# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atherosclerosis"
cohort = "GSE133601"

# Input paths
in_trait_dir = "../DATA/GEO/Atherosclerosis"
in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE133601"

# Output paths
out_data_file = "./output/preprocess/1/Atherosclerosis/GSE133601.csv"
out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE133601.csv"
out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE133601.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)
# 1) Gene Expression Data Availability
is_gene_available = True  # The background describes a transcriptional survey, so gene expression data is likely available.

# 2) Variable Availability and Data Type Conversion
# From the sample characteristics dictionary, no entries correspond to Atherosclerosis, age, or gender data.
trait_row = None
age_row = None
gender_row = None

def convert_trait(value: str) -> Optional[int]:
    # No specific data for the trait is present, so return None.
    return None

def convert_age(value: str) -> Optional[float]:
    # No age data found, so return None.
    return None

def convert_gender(value: str) -> Optional[int]:
    # No gender data found, so return None.
    return None

# 3) Save Metadata (Initial Filtering)
# If trait_row is None, then trait data is considered unavailable
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
# Since trait_row is None, we skip clinical feature extraction
# 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])
# The gene identifiers listed (e.g., '10000_at', '10001_at') are Affymetrix probe IDs, not standard human gene symbols.
# Hence, they require mapping to official gene symbols.
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 & 2. Identify the columns in `gene_annotation` that match the probe IDs and the gene symbols.
#    Based on the preview, 'ID' stores the probe identifiers (e.g., '10000_at'),
#    while 'Description' appears to store gene descriptions/symbols.
prob_col = 'ID'
gene_col = 'Description'

# Get the gene mapping dataframe from the annotation
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)

# 3. Convert probe-level measurements into gene-level measurements
gene_data = apply_gene_mapping(gene_data, mapping_df)
import os
import pandas as pd

# STEP 7

# 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)

# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
if not os.path.exists(out_clinical_data_file):
    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
    dummy_df = pd.DataFrame()
    trait_biased = True  # Mark as biased or unusable because we lack any trait information
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=trait_biased,
        df=dummy_df,
        note="No trait data found. This dataset is not usable for final analysis."
    )
    print("Clinical data file not found. Skipping linking and final data export.")
else:
    # 2. Link the clinical data with genetic data
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
    # By design, each row in this CSV might represent a clinical feature (e.g., trait, age, gender).
    # Since trait_row was None, we typically wouldn't have a valid trait row, but let's proceed safely:
    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,  # We do have a clinical file now
        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.")