# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE29796"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE29796"

# Output paths
out_data_file = "./output/preprocess/1/Epilepsy/GSE29796.csv"
out_gene_data_file = "./output/preprocess/1/Epilepsy/gene_data/GSE29796.csv"
out_clinical_data_file = "./output/preprocess/1/Epilepsy/clinical_data/GSE29796.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)
# Step 1: Gene Expression Data Availability
is_gene_available = True  # From the background, this appears to be gene expression (not pure miRNA or methylation).

# Step 2: Variable Availability and Data Type Conversion
# We locate the keys in the sample characteristics dictionary for trait/age/gender.
# For trait = "Epilepsy", row 1 has "pathology: epilepsy" among other pathologies.
trait_row = 1
age_row = None      # No row mentions 'age'
gender_row = None   # No row mentions 'gender'

# Define data conversion functions
def convert_trait(value: str):
    """
    Convert trait to binary.
    If the text after the colon is 'epilepsy', return 1; otherwise return 0.
    Unrecognized or missing values become None.
    """
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[-1].strip().lower()
    if val == 'epilepsy':
        return 1
    else:
        # Every non-'epilepsy' pathology is treated as 0
        return 0

def convert_age(value: str):
    # Not available here, so we won't actually use this.
    return None

def convert_gender(value: str):
    # Not available here, so we won't actually use this.
    return None

# Determine if trait is available
is_trait_available = (trait_row is not None)

# Step 3: Save Metadata (initial filtering)
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: Clinical Feature Extraction (only if trait data is available)
if trait_row is not None:
    # Assume 'clinical_data' is the DataFrame containing the sample characteristics.
    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 and save
    print("Selected Clinical Data Preview:")
    print(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', etc.) are Affymetrix probe set IDs, not human gene symbols.
# Hence, they typically require mapping to official 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: Gene Identifier Mapping

# 1 & 2. Identify the columns for probe IDs ("ID") and gene symbols ("Gene Symbol") in the annotation dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

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