# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypothyroidism"
cohort = "GSE75685"

# Input paths
in_trait_dir = "../DATA/GEO/Hypothyroidism"
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75685"

# Output paths
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE75685.csv"
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE75685.csv"
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE75685.csv"
json_path = "./output/preprocess/3/Hypothyroidism/cohort_info.json"

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

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene expression data availability check
# Study description suggests this is a breast cancer study with tumor samples
# There is RNA concentration and quality data (RQI Experion)
is_gene_available = True

# 2.1 Data row identification
trait_row = 21  # personal pathological history has 'Hypothyroidism' data
age_row = 19  # 'age at diagnosis'
gender_row = 1  # gender information

# 2.2 Data type conversion functions
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[-1]
    return 1 if value == 'Hypothyroidism' else 0

def convert_age(value):
    if pd.isna(value):
        return None
    try:
        age = int(value.split(': ')[-1])
        return age
    except:
        return None

def convert_gender(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# 3. Save metadata about dataset usability
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. Clinical feature extraction
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 and save clinical features
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The row IDs are numerical indices, not gene symbols or other identifiers
# Therefore, gene mapping will be required to convert these to meaningful gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract gene ID and gene symbol columns from annotation data
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview result
print("\nPreview of first few genes and their expression values:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# Get clinical features
clinical_features = 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
)

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

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from breast cancer patients, with clinical annotations including hypothyroidism status."
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
)

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