# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Autoinflammatory_Disorders"
cohort = "GSE43553"

# Input paths
in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders"
in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE43553"

# Output paths
out_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/GSE43553.csv"
out_gene_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/gene_data/GSE43553.csv"
out_clinical_data_file = "./output/preprocess/1/Autoinflammatory_Disorders/clinical_data/GSE43553.csv"
json_path = "./output/preprocess/1/Autoinflammatory_Disorders/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)
import pandas as pd
import numpy as np

# 1. Gene Expression Data Availability
is_gene_available = True  # Based on microarray-based gene expression profiling in the background info

# 2. Variable Availability and Data Type Conversion
#    Examining the sample characteristics dictionary, we see "disease state: CAPS" and 
#    "disease state: other autoinflammatory disease" in key=3, which vary across samples 
#    (not a constant feature). Hence, we'll use key=3 for our trait. 
trait_row = 3
age_row = None    # No age information found
gender_row = None # No gender information found

# Define the conversion functions
def convert_trait(value: str) -> int:
    if not isinstance(value, str) or pd.isna(value):
        return None
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if 'caps' in val or 'other autoinflammatory disease' in val:
        return 1
    # Otherwise, assume 0 (e.g., healthy or not the target condition)
    return 0

convert_age = None
convert_gender = None

# 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
)

# 4. Clinical Feature Extraction
#    Proceed only if trait_row is not None.
if is_trait_available:
    selected_clinical_df = geo_select_clinical_features(
        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 the resulting DataFrame
    print(preview_df(selected_clinical_df, n=5))
    # Save to CSV
    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])
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. Identify the columns in the gene annotation that match the probe IDs in the gene expression data ("ID")
#    and the column that stores the gene symbols ("Gene Symbol").
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Extract a gene mapping dataframe with the probe column and the gene symbol column.
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)

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

# (Optional) Preview a few rows of the mapped gene expression data
print("Preview of gene_data after mapping:")
print(gene_data.head(5))
# STEP7

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically using the actual trait name
linked_data_processed = handle_missing_values(linked_data, trait_col=trait)

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Final quality validation and metadata saving
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=linked_data_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

# 6. If dataset is usable, save the final linked data
if is_usable:
    linked_data_final.to_csv(out_data_file)