# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE212131"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212131"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE212131.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212131.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212131.csv"
json_path = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1) Determine if gene expression data is available
is_gene_available = True  # Based on the microarray transcriptomic analysis described

# 2) Determine variable availability
# The sample characteristics dictionary is {0: ['gender: Female', 'gender: Male']}
# Only one row (key=0) with gender info is found. 
# Trait and age are not found (or are constant/uninformative in this dataset).
trait_row = None
age_row = None
gender_row = 0  # "gender: Female" and "gender: Male" => two distinct values => available

# 2.2) Data Type Conversions
def convert_trait(x: str):
    # Not used because trait_row is None, but defining for completeness.
    # This function should extract relevant text after the colon, then convert.
    return None  # Return None since trait is not available.

def convert_age(x: str):
    # Not used because age_row is None, but defining for completeness.
    # This function should parse integer/float ages. Return None for invalid values.
    return None  # Return None since age is not available.

def convert_gender(x: str):
    # Example: x might be "gender: Male"
    parts = x.split(":")
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'male':
        return 1
    elif val == 'female':
        return 0
    return None

# 3) Initial filtering and saving metadata
# Trait data is not available => is_trait_available=False
is_trait_available = False

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
# Since trait_row is None, we SKIP extracting clinical features.