# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE46416"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46416"

# Output paths
out_data_file = "./output/preprocess/1/Bipolar_disorder/GSE46416.csv"
out_gene_data_file = "./output/preprocess/1/Bipolar_disorder/gene_data/GSE46416.csv"
out_clinical_data_file = "./output/preprocess/1/Bipolar_disorder/clinical_data/GSE46416.csv"
json_path = "./output/preprocess/1/Bipolar_disorder/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 background info, this dataset likely contains gene expression data.

# 2) Variable Availability
trait_row = 1     # Row where disease status (bipolar disorder vs. control) is stored.
age_row = None    # No age information found.
gender_row = None # No gender information found.

# 2.2) Data Type Conversion Functions
def convert_trait(raw_value: str):
    """
    Convert the raw trait value to a binary indicator (case=1, control=0).
    Extract the substring after the colon and compare.
    """
    parts = raw_value.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if "bipolar disorder" in val:
        return 1
    elif "control" in val:
        return 0
    return None

def convert_age(raw_value: str):
    """
    Since age data is not available (age_row=None),
    this function is defined but won't be used.
    """
    return None

def convert_gender(raw_value: str):
    """
    Since gender data is not available (gender_row=None),
    this function is defined but won't be used.
    """
    return None

# 3) Save Metadata (Initial filtering) using validate_and_save_cohort_info
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) Clinical Feature Extraction (only if trait_row is not None)
if is_trait_available:
    selected_clinical_df = geo_select_clinical_features(
        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 = preview_df(selected_clinical_df)
    print("Preview of extracted clinical data:", preview)
    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("These gene identifiers appear to be numeric probe IDs, not standard human 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. Based on the annotation preview, it appears that the 'ID' column in gene_annotation matches
#    the gene expression data's row identifiers, while 'gene_symbol' holds the symbols (though
#    many are NaN in the preview subset).

# 2. Get the gene mapping dataframe by extracting the two columns: 'ID' and 'gene_symbol'.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_symbol')

# 3. Convert probe-level measurements to gene-level expression using the mapping. 
#    Apply the many-to-many mapping scheme.
gene_data = apply_gene_mapping(gene_data, mapping_df)

print("Mapping completed. The gene_data now contains gene-level expression values.")
print("Preview of gene_data:", preview_df(gene_data))
# 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 column name (stored in variable trait)
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)