# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE185658"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE185658"

# Output paths
out_data_file = "./output/preprocess/1/Allergies/GSE185658.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE185658.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE185658.csv"
json_path = "./output/preprocess/1/Allergies/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) Check if gene expression data is available:
is_gene_available = True  # Based on microarray mention in the background info

# 2) Identify trait_row, age_row, gender_row, and define the conversion functions:
trait_row = 1  # "group" key likely indicates allergic status (AsthmaHDM vs. others)
age_row = None  # No age info found
gender_row = None  # No gender info found

def convert_trait(value: str):
    # Extract the substring after the colon
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    # Interpret "AsthmaHDM" as having allergies (1) and others as no allergies (0)
    if val == 'AsthmaHDM':
        return 1
    elif val in ['AsthmaHDMNeg', 'Healthy']:
        return 0
    return None

# Not used due to unavailability:
convert_age = None
convert_gender = None

# 3) Initial filtering and metadata saving:
is_trait_available = (trait_row is not None)
is_usable = 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_data,
        trait,
        trait_row,
        convert_trait,
        age_row,
        convert_age,
        gender_row,
        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])
# Based on the numeric indices (e.g., '7892501', '7892502') rather than standard gene symbols like 'CD69' or 'TNF',
# these identifiers appear to be probe IDs or some other non-human-gene-symbol identifiers that would require mapping.

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 6: Gene Identifier Mapping

# 1. The column "ID" in gene_annotation matches the probe IDs in the expression data,
#    and "gene_assignment" contains the relevant references for gene symbols.

mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Quick check of the resulting gene_data
print("Gene-level expression data shape:", gene_data.shape)
print("First 20 gene symbols:", gene_data.index[:20].tolist())
import pandas as pd

# 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, index=True)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Read the previously saved clinical data (which contains the trait) correctly:
#    Since we saved a single row (the trait) with multiple columns (sample IDs),
#    we read it as a normal CSV (no index_col) and then set the row index to the trait name.
clinical_df = pd.read_csv(out_clinical_data_file)
# Assign the single row index to the trait; columns are sample IDs.
clinical_df.index = [trait]

# 3. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

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

# 5. Check and remove biased features, and see if our trait is biased
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Final validation and saving 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,
    is_biased=is_biased,
    df=linked_data,
    note="Processed with correct trait indexing, missing-value handling, and bias checks."
)

# 7. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)
    print(f"Final linked data saved to {out_data_file}")
else:
    print("Dataset is not usable; final linked data not saved.")