# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE40912"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE40912"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE40912.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE40912.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE40912.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Background info shows it's a gene expression microarray study

# 2.1 Data Row Identification
trait_row = 14  # 'patient status' contains binary survival outcome
age_row = 4    # 'age at surgery' contains age data
gender_row = 3  # Gender info available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert patient status to binary: 0=alive, 1=dead"""
    if not value or ":" not in value:
        return None
    status = value.split(":")[1].strip().lower()
    if "death" in status:
        return 1
    elif "alive" in status:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age string to float"""
    if not value or ":" not in value:
        return None
    try:
        age = float(value.split(":")[1].strip().split()[0])
        return age
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0=female, 1=male"""
    if not value or ":" not in value:
        return None
    gender = value.split(":")[1].strip().lower()
    if "female" in gender:
        return 0
    elif "male" in gender:
        return 1
    return None

# 3. Save Initial 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. Clinical Feature Extraction
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 the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The row identifiers appear to be numerical indices rather than human gene symbols
# This indicates that gene mapping will be needed to convert identifiers to gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. & 2. Get gene mapping dataframe from gene annotation
# The 'ID' column in gene_metadata matches the row indices in genetic_data
# The 'GENE_SYMBOL' column contains the gene symbols we want to map to
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the results
print("First 10 gene symbols after mapping:")
print(gene_data.index[:10].tolist())

print("\nShape of gene expression data before and after mapping:")
print(f"Before: {genetic_data.shape}")
print(f"After: {gene_data.shape}")
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = 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
)

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

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

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples from various tissue types including kidney, lung, stomach and other organs, with both tumor and normal tissues."
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=note
)

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