# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE254707"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE254707"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE254707.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE254707.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE254707.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
is_gene_available = True  # The title and background summary indicate this is transcriptomic data (RNA-seq)

# 2. Variable Availability and Data Type Conversion
# Trait - from Feature 5 (diagnosis)
trait_row = 5

def convert_trait(value):
    if not value or ":" not in value:
        return None
    diagnosis = value.split(":")[1].strip()
    if diagnosis == "Psoriasis":
        return 1
    elif diagnosis == "Healthy":
        return 0
    return None

# Age - not available
age_row = None
convert_age = None

# Gender - not available 
gender_row = None
convert_gender = 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. Extract clinical features if available
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,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the data
    preview = preview_df(selected_clinical)
    print("Clinical data preview:", preview)
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# First inspect file structure more thoroughly
print("Scanning file for matrix data marker:")
marker_line_num = None
data_header = None
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    for i, line in enumerate(f):
        if i < 5:  # Show first few lines
            print(f"Line {i}: {line.strip()}")
        if "!series_matrix_table_begin" in line.lower():
            marker_line_num = i
            print(f"\nFound matrix marker at line {i}")
            # Get the next line which should be the header
            data_header = next(f).strip()
            print(f"Header line: {data_header}")
            # Get a few data lines
            print("\nFirst few data lines:")
            for _ in range(3):
                print(next(f).strip())
            break
    if marker_line_num is None:
        print("\nWarning: Matrix data marker not found!")

# Try reading the gene expression data
if marker_line_num is not None:
    try:
        gene_data = get_genetic_data(matrix_file)
        
        # Print first 20 row IDs and shape of data
        print("\nShape of gene expression data:", gene_data.shape)
        print("\nFirst few rows of data:")
        print(gene_data.head())
        print("\nFirst 20 gene/probe identifiers:")
        print(gene_data.index[:20])
        
    except Exception as e:
        print(f"\nError reading gene data: {str(e)}")
else:
    print("Cannot read gene data - matrix marker not found")
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# First check the data structure
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    # Skip to matrix line
    for line in f:
        if "!series_matrix_table_begin" in line:
            # Read header and first few lines to inspect format
            header = next(f).strip()
            print("\nPeeking at matrix data structure:")
            for _ in range(3): # Show first 3 data lines
                print(next(f).strip())
            break

# Modify read_csv parameters to handle the data format
def get_genetic_data_modified(file_path: str, marker: str = "!series_matrix_table_begin") -> pd.DataFrame:
    # Determine rows to skip
    with gzip.open(file_path, 'rt') as file:
        for i, line in enumerate(file):
            if marker in line:
                skip_rows = i + 1
                break
        else:
            raise ValueError(f"Marker '{marker}' not found")

    # Read with modified parameters for quoted data
    genetic_data = pd.read_csv(file_path,
                             compression='gzip',
                             skiprows=skip_rows,
                             comment='!',
                             delimiter='\t',
                             quotechar='"',
                             on_bad_lines='skip')
    
    # Process column names to remove quotes
    genetic_data.columns = genetic_data.columns.str.strip('"')
    
    # Rename and set index
    genetic_data = genetic_data.rename(columns={'ID_REF': 'ID'}).astype({'ID': 'str'})
    genetic_data.set_index('ID', inplace=True)
    
    return genetic_data

# Extract gene expression data using modified function
gene_data = get_genetic_data_modified(matrix_file)

# Print diagnostic information
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])