# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE32924"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE32924"

# Output paths
out_data_file = "./output/preprocess/1/Eczema/GSE32924.csv"
out_gene_data_file = "./output/preprocess/1/Eczema/gene_data/GSE32924.csv"
out_clinical_data_file = "./output/preprocess/1/Eczema/clinical_data/GSE32924.csv"
json_path = "./output/preprocess/1/Eczema/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
# From the background, it's clear this dataset includes gene expression microarray data.
is_gene_available = True

# 2) Variable Availability and Data Type Conversion
# Based on the sample characteristics dictionary, the trait ("Eczema") can be inferred from row 2.
# Age and gender info are not found, so they are set to None.

trait_row = 2
age_row = None
gender_row = None

# Define data conversion functions.

def convert_trait(value: str):
    # Typically, values are in the format "condition: AL", so split on ':' and strip.
    val = value.split(':')[-1].strip()
    if val in ["AL", "ANL"]:
        return 1
    elif val == "Normal":
        return 0
    return None

# Age and gender are not available, so we won't define or use those conversions.
convert_age = None
convert_gender = None

# 3) Save initial metadata to conduct basic usability filtering (not final).
is_usable_initial = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=(trait_row is not None)
)

# 4) Clinical Feature Extraction
# Only perform this step if trait_row is not None.
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_dict = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview_dict)
    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 
# that need to be mapped to standard gene symbols.

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. Identify columns for probe ID and gene symbol in the gene_annotation dataframe.
#    From the preview, the probe identifier column is 'ID' and the gene symbol column is 'Gene Symbol'.

# 2. Get the mapping from probe ID to gene symbol.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# 3. Convert probe-level data to gene-level data.
gene_data = apply_gene_mapping(gene_data, mapping_df)
import pandas as pd

# STEP7

# 1) Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2) Load the clinical data from step 2 and set "Eczema" as the row index
selected_clinical_df = pd.read_csv(out_clinical_data_file)
selected_clinical_df = selected_clinical_df.set_index(pd.Index([trait]))

# 3) Link the clinical dataframe with the normalized gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 4) Handle missing values using the trait column
final_data = handle_missing_values(linked_data, trait_col=trait)

# 5) Evaluate bias in the trait and remove any biased demographic features
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 6) Perform final validation and save relevant 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=trait_biased,
    df=final_data,
    note="Trait data successfully linked from step 2."
)

# 7) If the dataset is deemed usable, save the final linked data
if is_usable:
    final_data.to_csv(out_data_file)