# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bile_Duct_Cancer"
cohort = "GSE131027"

# Input paths
in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE131027"

# Output paths
out_data_file = "./output/preprocess/1/Bile_Duct_Cancer/GSE131027.csv"
out_gene_data_file = "./output/preprocess/1/Bile_Duct_Cancer/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/1/Bile_Duct_Cancer/clinical_data/GSE131027.csv"
json_path = "./output/preprocess/1/Bile_Duct_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 series description mentioning "expression features", we assume gene expression.

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics, "Bile duct cancer" appears under key=1 alongside other cancers.
# Hence, trait data is available and not constant. So:
trait_row = 1

# Age data is not present in the dictionary.
age_row = None

# Gender data is also absent in the dictionary.
gender_row = None

# Define conversion functions
def convert_trait(value: str):
    # Split by colon and take the rightmost part
    val = value.split(':')[-1].strip().lower()
    # Convert "bile duct cancer" to 1, everything else to 0, unknown to None
    if val == "bile duct cancer":
        return 1
    elif val:
        return 0
    return None

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

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

# 3. Save Metadata (initial filtering)
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 (only if trait_row is not None)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    # Preview the extracted features (just for inspection, not stored)
    print(preview_df(selected_clinical_df, n=5, max_items=200))
    # Save clinical data
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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("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))
# STEP6 - Gene Identifier Mapping

# 1. Identify columns in 'gene_annotation' which correspond to the probe ID and the gene symbol.
#    From the preview, the 'ID' column matches our gene_data index (e.g., '1007_s_at'),
#    and the 'Gene Symbol' column stores the gene symbols.

# 2. Get the gene mapping dataframe
gene_mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col="ID",
    gene_col="Gene Symbol"
)

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(
    expression_df=gene_data,
    mapping_df=gene_mapping_df
)

# Optional: Print shape or index to verify
print("Gene expression data after mapping:")
print("Shape:", gene_data.shape)
print("First 5 genes:\n", gene_data.index[:5])
# STEP7

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically using the actual trait name
linked_data_processed = handle_missing_values(linked_data, trait_col=trait)

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Final quality validation and metadata saving
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

# 6. If dataset is usable, save the final linked data
if is_usable:
    linked_data_final.to_csv(out_data_file)