# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE138118"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE138118"

# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE138118.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE138118.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE138118.csv"
json_path = "./output/preprocess/3/Bladder_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the series title and summary, this is gene expression data from blood

# 2.1 Data Availability
trait_row = 0  # "stage at sample" contains cancer status
age_row = 1    # Age information is available
gender_row = None  # Gender information is not available in the characteristics

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    value = x.split(': ')[-1].strip()
    # Convert to binary: Healthy=0, Any cancer stage=1
    if value == 'Healthy':
        return 0
    elif value in ['G1', 'G2', 'G3', 'G1 pTa', 'G2 pTa']:
        return 1
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    value = x.split(': ')[-1].strip()
    try:
        # Convert to continuous numeric value
        return float(value)
    except:
        return None

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

# 3. Save Initial Filtering Results
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
clinical_df = 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
)

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

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Observe the gene identifiers
# The identifiers are numeric strings starting with '16650', which appears to be probe IDs
# These are not standard human gene symbols and will need to be mapped
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Get gene mapping data from annotation
# For this dataset:
# ID column contains probe IDs matching gene expression data
# gene_assignment column contains gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Apply gene mapping to convert probe expression to gene expression
# The apply_gene_mapping function handles many-to-many mappings
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview data shape and first few rows
print("Shape of gene expression data:", gene_data.shape) 
print("\nFirst few rows:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
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)

# Load previously saved clinical data
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 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 and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
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="NanoString nCounter RNA profiling data for bladder cancer recurrence study"
)

# 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)