# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE75241"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE75241"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE75241.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE75241.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE75241.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()
# 1. Gene Expression Data Availability
# From title and summary, this is a gene expression profile dataset
is_gene_available = True

# 2.1 Data Availability
# trait (cancer status) is in row 1 (tissue type)
trait_row = 1

# age and gender not available in characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    # Extract value after colon and strip whitespace
    value = value.split(':')[1].strip()
    # Convert to binary: nonmalignant (0) vs tumor (1)
    if 'nonmalignant' in value.lower():
        return 0
    elif 'tumor' in value.lower():
        return 1
    return None

def convert_age(value):
    return None # Not used since age data not available

def convert_gender(value):
    return None # Not used since gender data not available

# 3. Save 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)
)

# 4. Clinical Feature Extraction
# Since trait_row is not None, we extract features
selected_clinical = 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
preview_result = preview_df(selected_clinical)

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These probes appear to be numerical IDs from Illumina platform
# rather than standardized gene symbols like "BRCA1", "TP53" etc.
# Therefore mapping to gene symbols will be required
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)
# 'ID' in gene_annotation matches the probe IDs in genetic_data
# 'gene_assignment' contains gene symbol information
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

# Apply gene mapping to get gene expression data 
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Save gene data
gene_data.to_csv(out_gene_data_file)

# Preview the gene data
preview_result = preview_df(gene_data)
# Read the processed clinical and gene data files 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data = pd.read_csv(out_gene_data_file, index_col=0)  # Already normalized in step 6

# 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 profiles in esophageal squamous cell carcinoma, "
        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
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:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")