# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE53543"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE53543"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE53543.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE53543.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE53543.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/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  # Based on the background info, this dataset contains gene expression data

# 2. Variable Availability
#   After examining the sample characteristics dictionary, we see:
#   - There is no row containing Cystic Fibrosis information, so trait_row = None
#   - There is no row containing age information, so age_row = None
#   - Row 1 contains gender information with two distinct values (Female, Male), so gender_row = 1
trait_row = None
age_row = None
gender_row = 1

# 2.2 Data Type Conversion Functions
def convert_trait(x: str):
    """
    Convert string to an appropriate trait value.
    Since we have no trait data, the function returns None for any input.
    """
    return None

def convert_age(x: str):
    """
    Convert string to a continuous value for age.
    Since age data is not available in this dataset, the function returns None for any input.
    """
    return None

def convert_gender(x: str):
    """
    Convert string to a binary value for gender: female -> 0, male -> 1.
    Any unknown token returns None.
    """
    if not x:
        return None
    val = x.split(":", 1)[-1].strip().lower()
    if val == "female":
        return 0
    elif val == "male":
        return 1
    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
#    This step is skipped because 'trait_row' is None 
#    (no trait data available 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])
# Observed gene identifiers (e.g., "ILMN_1651229") are Illumina probe IDs, not standard human gene symbols.
# They require mapping to official gene symbols.

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 in the annotation dataframe that match the gene_expression data's probe IDs and gene symbols
probe_col = "ID"     # This column in 'gene_annotation' matches the probe IDs in 'gene_data'
symbol_col = "Symbol"  # This column in 'gene_annotation' holds the gene symbols

# 2. Extract the mapping info from the annotation dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

# 3. Apply the gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP7
# Since trait_row = None, we have no trait data. Therefore, we cannot remove samples for a nonexistent trait,
# nor perform trait-based bias checks. We will still normalize and link the available clinical data (gender only)
# with gene data. Finally, we record that the dataset lacks trait data and is thus not fully usable.

# 1. Define the selected clinical data (which has gender info but no trait info).
selected_clinical_data = clinical_data

# 2. Normalize the obtained gene expression data using synonym information and save to CSV.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 3. Link the (gender-only) clinical data and gene data.
linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

# 4. We skip handle_missing_values() and bias checks because there's no trait column at all.
#    Attempting them would cause a KeyError on "Cystic_Fibrosis".

# 5. Perform final validation, marking that no trait data is available. This dataset is not fully usable for
#    trait-based analyses, but we still record its metadata in the JSON file.
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=None,
    df=None,
    note="No trait data available. Only gender is present."
)

# 6. Because the dataset lacks trait data, it won't be marked as fully usable, so we do not save any final linked CSV.
# STEP8
# As determined, this dataset lacks a valid trait column ("Cystic_Fibrosis"), so we cannot run
# trait-based missing value checks or bias assessments. We will still normalize and link
# the data, then record that the dataset is not fully usable (no trait data).

# 1. Normalize the obtained gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical (gender-only) and genetic data
#    Assuming 'selected_clinical_data' is simply 'clinical_data' from our previous steps.
selected_clinical_data = clinical_data
linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

# 3. Because there is no 'Cystic_Fibrosis' column, we skip handle_missing_values() and bias checks.

# 4. Conduct the final quality validation and record metadata.
#    The trait is not available, so we pass `is_trait_available=False`. The function requires is_biased to be boolean.
#    We set it to False to fulfill the function's requirements and note that the dataset lacks trait-based analysis.
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,  # Manually setting to False; no trait data => no trait bias check
    df=linked_data,
    note="No trait column found; cannot perform trait-based analysis. Only gender is present."
)

# 5. If the dataset were usable for trait-based analysis, we would save the final linked CSV.
#    But since it's not, we skip saving to `out_data_file`.
if is_usable:
    linked_data.to_csv(out_data_file)