# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE253531"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE253531"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE253531.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE253531.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE253531.csv"
json_path = "./output/z1/preprocess/Bladder_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)

# Step 2: Dataset Analysis and Clinical Feature Extraction
import re

# 1. Gene Expression Data Availability
is_gene_available = True  # Gene expression microarrays mentioned in background info

# 2. Variable Availability and Data Type Conversion

# From sample characteristics, only 'lab' and 'tcga_molecular_subtype' are available.
# Trait is Bladder_Cancer; all samples are bladder cancer (constant), so not available for association.
trait_row = None
age_row = None
gender_row = None

def _after_colon(value):
    if value is None:
        return None
    s = str(value)
    parts = s.split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(value):
    v = _after_colon(value)
    if v is None:
        return None
    v_low = v.lower()
    # Map controls/normal to 0; cancer/tumor-related to 1
    control_keys = ["normal", "healthy", "control", "benign", "non-cancer", "no cancer", "adjacent normal"]
    case_keys = ["cancer", "tumor", "carcinoma", "malignant", "mibc", "bladder cancer", "urothelial"]
    if any(k in v_low for k in control_keys):
        return 0
    if any(k in v_low for k in case_keys):
        return 1
    return None

def convert_age(value):
    v = _after_colon(value)
    if v is None:
        return None
    # Extract first number that looks like an age
    match = re.search(r"(\d+(\.\d+)?)", v)
    if not match:
        return None
    try:
        age = float(match.group(1))
        # Basic sanity check for human age
        if 0 <= age <= 120:
            return age
    except:
        pass
    return None

def convert_gender(value):
    v = _after_colon(value)
    if v is None:
        return None
    v_low = v.lower()
    if v_low in ["female", "f", "woman", "women"]:
        return 0
    if v_low in ["male", "m", "man", "men"]:
        return 1
    return None

# 3. Save Metadata (initial filtering)
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: skipped because trait_row is None

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
print("requires_gene_mapping = True")

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# Determine the appropriate columns for probe ID and gene symbols based on the annotation preview
probe_col = 'ID'  # Matches probe IDs in gene_data (e.g., '2315554')
gene_symbol_col = 'gene_assignment'  # Contains gene symbol info within mixed text

# 2. Build mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

# 3. Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize the obtained gene data and save to disk
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)

# Guard for missing clinical data (trait not available in this cohort per Step 2)
trait_row_val = globals().get('trait_row', None)
selected_clinical_data_obj = globals().get('selected_clinical_data', None)

linked_data = None  # Ensure the variable exists for downstream compatibility

if (trait_row_val is None) or (selected_clinical_data_obj is None):
    # Record unavailability and skip linking and downstream processing
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )
else:
    # 2. Link the clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data_obj, normalized_gene_data)

    # If trait column is missing after linking, treat as unavailable and skip
    if trait not in linked_data.columns:
        _ = validate_and_save_cohort_info(
            is_final=False,
            cohort=cohort,
            info_path=json_path,
            is_gene_available=True,
            is_trait_available=False
        )
    else:
        # 3. Handle missing values in the linked data
        linked_data = handle_missing_values(linked_data, trait)

        # 4. Determine whether the trait and demographic features are severely biased
        is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

        # 5. Conduct quality check and save the cohort information
        is_usable = validate_and_save_cohort_info(
            True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
        )

        # 6. If usable, save the linked data
        if is_usable:
            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
            unbiased_linked_data.to_csv(out_data_file)