# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cervical_Cancer"
cohort = "GSE146114"

# Input paths
in_trait_dir = "../DATA/GEO/Cervical_Cancer"
in_cohort_dir = "../DATA/GEO/Cervical_Cancer/GSE146114"

# Output paths
out_data_file = "./output/preprocess/1/Cervical_Cancer/GSE146114.csv"
out_gene_data_file = "./output/preprocess/1/Cervical_Cancer/gene_data/GSE146114.csv"
out_clinical_data_file = "./output/preprocess/1/Cervical_Cancer/clinical_data/GSE146114.csv"
json_path = "./output/preprocess/1/Cervical_Cancer/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. Gene Expression Data Availability
is_gene_available = True  # The dataset uses Illumina gene expression arrays.

# 2. Variable Availability
trait_row = None   # Dataset includes only cervical cancer patients, so "Cervical_Cancer" is constant or not recorded.
age_row = None     # No age-related information found.
gender_row = None  # Likely all female patients, thus constant or not recorded.

# 2.2 Data Type Conversion Functions
def convert_trait(value: str):
    # No trait data is actually available (or it's constant).
    return None

def convert_age(value: str):
    # No age data is available.
    return None

def convert_gender(value: str):
    # No gender data is available (or is constant).
    return None

# 3. Save Metadata with initial filtering
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
# Skipped because trait_row is None, so no clinical data extraction is performed.
# 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])
# The gene identifiers (e.g., "ILMN_1343291") 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. Decide which columns to use for mapping:
#    - The gene expression data uses "ILMN_####" style IDs, which match the "ID" column in 'gene_annotation'.
#    - The "Symbol" column in 'gene_annotation' appears to store the actual gene symbols.

# 2. Get a gene mapping dataframe using the library function, choosing 'ID' for probe IDs and 'Symbol' for the symbols.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Just print a short preview to confirm the transformation
print("Mapped gene_data preview:")
print(gene_data.head())
# STEP 7

import pandas as pd

# 1. Normalize gene symbols in the gene_data, then save to CSV.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Since 'trait_row' is None, no clinical trait data is available.
# We cannot perform linking or trait-based QC. The dataset is effectively unusable for trait analysis.

# 2 & 3 are skipped because we have no clinical data to link or trait to process.

# Prepare an empty DataFrame and a dummy bias indicator to pass final validation without error.
df_dummy = pd.DataFrame()
trait_biased_dummy = False  # This dummy value is required by the library.

# 5. Conduct final validation. We must supply a DataFrame and is_biased value if is_final=True.
#    Since there's no trait data, set is_trait_available=False and the dataset is not usable for trait-based analysis.
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=trait_biased_dummy,
    df=df_dummy,
    note="No trait data available; only gene data present."
)

# 6. The dataset won't be usable when trait data is missing. Do not save a final linked data CSV.
if is_usable:
    # If it were marked usable (unlikely), we would save the linked data here.
    pass