# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anorexia_Nervosa"
cohort = "GSE60190"

# Input paths
in_trait_dir = "../DATA/GEO/Anorexia_Nervosa"
in_cohort_dir = "../DATA/GEO/Anorexia_Nervosa/GSE60190"

# Output paths
out_data_file = "./output/z1/preprocess/Anorexia_Nervosa/GSE60190.csv"
out_gene_data_file = "./output/z1/preprocess/Anorexia_Nervosa/gene_data/GSE60190.csv"
out_clinical_data_file = "./output/z1/preprocess/Anorexia_Nervosa/clinical_data/GSE60190.csv"
json_path = "./output/z1/preprocess/Anorexia_Nervosa/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
# Step 1: Determine gene expression data availability
is_gene_available = True  # Illumina HumanHT-12 v3 microarray indicates gene expression data

# Step 2: Variable availability and conversion functions

# Trait (Anorexia Nervosa) availability:
# The dataset provides ED (eating disorder) but does not distinguish AN specifically.
trait_row = None  # Not available at the required specificity (AN vs BN)

# Age availability
age_row = 5  # 'age: <float>'
def convert_age(x):
    if x is None:
        return None
    try:
        val = x.split(":", 1)[1].strip()
    except Exception:
        val = str(x).strip()
    try:
        v = float(val)
        if 0 <= v < 120:
            return v
        return None
    except Exception:
        return None

# Gender availability
gender_row = 7  # 'Sex: M' / 'Sex: F'
def convert_gender(x):
    if x is None:
        return None
    try:
        val = x.split(":", 1)[1].strip().lower()
    except Exception:
        val = str(x).strip().lower()
    if val in {"m", "male"}:
        return 1
    if val in {"f", "female"}:
        return 0
    return None

# Placeholder for trait conversion (not used because trait_row is None)
def convert_trait(x):
    return None

# Step 3: Initial filtering and save 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
)

# Step 4: Clinical feature extraction (skip because trait_row is None)
# If trait_row becomes available in future, the following block can be used:
if trait_row is not None:
    selected_clinical_df = 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_df(selected_clinical_df)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# 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
requires_gene_mapping = True
print(f"requires_gene_mapping = {requires_gene_mapping}")

# 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
# Identify the columns for probe IDs and gene symbols in the annotation
probe_col = 'ID'       # Matches probe IDs like 'ILMN_1343291'
gene_symbol_col = 'Symbol'  # Contains human gene symbols

# 2. Build the gene mapping dataframe
gene_mapping = 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=gene_mapping)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols and save gene expression 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)

# 2-6. Proceed only if trait data is available; otherwise, skip linking and final validation
if ('trait_row' in globals()) and (trait_row is not None):
    # Build clinical feature dataframe from clinical_data
    selected_clinical_df = 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
    )

    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

    # Assess bias and remove biased demographic features
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Final validation and save cohort info
    is_usable = validate_and_save_cohort_info(
        True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
    )

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