# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Pancreatic_Cancer"
cohort = "GSE157494"

# Input paths
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE157494"

# Output paths
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE157494.csv"
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE157494.csv"
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE157494.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 = filter_content_by_prefix(matrix_file, 
                                                        prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
                                                        prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1'], 
                                                        unselect=False,
                                                        source_type='file',
                                                        return_df_a=False,
                                                        return_df_b=True,
                                                        transpose=True)

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

# Extract background info and clinical data 
background_info, clinical_data = filter_content_by_prefix(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_characteristics_ch'],  
    unselect=False,
    source_type='file',
    return_df_a=False,
    return_df_b=True
)

# 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 - the series summary mentions gene expression profiling with Affymetrix Gene Chip
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Sample Characteristics output is empty, indicating no clinical data available
trait_row = None  
age_row = None
gender_row = None

def convert_trait(x):
    return None

def convert_age(x): 
    return None

def convert_gender(x):
    return None

# 3. Save metadata
# Initial filtering - save info that this dataset has gene data but no clinical data
validate_and_save_cohort_info(is_final=False,
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=False)

# 4. Clinical Feature Extraction
# Skip since trait_row is None (no clinical data available)
# 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 IDs (e.g. 1007_s_at, 1053_at), these are Affymetrix probe IDs 
# from HG-U133_Plus_2 array platform, not gene symbols.
# They need to be mapped to human gene symbols for standardized 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
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# Looking at gene_data index ['1007_s_at', '1053_at', '117_at'...] and 
# gene_annotation dictionary preview, 'ID' column contains probe IDs matching gene_data index,
# and 'Gene Symbol' column contains the gene symbols we need

# Create mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# Normalize gene symbols to standard format using synonym dictionary
gene_data = normalize_gene_symbols_in_index(gene_data)
# First get the gene expression data again
gene_data = get_genetic_data(matrix_file)
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
gene_data = apply_gene_mapping(gene_data, gene_mapping)
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# Update cohort info - dataset unusable due to lack of clinical data
is_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=None,
    note="Gene expression data available but no clinical annotations/controls present."
)
# 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))
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get gene annotation first
gene_annotation = get_gene_annotation(soft_file)

# Get gene expression data
gene_data = get_genetic_data(matrix_file)

# Create mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# Print dimensions of result for verification 
print(f"\nShape of gene expression data after mapping: {gene_data.shape}")
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get gene annotation and expression data 
gene_annotation = get_gene_annotation(soft_file)
gene_data = get_genetic_data(matrix_file)

# Create mapping and apply it
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol') 
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Update cohort info - dataset unusable due to lack of clinical data
is_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=None,
    note="Gene expression data available but no clinical annotations/controls present."
)