# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_kidney_disease"
cohort = "GSE104954"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_kidney_disease"
in_cohort_dir = "../DATA/GEO/Chronic_kidney_disease/GSE104954"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_kidney_disease/GSE104954.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_kidney_disease/gene_data/GSE104954.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_kidney_disease/clinical_data/GSE104954.csv"
json_path = "./output/preprocess/1/Chronic_kidney_disease/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.
#    From the background, this dataset uses Affymetrix microarrays for RNA,
#    indicating gene expression data is present.
is_gene_available = True

# 2.1 Data Availability
#    Matching the provided sample characteristics dictionary, we see:
#      Key=0 => tissue: all the same, not useful.
#      Key=1 => multiple kidney disease diagnoses vs. tumor nephrectomy (control).
#    This can serve as a binary trait for "Chronic_kidney_disease" vs. non-CKD.
trait_row = 1      # diagnosis data is in row 1
age_row = None     # age not found
gender_row = None  # gender not found

# 2.2 Data Type Conversion
#    We choose 'binary' for the trait: 1 = Chronic Kidney Disease, 0 = non-CKD, None if unknown.
def convert_trait(x: Any) -> Optional[int]:
    if not isinstance(x, str):
        return None
    # Extract the part after the colon
    val = x.split(":", 1)[-1].strip().lower()
    if val in ["", "nan"]:
        return None
    if val == "tumor nephrectomy":
        return 0  # likely control group
    # Otherwise, treat all other diagnoses as CKD
    return 1

# Since age and gender are unavailable, define pass-through functions returning None.
def convert_age(x: Any) -> Optional[float]:
    return None

def convert_gender(x: Any) -> Optional[int]:
    return None

# 3. Initial filtering on dataset usability
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 (only if trait_row is available)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        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 result
    print(preview_df(selected_clinical_df, n=5))
    # Save the extracted clinical features
    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])
# The gene identifiers such as "10000_at", "10001_at", etc. indicate Affymetrix probe set IDs
# rather than human gene symbols, so they need to be mapped to 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. In the annotation DataFrame, the 'ID' column matches the probe identifiers in the gene expression data,
#    and the 'Symbol' column contains the gene symbols.
# 2. Create a mapping DataFrame for probe-to-gene mapping.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

# 3. Apply this mapping to convert probe-level measurements to gene-level data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Optionally, preview a small portion of the resulting gene expression DataFrame
print(preview_df(gene_data, n=5))
# STEP7
# 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. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the cohort information.
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=is_trait_biased,
    df=linked_data
)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)