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An
Atom Vector (AV) is created using the
AtomVector()
function. This function creates what is called an
Entire AV. See the [Subscript]
page for the Extract variant. The Extract variant
can also be created using the
Extract()
function; see the [Functions]
page.
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Function
Name
|
Argument
Keywords
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Return
Value
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AtomVector
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"template",
"numatom",
"numdimen",
"value",
"spread",
"copy",
"sample",
"seed"
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The
new Atom Vector
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Keyword
|
Argument
|
Value
|
Preset
Value
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|
template
|
exactly
one required
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The
root node of an Atom Map
|
-
|
|
numatom
|
Number
of Atoms
|
-
|
|
numdimen
|
optional
|
Number
of Dimensions
|
3
|
|
value
|
optional:
no more than one to be
specified
|
one
double precision floating point
value
|
0
|
|
spread
|
an
AV with one dimension of the same length
as that of the newly created AV and the
other dimension is one.
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-
|
|
copy
|
an
AV of the same shape as the newly created
AV
|
-
|
|
sample
|
"UNIFORM",
"GAUSSIAN", "INVERSEMASS" or
"CHARGE"
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-
|
|
seed
|
optional
|
seed
for the random number
generator
|
0
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An
AV may be associated with an Atom Map. Such AVs are
called Mapped AVs and are created by specifying the
root node of an Atom Map as the value for the
template
keyword. Mapped AVs can be extracted (see
Subscript)
and the extracts can be manipulated separately. In
the following example,
Top
is the root node of an Atom Map (see this
example
which will appear in a separate window).
>>> from Yup.Taro.AtomVect import *
>>> R = AtomVector( Top )
>>> R.numatom, R.numdimen, R.type
(16, 3, 'Entire-Mapped')
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Note
that template
is the keyword for the first argument and therefore
we can leave it out as we have done in the example.
After creating the new AV we print out three data
attributes. The number of atoms is equal to the
number of atoms in the Atom Map
Top.
The number of dimensions is three, which is the
preset value, that we did not override. We also
print out the type: R
is an Entire AV that is mapped.
In
the following example, we create an Anonymous AV,
i.e., an AV that is not associated with an Atom
Map.
>>> M = AtomVector( numatom=R.numatom, numdimen=1 )
>>> M.numatom, M.numdimen, M.type
(16, 1, 'Entire-Anonymous')
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An
anonymous AV is created by not specifying an Atom
Map in which case we have to specify the number of
atoms that we make equal to the number of atoms in
R
and which we created in the first example. We also
specify the number of dimensions to be one. When we
print out the type attribute we can see that
M
is still an Entire AV but it is Anonymous. An
Extract of this Atom Vector can be created using
the Extract()
function, see the Functions
page for the procedure.
The
last arguments are optional. See the
fill()
method in the Methods
page for details. Only one out of the last four
arguments can be specified or none of these
arguments is to be specified. If none is specified,
value=0
is assumed. This fills the newly created AV with
zeros.
In
the third line of the last example (below), we
create a new AV with the same number of atoms as in
R
(created in the first example) and we fill the new
AV with numbers from the Normal distribution. The
new AV is then combined with another AV called
SD
which has the same number of atoms as the new AV
but has only one dimension. See the
Operators
page for an explanation of the
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operator.
>>> SD.numatom, SD.numdimen, SD.type
(16, 1, 'Entire-Anonymous')
>>> V = AtomVector( numatom=R.numatom, sample='GAUSSIAN' ) | SD
>>> V.numatom, V.numdimen, V.type
(16, 3, 'Entire-Anonymous')
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The
AV created by the AtomVector()
function is not saved but is used in an operation
(and discarded after use) and the result of the
operation is saved in the variable
V.
Thus, AtomVector()
is still responsible for the creation of a new AV,
although indirectly.
The
last argument is optional. If a non-zero number is
specified, the random number generator is seeded
with this value.
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