# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Psoriasis"
cohort = "GSE158448"

# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE158448"

# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE158448.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE158448.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE158448.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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 the series title and design description, this is a gene expression study
# examining IL-17 family cytokines signaling in psoriasis
is_gene_available = True

# 2. Feature Analysis
# From sample characteristics, we can see treatment groups compared at the molecular level
# The untreated samples can serve as controls while treated samples represent cases
trait_row = 4  # treatment feature
age_row = None  # age not available 
gender_row = None  # gender not available

def convert_trait(value: str) -> int:
    """Convert treatment status to binary trait"""
    if not value or not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    # untreated samples are controls (0), treated samples are cases (1)
    return 0 if 'untreated' in value else 1

def convert_age(value: str) -> float:
    """Placeholder function since age is not available"""
    return None

def convert_gender(value: str) -> int:
    """Placeholder function since gender is not available"""
    return None

# 3. Save initial 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. Extract clinical features
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,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the processed clinical data
    preview = preview_df(selected_clinical_df)
    print("Preview of processed clinical data:")
    print(preview)
    
    # Save clinical data
    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 identifiers, they seem to be Illumina probe IDs starting with "16650"
# These need to be mapped to standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# 1. ID column in annotation data matches probe IDs in expression data
# gene_assignment column contains gene symbol info in format "SYMBOL // DESCRIPTION"
prob_col = 'ID'
gene_col = 'gene_assignment'

# 2. Extract mapping data from annotation 
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

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

# Preview output
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Clinical data is not available (trait_row was None), so skip remaining steps and mark dataset as not usable
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=None,
    df=gene_data,
    note="Contains gene expression data but lacks clinical information needed for trait association studies."
)