# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE164883"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE164883"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE164883.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE164883.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE164883.csv"
json_path = "./output/preprocess/3/Celiac_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
# Yes, it contains gene expression data as it mentions "transcriptomes" and total RNA isolation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
trait_row = 0  # Disease status in row 0
age_row = 2  # Age in row 2
gender_row = None  # Gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait values to binary: control=0, celiac disease=1"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if value == 'control':
        return 0
    elif value == 'celiac disease':
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age values to continuous numbers"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    try:
        return float(value)
    except:
        return None

# Gender conversion function not needed since gender data unavailable

# 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:
    # Extract clinical features
    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=None,
        convert_gender=None
    )
    
    # Preview the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.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])
# ILMN_* identifier prefixes indicate Illumina BeadArray probes 
# These are not standard human gene symbols and require mapping
requires_gene_mapping = True
# Read SOFT file first to inspect content
with gzip.open(soft_file, 'rt') as f:
    soft_content = f.readlines()
    
# Look for probe annotation section
probe_lines = []
started = False
for line in soft_content:
    if line.startswith('!platform_table_begin'):
        started = True
        continue
    elif line.startswith('!platform_table_end'):
        break
    elif started:
        probe_lines.append(line)

# Convert probe annotation lines to dataframe
probe_df = pd.read_csv(io.StringIO(''.join(probe_lines)), sep='\t')

# Check column names and non-null values
non_null_columns = probe_df.columns[probe_df.notnull().any()]
probe_df = probe_df[non_null_columns].dropna(how='all')

print("Column names with non-null values:")
print(non_null_columns)

print("\nPreview of probe annotation data:")
print(preview_df(probe_df))
# 1. Identify mapping columns: 
# 'ID' in probe_df matches ILMN_* identifiers in genetic_df
# 'Symbol' contains gene symbols
mapping_columns = ['ID', 'Symbol']

# 2. Get gene mapping dataframe
mapping_df = probe_df[mapping_columns]
mapping_df = mapping_df.rename(columns={'Symbol': 'Gene'})

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

# Preview results
print("Gene data shape:", gene_data.shape)
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
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

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

# 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=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data from duodenal biopsies of children with celiac disease 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)