# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Migraine"
cohort = "GSE67311"

# Input paths
in_trait_dir = "../DATA/GEO/Migraine"
in_cohort_dir = "../DATA/GEO/Migraine/GSE67311"

# Output paths
out_data_file = "./output/preprocess/3/Migraine/GSE67311.csv"
out_gene_data_file = "./output/preprocess/3/Migraine/gene_data/GSE67311.csv"
out_clinical_data_file = "./output/preprocess/3/Migraine/clinical_data/GSE67311.csv"
json_path = "./output/preprocess/3/Migraine/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
# From background info, this dataset contains whole blood gene expression data using Affymetrix arrays
is_gene_available = True

# 2.1 Data Availability
# Trait (Migraine) data is available in row 4 
trait_row = 4

# Age is not available in the sample characteristics
age_row = None  

# Gender is not available in the sample characteristics
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip()
    # Convert to binary where Yes=1, No=0, missing=None
    if value == 'Yes':
        return 1
    elif value == 'No': 
        return 0
    return None

def convert_age(value):
    return None  # Not used since age data not available

def convert_gender(value):
    return None  # Not used since gender data not available

# 3. Save 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. Clinical Feature Extraction 
# Since trait_row is not None, we extract clinical features
selected_clinical = geo_select_clinical_features(clinical_df=clinical_data,
                                              trait=trait,
                                              trait_row=trait_row,
                                              convert_trait=convert_trait)

# Preview the extracted features
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.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])
# The IDs appear to be identifiers that need mapping - they are numeric codes starting with "789" likely from a microarray
# These are not standard human gene symbols like BRCA1, TP53, etc.
# Based on the numeric format and length, these look like Illumina BeadArray probe IDs
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 mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Normalize gene symbols using the NCBI synonym information
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview results
print("Gene-level expression data shape:", gene_data.shape)
print("\nFirst 5 genes and their values across first 3 samples:")
print(gene_data.iloc[:5, :3])
# 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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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)