vs SR status
age_row = None  # Age not available 
gender_row = None  # Gender not available

# 2.2 Data Type Conversion
def convert_trait(value):
    if not isinstance(value, str):
        return None
    val = value.lower().split(': ')[-1].strip()
    if 'atrial fibrillation' in val:
        return 1
    elif 'sinus rhythm' in val:
        return 0
    return None

def convert_age(value):
    return None  # Not used but defined for completeness

def convert_gender(value):
    return None  # Not used but defined for completeness

# 3. Save Metadata
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
if trait_row is not None:
    clinical_features = 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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.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 appear to be Affymetrix probe IDs (e.g. '1007_s_at') rather than human gene symbols
# They will 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)
# 1. ID and Gene Symbol columns identified from the annotation preview
# ID column matches the probe IDs in gene expression data (e.g. '1007_s_at')
# Gene Symbol column contains the target gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

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

# 3. Apply gene mapping to convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the first few rows to verify the mapping
print("\nFirst few rows of mapped gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene data
genetic_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_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, genetic_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 postoperative atrial fibrillation vs sinus rhythm."
)

# 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)