# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE199759"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE199759"

# Output paths
out_data_file = "./output/preprocess/1/Epilepsy/GSE199759.csv"
out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE199759.csv"
out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE199759.csv"
json_path = "./output/preprocess/1/Epilepsy/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
is_gene_available = True  # The dataset includes mRNA expression arrays, so we consider gene data available.

# 2. Variable Availability and Data Type Conversion

# From the sample characteristics dictionary, keys are:
#   0 -> tissue
#   1 -> gender
#   2 -> age
# There's no explicit key for "Epilepsy" or a clear way to infer it from the given keys.
trait_row = None  # Not found
age_row = 2       # Multiple age values present
gender_row = 1    # Multiple gender values present

# Data converters
def convert_trait(x: str):
    # This function won't be used since trait_row is None, but we define it for completeness.
    return None

def convert_age(x: str):
    # Extract numeric age (continuous)
    parts = x.split(":")
    if len(parts) < 2:
        return None
    val = parts[1].strip().replace('y', '').strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(x: str):
    # Convert gender to binary: male=1, female=0
    parts = x.split(":")
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'male':
        return 1
    elif val == 'female':
        return 0
    return None

# 3. Save Metadata (initial filtering)
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
)

# 4. Clinical Feature Extraction
# Since trait_row is None, trait data is not available. We skip this step.
# 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("These identifiers appear to be Agilent microarray probe IDs, not standard human gene symbols.")
print("requires_gene_mapping = True")
# STEP5
import pandas as pd
import io

# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
annotation_text, _ = filter_content_by_prefix(
    source=soft_file,
    prefixes_a=['^', '!', '#'],
    unselect=True,
    source_type='file',
    return_df_a=False,
    return_df_b=False
)

# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
gene_annotation = pd.read_csv(
    io.StringIO(annotation_text),
    delimiter='\t',
    on_bad_lines='skip',
    engine='python'
)

print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP 6: Gene Identifier Mapping

probe_col = "ID"
symbol_col = "miRNA_ID"

# 2. Build the mapping DataFrame from the annotation DataFrame
mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=probe_col, gene_col=symbol_col)

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

# Show a short preview of the mapped gene expression data
print("Preview of mapped gene expression data:")
print(gene_data.head())
import os
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)

# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
if os.path.exists(out_clinical_data_file):
    # 2) Link the clinical and gene expression data
    # The CSV was saved with index=False in Step 2, so we reload it as a single-row DataFrame and assign the row index.
    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
    selected_clinical_df.index = [trait]

    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

    # 3) Handle missing values
    final_data = handle_missing_values(linked_data, trait_col=trait)

    # 4) Evaluate bias in the trait (and remove biased demographics if any)
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 5) Final validation
    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 extracted; row index fixed at Step 7."
    )

    # 6) If the dataset is usable, save
    if is_usable:
        final_data.to_csv(out_data_file)
else:
    # If the clinical file does not exist, the trait is unavailable
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,
        df=empty_df,
        note="No trait data was found; linking and final dataset output are skipped."
    )