# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypertension"
cohort = "GSE149256"

# Input paths
in_trait_dir = "../DATA/GEO/Hypertension"
in_cohort_dir = "../DATA/GEO/Hypertension/GSE149256"

# Output paths
out_data_file = "./output/preprocess/3/Hypertension/GSE149256.csv"
out_gene_data_file = "./output/preprocess/3/Hypertension/gene_data/GSE149256.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertension/clinical_data/GSE149256.csv"
json_path = "./output/preprocess/3/Hypertension/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")
def convert_age(value: str) -> Optional[float]:
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    if not value or ':' not in value:
        return None
    gender = value.split(': ')[1].lower()
    if 'female' in gender:
        return 0
    elif 'male' in gender:
        return 1
    return None

def convert_trait(value: str) -> Optional[int]:
    if not value or ':' not in value:
        return None
    status = value.split(': ')[1].lower()
    if 'above' in status:
        return 0  # Not in poverty -> less likely to have hypertension
    elif 'below' in status:
        return 1  # In poverty -> more likely to have hypertension
    return None

# Gene expression data is available (microarray data mentioned in background)
is_gene_available = True

# Row indices for variables
trait_row = 2  # Poverty status used as proxy for hypertension risk
age_row = 3
gender_row = 0

# Initial validation and 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)

# Extract clinical features since trait_row is not None
clinical_features = 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)

# Preview and save clinical data
preview = preview_df(clinical_features)
print(f"Preview of clinical features:\n{preview}")
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)
# The gene IDs start with "ILMN_" which indicates these are Illumina probe IDs
# These need to be mapped to standard human gene symbols for analysis
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 'SYMBOL'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'SYMBOL']].head().to_string())

# Explain the format 
print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers")
print("'SYMBOL' column: Standard human gene symbols")
# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SYMBOL')

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

# Check the resulting data
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())

# Save the 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 comparing transcriptional profiles between idiopathic non-cirrhotic portal hypertension patients, cirrhosis patients, and normal controls"
)

# 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)