# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE218109"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE218109"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE218109.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE218109.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE218109.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
is_gene_available = True  # Based on Series_title and Series_summary, this contains transcriptional profiling data

# 2.1 Data Availability
trait_row = 5  # p53 status indicates cancer condition
age_row = 1    # Age data available
gender_row = 0 # Sex data available 

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[-1].lower()
    if 'ns+' in value or 'nuclear-stabilized' in value:
        return 1
    elif 'ns-' in value or 'unstable' in value:
        return 0
    return None

def convert_age(value):
    if pd.isna(value):
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value):
    if pd.isna(value):
        return None
    value = value.split(': ')[1].upper()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    return None

# 3. 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)
)

# 4. Extract Clinical 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
print(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 identifiers rather than standard human gene symbols
# Human gene symbols typically follow a specific format (e.g., BRCA1, TP53, IL6)
# Therefore gene mapping will be required
requires_gene_mapping = True
# Since the SOFT file doesn't contain usable annotation data, load platform annotation from external source
annotation_file = "./metadata/GPL4133.tsv"

# Load and preview the platform annotation
gene_annotation = pd.read_csv(annotation_file, sep='\t', comment='#')

# Show column names and preview the data
print("Platform Annotation Preview:")
print("-" * 50)
print(f"Number of rows: {len(gene_annotation)}")
print(f"\nColumns:")
for col in gene_annotation.columns:
    print(col)
print("\nFirst few rows:")
print(preview_df(gene_annotation))
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview the data
print("Gene Annotation Preview:") 
print("-" * 50)
print(f"Number of rows: {len(gene_annotation)}")
print(f"\nColumns:")
for col in gene_annotation.columns:
    print(col)
print("\nFirst few rows:")
print(preview_df(gene_annotation))
# 1. The 'ID' column in gene annotation matches the gene expression data indices
# The 'GENE_SYMBOL' column contains the gene symbols we want to map to
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'GENE_SYMBOL')

# 2. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview results
print("Gene Expression Data Preview:")
print("-" * 50)
print(f"Number of genes: {len(gene_data)}")
print("\nFirst few rows:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save
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)

# 2. Link clinical and genetic data 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 4. Detect bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate data quality and save cohort info
note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma tumors, "
        "comparing nuclear-stabilized p53 (NS+) versus unstable p53 (NS-) protein tumor 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
)

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