# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE161532"

# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE161532"

# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE161532.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE161532.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE161532.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 - uses Affymetrix Human Transcriptome Array 2.0 for gene expression profiling
is_gene_available = True

# 2.1 Data Availability
# trait - disease state from feature 4 contains AML status
trait_row = 4
# age - age data available in feature 1 
age_row = 1
# gender - gender data available in feature 2
gender_row = 2

# 2.2 Data Type Conversion Functions 
def convert_trait(x):
    if not isinstance(x, str):
        return None
    # All samples have AML, but we can distinguish primary vs secondary
    if "de novo" in x.lower():
        return 0  # Primary AML
    elif any(t in x.lower() for t in ["secondary", "t-aml"]):
        return 1  # Secondary AML
    elif "aml" in x.lower():
        return None  # AML but type unknown
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        age = float(x.split(":")[1].strip())
        return age
    except:
        return None
        
def convert_gender(x):
    if not isinstance(x, str):
        return None
    x = x.lower()
    if "female" in x:
        return 0
    elif "male" in x:
        return 1
    return None

# 3. Save initial 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. Extract clinical features since trait_row is available
clinical_df = 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 clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
clinical_df.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 appear to be microarray probe IDs from Agilent platform ending in "_st"
# They are not standard human gene symbols and will need to be mapped
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))
# Looking at the gene expression data from step 3, the IDs end with "_st"
# Looking at the annotation data, the gene_assignment column contains gene names/symbols
# However, we need to extract the symbols from the complex assignments

# Extract probe IDs and gene assignments, and get mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

# Apply mapping to get gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_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_df, 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)