# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE230164"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE230164"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE230164.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE230164.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE230164.csv"
json_path = "./output/preprocess/1/Asthma/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)
# Step 1: Determine if gene expression data is likely available
is_gene_available = True  # Based on the title "Gene expression profiling of asthma"

# Step 2: Identify the rows for trait, age, and gender
# From the provided sample characteristics dictionary (only key 0 with gender info),
# we see no mention of the trait (asthma) or age, so these are not available.
trait_row = None
age_row = None
gender_row = 0  # "gender: female" and "gender: male" are present

# Data type conversion functions

def convert_trait(value: str) -> Optional[int]:
    """
    Convert trait values to binary (e.g., 'asthma' -> 1, 'control' or 'healthy' -> 0).
    Returns None if unknown.
    """
    # Extract the actual data after the colon if present
    parts = value.split(':', 1)
    val = parts[1].strip().lower() if len(parts) > 1 else value.lower()

    # Example mapping (if we had trait data)
    if 'asthma' in val:
        return 1
    if 'control' in val or 'healthy' in val:
        return 0

    return None

def convert_age(value: str) -> Optional[float]:
    """
    Convert age values to continuous floats.
    Returns None if parsing fails or data is unknown.
    """
    parts = value.split(':', 1)
    val = parts[1].strip() if len(parts) > 1 else value
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str) -> Optional[int]:
    """
    Convert gender to binary (female -> 0, male -> 1).
    Returns None if unknown.
    """
    parts = value.split(':', 1)
    val = parts[1].strip().lower() if len(parts) > 1 else value.lower()
    if 'female' in val:
        return 0
    if 'male' in val:
        return 1
    return None

# Step 3: Initial filtering and saving of metadata
is_trait_available = trait_row is not None

dataset_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: Since trait_row is None, we skip substep of 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])
# Based on the given identifiers (e.g., ILMN_1651199), these appear to be Illumina probe IDs
# rather than standard human gene symbols. Therefore, gene symbol mapping is required.

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 dataframe
#    - "ID" column contains Illumina probe IDs matching those in the expression data
#    - "Symbol" column contains the gene symbols
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get a gene mapping dataframe by extracting the two columns
mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

# 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)
print(f"Saved normalized gene data to {out_gene_data_file}")

# Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable.
# We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis.

empty_df = pd.DataFrame()  # Placeholder, as df must be provided to the validation function
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No trait data was found
    is_biased=True,            # Arbitrary True to pass validation, making the dataset not usable
    df=empty_df,
    note="Trait data is unavailable; skipping linking and final data steps."
)

print("Trait data unavailable. Skipping linking and final data output.")