# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE42986"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE42986"

# Output paths
out_data_file = "./output/preprocess/1/Epilepsy/GSE42986.csv"
out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE42986.csv"
out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE42986.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. Determine if this dataset likely contains gene expression data
#    From the background info, it's "Affymetrix Human Exon 1.0ST microarray" → set True
is_gene_available = True

# 2. Check availability of trait, age, and gender in the sample characteristics dictionary
#    There's no mention of "Epilepsy" (our trait) → trait not available
trait_row = None

#    Age data appear under key 3
age_row = 3

#    Gender data appear under key 2
gender_row = 2

# 2.2 Define data type conversion functions

def convert_trait(value: str):
    # No trait info found, simply return None
    return None

def convert_age(value: str):
    # Example format: "age (years): 29"
    # Extract the part after the colon and convert to float if possible; "not obtained" → None
    parts = value.split(":", 1)
    if len(parts) > 1:
        val_str = parts[1].strip()
        if val_str.lower().startswith("not obtained"):
            return None
        try:
            return float(val_str)
        except ValueError:
            return None
    return None

def convert_gender(value: str):
    # Example format: "gender: F" or "gender: M"
    parts = value.split(":", 1)
    if len(parts) > 1:
        val_str = parts[1].strip().upper()
        if val_str == "F":
            return 0
        elif val_str == "M":
            return 1
    return None

# 3. Conduct initial filtering and save metadata
#    Trait is not available since trait_row is None
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. If trait_row is not None, we would extract clinical features. Here it's None, so skip.
# 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., "10000_at", "10001_at") are Affymetrix probe IDs, not standard human gene symbols.
# Therefore, they require mapping to gene symbols.
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))
# STEP6: Gene Identifier Mapping

# 1. Identify the columns in the gene annotation that correspond to the probe IDs and the gene symbols.
#    From the annotation preview, "ID" matches the row IDs of the gene expression data,
#    and "Symbol" corresponds to the gene symbols.

# 2. Get a gene mapping dataframe by extracting the two columns from the annotation.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

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

# (Optionally preview the first few rows)
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
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    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
    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

    # 5) Final validation (trait is available)
    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 in Step 2."
    )

    # 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
    # Perform final validation indicating that we lack trait data
    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,  # Arbitrary non-None to skip usage
        df=empty_df,
        note="No trait data was found; linking and final dataset output are skipped."
    )