# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pancreatic_Cancer"
cohort = "GSE183795"

# Input paths
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE183795"

# Output paths
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE183795.csv"
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE183795.csv"
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE183795.csv"
json_path = "./output/preprocess/3/Pancreatic_Cancer/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 the background info, this is a microarray gene expression dataset
is_gene_available = True 

# 2.1 Data Availability
# For trait: Feature 0 contains tissue type info (tumor vs non-tumor)
trait_row = 0

# Age is not available in the data
age_row = None 

# Gender is not available in the data
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert tissue type to binary: 1 for tumor, 0 for non-tumor/normal"""
    if not value or 'tissue:' not in value:
        return None
    value = value.split('tissue:')[1].strip().lower()
    if 'tumor' in value and 'non' not in value:
        return 1
    elif 'non-tumor' in value or 'normal' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary"""
    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. Extract clinical features
if trait_row is not None:
    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("Preview 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)
# The identifiers appear to be probe IDs, not gene symbols
# The format is numerical IDs (e.g. 7896748, 7896754) which are probe identifiers 
# from the microarray platform
# These need to be mapped 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
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. Observe the IDs used in gene expression data and gene annotation data 
# In gene expression data, we see probe IDs like '7896748', '7896754', etc.
# In gene annotation, these probe IDs are stored in the 'ID' column
# Gene symbols are stored in 'gene_assignment' column

# 2. Extract probe-to-gene mapping 
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# 3. Convert probe-level data to gene-level data using mapping
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Save the processed gene data
gene_data.to_csv(out_gene_data_file)

# Preview the gene_data to verify the conversion
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save normalized gene data
# Remove "-mRNA" suffix from gene symbols before normalization
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data and trait
# First get selected clinical features using the extraction function from previous step
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
)

# Debug data structures before linking
print("\nPre-linking data shapes:")
print("Clinical data shape:", selected_clinical.shape)
print("Gene data shape:", gene_data.shape)
print("\nClinical data preview:")
print(selected_clinical.head())

# Transpose gene data to match clinical data orientation
gene_data_t = gene_data.T
linked_data = pd.concat([selected_clinical.T, gene_data_t], axis=1)

# 3. Handle missing values systematically
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 data quality and save metadata
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="Gene expression data from pancreatic cancer study. All samples are cancer cases (no controls)."
)

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