def test_alternating_order_insertion(self):
'''Test alternating order generation.'''
# Minimum possible depth = [log2(n)]+1 where n=size of data, so here 4
data = np.array([5, 1, 2, 7, 8, 3, 4, 6, 9])
n = np.size(data)
# Original list order
self._test_tree_depth(data, 5)
# Ordered insertion is worst case!
sorted_data = sort(data)
self._test_tree_depth(sorted_data, 9)
# Alternating order is not so good either
self._test_tree_depth(sorted_data[alternating_order(n)], 5)
# Neither does random order guarantee anything
self._test_tree_depth(data[np.random.permutation(n)], 4, 9)
# Best order. O(n log n) complexity to sort the list.
self._test_tree_depth(sorted_data[optimal_insertion_order(n)], 4)
self._test_tree_depth([8, 4, 2, 1, 3, 6, 5, 7, 9], 4)
# Best order holds for a general list. All numbers in the list must be different.
n = 100
data = np.random.uniform(size=n)#np.random.permutation(n)
sorted_data = sort(data)
depth = int(np.floor(np.log2(n)))+1
self._test_tree_depth(sorted_data[optimal_insertion_order(n)], depth)
# Same code using a util function
b = sequence_to_tree(sorted_data)
assert_equal(b.depth(), depth, 'Wrong tree depth')
def __init__(self, data, snp, sample_id):
'''
Construct a genotype set from data arrays:
- snp: SNP metadata record array (contains chromosome, name, morgans, base-pair location)
- data: a 3-D genotype data array: (individual x SNP x allele)
- sample_id: genotyped individuals' ID set
'''
# People's IDs
self.sample_id = sample_id
self.data = data
self._num_snps = self.data.shape[0]
self._num_samples = self.data.shape[1]
self._snp_range = None
# SNP metadata: SNP label, chromosome number, Genetic distance in Morgans, and
# base pair location for each SNP
self.snp = snp
# Base-pair-location to snp-index map, lazily-initialized + cached
base_pair = self.snp['base_pair']
self._base_pair = base_pair # np.array([int(base_pair)]) if base_pair.size == 1 else base_pair
self._bp_to_snp = dict_invert(dict(enumerate(self._base_pair)))
# Construct a BST for fast bp queries
self._snp_tree = BinarySearchTree(values=self._base_pair[optimal_insertion_order(self._num_snps)])
self._snp_index_tree = util.list_index_tree(self._base_pair)
# A genetic map: lists the two allele letters corresponding to 1 and 2 for each SNP, according
# their order in the self.snp array.
self.map = []
# General metadata, for easy handling of CGI data
self.metadata = []
# samples for which the parent-of-origin phase is determined
self.poo_phase = np.zeros((self._num_samples,), dtype=np.byte)
