# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE77790"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE77790"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE77790.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE77790.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE77790.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Create trait column from cell line information
cell_lines = clinical_data.iloc[0]
clinical_df = pd.DataFrame(index=cell_lines.index)
clinical_df[trait] = cell_lines.str.contains('TE8|TE9').astype(int)

# Normalize gene symbols and save to file
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Save clinical data to file
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) 
clinical_df.to_csv(out_clinical_data_file)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features 
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = ("This dataset studies gene expression changes in cancer cell lines after miRNA/siRNA treatments. " 
        "Data quality evaluation indicates the trait distribution is biased.")
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=is_biased,
    df=linked_data,
    note=note
)

# 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)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")
# Set initial availability flags
is_gene_available = False  # Cannot determine without data

# No data available yet
trait_row = None
age_row = None 
gender_row = None

def convert_trait(x):
    return None
    
def convert_age(x):
    return None

def convert_gender(x):
    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=False  # Since trait_row is None
)
# Check gene expression data availability (GPL570 platform indicates gene expression data)
is_gene_available = True

# Data availability from sample characteristics
trait_row = 2  # "source name: esophageal tumor or paired normal"
age_row = 9    # "age (years): [numeric values]"
gender_row = 8 # "sex: male/female"

def convert_trait(val: str) -> Optional[int]:
    if val is None:
        return None
    val = val.split(":")[-1].strip().lower()
    if "tumor" in val:
        return 1
    elif "normal" in val:
        return 0
    return None

def convert_age(val: str) -> Optional[float]:
    if val is None:
        return None
    val = val.split(":")[-1].strip()
    try:
        return float(val)
    except:
        return None

def convert_gender(val: str) -> Optional[int]:
    if val is None:
        return None
    val = val.split(":")[-1].strip().lower()
    if "female" in val:
        return 0
    elif "male" in val:
        return 1
    return None

# Save metadata for initial filtering
_ = 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 gene expression data
genetic_data = get_genetic_data(matrix_file_path)
genetic_data.index = genetic_data.index.astype(str)  # Convert probe IDs to strings

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# The indices appear to be just sequential numbers rather than any meaningful gene identifiers
# This indicates the gene identifiers need to be mapped to proper gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Extract probe-gene mapping columns
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')

# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Define clinical data parameters based on sample characteristics
trait_row = 1  # cell type row
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.lower()
    return 1 if 'esophageal cancer' in x else 0

# Extract clinical features 
clinical_df = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait
)

# Normalize gene symbols
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)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = ("This dataset studies gene expression in esophageal cancer cell lines. "
        "Data quality evaluation indicates potential trait distribution bias.")
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=is_biased,
    df=linked_data,
    note=note
)

# 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)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")