# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"
cohort = "GSE145037"

# Input paths
in_trait_dir = "../DATA/GEO/Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Rectal_Cancer/GSE145037"

# Output paths
out_data_file = "./output/preprocess/3/Rectal_Cancer/GSE145037.csv"
out_gene_data_file = "./output/preprocess/3/Rectal_Cancer/gene_data/GSE145037.csv"
out_clinical_data_file = "./output/preprocess/3/Rectal_Cancer/clinical_data/GSE145037.csv"
json_path = "./output/preprocess/3/Rectal_Cancer/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 gene expression data from rectal cancer patients
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identifiers
trait_row = 3  # response to CRT in row 3
age_row = 2    # age in row 2  
gender_row = 1 # gender in row 1

# 2.2 Conversion functions
def convert_trait(x):
    if not x:
        return None
    value = x.split(': ')[1].lower()
    return 0 if 'non-response' in value else 1

def convert_age(x):
    if not x:
        return None
    try:
        return int(x.split(': ')[1])
    except:
        return None

def convert_gender(x):
    if not x:
        return None
    value = x.split(': ')[1].lower()
    return 1 if 'male' in value else 0

# 3. Save metadata for initial filtering
is_trait_avail = 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_avail)

# 4. Extract clinical features
selected_clinical = geo_select_clinical_features(clinical_data,
                                               trait="Response",
                                               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 data
preview_dict = preview_df(selected_clinical)
print("Preview of extracted clinical data:")
print(preview_dict)

# 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 are human gene symbols (e.g. A1BG, A1CF, A2M) and do not need mapping
requires_gene_mapping = False
# 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(genetic_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, "Response")

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

# 5. Final validation and information saving
note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
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)