# Path Configuration
from tools.preprocess import *

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

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

# Output paths
out_data_file = "./output/preprocess/1/Cervical_Cancer/GSE131027.csv"
out_gene_data_file = "./output/preprocess/1/Cervical_Cancer/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/1/Cervical_Cancer/clinical_data/GSE131027.csv"
json_path = "./output/preprocess/1/Cervical_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) Determine if gene expression data is available
is_gene_available = True  # Based on the series description indicating "expression features", assume it's gene expression.

# 2) Determine data availability for 'trait', 'age', 'gender'.

# From the sample characteristics dictionary, we observe:
#   Key=1 has various "cancer: ..." entries, including 'cancer: Cervical cancer'.
#   There's no row for age or gender. Hence age_row = None and gender_row = None.
trait_row = 1
age_row = None
gender_row = None

# 2) Define data type conversions.
def convert_trait(value: str):
    """
    Extract the substring after the colon (':') and check if it matches 'Cervical cancer'.
    Return 1 if it is Cervical cancer, 0 otherwise. If the string is malformed or unknown, return None.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    cancer_type = parts[1].strip().lower()
    if "cervical cancer" in cancer_type:
        return 1
    else:
        return 0

def convert_age(value: str):
    """
    Age data is not available in this dataset, so always return None.
    """
    return None

def convert_gender(value: str):
    """
    Gender data is not available in this dataset, so always return None.
    """
    return None

# 3) Conduct initial filtering and save metadata.
#    Trait data is considered available if trait_row is not None.
is_trait_available = (trait_row is not None)

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) If we have trait data (trait_row is not None), extract clinical features and save.
if is_trait_available:
    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 resulting clinical features
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)

    # Save to CSV
    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))
# STEP: Gene Identifier Mapping

# 1. Identify which columns match the probe identifiers in the gene_data and the gene symbols in the annotation.
#    Based on the preview, "ID" holds the probe IDs, and "Gene Symbol" holds the gene symbols.

mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 2. Convert probe-level to gene-level expression.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (Optional) Print out basic info or preview for confirmation
print("Gene data shape after mapping:", gene_data.shape)
print("First 5 gene names in mapped data:")
print(gene_data.index[:5])
# STEP 7

# 1. Normalize gene symbols in the gene_data, then save to CSV.
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 on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and demographic features are biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final validation and save cohort info
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,
    note="Trait is available. Completed linking and QC steps."
)

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