# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sickle_Cell_Anemia"
cohort = "GSE117613"

# Input paths
in_trait_dir = "../DATA/GEO/Sickle_Cell_Anemia"
in_cohort_dir = "../DATA/GEO/Sickle_Cell_Anemia/GSE117613"

# Output paths
out_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/GSE117613.csv"
out_gene_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/gene_data/GSE117613.csv"
out_clinical_data_file = "./output/preprocess/1/Sickle_Cell_Anemia/clinical_data/GSE117613.csv"
json_path = "./output/preprocess/1/Sickle_Cell_Anemia/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 the dataset contains gene expression data
is_gene_available = True  # Based on the background info "genome-wide transcription profiles"

# 2. Identify rows for trait, age, and gender; define conversion functions

# For "Sickle Cell Anemia," row 8 has "hb genotype: AA, SS, AS" - we can map 'SS' as Sickle Cell Anemia = 1, otherwise 0
trait_row = 8
def convert_trait(value: str):
    # Example: "hb genotype: SS"
    part = value.split(':')
    if len(part) < 2:
        return None
    genotype = part[1].strip().upper()
    if genotype == 'SS':
        return 1
    elif genotype in ['AS', 'AA']:
        return 0
    return None

# For age, row 2 appears to provide numeric values of children's ages
age_row = 2
def convert_age(value: str):
    # Example: "age: 4.602"
    part = value.split(':')
    if len(part) < 2:
        return None
    try:
        return float(part[1].strip())
    except:
        return None

# For gender, row 4 has "Sex: Male" and "Sex: Female"
gender_row = 4
def convert_gender(value: str):
    # Example: "Sex: Male"
    part = value.split(':')
    if len(part) < 2:
        return None
    g = part[1].strip().lower()
    if g == 'male':
        return 1
    elif g == 'female':
        return 0
    return None

# Check trait availability
is_trait_available = (trait_row is not None)

# 3. Initial filtering and metadata recording
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. If trait data is available, extract clinical features, preview, and save
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 the extracted clinical DataFrame
    preview_data = preview_df(selected_clinical_df)
    print("Clinical data preview:", preview_data)

    # Save to CSV
    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])
# Based on the index labels (e.g., "ILMN_1343291"), these are Illumina probe IDs, not standard human gene symbols.
# Therefore, they require mapping to gene symbols.
print("\nrequires_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. We determined that 'ID' in the gene_annotation dataframe matches the probe identifiers 
#    in the gene expression data (which are like "ILMN_1343291"), and 'Symbol' holds the gene symbols.

# 2. Retrieve the mapping from probe ID to gene symbol
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe-level measurements to gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Show some basic information for verification
print("Gene-level data shape:", gene_data.shape)
print("First 10 gene symbols:", gene_data.index[:10].tolist())
# STEP7

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

# 2. Link clinical and gene expression data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Check for biased features (trait, age, gender)
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final quality validation and record metadata
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,
    note="Preprocessed with trait, age, and gender available."
)

# 6. If usable, save linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)