# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE99725"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE99725"

# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE99725.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE99725.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE99725.csv"
json_path = "./output/preprocess/3/Depression/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

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

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Dataset is about transcriptomic profiling from peripheral blood

# 2.1 Data Row Numbers
trait_row = 2  # MADRS (depression score)
age_row = None  # Age not available
gender_row = None  # Gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert MADRS score to binary depression status
    A: No/mild depression (0)
    B: Depression (1)"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1]
    if value == 'A':
        return 0
    elif value == 'B':
        return 1
    return None

def convert_age(x):
    return None  # Not used

def convert_gender(x):
    return None  # Not used

# 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. Clinical Feature Extraction
if trait_row is not None:
    clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
                                             trait=trait,
                                             trait_row=trait_row,
                                             convert_trait=convert_trait)
    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
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Based on the presence of "A_19_P" in the identifiers, these are Agilent probe IDs 
# that need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify mapping columns
# 'ID' column in gene_metadata contains the same Agilent probe IDs as in genetic_df
# 'GENE_SYMBOL' column contains the target gene symbols
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

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

# Print shape and preview
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nPreview of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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)

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

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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=trait_biased,
    df=linked_data,
    note="Study of depression in obese patients before and after bariatric surgery"
)

# 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)