# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE201395"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE201395"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE201395.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE201395.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE201395.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
# Based on the background information ("Expression array data..."), this dataset contains gene expression data.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# The sample characteristics dictionary suggests that these are urothelial carcinoma cell lines
# with no human subject-level information such as age or gender.
# Additionally, the trait ("Bladder_Cancer") is constant for all samples (they are all bladder cancer cell lines).
# Hence, no meaningful variation is present for trait, age, or gender.

trait_row = None
age_row = None
gender_row = None

# Since no data is available for trait, age, or gender, we do not need conversion functions.
# We'll define empty placeholders for completeness (they won't be used).

def convert_trait(x: str):
    return None

def convert_age(x: str):
    return None

def convert_gender(x: str):
    return None

# 3. Save Metadata (Initial Filtering)
# Trait data is considered absent if 'trait_row' is None.
is_trait_available = (trait_row is not None)

# Perform the initial filtering step and store the result in 'is_usable'
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
# This step is only performed if 'trait_row' is not None (i.e., trait data is available).
# Since 'trait_row' is None, we skip this step.
# 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])
print("\nrequires_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))
# STEP6: Gene Identifier Mapping

# 1. Decide columns for mapping
# Based on the preview, the 'ID' column of the annotation DataFrame corresponds to the probe identifiers,
# and the 'gene_assignment' column appears to contain text related to gene symbols.

mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="gene_assignment"
)

# 2. Convert probe-level measurements to gene-level by applying the gene mapping
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# (Optional) Print shape or a small preview for verification
print("Gene data shape after mapping:", gene_data.shape)
print("First 20 gene symbols in the mapped data:", gene_data.index[:20])
# 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)