# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE212134"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212134"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE212134.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212134.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212134.csv"
json_path = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/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
is_gene_available = True  # This dataset mentions mRNA expression, so we consider gene data available.

# 2. Variable Availability
#    From the provided info, only gender data is found at row 0 with two distinct values, so it's available.
trait_row = None     # No row found or constant trait (all ALS), thus not available for analysis
age_row = None       # No row found for age
gender_row = 0       # "gender: Female", "gender: Male"

# 2.2 Data Type Conversion
def convert_trait(value: str):
    # Trait data is not available; return None
    return None

def convert_age(value: str):
    # Age data is not available; return None
    return None

def convert_gender(value: str):
    # Extract the portion after 'gender:' if present
    if ':' in value:
        value = value.split(':', 1)[1].strip()
    # Convert to numeric: female -> 0, male -> 1
    if value.lower() == 'female':
        return 0
    elif value.lower() == 'male':
        return 1
    else:
        return None

# 3. Save Metadata (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
#    We only proceed if trait_row is not None; here, trait_row is None, so we skip.
# 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])
print("They appear to be numeric microarray probe IDs, not standard 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 for probe IDs and gene symbols
probe_col = "ID"              # Column storing the microarray probe identifiers
gene_col = "gene_assignment"  # Column storing the gene assignment or symbols

# 2. Get the gene mapping dataframe
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)

# 3. Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)

# Print the first few rows of the mapped gene_data for inspection
print(gene_data.head())
# STEP 7: Data Normalization and Linking

# Since we concluded in previous steps that there is no trait data (trait_row = None),
# we cannot link clinical data or perform trait-based analyses. We'll still normalize
# the gene data and then perform a final validation indicating that the dataset does
# not have trait information.

# 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. No trait data is available, so we skip linking genetic and clinical data.
#    We also skip handling missing trait values or checking trait bias.

# 3. Final Quality Validation
#    Since there's no trait, is_trait_available=False, so the dataset won't be deemed usable for trait-based analysis.
#    However, we still record the metadata. We must provide 'df' and 'is_biased' as the function requires.
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,            # Arbitrarily False because trait doesn't exist
    df=normalized_gene_data,    # We'll pass the gene data as the 'df'
    note="No trait data available, so cohort is not usable for association study."
)

# 4. If the dataset were usable, we'd save the final linked data. In this case, it's not usable for trait-based association.
if is_usable:
    # This branch will not execute because there's no trait
    linked_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Trait data not available. Skipping final output for association analysis.")