# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Intellectual_Disability"
cohort = "GSE100680"

# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE100680"

# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE100680.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE100680.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE100680.csv"
json_path = "./output/preprocess/3/Intellectual_Disability/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
# Yes, this dataset appears to contain gene expression data based on the background information
# which mentions measuring APP expression levels and genome-wide effects
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# Trait (DS vs Control) can be inferred from field 3 (description)
trait_row = 3
# Age is in field 2
age_row = 2  
# Gender is not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert description to binary indicating if sample is DS (1) or control (0)"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1]
    if 'DS Clone' in value:
        return 1 
    elif 'Euploid Clone' in value:
        return 0
    return None

def convert_age(value):
    """Convert age string to numeric days"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1]
    if 'Day' in value:
        try:
            return float(value.replace('Day ', ''))
        except:
            return None
    return None

def convert_gender(value):
    """Not used since gender data is not available"""
    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
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_df(clinical_features)

# Save 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())
# These identifiers are ILMN (Illumina) probe IDs, not gene symbols
# The ILMN_ prefix indicates they are from an Illumina microarray platform
# They need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Drop rows where Symbol is null or contains phage/virus/bacteria
gene_metadata = gene_metadata[gene_metadata['Symbol'].notna()]
gene_metadata = gene_metadata[~gene_metadata['Symbol'].str.contains('phage|virus|bacteria', 
                                                                   case=False, na=False)]

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Get gene mapping data from annotation
# 'ID' column matches the ILMN probe IDs in expression data
# 'Symbol' column contains the gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Symbol')

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_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, gene_data)

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

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 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 pediatric AML samples, focusing on Down syndrome cases versus other AML types."
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)