# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE57691"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE57691"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE57691.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE57691.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE57691.csv"
json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"

# STEP 1: Initial Data Loading

# 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,
    prefixes_a=background_prefixes,
    prefixes_b=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 gene availability
is_gene_available = True  # From the background info, it clearly states "Genome-wide expression analysis"

# Step 2: Identify data availability and define row indices for trait, age, and gender
# Based on inspection of the sample characteristics dictionary:
trait_row = 0   # Matches the "disease state" field
age_row = None  # Age info not provided
gender_row = None  # Gender info not provided

# Step 2 (continued): Define data type conversions
def convert_trait(value: str):
    # Extract the substring after colon
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[-1].strip().lower()
    # Map "control" to 0; all other known disease states to 1
    if 'control' in val:
        return 0
    elif 'aaa' in val or 'aod' in val:
        return 1
    return None

# No age or gender data available, so define stubs that always return None
def convert_age(value: str):
    return None

def convert_gender(value: str):
    return None

# Step 3: Conduct initial filtering and save metadata
# Trait data is considered available if trait_row is not None
is_trait_available = (trait_row is not None)

# We are in the middle of preprocessing, so is_final=False
# This function will record partial metadata if the dataset fails
# or return to continue if it passes (with is_gene_available & is_trait_available).
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
)

# Step 4: Extract clinical features if 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
    )
    # Preview and save the extracted clinical features
    preview_data = preview_df(selected_clinical_df)
    print("Clinical features preview:", preview_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])
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. Identify which columns in the gene annotation correspond to probe identifiers and gene symbols
#    From the preview, "ID" contains "ILMN_..." probe identifiers, and "Symbol" holds gene symbols.

# 2. Create a gene mapping dataframe from the annotation dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

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

# (Optionally preview the result if desired)
print("Mapped gene expression data shape:", gene_data.shape)
print("First 5 genes in the mapped data:", gene_data.index[:5].tolist())
# STEP 7

import pandas as pd

# 1. Normalize the gene expression data to standard gene symbols.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print("Normalized gene expression data saved to:", out_gene_data_file)

# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically.
df = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
trait_biased, df = judge_and_remove_biased_features(df, trait)

# 5. Perform final validation with full dataset information.
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=df,
    note="Final step with linking, missing-value handling, bias checks."
)

# 6. If the data is usable, save the final linked data.
if is_usable:
    df.to_csv(out_data_file)
    print(f"Final linked data saved to: {out_data_file}")
else:
    print("Dataset is not usable or severely biased. No final data saved.")