# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE65986"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE65986"

# Output paths
out_data_file = "./output/z2/preprocess/Endometrioid_Cancer/GSE65986.csv"
out_gene_data_file = "./output/z2/preprocess/Endometrioid_Cancer/gene_data/GSE65986.csv"
out_clinical_data_file = "./output/z2/preprocess/Endometrioid_Cancer/clinical_data/GSE65986.csv"
json_path = "./output/z2/preprocess/Endometrioid_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
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 2: Dataset Analysis and Clinical Feature Extraction
# 1. Gene Expression Data Availability
is_gene_available = True  # This dataset uses Affymetrix U133plus2 array for gene expression

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
trait_row = 0  # histology field contains Endometrioid vs other cancer types
age_row = 1    # age field contains age values
gender_row = None  # no gender information available in sample characteristics

# 2.2 Data Type Conversion Functions

def convert_trait(value):
    """Convert trait to binary: 1 for Endometrioid, 0 for others"""
    if value is None:
        return None
    # Extract value after colon
    val = value.split(':')[-1].strip() if ':' in value else value.strip()
    if val == 'Endometrioid':
        return 1
    elif val in ['Clear', 'Serous']:
        return 0
    else:
        return None

def convert_age(value):
    """Convert age to continuous numeric value"""
    if value is None:
        return None
    # Extract value after colon
    val = value.split(':')[-1].strip() if ':' in value else value.strip()
    try:
        return float(val)
    except (ValueError, TypeError):
        return None

def convert_gender(value):
    """Not applicable - gender data not available"""
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
save_result = 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
if trait_row is not None:
    selected_clinical_data = geo_select_clinical_features(
        clinical_data, trait, trait_row, convert_trait,
        age_row, convert_age, gender_row, convert_gender
    )
    
    # Preview the output dataframe
    print("Preview of selected clinical data:")
    print(preview_df(selected_clinical_data))
    
    # Save to CSV file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_data.to_csv(out_clinical_data_file, index=False)
    print(f"Clinical data saved to {out_clinical_data_file}")

# Step 3: Gene Data Extraction
# 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])

# Step 4: Gene Identifier Review
print("Sample gene identifiers from the dataset:")
print(gene_data.index[:20].tolist())

# These identifiers follow Affymetrix probe ID format (numbers + suffixes like _at, _s_at, _g_at, _i_at, _a_at)
# They are not human gene symbols, which would be in format like BRCA1, TP53, etc.
# Therefore, they need to be mapped to gene symbols

requires_gene_mapping = True

# Step 5: Gene Annotation
# 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 6: Gene Identifier Mapping
# 1. Identify the correct columns for mapping
# 'ID' contains the probe identifiers matching the gene expression data
# 'Gene Symbol' contains the gene symbols we want to map to
probe_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)

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

print(f"Original probe data shape: {get_genetic_data(matrix_file).shape}")
print(f"Mapped gene data shape: {gene_data.shape}")
print(f"First few gene symbols: {gene_data.index[:10].tolist()}")

# Step 7: Data Normalization and Linking
# 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)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
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_data, 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(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)