# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glioblastoma"
cohort = "GSE178236"

# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE178236"

# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE178236.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE178236.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE178236.csv"
json_path = "./output/preprocess/3/Glioblastoma/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 background info mentioning "gene expression analysis" and "gene expression profile"
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability 
trait_row = 5  # IDH status can indicate glioblastoma subtype
age_row = 2    # Age information available
gender_row = 1 # Gender information available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    # Convert IDH status to binary: 1 for wild-type (wt), 0 for mutant (mut)
    if 'wt' in value:
        return 1
    elif 'mut' in value:
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        # Extract numeric age value after colon
        age = int(x.split(': ')[-1])
        return age
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    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. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
clinical_features = 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
)

print("\nPreview of extracted clinical features:")
print(preview_df(clinical_features))

# Save clinical data
clinical_features.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)
# Observe the identifiers start with "ILMN_" - these are Illumina probe IDs, not 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
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Preview results 
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
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(clinical_features, 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="Clinical trial studying EGFR amplification in glioblastoma and response to gefitinib"
)

# 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)