# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE95425"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE95425"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE95425.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE95425.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE95425.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
# From the background info, it's clear this is a transcriptome study
# "Comprehensive transcriptome studies..." suggests gene expression data is available
is_gene_available = True

# 2.1 Data Availability
# For trait: In cell 1, all samples are "Normal kidney tissue", but this is a constant
# In cell 2, we have sampling depth which can serve as a proxy for kidney regions
trait_row = 2  

# For age and gender: Not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert sampling depth to binary:
    cortex = 0 (superficial)
    medulla/cortex_medulla = 1 (deeper)
    """
    if not value:
        return None
    value = value.split(': ')[-1].lower()
    if 'cortex' in value and 'medulla' not in value:
        return 0
    elif 'medulla' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> int:
    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
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 processed clinical data
    print("Preview of processed clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    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())
# These IDs are from Illumina microarray platform (ILMN prefix)
# They need to be mapped to official gene symbols for standardization
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))
# The 'ID' column in gene_metadata matches the ILMN identifiers in gene expression data
# The 'Symbol' column contains the gene symbols we want to map to
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

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

# Preview the results
print("First 20 gene symbols:")
print(gene_data.index[:20].tolist())
print("\nNumber of genes:", len(gene_data))
# 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,
    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 contains gene expression data from normal kidney tissue samples, comparing cortex and medulla regions."
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)