# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Aniridia"
cohort = "GSE137996"

# Input paths
in_trait_dir = "../DATA/GEO/Aniridia"
in_cohort_dir = "../DATA/GEO/Aniridia/GSE137996"

# Output paths
out_data_file = "./output/preprocess/1/Aniridia/GSE137996.csv"
out_gene_data_file = "./output/preprocess/1/Aniridia/gene_data/GSE137996.csv"
out_clinical_data_file = "./output/preprocess/1/Aniridia/clinical_data/GSE137996.csv"
json_path = "./output/preprocess/1/Aniridia/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 1: Determine if gene expression data is available
# Based on the background info (mRNA expression and microRNA data), we consider this dataset to have gene expression data.
is_gene_available = True

# Step 2: Identify availability for trait, age, and gender, and define conversion functions.

# From the sample characteristics:
#   row 0 => age
#   row 1 => gender
#   row 2 => disease (AAK / healthy control)
#
# We treat 'disease' as the trait variable, 'age' as continuous, and 'gender' as binary.

trait_row = 2
age_row = 0
gender_row = 1

def convert_trait(x: str) -> int:
    # Extract the raw value after the colon
    val = x.split(':')[-1].strip().lower()
    # Convert to binary: 1 for aniridia (AAK), 0 for control, None otherwise
    if val == 'aak':
        return 1
    elif val == 'healthy control':
        return 0
    return None

def convert_age(x: str) -> float:
    # Extract the raw value after the colon
    val = x.split(':')[-1].strip()
    # Convert to float if possible
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(x: str) -> int:
    # Extract the raw value after the colon
    val = x.split(':')[-1].strip().lower()
    # Convert F/M/W to binary: female => 0, male => 1
    if val in ['f', 'w']:
        return 0
    elif val == 'm':
        return 1
    return None

# Step 3: Initial filtering and saving metadata
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
)

# Step 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_data,
        trait=trait,  # "Aniridia"
        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 extracted clinical features
    previewed_data = preview_df(selected_clinical_df)
    print("Preview of selected clinical data:", previewed_data)

    # Save clinical features 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])
# After reviewing the identifiers such as "A_19_P00315452", they appear to be array probe IDs and not standard gene symbols.
# Therefore, gene symbol 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))
# Gene Identifier Mapping
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {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, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait (and potentially other features) is severely biased.
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final quality validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,   # We do have a trait column
    is_biased=trait_biased,
    df=linked_data,
    note="Cohort data successfully processed with trait-based analysis."
)

# 6. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")