# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE186963"

# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE186963"

# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE186963.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE186963.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE186963.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 availability
# Dataset contains whole blood gene expression data according to title and summary
is_gene_available = True

# 2. Variable availability and conversion functions
# Trait (patient response status) is available at index 3
trait_row = 3

def convert_trait(value):
    # Extract value after colon and strip whitespace
    if ':' in value:
        value = value.split(':')[1].strip()
    if value == 'Responder':
        return 0  # Negative case (control)
    elif value == 'Non-responder':  
        return 1  # Positive case
    return None

# Age and gender data are not available in sample characteristics
age_row = None
gender_row = None

def convert_age(value):
    return None

def convert_gender(value):
    return None

# 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=trait_row is not None
)

# 4. Extract clinical features since trait data is available
clinical_df = 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 and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))

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)
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract gene mapping data from annotation metadata
def extract_first_gene_symbol(desc):
    matches = re.findall(r'\[Source:HGNC Symbol;Acc:HGNC:\d+\]', str(desc))
    if matches:
        text_before = desc.split(matches[0])[0]
        gene = text_before.strip().split()[-1]
        return gene
    return None

# Create mapping dataframe with ID and extracted gene symbols
mapping_df = pd.DataFrame({
    'ID': gene_metadata['ID'],
    'Gene': gene_metadata['SPOT_ID.1'].apply(extract_first_gene_symbol)
})

# Apply gene mapping to convert probe-level data to gene-level data 
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) 

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

# 4. Check for bias
trait_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=trait_biased,
    df=linked_data,
    note="Study examining transcriptome profiles in rheumatoid arthritis."
)

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