# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Parkinsons_Disease"
cohort = "GSE101534"

# Input paths
in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE101534"

# Output paths
out_data_file = "./output/preprocess/3/Parkinsons_Disease/GSE101534.csv"
out_gene_data_file = "./output/preprocess/3/Parkinsons_Disease/gene_data/GSE101534.csv"
out_clinical_data_file = "./output/preprocess/3/Parkinsons_Disease/clinical_data/GSE101534.csv"
json_path = "./output/preprocess/3/Parkinsons_Disease/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)
# 1. Gene Expression Data
is_gene_available = True  # Based on background info mentioning "Genome-wide expression profiling"

# 2.1 Data Row Identification
trait_row = 0  # Based on mutation status in characteristic dictionary
age_row = None  # Age not available 
gender_row = None  # Gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if value == 'patient':
        return 1
    elif value == 'healthy':
        return 0
    return None

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

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

# 3. 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
)

# 4. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait
    )
    
    # Preview data
    preview = preview_df(selected_clinical)
    print("Preview of clinical data:", preview)
    
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file)
# 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])
# Looking at the identifiers "16650001", etc - these appear to be microarray probe IDs
# rather than standard human gene symbols (which would be like "SNCA", "PINK1", etc)
# Therefore we need to map these probe IDs to gene symbols
requires_gene_mapping = True
# First examine a portion of the SOFT file to locate gene annotation table
with gzip.open(soft_file_path, 'rt') as f:
    in_table = False
    table_lines = []
    for i, line in enumerate(f):
        if '!Platform_table_begin' in line:
            in_table = True
            table_lines.append(line)
            continue
        if in_table and '!Platform_table_end' in line:
            break
        if in_table:
            table_lines.append(line)
        if i < 5:  # Print first few lines for context
            print(line.strip())

# Parse table content into dataframe
table_content = ''.join(table_lines)
gene_annotation = pd.read_csv(io.StringIO(table_content), sep='\t', comment='!')

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

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Extract gene annotation data using the library function
gene_annotation = get_gene_annotation(soft_file_path)

# Load mapping between RefSeq accessions and gene symbols
# Since we see NM_ and NR_ accessions, this data likely needs mapping through external resources
# For now let's examine what columns and data we have
print("Column names in gene annotation data:")
print(gene_annotation.columns.tolist())

print("\nSample of gene annotation data:")
print(gene_annotation.head().to_dict('records'))

# Look at the distribution of GB_ACC patterns to understand what types of IDs we have
gb_acc_patterns = gene_annotation['GB_ACC'].dropna().str.extract(r'(^[A-Z]{2}_)')[0].value_counts()
print("\nDistribution of GB_ACC identifier types:")
print(gb_acc_patterns)

# Check total number of entries with GB_ACC
total = len(gene_annotation)
with_gb = gene_annotation['GB_ACC'].notna().sum()
print(f"\nTotal entries: {total}")
print(f"Entries with GB_ACC: {with_gb} ({(with_gb/total)*100:.1f}%)")
# Use the working annotation extraction from Step 6
gene_annotation = get_gene_annotation(soft_file_path)

# Create mapping dataframe focusing on ID and RefSeq accession
mapping_df = gene_annotation[['ID', 'GB_ACC']].dropna()

# Clean the RefSeq IDs and extract gene symbols
def get_gene_from_refseq(acc):
    if pd.isna(acc):
        return None
    # Keep only NM (mRNA) and NR (RNA) entries, strip version numbers
    acc = acc.split('.')[0]
    if acc.startswith(('NM_', 'NR_')):
        return acc
    return None
    
mapping_df['Gene'] = mapping_df['GB_ACC'].apply(get_gene_from_refseq)
mapping_df = mapping_df[['ID', 'Gene']].dropna()

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

print("Gene expression data shape after mapping:", gene_data.shape)
if len(gene_data) > 0:
    print("\nFirst few gene symbols:", list(gene_data.index)[:10])
    print("\nPreview of gene expression values:")
    print(gene_data.iloc[:5, :5])
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# First evaluate bias using the clinical data we have
trait_biased, clinical_df_processed = judge_and_remove_biased_features(selected_clinical_df, trait)

# Exit with appropriate metadata since gene mapping failed in previous step
note = "Gene mapping failed - no valid gene expression data obtained."
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=False,  # Set to False since we couldn't get valid gene data
    is_trait_available=True,
    is_biased=trait_biased,
    df=clinical_df_processed,
    note=note
)

# Do not save any output files since data processing failed