# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE143924"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE143924"

# Output paths
out_data_file = "./output/preprocess/1/Atrial_Fibrillation/GSE143924.csv"
out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE143924.csv"
out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE143924.csv"
json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"

# STEP1
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)
############################
# 1. Gene Expression Data Availability
############################
is_gene_available = True  # Based on "Whole-tissue gene expression patterns" in the series summary

############################
# 2. Variable Availability and Data Type Conversion
############################
# From the sample characteristics dictionary:
# {0: ['tissue: epicardial adipose tissue'],
#  1: ['patient diagnosis: sinus rhythm after surgery',
#      'patient diagnosis: postoperative atrial fibrillation after surgery (POAF)']}

# The trait "Atrial_Fibrillation" can be inferred from row 1 since it contains
# "sinus rhythm after surgery" vs. "postoperative atrial fibrillation after surgery (POAF)".
trait_row = 1

# There's no mention of age or gender information in the dictionary,
# thus they are considered not available.
age_row = None
gender_row = None

# Data Type Conversions
def convert_trait(value: str) -> Optional[int]:
    # Extract the value after the colon
    parts = value.split(':', 1)
    val_str = parts[1].strip() if len(parts) > 1 else value.strip()
    
    # Map recognized patterns to 0 or 1
    if val_str.lower() == 'sinus rhythm after surgery':
        return 0
    elif 'postoperative atrial fibrillation' in val_str.lower():
        return 1
    else:
        return None

def convert_age(value: str) -> Optional[float]:
    # No age data is truly available here; returning None
    return None

def convert_gender(value: str) -> Optional[int]:
    # No gender data is truly available here; returning None
    return None

############################
# 3. Save Metadata (Initial Filtering)
############################
is_trait_available = trait_row is not None
is_usable = 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:
    # Suppose clinical_data is our input DataFrame of sample characteristics
    selected_clinical = 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 clinical features
    preview = preview_df(selected_clinical, n=5)
    print("Selected Clinical Features Preview:", preview)
    
    # Save the selected clinical data
    selected_clinical.to_csv(out_clinical_data_file, index=False)
# STEP3
# 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])
# Based on biomedical knowledge, the listed gene identifiers appear to be recognized human gene symbols.
# Therefore, they do not require additional gene symbol mapping.
print("requires_gene_mapping = False")
# STEP5

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data
# Replace "selected_clinical_df" with the correct variable "selected_clinical"
linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

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

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Final quality validation and metadata saving
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=trait_biased,
    df=linked_data_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

# 6. If dataset is usable, save the final linked data
if is_usable:
    linked_data_final.to_csv(out_data_file)