# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE120340"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120340"

# Output paths
out_data_file = "./output/preprocess/1/Bipolar_disorder/GSE120340.csv"
out_gene_data_file = "./output/preprocess/1/Bipolar_disorder/gene_data/GSE120340.csv"
out_clinical_data_file = "./output/preprocess/1/Bipolar_disorder/clinical_data/GSE120340.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. Decide if gene expression data is available
#    From the background info, this is an Affymetrix gene expression dataset.
is_gene_available = True

# 2. Identify data availability and create row references
#    Row 0 has multiple disease states (control, SCZ, BD(-), BD(+)), use it as the trait data.
trait_row = 0
age_row = None
gender_row = None

# 2.2 Define data conversion functions
def convert_trait(x: str):
    """
    Convert disease state to a binary indicator for Bipolar_disorder:
    1 if BD(-) or BD(+), 0 for controls/SCZ, None otherwise.
    """
    # Parse the part after the colon
    parts = x.split(":")
    val = parts[-1].strip().lower() if len(parts) > 1 else x.strip().lower()

    if "bd" in val:
        return 1
    elif "control" in val or "scz" in val:
        return 0
    else:
        return None

def convert_age(x: str):
    return None  # No age data available

def convert_gender(x: str):
    return None  # No gender data available

# 3. Save metadata (initial filtering)
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 proceed if trait_row is not None (i.e., trait data is available).
if trait_row is not None:
    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
    )
    print(preview_df(selected_clinical_df, n=5))
    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])
# Observing the gene identifiers, they appear to be Affymetrix microarray probe IDs (e.g., '10000_at', '10009_at'),
# not standard gene symbols. Therefore, gene mapping is required.
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 & 2. Identify the columns in 'gene_annotation' that correspond to the probe IDs and gene symbols.
#    From our observation, 'ID' matches the probe IDs in the gene data, 
#    and 'Description' contains the gene symbol or gene name information.
mapping_df = get_gene_mapping(
    annotation=gene_annotation,
    prob_col='ID',        # Column storing probe IDs
    gene_col='Description'  # Column storing gene symbol (or gene name) information
)

# 3. Apply the mapping to convert probe-level measurements to gene-level expression data.
gene_data = apply_gene_mapping(
    expression_df=gene_data,
    mapping_df=mapping_df
)

# (The resulting 'gene_data' now contains the aggregated gene expression values, indexed by gene symbol.)
# 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
#    Replace df_clinical with the actual clinical dataframe from previous steps, i.e., selected_clinical_df
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)