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GeometricProgression

Create an array that contains a sequence of numbers that follow a geometric progression, i.e

., are evenly spaced on a log scale between a start and end value. This corresponds to the logspace function in Python and MATLAB(tm) if endpoints are specified as exponents, and otherwise to the geomspace function. One may optionally specify a template array to determine the num value if desired; in this case the shape of the created array will conform to that of the template array, which must be a vector. The node supports alternative compute backends, which can be used to speed up processing. Version 1.0.0

Ports/Properties

array

Output array.

verbose name
Array
default value
None
port type
DataPort
value type
AnyNumeric (can be None)
data direction
OUT

like

Optional template array.

verbose name
Like
default value
None
port type
DataPort
value type
AnyNumeric (can be None)
data direction
IN

start

Start of the range.

verbose name
Start
default value
0
port type
FloatPort
value type
float (can be None)

stop

End of the range, must be provided.

verbose name
Stop
default value
None
port type
FloatPort
value type
float (can be None)

endpoints_are

Whether the endpoints are specified as exponents to the given base (exponents mode), or as the actual start-stop values (simple mode).

verbose name
Endpoints Are
default value
simple
port type
EnumPort
value type
str (can be None)

num

Number of steps to use. If an array or list has been wired into the 'like' input of the node, then this value may be omitted and will then match the size of that array.

verbose name
Number Of Steps
default value
None
port type
IntPort
value type
int (can be None)

inclusive

Whether the stop value is inclusive or exclusive.

verbose name
Inclusive
default value
True
port type
BoolPort
value type
bool (can be None)

base

Base of the exponential progression. When using simple mode, this is inferred from the endpoints and the number of steps. Alternatively, when using exponents mode, a default of 10 is assumed, and the provided endpoints will be exponentiated with this base (i.e., the sequence runs from 10start to 10stop). In either case the resulting sequence of numbers will be spaced evenly when transformed to a log scale (i.e., when taking their logarithm).

verbose name
Base
default value
None
port type
FloatPort
value type
float (can be None)

backend

Optional compute backend to use. Keep is the current default, which resolves to that of the template array if one is provided and otherwise numpy unless overridden. Numpy is the standard CPU backend that underpins most of NeuroPype's operations. The others require one or more GPUs to be present on the system, except for torch-cpu. For best performance, keep all arrays that interact with each other (via processing nodes) on the same backend.

verbose name
Backend
default value
keep
port type
EnumPort
value type
str (can be None)

precision

Numeric precision to use. Keep resolves to the precision of the template array if one is provided, and otherwise to the current default (usually 64-bit). Can be reduced to save memory (e.g. if running on GPU).

verbose name
Precision
default value
keep
port type
EnumPort
value type
str (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

verbose name
Set Breakpoint (Debug Only)
default value
False
port type
BoolPort
value type
bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

verbose name
Metadata
default value
{}
port type
DictPort
value type
dict (can be None)