# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE84633"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE84633"

# Output paths
out_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/GSE84633.csv"
out_gene_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/gene_data/GSE84633.csv"
out_clinical_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/clinical_data/GSE84633.csv"
json_path = "./output/preprocess/1/Sickle_Cell_Anemia/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 the Series title "Gene expression of peripheral blood..."
# 2. Identify available variables in the sample characteristics dictionary
#    The dictionary is: 
#       0: ['tissue: peripheral blood']
#       1: ['cell type: mononuclear cells']
#       2: ['disease: sickle cell disease']
#    We see "sickle cell disease" is constant across samples (single unique value), hence not useful for association.
trait_row = None
age_row = None
gender_row = None

# 2.2 Write data type conversion functions.
# Since no data is available, these functions will simply return None.
def convert_trait(x: str):
    return None

def convert_age(x: str):
    return None

def convert_gender(x: str):
    return None

# 3. Conduct initial filtering and save metadata
is_trait_available = (trait_row is not None)
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. Since trait_row is None, we skip the clinical feature extraction step.
# 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 observed identifiers, they do not appear to be standard human gene symbols.
# They are likely array probe IDs that require mapping to human 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. The "ID" column in the annotation dataframe matches the probe IDs in the gene expression dataframe.
#    The "gene_assignment" column contains gene symbol information.

# 2. Get a gene mapping dataframe by extracting these two columns from gene_annotation.
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# (Optional) You can inspect the resulting gene_data shape or head for verification:
print("Mapped gene_data shape:", gene_data.shape)
print("Head of the mapped gene_data:")
print(gene_data.head())
# STEP7

# Since we previously determined that 'trait_row' is None, we do NOT have trait data available.
# We therefore skip the steps that require clinical data (linking, missing-value handling, bias checking).
# However, we can still normalize gene symbols and save the resulting gene expression data.

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Because trait data is not available, skip linking clinical to genetic data and any related processing.

# 3. Perform the initial filtering and record that the dataset is missing trait data (is_final=False).
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No trait data
    note="This dataset lacks trait information, so it is not usable for association analysis."
)

# 4. Since there is no trait data, the dataset cannot be used for association studies.
#    Hence, we do not proceed with final validation or produce a final linked data file.