# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE193442"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE193442"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE193442.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE193442.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE193442.csv"
json_path = "./output/preprocess/3/Multiple_sclerosis/cohort_info.json"

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

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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
# Based on the Series title and design, this appears to be a transcriptional profiling study of T cells
is_gene_available = True

# 2. Variable Availability and Data Analysis
# Looking at sample characteristics, there is no trait, age or gender data available
trait_row = None  
age_row = None
gender_row = None

# 2.2 Define conversion functions (even though not used in this case)
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip().lower()
    if value == "ms" or value == "multiple sclerosis":
        return 1
    elif value == "control" or value == "healthy" or value == "hc":
        return 0
    return None

def convert_age(value):
    if not isinstance(value, str):
        return None
    try:
        age = float(value.split(": ")[-1].strip())
        return age
    except:
        return None
        
def convert_gender(value):
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].strip().lower()
    if value in ["f", "female"]:
        return 0
    elif value in ["m", "male"]:
        return 1
    return None

# 3. Save metadata
# trait_row is None so is_trait_available is False
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. Skip clinical feature extraction since trait_row is None
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# First inspect the file structure
print("First few lines of matrix file:")
with gzip.open(matrix_file, 'rt') as f:
    for i, line in enumerate(f):
        print(line.strip())
        if i >= 10:
            break
            
# Manually find where the data table begins
with gzip.open(matrix_file, 'rt') as f:
    for i, line in enumerate(f):
        if "series_matrix_table_end" in line:
            end_marker = i
        if "series_matrix_table_begin" in line:
            begin_marker = i
            header = next(f).strip()  # Get column names
            print("\nFound data table at line", i)
            print("Header line:", header)
            break

# Now extract gene expression data with correct marker positioning
gene_data = get_genetic_data(matrix_file)

# Print shape and content information
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())

# Print first 20 gene/probe identifiers
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.head(20).index)
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# First inspect all files in directory
print("Files in directory:")
print(os.listdir(in_cohort_dir))

# First inspect the matrix file content since it may contain platform info
print("\nFirst few lines of matrix file:")
with gzip.open(matrix_file, 'rt') as f:
    for i, line in enumerate(f):
        if i < 15 or ('!platform_table_begin' in line.lower() and i < 30):
            print(line.strip())
        if i >= 30:
            break

# Try to extract platform annotation from matrix file
platform_lines = []
capturing = False
with gzip.open(matrix_file, 'rt') as f:
    for line in f:
        if '!platform_table_begin' in line.lower():
            capturing = True
            continue
        if '!platform_table_end' in line.lower():
            capturing = False
            break
        if capturing and line.strip():
            platform_lines.append(line)

# Convert platform lines to DataFrame if any exist
if platform_lines:
    try:
        gene_annotation = pd.read_csv(io.StringIO(''.join(platform_lines)), 
                                    delimiter='\t', low_memory=False)
        print("\nGene Annotation Preview:")
        print("Column names:", gene_annotation.columns.tolist())
        print("\nFirst few rows as dictionary:")
        print(preview_df(gene_annotation))
    except Exception as e:
        print(f"\nError reading platform annotation: {str(e)}")
else:
    print("\nNo platform annotation data found in matrix file")
# Check output from steps 3 and 5 
# Since gene identifiers preview is empty and there is no platform annotation in matrix file
# We will need to create empty dataframe for now and pass it through the pipeline
# The actual gene mapping will be done when proper data is available

gene_data = pd.DataFrame()