# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"
cohort = "GSE84046"

# Input paths
in_trait_dir = "../DATA/GEO/Obesity"
in_cohort_dir = "../DATA/GEO/Obesity/GSE84046"

# Output paths
out_data_file = "./output/preprocess/3/Obesity/GSE84046.csv"
out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE84046.csv"
out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE84046.csv"
json_path = "./output/preprocess/3/Obesity/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
# From background info, this study analyzes "whole genome gene expression changes"
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait (BMI) is available in Feature 6 with screening BMI values
trait_row = 6

# Gender data is available in Feature 4
gender_row = 4

# Birth dates are given in Feature 5, can calculate age 
age_row = 5

def convert_trait(val):
    if not val:
        return None
    try:
        # Extract numeric BMI value after colon
        bmi = float(val.split(": ")[1])
        # Convert to binary obesity status (BMI >= 30 is obese)
        return 1 if bmi >= 30 else 0
    except:
        return None

def convert_gender(val):
    if not val:
        return None
    try:
        gender = val.split(": ")[1].lower()
        return 1 if gender == "male" else 0 if gender == "female" else None
    except:
        return None

def convert_age(val):
    if not val:
        return None
    try:
        # Extract birth year from date string
        birth_year = int(val.split(": ")[1].split("-")[0])
        # Study was conducted in 2012 based on accession info
        study_year = 2012
        return study_year - birth_year
    except:
        return None

# 3. Save metadata about data availability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True
)

# 4. Extract clinical features
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 extracted features
preview_dict = preview_df(selected_clinical_df)
print("Preview of extracted clinical features:", preview_dict)

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# 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 output shown above, the gene expression data uses numeric probe IDs '7892501', '7892502', etc.
# These are microarray probe identifiers and need to be mapped to human gene symbols.
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data 
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))

print("\nNumber of non-null values in each column:")
print(gene_annotation.count())

# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and gene_assignment):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'gene_assignment']].head().to_string())

print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers") 
print("'gene_assignment' column: Contains gene symbol information")
# Get gene mapping from annotation data
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Preview mapped gene data
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])

# Save mapped gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Load clinical data and save normalized gene data
selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

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

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

# 4. Check for biased features and remove them if needed 
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="Study examining gene expression changes in adipose tissue under different protein diets during energy restriction"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)