# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Colon_and_Rectal_Cancer"
cohort = "GSE56699"

# Input paths
in_trait_dir = "../DATA/GEO/Colon_and_Rectal_Cancer"
in_cohort_dir = "../DATA/GEO/Colon_and_Rectal_Cancer/GSE56699"

# Output paths
out_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/GSE56699.csv"
out_gene_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv"
out_clinical_data_file = "./output/preprocess/1/Colon_and_Rectal_Cancer/clinical_data/GSE56699.csv"
json_path = "./output/preprocess/1/Colon_and_Rectal_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)
# Step 1: Determine gene expression data availability
is_gene_available = True  # Based on the series description, this is a microarray dataset.

# Step 2: Identify row indices for trait/age/gender and define conversion functions

# 2.1. Data Availability
trait_row = None     # All samples are rectal cancer; no variation across samples => not useful for association study
age_row = None       # No row found that clearly represents age
gender_row = None    # No row found that clearly represents gender

# 2.2. Define data type conversion functions
def convert_trait(value: str) -> Optional[int]:
    # This dataset does not provide a separate trait column,
    # but we still define a placeholder function.
    # If needed, parse the string after the colon:
    # For instance, if the dataset had a line like "Trait: case", we might do:
    # real_value = value.split(':')[-1].strip().lower()
    # return 1 if real_value == "case" else 0 if real_value == "control" else None
    return None

def convert_age(value: str) -> Optional[float]:
    # Placeholder function, since age is not available.
    # If we had real data, we would parse after the colon, convert to float, handle unknown as None.
    return None

def convert_gender(value: str) -> Optional[int]:
    # Placeholder function, since gender is not available.
    # Typically, we parse after the colon, convert female->0, male->1, else None
    return None

# Step 3: Initial filtering and record saving
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
)

# Step 4: Extract and preview clinical features if trait data is available
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,  # assuming 'clinical_data' is our DataFrame from the previous step
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    preview_result = preview_df(selected_clinical_df, n=5)
    print("Preview of selected clinical features:", preview_result)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# 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("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))
# STEP6: Gene Identifier Mapping

# 1. Decide which columns store the probe IDs and gene symbols, and create the mapping DataFrame
gene_mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

# 2. Convert probe-level measurements to gene-level data
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
# STEP 7
# Trait data is unavailable, so we cannot do a final validation or linking based on an absent trait.
# However, we can still normalize and save the gene expression data, then record partial metadata indicating that
# the dataset is not usable because it lacks trait information.

# 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)

# 2. Because trait data is unavailable, we skip linking, missing-value handling, and bias assessment.

# 3. Perform partial (initial) validation to record that trait data is unavailable.
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,   # We have gene data
    is_trait_available=False  # Trait data is unavailable
)

# 4. Since the dataset is missing trait data, it is not suitable for final analysis; do not save linked data.