# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lower_Grade_Glioma"
cohort = "GSE107850"

# Input paths
in_trait_dir = "../DATA/GEO/Lower_Grade_Glioma"
in_cohort_dir = "../DATA/GEO/Lower_Grade_Glioma/GSE107850"

# Output paths
out_data_file = "./output/preprocess/3/Lower_Grade_Glioma/GSE107850.csv"
out_gene_data_file = "./output/preprocess/3/Lower_Grade_Glioma/gene_data/GSE107850.csv"
out_clinical_data_file = "./output/preprocess/3/Lower_Grade_Glioma/clinical_data/GSE107850.csv"
json_path = "./output/preprocess/3/Lower_Grade_Glioma/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on background information mentioning "gene expression profiling", this dataset contains gene expression data
is_gene_available = True

# 2.1 Data Availability 
# trait_row: key 8 contains PFS event status (binary outcome for LGG progression)
trait_row = 8

# age_row: key 1 contains age data
age_row = 1

# gender_row: key 0 contains gender data
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.split(": ")[-1].strip()
    # Convert PFS event to binary (Yes=1, No=0)
    if x == "Yes":
        return 1
    elif x == "No": 
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    x = x.split(": ")[-1].strip()
    try:
        return float(x)
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    x = x.split(": ")[-1].strip()
    # Convert gender to binary (Female=0, Male=1)
    if x == "Female":
        return 0
    elif x == "Male":
        return 1
    return None

# 3. Save initial 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 data is available
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 extracted clinical features:")
    print(preview)
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# The identifiers in format "ILMN_XXXXXXX" are Illumina probe IDs
# These need to be mapped to official human gene symbols for analysis
requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))

# Preview additional rows to check for gene annotations
print("\nPreview of rows 100-105:")
print(preview_df(gene_annotation.iloc[100:105]))
# 1. Identify mapping columns
# 'ID' in gene annotation contains ILMN_* identifiers matching gene expression data
# 'Symbol' contains gene symbols to map to
probe_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)

# 3. Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

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

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

# 4. Check for biased features and remove biased demographic ones
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation 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=True,
    is_biased=is_biased,
    df=linked_data,
    note="Dataset contains gene expression data for gliomas. Trait is based on glioma grade (III vs IV/V)."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)