# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE123086"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE123086"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE123086.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123086.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123086.csv"
json_path = "./output/preprocess/3/Asthma/cohort_info.json"

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

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# Yes, this dataset contains gene expression data from CD4+ T cells using microarray
is_gene_available = True

# 2.1 Data Availability
# Trait is in Feature 1 (primary diagnosis), values include ASTHMA and others
trait_row = 1

# Gender is in Feature 2 and 3 (Sex appears in both)
gender_row = 2

# Age appears in Features 3 and 4 
age_row = 3

# 2.2 Data Type Conversion Functions

def convert_trait(value: str) -> int:
    """Convert trait values to binary (0 for control, 1 for case)"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].upper()
    if 'ASTHMA' in value:
        return 1
    elif value == 'HEALTHY_CONTROL':
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age values to continuous numeric"""
    if not isinstance(value, str):
        return None
    if not value.startswith('age: '):
        return None
    try:
        return float(value.split(': ')[-1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender values to binary (0 for female, 1 for male)"""
    if not isinstance(value, str):
        return None
    if not value.startswith('Sex: '):
        return None
    value = value.split(': ')[-1].upper()
    if value == 'FEMALE':
        return 0
    elif value == 'MALE':
        return 1
    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
if trait_row is not None:
    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
    )
    
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    clinical_features.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)
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# The IDs in gene annotation correspond to the row IDs in gene expression data
# The ENTREZ_GENE_ID contains IDs that we first map to
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')

# Apply the mapping to convert probe expression to gene expression
entrez_data = apply_gene_mapping(gene_data, mapping_df)

# Convert Entrez IDs to gene symbols using NCBI synonym database
gene_data = normalize_gene_symbols_in_index(entrez_data)

# Save the gene expression data 
gene_data.to_csv(out_gene_data_file)
# Load previously saved data
gene_data = pd.read_csv(out_gene_data_file, index_col=0)  
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

# Inspect data alignment
print("Clinical data shape:", clinical_data.shape)
print("Gene data shape:", gene_data.shape)
print("Clinical data columns:", clinical_data.columns[:5])
print("Gene data columns:", gene_data.columns[:5])

# Transpose data to get samples in rows, genes in columns
clinical_data = clinical_data.T
gene_data = gene_data.T
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Asthma')

# 5. Validate and save cohort info
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,
    note="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
# Load previously saved data
gene_data = pd.read_csv(out_gene_data_file, index_col=0)
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

# Verify data validity
if gene_data.empty:
    print("Gene expression data is empty. Previous preprocessing steps likely failed.")
    is_gene_available = False
    is_trait_available = True
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=is_gene_available,
        is_trait_available=is_trait_available,
        is_biased=False,  # Set a definite value
        df=clinical_data,  # Provide the clinical data
        note="Gene expression data processing failed, resulting in empty data."
    )
else:
    # Link clinical and genetic data 
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # Evaluate bias
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Validate and save cohort info
    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,
        note="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls."
    )

    # Save linked data if usable
    if is_usable:
        linked_data.to_csv(out_data_file)