# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Testicular_Cancer"
cohort = "GSE62523"

# Input paths
in_trait_dir = "../DATA/GEO/Testicular_Cancer"
in_cohort_dir = "../DATA/GEO/Testicular_Cancer/GSE62523"

# Output paths
out_data_file = "./output/preprocess/3/Testicular_Cancer/GSE62523.csv"
out_gene_data_file = "./output/preprocess/3/Testicular_Cancer/gene_data/GSE62523.csv"
out_clinical_data_file = "./output/preprocess/3/Testicular_Cancer/clinical_data/GSE62523.csv"
json_path = "./output/preprocess/3/Testicular_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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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)
# Gene expression data availability
is_gene_available = True  # This is a cDNA microarray study according to background info

# Clinical data availability assessment
trait_row = None # Cell line study, no real patient data 
age_row = None # No age information available
gender_row = None # No gender information available

# Data conversion functions 
def convert_trait(x):
    return None

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

# Save metadata about dataset usability
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))
# 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 identifiers appear to be custom probe IDs (e.g. '1.1.1.1', '1.1.1.10') rather than standard 
# human gene symbols like BRCA1, TP53 etc. These will need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Get gene mapping from annotation data 
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene symbol')

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

# Display results
print("\nGene data preview:")
print(preview_df(gene_data))
print("\nGene data shape:", gene_data.shape)
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# Since no clinical data is available (trait_row was None in Step 2),
# we create a minimal DataFrame with no clinical features
minimal_df = pd.DataFrame(index=gene_data.columns)
for column in gene_data.index:
    minimal_df[column] = gene_data.loc[column]

# Evaluate the gene data quality without trait analysis
trait_biased = True  # No trait analysis possible
minimal_df = minimal_df.iloc[:, :1000]  # Take subset of genes to reduce size
note = "This is a cell line study without patient trait data. While gene expression data was successfully preprocessed from probe-level to gene-level using NCBI Gene database, the dataset cannot be used for trait association analysis."

# Final validation reflecting lack of clinical data
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=trait_biased,
    df=minimal_df,
    note=note
)