# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE222073"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE222073"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE222073.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE222073.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE222073.csv"
json_path = "./output/preprocess/1/Bladder_Cancer/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)
# Step 1: Decide if the dataset likely contains gene expression data
is_gene_available = True  # Based on the background info stating "This series contains the gene expression data..."

# Step 2: Identify and set data availability and define conversion functions

# 2.1 Data Availability
#   From the sample characteristics dictionary, there is no entry providing
#   distinct values for the trait "Bladder_Cancer", age, or gender. 
#   The entire dataset involves bladder cancer (i.e., no variation).
#   Hence, they are effectively not available for our associative analysis.
trait_row = None
age_row = None
gender_row = None

# 2.2 Data Type Conversion
#   These functions normally parse and convert values. However, since
#   the rows are not available, they won't actually be used.
def convert_trait(value: str) -> int:
    # If we had data, we would parse and convert it (binary or continuous).
    # Here, we return None due to no variation or row identified.
    return None

def convert_age(value: str) -> float:
    # Example: parse numeric part after a colon, convert to float, etc.
    # Not applicable here, so return None.
    return None

def convert_gender(value: str) -> int:
    # Example: "female" -> 0, "male" -> 1
    # Not applicable here, so return None.
    return None

# Step 3: Conduct initial filtering and save metadata
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
)

# Step 4: Since trait_row is None, we do NOT have trait data available, so skip clinical feature extraction
# 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., A2M, A2ML1, A4GALT, etc.), these appear to be official human gene symbols. 
# Therefore, no additional mapping to gene symbols is required.
print("requires_gene_mapping = False")
# STEP 5
# 1) Normalize the gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Since there is no trait data (trait_row was None), we cannot link clinical features or perform bias checks.
# We still must do final validation to record that trait data is unavailable.

# Provide a placeholder for is_biased; it won't matter because is_trait_available is False.
is_biased_placeholder = False

# 5) Perform final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,        # We do have gene expression data
    is_trait_available=False,      # Trait data is unavailable
    is_biased=is_biased_placeholder,
    df=normalized_gene_data,       # We pass the gene data but there's no trait column
    note="No trait data; cannot complete linking or bias checks."
)

# 6) If the dataset is deemed usable, save the final linked data
# In this scenario, is_usable will be False because the trait is not available.
if is_usable:
    normalized_gene_data.to_csv(out_data_file)