# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE35925"

# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE35925"

# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE35925.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE35925.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE35925.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/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)
# 1. Gene Expression Data Availability
# From background info, this is a gene expression study using U133 Plus 2.0 GeneChip (Affymetrix)
is_gene_available = True

# 2. Clinical Features
# 2.1 Data Availability 
# All samples are breast cancer patients, so looking at rows 0-3 for clinical data
trait_row = None  # No vitamin D level data
age_row = 1  # Age data in row 1
gender_row = None  # Gender data unusable since all samples are female (constant feature)

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not used since trait data not available
    return None

def convert_age(x):
    try:
        return float(x.split(': ')[1])
    except:
        return None

def convert_gender(x):
    # Not used since gender data marked as unavailable
    return None

# 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=False  # trait_row is None
)

# 4. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# The identifiers shown are from Affymetrix Human Genome U133 Plus 2.0 Array probe IDs
# These are probe IDs and 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
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Preview first few rows of gene data
print("\nFirst few rows of gene expression data:")
print(preview_df(gene_data))
# 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 trait data is not available (trait_row was None in Step 2),
# set the trait bias to True since dataset lacks required trait data
note = "Dataset contains gene expression data but lacks vitamin D level measurements needed for trait analysis."
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=True,  # Dataset is biased/unusable due to missing trait data
    df=gene_data,    # Provide the gene expression data
    note=note
)