# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE87629"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE87629"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE87629.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE87629.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE87629.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 - based on background info, this is DNA microarray data from B and T cells
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Identify data rows
trait_row = 5  # Biopsy data (villus height to crypt depth ratio)
age_row = None  # Age not available
gender_row = None  # Gender not available

# 2.2 Data type conversion functions
def convert_trait(x):
    """Convert villus height to crypt depth ratio to continuous value"""
    try:
        # Extract numeric value after colon
        val = float(x.split(': ')[1])
        return val
    except:
        return None

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

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

# 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
if trait_row is not None:
    # Extract clinical features using library function
    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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical data
    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_ prefixes indicate these are Illumina probe IDs (BeadArray)
# These are not standard human gene symbols and need to be mapped
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Additional filtering to exclude control probes 
gene_metadata = gene_metadata[gene_metadata['Species'] != 'ILMN Controls']

# Preview filtered annotation data
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. From preview, we can see that 'ID' column in gene_metadata contains ILMN_ probe IDs like the gene expression data
# And 'Symbol' column contains gene symbols
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply mapping to convert probe-level measurements to gene expression values
gene_data = apply_gene_mapping(genetic_df, gene_mapping)

# Print info about the mapping results
print("\nShape before mapping (probes x samples):", genetic_df.shape)
print("Shape after mapping (genes x samples):", gene_data.shape)
print("\nFirst few mapped gene symbols:")
print(gene_data.index[:5])
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
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 and handle biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "Gene expression data from B and T cells of celiac disease patients during six-week gluten challenge. Contains continuous trait data (villus height to crypt depth ratio)."
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=note
)

# 6. Save linked data if usable and preview
if is_usable:
    print("\nFinal linked data shape:", linked_data.shape)
    print("\nPreview of linked data:")
    print(preview_df(linked_data))
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)