# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_Fatigue_Syndrome"
cohort = "GSE251792"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome"
in_cohort_dir = "../DATA/GEO/Chronic_Fatigue_Syndrome/GSE251792"

# Output paths
out_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/GSE251792.csv"
out_gene_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/gene_data/GSE251792.csv"
out_clinical_data_file = "./output/preprocess/3/Chronic_Fatigue_Syndrome/clinical_data/GSE251792.csv"
json_path = "./output/preprocess/3/Chronic_Fatigue_Syndrome/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))
# Gene expression data appears available given this is a deep phenotyping study
is_gene_available = True 

# Feature row identification 
trait_row = 2  # 'group' field contains trait data
age_row = 1    # 'age' field contains age data
gender_row = 0 # 'Sex' field contains gender data

# Convert trait (Patient=1, Control=0)
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1].strip()
    if 'patient' in x:
        return 1
    elif 'control' in x:
        return 0
    return None

# Convert age to float
def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(': ')[-1])
    except:
        return None

# Convert gender (Female=0, Male=1) 
def convert_gender(x):
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1].strip()
    if 'female' in x:
        return 0
    elif 'male' in x:
        return 1
    return None

# Save initial 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)

# Extract clinical features since trait data is available
clinical_df = 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)

# Preview the clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
clinical_df.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])
# Row IDs like 'HCE000104' are Helicos BioSciences Corporation Probe IDs, not standard gene symbols
# These need to be mapped to proper gene symbols for analysis
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))
# Get gene mapping using ID and EntrezGeneSymbol columns
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='EntrezGeneSymbol')

# Apply gene mapping to convert probe values to gene expression values
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview the resulting gene expression data
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
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  
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# 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=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data from blood samples used to study chronic fatigue syndrome"
)

# 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)