# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE84632"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84632"

# Output paths
out_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/GSE84632.csv"
out_gene_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/gene_data/GSE84632.csv"
out_clinical_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/clinical_data/GSE84632.csv"
json_path = "./output/preprocess/1/Sickle_Cell_Anemia/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  # Based on the background info "The gene expression of PBMC..."

# 2) Determine data availability for trait, age, and gender
#    From the sample characteristics dictionary, we see only a single constant value for disease (Sickle cell disease)
#    and no info about age or gender. Therefore, set rows to None and treat them as not available.
trait_row = None
age_row = None
gender_row = None

# 2.2) Define data type conversion functions as placeholders
def convert_trait(val: str) -> int:
    # No actual use because trait_row is None, but we still define the function
    return 1  # Arbitrary or pass

def convert_age(val: str) -> float:
    # No actual use because age_row is None, but we still define the function
    return None

def convert_gender(val: str) -> int:
    # No actual use because gender_row is None, but we still define the function
    return None

# 3) Initial filtering and save metadata
#    Trait is not available because trait_row is None
is_trait_available = False
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) Since trait_row is None, skip 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., "16650001"), these appear to be numeric probe IDs rather than standard human gene symbols.
# Hence, they need to be mapped to 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 keys in the gene annotation dataframe match the probe IDs and the gene symbols.
#    From the previews, the 'ID' column matches the numeric probe IDs (e.g. '16657436'),
#    and 'GB_ACC' appears to contain some form of gene-related information (e.g. 'NR_046018').
#    We will use 'ID' for probe identifiers and 'GB_ACC' for gene symbols.

# 2) Get a gene mapping dataframe by extracting the relevant pair of columns.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GB_ACC')

# 3) Convert probe-level measurements in gene_data to gene expression values by applying the mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP7

import pandas as pd

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Since we determined in a previous step that trait data is unavailable (trait_row = None),
# we have no clinical dataframe (selected_clinical_df) to link with gene data.
# Therefore, we skip linking clinical and genetic data and trait-based processing.

# For final validation with is_final=True, we must provide a boolean 'is_biased'.
# Because no trait data is available to assess bias, we set it to False by default.
is_trait_biased = False

# 5. Conduct final quality validation and save the cohort information.
#    Because trait data was deemed unavailable, is_trait_available=False.
#    The dataset is not usable for trait-based analysis, so the final data will not be saved.
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=is_trait_biased,
    df=normalized_gene_data,
    note="No trait data available; only gene expression data was processed."
)

# 6. If the dataset were usable, we would save the final linked data. But since is_usable is expected to be False, we skip.
if is_usable:
    # Normally we would save the final linked data, but we have no trait data.
    pass