# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE162253"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE162253"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE162253.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE162253.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE162253.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)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the title and context, this dataset likely contains gene expression data.

# 2. Variable Availability and Data Type Conversion
#    Checking sample characteristics, we see no mention of "Bladder_Cancer", age, or gender data.
#    Therefore, these are not available.
trait_row = None
age_row = None
gender_row = None

# Define conversion functions (though they will not be used here since trait_row, age_row, and gender_row are None).
def convert_trait(value: str):
    return None  # No trait data available

def convert_age(value: str):
    return None  # No age data available

def convert_gender(value: str):
    return None  # No gender data available

# 3. Initial filtering and saving metadata
#    Trait data is considered available if trait_row is not None. Here it is None.
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
#    Skip this step because trait_row is None (no clinical data to extract).
# 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])
# The provided IDs (e.g., '11715100_at') appear to be Affymetrix probe set identifiers,
# not standard human gene symbols. They therefore require mapping to standard gene symbols.
print("They appear to be Affymetrix probe set IDs.")
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP: Gene Identifier Mapping

# 1. From the annotation preview, we see the column "ID" matches the probe identifiers in the gene_data index,
#    and "Gene Symbol" stores the gene symbols.
probe_col = "ID"
symbol_col = "Gene Symbol"

# 2. Get a gene mapping dataframe using the library function get_gene_mapping.
gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

# 3. Convert the probe-level measurements in gene_data to a gene-level expression dataframe.
gene_data = apply_gene_mapping(gene_data, gene_mapping)

# (Optional) Inspect the shape of the remapped gene_data for a sanity check.
print(f"Remapped gene_data shape: {gene_data.shape}")
# 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)