# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriatic_Arthritis"
cohort = "GSE57383"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriatic_Arthritis"
in_cohort_dir = "../DATA/GEO/Psoriatic_Arthritis/GSE57383"

# Output paths
out_data_file = "./output/preprocess/3/Psoriatic_Arthritis/GSE57383.csv"
out_gene_data_file = "./output/preprocess/3/Psoriatic_Arthritis/gene_data/GSE57383.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriatic_Arthritis/clinical_data/GSE57383.csv"
json_path = "./output/preprocess/3/Psoriatic_Arthritis/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("Background Information:")
print(background_info)
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
# Dataset appears to be gene expression data from CD14+ cells
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Identify row indices
trait_row = 6  # 'disease' row contains trait info
age_row = 2    # 'age' row contains age info 
gender_row = 1 # 'Sex' row contains gender info

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (0=control, 1=case)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'Psoriatic Arthritis':
        return 1
    elif value == 'Health Control':
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age value to continuous numeric"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    return None

# 3. Save initial filtering metadata
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. Extract clinical features
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
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Checking identifiers - these are Affymetrix probe IDs (ending in "_PM_at" etc)
# rather than standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping dataframe using ID and Gene Symbol columns
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview data
print("Data shape after mapping:", gene_data.shape)
print("\nPreview of mapped gene data:")
print(preview_df(gene_data))
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

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

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Dataset contains gene expression data from CD14+ cells of Psoriatic Arthritis patients and healthy controls."
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=note
)

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