# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE228783"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE228783"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE228783.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE228783.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE228783.csv"
json_path = "./output/preprocess/3/Cardiovascular_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Dataset likely contains gene expression data based on experimental design
is_gene_available = True  

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability 
# For trait (Cardiovascular Disease): Not directly measured in this liver disease study
trait_row = None

# For age: Not available in characteristics 
age_row = None

# For gender: Not available in characteristics
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None  # No trait data

def convert_age(x):
    return None  # No age data

def convert_gender(x):
    return None  # No gender data

# 3. Save Metadata - Initial Filtering
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False  # trait_row is None
)

# 4. Skip Clinical Feature Extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Preview the DataFrame structure
print("DataFrame shape:", genetic_df.shape)
print("\nFirst few rows and columns:")
print(genetic_df.head().iloc[:, :5])

# Print first few lines from the matrix file to inspect format
print("\nRaw file preview:")
with gzip.open(matrix_file, 'rt') as f:
    for i, line in enumerate(f):
        if i > 30 and i < 35:  # Print a few lines around where data starts
            print(line.strip())
# Looking at the gene identifiers in the first few rows (e.g., 11715100_at, 11715101_s_at),
# these appear to be Affymetrix probe IDs, not gene symbols.
# They need to be mapped to official human gene symbols for analysis.

requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview column names and first few values
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# Get probe-to-gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview output
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save gene data
genetic_df = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_df.to_csv(out_gene_data_file)

# 2. Since clinical data is not available, create a placeholder dataframe for validation
placeholder_df = pd.DataFrame(index=genetic_df.columns, columns=['trait'])

# 3. Final validation and save cohort info
note = "Dataset contains gene expression data from liver tissue samples but lacks required trait information for cardiovascular disease analysis."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,  # Set to True since lacking trait data
    df=placeholder_df,
    note=note
)