# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE61225"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE61225"

# Output paths
out_data_file = "./output/preprocess/1/Eczema/GSE61225.csv"
out_gene_data_file = "./output/preprocess/1/Eczema/gene_data/GSE61225.csv"
out_clinical_data_file = "./output/preprocess/1/Eczema/clinical_data/GSE61225.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)
# Step 1: Determine gene expression data availability
is_gene_available = True  # According to the background info, it's an expression microarray.

# Step 2: Variable availability and data type conversion
# 2.1 Data availability
trait_row = None  # Eczema information is not found in the sample characteristics.
age_row = 6       # "age: ..." is found at key 6 with multiple unique values.
gender_row = 5    # "gender: female/male" is found at key 5 with multiple unique values.

# 2.2 Data type conversion
def convert_trait(value: str):
    # Trait not available, this function won't be used.
    return None

def convert_age(value: str):
    # Parse the substring after the first colon
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    try:
        return float(parts[1].strip())
    except ValueError:
        return None

def convert_gender(value: str):
    # Parse the substring after the first colon
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    gender_val = parts[1].strip().lower()
    if gender_val == 'female':
        return 0
    elif gender_val == 'male':
        return 1
    else:
        return None

# Step 3: Save metadata (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
)

# Step 4: Since trait_row is None, we skip the clinical feature extraction.
# 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])
# Gene Identifier Review
# Observing the index values (ILMN_xxx) reveals they are Illumina probe identifiers rather than standard gene symbols.
# Therefore we conclude:
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 store the probe identifiers and gene symbols:
#    From the annotation preview, 'ID' appears to contain the same probe identifiers (e.g., ILMN_1343295)
#    as in our gene expression data index, and 'ILMN_Gene' seems to hold the gene symbol.

# 2. Get a gene mapping dataframe by extracting 'ID' and 'ILMN_Gene' columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ILMN_Gene")

# 3. Convert probe-level measurements to gene-level expression data.
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)

# Define a placeholder clinical dataframe (since trait data is not actually available)
selected_clinical_df = pd.DataFrame()

# 2) Link the (empty) clinical dataframe with the normalized gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# Ensure the trait column exists, even though it's unavailable
if trait not in linked_data.columns:
    linked_data[trait] = None

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

# 4) Evaluate bias in the (non-existent) trait
trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)

# 5) Final validation. The dataset has no trait data, so is_trait_available=False
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=trait_biased,
    df=final_data,
    note="No trait data could be extracted for this dataset."
)

# 6) If the dataset is deemed usable (unlikely given no trait), save final data
if is_usable:
    final_data.to_csv(out_data_file)