# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypertension"
cohort = "GSE181339"

# Input paths
in_trait_dir = "../DATA/GEO/Hypertension"
in_cohort_dir = "../DATA/GEO/Hypertension/GSE181339"

# Output paths
out_data_file = "./output/preprocess/3/Hypertension/GSE181339.csv"
out_gene_data_file = "./output/preprocess/3/Hypertension/gene_data/GSE181339.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertension/clinical_data/GSE181339.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")
# 1. Gene Expression Data Availability
is_gene_available = True  # Background shows it's a microarray gene expression study on PBMCs

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability
trait_row = 1  # 'group' field contains hypertension info for MONW subjects
gender_row = 0  # 'gender' field
age_row = 2    # 'age' field

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Based on background, MONW group has hypertension
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip()
    if value == 'MONW':
        return 1  # Has hypertension
    elif value in ['NW', 'OW/OB']:
        return 0  # No hypertension
    return None

def convert_age(x):
    if not x or ':' not in x:
        return None
    try:
        value = x.split(':')[1].strip()
        return float(value)  # Convert to continuous numeric value
    except:
        return None

def convert_gender(x):
    if not x or ':' not in x:
        return None
    value = x.split(':')[1].strip().lower()
    if value == 'woman':
        return 0
    elif value == 'man':
        return 1
    return None

# 3. 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
)

# 4. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical = 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 selected features
    preview = preview_df(selected_clinical)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save to CSV
    selected_clinical.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 identifiers appear to be probe IDs (numeric format).
# They are not human gene symbols and need to be mapped.
# This is likely from an Illumina or Affymetrix microarray platform
# requiring mapping from probe IDs to 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_SYMBOL'):")
print("\nFirst 5 rows:")
print(gene_annotation[['ID', 'GENE_SYMBOL']].head().to_string())

# Explain the format
print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers")
print("'GENE_SYMBOL' column: Standard human gene symbols")
# Get gene mapping from annotation data
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Normalize gene symbols using synonym dictionary
gene_data = normalize_gene_symbols_in_index(gene_data)

# Print shape and preview to verify mapping results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows after mapping:")
print(gene_data.head())

# 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 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)