# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE205151"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"

# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE205151.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE205151.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE205151.csv"
json_path = "./output/preprocess/3/Asthma/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
# Yes, this dataset contains gene expression data from Nanostring array
is_gene_available = True

# 2.1. Data Availability
# Use cluster information as trait data since it represents asthma phenotypes
trait_row = 1  # Feature 1 contains cluster info
age_row = None # Age not explicitly provided
gender_row = None # Gender not provided

# 2.2. Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # Extract cluster number after colon and convert to binary
    # cluster 1 -> 0, cluster 2 -> 1
    try:
        cluster = int(x.split(':')[1].strip())
        if cluster == 1:
            return 0
        elif cluster == 2:
            return 1
        return None
    except:
        return None

def convert_age(x):
    # Not used since age data unavailable
    return None

def convert_gender(x):
    # Not used since gender data unavailable
    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_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait
    )
    
    # Preview the extracted features
    print("Preview of extracted clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    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)
# Looking at the gene identifiers in the first few rows, they appear to be valid human gene symbols
# For example, ABCB1, ABCF1, ABL1, ADA, AHR are all standard human gene symbols
# The ID column contains recognized official human gene symbols that do not need mapping
requires_gene_mapping = False
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
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 RNA transcriptome data in human sinonasal epithelial cells."
)

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