# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE98578"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE98578"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE98578.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE98578.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE98578.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
is_gene_available = True  # microarray gene expression data mentioned in summary

# 2.1 Data Availability
trait_row = 2  # cell subtype indicates AML type
age_row = None  # no age data
gender_row = None  # no gender data

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    # Convert to binary: AMKL=1, non-AMKL=0
    if value == 'AMKL':
        return 1
    elif value == 'non-AMKL': 
        return 0
    return None

# Age and gender conversion functions not needed since data unavailable
convert_age = None
convert_gender = None

# 3. Save metadata about data availability
is_initial = 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. Extract clinical features
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
    )
    
    # Preview the extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# 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])
# These identifiers are from Affymetrix HG-U133 Plus 2.0 array probe IDs
# 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)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Get gene mapping from annotation data
# 'ID' column matches probe IDs in gene expression data
# 'Gene Symbol' column contains the target gene symbols
mapping_data = get_gene_mapping(gene_annotation, 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 first few genes and samples
print("Preview of gene expression data after mapping:")
print(preview_df(gene_data))
# 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)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save metadata
# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined 
# based on cell subtypes (AMKL vs non-AMKL).
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="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
)

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