# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE115574"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE115574"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE115574.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE115574.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE115574.csv"
json_path = "./output/preprocess/1/Arrhythmia/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)
# Step 1: Determine if gene expression data is available
is_gene_available = True  # Based on the background info (Affymetrix human gene expression microarrays)

# Step 2: Identify data availability and define conversion functions

# After reviewing the sample characteristics dictionary:
# {0: ['disease state: atrial fibrillation patient with severe mitral regurgitation',
#      'disease state: sinus rhythm patient with severe mitral regurgitation'],
#  1: ['tissue: left atrium  - heart',
#      'tissue: right atrium  - heart']}

# The "trait" 'Arrhythmia' can be inferred from row 0, which distinguishes AFib vs. sinus rhythm.
trait_row = 0

# Age and gender data are not apparent in the dictionary, so set them to None.
age_row = None
gender_row = None

def convert_trait(value):
    """
    Convert the disease state string to a binary indicator for arrhythmia (AFib).
    Return 1 if the string indicates atrial fibrillation, 0 if sinus rhythm, else None.
    """
    try:
        after_colon = value.split(':', 1)[1].strip().lower()
    except IndexError:
        return None

    if 'atrial fibrillation' in after_colon:
        return 1
    elif 'sinus rhythm' in after_colon:
        return 0
    return None

def convert_age(value):
    """
    Age data is not available in this dataset. Return None.
    """
    return None

def convert_gender(value):
    """
    Gender data is not available in this dataset. Return None.
    """
    return None

# Step 3: Conduct initial filtering and save metadata
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
)

# Step 4: If trait data is available, extract clinical features and save
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_result = 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])
# These identifiers (e.g., '1007_s_at', '1053_at') are Affymetrix probe IDs, not standard gene symbols.
# Therefore, mapping to gene symbols is required.

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))
# Gene Identifier Mapping

# 1. Identify the columns in 'gene_annotation' that match the probe IDs in 'gene_data' and the gene symbols.
#    From the preview, the probe ID is stored in column "ID", and gene symbols are stored in "Gene Symbol".

# 2. Extract the gene mapping dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

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

# For verification, let's print the new gene_data shape and a sample of its row indices.
print("Mapped gene_data shape:", gene_data.shape)
print("Sample gene symbols:", list(gene_data.index[:10]))
# 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 in the linked data
linked_data = handle_missing_values(linked_data, trait_col=trait)

# 4. Determine whether the trait/demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=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,
    is_biased=trait_biased,
    df=linked_data,
    note="Trait data and gene data successfully linked."
)

# 6. If the dataset is deemed usable, save the final linked data as a CSV file
if is_usable:
    linked_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Dataset was not deemed usable; final linked data not saved.")