# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE185264"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE185264"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE185264.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE185264.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE185264.csv"
json_path = "./output/preprocess/1/Bladder_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  # This dataset appears to be RNA expression data.

# 2. Variable Availability
#   Check the sample characteristics dictionary for trait, age, and gender.
#   The dictionary key for the trait "Bladder_Cancer" has only one unique value ("Bladder Cancer"),
#   meaning there is no variability in the trait values. So trait_row is None.
trait_row = None

#   There is no apparent age information in the sample characteristics, so age_row is None.
age_row = None

#   Gender data is available at key 7: ["Sex: F", "Sex: M"]
gender_row = 7

# 2.2 Data Type Conversion
def convert_trait(x: str):
    """
    Convert the trait variable, but we have no variability in this dataset.
    We'll parse for demonstration, but it returns None because trait is not available.
    """
    return None

def convert_age(x: str):
    """
    Convert age to continuous values if it existed.
    In this dataset, age is not available, so return None.
    """
    return None

def convert_gender(x: str):
    """
    Convert gender to binary (female -> 0, male -> 1).
    Unknown values -> None
    """
    parts = x.split(":")
    if len(parts) < 2:
        return None
    val = parts[1].strip().upper()
    if val == 'F':
        return 0
    elif val == 'M':
        return 1
    else:
        return None

# 3. Save Metadata (Initial filtering)
#   Trait data availability is determined by whether 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 clinical feature extraction.
# 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])
# Observing the provided gene identifiers:
# - "53BP1" is an alias (official symbol is "TP53BP1"), and "AKT" is not fully resolved.
# Thus, they are not all in official HGNC symbol form. We recommend mapping to ensure correctness.
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 which columns store the same kind of identifiers as in our gene_data
#    and which store gene symbols. In this dataset, "ID" matches the row index
#    in gene_data (e.g., "53BP1", "ABCD3"), but there is no additional column
#    for gene symbols. We'll create a new column "Sym" (duplicate of "ID") so
#    that get_gene_mapping can operate without renaming the same column.

gene_annotation['Sym'] = gene_annotation['ID']

# 2. Obtain the gene mapping dataframe by extracting our primary ID column ("ID")
#    and our new "Sym" column. Then convert from probe-level to gene-level data.
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Sym")
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)

# For quick verification, let's print the shape and some index entries of the resulting gene_data:
print("Mapped gene_data shape:", gene_data.shape)
print("First 20 gene identifiers after mapping:")
print(gene_data.index[:20])
# STEP 5
# 1) Normalize the gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Since there is no trait data (trait_row was None), we cannot link clinical features or perform bias checks.
# We still must do final validation to record that trait data is unavailable.

# Provide a placeholder for is_biased; it won't matter because is_trait_available is False.
is_biased_placeholder = False

# 5) Perform final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,        # We do have gene expression data
    is_trait_available=False,      # Trait data is unavailable
    is_biased=is_biased_placeholder,
    df=normalized_gene_data,       # We pass the gene data but there's no trait column
    note="No trait data; cannot complete linking or bias checks."
)

# 6) If the dataset is deemed usable, save the final linked data
# In this scenario, is_usable will be False because the trait is not available.
if is_usable:
    normalized_gene_data.to_csv(out_data_file)