# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE61322"

# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE61322"

# Output paths
out_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/GSE61322.csv"
out_gene_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/gene_data/GSE61322.csv"
out_clinical_data_file = "./output/preprocess/1/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE61322.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
#    From the background info ("microarray", "RNA-sequencing"), this dataset likely contains gene expression data.
is_gene_available = True

# 2. Variable Availability and Conversion
#    Checking the sample characteristics dictionary:
#    {0: ['diagnosis: carrier', 'diagnosis: affected'],
#     1: ['disease: AOA2'],
#     2: ['definite analysis: definite', 'definite analysis: presumed']}
#    None of these keys mention "Amyotrophic_Lateral_Sclerosis" or an "ALS" variant.
#    Also, no keys show age or gender data. Hence, all are considered unavailable.
trait_row = None
age_row = None
gender_row = None

def convert_trait(value: str) -> Optional[float]:
    # Not used as trait_row is None, but we provide a stub.
    # Typically would parse the string after ':', then map to 0./1. or None appropriately.
    return None

def convert_age(value: str) -> Optional[float]:
    # Not used as age_row is None, but we provide a stub.
    return None

def convert_gender(value: str) -> Optional[int]:
    # Not used as gender_row is None, but we provide a stub.
    return None

# 3. Save Metadata with initial filtering
#    If trait_row is None => is_trait_available = False
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 for the trait).
# 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 above identifiers (e.g., ILMN_1343291, ILMN_1651209, etc.),
# they are Illumina probe IDs rather than 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. From the annotation preview and the gene expression row IDs, we see that
#    the "ID" column in gene_annotation matches probe IDs like "ILMN_1343291",
#    and the "Symbol" column stores the gene symbols.
prob_col = "ID"
gene_col = "Symbol"

# 2. Extract the mapping dataframe with these two columns
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Apply the mapping to convert probe-level data into gene-level expression
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 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.")