# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mitochondrial_Disorders"
cohort = "GSE65399"

# Input paths
in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE65399"

# Output paths
out_data_file = "./output/preprocess/3/Mitochondrial_Disorders/GSE65399.csv"
out_gene_data_file = "./output/preprocess/3/Mitochondrial_Disorders/gene_data/GSE65399.csv"
out_clinical_data_file = "./output/preprocess/3/Mitochondrial_Disorders/clinical_data/GSE65399.csv"
json_path = "./output/preprocess/3/Mitochondrial_Disorders/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Background info shows "Gene expression profiles were obtained using the Illumina HT12v4 Gene Expression BeadArray"
is_gene_available = True  

# 2. Variable Availability and Data Type Conversion
# From background info, this is a study about FRDA patients
# The differentiation/tissue type in row 0 indicates disease state information
trait_row = 0  
age_row = None # Age not available in characteristics
gender_row = None # Gender not available in characteristics

def convert_trait(x):
    if x is None or pd.isna(x):
        return None
    val = x.split(': ')[-1].lower()
    # Samples are neural progenitors or fetal tissues
    # Neural progenitors are FRDA patient-derived cells
    if 'neural progenitors' in val:
        return 1 # FRDA patient 
    elif 'fetal' in val:
        return 0 # Control tissue
    return None

def convert_age(x):
    if x is None or pd.isna(x):
        return None
    val = x.split(': ')[-1].lower()
    try:
        return float(val)
    except:
        return None

def convert_gender(x):
    if x is None or pd.isna(x):
        return None
    val = x.split(': ')[-1].lower()
    if 'female' in val or 'f' in val:
        return 0
    elif 'male' in val or 'm' in val:
        return 1
    return None

# 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
# Trait data is available, so extract clinical features
clinical_df = geo_select_clinical_features(
    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 extracted clinical data
print("\nPreview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The identifiers starting with ILMN_ are Illumina probe IDs, not gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping from annotation data
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Apply gene mapping to convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=note
)

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