# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE71799"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE71799"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE71799.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE71799.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE71799.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file 
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', '!Sample_source_name_ch1', '!Sample_description', '!Sample_title']
)

# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data, max_len=10)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on background info, this study measures gene expression in PBMC cells
is_gene_available = True

# 2.1 Data Availability 
# Trait can be derived from Sample_description (row 3)
trait_row = 3

# No age data available
age_row = None 

# No gender data available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Extract substring after colon if present
    if ':' in str(value):
        value = value.split(':', 1)[1].strip()
        
    # Convert based on description field
    if "unrelated healthy control" in value.lower():
        return 0
    elif "cystic fibrosis" in value.lower():
        return 1
    return None

def convert_age(value):
    return None

def convert_gender(value):
    return None

# 3. Save initial 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. 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
    )
    
    # Preview extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These identifiers are from Affymetrix microarray probes and need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# From the preview we can see that 'ID' in gene_metadata matches probe IDs in expression data
# and 'Gene Symbol' contains the target gene symbols

# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview the mapped gene expression data
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data) 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_features = clinical_features.T 
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Gene expression analysis comparing cystic fibrosis patients with healthy controls using PBMC samples"
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)