# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE245953"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE245953"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE245953.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE245953.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE245953.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. Decide whether gene expression data is available
#    Based on the background "Gene expression data from muscle-invasive bladder cancer" with a microarray platform,
#    we judge that it does provide gene expression data.
is_gene_available = True

# 2. Check sample characteristics for trait, age, and gender.
#    The dictionary provided is:
#    {0: ['condition: Muscle-invasive bladder cancer']}
#    There is only one unique value for the condition (i.e., it's constant across samples),
#    so, by instruction, we consider the trait not available.
trait_row = None
age_row = None
gender_row = None

# Prepare the conversion functions (though they will not be used since no data is available).
def convert_trait(value: str):
    # Typically we handle 'value after colon' and convert to a binary or continuous type.
    # But no trait data is actually used here, so we'll just return None.
    return None

def convert_age(value: str):
    # Similarly, no age data is present. Return None.
    return None

def convert_gender(value: str):
    # Similarly, no gender data is present. Return None.
    return None

# 3. Conduct initial filtering with validate_and_save_cohort_info
is_trait_available = (trait_row is not None)  # This is 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. Since trait_row is None, we do NOT extract and save clinical features.
#    (We skip calling geo_select_clinical_features because the instructions say to skip when 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 observed probe identifiers (AFFX-BkGr-GC##_st), these are Affymetrix control probes,
# not standard human gene symbols. They must be mapped to gene symbols.
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. Decide which columns in the gene_annotation match the probe IDs from the gene_data
#    and which column contains the raw text from which gene symbols can be extracted.
#    From the preview, "ID" appears to match the probe-level identifiers, and "SPOT_ID.1" contains text with gene symbols.

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="SPOT_ID.1")

# 3. Apply the gene mapping to convert probe-level data into gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7
# Since step 2 concluded that trait_row = None, there is no available (non-constant) trait data for this dataset.
# Therefore, we cannot link clinical data or proceed with trait-based analyses. We only normalize and save gene data,
# then mark the dataset as not usable for trait-based analysis (is_biased=True).

# 1) Normalize gene symbols in the gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Because trait data is unavailable (trait_row=None), we skip linking to clinical data and all steps 
#    dependent on the trait. We cannot do trait-based missing value handling or bias checks.

# 3) Perform final quality validation and save relevant info.
#    We set is_trait_available=False to indicate no usable clinical trait data.
#    We must provide a boolean value for is_biased when is_final=True, so we'll set it to True 
#    to indicate this dataset is too biased/unusable for trait-based studies.
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=True,
    df=normalized_gene_data,
    note="No non-constant trait data found. Dataset is not usable for trait-based analysis."
)

# 4) If the dataset were usable, we would save the final linked data. Here, it's unusable, so we skip.
if is_usable:
    # No final_data to save since trait data is unavailable.
    pass