# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE205151"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE205151.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE205151.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE205151.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"

# STEP 1

# 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. Gene expression data availability
#    Based on the metadata: "mRNA was analyzed using a targeted Nanostring immunology array," 
#    indicating this study involves gene expression data.
is_gene_available = True

# 2. Variable Availability and Conversion

#    From the sample characteristics, only two keys (0 and 1) are available:
#    0 -> polyic_stimulation, and 1 -> cluster
#    There's no mention of 'Asthma' variation, age, or gender. 
#    So, all samples are asthmatic, which yields no variability in 'trait', 
#    and age/gender aren't in the dictionary.

trait_row = None    # No variation in "Asthma" (everyone is asthmatic)
age_row = None      # Not found
gender_row = None   # Not found

def convert_trait(value: str) -> int:
    """
    Trait data is not available/variable here,
    so we won't actually use this function.
    """
    return None

def convert_age(value: str) -> float:
    """
    Age data not available.
    """
    return None

def convert_gender(value: str) -> int:
    """
    Gender data not available.
    """
    return None

# 3. Save Metadata (initial filtering)
#    Trait data is not available because there's no variability.
is_trait_available = False
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
#    Since trait_row is None, we skip extraction for this dataset.
# 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 inspection, these appear to be standard human gene symbols.
print("requires_gene_mapping = False")