Python Fixed-Point Arithmetic API¶

The doppler.arith module is the saturating fixed-point toolkit: elementwise Q15 (1.15, int16) and Q8 (1.7, int8) add / subtract / multiply, fractional dot products, saturating shifts, and small running accumulators. Every operation saturates on overflow rather than wrapping, matching how fixed-point DSP hardware and the rest of doppler's Q-format paths behave.

Source: src/doppler/arith/__init__.py

Fixed-point formats¶

Format	Storage	Range	Resolution	One-liner
Q15	`int16`	`[-1, +1)`	`2⁻¹⁵`	`1.0 → 32767`, `-1.0 → -32768`
Q8	`int8`	`[-1, +1)`	`2⁻⁷`	`1.0 → 127`, `-1.0 → -128`

A Q15 value v represents the real number v / 32768. Multiplication is the fractional product ((a·b) >> 15), so 0.5 × 0.5 = 0.25; addition saturates so 0.5 + 0.5 reads the codec ceiling 32767 (≈ +1.0), never wrapping to a negative.

Examples¶

Saturating Q15 elementwise ops¶

import numpy as np
from doppler.arith import add_q15, mul_q15

a = np.array([16384, 16384, -32768], dtype=np.int16)   # 0.5, 0.5, -1.0
b = np.array([16384, 32767,  16384], dtype=np.int16)   # 0.5, ~1.0, 0.5

add_q15(a, b)    # array([32767, 32767, -16384])  -> 0.5+0.5 saturates to +1
mul_q15(a, b)    # array([ 8192, 16384, -16384])  -> 0.5*0.5 = 0.25

Fractional dot product¶

dot_q15 accumulates the Q15 products in a wide integer and returns a Python int, so a long inner product never overflows mid-sum.

from doppler.arith import dot_q15

taps   = np.array([ 8192,  8192,  8192,  8192], dtype=np.int16)  # 0.25 each
sample = np.array([32767, 16384, -16384, 0],    dtype=np.int16)
acc = dot_q15(taps, sample)     # wide integer accumulator (no overflow)

Saturating shifts¶

from doppler.arith import shl_q15, shr_q15

x = np.array([16384, -32768], dtype=np.int16)   # 0.5, -1.0
shl_q15(x, 1)   # array([32767, -32768]) -> 0.5<<1 saturates to +1, -1<<1 to -1
shr_q15(x, 2)   # array([ 4096,  -8192]) -> arithmetic right shift

Running Q15 / Q8 accumulators¶

AccQ15 / AccQ8 keep a 64-bit running sum across calls — drive them sample-by-sample (step), in bulk (steps), or as a multiply-accumulate (madd), then read with get() (peek) or dump() (read-and-reset). The wide register means a long stream cannot overflow.

from doppler.arith import AccQ15

acc = AccQ15()
acc.steps(np.array([8192, 8192, 8192], dtype=np.int16))   # accumulate a block
acc.madd(np.array([16384], dtype=np.int16),               # acc += sum(a*b)
         np.array([16384], dtype=np.int16))
running = acc.get()      # peek without resetting
final   = acc.dump()     # read and reset to zero

Accumulators¶

AccQ15 ¶

Allocate and initialise an AccQ15 accumulator. The accumulator starts at the supplied initial value and may be driven sample-by-sample (step), in bulk (steps), or via multiply-accumulate (madd). The internal register is a 64-bit signed integer so it will not overflow in any realistic DSP workload.

Parameters:

Name	Type	Description	Default
`acc`	`int`	acc state variable.	`0`

Examples:

>>> from doppler.arith import AccQ15
>>> obj = AccQ15(100)
>>> obj.get_acc()
100

reset ¶

reset() -> None

Reset the accumulator to zero, mirroring the post-create state. Does not re-initialise to the constructor's acc value — always resets to zero, matching the default initial state for a clean sweep.

Examples:

>>> from doppler.arith import AccQ15
>>> obj = AccQ15(0)
>>> obj.step(42)
>>> obj.reset()
>>> obj.get()
0

step ¶

step(x: int) -> None

Accumulate one Q15 sample into the running total. The sample is sign-extended to 64 bits before addition, ensuring that negative samples subtract correctly from the accumulator without wrap.

Parameters:

Name	Type	Description	Default
`x`	`int`	Q15 input sample (int16_t, range `[-32768, 32767]`).	required

Examples:

>>> from doppler.arith import AccQ15
>>> obj = AccQ15(0)
>>> obj.step(100)
>>> obj.step(200)
>>> obj.get()
300

steps ¶

steps(x: NDArray[int16]) -> None

Accumulate a contiguous block of Q15 samples. Equivalent to calling step() n times but faster for large arrays because the loop can be auto-vectorised by the compiler.

Parameters:

Name	Type	Description	Default
`x`	`NDArray[int16]`	Input.	required

Examples:

>>> from doppler.arith import AccQ15
>>> import numpy as np
>>> obj = AccQ15(0)
>>> obj.steps(np.array([1, 2, 3, 4, 5], dtype=np.int16))
>>> obj.get()
15

get ¶

get() -> int

Return the current accumulated value without resetting.

Returns:

Type	Description
`int`	Current accumulator value (int64_t).

Examples:

>>> from doppler.arith import AccQ15
>>> import numpy as np
>>> obj = AccQ15(0)
>>> obj.steps(np.array([10, 20, 30], dtype=np.int16))
>>> obj.get()
60

dump ¶

dump() -> int

Return the accumulated value and reset to zero.

Returns:

Type	Description
`int`	Accumulator value before the reset (int64_t).

Examples:

>>> from doppler.arith import AccQ15
>>> import numpy as np
>>> obj = AccQ15(0)
>>> obj.steps(np.array([1, 2, 3, 4, 5], dtype=np.int16))
>>> obj.dump()
15
>>> obj.get()
0

madd ¶

madd(a: NDArray[int16], b: NDArray[int16]) -> None

Multiply-accumulate: acc += sum(a[i] * b[i]) for i in [0, len(a)). Uses AVX2 when available.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	First input array (int16_t).	required
`b`	`NDArray[int16]`	Second input array (int16_t), same length as a.	required

Examples:

>>> from doppler.arith import AccQ15
>>> import numpy as np
>>> obj = AccQ15(0)
>>> a = np.array([100, 200, 300], dtype=np.int16)
>>> b = np.array([10, 20, 30], dtype=np.int16)
>>> obj.madd(a, b)
>>> obj.get()
14000

state_bytes ¶

state_bytes() -> int

Size in bytes of this object's serialized state.

The exact length get_state returns and set_state requires. It depends on how the object was constructed (state arrays are sized at construction), so read it from the instance rather than assuming a constant.

Raises RuntimeError if the AccQ15 has already been destroyed.

Returns:

Type	Description
`int`	Byte length of one serialized state blob.

get_state ¶

get_state() -> bytes

Serialize this object's mutable state to bytes.

Captures exactly the state that evolves as the object runs, so a blob taken now and restored later resumes from this point. Construction parameters are not included: restore into an object built the same way.

The blob is opaque and always state_bytes() long. Its layout is an implementation detail of the C core and is not a stable format across builds.

Raises RuntimeError if the AccQ15 has already been destroyed.

Returns:

Type	Description
`bytes`	Opaque snapshot, `state_bytes()` bytes long.

set_state ¶

set_state(blob: bytes) -> None

Restore mutable state from a get_state() blob.

Overwrites the live state in place; the object keeps the parameters it was constructed with. Length is validated against state_bytes() before the blob is handed to the C core, and the core may reject it as well.

Raises TypeError if blob is not bytes, ValueError if its length differs from state_bytes() or the core rejects it, and RuntimeError if the AccQ15 has already been destroyed.

Parameters:

Name	Type	Description	Default
`blob`	`bytes`	A `get_state()` blob from this type, exactly `state_bytes()` long.	required

get_acc ¶

get_acc() -> int

Read the current accumulator value without modifying it. Use this when you need to snapshot the running total mid-stream and continue accumulating afterward.

Returns:

Type	Description
`int`	Output.

Examples:

>>> from doppler.arith import AccQ15
>>> obj = AccQ15(0)
>>> obj.step(100)
>>> obj.get()
100
>>> obj.step(200)
>>> obj.get()
300

set_acc ¶

set_acc(value: int) -> None

Overwrite the accumulator with a new value. Useful for setting a bias before a new accumulation window, or for restoring a previously checkpointed value.

Parameters:

Name	Type	Description	Default
`value`	`int`	Input.	required

Examples:

>>> from doppler.arith import AccQ15
>>> obj = AccQ15(0)
>>> obj.set_acc(1000)
>>> obj.get_acc()
1000

destroy ¶

destroy() -> None

Release the underlying C resources immediately.

Ordinarily unnecessary: the resources are freed when the object is garbage-collected. Call this to release them at a definite point instead, or use the object as a context manager, which calls it on exit.

Idempotent: calling it again on an already-released object does nothing. Every other method raises RuntimeError once it has run.

enter ¶

__enter__() -> AccQ15

Enter a context manager, returning this object.

Lets a AccQ15 be used in a with statement so its C resources are released deterministically on exit rather than at collection time.

Returns:

Type	Description
`AccQ15`	This same object, not a copy.

exit ¶

__exit__(
    exc_type: object | None = ...,
    exc: object | None = ...,
    tb: object | None = ...,
) -> None

Exit a context manager, releasing the AccQ15.

Equivalent to calling destroy(). Returns None, so an exception raised inside the with body propagates normally; this never suppresses one.

Parameters:

Name	Type	Description	Default
`exc_type`	`object \| None`	Exception class, or None. Ignored.	`...`
`exc`	`object \| None`	Exception instance, or None. Ignored.	`...`
`tb`	`object \| None`	Traceback object, or None. Ignored.	`...`

AccQ8 ¶

Allocate and initialise an AccQ8 accumulator. The accumulator starts at the supplied initial value and accepts Q8 (int8_t) samples via step(), steps(), or madd(). The 32-bit internal register handles up to roughly 16 million max-magnitude samples before wrap — sufficient for all standard DSP block sizes.

Parameters:

Name	Type	Description	Default
`acc`	`int`	acc state variable.	`0`

Examples:

>>> from doppler.arith import AccQ8
>>> obj = AccQ8(10)
>>> obj.get_acc()
10

reset ¶

reset() -> None

Reset the accumulator to zero, mirroring the post-create state. Always resets to zero regardless of the original constructor value, so it is safe to call at the start of any new accumulation window.

Examples:

>>> from doppler.arith import AccQ8
>>> obj = AccQ8(0)
>>> obj.step(42)
>>> obj.reset()
>>> obj.get()
0

step ¶

step(x: int) -> None

Accumulate one Q8 sample into the running total. The sample is sign-extended to 32 bits before addition so negative samples correctly subtract from the accumulator.

Parameters:

Name	Type	Description	Default
`x`	`int`	Q8 input sample (int8_t, range `[-128, 127]`).	required

Examples:

>>> from doppler.arith import AccQ8
>>> obj = AccQ8(0)
>>> obj.step(10)
>>> obj.step(20)
>>> obj.get()
30

steps ¶

steps(x: NDArray[int8]) -> None

Accumulate a contiguous block of Q8 samples. Equivalent to calling step() n times; the single loop is more amenable to auto-vectorisation than repeated method calls.

Parameters:

Name	Type	Description	Default
`x`	`NDArray[int8]`	Input.	required

Examples:

>>> from doppler.arith import AccQ8
>>> import numpy as np
>>> obj = AccQ8(0)
>>> obj.steps(np.array([1, 2, 3, 4, 5], dtype=np.int8))
>>> obj.get()
15

get ¶

get() -> int

Return the current accumulated value without resetting.

Returns:

Type	Description
`int`	Current accumulator value (int32_t).

Examples:

>>> from doppler.arith import AccQ8
>>> import numpy as np
>>> obj = AccQ8(0)
>>> obj.steps(np.array([10, 20, 30], dtype=np.int8))
>>> obj.get()
60

dump ¶

dump() -> int

Return the accumulated value and reset to zero.

Returns:

Type	Description
`int`	Accumulator value before the reset (int32_t).

Examples:

>>> from doppler.arith import AccQ8
>>> import numpy as np
>>> obj = AccQ8(0)
>>> obj.steps(np.array([1, 2, 3, 4, 5], dtype=np.int8))
>>> obj.dump()
15
>>> obj.get()
0

madd ¶

madd(a: NDArray[int8], b: NDArray[int8]) -> None

Multiply-accumulate: acc += sum(a[i] * b[i]) for i in [0, len(a)).

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	First input array (int8_t).	required
`b`	`NDArray[int8]`	Second input array (int8_t), same length as a.	required

Examples:

>>> from doppler.arith import AccQ8
>>> import numpy as np
>>> obj = AccQ8(0)
>>> a = np.array([10, 20, 30], dtype=np.int8)
>>> b = np.array([1, 2, 3], dtype=np.int8)
>>> obj.madd(a, b)
>>> obj.get()
140

state_bytes ¶

state_bytes() -> int

Size in bytes of this object's serialized state.

The exact length get_state returns and set_state requires. It depends on how the object was constructed (state arrays are sized at construction), so read it from the instance rather than assuming a constant.

Raises RuntimeError if the AccQ8 has already been destroyed.

Returns:

Type	Description
`int`	Byte length of one serialized state blob.

get_state ¶

get_state() -> bytes

Serialize this object's mutable state to bytes.

Captures exactly the state that evolves as the object runs, so a blob taken now and restored later resumes from this point. Construction parameters are not included: restore into an object built the same way.

The blob is opaque and always state_bytes() long. Its layout is an implementation detail of the C core and is not a stable format across builds.

Raises RuntimeError if the AccQ8 has already been destroyed.

Returns:

Type	Description
`bytes`	Opaque snapshot, `state_bytes()` bytes long.

set_state ¶

set_state(blob: bytes) -> None

Restore mutable state from a get_state() blob.

Overwrites the live state in place; the object keeps the parameters it was constructed with. Length is validated against state_bytes() before the blob is handed to the C core, and the core may reject it as well.

Raises TypeError if blob is not bytes, ValueError if its length differs from state_bytes() or the core rejects it, and RuntimeError if the AccQ8 has already been destroyed.

Parameters:

Name	Type	Description	Default
`blob`	`bytes`	A `get_state()` blob from this type, exactly `state_bytes()` long.	required

get_acc ¶

get_acc() -> int

Read the current accumulator value without modifying it. Permits repeated snapshots of the running sum mid-stream.

Returns:

Type	Description
`int`	Output.

Examples:

>>> from doppler.arith import AccQ8
>>> obj = AccQ8(0)
>>> obj.step(10)
>>> obj.get_acc()
10

set_acc ¶

set_acc(value: int) -> None

Overwrite the accumulator with a new value. Useful for applying a bias before a new accumulation window, or for restoring a checkpointed accumulator state.

Parameters:

Name	Type	Description	Default
`value`	`int`	Input.	required

Examples:

>>> from doppler.arith import AccQ8
>>> obj = AccQ8(0)
>>> obj.set_acc(50)
>>> obj.get_acc()
50

destroy ¶

destroy() -> None

Release the underlying C resources immediately.

Ordinarily unnecessary: the resources are freed when the object is garbage-collected. Call this to release them at a definite point instead, or use the object as a context manager, which calls it on exit.

Idempotent: calling it again on an already-released object does nothing. Every other method raises RuntimeError once it has run.

enter ¶

__enter__() -> AccQ8

Enter a context manager, returning this object.

Lets a AccQ8 be used in a with statement so its C resources are released deterministically on exit rather than at collection time.

Returns:

Type	Description
`AccQ8`	This same object, not a copy.

exit ¶

__exit__(
    exc_type: object | None = ...,
    exc: object | None = ...,
    tb: object | None = ...,
) -> None

Exit a context manager, releasing the AccQ8.

Equivalent to calling destroy(). Returns None, so an exception raised inside the with body propagates normally; this never suppresses one.

Parameters:

Name	Type	Description	Default
`exc_type`	`object \| None`	Exception class, or None. Ignored.	`...`
`exc`	`object \| None`	Exception instance, or None. Ignored.	`...`
`tb`	`object \| None`	Traceback object, or None. Ignored.	`...`

Q15 operations (1.15, `int16`)¶

add_q15 ¶

add_q15(
    a: NDArray[int16], b: NDArray[int16]
) -> NDArray[np.int16]

Elementwise saturating two's complement add of two Q15 arrays.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	First input array (int16_t).	required
`b`	`NDArray[int16]`	Second input array (int16_t), same length as a.	required

Returns:

Type	Description
`NDArray[int16]`	Output.

Examples:

>>> from doppler.arith import add_q15
>>> import numpy as np
>>> a = np.array([100, 20000, -20000], dtype=np.int16)
>>> b = np.array([50,  20000, -20000], dtype=np.int16)
>>> add_q15(a, b).tolist()
[150, 32767, -32768]

sub_q15 ¶

sub_q15(
    a: NDArray[int16], b: NDArray[int16]
) -> NDArray[np.int16]

Elementwise saturating two's complement subtract of two Q15 arrays.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	Minuend array (int16_t).	required
`b`	`NDArray[int16]`	Subtrahend array (int16_t), same length as a.	required

Returns:

Type	Description
`NDArray[int16]`	Output.

Examples:

>>> from doppler.arith import sub_q15
>>> import numpy as np
>>> a = np.array([100,  0, -32768], dtype=np.int16)
>>> b = np.array([50,   0,     10], dtype=np.int16)
>>> sub_q15(a, b).tolist()
[50, 0, -32768]

mul_q15 ¶

mul_q15(
    a: NDArray[int16], b: NDArray[int16]
) -> NDArray[np.int16]

Elementwise Q15 multiply with round-half-up: out[i] = sat16((a[i]*b[i] + 16384) >> 15).

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	First input array (int16_t).	required
`b`	`NDArray[int16]`	Second input array (int16_t), same length as a.	required

Returns:

Type	Description
`NDArray[int16]`	Output.

Examples:

>>> from doppler.arith import mul_q15
>>> import numpy as np
>>> a = np.array([16384, 16384, 32767], dtype=np.int16)
>>> b = np.array([16384, -16384, 32767], dtype=np.int16)
>>> mul_q15(a, b).tolist()
[8192, -8192, 32766]

dot_q15 ¶

dot_q15(a: NDArray[int16], b: NDArray[int16]) -> int

Inner product of two Q15 arrays. Returns the raw Q30 accumulation as int64_t. Shift right 15 to get a Q15 scalar.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	First input array (int16_t).	required
`b`	`NDArray[int16]`	Second input array (int16_t), same length as a.	required

Returns:

Type	Description
`int`	Raw Q30 accumulation (int64_t).

Examples:

>>> from doppler.arith import dot_q15
>>> import numpy as np
>>> a = np.array([100, 200, 300], dtype=np.int16)
>>> b = np.array([1, 2, 3], dtype=np.int16)
>>> dot_q15(a, b)
1400

shl_q15 ¶

shl_q15(a: NDArray[int16], n: int) -> NDArray[np.int16]

Elementwise arithmetic left shift of a Q15 array with saturation. Equivalent to multiplying by 2^n in fixed-point.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	Input array (int16_t).	required
`n`	`int`	Shift count (non-negative integer).	required

Returns:

Type	Description
`NDArray[int16]`	Output.

Examples:

>>> from doppler.arith import shl_q15
>>> import numpy as np
>>> a = np.array([8192, 16384, 20000], dtype=np.int16)
>>> shl_q15(a, 1).tolist()
[16384, 32767, 32767]

shr_q15 ¶

shr_q15(a: NDArray[int16], n: int) -> NDArray[np.int16]

Elementwise arithmetic right shift of a Q15 array with round-half-up. Equivalent to dividing by 2^n.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int16]`	Input array (int16_t).	required
`n`	`int`	Shift count (non-negative integer).	required

Returns:

Type	Description
`NDArray[int16]`	Output.

Examples:

>>> from doppler.arith import shr_q15
>>> import numpy as np
>>> a = np.array([100, 101, 102, -100], dtype=np.int16)
>>> shr_q15(a, 2).tolist()
[25, 25, 26, -25]

Q8 operations (1.7, `int8`)¶

add_q8 ¶

add_q8(
    a: NDArray[int8], b: NDArray[int8]
) -> NDArray[np.int8]

Elementwise saturating two's complement add of two Q8 arrays.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	First input array (int8_t).	required
`b`	`NDArray[int8]`	Second input array (int8_t), same length as a.	required

Returns:

Type	Description
`NDArray[int8]`	Output.

Examples:

>>> from doppler.arith import add_q8
>>> import numpy as np
>>> a = np.array([50, 100, -100], dtype=np.int8)
>>> b = np.array([50,  30,  -50], dtype=np.int8)
>>> add_q8(a, b).tolist()
[100, 127, -128]

sub_q8 ¶

sub_q8(
    a: NDArray[int8], b: NDArray[int8]
) -> NDArray[np.int8]

Elementwise saturating two's complement subtract of two Q8 arrays.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	Minuend array (int8_t).	required
`b`	`NDArray[int8]`	Subtrahend array (int8_t), same length as a.	required

Returns:

Type	Description
`NDArray[int8]`	Output.

Examples:

>>> from doppler.arith import sub_q8
>>> import numpy as np
>>> a = np.array([50,   0, -128], dtype=np.int8)
>>> b = np.array([30,   0,   10], dtype=np.int8)
>>> sub_q8(a, b).tolist()
[20, 0, -128]

mul_q8 ¶

mul_q8(
    a: NDArray[int8], b: NDArray[int8]
) -> NDArray[np.int8]

Elementwise Q8 multiply with round-half-up: out[i] = sat8((a[i]*b[i] + 64) >> 7).

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	First input array (int8_t).	required
`b`	`NDArray[int8]`	Second input array (int8_t), same length as a.	required

Returns:

Type	Description
`NDArray[int8]`	Output.

Examples:

>>> from doppler.arith import mul_q8
>>> import numpy as np
>>> a = np.array([64,  64, -64], dtype=np.int8)
>>> b = np.array([64, -64,  64], dtype=np.int8)
>>> mul_q8(a, b).tolist()
[32, -32, -32]

dot_q8 ¶

dot_q8(a: NDArray[int8], b: NDArray[int8]) -> int

Inner product of two Q8 arrays. Returns the raw Q14 accumulation as int32_t.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	First input array (int8_t).	required
`b`	`NDArray[int8]`	Second input array (int8_t), same length as a.	required

Returns:

Type	Description
`int`	Raw Q14 accumulation (int32_t).

Examples:

>>> from doppler.arith import dot_q8
>>> import numpy as np
>>> a = np.array([10, 20, 30], dtype=np.int8)
>>> b = np.array([1, 2, 3], dtype=np.int8)
>>> dot_q8(a, b)
140

shl_q8 ¶

shl_q8(a: NDArray[int8], n: int) -> NDArray[np.int8]

Elementwise arithmetic left shift of a Q8 array with saturation.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	Input array (int8_t).	required
`n`	`int`	Shift count (non-negative integer).	required

Returns:

Type	Description
`NDArray[int8]`	Output.

Examples:

>>> from doppler.arith import shl_q8
>>> import numpy as np
>>> a = np.array([10, 50, 64], dtype=np.int8)
>>> shl_q8(a, 1).tolist()
[20, 100, 127]

shr_q8 ¶

shr_q8(a: NDArray[int8], n: int) -> NDArray[np.int8]

Elementwise arithmetic right shift of a Q8 array with round-half-up.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int8]`	Input array (int8_t).	required
`n`	`int`	Shift count (non-negative integer).	required

Returns:

Type	Description
`NDArray[int8]`	Output.

Examples:

>>> from doppler.arith import shr_q8
>>> import numpy as np
>>> a = np.array([10, 11, 12, -10], dtype=np.int8)
>>> shr_q8(a, 2).tolist()
[3, 3, 3, -2]

64-bit saturating shifts¶

shl_i64 ¶

shl_i64(a: NDArray[int64], n: int) -> NDArray[np.int64]

Elementwise logical left shift of an int64_t array. No saturation (caller ensures no overflow).

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int64]`	Input array (int64_t).	required
`n`	`int`	Shift count (non-negative integer; >= 63 yields 0).	required

Returns:

Type	Description
`NDArray[int64]`	Output.

Examples:

>>> from doppler.arith import shl_i64
>>> import numpy as np
>>> a = np.array([100, 200, -200], dtype=np.int64)
>>> shl_i64(a, 3).tolist()
[800, 1600, -1600]

shr_i64 ¶

shr_i64(a: NDArray[int64], n: int) -> NDArray[np.int64]

Elementwise arithmetic right shift of an int64_t array with round-half-up. Useful for normalising dot_q15 Q30 results back to Q15.

Parameters:

Name	Type	Description	Default
`a`	`NDArray[int64]`	Input array (int64_t).	required
`n`	`int`	Shift count (non-negative integer; >= 63 is clamped to 63).	required

Returns:

Type	Description
`NDArray[int64]`	Output.

Examples:

>>> from doppler.arith import dot_q15, shr_i64
>>> import numpy as np
>>> raw = dot_q15(
...     np.array([16384, 16384], dtype=np.int16),
...     np.array([16384, 16384], dtype=np.int16),
... )
>>> shr_i64(np.array([raw], dtype=np.int64), 15).tolist()
[16384]

Guides — Getting Started with Fixed-Point Arithmetic Design — API taxonomy: the DSP building-block hierarchy and its naming axis

Python Fixed-Point Arithmetic API¶

Fixed-point formats¶

Examples¶

Saturating Q15 elementwise ops¶

Fractional dot product¶

Saturating shifts¶

Running Q15 / Q8 accumulators¶

Accumulators¶

AccQ15 ¶

reset ¶

step ¶

steps ¶

get ¶

dump ¶

madd ¶

state_bytes ¶

get_state ¶

set_state ¶

get_acc ¶

set_acc ¶

destroy ¶

__enter__ ¶

__exit__ ¶

AccQ8 ¶

reset ¶

step ¶

steps ¶

get ¶

dump ¶

madd ¶

state_bytes ¶

get_state ¶

set_state ¶

get_acc ¶

set_acc ¶

destroy ¶

__enter__ ¶

__exit__ ¶

Q15 operations (1.15, int16)¶

add_q15 ¶

sub_q15 ¶

mul_q15 ¶

dot_q15 ¶

shl_q15 ¶

shr_q15 ¶

Q8 operations (1.7, int8)¶

add_q8 ¶

sub_q8 ¶

mul_q8 ¶

dot_q8 ¶

shl_q8 ¶

shr_q8 ¶

64-bit saturating shifts¶

shl_i64 ¶

shr_i64 ¶

Related pages¶

enter ¶

exit ¶

enter ¶

exit ¶

Q15 operations (1.15, `int16`)¶

Q8 operations (1.7, `int8`)¶