# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE53622"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE53622"

# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE53622.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE53622.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE53622.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)
# 1) Determine if gene expression data is available
is_gene_available = True  # This is an lncRNA microarray study, so we consider it to have gene expression

# 2) Identify data availability and create conversion functions

# The sample characteristics dictionary shows:
# trait appears in row 10 with values "arrhythmia: no" or "arrhythmia: yes"
trait_row = 10

# age appears in row 1 with multiple numeric values
age_row = 1

# gender appears in row 2 with values "Sex: female" or "Sex: male"
gender_row = 2

def convert_trait(raw_value: str):
    """
    Convert arrhythmia values ('yes'/'no') to binary (1/0).
    """
    value = raw_value.split(':')[-1].strip().lower()
    if value == 'yes':
        return 1
    elif value == 'no':
        return 0
    return None

def convert_age(raw_value: str):
    """
    Convert age values to float. If conversion fails, returns None.
    """
    value = raw_value.split(':')[-1].strip()
    try:
        return float(value)
    except:
        return None

def convert_gender(raw_value: str):
    """
    Convert gender values ('male'/'female') to binary (1/0).
    """
    value = raw_value.split(':')[-1].strip().lower()
    if value == 'male':
        return 1
    elif value == 'female':
        return 0
    return None

# 3) Save metadata using initial filtering
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) Extract clinical features if trait data is available
if trait_row is not None:
    selected_clinical_features = 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 and save extracted clinical data
    preview_result = preview_df(selected_clinical_features, n=5)
    print("Preview of Selected Clinical Data:", preview_result)
    selected_clinical_features.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 format of the identifiers, they do not appear to match standard human gene symbols.
# Therefore, gene mapping is needed.
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 6: Gene Identifier Mapping

# 1. From observation, the 'ID' column in 'gene_annotation' matches the gene expression data 'ID'.
#    The 'SPOT_ID' column holds the gene symbols (though they look unusual, we assume they contain relevant symbol info).
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID')

# 2. Convert probe-level measurements to gene-level expression data using the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 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. We refer to the clinical data variable from step 2 as 'selected_clinical_features'
selected_clinical = selected_clinical_features

# 3. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

# 4. Handle missing values, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

# 5. Determine whether the trait (and potentially other features) is severely biased.
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. 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="Cohort data successfully processed with trait-based analysis."
)

# 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"Saved final linked data to {out_data_file}")
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")