NDArrayNumericExpression
- class hail.expr.NDArrayNumericExpression[source]
Expression of type
tndarray
with a numeric element type.Numeric ndarrays support arithmetic both with scalar values and other arrays. Arithmetic between two numeric ndarrays requires that the shapes of each ndarray be either identical or compatible for broadcasting. Operations are applied positionally (
nd1 * nd2
will multiply the first element ofnd1
by the first element ofnd2
, the second element ofnd1
by the second element ofnd2
, and so on). Arithmetic with a scalar will apply the operation to each element of the ndarray.Attributes
Reverse the dimensions of this ndarray.
The data type of the expression.
The number of dimensions of this ndarray.
The shape of this ndarray.
Methods
Sum out one or more axes of an ndarray.
- property T
Reverse the dimensions of this ndarray. For an n-dimensional array a, a[i_0, …, i_n-1, i_n] = a.T[i_n, i_n-1, …, i_0]. Same as self.transpose().
See also
transpose()
.- Returns:
- __add__(other)[source]
Positionally add an array or a scalar.
- Parameters:
other (
NumericExpression
orNDArrayNumericExpression
) – Value or ndarray to add.- Returns:
NDArrayNumericExpression
– NDArray of positional sums.
- __eq__(other)
Returns
True
if the two expressions are equal.Examples
>>> x = hl.literal(5) >>> y = hl.literal(5) >>> z = hl.literal(1)
>>> hl.eval(x == y) True
>>> hl.eval(x == z) False
Notes
This method will fail with an error if the two expressions are not of comparable types.
- Parameters:
other (
Expression
) – Expression for equality comparison.- Returns:
BooleanExpression
–True
if the two expressions are equal.
- __floordiv__(other)[source]
Positionally divide by a ndarray or a scalar using floor division.
- Parameters:
other (
NumericExpression
orNDArrayNumericExpression
)- Returns:
- __ge__(other)
Return self>=value.
- __gt__(other)
Return self>value.
- __le__(other)
Return self<=value.
- __lt__(other)
Return self<value.
- __matmul__(other)[source]
Matrix multiplication: a @ b, semantically equivalent to NumPy matmul. If a and b are vectors, the vector dot product is performed, returning a NumericExpression. If a and b are both 2-dimensional matrices, this performs normal matrix multiplication. If a and b have more than 2 dimensions, they are treated as multi-dimensional stacks of 2-dimensional matrices. Matrix multiplication is applied element-wise across the higher dimensions. E.g. if a has shape (3, 4, 5) and b has shape (3, 5, 6), a is treated as a stack of three matrices of shape (4, 5) and b as a stack of three matrices of shape (5, 6). a @ b would then have shape (3, 4, 6).
Notes
The last dimension of a and the second to last dimension of b (or only dimension if b is a vector) must have the same length. The dimensions to the left of the last two dimensions of a and b (for NDArrays of dimensionality > 2) must be equal or be compatible for broadcasting. Number of dimensions of both NDArrays must be at least 1.
- Parameters:
- Returns:
- __mul__(other)[source]
Positionally multiply by a ndarray or a scalar.
- Parameters:
other (
NumericExpression
orNDArrayNumericExpression
) – Value or ndarray to multiply by.- Returns:
NDArrayNumericExpression
– NDArray of positional products.
- __ne__(other)
Returns
True
if the two expressions are not equal.Examples
>>> x = hl.literal(5) >>> y = hl.literal(5) >>> z = hl.literal(1)
>>> hl.eval(x != y) False
>>> hl.eval(x != z) True
Notes
This method will fail with an error if the two expressions are not of comparable types.
- Parameters:
other (
Expression
) – Expression for inequality comparison.- Returns:
BooleanExpression
–True
if the two expressions are not equal.
- __neg__()[source]
Negate elements of the ndarray.
- Returns:
NDArrayNumericExpression
– Array expression of the same type.
- __sub__(other)[source]
Positionally subtract a ndarray or a scalar.
- Parameters:
other (
NumericExpression
orNDArrayNumericExpression
) – Value or ndarray to subtract.- Returns:
NDArrayNumericExpression
– NDArray of positional differences.
- __truediv__(other)[source]
Positionally divide by a ndarray or a scalar.
- Parameters:
other (
NumericExpression
orNDArrayNumericExpression
) – Value or ndarray to divide by.- Returns:
NDArrayNumericExpression
– NDArray of positional quotients.
- collect(_localize=True)
Collect all records of an expression into a local list.
Examples
Collect all the values from C1:
>>> table1.C1.collect() [2, 2, 10, 11]
Warning
Extremely experimental.
Warning
The list of records may be very large.
- Returns:
- describe(handler=<built-in function print>)
Print information about type, index, and dependencies.
- export(path, delimiter='\t', missing='NA', header=True)
Export a field to a text file.
Examples
>>> small_mt.GT.export('output/gt.tsv') >>> with open('output/gt.tsv', 'r') as f: ... for line in f: ... print(line, end='') locus alleles 0 1 2 3 1:1 ["A","C"] 0/1 0/0 0/1 0/0 1:2 ["A","C"] 1/1 0/1 0/1 0/1 1:3 ["A","C"] 0/0 0/1 0/0 0/0 1:4 ["A","C"] 0/1 1/1 0/1 0/1
>>> small_mt.GT.export('output/gt-no-header.tsv', header=False) >>> with open('output/gt-no-header.tsv', 'r') as f: ... for line in f: ... print(line, end='') 1:1 ["A","C"] 0/1 0/0 0/1 0/0 1:2 ["A","C"] 1/1 0/1 0/1 0/1 1:3 ["A","C"] 0/0 0/1 0/0 0/0 1:4 ["A","C"] 0/1 1/1 0/1 0/1
>>> small_mt.pop.export('output/pops.tsv') >>> with open('output/pops.tsv', 'r') as f: ... for line in f: ... print(line, end='') sample_idx pop 0 1 1 2 2 2 3 2
>>> small_mt.ancestral_af.export('output/ancestral_af.tsv') >>> with open('output/ancestral_af.tsv', 'r') as f: ... for line in f: ... print(line, end='') locus alleles ancestral_af 1:1 ["A","C"] 3.8152e-01 1:2 ["A","C"] 7.0588e-01 1:3 ["A","C"] 4.9991e-01 1:4 ["A","C"] 3.9616e-01
>>> small_mt.bn.export('output/bn.tsv') >>> with open('output/bn.tsv', 'r') as f: ... for line in f: ... print(line, end='') bn {"n_populations":3,"n_samples":4,"n_variants":4,"n_partitions":4,"pop_dist":[1,1,1],"fst":[0.1,0.1,0.1],"mixture":false}
Notes
For entry-indexed expressions, if there is one column key field, the result of calling
str()
on that field is used as the column header. Otherwise, each compound column key is converted to JSON and used as a column header. For example:>>> small_mt = small_mt.key_cols_by(s=small_mt.sample_idx, family='fam1') >>> small_mt.GT.export('output/gt-no-header.tsv') >>> with open('output/gt-no-header.tsv', 'r') as f: ... for line in f: ... print(line, end='') locus alleles {"s":0,"family":"fam1"} {"s":1,"family":"fam1"} {"s":2,"family":"fam1"} {"s":3,"family":"fam1"} 1:1 ["A","C"] 0/1 0/0 0/1 0/0 1:2 ["A","C"] 1/1 0/1 0/1 0/1 1:3 ["A","C"] 0/0 0/1 0/0 0/0 1:4 ["A","C"] 0/1 1/1 0/1 0/1
- map(f)
Applies an element-wise operation on an NDArray.
- Parameters:
f (function ( (arg) ->
Expression
)) – Function to transform each element of the NDArray.- Returns:
NDArrayExpression
. – NDArray where each element has been transformed according to f.
- map2(other, f)
Applies an element-wise binary operation on two NDArrays.
- Parameters:
other (class:.NDArrayExpression,
ArrayExpression
, numpy NDarray,) – or nested python list/tuples. Both NDArrays must be the same shape or broadcastable into common shape.f (function ((arg1, arg2)->
Expression
)) – Function to be applied to each element of both NDArrays.
- Returns:
NDArrayExpression
. – Element-wise result of applying f to each index in NDArrays.
- reshape(*shape)
Reshape this ndarray to a new shape.
- Parameters:
shape (
Expression
of typetint64
or) – :obj: tuple ofExpression
of typetint64
Examples
>>> v = hl.nd.array([1, 2, 3, 4]) >>> m = v.reshape((2, 2))
- Returns:
- property shape
The shape of this ndarray.
Examples
>>> hl.eval(nd.shape) (2, 2)
- Returns:
- show(n=None, width=None, truncate=None, types=True, handler=None, n_rows=None, n_cols=None)
Print the first few records of the expression to the console.
If the expression refers to a value on a keyed axis of a table or matrix table, then the accompanying keys will be shown along with the records.
Examples
>>> table1.SEX.show() +-------+-----+ | ID | SEX | +-------+-----+ | int32 | str | +-------+-----+ | 1 | "M" | | 2 | "M" | | 3 | "F" | | 4 | "F" | +-------+-----+
>>> hl.literal(123).show() +--------+ | <expr> | +--------+ | int32 | +--------+ | 123 | +--------+
Notes
The output can be passed piped to another output source using the handler argument:
>>> ht.foo.show(handler=lambda x: logging.info(x))
- Parameters:
- summarize(handler=None)
Compute and print summary information about the expression.
Danger
This functionality is experimental. It may not be tested as well as other parts of Hail and the interface is subject to change.
- take(n, _localize=True)
Collect the first n records of an expression.
Examples
Take the first three rows:
>>> table1.X.take(3) [5, 6, 7]
Warning
Extremely experimental.
- Parameters:
n (int) – Number of records to take.
- Returns:
- transpose(axes=None)
Permute the dimensions of this ndarray according to the ordering of axes. Axis j in the ith index of axes maps the jth dimension of the ndarray to the ith dimension of the output ndarray.
Notes
Does nothing on ndarrays of dimensionality 0 or 1.
- Returns: