# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE203196"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE203196"

# Output paths
out_data_file = "./output/preprocess/1/Allergies/GSE203196.csv"
out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE203196.csv"
out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE203196.csv"
json_path = "./output/preprocess/1/Allergies/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Determine if gene expression data is available
is_gene_available = True  # Based on the summary: "RNA ... used for transcriptomic studies"

# 2. Determine data availability for trait, age, and gender (row keys) and define type conversions

# From the sample characteristics dictionary:
# {0: ['cell type: ...'], 
#  1: ['gender: F','gender: M'],
#  2: ['individual: patient16', ...],
#  3: ['age: 28','age: 40',...],
#  4: ['allergy: mild','allergy: severe','allergy: control']}
trait_row = 4    # variable "allergy: mild/severe/control"
age_row = 3      # variable "age: NN"
gender_row = 1   # variable "gender: F/M"

def convert_trait(value: str) -> Optional[int]:
    """
    Convert allergy values to binary: 
      'control' -> 0, 'mild'/'severe' -> 1, otherwise None
    """
    # Expected format is 'allergy: something'
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'control':
        return 0
    elif val in ['mild', 'severe']:
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    """
    Convert age values to float; unknown or malformed -> None
    """
    # Expected format is 'age: NN'
    parts = value.split(':')
    if len(parts) < 2:
        return None
    try:
        return float(parts[1].strip())
    except ValueError:
        return None

def convert_gender(value: str) -> Optional[int]:
    """
    Convert gender values to binary:
       'F' -> 0, 'M' -> 1, otherwise None
    """
    # Expected format is 'gender: F' or 'gender: M'
    parts = value.split(':')
    if len(parts) < 2:
        return None
    val = parts[1].strip().upper()
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    return None

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

# 3. Initial filtering and saving 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. Clinical feature extraction (only if trait_row is not None)
if trait_row is not None:
    # Suppose 'clinical_data' DataFrame was obtained in a previous step
    # We'll assume it's already loaded in the environment
    df_clinical = 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
    )
    # Observe a preview of the extracted features
    clinical_preview = preview_df(df_clinical)
    print("Preview of clinical features:", clinical_preview)

    # Save the clinical dataframe to CSV
    df_clinical.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 nature of these IDs, they are not standard human gene symbols and require mapping.
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 6: Gene Identifier Mapping

# 1. Identify which annotation columns match the expression data and gene symbols
#    - The gene expression data index is stored in "ID"
#    - The likely column with gene symbols is "gene_assignment"

# 2. Get the gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# (Optional) Print a quick shape check to confirm mapping
print("Mapped gene expression data shape:", gene_data.shape)
# STEP 7: Data Normalization and Linking

import pandas as pd

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)
print(f"Saved normalized gene data to {out_gene_data_file}")

# 2. Link the refined clinical data (with "Allergies" column) to the normalized gene data
#    Recall that 'df_clinical' was created in an earlier step and contains the trait column "Allergies."
linked_data = geo_link_clinical_genetic_data(df_clinical, normalized_gene_data)

# 3. Handle missing values in the linked dataset
processed_data = handle_missing_values(linked_data, trait)

# 4. Judge if the trait (and covariates) are biased
trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)

# 5. Conduct final quality validation and save metadata
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=processed_data,
    note="Linked clinical and gene data successfully."
)

# 6. If the dataset is usable, save the final linked DataFrame
if is_usable:
    processed_data.to_csv(out_data_file, index=True)
    print(f"Final linked data saved to {out_data_file}")
else:
    print("Data was determined not to be usable; final dataset not saved.")