# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lower_Grade_Glioma"
cohort = "GSE35158"

# Input paths
in_trait_dir = "../DATA/GEO/Lower_Grade_Glioma"
in_cohort_dir = "../DATA/GEO/Lower_Grade_Glioma/GSE35158"

# Output paths
out_data_file = "./output/preprocess/3/Lower_Grade_Glioma/GSE35158.csv"
out_gene_data_file = "./output/preprocess/3/Lower_Grade_Glioma/gene_data/GSE35158.csv"
out_clinical_data_file = "./output/preprocess/3/Lower_Grade_Glioma/clinical_data/GSE35158.csv"
json_path = "./output/preprocess/3/Lower_Grade_Glioma/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on background info, this is gene expression profiling study
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# For trait: WHO grade (row 1) can be used as severity indicator 
trait_row = 1
# Age and gender not available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    if "who grade" not in x.lower():
        return None
    try:
        grade = x.lower().split(":")[-1].strip()
        if "ii" in grade:
            return 0  # Lower grade
        elif "iii" in grade:
            return 1  # Higher grade 
        return None
    except:
        return None

convert_age = None
convert_gender = 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
# Since trait_row is not None, we extract clinical features
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
print("Preview of clinical features:")
print(preview_df(clinical_features))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# These are Illumina probe IDs (starting with "ILMN_"), not human gene symbols
# They will need to be mapped to standard gene symbols for analysis

requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))

# Preview additional rows to check for gene annotations
print("\nPreview of rows 100-105:")
print(preview_df(gene_annotation.iloc[100:105]))
# 1. The column "ID" stores probe IDs matching gene expression data
# The column "Symbol" stores gene symbols
prob_col = "ID"
gene_col = "Symbol"

# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Print first few rows to verify
print("Preview of gene expression data:")
print(preview_df(gene_data))

# Save gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

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

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

# 4. Check for biased features and remove biased demographic ones
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata 
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="All subjects are male according to series summary. Age information not available."
)

# 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)