# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE107846"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE107846"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE107846.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE107846.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE107846.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/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
# Based on the background info - studying arachidonic acid metabolism, likely contains gene expression data
is_gene_available = True  

# 2.1 Data Availability
# Trait (CF status) is in row 5 "state"
trait_row = 5

# Age is in row 1
age_row = 1

# Gender is in row 2
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon
    if ':' in str(x):
        value = str(x).split(':')[1].strip()
        # Convert to binary: CF = 1, Healthy = 0
        if value == 'CF':
            return 1
        elif value == 'Healthy':
            return 0
    return None

def convert_age(x):
    # Extract value after colon and convert to float
    if ':' in str(x):
        value = str(x).split(':')[1].strip()
        try:
            return float(value)
        except:
            return None
    return None

def convert_gender(x):
    # Extract value after colon
    if ':' in str(x):
        value = str(x).split(':')[1].strip()
        # Convert to binary: M = 1, F = 0
        if value == 'M':
            return 1
        elif value == 'F':
            return 0
    return None

# 3. Save Metadata - Initial Filtering
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_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 to CSV
    clinical_features.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])
# The identifiers start with "ILMN_" which indicates these are Illumina probe IDs
# These are not standard human gene symbols and will need to be mapped
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 columns for mapping (ID column matches ILMN identifiers, SYMBOL contains gene symbols)
prob_col = 'ID'
gene_col = 'SYMBOL'

# 2. Get gene mapping dataframe containing probe IDs and corresponding gene symbols
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 info about the gene mapping results
print("Original probe data shape:", genetic_df.shape)
print("Gene mapping shape:", mapping_df.shape) 
print("Mapped gene data shape:", gene_data.shape)
print("\nPreview of mapped gene 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 using the concat function
linked_data = pd.concat([clinical_features, gene_data], axis=0).T

# 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 comparing gene expression in CF patients with and without secondhand smoke exposure"
)

# 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)