# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE121894"

# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE121894"

# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE121894.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE121894.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE121894.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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  # From series title and design, this is gene expression microarray data

# 2. Variable Availability and Data Type Conversion
trait_row = 0  # Subject status row contains RA/control info
age_row = None  # Age not available
gender_row = None  # Gender not available

# Convert trait: binary (0=control, 1=RA)
def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower().split(':')[-1].strip()
    if 'rheumatoid arthritis' in value:
        return 1
    elif 'healthy control' in value:
        return 0
    return None

# Skip convert_age and convert_gender since data not available

# 3. Save metadata
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=trait_row is not None)

# 4. Clinical feature extraction 
clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
                                         trait=trait,
                                         trait_row=trait_row,
                                         convert_trait=convert_trait)

# Preview and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))
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, they are ending with '_at' which indicates
# they are Affymetrix probe IDs, not standard human gene symbols.
# These need to be mapped to gene symbols for consistent downstream analysis.
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. Extract gene annotation data with enhanced preview to find gene symbol column
gene_metadata = get_gene_annotation(soft_file)
print("\nFirst lines of raw SOFT file to locate gene symbol column:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for i, line in enumerate(f):
        if not any(line.startswith(p) for p in ['^', '!', '#']):
            print(line.strip())
            print("-"*80)
            if i > 5:
                break

# Print all columns in gene_metadata            
print("\nAll columns in gene metadata:")
print(gene_metadata.columns.tolist())
print("\nFull preview of first row:")
print(gene_metadata.iloc[0].to_dict())

# Get gene symbol info from SOFT file using regex pattern
gene_metadata['Gene_Symbol'] = gene_metadata['Description'].apply(lambda x: extract_human_gene_symbols(x)[0] if extract_human_gene_symbols(x) else None)

# 2. Get gene mapping dataframe with probe ID and gene symbol columns
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene_Symbol')

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

# Print the shape and preview of the mapped gene data
print("\nShape of gene data after mapping:", gene_data.shape)
print("\nPreview of gene data after mapping:")
print(preview_df(gene_data))
# 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 
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for bias  
trait_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=trait_biased,
    df=linked_data,
    note="Study examining transcriptome profiles in rheumatoid arthritis."
)

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