def add_computed_fields(n, fields, rm_fields=[], fnname=None):
"""
Adds one or more new fields to a struct array,
using nd.elwise_map to create the deferred object.
Each field_expr should be a string or bit of code
that can be evaluated with an 'eval' call. It is called
with numpy/scipy in the globals, and the input
fields in the locals.
Parameters
----------
n : dynd array
This should have a struct data dtype. The
result will be a view of this data.
fields : list of (field_name, field_type, field_expr)
These are the fields which are added to 'n'.
rm_fields : list of string, optional
For fields that are in the input, but have no expression,
this removes them from the output struct instead of
keeping the value.
fnname : string, optional
The function name, which affects how the resulting
deferred expression's dtype is printed.
Examples
--------
>>> from dynd import nd, ndt
>>> import numpy as np
>>> x = np.array([(2, 0), (0, -2), (3, 5), (4, 4)],
... dtype=[('x', np.float64), ('y', np.float64)])
>>> y = nd.add_computed_fields(x,
... fields=[('r', np.float64, 'sqrt(x*x + y*y)'),
... ('theta', np.float64, 'arctan2(y, x)')],
... rm_fields=['x', 'y'],
... fnname='topolar')
>>> y.dtype
ndt.type('strided_dim<expr<cstruct<float64 r, float64 theta>, op0=cstruct<float64 x, float64 y>, expr=topolar(op0)>>')
>>> y.eval()
nd.array([[2, 0], [2, -1.5708], [5.83095, 1.03038], [5.65685, 0.785398]], strided_dim<cstruct<float64 r, float64 theta>>)
>>> x[0] = (-100, 0)
>>> y[0].eval()
nd.array([100, 3.14159], cstruct<float64 r, float64 theta>)
"""
n = array(n)
udt = dtype_of(n).value_type
if udt.kind != 'struct':
raise ValueError("parameter 'n' must have kind 'struct'")
# The field names and types of the input struct
field_names = as_py(udt.field_names)
field_types = as_py(udt.field_types)
# Put the new field names in a dict as well
new_field_dict = {}
for fn, ft, fe in fields:
new_field_dict[fn] = w_type(ft)
# Create the output struct type and corresponding expressions
new_field_names = []
new_field_types = []
new_field_expr = []
for fn, ft in zip(field_names, field_types):
if fn not in new_field_dict and fn not in rm_fields:
new_field_names.append(fn)
new_field_types.append(ft)
new_field_expr.append(fn)
for fn, ft, fe in fields:
new_field_names.append(fn)
new_field_types.append(ft)
new_field_expr.append(fe)
result_udt = make_cstruct(new_field_types, new_field_names)
fieldexpr = FieldExpr(new_field_expr, field_names, fnname)
return elwise_map([n], fieldexpr, result_udt)
def make_computed_fields(n, replace_ndim, fields, fnname=None):
"""
Creates a new struct type, with fields computed based
on the input fields. Leaves the requested number of
array dimensions in place, so the result has fewer
than the input if positive.
Each field_expr should be a string or bit of code
that can be evaluated with an 'eval' call. It is called
with numpy/scipy in the globals, and the input
fields in the locals.
Parameters
----------
n : dynd array
This should have a struct data type. The
result will be a view of this data.
replace_ndim : integer
The number of array dimensions to leave in the
input going to the fields. For example if the
input has shape (3,4,2) and replace_ndim is 1,
the result will have shape (3,4), and each operand
provided to the field expression will have shape (2).
fields : list of (field_name, field_type, field_expr)
These are the fields which are created in the output.
No fields are retained from the input.
fnname : string, optional
The function name, which affects how the resulting
deferred expression's type is printed.
Examples
--------
>>> from dynd import nd, ndt
>>> a = nd.array([
... ('A', 1, 2), ('A', 3, 4),
... ('B', 1.5, 2.5), ('A', 0.5, 9),
... ('C', 1, 5), ('B', 2, 2)],
... dtype='{cat: string; x: float32; y: float32}')
>>> gb = nd.groupby(a, a.cat)
>>> gb.groups
nd.array(["A", "B", "C"], strided_dim<string>)
>>> b = nd.make_computed_fields(gb.eval(), 1,
... fields=[('sum_x', ndt.float32, 'sum(x)'),
... ('mean_y', ndt.float32, 'mean(y)'),
... ('max_x', ndt.float32, 'max(x)'),
... ('max_y', ndt.float32, 'max(y)'),
... ('min_y', ndt.float32, 'min(y)')])
>>> from pprint import pprint
>>> pprint(nd.as_py(b))
[{u'max_x': 3.0, u'max_y': 9.0, u'mean_y': 5.0, u'min_y': 2.0, u'sum_x': 4.5},
{u'max_x': 2.0, u'max_y': 2.5, u'mean_y': 2.25, u'min_y': 2.0, u'sum_x': 3.5},
{u'max_x': 1.0, u'max_y': 5.0, u'mean_y': 5.0, u'min_y': 5.0, u'sum_x': 1.0}]
"""
n = array(n)
udt = dtype_of(n).value_type
if udt.kind != 'struct':
raise ValueError("parameter 'n' must have kind 'struct'")
# The field names and types of the input struct
field_names = as_py(udt.field_names)
field_types = as_py(udt.field_types)
# Create the output struct type and corresponding expressions
new_field_names = []
new_field_types = []
new_field_expr = []
for fn, ft, fe in fields:
new_field_names.append(fn)
new_field_types.append(ft)
new_field_expr.append(fe)
result_udt = make_cstruct(new_field_types, new_field_names)
src_udt = extract_dtype(type_of(n), replace_ndim)
fieldexpr = FieldExpr(new_field_expr, field_names, fnname)
return elwise_map([n], fieldexpr, result_udt, [src_udt])