# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE90713"

# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE90713"

# Output paths
out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE90713.csv"
out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE90713.csv"
out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE90713.csv"
json_path = "./output/preprocess/1/Adrenocortical_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) Determine if gene expression data is available
is_gene_available = True  # Based on the series description showing Affymetrix microarray gene expression

# 2) Identify availability of trait, age, and gender data
trait_row = 0   # "tissue: adrenocortical carcinoma" vs. "tissue: normal adrenal"
age_row = None  # No age-related information found
gender_row = None  # No gender-related information found

# 2) Data type conversion functions
def convert_trait(x: str) -> Optional[int]:
    """
    Convert the tissue annotation to binary values for adrenocortical carcinoma (1) or normal adrenal (0).
    Unknown values return None.
    """
    parts = x.split(':')
    if len(parts) < 2:
        return None
    val = parts[-1].strip().lower()
    if val in ["adrenocortical carcinoma", "acc", "tumor"]:
        return 1
    elif val in ["normal adrenal", "normal"]:
        return 0
    else:
        return None

def convert_age(x: str) -> Optional[float]:
    """No age data available, so always return None."""
    return None

def convert_gender(x: str) -> Optional[int]:
    """No gender data available, so always return None."""
    return None

# 3) Initial filtering and metadata saving
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) Extract clinical features if trait_row is not None
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        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 and save the extracted clinical features
    preview_result = preview_df(selected_clinical_df)
    print("Preview of Clinical Data:", 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])
# These identifiers (e.g., "11715100_at", "11715101_s_at") appear to be Affymetrix probe set IDs, 
# not standard human gene symbols. Hence, gene symbol mapping is required.

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 the columns in the annotation that match our probe IDs and gene symbols:
#    - Probe ID column: 'ID'
#    - Gene Symbol column: 'Gene Symbol'

probe_col = 'ID'
gene_symbol_col = 'Gene Symbol'

# 2. Generate a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Print a quick preview of the first few rows after mapping
print("Mapped Gene Expression Data (first 5 rows):")
print(gene_data.head(5))
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols and save the normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file, index=True)

# 2. Link clinical and genetic data on sample IDs
#    "selected_clinical_df" was defined in a previous step, so we can use it directly.
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically
processed_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait or demographic features are severely biased
trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)

# 5. Final quality validation and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=processed_data,
    note="Trait data present and mapped from step 2."
)

# 6. Save the final linked data only if usable
if is_usable:
    processed_data.to_csv(out_data_file, index=True)