LVecBase4f

from panda3d.core import LVecBase4f

class LVecBase4f

Bases:

Bases: MemoryBase

This is the base class for all three-component vectors and points.

Inheritance diagram

__add__(other: LVecBase4f) → LVecBase4f

__ceil__() → object

__div__(scalar: float) → LVecBase4f

__eq__(other: LVecBase4f) → bool

__floor__() → object

__floordiv__(scalar: float) → object

__getattr__(attr_name: str) → object

__getbuffer__(view: Py_buffer, flags: int) → int

__getitem__(i: int)

__getitem__(i: int) → float

__iadd__(other: LVecBase4f) → LVecBase4f

__idiv__(scalar: float) → LVecBase4f

__ifloordiv__(scalar: float) → object

__imul__(scalar: float) → LVecBase4f

__init__()

__init__(point: LPoint3f): Constructs an LVecBase4 from an LPoint3. The w coordinate is set to 1.0.

__init__(copy: LVecBase3f, w: float)

__init__(param0: LVecBase4f)

__init__(vector: LVector3f): Constructs an LVecBase4 from an LVector3. The w coordinate is set to 0.0.

__init__(copy: UnalignedLVecBase4f)

__init__(fill_value: float)

__init__(x: float, y: float, z: float, w: float)

__ipow__(exponent: float) → object

__isub__(other: LVecBase4f) → LVecBase4f

__lt__(other: LVecBase4f) → bool

__mul__(scalar: float) → LVecBase4f

__ne__(other: LVecBase4f) → bool

__pow__(exponent: float) → object

__reduce__() → object

__repr__() → str

__rmul__(scalar: float) → object

__round__() → object

__setattr__(attr_name: str, assign: object) → int

__sub__(other: LVecBase4f) → LVecBase4f

__sub__(other: LVecBase4f) → LVecBase4f

add_hash(hash: int) → int: Adds the vector into the running hash.

add_hash(hash: int, threshold: float) → int: Adds the vector into the running hash.

add_to_cell(i: int, value: float): These next functions add to an existing value. i.e. foo.set_x(foo.get_x() + value) These are useful to reduce overhead in scripting languages:

add_w(value: float)

add_x(value: float)

add_y(value: float)

add_z(value: float)

almost_equal(other: LVecBase4f) → bool: Returns true if two vectors are memberwise equal within a default tolerance based on the numeric type.

almost_equal(other: LVecBase4f, threshold: float) → bool: Returns true if two vectors are memberwise equal within a specified tolerance.

assign(copy: LVecBase4f) → LVecBase4f

assign(fill_value: float) → LVecBase4f

compare_to(other: LVecBase4f) → int: This flavor of compare_to uses a default threshold value based on the numeric type.

compare_to(other: LVecBase4f, threshold: float) → int: Sorts vectors lexicographically, componentwise. Returns a number less than 0 if this vector sorts before the other one, greater than zero if it sorts after, 0 if they are equivalent (within the indicated tolerance).

componentwise_mult(other: LVecBase4f)

dot(other: LVecBase4f) → float

fill(fill_value: float): Sets each element of the vector to the indicated fill_value. This is particularly useful for initializing to zero.

fmax(other: LVecBase4f) → LVecBase4f

fmin(other: LVecBase4f) → LVecBase4f

generate_hash(hashgen: ChecksumHashGenerator): Adds the vector to the indicated hash generator.

generate_hash(hashgen: ChecksumHashGenerator, threshold: float): Adds the vector to the indicated hash generator.

get_cell(i: int) → float

static get_class_type() → TypeHandle

get_data(): Returns the address of the first of the four data elements in the vector. The remaining elements occupy the next positions consecutively in memory.

get_hash() → int: Returns a suitable hash for phash_map.

get_hash(threshold: float) → int: Returns a suitable hash for phash_map.

static get_num_components() → int

get_w() → float

get_x() → float

get_xy() → LVecBase2f: Returns the x and y component of this vector

get_xyz() → LVecBase3f: Returns the x, y and z component of this vector

get_y() → float

get_z() → float

is_nan() → bool: Returns true if any component of the vector is not-a-number, false otherwise.

length() → float: Returns the length of the vector, by the Pythagorean theorem.

length_squared() → float: Returns the square of the vector’s length, cheap and easy.

normalize() → bool: Normalizes the vector in place. Returns true if the vector was normalized, false if it was a zero-length vector.

normalized() → LVecBase4f: Normalizes the vector and returns the normalized vector as a copy. If the vector was a zero-length vector, a zero length vector will be returned.

output(out: ostream)

project(onto: LVecBase4f) → LVecBase4f: Returns a new vector representing the projection of this vector onto another one. The resulting vector will be a scalar multiple of onto.

read_datagram(source: DatagramIterator): Reads the vector from the Datagram using get_stdfloat().

read_datagram_fixed(source: DatagramIterator): Reads the vector from the Datagram using get_float32() or get_float64(). See write_datagram_fixed().

set(x: float, y: float, z: float, w: float)

set_cell(i: int, value: float)

set_w(value: float)

set_x(value: float)

set_y(value: float)

set_z(value: float)

static size() → int

static unit_w() → LVecBase4f: Returns a unit W vector.

static unit_x() → LVecBase4f: Returns a unit X vector.

static unit_y() → LVecBase4f: Returns a unit Y vector.

static unit_z() → LVecBase4f: Returns a unit Z vector.

write_datagram(destination: Datagram): Writes the vector to the Datagram using add_stdfloat(). This is appropriate when you want to write the vector using the standard width setting, especially when you are writing a bam file.

write_datagram_fixed(destination: Datagram): Writes the vector to the Datagram using add_float32() or add_float64(), depending on the type of floats in the vector, regardless of the setting of Datagram.set_stdfloat_double(). This is appropriate when you want to write a fixed-width value to the datagram, especially when you are not writing a bam file.

property x → float

property xy → LVecBase2f: Returns the x and y component of this vector

property xyz → LVecBase3f: Returns the x, y and z component of this vector

property y → float

property z → float

static zero() → LVecBase4f: Returns a zero-length vector.