# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE67766"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"

# Output paths
out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE67766.csv"
out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE67766.csv"
out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE67766.csv"
json_path = "./output/preprocess/1/Adrenocortical_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. Determine if gene expression data is available
is_gene_available = True  # Based on background context, we assume gene expression data is present

# 2. Determine availability for trait, age, and gender from the sample characteristics dictionary
#    Given the dictionary: {0: ['cell line: SW-13']}, there is no variation or explicit mention
#    of trait, age, or gender. Hence, they are all considered unavailable.
trait_row = None
age_row = None
gender_row = None

# 2.2 Define data type conversion functions
def convert_trait(x: str):
    # No trait data available. Return None for any input.
    return None

def convert_age(x: str):
    # No age data available. Return None for any input.
    return None

def convert_gender(x: str):
    # No gender data available. Return None for any input.
    return None

# 3. Save Metadata (initial filtering)
#    'is_trait_available' is False because 'trait_row' is 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
#    Since 'trait_row' is None, we skip this step (no clinical data to extract).
# 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 gene identifiers ('ILMN_...') are Illumina probe IDs rather than standard human gene symbols.
# Hence, gene mapping to official symbols 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 for gene identifier and gene symbol based on the annotation preview.
probe_col = "ID"
symbol_col = "Symbol"

# 2) Build the gene mapping dataframe from the annotation dataframe.
mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)

# 3) Apply the mapping to convert probe-level expression to gene-level expression.
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, index=True)

# Since trait data is unavailable (trait_row = None), we cannot link or analyze trait/demographic features.
# We must finalize this dataset as unusable for downstream analysis.

# Provide a dummy dataframe and a boolean for is_biased to satisfy the library requirements.
import pandas as pd
empty_df = pd.DataFrame()

# 5. Perform final quality validation and save cohort info. 
#    We set is_biased=False to fulfill the function parameters; it will still result in is_usable=False 
#    because is_trait_available=False.
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=False,
    df=empty_df,
    note="No trait data available for this cohort."
)

# 6. Since no trait data is available, is_usable must be False, so we skip saving the final linked data.