# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE84633"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84633"

# Output paths
out_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/GSE84633.csv"
out_gene_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/gene_data/GSE84633.csv"
out_clinical_data_file = "./output/preprocess/3/Sickle_Cell_Anemia/clinical_data/GSE84633.csv"
json_path = "./output/preprocess/3/Sickle_Cell_Anemia/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
is_gene_available = True  # Study title indicates gene expression data from PBMCs

# 2. Variable Availability and Conversion
# Trait: Available from disease field
trait_row = 2  # Key for disease status
def convert_trait(val):
    if val is None:
        return None
    val = val.split(': ')[-1].lower()
    if 'sickle cell disease' in val:
        return 1
    return None

# Age: Not available
age_row = None 
convert_age = None

# Gender: Not available
gender_row = None
convert_gender = None

# 3. Save 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
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,
        age_row=age_row,
        convert_age=convert_age, 
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    print("Preview of clinical data:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    clinical_df.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 identifiers appear to be Illumina probe IDs starting with '23'
# These are not human gene symbols and need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Extract gene mapping info
# 'ID' column matches probe IDs in expression data
# 'gene_assignment' contains gene symbols in format "RefSeq // GeneSymbol // Description"
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Extract standard gene symbols
mapping_df['Gene'] = mapping_df['Gene'].apply(lambda x: x.split('//')[1].strip() if isinstance(x, str) and '//' in x else None)

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

# Preview the mapped gene data
print("\nGene expression data after mapping:")
print("Shape:", gene_data.shape)
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])
print("\nSample of expression values:")
print(gene_data.head())
# 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) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)