# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE42986"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE42986"

# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE42986.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE42986.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE42986.csv"
json_path = "./output/preprocess/3/Epilepsy/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
# The dataset uses Affymetrix Human Exon microarray which measures gene expression
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# The trait (respiratory chain deficiency) is recorded in row 1
trait_row = 1

# Gender is available in row 2
gender_row = 2

# Age is available in row 3
age_row = 3

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon
    value = x.split(': ')[1].lower()
    # Convert to binary: 0 for no deficiency, 1 for any deficiency
    if 'no respiratory chain complex deficiency' in value:
        return 0
    elif 'not determined' in value:
        return None
    else:
        return 1

def convert_gender(x):
    # Extract value after colon
    value = x.split(': ')[1].upper()
    # Convert F to 0, M to 1
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    return None

def convert_age(x):
    # Extract value after colon
    value = x.split(': ')[1].lower()
    # Convert to float, handle non-numeric values
    if value == 'not obtained':
        return None
    try:
        return float(value)
    except:
        return None

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
clinical_df = geo_select_clinical_features(clinical_data, 
                                         trait='Disease',
                                         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 results
preview_dict = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview_dict)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Looking at the identifier format (e.g. '100009676_at', '10000_at'), these appear to be mouse 
# probe identifiers from an Affymetrix array platform, not human gene symbols.
# We will need to map these to proper gene symbols.
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. The column 'ID' in gene_metadata matches the probe identifiers in genetic_df
# The 'Symbol' column contains the gene symbols we want to map to

# 2. Extract gene mapping information
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

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

# Print shape before and after mapping to verify
print("Shape before mapping (probes x samples):", genetic_df.shape)
print("Shape after mapping (genes x samples):", gene_data.shape)

# Preview the mapped data
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Disease')

# 5. Final validation and metadata saving
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="Dataset contains gene expression data from skeletal muscle and fibroblast samples of mitochondrial disease patients and 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)