# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE115574"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE115574"

# Output paths
out_data_file = "./output/preprocess/3/Arrhythmia/GSE115574.csv"
out_gene_data_file = "./output/preprocess/3/Arrhythmia/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/preprocess/3/Arrhythmia/clinical_data/GSE115574.csv"
json_path = "./output/preprocess/3/Arrhythmia/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")
# Gene expression data check
# The dataset uses Affymetrix human gene expression microarrays, so gene data should be available
is_gene_available = True

# For trait data, we can use the disease state which has two values - AFib vs SR
trait_row = 0

def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'atrial fibrillation' in value:
        return 1
    elif 'sinus rhythm' in value:
        return 0
    return None

# Age data not available
age_row = None
convert_age = None

# Gender data not available 
gender_row = None
convert_gender = None

# Validate and 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)
)

# Extract clinical features since trait data is available
selected_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 the results
preview_result = preview_df(selected_clinical_df)
print("Preview of selected clinical features:")
print(preview_result)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_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)
# Based on the identifiers like '1007_s_at', '1053_at', these are Affymetrix probe IDs
# They need to be mapped to human gene symbols for compatibility 
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 from annotation data
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Print shape and preview first few rows to verify mapping
print("Shape after mapping:", gene_data_mapped.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data_mapped.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data_mapped)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
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="Dataset contains gene expression data from human left and right atrial tissues in patients with degenerative MR in SR and AFib."
)

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