# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE121291"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE121291"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE121291.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE121291.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE121291.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/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 - the dataset contains microarray mRNA data according to series title
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# All samples are AML cell line - constant trait value
trait_row = None

# No age data available 
age_row = None

# No gender data available since this is cell line data
gender_row = None

def convert_trait(x):
    # Convert AML status to binary
    # If contains "Acute Myeloid Leukemia", return 1
    if isinstance(x, str) and "Acute Myeloid Leukemia" in x:
        return 1
    return None

def convert_age(x):
    pass

def convert_gender(x):
    pass

# 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. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(gene_data.index[:20])
# The gene identifiers look like Affymetrix probe IDs (format: digit+_at/s_at/x_at)
# These need to be mapped to gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. Observe columns - 'ID' for probe identifiers matching gene expression data, 'Gene Symbol' for gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'

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

# 3. Apply mapping to convert probe expression to gene expression
gene_data = apply_gene_mapping(gene_data, mapping)

# Preview the first few gene IDs to verify the mapping worked
print("First 20 mapped gene symbols:")
print(gene_data.index[:20])
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Create minimal linked data with just gene expression data
linked_data = gene_data.T 

# Add trait column initialized to None
linked_data[trait] = None

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

# Check for biased features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality 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=False,
    is_biased=is_biased,
    df=linked_data,
    note="Dataset contains gene expression data from cell lines but lacks AML trait information needed for analysis."
)