# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE62589"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE62589"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE62589.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE62589.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE62589.csv"
json_path = "./output/preprocess/3/Osteoporosis/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("Background Information:")
print(background_info)
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 check
# This is a SuperSeries and we don't have clear information about data type
is_gene_available = False

# Variable availability check
# Trait row: Not directly available in characteristics, can't be inferred
trait_row = None 

# Age row: Not available in characteristics
age_row = None

# Gender row: Available in row 2, but all female so not useful
gender_row = None

# Type conversion functions
def convert_trait(x):
    """Convert trait status to binary"""
    if x is None:
        return None
    x = str(x).lower()
    if ':' in x:
        x = x.split(':')[1].strip()
    if 'osteoporosis' in x:
        return 1
    elif 'control' in x or 'normal' in x:
        return 0
    return None

def convert_age(x):
    """Convert age to float"""
    if x is None:
        return None
    try:
        if ':' in x:
            x = x.split(':')[1].strip()
        return float(x)
    except:
        return None

def convert_gender(x):
    """Convert gender to binary"""
    if x is None:
        return None
    x = str(x).lower()
    if ':' in x:
        x = x.split(':')[1].strip()
    if 'female' in x:
        return 0
    elif 'male' in x:
        return 1
    return None

# Save metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)
# 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 row IDs appear to be probe IDs (numeric identifiers) rather than human gene symbols
# These will need to be mapped to standard gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# From examining previous outputs, we see:
# - Gene expression data uses numeric IDs in the 'ID' column
# - Gene annotation has 'ID' column with matching IDs and 'gene_assignment' with gene symbols

# Get gene mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

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

# Preview results
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# Create minimal dummy DataFrame
dummy_df = pd.DataFrame({trait: [0]})  # One row with trait value
is_biased = True  # Mark as biased/unusable

note = "This is a SuperSeries without clear data type information. No clinical trait data available."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=False,
    is_trait_available=False,
    is_biased=is_biased,
    df=dummy_df,
    note=note
)