Utilities

import allel
import numpy as np

# Use HDF5 cache decorator for functions
@allel.hdf5_cache(filepath='analysis_cache.h5', group='/results')
def expensive_calculation(genotype_data):
    g = allel.GenotypeArray(genotype_data)
    ac = g.count_alleles()
    return allel.sequence_diversity(positions, ac)

# Function results are automatically cached
result = expensive_calculation(my_genotype_data)

def hdf5_cache(filepath=None, parent=None, group=None, names=None, typed=False,
               hashed_key=False, **h5dcreate_kwargs):
    """
    HDF5 cache decorator for function results.
    
    Args:
        filepath (str, optional): Path to HDF5 file for caching
        parent (str, optional): Parent group path within HDF5 file
        group (str, optional): Specific group name for cache
        names (list, optional): Names for cached results
        typed (bool): Whether to consider argument types in cache key
        hashed_key (bool): Whether to hash the cache key
        **h5dcreate_kwargs: Additional HDF5 dataset creation arguments
        
    Returns:
        decorator: Function decorator that caches results
    """

import allel
import numpy as np

# Ensure array has correct dimensions
array_2d = allel.asarray_ndim(data, 2)
array_3d = allel.asarray_ndim(genotypes, 3)

# Validate array properties
allel.check_ndim(array, 2)  # Raises error if wrong dimensions
allel.check_shape(array, (100, 4))  # Check specific shape
allel.check_dtype(array, int)  # Validate data type
allel.check_integer_dtype(array)  # Check if integer type
allel.check_ploidy(2, 'genotypes')  # Validate ploidy parameter

# Check array alignment
allel.check_dim0_aligned(variants_array, genotypes_array)
allel.check_dim1_aligned(samples_array, genotypes_array)

def asarray_ndim(a, *ndims, **kwargs):
    """
    Ensure array has one of the specified numbers of dimensions.
    
    Args:
        a (array_like): Input array
        *ndims (int): Allowed numbers of dimensions
        **kwargs: Additional arguments for array conversion
        
    Returns:
        numpy.ndarray: Array with validated dimensionality
        
    Raises:
        TypeError: If array has wrong number of dimensions
    """

def check_ndim(a, ndim):
    """
    Check that array has expected number of dimensions.
    
    Args:
        a (array_like): Array to check
        ndim (int): Expected number of dimensions
        
    Raises:
        TypeError: If array has wrong number of dimensions
    """

def check_shape(a, shape):
    """
    Check that array has expected shape.
    
    Args:
        a (array_like): Array to check
        shape (tuple): Expected shape
        
    Raises:
        TypeError: If array shape doesn't match
    """

def check_dtype(a, *dtypes):
    """
    Check that array has one of the expected data types.
    
    Args:
        a (array_like): Array to check
        *dtypes: Expected data types
        
    Raises:
        TypeError: If array has wrong data type
    """

def check_integer_dtype(a):
    """
    Check that array has integer data type.
    
    Args:
        a (array_like): Array to check
        
    Raises:
        TypeError: If array is not integer type
    """

def check_ploidy(ploidy, name):
    """
    Validate ploidy parameter.
    
    Args:
        ploidy (int): Ploidy value to validate
        name (str): Name of the data for error messages
        
    Raises:
        ValueError: If ploidy is invalid
    """

def check_dim0_aligned(*arrays):
    """
    Check that multiple arrays are aligned on first dimension.
    
    Args:
        *arrays: Variable number of arrays to check
        
    Raises:
        ValueError: If arrays have different sizes in first dimension
    """

def check_dim1_aligned(*arrays):
    """
    Check that multiple arrays are aligned on second dimension.
    
    Args:
        *arrays: Variable number of arrays to check
        
    Raises:
        ValueError: If arrays have different sizes in second dimension
    """

# Handle invalid operations (e.g., division by zero)
with allel.ignore_invalid():
    # Calculations that might produce NaN/inf values
    frequencies = allele_counts / total_counts
    diversity = frequencies * (1 - frequencies)

def ignore_invalid():
    """
    Context manager to temporarily ignore numpy invalid value warnings.
    
    Useful for genetic calculations that may involve division by zero
    or other operations that produce NaN values as expected behavior.
    
    Returns:
        context manager: Context that ignores numpy invalid warnings
    """

import numpy as np
import allel

# Convert genetic arrays to NumPy for custom operations
g = allel.GenotypeArray(genotype_data)
numpy_array = np.array(g)

# Use NumPy functions directly
missing_mask = (numpy_array == -1)
mean_genotype = np.nanmean(numpy_array, axis=1)

# Statistical operations
allele_variance = np.var(numpy_array, axis=1)
sample_correlations = np.corrcoef(numpy_array.T)

import pandas as pd
import matplotlib.pyplot as plt

# Create analysis workflows
def basic_qc_workflow(vcf_file):
    """Basic quality control workflow."""
    # Read data
    variants, samples, calldata = allel.read_vcf(vcf_file)
    g = allel.GenotypeArray(calldata['GT'])
    
    # Calculate statistics
    ac = g.count_alleles()
    missing_rate = np.mean(g.is_missing(), axis=1)
    het_obs = allel.heterozygosity_observed(g)
    
    # Create summary DataFrame
    qc_stats = pd.DataFrame({
        'CHROM': variants['CHROM'],
        'POS': variants['POS'],
        'missing_rate': missing_rate,
        'het_obs': het_obs,
        'n_alleles': ac.allelism()
    })
    
    return qc_stats

def plot_diversity_along_chromosome(positions, diversity_values):
    """Plot diversity metrics along chromosome."""
    plt.figure(figsize=(12, 6))
    plt.plot(positions, diversity_values, alpha=0.7)
    plt.xlabel('Genomic Position')
    plt.ylabel('Nucleotide Diversity (π)')
    plt.title('Genetic Diversity Along Chromosome')
    plt.grid(True, alpha=0.3)
    return plt.gcf()

# Standard genetic constants used throughout scikit-allel
DIPLOID_PLOIDY = 2  # Standard diploid ploidy level

# Usage in validation
def validate_diploid_genotypes(g):
    """Validate that genotypes are diploid."""
    assert g.ploidy == allel.DIPLOID_PLOIDY
    return True