# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE174060"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE174060"

# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE174060.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE174060.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE174060.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()
# Gene Expression Data Availability
# Yes, this dataset contains gene expression data from CD34+ cells according to background information
is_gene_available = True

# Data Availability and Type Conversion
trait_row = 4  # Diagnosis is recorded in row 4
age_row = 2    # Age is recorded in row 2 
gender_row = 3 # Sex is recorded in row 3

def convert_trait(x):
    """Convert trait data to binary (0=control, 1=case)"""
    if not isinstance(x, str):
        return None
    value = x.split(": ")[-1].strip().lower()
    if value == "healthy control":
        return 0
    elif value == "et":
        return 1
    return None

def convert_age(x):
    """Convert age data to continuous values"""
    if not isinstance(x, str):
        return None
    try:
        age = int(x.split(": ")[-1])
        return age
    except:
        return None

def convert_gender(x):
    """Convert gender data to binary (0=female, 1=male)"""
    if not isinstance(x, str):
        return None
    value = x.split(": ")[-1].strip().upper()
    if value == "F":
        return 0
    elif value == "M":
        return 1
    return None

# Initial validation
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)

# Extract clinical features
selected_clinical = geo_select_clinical_features(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 data
preview = preview_df(selected_clinical)
print("Preview of selected clinical features:")
print(preview)

# Save clinical data
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])
# These are probe IDs from Affymetrix transcriptome arrays (TC prefix) that need to be mapped to gene symbols
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)
# Extract gene mapping from annotation data
# 'ID' column matches probe IDs in expression data
# 'gene_assignment' contains gene symbols in the format "RefSeq_ID // Gene_Symbol // Description"
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Normalize gene symbols using NCBI standard symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save the processed gene data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_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.")