# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE180395"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE180395"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE180395.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE180395.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE180395.csv"
json_path = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/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
# This dataset contains kidney biopsy data, likely containing gene expression data
is_gene_available = True 

# 2.1. Data Availability
# Looking at sample_group values under key 0, we can classify XLP status 
trait_row = 0 
# No age data available
age_row = None
# No gender data available 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if value is None or ':' not in value:
        return None
    group = value.split(': ')[1].lower()
    # Since XLP often presents with lymphoproliferative conditions
    if any(x in group for x in ['gn', 'glomerul', 'infiltration', 'lymph']):
        return 1  # Disease manifestation
    return 0  # Control/healthy

def convert_age(value):
    # Not needed since age data unavailable
    return None

def convert_gender(value):
    # Not needed since gender data unavailable
    return None

# 3. Save Initial Metadata 
is_usable = 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. Clinical Feature Extraction
if trait_row is not None:
    selected_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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the data
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview)
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if i < 10:  # Print first 10 lines
            print(line.strip())
        elif "!series_matrix_table_begin" in line:
            print("\nFound table marker at line", i)
            # Print next 3 lines after marker
            for _ in range(3):
                print(next(f).strip())
            break
# Based on the "_at" suffix in the gene IDs and the fact these appear to be mouse array probes (e.g. "100009613_at"),
# these identifiers need to be mapped to standard gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Print shape and column names for inspection
print("Gene annotation shape:", gene_annotation.shape)
print("\nColumn names:", list(gene_annotation.columns))

# Preview annotation structure 
preview = preview_df(gene_annotation)
print("\nGene annotation preview:")
print(preview)
# The annotation contains Entrez IDs which we'll use as interim gene identifiers
gene_annotation.columns = ['ID', 'Gene']

# Create mapping dataframe - get_gene_mapping handles the column renaming and filtering
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene')

# Apply gene mapping using Entrez IDs as interim gene identifiers
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print shape and preview mapped gene data 
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nPreview of mapped gene expression data:")
print(preview_df(gene_data))
# Create empty dataframe since gene mapping failed
gene_data = pd.DataFrame()

# 1. Save empty gene expression data 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Save metadata about dataset being unusable
note = "Gene mapping failed as SOFT file only contains Entrez IDs without gene symbols. Additionally, this dataset appears to be a kidney biopsy expression study not focused on XLP."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,  # Raw gene data exists but mapping failed
    is_trait_available=False,  # No XLP-specific data available  
    is_biased=False,  # Set explicit value as required
    df=gene_data,  # Provide empty DataFrame
    note=note
)

# Do not save linked data as it is not usable