# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Multiple_sclerosis"
cohort = "GSE141381"

# Input paths
in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE141381"

# Output paths
out_data_file = "./output/preprocess/3/Multiple_sclerosis/GSE141381.csv"
out_gene_data_file = "./output/preprocess/3/Multiple_sclerosis/gene_data/GSE141381.csv"
out_clinical_data_file = "./output/preprocess/3/Multiple_sclerosis/clinical_data/GSE141381.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 - from series title and design, this is a gene expression study
is_gene_available = True

# 2.1 Data Availability
# For trait - not explicitly given but we can infer from treatment status
trait_row = 2  # Use treatment status as indicator
# For age 
age_row = 1  # Age is recorded in feature 1
# For gender
gender_row = 0  # Gender is recorded in feature 0

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert treatment status to binary trait (0=healthy/control, 1=disease)"""
    if pd.isna(value):
        return None
    value = value.lower().split(': ')[1]
    if value == 'baseline':  # All patients have MS at baseline
        return 1
    return None  # Exclude treated/placebo since we only want baseline

def convert_age(value: str) -> Optional[float]:
    """Convert age to float"""
    if pd.isna(value):
        return None
    value = value.lower().split(': ')[1]
    if value == 'unknown':
        return None
    try:
        return float(value)
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary (0=female, 1=male)"""
    if pd.isna(value):
        return None
    value = value.lower().split(': ')[1]
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 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. Extract clinical features
if trait_row is not None:
    clinical_df = geo_select_clinical_features(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)
    
    print("Preview of extracted clinical data:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape 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])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# The identifiers appear to be microarray probe IDs (starting with '16650') 
# rather than standard human gene symbols
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Get mapping between probe IDs and gene symbols
# 'ID' column in annotation matches probe IDs from expression data 
# 'gene_assignment' column contains gene symbols embedded in complex strings
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Apply the mapping to convert probe data to gene data
# The function will automatically extract gene symbols from gene_assignment strings
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview results
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical data and convert trait based on treatment status
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

# Define trait: Baseline (1) vs Treated/Placebo (0)
def convert_treatment(x):
    if pd.isna(x):
        return None
    treatment = x.lower().split(': ')[1]
    return 1 if treatment == 'baseline' else 0

clinical_data.loc[trait] = clinical_data.loc[trait].apply(convert_treatment)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

# 3. Handle missing values  
linked_data = handle_missing_values(linked_data, trait)

# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Treatment comparison study in Multiple Sclerosis patients, comparing baseline vs treated/placebo groups using blood transcriptome data."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Load clinical data and convert trait based on age
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)

# Recalculate trait based on age (POMS: age ≤ 18 [1], AOMS: age > 18 [0])
clinical_data.loc[trait] = (clinical_data.loc['Age'] <= 18).astype(int)

# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

# 3. Handle missing values  
linked_data = handle_missing_values(linked_data, trait)

# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Pediatric vs Adult Onset Multiple Sclerosis comparison based on blood transcriptome data. Trait defined as POMS (1) vs AOMS (0) using age cutoff of 18 years."
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)