# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE180394"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE180394"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE180394.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE180394.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE180394.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
# Yes - based on series title and design info mentioning "transcriptome" and "Affymetrix microarray"
is_gene_available = True

# 2.1 Data Availability
# After reviewing sample characteristics, no X-Linked Lymphoproliferative Syndrome cases found
trait_row = None # Trait not available
age_row = None # Age not available  
gender_row = None # Gender not available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """No X-Linked Lymphoproliferative Syndrome cases in this dataset"""
    return None

def convert_age(value: str) -> Optional[float]:
    return None  # Not available

def convert_gender(value: str) -> Optional[int]:
    return None  # Not available

# 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)

# 4. Clinical Feature Extraction is skipped 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 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
requires_gene_mapping = True
# Extract gene annotation from SOFT file
# Try different prefix combinations to find gene symbol information
gene_annotation = get_gene_annotation(soft_file_path, prefixes=['^', '!'])

# Print all available columns to check for gene symbol information
print("Available columns in gene annotation:")
print(gene_annotation.columns.tolist())

# Print first few rows to inspect data
print("\nGene annotation preview (first 5 rows):")
print(gene_annotation.head())

# Look for Platform annotation section in SOFT file that may contain gene mapping
print("\nChecking SOFT file for Platform annotation:")
with gzip.open(soft_file_path, 'rt') as f:
    in_platform = False
    for i, line in enumerate(f):
        if line.startswith('^PLATFORM'):
            in_platform = True
            print("\nFound Platform section:")
        if in_platform and i < 100:  # Print first 100 lines after platform section
            print(line.strip())
# Get probe to ENTREZ mapping from annotation
mapping_df = gene_annotation.rename(columns={'ID': 'ID', 'ENTREZ_GENE_ID': 'Gene'})
mapping_df = mapping_df.astype({'ID': 'str', 'Gene': 'str'})

# Convert probe-level data to gene-level data using ENTREZ IDs first
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Convert ENTREZ IDs to gene symbols and aggregate data
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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 probe IDs that match with genetic_data index pattern
probe_pattern = r'[0-9]+_at$'  # Pattern matching probes like '10000_at'
probes = [id for id in genetic_data.index if re.match(probe_pattern, id)]

# Create mapping dataframe
mapping_df = pd.DataFrame()
mapping_df['ID'] = probes
mapping_df['Gene'] = [re.match(r'(\d+)_at', id).group(1) for id in probes]

# Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Convert ENTREZ IDs to gene symbols using built-in normalization
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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)
# Clean probe IDs to match annotation format by removing leading zeros
genetic_data.index = genetic_data.index.str.replace(r'0*([0-9]+_at)', r'\1', regex=True)

# Get probe-to-gene mapping from annotation data
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'ENTREZ_GENE_ID')

# Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Convert ENTREZ IDs to gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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)
# Print gene_annotation info to debug
print("Gene annotation info:")
print(gene_annotation.info())
print("\nGene annotation columns:")
print(gene_annotation.columns)

# Convert probe IDs in gene_annotation to match expression data format
mapping_df = gene_annotation.copy()
mapping_df.columns = ['ID', 'Gene']  # Rename columns to match expected format

# Clean probe IDs in expression data to match annotation format by removing leading zeros
genetic_data.index = genetic_data.index.str.replace(r'0*(\d+)(_at)', r'\1\2', regex=True)

# Convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Convert ENTREZ IDs to gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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)
# Create mapping dataframe from gene annotation
mapping_df = pd.DataFrame()
mapping_df['ID'] = gene_annotation['ID']
mapping_df['Gene'] = gene_annotation['ENTREZ_GENE_ID']

# Clean probe IDs in expression data to match annotation format
genetic_data.index = genetic_data.index.str.replace(r'0*(\d+)(_at)', r'\1\2', regex=True)

# Convert probe measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Convert ENTREZ IDs to gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())

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