# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Underweight"
cohort = "GSE57802"

# Input paths
in_trait_dir = "../DATA/GEO/Underweight"
in_cohort_dir = "../DATA/GEO/Underweight/GSE57802"

# Output paths
out_data_file = "./output/preprocess/3/Underweight/GSE57802.csv"
out_gene_data_file = "./output/preprocess/3/Underweight/gene_data/GSE57802.csv"
out_clinical_data_file = "./output/preprocess/3/Underweight/clinical_data/GSE57802.csv"
json_path = "./output/preprocess/3/Underweight/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)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene expression data availability - Analyzing background information
# From title and summary, this is transcriptome profiling data, so gene expression data should be available
is_gene_available = True

# 2.1. Data availability
# From sample characteristics, we can see:
# - Copy number and genotype info in rows 3 & 4 - can use for trait (underweight) 
# - Age info in row 2
# - Gender info in row 1
trait_row = 3  # Copy number row for determining underweight status
age_row = 2
gender_row = 1

# 2.2. Data type conversion functions
def convert_trait(x: str) -> int:
    """Convert copy number to binary underweight indicator
    From background info: deletion (copy number 1) is associated with underweight"""
    if not x or 'copy number 16p11.2' not in x:
        return None
    copy_num = x.split(': ')[1]
    if copy_num == '1':  # deletion = underweight
        return 1
    return 0

def convert_age(x: str) -> float:
    """Convert age string to float value"""
    if not x or 'age' not in x:
        return None
    age_str = x.split(': ')[1]
    if age_str == 'NA':
        return None
    try:
        return float(age_str)
    except:
        return None

def convert_gender(x: str) -> int:
    """Convert gender string to binary (0=female, 1=male)"""
    if not x or 'gender' not in x:
        return None
    gender = x.split(': ')[1]
    if gender == 'F':
        return 0
    elif gender == 'M':
        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 since 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)
    
    # Save clinical data
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These IDs are Affymetrix probe IDs with _PM_ pattern, not gene symbols
# Therefore gene ID mapping will be required
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. Identify relevant columns for mapping
# 'ID' column in gene_annotation matches the probe IDs in genetic_data (e.g., '1007_PM_s_at')
# 'Gene Symbol' column contains the standardized gene symbols (e.g., 'DDR1')

# 2. Extract mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3. Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the mapped data
print("\nFirst few mapped genes:")
print(list(gene_data.index[:10]))
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

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

# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data and clinical information from Type 1 Diabetes patients."
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_trait_biased,
    df=linked_data,
    note=note
)

# 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)