# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Aniridia"
cohort = "GSE137997"

# Input paths
in_trait_dir = "../DATA/GEO/Aniridia"
in_cohort_dir = "../DATA/GEO/Aniridia/GSE137997"

# Output paths
out_data_file = "./output/preprocess/1/Aniridia/GSE137997.csv"
out_gene_data_file = "./output/preprocess/1/Aniridia/gene_data/GSE137997.csv"
out_clinical_data_file = "./output/preprocess/1/Aniridia/clinical_data/GSE137997.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)
# 1. Determine if gene expression data is available
is_gene_available = True  # The title mentions "mRNA" alongside miRNA, so we consider gene expression data present.

# 2. Identify rows for trait, age, and gender, and define their conversion functions

# Based on the sample characteristics dictionary:
#   0 -> age data
#   1 -> gender data
#   2 -> "disease: AAK" or "disease: healthy control"
# This suggests:
trait_row = 2
age_row = 0
gender_row = 1

def convert_trait(value: str) -> int:
    """
    Convert 'disease: AAK' or 'disease: healthy control' to binary (1 for aniridia, 0 for control).
    Unknown or unexpected values become None.
    """
    try:
        val = value.split(':', 1)[1].strip().lower()
        if 'aak' in val:
            return 1
        elif 'healthy' in val:
            return 0
        else:
            return None
    except:
        return None

def convert_age(value: str) -> float:
    """
    Convert 'age: 20' etc. to a float (continuous). Unknown values become None.
    """
    try:
        val = value.split(':', 1)[1].strip()
        return float(val)
    except:
        return None

def convert_gender(value: str) -> int:
    """
    Convert 'gender: F', 'gender: M', 'gender: W' to binary (female=0, male=1).
    'W' presumed female. Unknown or unexpected become None.
    """
    try:
        val = value.split(':', 1)[1].strip().lower()
        if val in ['f', 'w', 'female', 'woman', 'women']:
            return 0
        elif val in ['m', 'male']:
            return 1
        else:
            return None
    except:
        return None

# 3. Conduct initial filtering and save 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
)

# 4. Clinical feature extraction if trait data is available
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
    preview_result = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_result)
    # 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])
# These are microRNA identifiers (e.g. hsa-miR-1-3p) rather than standard human gene symbols;
# they do not require further mapping to gene symbols.
print("requires_gene_mapping = False")
# 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.")