# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE95810"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE95810"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE95810.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE95810.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE95810.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 series title indicating "Gene expression..."

# 2) Identify the availability of trait, age, and gender data
trait_row = None      # No ALS-specific indicator in the sample characteristics
age_row = None        # No age data found
gender_row = None     # No gender data found

# 2) Create conversion functions (though they won't be used since rows are None)
def convert_trait(value: str):
    return None

def convert_age(value: str):
    return None

def convert_gender(value: str):
    return None

# 3) Initial metadata saving and validation
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) Skip clinical feature extraction because 'trait_row' is None
# 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 the provided gene identifiers (e.g., A1BG, A1CF, A2M, etc.), they appear to be standard human gene symbols.
# Consequently, a separate mapping step does not seem to be required.

print("requires_gene_mapping = False")
# STEP 5: Data Normalization and Linking

# Since trait data is not available based on the previous steps (trait_row=None),
# we cannot perform clinical-gene linking or meaningful trait-based analyses.

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2-6. Since there's no trait data, we skip linking and final output for association.
#      Perform final validation indicating the dataset is not usable for trait association.
is_trait_available = False
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=is_trait_available,
    is_biased=False,  # Provide a valid Boolean so it doesn't raise ValueError
    df=pd.DataFrame(),  # Empty since we can't link without trait
    note="No trait data found; skipping linking and final output."
)

print("Trait data is unavailable; no further steps for linking or final output can be performed.")