# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE103176"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE103176"

# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE103176.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE103176.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE103176.csv"
json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Series title mentions "Gene... expression profiles", so gene data is available
is_gene_available = True

# 2. Variable Availability and Data Row Identification
# 2.1 Data Type Selection and Data Row Identification
# Trait (ET vs Control) can be found in row 3 under 'disease'
trait_row = 3

# Age is not provided in the characteristics 
age_row = None

# Gender is in row 1 under 'Sex'
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert disease status to binary (0: control, 1: ET)"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip().lower()
    if 'et' in value:
        return 1
    elif 'healthy control' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float - not used since age not available"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (0: female, 1: male)"""
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].strip().lower()
    if value == 'f':
        return 0
    elif value == 'm':
        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. Extract Clinical Features
if trait_row is not None:
    selected_clinical = 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 the extracted features
    preview_result = preview_df(selected_clinical)
    print("Preview of extracted clinical features:")
    print(preview_result)
    
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# The identifiers appear to be probe IDs from a microarray platform, not standard gene symbols
# They need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Get unique probe IDs from gene expression data to understand the format
probe_examples = genetic_data.index[:5].tolist()

# Extract the complete platform annotation table
gene_annotation = get_gene_annotation(soft_file_path, prefixes=['!platform_table_begin', '!platform_table_end'])

# Extract columns for mapping and rename them
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID_REF', gene_col='Gene Symbol')

# Apply mapping to convert probe-level data to gene-level data  
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the result
print("\nExample probe IDs from expression data:")
print(probe_examples)

print("\nFirst 5 rows of mapping data:")
print(mapping_data.head())

print("\nFirst 5 rows and 3 columns of mapped gene expression data:")
print(gene_data.iloc[:5, :3])
# Get unique probe IDs from gene expression data to understand the format
probe_examples = genetic_data.index[:5].tolist()

# Extract the complete platform annotation table
gene_annotation = get_gene_annotation(soft_file_path)
print("\nRaw annotation data columns:")
print(gene_annotation.columns.tolist())

# Based on column names in the raw data, we can see that probe IDs are in the 'ID' column
# and gene symbols are in the 'Gene Symbol' column
mapping_data = pd.DataFrame({
    'ID': gene_annotation['ID'],
    'Gene': gene_annotation['Gene Symbol']
})

# Fix any NaN values that might cause mapping issues
mapping_data = mapping_data.dropna()

# Apply mapping to convert probe-level data to gene-level data  
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)

# Preview results
print("\nExample probe IDs from expression data:")
print(probe_examples)

print("\nFirst 5 rows of mapping data:")
print(mapping_data.head())

print("\nFirst 5 rows and 3 columns of mapped gene expression data:")
print(gene_data.iloc[:5, :3])
# Check if genetic data is empty
if genetic_data.empty:
    print("Gene expression data is empty - cannot proceed with linking and analysis")
    # Record failure in cohort info
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort, 
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=True,
        is_biased=None,
        df=None,
        note="Gene mapping failed - unable to match probe IDs between expression and annotation data"
    )
else:
    # 1. Normalize gene symbols and save normalized gene data 
    normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
    normalized_gene_data.to_csv(out_gene_data_file)

    # Read the processed clinical data file
    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

    # Link clinical and genetic data using the normalized gene data
    linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

    # Detect bias in trait and demographic features, remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Validate data quality and save cohort info
    note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available."
    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=note
    )

    # Save linked data if usable
    if is_usable:
        linked_data.to_csv(out_data_file)
    else:
        print(f"Dataset {cohort} did not pass quality validation and will not be saved.")