# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ankylosing_Spondylitis"
cohort = "GSE25101"

# Input paths
in_trait_dir = "../DATA/GEO/Ankylosing_Spondylitis"
in_cohort_dir = "../DATA/GEO/Ankylosing_Spondylitis/GSE25101"

# Output paths
out_data_file = "./output/preprocess/1/Ankylosing_Spondylitis/GSE25101.csv"
out_gene_data_file = "./output/preprocess/1/Ankylosing_Spondylitis/gene_data/GSE25101.csv"
out_clinical_data_file = "./output/preprocess/1/Ankylosing_Spondylitis/clinical_data/GSE25101.csv"
json_path = "./output/preprocess/1/Ankylosing_Spondylitis/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) Gene Expression Data Availability
# Based on the series description, this dataset uses "Illumina HT-12 Whole-Genome Expression BeadChips".
# Hence, we conclude that it likely contains gene expression data.
is_gene_available = True

# 2) Variable Availability and Data Type Conversion

# Inspecting the sample characteristics dictionary:
# {0: ['tissue: Whole blood'],
#  1: ['cell type: PBMC'],
#  2: ['disease status: Ankylosing spondylitis patient', 'disease status: Normal control']}

# -- Trait --
# The data for "Ankylosing_Spondylitis" can be inferred from key=2 (it has at least 2 unique values).
trait_row = 2

# -- Age --
# No age information is found. So:
age_row = None

# -- Gender --
# No gender information is found. So:
gender_row = None

# Data type choices:
# Since the "trait" variable has two categories (patient vs control), we treat it as binary.
# For "age" and "gender", no data is available, so we won't convert.

def convert_trait(value: str):
    """
    Convert disease status to binary:
      'Ankylosing spondylitis patient' -> 1
      'Normal control' -> 0
      Unknown -> None
    """
    # Split by ':', then take the part after the colon if present
    parts = value.split(':', 1)
    val = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()

    if 'ankylosing spondylitis patient' in val:
        return 1
    elif 'normal control' in val:
        return 0
    else:
        return None

def convert_age(value: str):
    # Age not available in this dataset, so all become None
    return None

def convert_gender(value: str):
    # Gender not available in this dataset, so all become None
    return None

# 3) Save Metadata with initial filtering
# Trait data is available if trait_row is not None
is_trait_available = (trait_row is not None)

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:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,  # assume 'clinical_data' was loaded in a previous step
        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 extracted clinical features
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical, n=5))

    # Save the clinical data
    selected_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])
# These ILMN_* identifiers are Illumina probe IDs, not standard human gene symbols.
# Therefore, they require mapping 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) Identify the columns in gene_annotation that match gene_data's "ID" and the gene symbol
#    From inspection, "ID" corresponds to the Illumina probe IDs in gene_data, and "Symbol" contains the gene symbols.

# 2) Create the gene mapping dataframe using probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3) Convert probe-level to gene-level data using the mapping
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Print out some information about the mapped gene data
print("Gene expression data after mapping:")
print("Shape of gene_data:", gene_data.shape)
print("First 20 mapped gene symbols:", list(gene_data.index[:20]))
# 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}")

# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

# 3. Handle missing values, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait (and potentially other features) is severely biased.
trait_biased, linked_data = judge_and_remove_biased_features(linked_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,   # We do have a trait column
    is_biased=trait_biased,
    df=linked_data,
    note="Cohort data successfully processed with trait-based analysis."
)

# 6. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")