# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE123086"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE123086"

# Output paths
out_data_file = "./output/preprocess/1/Eczema/GSE123086.csv"
out_gene_data_file = "./output/preprocess/1/Eczema/gene_data/GSE123086.csv"
out_clinical_data_file = "./output/preprocess/1/Eczema/clinical_data/GSE123086.csv"
json_path = "./output/preprocess/1/Eczema/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 gene expression data is available
is_gene_available = True  # Microarray-based gene expression data is present in the background info.

# 2. Identify rows and define conversion functions

# Based on the sample characteristics dictionary, we use:
#   trait_row = 1       # row with "primary diagnosis: ATOPIC_ECZEMA"
#   age_row = 3         # row containing multiple unique age values
#   gender_row = 2      # row containing "Sex: Male"/"Sex: Female"

trait_row = 1
age_row = 3
gender_row = 2

# Data availability checks
is_trait_available = (trait_row is not None)

def convert_trait(value: str):
    """Convert the trait value to binary (1 for atopic eczema, 0 otherwise)."""
    parts = value.split(':', 1)
    if len(parts) == 2:
        val = parts[1].strip().upper()
        return 1 if val == "ATOPIC_ECZEMA" else 0
    return None

def convert_age(value: str):
    """Convert the age value to continuous float. Unknown/unparsable values -> None."""
    parts = value.split(':', 1)
    if len(parts) == 2:
        val = parts[1].strip()
        try:
            return float(val)
        except ValueError:
            return None
    return None

def convert_gender(value: str):
    """
    Convert gender value to binary (female -> 0, male -> 1).
    If the string doesn't begin with 'Sex:', return None.
    """
    parts = value.split(':', 1)
    if len(parts) == 2:
        val = parts[1].strip().upper()
        if val == "MALE":
            return 1
        elif val == "FEMALE":
            return 0
    return None

# 3. Conduct initial filtering and save metadata
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 data is available, extract clinical features
if is_trait_available:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,            # This DataFrame is assumed to exist in the environment
        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
    )

    # Observe the output
    preview_output = preview_df(selected_clinical_df, n=5)

    # Save the clinical data
    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])
# Based on the numeric identifiers observed, they are not standard human gene symbols.
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: Gene Identifier Mapping

# 1. Decide which columns in the gene_annotation DataFrame correspond to the probe IDs
#    (matching the numeric 'ID' in our gene_data) and which correspond to the gene symbols.
#    From the annotation preview, we see 'ID' matches our gene_data.index and 'ENTREZ_GENE_ID' can serve as gene symbols.

# 2. Get a gene mapping DataFrame by extracting these two columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ENTREZ_GENE_ID")

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

# 2) Read the clinical data. It was saved with rows ["Eczema","Age","Gender"], so reassign them after loading.
selected_clinical_df = pd.read_csv(out_clinical_data_file)
selected_clinical_df.index = [trait, "Age", "Gender"]

# 3) Link the clinical and gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 4) Handle missing values using the trait column
final_data = handle_missing_values(linked_data, trait_col=trait)

# 5) Evaluate bias in the trait (and remove biased demographic features if they existed)
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 6) Final validation and saving if usable
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 and processed."
)

if is_usable:
    final_data.to_csv(out_data_file)