# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE182740"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE182740"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE182740.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE182740.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE182740.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on the background info, this is a microarray study analyzing gene expression profiles
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability
trait_row = 1  # Found in Feature 1: 'disease: ...'
age_row = None  # Age not available in sample characteristics
gender_row = None  # Gender not available in sample characteristics

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert disease status to binary (0: control, 1: Psoriasis/Mixed)"""
    if not isinstance(value, str):
        return None
    if ':' in value:
        value = value.split(':')[1].strip()
    # Based on summary, Mixed refers to overlap phenotype with psoriasis features
    if value in ['Psoriasis', 'Mixed']:
        return 1
    elif value in ['Normal_skin']:
        return 0
    elif value == 'Atopic_dermatitis':
        return 0  # AD patients are controls for psoriasis study
    return None

def convert_age(value: str) -> Optional[float]:
    return None  # Not used since age data unavailable

def convert_gender(value: str) -> Optional[int]:
    return None  # Not used since gender data unavailable

# 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. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait
    )
    
    # Preview the processed data
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical_df))
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Looking at the identifiers like '1007_s_at', '1053_at', these are Affymetrix probe IDs, not gene symbols
# They need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Get gene mapping using ID and Gene Symbol columns
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

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

# Preview the result
print("Shape of processed gene expression data:", gene_data.shape)
print("\nFirst few rows of processed gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols
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. Read and validate clinical data
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
if clinical_data.empty or clinical_data.isna().all().all():
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path, 
        is_gene_available=True,
        is_trait_available=False,
        is_biased=False,
        df=gene_data,
        note="Clinical data processing failed, resulting in empty or invalid data."
    )
else:
    # 2. Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # 4. Check for bias
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save metadata
    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=is_biased,
        df=linked_data
    )

    # 6. Save if usable
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)